stevenbucaille/image-segmentation-models-dataset

model_id stringlengths 12 92	model_card stringlengths 166 900k	model_labels listlengths 2 250
giuseppemartino/model2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model2 This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the giuseppemartino/isaid_sam_predicted dataset. It achieves the following results on the evaluation set: - Loss: 0.2318 - Mean Iou: 0.2504 - Mean Accuracy: 0.3019 - Overall Accuracy: 0.4542 - Accuracy Background: nan - Accuracy Ship: 0.6330 - Accuracy Small-vehicle: 0.4644 - Accuracy Tennis-court: 0.0280 - Accuracy Helicopter: nan - Accuracy Basketball-court: 0.0 - Accuracy Ground-track-field: 0.6010 - Accuracy Swimming-pool: nan - Accuracy Harbor: 0.4575 - Accuracy Soccer-ball-field: 0.7776 - Accuracy Plane: nan - Accuracy Storage-tank: 0.0 - Accuracy Baseball-diamond: nan - Accuracy Large-vehicle: 0.3594 - Accuracy Bridge: 0.0 - Accuracy Roundabout: 0.0 - Iou Background: 0.0 - Iou Ship: 0.5194 - Iou Small-vehicle: 0.4368 - Iou Tennis-court: 0.0280 - Iou Helicopter: nan - Iou Basketball-court: 0.0 - Iou Ground-track-field: 0.5492 - Iou Swimming-pool: nan - Iou Harbor: 0.3611 - Iou Soccer-ball-field: 0.7592 - Iou Plane: nan - Iou Storage-tank: 0.0 - Iou Baseball-diamond: nan - Iou Large-vehicle: 0.3508 - Iou Bridge: 0.0 - Iou Roundabout: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 1345 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Ship \| Accuracy Small-vehicle \| Accuracy Tennis-court \| Accuracy Helicopter \| Accuracy Basketball-court \| Accuracy Ground-track-field \| Accuracy Swimming-pool \| Accuracy Harbor \| Accuracy Soccer-ball-field \| Accuracy Plane \| Accuracy Storage-tank \| Accuracy Baseball-diamond \| Accuracy Large-vehicle \| Accuracy Bridge \| Accuracy Roundabout \| Iou Background \| Iou Ship \| Iou Small-vehicle \| Iou Tennis-court \| Iou Helicopter \| Iou Basketball-court \| Iou Ground-track-field \| Iou Swimming-pool \| Iou Harbor \| Iou Soccer-ball-field \| Iou Plane \| Iou Storage-tank \| Iou Baseball-diamond \| Iou Large-vehicle \| Iou Bridge \| Iou Roundabout \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:-------------:\|:----------------------:\|:---------------------:\|:-------------------:\|:-------------------------:\|:---------------------------:\|:----------------------:\|:---------------:\|:--------------------------:\|:--------------:\|:---------------------:\|:-------------------------:\|:----------------------:\|:---------------:\|:-------------------:\|:--------------:\|:--------:\|:-----------------:\|:----------------:\|:--------------:\|:--------------------:\|:----------------------:\|:-----------------:\|:----------:\|:---------------------:\|:---------:\|:----------------:\|:--------------------:\|:-----------------:\|:----------:\|:--------------:\| \| 1.1413 \| 1.0 \| 113 \| 0.5054 \| 0.0431 \| 0.0841 \| 0.0445 \| nan \| 0.0611 \| 0.0179 \| 0.0079 \| nan \| 0.0 \| 0.0 \| nan \| 0.8374 \| 0.0 \| nan \| 0.0 \| nan \| 0.0010 \| 0.0 \| 0.0 \| 0.0 \| 0.0592 \| 0.0178 \| 0.0079 \| nan \| 0.0 \| 0.0 \| nan \| 0.4319 \| 0.0 \| nan \| 0.0 \| nan \| 0.0010 \| 0.0 \| 0.0 \| \| 0.326 \| 2.0 \| 226 \| 0.3240 \| 0.0756 \| 0.1192 \| 0.2144 \| nan \| 0.0433 \| 0.1690 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| 0.8062 \| 0.0 \| nan \| 0.0 \| nan \| 0.2926 \| 0.0 \| 0.0 \| 0.0 \| 0.0430 \| 0.1614 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| 0.4129 \| 0.0 \| nan \| 0.0 \| nan \| 0.2904 \| 0.0 \| 0.0 \| \| 0.1849 \| 3.0 \| 339 \| 0.2807 \| 0.1589 \| 0.2164 \| 0.3238 \| nan \| 0.3520 \| 0.3125 \| 0.0 \| nan \| 0.0 \| 0.3563 \| nan \| 0.6252 \| 0.4509 \| nan \| 0.0 \| nan \| 0.2835 \| 0.0 \| 0.0 \| 0.0 \| 0.3236 \| 0.2894 \| 0.0 \| nan \| 0.0 \| 0.3265 \| 0.0 \| 0.3954 \| 0.4506 \| nan \| 0.0 \| nan \| 0.2807 \| 0.0 \| 0.0 \| \| 0.1341 \| 4.0 \| 452 \| 0.2694 \| 0.1618 \| 0.2309 \| 0.3055 \| nan \| 0.2089 \| 0.3628 \| 0.0188 \| nan \| 0.0 \| 0.4866 \| nan \| 0.7552 \| 0.5206 \| nan \| 0.0 \| nan \| 0.1866 \| 0.0 \| 0.0 \| 0.0 \| 0.2004 \| 0.3303 \| 0.0188 \| nan \| 0.0 \| 0.4268 \| 0.0 \| 0.4221 \| 0.5205 \| nan \| 0.0 \| nan \| 0.1840 \| 0.0 \| 0.0 \| \| 0.1282 \| 5.0 \| 565 \| 0.2631 \| 0.2057 \| 0.2726 \| 0.3396 \| nan \| 0.4061 \| 0.3347 \| 0.0292 \| nan \| 0.0 \| 0.6126 \| nan \| 0.6152 \| 0.8252 \| nan \| 0.0 \| nan \| 0.1751 \| 0.0 \| 0.0 \| 0.0 \| 0.3667 \| 0.3169 \| 0.0292 \| nan \| 0.0 \| 0.4767 \| nan \| 0.3995 \| 0.7049 \| nan \| 0.0 \| nan \| 0.1745 \| 0.0 \| 0.0 \| \| 0.1138 \| 6.0 \| 678 \| 0.2418 \| 0.1949 \| 0.2558 \| 0.3865 \| nan \| 0.2362 \| 0.3709 \| 0.0122 \| nan \| 0.0 \| 0.6128 \| nan \| 0.6627 \| 0.5823 \| nan \| 0.0 \| nan \| 0.3365 \| 0.0 \| 0.0 \| 0.0 \| 0.2249 \| 0.3444 \| 0.0122 \| nan \| 0.0 \| 0.4625 \| nan \| 0.3921 \| 0.5725 \| nan \| 0.0 \| nan \| 0.3301 \| 0.0 \| 0.0 \| \| 0.1049 \| 7.0 \| 791 \| 0.2345 \| 0.2013 \| 0.2623 \| 0.4725 \| nan \| 0.3186 \| 0.4071 \| 0.0827 \| nan \| 0.0 \| 0.1697 \| nan \| 0.7809 \| 0.6140 \| nan \| 0.0 \| nan \| 0.5118 \| 0.0 \| 0.0 \| 0.0 \| 0.2927 \| 0.3851 \| 0.0827 \| nan \| 0.0 \| 0.1679 \| nan \| 0.4702 \| 0.5212 \| nan \| 0.0 \| nan \| 0.4961 \| 0.0 \| 0.0 \| \| 0.0829 \| 8.0 \| 904 \| 0.2351 \| 0.2194 \| 0.2818 \| 0.4348 \| nan \| 0.1689 \| 0.4289 \| 0.0980 \| nan \| 0.0 \| 0.5547 \| nan \| 0.7050 \| 0.7860 \| nan \| 0.0 \| nan \| 0.3580 \| 0.0 \| 0.0 \| 0.0 \| 0.1619 \| 0.4048 \| 0.0980 \| nan \| 0.0 \| 0.5205 \| nan \| 0.3967 \| 0.7023 \| nan \| 0.0 \| nan \| 0.3490 \| 0.0 \| 0.0 \| \| 0.0922 \| 9.0 \| 1017 \| 0.2350 \| 0.2549 \| 0.3103 \| 0.5060 \| nan \| 0.4729 \| 0.4726 \| 0.0572 \| nan \| 0.0 \| 0.5679 \| nan \| 0.5794 \| 0.7942 \| nan \| 0.0 \| nan \| 0.4690 \| 0.0 \| 0.0 \| 0.0 \| 0.4143 \| 0.4398 \| 0.0572 \| nan \| 0.0 \| 0.5293 \| nan \| 0.4010 \| 0.7613 \| nan \| 0.0 \| nan \| 0.4563 \| 0.0 \| 0.0 \| \| 0.0717 \| 10.0 \| 1130 \| 0.2399 \| 0.2344 \| 0.2871 \| 0.4150 \| nan \| 0.4512 \| 0.4155 \| 0.0169 \| nan \| 0.0 \| 0.5706 \| nan \| 0.6279 \| 0.7676 \| nan \| 0.0 \| nan \| 0.3089 \| 0.0 \| 0.0 \| 0.0 \| 0.3995 \| 0.3949 \| 0.0169 \| nan \| 0.0 \| 0.5351 \| nan \| 0.4246 \| 0.7393 \| nan \| 0.0 \| nan \| 0.3023 \| 0.0 \| 0.0 \| \| 0.0787 \| 11.0 \| 1243 \| 0.2228 \| 0.2578 \| 0.3105 \| 0.4726 \| nan \| 0.6679 \| 0.4378 \| 0.0666 \| nan \| 0.0 \| 0.5865 \| nan \| 0.4684 \| 0.7796 \| nan \| 0.0 \| nan \| 0.4087 \| 0.0 \| 0.0 \| 0.0 \| 0.5359 \| 0.4172 \| 0.0666 \| nan \| 0.0 \| 0.5456 \| nan \| 0.3785 \| 0.7528 \| nan \| 0.0 \| nan \| 0.3975 \| 0.0 \| 0.0 \| \| 0.0787 \| 11.9 \| 1345 \| 0.2318 \| 0.2504 \| 0.3019 \| 0.4542 \| nan \| 0.6330 \| 0.4644 \| 0.0280 \| nan \| 0.0 \| 0.6010 \| nan \| 0.4575 \| 0.7776 \| nan \| 0.0 \| nan \| 0.3594 \| 0.0 \| 0.0 \| 0.0 \| 0.5194 \| 0.4368 \| 0.0280 \| nan \| 0.0 \| 0.5492 \| nan \| 0.3611 \| 0.7592 \| nan \| 0.0 \| nan \| 0.3508 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "background", "ship", "small-vehicle", "tennis-court", "helicopter", "basketball-court", "ground-track-field", "swimming-pool", "harbor", "soccer-ball-field", "plane", "storage-tank", "baseball-diamond", "large-vehicle", "bridge", "roundabout" ]
magitz/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7960 - eval_mean_iou: 0.2053 - eval_mean_accuracy: 0.2533 - eval_overall_accuracy: 0.7980 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8959 - eval_accuracy_flat-sidewalk: 0.9420 - eval_accuracy_flat-crosswalk: 0.0 - eval_accuracy_flat-cyclinglane: 0.4535 - eval_accuracy_flat-parkingdriveway: 0.2273 - eval_accuracy_flat-railtrack: nan - eval_accuracy_flat-curb: 0.3520 - eval_accuracy_human-person: 0.0 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.9346 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: nan - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.0 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.8792 - eval_accuracy_construction-door: 0.0 - eval_accuracy_construction-wall: 0.3746 - eval_accuracy_construction-fenceguardrail: 0.0 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: nan - eval_accuracy_construction-stairs: 0.0 - eval_accuracy_object-pole: 0.0065 - eval_accuracy_object-trafficsign: 0.0 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9279 - eval_accuracy_nature-terrain: 0.8991 - eval_accuracy_sky: 0.9585 - eval_accuracy_void-ground: 0.0 - eval_accuracy_void-dynamic: 0.0 - eval_accuracy_void-static: 0.0014 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.6699 - eval_iou_flat-sidewalk: 0.7999 - eval_iou_flat-crosswalk: 0.0 - eval_iou_flat-cyclinglane: 0.4306 - eval_iou_flat-parkingdriveway: 0.1874 - eval_iou_flat-railtrack: nan - eval_iou_flat-curb: 0.2837 - eval_iou_human-person: 0.0 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.6757 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: nan - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.0 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.6298 - eval_iou_construction-door: 0.0 - eval_iou_construction-wall: 0.2766 - eval_iou_construction-fenceguardrail: 0.0 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: nan - eval_iou_construction-stairs: 0.0 - eval_iou_object-pole: 0.0065 - eval_iou_object-trafficsign: 0.0 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.8333 - eval_iou_nature-terrain: 0.6916 - eval_iou_sky: 0.8777 - eval_iou_void-ground: 0.0 - eval_iou_void-dynamic: 0.0 - eval_iou_void-static: 0.0014 - eval_iou_void-unclear: 0.0 - eval_runtime: 34.3451 - eval_samples_per_second: 5.823 - eval_steps_per_second: 2.912 - epoch: 2.05 - step: 820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
wang1215/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
jericojr23/segformer-b0-finetuned-segments-sidewalk-2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.6325 - Mean Iou: 0.0535 - Mean Accuracy: 0.0868 - Overall Accuracy: 0.5176 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0813 - Accuracy Flat-sidewalk: 0.9451 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.0 - Accuracy Flat-parkingdriveway: 0.0 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.0017 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.2409 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: 0.0 - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9096 - Accuracy Nature-terrain: 0.0349 - Accuracy Sky: 0.5635 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.0721 - Iou Flat-sidewalk: 0.4933 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.0 - Iou Flat-parkingdriveway: 0.0 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.0017 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.1927 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.5226 - Iou Nature-terrain: 0.0332 - Iou Sky: 0.3971 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:------------------:\|:----------------------:\|:-----------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------:\|:---------------------:\|:--------------------:\|:--------------------:\|:----------------------:\|:--------------------:\|:--------------------------:\|:---------------------------:\|:------------------------:\|:------------------------:\|:---------------------------:\|:------------------------------:\|:--------------------------:\|:--------------------------:\|:------------------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:--------------------:\|:---------------------------:\|:----------------------------:\|:--------------------------:\|:-----------------------:\|:------------:\|:--------------------:\|:---------------------:\|:--------------------:\|:---------------------:\|:-------------:\|:-------------:\|:-----------------:\|:------------------:\|:--------------------:\|:------------------------:\|:------------------:\|:-------------:\|:----------------:\|:---------------:\|:---------------:\|:-----------------:\|:---------------:\|:---------------------:\|:----------------------:\|:-------------------:\|:-------------------:\|:----------------------:\|:-------------------------:\|:---------------------:\|:---------------------:\|:-------------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:---------------:\|:----------------------:\|:-----------------------:\|:---------------------:\|:------------------:\|:-------:\|:---------------:\|:----------------:\|:---------------:\|:----------------:\| \| 2.1406 \| 0.2 \| 20 \| 3.3169 \| 0.0250 \| 0.0555 \| 0.4250 \| nan \| 0.0 \| 0.8293 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0225 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9246 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4257 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0208 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3535 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.856 \| 0.4 \| 40 \| 1.9761 \| 0.0295 \| 0.0611 \| 0.4511 \| nan \| 0.0 \| 0.8624 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1301 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9616 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4700 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1012 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3725 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.9981 \| 0.6 \| 60 \| 2.2395 \| 0.0259 \| 0.0508 \| 0.4085 \| nan \| 0.0 \| 0.9972 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4062 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2213 \| 0.0000 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4001 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2248 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2034 \| 0.0000 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 2.1244 \| 0.8 \| 80 \| 1.7573 \| 0.0401 \| 0.0707 \| 0.4917 \| nan \| 0.0004 \| 0.9494 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2756 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9149 \| 0.0540 \| 0.0670 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0004 \| 0.4783 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1872 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5061 \| 0.0472 \| 0.0636 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.8822 \| 1.0 \| 100 \| 1.6325 \| 0.0535 \| 0.0868 \| 0.5176 \| nan \| 0.0813 \| 0.9451 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2409 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9096 \| 0.0349 \| 0.5635 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0721 \| 0.4933 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1927 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5226 \| 0.0332 \| 0.3971 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
wang1215/segformer-b3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b3 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.7826 - Mean Iou: 0.3995 - Mean Accuracy: 0.4977 - Overall Accuracy: 0.8759 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.9069 - Accuracy Flat-sidewalk: 0.9471 - Accuracy Flat-crosswalk: 0.5043 - Accuracy Flat-cyclinglane: 0.8684 - Accuracy Flat-parkingdriveway: 0.5057 - Accuracy Flat-railtrack: 0.0 - Accuracy Flat-curb: 0.7351 - Accuracy Human-person: 0.8662 - Accuracy Human-rider: 0.2599 - Accuracy Vehicle-car: 0.9494 - Accuracy Vehicle-truck: 0.1607 - Accuracy Vehicle-bus: 0.0044 - Accuracy Vehicle-tramtrain: 0.1992 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.7913 - Accuracy Vehicle-caravan: 0.4628 - Accuracy Vehicle-cartrailer: 0.0106 - Accuracy Construction-building: 0.9117 - Accuracy Construction-door: 0.2679 - Accuracy Construction-wall: 0.6351 - Accuracy Construction-fenceguardrail: 0.5893 - Accuracy Construction-bridge: 0.5639 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.4246 - Accuracy Object-pole: 0.6323 - Accuracy Object-trafficsign: 0.4266 - Accuracy Object-trafficlight: 0.2431 - Accuracy Nature-vegetation: 0.9540 - Accuracy Nature-terrain: 0.8819 - Accuracy Sky: 0.9827 - Accuracy Void-ground: 0.0045 - Accuracy Void-dynamic: 0.2006 - Accuracy Void-static: 0.5328 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.7947 - Iou Flat-sidewalk: 0.8656 - Iou Flat-crosswalk: 0.4529 - Iou Flat-cyclinglane: 0.6876 - Iou Flat-parkingdriveway: 0.4461 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.5989 - Iou Human-person: 0.6127 - Iou Human-rider: 0.2346 - Iou Vehicle-car: 0.8877 - Iou Vehicle-truck: 0.0662 - Iou Vehicle-bus: 0.0044 - Iou Vehicle-tramtrain: 0.1985 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.5765 - Iou Vehicle-caravan: 0.1495 - Iou Vehicle-cartrailer: 0.0106 - Iou Construction-building: 0.8060 - Iou Construction-door: 0.2190 - Iou Construction-wall: 0.5015 - Iou Construction-fenceguardrail: 0.4923 - Iou Construction-bridge: 0.3467 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.3908 - Iou Object-pole: 0.4693 - Iou Object-trafficsign: 0.3698 - Iou Object-trafficlight: 0.2052 - Iou Nature-vegetation: 0.8832 - Iou Nature-terrain: 0.7906 - Iou Sky: 0.9519 - Iou Void-ground: 0.0038 - Iou Void-dynamic: 0.1774 - Iou Void-static: 0.3885 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| 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\| 0.7275 \| 2.5 \| 500 \| 0.5765 \| 0.3050 \| 0.3654 \| 0.8441 \| nan \| 0.9561 \| 0.9153 \| 0.3719 \| 0.7164 \| 0.4360 \| 0.0 \| 0.3475 \| 0.8270 \| 0.0 \| 0.9318 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6843 \| 0.0 \| 0.0 \| 0.9160 \| 0.0667 \| 0.3893 \| 0.6512 \| 0.0 \| nan \| 0.0 \| 0.5447 \| 0.0525 \| 0.0 \| 0.9581 \| 0.8185 \| 0.9737 \| 0.0 \| 0.0262 \| 0.4752 \| 0.0 \| nan \| 0.7208 \| 0.8407 \| 0.3582 \| 0.6393 \| 0.3693 \| 0.0 \| 0.2705 \| 0.5291 \| 0.0 \| 0.8548 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5158 \| 0.0 \| 0.0 \| 0.7684 \| 0.0638 \| 0.3606 \| 0.4620 \| 0.0 \| nan \| 0.0 \| 0.3805 \| 0.0522 \| 0.0 \| 0.8572 \| 0.7657 \| 0.9225 \| 0.0 \| 0.0256 \| 0.3078 \| 0.0 \| \| 0.3654 \| 5.0 \| 1000 \| 0.5265 \| 0.3531 \| 0.4276 \| 0.8622 \| nan \| 0.9116 \| 0.9476 \| 0.4986 \| 0.8194 \| 0.4632 \| 0.0 \| 0.5613 \| 0.8672 \| 0.1407 \| 0.9399 \| 0.2129 \| 0.0 \| 0.0 \| 0.0 \| 0.7640 \| 0.0 \| 0.0 \| 0.8915 \| 0.1468 \| 0.5813 \| 0.5719 \| 0.0 \| nan \| 0.3336 \| 0.5583 \| 0.4068 \| 0.0 \| 0.9470 \| 0.8512 \| 0.9780 \| 0.0004 \| 0.1935 \| 0.5228 \| 0.0 \| nan \| 0.7904 \| 0.8491 \| 0.4455 \| 0.6935 \| 0.4013 \| 0.0 \| 0.4607 \| 0.5435 \| 0.1227 \| 0.8663 \| 0.1019 \| 0.0 \| 0.0 \| 0.0 \| 0.5464 \| 0.0 \| 0.0 \| 0.7740 \| 0.1198 \| 0.4746 \| 0.4587 \| 0.0 \| nan \| 0.2403 \| 0.4036 \| 0.2949 \| 0.0 \| 0.8663 \| 0.7614 \| 0.9334 \| 0.0003 \| 0.1492 \| 0.3544 \| 0.0 \| \| 0.2359 \| 7.5 \| 1500 \| 0.5790 \| 0.3584 \| 0.4296 \| 0.8649 \| nan \| 0.8646 \| 0.9501 \| 0.4466 \| 0.8506 \| 0.5513 \| 0.0 \| 0.7099 \| 0.8317 \| 0.2099 \| 0.9442 \| 0.2546 \| 0.0 \| 0.0 \| 0.0 \| 0.7862 \| 0.0087 \| 0.0 \| 0.9079 \| 0.1046 \| 0.6479 \| 0.5239 \| 0.0 \| nan \| 0.1543 \| 0.5674 \| 0.3864 \| 0.0 \| 0.9461 \| 0.8753 \| 0.9776 \| 0.0000 \| 0.1974 \| 0.4793 \| 0.0 \| nan \| 0.7917 \| 0.8414 \| 0.4214 \| 0.7012 \| 0.4494 \| 0.0 \| 0.5333 \| 0.5888 \| 0.1897 \| 0.8663 \| 0.1009 \| 0.0 \| 0.0 \| 0.0 \| 0.5427 \| 0.0042 \| 0.0 \| 0.7817 \| 0.0945 \| 0.4696 \| 0.4312 \| 0.0 \| nan \| 0.1477 \| 0.4314 \| 0.3152 \| 0.0 \| 0.8749 \| 0.7719 \| 0.9409 \| 0.0000 \| 0.1780 \| 0.3593 \| 0.0 \| \| 0.1708 \| 10.0 \| 2000 \| 0.6066 \| 0.3684 \| 0.4479 \| 0.8666 \| nan \| 0.8819 \| 0.9466 \| 0.5609 \| 0.8324 \| 0.4835 \| 0.0 \| 0.7200 \| 0.8575 \| 0.1404 \| 0.9422 \| 0.2656 \| 0.0 \| 0.0590 \| 0.0 \| 0.7505 \| 0.2619 \| 0.0 \| 0.8906 \| 0.2203 \| 0.6425 \| 0.5323 \| 0.0 \| nan \| 0.3455 \| 0.5923 \| 0.4085 \| 0.0 \| 0.9552 \| 0.8844 \| 0.9791 \| 0.0024 \| 0.0951 \| 0.5293 \| 0.0 \| nan \| 0.7930 \| 0.8537 \| 0.4562 \| 0.6519 \| 0.4311 \| 0.0 \| 0.5478 \| 0.5960 \| 0.1301 \| 0.8728 \| 0.1051 \| 0.0 \| 0.0590 \| 0.0 \| 0.5390 \| 0.0916 \| 0.0 \| 0.7864 \| 0.1779 \| 0.4949 \| 0.4467 \| 0.0 \| nan \| 0.3107 \| 0.4336 \| 0.3289 \| 0.0 \| 0.8757 \| 0.7694 \| 0.9416 \| 0.0019 \| 0.0893 \| 0.3741 \| 0.0 \| \| 0.1326 \| 12.5 \| 2500 \| 0.5934 \| 0.3969 \| 0.4877 \| 0.8753 \| nan \| 0.9227 \| 0.9490 \| 0.4762 \| 0.8499 \| 0.5255 \| 0.0 \| 0.6941 \| 0.8115 \| 0.3960 \| 0.9430 \| 0.3828 \| 0.0266 \| 0.0998 \| 0.0 \| 0.7963 \| 0.6565 \| 0.0003 \| 0.8988 \| 0.2880 \| 0.6352 \| 0.5442 \| 0.2746 \| nan \| 0.3048 \| 0.6133 \| 0.4269 \| 0.0 \| 0.9483 \| 0.9064 \| 0.9838 \| 0.0025 \| 0.2297 \| 0.5070 \| 0.0 \| nan \| 0.8032 \| 0.8631 \| 0.4521 \| 0.7678 \| 0.4440 \| 0.0 \| 0.5593 \| 0.6096 \| 0.2994 \| 0.8777 \| 0.1308 \| 0.0266 \| 0.0998 \| 0.0 \| 0.5663 \| 0.2016 \| 0.0003 \| 0.7931 \| 0.1957 \| 0.4838 \| 0.4475 \| 0.2094 \| nan \| 0.2849 \| 0.4530 \| 0.3424 \| 0.0 \| 0.8798 \| 0.7777 \| 0.9441 \| 0.0023 \| 0.1979 \| 0.3857 \| 0.0 \| \| 0.1116 \| 15.0 \| 3000 \| 0.6267 \| 0.3978 \| 0.4820 \| 0.8734 \| nan \| 0.9155 \| 0.9431 \| 0.5145 \| 0.8423 \| 0.4973 \| 0.0 \| 0.7284 \| 0.8513 \| 0.3146 \| 0.9492 \| 0.0753 \| 0.0 \| 0.2565 \| 0.0 \| 0.7845 \| 0.3853 \| 0.0220 \| 0.8998 \| 0.2497 \| 0.6306 \| 0.5571 \| 0.3186 \| nan \| 0.4912 \| 0.5902 \| 0.4795 \| 0.0 \| 0.9501 \| 0.9004 \| 0.9843 \| 0.0038 \| 0.2017 \| 0.5677 \| 0.0 \| nan \| 0.8037 \| 0.8591 \| 0.4665 \| 0.7147 \| 0.4311 \| 0.0 \| 0.5698 \| 0.5996 \| 0.2693 \| 0.8800 \| 0.0366 \| 0.0 \| 0.2554 \| 0.0 \| 0.5526 \| 0.1200 \| 0.0216 \| 0.7935 \| 0.1949 \| 0.4853 \| 0.4704 \| 0.2116 \| nan \| 0.3980 \| 0.4450 \| 0.3691 \| 0.0 \| 0.8815 \| 0.7857 \| 0.9439 \| 0.0031 \| 0.1751 \| 0.3899 \| 0.0 \| \| 0.098 \| 17.5 \| 3500 \| 0.6334 \| 0.3922 \| 0.5006 \| 0.8729 \| nan \| 0.8961 \| 0.9419 \| 0.5747 \| 0.8862 \| 0.4977 \| 0.0 \| 0.7428 \| 0.8491 \| 0.3477 \| 0.9464 \| 0.0952 \| 0.0 \| 0.2937 \| 0.0 \| 0.7908 \| 0.7738 \| 0.0 \| 0.8934 \| 0.2479 \| 0.6445 \| 0.6108 \| 0.4273 \| nan \| 0.4435 \| 0.6190 \| 0.4308 \| 0.0015 \| 0.9486 \| 0.9026 \| 0.9818 \| 0.0099 \| 0.2216 \| 0.4994 \| 0.0 \| 0.0 \| 0.7961 \| 0.8651 \| 0.5005 \| 0.6765 \| 0.4413 \| 0.0 \| 0.5751 \| 0.6176 \| 0.2944 \| 0.8811 \| 0.0373 \| 0.0 \| 0.2919 \| 0.0 \| 0.5578 \| 0.2307 \| 0.0 \| 0.7961 \| 0.1835 \| 0.4901 \| 0.4814 \| 0.2506 \| nan \| 0.3771 \| 0.4560 \| 0.3562 \| 0.0015 \| 0.8810 \| 0.7806 \| 0.9474 \| 0.0077 \| 0.1823 \| 0.3784 \| 0.0 \| \| 0.0894 \| 20.0 \| 4000 \| 0.6973 \| 0.3988 \| 0.4923 \| 0.8722 \| nan \| 0.8952 \| 0.9456 \| 0.5309 \| 0.8357 \| 0.4777 \| 0.0 \| 0.7630 \| 0.8291 \| 0.2785 \| 0.9467 \| 0.1712 \| 0.0047 \| 0.1377 \| 0.0 \| 0.7854 \| 0.8237 \| 0.0 \| 0.9282 \| 0.1899 \| 0.5904 \| 0.6020 \| 0.4761 \| nan \| 0.3323 \| 0.6192 \| 0.4047 \| 0.1381 \| 0.9522 \| 0.8851 \| 0.9767 \| 0.0050 \| 0.1916 \| 0.5277 \| 0.0 \| nan \| 0.8062 \| 0.8543 \| 0.4692 \| 0.6675 \| 0.4204 \| 0.0 \| 0.5710 \| 0.6083 \| 0.2366 \| 0.8855 \| 0.0677 \| 0.0046 \| 0.1374 \| 0.0 \| 0.5492 \| 0.2453 \| 0.0 \| 0.7996 \| 0.1571 \| 0.4887 \| 0.4847 \| 0.2656 \| nan \| 0.3148 \| 0.4670 \| 0.3491 \| 0.1264 \| 0.8815 \| 0.7829 \| 0.9496 \| 0.0043 \| 0.1718 \| 0.3936 \| 0.0 \| \| 0.0819 \| 22.5 \| 4500 \| 0.6867 \| 0.4098 \| 0.5001 \| 0.8778 \| nan \| 0.9344 \| 0.9410 \| 0.5690 \| 0.8783 \| 0.4856 \| 0.0 \| 0.7065 \| 0.8495 \| 0.2085 \| 0.9415 \| 0.1530 \| 0.0018 \| 0.2354 \| 0.0 \| 0.7829 \| 0.7796 \| 0.0 \| 0.9044 \| 0.2261 \| 0.6171 \| 0.6045 \| 0.4780 \| nan \| 0.4156 \| 0.6265 \| 0.4288 \| 0.1457 \| 0.9563 \| 0.8877 \| 0.9804 \| 0.0064 \| 0.2136 \| 0.5447 \| 0.0 \| nan \| 0.8016 \| 0.8702 \| 0.4902 \| 0.7597 \| 0.4279 \| 0.0 \| 0.5780 \| 0.6123 \| 0.1998 \| 0.8889 \| 0.0577 \| 0.0018 \| 0.2348 \| 0.0 \| 0.5898 \| 0.2436 \| 0.0 \| 0.7992 \| 0.1842 \| 0.4829 \| 0.4918 \| 0.2855 \| nan \| 0.3732 \| 0.4658 \| 0.3650 \| 0.1297 \| 0.8823 \| 0.7837 \| 0.9500 \| 0.0053 \| 0.1841 \| 0.3839 \| 0.0 \| \| 0.0767 \| 25.0 \| 5000 \| 0.7377 \| 0.4096 \| 0.5109 \| 0.8720 \| nan \| 0.8599 \| 0.9464 \| 0.5724 \| 0.9354 \| 0.4838 \| 0.0 \| 0.7392 \| 0.8475 \| 0.2679 \| 0.9530 \| 0.2438 \| 0.0 \| 0.2405 \| 0.0 \| 0.7879 \| 0.8364 \| 0.0 \| 0.9155 \| 0.2107 \| 0.5924 \| 0.5901 \| 0.5525 \| nan \| 0.3980 \| 0.6229 \| 0.4648 \| 0.2165 \| 0.9550 \| 0.8865 \| 0.9823 \| 0.0047 \| 0.1970 \| 0.5557 \| 0.0 \| nan \| 0.7881 \| 0.8643 \| 0.5042 \| 0.6317 \| 0.4280 \| 0.0 \| 0.5817 \| 0.6075 \| 0.2397 \| 0.8857 \| 0.1052 \| 0.0 \| 0.2384 \| 0.0 \| 0.5664 \| 0.2501 \| 0.0 \| 0.8056 \| 0.1813 \| 0.4878 \| 0.4863 \| 0.2871 \| nan \| 0.3652 \| 0.4725 \| 0.3883 \| 0.1660 \| 0.8804 \| 0.7905 \| 0.9503 \| 0.0040 \| 0.1704 \| 0.3891 \| 0.0 \| \| 0.0725 \| 27.5 \| 5500 \| 0.7085 \| 0.3977 \| 0.5056 \| 0.8782 \| nan \| 0.9177 \| 0.9482 \| 0.4916 \| 0.8966 \| 0.4989 \| 0.0 \| 0.7119 \| 0.8469 \| 0.2483 \| 0.9512 \| 0.2387 \| 0.0440 \| 0.1287 \| 0.0 \| 0.7947 \| 0.8184 \| 0.0 \| 0.9152 \| 0.2257 \| 0.6472 \| 0.5963 \| 0.5426 \| nan \| 0.3951 \| 0.6422 \| 0.4369 \| 0.2195 \| 0.9499 \| 0.8824 \| 0.9821 \| 0.0036 \| 0.1824 \| 0.5266 \| 0.0 \| 0.0 \| 0.8109 \| 0.8638 \| 0.4498 \| 0.7314 \| 0.4437 \| 0.0 \| 0.5797 \| 0.6047 \| 0.2215 \| 0.8861 \| 0.0855 \| 0.0430 \| 0.1284 \| 0.0 \| 0.5657 \| 0.2395 \| 0.0 \| 0.8058 \| 0.1939 \| 0.5113 \| 0.4913 \| 0.2943 \| nan \| 0.3732 \| 0.4773 \| 0.3770 \| 0.1643 \| 0.8836 \| 0.7864 \| 0.9509 \| 0.0029 \| 0.1639 \| 0.3905 \| 0.0 \| \| 0.0685 \| 30.0 \| 6000 \| 0.7388 \| 0.4115 \| 0.5051 \| 0.8738 \| nan \| 0.9135 \| 0.9420 \| 0.5290 \| 0.8405 \| 0.4909 \| 0.0 \| 0.7408 \| 0.8566 \| 0.3161 \| 0.9461 \| 0.1138 \| 0.0003 \| 0.1616 \| 0.0 \| 0.8061 \| 0.7486 \| 0.0001 \| 0.9074 \| 0.2986 \| 0.6418 \| 0.5669 \| 0.4769 \| nan \| 0.4607 \| 0.6454 \| 0.4717 \| 0.2320 \| 0.9531 \| 0.8849 \| 0.9802 \| 0.0037 \| 0.1983 \| 0.5417 \| 0.0 \| nan \| 0.7911 \| 0.8647 \| 0.4671 \| 0.6651 \| 0.4361 \| 0.0 \| 0.5848 \| 0.6127 \| 0.2642 \| 0.8885 \| 0.0453 \| 0.0003 \| 0.1613 \| 0.0 \| 0.5455 \| 0.2421 \| 0.0001 \| 0.8022 \| 0.2382 \| 0.4975 \| 0.4741 \| 0.3279 \| nan \| 0.4050 \| 0.4789 \| 0.3937 \| 0.1921 \| 0.8825 \| 0.7873 \| 0.9516 \| 0.0032 \| 0.1809 \| 0.3970 \| 0.0 \| \| 0.0654 \| 32.5 \| 6500 \| 0.7246 \| 0.4128 \| 0.5034 \| 0.8789 \| nan \| 0.9247 \| 0.9424 \| 0.5865 \| 0.8579 \| 0.5105 \| 0.0 \| 0.7409 \| 0.8799 \| 0.2449 \| 0.9462 \| 0.0922 \| 0.0 \| 0.1728 \| 0.0 \| 0.7762 \| 0.7085 \| 0.0 \| 0.9151 \| 0.2459 \| 0.6278 \| 0.6088 \| 0.5426 \| nan \| 0.4260 \| 0.6444 \| 0.4471 \| 0.2230 \| 0.9530 \| 0.8839 \| 0.9833 \| 0.0040 \| 0.1978 \| 0.5251 \| 0.0 \| nan \| 0.8010 \| 0.8705 \| 0.5132 \| 0.7193 \| 0.4466 \| 0.0 \| 0.5906 \| 0.5971 \| 0.2204 \| 0.8884 \| 0.0419 \| 0.0 \| 0.1724 \| 0.0 \| 0.5623 \| 0.2184 \| 0.0 \| 0.8044 \| 0.2015 \| 0.5037 \| 0.4964 \| 0.3206 \| nan \| 0.4032 \| 0.4828 \| 0.3859 \| 0.1802 \| 0.8828 \| 0.7909 \| 0.9505 \| 0.0033 \| 0.1792 \| 0.3959 \| 0.0 \| \| 0.0629 \| 35.0 \| 7000 \| 0.7655 \| 0.4168 \| 0.5105 \| 0.8741 \| nan \| 0.8961 \| 0.9470 \| 0.5214 \| 0.8906 \| 0.4982 \| 0.0 \| 0.7542 \| 0.8631 \| 0.2754 \| 0.9512 \| 0.1882 \| 0.0015 \| 0.3457 \| 0.0 \| 0.7778 \| 0.6418 \| 0.0144 \| 0.8908 \| 0.2816 \| 0.6612 \| 0.5910 \| 0.5330 \| nan \| 0.4434 \| 0.6305 \| 0.4273 \| 0.2421 \| 0.9516 \| 0.8805 \| 0.9821 \| 0.0036 \| 0.2172 \| 0.5444 \| 0.0 \| nan \| 0.7981 \| 0.8672 \| 0.4665 \| 0.6765 \| 0.4364 \| 0.0 \| 0.5934 \| 0.6114 \| 0.2489 \| 0.8877 \| 0.0831 \| 0.0013 \| 0.3436 \| 0.0 \| 0.5668 \| 0.2017 \| 0.0140 \| 0.7928 \| 0.2283 \| 0.4708 \| 0.4904 \| 0.3458 \| nan \| 0.4011 \| 0.4722 \| 0.3699 \| 0.1843 \| 0.8836 \| 0.7898 \| 0.9516 \| 0.0030 \| 0.1851 \| 0.3897 \| 0.0 \| \| 0.0607 \| 37.5 \| 7500 \| 0.7668 \| 0.4180 \| 0.5139 \| 0.8751 \| nan \| 0.8948 \| 0.9480 \| 0.5612 \| 0.8579 \| 0.4903 \| 0.0 \| 0.7432 \| 0.8676 \| 0.2619 \| 0.9495 \| 0.1718 \| 0.0165 \| 0.3359 \| 0.0010 \| 0.7738 \| 0.7077 \| 0.0304 \| 0.9104 \| 0.2826 \| 0.6353 \| 0.6045 \| 0.5609 \| nan \| 0.4406 \| 0.6293 \| 0.4355 \| 0.2376 \| 0.9511 \| 0.8940 \| 0.9818 \| 0.0033 \| 0.2277 \| 0.5530 \| 0.0 \| nan \| 0.7933 \| 0.8676 \| 0.4914 \| 0.6562 \| 0.4327 \| 0.0 \| 0.5956 \| 0.6059 \| 0.2348 \| 0.8875 \| 0.0739 \| 0.0161 \| 0.3343 \| 0.0010 \| 0.5696 \| 0.2086 \| 0.0295 \| 0.8084 \| 0.2268 \| 0.5014 \| 0.4962 \| 0.3297 \| nan \| 0.3948 \| 0.4702 \| 0.3754 \| 0.1918 \| 0.8836 \| 0.7857 \| 0.9519 \| 0.0029 \| 0.1886 \| 0.3900 \| 0.0 \| \| 0.0582 \| 40.0 \| 8000 \| 0.7562 \| 0.4049 \| 0.5074 \| 0.8780 \| nan \| 0.9204 \| 0.9463 \| 0.5052 \| 0.8643 \| 0.5082 \| 0.0 \| 0.7360 \| 0.8650 \| 0.2462 \| 0.9501 \| 0.1893 \| 0.0024 \| 0.2403 \| 0.0 \| 0.7814 \| 0.6631 \| 0.0242 \| 0.9169 \| 0.2821 \| 0.6356 \| 0.5984 \| 0.5609 \| nan \| 0.4218 \| 0.6299 \| 0.4414 \| 0.2421 \| 0.9504 \| 0.8835 \| 0.9796 \| 0.0043 \| 0.2138 \| 0.5395 \| 0.0 \| 0.0 \| 0.8004 \| 0.8683 \| 0.4553 \| 0.7073 \| 0.4478 \| 0.0 \| 0.6007 \| 0.6132 \| 0.2291 \| 0.8882 \| 0.0839 \| 0.0024 \| 0.2391 \| 0.0 \| 0.5777 \| 0.2020 \| 0.0237 \| 0.8060 \| 0.2276 \| 0.5058 \| 0.4990 \| 0.3415 \| nan \| 0.3905 \| 0.4699 \| 0.3815 \| 0.1952 \| 0.8837 \| 0.7908 \| 0.9522 \| 0.0036 \| 0.1849 \| 0.3941 \| 0.0 \| \| 0.0565 \| 42.5 \| 8500 \| 0.7834 \| 0.4004 \| 0.5024 \| 0.8762 \| nan \| 0.8960 \| 0.9474 \| 0.5430 \| 0.8894 \| 0.4937 \| 0.0 \| 0.7492 \| 0.8696 \| 0.2727 \| 0.9482 \| 0.1505 \| 0.0006 \| 0.1868 \| 0.0 \| 0.7945 \| 0.5042 \| 0.0051 \| 0.9155 \| 0.2834 \| 0.6369 \| 0.5958 \| 0.5811 \| nan \| 0.4136 \| 0.6419 \| 0.4457 \| 0.2481 \| 0.9510 \| 0.8887 \| 0.9822 \| 0.0041 \| 0.2054 \| 0.5358 \| 0.0 \| 0.0 \| 0.7955 \| 0.8676 \| 0.4613 \| 0.6877 \| 0.4390 \| 0.0 \| 0.6023 \| 0.6092 \| 0.2503 \| 0.8879 \| 0.0626 \| 0.0006 \| 0.1860 \| 0.0 \| 0.5802 \| 0.1628 \| 0.0051 \| 0.8061 \| 0.2307 \| 0.5003 \| 0.4961 \| 0.3290 \| nan \| 0.3820 \| 0.4752 \| 0.3819 \| 0.2070 \| 0.8838 \| 0.7914 \| 0.9520 \| 0.0034 \| 0.1813 \| 0.3944 \| 0.0 \| \| 0.0562 \| 45.0 \| 9000 \| 0.7812 \| 0.4015 \| 0.5008 \| 0.8772 \| nan \| 0.9064 \| 0.9466 \| 0.5058 \| 0.8872 \| 0.5059 \| 0.0 \| 0.7482 \| 0.8642 \| 0.2957 \| 0.9489 \| 0.1494 \| 0.0059 \| 0.1674 \| 0.0003 \| 0.8079 \| 0.4484 \| 0.0122 \| 0.9134 \| 0.2785 \| 0.6303 \| 0.6007 \| 0.5723 \| nan \| 0.4337 \| 0.6286 \| 0.4291 \| 0.2541 \| 0.9521 \| 0.8902 \| 0.9821 \| 0.0054 \| 0.2097 \| 0.5444 \| 0.0 \| 0.0 \| 0.7954 \| 0.8685 \| 0.4597 \| 0.7046 \| 0.4471 \| 0.0 \| 0.6024 \| 0.6174 \| 0.2611 \| 0.8881 \| 0.0630 \| 0.0057 \| 0.1667 \| 0.0003 \| 0.5844 \| 0.1441 \| 0.0120 \| 0.8059 \| 0.2279 \| 0.5021 \| 0.4963 \| 0.3439 \| nan \| 0.3981 \| 0.4694 \| 0.3714 \| 0.2134 \| 0.8841 \| 0.7883 \| 0.9522 \| 0.0044 \| 0.1836 \| 0.3913 \| 0.0 \| \| 0.0547 \| 47.5 \| 9500 \| 0.7899 \| 0.3997 \| 0.4971 \| 0.8759 \| nan \| 0.9053 \| 0.9472 \| 0.4999 \| 0.8752 \| 0.5002 \| 0.0 \| 0.7334 \| 0.8557 \| 0.2947 \| 0.9505 \| 0.1326 \| 0.0 \| 0.1843 \| 0.0 \| 0.8065 \| 0.3995 \| 0.0184 \| 0.9146 \| 0.2650 \| 0.6301 \| 0.6056 \| 0.5749 \| nan \| 0.4294 \| 0.6299 \| 0.4450 \| 0.2461 \| 0.9515 \| 0.8854 \| 0.9825 \| 0.0044 \| 0.2045 \| 0.5311 \| 0.0000 \| 0.0 \| 0.7939 \| 0.8655 \| 0.4530 \| 0.6865 \| 0.4427 \| 0.0 \| 0.5983 \| 0.6206 \| 0.2592 \| 0.8881 \| 0.0597 \| 0.0 \| 0.1837 \| 0.0 \| 0.5769 \| 0.1272 \| 0.0183 \| 0.8055 \| 0.2174 \| 0.5004 \| 0.4960 \| 0.3457 \| nan \| 0.3926 \| 0.4724 \| 0.3800 \| 0.2072 \| 0.8841 \| 0.7912 \| 0.9522 \| 0.0037 \| 0.1789 \| 0.3895 \| 0.0000 \| \| 0.0543 \| 50.0 \| 10000 \| 0.7826 \| 0.3995 \| 0.4977 \| 0.8759 \| nan \| 0.9069 \| 0.9471 \| 0.5043 \| 0.8684 \| 0.5057 \| 0.0 \| 0.7351 \| 0.8662 \| 0.2599 \| 0.9494 \| 0.1607 \| 0.0044 \| 0.1992 \| 0.0 \| 0.7913 \| 0.4628 \| 0.0106 \| 0.9117 \| 0.2679 \| 0.6351 \| 0.5893 \| 0.5639 \| nan \| 0.4246 \| 0.6323 \| 0.4266 \| 0.2431 \| 0.9540 \| 0.8819 \| 0.9827 \| 0.0045 \| 0.2006 \| 0.5328 \| 0.0 \| 0.0 \| 0.7947 \| 0.8656 \| 0.4529 \| 0.6876 \| 0.4461 \| 0.0 \| 0.5989 \| 0.6127 \| 0.2346 \| 0.8877 \| 0.0662 \| 0.0044 \| 0.1985 \| 0.0 \| 0.5765 \| 0.1495 \| 0.0106 \| 0.8060 \| 0.2190 \| 0.5015 \| 0.4923 \| 0.3467 \| nan \| 0.3908 \| 0.4693 \| 0.3698 \| 0.2052 \| 0.8832 \| 0.7906 \| 0.9519 \| 0.0038 \| 0.1774 \| 0.3885 \| 0.0 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
SatwikKambham/segformer-b0-finetuned-suim	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-suim This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the SatwikKambham/suim dataset. It achieves the following results on the evaluation set: - Loss: 0.5044 - Mean Iou: 0.6138 - Mean Accuracy: 0.7713 - Overall Accuracy: 0.8213 - Accuracy Background (waterbody): nan - Accuracy Human divers: 0.9139 - Accuracy Aquatic plants and sea-grass: 0.2842 - Accuracy Wrecks and ruins: 0.8156 - Accuracy Robots (auvs/rovs/instruments): 0.8117 - Accuracy Reefs and invertebrates: 0.9098 - Accuracy Fish and vertebrates: 0.8540 - Accuracy Sea-floor and rocks: 0.8096 - Iou Background (waterbody): 0.0 - Iou Human divers: 0.8428 - Iou Aquatic plants and sea-grass: 0.2638 - Iou Wrecks and ruins: 0.7560 - Iou Robots (auvs/rovs/instruments): 0.7896 - Iou Reefs and invertebrates: 0.7482 - Iou Fish and vertebrates: 0.7927 - Iou Sea-floor and rocks: 0.7176 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background (waterbody) \| Accuracy Human divers \| Accuracy Aquatic plants and sea-grass \| Accuracy Wrecks and ruins \| Accuracy Robots (auvs/rovs/instruments) \| Accuracy Reefs and invertebrates \| Accuracy Fish and vertebrates \| Accuracy Sea-floor and rocks \| Iou Background (waterbody) \| Iou Human divers \| Iou Aquatic plants and sea-grass \| Iou Wrecks and ruins \| Iou Robots (auvs/rovs/instruments) \| Iou Reefs and invertebrates \| Iou Fish and vertebrates \| Iou Sea-floor and rocks \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------------------:\|:---------------------:\|:-------------------------------------:\|:-------------------------:\|:---------------------------------------:\|:--------------------------------:\|:-----------------------------:\|:----------------------------:\|:--------------------------:\|:----------------:\|:--------------------------------:\|:--------------------:\|:----------------------------------:\|:---------------------------:\|:------------------------:\|:-----------------------:\| \| 1.1911 \| 0.54 \| 100 \| 0.9270 \| 0.3052 \| 0.4434 \| 0.6401 \| nan \| 0.2785 \| 0.0 \| 0.6857 \| 0.0 \| 0.7643 \| 0.7044 \| 0.6709 \| 0.0 \| 0.2555 \| 0.0 \| 0.4605 \| 0.0 \| 0.6111 \| 0.5394 \| 0.5753 \| \| 0.6852 \| 1.08 \| 200 \| 0.7299 \| 0.3457 \| 0.4967 \| 0.7065 \| nan \| 0.3429 \| 0.0 \| 0.8033 \| 0.0 \| 0.7829 \| 0.7653 \| 0.7826 \| 0.0 \| 0.3126 \| 0.0 \| 0.5458 \| 0.0 \| 0.6336 \| 0.5961 \| 0.6776 \| \| 0.854 \| 1.61 \| 300 \| 0.7453 \| 0.3420 \| 0.4937 \| 0.6506 \| nan \| 0.5488 \| 0.0 \| 0.7778 \| 0.0 \| 0.6528 \| 0.7659 \| 0.7103 \| 0.0 \| 0.4867 \| 0.0 \| 0.5156 \| 0.0 \| 0.5619 \| 0.6180 \| 0.5536 \| \| 0.5111 \| 2.15 \| 400 \| 0.6174 \| 0.3920 \| 0.5455 \| 0.7207 \| nan \| 0.7475 \| 0.0 \| 0.6988 \| 0.0 \| 0.8364 \| 0.7907 \| 0.7449 \| 0.0 \| 0.5704 \| 0.0 \| 0.5986 \| 0.0 \| 0.6422 \| 0.6796 \| 0.6451 \| \| 0.5311 \| 2.69 \| 500 \| 0.5811 \| 0.4084 \| 0.5606 \| 0.7368 \| nan \| 0.7697 \| 0.0 \| 0.7729 \| 0.0 \| 0.8658 \| 0.7824 \| 0.7333 \| 0.0 \| 0.6522 \| 0.0 \| 0.6000 \| 0.0 \| 0.6551 \| 0.7045 \| 0.6555 \| \| 0.5595 \| 3.23 \| 600 \| 0.5591 \| 0.4165 \| 0.5686 \| 0.7420 \| nan \| 0.8291 \| 0.0 \| 0.7557 \| 0.0 \| 0.8848 \| 0.7805 \| 0.7299 \| 0.0 \| 0.6524 \| 0.0 \| 0.6536 \| 0.0 \| 0.6760 \| 0.7011 \| 0.6489 \| \| 0.6847 \| 3.76 \| 700 \| 0.5657 \| 0.4068 \| 0.5740 \| 0.7336 \| nan \| 0.8233 \| 0.0 \| 0.8490 \| 0.0 \| 0.8649 \| 0.8157 \| 0.6648 \| 0.0 \| 0.6575 \| 0.0 \| 0.5982 \| 0.0 \| 0.6532 \| 0.7179 \| 0.6277 \| \| 0.3272 \| 4.3 \| 800 \| 0.5065 \| 0.4309 \| 0.5838 \| 0.7573 \| nan \| 0.7906 \| 0.0 \| 0.8117 \| 0.0467 \| 0.8729 \| 0.8021 \| 0.7629 \| 0.0 \| 0.6564 \| 0.0 \| 0.6608 \| 0.0467 \| 0.6666 \| 0.7242 \| 0.6925 \| \| 0.5065 \| 4.84 \| 900 \| 0.4745 \| 0.4420 \| 0.5978 \| 0.7675 \| nan \| 0.7969 \| 0.0000 \| 0.8235 \| 0.0879 \| 0.8301 \| 0.8187 \| 0.8273 \| 0.0 \| 0.6668 \| 0.0000 \| 0.6834 \| 0.0879 \| 0.6847 \| 0.7247 \| 0.6882 \| \| 0.3712 \| 5.38 \| 1000 \| 0.4567 \| 0.4691 \| 0.6296 \| 0.7824 \| nan \| 0.8468 \| 0.0004 \| 0.7933 \| 0.2299 \| 0.9005 \| 0.8431 \| 0.7929 \| 0.0 \| 0.6829 \| 0.0004 \| 0.7036 \| 0.2298 \| 0.6852 \| 0.7381 \| 0.7125 \| \| 0.6866 \| 5.91 \| 1100 \| 0.4453 \| 0.5352 \| 0.6932 \| 0.7843 \| nan \| 0.8487 \| 0.0236 \| 0.8462 \| 0.6450 \| 0.8373 \| 0.8198 \| 0.8317 \| 0.0 \| 0.7810 \| 0.0227 \| 0.7122 \| 0.6437 \| 0.6768 \| 0.7454 \| 0.7001 \| \| 0.4374 \| 6.45 \| 1200 \| 0.4806 \| 0.5279 \| 0.6836 \| 0.7705 \| nan \| 0.8392 \| 0.1452 \| 0.7508 \| 0.5700 \| 0.8615 \| 0.8452 \| 0.7732 \| 0.0 \| 0.7349 \| 0.1403 \| 0.6883 \| 0.5638 \| 0.6990 \| 0.7439 \| 0.6532 \| \| 0.5409 \| 6.99 \| 1300 \| 0.4671 \| 0.5403 \| 0.6987 \| 0.7768 \| nan \| 0.8636 \| 0.0181 \| 0.7956 \| 0.7149 \| 0.8739 \| 0.8502 \| 0.7746 \| 0.0 \| 0.7666 \| 0.0179 \| 0.7256 \| 0.7072 \| 0.6748 \| 0.7473 \| 0.6834 \| \| 0.3526 \| 7.53 \| 1400 \| 0.4691 \| 0.5517 \| 0.7168 \| 0.7811 \| nan \| 0.8670 \| 0.2005 \| 0.8345 \| 0.6704 \| 0.8837 \| 0.8260 \| 0.7353 \| 0.0 \| 0.7791 \| 0.1891 \| 0.6723 \| 0.6687 \| 0.6845 \| 0.7447 \| 0.6752 \| \| 0.2883 \| 8.06 \| 1500 \| 0.4418 \| 0.5744 \| 0.7341 \| 0.8013 \| nan \| 0.8526 \| 0.2870 \| 0.7820 \| 0.6996 \| 0.8971 \| 0.8211 \| 0.7993 \| 0.0 \| 0.7654 \| 0.2764 \| 0.7106 \| 0.6901 \| 0.6891 \| 0.7557 \| 0.7081 \| \| 0.645 \| 8.6 \| 1600 \| 0.4597 \| 0.5730 \| 0.7375 \| 0.7945 \| nan \| 0.8874 \| 0.1758 \| 0.8621 \| 0.7514 \| 0.8888 \| 0.8442 \| 0.7530 \| 0.0 \| 0.8018 \| 0.1683 \| 0.7251 \| 0.7413 \| 0.7048 \| 0.7556 \| 0.6869 \| \| 0.2953 \| 9.14 \| 1700 \| 0.4279 \| 0.5967 \| 0.7596 \| 0.8068 \| nan \| 0.8210 \| 0.4181 \| 0.8465 \| 0.7328 \| 0.8293 \| 0.8410 \| 0.8285 \| 0.0 \| 0.7607 \| 0.3735 \| 0.7601 \| 0.7259 \| 0.7098 \| 0.7495 \| 0.6944 \| \| 0.3441 \| 9.68 \| 1800 \| 0.4727 \| 0.5662 \| 0.7387 \| 0.7701 \| nan \| 0.8660 \| 0.2776 \| 0.8603 \| 0.7675 \| 0.6688 \| 0.8444 \| 0.8867 \| 0.0 \| 0.7965 \| 0.2577 \| 0.7043 \| 0.7602 \| 0.6180 \| 0.7374 \| 0.6556 \| \| 0.4354 \| 10.22 \| 1900 \| 0.4499 \| 0.5868 \| 0.7466 \| 0.8000 \| nan \| 0.7931 \| 0.3488 \| 0.8160 \| 0.7521 \| 0.8689 \| 0.8599 \| 0.7876 \| 0.0 \| 0.7399 \| 0.3263 \| 0.7469 \| 0.7342 \| 0.7090 \| 0.7542 \| 0.6836 \| \| 0.3476 \| 10.75 \| 2000 \| 0.4732 \| 0.5468 \| 0.7053 \| 0.7802 \| nan \| 0.8976 \| 0.0416 \| 0.8285 \| 0.6882 \| 0.8939 \| 0.8447 \| 0.7426 \| 0.0 \| 0.8055 \| 0.0402 \| 0.7180 \| 0.6837 \| 0.6855 \| 0.7632 \| 0.6781 \| \| 0.3416 \| 11.29 \| 2100 \| 0.4553 \| 0.5745 \| 0.7342 \| 0.7949 \| nan \| 0.8803 \| 0.1754 \| 0.7792 \| 0.7736 \| 0.9134 \| 0.8551 \| 0.7625 \| 0.0 \| 0.8009 \| 0.1632 \| 0.7275 \| 0.7484 \| 0.6939 \| 0.7627 \| 0.6996 \| \| 0.157 \| 11.83 \| 2200 \| 0.4684 \| 0.5814 \| 0.7432 \| 0.7927 \| nan \| 0.8915 \| 0.2169 \| 0.8390 \| 0.7693 \| 0.8894 \| 0.8532 \| 0.7434 \| 0.0 \| 0.8147 \| 0.2091 \| 0.7275 \| 0.7518 \| 0.7105 \| 0.7707 \| 0.6666 \| \| 0.1665 \| 12.37 \| 2300 \| 0.4369 \| 0.6031 \| 0.7710 \| 0.8076 \| nan \| 0.9014 \| 0.3852 \| 0.8488 \| 0.7526 \| 0.8590 \| 0.8721 \| 0.7780 \| 0.0 \| 0.8264 \| 0.3500 \| 0.7249 \| 0.7403 \| 0.7166 \| 0.7678 \| 0.6990 \| \| 0.3426 \| 12.9 \| 2400 \| 0.4458 \| 0.5814 \| 0.7495 \| 0.7925 \| nan \| 0.8842 \| 0.2608 \| 0.8393 \| 0.7845 \| 0.8458 \| 0.8491 \| 0.7826 \| 0.0 \| 0.8201 \| 0.2433 \| 0.6979 \| 0.7533 \| 0.6987 \| 0.7597 \| 0.6779 \| \| 0.2268 \| 13.44 \| 2500 \| 0.4612 \| 0.5776 \| 0.7355 \| 0.7929 \| nan \| 0.8722 \| 0.1872 \| 0.7989 \| 0.7789 \| 0.8675 \| 0.8461 \| 0.7978 \| 0.0 \| 0.7964 \| 0.1755 \| 0.7478 \| 0.7551 \| 0.7139 \| 0.7654 \| 0.6665 \| \| 0.3108 \| 13.98 \| 2600 \| 0.4449 \| 0.5926 \| 0.7490 \| 0.8112 \| nan \| 0.8756 \| 0.2111 \| 0.8021 \| 0.7897 \| 0.8921 \| 0.8473 \| 0.8256 \| 0.0 \| 0.8106 \| 0.2007 \| 0.7520 \| 0.7736 \| 0.7287 \| 0.7737 \| 0.7017 \| \| 0.1832 \| 14.52 \| 2700 \| 0.4271 \| 0.6254 \| 0.7965 \| 0.8277 \| nan \| 0.8930 \| 0.5784 \| 0.7930 \| 0.7627 \| 0.8712 \| 0.8446 \| 0.8323 \| 0.0 \| 0.8085 \| 0.4667 \| 0.7385 \| 0.7489 \| 0.7402 \| 0.7689 \| 0.7312 \| \| 0.1784 \| 15.05 \| 2800 \| 0.4531 \| 0.5858 \| 0.7448 \| 0.7987 \| nan \| 0.9073 \| 0.2227 \| 0.7956 \| 0.7764 \| 0.8722 \| 0.8323 \| 0.8069 \| 0.0 \| 0.8232 \| 0.2077 \| 0.7337 \| 0.7556 \| 0.7050 \| 0.7734 \| 0.6880 \| \| 0.2532 \| 15.59 \| 2900 \| 0.4925 \| 0.5712 \| 0.7273 \| 0.7948 \| nan \| 0.9059 \| 0.0955 \| 0.8082 \| 0.7507 \| 0.9018 \| 0.8558 \| 0.7736 \| 0.0 \| 0.8223 \| 0.0914 \| 0.7457 \| 0.7283 \| 0.7327 \| 0.7813 \| 0.6678 \| \| 0.2804 \| 16.13 \| 3000 \| 0.4406 \| 0.6236 \| 0.7967 \| 0.8169 \| nan \| 0.9282 \| 0.5433 \| 0.8292 \| 0.7638 \| 0.8782 \| 0.8735 \| 0.7609 \| 0.0 \| 0.8340 \| 0.4822 \| 0.7338 \| 0.7438 \| 0.7286 \| 0.7710 \| 0.6958 \| \| 0.2874 \| 16.67 \| 3100 \| 0.4576 \| 0.5876 \| 0.7474 \| 0.8073 \| nan \| 0.8891 \| 0.1620 \| 0.8209 \| 0.7999 \| 0.8955 \| 0.8642 \| 0.8001 \| 0.0 \| 0.8199 \| 0.1544 \| 0.7478 \| 0.7678 \| 0.7430 \| 0.7774 \| 0.6904 \| \| 0.2731 \| 17.2 \| 3200 \| 0.4212 \| 0.6263 \| 0.7914 \| 0.8341 \| nan \| 0.9025 \| 0.5544 \| 0.7921 \| 0.7143 \| 0.9193 \| 0.8514 \| 0.8057 \| 0.0 \| 0.8120 \| 0.4920 \| 0.7327 \| 0.6956 \| 0.7547 \| 0.7783 \| 0.7447 \| \| 0.1974 \| 17.74 \| 3300 \| 0.4423 \| 0.6215 \| 0.7846 \| 0.8138 \| nan \| 0.8722 \| 0.4908 \| 0.8068 \| 0.8134 \| 0.8748 \| 0.8356 \| 0.7988 \| 0.0 \| 0.8076 \| 0.4451 \| 0.7482 \| 0.7737 \| 0.7350 \| 0.7671 \| 0.6953 \| \| 0.1833 \| 18.28 \| 3400 \| 0.4207 \| 0.6284 \| 0.7944 \| 0.8250 \| nan \| 0.8809 \| 0.4986 \| 0.8445 \| 0.7943 \| 0.8829 \| 0.8643 \| 0.7955 \| 0.0 \| 0.8206 \| 0.4449 \| 0.7446 \| 0.7776 \| 0.7421 \| 0.7729 \| 0.7244 \| \| 0.1611 \| 18.82 \| 3500 \| 0.4327 \| 0.6085 \| 0.7733 \| 0.8244 \| nan \| 0.8563 \| 0.4147 \| 0.7980 \| 0.7706 \| 0.9201 \| 0.8495 \| 0.8041 \| 0.0 \| 0.7886 \| 0.3657 \| 0.7429 \| 0.7326 \| 0.7438 \| 0.7712 \| 0.7230 \| \| 0.1339 \| 19.35 \| 3600 \| 0.4795 \| 0.5796 \| 0.7369 \| 0.7978 \| nan \| 0.8572 \| 0.1929 \| 0.7776 \| 0.7929 \| 0.9016 \| 0.8480 \| 0.7882 \| 0.0 \| 0.7934 \| 0.1840 \| 0.7334 \| 0.7498 \| 0.7296 \| 0.7615 \| 0.6848 \| \| 0.1805 \| 19.89 \| 3700 \| 0.4722 \| 0.6137 \| 0.7739 \| 0.8134 \| nan \| 0.8924 \| 0.3871 \| 0.8223 \| 0.7928 \| 0.8949 \| 0.8441 \| 0.7836 \| 0.0 \| 0.8249 \| 0.3603 \| 0.7521 \| 0.7557 \| 0.7451 \| 0.7728 \| 0.6990 \| \| 0.173 \| 20.43 \| 3800 \| 0.4495 \| 0.6170 \| 0.7843 \| 0.8230 \| nan \| 0.9220 \| 0.3516 \| 0.8189 \| 0.8353 \| 0.9090 \| 0.8546 \| 0.7988 \| 0.0 \| 0.8318 \| 0.3314 \| 0.7584 \| 0.7729 \| 0.7453 \| 0.7761 \| 0.7202 \| \| 0.2476 \| 20.97 \| 3900 \| 0.4426 \| 0.6363 \| 0.8007 \| 0.8331 \| nan \| 0.9142 \| 0.4724 \| 0.8094 \| 0.8448 \| 0.9178 \| 0.8330 \| 0.8136 \| 0.0 \| 0.8485 \| 0.4259 \| 0.7481 \| 0.8003 \| 0.7454 \| 0.7840 \| 0.7380 \| \| 0.3163 \| 21.51 \| 4000 \| 0.4550 \| 0.6337 \| 0.8023 \| 0.8273 \| nan \| 0.8808 \| 0.5716 \| 0.8209 \| 0.8055 \| 0.9199 \| 0.8518 \| 0.7655 \| 0.0 \| 0.8129 \| 0.4923 \| 0.7503 \| 0.7815 \| 0.7402 \| 0.7748 \| 0.7180 \| \| 0.12 \| 22.04 \| 4100 \| 0.4396 \| 0.6142 \| 0.7772 \| 0.8249 \| nan \| 0.9254 \| 0.2952 \| 0.8118 \| 0.8303 \| 0.9143 \| 0.8460 \| 0.8174 \| 0.0 \| 0.8363 \| 0.2724 \| 0.7579 \| 0.7875 \| 0.7491 \| 0.7787 \| 0.7316 \| \| 0.2351 \| 22.58 \| 4200 \| 0.4622 \| 0.6020 \| 0.7612 \| 0.8087 \| nan \| 0.8992 \| 0.2520 \| 0.8300 \| 0.8150 \| 0.8647 \| 0.8478 \| 0.8200 \| 0.0 \| 0.8366 \| 0.2309 \| 0.7655 \| 0.7912 \| 0.7260 \| 0.7714 \| 0.6945 \| \| 0.106 \| 23.12 \| 4300 \| 0.4570 \| 0.6001 \| 0.7624 \| 0.8163 \| nan \| 0.8731 \| 0.2566 \| 0.7931 \| 0.8358 \| 0.8961 \| 0.8514 \| 0.8306 \| 0.0 \| 0.8110 \| 0.2326 \| 0.7431 \| 0.7773 \| 0.7413 \| 0.7771 \| 0.7182 \| \| 0.2408 \| 23.66 \| 4400 \| 0.4795 \| 0.5944 \| 0.7501 \| 0.8138 \| nan \| 0.9040 \| 0.1740 \| 0.8437 \| 0.7707 \| 0.8980 \| 0.8486 \| 0.8115 \| 0.0 \| 0.8454 \| 0.1631 \| 0.7700 \| 0.7579 \| 0.7352 \| 0.7742 \| 0.7091 \| \| 0.1285 \| 24.19 \| 4500 \| 0.4311 \| 0.6377 \| 0.8055 \| 0.8339 \| nan \| 0.9053 \| 0.5473 \| 0.8025 \| 0.8113 \| 0.9093 \| 0.8567 \| 0.8060 \| 0.0 \| 0.8279 \| 0.4738 \| 0.7405 \| 0.7775 \| 0.7588 \| 0.7855 \| 0.7380 \| \| 0.2967 \| 24.73 \| 4600 \| 0.4509 \| 0.6273 \| 0.7913 \| 0.8209 \| nan \| 0.8998 \| 0.4427 \| 0.8456 \| 0.8264 \| 0.8666 \| 0.8433 \| 0.8146 \| 0.0 \| 0.8437 \| 0.4058 \| 0.7616 \| 0.7873 \| 0.7382 \| 0.7723 \| 0.7098 \| \| 0.2716 \| 25.27 \| 4700 \| 0.4708 \| 0.6149 \| 0.7815 \| 0.8199 \| nan \| 0.8998 \| 0.3343 \| 0.8176 \| 0.8528 \| 0.9088 \| 0.8668 \| 0.7906 \| 0.0 \| 0.8345 \| 0.3116 \| 0.7546 \| 0.7633 \| 0.7474 \| 0.7928 \| 0.7152 \| \| 0.2685 \| 25.81 \| 4800 \| 0.4688 \| 0.6166 \| 0.7778 \| 0.8135 \| nan \| 0.9177 \| 0.3472 \| 0.8437 \| 0.8130 \| 0.9059 \| 0.8572 \| 0.7598 \| 0.0 \| 0.8452 \| 0.3205 \| 0.7512 \| 0.7864 \| 0.7475 \| 0.7879 \| 0.6937 \| \| 0.1678 \| 26.34 \| 4900 \| 0.4833 \| 0.6056 \| 0.7701 \| 0.8111 \| nan \| 0.9324 \| 0.2775 \| 0.8162 \| 0.8232 \| 0.8679 \| 0.8571 \| 0.8162 \| 0.0 \| 0.8521 \| 0.2610 \| 0.7384 \| 0.7874 \| 0.7107 \| 0.7817 \| 0.7138 \| \| 0.1904 \| 26.88 \| 5000 \| 0.4423 \| 0.6367 \| 0.8050 \| 0.8313 \| nan \| 0.9204 \| 0.5078 \| 0.8254 \| 0.8175 \| 0.8890 \| 0.8661 \| 0.8091 \| 0.0 \| 0.8452 \| 0.4385 \| 0.7538 \| 0.7841 \| 0.7505 \| 0.7884 \| 0.7333 \| \| 0.202 \| 27.42 \| 5100 \| 0.4582 \| 0.6143 \| 0.7755 \| 0.8206 \| nan \| 0.8995 \| 0.2772 \| 0.8510 \| 0.8392 \| 0.8871 \| 0.8583 \| 0.8159 \| 0.0 \| 0.8469 \| 0.2557 \| 0.7571 \| 0.8076 \| 0.7329 \| 0.7852 \| 0.7286 \| \| 0.1242 \| 27.96 \| 5200 \| 0.4692 \| 0.6143 \| 0.7766 \| 0.8192 \| nan \| 0.8887 \| 0.3377 \| 0.8350 \| 0.8156 \| 0.8718 \| 0.8649 \| 0.8222 \| 0.0 \| 0.8302 \| 0.3055 \| 0.7600 \| 0.7944 \| 0.7255 \| 0.7826 \| 0.7164 \| \| 0.1594 \| 28.49 \| 5300 \| 0.4588 \| 0.6187 \| 0.7860 \| 0.8190 \| nan \| 0.9347 \| 0.3829 \| 0.8410 \| 0.8051 \| 0.8479 \| 0.8606 \| 0.8297 \| 0.0 \| 0.8549 \| 0.3390 \| 0.7618 \| 0.7801 \| 0.7272 \| 0.7862 \| 0.7004 \| \| 0.1414 \| 29.03 \| 5400 \| 0.4591 \| 0.6293 \| 0.7923 \| 0.8344 \| nan \| 0.9064 \| 0.5202 \| 0.8183 \| 0.7327 \| 0.9143 \| 0.8469 \| 0.8073 \| 0.0 \| 0.8323 \| 0.4441 \| 0.7449 \| 0.7207 \| 0.7524 \| 0.7906 \| 0.7496 \| \| 0.1208 \| 29.57 \| 5500 \| 0.4611 \| 0.6370 \| 0.8006 \| 0.8280 \| nan \| 0.9187 \| 0.5037 \| 0.7983 \| 0.8194 \| 0.9176 \| 0.8641 \| 0.7823 \| 0.0 \| 0.8407 \| 0.4436 \| 0.7389 \| 0.7951 \| 0.7609 \| 0.7936 \| 0.7233 \| \| 0.227 \| 30.11 \| 5600 \| 0.4488 \| 0.6304 \| 0.7893 \| 0.8314 \| nan \| 0.8984 \| 0.4428 \| 0.8197 \| 0.7898 \| 0.9108 \| 0.8502 \| 0.8135 \| 0.0 \| 0.8355 \| 0.3967 \| 0.7621 \| 0.7707 \| 0.7496 \| 0.7875 \| 0.7410 \| \| 0.2627 \| 30.65 \| 5700 \| 0.4513 \| 0.6384 \| 0.8022 \| 0.8295 \| nan \| 0.8818 \| 0.5132 \| 0.8376 \| 0.8269 \| 0.9009 \| 0.8641 \| 0.7907 \| 0.0 \| 0.8304 \| 0.4484 \| 0.7652 \| 0.8068 \| 0.7548 \| 0.7772 \| 0.7245 \| \| 0.1512 \| 31.18 \| 5800 \| 0.4655 \| 0.6151 \| 0.7736 \| 0.8222 \| nan \| 0.9195 \| 0.3265 \| 0.7978 \| 0.8000 \| 0.9074 \| 0.8461 \| 0.8181 \| 0.0 \| 0.8427 \| 0.2993 \| 0.7452 \| 0.7803 \| 0.7358 \| 0.7824 \| 0.7347 \| \| 0.0861 \| 31.72 \| 5900 \| 0.4754 \| 0.6258 \| 0.7850 \| 0.8212 \| nan \| 0.9167 \| 0.3799 \| 0.8171 \| 0.8288 \| 0.9081 \| 0.8549 \| 0.7897 \| 0.0 \| 0.8467 \| 0.3493 \| 0.7586 \| 0.8013 \| 0.7459 \| 0.7807 \| 0.7235 \| \| 0.1755 \| 32.26 \| 6000 \| 0.5160 \| 0.5938 \| 0.7507 \| 0.8166 \| nan \| 0.9229 \| 0.1614 \| 0.8288 \| 0.7708 \| 0.9159 \| 0.8458 \| 0.8096 \| 0.0 \| 0.8410 \| 0.1522 \| 0.7619 \| 0.7540 \| 0.7284 \| 0.7849 \| 0.7280 \| \| 0.1556 \| 32.8 \| 6100 \| 0.4894 \| 0.6129 \| 0.7734 \| 0.8141 \| nan \| 0.8895 \| 0.3034 \| 0.8242 \| 0.8482 \| 0.8989 \| 0.8603 \| 0.7893 \| 0.0 \| 0.8294 \| 0.2825 \| 0.7532 \| 0.8083 \| 0.7317 \| 0.7820 \| 0.7158 \| \| 0.0927 \| 33.33 \| 6200 \| 0.4981 \| 0.6298 \| 0.7908 \| 0.8216 \| nan \| 0.9017 \| 0.4726 \| 0.8020 \| 0.8062 \| 0.9026 \| 0.8678 \| 0.7829 \| 0.0 \| 0.8340 \| 0.4196 \| 0.7415 \| 0.7908 \| 0.7476 \| 0.7910 \| 0.7138 \| \| 0.1746 \| 33.87 \| 6300 \| 0.4925 \| 0.6243 \| 0.7844 \| 0.8169 \| nan \| 0.9056 \| 0.4036 \| 0.8058 \| 0.8331 \| 0.8925 \| 0.8574 \| 0.7928 \| 0.0 \| 0.8421 \| 0.3654 \| 0.7480 \| 0.8046 \| 0.7419 \| 0.7887 \| 0.7037 \| \| 0.2803 \| 34.41 \| 6400 \| 0.5036 \| 0.6192 \| 0.7785 \| 0.8179 \| nan \| 0.9037 \| 0.4145 \| 0.8096 \| 0.7774 \| 0.9187 \| 0.8604 \| 0.7655 \| 0.0 \| 0.8204 \| 0.3805 \| 0.7519 \| 0.7603 \| 0.7509 \| 0.7855 \| 0.7038 \| \| 0.1777 \| 34.95 \| 6500 \| 0.4886 \| 0.6197 \| 0.7787 \| 0.8209 \| nan \| 0.9173 \| 0.3337 \| 0.8366 \| 0.8071 \| 0.9066 \| 0.8615 \| 0.7883 \| 0.0 \| 0.8405 \| 0.3112 \| 0.7654 \| 0.7895 \| 0.7486 \| 0.7875 \| 0.7149 \| \| 0.1073 \| 35.48 \| 6600 \| 0.4839 \| 0.6271 \| 0.7868 \| 0.8245 \| nan \| 0.9297 \| 0.3696 \| 0.8335 \| 0.8214 \| 0.9102 \| 0.8503 \| 0.7930 \| 0.0 \| 0.8519 \| 0.3424 \| 0.7647 \| 0.7957 \| 0.7553 \| 0.7908 \| 0.7156 \| \| 0.0958 \| 36.02 \| 6700 \| 0.5011 \| 0.6186 \| 0.7744 \| 0.8177 \| nan \| 0.9017 \| 0.3398 \| 0.8211 \| 0.8092 \| 0.8996 \| 0.8509 \| 0.7981 \| 0.0 \| 0.8383 \| 0.3189 \| 0.7575 \| 0.7915 \| 0.7508 \| 0.7882 \| 0.7037 \| \| 0.1107 \| 36.56 \| 6800 \| 0.4854 \| 0.6291 \| 0.7880 \| 0.8229 \| nan \| 0.9108 \| 0.3956 \| 0.8209 \| 0.8310 \| 0.9093 \| 0.8608 \| 0.7876 \| 0.0 \| 0.8423 \| 0.3649 \| 0.7552 \| 0.8064 \| 0.7546 \| 0.7949 \| 0.7143 \| \| 0.1541 \| 37.1 \| 6900 \| 0.4965 \| 0.6126 \| 0.7684 \| 0.8160 \| nan \| 0.9126 \| 0.2968 \| 0.7884 \| 0.8303 \| 0.9209 \| 0.8291 \| 0.8006 \| 0.0 \| 0.8398 \| 0.2769 \| 0.7381 \| 0.8048 \| 0.7358 \| 0.7817 \| 0.7240 \| \| 0.445 \| 37.63 \| 7000 \| 0.5060 \| 0.6189 \| 0.7769 \| 0.8191 \| nan \| 0.9039 \| 0.3292 \| 0.8121 \| 0.8313 \| 0.9127 \| 0.8582 \| 0.7910 \| 0.0 \| 0.8355 \| 0.3081 \| 0.7533 \| 0.8065 \| 0.7455 \| 0.7894 \| 0.7132 \| \| 0.2018 \| 38.17 \| 7100 \| 0.5000 \| 0.6144 \| 0.7723 \| 0.8270 \| nan \| 0.9077 \| 0.2693 \| 0.8312 \| 0.8125 \| 0.9103 \| 0.8466 \| 0.8282 \| 0.0 \| 0.8408 \| 0.2498 \| 0.7595 \| 0.7957 \| 0.7438 \| 0.7901 \| 0.7351 \| \| 0.3123 \| 38.71 \| 7200 \| 0.5074 \| 0.6111 \| 0.7691 \| 0.8184 \| nan \| 0.9025 \| 0.2503 \| 0.8243 \| 0.8337 \| 0.8975 \| 0.8623 \| 0.8133 \| 0.0 \| 0.8378 \| 0.2337 \| 0.7602 \| 0.8090 \| 0.7412 \| 0.7912 \| 0.7153 \| \| 0.1877 \| 39.25 \| 7300 \| 0.5227 \| 0.6147 \| 0.7722 \| 0.8159 \| nan \| 0.9113 \| 0.3006 \| 0.8216 \| 0.8232 \| 0.9182 \| 0.8541 \| 0.7766 \| 0.0 \| 0.8393 \| 0.2827 \| 0.7559 \| 0.7979 \| 0.7479 \| 0.7922 \| 0.7018 \| \| 0.1139 \| 39.78 \| 7400 \| 0.5134 \| 0.6146 \| 0.7727 \| 0.8208 \| nan \| 0.9234 \| 0.2839 \| 0.8203 \| 0.8118 \| 0.9109 \| 0.8565 \| 0.8021 \| 0.0 \| 0.8449 \| 0.2659 \| 0.7558 \| 0.7906 \| 0.7470 \| 0.7941 \| 0.7181 \| \| 0.2875 \| 40.32 \| 7500 \| 0.4953 \| 0.6309 \| 0.7919 \| 0.8246 \| nan \| 0.9165 \| 0.4221 \| 0.8274 \| 0.8228 \| 0.9026 \| 0.8602 \| 0.7917 \| 0.0 \| 0.8437 \| 0.3822 \| 0.7642 \| 0.7975 \| 0.7566 \| 0.7930 \| 0.7096 \| \| 0.1543 \| 40.86 \| 7600 \| 0.5131 \| 0.6227 \| 0.7814 \| 0.8225 \| nan \| 0.9159 \| 0.3537 \| 0.8144 \| 0.8157 \| 0.9106 \| 0.8651 \| 0.7943 \| 0.0 \| 0.8452 \| 0.3252 \| 0.7587 \| 0.7914 \| 0.7537 \| 0.7956 \| 0.7120 \| \| 0.0935 \| 41.4 \| 7700 \| 0.4870 \| 0.6333 \| 0.7958 \| 0.8326 \| nan \| 0.9115 \| 0.4304 \| 0.8214 \| 0.8199 \| 0.9069 \| 0.8677 \| 0.8127 \| 0.0 \| 0.8401 \| 0.3865 \| 0.7595 \| 0.7926 \| 0.7518 \| 0.7931 \| 0.7424 \| \| 0.1449 \| 41.94 \| 7800 \| 0.5064 \| 0.6131 \| 0.7718 \| 0.8177 \| nan \| 0.9140 \| 0.2785 \| 0.8342 \| 0.8206 \| 0.9070 \| 0.8563 \| 0.7919 \| 0.0 \| 0.8467 \| 0.2571 \| 0.7621 \| 0.7934 \| 0.7425 \| 0.7947 \| 0.7085 \| \| 0.196 \| 42.47 \| 7900 \| 0.4914 \| 0.6158 \| 0.7747 \| 0.8250 \| nan \| 0.9065 \| 0.3182 \| 0.8195 \| 0.7971 \| 0.9159 \| 0.8592 \| 0.8066 \| 0.0 \| 0.8378 \| 0.2900 \| 0.7554 \| 0.7732 \| 0.7429 \| 0.7941 \| 0.7334 \| \| 0.0902 \| 43.01 \| 8000 \| 0.5049 \| 0.6092 \| 0.7678 \| 0.8156 \| nan \| 0.9207 \| 0.2618 \| 0.8027 \| 0.8255 \| 0.9075 \| 0.8543 \| 0.8020 \| 0.0 \| 0.8510 \| 0.2407 \| 0.7415 \| 0.7976 \| 0.7388 \| 0.7934 \| 0.7108 \| \| 0.0981 \| 43.55 \| 8100 \| 0.5101 \| 0.6028 \| 0.7602 \| 0.8140 \| nan \| 0.9289 \| 0.2040 \| 0.8124 \| 0.8145 \| 0.9034 \| 0.8485 \| 0.8094 \| 0.0 \| 0.8519 \| 0.1903 \| 0.7504 \| 0.7913 \| 0.7421 \| 0.7891 \| 0.7070 \| \| 0.0804 \| 44.09 \| 8200 \| 0.5136 \| 0.6187 \| 0.7777 \| 0.8179 \| nan \| 0.9224 \| 0.3260 \| 0.8225 \| 0.8244 \| 0.9141 \| 0.8543 \| 0.7800 \| 0.0 \| 0.8514 \| 0.3008 \| 0.7543 \| 0.7957 \| 0.7521 \| 0.7924 \| 0.7033 \| \| 0.125 \| 44.62 \| 8300 \| 0.5089 \| 0.6182 \| 0.7770 \| 0.8184 \| nan \| 0.9165 \| 0.3390 \| 0.8121 \| 0.8162 \| 0.9054 \| 0.8558 \| 0.7939 \| 0.0 \| 0.8459 \| 0.3079 \| 0.7522 \| 0.7915 \| 0.7521 \| 0.7904 \| 0.7052 \| \| 0.1567 \| 45.16 \| 8400 \| 0.5128 \| 0.6093 \| 0.7676 \| 0.8178 \| nan \| 0.9259 \| 0.2550 \| 0.8216 \| 0.8056 \| 0.9131 \| 0.8573 \| 0.7945 \| 0.0 \| 0.8460 \| 0.2413 \| 0.7548 \| 0.7802 \| 0.7473 \| 0.7921 \| 0.7124 \| \| 0.1533 \| 45.7 \| 8500 \| 0.5073 \| 0.6144 \| 0.7719 \| 0.8183 \| nan \| 0.9250 \| 0.3017 \| 0.8238 \| 0.7985 \| 0.9106 \| 0.8532 \| 0.7905 \| 0.0 \| 0.8477 \| 0.2807 \| 0.7582 \| 0.7781 \| 0.7505 \| 0.7924 \| 0.7075 \| \| 0.1264 \| 46.24 \| 8600 \| 0.5117 \| 0.6175 \| 0.7771 \| 0.8281 \| nan \| 0.9186 \| 0.3045 \| 0.8219 \| 0.8005 \| 0.9130 \| 0.8633 \| 0.8178 \| 0.0 \| 0.8426 \| 0.2823 \| 0.7565 \| 0.7777 \| 0.7496 \| 0.7957 \| 0.7358 \| \| 0.196 \| 46.77 \| 8700 \| 0.5079 \| 0.6199 \| 0.7785 \| 0.8230 \| nan \| 0.9060 \| 0.3527 \| 0.8219 \| 0.8023 \| 0.9128 \| 0.8590 \| 0.7949 \| 0.0 \| 0.8362 \| 0.3234 \| 0.7564 \| 0.7803 \| 0.7515 \| 0.7915 \| 0.7200 \| \| 0.1576 \| 47.31 \| 8800 \| 0.5046 \| 0.6192 \| 0.7778 \| 0.8236 \| nan \| 0.9132 \| 0.3347 \| 0.8159 \| 0.8091 \| 0.9072 \| 0.8549 \| 0.8096 \| 0.0 \| 0.8410 \| 0.3079 \| 0.7548 \| 0.7871 \| 0.7504 \| 0.7915 \| 0.7211 \| \| 0.1362 \| 47.85 \| 8900 \| 0.5086 \| 0.6226 \| 0.7815 \| 0.8228 \| nan \| 0.9132 \| 0.3548 \| 0.8207 \| 0.8199 \| 0.9185 \| 0.8555 \| 0.7881 \| 0.0 \| 0.8427 \| 0.3250 \| 0.7574 \| 0.7954 \| 0.7543 \| 0.7932 \| 0.7124 \| \| 0.1221 \| 48.39 \| 9000 \| 0.5128 \| 0.6172 \| 0.7760 \| 0.8201 \| nan \| 0.9197 \| 0.3168 \| 0.8285 \| 0.8051 \| 0.9096 \| 0.8635 \| 0.7887 \| 0.0 \| 0.8438 \| 0.2946 \| 0.7603 \| 0.7838 \| 0.7511 \| 0.7937 \| 0.7101 \| \| 0.1495 \| 48.92 \| 9100 \| 0.5083 \| 0.6219 \| 0.7808 \| 0.8212 \| nan \| 0.9145 \| 0.3516 \| 0.8284 \| 0.8180 \| 0.9109 \| 0.8546 \| 0.7879 \| 0.0 \| 0.8451 \| 0.3218 \| 0.7593 \| 0.7943 \| 0.7525 \| 0.7924 \| 0.7096 \| \| 0.2303 \| 49.46 \| 9200 \| 0.5041 \| 0.6165 \| 0.7747 \| 0.8238 \| nan \| 0.9155 \| 0.3038 \| 0.8206 \| 0.8039 \| 0.9129 \| 0.8603 \| 0.8058 \| 0.0 \| 0.8434 \| 0.2804 \| 0.7582 \| 0.7845 \| 0.7494 \| 0.7953 \| 0.7211 \| \| 0.1028 \| 50.0 \| 9300 \| 0.5044 \| 0.6138 \| 0.7713 \| 0.8213 \| nan \| 0.9139 \| 0.2842 \| 0.8156 \| 0.8117 \| 0.9098 \| 0.8540 \| 0.8096 \| 0.0 \| 0.8428 \| 0.2638 \| 0.7560 \| 0.7896 \| 0.7482 \| 0.7927 \| 0.7176 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3	[ "background (waterbody)", "human divers", "aquatic plants and sea-grass", "wrecks and ruins", "robots (auvs/rovs/instruments)", "reefs and invertebrates", "fish and vertebrates", "sea-floor and rocks" ]
rishitunu/FINAL_ecc_segformer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # FINAL_ecc_segformer This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset_exhaustive dataset. It achieves the following results on the evaluation set: - Loss: 0.0749 - Mean Iou: 0.1968 - Mean Accuracy: 0.3939 - Overall Accuracy: 0.3939 - Accuracy Background: nan - Accuracy Crack: 0.3939 - Iou Background: 0.0 - Iou Crack: 0.3936 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Crack \| Iou Background \| Iou Crack \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\| \| 0.0534 \| 1.0 \| 548 \| 0.0614 \| 0.1368 \| 0.2750 \| 0.2750 \| nan \| 0.2750 \| 0.0 \| 0.2736 \| \| 0.058 \| 2.0 \| 1096 \| 0.1018 \| 0.2093 \| 0.4238 \| 0.4238 \| nan \| 0.4238 \| 0.0 \| 0.4186 \| \| 0.0482 \| 3.0 \| 1644 \| 0.0508 \| 0.1791 \| 0.4315 \| 0.4315 \| nan \| 0.4315 \| 0.0 \| 0.3582 \| \| 0.0338 \| 4.0 \| 2192 \| 0.0569 \| 0.1849 \| 0.3716 \| 0.3716 \| nan \| 0.3716 \| 0.0 \| 0.3698 \| \| 0.0395 \| 5.0 \| 2740 \| 0.0597 \| 0.1745 \| 0.3506 \| 0.3506 \| nan \| 0.3506 \| 0.0 \| 0.3490 \| \| 0.0372 \| 6.0 \| 3288 \| 0.0509 \| 0.2298 \| 0.4635 \| 0.4635 \| nan \| 0.4635 \| 0.0 \| 0.4597 \| \| 0.0402 \| 7.0 \| 3836 \| 0.0620 \| 0.1751 \| 0.3507 \| 0.3507 \| nan \| 0.3507 \| 0.0 \| 0.3503 \| \| 0.038 \| 8.0 \| 4384 \| 0.0681 \| 0.1905 \| 0.3815 \| 0.3815 \| nan \| 0.3815 \| 0.0 \| 0.3810 \| \| 0.0393 \| 9.0 \| 4932 \| 0.0685 \| 0.2213 \| 0.4433 \| 0.4433 \| nan \| 0.4433 \| 0.0 \| 0.4425 \| \| 0.0376 \| 10.0 \| 5480 \| 0.0590 \| 0.1962 \| 0.3929 \| 0.3929 \| nan \| 0.3929 \| 0.0 \| 0.3924 \| \| 0.0381 \| 11.0 \| 6028 \| 0.0626 \| 0.1891 \| 0.3801 \| 0.3801 \| nan \| 0.3801 \| 0.0 \| 0.3783 \| \| 0.034 \| 12.0 \| 6576 \| 0.0623 \| 0.2061 \| 0.4162 \| 0.4162 \| nan \| 0.4162 \| 0.0 \| 0.4122 \| \| 0.0301 \| 13.0 \| 7124 \| 0.0831 \| 0.1832 \| 0.3669 \| 0.3669 \| nan \| 0.3669 \| 0.0 \| 0.3664 \| \| 0.034 \| 14.0 \| 7672 \| 0.0636 \| 0.2059 \| 0.4119 \| 0.4119 \| nan \| 0.4119 \| 0.0 \| 0.4118 \| \| 0.0303 \| 15.0 \| 8220 \| 0.0705 \| 0.1931 \| 0.3864 \| 0.3864 \| nan \| 0.3864 \| 0.0 \| 0.3862 \| \| 0.0338 \| 16.0 \| 8768 \| 0.0685 \| 0.2101 \| 0.4206 \| 0.4206 \| nan \| 0.4206 \| 0.0 \| 0.4202 \| \| 0.0229 \| 17.0 \| 9316 \| 0.0706 \| 0.2099 \| 0.4204 \| 0.4204 \| nan \| 0.4204 \| 0.0 \| 0.4197 \| \| 0.0337 \| 18.0 \| 9864 \| 0.0742 \| 0.1982 \| 0.3968 \| 0.3968 \| nan \| 0.3968 \| 0.0 \| 0.3965 \| \| 0.0257 \| 18.25 \| 10000 \| 0.0749 \| 0.1968 \| 0.3939 \| 0.3939 \| nan \| 0.3939 \| 0.0 \| 0.3936 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1	[ "background", "crack" ]
bvaibhav83/segformer-b0-finetuned-segments-sidewalk-3	# SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic. SegFormer model fine-tuned on [Segments.ai](https://segments.ai) [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic). It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. ### How to use Here is how to use this model to classify an image of the sidewalk dataset: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") model = SegformerForSemanticSegmentation.from_pretrained("segments-tobias/segformer-b0-finetuned-segments-sidewalk") url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) ```	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
peldrak/segformer_finetuned_coasts	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer_finetuned_coasts This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coast dataset. It achieves the following results on the evaluation set: - Loss: 0.3304 - Mean Iou: 0.4794 - Mean Accuracy: 0.6650 - Overall Accuracy: 0.9144 - Accuracy Water: nan - Accuracy Whitewater: 0.4315 - Accuracy Sediment: 0.8895 - Accuracy Other Natural Terrain: 0.0 - Accuracy Vegetation: 0.8740 - Accuracy Development: 0.8271 - Accuracy Unknown: 0.9678 - Iou Water: 0.0 - Iou Whitewater: 0.2745 - Iou Sediment: 0.7784 - Iou Other Natural Terrain: 0.0 - Iou Vegetation: 0.7930 - Iou Development: 0.5438 - Iou Unknown: 0.9658 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sediment \| Accuracy Other Natural Terrain \| Accuracy Vegetation \| Accuracy Development \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sediment \| Iou Other Natural Terrain \| Iou Vegetation \| Iou Development \| Iou Unknown \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-----------------:\|:------------------------------:\|:-------------------:\|:--------------------:\|:----------------:\|:---------:\|:--------------:\|:------------:\|:-------------------------:\|:--------------:\|:---------------:\|:-----------:\| \| 1.5565 \| 0.01 \| 20 \| 1.3796 \| 0.2430 \| 0.3551 \| 0.7951 \| nan \| 0.0097 \| 0.1349 \| 0.0623 \| 0.8407 \| 0.1406 \| 0.9421 \| 0.0 \| 0.0051 \| 0.1067 \| 0.0232 \| 0.5193 \| 0.1049 \| 0.9414 \| \| 1.7203 \| 0.02 \| 40 \| 1.0580 \| 0.2474 \| 0.3601 \| 0.8312 \| nan \| 0.0000 \| 0.1687 \| 0.0012 \| 0.9812 \| 0.0552 \| 0.9541 \| 0.0 \| 0.0000 \| 0.1594 \| 0.0010 \| 0.5662 \| 0.0518 \| 0.9534 \| \| 1.5577 \| 0.04 \| 60 \| 0.9417 \| 0.2218 \| 0.3312 \| 0.8179 \| nan \| 0.0000 \| 0.0330 \| 0.0001 \| 0.9915 \| 0.0100 \| 0.9525 \| 0.0 \| 0.0000 \| 0.0323 \| 0.0001 \| 0.5585 \| 0.0099 \| 0.9517 \| \| 0.8823 \| 0.05 \| 80 \| 0.7899 \| 0.2293 \| 0.3345 \| 0.8217 \| nan \| 0.0000 \| 0.0599 \| 0.0 \| 0.9906 \| 0.0011 \| 0.9553 \| 0.0 \| 0.0000 \| 0.0596 \| 0.0 \| 0.5903 \| 0.0011 \| 0.9538 \| \| 1.2586 \| 0.06 \| 100 \| 0.6372 \| 0.2532 \| 0.3440 \| 0.8300 \| nan \| 0.0000 \| 0.0981 \| 0.0000 \| 0.9697 \| 0.0284 \| 0.9679 \| 0.0 \| 0.0000 \| 0.0971 \| 0.0000 \| 0.6818 \| 0.0284 \| 0.9653 \| \| 1.5022 \| 0.07 \| 120 \| 0.6110 \| 0.2431 \| 0.3372 \| 0.8258 \| nan \| 0.0 \| 0.0759 \| 0.0 \| 0.9823 \| 0.0029 \| 0.9619 \| 0.0 \| 0.0 \| 0.0757 \| 0.0 \| 0.6633 \| 0.0029 \| 0.9599 \| \| 0.7693 \| 0.08 \| 140 \| 0.5468 \| 0.2613 \| 0.3416 \| 0.8125 \| nan \| 0.0 \| 0.2451 \| 0.0 \| 0.8389 \| 0.0027 \| 0.9628 \| 0.0 \| 0.0 \| 0.2372 \| 0.0 \| 0.6285 \| 0.0027 \| 0.9606 \| \| 1.6587 \| 0.1 \| 160 \| 0.5876 \| 0.2717 \| 0.3736 \| 0.8444 \| nan \| 0.0 \| 0.3158 \| 0.0 \| 0.9600 \| 0.0029 \| 0.9628 \| 0.0 \| 0.0 \| 0.3106 \| 0.0 \| 0.6272 \| 0.0029 \| 0.9609 \| \| 1.259 \| 0.11 \| 180 \| 0.5015 \| 0.2883 \| 0.3752 \| 0.8292 \| nan \| 0.0 \| 0.4727 \| 0.0 \| 0.7987 \| 0.0120 \| 0.9679 \| 0.0 \| 0.0 \| 0.4317 \| 0.0 \| 0.6105 \| 0.0120 \| 0.9642 \| \| 1.1834 \| 0.12 \| 200 \| 0.5206 \| 0.3021 \| 0.4047 \| 0.8628 \| nan \| 0.0 \| 0.4986 \| 0.0 \| 0.9598 \| 0.0051 \| 0.9646 \| 0.0 \| 0.0 \| 0.4515 \| 0.0 \| 0.6958 \| 0.0051 \| 0.9620 \| \| 1.1998 \| 0.13 \| 220 \| 0.5969 \| 0.3095 \| 0.4198 \| 0.8740 \| nan \| 0.0 \| 0.5619 \| 0.0 \| 0.9853 \| 0.0075 \| 0.9642 \| 0.0 \| 0.0 \| 0.5167 \| 0.0 \| 0.6803 \| 0.0075 \| 0.9620 \| \| 1.2329 \| 0.15 \| 240 \| 0.4667 \| 0.3146 \| 0.4176 \| 0.8629 \| nan \| 0.0 \| 0.5946 \| 0.0 \| 0.9102 \| 0.0359 \| 0.9649 \| 0.0 \| 0.0 \| 0.5137 \| 0.0 \| 0.6915 \| 0.0348 \| 0.9622 \| \| 0.4256 \| 0.16 \| 260 \| 0.4695 \| 0.3513 \| 0.4674 \| 0.8878 \| nan \| 0.0 \| 0.7239 \| 0.0 \| 0.9465 \| 0.1678 \| 0.9660 \| 0.0 \| 0.0 \| 0.6265 \| 0.0 \| 0.7136 \| 0.1548 \| 0.9639 \| \| 0.6354 \| 0.17 \| 280 \| 0.4582 \| 0.3651 \| 0.4836 \| 0.8773 \| nan \| 0.0 \| 0.6636 \| 0.0 \| 0.8840 \| 0.3866 \| 0.9674 \| 0.0 \| 0.0 \| 0.5882 \| 0.0 \| 0.6976 \| 0.3052 \| 0.9648 \| \| 0.7103 \| 0.18 \| 300 \| 0.4466 \| 0.3736 \| 0.5041 \| 0.8699 \| nan \| 0.0 \| 0.7149 \| 0.0 \| 0.8020 \| 0.5409 \| 0.9668 \| 0.0 \| 0.0 \| 0.6068 \| 0.0 \| 0.6750 \| 0.3693 \| 0.9639 \| \| 0.7022 \| 0.19 \| 320 \| 0.4621 \| 0.3560 \| 0.4756 \| 0.8796 \| nan \| 0.0 \| 0.6003 \| 0.0 \| 0.9246 \| 0.3601 \| 0.9685 \| 0.0 \| 0.0 \| 0.5370 \| 0.0 \| 0.7056 \| 0.2843 \| 0.9653 \| \| 0.8337 \| 0.21 \| 340 \| 0.4500 \| 0.3678 \| 0.4897 \| 0.8754 \| nan \| 0.0 \| 0.6673 \| 0.0 \| 0.8694 \| 0.4355 \| 0.9660 \| 0.0 \| 0.0 \| 0.5735 \| 0.0 \| 0.7075 \| 0.3300 \| 0.9634 \| \| 0.3512 \| 0.22 \| 360 \| 0.4664 \| 0.3630 \| 0.4910 \| 0.8783 \| nan \| 0.0 \| 0.5888 \| 0.0 \| 0.8998 \| 0.4880 \| 0.9696 \| 0.0 \| 0.0 \| 0.5302 \| 0.0 \| 0.7033 \| 0.3433 \| 0.9645 \| \| 1.3383 \| 0.23 \| 380 \| 0.5411 \| 0.3447 \| 0.4592 \| 0.8802 \| nan \| 0.0 \| 0.5990 \| 0.0 \| 0.9552 \| 0.2349 \| 0.9663 \| 0.0 \| 0.0 \| 0.5463 \| 0.0 \| 0.7003 \| 0.2027 \| 0.9633 \| \| 1.421 \| 0.24 \| 400 \| 0.4386 \| 0.3646 \| 0.4884 \| 0.8661 \| nan \| 0.0 \| 0.6447 \| 0.0 \| 0.8233 \| 0.4958 \| 0.9668 \| 0.0 \| 0.0 \| 0.5597 \| 0.0 \| 0.6861 \| 0.3435 \| 0.9631 \| \| 0.5563 \| 0.25 \| 420 \| 0.4313 \| 0.3801 \| 0.5625 \| 0.8797 \| nan \| 0.0 \| 0.7973 \| 0.0 \| 0.7520 \| 0.8561 \| 0.9697 \| 0.0 \| 0.0 \| 0.6663 \| 0.0 \| 0.6742 \| 0.3552 \| 0.9652 \| \| 0.6055 \| 0.27 \| 440 \| 0.4203 \| 0.3731 \| 0.5480 \| 0.8733 \| nan \| 0.0 \| 0.7083 \| 0.0 \| 0.7656 \| 0.8454 \| 0.9690 \| 0.0 \| 0.0 \| 0.6071 \| 0.0 \| 0.6947 \| 0.3457 \| 0.9645 \| \| 1.0955 \| 0.28 \| 460 \| 0.4412 \| 0.3664 \| 0.5119 \| 0.8665 \| nan \| 0.0 \| 0.6004 \| 0.0 \| 0.8418 \| 0.6706 \| 0.9587 \| 0.0 \| 0.0 \| 0.5408 \| 0.0 \| 0.7123 \| 0.3561 \| 0.9554 \| \| 0.9308 \| 0.29 \| 480 \| 0.4208 \| 0.3845 \| 0.5381 \| 0.8852 \| nan \| 0.0 \| 0.7242 \| 0.0 \| 0.8578 \| 0.6826 \| 0.9637 \| 0.0 \| 0.0 \| 0.6415 \| 0.0 \| 0.7161 \| 0.3737 \| 0.9600 \| \| 0.3463 \| 0.3 \| 500 \| 0.4321 \| 0.3713 \| 0.5160 \| 0.8621 \| nan \| 0.0 \| 0.6749 \| 0.0 \| 0.7632 \| 0.6930 \| 0.9652 \| 0.0 \| 0.0 \| 0.5896 \| 0.0 \| 0.6635 \| 0.3833 \| 0.9627 \| \| 0.8166 \| 0.32 \| 520 \| 0.4851 \| 0.3999 \| 0.5399 \| 0.8955 \| nan \| 0.0 \| 0.7876 \| 0.0 \| 0.8940 \| 0.5942 \| 0.9633 \| 0.0 \| 0.0 \| 0.7062 \| 0.0 \| 0.7290 \| 0.4032 \| 0.9609 \| \| 0.5054 \| 0.33 \| 540 \| 0.4328 \| 0.3951 \| 0.5256 \| 0.8936 \| nan \| 0.0 \| 0.7356 \| 0.0 \| 0.9243 \| 0.5325 \| 0.9614 \| 0.0 \| 0.0 \| 0.6860 \| 0.0 \| 0.7262 \| 0.3955 \| 0.9579 \| \| 0.316 \| 0.34 \| 560 \| 0.3850 \| 0.3985 \| 0.5660 \| 0.8957 \| nan \| 0.0 \| 0.8014 \| 0.0 \| 0.8400 \| 0.7855 \| 0.9691 \| 0.0 \| 0.0 \| 0.7180 \| 0.0 \| 0.7286 \| 0.3782 \| 0.9647 \| \| 0.2616 \| 0.35 \| 580 \| 0.3974 \| 0.3831 \| 0.5087 \| 0.8852 \| nan \| 0.0 \| 0.6365 \| 0.0 \| 0.9089 \| 0.5391 \| 0.9677 \| 0.0 \| 0.0 \| 0.5653 \| 0.0 \| 0.7531 \| 0.3995 \| 0.9640 \| \| 0.4969 \| 0.36 \| 600 \| 0.4115 \| 0.3849 \| 0.5209 \| 0.8738 \| nan \| 0.0 \| 0.7301 \| 0.0 \| 0.8064 \| 0.6238 \| 0.9650 \| 0.0 \| 0.0 \| 0.6141 \| 0.0 \| 0.7030 \| 0.4155 \| 0.9618 \| \| 0.6554 \| 0.38 \| 620 \| 0.3927 \| 0.3997 \| 0.5634 \| 0.8927 \| nan \| 0.0 \| 0.8489 \| 0.0 \| 0.8074 \| 0.7544 \| 0.9698 \| 0.0 \| 0.0 \| 0.7254 \| 0.0 \| 0.7077 \| 0.4004 \| 0.9641 \| \| 0.5096 \| 0.39 \| 640 \| 0.4265 \| 0.3833 \| 0.5554 \| 0.8772 \| nan \| 0.0 \| 0.8562 \| 0.0 \| 0.7283 \| 0.7792 \| 0.9688 \| 0.0 \| 0.0 \| 0.6714 \| 0.0 \| 0.6556 \| 0.3905 \| 0.9655 \| \| 0.5453 \| 0.4 \| 660 \| 0.4163 \| 0.3830 \| 0.5356 \| 0.8763 \| nan \| 0.0 \| 0.7352 \| 0.0 \| 0.8036 \| 0.7106 \| 0.9644 \| 0.0 \| 0.0 \| 0.6194 \| 0.0 \| 0.6953 \| 0.4040 \| 0.9625 \| \| 0.8522 \| 0.41 \| 680 \| 0.3850 \| 0.3859 \| 0.5522 \| 0.8779 \| nan \| 0.0 \| 0.8238 \| 0.0 \| 0.7533 \| 0.7690 \| 0.9672 \| 0.0 \| 0.0 \| 0.6587 \| 0.0 \| 0.6785 \| 0.4009 \| 0.9634 \| \| 0.324 \| 0.42 \| 700 \| 0.3980 \| 0.3957 \| 0.5386 \| 0.8933 \| nan \| 0.0 \| 0.7033 \| 0.0 \| 0.8982 \| 0.6630 \| 0.9674 \| 0.0 \| 0.0 \| 0.6302 \| 0.0 \| 0.7372 \| 0.4384 \| 0.9644 \| \| 0.6783 \| 0.44 \| 720 \| 0.4155 \| 0.3798 \| 0.5332 \| 0.8771 \| nan \| 0.0 \| 0.6873 \| 0.0 \| 0.8122 \| 0.7309 \| 0.9689 \| 0.0 \| 0.0 \| 0.5793 \| 0.0 \| 0.7058 \| 0.4079 \| 0.9656 \| \| 0.6283 \| 0.45 \| 740 \| 0.4053 \| 0.3875 \| 0.5179 \| 0.8917 \| nan \| 0.0 \| 0.6808 \| 0.0 \| 0.9207 \| 0.5386 \| 0.9674 \| 0.0 \| 0.0 \| 0.6077 \| 0.0 \| 0.7409 \| 0.3986 \| 0.9649 \| \| 0.831 \| 0.46 \| 760 \| 0.3984 \| 0.3850 \| 0.5178 \| 0.8838 \| nan \| 0.0 \| 0.7712 \| 0.0 \| 0.8476 \| 0.5216 \| 0.9664 \| 0.0 \| 0.0 \| 0.5900 \| 0.0 \| 0.7380 \| 0.4044 \| 0.9629 \| \| 0.5993 \| 0.47 \| 780 \| 0.4069 \| 0.3891 \| 0.5129 \| 0.8961 \| nan \| 0.0 \| 0.7348 \| 0.0 \| 0.9287 \| 0.4453 \| 0.9687 \| 0.0 \| 0.0 \| 0.6417 \| 0.0 \| 0.7532 \| 0.3632 \| 0.9658 \| \| 0.719 \| 0.49 \| 800 \| 0.3856 \| 0.4053 \| 0.5597 \| 0.9006 \| nan \| 0.0 \| 0.8377 \| 0.0 \| 0.8641 \| 0.6875 \| 0.9688 \| 0.0 \| 0.0 \| 0.7007 \| 0.0 \| 0.7423 \| 0.4286 \| 0.9657 \| \| 0.4896 \| 0.5 \| 820 \| 0.3741 \| 0.4033 \| 0.5511 \| 0.8982 \| nan \| 0.0 \| 0.7926 \| 0.0 \| 0.8730 \| 0.6715 \| 0.9697 \| 0.0 \| 0.0 \| 0.6861 \| 0.0 \| 0.7405 \| 0.4302 \| 0.9662 \| \| 0.5632 \| 0.51 \| 840 \| 0.4503 \| 0.4085 \| 0.5607 \| 0.9069 \| nan \| 0.0 \| 0.8602 \| 0.0 \| 0.8788 \| 0.6530 \| 0.9724 \| 0.0 \| 0.0 \| 0.7133 \| 0.0 \| 0.7441 \| 0.4355 \| 0.9664 \| \| 0.2878 \| 0.52 \| 860 \| 0.4594 \| 0.3772 \| 0.4928 \| 0.8637 \| nan \| 0.0 \| 0.5859 \| 0.0 \| 0.8246 \| 0.5792 \| 0.9668 \| 0.0 \| 0.0 \| 0.5066 \| 0.0 \| 0.7162 \| 0.4538 \| 0.9639 \| \| 0.5433 \| 0.53 \| 880 \| 0.3916 \| 0.3899 \| 0.5519 \| 0.8832 \| nan \| 0.0 \| 0.7655 \| 0.0 \| 0.8304 \| 0.7561 \| 0.9596 \| 0.0 \| 0.0 \| 0.6246 \| 0.0 \| 0.7277 \| 0.4197 \| 0.9571 \| \| 1.1254 \| 0.55 \| 900 \| 0.3724 \| 0.4012 \| 0.5588 \| 0.8989 \| nan \| 0.0 \| 0.8798 \| 0.0 \| 0.8445 \| 0.6612 \| 0.9676 \| 0.0 \| 0.0 \| 0.6859 \| 0.0 \| 0.7355 \| 0.4222 \| 0.9645 \| \| 0.3224 \| 0.56 \| 920 \| 0.3896 \| 0.3832 \| 0.5236 \| 0.8848 \| nan \| 0.0 \| 0.6800 \| 0.0 \| 0.8722 \| 0.6211 \| 0.9682 \| 0.0 \| 0.0 \| 0.5748 \| 0.0 \| 0.7403 \| 0.4029 \| 0.9642 \| \| 0.5332 \| 0.57 \| 940 \| 0.4089 \| 0.3939 \| 0.5357 \| 0.9015 \| nan \| 0.0 \| 0.7886 \| 0.0 \| 0.9190 \| 0.5394 \| 0.9673 \| 0.0 \| 0.0 \| 0.6730 \| 0.0 \| 0.7391 \| 0.3803 \| 0.9647 \| \| 0.5894 \| 0.58 \| 960 \| 0.3700 \| 0.3927 \| 0.5405 \| 0.8839 \| nan \| 0.0 \| 0.7708 \| 0.0 \| 0.8235 \| 0.6824 \| 0.9660 \| 0.0 \| 0.0 \| 0.6556 \| 0.0 \| 0.7161 \| 0.4163 \| 0.9612 \| \| 1.036 \| 0.59 \| 980 \| 0.3671 \| 0.3944 \| 0.5432 \| 0.8837 \| nan \| 0.0 \| 0.7763 \| 0.0 \| 0.8179 \| 0.6990 \| 0.9658 \| 0.0 \| 0.0 \| 0.6802 \| 0.0 \| 0.7127 \| 0.4058 \| 0.9623 \| \| 1.5145 \| 0.61 \| 1000 \| 0.3916 \| 0.3965 \| 0.5456 \| 0.8958 \| nan \| 0.0 \| 0.7867 \| 0.0 \| 0.8846 \| 0.6375 \| 0.9647 \| 0.0 \| 0.0 \| 0.7018 \| 0.0 \| 0.7190 \| 0.3925 \| 0.9624 \| \| 0.4625 \| 0.62 \| 1020 \| 0.3603 \| 0.4067 \| 0.5496 \| 0.9010 \| nan \| 0.0 \| 0.8180 \| 0.0 \| 0.8992 \| 0.6160 \| 0.9646 \| 0.0 \| 0.0 \| 0.7095 \| 0.0 \| 0.7525 \| 0.4239 \| 0.9608 \| \| 0.3804 \| 0.63 \| 1040 \| 0.4219 \| 0.3928 \| 0.5328 \| 0.8789 \| nan \| 0.0 \| 0.8239 \| 0.0 \| 0.7882 \| 0.6196 \| 0.9652 \| 0.0 \| 0.0 \| 0.6778 \| 0.0 \| 0.6963 \| 0.4115 \| 0.9637 \| \| 0.1372 \| 0.64 \| 1060 \| 0.3774 \| 0.4034 \| 0.5582 \| 0.8978 \| nan \| 0.0 \| 0.8729 \| 0.0 \| 0.8348 \| 0.6721 \| 0.9696 \| 0.0 \| 0.0 \| 0.7169 \| 0.0 \| 0.7231 \| 0.4186 \| 0.9653 \| \| 0.4438 \| 0.65 \| 1080 \| 0.3409 \| 0.4042 \| 0.5501 \| 0.8971 \| nan \| 0.0 \| 0.7877 \| 0.0 \| 0.8704 \| 0.6733 \| 0.9694 \| 0.0 \| 0.0 \| 0.6900 \| 0.0 \| 0.7509 \| 0.4241 \| 0.9648 \| \| 0.3661 \| 0.67 \| 1100 \| 0.3662 \| 0.3994 \| 0.5728 \| 0.8936 \| nan \| 0.0039 \| 0.8665 \| 0.0 \| 0.7975 \| 0.7998 \| 0.9693 \| 0.0 \| 0.0039 \| 0.7029 \| 0.0 \| 0.7202 \| 0.4038 \| 0.9651 \| \| 0.3783 \| 0.68 \| 1120 \| 0.3625 \| 0.4003 \| 0.5673 \| 0.8893 \| nan \| 0.0 \| 0.8860 \| 0.0 \| 0.7856 \| 0.7669 \| 0.9654 \| 0.0 \| 0.0 \| 0.7155 \| 0.0 \| 0.7166 \| 0.4074 \| 0.9628 \| \| 0.3344 \| 0.69 \| 1140 \| 0.3889 \| 0.4142 \| 0.5535 \| 0.9062 \| nan \| 0.0 \| 0.8531 \| 0.0 \| 0.9107 \| 0.5919 \| 0.9651 \| 0.0 \| 0.0 \| 0.7437 \| 0.0 \| 0.7598 \| 0.4330 \| 0.9628 \| \| 0.448 \| 0.7 \| 1160 \| 0.3701 \| 0.3875 \| 0.5226 \| 0.8774 \| nan \| 0.0 \| 0.6806 \| 0.0 \| 0.8287 \| 0.6578 \| 0.9688 \| 0.0 \| 0.0 \| 0.5996 \| 0.0 \| 0.7207 \| 0.4273 \| 0.9648 \| \| 0.5724 \| 0.72 \| 1180 \| 0.3466 \| 0.4074 \| 0.5587 \| 0.8998 \| nan \| 0.0000 \| 0.8195 \| 0.0 \| 0.8653 \| 0.6982 \| 0.9692 \| 0.0 \| 0.0000 \| 0.7289 \| 0.0 \| 0.7380 \| 0.4198 \| 0.9648 \| \| 0.2868 \| 0.73 \| 1200 \| 0.3369 \| 0.4088 \| 0.5653 \| 0.8956 \| nan \| 0.0005 \| 0.8380 \| 0.0 \| 0.8254 \| 0.7584 \| 0.9697 \| 0.0 \| 0.0005 \| 0.7145 \| 0.0 \| 0.7399 \| 0.4414 \| 0.9652 \| \| 1.2485 \| 0.74 \| 1220 \| 0.3480 \| 0.4120 \| 0.5639 \| 0.9023 \| nan \| 0.0079 \| 0.8643 \| 0.0 \| 0.8543 \| 0.6865 \| 0.9706 \| 0.0 \| 0.0079 \| 0.7086 \| 0.0 \| 0.7548 \| 0.4469 \| 0.9659 \| \| 0.6551 \| 0.75 \| 1240 \| 0.3791 \| 0.4219 \| 0.5591 \| 0.9145 \| nan \| 0.0048 \| 0.8977 \| 0.0 \| 0.9139 \| 0.5662 \| 0.9718 \| 0.0 \| 0.0048 \| 0.7367 \| 0.0 \| 0.7844 \| 0.4620 \| 0.9651 \| \| 0.2599 \| 0.76 \| 1260 \| 0.4596 \| 0.3694 \| 0.5736 \| 0.8698 \| nan \| 0.0001 \| 0.9234 \| 0.0 \| 0.6502 \| 0.9018 \| 0.9664 \| 0.0 \| 0.0001 \| 0.6060 \| 0.0 \| 0.6179 \| 0.3974 \| 0.9644 \| \| 0.4005 \| 0.78 \| 1280 \| 0.3520 \| 0.4018 \| 0.5566 \| 0.8965 \| nan \| 0.0076 \| 0.7654 \| 0.0 \| 0.8698 \| 0.7278 \| 0.9690 \| 0.0 \| 0.0076 \| 0.6936 \| 0.0 \| 0.7161 \| 0.4306 \| 0.9648 \| \| 0.4001 \| 0.79 \| 1300 \| 0.3456 \| 0.4070 \| 0.5543 \| 0.8977 \| nan \| 0.0001 \| 0.7549 \| 0.0 \| 0.8813 \| 0.7203 \| 0.9692 \| 0.0 \| 0.0001 \| 0.6933 \| 0.0 \| 0.7277 \| 0.4620 \| 0.9658 \| \| 0.9039 \| 0.8 \| 1320 \| 0.3889 \| 0.4112 \| 0.5790 \| 0.8984 \| nan \| 0.0006 \| 0.8918 \| 0.0 \| 0.8236 \| 0.7933 \| 0.9650 \| 0.0 \| 0.0006 \| 0.7323 \| 0.0 \| 0.7349 \| 0.4479 \| 0.9626 \| \| 0.6388 \| 0.81 \| 1340 \| 0.4108 \| 0.4121 \| 0.5752 \| 0.9051 \| nan \| 0.0001 \| 0.8354 \| 0.0 \| 0.8712 \| 0.7752 \| 0.9692 \| 0.0 \| 0.0001 \| 0.7325 \| 0.0 \| 0.7351 \| 0.4512 \| 0.9655 \| \| 0.3616 \| 0.82 \| 1360 \| 0.4138 \| 0.4221 \| 0.5638 \| 0.9099 \| nan \| 0.0 \| 0.8630 \| 0.0 \| 0.9119 \| 0.6414 \| 0.9664 \| 0.0 \| 0.0 \| 0.7581 \| 0.0 \| 0.7689 \| 0.4631 \| 0.9646 \| \| 0.2287 \| 0.84 \| 1380 \| 0.3833 \| 0.4229 \| 0.5762 \| 0.9055 \| nan \| 0.0000 \| 0.8940 \| 0.0 \| 0.8637 \| 0.7335 \| 0.9658 \| 0.0 \| 0.0000 \| 0.7658 \| 0.0 \| 0.7545 \| 0.4779 \| 0.9620 \| \| 0.2687 \| 0.85 \| 1400 \| 0.3732 \| 0.4287 \| 0.5666 \| 0.9118 \| nan \| 0.0000 \| 0.8972 \| 0.0 \| 0.9157 \| 0.6226 \| 0.9641 \| 0.0 \| 0.0000 \| 0.7764 \| 0.0 \| 0.7815 \| 0.4814 \| 0.9618 \| \| 0.3827 \| 0.86 \| 1420 \| 0.3344 \| 0.4176 \| 0.5816 \| 0.9030 \| nan \| 0.0 \| 0.8511 \| 0.0 \| 0.8537 \| 0.8177 \| 0.9671 \| 0.0 \| 0.0 \| 0.7452 \| 0.0 \| 0.7485 \| 0.4653 \| 0.9641 \| \| 1.1798 \| 0.87 \| 1440 \| 0.3485 \| 0.4198 \| 0.5742 \| 0.9089 \| nan \| 0.0002 \| 0.8352 \| 0.0 \| 0.8893 \| 0.7500 \| 0.9705 \| 0.0 \| 0.0002 \| 0.7491 \| 0.0 \| 0.7514 \| 0.4729 \| 0.9653 \| \| 0.5062 \| 0.89 \| 1460 \| 0.3882 \| 0.4145 \| 0.5766 \| 0.9028 \| nan \| 0.0013 \| 0.8817 \| 0.0 \| 0.8337 \| 0.7717 \| 0.9712 \| 0.0 \| 0.0013 \| 0.7656 \| 0.0 \| 0.7303 \| 0.4389 \| 0.9656 \| \| 0.2002 \| 0.9 \| 1480 \| 0.3677 \| 0.4167 \| 0.5785 \| 0.9005 \| nan \| 0.0001 \| 0.8674 \| 0.0 \| 0.8450 \| 0.7934 \| 0.9649 \| 0.0 \| 0.0001 \| 0.7513 \| 0.0 \| 0.7391 \| 0.4645 \| 0.9620 \| \| 0.1993 \| 0.91 \| 1500 \| 0.3801 \| 0.4240 \| 0.5584 \| 0.9134 \| nan \| 0.0000 \| 0.8673 \| 0.0 \| 0.9311 \| 0.5842 \| 0.9680 \| 0.0 \| 0.0000 \| 0.7626 \| 0.0 \| 0.7714 \| 0.4687 \| 0.9653 \| \| 0.2609 \| 0.92 \| 1520 \| 0.3489 \| 0.4199 \| 0.5475 \| 0.9084 \| nan \| 0.0001 \| 0.8222 \| 0.0 \| 0.9304 \| 0.5642 \| 0.9682 \| 0.0 \| 0.0001 \| 0.7412 \| 0.0 \| 0.7755 \| 0.4568 \| 0.9657 \| \| 0.4571 \| 0.93 \| 1540 \| 0.3767 \| 0.4218 \| 0.5832 \| 0.9040 \| nan \| 0.0372 \| 0.8298 \| 0.0 \| 0.8774 \| 0.7894 \| 0.9651 \| 0.0 \| 0.0365 \| 0.7505 \| 0.0 \| 0.7451 \| 0.4583 \| 0.9621 \| \| 0.5643 \| 0.95 \| 1560 \| 0.3707 \| 0.4220 \| 0.5848 \| 0.9027 \| nan \| 0.0187 \| 0.8455 \| 0.0 \| 0.8446 \| 0.8307 \| 0.9695 \| 0.0 \| 0.0186 \| 0.7506 \| 0.0 \| 0.7459 \| 0.4715 \| 0.9670 \| \| 0.2607 \| 0.96 \| 1580 \| 0.3601 \| 0.4304 \| 0.5782 \| 0.9099 \| nan \| 0.0119 \| 0.8665 \| 0.0 \| 0.8921 \| 0.7312 \| 0.9675 \| 0.0 \| 0.0118 \| 0.7565 \| 0.0 \| 0.7761 \| 0.5029 \| 0.9652 \| \| 0.2481 \| 0.97 \| 1600 \| 0.3817 \| 0.4281 \| 0.5952 \| 0.9043 \| nan \| 0.0346 \| 0.8954 \| 0.0 \| 0.8582 \| 0.8225 \| 0.9604 \| 0.0 \| 0.0342 \| 0.7426 \| 0.0 \| 0.7691 \| 0.4917 \| 0.9589 \| \| 0.1986 \| 0.98 \| 1620 \| 0.3817 \| 0.4475 \| 0.5988 \| 0.9135 \| nan \| 0.1260 \| 0.8537 \| 0.0 \| 0.9194 \| 0.7288 \| 0.9651 \| 0.0 \| 0.1246 \| 0.7532 \| 0.0 \| 0.7756 \| 0.5166 \| 0.9626 \| \| 0.455 \| 0.99 \| 1640 \| 0.3812 \| 0.4526 \| 0.5958 \| 0.9144 \| nan \| 0.1518 \| 0.8918 \| 0.0 \| 0.9239 \| 0.6439 \| 0.9636 \| 0.0 \| 0.1467 \| 0.7665 \| 0.0 \| 0.7888 \| 0.5058 \| 0.9602 \| \| 0.4941 \| 1.01 \| 1660 \| 0.3751 \| 0.4389 \| 0.6153 \| 0.9094 \| nan \| 0.1142 \| 0.9261 \| 0.0 \| 0.8391 \| 0.8440 \| 0.9682 \| 0.0 \| 0.1110 \| 0.7601 \| 0.0 \| 0.7621 \| 0.4737 \| 0.9652 \| \| 0.2446 \| 1.02 \| 1680 \| 0.3794 \| 0.4389 \| 0.5817 \| 0.9136 \| nan \| 0.0160 \| 0.9137 \| 0.0 \| 0.8883 \| 0.7034 \| 0.9689 \| 0.0 \| 0.0160 \| 0.7765 \| 0.0 \| 0.7870 \| 0.5264 \| 0.9664 \| \| 0.3996 \| 1.03 \| 1700 \| 0.3408 \| 0.4193 \| 0.5478 \| 0.9004 \| nan \| 0.0060 \| 0.7699 \| 0.0 \| 0.8999 \| 0.6419 \| 0.9691 \| 0.0 \| 0.0060 \| 0.6761 \| 0.0 \| 0.7813 \| 0.5054 \| 0.9660 \| \| 0.6762 \| 1.04 \| 1720 \| 0.3653 \| 0.4203 \| 0.5646 \| 0.8983 \| nan \| 0.0554 \| 0.7069 \| 0.0 \| 0.8982 \| 0.7581 \| 0.9691 \| 0.0 \| 0.0550 \| 0.6118 \| 0.0 \| 0.7905 \| 0.5192 \| 0.9658 \| \| 0.3445 \| 1.06 \| 1740 \| 0.3179 \| 0.4460 \| 0.6063 \| 0.9131 \| nan \| 0.1284 \| 0.8453 \| 0.0 \| 0.8960 \| 0.7983 \| 0.9697 \| 0.0 \| 0.1275 \| 0.7422 \| 0.0 \| 0.7797 \| 0.5068 \| 0.9658 \| \| 0.5238 \| 1.07 \| 1760 \| 0.3420 \| 0.4456 \| 0.6022 \| 0.9178 \| nan \| 0.0626 \| 0.9129 \| 0.0 \| 0.8955 \| 0.7732 \| 0.9692 \| 0.0 \| 0.0616 \| 0.7810 \| 0.0 \| 0.7931 \| 0.5180 \| 0.9658 \| \| 0.4666 \| 1.08 \| 1780 \| 0.3410 \| 0.4379 \| 0.5994 \| 0.9099 \| nan \| 0.0695 \| 0.8674 \| 0.0 \| 0.8796 \| 0.8136 \| 0.9666 \| 0.0 \| 0.0678 \| 0.7510 \| 0.0 \| 0.7786 \| 0.5035 \| 0.9645 \| \| 0.1126 \| 1.09 \| 1800 \| 0.3464 \| 0.4383 \| 0.6157 \| 0.9101 \| nan \| 0.1208 \| 0.8865 \| 0.0 \| 0.8516 \| 0.8653 \| 0.9700 \| 0.0 \| 0.1180 \| 0.7454 \| 0.0 \| 0.7670 \| 0.4711 \| 0.9664 \| \| 0.7935 \| 1.1 \| 1820 \| 0.3999 \| 0.4382 \| 0.5737 \| 0.9179 \| nan \| 0.0529 \| 0.8790 \| 0.0 \| 0.9439 \| 0.5984 \| 0.9679 \| 0.0 \| 0.0528 \| 0.7719 \| 0.0 \| 0.7874 \| 0.4906 \| 0.9650 \| \| 0.6014 \| 1.12 \| 1840 \| 0.3176 \| 0.4707 \| 0.6222 \| 0.9193 \| nan \| 0.2149 \| 0.8795 \| 0.0 \| 0.9128 \| 0.7558 \| 0.9702 \| 0.0 \| 0.2033 \| 0.7731 \| 0.0 \| 0.7938 \| 0.5591 \| 0.9653 \| \| 1.1728 \| 1.13 \| 1860 \| 0.3165 \| 0.4715 \| 0.6205 \| 0.9205 \| nan \| 0.2344 \| 0.8965 \| 0.0 \| 0.9165 \| 0.7044 \| 0.9709 \| 0.0 \| 0.2151 \| 0.7453 \| 0.0 \| 0.8097 \| 0.5650 \| 0.9654 \| \| 0.1275 \| 1.14 \| 1880 \| 0.3705 \| 0.4586 \| 0.5965 \| 0.9140 \| nan \| 0.2139 \| 0.8187 \| 0.0 \| 0.9455 \| 0.6344 \| 0.9666 \| 0.0 \| 0.2070 \| 0.7223 \| 0.0 \| 0.7887 \| 0.5277 \| 0.9649 \| \| 0.2179 \| 1.15 \| 1900 \| 0.3268 \| 0.4608 \| 0.6240 \| 0.9126 \| nan \| 0.2176 \| 0.8507 \| 0.0 \| 0.8910 \| 0.8165 \| 0.9679 \| 0.0 \| 0.2054 \| 0.7202 \| 0.0 \| 0.7888 \| 0.5454 \| 0.9656 \| \| 0.1725 \| 1.16 \| 1920 \| 0.3277 \| 0.4655 \| 0.6146 \| 0.9203 \| nan \| 0.1809 \| 0.8656 \| 0.0 \| 0.9297 \| 0.7422 \| 0.9693 \| 0.0 \| 0.1753 \| 0.7456 \| 0.0 \| 0.8099 \| 0.5610 \| 0.9667 \| \| 0.3003 \| 1.18 \| 1940 \| 0.3347 \| 0.4741 \| 0.6311 \| 0.9203 \| nan \| 0.2239 \| 0.8903 \| 0.0 \| 0.9079 \| 0.7951 \| 0.9696 \| 0.0 \| 0.2144 \| 0.7493 \| 0.0 \| 0.8067 \| 0.5815 \| 0.9669 \| \| 0.4764 \| 1.19 \| 1960 \| 0.3413 \| 0.4478 \| 0.6279 \| 0.9079 \| nan \| 0.1854 \| 0.9016 \| 0.0 \| 0.8339 \| 0.8779 \| 0.9687 \| 0.0 \| 0.1690 \| 0.7441 \| 0.0 \| 0.7655 \| 0.4897 \| 0.9663 \| \| 0.1679 \| 1.2 \| 1980 \| 0.3516 \| 0.4473 \| 0.5798 \| 0.9191 \| nan \| 0.0754 \| 0.8674 \| 0.0 \| 0.9466 \| 0.6204 \| 0.9692 \| 0.0 \| 0.0724 \| 0.7752 \| 0.0 \| 0.7993 \| 0.5181 \| 0.9660 \| \| 0.1999 \| 1.21 \| 2000 \| 0.3341 \| 0.4721 \| 0.6457 \| 0.9175 \| nan \| 0.2688 \| 0.8973 \| 0.0 \| 0.8822 \| 0.8568 \| 0.9688 \| 0.0 \| 0.2286 \| 0.7781 \| 0.0 \| 0.7962 \| 0.5359 \| 0.9662 \| \| 0.8992 \| 1.22 \| 2020 \| 0.3121 \| 0.4757 \| 0.6409 \| 0.9179 \| nan \| 0.2915 \| 0.8910 \| 0.0 \| 0.8935 \| 0.8000 \| 0.9695 \| 0.0 \| 0.2634 \| 0.7798 \| 0.0 \| 0.7856 \| 0.5347 \| 0.9661 \| \| 0.7007 \| 1.24 \| 2040 \| 0.3041 \| 0.4618 \| 0.6127 \| 0.9128 \| nan \| 0.1999 \| 0.8653 \| 0.0 \| 0.8950 \| 0.7474 \| 0.9685 \| 0.0 \| 0.1702 \| 0.7512 \| 0.0 \| 0.7931 \| 0.5532 \| 0.9647 \| \| 0.5711 \| 1.25 \| 2060 \| 0.3104 \| 0.4758 \| 0.6277 \| 0.9190 \| nan \| 0.2760 \| 0.8877 \| 0.0 \| 0.9128 \| 0.7196 \| 0.9699 \| 0.0 \| 0.2357 \| 0.7700 \| 0.0 \| 0.8006 \| 0.5579 \| 0.9666 \| \| 0.7925 \| 1.26 \| 2080 \| 0.3465 \| 0.4605 \| 0.6418 \| 0.9091 \| nan \| 0.2878 \| 0.8570 \| 0.0 \| 0.8532 \| 0.8831 \| 0.9696 \| 0.0 \| 0.2437 \| 0.7217 \| 0.0 \| 0.7779 \| 0.5136 \| 0.9670 \| \| 0.1759 \| 1.27 \| 2100 \| 0.3406 \| 0.4619 \| 0.6235 \| 0.9078 \| nan \| 0.2492 \| 0.8230 \| 0.0 \| 0.8719 \| 0.8272 \| 0.9698 \| 0.0 \| 0.2157 \| 0.6976 \| 0.0 \| 0.7884 \| 0.5655 \| 0.9663 \| \| 0.6914 \| 1.29 \| 2120 \| 0.3209 \| 0.4694 \| 0.6229 \| 0.9168 \| nan \| 0.2340 \| 0.8629 \| 0.0 \| 0.9024 \| 0.7665 \| 0.9713 \| 0.0 \| 0.2126 \| 0.7390 \| 0.0 \| 0.8017 \| 0.5650 \| 0.9672 \| \| 0.1292 \| 1.3 \| 2140 \| 0.3151 \| 0.4735 \| 0.6349 \| 0.9147 \| nan \| 0.2670 \| 0.8724 \| 0.0 \| 0.8983 \| 0.8062 \| 0.9657 \| 0.0 \| 0.2358 \| 0.7487 \| 0.0 \| 0.8011 \| 0.5662 \| 0.9625 \| \| 0.5439 \| 1.31 \| 2160 \| 0.3343 \| 0.4617 \| 0.6058 \| 0.9133 \| nan \| 0.2228 \| 0.7989 \| 0.0 \| 0.9340 \| 0.7110 \| 0.9681 \| 0.0 \| 0.2091 \| 0.7154 \| 0.0 \| 0.7958 \| 0.5458 \| 0.9660 \| \| 0.5949 \| 1.32 \| 2180 \| 0.3260 \| 0.4561 \| 0.6419 \| 0.9072 \| nan \| 0.3080 \| 0.8721 \| 0.0 \| 0.8355 \| 0.8650 \| 0.9709 \| 0.0 \| 0.2166 \| 0.7527 \| 0.0 \| 0.7632 \| 0.4930 \| 0.9670 \| \| 0.9366 \| 1.33 \| 2200 \| 0.3182 \| 0.4748 \| 0.6430 \| 0.9190 \| nan \| 0.2967 \| 0.9100 \| 0.0 \| 0.8864 \| 0.7941 \| 0.9710 \| 0.0 \| 0.2381 \| 0.7813 \| 0.0 \| 0.7926 \| 0.5446 \| 0.9669 \| \| 0.4478 \| 1.35 \| 2220 \| 0.3531 \| 0.4596 \| 0.5997 \| 0.9223 \| nan \| 0.1622 \| 0.8843 \| 0.0 \| 0.9456 \| 0.6361 \| 0.9703 \| 0.0 \| 0.1565 \| 0.7919 \| 0.0 \| 0.7921 \| 0.5099 \| 0.9670 \| \| 0.2858 \| 1.36 \| 2240 \| 0.3627 \| 0.4607 \| 0.6228 \| 0.9173 \| nan \| 0.1578 \| 0.8976 \| 0.0 \| 0.8883 \| 0.8239 \| 0.9694 \| 0.0 \| 0.1500 \| 0.7902 \| 0.0 \| 0.7738 \| 0.5443 \| 0.9666 \| \| 0.4923 \| 1.37 \| 2260 \| 0.3367 \| 0.4498 \| 0.6047 \| 0.9082 \| nan \| 0.1380 \| 0.8348 \| 0.0 \| 0.8814 \| 0.8063 \| 0.9678 \| 0.0 \| 0.1299 \| 0.7364 \| 0.0 \| 0.7740 \| 0.5442 \| 0.9638 \| \| 0.1323 \| 1.38 \| 2280 \| 0.3380 \| 0.4515 \| 0.6301 \| 0.9084 \| nan \| 0.1971 \| 0.8926 \| 0.0 \| 0.8345 \| 0.8863 \| 0.9700 \| 0.0 \| 0.1751 \| 0.7494 \| 0.0 \| 0.7577 \| 0.5118 \| 0.9668 \| \| 0.3126 \| 1.39 \| 2300 \| 0.3519 \| 0.4753 \| 0.6187 \| 0.9234 \| nan \| 0.2345 \| 0.8849 \| 0.0 \| 0.9471 \| 0.6770 \| 0.9686 \| 0.0 \| 0.2226 \| 0.7876 \| 0.0 \| 0.7971 \| 0.5533 \| 0.9662 \| \| 1.8741 \| 1.41 \| 2320 \| 0.3483 \| 0.4766 \| 0.6352 \| 0.9194 \| nan \| 0.2550 \| 0.8999 \| 0.0 \| 0.9013 \| 0.7856 \| 0.9691 \| 0.0 \| 0.2242 \| 0.7747 \| 0.0 \| 0.7957 \| 0.5761 \| 0.9656 \| \| 0.3519 \| 1.42 \| 2340 \| 0.3390 \| 0.4794 \| 0.6391 \| 0.9205 \| nan \| 0.2594 \| 0.8966 \| 0.0 \| 0.9017 \| 0.8068 \| 0.9700 \| 0.0 \| 0.2378 \| 0.7732 \| 0.0 \| 0.7951 \| 0.5829 \| 0.9671 \| \| 0.4777 \| 1.43 \| 2360 \| 0.3234 \| 0.4707 \| 0.6410 \| 0.9149 \| nan \| 0.2605 \| 0.8687 \| 0.0 \| 0.8787 \| 0.8681 \| 0.9698 \| 0.0 \| 0.2422 \| 0.7715 \| 0.0 \| 0.7784 \| 0.5358 \| 0.9670 \| \| 0.7156 \| 1.44 \| 2380 \| 0.3451 \| 0.4794 \| 0.6418 \| 0.9201 \| nan \| 0.2929 \| 0.8798 \| 0.0 \| 0.9094 \| 0.7995 \| 0.9694 \| 0.0 \| 0.2575 \| 0.7733 \| 0.0 \| 0.7918 \| 0.5663 \| 0.9670 \| \| 0.3765 \| 1.46 \| 2400 \| 0.3339 \| 0.4639 \| 0.6238 \| 0.9133 \| nan \| 0.2550 \| 0.8921 \| 0.0 \| 0.8805 \| 0.7455 \| 0.9696 \| 0.0 \| 0.1751 \| 0.7569 \| 0.0 \| 0.7919 \| 0.5564 \| 0.9669 \| \| 0.4343 \| 1.47 \| 2420 \| 0.3374 \| 0.4630 \| 0.6168 \| 0.9135 \| nan \| 0.2179 \| 0.8541 \| 0.0 \| 0.9035 \| 0.7576 \| 0.9678 \| 0.0 \| 0.1775 \| 0.7531 \| 0.0 \| 0.7934 \| 0.5515 \| 0.9658 \| \| 0.2178 \| 1.48 \| 2440 \| 0.3254 \| 0.4825 \| 0.6499 \| 0.9219 \| nan \| 0.3424 \| 0.8816 \| 0.0 \| 0.9140 \| 0.7912 \| 0.9704 \| 0.0 \| 0.2639 \| 0.7733 \| 0.0 \| 0.8027 \| 0.5707 \| 0.9669 \| \| 0.1439 \| 1.49 \| 2460 \| 0.3176 \| 0.4752 \| 0.6409 \| 0.9175 \| nan \| 0.2942 \| 0.8787 \| 0.0 \| 0.8935 \| 0.8089 \| 0.9702 \| 0.0 \| 0.2160 \| 0.7786 \| 0.0 \| 0.7978 \| 0.5673 \| 0.9664 \| \| 0.2481 \| 1.5 \| 2480 \| 0.3195 \| 0.4773 \| 0.6265 \| 0.9190 \| nan \| 0.2645 \| 0.8625 \| 0.0 \| 0.9263 \| 0.7376 \| 0.9683 \| 0.0 \| 0.2089 \| 0.7689 \| 0.0 \| 0.8083 \| 0.5890 \| 0.9658 \| \| 0.9347 \| 1.52 \| 2500 \| 0.3430 \| 0.4789 \| 0.6464 \| 0.9207 \| nan \| 0.3009 \| 0.8885 \| 0.0 \| 0.9029 \| 0.8159 \| 0.9702 \| 0.0 \| 0.2626 \| 0.7724 \| 0.0 \| 0.7975 \| 0.5528 \| 0.9670 \| \| 0.1827 \| 1.53 \| 2520 \| 0.3459 \| 0.4726 \| 0.6328 \| 0.9181 \| nan \| 0.2484 \| 0.9093 \| 0.0 \| 0.8921 \| 0.7775 \| 0.9692 \| 0.0 \| 0.2042 \| 0.7741 \| 0.0 \| 0.7998 \| 0.5643 \| 0.9662 \| \| 0.3971 \| 1.54 \| 2540 \| 0.3276 \| 0.4661 \| 0.6353 \| 0.9144 \| nan \| 0.2600 \| 0.8791 \| 0.0 \| 0.8777 \| 0.8247 \| 0.9700 \| 0.0 \| 0.2198 \| 0.7730 \| 0.0 \| 0.7785 \| 0.5249 \| 0.9663 \| \| 0.173 \| 1.55 \| 2560 \| 0.3109 \| 0.4591 \| 0.6419 \| 0.9093 \| nan \| 0.2986 \| 0.8694 \| 0.0 \| 0.8536 \| 0.8611 \| 0.9690 \| 0.0 \| 0.2176 \| 0.7731 \| 0.0 \| 0.7626 \| 0.4941 \| 0.9662 \| \| 0.2705 \| 1.56 \| 2580 \| 0.3112 \| 0.4632 \| 0.6178 \| 0.9101 \| nan \| 0.2942 \| 0.8141 \| 0.0 \| 0.9057 \| 0.7240 \| 0.9687 \| 0.0 \| 0.2203 \| 0.7423 \| 0.0 \| 0.7818 \| 0.5318 \| 0.9661 \| \| 0.2656 \| 1.58 \| 2600 \| 0.3331 \| 0.4806 \| 0.6427 \| 0.9202 \| nan \| 0.3186 \| 0.8837 \| 0.0 \| 0.9166 \| 0.7691 \| 0.9680 \| 0.0 \| 0.2634 \| 0.7761 \| 0.0 \| 0.7986 \| 0.5600 \| 0.9661 \| \| 0.9206 \| 1.59 \| 2620 \| 0.3247 \| 0.4688 \| 0.6290 \| 0.9162 \| nan \| 0.3530 \| 0.8646 \| 0.0 \| 0.9130 \| 0.6725 \| 0.9706 \| 0.0 \| 0.2167 \| 0.7747 \| 0.0 \| 0.7877 \| 0.5357 \| 0.9667 \| \| 0.6181 \| 1.6 \| 2640 \| 0.4032 \| 0.4888 \| 0.6638 \| 0.9239 \| nan \| 0.3722 \| 0.9134 \| 0.0 \| 0.9041 \| 0.8231 \| 0.9701 \| 0.0 \| 0.3080 \| 0.7774 \| 0.0 \| 0.8039 \| 0.5652 \| 0.9668 \| \| 0.3185 \| 1.61 \| 2660 \| 0.3383 \| 0.4705 \| 0.6598 \| 0.9145 \| nan \| 0.3341 \| 0.9161 \| 0.0 \| 0.8448 \| 0.8926 \| 0.9711 \| 0.0 \| 0.2860 \| 0.7681 \| 0.0 \| 0.7732 \| 0.4997 \| 0.9667 \| \| 0.2155 \| 1.63 \| 2680 \| 0.3389 \| 0.4639 \| 0.6447 \| 0.9109 \| nan \| 0.3037 \| 0.8741 \| 0.0 \| 0.8593 \| 0.8625 \| 0.9689 \| 0.0 \| 0.2608 \| 0.7573 \| 0.0 \| 0.7696 \| 0.4933 \| 0.9664 \| \| 0.2003 \| 1.64 \| 2700 \| 0.3230 \| 0.4649 \| 0.6588 \| 0.9112 \| nan \| 0.3442 \| 0.8983 \| 0.0 \| 0.8350 \| 0.9045 \| 0.9710 \| 0.0 \| 0.2818 \| 0.7704 \| 0.0 \| 0.7626 \| 0.4723 \| 0.9672 \| \| 0.1279 \| 1.65 \| 2720 \| 0.3241 \| 0.4747 \| 0.6489 \| 0.9163 \| nan \| 0.3240 \| 0.8760 \| 0.0 \| 0.8863 \| 0.8379 \| 0.9693 \| 0.0 \| 0.2713 \| 0.7729 \| 0.0 \| 0.7876 \| 0.5243 \| 0.9666 \| \| 1.5163 \| 1.66 \| 2740 \| 0.3286 \| 0.4822 \| 0.6409 \| 0.9182 \| nan \| 0.3422 \| 0.8431 \| 0.0 \| 0.9203 \| 0.7703 \| 0.9694 \| 0.0 \| 0.2859 \| 0.7533 \| 0.0 \| 0.7992 \| 0.5702 \| 0.9667 \| \| 0.5542 \| 1.67 \| 2760 \| 0.3147 \| 0.4774 \| 0.6336 \| 0.9157 \| nan \| 0.3563 \| 0.8646 \| 0.0 \| 0.9065 \| 0.7039 \| 0.9702 \| 0.0 \| 0.2631 \| 0.7649 \| 0.0 \| 0.7932 \| 0.5544 \| 0.9664 \| \| 0.343 \| 1.69 \| 2780 \| 0.3632 \| 0.4858 \| 0.6494 \| 0.9212 \| nan \| 0.3345 \| 0.8857 \| 0.0 \| 0.9134 \| 0.7937 \| 0.9689 \| 0.0 \| 0.2855 \| 0.7758 \| 0.0 \| 0.7975 \| 0.5756 \| 0.9665 \| \| 0.4835 \| 1.7 \| 2800 \| 0.3339 \| 0.4753 \| 0.6437 \| 0.9181 \| nan \| 0.2891 \| 0.8746 \| 0.0 \| 0.8945 \| 0.8339 \| 0.9703 \| 0.0 \| 0.2399 \| 0.7766 \| 0.0 \| 0.7894 \| 0.5538 \| 0.9670 \| \| 0.1818 \| 1.71 \| 2820 \| 0.3292 \| 0.4655 \| 0.6247 \| 0.9133 \| nan \| 0.2390 \| 0.8420 \| 0.0 \| 0.8972 \| 0.8010 \| 0.9688 \| 0.0 \| 0.1741 \| 0.7599 \| 0.0 \| 0.7941 \| 0.5642 \| 0.9664 \| \| 0.9569 \| 1.72 \| 2840 \| 0.3529 \| 0.4755 \| 0.6446 \| 0.9220 \| nan \| 0.2926 \| 0.8872 \| 0.0 \| 0.9090 \| 0.8079 \| 0.9709 \| 0.0 \| 0.2297 \| 0.7690 \| 0.0 \| 0.8052 \| 0.5572 \| 0.9672 \| \| 0.1522 \| 1.73 \| 2860 \| 0.3493 \| 0.4606 \| 0.6375 \| 0.9130 \| nan \| 0.2946 \| 0.8384 \| 0.0 \| 0.8860 \| 0.8360 \| 0.9702 \| 0.0 \| 0.2283 \| 0.7457 \| 0.0 \| 0.7715 \| 0.5112 \| 0.9675 \| \| 0.1661 \| 1.75 \| 2880 \| 0.3477 \| 0.4546 \| 0.6333 \| 0.9095 \| nan \| 0.2724 \| 0.8481 \| 0.0 \| 0.8671 \| 0.8428 \| 0.9693 \| 0.0 \| 0.2106 \| 0.7487 \| 0.0 \| 0.7711 \| 0.4853 \| 0.9667 \| \| 0.2484 \| 1.76 \| 2900 \| 0.3435 \| 0.4515 \| 0.6292 \| 0.9050 \| nan \| 0.2757 \| 0.8926 \| 0.0 \| 0.8347 \| 0.8039 \| 0.9683 \| 0.0 \| 0.1935 \| 0.7419 \| 0.0 \| 0.7619 \| 0.4975 \| 0.9658 \| \| 0.1391 \| 1.77 \| 2920 \| 0.3083 \| 0.4621 \| 0.6332 \| 0.9138 \| nan \| 0.3116 \| 0.8488 \| 0.0 \| 0.8965 \| 0.7730 \| 0.9696 \| 0.0 \| 0.2033 \| 0.7516 \| 0.0 \| 0.7920 \| 0.5213 \| 0.9666 \| \| 0.1363 \| 1.78 \| 2940 \| 0.3371 \| 0.4638 \| 0.6463 \| 0.9110 \| nan \| 0.3354 \| 0.8500 \| 0.0 \| 0.8745 \| 0.8501 \| 0.9679 \| 0.0 \| 0.2511 \| 0.7327 \| 0.0 \| 0.7852 \| 0.5121 \| 0.9656 \| \| 0.3563 \| 1.8 \| 2960 \| 0.3271 \| 0.4584 \| 0.6325 \| 0.9096 \| nan \| 0.2815 \| 0.8248 \| 0.0 \| 0.8714 \| 0.8465 \| 0.9711 \| 0.0 \| 0.2268 \| 0.7214 \| 0.0 \| 0.7805 \| 0.5133 \| 0.9670 \| \| 0.7689 \| 1.81 \| 2980 \| 0.3054 \| 0.4593 \| 0.6192 \| 0.9096 \| nan \| 0.2350 \| 0.8486 \| 0.0 \| 0.8729 \| 0.7878 \| 0.9708 \| 0.0 \| 0.1854 \| 0.7343 \| 0.0 \| 0.7904 \| 0.5387 \| 0.9664 \| \| 0.333 \| 1.82 \| 3000 \| 0.3318 \| 0.4715 \| 0.6438 \| 0.9178 \| nan \| 0.2849 \| 0.8976 \| 0.0 \| 0.8858 \| 0.8248 \| 0.9698 \| 0.0 \| 0.2529 \| 0.7470 \| 0.0 \| 0.7938 \| 0.5400 \| 0.9669 \| \| 0.0664 \| 1.83 \| 3020 \| 0.3174 \| 0.4616 \| 0.6548 \| 0.9101 \| nan \| 0.3318 \| 0.8963 \| 0.0 \| 0.8294 \| 0.8993 \| 0.9718 \| 0.0 \| 0.2594 \| 0.7513 \| 0.0 \| 0.7654 \| 0.4884 \| 0.9670 \| \| 0.5802 \| 1.84 \| 3040 \| 0.3003 \| 0.4694 \| 0.6533 \| 0.9138 \| nan \| 0.3512 \| 0.8930 \| 0.0 \| 0.8604 \| 0.8446 \| 0.9707 \| 0.0 \| 0.2368 \| 0.7722 \| 0.0 \| 0.7820 \| 0.5277 \| 0.9668 \| \| 0.158 \| 1.86 \| 3060 \| 0.2986 \| 0.4762 \| 0.6403 \| 0.9197 \| nan \| 0.3481 \| 0.8854 \| 0.0 \| 0.9133 \| 0.7247 \| 0.9701 \| 0.0 \| 0.2633 \| 0.7703 \| 0.0 \| 0.7969 \| 0.5362 \| 0.9669 \| \| 0.1517 \| 1.87 \| 3080 \| 0.3548 \| 0.4843 \| 0.6427 \| 0.9240 \| nan \| 0.4025 \| 0.8833 \| 0.0 \| 0.9451 \| 0.6554 \| 0.9699 \| 0.0 \| 0.3066 \| 0.7910 \| 0.0 \| 0.7953 \| 0.5295 \| 0.9675 \| \| 0.5864 \| 1.88 \| 3100 \| 0.3026 \| 0.4797 \| 0.6438 \| 0.9193 \| nan \| 0.3682 \| 0.8682 \| 0.0 \| 0.9130 \| 0.7426 \| 0.9709 \| 0.0 \| 0.2850 \| 0.7875 \| 0.0 \| 0.7846 \| 0.5333 \| 0.9676 \| \| 0.4852 \| 1.89 \| 3120 \| 0.3118 \| 0.4811 \| 0.6588 \| 0.9188 \| nan \| 0.3660 \| 0.8840 \| 0.0 \| 0.8897 \| 0.8429 \| 0.9703 \| 0.0 \| 0.2981 \| 0.7871 \| 0.0 \| 0.7857 \| 0.5299 \| 0.9672 \| \| 0.2932 \| 1.9 \| 3140 \| 0.2926 \| 0.4920 \| 0.6502 \| 0.9245 \| nan \| 0.3744 \| 0.8931 \| 0.0 \| 0.9253 \| 0.7370 \| 0.9717 \| 0.0 \| 0.3025 \| 0.7904 \| 0.0 \| 0.8108 \| 0.5731 \| 0.9675 \| \| 0.2021 \| 1.92 \| 3160 \| 0.3038 \| 0.4779 \| 0.6211 \| 0.9178 \| nan \| 0.3510 \| 0.8491 \| 0.0 \| 0.9417 \| 0.6159 \| 0.9689 \| 0.0 \| 0.2831 \| 0.7536 \| 0.0 \| 0.8076 \| 0.5351 \| 0.9663 \| \| 0.3806 \| 1.93 \| 3180 \| 0.3182 \| 0.4760 \| 0.6492 \| 0.9141 \| nan \| 0.3445 \| 0.8570 \| 0.0 \| 0.8832 \| 0.8411 \| 0.9692 \| 0.0 \| 0.2641 \| 0.7392 \| 0.0 \| 0.7967 \| 0.5651 \| 0.9670 \| \| 0.1496 \| 1.94 \| 3200 \| 0.3361 \| 0.4753 \| 0.6422 \| 0.9156 \| nan \| 0.3138 \| 0.9011 \| 0.0 \| 0.8849 \| 0.7859 \| 0.9678 \| 0.0 \| 0.2435 \| 0.7803 \| 0.0 \| 0.7946 \| 0.5434 \| 0.9651 \| \| 0.0671 \| 1.95 \| 3220 \| 0.3265 \| 0.4742 \| 0.6578 \| 0.9147 \| nan \| 0.3656 \| 0.8798 \| 0.0 \| 0.8741 \| 0.8584 \| 0.9687 \| 0.0 \| 0.2816 \| 0.7842 \| 0.0 \| 0.7832 \| 0.5041 \| 0.9659 \| \| 0.15 \| 1.96 \| 3240 \| 0.3244 \| 0.4739 \| 0.6427 \| 0.9158 \| nan \| 0.3541 \| 0.8804 \| 0.0 \| 0.8972 \| 0.7564 \| 0.9682 \| 0.0 \| 0.2528 \| 0.7590 \| 0.0 \| 0.7978 \| 0.5417 \| 0.9660 \| \| 0.1933 \| 1.98 \| 3260 \| 0.3516 \| 0.4773 \| 0.6542 \| 0.9174 \| nan \| 0.3953 \| 0.9202 \| 0.0 \| 0.8821 \| 0.7583 \| 0.9693 \| 0.0 \| 0.2658 \| 0.7510 \| 0.0 \| 0.8026 \| 0.5550 \| 0.9665 \| \| 0.1333 \| 1.99 \| 3280 \| 0.3080 \| 0.4830 \| 0.6444 \| 0.9189 \| nan \| 0.3587 \| 0.9054 \| 0.0 \| 0.9009 \| 0.7318 \| 0.9695 \| 0.0 \| 0.3008 \| 0.7536 \| 0.0 \| 0.8027 \| 0.5574 \| 0.9667 \| \| 0.1016 \| 2.0 \| 3300 \| 0.3048 \| 0.4824 \| 0.6479 \| 0.9180 \| nan \| 0.3595 \| 0.8686 \| 0.0 \| 0.9005 \| 0.7879 \| 0.9707 \| 0.0 \| 0.3022 \| 0.7380 \| 0.0 \| 0.8046 \| 0.5642 \| 0.9675 \| \| 0.3692 \| 2.01 \| 3320 \| 0.3021 \| 0.4761 \| 0.6468 \| 0.9144 \| nan \| 0.3537 \| 0.8509 \| 0.0 \| 0.8868 \| 0.8186 \| 0.9706 \| 0.0 \| 0.2802 \| 0.7268 \| 0.0 \| 0.8011 \| 0.5570 \| 0.9672 \| \| 0.8706 \| 2.03 \| 3340 \| 0.3320 \| 0.4796 \| 0.6430 \| 0.9199 \| nan \| 0.3116 \| 0.9048 \| 0.0 \| 0.8987 \| 0.7727 \| 0.9701 \| 0.0 \| 0.2747 \| 0.7327 \| 0.0 \| 0.8107 \| 0.5718 \| 0.9674 \| \| 0.3265 \| 2.04 \| 3360 \| 0.3169 \| 0.4765 \| 0.6552 \| 0.9168 \| nan \| 0.3639 \| 0.9043 \| 0.0 \| 0.8762 \| 0.8171 \| 0.9698 \| 0.0 \| 0.2622 \| 0.7622 \| 0.0 \| 0.7963 \| 0.5480 \| 0.9671 \| \| 0.1013 \| 2.05 \| 3380 \| 0.3117 \| 0.4774 \| 0.6759 \| 0.9165 \| nan \| 0.4382 \| 0.9000 \| 0.0 \| 0.8647 \| 0.8830 \| 0.9696 \| 0.0 \| 0.2732 \| 0.7770 \| 0.0 \| 0.7877 \| 0.5370 \| 0.9668 \| \| 0.2655 \| 2.06 \| 3400 \| 0.3157 \| 0.4852 \| 0.6580 \| 0.9201 \| nan \| 0.3775 \| 0.8841 \| 0.0 \| 0.9011 \| 0.8152 \| 0.9698 \| 0.0 \| 0.3075 \| 0.7750 \| 0.0 \| 0.7963 \| 0.5505 \| 0.9673 \| \| 0.321 \| 2.07 \| 3420 \| 0.2988 \| 0.4807 \| 0.6601 \| 0.9180 \| nan \| 0.3850 \| 0.8639 \| 0.0 \| 0.8911 \| 0.8497 \| 0.9709 \| 0.0 \| 0.2850 \| 0.7833 \| 0.0 \| 0.7893 \| 0.5400 \| 0.9673 \| \| 0.1012 \| 2.09 \| 3440 \| 0.3125 \| 0.4939 \| 0.6538 \| 0.9251 \| nan \| 0.3966 \| 0.8959 \| 0.0 \| 0.9308 \| 0.7287 \| 0.9706 \| 0.0 \| 0.3223 \| 0.7911 \| 0.0 \| 0.8092 \| 0.5669 \| 0.9676 \| \| 0.412 \| 2.1 \| 3460 \| 0.3296 \| 0.4815 \| 0.6585 \| 0.9182 \| nan \| 0.3481 \| 0.9184 \| 0.0 \| 0.8726 \| 0.8417 \| 0.9701 \| 0.0 \| 0.2909 \| 0.7612 \| 0.0 \| 0.7971 \| 0.5547 \| 0.9666 \| \| 0.1383 \| 2.11 \| 3480 \| 0.3082 \| 0.4884 \| 0.6540 \| 0.9225 \| nan \| 0.3569 \| 0.9140 \| 0.0 \| 0.9056 \| 0.7776 \| 0.9699 \| 0.0 \| 0.2999 \| 0.7737 \| 0.0 \| 0.8086 \| 0.5698 \| 0.9665 \| \| 0.1925 \| 2.12 \| 3500 \| 0.3206 \| 0.4821 \| 0.6536 \| 0.9182 \| nan \| 0.3449 \| 0.9109 \| 0.0 \| 0.8844 \| 0.8124 \| 0.9688 \| 0.0 \| 0.2829 \| 0.7721 \| 0.0 \| 0.7977 \| 0.5564 \| 0.9659 \| \| 0.6483 \| 2.13 \| 3520 \| 0.3155 \| 0.4756 \| 0.6540 \| 0.9136 \| nan \| 0.3536 \| 0.8712 \| 0.0 \| 0.8698 \| 0.8595 \| 0.9697 \| 0.0 \| 0.2857 \| 0.7718 \| 0.0 \| 0.7790 \| 0.5269 \| 0.9660 \| \| 0.4534 \| 2.15 \| 3540 \| 0.3218 \| 0.4792 \| 0.6513 \| 0.9170 \| nan \| 0.3516 \| 0.9113 \| 0.0 \| 0.8804 \| 0.7955 \| 0.9690 \| 0.0 \| 0.2800 \| 0.7792 \| 0.0 \| 0.7901 \| 0.5392 \| 0.9657 \| \| 0.1899 \| 2.16 \| 3560 \| 0.3131 \| 0.4741 \| 0.6607 \| 0.9143 \| nan \| 0.3562 \| 0.9017 \| 0.0 \| 0.8519 \| 0.8837 \| 0.9708 \| 0.0 \| 0.2942 \| 0.7720 \| 0.0 \| 0.7761 \| 0.5095 \| 0.9666 \| \| 0.6685 \| 2.17 \| 3580 \| 0.3230 \| 0.4836 \| 0.6603 \| 0.9195 \| nan \| 0.3811 \| 0.9106 \| 0.0 \| 0.8880 \| 0.8125 \| 0.9694 \| 0.0 \| 0.3074 \| 0.7791 \| 0.0 \| 0.7905 \| 0.5417 \| 0.9664 \| \| 0.1743 \| 2.18 \| 3600 \| 0.3060 \| 0.4764 \| 0.6571 \| 0.9157 \| nan \| 0.3722 \| 0.9087 \| 0.0 \| 0.8655 \| 0.8258 \| 0.9704 \| 0.0 \| 0.2614 \| 0.7796 \| 0.0 \| 0.7862 \| 0.5412 \| 0.9661 \| \| 0.2988 \| 2.2 \| 3620 \| 0.3150 \| 0.4883 \| 0.6569 \| 0.9234 \| nan \| 0.3869 \| 0.8954 \| 0.0 \| 0.9154 \| 0.7728 \| 0.9709 \| 0.0 \| 0.2894 \| 0.7913 \| 0.0 \| 0.8052 \| 0.5647 \| 0.9671 \| \| 0.199 \| 2.21 \| 3640 \| 0.3073 \| 0.4799 \| 0.6563 \| 0.9205 \| nan \| 0.3823 \| 0.9094 \| 0.0 \| 0.8966 \| 0.7794 \| 0.9700 \| 0.0 \| 0.2462 \| 0.7752 \| 0.0 \| 0.8058 \| 0.5652 \| 0.9668 \| \| 0.356 \| 2.22 \| 3660 \| 0.3088 \| 0.4787 \| 0.6539 \| 0.9202 \| nan \| 0.3759 \| 0.8945 \| 0.0 \| 0.9014 \| 0.7813 \| 0.9701 \| 0.0 \| 0.2406 \| 0.7759 \| 0.0 \| 0.8051 \| 0.5631 \| 0.9663 \| \| 1.2003 \| 2.23 \| 3680 \| 0.3037 \| 0.4829 \| 0.6551 \| 0.9212 \| nan \| 0.4053 \| 0.8855 \| 0.0 \| 0.9139 \| 0.7558 \| 0.9701 \| 0.0 \| 0.2744 \| 0.7755 \| 0.0 \| 0.8075 \| 0.5561 \| 0.9666 \| \| 0.1801 \| 2.24 \| 3700 \| 0.3155 \| 0.4837 \| 0.6570 \| 0.9202 \| nan \| 0.4074 \| 0.8924 \| 0.0 \| 0.9082 \| 0.7655 \| 0.9688 \| 0.0 \| 0.3066 \| 0.7867 \| 0.0 \| 0.7995 \| 0.5269 \| 0.9664 \| \| 0.2767 \| 2.26 \| 3720 \| 0.3365 \| 0.4783 \| 0.6767 \| 0.9163 \| nan \| 0.4490 \| 0.9065 \| 0.0000 \| 0.8610 \| 0.8735 \| 0.9699 \| 0.0 \| 0.2931 \| 0.7931 \| 0.0000 \| 0.7863 \| 0.5086 \| 0.9666 \| \| 0.2641 \| 2.27 \| 3740 \| 0.3103 \| 0.4783 \| 0.6719 \| 0.9153 \| nan \| 0.4315 \| 0.8901 \| 0.0 \| 0.8631 \| 0.8765 \| 0.9700 \| 0.0 \| 0.2993 \| 0.7883 \| 0.0 \| 0.7826 \| 0.5114 \| 0.9668 \| \| 0.7382 \| 2.28 \| 3760 \| 0.3684 \| 0.4819 \| 0.6608 \| 0.9156 \| nan \| 0.3913 \| 0.8903 \| 0.0 \| 0.8832 \| 0.8335 \| 0.9667 \| 0.0 \| 0.3122 \| 0.7635 \| 0.0 \| 0.7916 \| 0.5410 \| 0.9651 \| \| 0.1887 \| 2.29 \| 3780 \| 0.3380 \| 0.4850 \| 0.6672 \| 0.9190 \| nan \| 0.3872 \| 0.9062 \| 0.0 \| 0.8795 \| 0.8607 \| 0.9693 \| 0.0 \| 0.3182 \| 0.7798 \| 0.0 \| 0.7920 \| 0.5385 \| 0.9668 \| \| 0.4301 \| 2.3 \| 3800 \| 0.3126 \| 0.4902 \| 0.6655 \| 0.9219 \| nan \| 0.4399 \| 0.8957 \| 0.0 \| 0.9040 \| 0.7823 \| 0.9711 \| 0.0 \| 0.3320 \| 0.7915 \| 0.0 \| 0.7986 \| 0.5423 \| 0.9673 \| \| 0.0796 \| 2.32 \| 3820 \| 0.3078 \| 0.4931 \| 0.6700 \| 0.9231 \| nan \| 0.4574 \| 0.8945 \| 0.0 \| 0.9082 \| 0.7887 \| 0.9712 \| 0.0 \| 0.3244 \| 0.7902 \| 0.0 \| 0.8045 \| 0.5654 \| 0.9673 \| \| 0.5856 \| 2.33 \| 3840 \| 0.3280 \| 0.4860 \| 0.6739 \| 0.9186 \| nan \| 0.4448 \| 0.8992 \| 0.0 \| 0.8803 \| 0.8492 \| 0.9697 \| 0.0 \| 0.3209 \| 0.7944 \| 0.0 \| 0.7874 \| 0.5328 \| 0.9665 \| \| 0.3133 \| 2.34 \| 3860 \| 0.3306 \| 0.4901 \| 0.6625 \| 0.9220 \| nan \| 0.4158 \| 0.8912 \| 0.0 \| 0.9104 \| 0.7877 \| 0.9698 \| 0.0 \| 0.3321 \| 0.7919 \| 0.0 \| 0.7933 \| 0.5461 \| 0.9669 \| \| 0.1759 \| 2.35 \| 3880 \| 0.3318 \| 0.4893 \| 0.6707 \| 0.9209 \| nan \| 0.4327 \| 0.9019 \| 0.0 \| 0.8910 \| 0.8278 \| 0.9705 \| 0.0 \| 0.3309 \| 0.7941 \| 0.0 \| 0.7933 \| 0.5402 \| 0.9669 \| \| 0.2173 \| 2.37 \| 3900 \| 0.3227 \| 0.4842 \| 0.6738 \| 0.9186 \| nan \| 0.4241 \| 0.9066 \| 0.0 \| 0.8627 \| 0.8765 \| 0.9731 \| 0.0 \| 0.3222 \| 0.7925 \| 0.0 \| 0.7836 \| 0.5238 \| 0.9670 \| \| 0.3338 \| 2.38 \| 3920 \| 0.3146 \| 0.4892 \| 0.6725 \| 0.9208 \| nan \| 0.4330 \| 0.8966 \| 0.0 \| 0.8909 \| 0.8443 \| 0.9704 \| 0.0 \| 0.3364 \| 0.7922 \| 0.0 \| 0.7910 \| 0.5381 \| 0.9671 \| \| 0.1098 \| 2.39 \| 3940 \| 0.3051 \| 0.4850 \| 0.6648 \| 0.9186 \| nan \| 0.4086 \| 0.9006 \| 0.0 \| 0.8823 \| 0.8271 \| 0.9703 \| 0.0 \| 0.2986 \| 0.7912 \| 0.0 \| 0.7915 \| 0.5466 \| 0.9668 \| \| 0.2892 \| 2.4 \| 3960 \| 0.3277 \| 0.4914 \| 0.6582 \| 0.9235 \| nan \| 0.4131 \| 0.9019 \| 0.0 \| 0.9224 \| 0.7429 \| 0.9691 \| 0.0 \| 0.3010 \| 0.7897 \| 0.0 \| 0.8109 \| 0.5718 \| 0.9666 \| \| 0.7496 \| 2.41 \| 3980 \| 0.3239 \| 0.4799 \| 0.6699 \| 0.9166 \| nan \| 0.4203 \| 0.9044 \| 0.0000 \| 0.8701 \| 0.8556 \| 0.9690 \| 0.0 \| 0.2835 \| 0.7871 \| 0.0000 \| 0.7897 \| 0.5325 \| 0.9666 \| \| 0.1267 \| 2.43 \| 4000 \| 0.3030 \| 0.4849 \| 0.6719 \| 0.9188 \| nan \| 0.4334 \| 0.8752 \| 0.0 \| 0.8889 \| 0.8636 \| 0.9700 \| 0.0 \| 0.3219 \| 0.7904 \| 0.0 \| 0.7879 \| 0.5268 \| 0.9670 \| \| 0.1651 \| 2.44 \| 4020 \| 0.3301 \| 0.4801 \| 0.6728 \| 0.9161 \| nan \| 0.4176 \| 0.8973 \| 0.0 \| 0.8652 \| 0.8877 \| 0.9692 \| 0.0 \| 0.2969 \| 0.7938 \| 0.0 \| 0.7830 \| 0.5201 \| 0.9669 \| \| 0.3399 \| 2.45 \| 4040 \| 0.3104 \| 0.4728 \| 0.6705 \| 0.9147 \| nan \| 0.4008 \| 0.8929 \| 0.0000 \| 0.8517 \| 0.9064 \| 0.9712 \| 0.0 \| 0.2694 \| 0.7927 \| 0.0000 \| 0.7785 \| 0.5023 \| 0.9669 \| \| 0.399 \| 2.46 \| 4060 \| 0.2932 \| 0.4825 \| 0.6653 \| 0.9207 \| nan \| 0.4182 \| 0.8889 \| 0.0000 \| 0.8964 \| 0.8174 \| 0.9711 \| 0.0 \| 0.2890 \| 0.7885 \| 0.0000 \| 0.7953 \| 0.5376 \| 0.9672 \| \| 0.1291 \| 2.47 \| 4080 \| 0.3093 \| 0.4793 \| 0.6726 \| 0.9178 \| nan \| 0.4242 \| 0.8888 \| 0.0 \| 0.8791 \| 0.8745 \| 0.9691 \| 0.0 \| 0.2900 \| 0.7911 \| 0.0 \| 0.7879 \| 0.5195 \| 0.9669 \| \| 0.0833 \| 2.49 \| 4100 \| 0.3038 \| 0.4784 \| 0.6705 \| 0.9162 \| nan \| 0.4206 \| 0.8873 \| 0.0000 \| 0.8697 \| 0.8757 \| 0.9699 \| 0.0 \| 0.2837 \| 0.7942 \| 0.0000 \| 0.7854 \| 0.5195 \| 0.9664 \| \| 0.4351 \| 2.5 \| 4120 \| 0.3084 \| 0.4812 \| 0.6710 \| 0.9181 \| nan \| 0.4290 \| 0.8976 \| 0.0 \| 0.8771 \| 0.8520 \| 0.9702 \| 0.0 \| 0.2869 \| 0.7811 \| 0.0 \| 0.7942 \| 0.5398 \| 0.9666 \| \| 0.6208 \| 2.51 \| 4140 \| 0.3039 \| 0.4776 \| 0.6523 \| 0.9168 \| nan \| 0.3598 \| 0.8827 \| 0.0 \| 0.8828 \| 0.8175 \| 0.9710 \| 0.0 \| 0.2525 \| 0.7696 \| 0.0 \| 0.7989 \| 0.5560 \| 0.9663 \| \| 0.3642 \| 2.52 \| 4160 \| 0.3249 \| 0.4892 \| 0.6676 \| 0.9218 \| nan \| 0.4016 \| 0.9010 \| 0.0 \| 0.8974 \| 0.8360 \| 0.9699 \| 0.0 \| 0.3151 \| 0.7717 \| 0.0 \| 0.8056 \| 0.5650 \| 0.9670 \| \| 0.6865 \| 2.53 \| 4180 \| 0.3203 \| 0.4934 \| 0.6659 \| 0.9243 \| nan \| 0.4052 \| 0.8954 \| 0.0 \| 0.9134 \| 0.8108 \| 0.9708 \| 0.0 \| 0.3057 \| 0.7841 \| 0.0 \| 0.8130 \| 0.5842 \| 0.9672 \| \| 1.0893 \| 2.55 \| 4200 \| 0.2951 \| 0.4863 \| 0.6613 \| 0.9193 \| nan \| 0.4150 \| 0.8548 \| 0.0000 \| 0.9060 \| 0.8212 \| 0.9707 \| 0.0 \| 0.2905 \| 0.7699 \| 0.0000 \| 0.8057 \| 0.5707 \| 0.9670 \| \| 0.4914 \| 2.56 \| 4220 \| 0.2988 \| 0.4951 \| 0.6731 \| 0.9224 \| nan \| 0.4553 \| 0.8745 \| 0.0000 \| 0.9001 \| 0.8352 \| 0.9734 \| 0.0 \| 0.3491 \| 0.7804 \| 0.0000 \| 0.8038 \| 0.5630 \| 0.9690 \| \| 0.2864 \| 2.57 \| 4240 \| 0.3029 \| 0.4946 \| 0.6670 \| 0.9232 \| nan \| 0.4448 \| 0.8753 \| 0.0 \| 0.9182 \| 0.7926 \| 0.9710 \| 0.0 \| 0.3187 \| 0.7765 \| 0.0 \| 0.8127 \| 0.5870 \| 0.9673 \| \| 0.1695 \| 2.58 \| 4260 \| 0.3356 \| 0.5014 \| 0.6749 \| 0.9263 \| nan \| 0.4545 \| 0.8932 \| 0.0 \| 0.9249 \| 0.8066 \| 0.9702 \| 0.0 \| 0.3657 \| 0.7799 \| 0.0 \| 0.8119 \| 0.5847 \| 0.9676 \| \| 0.5746 \| 2.6 \| 4280 \| 0.3106 \| 0.5024 \| 0.6754 \| 0.9260 \| nan \| 0.4610 \| 0.8796 \| 0.0 \| 0.9206 \| 0.8187 \| 0.9723 \| 0.0 \| 0.3690 \| 0.7837 \| 0.0 \| 0.8137 \| 0.5818 \| 0.9683 \| \| 0.2629 \| 2.61 \| 4300 \| 0.3153 \| 0.5020 \| 0.6771 \| 0.9269 \| nan \| 0.4768 \| 0.9000 \| 0.0 \| 0.9266 \| 0.7890 \| 0.9702 \| 0.0 \| 0.3420 \| 0.7826 \| 0.0 \| 0.8218 \| 0.6005 \| 0.9671 \| \| 0.4151 \| 2.62 \| 4320 \| 0.2932 \| 0.4957 \| 0.6677 \| 0.9229 \| nan \| 0.4382 \| 0.8798 \| 0.0 \| 0.9142 \| 0.8031 \| 0.9706 \| 0.0 \| 0.3086 \| 0.7840 \| 0.0 \| 0.8159 \| 0.5944 \| 0.9668 \| \| 0.1169 \| 2.63 \| 4340 \| 0.2940 \| 0.4995 \| 0.6683 \| 0.9244 \| nan \| 0.4620 \| 0.8738 \| 0.0 \| 0.9295 \| 0.7744 \| 0.9701 \| 0.0 \| 0.3315 \| 0.7860 \| 0.0 \| 0.8161 \| 0.5960 \| 0.9668 \| \| 0.1379 \| 2.64 \| 4360 \| 0.2958 \| 0.5047 \| 0.6750 \| 0.9264 \| nan \| 0.4636 \| 0.8776 \| 0.0000 \| 0.9138 \| 0.8195 \| 0.9754 \| 0.0 \| 0.3667 \| 0.7920 \| 0.0000 \| 0.8160 \| 0.5891 \| 0.9689 \| \| 0.1106 \| 2.66 \| 4380 \| 0.3271 \| 0.5063 \| 0.6866 \| 0.9278 \| nan \| 0.5061 \| 0.9005 \| 0.0 \| 0.9251 \| 0.8175 \| 0.9702 \| 0.0 \| 0.3773 \| 0.7976 \| 0.0 \| 0.8143 \| 0.5878 \| 0.9673 \| \| 0.6561 \| 2.67 \| 4400 \| 0.3072 \| 0.4820 \| 0.6689 \| 0.9179 \| nan \| 0.4274 \| 0.9043 \| 0.0001 \| 0.8736 \| 0.8372 \| 0.9707 \| 0.0 \| 0.2704 \| 0.7896 \| 0.0001 \| 0.7980 \| 0.5493 \| 0.9665 \| \| 1.0281 \| 2.68 \| 4420 \| 0.3102 \| 0.4815 \| 0.6878 \| 0.9179 \| nan \| 0.4964 \| 0.9069 \| 0.0000 \| 0.8569 \| 0.8942 \| 0.9721 \| 0.0 \| 0.3072 \| 0.7945 \| 0.0000 \| 0.7899 \| 0.5115 \| 0.9676 \| \| 0.2507 \| 2.69 \| 4440 \| 0.3143 \| 0.4839 \| 0.6758 \| 0.9200 \| nan \| 0.4543 \| 0.9103 \| 0.0 \| 0.8806 \| 0.8390 \| 0.9704 \| 0.0 \| 0.2914 \| 0.7904 \| 0.0 \| 0.8009 \| 0.5380 \| 0.9668 \| \| 1.8848 \| 2.7 \| 4460 \| 0.3204 \| 0.4827 \| 0.6763 \| 0.9186 \| nan \| 0.4533 \| 0.9111 \| 0.0000 \| 0.8766 \| 0.8475 \| 0.9690 \| 0.0 \| 0.2813 \| 0.7838 \| 0.0000 \| 0.8023 \| 0.5453 \| 0.9664 \| \| 0.2177 \| 2.72 \| 4480 \| 0.3152 \| 0.4841 \| 0.6658 \| 0.9196 \| nan \| 0.4292 \| 0.8986 \| 0.0 \| 0.8922 \| 0.8050 \| 0.9699 \| 0.0 \| 0.2702 \| 0.7881 \| 0.0 \| 0.8063 \| 0.5579 \| 0.9666 \| \| 0.2683 \| 2.73 \| 4500 \| 0.3165 \| 0.4810 \| 0.6710 \| 0.9176 \| nan \| 0.4378 \| 0.9003 \| 0.0 \| 0.8797 \| 0.8395 \| 0.9687 \| 0.0 \| 0.2755 \| 0.7847 \| 0.0 \| 0.7996 \| 0.5408 \| 0.9661 \| \| 0.4168 \| 2.74 \| 4520 \| 0.3347 \| 0.4920 \| 0.6748 \| 0.9233 \| nan \| 0.4527 \| 0.9122 \| 0.0 \| 0.9021 \| 0.8121 \| 0.9698 \| 0.0 \| 0.3271 \| 0.7776 \| 0.0 \| 0.8105 \| 0.5616 \| 0.9670 \| \| 0.89 \| 2.75 \| 4540 \| 0.3345 \| 0.4875 \| 0.6709 \| 0.9211 \| nan \| 0.4383 \| 0.9006 \| 0.0 \| 0.8985 \| 0.8189 \| 0.9690 \| 0.0 \| 0.2903 \| 0.7895 \| 0.0 \| 0.8071 \| 0.5591 \| 0.9666 \| \| 0.3392 \| 2.77 \| 4560 \| 0.3020 \| 0.4852 \| 0.6730 \| 0.9197 \| nan \| 0.4509 \| 0.8977 \| 0.0 \| 0.8886 \| 0.8312 \| 0.9698 \| 0.0 \| 0.2847 \| 0.7921 \| 0.0 \| 0.8024 \| 0.5506 \| 0.9665 \| \| 0.4607 \| 2.78 \| 4580 \| 0.3230 \| 0.4935 \| 0.6757 \| 0.9238 \| nan \| 0.4640 \| 0.9097 \| 0.0 \| 0.9075 \| 0.8036 \| 0.9696 \| 0.0 \| 0.3195 \| 0.7866 \| 0.0 \| 0.8116 \| 0.5697 \| 0.9670 \| \| 0.3654 \| 2.79 \| 4600 \| 0.3109 \| 0.4928 \| 0.6672 \| 0.9231 \| nan \| 0.4508 \| 0.8907 \| 0.0 \| 0.9169 \| 0.7750 \| 0.9698 \| 0.0 \| 0.3078 \| 0.7942 \| 0.0 \| 0.8102 \| 0.5704 \| 0.9670 \| \| 0.4694 \| 2.8 \| 4620 \| 0.3250 \| 0.4919 \| 0.6806 \| 0.9221 \| nan \| 0.4773 \| 0.9033 \| 0.0 \| 0.8870 \| 0.8435 \| 0.9722 \| 0.0 \| 0.3328 \| 0.7919 \| 0.0 \| 0.8015 \| 0.5489 \| 0.9679 \| \| 0.7987 \| 2.81 \| 4640 \| 0.3292 \| 0.4954 \| 0.6686 \| 0.9240 \| nan \| 0.4267 \| 0.8898 \| 0.0 \| 0.9178 \| 0.8078 \| 0.9696 \| 0.0 \| 0.3340 \| 0.7982 \| 0.0 \| 0.8063 \| 0.5622 \| 0.9672 \| \| 0.1422 \| 2.83 \| 4660 \| 0.3098 \| 0.4980 \| 0.6793 \| 0.9252 \| nan \| 0.4912 \| 0.9051 \| 0.0 \| 0.9137 \| 0.7954 \| 0.9706 \| 0.0 \| 0.3358 \| 0.7895 \| 0.0 \| 0.8146 \| 0.5789 \| 0.9674 \| \| 0.2764 \| 2.84 \| 4680 \| 0.2950 \| 0.4955 \| 0.6780 \| 0.9236 \| nan \| 0.4777 \| 0.8876 \| 0.0 \| 0.9065 \| 0.8249 \| 0.9715 \| 0.0 \| 0.3347 \| 0.7929 \| 0.0 \| 0.8095 \| 0.5634 \| 0.9678 \| \| 0.7027 \| 2.85 \| 4700 \| 0.3178 \| 0.4956 \| 0.6811 \| 0.9237 \| nan \| 0.4798 \| 0.9043 \| 0.0 \| 0.9009 \| 0.8310 \| 0.9708 \| 0.0 \| 0.3432 \| 0.7879 \| 0.0 \| 0.8083 \| 0.5622 \| 0.9676 \| \| 0.1181 \| 2.86 \| 4720 \| 0.3131 \| 0.4926 \| 0.6812 \| 0.9229 \| nan \| 0.4841 \| 0.9019 \| 0.0 \| 0.8987 \| 0.8317 \| 0.9705 \| 0.0 \| 0.3250 \| 0.7894 \| 0.0 \| 0.8072 \| 0.5593 \| 0.9673 \| \| 0.1929 \| 2.87 \| 4740 \| 0.3345 \| 0.4983 \| 0.6796 \| 0.9246 \| nan \| 0.4671 \| 0.9066 \| 0.0 \| 0.9087 \| 0.8258 \| 0.9697 \| 0.0 \| 0.3638 \| 0.7872 \| 0.0 \| 0.8075 \| 0.5620 \| 0.9673 \| \| 0.1227 \| 2.89 \| 4760 \| 0.3008 \| 0.4923 \| 0.6766 \| 0.9215 \| nan \| 0.4699 \| 0.8828 \| 0.0 \| 0.8972 \| 0.8386 \| 0.9714 \| 0.0 \| 0.3415 \| 0.7942 \| 0.0 \| 0.8013 \| 0.5408 \| 0.9681 \| \| 0.2807 \| 2.9 \| 4780 \| 0.3065 \| 0.4867 \| 0.6674 \| 0.9185 \| nan \| 0.4280 \| 0.8885 \| 0.0 \| 0.8864 \| 0.8315 \| 0.9701 \| 0.0 \| 0.3093 \| 0.7890 \| 0.0 \| 0.7973 \| 0.5438 \| 0.9672 \| \| 0.1685 \| 2.91 \| 4800 \| 0.3054 \| 0.4890 \| 0.6712 \| 0.9206 \| nan \| 0.4508 \| 0.8940 \| 0.0 \| 0.8947 \| 0.8176 \| 0.9704 \| 0.0 \| 0.3112 \| 0.7901 \| 0.0 \| 0.8020 \| 0.5527 \| 0.9672 \| \| 0.2884 \| 2.92 \| 4820 \| 0.3072 \| 0.4896 \| 0.6770 \| 0.9201 \| nan \| 0.4773 \| 0.8843 \| 0.0 \| 0.8932 \| 0.8372 \| 0.9703 \| 0.0 \| 0.3251 \| 0.7914 \| 0.0 \| 0.7997 \| 0.5440 \| 0.9673 \| \| 0.6886 \| 2.94 \| 4840 \| 0.3077 \| 0.4936 \| 0.6643 \| 0.9225 \| nan \| 0.4469 \| 0.8662 \| 0.0 \| 0.9228 \| 0.7796 \| 0.9704 \| 0.0 \| 0.3339 \| 0.7868 \| 0.0 \| 0.8057 \| 0.5614 \| 0.9676 \| \| 0.3344 \| 2.95 \| 4860 \| 0.3017 \| 0.4943 \| 0.6733 \| 0.9225 \| nan \| 0.4660 \| 0.8746 \| 0.0 \| 0.9098 \| 0.8184 \| 0.9711 \| 0.0 \| 0.3488 \| 0.7900 \| 0.0 \| 0.8032 \| 0.5498 \| 0.9681 \| \| 0.1385 \| 2.96 \| 4880 \| 0.3149 \| 0.4846 \| 0.6688 \| 0.9180 \| nan \| 0.4436 \| 0.8811 \| 0.0000 \| 0.8905 \| 0.8288 \| 0.9692 \| 0.0 \| 0.2956 \| 0.7873 \| 0.0000 \| 0.7986 \| 0.5441 \| 0.9667 \| \| 0.559 \| 2.97 \| 4900 \| 0.3012 \| 0.4827 \| 0.6598 \| 0.9172 \| nan \| 0.4204 \| 0.8584 \| 0.0000 \| 0.8997 \| 0.8108 \| 0.9693 \| 0.0 \| 0.2854 \| 0.7789 \| 0.0000 \| 0.7992 \| 0.5489 \| 0.9666 \| \| 0.0576 \| 2.98 \| 4920 \| 0.3116 \| 0.4880 \| 0.6794 \| 0.9200 \| nan \| 0.4841 \| 0.8852 \| 0.0 \| 0.8866 \| 0.8489 \| 0.9714 \| 0.0 \| 0.3229 \| 0.7898 \| 0.0 \| 0.7986 \| 0.5372 \| 0.9678 \| \| 0.1294 \| 3.0 \| 4940 \| 0.3304 \| 0.4794 \| 0.6650 \| 0.9144 \| nan \| 0.4315 \| 0.8895 \| 0.0 \| 0.8740 \| 0.8271 \| 0.9678 \| 0.0 \| 0.2745 \| 0.7784 \| 0.0 \| 0.7930 \| 0.5438 \| 0.9658 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3	[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]
Aassemtkt/segformer-b0-finetuned-drugs-in-bins-nov-23	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Aassemtkt/v0.1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "unlabeled", "drug-blister" ]
Aassemtkt/segformer-b3-finetuned-drugs-in-bins-nov-23	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b3-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the Aassemtkt/v0.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.0494 - Mean Iou: 0.4900 - Mean Accuracy: 0.9799 - Overall Accuracy: 0.9799 - Accuracy Unlabeled: nan - Accuracy Drug-blister: 0.9799 - Iou Unlabeled: 0.0 - Iou Drug-blister: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Drug-blister \| Iou Unlabeled \| Iou Drug-blister \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:---------------------:\|:-------------:\|:----------------:\| \| 0.4816 \| 0.14 \| 20 \| 0.2387 \| 0.4744 \| 0.9488 \| 0.9488 \| nan \| 0.9488 \| 0.0 \| 0.9488 \| \| 0.1071 \| 0.29 \| 40 \| 0.0969 \| 0.4766 \| 0.9532 \| 0.9532 \| nan \| 0.9532 \| 0.0 \| 0.9532 \| \| 0.102 \| 0.43 \| 60 \| 0.0701 \| 0.4799 \| 0.9599 \| 0.9599 \| nan \| 0.9599 \| 0.0 \| 0.9599 \| \| 0.0828 \| 0.58 \| 80 \| 0.0748 \| 0.4865 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \| \| 0.2944 \| 0.72 \| 100 \| 0.0517 \| 0.4816 \| 0.9633 \| 0.9633 \| nan \| 0.9633 \| 0.0 \| 0.9633 \| \| 0.0308 \| 0.86 \| 120 \| 0.0493 \| 0.4854 \| 0.9709 \| 0.9709 \| nan \| 0.9709 \| 0.0 \| 0.9709 \| \| 0.0247 \| 1.01 \| 140 \| 0.0488 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \| \| 0.0194 \| 1.15 \| 160 \| 0.0447 \| 0.4864 \| 0.9728 \| 0.9728 \| nan \| 0.9728 \| 0.0 \| 0.9728 \| \| 0.1873 \| 1.29 \| 180 \| 0.0496 \| 0.4789 \| 0.9579 \| 0.9579 \| nan \| 0.9579 \| 0.0 \| 0.9579 \| \| 0.0984 \| 1.44 \| 200 \| 0.0442 \| 0.4838 \| 0.9676 \| 0.9676 \| nan \| 0.9676 \| 0.0 \| 0.9676 \| \| 0.4066 \| 1.58 \| 220 \| 0.0384 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0197 \| 1.73 \| 240 \| 0.0567 \| 0.4809 \| 0.9619 \| 0.9619 \| nan \| 0.9619 \| 0.0 \| 0.9619 \| \| 0.068 \| 1.87 \| 260 \| 0.0389 \| 0.4849 \| 0.9698 \| 0.9698 \| nan \| 0.9698 \| 0.0 \| 0.9698 \| \| 0.029 \| 2.01 \| 280 \| 0.0351 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \| \| 0.016 \| 2.16 \| 300 \| 0.0373 \| 0.4821 \| 0.9642 \| 0.9642 \| nan \| 0.9642 \| 0.0 \| 0.9642 \| \| 0.0146 \| 2.3 \| 320 \| 0.0367 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0123 \| 2.45 \| 340 \| 0.0388 \| 0.4872 \| 0.9745 \| 0.9745 \| nan \| 0.9745 \| 0.0 \| 0.9745 \| \| 0.1359 \| 2.59 \| 360 \| 0.0360 \| 0.4858 \| 0.9715 \| 0.9715 \| nan \| 0.9715 \| 0.0 \| 0.9715 \| \| 0.0142 \| 2.73 \| 380 \| 0.0337 \| 0.4882 \| 0.9765 \| 0.9765 \| nan \| 0.9765 \| 0.0 \| 0.9765 \| \| 0.012 \| 2.88 \| 400 \| 0.0357 \| 0.4865 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \| \| 0.0101 \| 3.02 \| 420 \| 0.0370 \| 0.4864 \| 0.9728 \| 0.9728 \| nan \| 0.9728 \| 0.0 \| 0.9728 \| \| 0.0098 \| 3.17 \| 440 \| 0.0361 \| 0.4870 \| 0.9740 \| 0.9740 \| nan \| 0.9740 \| 0.0 \| 0.9740 \| \| 0.0226 \| 3.31 \| 460 \| 0.0349 \| 0.4895 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0157 \| 3.45 \| 480 \| 0.0362 \| 0.4856 \| 0.9712 \| 0.9712 \| nan \| 0.9712 \| 0.0 \| 0.9712 \| \| 0.0145 \| 3.6 \| 500 \| 0.0468 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \| \| 0.1801 \| 3.74 \| 520 \| 0.0324 \| 0.4906 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0129 \| 3.88 \| 540 \| 0.0314 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0159 \| 4.03 \| 560 \| 0.0310 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0132 \| 4.17 \| 580 \| 0.0321 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0126 \| 4.32 \| 600 \| 0.0329 \| 0.4874 \| 0.9747 \| 0.9747 \| nan \| 0.9747 \| 0.0 \| 0.9747 \| \| 0.0156 \| 4.46 \| 620 \| 0.0381 \| 0.4876 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 0.0147 \| 4.6 \| 640 \| 0.0322 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0174 \| 4.75 \| 660 \| 0.0344 \| 0.4886 \| 0.9772 \| 0.9772 \| nan \| 0.9772 \| 0.0 \| 0.9772 \| \| 0.1191 \| 4.89 \| 680 \| 0.0378 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \| \| 0.0117 \| 5.04 \| 700 \| 0.0386 \| 0.4873 \| 0.9745 \| 0.9745 \| nan \| 0.9745 \| 0.0 \| 0.9745 \| \| 0.0193 \| 5.18 \| 720 \| 0.0361 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0214 \| 5.32 \| 740 \| 0.0360 \| 0.4886 \| 0.9772 \| 0.9772 \| nan \| 0.9772 \| 0.0 \| 0.9772 \| \| 0.0184 \| 5.47 \| 760 \| 0.0322 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0262 \| 5.61 \| 780 \| 0.0357 \| 0.4907 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0115 \| 5.76 \| 800 \| 0.0386 \| 0.4887 \| 0.9774 \| 0.9774 \| nan \| 0.9774 \| 0.0 \| 0.9774 \| \| 0.0145 \| 5.9 \| 820 \| 0.0394 \| 0.4879 \| 0.9759 \| 0.9759 \| nan \| 0.9759 \| 0.0 \| 0.9759 \| \| 0.0097 \| 6.04 \| 840 \| 0.0322 \| 0.4889 \| 0.9777 \| 0.9777 \| nan \| 0.9777 \| 0.0 \| 0.9777 \| \| 0.0101 \| 6.19 \| 860 \| 0.0313 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0099 \| 6.33 \| 880 \| 0.0336 \| 0.4876 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 0.0092 \| 6.47 \| 900 \| 0.0342 \| 0.4894 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0087 \| 6.62 \| 920 \| 0.0352 \| 0.4913 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.019 \| 6.76 \| 940 \| 0.0516 \| 0.4871 \| 0.9742 \| 0.9742 \| nan \| 0.9742 \| 0.0 \| 0.9742 \| \| 0.0104 \| 6.91 \| 960 \| 0.0364 \| 0.4877 \| 0.9754 \| 0.9754 \| nan \| 0.9754 \| 0.0 \| 0.9754 \| \| 0.0079 \| 7.05 \| 980 \| 0.0300 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.0107 \| 7.19 \| 1000 \| 0.0327 \| 0.4939 \| 0.9878 \| 0.9878 \| nan \| 0.9878 \| 0.0 \| 0.9878 \| \| 0.0097 \| 7.34 \| 1020 \| 0.0294 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0359 \| 7.48 \| 1040 \| 0.0321 \| 0.4908 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0674 \| 7.63 \| 1060 \| 0.0321 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.1484 \| 7.77 \| 1080 \| 0.0428 \| 0.4868 \| 0.9737 \| 0.9737 \| nan \| 0.9737 \| 0.0 \| 0.9737 \| \| 0.188 \| 7.91 \| 1100 \| 0.0338 \| 0.4945 \| 0.9890 \| 0.9890 \| nan \| 0.9890 \| 0.0 \| 0.9890 \| \| 0.0124 \| 8.06 \| 1120 \| 0.0345 \| 0.4873 \| 0.9746 \| 0.9746 \| nan \| 0.9746 \| 0.0 \| 0.9746 \| \| 0.011 \| 8.2 \| 1140 \| 0.0350 \| 0.4913 \| 0.9827 \| 0.9827 \| nan \| 0.9827 \| 0.0 \| 0.9827 \| \| 0.0076 \| 8.35 \| 1160 \| 0.0373 \| 0.4884 \| 0.9767 \| 0.9767 \| nan \| 0.9767 \| 0.0 \| 0.9767 \| \| 0.0074 \| 8.49 \| 1180 \| 0.0378 \| 0.4931 \| 0.9862 \| 0.9862 \| nan \| 0.9862 \| 0.0 \| 0.9862 \| \| 0.0757 \| 8.63 \| 1200 \| 0.0364 \| 0.4880 \| 0.9761 \| 0.9761 \| nan \| 0.9761 \| 0.0 \| 0.9761 \| \| 0.0276 \| 8.78 \| 1220 \| 0.0297 \| 0.4906 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0072 \| 8.92 \| 1240 \| 0.0308 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0061 \| 9.06 \| 1260 \| 0.0308 \| 0.4912 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.0063 \| 9.21 \| 1280 \| 0.0323 \| 0.4894 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0088 \| 9.35 \| 1300 \| 0.0308 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0129 \| 9.5 \| 1320 \| 0.0295 \| 0.4911 \| 0.9823 \| 0.9823 \| nan \| 0.9823 \| 0.0 \| 0.9823 \| \| 0.0277 \| 9.64 \| 1340 \| 0.0388 \| 0.4876 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 0.0115 \| 9.78 \| 1360 \| 0.0345 \| 0.4894 \| 0.9787 \| 0.9787 \| nan \| 0.9787 \| 0.0 \| 0.9787 \| \| 0.0129 \| 9.93 \| 1380 \| 0.0394 \| 0.4879 \| 0.9758 \| 0.9758 \| nan \| 0.9758 \| 0.0 \| 0.9758 \| \| 0.0092 \| 10.07 \| 1400 \| 0.0335 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.0107 \| 10.22 \| 1420 \| 0.0348 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0072 \| 10.36 \| 1440 \| 0.0334 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0081 \| 10.5 \| 1460 \| 0.0409 \| 0.4886 \| 0.9772 \| 0.9772 \| nan \| 0.9772 \| 0.0 \| 0.9772 \| \| 0.0158 \| 10.65 \| 1480 \| 0.0337 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0058 \| 10.79 \| 1500 \| 0.0364 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \| \| 0.0102 \| 10.94 \| 1520 \| 0.0354 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.0098 \| 11.08 \| 1540 \| 0.0515 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \| \| 0.0063 \| 11.22 \| 1560 \| 0.0337 \| 0.4882 \| 0.9763 \| 0.9763 \| nan \| 0.9763 \| 0.0 \| 0.9763 \| \| 0.0151 \| 11.37 \| 1580 \| 0.0313 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0197 \| 11.51 \| 1600 \| 0.0384 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0093 \| 11.65 \| 1620 \| 0.0328 \| 0.4910 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.2493 \| 11.8 \| 1640 \| 0.0413 \| 0.4880 \| 0.9759 \| 0.9759 \| nan \| 0.9759 \| 0.0 \| 0.9759 \| \| 0.0133 \| 11.94 \| 1660 \| 0.0385 \| 0.4877 \| 0.9754 \| 0.9754 \| nan \| 0.9754 \| 0.0 \| 0.9754 \| \| 0.0484 \| 12.09 \| 1680 \| 0.0364 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0074 \| 12.23 \| 1700 \| 0.0334 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.0202 \| 12.37 \| 1720 \| 0.0409 \| 0.4876 \| 0.9752 \| 0.9752 \| nan \| 0.9752 \| 0.0 \| 0.9752 \| \| 0.006 \| 12.52 \| 1740 \| 0.0540 \| 0.4860 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \| \| 0.0059 \| 12.66 \| 1760 \| 0.0601 \| 0.4857 \| 0.9714 \| 0.9714 \| nan \| 0.9714 \| 0.0 \| 0.9714 \| \| 0.0083 \| 12.81 \| 1780 \| 0.0348 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.011 \| 12.95 \| 1800 \| 0.0402 \| 0.4885 \| 0.9770 \| 0.9770 \| nan \| 0.9770 \| 0.0 \| 0.9770 \| \| 0.045 \| 13.09 \| 1820 \| 0.0322 \| 0.4911 \| 0.9822 \| 0.9822 \| nan \| 0.9822 \| 0.0 \| 0.9822 \| \| 0.043 \| 13.24 \| 1840 \| 0.0331 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0061 \| 13.38 \| 1860 \| 0.0314 \| 0.4913 \| 0.9826 \| 0.9826 \| nan \| 0.9826 \| 0.0 \| 0.9826 \| \| 0.0062 \| 13.53 \| 1880 \| 0.0358 \| 0.4890 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0087 \| 13.67 \| 1900 \| 0.0334 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0106 \| 13.81 \| 1920 \| 0.0341 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0554 \| 13.96 \| 1940 \| 0.0359 \| 0.4881 \| 0.9762 \| 0.9762 \| nan \| 0.9762 \| 0.0 \| 0.9762 \| \| 0.009 \| 14.1 \| 1960 \| 0.0424 \| 0.4865 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \| \| 0.0078 \| 14.24 \| 1980 \| 0.0329 \| 0.4885 \| 0.9770 \| 0.9770 \| nan \| 0.9770 \| 0.0 \| 0.9770 \| \| 0.012 \| 14.39 \| 2000 \| 0.0346 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0064 \| 14.53 \| 2020 \| 0.0362 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0345 \| 14.68 \| 2040 \| 0.0309 \| 0.4919 \| 0.9838 \| 0.9838 \| nan \| 0.9838 \| 0.0 \| 0.9838 \| \| 0.0075 \| 14.82 \| 2060 \| 0.0389 \| 0.4884 \| 0.9768 \| 0.9768 \| nan \| 0.9768 \| 0.0 \| 0.9768 \| \| 0.0066 \| 14.96 \| 2080 \| 0.0337 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \| \| 0.0081 \| 15.11 \| 2100 \| 0.0365 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0071 \| 15.25 \| 2120 \| 0.0349 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0054 \| 15.4 \| 2140 \| 0.0388 \| 0.4885 \| 0.9769 \| 0.9769 \| nan \| 0.9769 \| 0.0 \| 0.9769 \| \| 0.4004 \| 15.54 \| 2160 \| 0.0339 \| 0.4909 \| 0.9819 \| 0.9819 \| nan \| 0.9819 \| 0.0 \| 0.9819 \| \| 0.008 \| 15.68 \| 2180 \| 0.0422 \| 0.4896 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0365 \| 15.83 \| 2200 \| 0.0468 \| 0.4887 \| 0.9774 \| 0.9774 \| nan \| 0.9774 \| 0.0 \| 0.9774 \| \| 0.0067 \| 15.97 \| 2220 \| 0.0416 \| 0.4890 \| 0.9780 \| 0.9780 \| nan \| 0.9780 \| 0.0 \| 0.9780 \| \| 0.0079 \| 16.12 \| 2240 \| 0.0377 \| 0.4908 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0075 \| 16.26 \| 2260 \| 0.0420 \| 0.4889 \| 0.9779 \| 0.9779 \| nan \| 0.9779 \| 0.0 \| 0.9779 \| \| 0.0063 \| 16.4 \| 2280 \| 0.0422 \| 0.4889 \| 0.9777 \| 0.9777 \| nan \| 0.9777 \| 0.0 \| 0.9777 \| \| 0.0062 \| 16.55 \| 2300 \| 0.0338 \| 0.4912 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.0413 \| 16.69 \| 2320 \| 0.0345 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0411 \| 16.83 \| 2340 \| 0.0387 \| 0.4891 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0548 \| 16.98 \| 2360 \| 0.0333 \| 0.4936 \| 0.9872 \| 0.9872 \| nan \| 0.9872 \| 0.0 \| 0.9872 \| \| 0.0431 \| 17.12 \| 2380 \| 0.0352 \| 0.4887 \| 0.9773 \| 0.9773 \| nan \| 0.9773 \| 0.0 \| 0.9773 \| \| 0.0069 \| 17.27 \| 2400 \| 0.0327 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0059 \| 17.41 \| 2420 \| 0.0406 \| 0.4881 \| 0.9763 \| 0.9763 \| nan \| 0.9763 \| 0.0 \| 0.9763 \| \| 0.0062 \| 17.55 \| 2440 \| 0.0434 \| 0.4875 \| 0.9750 \| 0.9750 \| nan \| 0.9750 \| 0.0 \| 0.9750 \| \| 0.0064 \| 17.7 \| 2460 \| 0.0350 \| 0.4910 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0077 \| 17.84 \| 2480 \| 0.0390 \| 0.4894 \| 0.9788 \| 0.9788 \| nan \| 0.9788 \| 0.0 \| 0.9788 \| \| 0.0061 \| 17.99 \| 2500 \| 0.0395 \| 0.4906 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0073 \| 18.13 \| 2520 \| 0.0370 \| 0.4911 \| 0.9822 \| 0.9822 \| nan \| 0.9822 \| 0.0 \| 0.9822 \| \| 0.0038 \| 18.27 \| 2540 \| 0.0383 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0066 \| 18.42 \| 2560 \| 0.0394 \| 0.4888 \| 0.9776 \| 0.9776 \| nan \| 0.9776 \| 0.0 \| 0.9776 \| \| 0.0232 \| 18.56 \| 2580 \| 0.0384 \| 0.4891 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0066 \| 18.71 \| 2600 \| 0.0408 \| 0.4887 \| 0.9773 \| 0.9773 \| nan \| 0.9773 \| 0.0 \| 0.9773 \| \| 0.0355 \| 18.85 \| 2620 \| 0.0367 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0048 \| 18.99 \| 2640 \| 0.0366 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0083 \| 19.14 \| 2660 \| 0.0422 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0215 \| 19.28 \| 2680 \| 0.0376 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0315 \| 19.42 \| 2700 \| 0.0370 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0061 \| 19.57 \| 2720 \| 0.0380 \| 0.4909 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0048 \| 19.71 \| 2740 \| 0.0371 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0058 \| 19.86 \| 2760 \| 0.0389 \| 0.4892 \| 0.9785 \| 0.9785 \| nan \| 0.9785 \| 0.0 \| 0.9785 \| \| 0.01 \| 20.0 \| 2780 \| 0.0354 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.0051 \| 20.14 \| 2800 \| 0.0380 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0053 \| 20.29 \| 2820 \| 0.0426 \| 0.4889 \| 0.9779 \| 0.9779 \| nan \| 0.9779 \| 0.0 \| 0.9779 \| \| 0.0062 \| 20.43 \| 2840 \| 0.0359 \| 0.4913 \| 0.9827 \| 0.9827 \| nan \| 0.9827 \| 0.0 \| 0.9827 \| \| 0.0573 \| 20.58 \| 2860 \| 0.0370 \| 0.4909 \| 0.9819 \| 0.9819 \| nan \| 0.9819 \| 0.0 \| 0.9819 \| \| 0.0087 \| 20.72 \| 2880 \| 0.0418 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0144 \| 20.86 \| 2900 \| 0.0398 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0157 \| 21.01 \| 2920 \| 0.0463 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0063 \| 21.15 \| 2940 \| 0.0357 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0079 \| 21.29 \| 2960 \| 0.0339 \| 0.4928 \| 0.9856 \| 0.9856 \| nan \| 0.9856 \| 0.0 \| 0.9856 \| \| 0.0052 \| 21.44 \| 2980 \| 0.0419 \| 0.4888 \| 0.9775 \| 0.9775 \| nan \| 0.9775 \| 0.0 \| 0.9775 \| \| 0.0068 \| 21.58 \| 3000 \| 0.0370 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0109 \| 21.73 \| 3020 \| 0.0350 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0048 \| 21.87 \| 3040 \| 0.0353 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0053 \| 22.01 \| 3060 \| 0.0369 \| 0.4911 \| 0.9823 \| 0.9823 \| nan \| 0.9823 \| 0.0 \| 0.9823 \| \| 0.006 \| 22.16 \| 3080 \| 0.0339 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0336 \| 22.3 \| 3100 \| 0.0339 \| 0.4914 \| 0.9828 \| 0.9828 \| nan \| 0.9828 \| 0.0 \| 0.9828 \| \| 0.0222 \| 22.45 \| 3120 \| 0.0513 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \| \| 0.0072 \| 22.59 \| 3140 \| 0.0328 \| 0.4920 \| 0.9840 \| 0.9840 \| nan \| 0.9840 \| 0.0 \| 0.9840 \| \| 0.0046 \| 22.73 \| 3160 \| 0.0334 \| 0.4907 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.0039 \| 22.88 \| 3180 \| 0.0352 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0059 \| 23.02 \| 3200 \| 0.0359 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0049 \| 23.17 \| 3220 \| 0.0425 \| 0.4881 \| 0.9762 \| 0.9762 \| nan \| 0.9762 \| 0.0 \| 0.9762 \| \| 0.0244 \| 23.31 \| 3240 \| 0.0351 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0047 \| 23.45 \| 3260 \| 0.0339 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0074 \| 23.6 \| 3280 \| 0.0382 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0062 \| 23.74 \| 3300 \| 0.0366 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0339 \| 23.88 \| 3320 \| 0.0378 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.005 \| 24.03 \| 3340 \| 0.0395 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0038 \| 24.17 \| 3360 \| 0.0455 \| 0.4887 \| 0.9773 \| 0.9773 \| nan \| 0.9773 \| 0.0 \| 0.9773 \| \| 0.008 \| 24.32 \| 3380 \| 0.0389 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0071 \| 24.46 \| 3400 \| 0.0367 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0308 \| 24.6 \| 3420 \| 0.0390 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0062 \| 24.75 \| 3440 \| 0.0368 \| 0.4918 \| 0.9837 \| 0.9837 \| nan \| 0.9837 \| 0.0 \| 0.9837 \| \| 0.0062 \| 24.89 \| 3460 \| 0.0378 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.006 \| 25.04 \| 3480 \| 0.0413 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0057 \| 25.18 \| 3500 \| 0.0383 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0149 \| 25.32 \| 3520 \| 0.0367 \| 0.4911 \| 0.9822 \| 0.9822 \| nan \| 0.9822 \| 0.0 \| 0.9822 \| \| 0.0185 \| 25.47 \| 3540 \| 0.0409 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0057 \| 25.61 \| 3560 \| 0.0390 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.005 \| 25.76 \| 3580 \| 0.0383 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0109 \| 25.9 \| 3600 \| 0.0379 \| 0.4909 \| 0.9819 \| 0.9819 \| nan \| 0.9819 \| 0.0 \| 0.9819 \| \| 0.0055 \| 26.04 \| 3620 \| 0.0471 \| 0.4883 \| 0.9767 \| 0.9767 \| nan \| 0.9767 \| 0.0 \| 0.9767 \| \| 0.042 \| 26.19 \| 3640 \| 0.0481 \| 0.4877 \| 0.9755 \| 0.9755 \| nan \| 0.9755 \| 0.0 \| 0.9755 \| \| 0.0226 \| 26.33 \| 3660 \| 0.0383 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0143 \| 26.47 \| 3680 \| 0.0402 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.008 \| 26.62 \| 3700 \| 0.0381 \| 0.4908 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0241 \| 26.76 \| 3720 \| 0.0411 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0055 \| 26.91 \| 3740 \| 0.0386 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0109 \| 27.05 \| 3760 \| 0.0376 \| 0.4913 \| 0.9826 \| 0.9826 \| nan \| 0.9826 \| 0.0 \| 0.9826 \| \| 0.0072 \| 27.19 \| 3780 \| 0.0457 \| 0.4890 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0048 \| 27.34 \| 3800 \| 0.0512 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \| \| 0.006 \| 27.48 \| 3820 \| 0.0430 \| 0.4891 \| 0.9783 \| 0.9783 \| nan \| 0.9783 \| 0.0 \| 0.9783 \| \| 0.0161 \| 27.63 \| 3840 \| 0.0404 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0169 \| 27.77 \| 3860 \| 0.0386 \| 0.4903 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0041 \| 27.91 \| 3880 \| 0.0375 \| 0.4917 \| 0.9835 \| 0.9835 \| nan \| 0.9835 \| 0.0 \| 0.9835 \| \| 0.0068 \| 28.06 \| 3900 \| 0.0381 \| 0.4917 \| 0.9834 \| 0.9834 \| nan \| 0.9834 \| 0.0 \| 0.9834 \| \| 0.0122 \| 28.2 \| 3920 \| 0.0463 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0055 \| 28.35 \| 3940 \| 0.0456 \| 0.4887 \| 0.9774 \| 0.9774 \| nan \| 0.9774 \| 0.0 \| 0.9774 \| \| 0.0048 \| 28.49 \| 3960 \| 0.0398 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0126 \| 28.63 \| 3980 \| 0.0401 \| 0.4917 \| 0.9834 \| 0.9834 \| nan \| 0.9834 \| 0.0 \| 0.9834 \| \| 0.0134 \| 28.78 \| 4000 \| 0.0404 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0109 \| 28.92 \| 4020 \| 0.0414 \| 0.4894 \| 0.9787 \| 0.9787 \| nan \| 0.9787 \| 0.0 \| 0.9787 \| \| 0.0479 \| 29.06 \| 4040 \| 0.0409 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0061 \| 29.21 \| 4060 \| 0.0422 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0278 \| 29.35 \| 4080 \| 0.0416 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0049 \| 29.5 \| 4100 \| 0.0400 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0438 \| 29.64 \| 4120 \| 0.0390 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0041 \| 29.78 \| 4140 \| 0.0425 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0078 \| 29.93 \| 4160 \| 0.0411 \| 0.4907 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0057 \| 30.07 \| 4180 \| 0.0375 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.0053 \| 30.22 \| 4200 \| 0.0423 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0133 \| 30.36 \| 4220 \| 0.0429 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0045 \| 30.5 \| 4240 \| 0.0454 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0044 \| 30.65 \| 4260 \| 0.0415 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.006 \| 30.79 \| 4280 \| 0.0420 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0043 \| 30.94 \| 4300 \| 0.0428 \| 0.4899 \| 0.9797 \| 0.9797 \| nan \| 0.9797 \| 0.0 \| 0.9797 \| \| 0.017 \| 31.08 \| 4320 \| 0.0421 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0043 \| 31.22 \| 4340 \| 0.0400 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0061 \| 31.37 \| 4360 \| 0.0383 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0378 \| 31.51 \| 4380 \| 0.0371 \| 0.4913 \| 0.9826 \| 0.9826 \| nan \| 0.9826 \| 0.0 \| 0.9826 \| \| 0.0052 \| 31.65 \| 4400 \| 0.0382 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0046 \| 31.8 \| 4420 \| 0.0398 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0076 \| 31.94 \| 4440 \| 0.0400 \| 0.4904 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0062 \| 32.09 \| 4460 \| 0.0396 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0152 \| 32.23 \| 4480 \| 0.0399 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0044 \| 32.37 \| 4500 \| 0.0426 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0104 \| 32.52 \| 4520 \| 0.0431 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0041 \| 32.66 \| 4540 \| 0.0458 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0084 \| 32.81 \| 4560 \| 0.0457 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0046 \| 32.95 \| 4580 \| 0.0465 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0038 \| 33.09 \| 4600 \| 0.0422 \| 0.4907 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.0039 \| 33.24 \| 4620 \| 0.0410 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.004 \| 33.38 \| 4640 \| 0.0427 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.006 \| 33.53 \| 4660 \| 0.0458 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0039 \| 33.67 \| 4680 \| 0.0484 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0065 \| 33.81 \| 4700 \| 0.0516 \| 0.4894 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0065 \| 33.96 \| 4720 \| 0.0525 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0041 \| 34.1 \| 4740 \| 0.0462 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0031 \| 34.24 \| 4760 \| 0.0458 \| 0.4909 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0039 \| 34.39 \| 4780 \| 0.0493 \| 0.4895 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0125 \| 34.53 \| 4800 \| 0.0467 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0038 \| 34.68 \| 4820 \| 0.0456 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0043 \| 34.82 \| 4840 \| 0.0484 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0098 \| 34.96 \| 4860 \| 0.0460 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.004 \| 35.11 \| 4880 \| 0.0475 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0087 \| 35.25 \| 4900 \| 0.0460 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0093 \| 35.4 \| 4920 \| 0.0455 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0052 \| 35.54 \| 4940 \| 0.0500 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0045 \| 35.68 \| 4960 \| 0.0482 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0036 \| 35.83 \| 4980 \| 0.0443 \| 0.4906 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0034 \| 35.97 \| 5000 \| 0.0426 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0041 \| 36.12 \| 5020 \| 0.0415 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0043 \| 36.26 \| 5040 \| 0.0450 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.007 \| 36.4 \| 5060 \| 0.0467 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.006 \| 36.55 \| 5080 \| 0.0463 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.006 \| 36.69 \| 5100 \| 0.0468 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0043 \| 36.83 \| 5120 \| 0.0428 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0073 \| 36.98 \| 5140 \| 0.0417 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0188 \| 37.12 \| 5160 \| 0.0418 \| 0.4908 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.0052 \| 37.27 \| 5180 \| 0.0450 \| 0.4907 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0089 \| 37.41 \| 5200 \| 0.0476 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0041 \| 37.55 \| 5220 \| 0.0505 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0062 \| 37.7 \| 5240 \| 0.0478 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0035 \| 37.84 \| 5260 \| 0.0463 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0163 \| 37.99 \| 5280 \| 0.0453 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0054 \| 38.13 \| 5300 \| 0.0462 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0132 \| 38.27 \| 5320 \| 0.0481 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \| \| 0.0056 \| 38.42 \| 5340 \| 0.0460 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0054 \| 38.56 \| 5360 \| 0.0449 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0037 \| 38.71 \| 5380 \| 0.0432 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0049 \| 38.85 \| 5400 \| 0.0449 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0044 \| 38.99 \| 5420 \| 0.0448 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0037 \| 39.14 \| 5440 \| 0.0462 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0079 \| 39.28 \| 5460 \| 0.0490 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0033 \| 39.42 \| 5480 \| 0.0494 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0066 \| 39.57 \| 5500 \| 0.0458 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0053 \| 39.71 \| 5520 \| 0.0482 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0044 \| 39.86 \| 5540 \| 0.0483 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0044 \| 40.0 \| 5560 \| 0.0497 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0062 \| 40.14 \| 5580 \| 0.0476 \| 0.4894 \| 0.9788 \| 0.9788 \| nan \| 0.9788 \| 0.0 \| 0.9788 \| \| 0.0047 \| 40.29 \| 5600 \| 0.0467 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.006 \| 40.43 \| 5620 \| 0.0444 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0041 \| 40.58 \| 5640 \| 0.0459 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0098 \| 40.72 \| 5660 \| 0.0447 \| 0.4903 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0026 \| 40.86 \| 5680 \| 0.0439 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0043 \| 41.01 \| 5700 \| 0.0466 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0044 \| 41.15 \| 5720 \| 0.0444 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0041 \| 41.29 \| 5740 \| 0.0452 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0043 \| 41.44 \| 5760 \| 0.0468 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0071 \| 41.58 \| 5780 \| 0.0482 \| 0.4897 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0187 \| 41.73 \| 5800 \| 0.0463 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0034 \| 41.87 \| 5820 \| 0.0456 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0238 \| 42.01 \| 5840 \| 0.0450 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0048 \| 42.16 \| 5860 \| 0.0464 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0116 \| 42.3 \| 5880 \| 0.0475 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0039 \| 42.45 \| 5900 \| 0.0475 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0042 \| 42.59 \| 5920 \| 0.0446 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0069 \| 42.73 \| 5940 \| 0.0441 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0045 \| 42.88 \| 5960 \| 0.0460 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0038 \| 43.02 \| 5980 \| 0.0501 \| 0.4896 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0123 \| 43.17 \| 6000 \| 0.0490 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0079 \| 43.31 \| 6020 \| 0.0471 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.004 \| 43.45 \| 6040 \| 0.0453 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0145 \| 43.6 \| 6060 \| 0.0439 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0038 \| 43.74 \| 6080 \| 0.0466 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.004 \| 43.88 \| 6100 \| 0.0467 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0044 \| 44.03 \| 6120 \| 0.0480 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0193 \| 44.17 \| 6140 \| 0.0458 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0036 \| 44.32 \| 6160 \| 0.0470 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0042 \| 44.46 \| 6180 \| 0.0456 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0031 \| 44.6 \| 6200 \| 0.0454 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0117 \| 44.75 \| 6220 \| 0.0478 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0036 \| 44.89 \| 6240 \| 0.0482 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0036 \| 45.04 \| 6260 \| 0.0506 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0052 \| 45.18 \| 6280 \| 0.0485 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0035 \| 45.32 \| 6300 \| 0.0496 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0056 \| 45.47 \| 6320 \| 0.0494 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0172 \| 45.61 \| 6340 \| 0.0482 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0041 \| 45.76 \| 6360 \| 0.0484 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0034 \| 45.9 \| 6380 \| 0.0492 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0108 \| 46.04 \| 6400 \| 0.0481 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0054 \| 46.19 \| 6420 \| 0.0474 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0102 \| 46.33 \| 6440 \| 0.0483 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0036 \| 46.47 \| 6460 \| 0.0493 \| 0.4903 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0057 \| 46.62 \| 6480 \| 0.0496 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.003 \| 46.76 \| 6500 \| 0.0504 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0057 \| 46.91 \| 6520 \| 0.0492 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0048 \| 47.05 \| 6540 \| 0.0524 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.004 \| 47.19 \| 6560 \| 0.0500 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0218 \| 47.34 \| 6580 \| 0.0502 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0038 \| 47.48 \| 6600 \| 0.0532 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0029 \| 47.63 \| 6620 \| 0.0496 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0035 \| 47.77 \| 6640 \| 0.0508 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0049 \| 47.91 \| 6660 \| 0.0501 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0056 \| 48.06 \| 6680 \| 0.0488 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0182 \| 48.2 \| 6700 \| 0.0482 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0056 \| 48.35 \| 6720 \| 0.0494 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0029 \| 48.49 \| 6740 \| 0.0501 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0076 \| 48.63 \| 6760 \| 0.0480 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0042 \| 48.78 \| 6780 \| 0.0514 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0069 \| 48.92 \| 6800 \| 0.0483 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0144 \| 49.06 \| 6820 \| 0.0472 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0041 \| 49.21 \| 6840 \| 0.0491 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0034 \| 49.35 \| 6860 \| 0.0481 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0117 \| 49.5 \| 6880 \| 0.0482 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0042 \| 49.64 \| 6900 \| 0.0499 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0057 \| 49.78 \| 6920 \| 0.0507 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0183 \| 49.93 \| 6940 \| 0.0494 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "unlabeled", "drug-blister" ]
gdurkin/segformer-b0-finetuned-segments-floods-S2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-floods-S2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the gdurkin/flood_dataset_S2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu121 - Datasets 2.11.0 - Tokenizers 0.13.3	[ "invalid", "not water", "water" ]
peldrak/segformer-finetuned-coastal	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-coastal This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coastal3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7456 - Mean Iou: 0.0841 - Mean Accuracy: 0.1629 - Overall Accuracy: 0.3309 - Accuracy Water: 0.2728 - Accuracy Whitewater: 0.0 - Accuracy Sediment: 0.0194 - Accuracy Other Natural Terrain: 0.0 - Accuracy Vegetation: 0.7325 - Accuracy Development: 0.0055 - Accuracy Unknown: 0.1102 - Iou Water: 0.1767 - Iou Whitewater: 0.0 - Iou Sediment: 0.0157 - Iou Other Natural Terrain: 0.0 - Iou Vegetation: 0.3002 - Iou Development: 0.0052 - Iou Unknown: 0.0908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sediment \| Accuracy Other Natural Terrain \| Accuracy Vegetation \| Accuracy Development \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sediment \| Iou Other Natural Terrain \| Iou Vegetation \| Iou Development \| Iou Unknown \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-----------------:\|:------------------------------:\|:-------------------:\|:--------------------:\|:----------------:\|:---------:\|:--------------:\|:------------:\|:-------------------------:\|:--------------:\|:---------------:\|:-----------:\| \| 1.8429 \| 0.12 \| 20 \| 1.8742 \| 0.0623 \| 0.1432 \| 0.2198 \| 0.5172 \| 0.0044 \| 0.3137 \| 0.0050 \| 0.1165 \| 0.0019 \| 0.0435 \| 0.2514 \| 0.0037 \| 0.0521 \| 0.0034 \| 0.0889 \| 0.0019 \| 0.0348 \| \| 1.6623 \| 0.25 \| 40 \| 1.8297 \| 0.0731 \| 0.1572 \| 0.2536 \| 0.4349 \| 0.0007 \| 0.3336 \| 0.0010 \| 0.3158 \| 0.0011 \| 0.0135 \| 0.2462 \| 0.0006 \| 0.0569 \| 0.0009 \| 0.1940 \| 0.0011 \| 0.0120 \| \| 1.6173 \| 0.37 \| 60 \| 1.7856 \| 0.0812 \| 0.1585 \| 0.3080 \| 0.3151 \| 0.0007 \| 0.1179 \| 0.0005 \| 0.6339 \| 0.0012 \| 0.0405 \| 0.2056 \| 0.0007 \| 0.0357 \| 0.0005 \| 0.2859 \| 0.0012 \| 0.0388 \| \| 1.5078 \| 0.49 \| 80 \| 1.7805 \| 0.0764 \| 0.1537 \| 0.3038 \| 0.2792 \| 0.0 \| 0.1036 \| 0.0000 \| 0.6639 \| 0.0003 \| 0.0290 \| 0.1839 \| 0.0 \| 0.0338 \| 0.0000 \| 0.2895 \| 0.0003 \| 0.0274 \| \| 1.3927 \| 0.62 \| 100 \| 1.7549 \| 0.0725 \| 0.1521 \| 0.3227 \| 0.2589 \| 0.0 \| 0.0222 \| 0.0001 \| 0.7643 \| 0.0005 \| 0.0187 \| 0.1703 \| 0.0 \| 0.0133 \| 0.0001 \| 0.3055 \| 0.0005 \| 0.0179 \| \| 1.6024 \| 0.74 \| 120 \| 1.7348 \| 0.0835 \| 0.1598 \| 0.3271 \| 0.3514 \| 0.0 \| 0.0204 \| 0.0001 \| 0.6538 \| 0.0005 \| 0.0926 \| 0.2021 \| 0.0 \| 0.0147 \| 0.0001 \| 0.2873 \| 0.0005 \| 0.0796 \| \| 1.3287 \| 0.86 \| 140 \| 1.7352 \| 0.0776 \| 0.1556 \| 0.3267 \| 0.3309 \| 0.0 \| 0.0083 \| 0.0 \| 0.6910 \| 0.0000 \| 0.0591 \| 0.1935 \| 0.0 \| 0.0076 \| 0.0 \| 0.2849 \| 0.0000 \| 0.0573 \| \| 1.4153 \| 0.99 \| 160 \| 1.7024 \| 0.0746 \| 0.1567 \| 0.3356 \| 0.2947 \| 0.0 \| 0.0011 \| 0.0 \| 0.7683 \| 0.0006 \| 0.0320 \| 0.1879 \| 0.0 \| 0.0011 \| 0.0 \| 0.3021 \| 0.0006 \| 0.0309 \| \| 1.3334 \| 1.11 \| 180 \| 1.7262 \| 0.0744 \| 0.1567 \| 0.3374 \| 0.3130 \| 0.0 \| 0.0018 \| 0.0 \| 0.7620 \| 0.0001 \| 0.0203 \| 0.1988 \| 0.0 \| 0.0017 \| 0.0 \| 0.3004 \| 0.0001 \| 0.0198 \| \| 1.3956 \| 1.23 \| 200 \| 1.7304 \| 0.0858 \| 0.1622 \| 0.3326 \| 0.4838 \| 0.0 \| 0.0127 \| 0.0 \| 0.5432 \| 0.0015 \| 0.0944 \| 0.2373 \| 0.0 \| 0.0112 \| 0.0 \| 0.2731 \| 0.0015 \| 0.0777 \| \| 1.5776 \| 1.36 \| 220 \| 1.7300 \| 0.0791 \| 0.1622 \| 0.3411 \| 0.2581 \| 0.0 \| 0.0010 \| 0.0 \| 0.8012 \| 0.0003 \| 0.0748 \| 0.1734 \| 0.0 \| 0.0010 \| 0.0 \| 0.3144 \| 0.0003 \| 0.0647 \| \| 1.1656 \| 1.48 \| 240 \| 1.7248 \| 0.0831 \| 0.1657 \| 0.3440 \| 0.2687 \| 0.0 \| 0.0026 \| 0.0 \| 0.7879 \| 0.0014 \| 0.0995 \| 0.1775 \| 0.0 \| 0.0025 \| 0.0 \| 0.3183 \| 0.0014 \| 0.0822 \| \| 1.4429 \| 1.6 \| 260 \| 1.7308 \| 0.0764 \| 0.1616 \| 0.3408 \| 0.2091 \| 0.0 \| 0.0029 \| 0.0 \| 0.8518 \| 0.0037 \| 0.0637 \| 0.1507 \| 0.0 \| 0.0028 \| 0.0 \| 0.3200 \| 0.0036 \| 0.0578 \| \| 1.6649 \| 1.73 \| 280 \| 1.7282 \| 0.0743 \| 0.1564 \| 0.3372 \| 0.3261 \| 0.0 \| 0.0033 \| 0.0 \| 0.7514 \| 0.0015 \| 0.0128 \| 0.1994 \| 0.0 \| 0.0031 \| 0.0 \| 0.3037 \| 0.0015 \| 0.0125 \| \| 1.3634 \| 1.85 \| 300 \| 1.7216 \| 0.0847 \| 0.1653 \| 0.3413 \| 0.3196 \| 0.0 \| 0.0273 \| 0.0 \| 0.7402 \| 0.0036 \| 0.0665 \| 0.2012 \| 0.0 \| 0.0204 \| 0.0 \| 0.3101 \| 0.0035 \| 0.0579 \| \| 1.5224 \| 1.98 \| 320 \| 1.7343 \| 0.0822 \| 0.1626 \| 0.3410 \| 0.3793 \| 0.0 \| 0.0277 \| 0.0 \| 0.6985 \| 0.0017 \| 0.0311 \| 0.2236 \| 0.0 \| 0.0210 \| 0.0 \| 0.3000 \| 0.0016 \| 0.0293 \| \| 1.2527 \| 2.1 \| 340 \| 1.7149 \| 0.0759 \| 0.1559 \| 0.3344 \| 0.4108 \| 0.0 \| 0.0119 \| 0.0 \| 0.6629 \| 0.0021 \| 0.0034 \| 0.2244 \| 0.0 \| 0.0105 \| 0.0 \| 0.2913 \| 0.0021 \| 0.0033 \| \| 1.5931 \| 2.22 \| 360 \| 1.7170 \| 0.0838 \| 0.1619 \| 0.3365 \| 0.4061 \| 0.0 \| 0.0098 \| 0.0 \| 0.6414 \| 0.0028 \| 0.0730 \| 0.2228 \| 0.0 \| 0.0090 \| 0.0 \| 0.2882 \| 0.0027 \| 0.0641 \| \| 1.2434 \| 2.35 \| 380 \| 1.7437 \| 0.0844 \| 0.1619 \| 0.3364 \| 0.4734 \| 0.0 \| 0.0118 \| 0.0 \| 0.5785 \| 0.0028 \| 0.0667 \| 0.2391 \| 0.0 \| 0.0105 \| 0.0 \| 0.2777 \| 0.0027 \| 0.0607 \| \| 1.4071 \| 2.47 \| 400 \| 1.7316 \| 0.0823 \| 0.1639 \| 0.3433 \| 0.3054 \| 0.0 \| 0.0053 \| 0.0 \| 0.7633 \| 0.0057 \| 0.0674 \| 0.1933 \| 0.0 \| 0.0051 \| 0.0 \| 0.3125 \| 0.0054 \| 0.0596 \| \| 1.2177 \| 2.59 \| 420 \| 1.7195 \| 0.0848 \| 0.1657 \| 0.3459 \| 0.3442 \| 0.0 \| 0.0030 \| 0.0 \| 0.7315 \| 0.0110 \| 0.0705 \| 0.2084 \| 0.0 \| 0.0029 \| 0.0 \| 0.3111 \| 0.0102 \| 0.0613 \| \| 1.3724 \| 2.72 \| 440 \| 1.7359 \| 0.0843 \| 0.1660 \| 0.3455 \| 0.3091 \| 0.0 \| 0.0089 \| 0.0 \| 0.7620 \| 0.0057 \| 0.0761 \| 0.1946 \| 0.0 \| 0.0084 \| 0.0 \| 0.3147 \| 0.0054 \| 0.0669 \| \| 1.3973 \| 2.84 \| 460 \| 1.7469 \| 0.0827 \| 0.1617 \| 0.3352 \| 0.3153 \| 0.0 \| 0.0101 \| 0.0 \| 0.7231 \| 0.0109 \| 0.0724 \| 0.1922 \| 0.0 \| 0.0088 \| 0.0 \| 0.3045 \| 0.0099 \| 0.0638 \| \| 1.3098 \| 2.96 \| 480 \| 1.7193 \| 0.0852 \| 0.1658 \| 0.3447 \| 0.3412 \| 0.0 \| 0.0032 \| 0.0 \| 0.7240 \| 0.0038 \| 0.0887 \| 0.2039 \| 0.0 \| 0.0031 \| 0.0 \| 0.3076 \| 0.0037 \| 0.0779 \| \| 0.9545 \| 3.09 \| 500 \| 1.7256 \| 0.0840 \| 0.1627 \| 0.3359 \| 0.3366 \| 0.0 \| 0.0026 \| 0.0 \| 0.6959 \| 0.0077 \| 0.0960 \| 0.2007 \| 0.0 \| 0.0025 \| 0.0 \| 0.2969 \| 0.0072 \| 0.0808 \| \| 1.176 \| 3.21 \| 520 \| 1.7334 \| 0.0827 \| 0.1616 \| 0.3357 \| 0.3396 \| 0.0 \| 0.0024 \| 0.0 \| 0.6963 \| 0.0016 \| 0.0915 \| 0.1999 \| 0.0 \| 0.0024 \| 0.0 \| 0.2966 \| 0.0015 \| 0.0785 \| \| 1.5622 \| 3.33 \| 540 \| 1.7790 \| 0.0689 \| 0.1528 \| 0.3311 \| 0.2393 \| 0.0 \| 0.0015 \| 0.0 \| 0.8186 \| 0.0009 \| 0.0091 \| 0.1638 \| 0.0 \| 0.0015 \| 0.0 \| 0.3073 \| 0.0009 \| 0.0089 \| \| 1.2673 \| 3.46 \| 560 \| 1.7339 \| 0.0803 \| 0.1585 \| 0.3302 \| 0.3228 \| 0.0 \| 0.0113 \| 0.0 \| 0.7035 \| 0.0036 \| 0.0686 \| 0.1930 \| 0.0 \| 0.0104 \| 0.0 \| 0.2942 \| 0.0034 \| 0.0613 \| \| 1.418 \| 3.58 \| 580 \| 1.7648 \| 0.0760 \| 0.1563 \| 0.3325 \| 0.3074 \| 0.0 \| 0.0022 \| 0.0 \| 0.7416 \| 0.0041 \| 0.0386 \| 0.1902 \| 0.0 \| 0.0022 \| 0.0 \| 0.2994 \| 0.0038 \| 0.0363 \| \| 1.3578 \| 3.7 \| 600 \| 1.7338 \| 0.0845 \| 0.1619 \| 0.3327 \| 0.3548 \| 0.0 \| 0.0119 \| 0.0 \| 0.6693 \| 0.0076 \| 0.0898 \| 0.2039 \| 0.0 \| 0.0108 \| 0.0 \| 0.2940 \| 0.0070 \| 0.0758 \| \| 1.1991 \| 3.83 \| 620 \| 1.7711 \| 0.0761 \| 0.1546 \| 0.3285 \| 0.3473 \| 0.0 \| 0.0049 \| 0.0 \| 0.6906 \| 0.0018 \| 0.0379 \| 0.1995 \| 0.0 \| 0.0046 \| 0.0 \| 0.2912 \| 0.0017 \| 0.0356 \| \| 1.3699 \| 3.95 \| 640 \| 1.7421 \| 0.0829 \| 0.1595 \| 0.3312 \| 0.4290 \| 0.0 \| 0.0048 \| 0.0 \| 0.5994 \| 0.0032 \| 0.0804 \| 0.2223 \| 0.0 \| 0.0045 \| 0.0 \| 0.2819 \| 0.0031 \| 0.0687 \| \| 1.308 \| 4.07 \| 660 \| 1.7769 \| 0.0709 \| 0.1518 \| 0.3250 \| 0.2512 \| 0.0 \| 0.0035 \| 0.0 \| 0.7784 \| 0.0034 \| 0.0260 \| 0.1656 \| 0.0 \| 0.0033 \| 0.0 \| 0.2996 \| 0.0033 \| 0.0245 \| \| 1.3746 \| 4.2 \| 680 \| 1.7811 \| 0.0749 \| 0.1538 \| 0.3283 \| 0.3497 \| 0.0 \| 0.0077 \| 0.0 \| 0.6953 \| 0.0037 \| 0.0200 \| 0.2022 \| 0.0 \| 0.0070 \| 0.0 \| 0.2928 \| 0.0035 \| 0.0191 \| \| 1.2085 \| 4.32 \| 700 \| 1.7401 \| 0.0825 \| 0.1600 \| 0.3319 \| 0.3632 \| 0.0 \| 0.0106 \| 0.0 \| 0.6663 \| 0.0042 \| 0.0759 \| 0.2064 \| 0.0 \| 0.0096 \| 0.0 \| 0.2912 \| 0.0040 \| 0.0664 \| \| 0.8119 \| 4.44 \| 720 \| 1.7638 \| 0.0755 \| 0.1539 \| 0.3284 \| 0.3660 \| 0.0 \| 0.0060 \| 0.0 \| 0.6784 \| 0.0034 \| 0.0237 \| 0.2073 \| 0.0 \| 0.0053 \| 0.0 \| 0.2902 \| 0.0033 \| 0.0226 \| \| 1.1547 \| 4.57 \| 740 \| 1.7581 \| 0.0795 \| 0.1573 \| 0.3289 \| 0.3410 \| 0.0 \| 0.0140 \| 0.0 \| 0.6879 \| 0.0030 \| 0.0550 \| 0.1996 \| 0.0 \| 0.0116 \| 0.0 \| 0.2929 \| 0.0029 \| 0.0497 \| \| 1.2229 \| 4.69 \| 760 \| 1.7817 \| 0.0730 \| 0.1550 \| 0.3243 \| 0.1861 \| 0.0 \| 0.0124 \| 0.0 \| 0.8198 \| 0.0036 \| 0.0631 \| 0.1365 \| 0.0 \| 0.0103 \| 0.0 \| 0.3050 \| 0.0034 \| 0.0557 \| \| 1.3332 \| 4.81 \| 780 \| 1.7580 \| 0.0769 \| 0.1563 \| 0.3276 \| 0.2656 \| 0.0 \| 0.0044 \| 0.0 \| 0.7524 \| 0.0042 \| 0.0677 \| 0.1721 \| 0.0 \| 0.0041 \| 0.0 \| 0.2998 \| 0.0040 \| 0.0581 \| \| 1.1668 \| 4.94 \| 800 \| 1.7456 \| 0.0841 \| 0.1629 \| 0.3309 \| 0.2728 \| 0.0 \| 0.0194 \| 0.0 \| 0.7325 \| 0.0055 \| 0.1102 \| 0.1767 \| 0.0 \| 0.0157 \| 0.0 \| 0.3002 \| 0.0052 \| 0.0908 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3	[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]
rwood-97/test_os_counties	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_os_counties This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rwood-97/os_counties dataset. It achieves the following results on the evaluation set: - Loss: 1.8997 - Mean Iou: 0.1075 - Mean Accuracy: 0.4992 - Overall Accuracy: 0.2118 - Accuracy Non-map: 0.9899 - Accuracy Map: 0.0084 - Iou Non-map: 0.2065 - Iou Map: 0.0084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Non-map \| Accuracy Map \| Iou Non-map \| Iou Map \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:----------------:\|:------------:\|:-----------:\|:-------:\| \| 0.4081 \| 0.41 \| 20 \| 0.8267 \| 0.2101 \| 0.4981 \| 0.3478 \| 0.7548 \| 0.2414 \| 0.1934 \| 0.2268 \| \| 0.3939 \| 0.82 \| 40 \| 0.8190 \| 0.2387 \| 0.4978 \| 0.3898 \| 0.6821 \| 0.3134 \| 0.1881 \| 0.2894 \| \| 0.3319 \| 1.22 \| 60 \| 0.9487 \| 0.1579 \| 0.4976 \| 0.2759 \| 0.8762 \| 0.1191 \| 0.2005 \| 0.1153 \| \| 0.3234 \| 1.63 \| 80 \| 0.9275 \| 0.1847 \| 0.4974 \| 0.3120 \| 0.8138 \| 0.1809 \| 0.1969 \| 0.1725 \| \| 0.3932 \| 2.04 \| 100 \| 0.9053 \| 0.1892 \| 0.4974 \| 0.3183 \| 0.8031 \| 0.1916 \| 0.1962 \| 0.1822 \| \| 0.3412 \| 2.45 \| 120 \| 0.8265 \| 0.2385 \| 0.4973 \| 0.3895 \| 0.6814 \| 0.3132 \| 0.1878 \| 0.2891 \| \| 0.2976 \| 2.86 \| 140 \| 1.3713 \| 0.1127 \| 0.4987 \| 0.2182 \| 0.9778 \| 0.0197 \| 0.2058 \| 0.0195 \| \| 0.2803 \| 3.27 \| 160 \| 1.6436 \| 0.1095 \| 0.4993 \| 0.2143 \| 0.9860 \| 0.0126 \| 0.2064 \| 0.0125 \| \| 0.3686 \| 3.67 \| 180 \| 1.2379 \| 0.1221 \| 0.4982 \| 0.2298 \| 0.9564 \| 0.0399 \| 0.2047 \| 0.0395 \| \| 0.3434 \| 4.08 \| 200 \| 1.1857 \| 0.1385 \| 0.4978 \| 0.2507 \| 0.9197 \| 0.0758 \| 0.2028 \| 0.0743 \| \| 0.2951 \| 4.49 \| 220 \| 1.3947 \| 0.1160 \| 0.4986 \| 0.2223 \| 0.9704 \| 0.0268 \| 0.2054 \| 0.0266 \| \| 0.2333 \| 4.9 \| 240 \| 1.5480 \| 0.1170 \| 0.4988 \| 0.2236 \| 0.9687 \| 0.0288 \| 0.2054 \| 0.0286 \| \| 0.2491 \| 5.31 \| 260 \| 1.6563 \| 0.1136 \| 0.4990 \| 0.2194 \| 0.9764 \| 0.0215 \| 0.2058 \| 0.0214 \| \| 0.2706 \| 5.71 \| 280 \| 1.9766 \| 0.1058 \| 0.4995 \| 0.2098 \| 0.9942 \| 0.0048 \| 0.2068 \| 0.0048 \| \| 0.2171 \| 6.12 \| 300 \| 1.6191 \| 0.1117 \| 0.4989 \| 0.2170 \| 0.9804 \| 0.0175 \| 0.2060 \| 0.0174 \| \| 0.2352 \| 6.53 \| 320 \| 1.8075 \| 0.1102 \| 0.4992 \| 0.2151 \| 0.9842 \| 0.0141 \| 0.2062 \| 0.0141 \| \| 0.2953 \| 6.94 \| 340 \| 1.4709 \| 0.1178 \| 0.4986 \| 0.2245 \| 0.9667 \| 0.0305 \| 0.2053 \| 0.0303 \| \| 0.2799 \| 7.35 \| 360 \| 1.3843 \| 0.1300 \| 0.4982 \| 0.2399 \| 0.9392 \| 0.0571 \| 0.2038 \| 0.0562 \| \| 0.2285 \| 7.76 \| 380 \| 1.7799 \| 0.1101 \| 0.4991 \| 0.2150 \| 0.9842 \| 0.0140 \| 0.2062 \| 0.0139 \| \| 0.3461 \| 8.16 \| 400 \| 1.7282 \| 0.1106 \| 0.4989 \| 0.2157 \| 0.9826 \| 0.0153 \| 0.2061 \| 0.0152 \| \| 0.1867 \| 8.57 \| 420 \| 2.3356 \| 0.1042 \| 0.4997 \| 0.2079 \| 0.9981 \| 0.0014 \| 0.2070 \| 0.0014 \| \| 0.1731 \| 8.98 \| 440 \| 2.1465 \| 0.1050 \| 0.4996 \| 0.2089 \| 0.9959 \| 0.0032 \| 0.2069 \| 0.0032 \| \| 0.224 \| 9.39 \| 460 \| 2.3467 \| 0.1047 \| 0.4996 \| 0.2084 \| 0.9969 \| 0.0024 \| 0.2070 \| 0.0024 \| \| 0.2199 \| 9.8 \| 480 \| 1.8997 \| 0.1075 \| 0.4992 \| 0.2118 \| 0.9899 \| 0.0084 \| 0.2065 \| 0.0084 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0 - Datasets 2.14.5 - Tokenizers 0.14.1	[ "non-map", "map" ]
rishitunu/new_ecc_segformer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # new_ecc_segformer This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ECC_crackdataset_withsplit dataset. It achieves the following results on the evaluation set: - Loss: 0.0663 - Mean Iou: 0.1943 - Mean Accuracy: 0.3915 - Overall Accuracy: 0.3915 - Accuracy Background: nan - Accuracy Crack: 0.3915 - Iou Background: 0.0 - Iou Crack: 0.3887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Crack \| Iou Background \| Iou Crack \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\| \| 0.0489 \| 1.0 \| 438 \| 0.0634 \| 0.1464 \| 0.2933 \| 0.2933 \| nan \| 0.2933 \| 0.0 \| 0.2929 \| \| 0.0542 \| 2.0 \| 876 \| 0.0439 \| 0.1956 \| 0.3917 \| 0.3917 \| nan \| 0.3917 \| 0.0 \| 0.3912 \| \| 0.0484 \| 3.0 \| 1314 \| 0.0434 \| 0.1719 \| 0.3551 \| 0.3551 \| nan \| 0.3551 \| 0.0 \| 0.3439 \| \| 0.0539 \| 4.0 \| 1752 \| 0.0447 \| 0.1871 \| 0.3820 \| 0.3820 \| nan \| 0.3820 \| 0.0 \| 0.3741 \| \| 0.0565 \| 5.0 \| 2190 \| 0.0435 \| 0.1888 \| 0.3937 \| 0.3937 \| nan \| 0.3937 \| 0.0 \| 0.3777 \| \| 0.0544 \| 6.0 \| 2628 \| 0.0442 \| 0.1904 \| 0.3930 \| 0.3930 \| nan \| 0.3930 \| 0.0 \| 0.3808 \| \| 0.0421 \| 7.0 \| 3066 \| 0.0449 \| 0.2256 \| 0.4651 \| 0.4651 \| nan \| 0.4651 \| 0.0 \| 0.4513 \| \| 0.0352 \| 8.0 \| 3504 \| 0.0587 \| 0.1569 \| 0.3165 \| 0.3165 \| nan \| 0.3165 \| 0.0 \| 0.3138 \| \| 0.0394 \| 9.0 \| 3942 \| 0.0442 \| 0.1842 \| 0.3710 \| 0.3710 \| nan \| 0.3710 \| 0.0 \| 0.3684 \| \| 0.0445 \| 10.0 \| 4380 \| 0.0609 \| 0.1167 \| 0.4173 \| 0.4173 \| nan \| 0.4173 \| 0.0 \| 0.2334 \| \| 0.0503 \| 11.0 \| 4818 \| 0.0504 \| 0.1702 \| 0.3714 \| 0.3714 \| nan \| 0.3714 \| 0.0 \| 0.3403 \| \| 0.0379 \| 12.0 \| 5256 \| 0.0460 \| 0.1903 \| 0.3869 \| 0.3869 \| nan \| 0.3869 \| 0.0 \| 0.3807 \| \| 0.0405 \| 13.0 \| 5694 \| 0.0452 \| 0.2017 \| 0.4084 \| 0.4084 \| nan \| 0.4084 \| 0.0 \| 0.4034 \| \| 0.0367 \| 14.0 \| 6132 \| 0.0477 \| 0.1995 \| 0.4060 \| 0.4060 \| nan \| 0.4060 \| 0.0 \| 0.3990 \| \| 0.0315 \| 15.0 \| 6570 \| 0.0498 \| 0.2073 \| 0.4208 \| 0.4208 \| nan \| 0.4208 \| 0.0 \| 0.4147 \| \| 0.0244 \| 16.0 \| 7008 \| 0.0486 \| 0.1963 \| 0.4029 \| 0.4029 \| nan \| 0.4029 \| 0.0 \| 0.3926 \| \| 0.031 \| 17.0 \| 7446 \| 0.0568 \| 0.1927 \| 0.3892 \| 0.3892 \| nan \| 0.3892 \| 0.0 \| 0.3855 \| \| 0.0288 \| 18.0 \| 7884 \| 0.0560 \| 0.2033 \| 0.4092 \| 0.4092 \| nan \| 0.4092 \| 0.0 \| 0.4067 \| \| 0.0354 \| 19.0 \| 8322 \| 0.0613 \| 0.2007 \| 0.4056 \| 0.4056 \| nan \| 0.4056 \| 0.0 \| 0.4013 \| \| 0.0315 \| 20.0 \| 8760 \| 0.0605 \| 0.1865 \| 0.3752 \| 0.3752 \| nan \| 0.3752 \| 0.0 \| 0.3731 \| \| 0.0343 \| 21.0 \| 9198 \| 0.0653 \| 0.1991 \| 0.4019 \| 0.4019 \| nan \| 0.4019 \| 0.0 \| 0.3981 \| \| 0.0327 \| 22.0 \| 9636 \| 0.0660 \| 0.1945 \| 0.3924 \| 0.3924 \| nan \| 0.3924 \| 0.0 \| 0.3891 \| \| 0.0252 \| 22.83 \| 10000 \| 0.0663 \| 0.1943 \| 0.3915 \| 0.3915 \| nan \| 0.3915 \| 0.0 \| 0.3887 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1	[ "background", "crack" ]
peldrak/segformer-finetuned-coastalDataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-coastalDataset This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coastal_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.6091 - Mean Iou: 0.6876 - Mean Accuracy: 0.7945 - Overall Accuracy: 0.8704 - Accuracy Water: 0.9332 - Accuracy Whitewater: 0.7904 - Accuracy Sediment: 0.8591 - Accuracy Other Natural Terrain: 0.4778 - Accuracy Vegetation: 0.9017 - Accuracy Development: 0.8549 - Accuracy Unknown: 0.7443 - Iou Water: 0.8671 - Iou Whitewater: 0.6713 - Iou Sediment: 0.7452 - Iou Other Natural Terrain: 0.3782 - Iou Vegetation: 0.7799 - Iou Development: 0.6736 - Iou Unknown: 0.6978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sediment \| Accuracy Other Natural Terrain \| Accuracy Vegetation \| Accuracy Development \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sediment \| Iou Other Natural Terrain \| Iou Vegetation \| Iou Development \| Iou Unknown \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-----------------:\|:------------------------------:\|:-------------------:\|:--------------------:\|:----------------:\|:---------:\|:--------------:\|:------------:\|:-------------------------:\|:--------------:\|:---------------:\|:-----------:\| \| 1.7886 \| 0.05 \| 20 \| 1.6491 \| 0.1498 \| 0.2460 \| 0.4112 \| 0.4687 \| 0.0287 \| 0.0048 \| 0.0144 \| 0.6736 \| 0.4392 \| 0.0923 \| 0.3518 \| 0.0098 \| 0.0048 \| 0.0058 \| 0.3662 \| 0.2469 \| 0.0632 \| \| 1.7534 \| 0.11 \| 40 \| 1.4097 \| 0.2254 \| 0.3388 \| 0.5545 \| 0.7079 \| 0.0074 \| 0.0185 \| 0.0066 \| 0.7428 \| 0.5723 \| 0.3159 \| 0.4839 \| 0.0069 \| 0.0184 \| 0.0047 \| 0.4440 \| 0.3124 \| 0.3073 \| \| 1.3223 \| 0.16 \| 60 \| 1.3142 \| 0.2096 \| 0.3198 \| 0.5687 \| 0.7425 \| 0.0013 \| 0.0487 \| 0.0054 \| 0.8736 \| 0.5420 \| 0.0248 \| 0.5201 \| 0.0013 \| 0.0468 \| 0.0041 \| 0.4890 \| 0.3818 \| 0.0242 \| \| 1.2429 \| 0.22 \| 80 \| 1.1734 \| 0.2616 \| 0.3747 \| 0.6300 \| 0.8271 \| 0.0011 \| 0.0271 \| 0.0000 \| 0.8500 \| 0.6156 \| 0.3021 \| 0.5954 \| 0.0011 \| 0.0265 \| 0.0000 \| 0.5267 \| 0.3806 \| 0.3011 \| \| 1.3556 \| 0.27 \| 100 \| 1.1141 \| 0.2878 \| 0.4016 \| 0.6536 \| 0.8384 \| 0.0114 \| 0.0710 \| 0.0000 \| 0.8670 \| 0.6799 \| 0.3432 \| 0.6140 \| 0.0113 \| 0.0655 \| 0.0000 \| 0.5632 \| 0.4226 \| 0.3381 \| \| 0.8995 \| 0.32 \| 120 \| 1.0387 \| 0.3096 \| 0.4245 \| 0.6724 \| 0.8439 \| 0.0148 \| 0.1242 \| 0.0 \| 0.8742 \| 0.7178 \| 0.3966 \| 0.6438 \| 0.0147 \| 0.1147 \| 0.0 \| 0.5733 \| 0.4289 \| 0.3921 \| \| 1.0435 \| 0.38 \| 140 \| 1.0234 \| 0.3135 \| 0.4294 \| 0.6615 \| 0.7801 \| 0.0023 \| 0.1835 \| 0.0000 \| 0.8794 \| 0.7321 \| 0.4284 \| 0.6033 \| 0.0023 \| 0.1685 \| 0.0000 \| 0.5538 \| 0.4573 \| 0.4094 \| \| 1.7516 \| 0.43 \| 160 \| 1.0258 \| 0.3287 \| 0.4534 \| 0.6665 \| 0.7874 \| 0.0122 \| 0.3467 \| 0.0 \| 0.8209 \| 0.7619 \| 0.4449 \| 0.6094 \| 0.0121 \| 0.3020 \| 0.0 \| 0.5660 \| 0.4476 \| 0.3638 \| \| 1.1561 \| 0.49 \| 180 \| 0.9637 \| 0.3662 \| 0.4967 \| 0.6989 \| 0.8001 \| 0.0003 \| 0.5674 \| 0.0 \| 0.8247 \| 0.8164 \| 0.4682 \| 0.6765 \| 0.0003 \| 0.4476 \| 0.0 \| 0.5738 \| 0.4173 \| 0.4477 \| \| 1.2007 \| 0.54 \| 200 \| 0.8883 \| 0.3867 \| 0.5175 \| 0.7210 \| 0.8696 \| 0.0006 \| 0.6747 \| 0.0 \| 0.7787 \| 0.8218 \| 0.4771 \| 0.6801 \| 0.0006 \| 0.4996 \| 0.0 \| 0.6154 \| 0.4621 \| 0.4495 \| \| 0.8308 \| 0.59 \| 220 \| 0.8686 \| 0.3936 \| 0.5091 \| 0.7279 \| 0.8239 \| 0.0035 \| 0.6613 \| 0.0 \| 0.8950 \| 0.7065 \| 0.4738 \| 0.6718 \| 0.0035 \| 0.4924 \| 0.0 \| 0.6390 \| 0.5353 \| 0.4134 \| \| 1.3709 \| 0.65 \| 240 \| 0.8437 \| 0.3847 \| 0.5166 \| 0.7198 \| 0.8709 \| 0.0081 \| 0.6150 \| 0.0 \| 0.7759 \| 0.8590 \| 0.4872 \| 0.6789 \| 0.0081 \| 0.4558 \| 0.0 \| 0.6192 \| 0.4765 \| 0.4545 \| \| 1.0652 \| 0.7 \| 260 \| 0.8299 \| 0.3842 \| 0.4930 \| 0.7246 \| 0.8817 \| 0.0420 \| 0.4268 \| 0.0 \| 0.8581 \| 0.7229 \| 0.5196 \| 0.6779 \| 0.0418 \| 0.3285 \| 0.0 \| 0.6367 \| 0.5153 \| 0.4892 \| \| 0.8973 \| 0.76 \| 280 \| 0.8115 \| 0.4030 \| 0.5232 \| 0.7436 \| 0.8562 \| 0.0126 \| 0.6910 \| 0.0 \| 0.8836 \| 0.7093 \| 0.5098 \| 0.7304 \| 0.0126 \| 0.4955 \| 0.0 \| 0.6481 \| 0.4535 \| 0.4811 \| \| 0.6368 \| 0.81 \| 300 \| 0.8043 \| 0.4291 \| 0.5447 \| 0.7516 \| 0.8700 \| 0.1096 \| 0.6775 \| 0.0 \| 0.8668 \| 0.7688 \| 0.5198 \| 0.7217 \| 0.1079 \| 0.5182 \| 0.0 \| 0.6438 \| 0.5261 \| 0.4860 \| \| 1.946 \| 0.86 \| 320 \| 0.7983 \| 0.4245 \| 0.5350 \| 0.7481 \| 0.8194 \| 0.0592 \| 0.6388 \| 0.0 \| 0.9216 \| 0.7385 \| 0.5671 \| 0.7116 \| 0.0582 \| 0.5212 \| 0.0 \| 0.6329 \| 0.5278 \| 0.5199 \| \| 0.9624 \| 0.92 \| 340 \| 0.8263 \| 0.4067 \| 0.5372 \| 0.7345 \| 0.8064 \| 0.0394 \| 0.7976 \| 0.0 \| 0.8713 \| 0.7507 \| 0.4953 \| 0.6855 \| 0.0391 \| 0.4348 \| 0.0 \| 0.6591 \| 0.5461 \| 0.4821 \| \| 0.7984 \| 0.97 \| 360 \| 0.7752 \| 0.4175 \| 0.5430 \| 0.7456 \| 0.8623 \| 0.0198 \| 0.7372 \| 0.0 \| 0.8328 \| 0.8482 \| 0.5007 \| 0.7137 \| 0.0198 \| 0.5537 \| 0.0 \| 0.6271 \| 0.5259 \| 0.4821 \| \| 0.7808 \| 1.03 \| 380 \| 0.7499 \| 0.4329 \| 0.5442 \| 0.7642 \| 0.8970 \| 0.0194 \| 0.7168 \| 0.0 \| 0.8745 \| 0.8007 \| 0.5010 \| 0.7389 \| 0.0193 \| 0.5562 \| 0.0 \| 0.6543 \| 0.5754 \| 0.4860 \| \| 0.9687 \| 1.08 \| 400 \| 0.7386 \| 0.4288 \| 0.5348 \| 0.7645 \| 0.8957 \| 0.0001 \| 0.7287 \| 0.0 \| 0.8980 \| 0.7178 \| 0.5034 \| 0.7365 \| 0.0001 \| 0.5451 \| 0.0 \| 0.6627 \| 0.5687 \| 0.4884 \| \| 0.7036 \| 1.14 \| 420 \| 0.7221 \| 0.4424 \| 0.5739 \| 0.7627 \| 0.8876 \| 0.1237 \| 0.8011 \| 0.0 \| 0.8096 \| 0.8442 \| 0.5511 \| 0.7576 \| 0.1223 \| 0.5346 \| 0.0 \| 0.6532 \| 0.5191 \| 0.5103 \| \| 0.5789 \| 1.19 \| 440 \| 0.7387 \| 0.4409 \| 0.5588 \| 0.7688 \| 0.9018 \| 0.0959 \| 0.8107 \| 0.0 \| 0.8672 \| 0.7453 \| 0.4904 \| 0.7527 \| 0.0945 \| 0.5342 \| 0.0 \| 0.6773 \| 0.5507 \| 0.4773 \| \| 0.5338 \| 1.24 \| 460 \| 0.6946 \| 0.4416 \| 0.5639 \| 0.7645 \| 0.9112 \| 0.1294 \| 0.7190 \| 0.0 \| 0.8238 \| 0.8345 \| 0.5296 \| 0.7535 \| 0.1236 \| 0.5391 \| 0.0 \| 0.6670 \| 0.5441 \| 0.4639 \| \| 0.7953 \| 1.3 \| 480 \| 0.7493 \| 0.4686 \| 0.5872 \| 0.7794 \| 0.9053 \| 0.2310 \| 0.8043 \| 0.0 \| 0.8665 \| 0.8091 \| 0.4944 \| 0.7708 \| 0.2220 \| 0.5434 \| 0.0 \| 0.6828 \| 0.5814 \| 0.4798 \| \| 1.0133 \| 1.35 \| 500 \| 0.7158 \| 0.4634 \| 0.5757 \| 0.7767 \| 0.9061 \| 0.1974 \| 0.7773 \| 0.0 \| 0.8755 \| 0.7682 \| 0.5051 \| 0.7564 \| 0.1901 \| 0.5548 \| 0.0 \| 0.6792 \| 0.5743 \| 0.4893 \| \| 0.6369 \| 1.41 \| 520 \| 0.7021 \| 0.4645 \| 0.5829 \| 0.7781 \| 0.8936 \| 0.2029 \| 0.7963 \| 0.0 \| 0.8787 \| 0.8036 \| 0.5054 \| 0.7735 \| 0.1920 \| 0.5585 \| 0.0 \| 0.6762 \| 0.5605 \| 0.4907 \| \| 0.5932 \| 1.46 \| 540 \| 0.6935 \| 0.4591 \| 0.5654 \| 0.7807 \| 0.9025 \| 0.1195 \| 0.7795 \| 0.0 \| 0.9085 \| 0.7415 \| 0.5061 \| 0.7705 \| 0.1135 \| 0.5608 \| 0.0 \| 0.6785 \| 0.5978 \| 0.4927 \| \| 0.7677 \| 1.51 \| 560 \| 0.6552 \| 0.4872 \| 0.6003 \| 0.7875 \| 0.9044 \| 0.3022 \| 0.7881 \| 0.0006 \| 0.8814 \| 0.7990 \| 0.5267 \| 0.7754 \| 0.2845 \| 0.5843 \| 0.0006 \| 0.6876 \| 0.5780 \| 0.4998 \| \| 0.5607 \| 1.57 \| 580 \| 0.6682 \| 0.4871 \| 0.5980 \| 0.7867 \| 0.8976 \| 0.3117 \| 0.7955 \| 0.0 \| 0.8992 \| 0.7793 \| 0.5028 \| 0.7697 \| 0.2911 \| 0.5902 \| 0.0 \| 0.6878 \| 0.5807 \| 0.4901 \| \| 0.7269 \| 1.62 \| 600 \| 0.6849 \| 0.4823 \| 0.5975 \| 0.7853 \| 0.9091 \| 0.2905 \| 0.8256 \| 0.0003 \| 0.8768 \| 0.7944 \| 0.4861 \| 0.7729 \| 0.2647 \| 0.5936 \| 0.0003 \| 0.6846 \| 0.5836 \| 0.4761 \| \| 0.4449 \| 1.68 \| 620 \| 0.6690 \| 0.4930 \| 0.6175 \| 0.7831 \| 0.8831 \| 0.3929 \| 0.8275 \| 0.0009 \| 0.8606 \| 0.8298 \| 0.5280 \| 0.7744 \| 0.3556 \| 0.5759 \| 0.0009 \| 0.6753 \| 0.5654 \| 0.5040 \| \| 0.6588 \| 1.73 \| 640 \| 0.6417 \| 0.5054 \| 0.6210 \| 0.7914 \| 0.9059 \| 0.4289 \| 0.7604 \| 0.0 \| 0.8640 \| 0.8192 \| 0.5685 \| 0.7932 \| 0.3960 \| 0.5968 \| 0.0 \| 0.6779 \| 0.5516 \| 0.5226 \| \| 0.8525 \| 1.78 \| 660 \| 0.6499 \| 0.4952 \| 0.6075 \| 0.7905 \| 0.8953 \| 0.3919 \| 0.7930 \| 0.0 \| 0.9060 \| 0.7254 \| 0.5409 \| 0.7884 \| 0.3341 \| 0.5840 \| 0.0 \| 0.6871 \| 0.5555 \| 0.5175 \| \| 0.7697 \| 1.84 \| 680 \| 0.6378 \| 0.5138 \| 0.6339 \| 0.7969 \| 0.8922 \| 0.4613 \| 0.8212 \| 0.0004 \| 0.8760 \| 0.8083 \| 0.5777 \| 0.7860 \| 0.3819 \| 0.6104 \| 0.0004 \| 0.6916 \| 0.5844 \| 0.5424 \| \| 0.5325 \| 1.89 \| 700 \| 0.6448 \| 0.5178 \| 0.6301 \| 0.8011 \| 0.8963 \| 0.4397 \| 0.8236 \| 0.0072 \| 0.8988 \| 0.7742 \| 0.5709 \| 0.7955 \| 0.3818 \| 0.6057 \| 0.0072 \| 0.6929 \| 0.5915 \| 0.5502 \| \| 0.7487 \| 1.95 \| 720 \| 0.5989 \| 0.5416 \| 0.6563 \| 0.8108 \| 0.8980 \| 0.5287 \| 0.8194 \| 0.0077 \| 0.8714 \| 0.7934 \| 0.6758 \| 0.7995 \| 0.4509 \| 0.6198 \| 0.0077 \| 0.6996 \| 0.6079 \| 0.6059 \| \| 1.8711 \| 2.0 \| 740 \| 0.6323 \| 0.5173 \| 0.6399 \| 0.7992 \| 0.9035 \| 0.5213 \| 0.7845 \| 0.0088 \| 0.8817 \| 0.8414 \| 0.5378 \| 0.7977 \| 0.4148 \| 0.6028 \| 0.0088 \| 0.6987 \| 0.5864 \| 0.5123 \| \| 0.4823 \| 2.05 \| 760 \| 0.6463 \| 0.5050 \| 0.6120 \| 0.7958 \| 0.9088 \| 0.4049 \| 0.7904 \| 0.0078 \| 0.9076 \| 0.7275 \| 0.5368 \| 0.7895 \| 0.3503 \| 0.5980 \| 0.0078 \| 0.6907 \| 0.5788 \| 0.5196 \| \| 0.6854 \| 2.11 \| 780 \| 0.6507 \| 0.5066 \| 0.6351 \| 0.7889 \| 0.9002 \| 0.5056 \| 0.8252 \| 0.0130 \| 0.8507 \| 0.8364 \| 0.5145 \| 0.7809 \| 0.4048 \| 0.6104 \| 0.0130 \| 0.6831 \| 0.5572 \| 0.4964 \| \| 0.5534 \| 2.16 \| 800 \| 0.6499 \| 0.4977 \| 0.6310 \| 0.7870 \| 0.8875 \| 0.4178 \| 0.8700 \| 0.0133 \| 0.8414 \| 0.8439 \| 0.5428 \| 0.7735 \| 0.3444 \| 0.5597 \| 0.0133 \| 0.6990 \| 0.5860 \| 0.5082 \| \| 1.6573 \| 2.22 \| 820 \| 0.6379 \| 0.5041 \| 0.6137 \| 0.7944 \| 0.9105 \| 0.4303 \| 0.7719 \| 0.0039 \| 0.9020 \| 0.7513 \| 0.5256 \| 0.7882 \| 0.3374 \| 0.6124 \| 0.0039 \| 0.6883 \| 0.6030 \| 0.4955 \| \| 0.422 \| 2.27 \| 840 \| 0.6730 \| 0.4999 \| 0.6418 \| 0.7800 \| 0.8509 \| 0.5520 \| 0.8265 \| 0.0241 \| 0.8649 \| 0.8507 \| 0.5237 \| 0.7707 \| 0.4076 \| 0.5670 \| 0.0240 \| 0.6808 \| 0.5550 \| 0.4941 \| \| 0.7256 \| 2.32 \| 860 \| 0.6374 \| 0.5134 \| 0.6478 \| 0.7898 \| 0.8793 \| 0.5758 \| 0.8032 \| 0.0284 \| 0.8657 \| 0.8556 \| 0.5266 \| 0.7931 \| 0.4553 \| 0.5835 \| 0.0276 \| 0.6888 \| 0.5477 \| 0.4980 \| \| 0.3261 \| 2.38 \| 880 \| 0.6084 \| 0.5307 \| 0.6678 \| 0.8026 \| 0.8987 \| 0.7237 \| 0.7705 \| 0.0299 \| 0.8830 \| 0.8401 \| 0.5288 \| 0.8012 \| 0.4855 \| 0.6110 \| 0.0299 \| 0.7123 \| 0.5715 \| 0.5037 \| \| 0.9935 \| 2.43 \| 900 \| 0.6262 \| 0.5298 \| 0.6550 \| 0.8025 \| 0.8743 \| 0.5612 \| 0.8547 \| 0.0157 \| 0.8966 \| 0.8177 \| 0.5645 \| 0.7868 \| 0.4467 \| 0.6069 \| 0.0157 \| 0.7097 \| 0.6212 \| 0.5217 \| \| 1.2977 \| 2.49 \| 920 \| 0.6661 \| 0.5119 \| 0.6417 \| 0.7924 \| 0.8757 \| 0.5019 \| 0.8213 \| 0.0103 \| 0.8746 \| 0.8719 \| 0.5365 \| 0.7896 \| 0.4260 \| 0.5785 \| 0.0103 \| 0.6917 \| 0.5665 \| 0.5211 \| \| 0.4984 \| 2.54 \| 940 \| 0.5994 \| 0.5452 \| 0.6678 \| 0.8086 \| 0.9115 \| 0.6257 \| 0.8113 \| 0.0159 \| 0.8524 \| 0.8568 \| 0.6009 \| 0.8123 \| 0.5189 \| 0.6264 \| 0.0159 \| 0.6904 \| 0.6049 \| 0.5473 \| \| 0.6221 \| 2.59 \| 960 \| 0.6465 \| 0.5342 \| 0.6660 \| 0.7959 \| 0.9048 \| 0.6706 \| 0.8602 \| 0.0221 \| 0.8192 \| 0.7986 \| 0.5866 \| 0.7858 \| 0.5098 \| 0.5983 \| 0.0221 \| 0.6700 \| 0.6201 \| 0.5335 \| \| 0.3674 \| 2.65 \| 980 \| 0.6477 \| 0.5323 \| 0.6529 \| 0.8017 \| 0.8813 \| 0.6333 \| 0.8210 \| 0.0130 \| 0.9148 \| 0.7782 \| 0.5286 \| 0.8052 \| 0.5073 \| 0.6330 \| 0.0130 \| 0.6904 \| 0.5653 \| 0.5121 \| \| 0.4939 \| 2.7 \| 1000 \| 0.6064 \| 0.5424 \| 0.6765 \| 0.8087 \| 0.9061 \| 0.6742 \| 0.8566 \| 0.0241 \| 0.8567 \| 0.8603 \| 0.5579 \| 0.8141 \| 0.5124 \| 0.6231 \| 0.0241 \| 0.7060 \| 0.5928 \| 0.5246 \| \| 0.4825 \| 2.76 \| 1020 \| 0.6061 \| 0.5371 \| 0.6714 \| 0.8050 \| 0.9102 \| 0.6962 \| 0.8545 \| 0.0245 \| 0.8540 \| 0.8060 \| 0.5546 \| 0.8184 \| 0.5311 \| 0.6143 \| 0.0245 \| 0.7033 \| 0.5607 \| 0.5077 \| \| 0.2858 \| 2.81 \| 1040 \| 0.6032 \| 0.5408 \| 0.6806 \| 0.8019 \| 0.8807 \| 0.7392 \| 0.8252 \| 0.0246 \| 0.8606 \| 0.8663 \| 0.5675 \| 0.8098 \| 0.5620 \| 0.6315 \| 0.0245 \| 0.6985 \| 0.5374 \| 0.5220 \| \| 0.6248 \| 2.86 \| 1060 \| 0.6321 \| 0.5317 \| 0.6489 \| 0.8059 \| 0.9103 \| 0.6419 \| 0.7859 \| 0.0122 \| 0.9188 \| 0.7799 \| 0.4933 \| 0.8025 \| 0.4770 \| 0.6247 \| 0.0122 \| 0.7102 \| 0.6242 \| 0.4709 \| \| 0.5328 \| 2.92 \| 1080 \| 0.5913 \| 0.5412 \| 0.6815 \| 0.8091 \| 0.9023 \| 0.7421 \| 0.8144 \| 0.0280 \| 0.8715 \| 0.8700 \| 0.5422 \| 0.8102 \| 0.4764 \| 0.6364 \| 0.0279 \| 0.7111 \| 0.6099 \| 0.5161 \| \| 0.3748 \| 2.97 \| 1100 \| 0.6328 \| 0.5279 \| 0.6739 \| 0.7984 \| 0.9168 \| 0.7236 \| 0.8730 \| 0.0248 \| 0.8217 \| 0.8466 \| 0.5111 \| 0.8111 \| 0.5002 \| 0.6253 \| 0.0247 \| 0.6869 \| 0.5492 \| 0.4976 \| \| 0.5079 \| 3.03 \| 1120 \| 0.6226 \| 0.5361 \| 0.6568 \| 0.8084 \| 0.9069 \| 0.6303 \| 0.8483 \| 0.0226 \| 0.9101 \| 0.7806 \| 0.4986 \| 0.8151 \| 0.4983 \| 0.6206 \| 0.0225 \| 0.7104 \| 0.5992 \| 0.4862 \| \| 1.1987 \| 3.08 \| 1140 \| 0.5712 \| 0.5714 \| 0.6993 \| 0.8244 \| 0.8928 \| 0.7590 \| 0.8263 \| 0.0269 \| 0.8931 \| 0.8668 \| 0.6305 \| 0.8213 \| 0.5669 \| 0.6592 \| 0.0268 \| 0.7241 \| 0.6041 \| 0.5972 \| \| 0.7254 \| 3.14 \| 1160 \| 0.6330 \| 0.5416 \| 0.6696 \| 0.8088 \| 0.8986 \| 0.6701 \| 0.8139 \| 0.0237 \| 0.8958 \| 0.8757 \| 0.5094 \| 0.8103 \| 0.5280 \| 0.6353 \| 0.0236 \| 0.7146 \| 0.5816 \| 0.4980 \| \| 0.4003 \| 3.19 \| 1180 \| 0.6043 \| 0.5385 \| 0.6659 \| 0.8092 \| 0.9123 \| 0.6244 \| 0.8237 \| 0.0240 \| 0.8793 \| 0.8897 \| 0.5081 \| 0.8140 \| 0.4894 \| 0.6426 \| 0.0239 \| 0.7104 \| 0.5954 \| 0.4938 \| \| 0.3748 \| 3.24 \| 1200 \| 0.5919 \| 0.5438 \| 0.6621 \| 0.8102 \| 0.9296 \| 0.5921 \| 0.8031 \| 0.0300 \| 0.8633 \| 0.8873 \| 0.5289 \| 0.8136 \| 0.5033 \| 0.6481 \| 0.0299 \| 0.6993 \| 0.6011 \| 0.5113 \| \| 0.6813 \| 3.3 \| 1220 \| 0.5742 \| 0.5589 \| 0.6683 \| 0.8179 \| 0.9073 \| 0.5921 \| 0.8173 \| 0.0311 \| 0.8975 \| 0.8354 \| 0.5973 \| 0.8079 \| 0.5053 \| 0.6427 \| 0.0309 \| 0.7055 \| 0.6439 \| 0.5764 \| \| 0.3824 \| 3.35 \| 1240 \| 0.5503 \| 0.5679 \| 0.6963 \| 0.8207 \| 0.9115 \| 0.7776 \| 0.8276 \| 0.0345 \| 0.8744 \| 0.8579 \| 0.5906 \| 0.8203 \| 0.5693 \| 0.6410 \| 0.0342 \| 0.7103 \| 0.6310 \| 0.5693 \| \| 0.7603 \| 3.41 \| 1260 \| 0.5712 \| 0.5638 \| 0.6821 \| 0.8174 \| 0.8986 \| 0.7138 \| 0.8280 \| 0.0439 \| 0.9093 \| 0.8306 \| 0.5506 \| 0.8160 \| 0.5587 \| 0.6624 \| 0.0433 \| 0.7065 \| 0.6286 \| 0.5310 \| \| 0.7926 \| 3.46 \| 1280 \| 0.5728 \| 0.5673 \| 0.6926 \| 0.8185 \| 0.9123 \| 0.6875 \| 0.8869 \| 0.0490 \| 0.8531 \| 0.8686 \| 0.5909 \| 0.8117 \| 0.5581 \| 0.6424 \| 0.0483 \| 0.7068 \| 0.6355 \| 0.5681 \| \| 0.5149 \| 3.51 \| 1300 \| 0.5715 \| 0.5679 \| 0.6958 \| 0.8194 \| 0.9052 \| 0.6954 \| 0.8254 \| 0.0548 \| 0.8556 \| 0.9054 \| 0.6290 \| 0.8223 \| 0.5488 \| 0.6569 \| 0.0544 \| 0.7048 \| 0.5916 \| 0.5967 \| \| 0.623 \| 3.57 \| 1320 \| 0.5723 \| 0.5752 \| 0.6931 \| 0.8245 \| 0.9129 \| 0.6938 \| 0.8519 \| 0.0379 \| 0.8771 \| 0.8737 \| 0.6042 \| 0.8195 \| 0.5656 \| 0.6468 \| 0.0371 \| 0.7112 \| 0.6614 \| 0.5844 \| \| 0.5331 \| 3.62 \| 1340 \| 0.5802 \| 0.5694 \| 0.6758 \| 0.8221 \| 0.9283 \| 0.6637 \| 0.8086 \| 0.0427 \| 0.8874 \| 0.7865 \| 0.6136 \| 0.8052 \| 0.5426 \| 0.6474 \| 0.0418 \| 0.7151 \| 0.6460 \| 0.5877 \| \| 0.4966 \| 3.68 \| 1360 \| 0.5776 \| 0.5653 \| 0.6944 \| 0.8189 \| 0.9041 \| 0.7890 \| 0.8019 \| 0.0577 \| 0.8948 \| 0.8461 \| 0.5674 \| 0.8222 \| 0.5665 \| 0.6527 \| 0.0565 \| 0.7171 \| 0.5995 \| 0.5425 \| \| 0.7875 \| 3.73 \| 1380 \| 0.5500 \| 0.5825 \| 0.7075 \| 0.8249 \| 0.9192 \| 0.7436 \| 0.8403 \| 0.1109 \| 0.8625 \| 0.8702 \| 0.6056 \| 0.8167 \| 0.5658 \| 0.6609 \| 0.1036 \| 0.7230 \| 0.6398 \| 0.5679 \| \| 0.4906 \| 3.78 \| 1400 \| 0.5681 \| 0.5805 \| 0.7036 \| 0.8191 \| 0.8903 \| 0.6878 \| 0.7936 \| 0.1968 \| 0.8973 \| 0.8741 \| 0.5855 \| 0.8242 \| 0.5382 \| 0.6658 \| 0.1750 \| 0.7098 \| 0.6022 \| 0.5484 \| \| 0.3565 \| 3.84 \| 1420 \| 0.6125 \| 0.5640 \| 0.6908 \| 0.8155 \| 0.9136 \| 0.7074 \| 0.8146 \| 0.1141 \| 0.8803 \| 0.8820 \| 0.5234 \| 0.8172 \| 0.5596 \| 0.6229 \| 0.1071 \| 0.7207 \| 0.6137 \| 0.5068 \| \| 1.3393 \| 3.89 \| 1440 \| 0.5608 \| 0.5915 \| 0.7116 \| 0.8272 \| 0.8975 \| 0.7789 \| 0.8623 \| 0.1120 \| 0.8902 \| 0.7977 \| 0.6430 \| 0.8131 \| 0.6059 \| 0.6366 \| 0.1083 \| 0.7236 \| 0.6506 \| 0.6027 \| \| 0.864 \| 3.95 \| 1460 \| 0.5728 \| 0.5831 \| 0.7010 \| 0.8272 \| 0.9050 \| 0.7358 \| 0.7942 \| 0.0735 \| 0.8930 \| 0.8686 \| 0.6372 \| 0.8239 \| 0.5722 \| 0.6571 \| 0.0716 \| 0.7173 \| 0.6471 \| 0.5923 \| \| 0.4925 \| 4.0 \| 1480 \| 0.5538 \| 0.5883 \| 0.6994 \| 0.8313 \| 0.9121 \| 0.6628 \| 0.8424 \| 0.0866 \| 0.8914 \| 0.8586 \| 0.6421 \| 0.8283 \| 0.5531 \| 0.6734 \| 0.0811 \| 0.7206 \| 0.6540 \| 0.6076 \| \| 0.4559 \| 4.05 \| 1500 \| 0.5789 \| 0.5725 \| 0.6901 \| 0.8233 \| 0.9219 \| 0.6579 \| 0.8358 \| 0.0760 \| 0.8747 \| 0.8848 \| 0.5795 \| 0.8222 \| 0.5401 \| 0.6579 \| 0.0735 \| 0.7155 \| 0.6462 \| 0.5521 \| \| 0.4295 \| 4.11 \| 1520 \| 0.6088 \| 0.5705 \| 0.6949 \| 0.8165 \| 0.9330 \| 0.6590 \| 0.8607 \| 0.0876 \| 0.8127 \| 0.9066 \| 0.6045 \| 0.7917 \| 0.5603 \| 0.6283 \| 0.0844 \| 0.7159 \| 0.6390 \| 0.5737 \| \| 0.5591 \| 4.16 \| 1540 \| 0.5385 \| 0.6038 \| 0.7106 \| 0.8364 \| 0.9136 \| 0.6978 \| 0.7744 \| 0.1493 \| 0.8992 \| 0.8430 \| 0.6971 \| 0.8240 \| 0.5594 \| 0.6594 \| 0.1425 \| 0.7283 \| 0.6668 \| 0.6462 \| \| 0.536 \| 4.22 \| 1560 \| 0.6030 \| 0.5814 \| 0.7128 \| 0.8229 \| 0.8963 \| 0.7285 \| 0.8589 \| 0.1084 \| 0.8596 \| 0.9171 \| 0.6206 \| 0.8224 \| 0.5719 \| 0.6460 \| 0.1042 \| 0.7128 \| 0.6234 \| 0.5890 \| \| 1.0689 \| 4.27 \| 1580 \| 0.5964 \| 0.5930 \| 0.7215 \| 0.8262 \| 0.9036 \| 0.7625 \| 0.8329 \| 0.1609 \| 0.8663 \| 0.9100 \| 0.6144 \| 0.8191 \| 0.5807 \| 0.6749 \| 0.1523 \| 0.7192 \| 0.6189 \| 0.5861 \| \| 0.2286 \| 4.32 \| 1600 \| 0.5731 \| 0.6002 \| 0.7168 \| 0.8319 \| 0.9089 \| 0.7182 \| 0.8832 \| 0.1414 \| 0.8752 \| 0.8408 \| 0.6502 \| 0.8171 \| 0.5893 \| 0.6404 \| 0.1364 \| 0.7279 \| 0.6738 \| 0.6166 \| \| 0.4283 \| 4.38 \| 1620 \| 0.5744 \| 0.6147 \| 0.7181 \| 0.8372 \| 0.8998 \| 0.6260 \| 0.8260 \| 0.2497 \| 0.9142 \| 0.8480 \| 0.6631 \| 0.8316 \| 0.5335 \| 0.6976 \| 0.2223 \| 0.7171 \| 0.6660 \| 0.6351 \| \| 0.4222 \| 4.43 \| 1640 \| 0.5881 \| 0.6014 \| 0.6982 \| 0.8337 \| 0.9133 \| 0.6604 \| 0.8085 \| 0.1416 \| 0.9237 \| 0.8171 \| 0.6224 \| 0.8244 \| 0.5747 \| 0.6702 \| 0.1357 \| 0.7182 \| 0.6822 \| 0.6041 \| \| 0.7353 \| 4.49 \| 1660 \| 0.5441 \| 0.6086 \| 0.7291 \| 0.8298 \| 0.9064 \| 0.7696 \| 0.8084 \| 0.2185 \| 0.8724 \| 0.8933 \| 0.6352 \| 0.8266 \| 0.6096 \| 0.6655 \| 0.2029 \| 0.7105 \| 0.6564 \| 0.5887 \| \| 0.3957 \| 4.54 \| 1680 \| 0.6038 \| 0.5958 \| 0.7275 \| 0.8192 \| 0.8897 \| 0.7436 \| 0.8966 \| 0.2666 \| 0.8646 \| 0.8630 \| 0.5688 \| 0.8015 \| 0.5760 \| 0.6355 \| 0.2409 \| 0.7136 \| 0.6558 \| 0.5474 \| \| 0.2487 \| 4.59 \| 1700 \| 0.5658 \| 0.6187 \| 0.7409 \| 0.8320 \| 0.9057 \| 0.7238 \| 0.8779 \| 0.3069 \| 0.8613 \| 0.8739 \| 0.6365 \| 0.8163 \| 0.6037 \| 0.6622 \| 0.2484 \| 0.7229 \| 0.6738 \| 0.6033 \| \| 0.3008 \| 4.65 \| 1720 \| 0.5535 \| 0.6035 \| 0.7189 \| 0.8289 \| 0.9121 \| 0.7453 \| 0.7740 \| 0.2170 \| 0.8902 \| 0.8903 \| 0.6032 \| 0.8225 \| 0.5837 \| 0.6813 \| 0.1916 \| 0.7119 \| 0.6491 \| 0.5841 \| \| 0.6365 \| 4.7 \| 1740 \| 0.5208 \| 0.6136 \| 0.7279 \| 0.8371 \| 0.9263 \| 0.7461 \| 0.8308 \| 0.1831 \| 0.8638 \| 0.8811 \| 0.6639 \| 0.8202 \| 0.6155 \| 0.6586 \| 0.1628 \| 0.7354 \| 0.6755 \| 0.6269 \| \| 0.4279 \| 4.76 \| 1760 \| 0.6134 \| 0.5704 \| 0.6952 \| 0.8183 \| 0.9179 \| 0.6845 \| 0.8761 \| 0.1342 \| 0.8760 \| 0.8668 \| 0.5109 \| 0.8247 \| 0.5764 \| 0.6442 \| 0.1245 \| 0.7225 \| 0.5997 \| 0.5010 \| \| 0.6908 \| 4.81 \| 1780 \| 0.5553 \| 0.6059 \| 0.7304 \| 0.8273 \| 0.9250 \| 0.7461 \| 0.8168 \| 0.2998 \| 0.8635 \| 0.9004 \| 0.5613 \| 0.8287 \| 0.6168 \| 0.6874 \| 0.2536 \| 0.7293 \| 0.5799 \| 0.5453 \| \| 0.5609 \| 4.86 \| 1800 \| 0.5144 \| 0.6264 \| 0.7445 \| 0.8389 \| 0.9163 \| 0.7618 \| 0.8524 \| 0.3185 \| 0.8861 \| 0.8515 \| 0.6247 \| 0.8349 \| 0.6154 \| 0.7107 \| 0.2581 \| 0.7332 \| 0.6332 \| 0.5991 \| \| 1.0128 \| 4.92 \| 1820 \| 0.5699 \| 0.6074 \| 0.7300 \| 0.8298 \| 0.9006 \| 0.7923 \| 0.8483 \| 0.2638 \| 0.9048 \| 0.8210 \| 0.5789 \| 0.8302 \| 0.6199 \| 0.6829 \| 0.2303 \| 0.7278 \| 0.5999 \| 0.5610 \| \| 0.516 \| 4.97 \| 1840 \| 0.5578 \| 0.6022 \| 0.7351 \| 0.8286 \| 0.9112 \| 0.7915 \| 0.8450 \| 0.2377 \| 0.8619 \| 0.9054 \| 0.5928 \| 0.8346 \| 0.6146 \| 0.6743 \| 0.1882 \| 0.7268 \| 0.6007 \| 0.5763 \| \| 0.3054 \| 5.03 \| 1860 \| 0.5368 \| 0.6154 \| 0.7584 \| 0.8301 \| 0.9143 \| 0.7967 \| 0.8678 \| 0.4105 \| 0.8507 \| 0.8955 \| 0.5728 \| 0.8363 \| 0.6110 \| 0.6948 \| 0.2822 \| 0.7338 \| 0.5973 \| 0.5523 \| \| 0.6332 \| 5.08 \| 1880 \| 0.5266 \| 0.6299 \| 0.7423 \| 0.8414 \| 0.9295 \| 0.7338 \| 0.8080 \| 0.3166 \| 0.8789 \| 0.8939 \| 0.6356 \| 0.8371 \| 0.6080 \| 0.6962 \| 0.2749 \| 0.7321 \| 0.6483 \| 0.6128 \| \| 0.4124 \| 5.14 \| 1900 \| 0.5191 \| 0.6227 \| 0.7240 \| 0.8400 \| 0.9238 \| 0.6855 \| 0.7834 \| 0.2553 \| 0.8945 \| 0.8449 \| 0.6803 \| 0.8282 \| 0.5864 \| 0.6731 \| 0.2227 \| 0.7297 \| 0.6979 \| 0.6208 \| \| 0.3046 \| 5.19 \| 1920 \| 0.5244 \| 0.6272 \| 0.7438 \| 0.8437 \| 0.9192 \| 0.7810 \| 0.8585 \| 0.2385 \| 0.8838 \| 0.8707 \| 0.6552 \| 0.8372 \| 0.6193 \| 0.6793 \| 0.1982 \| 0.7389 \| 0.6853 \| 0.6323 \| \| 0.5471 \| 5.24 \| 1940 \| 0.4855 \| 0.6292 \| 0.7565 \| 0.8412 \| 0.8930 \| 0.8240 \| 0.8479 \| 0.2628 \| 0.8820 \| 0.9020 \| 0.6839 \| 0.8266 \| 0.6157 \| 0.6929 \| 0.2276 \| 0.7393 \| 0.6698 \| 0.6326 \| \| 0.2924 \| 5.3 \| 1960 \| 0.5475 \| 0.6145 \| 0.7243 \| 0.8377 \| 0.9260 \| 0.6746 \| 0.8684 \| 0.2452 \| 0.8921 \| 0.8872 \| 0.5767 \| 0.8220 \| 0.5689 \| 0.6825 \| 0.2239 \| 0.7409 \| 0.6992 \| 0.5639 \| \| 0.7133 \| 5.35 \| 1980 \| 0.4943 \| 0.6401 \| 0.7533 \| 0.8468 \| 0.9030 \| 0.7853 \| 0.8514 \| 0.2992 \| 0.9117 \| 0.8727 \| 0.6501 \| 0.8375 \| 0.6315 \| 0.7148 \| 0.2638 \| 0.7475 \| 0.6811 \| 0.6041 \| \| 0.3775 \| 5.41 \| 2000 \| 0.5023 \| 0.6105 \| 0.7239 \| 0.8412 \| 0.9024 \| 0.7428 \| 0.8539 \| 0.1445 \| 0.9132 \| 0.8469 \| 0.6636 \| 0.8286 \| 0.6235 \| 0.6728 \| 0.1400 \| 0.7618 \| 0.6342 \| 0.6125 \| \| 0.8475 \| 5.46 \| 2020 \| 0.5247 \| 0.6125 \| 0.7384 \| 0.8312 \| 0.8990 \| 0.7531 \| 0.8370 \| 0.2787 \| 0.8786 \| 0.9103 \| 0.6121 \| 0.8195 \| 0.6126 \| 0.7011 \| 0.2521 \| 0.7470 \| 0.5952 \| 0.5598 \| \| 0.2725 \| 5.51 \| 2040 \| 0.5264 \| 0.6263 \| 0.7294 \| 0.8445 \| 0.9250 \| 0.7440 \| 0.8590 \| 0.2303 \| 0.9168 \| 0.8122 \| 0.6183 \| 0.8321 \| 0.6192 \| 0.6931 \| 0.2099 \| 0.7482 \| 0.6815 \| 0.5999 \| \| 0.5317 \| 5.57 \| 2060 \| 0.5177 \| 0.6208 \| 0.7396 \| 0.8372 \| 0.9330 \| 0.7005 \| 0.8587 \| 0.2808 \| 0.8339 \| 0.8877 \| 0.6823 \| 0.8095 \| 0.5866 \| 0.6802 \| 0.2380 \| 0.7451 \| 0.6555 \| 0.6306 \| \| 0.6395 \| 5.62 \| 2080 \| 0.5292 \| 0.6252 \| 0.7410 \| 0.8365 \| 0.8861 \| 0.7238 \| 0.8425 \| 0.3135 \| 0.9041 \| 0.8495 \| 0.6674 \| 0.8211 \| 0.6161 \| 0.6870 \| 0.2707 \| 0.7388 \| 0.6309 \| 0.6120 \| \| 0.3586 \| 5.68 \| 2100 \| 0.4814 \| 0.6412 \| 0.7579 \| 0.8492 \| 0.9053 \| 0.8220 \| 0.8115 \| 0.2991 \| 0.9051 \| 0.8471 \| 0.7149 \| 0.8402 \| 0.6208 \| 0.6996 \| 0.2540 \| 0.7490 \| 0.6618 \| 0.6634 \| \| 0.3127 \| 5.73 \| 2120 \| 0.4579 \| 0.6571 \| 0.7653 \| 0.8566 \| 0.9160 \| 0.8202 \| 0.8085 \| 0.3566 \| 0.9232 \| 0.8290 \| 0.7034 \| 0.8397 \| 0.6271 \| 0.7127 \| 0.2927 \| 0.7648 \| 0.6997 \| 0.6628 \| \| 0.6651 \| 5.78 \| 2140 \| 0.5767 \| 0.6326 \| 0.7389 \| 0.8437 \| 0.9129 \| 0.7090 \| 0.8142 \| 0.3383 \| 0.9263 \| 0.8663 \| 0.6056 \| 0.8355 \| 0.5889 \| 0.7222 \| 0.2958 \| 0.7460 \| 0.6529 \| 0.5868 \| \| 0.2858 \| 5.84 \| 2160 \| 0.5401 \| 0.6216 \| 0.7492 \| 0.8346 \| 0.9231 \| 0.6579 \| 0.8866 \| 0.3958 \| 0.8346 \| 0.9036 \| 0.6429 \| 0.8273 \| 0.5633 \| 0.6981 \| 0.3002 \| 0.7283 \| 0.6183 \| 0.6156 \| \| 0.2566 \| 5.89 \| 2180 \| 0.5070 \| 0.6388 \| 0.7608 \| 0.8433 \| 0.9055 \| 0.7782 \| 0.8674 \| 0.3469 \| 0.8775 \| 0.8787 \| 0.6715 \| 0.8361 \| 0.6347 \| 0.7050 \| 0.2857 \| 0.7392 \| 0.6408 \| 0.6301 \| \| 0.4278 \| 5.95 \| 2200 \| 0.5319 \| 0.6431 \| 0.7485 \| 0.8506 \| 0.8914 \| 0.7504 \| 0.8633 \| 0.2503 \| 0.9239 \| 0.8485 \| 0.7118 \| 0.8342 \| 0.6068 \| 0.7183 \| 0.2295 \| 0.7408 \| 0.6970 \| 0.6752 \| \| 0.3374 \| 6.0 \| 2220 \| 0.5220 \| 0.6446 \| 0.7587 \| 0.8504 \| 0.9149 \| 0.7802 \| 0.8598 \| 0.3481 \| 0.9145 \| 0.8635 \| 0.6300 \| 0.8448 \| 0.6248 \| 0.7057 \| 0.3009 \| 0.7573 \| 0.6588 \| 0.6198 \| \| 0.4162 \| 6.05 \| 2240 \| 0.5373 \| 0.6312 \| 0.7413 \| 0.8445 \| 0.9082 \| 0.7615 \| 0.8502 \| 0.2900 \| 0.9255 \| 0.8356 \| 0.6185 \| 0.8390 \| 0.6241 \| 0.7017 \| 0.2609 \| 0.7497 \| 0.6446 \| 0.5982 \| \| 0.3659 \| 6.11 \| 2260 \| 0.5100 \| 0.6503 \| 0.7555 \| 0.8558 \| 0.9271 \| 0.7673 \| 0.8519 \| 0.3182 \| 0.9162 \| 0.8447 \| 0.6630 \| 0.8468 \| 0.6269 \| 0.7024 \| 0.2766 \| 0.7626 \| 0.6856 \| 0.6513 \| \| 0.3019 \| 6.16 \| 2280 \| 0.5076 \| 0.6437 \| 0.7600 \| 0.8502 \| 0.9256 \| 0.7714 \| 0.8497 \| 0.3067 \| 0.8644 \| 0.8754 \| 0.7266 \| 0.8413 \| 0.6254 \| 0.6728 \| 0.2581 \| 0.7460 \| 0.6784 \| 0.6838 \| \| 0.419 \| 6.22 \| 2300 \| 0.5132 \| 0.6403 \| 0.7579 \| 0.8483 \| 0.9121 \| 0.7802 \| 0.8602 \| 0.3012 \| 0.8819 \| 0.8600 \| 0.7096 \| 0.8394 \| 0.6169 \| 0.6742 \| 0.2599 \| 0.7437 \| 0.6698 \| 0.6782 \| \| 0.3698 \| 6.27 \| 2320 \| 0.5154 \| 0.6424 \| 0.7488 \| 0.8505 \| 0.9401 \| 0.7680 \| 0.8024 \| 0.3329 \| 0.9022 \| 0.8523 \| 0.6434 \| 0.8390 \| 0.6141 \| 0.6964 \| 0.2846 \| 0.7549 \| 0.6732 \| 0.6348 \| \| 0.3336 \| 6.32 \| 2340 \| 0.5687 \| 0.6079 \| 0.7314 \| 0.8337 \| 0.9302 \| 0.7395 \| 0.8798 \| 0.2330 \| 0.8582 \| 0.8943 \| 0.5849 \| 0.8411 \| 0.6169 \| 0.6405 \| 0.2012 \| 0.7290 \| 0.6507 \| 0.5757 \| \| 0.3567 \| 6.38 \| 2360 \| 0.5166 \| 0.6322 \| 0.7639 \| 0.8435 \| 0.9016 \| 0.8075 \| 0.8436 \| 0.3007 \| 0.8602 \| 0.9100 \| 0.7236 \| 0.8399 \| 0.6166 \| 0.6671 \| 0.2515 \| 0.7433 \| 0.6095 \| 0.6972 \| \| 0.4141 \| 6.43 \| 2380 \| 0.4746 \| 0.6610 \| 0.7868 \| 0.8559 \| 0.9129 \| 0.8145 \| 0.8264 \| 0.4202 \| 0.8670 \| 0.9008 \| 0.7660 \| 0.8423 \| 0.6173 \| 0.6954 \| 0.3593 \| 0.7663 \| 0.6171 \| 0.7296 \| \| 1.0648 \| 6.49 \| 2400 \| 0.4916 \| 0.6551 \| 0.7625 \| 0.8531 \| 0.9221 \| 0.7768 \| 0.8518 \| 0.3922 \| 0.9069 \| 0.7967 \| 0.6909 \| 0.8407 \| 0.6314 \| 0.7027 \| 0.3444 \| 0.7561 \| 0.6446 \| 0.6659 \| \| 0.3123 \| 6.54 \| 2420 \| 0.4354 \| 0.6802 \| 0.7896 \| 0.8663 \| 0.8954 \| 0.7670 \| 0.8441 \| 0.4503 \| 0.9090 \| 0.8037 \| 0.8579 \| 0.8356 \| 0.6152 \| 0.7331 \| 0.3740 \| 0.7816 \| 0.6607 \| 0.7611 \| \| 0.3032 \| 6.59 \| 2440 \| 0.4324 \| 0.6820 \| 0.7943 \| 0.8670 \| 0.9154 \| 0.7967 \| 0.8428 \| 0.4614 \| 0.8942 \| 0.8423 \| 0.8070 \| 0.8411 \| 0.6355 \| 0.7143 \| 0.3655 \| 0.7800 \| 0.6673 \| 0.7703 \| \| 0.4145 \| 6.65 \| 2460 \| 0.5008 \| 0.6531 \| 0.7604 \| 0.8555 \| 0.9128 \| 0.7721 \| 0.8558 \| 0.3218 \| 0.9130 \| 0.8410 \| 0.7061 \| 0.8412 \| 0.6222 \| 0.7058 \| 0.2820 \| 0.7575 \| 0.6801 \| 0.6831 \| \| 0.241 \| 6.7 \| 2480 \| 0.5001 \| 0.6470 \| 0.7684 \| 0.8514 \| 0.9097 \| 0.8170 \| 0.8686 \| 0.3234 \| 0.8854 \| 0.8659 \| 0.7087 \| 0.8413 \| 0.6228 \| 0.7038 \| 0.2625 \| 0.7491 \| 0.6655 \| 0.6840 \| \| 0.5881 \| 6.76 \| 2500 \| 0.4669 \| 0.6416 \| 0.7549 \| 0.8518 \| 0.9329 \| 0.8055 \| 0.8571 \| 0.2522 \| 0.8717 \| 0.8462 \| 0.7189 \| 0.8367 \| 0.6359 \| 0.6855 \| 0.2267 \| 0.7549 \| 0.6625 \| 0.6889 \| \| 0.6851 \| 6.81 \| 2520 \| 0.5335 \| 0.6332 \| 0.7561 \| 0.8480 \| 0.9172 \| 0.8463 \| 0.8290 \| 0.2526 \| 0.8877 \| 0.8747 \| 0.6852 \| 0.8404 \| 0.6112 \| 0.6837 \| 0.2164 \| 0.7485 \| 0.6639 \| 0.6686 \| \| 0.3773 \| 6.86 \| 2540 \| 0.5191 \| 0.6317 \| 0.7559 \| 0.8453 \| 0.9217 \| 0.7878 \| 0.8746 \| 0.2958 \| 0.8702 \| 0.8793 \| 0.6617 \| 0.8440 \| 0.6182 \| 0.6861 \| 0.2415 \| 0.7457 \| 0.6392 \| 0.6474 \| \| 0.5092 \| 6.92 \| 2560 \| 0.4745 \| 0.6505 \| 0.7763 \| 0.8521 \| 0.9234 \| 0.8219 \| 0.8182 \| 0.3888 \| 0.8668 \| 0.8939 \| 0.7211 \| 0.8481 \| 0.6088 \| 0.6894 \| 0.3175 \| 0.7488 \| 0.6412 \| 0.6999 \| \| 0.3652 \| 6.97 \| 2580 \| 0.4222 \| 0.6749 \| 0.7796 \| 0.8686 \| 0.9326 \| 0.7960 \| 0.8283 \| 0.3546 \| 0.8916 \| 0.8440 \| 0.8104 \| 0.8442 \| 0.6262 \| 0.7074 \| 0.3021 \| 0.7813 \| 0.6852 \| 0.7777 \| \| 0.3016 \| 7.03 \| 2600 \| 0.4632 \| 0.6570 \| 0.7702 \| 0.8602 \| 0.9343 \| 0.8038 \| 0.8335 \| 0.3145 \| 0.8827 \| 0.8947 \| 0.7276 \| 0.8449 \| 0.6266 \| 0.6963 \| 0.2810 \| 0.7739 \| 0.6594 \| 0.7169 \| \| 0.9354 \| 7.08 \| 2620 \| 0.4494 \| 0.6551 \| 0.7692 \| 0.8545 \| 0.9277 \| 0.7874 \| 0.8065 \| 0.3745 \| 0.8844 \| 0.8996 \| 0.7043 \| 0.8435 \| 0.6262 \| 0.7053 \| 0.3194 \| 0.7577 \| 0.6523 \| 0.6814 \| \| 0.8554 \| 7.14 \| 2640 \| 0.4588 \| 0.6492 \| 0.7579 \| 0.8527 \| 0.9130 \| 0.7053 \| 0.8017 \| 0.4151 \| 0.9105 \| 0.8226 \| 0.7374 \| 0.8395 \| 0.5736 \| 0.7068 \| 0.3074 \| 0.7560 \| 0.6831 \| 0.6783 \| \| 0.1009 \| 7.19 \| 2660 \| 0.4967 \| 0.6422 \| 0.7690 \| 0.8439 \| 0.9088 \| 0.8026 \| 0.8285 \| 0.3227 \| 0.8242 \| 0.8732 \| 0.8231 \| 0.8260 \| 0.6197 \| 0.6833 \| 0.2669 \| 0.7236 \| 0.6736 \| 0.7024 \| \| 0.5878 \| 7.24 \| 2680 \| 0.4940 \| 0.6499 \| 0.7592 \| 0.8555 \| 0.9238 \| 0.7939 \| 0.8104 \| 0.3196 \| 0.9069 \| 0.8481 \| 0.7119 \| 0.8408 \| 0.6032 \| 0.6907 \| 0.2835 \| 0.7620 \| 0.6881 \| 0.6809 \| \| 0.3527 \| 7.3 \| 2700 \| 0.4924 \| 0.6378 \| 0.7595 \| 0.8482 \| 0.9264 \| 0.8187 \| 0.8311 \| 0.2991 \| 0.8702 \| 0.8720 \| 0.6992 \| 0.8383 \| 0.6033 \| 0.6794 \| 0.2624 \| 0.7467 \| 0.6595 \| 0.6750 \| \| 0.3733 \| 7.35 \| 2720 \| 0.5420 \| 0.6321 \| 0.7384 \| 0.8446 \| 0.9223 \| 0.7199 \| 0.8052 \| 0.3394 \| 0.9110 \| 0.8042 \| 0.6671 \| 0.8367 \| 0.5826 \| 0.6743 \| 0.2822 \| 0.7412 \| 0.6629 \| 0.6450 \| \| 0.2743 \| 7.41 \| 2740 \| 0.5190 \| 0.6463 \| 0.7655 \| 0.8483 \| 0.9182 \| 0.7957 \| 0.8177 \| 0.3926 \| 0.8902 \| 0.8732 \| 0.6707 \| 0.8416 \| 0.6282 \| 0.7044 \| 0.3097 \| 0.7491 \| 0.6528 \| 0.6385 \| \| 0.6321 \| 7.46 \| 2760 \| 0.5105 \| 0.6427 \| 0.7447 \| 0.8529 \| 0.9358 \| 0.7341 \| 0.8344 \| 0.2998 \| 0.9051 \| 0.8230 \| 0.6807 \| 0.8437 \| 0.6225 \| 0.6947 \| 0.2592 \| 0.7593 \| 0.6632 \| 0.6567 \| \| 0.7369 \| 7.51 \| 2780 \| 0.5156 \| 0.6364 \| 0.7543 \| 0.8434 \| 0.9160 \| 0.7795 \| 0.8035 \| 0.3590 \| 0.8889 \| 0.8646 \| 0.6685 \| 0.8289 \| 0.6335 \| 0.6704 \| 0.3000 \| 0.7561 \| 0.6414 \| 0.6245 \| \| 0.2981 \| 7.57 \| 2800 \| 0.5989 \| 0.6172 \| 0.7461 \| 0.8338 \| 0.8977 \| 0.6911 \| 0.8723 \| 0.3880 \| 0.8857 \| 0.8817 \| 0.6059 \| 0.8271 \| 0.5719 \| 0.6510 \| 0.3053 \| 0.7454 \| 0.6301 \| 0.5892 \| \| 0.4012 \| 7.62 \| 2820 \| 0.5105 \| 0.6539 \| 0.7702 \| 0.8487 \| 0.9265 \| 0.8119 \| 0.7934 \| 0.4344 \| 0.8756 \| 0.8344 \| 0.7152 \| 0.8218 \| 0.6226 \| 0.6768 \| 0.3363 \| 0.7478 \| 0.6871 \| 0.6846 \| \| 0.3812 \| 7.68 \| 2840 \| 0.5101 \| 0.6602 \| 0.7640 \| 0.8547 \| 0.9132 \| 0.7615 \| 0.7728 \| 0.4581 \| 0.9319 \| 0.7872 \| 0.7229 \| 0.8344 \| 0.6247 \| 0.6867 \| 0.3336 \| 0.7610 \| 0.6983 \| 0.6824 \| \| 2.1456 \| 7.73 \| 2860 \| 0.5119 \| 0.6437 \| 0.7641 \| 0.8458 \| 0.9168 \| 0.7538 \| 0.8622 \| 0.3724 \| 0.8550 \| 0.8562 \| 0.7322 \| 0.8331 \| 0.6207 \| 0.6904 \| 0.3005 \| 0.7397 \| 0.6531 \| 0.6685 \| \| 0.3138 \| 7.78 \| 2880 \| 0.5153 \| 0.6446 \| 0.7572 \| 0.8519 \| 0.9389 \| 0.7837 \| 0.8490 \| 0.3067 \| 0.8751 \| 0.8660 \| 0.6808 \| 0.8358 \| 0.6323 \| 0.6872 \| 0.2656 \| 0.7627 \| 0.6731 \| 0.6553 \| \| 0.3975 \| 7.84 \| 2900 \| 0.4575 \| 0.6640 \| 0.7735 \| 0.8570 \| 0.9125 \| 0.7619 \| 0.8285 \| 0.3969 \| 0.8894 \| 0.8522 \| 0.7731 \| 0.8417 \| 0.6410 \| 0.7047 \| 0.3399 \| 0.7579 \| 0.6468 \| 0.7160 \| \| 0.138 \| 7.89 \| 2920 \| 0.4832 \| 0.6531 \| 0.7794 \| 0.8500 \| 0.9183 \| 0.8233 \| 0.8272 \| 0.4431 \| 0.8725 \| 0.8669 \| 0.7047 \| 0.8386 \| 0.6239 \| 0.6978 \| 0.3327 \| 0.7489 \| 0.6565 \| 0.6735 \| \| 1.2288 \| 7.95 \| 2940 \| 0.5300 \| 0.6530 \| 0.7646 \| 0.8526 \| 0.9186 \| 0.8154 \| 0.7786 \| 0.3941 \| 0.9151 \| 0.8382 \| 0.6919 \| 0.8355 \| 0.6158 \| 0.6870 \| 0.3192 \| 0.7584 \| 0.6955 \| 0.6596 \| \| 0.2467 \| 8.0 \| 2960 \| 0.4991 \| 0.6630 \| 0.7906 \| 0.8548 \| 0.9189 \| 0.8304 \| 0.8237 \| 0.4906 \| 0.8798 \| 0.8931 \| 0.6975 \| 0.8475 \| 0.6185 \| 0.7209 \| 0.3837 \| 0.7599 \| 0.6494 \| 0.6610 \| \| 0.4246 \| 8.05 \| 2980 \| 0.5756 \| 0.6489 \| 0.7639 \| 0.8513 \| 0.9129 \| 0.7837 \| 0.8474 \| 0.3609 \| 0.8986 \| 0.8498 \| 0.6938 \| 0.8375 \| 0.6280 \| 0.6816 \| 0.2919 \| 0.7553 \| 0.6776 \| 0.6706 \| \| 0.6102 \| 8.11 \| 3000 \| 0.5444 \| 0.6359 \| 0.7444 \| 0.8520 \| 0.9323 \| 0.7862 \| 0.8062 \| 0.2210 \| 0.8929 \| 0.8628 \| 0.7094 \| 0.8397 \| 0.6343 \| 0.6563 \| 0.2024 \| 0.7613 \| 0.6760 \| 0.6813 \| \| 0.5901 \| 8.16 \| 3020 \| 0.5296 \| 0.6434 \| 0.7519 \| 0.8538 \| 0.9221 \| 0.8084 \| 0.8229 \| 0.2617 \| 0.9094 \| 0.8298 \| 0.7092 \| 0.8441 \| 0.6404 \| 0.6818 \| 0.2361 \| 0.7613 \| 0.6664 \| 0.6739 \| \| 0.7706 \| 8.22 \| 3040 \| 0.5710 \| 0.6410 \| 0.7500 \| 0.8467 \| 0.9227 \| 0.7546 \| 0.8326 \| 0.3445 \| 0.8991 \| 0.8377 \| 0.6586 \| 0.8363 \| 0.6332 \| 0.6955 \| 0.2874 \| 0.7468 \| 0.6499 \| 0.6377 \| \| 0.2613 \| 8.27 \| 3060 \| 0.4850 \| 0.6706 \| 0.7869 \| 0.8569 \| 0.9173 \| 0.7762 \| 0.8292 \| 0.5361 \| 0.8933 \| 0.8203 \| 0.7362 \| 0.8406 \| 0.6317 \| 0.7262 \| 0.3816 \| 0.7588 \| 0.6762 \| 0.6791 \| \| 0.3787 \| 8.32 \| 3080 \| 0.5123 \| 0.6724 \| 0.7794 \| 0.8608 \| 0.9303 \| 0.7850 \| 0.8282 \| 0.4586 \| 0.9080 \| 0.8630 \| 0.6827 \| 0.8453 \| 0.6354 \| 0.7207 \| 0.3831 \| 0.7686 \| 0.6930 \| 0.6607 \| \| 0.2027 \| 8.38 \| 3100 \| 0.5467 \| 0.6577 \| 0.7658 \| 0.8547 \| 0.9104 \| 0.7148 \| 0.8596 \| 0.4194 \| 0.9114 \| 0.8503 \| 0.6949 \| 0.8470 \| 0.5904 \| 0.7081 \| 0.3613 \| 0.7506 \| 0.6764 \| 0.6699 \| \| 0.2585 \| 8.43 \| 3120 \| 0.5285 \| 0.6540 \| 0.7727 \| 0.8509 \| 0.9137 \| 0.7336 \| 0.8450 \| 0.4543 \| 0.8851 \| 0.8879 \| 0.6896 \| 0.8468 \| 0.6090 \| 0.6951 \| 0.3664 \| 0.7487 \| 0.6517 \| 0.6603 \| \| 1.3011 \| 8.49 \| 3140 \| 0.5199 \| 0.6615 \| 0.7847 \| 0.8541 \| 0.9062 \| 0.8103 \| 0.8696 \| 0.4618 \| 0.8940 \| 0.8486 \| 0.7024 \| 0.8446 \| 0.6277 \| 0.7115 \| 0.3608 \| 0.7552 \| 0.6605 \| 0.6703 \| \| 0.4755 \| 8.54 \| 3160 \| 0.4897 \| 0.6631 \| 0.7860 \| 0.8533 \| 0.9125 \| 0.8131 \| 0.8255 \| 0.4825 \| 0.8793 \| 0.8535 \| 0.7360 \| 0.8390 \| 0.6284 \| 0.7110 \| 0.3724 \| 0.7533 \| 0.6619 \| 0.6759 \| \| 1.0989 \| 8.59 \| 3180 \| 0.5838 \| 0.6376 \| 0.7531 \| 0.8451 \| 0.9081 \| 0.7302 \| 0.8004 \| 0.4229 \| 0.9152 \| 0.8258 \| 0.6692 \| 0.8425 \| 0.5774 \| 0.7018 \| 0.3407 \| 0.7489 \| 0.6215 \| 0.6307 \| \| 0.2332 \| 8.65 \| 3200 \| 0.4615 \| 0.6701 \| 0.7911 \| 0.8600 \| 0.9218 \| 0.7880 \| 0.8669 \| 0.4674 \| 0.8654 \| 0.8479 \| 0.7801 \| 0.8409 \| 0.6291 \| 0.7166 \| 0.3506 \| 0.7689 \| 0.6726 \| 0.7122 \| \| 0.8864 \| 8.7 \| 3220 \| 0.5110 \| 0.6600 \| 0.7710 \| 0.8540 \| 0.9048 \| 0.7695 \| 0.8619 \| 0.4088 \| 0.9046 \| 0.8200 \| 0.7270 \| 0.8333 \| 0.6345 \| 0.6866 \| 0.3386 \| 0.7581 \| 0.6790 \| 0.6897 \| \| 0.1611 \| 8.76 \| 3240 \| 0.4700 \| 0.6711 \| 0.7814 \| 0.8612 \| 0.9116 \| 0.7684 \| 0.8589 \| 0.4298 \| 0.9013 \| 0.8437 \| 0.7561 \| 0.8420 \| 0.6227 \| 0.7142 \| 0.3619 \| 0.7678 \| 0.6764 \| 0.7128 \| \| 0.4228 \| 8.81 \| 3260 \| 0.4747 \| 0.6717 \| 0.7968 \| 0.8588 \| 0.9180 \| 0.8397 \| 0.8264 \| 0.4786 \| 0.8719 \| 0.8969 \| 0.7463 \| 0.8504 \| 0.6326 \| 0.7152 \| 0.3934 \| 0.7592 \| 0.6400 \| 0.7109 \| \| 0.2511 \| 8.86 \| 3280 \| 0.5220 \| 0.6704 \| 0.7833 \| 0.8600 \| 0.9261 \| 0.8208 \| 0.8527 \| 0.4542 \| 0.9033 \| 0.8320 \| 0.6943 \| 0.8505 \| 0.6246 \| 0.7248 \| 0.3880 \| 0.7629 \| 0.6744 \| 0.6678 \| \| 0.2103 \| 8.92 \| 3300 \| 0.4626 \| 0.6784 \| 0.7827 \| 0.8642 \| 0.9136 \| 0.7540 \| 0.8653 \| 0.4562 \| 0.9063 \| 0.7931 \| 0.7904 \| 0.8421 \| 0.6237 \| 0.7221 \| 0.3783 \| 0.7716 \| 0.6970 \| 0.7139 \| \| 0.3643 \| 8.97 \| 3320 \| 0.4803 \| 0.6726 \| 0.7760 \| 0.8625 \| 0.9124 \| 0.7542 \| 0.8562 \| 0.3939 \| 0.9034 \| 0.8287 \| 0.7829 \| 0.8432 \| 0.6269 \| 0.7278 \| 0.3554 \| 0.7672 \| 0.6791 \| 0.7084 \| \| 0.2641 \| 9.03 \| 3340 \| 0.4765 \| 0.6783 \| 0.8001 \| 0.8637 \| 0.9147 \| 0.8312 \| 0.8584 \| 0.4151 \| 0.8551 \| 0.9011 \| 0.8251 \| 0.8528 \| 0.6453 \| 0.7192 \| 0.3693 \| 0.7606 \| 0.6432 \| 0.7575 \| \| 0.2143 \| 9.08 \| 3360 \| 0.5518 \| 0.6610 \| 0.7748 \| 0.8540 \| 0.9158 \| 0.7887 \| 0.8516 \| 0.4334 \| 0.9013 \| 0.8551 \| 0.6778 \| 0.8508 \| 0.6459 \| 0.7176 \| 0.3734 \| 0.7556 \| 0.6398 \| 0.6439 \| \| 0.3505 \| 9.14 \| 3380 \| 0.5120 \| 0.6642 \| 0.7757 \| 0.8588 \| 0.9292 \| 0.7881 \| 0.8391 \| 0.4038 \| 0.8775 \| 0.8161 \| 0.7764 \| 0.8389 \| 0.6293 \| 0.7024 \| 0.3511 \| 0.7709 \| 0.6456 \| 0.7114 \| \| 0.358 \| 9.19 \| 3400 \| 0.5251 \| 0.6604 \| 0.7720 \| 0.8579 \| 0.9340 \| 0.7911 \| 0.8394 \| 0.4064 \| 0.8972 \| 0.8521 \| 0.6837 \| 0.8526 \| 0.6403 \| 0.7061 \| 0.3513 \| 0.7708 \| 0.6472 \| 0.6547 \| \| 0.5739 \| 9.24 \| 3420 \| 0.4954 \| 0.6654 \| 0.7883 \| 0.8615 \| 0.9251 \| 0.8190 \| 0.8580 \| 0.4015 \| 0.8678 \| 0.8745 \| 0.7726 \| 0.8513 \| 0.6423 \| 0.6940 \| 0.3262 \| 0.7799 \| 0.6355 \| 0.7285 \| \| 0.3859 \| 9.3 \| 3440 \| 0.5409 \| 0.6563 \| 0.7677 \| 0.8556 \| 0.9375 \| 0.7684 \| 0.8450 \| 0.4151 \| 0.8911 \| 0.8357 \| 0.6808 \| 0.8483 \| 0.6370 \| 0.7071 \| 0.3394 \| 0.7688 \| 0.6456 \| 0.6483 \| \| 0.2072 \| 9.35 \| 3460 \| 0.4515 \| 0.6701 \| 0.7792 \| 0.8644 \| 0.9235 \| 0.7975 \| 0.8361 \| 0.3910 \| 0.9044 \| 0.8436 \| 0.7580 \| 0.8473 \| 0.6406 \| 0.7065 \| 0.3286 \| 0.7822 \| 0.6679 \| 0.7177 \| \| 0.2763 \| 9.41 \| 3480 \| 0.4903 \| 0.6651 \| 0.7767 \| 0.8597 \| 0.9285 \| 0.8032 \| 0.8346 \| 0.4003 \| 0.8937 \| 0.8548 \| 0.7218 \| 0.8446 \| 0.6381 \| 0.7099 \| 0.3249 \| 0.7675 \| 0.6788 \| 0.6920 \| \| 0.4056 \| 9.46 \| 3500 \| 0.4582 \| 0.6714 \| 0.7913 \| 0.8648 \| 0.9196 \| 0.8358 \| 0.8390 \| 0.3835 \| 0.8786 \| 0.8930 \| 0.7896 \| 0.8485 \| 0.6269 \| 0.7106 \| 0.3245 \| 0.7766 \| 0.6624 \| 0.7503 \| \| 0.2243 \| 9.51 \| 3520 \| 0.5132 \| 0.6559 \| 0.7656 \| 0.8591 \| 0.9275 \| 0.7915 \| 0.8485 \| 0.3016 \| 0.8904 \| 0.8661 \| 0.7339 \| 0.8451 \| 0.6310 \| 0.7043 \| 0.2617 \| 0.7657 \| 0.6894 \| 0.6945 \| \| 0.7284 \| 9.57 \| 3540 \| 0.4846 \| 0.6554 \| 0.7593 \| 0.8617 \| 0.9298 \| 0.7302 \| 0.8479 \| 0.2907 \| 0.8971 \| 0.8715 \| 0.7482 \| 0.8547 \| 0.5976 \| 0.7054 \| 0.2533 \| 0.7619 \| 0.7019 \| 0.7130 \| \| 0.3212 \| 9.62 \| 3560 \| 0.4803 \| 0.6572 \| 0.7699 \| 0.8608 \| 0.9212 \| 0.7555 \| 0.8622 \| 0.3283 \| 0.8883 \| 0.8770 \| 0.7572 \| 0.8590 \| 0.5987 \| 0.7006 \| 0.2688 \| 0.7570 \| 0.6950 \| 0.7215 \| \| 0.6291 \| 9.68 \| 3580 \| 0.4862 \| 0.6426 \| 0.7535 \| 0.8544 \| 0.9147 \| 0.7223 \| 0.8381 \| 0.2837 \| 0.8865 \| 0.8410 \| 0.7880 \| 0.8526 \| 0.5934 \| 0.6839 \| 0.2294 \| 0.7511 \| 0.6794 \| 0.7087 \| \| 0.3655 \| 9.73 \| 3600 \| 0.4545 \| 0.6705 \| 0.7891 \| 0.8621 \| 0.8971 \| 0.8076 \| 0.8626 \| 0.3888 \| 0.8789 \| 0.8207 \| 0.8681 \| 0.8355 \| 0.6397 \| 0.6816 \| 0.3088 \| 0.7717 \| 0.6847 \| 0.7717 \| \| 0.2223 \| 9.78 \| 3620 \| 0.4530 \| 0.6666 \| 0.7888 \| 0.8596 \| 0.9038 \| 0.8096 \| 0.8744 \| 0.4158 \| 0.8844 \| 0.8551 \| 0.7787 \| 0.8421 \| 0.6465 \| 0.6691 \| 0.3365 \| 0.7757 \| 0.6604 \| 0.7361 \| \| 0.2464 \| 9.84 \| 3640 \| 0.5492 \| 0.6501 \| 0.7693 \| 0.8513 \| 0.9188 \| 0.7944 \| 0.8741 \| 0.3862 \| 0.8928 \| 0.8657 \| 0.6531 \| 0.8540 \| 0.6479 \| 0.7151 \| 0.3299 \| 0.7584 \| 0.6197 \| 0.6259 \| \| 0.3132 \| 9.89 \| 3660 \| 0.4718 \| 0.6706 \| 0.7842 \| 0.8630 \| 0.9278 \| 0.8222 \| 0.8293 \| 0.3615 \| 0.8638 \| 0.8602 \| 0.8244 \| 0.8415 \| 0.6531 \| 0.7033 \| 0.3108 \| 0.7700 \| 0.6474 \| 0.7678 \| \| 0.3324 \| 9.95 \| 3680 \| 0.4550 \| 0.6790 \| 0.7803 \| 0.8673 \| 0.9217 \| 0.8007 \| 0.8257 \| 0.4419 \| 0.9299 \| 0.7736 \| 0.7688 \| 0.8515 \| 0.6404 \| 0.7304 \| 0.3462 \| 0.7750 \| 0.6775 \| 0.7321 \| \| 0.2938 \| 10.0 \| 3700 \| 0.4770 \| 0.6700 \| 0.8004 \| 0.8599 \| 0.9264 \| 0.8356 \| 0.8404 \| 0.4655 \| 0.8441 \| 0.9064 \| 0.7846 \| 0.8502 \| 0.6429 \| 0.7106 \| 0.3299 \| 0.7600 \| 0.6462 \| 0.7503 \| \| 0.1729 \| 10.05 \| 3720 \| 0.5693 \| 0.6432 \| 0.7511 \| 0.8483 \| 0.9290 \| 0.7376 \| 0.8531 \| 0.3331 \| 0.8826 \| 0.8375 \| 0.6846 \| 0.8417 \| 0.6330 \| 0.6959 \| 0.2686 \| 0.7390 \| 0.6639 \| 0.6601 \| \| 0.2335 \| 10.11 \| 3740 \| 0.5438 \| 0.6523 \| 0.7610 \| 0.8543 \| 0.9426 \| 0.7742 \| 0.8817 \| 0.3151 \| 0.8659 \| 0.8432 \| 0.7042 \| 0.8419 \| 0.6442 \| 0.6913 \| 0.2620 \| 0.7497 \| 0.6928 \| 0.6843 \| \| 0.2832 \| 10.16 \| 3760 \| 0.5138 \| 0.6525 \| 0.7706 \| 0.8527 \| 0.9438 \| 0.8356 \| 0.7781 \| 0.3821 \| 0.8624 \| 0.8700 \| 0.7221 \| 0.8392 \| 0.6126 \| 0.6875 \| 0.3230 \| 0.7502 \| 0.6561 \| 0.6986 \| \| 0.8137 \| 10.22 \| 3780 \| 0.5498 \| 0.6492 \| 0.7599 \| 0.8514 \| 0.9200 \| 0.7925 \| 0.8534 \| 0.3559 \| 0.9091 \| 0.8304 \| 0.6579 \| 0.8508 \| 0.6445 \| 0.7176 \| 0.3250 \| 0.7553 \| 0.6220 \| 0.6292 \| \| 0.3137 \| 10.27 \| 3800 \| 0.5088 \| 0.6593 \| 0.7719 \| 0.8540 \| 0.9410 \| 0.8104 \| 0.8620 \| 0.4019 \| 0.8722 \| 0.8296 \| 0.6862 \| 0.8435 \| 0.6484 \| 0.7093 \| 0.3554 \| 0.7551 \| 0.6471 \| 0.6563 \| \| 0.2471 \| 10.32 \| 3820 \| 0.5060 \| 0.6601 \| 0.7856 \| 0.8546 \| 0.9305 \| 0.8251 \| 0.8578 \| 0.4243 \| 0.8544 \| 0.8923 \| 0.7148 \| 0.8498 \| 0.6390 \| 0.7031 \| 0.3558 \| 0.7563 \| 0.6367 \| 0.6798 \| \| 0.2507 \| 10.38 \| 3840 \| 0.5004 \| 0.6596 \| 0.7779 \| 0.8572 \| 0.9356 \| 0.8184 \| 0.8547 \| 0.4060 \| 0.8776 \| 0.8508 \| 0.7022 \| 0.8528 \| 0.6355 \| 0.7072 \| 0.3361 \| 0.7627 \| 0.6493 \| 0.6736 \| \| 0.2734 \| 10.43 \| 3860 \| 0.5179 \| 0.6519 \| 0.7642 \| 0.8567 \| 0.9220 \| 0.8118 \| 0.8682 \| 0.2989 \| 0.8972 \| 0.8345 \| 0.7170 \| 0.8491 \| 0.6421 \| 0.7026 \| 0.2487 \| 0.7605 \| 0.6748 \| 0.6852 \| \| 0.9895 \| 10.49 \| 3880 \| 0.4962 \| 0.6658 \| 0.7818 \| 0.8615 \| 0.9164 \| 0.8324 \| 0.8711 \| 0.3679 \| 0.8991 \| 0.8569 \| 0.7290 \| 0.8500 \| 0.6467 \| 0.7104 \| 0.2938 \| 0.7691 \| 0.6931 \| 0.6973 \| \| 0.1867 \| 10.54 \| 3900 \| 0.4848 \| 0.6646 \| 0.7732 \| 0.8621 \| 0.9189 \| 0.7909 \| 0.8550 \| 0.3523 \| 0.9082 \| 0.8533 \| 0.7337 \| 0.8511 \| 0.6524 \| 0.7081 \| 0.2877 \| 0.7739 \| 0.6864 \| 0.6926 \| \| 0.2285 \| 10.59 \| 3920 \| 0.4964 \| 0.6639 \| 0.7690 \| 0.8601 \| 0.9294 \| 0.7393 \| 0.8069 \| 0.4343 \| 0.9073 \| 0.8280 \| 0.7377 \| 0.8440 \| 0.6155 \| 0.7073 \| 0.3567 \| 0.7764 \| 0.6515 \| 0.6956 \| \| 0.236 \| 10.65 \| 3940 \| 0.5185 \| 0.6739 \| 0.7955 \| 0.8625 \| 0.9266 \| 0.8106 \| 0.8428 \| 0.4827 \| 0.8763 \| 0.8845 \| 0.7446 \| 0.8541 \| 0.6558 \| 0.7205 \| 0.3622 \| 0.7763 \| 0.6455 \| 0.7026 \| \| 0.1239 \| 10.7 \| 3960 \| 0.5523 \| 0.6651 \| 0.7828 \| 0.8568 \| 0.9286 \| 0.7897 \| 0.8435 \| 0.4805 \| 0.8873 \| 0.8628 \| 0.6874 \| 0.8569 \| 0.6587 \| 0.7208 \| 0.3616 \| 0.7617 \| 0.6427 \| 0.6534 \| \| 0.2032 \| 10.76 \| 3980 \| 0.5506 \| 0.6614 \| 0.7776 \| 0.8553 \| 0.9275 \| 0.7965 \| 0.8530 \| 0.4918 \| 0.9019 \| 0.7928 \| 0.6798 \| 0.8537 \| 0.6437 \| 0.7292 \| 0.3746 \| 0.7632 \| 0.6240 \| 0.6416 \| \| 0.226 \| 10.81 \| 4000 \| 0.4977 \| 0.6652 \| 0.7906 \| 0.8569 \| 0.9294 \| 0.7910 \| 0.8634 \| 0.5039 \| 0.8687 \| 0.8779 \| 0.7002 \| 0.8540 \| 0.6425 \| 0.7169 \| 0.3600 \| 0.7622 \| 0.6504 \| 0.6706 \| \| 0.2067 \| 10.86 \| 4020 \| 0.5152 \| 0.6573 \| 0.7743 \| 0.8513 \| 0.9192 \| 0.7888 \| 0.8503 \| 0.4305 \| 0.8782 \| 0.8423 \| 0.7110 \| 0.8364 \| 0.6244 \| 0.7229 \| 0.3532 \| 0.7514 \| 0.6666 \| 0.6465 \| \| 0.4254 \| 10.92 \| 4040 \| 0.4596 \| 0.6791 \| 0.8022 \| 0.8614 \| 0.9125 \| 0.8033 \| 0.8455 \| 0.5592 \| 0.8885 \| 0.8584 \| 0.7476 \| 0.8421 \| 0.6433 \| 0.7377 \| 0.3891 \| 0.7717 \| 0.6739 \| 0.6958 \| \| 1.076 \| 10.97 \| 4060 \| 0.4386 \| 0.6890 \| 0.7967 \| 0.8683 \| 0.9162 \| 0.7783 \| 0.8235 \| 0.5060 \| 0.8973 \| 0.8320 \| 0.8238 \| 0.8371 \| 0.6368 \| 0.7309 \| 0.3817 \| 0.7783 \| 0.7000 \| 0.7585 \| \| 0.4622 \| 11.03 \| 4080 \| 0.4688 \| 0.6831 \| 0.7961 \| 0.8663 \| 0.9228 \| 0.7979 \| 0.8488 \| 0.4770 \| 0.8826 \| 0.8460 \| 0.7975 \| 0.8440 \| 0.6515 \| 0.7273 \| 0.3719 \| 0.7770 \| 0.6573 \| 0.7529 \| \| 0.2557 \| 11.08 \| 4100 \| 0.5033 \| 0.6658 \| 0.7796 \| 0.8615 \| 0.9334 \| 0.8058 \| 0.8427 \| 0.4212 \| 0.8923 \| 0.8325 \| 0.7292 \| 0.8470 \| 0.6434 \| 0.7070 \| 0.3484 \| 0.7860 \| 0.6294 \| 0.6995 \| \| 0.2339 \| 11.14 \| 4120 \| 0.4985 \| 0.6627 \| 0.7835 \| 0.8575 \| 0.9314 \| 0.8093 \| 0.8638 \| 0.4633 \| 0.8814 \| 0.8293 \| 0.7056 \| 0.8550 \| 0.6525 \| 0.7165 \| 0.3499 \| 0.7696 \| 0.6218 \| 0.6737 \| \| 0.293 \| 11.19 \| 4140 \| 0.5416 \| 0.6486 \| 0.7653 \| 0.8508 \| 0.9307 \| 0.8219 \| 0.8263 \| 0.3641 \| 0.8809 \| 0.8411 \| 0.6920 \| 0.8436 \| 0.6564 \| 0.7103 \| 0.3014 \| 0.7591 \| 0.6107 \| 0.6587 \| \| 0.1347 \| 11.24 \| 4160 \| 0.4919 \| 0.6628 \| 0.7802 \| 0.8581 \| 0.9256 \| 0.8205 \| 0.8425 \| 0.4267 \| 0.8878 \| 0.8205 \| 0.7377 \| 0.8457 \| 0.6563 \| 0.7114 \| 0.3152 \| 0.7709 \| 0.6407 \| 0.6992 \| \| 0.3583 \| 11.3 \| 4180 \| 0.4759 \| 0.6615 \| 0.7760 \| 0.8565 \| 0.9060 \| 0.8063 \| 0.8523 \| 0.4017 \| 0.9049 \| 0.8172 \| 0.7439 \| 0.8435 \| 0.6547 \| 0.7142 \| 0.3172 \| 0.7643 \| 0.6492 \| 0.6876 \| \| 0.4382 \| 11.35 \| 4200 \| 0.6038 \| 0.6373 \| 0.7558 \| 0.8421 \| 0.9127 \| 0.7383 \| 0.8616 \| 0.4178 \| 0.8920 \| 0.8279 \| 0.6399 \| 0.8332 \| 0.6320 \| 0.6805 \| 0.3458 \| 0.7562 \| 0.6042 \| 0.6096 \| \| 0.3586 \| 11.41 \| 4220 \| 0.5314 \| 0.6627 \| 0.7782 \| 0.8553 \| 0.9249 \| 0.8193 \| 0.8616 \| 0.4531 \| 0.8962 \| 0.8018 \| 0.6907 \| 0.8483 \| 0.6569 \| 0.7091 \| 0.3746 \| 0.7638 \| 0.6338 \| 0.6521 \| \| 0.2528 \| 11.46 \| 4240 \| 0.5731 \| 0.6546 \| 0.7763 \| 0.8495 \| 0.9296 \| 0.8141 \| 0.8520 \| 0.4677 \| 0.8737 \| 0.8220 \| 0.6754 \| 0.8366 \| 0.6431 \| 0.7088 \| 0.3676 \| 0.7566 \| 0.6234 \| 0.6460 \| \| 0.4401 \| 11.51 \| 4260 \| 0.5289 \| 0.6641 \| 0.7897 \| 0.8533 \| 0.9213 \| 0.8162 \| 0.8542 \| 0.5106 \| 0.8659 \| 0.8265 \| 0.7332 \| 0.8398 \| 0.6423 \| 0.7196 \| 0.3737 \| 0.7539 \| 0.6285 \| 0.6911 \| \| 0.1793 \| 11.57 \| 4280 \| 0.5084 \| 0.6584 \| 0.7837 \| 0.8504 \| 0.9197 \| 0.7662 \| 0.8610 \| 0.5191 \| 0.8659 \| 0.8455 \| 0.7083 \| 0.8411 \| 0.6381 \| 0.7032 \| 0.3661 \| 0.7500 \| 0.6359 \| 0.6740 \| \| 0.4799 \| 11.62 \| 4300 \| 0.5034 \| 0.6746 \| 0.8053 \| 0.8576 \| 0.9170 \| 0.7807 \| 0.8470 \| 0.5406 \| 0.8247 \| 0.8734 \| 0.8540 \| 0.8432 \| 0.6430 \| 0.7224 \| 0.3701 \| 0.7474 \| 0.6522 \| 0.7442 \| \| 0.1624 \| 11.68 \| 4320 \| 0.4906 \| 0.6740 \| 0.7886 \| 0.8598 \| 0.9088 \| 0.7923 \| 0.8441 \| 0.4827 \| 0.8826 \| 0.7731 \| 0.8367 \| 0.8399 \| 0.6449 \| 0.7167 \| 0.3352 \| 0.7557 \| 0.6873 \| 0.7381 \| \| 0.8102 \| 11.73 \| 4340 \| 0.5751 \| 0.6489 \| 0.7797 \| 0.8484 \| 0.9225 \| 0.8260 \| 0.8834 \| 0.4228 \| 0.8568 \| 0.8642 \| 0.6826 \| 0.8541 \| 0.6489 \| 0.7003 \| 0.3127 \| 0.7414 \| 0.6291 \| 0.6557 \| \| 0.276 \| 11.78 \| 4360 \| 0.5836 \| 0.6572 \| 0.7753 \| 0.8508 \| 0.9281 \| 0.7902 \| 0.8354 \| 0.5140 \| 0.8948 \| 0.8049 \| 0.6599 \| 0.8430 \| 0.6425 \| 0.7164 \| 0.3771 \| 0.7560 \| 0.6329 \| 0.6327 \| \| 0.1517 \| 11.84 \| 4380 \| 0.5328 \| 0.6633 \| 0.7821 \| 0.8551 \| 0.9274 \| 0.8103 \| 0.8505 \| 0.4561 \| 0.8747 \| 0.8357 \| 0.7202 \| 0.8449 \| 0.6465 \| 0.7077 \| 0.3596 \| 0.7565 \| 0.6448 \| 0.6831 \| \| 0.3974 \| 11.89 \| 4400 \| 0.5615 \| 0.6587 \| 0.7745 \| 0.8535 \| 0.9407 \| 0.8102 \| 0.8532 \| 0.4042 \| 0.8589 \| 0.8444 \| 0.7100 \| 0.8412 \| 0.6467 \| 0.7079 \| 0.3426 \| 0.7524 \| 0.6433 \| 0.6766 \| \| 0.2477 \| 11.95 \| 4420 \| 0.5036 \| 0.6828 \| 0.7966 \| 0.8658 \| 0.9258 \| 0.8122 \| 0.8544 \| 0.4738 \| 0.8794 \| 0.8440 \| 0.7865 \| 0.8471 \| 0.6419 \| 0.7226 \| 0.3875 \| 0.7715 \| 0.6759 \| 0.7329 \| \| 0.1876 \| 12.0 \| 4440 \| 0.5161 \| 0.6774 \| 0.8080 \| 0.8608 \| 0.9253 \| 0.8386 \| 0.8543 \| 0.5258 \| 0.8468 \| 0.8885 \| 0.7765 \| 0.8491 \| 0.6436 \| 0.7152 \| 0.3906 \| 0.7604 \| 0.6463 \| 0.7364 \| \| 0.3552 \| 12.05 \| 4460 \| 0.5186 \| 0.6651 \| 0.7778 \| 0.8574 \| 0.9117 \| 0.7749 \| 0.8590 \| 0.4480 \| 0.9008 \| 0.8023 \| 0.7483 \| 0.8461 \| 0.6384 \| 0.7082 \| 0.3417 \| 0.7580 \| 0.6591 \| 0.7042 \| \| 0.3803 \| 12.11 \| 4480 \| 0.4803 \| 0.6761 \| 0.7897 \| 0.8669 \| 0.9237 \| 0.8025 \| 0.8526 \| 0.4206 \| 0.8850 \| 0.8320 \| 0.8112 \| 0.8475 \| 0.6370 \| 0.7145 \| 0.3249 \| 0.7813 \| 0.6757 \| 0.7520 \| \| 0.3355 \| 12.16 \| 4500 \| 0.5312 \| 0.6579 \| 0.7699 \| 0.8559 \| 0.9315 \| 0.7728 \| 0.8423 \| 0.3943 \| 0.8790 \| 0.8367 \| 0.7328 \| 0.8388 \| 0.6299 \| 0.7106 \| 0.2987 \| 0.7625 \| 0.6824 \| 0.6820 \| \| 0.2417 \| 12.22 \| 4520 \| 0.5646 \| 0.6554 \| 0.7789 \| 0.8537 \| 0.9142 \| 0.8015 \| 0.8827 \| 0.4214 \| 0.8845 \| 0.8463 \| 0.7018 \| 0.8546 \| 0.6449 \| 0.7107 \| 0.3123 \| 0.7550 \| 0.6418 \| 0.6685 \| \| 0.5121 \| 12.27 \| 4540 \| 0.5256 \| 0.6580 \| 0.7809 \| 0.8532 \| 0.9073 \| 0.7828 \| 0.8720 \| 0.4260 \| 0.8725 \| 0.8535 \| 0.7526 \| 0.8491 \| 0.6504 \| 0.7196 \| 0.3252 \| 0.7559 \| 0.6362 \| 0.6699 \| \| 0.2514 \| 12.32 \| 4560 \| 0.5088 \| 0.6662 \| 0.7933 \| 0.8565 \| 0.9103 \| 0.8052 \| 0.8638 \| 0.4647 \| 0.8684 \| 0.8983 \| 0.7422 \| 0.8549 \| 0.6506 \| 0.7166 \| 0.3664 \| 0.7602 \| 0.6327 \| 0.6825 \| \| 0.1052 \| 12.38 \| 4580 \| 0.4722 \| 0.6896 \| 0.8055 \| 0.8706 \| 0.9056 \| 0.8071 \| 0.8511 \| 0.4913 \| 0.8924 \| 0.8311 \| 0.8599 \| 0.8479 \| 0.6509 \| 0.7136 \| 0.3808 \| 0.7866 \| 0.6746 \| 0.7729 \| \| 0.1715 \| 12.43 \| 4600 \| 0.5683 \| 0.6530 \| 0.7754 \| 0.8539 \| 0.9251 \| 0.8011 \| 0.8864 \| 0.3906 \| 0.8757 \| 0.8612 \| 0.6881 \| 0.8552 \| 0.6512 \| 0.7004 \| 0.2957 \| 0.7584 \| 0.6524 \| 0.6581 \| \| 0.3604 \| 12.49 \| 4620 \| 0.5680 \| 0.6696 \| 0.7885 \| 0.8616 \| 0.9308 \| 0.8331 \| 0.8639 \| 0.4597 \| 0.8946 \| 0.8443 \| 0.6931 \| 0.8578 \| 0.6440 \| 0.7312 \| 0.3553 \| 0.7723 \| 0.6691 \| 0.6575 \| \| 0.3714 \| 12.54 \| 4640 \| 0.5835 \| 0.6659 \| 0.7884 \| 0.8562 \| 0.9224 \| 0.8000 \| 0.8512 \| 0.4935 \| 0.8794 \| 0.8691 \| 0.7033 \| 0.8538 \| 0.6472 \| 0.7285 \| 0.3717 \| 0.7599 \| 0.6454 \| 0.6545 \| \| 0.2263 \| 12.59 \| 4660 \| 0.4759 \| 0.6725 \| 0.7893 \| 0.8652 \| 0.9259 \| 0.7661 \| 0.8610 \| 0.4304 \| 0.8656 \| 0.8497 \| 0.8266 \| 0.8538 \| 0.6425 \| 0.7132 \| 0.3036 \| 0.7750 \| 0.6634 \| 0.7561 \| \| 0.5225 \| 12.65 \| 4680 \| 0.4786 \| 0.6851 \| 0.8043 \| 0.8688 \| 0.9180 \| 0.7921 \| 0.8680 \| 0.4940 \| 0.8808 \| 0.8818 \| 0.7950 \| 0.8538 \| 0.6470 \| 0.7295 \| 0.3847 \| 0.7884 \| 0.6532 \| 0.7388 \| \| 0.3626 \| 12.7 \| 4700 \| 0.5585 \| 0.6550 \| 0.7612 \| 0.8589 \| 0.9307 \| 0.6914 \| 0.8602 \| 0.3850 \| 0.9027 \| 0.8599 \| 0.6985 \| 0.8588 \| 0.5957 \| 0.7259 \| 0.3158 \| 0.7659 \| 0.6579 \| 0.6651 \| \| 0.3352 \| 12.76 \| 4720 \| 0.5811 \| 0.6610 \| 0.7714 \| 0.8576 \| 0.9286 \| 0.6931 \| 0.8372 \| 0.4718 \| 0.8974 \| 0.8828 \| 0.6887 \| 0.8534 \| 0.5920 \| 0.7401 \| 0.3678 \| 0.7630 \| 0.6525 \| 0.6585 \| \| 0.1431 \| 12.81 \| 4740 \| 0.5148 \| 0.6730 \| 0.7917 \| 0.8621 \| 0.9251 \| 0.8162 \| 0.8604 \| 0.5046 \| 0.8997 \| 0.8249 \| 0.7111 \| 0.8538 \| 0.6535 \| 0.7287 \| 0.3494 \| 0.7734 \| 0.6723 \| 0.6799 \| \| 0.2278 \| 12.86 \| 4760 \| 0.5648 \| 0.6589 \| 0.7866 \| 0.8529 \| 0.9235 \| 0.7984 \| 0.8661 \| 0.4670 \| 0.8616 \| 0.8986 \| 0.6910 \| 0.8515 \| 0.6571 \| 0.7189 \| 0.3448 \| 0.7587 \| 0.6227 \| 0.6589 \| \| 0.2733 \| 12.92 \| 4780 \| 0.5890 \| 0.6732 \| 0.7910 \| 0.8599 \| 0.9260 \| 0.8214 \| 0.8534 \| 0.5203 \| 0.9015 \| 0.8293 \| 0.6848 \| 0.8503 \| 0.6511 \| 0.7426 \| 0.3901 \| 0.7709 \| 0.6560 \| 0.6512 \| \| 0.2281 \| 12.97 \| 4800 \| 0.5588 \| 0.6596 \| 0.7686 \| 0.8568 \| 0.9229 \| 0.7174 \| 0.8451 \| 0.4202 \| 0.8968 \| 0.8708 \| 0.7069 \| 0.8519 \| 0.6191 \| 0.7290 \| 0.3404 \| 0.7612 \| 0.6532 \| 0.6625 \| \| 0.183 \| 13.03 \| 4820 \| 0.5286 \| 0.6607 \| 0.7743 \| 0.8578 \| 0.9355 \| 0.7726 \| 0.8733 \| 0.4067 \| 0.8809 \| 0.8563 \| 0.6949 \| 0.8523 \| 0.6487 \| 0.7186 \| 0.3214 \| 0.7671 \| 0.6575 \| 0.6593 \| \| 0.2967 \| 13.08 \| 4840 \| 0.5679 \| 0.6615 \| 0.7843 \| 0.8564 \| 0.9251 \| 0.8120 \| 0.8757 \| 0.4203 \| 0.8735 \| 0.8922 \| 0.6910 \| 0.8546 \| 0.6581 \| 0.7157 \| 0.3374 \| 0.7639 \| 0.6437 \| 0.6574 \| \| 0.2665 \| 13.14 \| 4860 \| 0.5360 \| 0.6636 \| 0.7856 \| 0.8584 \| 0.9333 \| 0.8339 \| 0.8712 \| 0.4552 \| 0.8844 \| 0.8341 \| 0.6873 \| 0.8544 \| 0.6576 \| 0.7082 \| 0.3394 \| 0.7732 \| 0.6580 \| 0.6542 \| \| 0.1838 \| 13.19 \| 4880 \| 0.5524 \| 0.6748 \| 0.7894 \| 0.8623 \| 0.9380 \| 0.8202 \| 0.8343 \| 0.5153 \| 0.9034 \| 0.8439 \| 0.6706 \| 0.8562 \| 0.6601 \| 0.7390 \| 0.3860 \| 0.7759 \| 0.6547 \| 0.6517 \| \| 0.4542 \| 13.24 \| 4900 \| 0.4713 \| 0.6811 \| 0.7893 \| 0.8673 \| 0.9307 \| 0.8204 \| 0.8339 \| 0.4459 \| 0.9051 \| 0.8505 \| 0.7387 \| 0.8562 \| 0.6606 \| 0.7316 \| 0.3687 \| 0.7802 \| 0.6740 \| 0.6964 \| \| 0.1641 \| 13.3 \| 4920 \| 0.4515 \| 0.6862 \| 0.8015 \| 0.8679 \| 0.9280 \| 0.8041 \| 0.8644 \| 0.4573 \| 0.8587 \| 0.8722 \| 0.8255 \| 0.8481 \| 0.6610 \| 0.7254 \| 0.3649 \| 0.7742 \| 0.6789 \| 0.7509 \| \| 0.3412 \| 13.35 \| 4940 \| 0.5416 \| 0.6607 \| 0.7873 \| 0.8550 \| 0.9109 \| 0.8503 \| 0.8744 \| 0.4282 \| 0.8865 \| 0.8621 \| 0.6985 \| 0.8449 \| 0.6412 \| 0.7099 \| 0.3406 \| 0.7618 \| 0.6590 \| 0.6675 \| \| 0.4612 \| 13.41 \| 4960 \| 0.5605 \| 0.6679 \| 0.7859 \| 0.8566 \| 0.9213 \| 0.8035 \| 0.8725 \| 0.4759 \| 0.8875 \| 0.8487 \| 0.6918 \| 0.8472 \| 0.6569 \| 0.7228 \| 0.3761 \| 0.7624 \| 0.6588 \| 0.6510 \| \| 0.2196 \| 13.46 \| 4980 \| 0.5417 \| 0.6690 \| 0.7952 \| 0.8569 \| 0.9264 \| 0.7930 \| 0.8688 \| 0.5372 \| 0.8706 \| 0.8781 \| 0.6920 \| 0.8500 \| 0.6441 \| 0.7425 \| 0.3886 \| 0.7678 \| 0.6499 \| 0.6399 \| \| 0.7025 \| 13.51 \| 5000 \| 0.5168 \| 0.6712 \| 0.7874 \| 0.8581 \| 0.9288 \| 0.8091 \| 0.8454 \| 0.5175 \| 0.8924 \| 0.8165 \| 0.7019 \| 0.8416 \| 0.6440 \| 0.7388 \| 0.3998 \| 0.7765 \| 0.6671 \| 0.6308 \| \| 0.1238 \| 13.57 \| 5020 \| 0.5405 \| 0.6739 \| 0.7896 \| 0.8613 \| 0.9134 \| 0.7864 \| 0.8344 \| 0.4747 \| 0.8913 \| 0.8576 \| 0.7696 \| 0.8390 \| 0.6454 \| 0.7264 \| 0.3818 \| 0.7845 \| 0.6485 \| 0.6919 \| \| 0.1813 \| 13.62 \| 5040 \| 0.6150 \| 0.6523 \| 0.7701 \| 0.8504 \| 0.9111 \| 0.7670 \| 0.8743 \| 0.4085 \| 0.8852 \| 0.8403 \| 0.7043 \| 0.8415 \| 0.6351 \| 0.7191 \| 0.3253 \| 0.7541 \| 0.6522 \| 0.6387 \| \| 0.5263 \| 13.68 \| 5060 \| 0.5872 \| 0.6592 \| 0.7730 \| 0.8501 \| 0.9338 \| 0.7673 \| 0.8176 \| 0.4907 \| 0.8729 \| 0.8338 \| 0.6946 \| 0.8341 \| 0.6418 \| 0.7174 \| 0.3718 \| 0.7489 \| 0.6506 \| 0.6496 \| \| 0.3174 \| 13.73 \| 5080 \| 0.5468 \| 0.6728 \| 0.7925 \| 0.8599 \| 0.9251 \| 0.7621 \| 0.8633 \| 0.5261 \| 0.8801 \| 0.8816 \| 0.7095 \| 0.8532 \| 0.6392 \| 0.7456 \| 0.4023 \| 0.7725 \| 0.6278 \| 0.6690 \| \| 0.6582 \| 13.78 \| 5100 \| 0.5299 \| 0.6786 \| 0.7957 \| 0.8638 \| 0.9292 \| 0.7859 \| 0.8641 \| 0.5234 \| 0.8881 \| 0.8596 \| 0.7194 \| 0.8553 \| 0.6478 \| 0.7414 \| 0.3962 \| 0.7765 \| 0.6479 \| 0.6848 \| \| 0.8889 \| 13.84 \| 5120 \| 0.5259 \| 0.6615 \| 0.7793 \| 0.8560 \| 0.9346 \| 0.7669 \| 0.8527 \| 0.4365 \| 0.8553 \| 0.8574 \| 0.7519 \| 0.8439 \| 0.6393 \| 0.7169 \| 0.3305 \| 0.7599 \| 0.6424 \| 0.6978 \| \| 0.2472 \| 13.89 \| 5140 \| 0.5258 \| 0.6621 \| 0.7770 \| 0.8572 \| 0.9363 \| 0.7882 \| 0.8225 \| 0.4469 \| 0.8850 \| 0.8630 \| 0.6967 \| 0.8522 \| 0.6564 \| 0.7134 \| 0.3403 \| 0.7683 \| 0.6416 \| 0.6626 \| \| 0.1984 \| 13.95 \| 5160 \| 0.5231 \| 0.6743 \| 0.7961 \| 0.8606 \| 0.9256 \| 0.8202 \| 0.8650 \| 0.5210 \| 0.8900 \| 0.8627 \| 0.6882 \| 0.8602 \| 0.6571 \| 0.7492 \| 0.3873 \| 0.7665 \| 0.6384 \| 0.6611 \| \| 0.293 \| 14.0 \| 5180 \| 0.5337 \| 0.6850 \| 0.7922 \| 0.8675 \| 0.9405 \| 0.7842 \| 0.8660 \| 0.5240 \| 0.9062 \| 0.8357 \| 0.6889 \| 0.8591 \| 0.6501 \| 0.7540 \| 0.4165 \| 0.7804 \| 0.6725 \| 0.6622 \| \| 0.0932 \| 14.05 \| 5200 \| 0.5744 \| 0.6686 \| 0.8039 \| 0.8546 \| 0.9212 \| 0.8140 \| 0.8778 \| 0.5835 \| 0.8628 \| 0.8955 \| 0.6726 \| 0.8531 \| 0.6446 \| 0.7479 \| 0.4070 \| 0.7589 \| 0.6220 \| 0.6466 \| \| 1.7961 \| 14.11 \| 5220 \| 0.5400 \| 0.6665 \| 0.7940 \| 0.8557 \| 0.9216 \| 0.7976 \| 0.8808 \| 0.5004 \| 0.8603 \| 0.8823 \| 0.7155 \| 0.8542 \| 0.6424 \| 0.7432 \| 0.3609 \| 0.7505 \| 0.6406 \| 0.6741 \| \| 0.2783 \| 14.16 \| 5240 \| 0.6033 \| 0.6572 \| 0.7874 \| 0.8493 \| 0.9334 \| 0.7866 \| 0.8922 \| 0.5057 \| 0.8404 \| 0.8819 \| 0.6718 \| 0.8494 \| 0.6407 \| 0.7336 \| 0.3738 \| 0.7428 \| 0.6120 \| 0.6477 \| \| 0.3782 \| 14.22 \| 5260 \| 0.5731 \| 0.6678 \| 0.7849 \| 0.8557 \| 0.9300 \| 0.7987 \| 0.8899 \| 0.4775 \| 0.8734 \| 0.8322 \| 0.6927 \| 0.8434 \| 0.6574 \| 0.7411 \| 0.3627 \| 0.7539 \| 0.6534 \| 0.6625 \| \| 0.0964 \| 14.27 \| 5280 \| 0.5934 \| 0.6485 \| 0.7755 \| 0.8480 \| 0.9251 \| 0.7968 \| 0.8610 \| 0.4235 \| 0.8593 \| 0.8934 \| 0.6696 \| 0.8509 \| 0.6531 \| 0.7278 \| 0.2966 \| 0.7442 \| 0.6246 \| 0.6419 \| \| 0.2305 \| 14.32 \| 5300 \| 0.4892 \| 0.6904 \| 0.7988 \| 0.8707 \| 0.9198 \| 0.7801 \| 0.8533 \| 0.5252 \| 0.9124 \| 0.8244 \| 0.7762 \| 0.8525 \| 0.6445 \| 0.7501 \| 0.3770 \| 0.7856 \| 0.7124 \| 0.7106 \| \| 0.3662 \| 14.38 \| 5320 \| 0.4420 \| 0.7077 \| 0.8096 \| 0.8819 \| 0.9296 \| 0.7715 \| 0.8475 \| 0.5408 \| 0.9138 \| 0.8296 \| 0.8347 \| 0.8577 \| 0.6444 \| 0.7556 \| 0.4044 \| 0.8066 \| 0.7094 \| 0.7760 \| \| 0.1456 \| 14.43 \| 5340 \| 0.4490 \| 0.7042 \| 0.8103 \| 0.8782 \| 0.9334 \| 0.7755 \| 0.8288 \| 0.5435 \| 0.8972 \| 0.8859 \| 0.8080 \| 0.8624 \| 0.6527 \| 0.7505 \| 0.4182 \| 0.7931 \| 0.6729 \| 0.7792 \| \| 0.2907 \| 14.49 \| 5360 \| 0.4646 \| 0.6876 \| 0.8047 \| 0.8699 \| 0.9373 \| 0.7725 \| 0.8833 \| 0.5180 \| 0.8584 \| 0.8532 \| 0.8101 \| 0.8522 \| 0.6453 \| 0.7350 \| 0.3536 \| 0.7792 \| 0.6911 \| 0.7568 \| \| 0.2382 \| 14.54 \| 5380 \| 0.5256 \| 0.6661 \| 0.7936 \| 0.8540 \| 0.9247 \| 0.7901 \| 0.8871 \| 0.5412 \| 0.8609 \| 0.8475 \| 0.7034 \| 0.8390 \| 0.6473 \| 0.7149 \| 0.3565 \| 0.7585 \| 0.6907 \| 0.6558 \| \| 0.5997 \| 14.59 \| 5400 \| 0.5506 \| 0.6638 \| 0.7806 \| 0.8569 \| 0.9306 \| 0.7989 \| 0.8488 \| 0.4217 \| 0.8792 \| 0.9043 \| 0.6806 \| 0.8595 \| 0.6680 \| 0.7178 \| 0.3565 \| 0.7619 \| 0.6256 \| 0.6575 \| \| 0.2169 \| 14.65 \| 5420 \| 0.4596 \| 0.6812 \| 0.7941 \| 0.8719 \| 0.9319 \| 0.8269 \| 0.8532 \| 0.3951 \| 0.8856 \| 0.8563 \| 0.8099 \| 0.8611 \| 0.6533 \| 0.7260 \| 0.3334 \| 0.7942 \| 0.6397 \| 0.7606 \| \| 0.4158 \| 14.7 \| 5440 \| 0.4825 \| 0.6757 \| 0.7914 \| 0.8663 \| 0.9302 \| 0.8297 \| 0.8431 \| 0.4104 \| 0.8852 \| 0.8899 \| 0.7515 \| 0.8589 \| 0.6603 \| 0.7279 \| 0.3425 \| 0.7823 \| 0.6390 \| 0.7190 \| \| 0.2731 \| 14.76 \| 5460 \| 0.4921 \| 0.6693 \| 0.7758 \| 0.8654 \| 0.9363 \| 0.7729 \| 0.8511 \| 0.3669 \| 0.8939 \| 0.8740 \| 0.7355 \| 0.8595 \| 0.6582 \| 0.7365 \| 0.3036 \| 0.7804 \| 0.6435 \| 0.7032 \| \| 0.2632 \| 14.81 \| 5480 \| 0.5184 \| 0.6608 \| 0.7736 \| 0.8576 \| 0.9230 \| 0.7621 \| 0.8457 \| 0.4050 \| 0.8904 \| 0.8685 \| 0.7206 \| 0.8540 \| 0.6451 \| 0.7336 \| 0.3116 \| 0.7644 \| 0.6550 \| 0.6616 \| \| 0.251 \| 14.86 \| 5500 \| 0.5260 \| 0.6573 \| 0.7853 \| 0.8539 \| 0.9166 \| 0.8257 \| 0.8636 \| 0.4380 \| 0.8732 \| 0.8650 \| 0.7147 \| 0.8556 \| 0.6489 \| 0.7266 \| 0.3015 \| 0.7549 \| 0.6576 \| 0.6559 \| \| 1.2515 \| 14.92 \| 5520 \| 0.5372 \| 0.6612 \| 0.7934 \| 0.8557 \| 0.9341 \| 0.8601 \| 0.8507 \| 0.4872 \| 0.8631 \| 0.8660 \| 0.6925 \| 0.8518 \| 0.6443 \| 0.7224 \| 0.3354 \| 0.7645 \| 0.6509 \| 0.6593 \| \| 0.3045 \| 14.97 \| 5540 \| 0.5962 \| 0.6381 \| 0.7591 \| 0.8482 \| 0.9409 \| 0.7581 \| 0.8722 \| 0.3525 \| 0.8610 \| 0.8802 \| 0.6490 \| 0.8545 \| 0.6279 \| 0.7132 \| 0.2629 \| 0.7500 \| 0.6308 \| 0.6273 \| \| 0.3704 \| 15.03 \| 5560 \| 0.5266 \| 0.6592 \| 0.7633 \| 0.8630 \| 0.9323 \| 0.7273 \| 0.8722 \| 0.3523 \| 0.9088 \| 0.8266 \| 0.7236 \| 0.8633 \| 0.6164 \| 0.7280 \| 0.2874 \| 0.7715 \| 0.6555 \| 0.6927 \| \| 0.2794 \| 15.08 \| 5580 \| 0.5012 \| 0.6792 \| 0.7915 \| 0.8685 \| 0.9195 \| 0.7160 \| 0.8471 \| 0.4489 \| 0.8742 \| 0.8946 \| 0.8405 \| 0.8594 \| 0.6204 \| 0.7412 \| 0.3444 \| 0.7757 \| 0.6530 \| 0.7606 \| \| 0.2056 \| 15.14 \| 5600 \| 0.5639 \| 0.6621 \| 0.7691 \| 0.8613 \| 0.9277 \| 0.7933 \| 0.8774 \| 0.3491 \| 0.9046 \| 0.8036 \| 0.7281 \| 0.8611 \| 0.6590 \| 0.7253 \| 0.2813 \| 0.7655 \| 0.6626 \| 0.6801 \| \| 0.2394 \| 15.19 \| 5620 \| 0.5071 \| 0.6802 \| 0.8040 \| 0.8661 \| 0.9105 \| 0.8203 \| 0.8734 \| 0.4962 \| 0.8814 \| 0.8298 \| 0.8163 \| 0.8554 \| 0.6479 \| 0.7288 \| 0.3451 \| 0.7744 \| 0.6686 \| 0.7416 \| \| 0.1558 \| 15.24 \| 5640 \| 0.4703 \| 0.6941 \| 0.8137 \| 0.8737 \| 0.9194 \| 0.8197 \| 0.8654 \| 0.5217 \| 0.8806 \| 0.8437 \| 0.8452 \| 0.8587 \| 0.6512 \| 0.7389 \| 0.3639 \| 0.7848 \| 0.6852 \| 0.7759 \| \| 0.4394 \| 15.3 \| 5660 \| 0.4602 \| 0.7047 \| 0.8105 \| 0.8796 \| 0.9243 \| 0.7421 \| 0.8635 \| 0.5609 \| 0.8994 \| 0.8189 \| 0.8641 \| 0.8556 \| 0.6480 \| 0.7427 \| 0.3821 \| 0.7983 \| 0.7087 \| 0.7976 \| \| 0.1494 \| 15.35 \| 5680 \| 0.5335 \| 0.6770 \| 0.8024 \| 0.8617 \| 0.9188 \| 0.7986 \| 0.8514 \| 0.5649 \| 0.8821 \| 0.8571 \| 0.7442 \| 0.8588 \| 0.6418 \| 0.7489 \| 0.3658 \| 0.7623 \| 0.6771 \| 0.6846 \| \| 0.1822 \| 15.41 \| 5700 \| 0.5092 \| 0.6811 \| 0.7989 \| 0.8656 \| 0.9255 \| 0.8020 \| 0.8331 \| 0.5376 \| 0.8932 \| 0.8421 \| 0.7585 \| 0.8585 \| 0.6473 \| 0.7361 \| 0.3856 \| 0.7764 \| 0.6664 \| 0.6972 \| \| 0.3058 \| 15.46 \| 5720 \| 0.4918 \| 0.6802 \| 0.7954 \| 0.8706 \| 0.9194 \| 0.7899 \| 0.8618 \| 0.4001 \| 0.8777 \| 0.8769 \| 0.8417 \| 0.8589 \| 0.6568 \| 0.7226 \| 0.3124 \| 0.7887 \| 0.6539 \| 0.7681 \| \| 0.3935 \| 15.51 \| 5740 \| 0.4652 \| 0.6979 \| 0.8067 \| 0.8774 \| 0.9232 \| 0.8056 \| 0.8448 \| 0.4917 \| 0.9065 \| 0.8527 \| 0.8223 \| 0.8610 \| 0.6478 \| 0.7408 \| 0.4001 \| 0.8026 \| 0.6846 \| 0.7481 \| \| 0.2512 \| 15.57 \| 5760 \| 0.4909 \| 0.6962 \| 0.8121 \| 0.8750 \| 0.9275 \| 0.8334 \| 0.8500 \| 0.5508 \| 0.9036 \| 0.8298 \| 0.7896 \| 0.8591 \| 0.6628 \| 0.7455 \| 0.4008 \| 0.8035 \| 0.6663 \| 0.7356 \| \| 0.2733 \| 15.62 \| 5780 \| 0.5788 \| 0.6696 \| 0.7811 \| 0.8604 \| 0.9341 \| 0.7313 \| 0.8732 \| 0.5224 \| 0.9025 \| 0.8375 \| 0.6670 \| 0.8598 \| 0.6275 \| 0.7424 \| 0.3998 \| 0.7718 \| 0.6556 \| 0.6304 \| \| 0.169 \| 15.68 \| 5800 \| 0.5864 \| 0.6691 \| 0.7881 \| 0.8567 \| 0.9258 \| 0.7840 \| 0.8879 \| 0.4849 \| 0.8772 \| 0.8845 \| 0.6727 \| 0.8595 \| 0.6625 \| 0.7433 \| 0.3931 \| 0.7578 \| 0.6336 \| 0.6342 \| \| 0.1417 \| 15.73 \| 5820 \| 0.5768 \| 0.6779 \| 0.7897 \| 0.8648 \| 0.9303 \| 0.7753 \| 0.8797 \| 0.4615 \| 0.8854 \| 0.8617 \| 0.7343 \| 0.8603 \| 0.6483 \| 0.7433 \| 0.3798 \| 0.7725 \| 0.6672 \| 0.6742 \| \| 0.167 \| 15.78 \| 5840 \| 0.5437 \| 0.6705 \| 0.7821 \| 0.8620 \| 0.9320 \| 0.7385 \| 0.8467 \| 0.4932 \| 0.8902 \| 0.8329 \| 0.7414 \| 0.8533 \| 0.6186 \| 0.7282 \| 0.3730 \| 0.7755 \| 0.6743 \| 0.6702 \| \| 0.1848 \| 15.84 \| 5860 \| 0.5523 \| 0.6718 \| 0.7834 \| 0.8637 \| 0.9479 \| 0.7919 \| 0.8362 \| 0.4667 \| 0.8878 \| 0.8478 \| 0.7055 \| 0.8525 \| 0.6592 \| 0.7111 \| 0.3435 \| 0.7841 \| 0.6711 \| 0.6813 \| \| 0.2633 \| 15.89 \| 5880 \| 0.5593 \| 0.6719 \| 0.7908 \| 0.8619 \| 0.9277 \| 0.8281 \| 0.8055 \| 0.5113 \| 0.9051 \| 0.8474 \| 0.7102 \| 0.8546 \| 0.6527 \| 0.7220 \| 0.3589 \| 0.7781 \| 0.6629 \| 0.6743 \| \| 0.4253 \| 15.95 \| 5900 \| 0.5592 \| 0.6702 \| 0.7819 \| 0.8634 \| 0.9234 \| 0.7766 \| 0.8696 \| 0.4417 \| 0.9088 \| 0.8397 \| 0.7135 \| 0.8611 \| 0.6580 \| 0.7278 \| 0.3209 \| 0.7759 \| 0.6673 \| 0.6801 \| \| 0.2215 \| 16.0 \| 5920 \| 0.5557 \| 0.6733 \| 0.7792 \| 0.8650 \| 0.9244 \| 0.7379 \| 0.8344 \| 0.4831 \| 0.9254 \| 0.8374 \| 0.7120 \| 0.8621 \| 0.6246 \| 0.7419 \| 0.3600 \| 0.7752 \| 0.6696 \| 0.6797 \| \| 0.1312 \| 16.05 \| 5940 \| 0.5140 \| 0.6661 \| 0.7905 \| 0.8577 \| 0.9235 \| 0.8223 \| 0.8654 \| 0.4607 \| 0.8732 \| 0.8718 \| 0.7166 \| 0.8513 \| 0.6364 \| 0.7172 \| 0.3534 \| 0.7601 \| 0.6666 \| 0.6773 \| \| 0.2601 \| 16.11 \| 5960 \| 0.5589 \| 0.6644 \| 0.7804 \| 0.8577 \| 0.9311 \| 0.7976 \| 0.8697 \| 0.4033 \| 0.8717 \| 0.8812 \| 0.7081 \| 0.8468 \| 0.6647 \| 0.7076 \| 0.3282 \| 0.7644 \| 0.6586 \| 0.6807 \| \| 0.2015 \| 16.16 \| 5980 \| 0.5697 \| 0.6602 \| 0.7921 \| 0.8577 \| 0.9164 \| 0.8626 \| 0.8408 \| 0.4628 \| 0.8911 \| 0.8656 \| 0.7056 \| 0.8520 \| 0.6123 \| 0.7242 \| 0.3098 \| 0.7704 \| 0.6842 \| 0.6689 \| \| 0.7605 \| 16.22 \| 6000 \| 0.5931 \| 0.6614 \| 0.7788 \| 0.8597 \| 0.9300 \| 0.7898 \| 0.8712 \| 0.3997 \| 0.8861 \| 0.8765 \| 0.6981 \| 0.8554 \| 0.6396 \| 0.7276 \| 0.2938 \| 0.7699 \| 0.6805 \| 0.6631 \| \| 0.2138 \| 16.27 \| 6020 \| 0.5283 \| 0.6785 \| 0.7962 \| 0.8628 \| 0.9194 \| 0.7919 \| 0.8577 \| 0.5364 \| 0.9008 \| 0.8538 \| 0.7131 \| 0.8529 \| 0.6356 \| 0.7431 \| 0.4098 \| 0.7757 \| 0.6747 \| 0.6581 \| \| 0.1078 \| 16.32 \| 6040 \| 0.5326 \| 0.6731 \| 0.7963 \| 0.8601 \| 0.9164 \| 0.8080 \| 0.8588 \| 0.5105 \| 0.8852 \| 0.8675 \| 0.7278 \| 0.8514 \| 0.6393 \| 0.7371 \| 0.3786 \| 0.7677 \| 0.6686 \| 0.6692 \| \| 0.2353 \| 16.38 \| 6060 \| 0.6190 \| 0.6729 \| 0.7833 \| 0.8617 \| 0.9301 \| 0.7918 \| 0.8480 \| 0.4871 \| 0.9120 \| 0.8401 \| 0.6740 \| 0.8497 \| 0.6474 \| 0.7350 \| 0.3857 \| 0.7782 \| 0.6665 \| 0.6477 \| \| 0.2366 \| 16.43 \| 6080 \| 0.5435 \| 0.6764 \| 0.7883 \| 0.8650 \| 0.9354 \| 0.8085 \| 0.8590 \| 0.4732 \| 0.9044 \| 0.8483 \| 0.6893 \| 0.8548 \| 0.6452 \| 0.7389 \| 0.3762 \| 0.7820 \| 0.6741 \| 0.6633 \| \| 0.2295 \| 16.49 \| 6100 \| 0.5347 \| 0.6782 \| 0.7865 \| 0.8673 \| 0.9395 \| 0.8152 \| 0.8355 \| 0.4706 \| 0.9174 \| 0.8422 \| 0.6852 \| 0.8580 \| 0.6427 \| 0.7344 \| 0.3860 \| 0.7892 \| 0.6746 \| 0.6629 \| \| 0.1413 \| 16.54 \| 6120 \| 0.5431 \| 0.6763 \| 0.7882 \| 0.8669 \| 0.9277 \| 0.8083 \| 0.8725 \| 0.4629 \| 0.9205 \| 0.8386 \| 0.6871 \| 0.8616 \| 0.6501 \| 0.7270 \| 0.3681 \| 0.7885 \| 0.6747 \| 0.6642 \| \| 0.967 \| 16.59 \| 6140 \| 0.5356 \| 0.6685 \| 0.7755 \| 0.8641 \| 0.9359 \| 0.7922 \| 0.8503 \| 0.3939 \| 0.9090 \| 0.8533 \| 0.6940 \| 0.8561 \| 0.6626 \| 0.7212 \| 0.3244 \| 0.7854 \| 0.6656 \| 0.6646 \| \| 0.1501 \| 16.65 \| 6160 \| 0.5186 \| 0.6858 \| 0.7926 \| 0.8682 \| 0.9315 \| 0.8167 \| 0.8330 \| 0.4982 \| 0.9193 \| 0.8465 \| 0.7030 \| 0.8566 \| 0.6663 \| 0.7399 \| 0.4115 \| 0.7886 \| 0.6676 \| 0.6700 \| \| 0.1527 \| 16.7 \| 6180 \| 0.4952 \| 0.6802 \| 0.7988 \| 0.8623 \| 0.9312 \| 0.8229 \| 0.8458 \| 0.4948 \| 0.8677 \| 0.8908 \| 0.7386 \| 0.8549 \| 0.6666 \| 0.7339 \| 0.3974 \| 0.7634 \| 0.6400 \| 0.7050 \| \| 0.2878 \| 16.76 \| 6200 \| 0.4871 \| 0.6759 \| 0.7870 \| 0.8634 \| 0.9276 \| 0.7620 \| 0.8634 \| 0.4550 \| 0.8841 \| 0.8775 \| 0.7392 \| 0.8604 \| 0.6593 \| 0.7302 \| 0.3735 \| 0.7686 \| 0.6468 \| 0.6926 \| \| 0.3001 \| 16.81 \| 6220 \| 0.4844 \| 0.6884 \| 0.8021 \| 0.8665 \| 0.9231 \| 0.7721 \| 0.8516 \| 0.5451 \| 0.8885 \| 0.8888 \| 0.7458 \| 0.8591 \| 0.6591 \| 0.7514 \| 0.4375 \| 0.7779 \| 0.6351 \| 0.6984 \| \| 2.424 \| 16.86 \| 6240 \| 0.5032 \| 0.6759 \| 0.7760 \| 0.8693 \| 0.9355 \| 0.7415 \| 0.8712 \| 0.3962 \| 0.9116 \| 0.8358 \| 0.7404 \| 0.8582 \| 0.6502 \| 0.7282 \| 0.3402 \| 0.7949 \| 0.6654 \| 0.6946 \| \| 0.1105 \| 16.92 \| 6260 \| 0.4684 \| 0.6984 \| 0.8042 \| 0.8785 \| 0.9209 \| 0.8012 \| 0.8217 \| 0.4430 \| 0.8964 \| 0.8652 \| 0.8811 \| 0.8552 \| 0.6593 \| 0.7190 \| 0.3668 \| 0.8010 \| 0.6967 \| 0.7907 \| \| 0.3355 \| 16.97 \| 6280 \| 0.4982 \| 0.6788 \| 0.7843 \| 0.8656 \| 0.9386 \| 0.7810 \| 0.8146 \| 0.4848 \| 0.9087 \| 0.8580 \| 0.7045 \| 0.8520 \| 0.6508 \| 0.7215 \| 0.3874 \| 0.7825 \| 0.6807 \| 0.6768 \| \| 0.1191 \| 17.03 \| 6300 \| 0.4687 \| 0.6812 \| 0.7999 \| 0.8670 \| 0.9303 \| 0.8004 \| 0.8551 \| 0.5189 \| 0.8870 \| 0.8598 \| 0.7475 \| 0.8538 \| 0.6336 \| 0.7457 \| 0.3761 \| 0.7847 \| 0.6798 \| 0.6946 \| \| 0.2914 \| 17.08 \| 6320 \| 0.4668 \| 0.6869 \| 0.8088 \| 0.8695 \| 0.9212 \| 0.7948 \| 0.8863 \| 0.5264 \| 0.8773 \| 0.8666 \| 0.7893 \| 0.8570 \| 0.6411 \| 0.7525 \| 0.3733 \| 0.7843 \| 0.6786 \| 0.7214 \| \| 0.1232 \| 17.14 \| 6340 \| 0.4774 \| 0.6801 \| 0.7939 \| 0.8704 \| 0.9307 \| 0.7988 \| 0.8856 \| 0.4509 \| 0.8972 \| 0.8307 \| 0.7631 \| 0.8620 \| 0.6321 \| 0.7414 \| 0.3314 \| 0.7876 \| 0.6911 \| 0.7151 \| \| 0.2338 \| 17.19 \| 6360 \| 0.5762 \| 0.6572 \| 0.7692 \| 0.8579 \| 0.9362 \| 0.8172 \| 0.8860 \| 0.3874 \| 0.9167 \| 0.8342 \| 0.6068 \| 0.8522 \| 0.6441 \| 0.7254 \| 0.3293 \| 0.7787 \| 0.6821 \| 0.5886 \| \| 0.6755 \| 17.24 \| 6380 \| 0.5811 \| 0.6644 \| 0.7687 \| 0.8618 \| 0.9434 \| 0.7529 \| 0.8458 \| 0.4083 \| 0.9060 \| 0.8501 \| 0.6745 \| 0.8556 \| 0.6378 \| 0.7335 \| 0.3314 \| 0.7767 \| 0.6644 \| 0.6515 \| \| 1.194 \| 17.3 \| 6400 \| 0.5682 \| 0.6739 \| 0.7769 \| 0.8665 \| 0.9349 \| 0.7822 \| 0.8431 \| 0.4116 \| 0.9165 \| 0.8349 \| 0.7150 \| 0.8586 \| 0.6539 \| 0.7349 \| 0.3380 \| 0.7835 \| 0.6700 \| 0.6782 \| \| 0.1116 \| 17.35 \| 6420 \| 0.5574 \| 0.6875 \| 0.7958 \| 0.8693 \| 0.9329 \| 0.8225 \| 0.8437 \| 0.4951 \| 0.9105 \| 0.8437 \| 0.7219 \| 0.8620 \| 0.6686 \| 0.7506 \| 0.3975 \| 0.7825 \| 0.6649 \| 0.6864 \| \| 0.5987 \| 17.41 \| 6440 \| 0.5782 \| 0.6833 \| 0.7990 \| 0.8666 \| 0.9311 \| 0.8397 \| 0.8033 \| 0.5404 \| 0.9096 \| 0.8529 \| 0.7161 \| 0.8603 \| 0.6482 \| 0.7287 \| 0.4138 \| 0.7792 \| 0.6665 \| 0.6864 \| \| 0.5289 \| 17.46 \| 6460 \| 0.5611 \| 0.6843 \| 0.7995 \| 0.8665 \| 0.9344 \| 0.8108 \| 0.8677 \| 0.5384 \| 0.8972 \| 0.8494 \| 0.6990 \| 0.8619 \| 0.6737 \| 0.7491 \| 0.3982 \| 0.7796 \| 0.6570 \| 0.6708 \| \| 0.1055 \| 17.51 \| 6480 \| 0.5299 \| 0.6816 \| 0.8016 \| 0.8634 \| 0.9276 \| 0.7722 \| 0.8812 \| 0.5540 \| 0.8755 \| 0.8793 \| 0.7215 \| 0.8606 \| 0.6600 \| 0.7496 \| 0.4094 \| 0.7708 \| 0.6508 \| 0.6698 \| \| 0.1996 \| 17.57 \| 6500 \| 0.5186 \| 0.6846 \| 0.8096 \| 0.8669 \| 0.9139 \| 0.8119 \| 0.8522 \| 0.5283 \| 0.8727 \| 0.8726 \| 0.8154 \| 0.8536 \| 0.6580 \| 0.7417 \| 0.3706 \| 0.7813 \| 0.6467 \| 0.7401 \| \| 0.8667 \| 17.62 \| 6520 \| 0.4990 \| 0.6866 \| 0.8012 \| 0.8694 \| 0.9243 \| 0.7851 \| 0.8510 \| 0.4927 \| 0.8820 \| 0.8749 \| 0.7984 \| 0.8600 \| 0.6594 \| 0.7428 \| 0.3791 \| 0.7827 \| 0.6482 \| 0.7342 \| \| 0.241 \| 17.68 \| 6540 \| 0.5401 \| 0.6882 \| 0.8099 \| 0.8681 \| 0.9116 \| 0.8045 \| 0.8465 \| 0.5455 \| 0.8792 \| 0.8454 \| 0.8365 \| 0.8513 \| 0.6577 \| 0.7394 \| 0.4010 \| 0.7885 \| 0.6561 \| 0.7231 \| \| 0.2098 \| 17.73 \| 6560 \| 0.5664 \| 0.6702 \| 0.7946 \| 0.8594 \| 0.9256 \| 0.7894 \| 0.8642 \| 0.5095 \| 0.8676 \| 0.8668 \| 0.7389 \| 0.8593 \| 0.6539 \| 0.7301 \| 0.3559 \| 0.7628 \| 0.6490 \| 0.6806 \| \| 0.5638 \| 17.78 \| 6580 \| 0.5683 \| 0.6764 \| 0.7958 \| 0.8628 \| 0.9150 \| 0.8131 \| 0.8525 \| 0.4853 \| 0.8941 \| 0.8735 \| 0.7370 \| 0.8587 \| 0.6467 \| 0.7469 \| 0.3802 \| 0.7699 \| 0.6521 \| 0.6804 \| \| 0.2553 \| 17.84 \| 6600 \| 0.6075 \| 0.6721 \| 0.7817 \| 0.8644 \| 0.9213 \| 0.7710 \| 0.8738 \| 0.4099 \| 0.9017 \| 0.8573 \| 0.7372 \| 0.8562 \| 0.6562 \| 0.7240 \| 0.3281 \| 0.7796 \| 0.6819 \| 0.6784 \| \| 0.4949 \| 17.89 \| 6620 \| 0.5938 \| 0.6702 \| 0.7857 \| 0.8627 \| 0.9278 \| 0.7916 \| 0.8809 \| 0.4344 \| 0.8916 \| 0.8662 \| 0.7078 \| 0.8599 \| 0.6583 \| 0.7253 \| 0.3382 \| 0.7766 \| 0.6735 \| 0.6599 \| \| 0.3578 \| 17.95 \| 6640 \| 0.6084 \| 0.6767 \| 0.7912 \| 0.8650 \| 0.9183 \| 0.8099 \| 0.8671 \| 0.4311 \| 0.8997 \| 0.8854 \| 0.7265 \| 0.8619 \| 0.6534 \| 0.7510 \| 0.3597 \| 0.7749 \| 0.6652 \| 0.6705 \| \| 0.2771 \| 18.0 \| 6660 \| 0.5365 \| 0.6951 \| 0.8192 \| 0.8708 \| 0.9139 \| 0.8194 \| 0.8766 \| 0.5758 \| 0.8843 \| 0.8783 \| 0.7862 \| 0.8577 \| 0.6546 \| 0.7497 \| 0.4320 \| 0.7889 \| 0.6627 \| 0.7200 \| \| 0.1865 \| 18.05 \| 6680 \| 0.5234 \| 0.6917 \| 0.7939 \| 0.8786 \| 0.9192 \| 0.8008 \| 0.8629 \| 0.3839 \| 0.9192 \| 0.8337 \| 0.8374 \| 0.8585 \| 0.6700 \| 0.7384 \| 0.3171 \| 0.8105 \| 0.6856 \| 0.7621 \| \| 0.3439 \| 18.11 \| 6700 \| 0.5187 \| 0.7022 \| 0.8172 \| 0.8768 \| 0.9080 \| 0.8281 \| 0.8201 \| 0.5452 \| 0.9090 \| 0.8610 \| 0.8493 \| 0.8541 \| 0.6545 \| 0.7509 \| 0.4222 \| 0.8047 \| 0.6654 \| 0.7633 \| \| 0.4716 \| 18.16 \| 6720 \| 0.5480 \| 0.6704 \| 0.7856 \| 0.8639 \| 0.9208 \| 0.7829 \| 0.8962 \| 0.4060 \| 0.8920 \| 0.8730 \| 0.7283 \| 0.8575 \| 0.6527 \| 0.7335 \| 0.3266 \| 0.7814 \| 0.6738 \| 0.6677 \| \| 0.2285 \| 18.22 \| 6740 \| 0.5800 \| 0.6786 \| 0.7927 \| 0.8665 \| 0.9309 \| 0.8055 \| 0.8851 \| 0.4608 \| 0.8981 \| 0.8685 \| 0.7001 \| 0.8623 \| 0.6682 \| 0.7501 \| 0.3433 \| 0.7805 \| 0.6806 \| 0.6655 \| \| 0.2874 \| 18.27 \| 6760 \| 0.5568 \| 0.6854 \| 0.7961 \| 0.8692 \| 0.9238 \| 0.8069 \| 0.8568 \| 0.5064 \| 0.9231 \| 0.8517 \| 0.7040 \| 0.8607 \| 0.6629 \| 0.7494 \| 0.3803 \| 0.7894 \| 0.6884 \| 0.6670 \| \| 0.2325 \| 18.32 \| 6780 \| 0.5335 \| 0.6784 \| 0.7921 \| 0.8678 \| 0.9300 \| 0.8023 \| 0.8615 \| 0.4633 \| 0.9001 \| 0.8499 \| 0.7378 \| 0.8611 \| 0.6612 \| 0.7392 \| 0.3237 \| 0.7850 \| 0.6803 \| 0.6981 \| \| 0.4917 \| 18.38 \| 6800 \| 0.5157 \| 0.6791 \| 0.7932 \| 0.8678 \| 0.9295 \| 0.7975 \| 0.8441 \| 0.4491 \| 0.8877 \| 0.8765 \| 0.7678 \| 0.8603 \| 0.6539 \| 0.7362 \| 0.3412 \| 0.7829 \| 0.6722 \| 0.7069 \| \| 0.2626 \| 18.43 \| 6820 \| 0.5124 \| 0.6862 \| 0.8043 \| 0.8704 \| 0.9235 \| 0.8027 \| 0.8406 \| 0.4731 \| 0.8755 \| 0.9020 \| 0.8128 \| 0.8581 \| 0.6539 \| 0.7425 \| 0.3670 \| 0.7898 \| 0.6569 \| 0.7354 \| \| 0.2628 \| 18.49 \| 6840 \| 0.5533 \| 0.6837 \| 0.7922 \| 0.8687 \| 0.9340 \| 0.7953 \| 0.8593 \| 0.4783 \| 0.9067 \| 0.8609 \| 0.7109 \| 0.8617 \| 0.6657 \| 0.7457 \| 0.3766 \| 0.7853 \| 0.6769 \| 0.6740 \| \| 0.6479 \| 18.54 \| 6860 \| 0.6086 \| 0.6772 \| 0.7953 \| 0.8621 \| 0.9180 \| 0.7915 \| 0.8750 \| 0.5212 \| 0.8993 \| 0.8629 \| 0.6995 \| 0.8614 \| 0.6602 \| 0.7503 \| 0.3844 \| 0.7669 \| 0.6552 \| 0.6620 \| \| 0.1974 \| 18.59 \| 6880 \| 0.5783 \| 0.6766 \| 0.7891 \| 0.8652 \| 0.9379 \| 0.8097 \| 0.8820 \| 0.4458 \| 0.8880 \| 0.8511 \| 0.7092 \| 0.8621 \| 0.6774 \| 0.7359 \| 0.3479 \| 0.7762 \| 0.6608 \| 0.6759 \| \| 0.3145 \| 18.65 \| 6900 \| 0.5640 \| 0.6844 \| 0.7992 \| 0.8661 \| 0.9178 \| 0.7956 \| 0.8842 \| 0.5153 \| 0.8987 \| 0.8417 \| 0.7411 \| 0.8573 \| 0.6348 \| 0.7569 \| 0.4158 \| 0.7746 \| 0.6763 \| 0.6749 \| \| 0.1216 \| 18.7 \| 6920 \| 0.4967 \| 0.6987 \| 0.8231 \| 0.8751 \| 0.9126 \| 0.8145 \| 0.8771 \| 0.5640 \| 0.8812 \| 0.8753 \| 0.8371 \| 0.8588 \| 0.6335 \| 0.7599 \| 0.4134 \| 0.7943 \| 0.6746 \| 0.7565 \| \| 0.1664 \| 18.76 \| 6940 \| 0.5310 \| 0.6900 \| 0.8060 \| 0.8679 \| 0.9170 \| 0.7942 \| 0.8529 \| 0.5591 \| 0.8988 \| 0.8622 \| 0.7575 \| 0.8591 \| 0.6545 \| 0.7631 \| 0.4149 \| 0.7766 \| 0.6683 \| 0.6936 \| \| 0.2653 \| 18.81 \| 6960 \| 0.5674 \| 0.6819 \| 0.7923 \| 0.8660 \| 0.9224 \| 0.7480 \| 0.8707 \| 0.5060 \| 0.8992 \| 0.8637 \| 0.7362 \| 0.8612 \| 0.6425 \| 0.7551 \| 0.3906 \| 0.7732 \| 0.6745 \| 0.6761 \| \| 0.1461 \| 18.86 \| 6980 \| 0.5890 \| 0.6794 \| 0.7804 \| 0.8693 \| 0.9328 \| 0.7151 \| 0.8666 \| 0.4832 \| 0.9264 \| 0.8316 \| 0.7068 \| 0.8629 \| 0.6129 \| 0.7558 \| 0.3803 \| 0.7851 \| 0.6818 \| 0.6770 \| \| 0.2221 \| 18.92 \| 7000 \| 0.5067 \| 0.6993 \| 0.8129 \| 0.8735 \| 0.9338 \| 0.7829 \| 0.8681 \| 0.5879 \| 0.8860 \| 0.8581 \| 0.7738 \| 0.8635 \| 0.6556 \| 0.7693 \| 0.4326 \| 0.7850 \| 0.6695 \| 0.7195 \| \| 0.2125 \| 18.97 \| 7020 \| 0.5190 \| 0.7000 \| 0.8024 \| 0.8755 \| 0.9392 \| 0.7639 \| 0.8447 \| 0.5359 \| 0.9017 \| 0.8645 \| 0.7666 \| 0.8657 \| 0.6558 \| 0.7583 \| 0.4329 \| 0.7883 \| 0.6770 \| 0.7221 \| \| 0.1904 \| 19.03 \| 7040 \| 0.5813 \| 0.6759 \| 0.7899 \| 0.8618 \| 0.9322 \| 0.7705 \| 0.8519 \| 0.4998 \| 0.8815 \| 0.8776 \| 0.7160 \| 0.8604 \| 0.6552 \| 0.7470 \| 0.3914 \| 0.7667 \| 0.6452 \| 0.6655 \| \| 0.2587 \| 19.08 \| 7060 \| 0.5817 \| 0.6675 \| 0.7925 \| 0.8587 \| 0.9359 \| 0.8122 \| 0.8658 \| 0.5069 \| 0.8690 \| 0.8550 \| 0.7027 \| 0.8628 \| 0.6479 \| 0.7377 \| 0.3555 \| 0.7601 \| 0.6446 \| 0.6638 \| \| 0.1773 \| 19.14 \| 7080 \| 0.6150 \| 0.6701 \| 0.7901 \| 0.8583 \| 0.9266 \| 0.8001 \| 0.8581 \| 0.5196 \| 0.8907 \| 0.8551 \| 0.6805 \| 0.8601 \| 0.6697 \| 0.7471 \| 0.3655 \| 0.7629 \| 0.6369 \| 0.6487 \| \| 0.32 \| 19.19 \| 7100 \| 0.5867 \| 0.6727 \| 0.7812 \| 0.8661 \| 0.9307 \| 0.7993 \| 0.8512 \| 0.3972 \| 0.9027 \| 0.8471 \| 0.7406 \| 0.8645 \| 0.6655 \| 0.7247 \| 0.2904 \| 0.7725 \| 0.6829 \| 0.7082 \| \| 0.1643 \| 19.24 \| 7120 \| 0.5770 \| 0.6743 \| 0.7900 \| 0.8651 \| 0.9329 \| 0.8131 \| 0.8479 \| 0.4639 \| 0.8926 \| 0.8417 \| 0.7377 \| 0.8608 \| 0.6356 \| 0.7377 \| 0.3433 \| 0.7742 \| 0.6839 \| 0.6846 \| \| 0.2318 \| 19.3 \| 7140 \| 0.6071 \| 0.6768 \| 0.7972 \| 0.8651 \| 0.9316 \| 0.8415 \| 0.8710 \| 0.4708 \| 0.8879 \| 0.8668 \| 0.7106 \| 0.8631 \| 0.6620 \| 0.7394 \| 0.3453 \| 0.7732 \| 0.6689 \| 0.6858 \| \| 0.2215 \| 19.35 \| 7160 \| 0.5905 \| 0.6844 \| 0.7986 \| 0.8671 \| 0.9299 \| 0.8322 \| 0.8591 \| 0.4789 \| 0.8946 \| 0.8795 \| 0.7163 \| 0.8611 \| 0.6652 \| 0.7432 \| 0.3924 \| 0.7757 \| 0.6682 \| 0.6850 \| \| 0.1181 \| 19.41 \| 7180 \| 0.5889 \| 0.6876 \| 0.7907 \| 0.8691 \| 0.9314 \| 0.7910 \| 0.8487 \| 0.4678 \| 0.9082 \| 0.8539 \| 0.7337 \| 0.8629 \| 0.6766 \| 0.7420 \| 0.3876 \| 0.7752 \| 0.6697 \| 0.6993 \| \| 0.2127 \| 19.46 \| 7200 \| 0.6140 \| 0.6876 \| 0.7902 \| 0.8686 \| 0.9233 \| 0.7681 \| 0.8369 \| 0.5085 \| 0.9208 \| 0.8268 \| 0.7468 \| 0.8609 \| 0.6536 \| 0.7440 \| 0.4109 \| 0.7763 \| 0.6677 \| 0.6997 \| \| 0.1767 \| 19.51 \| 7220 \| 0.5347 \| 0.6865 \| 0.8045 \| 0.8655 \| 0.9298 \| 0.8068 \| 0.8362 \| 0.5574 \| 0.8796 \| 0.8711 \| 0.7504 \| 0.8545 \| 0.6559 \| 0.7433 \| 0.4151 \| 0.7713 \| 0.6658 \| 0.6998 \| \| 0.1436 \| 19.57 \| 7240 \| 0.5328 \| 0.6897 \| 0.8028 \| 0.8693 \| 0.9276 \| 0.7944 \| 0.8557 \| 0.5430 \| 0.8988 \| 0.8528 \| 0.7470 \| 0.8604 \| 0.6553 \| 0.7435 \| 0.4161 \| 0.7815 \| 0.6687 \| 0.7023 \| \| 0.3826 \| 19.62 \| 7260 \| 0.5324 \| 0.6842 \| 0.7933 \| 0.8691 \| 0.9348 \| 0.7950 \| 0.8648 \| 0.4719 \| 0.8966 \| 0.8462 \| 0.7434 \| 0.8652 \| 0.6548 \| 0.7315 \| 0.3897 \| 0.7796 \| 0.6686 \| 0.7003 \| \| 0.1237 \| 19.68 \| 7280 \| 0.5873 \| 0.6829 \| 0.7902 \| 0.8679 \| 0.9212 \| 0.7642 \| 0.8508 \| 0.4830 \| 0.9112 \| 0.8521 \| 0.7490 \| 0.8627 \| 0.6414 \| 0.7475 \| 0.3882 \| 0.7758 \| 0.6633 \| 0.7012 \| \| 0.3202 \| 19.73 \| 7300 \| 0.5673 \| 0.6791 \| 0.7928 \| 0.8647 \| 0.9204 \| 0.7484 \| 0.8683 \| 0.5007 \| 0.8901 \| 0.8702 \| 0.7515 \| 0.8628 \| 0.6275 \| 0.7545 \| 0.3923 \| 0.7672 \| 0.6612 \| 0.6880 \| \| 0.151 \| 19.78 \| 7320 \| 0.5385 \| 0.6796 \| 0.7916 \| 0.8648 \| 0.9233 \| 0.7913 \| 0.8560 \| 0.4815 \| 0.8975 \| 0.8501 \| 0.7412 \| 0.8572 \| 0.6437 \| 0.7462 \| 0.3911 \| 0.7732 \| 0.6667 \| 0.6787 \| \| 1.8943 \| 19.84 \| 7340 \| 0.5848 \| 0.6687 \| 0.7813 \| 0.8632 \| 0.9277 \| 0.8039 \| 0.8795 \| 0.3675 \| 0.8863 \| 0.8732 \| 0.7314 \| 0.8603 \| 0.6622 \| 0.7305 \| 0.3137 \| 0.7705 \| 0.6628 \| 0.6812 \| \| 2.0602 \| 19.89 \| 7360 \| 0.6545 \| 0.6648 \| 0.7714 \| 0.8624 \| 0.9365 \| 0.7130 \| 0.8754 \| 0.4220 \| 0.9027 \| 0.8739 \| 0.6764 \| 0.8619 \| 0.6179 \| 0.7413 \| 0.3474 \| 0.7738 \| 0.6558 \| 0.6558 \| \| 0.1774 \| 19.95 \| 7380 \| 0.5291 \| 0.6811 \| 0.7850 \| 0.8668 \| 0.9319 \| 0.7039 \| 0.8638 \| 0.4985 \| 0.8997 \| 0.8678 \| 0.7296 \| 0.8574 \| 0.6120 \| 0.7519 \| 0.4044 \| 0.7726 \| 0.6769 \| 0.6924 \| \| 0.4491 \| 20.0 \| 7400 \| 0.5457 \| 0.6885 \| 0.8092 \| 0.8668 \| 0.9288 \| 0.7781 \| 0.8755 \| 0.5600 \| 0.8597 \| 0.8784 \| 0.7842 \| 0.8537 \| 0.6507 \| 0.7424 \| 0.4132 \| 0.7719 \| 0.6517 \| 0.7357 \| \| 0.2327 \| 20.05 \| 7420 \| 0.4986 \| 0.6951 \| 0.8138 \| 0.8718 \| 0.9203 \| 0.7875 \| 0.8774 \| 0.5437 \| 0.8700 \| 0.8639 \| 0.8341 \| 0.8540 \| 0.6482 \| 0.7439 \| 0.4032 \| 0.7815 \| 0.6722 \| 0.7627 \| \| 0.174 \| 20.11 \| 7440 \| 0.5225 \| 0.6850 \| 0.7940 \| 0.8693 \| 0.9285 \| 0.7177 \| 0.8441 \| 0.5245 \| 0.8854 \| 0.8430 \| 0.8149 \| 0.8542 \| 0.6089 \| 0.7398 \| 0.3890 \| 0.7762 \| 0.6749 \| 0.7516 \| \| 0.223 \| 20.16 \| 7460 \| 0.5617 \| 0.6735 \| 0.7814 \| 0.8641 \| 0.9207 \| 0.7170 \| 0.8325 \| 0.4816 \| 0.9066 \| 0.8579 \| 0.7533 \| 0.8607 \| 0.6110 \| 0.7362 \| 0.3626 \| 0.7656 \| 0.6743 \| 0.7039 \| \| 0.1366 \| 20.22 \| 7480 \| 0.5322 \| 0.6753 \| 0.7865 \| 0.8674 \| 0.9287 \| 0.7346 \| 0.8706 \| 0.4498 \| 0.8929 \| 0.8790 \| 0.7499 \| 0.8666 \| 0.6240 \| 0.7352 \| 0.3376 \| 0.7741 \| 0.6781 \| 0.7117 \| \| 0.1194 \| 20.27 \| 7500 \| 0.5123 \| 0.6821 \| 0.7900 \| 0.8729 \| 0.9274 \| 0.7172 \| 0.8794 \| 0.4368 \| 0.8893 \| 0.8580 \| 0.8217 \| 0.8618 \| 0.6174 \| 0.7346 \| 0.3412 \| 0.7914 \| 0.6706 \| 0.7576 \| \| 0.4355 \| 20.32 \| 7520 \| 0.5022 \| 0.6883 \| 0.7942 \| 0.8700 \| 0.9291 \| 0.7423 \| 0.8283 \| 0.5127 \| 0.8953 \| 0.8612 \| 0.7909 \| 0.8556 \| 0.6275 \| 0.7459 \| 0.4057 \| 0.7788 \| 0.6703 \| 0.7344 \| \| 0.2599 \| 20.38 \| 7540 \| 0.5213 \| 0.6843 \| 0.7884 \| 0.8699 \| 0.9255 \| 0.7675 \| 0.8745 \| 0.4440 \| 0.9038 \| 0.8239 \| 0.7798 \| 0.8591 \| 0.6403 \| 0.7475 \| 0.3666 \| 0.7765 \| 0.6735 \| 0.7268 \| \| 0.1313 \| 20.43 \| 7560 \| 0.5307 \| 0.6870 \| 0.7970 \| 0.8715 \| 0.9309 \| 0.8263 \| 0.8690 \| 0.4340 \| 0.8996 \| 0.8745 \| 0.7449 \| 0.8690 \| 0.6708 \| 0.7400 \| 0.3684 \| 0.7822 \| 0.6607 \| 0.7177 \| \| 0.2357 \| 20.49 \| 7580 \| 0.5712 \| 0.6687 \| 0.7818 \| 0.8625 \| 0.9381 \| 0.7413 \| 0.8777 \| 0.4756 \| 0.8956 \| 0.8785 \| 0.6658 \| 0.8660 \| 0.6395 \| 0.7327 \| 0.3683 \| 0.7771 \| 0.6526 \| 0.6443 \| \| 0.0972 \| 20.54 \| 7600 \| 0.5153 \| 0.6886 \| 0.8072 \| 0.8707 \| 0.9321 \| 0.8320 \| 0.8792 \| 0.5420 \| 0.8992 \| 0.8391 \| 0.7266 \| 0.8704 \| 0.6725 \| 0.7292 \| 0.3949 \| 0.7864 \| 0.6665 \| 0.7001 \| \| 0.1029 \| 20.59 \| 7620 \| 0.5067 \| 0.6902 \| 0.7927 \| 0.8727 \| 0.9382 \| 0.7702 \| 0.8274 \| 0.4957 \| 0.9044 \| 0.8297 \| 0.7829 \| 0.8599 \| 0.6495 \| 0.7447 \| 0.3951 \| 0.7903 \| 0.6682 \| 0.7236 \| \| 0.5238 \| 20.65 \| 7640 \| 0.5782 \| 0.6799 \| 0.7993 \| 0.8650 \| 0.9307 \| 0.8141 \| 0.8524 \| 0.4980 \| 0.8774 \| 0.8783 \| 0.7441 \| 0.8642 \| 0.6526 \| 0.7503 \| 0.3906 \| 0.7734 \| 0.6402 \| 0.6883 \| \| 1.6773 \| 20.7 \| 7660 \| 0.5609 \| 0.6822 \| 0.8005 \| 0.8664 \| 0.9312 \| 0.8056 \| 0.8660 \| 0.5051 \| 0.8798 \| 0.8725 \| 0.7432 \| 0.8641 \| 0.6569 \| 0.7514 \| 0.3921 \| 0.7781 \| 0.6436 \| 0.6894 \| \| 0.1892 \| 20.76 \| 7680 \| 0.5891 \| 0.6798 \| 0.7864 \| 0.8684 \| 0.9357 \| 0.7842 \| 0.8568 \| 0.4363 \| 0.9010 \| 0.8586 \| 0.7325 \| 0.8671 \| 0.6601 \| 0.7397 \| 0.3563 \| 0.7793 \| 0.6701 \| 0.6859 \| \| 0.8756 \| 20.81 \| 7700 \| 0.6172 \| 0.6740 \| 0.7825 \| 0.8664 \| 0.9333 \| 0.8033 \| 0.8448 \| 0.3998 \| 0.9053 \| 0.8823 \| 0.7090 \| 0.8657 \| 0.6639 \| 0.7382 \| 0.3314 \| 0.7780 \| 0.6583 \| 0.6828 \| \| 0.2574 \| 20.86 \| 7720 \| 0.5685 \| 0.6730 \| 0.7848 \| 0.8657 \| 0.9321 \| 0.8291 \| 0.8601 \| 0.3877 \| 0.9004 \| 0.8768 \| 0.7071 \| 0.8641 \| 0.6729 \| 0.7365 \| 0.3251 \| 0.7792 \| 0.6542 \| 0.6789 \| \| 0.2701 \| 20.92 \| 7740 \| 0.5149 \| 0.6892 \| 0.8032 \| 0.8752 \| 0.9224 \| 0.8254 \| 0.8539 \| 0.4344 \| 0.8943 \| 0.8636 \| 0.8288 \| 0.8631 \| 0.6578 \| 0.7363 \| 0.3458 \| 0.7994 \| 0.6619 \| 0.7604 \| \| 0.5788 \| 20.97 \| 7760 \| 0.5510 \| 0.6740 \| 0.7774 \| 0.8681 \| 0.9259 \| 0.7662 \| 0.8814 \| 0.3868 \| 0.9117 \| 0.8002 \| 0.7697 \| 0.8639 \| 0.6620 \| 0.7312 \| 0.3048 \| 0.7825 \| 0.6629 \| 0.7104 \| \| 0.3711 \| 21.03 \| 7780 \| 0.5231 \| 0.6849 \| 0.7949 \| 0.8718 \| 0.9327 \| 0.7757 \| 0.8866 \| 0.4610 \| 0.8855 \| 0.8102 \| 0.8124 \| 0.8622 \| 0.6629 \| 0.7369 \| 0.3397 \| 0.7892 \| 0.6596 \| 0.7439 \| \| 0.2244 \| 21.08 \| 7800 \| 0.5435 \| 0.6822 \| 0.8026 \| 0.8675 \| 0.9217 \| 0.7945 \| 0.8675 \| 0.4906 \| 0.8746 \| 0.8746 \| 0.7946 \| 0.8641 \| 0.6620 \| 0.7449 \| 0.3363 \| 0.7738 \| 0.6658 \| 0.7286 \| \| 0.2711 \| 21.14 \| 7820 \| 0.6044 \| 0.6836 \| 0.7940 \| 0.8682 \| 0.9306 \| 0.8000 \| 0.8592 \| 0.5011 \| 0.9066 \| 0.8271 \| 0.7334 \| 0.8685 \| 0.6619 \| 0.7554 \| 0.3755 \| 0.7750 \| 0.6568 \| 0.6922 \| \| 0.1413 \| 21.19 \| 7840 \| 0.5306 \| 0.6918 \| 0.8031 \| 0.8733 \| 0.9320 \| 0.7765 \| 0.8831 \| 0.4888 \| 0.8802 \| 0.8552 \| 0.8056 \| 0.8615 \| 0.6591 \| 0.7465 \| 0.3789 \| 0.7903 \| 0.6675 \| 0.7387 \| \| 0.2264 \| 21.24 \| 7860 \| 0.5182 \| 0.6880 \| 0.8088 \| 0.8700 \| 0.9337 \| 0.8223 \| 0.8892 \| 0.5332 \| 0.8845 \| 0.8725 \| 0.7264 \| 0.8712 \| 0.6752 \| 0.7285 \| 0.3943 \| 0.7840 \| 0.6610 \| 0.7017 \| \| 0.0717 \| 21.3 \| 7880 \| 0.5174 \| 0.6903 \| 0.8026 \| 0.8729 \| 0.9256 \| 0.8152 \| 0.8579 \| 0.4825 \| 0.9021 \| 0.8608 \| 0.7740 \| 0.8675 \| 0.6609 \| 0.7434 \| 0.3772 \| 0.7865 \| 0.6670 \| 0.7296 \| \| 0.2197 \| 21.35 \| 7900 \| 0.5787 \| 0.6807 \| 0.7905 \| 0.8691 \| 0.9341 \| 0.8186 \| 0.8830 \| 0.4429 \| 0.9089 \| 0.8356 \| 0.7103 \| 0.8681 \| 0.6762 \| 0.7340 \| 0.3512 \| 0.7846 \| 0.6679 \| 0.6827 \| \| 0.316 \| 21.41 \| 7920 \| 0.5727 \| 0.6830 \| 0.7970 \| 0.8686 \| 0.9316 \| 0.8351 \| 0.8586 \| 0.4615 \| 0.8985 \| 0.8697 \| 0.7243 \| 0.8680 \| 0.6698 \| 0.7498 \| 0.3609 \| 0.7777 \| 0.6645 \| 0.6907 \| \| 0.2365 \| 21.46 \| 7940 \| 0.5699 \| 0.6770 \| 0.7762 \| 0.8693 \| 0.9408 \| 0.7563 \| 0.8860 \| 0.3834 \| 0.9086 \| 0.8411 \| 0.7171 \| 0.8658 \| 0.6654 \| 0.7459 \| 0.3222 \| 0.7818 \| 0.6689 \| 0.6889 \| \| 0.2017 \| 21.51 \| 7960 \| 0.5048 \| 0.6899 \| 0.7966 \| 0.8736 \| 0.9328 \| 0.8088 \| 0.8570 \| 0.4467 \| 0.9042 \| 0.8644 \| 0.7625 \| 0.8659 \| 0.6671 \| 0.7526 \| 0.3645 \| 0.7899 \| 0.6704 \| 0.7190 \| \| 0.1801 \| 21.57 \| 7980 \| 0.5304 \| 0.6847 \| 0.7873 \| 0.8729 \| 0.9377 \| 0.7963 \| 0.8375 \| 0.4198 \| 0.9089 \| 0.8376 \| 0.7732 \| 0.8617 \| 0.6613 \| 0.7328 \| 0.3441 \| 0.7944 \| 0.6750 \| 0.7235 \| \| 0.1469 \| 21.62 \| 8000 \| 0.5277 \| 0.6891 \| 0.8025 \| 0.8738 \| 0.9288 \| 0.8013 \| 0.8589 \| 0.4444 \| 0.8796 \| 0.8819 \| 0.8228 \| 0.8600 \| 0.6624 \| 0.7352 \| 0.3509 \| 0.7964 \| 0.6605 \| 0.7580 \| \| 0.1851 \| 21.68 \| 8020 \| 0.5037 \| 0.6917 \| 0.8026 \| 0.8752 \| 0.9236 \| 0.7849 \| 0.8607 \| 0.4541 \| 0.8874 \| 0.8659 \| 0.8417 \| 0.8595 \| 0.6612 \| 0.7352 \| 0.3574 \| 0.7997 \| 0.6688 \| 0.7600 \| \| 0.2767 \| 21.73 \| 8040 \| 0.5497 \| 0.6743 \| 0.7824 \| 0.8654 \| 0.9295 \| 0.7648 \| 0.8667 \| 0.4335 \| 0.9000 \| 0.8474 \| 0.7347 \| 0.8657 \| 0.6586 \| 0.7309 \| 0.3443 \| 0.7752 \| 0.6644 \| 0.6809 \| \| 0.4517 \| 21.78 \| 8060 \| 0.5216 \| 0.6863 \| 0.8001 \| 0.8694 \| 0.9328 \| 0.7981 \| 0.8670 \| 0.4973 \| 0.8863 \| 0.8636 \| 0.7556 \| 0.8637 \| 0.6652 \| 0.7368 \| 0.3809 \| 0.7812 \| 0.6670 \| 0.7091 \| \| 0.1079 \| 21.84 \| 8080 \| 0.5996 \| 0.6822 \| 0.7995 \| 0.8670 \| 0.9256 \| 0.8301 \| 0.8706 \| 0.4783 \| 0.8941 \| 0.8730 \| 0.7251 \| 0.8654 \| 0.6743 \| 0.7266 \| 0.3776 \| 0.7783 \| 0.6619 \| 0.6910 \| \| 0.1717 \| 21.89 \| 8100 \| 0.5482 \| 0.6819 \| 0.7917 \| 0.8708 \| 0.9316 \| 0.8331 \| 0.8817 \| 0.4027 \| 0.9023 \| 0.8436 \| 0.7470 \| 0.8700 \| 0.6738 \| 0.7327 \| 0.3401 \| 0.7833 \| 0.6612 \| 0.7121 \| \| 0.3239 \| 21.95 \| 8120 \| 0.5427 \| 0.6794 \| 0.7976 \| 0.8651 \| 0.8876 \| 0.7914 \| 0.8811 \| 0.4234 \| 0.8909 \| 0.8623 \| 0.8468 \| 0.8398 \| 0.6644 \| 0.7446 \| 0.3468 \| 0.7923 \| 0.6618 \| 0.7062 \| \| 0.1983 \| 22.0 \| 8140 \| 0.6081 \| 0.6780 \| 0.7984 \| 0.8627 \| 0.8777 \| 0.7846 \| 0.8739 \| 0.4317 \| 0.8891 \| 0.8768 \| 0.8552 \| 0.8323 \| 0.6606 \| 0.7464 \| 0.3587 \| 0.7932 \| 0.6566 \| 0.6980 \| \| 0.1484 \| 22.05 \| 8160 \| 0.5349 \| 0.6790 \| 0.7993 \| 0.8633 \| 0.8994 \| 0.8141 \| 0.8562 \| 0.4765 \| 0.8940 \| 0.8652 \| 0.7899 \| 0.8491 \| 0.6716 \| 0.7440 \| 0.3733 \| 0.7850 \| 0.6449 \| 0.6849 \| \| 0.1894 \| 22.11 \| 8180 \| 0.5522 \| 0.6756 \| 0.7889 \| 0.8596 \| 0.9088 \| 0.7743 \| 0.8673 \| 0.4937 \| 0.8993 \| 0.8464 \| 0.7326 \| 0.8393 \| 0.6720 \| 0.7426 \| 0.3832 \| 0.7810 \| 0.6632 \| 0.6477 \| \| 0.3145 \| 22.16 \| 8200 \| 0.5572 \| 0.6822 \| 0.7993 \| 0.8665 \| 0.9307 \| 0.8412 \| 0.8772 \| 0.4812 \| 0.8923 \| 0.8619 \| 0.7103 \| 0.8636 \| 0.6720 \| 0.7410 \| 0.3873 \| 0.7780 \| 0.6549 \| 0.6784 \| \| 0.2407 \| 22.22 \| 8220 \| 0.5461 \| 0.6860 \| 0.7994 \| 0.8673 \| 0.9205 \| 0.8052 \| 0.8697 \| 0.4979 \| 0.8992 \| 0.8660 \| 0.7374 \| 0.8618 \| 0.6721 \| 0.7515 \| 0.3905 \| 0.7765 \| 0.6633 \| 0.6864 \| \| 0.2316 \| 22.27 \| 8240 \| 0.5117 \| 0.6899 \| 0.8124 \| 0.8722 \| 0.9228 \| 0.8234 \| 0.8740 \| 0.5053 \| 0.8763 \| 0.8768 \| 0.8084 \| 0.8632 \| 0.6560 \| 0.7452 \| 0.3687 \| 0.7897 \| 0.6626 \| 0.7437 \| \| 0.1487 \| 22.32 \| 8260 \| 0.5589 \| 0.6807 \| 0.7897 \| 0.8691 \| 0.9372 \| 0.7995 \| 0.8774 \| 0.4483 \| 0.9010 \| 0.8372 \| 0.7271 \| 0.8645 \| 0.6632 \| 0.7359 \| 0.3582 \| 0.7870 \| 0.6715 \| 0.6844 \| \| 0.5139 \| 22.38 \| 8280 \| 0.6136 \| 0.6741 \| 0.7883 \| 0.8648 \| 0.9278 \| 0.8190 \| 0.8847 \| 0.4439 \| 0.9060 \| 0.8414 \| 0.6949 \| 0.8632 \| 0.6656 \| 0.7263 \| 0.3530 \| 0.7808 \| 0.6715 \| 0.6581 \| \| 0.1885 \| 22.43 \| 8300 \| 0.5952 \| 0.6781 \| 0.7890 \| 0.8660 \| 0.9278 \| 0.8208 \| 0.8729 \| 0.4469 \| 0.9098 \| 0.8419 \| 0.7027 \| 0.8622 \| 0.6664 \| 0.7447 \| 0.3691 \| 0.7802 \| 0.6591 \| 0.6653 \| \| 0.1655 \| 22.49 \| 8320 \| 0.5607 \| 0.6828 \| 0.7937 \| 0.8679 \| 0.9396 \| 0.8111 \| 0.8783 \| 0.4798 \| 0.8965 \| 0.8448 \| 0.7055 \| 0.8655 \| 0.6729 \| 0.7423 \| 0.3851 \| 0.7813 \| 0.6571 \| 0.6754 \| \| 0.2207 \| 22.54 \| 8340 \| 0.5650 \| 0.6836 \| 0.7969 \| 0.8673 \| 0.9392 \| 0.8329 \| 0.8358 \| 0.5001 \| 0.8973 \| 0.8690 \| 0.7041 \| 0.8633 \| 0.6696 \| 0.7397 \| 0.3907 \| 0.7790 \| 0.6637 \| 0.6792 \| \| 0.1021 \| 22.59 \| 8360 \| 0.5846 \| 0.6821 \| 0.7936 \| 0.8673 \| 0.9382 \| 0.8232 \| 0.8513 \| 0.4894 \| 0.9030 \| 0.8514 \| 0.6990 \| 0.8629 \| 0.6707 \| 0.7395 \| 0.3838 \| 0.7821 \| 0.6650 \| 0.6711 \| \| 0.2844 \| 22.65 \| 8380 \| 0.5945 \| 0.6737 \| 0.7886 \| 0.8641 \| 0.9414 \| 0.8176 \| 0.8566 \| 0.4632 \| 0.8908 \| 0.8607 \| 0.6901 \| 0.8643 \| 0.6701 \| 0.7218 \| 0.3506 \| 0.7753 \| 0.6670 \| 0.6671 \| \| 0.2364 \| 22.7 \| 8400 \| 0.5675 \| 0.6828 \| 0.7980 \| 0.8668 \| 0.9386 \| 0.8129 \| 0.8429 \| 0.5250 \| 0.8927 \| 0.8625 \| 0.7115 \| 0.8599 \| 0.6651 \| 0.7359 \| 0.3909 \| 0.7825 \| 0.6661 \| 0.6791 \| \| 0.0931 \| 22.76 \| 8420 \| 0.5758 \| 0.6814 \| 0.7965 \| 0.8677 \| 0.9280 \| 0.8355 \| 0.8542 \| 0.4923 \| 0.9146 \| 0.8567 \| 0.6943 \| 0.8600 \| 0.6565 \| 0.7365 \| 0.3779 \| 0.7881 \| 0.6878 \| 0.6632 \| \| 1.9535 \| 22.81 \| 8440 \| 0.5744 \| 0.6708 \| 0.7846 \| 0.8615 \| 0.9414 \| 0.8052 \| 0.8589 \| 0.4741 \| 0.8864 \| 0.8198 \| 0.7064 \| 0.8527 \| 0.6636 \| 0.7285 \| 0.3274 \| 0.7738 \| 0.6879 \| 0.6616 \| \| 0.089 \| 22.86 \| 8460 \| 0.5871 \| 0.6750 \| 0.7902 \| 0.8621 \| 0.9236 \| 0.8017 \| 0.8604 \| 0.4905 \| 0.8994 \| 0.8477 \| 0.7080 \| 0.8547 \| 0.6580 \| 0.7415 \| 0.3755 \| 0.7767 \| 0.6754 \| 0.6433 \| \| 0.4797 \| 22.92 \| 8480 \| 0.5729 \| 0.6792 \| 0.7963 \| 0.8640 \| 0.9317 \| 0.7961 \| 0.8674 \| 0.5229 \| 0.8887 \| 0.8601 \| 0.7072 \| 0.8603 \| 0.6589 \| 0.7385 \| 0.3936 \| 0.7750 \| 0.6674 \| 0.6609 \| \| 0.3645 \| 22.97 \| 8500 \| 0.5924 \| 0.6621 \| 0.7854 \| 0.8574 \| 0.9327 \| 0.8002 \| 0.8649 \| 0.4443 \| 0.8653 \| 0.8807 \| 0.7099 \| 0.8588 \| 0.6602 \| 0.7243 \| 0.3064 \| 0.7636 \| 0.6646 \| 0.6571 \| \| 1.9274 \| 23.03 \| 8520 \| 0.5832 \| 0.6716 \| 0.7923 \| 0.8619 \| 0.9191 \| 0.8040 \| 0.8636 \| 0.4716 \| 0.8842 \| 0.8479 \| 0.7558 \| 0.8588 \| 0.6626 \| 0.7375 \| 0.3162 \| 0.7693 \| 0.6642 \| 0.6928 \| \| 0.4846 \| 23.08 \| 8540 \| 0.5686 \| 0.6763 \| 0.8021 \| 0.8606 \| 0.9225 \| 0.8182 \| 0.8364 \| 0.5516 \| 0.8752 \| 0.8683 \| 0.7425 \| 0.8571 \| 0.6578 \| 0.7466 \| 0.3835 \| 0.7664 \| 0.6399 \| 0.6830 \| \| 0.238 \| 23.14 \| 8560 \| 0.5864 \| 0.6750 \| 0.7964 \| 0.8601 \| 0.9281 \| 0.8056 \| 0.8387 \| 0.5468 \| 0.8832 \| 0.8638 \| 0.7090 \| 0.8589 \| 0.6643 \| 0.7471 \| 0.3803 \| 0.7640 \| 0.6465 \| 0.6642 \| \| 0.1989 \| 23.19 \| 8580 \| 0.5883 \| 0.6800 \| 0.7955 \| 0.8633 \| 0.9314 \| 0.8008 \| 0.8531 \| 0.5379 \| 0.8912 \| 0.8374 \| 0.7170 \| 0.8609 \| 0.6653 \| 0.7501 \| 0.3932 \| 0.7695 \| 0.6599 \| 0.6609 \| \| 0.1894 \| 23.24 \| 8600 \| 0.6118 \| 0.6843 \| 0.7944 \| 0.8675 \| 0.9321 \| 0.8160 \| 0.8606 \| 0.4999 \| 0.9119 \| 0.8475 \| 0.6927 \| 0.8617 \| 0.6717 \| 0.7498 \| 0.3885 \| 0.7821 \| 0.6766 \| 0.6594 \| \| 0.2921 \| 23.3 \| 8620 \| 0.6391 \| 0.6731 \| 0.7798 \| 0.8643 \| 0.9367 \| 0.7823 \| 0.8612 \| 0.4541 \| 0.9168 \| 0.8444 \| 0.6629 \| 0.8619 \| 0.6692 \| 0.7416 \| 0.3576 \| 0.7836 \| 0.6656 \| 0.6324 \| \| 0.1991 \| 23.35 \| 8640 \| 0.5604 \| 0.6692 \| 0.7986 \| 0.8594 \| 0.9283 \| 0.8299 \| 0.8807 \| 0.4897 \| 0.8591 \| 0.8730 \| 0.7292 \| 0.8630 \| 0.6569 \| 0.7372 \| 0.3387 \| 0.7609 \| 0.6585 \| 0.6690 \| \| 2.1941 \| 23.41 \| 8660 \| 0.6231 \| 0.6726 \| 0.7960 \| 0.8598 \| 0.9252 \| 0.7916 \| 0.8704 \| 0.5168 \| 0.8739 \| 0.8811 \| 0.7128 \| 0.8600 \| 0.6560 \| 0.7499 \| 0.3834 \| 0.7690 \| 0.6361 \| 0.6540 \| \| 1.0263 \| 23.46 \| 8680 \| 0.5725 \| 0.6812 \| 0.7969 \| 0.8661 \| 0.9317 \| 0.7970 \| 0.8727 \| 0.4957 \| 0.8853 \| 0.8666 \| 0.7292 \| 0.8650 \| 0.6602 \| 0.7414 \| 0.3854 \| 0.7752 \| 0.6646 \| 0.6764 \| \| 0.3965 \| 23.51 \| 8700 \| 0.6046 \| 0.6701 \| 0.7845 \| 0.8601 \| 0.9275 \| 0.7791 \| 0.8538 \| 0.4748 \| 0.8911 \| 0.8554 \| 0.7099 \| 0.8609 \| 0.6647 \| 0.7444 \| 0.3495 \| 0.7669 \| 0.6535 \| 0.6511 \| \| 0.3083 \| 23.57 \| 8720 \| 0.5639 \| 0.6899 \| 0.8086 \| 0.8714 \| 0.9230 \| 0.8020 \| 0.8668 \| 0.5128 \| 0.8775 \| 0.8573 \| 0.8208 \| 0.8613 \| 0.6656 \| 0.7423 \| 0.3584 \| 0.7845 \| 0.6696 \| 0.7476 \| \| 0.348 \| 23.62 \| 8740 \| 0.5833 \| 0.6772 \| 0.8023 \| 0.8639 \| 0.9258 \| 0.8096 \| 0.8594 \| 0.5035 \| 0.8651 \| 0.8836 \| 0.7693 \| 0.8626 \| 0.6613 \| 0.7440 \| 0.3460 \| 0.7708 \| 0.6550 \| 0.7010 \| \| 0.2902 \| 23.68 \| 8760 \| 0.6245 \| 0.6601 \| 0.7887 \| 0.8543 \| 0.9189 \| 0.8138 \| 0.8727 \| 0.4692 \| 0.8708 \| 0.8761 \| 0.6994 \| 0.8602 \| 0.6599 \| 0.7390 \| 0.3228 \| 0.7571 \| 0.6401 \| 0.6419 \| \| 0.1974 \| 23.73 \| 8780 \| 0.5838 \| 0.6695 \| 0.7817 \| 0.8603 \| 0.9269 \| 0.7834 \| 0.8570 \| 0.4585 \| 0.8937 \| 0.8266 \| 0.7255 \| 0.8591 \| 0.6629 \| 0.7404 \| 0.3419 \| 0.7653 \| 0.6539 \| 0.6629 \| \| 0.206 \| 23.78 \| 8800 \| 0.5512 \| 0.6790 \| 0.7919 \| 0.8634 \| 0.9296 \| 0.7790 \| 0.8484 \| 0.5005 \| 0.8879 \| 0.8753 \| 0.7230 \| 0.8614 \| 0.6625 \| 0.7393 \| 0.3936 \| 0.7701 \| 0.6631 \| 0.6630 \| \| 0.0565 \| 23.84 \| 8820 \| 0.5502 \| 0.6865 \| 0.8006 \| 0.8691 \| 0.9266 \| 0.8180 \| 0.8576 \| 0.4931 \| 0.8943 \| 0.8524 \| 0.7625 \| 0.8607 \| 0.6622 \| 0.7458 \| 0.3915 \| 0.7857 \| 0.6619 \| 0.6976 \| \| 0.1328 \| 23.89 \| 8840 \| 0.5634 \| 0.6827 \| 0.7948 \| 0.8668 \| 0.9207 \| 0.7875 \| 0.8474 \| 0.5038 \| 0.9039 \| 0.8439 \| 0.7562 \| 0.8580 \| 0.6475 \| 0.7534 \| 0.3939 \| 0.7817 \| 0.6573 \| 0.6872 \| \| 0.1884 \| 23.95 \| 8860 \| 0.6300 \| 0.6704 \| 0.7872 \| 0.8606 \| 0.9145 \| 0.8043 \| 0.8633 \| 0.4493 \| 0.9001 \| 0.8627 \| 0.7165 \| 0.8571 \| 0.6550 \| 0.7494 \| 0.3593 \| 0.7726 \| 0.6456 \| 0.6540 \| \| 0.2707 \| 24.0 \| 8880 \| 0.6569 \| 0.6586 \| 0.7709 \| 0.8575 \| 0.9294 \| 0.7927 \| 0.8687 \| 0.3950 \| 0.9010 \| 0.8286 \| 0.6807 \| 0.8605 \| 0.6619 \| 0.7332 \| 0.3093 \| 0.7674 \| 0.6457 \| 0.6322 \| \| 0.3871 \| 24.05 \| 8900 \| 0.6504 \| 0.6578 \| 0.7787 \| 0.8564 \| 0.9356 \| 0.8423 \| 0.8347 \| 0.4222 \| 0.8869 \| 0.8495 \| 0.6795 \| 0.8592 \| 0.6567 \| 0.7318 \| 0.3005 \| 0.7635 \| 0.6441 \| 0.6486 \| \| 0.3144 \| 24.11 \| 8920 \| 0.6706 \| 0.6548 \| 0.7795 \| 0.8536 \| 0.9291 \| 0.8228 \| 0.8620 \| 0.4337 \| 0.8836 \| 0.8777 \| 0.6475 \| 0.8604 \| 0.6646 \| 0.7307 \| 0.3255 \| 0.7670 \| 0.6234 \| 0.6122 \| \| 0.0824 \| 24.16 \| 8940 \| 0.6211 \| 0.6640 \| 0.7859 \| 0.8568 \| 0.9224 \| 0.7709 \| 0.8858 \| 0.4967 \| 0.8808 \| 0.8346 \| 0.7101 \| 0.8536 \| 0.6410 \| 0.7348 \| 0.3514 \| 0.7663 \| 0.6524 \| 0.6482 \| \| 0.147 \| 24.22 \| 8960 \| 0.6018 \| 0.6663 \| 0.7807 \| 0.8599 \| 0.9246 \| 0.6985 \| 0.8578 \| 0.5418 \| 0.9017 \| 0.8264 \| 0.7142 \| 0.8596 \| 0.6068 \| 0.7480 \| 0.3659 \| 0.7694 \| 0.6577 \| 0.6565 \| \| 0.2046 \| 24.27 \| 8980 \| 0.5952 \| 0.6760 \| 0.7914 \| 0.8632 \| 0.9212 \| 0.7512 \| 0.8642 \| 0.5368 \| 0.8980 \| 0.8256 \| 0.7427 \| 0.8591 \| 0.6417 \| 0.7422 \| 0.3755 \| 0.7736 \| 0.6646 \| 0.6755 \| \| 0.2014 \| 24.32 \| 9000 \| 0.5944 \| 0.6783 \| 0.7980 \| 0.8641 \| 0.9273 \| 0.8022 \| 0.8638 \| 0.5247 \| 0.8844 \| 0.8371 \| 0.7463 \| 0.8614 \| 0.6586 \| 0.7374 \| 0.3680 \| 0.7722 \| 0.6679 \| 0.6826 \| \| 0.0533 \| 24.38 \| 9020 \| 0.6587 \| 0.6734 \| 0.7872 \| 0.8619 \| 0.9203 \| 0.7801 \| 0.8606 \| 0.4876 \| 0.9016 \| 0.8332 \| 0.7268 \| 0.8595 \| 0.6487 \| 0.7451 \| 0.3628 \| 0.7684 \| 0.6648 \| 0.6641 \| \| 0.2093 \| 24.43 \| 9040 \| 0.6173 \| 0.6763 \| 0.7903 \| 0.8648 \| 0.9328 \| 0.8158 \| 0.8373 \| 0.4630 \| 0.8884 \| 0.8442 \| 0.7507 \| 0.8599 \| 0.6587 \| 0.7291 \| 0.3529 \| 0.7738 \| 0.6708 \| 0.6886 \| \| 0.1713 \| 24.49 \| 9060 \| 0.5820 \| 0.6747 \| 0.7958 \| 0.8626 \| 0.9317 \| 0.7998 \| 0.8558 \| 0.4790 \| 0.8616 \| 0.8773 \| 0.7652 \| 0.8635 \| 0.6673 \| 0.7396 \| 0.3206 \| 0.7610 \| 0.6686 \| 0.7023 \| \| 0.1691 \| 24.54 \| 9080 \| 0.5721 \| 0.6751 \| 0.7924 \| 0.8631 \| 0.9324 \| 0.8007 \| 0.8585 \| 0.4802 \| 0.8762 \| 0.8532 \| 0.7460 \| 0.8643 \| 0.6645 \| 0.7445 \| 0.3347 \| 0.7627 \| 0.6669 \| 0.6883 \| \| 0.1106 \| 24.59 \| 9100 \| 0.5679 \| 0.6808 \| 0.7961 \| 0.8660 \| 0.9313 \| 0.8225 \| 0.8635 \| 0.4743 \| 0.8893 \| 0.8673 \| 0.7246 \| 0.8669 \| 0.6724 \| 0.7418 \| 0.3643 \| 0.7698 \| 0.6645 \| 0.6859 \| \| 0.1896 \| 24.65 \| 9120 \| 0.5785 \| 0.6783 \| 0.7910 \| 0.8649 \| 0.9234 \| 0.7689 \| 0.8535 \| 0.4665 \| 0.8878 \| 0.8821 \| 0.7548 \| 0.8615 \| 0.6394 \| 0.7505 \| 0.3741 \| 0.7690 \| 0.6665 \| 0.6871 \| \| 0.5134 \| 24.7 \| 9140 \| 0.5299 \| 0.6942 \| 0.8100 \| 0.8722 \| 0.9225 \| 0.8044 \| 0.8590 \| 0.5211 \| 0.8837 \| 0.8726 \| 0.8068 \| 0.8600 \| 0.6590 \| 0.7510 \| 0.4038 \| 0.7866 \| 0.6650 \| 0.7339 \| \| 0.4509 \| 24.76 \| 9160 \| 0.5697 \| 0.6858 \| 0.8003 \| 0.8677 \| 0.9174 \| 0.8081 \| 0.8697 \| 0.5085 \| 0.9011 \| 0.8324 \| 0.7648 \| 0.8602 \| 0.6603 \| 0.7489 \| 0.3869 \| 0.7777 \| 0.6692 \| 0.6972 \| \| 0.1456 \| 24.81 \| 9180 \| 0.6313 \| 0.6818 \| 0.7974 \| 0.8656 \| 0.9252 \| 0.8321 \| 0.8474 \| 0.5258 \| 0.9130 \| 0.8379 \| 0.7006 \| 0.8625 \| 0.6669 \| 0.7485 \| 0.3889 \| 0.7775 \| 0.6627 \| 0.6657 \| \| 0.3161 \| 24.86 \| 9200 \| 0.5902 \| 0.6805 \| 0.7989 \| 0.8655 \| 0.9239 \| 0.8146 \| 0.8581 \| 0.5054 \| 0.8938 \| 0.8657 \| 0.7306 \| 0.8655 \| 0.6603 \| 0.7480 \| 0.3758 \| 0.7729 \| 0.6618 \| 0.6794 \| \| 0.1412 \| 24.92 \| 9220 \| 0.6194 \| 0.6728 \| 0.7906 \| 0.8624 \| 0.9236 \| 0.8272 \| 0.8365 \| 0.4650 \| 0.8953 \| 0.8559 \| 0.7307 \| 0.8560 \| 0.6528 \| 0.7384 \| 0.3507 \| 0.7740 \| 0.6678 \| 0.6698 \| \| 0.169 \| 24.97 \| 9240 \| 0.5721 \| 0.6742 \| 0.7854 \| 0.8649 \| 0.9276 \| 0.8090 \| 0.8546 \| 0.4162 \| 0.8956 \| 0.8470 \| 0.7475 \| 0.8569 \| 0.6604 \| 0.7383 \| 0.3288 \| 0.7776 \| 0.6754 \| 0.6818 \| \| 0.1168 \| 25.03 \| 9260 \| 0.5408 \| 0.6758 \| 0.7857 \| 0.8653 \| 0.9315 \| 0.8146 \| 0.8371 \| 0.4369 \| 0.8992 \| 0.8365 \| 0.7444 \| 0.8570 \| 0.6611 \| 0.7355 \| 0.3402 \| 0.7776 \| 0.6756 \| 0.6834 \| \| 0.1236 \| 25.08 \| 9280 \| 0.5989 \| 0.6796 \| 0.7947 \| 0.8637 \| 0.9337 \| 0.8156 \| 0.8602 \| 0.5125 \| 0.8951 \| 0.8586 \| 0.6868 \| 0.8574 \| 0.6684 \| 0.7470 \| 0.3954 \| 0.7758 \| 0.6597 \| 0.6533 \| \| 0.1402 \| 25.14 \| 9300 \| 0.5337 \| 0.6828 \| 0.7902 \| 0.8682 \| 0.9369 \| 0.8239 \| 0.8565 \| 0.4630 \| 0.9081 \| 0.8272 \| 0.7161 \| 0.8596 \| 0.6726 \| 0.7423 \| 0.3713 \| 0.7842 \| 0.6732 \| 0.6764 \| \| 0.0659 \| 25.19 \| 9320 \| 0.5782 \| 0.6736 \| 0.7954 \| 0.8614 \| 0.9302 \| 0.8341 \| 0.8540 \| 0.4856 \| 0.8799 \| 0.8762 \| 0.7081 \| 0.8572 \| 0.6569 \| 0.7373 \| 0.3741 \| 0.7711 \| 0.6594 \| 0.6595 \| \| 0.3301 \| 25.24 \| 9340 \| 0.6045 \| 0.6715 \| 0.7866 \| 0.8620 \| 0.9276 \| 0.7994 \| 0.8715 \| 0.4603 \| 0.8958 \| 0.8467 \| 0.7047 \| 0.8590 \| 0.6512 \| 0.7431 \| 0.3611 \| 0.7735 \| 0.6582 \| 0.6542 \| \| 0.2523 \| 25.3 \| 9360 \| 0.5964 \| 0.6754 \| 0.7884 \| 0.8641 \| 0.9323 \| 0.8082 \| 0.8618 \| 0.4527 \| 0.8947 \| 0.8632 \| 0.7063 \| 0.8602 \| 0.6700 \| 0.7370 \| 0.3532 \| 0.7760 \| 0.6639 \| 0.6673 \| \| 0.2483 \| 25.35 \| 9380 \| 0.5981 \| 0.6707 \| 0.7816 \| 0.8634 \| 0.9321 \| 0.7652 \| 0.8546 \| 0.4422 \| 0.8953 \| 0.8623 \| 0.7191 \| 0.8631 \| 0.6526 \| 0.7336 \| 0.3334 \| 0.7730 \| 0.6677 \| 0.6716 \| \| 0.1621 \| 25.41 \| 9400 \| 0.5591 \| 0.6763 \| 0.7884 \| 0.8664 \| 0.9338 \| 0.7969 \| 0.8472 \| 0.4417 \| 0.8910 \| 0.8637 \| 0.7442 \| 0.8621 \| 0.6397 \| 0.7300 \| 0.3486 \| 0.7741 \| 0.6882 \| 0.6915 \| \| 0.1596 \| 25.46 \| 9420 \| 0.6054 \| 0.6797 \| 0.7894 \| 0.8674 \| 0.9307 \| 0.8010 \| 0.8502 \| 0.4750 \| 0.9109 \| 0.8343 \| 0.7235 \| 0.8612 \| 0.6403 \| 0.7309 \| 0.3735 \| 0.7782 \| 0.6834 \| 0.6904 \| \| 0.3227 \| 25.51 \| 9440 \| 0.5901 \| 0.6776 \| 0.7910 \| 0.8658 \| 0.9212 \| 0.7572 \| 0.8674 \| 0.4964 \| 0.9057 \| 0.8652 \| 0.7235 \| 0.8660 \| 0.6386 \| 0.7388 \| 0.3695 \| 0.7733 \| 0.6707 \| 0.6865 \| \| 0.2153 \| 25.57 \| 9460 \| 0.5758 \| 0.6730 \| 0.7875 \| 0.8648 \| 0.9244 \| 0.7906 \| 0.8777 \| 0.4487 \| 0.9020 \| 0.8477 \| 0.7211 \| 0.8647 \| 0.6502 \| 0.7290 \| 0.3387 \| 0.7741 \| 0.6667 \| 0.6879 \| \| 0.3868 \| 25.62 \| 9480 \| 0.5591 \| 0.6795 \| 0.7912 \| 0.8671 \| 0.9252 \| 0.8109 \| 0.8717 \| 0.4495 \| 0.9042 \| 0.8442 \| 0.7330 \| 0.8641 \| 0.6580 \| 0.7303 \| 0.3576 \| 0.7743 \| 0.6755 \| 0.6967 \| \| 0.2817 \| 25.68 \| 9500 \| 0.5215 \| 0.6878 \| 0.7959 \| 0.8738 \| 0.9336 \| 0.8251 \| 0.8433 \| 0.4297 \| 0.9011 \| 0.8513 \| 0.7875 \| 0.8663 \| 0.6666 \| 0.7304 \| 0.3412 \| 0.7889 \| 0.6801 \| 0.7408 \| \| 0.2824 \| 25.73 \| 9520 \| 0.5074 \| 0.6917 \| 0.8034 \| 0.8757 \| 0.9264 \| 0.8140 \| 0.8525 \| 0.4297 \| 0.8823 \| 0.8657 \| 0.8532 \| 0.8594 \| 0.6615 \| 0.7304 \| 0.3441 \| 0.7954 \| 0.6741 \| 0.7768 \| \| 0.1215 \| 25.78 \| 9540 \| 0.5113 \| 0.6956 \| 0.8073 \| 0.8768 \| 0.9241 \| 0.8304 \| 0.8311 \| 0.4732 \| 0.8996 \| 0.8544 \| 0.8380 \| 0.8596 \| 0.6533 \| 0.7364 \| 0.3791 \| 0.8016 \| 0.6742 \| 0.7651 \| \| 0.2846 \| 25.84 \| 9560 \| 0.5507 \| 0.6867 \| 0.7933 \| 0.8711 \| 0.9280 \| 0.8186 \| 0.8677 \| 0.4674 \| 0.9230 \| 0.8247 \| 0.7234 \| 0.8678 \| 0.6740 \| 0.7375 \| 0.3711 \| 0.7854 \| 0.6774 \| 0.6935 \| \| 0.4948 \| 25.89 \| 9580 \| 0.5195 \| 0.6907 \| 0.7986 \| 0.8729 \| 0.9382 \| 0.8081 \| 0.8478 \| 0.4941 \| 0.9044 \| 0.8507 \| 0.7467 \| 0.8678 \| 0.6684 \| 0.7407 \| 0.3734 \| 0.7855 \| 0.6878 \| 0.7113 \| \| 0.3136 \| 25.95 \| 9600 \| 0.5447 \| 0.6889 \| 0.7947 \| 0.8721 \| 0.9387 \| 0.8271 \| 0.8304 \| 0.4608 \| 0.9087 \| 0.8594 \| 0.7375 \| 0.8647 \| 0.6735 \| 0.7420 \| 0.3706 \| 0.7852 \| 0.6748 \| 0.7117 \| \| 0.1665 \| 26.0 \| 9620 \| 0.5755 \| 0.6885 \| 0.7950 \| 0.8711 \| 0.9373 \| 0.8130 \| 0.8596 \| 0.4627 \| 0.9026 \| 0.8602 \| 0.7300 \| 0.8679 \| 0.6796 \| 0.7482 \| 0.3707 \| 0.7794 \| 0.6681 \| 0.7055 \| \| 0.0865 \| 26.05 \| 9640 \| 0.5778 \| 0.6861 \| 0.7970 \| 0.8684 \| 0.9328 \| 0.7874 \| 0.8652 \| 0.4998 \| 0.8920 \| 0.8654 \| 0.7365 \| 0.8684 \| 0.6677 \| 0.7499 \| 0.3870 \| 0.7731 \| 0.6623 \| 0.6941 \| \| 0.1036 \| 26.11 \| 9660 \| 0.5929 \| 0.6828 \| 0.7923 \| 0.8666 \| 0.9384 \| 0.7990 \| 0.8381 \| 0.4884 \| 0.8910 \| 0.8574 \| 0.7335 \| 0.8626 \| 0.6687 \| 0.7389 \| 0.3858 \| 0.7744 \| 0.6637 \| 0.6854 \| \| 0.2062 \| 26.16 \| 9680 \| 0.6145 \| 0.6766 \| 0.7881 \| 0.8658 \| 0.9364 \| 0.8016 \| 0.8480 \| 0.4344 \| 0.8859 \| 0.8719 \| 0.7387 \| 0.8637 \| 0.6647 \| 0.7360 \| 0.3431 \| 0.7736 \| 0.6675 \| 0.6879 \| \| 0.2066 \| 26.22 \| 9700 \| 0.6302 \| 0.6749 \| 0.7899 \| 0.8650 \| 0.9310 \| 0.7961 \| 0.8787 \| 0.4181 \| 0.8760 \| 0.8837 \| 0.7460 \| 0.8641 \| 0.6654 \| 0.7349 \| 0.3311 \| 0.7713 \| 0.6681 \| 0.6891 \| \| 0.3122 \| 26.27 \| 9720 \| 0.5919 \| 0.6790 \| 0.7833 \| 0.8695 \| 0.9322 \| 0.7807 \| 0.8695 \| 0.3970 \| 0.9022 \| 0.8424 \| 0.7595 \| 0.8660 \| 0.6666 \| 0.7363 \| 0.3249 \| 0.7820 \| 0.6801 \| 0.6968 \| \| 0.2804 \| 26.32 \| 9740 \| 0.5657 \| 0.6853 \| 0.7938 \| 0.8715 \| 0.9325 \| 0.8105 \| 0.8793 \| 0.4290 \| 0.8968 \| 0.8425 \| 0.7658 \| 0.8653 \| 0.6692 \| 0.7429 \| 0.3469 \| 0.7857 \| 0.6797 \| 0.7073 \| \| 0.3325 \| 26.38 \| 9760 \| 0.5954 \| 0.6892 \| 0.8015 \| 0.8691 \| 0.9321 \| 0.8292 \| 0.8377 \| 0.5225 \| 0.9006 \| 0.8510 \| 0.7375 \| 0.8623 \| 0.6595 \| 0.7504 \| 0.4156 \| 0.7805 \| 0.6716 \| 0.6847 \| \| 0.6275 \| 26.43 \| 9780 \| 0.5847 \| 0.6860 \| 0.7970 \| 0.8684 \| 0.9342 \| 0.8112 \| 0.8649 \| 0.4878 \| 0.8961 \| 0.8643 \| 0.7204 \| 0.8665 \| 0.6660 \| 0.7513 \| 0.3979 \| 0.7769 \| 0.6660 \| 0.6776 \| \| 0.3794 \| 26.49 \| 9800 \| 0.5874 \| 0.6800 \| 0.7975 \| 0.8655 \| 0.9268 \| 0.8132 \| 0.8603 \| 0.4910 \| 0.8874 \| 0.8596 \| 0.7441 \| 0.8640 \| 0.6618 \| 0.7541 \| 0.3529 \| 0.7702 \| 0.6686 \| 0.6883 \| \| 0.3265 \| 26.54 \| 9820 \| 0.5431 \| 0.6868 \| 0.7962 \| 0.8708 \| 0.9312 \| 0.8130 \| 0.8648 \| 0.4739 \| 0.9030 \| 0.8283 \| 0.7593 \| 0.8628 \| 0.6600 \| 0.7500 \| 0.3676 \| 0.7845 \| 0.6808 \| 0.7021 \| \| 0.2163 \| 26.59 \| 9840 \| 0.5910 \| 0.6866 \| 0.7966 \| 0.8696 \| 0.9273 \| 0.8196 \| 0.8565 \| 0.4767 \| 0.9066 \| 0.8479 \| 0.7419 \| 0.8645 \| 0.6613 \| 0.7493 \| 0.3847 \| 0.7799 \| 0.6765 \| 0.6901 \| \| 0.2469 \| 26.65 \| 9860 \| 0.6472 \| 0.6810 \| 0.7872 \| 0.8665 \| 0.9281 \| 0.8071 \| 0.8293 \| 0.4536 \| 0.9103 \| 0.8488 \| 0.7333 \| 0.8567 \| 0.6656 \| 0.7254 \| 0.3819 \| 0.7806 \| 0.6762 \| 0.6803 \| \| 0.2078 \| 26.7 \| 9880 \| 0.5434 \| 0.6868 \| 0.7946 \| 0.8705 \| 0.9330 \| 0.8069 \| 0.8265 \| 0.4754 \| 0.8997 \| 0.8422 \| 0.7785 \| 0.8561 \| 0.6640 \| 0.7201 \| 0.3839 \| 0.7911 \| 0.6748 \| 0.7178 \| \| 0.2536 \| 26.76 \| 9900 \| 0.5317 \| 0.6955 \| 0.8006 \| 0.8779 \| 0.9326 \| 0.8000 \| 0.8603 \| 0.4675 \| 0.9055 \| 0.8162 \| 0.8222 \| 0.8635 \| 0.6658 \| 0.7390 \| 0.3615 \| 0.8027 \| 0.6718 \| 0.7643 \| \| 0.1666 \| 26.81 \| 9920 \| 0.5297 \| 0.6903 \| 0.7972 \| 0.8721 \| 0.9321 \| 0.7830 \| 0.8653 \| 0.4971 \| 0.9042 \| 0.8388 \| 0.7595 \| 0.8693 \| 0.6671 \| 0.7499 \| 0.3844 \| 0.7824 \| 0.6657 \| 0.7136 \| \| 0.3875 \| 26.86 \| 9940 \| 0.6049 \| 0.6835 \| 0.7915 \| 0.8701 \| 0.9437 \| 0.8191 \| 0.8704 \| 0.4413 \| 0.8953 \| 0.8433 \| 0.7278 \| 0.8678 \| 0.6791 \| 0.7416 \| 0.3449 \| 0.7801 \| 0.6683 \| 0.7030 \| \| 0.1804 \| 26.92 \| 9960 \| 0.5804 \| 0.6904 \| 0.7902 \| 0.8738 \| 0.9399 \| 0.8023 \| 0.8600 \| 0.4524 \| 0.9183 \| 0.8191 \| 0.7393 \| 0.8686 \| 0.6755 \| 0.7466 \| 0.3702 \| 0.7885 \| 0.6844 \| 0.6988 \| \| 0.5588 \| 26.97 \| 9980 \| 0.5892 \| 0.6911 \| 0.7966 \| 0.8728 \| 0.9391 \| 0.8084 \| 0.8569 \| 0.4798 \| 0.9074 \| 0.8526 \| 0.7320 \| 0.8699 \| 0.6801 \| 0.7471 \| 0.3797 \| 0.7849 \| 0.6690 \| 0.7068 \| \| 0.2821 \| 27.03 \| 10000 \| 0.6022 \| 0.6881 \| 0.7945 \| 0.8725 \| 0.9408 \| 0.8337 \| 0.8606 \| 0.4497 \| 0.9088 \| 0.8421 \| 0.7255 \| 0.8673 \| 0.6786 \| 0.7456 \| 0.3603 \| 0.7874 \| 0.6745 \| 0.7029 \| \| 0.3519 \| 27.08 \| 10020 \| 0.5578 \| 0.6892 \| 0.7975 \| 0.8707 \| 0.9388 \| 0.8167 \| 0.8569 \| 0.4875 \| 0.8977 \| 0.8465 \| 0.7385 \| 0.8657 \| 0.6727 \| 0.7516 \| 0.3869 \| 0.7808 \| 0.6718 \| 0.6952 \| \| 0.1704 \| 27.14 \| 10040 \| 0.5708 \| 0.6861 \| 0.7956 \| 0.8689 \| 0.9361 \| 0.8084 \| 0.8535 \| 0.4892 \| 0.8961 \| 0.8440 \| 0.7416 \| 0.8657 \| 0.6736 \| 0.7454 \| 0.3780 \| 0.7770 \| 0.6687 \| 0.6943 \| \| 0.3597 \| 27.19 \| 10060 \| 0.5695 \| 0.6876 \| 0.7976 \| 0.8695 \| 0.9339 \| 0.7896 \| 0.8656 \| 0.4934 \| 0.8918 \| 0.8648 \| 0.7440 \| 0.8703 \| 0.6722 \| 0.7468 \| 0.3854 \| 0.7742 \| 0.6671 \| 0.6975 \| \| 0.3913 \| 27.24 \| 10080 \| 0.5464 \| 0.6787 \| 0.7917 \| 0.8608 \| 0.9082 \| 0.8047 \| 0.8496 \| 0.4676 \| 0.8936 \| 0.8591 \| 0.7592 \| 0.8406 \| 0.6733 \| 0.7477 \| 0.3879 \| 0.7782 \| 0.6667 \| 0.6563 \| \| 0.3059 \| 27.3 \| 10100 \| 0.5639 \| 0.6874 \| 0.7933 \| 0.8717 \| 0.9390 \| 0.8386 \| 0.8589 \| 0.4450 \| 0.9122 \| 0.8430 \| 0.7161 \| 0.8675 \| 0.6804 \| 0.7375 \| 0.3737 \| 0.7863 \| 0.6743 \| 0.6922 \| \| 0.2716 \| 27.35 \| 10120 \| 0.5449 \| 0.6894 \| 0.7902 \| 0.8746 \| 0.9436 \| 0.8183 \| 0.8533 \| 0.4371 \| 0.9208 \| 0.8367 \| 0.7217 \| 0.8678 \| 0.6694 \| 0.7321 \| 0.3694 \| 0.7947 \| 0.6974 \| 0.6953 \| \| 0.6047 \| 27.41 \| 10140 \| 0.5346 \| 0.6943 \| 0.7947 \| 0.8758 \| 0.9383 \| 0.8108 \| 0.8538 \| 0.4517 \| 0.9177 \| 0.8405 \| 0.7500 \| 0.8708 \| 0.6828 \| 0.7374 \| 0.3747 \| 0.7913 \| 0.6854 \| 0.7174 \| \| 0.151 \| 27.46 \| 10160 \| 0.5572 \| 0.6890 \| 0.7934 \| 0.8717 \| 0.9355 \| 0.8047 \| 0.8545 \| 0.4600 \| 0.9073 \| 0.8513 \| 0.7409 \| 0.8698 \| 0.6781 \| 0.7413 \| 0.3766 \| 0.7808 \| 0.6734 \| 0.7026 \| \| 0.1925 \| 27.51 \| 10180 \| 0.5290 \| 0.6971 \| 0.8094 \| 0.8775 \| 0.9312 \| 0.8126 \| 0.8643 \| 0.4669 \| 0.8834 \| 0.8820 \| 0.8253 \| 0.8688 \| 0.6679 \| 0.7391 \| 0.3718 \| 0.7955 \| 0.6638 \| 0.7726 \| \| 0.2092 \| 27.57 \| 10200 \| 0.5235 \| 0.6899 \| 0.7992 \| 0.8743 \| 0.9368 \| 0.8169 \| 0.8616 \| 0.4508 \| 0.8914 \| 0.8400 \| 0.7967 \| 0.8674 \| 0.6691 \| 0.7364 \| 0.3548 \| 0.7895 \| 0.6677 \| 0.7446 \| \| 0.1342 \| 27.62 \| 10220 \| 0.5449 \| 0.6871 \| 0.7969 \| 0.8709 \| 0.9362 \| 0.8072 \| 0.8619 \| 0.4596 \| 0.8907 \| 0.8692 \| 0.7537 \| 0.8680 \| 0.6829 \| 0.7420 \| 0.3525 \| 0.7800 \| 0.6626 \| 0.7218 \| \| 0.2301 \| 27.68 \| 10240 \| 0.5375 \| 0.6890 \| 0.8043 \| 0.8713 \| 0.9345 \| 0.8359 \| 0.8612 \| 0.4908 \| 0.8915 \| 0.8715 \| 0.7447 \| 0.8686 \| 0.6741 \| 0.7481 \| 0.3662 \| 0.7806 \| 0.6661 \| 0.7193 \| \| 0.1528 \| 27.73 \| 10260 \| 0.5133 \| 0.6878 \| 0.7960 \| 0.8723 \| 0.9392 \| 0.8050 \| 0.8680 \| 0.4585 \| 0.8929 \| 0.8435 \| 0.7649 \| 0.8703 \| 0.6833 \| 0.7429 \| 0.3345 \| 0.7807 \| 0.6716 \| 0.7310 \| \| 0.1313 \| 27.78 \| 10280 \| 0.5783 \| 0.6771 \| 0.7860 \| 0.8671 \| 0.9423 \| 0.8021 \| 0.8589 \| 0.4187 \| 0.8882 \| 0.8644 \| 0.7276 \| 0.8679 \| 0.6851 \| 0.7331 \| 0.3114 \| 0.7731 \| 0.6649 \| 0.7039 \| \| 0.0945 \| 27.84 \| 10300 \| 0.5292 \| 0.6898 \| 0.7973 \| 0.8734 \| 0.9356 \| 0.8261 \| 0.8556 \| 0.4516 \| 0.9036 \| 0.8470 \| 0.7613 \| 0.8689 \| 0.6769 \| 0.7382 \| 0.3610 \| 0.7861 \| 0.6681 \| 0.7293 \| \| 0.1894 \| 27.89 \| 10320 \| 0.5126 \| 0.6927 \| 0.8039 \| 0.8739 \| 0.9350 \| 0.8048 \| 0.8570 \| 0.4898 \| 0.8885 \| 0.8658 \| 0.7861 \| 0.8686 \| 0.6668 \| 0.7428 \| 0.3762 \| 0.7854 \| 0.6741 \| 0.7351 \| \| 0.494 \| 27.95 \| 10340 \| 0.5499 \| 0.6927 \| 0.8089 \| 0.8733 \| 0.9358 \| 0.7857 \| 0.8717 \| 0.5165 \| 0.8665 \| 0.8672 \| 0.8185 \| 0.8648 \| 0.6660 \| 0.7457 \| 0.3595 \| 0.7839 \| 0.6729 \| 0.7561 \| \| 0.1046 \| 28.0 \| 10360 \| 0.5496 \| 0.6962 \| 0.8084 \| 0.8731 \| 0.9251 \| 0.7879 \| 0.8410 \| 0.5496 \| 0.8945 \| 0.8596 \| 0.8012 \| 0.8606 \| 0.6626 \| 0.7457 \| 0.4125 \| 0.7902 \| 0.6745 \| 0.7276 \| \| 0.196 \| 28.05 \| 10380 \| 0.6126 \| 0.6778 \| 0.7928 \| 0.8640 \| 0.9287 \| 0.8225 \| 0.8503 \| 0.5001 \| 0.9049 \| 0.8465 \| 0.6965 \| 0.8619 \| 0.6751 \| 0.7386 \| 0.3830 \| 0.7784 \| 0.6479 \| 0.6597 \| \| 0.5045 \| 28.11 \| 10400 \| 0.5943 \| 0.6726 \| 0.7889 \| 0.8614 \| 0.9384 \| 0.8178 \| 0.8455 \| 0.5025 \| 0.8972 \| 0.8583 \| 0.6626 \| 0.8610 \| 0.6741 \| 0.7375 \| 0.3788 \| 0.7771 \| 0.6459 \| 0.6336 \| \| 0.1568 \| 28.16 \| 10420 \| 0.5737 \| 0.6751 \| 0.7883 \| 0.8654 \| 0.9394 \| 0.8093 \| 0.8596 \| 0.4498 \| 0.8935 \| 0.8680 \| 0.6983 \| 0.8628 \| 0.6736 \| 0.7297 \| 0.3467 \| 0.7823 \| 0.6611 \| 0.6698 \| \| 0.2164 \| 28.22 \| 10440 \| 0.5116 \| 0.6823 \| 0.7975 \| 0.8708 \| 0.9333 \| 0.8081 \| 0.8632 \| 0.4418 \| 0.8809 \| 0.8672 \| 0.7880 \| 0.8710 \| 0.6666 \| 0.7358 \| 0.3057 \| 0.7778 \| 0.6720 \| 0.7472 \| \| 0.2651 \| 28.27 \| 10460 \| 0.5385 \| 0.6866 \| 0.7999 \| 0.8708 \| 0.9318 \| 0.8073 \| 0.8674 \| 0.4666 \| 0.8846 \| 0.8628 \| 0.7789 \| 0.8699 \| 0.6695 \| 0.7466 \| 0.3412 \| 0.7748 \| 0.6752 \| 0.7286 \| \| 0.1581 \| 28.32 \| 10480 \| 0.5258 \| 0.7004 \| 0.8087 \| 0.8776 \| 0.9289 \| 0.7974 \| 0.8575 \| 0.4937 \| 0.8935 \| 0.8703 \| 0.8197 \| 0.8649 \| 0.6687 \| 0.7515 \| 0.3923 \| 0.7967 \| 0.6763 \| 0.7522 \| \| 0.161 \| 28.38 \| 10500 \| 0.5039 \| 0.6967 \| 0.8080 \| 0.8749 \| 0.9339 \| 0.7980 \| 0.8717 \| 0.5228 \| 0.8884 \| 0.8471 \| 0.7941 \| 0.8685 \| 0.6661 \| 0.7482 \| 0.3973 \| 0.7868 \| 0.6734 \| 0.7366 \| \| 0.2177 \| 28.43 \| 10520 \| 0.5610 \| 0.6881 \| 0.8035 \| 0.8698 \| 0.9305 \| 0.8322 \| 0.8692 \| 0.5056 \| 0.8966 \| 0.8588 \| 0.7318 \| 0.8702 \| 0.6773 \| 0.7482 \| 0.3806 \| 0.7772 \| 0.6648 \| 0.6985 \| \| 0.1825 \| 28.49 \| 10540 \| 0.5600 \| 0.6819 \| 0.7925 \| 0.8669 \| 0.9374 \| 0.8129 \| 0.8308 \| 0.4943 \| 0.8953 \| 0.8302 \| 0.7467 \| 0.8687 \| 0.6701 \| 0.7466 \| 0.3572 \| 0.7675 \| 0.6661 \| 0.6971 \| \| 0.3412 \| 28.54 \| 10560 \| 0.5749 \| 0.6811 \| 0.7952 \| 0.8669 \| 0.9277 \| 0.8007 \| 0.8613 \| 0.4673 \| 0.8851 \| 0.8556 \| 0.7684 \| 0.8639 \| 0.6714 \| 0.7460 \| 0.3381 \| 0.7720 \| 0.6707 \| 0.7060 \| \| 0.1687 \| 28.59 \| 10580 \| 0.5676 \| 0.6847 \| 0.8014 \| 0.8683 \| 0.9322 \| 0.7985 \| 0.8455 \| 0.5046 \| 0.8758 \| 0.8706 \| 0.7828 \| 0.8638 \| 0.6689 \| 0.7457 \| 0.3469 \| 0.7749 \| 0.6728 \| 0.7199 \| \| 0.2223 \| 28.65 \| 10600 \| 0.5701 \| 0.6774 \| 0.7878 \| 0.8648 \| 0.9342 \| 0.8000 \| 0.8401 \| 0.4748 \| 0.8960 \| 0.8328 \| 0.7369 \| 0.8625 \| 0.6639 \| 0.7426 \| 0.3513 \| 0.7699 \| 0.6685 \| 0.6830 \| \| 0.1686 \| 28.7 \| 10620 \| 0.5562 \| 0.6936 \| 0.8015 \| 0.8732 \| 0.9310 \| 0.7666 \| 0.8577 \| 0.5019 \| 0.8869 \| 0.8590 \| 0.8072 \| 0.8607 \| 0.6638 \| 0.7470 \| 0.3897 \| 0.7890 \| 0.6721 \| 0.7326 \| \| 0.1468 \| 28.76 \| 10640 \| 0.5762 \| 0.6844 \| 0.7940 \| 0.8680 \| 0.9349 \| 0.7952 \| 0.8749 \| 0.4870 \| 0.8933 \| 0.8312 \| 0.7411 \| 0.8633 \| 0.6666 \| 0.7430 \| 0.3805 \| 0.7766 \| 0.6717 \| 0.6888 \| \| 0.2564 \| 28.81 \| 10660 \| 0.5444 \| 0.7031 \| 0.8104 \| 0.8793 \| 0.9220 \| 0.7982 \| 0.8617 \| 0.4997 \| 0.9015 \| 0.8416 \| 0.8484 \| 0.8618 \| 0.6682 \| 0.7459 \| 0.3963 \| 0.8024 \| 0.6746 \| 0.7727 \| \| 0.1094 \| 28.86 \| 10680 \| 0.5362 \| 0.6983 \| 0.8109 \| 0.8771 \| 0.9224 \| 0.7900 \| 0.8721 \| 0.5041 \| 0.8903 \| 0.8609 \| 0.8362 \| 0.8627 \| 0.6654 \| 0.7475 \| 0.3752 \| 0.7983 \| 0.6734 \| 0.7654 \| \| 0.2855 \| 28.92 \| 10700 \| 0.5108 \| 0.6925 \| 0.8033 \| 0.8755 \| 0.9335 \| 0.7491 \| 0.8749 \| 0.4847 \| 0.8740 \| 0.8681 \| 0.8390 \| 0.8638 \| 0.6534 \| 0.7505 \| 0.3432 \| 0.7913 \| 0.6772 \| 0.7683 \| \| 0.121 \| 28.97 \| 10720 \| 0.5407 \| 0.6888 \| 0.8004 \| 0.8717 \| 0.9329 \| 0.8138 \| 0.8687 \| 0.4840 \| 0.8944 \| 0.8419 \| 0.7670 \| 0.8660 \| 0.6689 \| 0.7485 \| 0.3754 \| 0.7861 \| 0.6645 \| 0.7124 \| \| 0.5389 \| 29.03 \| 10740 \| 0.5872 \| 0.6803 \| 0.7784 \| 0.8701 \| 0.9412 \| 0.7895 \| 0.8622 \| 0.3998 \| 0.9191 \| 0.8209 \| 0.7163 \| 0.8677 \| 0.6793 \| 0.7382 \| 0.3383 \| 0.7835 \| 0.6663 \| 0.6887 \| \| 0.2407 \| 29.08 \| 10760 \| 0.5927 \| 0.6761 \| 0.7843 \| 0.8656 \| 0.9352 \| 0.8173 \| 0.8767 \| 0.4338 \| 0.9091 \| 0.8260 \| 0.6917 \| 0.8661 \| 0.6843 \| 0.7311 \| 0.3475 \| 0.7762 \| 0.6627 \| 0.6652 \| \| 0.0844 \| 29.14 \| 10780 \| 0.5656 \| 0.6827 \| 0.7954 \| 0.8672 \| 0.9407 \| 0.8211 \| 0.8714 \| 0.4765 \| 0.8884 \| 0.8615 \| 0.7078 \| 0.8616 \| 0.6856 \| 0.7376 \| 0.3630 \| 0.7793 \| 0.6693 \| 0.6826 \| \| 0.3771 \| 29.19 \| 10800 \| 0.5372 \| 0.6878 \| 0.7945 \| 0.8720 \| 0.9441 \| 0.8078 \| 0.8704 \| 0.4439 \| 0.8889 \| 0.8554 \| 0.7510 \| 0.8645 \| 0.6810 \| 0.7412 \| 0.3544 \| 0.7860 \| 0.6720 \| 0.7154 \| \| 1.0276 \| 29.24 \| 10820 \| 0.5559 \| 0.6891 \| 0.8020 \| 0.8731 \| 0.9295 \| 0.7942 \| 0.8719 \| 0.4453 \| 0.8755 \| 0.8764 \| 0.8212 \| 0.8604 \| 0.6669 \| 0.7405 \| 0.3507 \| 0.7921 \| 0.6630 \| 0.7501 \| \| 0.2003 \| 29.3 \| 10840 \| 0.6174 \| 0.6756 \| 0.7818 \| 0.8656 \| 0.9368 \| 0.8194 \| 0.8466 \| 0.4171 \| 0.9106 \| 0.8493 \| 0.6932 \| 0.8639 \| 0.6785 \| 0.7393 \| 0.3413 \| 0.7760 \| 0.6667 \| 0.6636 \| \| 0.0898 \| 29.35 \| 10860 \| 0.6115 \| 0.6727 \| 0.7790 \| 0.8647 \| 0.9395 \| 0.7981 \| 0.8618 \| 0.4070 \| 0.9027 \| 0.8527 \| 0.6911 \| 0.8636 \| 0.6768 \| 0.7372 \| 0.3321 \| 0.7756 \| 0.6629 \| 0.6608 \| \| 0.2334 \| 29.41 \| 10880 \| 0.6047 \| 0.6724 \| 0.7800 \| 0.8653 \| 0.9387 \| 0.8099 \| 0.8712 \| 0.3895 \| 0.9004 \| 0.8511 \| 0.6989 \| 0.8659 \| 0.6842 \| 0.7330 \| 0.3197 \| 0.7772 \| 0.6620 \| 0.6649 \| \| 0.0804 \| 29.46 \| 10900 \| 0.5706 \| 0.6802 \| 0.7888 \| 0.8679 \| 0.9416 \| 0.7941 \| 0.8703 \| 0.4362 \| 0.8853 \| 0.8550 \| 0.7389 \| 0.8680 \| 0.6767 \| 0.7409 \| 0.3356 \| 0.7729 \| 0.6741 \| 0.6933 \| \| 0.1789 \| 29.51 \| 10920 \| 0.5623 \| 0.6849 \| 0.7960 \| 0.8688 \| 0.9329 \| 0.8047 \| 0.8644 \| 0.4843 \| 0.8936 \| 0.8412 \| 0.7512 \| 0.8691 \| 0.6729 \| 0.7498 \| 0.3684 \| 0.7749 \| 0.6640 \| 0.6956 \| \| 0.1435 \| 29.57 \| 10940 \| 0.5850 \| 0.6812 \| 0.7944 \| 0.8671 \| 0.9321 \| 0.8034 \| 0.8794 \| 0.4527 \| 0.8837 \| 0.8672 \| 0.7426 \| 0.8679 \| 0.6755 \| 0.7446 \| 0.3683 \| 0.7766 \| 0.6464 \| 0.6892 \| \| 0.2155 \| 29.62 \| 10960 \| 0.6244 \| 0.6839 \| 0.7895 \| 0.8685 \| 0.9388 \| 0.7877 \| 0.8621 \| 0.4645 \| 0.9001 \| 0.8552 \| 0.7180 \| 0.8682 \| 0.6809 \| 0.7439 \| 0.3787 \| 0.7786 \| 0.6521 \| 0.6850 \| \| 0.2224 \| 29.68 \| 10980 \| 0.6111 \| 0.6886 \| 0.7970 \| 0.8692 \| 0.9322 \| 0.7919 \| 0.8653 \| 0.5128 \| 0.9025 \| 0.8449 \| 0.7296 \| 0.8696 \| 0.6832 \| 0.7454 \| 0.3983 \| 0.7768 \| 0.6573 \| 0.6897 \| \| 0.1149 \| 29.73 \| 11000 \| 0.5952 \| 0.6806 \| 0.7983 \| 0.8665 \| 0.9384 \| 0.8183 \| 0.8729 \| 0.4753 \| 0.8747 \| 0.8806 \| 0.7282 \| 0.8712 \| 0.6840 \| 0.7324 \| 0.3515 \| 0.7705 \| 0.6608 \| 0.6935 \| \| 0.1818 \| 29.78 \| 11020 \| 0.5987 \| 0.6814 \| 0.7965 \| 0.8671 \| 0.9375 \| 0.8148 \| 0.8667 \| 0.4696 \| 0.8807 \| 0.8742 \| 0.7321 \| 0.8725 \| 0.6840 \| 0.7402 \| 0.3450 \| 0.7688 \| 0.6630 \| 0.6959 \| \| 0.1756 \| 29.84 \| 11040 \| 0.5670 \| 0.6774 \| 0.7905 \| 0.8668 \| 0.9377 \| 0.7966 \| 0.8599 \| 0.4413 \| 0.8791 \| 0.8631 \| 0.7559 \| 0.8693 \| 0.6710 \| 0.7368 \| 0.3171 \| 0.7699 \| 0.6703 \| 0.7072 \| \| 0.1492 \| 29.89 \| 11060 \| 0.5367 \| 0.6823 \| 0.7962 \| 0.8708 \| 0.9361 \| 0.8053 \| 0.8733 \| 0.4252 \| 0.8708 \| 0.8506 \| 0.8121 \| 0.8690 \| 0.6733 \| 0.7339 \| 0.2975 \| 0.7764 \| 0.6697 \| 0.7562 \| \| 0.1751 \| 29.95 \| 11080 \| 0.5206 \| 0.6859 \| 0.7955 \| 0.8723 \| 0.9391 \| 0.8241 \| 0.8631 \| 0.4284 \| 0.8892 \| 0.8497 \| 0.7746 \| 0.8672 \| 0.6816 \| 0.7384 \| 0.3159 \| 0.7823 \| 0.6747 \| 0.7412 \| \| 1.3064 \| 30.0 \| 11100 \| 0.5996 \| 0.6743 \| 0.7801 \| 0.8670 \| 0.9437 \| 0.8023 \| 0.8694 \| 0.4123 \| 0.9054 \| 0.8260 \| 0.7016 \| 0.8651 \| 0.6754 \| 0.7186 \| 0.3279 \| 0.7822 \| 0.6724 \| 0.6786 \| \| 0.3603 \| 30.05 \| 11120 \| 0.6017 \| 0.6762 \| 0.7899 \| 0.8670 \| 0.9342 \| 0.8223 \| 0.8725 \| 0.4273 \| 0.8954 \| 0.8571 \| 0.7206 \| 0.8687 \| 0.6756 \| 0.7215 \| 0.3337 \| 0.7795 \| 0.6623 \| 0.6918 \| \| 0.2207 \| 30.11 \| 11140 \| 0.5756 \| 0.6827 \| 0.7851 \| 0.8719 \| 0.9386 \| 0.8051 \| 0.8549 \| 0.4086 \| 0.9150 \| 0.8366 \| 0.7368 \| 0.8692 \| 0.6788 \| 0.7276 \| 0.3323 \| 0.7877 \| 0.6754 \| 0.7082 \| \| 0.3663 \| 30.16 \| 11160 \| 0.5422 \| 0.6897 \| 0.7993 \| 0.8723 \| 0.9384 \| 0.8169 \| 0.8639 \| 0.4655 \| 0.8914 \| 0.8623 \| 0.7569 \| 0.8694 \| 0.6825 \| 0.7404 \| 0.3611 \| 0.7819 \| 0.6684 \| 0.7244 \| \| 0.1134 \| 30.22 \| 11180 \| 0.5514 \| 0.6917 \| 0.8059 \| 0.8723 \| 0.9295 \| 0.8160 \| 0.8783 \| 0.5033 \| 0.8933 \| 0.8629 \| 0.7581 \| 0.8684 \| 0.6767 \| 0.7455 \| 0.3758 \| 0.7831 \| 0.6704 \| 0.7219 \| \| 0.1779 \| 30.27 \| 11200 \| 0.6142 \| 0.6754 \| 0.7894 \| 0.8651 \| 0.9376 \| 0.8027 \| 0.8878 \| 0.4476 \| 0.8880 \| 0.8635 \| 0.6985 \| 0.8634 \| 0.6782 \| 0.7282 \| 0.3454 \| 0.7792 \| 0.6618 \| 0.6716 \| \| 0.1774 \| 30.32 \| 11220 \| 0.6221 \| 0.6834 \| 0.7954 \| 0.8677 \| 0.9317 \| 0.8304 \| 0.8640 \| 0.4816 \| 0.9048 \| 0.8428 \| 0.7128 \| 0.8604 \| 0.6786 \| 0.7377 \| 0.3705 \| 0.7826 \| 0.6754 \| 0.6785 \| \| 0.0996 \| 30.38 \| 11240 \| 0.5661 \| 0.6912 \| 0.7990 \| 0.8715 \| 0.9347 \| 0.8160 \| 0.8392 \| 0.4970 \| 0.9044 \| 0.8581 \| 0.7438 \| 0.8625 \| 0.6807 \| 0.7443 \| 0.3849 \| 0.7860 \| 0.6744 \| 0.7056 \| \| 0.1338 \| 30.43 \| 11260 \| 0.5457 \| 0.6903 \| 0.7959 \| 0.8731 \| 0.9344 \| 0.8023 \| 0.8667 \| 0.4449 \| 0.8995 \| 0.8613 \| 0.7622 \| 0.8651 \| 0.6802 \| 0.7445 \| 0.3611 \| 0.7879 \| 0.6738 \| 0.7193 \| \| 0.1228 \| 30.49 \| 11280 \| 0.5527 \| 0.6908 \| 0.7976 \| 0.8727 \| 0.9291 \| 0.7971 \| 0.8688 \| 0.4670 \| 0.9044 \| 0.8569 \| 0.7600 \| 0.8664 \| 0.6797 \| 0.7432 \| 0.3680 \| 0.7855 \| 0.6748 \| 0.7183 \| \| 0.1422 \| 30.54 \| 11300 \| 0.5595 \| 0.6907 \| 0.8010 \| 0.8712 \| 0.9351 \| 0.8085 \| 0.8636 \| 0.4905 \| 0.8920 \| 0.8701 \| 0.7472 \| 0.8652 \| 0.6841 \| 0.7470 \| 0.3814 \| 0.7839 \| 0.6653 \| 0.7078 \| \| 0.1277 \| 30.59 \| 11320 \| 0.5841 \| 0.6835 \| 0.7978 \| 0.8687 \| 0.9369 \| 0.7791 \| 0.8686 \| 0.5062 \| 0.8874 \| 0.8764 \| 0.7298 \| 0.8653 \| 0.6621 \| 0.7434 \| 0.3664 \| 0.7820 \| 0.6648 \| 0.7002 \| \| 0.3679 \| 30.65 \| 11340 \| 0.5968 \| 0.6760 \| 0.7887 \| 0.8655 \| 0.9362 \| 0.8030 \| 0.8731 \| 0.4540 \| 0.8889 \| 0.8327 \| 0.7330 \| 0.8673 \| 0.6802 \| 0.7292 \| 0.3118 \| 0.7687 \| 0.6675 \| 0.7075 \| \| 0.1493 \| 30.7 \| 11360 \| 0.5796 \| 0.6791 \| 0.7888 \| 0.8671 \| 0.9404 \| 0.8160 \| 0.8517 \| 0.4411 \| 0.8890 \| 0.8368 \| 0.7467 \| 0.8671 \| 0.6856 \| 0.7335 \| 0.3129 \| 0.7696 \| 0.6674 \| 0.7174 \| \| 0.1479 \| 30.76 \| 11380 \| 0.5848 \| 0.6839 \| 0.7953 \| 0.8691 \| 0.9403 \| 0.8206 \| 0.8591 \| 0.4617 \| 0.8880 \| 0.8564 \| 0.7407 \| 0.8673 \| 0.6859 \| 0.7356 \| 0.3426 \| 0.7764 \| 0.6680 \| 0.7117 \| \| 0.1328 \| 30.81 \| 11400 \| 0.5847 \| 0.6846 \| 0.7904 \| 0.8705 \| 0.9383 \| 0.8054 \| 0.8517 \| 0.4391 \| 0.9000 \| 0.8512 \| 0.7471 \| 0.8684 \| 0.6805 \| 0.7387 \| 0.3422 \| 0.7781 \| 0.6691 \| 0.7154 \| \| 0.2385 \| 30.86 \| 11420 \| 0.5694 \| 0.6856 \| 0.7946 \| 0.8709 \| 0.9389 \| 0.8115 \| 0.8643 \| 0.4473 \| 0.8931 \| 0.8599 \| 0.7470 \| 0.8689 \| 0.6834 \| 0.7410 \| 0.3447 \| 0.7813 \| 0.6652 \| 0.7147 \| \| 0.3155 \| 30.92 \| 11440 \| 0.5510 \| 0.6868 \| 0.7946 \| 0.8707 \| 0.9316 \| 0.7732 \| 0.8801 \| 0.4731 \| 0.8983 \| 0.8581 \| 0.7477 \| 0.8668 \| 0.6722 \| 0.7457 \| 0.3686 \| 0.7833 \| 0.6634 \| 0.7075 \| \| 0.1535 \| 30.97 \| 11460 \| 0.5864 \| 0.6868 \| 0.7928 \| 0.8701 \| 0.9311 \| 0.7825 \| 0.8671 \| 0.4806 \| 0.9058 \| 0.8358 \| 0.7467 \| 0.8659 \| 0.6644 \| 0.7488 \| 0.3787 \| 0.7789 \| 0.6664 \| 0.7042 \| \| 0.097 \| 31.03 \| 11480 \| 0.5224 \| 0.6941 \| 0.7999 \| 0.8750 \| 0.9330 \| 0.7874 \| 0.8563 \| 0.4869 \| 0.8984 \| 0.8317 \| 0.8057 \| 0.8648 \| 0.6619 \| 0.7487 \| 0.3868 \| 0.7914 \| 0.6641 \| 0.7413 \| \| 0.3133 \| 31.08 \| 11500 \| 0.5746 \| 0.6834 \| 0.7888 \| 0.8705 \| 0.9364 \| 0.8264 \| 0.8609 \| 0.3981 \| 0.9002 \| 0.8511 \| 0.7486 \| 0.8646 \| 0.6800 \| 0.7402 \| 0.3364 \| 0.7805 \| 0.6718 \| 0.7100 \| \| 0.0855 \| 31.14 \| 11520 \| 0.5645 \| 0.6903 \| 0.7952 \| 0.8723 \| 0.9404 \| 0.8164 \| 0.8666 \| 0.4516 \| 0.8986 \| 0.8471 \| 0.7453 \| 0.8673 \| 0.6853 \| 0.7445 \| 0.3686 \| 0.7816 \| 0.6715 \| 0.7131 \| \| 0.0744 \| 31.19 \| 11540 \| 0.5626 \| 0.6893 \| 0.7952 \| 0.8718 \| 0.9387 \| 0.8155 \| 0.8578 \| 0.4492 \| 0.8961 \| 0.8562 \| 0.7528 \| 0.8696 \| 0.6861 \| 0.7414 \| 0.3639 \| 0.7785 \| 0.6722 \| 0.7132 \| \| 0.2231 \| 31.24 \| 11560 \| 0.5724 \| 0.6874 \| 0.7940 \| 0.8699 \| 0.9358 \| 0.8015 \| 0.8501 \| 0.4769 \| 0.8988 \| 0.8444 \| 0.7507 \| 0.8685 \| 0.6683 \| 0.7460 \| 0.3839 \| 0.7751 \| 0.6713 \| 0.6982 \| \| 0.1109 \| 31.3 \| 11580 \| 0.6260 \| 0.6867 \| 0.7934 \| 0.8701 \| 0.9380 \| 0.7974 \| 0.8640 \| 0.4617 \| 0.8965 \| 0.8635 \| 0.7329 \| 0.8675 \| 0.6758 \| 0.7450 \| 0.3734 \| 0.7776 \| 0.6658 \| 0.7019 \| \| 1.0078 \| 31.35 \| 11600 \| 0.6212 \| 0.6856 \| 0.7888 \| 0.8703 \| 0.9424 \| 0.7945 \| 0.8673 \| 0.4464 \| 0.8997 \| 0.8385 \| 0.7329 \| 0.8675 \| 0.6846 \| 0.7420 \| 0.3578 \| 0.7787 \| 0.6654 \| 0.7027 \| \| 0.1772 \| 31.41 \| 11620 \| 0.6059 \| 0.6872 \| 0.7913 \| 0.8718 \| 0.9407 \| 0.8118 \| 0.8641 \| 0.4315 \| 0.9000 \| 0.8440 \| 0.7466 \| 0.8688 \| 0.6868 \| 0.7420 \| 0.3489 \| 0.7805 \| 0.6684 \| 0.7152 \| \| 0.1711 \| 31.46 \| 11640 \| 0.6070 \| 0.6863 \| 0.7947 \| 0.8700 \| 0.9350 \| 0.8271 \| 0.8735 \| 0.4440 \| 0.8963 \| 0.8411 \| 0.7457 \| 0.8645 \| 0.6850 \| 0.7380 \| 0.3600 \| 0.7794 \| 0.6686 \| 0.7084 \| \| 0.1414 \| 31.51 \| 11660 \| 0.5673 \| 0.6927 \| 0.7979 \| 0.8724 \| 0.9349 \| 0.8041 \| 0.8693 \| 0.4685 \| 0.8992 \| 0.8598 \| 0.7493 \| 0.8678 \| 0.6843 \| 0.7474 \| 0.3852 \| 0.7800 \| 0.6699 \| 0.7143 \| \| 0.2769 \| 31.57 \| 11680 \| 0.5566 \| 0.6923 \| 0.7990 \| 0.8727 \| 0.9313 \| 0.8129 \| 0.8601 \| 0.4634 \| 0.9009 \| 0.8642 \| 0.7603 \| 0.8669 \| 0.6812 \| 0.7465 \| 0.3811 \| 0.7832 \| 0.6681 \| 0.7188 \| \| 0.1607 \| 31.62 \| 11700 \| 0.5462 \| 0.6854 \| 0.7928 \| 0.8716 \| 0.9336 \| 0.8169 \| 0.8736 \| 0.4127 \| 0.8973 \| 0.8578 \| 0.7578 \| 0.8671 \| 0.6828 \| 0.7397 \| 0.3381 \| 0.7831 \| 0.6684 \| 0.7189 \| \| 0.4752 \| 31.68 \| 11720 \| 0.5840 \| 0.6771 \| 0.7857 \| 0.8678 \| 0.9354 \| 0.8028 \| 0.8720 \| 0.4040 \| 0.8940 \| 0.8490 \| 0.7424 \| 0.8708 \| 0.6820 \| 0.7388 \| 0.2953 \| 0.7704 \| 0.6695 \| 0.7128 \| \| 0.2187 \| 31.73 \| 11740 \| 0.5990 \| 0.6799 \| 0.7890 \| 0.8683 \| 0.9355 \| 0.8061 \| 0.8600 \| 0.4239 \| 0.8928 \| 0.8570 \| 0.7475 \| 0.8693 \| 0.6805 \| 0.7430 \| 0.3093 \| 0.7710 \| 0.6734 \| 0.7130 \| \| 0.2686 \| 31.78 \| 11760 \| 0.6126 \| 0.6797 \| 0.7898 \| 0.8681 \| 0.9337 \| 0.8209 \| 0.8567 \| 0.4304 \| 0.8976 \| 0.8441 \| 0.7454 \| 0.8700 \| 0.6787 \| 0.7415 \| 0.3112 \| 0.7701 \| 0.6702 \| 0.7161 \| \| 0.1204 \| 31.84 \| 11780 \| 0.6013 \| 0.6757 \| 0.7905 \| 0.8657 \| 0.9353 \| 0.8212 \| 0.8707 \| 0.4238 \| 0.8796 \| 0.8572 \| 0.7459 \| 0.8702 \| 0.6781 \| 0.7393 \| 0.2985 \| 0.7638 \| 0.6724 \| 0.7075 \| \| 0.1468 \| 31.89 \| 11800 \| 0.6401 \| 0.6777 \| 0.7915 \| 0.8665 \| 0.9385 \| 0.8270 \| 0.8623 \| 0.4304 \| 0.8835 \| 0.8695 \| 0.7291 \| 0.8679 \| 0.6817 \| 0.7453 \| 0.3132 \| 0.7690 \| 0.6639 \| 0.7032 \| \| 0.232 \| 31.95 \| 11820 \| 0.6002 \| 0.6923 \| 0.8004 \| 0.8712 \| 0.9326 \| 0.8146 \| 0.8547 \| 0.5084 \| 0.9039 \| 0.8416 \| 0.7468 \| 0.8673 \| 0.6807 \| 0.7504 \| 0.3936 \| 0.7786 \| 0.6689 \| 0.7065 \| \| 0.1691 \| 32.0 \| 11840 \| 0.5936 \| 0.6901 \| 0.8007 \| 0.8695 \| 0.9308 \| 0.8029 \| 0.8417 \| 0.5180 \| 0.8970 \| 0.8671 \| 0.7476 \| 0.8663 \| 0.6722 \| 0.7471 \| 0.4006 \| 0.7754 \| 0.6637 \| 0.7055 \| \| 0.2323 \| 32.05 \| 11860 \| 0.6082 \| 0.6801 \| 0.7981 \| 0.8658 \| 0.9372 \| 0.8277 \| 0.8615 \| 0.4913 \| 0.8800 \| 0.8580 \| 0.7308 \| 0.8672 \| 0.6782 \| 0.7418 \| 0.3405 \| 0.7674 \| 0.6603 \| 0.7052 \| \| 2.4548 \| 32.11 \| 11880 \| 0.6021 \| 0.6788 \| 0.7962 \| 0.8651 \| 0.9351 \| 0.8213 \| 0.8721 \| 0.4859 \| 0.8830 \| 0.8548 \| 0.7208 \| 0.8633 \| 0.6791 \| 0.7419 \| 0.3397 \| 0.7707 \| 0.6677 \| 0.6894 \| \| 0.2158 \| 32.16 \| 11900 \| 0.5693 \| 0.6823 \| 0.7965 \| 0.8679 \| 0.9374 \| 0.8008 \| 0.8740 \| 0.4830 \| 0.8836 \| 0.8587 \| 0.7379 \| 0.8674 \| 0.6773 \| 0.7381 \| 0.3467 \| 0.7750 \| 0.6658 \| 0.7058 \| \| 0.1927 \| 32.22 \| 11920 \| 0.5284 \| 0.6910 \| 0.8004 \| 0.8726 \| 0.9366 \| 0.8123 \| 0.8556 \| 0.4924 \| 0.8981 \| 0.8492 \| 0.7589 \| 0.8697 \| 0.6735 \| 0.7471 \| 0.3741 \| 0.7822 \| 0.6657 \| 0.7245 \| \| 0.2838 \| 32.27 \| 11940 \| 0.5619 \| 0.6909 \| 0.8074 \| 0.8702 \| 0.9284 \| 0.8075 \| 0.8623 \| 0.5499 \| 0.8952 \| 0.8694 \| 0.7393 \| 0.8664 \| 0.6686 \| 0.7547 \| 0.4031 \| 0.7818 \| 0.6570 \| 0.7049 \| \| 0.2922 \| 32.32 \| 11960 \| 0.5656 \| 0.6856 \| 0.8001 \| 0.8685 \| 0.9310 \| 0.8307 \| 0.8601 \| 0.4979 \| 0.9003 \| 0.8625 \| 0.7179 \| 0.8621 \| 0.6717 \| 0.7440 \| 0.3858 \| 0.7831 \| 0.6659 \| 0.6866 \| \| 0.1937 \| 32.38 \| 11980 \| 0.5836 \| 0.6818 \| 0.7950 \| 0.8690 \| 0.9286 \| 0.8243 \| 0.8696 \| 0.4326 \| 0.8948 \| 0.8685 \| 0.7468 \| 0.8660 \| 0.6764 \| 0.7286 \| 0.3389 \| 0.7805 \| 0.6751 \| 0.7068 \| \| 0.7299 \| 32.43 \| 12000 \| 0.5913 \| 0.6810 \| 0.7865 \| 0.8692 \| 0.9312 \| 0.8205 \| 0.8379 \| 0.4219 \| 0.9128 \| 0.8312 \| 0.7500 \| 0.8647 \| 0.6747 \| 0.7242 \| 0.3392 \| 0.7809 \| 0.6781 \| 0.7049 \| \| 0.1485 \| 32.49 \| 12020 \| 0.5512 \| 0.6889 \| 0.7972 \| 0.8717 \| 0.9347 \| 0.8325 \| 0.8413 \| 0.4624 \| 0.9041 \| 0.8560 \| 0.7495 \| 0.8646 \| 0.6797 \| 0.7385 \| 0.3651 \| 0.7850 \| 0.6777 \| 0.7117 \| \| 0.2181 \| 32.54 \| 12040 \| 0.5926 \| 0.6830 \| 0.7958 \| 0.8697 \| 0.9364 \| 0.8359 \| 0.8676 \| 0.4369 \| 0.8932 \| 0.8717 \| 0.7292 \| 0.8626 \| 0.6799 \| 0.7326 \| 0.3465 \| 0.7872 \| 0.6722 \| 0.6997 \| \| 0.1291 \| 32.59 \| 12060 \| 0.5869 \| 0.6820 \| 0.7940 \| 0.8692 \| 0.9371 \| 0.8218 \| 0.8700 \| 0.4356 \| 0.8916 \| 0.8759 \| 0.7264 \| 0.8644 \| 0.6830 \| 0.7377 \| 0.3383 \| 0.7844 \| 0.6696 \| 0.6964 \| \| 0.1469 \| 32.65 \| 12080 \| 0.5852 \| 0.6802 \| 0.7831 \| 0.8701 \| 0.9389 \| 0.7942 \| 0.8545 \| 0.3930 \| 0.9051 \| 0.8628 \| 0.7331 \| 0.8666 \| 0.6800 \| 0.7414 \| 0.3209 \| 0.7831 \| 0.6715 \| 0.6982 \| \| 0.263 \| 32.7 \| 12100 \| 0.5941 \| 0.6803 \| 0.7892 \| 0.8696 \| 0.9372 \| 0.8100 \| 0.8631 \| 0.4090 \| 0.8958 \| 0.8766 \| 0.7331 \| 0.8664 \| 0.6774 \| 0.7397 \| 0.3267 \| 0.7838 \| 0.6699 \| 0.6981 \| \| 1.5095 \| 32.76 \| 12120 \| 0.5723 \| 0.6848 \| 0.7946 \| 0.8715 \| 0.9285 \| 0.8299 \| 0.8757 \| 0.4208 \| 0.9067 \| 0.8592 \| 0.7414 \| 0.8675 \| 0.6780 \| 0.7456 \| 0.3376 \| 0.7858 \| 0.6736 \| 0.7052 \| \| 0.1808 \| 32.81 \| 12140 \| 0.5699 \| 0.6850 \| 0.7913 \| 0.8717 \| 0.9332 \| 0.8255 \| 0.8600 \| 0.4157 \| 0.9094 \| 0.8537 \| 0.7413 \| 0.8668 \| 0.6807 \| 0.7435 \| 0.3382 \| 0.7862 \| 0.6737 \| 0.7063 \| \| 0.235 \| 32.86 \| 12160 \| 0.5363 \| 0.6912 \| 0.7951 \| 0.8751 \| 0.9378 \| 0.7900 \| 0.8752 \| 0.4286 \| 0.8944 \| 0.8517 \| 0.7878 \| 0.8678 \| 0.6817 \| 0.7446 \| 0.3422 \| 0.7900 \| 0.6728 \| 0.7392 \| \| 1.1158 \| 32.92 \| 12180 \| 0.5303 \| 0.6872 \| 0.8004 \| 0.8718 \| 0.9367 \| 0.8376 \| 0.8672 \| 0.4419 \| 0.8821 \| 0.8578 \| 0.7795 \| 0.8657 \| 0.6833 \| 0.7439 \| 0.3319 \| 0.7831 \| 0.6705 \| 0.7317 \| \| 0.1304 \| 32.97 \| 12200 \| 0.5607 \| 0.6809 \| 0.7900 \| 0.8689 \| 0.9395 \| 0.8156 \| 0.8663 \| 0.4354 \| 0.8962 \| 0.8458 \| 0.7310 \| 0.8681 \| 0.6881 \| 0.7349 \| 0.3345 \| 0.7811 \| 0.6597 \| 0.7001 \| \| 0.2281 \| 33.03 \| 12220 \| 0.5931 \| 0.6836 \| 0.7949 \| 0.8671 \| 0.9305 \| 0.8126 \| 0.8581 \| 0.4859 \| 0.8990 \| 0.8518 \| 0.7267 \| 0.8656 \| 0.6799 \| 0.7423 \| 0.3775 \| 0.7750 \| 0.6616 \| 0.6833 \| \| 0.1557 \| 33.08 \| 12240 \| 0.5808 \| 0.6903 \| 0.7965 \| 0.8705 \| 0.9294 \| 0.8090 \| 0.8536 \| 0.4897 \| 0.9086 \| 0.8380 \| 0.7475 \| 0.8660 \| 0.6826 \| 0.7468 \| 0.3837 \| 0.7779 \| 0.6734 \| 0.7018 \| \| 0.6017 \| 33.14 \| 12260 \| 0.5959 \| 0.6844 \| 0.7954 \| 0.8695 \| 0.9363 \| 0.8391 \| 0.8658 \| 0.4471 \| 0.8974 \| 0.8538 \| 0.7281 \| 0.8639 \| 0.6804 \| 0.7448 \| 0.3591 \| 0.7825 \| 0.6646 \| 0.6957 \| \| 0.2142 \| 33.19 \| 12280 \| 0.6027 \| 0.6835 \| 0.7967 \| 0.8679 \| 0.9284 \| 0.8333 \| 0.8645 \| 0.4702 \| 0.9038 \| 0.8604 \| 0.7161 \| 0.8653 \| 0.6795 \| 0.7515 \| 0.3699 \| 0.7802 \| 0.6537 \| 0.6843 \| \| 0.1474 \| 33.24 \| 12300 \| 0.5862 \| 0.6777 \| 0.7958 \| 0.8650 \| 0.9330 \| 0.8353 \| 0.8693 \| 0.4818 \| 0.8901 \| 0.8462 \| 0.7150 \| 0.8669 \| 0.6789 \| 0.7468 \| 0.3554 \| 0.7752 \| 0.6441 \| 0.6768 \| \| 0.1682 \| 33.3 \| 12320 \| 0.5766 \| 0.6800 \| 0.7929 \| 0.8660 \| 0.9408 \| 0.7946 \| 0.8645 \| 0.4906 \| 0.8837 \| 0.8518 \| 0.7241 \| 0.8663 \| 0.6744 \| 0.7485 \| 0.3706 \| 0.7762 \| 0.6440 \| 0.6800 \| \| 0.1448 \| 33.35 \| 12340 \| 0.5752 \| 0.6824 \| 0.7950 \| 0.8667 \| 0.9330 \| 0.8126 \| 0.8599 \| 0.4810 \| 0.8927 \| 0.8587 \| 0.7270 \| 0.8643 \| 0.6770 \| 0.7509 \| 0.3804 \| 0.7793 \| 0.6449 \| 0.6800 \| \| 0.1857 \| 33.41 \| 12360 \| 0.6157 \| 0.6804 \| 0.7852 \| 0.8665 \| 0.9366 \| 0.7806 \| 0.8492 \| 0.4680 \| 0.9048 \| 0.8367 \| 0.7202 \| 0.8631 \| 0.6720 \| 0.7459 \| 0.3743 \| 0.7777 \| 0.6549 \| 0.6750 \| \| 0.1125 \| 33.46 \| 12380 \| 0.5973 \| 0.6810 \| 0.7933 \| 0.8663 \| 0.9359 \| 0.8104 \| 0.8734 \| 0.4840 \| 0.8948 \| 0.8440 \| 0.7105 \| 0.8599 \| 0.6790 \| 0.7413 \| 0.3753 \| 0.7828 \| 0.6546 \| 0.6740 \| \| 0.2302 \| 33.51 \| 12400 \| 0.5912 \| 0.6799 \| 0.7960 \| 0.8649 \| 0.9247 \| 0.8093 \| 0.8589 \| 0.5027 \| 0.8975 \| 0.8561 \| 0.7231 \| 0.8639 \| 0.6605 \| 0.7535 \| 0.3885 \| 0.7751 \| 0.6447 \| 0.6734 \| \| 0.2708 \| 33.57 \| 12420 \| 0.5855 \| 0.6800 \| 0.7918 \| 0.8669 \| 0.9294 \| 0.7983 \| 0.8735 \| 0.4647 \| 0.8996 \| 0.8533 \| 0.7234 \| 0.8654 \| 0.6682 \| 0.7515 \| 0.3720 \| 0.7817 \| 0.6398 \| 0.6815 \| \| 0.2264 \| 33.62 \| 12440 \| 0.5963 \| 0.6825 \| 0.7979 \| 0.8658 \| 0.9302 \| 0.8181 \| 0.8566 \| 0.5026 \| 0.8944 \| 0.8739 \| 0.7095 \| 0.8654 \| 0.6781 \| 0.7531 \| 0.3897 \| 0.7770 \| 0.6388 \| 0.6756 \| \| 0.206 \| 33.68 \| 12460 \| 0.5587 \| 0.6852 \| 0.7922 \| 0.8695 \| 0.9287 \| 0.7938 \| 0.8625 \| 0.4779 \| 0.9121 \| 0.8362 \| 0.7343 \| 0.8677 \| 0.6742 \| 0.7522 \| 0.3769 \| 0.7820 \| 0.6499 \| 0.6936 \| \| 0.1734 \| 33.73 \| 12480 \| 0.5269 \| 0.6910 \| 0.7995 \| 0.8734 \| 0.9357 \| 0.8040 \| 0.8626 \| 0.4864 \| 0.9002 \| 0.8428 \| 0.7648 \| 0.8704 \| 0.6659 \| 0.7543 \| 0.3811 \| 0.7869 \| 0.6629 \| 0.7156 \| \| 0.1171 \| 33.78 \| 12500 \| 0.5463 \| 0.6876 \| 0.7964 \| 0.8715 \| 0.9350 \| 0.7956 \| 0.8662 \| 0.4893 \| 0.9022 \| 0.8371 \| 0.7495 \| 0.8707 \| 0.6670 \| 0.7534 \| 0.3737 \| 0.7832 \| 0.6624 \| 0.7030 \| \| 0.2482 \| 33.84 \| 12520 \| 0.5667 \| 0.6851 \| 0.7959 \| 0.8694 \| 0.9250 \| 0.8061 \| 0.8682 \| 0.4704 \| 0.9049 \| 0.8508 \| 0.7461 \| 0.8671 \| 0.6662 \| 0.7539 \| 0.3678 \| 0.7798 \| 0.6688 \| 0.6923 \| \| 0.162 \| 33.89 \| 12540 \| 0.5679 \| 0.6802 \| 0.7933 \| 0.8679 \| 0.9215 \| 0.8198 \| 0.8724 \| 0.4372 \| 0.9024 \| 0.8493 \| 0.7507 \| 0.8641 \| 0.6646 \| 0.7526 \| 0.3456 \| 0.7807 \| 0.6640 \| 0.6899 \| \| 0.3138 \| 33.95 \| 12560 \| 0.5669 \| 0.6866 \| 0.7918 \| 0.8715 \| 0.9281 \| 0.7997 \| 0.8617 \| 0.4423 \| 0.9106 \| 0.8405 \| 0.7597 \| 0.8649 \| 0.6689 \| 0.7510 \| 0.3551 \| 0.7857 \| 0.6824 \| 0.6984 \| \| 0.1161 \| 34.0 \| 12580 \| 0.5417 \| 0.6924 \| 0.7947 \| 0.8745 \| 0.9312 \| 0.7930 \| 0.8545 \| 0.4611 \| 0.9144 \| 0.8362 \| 0.7722 \| 0.8672 \| 0.6676 \| 0.7518 \| 0.3713 \| 0.7895 \| 0.6849 \| 0.7144 \| \| 0.2788 \| 34.05 \| 12600 \| 0.5523 \| 0.6907 \| 0.7943 \| 0.8741 \| 0.9342 \| 0.8215 \| 0.8531 \| 0.4377 \| 0.9138 \| 0.8457 \| 0.7540 \| 0.8668 \| 0.6792 \| 0.7494 \| 0.3577 \| 0.7906 \| 0.6810 \| 0.7101 \| \| 0.3315 \| 34.11 \| 12620 \| 0.5840 \| 0.6890 \| 0.7910 \| 0.8734 \| 0.9300 \| 0.8029 \| 0.8621 \| 0.4280 \| 0.9186 \| 0.8452 \| 0.7498 \| 0.8672 \| 0.6793 \| 0.7465 \| 0.3509 \| 0.7883 \| 0.6815 \| 0.7095 \| \| 0.2396 \| 34.16 \| 12640 \| 0.5434 \| 0.6875 \| 0.8004 \| 0.8720 \| 0.9292 \| 0.8108 \| 0.8679 \| 0.4665 \| 0.8898 \| 0.8431 \| 0.7956 \| 0.8667 \| 0.6651 \| 0.7541 \| 0.3331 \| 0.7814 \| 0.6751 \| 0.7367 \| \| 0.1696 \| 34.22 \| 12660 \| 0.5481 \| 0.6887 \| 0.8029 \| 0.8734 \| 0.9292 \| 0.7997 \| 0.8789 \| 0.4458 \| 0.8764 \| 0.8718 \| 0.8184 \| 0.8659 \| 0.6684 \| 0.7514 \| 0.3227 \| 0.7849 \| 0.6672 \| 0.7601 \| \| 0.1219 \| 34.27 \| 12680 \| 0.5375 \| 0.6820 \| 0.7902 \| 0.8727 \| 0.9330 \| 0.7890 \| 0.8734 \| 0.3810 \| 0.8846 \| 0.8562 \| 0.8140 \| 0.8677 \| 0.6714 \| 0.7417 \| 0.2785 \| 0.7840 \| 0.6740 \| 0.7569 \| \| 0.1933 \| 34.32 \| 12700 \| 0.5553 \| 0.6836 \| 0.7926 \| 0.8707 \| 0.9335 \| 0.8171 \| 0.8641 \| 0.4131 \| 0.8941 \| 0.8681 \| 0.7581 \| 0.8672 \| 0.6821 \| 0.7432 \| 0.3223 \| 0.7801 \| 0.6697 \| 0.7203 \| \| 0.1571 \| 34.38 \| 12720 \| 0.5774 \| 0.6904 \| 0.7963 \| 0.8726 \| 0.9342 \| 0.8166 \| 0.8529 \| 0.4615 \| 0.9076 \| 0.8535 \| 0.7476 \| 0.8703 \| 0.6839 \| 0.7477 \| 0.3588 \| 0.7805 \| 0.6729 \| 0.7185 \| \| 0.0608 \| 34.43 \| 12740 \| 0.5362 \| 0.6865 \| 0.8022 \| 0.8711 \| 0.9334 \| 0.8206 \| 0.8708 \| 0.4746 \| 0.8821 \| 0.8506 \| 0.7833 \| 0.8711 \| 0.6691 \| 0.7450 \| 0.3349 \| 0.7752 \| 0.6730 \| 0.7373 \| \| 0.2141 \| 34.49 \| 12760 \| 0.5540 \| 0.6939 \| 0.8000 \| 0.8760 \| 0.9406 \| 0.7782 \| 0.8619 \| 0.4693 \| 0.8877 \| 0.8645 \| 0.7978 \| 0.8699 \| 0.6696 \| 0.7435 \| 0.3612 \| 0.7894 \| 0.6749 \| 0.7489 \| \| 0.1735 \| 34.54 \| 12780 \| 0.5424 \| 0.6879 \| 0.7928 \| 0.8738 \| 0.9372 \| 0.7928 \| 0.8580 \| 0.4286 \| 0.8995 \| 0.8605 \| 0.7729 \| 0.8702 \| 0.6691 \| 0.7420 \| 0.3386 \| 0.7852 \| 0.6812 \| 0.7288 \| \| 0.1217 \| 34.59 \| 12800 \| 0.5753 \| 0.6933 \| 0.7980 \| 0.8744 \| 0.9404 \| 0.8221 \| 0.8491 \| 0.4777 \| 0.9107 \| 0.8456 \| 0.7403 \| 0.8691 \| 0.6794 \| 0.7448 \| 0.3714 \| 0.7865 \| 0.6836 \| 0.7180 \| \| 0.1423 \| 34.65 \| 12820 \| 0.5989 \| 0.6918 \| 0.7979 \| 0.8728 \| 0.9357 \| 0.8113 \| 0.8612 \| 0.4718 \| 0.9046 \| 0.8596 \| 0.7411 \| 0.8699 \| 0.6801 \| 0.7434 \| 0.3774 \| 0.7808 \| 0.6737 \| 0.7173 \| \| 0.1074 \| 34.7 \| 12840 \| 0.5823 \| 0.6906 \| 0.7938 \| 0.8728 \| 0.9386 \| 0.8146 \| 0.8467 \| 0.4384 \| 0.9038 \| 0.8714 \| 0.7433 \| 0.8685 \| 0.6807 \| 0.7451 \| 0.3731 \| 0.7820 \| 0.6715 \| 0.7132 \| \| 0.1764 \| 34.76 \| 12860 \| 0.6007 \| 0.6936 \| 0.7986 \| 0.8733 \| 0.9357 \| 0.8238 \| 0.8581 \| 0.4647 \| 0.9060 \| 0.8608 \| 0.7413 \| 0.8687 \| 0.6820 \| 0.7520 \| 0.3894 \| 0.7832 \| 0.6704 \| 0.7095 \| \| 0.1833 \| 34.81 \| 12880 \| 0.5795 \| 0.6925 \| 0.7981 \| 0.8732 \| 0.9340 \| 0.8254 \| 0.8625 \| 0.4631 \| 0.9084 \| 0.8488 \| 0.7446 \| 0.8684 \| 0.6820 \| 0.7482 \| 0.3796 \| 0.7840 \| 0.6739 \| 0.7114 \| \| 0.1584 \| 34.86 \| 12900 \| 0.6025 \| 0.6867 \| 0.7946 \| 0.8703 \| 0.9357 \| 0.8123 \| 0.8721 \| 0.4592 \| 0.8965 \| 0.8376 \| 0.7491 \| 0.8684 \| 0.6769 \| 0.7440 \| 0.3667 \| 0.7765 \| 0.6677 \| 0.7068 \| \| 0.129 \| 34.92 \| 12920 \| 0.5499 \| 0.6906 \| 0.7959 \| 0.8721 \| 0.9341 \| 0.7971 \| 0.8729 \| 0.4661 \| 0.9007 \| 0.8455 \| 0.7552 \| 0.8663 \| 0.6828 \| 0.7443 \| 0.3728 \| 0.7828 \| 0.6686 \| 0.7164 \| \| 0.1255 \| 34.97 \| 12940 \| 0.5757 \| 0.6861 \| 0.7956 \| 0.8704 \| 0.9365 \| 0.8204 \| 0.8667 \| 0.4516 \| 0.8970 \| 0.8656 \| 0.7316 \| 0.8661 \| 0.6835 \| 0.7382 \| 0.3643 \| 0.7840 \| 0.6617 \| 0.7050 \| \| 0.2092 \| 35.03 \| 12960 \| 0.5626 \| 0.6938 \| 0.8046 \| 0.8723 \| 0.9337 \| 0.8250 \| 0.8536 \| 0.5120 \| 0.8993 \| 0.8605 \| 0.7481 \| 0.8663 \| 0.6784 \| 0.7466 \| 0.4004 \| 0.7841 \| 0.6658 \| 0.7152 \| \| 0.1811 \| 35.08 \| 12980 \| 0.5657 \| 0.6919 \| 0.7958 \| 0.8721 \| 0.9384 \| 0.7799 \| 0.8454 \| 0.4902 \| 0.9006 \| 0.8713 \| 0.7447 \| 0.8670 \| 0.6760 \| 0.7436 \| 0.3916 \| 0.7810 \| 0.6680 \| 0.7164 \| \| 0.2131 \| 35.14 \| 13000 \| 0.5284 \| 0.6957 \| 0.8061 \| 0.8744 \| 0.9357 \| 0.8163 \| 0.8580 \| 0.5018 \| 0.8940 \| 0.8721 \| 0.7649 \| 0.8698 \| 0.6769 \| 0.7495 \| 0.3824 \| 0.7836 \| 0.6734 \| 0.7345 \| \| 0.1875 \| 35.19 \| 13020 \| 0.5665 \| 0.6945 \| 0.8023 \| 0.8731 \| 0.9382 \| 0.8077 \| 0.8550 \| 0.5006 \| 0.8965 \| 0.8716 \| 0.7464 \| 0.8685 \| 0.6763 \| 0.7515 \| 0.3943 \| 0.7814 \| 0.6722 \| 0.7171 \| \| 0.1252 \| 35.24 \| 13040 \| 0.5460 \| 0.6942 \| 0.8021 \| 0.8724 \| 0.9346 \| 0.8044 \| 0.8655 \| 0.5060 \| 0.8979 \| 0.8550 \| 0.7510 \| 0.8685 \| 0.6785 \| 0.7549 \| 0.3956 \| 0.7792 \| 0.6715 \| 0.7116 \| \| 0.9007 \| 35.3 \| 13060 \| 0.5500 \| 0.6894 \| 0.7948 \| 0.8715 \| 0.9381 \| 0.7895 \| 0.8665 \| 0.4685 \| 0.8925 \| 0.8409 \| 0.7677 \| 0.8652 \| 0.6714 \| 0.7466 \| 0.3765 \| 0.7801 \| 0.6676 \| 0.7184 \| \| 0.4496 \| 35.35 \| 13080 \| 0.5447 \| 0.6946 \| 0.8039 \| 0.8742 \| 0.9275 \| 0.8035 \| 0.8622 \| 0.4909 \| 0.8957 \| 0.8432 \| 0.8040 \| 0.8646 \| 0.6690 \| 0.7453 \| 0.3884 \| 0.7882 \| 0.6636 \| 0.7428 \| \| 0.2982 \| 35.41 \| 13100 \| 0.5485 \| 0.6925 \| 0.7993 \| 0.8724 \| 0.9335 \| 0.7963 \| 0.8511 \| 0.4910 \| 0.8967 \| 0.8507 \| 0.7760 \| 0.8675 \| 0.6710 \| 0.7460 \| 0.3931 \| 0.7811 \| 0.6651 \| 0.7237 \| \| 0.0562 \| 35.46 \| 13120 \| 0.5580 \| 0.6918 \| 0.8072 \| 0.8716 \| 0.9344 \| 0.8218 \| 0.8702 \| 0.5000 \| 0.8812 \| 0.8844 \| 0.7582 \| 0.8686 \| 0.6842 \| 0.7443 \| 0.3822 \| 0.7813 \| 0.6536 \| 0.7282 \| \| 0.4671 \| 35.51 \| 13140 \| 0.5683 \| 0.6872 \| 0.8005 \| 0.8702 \| 0.9335 \| 0.8228 \| 0.8739 \| 0.4743 \| 0.8906 \| 0.8659 \| 0.7425 \| 0.8683 \| 0.6857 \| 0.7382 \| 0.3660 \| 0.7808 \| 0.6611 \| 0.7104 \| \| 0.1878 \| 35.57 \| 13160 \| 0.6551 \| 0.6788 \| 0.7907 \| 0.8655 \| 0.9340 \| 0.8225 \| 0.8581 \| 0.4803 \| 0.9071 \| 0.8440 \| 0.6887 \| 0.8648 \| 0.6817 \| 0.7419 \| 0.3751 \| 0.7795 \| 0.6427 \| 0.6659 \| \| 0.0784 \| 35.62 \| 13180 \| 0.5837 \| 0.6837 \| 0.7996 \| 0.8671 \| 0.9367 \| 0.8309 \| 0.8542 \| 0.5127 \| 0.8948 \| 0.8661 \| 0.7021 \| 0.8665 \| 0.6833 \| 0.7454 \| 0.3876 \| 0.7812 \| 0.6468 \| 0.6753 \| \| 0.15 \| 35.68 \| 13200 \| 0.5714 \| 0.6864 \| 0.7937 \| 0.8705 \| 0.9408 \| 0.8253 \| 0.8461 \| 0.4674 \| 0.9046 \| 0.8472 \| 0.7247 \| 0.8672 \| 0.6855 \| 0.7445 \| 0.3744 \| 0.7863 \| 0.6517 \| 0.6954 \| \| 0.113 \| 35.73 \| 13220 \| 0.5633 \| 0.6879 \| 0.7957 \| 0.8714 \| 0.9385 \| 0.8247 \| 0.8559 \| 0.4693 \| 0.9055 \| 0.8475 \| 0.7285 \| 0.8687 \| 0.6867 \| 0.7479 \| 0.3715 \| 0.7866 \| 0.6522 \| 0.7017 \| \| 0.2453 \| 35.78 \| 13240 \| 0.5593 \| 0.6869 \| 0.8005 \| 0.8715 \| 0.9345 \| 0.8308 \| 0.8694 \| 0.4542 \| 0.8902 \| 0.8711 \| 0.7532 \| 0.8706 \| 0.6846 \| 0.7427 \| 0.3448 \| 0.7827 \| 0.6578 \| 0.7253 \| \| 0.1704 \| 35.84 \| 13260 \| 0.5220 \| 0.6978 \| 0.8067 \| 0.8764 \| 0.9364 \| 0.8005 \| 0.8554 \| 0.5021 \| 0.8921 \| 0.8680 \| 0.7924 \| 0.8708 \| 0.6704 \| 0.7485 \| 0.3915 \| 0.7905 \| 0.6676 \| 0.7454 \| \| 0.3966 \| 35.89 \| 13280 \| 0.5495 \| 0.6943 \| 0.7998 \| 0.8733 \| 0.9351 \| 0.7967 \| 0.8509 \| 0.5202 \| 0.9086 \| 0.8287 \| 0.7588 \| 0.8712 \| 0.6678 \| 0.7512 \| 0.4067 \| 0.7818 \| 0.6676 \| 0.7140 \| \| 0.2348 \| 35.95 \| 13300 \| 0.5630 \| 0.6864 \| 0.7984 \| 0.8706 \| 0.9355 \| 0.8080 \| 0.8558 \| 0.4874 \| 0.8900 \| 0.8395 \| 0.7726 \| 0.8714 \| 0.6699 \| 0.7451 \| 0.3509 \| 0.7740 \| 0.6662 \| 0.7275 \| \| 0.1803 \| 36.0 \| 13320 \| 0.5926 \| 0.6799 \| 0.7877 \| 0.8672 \| 0.9423 \| 0.7878 \| 0.8591 \| 0.4682 \| 0.8944 \| 0.8373 \| 0.7245 \| 0.8678 \| 0.6819 \| 0.7356 \| 0.3364 \| 0.7728 \| 0.6653 \| 0.6993 \| \| 0.1647 \| 36.05 \| 13340 \| 0.5567 \| 0.6929 \| 0.8017 \| 0.8723 \| 0.9345 \| 0.8153 \| 0.8514 \| 0.5048 \| 0.9030 \| 0.8593 \| 0.7435 \| 0.8706 \| 0.6842 \| 0.7461 \| 0.3918 \| 0.7819 \| 0.6626 \| 0.7130 \| \| 0.1053 \| 36.11 \| 13360 \| 0.5655 \| 0.6904 \| 0.7969 \| 0.8721 \| 0.9346 \| 0.8230 \| 0.8569 \| 0.4639 \| 0.9070 \| 0.8573 \| 0.7358 \| 0.8699 \| 0.6844 \| 0.7485 \| 0.3822 \| 0.7836 \| 0.6575 \| 0.7065 \| \| 0.1856 \| 36.16 \| 13380 \| 0.5469 \| 0.6876 \| 0.7972 \| 0.8718 \| 0.9382 \| 0.8268 \| 0.8705 \| 0.4523 \| 0.8964 \| 0.8550 \| 0.7413 \| 0.8701 \| 0.6858 \| 0.7415 \| 0.3572 \| 0.7832 \| 0.6617 \| 0.7138 \| \| 0.228 \| 36.22 \| 13400 \| 0.5294 \| 0.7021 \| 0.8100 \| 0.8801 \| 0.9366 \| 0.8260 \| 0.8586 \| 0.4713 \| 0.8916 \| 0.8604 \| 0.8254 \| 0.8714 \| 0.6841 \| 0.7491 \| 0.3654 \| 0.7966 \| 0.6686 \| 0.7798 \| \| 0.2333 \| 36.27 \| 13420 \| 0.5561 \| 0.6968 \| 0.8054 \| 0.8753 \| 0.9340 \| 0.8265 \| 0.8667 \| 0.4814 \| 0.8983 \| 0.8607 \| 0.7703 \| 0.8710 \| 0.6830 \| 0.7528 \| 0.3852 \| 0.7864 \| 0.6664 \| 0.7329 \| \| 0.157 \| 36.32 \| 13440 \| 0.5574 \| 0.6951 \| 0.8006 \| 0.8743 \| 0.9385 \| 0.7941 \| 0.8679 \| 0.4821 \| 0.8959 \| 0.8671 \| 0.7583 \| 0.8694 \| 0.6798 \| 0.7521 \| 0.3887 \| 0.7854 \| 0.6646 \| 0.7260 \| \| 0.3018 \| 36.38 \| 13460 \| 0.5771 \| 0.6896 \| 0.7940 \| 0.8713 \| 0.9288 \| 0.7818 \| 0.8672 \| 0.4633 \| 0.9064 \| 0.8647 \| 0.7459 \| 0.8672 \| 0.6786 \| 0.7469 \| 0.3785 \| 0.7809 \| 0.6670 \| 0.7079 \| \| 0.2048 \| 36.43 \| 13480 \| 0.5862 \| 0.6917 \| 0.7952 \| 0.8727 \| 0.9341 \| 0.7953 \| 0.8683 \| 0.4584 \| 0.9043 \| 0.8582 \| 0.7481 \| 0.8689 \| 0.6857 \| 0.7472 \| 0.3734 \| 0.7810 \| 0.6718 \| 0.7141 \| \| 0.0848 \| 36.49 \| 13500 \| 0.5357 \| 0.6944 \| 0.8027 \| 0.8735 \| 0.9390 \| 0.8168 \| 0.8557 \| 0.4818 \| 0.8887 \| 0.8676 \| 0.7695 \| 0.8701 \| 0.6841 \| 0.7480 \| 0.3829 \| 0.7812 \| 0.6706 \| 0.7237 \| \| 0.1358 \| 36.54 \| 13520 \| 0.5912 \| 0.6908 \| 0.8002 \| 0.8717 \| 0.9312 \| 0.8253 \| 0.8684 \| 0.4716 \| 0.9010 \| 0.8579 \| 0.7459 \| 0.8702 \| 0.6827 \| 0.7495 \| 0.3764 \| 0.7786 \| 0.6677 \| 0.7105 \| \| 0.2787 \| 36.59 \| 13540 \| 0.5802 \| 0.6898 \| 0.8003 \| 0.8712 \| 0.9346 \| 0.8261 \| 0.8675 \| 0.4779 \| 0.8946 \| 0.8488 \| 0.7525 \| 0.8693 \| 0.6790 \| 0.7451 \| 0.3759 \| 0.7783 \| 0.6728 \| 0.7078 \| \| 0.1792 \| 36.65 \| 13560 \| 0.5838 \| 0.6875 \| 0.7981 \| 0.8701 \| 0.9352 \| 0.7941 \| 0.8721 \| 0.4879 \| 0.8900 \| 0.8588 \| 0.7483 \| 0.8684 \| 0.6681 \| 0.7488 \| 0.3779 \| 0.7769 \| 0.6712 \| 0.7011 \| \| 0.31 \| 36.7 \| 13580 \| 0.5766 \| 0.6867 \| 0.8050 \| 0.8679 \| 0.9249 \| 0.8010 \| 0.8752 \| 0.5418 \| 0.8880 \| 0.8490 \| 0.7552 \| 0.8660 \| 0.6669 \| 0.7520 \| 0.3778 \| 0.7732 \| 0.6712 \| 0.6998 \| \| 0.1493 \| 36.76 \| 13600 \| 0.5914 \| 0.6910 \| 0.7988 \| 0.8716 \| 0.9309 \| 0.8183 \| 0.8561 \| 0.4823 \| 0.9065 \| 0.8535 \| 0.7443 \| 0.8686 \| 0.6843 \| 0.7493 \| 0.3789 \| 0.7808 \| 0.6680 \| 0.7072 \| \| 0.1263 \| 36.81 \| 13620 \| 0.5895 \| 0.6931 \| 0.7997 \| 0.8721 \| 0.9355 \| 0.8179 \| 0.8394 \| 0.5075 \| 0.9067 \| 0.8425 \| 0.7487 \| 0.8692 \| 0.6775 \| 0.7478 \| 0.3973 \| 0.7796 \| 0.6723 \| 0.7078 \| \| 0.1087 \| 36.86 \| 13640 \| 0.5736 \| 0.6880 \| 0.7967 \| 0.8705 \| 0.9381 \| 0.7949 \| 0.8681 \| 0.4828 \| 0.8910 \| 0.8527 \| 0.7495 \| 0.8685 \| 0.6745 \| 0.7450 \| 0.3713 \| 0.7769 \| 0.6742 \| 0.7057 \| \| 0.1361 \| 36.92 \| 13660 \| 0.5874 \| 0.6901 \| 0.7968 \| 0.8720 \| 0.9342 \| 0.7967 \| 0.8643 \| 0.4772 \| 0.9022 \| 0.8556 \| 0.7475 \| 0.8682 \| 0.6808 \| 0.7457 \| 0.3701 \| 0.7827 \| 0.6734 \| 0.7100 \| \| 0.2133 \| 36.97 \| 13680 \| 0.6268 \| 0.6845 \| 0.7971 \| 0.8679 \| 0.9323 \| 0.8224 \| 0.8718 \| 0.4855 \| 0.8983 \| 0.8508 \| 0.7184 \| 0.8622 \| 0.6796 \| 0.7454 \| 0.3773 \| 0.7818 \| 0.6636 \| 0.6816 \| \| 0.118 \| 37.03 \| 13700 \| 0.6365 \| 0.6843 \| 0.7959 \| 0.8669 \| 0.9297 \| 0.8242 \| 0.8565 \| 0.4941 \| 0.9071 \| 0.8601 \| 0.6995 \| 0.8618 \| 0.6778 \| 0.7497 \| 0.3953 \| 0.7813 \| 0.6603 \| 0.6637 \| \| 0.2593 \| 37.08 \| 13720 \| 0.5960 \| 0.6911 \| 0.8020 \| 0.8703 \| 0.9289 \| 0.8211 \| 0.8531 \| 0.4929 \| 0.8975 \| 0.8760 \| 0.7444 \| 0.8647 \| 0.6801 \| 0.7497 \| 0.3965 \| 0.7801 \| 0.6667 \| 0.7002 \| \| 0.0827 \| 37.14 \| 13740 \| 0.5955 \| 0.6934 \| 0.8019 \| 0.8715 \| 0.9332 \| 0.8024 \| 0.8570 \| 0.5219 \| 0.9006 \| 0.8523 \| 0.7461 \| 0.8647 \| 0.6788 \| 0.7485 \| 0.3990 \| 0.7816 \| 0.6778 \| 0.7032 \| \| 0.0814 \| 37.19 \| 13760 \| 0.5991 \| 0.6895 \| 0.8009 \| 0.8703 \| 0.9325 \| 0.8252 \| 0.8515 \| 0.4945 \| 0.8987 \| 0.8662 \| 0.7377 \| 0.8637 \| 0.6799 \| 0.7460 \| 0.3810 \| 0.7821 \| 0.6734 \| 0.7003 \| \| 0.1256 \| 37.24 \| 13780 \| 0.6215 \| 0.6886 \| 0.7992 \| 0.8704 \| 0.9319 \| 0.8285 \| 0.8542 \| 0.4980 \| 0.9101 \| 0.8535 \| 0.7182 \| 0.8646 \| 0.6778 \| 0.7464 \| 0.3842 \| 0.7848 \| 0.6722 \| 0.6903 \| \| 0.1831 \| 37.3 \| 13800 \| 0.5965 \| 0.6921 \| 0.8039 \| 0.8722 \| 0.9328 \| 0.8406 \| 0.8499 \| 0.5007 \| 0.9045 \| 0.8615 \| 0.7375 \| 0.8694 \| 0.6795 \| 0.7478 \| 0.3885 \| 0.7837 \| 0.6690 \| 0.7066 \| \| 0.1468 \| 37.35 \| 13820 \| 0.6292 \| 0.6810 \| 0.7915 \| 0.8674 \| 0.9363 \| 0.8282 \| 0.8616 \| 0.4482 \| 0.9026 \| 0.8689 \| 0.6948 \| 0.8663 \| 0.6815 \| 0.7447 \| 0.3665 \| 0.7804 \| 0.6584 \| 0.6689 \| \| 0.2897 \| 37.41 \| 13840 \| 0.6377 \| 0.6749 \| 0.7849 \| 0.8648 \| 0.9336 \| 0.8309 \| 0.8658 \| 0.4189 \| 0.9080 \| 0.8592 \| 0.6781 \| 0.8650 \| 0.6811 \| 0.7412 \| 0.3519 \| 0.7772 \| 0.6566 \| 0.6512 \| \| 0.1732 \| 37.46 \| 13860 \| 0.6302 \| 0.6813 \| 0.7866 \| 0.8683 \| 0.9395 \| 0.8019 \| 0.8601 \| 0.4386 \| 0.9064 \| 0.8596 \| 0.6999 \| 0.8660 \| 0.6827 \| 0.7401 \| 0.3569 \| 0.7809 \| 0.6685 \| 0.6740 \| \| 0.3542 \| 37.51 \| 13880 \| 0.6502 \| 0.6835 \| 0.7933 \| 0.8673 \| 0.9379 \| 0.8145 \| 0.8609 \| 0.4908 \| 0.9034 \| 0.8516 \| 0.6937 \| 0.8639 \| 0.6811 \| 0.7438 \| 0.3849 \| 0.7800 \| 0.6610 \| 0.6694 \| \| 0.1036 \| 37.57 \| 13900 \| 0.6033 \| 0.6828 \| 0.7972 \| 0.8675 \| 0.9271 \| 0.8177 \| 0.8730 \| 0.4687 \| 0.8950 \| 0.8699 \| 0.7291 \| 0.8645 \| 0.6811 \| 0.7420 \| 0.3650 \| 0.7798 \| 0.6560 \| 0.6914 \| \| 0.409 \| 37.62 \| 13920 \| 0.6042 \| 0.6853 \| 0.7915 \| 0.8705 \| 0.9315 \| 0.7980 \| 0.8674 \| 0.4327 \| 0.9030 \| 0.8662 \| 0.7415 \| 0.8660 \| 0.6811 \| 0.7433 \| 0.3500 \| 0.7820 \| 0.6694 \| 0.7054 \| \| 0.1406 \| 37.68 \| 13940 \| 0.6144 \| 0.6794 \| 0.7867 \| 0.8687 \| 0.9320 \| 0.8037 \| 0.8743 \| 0.3970 \| 0.9010 \| 0.8697 \| 0.7289 \| 0.8639 \| 0.6812 \| 0.7400 \| 0.3293 \| 0.7831 \| 0.6650 \| 0.6934 \| \| 1.313 \| 37.73 \| 13960 \| 0.6022 \| 0.6820 \| 0.7912 \| 0.8691 \| 0.9333 \| 0.8145 \| 0.8752 \| 0.4238 \| 0.8973 \| 0.8589 \| 0.7354 \| 0.8657 \| 0.6834 \| 0.7369 \| 0.3427 \| 0.7814 \| 0.6650 \| 0.6992 \| \| 0.1975 \| 37.78 \| 13980 \| 0.6037 \| 0.6833 \| 0.7923 \| 0.8701 \| 0.9341 \| 0.8190 \| 0.8761 \| 0.4237 \| 0.8992 \| 0.8615 \| 0.7326 \| 0.8679 \| 0.6853 \| 0.7358 \| 0.3423 \| 0.7827 \| 0.6690 \| 0.6998 \| \| 0.2584 \| 37.84 \| 14000 \| 0.5807 \| 0.6901 \| 0.7957 \| 0.8727 \| 0.9347 \| 0.8231 \| 0.8562 \| 0.4460 \| 0.9069 \| 0.8583 \| 0.7448 \| 0.8675 \| 0.6826 \| 0.7458 \| 0.3671 \| 0.7856 \| 0.6739 \| 0.7084 \| \| 0.2032 \| 37.89 \| 14020 \| 0.5551 \| 0.6926 \| 0.7968 \| 0.8737 \| 0.9390 \| 0.8200 \| 0.8492 \| 0.4610 \| 0.9058 \| 0.8541 \| 0.7482 \| 0.8674 \| 0.6827 \| 0.7500 \| 0.3784 \| 0.7873 \| 0.6681 \| 0.7144 \| \| 0.1317 \| 37.95 \| 14040 \| 0.5509 \| 0.6899 \| 0.7980 \| 0.8715 \| 0.9342 \| 0.8237 \| 0.8539 \| 0.4756 \| 0.9026 \| 0.8470 \| 0.7486 \| 0.8657 \| 0.6836 \| 0.7505 \| 0.3788 \| 0.7849 \| 0.6588 \| 0.7072 \| \| 0.3604 \| 38.0 \| 14060 \| 0.5349 \| 0.6912 \| 0.7985 \| 0.8731 \| 0.9346 \| 0.7841 \| 0.8664 \| 0.4658 \| 0.8895 \| 0.8672 \| 0.7822 \| 0.8660 \| 0.6780 \| 0.7456 \| 0.3673 \| 0.7874 \| 0.6647 \| 0.7294 \| \| 0.2222 \| 38.05 \| 14080 \| 0.5328 \| 0.6934 \| 0.8066 \| 0.8727 \| 0.9309 \| 0.8128 \| 0.8685 \| 0.4980 \| 0.8868 \| 0.8755 \| 0.7734 \| 0.8666 \| 0.6802 \| 0.7526 \| 0.3808 \| 0.7849 \| 0.6649 \| 0.7238 \| \| 0.2449 \| 38.11 \| 14100 \| 0.5987 \| 0.6862 \| 0.7949 \| 0.8699 \| 0.9331 \| 0.8197 \| 0.8593 \| 0.4756 \| 0.9094 \| 0.8469 \| 0.7204 \| 0.8669 \| 0.6817 \| 0.7497 \| 0.3721 \| 0.7831 \| 0.6583 \| 0.6917 \| \| 0.1192 \| 38.16 \| 14120 \| 0.5685 \| 0.6854 \| 0.7936 \| 0.8716 \| 0.9370 \| 0.8237 \| 0.8656 \| 0.4263 \| 0.9006 \| 0.8654 \| 0.7364 \| 0.8683 \| 0.6840 \| 0.7460 \| 0.3447 \| 0.7864 \| 0.6622 \| 0.7064 \| \| 0.1802 \| 38.22 \| 14140 \| 0.5572 \| 0.6864 \| 0.7974 \| 0.8706 \| 0.9384 \| 0.8201 \| 0.8507 \| 0.4702 \| 0.8938 \| 0.8650 \| 0.7433 \| 0.8693 \| 0.6860 \| 0.7460 \| 0.3442 \| 0.7802 \| 0.6655 \| 0.7138 \| \| 0.1099 \| 38.27 \| 14160 \| 0.5273 \| 0.6947 \| 0.8008 \| 0.8757 \| 0.9340 \| 0.8020 \| 0.8591 \| 0.4683 \| 0.8998 \| 0.8542 \| 0.7885 \| 0.8683 \| 0.6787 \| 0.7498 \| 0.3568 \| 0.7892 \| 0.6763 \| 0.7435 \| \| 0.1271 \| 38.32 \| 14180 \| 0.5254 \| 0.6958 \| 0.8033 \| 0.8761 \| 0.9346 \| 0.8124 \| 0.8634 \| 0.4681 \| 0.8967 \| 0.8600 \| 0.7880 \| 0.8691 \| 0.6809 \| 0.7508 \| 0.3583 \| 0.7892 \| 0.6761 \| 0.7461 \| \| 0.2621 \| 38.38 \| 14200 \| 0.5331 \| 0.6914 \| 0.8033 \| 0.8727 \| 0.9300 \| 0.8093 \| 0.8749 \| 0.4784 \| 0.8887 \| 0.8572 \| 0.7849 \| 0.8678 \| 0.6814 \| 0.7501 \| 0.3479 \| 0.7809 \| 0.6761 \| 0.7354 \| \| 0.1324 \| 38.43 \| 14220 \| 0.5733 \| 0.6884 \| 0.7913 \| 0.8725 \| 0.9396 \| 0.7890 \| 0.8555 \| 0.4533 \| 0.9036 \| 0.8445 \| 0.7535 \| 0.8690 \| 0.6752 \| 0.7446 \| 0.3556 \| 0.7818 \| 0.6776 \| 0.7145 \| \| 0.2456 \| 38.49 \| 14240 \| 0.5537 \| 0.6811 \| 0.7909 \| 0.8691 \| 0.9311 \| 0.7956 \| 0.8796 \| 0.4251 \| 0.8921 \| 0.8550 \| 0.7580 \| 0.8669 \| 0.6773 \| 0.7350 \| 0.3256 \| 0.7791 \| 0.6777 \| 0.7062 \| \| 0.1776 \| 38.54 \| 14260 \| 0.5484 \| 0.6846 \| 0.7967 \| 0.8702 \| 0.9346 \| 0.8246 \| 0.8577 \| 0.4512 \| 0.8922 \| 0.8647 \| 0.7521 \| 0.8666 \| 0.6817 \| 0.7389 \| 0.3411 \| 0.7820 \| 0.6697 \| 0.7125 \| \| 0.0734 \| 38.59 \| 14280 \| 0.5084 \| 0.6870 \| 0.8004 \| 0.8719 \| 0.9307 \| 0.8319 \| 0.8716 \| 0.4560 \| 0.8947 \| 0.8509 \| 0.7672 \| 0.8691 \| 0.6835 \| 0.7401 \| 0.3239 \| 0.7826 \| 0.6831 \| 0.7267 \| \| 0.148 \| 38.65 \| 14300 \| 0.5462 \| 0.6909 \| 0.7990 \| 0.8734 \| 0.9311 \| 0.8299 \| 0.8548 \| 0.4524 \| 0.9069 \| 0.8641 \| 0.7541 \| 0.8685 \| 0.6792 \| 0.7504 \| 0.3577 \| 0.7858 \| 0.6809 \| 0.7136 \| \| 0.2257 \| 38.7 \| 14320 \| 0.5883 \| 0.6896 \| 0.7977 \| 0.8735 \| 0.9362 \| 0.8310 \| 0.8678 \| 0.4374 \| 0.9021 \| 0.8645 \| 0.7452 \| 0.8677 \| 0.6782 \| 0.7464 \| 0.3489 \| 0.7870 \| 0.6866 \| 0.7121 \| \| 0.2438 \| 38.76 \| 14340 \| 0.5733 \| 0.6899 \| 0.7938 \| 0.8737 \| 0.9393 \| 0.8069 \| 0.8720 \| 0.4422 \| 0.9046 \| 0.8429 \| 0.7489 \| 0.8688 \| 0.6787 \| 0.7507 \| 0.3463 \| 0.7847 \| 0.6856 \| 0.7149 \| \| 0.3243 \| 38.81 \| 14360 \| 0.5874 \| 0.6858 \| 0.7993 \| 0.8701 \| 0.9368 \| 0.8230 \| 0.8774 \| 0.4547 \| 0.8836 \| 0.8765 \| 0.7430 \| 0.8699 \| 0.6823 \| 0.7501 \| 0.3330 \| 0.7733 \| 0.6795 \| 0.7122 \| \| 1.404 \| 38.86 \| 14380 \| 0.6100 \| 0.6864 \| 0.7991 \| 0.8700 \| 0.9364 \| 0.8289 \| 0.8609 \| 0.4629 \| 0.8904 \| 0.8808 \| 0.7335 \| 0.8682 \| 0.6790 \| 0.7495 \| 0.3527 \| 0.7767 \| 0.6739 \| 0.7045 \| \| 0.1701 \| 38.92 \| 14400 \| 0.5984 \| 0.6881 \| 0.7996 \| 0.8700 \| 0.9313 \| 0.8191 \| 0.8529 \| 0.4826 \| 0.8977 \| 0.8748 \| 0.7388 \| 0.8657 \| 0.6784 \| 0.7495 \| 0.3718 \| 0.7800 \| 0.6704 \| 0.7010 \| \| 0.1059 \| 38.97 \| 14420 \| 0.5984 \| 0.6861 \| 0.7914 \| 0.8710 \| 0.9374 \| 0.8123 \| 0.8498 \| 0.4406 \| 0.9055 \| 0.8613 \| 0.7328 \| 0.8665 \| 0.6823 \| 0.7450 \| 0.3535 \| 0.7830 \| 0.6716 \| 0.7012 \| \| 0.12 \| 39.03 \| 14440 \| 0.6271 \| 0.6814 \| 0.7880 \| 0.8693 \| 0.9408 \| 0.8146 \| 0.8609 \| 0.4222 \| 0.9020 \| 0.8635 \| 0.7120 \| 0.8637 \| 0.6826 \| 0.7393 \| 0.3380 \| 0.7841 \| 0.6756 \| 0.6866 \| \| 0.1683 \| 39.08 \| 14460 \| 0.5926 \| 0.6871 \| 0.7967 \| 0.8710 \| 0.9393 \| 0.8177 \| 0.8623 \| 0.4680 \| 0.8966 \| 0.8592 \| 0.7340 \| 0.8662 \| 0.6814 \| 0.7366 \| 0.3594 \| 0.7843 \| 0.6762 \| 0.7055 \| \| 0.2943 \| 39.14 \| 14480 \| 0.5866 \| 0.6918 \| 0.8027 \| 0.8716 \| 0.9340 \| 0.8184 \| 0.8565 \| 0.5067 \| 0.8983 \| 0.8610 \| 0.7440 \| 0.8657 \| 0.6794 \| 0.7444 \| 0.3851 \| 0.7827 \| 0.6765 \| 0.7092 \| \| 0.1408 \| 39.19 \| 14500 \| 0.5564 \| 0.6949 \| 0.8101 \| 0.8720 \| 0.9309 \| 0.8207 \| 0.8574 \| 0.5468 \| 0.8937 \| 0.8708 \| 0.7504 \| 0.8662 \| 0.6776 \| 0.7480 \| 0.3995 \| 0.7821 \| 0.6761 \| 0.7151 \| \| 0.1082 \| 39.24 \| 14520 \| 0.5643 \| 0.6929 \| 0.8061 \| 0.8712 \| 0.9298 \| 0.8192 \| 0.8561 \| 0.5210 \| 0.8954 \| 0.8730 \| 0.7483 \| 0.8654 \| 0.6766 \| 0.7484 \| 0.3964 \| 0.7805 \| 0.6728 \| 0.7101 \| \| 0.0968 \| 39.3 \| 14540 \| 0.5755 \| 0.6852 \| 0.8011 \| 0.8686 \| 0.9351 \| 0.8134 \| 0.8756 \| 0.4924 \| 0.8807 \| 0.8623 \| 0.7483 \| 0.8651 \| 0.6733 \| 0.7371 \| 0.3614 \| 0.7761 \| 0.6791 \| 0.7044 \| \| 0.2545 \| 39.35 \| 14560 \| 0.5966 \| 0.6834 \| 0.7974 \| 0.8674 \| 0.9279 \| 0.8092 \| 0.8527 \| 0.4868 \| 0.8930 \| 0.8675 \| 0.7449 \| 0.8660 \| 0.6733 \| 0.7499 \| 0.3469 \| 0.7702 \| 0.6733 \| 0.7040 \| \| 0.1329 \| 39.41 \| 14580 \| 0.5686 \| 0.6871 \| 0.7964 \| 0.8703 \| 0.9331 \| 0.8087 \| 0.8529 \| 0.4747 \| 0.8990 \| 0.8579 \| 0.7486 \| 0.8675 \| 0.6719 \| 0.7497 \| 0.3583 \| 0.7761 \| 0.6762 \| 0.7100 \| \| 0.2681 \| 39.46 \| 14600 \| 0.5751 \| 0.6901 \| 0.7980 \| 0.8719 \| 0.9340 \| 0.8132 \| 0.8697 \| 0.4691 \| 0.8993 \| 0.8493 \| 0.7515 \| 0.8660 \| 0.6751 \| 0.7442 \| 0.3771 \| 0.7826 \| 0.6776 \| 0.7084 \| \| 0.2 \| 39.51 \| 14620 \| 0.5701 \| 0.6920 \| 0.8018 \| 0.8713 \| 0.9335 \| 0.8146 \| 0.8519 \| 0.4990 \| 0.8949 \| 0.8640 \| 0.7546 \| 0.8642 \| 0.6691 \| 0.7492 \| 0.3978 \| 0.7803 \| 0.6759 \| 0.7074 \| \| 0.2699 \| 39.57 \| 14640 \| 0.5471 \| 0.6912 \| 0.7975 \| 0.8729 \| 0.9307 \| 0.7951 \| 0.8601 \| 0.4642 \| 0.8994 \| 0.8551 \| 0.7780 \| 0.8644 \| 0.6666 \| 0.7483 \| 0.3761 \| 0.7850 \| 0.6755 \| 0.7225 \| \| 0.3498 \| 39.62 \| 14660 \| 0.5512 \| 0.6915 \| 0.7995 \| 0.8733 \| 0.9313 \| 0.8081 \| 0.8764 \| 0.4658 \| 0.9028 \| 0.8590 \| 0.7531 \| 0.8672 \| 0.6789 \| 0.7416 \| 0.3679 \| 0.7869 \| 0.6813 \| 0.7165 \| \| 0.2973 \| 39.68 \| 14680 \| 0.5437 \| 0.6932 \| 0.8031 \| 0.8732 \| 0.9291 \| 0.8143 \| 0.8607 \| 0.4818 \| 0.8990 \| 0.8706 \| 0.7660 \| 0.8667 \| 0.6742 \| 0.7480 \| 0.3785 \| 0.7840 \| 0.6784 \| 0.7226 \| \| 0.1419 \| 39.73 \| 14700 \| 0.5355 \| 0.6952 \| 0.8051 \| 0.8753 \| 0.9312 \| 0.8024 \| 0.8720 \| 0.4789 \| 0.8898 \| 0.8601 \| 0.8017 \| 0.8662 \| 0.6684 \| 0.7473 \| 0.3741 \| 0.7893 \| 0.6771 \| 0.7443 \| \| 0.0852 \| 39.78 \| 14720 \| 0.5293 \| 0.6959 \| 0.8066 \| 0.8756 \| 0.9318 \| 0.8143 \| 0.8604 \| 0.4872 \| 0.8932 \| 0.8656 \| 0.7939 \| 0.8680 \| 0.6670 \| 0.7500 \| 0.3789 \| 0.7895 \| 0.6785 \| 0.7393 \| \| 0.2802 \| 39.84 \| 14740 \| 0.5564 \| 0.6967 \| 0.8063 \| 0.8765 \| 0.9361 \| 0.8137 \| 0.8491 \| 0.4888 \| 0.8909 \| 0.8590 \| 0.8065 \| 0.8668 \| 0.6649 \| 0.7473 \| 0.3778 \| 0.7922 \| 0.6751 \| 0.7525 \| \| 0.2943 \| 39.89 \| 14760 \| 0.5502 \| 0.6917 \| 0.8007 \| 0.8729 \| 0.9309 \| 0.8013 \| 0.8420 \| 0.4998 \| 0.9022 \| 0.8526 \| 0.7759 \| 0.8675 \| 0.6637 \| 0.7483 \| 0.3821 \| 0.7843 \| 0.6731 \| 0.7233 \| \| 1.6794 \| 39.95 \| 14780 \| 0.5218 \| 0.6937 \| 0.8051 \| 0.8750 \| 0.9322 \| 0.8053 \| 0.8602 \| 0.4909 \| 0.8909 \| 0.8536 \| 0.8028 \| 0.8683 \| 0.6670 \| 0.7508 \| 0.3589 \| 0.7873 \| 0.6775 \| 0.7463 \| \| 0.1365 \| 40.0 \| 14800 \| 0.5598 \| 0.6912 \| 0.8028 \| 0.8714 \| 0.9296 \| 0.8137 \| 0.8529 \| 0.5126 \| 0.9004 \| 0.8539 \| 0.7566 \| 0.8667 \| 0.6711 \| 0.7530 \| 0.3841 \| 0.7810 \| 0.6735 \| 0.7090 \| \| 0.2468 \| 40.05 \| 14820 \| 0.5624 \| 0.6893 \| 0.8013 \| 0.8703 \| 0.9332 \| 0.7991 \| 0.8578 \| 0.5107 \| 0.8911 \| 0.8601 \| 0.7568 \| 0.8657 \| 0.6739 \| 0.7522 \| 0.3772 \| 0.7799 \| 0.6658 \| 0.7102 \| \| 0.1003 \| 40.11 \| 14840 \| 0.5568 \| 0.6938 \| 0.8027 \| 0.8731 \| 0.9307 \| 0.8190 \| 0.8441 \| 0.4911 \| 0.9014 \| 0.8655 \| 0.7668 \| 0.8659 \| 0.6782 \| 0.7516 \| 0.3845 \| 0.7851 \| 0.6677 \| 0.7235 \| \| 0.1876 \| 40.16 \| 14860 \| 0.5569 \| 0.6948 \| 0.8014 \| 0.8735 \| 0.9317 \| 0.8163 \| 0.8593 \| 0.4999 \| 0.9088 \| 0.8370 \| 0.7569 \| 0.8669 \| 0.6796 \| 0.7488 \| 0.3910 \| 0.7847 \| 0.6742 \| 0.7186 \| \| 0.1911 \| 40.22 \| 14880 \| 0.5425 \| 0.6941 \| 0.8083 \| 0.8738 \| 0.9302 \| 0.8237 \| 0.8678 \| 0.4900 \| 0.8875 \| 0.8798 \| 0.7791 \| 0.8681 \| 0.6750 \| 0.7483 \| 0.3762 \| 0.7852 \| 0.6759 \| 0.7304 \| \| 0.2452 \| 40.27 \| 14900 \| 0.5416 \| 0.6949 \| 0.8067 \| 0.8761 \| 0.9335 \| 0.8207 \| 0.8548 \| 0.4748 \| 0.8882 \| 0.8637 \| 0.8113 \| 0.8696 \| 0.6707 \| 0.7449 \| 0.3573 \| 0.7889 \| 0.6750 \| 0.7582 \| \| 0.1584 \| 40.32 \| 14920 \| 0.5212 \| 0.6915 \| 0.8043 \| 0.8746 \| 0.9345 \| 0.8157 \| 0.8682 \| 0.4560 \| 0.8813 \| 0.8720 \| 0.8024 \| 0.8679 \| 0.6698 \| 0.7462 \| 0.3467 \| 0.7874 \| 0.6759 \| 0.7467 \| \| 0.1308 \| 40.38 \| 14940 \| 0.5571 \| 0.6847 \| 0.7996 \| 0.8701 \| 0.9315 \| 0.8329 \| 0.8717 \| 0.4544 \| 0.8927 \| 0.8726 \| 0.7413 \| 0.8677 \| 0.6802 \| 0.7430 \| 0.3440 \| 0.7825 \| 0.6649 \| 0.7103 \| \| 0.1618 \| 40.43 \| 14960 \| 0.5995 \| 0.6829 \| 0.7980 \| 0.8685 \| 0.9332 \| 0.8245 \| 0.8659 \| 0.4842 \| 0.8970 \| 0.8592 \| 0.7217 \| 0.8683 \| 0.6802 \| 0.7435 \| 0.3530 \| 0.7801 \| 0.6599 \| 0.6956 \| \| 0.1534 \| 40.49 \| 14980 \| 0.5638 \| 0.6891 \| 0.7991 \| 0.8720 \| 0.9338 \| 0.8280 \| 0.8527 \| 0.4667 \| 0.9008 \| 0.8612 \| 0.7509 \| 0.8675 \| 0.6793 \| 0.7472 \| 0.3558 \| 0.7829 \| 0.6735 \| 0.7177 \| \| 0.2106 \| 40.54 \| 15000 \| 0.5425 \| 0.6857 \| 0.7977 \| 0.8703 \| 0.9336 \| 0.8019 \| 0.8692 \| 0.4726 \| 0.8889 \| 0.8523 \| 0.7654 \| 0.8677 \| 0.6708 \| 0.7480 \| 0.3436 \| 0.7780 \| 0.6774 \| 0.7144 \| \| 0.354 \| 40.59 \| 15020 \| 0.5624 \| 0.6866 \| 0.8001 \| 0.8704 \| 0.9353 \| 0.8050 \| 0.8576 \| 0.4813 \| 0.8845 \| 0.8732 \| 0.7634 \| 0.8688 \| 0.6659 \| 0.7478 \| 0.3570 \| 0.7772 \| 0.6741 \| 0.7150 \| \| 0.2076 \| 40.65 \| 15040 \| 0.5313 \| 0.6878 \| 0.7973 \| 0.8718 \| 0.9335 \| 0.7936 \| 0.8647 \| 0.4629 \| 0.8919 \| 0.8648 \| 0.7696 \| 0.8685 \| 0.6698 \| 0.7495 \| 0.3525 \| 0.7810 \| 0.6749 \| 0.7187 \| \| 0.2259 \| 40.7 \| 15060 \| 0.5863 \| 0.6810 \| 0.7920 \| 0.8680 \| 0.9389 \| 0.7749 \| 0.8614 \| 0.4676 \| 0.8837 \| 0.8760 \| 0.7416 \| 0.8690 \| 0.6671 \| 0.7470 \| 0.3448 \| 0.7737 \| 0.6645 \| 0.7009 \| \| 0.1089 \| 40.76 \| 15080 \| 0.5667 \| 0.6846 \| 0.7934 \| 0.8700 \| 0.9306 \| 0.7920 \| 0.8619 \| 0.4550 \| 0.8983 \| 0.8608 \| 0.7552 \| 0.8689 \| 0.6689 \| 0.7460 \| 0.3516 \| 0.7774 \| 0.6731 \| 0.7064 \| \| 0.1985 \| 40.81 \| 15100 \| 0.5746 \| 0.6852 \| 0.7922 \| 0.8710 \| 0.9395 \| 0.8048 \| 0.8625 \| 0.4457 \| 0.9008 \| 0.8620 \| 0.7298 \| 0.8702 \| 0.6842 \| 0.7426 \| 0.3464 \| 0.7827 \| 0.6672 \| 0.7032 \| \| 0.2077 \| 40.86 \| 15120 \| 0.6194 \| 0.6860 \| 0.7942 \| 0.8708 \| 0.9381 \| 0.8099 \| 0.8644 \| 0.4613 \| 0.9026 \| 0.8608 \| 0.7223 \| 0.8702 \| 0.6848 \| 0.7435 \| 0.3577 \| 0.7832 \| 0.6654 \| 0.6969 \| \| 0.3211 \| 40.92 \| 15140 \| 0.5721 \| 0.6908 \| 0.8018 \| 0.8724 \| 0.9329 \| 0.7984 \| 0.8731 \| 0.4909 \| 0.8950 \| 0.8706 \| 0.7519 \| 0.8700 \| 0.6818 \| 0.7414 \| 0.3629 \| 0.7829 \| 0.6786 \| 0.7181 \| \| 0.1905 \| 40.97 \| 15160 \| 0.6226 \| 0.6913 \| 0.8014 \| 0.8726 \| 0.9336 \| 0.8158 \| 0.8653 \| 0.4835 \| 0.8994 \| 0.8663 \| 0.7461 \| 0.8706 \| 0.6841 \| 0.7474 \| 0.3639 \| 0.7819 \| 0.6761 \| 0.7149 \| \| 0.1425 \| 41.03 \| 15180 \| 0.6056 \| 0.6930 \| 0.7992 \| 0.8737 \| 0.9356 \| 0.8152 \| 0.8504 \| 0.4829 \| 0.9080 \| 0.8536 \| 0.7489 \| 0.8702 \| 0.6834 \| 0.7472 \| 0.3659 \| 0.7834 \| 0.6829 \| 0.7179 \| \| 0.1168 \| 41.08 \| 15200 \| 0.5751 \| 0.6940 \| 0.8069 \| 0.8735 \| 0.9348 \| 0.8303 \| 0.8649 \| 0.5107 \| 0.8983 \| 0.8643 \| 0.7448 \| 0.8706 \| 0.6811 \| 0.7456 \| 0.3795 \| 0.7845 \| 0.6799 \| 0.7166 \| \| 0.086 \| 41.14 \| 15220 \| 0.5493 \| 0.6947 \| 0.8031 \| 0.8743 \| 0.9343 \| 0.8231 \| 0.8562 \| 0.4884 \| 0.9041 \| 0.8623 \| 0.7533 \| 0.8703 \| 0.6848 \| 0.7461 \| 0.3760 \| 0.7863 \| 0.6785 \| 0.7208 \| \| 0.1855 \| 41.19 \| 15240 \| 0.5724 \| 0.6924 \| 0.7966 \| 0.8740 \| 0.9333 \| 0.8292 \| 0.8535 \| 0.4596 \| 0.9158 \| 0.8325 \| 0.7525 \| 0.8691 \| 0.6858 \| 0.7451 \| 0.3649 \| 0.7859 \| 0.6784 \| 0.7174 \| \| 0.0492 \| 41.24 \| 15260 \| 0.6137 \| 0.6902 \| 0.7969 \| 0.8727 \| 0.9405 \| 0.8323 \| 0.8365 \| 0.4716 \| 0.9080 \| 0.8575 \| 0.7321 \| 0.8679 \| 0.6820 \| 0.7423 \| 0.3731 \| 0.7871 \| 0.6706 \| 0.7081 \| \| 0.1612 \| 41.3 \| 15280 \| 0.5675 \| 0.6914 \| 0.8014 \| 0.8733 \| 0.9367 \| 0.8226 \| 0.8679 \| 0.4751 \| 0.8979 \| 0.8623 \| 0.7475 \| 0.8702 \| 0.6836 \| 0.7426 \| 0.3659 \| 0.7861 \| 0.6748 \| 0.7164 \| \| 0.1317 \| 41.35 \| 15300 \| 0.6373 \| 0.6892 \| 0.8021 \| 0.8703 \| 0.9340 \| 0.8263 \| 0.8559 \| 0.5042 \| 0.8989 \| 0.8713 \| 0.7244 \| 0.8683 \| 0.6790 \| 0.7479 \| 0.3902 \| 0.7821 \| 0.6606 \| 0.6961 \| \| 0.2445 \| 41.41 \| 15320 \| 0.5977 \| 0.6866 \| 0.8012 \| 0.8681 \| 0.9275 \| 0.8100 \| 0.8435 \| 0.5090 \| 0.8927 \| 0.8800 \| 0.7459 \| 0.8665 \| 0.6677 \| 0.7469 \| 0.3924 \| 0.7757 \| 0.6653 \| 0.6918 \| \| 0.1636 \| 41.46 \| 15340 \| 0.5728 \| 0.6885 \| 0.8013 \| 0.8701 \| 0.9312 \| 0.7956 \| 0.8557 \| 0.5054 \| 0.8891 \| 0.8701 \| 0.7623 \| 0.8667 \| 0.6663 \| 0.7506 \| 0.3795 \| 0.7786 \| 0.6739 \| 0.7043 \| \| 0.1409 \| 41.51 \| 15360 \| 0.6116 \| 0.6890 \| 0.7989 \| 0.8703 \| 0.9343 \| 0.8016 \| 0.8489 \| 0.5056 \| 0.8979 \| 0.8582 \| 0.7456 \| 0.8677 \| 0.6651 \| 0.7539 \| 0.3910 \| 0.7786 \| 0.6701 \| 0.6964 \| \| 0.1588 \| 41.57 \| 15380 \| 0.5694 \| 0.6898 \| 0.7994 \| 0.8714 \| 0.9346 \| 0.7957 \| 0.8644 \| 0.4974 \| 0.8945 \| 0.8505 \| 0.7590 \| 0.8684 \| 0.6684 \| 0.7535 \| 0.3769 \| 0.7800 \| 0.6750 \| 0.7063 \| \| 0.1663 \| 41.62 \| 15400 \| 0.5666 \| 0.6901 \| 0.8053 \| 0.8707 \| 0.9302 \| 0.8135 \| 0.8689 \| 0.5097 \| 0.8881 \| 0.8691 \| 0.7574 \| 0.8670 \| 0.6729 \| 0.7533 \| 0.3769 \| 0.7791 \| 0.6741 \| 0.7077 \| \| 0.1314 \| 41.68 \| 15420 \| 0.5718 \| 0.6937 \| 0.8029 \| 0.8727 \| 0.9314 \| 0.8171 \| 0.8418 \| 0.5087 \| 0.9047 \| 0.8625 \| 0.7544 \| 0.8666 \| 0.6782 \| 0.7517 \| 0.3845 \| 0.7834 \| 0.6774 \| 0.7139 \| \| 0.2941 \| 41.73 \| 15440 \| 0.5796 \| 0.6922 \| 0.8003 \| 0.8725 \| 0.9328 \| 0.8158 \| 0.8496 \| 0.4832 \| 0.9020 \| 0.8643 \| 0.7542 \| 0.8663 \| 0.6794 \| 0.7494 \| 0.3763 \| 0.7830 \| 0.6768 \| 0.7143 \| \| 0.1006 \| 41.78 \| 15460 \| 0.5848 \| 0.6879 \| 0.7983 \| 0.8705 \| 0.9349 \| 0.8157 \| 0.8510 \| 0.4731 \| 0.8927 \| 0.8676 \| 0.7532 \| 0.8661 \| 0.6816 \| 0.7458 \| 0.3534 \| 0.7778 \| 0.6742 \| 0.7163 \| \| 0.083 \| 41.84 \| 15480 \| 0.5681 \| 0.6853 \| 0.8023 \| 0.8688 \| 0.9312 \| 0.8305 \| 0.8626 \| 0.4771 \| 0.8814 \| 0.8734 \| 0.7595 \| 0.8661 \| 0.6839 \| 0.7471 \| 0.3324 \| 0.7733 \| 0.6750 \| 0.7195 \| \| 0.133 \| 41.89 \| 15500 \| 0.5777 \| 0.6856 \| 0.7990 \| 0.8691 \| 0.9364 \| 0.8230 \| 0.8427 \| 0.4706 \| 0.8831 \| 0.8800 \| 0.7569 \| 0.8654 \| 0.6831 \| 0.7483 \| 0.3344 \| 0.7736 \| 0.6757 \| 0.7190 \| \| 0.2027 \| 41.95 \| 15520 \| 0.5768 \| 0.6863 \| 0.8004 \| 0.8686 \| 0.9301 \| 0.8058 \| 0.8600 \| 0.5054 \| 0.8898 \| 0.8587 \| 0.7532 \| 0.8669 \| 0.6841 \| 0.7468 \| 0.3422 \| 0.7715 \| 0.6754 \| 0.7173 \| \| 0.2747 \| 42.0 \| 15540 \| 0.5677 \| 0.6844 \| 0.7915 \| 0.8694 \| 0.9351 \| 0.7751 \| 0.8569 \| 0.4625 \| 0.8912 \| 0.8537 \| 0.7657 \| 0.8663 \| 0.6772 \| 0.7467 \| 0.3274 \| 0.7734 \| 0.6753 \| 0.7242 \| \| 0.1922 \| 42.05 \| 15560 \| 0.5835 \| 0.6852 \| 0.7977 \| 0.8685 \| 0.9404 \| 0.8020 \| 0.8617 \| 0.4933 \| 0.8807 \| 0.8569 \| 0.7491 \| 0.8654 \| 0.6851 \| 0.7424 \| 0.3382 \| 0.7725 \| 0.6742 \| 0.7185 \| \| 0.0675 \| 42.11 \| 15580 \| 0.6323 \| 0.6829 \| 0.7929 \| 0.8682 \| 0.9452 \| 0.8078 \| 0.8576 \| 0.4661 \| 0.8825 \| 0.8532 \| 0.7377 \| 0.8639 \| 0.6849 \| 0.7410 \| 0.3311 \| 0.7731 \| 0.6738 \| 0.7128 \| \| 0.344 \| 42.16 \| 15600 \| 0.5709 \| 0.6870 \| 0.7964 \| 0.8701 \| 0.9394 \| 0.7835 \| 0.8659 \| 0.4898 \| 0.8870 \| 0.8564 \| 0.7524 \| 0.8661 \| 0.6786 \| 0.7474 \| 0.3460 \| 0.7764 \| 0.6774 \| 0.7173 \| \| 0.125 \| 42.22 \| 15620 \| 0.5475 \| 0.6960 \| 0.8002 \| 0.8737 \| 0.9331 \| 0.7964 \| 0.8468 \| 0.5046 \| 0.9077 \| 0.8527 \| 0.7598 \| 0.8662 \| 0.6806 \| 0.7539 \| 0.3933 \| 0.7843 \| 0.6768 \| 0.7171 \| \| 0.1713 \| 42.27 \| 15640 \| 0.5458 \| 0.6953 \| 0.8001 \| 0.8743 \| 0.9405 \| 0.8079 \| 0.8563 \| 0.4827 \| 0.8983 \| 0.8567 \| 0.7584 \| 0.8664 \| 0.6831 \| 0.7485 \| 0.3822 \| 0.7861 \| 0.6786 \| 0.7221 \| \| 1.7043 \| 42.32 \| 15660 \| 0.5768 \| 0.6964 \| 0.8020 \| 0.8740 \| 0.9388 \| 0.8151 \| 0.8449 \| 0.4936 \| 0.9001 \| 0.8707 \| 0.7506 \| 0.8667 \| 0.6813 \| 0.7503 \| 0.3968 \| 0.7842 \| 0.6772 \| 0.7182 \| \| 0.2095 \| 42.38 \| 15680 \| 0.5594 \| 0.6969 \| 0.8035 \| 0.8747 \| 0.9433 \| 0.8076 \| 0.8542 \| 0.5005 \| 0.8914 \| 0.8655 \| 0.7617 \| 0.8656 \| 0.6815 \| 0.7481 \| 0.3907 \| 0.7876 \| 0.6779 \| 0.7273 \| \| 0.2733 \| 42.43 \| 15700 \| 0.5576 \| 0.6942 \| 0.8026 \| 0.8739 \| 0.9435 \| 0.8113 \| 0.8656 \| 0.4881 \| 0.8876 \| 0.8656 \| 0.7568 \| 0.8680 \| 0.6844 \| 0.7441 \| 0.3746 \| 0.7846 \| 0.6772 \| 0.7261 \| \| 0.1815 \| 42.49 \| 15720 \| 0.5334 \| 0.6928 \| 0.8016 \| 0.8730 \| 0.9367 \| 0.8150 \| 0.8411 \| 0.4963 \| 0.8961 \| 0.8553 \| 0.7707 \| 0.8689 \| 0.6745 \| 0.7472 \| 0.3774 \| 0.7808 \| 0.6770 \| 0.7239 \| \| 0.6893 \| 42.54 \| 15740 \| 0.5713 \| 0.6868 \| 0.8000 \| 0.8697 \| 0.9306 \| 0.7965 \| 0.8702 \| 0.4901 \| 0.8868 \| 0.8600 \| 0.7657 \| 0.8692 \| 0.6693 \| 0.7456 \| 0.3598 \| 0.7730 \| 0.6764 \| 0.7146 \| \| 0.2895 \| 42.59 \| 15760 \| 0.5742 \| 0.6908 \| 0.8016 \| 0.8712 \| 0.9326 \| 0.8033 \| 0.8497 \| 0.5158 \| 0.8978 \| 0.8543 \| 0.7574 \| 0.8693 \| 0.6655 \| 0.7480 \| 0.3885 \| 0.7770 \| 0.6769 \| 0.7103 \| \| 0.1651 \| 42.65 \| 15780 \| 0.5621 \| 0.6913 \| 0.8010 \| 0.8726 \| 0.9350 \| 0.7999 \| 0.8561 \| 0.5020 \| 0.8951 \| 0.8461 \| 0.7726 \| 0.8686 \| 0.6651 \| 0.7467 \| 0.3770 \| 0.7808 \| 0.6783 \| 0.7230 \| \| 0.449 \| 42.7 \| 15800 \| 0.5858 \| 0.6869 \| 0.8018 \| 0.8699 \| 0.9356 \| 0.8069 \| 0.8563 \| 0.5081 \| 0.8846 \| 0.8587 \| 0.7623 \| 0.8693 \| 0.6651 \| 0.7477 \| 0.3575 \| 0.7732 \| 0.6769 \| 0.7184 \| \| 0.1698 \| 42.76 \| 15820 \| 0.5816 \| 0.6848 \| 0.7945 \| 0.8693 \| 0.9375 \| 0.7910 \| 0.8581 \| 0.4779 \| 0.8894 \| 0.8547 \| 0.7528 \| 0.8690 \| 0.6786 \| 0.7444 \| 0.3352 \| 0.7715 \| 0.6771 \| 0.7180 \| \| 0.2006 \| 42.81 \| 15840 \| 0.6001 \| 0.6854 \| 0.7968 \| 0.8691 \| 0.9409 \| 0.8045 \| 0.8469 \| 0.4956 \| 0.8878 \| 0.8578 \| 0.7444 \| 0.8687 \| 0.6803 \| 0.7442 \| 0.3423 \| 0.7725 \| 0.6735 \| 0.7161 \| \| 0.1577 \| 42.86 \| 15860 \| 0.5450 \| 0.6840 \| 0.7929 \| 0.8694 \| 0.9403 \| 0.7646 \| 0.8591 \| 0.4852 \| 0.8863 \| 0.8611 \| 0.7537 \| 0.8685 \| 0.6691 \| 0.7435 \| 0.3371 \| 0.7739 \| 0.6774 \| 0.7185 \| \| 0.1936 \| 42.92 \| 15880 \| 0.5545 \| 0.6879 \| 0.7945 \| 0.8718 \| 0.9361 \| 0.7968 \| 0.8605 \| 0.4570 \| 0.8976 \| 0.8518 \| 0.7614 \| 0.8684 \| 0.6781 \| 0.7421 \| 0.3501 \| 0.7804 \| 0.6758 \| 0.7205 \| \| 0.187 \| 42.97 \| 15900 \| 0.5604 \| 0.6892 \| 0.8003 \| 0.8719 \| 0.9387 \| 0.8140 \| 0.8597 \| 0.4801 \| 0.8886 \| 0.8594 \| 0.7619 \| 0.8692 \| 0.6803 \| 0.7403 \| 0.3501 \| 0.7794 \| 0.6754 \| 0.7298 \| \| 0.1292 \| 43.03 \| 15920 \| 0.5918 \| 0.6899 \| 0.7987 \| 0.8723 \| 0.9343 \| 0.8061 \| 0.8655 \| 0.4822 \| 0.9014 \| 0.8520 \| 0.7494 \| 0.8705 \| 0.6816 \| 0.7414 \| 0.3591 \| 0.7811 \| 0.6760 \| 0.7198 \| \| 0.2654 \| 43.08 \| 15940 \| 0.5506 \| 0.6924 \| 0.8040 \| 0.8733 \| 0.9316 \| 0.8047 \| 0.8668 \| 0.5038 \| 0.8963 \| 0.8558 \| 0.7690 \| 0.8702 \| 0.6782 \| 0.7449 \| 0.3624 \| 0.7832 \| 0.6761 \| 0.7316 \| \| 0.1696 \| 43.14 \| 15960 \| 0.5600 \| 0.6910 \| 0.8012 \| 0.8727 \| 0.9401 \| 0.7865 \| 0.8614 \| 0.4967 \| 0.8849 \| 0.8721 \| 0.7668 \| 0.8702 \| 0.6720 \| 0.7463 \| 0.3690 \| 0.7820 \| 0.6737 \| 0.7236 \| \| 0.2917 \| 43.19 \| 15980 \| 0.5430 \| 0.6926 \| 0.7988 \| 0.8744 \| 0.9379 \| 0.7929 \| 0.8628 \| 0.4769 \| 0.8968 \| 0.8486 \| 0.7756 \| 0.8706 \| 0.6744 \| 0.7458 \| 0.3646 \| 0.7850 \| 0.6774 \| 0.7302 \| \| 0.0827 \| 43.24 \| 16000 \| 0.5455 \| 0.6914 \| 0.7944 \| 0.8734 \| 0.9381 \| 0.7788 \| 0.8395 \| 0.4787 \| 0.9044 \| 0.8579 \| 0.7635 \| 0.8699 \| 0.6717 \| 0.7451 \| 0.3722 \| 0.7834 \| 0.6767 \| 0.7209 \| \| 0.2404 \| 43.3 \| 16020 \| 0.5516 \| 0.6906 \| 0.7971 \| 0.8732 \| 0.9376 \| 0.7851 \| 0.8611 \| 0.4708 \| 0.8933 \| 0.8598 \| 0.7722 \| 0.8706 \| 0.6717 \| 0.7443 \| 0.3645 \| 0.7817 \| 0.6772 \| 0.7243 \| \| 0.1556 \| 43.35 \| 16040 \| 0.5462 \| 0.6925 \| 0.8005 \| 0.8740 \| 0.9348 \| 0.8013 \| 0.8643 \| 0.4726 \| 0.8955 \| 0.8622 \| 0.7731 \| 0.8710 \| 0.6781 \| 0.7439 \| 0.3634 \| 0.7834 \| 0.6764 \| 0.7314 \| \| 0.1845 \| 43.41 \| 16060 \| 0.5758 \| 0.6900 \| 0.7977 \| 0.8726 \| 0.9373 \| 0.8043 \| 0.8549 \| 0.4728 \| 0.8985 \| 0.8616 \| 0.7542 \| 0.8716 \| 0.6811 \| 0.7436 \| 0.3533 \| 0.7796 \| 0.6770 \| 0.7236 \| \| 0.0959 \| 43.46 \| 16080 \| 0.5496 \| 0.6879 \| 0.7945 \| 0.8715 \| 0.9385 \| 0.7898 \| 0.8393 \| 0.4796 \| 0.8961 \| 0.8472 \| 0.7711 \| 0.8710 \| 0.6724 \| 0.7427 \| 0.3525 \| 0.7764 \| 0.6759 \| 0.7241 \| \| 0.1834 \| 43.51 \| 16100 \| 0.5518 \| 0.6892 \| 0.7975 \| 0.8726 \| 0.9407 \| 0.7930 \| 0.8585 \| 0.4830 \| 0.8892 \| 0.8362 \| 0.7816 \| 0.8708 \| 0.6710 \| 0.7472 \| 0.3442 \| 0.7781 \| 0.6783 \| 0.7348 \| \| 0.2178 \| 43.57 \| 16120 \| 0.5328 \| 0.6921 \| 0.8030 \| 0.8734 \| 0.9350 \| 0.8053 \| 0.8563 \| 0.5076 \| 0.8924 \| 0.8315 \| 0.7927 \| 0.8709 \| 0.6699 \| 0.7490 \| 0.3533 \| 0.7788 \| 0.6799 \| 0.7427 \| \| 0.1988 \| 43.62 \| 16140 \| 0.5387 \| 0.6941 \| 0.8093 \| 0.8742 \| 0.9337 \| 0.8106 \| 0.8586 \| 0.5149 \| 0.8813 \| 0.8615 \| 0.8042 \| 0.8718 \| 0.6710 \| 0.7514 \| 0.3541 \| 0.7794 \| 0.6751 \| 0.7559 \| \| 0.3371 \| 43.68 \| 16160 \| 0.5451 \| 0.6948 \| 0.8107 \| 0.8745 \| 0.9361 \| 0.8125 \| 0.8608 \| 0.5162 \| 0.8753 \| 0.8655 \| 0.8087 \| 0.8707 \| 0.6722 \| 0.7527 \| 0.3544 \| 0.7810 \| 0.6747 \| 0.7576 \| \| 1.6466 \| 43.73 \| 16180 \| 0.5541 \| 0.6939 \| 0.8030 \| 0.8738 \| 0.9353 \| 0.8098 \| 0.8611 \| 0.4924 \| 0.8941 \| 0.8530 \| 0.7751 \| 0.8704 \| 0.6773 \| 0.7537 \| 0.3695 \| 0.7808 \| 0.6745 \| 0.7312 \| \| 0.2617 \| 43.78 \| 16200 \| 0.5834 \| 0.6951 \| 0.8019 \| 0.8739 \| 0.9381 \| 0.8178 \| 0.8479 \| 0.4975 \| 0.9021 \| 0.8580 \| 0.7523 \| 0.8700 \| 0.6792 \| 0.7536 \| 0.3892 \| 0.7828 \| 0.6701 \| 0.7209 \| \| 0.2101 \| 43.84 \| 16220 \| 0.5582 \| 0.6940 \| 0.8025 \| 0.8735 \| 0.9336 \| 0.8114 \| 0.8511 \| 0.4866 \| 0.8968 \| 0.8705 \| 0.7675 \| 0.8678 \| 0.6763 \| 0.7535 \| 0.3827 \| 0.7837 \| 0.6701 \| 0.7243 \| \| 0.248 \| 43.89 \| 16240 \| 0.5753 \| 0.6913 \| 0.8026 \| 0.8719 \| 0.9363 \| 0.8156 \| 0.8578 \| 0.4772 \| 0.8858 \| 0.8880 \| 0.7575 \| 0.8660 \| 0.6777 \| 0.7501 \| 0.3793 \| 0.7831 \| 0.6637 \| 0.7189 \| \| 0.0976 \| 43.95 \| 16260 \| 0.5836 \| 0.6927 \| 0.7998 \| 0.8728 \| 0.9350 \| 0.8089 \| 0.8572 \| 0.4870 \| 0.9019 \| 0.8582 \| 0.7504 \| 0.8679 \| 0.6799 \| 0.7492 \| 0.3808 \| 0.7828 \| 0.6723 \| 0.7159 \| \| 0.1359 \| 44.0 \| 16280 \| 0.5986 \| 0.6928 \| 0.8023 \| 0.8729 \| 0.9355 \| 0.8258 \| 0.8575 \| 0.4942 \| 0.9022 \| 0.8580 \| 0.7430 \| 0.8687 \| 0.6795 \| 0.7498 \| 0.3827 \| 0.7839 \| 0.6713 \| 0.7136 \| \| 0.218 \| 44.05 \| 16300 \| 0.5782 \| 0.6934 \| 0.8002 \| 0.8732 \| 0.9368 \| 0.8057 \| 0.8505 \| 0.4946 \| 0.9012 \| 0.8595 \| 0.7528 \| 0.8682 \| 0.6810 \| 0.7469 \| 0.3838 \| 0.7839 \| 0.6711 \| 0.7190 \| \| 0.0846 \| 44.11 \| 16320 \| 0.5503 \| 0.6929 \| 0.8037 \| 0.8732 \| 0.9361 \| 0.8267 \| 0.8657 \| 0.4897 \| 0.8946 \| 0.8532 \| 0.7598 \| 0.8685 \| 0.6821 \| 0.7442 \| 0.3739 \| 0.7838 \| 0.6730 \| 0.7250 \| \| 0.0841 \| 44.16 \| 16340 \| 0.5649 \| 0.6924 \| 0.8015 \| 0.8730 \| 0.9346 \| 0.8188 \| 0.8560 \| 0.4862 \| 0.8994 \| 0.8579 \| 0.7576 \| 0.8685 \| 0.6774 \| 0.7467 \| 0.3787 \| 0.7833 \| 0.6699 \| 0.7220 \| \| 0.138 \| 44.22 \| 16360 \| 0.5352 \| 0.6917 \| 0.8020 \| 0.8722 \| 0.9318 \| 0.7998 \| 0.8597 \| 0.5036 \| 0.8948 \| 0.8518 \| 0.7726 \| 0.8673 \| 0.6706 \| 0.7498 \| 0.3805 \| 0.7817 \| 0.6724 \| 0.7198 \| \| 0.2396 \| 44.27 \| 16380 \| 0.5898 \| 0.6893 \| 0.7971 \| 0.8713 \| 0.9356 \| 0.7989 \| 0.8599 \| 0.4779 \| 0.8967 \| 0.8606 \| 0.7503 \| 0.8678 \| 0.6759 \| 0.7459 \| 0.3729 \| 0.7798 \| 0.6699 \| 0.7128 \| \| 0.1535 \| 44.32 \| 16400 \| 0.5528 \| 0.6914 \| 0.8003 \| 0.8720 \| 0.9319 \| 0.8086 \| 0.8525 \| 0.4884 \| 0.8988 \| 0.8591 \| 0.7626 \| 0.8666 \| 0.6793 \| 0.7472 \| 0.3750 \| 0.7816 \| 0.6698 \| 0.7205 \| \| 0.1736 \| 44.38 \| 16420 \| 0.5528 \| 0.6919 \| 0.7969 \| 0.8735 \| 0.9377 \| 0.8168 \| 0.8477 \| 0.4540 \| 0.9000 \| 0.8561 \| 0.7660 \| 0.8655 \| 0.6819 \| 0.7432 \| 0.3698 \| 0.7864 \| 0.6703 \| 0.7263 \| \| 0.1992 \| 44.43 \| 16440 \| 0.5871 \| 0.6912 \| 0.7981 \| 0.8723 \| 0.9371 \| 0.8070 \| 0.8657 \| 0.4709 \| 0.8975 \| 0.8629 \| 0.7452 \| 0.8669 \| 0.6839 \| 0.7436 \| 0.3786 \| 0.7839 \| 0.6676 \| 0.7137 \| \| 0.2313 \| 44.49 \| 16460 \| 0.5677 \| 0.6935 \| 0.8018 \| 0.8732 \| 0.9371 \| 0.8135 \| 0.8642 \| 0.4868 \| 0.8965 \| 0.8653 \| 0.7494 \| 0.8665 \| 0.6815 \| 0.7453 \| 0.3865 \| 0.7861 \| 0.6714 \| 0.7173 \| \| 0.124 \| 44.54 \| 16480 \| 0.6107 \| 0.6914 \| 0.8017 \| 0.8711 \| 0.9335 \| 0.8205 \| 0.8562 \| 0.5112 \| 0.9042 \| 0.8585 \| 0.7275 \| 0.8683 \| 0.6794 \| 0.7500 \| 0.3988 \| 0.7815 \| 0.6621 \| 0.6997 \| \| 0.1359 \| 44.59 \| 16500 \| 0.5790 \| 0.6935 \| 0.8042 \| 0.8727 \| 0.9342 \| 0.8224 \| 0.8697 \| 0.5065 \| 0.8998 \| 0.8527 \| 0.7441 \| 0.8671 \| 0.6789 \| 0.7465 \| 0.3922 \| 0.7851 \| 0.6728 \| 0.7117 \| \| 0.2572 \| 44.65 \| 16520 \| 0.5907 \| 0.6919 \| 0.8025 \| 0.8724 \| 0.9346 \| 0.8322 \| 0.8607 \| 0.4982 \| 0.9031 \| 0.8458 \| 0.7428 \| 0.8675 \| 0.6778 \| 0.7443 \| 0.3847 \| 0.7843 \| 0.6715 \| 0.7128 \| \| 0.1618 \| 44.7 \| 16540 \| 0.5507 \| 0.6949 \| 0.8049 \| 0.8737 \| 0.9316 \| 0.8115 \| 0.8589 \| 0.5026 \| 0.8969 \| 0.8590 \| 0.7734 \| 0.8672 \| 0.6752 \| 0.7483 \| 0.3889 \| 0.7855 \| 0.6723 \| 0.7270 \| \| 0.1022 \| 44.76 \| 16560 \| 0.5530 \| 0.6934 \| 0.8010 \| 0.8735 \| 0.9352 \| 0.7987 \| 0.8503 \| 0.4847 \| 0.8931 \| 0.8648 \| 0.7801 \| 0.8665 \| 0.6719 \| 0.7473 \| 0.3849 \| 0.7856 \| 0.6711 \| 0.7265 \| \| 0.0975 \| 44.81 \| 16580 \| 0.5464 \| 0.6923 \| 0.7998 \| 0.8733 \| 0.9321 \| 0.8051 \| 0.8521 \| 0.4701 \| 0.8961 \| 0.8606 \| 0.7827 \| 0.8662 \| 0.6718 \| 0.7458 \| 0.3799 \| 0.7855 \| 0.6720 \| 0.7252 \| \| 0.1967 \| 44.86 \| 16600 \| 0.5835 \| 0.6893 \| 0.8004 \| 0.8704 \| 0.9298 \| 0.8013 \| 0.8634 \| 0.5021 \| 0.8976 \| 0.8577 \| 0.7508 \| 0.8662 \| 0.6693 \| 0.7460 \| 0.3878 \| 0.7799 \| 0.6712 \| 0.7049 \| \| 0.1643 \| 44.92 \| 16620 \| 0.5923 \| 0.6886 \| 0.7946 \| 0.8704 \| 0.9351 \| 0.7896 \| 0.8547 \| 0.4878 \| 0.9003 \| 0.8447 \| 0.7501 \| 0.8662 \| 0.6708 \| 0.7443 \| 0.3847 \| 0.7786 \| 0.6743 \| 0.7012 \| \| 0.2977 \| 44.97 \| 16640 \| 0.5870 \| 0.6887 \| 0.7951 \| 0.8709 \| 0.9369 \| 0.7886 \| 0.8553 \| 0.4781 \| 0.8971 \| 0.8644 \| 0.7453 \| 0.8680 \| 0.6709 \| 0.7468 \| 0.3824 \| 0.7792 \| 0.6728 \| 0.7007 \| \| 0.1541 \| 45.03 \| 16660 \| 0.5820 \| 0.6859 \| 0.7942 \| 0.8699 \| 0.9342 \| 0.7986 \| 0.8597 \| 0.4528 \| 0.8924 \| 0.8690 \| 0.7524 \| 0.8667 \| 0.6706 \| 0.7448 \| 0.3690 \| 0.7776 \| 0.6711 \| 0.7018 \| \| 0.1896 \| 45.08 \| 16680 \| 0.5527 \| 0.6892 \| 0.7964 \| 0.8718 \| 0.9328 \| 0.8019 \| 0.8546 \| 0.4620 \| 0.8974 \| 0.8574 \| 0.7687 \| 0.8670 \| 0.6703 \| 0.7458 \| 0.3736 \| 0.7821 \| 0.6740 \| 0.7117 \| \| 1.3552 \| 45.14 \| 16700 \| 0.5711 \| 0.6900 \| 0.8013 \| 0.8716 \| 0.9352 \| 0.8069 \| 0.8606 \| 0.4923 \| 0.8914 \| 0.8660 \| 0.7568 \| 0.8686 \| 0.6672 \| 0.7452 \| 0.3851 \| 0.7815 \| 0.6740 \| 0.7088 \| \| 0.2574 \| 45.19 \| 16720 \| 0.5742 \| 0.6898 \| 0.8009 \| 0.8712 \| 0.9328 \| 0.8003 \| 0.8590 \| 0.4953 \| 0.8929 \| 0.8698 \| 0.7563 \| 0.8690 \| 0.6677 \| 0.7465 \| 0.3846 \| 0.7797 \| 0.6740 \| 0.7068 \| \| 0.0881 \| 45.24 \| 16740 \| 0.5674 \| 0.6899 \| 0.7971 \| 0.8714 \| 0.9339 \| 0.7903 \| 0.8497 \| 0.4926 \| 0.8995 \| 0.8543 \| 0.7595 \| 0.8684 \| 0.6678 \| 0.7465 \| 0.3848 \| 0.7800 \| 0.6756 \| 0.7059 \| \| 0.1921 \| 45.3 \| 16760 \| 0.5661 \| 0.6893 \| 0.7965 \| 0.8721 \| 0.9360 \| 0.7912 \| 0.8675 \| 0.4678 \| 0.8936 \| 0.8550 \| 0.7646 \| 0.8683 \| 0.6688 \| 0.7459 \| 0.3729 \| 0.7818 \| 0.6767 \| 0.7106 \| \| 0.2151 \| 45.35 \| 16780 \| 0.5675 \| 0.6882 \| 0.7980 \| 0.8706 \| 0.9294 \| 0.8043 \| 0.8658 \| 0.4780 \| 0.8980 \| 0.8512 \| 0.7594 \| 0.8679 \| 0.6692 \| 0.7463 \| 0.3759 \| 0.7789 \| 0.6771 \| 0.7020 \| \| 0.2561 \| 45.41 \| 16800 \| 0.5671 \| 0.6863 \| 0.7922 \| 0.8707 \| 0.9321 \| 0.7896 \| 0.8597 \| 0.4527 \| 0.9011 \| 0.8521 \| 0.7581 \| 0.8678 \| 0.6706 \| 0.7433 \| 0.3635 \| 0.7798 \| 0.6768 \| 0.7022 \| \| 0.2134 \| 45.46 \| 16820 \| 0.5914 \| 0.6850 \| 0.7933 \| 0.8697 \| 0.9293 \| 0.7952 \| 0.8649 \| 0.4569 \| 0.9003 \| 0.8540 \| 0.7524 \| 0.8677 \| 0.6685 \| 0.7414 \| 0.3652 \| 0.7775 \| 0.6756 \| 0.6994 \| \| 0.1302 \| 45.51 \| 16840 \| 0.5701 \| 0.6869 \| 0.7950 \| 0.8701 \| 0.9313 \| 0.7960 \| 0.8615 \| 0.4770 \| 0.8996 \| 0.8421 \| 0.7574 \| 0.8668 \| 0.6689 \| 0.7441 \| 0.3744 \| 0.7788 \| 0.6752 \| 0.7002 \| \| 0.1511 \| 45.57 \| 16860 \| 0.5676 \| 0.6885 \| 0.7989 \| 0.8704 \| 0.9319 \| 0.7977 \| 0.8592 \| 0.4974 \| 0.8958 \| 0.8547 \| 0.7556 \| 0.8666 \| 0.6675 \| 0.7457 \| 0.3834 \| 0.7801 \| 0.6762 \| 0.7004 \| \| 0.107 \| 45.62 \| 16880 \| 0.5839 \| 0.6881 \| 0.7957 \| 0.8710 \| 0.9380 \| 0.7988 \| 0.8588 \| 0.4721 \| 0.8950 \| 0.8595 \| 0.7477 \| 0.8681 \| 0.6709 \| 0.7444 \| 0.3747 \| 0.7800 \| 0.6769 \| 0.7015 \| \| 0.1537 \| 45.68 \| 16900 \| 0.5476 \| 0.6871 \| 0.7989 \| 0.8701 \| 0.9338 \| 0.7988 \| 0.8663 \| 0.4889 \| 0.8887 \| 0.8529 \| 0.7627 \| 0.8672 \| 0.6707 \| 0.7451 \| 0.3635 \| 0.7779 \| 0.6774 \| 0.7080 \| \| 0.2827 \| 45.73 \| 16920 \| 0.5664 \| 0.6887 \| 0.7985 \| 0.8711 \| 0.9355 \| 0.8167 \| 0.8571 \| 0.4762 \| 0.8957 \| 0.8578 \| 0.7505 \| 0.8672 \| 0.6798 \| 0.7419 \| 0.3660 \| 0.7808 \| 0.6775 \| 0.7077 \| \| 0.1563 \| 45.78 \| 16940 \| 0.5821 \| 0.6871 \| 0.7969 \| 0.8700 \| 0.9316 \| 0.7993 \| 0.8576 \| 0.4792 \| 0.8967 \| 0.8643 \| 0.7493 \| 0.8675 \| 0.6746 \| 0.7431 \| 0.3696 \| 0.7782 \| 0.6743 \| 0.7022 \| \| 0.2185 \| 45.84 \| 16960 \| 0.5599 \| 0.6836 \| 0.7929 \| 0.8692 \| 0.9370 \| 0.7951 \| 0.8652 \| 0.4628 \| 0.8910 \| 0.8473 \| 0.7516 \| 0.8663 \| 0.6747 \| 0.7374 \| 0.3487 \| 0.7781 \| 0.6768 \| 0.7032 \| \| 0.222 \| 45.89 \| 16980 \| 0.6002 \| 0.6813 \| 0.7913 \| 0.8685 \| 0.9413 \| 0.7986 \| 0.8563 \| 0.4529 \| 0.8878 \| 0.8653 \| 0.7372 \| 0.8660 \| 0.6771 \| 0.7349 \| 0.3389 \| 0.7782 \| 0.6722 \| 0.7019 \| \| 0.1177 \| 45.95 \| 17000 \| 0.5830 \| 0.6806 \| 0.7892 \| 0.8682 \| 0.9355 \| 0.7860 \| 0.8612 \| 0.4440 \| 0.8911 \| 0.8532 \| 0.7537 \| 0.8660 \| 0.6708 \| 0.7367 \| 0.3370 \| 0.7763 \| 0.6765 \| 0.7010 \| \| 0.1475 \| 46.0 \| 17020 \| 0.6041 \| 0.6798 \| 0.7864 \| 0.8690 \| 0.9365 \| 0.7879 \| 0.8659 \| 0.4130 \| 0.8942 \| 0.8586 \| 0.7485 \| 0.8657 \| 0.6742 \| 0.7364 \| 0.3253 \| 0.7791 \| 0.6752 \| 0.7028 \| \| 0.1955 \| 46.05 \| 17040 \| 0.5875 \| 0.6802 \| 0.7932 \| 0.8670 \| 0.9361 \| 0.7959 \| 0.8552 \| 0.4665 \| 0.8825 \| 0.8654 \| 0.7506 \| 0.8647 \| 0.6700 \| 0.7418 \| 0.3382 \| 0.7731 \| 0.6742 \| 0.6997 \| \| 0.1266 \| 46.11 \| 17060 \| 0.5758 \| 0.6832 \| 0.7978 \| 0.8680 \| 0.9279 \| 0.8001 \| 0.8576 \| 0.4831 \| 0.8856 \| 0.8557 \| 0.7744 \| 0.8660 \| 0.6712 \| 0.7465 \| 0.3394 \| 0.7728 \| 0.6729 \| 0.7134 \| \| 0.1401 \| 46.16 \| 17080 \| 0.5915 \| 0.6821 \| 0.7904 \| 0.8681 \| 0.9319 \| 0.7868 \| 0.8599 \| 0.4571 \| 0.8955 \| 0.8477 \| 0.7542 \| 0.8658 \| 0.6716 \| 0.7430 \| 0.3456 \| 0.7749 \| 0.6748 \| 0.6988 \| \| 0.1667 \| 46.22 \| 17100 \| 0.6235 \| 0.6830 \| 0.7912 \| 0.8685 \| 0.9305 \| 0.7992 \| 0.8465 \| 0.4527 \| 0.8998 \| 0.8588 \| 0.7507 \| 0.8658 \| 0.6710 \| 0.7446 \| 0.3528 \| 0.7758 \| 0.6744 \| 0.6963 \| \| 0.3457 \| 46.27 \| 17120 \| 0.5880 \| 0.6838 \| 0.7940 \| 0.8684 \| 0.9266 \| 0.7901 \| 0.8612 \| 0.4706 \| 0.8981 \| 0.8572 \| 0.7539 \| 0.8662 \| 0.6699 \| 0.7492 \| 0.3538 \| 0.7748 \| 0.6751 \| 0.6976 \| \| 0.1532 \| 46.32 \| 17140 \| 0.5769 \| 0.6822 \| 0.7960 \| 0.8676 \| 0.9343 \| 0.8066 \| 0.8563 \| 0.4731 \| 0.8835 \| 0.8635 \| 0.7543 \| 0.8653 \| 0.6705 \| 0.7443 \| 0.3464 \| 0.7732 \| 0.6757 \| 0.6998 \| \| 0.0841 \| 46.38 \| 17160 \| 0.5844 \| 0.6853 \| 0.7975 \| 0.8691 \| 0.9308 \| 0.8124 \| 0.8582 \| 0.4786 \| 0.8940 \| 0.8537 \| 0.7544 \| 0.8657 \| 0.6693 \| 0.7440 \| 0.3639 \| 0.7776 \| 0.6767 \| 0.6997 \| \| 0.1693 \| 46.43 \| 17180 \| 0.5783 \| 0.6858 \| 0.7952 \| 0.8695 \| 0.9338 \| 0.8027 \| 0.8494 \| 0.4747 \| 0.8957 \| 0.8583 \| 0.7518 \| 0.8658 \| 0.6697 \| 0.7443 \| 0.3679 \| 0.7785 \| 0.6771 \| 0.6971 \| \| 0.301 \| 46.49 \| 17200 \| 0.5805 \| 0.6854 \| 0.7922 \| 0.8697 \| 0.9351 \| 0.8009 \| 0.8429 \| 0.4677 \| 0.9017 \| 0.8494 \| 0.7479 \| 0.8657 \| 0.6696 \| 0.7444 \| 0.3678 \| 0.7798 \| 0.6750 \| 0.6954 \| \| 0.2313 \| 46.54 \| 17220 \| 0.6168 \| 0.6833 \| 0.7940 \| 0.8685 \| 0.9329 \| 0.8082 \| 0.8601 \| 0.4705 \| 0.8977 \| 0.8502 \| 0.7383 \| 0.8659 \| 0.6692 \| 0.7442 \| 0.3659 \| 0.7786 \| 0.6701 \| 0.6893 \| \| 0.0797 \| 46.59 \| 17240 \| 0.5888 \| 0.6855 \| 0.7917 \| 0.8697 \| 0.9337 \| 0.7843 \| 0.8561 \| 0.4666 \| 0.9007 \| 0.8532 \| 0.7472 \| 0.8663 \| 0.6702 \| 0.7459 \| 0.3682 \| 0.7795 \| 0.6745 \| 0.6941 \| \| 0.1081 \| 46.65 \| 17260 \| 0.5985 \| 0.6837 \| 0.7910 \| 0.8697 \| 0.9352 \| 0.8008 \| 0.8645 \| 0.4397 \| 0.8965 \| 0.8499 \| 0.7502 \| 0.8659 \| 0.6712 \| 0.7410 \| 0.3553 \| 0.7799 \| 0.6765 \| 0.6965 \| \| 0.1764 \| 46.7 \| 17280 \| 0.5906 \| 0.6859 \| 0.7944 \| 0.8698 \| 0.9319 \| 0.7982 \| 0.8652 \| 0.4635 \| 0.8976 \| 0.8563 \| 0.7484 \| 0.8669 \| 0.6721 \| 0.7459 \| 0.3668 \| 0.7787 \| 0.6742 \| 0.6965 \| \| 0.2976 \| 46.76 \| 17300 \| 0.5939 \| 0.6864 \| 0.7952 \| 0.8698 \| 0.9328 \| 0.7964 \| 0.8673 \| 0.4718 \| 0.8958 \| 0.8514 \| 0.7507 \| 0.8668 \| 0.6716 \| 0.7455 \| 0.3705 \| 0.7785 \| 0.6752 \| 0.6967 \| \| 0.1649 \| 46.81 \| 17320 \| 0.6012 \| 0.6851 \| 0.7915 \| 0.8698 \| 0.9349 \| 0.8003 \| 0.8531 \| 0.4458 \| 0.8986 \| 0.8610 \| 0.7467 \| 0.8662 \| 0.6728 \| 0.7438 \| 0.3637 \| 0.7792 \| 0.6741 \| 0.6957 \| \| 0.3159 \| 46.86 \| 17340 \| 0.5776 \| 0.6861 \| 0.7962 \| 0.8695 \| 0.9327 \| 0.7969 \| 0.8685 \| 0.4762 \| 0.8924 \| 0.8533 \| 0.7535 \| 0.8661 \| 0.6711 \| 0.7428 \| 0.3709 \| 0.7787 \| 0.6760 \| 0.6975 \| \| 0.0889 \| 46.92 \| 17360 \| 0.5593 \| 0.6858 \| 0.7952 \| 0.8695 \| 0.9327 \| 0.7738 \| 0.8665 \| 0.4856 \| 0.8908 \| 0.8584 \| 0.7587 \| 0.8658 \| 0.6671 \| 0.7409 \| 0.3702 \| 0.7788 \| 0.6769 \| 0.7009 \| \| 0.1136 \| 46.97 \| 17380 \| 0.5620 \| 0.6860 \| 0.7935 \| 0.8697 \| 0.9372 \| 0.7844 \| 0.8580 \| 0.4767 \| 0.8920 \| 0.8514 \| 0.7551 \| 0.8647 \| 0.6699 \| 0.7409 \| 0.3701 \| 0.7796 \| 0.6774 \| 0.6998 \| \| 0.0898 \| 47.03 \| 17400 \| 0.6127 \| 0.6875 \| 0.7960 \| 0.8705 \| 0.9371 \| 0.8083 \| 0.8603 \| 0.4688 \| 0.8956 \| 0.8601 \| 0.7417 \| 0.8651 \| 0.6765 \| 0.7419 \| 0.3717 \| 0.7821 \| 0.6742 \| 0.7007 \| \| 0.2659 \| 47.08 \| 17420 \| 0.5871 \| 0.6894 \| 0.7979 \| 0.8712 \| 0.9316 \| 0.8010 \| 0.8654 \| 0.4770 \| 0.8997 \| 0.8614 \| 0.7489 \| 0.8665 \| 0.6771 \| 0.7450 \| 0.3736 \| 0.7817 \| 0.6756 \| 0.7062 \| \| 0.1993 \| 47.14 \| 17440 \| 0.6110 \| 0.6910 \| 0.7991 \| 0.8719 \| 0.9334 \| 0.8201 \| 0.8481 \| 0.4852 \| 0.9056 \| 0.8585 \| 0.7429 \| 0.8666 \| 0.6801 \| 0.7452 \| 0.3807 \| 0.7836 \| 0.6729 \| 0.7081 \| \| 0.6759 \| 47.19 \| 17460 \| 0.6108 \| 0.6898 \| 0.7991 \| 0.8715 \| 0.9319 \| 0.8170 \| 0.8590 \| 0.4770 \| 0.9020 \| 0.8624 \| 0.7440 \| 0.8666 \| 0.6792 \| 0.7455 \| 0.3756 \| 0.7828 \| 0.6720 \| 0.7072 \| \| 0.3493 \| 47.24 \| 17480 \| 0.5807 \| 0.6884 \| 0.7973 \| 0.8715 \| 0.9325 \| 0.8143 \| 0.8716 \| 0.4591 \| 0.8995 \| 0.8553 \| 0.7490 \| 0.8666 \| 0.6794 \| 0.7428 \| 0.3630 \| 0.7836 \| 0.6768 \| 0.7065 \| \| 1.1969 \| 47.3 \| 17500 \| 0.5830 \| 0.6894 \| 0.8009 \| 0.8716 \| 0.9343 \| 0.8273 \| 0.8659 \| 0.4762 \| 0.8958 \| 0.8589 \| 0.7475 \| 0.8660 \| 0.6793 \| 0.7426 \| 0.3681 \| 0.7841 \| 0.6766 \| 0.7088 \| \| 0.2925 \| 47.35 \| 17520 \| 0.5777 \| 0.6890 \| 0.7957 \| 0.8710 \| 0.9350 \| 0.7949 \| 0.8508 \| 0.4839 \| 0.9005 \| 0.8514 \| 0.7531 \| 0.8664 \| 0.6765 \| 0.7452 \| 0.3728 \| 0.7812 \| 0.6753 \| 0.7056 \| \| 0.1624 \| 47.41 \| 17540 \| 0.5718 \| 0.6887 \| 0.7983 \| 0.8709 \| 0.9360 \| 0.8097 \| 0.8584 \| 0.4825 \| 0.8944 \| 0.8563 \| 0.7509 \| 0.8659 \| 0.6795 \| 0.7429 \| 0.3686 \| 0.7816 \| 0.6760 \| 0.7063 \| \| 0.1431 \| 47.46 \| 17560 \| 0.5648 \| 0.6864 \| 0.7947 \| 0.8703 \| 0.9367 \| 0.8056 \| 0.8620 \| 0.4635 \| 0.8951 \| 0.8503 \| 0.7495 \| 0.8654 \| 0.6778 \| 0.7402 \| 0.3609 \| 0.7816 \| 0.6755 \| 0.7032 \| \| 0.3399 \| 47.51 \| 17580 \| 0.5932 \| 0.6888 \| 0.7999 \| 0.8708 \| 0.9332 \| 0.8139 \| 0.8631 \| 0.4861 \| 0.8959 \| 0.8598 \| 0.7474 \| 0.8665 \| 0.6779 \| 0.7434 \| 0.3730 \| 0.7818 \| 0.6751 \| 0.7040 \| \| 0.2807 \| 47.57 \| 17600 \| 0.6147 \| 0.6890 \| 0.7964 \| 0.8713 \| 0.9327 \| 0.8105 \| 0.8611 \| 0.4676 \| 0.9040 \| 0.8567 \| 0.7421 \| 0.8670 \| 0.6795 \| 0.7461 \| 0.3726 \| 0.7826 \| 0.6718 \| 0.7032 \| \| 0.1749 \| 47.62 \| 17620 \| 0.5876 \| 0.6866 \| 0.7996 \| 0.8696 \| 0.9365 \| 0.8126 \| 0.8634 \| 0.4794 \| 0.8840 \| 0.8724 \| 0.7493 \| 0.8651 \| 0.6754 \| 0.7383 \| 0.3706 \| 0.7801 \| 0.6764 \| 0.7002 \| \| 0.2923 \| 47.68 \| 17640 \| 0.6136 \| 0.6892 \| 0.7968 \| 0.8711 \| 0.9332 \| 0.8137 \| 0.8570 \| 0.4787 \| 0.9054 \| 0.8508 \| 0.7390 \| 0.8666 \| 0.6781 \| 0.7455 \| 0.3797 \| 0.7821 \| 0.6700 \| 0.7020 \| \| 0.11 \| 47.73 \| 17660 \| 0.6215 \| 0.6886 \| 0.7977 \| 0.8710 \| 0.9334 \| 0.8180 \| 0.8694 \| 0.4789 \| 0.9041 \| 0.8476 \| 0.7327 \| 0.8668 \| 0.6787 \| 0.7454 \| 0.3778 \| 0.7833 \| 0.6690 \| 0.6992 \| \| 0.1884 \| 47.78 \| 17680 \| 0.5969 \| 0.6886 \| 0.7962 \| 0.8707 \| 0.9348 \| 0.8053 \| 0.8524 \| 0.4755 \| 0.8989 \| 0.8604 \| 0.7464 \| 0.8665 \| 0.6752 \| 0.7465 \| 0.3785 \| 0.7804 \| 0.6724 \| 0.7005 \| \| 0.2251 \| 47.84 \| 17700 \| 0.5911 \| 0.6846 \| 0.7895 \| 0.8701 \| 0.9340 \| 0.8002 \| 0.8500 \| 0.4295 \| 0.9015 \| 0.8602 \| 0.7509 \| 0.8664 \| 0.6723 \| 0.7441 \| 0.3568 \| 0.7790 \| 0.6746 \| 0.6986 \| \| 0.2057 \| 47.89 \| 17720 \| 0.5940 \| 0.6863 \| 0.7949 \| 0.8699 \| 0.9365 \| 0.8072 \| 0.8506 \| 0.4683 \| 0.8947 \| 0.8569 \| 0.7498 \| 0.8658 \| 0.6692 \| 0.7432 \| 0.3744 \| 0.7794 \| 0.6750 \| 0.6972 \| \| 0.1724 \| 47.95 \| 17740 \| 0.5831 \| 0.6872 \| 0.7950 \| 0.8701 \| 0.9323 \| 0.8035 \| 0.8605 \| 0.4827 \| 0.9031 \| 0.8323 \| 0.7508 \| 0.8667 \| 0.6696 \| 0.7456 \| 0.3780 \| 0.7792 \| 0.6732 \| 0.6980 \| \| 0.1173 \| 48.0 \| 17760 \| 0.6074 \| 0.6874 \| 0.7990 \| 0.8698 \| 0.9319 \| 0.8008 \| 0.8714 \| 0.4907 \| 0.8939 \| 0.8605 \| 0.7435 \| 0.8673 \| 0.6719 \| 0.7454 \| 0.3795 \| 0.7793 \| 0.6723 \| 0.6961 \| \| 0.8592 \| 48.05 \| 17780 \| 0.5757 \| 0.6849 \| 0.7921 \| 0.8699 \| 0.9348 \| 0.8001 \| 0.8577 \| 0.4457 \| 0.8958 \| 0.8550 \| 0.7555 \| 0.8655 \| 0.6690 \| 0.7424 \| 0.3621 \| 0.7799 \| 0.6757 \| 0.6995 \| \| 0.1753 \| 48.11 \| 17800 \| 0.5664 \| 0.6867 \| 0.7929 \| 0.8704 \| 0.9332 \| 0.7859 \| 0.8596 \| 0.4656 \| 0.8998 \| 0.8509 \| 0.7554 \| 0.8666 \| 0.6685 \| 0.7448 \| 0.3710 \| 0.7806 \| 0.6761 \| 0.6991 \| \| 0.1293 \| 48.16 \| 17820 \| 0.5801 \| 0.6847 \| 0.7894 \| 0.8702 \| 0.9362 \| 0.7863 \| 0.8680 \| 0.4540 \| 0.9021 \| 0.8296 \| 0.7498 \| 0.8659 \| 0.6696 \| 0.7414 \| 0.3637 \| 0.7818 \| 0.6726 \| 0.6980 \| \| 0.1466 \| 48.22 \| 17840 \| 0.6018 \| 0.6865 \| 0.7967 \| 0.8699 \| 0.9352 \| 0.8069 \| 0.8598 \| 0.4755 \| 0.8944 \| 0.8620 \| 0.7429 \| 0.8661 \| 0.6711 \| 0.7429 \| 0.3759 \| 0.7812 \| 0.6732 \| 0.6951 \| \| 0.1305 \| 48.27 \| 17860 \| 0.5926 \| 0.6869 \| 0.7944 \| 0.8700 \| 0.9330 \| 0.7882 \| 0.8580 \| 0.4770 \| 0.8985 \| 0.8558 \| 0.7502 \| 0.8668 \| 0.6682 \| 0.7448 \| 0.3776 \| 0.7791 \| 0.6749 \| 0.6969 \| \| 0.252 \| 48.32 \| 17880 \| 0.6012 \| 0.6852 \| 0.7916 \| 0.8692 \| 0.9317 \| 0.7725 \| 0.8558 \| 0.4697 \| 0.8987 \| 0.8640 \| 0.7490 \| 0.8667 \| 0.6671 \| 0.7437 \| 0.3736 \| 0.7775 \| 0.6720 \| 0.6959 \| \| 0.176 \| 48.38 \| 17900 \| 0.5816 \| 0.6857 \| 0.7896 \| 0.8704 \| 0.9382 \| 0.7821 \| 0.8497 \| 0.4503 \| 0.8985 \| 0.8567 \| 0.7516 \| 0.8653 \| 0.6686 \| 0.7437 \| 0.3667 \| 0.7814 \| 0.6756 \| 0.6984 \| \| 0.1354 \| 48.43 \| 17920 \| 0.5885 \| 0.6866 \| 0.7965 \| 0.8698 \| 0.9383 \| 0.7939 \| 0.8655 \| 0.4795 \| 0.8873 \| 0.8632 \| 0.7481 \| 0.8653 \| 0.6703 \| 0.7420 \| 0.3758 \| 0.7805 \| 0.6744 \| 0.6981 \| \| 0.0773 \| 48.49 \| 17940 \| 0.5890 \| 0.6846 \| 0.7979 \| 0.8692 \| 0.9380 \| 0.8073 \| 0.8778 \| 0.4728 \| 0.8817 \| 0.8590 \| 0.7486 \| 0.8643 \| 0.6719 \| 0.7348 \| 0.3646 \| 0.7812 \| 0.6761 \| 0.6993 \| \| 0.1057 \| 48.54 \| 17960 \| 0.5929 \| 0.6855 \| 0.7928 \| 0.8703 \| 0.9382 \| 0.8082 \| 0.8636 \| 0.4427 \| 0.8942 \| 0.8564 \| 0.7460 \| 0.8652 \| 0.6748 \| 0.7406 \| 0.3605 \| 0.7815 \| 0.6761 \| 0.6998 \| \| 0.1428 \| 48.59 \| 17980 \| 0.5724 \| 0.6871 \| 0.7995 \| 0.8699 \| 0.9311 \| 0.8045 \| 0.8725 \| 0.4847 \| 0.8916 \| 0.8622 \| 0.7498 \| 0.8666 \| 0.6723 \| 0.7415 \| 0.3745 \| 0.7800 \| 0.6748 \| 0.6997 \| \| 0.1607 \| 48.65 \| 18000 \| 0.5906 \| 0.6874 \| 0.7968 \| 0.8705 \| 0.9340 \| 0.8172 \| 0.8630 \| 0.4680 \| 0.8982 \| 0.8524 \| 0.7449 \| 0.8655 \| 0.6750 \| 0.7424 \| 0.3728 \| 0.7821 \| 0.6742 \| 0.6998 \| \| 0.159 \| 48.7 \| 18020 \| 0.6010 \| 0.6882 \| 0.8004 \| 0.8701 \| 0.9354 \| 0.8119 \| 0.8650 \| 0.4949 \| 0.8912 \| 0.8634 \| 0.7409 \| 0.8658 \| 0.6757 \| 0.7414 \| 0.3813 \| 0.7810 \| 0.6721 \| 0.6999 \| \| 0.8972 \| 48.76 \| 18040 \| 0.5830 \| 0.6852 \| 0.7904 \| 0.8705 \| 0.9348 \| 0.8012 \| 0.8621 \| 0.4320 \| 0.9007 \| 0.8526 \| 0.7492 \| 0.8659 \| 0.6747 \| 0.7424 \| 0.3570 \| 0.7810 \| 0.6744 \| 0.7008 \| \| 0.1339 \| 48.81 \| 18060 \| 0.5907 \| 0.6863 \| 0.7959 \| 0.8700 \| 0.9335 \| 0.7957 \| 0.8694 \| 0.4691 \| 0.8929 \| 0.8591 \| 0.7513 \| 0.8662 \| 0.6717 \| 0.7408 \| 0.3699 \| 0.7802 \| 0.6751 \| 0.7005 \| \| 0.1247 \| 48.86 \| 18080 \| 0.5731 \| 0.6889 \| 0.7992 \| 0.8706 \| 0.9324 \| 0.8044 \| 0.8637 \| 0.4928 \| 0.8965 \| 0.8554 \| 0.7496 \| 0.8665 \| 0.6725 \| 0.7444 \| 0.3816 \| 0.7807 \| 0.6754 \| 0.7010 \| \| 0.2777 \| 48.92 \| 18100 \| 0.5914 \| 0.6881 \| 0.7955 \| 0.8706 \| 0.9306 \| 0.7950 \| 0.8613 \| 0.4777 \| 0.9030 \| 0.8501 \| 0.7509 \| 0.8671 \| 0.6716 \| 0.7448 \| 0.3763 \| 0.7797 \| 0.6759 \| 0.7013 \| \| 0.1174 \| 48.97 \| 18120 \| 0.5918 \| 0.6879 \| 0.7934 \| 0.8710 \| 0.9356 \| 0.8026 \| 0.8500 \| 0.4661 \| 0.9041 \| 0.8506 \| 0.7451 \| 0.8664 \| 0.6750 \| 0.7432 \| 0.3734 \| 0.7818 \| 0.6741 \| 0.7012 \| \| 0.1679 \| 49.03 \| 18140 \| 0.6104 \| 0.6886 \| 0.7963 \| 0.8708 \| 0.9340 \| 0.8055 \| 0.8518 \| 0.4844 \| 0.9041 \| 0.8542 \| 0.7403 \| 0.8664 \| 0.6750 \| 0.7451 \| 0.3803 \| 0.7825 \| 0.6717 \| 0.6993 \| \| 0.1887 \| 49.08 \| 18160 \| 0.6064 \| 0.6856 \| 0.7934 \| 0.8696 \| 0.9332 \| 0.7898 \| 0.8612 \| 0.4606 \| 0.8952 \| 0.8644 \| 0.7493 \| 0.8668 \| 0.6719 \| 0.7422 \| 0.3693 \| 0.7783 \| 0.6729 \| 0.6981 \| \| 0.2318 \| 49.14 \| 18180 \| 0.5992 \| 0.6876 \| 0.7973 \| 0.8701 \| 0.9329 \| 0.8117 \| 0.8590 \| 0.4754 \| 0.8966 \| 0.8567 \| 0.7490 \| 0.8664 \| 0.6727 \| 0.7442 \| 0.3777 \| 0.7794 \| 0.6744 \| 0.6986 \| \| 0.298 \| 49.19 \| 18200 \| 0.5877 \| 0.6846 \| 0.7942 \| 0.8695 \| 0.9321 \| 0.8075 \| 0.8668 \| 0.4455 \| 0.8935 \| 0.8625 \| 0.7517 \| 0.8663 \| 0.6718 \| 0.7405 \| 0.3614 \| 0.7788 \| 0.6750 \| 0.6985 \| \| 0.0678 \| 49.24 \| 18220 \| 0.5772 \| 0.6867 \| 0.7934 \| 0.8705 \| 0.9371 \| 0.8053 \| 0.8553 \| 0.4609 \| 0.8981 \| 0.8475 \| 0.7496 \| 0.8654 \| 0.6719 \| 0.7419 \| 0.3718 \| 0.7813 \| 0.6760 \| 0.6989 \| \| 0.156 \| 49.3 \| 18240 \| 0.5871 \| 0.6862 \| 0.7925 \| 0.8702 \| 0.9372 \| 0.8002 \| 0.8573 \| 0.4599 \| 0.8979 \| 0.8466 \| 0.7483 \| 0.8657 \| 0.6720 \| 0.7417 \| 0.3707 \| 0.7802 \| 0.6745 \| 0.6984 \| \| 0.1281 \| 49.35 \| 18260 \| 0.5737 \| 0.6857 \| 0.7913 \| 0.8702 \| 0.9357 \| 0.7840 \| 0.8605 \| 0.4548 \| 0.8979 \| 0.8570 \| 0.7494 \| 0.8660 \| 0.6711 \| 0.7417 \| 0.3669 \| 0.7811 \| 0.6754 \| 0.6980 \| \| 0.1969 \| 49.41 \| 18280 \| 0.5778 \| 0.6881 \| 0.7983 \| 0.8701 \| 0.9323 \| 0.7933 \| 0.8588 \| 0.4953 \| 0.8948 \| 0.8609 \| 0.7525 \| 0.8669 \| 0.6693 \| 0.7445 \| 0.3819 \| 0.7794 \| 0.6754 \| 0.6992 \| \| 0.6113 \| 49.46 \| 18300 \| 0.5947 \| 0.6868 \| 0.7978 \| 0.8696 \| 0.9324 \| 0.7983 \| 0.8647 \| 0.4854 \| 0.8932 \| 0.8637 \| 0.7466 \| 0.8669 \| 0.6712 \| 0.7427 \| 0.3775 \| 0.7788 \| 0.6726 \| 0.6976 \| \| 0.1182 \| 49.51 \| 18320 \| 0.6038 \| 0.6865 \| 0.7949 \| 0.8700 \| 0.9366 \| 0.8004 \| 0.8610 \| 0.4717 \| 0.8948 \| 0.8539 \| 0.7455 \| 0.8660 \| 0.6720 \| 0.7418 \| 0.3743 \| 0.7801 \| 0.6738 \| 0.6977 \| \| 0.2847 \| 49.57 \| 18340 \| 0.5843 \| 0.6879 \| 0.7966 \| 0.8701 \| 0.9318 \| 0.7919 \| 0.8567 \| 0.4863 \| 0.8970 \| 0.8583 \| 0.7545 \| 0.8663 \| 0.6683 \| 0.7453 \| 0.3815 \| 0.7793 \| 0.6757 \| 0.6989 \| \| 0.1221 \| 49.62 \| 18360 \| 0.5991 \| 0.6883 \| 0.7973 \| 0.8702 \| 0.9357 \| 0.8075 \| 0.8487 \| 0.4922 \| 0.8977 \| 0.8538 \| 0.7456 \| 0.8660 \| 0.6707 \| 0.7450 \| 0.3860 \| 0.7800 \| 0.6730 \| 0.6975 \| \| 0.3461 \| 49.68 \| 18380 \| 0.6064 \| 0.6875 \| 0.7930 \| 0.8702 \| 0.9378 \| 0.7899 \| 0.8451 \| 0.4777 \| 0.8990 \| 0.8562 \| 0.7456 \| 0.8659 \| 0.6714 \| 0.7432 \| 0.3807 \| 0.7798 \| 0.6736 \| 0.6977 \| \| 0.3113 \| 49.73 \| 18400 \| 0.5789 \| 0.6872 \| 0.7957 \| 0.8701 \| 0.9312 \| 0.8022 \| 0.8621 \| 0.4767 \| 0.9000 \| 0.8423 \| 0.7555 \| 0.8663 \| 0.6698 \| 0.7435 \| 0.3764 \| 0.7796 \| 0.6755 \| 0.6991 \| \| 0.143 \| 49.78 \| 18420 \| 0.5968 \| 0.6831 \| 0.7882 \| 0.8699 \| 0.9359 \| 0.7844 \| 0.8689 \| 0.4298 \| 0.8975 \| 0.8514 \| 0.7497 \| 0.8661 \| 0.6701 \| 0.7386 \| 0.3526 \| 0.7804 \| 0.6758 \| 0.6983 \| \| 0.1059 \| 49.84 \| 18440 \| 0.6216 \| 0.6848 \| 0.7897 \| 0.8704 \| 0.9392 \| 0.7951 \| 0.8648 \| 0.4421 \| 0.9001 \| 0.8480 \| 0.7386 \| 0.8656 \| 0.6749 \| 0.7390 \| 0.3603 \| 0.7832 \| 0.6731 \| 0.6977 \| \| 0.1687 \| 49.89 \| 18460 \| 0.5976 \| 0.6875 \| 0.7969 \| 0.8700 \| 0.9327 \| 0.8021 \| 0.8562 \| 0.4814 \| 0.8967 \| 0.8616 \| 0.7479 \| 0.8665 \| 0.6708 \| 0.7448 \| 0.3803 \| 0.7790 \| 0.6739 \| 0.6973 \| \| 0.1931 \| 49.95 \| 18480 \| 0.6122 \| 0.6876 \| 0.7979 \| 0.8699 \| 0.9317 \| 0.7972 \| 0.8658 \| 0.4922 \| 0.8967 \| 0.8553 \| 0.7464 \| 0.8671 \| 0.6702 \| 0.7440 \| 0.3812 \| 0.7790 \| 0.6737 \| 0.6980 \| \| 0.1159 \| 50.0 \| 18500 \| 0.6091 \| 0.6876 \| 0.7945 \| 0.8704 \| 0.9332 \| 0.7904 \| 0.8591 \| 0.4778 \| 0.9017 \| 0.8549 \| 0.7443 \| 0.8671 \| 0.6713 \| 0.7452 \| 0.3782 \| 0.7799 \| 0.6736 \| 0.6978 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3	[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]
peldrak/segformer-finetuned-coasts-final	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-coasts-final This model is a fine-tuned version of [peldrak/segformer-finetuned-coastalDataset](https://huggingface.co/peldrak/segformer-finetuned-coastalDataset) on the peldrak/coastal2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Mean Iou: 0.5765 - Mean Accuracy: 0.7934 - Overall Accuracy: 0.8942 - Accuracy Water: 0.9204 - Accuracy Whitewater: 0.6450 - Accuracy Sediment: 0.8936 - Accuracy Other Natural Terrain: 0.5526 - Accuracy Vegetation: 0.9077 - Accuracy Development: 0.8415 - Accuracy Unknown: nan - Iou Water: 0.8847 - Iou Whitewater: 0.4614 - Iou Sediment: 0.7695 - Iou Other Natural Terrain: 0.4632 - Iou Vegetation: 0.8233 - Iou Development: 0.6331 - Iou Unknown: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sediment \| Accuracy Other Natural Terrain \| Accuracy Vegetation \| Accuracy Development \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sediment \| Iou Other Natural Terrain \| Iou Vegetation \| Iou Development \| Iou Unknown \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-----------------:\|:------------------------------:\|:-------------------:\|:--------------------:\|:----------------:\|:---------:\|:--------------:\|:------------:\|:-------------------------:\|:--------------:\|:---------------:\|:-----------:\| \| 0.6692 \| 0.01 \| 20 \| 0.4614 \| 0.4642 \| 0.6595 \| 0.8193 \| 0.8313 \| 0.4104 \| 0.7626 \| 0.3112 \| 0.9007 \| 0.7410 \| nan \| 0.7682 \| 0.3175 \| 0.6041 \| 0.2766 \| 0.7352 \| 0.5478 \| 0.0 \| \| 0.6263 \| 0.02 \| 40 \| 0.4235 \| 0.4592 \| 0.6471 \| 0.8295 \| 0.9136 \| 0.3421 \| 0.7720 \| 0.2776 \| 0.8233 \| 0.7542 \| nan \| 0.8087 \| 0.2990 \| 0.6199 \| 0.2471 \| 0.7340 \| 0.5055 \| 0.0 \| \| 1.3017 \| 0.04 \| 60 \| 0.4394 \| 0.4653 \| 0.6590 \| 0.8330 \| 0.8610 \| 0.3290 \| 0.9077 \| 0.2547 \| 0.8425 \| 0.7591 \| nan \| 0.8012 \| 0.2886 \| 0.6560 \| 0.2308 \| 0.7409 \| 0.5394 \| 0.0 \| \| 0.4226 \| 0.05 \| 80 \| 0.4617 \| 0.4769 \| 0.6540 \| 0.8418 \| 0.9305 \| 0.4493 \| 0.7371 \| 0.2628 \| 0.8645 \| 0.6799 \| nan \| 0.8198 \| 0.3649 \| 0.6261 \| 0.2443 \| 0.7488 \| 0.5347 \| 0.0 \| \| 0.8408 \| 0.06 \| 100 \| 0.4295 \| 0.4505 \| 0.6816 \| 0.8087 \| 0.8050 \| 0.4237 \| 0.8763 \| 0.2882 \| 0.8330 \| 0.8634 \| nan \| 0.7757 \| 0.3119 \| 0.6338 \| 0.2439 \| 0.7017 \| 0.4861 \| 0.0 \| \| 1.474 \| 0.07 \| 120 \| 0.4297 \| 0.4708 \| 0.6926 \| 0.8290 \| 0.8725 \| 0.5252 \| 0.8807 \| 0.2439 \| 0.8128 \| 0.8206 \| nan \| 0.8183 \| 0.3891 \| 0.6635 \| 0.2246 \| 0.7283 \| 0.4714 \| 0.0 \| \| 0.5739 \| 0.08 \| 140 \| 0.3918 \| 0.4891 \| 0.6964 \| 0.8398 \| 0.8682 \| 0.4291 \| 0.8989 \| 0.3777 \| 0.8434 \| 0.7609 \| nan \| 0.8206 \| 0.3611 \| 0.6719 \| 0.3271 \| 0.7499 \| 0.4930 \| 0.0 \| \| 1.8433 \| 0.1 \| 160 \| 0.4038 \| 0.4801 \| 0.7087 \| 0.8276 \| 0.8590 \| 0.4685 \| 0.9100 \| 0.3518 \| 0.7974 \| 0.8656 \| nan \| 0.8192 \| 0.3831 \| 0.6661 \| 0.3156 \| 0.7184 \| 0.4585 \| 0.0 \| \| 0.3492 \| 0.11 \| 180 \| 0.3186 \| 0.5025 \| 0.7143 \| 0.8507 \| 0.9192 \| 0.5610 \| 0.8231 \| 0.3873 \| 0.8404 \| 0.7547 \| nan \| 0.8615 \| 0.3949 \| 0.7214 \| 0.3523 \| 0.7304 \| 0.4573 \| 0.0 \| \| 1.4639 \| 0.12 \| 200 \| 0.4100 \| 0.4907 \| 0.7152 \| 0.8368 \| 0.8950 \| 0.5659 \| 0.8607 \| 0.3800 \| 0.8092 \| 0.7800 \| nan \| 0.8302 \| 0.4098 \| 0.6733 \| 0.3275 \| 0.7245 \| 0.4700 \| 0.0 \| \| 0.4228 \| 0.13 \| 220 \| 0.4499 \| 0.4825 \| 0.7167 \| 0.8290 \| 0.8528 \| 0.6444 \| 0.9074 \| 0.3113 \| 0.8265 \| 0.7581 \| nan \| 0.8071 \| 0.4126 \| 0.6496 \| 0.2843 \| 0.7270 \| 0.4967 \| 0.0 \| \| 0.4334 \| 0.15 \| 240 \| 0.3982 \| 0.4789 \| 0.7042 \| 0.8384 \| 0.8739 \| 0.6612 \| 0.8529 \| 0.2549 \| 0.8633 \| 0.7188 \| nan \| 0.8264 \| 0.3878 \| 0.6766 \| 0.2461 \| 0.7383 \| 0.4771 \| 0.0 \| \| 0.2844 \| 0.16 \| 260 \| 0.4778 \| 0.4594 \| 0.6958 \| 0.8264 \| 0.8630 \| 0.6156 \| 0.8865 \| 0.2021 \| 0.8209 \| 0.7869 \| nan \| 0.8210 \| 0.3735 \| 0.6681 \| 0.1918 \| 0.7157 \| 0.4459 \| 0.0 \| \| 0.4044 \| 0.17 \| 280 \| 0.4410 \| 0.4585 \| 0.6861 \| 0.8289 \| 0.8873 \| 0.5803 \| 0.8906 \| 0.1865 \| 0.8000 \| 0.7719 \| nan \| 0.8319 \| 0.3753 \| 0.6804 \| 0.1753 \| 0.7113 \| 0.4354 \| 0.0 \| \| 0.3748 \| 0.18 \| 300 \| 0.4839 \| 0.4393 \| 0.6829 \| 0.7970 \| 0.8379 \| 0.5714 \| 0.9223 \| 0.2301 \| 0.7465 \| 0.7892 \| nan \| 0.7826 \| 0.3613 \| 0.5881 \| 0.1983 \| 0.6774 \| 0.4673 \| 0.0 \| \| 0.4755 \| 0.19 \| 320 \| 0.3532 \| 0.4889 \| 0.7063 \| 0.8552 \| 0.8869 \| 0.5985 \| 0.8407 \| 0.2399 \| 0.8972 \| 0.7746 \| nan \| 0.8519 \| 0.3580 \| 0.7004 \| 0.2270 \| 0.7688 \| 0.5159 \| 0.0 \| \| 0.1647 \| 0.21 \| 340 \| 0.4263 \| 0.4482 \| 0.6750 \| 0.8275 \| 0.8739 \| 0.4728 \| 0.8689 \| 0.1326 \| 0.8123 \| 0.8897 \| nan \| 0.8275 \| 0.3375 \| 0.6691 \| 0.1279 \| 0.7168 \| 0.4589 \| 0.0 \| \| 0.1324 \| 0.22 \| 360 \| 0.3186 \| 0.4722 \| 0.7102 \| 0.8414 \| 0.9062 \| 0.6599 \| 0.8066 \| 0.1877 \| 0.8335 \| 0.8674 \| nan \| 0.8473 \| 0.3452 \| 0.6860 \| 0.1817 \| 0.7490 \| 0.4958 \| 0.0 \| \| 1.2721 \| 0.23 \| 380 \| 0.4527 \| 0.4562 \| 0.6524 \| 0.8415 \| 0.9224 \| 0.5242 \| 0.7128 \| 0.0855 \| 0.8813 \| 0.7885 \| nan \| 0.8233 \| 0.3347 \| 0.6008 \| 0.0845 \| 0.7683 \| 0.5818 \| 0.0 \| \| 1.5569 \| 0.24 \| 400 \| 0.3610 \| 0.4686 \| 0.7072 \| 0.8468 \| 0.8868 \| 0.6755 \| 0.8329 \| 0.1835 \| 0.8760 \| 0.7884 \| nan \| 0.8391 \| 0.3119 \| 0.6641 \| 0.1672 \| 0.7827 \| 0.5152 \| 0.0 \| \| 0.3027 \| 0.25 \| 420 \| 0.3637 \| 0.4659 \| 0.7499 \| 0.8159 \| 0.8104 \| 0.7363 \| 0.8482 \| 0.4076 \| 0.8415 \| 0.8554 \| nan \| 0.7855 \| 0.2747 \| 0.7003 \| 0.3160 \| 0.7191 \| 0.4658 \| 0.0 \| \| 0.2337 \| 0.27 \| 440 \| 0.3187 \| 0.4821 \| 0.7265 \| 0.8479 \| 0.9054 \| 0.5871 \| 0.8284 \| 0.3716 \| 0.8357 \| 0.8309 \| nan \| 0.8486 \| 0.2510 \| 0.7033 \| 0.3185 \| 0.7569 \| 0.4964 \| 0.0 \| \| 0.9241 \| 0.28 \| 460 \| 0.3566 \| 0.4981 \| 0.6725 \| 0.8624 \| 0.9099 \| 0.4308 \| 0.7761 \| 0.3893 \| 0.9400 \| 0.5890 \| nan \| 0.8466 \| 0.3307 \| 0.6713 \| 0.3407 \| 0.7924 \| 0.5048 \| 0.0 \| \| 0.3494 \| 0.29 \| 480 \| 0.3679 \| 0.5070 \| 0.7207 \| 0.8460 \| 0.8849 \| 0.5082 \| 0.7560 \| 0.4808 \| 0.8855 \| 0.8088 \| nan \| 0.8284 \| 0.3782 \| 0.6844 \| 0.4029 \| 0.7387 \| 0.5163 \| 0.0 \| \| 0.1691 \| 0.3 \| 500 \| 0.3436 \| 0.5198 \| 0.7262 \| 0.8638 \| 0.9274 \| 0.5366 \| 0.7800 \| 0.4651 \| 0.8808 \| 0.7675 \| nan \| 0.8564 \| 0.3869 \| 0.6920 \| 0.4037 \| 0.7766 \| 0.5232 \| 0.0 \| \| 0.5782 \| 0.32 \| 520 \| 0.4702 \| 0.5067 \| 0.7430 \| 0.8398 \| 0.8207 \| 0.5963 \| 0.8942 \| 0.4582 \| 0.8883 \| 0.8005 \| nan \| 0.7941 \| 0.3921 \| 0.6395 \| 0.4094 \| 0.7838 \| 0.5280 \| 0.0 \| \| 0.7752 \| 0.33 \| 540 \| 0.6535 \| 0.4591 \| 0.6686 \| 0.7923 \| 0.7106 \| 0.5371 \| 0.8396 \| 0.3616 \| 0.9534 \| 0.6090 \| nan \| 0.6940 \| 0.4164 \| 0.5151 \| 0.3090 \| 0.7622 \| 0.5171 \| 0.0 \| \| 0.2089 \| 0.34 \| 560 \| 0.3457 \| 0.5121 \| 0.7382 \| 0.8549 \| 0.8789 \| 0.6058 \| 0.8199 \| 0.4140 \| 0.8932 \| 0.8171 \| nan \| 0.8497 \| 0.3949 \| 0.7054 \| 0.3535 \| 0.7516 \| 0.5294 \| 0.0 \| \| 0.1504 \| 0.35 \| 580 \| 0.3299 \| 0.5208 \| 0.7438 \| 0.8605 \| 0.8912 \| 0.6374 \| 0.8511 \| 0.3967 \| 0.8831 \| 0.8036 \| nan \| 0.8542 \| 0.4207 \| 0.7280 \| 0.3507 \| 0.7619 \| 0.5301 \| 0.0 \| \| 0.1052 \| 0.36 \| 600 \| 0.3514 \| 0.5118 \| 0.7143 \| 0.8557 \| 0.9408 \| 0.5685 \| 0.8064 \| 0.3304 \| 0.8314 \| 0.8086 \| nan \| 0.8346 \| 0.4004 \| 0.7211 \| 0.3147 \| 0.7433 \| 0.5682 \| 0.0 \| \| 0.3436 \| 0.38 \| 620 \| 0.3174 \| 0.5293 \| 0.7225 \| 0.8702 \| 0.9170 \| 0.5454 \| 0.8581 \| 0.3863 \| 0.8901 \| 0.7380 \| nan \| 0.8482 \| 0.3866 \| 0.7412 \| 0.3614 \| 0.7797 \| 0.5882 \| 0.0 \| \| 0.2951 \| 0.39 \| 640 \| 0.3557 \| 0.5297 \| 0.7490 \| 0.8605 \| 0.8547 \| 0.6049 \| 0.8916 \| 0.4654 \| 0.9130 \| 0.7643 \| nan \| 0.8272 \| 0.4138 \| 0.7145 \| 0.3913 \| 0.7777 \| 0.5834 \| 0.0 \| \| 0.3383 \| 0.4 \| 660 \| 0.3802 \| 0.5108 \| 0.7373 \| 0.8460 \| 0.9167 \| 0.5489 \| 0.8977 \| 0.4706 \| 0.7794 \| 0.8104 \| nan \| 0.8249 \| 0.3975 \| 0.7184 \| 0.3596 \| 0.7186 \| 0.5569 \| 0.0 \| \| 0.4272 \| 0.41 \| 680 \| 0.2963 \| 0.5330 \| 0.7518 \| 0.8669 \| 0.9489 \| 0.6026 \| 0.7575 \| 0.5282 \| 0.8565 \| 0.8174 \| nan \| 0.8580 \| 0.4109 \| 0.7019 \| 0.4198 \| 0.7806 \| 0.5601 \| 0.0 \| \| 0.3004 \| 0.42 \| 700 \| 0.3287 \| 0.5242 \| 0.7068 \| 0.8728 \| 0.9485 \| 0.4648 \| 0.7569 \| 0.4087 \| 0.8993 \| 0.7628 \| nan \| 0.8661 \| 0.3948 \| 0.6820 \| 0.3687 \| 0.7975 \| 0.5607 \| 0.0 \| \| 0.368 \| 0.44 \| 720 \| 0.3581 \| 0.5145 \| 0.7082 \| 0.8678 \| 0.8976 \| 0.4916 \| 0.9031 \| 0.3196 \| 0.8945 \| 0.7427 \| nan \| 0.8625 \| 0.4132 \| 0.6936 \| 0.2926 \| 0.7928 \| 0.5464 \| 0.0 \| \| 0.5273 \| 0.45 \| 740 \| 0.3392 \| 0.5172 \| 0.7461 \| 0.8659 \| 0.8987 \| 0.6086 \| 0.8829 \| 0.3754 \| 0.8723 \| 0.8384 \| nan \| 0.8710 \| 0.3943 \| 0.7320 \| 0.3510 \| 0.7704 \| 0.5018 \| 0.0 \| \| 0.8596 \| 0.46 \| 760 \| 0.2994 \| 0.5144 \| 0.7075 \| 0.8733 \| 0.9402 \| 0.5261 \| 0.8305 \| 0.2849 \| 0.8849 \| 0.7781 \| nan \| 0.8820 \| 0.4074 \| 0.7324 \| 0.2753 \| 0.7808 \| 0.5227 \| 0.0 \| \| 0.3141 \| 0.47 \| 780 \| 0.2862 \| 0.5220 \| 0.7148 \| 0.8762 \| 0.9304 \| 0.6100 \| 0.8318 \| 0.2835 \| 0.9128 \| 0.7206 \| nan \| 0.8776 \| 0.4229 \| 0.7512 \| 0.2734 \| 0.7838 \| 0.5453 \| 0.0 \| \| 0.4501 \| 0.49 \| 800 \| 0.3087 \| 0.5179 \| 0.7518 \| 0.8713 \| 0.9077 \| 0.7417 \| 0.8486 \| 0.3256 \| 0.9016 \| 0.7858 \| nan \| 0.8720 \| 0.3803 \| 0.7325 \| 0.3019 \| 0.7876 \| 0.5509 \| 0.0 \| \| 0.5333 \| 0.5 \| 820 \| 0.3242 \| 0.5222 \| 0.7365 \| 0.8736 \| 0.9122 \| 0.6724 \| 0.8373 \| 0.2926 \| 0.9118 \| 0.7930 \| nan \| 0.8753 \| 0.4221 \| 0.7275 \| 0.2756 \| 0.7903 \| 0.5645 \| 0.0 \| \| 0.4082 \| 0.51 \| 840 \| 0.3147 \| 0.5108 \| 0.7301 \| 0.8651 \| 0.8951 \| 0.6418 \| 0.8918 \| 0.2432 \| 0.8836 \| 0.8253 \| nan \| 0.8640 \| 0.4205 \| 0.7213 \| 0.2295 \| 0.7843 \| 0.5561 \| 0.0 \| \| 0.1804 \| 0.52 \| 860 \| 0.3430 \| 0.5104 \| 0.7248 \| 0.8642 \| 0.8920 \| 0.6020 \| 0.8910 \| 0.2571 \| 0.8860 \| 0.8208 \| nan \| 0.8544 \| 0.4244 \| 0.7103 \| 0.2378 \| 0.7834 \| 0.5628 \| 0.0 \| \| 0.2636 \| 0.53 \| 880 \| 0.2953 \| 0.5190 \| 0.7434 \| 0.8647 \| 0.9241 \| 0.6135 \| 0.8433 \| 0.3692 \| 0.8507 \| 0.8594 \| nan \| 0.8685 \| 0.4108 \| 0.7447 \| 0.3300 \| 0.7575 \| 0.5215 \| 0.0 \| \| 0.9171 \| 0.55 \| 900 \| 0.2782 \| 0.5373 \| 0.7328 \| 0.8752 \| 0.9118 \| 0.5616 \| 0.8318 \| 0.4369 \| 0.9192 \| 0.7355 \| nan \| 0.8710 \| 0.4169 \| 0.7506 \| 0.3880 \| 0.7798 \| 0.5546 \| 0.0 \| \| 0.1809 \| 0.56 \| 920 \| 0.2736 \| 0.5392 \| 0.7376 \| 0.8749 \| 0.9108 \| 0.5501 \| 0.8474 \| 0.4279 \| 0.9061 \| 0.7835 \| nan \| 0.8691 \| 0.4246 \| 0.7473 \| 0.3769 \| 0.7837 \| 0.5728 \| 0.0 \| \| 0.3095 \| 0.57 \| 940 \| 0.2762 \| 0.5405 \| 0.7460 \| 0.8771 \| 0.9084 \| 0.5879 \| 0.8590 \| 0.4068 \| 0.9076 \| 0.8066 \| nan \| 0.8716 \| 0.4199 \| 0.7538 \| 0.3667 \| 0.7874 \| 0.5842 \| 0.0 \| \| 0.6095 \| 0.58 \| 960 \| 0.2831 \| 0.5220 \| 0.7405 \| 0.8655 \| 0.9084 \| 0.6134 \| 0.8646 \| 0.3468 \| 0.8689 \| 0.8412 \| nan \| 0.8645 \| 0.4026 \| 0.7262 \| 0.3220 \| 0.7696 \| 0.5695 \| 0.0 \| \| 0.6369 \| 0.59 \| 980 \| 0.3259 \| 0.5190 \| 0.7511 \| 0.8612 \| 0.8955 \| 0.6528 \| 0.9060 \| 0.3588 \| 0.8511 \| 0.8425 \| nan \| 0.8545 \| 0.4057 \| 0.7171 \| 0.3390 \| 0.7648 \| 0.5522 \| 0.0 \| \| 1.711 \| 0.61 \| 1000 \| 0.3439 \| 0.5268 \| 0.7580 \| 0.8615 \| 0.8617 \| 0.6757 \| 0.8944 \| 0.4164 \| 0.9026 \| 0.7975 \| nan \| 0.8434 \| 0.4338 \| 0.7032 \| 0.3844 \| 0.7818 \| 0.5411 \| 0.0 \| \| 0.2443 \| 0.62 \| 1020 \| 0.2789 \| 0.5429 \| 0.7415 \| 0.8809 \| 0.8993 \| 0.6534 \| 0.8807 \| 0.4180 \| 0.9425 \| 0.6553 \| nan \| 0.8714 \| 0.4214 \| 0.7501 \| 0.3893 \| 0.8031 \| 0.5651 \| 0.0 \| \| 0.1833 \| 0.63 \| 1040 \| 0.3325 \| 0.5356 \| 0.7394 \| 0.8787 \| 0.9275 \| 0.6594 \| 0.8686 \| 0.3051 \| 0.8942 \| 0.7819 \| nan \| 0.8675 \| 0.4338 \| 0.7404 \| 0.2979 \| 0.7905 \| 0.6189 \| 0.0 \| \| 0.0592 \| 0.64 \| 1060 \| 0.3044 \| 0.5287 \| 0.7271 \| 0.8714 \| 0.9078 \| 0.6267 \| 0.8723 \| 0.2993 \| 0.9020 \| 0.7541 \| nan \| 0.8564 \| 0.4381 \| 0.7202 \| 0.2867 \| 0.7899 \| 0.6094 \| 0.0 \| \| 0.5377 \| 0.65 \| 1080 \| 0.2896 \| 0.5328 \| 0.7307 \| 0.8780 \| 0.9091 \| 0.5475 \| 0.8544 \| 0.3355 \| 0.9168 \| 0.8209 \| nan \| 0.8693 \| 0.4136 \| 0.7217 \| 0.3155 \| 0.8081 \| 0.6012 \| 0.0 \| \| 0.25 \| 0.67 \| 1100 \| 0.3252 \| 0.5161 \| 0.7578 \| 0.8583 \| 0.8770 \| 0.6339 \| 0.9036 \| 0.3850 \| 0.8583 \| 0.8890 \| nan \| 0.8596 \| 0.4195 \| 0.7235 \| 0.3470 \| 0.7676 \| 0.4955 \| 0.0 \| \| 0.3291 \| 0.68 \| 1120 \| 0.3295 \| 0.5100 \| 0.7740 \| 0.8550 \| 0.8794 \| 0.6870 \| 0.9169 \| 0.4707 \| 0.8352 \| 0.8547 \| nan \| 0.8526 \| 0.3684 \| 0.7032 \| 0.3674 \| 0.7645 \| 0.5141 \| 0.0 \| \| 0.1965 \| 0.69 \| 1140 \| 0.2703 \| 0.5336 \| 0.7583 \| 0.8752 \| 0.9154 \| 0.6437 \| 0.8906 \| 0.4315 \| 0.8769 \| 0.7919 \| nan \| 0.8785 \| 0.4010 \| 0.7532 \| 0.3803 \| 0.7833 \| 0.5385 \| 0.0 \| \| 0.129 \| 0.7 \| 1160 \| 0.2486 \| 0.5496 \| 0.7453 \| 0.8864 \| 0.9272 \| 0.5909 \| 0.8937 \| 0.4061 \| 0.9048 \| 0.7493 \| nan \| 0.8838 \| 0.4231 \| 0.7696 \| 0.3737 \| 0.7984 \| 0.5990 \| 0.0 \| \| 0.5261 \| 0.72 \| 1180 \| 0.2643 \| 0.5498 \| 0.7565 \| 0.8759 \| 0.9306 \| 0.6428 \| 0.8678 \| 0.4760 \| 0.8724 \| 0.7493 \| nan \| 0.8654 \| 0.4343 \| 0.7636 \| 0.4222 \| 0.7719 \| 0.5914 \| 0.0 \| \| 0.1468 \| 0.73 \| 1200 \| 0.2658 \| 0.5414 \| 0.7664 \| 0.8706 \| 0.9212 \| 0.6083 \| 0.9012 \| 0.5039 \| 0.8395 \| 0.8244 \| nan \| 0.8717 \| 0.4338 \| 0.7665 \| 0.4223 \| 0.7572 \| 0.5382 \| 0.0 \| \| 1.0941 \| 0.74 \| 1220 \| 0.2715 \| 0.5423 \| 0.7611 \| 0.8718 \| 0.9043 \| 0.5967 \| 0.8793 \| 0.4764 \| 0.8766 \| 0.8333 \| nan \| 0.8692 \| 0.4257 \| 0.7705 \| 0.4168 \| 0.7701 \| 0.5438 \| 0.0 \| \| 0.418 \| 0.75 \| 1240 \| 0.2988 \| 0.5385 \| 0.7575 \| 0.8631 \| 0.8481 \| 0.6181 \| 0.8973 \| 0.4993 \| 0.9246 \| 0.7573 \| nan \| 0.8263 \| 0.4090 \| 0.6882 \| 0.4314 \| 0.8081 \| 0.6062 \| 0.0 \| \| 0.173 \| 0.76 \| 1260 \| 0.2824 \| 0.5602 \| 0.7812 \| 0.8816 \| 0.8983 \| 0.6148 \| 0.9039 \| 0.5562 \| 0.8982 \| 0.8157 \| nan \| 0.8658 \| 0.4415 \| 0.7486 \| 0.4542 \| 0.8029 \| 0.6083 \| 0.0 \| \| 0.2501 \| 0.78 \| 1280 \| 0.2893 \| 0.5345 \| 0.7801 \| 0.8579 \| 0.8856 \| 0.6174 \| 0.8920 \| 0.5393 \| 0.8337 \| 0.9126 \| nan \| 0.8537 \| 0.4336 \| 0.7198 \| 0.4689 \| 0.7618 \| 0.5034 \| 0.0 \| \| 0.2213 \| 0.79 \| 1300 \| 0.2579 \| 0.5536 \| 0.7501 \| 0.8840 \| 0.9223 \| 0.5794 \| 0.8305 \| 0.4354 \| 0.9198 \| 0.8130 \| nan \| 0.8825 \| 0.4336 \| 0.7614 \| 0.4142 \| 0.7924 \| 0.5915 \| 0.0 \| \| 0.6845 \| 0.8 \| 1320 \| 0.2766 \| 0.5574 \| 0.7593 \| 0.8816 \| 0.9103 \| 0.5735 \| 0.8930 \| 0.4722 \| 0.8971 \| 0.8099 \| nan \| 0.8684 \| 0.4448 \| 0.7728 \| 0.4200 \| 0.7871 \| 0.6083 \| 0.0 \| \| 0.3139 \| 0.81 \| 1340 \| 0.2591 \| 0.5600 \| 0.7617 \| 0.8861 \| 0.9095 \| 0.5474 \| 0.9057 \| 0.4920 \| 0.9052 \| 0.8103 \| nan \| 0.8801 \| 0.4356 \| 0.7602 \| 0.4273 \| 0.8075 \| 0.6096 \| 0.0 \| \| 0.2207 \| 0.82 \| 1360 \| 0.3023 \| 0.5519 \| 0.7398 \| 0.8842 \| 0.9065 \| 0.4921 \| 0.8773 \| 0.4597 \| 0.9261 \| 0.7774 \| nan \| 0.8717 \| 0.4113 \| 0.7559 \| 0.4242 \| 0.8022 \| 0.5979 \| 0.0 \| \| 0.055 \| 0.84 \| 1380 \| 0.2899 \| 0.5583 \| 0.7640 \| 0.8801 \| 0.9062 \| 0.6091 \| 0.8915 \| 0.4858 \| 0.8992 \| 0.7923 \| nan \| 0.8653 \| 0.4651 \| 0.7603 \| 0.4424 \| 0.7933 \| 0.5815 \| 0.0 \| \| 0.1648 \| 0.85 \| 1400 \| 0.3026 \| 0.5493 \| 0.7529 \| 0.8830 \| 0.9021 \| 0.6004 \| 0.9090 \| 0.4134 \| 0.9147 \| 0.7780 \| nan \| 0.8722 \| 0.4553 \| 0.7478 \| 0.3737 \| 0.8087 \| 0.5877 \| 0.0 \| \| 0.1717 \| 0.86 \| 1420 \| 0.2719 \| 0.5577 \| 0.7945 \| 0.8802 \| 0.9051 \| 0.6636 \| 0.8980 \| 0.5765 \| 0.8797 \| 0.8441 \| nan \| 0.8782 \| 0.4527 \| 0.7591 \| 0.4554 \| 0.7931 \| 0.5656 \| 0.0 \| \| 1.2901 \| 0.87 \| 1440 \| 0.2583 \| 0.5666 \| 0.7906 \| 0.8837 \| 0.9008 \| 0.6385 \| 0.8771 \| 0.5761 \| 0.9061 \| 0.8453 \| nan \| 0.8801 \| 0.4551 \| 0.7863 \| 0.4752 \| 0.7929 \| 0.5766 \| 0.0 \| \| 0.3958 \| 0.89 \| 1460 \| 0.2567 \| 0.5636 \| 0.7722 \| 0.8856 \| 0.9209 \| 0.5674 \| 0.8749 \| 0.5567 \| 0.8950 \| 0.8186 \| nan \| 0.8795 \| 0.4382 \| 0.7648 \| 0.4645 \| 0.8065 \| 0.5913 \| 0.0 \| \| 0.132 \| 0.9 \| 1480 \| 0.3065 \| 0.5529 \| 0.7824 \| 0.8769 \| 0.8965 \| 0.6280 \| 0.8995 \| 0.5739 \| 0.8871 \| 0.8096 \| nan \| 0.8637 \| 0.4402 \| 0.7403 \| 0.4606 \| 0.8000 \| 0.5655 \| 0.0 \| \| 0.1086 \| 0.91 \| 1500 \| 0.3508 \| 0.5322 \| 0.7658 \| 0.8560 \| 0.8395 \| 0.6086 \| 0.9102 \| 0.5617 \| 0.9003 \| 0.7743 \| nan \| 0.8166 \| 0.4316 \| 0.6703 \| 0.4462 \| 0.7977 \| 0.5629 \| 0.0 \| \| 0.1127 \| 0.92 \| 1520 \| 0.3589 \| 0.5310 \| 0.7720 \| 0.8529 \| 0.8399 \| 0.6515 \| 0.9186 \| 0.5537 \| 0.8843 \| 0.7840 \| nan \| 0.8100 \| 0.4318 \| 0.6527 \| 0.4291 \| 0.7970 \| 0.5963 \| 0.0 \| \| 0.3807 \| 0.93 \| 1540 \| 0.2715 \| 0.5554 \| 0.7927 \| 0.8792 \| 0.8975 \| 0.6704 \| 0.8713 \| 0.5857 \| 0.8995 \| 0.8319 \| nan \| 0.8742 \| 0.4380 \| 0.7658 \| 0.4537 \| 0.7898 \| 0.5663 \| 0.0 \| \| 0.3847 \| 0.95 \| 1560 \| 0.2537 \| 0.5589 \| 0.7988 \| 0.8847 \| 0.9184 \| 0.6811 \| 0.8729 \| 0.5827 \| 0.8849 \| 0.8530 \| nan \| 0.8863 \| 0.4253 \| 0.7846 \| 0.4582 \| 0.7933 \| 0.5645 \| 0.0 \| \| 0.2076 \| 0.96 \| 1580 \| 0.2669 \| 0.5517 \| 0.7704 \| 0.8865 \| 0.9249 \| 0.6891 \| 0.8583 \| 0.4244 \| 0.9084 \| 0.8171 \| nan \| 0.8811 \| 0.4304 \| 0.7516 \| 0.3920 \| 0.8123 \| 0.5943 \| 0.0 \| \| 0.1031 \| 0.97 \| 1600 \| 0.2806 \| 0.5474 \| 0.7524 \| 0.8853 \| 0.9163 \| 0.6332 \| 0.8666 \| 0.3833 \| 0.9215 \| 0.7932 \| nan \| 0.8782 \| 0.4432 \| 0.7448 \| 0.3527 \| 0.8112 \| 0.6020 \| 0.0 \| \| 0.2727 \| 0.98 \| 1620 \| 0.3023 \| 0.5370 \| 0.7711 \| 0.8710 \| 0.8763 \| 0.6717 \| 0.9048 \| 0.4370 \| 0.8998 \| 0.8370 \| nan \| 0.8557 \| 0.4296 \| 0.7258 \| 0.3803 \| 0.7962 \| 0.5714 \| 0.0 \| \| 0.2601 \| 0.99 \| 1640 \| 0.3792 \| 0.5187 \| 0.7649 \| 0.8554 \| 0.8331 \| 0.7026 \| 0.9221 \| 0.4155 \| 0.9061 \| 0.8097 \| nan \| 0.8181 \| 0.3941 \| 0.6737 \| 0.3592 \| 0.7989 \| 0.5868 \| 0.0 \| \| 0.239 \| 1.01 \| 1660 \| 0.3111 \| 0.5377 \| 0.7876 \| 0.8674 \| 0.8753 \| 0.6883 \| 0.9208 \| 0.5076 \| 0.8718 \| 0.8621 \| nan \| 0.8552 \| 0.4240 \| 0.7277 \| 0.4169 \| 0.7868 \| 0.5533 \| 0.0 \| \| 0.2939 \| 1.02 \| 1680 \| 0.2581 \| 0.5698 \| 0.7874 \| 0.8910 \| 0.9218 \| 0.6397 \| 0.9075 \| 0.5464 \| 0.8947 \| 0.8144 \| nan \| 0.8834 \| 0.4488 \| 0.7785 \| 0.4475 \| 0.8142 \| 0.6161 \| 0.0 \| \| 0.1292 \| 1.03 \| 1700 \| 0.2495 \| 0.5694 \| 0.7724 \| 0.8886 \| 0.9291 \| 0.6174 \| 0.8777 \| 0.5010 \| 0.8964 \| 0.8129 \| nan \| 0.8768 \| 0.4587 \| 0.7841 \| 0.4434 \| 0.8067 \| 0.6160 \| 0.0 \| \| 0.4733 \| 1.04 \| 1720 \| 0.2527 \| 0.5662 \| 0.7743 \| 0.8881 \| 0.9246 \| 0.6255 \| 0.8860 \| 0.4842 \| 0.8958 \| 0.8297 \| nan \| 0.8793 \| 0.4605 \| 0.7759 \| 0.4335 \| 0.8041 \| 0.6104 \| 0.0 \| \| 0.1835 \| 1.06 \| 1740 \| 0.2607 \| 0.5558 \| 0.7705 \| 0.8842 \| 0.8955 \| 0.6777 \| 0.8876 \| 0.4263 \| 0.9280 \| 0.8080 \| nan \| 0.8712 \| 0.4401 \| 0.7581 \| 0.4051 \| 0.8096 \| 0.6061 \| 0.0 \| \| 0.4408 \| 1.07 \| 1760 \| 0.2765 \| 0.5536 \| 0.7802 \| 0.8793 \| 0.8830 \| 0.6812 \| 0.8959 \| 0.4971 \| 0.9197 \| 0.8046 \| nan \| 0.8569 \| 0.4205 \| 0.7288 \| 0.4439 \| 0.8175 \| 0.6073 \| 0.0 \| \| 0.1692 \| 1.08 \| 1780 \| 0.2987 \| 0.5517 \| 0.7740 \| 0.8819 \| 0.8844 \| 0.6921 \| 0.9004 \| 0.4412 \| 0.9298 \| 0.7961 \| nan \| 0.8635 \| 0.4247 \| 0.7442 \| 0.4119 \| 0.8119 \| 0.6054 \| 0.0 \| \| 0.0829 \| 1.09 \| 1800 \| 0.2804 \| 0.5394 \| 0.7624 \| 0.8797 \| 0.9094 \| 0.7067 \| 0.8563 \| 0.3608 \| 0.9121 \| 0.8289 \| nan \| 0.8706 \| 0.4162 \| 0.7470 \| 0.3457 \| 0.8019 \| 0.5943 \| 0.0 \| \| 0.6807 \| 1.1 \| 1820 \| 0.3080 \| 0.5373 \| 0.7638 \| 0.8729 \| 0.8821 \| 0.6748 \| 0.9134 \| 0.4184 \| 0.9030 \| 0.7915 \| nan \| 0.8532 \| 0.4092 \| 0.7250 \| 0.3750 \| 0.7992 \| 0.5993 \| 0.0 \| \| 0.2518 \| 1.12 \| 1840 \| 0.2835 \| 0.5332 \| 0.7744 \| 0.8669 \| 0.8880 \| 0.6827 \| 0.9181 \| 0.4495 \| 0.8632 \| 0.8447 \| nan \| 0.8575 \| 0.4012 \| 0.7213 \| 0.3993 \| 0.7805 \| 0.5729 \| 0.0 \| \| 0.7864 \| 1.13 \| 1860 \| 0.2637 \| 0.5532 \| 0.7737 \| 0.8879 \| 0.9086 \| 0.6829 \| 0.9120 \| 0.4406 \| 0.9137 \| 0.7842 \| nan \| 0.8832 \| 0.4071 \| 0.7702 \| 0.3902 \| 0.8153 \| 0.6065 \| 0.0 \| \| 0.0924 \| 1.14 \| 1880 \| 0.2864 \| 0.5621 \| 0.7705 \| 0.8949 \| 0.9200 \| 0.6569 \| 0.8994 \| 0.4515 \| 0.9284 \| 0.7665 \| nan \| 0.8908 \| 0.4259 \| 0.7806 \| 0.4054 \| 0.8209 \| 0.6114 \| 0.0 \| \| 0.1686 \| 1.15 \| 1900 \| 0.2654 \| 0.5637 \| 0.7854 \| 0.8912 \| 0.9187 \| 0.6555 \| 0.8953 \| 0.4942 \| 0.9044 \| 0.8445 \| nan \| 0.8892 \| 0.4345 \| 0.7787 \| 0.4371 \| 0.8110 \| 0.5950 \| 0.0 \| \| 0.1225 \| 1.16 \| 1920 \| 0.2667 \| 0.5587 \| 0.7855 \| 0.8852 \| 0.9104 \| 0.6479 \| 0.9008 \| 0.5066 \| 0.8918 \| 0.8552 \| nan \| 0.8832 \| 0.4434 \| 0.7668 \| 0.4407 \| 0.8031 \| 0.5738 \| 0.0 \| \| 0.1435 \| 1.18 \| 1940 \| 0.2704 \| 0.5619 \| 0.7807 \| 0.8860 \| 0.8959 \| 0.6520 \| 0.8953 \| 0.5080 \| 0.9226 \| 0.8107 \| nan \| 0.8726 \| 0.4417 \| 0.7566 \| 0.4386 \| 0.8158 \| 0.6082 \| 0.0 \| \| 0.2853 \| 1.19 \| 1960 \| 0.2792 \| 0.5523 \| 0.7959 \| 0.8822 \| 0.9074 \| 0.7239 \| 0.8853 \| 0.5060 \| 0.8900 \| 0.8628 \| nan \| 0.8801 \| 0.4154 \| 0.7708 \| 0.4392 \| 0.7978 \| 0.5625 \| 0.0 \| \| 0.1169 \| 1.2 \| 1980 \| 0.2879 \| 0.5629 \| 0.7787 \| 0.8900 \| 0.9085 \| 0.6766 \| 0.8683 \| 0.4738 \| 0.9324 \| 0.8124 \| nan \| 0.8797 \| 0.4266 \| 0.7629 \| 0.4292 \| 0.8164 \| 0.6251 \| 0.0 \| \| 0.1685 \| 1.21 \| 2000 \| 0.2960 \| 0.5645 \| 0.7948 \| 0.8899 \| 0.9075 \| 0.6827 \| 0.8744 \| 0.5332 \| 0.9189 \| 0.8520 \| nan \| 0.8793 \| 0.4256 \| 0.7577 \| 0.4546 \| 0.8224 \| 0.6117 \| 0.0 \| \| 0.7038 \| 1.22 \| 2020 \| 0.2923 \| 0.5641 \| 0.7853 \| 0.8874 \| 0.8949 \| 0.6525 \| 0.8828 \| 0.5251 \| 0.9296 \| 0.8266 \| nan \| 0.8714 \| 0.4343 \| 0.7489 \| 0.4541 \| 0.8219 \| 0.6178 \| 0.0 \| \| 0.6504 \| 1.24 \| 2040 \| 0.2727 \| 0.5656 \| 0.7799 \| 0.8906 \| 0.9181 \| 0.6237 \| 0.9048 \| 0.5272 \| 0.9039 \| 0.8017 \| nan \| 0.8771 \| 0.4327 \| 0.7616 \| 0.4410 \| 0.8245 \| 0.6224 \| 0.0 \| \| 0.3215 \| 1.25 \| 2060 \| 0.2697 \| 0.5692 \| 0.7798 \| 0.8921 \| 0.9109 \| 0.6537 \| 0.9027 \| 0.4922 \| 0.9214 \| 0.7980 \| nan \| 0.8794 \| 0.4591 \| 0.7621 \| 0.4293 \| 0.8272 \| 0.6271 \| 0.0 \| \| 0.7559 \| 1.26 \| 2080 \| 0.2841 \| 0.5703 \| 0.7909 \| 0.8938 \| 0.9121 \| 0.6957 \| 0.8969 \| 0.4812 \| 0.9210 \| 0.8386 \| nan \| 0.8861 \| 0.4526 \| 0.7802 \| 0.4258 \| 0.8202 \| 0.6275 \| 0.0 \| \| 0.0888 \| 1.27 \| 2100 \| 0.2607 \| 0.5670 \| 0.7677 \| 0.8935 \| 0.9342 \| 0.6483 \| 0.8855 \| 0.4075 \| 0.9070 \| 0.8234 \| nan \| 0.8861 \| 0.4785 \| 0.7798 \| 0.3772 \| 0.8168 \| 0.6305 \| 0.0 \| \| 0.4145 \| 1.29 \| 2120 \| 0.2646 \| 0.5615 \| 0.7702 \| 0.8867 \| 0.9221 \| 0.6530 \| 0.8885 \| 0.4237 \| 0.8969 \| 0.8367 \| nan \| 0.8752 \| 0.4703 \| 0.7690 \| 0.3822 \| 0.8086 \| 0.6253 \| 0.0 \| \| 0.059 \| 1.3 \| 2140 \| 0.2673 \| 0.5619 \| 0.7662 \| 0.8893 \| 0.9142 \| 0.6207 \| 0.9007 \| 0.4401 \| 0.9140 \| 0.8076 \| nan \| 0.8776 \| 0.4666 \| 0.7564 \| 0.3822 \| 0.8222 \| 0.6286 \| 0.0 \| \| 0.2973 \| 1.31 \| 2160 \| 0.2817 \| 0.5660 \| 0.7736 \| 0.8912 \| 0.9093 \| 0.6434 \| 0.8961 \| 0.4758 \| 0.9269 \| 0.7901 \| nan \| 0.8798 \| 0.4608 \| 0.7590 \| 0.4140 \| 0.8216 \| 0.6265 \| 0.0 \| \| 0.4088 \| 1.32 \| 2180 \| 0.2859 \| 0.5479 \| 0.7895 \| 0.8790 \| 0.9071 \| 0.6934 \| 0.9058 \| 0.4804 \| 0.8722 \| 0.8782 \| nan \| 0.8749 \| 0.4309 \| 0.7583 \| 0.4013 \| 0.8000 \| 0.5703 \| 0.0 \| \| 0.6954 \| 1.33 \| 2200 \| 0.2905 \| 0.5541 \| 0.7861 \| 0.8764 \| 0.8848 \| 0.6835 \| 0.8968 \| 0.5118 \| 0.9013 \| 0.8386 \| nan \| 0.8572 \| 0.4295 \| 0.7496 \| 0.4346 \| 0.8012 \| 0.6064 \| 0.0 \| \| 0.5832 \| 1.35 \| 2220 \| 0.3119 \| 0.5508 \| 0.7683 \| 0.8747 \| 0.8692 \| 0.6771 \| 0.8894 \| 0.4628 \| 0.9345 \| 0.7771 \| nan \| 0.8459 \| 0.4351 \| 0.7185 \| 0.4202 \| 0.8111 \| 0.6249 \| 0.0 \| \| 0.0921 \| 1.36 \| 2240 \| 0.3298 \| 0.5461 \| 0.7714 \| 0.8718 \| 0.8627 \| 0.7034 \| 0.8801 \| 0.4590 \| 0.9362 \| 0.7869 \| nan \| 0.8396 \| 0.4272 \| 0.6938 \| 0.4216 \| 0.8149 \| 0.6257 \| 0.0 \| \| 0.3714 \| 1.37 \| 2260 \| 0.2859 \| 0.5629 \| 0.7798 \| 0.8866 \| 0.9126 \| 0.6515 \| 0.8707 \| 0.4921 \| 0.9104 \| 0.8414 \| nan \| 0.8783 \| 0.4474 \| 0.7555 \| 0.4428 \| 0.8119 \| 0.6045 \| 0.0 \| \| 0.1119 \| 1.38 \| 2280 \| 0.2804 \| 0.5521 \| 0.7694 \| 0.8821 \| 0.9107 \| 0.6123 \| 0.8938 \| 0.4466 \| 0.8905 \| 0.8629 \| nan \| 0.8753 \| 0.4630 \| 0.7505 \| 0.3926 \| 0.8054 \| 0.5777 \| 0.0 \| \| 0.256 \| 1.39 \| 2300 \| 0.2871 \| 0.5641 \| 0.7823 \| 0.8843 \| 0.8895 \| 0.6507 \| 0.8879 \| 0.5237 \| 0.9270 \| 0.8150 \| nan \| 0.8622 \| 0.4488 \| 0.7299 \| 0.4582 \| 0.8236 \| 0.6261 \| 0.0 \| \| 1.856 \| 1.41 \| 2320 \| 0.3132 \| 0.5445 \| 0.7815 \| 0.8742 \| 0.8851 \| 0.6856 \| 0.9064 \| 0.4533 \| 0.8902 \| 0.8684 \| nan \| 0.8555 \| 0.4349 \| 0.7115 \| 0.4077 \| 0.8106 \| 0.5913 \| 0.0 \| \| 0.2095 \| 1.42 \| 2340 \| 0.2954 \| 0.5624 \| 0.7891 \| 0.8844 \| 0.8890 \| 0.6993 \| 0.8840 \| 0.4935 \| 0.9264 \| 0.8427 \| nan \| 0.8638 \| 0.4331 \| 0.7320 \| 0.4606 \| 0.8214 \| 0.6255 \| 0.0 \| \| 0.4009 \| 1.43 \| 2360 \| 0.2798 \| 0.5565 \| 0.8003 \| 0.8818 \| 0.9017 \| 0.7110 \| 0.9015 \| 0.5252 \| 0.8853 \| 0.8769 \| nan \| 0.8714 \| 0.4175 \| 0.7538 \| 0.4672 \| 0.8064 \| 0.5789 \| 0.0 \| \| 0.324 \| 1.44 \| 2380 \| 0.2871 \| 0.5622 \| 0.7878 \| 0.8838 \| 0.8877 \| 0.6549 \| 0.9034 \| 0.5290 \| 0.9171 \| 0.8345 \| nan \| 0.8595 \| 0.4275 \| 0.7316 \| 0.4622 \| 0.8243 \| 0.6302 \| 0.0 \| \| 0.1967 \| 1.46 \| 2400 \| 0.2866 \| 0.5607 \| 0.7792 \| 0.8862 \| 0.8918 \| 0.6889 \| 0.9070 \| 0.5223 \| 0.9336 \| 0.7318 \| nan \| 0.8663 \| 0.4122 \| 0.7426 \| 0.4698 \| 0.8234 \| 0.6108 \| 0.0 \| \| 0.3549 \| 1.47 \| 2420 \| 0.2846 \| 0.5661 \| 0.7802 \| 0.8907 \| 0.9066 \| 0.6648 \| 0.8892 \| 0.4930 \| 0.9283 \| 0.7996 \| nan \| 0.8749 \| 0.4221 \| 0.7503 \| 0.4583 \| 0.8237 \| 0.6336 \| 0.0 \| \| 0.1298 \| 1.48 \| 2440 \| 0.2927 \| 0.5576 \| 0.7861 \| 0.8844 \| 0.8865 \| 0.7135 \| 0.9022 \| 0.4713 \| 0.9275 \| 0.8154 \| nan \| 0.8638 \| 0.4191 \| 0.7356 \| 0.4254 \| 0.8226 \| 0.6368 \| 0.0 \| \| 0.1442 \| 1.49 \| 2460 \| 0.2714 \| 0.5632 \| 0.7953 \| 0.8880 \| 0.9137 \| 0.6747 \| 0.8955 \| 0.5360 \| 0.8946 \| 0.8574 \| nan \| 0.8757 \| 0.4123 \| 0.7575 \| 0.4568 \| 0.8186 \| 0.6218 \| 0.0 \| \| 0.1179 \| 1.5 \| 2480 \| 0.2873 \| 0.5739 \| 0.7854 \| 0.8936 \| 0.9142 \| 0.6517 \| 0.8819 \| 0.5194 \| 0.9252 \| 0.8201 \| nan \| 0.8803 \| 0.4460 \| 0.7597 \| 0.4696 \| 0.8249 \| 0.6369 \| 0.0 \| \| 0.8153 \| 1.52 \| 2500 \| 0.3244 \| 0.5628 \| 0.7819 \| 0.8828 \| 0.8855 \| 0.6434 \| 0.8990 \| 0.4903 \| 0.9196 \| 0.8538 \| nan \| 0.8601 \| 0.4594 \| 0.7278 \| 0.4458 \| 0.8205 \| 0.6262 \| 0.0 \| \| 0.1386 \| 1.53 \| 2520 \| 0.3220 \| 0.5548 \| 0.7648 \| 0.8788 \| 0.8843 \| 0.6161 \| 0.9062 \| 0.4492 \| 0.9163 \| 0.8168 \| nan \| 0.8494 \| 0.4596 \| 0.7033 \| 0.4101 \| 0.8261 \| 0.6350 \| 0.0 \| \| 0.3631 \| 1.54 \| 2540 \| 0.2927 \| 0.5611 \| 0.7734 \| 0.8852 \| 0.9010 \| 0.6518 \| 0.8930 \| 0.4558 \| 0.9176 \| 0.8214 \| nan \| 0.8666 \| 0.4574 \| 0.7316 \| 0.4180 \| 0.8232 \| 0.6306 \| 0.0 \| \| 0.1358 \| 1.55 \| 2560 \| 0.2741 \| 0.5618 \| 0.7824 \| 0.8875 \| 0.9088 \| 0.6661 \| 0.8972 \| 0.4611 \| 0.9061 \| 0.8551 \| nan \| 0.8760 \| 0.4523 \| 0.7497 \| 0.4236 \| 0.8184 \| 0.6127 \| 0.0 \| \| 0.1636 \| 1.56 \| 2580 \| 0.2639 \| 0.5716 \| 0.7802 \| 0.8943 \| 0.9340 \| 0.6282 \| 0.8823 \| 0.4880 \| 0.9004 \| 0.8484 \| nan \| 0.8878 \| 0.4554 \| 0.7736 \| 0.4482 \| 0.8186 \| 0.6174 \| 0.0 \| \| 0.218 \| 1.58 \| 2600 \| 0.2771 \| 0.5722 \| 0.7804 \| 0.8914 \| 0.9113 \| 0.6134 \| 0.8914 \| 0.4984 \| 0.9160 \| 0.8517 \| nan \| 0.8823 \| 0.4693 \| 0.7641 \| 0.4576 \| 0.8154 \| 0.6165 \| 0.0 \| \| 0.6271 \| 1.59 \| 2620 \| 0.2715 \| 0.5713 \| 0.7803 \| 0.8871 \| 0.8985 \| 0.6390 \| 0.8907 \| 0.5256 \| 0.9244 \| 0.8038 \| nan \| 0.8682 \| 0.4587 \| 0.7446 \| 0.4759 \| 0.8199 \| 0.6316 \| 0.0 \| \| 0.307 \| 1.6 \| 2640 \| 0.3499 \| 0.5620 \| 0.7852 \| 0.8789 \| 0.8686 \| 0.6577 \| 0.9099 \| 0.5258 \| 0.9260 \| 0.8234 \| nan \| 0.8479 \| 0.4454 \| 0.7133 \| 0.4692 \| 0.8217 \| 0.6363 \| 0.0 \| \| 0.2482 \| 1.61 \| 2660 \| 0.2980 \| 0.5654 \| 0.7890 \| 0.8830 \| 0.8834 \| 0.6598 \| 0.9082 \| 0.5263 \| 0.9173 \| 0.8390 \| nan \| 0.8598 \| 0.4448 \| 0.7340 \| 0.4643 \| 0.8210 \| 0.6338 \| 0.0 \| \| 0.1496 \| 1.63 \| 2680 \| 0.2893 \| 0.5750 \| 0.7881 \| 0.8922 \| 0.9088 \| 0.6366 \| 0.8930 \| 0.5268 \| 0.9191 \| 0.8445 \| nan \| 0.8799 \| 0.4588 \| 0.7638 \| 0.4720 \| 0.8201 \| 0.6304 \| 0.0 \| \| 0.2405 \| 1.64 \| 2700 \| 0.2712 \| 0.5738 \| 0.7958 \| 0.8884 \| 0.8965 \| 0.6660 \| 0.8927 \| 0.5553 \| 0.9199 \| 0.8441 \| nan \| 0.8731 \| 0.4552 \| 0.7597 \| 0.4861 \| 0.8167 \| 0.6260 \| 0.0 \| \| 0.0689 \| 1.65 \| 2720 \| 0.2699 \| 0.5773 \| 0.8004 \| 0.8916 \| 0.9093 \| 0.6645 \| 0.8839 \| 0.5675 \| 0.9127 \| 0.8645 \| nan \| 0.8818 \| 0.4697 \| 0.7649 \| 0.4878 \| 0.8179 \| 0.6192 \| 0.0 \| \| 1.3023 \| 1.66 \| 2740 \| 0.2776 \| 0.5739 \| 0.8043 \| 0.8902 \| 0.9055 \| 0.6846 \| 0.8874 \| 0.5705 \| 0.9107 \| 0.8673 \| nan \| 0.8798 \| 0.4549 \| 0.7620 \| 0.4845 \| 0.8171 \| 0.6193 \| 0.0 \| \| 0.401 \| 1.67 \| 2760 \| 0.2705 \| 0.5721 \| 0.7931 \| 0.8883 \| 0.9074 \| 0.6542 \| 0.8896 \| 0.5540 \| 0.9072 \| 0.8462 \| nan \| 0.8721 \| 0.4551 \| 0.7475 \| 0.4782 \| 0.8221 \| 0.6295 \| 0.0 \| \| 0.2472 \| 1.69 \| 2780 \| 0.3258 \| 0.5705 \| 0.7939 \| 0.8852 \| 0.8866 \| 0.6562 \| 0.9042 \| 0.5490 \| 0.9180 \| 0.8494 \| nan \| 0.8631 \| 0.4681 \| 0.7336 \| 0.4728 \| 0.8223 \| 0.6338 \| 0.0 \| \| 0.4908 \| 1.7 \| 2800 \| 0.3074 \| 0.5651 \| 0.7893 \| 0.8807 \| 0.8882 \| 0.6278 \| 0.9025 \| 0.5567 \| 0.9016 \| 0.8588 \| nan \| 0.8577 \| 0.4620 \| 0.7193 \| 0.4756 \| 0.8178 \| 0.6234 \| 0.0 \| \| 0.1247 \| 1.71 \| 2820 \| 0.2845 \| 0.5739 \| 0.7789 \| 0.8909 \| 0.9063 \| 0.6080 \| 0.8883 \| 0.5422 \| 0.9268 \| 0.8014 \| nan \| 0.8726 \| 0.4572 \| 0.7445 \| 0.4782 \| 0.8260 \| 0.6386 \| 0.0 \| \| 0.9431 \| 1.72 \| 2840 \| 0.2788 \| 0.5753 \| 0.7823 \| 0.8900 \| 0.8967 \| 0.6380 \| 0.8976 \| 0.5355 \| 0.9333 \| 0.7929 \| nan \| 0.8721 \| 0.4694 \| 0.7523 \| 0.4729 \| 0.8222 \| 0.6386 \| 0.0 \| \| 0.1346 \| 1.73 \| 2860 \| 0.2942 \| 0.5749 \| 0.7819 \| 0.8907 \| 0.8988 \| 0.6226 \| 0.9048 \| 0.5251 \| 0.9280 \| 0.8121 \| nan \| 0.8736 \| 0.4707 \| 0.7525 \| 0.4630 \| 0.8226 \| 0.6417 \| 0.0 \| \| 0.1469 \| 1.75 \| 2880 \| 0.2782 \| 0.5762 \| 0.7808 \| 0.8933 \| 0.9093 \| 0.6225 \| 0.8923 \| 0.5319 \| 0.9289 \| 0.8001 \| nan \| 0.8793 \| 0.4653 \| 0.7595 \| 0.4657 \| 0.8239 \| 0.6395 \| 0.0 \| \| 0.1829 \| 1.76 \| 2900 \| 0.2748 \| 0.5727 \| 0.7899 \| 0.8908 \| 0.9105 \| 0.6307 \| 0.9093 \| 0.5512 \| 0.9050 \| 0.8326 \| nan \| 0.8795 \| 0.4647 \| 0.7606 \| 0.4629 \| 0.8195 \| 0.6217 \| 0.0 \| \| 0.1476 \| 1.77 \| 2920 \| 0.2591 \| 0.5729 \| 0.7936 \| 0.8911 \| 0.9130 \| 0.6489 \| 0.8955 \| 0.5441 \| 0.9053 \| 0.8549 \| nan \| 0.8828 \| 0.4653 \| 0.7657 \| 0.4666 \| 0.8169 \| 0.6131 \| 0.0 \| \| 0.09 \| 1.78 \| 2940 \| 0.2802 \| 0.5692 \| 0.7887 \| 0.8892 \| 0.9127 \| 0.6333 \| 0.9030 \| 0.5209 \| 0.8974 \| 0.8651 \| nan \| 0.8830 \| 0.4768 \| 0.7639 \| 0.4502 \| 0.8116 \| 0.5989 \| 0.0 \| \| 0.2123 \| 1.8 \| 2960 \| 0.2605 \| 0.5653 \| 0.7857 \| 0.8868 \| 0.9139 \| 0.6362 \| 0.8964 \| 0.5169 \| 0.8930 \| 0.8581 \| nan \| 0.8819 \| 0.4653 \| 0.7632 \| 0.4462 \| 0.8091 \| 0.5913 \| 0.0 \| \| 0.4904 \| 1.81 \| 2980 \| 0.2484 \| 0.5707 \| 0.7754 \| 0.8939 \| 0.9314 \| 0.5966 \| 0.8876 \| 0.5218 \| 0.9042 \| 0.8109 \| nan \| 0.8847 \| 0.4367 \| 0.7715 \| 0.4485 \| 0.8230 \| 0.6308 \| 0.0 \| \| 0.3414 \| 1.82 \| 3000 \| 0.2569 \| 0.5722 \| 0.7811 \| 0.8929 \| 0.9166 \| 0.6386 \| 0.8934 \| 0.5163 \| 0.9180 \| 0.8038 \| nan \| 0.8820 \| 0.4596 \| 0.7572 \| 0.4440 \| 0.8246 \| 0.6380 \| 0.0 \| \| 0.0498 \| 1.83 \| 3020 \| 0.2547 \| 0.5738 \| 0.7870 \| 0.8923 \| 0.9148 \| 0.6430 \| 0.8918 \| 0.5312 \| 0.9144 \| 0.8268 \| nan \| 0.8817 \| 0.4658 \| 0.7594 \| 0.4496 \| 0.8235 \| 0.6365 \| 0.0 \| \| 0.3362 \| 1.84 \| 3040 \| 0.2595 \| 0.5756 \| 0.7856 \| 0.8937 \| 0.9173 \| 0.6259 \| 0.8873 \| 0.5339 \| 0.9167 \| 0.8325 \| nan \| 0.8833 \| 0.4694 \| 0.7628 \| 0.4537 \| 0.8248 \| 0.6352 \| 0.0 \| \| 0.1219 \| 1.86 \| 3060 \| 0.2537 \| 0.5750 \| 0.7841 \| 0.8947 \| 0.9217 \| 0.6318 \| 0.8915 \| 0.5239 \| 0.9145 \| 0.8211 \| nan \| 0.8846 \| 0.4633 \| 0.7649 \| 0.4475 \| 0.8255 \| 0.6395 \| 0.0 \| \| 0.123 \| 1.87 \| 3080 \| 0.2808 \| 0.5758 \| 0.7945 \| 0.8934 \| 0.9069 \| 0.6636 \| 0.9036 \| 0.5391 \| 0.9199 \| 0.8339 \| nan \| 0.8822 \| 0.4694 \| 0.7664 \| 0.4539 \| 0.8238 \| 0.6346 \| 0.0 \| \| 0.3721 \| 1.88 \| 3100 \| 0.2523 \| 0.5777 \| 0.7885 \| 0.8954 \| 0.9207 \| 0.6431 \| 0.8826 \| 0.5526 \| 0.9209 \| 0.8111 \| nan \| 0.8858 \| 0.4599 \| 0.7679 \| 0.4677 \| 0.8257 \| 0.6367 \| 0.0 \| \| 0.284 \| 1.89 \| 3120 \| 0.2545 \| 0.5786 \| 0.7927 \| 0.8948 \| 0.9151 \| 0.6464 \| 0.8905 \| 0.5516 \| 0.9188 \| 0.8336 \| nan \| 0.8854 \| 0.4687 \| 0.7707 \| 0.4664 \| 0.8241 \| 0.6350 \| 0.0 \| \| 0.5131 \| 1.9 \| 3140 \| 0.2502 \| 0.5768 \| 0.7875 \| 0.8948 \| 0.9234 \| 0.6250 \| 0.8978 \| 0.5439 \| 0.9072 \| 0.8275 \| nan \| 0.8856 \| 0.4619 \| 0.7722 \| 0.4582 \| 0.8243 \| 0.6357 \| 0.0 \| \| 0.1381 \| 1.92 \| 3160 \| 0.2580 \| 0.5760 \| 0.7772 \| 0.8954 \| 0.9194 \| 0.6006 \| 0.9047 \| 0.5318 \| 0.9194 \| 0.7870 \| nan \| 0.8853 \| 0.4661 \| 0.7702 \| 0.4505 \| 0.8260 \| 0.6338 \| 0.0 \| \| 0.3162 \| 1.93 \| 3180 \| 0.2739 \| 0.5775 \| 0.7918 \| 0.8935 \| 0.9066 \| 0.6439 \| 0.9006 \| 0.5423 \| 0.9222 \| 0.8350 \| nan \| 0.8822 \| 0.4750 \| 0.7676 \| 0.4609 \| 0.8230 \| 0.6338 \| 0.0 \| \| 0.1153 \| 1.94 \| 3200 \| 0.2678 \| 0.5704 \| 0.7631 \| 0.8953 \| 0.9273 \| 0.5727 \| 0.8881 \| 0.5334 \| 0.9260 \| 0.7312 \| nan \| 0.8843 \| 0.4380 \| 0.7696 \| 0.4600 \| 0.8251 \| 0.6161 \| 0.0 \| \| 0.0417 \| 1.95 \| 3220 \| 0.2650 \| 0.5775 \| 0.7906 \| 0.8950 \| 0.9137 \| 0.6581 \| 0.8944 \| 0.5503 \| 0.9244 \| 0.8028 \| nan \| 0.8844 \| 0.4627 \| 0.7687 \| 0.4657 \| 0.8250 \| 0.6359 \| 0.0 \| \| 0.1364 \| 1.96 \| 3240 \| 0.2696 \| 0.5771 \| 0.7861 \| 0.8953 \| 0.9133 \| 0.6531 \| 0.8970 \| 0.5482 \| 0.9295 \| 0.7756 \| nan \| 0.8844 \| 0.4652 \| 0.7692 \| 0.4642 \| 0.8254 \| 0.6317 \| 0.0 \| \| 0.0806 \| 1.98 \| 3260 \| 0.2718 \| 0.5771 \| 0.7957 \| 0.8945 \| 0.9151 \| 0.6654 \| 0.9009 \| 0.5405 \| 0.9130 \| 0.8391 \| nan \| 0.8844 \| 0.4683 \| 0.7680 \| 0.4590 \| 0.8247 \| 0.6356 \| 0.0 \| \| 0.1196 \| 1.99 \| 3280 \| 0.2563 \| 0.5765 \| 0.7934 \| 0.8942 \| 0.9204 \| 0.6450 \| 0.8936 \| 0.5526 \| 0.9077 \| 0.8415 \| nan \| 0.8847 \| 0.4614 \| 0.7695 \| 0.4632 \| 0.8233 \| 0.6331 \| 0.0 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3	[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]
vigneshgs7/segformer-b0-finetuned-segments-docboundary-nov-13	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-docboundary-nov-13 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the vigneshgs7/doc-boundary dataset. It achieves the following results on the evaluation set: - Loss: 0.2806 - Mean Iou: 0.4886 - Mean Accuracy: 0.9771 - Overall Accuracy: 0.9771 - Accuracy Page: nan - Accuracy Surface: 0.9771 - Iou Page: 0.0 - Iou Surface: 0.9771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Page \| Accuracy Surface \| Iou Page \| Iou Surface \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------:\|:----------------:\|:--------:\|:-----------:\| \| 0.4191 \| 2.22 \| 20 \| 0.5643 \| 0.4651 \| 0.9301 \| 0.9301 \| nan \| 0.9301 \| 0.0 \| 0.9301 \| \| 0.3141 \| 4.44 \| 40 \| 0.3959 \| 0.4866 \| 0.9733 \| 0.9733 \| nan \| 0.9733 \| 0.0 \| 0.9733 \| \| 0.2865 \| 6.67 \| 60 \| 0.2889 \| 0.4870 \| 0.9740 \| 0.9740 \| nan \| 0.9740 \| 0.0 \| 0.9740 \| \| 0.3955 \| 8.89 \| 80 \| 0.2806 \| 0.4886 \| 0.9771 \| 0.9771 \| nan \| 0.9771 \| 0.0 \| 0.9771 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "page", "surface" ]
piecurus/segformer-finetuned-segments-opit	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-segments-opit This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.3483 - Mean Iou: 0.1474 - Mean Accuracy: 0.1958 - Overall Accuracy: 0.7227 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.7892 - Accuracy Flat-sidewalk: 0.9075 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.1029 - Accuracy Flat-parkingdriveway: 0.0001 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.8749 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8943 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9118 - Accuracy Nature-terrain: 0.6546 - Accuracy Sky: 0.9352 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.4282 - Iou Flat-sidewalk: 0.7768 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.1021 - Iou Flat-parkingdriveway: 0.0001 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.6372 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.5530 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.7392 - Iou Nature-terrain: 0.5009 - Iou Sky: 0.8328 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| 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\| 2.8103 \| 0.06 \| 25 \| 3.0462 \| 0.0835 \| 0.1351 \| 0.5790 \| nan \| 0.1892 \| 0.9330 \| 0.0 \| 0.0048 \| 0.0006 \| nan \| 0.0005 \| 0.0002 \| 0.0 \| 0.5793 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8849 \| 0.0 \| 0.0000 \| 0.0002 \| 0.0 \| nan \| 0.0003 \| 0.0009 \| 0.0 \| 0.0 \| 0.6599 \| 0.4398 \| 0.4779 \| 0.0004 \| 0.0 \| 0.0151 \| 0.0 \| 0.0 \| 0.1368 \| 0.6091 \| 0.0 \| 0.0046 \| 0.0006 \| 0.0 \| 0.0005 \| 0.0002 \| 0.0 \| 0.4916 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3537 \| 0.0 \| 0.0000 \| 0.0002 \| 0.0 \| 0.0 \| 0.0003 \| 0.0008 \| 0.0 \| 0.0 \| 0.5851 \| 0.2698 \| 0.4572 \| 0.0004 \| 0.0 \| 0.0123 \| 0.0 \| \| 2.3833 \| 0.12 \| 50 \| 2.3708 \| 0.1025 \| 0.1539 \| 0.6295 \| nan \| 0.6185 \| 0.8261 \| 0.0 \| 0.0007 \| 0.0003 \| nan \| 0.0000 \| 0.0 \| 0.0 \| 0.7749 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9283 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8688 \| 0.0568 \| 0.6944 \| 0.0 \| 0.0 \| 0.0016 \| 0.0 \| nan \| 0.3347 \| 0.6672 \| 0.0 \| 0.0007 \| 0.0003 \| nan \| 0.0000 \| 0.0 \| 0.0 \| 0.5428 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4369 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6274 \| 0.0451 \| 0.6247 \| 0.0 \| 0.0 \| 0.0015 \| 0.0 \| \| 2.1946 \| 0.19 \| 75 \| 1.9680 \| 0.1145 \| 0.1612 \| 0.6615 \| nan \| 0.6725 \| 0.8726 \| 0.0 \| 0.0011 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.7496 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9138 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9185 \| 0.0333 \| 0.8355 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.3738 \| 0.7037 \| 0.0 \| 0.0011 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5385 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4836 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6493 \| 0.0311 \| 0.7684 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 1.959 \| 0.25 \| 100 \| 1.8828 \| 0.1179 \| 0.1636 \| 0.6727 \| nan \| 0.6801 \| 0.8928 \| 0.0 \| 0.0006 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.7736 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8784 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9482 \| 0.0389 \| 0.8584 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.3884 \| 0.7151 \| 0.0 \| 0.0006 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5705 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5160 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6418 \| 0.0360 \| 0.7878 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 1.8759 \| 0.31 \| 125 \| 1.7092 \| 0.1260 \| 0.1758 \| 0.6862 \| nan \| 0.7247 \| 0.8976 \| 0.0 \| 0.0000 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9094 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8479 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9024 \| 0.2625 \| 0.9054 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.3977 \| 0.7307 \| 0.0 \| 0.0000 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5237 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5329 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6911 \| 0.2245 \| 0.8065 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 2.0333 \| 0.38 \| 150 \| 1.5558 \| 0.1267 \| 0.1751 \| 0.6898 \| nan \| 0.7565 \| 0.8952 \| 0.0 \| 0.0007 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8792 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8874 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9104 \| 0.1928 \| 0.9055 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4083 \| 0.7455 \| 0.0 \| 0.0007 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5928 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5267 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6820 \| 0.1637 \| 0.8069 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.8985 \| 0.44 \| 175 \| 1.5370 \| 0.1277 \| 0.1752 \| 0.6939 \| nan \| 0.7438 \| 0.9025 \| 0.0 \| 0.0068 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8237 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8984 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9307 \| 0.1976 \| 0.9266 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4223 \| 0.7553 \| 0.0 \| 0.0068 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6077 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5131 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6724 \| 0.1691 \| 0.8133 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.7908 \| 0.5 \| 200 \| 1.4854 \| 0.1339 \| 0.1843 \| 0.7020 \| nan \| 0.7612 \| 0.9068 \| 0.0 \| 0.0012 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9215 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8381 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9078 \| 0.4480 \| 0.9274 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4095 \| 0.7591 \| 0.0 \| 0.0012 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5471 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5386 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7168 \| 0.3673 \| 0.8119 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4371 \| 0.56 \| 225 \| 1.4176 \| 0.1367 \| 0.1830 \| 0.7079 \| nan \| 0.7035 \| 0.9303 \| 0.0 \| 0.0255 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8694 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8848 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9287 \| 0.4230 \| 0.9065 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4233 \| 0.7534 \| 0.0 \| 0.0255 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6246 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5331 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7049 \| 0.3427 \| 0.8312 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4506 \| 0.62 \| 250 \| 1.4011 \| 0.1350 \| 0.1827 \| 0.7079 \| nan \| 0.6936 \| 0.9362 \| 0.0 \| 0.0364 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8955 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8612 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9299 \| 0.3720 \| 0.9394 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4251 \| 0.7533 \| 0.0 \| 0.0363 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5961 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5472 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6990 \| 0.3026 \| 0.8250 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3095 \| 0.69 \| 275 \| 1.4039 \| 0.1398 \| 0.1861 \| 0.7134 \| nan \| 0.6873 \| 0.9430 \| 0.0 \| 0.0260 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8807 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8960 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9156 \| 0.5055 \| 0.9137 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4187 \| 0.7531 \| 0.0 \| 0.0260 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6172 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5457 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7261 \| 0.4143 \| 0.8343 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4218 \| 0.75 \| 300 \| 1.3735 \| 0.1370 \| 0.1856 \| 0.7082 \| nan \| 0.7701 \| 0.8980 \| 0.0 \| 0.0524 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8776 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8762 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9444 \| 0.4052 \| 0.9279 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4267 \| 0.7668 \| 0.0 \| 0.0522 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6148 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5481 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6931 \| 0.3263 \| 0.8172 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3986 \| 0.81 \| 325 \| 1.3224 \| 0.1451 \| 0.1917 \| 0.7223 \| nan \| 0.7354 \| 0.9296 \| 0.0 \| 0.1261 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8747 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8946 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9269 \| 0.5292 \| 0.9261 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4389 \| 0.7700 \| 0.0 \| 0.1253 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6395 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5541 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7200 \| 0.4083 \| 0.8409 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3335 \| 0.88 \| 350 \| 1.2909 \| 0.1454 \| 0.1921 \| 0.7230 \| nan \| 0.7157 \| 0.9388 \| 0.0 \| 0.1024 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8896 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8932 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9207 \| 0.5667 \| 0.9276 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4347 \| 0.7657 \| 0.0 \| 0.1020 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6232 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5533 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7300 \| 0.4607 \| 0.8376 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.6376 \| 0.94 \| 375 \| 1.3109 \| 0.1476 \| 0.1964 \| 0.7253 \| nan \| 0.7617 \| 0.9215 \| 0.0 \| 0.1225 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9008 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8842 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9118 \| 0.6529 \| 0.9335 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4357 \| 0.7750 \| 0.0 \| 0.1216 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6139 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5572 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7387 \| 0.5011 \| 0.8333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4354 \| 1.0 \| 400 \| 1.3483 \| 0.1474 \| 0.1958 \| 0.7227 \| nan \| 0.7892 \| 0.9075 \| 0.0 \| 0.1029 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8749 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8943 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9118 \| 0.6546 \| 0.9352 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4282 \| 0.7768 \| 0.0 \| 0.1021 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6372 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5530 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7392 \| 0.5009 \| 0.8328 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
jfdpastor/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.0243 - Mean Iou: 0.9582 - Mean Accuracy: 0.9792 - Overall Accuracy: 0.9965 - Accuracy Unlabeled: 0.9981 - Accuracy Numero: 0.9603 - Iou Unlabeled: 0.9963 - Iou Numero: 0.9200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Numero \| Iou Unlabeled \| Iou Numero \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:---------------:\|:-------------:\|:----------:\| \| 0.1406 \| 5.0 \| 20 \| 0.1672 \| 0.7389 \| 0.7497 \| 0.9790 \| 1.0000 \| 0.4994 \| 0.9785 \| 0.4993 \| \| 0.045 \| 10.0 \| 40 \| 0.0498 \| 0.9398 \| 0.9476 \| 0.9951 \| 0.9994 \| 0.8958 \| 0.9949 \| 0.8846 \| \| 0.0361 \| 15.0 \| 60 \| 0.0296 \| 0.9575 \| 0.9811 \| 0.9964 \| 0.9978 \| 0.9643 \| 0.9963 \| 0.9187 \| \| 0.026 \| 20.0 \| 80 \| 0.0243 \| 0.9582 \| 0.9792 \| 0.9965 \| 0.9981 \| 0.9603 \| 0.9963 \| 0.9200 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "numero" ]
peldrak/segformer-finetuned-riviera	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-riviera This model is a fine-tuned version of [peldrak/segformer-finetuned-coasts-final](https://huggingface.co/peldrak/segformer-finetuned-coasts-final) on the peldrak/riviera_labeled_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1898 - Mean Iou: 0.5029 - Mean Accuracy: 0.6197 - Overall Accuracy: 0.9030 - Accuracy Water: 0.9790 - Accuracy Whitewater: 0.0 - Accuracy Sand: 0.8826 - Accuracy Rocky Terrain: 0.8951 - Accuracy Agricultural: 0.8809 - Accuracy Vegetation: 0.9396 - Accuracy Road: 0.6302 - Accuracy Building: 0.5410 - Accuracy Soil: 0.4486 - Accuracy Boat: nan - Accuracy Development: 0.0 - Accuracy Unknown: nan - Iou Water: 0.9539 - Iou Whitewater: 0.0 - Iou Sand: 0.8064 - Iou Rocky Terrain: 0.8292 - Iou Agricultural: 0.8190 - Iou Vegetation: 0.7979 - Iou Road: 0.5135 - Iou Building: 0.4346 - Iou Soil: 0.3769 - Iou Boat: nan - Iou Development: 0.0 - Iou Unknown: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sand \| Accuracy Rocky Terrain \| Accuracy Agricultural \| Accuracy Vegetation \| Accuracy Road \| Accuracy Building \| Accuracy Soil \| Accuracy Boat \| Accuracy Development \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sand \| Iou Rocky Terrain \| Iou Agricultural \| Iou Vegetation \| Iou Road \| Iou Building \| Iou Soil \| Iou Boat \| Iou Development \| Iou Unknown \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-------------:\|:----------------------:\|:---------------------:\|:-------------------:\|:-------------:\|:-----------------:\|:-------------:\|:-------------:\|:--------------------:\|:----------------:\|:---------:\|:--------------:\|:--------:\|:-----------------:\|:----------------:\|:--------------:\|:--------:\|:------------:\|:--------:\|:--------:\|:---------------:\|:-----------:\| \| 1.908 \| 0.43 \| 20 \| 1.5006 \| 0.1386 \| 0.2716 \| 0.5245 \| 0.5673 \| 0.0015 \| 0.1051 \| 0.7783 \| 0.0605 \| 0.9687 \| 0.0004 \| 0.2337 \| 0.0003 \| nan \| 0.0 \| nan \| 0.5526 \| 0.0002 \| 0.0967 \| 0.5020 \| 0.0515 \| 0.4225 \| 0.0004 \| 0.0371 \| 0.0003 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4398 \| 0.87 \| 40 \| 0.9640 \| 0.2528 \| 0.3806 \| 0.7203 \| 0.9531 \| 0.0 \| 0.8145 \| 0.9034 \| 0.1560 \| 0.9690 \| 0.0 \| 0.0075 \| 0.0027 \| nan \| 0.0 \| nan \| 0.9207 \| 0.0 \| 0.7481 \| 0.6936 \| 0.1539 \| 0.5106 \| 0.0 \| 0.0046 \| 0.0027 \| 0.0 \| 0.0 \| 0.0 \| \| 1.0307 \| 1.3 \| 60 \| 0.7848 \| 0.2878 \| 0.3859 \| 0.7291 \| 0.9583 \| 0.0 \| 0.8569 \| 0.8883 \| 0.1763 \| 0.9791 \| 0.0 \| 0.0 \| 0.0000 \| nan \| 0.0 \| nan \| 0.9395 \| 0.0 \| 0.8017 \| 0.7539 \| 0.1747 \| 0.4960 \| 0.0 \| 0.0 \| 0.0000 \| nan \| 0.0 \| 0.0 \| \| 0.8326 \| 1.74 \| 80 \| 0.6153 \| 0.2889 \| 0.3878 \| 0.7374 \| 0.9785 \| 0.0 \| 0.8393 \| 0.8917 \| 0.1999 \| 0.9681 \| 0.0 \| 0.0 \| 0.0000 \| nan \| 0.0 \| nan \| 0.9483 \| 0.0 \| 0.8011 \| 0.7193 \| 0.1976 \| 0.5110 \| 0.0 \| 0.0 \| 0.0000 \| nan \| 0.0 \| 0.0 \| \| 0.8073 \| 2.17 \| 100 \| 0.4691 \| 0.3205 \| 0.4189 \| 0.8037 \| 0.9808 \| 0.0 \| 0.8498 \| 0.8368 \| 0.5480 \| 0.9713 \| 0.0 \| 0.0 \| 0.0022 \| nan \| 0.0 \| nan \| 0.9307 \| 0.0 \| 0.6741 \| 0.7811 \| 0.5204 \| 0.6174 \| 0.0 \| 0.0 \| 0.0022 \| nan \| 0.0 \| 0.0 \| \| 0.704 \| 2.61 \| 120 \| 0.4258 \| 0.3332 \| 0.4358 \| 0.8194 \| 0.9702 \| 0.0 \| 0.8729 \| 0.8962 \| 0.6086 \| 0.9639 \| 0.0 \| 0.0 \| 0.0457 \| nan \| 0.0 \| nan \| 0.9486 \| 0.0 \| 0.6774 \| 0.7899 \| 0.5665 \| 0.6374 \| 0.0 \| 0.0 \| 0.0455 \| nan \| 0.0 \| 0.0 \| \| 0.6908 \| 3.04 \| 140 \| 0.3838 \| 0.3652 \| 0.4545 \| 0.8613 \| 0.9801 \| 0.0 \| 0.8479 \| 0.8894 \| 0.8547 \| 0.9289 \| 0.0 \| 0.0 \| 0.0435 \| nan \| 0.0 \| nan \| 0.9418 \| 0.0 \| 0.8003 \| 0.7730 \| 0.7470 \| 0.7114 \| 0.0 \| 0.0 \| 0.0434 \| nan \| 0.0 \| 0.0 \| \| 0.8322 \| 3.48 \| 160 \| 0.3320 \| 0.3723 \| 0.4625 \| 0.8673 \| 0.9825 \| 0.0 \| 0.8877 \| 0.8569 \| 0.8891 \| 0.9189 \| 0.0 \| 0.0 \| 0.0898 \| nan \| 0.0 \| nan \| 0.9352 \| 0.0 \| 0.7833 \| 0.7893 \| 0.7687 \| 0.7353 \| 0.0 \| 0.0 \| 0.0832 \| nan \| 0.0 \| 0.0 \| \| 0.4966 \| 3.91 \| 180 \| 0.2892 \| 0.3848 \| 0.4793 \| 0.8732 \| 0.9715 \| 0.0 \| 0.8962 \| 0.8724 \| 0.8856 \| 0.9257 \| 0.0 \| 0.0 \| 0.2418 \| nan \| 0.0 \| nan \| 0.9447 \| 0.0 \| 0.7618 \| 0.7963 \| 0.7712 \| 0.7571 \| 0.0 \| 0.0 \| 0.2014 \| nan \| 0.0 \| 0.0 \| \| 0.5418 \| 4.35 \| 200 \| 0.2968 \| 0.3658 \| 0.4564 \| 0.8647 \| 0.9697 \| 0.0 \| 0.8268 \| 0.8930 \| 0.8605 \| 0.9519 \| 0.0 \| 0.0 \| 0.0625 \| nan \| 0.0 \| nan \| 0.9464 \| 0.0 \| 0.7798 \| 0.7485 \| 0.7633 \| 0.7261 \| 0.0 \| 0.0 \| 0.0601 \| nan \| 0.0 \| 0.0 \| \| 0.6316 \| 4.78 \| 220 \| 0.2704 \| 0.3803 \| 0.4742 \| 0.8606 \| 0.9748 \| 0.0 \| 0.8969 \| 0.9011 \| 0.7804 \| 0.9430 \| 0.0 \| 0.0 \| 0.2453 \| nan \| 0.0 \| nan \| 0.9473 \| 0.0 \| 0.7903 \| 0.8128 \| 0.7240 \| 0.7156 \| 0.0 \| 0.0 \| 0.1931 \| nan \| 0.0 \| 0.0 \| \| 0.6079 \| 5.22 \| 240 \| 0.2406 \| 0.3930 \| 0.4851 \| 0.8765 \| 0.9742 \| 0.0 \| 0.8936 \| 0.8915 \| 0.9013 \| 0.9088 \| 0.0 \| 0.0 \| 0.2812 \| nan \| 0.0 \| nan \| 0.9356 \| 0.0 \| 0.7964 \| 0.8103 \| 0.7827 \| 0.7700 \| 0.0 \| 0.0 \| 0.2281 \| nan \| 0.0 \| 0.0 \| \| 0.3053 \| 5.65 \| 260 \| 0.2500 \| 0.3926 \| 0.4892 \| 0.8681 \| 0.9674 \| 0.0 \| 0.8905 \| 0.9148 \| 0.9038 \| 0.8597 \| 0.0 \| 0.0 \| 0.3563 \| nan \| 0.0 \| nan \| 0.9246 \| 0.0 \| 0.8017 \| 0.8123 \| 0.7713 \| 0.7575 \| 0.0 \| 0.0 \| 0.2513 \| nan \| 0.0 \| 0.0 \| \| 0.2995 \| 6.09 \| 280 \| 0.2654 \| 0.3756 \| 0.4619 \| 0.8622 \| 0.9758 \| 0.0 \| 0.8820 \| 0.8759 \| 0.8019 \| 0.9671 \| 0.0 \| 0.0 \| 0.1161 \| nan \| 0.0 \| nan \| 0.9458 \| 0.0 \| 0.8107 \| 0.8151 \| 0.7566 \| 0.6975 \| 0.0 \| 0.0 \| 0.1056 \| nan \| 0.0 \| 0.0 \| \| 0.2772 \| 6.52 \| 300 \| 0.2242 \| 0.4013 \| 0.4916 \| 0.8844 \| 0.9834 \| 0.0 \| 0.8714 \| 0.8650 \| 0.9075 \| 0.9231 \| 0.0 \| 0.0 \| 0.3653 \| nan \| 0.0 \| nan \| 0.9429 \| 0.0 \| 0.8099 \| 0.7963 \| 0.8043 \| 0.7682 \| 0.0 \| 0.0 \| 0.2922 \| nan \| 0.0 \| 0.0 \| \| 0.4586 \| 6.96 \| 320 \| 0.2596 \| 0.3783 \| 0.4670 \| 0.8555 \| 0.9771 \| 0.0 \| 0.8875 \| 0.8424 \| 0.7514 \| 0.9690 \| 0.0 \| 0.0 \| 0.2426 \| nan \| 0.0 \| nan \| 0.9414 \| 0.0 \| 0.8145 \| 0.7988 \| 0.7052 \| 0.6870 \| 0.0 \| 0.0 \| 0.2148 \| nan \| 0.0 \| 0.0 \| \| 0.4574 \| 7.39 \| 340 \| 0.2196 \| 0.4007 \| 0.4989 \| 0.8832 \| 0.9701 \| 0.0 \| 0.8993 \| 0.9037 \| 0.9086 \| 0.9088 \| 0.0 \| 0.0 \| 0.3981 \| nan \| 0.0 \| nan \| 0.9464 \| 0.0 \| 0.7823 \| 0.8113 \| 0.8016 \| 0.7711 \| 0.0 \| 0.0 \| 0.2950 \| nan \| 0.0 \| 0.0 \| \| 0.5272 \| 7.83 \| 360 \| 0.2229 \| 0.3997 \| 0.4979 \| 0.8804 \| 0.9830 \| 0.0 \| 0.9013 \| 0.8904 \| 0.8637 \| 0.9164 \| 0.0 \| 0.0 \| 0.4245 \| nan \| 0.0 \| nan \| 0.9426 \| 0.0 \| 0.7984 \| 0.8113 \| 0.7873 \| 0.7681 \| 0.0 \| 0.0 \| 0.2895 \| nan \| 0.0 \| 0.0 \| \| 0.3955 \| 8.26 \| 380 \| 0.2135 \| 0.4035 \| 0.5020 \| 0.8833 \| 0.9786 \| 0.0 \| 0.8921 \| 0.8926 \| 0.9247 \| 0.8807 \| 0.0 \| 0.0 \| 0.4515 \| nan \| 0.0 \| nan \| 0.9452 \| 0.0 \| 0.7945 \| 0.8264 \| 0.7946 \| 0.7726 \| 0.0 \| 0.0 \| 0.3048 \| nan \| 0.0 \| 0.0 \| \| 0.5612 \| 8.7 \| 400 \| 0.2228 \| 0.4009 \| 0.4919 \| 0.8849 \| 0.9892 \| 0.0 \| 0.8790 \| 0.8097 \| 0.8977 \| 0.9389 \| 0.0 \| 0.0 \| 0.4042 \| nan \| 0.0 \| nan \| 0.9419 \| 0.0 \| 0.8002 \| 0.7755 \| 0.8134 \| 0.7698 \| 0.0 \| 0.0 \| 0.3086 \| nan \| 0.0 \| 0.0 \| \| 0.1692 \| 9.13 \| 420 \| 0.2094 \| 0.4046 \| 0.5041 \| 0.8853 \| 0.9658 \| 0.0 \| 0.9087 \| 0.9039 \| 0.8833 \| 0.9330 \| 0.0 \| 0.0 \| 0.4467 \| nan \| 0.0 \| nan \| 0.9479 \| 0.0 \| 0.7794 \| 0.8138 \| 0.8039 \| 0.7686 \| 0.0 \| 0.0 \| 0.3368 \| nan \| 0.0 \| 0.0 \| \| 0.4642 \| 9.57 \| 440 \| 0.2251 \| 0.3966 \| 0.4857 \| 0.8790 \| 0.9910 \| 0.0 \| 0.8899 \| 0.8028 \| 0.8914 \| 0.9269 \| 0.0 \| 0.0 \| 0.3552 \| nan \| 0.0 \| nan \| 0.9288 \| 0.0 \| 0.8090 \| 0.7718 \| 0.7912 \| 0.7577 \| 0.0 \| 0.0 \| 0.3036 \| nan \| 0.0 \| 0.0 \| \| 0.2049 \| 10.0 \| 460 \| 0.1939 \| 0.4101 \| 0.5068 \| 0.8893 \| 0.9763 \| 0.0 \| 0.8918 \| 0.8915 \| 0.8887 \| 0.9321 \| 0.0061 \| 0.0 \| 0.4817 \| nan \| 0.0 \| nan \| 0.9501 \| 0.0 \| 0.8010 \| 0.8156 \| 0.8126 \| 0.7766 \| 0.0061 \| 0.0 \| 0.3493 \| nan \| 0.0 \| 0.0 \| \| 0.2509 \| 10.43 \| 480 \| 0.2103 \| 0.4012 \| 0.4950 \| 0.8773 \| 0.9847 \| 0.0 \| 0.8763 \| 0.8441 \| 0.8569 \| 0.9243 \| 0.0069 \| 0.0 \| 0.4566 \| nan \| 0.0 \| nan \| 0.9417 \| 0.0 \| 0.8099 \| 0.7984 \| 0.7737 \| 0.7506 \| 0.0069 \| 0.0 \| 0.3319 \| nan \| 0.0 \| 0.0 \| \| 0.2327 \| 10.87 \| 500 \| 0.2140 \| 0.4026 \| 0.4977 \| 0.8841 \| 0.9813 \| 0.0 \| 0.8971 \| 0.8575 \| 0.8907 \| 0.9270 \| 0.0105 \| 0.0 \| 0.4126 \| nan \| 0.0 \| nan \| 0.9486 \| 0.0 \| 0.7858 \| 0.8034 \| 0.7947 \| 0.7634 \| 0.0105 \| 0.0 \| 0.3218 \| nan \| 0.0 \| 0.0 \| \| 0.2991 \| 11.3 \| 520 \| 0.2111 \| 0.3995 \| 0.4917 \| 0.8787 \| 0.9828 \| 0.0 \| 0.8797 \| 0.8606 \| 0.8767 \| 0.9223 \| 0.0151 \| 0.0 \| 0.3794 \| nan \| 0.0 \| nan \| 0.9402 \| 0.0 \| 0.8020 \| 0.7913 \| 0.7812 \| 0.7541 \| 0.0151 \| 0.0 \| 0.3105 \| nan \| 0.0 \| 0.0 \| \| 0.2296 \| 11.74 \| 540 \| 0.2011 \| 0.4054 \| 0.4998 \| 0.8862 \| 0.9755 \| 0.0 \| 0.8746 \| 0.8879 \| 0.8974 \| 0.9281 \| 0.0060 \| 0.0 \| 0.4286 \| nan \| 0.0 \| nan \| 0.9508 \| 0.0 \| 0.8014 \| 0.8092 \| 0.8019 \| 0.7688 \| 0.0060 \| 0.0 \| 0.3209 \| nan \| 0.0 \| 0.0 \| \| 1.0788 \| 12.17 \| 560 \| 0.1957 \| 0.4251 \| 0.5366 \| 0.8836 \| 0.9853 \| 0.0 \| 0.9076 \| 0.8684 \| 0.8856 \| 0.8582 \| 0.1839 \| 0.0 \| 0.6768 \| nan \| 0.0 \| nan \| 0.9360 \| 0.0 \| 0.7905 \| 0.8101 \| 0.8059 \| 0.7701 \| 0.1835 \| 0.0 \| 0.3802 \| nan \| 0.0 \| 0.0 \| \| 0.2605 \| 12.61 \| 580 \| 0.1892 \| 0.4191 \| 0.5156 \| 0.8896 \| 0.9833 \| 0.0 \| 0.8800 \| 0.8779 \| 0.8794 \| 0.9283 \| 0.0863 \| 0.0121 \| 0.5091 \| nan \| 0.0 \| nan \| 0.9490 \| 0.0 \| 0.7897 \| 0.8130 \| 0.8217 \| 0.7759 \| 0.0862 \| 0.0121 \| 0.3627 \| nan \| 0.0 \| 0.0 \| \| 0.0813 \| 13.04 \| 600 \| 0.1897 \| 0.4230 \| 0.5230 \| 0.8907 \| 0.9813 \| 0.0 \| 0.8939 \| 0.8806 \| 0.8869 \| 0.9180 \| 0.1234 \| 0.0078 \| 0.5375 \| nan \| 0.0 \| nan \| 0.9482 \| 0.0 \| 0.7897 \| 0.8206 \| 0.8134 \| 0.7772 \| 0.1234 \| 0.0078 \| 0.3729 \| nan \| 0.0 \| 0.0 \| \| 0.1048 \| 13.48 \| 620 \| 0.1922 \| 0.4193 \| 0.5126 \| 0.8877 \| 0.9825 \| 0.0 \| 0.8779 \| 0.8872 \| 0.8628 \| 0.9418 \| 0.1405 \| 0.0 \| 0.4337 \| nan \| 0.0 \| nan \| 0.9486 \| 0.0 \| 0.7928 \| 0.8189 \| 0.7970 \| 0.7656 \| 0.1403 \| 0.0 \| 0.3486 \| nan \| 0.0 \| 0.0 \| \| 0.5566 \| 13.91 \| 640 \| 0.1864 \| 0.4260 \| 0.5238 \| 0.8922 \| 0.9771 \| 0.0 \| 0.9038 \| 0.8912 \| 0.9021 \| 0.9222 \| 0.1904 \| 0.0 \| 0.4507 \| nan \| 0.0 \| nan \| 0.9487 \| 0.0 \| 0.7827 \| 0.8262 \| 0.8114 \| 0.7797 \| 0.1894 \| 0.0 \| 0.3479 \| nan \| 0.0 \| 0.0 \| \| 0.1057 \| 14.35 \| 660 \| 0.1946 \| 0.4181 \| 0.5078 \| 0.8910 \| 0.9873 \| 0.0 \| 0.8601 \| 0.8752 \| 0.8917 \| 0.9425 \| 0.1355 \| 0.0 \| 0.3856 \| nan \| 0.0 \| nan \| 0.9483 \| 0.0 \| 0.7937 \| 0.8148 \| 0.8133 \| 0.7715 \| 0.1349 \| 0.0 \| 0.3226 \| nan \| 0.0 \| 0.0 \| \| 0.1439 \| 14.78 \| 680 \| 0.1795 \| 0.4398 \| 0.5410 \| 0.8934 \| 0.9836 \| 0.0 \| 0.8812 \| 0.8729 \| 0.9105 \| 0.8974 \| 0.3216 \| 0.0 \| 0.5430 \| nan \| 0.0 \| nan \| 0.9457 \| 0.0 \| 0.7858 \| 0.8205 \| 0.8126 \| 0.7930 \| 0.3162 \| 0.0 \| 0.3637 \| nan \| 0.0 \| 0.0 \| \| 0.2824 \| 15.22 \| 700 \| 0.1861 \| 0.4336 \| 0.5321 \| 0.8931 \| 0.9800 \| 0.0 \| 0.8835 \| 0.9116 \| 0.8853 \| 0.9227 \| 0.2573 \| 0.0090 \| 0.4711 \| nan \| 0.0 \| nan \| 0.9529 \| 0.0 \| 0.7882 \| 0.8265 \| 0.8065 \| 0.7834 \| 0.2549 \| 0.0090 \| 0.3478 \| nan \| 0.0 \| 0.0 \| \| 0.1526 \| 15.65 \| 720 \| 0.1906 \| 0.4393 \| 0.5373 \| 0.8937 \| 0.9825 \| 0.0 \| 0.9053 \| 0.8791 \| 0.9102 \| 0.9116 \| 0.3373 \| 0.0175 \| 0.4292 \| nan \| 0.0 \| nan \| 0.9494 \| 0.0 \| 0.7948 \| 0.8218 \| 0.7996 \| 0.7924 \| 0.3272 \| 0.0175 \| 0.3300 \| nan \| 0.0 \| 0.0 \| \| 0.2005 \| 16.09 \| 740 \| 0.2003 \| 0.4357 \| 0.5370 \| 0.8935 \| 0.9823 \| 0.0 \| 0.9005 \| 0.8834 \| 0.8703 \| 0.9318 \| 0.2638 \| 0.0170 \| 0.5209 \| nan \| 0.0 \| nan \| 0.9518 \| 0.0 \| 0.7887 \| 0.8264 \| 0.8059 \| 0.7930 \| 0.2606 \| 0.0168 \| 0.3492 \| nan \| 0.0 \| 0.0 \| \| 0.2036 \| 16.52 \| 760 \| 0.1838 \| 0.4435 \| 0.5415 \| 0.8945 \| 0.9871 \| 0.0 \| 0.8860 \| 0.8864 \| 0.9123 \| 0.9028 \| 0.3602 \| 0.0335 \| 0.4468 \| nan \| 0.0 \| nan \| 0.9537 \| 0.0 \| 0.7910 \| 0.8258 \| 0.8032 \| 0.7884 \| 0.3456 \| 0.0334 \| 0.3371 \| nan \| 0.0 \| 0.0 \| \| 0.2055 \| 16.96 \| 780 \| 0.1869 \| 0.4504 \| 0.5608 \| 0.8918 \| 0.9767 \| 0.0 \| 0.9097 \| 0.9289 \| 0.8528 \| 0.9078 \| 0.4082 \| 0.0591 \| 0.5649 \| nan \| 0.0 \| nan \| 0.9562 \| 0.0 \| 0.8009 \| 0.8215 \| 0.7875 \| 0.7943 \| 0.3743 \| 0.0567 \| 0.3631 \| nan \| 0.0 \| 0.0 \| \| 0.3388 \| 17.39 \| 800 \| 0.1877 \| 0.4429 \| 0.5406 \| 0.8969 \| 0.9833 \| 0.0 \| 0.9198 \| 0.8840 \| 0.9019 \| 0.9237 \| 0.3276 \| 0.0213 \| 0.4441 \| nan \| 0.0 \| nan \| 0.9538 \| 0.0 \| 0.8057 \| 0.8268 \| 0.8104 \| 0.7897 \| 0.3181 \| 0.0213 \| 0.3467 \| nan \| 0.0 \| 0.0 \| \| 0.2979 \| 17.83 \| 820 \| 0.1659 \| 0.4508 \| 0.5498 \| 0.8986 \| 0.9788 \| 0.0 \| 0.9080 \| 0.8993 \| 0.9090 \| 0.9164 \| 0.4007 \| 0.0071 \| 0.4783 \| nan \| 0.0 \| nan \| 0.9526 \| 0.0 \| 0.8108 \| 0.8275 \| 0.8099 \| 0.7985 \| 0.3902 \| 0.0071 \| 0.3619 \| nan \| 0.0 \| 0.0 \| \| 0.2011 \| 18.26 \| 840 \| 0.1894 \| 0.4475 \| 0.5465 \| 0.8967 \| 0.9815 \| 0.0 \| 0.9013 \| 0.8978 \| 0.8898 \| 0.9281 \| 0.3918 \| 0.0271 \| 0.4472 \| nan \| 0.0 \| nan \| 0.9541 \| 0.0 \| 0.8001 \| 0.8326 \| 0.8058 \| 0.7907 \| 0.3727 \| 0.0268 \| 0.3394 \| nan \| 0.0 \| 0.0 \| \| 0.2616 \| 18.7 \| 860 \| 0.1768 \| 0.4599 \| 0.5629 \| 0.9003 \| 0.9787 \| 0.0 \| 0.8992 \| 0.9177 \| 0.9072 \| 0.9140 \| 0.4964 \| 0.0517 \| 0.4638 \| nan \| 0.0 \| nan \| 0.9548 \| 0.0 \| 0.7989 \| 0.8413 \| 0.8094 \| 0.7996 \| 0.4472 \| 0.0507 \| 0.3566 \| nan \| 0.0 \| 0.0 \| \| 0.1374 \| 19.13 \| 880 \| 0.1818 \| 0.4574 \| 0.5580 \| 0.8986 \| 0.9885 \| 0.0 \| 0.8604 \| 0.8578 \| 0.8910 \| 0.9292 \| 0.5430 \| 0.0049 \| 0.5051 \| nan \| 0.0 \| nan \| 0.9514 \| 0.0 \| 0.8067 \| 0.8207 \| 0.8126 \| 0.7994 \| 0.4787 \| 0.0049 \| 0.3568 \| nan \| 0.0 \| 0.0 \| \| 0.4279 \| 19.57 \| 900 \| 0.1698 \| 0.4761 \| 0.5908 \| 0.8989 \| 0.9739 \| 0.0 \| 0.9051 \| 0.9403 \| 0.9099 \| 0.8918 \| 0.5825 \| 0.2412 \| 0.4630 \| nan \| 0.0 \| nan \| 0.9536 \| 0.0 \| 0.8065 \| 0.8213 \| 0.8168 \| 0.8022 \| 0.4816 \| 0.2106 \| 0.3449 \| nan \| 0.0 \| 0.0 \| \| 0.1616 \| 20.0 \| 920 \| 0.1756 \| 0.4670 \| 0.5709 \| 0.8988 \| 0.9765 \| 0.0 \| 0.8972 \| 0.9157 \| 0.9099 \| 0.9152 \| 0.4892 \| 0.1875 \| 0.4178 \| nan \| 0.0 \| nan \| 0.9540 \| 0.0 \| 0.7955 \| 0.8372 \| 0.8129 \| 0.7971 \| 0.4375 \| 0.1709 \| 0.3319 \| nan \| 0.0 \| 0.0 \| \| 0.1919 \| 20.43 \| 940 \| 0.1604 \| 0.4676 \| 0.5748 \| 0.9024 \| 0.9800 \| 0.0 \| 0.8835 \| 0.9098 \| 0.9301 \| 0.8918 \| 0.5902 \| 0.0423 \| 0.5207 \| nan \| 0.0 \| nan \| 0.9535 \| 0.0 \| 0.8118 \| 0.8462 \| 0.8284 \| 0.8078 \| 0.4979 \| 0.0412 \| 0.3564 \| nan \| 0.0 \| 0.0 \| \| 0.2451 \| 20.87 \| 960 \| 0.1640 \| 0.4733 \| 0.5909 \| 0.8996 \| 0.9784 \| 0.0 \| 0.8962 \| 0.9061 \| 0.9132 \| 0.8778 \| 0.6155 \| 0.1235 \| 0.5980 \| nan \| 0.0 \| nan \| 0.9502 \| 0.0 \| 0.8104 \| 0.8375 \| 0.8307 \| 0.8073 \| 0.4862 \| 0.1120 \| 0.3723 \| nan \| 0.0 \| 0.0 \| \| 0.3232 \| 21.3 \| 980 \| 0.1749 \| 0.4743 \| 0.5760 \| 0.9007 \| 0.9839 \| 0.0 \| 0.8839 \| 0.8859 \| 0.9054 \| 0.9281 \| 0.5201 \| 0.2357 \| 0.4168 \| nan \| 0.0 \| nan \| 0.9558 \| 0.0 \| 0.8040 \| 0.8320 \| 0.8197 \| 0.7934 \| 0.4538 \| 0.2167 \| 0.3415 \| nan \| 0.0 \| 0.0 \| \| 0.1096 \| 21.74 \| 1000 \| 0.1663 \| 0.4741 \| 0.5870 \| 0.8996 \| 0.9817 \| 0.0 \| 0.8991 \| 0.8940 \| 0.8923 \| 0.9046 \| 0.5485 \| 0.1900 \| 0.5595 \| nan \| 0.0 \| nan \| 0.9524 \| 0.0 \| 0.8085 \| 0.8322 \| 0.8204 \| 0.8010 \| 0.4463 \| 0.1727 \| 0.3818 \| nan \| 0.0 \| 0.0 \| \| 0.0849 \| 22.17 \| 1020 \| 0.1842 \| 0.4726 \| 0.5788 \| 0.9019 \| 0.9792 \| 0.0 \| 0.8916 \| 0.9083 \| 0.9011 \| 0.9196 \| 0.5674 \| 0.1386 \| 0.4818 \| nan \| 0.0 \| nan \| 0.9548 \| 0.0 \| 0.8071 \| 0.8381 \| 0.8157 \| 0.8027 \| 0.4867 \| 0.1327 \| 0.3609 \| nan \| 0.0 \| 0.0 \| \| 0.28 \| 22.61 \| 1040 \| 0.1841 \| 0.4814 \| 0.5925 \| 0.9031 \| 0.9753 \| 0.0 \| 0.9142 \| 0.9124 \| 0.9188 \| 0.9145 \| 0.5855 \| 0.2764 \| 0.4275 \| nan \| 0.0 \| nan \| 0.9542 \| 0.0 \| 0.8005 \| 0.8341 \| 0.8146 \| 0.8048 \| 0.4811 \| 0.2472 \| 0.3589 \| nan \| 0.0 \| 0.0 \| \| 0.3515 \| 23.04 \| 1060 \| 0.1875 \| 0.4773 \| 0.5847 \| 0.8997 \| 0.9851 \| 0.0 \| 0.8938 \| 0.8555 \| 0.9027 \| 0.9324 \| 0.5401 \| 0.3279 \| 0.4096 \| nan \| 0.0 \| nan \| 0.9494 \| 0.0 \| 0.7907 \| 0.8135 \| 0.8221 \| 0.7940 \| 0.4402 \| 0.2804 \| 0.3601 \| nan \| 0.0 \| 0.0 \| \| 0.2499 \| 23.48 \| 1080 \| 0.1807 \| 0.4796 \| 0.5940 \| 0.8965 \| 0.9705 \| 0.0 \| 0.8706 \| 0.9246 \| 0.8663 \| 0.9262 \| 0.5712 \| 0.3009 \| 0.5100 \| nan \| 0.0 \| nan \| 0.9511 \| 0.0 \| 0.7995 \| 0.8286 \| 0.8060 \| 0.8026 \| 0.4593 \| 0.2677 \| 0.3605 \| nan \| 0.0 \| 0.0 \| \| 0.1632 \| 23.91 \| 1100 \| 0.1788 \| 0.4839 \| 0.5988 \| 0.8990 \| 0.9779 \| 0.0 \| 0.9129 \| 0.9039 \| 0.8835 \| 0.9191 \| 0.6179 \| 0.3242 \| 0.4487 \| nan \| 0.0 \| nan \| 0.9497 \| 0.0 \| 0.8102 \| 0.8378 \| 0.8036 \| 0.8011 \| 0.4888 \| 0.2808 \| 0.3514 \| nan \| 0.0 \| 0.0 \| \| 0.099 \| 24.35 \| 1120 \| 0.1757 \| 0.4872 \| 0.6020 \| 0.9007 \| 0.9820 \| 0.0 \| 0.8959 \| 0.9089 \| 0.8962 \| 0.9048 \| 0.6043 \| 0.3368 \| 0.4915 \| nan \| 0.0 \| nan \| 0.9517 \| 0.0 \| 0.8099 \| 0.8424 \| 0.8083 \| 0.8020 \| 0.5038 \| 0.2829 \| 0.3583 \| nan \| 0.0 \| 0.0 \| \| 0.4929 \| 24.78 \| 1140 \| 0.1815 \| 0.4739 \| 0.5880 \| 0.8978 \| 0.9747 \| 0.0 \| 0.8931 \| 0.9110 \| 0.9018 \| 0.8914 \| 0.6130 \| 0.1439 \| 0.5510 \| nan \| 0.0 \| nan \| 0.9482 \| 0.0 \| 0.7966 \| 0.8351 \| 0.8117 \| 0.7985 \| 0.5134 \| 0.1375 \| 0.3723 \| nan \| 0.0 \| 0.0 \| \| 0.1068 \| 25.22 \| 1160 \| 0.1735 \| 0.4870 \| 0.6002 \| 0.9025 \| 0.9788 \| 0.0 \| 0.9070 \| 0.9055 \| 0.9000 \| 0.9107 \| 0.6189 \| 0.2777 \| 0.5030 \| nan \| 0.0 \| nan \| 0.9528 \| 0.0 \| 0.8086 \| 0.8388 \| 0.8150 \| 0.8055 \| 0.5062 \| 0.2559 \| 0.3739 \| nan \| 0.0 \| 0.0 \| \| 0.1619 \| 25.65 \| 1180 \| 0.2112 \| 0.4802 \| 0.5975 \| 0.8927 \| 0.9747 \| 0.0 \| 0.8930 \| 0.9126 \| 0.8258 \| 0.9279 \| 0.5779 \| 0.2974 \| 0.5657 \| nan \| 0.0 \| nan \| 0.9543 \| 0.0 \| 0.8057 \| 0.8380 \| 0.7781 \| 0.7911 \| 0.4740 \| 0.2635 \| 0.3775 \| nan \| 0.0 \| 0.0 \| \| 0.1147 \| 26.09 \| 1200 \| 0.1840 \| 0.4895 \| 0.6056 \| 0.8986 \| 0.9826 \| 0.0 \| 0.8927 \| 0.8858 \| 0.8902 \| 0.9032 \| 0.6027 \| 0.3710 \| 0.5275 \| nan \| 0.0 \| nan \| 0.9497 \| 0.0 \| 0.8161 \| 0.8296 \| 0.8054 \| 0.8056 \| 0.5088 \| 0.2954 \| 0.3736 \| nan \| 0.0 \| 0.0 \| \| 0.1106 \| 26.52 \| 1220 \| 0.2082 \| 0.4780 \| 0.5868 \| 0.8931 \| 0.9814 \| 0.0 \| 0.8777 \| 0.8927 \| 0.8463 \| 0.9429 \| 0.5490 \| 0.3677 \| 0.4107 \| nan \| 0.0 \| nan \| 0.9539 \| 0.0 \| 0.8048 \| 0.8265 \| 0.7864 \| 0.7734 \| 0.4618 \| 0.3047 \| 0.3467 \| nan \| 0.0 \| 0.0 \| \| 0.1019 \| 26.96 \| 1240 \| 0.1741 \| 0.4906 \| 0.6002 \| 0.9021 \| 0.9802 \| 0.0 \| 0.8946 \| 0.8912 \| 0.9047 \| 0.9228 \| 0.5771 \| 0.3889 \| 0.4420 \| nan \| 0.0 \| nan \| 0.9546 \| 0.0 \| 0.8079 \| 0.8331 \| 0.8130 \| 0.8005 \| 0.4971 \| 0.3282 \| 0.3624 \| nan \| 0.0 \| 0.0 \| \| 0.1566 \| 27.39 \| 1260 \| 0.1982 \| 0.4930 \| 0.6067 \| 0.8989 \| 0.9807 \| 0.0 \| 0.8996 \| 0.8879 \| 0.8647 \| 0.9430 \| 0.5618 \| 0.5036 \| 0.4259 \| nan \| 0.0 \| nan \| 0.9546 \| 0.0 \| 0.8068 \| 0.8311 \| 0.8025 \| 0.7808 \| 0.4765 \| 0.4065 \| 0.3646 \| nan \| 0.0 \| 0.0 \| \| 0.1307 \| 27.83 \| 1280 \| 0.1957 \| 0.4988 \| 0.6173 \| 0.8993 \| 0.9837 \| 0.0 \| 0.8992 \| 0.8860 \| 0.8612 \| 0.9329 \| 0.6201 \| 0.5265 \| 0.4633 \| nan \| 0.0 \| nan \| 0.9533 \| 0.0 \| 0.8102 \| 0.8357 \| 0.7989 \| 0.7835 \| 0.5001 \| 0.4210 \| 0.3846 \| nan \| 0.0 \| 0.0 \| \| 0.3709 \| 28.26 \| 1300 \| 0.1761 \| 0.4998 \| 0.6132 \| 0.9042 \| 0.9854 \| 0.0 \| 0.8862 \| 0.8905 \| 0.8969 \| 0.9201 \| 0.6066 \| 0.4549 \| 0.4919 \| nan \| 0.0 \| nan \| 0.9519 \| 0.0 \| 0.8211 \| 0.8331 \| 0.8191 \| 0.8051 \| 0.5097 \| 0.3766 \| 0.3807 \| nan \| 0.0 \| 0.0 \| \| 0.1188 \| 28.7 \| 1320 \| 0.1788 \| 0.4940 \| 0.6100 \| 0.8998 \| 0.9852 \| 0.0 \| 0.8999 \| 0.8697 \| 0.8697 \| 0.9414 \| 0.6017 \| 0.5108 \| 0.4218 \| nan \| 0.0 \| nan \| 0.9493 \| 0.0 \| 0.8232 \| 0.8243 \| 0.8091 \| 0.7932 \| 0.4950 \| 0.3705 \| 0.3695 \| nan \| 0.0 \| 0.0 \| \| 0.1173 \| 29.13 \| 1340 \| 0.1626 \| 0.5031 \| 0.6225 \| 0.9073 \| 0.9794 \| 0.0 \| 0.9253 \| 0.9190 \| 0.9075 \| 0.9157 \| 0.6297 \| 0.4858 \| 0.4621 \| nan \| 0.0 \| nan \| 0.9551 \| 0.0 \| 0.8261 \| 0.8429 \| 0.8273 \| 0.8112 \| 0.4861 \| 0.4026 \| 0.3823 \| nan \| 0.0 \| 0.0 \| \| 0.113 \| 29.57 \| 1360 \| 0.1880 \| 0.5000 \| 0.6237 \| 0.9023 \| 0.9809 \| 0.0 \| 0.9132 \| 0.9085 \| 0.8644 \| 0.9310 \| 0.6079 \| 0.5466 \| 0.4847 \| nan \| 0.0 \| nan \| 0.9543 \| 0.0 \| 0.8245 \| 0.8391 \| 0.8084 \| 0.8025 \| 0.4856 \| 0.3998 \| 0.3854 \| nan \| 0.0 \| 0.0 \| \| 0.1002 \| 30.0 \| 1380 \| 0.1767 \| 0.5000 \| 0.6287 \| 0.9011 \| 0.9794 \| 0.0 \| 0.9151 \| 0.8999 \| 0.8781 \| 0.9104 \| 0.6512 \| 0.5282 \| 0.5245 \| nan \| 0.0 \| nan \| 0.9525 \| 0.0 \| 0.8241 \| 0.8392 \| 0.8049 \| 0.8057 \| 0.5064 \| 0.3795 \| 0.3881 \| nan \| 0.0 \| 0.0 \| \| 0.1108 \| 30.43 \| 1400 \| 0.1767 \| 0.5017 \| 0.6256 \| 0.9023 \| 0.9836 \| 0.0 \| 0.9006 \| 0.9011 \| 0.8708 \| 0.9210 \| 0.6497 \| 0.5210 \| 0.5086 \| nan \| 0.0 \| nan \| 0.9544 \| 0.0 \| 0.8193 \| 0.8402 \| 0.8055 \| 0.8035 \| 0.5106 \| 0.3919 \| 0.3932 \| nan \| 0.0 \| 0.0 \| \| 0.3149 \| 30.87 \| 1420 \| 0.1749 \| 0.4987 \| 0.6121 \| 0.9019 \| 0.9800 \| 0.0 \| 0.8933 \| 0.8955 \| 0.8844 \| 0.9353 \| 0.6261 \| 0.4876 \| 0.4187 \| nan \| 0.0 \| nan \| 0.9521 \| 0.0 \| 0.8120 \| 0.8369 \| 0.8133 \| 0.8002 \| 0.4998 \| 0.4099 \| 0.3621 \| nan \| 0.0 \| 0.0 \| \| 0.0742 \| 31.3 \| 1440 \| 0.1684 \| 0.4996 \| 0.6206 \| 0.9022 \| 0.9802 \| 0.0 \| 0.8933 \| 0.9001 \| 0.8860 \| 0.9096 \| 0.6485 \| 0.4379 \| 0.5506 \| nan \| 0.0 \| nan \| 0.9509 \| 0.0 \| 0.8178 \| 0.8327 \| 0.8114 \| 0.8110 \| 0.5176 \| 0.3538 \| 0.4006 \| nan \| 0.0 \| 0.0 \| \| 0.1199 \| 31.74 \| 1460 \| 0.1784 \| 0.4989 \| 0.6242 \| 0.9008 \| 0.9795 \| 0.0 \| 0.9031 \| 0.8940 \| 0.8800 \| 0.9248 \| 0.6414 \| 0.5655 \| 0.4540 \| nan \| 0.0 \| nan \| 0.9517 \| 0.0 \| 0.8162 \| 0.8322 \| 0.8076 \| 0.8021 \| 0.5085 \| 0.3951 \| 0.3748 \| nan \| 0.0 \| 0.0 \| \| 0.0889 \| 32.17 \| 1480 \| 0.1994 \| 0.4930 \| 0.6116 \| 0.8949 \| 0.9809 \| 0.0 \| 0.9031 \| 0.8725 \| 0.8473 \| 0.9406 \| 0.6060 \| 0.5287 \| 0.4367 \| nan \| 0.0 \| nan \| 0.9497 \| 0.0 \| 0.8134 \| 0.8244 \| 0.7915 \| 0.7827 \| 0.4829 \| 0.4020 \| 0.3765 \| nan \| 0.0 \| 0.0 \| \| 0.1348 \| 32.61 \| 1500 \| 0.1751 \| 0.4897 \| 0.6067 \| 0.8997 \| 0.9801 \| 0.0 \| 0.9140 \| 0.8786 \| 0.8865 \| 0.9283 \| 0.6049 \| 0.4488 \| 0.4262 \| nan \| 0.0 \| nan \| 0.9516 \| 0.0 \| 0.8119 \| 0.8287 \| 0.8064 \| 0.8012 \| 0.4851 \| 0.3438 \| 0.3585 \| nan \| 0.0 \| 0.0 \| \| 0.0824 \| 33.04 \| 1520 \| 0.2008 \| 0.4911 \| 0.6102 \| 0.8963 \| 0.9818 \| 0.0 \| 0.8937 \| 0.8893 \| 0.8384 \| 0.9462 \| 0.5993 \| 0.5037 \| 0.4493 \| nan \| 0.0 \| nan \| 0.9547 \| 0.0 \| 0.8114 \| 0.8321 \| 0.7839 \| 0.7881 \| 0.4963 \| 0.3691 \| 0.3666 \| nan \| 0.0 \| 0.0 \| \| 0.2206 \| 33.48 \| 1540 \| 0.1816 \| 0.4997 \| 0.6190 \| 0.9049 \| 0.9793 \| 0.0 \| 0.9133 \| 0.9083 \| 0.9027 \| 0.9249 \| 0.6156 \| 0.5286 \| 0.4172 \| nan \| 0.0 \| nan \| 0.9560 \| 0.0 \| 0.8168 \| 0.8359 \| 0.8192 \| 0.8045 \| 0.4901 \| 0.4187 \| 0.3556 \| nan \| 0.0 \| 0.0 \| \| 0.2564 \| 33.91 \| 1560 \| 0.1816 \| 0.5030 \| 0.6190 \| 0.9055 \| 0.9793 \| 0.0 \| 0.8910 \| 0.9106 \| 0.8974 \| 0.9267 \| 0.6259 \| 0.4937 \| 0.4653 \| nan \| 0.0 \| nan \| 0.9575 \| 0.0 \| 0.8166 \| 0.8366 \| 0.8212 \| 0.8020 \| 0.5080 \| 0.4151 \| 0.3756 \| nan \| 0.0 \| 0.0 \| \| 0.7781 \| 34.35 \| 1580 \| 0.1789 \| 0.5015 \| 0.6268 \| 0.9026 \| 0.9822 \| 0.0 \| 0.9084 \| 0.8930 \| 0.8861 \| 0.9127 \| 0.6613 \| 0.5154 \| 0.5090 \| nan \| 0.0 \| nan \| 0.9517 \| 0.0 \| 0.8238 \| 0.8372 \| 0.8176 \| 0.8066 \| 0.4996 \| 0.3958 \| 0.3838 \| nan \| 0.0 \| 0.0 \| \| 0.1307 \| 34.78 \| 1600 \| 0.1819 \| 0.4971 \| 0.6269 \| 0.9005 \| 0.9814 \| 0.0 \| 0.9058 \| 0.8986 \| 0.8647 \| 0.9231 \| 0.6565 \| 0.5438 \| 0.4953 \| nan \| 0.0 \| nan \| 0.9520 \| 0.0 \| 0.8230 \| 0.8387 \| 0.8107 \| 0.8020 \| 0.4825 \| 0.3801 \| 0.3787 \| nan \| 0.0 \| 0.0 \| \| 0.1537 \| 35.22 \| 1620 \| 0.1882 \| 0.4975 \| 0.6133 \| 0.9003 \| 0.9849 \| 0.0 \| 0.8850 \| 0.8805 \| 0.8719 \| 0.9344 \| 0.6117 \| 0.5106 \| 0.4542 \| nan \| 0.0 \| nan \| 0.9539 \| 0.0 \| 0.8155 \| 0.8300 \| 0.8080 \| 0.7942 \| 0.4903 \| 0.4111 \| 0.3691 \| nan \| 0.0 \| 0.0 \| \| 0.3649 \| 35.65 \| 1640 \| 0.1842 \| 0.4998 \| 0.6161 \| 0.9028 \| 0.9850 \| 0.0 \| 0.8915 \| 0.8816 \| 0.8819 \| 0.9255 \| 0.6284 \| 0.4610 \| 0.5064 \| nan \| 0.0 \| nan \| 0.9535 \| 0.0 \| 0.8140 \| 0.8306 \| 0.8151 \| 0.7999 \| 0.5073 \| 0.3892 \| 0.3882 \| nan \| 0.0 \| 0.0 \| \| 0.1117 \| 36.09 \| 1660 \| 0.1835 \| 0.4993 \| 0.6205 \| 0.9012 \| 0.9844 \| 0.0 \| 0.8968 \| 0.8939 \| 0.8717 \| 0.9304 \| 0.6331 \| 0.5485 \| 0.4457 \| nan \| 0.0 \| nan \| 0.9548 \| 0.0 \| 0.8168 \| 0.8367 \| 0.8063 \| 0.7964 \| 0.4972 \| 0.4147 \| 0.3689 \| nan \| 0.0 \| 0.0 \| \| 0.3235 \| 36.52 \| 1680 \| 0.1805 \| 0.5016 \| 0.6294 \| 0.9012 \| 0.9789 \| 0.0 \| 0.8928 \| 0.9158 \| 0.8713 \| 0.9192 \| 0.6496 \| 0.5691 \| 0.4976 \| nan \| 0.0 \| nan \| 0.9543 \| 0.0 \| 0.8171 \| 0.8398 \| 0.8126 \| 0.8037 \| 0.4998 \| 0.4098 \| 0.3807 \| nan \| 0.0 \| 0.0 \| \| 0.1136 \| 36.96 \| 1700 \| 0.1833 \| 0.5018 \| 0.6181 \| 0.9033 \| 0.9837 \| 0.0 \| 0.8949 \| 0.9012 \| 0.8819 \| 0.9230 \| 0.6126 \| 0.4889 \| 0.4946 \| nan \| 0.0 \| nan \| 0.9564 \| 0.0 \| 0.8155 \| 0.8392 \| 0.8128 \| 0.8028 \| 0.5070 \| 0.4067 \| 0.3794 \| nan \| 0.0 \| 0.0 \| \| 0.0901 \| 37.39 \| 1720 \| 0.1862 \| 0.4974 \| 0.6077 \| 0.9026 \| 0.9816 \| 0.0 \| 0.8790 \| 0.8881 \| 0.8917 \| 0.9380 \| 0.5725 \| 0.4988 \| 0.4273 \| nan \| 0.0 \| nan \| 0.9552 \| 0.0 \| 0.8102 \| 0.8308 \| 0.8218 \| 0.7934 \| 0.4915 \| 0.4088 \| 0.3594 \| nan \| 0.0 \| 0.0 \| \| 0.2317 \| 37.83 \| 1740 \| 0.1824 \| 0.5044 \| 0.6259 \| 0.9042 \| 0.9829 \| 0.0 \| 0.8811 \| 0.9026 \| 0.8869 \| 0.9203 \| 0.6555 \| 0.5244 \| 0.5049 \| nan \| 0.0 \| nan \| 0.9548 \| 0.0 \| 0.8146 \| 0.8359 \| 0.8189 \| 0.8042 \| 0.5142 \| 0.4207 \| 0.3854 \| nan \| 0.0 \| 0.0 \| \| 0.0893 \| 38.26 \| 1760 \| 0.1893 \| 0.5002 \| 0.6251 \| 0.9036 \| 0.9810 \| 0.0 \| 0.8931 \| 0.9047 \| 0.8649 \| 0.9285 \| 0.6431 \| 0.4835 \| 0.5527 \| nan \| 0.0 \| nan \| 0.9565 \| 0.0 \| 0.8097 \| 0.8389 \| 0.8180 \| 0.8079 \| 0.4992 \| 0.3713 \| 0.4004 \| nan \| 0.0 \| 0.0 \| \| 0.2952 \| 38.7 \| 1780 \| 0.1832 \| 0.4990 \| 0.6201 \| 0.9018 \| 0.9811 \| 0.0 \| 0.8979 \| 0.8879 \| 0.8947 \| 0.9246 \| 0.6275 \| 0.5634 \| 0.4235 \| nan \| 0.0 \| nan \| 0.9522 \| 0.0 \| 0.8077 \| 0.8323 \| 0.8147 \| 0.8033 \| 0.5077 \| 0.4146 \| 0.3562 \| nan \| 0.0 \| 0.0 \| \| 0.1111 \| 39.13 \| 1800 \| 0.1769 \| 0.5015 \| 0.6316 \| 0.9018 \| 0.9818 \| 0.0 \| 0.9004 \| 0.8883 \| 0.8842 \| 0.9185 \| 0.6708 \| 0.5974 \| 0.4743 \| nan \| 0.0 \| nan \| 0.9525 \| 0.0 \| 0.8107 \| 0.8304 \| 0.8132 \| 0.8045 \| 0.5118 \| 0.4135 \| 0.3799 \| nan \| 0.0 \| 0.0 \| \| 0.1 \| 39.57 \| 1820 \| 0.1738 \| 0.5041 \| 0.6271 \| 0.9042 \| 0.9794 \| 0.0 \| 0.8911 \| 0.8970 \| 0.8915 \| 0.9162 \| 0.6539 \| 0.5031 \| 0.5385 \| nan \| 0.0 \| nan \| 0.9555 \| 0.0 \| 0.8088 \| 0.8301 \| 0.8191 \| 0.8064 \| 0.5209 \| 0.4062 \| 0.3979 \| nan \| 0.0 \| 0.0 \| \| 0.2137 \| 40.0 \| 1840 \| 0.1747 \| 0.5048 \| 0.6263 \| 0.9047 \| 0.9846 \| 0.0 \| 0.8822 \| 0.8788 \| 0.9000 \| 0.9183 \| 0.6654 \| 0.5311 \| 0.5028 \| nan \| 0.0 \| nan \| 0.9527 \| 0.0 \| 0.8094 \| 0.8257 \| 0.8270 \| 0.8071 \| 0.5224 \| 0.4186 \| 0.3901 \| nan \| 0.0 \| 0.0 \| \| 0.0432 \| 40.43 \| 1860 \| 0.1944 \| 0.4978 \| 0.6195 \| 0.9009 \| 0.9813 \| 0.0 \| 0.8832 \| 0.8845 \| 0.8725 \| 0.9384 \| 0.6305 \| 0.5560 \| 0.4488 \| nan \| 0.0 \| nan \| 0.9550 \| 0.0 \| 0.8099 \| 0.8251 \| 0.8098 \| 0.7980 \| 0.5071 \| 0.4025 \| 0.3683 \| nan \| 0.0 \| 0.0 \| \| 0.0705 \| 40.87 \| 1880 \| 0.1915 \| 0.4989 \| 0.6289 \| 0.8996 \| 0.9818 \| 0.0 \| 0.8845 \| 0.8985 \| 0.8555 \| 0.9340 \| 0.6647 \| 0.6050 \| 0.4652 \| nan \| 0.0 \| nan \| 0.9559 \| 0.0 \| 0.8117 \| 0.8332 \| 0.8002 \| 0.7950 \| 0.5122 \| 0.4033 \| 0.3761 \| nan \| 0.0 \| 0.0 \| \| 0.1875 \| 41.3 \| 1900 \| 0.1808 \| 0.5035 \| 0.6284 \| 0.9014 \| 0.9784 \| 0.0 \| 0.8915 \| 0.8955 \| 0.8781 \| 0.9244 \| 0.6737 \| 0.5559 \| 0.4863 \| nan \| 0.0 \| nan \| 0.9538 \| 0.0 \| 0.8075 \| 0.8298 \| 0.8104 \| 0.7990 \| 0.5307 \| 0.4133 \| 0.3934 \| nan \| 0.0 \| 0.0 \| \| 0.041 \| 41.74 \| 1920 \| 0.1772 \| 0.5040 \| 0.6316 \| 0.9030 \| 0.9790 \| 0.0 \| 0.8964 \| 0.9038 \| 0.8777 \| 0.9238 \| 0.6675 \| 0.5689 \| 0.4989 \| nan \| 0.0 \| nan \| 0.9555 \| 0.0 \| 0.8123 \| 0.8319 \| 0.8131 \| 0.8050 \| 0.5199 \| 0.4136 \| 0.3931 \| nan \| 0.0 \| 0.0 \| \| 0.1552 \| 42.17 \| 1940 \| 0.1809 \| 0.5044 \| 0.6265 \| 0.9025 \| 0.9807 \| 0.0 \| 0.8953 \| 0.8909 \| 0.8869 \| 0.9270 \| 0.6385 \| 0.5922 \| 0.4534 \| nan \| 0.0 \| nan \| 0.9529 \| 0.0 \| 0.8134 \| 0.8321 \| 0.8114 \| 0.8023 \| 0.5260 \| 0.4335 \| 0.3768 \| nan \| 0.0 \| 0.0 \| \| 0.5015 \| 42.61 \| 1960 \| 0.1770 \| 0.5030 \| 0.6263 \| 0.9025 \| 0.9782 \| 0.0 \| 0.8934 \| 0.8864 \| 0.8939 \| 0.9262 \| 0.6580 \| 0.5736 \| 0.4532 \| nan \| 0.0 \| nan \| 0.9517 \| 0.0 \| 0.8116 \| 0.8269 \| 0.8184 \| 0.8047 \| 0.5204 \| 0.4251 \| 0.3737 \| nan \| 0.0 \| 0.0 \| \| 0.1057 \| 43.04 \| 1980 \| 0.1809 \| 0.4991 \| 0.6197 \| 0.9006 \| 0.9812 \| 0.0 \| 0.8849 \| 0.8871 \| 0.8775 \| 0.9361 \| 0.6232 \| 0.5822 \| 0.4248 \| nan \| 0.0 \| nan \| 0.9531 \| 0.0 \| 0.8106 \| 0.8272 \| 0.8123 \| 0.7967 \| 0.5061 \| 0.4258 \| 0.3585 \| nan \| 0.0 \| 0.0 \| \| 0.0832 \| 43.48 \| 2000 \| 0.1802 \| 0.5042 \| 0.6240 \| 0.9032 \| 0.9776 \| 0.0 \| 0.8946 \| 0.8932 \| 0.8916 \| 0.9287 \| 0.6458 \| 0.5483 \| 0.4599 \| nan \| 0.0 \| nan \| 0.9519 \| 0.0 \| 0.8075 \| 0.8274 \| 0.8211 \| 0.8033 \| 0.5229 \| 0.4312 \| 0.3810 \| nan \| 0.0 \| 0.0 \| \| 0.0851 \| 43.91 \| 2020 \| 0.1826 \| 0.5033 \| 0.6257 \| 0.9037 \| 0.9795 \| 0.0 \| 0.9015 \| 0.8938 \| 0.8928 \| 0.9311 \| 0.6515 \| 0.5854 \| 0.4217 \| nan \| 0.0 \| nan \| 0.9539 \| 0.0 \| 0.8098 \| 0.8285 \| 0.8192 \| 0.8015 \| 0.5246 \| 0.4349 \| 0.3637 \| nan \| 0.0 \| 0.0 \| \| 0.3671 \| 44.35 \| 2040 \| 0.1850 \| 0.5028 \| 0.6233 \| 0.9007 \| 0.9792 \| 0.0 \| 0.8862 \| 0.8917 \| 0.8732 \| 0.9317 \| 0.6350 \| 0.5619 \| 0.4743 \| nan \| 0.0 \| nan \| 0.9503 \| 0.0 \| 0.8076 \| 0.8289 \| 0.8138 \| 0.7987 \| 0.5204 \| 0.4218 \| 0.3889 \| nan \| 0.0 \| 0.0 \| \| 0.0769 \| 44.78 \| 2060 \| 0.1957 \| 0.5002 \| 0.6240 \| 0.9016 \| 0.9768 \| 0.0 \| 0.8906 \| 0.8940 \| 0.8726 \| 0.9344 \| 0.6404 \| 0.5415 \| 0.4901 \| nan \| 0.0 \| nan \| 0.9531 \| 0.0 \| 0.8011 \| 0.8250 \| 0.8149 \| 0.7996 \| 0.5125 \| 0.4037 \| 0.3928 \| nan \| 0.0 \| 0.0 \| \| 0.0754 \| 45.22 \| 2080 \| 0.1943 \| 0.4992 \| 0.6205 \| 0.9007 \| 0.9772 \| 0.0 \| 0.8831 \| 0.8980 \| 0.8669 \| 0.9415 \| 0.6323 \| 0.5524 \| 0.4536 \| nan \| 0.0 \| nan \| 0.9544 \| 0.0 \| 0.8029 \| 0.8287 \| 0.8108 \| 0.7929 \| 0.5148 \| 0.4063 \| 0.3805 \| nan \| 0.0 \| 0.0 \| \| 0.1296 \| 45.65 \| 2100 \| 0.1879 \| 0.5014 \| 0.6278 \| 0.9008 \| 0.9763 \| 0.0 \| 0.8937 \| 0.9025 \| 0.8724 \| 0.9312 \| 0.6504 \| 0.5896 \| 0.4622 \| nan \| 0.0 \| nan \| 0.9534 \| 0.0 \| 0.8029 \| 0.8333 \| 0.8086 \| 0.7965 \| 0.5233 \| 0.4111 \| 0.3863 \| nan \| 0.0 \| 0.0 \| \| 0.1158 \| 46.09 \| 2120 \| 0.1835 \| 0.5033 \| 0.6266 \| 0.9021 \| 0.9833 \| 0.0 \| 0.8874 \| 0.8859 \| 0.8737 \| 0.9294 \| 0.6400 \| 0.5732 \| 0.4934 \| nan \| 0.0 \| nan \| 0.9531 \| 0.0 \| 0.8069 \| 0.8320 \| 0.8101 \| 0.8015 \| 0.5208 \| 0.4155 \| 0.3966 \| nan \| 0.0 \| 0.0 \| \| 0.0872 \| 46.52 \| 2140 \| 0.1891 \| 0.5017 \| 0.6269 \| 0.9026 \| 0.9845 \| 0.0 \| 0.8937 \| 0.8879 \| 0.8760 \| 0.9310 \| 0.6513 \| 0.5860 \| 0.4583 \| nan \| 0.0 \| nan \| 0.9537 \| 0.0 \| 0.8097 \| 0.8323 \| 0.8113 \| 0.8016 \| 0.5129 \| 0.4153 \| 0.3815 \| nan \| 0.0 \| 0.0 \| \| 0.0851 \| 46.96 \| 2160 \| 0.1803 \| 0.5039 \| 0.6309 \| 0.9024 \| 0.9799 \| 0.0 \| 0.8952 \| 0.9048 \| 0.8685 \| 0.9292 \| 0.6604 \| 0.5796 \| 0.4910 \| nan \| 0.0 \| nan \| 0.9533 \| 0.0 \| 0.8096 \| 0.8372 \| 0.8117 \| 0.8031 \| 0.5144 \| 0.4165 \| 0.3975 \| nan \| 0.0 \| 0.0 \| \| 0.061 \| 47.39 \| 2180 \| 0.1820 \| 0.5061 \| 0.6296 \| 0.9045 \| 0.9792 \| 0.0 \| 0.9011 \| 0.9064 \| 0.8802 \| 0.9305 \| 0.6656 \| 0.5608 \| 0.4717 \| nan \| 0.0 \| nan \| 0.9546 \| 0.0 \| 0.8103 \| 0.8377 \| 0.8172 \| 0.8049 \| 0.5148 \| 0.4388 \| 0.3886 \| nan \| 0.0 \| 0.0 \| \| 0.094 \| 47.83 \| 2200 \| 0.1817 \| 0.5077 \| 0.6281 \| 0.9056 \| 0.9821 \| 0.0 \| 0.8850 \| 0.8979 \| 0.8903 \| 0.9287 \| 0.6542 \| 0.5599 \| 0.4831 \| nan \| 0.0 \| nan \| 0.9555 \| 0.0 \| 0.8091 \| 0.8353 \| 0.8209 \| 0.8058 \| 0.5259 \| 0.4400 \| 0.3926 \| nan \| 0.0 \| 0.0 \| \| 0.0832 \| 48.26 \| 2220 \| 0.1822 \| 0.5050 \| 0.6208 \| 0.9050 \| 0.9810 \| 0.0 \| 0.8805 \| 0.9008 \| 0.8954 \| 0.9313 \| 0.6348 \| 0.5361 \| 0.4480 \| nan \| 0.0 \| nan \| 0.9550 \| 0.0 \| 0.8100 \| 0.8339 \| 0.8193 \| 0.8038 \| 0.5261 \| 0.4307 \| 0.3757 \| nan \| 0.0 \| 0.0 \| \| 0.4708 \| 48.7 \| 2240 \| 0.1810 \| 0.5065 \| 0.6273 \| 0.9048 \| 0.9828 \| 0.0 \| 0.8856 \| 0.8951 \| 0.8892 \| 0.9279 \| 0.6487 \| 0.5682 \| 0.4758 \| nan \| 0.0 \| nan \| 0.9544 \| 0.0 \| 0.8085 \| 0.8336 \| 0.8189 \| 0.8051 \| 0.5249 \| 0.4356 \| 0.3900 \| nan \| 0.0 \| 0.0 \| \| 0.0847 \| 49.13 \| 2260 \| 0.1841 \| 0.5043 \| 0.6209 \| 0.9036 \| 0.9783 \| 0.0 \| 0.8842 \| 0.8957 \| 0.8915 \| 0.9332 \| 0.6351 \| 0.5396 \| 0.4515 \| nan \| 0.0 \| nan \| 0.9531 \| 0.0 \| 0.8072 \| 0.8299 \| 0.8201 \| 0.8014 \| 0.5221 \| 0.4353 \| 0.3777 \| nan \| 0.0 \| 0.0 \| \| 0.0518 \| 49.57 \| 2280 \| 0.1782 \| 0.5066 \| 0.6298 \| 0.9046 \| 0.9814 \| 0.0 \| 0.8872 \| 0.8958 \| 0.8905 \| 0.9256 \| 0.6696 \| 0.5721 \| 0.4752 \| nan \| 0.0 \| nan \| 0.9532 \| 0.0 \| 0.8083 \| 0.8334 \| 0.8209 \| 0.8067 \| 0.5232 \| 0.4383 \| 0.3889 \| nan \| 0.0 \| 0.0 \| \| 0.109 \| 50.0 \| 2300 \| 0.1898 \| 0.5029 \| 0.6197 \| 0.9030 \| 0.9790 \| 0.0 \| 0.8826 \| 0.8951 \| 0.8809 \| 0.9396 \| 0.6302 \| 0.5410 \| 0.4486 \| nan \| 0.0 \| nan \| 0.9539 \| 0.0 \| 0.8064 \| 0.8292 \| 0.8190 \| 0.7979 \| 0.5135 \| 0.4346 \| 0.3769 \| nan \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1	[ "water", "whitewater", "sand", "rocky_terrain", "agricultural", "vegetation", "road", "building", "soil", "boat", "development", "unknown" ]
maratuly/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the maratuly/Pseudo-echo dataset. It achieves the following results on the evaluation set: - Loss: 0.3463 - Mean Iou: 0.7622 - Mean Accuracy: 0.9528 - Overall Accuracy: 0.9581 - Accuracy Unlabeled: nan - Accuracy Lv: 0.9931 - Accuracy Rv: 0.9354 - Accuracy La: 0.9533 - Accuracy Ra: 0.9293 - Iou Unlabeled: 0.0 - Iou Lv: 0.9931 - Iou Rv: 0.9354 - Iou La: 0.9533 - Iou Ra: 0.9293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Lv \| Accuracy Rv \| Accuracy La \| Accuracy Ra \| Iou Unlabeled \| Iou Lv \| Iou Rv \| Iou La \| Iou Ra \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-------------:\|:------:\|:------:\|:------:\|:------:\| \| 1.0046 \| 5.0 \| 20 \| 1.3540 \| 0.4931 \| 0.7263 \| 0.7606 \| nan \| 0.9742 \| 0.6789 \| 0.5862 \| 0.6657 \| 0.0 \| 0.6545 \| 0.6789 \| 0.5540 \| 0.5783 \| \| 0.7304 \| 10.0 \| 40 \| 0.8262 \| 0.6901 \| 0.8908 \| 0.9017 \| nan \| 0.9906 \| 0.8147 \| 0.9192 \| 0.8387 \| 0.0 \| 0.9028 \| 0.8145 \| 0.9046 \| 0.8287 \| \| 0.5957 \| 15.0 \| 60 \| 0.5683 \| 0.7395 \| 0.9277 \| 0.9367 \| nan \| 0.9896 \| 0.9141 \| 0.9225 \| 0.8845 \| 0.0 \| 0.9766 \| 0.9141 \| 0.9225 \| 0.8845 \| \| 0.5094 \| 20.0 \| 80 \| 0.4881 \| 0.7541 \| 0.9426 \| 0.9499 \| nan \| 0.9909 \| 0.9368 \| 0.9312 \| 0.9116 \| 0.0 \| 0.9909 \| 0.9368 \| 0.9312 \| 0.9116 \| \| 0.4681 \| 25.0 \| 100 \| 0.4384 \| 0.7689 \| 0.9612 \| 0.9653 \| nan \| 0.9942 \| 0.9434 \| 0.9650 \| 0.9421 \| 0.0 \| 0.9942 \| 0.9434 \| 0.9650 \| 0.9421 \| \| 0.4045 \| 30.0 \| 120 \| 0.4078 \| 0.7639 \| 0.9549 \| 0.9602 \| nan \| 0.9933 \| 0.9418 \| 0.9519 \| 0.9328 \| 0.0 \| 0.9933 \| 0.9418 \| 0.9519 \| 0.9328 \| \| 0.3956 \| 35.0 \| 140 \| 0.3844 \| 0.7664 \| 0.9580 \| 0.9625 \| nan \| 0.9939 \| 0.9406 \| 0.9551 \| 0.9425 \| 0.0 \| 0.9939 \| 0.9406 \| 0.9551 \| 0.9425 \| \| 0.3736 \| 40.0 \| 160 \| 0.3736 \| 0.7687 \| 0.9609 \| 0.9652 \| nan \| 0.9961 \| 0.9409 \| 0.9631 \| 0.9436 \| 0.0 \| 0.9961 \| 0.9409 \| 0.9631 \| 0.9436 \| \| 0.3431 \| 45.0 \| 180 \| 0.3528 \| 0.7622 \| 0.9528 \| 0.9577 \| nan \| 0.9923 \| 0.9321 \| 0.9539 \| 0.9327 \| 0.0 \| 0.9923 \| 0.9321 \| 0.9539 \| 0.9327 \| \| 0.3428 \| 50.0 \| 200 \| 0.3463 \| 0.7622 \| 0.9528 \| 0.9581 \| nan \| 0.9931 \| 0.9354 \| 0.9533 \| 0.9293 \| 0.0 \| 0.9931 \| 0.9354 \| 0.9533 \| 0.9293 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "lv", "rv", "la", "ra" ]
khalidddd/dinat_l_oneformer_coco	# OneFormer OneFormer model trained on the COCO dataset (large-sized version, Dinat backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer). ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_teaser.png) ## Model description OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset. ### How to use Here is how to use this model: ```python from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.jpeg" image = Image.open(requests.get(url, stream=True).raw) # Loading a single model for all three tasks processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_coco_dinat_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_dinat_large") # Semantic Segmentation semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt") semantic_outputs = model(semantic_inputs) # pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # Instance Segmentation instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt") instance_outputs = model(instance_inputs) # pass through image_processor for postprocessing predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] # Panoptic Segmentation panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt") panoptic_outputs = model(**panoptic_inputs) # pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] ``` For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer). ### Citation ```bibtex @article{jain2022oneformer, title={{OneFormer: One Transformer to Rule Universal Image Segmentation}}, author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi}, journal={arXiv}, year={2022} } ```	[ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "bridge", "cardboard", "counter", "curtain", "door-stuff", "floor-wood", "flower", "fruit", "gravel", "house", "light", "mirror-stuff", "net", "pillow", "platform", "playingfield", "railroad", "river", "road", "roof", "sand", "sea", "shelf", "snow", "stairs", "tent", "towel", "wall-brick", "wall-stone", "wall-tile", "wall-wood", "water-other", "window-blind", "window-other", "tree-merged", "fence-merged", "ceiling-merged", "sky-other-merged", "cabinet-merged", "table-merged", "floor-other-merged", "pavement-merged", "mountain-merged", "grass-merged", "dirt-merged", "paper-merged", "food-other-merged", "building-other-merged", "rock-merged", "wall-other-merged", "rug-merged" ]
peldrak/segformer-finetuned-riviera2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-riviera2 This model is a fine-tuned version of [peldrak/segformer-finetuned-coasts-final](https://huggingface.co/peldrak/segformer-finetuned-coasts-final) on the peldrak/riviera_labeled_split2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2172 - Mean Iou: 0.5684 - Mean Accuracy: 0.7041 - Overall Accuracy: 0.9037 - Accuracy Water: 0.9782 - Accuracy Whitewater: 0.0031 - Accuracy Sand: 0.9694 - Accuracy Rocky Terrain: 0.8474 - Accuracy Agricultural: 0.8818 - Accuracy Vegetation: 0.9453 - Accuracy Road: 0.5085 - Accuracy Development: 0.7910 - Accuracy Other Natural Terrain: 0.4118 - Accuracy Unknown: nan - Iou Water: 0.9541 - Iou Whitewater: 0.0031 - Iou Sand: 0.8472 - Iou Rocky Terrain: 0.7939 - Iou Agricultural: 0.7881 - Iou Vegetation: 0.8610 - Iou Road: 0.4506 - Iou Development: 0.6761 - Iou Other Natural Terrain: 0.3104 - Iou Unknown: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sand \| Accuracy Rocky Terrain \| Accuracy Agricultural \| Accuracy Vegetation \| Accuracy Road \| Accuracy Development \| Accuracy Other Natural Terrain \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sand \| Iou Rocky Terrain \| Iou Agricultural \| Iou Vegetation \| Iou Road \| Iou Development \| Iou Other Natural Terrain \| Iou Unknown \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-------------:\|:----------------------:\|:---------------------:\|:-------------------:\|:-------------:\|:--------------------:\|:------------------------------:\|:----------------:\|:---------:\|:--------------:\|:--------:\|:-----------------:\|:----------------:\|:--------------:\|:--------:\|:---------------:\|:-------------------------:\|:-----------:\| \| 1.6151 \| 0.24 \| 20 \| 1.4156 \| 0.0850 \| 0.1749 \| 0.3514 \| 0.9391 \| 0.0113 \| 0.0 \| 0.1553 \| 0.0248 \| 0.1658 \| 0.0035 \| 0.1890 \| 0.0857 \| nan \| 0.3854 \| 0.0012 \| 0.0 \| 0.1345 \| 0.0110 \| 0.1615 \| 0.0017 \| 0.1034 \| 0.0518 \| 0.0 \| \| 1.3149 \| 0.49 \| 40 \| 1.0378 \| 0.2239 \| 0.3363 \| 0.6043 \| 0.9571 \| 0.0318 \| 0.2679 \| 0.5472 \| 0.0248 \| 0.6780 \| 0.0 \| 0.4390 \| 0.0811 \| nan \| 0.6123 \| 0.0184 \| 0.2520 \| 0.5089 \| 0.0141 \| 0.5632 \| 0.0 \| 0.2247 \| 0.0456 \| 0.0 \| \| 1.1554 \| 0.73 \| 60 \| 0.7989 \| 0.3289 \| 0.4401 \| 0.7722 \| 0.9818 \| 0.0 \| 0.8295 \| 0.6482 \| 0.0581 \| 0.9606 \| 0.0008 \| 0.4676 \| 0.0146 \| nan \| 0.8261 \| 0.0 \| 0.7142 \| 0.6253 \| 0.0523 \| 0.7241 \| 0.0008 \| 0.3334 \| 0.0130 \| 0.0 \| \| 1.0181 \| 0.98 \| 80 \| 0.6544 \| 0.3592 \| 0.4747 \| 0.8043 \| 0.9692 \| 0.0 \| 0.8957 \| 0.7580 \| 0.3678 \| 0.9693 \| 0.0001 \| 0.3088 \| 0.0032 \| nan \| 0.9121 \| 0.0 \| 0.6664 \| 0.7171 \| 0.2835 \| 0.7605 \| 0.0001 \| 0.2489 \| 0.0031 \| 0.0 \| \| 1.119 \| 1.22 \| 100 \| 0.5360 \| 0.3739 \| 0.4880 \| 0.8252 \| 0.9764 \| 0.0 \| 0.9123 \| 0.7932 \| 0.5894 \| 0.9776 \| 0.0 \| 0.1422 \| 0.0008 \| nan \| 0.9300 \| 0.0 \| 0.7082 \| 0.7339 \| 0.4630 \| 0.7700 \| 0.0 \| 0.1329 \| 0.0008 \| 0.0 \| \| 0.8191 \| 1.46 \| 120 \| 0.4732 \| 0.4115 \| 0.5270 \| 0.8487 \| 0.9860 \| 0.0 \| 0.8930 \| 0.8134 \| 0.7485 \| 0.9750 \| 0.0 \| 0.3256 \| 0.0019 \| nan \| 0.9360 \| 0.0 \| 0.7421 \| 0.7490 \| 0.6024 \| 0.8003 \| 0.0 \| 0.2832 \| 0.0018 \| 0.0 \| \| 0.7274 \| 1.71 \| 140 \| 0.4744 \| 0.4010 \| 0.5172 \| 0.8459 \| 0.9847 \| 0.0 \| 0.8945 \| 0.7845 \| 0.7891 \| 0.9754 \| 0.0 \| 0.2258 \| 0.0011 \| nan \| 0.9485 \| 0.0 \| 0.7668 \| 0.7512 \| 0.5327 \| 0.8076 \| 0.0 \| 0.2025 \| 0.0011 \| 0.0 \| \| 0.3963 \| 1.95 \| 160 \| 0.4212 \| 0.4143 \| 0.5289 \| 0.8491 \| 0.9835 \| 0.0 \| 0.9249 \| 0.8669 \| 0.6654 \| 0.9796 \| 0.0 \| 0.3312 \| 0.0084 \| nan \| 0.9514 \| 0.0 \| 0.7786 \| 0.7736 \| 0.5481 \| 0.7861 \| 0.0 \| 0.2971 \| 0.0082 \| 0.0 \| \| 0.8763 \| 2.2 \| 180 \| 0.3832 \| 0.4210 \| 0.5390 \| 0.8587 \| 0.9798 \| 0.0 \| 0.9471 \| 0.7914 \| 0.9032 \| 0.9688 \| 0.0 \| 0.2596 \| 0.0012 \| nan \| 0.9483 \| 0.0 \| 0.8401 \| 0.7517 \| 0.6183 \| 0.8151 \| 0.0 \| 0.2348 \| 0.0012 \| 0.0 \| \| 0.868 \| 2.44 \| 200 \| 0.3764 \| 0.4061 \| 0.5216 \| 0.8472 \| 0.9793 \| 0.0 \| 0.9666 \| 0.8200 \| 0.7151 \| 0.9764 \| 0.0 \| 0.2267 \| 0.0103 \| nan \| 0.9514 \| 0.0 \| 0.6926 \| 0.7764 \| 0.6398 \| 0.7901 \| 0.0 \| 0.2007 \| 0.0102 \| 0.0 \| \| 0.7492 \| 2.68 \| 220 \| 0.3502 \| 0.4267 \| 0.5626 \| 0.8629 \| 0.9742 \| 0.0 \| 0.9717 \| 0.8473 \| 0.9721 \| 0.9408 \| 0.0 \| 0.3376 \| 0.0200 \| nan \| 0.9493 \| 0.0 \| 0.7071 \| 0.7902 \| 0.6701 \| 0.8511 \| 0.0 \| 0.2796 \| 0.0192 \| 0.0 \| \| 0.9957 \| 2.93 \| 240 \| 0.3382 \| 0.4572 \| 0.5778 \| 0.8688 \| 0.9842 \| 0.0 \| 0.9534 \| 0.8633 \| 0.7888 \| 0.9593 \| 0.0 \| 0.5910 \| 0.0602 \| nan \| 0.9527 \| 0.0 \| 0.8231 \| 0.8012 \| 0.6499 \| 0.8112 \| 0.0 \| 0.4790 \| 0.0545 \| 0.0 \| \| 0.416 \| 3.17 \| 260 \| 0.3426 \| 0.4475 \| 0.5700 \| 0.8617 \| 0.9725 \| 0.0 \| 0.9648 \| 0.8810 \| 0.7076 \| 0.9640 \| 0.0 \| 0.5665 \| 0.0738 \| nan \| 0.9499 \| 0.0 \| 0.7928 \| 0.7900 \| 0.6095 \| 0.8113 \| 0.0 \| 0.4584 \| 0.0628 \| 0.0 \| \| 0.3574 \| 3.41 \| 280 \| 0.3294 \| 0.4534 \| 0.5701 \| 0.8659 \| 0.9835 \| 0.0 \| 0.9498 \| 0.7931 \| 0.7716 \| 0.9674 \| 0.0 \| 0.6177 \| 0.0481 \| nan \| 0.9441 \| 0.0 \| 0.8365 \| 0.7578 \| 0.6388 \| 0.8089 \| 0.0 \| 0.5038 \| 0.0438 \| 0.0 \| \| 0.2504 \| 3.66 \| 300 \| 0.3045 \| 0.4381 \| 0.5590 \| 0.8596 \| 0.9787 \| 0.0 \| 0.9376 \| 0.8223 \| 0.8269 \| 0.9575 \| 0.0 \| 0.4316 \| 0.0765 \| nan \| 0.9492 \| 0.0 \| 0.7856 \| 0.7767 \| 0.6163 \| 0.8214 \| 0.0 \| 0.3681 \| 0.0637 \| 0.0 \| \| 0.3342 \| 3.9 \| 320 \| 0.3037 \| 0.4675 \| 0.5986 \| 0.8712 \| 0.9712 \| 0.0 \| 0.9625 \| 0.9100 \| 0.7707 \| 0.9492 \| 0.0 \| 0.7293 \| 0.0942 \| nan \| 0.9492 \| 0.0 \| 0.8752 \| 0.7814 \| 0.6541 \| 0.8159 \| 0.0 \| 0.5153 \| 0.0836 \| 0.0 \| \| 0.7272 \| 4.15 \| 340 \| 0.3025 \| 0.4617 \| 0.5795 \| 0.8694 \| 0.9742 \| 0.0 \| 0.9665 \| 0.8396 \| 0.7763 \| 0.9678 \| 0.0 \| 0.6223 \| 0.0689 \| nan \| 0.9486 \| 0.0 \| 0.8327 \| 0.7980 \| 0.6451 \| 0.8157 \| 0.0 \| 0.5163 \| 0.0605 \| 0.0 \| \| 0.452 \| 4.39 \| 360 \| 0.2799 \| 0.4589 \| 0.5891 \| 0.8688 \| 0.9723 \| 0.0 \| 0.9796 \| 0.8554 \| 0.8382 \| 0.9439 \| 0.0 \| 0.5294 \| 0.1836 \| nan \| 0.9510 \| 0.0 \| 0.7637 \| 0.7978 \| 0.6793 \| 0.8367 \| 0.0 \| 0.4202 \| 0.1405 \| 0.0 \| \| 0.2372 \| 4.63 \| 380 \| 0.2749 \| 0.4642 \| 0.5891 \| 0.8765 \| 0.9755 \| 0.0 \| 0.9684 \| 0.8436 \| 0.9190 \| 0.9569 \| 0.0 \| 0.5282 \| 0.1100 \| nan \| 0.9493 \| 0.0 \| 0.7809 \| 0.7960 \| 0.7737 \| 0.8389 \| 0.0 \| 0.4060 \| 0.0969 \| 0.0 \| \| 1.3141 \| 4.88 \| 400 \| 0.2875 \| 0.4723 \| 0.6004 \| 0.8726 \| 0.9700 \| 0.0 \| 0.9573 \| 0.8714 \| 0.7878 \| 0.9529 \| 0.0 \| 0.7587 \| 0.1054 \| nan \| 0.9449 \| 0.0 \| 0.8715 \| 0.7964 \| 0.6752 \| 0.8177 \| 0.0 \| 0.5243 \| 0.0929 \| 0.0 \| \| 0.605 \| 5.12 \| 420 \| 0.2752 \| 0.4653 \| 0.6031 \| 0.8722 \| 0.9752 \| 0.0 \| 0.9356 \| 0.8359 \| 0.9082 \| 0.9332 \| 0.0010 \| 0.7404 \| 0.0986 \| nan \| 0.9462 \| 0.0 \| 0.8710 \| 0.7879 \| 0.6188 \| 0.8482 \| 0.0010 \| 0.4905 \| 0.0892 \| 0.0 \| \| 0.3456 \| 5.37 \| 440 \| 0.2907 \| 0.4721 \| 0.5955 \| 0.8717 \| 0.9761 \| 0.0 \| 0.9655 \| 0.8499 \| 0.7721 \| 0.9535 \| 0.0 \| 0.6785 \| 0.1641 \| nan \| 0.9531 \| 0.0 \| 0.8718 \| 0.7986 \| 0.6161 \| 0.8207 \| 0.0 \| 0.5316 \| 0.1291 \| 0.0 \| \| 0.4065 \| 5.61 \| 460 \| 0.2588 \| 0.4707 \| 0.6035 \| 0.8751 \| 0.9735 \| 0.0 \| 0.9728 \| 0.8768 \| 0.8025 \| 0.9485 \| 0.0 \| 0.5470 \| 0.3100 \| nan \| 0.9528 \| 0.0 \| 0.7724 \| 0.8075 \| 0.6647 \| 0.8490 \| 0.0 \| 0.4260 \| 0.2346 \| 0.0 \| \| 0.5515 \| 5.85 \| 480 \| 0.2509 \| 0.4817 \| 0.6075 \| 0.8803 \| 0.9607 \| 0.0 \| 0.9654 \| 0.8643 \| 0.8910 \| 0.9597 \| 0.0000 \| 0.6962 \| 0.1303 \| nan \| 0.9444 \| 0.0 \| 0.8502 \| 0.7854 \| 0.7400 \| 0.8362 \| 0.0000 \| 0.5515 \| 0.1096 \| 0.0 \| \| 0.7913 \| 6.1 \| 500 \| 0.2392 \| 0.4835 \| 0.6074 \| 0.8854 \| 0.9772 \| 0.0 \| 0.9578 \| 0.8821 \| 0.9384 \| 0.9594 \| 0.0244 \| 0.6102 \| 0.1169 \| nan \| 0.9511 \| 0.0 \| 0.8279 \| 0.8049 \| 0.7936 \| 0.8365 \| 0.0244 \| 0.4897 \| 0.1072 \| 0.0 \| \| 0.3186 \| 6.34 \| 520 \| 0.2556 \| 0.4736 \| 0.6009 \| 0.8775 \| 0.9769 \| 0.0 \| 0.9765 \| 0.8601 \| 0.9007 \| 0.9478 \| 0.0386 \| 0.5438 \| 0.1636 \| nan \| 0.9521 \| 0.0 \| 0.7805 \| 0.8029 \| 0.7483 \| 0.8328 \| 0.0385 \| 0.4392 \| 0.1420 \| 0.0 \| \| 0.2549 \| 6.59 \| 540 \| 0.2342 \| 0.5138 \| 0.6555 \| 0.8909 \| 0.9748 \| 0.0 \| 0.9533 \| 0.8514 \| 0.8781 \| 0.9276 \| 0.0280 \| 0.7772 \| 0.5094 \| nan \| 0.9512 \| 0.0 \| 0.8886 \| 0.7891 \| 0.7547 \| 0.8588 \| 0.0279 \| 0.5303 \| 0.3371 \| 0.0 \| \| 0.3034 \| 6.83 \| 560 \| 0.2574 \| 0.4892 \| 0.6058 \| 0.8794 \| 0.9819 \| 0.0 \| 0.9563 \| 0.7993 \| 0.7777 \| 0.9666 \| 0.0356 \| 0.7305 \| 0.2041 \| nan \| 0.9505 \| 0.0 \| 0.8925 \| 0.7697 \| 0.6993 \| 0.8226 \| 0.0355 \| 0.5656 \| 0.1561 \| 0.0 \| \| 0.2759 \| 7.07 \| 580 \| 0.2417 \| 0.5055 \| 0.6355 \| 0.8899 \| 0.9735 \| 0.0 \| 0.9789 \| 0.8397 \| 0.9129 \| 0.9483 \| 0.0845 \| 0.6354 \| 0.3465 \| nan \| 0.9530 \| 0.0 \| 0.8171 \| 0.7897 \| 0.7572 \| 0.8530 \| 0.0840 \| 0.5311 \| 0.2698 \| 0.0 \| \| 0.4661 \| 7.32 \| 600 \| 0.2272 \| 0.5198 \| 0.6647 \| 0.8944 \| 0.9787 \| 0.0 \| 0.9780 \| 0.8505 \| 0.8440 \| 0.9286 \| 0.0697 \| 0.6809 \| 0.6515 \| nan \| 0.9552 \| 0.0 \| 0.8088 \| 0.7960 \| 0.7438 \| 0.8682 \| 0.0691 \| 0.5247 \| 0.4328 \| 0.0 \| \| 0.1629 \| 7.56 \| 620 \| 0.2331 \| 0.5248 \| 0.6639 \| 0.8916 \| 0.9782 \| 0.0 \| 0.9591 \| 0.8574 \| 0.8627 \| 0.9262 \| 0.1268 \| 0.7364 \| 0.5286 \| nan \| 0.9539 \| 0.0 \| 0.8723 \| 0.8011 \| 0.6613 \| 0.8493 \| 0.1252 \| 0.5734 \| 0.4116 \| 0.0 \| \| 0.1212 \| 7.8 \| 640 \| 0.2431 \| 0.5138 \| 0.6604 \| 0.8841 \| 0.9727 \| 0.0 \| 0.9811 \| 0.8664 \| 0.7576 \| 0.9201 \| 0.1052 \| 0.7484 \| 0.5919 \| nan \| 0.9543 \| 0.0 \| 0.8160 \| 0.7970 \| 0.6137 \| 0.8406 \| 0.1039 \| 0.5809 \| 0.4314 \| 0.0 \| \| 0.4444 \| 8.05 \| 660 \| 0.2277 \| 0.5174 \| 0.6470 \| 0.8938 \| 0.9807 \| 0.0 \| 0.9616 \| 0.8141 \| 0.9088 \| 0.9461 \| 0.0907 \| 0.7961 \| 0.3247 \| nan \| 0.9518 \| 0.0 \| 0.8687 \| 0.7832 \| 0.7780 \| 0.8490 \| 0.0901 \| 0.5943 \| 0.2586 \| 0.0 \| \| 0.2176 \| 8.29 \| 680 \| 0.2123 \| 0.5327 \| 0.6734 \| 0.9001 \| 0.9774 \| 0.0 \| 0.9701 \| 0.8768 \| 0.8724 \| 0.9365 \| 0.1111 \| 0.8156 \| 0.5004 \| nan \| 0.9541 \| 0.0 \| 0.8617 \| 0.8040 \| 0.7823 \| 0.8657 \| 0.1092 \| 0.5785 \| 0.3715 \| 0.0 \| \| 0.4515 \| 8.54 \| 700 \| 0.2545 \| 0.5198 \| 0.6586 \| 0.8886 \| 0.9852 \| 0.0 \| 0.9652 \| 0.8208 \| 0.9443 \| 0.9214 \| 0.2593 \| 0.7064 \| 0.3252 \| nan \| 0.9517 \| 0.0 \| 0.8370 \| 0.7896 \| 0.6680 \| 0.8531 \| 0.2441 \| 0.5716 \| 0.2833 \| 0.0 \| \| 0.2276 \| 8.78 \| 720 \| 0.2427 \| 0.5161 \| 0.6488 \| 0.8869 \| 0.9730 \| 0.0 \| 0.9757 \| 0.8662 \| 0.8668 \| 0.9466 \| 0.2834 \| 0.7261 \| 0.2011 \| nan \| 0.9521 \| 0.0 \| 0.8300 \| 0.8106 \| 0.7177 \| 0.8432 \| 0.2632 \| 0.5765 \| 0.1681 \| 0.0 \| \| 0.1664 \| 9.02 \| 740 \| 0.2403 \| 0.5174 \| 0.6477 \| 0.8924 \| 0.9731 \| 0.0 \| 0.9722 \| 0.8425 \| 0.8760 \| 0.9551 \| 0.1686 \| 0.7817 \| 0.2597 \| nan \| 0.9516 \| 0.0 \| 0.8776 \| 0.7910 \| 0.7536 \| 0.8527 \| 0.1648 \| 0.5767 \| 0.2062 \| 0.0 \| \| 0.1256 \| 9.27 \| 760 \| 0.2232 \| 0.5293 \| 0.6719 \| 0.8913 \| 0.9753 \| 0.0 \| 0.9694 \| 0.8536 \| 0.8344 \| 0.9224 \| 0.1563 \| 0.7175 \| 0.6187 \| nan \| 0.9520 \| 0.0 \| 0.8567 \| 0.7925 \| 0.7405 \| 0.8617 \| 0.1530 \| 0.5946 \| 0.3420 \| 0.0 \| \| 0.336 \| 9.51 \| 780 \| 0.2125 \| 0.5451 \| 0.6873 \| 0.9017 \| 0.9794 \| 0.0 \| 0.9741 \| 0.8796 \| 0.8795 \| 0.9323 \| 0.2422 \| 0.7836 \| 0.5153 \| nan \| 0.9522 \| 0.0 \| 0.8579 \| 0.7945 \| 0.7699 \| 0.8705 \| 0.2256 \| 0.5934 \| 0.3870 \| 0.0 \| \| 0.2018 \| 9.76 \| 800 \| 0.2224 \| 0.5404 \| 0.6681 \| 0.8984 \| 0.9822 \| 0.0 \| 0.9652 \| 0.8555 \| 0.8950 \| 0.9519 \| 0.3185 \| 0.7221 \| 0.3220 \| nan \| 0.9531 \| 0.0 \| 0.8535 \| 0.8067 \| 0.7567 \| 0.8584 \| 0.2949 \| 0.6183 \| 0.2621 \| 0.0 \| \| 0.1682 \| 10.0 \| 820 \| 0.2187 \| 0.5320 \| 0.6820 \| 0.8924 \| 0.9718 \| 0.0 \| 0.9814 \| 0.8696 \| 0.8334 \| 0.9177 \| 0.1967 \| 0.8444 \| 0.5228 \| nan \| 0.9521 \| 0.0 \| 0.8742 \| 0.7917 \| 0.7417 \| 0.8515 \| 0.1852 \| 0.5529 \| 0.3707 \| 0.0 \| \| 0.176 \| 10.24 \| 840 \| 0.2228 \| 0.5335 \| 0.6621 \| 0.8964 \| 0.9811 \| 0.0 \| 0.9741 \| 0.8623 \| 0.8578 \| 0.9513 \| 0.2482 \| 0.7291 \| 0.3547 \| nan \| 0.9553 \| 0.0 \| 0.8520 \| 0.8082 \| 0.7474 \| 0.8587 \| 0.2369 \| 0.6102 \| 0.2662 \| 0.0 \| \| 0.4021 \| 10.49 \| 860 \| 0.2221 \| 0.5370 \| 0.6793 \| 0.8973 \| 0.9742 \| 0.0 \| 0.9634 \| 0.8676 \| 0.9133 \| 0.9356 \| 0.2970 \| 0.7645 \| 0.3982 \| nan \| 0.9533 \| 0.0 \| 0.8641 \| 0.7969 \| 0.7135 \| 0.8725 \| 0.2745 \| 0.5827 \| 0.3125 \| 0.0 \| \| 0.2189 \| 10.73 \| 880 \| 0.2594 \| 0.5157 \| 0.6489 \| 0.8857 \| 0.9846 \| 0.0 \| 0.9684 \| 0.8699 \| 0.7198 \| 0.9467 \| 0.2024 \| 0.8028 \| 0.3458 \| nan \| 0.9562 \| 0.0 \| 0.8654 \| 0.8160 \| 0.6462 \| 0.8367 \| 0.1928 \| 0.5791 \| 0.2648 \| 0.0 \| \| 0.218 \| 10.98 \| 900 \| 0.2445 \| 0.5208 \| 0.6623 \| 0.8853 \| 0.9734 \| 0.0 \| 0.9834 \| 0.8650 \| 0.7271 \| 0.9380 \| 0.2457 \| 0.7985 \| 0.4296 \| nan \| 0.9561 \| 0.0 \| 0.8233 \| 0.8043 \| 0.6560 \| 0.8406 \| 0.2276 \| 0.5827 \| 0.3177 \| 0.0 \| \| 0.3402 \| 11.22 \| 920 \| 0.2789 \| 0.5181 \| 0.6438 \| 0.8821 \| 0.9728 \| 0.0 \| 0.9665 \| 0.8725 \| 0.6840 \| 0.9599 \| 0.2502 \| 0.7863 \| 0.3024 \| nan \| 0.9507 \| 0.0 \| 0.8598 \| 0.8023 \| 0.6413 \| 0.8265 \| 0.2387 \| 0.6396 \| 0.2225 \| 0.0 \| \| 0.2232 \| 11.46 \| 940 \| 0.2219 \| 0.5387 \| 0.6810 \| 0.8920 \| 0.9705 \| 0.0 \| 0.9498 \| 0.9055 \| 0.7311 \| 0.9404 \| 0.2678 \| 0.7632 \| 0.6004 \| nan \| 0.9490 \| 0.0 \| 0.8674 \| 0.7808 \| 0.6552 \| 0.8561 \| 0.2553 \| 0.6268 \| 0.3965 \| 0.0 \| \| 0.1727 \| 11.71 \| 960 \| 0.2741 \| 0.5328 \| 0.6593 \| 0.8880 \| 0.9812 \| 0.0 \| 0.9682 \| 0.8166 \| 0.7600 \| 0.9648 \| 0.4642 \| 0.7672 \| 0.2112 \| nan \| 0.9554 \| 0.0 \| 0.8701 \| 0.7751 \| 0.6763 \| 0.8300 \| 0.4070 \| 0.6312 \| 0.1824 \| 0.0 \| \| 0.3027 \| 11.95 \| 980 \| 0.2126 \| 0.5477 \| 0.6784 \| 0.9005 \| 0.9774 \| 0.0 \| 0.9702 \| 0.8705 \| 0.8597 \| 0.9570 \| 0.3690 \| 0.7887 \| 0.3127 \| nan \| 0.9542 \| 0.0 \| 0.8645 \| 0.8041 \| 0.7646 \| 0.8582 \| 0.3354 \| 0.6361 \| 0.2600 \| 0.0 \| \| 0.2245 \| 12.2 \| 1000 \| 0.2490 \| 0.5254 \| 0.6482 \| 0.8899 \| 0.9833 \| 0.0 \| 0.9638 \| 0.8135 \| 0.7925 \| 0.9626 \| 0.2752 \| 0.8146 \| 0.2286 \| nan \| 0.9548 \| 0.0 \| 0.8727 \| 0.7852 \| 0.7239 \| 0.8352 \| 0.2619 \| 0.6371 \| 0.1834 \| 0.0 \| \| 0.1551 \| 12.44 \| 1020 \| 0.2332 \| 0.5364 \| 0.6875 \| 0.8924 \| 0.9766 \| 0.0 \| 0.9856 \| 0.8760 \| 0.8783 \| 0.9118 \| 0.3322 \| 0.6270 \| 0.5995 \| nan \| 0.9571 \| 0.0 \| 0.7598 \| 0.8028 \| 0.7604 \| 0.8562 \| 0.2982 \| 0.5043 \| 0.4247 \| 0.0 \| \| 0.3828 \| 12.68 \| 1040 \| 0.2138 \| 0.5437 \| 0.6753 \| 0.8979 \| 0.9799 \| 0.0 \| 0.9477 \| 0.8984 \| 0.8723 \| 0.9574 \| 0.4473 \| 0.6534 \| 0.3209 \| nan \| 0.9542 \| 0.0 \| 0.8187 \| 0.8072 \| 0.7887 \| 0.8580 \| 0.3793 \| 0.5674 \| 0.2640 \| 0.0 \| \| 0.0929 \| 12.93 \| 1060 \| 0.2544 \| 0.5186 \| 0.6501 \| 0.8855 \| 0.9783 \| 0.0 \| 0.9709 \| 0.8634 \| 0.7491 \| 0.9542 \| 0.3067 \| 0.6526 \| 0.3757 \| nan \| 0.9549 \| 0.0 \| 0.8048 \| 0.8069 \| 0.6771 \| 0.8418 \| 0.2878 \| 0.5185 \| 0.2948 \| 0.0 \| \| 0.2362 \| 13.17 \| 1080 \| 0.2353 \| 0.5278 \| 0.6661 \| 0.8877 \| 0.9763 \| 0.0 \| 0.9807 \| 0.8577 \| 0.8281 \| 0.9374 \| 0.3786 \| 0.6547 \| 0.3816 \| nan \| 0.9531 \| 0.0 \| 0.7907 \| 0.8045 \| 0.7260 \| 0.8447 \| 0.3395 \| 0.5196 \| 0.3001 \| 0.0 \| \| 0.1954 \| 13.41 \| 1100 \| 0.2073 \| 0.5580 \| 0.7009 \| 0.9019 \| 0.9762 \| 0.0 \| 0.9681 \| 0.8902 \| 0.8184 \| 0.9348 \| 0.3367 \| 0.7731 \| 0.6111 \| nan \| 0.9548 \| 0.0 \| 0.8642 \| 0.8120 \| 0.7373 \| 0.8679 \| 0.3120 \| 0.6404 \| 0.3918 \| 0.0 \| \| 0.2412 \| 13.66 \| 1120 \| 0.2144 \| 0.5520 \| 0.6946 \| 0.9022 \| 0.9808 \| 0.0 \| 0.9764 \| 0.8460 \| 0.8418 \| 0.9352 \| 0.3048 \| 0.7065 \| 0.6605 \| nan \| 0.9554 \| 0.0 \| 0.8265 \| 0.7963 \| 0.7536 \| 0.8773 \| 0.2868 \| 0.6078 \| 0.4162 \| 0.0 \| \| 0.2167 \| 13.9 \| 1140 \| 0.2111 \| 0.5577 \| 0.6947 \| 0.9005 \| 0.9753 \| 0.0 \| 0.9744 \| 0.8242 \| 0.8303 \| 0.9442 \| 0.3808 \| 0.7631 \| 0.5597 \| nan \| 0.9516 \| 0.0 \| 0.8487 \| 0.7825 \| 0.7616 \| 0.8716 \| 0.3440 \| 0.6640 \| 0.3534 \| 0.0 \| \| 0.0952 \| 14.15 \| 1160 \| 0.2637 \| 0.5279 \| 0.6724 \| 0.8863 \| 0.9740 \| 0.0 \| 0.9860 \| 0.8767 \| 0.7933 \| 0.9334 \| 0.4219 \| 0.5904 \| 0.4754 \| nan \| 0.9531 \| 0.0 \| 0.7480 \| 0.8056 \| 0.7181 \| 0.8535 \| 0.3615 \| 0.4894 \| 0.3497 \| 0.0 \| \| 0.1032 \| 14.39 \| 1180 \| 0.2484 \| 0.5403 \| 0.6730 \| 0.8921 \| 0.9769 \| 0.0 \| 0.9724 \| 0.8537 \| 0.7452 \| 0.9591 \| 0.4251 \| 0.8313 \| 0.2934 \| nan \| 0.9553 \| 0.0 \| 0.8613 \| 0.7993 \| 0.6934 \| 0.8405 \| 0.3786 \| 0.6417 \| 0.2331 \| 0.0 \| \| 0.2301 \| 14.63 \| 1200 \| 0.2167 \| 0.5450 \| 0.6843 \| 0.8969 \| 0.9752 \| 0.0 \| 0.9785 \| 0.8525 \| 0.9088 \| 0.9402 \| 0.4511 \| 0.6742 \| 0.3782 \| nan \| 0.9534 \| 0.0 \| 0.8015 \| 0.7952 \| 0.7726 \| 0.8610 \| 0.3911 \| 0.5705 \| 0.3044 \| 0.0 \| \| 0.3602 \| 14.88 \| 1220 \| 0.2154 \| 0.5522 \| 0.7015 \| 0.8960 \| 0.9738 \| 0.0 \| 0.9770 \| 0.8642 \| 0.7977 \| 0.9202 \| 0.3336 \| 0.7938 \| 0.6533 \| nan \| 0.9495 \| 0.0 \| 0.8433 \| 0.7947 \| 0.7288 \| 0.8638 \| 0.3085 \| 0.6520 \| 0.3817 \| 0.0 \| \| 0.1081 \| 15.12 \| 1240 \| 0.2512 \| 0.5321 \| 0.6634 \| 0.8872 \| 0.9717 \| 0.0 \| 0.9707 \| 0.8631 \| 0.7590 \| 0.9627 \| 0.4839 \| 0.6768 \| 0.2828 \| nan \| 0.9508 \| 0.0 \| 0.8181 \| 0.7973 \| 0.6834 \| 0.8401 \| 0.4158 \| 0.5872 \| 0.2285 \| 0.0 \| \| 0.138 \| 15.37 \| 1260 \| 0.1995 \| 0.5660 \| 0.7144 \| 0.9028 \| 0.9747 \| 0.0 \| 0.9687 \| 0.8627 \| 0.8566 \| 0.9240 \| 0.3978 \| 0.8400 \| 0.6048 \| nan \| 0.9511 \| 0.0 \| 0.8700 \| 0.7965 \| 0.7700 \| 0.8698 \| 0.3606 \| 0.6542 \| 0.3876 \| 0.0 \| \| 0.1254 \| 15.61 \| 1280 \| 0.2302 \| 0.5517 \| 0.6866 \| 0.8994 \| 0.9652 \| 0.0 \| 0.9730 \| 0.8691 \| 0.9000 \| 0.9510 \| 0.4196 \| 0.7253 \| 0.3762 \| nan \| 0.9483 \| 0.0 \| 0.8413 \| 0.7915 \| 0.7688 \| 0.8603 \| 0.3770 \| 0.6323 \| 0.2976 \| 0.0 \| \| 0.0939 \| 15.85 \| 1300 \| 0.2252 \| 0.5506 \| 0.6884 \| 0.8930 \| 0.9853 \| 0.0 \| 0.9585 \| 0.8488 \| 0.7720 \| 0.9348 \| 0.4332 \| 0.7397 \| 0.5235 \| nan \| 0.9494 \| 0.0 \| 0.8510 \| 0.8047 \| 0.6791 \| 0.8478 \| 0.3833 \| 0.6327 \| 0.3582 \| 0.0 \| \| 0.079 \| 16.1 \| 1320 \| 0.2439 \| 0.5381 \| 0.6686 \| 0.8888 \| 0.9764 \| 0.0 \| 0.9686 \| 0.8422 \| 0.7464 \| 0.9577 \| 0.4522 \| 0.6807 \| 0.3930 \| nan \| 0.9544 \| 0.0 \| 0.8271 \| 0.7930 \| 0.6805 \| 0.8475 \| 0.3958 \| 0.6125 \| 0.2706 \| 0.0 \| \| 0.1409 \| 16.34 \| 1340 \| 0.2244 \| 0.5567 \| 0.7105 \| 0.8928 \| 0.9660 \| 0.0 \| 0.9787 \| 0.8779 \| 0.7602 \| 0.9191 \| 0.4474 \| 0.7951 \| 0.6504 \| nan \| 0.9501 \| 0.0 \| 0.8409 \| 0.7902 \| 0.6957 \| 0.8539 \| 0.3926 \| 0.6588 \| 0.3851 \| 0.0 \| \| 0.3714 \| 16.59 \| 1360 \| 0.2480 \| 0.5451 \| 0.6732 \| 0.8965 \| 0.9827 \| 0.0 \| 0.9692 \| 0.8453 \| 0.9122 \| 0.9530 \| 0.4942 \| 0.7125 \| 0.1900 \| nan \| 0.9553 \| 0.0 \| 0.8439 \| 0.8058 \| 0.7744 \| 0.8417 \| 0.4262 \| 0.6336 \| 0.1704 \| 0.0 \| \| 0.2288 \| 16.83 \| 1380 \| 0.2100 \| 0.5634 \| 0.7029 \| 0.9011 \| 0.9767 \| 0.0 \| 0.9704 \| 0.8758 \| 0.8995 \| 0.9382 \| 0.5412 \| 0.7601 \| 0.3645 \| nan \| 0.9520 \| 0.0 \| 0.8497 \| 0.8046 \| 0.7688 \| 0.8513 \| 0.4448 \| 0.6575 \| 0.3057 \| 0.0 \| \| 0.2297 \| 17.07 \| 1400 \| 0.2083 \| 0.5629 \| 0.7053 \| 0.9023 \| 0.9720 \| 0.0 \| 0.9721 \| 0.8785 \| 0.9438 \| 0.9305 \| 0.4819 \| 0.7221 \| 0.4466 \| nan \| 0.9514 \| 0.0 \| 0.8451 \| 0.7942 \| 0.7728 \| 0.8596 \| 0.4149 \| 0.6409 \| 0.3503 \| 0.0 \| \| 0.1961 \| 17.32 \| 1420 \| 0.2102 \| 0.5588 \| 0.6962 \| 0.9023 \| 0.9768 \| 0.0 \| 0.9704 \| 0.8353 \| 0.8819 \| 0.9435 \| 0.4188 \| 0.7559 \| 0.4831 \| nan \| 0.9528 \| 0.0 \| 0.8311 \| 0.7837 \| 0.7888 \| 0.8673 \| 0.3779 \| 0.6372 \| 0.3493 \| 0.0 \| \| 0.1938 \| 17.56 \| 1440 \| 0.2151 \| 0.5554 \| 0.6917 \| 0.8999 \| 0.9775 \| 0.0 \| 0.9638 \| 0.8859 \| 0.8447 \| 0.9448 \| 0.4199 \| 0.8143 \| 0.3742 \| nan \| 0.9525 \| 0.0 \| 0.8517 \| 0.8075 \| 0.7688 \| 0.8574 \| 0.3760 \| 0.6574 \| 0.2831 \| 0.0 \| \| 0.283 \| 17.8 \| 1460 \| 0.2285 \| 0.5502 \| 0.6886 \| 0.8971 \| 0.9752 \| 0.0 \| 0.9658 \| 0.8285 \| 0.7796 \| 0.9470 \| 0.3540 \| 0.8388 \| 0.5087 \| nan \| 0.9553 \| 0.0 \| 0.8671 \| 0.7779 \| 0.7223 \| 0.8636 \| 0.3317 \| 0.6615 \| 0.3227 \| 0.0 \| \| 0.2744 \| 18.05 \| 1480 \| 0.2301 \| 0.5532 \| 0.6929 \| 0.8974 \| 0.9741 \| 0.0 \| 0.9831 \| 0.8644 \| 0.7817 \| 0.9473 \| 0.4503 \| 0.7601 \| 0.4751 \| nan \| 0.9550 \| 0.0 \| 0.8186 \| 0.7977 \| 0.7268 \| 0.8598 \| 0.3918 \| 0.6399 \| 0.3421 \| 0.0 \| \| 0.0956 \| 18.29 \| 1500 \| 0.2140 \| 0.5637 \| 0.7149 \| 0.9026 \| 0.9717 \| 0.0 \| 0.9813 \| 0.8585 \| 0.9085 \| 0.9223 \| 0.4672 \| 0.7189 \| 0.6055 \| nan \| 0.9535 \| 0.0 \| 0.8169 \| 0.7947 \| 0.7597 \| 0.8727 \| 0.3985 \| 0.6146 \| 0.4264 \| 0.0 \| \| 0.0477 \| 18.54 \| 1520 \| 0.2209 \| 0.5551 \| 0.6878 \| 0.8975 \| 0.9692 \| 0.0 \| 0.9696 \| 0.8742 \| 0.8338 \| 0.9573 \| 0.5019 \| 0.7648 \| 0.3190 \| nan \| 0.9495 \| 0.0 \| 0.8549 \| 0.7934 \| 0.7518 \| 0.8579 \| 0.4426 \| 0.6592 \| 0.2416 \| 0.0 \| \| 0.3598 \| 18.78 \| 1540 \| 0.2430 \| 0.5330 \| 0.6704 \| 0.8890 \| 0.9762 \| 0.0 \| 0.9813 \| 0.8502 \| 0.8685 \| 0.9383 \| 0.4623 \| 0.5791 \| 0.3782 \| nan \| 0.9527 \| 0.0 \| 0.7785 \| 0.7953 \| 0.7300 \| 0.8520 \| 0.4042 \| 0.5312 \| 0.2861 \| 0.0 \| \| 0.1007 \| 19.02 \| 1560 \| 0.2182 \| 0.5626 \| 0.7106 \| 0.8983 \| 0.9772 \| 0.0 \| 0.9776 \| 0.8621 \| 0.8359 \| 0.9232 \| 0.4950 \| 0.7717 \| 0.5527 \| nan \| 0.9560 \| 0.0 \| 0.8457 \| 0.7943 \| 0.7189 \| 0.8602 \| 0.4321 \| 0.6575 \| 0.3613 \| 0.0 \| \| 0.1631 \| 19.27 \| 1580 \| 0.2335 \| 0.5543 \| 0.6887 \| 0.8983 \| 0.9767 \| 0.0 \| 0.9741 \| 0.8444 \| 0.8350 \| 0.9533 \| 0.5018 \| 0.6999 \| 0.4135 \| nan \| 0.9555 \| 0.0 \| 0.8328 \| 0.7923 \| 0.7482 \| 0.8629 \| 0.4291 \| 0.6299 \| 0.2924 \| 0.0 \| \| 0.4344 \| 19.51 \| 1600 \| 0.2087 \| 0.5602 \| 0.6987 \| 0.9025 \| 0.9797 \| 0.0 \| 0.9657 \| 0.8446 \| 0.8997 \| 0.9404 \| 0.4632 \| 0.7297 \| 0.4654 \| nan \| 0.9533 \| 0.0 \| 0.8205 \| 0.7975 \| 0.7871 \| 0.8685 \| 0.4160 \| 0.6058 \| 0.3531 \| 0.0 \| \| 0.0956 \| 19.76 \| 1620 \| 0.2287 \| 0.5574 \| 0.6894 \| 0.9007 \| 0.9774 \| 0.0 \| 0.9667 \| 0.8465 \| 0.8975 \| 0.9511 \| 0.4834 \| 0.8058 \| 0.2760 \| nan \| 0.9546 \| 0.0 \| 0.8582 \| 0.7967 \| 0.7856 \| 0.8517 \| 0.4333 \| 0.6646 \| 0.2291 \| 0.0 \| \| 0.1634 \| 20.0 \| 1640 \| 0.2126 \| 0.5576 \| 0.6997 \| 0.9003 \| 0.9725 \| 0.0 \| 0.9783 \| 0.8538 \| 0.8832 \| 0.9374 \| 0.4600 \| 0.7096 \| 0.5020 \| nan \| 0.9523 \| 0.0 \| 0.8042 \| 0.7903 \| 0.7842 \| 0.8664 \| 0.4073 \| 0.5994 \| 0.3719 \| 0.0 \| \| 0.1048 \| 20.24 \| 1660 \| 0.2254 \| 0.5588 \| 0.6831 \| 0.9017 \| 0.9784 \| 0.0 \| 0.9590 \| 0.8543 \| 0.8564 \| 0.9646 \| 0.4542 \| 0.7924 \| 0.2885 \| nan \| 0.9534 \| 0.0 \| 0.8616 \| 0.7995 \| 0.7886 \| 0.8552 \| 0.4168 \| 0.6842 \| 0.2289 \| 0.0 \| \| 0.1636 \| 20.49 \| 1680 \| 0.2063 \| 0.5713 \| 0.7097 \| 0.9046 \| 0.9746 \| 0.0 \| 0.9741 \| 0.8597 \| 0.8250 \| 0.9483 \| 0.5023 \| 0.7629 \| 0.5409 \| nan \| 0.9537 \| 0.0 \| 0.8468 \| 0.7972 \| 0.7660 \| 0.8728 \| 0.4351 \| 0.6817 \| 0.3597 \| 0.0 \| \| 0.0753 \| 20.73 \| 1700 \| 0.2005 \| 0.5738 \| 0.7122 \| 0.9053 \| 0.9742 \| 0.0 \| 0.9762 \| 0.8687 \| 0.8586 \| 0.9470 \| 0.5485 \| 0.7920 \| 0.4442 \| nan \| 0.9536 \| 0.0 \| 0.8456 \| 0.8049 \| 0.7854 \| 0.8662 \| 0.4710 \| 0.6828 \| 0.3289 \| 0.0 \| \| 0.1346 \| 20.98 \| 1720 \| 0.1977 \| 0.5762 \| 0.7246 \| 0.9065 \| 0.9759 \| 0.0 \| 0.9775 \| 0.8651 \| 0.8993 \| 0.9239 \| 0.5007 \| 0.7995 \| 0.5794 \| nan \| 0.9548 \| 0.0 \| 0.8379 \| 0.7961 \| 0.7913 \| 0.8679 \| 0.4392 \| 0.6660 \| 0.4089 \| 0.0 \| \| 0.1527 \| 21.22 \| 1740 \| 0.2123 \| 0.5662 \| 0.7087 \| 0.9025 \| 0.9759 \| 0.0002 \| 0.9621 \| 0.8524 \| 0.9264 \| 0.9353 \| 0.5505 \| 0.7677 \| 0.4078 \| nan \| 0.9542 \| 0.0002 \| 0.8533 \| 0.7906 \| 0.7497 \| 0.8610 \| 0.4724 \| 0.6514 \| 0.3290 \| 0.0 \| \| 0.1149 \| 21.46 \| 1760 \| 0.2262 \| 0.5623 \| 0.6926 \| 0.9003 \| 0.9734 \| 0.0 \| 0.9632 \| 0.8380 \| 0.8228 \| 0.9623 \| 0.5167 \| 0.7898 \| 0.3669 \| nan \| 0.9523 \| 0.0 \| 0.8536 \| 0.7847 \| 0.7638 \| 0.8602 \| 0.4552 \| 0.6804 \| 0.2725 \| 0.0 \| \| 0.3336 \| 21.71 \| 1780 \| 0.2176 \| 0.5647 \| 0.7053 \| 0.9023 \| 0.9713 \| 0.0003 \| 0.9803 \| 0.8546 \| 0.8731 \| 0.9426 \| 0.4990 \| 0.7902 \| 0.4361 \| nan \| 0.9543 \| 0.0003 \| 0.8273 \| 0.7953 \| 0.7841 \| 0.8632 \| 0.4397 \| 0.6565 \| 0.3267 \| 0.0 \| \| 0.0924 \| 21.95 \| 1800 \| 0.2271 \| 0.5608 \| 0.6933 \| 0.9011 \| 0.9834 \| 0.0 \| 0.9709 \| 0.8338 \| 0.8657 \| 0.9472 \| 0.4902 \| 0.7430 \| 0.4059 \| nan \| 0.9577 \| 0.0 \| 0.8354 \| 0.7946 \| 0.7788 \| 0.8591 \| 0.4359 \| 0.6520 \| 0.2945 \| 0.0 \| \| 0.256 \| 22.2 \| 1820 \| 0.2140 \| 0.5659 \| 0.7010 \| 0.9038 \| 0.9820 \| 0.0 \| 0.9697 \| 0.8401 \| 0.9119 \| 0.9435 \| 0.5189 \| 0.7867 \| 0.3560 \| nan \| 0.9560 \| 0.0 \| 0.8462 \| 0.7987 \| 0.8078 \| 0.8582 \| 0.4515 \| 0.6594 \| 0.2817 \| 0.0 \| \| 0.202 \| 22.44 \| 1840 \| 0.2358 \| 0.5589 \| 0.6907 \| 0.9001 \| 0.9766 \| 0.0 \| 0.9760 \| 0.8359 \| 0.8945 \| 0.9526 \| 0.5406 \| 0.7420 \| 0.2981 \| nan \| 0.9553 \| 0.0 \| 0.8391 \| 0.7915 \| 0.7924 \| 0.8522 \| 0.4572 \| 0.6613 \| 0.2397 \| 0.0 \| \| 0.1456 \| 22.68 \| 1860 \| 0.2115 \| 0.5669 \| 0.7125 \| 0.9016 \| 0.9762 \| 0.0 \| 0.9801 \| 0.8582 \| 0.8871 \| 0.9267 \| 0.5130 \| 0.8054 \| 0.4657 \| nan \| 0.9556 \| 0.0 \| 0.8310 \| 0.8008 \| 0.7886 \| 0.8585 \| 0.4425 \| 0.6588 \| 0.3335 \| 0.0 \| \| 0.1198 \| 22.93 \| 1880 \| 0.2233 \| 0.5631 \| 0.6997 \| 0.9020 \| 0.9745 \| 0.0 \| 0.9731 \| 0.8563 \| 0.9047 \| 0.9484 \| 0.5580 \| 0.7858 \| 0.2967 \| nan \| 0.9529 \| 0.0 \| 0.8405 \| 0.8011 \| 0.7914 \| 0.8561 \| 0.4715 \| 0.6652 \| 0.2520 \| 0.0 \| \| 0.0804 \| 23.17 \| 1900 \| 0.2075 \| 0.5719 \| 0.7090 \| 0.9047 \| 0.9791 \| 0.0 \| 0.9686 \| 0.8542 \| 0.8664 \| 0.9434 \| 0.5174 \| 0.7723 \| 0.4800 \| nan \| 0.9552 \| 0.0 \| 0.8456 \| 0.7995 \| 0.7842 \| 0.8636 \| 0.4512 \| 0.6765 \| 0.3429 \| 0.0 \| \| 0.0779 \| 23.41 \| 1920 \| 0.2217 \| 0.5630 \| 0.7025 \| 0.9007 \| 0.9736 \| 0.0001 \| 0.9774 \| 0.8596 \| 0.8837 \| 0.9422 \| 0.5470 \| 0.7558 \| 0.3832 \| nan \| 0.9544 \| 0.0001 \| 0.8258 \| 0.7991 \| 0.7705 \| 0.8559 \| 0.4602 \| 0.6611 \| 0.3032 \| 0.0 \| \| 0.1159 \| 23.66 \| 1940 \| 0.2122 \| 0.5690 \| 0.7134 \| 0.9030 \| 0.9772 \| 0.0002 \| 0.9767 \| 0.8574 \| 0.8913 \| 0.9275 \| 0.4955 \| 0.7959 \| 0.4990 \| nan \| 0.9538 \| 0.0002 \| 0.8307 \| 0.8004 \| 0.7766 \| 0.8612 \| 0.4343 \| 0.6650 \| 0.3683 \| 0.0 \| \| 0.1 \| 23.9 \| 1960 \| 0.1988 \| 0.5762 \| 0.7116 \| 0.9078 \| 0.9834 \| 0.0 \| 0.9523 \| 0.8439 \| 0.8919 \| 0.9463 \| 0.5231 \| 0.8031 \| 0.4608 \| nan \| 0.9543 \| 0.0 \| 0.8620 \| 0.7983 \| 0.7991 \| 0.8662 \| 0.4611 \| 0.6719 \| 0.3492 \| 0.0 \| \| 0.1052 \| 24.15 \| 1980 \| 0.2147 \| 0.5672 \| 0.7012 \| 0.9048 \| 0.9778 \| 0.0 \| 0.9704 \| 0.8441 \| 0.8915 \| 0.9499 \| 0.5004 \| 0.7622 \| 0.4146 \| nan \| 0.9550 \| 0.0 \| 0.8396 \| 0.7916 \| 0.8009 \| 0.8624 \| 0.4403 \| 0.6608 \| 0.3219 \| 0.0 \| \| 0.1478 \| 24.39 \| 2000 \| 0.2206 \| 0.5638 \| 0.6950 \| 0.9040 \| 0.9815 \| 0.0 \| 0.9657 \| 0.8313 \| 0.8904 \| 0.9530 \| 0.4869 \| 0.7536 \| 0.3929 \| nan \| 0.9557 \| 0.0 \| 0.8433 \| 0.7884 \| 0.7959 \| 0.8606 \| 0.4347 \| 0.6515 \| 0.3081 \| 0.0 \| \| 0.1752 \| 24.63 \| 2020 \| 0.2048 \| 0.5700 \| 0.7085 \| 0.9058 \| 0.9793 \| 0.0 \| 0.9668 \| 0.8566 \| 0.9095 \| 0.9430 \| 0.5319 \| 0.7519 \| 0.4372 \| nan \| 0.9535 \| 0.0 \| 0.8458 \| 0.7987 \| 0.7801 \| 0.8670 \| 0.4597 \| 0.6460 \| 0.3492 \| 0.0 \| \| 0.0451 \| 24.88 \| 2040 \| 0.2171 \| 0.5666 \| 0.7053 \| 0.9025 \| 0.9799 \| 0.0 \| 0.9748 \| 0.8552 \| 0.8761 \| 0.9405 \| 0.5371 \| 0.7711 \| 0.4131 \| nan \| 0.9545 \| 0.0 \| 0.8309 \| 0.8008 \| 0.7878 \| 0.8586 \| 0.4534 \| 0.6644 \| 0.3154 \| 0.0 \| \| 0.4243 \| 25.12 \| 2060 \| 0.2341 \| 0.5595 \| 0.6916 \| 0.8999 \| 0.9746 \| 0.0 \| 0.9764 \| 0.8444 \| 0.8887 \| 0.9491 \| 0.4996 \| 0.7703 \| 0.3211 \| nan \| 0.9515 \| 0.0 \| 0.8327 \| 0.7956 \| 0.7984 \| 0.8537 \| 0.4397 \| 0.6689 \| 0.2545 \| 0.0 \| \| 0.0585 \| 25.37 \| 2080 \| 0.2213 \| 0.5660 \| 0.7007 \| 0.9033 \| 0.9820 \| 0.0 \| 0.9652 \| 0.8551 \| 0.9015 \| 0.9435 \| 0.5187 \| 0.7915 \| 0.3485 \| nan \| 0.9542 \| 0.0 \| 0.8466 \| 0.8024 \| 0.7984 \| 0.8564 \| 0.4574 \| 0.6699 \| 0.2748 \| 0.0 \| \| 0.2001 \| 25.61 \| 2100 \| 0.2246 \| 0.5644 \| 0.7026 \| 0.9009 \| 0.9783 \| 0.0 \| 0.9724 \| 0.8691 \| 0.8753 \| 0.9397 \| 0.5349 \| 0.7871 \| 0.3667 \| nan \| 0.9539 \| 0.0 \| 0.8358 \| 0.8023 \| 0.7863 \| 0.8541 \| 0.4617 \| 0.6681 \| 0.2817 \| 0.0 \| \| 0.0681 \| 25.85 \| 2120 \| 0.2243 \| 0.5653 \| 0.7001 \| 0.9033 \| 0.9774 \| 0.0071 \| 0.9602 \| 0.8607 \| 0.8942 \| 0.9492 \| 0.5104 \| 0.8085 \| 0.3328 \| nan \| 0.9538 \| 0.0071 \| 0.8526 \| 0.7975 \| 0.7950 \| 0.8573 \| 0.4517 \| 0.6706 \| 0.2679 \| 0.0 \| \| 0.3011 \| 26.1 \| 2140 \| 0.2142 \| 0.5695 \| 0.7099 \| 0.9029 \| 0.9755 \| 0.0022 \| 0.9730 \| 0.8624 \| 0.8863 \| 0.9383 \| 0.5417 \| 0.7982 \| 0.4115 \| nan \| 0.9541 \| 0.0021 \| 0.8424 \| 0.8006 \| 0.7913 \| 0.8590 \| 0.4633 \| 0.6755 \| 0.3067 \| 0.0 \| \| 0.1124 \| 26.34 \| 2160 \| 0.2134 \| 0.5678 \| 0.7069 \| 0.9014 \| 0.9740 \| 0.0097 \| 0.9692 \| 0.8475 \| 0.8710 \| 0.9399 \| 0.5086 \| 0.8119 \| 0.4309 \| nan \| 0.9526 \| 0.0097 \| 0.8497 \| 0.7935 \| 0.7773 \| 0.8598 \| 0.4480 \| 0.6745 \| 0.3125 \| 0.0 \| \| 0.2631 \| 26.59 \| 2180 \| 0.2171 \| 0.5685 \| 0.7043 \| 0.9042 \| 0.9764 \| 0.0165 \| 0.9626 \| 0.8486 \| 0.8985 \| 0.9481 \| 0.5077 \| 0.8124 \| 0.3682 \| nan \| 0.9540 \| 0.0164 \| 0.8554 \| 0.7937 \| 0.7880 \| 0.8606 \| 0.4499 \| 0.6734 \| 0.2935 \| 0.0 \| \| 0.0783 \| 26.83 \| 2200 \| 0.2094 \| 0.5699 \| 0.7120 \| 0.9031 \| 0.9788 \| 0.0 \| 0.9649 \| 0.8464 \| 0.9089 \| 0.9335 \| 0.5525 \| 0.8184 \| 0.4045 \| nan \| 0.9525 \| 0.0 \| 0.8521 \| 0.7966 \| 0.7837 \| 0.8604 \| 0.4736 \| 0.6675 \| 0.3123 \| 0.0 \| \| 0.0412 \| 27.07 \| 2220 \| 0.2115 \| 0.5688 \| 0.7096 \| 0.9041 \| 0.9772 \| 0.0057 \| 0.9679 \| 0.8448 \| 0.8925 \| 0.9363 \| 0.4727 \| 0.8228 \| 0.4667 \| nan \| 0.9545 \| 0.0057 \| 0.8544 \| 0.7909 \| 0.7903 \| 0.8633 \| 0.4230 \| 0.6677 \| 0.3380 \| 0.0 \| \| 0.096 \| 27.32 \| 2240 \| 0.2224 \| 0.5626 \| 0.6939 \| 0.9029 \| 0.9788 \| 0.0 \| 0.9699 \| 0.8440 \| 0.8945 \| 0.9502 \| 0.4758 \| 0.7877 \| 0.3443 \| nan \| 0.9551 \| 0.0 \| 0.8461 \| 0.7956 \| 0.7958 \| 0.8576 \| 0.4303 \| 0.6747 \| 0.2704 \| 0.0 \| \| 0.1542 \| 27.56 \| 2260 \| 0.2251 \| 0.5646 \| 0.6958 \| 0.9027 \| 0.9755 \| 0.0040 \| 0.9674 \| 0.8465 \| 0.9004 \| 0.9548 \| 0.5310 \| 0.7762 \| 0.3064 \| nan \| 0.9531 \| 0.0040 \| 0.8489 \| 0.7935 \| 0.7997 \| 0.8566 \| 0.4623 \| 0.6790 \| 0.2493 \| 0.0 \| \| 0.2322 \| 27.8 \| 2280 \| 0.2243 \| 0.5641 \| 0.6943 \| 0.9029 \| 0.9739 \| 0.0024 \| 0.9733 \| 0.8549 \| 0.8847 \| 0.9570 \| 0.5096 \| 0.7569 \| 0.3360 \| nan \| 0.9533 \| 0.0024 \| 0.8427 \| 0.7961 \| 0.7998 \| 0.8584 \| 0.4475 \| 0.6741 \| 0.2668 \| 0.0 \| \| 0.1025 \| 28.05 \| 2300 \| 0.2217 \| 0.5665 \| 0.7022 \| 0.9034 \| 0.9726 \| 0.0077 \| 0.9750 \| 0.8541 \| 0.9116 \| 0.9503 \| 0.5514 \| 0.7723 \| 0.3243 \| nan \| 0.9519 \| 0.0077 \| 0.8415 \| 0.7960 \| 0.7986 \| 0.8598 \| 0.4700 \| 0.6711 \| 0.2689 \| 0.0 \| \| 0.1405 \| 28.29 \| 2320 \| 0.2244 \| 0.5680 \| 0.7009 \| 0.9042 \| 0.9761 \| 0.0087 \| 0.9650 \| 0.8600 \| 0.8864 \| 0.9543 \| 0.5317 \| 0.7668 \| 0.3590 \| nan \| 0.9539 \| 0.0087 \| 0.8512 \| 0.7963 \| 0.7881 \| 0.8603 \| 0.4623 \| 0.6746 \| 0.2846 \| 0.0 \| \| 0.1002 \| 28.54 \| 2340 \| 0.2165 \| 0.5682 \| 0.7088 \| 0.9031 \| 0.9742 \| 0.0035 \| 0.9769 \| 0.8598 \| 0.8865 \| 0.9408 \| 0.5402 \| 0.7832 \| 0.4139 \| nan \| 0.9535 \| 0.0035 \| 0.8365 \| 0.7943 \| 0.7853 \| 0.8610 \| 0.4612 \| 0.6691 \| 0.3179 \| 0.0 \| \| 0.0803 \| 28.78 \| 2360 \| 0.2233 \| 0.5655 \| 0.7000 \| 0.9019 \| 0.9759 \| 0.0059 \| 0.9706 \| 0.8529 \| 0.8577 \| 0.9497 \| 0.5112 \| 0.7560 \| 0.4199 \| nan \| 0.9541 \| 0.0059 \| 0.8452 \| 0.7923 \| 0.7727 \| 0.8604 \| 0.4468 \| 0.6716 \| 0.3062 \| 0.0 \| \| 0.1149 \| 29.02 \| 2380 \| 0.2215 \| 0.5663 \| 0.7008 \| 0.9027 \| 0.9749 \| 0.0078 \| 0.9712 \| 0.8512 \| 0.8789 \| 0.9503 \| 0.5218 \| 0.7721 \| 0.3787 \| nan \| 0.9534 \| 0.0078 \| 0.8440 \| 0.7935 \| 0.7823 \| 0.8596 \| 0.4542 \| 0.6740 \| 0.2941 \| 0.0 \| \| 0.0773 \| 29.27 \| 2400 \| 0.2228 \| 0.5675 \| 0.7068 \| 0.9027 \| 0.9762 \| 0.0070 \| 0.9763 \| 0.8517 \| 0.8750 \| 0.9405 \| 0.5117 \| 0.7937 \| 0.4288 \| nan \| 0.9547 \| 0.0070 \| 0.8381 \| 0.7930 \| 0.7838 \| 0.8606 \| 0.4480 \| 0.6735 \| 0.3166 \| 0.0 \| \| 0.1085 \| 29.51 \| 2420 \| 0.2225 \| 0.5650 \| 0.6974 \| 0.9033 \| 0.9811 \| 0.0 \| 0.9693 \| 0.8404 \| 0.8858 \| 0.9493 \| 0.5061 \| 0.7632 \| 0.3816 \| nan \| 0.9550 \| 0.0 \| 0.8423 \| 0.7940 \| 0.7873 \| 0.8597 \| 0.4464 \| 0.6708 \| 0.2944 \| 0.0 \| \| 0.0874 \| 29.76 \| 2440 \| 0.2147 \| 0.5680 \| 0.7047 \| 0.9033 \| 0.9776 \| 0.0012 \| 0.9709 \| 0.8514 \| 0.8785 \| 0.9432 \| 0.5078 \| 0.7886 \| 0.4235 \| nan \| 0.9536 \| 0.0012 \| 0.8450 \| 0.7948 \| 0.7865 \| 0.8610 \| 0.4482 \| 0.6751 \| 0.3149 \| 0.0 \| \| 0.1308 \| 30.0 \| 2460 \| 0.2172 \| 0.5684 \| 0.7041 \| 0.9037 \| 0.9782 \| 0.0031 \| 0.9694 \| 0.8474 \| 0.8818 \| 0.9453 \| 0.5085 \| 0.7910 \| 0.4118 \| nan \| 0.9541 \| 0.0031 \| 0.8472 \| 0.7939 \| 0.7881 \| 0.8610 \| 0.4506 \| 0.6761 \| 0.3104 \| 0.0 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1	[ "water", "whitewater", "sand", "rocky_terrain", "agricultural", "vegetation", "road", "development", "other_natural_terrain", "unknown" ]
unreal-hug/segformer-b0-finetuned-segments-ECHO-dev-01-v1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-ECHO-dev-01-v1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the unreal-hug/SYNTH_DATASET_SEG dataset. It achieves the following results on the evaluation set: - Loss: 0.1147 - Mean Iou: 0.7718 - Mean Accuracy: 0.9647 - Overall Accuracy: 0.9651 - Accuracy Unlabeled: nan - Accuracy Lv: 0.9592 - Accuracy Lr: 0.9722 - Accuracy Ra: 0.9540 - Accuracy La: 0.9733 - Iou Unlabeled: 0.0 - Iou Lv: 0.9592 - Iou Lr: 0.9722 - Iou Ra: 0.9540 - Iou La: 0.9733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Lv \| Accuracy Lr \| Accuracy Ra \| Accuracy La \| Iou Unlabeled \| Iou Lv \| Iou Lr \| Iou Ra \| Iou La \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-------------:\|:------:\|:------:\|:------:\|:------:\| \| 0.9325 \| 2.5 \| 20 \| 1.3095 \| 0.4524 \| 0.7068 \| 0.7433 \| nan \| 0.9170 \| 0.8666 \| 0.2895 \| 0.7542 \| 0.0 \| 0.7498 \| 0.6467 \| 0.2538 \| 0.6116 \| \| 0.6633 \| 5.0 \| 40 \| 0.7431 \| 0.6359 \| 0.8411 \| 0.8600 \| nan \| 0.9559 \| 0.9196 \| 0.6304 \| 0.8583 \| 0.0 \| 0.8810 \| 0.8526 \| 0.6270 \| 0.8191 \| \| 0.5492 \| 7.5 \| 60 \| 0.4682 \| 0.7583 \| 0.9515 \| 0.9548 \| nan \| 0.9702 \| 0.9610 \| 0.9452 \| 0.9295 \| 0.0 \| 0.9630 \| 0.9610 \| 0.9380 \| 0.9295 \| \| 0.5418 \| 10.0 \| 80 \| 0.3925 \| 0.7453 \| 0.9323 \| 0.9367 \| nan \| 0.9520 \| 0.9515 \| 0.9050 \| 0.9206 \| 0.0 \| 0.9509 \| 0.9515 \| 0.9036 \| 0.9206 \| \| 0.3586 \| 12.5 \| 100 \| 0.3216 \| 0.7612 \| 0.9515 \| 0.9561 \| nan \| 0.9594 \| 0.9767 \| 0.9359 \| 0.9340 \| 0.0 \| 0.9594 \| 0.9767 \| 0.9359 \| 0.9340 \| \| 0.2943 \| 15.0 \| 120 \| 0.2719 \| 0.7615 \| 0.9518 \| 0.9541 \| nan \| 0.9480 \| 0.9691 \| 0.9413 \| 0.9490 \| 0.0 \| 0.9480 \| 0.9691 \| 0.9413 \| 0.9490 \| \| 0.3039 \| 17.5 \| 140 \| 0.2505 \| 0.7765 \| 0.9763 \| 0.9743 \| nan \| 0.9742 \| 0.9650 \| 0.9799 \| 0.9861 \| 0.0 \| 0.9742 \| 0.9650 \| 0.9572 \| 0.9861 \| \| 0.2639 \| 20.0 \| 160 \| 0.2208 \| 0.7743 \| 0.9679 \| 0.9690 \| nan \| 0.9676 \| 0.9784 \| 0.9477 \| 0.9778 \| 0.0 \| 0.9676 \| 0.9784 \| 0.9477 \| 0.9778 \| \| 0.2252 \| 22.5 \| 180 \| 0.1928 \| 0.7710 \| 0.9637 \| 0.9655 \| nan \| 0.9618 \| 0.9784 \| 0.9483 \| 0.9664 \| 0.0 \| 0.9618 \| 0.9784 \| 0.9483 \| 0.9664 \| \| 0.1816 \| 25.0 \| 200 \| 0.1756 \| 0.7690 \| 0.9612 \| 0.9619 \| nan \| 0.9574 \| 0.9696 \| 0.9501 \| 0.9679 \| 0.0 \| 0.9574 \| 0.9696 \| 0.9501 \| 0.9679 \| \| 0.1676 \| 27.5 \| 220 \| 0.1556 \| 0.7610 \| 0.9513 \| 0.9541 \| nan \| 0.9483 \| 0.9721 \| 0.9387 \| 0.9460 \| 0.0 \| 0.9483 \| 0.9721 \| 0.9387 \| 0.9460 \| \| 0.1833 \| 30.0 \| 240 \| 0.1468 \| 0.7786 \| 0.9733 \| 0.9742 \| nan \| 0.9669 \| 0.9837 \| 0.9639 \| 0.9786 \| 0.0 \| 0.9669 \| 0.9837 \| 0.9639 \| 0.9786 \| \| 0.1487 \| 32.5 \| 260 \| 0.1367 \| 0.7708 \| 0.9635 \| 0.9649 \| nan \| 0.9580 \| 0.9776 \| 0.9479 \| 0.9705 \| 0.0 \| 0.9580 \| 0.9776 \| 0.9479 \| 0.9705 \| \| 0.1482 \| 35.0 \| 280 \| 0.1320 \| 0.7712 \| 0.9641 \| 0.9655 \| nan \| 0.9576 \| 0.9779 \| 0.9555 \| 0.9653 \| 0.0 \| 0.9576 \| 0.9779 \| 0.9555 \| 0.9653 \| \| 0.1412 \| 37.5 \| 300 \| 0.1241 \| 0.7763 \| 0.9704 \| 0.9706 \| nan \| 0.9696 \| 0.9738 \| 0.9590 \| 0.9791 \| 0.0 \| 0.9696 \| 0.9738 \| 0.9590 \| 0.9791 \| \| 0.1282 \| 40.0 \| 320 \| 0.1213 \| 0.7724 \| 0.9655 \| 0.9665 \| nan \| 0.9588 \| 0.9778 \| 0.9512 \| 0.9742 \| 0.0 \| 0.9588 \| 0.9778 \| 0.9512 \| 0.9742 \| \| 0.133 \| 42.5 \| 340 \| 0.1155 \| 0.7745 \| 0.9681 \| 0.9686 \| nan \| 0.9640 \| 0.9752 \| 0.9580 \| 0.9751 \| 0.0 \| 0.9640 \| 0.9752 \| 0.9580 \| 0.9751 \| \| 0.1172 \| 45.0 \| 360 \| 0.1178 \| 0.7673 \| 0.9603 \| 0.9607 \| nan \| 0.9562 \| 0.9665 \| 0.9510 \| 0.9676 \| 0.0 \| 0.9562 \| 0.9665 \| 0.9460 \| 0.9676 \| \| 0.1469 \| 47.5 \| 380 \| 0.1138 \| 0.7734 \| 0.9668 \| 0.9673 \| nan \| 0.9629 \| 0.9743 \| 0.9545 \| 0.9753 \| 0.0 \| 0.9629 \| 0.9743 \| 0.9545 \| 0.9753 \| \| 0.1247 \| 50.0 \| 400 \| 0.1147 \| 0.7718 \| 0.9647 \| 0.9651 \| nan \| 0.9592 \| 0.9722 \| 0.9540 \| 0.9733 \| 0.0 \| 0.9592 \| 0.9722 \| 0.9540 \| 0.9733 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "lv", "lr", "ra", "la" ]
jinhong426/test	# Mask2Former Mask2Former model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on COCO panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-coco-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-coco-panoptic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).	[ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "bridge", "cardboard", "counter", "curtain", "door-stuff", "floor-wood", "flower", "fruit", "gravel", "house", "light", "mirror-stuff", "net", "pillow", "platform", "playingfield", "railroad", "river", "road", "roof", "sand", "sea", "shelf", "snow", "stairs", "tent", "towel", "wall-brick", "wall-stone", "wall-tile", "wall-wood", "water-other", "window-blind", "window-other", "tree-merged", "fence-merged", "ceiling-merged", "sky-other-merged", "cabinet-merged", "table-merged", "floor-other-merged", "pavement-merged", "mountain-merged", "grass-merged", "dirt-merged", "paper-merged", "food-other-merged", "building-other-merged", "rock-merged", "wall-other-merged", "rug-merged" ]
unreal-hug/segformer-b0-finetuned-segments-ECHO-dev-05-v1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-ECHO-dev-05-v1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the unreal-hug/REAL_DATASET_SEG dataset. It achieves the following results on the evaluation set: - Loss: 0.4592 - Mean Iou: 0.3826 - Mean Accuracy: 0.5892 - Overall Accuracy: 0.5467 - Accuracy Unlabeled: nan - Accuracy Lv: 0.7143 - Accuracy Lr: 0.4323 - Accuracy Ra: 0.7629 - Accuracy La: 0.4472 - Iou Unlabeled: 0.0 - Iou Lv: 0.7065 - Iou Lr: 0.4317 - Iou Ra: 0.4223 - Iou La: 0.3527 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Lv \| Accuracy Lr \| Accuracy Ra \| Accuracy La \| Iou Unlabeled \| Iou Lv \| Iou Lr \| Iou Ra \| Iou La \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-------------:\|:------:\|:------:\|:------:\|:------:\| \| 1.1252 \| 2.86 \| 20 \| 1.3259 \| 0.1971 \| 0.3379 \| 0.4375 \| nan \| 0.0 \| 0.6365 \| 0.5291 \| 0.1860 \| 0.0 \| 0.0 \| 0.4923 \| 0.3492 \| 0.1439 \| \| 0.9104 \| 5.71 \| 40 \| 0.9589 \| 0.1818 \| 0.3421 \| 0.3596 \| nan \| 0.0145 \| 0.3590 \| 0.7644 \| 0.2304 \| 0.0 \| 0.0144 \| 0.3436 \| 0.3778 \| 0.1731 \| \| 0.7567 \| 8.57 \| 60 \| 0.7761 \| 0.2203 \| 0.3739 \| 0.3852 \| nan \| 0.0808 \| 0.3882 \| 0.6422 \| 0.3844 \| 0.0 \| 0.0803 \| 0.3778 \| 0.4073 \| 0.2360 \| \| 0.7035 \| 11.43 \| 80 \| 0.7442 \| 0.2729 \| 0.4718 \| 0.4941 \| nan \| 0.2145 \| 0.5077 \| 0.8370 \| 0.3279 \| 0.0 \| 0.2134 \| 0.4817 \| 0.4073 \| 0.2619 \| \| 0.5781 \| 14.29 \| 100 \| 0.6260 \| 0.2876 \| 0.4446 \| 0.4279 \| nan \| 0.4235 \| 0.3777 \| 0.5787 \| 0.3986 \| 0.0 \| 0.3683 \| 0.3761 \| 0.4063 \| 0.2873 \| \| 0.5438 \| 17.14 \| 120 \| 0.5559 \| 0.3877 \| 0.5412 \| 0.5761 \| nan \| 0.5803 \| 0.6504 \| 0.5190 \| 0.4149 \| 0.0 \| 0.5671 \| 0.6193 \| 0.4171 \| 0.3352 \| \| 0.5198 \| 20.0 \| 140 \| 0.5617 \| 0.3724 \| 0.5617 \| 0.5335 \| nan \| 0.6661 \| 0.4532 \| 0.7059 \| 0.4216 \| 0.0 \| 0.6419 \| 0.4532 \| 0.4129 \| 0.3540 \| \| 0.4435 \| 22.86 \| 160 \| 0.5393 \| 0.4160 \| 0.6198 \| 0.6126 \| nan \| 0.7555 \| 0.5832 \| 0.6962 \| 0.4442 \| 0.0 \| 0.7000 \| 0.5705 \| 0.4873 \| 0.3221 \| \| 0.5002 \| 25.71 \| 180 \| 0.5126 \| 0.4094 \| 0.6080 \| 0.6043 \| nan \| 0.6854 \| 0.5833 \| 0.6945 \| 0.4687 \| 0.0 \| 0.6771 \| 0.5761 \| 0.4762 \| 0.3176 \| \| 0.4142 \| 28.57 \| 200 \| 0.4874 \| 0.3503 \| 0.5361 \| 0.4949 \| nan \| 0.6967 \| 0.3895 \| 0.6436 \| 0.4147 \| 0.0 \| 0.6287 \| 0.3895 \| 0.4106 \| 0.3228 \| \| 0.3092 \| 31.43 \| 220 \| 0.4819 \| 0.3857 \| 0.6001 \| 0.5534 \| nan \| 0.7296 \| 0.4267 \| 0.8020 \| 0.4423 \| 0.0 \| 0.7157 \| 0.4267 \| 0.4266 \| 0.3595 \| \| 0.2895 \| 34.29 \| 240 \| 0.4969 \| 0.3983 \| 0.6220 \| 0.5809 \| nan \| 0.7353 \| 0.4689 \| 0.8050 \| 0.4787 \| 0.0 \| 0.7265 \| 0.4677 \| 0.4474 \| 0.3498 \| \| 0.3046 \| 37.14 \| 260 \| 0.4767 \| 0.4248 \| 0.6412 \| 0.6115 \| nan \| 0.7853 \| 0.5270 \| 0.7814 \| 0.4711 \| 0.0 \| 0.7712 \| 0.5199 \| 0.4587 \| 0.3742 \| \| 0.3514 \| 40.0 \| 280 \| 0.4531 \| 0.3978 \| 0.5989 \| 0.5767 \| nan \| 0.7112 \| 0.5082 \| 0.7478 \| 0.4282 \| 0.0 \| 0.6979 \| 0.5024 \| 0.4353 \| 0.3537 \| \| 0.2891 \| 42.86 \| 300 \| 0.4629 \| 0.3842 \| 0.5885 \| 0.5488 \| nan \| 0.7046 \| 0.4397 \| 0.7693 \| 0.4403 \| 0.0 \| 0.6982 \| 0.4366 \| 0.4237 \| 0.3623 \| \| 0.2512 \| 45.71 \| 320 \| 0.4584 \| 0.3783 \| 0.5794 \| 0.5357 \| nan \| 0.7144 \| 0.4199 \| 0.7390 \| 0.4443 \| 0.0 \| 0.7016 \| 0.4196 \| 0.4134 \| 0.3568 \| \| 0.2695 \| 48.57 \| 340 \| 0.4592 \| 0.3826 \| 0.5892 \| 0.5467 \| nan \| 0.7143 \| 0.4323 \| 0.7629 \| 0.4472 \| 0.0 \| 0.7065 \| 0.4317 \| 0.4223 \| 0.3527 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "lv", "lr", "ra", "la" ]
yolo12138/segformer-b2-human-parse-24	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b2-human-parse-24 This model is a fine-tuned version of [mattmdjaga/segformer_b2_clothes](https://huggingface.co/mattmdjaga/segformer_b2_clothes) on the human_parsing_29_mix dataset. It achieves the following results on the evaluation set: - Loss: 0.0818 - Mean Iou: 0.6023 - Mean Accuracy: 0.6321 - Overall Accuracy: 0.9780 - Accuracy Background: 0.9969 - Accuracy Hat: nan - Accuracy Hair: 0.9646 - Accuracy Glove: 0.0 - Accuracy Glasses: 0.0 - Accuracy Upper Only Torso Region: 0.9747 - Accuracy Dresses Only Torso Region: 0.4939 - Accuracy Coat Only Torso Region: 0.0039 - Accuracy Socks: 0.0 - Accuracy Left Pants: 0.9604 - Accuracy Right Patns: 0.9646 - Accuracy Skin Around Neck Region: 0.9585 - Accuracy Scarf: nan - Accuracy Skirts: 0.8904 - Accuracy Face: 0.9796 - Accuracy Left Arm: 0.9703 - Accuracy Right Arm: 0.9700 - Accuracy Left Leg: 0.9267 - Accuracy Right Leg: 0.9297 - Accuracy Left Shoe: 0.0 - Accuracy Right Shoe: 0.0 - Accuracy Left Sleeve For Upper: 0.9462 - Accuracy Right Sleeve For Upper: 0.9517 - Accuracy Bag: 0.0234 - Iou Background: 0.9941 - Iou Hat: nan - Iou Hair: 0.9268 - Iou Glove: 0.0 - Iou Glasses: 0.0 - Iou Upper Only Torso Region: 0.9351 - Iou Dresses Only Torso Region: 0.4059 - Iou Coat Only Torso Region: 0.0035 - Iou Socks: 0.0 - Iou Left Pants: 0.9232 - Iou Right Patns: 0.9217 - Iou Skin Around Neck Region: 0.9227 - Iou Scarf: nan - Iou Skirts: 0.7887 - Iou Face: 0.9582 - Iou Left Arm: 0.9436 - Iou Right Arm: 0.9426 - Iou Left Leg: 0.8836 - Iou Right Leg: 0.8767 - Iou Left Shoe: 0.0 - Iou Right Shoe: 0.0 - Iou Left Sleeve For Upper: 0.9005 - Iou Right Sleeve For Upper: 0.9012 - Iou Bag: 0.0232 ## Model description More information needed ``` "id2label": { "0": "background", "1": "hat", "2": "hair", "3": "glove", "4": "glasses", "5": "upper_only_torso_region", "6": "dresses_only_torso_region", "7": "coat_only_torso_region", "8": "socks", "9": "left_pants", "10": "right_patns", "11": "skin_around_neck_region", "12": "scarf", "13": "skirts", "14": "face", "15": "left_arm", "16": "right_arm", "17": "left_leg", "18": "right_leg", "19": "left_shoe", "20": "right_shoe", "21": "left_sleeve_for_upper", "22": "right_sleeve_for_upper", "23": "bag" } ``` ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Hat \| Accuracy Hair \| Accuracy Glove \| Accuracy Glasses \| Accuracy Upper Only Torso Region \| Accuracy Dresses Only Torso Region \| Accuracy Coat Only Torso Region \| Accuracy Socks \| Accuracy Left Pants \| Accuracy Right Patns \| Accuracy Skin Around Neck Region \| Accuracy Scarf \| Accuracy Skirts \| Accuracy Face \| Accuracy Left Arm \| Accuracy Right Arm \| Accuracy Left Leg \| Accuracy Right Leg \| Accuracy Left Shoe \| Accuracy Right Shoe \| Accuracy Left Sleeve For Upper \| Accuracy Right Sleeve For Upper \| Accuracy Bag \| Iou Background \| Iou Hat \| Iou Hair \| Iou Glove \| Iou Glasses \| Iou Upper Only Torso Region \| Iou Dresses Only Torso Region \| Iou Coat Only Torso Region \| Iou Socks \| Iou Left Pants \| Iou Right Patns \| Iou Skin Around Neck Region \| Iou Scarf \| Iou Skirts \| Iou Face \| Iou Left Arm \| Iou Right Arm \| Iou Left Leg \| Iou Right Leg \| Iou Left Shoe \| Iou Right Shoe \| Iou Left Sleeve For Upper \| Iou Right Sleeve For Upper \| Iou Bag \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:------------:\|:-------------:\|:--------------:\|:----------------:\|:--------------------------------:\|:----------------------------------:\|:-------------------------------:\|:--------------:\|:-------------------:\|:--------------------:\|:--------------------------------:\|:--------------:\|:---------------:\|:-------------:\|:-----------------:\|:------------------:\|:-----------------:\|:------------------:\|:------------------:\|:-------------------:\|:------------------------------:\|:-------------------------------:\|:------------:\|:--------------:\|:-------:\|:--------:\|:---------:\|:-----------:\|:---------------------------:\|:-----------------------------:\|:--------------------------:\|:---------:\|:--------------:\|:---------------:\|:---------------------------:\|:---------:\|:----------:\|:--------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:-------------:\|:--------------:\|:-------------------------:\|:--------------------------:\|:-------:\| \| 0.0652 \| 1.62 \| 1000 \| 0.0802 \| 0.5857 \| 0.6166 \| 0.9737 \| 0.9963 \| nan \| 0.9490 \| 0.0 \| 0.0 \| 0.9801 \| 0.4034 \| 0.0 \| 0.0 \| 0.9487 \| 0.9574 \| 0.9272 \| nan \| 0.8783 \| 0.9782 \| 0.9628 \| 0.9534 \| 0.8874 \| 0.9012 \| 0.0 \| 0.0 \| 0.9227 \| 0.9197 \| 0.0 \| 0.9926 \| nan \| 0.9117 \| 0.0 \| 0.0 \| 0.9217 \| 0.3541 \| 0.0 \| 0.0 \| 0.9084 \| 0.9073 \| 0.8963 \| nan \| 0.7766 \| 0.9455 \| 0.9210 \| 0.9191 \| 0.8405 \| 0.8496 \| 0.0 \| 0.0 \| 0.8673 \| 0.8728 \| 0.0 \| \| 0.061 \| 3.23 \| 2000 \| 0.0843 \| 0.5977 \| 0.6335 \| 0.9747 \| 0.9967 \| nan \| 0.9580 \| 0.0 \| 0.0 \| 0.9657 \| 0.5733 \| 0.1504 \| 0.0 \| 0.9591 \| 0.9600 \| 0.9497 \| nan \| 0.8169 \| 0.9789 \| 0.9667 \| 0.9645 \| 0.8906 \| 0.9165 \| 0.0 \| 0.0 \| 0.9444 \| 0.9445 \| 0.0003 \| 0.9935 \| nan \| 0.9199 \| 0.0 \| 0.0 \| 0.9273 \| 0.4058 \| 0.1206 \| 0.0 \| 0.9131 \| 0.9082 \| 0.9128 \| nan \| 0.7330 \| 0.9527 \| 0.9355 \| 0.9343 \| 0.8534 \| 0.8651 \| 0.0 \| 0.0 \| 0.8860 \| 0.8879 \| 0.0003 \| \| 0.0653 \| 4.85 \| 3000 \| 0.0823 \| 0.6000 \| 0.6295 \| 0.9775 \| 0.9967 \| nan \| 0.9621 \| 0.0 \| 0.0 \| 0.9780 \| 0.4991 \| 0.0044 \| 0.0 \| 0.9587 \| 0.9649 \| 0.9562 \| nan \| 0.8842 \| 0.9769 \| 0.9692 \| 0.9651 \| 0.9198 \| 0.9273 \| 0.0 \| 0.0 \| 0.9422 \| 0.9415 \| 0.0037 \| 0.9939 \| nan \| 0.9247 \| 0.0 \| 0.0 \| 0.9341 \| 0.4136 \| 0.0042 \| 0.0 \| 0.9202 \| 0.9193 \| 0.9193 \| nan \| 0.7899 \| 0.9563 \| 0.9403 \| 0.9388 \| 0.8745 \| 0.8741 \| 0.0 \| 0.0 \| 0.8963 \| 0.8970 \| 0.0037 \| \| 0.0402 \| 6.46 \| 4000 \| 0.0818 \| 0.6023 \| 0.6321 \| 0.9780 \| 0.9969 \| nan \| 0.9646 \| 0.0 \| 0.0 \| 0.9747 \| 0.4939 \| 0.0039 \| 0.0 \| 0.9604 \| 0.9646 \| 0.9585 \| nan \| 0.8904 \| 0.9796 \| 0.9703 \| 0.9700 \| 0.9267 \| 0.9297 \| 0.0 \| 0.0 \| 0.9462 \| 0.9517 \| 0.0234 \| 0.9941 \| nan \| 0.9268 \| 0.0 \| 0.0 \| 0.9351 \| 0.4059 \| 0.0035 \| 0.0 \| 0.9232 \| 0.9217 \| 0.9227 \| nan \| 0.7887 \| 0.9582 \| 0.9436 \| 0.9426 \| 0.8836 \| 0.8767 \| 0.0 \| 0.0 \| 0.9005 \| 0.9012 \| 0.0232 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "background", "hat", "hair", "glove", "glasses", "upper_only_torso_region", "dresses_only_torso_region", "coat_only_torso_region", "socks", "left_pants", "right_patns", "skin_around_neck_region", "scarf", "skirts", "face", "left_arm", "right_arm", "left_leg", "right_leg", "left_shoe", "right_shoe", "left_sleeve_for_upper", "right_sleeve_for_upper", "bag" ]
sam1120/safety-utcustom-terrain	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-terrain-final This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-terrain-jackal-full-391 dataset. It achieves the following results on the evaluation set: - Loss: 0.0892 - Mean Iou: 0.7759 - Mean Accuracy: 0.8195 - Overall Accuracy: 0.9811 - Accuracy Unlabeled: nan - Accuracy Nat: 0.9942 - Accuracy Concrete: 0.9592 - Accuracy Grass: 0.9021 - Accuracy Speedway bricks: 0.9902 - Accuracy Steel: 0.9315 - Accuracy Rough concrete: 0.6330 - Accuracy Dark bricks: 0.8554 - Accuracy Road: 0.9821 - Accuracy Rough red sidewalk: 0.7662 - Accuracy Tiles: 0.2392 - Accuracy Red bricks: 0.9438 - Accuracy Concrete tiles: 0.9536 - Accuracy Rest: 0.5024 - Iou Unlabeled: nan - Iou Nat: 0.9848 - Iou Concrete: 0.8976 - Iou Grass: 0.8486 - Iou Speedway bricks: 0.9809 - Iou Steel: 0.8822 - Iou Rough concrete: 0.6278 - Iou Dark bricks: 0.8418 - Iou Road: 0.9618 - Iou Rough red sidewalk: 0.6046 - Iou Tiles: 0.2381 - Iou Red bricks: 0.9093 - Iou Concrete tiles: 0.8939 - Iou Rest: 0.4153 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 500 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Nat \| Accuracy Rest \| Accuracy Concrete \| Accuracy Concrete tiles \| Accuracy Dark bricks \| Accuracy Grass \| Accuracy Red bricks \| Accuracy Road \| Accuracy Rough concrete \| Accuracy Rough red sidewalk \| Accuracy Speedway bricks \| Accuracy Steel \| Accuracy Tiles \| Accuracy Unlabeled \| Iou Nat \| Iou Rest \| Iou Concrete \| Iou Concrete tiles \| Iou Dark bricks \| Iou Grass \| Iou Red bricks \| Iou Road \| Iou Rough concrete \| Iou Rough red sidewalk \| Iou Speedway bricks \| Iou Steel \| Iou Tiles \| Iou Unlabeled \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:------------:\|:-------------:\|:-----------------:\|:-----------------------:\|:--------------------:\|:--------------:\|:-------------------:\|:-------------:\|:-----------------------:\|:---------------------------:\|:------------------------:\|:--------------:\|:--------------:\|:------------------:\|:-------:\|:--------:\|:------------:\|:------------------:\|:---------------:\|:---------:\|:--------------:\|:--------:\|:------------------:\|:----------------------:\|:-------------------:\|:---------:\|:---------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 2.51 \| 1.18 \| 20 \| 0.0902 \| 0.1772 \| 0.2184 \| 0.1090 \| 0.0127 \| 0.0271 \| 0.0004 \| 0.0071 \| 0.0027 \| 0.0 \| 0.6742 \| 0.0008 \| 0.2799 \| nan \| 0.0901 \| 0.0000 \| 0.0797 \| 0.0012 \| 0.0031 \| 0.0192 \| 0.0000 \| 0.0068 \| 0.0001 \| 0.0 \| 0.4633 \| 0.0007 \| 0.0032 \| 0.0 \| 2.5071 \| 0.1230 \| 0.0477 \| 0.2016 \| \| 1.5504 \| 2.35 \| 40 \| 0.7602 \| 0.0 \| 0.3387 \| 0.0079 \| 0.0014 \| 0.0745 \| 0.0 \| 0.3332 \| 0.0010 \| 0.0 \| 0.9880 \| 0.0 \| 0.0088 \| nan \| 0.7577 \| 0.0 \| 0.2038 \| 0.0003 \| 0.0010 \| 0.0682 \| 0.0 \| 0.3127 \| 0.0002 \| 0.0 \| 0.5711 \| 0.0 \| 0.0008 \| 0.0 \| 1.8408 \| 0.1934 \| 0.1368 \| 0.6936 \| \| 0.6271 \| 3.53 \| 60 \| 0.9551 \| 0.0 \| 0.8127 \| 0.0 \| 0.0 \| 0.2590 \| 0.0 \| 0.8979 \| 0.0 \| 0.0 \| 0.9793 \| 0.0 \| 0.0000 \| nan \| 0.9405 \| 0.0 \| 0.5259 \| 0.0 \| 0.0 \| 0.2392 \| 0.0 \| 0.6993 \| 0.0 \| 0.0 \| 0.8263 \| 0.0 \| 0.0000 \| nan \| 0.8083 \| 0.3003 \| 0.2486 \| 0.8894 \| \| 0.3963 \| 4.71 \| 80 \| 0.9698 \| 0.0 \| 0.7973 \| 0.0443 \| 0.0 \| 0.7826 \| 0.0 \| 0.9651 \| 0.0 \| 0.0 \| 0.9777 \| 0.0004 \| 0.0 \| nan \| 0.9532 \| 0.0 \| 0.6901 \| 0.0443 \| 0.0 \| 0.5632 \| 0.0 \| 0.7280 \| 0.0 \| 0.0 \| 0.8641 \| 0.0004 \| 0.0 \| nan \| 0.4204 \| 0.3490 \| 0.2956 \| 0.9186 \| \| 0.3558 \| 5.88 \| 100 \| 0.9806 \| 0.0 \| 0.9025 \| 0.3050 \| 0.0 \| 0.7816 \| 0.0 \| 0.9135 \| 0.0031 \| 0.0 \| 0.9632 \| 0.2874 \| 0.0 \| nan \| 0.9618 \| 0.0 \| 0.6868 \| 0.3027 \| 0.0 \| 0.6345 \| 0.0 \| 0.8305 \| 0.0031 \| 0.0 \| 0.8915 \| 0.2804 \| 0.0 \| nan \| 0.3091 \| 0.3952 \| 0.3532 \| 0.9322 \| \| 0.2654 \| 7.06 \| 120 \| 0.9793 \| 0.0 \| 0.8161 \| 0.3875 \| 0.0 \| 0.8487 \| 0.0 \| 0.9769 \| 0.0002 \| 0.0009 \| 0.9862 \| 0.4149 \| 0.0 \| nan \| 0.9636 \| 0.0 \| 0.7301 \| 0.3046 \| 0.0 \| 0.6491 \| 0.0 \| 0.7846 \| 0.0002 \| 0.0009 \| 0.9014 \| 0.4015 \| 0.0 \| nan \| 0.2554 \| 0.4162 \| 0.3643 \| 0.9363 \| \| 0.1938 \| 8.24 \| 140 \| 0.9830 \| 0.0 \| 0.9010 \| 0.6649 \| 0.0 \| 0.8719 \| 0.0 \| 0.9517 \| 0.1663 \| 0.0374 \| 0.9779 \| 0.6445 \| 0.0 \| nan \| 0.9681 \| 0.0 \| 0.7740 \| 0.1887 \| 0.0 \| 0.6642 \| 0.0 \| 0.8746 \| 0.1660 \| 0.0374 \| 0.9354 \| 0.5996 \| 0.0 \| nan \| 0.2059 \| 0.4768 \| 0.4006 \| 0.9478 \| \| 0.202 \| 9.41 \| 160 \| 0.9873 \| 0.0 \| 0.8667 \| 0.7499 \| 0.0282 \| 0.8553 \| 0.0 \| 0.9539 \| 0.5565 \| 0.1631 \| 0.9878 \| 0.6794 \| 0.0172 \| nan \| 0.9705 \| 0.0 \| 0.7775 \| 0.2007 \| 0.0282 \| 0.7107 \| 0.0 \| 0.9073 \| 0.4995 \| 0.1493 \| 0.9308 \| 0.6268 \| 0.0166 \| nan \| 0.1785 \| 0.5266 \| 0.4475 \| 0.9524 \| \| 0.174 \| 10.59 \| 180 \| 0.9880 \| 0.0 \| 0.9067 \| 0.7555 \| 0.0684 \| 0.8559 \| 0.0881 \| 0.9470 \| 0.5194 \| 0.4768 \| 0.9831 \| 0.7462 \| 0.0 \| nan \| 0.9711 \| 0.0 \| 0.7934 \| 0.2110 \| 0.0680 \| 0.7406 \| 0.0881 \| 0.9141 \| 0.5091 \| 0.3846 \| 0.9482 \| 0.6883 \| 0.0 \| nan \| 0.1571 \| 0.5642 \| 0.4859 \| 0.9572 \| \| 0.1317 \| 11.76 \| 200 \| 0.9865 \| 0.0 \| 0.9250 \| 0.7600 \| 0.7836 \| 0.8949 \| 0.1046 \| 0.9492 \| 0.5791 \| 0.5757 \| 0.9715 \| 0.7851 \| 0.0 \| nan \| 0.9720 \| 0.0 \| 0.8087 \| 0.6147 \| 0.7372 \| 0.7440 \| 0.1044 \| 0.9256 \| 0.5756 \| 0.4397 \| 0.9522 \| 0.7135 \| 0.0 \| nan \| 0.1391 \| 0.6396 \| 0.5837 \| 0.9632 \| \| 0.0915 \| 12.94 \| 220 \| 0.9860 \| 0.0 \| 0.9262 \| 0.7639 \| 0.7600 \| 0.9003 \| 0.5878 \| 0.9337 \| 0.6388 \| 0.5331 \| 0.9805 \| 0.7937 \| 0.0115 \| nan \| 0.9733 \| 0.0 \| 0.8116 \| 0.6127 \| 0.7370 \| 0.7638 \| 0.5787 \| 0.9065 \| 0.5673 \| 0.4644 \| 0.9561 \| 0.7260 \| 0.0114 \| nan \| 0.1252 \| 0.6781 \| 0.6238 \| 0.9644 \| \| 0.1027 \| 14.12 \| 240 \| 0.9917 \| 0.0 \| 0.9190 \| 0.7523 \| 0.7272 \| 0.8350 \| 0.6341 \| 0.9736 \| 0.6123 \| 0.5560 \| 0.9814 \| 0.7842 \| 0.0590 \| nan \| 0.9741 \| 0.0 \| 0.8297 \| 0.6982 \| 0.7163 \| 0.7690 \| 0.6251 \| 0.9232 \| 0.5949 \| 0.4768 \| 0.9583 \| 0.7348 \| 0.0588 \| nan \| 0.1175 \| 0.6789 \| 0.6430 \| 0.9672 \| \| 0.1055 \| 15.29 \| 260 \| 0.9881 \| 0.0 \| 0.9170 \| 0.6841 \| 0.7566 \| 0.8499 \| 0.6607 \| 0.9807 \| 0.6126 \| 0.6318 \| 0.9881 \| 0.7435 \| 0.0304 \| nan \| 0.9746 \| 0.0 \| 0.8274 \| 0.6670 \| 0.7390 \| 0.7777 \| 0.6508 \| 0.9133 \| 0.5831 \| 0.5124 \| 0.9579 \| 0.7130 \| 0.0301 \| nan \| 0.1139 \| 0.6803 \| 0.6420 \| 0.9669 \| \| 0.089 \| 16.47 \| 280 \| 0.9883 \| 0.0 \| 0.9266 \| 0.7338 \| 0.7679 \| 0.8684 \| 0.7259 \| 0.9741 \| 0.6241 \| 0.7053 \| 0.9872 \| 0.7572 \| 0.0675 \| nan \| 0.9757 \| 0.0 \| 0.8339 \| 0.6869 \| 0.7546 \| 0.7875 \| 0.7145 \| 0.9367 \| 0.5637 \| 0.5270 \| 0.9598 \| 0.7243 \| 0.0665 \| nan \| 0.1049 \| 0.7020 \| 0.6562 \| 0.9687 \| \| 0.0982 \| 17.65 \| 300 \| 0.9932 \| 0.0 \| 0.9315 \| 0.7460 \| 0.8016 \| 0.8161 \| 0.8457 \| 0.9702 \| 0.6211 \| 0.6153 \| 0.9799 \| 0.8352 \| 0.0917 \| nan \| 0.9753 \| 0.0 \| 0.8406 \| 0.7205 \| 0.7828 \| 0.7713 \| 0.8009 \| 0.9369 \| 0.5512 \| 0.5013 \| 0.9641 \| 0.7745 \| 0.0881 \| nan \| 0.1024 \| 0.7113 \| 0.6698 \| 0.9696 \| \| 0.0787 \| 18.82 \| 320 \| 0.9902 \| 0.0 \| 0.9380 \| 0.7392 \| 0.8253 \| 0.8625 \| 0.8736 \| 0.9765 \| 0.6109 \| 0.7008 \| 0.9773 \| 0.8889 \| 0.1198 \| nan \| 0.9772 \| 0.0 \| 0.8491 \| 0.6967 \| 0.8024 \| 0.7964 \| 0.8242 \| 0.9342 \| 0.5884 \| 0.5514 \| 0.9625 \| 0.7733 \| 0.1164 \| nan \| 0.1005 \| 0.7310 \| 0.6825 \| 0.9711 \| \| 0.0659 \| 20.0 \| 340 \| 0.9924 \| 0.0 \| 0.9213 \| 0.8308 \| 0.8275 \| 0.8675 \| 0.8678 \| 0.9701 \| 0.6168 \| 0.7045 \| 0.9838 \| 0.8907 \| 0.1436 \| nan \| 0.9771 \| 0.0 \| 0.8576 \| 0.7540 \| 0.8017 \| 0.8037 \| 0.8045 \| 0.9477 \| 0.5888 \| 0.5288 \| 0.9634 \| 0.7818 \| 0.1388 \| nan \| 0.0948 \| 0.7398 \| 0.6883 \| 0.9722 \| \| 0.0615 \| 21.18 \| 360 \| 0.9914 \| 0.0 \| 0.9523 \| 0.7668 \| 0.8173 \| 0.8567 \| 0.7849 \| 0.9601 \| 0.6088 \| 0.6383 \| 0.9756 \| 0.8970 \| 0.0394 \| nan \| 0.9766 \| 0.0 \| 0.8440 \| 0.7240 \| 0.8007 \| 0.7904 \| 0.7613 \| 0.9455 \| 0.6013 \| 0.5302 \| 0.9648 \| 0.7870 \| 0.0386 \| nan \| 0.0998 \| 0.7145 \| 0.6742 \| 0.9711 \| \| 0.0671 \| 22.35 \| 380 \| 0.9918 \| 0.0 \| 0.9463 \| 0.7548 \| 0.8054 \| 0.8489 \| 0.8876 \| 0.9673 \| 0.6129 \| 0.7477 \| 0.9791 \| 0.8704 \| 0.1060 \| nan \| 0.9774 \| 0.0 \| 0.8497 \| 0.7045 \| 0.7850 \| 0.7915 \| 0.8271 \| 0.9405 \| 0.5940 \| 0.5749 \| 0.9671 \| 0.7935 \| 0.1040 \| nan \| 0.0957 \| 0.7322 \| 0.6853 \| 0.9719 \| \| 0.0917 \| 23.53 \| 400 \| 0.9909 \| 0.0 \| 0.9336 \| 0.7850 \| 0.8096 \| 0.8687 \| 0.8738 \| 0.9780 \| 0.6158 \| 0.6849 \| 0.9868 \| 0.8362 \| 0.1046 \| nan \| 0.9777 \| 0.0 \| 0.8545 \| 0.7320 \| 0.7838 \| 0.8004 \| 0.8380 \| 0.9417 \| 0.5996 \| 0.5411 \| 0.9665 \| 0.7895 \| 0.1043 \| nan \| 0.0935 \| 0.7283 \| 0.6869 \| 0.9725 \| \| 0.0649 \| 24.71 \| 420 \| 0.9925 \| 0.0 \| 0.9283 \| 0.8514 \| 0.8465 \| 0.8542 \| 0.9041 \| 0.9788 \| 0.6238 \| 0.6471 \| 0.9883 \| 0.8563 \| 0.1356 \| nan \| 0.9783 \| 0.0 \| 0.8609 \| 0.7683 \| 0.8168 \| 0.8034 \| 0.8423 \| 0.9410 \| 0.6038 \| 0.5446 \| 0.9664 \| 0.7955 \| 0.1335 \| nan \| 0.0880 \| 0.7390 \| 0.6965 \| 0.9733 \| \| 0.0648 \| 25.88 \| 440 \| 0.9920 \| 0.0 \| 0.9411 \| 0.8344 \| 0.8282 \| 0.8705 \| 0.8488 \| 0.9759 \| 0.6163 \| 0.7026 \| 0.9870 \| 0.8342 \| 0.1031 \| nan \| 0.9789 \| 0.0 \| 0.8594 \| 0.7905 \| 0.8074 \| 0.8149 \| 0.8212 \| 0.9523 \| 0.5988 \| 0.5236 \| 0.9685 \| 0.7926 \| 0.1028 \| nan \| 0.0886 \| 0.7334 \| 0.6931 \| 0.9739 \| \| 0.0633 \| 27.06 \| 460 \| 0.9913 \| 0.0 \| 0.9298 \| 0.8931 \| 0.8600 \| 0.8680 \| 0.9230 \| 0.9770 \| 0.6187 \| 0.7837 \| 0.9905 \| 0.8313 \| 0.1321 \| nan \| 0.9785 \| 0.0 \| 0.8658 \| 0.7637 \| 0.8113 \| 0.8027 \| 0.8376 \| 0.9457 \| 0.6000 \| 0.6097 \| 0.9674 \| 0.7922 \| 0.1316 \| nan \| 0.0885 \| 0.7537 \| 0.7005 \| 0.9739 \| \| 0.0504 \| 28.24 \| 480 \| 0.9911 \| 0.0 \| 0.9332 \| 0.8848 \| 0.8469 \| 0.8925 \| 0.9045 \| 0.9779 \| 0.6205 \| 0.7196 \| 0.9882 \| 0.8626 \| 0.1751 \| nan \| 0.9790 \| 0.0 \| 0.8676 \| 0.7879 \| 0.8161 \| 0.8156 \| 0.8464 \| 0.9503 \| 0.5961 \| 0.5367 \| 0.9697 \| 0.8085 \| 0.1739 \| nan \| 0.0862 \| 0.7536 \| 0.7037 \| 0.9746 \| \| 0.0603 \| 29.41 \| 500 \| 0.9932 \| 0.0 \| 0.9429 \| 0.8191 \| 0.8274 \| 0.8523 \| 0.8900 \| 0.9719 \| 0.6164 \| 0.7170 \| 0.9858 \| 0.8697 \| 0.1381 \| nan \| 0.9783 \| 0.0 \| 0.8635 \| 0.7952 \| 0.8105 \| 0.8027 \| 0.8526 \| 0.9460 \| 0.6043 \| 0.5907 \| 0.9711 \| 0.8125 \| 0.1370 \| nan \| 0.0885 \| 0.7403 \| 0.7050 \| 0.9743 \| \| 0.0447 \| 30.59 \| 520 \| 0.9902 \| 0.0 \| 0.9484 \| 0.8591 \| 0.8759 \| 0.8866 \| 0.9197 \| 0.9788 \| 0.6183 \| 0.7369 \| 0.9854 \| 0.8896 \| 0.2666 \| nan \| 0.9795 \| 0.0 \| 0.8699 \| 0.8193 \| 0.8352 \| 0.8187 \| 0.8540 \| 0.9470 \| 0.6008 \| 0.5913 \| 0.9714 \| 0.8157 \| 0.2453 \| nan \| 0.0824 \| 0.7658 \| 0.7191 \| 0.9753 \| \| 0.0517 \| 31.76 \| 540 \| 0.9910 \| 0.0 \| 0.9372 \| 0.8937 \| 0.8920 \| 0.8815 \| 0.9331 \| 0.9784 \| 0.6245 \| 0.8062 \| 0.9904 \| 0.8431 \| 0.3040 \| nan \| 0.9799 \| 0.0 \| 0.8732 \| 0.8217 \| 0.8341 \| 0.8191 \| 0.8354 \| 0.9495 \| 0.6024 \| 0.5985 \| 0.9702 \| 0.8045 \| 0.2819 \| nan \| 0.0807 \| 0.7750 \| 0.7208 \| 0.9755 \| \| 0.0512 \| 32.94 \| 560 \| 0.9934 \| 0.0 \| 0.9411 \| 0.8627 \| 0.7835 \| 0.8619 \| 0.8360 \| 0.9789 \| 0.6166 \| 0.7804 \| 0.9853 \| 0.8938 \| 0.1282 \| nan \| 0.9793 \| 0.0 \| 0.8740 \| 0.8110 \| 0.7669 \| 0.8082 \| 0.8223 \| 0.9476 \| 0.6077 \| 0.6209 \| 0.9704 \| 0.8202 \| 0.1262 \| nan \| 0.0856 \| 0.7432 \| 0.7042 \| 0.9752 \| \| 0.0474 \| 34.12 \| 580 \| 0.9925 \| 0.0 \| 0.9511 \| 0.8677 \| 0.8661 \| 0.8697 \| 0.8806 \| 0.9743 \| 0.6254 \| 0.7290 \| 0.9838 \| 0.8987 \| 0.2127 \| nan \| 0.9795 \| 0.0 \| 0.8724 \| 0.8249 \| 0.8305 \| 0.8163 \| 0.8461 \| 0.9532 \| 0.5900 \| 0.6061 \| 0.9724 \| 0.8224 \| 0.2101 \| nan \| 0.0796 \| 0.7578 \| 0.7172 \| 0.9758 \| \| 0.0471 \| 35.29 \| 600 \| 0.9922 \| 0.0 \| 0.9352 \| 0.9168 \| 0.8880 \| 0.8596 \| 0.9220 \| 0.9797 \| 0.6205 \| 0.8090 \| 0.9876 \| 0.8911 \| 0.4564 \| nan \| 0.9789 \| 0.0 \| 0.8785 \| 0.8174 \| 0.8203 \| 0.8034 \| 0.8567 \| 0.9530 \| 0.6048 \| 0.5828 \| 0.9728 \| 0.8208 \| 0.4101 \| nan \| 0.0815 \| 0.7891 \| 0.7307 \| 0.9757 \| \| 0.0522 \| 36.47 \| 620 \| 0.9943 \| 0.0 \| 0.9353 \| 0.8650 \| 0.8185 \| 0.8543 \| 0.8783 \| 0.9783 \| 0.6238 \| 0.7479 \| 0.9850 \| 0.9008 \| 0.1075 \| nan \| 0.9786 \| 0.0 \| 0.8728 \| 0.8187 \| 0.7978 \| 0.8036 \| 0.8519 \| 0.9475 \| 0.6092 \| 0.6031 \| 0.9727 \| 0.8254 \| 0.1075 \| nan \| 0.0846 \| 0.7453 \| 0.7068 \| 0.9752 \| \| 0.043 \| 37.65 \| 640 \| 0.9938 \| 0.0 \| 0.9391 \| 0.9101 \| 0.8167 \| 0.8672 \| 0.9004 \| 0.9800 \| 0.6179 \| 0.7609 \| 0.9853 \| 0.9130 \| 0.1707 \| nan \| 0.9796 \| 0.0 \| 0.8763 \| 0.8325 \| 0.7965 \| 0.8145 \| 0.8582 \| 0.9520 \| 0.6101 \| 0.6211 \| 0.9728 \| 0.8251 \| 0.1693 \| nan \| 0.0813 \| 0.7581 \| 0.7160 \| 0.9761 \| \| 0.0429 \| 38.82 \| 660 \| 0.9931 \| 0.0 \| 0.9416 \| 0.8665 \| 0.8163 \| 0.8894 \| 0.9095 \| 0.9792 \| 0.6195 \| 0.7703 \| 0.9876 \| 0.8956 \| 0.1931 \| nan \| 0.9804 \| 0.0 \| 0.8790 \| 0.8424 \| 0.7970 \| 0.8264 \| 0.8589 \| 0.9544 \| 0.6086 \| 0.6180 \| 0.9729 \| 0.8282 \| 0.1904 \| nan \| 0.0792 \| 0.7586 \| 0.7197 \| 0.9768 \| \| 0.0506 \| 40.0 \| 680 \| 0.9916 \| 0.0 \| 0.9474 \| 0.8906 \| 0.8488 \| 0.8903 \| 0.8936 \| 0.9796 \| 0.6164 \| 0.7507 \| 0.9894 \| 0.8788 \| 0.2110 \| nan \| 0.9807 \| 0.0 \| 0.8770 \| 0.8471 \| 0.8198 \| 0.8269 \| 0.8523 \| 0.9516 \| 0.6061 \| 0.6140 \| 0.9726 \| 0.8269 \| 0.2069 \| nan \| 0.0794 \| 0.7606 \| 0.7217 \| 0.9767 \| \| 0.0426 \| 41.18 \| 700 \| 0.9921 \| 0.0 \| 0.9513 \| 0.9062 \| 0.8437 \| 0.8701 \| 0.9256 \| 0.9788 \| 0.6143 \| 0.7620 \| 0.9887 \| 0.8859 \| 0.2141 \| nan \| 0.9804 \| 0.0 \| 0.8763 \| 0.8417 \| 0.8206 \| 0.8174 \| 0.8579 \| 0.9524 \| 0.6066 \| 0.6145 \| 0.9741 \| 0.8318 \| 0.2104 \| nan \| 0.0813 \| 0.7641 \| 0.7219 \| 0.9766 \| \| 0.043 \| 42.35 \| 720 \| 0.9924 \| 0.0 \| 0.9485 \| 0.8467 \| 0.8488 \| 0.8918 \| 0.9142 \| 0.9799 \| 0.6665 \| 0.7487 \| 0.9883 \| 0.8804 \| 0.1911 \| nan \| 0.9810 \| 0.0 \| 0.8794 \| 0.8212 \| 0.8281 \| 0.8290 \| 0.8633 \| 0.9536 \| 0.6521 \| 0.6152 \| 0.9744 \| 0.8307 \| 0.1892 \| nan \| 0.0758 \| 0.7613 \| 0.7244 \| 0.9773 \| \| 0.0369 \| 43.53 \| 740 \| 0.9928 \| 0.0 \| 0.9526 \| 0.8837 \| 0.8391 \| 0.8761 \| 0.9155 \| 0.9745 \| 0.6171 \| 0.7684 \| 0.9882 \| 0.8889 \| 0.2243 \| nan \| 0.9808 \| 0.0 \| 0.8761 \| 0.8354 \| 0.8189 \| 0.8243 \| 0.8648 \| 0.9536 \| 0.6082 \| 0.6259 \| 0.9747 \| 0.8328 \| 0.2171 \| nan \| 0.0800 \| 0.7632 \| 0.7241 \| 0.9770 \| \| 0.0373 \| 44.71 \| 760 \| 0.9928 \| 0.0 \| 0.9444 \| 0.8855 \| 0.8561 \| 0.8601 \| 0.8866 \| 0.9836 \| 0.6149 \| 0.7557 \| 0.9886 \| 0.8776 \| 0.2033 \| nan \| 0.9803 \| 0.0 \| 0.8743 \| 0.8405 \| 0.8347 \| 0.8131 \| 0.8579 \| 0.9458 \| 0.6082 \| 0.6064 \| 0.9741 \| 0.8309 \| 0.1965 \| nan \| 0.0832 \| 0.7576 \| 0.7202 \| 0.9762 \| \| 0.0332 \| 45.88 \| 780 \| 0.9923 \| 0.0 \| 0.9401 \| 0.8656 \| 0.8542 \| 0.8879 \| 0.8911 \| 0.9791 \| 0.6248 \| 0.8070 \| 0.9859 \| 0.9102 \| 0.2295 \| nan \| 0.9807 \| 0.0 \| 0.8770 \| 0.8293 \| 0.8286 \| 0.8240 \| 0.8598 \| 0.9451 \| 0.5887 \| 0.5983 \| 0.9746 \| 0.8353 \| 0.2259 \| nan \| 0.0818 \| 0.7668 \| 0.7205 \| 0.9765 \| \| 0.0386 \| 47.06 \| 800 \| 0.9926 \| 0.0325 \| 0.9593 \| 0.8440 \| 0.8593 \| 0.8725 \| 0.8980 \| 0.9703 \| 0.6201 \| 0.7499 \| 0.9851 \| 0.9043 \| 0.1994 \| nan \| 0.9810 \| 0.0325 \| 0.8732 \| 0.8141 \| 0.8340 \| 0.8222 \| 0.8616 \| 0.9529 \| 0.5787 \| 0.5951 \| 0.9753 \| 0.8392 \| 0.1945 \| nan \| 0.0835 \| 0.7606 \| 0.7196 \| 0.9767 \| \| 0.0457 \| 48.24 \| 820 \| 0.9933 \| 0.0114 \| 0.9413 \| 0.9301 \| 0.8847 \| 0.8748 \| 0.9043 \| 0.9777 \| 0.6238 \| 0.7846 \| 0.9887 \| 0.8896 \| 0.2452 \| nan \| 0.9810 \| 0.0114 \| 0.8789 \| 0.8180 \| 0.8470 \| 0.8198 \| 0.8569 \| 0.9515 \| 0.6110 \| 0.6043 \| 0.9751 \| 0.8365 \| 0.2376 \| nan \| 0.0799 \| 0.7730 \| 0.7253 \| 0.9771 \| \| 0.045 \| 49.41 \| 840 \| 0.9927 \| 0.0 \| 0.9411 \| 0.8221 \| 0.8123 \| 0.8807 \| 0.8406 \| 0.9732 \| 0.6200 \| 0.7598 \| 0.9920 \| 0.8704 \| 0.1768 \| nan \| 0.9807 \| 0.0 \| 0.8722 \| 0.7926 \| 0.7954 \| 0.8200 \| 0.8261 \| 0.9524 \| 0.6057 \| 0.6041 \| 0.9720 \| 0.8282 \| 0.1756 \| nan \| 0.0856 \| 0.7447 \| 0.7096 \| 0.9762 \| \| 0.0414 \| 50.59 \| 860 \| 0.9926 \| 0.1220 \| 0.9469 \| 0.8722 \| 0.8333 \| 0.8965 \| 0.8889 \| 0.9775 \| 0.6201 \| 0.7202 \| 0.9903 \| 0.8716 \| 0.2122 \| nan \| 0.9814 \| 0.1218 \| 0.8780 \| 0.8280 \| 0.8171 \| 0.8290 \| 0.8561 \| 0.9514 \| 0.6120 \| 0.5965 \| 0.9745 \| 0.8299 \| 0.2088 \| nan \| 0.0802 \| 0.7649 \| 0.7296 \| 0.9772 \| \| 0.038 \| 51.76 \| 880 \| 0.9934 \| 0.0341 \| 0.9430 \| 0.8840 \| 0.8400 \| 0.8810 \| 0.8959 \| 0.9795 \| 0.6205 \| 0.7668 \| 0.9909 \| 0.8688 \| 0.1805 \| nan \| 0.9812 \| 0.0341 \| 0.8798 \| 0.8270 \| 0.8212 \| 0.8243 \| 0.8576 \| 0.9539 \| 0.6108 \| 0.6118 \| 0.9746 \| 0.8286 \| 0.1792 \| nan \| 0.0805 \| 0.7599 \| 0.7219 \| 0.9773 \| \| 0.0348 \| 52.94 \| 900 \| 0.9889 \| 0.3008 \| 0.9530 \| 0.9012 \| 0.8565 \| 0.9122 \| 0.9022 \| 0.9801 \| 0.6242 \| 0.8273 \| 0.9888 \| 0.8903 \| 0.2021 \| nan \| 0.9810 \| 0.2998 \| 0.8773 \| 0.8303 \| 0.8292 \| 0.8277 \| 0.8710 \| 0.9463 \| 0.5850 \| 0.6260 \| 0.9757 \| 0.8368 \| 0.1981 \| nan \| 0.0810 \| 0.7944 \| 0.7449 \| 0.9768 \| \| 0.0386 \| 54.12 \| 920 \| 0.9938 \| 0.0537 \| 0.9512 \| 0.8835 \| 0.8350 \| 0.8486 \| 0.9054 \| 0.9783 \| 0.6164 \| 0.7138 \| 0.9896 \| 0.8807 \| 0.1991 \| nan \| 0.9803 \| 0.0537 \| 0.8779 \| 0.8351 \| 0.8194 \| 0.8058 \| 0.8772 \| 0.9517 \| 0.6106 \| 0.6058 \| 0.9751 \| 0.8357 \| 0.1963 \| nan \| 0.0854 \| 0.7576 \| 0.7250 \| 0.9766 \| \| 0.0387 \| 55.29 \| 940 \| 0.9936 \| 0.0829 \| 0.9422 \| 0.8882 \| 0.8625 \| 0.8795 \| 0.8723 \| 0.9796 \| 0.6231 \| 0.8037 \| 0.9910 \| 0.8757 \| 0.2241 \| nan \| 0.9811 \| 0.0828 \| 0.8820 \| 0.8280 \| 0.8400 \| 0.8260 \| 0.8475 \| 0.9557 \| 0.6126 \| 0.6468 \| 0.9754 \| 0.8335 \| 0.2217 \| nan \| 0.0794 \| 0.7706 \| 0.7333 \| 0.9777 \| \| 0.0283 \| 56.47 \| 960 \| 0.9940 \| 0.1073 \| 0.9472 \| 0.8996 \| 0.8596 \| 0.8739 \| 0.9006 \| 0.9807 \| 0.6203 \| 0.7863 \| 0.9864 \| 0.8994 \| 0.2271 \| nan \| 0.9810 \| 0.1070 \| 0.8836 \| 0.8254 \| 0.8396 \| 0.8187 \| 0.8586 \| 0.9543 \| 0.6134 \| 0.6249 \| 0.9763 \| 0.8406 \| 0.2245 \| nan \| 0.0813 \| 0.7756 \| 0.7345 \| 0.9776 \| \| 0.0384 \| 57.65 \| 980 \| 0.9925 \| 0.1577 \| 0.9499 \| 0.9201 \| 0.8493 \| 0.8903 \| 0.8871 \| 0.9788 \| 0.6250 \| 0.7860 \| 0.9886 \| 0.8914 \| 0.2491 \| nan \| 0.9817 \| 0.1575 \| 0.8825 \| 0.8383 \| 0.8280 \| 0.8280 \| 0.8572 \| 0.9502 \| 0.5993 \| 0.6357 \| 0.9760 \| 0.8399 \| 0.2445 \| nan \| 0.0800 \| 0.7820 \| 0.7399 \| 0.9777 \| \| 0.0284 \| 58.82 \| 1000 \| 0.9922 \| 0.4341 \| 0.9423 \| 0.8988 \| 0.8606 \| 0.8817 \| 0.8903 \| 0.9850 \| 0.6222 \| 0.7714 \| 0.9912 \| 0.8787 \| 0.2188 \| nan \| 0.9813 \| 0.4070 \| 0.8806 \| 0.8373 \| 0.8310 \| 0.8225 \| 0.8639 \| 0.9464 \| 0.6076 \| 0.6204 \| 0.9753 \| 0.8363 \| 0.2159 \| nan \| 0.0827 \| 0.7975 \| 0.7558 \| 0.9772 \| \| 0.0282 \| 60.0 \| 1020 \| 0.9918 \| 0.4016 \| 0.9464 \| 0.9251 \| 0.8827 \| 0.8950 \| 0.8991 \| 0.9836 \| 0.6233 \| 0.7761 \| 0.9871 \| 0.9167 \| 0.2432 \| nan \| 0.9815 \| 0.3927 \| 0.8841 \| 0.8398 \| 0.8436 \| 0.8236 \| 0.8689 \| 0.9497 \| 0.6084 \| 0.6328 \| 0.9760 \| 0.8398 \| 0.2396 \| nan \| 0.0809 \| 0.8055 \| 0.7600 \| 0.9777 \| \| 0.0327 \| 61.18 \| 1040 \| 0.9918 \| 0.4114 \| 0.9499 \| 0.9102 \| 0.8241 \| 0.9009 \| 0.8756 \| 0.9790 \| 0.6216 \| 0.7633 \| 0.9883 \| 0.9068 \| 0.2295 \| nan \| 0.9817 \| 0.4081 \| 0.8796 \| 0.8338 \| 0.8084 \| 0.8279 \| 0.8530 \| 0.9510 \| 0.6062 \| 0.6306 \| 0.9756 \| 0.8438 \| 0.2259 \| nan \| 0.0825 \| 0.7963 \| 0.7558 \| 0.9775 \| \| 0.033 \| 62.35 \| 1060 \| 0.9924 \| 0.3024 \| 0.9527 \| 0.8912 \| 0.8402 \| 0.8845 \| 0.9115 \| 0.9813 \| 0.6220 \| 0.7280 \| 0.9887 \| 0.9029 \| 0.2298 \| nan \| 0.9816 \| 0.2995 \| 0.8806 \| 0.8413 \| 0.8209 \| 0.8232 \| 0.8705 \| 0.9529 \| 0.6107 \| 0.6132 \| 0.9762 \| 0.8435 \| 0.2262 \| nan \| 0.0824 \| 0.7868 \| 0.7493 \| 0.9776 \| \| 0.0368 \| 63.53 \| 1080 \| 0.9934 \| 0.1106 \| 0.9447 \| 0.8896 \| 0.8379 \| 0.8801 \| 0.9108 \| 0.9836 \| 0.6245 \| 0.7779 \| 0.9905 \| 0.8842 \| 0.2147 \| nan \| 0.9817 \| 0.1106 \| 0.8837 \| 0.8379 \| 0.8201 \| 0.8246 \| 0.8741 \| 0.9522 \| 0.6121 \| 0.6342 \| 0.9760 \| 0.8399 \| 0.2128 \| nan \| 0.0803 \| 0.7725 \| 0.7354 \| 0.9779 \| \| 0.0395 \| 64.71 \| 1100 \| 0.9933 \| 0.0553 \| 0.9391 \| 0.9201 \| 0.8681 \| 0.8875 \| 0.9200 \| 0.9820 \| 0.6582 \| 0.7844 \| 0.9909 \| 0.8916 \| 0.2336 \| nan \| 0.9816 \| 0.0553 \| 0.8854 \| 0.8479 \| 0.8407 \| 0.8273 \| 0.8752 \| 0.9526 \| 0.6441 \| 0.6288 \| 0.9760 \| 0.8418 \| 0.2306 \| nan \| 0.0766 \| 0.7788 \| 0.7375 \| 0.9782 \| \| 0.0355 \| 65.88 \| 1120 \| 0.9935 \| 0.1577 \| 0.9472 \| 0.9118 \| 0.8493 \| 0.8630 \| 0.9045 \| 0.9832 \| 0.6617 \| 0.7930 \| 0.9891 \| 0.8981 \| 0.2304 \| nan \| 0.9811 \| 0.1575 \| 0.8863 \| 0.8400 \| 0.8324 \| 0.8154 \| 0.8784 \| 0.9513 \| 0.6451 \| 0.6217 \| 0.9769 \| 0.8456 \| 0.2256 \| nan \| 0.0792 \| 0.7833 \| 0.7429 \| 0.9778 \| \| 0.0326 \| 67.06 \| 1140 \| 0.9919 \| 0.1919 \| 0.9547 \| 0.8761 \| 0.8499 \| 0.8893 \| 0.9099 \| 0.9822 \| 0.6300 \| 0.7588 \| 0.9882 \| 0.9022 \| 0.1801 \| nan \| 0.9820 \| 0.1919 \| 0.8805 \| 0.8213 \| 0.8275 \| 0.8292 \| 0.8831 \| 0.9492 \| 0.6199 \| 0.6253 \| 0.9767 \| 0.8458 \| 0.1768 \| nan \| 0.0798 \| 0.7773 \| 0.7392 \| 0.9778 \| \| 0.03 \| 68.24 \| 1160 \| 0.9935 \| 0.3057 \| 0.9438 \| 0.9097 \| 0.8682 \| 0.8853 \| 0.9112 \| 0.9814 \| 0.7520 \| 0.7964 \| 0.9891 \| 0.9062 \| 0.2278 \| nan \| 0.9820 \| 0.2998 \| 0.8900 \| 0.8445 \| 0.8443 \| 0.8281 \| 0.8803 \| 0.9556 \| 0.7380 \| 0.6151 \| 0.9768 \| 0.8482 \| 0.2242 \| nan \| 0.0735 \| 0.8054 \| 0.7636 \| 0.9789 \| \| 0.0304 \| 69.41 \| 1180 \| 0.9931 \| 0.2634 \| 0.9452 \| 0.9119 \| 0.8890 \| 0.8894 \| 0.9082 \| 0.9829 \| 0.6426 \| 0.7216 \| 0.9899 \| 0.9068 \| 0.2100 \| nan \| 0.9820 \| 0.2613 \| 0.8847 \| 0.8445 \| 0.8600 \| 0.8291 \| 0.8779 \| 0.9486 \| 0.6299 \| 0.6227 \| 0.9780 \| 0.8495 \| 0.2039 \| nan \| 0.0749 \| 0.7888 \| 0.7517 \| 0.9784 \| \| 0.0366 \| 70.59 \| 1200 \| 0.9927 \| 0.0472 \| 0.9582 \| 0.8675 \| 0.8251 \| 0.8619 \| 0.9024 \| 0.9774 \| 0.6224 \| 0.8008 \| 0.9870 \| 0.9060 \| 0.1424 \| nan \| 0.9819 \| 0.0471 \| 0.8767 \| 0.8272 \| 0.8106 \| 0.8130 \| 0.8767 \| 0.9599 \| 0.6055 \| 0.5239 \| 0.9777 \| 0.8490 \| 0.1354 \| nan \| 0.0830 \| 0.7609 \| 0.7142 \| 0.9771 \| \| 0.0296 \| 71.76 \| 1220 \| 0.9916 \| 0.1431 \| 0.9524 \| 0.9023 \| 0.8712 \| 0.9014 \| 0.8903 \| 0.9791 \| 0.6243 \| 0.7556 \| 0.9916 \| 0.8860 \| 0.1754 \| nan \| 0.9819 \| 0.1431 \| 0.8808 \| 0.8397 \| 0.8471 \| 0.8321 \| 0.8665 \| 0.9586 \| 0.6103 \| 0.6133 \| 0.9772 \| 0.8449 \| 0.1710 \| nan \| 0.0791 \| 0.7742 \| 0.7359 \| 0.9782 \| \| 0.0313 \| 72.94 \| 1240 \| 0.9927 \| 0.0894 \| 0.9468 \| 0.9155 \| 0.8566 \| 0.8993 \| 0.8841 \| 0.9796 \| 0.6253 \| 0.7843 \| 0.9926 \| 0.8653 \| 0.2090 \| nan \| 0.9821 \| 0.0894 \| 0.8860 \| 0.8653 \| 0.8393 \| 0.8309 \| 0.8594 \| 0.9601 \| 0.6150 \| 0.6127 \| 0.9755 \| 0.8342 \| 0.2065 \| nan \| 0.0774 \| 0.7724 \| 0.7351 \| 0.9784 \| \| 0.0272 \| 74.12 \| 1260 \| 0.9942 \| 0.2228 \| 0.9498 \| 0.9141 \| 0.8849 \| 0.8712 \| 0.9235 \| 0.9781 \| 0.6261 \| 0.7730 \| 0.9907 \| 0.8943 \| 0.2644 \| nan \| 0.9817 \| 0.2164 \| 0.8902 \| 0.8554 \| 0.8587 \| 0.8241 \| 0.8853 \| 0.9617 \| 0.6170 \| 0.6001 \| 0.9772 \| 0.8477 \| 0.2536 \| nan \| 0.0761 \| 0.7913 \| 0.7515 \| 0.9787 \| \| 0.0329 \| 75.29 \| 1280 \| 0.9928 \| 0.1902 \| 0.9563 \| 0.8935 \| 0.8582 \| 0.8855 \| 0.8926 \| 0.9794 \| 0.6249 \| 0.7687 \| 0.9893 \| 0.8970 \| 0.2109 \| nan \| 0.9821 \| 0.1896 \| 0.8856 \| 0.8492 \| 0.8390 \| 0.8261 \| 0.8720 \| 0.9610 \| 0.6129 \| 0.5996 \| 0.9775 \| 0.8492 \| 0.2071 \| nan \| 0.0783 \| 0.7800 \| 0.7424 \| 0.9785 \| \| 0.0297 \| 76.47 \| 1300 \| 0.9925 \| 0.2602 \| 0.9522 \| 0.9211 \| 0.8558 \| 0.9005 \| 0.8835 \| 0.9813 \| 0.6233 \| 0.7873 \| 0.9882 \| 0.9161 \| 0.2259 \| nan \| 0.9823 \| 0.2581 \| 0.8877 \| 0.8522 \| 0.8369 \| 0.8292 \| 0.8674 \| 0.9597 \| 0.6142 \| 0.6165 \| 0.9772 \| 0.8511 \| 0.2211 \| nan \| 0.0783 \| 0.7914 \| 0.7503 \| 0.9787 \| \| 0.0316 \| 77.65 \| 1320 \| 0.9950 \| 0.0683 \| 0.9489 \| 0.9051 \| 0.8369 \| 0.8689 \| 0.8564 \| 0.9788 \| 0.6222 \| 0.7373 \| 0.9890 \| 0.9112 \| 0.2202 \| nan \| 0.9813 \| 0.0681 \| 0.8873 \| 0.8526 \| 0.8250 \| 0.8239 \| 0.8418 \| 0.9599 \| 0.6145 \| 0.6000 \| 0.9773 \| 0.8518 \| 0.2182 \| nan \| 0.0802 \| 0.7645 \| 0.7309 \| 0.9783 \| \| 0.0295 \| 78.82 \| 1340 \| 0.9931 \| 0.2309 \| 0.9575 \| 0.8918 \| 0.8445 \| 0.8918 \| 0.8780 \| 0.9784 \| 0.6227 \| 0.7670 \| 0.9876 \| 0.9198 \| 0.2125 \| nan \| 0.9825 \| 0.2276 \| 0.8858 \| 0.8543 \| 0.8294 \| 0.8329 \| 0.8619 \| 0.9603 \| 0.6136 \| 0.6130 \| 0.9771 \| 0.8528 \| 0.2104 \| nan \| 0.0793 \| 0.7827 \| 0.7463 \| 0.9787 \| \| 0.0363 \| 80.0 \| 1360 \| 0.9935 \| 0.2293 \| 0.9455 \| 0.9084 \| 0.8572 \| 0.8976 \| 0.8853 \| 0.9827 \| 0.6365 \| 0.7868 \| 0.9908 \| 0.8975 \| 0.2276 \| nan \| 0.9824 \| 0.2267 \| 0.8881 \| 0.8519 \| 0.8387 \| 0.8352 \| 0.8649 \| 0.9615 \| 0.6243 \| 0.6143 \| 0.9772 \| 0.8497 \| 0.2253 \| nan \| 0.0758 \| 0.7876 \| 0.7493 \| 0.9790 \| \| 0.0278 \| 81.18 \| 1380 \| 0.9933 \| 0.1659 \| 0.9558 \| 0.9068 \| 0.8466 \| 0.8911 \| 0.8826 \| 0.9817 \| 0.6237 \| 0.7761 \| 0.9879 \| 0.8980 \| 0.2160 \| nan \| 0.9825 \| 0.1656 \| 0.8856 \| 0.8531 \| 0.8324 \| 0.8328 \| 0.8661 \| 0.9615 \| 0.6169 \| 0.6027 \| 0.9773 \| 0.8501 \| 0.2138 \| nan \| 0.0779 \| 0.7789 \| 0.7416 \| 0.9787 \| \| 0.0349 \| 82.35 \| 1400 \| 0.9919 \| 0.2244 \| 0.9584 \| 0.8933 \| 0.8434 \| 0.9011 \| 0.8807 \| 0.9809 \| 0.6226 \| 0.7732 \| 0.9893 \| 0.8945 \| 0.2060 \| nan \| 0.9826 \| 0.2240 \| 0.8839 \| 0.8456 \| 0.8265 \| 0.8310 \| 0.8619 \| 0.9604 \| 0.6152 \| 0.6070 \| 0.9772 \| 0.8500 \| 0.2038 \| nan \| 0.0789 \| 0.7815 \| 0.7438 \| 0.9785 \| \| 0.0334 \| 83.53 \| 1420 \| 0.9938 \| 0.2081 \| 0.9553 \| 0.9087 \| 0.8442 \| 0.8773 \| 0.8931 \| 0.9789 \| 0.6275 \| 0.7603 \| 0.9896 \| 0.8964 \| 0.2237 \| nan \| 0.9822 \| 0.2055 \| 0.8865 \| 0.8505 \| 0.8299 \| 0.8272 \| 0.8748 \| 0.9599 \| 0.6198 \| 0.6036 \| 0.9772 \| 0.8502 \| 0.2211 \| nan \| 0.0789 \| 0.7813 \| 0.7452 \| 0.9786 \| \| 0.0271 \| 84.71 \| 1440 \| 0.9929 \| 0.3496 \| 0.9541 \| 0.9276 \| 0.8700 \| 0.8941 \| 0.8969 \| 0.9810 \| 0.6276 \| 0.7772 \| 0.9882 \| 0.9110 \| 0.2436 \| nan \| 0.9825 \| 0.3418 \| 0.8882 \| 0.8426 \| 0.8466 \| 0.8327 \| 0.8717 \| 0.9594 \| 0.6172 \| 0.6108 \| 0.9775 \| 0.8514 \| 0.2407 \| nan \| 0.0779 \| 0.8011 \| 0.7587 \| 0.9789 \| \| 0.0269 \| 85.88 \| 1460 \| 0.9929 \| 0.2959 \| 0.9507 \| 0.9229 \| 0.8474 \| 0.8995 \| 0.9106 \| 0.9824 \| 0.6299 \| 0.7745 \| 0.9895 \| 0.9106 \| 0.2364 \| nan \| 0.9826 \| 0.2940 \| 0.8884 \| 0.8451 \| 0.8318 \| 0.8361 \| 0.8758 \| 0.9576 \| 0.6199 \| 0.6215 \| 0.9774 \| 0.8516 \| 0.2340 \| nan \| 0.0778 \| 0.7956 \| 0.7551 \| 0.9790 \| \| 0.0273 \| 87.06 \| 1480 \| 0.9937 \| 0.0894 \| 0.9506 \| 0.9190 \| 0.8680 \| 0.8860 \| 0.9251 \| 0.9807 \| 0.6401 \| 0.7765 \| 0.9898 \| 0.9120 \| 0.2603 \| nan \| 0.9826 \| 0.0894 \| 0.8905 \| 0.8519 \| 0.8466 \| 0.8315 \| 0.8874 \| 0.9592 \| 0.6285 \| 0.6164 \| 0.9774 \| 0.8540 \| 0.2562 \| nan \| 0.0758 \| 0.7839 \| 0.7440 \| 0.9791 \| \| 0.0288 \| 88.24 \| 1500 \| 0.9932 \| 0.1854 \| 0.9524 \| 0.9075 \| 0.8556 \| 0.8900 \| 0.8910 \| 0.9808 \| 0.6311 \| 0.7878 \| 0.9903 \| 0.9093 \| 0.2340 \| nan \| 0.9828 \| 0.1848 \| 0.8889 \| 0.8508 \| 0.8370 \| 0.8321 \| 0.8751 \| 0.9591 \| 0.6224 \| 0.6146 \| 0.9774 \| 0.8555 \| 0.2317 \| nan \| 0.0780 \| 0.7852 \| 0.7471 \| 0.9790 \| \| 0.0258 \| 89.41 \| 1520 \| 0.9924 \| 0.3057 \| 0.9572 \| 0.9354 \| 0.8573 \| 0.8933 \| 0.8876 \| 0.9823 \| 0.6272 \| 0.7935 \| 0.9876 \| 0.9146 \| 0.2306 \| nan \| 0.9826 \| 0.3003 \| 0.8866 \| 0.8465 \| 0.8400 \| 0.8322 \| 0.8710 \| 0.9575 \| 0.6192 \| 0.6181 \| 0.9778 \| 0.8540 \| 0.2259 \| nan \| 0.0794 \| 0.7973 \| 0.7548 \| 0.9788 \| \| 0.023 \| 90.59 \| 1540 \| 0.9927 \| 0.3252 \| 0.9555 \| 0.9297 \| 0.8655 \| 0.8867 \| 0.9231 \| 0.9818 \| 0.6278 \| 0.7999 \| 0.9899 \| 0.9057 \| 0.2339 \| nan \| 0.9828 \| 0.3135 \| 0.8881 \| 0.8506 \| 0.8438 \| 0.8323 \| 0.8904 \| 0.9598 \| 0.6192 \| 0.6107 \| 0.9778 \| 0.8548 \| 0.2281 \| nan \| 0.0783 \| 0.8014 \| 0.7578 \| 0.9790 \| \| 0.0263 \| 91.76 \| 1560 \| 0.9935 \| 0.2634 \| 0.9549 \| 0.9192 \| 0.8576 \| 0.8835 \| 0.9122 \| 0.9809 \| 0.6293 \| 0.7828 \| 0.9882 \| 0.9137 \| 0.2440 \| nan \| 0.9826 \| 0.2576 \| 0.8877 \| 0.8496 \| 0.8401 \| 0.8323 \| 0.8877 \| 0.9592 \| 0.6229 \| 0.6039 \| 0.9777 \| 0.8543 \| 0.2387 \| nan \| 0.0788 \| 0.7941 \| 0.7534 \| 0.9789 \| \| 0.0308 \| 92.94 \| 1580 \| 0.9933 \| 0.2195 \| 0.9518 \| 0.9267 \| 0.8570 \| 0.8981 \| 0.8983 \| 0.9813 \| 0.6285 \| 0.7817 \| 0.9884 \| 0.9184 \| 0.2340 \| nan \| 0.9828 \| 0.2181 \| 0.8883 \| 0.8554 \| 0.8391 \| 0.8335 \| 0.8765 \| 0.9603 \| 0.6199 \| 0.6176 \| 0.9778 \| 0.8563 \| 0.2311 \| nan \| 0.0786 \| 0.7905 \| 0.7505 \| 0.9791 \| \| 0.0255 \| 94.12 \| 1600 \| 0.9936 \| 0.2065 \| 0.9601 \| 0.9187 \| 0.8598 \| 0.8876 \| 0.8846 \| 0.9775 \| 0.6275 \| 0.7880 \| 0.9860 \| 0.9239 \| 0.2229 \| nan \| 0.9829 \| 0.2058 \| 0.8874 \| 0.8559 \| 0.8419 \| 0.8333 \| 0.8670 \| 0.9607 \| 0.6197 \| 0.6095 \| 0.9777 \| 0.8581 \| 0.2203 \| nan \| 0.0799 \| 0.7874 \| 0.7477 \| 0.9791 \| \| 0.0288 \| 95.29 \| 1620 \| 0.9932 \| 0.2878 \| 0.9565 \| 0.9128 \| 0.8604 \| 0.8750 \| 0.9047 \| 0.9825 \| 0.6288 \| 0.7858 \| 0.9910 \| 0.8878 \| 0.2249 \| nan \| 0.9828 \| 0.2846 \| 0.8870 \| 0.8520 \| 0.8408 \| 0.8290 \| 0.8824 \| 0.9594 \| 0.6188 \| 0.6095 \| 0.9775 \| 0.8493 \| 0.2224 \| nan \| 0.0808 \| 0.7916 \| 0.7535 \| 0.9789 \| \| 0.0278 \| 96.47 \| 1640 \| 0.9944 \| 0.1593 \| 0.9515 \| 0.9112 \| 0.8437 \| 0.8814 \| 0.8830 \| 0.9805 \| 0.6292 \| 0.7454 \| 0.9892 \| 0.9099 \| 0.2248 \| nan \| 0.9825 \| 0.1593 \| 0.8880 \| 0.8545 \| 0.8285 \| 0.8316 \| 0.8652 \| 0.9586 \| 0.6228 \| 0.5959 \| 0.9778 \| 0.8548 \| 0.2228 \| nan \| 0.0802 \| 0.7772 \| 0.7417 \| 0.9789 \| \| 0.0266 \| 97.65 \| 1660 \| 0.9933 \| 0.2033 \| 0.9569 \| 0.9173 \| 0.8506 \| 0.8898 \| 0.8823 \| 0.9815 \| 0.6297 \| 0.7677 \| 0.9875 \| 0.9221 \| 0.2112 \| nan \| 0.9829 \| 0.2029 \| 0.8874 \| 0.8550 \| 0.8348 \| 0.8321 \| 0.8654 \| 0.9597 \| 0.6215 \| 0.6081 \| 0.9780 \| 0.8558 \| 0.2093 \| nan \| 0.0801 \| 0.7841 \| 0.7456 \| 0.9790 \| \| 0.025 \| 98.82 \| 1680 \| 0.9930 \| 0.2081 \| 0.9597 \| 0.8978 \| 0.8492 \| 0.9013 \| 0.8684 \| 0.9799 \| 0.6333 \| 0.7604 \| 0.9864 \| 0.9254 \| 0.2005 \| nan \| 0.9832 \| 0.2075 \| 0.8871 \| 0.8506 \| 0.8329 \| 0.8398 \| 0.8552 \| 0.9606 \| 0.6287 \| 0.6087 \| 0.9772 \| 0.8545 \| 0.1986 \| nan \| 0.0787 \| 0.7818 \| 0.7450 \| 0.9792 \| \| 0.0268 \| 100.0 \| 1700 \| 0.9931 \| 0.1870 \| 0.9549 \| 0.9206 \| 0.8659 \| 0.8962 \| 0.8848 \| 0.9826 \| 0.6367 \| 0.7757 \| 0.9882 \| 0.9147 \| 0.2171 \| nan \| 0.9830 \| 0.1861 \| 0.8891 \| 0.8483 \| 0.8460 \| 0.8333 \| 0.8671 \| 0.9596 \| 0.6281 \| 0.6210 \| 0.9784 \| 0.8557 \| 0.2148 \| nan \| 0.0788 \| 0.7860 \| 0.7470 \| 0.9793 \| \| 0.0256 \| 101.18 \| 1720 \| 0.9942 \| 0.1902 \| 0.9516 \| 0.9301 \| 0.8468 \| 0.8849 \| 0.9161 \| 0.9808 \| 0.6354 \| 0.7714 \| 0.9889 \| 0.9183 \| 0.2331 \| nan \| 0.9826 \| 0.1896 \| 0.8895 \| 0.8524 \| 0.8318 \| 0.8335 \| 0.8921 \| 0.9605 \| 0.6271 \| 0.6191 \| 0.9780 \| 0.8559 \| 0.2303 \| nan \| 0.0785 \| 0.7878 \| 0.7494 \| 0.9792 \| \| 0.0256 \| 102.35 \| 1740 \| 0.9930 \| 0.3220 \| 0.9563 \| 0.9174 \| 0.8542 \| 0.8969 \| 0.9228 \| 0.9798 \| 0.6242 \| 0.7796 \| 0.9893 \| 0.9139 \| 0.2347 \| nan \| 0.9831 \| 0.3163 \| 0.8872 \| 0.8580 \| 0.8359 \| 0.8384 \| 0.8959 \| 0.9590 \| 0.6187 \| 0.6221 \| 0.9785 \| 0.8583 \| 0.2265 \| nan \| 0.0795 \| 0.7988 \| 0.7598 \| 0.9793 \| \| 0.0243 \| 103.53 \| 1760 \| 0.9933 \| 0.1967 \| 0.9556 \| 0.9194 \| 0.8592 \| 0.9019 \| 0.9078 \| 0.9791 \| 0.6303 \| 0.7629 \| 0.9883 \| 0.9218 \| 0.2304 \| nan \| 0.9830 \| 0.1961 \| 0.8895 \| 0.8594 \| 0.8390 \| 0.8391 \| 0.8899 \| 0.9602 \| 0.6230 \| 0.6181 \| 0.9782 \| 0.8581 \| 0.2273 \| nan \| 0.0781 \| 0.7882 \| 0.7509 \| 0.9794 \| \| 0.0299 \| 104.71 \| 1780 \| 0.9931 \| 0.2033 \| 0.9584 \| 0.9250 \| 0.8485 \| 0.9021 \| 0.8709 \| 0.9783 \| 0.6225 \| 0.7794 \| 0.9884 \| 0.9216 \| 0.2051 \| nan \| 0.9831 \| 0.2026 \| 0.8884 \| 0.8548 \| 0.8335 \| 0.8379 \| 0.8556 \| 0.9612 \| 0.6179 \| 0.6252 \| 0.9788 \| 0.8587 \| 0.2033 \| nan \| 0.0785 \| 0.7843 \| 0.7462 \| 0.9794 \| \| 0.0238 \| 105.88 \| 1800 \| 0.9939 \| 0.1789 \| 0.9530 \| 0.9209 \| 0.8513 \| 0.8998 \| 0.8795 \| 0.9815 \| 0.6352 \| 0.7671 \| 0.9888 \| 0.9116 \| 0.2369 \| nan \| 0.9830 \| 0.1789 \| 0.8912 \| 0.8586 \| 0.8351 \| 0.8372 \| 0.8654 \| 0.9615 \| 0.6288 \| 0.6171 \| 0.9785 \| 0.8580 \| 0.2351 \| nan \| 0.0770 \| 0.7845 \| 0.7483 \| 0.9796 \| \| 0.0283 \| 107.06 \| 1820 \| 0.9930 \| 0.2358 \| 0.9605 \| 0.9187 \| 0.8514 \| 0.8910 \| 0.9089 \| 0.9780 \| 0.6273 \| 0.8059 \| 0.9888 \| 0.9097 \| 0.2182 \| nan \| 0.9829 \| 0.2342 \| 0.8885 \| 0.8595 \| 0.8352 \| 0.8344 \| 0.8892 \| 0.9608 \| 0.6210 \| 0.6203 \| 0.9786 \| 0.8585 \| 0.2165 \| nan \| 0.0792 \| 0.7913 \| 0.7523 \| 0.9792 \| \| 0.0236 \| 108.24 \| 1840 \| 0.9934 \| 0.3024 \| 0.9579 \| 0.9291 \| 0.8560 \| 0.8895 \| 0.9201 \| 0.9805 \| 0.6352 \| 0.7655 \| 0.9884 \| 0.9079 \| 0.2409 \| nan \| 0.9828 \| 0.2981 \| 0.8888 \| 0.8550 \| 0.8388 \| 0.8342 \| 0.8929 \| 0.9592 \| 0.6231 \| 0.6139 \| 0.9783 \| 0.8570 \| 0.2367 \| nan \| 0.0789 \| 0.7974 \| 0.7584 \| 0.9792 \| \| 0.0248 \| 109.41 \| 1860 \| 0.9936 \| 0.2927 \| 0.9553 \| 0.9286 \| 0.8521 \| 0.8933 \| 0.9070 \| 0.9819 \| 0.6299 \| 0.7726 \| 0.9893 \| 0.9061 \| 0.2390 \| nan \| 0.9830 \| 0.2885 \| 0.8901 \| 0.8463 \| 0.8344 \| 0.8375 \| 0.8853 \| 0.9592 \| 0.6220 \| 0.6224 \| 0.9784 \| 0.8568 \| 0.2365 \| nan \| 0.0784 \| 0.7955 \| 0.7570 \| 0.9794 \| \| 0.0296 \| 110.59 \| 1880 \| 0.9933 \| 0.1561 \| 0.9523 \| 0.9349 \| 0.8549 \| 0.9022 \| 0.8833 \| 0.9830 \| 0.6291 \| 0.7902 \| 0.9888 \| 0.9162 \| 0.2348 \| nan \| 0.9831 \| 0.1561 \| 0.8907 \| 0.8562 \| 0.8386 \| 0.8375 \| 0.8652 \| 0.9607 \| 0.6205 \| 0.6305 \| 0.9783 \| 0.8588 \| 0.2331 \| nan \| 0.0777 \| 0.7861 \| 0.7469 \| 0.9795 \| \| 0.0234 \| 111.76 \| 1900 \| 0.9937 \| 0.2764 \| 0.9536 \| 0.9374 \| 0.8559 \| 0.8959 \| 0.9211 \| 0.9791 \| 0.6245 \| 0.7706 \| 0.9896 \| 0.9138 \| 0.2421 \| nan \| 0.9828 \| 0.2742 \| 0.8901 \| 0.8577 \| 0.8386 \| 0.8378 \| 0.8941 \| 0.9596 \| 0.6196 \| 0.6235 \| 0.9785 \| 0.8596 \| 0.2400 \| nan \| 0.0799 \| 0.7964 \| 0.7582 \| 0.9794 \| \| 0.0256 \| 112.94 \| 1920 \| 0.9931 \| 0.3415 \| 0.9587 \| 0.9101 \| 0.8473 \| 0.8977 \| 0.9016 \| 0.9780 \| 0.6255 \| 0.7751 \| 0.9892 \| 0.9108 \| 0.2290 \| nan \| 0.9830 \| 0.3371 \| 0.8884 \| 0.8634 \| 0.8312 \| 0.8370 \| 0.8852 \| 0.9599 \| 0.6191 \| 0.6203 \| 0.9783 \| 0.8605 \| 0.2273 \| nan \| 0.0801 \| 0.7967 \| 0.7608 \| 0.9793 \| \| 0.022 \| 114.12 \| 1940 \| 0.9933 \| 0.3789 \| 0.9556 \| 0.9383 \| 0.8550 \| 0.8948 \| 0.9147 \| 0.9789 \| 0.6288 \| 0.7992 \| 0.9892 \| 0.9181 \| 0.2431 \| nan \| 0.9830 \| 0.3612 \| 0.8901 \| 0.8594 \| 0.8375 \| 0.8357 \| 0.8922 \| 0.9607 \| 0.6224 \| 0.6209 \| 0.9785 \| 0.8616 \| 0.2407 \| nan \| 0.0786 \| 0.8068 \| 0.7649 \| 0.9794 \| \| 0.0235 \| 115.29 \| 1960 \| 0.9932 \| 0.3480 \| 0.9560 \| 0.9251 \| 0.8506 \| 0.8939 \| 0.9053 \| 0.9799 \| 0.6274 \| 0.7836 \| 0.9905 \| 0.9063 \| 0.2364 \| nan \| 0.9830 \| 0.3424 \| 0.8901 \| 0.8615 \| 0.8324 \| 0.8352 \| 0.8878 \| 0.9598 \| 0.6207 \| 0.6235 \| 0.9783 \| 0.8597 \| 0.2343 \| nan \| 0.0790 \| 0.7997 \| 0.7622 \| 0.9794 \| \| 0.0292 \| 116.47 \| 1980 \| 0.9939 \| 0.3545 \| 0.9550 \| 0.9267 \| 0.8588 \| 0.8872 \| 0.9186 \| 0.9809 \| 0.6221 \| 0.7914 \| 0.9883 \| 0.9215 \| 0.2433 \| nan \| 0.9829 \| 0.3455 \| 0.8907 \| 0.8650 \| 0.8402 \| 0.8341 \| 0.8967 \| 0.9594 \| 0.6182 \| 0.6286 \| 0.9787 \| 0.8614 \| 0.2409 \| nan \| 0.0803 \| 0.8032 \| 0.7648 \| 0.9795 \| \| 0.0252 \| 117.65 \| 2000 \| 0.9940 \| 0.3480 \| 0.9533 \| 0.9367 \| 0.8492 \| 0.8846 \| 0.9283 \| 0.9816 \| 0.6248 \| 0.7847 \| 0.9894 \| 0.9175 \| 0.2436 \| nan \| 0.9829 \| 0.3386 \| 0.8908 \| 0.8639 \| 0.8326 \| 0.8315 \| 0.8974 \| 0.9589 \| 0.6189 \| 0.6293 \| 0.9788 \| 0.8600 \| 0.2391 \| nan \| 0.0801 \| 0.8027 \| 0.7633 \| 0.9794 \| \| 0.0266 \| 118.82 \| 2020 \| 0.9924 \| 0.4439 \| 0.9605 \| 0.9216 \| 0.8813 \| 0.9003 \| 0.9196 \| 0.9826 \| 0.6329 \| 0.7891 \| 0.9875 \| 0.9215 \| 0.1686 \| nan \| 0.9834 \| 0.4326 \| 0.8888 \| 0.8657 \| 0.8562 \| 0.8397 \| 0.8957 \| 0.9589 \| 0.6227 \| 0.6283 \| 0.9782 \| 0.8601 \| 0.1679 \| nan \| 0.0790 \| 0.8078 \| 0.7676 \| 0.9795 \| \| 0.0257 \| 120.0 \| 2040 \| 0.9935 \| 0.3317 \| 0.9584 \| 0.9307 \| 0.8576 \| 0.8875 \| 0.9198 \| 0.9791 \| 0.6243 \| 0.8030 \| 0.9882 \| 0.9262 \| 0.1780 \| nan \| 0.9831 \| 0.3259 \| 0.8903 \| 0.8655 \| 0.8413 \| 0.8344 \| 0.8958 \| 0.9607 \| 0.6169 \| 0.6193 \| 0.9788 \| 0.8634 \| 0.1772 \| nan \| 0.0813 \| 0.7983 \| 0.7579 \| 0.9795 \| \| 0.0246 \| 121.18 \| 2060 \| 0.9937 \| 0.2504 \| 0.9544 \| 0.9303 \| 0.8725 \| 0.8938 \| 0.9228 \| 0.9800 \| 0.6265 \| 0.7641 \| 0.9910 \| 0.9045 \| 0.2586 \| nan \| 0.9833 \| 0.2476 \| 0.8922 \| 0.8671 \| 0.8509 \| 0.8386 \| 0.9003 \| 0.9603 \| 0.6180 \| 0.6211 \| 0.9782 \| 0.8594 \| 0.2520 \| nan \| 0.0773 \| 0.7956 \| 0.7592 \| 0.9797 \| \| 0.0273 \| 122.35 \| 2080 \| 0.9939 \| 0.1935 \| 0.9529 \| 0.9174 \| 0.8531 \| 0.8845 \| 0.9048 \| 0.9814 \| 0.6249 \| 0.7741 \| 0.9913 \| 0.9008 \| 0.2092 \| nan \| 0.9832 \| 0.1926 \| 0.8896 \| 0.8640 \| 0.8383 \| 0.8356 \| 0.8907 \| 0.9592 \| 0.6192 \| 0.6205 \| 0.9774 \| 0.8581 \| 0.2077 \| nan \| 0.0812 \| 0.7832 \| 0.7489 \| 0.9794 \| \| 0.0209 \| 123.53 \| 2100 \| 0.9933 \| 0.2455 \| 0.9573 \| 0.9207 \| 0.8545 \| 0.9000 \| 0.9154 \| 0.9801 \| 0.6232 \| 0.7778 \| 0.9897 \| 0.9137 \| 0.2016 \| nan \| 0.9834 \| 0.2439 \| 0.8904 \| 0.8679 \| 0.8394 \| 0.8419 \| 0.8981 \| 0.9600 \| 0.6177 \| 0.6289 \| 0.9784 \| 0.8627 \| 0.2002 \| nan \| 0.0805 \| 0.7902 \| 0.7548 \| 0.9797 \| \| 0.0233 \| 124.71 \| 2120 \| 0.9930 \| 0.2423 \| 0.9551 \| 0.9354 \| 0.8548 \| 0.9022 \| 0.9183 \| 0.9807 \| 0.6224 \| 0.7888 \| 0.9906 \| 0.9106 \| 0.2290 \| nan \| 0.9834 \| 0.2407 \| 0.8903 \| 0.8648 \| 0.8388 \| 0.8394 \| 0.8962 \| 0.9600 \| 0.6161 \| 0.6274 \| 0.9785 \| 0.8623 \| 0.2271 \| nan \| 0.0803 \| 0.7941 \| 0.7558 \| 0.9797 \| \| 0.0206 \| 125.88 \| 2140 \| 0.9938 \| 0.3089 \| 0.9518 \| 0.9177 \| 0.8557 \| 0.8886 \| 0.9249 \| 0.9814 \| 0.6287 \| 0.7948 \| 0.9914 \| 0.9048 \| 0.2116 \| nan \| 0.9832 \| 0.3035 \| 0.8902 \| 0.8658 \| 0.8395 \| 0.8396 \| 0.9013 \| 0.9591 \| 0.6217 \| 0.6244 \| 0.9783 \| 0.8598 \| 0.2103 \| nan \| 0.0804 \| 0.7965 \| 0.7598 \| 0.9796 \| \| 0.0252 \| 127.06 \| 2160 \| 0.9933 \| 0.3285 \| 0.9544 \| 0.9381 \| 0.8706 \| 0.8985 \| 0.9303 \| 0.9813 \| 0.6272 \| 0.7816 \| 0.9902 \| 0.9175 \| 0.2381 \| nan \| 0.9834 \| 0.3211 \| 0.8908 \| 0.8686 \| 0.8502 \| 0.8415 \| 0.9023 \| 0.9597 \| 0.6207 \| 0.6264 \| 0.9788 \| 0.8633 \| 0.2337 \| nan \| 0.0794 \| 0.8038 \| 0.7646 \| 0.9798 \| \| 0.0274 \| 128.24 \| 2180 \| 0.9935 \| 0.3366 \| 0.9562 \| 0.9205 \| 0.8627 \| 0.8976 \| 0.9170 \| 0.9812 \| 0.6234 \| 0.7904 \| 0.9889 \| 0.9198 \| 0.2291 \| nan \| 0.9833 \| 0.3312 \| 0.8904 \| 0.8680 \| 0.8469 \| 0.8408 \| 0.8969 \| 0.9593 \| 0.6176 \| 0.6297 \| 0.9789 \| 0.8639 \| 0.2272 \| nan \| 0.0801 \| 0.8013 \| 0.7642 \| 0.9797 \| \| 0.0254 \| 129.41 \| 2200 \| 0.9936 \| 0.2537 \| 0.9551 \| 0.9270 \| 0.8635 \| 0.8960 \| 0.9123 \| 0.9819 \| 0.6250 \| 0.7929 \| 0.9886 \| 0.9260 \| 0.2223 \| nan \| 0.9833 \| 0.2512 \| 0.8911 \| 0.8658 \| 0.8461 \| 0.8386 \| 0.8925 \| 0.9590 \| 0.6188 \| 0.6304 \| 0.9792 \| 0.8636 \| 0.2206 \| nan \| 0.0796 \| 0.7952 \| 0.7569 \| 0.9797 \| \| 0.024 \| 130.59 \| 2220 \| 0.9936 \| 0.1951 \| 0.9572 \| 0.9302 \| 0.8622 \| 0.8970 \| 0.9229 \| 0.9811 \| 0.6275 \| 0.7900 \| 0.9893 \| 0.9206 \| 0.2262 \| nan \| 0.9835 \| 0.1942 \| 0.8927 \| 0.8688 \| 0.8467 \| 0.8410 \| 0.8989 \| 0.9612 \| 0.6217 \| 0.6272 \| 0.9791 \| 0.8650 \| 0.2247 \| nan \| 0.0783 \| 0.7918 \| 0.7542 \| 0.9800 \| \| 0.027 \| 131.76 \| 2240 \| 0.9933 \| 0.2813 \| 0.9517 \| 0.9241 \| 0.8712 \| 0.9027 \| 0.9345 \| 0.9833 \| 0.6333 \| 0.7869 \| 0.9902 \| 0.9146 \| 0.2275 \| nan \| 0.9834 \| 0.2777 \| 0.8926 \| 0.8661 \| 0.8497 \| 0.8405 \| 0.9018 \| 0.9597 \| 0.6237 \| 0.6262 \| 0.9790 \| 0.8631 \| 0.2257 \| nan \| 0.0786 \| 0.7996 \| 0.7607 \| 0.9799 \| \| 0.0208 \| 132.94 \| 2260 \| 0.9935 \| 0.2358 \| 0.9551 \| 0.9257 \| 0.8583 \| 0.8920 \| 0.9265 \| 0.9821 \| 0.6282 \| 0.8028 \| 0.9903 \| 0.9136 \| 0.2308 \| nan \| 0.9833 \| 0.2339 \| 0.8921 \| 0.8694 \| 0.8426 \| 0.8365 \| 0.8995 \| 0.9604 \| 0.6216 \| 0.6271 \| 0.9791 \| 0.8638 \| 0.2291 \| nan \| 0.0801 \| 0.7950 \| 0.7568 \| 0.9798 \| \| 0.0251 \| 134.12 \| 2280 \| 0.9937 \| 0.2894 \| 0.9541 \| 0.9327 \| 0.8687 \| 0.8883 \| 0.9182 \| 0.9822 \| 0.6234 \| 0.7846 \| 0.9905 \| 0.9137 \| 0.2341 \| nan \| 0.9833 \| 0.2848 \| 0.8915 \| 0.8688 \| 0.8491 \| 0.8362 \| 0.8970 \| 0.9603 \| 0.6178 \| 0.6223 \| 0.9790 \| 0.8634 \| 0.2323 \| nan \| 0.0810 \| 0.7980 \| 0.7604 \| 0.9797 \| \| 0.0217 \| 135.29 \| 2300 \| 0.9942 \| 0.3285 \| 0.9531 \| 0.9385 \| 0.8568 \| 0.8883 \| 0.9183 \| 0.9803 \| 0.6244 \| 0.7928 \| 0.9904 \| 0.9173 \| 0.2393 \| nan \| 0.9833 \| 0.3201 \| 0.8922 \| 0.8765 \| 0.8403 \| 0.8374 \| 0.8983 \| 0.9603 \| 0.6188 \| 0.6297 \| 0.9789 \| 0.8644 \| 0.2366 \| nan \| 0.0809 \| 0.8017 \| 0.7644 \| 0.9798 \| \| 0.0249 \| 136.47 \| 2320 \| 0.9937 \| 0.2976 \| 0.9560 \| 0.9356 \| 0.8588 \| 0.8969 \| 0.8910 \| 0.9818 \| 0.6262 \| 0.7744 \| 0.9887 \| 0.9167 \| 0.2345 \| nan \| 0.9833 \| 0.2928 \| 0.8913 \| 0.8725 \| 0.8419 \| 0.8377 \| 0.8746 \| 0.9608 \| 0.6198 \| 0.6203 \| 0.9789 \| 0.8643 \| 0.2324 \| nan \| 0.0812 \| 0.7963 \| 0.7593 \| 0.9797 \| \| 0.0229 \| 137.65 \| 2340 \| 0.9940 \| 0.3398 \| 0.9522 \| 0.9262 \| 0.8622 \| 0.8958 \| 0.9253 \| 0.9809 \| 0.6251 \| 0.7975 \| 0.9905 \| 0.9102 \| 0.2338 \| nan \| 0.9834 \| 0.3333 \| 0.8922 \| 0.8732 \| 0.8430 \| 0.8405 \| 0.9013 \| 0.9609 \| 0.6189 \| 0.6253 \| 0.9788 \| 0.8638 \| 0.2321 \| nan \| 0.0800 \| 0.8026 \| 0.7651 \| 0.9799 \| \| 0.0255 \| 138.82 \| 2360 \| 0.9942 \| 0.3220 \| 0.9547 \| 0.9288 \| 0.8616 \| 0.8916 \| 0.9138 \| 0.9814 \| 0.6246 \| 0.7646 \| 0.9898 \| 0.9134 \| 0.2408 \| nan \| 0.9832 \| 0.3153 \| 0.8923 \| 0.8761 \| 0.8445 \| 0.8383 \| 0.8958 \| 0.9602 \| 0.6180 \| 0.6198 \| 0.9792 \| 0.8644 \| 0.2385 \| nan \| 0.0807 \| 0.7986 \| 0.7635 \| 0.9798 \| \| 0.025 \| 140.0 \| 2380 \| 0.9932 \| 0.3626 \| 0.9544 \| 0.9285 \| 0.8567 \| 0.8978 \| 0.9276 \| 0.9807 \| 0.6266 \| 0.7963 \| 0.9910 \| 0.9159 \| 0.2400 \| nan \| 0.9836 \| 0.3551 \| 0.8904 \| 0.8750 \| 0.8394 \| 0.8405 \| 0.9008 \| 0.9601 \| 0.6177 \| 0.6291 \| 0.9785 \| 0.8647 \| 0.2366 \| nan \| 0.0811 \| 0.8055 \| 0.7670 \| 0.9798 \| \| 0.0203 \| 141.18 \| 2400 \| 0.9937 \| 0.3220 \| 0.9577 \| 0.9230 \| 0.8747 \| 0.8940 \| 0.9281 \| 0.9797 \| 0.6233 \| 0.7675 \| 0.9893 \| 0.9159 \| 0.2408 \| nan \| 0.9834 \| 0.3163 \| 0.8907 \| 0.8699 \| 0.8540 \| 0.8388 \| 0.8999 \| 0.9594 \| 0.6165 \| 0.6187 \| 0.9794 \| 0.8643 \| 0.2345 \| nan \| 0.0815 \| 0.8007 \| 0.7635 \| 0.9798 \| \| 0.0245 \| 142.35 \| 2420 \| 0.9939 \| 0.3122 \| 0.9541 \| 0.9387 \| 0.8693 \| 0.8924 \| 0.9261 \| 0.9827 \| 0.6251 \| 0.7980 \| 0.9883 \| 0.9334 \| 0.2442 \| nan \| 0.9834 \| 0.3077 \| 0.8928 \| 0.8696 \| 0.8511 \| 0.8393 \| 0.9012 \| 0.9599 \| 0.6196 \| 0.6327 \| 0.9793 \| 0.8639 \| 0.2411 \| nan \| 0.0800 \| 0.8045 \| 0.7647 \| 0.9800 \| \| 0.021 \| 143.53 \| 2440 \| 0.9934 \| 0.3626 \| 0.9557 \| 0.9449 \| 0.8650 \| 0.8963 \| 0.9220 \| 0.9824 \| 0.6284 \| 0.7961 \| 0.9896 \| 0.9210 \| 0.2369 \| nan \| 0.9836 \| 0.3562 \| 0.8920 \| 0.8759 \| 0.8457 \| 0.8395 \| 0.8999 \| 0.9591 \| 0.6215 \| 0.6309 \| 0.9794 \| 0.8647 \| 0.2342 \| nan \| 0.0797 \| 0.8073 \| 0.7679 \| 0.9799 \| \| 0.0235 \| 144.71 \| 2460 \| 0.9943 \| 0.2911 \| 0.9581 \| 0.9411 \| 0.8603 \| 0.8844 \| 0.9149 \| 0.9794 \| 0.6232 \| 0.7764 \| 0.9884 \| 0.9207 \| 0.2400 \| nan \| 0.9832 \| 0.2869 \| 0.8910 \| 0.8680 \| 0.8450 \| 0.8354 \| 0.8936 \| 0.9600 \| 0.6179 \| 0.6198 \| 0.9793 \| 0.8658 \| 0.2369 \| nan \| 0.0825 \| 0.7979 \| 0.7602 \| 0.9797 \| \| 0.0254 \| 145.88 \| 2480 \| 0.9941 \| 0.2846 \| 0.9532 \| 0.9409 \| 0.8680 \| 0.8930 \| 0.9247 \| 0.9798 \| 0.6242 \| 0.7814 \| 0.9903 \| 0.9162 \| 0.2461 \| nan \| 0.9833 \| 0.2809 \| 0.8925 \| 0.8708 \| 0.8467 \| 0.8382 \| 0.9010 \| 0.9607 \| 0.6183 \| 0.6232 \| 0.9792 \| 0.8653 \| 0.2431 \| nan \| 0.0812 \| 0.7997 \| 0.7618 \| 0.9799 \| \| 0.0216 \| 147.06 \| 2500 \| 0.9935 \| 0.2927 \| 0.9526 \| 0.9281 \| 0.8561 \| 0.8995 \| 0.9277 \| 0.9810 \| 0.6235 \| 0.7848 \| 0.9913 \| 0.9075 \| 0.2323 \| nan \| 0.9834 \| 0.2903 \| 0.8914 \| 0.8714 \| 0.8393 \| 0.8375 \| 0.9033 \| 0.9601 \| 0.6180 \| 0.6270 \| 0.9788 \| 0.8628 \| 0.2305 \| nan \| 0.0810 \| 0.7977 \| 0.7611 \| 0.9798 \| \| 0.0229 \| 148.24 \| 2520 \| 0.9936 \| 0.3642 \| 0.9542 \| 0.9384 \| 0.8601 \| 0.8935 \| 0.9304 \| 0.9833 \| 0.6248 \| 0.7977 \| 0.9898 \| 0.9155 \| 0.2459 \| nan \| 0.9835 \| 0.3544 \| 0.8921 \| 0.8637 \| 0.8429 \| 0.8380 \| 0.8966 \| 0.9593 \| 0.6182 \| 0.6257 \| 0.9792 \| 0.8650 \| 0.2430 \| nan \| 0.0810 \| 0.8070 \| 0.7663 \| 0.9798 \| \| 0.022 \| 149.41 \| 2540 \| 0.9934 \| 0.3138 \| 0.9572 \| 0.9460 \| 0.8611 \| 0.8924 \| 0.9241 \| 0.9815 \| 0.6235 \| 0.7865 \| 0.9897 \| 0.9176 \| 0.2397 \| nan \| 0.9835 \| 0.3030 \| 0.8909 \| 0.8671 \| 0.8420 \| 0.8378 \| 0.8953 \| 0.9597 \| 0.6182 \| 0.6212 \| 0.9792 \| 0.8652 \| 0.2377 \| nan \| 0.0817 \| 0.8020 \| 0.7616 \| 0.9798 \| \| 0.0235 \| 150.59 \| 2560 \| 0.9938 \| 0.2667 \| 0.9539 \| 0.9397 \| 0.8590 \| 0.8940 \| 0.9200 \| 0.9818 \| 0.6240 \| 0.7736 \| 0.9897 \| 0.9229 \| 0.2428 \| nan \| 0.9834 \| 0.2641 \| 0.8916 \| 0.8688 \| 0.8425 \| 0.8378 \| 0.8992 \| 0.9601 \| 0.6176 \| 0.6177 \| 0.9790 \| 0.8660 \| 0.2402 \| nan \| 0.0817 \| 0.7971 \| 0.7591 \| 0.9798 \| \| 0.0224 \| 151.76 \| 2580 \| 0.9938 \| 0.3561 \| 0.9555 \| 0.9403 \| 0.8661 \| 0.8892 \| 0.9244 \| 0.9814 \| 0.6238 \| 0.7724 \| 0.9899 \| 0.9214 \| 0.2453 \| nan \| 0.9834 \| 0.3471 \| 0.8912 \| 0.8688 \| 0.8470 \| 0.8374 \| 0.8984 \| 0.9600 \| 0.6176 \| 0.6209 \| 0.9793 \| 0.8658 \| 0.2398 \| nan \| 0.0820 \| 0.8046 \| 0.7659 \| 0.9798 \| \| 0.0205 \| 152.94 \| 2600 \| 0.9939 \| 0.2943 \| 0.9546 \| 0.9375 \| 0.8547 \| 0.8966 \| 0.9193 \| 0.9801 \| 0.6246 \| 0.7738 \| 0.9904 \| 0.9152 \| 0.2378 \| nan \| 0.9835 \| 0.2896 \| 0.8918 \| 0.8715 \| 0.8392 \| 0.8383 \| 0.8984 \| 0.9611 \| 0.6185 \| 0.6206 \| 0.9793 \| 0.8657 \| 0.2359 \| nan \| 0.0810 \| 0.7979 \| 0.7610 \| 0.9799 \| \| 0.0197 \| 154.12 \| 2620 \| 0.9929 \| 0.3382 \| 0.9570 \| 0.9462 \| 0.8656 \| 0.9036 \| 0.9324 \| 0.9828 \| 0.6258 \| 0.7921 \| 0.9891 \| 0.9219 \| 0.2389 \| nan \| 0.9836 \| 0.3323 \| 0.8919 \| 0.8674 \| 0.8475 \| 0.8405 \| 0.9058 \| 0.9604 \| 0.6171 \| 0.6255 \| 0.9794 \| 0.8656 \| 0.2358 \| nan \| 0.0808 \| 0.8067 \| 0.7656 \| 0.9799 \| \| 0.0209 \| 155.29 \| 2640 \| 0.9943 \| 0.2423 \| 0.9555 \| 0.9340 \| 0.8575 \| 0.8939 \| 0.9254 \| 0.9817 \| 0.6240 \| 0.7638 \| 0.9886 \| 0.9252 \| 0.2443 \| nan \| 0.9835 \| 0.2399 \| 0.8926 \| 0.8723 \| 0.8421 \| 0.8398 \| 0.9027 \| 0.9610 \| 0.6175 \| 0.6173 \| 0.9793 \| 0.8657 \| 0.2419 \| nan \| 0.0812 \| 0.7946 \| 0.7581 \| 0.9800 \| \| 0.0203 \| 156.47 \| 2660 \| 0.9940 \| 0.3496 \| 0.9558 \| 0.9387 \| 0.8629 \| 0.8939 \| 0.9251 \| 0.9808 \| 0.6245 \| 0.7735 \| 0.9892 \| 0.9231 \| 0.2499 \| nan \| 0.9834 \| 0.3429 \| 0.8926 \| 0.8695 \| 0.8463 \| 0.8400 \| 0.9050 \| 0.9608 \| 0.6177 \| 0.6220 \| 0.9796 \| 0.8664 \| 0.2460 \| nan \| 0.0809 \| 0.8047 \| 0.7671 \| 0.9800 \| \| 0.0225 \| 157.65 \| 2680 \| 0.9938 \| 0.3203 \| 0.9571 \| 0.9292 \| 0.8643 \| 0.8980 \| 0.9265 \| 0.9810 \| 0.6220 \| 0.7662 \| 0.9885 \| 0.9237 \| 0.2427 \| nan \| 0.9836 \| 0.3152 \| 0.8912 \| 0.8653 \| 0.8467 \| 0.8419 \| 0.9053 \| 0.9602 \| 0.6169 \| 0.6222 \| 0.9794 \| 0.8654 \| 0.2388 \| nan \| 0.0808 \| 0.8010 \| 0.7640 \| 0.9800 \| \| 0.0269 \| 158.82 \| 2700 \| 0.9943 \| 0.2553 \| 0.9558 \| 0.9247 \| 0.8603 \| 0.8894 \| 0.9212 \| 0.9807 \| 0.6250 \| 0.7761 \| 0.9899 \| 0.9177 \| 0.2444 \| nan \| 0.9834 \| 0.2516 \| 0.8928 \| 0.8697 \| 0.8439 \| 0.8390 \| 0.9014 \| 0.9609 \| 0.6184 \| 0.6272 \| 0.9795 \| 0.8653 \| 0.2418 \| nan \| 0.0806 \| 0.7950 \| 0.7596 \| 0.9800 \| \| 0.0204 \| 160.0 \| 2720 \| 0.9940 \| 0.3480 \| 0.9543 \| 0.9310 \| 0.8668 \| 0.8949 \| 0.9281 \| 0.9816 \| 0.6257 \| 0.7981 \| 0.9892 \| 0.9248 \| 0.2527 \| nan \| 0.9835 \| 0.3391 \| 0.8932 \| 0.8665 \| 0.8497 \| 0.8404 \| 0.9049 \| 0.9605 \| 0.6187 \| 0.6308 \| 0.9796 \| 0.8656 \| 0.2475 \| nan \| 0.0805 \| 0.8069 \| 0.7677 \| 0.9801 \| \| 0.0188 \| 161.18 \| 2740 \| 0.9934 \| 0.3659 \| 0.9579 \| 0.9386 \| 0.8677 \| 0.8992 \| 0.9274 \| 0.9823 \| 0.6255 \| 0.7694 \| 0.9896 \| 0.9168 \| 0.2531 \| nan \| 0.9837 \| 0.3571 \| 0.8920 \| 0.8709 \| 0.8494 \| 0.8420 \| 0.9019 \| 0.9608 \| 0.6191 \| 0.6213 \| 0.9794 \| 0.8654 \| 0.2484 \| nan \| 0.0801 \| 0.8067 \| 0.7686 \| 0.9801 \| \| 0.0218 \| 162.35 \| 2760 \| 0.9936 \| 0.3577 \| 0.9578 \| 0.9321 \| 0.8665 \| 0.9000 \| 0.9290 \| 0.9800 \| 0.6247 \| 0.7756 \| 0.9894 \| 0.9216 \| 0.2515 \| nan \| 0.9837 \| 0.3514 \| 0.8919 \| 0.8732 \| 0.8478 \| 0.8421 \| 0.9041 \| 0.9609 \| 0.6183 \| 0.6240 \| 0.9795 \| 0.8674 \| 0.2469 \| nan \| 0.0805 \| 0.8061 \| 0.7686 \| 0.9801 \| \| 0.0203 \| 163.53 \| 2780 \| 0.9931 \| 0.3268 \| 0.9580 \| 0.9177 \| 0.8569 \| 0.9023 \| 0.9230 \| 0.9822 \| 0.6227 \| 0.7698 \| 0.9898 \| 0.9155 \| 0.2411 \| nan \| 0.9836 \| 0.3216 \| 0.8910 \| 0.8713 \| 0.8405 \| 0.8406 \| 0.9018 \| 0.9599 \| 0.6167 \| 0.6242 \| 0.9795 \| 0.8664 \| 0.2384 \| nan \| 0.0815 \| 0.7999 \| 0.7643 \| 0.9799 \| \| 0.0229 \| 164.71 \| 2800 \| 0.9933 \| 0.3301 \| 0.9548 \| 0.9399 \| 0.8574 \| 0.9024 \| 0.9303 \| 0.9801 \| 0.6251 \| 0.7891 \| 0.9912 \| 0.9162 \| 0.2512 \| nan \| 0.9836 \| 0.3248 \| 0.8924 \| 0.8793 \| 0.8401 \| 0.8410 \| 0.9061 \| 0.9611 \| 0.6190 \| 0.6316 \| 0.9792 \| 0.8669 \| 0.2481 \| nan \| 0.0805 \| 0.8047 \| 0.7672 \| 0.9801 \| \| 0.0189 \| 165.88 \| 2820 \| 0.9940 \| 0.3350 \| 0.9556 \| 0.9351 \| 0.8622 \| 0.8921 \| 0.9278 \| 0.9821 \| 0.6248 \| 0.7816 \| 0.9890 \| 0.9258 \| 0.2524 \| nan \| 0.9835 \| 0.3285 \| 0.8930 \| 0.8755 \| 0.8450 \| 0.8390 \| 0.9041 \| 0.9605 \| 0.6187 \| 0.6255 \| 0.9795 \| 0.8665 \| 0.2472 \| nan \| 0.0816 \| 0.8044 \| 0.7667 \| 0.9800 \| \| 0.0235 \| 167.06 \| 2840 \| 0.9933 \| 0.3772 \| 0.9555 \| 0.9379 \| 0.8622 \| 0.9032 \| 0.9265 \| 0.9822 \| 0.6231 \| 0.7864 \| 0.9899 \| 0.9224 \| 0.2539 \| nan \| 0.9837 \| 0.3700 \| 0.8925 \| 0.8772 \| 0.8443 \| 0.8412 \| 0.9049 \| 0.9603 \| 0.6176 \| 0.6272 \| 0.9795 \| 0.8669 \| 0.2486 \| nan \| 0.0809 \| 0.8087 \| 0.7703 \| 0.9801 \| \| 0.0218 \| 168.24 \| 2860 \| 0.9935 \| 0.3350 \| 0.9582 \| 0.9314 \| 0.8572 \| 0.8961 \| 0.9207 \| 0.9809 \| 0.6220 \| 0.7770 \| 0.9906 \| 0.9125 \| 0.2426 \| nan \| 0.9837 \| 0.3291 \| 0.8914 \| 0.8749 \| 0.8395 \| 0.8412 \| 0.9003 \| 0.9613 \| 0.6170 \| 0.6243 \| 0.9792 \| 0.8658 \| 0.2400 \| nan \| 0.0825 \| 0.8013 \| 0.7652 \| 0.9800 \| \| 0.0225 \| 169.41 \| 2880 \| 0.9941 \| 0.3089 \| 0.9553 \| 0.9303 \| 0.8597 \| 0.8913 \| 0.9232 \| 0.9819 \| 0.6236 \| 0.7837 \| 0.9896 \| 0.9214 \| 0.2450 \| nan \| 0.9835 \| 0.304 \| 0.8928 \| 0.8759 \| 0.8426 \| 0.8392 \| 0.9029 \| 0.9611 \| 0.6185 \| 0.6286 \| 0.9794 \| 0.8665 \| 0.2414 \| nan \| 0.0818 \| 0.8006 \| 0.7643 \| 0.9800 \| \| 0.0204 \| 170.59 \| 2900 \| 0.9937 \| 0.3870 \| 0.9557 \| 0.9338 \| 0.8589 \| 0.8986 \| 0.9322 \| 0.9815 \| 0.6253 \| 0.7736 \| 0.9902 \| 0.9196 \| 0.2531 \| nan \| 0.9837 \| 0.3766 \| 0.8928 \| 0.8770 \| 0.8424 \| 0.8412 \| 0.9061 \| 0.9611 \| 0.6192 \| 0.6238 \| 0.9793 \| 0.8667 \| 0.2497 \| nan \| 0.0808 \| 0.8079 \| 0.7707 \| 0.9801 \| \| 0.0184 \| 171.76 \| 2920 \| 0.9939 \| 0.3577 \| 0.9575 \| 0.9346 \| 0.8621 \| 0.8910 \| 0.9337 \| 0.9813 \| 0.6253 \| 0.7793 \| 0.9892 \| 0.9239 \| 0.2545 \| nan \| 0.9836 \| 0.3487 \| 0.8927 \| 0.8715 \| 0.8450 \| 0.8385 \| 0.9022 \| 0.9612 \| 0.6189 \| 0.6238 \| 0.9795 \| 0.8668 \| 0.2488 \| nan \| 0.0817 \| 0.8065 \| 0.7678 \| 0.9800 \| \| 0.0234 \| 172.94 \| 2940 \| 0.9938 \| 0.3301 \| 0.9576 \| 0.9343 \| 0.8640 \| 0.8945 \| 0.9314 \| 0.9801 \| 0.6259 \| 0.7919 \| 0.9890 \| 0.9268 \| 0.2530 \| nan \| 0.9837 \| 0.3232 \| 0.8928 \| 0.8738 \| 0.8453 \| 0.8400 \| 0.9008 \| 0.9615 \| 0.6194 \| 0.6259 \| 0.9794 \| 0.8671 \| 0.2474 \| nan \| 0.0814 \| 0.8056 \| 0.7662 \| 0.9801 \| \| 0.0213 \| 174.12 \| 2960 \| 0.9934 \| 0.3528 \| 0.9570 \| 0.9312 \| 0.8599 \| 0.9020 \| 0.9337 \| 0.9818 \| 0.6259 \| 0.7751 \| 0.9894 \| 0.9218 \| 0.2475 \| nan \| 0.9837 \| 0.3461 \| 0.8921 \| 0.8755 \| 0.8432 \| 0.8416 \| 0.9035 \| 0.9609 \| 0.6192 \| 0.6199 \| 0.9796 \| 0.8672 \| 0.2440 \| nan \| 0.0815 \| 0.8055 \| 0.7674 \| 0.9801 \| \| 0.0182 \| 175.29 \| 2980 \| 0.9934 \| 0.3610 \| 0.9574 \| 0.9413 \| 0.8613 \| 0.8957 \| 0.9366 \| 0.9802 \| 0.6249 \| 0.7775 \| 0.9902 \| 0.9251 \| 0.2460 \| nan \| 0.9836 \| 0.3535 \| 0.8918 \| 0.8758 \| 0.8435 \| 0.8402 \| 0.9023 \| 0.9612 \| 0.6179 \| 0.6216 \| 0.9794 \| 0.8677 \| 0.2437 \| nan \| 0.0820 \| 0.8070 \| 0.7679 \| 0.9800 \| \| 0.0203 \| 176.47 \| 3000 \| 0.9934 \| 0.3236 \| 0.9573 \| 0.9337 \| 0.8583 \| 0.8992 \| 0.9322 \| 0.9811 \| 0.6247 \| 0.7840 \| 0.9893 \| 0.9246 \| 0.2318 \| nan \| 0.9836 \| 0.3184 \| 0.8917 \| 0.8729 \| 0.8421 \| 0.8399 \| 0.9037 \| 0.9607 \| 0.6183 \| 0.6227 \| 0.9795 \| 0.8674 \| 0.2303 \| nan \| 0.0825 \| 0.8026 \| 0.7639 \| 0.9800 \| \| 0.024 \| 177.65 \| 3020 \| 0.9938 \| 0.3236 \| 0.9539 \| 0.9346 \| 0.8584 \| 0.8971 \| 0.9300 \| 0.9825 \| 0.6259 \| 0.7842 \| 0.9899 \| 0.9241 \| 0.2416 \| nan \| 0.9837 \| 0.3179 \| 0.8926 \| 0.8711 \| 0.8415 \| 0.8404 \| 0.9034 \| 0.9607 \| 0.6197 \| 0.6244 \| 0.9794 \| 0.8673 \| 0.2400 \| nan \| 0.0821 \| 0.8030 \| 0.7648 \| 0.9800 \| \| 0.0182 \| 178.82 \| 3040 \| 0.9933 \| 0.3740 \| 0.9574 \| 0.9400 \| 0.8569 \| 0.9011 \| 0.9271 \| 0.9818 \| 0.6261 \| 0.7775 \| 0.9900 \| 0.9205 \| 0.2384 \| nan \| 0.9838 \| 0.3668 \| 0.8916 \| 0.8713 \| 0.8399 \| 0.8421 \| 0.9023 \| 0.9607 \| 0.6196 \| 0.6247 \| 0.9794 \| 0.8675 \| 0.2366 \| nan \| 0.0814 \| 0.8065 \| 0.7682 \| 0.9801 \| \| 0.0227 \| 180.0 \| 3060 \| 0.9932 \| 0.3447 \| 0.9574 \| 0.9368 \| 0.8584 \| 0.9025 \| 0.9287 \| 0.9817 \| 0.6249 \| 0.7810 \| 0.9896 \| 0.9228 \| 0.2408 \| nan \| 0.9838 \| 0.3387 \| 0.8918 \| 0.8728 \| 0.8409 \| 0.8411 \| 0.9050 \| 0.9605 \| 0.6186 \| 0.6261 \| 0.9795 \| 0.8672 \| 0.2391 \| nan \| 0.0813 \| 0.8048 \| 0.7665 \| 0.9800 \| \| 0.0198 \| 181.18 \| 3080 \| 0.9940 \| 0.3593 \| 0.9564 \| 0.9417 \| 0.8583 \| 0.8938 \| 0.9340 \| 0.9814 \| 0.6248 \| 0.7771 \| 0.9889 \| 0.9282 \| 0.2478 \| nan \| 0.9837 \| 0.3508 \| 0.8918 \| 0.8712 \| 0.8429 \| 0.8406 \| 0.9037 \| 0.9606 \| 0.6177 \| 0.6240 \| 0.9794 \| 0.8673 \| 0.2456 \| nan \| 0.0816 \| 0.8066 \| 0.7676 \| 0.9800 \| \| 0.0249 \| 182.35 \| 3100 \| 0.9939 \| 0.3122 \| 0.9550 \| 0.9416 \| 0.8634 \| 0.8990 \| 0.9334 \| 0.9814 \| 0.6256 \| 0.7774 \| 0.9899 \| 0.9186 \| 0.2534 \| nan \| 0.9838 \| 0.3062 \| 0.8929 \| 0.8700 \| 0.8448 \| 0.8416 \| 0.9050 \| 0.9611 \| 0.6180 \| 0.6212 \| 0.9795 \| 0.8677 \| 0.2501 \| nan \| 0.0812 \| 0.8034 \| 0.7648 \| 0.9801 \| \| 0.0201 \| 183.53 \| 3120 \| 0.9934 \| 0.3008 \| 0.9578 \| 0.9378 \| 0.8544 \| 0.8992 \| 0.9277 \| 0.9808 \| 0.6233 \| 0.7769 \| 0.9899 \| 0.9199 \| 0.2465 \| nan \| 0.9838 \| 0.2955 \| 0.8920 \| 0.8724 \| 0.8382 \| 0.8405 \| 0.9022 \| 0.9609 \| 0.6166 \| 0.6208 \| 0.9794 \| 0.8678 \| 0.2438 \| nan \| 0.0822 \| 0.8007 \| 0.7626 \| 0.9800 \| \| 0.0262 \| 184.71 \| 3140 \| 0.9936 \| 0.3382 \| 0.9554 \| 0.9305 \| 0.8554 \| 0.8991 \| 0.9308 \| 0.9814 \| 0.6254 \| 0.7831 \| 0.9905 \| 0.9193 \| 0.2475 \| nan \| 0.9838 \| 0.3323 \| 0.8927 \| 0.8766 \| 0.8390 \| 0.8408 \| 0.9037 \| 0.9610 \| 0.6180 \| 0.6240 \| 0.9794 \| 0.8674 \| 0.2455 \| nan \| 0.0807 \| 0.8039 \| 0.7665 \| 0.9801 \| \| 0.0217 \| 185.88 \| 3160 \| 0.9937 \| 0.3740 \| 0.9571 \| 0.9372 \| 0.8574 \| 0.8971 \| 0.9322 \| 0.9816 \| 0.6257 \| 0.7808 \| 0.9894 \| 0.9236 \| 0.2459 \| nan \| 0.9838 \| 0.3651 \| 0.8924 \| 0.8744 \| 0.8419 \| 0.8408 \| 0.9036 \| 0.9608 \| 0.6183 \| 0.6234 \| 0.9796 \| 0.8684 \| 0.2437 \| nan \| 0.0813 \| 0.8073 \| 0.7689 \| 0.9801 \| \| 0.0193 \| 187.06 \| 3180 \| 0.9937 \| 0.3854 \| 0.9561 \| 0.9362 \| 0.8592 \| 0.8967 \| 0.9305 \| 0.9825 \| 0.6256 \| 0.7832 \| 0.9895 \| 0.9242 \| 0.2482 \| nan \| 0.9837 \| 0.375 \| 0.8928 \| 0.8725 \| 0.8426 \| 0.8408 \| 0.9029 \| 0.9606 \| 0.6183 \| 0.6244 \| 0.9796 \| 0.8681 \| 0.2457 \| nan \| 0.0813 \| 0.8085 \| 0.7698 \| 0.9801 \| \| 0.0252 \| 188.24 \| 3200 \| 0.9938 \| 0.3691 \| 0.9568 \| 0.9326 \| 0.8565 \| 0.8948 \| 0.9250 \| 0.9813 \| 0.6246 \| 0.7735 \| 0.9899 \| 0.9238 \| 0.2452 \| nan \| 0.9837 \| 0.3603 \| 0.8923 \| 0.8751 \| 0.8404 \| 0.8403 \| 0.9006 \| 0.9608 \| 0.6178 \| 0.6210 \| 0.9794 \| 0.8683 \| 0.2428 \| nan \| 0.0817 \| 0.8051 \| 0.7679 \| 0.9801 \| \| 0.0204 \| 189.41 \| 3220 \| 0.9938 \| 0.3626 \| 0.9558 \| 0.9326 \| 0.8602 \| 0.8968 \| 0.9294 \| 0.9805 \| 0.6239 \| 0.7823 \| 0.9902 \| 0.9197 \| 0.2490 \| nan \| 0.9837 \| 0.3557 \| 0.8927 \| 0.8750 \| 0.8431 \| 0.8403 \| 0.9029 \| 0.9610 \| 0.6175 \| 0.6246 \| 0.9795 \| 0.8682 \| 0.2456 \| nan \| 0.0819 \| 0.8059 \| 0.7684 \| 0.9801 \| \| 0.0249 \| 190.59 \| 3240 \| 0.9936 \| 0.3366 \| 0.9576 \| 0.9339 \| 0.8609 \| 0.8992 \| 0.9281 \| 0.9814 \| 0.6233 \| 0.7887 \| 0.9894 \| 0.9215 \| 0.2500 \| nan \| 0.9838 \| 0.3307 \| 0.8927 \| 0.8725 \| 0.8438 \| 0.8409 \| 0.9025 \| 0.9609 \| 0.6170 \| 0.6282 \| 0.9796 \| 0.8679 \| 0.2464 \| nan \| 0.0822 \| 0.8049 \| 0.7667 \| 0.9801 \| \| 0.0215 \| 191.76 \| 3260 \| 0.9939 \| 0.3463 \| 0.9557 \| 0.9361 \| 0.8647 \| 0.8962 \| 0.9289 \| 0.9821 \| 0.6244 \| 0.7839 \| 0.9894 \| 0.9245 \| 0.2526 \| nan \| 0.9837 \| 0.3397 \| 0.8932 \| 0.8727 \| 0.8466 \| 0.8406 \| 0.9027 \| 0.9609 \| 0.6172 \| 0.6255 \| 0.9796 \| 0.8678 \| 0.2487 \| nan \| 0.0820 \| 0.8060 \| 0.7676 \| 0.9801 \| \| 0.0225 \| 192.94 \| 3280 \| 0.9937 \| 0.3821 \| 0.9572 \| 0.9380 \| 0.8615 \| 0.8938 \| 0.9264 \| 0.9820 \| 0.6240 \| 0.7830 \| 0.9897 \| 0.9226 \| 0.2461 \| nan \| 0.9838 \| 0.3718 \| 0.8921 \| 0.8718 \| 0.8438 \| 0.8402 \| 0.8987 \| 0.9607 \| 0.6166 \| 0.6233 \| 0.9795 \| 0.8679 \| 0.2435 \| nan \| 0.0824 \| 0.8077 \| 0.7687 \| 0.9801 \| \| 0.0221 \| 194.12 \| 3300 \| 0.9937 \| 0.3675 \| 0.9554 \| 0.9395 \| 0.8611 \| 0.8971 \| 0.9268 \| 0.9819 \| 0.6245 \| 0.7911 \| 0.9899 \| 0.9238 \| 0.2495 \| nan \| 0.9838 \| 0.3604 \| 0.8930 \| 0.8720 \| 0.8434 \| 0.8408 \| 0.9010 \| 0.9610 \| 0.6170 \| 0.6262 \| 0.9795 \| 0.8679 \| 0.2472 \| nan \| 0.0816 \| 0.8078 \| 0.7687 \| 0.9801 \| \| 0.0198 \| 195.29 \| 3320 \| 0.9939 \| 0.3561 \| 0.9560 \| 0.9346 \| 0.8574 \| 0.8955 \| 0.9280 \| 0.9815 \| 0.6237 \| 0.7885 \| 0.9897 \| 0.9187 \| 0.2494 \| nan \| 0.9837 \| 0.3493 \| 0.8926 \| 0.8724 \| 0.8411 \| 0.8404 \| 0.9010 \| 0.9609 \| 0.6173 \| 0.6257 \| 0.9796 \| 0.8679 \| 0.2463 \| nan \| 0.0821 \| 0.8056 \| 0.7675 \| 0.9801 \| \| 0.0182 \| 196.47 \| 3340 \| 0.9936 \| 0.3545 \| 0.9579 \| 0.9313 \| 0.8589 \| 0.8976 \| 0.9259 \| 0.9813 \| 0.6236 \| 0.7862 \| 0.9891 \| 0.9238 \| 0.2451 \| nan \| 0.9838 \| 0.3482 \| 0.8920 \| 0.8730 \| 0.8417 \| 0.8409 \| 0.9003 \| 0.9609 \| 0.6171 \| 0.6250 \| 0.9795 \| 0.8682 \| 0.2426 \| nan \| 0.0824 \| 0.8053 \| 0.7672 \| 0.9801 \| \| 0.0186 \| 197.65 \| 3360 \| 0.9936 \| 0.3593 \| 0.9573 \| 0.9342 \| 0.8621 \| 0.8982 \| 0.9247 \| 0.9811 \| 0.6239 \| 0.7856 \| 0.9894 \| 0.9232 \| 0.2462 \| nan \| 0.9838 \| 0.3530 \| 0.8923 \| 0.8729 \| 0.8439 \| 0.8413 \| 0.9004 \| 0.9610 \| 0.6173 \| 0.6248 \| 0.9796 \| 0.8684 \| 0.2439 \| nan \| 0.0821 \| 0.8061 \| 0.7679 \| 0.9801 \| \| 0.0241 \| 198.82 \| 3380 \| 0.9940 \| 0.3431 \| 0.9557 \| 0.9396 \| 0.8677 \| 0.8904 \| 0.9302 \| 0.9817 \| 0.6260 \| 0.7848 \| 0.9899 \| 0.9173 \| 0.2559 \| nan \| 0.9836 \| 0.3344 \| 0.8929 \| 0.8736 \| 0.8493 \| 0.8387 \| 0.9028 \| 0.9606 \| 0.6195 \| 0.6250 \| 0.9796 \| 0.8669 \| 0.2493 \| nan \| 0.0823 \| 0.8059 \| 0.7674 \| 0.9801 \| \| 0.0212 \| 200.0 \| 3400 \| 0.9945 \| 0.3252 \| 0.9566 \| 0.9141 \| 0.8484 \| 0.8866 \| 0.9209 \| 0.9818 \| 0.6251 \| 0.7763 \| 0.9894 \| 0.9187 \| 0.2221 \| nan \| 0.9835 \| 0.3175 \| 0.8928 \| 0.8665 \| 0.8352 \| 0.8386 \| 0.8999 \| 0.9615 \| 0.6194 \| 0.6165 \| 0.9793 \| 0.8673 \| 0.2204 \| nan \| 0.0827 \| 0.7969 \| 0.7614 \| 0.9800 \| \| 0.0204 \| 201.18 \| 3420 \| 0.9936 \| 0.3870 \| 0.9568 \| 0.9381 \| 0.8583 \| 0.9025 \| 0.9272 \| 0.9818 \| 0.6253 \| 0.7838 \| 0.9885 \| 0.9281 \| 0.2431 \| nan \| 0.9839 \| 0.3802 \| 0.8919 \| 0.8633 \| 0.8419 \| 0.8438 \| 0.9017 \| 0.9610 \| 0.6190 \| 0.6216 \| 0.9794 \| 0.8688 \| 0.2403 \| nan \| 0.0817 \| 0.8088 \| 0.7690 \| 0.9801 \| \| 0.0216 \| 202.35 \| 3440 \| 0.9938 \| 0.3919 \| 0.9606 \| 0.9362 \| 0.8438 \| 0.8912 \| 0.9277 \| 0.9803 \| 0.6227 \| 0.7608 \| 0.9890 \| 0.9203 \| 0.2388 \| nan \| 0.9837 \| 0.3813 \| 0.8904 \| 0.8701 \| 0.8296 \| 0.8408 \| 0.9007 \| 0.9610 \| 0.6166 \| 0.6171 \| 0.9791 \| 0.8668 \| 0.2372 \| nan \| 0.0829 \| 0.8044 \| 0.7673 \| 0.9799 \| \| 0.0192 \| 203.53 \| 3460 \| 0.9938 \| 0.3951 \| 0.9581 \| 0.9246 \| 0.8510 \| 0.8967 \| 0.9139 \| 0.9803 \| 0.6228 \| 0.7711 \| 0.9886 \| 0.9360 \| 0.2317 \| nan \| 0.9839 \| 0.3857 \| 0.8910 \| 0.8700 \| 0.8356 \| 0.8436 \| 0.8956 \| 0.9600 \| 0.6176 \| 0.6196 \| 0.9792 \| 0.8666 \| 0.2299 \| nan \| 0.0827 \| 0.8049 \| 0.7676 \| 0.9800 \| \| 0.0189 \| 204.71 \| 3480 \| 0.9939 \| 0.3561 \| 0.9578 \| 0.9363 \| 0.8616 \| 0.8993 \| 0.9235 \| 0.9806 \| 0.6256 \| 0.7930 \| 0.9867 \| 0.9351 \| 0.2401 \| nan \| 0.9837 \| 0.3471 \| 0.8919 \| 0.8699 \| 0.8441 \| 0.8427 \| 0.8997 \| 0.9608 \| 0.6185 \| 0.6242 \| 0.9791 \| 0.8657 \| 0.2386 \| nan \| 0.0822 \| 0.8069 \| 0.7666 \| 0.9800 \| \| 0.0226 \| 205.88 \| 3500 \| 0.9929 \| 0.3024 \| 0.9535 \| 0.9352 \| 0.8572 \| 0.9105 \| 0.9212 \| 0.9821 \| 0.6284 \| 0.7859 \| 0.9912 \| 0.9195 \| 0.2203 \| nan \| 0.9840 \| 0.2948 \| 0.8917 \| 0.8777 \| 0.8367 \| 0.8440 \| 0.8974 \| 0.9606 \| 0.6222 \| 0.6266 \| 0.9788 \| 0.8678 \| 0.2190 \| nan \| 0.0807 \| 0.8000 \| 0.7616 \| 0.9801 \| \| 0.019 \| 207.06 \| 3520 \| 0.9932 \| 0.2927 \| 0.9541 \| 0.9493 \| 0.8537 \| 0.9081 \| 0.9246 \| 0.9808 \| 0.6292 \| 0.7824 \| 0.9918 \| 0.9102 \| 0.2333 \| nan \| 0.9840 \| 0.2894 \| 0.8920 \| 0.8771 \| 0.8380 \| 0.8451 \| 0.9021 \| 0.9604 \| 0.6222 \| 0.6305 \| 0.9791 \| 0.8668 \| 0.2318 \| nan \| 0.0816 \| 0.8003 \| 0.7630 \| 0.9802 \| \| 0.0192 \| 208.24 \| 3540 \| 0.9940 \| 0.3691 \| 0.9518 \| 0.9420 \| 0.8676 \| 0.8984 \| 0.9314 \| 0.9820 \| 0.6263 \| 0.7955 \| 0.9912 \| 0.9161 \| 0.2501 \| nan \| 0.9838 \| 0.3586 \| 0.8937 \| 0.8717 \| 0.8469 \| 0.8416 \| 0.9064 \| 0.9613 \| 0.6197 \| 0.6279 \| 0.9798 \| 0.8693 \| 0.2475 \| nan \| 0.0819 \| 0.8089 \| 0.7699 \| 0.9803 \| \| 0.0183 \| 209.41 \| 3560 \| 0.9934 \| 0.3593 \| 0.9579 \| 0.9417 \| 0.8505 \| 0.9046 \| 0.9235 \| 0.9808 \| 0.6222 \| 0.7652 \| 0.9898 \| 0.9218 \| 0.2416 \| nan \| 0.9839 \| 0.3469 \| 0.8911 \| 0.8719 \| 0.8347 \| 0.8452 \| 0.9017 \| 0.9597 \| 0.6160 \| 0.6251 \| 0.9797 \| 0.8693 \| 0.2332 \| nan \| 0.0829 \| 0.8040 \| 0.7660 \| 0.9801 \| \| 0.0203 \| 210.59 \| 3580 \| 0.9936 \| 0.2732 \| 0.9575 \| 0.9368 \| 0.8535 \| 0.8987 \| 0.9253 \| 0.9809 \| 0.6303 \| 0.8001 \| 0.9898 \| 0.9163 \| 0.2412 \| nan \| 0.9838 \| 0.2701 \| 0.8924 \| 0.8761 \| 0.8377 \| 0.8418 \| 0.9035 \| 0.9605 \| 0.6226 \| 0.6354 \| 0.9795 \| 0.8694 \| 0.2368 \| nan \| 0.0813 \| 0.7998 \| 0.7623 \| 0.9801 \| \| 0.0202 \| 211.76 \| 3600 \| 0.9940 \| 0.4033 \| 0.9544 \| 0.9563 \| 0.8578 \| 0.8902 \| 0.9383 \| 0.9820 \| 0.6282 \| 0.7908 \| 0.9894 \| 0.9287 \| 0.2551 \| nan \| 0.9837 \| 0.3899 \| 0.8925 \| 0.8662 \| 0.8412 \| 0.8385 \| 0.9049 \| 0.9599 \| 0.6211 \| 0.6218 \| 0.9795 \| 0.8693 \| 0.2502 \| nan \| 0.0827 \| 0.8130 \| 0.7707 \| 0.9800 \| \| 0.0166 \| 212.94 \| 3620 \| 0.9944 \| 0.2878 \| 0.9547 \| 0.9338 \| 0.8550 \| 0.8885 \| 0.9356 \| 0.9824 \| 0.6238 \| 0.7580 \| 0.9892 \| 0.9279 \| 0.2508 \| nan \| 0.9836 \| 0.2823 \| 0.8923 \| 0.8738 \| 0.8384 \| 0.8381 \| 0.9061 \| 0.9597 \| 0.6176 \| 0.6064 \| 0.9797 \| 0.8700 \| 0.2483 \| nan \| 0.0835 \| 0.7986 \| 0.7612 \| 0.9799 \| \| 0.0211 \| 214.12 \| 3640 \| 0.9942 \| 0.3252 \| 0.9542 \| 0.9483 \| 0.8648 \| 0.8890 \| 0.9359 \| 0.9806 \| 0.6263 \| 0.7818 \| 0.9906 \| 0.9218 \| 0.2551 \| nan \| 0.9836 \| 0.3180 \| 0.8936 \| 0.8714 \| 0.8477 \| 0.8378 \| 0.9079 \| 0.9612 \| 0.6199 \| 0.6162 \| 0.9796 \| 0.8705 \| 0.2513 \| nan \| 0.0829 \| 0.8052 \| 0.7661 \| 0.9801 \| \| 0.0217 \| 215.29 \| 3660 \| 0.9938 \| 0.4683 \| 0.9548 \| 0.9303 \| 0.8702 \| 0.8987 \| 0.9307 \| 0.9829 \| 0.6275 \| 0.7809 \| 0.9910 \| 0.9140 \| 0.2478 \| nan \| 0.9841 \| 0.4486 \| 0.8930 \| 0.8733 \| 0.8507 \| 0.8446 \| 0.9020 \| 0.9605 \| 0.6205 \| 0.6242 \| 0.9797 \| 0.8681 \| 0.2431 \| nan \| 0.0806 \| 0.8147 \| 0.7763 \| 0.9804 \| \| 0.0191 \| 216.47 \| 3680 \| 0.9941 \| 0.3772 \| 0.9546 \| 0.9322 \| 0.8528 \| 0.8988 \| 0.9296 \| 0.9824 \| 0.6285 \| 0.7986 \| 0.9893 \| 0.9253 \| 0.2534 \| nan \| 0.9839 \| 0.3694 \| 0.8941 \| 0.8726 \| 0.8372 \| 0.8427 \| 0.9067 \| 0.9605 \| 0.6208 \| 0.6240 \| 0.9797 \| 0.8702 \| 0.2514 \| nan \| 0.0811 \| 0.8090 \| 0.7703 \| 0.9803 \| \| 0.0202 \| 217.65 \| 3700 \| 0.9936 \| 0.3724 \| 0.9585 \| 0.9363 \| 0.8553 \| 0.9000 \| 0.9307 \| 0.9813 \| 0.6279 \| 0.7648 \| 0.9895 \| 0.9255 \| 0.2396 \| nan \| 0.9840 \| 0.3658 \| 0.8923 \| 0.8793 \| 0.8386 \| 0.8432 \| 0.9073 \| 0.9599 \| 0.6208 \| 0.6136 \| 0.9798 \| 0.8708 \| 0.2372 \| nan \| 0.0823 \| 0.8058 \| 0.7687 \| 0.9802 \| \| 0.0192 \| 218.82 \| 3720 \| 0.9938 \| 0.3886 \| 0.9556 \| 0.9488 \| 0.8705 \| 0.8988 \| 0.9315 \| 0.9813 \| 0.6290 \| 0.7774 \| 0.9897 \| 0.9304 \| 0.2487 \| nan \| 0.9840 \| 0.3794 \| 0.8944 \| 0.8817 \| 0.8481 \| 0.8429 \| 0.9072 \| 0.9610 \| 0.6228 \| 0.6140 \| 0.9797 \| 0.8704 \| 0.2451 \| nan \| 0.0816 \| 0.8111 \| 0.7716 \| 0.9803 \| \| 0.0211 \| 220.0 \| 3740 \| 0.9943 \| 0.3447 \| 0.9568 \| 0.9372 \| 0.8628 \| 0.8932 \| 0.9251 \| 0.9793 \| 0.6242 \| 0.7699 \| 0.9897 \| 0.9231 \| 0.2349 \| nan \| 0.9839 \| 0.3387 \| 0.8930 \| 0.8822 \| 0.8455 \| 0.8413 \| 0.9037 \| 0.9600 \| 0.6190 \| 0.6111 \| 0.9799 \| 0.8703 \| 0.2297 \| nan \| 0.0837 \| 0.8027 \| 0.7660 \| 0.9802 \| \| 0.0169 \| 221.18 \| 3760 \| 0.9934 \| 0.4260 \| 0.9574 \| 0.9449 \| 0.8633 \| 0.9003 \| 0.9379 \| 0.9820 \| 0.6276 \| 0.7915 \| 0.9893 \| 0.9292 \| 0.2544 \| nan \| 0.9840 \| 0.4031 \| 0.8935 \| 0.8798 \| 0.8463 \| 0.8396 \| 0.9068 \| 0.9605 \| 0.6209 \| 0.6207 \| 0.9801 \| 0.8713 \| 0.2485 \| nan \| 0.0826 \| 0.8152 \| 0.7735 \| 0.9802 \| \| 0.0179 \| 222.35 \| 3780 \| 0.9943 \| 0.4455 \| 0.9546 \| 0.9557 \| 0.8707 \| 0.8969 \| 0.9343 \| 0.9812 \| 0.6284 \| 0.7858 \| 0.9895 \| 0.9230 \| 0.2580 \| nan \| 0.9838 \| 0.4295 \| 0.8944 \| 0.8720 \| 0.8492 \| 0.8404 \| 0.9090 \| 0.9619 \| 0.6224 \| 0.6242 \| 0.9801 \| 0.8724 \| 0.2549 \| nan \| 0.0818 \| 0.8168 \| 0.7765 \| 0.9804 \| \| 0.0174 \| 223.53 \| 3800 \| 0.9941 \| 0.2683 \| 0.9578 \| 0.9291 \| 0.8487 \| 0.8960 \| 0.9115 \| 0.9813 \| 0.6218 \| 0.7664 \| 0.9891 \| 0.9271 \| 0.2324 \| nan \| 0.9839 \| 0.2657 \| 0.8924 \| 0.8757 \| 0.8345 \| 0.8414 \| 0.8948 \| 0.9607 \| 0.6180 \| 0.6167 \| 0.9797 \| 0.8718 \| 0.2309 \| nan \| 0.0845 \| 0.7941 \| 0.7589 \| 0.9801 \| \| 0.0177 \| 224.71 \| 3820 \| 0.9939 \| 0.3512 \| 0.9566 \| 0.9368 \| 0.8628 \| 0.8967 \| 0.9329 \| 0.9826 \| 0.6266 \| 0.7956 \| 0.9894 \| 0.9307 \| 0.2566 \| nan \| 0.9840 \| 0.3445 \| 0.8948 \| 0.8795 \| 0.8452 \| 0.8426 \| 0.9045 \| 0.9618 \| 0.6203 \| 0.6299 \| 0.9799 \| 0.8718 \| 0.2531 \| nan \| 0.0815 \| 0.8086 \| 0.7701 \| 0.9805 \| \| 0.0207 \| 225.88 \| 3840 \| 0.9934 \| 0.4341 \| 0.9566 \| 0.9327 \| 0.8625 \| 0.8968 \| 0.9374 \| 0.9825 \| 0.6270 \| 0.7652 \| 0.9907 \| 0.9247 \| 0.2518 \| nan \| 0.9840 \| 0.4205 \| 0.8920 \| 0.8749 \| 0.8435 \| 0.8427 \| 0.8982 \| 0.9601 \| 0.6198 \| 0.6150 \| 0.9796 \| 0.8708 \| 0.2468 \| nan \| 0.0825 \| 0.8120 \| 0.7729 \| 0.9802 \| \| 0.0186 \| 227.06 \| 3860 \| 0.9944 \| 0.3593 \| 0.9568 \| 0.9338 \| 0.8586 \| 0.8876 \| 0.9340 \| 0.9812 \| 0.6258 \| 0.7708 \| 0.9903 \| 0.9223 \| 0.2453 \| nan \| 0.9839 \| 0.3491 \| 0.8931 \| 0.8787 \| 0.8429 \| 0.8409 \| 0.8995 \| 0.9609 \| 0.6192 \| 0.6172 \| 0.9798 \| 0.8716 \| 0.2401 \| nan \| 0.0832 \| 0.8046 \| 0.7675 \| 0.9802 \| \| 0.0215 \| 228.24 \| 3880 \| 0.9938 \| 0.4341 \| 0.9571 \| 0.9345 \| 0.8621 \| 0.8989 \| 0.9379 \| 0.9819 \| 0.6253 \| 0.7557 \| 0.9900 \| 0.9258 \| 0.2545 \| nan \| 0.9839 \| 0.4192 \| 0.8930 \| 0.8791 \| 0.8442 \| 0.8447 \| 0.9060 \| 0.9598 \| 0.6205 \| 0.6098 \| 0.9800 \| 0.8715 \| 0.2500 \| nan \| 0.0826 \| 0.8117 \| 0.7740 \| 0.9803 \| \| 0.0191 \| 229.41 \| 3900 \| 0.9935 \| 0.4228 \| 0.9609 \| 0.9298 \| 0.8530 \| 0.8971 \| 0.9220 \| 0.9810 \| 0.6242 \| 0.7614 \| 0.9892 \| 0.9239 \| 0.2450 \| nan \| 0.9840 \| 0.4107 \| 0.8910 \| 0.8748 \| 0.8368 \| 0.8431 \| 0.8969 \| 0.9596 \| 0.6181 \| 0.6114 \| 0.9798 \| 0.8712 \| 0.2416 \| nan \| 0.0839 \| 0.8080 \| 0.7707 \| 0.9801 \| \| 0.0194 \| 230.59 \| 3920 \| 0.9935 \| 0.4114 \| 0.9572 \| 0.9463 \| 0.8608 \| 0.9017 \| 0.9329 \| 0.9809 \| 0.6252 \| 0.7815 \| 0.9901 \| 0.9263 \| 0.2493 \| nan \| 0.9841 \| 0.4003 \| 0.8930 \| 0.8789 \| 0.8421 \| 0.8443 \| 0.9048 \| 0.9610 \| 0.6189 \| 0.6224 \| 0.9797 \| 0.8721 \| 0.2465 \| nan \| 0.0825 \| 0.8121 \| 0.7729 \| 0.9803 \| \| 0.0181 \| 231.76 \| 3940 \| 0.9935 \| 0.3984 \| 0.9567 \| 0.9417 \| 0.8638 \| 0.9068 \| 0.9288 \| 0.9793 \| 0.6239 \| 0.7886 \| 0.9905 \| 0.9218 \| 0.2480 \| nan \| 0.9841 \| 0.3907 \| 0.8936 \| 0.8811 \| 0.8454 \| 0.8443 \| 0.9052 \| 0.9609 \| 0.6184 \| 0.6328 \| 0.9800 \| 0.8721 \| 0.2439 \| nan \| 0.0813 \| 0.8109 \| 0.7733 \| 0.9804 \| \| 0.0219 \| 232.94 \| 3960 \| 0.9933 \| 0.4797 \| 0.9603 \| 0.9400 \| 0.8654 \| 0.9075 \| 0.9241 \| 0.9802 \| 0.6246 \| 0.7859 \| 0.9884 \| 0.9301 \| 0.2488 \| nan \| 0.9841 \| 0.4531 \| 0.8924 \| 0.8777 \| 0.8482 \| 0.8453 \| 0.9005 \| 0.9612 \| 0.6176 \| 0.6328 \| 0.9800 \| 0.8722 \| 0.2445 \| nan \| 0.0816 \| 0.8176 \| 0.7777 \| 0.9804 \| \| 0.017 \| 234.12 \| 3980 \| 0.9943 \| 0.3984 \| 0.9591 \| 0.9360 \| 0.8552 \| 0.8959 \| 0.9249 \| 0.9810 \| 0.6254 \| 0.7733 \| 0.9886 \| 0.9256 \| 0.2392 \| nan \| 0.9840 \| 0.3834 \| 0.8924 \| 0.8772 \| 0.8409 \| 0.8454 \| 0.9022 \| 0.9609 \| 0.6199 \| 0.6260 \| 0.9798 \| 0.8721 \| 0.2357 \| nan \| 0.0822 \| 0.8075 \| 0.7708 \| 0.9803 \| \| 0.0178 \| 235.29 \| 4000 \| 0.9938 \| 0.4504 \| 0.9568 \| 0.9284 \| 0.8601 \| 0.8952 \| 0.9263 \| 0.9842 \| 0.6245 \| 0.7851 \| 0.9895 \| 0.9196 \| 0.2356 \| nan \| 0.9841 \| 0.4321 \| 0.8926 \| 0.8717 \| 0.8432 \| 0.8439 \| 0.9021 \| 0.9587 \| 0.6172 \| 0.6180 \| 0.9801 \| 0.8699 \| 0.2261 \| nan \| 0.0836 \| 0.8115 \| 0.7723 \| 0.9802 \| \| 0.0198 \| 236.47 \| 4020 \| 0.9939 \| 0.3886 \| 0.9591 \| 0.9406 \| 0.8569 \| 0.8932 \| 0.9280 \| 0.9807 \| 0.6237 \| 0.7595 \| 0.9902 \| 0.9249 \| 0.2407 \| nan \| 0.9842 \| 0.3776 \| 0.8928 \| 0.8755 \| 0.8403 \| 0.8432 \| 0.9005 \| 0.9603 \| 0.6186 \| 0.6093 \| 0.9797 \| 0.8717 \| 0.2346 \| nan \| 0.0836 \| 0.8062 \| 0.7683 \| 0.9803 \| \| 0.017 \| 237.65 \| 4040 \| 0.9941 \| 0.4065 \| 0.9591 \| 0.9322 \| 0.8549 \| 0.8945 \| 0.9281 \| 0.9806 \| 0.6263 \| 0.7652 \| 0.9907 \| 0.9183 \| 0.2460 \| nan \| 0.9842 \| 0.3937 \| 0.8937 \| 0.8821 \| 0.8395 \| 0.8443 \| 0.9029 \| 0.9611 \| 0.6205 \| 0.6150 \| 0.9798 \| 0.8723 \| 0.2421 \| nan \| 0.0826 \| 0.8074 \| 0.7716 \| 0.9804 \| \| 0.0195 \| 238.82 \| 4060 \| 0.9942 \| 0.3805 \| 0.9595 \| 0.9244 \| 0.8475 \| 0.8913 \| 0.9235 \| 0.9799 \| 0.6239 \| 0.7692 \| 0.9901 \| 0.9209 \| 0.2439 \| nan \| 0.9840 \| 0.3685 \| 0.8931 \| 0.8770 \| 0.8345 \| 0.8419 \| 0.9021 \| 0.9607 \| 0.6195 \| 0.6139 \| 0.9798 \| 0.8725 \| 0.2412 \| nan \| 0.0848 \| 0.8037 \| 0.7684 \| 0.9803 \| \| 0.0195 \| 240.0 \| 4080 \| 0.9939 \| 0.3740 \| 0.9576 \| 0.9336 \| 0.8518 \| 0.9005 \| 0.9294 \| 0.9802 \| 0.6219 \| 0.7682 \| 0.9900 \| 0.9267 \| 0.2434 \| nan \| 0.9841 \| 0.3668 \| 0.8932 \| 0.8810 \| 0.8362 \| 0.8436 \| 0.9046 \| 0.9609 \| 0.6179 \| 0.6148 \| 0.9798 \| 0.8731 \| 0.2409 \| nan \| 0.0841 \| 0.8055 \| 0.7690 \| 0.9803 \| \| 0.019 \| 241.18 \| 4100 \| 0.9934 \| 0.4130 \| 0.9557 \| 0.9383 \| 0.8568 \| 0.9091 \| 0.9265 \| 0.9816 \| 0.6237 \| 0.7526 \| 0.9907 \| 0.9207 \| 0.2527 \| nan \| 0.9842 \| 0.4032 \| 0.8923 \| 0.8823 \| 0.8388 \| 0.8452 \| 0.9044 \| 0.9596 \| 0.6181 \| 0.6075 \| 0.9801 \| 0.8726 \| 0.2473 \| nan \| 0.0839 \| 0.8088 \| 0.7720 \| 0.9803 \| \| 0.0155 \| 242.35 \| 4120 \| 0.9937 \| 0.4146 \| 0.9588 \| 0.9401 \| 0.8506 \| 0.8980 \| 0.9292 \| 0.9800 \| 0.6262 \| 0.7814 \| 0.9903 \| 0.9263 \| 0.2435 \| nan \| 0.9841 \| 0.3966 \| 0.8933 \| 0.8803 \| 0.8337 \| 0.8436 \| 0.9036 \| 0.9615 \| 0.6203 \| 0.6218 \| 0.9796 \| 0.8736 \| 0.2413 \| nan \| 0.0827 \| 0.8102 \| 0.7718 \| 0.9803 \| \| 0.0185 \| 243.53 \| 4140 \| 0.9933 \| 0.4407 \| 0.9586 \| 0.9358 \| 0.8511 \| 0.9064 \| 0.9302 \| 0.9809 \| 0.6269 \| 0.7668 \| 0.9902 \| 0.9265 \| 0.2348 \| nan \| 0.9842 \| 0.4274 \| 0.8925 \| 0.8811 \| 0.8337 \| 0.8460 \| 0.9038 \| 0.9601 \| 0.6209 \| 0.6185 \| 0.9797 \| 0.8737 \| 0.2329 \| nan \| 0.0827 \| 0.8109 \| 0.7734 \| 0.9803 \| \| 0.0197 \| 244.71 \| 4160 \| 0.9941 \| 0.4016 \| 0.9574 \| 0.9368 \| 0.8594 \| 0.8953 \| 0.9265 \| 0.9816 \| 0.6265 \| 0.7806 \| 0.9889 \| 0.9315 \| 0.2460 \| nan \| 0.9840 \| 0.3914 \| 0.8931 \| 0.8748 \| 0.8421 \| 0.8429 \| 0.9029 \| 0.9601 \| 0.6198 \| 0.6194 \| 0.9800 \| 0.8737 \| 0.2438 \| nan \| 0.0838 \| 0.8097 \| 0.7714 \| 0.9803 \| \| 0.0178 \| 245.88 \| 4180 \| 0.9942 \| 0.4211 \| 0.9549 \| 0.9475 \| 0.8526 \| 0.8957 \| 0.9281 \| 0.9834 \| 0.6257 \| 0.7645 \| 0.9887 \| 0.9300 \| 0.2505 \| nan \| 0.9838 \| 0.4098 \| 0.8933 \| 0.8740 \| 0.8387 \| 0.8414 \| 0.9039 \| 0.9585 \| 0.6200 \| 0.6099 \| 0.9801 \| 0.8723 \| 0.2476 \| nan \| 0.0851 \| 0.8105 \| 0.7718 \| 0.9802 \| \| 0.0181 \| 247.06 \| 4200 \| 0.9934 \| 0.4797 \| 0.9565 \| 0.9473 \| 0.8639 \| 0.9014 \| 0.9278 \| 0.9820 \| 0.6290 \| 0.7834 \| 0.9903 \| 0.9272 \| 0.2492 \| nan \| 0.9841 \| 0.4631 \| 0.8940 \| 0.8791 \| 0.8450 \| 0.8423 \| 0.9042 \| 0.9606 \| 0.6226 \| 0.6200 \| 0.9799 \| 0.8731 \| 0.2465 \| nan \| 0.0819 \| 0.8178 \| 0.7780 \| 0.9804 \| \| 0.0222 \| 248.24 \| 4220 \| 0.9938 \| 0.4049 \| 0.9570 \| 0.9411 \| 0.8531 \| 0.9025 \| 0.9344 \| 0.9807 \| 0.6242 \| 0.7708 \| 0.9900 \| 0.9230 \| 0.2420 \| nan \| 0.9841 \| 0.3934 \| 0.8930 \| 0.8778 \| 0.8377 \| 0.8430 \| 0.9060 \| 0.9607 \| 0.6193 \| 0.6136 \| 0.9800 \| 0.8723 \| 0.2393 \| nan \| 0.0837 \| 0.8090 \| 0.7708 \| 0.9803 \| \| 0.0197 \| 249.41 \| 4240 \| 0.9936 \| 0.3740 \| 0.9589 \| 0.9474 \| 0.8535 \| 0.9008 \| 0.9293 \| 0.9819 \| 0.6252 \| 0.7759 \| 0.9897 \| 0.9225 \| 0.2403 \| nan \| 0.9841 \| 0.3633 \| 0.8932 \| 0.8757 \| 0.8384 \| 0.8433 \| 0.9037 \| 0.9607 \| 0.6193 \| 0.6239 \| 0.9800 \| 0.8733 \| 0.2382 \| nan \| 0.0835 \| 0.8072 \| 0.7690 \| 0.9803 \| \| 0.0198 \| 250.59 \| 4260 \| 0.9939 \| 0.3854 \| 0.9596 \| 0.9399 \| 0.8664 \| 0.8952 \| 0.9290 \| 0.9808 \| 0.6238 \| 0.7739 \| 0.9886 \| 0.9305 \| 0.2437 \| nan \| 0.9840 \| 0.3652 \| 0.8927 \| 0.8747 \| 0.8457 \| 0.8428 \| 0.9030 \| 0.9609 \| 0.6167 \| 0.6180 \| 0.9799 \| 0.8736 \| 0.2415 \| nan \| 0.0848 \| 0.8085 \| 0.7691 \| 0.9803 \| \| 0.0194 \| 251.76 \| 4280 \| 0.9936 \| 0.4553 \| 0.9588 \| 0.9327 \| 0.8496 \| 0.9022 \| 0.9293 \| 0.9809 \| 0.6247 \| 0.7580 \| 0.9897 \| 0.9296 \| 0.2382 \| nan \| 0.9842 \| 0.4355 \| 0.8918 \| 0.8756 \| 0.8360 \| 0.8450 \| 0.9026 \| 0.9606 \| 0.6185 \| 0.6111 \| 0.9799 \| 0.8738 \| 0.2361 \| nan \| 0.0835 \| 0.8110 \| 0.7731 \| 0.9803 \| \| 0.0181 \| 252.94 \| 4300 \| 0.9935 \| 0.4602 \| 0.9567 \| 0.9443 \| 0.8640 \| 0.9062 \| 0.9366 \| 0.9819 \| 0.6251 \| 0.7717 \| 0.9900 \| 0.9281 \| 0.2511 \| nan \| 0.9842 \| 0.4388 \| 0.8939 \| 0.8769 \| 0.8460 \| 0.8454 \| 0.9043 \| 0.9610 \| 0.6190 \| 0.6160 \| 0.9800 \| 0.8732 \| 0.2487 \| nan \| 0.0834 \| 0.8161 \| 0.7760 \| 0.9805 \| \| 0.024 \| 254.12 \| 4320 \| 0.9944 \| 0.3919 \| 0.9566 \| 0.9335 \| 0.8590 \| 0.8940 \| 0.9317 \| 0.9804 \| 0.6241 \| 0.7724 \| 0.9891 \| 0.9283 \| 0.2530 \| nan \| 0.9839 \| 0.3813 \| 0.8934 \| 0.8777 \| 0.8436 \| 0.8412 \| 0.9047 \| 0.9602 \| 0.6196 \| 0.6129 \| 0.9800 \| 0.8729 \| 0.2507 \| nan \| 0.0847 \| 0.8083 \| 0.7709 \| 0.9803 \| \| 0.0186 \| 255.29 \| 4340 \| 0.9942 \| 0.4455 \| 0.9557 \| 0.9383 \| 0.8635 \| 0.9019 \| 0.9373 \| 0.9813 \| 0.6262 \| 0.7775 \| 0.9895 \| 0.9279 \| 0.2574 \| nan \| 0.9842 \| 0.4281 \| 0.8947 \| 0.8749 \| 0.8463 \| 0.8457 \| 0.9051 \| 0.9605 \| 0.6209 \| 0.6189 \| 0.9801 \| 0.8731 \| 0.2546 \| nan \| 0.0830 \| 0.8151 \| 0.7759 \| 0.9805 \| \| 0.0222 \| 256.47 \| 4360 \| 0.9940 \| 0.4325 \| 0.9572 \| 0.9444 \| 0.8710 \| 0.8992 \| 0.9341 \| 0.9807 \| 0.6243 \| 0.7862 \| 0.9897 \| 0.9272 \| 0.2564 \| nan \| 0.9841 \| 0.4130 \| 0.8945 \| 0.8743 \| 0.8524 \| 0.8440 \| 0.9022 \| 0.9603 \| 0.6192 \| 0.6287 \| 0.9804 \| 0.8735 \| 0.2526 \| nan \| 0.0845 \| 0.8152 \| 0.7753 \| 0.9805 \| \| 0.0218 \| 257.65 \| 4380 \| 0.9937 \| 0.4309 \| 0.9583 \| 0.9415 \| 0.8610 \| 0.8997 \| 0.9356 \| 0.9813 \| 0.6261 \| 0.7693 \| 0.9900 \| 0.9263 \| 0.2509 \| nan \| 0.9842 \| 0.4033 \| 0.8936 \| 0.8816 \| 0.8437 \| 0.8442 \| 0.9018 \| 0.9600 \| 0.6198 \| 0.6182 \| 0.9800 \| 0.8737 \| 0.2489 \| nan \| 0.0837 \| 0.8127 \| 0.7733 \| 0.9804 \| \| 0.0192 \| 258.82 \| 4400 \| 0.9938 \| 0.4650 \| 0.9569 \| 0.9477 \| 0.8656 \| 0.8980 \| 0.9341 \| 0.9821 \| 0.6272 \| 0.7713 \| 0.9900 \| 0.9276 \| 0.2486 \| nan \| 0.9841 \| 0.4269 \| 0.8939 \| 0.8792 \| 0.8465 \| 0.8449 \| 0.9027 \| 0.9603 \| 0.6197 \| 0.6161 \| 0.9799 \| 0.8731 \| 0.2468 \| nan \| 0.0834 \| 0.8160 \| 0.7749 \| 0.9804 \| \| 0.0184 \| 260.0 \| 4420 \| 0.9941 \| 0.4472 \| 0.9547 \| 0.9486 \| 0.8584 \| 0.8966 \| 0.9336 \| 0.9813 \| 0.6281 \| 0.7855 \| 0.9907 \| 0.9238 \| 0.2488 \| nan \| 0.9841 \| 0.4224 \| 0.8939 \| 0.8808 \| 0.8417 \| 0.8439 \| 0.9044 \| 0.9600 \| 0.6214 \| 0.6226 \| 0.9801 \| 0.8740 \| 0.2469 \| nan \| 0.0842 \| 0.8147 \| 0.7751 \| 0.9804 \| \| 0.022 \| 261.18 \| 4440 \| 0.9941 \| 0.3902 \| 0.9557 \| 0.9395 \| 0.8583 \| 0.8976 \| 0.9382 \| 0.9808 \| 0.6246 \| 0.7736 \| 0.9910 \| 0.9207 \| 0.2465 \| nan \| 0.9841 \| 0.3756 \| 0.8935 \| 0.8816 \| 0.8425 \| 0.8451 \| 0.9033 \| 0.9606 \| 0.6192 \| 0.6170 \| 0.9798 \| 0.8739 \| 0.2445 \| nan \| 0.0853 \| 0.8085 \| 0.7708 \| 0.9804 \| \| 0.0171 \| 262.35 \| 4460 \| 0.9938 \| 0.3902 \| 0.9571 \| 0.9364 \| 0.8589 \| 0.9032 \| 0.9348 \| 0.9815 \| 0.6240 \| 0.7598 \| 0.9902 \| 0.9245 \| 0.2466 \| nan \| 0.9842 \| 0.3785 \| 0.8933 \| 0.8816 \| 0.8419 \| 0.8455 \| 0.9045 \| 0.9605 \| 0.6179 \| 0.6089 \| 0.9801 \| 0.8745 \| 0.2446 \| nan \| 0.0846 \| 0.8078 \| 0.7705 \| 0.9804 \| \| 0.0186 \| 263.53 \| 4480 \| 0.9938 \| 0.4049 \| 0.9589 \| 0.9366 \| 0.8536 \| 0.8974 \| 0.9305 \| 0.9810 \| 0.6224 \| 0.7809 \| 0.9894 \| 0.9279 \| 0.2440 \| nan \| 0.9841 \| 0.3915 \| 0.8926 \| 0.8789 \| 0.8384 \| 0.8423 \| 0.9034 \| 0.9601 \| 0.6177 \| 0.6193 \| 0.9802 \| 0.8740 \| 0.2417 \| nan \| 0.0860 \| 0.8093 \| 0.7711 \| 0.9803 \| \| 0.0161 \| 264.71 \| 4500 \| 0.9936 \| 0.4325 \| 0.9587 \| 0.9336 \| 0.8584 \| 0.9029 \| 0.9324 \| 0.9817 \| 0.6250 \| 0.7839 \| 0.9893 \| 0.9286 \| 0.2442 \| nan \| 0.9842 \| 0.4111 \| 0.8937 \| 0.8809 \| 0.8413 \| 0.8443 \| 0.9049 \| 0.9605 \| 0.6189 \| 0.6239 \| 0.9801 \| 0.8744 \| 0.2420 \| nan \| 0.0846 \| 0.8127 \| 0.7739 \| 0.9804 \| \| 0.0159 \| 265.88 \| 4520 \| 0.9938 \| 0.4163 \| 0.9579 \| 0.9452 \| 0.8556 \| 0.9034 \| 0.9332 \| 0.9812 \| 0.6251 \| 0.7862 \| 0.9897 \| 0.9289 \| 0.2462 \| nan \| 0.9842 \| 0.4 \| 0.8942 \| 0.8814 \| 0.8398 \| 0.8449 \| 0.9062 \| 0.9611 \| 0.6194 \| 0.6278 \| 0.9802 \| 0.8750 \| 0.2443 \| nan \| 0.0841 \| 0.8125 \| 0.7737 \| 0.9805 \| \| 0.0195 \| 267.06 \| 4540 \| 0.9940 \| 0.4407 \| 0.9593 \| 0.9412 \| 0.8514 \| 0.8952 \| 0.9284 \| 0.9814 \| 0.6239 \| 0.7695 \| 0.9897 \| 0.9249 \| 0.2377 \| nan \| 0.9842 \| 0.4215 \| 0.8929 \| 0.8807 \| 0.8358 \| 0.8433 \| 0.9019 \| 0.9603 \| 0.6183 \| 0.6176 \| 0.9800 \| 0.8746 \| 0.2360 \| nan \| 0.0858 \| 0.8105 \| 0.7729 \| 0.9803 \| \| 0.0172 \| 268.24 \| 4560 \| 0.9934 \| 0.4634 \| 0.9586 \| 0.9494 \| 0.8576 \| 0.9058 \| 0.9289 \| 0.9817 \| 0.6260 \| 0.7862 \| 0.9899 \| 0.9261 \| 0.2372 \| nan \| 0.9843 \| 0.4286 \| 0.8937 \| 0.8813 \| 0.8384 \| 0.8461 \| 0.9010 \| 0.9609 \| 0.6199 \| 0.6251 \| 0.9800 \| 0.8740 \| 0.2354 \| nan \| 0.0835 \| 0.8157 \| 0.7745 \| 0.9805 \| \| 0.019 \| 269.41 \| 4580 \| 0.9940 \| 0.4081 \| 0.9559 \| 0.9444 \| 0.8570 \| 0.9055 \| 0.9306 \| 0.9818 \| 0.6256 \| 0.7708 \| 0.9897 \| 0.9284 \| 0.2432 \| nan \| 0.9843 \| 0.3885 \| 0.8943 \| 0.8814 \| 0.8404 \| 0.8466 \| 0.9033 \| 0.9613 \| 0.6203 \| 0.6223 \| 0.9800 \| 0.8742 \| 0.2415 \| nan \| 0.0834 \| 0.8104 \| 0.7722 \| 0.9806 \| \| 0.018 \| 270.59 \| 4600 \| 0.9938 \| 0.4293 \| 0.9568 \| 0.9400 \| 0.8524 \| 0.9018 \| 0.9287 \| 0.9821 \| 0.6257 \| 0.7869 \| 0.9904 \| 0.9227 \| 0.2394 \| nan \| 0.9843 \| 0.3923 \| 0.8938 \| 0.8821 \| 0.8364 \| 0.8462 \| 0.9013 \| 0.9614 \| 0.6200 \| 0.6244 \| 0.9799 \| 0.8736 \| 0.2377 \| nan \| 0.0835 \| 0.8115 \| 0.7718 \| 0.9805 \| \| 0.0223 \| 271.76 \| 4620 \| 0.9937 \| 0.4081 \| 0.9571 \| 0.9406 \| 0.8528 \| 0.9023 \| 0.9291 \| 0.9811 \| 0.6254 \| 0.7921 \| 0.9903 \| 0.9257 \| 0.2447 \| nan \| 0.9843 \| 0.3769 \| 0.8937 \| 0.8822 \| 0.8364 \| 0.8446 \| 0.9017 \| 0.9610 \| 0.6194 \| 0.6266 \| 0.9799 \| 0.8734 \| 0.2425 \| nan \| 0.0839 \| 0.8110 \| 0.7710 \| 0.9805 \| \| 0.0227 \| 272.94 \| 4640 \| 0.9940 \| 0.4 \| 0.9574 \| 0.9357 \| 0.8553 \| 0.8968 \| 0.9276 \| 0.9807 \| 0.6246 \| 0.7851 \| 0.9906 \| 0.9236 \| 0.2449 \| nan \| 0.9842 \| 0.3814 \| 0.8941 \| 0.8830 \| 0.8386 \| 0.8435 \| 0.9026 \| 0.9612 \| 0.6195 \| 0.6246 \| 0.9801 \| 0.8743 \| 0.2430 \| nan \| 0.0845 \| 0.8089 \| 0.7715 \| 0.9805 \| \| 0.0165 \| 274.12 \| 4660 \| 0.9940 \| 0.4033 \| 0.9576 \| 0.9453 \| 0.8601 \| 0.8980 \| 0.9318 \| 0.9805 \| 0.6254 \| 0.7957 \| 0.9900 \| 0.9278 \| 0.2541 \| nan \| 0.9842 \| 0.3758 \| 0.8946 \| 0.8774 \| 0.8433 \| 0.8438 \| 0.9025 \| 0.9615 \| 0.6199 \| 0.6288 \| 0.9802 \| 0.8743 \| 0.2517 \| nan \| 0.0850 \| 0.8126 \| 0.7722 \| 0.9806 \| \| 0.0192 \| 275.29 \| 4680 \| 0.9941 \| 0.4033 \| 0.9571 \| 0.9376 \| 0.8547 \| 0.8986 \| 0.9298 \| 0.9805 \| 0.6271 \| 0.7904 \| 0.9903 \| 0.9243 \| 0.2444 \| nan \| 0.9842 \| 0.3887 \| 0.8946 \| 0.8788 \| 0.8394 \| 0.8444 \| 0.9025 \| 0.9617 \| 0.6214 \| 0.6275 \| 0.9801 \| 0.8747 \| 0.2426 \| nan \| 0.0836 \| 0.8102 \| 0.7724 \| 0.9806 \| \| 0.0172 \| 276.47 \| 4700 \| 0.9940 \| 0.4049 \| 0.9573 \| 0.9333 \| 0.8525 \| 0.8995 \| 0.9312 \| 0.9818 \| 0.6278 \| 0.7866 \| 0.9894 \| 0.9315 \| 0.2391 \| nan \| 0.9842 \| 0.3879 \| 0.8943 \| 0.8779 \| 0.8361 \| 0.8443 \| 0.9031 \| 0.9609 \| 0.6212 \| 0.6245 \| 0.9798 \| 0.8740 \| 0.2374 \| nan \| 0.0843 \| 0.8099 \| 0.7712 \| 0.9804 \| \| 0.0173 \| 277.65 \| 4720 \| 0.9942 \| 0.3984 \| 0.9576 \| 0.9424 \| 0.8548 \| 0.8968 \| 0.9321 \| 0.9812 \| 0.6268 \| 0.7832 \| 0.9892 \| 0.9319 \| 0.2483 \| nan \| 0.9842 \| 0.3834 \| 0.8948 \| 0.8782 \| 0.8391 \| 0.8436 \| 0.9037 \| 0.9609 \| 0.6215 \| 0.6230 \| 0.9800 \| 0.8738 \| 0.2464 \| nan \| 0.0851 \| 0.8105 \| 0.7717 \| 0.9805 \| \| 0.0183 \| 278.82 \| 4740 \| 0.9941 \| 0.3951 \| 0.9570 \| 0.9430 \| 0.8546 \| 0.8972 \| 0.9328 \| 0.9814 \| 0.6268 \| 0.7811 \| 0.9901 \| 0.9249 \| 0.2505 \| nan \| 0.9842 \| 0.3767 \| 0.8947 \| 0.8781 \| 0.8386 \| 0.8437 \| 0.9043 \| 0.9609 \| 0.6208 \| 0.6215 \| 0.9799 \| 0.8737 \| 0.2485 \| nan \| 0.0844 \| 0.8099 \| 0.7712 \| 0.9805 \| \| 0.0193 \| 280.0 \| 4760 \| 0.9941 \| 0.4179 \| 0.9565 \| 0.9445 \| 0.8497 \| 0.8988 \| 0.9308 \| 0.9818 \| 0.6264 \| 0.7781 \| 0.9901 \| 0.9254 \| 0.2521 \| nan \| 0.9842 \| 0.3984 \| 0.8945 \| 0.8806 \| 0.8353 \| 0.8440 \| 0.9032 \| 0.9607 \| 0.6205 \| 0.6244 \| 0.9799 \| 0.8737 \| 0.2500 \| nan \| 0.0838 \| 0.8113 \| 0.7730 \| 0.9805 \| \| 0.0207 \| 281.18 \| 4780 \| 0.9939 \| 0.4081 \| 0.9582 \| 0.9366 \| 0.8501 \| 0.9014 \| 0.9304 \| 0.9805 \| 0.6262 \| 0.7810 \| 0.9905 \| 0.9217 \| 0.2484 \| nan \| 0.9843 \| 0.3928 \| 0.8940 \| 0.8790 \| 0.8351 \| 0.8462 \| 0.9038 \| 0.9610 \| 0.6204 \| 0.6277 \| 0.9800 \| 0.8740 \| 0.2463 \| nan \| 0.0842 \| 0.8098 \| 0.7727 \| 0.9805 \| \| 0.0166 \| 282.35 \| 4800 \| 0.9942 \| 0.4228 \| 0.9571 \| 0.9356 \| 0.8498 \| 0.8966 \| 0.9317 \| 0.9815 \| 0.6259 \| 0.7806 \| 0.9900 \| 0.9290 \| 0.2540 \| nan \| 0.9842 \| 0.4075 \| 0.8946 \| 0.8781 \| 0.8356 \| 0.8444 \| 0.9057 \| 0.9609 \| 0.6203 \| 0.6242 \| 0.9800 \| 0.8741 \| 0.2518 \| nan \| 0.0846 \| 0.8114 \| 0.7740 \| 0.9805 \| \| 0.0203 \| 283.53 \| 4820 \| 0.9941 \| 0.4244 \| 0.9571 \| 0.9421 \| 0.8525 \| 0.8984 \| 0.9327 \| 0.9813 \| 0.6270 \| 0.7913 \| 0.9901 \| 0.9257 \| 0.2532 \| nan \| 0.9842 \| 0.4078 \| 0.8948 \| 0.8788 \| 0.8370 \| 0.8445 \| 0.9065 \| 0.9613 \| 0.6204 \| 0.6273 \| 0.9801 \| 0.8745 \| 0.2510 \| nan \| 0.0843 \| 0.8131 \| 0.7745 \| 0.9805 \| \| 0.0153 \| 284.71 \| 4840 \| 0.9939 \| 0.4 \| 0.9586 \| 0.9311 \| 0.8477 \| 0.9007 \| 0.9293 \| 0.9805 \| 0.6264 \| 0.7916 \| 0.9900 \| 0.9249 \| 0.2477 \| nan \| 0.9842 \| 0.3862 \| 0.8942 \| 0.8752 \| 0.8333 \| 0.8451 \| 0.9043 \| 0.9614 \| 0.6202 \| 0.6283 \| 0.9799 \| 0.8742 \| 0.2458 \| nan \| 0.0842 \| 0.8094 \| 0.7717 \| 0.9805 \| \| 0.017 \| 285.88 \| 4860 \| 0.9938 \| 0.4049 \| 0.9583 \| 0.9444 \| 0.8532 \| 0.9027 \| 0.9308 \| 0.9802 \| 0.6260 \| 0.7857 \| 0.9899 \| 0.9276 \| 0.2542 \| nan \| 0.9843 \| 0.3921 \| 0.8941 \| 0.8785 \| 0.8372 \| 0.8460 \| 0.9050 \| 0.9611 \| 0.6199 \| 0.6283 \| 0.9800 \| 0.8744 \| 0.2516 \| nan \| 0.0843 \| 0.8117 \| 0.7733 \| 0.9805 \| \| 0.0192 \| 287.06 \| 4880 \| 0.9941 \| 0.4065 \| 0.9575 \| 0.9453 \| 0.8526 \| 0.8979 \| 0.9343 \| 0.9813 \| 0.6260 \| 0.7883 \| 0.9896 \| 0.9279 \| 0.2535 \| nan \| 0.9842 \| 0.3925 \| 0.8945 \| 0.8776 \| 0.8372 \| 0.8446 \| 0.9069 \| 0.9612 \| 0.6202 \| 0.6266 \| 0.9801 \| 0.8744 \| 0.2513 \| nan \| 0.0848 \| 0.8119 \| 0.7732 \| 0.9805 \| \| 0.0173 \| 288.24 \| 4900 \| 0.9941 \| 0.4098 \| 0.9586 \| 0.9355 \| 0.8501 \| 0.8983 \| 0.9294 \| 0.9805 \| 0.6255 \| 0.7836 \| 0.9899 \| 0.9256 \| 0.2480 \| nan \| 0.9842 \| 0.3962 \| 0.8940 \| 0.8788 \| 0.8350 \| 0.8452 \| 0.9048 \| 0.9611 \| 0.6197 \| 0.6256 \| 0.9800 \| 0.8744 \| 0.2459 \| nan \| 0.0854 \| 0.8099 \| 0.7727 \| 0.9805 \| \| 0.0162 \| 289.41 \| 4920 \| 0.9940 \| 0.4390 \| 0.9584 \| 0.9386 \| 0.8496 \| 0.9000 \| 0.9311 \| 0.9803 \| 0.6253 \| 0.7781 \| 0.9900 \| 0.9283 \| 0.2452 \| nan \| 0.9843 \| 0.4225 \| 0.8938 \| 0.8789 \| 0.8341 \| 0.8458 \| 0.9055 \| 0.9611 \| 0.6198 \| 0.6240 \| 0.9800 \| 0.8746 \| 0.2433 \| nan \| 0.0848 \| 0.8121 \| 0.7744 \| 0.9805 \| \| 0.0181 \| 290.59 \| 4940 \| 0.9940 \| 0.4244 \| 0.9578 \| 0.9367 \| 0.8518 \| 0.8986 \| 0.9304 \| 0.9817 \| 0.6253 \| 0.7813 \| 0.9900 \| 0.9262 \| 0.2500 \| nan \| 0.9842 \| 0.4085 \| 0.8941 \| 0.8775 \| 0.8356 \| 0.8452 \| 0.9054 \| 0.9608 \| 0.6197 \| 0.6246 \| 0.9801 \| 0.8743 \| 0.2476 \| nan \| 0.0856 \| 0.8114 \| 0.7737 \| 0.9805 \| \| 0.02 \| 291.76 \| 4960 \| 0.9938 \| 0.4293 \| 0.9577 \| 0.9439 \| 0.8527 \| 0.9013 \| 0.9346 \| 0.9820 \| 0.6250 \| 0.7868 \| 0.9899 \| 0.9269 \| 0.2499 \| nan \| 0.9843 \| 0.4106 \| 0.8941 \| 0.8791 \| 0.8363 \| 0.8456 \| 0.9060 \| 0.9609 \| 0.6197 \| 0.6259 \| 0.9801 \| 0.8745 \| 0.2476 \| nan \| 0.0854 \| 0.8134 \| 0.7742 \| 0.9805 \| \| 0.0202 \| 292.94 \| 4980 \| 0.9939 \| 0.4244 \| 0.9585 \| 0.9413 \| 0.8516 \| 0.9000 \| 0.9309 \| 0.9804 \| 0.6256 \| 0.7881 \| 0.9902 \| 0.9257 \| 0.2459 \| nan \| 0.9843 \| 0.4078 \| 0.8940 \| 0.8797 \| 0.8353 \| 0.8456 \| 0.9054 \| 0.9615 \| 0.6198 \| 0.6272 \| 0.9801 \| 0.8748 \| 0.2440 \| nan \| 0.0845 \| 0.8120 \| 0.7738 \| 0.9805 \| \| 0.0179 \| 294.12 \| 5000 \| 0.9939 \| 0.4423 \| 0.9586 \| 0.9298 \| 0.8508 \| 0.9009 \| 0.9313 \| 0.9810 \| 0.6256 \| 0.7896 \| 0.9899 \| 0.9268 \| 0.2483 \| nan \| 0.9843 \| 0.4243 \| 0.8940 \| 0.8800 \| 0.8353 \| 0.8459 \| 0.9060 \| 0.9614 \| 0.6199 \| 0.6280 \| 0.9801 \| 0.8749 \| 0.2463 \| nan \| 0.0847 \| 0.8130 \| 0.7754 \| 0.9805 \| \| 0.0163 \| 295.29 \| 5020 \| 0.9938 \| 0.4472 \| 0.9584 \| 0.9438 \| 0.8520 \| 0.9005 \| 0.9351 \| 0.9814 \| 0.6257 \| 0.7885 \| 0.9902 \| 0.9256 \| 0.2510 \| nan \| 0.9843 \| 0.4198 \| 0.8940 \| 0.8804 \| 0.8358 \| 0.8459 \| 0.9061 \| 0.9613 \| 0.6200 \| 0.6274 \| 0.9801 \| 0.8747 \| 0.2488 \| nan \| 0.0845 \| 0.8149 \| 0.7753 \| 0.9805 \| \| 0.0172 \| 296.47 \| 5040 \| 0.9939 \| 0.4423 \| 0.9577 \| 0.9471 \| 0.8509 \| 0.9005 \| 0.9342 \| 0.9814 \| 0.6260 \| 0.7869 \| 0.9902 \| 0.9259 \| 0.2548 \| nan \| 0.9843 \| 0.4146 \| 0.8944 \| 0.8798 \| 0.8355 \| 0.8456 \| 0.9066 \| 0.9613 \| 0.6198 \| 0.6271 \| 0.9801 \| 0.8747 \| 0.2524 \| nan \| 0.0843 \| 0.8148 \| 0.7751 \| 0.9806 \| \| 0.0203 \| 297.65 \| 5060 \| 0.0854 \| 0.7753 \| 0.8134 \| 0.9804 \| nan \| 0.9945 \| 0.9549 \| 0.8916 \| 0.9913 \| 0.9137 \| 0.6245 \| 0.8581 \| 0.9801 \| 0.7858 \| 0.2508 \| 0.9343 \| 0.9540 \| 0.4407 \| nan \| 0.9839 \| 0.8947 \| 0.8421 \| 0.9799 \| 0.8705 \| 0.6197 \| 0.8416 \| 0.9614 \| 0.6280 \| 0.2472 \| 0.9054 \| 0.8846 \| 0.4195 \| \| 0.0205 \| 298.82 \| 5080 \| 0.0871 \| 0.7626 \| 0.8020 \| 0.9801 \| nan \| 0.9931 \| 0.9611 \| 0.9024 \| 0.9895 \| 0.9241 \| 0.6243 \| 0.8558 \| 0.9790 \| 0.7904 \| 0.2323 \| 0.9215 \| 0.9416 \| 0.3106 \| nan \| 0.9840 \| 0.8921 \| 0.8393 \| 0.9800 \| 0.8719 \| 0.6151 \| 0.8377 \| 0.9609 \| 0.6236 \| 0.2302 \| 0.9017 \| 0.8872 \| 0.2898 \| \| 0.0188 \| 300.0 \| 5100 \| 0.0845 \| 0.7689 \| 0.8070 \| 0.9804 \| nan \| 0.9940 \| 0.9574 \| 0.8962 \| 0.9902 \| 0.9197 \| 0.6269 \| 0.8422 \| 0.9827 \| 0.7988 \| 0.2310 \| 0.9154 \| 0.9435 \| 0.3935 \| nan \| 0.9842 \| 0.8939 \| 0.8437 \| 0.9794 \| 0.8726 \| 0.6211 \| 0.8274 \| 0.9616 \| 0.6333 \| 0.2295 \| 0.8987 \| 0.8783 \| 0.3717 \| \| 0.0206 \| 301.18 \| 5120 \| 0.0832 \| 0.7768 \| 0.8159 \| 0.9805 \| nan \| 0.9936 \| 0.9556 \| 0.9060 \| 0.9911 \| 0.9237 \| 0.6286 \| 0.8542 \| 0.9815 \| 0.7855 \| 0.2610 \| 0.9257 \| 0.9445 \| 0.4553 \| nan \| 0.9844 \| 0.8934 \| 0.8474 \| 0.9795 \| 0.8737 \| 0.6225 \| 0.8381 \| 0.9608 \| 0.6310 \| 0.2587 \| 0.9043 \| 0.8865 \| 0.4179 \| \| 0.0193 \| 302.35 \| 5140 \| 0.0867 \| 0.7780 \| 0.8209 \| 0.9801 \| nan \| 0.9937 \| 0.9592 \| 0.8986 \| 0.9880 \| 0.9259 \| 0.6240 \| 0.8541 \| 0.9825 \| 0.7898 \| 0.2379 \| 0.9193 \| 0.9365 \| 0.5626 \| nan \| 0.9841 \| 0.8905 \| 0.8436 \| 0.9801 \| 0.8725 \| 0.6174 \| 0.8376 \| 0.9586 \| 0.6280 \| 0.2327 \| 0.8946 \| 0.8769 \| 0.4971 \| \| 0.0208 \| 303.53 \| 5160 \| 0.0848 \| 0.7725 \| 0.8100 \| 0.9805 \| nan \| 0.9938 \| 0.9579 \| 0.8993 \| 0.9905 \| 0.9238 \| 0.6241 \| 0.8569 \| 0.9811 \| 0.7872 \| 0.2388 \| 0.9215 \| 0.9274 \| 0.4276 \| nan \| 0.9844 \| 0.8929 \| 0.8451 \| 0.9798 \| 0.8744 \| 0.6190 \| 0.8393 \| 0.9609 \| 0.6293 \| 0.2325 \| 0.8954 \| 0.8792 \| 0.4103 \| \| 0.0217 \| 304.71 \| 5180 \| 0.0816 \| 0.7778 \| 0.8182 \| 0.9806 \| nan \| 0.9930 \| 0.9544 \| 0.9174 \| 0.9897 \| 0.9243 \| 0.6281 \| 0.8733 \| 0.9847 \| 0.7901 \| 0.2384 \| 0.9351 \| 0.9500 \| 0.4585 \| nan \| 0.9844 \| 0.8931 \| 0.8497 \| 0.9803 \| 0.8737 \| 0.6188 \| 0.8540 \| 0.9584 \| 0.6310 \| 0.2370 \| 0.9060 \| 0.8821 \| 0.4434 \| \| 0.02 \| 305.88 \| 5200 \| 0.0823 \| 0.7771 \| 0.8183 \| 0.9805 \| nan \| 0.9935 \| 0.9587 \| 0.9048 \| 0.9891 \| 0.9334 \| 0.6309 \| 0.8489 \| 0.9818 \| 0.7880 \| 0.2463 \| 0.9414 \| 0.9495 \| 0.4715 \| nan \| 0.9844 \| 0.8931 \| 0.8467 \| 0.9798 \| 0.8730 \| 0.6252 \| 0.8337 \| 0.9604 \| 0.6265 \| 0.2440 \| 0.9043 \| 0.8854 \| 0.4462 \| \| 0.0216 \| 307.06 \| 5220 \| 0.0838 \| 0.7722 \| 0.8099 \| 0.9804 \| nan \| 0.9936 \| 0.9595 \| 0.8957 \| 0.9918 \| 0.9096 \| 0.6247 \| 0.8559 \| 0.9791 \| 0.8019 \| 0.2321 \| 0.9240 \| 0.9352 \| 0.4260 \| nan \| 0.9843 \| 0.8921 \| 0.8419 \| 0.9799 \| 0.8723 \| 0.6176 \| 0.8409 \| 0.9613 \| 0.6354 \| 0.2293 \| 0.8969 \| 0.8748 \| 0.4119 \| \| 0.0181 \| 308.24 \| 5240 \| 0.0851 \| 0.7724 \| 0.8131 \| 0.9804 \| nan \| 0.9938 \| 0.9602 \| 0.8987 \| 0.9875 \| 0.9376 \| 0.6258 \| 0.8607 \| 0.9818 \| 0.7931 \| 0.2409 \| 0.9377 \| 0.9427 \| 0.4098 \| nan \| 0.9843 \| 0.8927 \| 0.8436 \| 0.9799 \| 0.8725 \| 0.6198 \| 0.8439 \| 0.9611 \| 0.6237 \| 0.2393 \| 0.9071 \| 0.8850 \| 0.3877 \| \| 0.0206 \| 309.41 \| 5260 \| 0.0842 \| 0.7819 \| 0.8284 \| 0.9806 \| nan \| 0.9933 \| 0.9567 \| 0.9048 \| 0.9903 \| 0.9186 \| 0.6272 \| 0.8752 \| 0.9835 \| 0.8066 \| 0.2561 \| 0.9462 \| 0.9472 \| 0.5642 \| nan \| 0.9843 \| 0.8949 \| 0.8457 \| 0.9805 \| 0.8736 \| 0.6186 \| 0.8517 \| 0.9598 \| 0.6297 \| 0.2533 \| 0.9011 \| 0.8833 \| 0.4880 \| \| 0.0168 \| 310.59 \| 5280 \| 0.0851 \| 0.7785 \| 0.8190 \| 0.9805 \| nan \| 0.9944 \| 0.9578 \| 0.8907 \| 0.9885 \| 0.9279 \| 0.6256 \| 0.8771 \| 0.9826 \| 0.7949 \| 0.2443 \| 0.9371 \| 0.9498 \| 0.4764 \| nan \| 0.9841 \| 0.8934 \| 0.8397 \| 0.9808 \| 0.8769 \| 0.6197 \| 0.8586 \| 0.9610 \| 0.6243 \| 0.2429 \| 0.9081 \| 0.8825 \| 0.4480 \| \| 0.0153 \| 311.76 \| 5300 \| 0.0753 \| 0.7878 \| 0.8268 \| 0.9809 \| nan \| 0.9948 \| 0.9401 \| 0.8909 \| 0.9905 \| 0.9115 \| 0.8866 \| 0.8532 \| 0.9862 \| 0.8041 \| 0.2192 \| 0.9210 \| 0.9295 \| 0.4211 \| nan \| 0.9841 \| 0.8982 \| 0.8439 \| 0.9787 \| 0.8682 \| 0.8549 \| 0.8367 \| 0.9515 \| 0.6267 \| 0.2180 \| 0.9013 \| 0.8796 \| 0.3991 \| \| 0.0199 \| 312.94 \| 5320 \| 0.0868 \| 0.7613 \| 0.7949 \| 0.9792 \| nan \| 0.9942 \| 0.9650 \| 0.8893 \| 0.9877 \| 0.9383 \| 0.6267 \| 0.8545 \| 0.9722 \| 0.5985 \| 0.2032 \| 0.9396 \| 0.9374 \| 0.4276 \| nan \| 0.9840 \| 0.8846 \| 0.8418 \| 0.9790 \| 0.8723 \| 0.6225 \| 0.8339 \| 0.9520 \| 0.5141 \| 0.2010 \| 0.9139 \| 0.8863 \| 0.4116 \| \| 0.0178 \| 314.12 \| 5340 \| 0.0850 \| 0.7697 \| 0.8068 \| 0.9805 \| nan \| 0.9939 \| 0.9599 \| 0.9062 \| 0.9907 \| 0.9161 \| 0.6306 \| 0.8499 \| 0.9758 \| 0.7884 \| 0.2163 \| 0.9081 \| 0.9348 \| 0.4179 \| nan \| 0.9842 \| 0.8935 \| 0.8489 \| 0.9799 \| 0.8708 \| 0.6261 \| 0.8356 \| 0.9646 \| 0.6107 \| 0.2151 \| 0.8895 \| 0.8810 \| 0.4060 \| \| 0.017 \| 315.29 \| 5360 \| 0.0872 \| 0.7739 \| 0.8119 \| 0.9804 \| nan \| 0.9942 \| 0.9653 \| 0.8954 \| 0.9886 \| 0.9236 \| 0.6239 \| 0.8803 \| 0.9739 \| 0.7395 \| 0.2294 \| 0.9410 \| 0.9470 \| 0.4520 \| nan \| 0.9838 \| 0.8924 \| 0.8439 \| 0.9810 \| 0.8726 \| 0.6182 \| 0.8629 \| 0.9623 \| 0.6111 \| 0.2177 \| 0.9017 \| 0.8837 \| 0.4297 \| \| 0.0214 \| 316.47 \| 5380 \| 0.0780 \| 0.7554 \| 0.7923 \| 0.9805 \| nan \| 0.9944 \| 0.9553 \| 0.8832 \| 0.9898 \| 0.9253 \| 0.7320 \| 0.8456 \| 0.9838 \| 0.7827 \| 0.1308 \| 0.9238 \| 0.9396 \| 0.2130 \| nan \| 0.9835 \| 0.8995 \| 0.8368 \| 0.9787 \| 0.8746 \| 0.7223 \| 0.8282 \| 0.9635 \| 0.6154 \| 0.1301 \| 0.8980 \| 0.8782 \| 0.2120 \| \| 0.0192 \| 317.65 \| 5400 \| 0.0794 \| 0.7672 \| 0.8048 \| 0.9801 \| nan \| 0.9931 \| 0.9617 \| 0.9034 \| 0.9888 \| 0.9378 \| 0.7786 \| 0.8047 \| 0.9713 \| 0.7534 \| 0.1816 \| 0.9131 \| 0.9216 \| 0.3528 \| nan \| 0.9841 \| 0.8940 \| 0.8434 \| 0.9781 \| 0.8732 \| 0.7684 \| 0.7948 \| 0.9531 \| 0.5930 \| 0.1806 \| 0.8918 \| 0.8682 \| 0.3506 \| \| 0.0199 \| 318.82 \| 5420 \| 0.0806 \| 0.7680 \| 0.7966 \| 0.9800 \| nan \| 0.9934 \| 0.9598 \| 0.9019 \| 0.9903 \| 0.9157 \| 0.7492 \| 0.8477 \| 0.9818 \| 0.5849 \| 0.1813 \| 0.8985 \| 0.9140 \| 0.4374 \| nan \| 0.9841 \| 0.8917 \| 0.8419 \| 0.9800 \| 0.8735 \| 0.7441 \| 0.8360 \| 0.9487 \| 0.5177 \| 0.1794 \| 0.8826 \| 0.8756 \| 0.4290 \| \| 0.0522 \| 320.0 \| 5440 \| 0.0767 \| 0.7813 \| 0.8402 \| 0.9796 \| nan \| 0.9939 \| 0.9494 \| 0.9045 \| 0.9897 \| 0.9328 \| 0.8251 \| 0.8745 \| 0.9511 \| 0.8380 \| 0.2580 \| 0.9385 \| 0.9516 \| 0.5154 \| nan \| 0.9835 \| 0.9003 \| 0.8501 \| 0.9807 \| 0.8752 \| 0.8062 \| 0.8536 \| 0.9432 \| 0.4502 \| 0.2562 \| 0.9162 \| 0.8516 \| 0.4900 \| \| 0.0183 \| 321.18 \| 5460 \| 0.0721 \| 0.7875 \| 0.8286 \| 0.9813 \| nan \| 0.9938 \| 0.9601 \| 0.9028 \| 0.9891 \| 0.9246 \| 0.8399 \| 0.8574 \| 0.9798 \| 0.7226 \| 0.1786 \| 0.9456 \| 0.9131 \| 0.5642 \| nan \| 0.9842 \| 0.9066 \| 0.8467 \| 0.9800 \| 0.8756 \| 0.8286 \| 0.8383 \| 0.9568 \| 0.5555 \| 0.1776 \| 0.9117 \| 0.8482 \| 0.5282 \| \| 0.0224 \| 322.35 \| 5480 \| 0.0707 \| 0.7849 \| 0.8216 \| 0.9813 \| nan \| 0.9940 \| 0.9585 \| 0.9074 \| 0.9907 \| 0.9264 \| 0.7408 \| 0.8514 \| 0.9840 \| 0.6871 \| 0.2202 \| 0.9366 \| 0.9440 \| 0.5398 \| nan \| 0.9843 \| 0.9055 \| 0.8501 \| 0.9803 \| 0.8770 \| 0.7348 \| 0.8349 \| 0.9602 \| 0.5580 \| 0.2191 \| 0.9116 \| 0.8847 \| 0.5038 \| \| 0.0145 \| 323.53 \| 5500 \| 0.0739 \| 0.7783 \| 0.8199 \| 0.9812 \| nan \| 0.9939 \| 0.9629 \| 0.8979 \| 0.9902 \| 0.9209 \| 0.6621 \| 0.8560 \| 0.9834 \| 0.8037 \| 0.1874 \| 0.9307 \| 0.9302 \| 0.5398 \| nan \| 0.9844 \| 0.9003 \| 0.8474 \| 0.9802 \| 0.8753 \| 0.6562 \| 0.8379 \| 0.9712 \| 0.5973 \| 0.1862 \| 0.9012 \| 0.8796 \| 0.5 \| \| 0.0167 \| 324.71 \| 5520 \| 0.0751 \| 0.7809 \| 0.8219 \| 0.9811 \| nan \| 0.9940 \| 0.9595 \| 0.9052 \| 0.9896 \| 0.9284 \| 0.6530 \| 0.8600 \| 0.9835 \| 0.7705 \| 0.2407 \| 0.9443 \| 0.9504 \| 0.5057 \| nan \| 0.9842 \| 0.9006 \| 0.8473 \| 0.9804 \| 0.8766 \| 0.6480 \| 0.8405 \| 0.9679 \| 0.5969 \| 0.2393 \| 0.9117 \| 0.8859 \| 0.4726 \| \| 0.0167 \| 325.88 \| 5540 \| 0.0757 \| 0.7792 \| 0.8172 \| 0.9810 \| nan \| 0.9943 \| 0.9591 \| 0.9005 \| 0.9897 \| 0.9241 \| 0.6657 \| 0.8523 \| 0.9829 \| 0.7736 \| 0.2321 \| 0.9343 \| 0.9331 \| 0.4813 \| nan \| 0.9841 \| 0.9011 \| 0.8447 \| 0.9802 \| 0.8778 \| 0.6593 \| 0.8347 \| 0.9667 \| 0.5967 \| 0.2306 \| 0.9053 \| 0.8844 \| 0.4639 \| \| 0.018 \| 327.06 \| 5560 \| 0.0733 \| 0.7836 \| 0.8218 \| 0.9815 \| nan \| 0.9938 \| 0.9587 \| 0.9098 \| 0.9896 \| 0.9323 \| 0.7120 \| 0.8470 \| 0.9847 \| 0.7838 \| 0.2247 \| 0.9277 \| 0.9430 \| 0.4764 \| nan \| 0.9845 \| 0.9039 \| 0.8514 \| 0.9800 \| 0.8768 \| 0.7052 \| 0.8317 \| 0.9655 \| 0.6138 \| 0.2237 \| 0.9037 \| 0.8878 \| 0.4585 \| \| 0.0168 \| 328.24 \| 5580 \| 0.0745 \| 0.7829 \| 0.8221 \| 0.9813 \| nan \| 0.9936 \| 0.9593 \| 0.9110 \| 0.9905 \| 0.9306 \| 0.7000 \| 0.8505 \| 0.9825 \| 0.7681 \| 0.2374 \| 0.9332 \| 0.9366 \| 0.4943 \| nan \| 0.9845 \| 0.9028 \| 0.8487 \| 0.9801 \| 0.8782 \| 0.6912 \| 0.8345 \| 0.9664 \| 0.5946 \| 0.2362 \| 0.9035 \| 0.8832 \| 0.4735 \| \| 0.0175 \| 329.41 \| 5600 \| 0.0757 \| 0.7841 \| 0.8254 \| 0.9811 \| nan \| 0.9938 \| 0.9617 \| 0.9038 \| 0.9894 \| 0.9297 \| 0.6870 \| 0.8482 \| 0.9825 \| 0.7651 \| 0.2317 \| 0.9259 \| 0.9476 \| 0.5642 \| nan \| 0.9845 \| 0.9005 \| 0.8476 \| 0.9800 \| 0.8785 \| 0.6805 \| 0.8328 \| 0.9647 \| 0.5972 \| 0.2304 \| 0.9003 \| 0.8830 \| 0.5133 \| \| 0.0164 \| 330.59 \| 5620 \| 0.0787 \| 0.7808 \| 0.8214 \| 0.9808 \| nan \| 0.9940 \| 0.9603 \| 0.9011 \| 0.9895 \| 0.9351 \| 0.6607 \| 0.8453 \| 0.9814 \| 0.7521 \| 0.2425 \| 0.9304 \| 0.9317 \| 0.5545 \| nan \| 0.9843 \| 0.8983 \| 0.8475 \| 0.9798 \| 0.8765 \| 0.6549 \| 0.8299 \| 0.9633 \| 0.5923 \| 0.2410 \| 0.9038 \| 0.8818 \| 0.4964 \| \| 0.0179 \| 331.76 \| 5640 \| 0.0778 \| 0.7818 \| 0.8227 \| 0.9810 \| nan \| 0.9938 \| 0.9612 \| 0.9026 \| 0.9895 \| 0.9322 \| 0.6636 \| 0.8488 \| 0.9825 \| 0.7723 \| 0.2528 \| 0.9212 \| 0.9530 \| 0.5220 \| nan \| 0.9844 \| 0.8995 \| 0.8489 \| 0.9797 \| 0.8779 \| 0.6562 \| 0.8323 \| 0.9637 \| 0.6118 \| 0.2507 \| 0.9000 \| 0.8859 \| 0.4728 \| \| 0.019 \| 332.94 \| 5660 \| 0.0804 \| 0.7753 \| 0.8132 \| 0.9807 \| nan \| 0.9940 \| 0.9583 \| 0.8986 \| 0.9906 \| 0.9298 \| 0.6477 \| 0.8399 \| 0.9824 \| 0.7718 \| 0.2279 \| 0.9242 \| 0.9402 \| 0.4667 \| nan \| 0.9841 \| 0.8987 \| 0.8444 \| 0.9797 \| 0.8781 \| 0.6414 \| 0.8257 \| 0.9641 \| 0.6016 \| 0.2267 \| 0.8997 \| 0.8870 \| 0.4477 \| \| 0.0126 \| 334.12 \| 5680 \| 0.0798 \| 0.7735 \| 0.8106 \| 0.9809 \| nan \| 0.9941 \| 0.9575 \| 0.9043 \| 0.9906 \| 0.9254 \| 0.6521 \| 0.8461 \| 0.9820 \| 0.7726 \| 0.2260 \| 0.9259 \| 0.9372 \| 0.4244 \| nan \| 0.9843 \| 0.8987 \| 0.8474 \| 0.9800 \| 0.8780 \| 0.6472 \| 0.8309 \| 0.9637 \| 0.6034 \| 0.2248 \| 0.9020 \| 0.8843 \| 0.4104 \| \| 0.0167 \| 335.29 \| 5700 \| 0.0788 \| 0.7771 \| 0.8177 \| 0.9809 \| nan \| 0.9941 \| 0.9593 \| 0.9045 \| 0.9896 \| 0.9284 \| 0.6608 \| 0.8483 \| 0.9817 \| 0.7561 \| 0.2539 \| 0.9400 \| 0.9584 \| 0.4553 \| nan \| 0.9844 \| 0.8990 \| 0.8486 \| 0.9801 \| 0.8784 \| 0.6551 \| 0.8324 \| 0.9629 \| 0.5952 \| 0.2523 \| 0.9068 \| 0.8795 \| 0.4281 \| \| 0.0199 \| 336.47 \| 5720 \| 0.0800 \| 0.7829 \| 0.8225 \| 0.9809 \| nan \| 0.9933 \| 0.9610 \| 0.9132 \| 0.9895 \| 0.9275 \| 0.6461 \| 0.8501 \| 0.9814 \| 0.7706 \| 0.2532 \| 0.9303 \| 0.9451 \| 0.5317 \| nan \| 0.9843 \| 0.8977 \| 0.8488 \| 0.9802 \| 0.8786 \| 0.6404 \| 0.8345 \| 0.9625 \| 0.6099 \| 0.2511 \| 0.9028 \| 0.8930 \| 0.4940 \| \| 0.0148 \| 337.65 \| 5740 \| 0.0814 \| 0.7736 \| 0.8127 \| 0.9808 \| nan \| 0.9946 \| 0.9606 \| 0.8911 \| 0.9900 \| 0.9238 \| 0.6395 \| 0.8454 \| 0.9811 \| 0.7812 \| 0.2523 \| 0.9277 \| 0.9406 \| 0.4374 \| nan \| 0.9841 \| 0.8981 \| 0.8436 \| 0.9801 \| 0.8773 \| 0.6347 \| 0.8311 \| 0.9651 \| 0.6175 \| 0.2502 \| 0.9010 \| 0.8881 \| 0.3854 \| \| 0.016 \| 338.82 \| 5760 \| 0.0792 \| 0.7829 \| 0.8301 \| 0.9810 \| nan \| 0.9931 \| 0.9610 \| 0.9094 \| 0.9907 \| 0.9274 \| 0.6476 \| 0.8547 \| 0.9813 \| 0.8063 \| 0.2462 \| 0.9472 \| 0.9573 \| 0.5691 \| nan \| 0.9845 \| 0.8977 \| 0.8480 \| 0.9801 \| 0.8788 \| 0.6406 \| 0.8354 \| 0.9653 \| 0.6268 \| 0.2439 \| 0.9063 \| 0.8886 \| 0.4821 \| \| 0.0199 \| 340.0 \| 5780 \| 0.0800 \| 0.7762 \| 0.8147 \| 0.9810 \| nan \| 0.9941 \| 0.9587 \| 0.9087 \| 0.9896 \| 0.9298 \| 0.6454 \| 0.8490 \| 0.9809 \| 0.7833 \| 0.2335 \| 0.9356 \| 0.9332 \| 0.4488 \| nan \| 0.9844 \| 0.8985 \| 0.8488 \| 0.9802 \| 0.8788 \| 0.6408 \| 0.8330 \| 0.9650 \| 0.6192 \| 0.2319 \| 0.9081 \| 0.8861 \| 0.4157 \| \| 0.0174 \| 341.18 \| 5800 \| 0.0804 \| 0.7764 \| 0.8164 \| 0.9809 \| nan \| 0.9939 \| 0.9600 \| 0.9025 \| 0.9896 \| 0.9298 \| 0.6367 \| 0.8582 \| 0.9819 \| 0.7819 \| 0.2368 \| 0.9369 \| 0.9449 \| 0.4602 \| nan \| 0.9843 \| 0.8980 \| 0.8454 \| 0.9805 \| 0.8793 \| 0.6314 \| 0.8407 \| 0.9639 \| 0.6147 \| 0.2351 \| 0.9070 \| 0.8868 \| 0.4256 \| \| 0.0154 \| 342.35 \| 5820 \| 0.0806 \| 0.7798 \| 0.8230 \| 0.9809 \| nan \| 0.9937 \| 0.9583 \| 0.9049 \| 0.9910 \| 0.9229 \| 0.6410 \| 0.8561 \| 0.9834 \| 0.7681 \| 0.2487 \| 0.9395 \| 0.9550 \| 0.5366 \| nan \| 0.9846 \| 0.8978 \| 0.8499 \| 0.9803 \| 0.8778 \| 0.6348 \| 0.8378 \| 0.9621 \| 0.6047 \| 0.2465 \| 0.9055 \| 0.8833 \| 0.4728 \| \| 0.0183 \| 343.53 \| 5840 \| 0.0813 \| 0.7791 \| 0.8207 \| 0.9809 \| nan \| 0.9940 \| 0.9606 \| 0.9000 \| 0.9899 \| 0.9282 \| 0.6387 \| 0.8518 \| 0.9822 \| 0.7755 \| 0.2453 \| 0.9338 \| 0.9375 \| 0.5317 \| nan \| 0.9844 \| 0.8975 \| 0.8469 \| 0.9802 \| 0.8789 \| 0.6325 \| 0.8372 \| 0.9643 \| 0.6081 \| 0.2434 \| 0.9069 \| 0.8862 \| 0.4619 \| \| 0.016 \| 344.71 \| 5860 \| 0.0784 \| 0.7812 \| 0.8259 \| 0.9811 \| nan \| 0.9939 \| 0.9609 \| 0.9019 \| 0.9902 \| 0.9314 \| 0.6573 \| 0.8590 \| 0.9817 \| 0.7669 \| 0.2522 \| 0.9553 \| 0.9484 \| 0.5382 \| nan \| 0.9844 \| 0.8996 \| 0.8478 \| 0.9804 \| 0.8786 \| 0.6501 \| 0.8423 \| 0.9639 \| 0.6110 \| 0.2502 \| 0.8980 \| 0.8922 \| 0.4566 \| \| 0.0149 \| 345.88 \| 5880 \| 0.0794 \| 0.7759 \| 0.8167 \| 0.9811 \| nan \| 0.9938 \| 0.9576 \| 0.9067 \| 0.9907 \| 0.9308 \| 0.6571 \| 0.8498 \| 0.9834 \| 0.7812 \| 0.2183 \| 0.9416 \| 0.9471 \| 0.4585 \| nan \| 0.9846 \| 0.8996 \| 0.8499 \| 0.9802 \| 0.8787 \| 0.6509 \| 0.8342 \| 0.9627 \| 0.6134 \| 0.2174 \| 0.9059 \| 0.8921 \| 0.4165 \| \| 0.0163 \| 347.06 \| 5900 \| 0.0789 \| 0.7814 \| 0.8240 \| 0.9811 \| nan \| 0.9939 \| 0.9579 \| 0.9034 \| 0.9911 \| 0.9261 \| 0.6744 \| 0.8515 \| 0.9834 \| 0.7660 \| 0.2308 \| 0.9402 \| 0.9386 \| 0.5545 \| nan \| 0.9845 \| 0.9000 \| 0.8485 \| 0.9800 \| 0.8791 \| 0.6665 \| 0.8360 \| 0.9634 \| 0.6086 \| 0.2294 \| 0.9029 \| 0.8884 \| 0.4703 \| \| 0.0145 \| 348.24 \| 5920 \| 0.0813 \| 0.7778 \| 0.8159 \| 0.9809 \| nan \| 0.9945 \| 0.9596 \| 0.8964 \| 0.9898 \| 0.9303 \| 0.6516 \| 0.8508 \| 0.9812 \| 0.7580 \| 0.2286 \| 0.9312 \| 0.9455 \| 0.4894 \| nan \| 0.9843 \| 0.8992 \| 0.8457 \| 0.9802 \| 0.8800 \| 0.6468 \| 0.8360 \| 0.9636 \| 0.6023 \| 0.2273 \| 0.9038 \| 0.8864 \| 0.4561 \| \| 0.0131 \| 349.41 \| 5940 \| 0.0812 \| 0.7794 \| 0.8175 \| 0.9809 \| nan \| 0.9942 \| 0.9605 \| 0.8982 \| 0.9905 \| 0.9285 \| 0.6388 \| 0.8540 \| 0.9813 \| 0.7538 \| 0.2441 \| 0.9348 \| 0.9416 \| 0.5073 \| nan \| 0.9844 \| 0.8984 \| 0.8468 \| 0.9805 \| 0.8793 \| 0.6339 \| 0.8375 \| 0.9627 \| 0.6020 \| 0.2424 \| 0.9083 \| 0.8903 \| 0.4650 \| \| 0.0161 \| 350.59 \| 5960 \| 0.0798 \| 0.7816 \| 0.8190 \| 0.9811 \| nan \| 0.9941 \| 0.9602 \| 0.9037 \| 0.9905 \| 0.9309 \| 0.6560 \| 0.8494 \| 0.9810 \| 0.7680 \| 0.2377 \| 0.9310 \| 0.9369 \| 0.5073 \| nan \| 0.9846 \| 0.8992 \| 0.8494 \| 0.9802 \| 0.8792 \| 0.6491 \| 0.8345 \| 0.9637 \| 0.6106 \| 0.2360 \| 0.9081 \| 0.8880 \| 0.4778 \| \| 0.0155 \| 351.76 \| 5980 \| 0.0787 \| 0.7809 \| 0.8176 \| 0.9813 \| nan \| 0.9937 \| 0.9589 \| 0.9104 \| 0.9918 \| 0.9208 \| 0.6665 \| 0.8498 \| 0.9817 \| 0.7653 \| 0.2358 \| 0.9344 \| 0.9451 \| 0.4748 \| nan \| 0.9847 \| 0.8998 \| 0.8526 \| 0.9797 \| 0.8781 \| 0.6597 \| 0.8339 \| 0.9647 \| 0.6131 \| 0.2344 \| 0.9110 \| 0.8917 \| 0.4485 \| \| 0.0143 \| 352.94 \| 6000 \| 0.0802 \| 0.7834 \| 0.8233 \| 0.9811 \| nan \| 0.9939 \| 0.9601 \| 0.9068 \| 0.9892 \| 0.9346 \| 0.6400 \| 0.8534 \| 0.9825 \| 0.7932 \| 0.2468 \| 0.9422 \| 0.9463 \| 0.5138 \| nan \| 0.9845 \| 0.8987 \| 0.8502 \| 0.9801 \| 0.8784 \| 0.6352 \| 0.8373 \| 0.9650 \| 0.6210 \| 0.2448 \| 0.9135 \| 0.8887 \| 0.4869 \| \| 0.0142 \| 354.12 \| 6020 \| 0.0830 \| 0.7801 \| 0.8185 \| 0.9809 \| nan \| 0.9942 \| 0.9580 \| 0.8996 \| 0.9906 \| 0.9292 \| 0.6345 \| 0.8529 \| 0.9820 \| 0.7825 \| 0.2270 \| 0.9368 \| 0.9447 \| 0.5089 \| nan \| 0.9844 \| 0.8980 \| 0.8473 \| 0.9803 \| 0.8797 \| 0.6287 \| 0.8374 \| 0.9634 \| 0.6151 \| 0.2255 \| 0.9101 \| 0.8900 \| 0.4808 \| \| 0.0176 \| 355.29 \| 6040 \| 0.0822 \| 0.7770 \| 0.8179 \| 0.9809 \| nan \| 0.9938 \| 0.9582 \| 0.9086 \| 0.9904 \| 0.9291 \| 0.6323 \| 0.8537 \| 0.9827 \| 0.7620 \| 0.2408 \| 0.9336 \| 0.9404 \| 0.5073 \| nan \| 0.9845 \| 0.8972 \| 0.8504 \| 0.9802 \| 0.8788 \| 0.6268 \| 0.8372 \| 0.9625 \| 0.6082 \| 0.2393 \| 0.9082 \| 0.8906 \| 0.4370 \| \| 0.0155 \| 356.47 \| 6060 \| 0.0822 \| 0.7834 \| 0.8251 \| 0.9809 \| nan \| 0.9940 \| 0.9606 \| 0.9042 \| 0.9901 \| 0.9284 \| 0.6369 \| 0.8456 \| 0.9803 \| 0.7700 \| 0.2431 \| 0.9318 \| 0.9489 \| 0.5919 \| nan \| 0.9845 \| 0.8967 \| 0.8493 \| 0.9804 \| 0.8797 \| 0.6313 \| 0.8330 \| 0.9631 \| 0.6089 \| 0.2417 \| 0.9055 \| 0.8927 \| 0.5170 \| \| 0.0157 \| 357.65 \| 6080 \| 0.0823 \| 0.7775 \| 0.8172 \| 0.9808 \| nan \| 0.9941 \| 0.9604 \| 0.8986 \| 0.9902 \| 0.9303 \| 0.6334 \| 0.8564 \| 0.9814 \| 0.7666 \| 0.2261 \| 0.9380 \| 0.9454 \| 0.5024 \| nan \| 0.9845 \| 0.8965 \| 0.8475 \| 0.9803 \| 0.8801 \| 0.6285 \| 0.8363 \| 0.9628 \| 0.6066 \| 0.2250 \| 0.9051 \| 0.8907 \| 0.4640 \| \| 0.0183 \| 358.82 \| 6100 \| 0.0830 \| 0.7770 \| 0.8150 \| 0.9807 \| nan \| 0.9941 \| 0.9597 \| 0.9015 \| 0.9898 \| 0.9287 \| 0.6385 \| 0.8428 \| 0.9826 \| 0.7569 \| 0.2303 \| 0.9378 \| 0.9431 \| 0.4894 \| nan \| 0.9844 \| 0.8967 \| 0.8470 \| 0.9803 \| 0.8796 \| 0.6318 \| 0.8298 \| 0.9619 \| 0.6016 \| 0.2294 \| 0.9082 \| 0.8910 \| 0.4588 \| \| 0.0193 \| 360.0 \| 6120 \| 0.0827 \| 0.7779 \| 0.8179 \| 0.9808 \| nan \| 0.9942 \| 0.9596 \| 0.8990 \| 0.9909 \| 0.9198 \| 0.6361 \| 0.8494 \| 0.9809 \| 0.7656 \| 0.2428 \| 0.9407 \| 0.9479 \| 0.5057 \| nan \| 0.9844 \| 0.8968 \| 0.8472 \| 0.9802 \| 0.8780 \| 0.6311 \| 0.8345 \| 0.9629 \| 0.6084 \| 0.2412 \| 0.9063 \| 0.8930 \| 0.4481 \| \| 0.0175 \| 361.18 \| 6140 \| 0.0824 \| 0.7758 \| 0.8156 \| 0.9808 \| nan \| 0.9941 \| 0.9581 \| 0.9025 \| 0.9905 \| 0.9268 \| 0.6345 \| 0.8485 \| 0.9820 \| 0.7693 \| 0.2345 \| 0.9352 \| 0.9371 \| 0.4894 \| nan \| 0.9844 \| 0.8971 \| 0.8471 \| 0.9804 \| 0.8793 \| 0.6291 \| 0.8344 \| 0.9623 \| 0.6060 \| 0.2334 \| 0.9054 \| 0.8897 \| 0.4362 \| \| 0.0188 \| 362.35 \| 6160 \| 0.0828 \| 0.7759 \| 0.8161 \| 0.9809 \| nan \| 0.9940 \| 0.9591 \| 0.9031 \| 0.9909 \| 0.9237 \| 0.6369 \| 0.8463 \| 0.9817 \| 0.7660 \| 0.2411 \| 0.9363 \| 0.9467 \| 0.4829 \| nan \| 0.9844 \| 0.8975 \| 0.8469 \| 0.9803 \| 0.8798 \| 0.6303 \| 0.8325 \| 0.9628 \| 0.6088 \| 0.2396 \| 0.9072 \| 0.8898 \| 0.4267 \| \| 0.018 \| 363.53 \| 6180 \| 0.0823 \| 0.7776 \| 0.8196 \| 0.9810 \| nan \| 0.9942 \| 0.9583 \| 0.9025 \| 0.9905 \| 0.9247 \| 0.6379 \| 0.8545 \| 0.9819 \| 0.7668 \| 0.2543 \| 0.9499 \| 0.9477 \| 0.4911 \| nan \| 0.9845 \| 0.8980 \| 0.8489 \| 0.9804 \| 0.8785 \| 0.6320 \| 0.8376 \| 0.9624 \| 0.6121 \| 0.2526 \| 0.9085 \| 0.8856 \| 0.4272 \| \| 0.0182 \| 364.71 \| 6200 \| 0.0805 \| 0.7784 \| 0.8179 \| 0.9811 \| nan \| 0.9938 \| 0.9591 \| 0.9102 \| 0.9906 \| 0.9244 \| 0.6425 \| 0.8608 \| 0.9818 \| 0.7789 \| 0.2407 \| 0.9394 \| 0.9405 \| 0.4699 \| nan \| 0.9846 \| 0.8986 \| 0.8499 \| 0.9806 \| 0.8794 \| 0.6369 \| 0.8443 \| 0.9626 \| 0.6202 \| 0.2393 \| 0.9092 \| 0.8896 \| 0.4244 \| \| 0.0134 \| 365.88 \| 6220 \| 0.0818 \| 0.7802 \| 0.8213 \| 0.9810 \| nan \| 0.9939 \| 0.9608 \| 0.9063 \| 0.9895 \| 0.9316 \| 0.6353 \| 0.8563 \| 0.9826 \| 0.7626 \| 0.2332 \| 0.9301 \| 0.9447 \| 0.5496 \| nan \| 0.9846 \| 0.8969 \| 0.8505 \| 0.9805 \| 0.8805 \| 0.6308 \| 0.8411 \| 0.9620 \| 0.6115 \| 0.2321 \| 0.9018 \| 0.8917 \| 0.4788 \| \| 0.0151 \| 367.06 \| 6240 \| 0.0829 \| 0.7799 \| 0.8221 \| 0.9809 \| nan \| 0.9937 \| 0.9595 \| 0.9031 \| 0.9901 \| 0.9348 \| 0.6398 \| 0.8571 \| 0.9827 \| 0.7771 \| 0.2305 \| 0.9308 \| 0.9515 \| 0.5366 \| nan \| 0.9846 \| 0.8973 \| 0.8482 \| 0.9802 \| 0.8796 \| 0.6346 \| 0.8409 \| 0.9620 \| 0.6127 \| 0.2292 \| 0.9017 \| 0.8930 \| 0.4741 \| \| 0.0191 \| 368.24 \| 6260 \| 0.0838 \| 0.7805 \| 0.8252 \| 0.9808 \| nan \| 0.9936 \| 0.9591 \| 0.9048 \| 0.9901 \| 0.9324 \| 0.6411 \| 0.8566 \| 0.9825 \| 0.7689 \| 0.2388 \| 0.9461 \| 0.9518 \| 0.5626 \| nan \| 0.9845 \| 0.8968 \| 0.8469 \| 0.9803 \| 0.8801 \| 0.6341 \| 0.8403 \| 0.9610 \| 0.6096 \| 0.2375 \| 0.9042 \| 0.8904 \| 0.4806 \| \| 0.0149 \| 369.41 \| 6280 \| 0.0830 \| 0.7762 \| 0.8152 \| 0.9809 \| nan \| 0.9942 \| 0.9584 \| 0.9036 \| 0.9899 \| 0.9309 \| 0.6430 \| 0.8511 \| 0.9819 \| 0.7671 \| 0.2318 \| 0.9366 \| 0.9493 \| 0.4602 \| nan \| 0.9845 \| 0.8981 \| 0.8485 \| 0.9802 \| 0.8802 \| 0.6382 \| 0.8359 \| 0.9618 \| 0.6090 \| 0.2306 \| 0.9093 \| 0.8882 \| 0.4256 \| \| 0.0159 \| 370.59 \| 6300 \| 0.0831 \| 0.7828 \| 0.8245 \| 0.9811 \| nan \| 0.9940 \| 0.9601 \| 0.9058 \| 0.9898 \| 0.9324 \| 0.6392 \| 0.8581 \| 0.9819 \| 0.7696 \| 0.2508 \| 0.9445 \| 0.9494 \| 0.5431 \| nan \| 0.9847 \| 0.8975 \| 0.8497 \| 0.9805 \| 0.8806 \| 0.6338 \| 0.8429 \| 0.9625 \| 0.6132 \| 0.2492 \| 0.9053 \| 0.8870 \| 0.4890 \| \| 0.0168 \| 371.76 \| 6320 \| 0.0826 \| 0.7797 \| 0.8191 \| 0.9810 \| nan \| 0.9946 \| 0.9582 \| 0.8949 \| 0.9904 \| 0.9307 \| 0.6373 \| 0.8570 \| 0.9820 \| 0.7708 \| 0.2460 \| 0.9435 \| 0.9518 \| 0.4911 \| nan \| 0.9844 \| 0.8986 \| 0.8460 \| 0.9806 \| 0.8804 \| 0.6325 \| 0.8417 \| 0.9624 \| 0.6131 \| 0.2448 \| 0.9056 \| 0.8869 \| 0.4590 \| \| 0.017 \| 372.94 \| 6340 \| 0.0827 \| 0.7824 \| 0.8220 \| 0.9810 \| nan \| 0.9938 \| 0.9587 \| 0.9091 \| 0.9897 \| 0.9373 \| 0.6388 \| 0.8545 \| 0.9818 \| 0.7691 \| 0.2442 \| 0.9389 \| 0.9406 \| 0.5301 \| nan \| 0.9847 \| 0.8976 \| 0.8496 \| 0.9805 \| 0.8796 \| 0.6334 \| 0.8399 \| 0.9615 \| 0.6071 \| 0.2429 \| 0.9090 \| 0.8898 \| 0.4962 \| \| 0.0165 \| 374.12 \| 6360 \| 0.0834 \| 0.7825 \| 0.8236 \| 0.9810 \| nan \| 0.9938 \| 0.9597 \| 0.9054 \| 0.9905 \| 0.9282 \| 0.6359 \| 0.8550 \| 0.9821 \| 0.7719 \| 0.2534 \| 0.9416 \| 0.9532 \| 0.5366 \| nan \| 0.9847 \| 0.8976 \| 0.8496 \| 0.9806 \| 0.8804 \| 0.6301 \| 0.8408 \| 0.9617 \| 0.6117 \| 0.2517 \| 0.9045 \| 0.8926 \| 0.4867 \| \| 0.0159 \| 375.29 \| 6380 \| 0.0834 \| 0.7815 \| 0.8244 \| 0.9810 \| nan \| 0.9942 \| 0.9586 \| 0.8998 \| 0.9900 \| 0.9282 \| 0.6385 \| 0.8632 \| 0.9827 \| 0.7744 \| 0.2471 \| 0.9433 \| 0.9503 \| 0.5463 \| nan \| 0.9845 \| 0.8980 \| 0.8473 \| 0.9809 \| 0.8802 \| 0.6320 \| 0.8461 \| 0.9613 \| 0.6087 \| 0.2457 \| 0.9106 \| 0.8898 \| 0.4739 \| \| 0.0138 \| 376.47 \| 6400 \| 0.0830 \| 0.7878 \| 0.8311 \| 0.9809 \| nan \| 0.9932 \| 0.9580 \| 0.9116 \| 0.9912 \| 0.9286 \| 0.6430 \| 0.8590 \| 0.9831 \| 0.7723 \| 0.2505 \| 0.9405 \| 0.9516 \| 0.6211 \| nan \| 0.9847 \| 0.8970 \| 0.8497 \| 0.9804 \| 0.8802 \| 0.6352 \| 0.8422 \| 0.9605 \| 0.6079 \| 0.2486 \| 0.9064 \| 0.8916 \| 0.5569 \| \| 0.0122 \| 377.65 \| 6420 \| 0.0841 \| 0.7819 \| 0.8203 \| 0.9809 \| nan \| 0.9942 \| 0.9597 \| 0.8999 \| 0.9896 \| 0.9349 \| 0.6352 \| 0.8539 \| 0.9814 \| 0.7718 \| 0.2446 \| 0.9339 \| 0.9450 \| 0.5203 \| nan \| 0.9845 \| 0.8973 \| 0.8475 \| 0.9807 \| 0.8809 \| 0.6299 \| 0.8395 \| 0.9616 \| 0.6135 \| 0.2431 \| 0.9070 \| 0.8900 \| 0.4893 \| \| 0.0182 \| 378.82 \| 6440 \| 0.0838 \| 0.7803 \| 0.8197 \| 0.9809 \| nan \| 0.9940 \| 0.9587 \| 0.9042 \| 0.9905 \| 0.9276 \| 0.6341 \| 0.8534 \| 0.9815 \| 0.7783 \| 0.2354 \| 0.9376 \| 0.9534 \| 0.5073 \| nan \| 0.9845 \| 0.8976 \| 0.8480 \| 0.9806 \| 0.8795 \| 0.6292 \| 0.8388 \| 0.9614 \| 0.6157 \| 0.2341 \| 0.9103 \| 0.8939 \| 0.4699 \| \| 0.0177 \| 380.0 \| 6460 \| 0.0830 \| 0.7854 \| 0.8328 \| 0.9810 \| nan \| 0.9931 \| 0.9604 \| 0.9114 \| 0.9906 \| 0.9274 \| 0.6435 \| 0.8592 \| 0.9823 \| 0.7762 \| 0.2498 \| 0.9456 \| 0.9515 \| 0.6358 \| nan \| 0.9847 \| 0.8973 \| 0.8493 \| 0.9805 \| 0.8794 \| 0.6360 \| 0.8425 \| 0.9613 \| 0.6131 \| 0.2480 \| 0.9113 \| 0.8956 \| 0.5118 \| \| 0.0144 \| 381.18 \| 6480 \| 0.0834 \| 0.7799 \| 0.8235 \| 0.9810 \| nan \| 0.9941 \| 0.9593 \| 0.8995 \| 0.9904 \| 0.9250 \| 0.6398 \| 0.8570 \| 0.9831 \| 0.7834 \| 0.2409 \| 0.9433 \| 0.9473 \| 0.5431 \| nan \| 0.9846 \| 0.8980 \| 0.8474 \| 0.9807 \| 0.8801 \| 0.6341 \| 0.8414 \| 0.9621 \| 0.6104 \| 0.2397 \| 0.9097 \| 0.8909 \| 0.4601 \| \| 0.0158 \| 382.35 \| 6500 \| 0.0878 \| 0.7685 \| 0.8145 \| 0.9806 \| nan \| 0.9942 \| 0.9590 \| 0.8857 \| 0.9900 \| 0.9321 \| 0.6415 \| 0.8506 \| 0.9834 \| 0.7825 \| 0.2307 \| 0.9454 \| 0.9544 \| 0.4390 \| nan \| 0.9843 \| 0.8984 \| 0.8365 \| 0.9803 \| 0.8799 \| 0.6354 \| 0.8363 \| 0.9624 \| 0.5821 \| 0.2293 \| 0.9056 \| 0.8859 \| 0.3734 \| \| 0.0166 \| 383.53 \| 6520 \| 0.0841 \| 0.7749 \| 0.8166 \| 0.9809 \| nan \| 0.9941 \| 0.9565 \| 0.8992 \| 0.9913 \| 0.9226 \| 0.6385 \| 0.8618 \| 0.9833 \| 0.7698 \| 0.2447 \| 0.9405 \| 0.9533 \| 0.4602 \| nan \| 0.9845 \| 0.8983 \| 0.8447 \| 0.9805 \| 0.8796 \| 0.6329 \| 0.8449 \| 0.9621 \| 0.6013 \| 0.2433 \| 0.9085 \| 0.8908 \| 0.4026 \| \| 0.0175 \| 384.71 \| 6540 \| 0.0854 \| 0.7701 \| 0.8109 \| 0.9809 \| nan \| 0.9941 \| 0.9587 \| 0.9005 \| 0.9906 \| 0.9271 \| 0.6375 \| 0.8499 \| 0.9820 \| 0.7882 \| 0.2268 \| 0.9342 \| 0.9389 \| 0.4130 \| nan \| 0.9845 \| 0.8986 \| 0.8455 \| 0.9806 \| 0.8808 \| 0.6315 \| 0.8366 \| 0.9631 \| 0.6051 \| 0.2258 \| 0.9090 \| 0.8870 \| 0.3629 \| \| 0.0183 \| 385.88 \| 6560 \| 0.0850 \| 0.7753 \| 0.8173 \| 0.9808 \| nan \| 0.9937 \| 0.9593 \| 0.9063 \| 0.9908 \| 0.9280 \| 0.6389 \| 0.8468 \| 0.9820 \| 0.7561 \| 0.2393 \| 0.9362 \| 0.9504 \| 0.4976 \| nan \| 0.9847 \| 0.8974 \| 0.8464 \| 0.9804 \| 0.8799 \| 0.6340 \| 0.8340 \| 0.9614 \| 0.5945 \| 0.2379 \| 0.9073 \| 0.8919 \| 0.4286 \| \| 0.0132 \| 387.06 \| 6580 \| 0.0858 \| 0.7780 \| 0.8173 \| 0.9808 \| nan \| 0.9945 \| 0.9573 \| 0.8936 \| 0.9905 \| 0.9336 \| 0.6400 \| 0.8506 \| 0.9823 \| 0.7634 \| 0.2441 \| 0.9468 \| 0.9480 \| 0.4797 \| nan \| 0.9844 \| 0.8981 \| 0.8433 \| 0.9806 \| 0.8810 \| 0.6353 \| 0.8376 \| 0.9619 \| 0.5958 \| 0.2427 \| 0.9079 \| 0.8894 \| 0.4560 \| \| 0.0164 \| 388.24 \| 6600 \| 0.0835 \| 0.7774 \| 0.8197 \| 0.9810 \| nan \| 0.9942 \| 0.9572 \| 0.8993 \| 0.9910 \| 0.9280 \| 0.6455 \| 0.8550 \| 0.9830 \| 0.7761 \| 0.2508 \| 0.9418 \| 0.9484 \| 0.4862 \| nan \| 0.9846 \| 0.8988 \| 0.8451 \| 0.9807 \| 0.8811 \| 0.6402 \| 0.8416 \| 0.9633 \| 0.6048 \| 0.2493 \| 0.9116 \| 0.8885 \| 0.4164 \| \| 0.0169 \| 389.41 \| 6620 \| 0.0826 \| 0.7791 \| 0.8170 \| 0.9812 \| nan \| 0.9944 \| 0.9582 \| 0.8922 \| 0.9910 \| 0.9285 \| 0.6970 \| 0.8513 \| 0.9819 \| 0.7809 \| 0.2323 \| 0.9360 \| 0.9385 \| 0.4390 \| nan \| 0.9844 \| 0.9009 \| 0.8434 \| 0.9806 \| 0.8812 \| 0.6911 \| 0.8371 \| 0.9622 \| 0.6115 \| 0.2314 \| 0.9040 \| 0.8857 \| 0.4147 \| \| 0.0174 \| 390.59 \| 6640 \| 0.0850 \| 0.7770 \| 0.8154 \| 0.9809 \| nan \| 0.9943 \| 0.9611 \| 0.8892 \| 0.9902 \| 0.9333 \| 0.6636 \| 0.8497 \| 0.9806 \| 0.7781 \| 0.2386 \| 0.9328 \| 0.9409 \| 0.4472 \| nan \| 0.9844 \| 0.8983 \| 0.8408 \| 0.9805 \| 0.8816 \| 0.6591 \| 0.8373 \| 0.9629 \| 0.6062 \| 0.2376 \| 0.9021 \| 0.8848 \| 0.4257 \| \| 0.0136 \| 391.76 \| 6660 \| 0.0832 \| 0.7800 \| 0.8234 \| 0.9811 \| nan \| 0.9939 \| 0.9601 \| 0.8985 \| 0.9905 \| 0.9343 \| 0.6811 \| 0.8530 \| 0.9808 \| 0.7674 \| 0.2534 \| 0.9437 \| 0.9554 \| 0.4927 \| nan \| 0.9846 \| 0.8998 \| 0.8440 \| 0.9806 \| 0.8804 \| 0.6752 \| 0.8382 \| 0.9619 \| 0.6016 \| 0.2518 \| 0.9071 \| 0.8898 \| 0.4256 \| \| 0.0154 \| 392.94 \| 6680 \| 0.0839 \| 0.7790 \| 0.8178 \| 0.9810 \| nan \| 0.9942 \| 0.9600 \| 0.8961 \| 0.9901 \| 0.9288 \| 0.6741 \| 0.8511 \| 0.9816 \| 0.7804 \| 0.2303 \| 0.9383 \| 0.9465 \| 0.4602 \| nan \| 0.9845 \| 0.8989 \| 0.8447 \| 0.9807 \| 0.8813 \| 0.6691 \| 0.8380 \| 0.9618 \| 0.6088 \| 0.2293 \| 0.9087 \| 0.8900 \| 0.4307 \| \| 0.017 \| 394.12 \| 6700 \| 0.0831 \| 0.7782 \| 0.8191 \| 0.9811 \| nan \| 0.9941 \| 0.9573 \| 0.8999 \| 0.9906 \| 0.9314 \| 0.6706 \| 0.8559 \| 0.9826 \| 0.7835 \| 0.2388 \| 0.9406 \| 0.9473 \| 0.4553 \| nan \| 0.9845 \| 0.8998 \| 0.8445 \| 0.9806 \| 0.8809 \| 0.6654 \| 0.8407 \| 0.9622 \| 0.6109 \| 0.2378 \| 0.9084 \| 0.8928 \| 0.4076 \| \| 0.0167 \| 395.29 \| 6720 \| 0.0841 \| 0.7793 \| 0.8205 \| 0.9810 \| nan \| 0.9944 \| 0.9576 \| 0.8970 \| 0.9897 \| 0.9379 \| 0.6649 \| 0.8526 \| 0.9817 \| 0.7850 \| 0.2475 \| 0.9416 \| 0.9481 \| 0.4683 \| nan \| 0.9844 \| 0.8996 \| 0.8437 \| 0.9807 \| 0.8800 \| 0.6595 \| 0.8390 \| 0.9622 \| 0.6118 \| 0.2463 \| 0.9091 \| 0.8902 \| 0.4248 \| \| 0.0158 \| 396.47 \| 6740 \| 0.0833 \| 0.7808 \| 0.8225 \| 0.9812 \| nan \| 0.9938 \| 0.9576 \| 0.9064 \| 0.9907 \| 0.9314 \| 0.6662 \| 0.8514 \| 0.9828 \| 0.7837 \| 0.2379 \| 0.9403 \| 0.9485 \| 0.5024 \| nan \| 0.9847 \| 0.8993 \| 0.8481 \| 0.9806 \| 0.8806 \| 0.6605 \| 0.8369 \| 0.9623 \| 0.6130 \| 0.2367 \| 0.9103 \| 0.8936 \| 0.4433 \| \| 0.0183 \| 397.65 \| 6760 \| 0.0830 \| 0.7765 \| 0.8210 \| 0.9811 \| nan \| 0.9939 \| 0.9580 \| 0.9014 \| 0.9905 \| 0.9318 \| 0.6794 \| 0.8495 \| 0.9832 \| 0.7843 \| 0.2060 \| 0.9401 \| 0.9481 \| 0.5073 \| nan \| 0.9846 \| 0.9003 \| 0.8466 \| 0.9797 \| 0.8786 \| 0.6724 \| 0.8336 \| 0.9628 \| 0.6114 \| 0.2051 \| 0.9017 \| 0.8936 \| 0.4245 \| \| 0.0155 \| 398.82 \| 6780 \| 0.0842 \| 0.7763 \| 0.8177 \| 0.9811 \| nan \| 0.9944 \| 0.9598 \| 0.8961 \| 0.9898 \| 0.9287 \| 0.6737 \| 0.8478 \| 0.9829 \| 0.7680 \| 0.2345 \| 0.9411 \| 0.9471 \| 0.4667 \| nan \| 0.9846 \| 0.9004 \| 0.8456 \| 0.9802 \| 0.8793 \| 0.6689 \| 0.8338 \| 0.9628 \| 0.6017 \| 0.2334 \| 0.9042 \| 0.8903 \| 0.4065 \| \| 0.0149 \| 400.0 \| 6800 \| 0.0845 \| 0.7825 \| 0.8292 \| 0.9811 \| nan \| 0.9939 \| 0.9578 \| 0.9039 \| 0.9903 \| 0.9277 \| 0.6656 \| 0.8527 \| 0.9839 \| 0.7760 \| 0.2570 \| 0.9447 \| 0.9540 \| 0.5724 \| nan \| 0.9847 \| 0.8996 \| 0.8469 \| 0.9805 \| 0.8805 \| 0.6588 \| 0.8382 \| 0.9619 \| 0.6028 \| 0.2550 \| 0.9095 \| 0.8910 \| 0.4632 \| \| 0.0184 \| 401.18 \| 6820 \| 0.0845 \| 0.7789 \| 0.8230 \| 0.9810 \| nan \| 0.9941 \| 0.9596 \| 0.8991 \| 0.9900 \| 0.9340 \| 0.6628 \| 0.8511 \| 0.9818 \| 0.7708 \| 0.2505 \| 0.9413 \| 0.9530 \| 0.5106 \| nan \| 0.9845 \| 0.8991 \| 0.8445 \| 0.9807 \| 0.8808 \| 0.6573 \| 0.8382 \| 0.9632 \| 0.6002 \| 0.2489 \| 0.9130 \| 0.8904 \| 0.4249 \| \| 0.0162 \| 402.35 \| 6840 \| 0.0857 \| 0.7757 \| 0.8189 \| 0.9810 \| nan \| 0.9938 \| 0.9589 \| 0.9035 \| 0.9899 \| 0.9319 \| 0.6466 \| 0.8547 \| 0.9838 \| 0.7756 \| 0.2385 \| 0.9432 \| 0.9419 \| 0.4829 \| nan \| 0.9846 \| 0.8982 \| 0.8459 \| 0.9806 \| 0.8811 \| 0.6418 \| 0.8401 \| 0.9627 \| 0.6004 \| 0.2374 \| 0.9112 \| 0.8876 \| 0.4119 \| \| 0.0157 \| 403.53 \| 6860 \| 0.0855 \| 0.7766 \| 0.8192 \| 0.9810 \| nan \| 0.9942 \| 0.9586 \| 0.8980 \| 0.9901 \| 0.9347 \| 0.6456 \| 0.8568 \| 0.9830 \| 0.7649 \| 0.2415 \| 0.9432 \| 0.9477 \| 0.4911 \| nan \| 0.9847 \| 0.8980 \| 0.8453 \| 0.9807 \| 0.8806 \| 0.6403 \| 0.8419 \| 0.9628 \| 0.5964 \| 0.2402 \| 0.9092 \| 0.8897 \| 0.4260 \| \| 0.0167 \| 404.71 \| 6880 \| 0.0847 \| 0.7781 \| 0.8199 \| 0.9811 \| nan \| 0.9942 \| 0.9586 \| 0.9025 \| 0.9902 \| 0.9307 \| 0.6442 \| 0.8535 \| 0.9831 \| 0.7756 \| 0.2390 \| 0.9388 \| 0.9479 \| 0.5008 \| nan \| 0.9847 \| 0.8988 \| 0.8471 \| 0.9807 \| 0.8811 \| 0.6389 \| 0.8401 \| 0.9631 \| 0.6059 \| 0.2378 \| 0.9088 \| 0.8913 \| 0.4369 \| \| 0.0145 \| 405.88 \| 6900 \| 0.0845 \| 0.7806 \| 0.8236 \| 0.9811 \| nan \| 0.9942 \| 0.9569 \| 0.9016 \| 0.9908 \| 0.9292 \| 0.6595 \| 0.8572 \| 0.9826 \| 0.7626 \| 0.2518 \| 0.9434 \| 0.9532 \| 0.5236 \| nan \| 0.9846 \| 0.8990 \| 0.8467 \| 0.9806 \| 0.8809 \| 0.6513 \| 0.8423 \| 0.9626 \| 0.5981 \| 0.2502 \| 0.9122 \| 0.8929 \| 0.4466 \| \| 0.0154 \| 407.06 \| 6920 \| 0.0846 \| 0.7780 \| 0.8198 \| 0.9810 \| nan \| 0.9940 \| 0.9580 \| 0.9016 \| 0.9909 \| 0.9292 \| 0.6557 \| 0.8550 \| 0.9827 \| 0.7667 \| 0.2377 \| 0.9383 \| 0.9445 \| 0.5024 \| nan \| 0.9846 \| 0.8985 \| 0.8464 \| 0.9807 \| 0.8818 \| 0.6496 \| 0.8409 \| 0.9625 \| 0.5998 \| 0.2364 \| 0.9099 \| 0.8916 \| 0.4316 \| \| 0.0159 \| 408.24 \| 6940 \| 0.0859 \| 0.7788 \| 0.8219 \| 0.9811 \| nan \| 0.9942 \| 0.9589 \| 0.9006 \| 0.9906 \| 0.9286 \| 0.6487 \| 0.8535 \| 0.9823 \| 0.7609 \| 0.2400 \| 0.9457 \| 0.9549 \| 0.5252 \| nan \| 0.9847 \| 0.8986 \| 0.8475 \| 0.9807 \| 0.8805 \| 0.6431 \| 0.8395 \| 0.9621 \| 0.5973 \| 0.2386 \| 0.9077 \| 0.8927 \| 0.4511 \| \| 0.0152 \| 409.41 \| 6960 \| 0.0859 \| 0.7766 \| 0.8179 \| 0.9809 \| nan \| 0.9945 \| 0.9577 \| 0.8985 \| 0.9903 \| 0.9332 \| 0.6384 \| 0.8520 \| 0.9815 \| 0.7575 \| 0.2407 \| 0.9382 \| 0.9442 \| 0.5057 \| nan \| 0.9845 \| 0.8982 \| 0.8462 \| 0.9807 \| 0.8808 \| 0.6337 \| 0.8390 \| 0.9615 \| 0.5976 \| 0.2394 \| 0.9080 \| 0.8938 \| 0.4319 \| \| 0.014 \| 410.59 \| 6980 \| 0.0862 \| 0.7784 \| 0.8199 \| 0.9810 \| nan \| 0.9943 \| 0.9577 \| 0.9012 \| 0.9907 \| 0.9288 \| 0.6393 \| 0.8494 \| 0.9817 \| 0.7704 \| 0.2373 \| 0.9383 \| 0.9504 \| 0.5187 \| nan \| 0.9847 \| 0.8976 \| 0.8476 \| 0.9807 \| 0.8812 \| 0.6343 \| 0.8363 \| 0.9617 \| 0.6033 \| 0.2360 \| 0.9078 \| 0.8940 \| 0.4538 \| \| 0.0155 \| 411.76 \| 7000 \| 0.0866 \| 0.7776 \| 0.8204 \| 0.9809 \| nan \| 0.9941 \| 0.9595 \| 0.8999 \| 0.9903 \| 0.9335 \| 0.6357 \| 0.8580 \| 0.9816 \| 0.7542 \| 0.2504 \| 0.9424 \| 0.9521 \| 0.5138 \| nan \| 0.9847 \| 0.8972 \| 0.8465 \| 0.9808 \| 0.8807 \| 0.6306 \| 0.8432 \| 0.9614 \| 0.5978 \| 0.2488 \| 0.9089 \| 0.8948 \| 0.4335 \| \| 0.0165 \| 412.94 \| 7020 \| 0.0861 \| 0.7777 \| 0.8232 \| 0.9810 \| nan \| 0.9939 \| 0.9580 \| 0.9050 \| 0.9909 \| 0.9307 \| 0.6354 \| 0.8595 \| 0.9819 \| 0.7652 \| 0.2393 \| 0.9467 \| 0.9501 \| 0.5447 \| nan \| 0.9848 \| 0.8973 \| 0.8482 \| 0.9808 \| 0.8813 \| 0.6300 \| 0.8442 \| 0.9620 \| 0.5964 \| 0.2380 \| 0.9081 \| 0.8922 \| 0.4473 \| \| 0.0147 \| 414.12 \| 7040 \| 0.0868 \| 0.7762 \| 0.8173 \| 0.9809 \| nan \| 0.9943 \| 0.9576 \| 0.9003 \| 0.9908 \| 0.9307 \| 0.6357 \| 0.8572 \| 0.9813 \| 0.7540 \| 0.2318 \| 0.9396 \| 0.9442 \| 0.5073 \| nan \| 0.9846 \| 0.8969 \| 0.8462 \| 0.9807 \| 0.8819 \| 0.6305 \| 0.8419 \| 0.9614 \| 0.5952 \| 0.2306 \| 0.9080 \| 0.8922 \| 0.4401 \| \| 0.0147 \| 415.29 \| 7060 \| 0.0859 \| 0.7789 \| 0.8220 \| 0.9810 \| nan \| 0.9942 \| 0.9590 \| 0.9003 \| 0.9901 \| 0.9344 \| 0.6386 \| 0.8597 \| 0.9813 \| 0.7823 \| 0.2422 \| 0.9429 \| 0.9514 \| 0.5089 \| nan \| 0.9847 \| 0.8983 \| 0.8453 \| 0.9809 \| 0.8819 \| 0.6343 \| 0.8453 \| 0.9627 \| 0.6063 \| 0.2409 \| 0.9091 \| 0.8923 \| 0.4433 \| \| 0.0158 \| 416.47 \| 7080 \| 0.0858 \| 0.7813 \| 0.8262 \| 0.9811 \| nan \| 0.9939 \| 0.9586 \| 0.9062 \| 0.9911 \| 0.9282 \| 0.6393 \| 0.8540 \| 0.9814 \| 0.7755 \| 0.2555 \| 0.9404 \| 0.9571 \| 0.5593 \| nan \| 0.9849 \| 0.8979 \| 0.8486 \| 0.9807 \| 0.8821 \| 0.6345 \| 0.8402 \| 0.9629 \| 0.6057 \| 0.2538 \| 0.9084 \| 0.8935 \| 0.4642 \| \| 0.0151 \| 417.65 \| 7100 \| 0.0854 \| 0.7789 \| 0.8210 \| 0.9811 \| nan \| 0.9943 \| 0.9591 \| 0.9029 \| 0.9899 \| 0.9325 \| 0.6451 \| 0.8519 \| 0.9822 \| 0.7760 \| 0.2511 \| 0.9344 \| 0.9516 \| 0.5024 \| nan \| 0.9848 \| 0.8983 \| 0.8483 \| 0.9809 \| 0.8823 \| 0.6402 \| 0.8389 \| 0.9633 \| 0.6066 \| 0.2495 \| 0.9069 \| 0.8937 \| 0.4322 \| \| 0.0163 \| 418.82 \| 7120 \| 0.0861 \| 0.7775 \| 0.8202 \| 0.9811 \| nan \| 0.9940 \| 0.9587 \| 0.9058 \| 0.9903 \| 0.9305 \| 0.6370 \| 0.8553 \| 0.9830 \| 0.7598 \| 0.2476 \| 0.9382 \| 0.9567 \| 0.5057 \| nan \| 0.9848 \| 0.8978 \| 0.8482 \| 0.9809 \| 0.8820 \| 0.6311 \| 0.8414 \| 0.9618 \| 0.5989 \| 0.2462 \| 0.9092 \| 0.8933 \| 0.4319 \| \| 0.0171 \| 420.0 \| 7140 \| 0.0856 \| 0.7772 \| 0.8200 \| 0.9811 \| nan \| 0.9939 \| 0.9580 \| 0.9083 \| 0.9903 \| 0.9342 \| 0.6387 \| 0.8617 \| 0.9827 \| 0.7644 \| 0.2490 \| 0.9373 \| 0.9507 \| 0.4911 \| nan \| 0.9848 \| 0.8985 \| 0.8481 \| 0.9808 \| 0.8814 \| 0.6331 \| 0.8453 \| 0.9621 \| 0.6018 \| 0.2476 \| 0.9088 \| 0.8942 \| 0.4177 \| \| 0.0154 \| 421.18 \| 7160 \| 0.0854 \| 0.7797 \| 0.8232 \| 0.9811 \| nan \| 0.9940 \| 0.9592 \| 0.9080 \| 0.9899 \| 0.9359 \| 0.6397 \| 0.8581 \| 0.9822 \| 0.7639 \| 0.2410 \| 0.9424 \| 0.9495 \| 0.5382 \| nan \| 0.9848 \| 0.8983 \| 0.8497 \| 0.9808 \| 0.8815 \| 0.6349 \| 0.8422 \| 0.9623 \| 0.6027 \| 0.2399 \| 0.9120 \| 0.8955 \| 0.4510 \| \| 0.0132 \| 422.35 \| 7180 \| 0.0857 \| 0.7810 \| 0.8238 \| 0.9811 \| nan \| 0.9942 \| 0.9590 \| 0.9042 \| 0.9902 \| 0.9314 \| 0.6393 \| 0.8514 \| 0.9818 \| 0.7735 \| 0.2489 \| 0.9383 \| 0.9541 \| 0.5431 \| nan \| 0.9848 \| 0.8983 \| 0.8485 \| 0.9806 \| 0.8806 \| 0.6338 \| 0.8375 \| 0.9625 \| 0.6084 \| 0.2473 \| 0.9103 \| 0.8953 \| 0.4645 \| \| 0.0146 \| 423.53 \| 7200 \| 0.0858 \| 0.7792 \| 0.8235 \| 0.9811 \| nan \| 0.9940 \| 0.9591 \| 0.9052 \| 0.9900 \| 0.9371 \| 0.6385 \| 0.8489 \| 0.9822 \| 0.7739 \| 0.2488 \| 0.9457 \| 0.9539 \| 0.5285 \| nan \| 0.9848 \| 0.8983 \| 0.8487 \| 0.9805 \| 0.8800 \| 0.6331 \| 0.8356 \| 0.9627 \| 0.6083 \| 0.2472 \| 0.9115 \| 0.8948 \| 0.4440 \| \| 0.0172 \| 424.71 \| 7220 \| 0.0854 \| 0.7790 \| 0.8231 \| 0.9811 \| nan \| 0.9940 \| 0.9597 \| 0.9055 \| 0.9893 \| 0.9388 \| 0.6390 \| 0.8526 \| 0.9825 \| 0.7776 \| 0.2414 \| 0.9422 \| 0.9490 \| 0.5285 \| nan \| 0.9848 \| 0.8983 \| 0.8488 \| 0.9804 \| 0.8796 \| 0.6333 \| 0.8386 \| 0.9629 \| 0.6116 \| 0.2400 \| 0.9114 \| 0.8941 \| 0.4434 \| \| 0.0169 \| 425.88 \| 7240 \| 0.0869 \| 0.7769 \| 0.8230 \| 0.9810 \| nan \| 0.9942 \| 0.9600 \| 0.9021 \| 0.9894 \| 0.9341 \| 0.6328 \| 0.8560 \| 0.9824 \| 0.7743 \| 0.2400 \| 0.9456 \| 0.9583 \| 0.5301 \| nan \| 0.9847 \| 0.8978 \| 0.8481 \| 0.9806 \| 0.8801 \| 0.6279 \| 0.8419 \| 0.9623 \| 0.6093 \| 0.2387 \| 0.9108 \| 0.8897 \| 0.4278 \| \| 0.0159 \| 427.06 \| 7260 \| 0.0870 \| 0.7777 \| 0.8192 \| 0.9811 \| nan \| 0.9943 \| 0.9567 \| 0.9060 \| 0.9904 \| 0.9334 \| 0.6365 \| 0.8581 \| 0.9830 \| 0.7613 \| 0.2368 \| 0.9406 \| 0.9463 \| 0.5057 \| nan \| 0.9847 \| 0.8981 \| 0.8493 \| 0.9808 \| 0.8816 \| 0.6315 \| 0.8431 \| 0.9620 \| 0.6033 \| 0.2358 \| 0.9098 \| 0.8946 \| 0.4350 \| \| 0.0171 \| 428.24 \| 7280 \| 0.0865 \| 0.7808 \| 0.8271 \| 0.9811 \| nan \| 0.9939 \| 0.9589 \| 0.9037 \| 0.9912 \| 0.9259 \| 0.6339 \| 0.8552 \| 0.9822 \| 0.7752 \| 0.2475 \| 0.9462 \| 0.9557 \| 0.5821 \| nan \| 0.9848 \| 0.8977 \| 0.8483 \| 0.9807 \| 0.8815 \| 0.6286 \| 0.8404 \| 0.9625 \| 0.6075 \| 0.2459 \| 0.9050 \| 0.8970 \| 0.4711 \| \| 0.0131 \| 429.41 \| 7300 \| 0.0865 \| 0.7791 \| 0.8237 \| 0.9811 \| nan \| 0.9938 \| 0.9621 \| 0.9062 \| 0.9896 \| 0.9333 \| 0.6360 \| 0.8605 \| 0.9818 \| 0.7703 \| 0.2459 \| 0.9388 \| 0.9502 \| 0.5398 \| nan \| 0.9849 \| 0.8970 \| 0.8497 \| 0.9809 \| 0.8819 \| 0.6299 \| 0.8469 \| 0.9627 \| 0.6100 \| 0.2445 \| 0.9079 \| 0.8959 \| 0.4363 \| \| 0.0164 \| 430.59 \| 7320 \| 0.0864 \| 0.7792 \| 0.8261 \| 0.9810 \| nan \| 0.9938 \| 0.9598 \| 0.9064 \| 0.9904 \| 0.9271 \| 0.6358 \| 0.8581 \| 0.9822 \| 0.7538 \| 0.2504 \| 0.9422 \| 0.9560 \| 0.5837 \| nan \| 0.9849 \| 0.8967 \| 0.8489 \| 0.9807 \| 0.8810 \| 0.6313 \| 0.8426 \| 0.9610 \| 0.5967 \| 0.2490 \| 0.9051 \| 0.8957 \| 0.4556 \| \| 0.0132 \| 431.76 \| 7340 \| 0.0873 \| 0.7747 \| 0.8162 \| 0.9811 \| nan \| 0.9939 \| 0.9582 \| 0.9055 \| 0.9910 \| 0.9262 \| 0.6361 \| 0.8753 \| 0.9827 \| 0.7511 \| 0.2465 \| 0.9367 \| 0.9509 \| 0.4569 \| nan \| 0.9848 \| 0.8975 \| 0.8471 \| 0.9813 \| 0.8818 \| 0.6313 \| 0.8594 \| 0.9605 \| 0.5933 \| 0.2454 \| 0.9084 \| 0.8957 \| 0.3844 \| \| 0.0127 \| 432.94 \| 7360 \| 0.0863 \| 0.7789 \| 0.8217 \| 0.9811 \| nan \| 0.9941 \| 0.9588 \| 0.9027 \| 0.9909 \| 0.9249 \| 0.6431 \| 0.8660 \| 0.9820 \| 0.7604 \| 0.2497 \| 0.9388 \| 0.9556 \| 0.5154 \| nan \| 0.9848 \| 0.8984 \| 0.8481 \| 0.9809 \| 0.8808 \| 0.6372 \| 0.8503 \| 0.9609 \| 0.6001 \| 0.2484 \| 0.9068 \| 0.8972 \| 0.4319 \| \| 0.0152 \| 434.12 \| 7380 \| 0.0877 \| 0.7794 \| 0.8228 \| 0.9810 \| nan \| 0.9944 \| 0.9600 \| 0.8963 \| 0.9905 \| 0.9251 \| 0.6386 \| 0.8668 \| 0.9813 \| 0.7583 \| 0.2581 \| 0.9397 \| 0.9525 \| 0.5350 \| nan \| 0.9848 \| 0.8974 \| 0.8471 \| 0.9810 \| 0.8809 \| 0.6328 \| 0.8512 \| 0.9611 \| 0.5993 \| 0.2562 \| 0.9069 \| 0.8945 \| 0.4393 \| \| 0.018 \| 435.29 \| 7400 \| 0.0881 \| 0.7752 \| 0.8171 \| 0.9810 \| nan \| 0.9940 \| 0.9590 \| 0.9032 \| 0.9912 \| 0.9265 \| 0.6346 \| 0.8597 \| 0.9815 \| 0.7511 \| 0.2454 \| 0.9358 \| 0.9511 \| 0.4894 \| nan \| 0.9848 \| 0.8968 \| 0.8484 \| 0.9808 \| 0.8807 \| 0.6296 \| 0.8455 \| 0.9607 \| 0.5962 \| 0.2441 \| 0.9066 \| 0.8951 \| 0.4079 \| \| 0.0148 \| 436.47 \| 7420 \| 0.0879 \| 0.7788 \| 0.8208 \| 0.9810 \| nan \| 0.9942 \| 0.9594 \| 0.8996 \| 0.9906 \| 0.9274 \| 0.6353 \| 0.8614 \| 0.9816 \| 0.7678 \| 0.2475 \| 0.9396 \| 0.9505 \| 0.5154 \| nan \| 0.9848 \| 0.8972 \| 0.8474 \| 0.9810 \| 0.8812 \| 0.6303 \| 0.8471 \| 0.9617 \| 0.6016 \| 0.2462 \| 0.9093 \| 0.8943 \| 0.4421 \| \| 0.0144 \| 437.65 \| 7440 \| 0.0879 \| 0.7763 \| 0.8174 \| 0.9810 \| nan \| 0.9945 \| 0.9593 \| 0.8979 \| 0.9901 \| 0.9302 \| 0.6345 \| 0.8570 \| 0.9816 \| 0.7623 \| 0.2468 \| 0.9385 \| 0.9495 \| 0.4846 \| nan \| 0.9847 \| 0.8974 \| 0.8472 \| 0.9808 \| 0.8812 \| 0.6298 \| 0.8431 \| 0.9615 \| 0.6031 \| 0.2455 \| 0.9074 \| 0.8924 \| 0.4180 \| \| 0.0135 \| 438.82 \| 7460 \| 0.0870 \| 0.7788 \| 0.8227 \| 0.9810 \| nan \| 0.9938 \| 0.9582 \| 0.9080 \| 0.9908 \| 0.9288 \| 0.6338 \| 0.8619 \| 0.9823 \| 0.7745 \| 0.2426 \| 0.9383 \| 0.9475 \| 0.5350 \| nan \| 0.9849 \| 0.8974 \| 0.8482 \| 0.9809 \| 0.8813 \| 0.6276 \| 0.8461 \| 0.9613 \| 0.6064 \| 0.2414 \| 0.9074 \| 0.8957 \| 0.4452 \| \| 0.0163 \| 440.0 \| 7480 \| 0.0866 \| 0.7789 \| 0.8234 \| 0.9810 \| nan \| 0.9938 \| 0.9589 \| 0.9074 \| 0.9913 \| 0.9244 \| 0.6372 \| 0.8584 \| 0.9818 \| 0.7663 \| 0.2434 \| 0.9421 \| 0.9501 \| 0.5496 \| nan \| 0.9848 \| 0.8973 \| 0.8492 \| 0.9807 \| 0.8805 \| 0.6311 \| 0.8438 \| 0.9616 \| 0.6055 \| 0.2423 \| 0.9108 \| 0.8945 \| 0.4436 \| \| 0.0163 \| 441.18 \| 7500 \| 0.0880 \| 0.7785 \| 0.8228 \| 0.9810 \| nan \| 0.9942 \| 0.9596 \| 0.9017 \| 0.9901 \| 0.9297 \| 0.6381 \| 0.8557 \| 0.9820 \| 0.7674 \| 0.2384 \| 0.9387 \| 0.9451 \| 0.5561 \| nan \| 0.9847 \| 0.8976 \| 0.8473 \| 0.9808 \| 0.8806 \| 0.6324 \| 0.8425 \| 0.9619 \| 0.6035 \| 0.2372 \| 0.9088 \| 0.8933 \| 0.4494 \| \| 0.0161 \| 442.35 \| 7520 \| 0.0883 \| 0.7755 \| 0.8213 \| 0.9809 \| nan \| 0.9941 \| 0.9604 \| 0.9010 \| 0.9900 \| 0.9294 \| 0.6327 \| 0.8547 \| 0.9819 \| 0.7662 \| 0.2359 \| 0.9429 \| 0.9515 \| 0.5366 \| nan \| 0.9847 \| 0.8970 \| 0.8475 \| 0.9808 \| 0.8810 \| 0.6276 \| 0.8416 \| 0.9614 \| 0.6022 \| 0.2348 \| 0.9081 \| 0.8918 \| 0.4231 \| \| 0.0139 \| 443.53 \| 7540 \| 0.0880 \| 0.7763 \| 0.8199 \| 0.9810 \| nan \| 0.9941 \| 0.9591 \| 0.9030 \| 0.9903 \| 0.9309 \| 0.6363 \| 0.8543 \| 0.9827 \| 0.7530 \| 0.2365 \| 0.9432 \| 0.9456 \| 0.5301 \| nan \| 0.9848 \| 0.8972 \| 0.8486 \| 0.9809 \| 0.8813 \| 0.6311 \| 0.8415 \| 0.9612 \| 0.5959 \| 0.2355 \| 0.9104 \| 0.8932 \| 0.4301 \| \| 0.0131 \| 444.71 \| 7560 \| 0.0876 \| 0.7763 \| 0.8197 \| 0.9810 \| nan \| 0.9942 \| 0.9587 \| 0.8992 \| 0.9910 \| 0.9280 \| 0.6356 \| 0.8559 \| 0.9820 \| 0.7614 \| 0.2425 \| 0.9400 \| 0.9472 \| 0.5203 \| nan \| 0.9848 \| 0.8972 \| 0.8478 \| 0.9807 \| 0.8809 \| 0.6303 \| 0.8410 \| 0.9610 \| 0.6026 \| 0.2412 \| 0.9092 \| 0.8955 \| 0.4194 \| \| 0.0159 \| 445.88 \| 7580 \| 0.0872 \| 0.7756 \| 0.8196 \| 0.9810 \| nan \| 0.9939 \| 0.9604 \| 0.9046 \| 0.9901 \| 0.9297 \| 0.6339 \| 0.8545 \| 0.9825 \| 0.7557 \| 0.2425 \| 0.9373 \| 0.9463 \| 0.5236 \| nan \| 0.9848 \| 0.8969 \| 0.8490 \| 0.9808 \| 0.8810 \| 0.6285 \| 0.8404 \| 0.9610 \| 0.5993 \| 0.2411 \| 0.9099 \| 0.8946 \| 0.4149 \| \| 0.0148 \| 447.06 \| 7600 \| 0.0879 \| 0.7775 \| 0.8218 \| 0.9810 \| nan \| 0.9941 \| 0.9594 \| 0.9045 \| 0.9906 \| 0.9247 \| 0.6363 \| 0.8524 \| 0.9819 \| 0.7584 \| 0.2471 \| 0.9341 \| 0.9502 \| 0.5496 \| nan \| 0.9848 \| 0.8969 \| 0.8491 \| 0.9808 \| 0.8802 \| 0.6306 \| 0.8393 \| 0.9613 \| 0.6004 \| 0.2453 \| 0.9100 \| 0.8955 \| 0.4339 \| \| 0.0134 \| 448.24 \| 7620 \| 0.0879 \| 0.7771 \| 0.8228 \| 0.9810 \| nan \| 0.9941 \| 0.9592 \| 0.9036 \| 0.9909 \| 0.9285 \| 0.6344 \| 0.8575 \| 0.9804 \| 0.7698 \| 0.2428 \| 0.9385 \| 0.9567 \| 0.5398 \| nan \| 0.9848 \| 0.8967 \| 0.8490 \| 0.9809 \| 0.8812 \| 0.6284 \| 0.8430 \| 0.9617 \| 0.6081 \| 0.2415 \| 0.9081 \| 0.8957 \| 0.4235 \| \| 0.0147 \| 449.41 \| 7640 \| 0.0888 \| 0.7747 \| 0.8171 \| 0.9810 \| nan \| 0.9946 \| 0.9583 \| 0.8969 \| 0.9905 \| 0.9287 \| 0.6332 \| 0.8589 \| 0.9821 \| 0.7671 \| 0.2446 \| 0.9396 \| 0.9494 \| 0.4780 \| nan \| 0.9847 \| 0.8973 \| 0.8473 \| 0.9810 \| 0.8814 \| 0.6283 \| 0.8446 \| 0.9620 \| 0.6066 \| 0.2435 \| 0.9079 \| 0.8920 \| 0.3941 \| \| 0.0166 \| 450.59 \| 7660 \| 0.0864 \| 0.7770 \| 0.8206 \| 0.9811 \| nan \| 0.9942 \| 0.9587 \| 0.9036 \| 0.9910 \| 0.9256 \| 0.6334 \| 0.8584 \| 0.9817 \| 0.7694 \| 0.2435 \| 0.9328 \| 0.9501 \| 0.5252 \| nan \| 0.9848 \| 0.8971 \| 0.8498 \| 0.9809 \| 0.8809 \| 0.6286 \| 0.8440 \| 0.9622 \| 0.6110 \| 0.2423 \| 0.9077 \| 0.8956 \| 0.4157 \| \| 0.0154 \| 451.76 \| 7680 \| 0.0869 \| 0.7774 \| 0.8202 \| 0.9811 \| nan \| 0.9942 \| 0.9594 \| 0.9022 \| 0.9907 \| 0.9295 \| 0.6352 \| 0.8584 \| 0.9813 \| 0.7750 \| 0.2382 \| 0.9341 \| 0.9468 \| 0.5171 \| nan \| 0.9848 \| 0.8973 \| 0.8489 \| 0.9809 \| 0.8814 \| 0.6299 \| 0.8439 \| 0.9619 \| 0.6129 \| 0.2371 \| 0.9071 \| 0.8967 \| 0.4229 \| \| 0.0117 \| 452.94 \| 7700 \| 0.0869 \| 0.7781 \| 0.8221 \| 0.9811 \| nan \| 0.9942 \| 0.9585 \| 0.9022 \| 0.9906 \| 0.9303 \| 0.6360 \| 0.8611 \| 0.9819 \| 0.7771 \| 0.2489 \| 0.9394 \| 0.9513 \| 0.5154 \| nan \| 0.9848 \| 0.8981 \| 0.8487 \| 0.9809 \| 0.8816 \| 0.6306 \| 0.8460 \| 0.9621 \| 0.6115 \| 0.2475 \| 0.9078 \| 0.8934 \| 0.4221 \| \| 0.0128 \| 454.12 \| 7720 \| 0.0884 \| 0.7798 \| 0.8240 \| 0.9811 \| nan \| 0.9940 \| 0.9583 \| 0.9037 \| 0.9907 \| 0.9270 \| 0.6335 \| 0.8599 \| 0.9833 \| 0.7809 \| 0.2439 \| 0.9433 \| 0.9506 \| 0.5431 \| nan \| 0.9848 \| 0.8976 \| 0.8489 \| 0.9810 \| 0.8810 \| 0.6280 \| 0.8452 \| 0.9620 \| 0.6110 \| 0.2427 \| 0.9089 \| 0.8950 \| 0.4507 \| \| 0.0139 \| 455.29 \| 7740 \| 0.0883 \| 0.7783 \| 0.8237 \| 0.9810 \| nan \| 0.9938 \| 0.9600 \| 0.9046 \| 0.9906 \| 0.9306 \| 0.6308 \| 0.8584 \| 0.9814 \| 0.7676 \| 0.2412 \| 0.9439 \| 0.9537 \| 0.5512 \| nan \| 0.9848 \| 0.8965 \| 0.8491 \| 0.9807 \| 0.8813 \| 0.6252 \| 0.8438 \| 0.9618 \| 0.6057 \| 0.2398 \| 0.9089 \| 0.8956 \| 0.4443 \| \| 0.0134 \| 456.47 \| 7760 \| 0.0879 \| 0.7775 \| 0.8208 \| 0.9810 \| nan \| 0.9941 \| 0.9589 \| 0.9037 \| 0.9906 \| 0.9296 \| 0.6326 \| 0.8550 \| 0.9823 \| 0.7643 \| 0.2371 \| 0.9394 \| 0.9466 \| 0.5366 \| nan \| 0.9849 \| 0.8969 \| 0.8496 \| 0.9807 \| 0.8809 \| 0.6271 \| 0.8410 \| 0.9618 \| 0.6053 \| 0.2356 \| 0.9073 \| 0.8953 \| 0.4412 \| \| 0.0115 \| 457.65 \| 7780 \| 0.0886 \| 0.7789 \| 0.8224 \| 0.9811 \| nan \| 0.9941 \| 0.9591 \| 0.9027 \| 0.9908 \| 0.9295 \| 0.6308 \| 0.8579 \| 0.9818 \| 0.7714 \| 0.2381 \| 0.9397 \| 0.9502 \| 0.5447 \| nan \| 0.9849 \| 0.8970 \| 0.8495 \| 0.9808 \| 0.8814 \| 0.6263 \| 0.8435 \| 0.9618 \| 0.6085 \| 0.2369 \| 0.9082 \| 0.8955 \| 0.4521 \| \| 0.0123 \| 458.82 \| 7800 \| 0.0888 \| 0.7774 \| 0.8198 \| 0.9810 \| nan \| 0.9940 \| 0.9594 \| 0.9044 \| 0.9908 \| 0.9286 \| 0.6295 \| 0.8554 \| 0.9819 \| 0.7670 \| 0.2368 \| 0.9354 \| 0.9530 \| 0.5220 \| nan \| 0.9848 \| 0.8965 \| 0.8496 \| 0.9808 \| 0.8814 \| 0.6249 \| 0.8415 \| 0.9615 \| 0.6062 \| 0.2356 \| 0.9062 \| 0.8941 \| 0.4428 \| \| 0.0149 \| 460.0 \| 7820 \| 0.0879 \| 0.7799 \| 0.8228 \| 0.9811 \| nan \| 0.9941 \| 0.9588 \| 0.9047 \| 0.9907 \| 0.9300 \| 0.6342 \| 0.8599 \| 0.9815 \| 0.7671 \| 0.2414 \| 0.9422 \| 0.9521 \| 0.5398 \| nan \| 0.9849 \| 0.8971 \| 0.8501 \| 0.9808 \| 0.8813 \| 0.6289 \| 0.8448 \| 0.9619 \| 0.6067 \| 0.2401 \| 0.9084 \| 0.8938 \| 0.4598 \| \| 0.0139 \| 461.18 \| 7840 \| 0.0878 \| 0.7785 \| 0.8218 \| 0.9811 \| nan \| 0.9941 \| 0.9592 \| 0.9047 \| 0.9904 \| 0.9328 \| 0.6346 \| 0.8575 \| 0.9819 \| 0.7639 \| 0.2464 \| 0.9443 \| 0.9531 \| 0.5203 \| nan \| 0.9848 \| 0.8975 \| 0.8496 \| 0.9809 \| 0.8814 \| 0.6294 \| 0.8430 \| 0.9619 \| 0.6070 \| 0.2451 \| 0.9101 \| 0.8956 \| 0.4348 \| \| 0.0143 \| 462.35 \| 7860 \| 0.0888 \| 0.7793 \| 0.8223 \| 0.9810 \| nan \| 0.9942 \| 0.9596 \| 0.9033 \| 0.9901 \| 0.9313 \| 0.6324 \| 0.8589 \| 0.9813 \| 0.7688 \| 0.2494 \| 0.9387 \| 0.9505 \| 0.5317 \| nan \| 0.9848 \| 0.8970 \| 0.8490 \| 0.9809 \| 0.8815 \| 0.6275 \| 0.8452 \| 0.9617 \| 0.6075 \| 0.2479 \| 0.9063 \| 0.8942 \| 0.4479 \| \| 0.0155 \| 463.53 \| 7880 \| 0.0878 \| 0.7807 \| 0.8242 \| 0.9812 \| nan \| 0.9941 \| 0.9584 \| 0.9066 \| 0.9905 \| 0.9315 \| 0.6383 \| 0.8593 \| 0.9817 \| 0.7721 \| 0.2556 \| 0.9436 \| 0.9568 \| 0.5268 \| nan \| 0.9848 \| 0.8979 \| 0.8499 \| 0.9810 \| 0.8818 \| 0.6327 \| 0.8452 \| 0.9620 \| 0.6103 \| 0.2540 \| 0.9084 \| 0.8965 \| 0.4451 \| \| 0.0152 \| 464.71 \| 7900 \| 0.0876 \| 0.7804 \| 0.8217 \| 0.9811 \| nan \| 0.9942 \| 0.9595 \| 0.9025 \| 0.9901 \| 0.9319 \| 0.6367 \| 0.8621 \| 0.9825 \| 0.7643 \| 0.2459 \| 0.9366 \| 0.9553 \| 0.5203 \| nan \| 0.9847 \| 0.8978 \| 0.8482 \| 0.9812 \| 0.8817 \| 0.6314 \| 0.8486 \| 0.9623 \| 0.6089 \| 0.2446 \| 0.9103 \| 0.8950 \| 0.4501 \| \| 0.0137 \| 465.88 \| 7920 \| 0.0880 \| 0.7804 \| 0.8231 \| 0.9812 \| nan \| 0.9942 \| 0.9587 \| 0.9029 \| 0.9906 \| 0.9292 \| 0.6363 \| 0.8634 \| 0.9827 \| 0.7728 \| 0.2493 \| 0.9413 \| 0.9501 \| 0.5285 \| nan \| 0.9848 \| 0.8981 \| 0.8488 \| 0.9811 \| 0.8818 \| 0.6312 \| 0.8486 \| 0.9624 \| 0.6111 \| 0.2480 \| 0.9110 \| 0.8958 \| 0.4422 \| \| 0.0164 \| 467.06 \| 7940 \| 0.0887 \| 0.7780 \| 0.8214 \| 0.9811 \| nan \| 0.9942 \| 0.9599 \| 0.9015 \| 0.9902 \| 0.9318 \| 0.6311 \| 0.8595 \| 0.9821 \| 0.7624 \| 0.2502 \| 0.9410 \| 0.9508 \| 0.5236 \| nan \| 0.9848 \| 0.8972 \| 0.8484 \| 0.9811 \| 0.8819 \| 0.6262 \| 0.8456 \| 0.9620 \| 0.6046 \| 0.2487 \| 0.9097 \| 0.8948 \| 0.4293 \| \| 0.0142 \| 468.24 \| 7960 \| 0.0887 \| 0.7770 \| 0.8194 \| 0.9810 \| nan \| 0.9941 \| 0.9595 \| 0.9015 \| 0.9906 \| 0.9295 \| 0.6287 \| 0.8614 \| 0.9816 \| 0.7750 \| 0.2461 \| 0.9390 \| 0.9504 \| 0.4943 \| nan \| 0.9847 \| 0.8974 \| 0.8472 \| 0.9811 \| 0.8820 \| 0.6239 \| 0.8472 \| 0.9623 \| 0.6098 \| 0.2449 \| 0.9106 \| 0.8934 \| 0.4164 \| \| 0.0152 \| 469.41 \| 7980 \| 0.0887 \| 0.7778 \| 0.8212 \| 0.9811 \| nan \| 0.9943 \| 0.9591 \| 0.9029 \| 0.9903 \| 0.9306 \| 0.6300 \| 0.8576 \| 0.9814 \| 0.7729 \| 0.2470 \| 0.9437 \| 0.9521 \| 0.5138 \| nan \| 0.9847 \| 0.8978 \| 0.8485 \| 0.9810 \| 0.8822 \| 0.6249 \| 0.8437 \| 0.9624 \| 0.6101 \| 0.2457 \| 0.9109 \| 0.8944 \| 0.4247 \| \| 0.0134 \| 470.59 \| 8000 \| 0.0885 \| 0.7757 \| 0.8181 \| 0.9810 \| nan \| 0.9944 \| 0.9594 \| 0.8983 \| 0.9904 \| 0.9307 \| 0.6323 \| 0.8549 \| 0.9819 \| 0.7692 \| 0.2428 \| 0.9365 \| 0.9536 \| 0.4911 \| nan \| 0.9847 \| 0.8979 \| 0.8465 \| 0.9808 \| 0.8819 \| 0.6273 \| 0.8411 \| 0.9621 \| 0.6099 \| 0.2417 \| 0.9091 \| 0.8937 \| 0.4070 \| \| 0.0132 \| 471.76 \| 8020 \| 0.0883 \| 0.7795 \| 0.8240 \| 0.9811 \| nan \| 0.9942 \| 0.9598 \| 0.9034 \| 0.9904 \| 0.9288 \| 0.6340 \| 0.8547 \| 0.9808 \| 0.7711 \| 0.2508 \| 0.9408 \| 0.9569 \| 0.5463 \| nan \| 0.9848 \| 0.8978 \| 0.8489 \| 0.9808 \| 0.8822 \| 0.6286 \| 0.8408 \| 0.9620 \| 0.6114 \| 0.2492 \| 0.9090 \| 0.8932 \| 0.4450 \| \| 0.0136 \| 472.94 \| 8040 \| 0.0882 \| 0.7783 \| 0.8230 \| 0.9810 \| nan \| 0.9941 \| 0.9588 \| 0.9048 \| 0.9906 \| 0.9312 \| 0.6301 \| 0.8533 \| 0.9820 \| 0.7679 \| 0.2477 \| 0.9404 \| 0.9491 \| 0.5496 \| nan \| 0.9848 \| 0.8974 \| 0.8494 \| 0.9808 \| 0.8819 \| 0.6249 \| 0.8391 \| 0.9617 \| 0.6097 \| 0.2462 \| 0.9087 \| 0.8952 \| 0.4390 \| \| 0.0149 \| 474.12 \| 8060 \| 0.0882 \| 0.7777 \| 0.8217 \| 0.9811 \| nan \| 0.9942 \| 0.9596 \| 0.9020 \| 0.9904 \| 0.9313 \| 0.6301 \| 0.8545 \| 0.9815 \| 0.7732 \| 0.2458 \| 0.9380 \| 0.9464 \| 0.5350 \| nan \| 0.9848 \| 0.8974 \| 0.8488 \| 0.9808 \| 0.8822 \| 0.6250 \| 0.8401 \| 0.9621 \| 0.6110 \| 0.2444 \| 0.9086 \| 0.8937 \| 0.4306 \| \| 0.0139 \| 475.29 \| 8080 \| 0.0883 \| 0.7786 \| 0.8224 \| 0.9811 \| nan \| 0.9940 \| 0.9599 \| 0.9042 \| 0.9908 \| 0.9271 \| 0.6312 \| 0.8554 \| 0.9822 \| 0.7699 \| 0.2503 \| 0.9405 \| 0.9458 \| 0.5398 \| nan \| 0.9849 \| 0.8973 \| 0.8495 \| 0.9809 \| 0.8817 \| 0.6257 \| 0.8413 \| 0.9620 \| 0.6087 \| 0.2488 \| 0.9095 \| 0.8944 \| 0.4374 \| \| 0.0164 \| 476.47 \| 8100 \| 0.0886 \| 0.7773 \| 0.8202 \| 0.9810 \| nan \| 0.9943 \| 0.9591 \| 0.9030 \| 0.9903 \| 0.9299 \| 0.6293 \| 0.8524 \| 0.9818 \| 0.7669 \| 0.2504 \| 0.9393 \| 0.9485 \| 0.5171 \| nan \| 0.9848 \| 0.8974 \| 0.8490 \| 0.9809 \| 0.8820 \| 0.6246 \| 0.8395 \| 0.9618 \| 0.6073 \| 0.2491 \| 0.9096 \| 0.8938 \| 0.4251 \| \| 0.0168 \| 477.65 \| 8120 \| 0.0890 \| 0.7771 \| 0.8195 \| 0.9810 \| nan \| 0.9942 \| 0.9592 \| 0.9030 \| 0.9903 \| 0.9321 \| 0.6308 \| 0.8556 \| 0.9819 \| 0.7576 \| 0.2493 \| 0.9401 \| 0.9501 \| 0.5089 \| nan \| 0.9848 \| 0.8974 \| 0.8489 \| 0.9809 \| 0.8823 \| 0.6258 \| 0.8412 \| 0.9614 \| 0.6026 \| 0.2479 \| 0.9096 \| 0.8945 \| 0.4247 \| \| 0.0148 \| 478.82 \| 8140 \| 0.0888 \| 0.7784 \| 0.8230 \| 0.9811 \| nan \| 0.9942 \| 0.9589 \| 0.9036 \| 0.9904 \| 0.9303 \| 0.6333 \| 0.8583 \| 0.9821 \| 0.7736 \| 0.2514 \| 0.9442 \| 0.9536 \| 0.5252 \| nan \| 0.9848 \| 0.8978 \| 0.8492 \| 0.9809 \| 0.8824 \| 0.6278 \| 0.8434 \| 0.9618 \| 0.6089 \| 0.2499 \| 0.9104 \| 0.8933 \| 0.4284 \| \| 0.0141 \| 480.0 \| 8160 \| 0.0887 \| 0.7777 \| 0.8221 \| 0.9811 \| nan \| 0.9942 \| 0.9586 \| 0.9028 \| 0.9907 \| 0.9298 \| 0.6325 \| 0.8558 \| 0.9816 \| 0.7710 \| 0.2474 \| 0.9437 \| 0.9552 \| 0.5236 \| nan \| 0.9848 \| 0.8973 \| 0.8490 \| 0.9809 \| 0.8823 \| 0.6271 \| 0.8415 \| 0.9616 \| 0.6069 \| 0.2459 \| 0.9086 \| 0.8943 \| 0.4299 \| \| 0.0175 \| 481.18 \| 8180 \| 0.0880 \| 0.7778 \| 0.8223 \| 0.9811 \| nan \| 0.9942 \| 0.9593 \| 0.9033 \| 0.9904 \| 0.9292 \| 0.6337 \| 0.8569 \| 0.9818 \| 0.7735 \| 0.2448 \| 0.9433 \| 0.9524 \| 0.5268 \| nan \| 0.9848 \| 0.8976 \| 0.8491 \| 0.9809 \| 0.8822 \| 0.6289 \| 0.8424 \| 0.9619 \| 0.6098 \| 0.2434 \| 0.9090 \| 0.8949 \| 0.4269 \| \| 0.0141 \| 482.35 \| 8200 \| 0.0883 \| 0.7767 \| 0.8201 \| 0.9811 \| nan \| 0.9942 \| 0.9591 \| 0.9027 \| 0.9905 \| 0.9316 \| 0.6338 \| 0.8564 \| 0.9816 \| 0.7668 \| 0.2413 \| 0.9422 \| 0.9524 \| 0.5089 \| nan \| 0.9848 \| 0.8976 \| 0.8489 \| 0.9808 \| 0.8822 \| 0.6285 \| 0.8417 \| 0.9619 \| 0.6074 \| 0.2401 \| 0.9085 \| 0.8960 \| 0.4184 \| \| 0.0141 \| 483.53 \| 8220 \| 0.0890 \| 0.7767 \| 0.8201 \| 0.9811 \| nan \| 0.9942 \| 0.9594 \| 0.9033 \| 0.9905 \| 0.9297 \| 0.6310 \| 0.8546 \| 0.9821 \| 0.7660 \| 0.2430 \| 0.9413 \| 0.9527 \| 0.5138 \| nan \| 0.9848 \| 0.8974 \| 0.8490 \| 0.9809 \| 0.8819 \| 0.6262 \| 0.8404 \| 0.9617 \| 0.6068 \| 0.2416 \| 0.9091 \| 0.8956 \| 0.4213 \| \| 0.0132 \| 484.71 \| 8240 \| 0.0887 \| 0.7767 \| 0.8211 \| 0.9810 \| nan \| 0.9940 \| 0.9598 \| 0.9033 \| 0.9906 \| 0.9322 \| 0.6318 \| 0.8543 \| 0.9818 \| 0.7626 \| 0.2416 \| 0.9427 \| 0.9556 \| 0.5236 \| nan \| 0.9848 \| 0.8973 \| 0.8491 \| 0.9808 \| 0.8820 \| 0.6268 \| 0.8403 \| 0.9616 \| 0.6045 \| 0.2402 \| 0.9091 \| 0.8956 \| 0.4248 \| \| 0.0148 \| 485.88 \| 8260 \| 0.0885 \| 0.7773 \| 0.8210 \| 0.9810 \| nan \| 0.9941 \| 0.9599 \| 0.9028 \| 0.9906 \| 0.9291 \| 0.6326 \| 0.8543 \| 0.9812 \| 0.7682 \| 0.2418 \| 0.9413 \| 0.9500 \| 0.5268 \| nan \| 0.9848 \| 0.8972 \| 0.8490 \| 0.9808 \| 0.8822 \| 0.6273 \| 0.8407 \| 0.9617 \| 0.6073 \| 0.2404 \| 0.9091 \| 0.8946 \| 0.4297 \| \| 0.0138 \| 487.06 \| 8280 \| 0.0884 \| 0.7781 \| 0.8226 \| 0.9811 \| nan \| 0.9942 \| 0.9592 \| 0.9034 \| 0.9903 \| 0.9320 \| 0.6341 \| 0.8603 \| 0.9815 \| 0.7740 \| 0.2397 \| 0.9443 \| 0.9528 \| 0.5285 \| nan \| 0.9848 \| 0.8977 \| 0.8492 \| 0.9809 \| 0.8825 \| 0.6285 \| 0.8451 \| 0.9619 \| 0.6098 \| 0.2385 \| 0.9103 \| 0.8943 \| 0.4316 \| \| 0.0122 \| 488.24 \| 8300 \| 0.0885 \| 0.7777 \| 0.8220 \| 0.9811 \| nan \| 0.9941 \| 0.9592 \| 0.9034 \| 0.9906 \| 0.9321 \| 0.6326 \| 0.8576 \| 0.9816 \| 0.7695 \| 0.2422 \| 0.9447 \| 0.9484 \| 0.5301 \| nan \| 0.9848 \| 0.8976 \| 0.8488 \| 0.9809 \| 0.8823 \| 0.6273 \| 0.8428 \| 0.9617 \| 0.6080 \| 0.2408 \| 0.9102 \| 0.8965 \| 0.4289 \| \| 0.0138 \| 489.41 \| 8320 \| 0.0889 \| 0.7774 \| 0.8212 \| 0.9811 \| nan \| 0.9942 \| 0.9594 \| 0.9023 \| 0.9906 \| 0.9290 \| 0.6331 \| 0.8563 \| 0.9818 \| 0.7680 \| 0.2399 \| 0.9424 \| 0.9547 \| 0.5236 \| nan \| 0.9848 \| 0.8976 \| 0.8486 \| 0.9809 \| 0.8821 \| 0.6278 \| 0.8425 \| 0.9617 \| 0.6062 \| 0.2387 \| 0.9091 \| 0.8949 \| 0.4311 \| \| 0.0136 \| 490.59 \| 8340 \| 0.0885 \| 0.7769 \| 0.8208 \| 0.9811 \| nan \| 0.9942 \| 0.9585 \| 0.9028 \| 0.9907 \| 0.9299 \| 0.6321 \| 0.8574 \| 0.9820 \| 0.7626 \| 0.2412 \| 0.9436 \| 0.9551 \| 0.5203 \| nan \| 0.9848 \| 0.8977 \| 0.8485 \| 0.9809 \| 0.8821 \| 0.6270 \| 0.8432 \| 0.9615 \| 0.6035 \| 0.2400 \| 0.9091 \| 0.8956 \| 0.4261 \| \| 0.0162 \| 491.76 \| 8360 \| 0.0891 \| 0.7772 \| 0.8214 \| 0.9810 \| nan \| 0.9942 \| 0.9593 \| 0.9029 \| 0.9906 \| 0.9295 \| 0.6303 \| 0.8574 \| 0.9814 \| 0.7630 \| 0.2443 \| 0.9431 \| 0.9567 \| 0.5252 \| nan \| 0.9848 \| 0.8972 \| 0.8486 \| 0.9809 \| 0.8822 \| 0.6255 \| 0.8432 \| 0.9615 \| 0.6032 \| 0.2429 \| 0.9089 \| 0.8944 \| 0.4301 \| \| 0.0159 \| 492.94 \| 8380 \| 0.0889 \| 0.7770 \| 0.8215 \| 0.9811 \| nan \| 0.9942 \| 0.9589 \| 0.9030 \| 0.9904 \| 0.9309 \| 0.6332 \| 0.8590 \| 0.9819 \| 0.7676 \| 0.2435 \| 0.9446 \| 0.9580 \| 0.5138 \| nan \| 0.9848 \| 0.8978 \| 0.8487 \| 0.9809 \| 0.8821 \| 0.6279 \| 0.8444 \| 0.9618 \| 0.6055 \| 0.2423 \| 0.9100 \| 0.8933 \| 0.4219 \| \| 0.0154 \| 494.12 \| 8400 \| 0.0900 \| 0.7764 \| 0.8206 \| 0.9810 \| nan \| 0.9941 \| 0.9602 \| 0.9023 \| 0.9905 \| 0.9297 \| 0.6290 \| 0.8555 \| 0.9816 \| 0.7684 \| 0.2415 \| 0.9439 \| 0.9562 \| 0.5154 \| nan \| 0.9848 \| 0.8970 \| 0.8487 \| 0.9808 \| 0.8821 \| 0.6248 \| 0.8417 \| 0.9618 \| 0.6054 \| 0.2402 \| 0.9085 \| 0.8940 \| 0.4238 \| \| 0.0135 \| 495.29 \| 8420 \| 0.0890 \| 0.7765 \| 0.8210 \| 0.9810 \| nan \| 0.9941 \| 0.9596 \| 0.9028 \| 0.9905 \| 0.9307 \| 0.6315 \| 0.8555 \| 0.9819 \| 0.7657 \| 0.2423 \| 0.9442 \| 0.9567 \| 0.5171 \| nan \| 0.9848 \| 0.8974 \| 0.8488 \| 0.9808 \| 0.8821 \| 0.6266 \| 0.8415 \| 0.9617 \| 0.6040 \| 0.2410 \| 0.9091 \| 0.8943 \| 0.4229 \| \| 0.0148 \| 496.47 \| 8440 \| 0.0886 \| 0.7767 \| 0.8206 \| 0.9811 \| nan \| 0.9942 \| 0.9591 \| 0.9022 \| 0.9906 \| 0.9283 \| 0.6328 \| 0.8574 \| 0.9821 \| 0.7661 \| 0.2425 \| 0.9424 \| 0.9525 \| 0.5171 \| nan \| 0.9848 \| 0.8976 \| 0.8487 \| 0.9809 \| 0.8820 \| 0.6277 \| 0.8427 \| 0.9617 \| 0.6046 \| 0.2412 \| 0.9089 \| 0.8946 \| 0.4218 \| \| 0.0165 \| 497.65 \| 8460 \| 0.0885 \| 0.7771 \| 0.8214 \| 0.9811 \| nan \| 0.9941 \| 0.9591 \| 0.9037 \| 0.9905 \| 0.9306 \| 0.6345 \| 0.8564 \| 0.9823 \| 0.7623 \| 0.2447 \| 0.9447 \| 0.9545 \| 0.5203 \| nan \| 0.9848 \| 0.8977 \| 0.8490 \| 0.9809 \| 0.8821 \| 0.6287 \| 0.8422 \| 0.9615 \| 0.6034 \| 0.2433 \| 0.9095 \| 0.8950 \| 0.4238 \| \| 0.0138 \| 498.82 \| 8480 \| 0.0890 \| 0.7763 \| 0.8202 \| 0.9810 \| nan \| 0.9941 \| 0.9594 \| 0.9033 \| 0.9905 \| 0.9302 \| 0.6320 \| 0.8534 \| 0.9822 \| 0.7664 \| 0.2426 \| 0.9419 \| 0.9531 \| 0.5138 \| nan \| 0.9848 \| 0.8974 \| 0.8489 \| 0.9809 \| 0.8821 \| 0.6269 \| 0.8401 \| 0.9617 \| 0.6046 \| 0.2413 \| 0.9087 \| 0.8949 \| 0.4191 \| \| 0.0162 \| 500.0 \| 8500 \| 0.0892 \| 0.7759 \| 0.8195 \| 0.9811 \| nan \| 0.9942 \| 0.9592 \| 0.9021 \| 0.9902 \| 0.9315 \| 0.6330 \| 0.8554 \| 0.9821 \| 0.7662 \| 0.2392 \| 0.9438 \| 0.9536 \| 0.5024 \| nan \| 0.9848 \| 0.8976 \| 0.8486 \| 0.9809 \| 0.8822 \| 0.6278 \| 0.8418 \| 0.9618 \| 0.6046 \| 0.2381 \| 0.9093 \| 0.8939 \| 0.4153 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "nat", "concrete", "grass", "speedway bricks", "steel", "rough concrete", "dark bricks", "road", "rough red sidewalk", "tiles", "red bricks", "concrete tiles", "rest" ]
fshala/segformer-cloud	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-cloud This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
johanhag/segformer-b0-winter	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-winter This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the johanhag/winter-test dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - Mean Iou: 0.8861 - Mean Accuracy: 0.9456 - Overall Accuracy: 0.9660 - Accuracy Unlabeled: nan - Accuracy Object: nan - Accuracy Road: 0.9769 - Accuracy Side walk: 0.8930 - Accuracy Car: 0.9347 - Accuracy Pedestrian: nan - Accuracy Other: 0.9779 - Iou Unlabeled: nan - Iou Object: nan - Iou Road: 0.9197 - Iou Side walk: 0.8250 - Iou Car: 0.8319 - Iou Pedestrian: nan - Iou Other: 0.9678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Object \| Accuracy Road \| Accuracy Side walk \| Accuracy Car \| Accuracy Pedestrian \| Accuracy Other \| Iou Unlabeled \| Iou Object \| Iou Road \| Iou Side walk \| Iou Car \| Iou Pedestrian \| Iou Other \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:---------------:\|:-------------:\|:------------------:\|:------------:\|:-------------------:\|:--------------:\|:-------------:\|:----------:\|:--------:\|:-------------:\|:-------:\|:--------------:\|:---------:\| \| 0.2207 \| 4.0 \| 20 \| 0.2300 \| 0.8576 \| 0.9439 \| 0.9583 \| nan \| nan \| 0.9738 \| 0.8767 \| 0.9564 \| nan \| 0.9686 \| nan \| nan \| 0.9018 \| 0.8000 \| 0.7670 \| nan \| 0.9617 \| \| 0.1792 \| 8.0 \| 40 \| 0.2126 \| 0.8696 \| 0.9457 \| 0.9614 \| nan \| nan \| 0.9768 \| 0.8911 \| 0.9444 \| nan \| 0.9706 \| nan \| nan \| 0.9106 \| 0.8122 \| 0.7924 \| nan \| 0.9633 \| \| 0.1527 \| 12.0 \| 60 \| 0.1869 \| 0.8769 \| 0.9470 \| 0.9634 \| nan \| nan \| 0.9776 \| 0.9023 \| 0.9364 \| nan \| 0.9718 \| nan \| nan \| 0.9180 \| 0.8165 \| 0.8085 \| nan \| 0.9647 \| \| 0.1329 \| 16.0 \| 80 \| 0.1787 \| 0.8783 \| 0.9429 \| 0.9634 \| nan \| nan \| 0.9772 \| 0.8880 \| 0.9314 \| nan \| 0.9749 \| nan \| nan \| 0.9126 \| 0.8117 \| 0.8229 \| nan \| 0.9661 \| \| 0.1746 \| 20.0 \| 100 \| 0.1651 \| 0.8864 \| 0.9511 \| 0.9668 \| nan \| nan \| 0.9771 \| 0.9126 \| 0.9395 \| nan \| 0.9751 \| nan \| nan \| 0.9258 \| 0.8369 \| 0.8157 \| nan \| 0.9671 \| \| 0.1218 \| 24.0 \| 120 \| 0.1652 \| 0.8798 \| 0.9444 \| 0.9643 \| nan \| nan \| 0.9791 \| 0.8858 \| 0.9370 \| nan \| 0.9757 \| nan \| nan \| 0.9140 \| 0.8156 \| 0.8224 \| nan \| 0.9673 \| \| 0.0816 \| 28.0 \| 140 \| 0.1473 \| 0.8921 \| 0.9521 \| 0.9684 \| nan \| nan \| 0.9723 \| 0.9199 \| 0.9383 \| nan \| 0.9780 \| nan \| nan \| 0.9299 \| 0.8478 \| 0.8231 \| nan \| 0.9676 \| \| 0.0893 \| 32.0 \| 160 \| 0.1490 \| 0.8892 \| 0.9502 \| 0.9672 \| nan \| nan \| 0.9749 \| 0.9140 \| 0.9354 \| nan \| 0.9766 \| nan \| nan \| 0.9260 \| 0.8384 \| 0.825 \| nan \| 0.9673 \| \| 0.0849 \| 36.0 \| 180 \| 0.1517 \| 0.8861 \| 0.9476 \| 0.9660 \| nan \| nan \| 0.9791 \| 0.8987 \| 0.9367 \| nan \| 0.9760 \| nan \| nan \| 0.9205 \| 0.8258 \| 0.8308 \| nan \| 0.9674 \| \| 0.1625 \| 40.0 \| 200 \| 0.1519 \| 0.8843 \| 0.9468 \| 0.9654 \| nan \| nan \| 0.9777 \| 0.8938 \| 0.9394 \| nan \| 0.9763 \| nan \| nan \| 0.9176 \| 0.8235 \| 0.8289 \| nan \| 0.9673 \| \| 0.1396 \| 44.0 \| 220 \| 0.1500 \| 0.8850 \| 0.9476 \| 0.9655 \| nan \| nan \| 0.9791 \| 0.8949 \| 0.9408 \| nan \| 0.9757 \| nan \| nan \| 0.9187 \| 0.8223 \| 0.8317 \| nan \| 0.9674 \| \| 0.0931 \| 48.0 \| 240 \| 0.1441 \| 0.8861 \| 0.9456 \| 0.9660 \| nan \| nan \| 0.9769 \| 0.8930 \| 0.9347 \| nan \| 0.9779 \| nan \| nan \| 0.9197 \| 0.8250 \| 0.8319 \| nan \| 0.9678 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "object", "road", "side walk", "car", "pedestrian", "other" ]
yolo12138/segformer-b2-cloth-parse-9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b2-cloth-parse-9 This model is a fine-tuned version of [mattmdjaga/segformer_b2_clothes](https://huggingface.co/mattmdjaga/segformer_b2_clothes) on the cloth_parsing_mix dataset. It achieves the following results on the evaluation set: - Loss: 0.0433 - Mean Iou: 0.8611 - Mean Accuracy: 0.9107 - Overall Accuracy: 0.9846 - Accuracy Background: 0.9964 - Accuracy Upper Torso: 0.9857 - Accuracy Left Pants: 0.9654 - Accuracy Right Patns: 0.9664 - Accuracy Skirts: 0.9065 - Accuracy Left Sleeve: 0.9591 - Accuracy Right Sleeve: 0.9662 - Accuracy Outer Collar: 0.6491 - Accuracy Inner Collar: 0.8015 - Iou Background: 0.9923 - Iou Upper Torso: 0.9655 - Iou Left Pants: 0.9017 - Iou Right Patns: 0.9085 - Iou Skirts: 0.8749 - Iou Left Sleeve: 0.9223 - Iou Right Sleeve: 0.9289 - Iou Outer Collar: 0.5394 - Iou Inner Collar: 0.7160 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Upper Torso \| Accuracy Left Pants \| Accuracy Right Patns \| Accuracy Skirts \| Accuracy Left Sleeve \| Accuracy Right Sleeve \| Accuracy Outer Collar \| Accuracy Inner Collar \| Iou Background \| Iou Upper Torso \| Iou Left Pants \| Iou Right Patns \| Iou Skirts \| Iou Left Sleeve \| Iou Right Sleeve \| Iou Outer Collar \| Iou Inner Collar \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------------:\|:-------------------:\|:--------------------:\|:---------------:\|:--------------------:\|:---------------------:\|:---------------------:\|:---------------------:\|:--------------:\|:---------------:\|:--------------:\|:---------------:\|:----------:\|:---------------:\|:----------------:\|:----------------:\|:----------------:\| \| 0.1054 \| 0.11 \| 500 \| 0.1180 \| 0.7305 \| 0.7971 \| 0.9670 \| 0.9902 \| 0.9720 \| 0.9654 \| 0.9756 \| 0.8036 \| 0.9226 \| 0.9289 \| 0.0716 \| 0.5444 \| 0.9830 \| 0.9234 \| 0.8752 \| 0.8765 \| 0.7370 \| 0.8236 \| 0.8232 \| 0.0703 \| 0.4628 \| \| 0.1033 \| 0.22 \| 1000 \| 0.0851 \| 0.7862 \| 0.8418 \| 0.9746 \| 0.9924 \| 0.9829 \| 0.9665 \| 0.9653 \| 0.8491 \| 0.9145 \| 0.9226 \| 0.3219 \| 0.6608 \| 0.9866 \| 0.9424 \| 0.8858 \| 0.8875 \| 0.8105 \| 0.8538 \| 0.8614 \| 0.2833 \| 0.5642 \| \| 0.0944 \| 0.32 \| 1500 \| 0.0713 \| 0.8077 \| 0.8595 \| 0.9773 \| 0.9941 \| 0.9833 \| 0.9566 \| 0.9625 \| 0.8924 \| 0.9094 \| 0.9181 \| 0.4414 \| 0.6774 \| 0.9880 \| 0.9481 \| 0.8937 \| 0.8950 \| 0.8437 \| 0.8668 \| 0.8751 \| 0.3629 \| 0.5958 \| \| 0.0746 \| 0.43 \| 2000 \| 0.0683 \| 0.8190 \| 0.8770 \| 0.9783 \| 0.9941 \| 0.9796 \| 0.9652 \| 0.9722 \| 0.8656 \| 0.9480 \| 0.9562 \| 0.4882 \| 0.7236 \| 0.9888 \| 0.9497 \| 0.9070 \| 0.9127 \| 0.8306 \| 0.8790 \| 0.8870 \| 0.3945 \| 0.6218 \| \| 0.0548 \| 0.54 \| 2500 \| 0.0666 \| 0.8187 \| 0.8713 \| 0.9787 \| 0.9951 \| 0.9831 \| 0.9580 \| 0.9606 \| 0.8651 \| 0.9215 \| 0.9453 \| 0.4839 \| 0.7293 \| 0.9893 \| 0.9514 \| 0.8939 \| 0.9006 \| 0.8245 \| 0.8812 \| 0.8964 \| 0.4010 \| 0.6298 \| \| 0.0728 \| 0.65 \| 3000 \| 0.0591 \| 0.8271 \| 0.8806 \| 0.9804 \| 0.9945 \| 0.9839 \| 0.9624 \| 0.9659 \| 0.8982 \| 0.9399 \| 0.9430 \| 0.4884 \| 0.7493 \| 0.9900 \| 0.9551 \| 0.8940 \| 0.8966 \| 0.8583 \| 0.8930 \| 0.9011 \| 0.4100 \| 0.6458 \| \| 0.0505 \| 0.75 \| 3500 \| 0.0648 \| 0.8218 \| 0.8745 \| 0.9797 \| 0.9947 \| 0.9847 \| 0.9858 \| 0.9905 \| 0.8402 \| 0.9500 \| 0.9587 \| 0.4480 \| 0.7178 \| 0.9900 \| 0.9534 \| 0.9022 \| 0.9037 \| 0.8223 \| 0.8944 \| 0.9017 \| 0.3881 \| 0.6402 \| \| 0.0601 \| 0.86 \| 4000 \| 0.0568 \| 0.8415 \| 0.8951 \| 0.9817 \| 0.9952 \| 0.9817 \| 0.9632 \| 0.9640 \| 0.9170 \| 0.9521 \| 0.9541 \| 0.5781 \| 0.7508 \| 0.9903 \| 0.9576 \| 0.9138 \| 0.9199 \| 0.8716 \| 0.9010 \| 0.9106 \| 0.4562 \| 0.6529 \| \| 0.0438 \| 0.97 \| 4500 \| 0.0569 \| 0.8431 \| 0.8925 \| 0.9815 \| 0.9947 \| 0.9844 \| 0.9764 \| 0.9838 \| 0.8870 \| 0.9492 \| 0.9595 \| 0.5561 \| 0.7416 \| 0.9903 \| 0.9560 \| 0.9287 \| 0.9370 \| 0.8585 \| 0.9000 \| 0.9089 \| 0.4524 \| 0.6559 \| \| 0.0617 \| 1.08 \| 5000 \| 0.0529 \| 0.8417 \| 0.8933 \| 0.9816 \| 0.9952 \| 0.9841 \| 0.9602 \| 0.9631 \| 0.8922 \| 0.9475 \| 0.9533 \| 0.5797 \| 0.7642 \| 0.9907 \| 0.9571 \| 0.9097 \| 0.9126 \| 0.8488 \| 0.9044 \| 0.9158 \| 0.4687 \| 0.6678 \| \| 0.0452 \| 1.19 \| 5500 \| 0.0557 \| 0.8351 \| 0.8935 \| 0.9812 \| 0.9949 \| 0.9842 \| 0.9644 \| 0.9667 \| 0.8781 \| 0.9494 \| 0.9604 \| 0.5961 \| 0.7471 \| 0.9906 \| 0.9588 \| 0.8803 \| 0.8885 \| 0.8349 \| 0.9069 \| 0.9169 \| 0.4743 \| 0.6645 \| \| 0.0571 \| 1.29 \| 6000 \| 0.0551 \| 0.8351 \| 0.8934 \| 0.9810 \| 0.9957 \| 0.9831 \| 0.9652 \| 0.9693 \| 0.8562 \| 0.9593 \| 0.9569 \| 0.5959 \| 0.7586 \| 0.9910 \| 0.9579 \| 0.8842 \| 0.8879 \| 0.8188 \| 0.9084 \| 0.9155 \| 0.4774 \| 0.6749 \| \| 0.0778 \| 1.4 \| 6500 \| 0.0537 \| 0.8430 \| 0.8994 \| 0.9818 \| 0.9948 \| 0.9839 \| 0.9872 \| 0.9921 \| 0.8702 \| 0.9587 \| 0.9635 \| 0.5790 \| 0.7656 \| 0.9911 \| 0.9579 \| 0.9044 \| 0.9093 \| 0.8458 \| 0.9060 \| 0.9157 \| 0.4760 \| 0.6808 \| \| 0.0392 \| 1.51 \| 7000 \| 0.0491 \| 0.8503 \| 0.9069 \| 0.9830 \| 0.9954 \| 0.9823 \| 0.9645 \| 0.9666 \| 0.9205 \| 0.9534 \| 0.9599 \| 0.6214 \| 0.7984 \| 0.9916 \| 0.9607 \| 0.9123 \| 0.9139 \| 0.8755 \| 0.9072 \| 0.9180 \| 0.4907 \| 0.6830 \| \| 0.0376 \| 1.62 \| 7500 \| 0.0514 \| 0.8442 \| 0.9010 \| 0.9819 \| 0.9954 \| 0.9832 \| 0.9652 \| 0.9660 \| 0.8850 \| 0.9525 \| 0.9598 \| 0.6257 \| 0.7762 \| 0.9914 \| 0.9586 \| 0.8944 \| 0.9053 \| 0.8355 \| 0.9104 \| 0.9215 \| 0.4965 \| 0.6838 \| \| 0.0391 \| 1.73 \| 8000 \| 0.0492 \| 0.8422 \| 0.8993 \| 0.9819 \| 0.9958 \| 0.9836 \| 0.9641 \| 0.9671 \| 0.8692 \| 0.9561 \| 0.9661 \| 0.6159 \| 0.7756 \| 0.9916 \| 0.9596 \| 0.8882 \| 0.8930 \| 0.8338 \| 0.9103 \| 0.9189 \| 0.4982 \| 0.6860 \| \| 0.0446 \| 1.83 \| 8500 \| 0.0491 \| 0.8515 \| 0.9079 \| 0.9829 \| 0.9960 \| 0.9836 \| 0.9890 \| 0.9913 \| 0.8770 \| 0.9505 \| 0.9631 \| 0.6458 \| 0.7751 \| 0.9916 \| 0.9603 \| 0.9114 \| 0.9161 \| 0.8559 \| 0.9100 \| 0.9217 \| 0.5096 \| 0.6867 \| \| 0.041 \| 1.94 \| 9000 \| 0.0482 \| 0.8464 \| 0.8978 \| 0.9825 \| 0.9958 \| 0.9848 \| 0.9619 \| 0.9668 \| 0.8822 \| 0.9569 \| 0.9659 \| 0.5961 \| 0.7703 \| 0.9916 \| 0.9602 \| 0.8958 \| 0.9018 \| 0.8438 \| 0.9148 \| 0.9231 \| 0.4966 \| 0.6899 \| \| 0.0744 \| 2.05 \| 9500 \| 0.0474 \| 0.8523 \| 0.9018 \| 0.9834 \| 0.9961 \| 0.9840 \| 0.9598 \| 0.9633 \| 0.9195 \| 0.9471 \| 0.9644 \| 0.6055 \| 0.7766 \| 0.9919 \| 0.9619 \| 0.9095 \| 0.9125 \| 0.8697 \| 0.9113 \| 0.9238 \| 0.5010 \| 0.6889 \| \| 0.0433 \| 2.16 \| 10000 \| 0.0471 \| 0.8581 \| 0.9103 \| 0.9842 \| 0.9951 \| 0.9843 \| 0.9617 \| 0.9646 \| 0.9416 \| 0.9549 \| 0.9718 \| 0.6305 \| 0.7879 \| 0.9915 \| 0.9644 \| 0.9100 \| 0.9155 \| 0.8976 \| 0.9145 \| 0.9245 \| 0.5127 \| 0.6920 \| \| 0.0412 \| 2.26 \| 10500 \| 0.0468 \| 0.8574 \| 0.9042 \| 0.9835 \| 0.9956 \| 0.9848 \| 0.9628 \| 0.9669 \| 0.9023 \| 0.9615 \| 0.9677 \| 0.6115 \| 0.7847 \| 0.9918 \| 0.9601 \| 0.9248 \| 0.9286 \| 0.8656 \| 0.9177 \| 0.9245 \| 0.5073 \| 0.6964 \| \| 0.0489 \| 2.37 \| 11000 \| 0.0496 \| 0.8511 \| 0.9029 \| 0.9832 \| 0.9956 \| 0.9858 \| 0.9905 \| 0.9948 \| 0.8694 \| 0.9574 \| 0.9654 \| 0.5748 \| 0.7926 \| 0.9921 \| 0.9604 \| 0.9066 \| 0.9086 \| 0.8615 \| 0.9167 \| 0.9228 \| 0.4913 \| 0.7004 \| \| 0.0388 \| 2.48 \| 11500 \| 0.0450 \| 0.8594 \| 0.9036 \| 0.9849 \| 0.9957 \| 0.9857 \| 0.9621 \| 0.9648 \| 0.9620 \| 0.9493 \| 0.9604 \| 0.5733 \| 0.7793 \| 0.9922 \| 0.9649 \| 0.9155 \| 0.9205 \| 0.9076 \| 0.9138 \| 0.9257 \| 0.4941 \| 0.7002 \| \| 0.0409 \| 2.59 \| 12000 \| 0.0493 \| 0.8579 \| 0.9124 \| 0.9844 \| 0.9955 \| 0.9853 \| 0.9928 \| 0.9929 \| 0.9083 \| 0.9573 \| 0.9671 \| 0.6288 \| 0.7832 \| 0.9921 \| 0.9651 \| 0.9046 \| 0.9086 \| 0.8842 \| 0.9196 \| 0.9267 \| 0.5175 \| 0.7026 \| \| 0.0477 \| 2.7 \| 12500 \| 0.0436 \| 0.8610 \| 0.9051 \| 0.9848 \| 0.9957 \| 0.9868 \| 0.9639 \| 0.9675 \| 0.9478 \| 0.9445 \| 0.9590 \| 0.5972 \| 0.7831 \| 0.9919 \| 0.9654 \| 0.9187 \| 0.9251 \| 0.9029 \| 0.9126 \| 0.9253 \| 0.5035 \| 0.7034 \| \| 0.0488 \| 2.8 \| 13000 \| 0.0450 \| 0.8577 \| 0.9076 \| 0.9842 \| 0.9963 \| 0.9848 \| 0.9712 \| 0.9695 \| 0.9132 \| 0.9493 \| 0.9621 \| 0.6188 \| 0.8026 \| 0.9924 \| 0.9635 \| 0.9095 \| 0.9124 \| 0.8742 \| 0.9172 \| 0.9276 \| 0.5157 \| 0.7065 \| \| 0.0879 \| 2.91 \| 13500 \| 0.0516 \| 0.8453 \| 0.8949 \| 0.9819 \| 0.9960 \| 0.9867 \| 0.9631 \| 0.9665 \| 0.8325 \| 0.9618 \| 0.9678 \| 0.6033 \| 0.7763 \| 0.9919 \| 0.9574 \| 0.8955 \| 0.9007 \| 0.8088 \| 0.9206 \| 0.9245 \| 0.5069 \| 0.7013 \| \| 0.0525 \| 3.02 \| 14000 \| 0.0474 \| 0.8521 \| 0.9053 \| 0.9830 \| 0.9959 \| 0.9849 \| 0.9850 \| 0.9925 \| 0.8703 \| 0.9481 \| 0.9597 \| 0.6076 \| 0.8038 \| 0.9923 \| 0.9600 \| 0.9050 \| 0.9099 \| 0.8420 \| 0.9143 \| 0.9263 \| 0.5148 \| 0.7044 \| \| 0.0455 \| 3.13 \| 14500 \| 0.0435 \| 0.8579 \| 0.9111 \| 0.9842 \| 0.9953 \| 0.9852 \| 0.9646 \| 0.9672 \| 0.9255 \| 0.9569 \| 0.9654 \| 0.6514 \| 0.7888 \| 0.9923 \| 0.9642 \| 0.8971 \| 0.9055 \| 0.8780 \| 0.9182 \| 0.9284 \| 0.5327 \| 0.7046 \| \| 0.0454 \| 3.24 \| 15000 \| 0.0451 \| 0.8599 \| 0.9161 \| 0.9844 \| 0.9953 \| 0.9858 \| 0.9895 \| 0.9907 \| 0.8944 \| 0.9635 \| 0.9692 \| 0.6643 \| 0.7925 \| 0.9924 \| 0.9645 \| 0.9061 \| 0.9107 \| 0.8803 \| 0.9202 \| 0.9236 \| 0.5356 \| 0.7058 \| \| 0.0687 \| 3.34 \| 15500 \| 0.0496 \| 0.8482 \| 0.9017 \| 0.9827 \| 0.9959 \| 0.9869 \| 0.9715 \| 0.9676 \| 0.8483 \| 0.9616 \| 0.9672 \| 0.6235 \| 0.7932 \| 0.9922 \| 0.9614 \| 0.8904 \| 0.8909 \| 0.8269 \| 0.9187 \| 0.9218 \| 0.5249 \| 0.7069 \| \| 0.0555 \| 3.45 \| 16000 \| 0.0445 \| 0.8568 \| 0.9081 \| 0.9838 \| 0.9964 \| 0.9858 \| 0.9649 \| 0.9681 \| 0.8880 \| 0.9585 \| 0.9610 \| 0.6510 \| 0.7995 \| 0.9922 \| 0.9635 \| 0.8996 \| 0.9073 \| 0.8582 \| 0.9230 \| 0.9257 \| 0.5328 \| 0.7093 \| \| 0.0528 \| 3.56 \| 16500 \| 0.0477 \| 0.8549 \| 0.9053 \| 0.9833 \| 0.9958 \| 0.9875 \| 0.9668 \| 0.9677 \| 0.8740 \| 0.9512 \| 0.9631 \| 0.6512 \| 0.7902 \| 0.9920 \| 0.9618 \| 0.9021 \| 0.9036 \| 0.8486 \| 0.9185 \| 0.9254 \| 0.5348 \| 0.7070 \| \| 0.043 \| 3.67 \| 17000 \| 0.0439 \| 0.8633 \| 0.9173 \| 0.9849 \| 0.9960 \| 0.9851 \| 0.9860 \| 0.9893 \| 0.9114 \| 0.9555 \| 0.9656 \| 0.6623 \| 0.8046 \| 0.9921 \| 0.9666 \| 0.9083 \| 0.9158 \| 0.8910 \| 0.9197 \| 0.9262 \| 0.5391 \| 0.7111 \| \| 0.0372 \| 3.77 \| 17500 \| 0.0474 \| 0.8555 \| 0.9039 \| 0.9836 \| 0.9959 \| 0.9876 \| 0.9626 \| 0.9647 \| 0.8818 \| 0.9556 \| 0.9623 \| 0.6393 \| 0.7858 \| 0.9921 \| 0.9623 \| 0.8999 \| 0.9065 \| 0.8526 \| 0.9218 \| 0.9264 \| 0.5299 \| 0.7082 \| \| 0.0614 \| 3.88 \| 18000 \| 0.0463 \| 0.8564 \| 0.9088 \| 0.9839 \| 0.9959 \| 0.9853 \| 0.9644 \| 0.9662 \| 0.9035 \| 0.9569 \| 0.9638 \| 0.6413 \| 0.8025 \| 0.9921 \| 0.9643 \| 0.8967 \| 0.9020 \| 0.8607 \| 0.9202 \| 0.9276 \| 0.5330 \| 0.7111 \| \| 0.0413 \| 3.99 \| 18500 \| 0.0453 \| 0.8579 \| 0.9123 \| 0.9841 \| 0.9963 \| 0.9848 \| 0.9794 \| 0.9828 \| 0.8865 \| 0.9613 \| 0.9695 \| 0.6526 \| 0.7977 \| 0.9922 \| 0.9648 \| 0.8991 \| 0.9047 \| 0.8629 \| 0.9221 \| 0.9274 \| 0.5369 \| 0.7112 \| \| 0.0386 \| 4.1 \| 19000 \| 0.0438 \| 0.8578 \| 0.9109 \| 0.9842 \| 0.9959 \| 0.9844 \| 0.9649 \| 0.9667 \| 0.9154 \| 0.9580 \| 0.9662 \| 0.6408 \| 0.8062 \| 0.9924 \| 0.9644 \| 0.8973 \| 0.9025 \| 0.8683 \| 0.9196 \| 0.9279 \| 0.5340 \| 0.7134 \| \| 0.0541 \| 4.21 \| 19500 \| 0.0443 \| 0.8577 \| 0.9118 \| 0.9840 \| 0.9957 \| 0.9847 \| 0.9829 \| 0.9872 \| 0.8935 \| 0.9594 \| 0.9686 \| 0.6265 \| 0.8077 \| 0.9921 \| 0.9641 \| 0.9017 \| 0.9079 \| 0.8621 \| 0.9203 \| 0.9277 \| 0.5298 \| 0.7133 \| \| 0.0409 \| 4.31 \| 20000 \| 0.0433 \| 0.8560 \| 0.9083 \| 0.9840 \| 0.9959 \| 0.9860 \| 0.9670 \| 0.9687 \| 0.9020 \| 0.9578 \| 0.9632 \| 0.6421 \| 0.7918 \| 0.9922 \| 0.9652 \| 0.8921 \| 0.8966 \| 0.8633 \| 0.9206 \| 0.9278 \| 0.5349 \| 0.7117 \| \| 0.0398 \| 4.42 \| 20500 \| 0.0451 \| 0.8581 \| 0.9102 \| 0.9840 \| 0.9960 \| 0.9859 \| 0.9687 \| 0.9685 \| 0.8885 \| 0.9597 \| 0.9684 \| 0.6554 \| 0.8004 \| 0.9922 \| 0.9638 \| 0.9000 \| 0.9042 \| 0.8595 \| 0.9232 \| 0.9266 \| 0.5395 \| 0.7144 \| \| 0.038 \| 4.53 \| 21000 \| 0.0464 \| 0.8608 \| 0.9123 \| 0.9843 \| 0.9959 \| 0.9866 \| 0.9885 \| 0.9907 \| 0.8739 \| 0.9616 \| 0.9678 \| 0.6398 \| 0.8056 \| 0.9921 \| 0.9639 \| 0.9088 \| 0.9160 \| 0.8657 \| 0.9238 \| 0.9273 \| 0.5347 \| 0.7150 \| \| 0.0295 \| 4.64 \| 21500 \| 0.0433 \| 0.8596 \| 0.9094 \| 0.9840 \| 0.9960 \| 0.9864 \| 0.9641 \| 0.9664 \| 0.8985 \| 0.9535 \| 0.9582 \| 0.6581 \| 0.8033 \| 0.9922 \| 0.9633 \| 0.9056 \| 0.9102 \| 0.8619 \| 0.9195 \| 0.9276 \| 0.5408 \| 0.7151 \| \| 0.0318 \| 4.75 \| 22000 \| 0.0439 \| 0.8600 \| 0.9127 \| 0.9842 \| 0.9964 \| 0.9848 \| 0.9665 \| 0.9676 \| 0.8929 \| 0.9627 \| 0.9689 \| 0.6656 \| 0.8089 \| 0.9923 \| 0.9643 \| 0.9007 \| 0.9080 \| 0.8645 \| 0.9223 \| 0.9283 \| 0.5444 \| 0.7156 \| \| 0.0377 \| 4.85 \| 22500 \| 0.0429 \| 0.8619 \| 0.9125 \| 0.9846 \| 0.9963 \| 0.9849 \| 0.9633 \| 0.9666 \| 0.9115 \| 0.9609 \| 0.9689 \| 0.6527 \| 0.8069 \| 0.9923 \| 0.9654 \| 0.9052 \| 0.9104 \| 0.8762 \| 0.9217 \| 0.9288 \| 0.5407 \| 0.7166 \| \| 0.0419 \| 4.96 \| 23000 \| 0.0433 \| 0.8611 \| 0.9107 \| 0.9846 \| 0.9964 \| 0.9857 \| 0.9654 \| 0.9664 \| 0.9065 \| 0.9591 \| 0.9662 \| 0.6491 \| 0.8015 \| 0.9923 \| 0.9655 \| 0.9017 \| 0.9085 \| 0.8749 \| 0.9223 \| 0.9289 \| 0.5394 \| 0.7160 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "background", "upper_torso", "left_pants", "right_patns", "skirts", "left_sleeve", "right_sleeve", "outer_collar", "inner_collar" ]
fshala/segformer-finetuned-sidewalk-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-sidewalk-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.3528 - Mean Iou: 0.0705 - Mean Accuracy: 0.1115 - Overall Accuracy: 0.5632 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.5861 - Accuracy Flat-sidewalk: 0.8653 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.0136 - Accuracy Flat-parkingdriveway: 0.0078 - Accuracy Flat-railtrack: 0.0 - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.0737 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.9444 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0012 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.8841 - Accuracy Nature-terrain: 0.2830 - Accuracy Sky: 0.0216 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.3993 - Iou Flat-sidewalk: 0.6672 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.0135 - Iou Flat-parkingdriveway: 0.0076 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.0712 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.2455 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0011 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.6484 - Iou Nature-terrain: 0.2515 - Iou Sky: 0.0216 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:------------------:\|:----------------------:\|:-----------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------:\|:---------------------:\|:--------------------:\|:--------------------:\|:----------------------:\|:--------------------:\|:--------------------------:\|:---------------------------:\|:------------------------:\|:------------------------:\|:---------------------------:\|:------------------------------:\|:--------------------------:\|:--------------------------:\|:------------------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:--------------------:\|:---------------------------:\|:----------------------------:\|:--------------------------:\|:-----------------------:\|:------------:\|:--------------------:\|:---------------------:\|:--------------------:\|:---------------------:\|:-------------:\|:-------------:\|:-----------------:\|:------------------:\|:--------------------:\|:------------------------:\|:------------------:\|:-------------:\|:----------------:\|:---------------:\|:---------------:\|:-----------------:\|:---------------:\|:---------------------:\|:----------------------:\|:-------------------:\|:-------------------:\|:----------------------:\|:-------------------------:\|:---------------------:\|:---------------------:\|:-------------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:---------------:\|:----------------------:\|:-----------------------:\|:---------------------:\|:------------------:\|:-------:\|:---------------:\|:----------------:\|:---------------:\|:----------------:\| \| 2.8114 \| 0.93 \| 100 \| 2.3528 \| 0.0705 \| 0.1115 \| 0.5632 \| nan \| 0.5861 \| 0.8653 \| 0.0 \| 0.0136 \| 0.0078 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0737 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9444 \| 0.0 \| 0.0012 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8841 \| 0.2830 \| 0.0216 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.3993 \| 0.6672 \| 0.0 \| 0.0135 \| 0.0076 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0712 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2455 \| 0.0 \| 0.0011 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6484 \| 0.2515 \| 0.0216 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.0.0+cpu - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
vigneshgs7/segformer-b0-finetuned-segments-pageboundet-finaltry	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-pageboundet-finaltry This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the vigneshgs7/Boundary_detection_onelasttry dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - Mean Iou: nan - Mean Accuracy: nan - Overall Accuracy: nan - Accuracy Object: nan - Accuracy Surface: nan - Iou Object: nan - Iou Surface: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Object \| Accuracy Surface \| Iou Object \| Iou Surface \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:---------------:\|:----------------:\|:----------:\|:-----------:\| \| 0.4616 \| 2.0 \| 20 \| 0.4828 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| \| 0.3359 \| 4.0 \| 40 \| 0.3351 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| \| 0.2898 \| 6.0 \| 60 \| 0.2885 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| \| 0.3041 \| 8.0 \| 80 \| 0.2671 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| \| 0.2429 \| 10.0 \| 100 \| 0.2569 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1	[ "object", "surface" ]
eungitoto/segformer-b0-finetuned-segments-sidewalk-2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.6351 - Mean Iou: 0.2834 - Mean Accuracy: 0.3374 - Overall Accuracy: 0.8443 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.9045 - Accuracy Flat-sidewalk: 0.9530 - Accuracy Flat-crosswalk: 0.6075 - Accuracy Flat-cyclinglane: 0.7798 - Accuracy Flat-parkingdriveway: 0.3400 - Accuracy Flat-railtrack: 0.0 - Accuracy Flat-curb: 0.5121 - Accuracy Human-person: 0.6170 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9458 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.6956 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.9045 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.4037 - Accuracy Construction-fenceguardrail: 0.3503 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: 0.0 - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3679 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9274 - Accuracy Nature-terrain: 0.8889 - Accuracy Sky: 0.9722 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0119 - Accuracy Void-static: 0.2879 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7249 - Iou Flat-sidewalk: 0.8500 - Iou Flat-crosswalk: 0.5367 - Iou Flat-cyclinglane: 0.7300 - Iou Flat-parkingdriveway: 0.2828 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.4008 - Iou Human-person: 0.4039 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.8291 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.5268 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7142 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3242 - Iou Construction-fenceguardrail: 0.2450 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.3031 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8429 - Iou Nature-terrain: 0.7587 - Iou Sky: 0.9308 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0117 - Iou Void-static: 0.2194 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| 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\| 2.6363 \| 0.4 \| 20 \| 3.0746 \| 0.0958 \| 0.1387 \| 0.6097 \| nan \| 0.4923 \| 0.9140 \| 0.0023 \| 0.0115 \| 0.0163 \| 0.0 \| 0.0012 \| 0.0 \| 0.0 \| 0.7446 \| 0.0010 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9002 \| 0.0 \| 0.0074 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8330 \| 0.0676 \| 0.7219 \| 0.0 \| 0.0 \| 0.0033 \| 0.0 \| nan \| 0.3615 \| 0.6272 \| 0.0023 \| 0.0115 \| 0.0138 \| 0.0 \| 0.0012 \| 0.0 \| 0.0 \| 0.4781 \| 0.0004 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3994 \| 0.0 \| 0.0072 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6074 \| 0.0646 \| 0.6789 \| 0.0 \| 0.0 \| 0.0033 \| 0.0 \| \| 2.1728 \| 0.8 \| 40 \| 2.1331 \| 0.1057 \| 0.1504 \| 0.6493 \| nan \| 0.7183 \| 0.9087 \| 0.0 \| 0.0011 \| 0.0031 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.7912 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8617 \| 0.0 \| 0.0025 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9374 \| 0.0054 \| 0.8823 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| nan \| 0.4086 \| 0.6905 \| 0.0 \| 0.0011 \| 0.0030 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.5711 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4771 \| 0.0 \| 0.0025 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6142 \| 0.0053 \| 0.8190 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| \| 1.8992 \| 1.2 \| 60 \| 1.7800 \| 0.1102 \| 0.1549 \| 0.6621 \| nan \| 0.6924 \| 0.9401 \| 0.0 \| 0.0144 \| 0.0013 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.8627 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8730 \| 0.0 \| 0.0008 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9193 \| 0.0615 \| 0.9003 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4255 \| 0.6860 \| 0.0 \| 0.0144 \| 0.0013 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.5819 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4994 \| 0.0 \| 0.0008 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6468 \| 0.0608 \| 0.8313 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.8919 \| 1.6 \| 80 \| 1.6335 \| 0.1201 \| 0.1629 \| 0.6809 \| nan \| 0.7313 \| 0.9424 \| 0.0 \| 0.0501 \| 0.0008 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.8569 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8876 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9397 \| 0.2331 \| 0.8972 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4467 \| 0.7033 \| 0.0 \| 0.0501 \| 0.0008 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.6179 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5237 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6657 \| 0.2237 \| 0.8501 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.63 \| 2.0 \| 100 \| 1.4813 \| 0.1342 \| 0.1756 \| 0.7089 \| nan \| 0.8218 \| 0.9389 \| 0.0 \| 0.2047 \| 0.0006 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8384 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8802 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9479 \| 0.4267 \| 0.9098 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4844 \| 0.7364 \| 0.0 \| 0.2025 \| 0.0006 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6453 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5371 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6948 \| 0.4056 \| 0.8548 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3523 \| 2.4 \| 120 \| 1.3727 \| 0.1467 \| 0.1884 \| 0.7335 \| nan \| 0.8524 \| 0.9382 \| 0.0 \| 0.4096 \| 0.0009 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8653 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8659 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9450 \| 0.5996 \| 0.9273 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5341 \| 0.7489 \| 0.0 \| 0.4011 \| 0.0009 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6377 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5535 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7173 \| 0.5393 \| 0.8549 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.2951 \| 2.8 \| 140 \| 1.2957 \| 0.1511 \| 0.1932 \| 0.7407 \| nan \| 0.8465 \| 0.9414 \| 0.0 \| 0.4159 \| 0.0029 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8951 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8775 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9327 \| 0.7282 \| 0.9275 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5327 \| 0.7570 \| 0.0 \| 0.4072 \| 0.0029 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6595 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5523 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7399 \| 0.6208 \| 0.8658 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3024 \| 3.2 \| 160 \| 1.2389 \| 0.1536 \| 0.1941 \| 0.7464 \| nan \| 0.8844 \| 0.9356 \| 0.0 \| 0.5074 \| 0.0044 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8589 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8984 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9380 \| 0.6688 \| 0.9035 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5543 \| 0.7672 \| 0.0 \| 0.4812 \| 0.0043 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6749 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5517 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7351 \| 0.5888 \| 0.8655 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3291 \| 3.6 \| 180 \| 1.1719 \| 0.1582 \| 0.1970 \| 0.7516 \| nan \| 0.7975 \| 0.9658 \| 0.0001 \| 0.5346 \| 0.0113 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8905 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9044 \| 0.0 \| 0.0003 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9127 \| 0.7531 \| 0.9290 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5791 \| 0.7373 \| 0.0001 \| 0.5120 \| 0.0111 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6910 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5522 \| 0.0 \| 0.0003 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7708 \| 0.6519 \| 0.8725 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.171 \| 4.0 \| 200 \| 1.1129 \| 0.1583 \| 0.1982 \| 0.7564 \| nan \| 0.8861 \| 0.9498 \| 0.0 \| 0.5324 \| 0.0266 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8824 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8648 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9478 \| 0.7150 \| 0.9343 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5772 \| 0.7708 \| 0.0 \| 0.5090 \| 0.0259 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6945 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5713 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7383 \| 0.6220 \| 0.8744 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.2553 \| 4.4 \| 220 \| 1.0982 \| 0.1597 \| 0.2022 \| 0.7524 \| nan \| 0.9524 \| 0.8937 \| 0.0 \| 0.5760 \| 0.0539 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.8626 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9018 \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9364 \| 0.7568 \| 0.9400 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5187 \| 0.7967 \| 0.0 \| 0.5249 \| 0.0509 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.6982 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5609 \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7580 \| 0.6483 \| 0.8736 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9428 \| 4.8 \| 240 \| 1.0357 \| 0.1662 \| 0.2074 \| 0.7694 \| nan \| 0.9300 \| 0.9359 \| 0.0074 \| 0.6225 \| 0.0780 \| 0.0 \| 0.0007 \| 0.0 \| 0.0 \| 0.8966 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9012 \| 0.0 \| 0.0023 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9207 \| 0.8155 \| 0.9398 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5795 \| 0.7982 \| 0.0074 \| 0.5725 \| 0.0727 \| 0.0 \| 0.0007 \| 0.0 \| 0.0 \| 0.7107 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5673 \| 0.0 \| 0.0023 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7794 \| 0.6826 \| 0.8785 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9843 \| 5.2 \| 260 \| 0.9881 \| 0.1689 \| 0.2087 \| 0.7706 \| nan \| 0.8682 \| 0.9574 \| 0.0704 \| 0.6251 \| 0.0810 \| 0.0 \| 0.0020 \| 0.0 \| 0.0 \| 0.9139 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8783 \| 0.0 \| 0.0051 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9202 \| 0.8331 \| 0.9406 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6115 \| 0.7686 \| 0.0704 \| 0.5919 \| 0.0731 \| 0.0 \| 0.0020 \| 0.0 \| 0.0 \| 0.7004 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5797 \| 0.0 \| 0.0050 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7766 \| 0.6863 \| 0.8784 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9274 \| 5.6 \| 280 \| 0.9701 \| 0.1738 \| 0.2166 \| 0.7794 \| nan \| 0.9171 \| 0.9276 \| 0.1062 \| 0.7376 \| 0.1365 \| 0.0 \| 0.0097 \| 0.0 \| 0.0 \| 0.9144 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8870 \| 0.0 \| 0.0102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9297 \| 0.8383 \| 0.9493 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6165 \| 0.8027 \| 0.1059 \| 0.6193 \| 0.1201 \| 0.0 \| 0.0096 \| 0.0 \| 0.0 \| 0.6958 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5848 \| 0.0 \| 0.0101 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7777 \| 0.6882 \| 0.8794 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8762 \| 6.0 \| 300 \| 0.9369 \| 0.1850 \| 0.2257 \| 0.7864 \| nan \| 0.9056 \| 0.9452 \| 0.4152 \| 0.6530 \| 0.1945 \| 0.0 \| 0.0313 \| 0.0 \| 0.0 \| 0.9224 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8770 \| 0.0 \| 0.0269 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9266 \| 0.8372 \| 0.9387 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6408 \| 0.8042 \| 0.3904 \| 0.6126 \| 0.1521 \| 0.0 \| 0.0310 \| 0.0 \| 0.0 \| 0.6888 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5874 \| 0.0 \| 0.0257 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7821 \| 0.6954 \| 0.8809 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8044 \| 6.4 \| 320 \| 0.9197 \| 0.1903 \| 0.2308 \| 0.7896 \| nan \| 0.9174 \| 0.9437 \| 0.5151 \| 0.6212 \| 0.2095 \| 0.0 \| 0.0733 \| 0.0 \| 0.0 \| 0.9091 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8953 \| 0.0 \| 0.0558 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9174 \| 0.8372 \| 0.9511 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6400 \| 0.8072 \| 0.4651 \| 0.5881 \| 0.1706 \| 0.0 \| 0.0714 \| 0.0 \| 0.0 \| 0.7168 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5855 \| 0.0 \| 0.0512 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7905 \| 0.7010 \| 0.8817 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7438 \| 6.8 \| 340 \| 0.8776 \| 0.1934 \| 0.2344 \| 0.7940 \| nan \| 0.9134 \| 0.9414 \| 0.5616 \| 0.6671 \| 0.2014 \| 0.0 \| 0.0966 \| 0.0 \| 0.0 \| 0.9162 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9011 \| 0.0 \| 0.0716 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9231 \| 0.8347 \| 0.9422 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6545 \| 0.8114 \| 0.4818 \| 0.6313 \| 0.1657 \| 0.0 \| 0.0933 \| 0.0 \| 0.0 \| 0.7053 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5874 \| 0.0 \| 0.0639 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7945 \| 0.6990 \| 0.8886 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9671 \| 7.2 \| 360 \| 0.8704 \| 0.1975 \| 0.2399 \| 0.7955 \| nan \| 0.8871 \| 0.9408 \| 0.5863 \| 0.6940 \| 0.2363 \| 0.0 \| 0.1439 \| 0.0 \| 0.0 \| 0.9029 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8684 \| 0.0 \| 0.1670 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9291 \| 0.8532 \| 0.9471 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6532 \| 0.8126 \| 0.4632 \| 0.6345 \| 0.1855 \| 0.0 \| 0.1361 \| 0.0 \| 0.0 \| 0.7283 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5977 \| 0.0 \| 0.1406 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7862 \| 0.6935 \| 0.8840 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8191 \| 7.6 \| 380 \| 0.8422 \| 0.2048 \| 0.2442 \| 0.8033 \| nan \| 0.9159 \| 0.9441 \| 0.5265 \| 0.7238 \| 0.2684 \| 0.0 \| 0.1784 \| 0.0003 \| 0.0 \| 0.8799 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8603 \| 0.0 \| 0.2762 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.9402 \| 0.8318 \| 0.9551 \| 0.0 \| 0.0 \| 0.0005 \| 0.0 \| nan \| 0.6939 \| 0.8112 \| 0.4965 \| 0.6614 \| 0.2087 \| 0.0 \| 0.1667 \| 0.0003 \| 0.0 \| 0.7490 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6063 \| 0.0 \| 0.2037 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.7830 \| 0.7000 \| 0.8823 \| 0.0 \| 0.0 \| 0.0005 \| 0.0 \| \| 0.8005 \| 8.0 \| 400 \| 0.8414 \| 0.1996 \| 0.2372 \| 0.7972 \| nan \| 0.9003 \| 0.9492 \| 0.5198 \| 0.6659 \| 0.1888 \| 0.0 \| 0.2424 \| 0.0000 \| 0.0 \| 0.9048 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9291 \| 0.0 \| 0.1105 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.9216 \| 0.7883 \| 0.9434 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| nan \| 0.6719 \| 0.8049 \| 0.4991 \| 0.6372 \| 0.1537 \| 0.0 \| 0.2152 \| 0.0000 \| 0.0 \| 0.7254 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5841 \| 0.0 \| 0.0940 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.8047 \| 0.7032 \| 0.8927 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| \| 0.9294 \| 8.4 \| 420 \| 0.8182 \| 0.2036 \| 0.2430 \| 0.8029 \| nan \| 0.9269 \| 0.9482 \| 0.5270 \| 0.6550 \| 0.2482 \| 0.0 \| 0.2108 \| 0.0002 \| 0.0 \| 0.9167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8894 \| 0.0 \| 0.2316 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| 0.9295 \| 0.8273 \| 0.9512 \| 0.0 \| 0.0 \| 0.0007 \| 0.0 \| nan \| 0.6841 \| 0.8133 \| 0.4924 \| 0.6336 \| 0.1979 \| 0.0 \| 0.1916 \| 0.0002 \| 0.0 \| 0.7197 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6065 \| 0.0 \| 0.1800 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| 0.8014 \| 0.7111 \| 0.8901 \| 0.0 \| 0.0 \| 0.0007 \| 0.0 \| \| 0.7301 \| 8.8 \| 440 \| 0.8036 \| 0.2086 \| 0.2527 \| 0.8061 \| nan \| 0.9223 \| 0.9419 \| 0.5814 \| 0.6931 \| 0.2747 \| 0.0 \| 0.2847 \| 0.0053 \| 0.0 \| 0.9361 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8273 \| 0.0 \| 0.3837 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.9300 \| 0.8531 \| 0.9561 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| nan \| 0.6964 \| 0.8218 \| 0.5116 \| 0.6603 \| 0.2195 \| 0.0 \| 0.2440 \| 0.0053 \| 0.0 \| 0.6847 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6037 \| 0.0 \| 0.2526 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.7979 \| 0.7074 \| 0.8852 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| \| 0.6754 \| 9.2 \| 460 \| 0.7868 \| 0.2083 \| 0.2482 \| 0.8070 \| nan \| 0.9098 \| 0.9432 \| 0.5608 \| 0.7545 \| 0.2089 \| 0.0 \| 0.2884 \| 0.0073 \| 0.0 \| 0.9000 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8991 \| 0.0 \| 0.2473 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.9193 \| 0.8499 \| 0.9478 \| 0.0 \| 0.0 \| 0.0023 \| 0.0 \| nan \| 0.6748 \| 0.8194 \| 0.5305 \| 0.6745 \| 0.1787 \| 0.0 \| 0.2438 \| 0.0073 \| 0.0 \| 0.7486 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6100 \| 0.0 \| 0.1831 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.8067 \| 0.7069 \| 0.8942 \| 0.0 \| 0.0 \| 0.0023 \| 0.0 \| \| 0.7499 \| 9.6 \| 480 \| 0.7756 \| 0.2112 \| 0.2511 \| 0.8109 \| nan \| 0.9109 \| 0.9429 \| 0.5288 \| 0.7550 \| 0.2927 \| 0.0 \| 0.2838 \| 0.0126 \| 0.0 \| 0.9117 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9054 \| 0.0 \| 0.2797 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0005 \| 0.0 \| 0.0 \| 0.9263 \| 0.8295 \| 0.9522 \| 0.0 \| 0.0 \| 0.0039 \| 0.0 \| nan \| 0.7098 \| 0.8209 \| 0.4936 \| 0.7004 \| 0.2259 \| 0.0 \| 0.2461 \| 0.0126 \| 0.0 \| 0.7399 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6058 \| 0.0 \| 0.1980 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0005 \| 0.0 \| 0.0 \| 0.8077 \| 0.7226 \| 0.8942 \| 0.0 \| 0.0 \| 0.0039 \| 0.0 \| \| 0.5878 \| 10.0 \| 500 \| 0.7564 \| 0.2131 \| 0.2568 \| 0.8123 \| nan \| 0.9099 \| 0.9388 \| 0.5922 \| 0.7452 \| 0.3135 \| 0.0 \| 0.2995 \| 0.0235 \| 0.0 \| 0.9165 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8776 \| 0.0 \| 0.3744 \| 0.0000 \| 0.0 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| 0.0 \| 0.9301 \| 0.8555 \| 0.9495 \| 0.0 \| 0.0 \| 0.0047 \| 0.0 \| nan \| 0.6945 \| 0.8339 \| 0.4657 \| 0.6925 \| 0.2436 \| 0.0 \| 0.2602 \| 0.0235 \| 0.0 \| 0.7350 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6171 \| 0.0 \| 0.2630 \| 0.0000 \| 0.0 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| 0.0 \| 0.7991 \| 0.7150 \| 0.8956 \| 0.0 \| 0.0 \| 0.0046 \| 0.0 \| \| 0.874 \| 10.4 \| 520 \| 0.7601 \| 0.2169 \| 0.2584 \| 0.8133 \| nan \| 0.9144 \| 0.9378 \| 0.5474 \| 0.7449 \| 0.3350 \| 0.0 \| 0.3237 \| 0.0738 \| 0.0 \| 0.9292 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8918 \| 0.0 \| 0.3508 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| 0.9232 \| 0.8450 \| 0.9579 \| 0.0 \| 0.0 \| 0.0086 \| 0.0 \| nan \| 0.7030 \| 0.8214 \| 0.5211 \| 0.6962 \| 0.2497 \| 0.0 \| 0.2740 \| 0.0735 \| 0.0 \| 0.7398 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6252 \| 0.0 \| 0.2396 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| 0.8120 \| 0.7134 \| 0.8942 \| 0.0 \| 0.0 \| 0.0084 \| 0.0 \| \| 0.5957 \| 10.8 \| 540 \| 0.7557 \| 0.2115 \| 0.2534 \| 0.8114 \| nan \| 0.8494 \| 0.9518 \| 0.6034 \| 0.7916 \| 0.2467 \| 0.0 \| 0.3290 \| 0.0558 \| 0.0 \| 0.9140 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9268 \| 0.0 \| 0.2114 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0043 \| 0.0 \| 0.0 \| 0.9189 \| 0.8608 \| 0.9472 \| 0.0 \| 0.0 \| 0.0041 \| 0.0 \| nan \| 0.7069 \| 0.8234 \| 0.4749 \| 0.6717 \| 0.2108 \| 0.0 \| 0.2916 \| 0.0557 \| 0.0 \| 0.7444 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6064 \| 0.0 \| 0.1714 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0043 \| 0.0 \| 0.0 \| 0.8108 \| 0.7131 \| 0.9005 \| 0.0 \| 0.0 \| 0.0040 \| 0.0 \| \| 0.5981 \| 11.2 \| 560 \| 0.7396 \| 0.2223 \| 0.2641 \| 0.8176 \| nan \| 0.8798 \| 0.9418 \| 0.5376 \| 0.8258 \| 0.2915 \| 0.0 \| 0.3517 \| 0.1215 \| 0.0 \| 0.9240 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8754 \| 0.0 \| 0.4492 \| 0.0029 \| 0.0 \| 0.0 \| 0.0 \| 0.0095 \| 0.0 \| 0.0 \| 0.9420 \| 0.8361 \| 0.9583 \| 0.0 \| 0.0 \| 0.0309 \| 0.0 \| nan \| 0.7188 \| 0.8224 \| 0.5170 \| 0.7030 \| 0.2402 \| 0.0 \| 0.2981 \| 0.1202 \| 0.0 \| 0.7507 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6314 \| 0.0 \| 0.3022 \| 0.0028 \| 0.0 \| 0.0 \| 0.0 \| 0.0095 \| 0.0 \| 0.0 \| 0.8028 \| 0.7141 \| 0.8963 \| 0.0 \| 0.0 \| 0.0295 \| 0.0 \| \| 0.5177 \| 11.6 \| 580 \| 0.7403 \| 0.2227 \| 0.2665 \| 0.8144 \| nan \| 0.9070 \| 0.9314 \| 0.6047 \| 0.7349 \| 0.3815 \| 0.0 \| 0.3395 \| 0.1778 \| 0.0 \| 0.9082 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8715 \| 0.0 \| 0.4334 \| 0.0040 \| 0.0 \| 0.0 \| 0.0 \| 0.0100 \| 0.0 \| 0.0 \| 0.9464 \| 0.8180 \| 0.9571 \| 0.0 \| 0.0 \| 0.0353 \| 0.0 \| nan \| 0.6983 \| 0.8321 \| 0.4714 \| 0.6965 \| 0.2653 \| 0.0 \| 0.2899 \| 0.1731 \| 0.0 \| 0.7622 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6327 \| 0.0 \| 0.2963 \| 0.0038 \| 0.0 \| 0.0 \| 0.0 \| 0.0100 \| 0.0 \| 0.0 \| 0.7972 \| 0.7113 \| 0.8992 \| 0.0 \| 0.0 \| 0.0334 \| 0.0 \| \| 0.5689 \| 12.0 \| 600 \| 0.7382 \| 0.2237 \| 0.2642 \| 0.8176 \| nan \| 0.9339 \| 0.9440 \| 0.5055 \| 0.7365 \| 0.3006 \| 0.0 \| 0.3610 \| 0.1763 \| 0.0 \| 0.9328 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.8772 \| 0.0 \| 0.4224 \| 0.0153 \| 0.0 \| 0.0 \| 0.0 \| 0.0108 \| 0.0 \| 0.0 \| 0.9320 \| 0.8401 \| 0.9553 \| 0.0 \| 0.0 \| 0.0391 \| 0.0 \| nan \| 0.7077 \| 0.8244 \| 0.4863 \| 0.7017 \| 0.2403 \| 0.0 \| 0.3038 \| 0.1720 \| 0.0 \| 0.7422 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.6333 \| 0.0 \| 0.2928 \| 0.0142 \| 0.0 \| 0.0 \| 0.0 \| 0.0108 \| 0.0 \| 0.0 \| 0.8130 \| 0.7274 \| 0.8999 \| 0.0 \| 0.0 \| 0.0366 \| 0.0 \| \| 0.6379 \| 12.4 \| 620 \| 0.7284 \| 0.2260 \| 0.2674 \| 0.8171 \| nan \| 0.8613 \| 0.9554 \| 0.5848 \| 0.7909 \| 0.2547 \| 0.0 \| 0.3498 \| 0.2235 \| 0.0 \| 0.9240 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0011 \| 0.0 \| 0.0 \| 0.8959 \| 0.0 \| 0.4200 \| 0.0197 \| 0.0 \| 0.0 \| 0.0 \| 0.0173 \| 0.0 \| 0.0 \| 0.9153 \| 0.8660 \| 0.9535 \| 0.0 \| 0.0 \| 0.0598 \| 0.0 \| nan \| 0.7209 \| 0.8117 \| 0.5001 \| 0.7144 \| 0.2080 \| 0.0 \| 0.3020 \| 0.2141 \| 0.0 \| 0.7682 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0011 \| 0.0 \| 0.0 \| 0.6390 \| 0.0 \| 0.2812 \| 0.0188 \| 0.0 \| 0.0 \| 0.0 \| 0.0172 \| 0.0 \| 0.0 \| 0.8154 \| 0.7159 \| 0.9029 \| 0.0 \| 0.0 \| 0.0546 \| 0.0 \| \| 0.5772 \| 12.8 \| 640 \| 0.7187 \| 0.2317 \| 0.2755 \| 0.8213 \| nan \| 0.9200 \| 0.9479 \| 0.5902 \| 0.7480 \| 0.2776 \| 0.0 \| 0.3621 \| 0.3136 \| 0.0 \| 0.9321 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0023 \| 0.0 \| 0.0 \| 0.8618 \| 0.0 \| 0.4991 \| 0.0422 \| 0.0 \| 0.0 \| 0.0 \| 0.0430 \| 0.0 \| 0.0 \| 0.9282 \| 0.8676 \| 0.9574 \| 0.0 \| 0.0 \| 0.0742 \| 0.0 \| nan \| 0.7049 \| 0.8337 \| 0.4961 \| 0.7056 \| 0.2336 \| 0.0 \| 0.3085 \| 0.2828 \| 0.0 \| 0.7610 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0023 \| 0.0 \| 0.0 \| 0.6438 \| 0.0 \| 0.3241 \| 0.0375 \| 0.0 \| 0.0 \| 0.0 \| 0.0427 \| 0.0 \| 0.0 \| 0.8137 \| 0.7203 \| 0.9018 \| 0.0 \| 0.0 \| 0.0667 \| 0.0 \| \| 0.6958 \| 13.2 \| 660 \| 0.6981 \| 0.2346 \| 0.2795 \| 0.8243 \| nan \| 0.9034 \| 0.9373 \| 0.6079 \| 0.8121 \| 0.3564 \| 0.0 \| 0.3662 \| 0.3246 \| 0.0 \| 0.9333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0022 \| 0.0 \| 0.0 \| 0.8780 \| 0.0 \| 0.4743 \| 0.0491 \| 0.0 \| 0.0 \| 0.0 \| 0.0335 \| 0.0 \| 0.0 \| 0.9305 \| 0.8584 \| 0.9573 \| 0.0 \| 0.0 \| 0.0779 \| 0.0 \| nan \| 0.7277 \| 0.8370 \| 0.4877 \| 0.7263 \| 0.2627 \| 0.0 \| 0.3095 \| 0.2965 \| 0.0 \| 0.7655 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0022 \| 0.0 \| 0.0 \| 0.6538 \| 0.0 \| 0.3213 \| 0.0434 \| 0.0 \| 0.0 \| 0.0 \| 0.0333 \| 0.0 \| 0.0 \| 0.8126 \| 0.7263 \| 0.9021 \| 0.0 \| 0.0 \| 0.0692 \| 0.0 \| \| 0.6015 \| 13.6 \| 680 \| 0.7058 \| 0.2382 \| 0.2808 \| 0.8256 \| nan \| 0.9422 \| 0.9404 \| 0.5065 \| 0.7814 \| 0.3153 \| 0.0 \| 0.3763 \| 0.3868 \| 0.0 \| 0.9294 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0052 \| 0.0 \| 0.0 \| 0.9027 \| 0.0 \| 0.4368 \| 0.1332 \| 0.0 \| 0.0 \| 0.0 \| 0.0725 \| 0.0 \| 0.0 \| 0.9198 \| 0.8596 \| 0.9627 \| 0.0 \| 0.0 \| 0.0749 \| 0.0 \| nan \| 0.7195 \| 0.8358 \| 0.4934 \| 0.7243 \| 0.2502 \| 0.0 \| 0.3068 \| 0.3232 \| 0.0 \| 0.7693 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0052 \| 0.0 \| 0.0 \| 0.6496 \| 0.0 \| 0.3222 \| 0.1099 \| 0.0 \| 0.0 \| 0.0 \| 0.0713 \| 0.0 \| 0.0 \| 0.8216 \| 0.7279 \| 0.9005 \| 0.0 \| 0.0 \| 0.0679 \| 0.0 \| \| 0.5698 \| 14.0 \| 700 \| 0.6973 \| 0.2374 \| 0.2806 \| 0.8240 \| nan \| 0.9140 \| 0.9468 \| 0.5788 \| 0.7557 \| 0.3021 \| 0.0 \| 0.3873 \| 0.3988 \| 0.0 \| 0.9359 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0113 \| 0.0 \| 0.0 \| 0.9088 \| 0.0 \| 0.3731 \| 0.1032 \| 0.0 \| 0.0 \| 0.0 \| 0.0816 \| 0.0 \| 0.0 \| 0.9156 \| 0.8763 \| 0.9563 \| 0.0 \| 0.0 \| 0.0937 \| 0.0 \| nan \| 0.7136 \| 0.8338 \| 0.4892 \| 0.7176 \| 0.2411 \| 0.0 \| 0.3250 \| 0.3327 \| 0.0 \| 0.7658 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0113 \| 0.0 \| 0.0 \| 0.6525 \| 0.0 \| 0.3009 \| 0.0915 \| 0.0 \| 0.0 \| 0.0 \| 0.0804 \| 0.0 \| 0.0 \| 0.8157 \| 0.7135 \| 0.9056 \| 0.0 \| 0.0 \| 0.0825 \| 0.0 \| \| 0.6438 \| 14.4 \| 720 \| 0.6984 \| 0.2417 \| 0.2849 \| 0.8258 \| nan \| 0.9185 \| 0.9511 \| 0.5585 \| 0.7586 \| 0.3002 \| 0.0 \| 0.3864 \| 0.4324 \| 0.0 \| 0.9399 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0168 \| 0.0 \| 0.0 \| 0.8707 \| 0.0 \| 0.4506 \| 0.1372 \| 0.0 \| 0.0 \| 0.0 \| 0.0670 \| 0.0 \| 0.0 \| 0.9270 \| 0.8638 \| 0.9586 \| 0.0 \| 0.0 \| 0.1479 \| 0.0 \| nan \| 0.7374 \| 0.8238 \| 0.5275 \| 0.7116 \| 0.2414 \| 0.0 \| 0.3184 \| 0.3475 \| 0.0 \| 0.7622 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0167 \| 0.0 \| 0.0 \| 0.6636 \| 0.0 \| 0.3196 \| 0.1092 \| 0.0 \| 0.0 \| 0.0 \| 0.0661 \| 0.0 \| 0.0 \| 0.8193 \| 0.7246 \| 0.9037 \| 0.0 \| 0.0 \| 0.1249 \| 0.0 \| \| 0.4882 \| 14.8 \| 740 \| 0.6937 \| 0.2419 \| 0.2838 \| 0.8280 \| nan \| 0.9320 \| 0.9493 \| 0.5035 \| 0.7776 \| 0.2815 \| 0.0 \| 0.4022 \| 0.4547 \| 0.0 \| 0.9293 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0157 \| 0.0 \| 0.0 \| 0.9167 \| 0.0 \| 0.3765 \| 0.1535 \| 0.0 \| 0.0 \| 0.0 \| 0.0903 \| 0.0 \| 0.0 \| 0.9199 \| 0.8638 \| 0.9618 \| 0.0 \| 0.0 \| 0.1214 \| 0.0 \| nan \| 0.7364 \| 0.8293 \| 0.4922 \| 0.7276 \| 0.2350 \| 0.0 \| 0.3321 \| 0.3528 \| 0.0 \| 0.7749 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0157 \| 0.0 \| 0.0 \| 0.6534 \| 0.0 \| 0.2906 \| 0.1250 \| 0.0 \| 0.0 \| 0.0 \| 0.0889 \| 0.0 \| 0.0 \| 0.8270 \| 0.7322 \| 0.9064 \| 0.0 \| 0.0 \| 0.1054 \| 0.0 \| \| 0.4693 \| 15.2 \| 760 \| 0.6931 \| 0.2437 \| 0.2883 \| 0.8248 \| nan \| 0.9276 \| 0.9436 \| 0.5805 \| 0.7401 \| 0.3161 \| 0.0 \| 0.4023 \| 0.4731 \| 0.0 \| 0.9225 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0212 \| 0.0 \| 0.0 \| 0.8888 \| 0.0 \| 0.4353 \| 0.1921 \| 0.0 \| 0.0 \| 0.0 \| 0.0994 \| 0.0 \| 0.0 \| 0.9260 \| 0.8468 \| 0.9584 \| 0.0 \| 0.0 \| 0.1281 \| 0.0 \| nan \| 0.7061 \| 0.8324 \| 0.5132 \| 0.7048 \| 0.2545 \| 0.0 \| 0.3326 \| 0.3620 \| 0.0 \| 0.7827 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0212 \| 0.0 \| 0.0 \| 0.6548 \| 0.0 \| 0.3164 \| 0.1357 \| 0.0 \| 0.0 \| 0.0 \| 0.0970 \| 0.0 \| 0.0 \| 0.8211 \| 0.7338 \| 0.9070 \| 0.0 \| 0.0 \| 0.1113 \| 0.0 \| \| 0.5884 \| 15.6 \| 780 \| 0.6870 \| 0.2460 \| 0.2919 \| 0.8280 \| nan \| 0.8965 \| 0.9478 \| 0.5938 \| 0.7779 \| 0.3113 \| 0.0 \| 0.4136 \| 0.5281 \| 0.0 \| 0.9223 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0379 \| 0.0 \| 0.0 \| 0.9103 \| 0.0 \| 0.3960 \| 0.1587 \| 0.0 \| 0.0 \| 0.0 \| 0.1163 \| 0.0 \| 0.0 \| 0.9203 \| 0.8756 \| 0.9641 \| 0.0 \| 0.0 \| 0.1551 \| 0.0 \| nan \| 0.7291 \| 0.8308 \| 0.5095 \| 0.7130 \| 0.2456 \| 0.0 \| 0.3393 \| 0.3730 \| 0.0 \| 0.7832 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0378 \| 0.0 \| 0.0 \| 0.6676 \| 0.0 \| 0.3097 \| 0.1280 \| 0.0 \| 0.0 \| 0.0 \| 0.1127 \| 0.0 \| 0.0 \| 0.8267 \| 0.7249 \| 0.9050 \| 0.0 \| 0.0 \| 0.1296 \| 0.0 \| \| 0.4822 \| 16.0 \| 800 \| 0.6822 \| 0.2490 \| 0.2929 \| 0.8277 \| nan \| 0.8592 \| 0.9535 \| 0.5867 \| 0.8150 \| 0.2845 \| 0.0 \| 0.4140 \| 0.5151 \| 0.0 \| 0.9363 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0563 \| 0.0 \| 0.0 \| 0.8924 \| 0.0 \| 0.3955 \| 0.2003 \| 0.0 \| 0.0 \| 0.0 \| 0.1470 \| 0.0 \| 0.0 \| 0.9358 \| 0.8603 \| 0.9569 \| 0.0 \| 0.0 \| 0.1481 \| 0.0 \| nan \| 0.7180 \| 0.8218 \| 0.5548 \| 0.7288 \| 0.2424 \| 0.0 \| 0.3294 \| 0.3797 \| 0.0 \| 0.7752 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0562 \| 0.0 \| 0.0 \| 0.6685 \| 0.0 \| 0.3157 \| 0.1505 \| 0.0 \| 0.0 \| 0.0 \| 0.1400 \| 0.0 \| 0.0 \| 0.8201 \| 0.7296 \| 0.9095 \| 0.0 \| 0.0 \| 0.1267 \| 0.0 \| \| 0.4741 \| 16.4 \| 820 \| 0.6827 \| 0.2488 \| 0.2926 \| 0.8283 \| nan \| 0.8902 \| 0.9524 \| 0.5983 \| 0.7742 \| 0.3112 \| 0.0 \| 0.4137 \| 0.4934 \| 0.0 \| 0.9314 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0730 \| 0.0 \| 0.0 \| 0.8856 \| 0.0 \| 0.4433 \| 0.1811 \| 0.0 \| 0.0 \| 0.0 \| 0.1122 \| 0.0 \| 0.0 \| 0.9397 \| 0.8325 \| 0.9600 \| 0.0 \| 0.0 \| 0.1564 \| 0.0 \| nan \| 0.7279 \| 0.8282 \| 0.5455 \| 0.7332 \| 0.2494 \| 0.0 \| 0.3343 \| 0.3734 \| 0.0 \| 0.7841 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0729 \| 0.0 \| 0.0 \| 0.6686 \| 0.0 \| 0.3175 \| 0.1309 \| 0.0 \| 0.0 \| 0.0 \| 0.1090 \| 0.0 \| 0.0 \| 0.8181 \| 0.7275 \| 0.9094 \| 0.0 \| 0.0 \| 0.1289 \| 0.0 \| \| 0.5164 \| 16.8 \| 840 \| 0.6803 \| 0.2488 \| 0.2928 \| 0.8309 \| nan \| 0.9067 \| 0.9498 \| 0.5655 \| 0.8037 \| 0.3081 \| 0.0 \| 0.4061 \| 0.5493 \| 0.0 \| 0.9276 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0686 \| 0.0 \| 0.0 \| 0.9156 \| 0.0 \| 0.3162 \| 0.1933 \| 0.0 \| 0.0 \| 0.0 \| 0.1543 \| 0.0 \| 0.0 \| 0.9273 \| 0.8698 \| 0.9604 \| 0.0 \| 0.0 \| 0.1324 \| 0.0 \| nan \| 0.7404 \| 0.8309 \| 0.5307 \| 0.7191 \| 0.2548 \| 0.0 \| 0.3371 \| 0.3740 \| 0.0 \| 0.7897 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0685 \| 0.0 \| 0.0 \| 0.6642 \| 0.0 \| 0.2698 \| 0.1508 \| 0.0 \| 0.0 \| 0.0 \| 0.1469 \| 0.0 \| 0.0 \| 0.8258 \| 0.7322 \| 0.9105 \| 0.0 \| 0.0 \| 0.1132 \| 0.0 \| \| 0.4384 \| 17.2 \| 860 \| 0.6711 \| 0.2512 \| 0.2978 \| 0.8313 \| nan \| 0.9294 \| 0.9412 \| 0.5986 \| 0.7805 \| 0.3288 \| 0.0 \| 0.4120 \| 0.5585 \| 0.0 \| 0.9350 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0621 \| 0.0 \| 0.0 \| 0.8890 \| 0.0 \| 0.4228 \| 0.1557 \| 0.0 \| 0.0 \| 0.0 \| 0.1642 \| 0.0 \| 0.0 \| 0.9272 \| 0.8743 \| 0.9600 \| 0.0 \| 0.0 \| 0.1852 \| 0.0 \| nan \| 0.7282 \| 0.8363 \| 0.5214 \| 0.7324 \| 0.2651 \| 0.0 \| 0.3417 \| 0.3806 \| 0.0 \| 0.7894 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0620 \| 0.0 \| 0.0 \| 0.6730 \| 0.0 \| 0.3112 \| 0.1257 \| 0.0 \| 0.0 \| 0.0 \| 0.1559 \| 0.0 \| 0.0 \| 0.8215 \| 0.7310 \| 0.9109 \| 0.0 \| 0.0 \| 0.1533 \| 0.0 \| \| 0.4316 \| 17.6 \| 880 \| 0.6659 \| 0.2556 \| 0.3047 \| 0.8338 \| nan \| 0.9368 \| 0.9390 \| 0.5718 \| 0.8247 \| 0.3282 \| 0.0 \| 0.4395 \| 0.5804 \| 0.0 \| 0.9423 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0993 \| 0.0 \| 0.0 \| 0.8502 \| 0.0 \| 0.4347 \| 0.2537 \| 0.0 \| 0.0 \| 0.0 \| 0.2004 \| 0.0 \| 0.0 \| 0.9375 \| 0.8574 \| 0.9656 \| 0.0 \| 0.0 \| 0.1969 \| 0.0 \| nan \| 0.7368 \| 0.8437 \| 0.5323 \| 0.7437 \| 0.2656 \| 0.0 \| 0.3588 \| 0.3788 \| 0.0 \| 0.7690 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0989 \| 0.0 \| 0.0 \| 0.6726 \| 0.0 \| 0.3185 \| 0.1661 \| 0.0 \| 0.0 \| 0.0 \| 0.1845 \| 0.0 \| 0.0 \| 0.8227 \| 0.7293 \| 0.9084 \| 0.0 \| 0.0 \| 0.1607 \| 0.0 \| \| 0.4505 \| 18.0 \| 900 \| 0.6758 \| 0.2542 \| 0.2989 \| 0.8298 \| nan \| 0.9136 \| 0.9505 \| 0.5574 \| 0.7432 \| 0.2982 \| 0.0 \| 0.4386 \| 0.5360 \| 0.0 \| 0.9437 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1767 \| 0.0 \| 0.0 \| 0.9093 \| 0.0 \| 0.4136 \| 0.2006 \| 0.0 \| 0.0 \| 0.0 \| 0.1824 \| 0.0 \| 0.0 \| 0.9305 \| 0.8321 \| 0.9575 \| 0.0 \| 0.0 \| 0.1794 \| 0.0 \| nan \| 0.7194 \| 0.8331 \| 0.5212 \| 0.7112 \| 0.2453 \| 0.0 \| 0.3458 \| 0.3758 \| 0.0 \| 0.7782 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1753 \| 0.0 \| 0.0 \| 0.6756 \| 0.0 \| 0.3193 \| 0.1549 \| 0.0 \| 0.0 \| 0.0 \| 0.1715 \| 0.0 \| 0.0 \| 0.8259 \| 0.7307 \| 0.9115 \| 0.0 \| 0.0 \| 0.1480 \| 0.0 \| \| 0.4388 \| 18.4 \| 920 \| 0.6661 \| 0.2565 \| 0.3072 \| 0.8297 \| nan \| 0.8822 \| 0.9428 \| 0.6160 \| 0.8143 \| 0.3081 \| 0.0 \| 0.4454 \| 0.5414 \| 0.0 \| 0.9279 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2064 \| 0.0 \| 0.0 \| 0.8709 \| 0.0 \| 0.4429 \| 0.2824 \| 0.0 \| 0.0 \| 0.0 \| 0.1963 \| 0.0 \| 0.0 \| 0.9268 \| 0.8822 \| 0.9626 \| 0.0 \| 0.0 \| 0.1975 \| 0.0 \| nan \| 0.7157 \| 0.8393 \| 0.4520 \| 0.7274 \| 0.2555 \| 0.0 \| 0.3630 \| 0.3766 \| 0.0 \| 0.8002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2041 \| 0.0 \| 0.0 \| 0.6728 \| 0.0 \| 0.3199 \| 0.1873 \| 0.0 \| 0.0 \| 0.0 \| 0.1827 \| 0.0 \| 0.0 \| 0.8220 \| 0.7298 \| 0.9113 \| 0.0 \| 0.0 \| 0.1622 \| 0.0 \| \| 0.361 \| 18.8 \| 940 \| 0.6628 \| 0.2605 \| 0.3096 \| 0.8323 \| nan \| 0.8951 \| 0.9452 \| 0.5626 \| 0.8170 \| 0.3278 \| 0.0 \| 0.4493 \| 0.5721 \| 0.0 \| 0.9407 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2403 \| 0.0 \| 0.0 \| 0.8579 \| 0.0 \| 0.4486 \| 0.2506 \| 0.0 \| 0.0 \| 0.0 \| 0.1961 \| 0.0 \| 0.0 \| 0.9287 \| 0.8857 \| 0.9658 \| 0.0 \| 0.0 \| 0.2412 \| 0.0 \| nan \| 0.7357 \| 0.8323 \| 0.5378 \| 0.7362 \| 0.2625 \| 0.0 \| 0.3491 \| 0.3683 \| 0.0 \| 0.7867 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2364 \| 0.0 \| 0.0 \| 0.6806 \| 0.0 \| 0.3224 \| 0.1758 \| 0.0 \| 0.0 \| 0.0 \| 0.1821 \| 0.0 \| 0.0 \| 0.8223 \| 0.7321 \| 0.9113 \| 0.0 \| 0.0 \| 0.1866 \| 0.0 \| \| 0.3828 \| 19.2 \| 960 \| 0.6739 \| 0.2605 \| 0.3097 \| 0.8287 \| nan \| 0.8388 \| 0.9556 \| 0.5834 \| 0.8373 \| 0.2736 \| 0.0 \| 0.4234 \| 0.6044 \| 0.0 \| 0.9343 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2668 \| 0.0 \| 0.0 \| 0.8799 \| 0.0 \| 0.4288 \| 0.2738 \| 0.0 \| 0.0 \| 0.0 \| 0.2310 \| 0.0 \| 0.0 \| 0.9154 \| 0.8914 \| 0.9607 \| 0.0 \| 0.0 \| 0.2327 \| 0.0 \| nan \| 0.7156 \| 0.8243 \| 0.5543 \| 0.6957 \| 0.2336 \| 0.0 \| 0.3482 \| 0.3805 \| 0.0 \| 0.7961 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2613 \| 0.0 \| 0.0 \| 0.6838 \| 0.0 \| 0.3288 \| 0.1820 \| 0.0 \| 0.0 \| 0.0 \| 0.2107 \| 0.0 \| 0.0 \| 0.8227 \| 0.7190 \| 0.9134 \| 0.0 \| 0.0 \| 0.1857 \| 0.0 \| \| 0.6092 \| 19.6 \| 980 \| 0.6651 \| 0.2619 \| 0.3102 \| 0.8331 \| nan \| 0.9454 \| 0.9455 \| 0.5339 \| 0.7741 \| 0.3170 \| 0.0 \| 0.4542 \| 0.6123 \| 0.0 \| 0.9329 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2972 \| 0.0 \| 0.0 \| 0.9051 \| 0.0 \| 0.3950 \| 0.2431 \| 0.0 \| 0.0 \| 0.0 \| 0.2317 \| 0.0 \| 0.0 \| 0.9022 \| 0.8711 \| 0.9631 \| 0.0 \| 0.0 \| 0.2214 \| 0.0 \| nan \| 0.7209 \| 0.8382 \| 0.5155 \| 0.7300 \| 0.2616 \| 0.0 \| 0.3551 \| 0.3647 \| 0.0 \| 0.7986 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2900 \| 0.0 \| 0.0 \| 0.6842 \| 0.0 \| 0.3113 \| 0.1817 \| 0.0 \| 0.0 \| 0.0 \| 0.2123 \| 0.0 \| 0.0 \| 0.8260 \| 0.7207 \| 0.9135 \| 0.0 \| 0.0 \| 0.1808 \| 0.0 \| \| 0.4141 \| 20.0 \| 1000 \| 0.6641 \| 0.2647 \| 0.3139 \| 0.8316 \| nan \| 0.9264 \| 0.9481 \| 0.5341 \| 0.7649 \| 0.3226 \| 0.0 \| 0.4334 \| 0.5668 \| 0.0 \| 0.9405 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3605 \| 0.0 \| 0.0 \| 0.8534 \| 0.0 \| 0.4698 \| 0.2992 \| 0.0 \| 0.0 \| 0.0 \| 0.2259 \| 0.0 \| 0.0 \| 0.9166 \| 0.8909 \| 0.9625 \| 0.0 \| 0.0 \| 0.2556 \| 0.0 \| nan \| 0.7208 \| 0.8365 \| 0.5058 \| 0.7259 \| 0.2609 \| 0.0 \| 0.3563 \| 0.3898 \| 0.0 \| 0.7969 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3435 \| 0.0 \| 0.0 \| 0.6834 \| 0.0 \| 0.3347 \| 0.1917 \| 0.0 \| 0.0 \| 0.0 \| 0.2066 \| 0.0 \| 0.0 \| 0.8200 \| 0.7153 \| 0.9160 \| 0.0 \| 0.0 \| 0.1970 \| 0.0 \| \| 0.4172 \| 20.4 \| 1020 \| 0.6580 \| 0.2638 \| 0.3124 \| 0.8320 \| nan \| 0.8716 \| 0.9580 \| 0.6106 \| 0.7824 \| 0.2688 \| 0.0 \| 0.4657 \| 0.5675 \| 0.0 \| 0.9364 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3450 \| 0.0 \| 0.0 \| 0.8929 \| 0.0 \| 0.4323 \| 0.3108 \| 0.0 \| 0.0 \| 0.0 \| 0.2097 \| 0.0 \| 0.0 \| 0.9226 \| 0.8574 \| 0.9667 \| 0.0 \| 0.0 \| 0.2240 \| 0.0 \| nan \| 0.7318 \| 0.8241 \| 0.5236 \| 0.7430 \| 0.2310 \| 0.0 \| 0.3535 \| 0.3745 \| 0.0 \| 0.8019 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3327 \| 0.0 \| 0.0 \| 0.6804 \| 0.0 \| 0.3154 \| 0.2001 \| 0.0 \| 0.0 \| 0.0 \| 0.1933 \| 0.0 \| 0.0 \| 0.8345 \| 0.7404 \| 0.9137 \| 0.0 \| 0.0 \| 0.1768 \| 0.0 \| \| 0.4116 \| 20.8 \| 1040 \| 0.6727 \| 0.2634 \| 0.3088 \| 0.8344 \| nan \| 0.8931 \| 0.9598 \| 0.6058 \| 0.7902 \| 0.2737 \| 0.0 \| 0.4378 \| 0.5670 \| 0.0 \| 0.9385 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3090 \| 0.0 \| 0.0 \| 0.8957 \| 0.0 \| 0.3829 \| 0.2471 \| 0.0 \| 0.0 \| 0.0 \| 0.2405 \| 0.0 \| 0.0 \| 0.9260 \| 0.8616 \| 0.9612 \| 0.0 \| 0.0 \| 0.2085 \| 0.0 \| nan \| 0.7426 \| 0.8232 \| 0.5412 \| 0.7417 \| 0.2388 \| 0.0 \| 0.3514 \| 0.3729 \| 0.0 \| 0.7984 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2989 \| 0.0 \| 0.0 \| 0.6842 \| 0.0 \| 0.3072 \| 0.1826 \| 0.0 \| 0.0 \| 0.0 \| 0.2174 \| 0.0 \| 0.0 \| 0.8303 \| 0.7357 \| 0.9163 \| 0.0 \| 0.0 \| 0.1736 \| 0.0 \| \| 0.422 \| 21.2 \| 1060 \| 0.6688 \| 0.2681 \| 0.3141 \| 0.8333 \| nan \| 0.8560 \| 0.9509 \| 0.5973 \| 0.8216 \| 0.3325 \| 0.0 \| 0.4396 \| 0.5635 \| 0.0 \| 0.9370 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4310 \| 0.0 \| 0.0 \| 0.9214 \| 0.0 \| 0.3744 \| 0.2274 \| 0.0 \| 0.0 \| 0.0 \| 0.2551 \| 0.0 \| 0.0 \| 0.9186 \| 0.8724 \| 0.9636 \| 0.0 \| 0.0 \| 0.2176 \| 0.0 \| nan \| 0.7133 \| 0.8257 \| 0.5657 \| 0.7377 \| 0.2744 \| 0.0 \| 0.3530 \| 0.3860 \| 0.0 \| 0.8069 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4029 \| 0.0 \| 0.0 \| 0.6819 \| 0.0 \| 0.2941 \| 0.1799 \| 0.0 \| 0.0 \| 0.0 \| 0.2286 \| 0.0 \| 0.0 \| 0.8336 \| 0.7400 \| 0.9167 \| 0.0 \| 0.0 \| 0.1740 \| 0.0 \| \| 0.3686 \| 21.6 \| 1080 \| 0.6515 \| 0.2695 \| 0.3171 \| 0.8381 \| nan \| 0.9222 \| 0.9530 \| 0.6073 \| 0.7765 \| 0.3097 \| 0.0 \| 0.4667 \| 0.6143 \| 0.0 \| 0.9413 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4369 \| 0.0 \| 0.0 \| 0.9051 \| 0.0 \| 0.3810 \| 0.2432 \| 0.0 \| 0.0 \| 0.0 \| 0.2461 \| 0.0 \| 0.0 \| 0.9233 \| 0.8697 \| 0.9598 \| 0.0 \| 0.0 \| 0.2258 \| 0.0 \| nan \| 0.7351 \| 0.8391 \| 0.5477 \| 0.7423 \| 0.2621 \| 0.0 \| 0.3677 \| 0.3792 \| 0.0 \| 0.7985 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4059 \| 0.0 \| 0.0 \| 0.6838 \| 0.0 \| 0.3067 \| 0.1912 \| 0.0 \| 0.0 \| 0.0 \| 0.2229 \| 0.0 \| 0.0 \| 0.8352 \| 0.7458 \| 0.9177 \| 0.0 \| 0.0 \| 0.1822 \| 0.0 \| \| 0.3979 \| 22.0 \| 1100 \| 0.6568 \| 0.2693 \| 0.3208 \| 0.8330 \| nan \| 0.8615 \| 0.9551 \| 0.6160 \| 0.7842 \| 0.3245 \| 0.0 \| 0.4468 \| 0.5568 \| 0.0 \| 0.9365 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4700 \| 0.0 \| 0.0 \| 0.8862 \| 0.0 \| 0.4119 \| 0.3784 \| 0.0 \| 0.0 \| 0.0 \| 0.2737 \| 0.0 \| 0.0 \| 0.9224 \| 0.8753 \| 0.9673 \| 0.0 \| 0.0 \| 0.2389 \| 0.0 \| nan \| 0.7164 \| 0.8363 \| 0.4831 \| 0.7147 \| 0.2654 \| 0.0 \| 0.3574 \| 0.3850 \| 0.0 \| 0.8012 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4289 \| 0.0 \| 0.0 \| 0.6866 \| 0.0 \| 0.3288 \| 0.2315 \| 0.0 \| 0.0 \| 0.0 \| 0.2423 \| 0.0 \| 0.0 \| 0.8331 \| 0.7420 \| 0.9164 \| 0.0 \| 0.0 \| 0.1857 \| 0.0 \| \| 0.4441 \| 22.4 \| 1120 \| 0.6608 \| 0.2695 \| 0.3167 \| 0.8365 \| nan \| 0.9346 \| 0.9529 \| 0.5573 \| 0.7570 \| 0.2894 \| 0.0 \| 0.4630 \| 0.5627 \| 0.0 \| 0.9363 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4252 \| 0.0 \| 0.0 \| 0.8931 \| 0.0 \| 0.4402 \| 0.3039 \| 0.0 \| 0.0 \| 0.0 \| 0.2724 \| 0.0 \| 0.0 \| 0.9202 \| 0.8783 \| 0.9636 \| 0.0 \| 0.0 \| 0.2175 \| 0.0 \| nan \| 0.7278 \| 0.8391 \| 0.5251 \| 0.7202 \| 0.2519 \| 0.0 \| 0.3607 \| 0.3867 \| 0.0 \| 0.8006 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3984 \| 0.0 \| 0.0 \| 0.6891 \| 0.0 \| 0.3342 \| 0.2181 \| 0.0 \| 0.0 \| 0.0 \| 0.2424 \| 0.0 \| 0.0 \| 0.8331 \| 0.7397 \| 0.9175 \| 0.0 \| 0.0 \| 0.1767 \| 0.0 \| \| 0.4551 \| 22.8 \| 1140 \| 0.6578 \| 0.2728 \| 0.3240 \| 0.8349 \| nan \| 0.9032 \| 0.9512 \| 0.5769 \| 0.7527 \| 0.3419 \| 0.0 \| 0.4811 \| 0.6010 \| 0.0 \| 0.9427 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5591 \| 0.0 \| 0.0 \| 0.9050 \| 0.0 \| 0.4264 \| 0.3202 \| 0.0 \| 0.0 \| 0.0 \| 0.2784 \| 0.0 \| 0.0 \| 0.9086 \| 0.8781 \| 0.9624 \| 0.0 \| 0.0 \| 0.2286 \| 0.0 \| nan \| 0.7093 \| 0.8423 \| 0.5256 \| 0.7125 \| 0.2784 \| 0.0 \| 0.3686 \| 0.3908 \| 0.0 \| 0.8046 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4909 \| 0.0 \| 0.0 \| 0.6934 \| 0.0 \| 0.3288 \| 0.2186 \| 0.0 \| 0.0 \| 0.0 \| 0.2458 \| 0.0 \| 0.0 \| 0.8315 \| 0.7326 \| 0.9183 \| 0.0 \| 0.0 \| 0.1832 \| 0.0 \| \| 0.4407 \| 23.2 \| 1160 \| 0.6450 \| 0.2690 \| 0.3193 \| 0.8365 \| nan \| 0.9042 \| 0.9432 \| 0.6099 \| 0.7794 \| 0.3788 \| 0.0 \| 0.4657 \| 0.6034 \| 0.0 \| 0.9444 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4627 \| 0.0 \| 0.0 \| 0.9219 \| 0.0 \| 0.3212 \| 0.2968 \| 0.0 \| 0.0 \| 0.0 \| 0.2736 \| 0.0 \| 0.0 \| 0.9216 \| 0.8775 \| 0.9650 \| 0.0 \| 0.0 \| 0.1872 \| 0.0 \| nan \| 0.7256 \| 0.8449 \| 0.5092 \| 0.7254 \| 0.2939 \| 0.0 \| 0.3773 \| 0.3916 \| 0.0 \| 0.7977 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4162 \| 0.0 \| 0.0 \| 0.6820 \| 0.0 \| 0.2634 \| 0.2125 \| 0.0 \| 0.0 \| 0.0 \| 0.2438 \| 0.0 \| 0.0 \| 0.8376 \| 0.7448 \| 0.9180 \| 0.0 \| 0.0 \| 0.1607 \| 0.0 \| \| 0.4254 \| 23.6 \| 1180 \| 0.6625 \| 0.2688 \| 0.3188 \| 0.8334 \| nan \| 0.9195 \| 0.9530 \| 0.6182 \| 0.6861 \| 0.3235 \| 0.0 \| 0.4638 \| 0.5651 \| 0.0 \| 0.9376 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5024 \| 0.0 \| 0.0 \| 0.8938 \| 0.0 \| 0.3684 \| 0.3308 \| 0.0 \| 0.0 \| 0.0 \| 0.2762 \| 0.0 \| 0.0 \| 0.9336 \| 0.8559 \| 0.9677 \| 0.0 \| 0.0 \| 0.2428 \| 0.0 \| nan \| 0.6894 \| 0.8508 \| 0.4903 \| 0.6638 \| 0.2781 \| 0.0 \| 0.3752 \| 0.3880 \| 0.0 \| 0.8096 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4447 \| 0.0 \| 0.0 \| 0.6880 \| 0.0 \| 0.3020 \| 0.2205 \| 0.0 \| 0.0 \| 0.0 \| 0.2448 \| 0.0 \| 0.0 \| 0.8333 \| 0.7482 \| 0.9180 \| 0.0 \| 0.0 \| 0.1936 \| 0.0 \| \| 0.3237 \| 24.0 \| 1200 \| 0.6369 \| 0.2716 \| 0.3205 \| 0.8382 \| nan \| 0.9325 \| 0.9453 \| 0.5367 \| 0.7718 \| 0.3618 \| 0.0 \| 0.4612 \| 0.6096 \| 0.0 \| 0.9404 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4799 \| 0.0 \| 0.0 \| 0.8972 \| 0.0 \| 0.4341 \| 0.2409 \| 0.0 \| 0.0 \| 0.0 \| 0.2640 \| 0.0 \| 0.0 \| 0.9304 \| 0.8631 \| 0.9614 \| 0.0 \| 0.0 \| 0.2649 \| 0.0 \| nan \| 0.7146 \| 0.8491 \| 0.5225 \| 0.7157 \| 0.2907 \| 0.0 \| 0.3681 \| 0.3802 \| 0.0 \| 0.8074 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4313 \| 0.0 \| 0.0 \| 0.6964 \| 0.0 \| 0.3215 \| 0.1884 \| 0.0 \| 0.0 \| 0.0 \| 0.2369 \| 0.0 \| 0.0 \| 0.8355 \| 0.7482 \| 0.9192 \| 0.0 \| 0.0 \| 0.2078 \| 0.0 \| \| 0.365 \| 24.4 \| 1220 \| 0.6506 \| 0.2730 \| 0.3214 \| 0.8385 \| nan \| 0.9277 \| 0.9542 \| 0.5852 \| 0.7626 \| 0.2932 \| 0.0 \| 0.4719 \| 0.5930 \| 0.0 \| 0.9376 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5711 \| 0.0 \| 0.0 \| 0.9051 \| 0.0 \| 0.4031 \| 0.2609 \| 0.0 \| 0.0 \| 0.0 \| 0.2769 \| 0.0 \| 0.0 \| 0.9232 \| 0.8704 \| 0.9705 \| 0.0 \| 0.0 \| 0.2199 \| 0.0 \| nan \| 0.7176 \| 0.8458 \| 0.5448 \| 0.7225 \| 0.2537 \| 0.0 \| 0.3714 \| 0.3947 \| 0.0 \| 0.8089 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4881 \| 0.0 \| 0.0 \| 0.6931 \| 0.0 \| 0.3242 \| 0.1968 \| 0.0 \| 0.0 \| 0.0 \| 0.2446 \| 0.0 \| 0.0 \| 0.8333 \| 0.7446 \| 0.9172 \| 0.0 \| 0.0 \| 0.1814 \| 0.0 \| \| 0.4789 \| 24.8 \| 1240 \| 0.6483 \| 0.2759 \| 0.3270 \| 0.8389 \| nan \| 0.9107 \| 0.9543 \| 0.6023 \| 0.7839 \| 0.2900 \| 0.0 \| 0.4549 \| 0.5867 \| 0.0 \| 0.9412 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6129 \| 0.0 \| 0.0 \| 0.8747 \| 0.0 \| 0.4464 \| 0.3362 \| 0.0 \| 0.0 \| 0.0 \| 0.2824 \| 0.0 \| 0.0 \| 0.9350 \| 0.8723 \| 0.9630 \| 0.0 \| 0.0 \| 0.2702 \| 0.0 \| nan \| 0.7231 \| 0.8439 \| 0.5571 \| 0.7132 \| 0.2529 \| 0.0 \| 0.3650 \| 0.3984 \| 0.0 \| 0.8099 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5013 \| 0.0 \| 0.0 \| 0.7005 \| 0.0 \| 0.3384 \| 0.2177 \| 0.0 \| 0.0 \| 0.0 \| 0.2474 \| 0.0 \| 0.0 \| 0.8330 \| 0.7510 \| 0.9199 \| 0.0 \| 0.0 \| 0.2094 \| 0.0 \| \| 0.5162 \| 25.2 \| 1260 \| 0.6464 \| 0.2760 \| 0.3278 \| 0.8383 \| nan \| 0.9063 \| 0.9529 \| 0.6199 \| 0.7501 \| 0.3129 \| 0.0 \| 0.4830 \| 0.6013 \| 0.0 \| 0.9348 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6178 \| 0.0 \| 0.0 \| 0.8908 \| 0.0 \| 0.4529 \| 0.2548 \| 0.0 \| 0.0 \| 0.0 \| 0.3034 \| 0.0 \| 0.0 \| 0.9285 \| 0.8805 \| 0.9658 \| 0.0 \| 0.0 \| 0.2889 \| 0.0 \| nan \| 0.7113 \| 0.8456 \| 0.5127 \| 0.7169 \| 0.2679 \| 0.0 \| 0.3825 \| 0.3913 \| 0.0 \| 0.8162 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5243 \| 0.0 \| 0.0 \| 0.7037 \| 0.0 \| 0.3354 \| 0.1923 \| 0.0 \| 0.0 \| 0.0 \| 0.2616 \| 0.0 \| 0.0 \| 0.8367 \| 0.7464 \| 0.9201 \| 0.0 \| 0.0 \| 0.2177 \| 0.0 \| \| 0.3914 \| 25.6 \| 1280 \| 0.6455 \| 0.2748 \| 0.3256 \| 0.8379 \| nan \| 0.9405 \| 0.9451 \| 0.5647 \| 0.7560 \| 0.3129 \| 0.0 \| 0.4521 \| 0.5869 \| 0.0 \| 0.9434 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6490 \| 0.0 \| 0.0 \| 0.8990 \| 0.0 \| 0.3980 \| 0.3299 \| 0.0 \| 0.0 \| 0.0 \| 0.3075 \| 0.0 \| 0.0 \| 0.9318 \| 0.8666 \| 0.9697 \| 0.0 \| 0.0 \| 0.2156 \| 0.0 \| nan \| 0.7086 \| 0.8472 \| 0.5238 \| 0.7070 \| 0.2627 \| 0.0 \| 0.3769 \| 0.4007 \| 0.0 \| 0.8069 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5155 \| 0.0 \| 0.0 \| 0.6933 \| 0.0 \| 0.3223 \| 0.2192 \| 0.0 \| 0.0 \| 0.0 \| 0.2646 \| 0.0 \| 0.0 \| 0.8382 \| 0.7529 \| 0.9194 \| 0.0 \| 0.0 \| 0.1835 \| 0.0 \| \| 0.3738 \| 26.0 \| 1300 \| 0.6373 \| 0.2726 \| 0.3267 \| 0.8361 \| nan \| 0.8998 \| 0.9382 \| 0.5957 \| 0.7798 \| 0.3951 \| 0.0 \| 0.5195 \| 0.5915 \| 0.0 \| 0.9516 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5415 \| 0.0 \| 0.0 \| 0.8902 \| 0.0 \| 0.4308 \| 0.2702 \| 0.0 \| 0.0 \| 0.0 \| 0.2883 \| 0.0 \| 0.0 \| 0.9131 \| 0.9061 \| 0.9652 \| 0.0 \| 0.0 \| 0.2309 \| 0.0 \| nan \| 0.7155 \| 0.8478 \| 0.5211 \| 0.7238 \| 0.2994 \| 0.0 \| 0.3901 \| 0.3975 \| 0.0 \| 0.7894 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4424 \| 0.0 \| 0.0 \| 0.6989 \| 0.0 \| 0.3224 \| 0.2038 \| 0.0 \| 0.0 \| 0.0 \| 0.2529 \| 0.0 \| 0.0 \| 0.8274 \| 0.7245 \| 0.9210 \| 0.0 \| 0.0 \| 0.1919 \| 0.0 \| \| 0.3512 \| 26.4 \| 1320 \| 0.6418 \| 0.2763 \| 0.3321 \| 0.8362 \| nan \| 0.9184 \| 0.9437 \| 0.6087 \| 0.7657 \| 0.3338 \| 0.0 \| 0.4730 \| 0.5723 \| 0.0 \| 0.9370 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6904 \| 0.0 \| 0.0 \| 0.8705 \| 0.0 \| 0.4566 \| 0.3468 \| 0.0 \| 0.0 \| 0.0 \| 0.3086 \| 0.0 \| 0.0 \| 0.9188 \| 0.8848 \| 0.9680 \| 0.0 \| 0.0 \| 0.2939 \| 0.0 \| nan \| 0.7141 \| 0.8468 \| 0.5263 \| 0.6994 \| 0.2719 \| 0.0 \| 0.3787 \| 0.4026 \| 0.0 \| 0.8163 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5194 \| 0.0 \| 0.0 \| 0.6977 \| 0.0 \| 0.3261 \| 0.2179 \| 0.0 \| 0.0 \| 0.0 \| 0.2645 \| 0.0 \| 0.0 \| 0.8347 \| 0.7424 \| 0.9208 \| 0.0 \| 0.0 \| 0.2151 \| 0.0 \| \| 0.4588 \| 26.8 \| 1340 \| 0.6435 \| 0.2748 \| 0.3252 \| 0.8395 \| nan \| 0.8778 \| 0.9551 \| 0.6037 \| 0.8296 \| 0.2888 \| 0.0 \| 0.4736 \| 0.6074 \| 0.0 \| 0.9403 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5804 \| 0.0 \| 0.0 \| 0.8944 \| 0.0 \| 0.4158 \| 0.2494 \| 0.0 \| 0.0 \| 0.0 \| 0.2931 \| 0.0 \| 0.0 \| 0.9314 \| 0.8776 \| 0.9691 \| 0.0 \| 0.0 \| 0.2711 \| 0.0 \| nan \| 0.7259 \| 0.8395 \| 0.5360 \| 0.7373 \| 0.2532 \| 0.0 \| 0.3753 \| 0.3901 \| 0.0 \| 0.8111 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4937 \| 0.0 \| 0.0 \| 0.7002 \| 0.0 \| 0.3175 \| 0.1880 \| 0.0 \| 0.0 \| 0.0 \| 0.2560 \| 0.0 \| 0.0 \| 0.8377 \| 0.7488 \| 0.9216 \| 0.0 \| 0.0 \| 0.2116 \| 0.0 \| \| 0.3339 \| 27.2 \| 1360 \| 0.6434 \| 0.2732 \| 0.3252 \| 0.8375 \| nan \| 0.9120 \| 0.9427 \| 0.6126 \| 0.7717 \| 0.3484 \| 0.0 \| 0.4983 \| 0.5931 \| 0.0 \| 0.9441 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6112 \| 0.0 \| 0.0 \| 0.9134 \| 0.0 \| 0.3259 \| 0.2733 \| 0.0 \| 0.0 \| 0.0 \| 0.2995 \| 0.0 \| 0.0 \| 0.9217 \| 0.8908 \| 0.9681 \| 0.0 \| 0.0 \| 0.2298 \| 0.0 \| nan \| 0.7060 \| 0.8513 \| 0.5062 \| 0.7258 \| 0.2805 \| 0.0 \| 0.3963 \| 0.3985 \| 0.0 \| 0.8093 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4980 \| 0.0 \| 0.0 \| 0.6925 \| 0.0 \| 0.2695 \| 0.2074 \| 0.0 \| 0.0 \| 0.0 \| 0.2605 \| 0.0 \| 0.0 \| 0.8341 \| 0.7418 \| 0.9222 \| 0.0 \| 0.0 \| 0.1907 \| 0.0 \| \| 0.3599 \| 27.6 \| 1380 \| 0.6405 \| 0.2755 \| 0.3266 \| 0.8384 \| nan \| 0.9207 \| 0.9422 \| 0.6103 \| 0.7769 \| 0.3508 \| 0.0 \| 0.4741 \| 0.5936 \| 0.0 \| 0.9373 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6317 \| 0.0 \| 0.0 \| 0.9056 \| 0.0 \| 0.3968 \| 0.2528 \| 0.0 \| 0.0 \| 0.0 \| 0.3090 \| 0.0 \| 0.0 \| 0.9369 \| 0.8342 \| 0.9656 \| 0.0 \| 0.0 \| 0.2645 \| 0.0 \| nan \| 0.7140 \| 0.8470 \| 0.5370 \| 0.7255 \| 0.2798 \| 0.0 \| 0.3807 \| 0.3961 \| 0.0 \| 0.8127 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4994 \| 0.0 \| 0.0 \| 0.6990 \| 0.0 \| 0.3122 \| 0.1956 \| 0.0 \| 0.0 \| 0.0 \| 0.2654 \| 0.0 \| 0.0 \| 0.8335 \| 0.7405 \| 0.9227 \| 0.0 \| 0.0 \| 0.2059 \| 0.0 \| \| 0.4722 \| 28.0 \| 1400 \| 0.6423 \| 0.2776 \| 0.3307 \| 0.8391 \| nan \| 0.8828 \| 0.9548 \| 0.6152 \| 0.7934 \| 0.3135 \| 0.0 \| 0.4904 \| 0.5925 \| 0.0 \| 0.9444 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6486 \| 0.0 \| 0.0 \| 0.8909 \| 0.0 \| 0.4174 \| 0.3296 \| 0.0 \| 0.0 \| 0.0 \| 0.3117 \| 0.0 \| 0.0 \| 0.9236 \| 0.8846 \| 0.9647 \| 0.0 \| 0.0 \| 0.2867 \| 0.0 \| nan \| 0.7319 \| 0.8373 \| 0.5339 \| 0.7168 \| 0.2670 \| 0.0 \| 0.3887 \| 0.3930 \| 0.0 \| 0.8085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5058 \| 0.0 \| 0.0 \| 0.7043 \| 0.0 \| 0.3277 \| 0.2334 \| 0.0 \| 0.0 \| 0.0 \| 0.2652 \| 0.0 \| 0.0 \| 0.8358 \| 0.7486 \| 0.9243 \| 0.0 \| 0.0 \| 0.2164 \| 0.0 \| \| 0.3999 \| 28.4 \| 1420 \| 0.6573 \| 0.2758 \| 0.3279 \| 0.8344 \| nan \| 0.9074 \| 0.9564 \| 0.6044 \| 0.6972 \| 0.3170 \| 0.0 \| 0.4638 \| 0.5768 \| 0.0 \| 0.9393 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6698 \| 0.0 \| 0.0 \| 0.8872 \| 0.0 \| 0.4259 \| 0.3229 \| 0.0 \| 0.0 \| 0.0 \| 0.3066 \| 0.0 \| 0.0 \| 0.9183 \| 0.8809 \| 0.9685 \| 0.0 \| 0.0 \| 0.3048 \| 0.0 \| nan \| 0.7038 \| 0.8367 \| 0.5552 \| 0.6687 \| 0.2655 \| 0.0 \| 0.3707 \| 0.3920 \| 0.0 \| 0.8207 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5299 \| 0.0 \| 0.0 \| 0.7058 \| 0.0 \| 0.3154 \| 0.2230 \| 0.0 \| 0.0 \| 0.0 \| 0.2644 \| 0.0 \| 0.0 \| 0.8368 \| 0.7440 \| 0.9234 \| 0.0 \| 0.0 \| 0.2197 \| 0.0 \| \| 0.3251 \| 28.8 \| 1440 \| 0.6375 \| 0.2770 \| 0.3291 \| 0.8408 \| nan \| 0.9199 \| 0.9489 \| 0.6086 \| 0.7769 \| 0.3191 \| 0.0 \| 0.4958 \| 0.5862 \| 0.0 \| 0.9461 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6409 \| 0.0 \| 0.0 \| 0.8994 \| 0.0 \| 0.4118 \| 0.2941 \| 0.0 \| 0.0 \| 0.0 \| 0.3050 \| 0.0 \| 0.0 \| 0.9265 \| 0.8786 \| 0.9683 \| 0.0 \| 0.0 \| 0.2619 \| 0.0 \| nan \| 0.7236 \| 0.8482 \| 0.5358 \| 0.7290 \| 0.2666 \| 0.0 \| 0.3854 \| 0.3929 \| 0.0 \| 0.8090 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5036 \| 0.0 \| 0.0 \| 0.7042 \| 0.0 \| 0.3149 \| 0.2207 \| 0.0 \| 0.0 \| 0.0 \| 0.2649 \| 0.0 \| 0.0 \| 0.8387 \| 0.7519 \| 0.9235 \| 0.0 \| 0.0 \| 0.2062 \| 0.0 \| \| 0.3289 \| 29.2 \| 1460 \| 0.6556 \| 0.2768 \| 0.3296 \| 0.8362 \| nan \| 0.9273 \| 0.9485 \| 0.5861 \| 0.6879 \| 0.3439 \| 0.0 \| 0.4798 \| 0.6059 \| 0.0 \| 0.9416 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6749 \| 0.0 \| 0.0 \| 0.8971 \| 0.0 \| 0.4096 \| 0.3502 \| 0.0 \| 0.0 \| 0.0 \| 0.3222 \| 0.0 \| 0.0 \| 0.9308 \| 0.8716 \| 0.9684 \| 0.0 \| 0.0 \| 0.2596 \| 0.0 \| nan \| 0.6921 \| 0.8489 \| 0.5571 \| 0.6606 \| 0.2774 \| 0.0 \| 0.3801 \| 0.3953 \| 0.0 \| 0.8177 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5192 \| 0.0 \| 0.0 \| 0.7043 \| 0.0 \| 0.3281 \| 0.2382 \| 0.0 \| 0.0 \| 0.0 \| 0.2725 \| 0.0 \| 0.0 \| 0.8351 \| 0.7541 \| 0.9241 \| 0.0 \| 0.0 \| 0.2073 \| 0.0 \| \| 0.3227 \| 29.6 \| 1480 \| 0.6274 \| 0.2803 \| 0.3334 \| 0.8430 \| nan \| 0.9079 \| 0.9456 \| 0.6035 \| 0.8309 \| 0.3371 \| 0.0 \| 0.4808 \| 0.5824 \| 0.0 \| 0.9394 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6828 \| 0.0 \| 0.0 \| 0.8969 \| 0.0 \| 0.4624 \| 0.3205 \| 0.0 \| 0.0 \| 0.0 \| 0.3089 \| 0.0 \| 0.0 \| 0.9276 \| 0.8832 \| 0.9688 \| 0.0 \| 0.0 \| 0.2577 \| 0.0 \| nan \| 0.7452 \| 0.8459 \| 0.5544 \| 0.7339 \| 0.2753 \| 0.0 \| 0.3877 \| 0.3953 \| 0.0 \| 0.8192 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5186 \| 0.0 \| 0.0 \| 0.7039 \| 0.0 \| 0.3355 \| 0.2291 \| 0.0 \| 0.0 \| 0.0 \| 0.2654 \| 0.0 \| 0.0 \| 0.8382 \| 0.7490 \| 0.9239 \| 0.0 \| 0.0 \| 0.2088 \| 0.0 \| \| 0.4271 \| 30.0 \| 1500 \| 0.6412 \| 0.2769 \| 0.3306 \| 0.8382 \| nan \| 0.9016 \| 0.9544 \| 0.6153 \| 0.7497 \| 0.3307 \| 0.0 \| 0.4845 \| 0.5851 \| 0.0 \| 0.9434 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6663 \| 0.0 \| 0.0 \| 0.9019 \| 0.0 \| 0.4165 \| 0.3538 \| 0.0 \| 0.0 \| 0.0 \| 0.3151 \| 0.0 \| 0.0 \| 0.9151 \| 0.8806 \| 0.9701 \| 0.0 \| 0.0 \| 0.2557 \| 0.0 \| nan \| 0.7166 \| 0.8431 \| 0.5148 \| 0.7143 \| 0.2745 \| 0.0 \| 0.3858 \| 0.3937 \| 0.0 \| 0.8110 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5152 \| 0.0 \| 0.0 \| 0.7040 \| 0.0 \| 0.3288 \| 0.2387 \| 0.0 \| 0.0 \| 0.0 \| 0.2693 \| 0.0 \| 0.0 \| 0.8363 \| 0.7431 \| 0.9235 \| 0.0 \| 0.0 \| 0.2023 \| 0.0 \| \| 0.2996 \| 30.4 \| 1520 \| 0.6320 \| 0.2803 \| 0.3323 \| 0.8430 \| nan \| 0.9176 \| 0.9528 \| 0.5870 \| 0.8010 \| 0.3246 \| 0.0 \| 0.4829 \| 0.5975 \| 0.0 \| 0.9428 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6519 \| 0.0 \| 0.0 \| 0.8888 \| 0.0 \| 0.4324 \| 0.3396 \| 0.0 \| 0.0 \| 0.0 \| 0.3139 \| 0.0 \| 0.0 \| 0.9282 \| 0.8780 \| 0.9651 \| 0.0 \| 0.0 \| 0.2940 \| 0.0 \| nan \| 0.7387 \| 0.8443 \| 0.5328 \| 0.7418 \| 0.2720 \| 0.0 \| 0.3946 \| 0.3908 \| 0.0 \| 0.8167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5156 \| 0.0 \| 0.0 \| 0.7091 \| 0.0 \| 0.3351 \| 0.2379 \| 0.0 \| 0.0 \| 0.0 \| 0.2681 \| 0.0 \| 0.0 \| 0.8362 \| 0.7528 \| 0.9250 \| 0.0 \| 0.0 \| 0.2202 \| 0.0 \| \| 0.3392 \| 30.8 \| 1540 \| 0.6255 \| 0.2797 \| 0.3345 \| 0.8414 \| nan \| 0.8964 \| 0.9477 \| 0.6256 \| 0.7851 \| 0.3788 \| 0.0 \| 0.4961 \| 0.5916 \| 0.0 \| 0.9394 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6616 \| 0.0 \| 0.0 \| 0.8897 \| 0.0 \| 0.4526 \| 0.3530 \| 0.0 \| 0.0 \| 0.0 \| 0.3145 \| 0.0 \| 0.0 \| 0.9310 \| 0.8777 \| 0.9663 \| 0.0 \| 0.0 \| 0.2673 \| 0.0 \| nan \| 0.7268 \| 0.8503 \| 0.4849 \| 0.7355 \| 0.3015 \| 0.0 \| 0.3984 \| 0.3966 \| 0.0 \| 0.8222 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5235 \| 0.0 \| 0.0 \| 0.7054 \| 0.0 \| 0.3339 \| 0.2396 \| 0.0 \| 0.0 \| 0.0 \| 0.2685 \| 0.0 \| 0.0 \| 0.8372 \| 0.7534 \| 0.9251 \| 0.0 \| 0.0 \| 0.2059 \| 0.0 \| \| 0.2965 \| 31.2 \| 1560 \| 0.6367 \| 0.2789 \| 0.3312 \| 0.8415 \| nan \| 0.9269 \| 0.9523 \| 0.5961 \| 0.7622 \| 0.3118 \| 0.0 \| 0.4969 \| 0.6140 \| 0.0 \| 0.9472 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6352 \| 0.0 \| 0.0 \| 0.8980 \| 0.0 \| 0.4180 \| 0.3364 \| 0.0 \| 0.0 \| 0.0 \| 0.3069 \| 0.0 \| 0.0 \| 0.9211 \| 0.8850 \| 0.9695 \| 0.0 \| 0.0 \| 0.2831 \| 0.0 \| nan \| 0.7329 \| 0.8441 \| 0.5433 \| 0.7287 \| 0.2676 \| 0.0 \| 0.3809 \| 0.3910 \| 0.0 \| 0.8149 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5191 \| 0.0 \| 0.0 \| 0.7090 \| 0.0 \| 0.3280 \| 0.2362 \| 0.0 \| 0.0 \| 0.0 \| 0.2642 \| 0.0 \| 0.0 \| 0.8376 \| 0.7476 \| 0.9244 \| 0.0 \| 0.0 \| 0.2140 \| 0.0 \| \| 0.3831 \| 31.6 \| 1580 \| 0.6300 \| 0.2812 \| 0.3332 \| 0.8429 \| nan \| 0.9094 \| 0.9529 \| 0.5967 \| 0.7851 \| 0.3383 \| 0.0 \| 0.4985 \| 0.5956 \| 0.0 \| 0.9411 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6727 \| 0.0 \| 0.0 \| 0.9011 \| 0.0 \| 0.4275 \| 0.3208 \| 0.0 \| 0.0 \| 0.0 \| 0.3346 \| 0.0 \| 0.0 \| 0.9285 \| 0.8705 \| 0.9694 \| 0.0 \| 0.0 \| 0.2851 \| 0.0 \| nan \| 0.7337 \| 0.8445 \| 0.5445 \| 0.7378 \| 0.2787 \| 0.0 \| 0.3846 \| 0.3957 \| 0.0 \| 0.8222 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5248 \| 0.0 \| 0.0 \| 0.7076 \| 0.0 \| 0.3366 \| 0.2305 \| 0.0 \| 0.0 \| 0.0 \| 0.2797 \| 0.0 \| 0.0 \| 0.8416 \| 0.7545 \| 0.9260 \| 0.0 \| 0.0 \| 0.2181 \| 0.0 \| \| 0.3493 \| 32.0 \| 1600 \| 0.6407 \| 0.2785 \| 0.3356 \| 0.8400 \| nan \| 0.9012 \| 0.9423 \| 0.6217 \| 0.7873 \| 0.3749 \| 0.0 \| 0.4880 \| 0.5983 \| 0.0 \| 0.9465 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6982 \| 0.0 \| 0.0 \| 0.8913 \| 0.0 \| 0.4313 \| 0.3725 \| 0.0 \| 0.0 \| 0.0 \| 0.3320 \| 0.0 \| 0.0 \| 0.9243 \| 0.8959 \| 0.9695 \| 0.0 \| 0.0 \| 0.2344 \| 0.0 \| nan \| 0.7114 \| 0.8536 \| 0.5136 \| 0.7126 \| 0.2994 \| 0.0 \| 0.3924 \| 0.3971 \| 0.0 \| 0.8130 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5163 \| 0.0 \| 0.0 \| 0.7068 \| 0.0 \| 0.3303 \| 0.2404 \| 0.0 \| 0.0 \| 0.0 \| 0.2795 \| 0.0 \| 0.0 \| 0.8369 \| 0.7481 \| 0.9259 \| 0.0 \| 0.0 \| 0.1920 \| 0.0 \| \| 0.3436 \| 32.4 \| 1620 \| 0.6411 \| 0.2797 \| 0.3326 \| 0.8412 \| nan \| 0.9267 \| 0.9499 \| 0.5514 \| 0.7637 \| 0.3525 \| 0.0 \| 0.5014 \| 0.6383 \| 0.0 \| 0.9393 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6488 \| 0.0 \| 0.0 \| 0.8973 \| 0.0 \| 0.4156 \| 0.3836 \| 0.0 \| 0.0 \| 0.0 \| 0.3289 \| 0.0 \| 0.0 \| 0.9348 \| 0.8434 \| 0.9690 \| 0.0 \| 0.0 \| 0.2623 \| 0.0 \| nan \| 0.7220 \| 0.8468 \| 0.5165 \| 0.7270 \| 0.2840 \| 0.0 \| 0.3934 \| 0.3914 \| 0.0 \| 0.8211 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5100 \| 0.0 \| 0.0 \| 0.7055 \| 0.0 \| 0.3275 \| 0.2602 \| 0.0 \| 0.0 \| 0.0 \| 0.2775 \| 0.0 \| 0.0 \| 0.8389 \| 0.7518 \| 0.9259 \| 0.0 \| 0.0 \| 0.2099 \| 0.0 \| \| 0.3519 \| 32.8 \| 1640 \| 0.6279 \| 0.2803 \| 0.3326 \| 0.8424 \| nan \| 0.9006 \| 0.9528 \| 0.5944 \| 0.8234 \| 0.3076 \| 0.0 \| 0.4970 \| 0.5845 \| 0.0 \| 0.9431 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6655 \| 0.0 \| 0.0 \| 0.8913 \| 0.0 \| 0.4559 \| 0.3274 \| 0.0 \| 0.0 \| 0.0 \| 0.3196 \| 0.0 \| 0.0 \| 0.9211 \| 0.8900 \| 0.9693 \| 0.0 \| 0.0 \| 0.2651 \| 0.0 \| nan \| 0.7445 \| 0.8418 \| 0.5386 \| 0.7479 \| 0.2611 \| 0.0 \| 0.3900 \| 0.4008 \| 0.0 \| 0.8198 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5284 \| 0.0 \| 0.0 \| 0.7091 \| 0.0 \| 0.3360 \| 0.2321 \| 0.0 \| 0.0 \| 0.0 \| 0.2734 \| 0.0 \| 0.0 \| 0.8349 \| 0.7426 \| 0.9262 \| 0.0 \| 0.0 \| 0.2026 \| 0.0 \| \| 0.4359 \| 33.2 \| 1660 \| 0.6426 \| 0.2794 \| 0.3331 \| 0.8414 \| nan \| 0.9304 \| 0.9499 \| 0.6014 \| 0.7611 \| 0.3188 \| 0.0 \| 0.4850 \| 0.6366 \| 0.0 \| 0.9443 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6564 \| 0.0 \| 0.0 \| 0.8743 \| 0.0 \| 0.4532 \| 0.3225 \| 0.0 \| 0.0 \| 0.0 \| 0.3332 \| 0.0 \| 0.0 \| 0.9382 \| 0.8668 \| 0.9680 \| 0.0 \| 0.0 \| 0.2871 \| 0.0 \| nan \| 0.7254 \| 0.8473 \| 0.5447 \| 0.7218 \| 0.2699 \| 0.0 \| 0.3871 \| 0.3876 \| 0.0 \| 0.8191 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5169 \| 0.0 \| 0.0 \| 0.7093 \| 0.0 \| 0.3314 \| 0.2280 \| 0.0 \| 0.0 \| 0.0 \| 0.2796 \| 0.0 \| 0.0 \| 0.8370 \| 0.7538 \| 0.9272 \| 0.0 \| 0.0 \| 0.2141 \| 0.0 \| \| 0.2864 \| 33.6 \| 1680 \| 0.6615 \| 0.2758 \| 0.3289 \| 0.8372 \| nan \| 0.9209 \| 0.9509 \| 0.6063 \| 0.6941 \| 0.3526 \| 0.0 \| 0.5022 \| 0.6105 \| 0.0 \| 0.9401 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6613 \| 0.0 \| 0.0 \| 0.9223 \| 0.0 \| 0.3529 \| 0.3426 \| 0.0 \| 0.0 \| 0.0 \| 0.3352 \| 0.0 \| 0.0 \| 0.9148 \| 0.8885 \| 0.9698 \| 0.0 \| 0.0 \| 0.2183 \| 0.0 \| nan \| 0.7027 \| 0.8478 \| 0.5329 \| 0.6720 \| 0.2889 \| 0.0 \| 0.3945 \| 0.3956 \| 0.0 \| 0.8200 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5033 \| 0.0 \| 0.0 \| 0.6960 \| 0.0 \| 0.2995 \| 0.2472 \| 0.0 \| 0.0 \| 0.0 \| 0.2831 \| 0.0 \| 0.0 \| 0.8392 \| 0.7452 \| 0.9262 \| 0.0 \| 0.0 \| 0.1834 \| 0.0 \| \| 0.2795 \| 34.0 \| 1700 \| 0.6369 \| 0.2810 \| 0.3350 \| 0.8426 \| nan \| 0.9120 \| 0.9520 \| 0.6168 \| 0.7708 \| 0.3537 \| 0.0 \| 0.4978 \| 0.5937 \| 0.0 \| 0.9442 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6760 \| 0.0 \| 0.0 \| 0.8977 \| 0.0 \| 0.4292 \| 0.3539 \| 0.0 \| 0.0 \| 0.0 \| 0.3445 \| 0.0 \| 0.0 \| 0.9238 \| 0.8757 \| 0.9703 \| 0.0 \| 0.0 \| 0.2783 \| 0.0 \| nan \| 0.7260 \| 0.8481 \| 0.5226 \| 0.7362 \| 0.2894 \| 0.0 \| 0.3964 \| 0.3960 \| 0.0 \| 0.8202 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5116 \| 0.0 \| 0.0 \| 0.7095 \| 0.0 \| 0.3331 \| 0.2424 \| 0.0 \| 0.0 \| 0.0 \| 0.2883 \| 0.0 \| 0.0 \| 0.8397 \| 0.7539 \| 0.9264 \| 0.0 \| 0.0 \| 0.2139 \| 0.0 \| \| 0.2809 \| 34.4 \| 1720 \| 0.6342 \| 0.2803 \| 0.3341 \| 0.8432 \| nan \| 0.9144 \| 0.9513 \| 0.5907 \| 0.7914 \| 0.3386 \| 0.0 \| 0.5002 \| 0.6073 \| 0.0 \| 0.9444 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6870 \| 0.0 \| 0.0 \| 0.8900 \| 0.0 \| 0.4229 \| 0.3337 \| 0.0 \| 0.0 \| 0.0 \| 0.3399 \| 0.0 \| 0.0 \| 0.9301 \| 0.8865 \| 0.9689 \| 0.0 \| 0.0 \| 0.2613 \| 0.0 \| nan \| 0.7315 \| 0.8487 \| 0.5274 \| 0.7364 \| 0.2840 \| 0.0 \| 0.3930 \| 0.3953 \| 0.0 \| 0.8166 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5111 \| 0.0 \| 0.0 \| 0.7087 \| 0.0 \| 0.3336 \| 0.2379 \| 0.0 \| 0.0 \| 0.0 \| 0.2834 \| 0.0 \| 0.0 \| 0.8369 \| 0.7519 \| 0.9277 \| 0.0 \| 0.0 \| 0.2047 \| 0.0 \| \| 0.2926 \| 34.8 \| 1740 \| 0.6361 \| 0.2805 \| 0.3333 \| 0.8417 \| nan \| 0.9156 \| 0.9475 \| 0.6039 \| 0.7719 \| 0.3567 \| 0.0 \| 0.5087 \| 0.5719 \| 0.0 \| 0.9427 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6924 \| 0.0 \| 0.0 \| 0.9058 \| 0.0 \| 0.3941 \| 0.3460 \| 0.0 \| 0.0 \| 0.0 \| 0.3368 \| 0.0 \| 0.0 \| 0.9269 \| 0.8690 \| 0.9715 \| 0.0 \| 0.0 \| 0.2698 \| 0.0 \| nan \| 0.7200 \| 0.8492 \| 0.5450 \| 0.7109 \| 0.2920 \| 0.0 \| 0.3954 \| 0.4026 \| 0.0 \| 0.8197 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5170 \| 0.0 \| 0.0 \| 0.7056 \| 0.0 \| 0.3175 \| 0.2451 \| 0.0 \| 0.0 \| 0.0 \| 0.2846 \| 0.0 \| 0.0 \| 0.8415 \| 0.7558 \| 0.9275 \| 0.0 \| 0.0 \| 0.2081 \| 0.0 \| \| 0.3475 \| 35.2 \| 1760 \| 0.6425 \| 0.2810 \| 0.3359 \| 0.8420 \| nan \| 0.9072 \| 0.9492 \| 0.6142 \| 0.7957 \| 0.3298 \| 0.0 \| 0.4928 \| 0.5961 \| 0.0 \| 0.9413 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7089 \| 0.0 \| 0.0 \| 0.8849 \| 0.0 \| 0.4386 \| 0.3497 \| 0.0 \| 0.0 \| 0.0 \| 0.3410 \| 0.0 \| 0.0 \| 0.9280 \| 0.8849 \| 0.9715 \| 0.0 \| 0.0 \| 0.2870 \| 0.0 \| nan \| 0.7278 \| 0.8464 \| 0.5424 \| 0.7240 \| 0.2750 \| 0.0 \| 0.3918 \| 0.3997 \| 0.0 \| 0.8241 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5166 \| 0.0 \| 0.0 \| 0.7098 \| 0.0 \| 0.3390 \| 0.2400 \| 0.0 \| 0.0 \| 0.0 \| 0.2865 \| 0.0 \| 0.0 \| 0.8361 \| 0.7514 \| 0.9274 \| 0.0 \| 0.0 \| 0.2172 \| 0.0 \| \| 0.3079 \| 35.6 \| 1780 \| 0.6318 \| 0.2815 \| 0.3353 \| 0.8421 \| nan \| 0.8986 \| 0.9491 \| 0.6034 \| 0.7912 \| 0.3674 \| 0.0 \| 0.5018 \| 0.5996 \| 0.0 \| 0.9460 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6846 \| 0.0 \| 0.0 \| 0.8921 \| 0.0 \| 0.4470 \| 0.3286 \| 0.0 \| 0.0 \| 0.0 \| 0.3315 \| 0.0 \| 0.0 \| 0.9285 \| 0.8754 \| 0.9703 \| 0.0 \| 0.0 \| 0.2843 \| 0.0 \| nan \| 0.7219 \| 0.8490 \| 0.5500 \| 0.7212 \| 0.2938 \| 0.0 \| 0.3942 \| 0.3987 \| 0.0 \| 0.8225 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5247 \| 0.0 \| 0.0 \| 0.7107 \| 0.0 \| 0.3360 \| 0.2305 \| 0.0 \| 0.0 \| 0.0 \| 0.2817 \| 0.0 \| 0.0 \| 0.8399 \| 0.7553 \| 0.9282 \| 0.0 \| 0.0 \| 0.2137 \| 0.0 \| \| 0.3387 \| 36.0 \| 1800 \| 0.6398 \| 0.2806 \| 0.3351 \| 0.8414 \| nan \| 0.9147 \| 0.9487 \| 0.6065 \| 0.7587 \| 0.3486 \| 0.0 \| 0.5085 \| 0.6111 \| 0.0 \| 0.9467 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6779 \| 0.0 \| 0.0 \| 0.8984 \| 0.0 \| 0.3925 \| 0.3676 \| 0.0 \| 0.0 \| 0.0 \| 0.3387 \| 0.0 \| 0.0 \| 0.9234 \| 0.8892 \| 0.9718 \| 0.0 \| 0.0 \| 0.2900 \| 0.0 \| nan \| 0.7073 \| 0.8535 \| 0.5247 \| 0.7137 \| 0.2883 \| 0.0 \| 0.4003 \| 0.4007 \| 0.0 \| 0.8207 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5182 \| 0.0 \| 0.0 \| 0.7085 \| 0.0 \| 0.3295 \| 0.2525 \| 0.0 \| 0.0 \| 0.0 \| 0.2841 \| 0.0 \| 0.0 \| 0.8374 \| 0.7504 \| 0.9273 \| 0.0 \| 0.0 \| 0.2232 \| 0.0 \| \| 0.353 \| 36.4 \| 1820 \| 0.6366 \| 0.2812 \| 0.3340 \| 0.8429 \| nan \| 0.8990 \| 0.9528 \| 0.5851 \| 0.7833 \| 0.3531 \| 0.0 \| 0.5036 \| 0.6333 \| 0.0 \| 0.9468 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6623 \| 0.0 \| 0.0 \| 0.9047 \| 0.0 \| 0.3893 \| 0.3411 \| 0.0 \| 0.0 \| 0.0 \| 0.3444 \| 0.0 \| 0.0 \| 0.9330 \| 0.8748 \| 0.9680 \| 0.0 \| 0.0 \| 0.2812 \| 0.0 \| nan \| 0.7231 \| 0.8457 \| 0.5366 \| 0.7293 \| 0.2918 \| 0.0 \| 0.3941 \| 0.3964 \| 0.0 \| 0.8177 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5151 \| 0.0 \| 0.0 \| 0.7096 \| 0.0 \| 0.3197 \| 0.2451 \| 0.0 \| 0.0 \| 0.0 \| 0.2880 \| 0.0 \| 0.0 \| 0.8415 \| 0.7584 \| 0.9291 \| 0.0 \| 0.0 \| 0.2203 \| 0.0 \| \| 0.3651 \| 36.8 \| 1840 \| 0.6441 \| 0.2809 \| 0.3325 \| 0.8417 \| nan \| 0.9112 \| 0.9519 \| 0.6114 \| 0.7513 \| 0.3439 \| 0.0 \| 0.5112 \| 0.5639 \| 0.0 \| 0.9406 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6871 \| 0.0 \| 0.0 \| 0.9136 \| 0.0 \| 0.4066 \| 0.3437 \| 0.0 \| 0.0 \| 0.0 \| 0.3372 \| 0.0 \| 0.0 \| 0.9259 \| 0.8716 \| 0.9707 \| 0.0 \| 0.0 \| 0.2644 \| 0.0 \| nan \| 0.7087 \| 0.8503 \| 0.5294 \| 0.7131 \| 0.2885 \| 0.0 \| 0.4001 \| 0.4039 \| 0.0 \| 0.8261 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5293 \| 0.0 \| 0.0 \| 0.7053 \| 0.0 \| 0.3313 \| 0.2438 \| 0.0 \| 0.0 \| 0.0 \| 0.2856 \| 0.0 \| 0.0 \| 0.8430 \| 0.7564 \| 0.9282 \| 0.0 \| 0.0 \| 0.2072 \| 0.0 \| \| 0.3073 \| 37.2 \| 1860 \| 0.6468 \| 0.2810 \| 0.3346 \| 0.8406 \| nan \| 0.9223 \| 0.9433 \| 0.6039 \| 0.7368 \| 0.3718 \| 0.0 \| 0.5138 \| 0.6017 \| 0.0 \| 0.9489 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6509 \| 0.0 \| 0.0 \| 0.8915 \| 0.0 \| 0.4196 \| 0.3052 \| 0.0 \| 0.0 \| 0.0 \| 0.3519 \| 0.0 \| 0.0 \| 0.9278 \| 0.8895 \| 0.9719 \| 0.0 \| 0.0 \| 0.3263 \| 0.0 \| nan \| 0.7052 \| 0.8516 \| 0.5366 \| 0.7012 \| 0.2969 \| 0.0 \| 0.4010 \| 0.4007 \| 0.0 \| 0.8187 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5200 \| 0.0 \| 0.0 \| 0.7125 \| 0.0 \| 0.3332 \| 0.2251 \| 0.0 \| 0.0 \| 0.0 \| 0.2916 \| 0.0 \| 0.0 \| 0.8387 \| 0.7544 \| 0.9276 \| 0.0 \| 0.0 \| 0.2398 \| 0.0 \| \| 0.4331 \| 37.6 \| 1880 \| 0.6398 \| 0.2816 \| 0.3362 \| 0.8424 \| nan \| 0.9012 \| 0.9534 \| 0.6108 \| 0.7906 \| 0.3207 \| 0.0 \| 0.5132 \| 0.5980 \| 0.0 \| 0.9424 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7046 \| 0.0 \| 0.0 \| 0.8988 \| 0.0 \| 0.4445 \| 0.3527 \| 0.0 \| 0.0 \| 0.0 \| 0.3542 \| 0.0 \| 0.0 \| 0.9206 \| 0.8769 \| 0.9700 \| 0.0 \| 0.0 \| 0.2780 \| 0.0 \| nan \| 0.7220 \| 0.8473 \| 0.5391 \| 0.7306 \| 0.2707 \| 0.0 \| 0.3963 \| 0.4018 \| 0.0 \| 0.8242 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5264 \| 0.0 \| 0.0 \| 0.7122 \| 0.0 \| 0.3363 \| 0.2354 \| 0.0 \| 0.0 \| 0.0 \| 0.2949 \| 0.0 \| 0.0 \| 0.8415 \| 0.7557 \| 0.9286 \| 0.0 \| 0.0 \| 0.2124 \| 0.0 \| \| 0.3336 \| 38.0 \| 1900 \| 0.6402 \| 0.2813 \| 0.3366 \| 0.8419 \| nan \| 0.9057 \| 0.9474 \| 0.6130 \| 0.7764 \| 0.3629 \| 0.0 \| 0.4953 \| 0.6091 \| 0.0 \| 0.9453 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7066 \| 0.0 \| 0.0 \| 0.8942 \| 0.0 \| 0.4472 \| 0.3294 \| 0.0 \| 0.0 \| 0.0 \| 0.3447 \| 0.0 \| 0.0 \| 0.9256 \| 0.8864 \| 0.9735 \| 0.0 \| 0.0 \| 0.2811 \| 0.0 \| nan \| 0.7148 \| 0.8503 \| 0.5302 \| 0.7213 \| 0.2942 \| 0.0 \| 0.3992 \| 0.4010 \| 0.0 \| 0.8215 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5246 \| 0.0 \| 0.0 \| 0.7111 \| 0.0 \| 0.3400 \| 0.2281 \| 0.0 \| 0.0 \| 0.0 \| 0.2888 \| 0.0 \| 0.0 \| 0.8405 \| 0.7575 \| 0.9275 \| 0.0 \| 0.0 \| 0.2128 \| 0.0 \| \| 0.3628 \| 38.4 \| 1920 \| 0.6451 \| 0.2803 \| 0.3339 \| 0.8419 \| nan \| 0.9157 \| 0.9506 \| 0.6013 \| 0.7584 \| 0.3272 \| 0.0 \| 0.5187 \| 0.6031 \| 0.0 \| 0.9455 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6922 \| 0.0 \| 0.0 \| 0.9004 \| 0.0 \| 0.4047 \| 0.3112 \| 0.0 \| 0.0 \| 0.0 \| 0.3390 \| 0.0 \| 0.0 \| 0.9255 \| 0.8911 \| 0.9729 \| 0.0 \| 0.0000 \| 0.2961 \| 0.0 \| nan \| 0.7134 \| 0.8508 \| 0.5294 \| 0.7156 \| 0.2770 \| 0.0 \| 0.4017 \| 0.4011 \| 0.0 \| 0.8237 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5241 \| 0.0 \| 0.0 \| 0.7121 \| 0.0 \| 0.3262 \| 0.2266 \| 0.0 \| 0.0 \| 0.0 \| 0.2849 \| 0.0 \| 0.0 \| 0.8403 \| 0.7520 \| 0.9276 \| 0.0 \| 0.0000 \| 0.2235 \| 0.0 \| \| 0.3378 \| 38.8 \| 1940 \| 0.6329 \| 0.2818 \| 0.3348 \| 0.8434 \| nan \| 0.9127 \| 0.9465 \| 0.5858 \| 0.7964 \| 0.3750 \| 0.0 \| 0.5024 \| 0.6098 \| 0.0 \| 0.9438 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6808 \| 0.0 \| 0.0 \| 0.8976 \| 0.0 \| 0.4174 \| 0.3276 \| 0.0 \| 0.0 \| 0.0 \| 0.3451 \| 0.0 \| 0.0 \| 0.9327 \| 0.8730 \| 0.9706 \| 0.0 \| 0.0005 \| 0.2661 \| 0.0 \| nan \| 0.7267 \| 0.8480 \| 0.5227 \| 0.7435 \| 0.2949 \| 0.0 \| 0.4038 \| 0.4030 \| 0.0 \| 0.8264 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5204 \| 0.0 \| 0.0 \| 0.7092 \| 0.0 \| 0.3240 \| 0.2280 \| 0.0 \| 0.0 \| 0.0 \| 0.2899 \| 0.0 \| 0.0 \| 0.8409 \| 0.7582 \| 0.9292 \| 0.0 \| 0.0005 \| 0.2104 \| 0.0 \| \| 0.3523 \| 39.2 \| 1960 \| 0.6349 \| 0.2824 \| 0.3353 \| 0.8436 \| nan \| 0.9112 \| 0.9520 \| 0.5920 \| 0.7852 \| 0.3394 \| 0.0 \| 0.5104 \| 0.6062 \| 0.0 \| 0.9452 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6808 \| 0.0 \| 0.0 \| 0.8983 \| 0.0 \| 0.4215 \| 0.3371 \| 0.0 \| 0.0 \| 0.0 \| 0.3531 \| 0.0 \| 0.0 \| 0.9274 \| 0.8815 \| 0.9701 \| 0.0 \| 0.0000 \| 0.2872 \| 0.0 \| nan \| 0.7310 \| 0.8457 \| 0.5393 \| 0.7385 \| 0.2799 \| 0.0 \| 0.3992 \| 0.4024 \| 0.0 \| 0.8248 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5245 \| 0.0 \| 0.0 \| 0.7115 \| 0.0 \| 0.3345 \| 0.2331 \| 0.0 \| 0.0 \| 0.0 \| 0.2944 \| 0.0 \| 0.0 \| 0.8416 \| 0.7565 \| 0.9298 \| 0.0 \| 0.0000 \| 0.2159 \| 0.0 \| \| 0.3157 \| 39.6 \| 1980 \| 0.6250 \| 0.2842 \| 0.3374 \| 0.8460 \| nan \| 0.9196 \| 0.9467 \| 0.5922 \| 0.8099 \| 0.3500 \| 0.0 \| 0.5136 \| 0.6095 \| 0.0 \| 0.9401 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6812 \| 0.0 \| 0.0 \| 0.8994 \| 0.0 \| 0.4322 \| 0.3447 \| 0.0 \| 0.0 \| 0.0 \| 0.3562 \| 0.0 \| 0.0 \| 0.9348 \| 0.8775 \| 0.9712 \| 0.0 \| 0.0002 \| 0.2918 \| 0.0 \| nan \| 0.7379 \| 0.8502 \| 0.5435 \| 0.7448 \| 0.2894 \| 0.0 \| 0.4010 \| 0.4024 \| 0.0 \| 0.8284 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5241 \| 0.0 \| 0.0 \| 0.7128 \| 0.0 \| 0.3439 \| 0.2390 \| 0.0 \| 0.0 \| 0.0 \| 0.2957 \| 0.0 \| 0.0 \| 0.8413 \| 0.7601 \| 0.9295 \| 0.0 \| 0.0001 \| 0.2202 \| 0.0 \| \| 0.3091 \| 40.0 \| 2000 \| 0.6428 \| 0.2819 \| 0.3352 \| 0.8439 \| nan \| 0.9164 \| 0.9545 \| 0.5811 \| 0.7845 \| 0.3218 \| 0.0 \| 0.5088 \| 0.6127 \| 0.0 \| 0.9482 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6690 \| 0.0 \| 0.0 \| 0.9004 \| 0.0 \| 0.4326 \| 0.3583 \| 0.0 \| 0.0 \| 0.0 \| 0.3424 \| 0.0 \| 0.0 \| 0.9202 \| 0.8846 \| 0.9727 \| 0.0 \| 0.0004 \| 0.2870 \| 0.0 \| nan \| 0.7315 \| 0.8476 \| 0.5387 \| 0.7317 \| 0.2720 \| 0.0 \| 0.3982 \| 0.3976 \| 0.0 \| 0.8193 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5167 \| 0.0 \| 0.0 \| 0.7140 \| 0.0 \| 0.3426 \| 0.2420 \| 0.0 \| 0.0 \| 0.0 \| 0.2883 \| 0.0 \| 0.0 \| 0.8415 \| 0.7529 \| 0.9287 \| 0.0 \| 0.0004 \| 0.2197 \| 0.0 \| \| 0.3324 \| 40.4 \| 2020 \| 0.6305 \| 0.2832 \| 0.3382 \| 0.8453 \| nan \| 0.9081 \| 0.9501 \| 0.6112 \| 0.8081 \| 0.3387 \| 0.0 \| 0.5193 \| 0.6078 \| 0.0 \| 0.9427 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6990 \| 0.0 \| 0.0 \| 0.9021 \| 0.0 \| 0.4415 \| 0.3691 \| 0.0 \| 0.0 \| 0.0 \| 0.3462 \| 0.0 \| 0.0 \| 0.9252 \| 0.8865 \| 0.9728 \| 0.0 \| 0.0017 \| 0.2685 \| 0.0 \| nan \| 0.7342 \| 0.8523 \| 0.5291 \| 0.7446 \| 0.2823 \| 0.0 \| 0.4023 \| 0.4058 \| 0.0 \| 0.8258 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5201 \| 0.0 \| 0.0 \| 0.7108 \| 0.0 \| 0.3504 \| 0.2445 \| 0.0 \| 0.0 \| 0.0 \| 0.2902 \| 0.0 \| 0.0 \| 0.8413 \| 0.7568 \| 0.9290 \| 0.0 \| 0.0017 \| 0.2092 \| 0.0 \| \| 0.446 \| 40.8 \| 2040 \| 0.6335 \| 0.2823 \| 0.3354 \| 0.8436 \| nan \| 0.9158 \| 0.9466 \| 0.6004 \| 0.7753 \| 0.3634 \| 0.0 \| 0.5087 \| 0.6236 \| 0.0 \| 0.9416 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6766 \| 0.0 \| 0.0 \| 0.9036 \| 0.0 \| 0.3974 \| 0.3363 \| 0.0 \| 0.0 \| 0.0 \| 0.3486 \| 0.0 \| 0.0 \| 0.9336 \| 0.8864 \| 0.9729 \| 0.0 \| 0.0003 \| 0.2717 \| 0.0 \| nan \| 0.7228 \| 0.8505 \| 0.5378 \| 0.7275 \| 0.2928 \| 0.0 \| 0.3973 \| 0.4040 \| 0.0 \| 0.8271 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5219 \| 0.0 \| 0.0 \| 0.7113 \| 0.0 \| 0.3299 \| 0.2440 \| 0.0 \| 0.0 \| 0.0 \| 0.2917 \| 0.0 \| 0.0 \| 0.8392 \| 0.7599 \| 0.9290 \| 0.0 \| 0.0003 \| 0.2106 \| 0.0 \| \| 0.2991 \| 41.2 \| 2060 \| 0.6400 \| 0.2819 \| 0.3346 \| 0.8430 \| nan \| 0.9021 \| 0.9569 \| 0.6020 \| 0.7808 \| 0.3141 \| 0.0 \| 0.5107 \| 0.6066 \| 0.0 \| 0.9434 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6738 \| 0.0 \| 0.0 \| 0.8978 \| 0.0 \| 0.4379 \| 0.3122 \| 0.0 \| 0.0 \| 0.0 \| 0.3561 \| 0.0 \| 0.0 \| 0.9229 \| 0.8815 \| 0.9712 \| 0.0 \| 0.0002 \| 0.3062 \| 0.0 \| nan \| 0.7281 \| 0.8460 \| 0.5370 \| 0.7257 \| 0.2681 \| 0.0 \| 0.3941 \| 0.4008 \| 0.0 \| 0.8270 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5334 \| 0.0 \| 0.0 \| 0.7140 \| 0.0 \| 0.3370 \| 0.2272 \| 0.0 \| 0.0 \| 0.0 \| 0.2963 \| 0.0 \| 0.0 \| 0.8422 \| 0.7558 \| 0.9299 \| 0.0 \| 0.0002 \| 0.2222 \| 0.0 \| \| 0.2577 \| 41.6 \| 2080 \| 0.6273 \| 0.2827 \| 0.3367 \| 0.8433 \| nan \| 0.9083 \| 0.9458 \| 0.6065 \| 0.7727 \| 0.3782 \| 0.0 \| 0.5244 \| 0.5984 \| 0.0 \| 0.9455 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6865 \| 0.0 \| 0.0 \| 0.9076 \| 0.0 \| 0.4176 \| 0.3493 \| 0.0 \| 0.0 \| 0.0 \| 0.3545 \| 0.0 \| 0.0 \| 0.9272 \| 0.8876 \| 0.9708 \| 0.0 \| 0.0003 \| 0.2670 \| 0.0 \| nan \| 0.7190 \| 0.8537 \| 0.5356 \| 0.7125 \| 0.3026 \| 0.0 \| 0.4038 \| 0.4057 \| 0.0 \| 0.8259 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5295 \| 0.0 \| 0.0 \| 0.7117 \| 0.0 \| 0.3325 \| 0.2436 \| 0.0 \| 0.0 \| 0.0 \| 0.2964 \| 0.0 \| 0.0 \| 0.8426 \| 0.7589 \| 0.9302 \| 0.0 \| 0.0003 \| 0.2075 \| 0.0 \| \| 0.2437 \| 42.0 \| 2100 \| 0.6321 \| 0.2827 \| 0.3357 \| 0.8439 \| nan \| 0.9057 \| 0.9521 \| 0.6023 \| 0.7836 \| 0.3366 \| 0.0 \| 0.5205 \| 0.6125 \| 0.0 \| 0.9438 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6864 \| 0.0 \| 0.0 \| 0.9087 \| 0.0 \| 0.4109 \| 0.3239 \| 0.0 \| 0.0 \| 0.0 \| 0.3558 \| 0.0 \| 0.0 \| 0.9261 \| 0.8823 \| 0.9705 \| 0.0 \| 0.0011 \| 0.2920 \| 0.0 \| nan \| 0.7288 \| 0.8478 \| 0.5416 \| 0.7320 \| 0.2805 \| 0.0 \| 0.3960 \| 0.4063 \| 0.0 \| 0.8286 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5299 \| 0.0 \| 0.0 \| 0.7129 \| 0.0 \| 0.3234 \| 0.2309 \| 0.0 \| 0.0 \| 0.0 \| 0.2972 \| 0.0 \| 0.0 \| 0.8438 \| 0.7595 \| 0.9304 \| 0.0 \| 0.0011 \| 0.2199 \| 0.0 \| \| 0.3548 \| 42.4 \| 2120 \| 0.6317 \| 0.2831 \| 0.3370 \| 0.8445 \| nan \| 0.9133 \| 0.9517 \| 0.6028 \| 0.7868 \| 0.3391 \| 0.0 \| 0.5153 \| 0.5879 \| 0.0 \| 0.9435 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7178 \| 0.0 \| 0.0 \| 0.9042 \| 0.0 \| 0.3861 \| 0.3665 \| 0.0 \| 0.0 \| 0.0 \| 0.3643 \| 0.0 \| 0.0 \| 0.9244 \| 0.8901 \| 0.9723 \| 0.0 \| 0.0010 \| 0.2925 \| 0.0 \| nan \| 0.7290 \| 0.8519 \| 0.5360 \| 0.7372 \| 0.2841 \| 0.0 \| 0.3997 \| 0.4093 \| 0.0 \| 0.8285 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5271 \| 0.0 \| 0.0 \| 0.7117 \| 0.0 \| 0.3179 \| 0.2480 \| 0.0 \| 0.0 \| 0.0 \| 0.3012 \| 0.0 \| 0.0 \| 0.8393 \| 0.7536 \| 0.9296 \| 0.0 \| 0.0010 \| 0.2212 \| 0.0 \| \| 0.2519 \| 42.8 \| 2140 \| 0.6322 \| 0.2827 \| 0.3354 \| 0.8445 \| nan \| 0.9146 \| 0.9501 \| 0.6036 \| 0.7848 \| 0.3492 \| 0.0 \| 0.5133 \| 0.6009 \| 0.0 \| 0.9451 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6921 \| 0.0 \| 0.0 \| 0.9111 \| 0.0 \| 0.3731 \| 0.3440 \| 0.0 \| 0.0 \| 0.0 \| 0.3634 \| 0.0 \| 0.0 \| 0.9296 \| 0.8763 \| 0.9724 \| 0.0 \| 0.0010 \| 0.2798 \| 0.0 \| nan \| 0.7241 \| 0.8515 \| 0.5364 \| 0.7345 \| 0.2894 \| 0.0 \| 0.4034 \| 0.4073 \| 0.0 \| 0.8253 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5263 \| 0.0 \| 0.0 \| 0.7099 \| 0.0 \| 0.3078 \| 0.2443 \| 0.0 \| 0.0 \| 0.0 \| 0.3013 \| 0.0 \| 0.0 \| 0.8437 \| 0.7608 \| 0.9298 \| 0.0 \| 0.0010 \| 0.2159 \| 0.0 \| \| 0.275 \| 43.2 \| 2160 \| 0.6368 \| 0.2833 \| 0.3374 \| 0.8434 \| nan \| 0.9058 \| 0.9532 \| 0.6054 \| 0.7740 \| 0.3447 \| 0.0 \| 0.5067 \| 0.5989 \| 0.0 \| 0.9488 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7074 \| 0.0 \| 0.0 \| 0.9007 \| 0.0 \| 0.4272 \| 0.3609 \| 0.0 \| 0.0 \| 0.0 \| 0.3579 \| 0.0 \| 0.0 \| 0.9222 \| 0.8823 \| 0.9725 \| 0.0 \| 0.0051 \| 0.2991 \| 0.0 \| nan \| 0.7228 \| 0.8475 \| 0.5442 \| 0.7262 \| 0.2855 \| 0.0 \| 0.3968 \| 0.4046 \| 0.0 \| 0.8230 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5262 \| 0.0 \| 0.0 \| 0.7152 \| 0.0 \| 0.3375 \| 0.2463 \| 0.0 \| 0.0 \| 0.0 \| 0.2984 \| 0.0 \| 0.0 \| 0.8432 \| 0.7559 \| 0.9298 \| 0.0 \| 0.0051 \| 0.2229 \| 0.0 \| \| 0.357 \| 43.6 \| 2180 \| 0.6373 \| 0.2837 \| 0.3380 \| 0.8440 \| nan \| 0.9100 \| 0.9519 \| 0.5992 \| 0.7804 \| 0.3476 \| 0.0 \| 0.5014 \| 0.6046 \| 0.0 \| 0.9414 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7171 \| 0.0 \| 0.0 \| 0.8995 \| 0.0 \| 0.4276 \| 0.3742 \| 0.0 \| 0.0 \| 0.0 \| 0.3613 \| 0.0 \| 0.0 \| 0.9259 \| 0.8901 \| 0.9719 \| 0.0 \| 0.0077 \| 0.2811 \| 0.0 \| nan \| 0.7280 \| 0.8485 \| 0.5382 \| 0.7278 \| 0.2877 \| 0.0 \| 0.3975 \| 0.4056 \| 0.0 \| 0.8300 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5271 \| 0.0 \| 0.0 \| 0.7146 \| 0.0 \| 0.3410 \| 0.2515 \| 0.0 \| 0.0 \| 0.0 \| 0.2997 \| 0.0 \| 0.0 \| 0.8392 \| 0.7543 \| 0.9303 \| 0.0 \| 0.0076 \| 0.2168 \| 0.0 \| \| 0.2715 \| 44.0 \| 2200 \| 0.6300 \| 0.2841 \| 0.3390 \| 0.8446 \| nan \| 0.9019 \| 0.9507 \| 0.6033 \| 0.7953 \| 0.3516 \| 0.0 \| 0.5189 \| 0.6222 \| 0.0 \| 0.9445 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7024 \| 0.0 \| 0.0 \| 0.8993 \| 0.0 \| 0.4278 \| 0.3749 \| 0.0 \| 0.0 \| 0.0 \| 0.3613 \| 0.0 \| 0.0 \| 0.9300 \| 0.8815 \| 0.9718 \| 0.0 \| 0.0074 \| 0.2824 \| 0.0 \| nan \| 0.7301 \| 0.8489 \| 0.5301 \| 0.7339 \| 0.2892 \| 0.0 \| 0.4044 \| 0.4043 \| 0.0 \| 0.8268 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5254 \| 0.0 \| 0.0 \| 0.7149 \| 0.0 \| 0.3419 \| 0.2532 \| 0.0 \| 0.0 \| 0.0 \| 0.2997 \| 0.0 \| 0.0 \| 0.8414 \| 0.7608 \| 0.9304 \| 0.0 \| 0.0073 \| 0.2169 \| 0.0 \| \| 0.296 \| 44.4 \| 2220 \| 0.6370 \| 0.2836 \| 0.3366 \| 0.8448 \| nan \| 0.9177 \| 0.9524 \| 0.5981 \| 0.7768 \| 0.3251 \| 0.0 \| 0.5173 \| 0.6082 \| 0.0 \| 0.9434 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7014 \| 0.0 \| 0.0 \| 0.9090 \| 0.0 \| 0.3956 \| 0.3633 \| 0.0 \| 0.0 \| 0.0 \| 0.3607 \| 0.0 \| 0.0 \| 0.9299 \| 0.8809 \| 0.9714 \| 0.0 \| 0.0099 \| 0.2825 \| 0.0 \| nan \| 0.7277 \| 0.8500 \| 0.5310 \| 0.7310 \| 0.2768 \| 0.0 \| 0.4025 \| 0.4067 \| 0.0 \| 0.8290 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5305 \| 0.0 \| 0.0 \| 0.7134 \| 0.0 \| 0.3306 \| 0.2546 \| 0.0 \| 0.0 \| 0.0 \| 0.2995 \| 0.0 \| 0.0 \| 0.8416 \| 0.7599 \| 0.9306 \| 0.0 \| 0.0098 \| 0.2172 \| 0.0 \| \| 0.2938 \| 44.8 \| 2240 \| 0.6356 \| 0.2838 \| 0.3381 \| 0.8450 \| nan \| 0.9084 \| 0.9523 \| 0.5857 \| 0.7985 \| 0.3389 \| 0.0 \| 0.5128 \| 0.6084 \| 0.0 \| 0.9490 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7049 \| 0.0 \| 0.0 \| 0.8964 \| 0.0 \| 0.4398 \| 0.3642 \| 0.0 \| 0.0 \| 0.0 \| 0.3571 \| 0.0 \| 0.0 \| 0.9269 \| 0.8862 \| 0.9726 \| 0.0 \| 0.0085 \| 0.2834 \| 0.0 \| nan \| 0.7298 \| 0.8499 \| 0.5354 \| 0.7345 \| 0.2844 \| 0.0 \| 0.4013 \| 0.4050 \| 0.0 \| 0.8243 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5253 \| 0.0 \| 0.0 \| 0.7163 \| 0.0 \| 0.3436 \| 0.2459 \| 0.0 \| 0.0 \| 0.0 \| 0.2973 \| 0.0 \| 0.0 \| 0.8423 \| 0.7578 \| 0.9302 \| 0.0 \| 0.0084 \| 0.2171 \| 0.0 \| \| 0.3444 \| 45.2 \| 2260 \| 0.6378 \| 0.2838 \| 0.3366 \| 0.8450 \| nan \| 0.9139 \| 0.9506 \| 0.5811 \| 0.7960 \| 0.3489 \| 0.0 \| 0.5043 \| 0.6117 \| 0.0 \| 0.9429 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6932 \| 0.0 \| 0.0 \| 0.9158 \| 0.0 \| 0.3896 \| 0.3592 \| 0.0 \| 0.0 \| 0.0 \| 0.3710 \| 0.0 \| 0.0 \| 0.9246 \| 0.8836 \| 0.9719 \| 0.0 \| 0.0115 \| 0.2739 \| 0.0 \| nan \| 0.7284 \| 0.8503 \| 0.5353 \| 0.7357 \| 0.2867 \| 0.0 \| 0.3994 \| 0.4063 \| 0.0 \| 0.8309 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5304 \| 0.0 \| 0.0 \| 0.7117 \| 0.0 \| 0.3196 \| 0.2487 \| 0.0 \| 0.0 \| 0.0 \| 0.3047 \| 0.0 \| 0.0 \| 0.8438 \| 0.7605 \| 0.9305 \| 0.0 \| 0.0114 \| 0.2156 \| 0.0 \| \| 0.3901 \| 45.6 \| 2280 \| 0.6350 \| 0.2841 \| 0.3381 \| 0.8447 \| nan \| 0.9130 \| 0.9486 \| 0.5974 \| 0.7909 \| 0.3553 \| 0.0 \| 0.5118 \| 0.6193 \| 0.0 \| 0.9459 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6916 \| 0.0 \| 0.0 \| 0.8993 \| 0.0 \| 0.3965 \| 0.3624 \| 0.0 \| 0.0 \| 0.0 \| 0.3728 \| 0.0 \| 0.0 \| 0.9318 \| 0.8800 \| 0.9722 \| 0.0 \| 0.0117 \| 0.2949 \| 0.0 \| nan \| 0.7220 \| 0.8523 \| 0.5333 \| 0.7371 \| 0.2906 \| 0.0 \| 0.4042 \| 0.4047 \| 0.0 \| 0.8284 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5288 \| 0.0 \| 0.0 \| 0.7136 \| 0.0 \| 0.3227 \| 0.2481 \| 0.0 \| 0.0 \| 0.0 \| 0.3042 \| 0.0 \| 0.0 \| 0.8416 \| 0.7623 \| 0.9306 \| 0.0 \| 0.0115 \| 0.2240 \| 0.0 \| \| 0.2613 \| 46.0 \| 2300 \| 0.6456 \| 0.2825 \| 0.3352 \| 0.8438 \| nan \| 0.9255 \| 0.9513 \| 0.5876 \| 0.7639 \| 0.3329 \| 0.0 \| 0.5091 \| 0.6051 \| 0.0 \| 0.9460 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6952 \| 0.0 \| 0.0 \| 0.9115 \| 0.0 \| 0.3870 \| 0.3567 \| 0.0 \| 0.0 \| 0.0 \| 0.3589 \| 0.0 \| 0.0 \| 0.9250 \| 0.8831 \| 0.9721 \| 0.0 \| 0.0129 \| 0.2745 \| 0.0 \| nan \| 0.7192 \| 0.8508 \| 0.5345 \| 0.7288 \| 0.2799 \| 0.0 \| 0.3986 \| 0.4038 \| 0.0 \| 0.8275 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5291 \| 0.0 \| 0.0 \| 0.7122 \| 0.0 \| 0.3142 \| 0.2470 \| 0.0 \| 0.0 \| 0.0 \| 0.2997 \| 0.0 \| 0.0 \| 0.8435 \| 0.7592 \| 0.9305 \| 0.0 \| 0.0127 \| 0.2152 \| 0.0 \| \| 0.2849 \| 46.4 \| 2320 \| 0.6370 \| 0.2838 \| 0.3371 \| 0.8447 \| nan \| 0.9062 \| 0.9524 \| 0.6058 \| 0.7879 \| 0.3519 \| 0.0 \| 0.5141 \| 0.6140 \| 0.0 \| 0.9449 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6878 \| 0.0 \| 0.0 \| 0.9100 \| 0.0 \| 0.3961 \| 0.3550 \| 0.0 \| 0.0 \| 0.0 \| 0.3628 \| 0.0 \| 0.0 \| 0.9247 \| 0.8827 \| 0.9722 \| 0.0 \| 0.0138 \| 0.2791 \| 0.0 \| nan \| 0.7274 \| 0.8498 \| 0.5370 \| 0.7366 \| 0.2880 \| 0.0 \| 0.4025 \| 0.4042 \| 0.0 \| 0.8285 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5304 \| 0.0 \| 0.0 \| 0.7132 \| 0.0 \| 0.3211 \| 0.2469 \| 0.0 \| 0.0 \| 0.0 \| 0.3011 \| 0.0 \| 0.0 \| 0.8429 \| 0.7607 \| 0.9306 \| 0.0 \| 0.0136 \| 0.2164 \| 0.0 \| \| 0.2599 \| 46.8 \| 2340 \| 0.6385 \| 0.2839 \| 0.3383 \| 0.8446 \| nan \| 0.9125 \| 0.9515 \| 0.5988 \| 0.7867 \| 0.3383 \| 0.0 \| 0.5109 \| 0.6167 \| 0.0 \| 0.9444 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6999 \| 0.0 \| 0.0 \| 0.8998 \| 0.0 \| 0.4217 \| 0.3582 \| 0.0 \| 0.0 \| 0.0 \| 0.3618 \| 0.0 \| 0.0 \| 0.9241 \| 0.8944 \| 0.9728 \| 0.0 \| 0.0140 \| 0.2960 \| 0.0 \| nan \| 0.7253 \| 0.8508 \| 0.5379 \| 0.7364 \| 0.2833 \| 0.0 \| 0.4010 \| 0.4031 \| 0.0 \| 0.8290 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5276 \| 0.0 \| 0.0 \| 0.7151 \| 0.0 \| 0.3357 \| 0.2444 \| 0.0 \| 0.0 \| 0.0 \| 0.2996 \| 0.0 \| 0.0 \| 0.8416 \| 0.7563 \| 0.9304 \| 0.0 \| 0.0138 \| 0.2217 \| 0.0 \| \| 0.3051 \| 47.2 \| 2360 \| 0.6322 \| 0.2843 \| 0.3384 \| 0.8449 \| nan \| 0.9056 \| 0.9501 \| 0.6024 \| 0.7992 \| 0.3467 \| 0.0 \| 0.5156 \| 0.6115 \| 0.0 \| 0.9455 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7007 \| 0.0 \| 0.0 \| 0.9005 \| 0.0 \| 0.4145 \| 0.3529 \| 0.0 \| 0.0 \| 0.0 \| 0.3639 \| 0.0 \| 0.0 \| 0.9305 \| 0.8834 \| 0.9705 \| 0.0 \| 0.0145 \| 0.2967 \| 0.0 \| nan \| 0.7256 \| 0.8512 \| 0.5395 \| 0.7363 \| 0.2865 \| 0.0 \| 0.4030 \| 0.4059 \| 0.0 \| 0.8285 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5279 \| 0.0 \| 0.0 \| 0.7145 \| 0.0 \| 0.3315 \| 0.2443 \| 0.0 \| 0.0 \| 0.0 \| 0.3009 \| 0.0 \| 0.0 \| 0.8422 \| 0.7621 \| 0.9314 \| 0.0 \| 0.0143 \| 0.2224 \| 0.0 \| \| 0.3295 \| 47.6 \| 2380 \| 0.6335 \| 0.2843 \| 0.3385 \| 0.8448 \| nan \| 0.9076 \| 0.9511 \| 0.5946 \| 0.7985 \| 0.3416 \| 0.0 \| 0.5124 \| 0.6087 \| 0.0 \| 0.9466 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7039 \| 0.0 \| 0.0 \| 0.8969 \| 0.0 \| 0.4223 \| 0.3597 \| 0.0 \| 0.0 \| 0.0 \| 0.3662 \| 0.0 \| 0.0 \| 0.9273 \| 0.8890 \| 0.9726 \| 0.0 \| 0.0139 \| 0.2955 \| 0.0 \| nan \| 0.7261 \| 0.8504 \| 0.5418 \| 0.7353 \| 0.2840 \| 0.0 \| 0.3997 \| 0.4052 \| 0.0 \| 0.8276 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5280 \| 0.0 \| 0.0 \| 0.7159 \| 0.0 \| 0.3352 \| 0.2454 \| 0.0 \| 0.0 \| 0.0 \| 0.3018 \| 0.0 \| 0.0 \| 0.8429 \| 0.7597 \| 0.9307 \| 0.0 \| 0.0137 \| 0.2220 \| 0.0 \| \| 0.3076 \| 48.0 \| 2400 \| 0.6354 \| 0.2839 \| 0.3371 \| 0.8449 \| nan \| 0.9013 \| 0.9540 \| 0.5975 \| 0.8009 \| 0.3376 \| 0.0 \| 0.5096 \| 0.6170 \| 0.0 \| 0.9443 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6890 \| 0.0 \| 0.0 \| 0.9068 \| 0.0 \| 0.4008 \| 0.3547 \| 0.0 \| 0.0 \| 0.0 \| 0.3703 \| 0.0 \| 0.0 \| 0.9272 \| 0.8803 \| 0.9721 \| 0.0 \| 0.0159 \| 0.2833 \| 0.0 \| nan \| 0.7289 \| 0.8493 \| 0.5403 \| 0.7362 \| 0.2811 \| 0.0 \| 0.3987 \| 0.4042 \| 0.0 \| 0.8300 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5282 \| 0.0 \| 0.0 \| 0.7131 \| 0.0 \| 0.3234 \| 0.2461 \| 0.0 \| 0.0 \| 0.0 \| 0.3041 \| 0.0 \| 0.0 \| 0.8440 \| 0.7614 \| 0.9308 \| 0.0 \| 0.0155 \| 0.2175 \| 0.0 \| \| 0.306 \| 48.4 \| 2420 \| 0.6358 \| 0.2836 \| 0.3364 \| 0.8446 \| nan \| 0.9125 \| 0.9514 \| 0.6005 \| 0.7840 \| 0.3395 \| 0.0 \| 0.5120 \| 0.6077 \| 0.0 \| 0.9452 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6902 \| 0.0 \| 0.0 \| 0.9049 \| 0.0 \| 0.4001 \| 0.3406 \| 0.0 \| 0.0 \| 0.0 \| 0.3646 \| 0.0 \| 0.0 \| 0.9304 \| 0.8802 \| 0.9712 \| 0.0 \| 0.0157 \| 0.2882 \| 0.0 \| nan \| 0.7236 \| 0.8512 \| 0.5370 \| 0.7335 \| 0.2827 \| 0.0 \| 0.4008 \| 0.4065 \| 0.0 \| 0.8291 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5290 \| 0.0 \| 0.0 \| 0.7132 \| 0.0 \| 0.3206 \| 0.2414 \| 0.0 \| 0.0 \| 0.0 \| 0.3014 \| 0.0 \| 0.0 \| 0.8427 \| 0.7619 \| 0.9312 \| 0.0 \| 0.0154 \| 0.2195 \| 0.0 \| \| 0.2623 \| 48.8 \| 2440 \| 0.6342 \| 0.2839 \| 0.3381 \| 0.8444 \| nan \| 0.9127 \| 0.9487 \| 0.6027 \| 0.7876 \| 0.3482 \| 0.0 \| 0.5157 \| 0.6140 \| 0.0 \| 0.9431 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6961 \| 0.0 \| 0.0 \| 0.9053 \| 0.0 \| 0.4075 \| 0.3583 \| 0.0 \| 0.0 \| 0.0 \| 0.3654 \| 0.0 \| 0.0 \| 0.9251 \| 0.8914 \| 0.9738 \| 0.0 \| 0.0143 \| 0.2854 \| 0.0 \| nan \| 0.7237 \| 0.8518 \| 0.5387 \| 0.7316 \| 0.2869 \| 0.0 \| 0.4022 \| 0.4066 \| 0.0 \| 0.8309 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5283 \| 0.0 \| 0.0 \| 0.7137 \| 0.0 \| 0.3254 \| 0.2469 \| 0.0 \| 0.0 \| 0.0 \| 0.3021 \| 0.0 \| 0.0 \| 0.8428 \| 0.7587 \| 0.9298 \| 0.0 \| 0.0140 \| 0.2182 \| 0.0 \| \| 0.2494 \| 49.2 \| 2460 \| 0.6372 \| 0.2839 \| 0.3377 \| 0.8441 \| nan \| 0.9121 \| 0.9506 \| 0.6043 \| 0.7734 \| 0.3497 \| 0.0 \| 0.5153 \| 0.6076 \| 0.0 \| 0.9462 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6948 \| 0.0 \| 0.0 \| 0.9019 \| 0.0 \| 0.4125 \| 0.3576 \| 0.0 \| 0.0 \| 0.0 \| 0.3646 \| 0.0 \| 0.0 \| 0.9270 \| 0.8869 \| 0.9730 \| 0.0 \| 0.0143 \| 0.2903 \| 0.0 \| nan \| 0.7210 \| 0.8511 \| 0.5373 \| 0.7297 \| 0.2867 \| 0.0 \| 0.4017 \| 0.4059 \| 0.0 \| 0.8284 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5309 \| 0.0 \| 0.0 \| 0.7149 \| 0.0 \| 0.3281 \| 0.2462 \| 0.0 \| 0.0 \| 0.0 \| 0.3017 \| 0.0 \| 0.0 \| 0.8432 \| 0.7605 \| 0.9304 \| 0.0 \| 0.0141 \| 0.2201 \| 0.0 \| \| 0.2411 \| 49.6 \| 2480 \| 0.6353 \| 0.2838 \| 0.3373 \| 0.8443 \| nan \| 0.9128 \| 0.9496 \| 0.6019 \| 0.7793 \| 0.3468 \| 0.0 \| 0.5182 \| 0.6107 \| 0.0 \| 0.9421 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6947 \| 0.0 \| 0.0 \| 0.9064 \| 0.0 \| 0.4011 \| 0.3651 \| 0.0 \| 0.0 \| 0.0 \| 0.3610 \| 0.0 \| 0.0 \| 0.9318 \| 0.8792 \| 0.9705 \| 0.0 \| 0.0149 \| 0.2827 \| 0.0 \| nan \| 0.7221 \| 0.8517 \| 0.5390 \| 0.7298 \| 0.2859 \| 0.0 \| 0.4017 \| 0.4064 \| 0.0 \| 0.8315 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5277 \| 0.0 \| 0.0 \| 0.7128 \| 0.0 \| 0.3236 \| 0.2505 \| 0.0 \| 0.0 \| 0.0 \| 0.3001 \| 0.0 \| 0.0 \| 0.8418 \| 0.7627 \| 0.9313 \| 0.0 \| 0.0147 \| 0.2176 \| 0.0 \| \| 0.2768 \| 50.0 \| 2500 \| 0.6351 \| 0.2834 \| 0.3374 \| 0.8443 \| nan \| 0.9045 \| 0.9530 \| 0.6075 \| 0.7798 \| 0.3400 \| 0.0 \| 0.5121 \| 0.6170 \| 0.0 \| 0.9458 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6956 \| 0.0 \| 0.0 \| 0.9045 \| 0.0 \| 0.4037 \| 0.3503 \| 0.0 \| 0.0 \| 0.0 \| 0.3679 \| 0.0 \| 0.0 \| 0.9274 \| 0.8889 \| 0.9722 \| 0.0 \| 0.0119 \| 0.2879 \| 0.0 \| nan \| 0.7249 \| 0.8500 \| 0.5367 \| 0.7300 \| 0.2828 \| 0.0 \| 0.4008 \| 0.4039 \| 0.0 \| 0.8291 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5268 \| 0.0 \| 0.0 \| 0.7142 \| 0.0 \| 0.3242 \| 0.2450 \| 0.0 \| 0.0 \| 0.0 \| 0.3031 \| 0.0 \| 0.0 \| 0.8429 \| 0.7587 \| 0.9308 \| 0.0 \| 0.0117 \| 0.2194 \| 0.0 \| ### Framework versions - Transformers 4.34.1 - Pytorch 1.11.0+cu113 - Datasets 2.15.0 - Tokenizers 0.14.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
yijisuk/segformer-b0-finetuned-segments-ic-chip-sample	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-ic-chip-sample This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Mean Iou: 0.4863 - Mean Accuracy: 0.9725 - Overall Accuracy: 0.9725 - Accuracy Unlabeled: nan - Accuracy Circuit: 0.9725 - Iou Unlabeled: 0.0 - Iou Circuit: 0.9725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Circuit \| Iou Unlabeled \| Iou Circuit \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:-------------:\|:-----------:\| \| 0.0885 \| 1.0 \| 20 \| 0.0858 \| 0.4907 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.1764 \| 2.0 \| 40 \| 0.0854 \| 0.4734 \| 0.9469 \| 0.9469 \| nan \| 0.9469 \| 0.0 \| 0.9469 \| \| 0.0569 \| 3.0 \| 60 \| 0.0854 \| 0.4702 \| 0.9404 \| 0.9404 \| nan \| 0.9404 \| 0.0 \| 0.9404 \| \| 0.0959 \| 4.0 \| 80 \| 0.0851 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.2969 \| 5.0 \| 100 \| 0.0825 \| 0.4863 \| 0.9727 \| 0.9727 \| nan \| 0.9727 \| 0.0 \| 0.9727 \| \| 0.1979 \| 6.0 \| 120 \| 0.0824 \| 0.4873 \| 0.9746 \| 0.9746 \| nan \| 0.9746 \| 0.0 \| 0.9746 \| \| 0.0906 \| 7.0 \| 140 \| 0.0824 \| 0.4740 \| 0.9480 \| 0.9480 \| nan \| 0.9480 \| 0.0 \| 0.9480 \| \| 0.2879 \| 8.0 \| 160 \| 0.0821 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \| \| 0.1366 \| 9.0 \| 180 \| 0.0807 \| 0.4833 \| 0.9666 \| 0.9666 \| nan \| 0.9666 \| 0.0 \| 0.9666 \| \| 0.1664 \| 10.0 \| 200 \| 0.0813 \| 0.4860 \| 0.9720 \| 0.9720 \| nan \| 0.9720 \| 0.0 \| 0.9720 \| \| 0.1521 \| 11.0 \| 220 \| 0.0831 \| 0.4830 \| 0.9660 \| 0.9660 \| nan \| 0.9660 \| 0.0 \| 0.9660 \| \| 0.2004 \| 12.0 \| 240 \| 0.0795 \| 0.4825 \| 0.9651 \| 0.9651 \| nan \| 0.9651 \| 0.0 \| 0.9651 \| \| 0.1547 \| 13.0 \| 260 \| 0.0793 \| 0.4812 \| 0.9625 \| 0.9625 \| nan \| 0.9625 \| 0.0 \| 0.9625 \| \| 0.4191 \| 14.0 \| 280 \| 0.0788 \| 0.4830 \| 0.9659 \| 0.9659 \| nan \| 0.9659 \| 0.0 \| 0.9659 \| \| 0.0431 \| 15.0 \| 300 \| 0.0782 \| 0.4815 \| 0.9630 \| 0.9630 \| nan \| 0.9630 \| 0.0 \| 0.9630 \| \| 1.3911 \| 16.0 \| 320 \| 0.0793 \| 0.4820 \| 0.9640 \| 0.9640 \| nan \| 0.9640 \| 0.0 \| 0.9640 \| \| 0.0217 \| 17.0 \| 340 \| 0.0814 \| 0.4836 \| 0.9671 \| 0.9671 \| nan \| 0.9671 \| 0.0 \| 0.9671 \| \| 0.1116 \| 18.0 \| 360 \| 0.0789 \| 0.4839 \| 0.9678 \| 0.9678 \| nan \| 0.9678 \| 0.0 \| 0.9678 \| \| 0.3295 \| 19.0 \| 380 \| 0.0791 \| 0.4763 \| 0.9526 \| 0.9526 \| nan \| 0.9526 \| 0.0 \| 0.9526 \| \| 0.0327 \| 20.0 \| 400 \| 0.0792 \| 0.4829 \| 0.9658 \| 0.9658 \| nan \| 0.9658 \| 0.0 \| 0.9658 \| \| 0.2542 \| 21.0 \| 420 \| 0.0787 \| 0.4861 \| 0.9722 \| 0.9722 \| nan \| 0.9722 \| 0.0 \| 0.9722 \| \| 0.1587 \| 22.0 \| 440 \| 0.0783 \| 0.4772 \| 0.9543 \| 0.9543 \| nan \| 0.9543 \| 0.0 \| 0.9543 \| \| 0.2721 \| 23.0 \| 460 \| 0.0804 \| 0.4913 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.0505 \| 24.0 \| 480 \| 0.0781 \| 0.4827 \| 0.9655 \| 0.9655 \| nan \| 0.9655 \| 0.0 \| 0.9655 \| \| 0.1417 \| 25.0 \| 500 \| 0.0801 \| 0.4834 \| 0.9669 \| 0.9669 \| nan \| 0.9669 \| 0.0 \| 0.9669 \| \| 0.1371 \| 26.0 \| 520 \| 0.0777 \| 0.4838 \| 0.9676 \| 0.9676 \| nan \| 0.9676 \| 0.0 \| 0.9676 \| \| 0.1282 \| 27.0 \| 540 \| 0.0773 \| 0.4807 \| 0.9613 \| 0.9613 \| nan \| 0.9613 \| 0.0 \| 0.9613 \| \| 0.057 \| 28.0 \| 560 \| 0.0772 \| 0.4829 \| 0.9657 \| 0.9657 \| nan \| 0.9657 \| 0.0 \| 0.9657 \| \| 0.2592 \| 29.0 \| 580 \| 0.0807 \| 0.4872 \| 0.9744 \| 0.9744 \| nan \| 0.9744 \| 0.0 \| 0.9744 \| \| 0.1687 \| 30.0 \| 600 \| 0.0794 \| 0.4825 \| 0.9649 \| 0.9649 \| nan \| 0.9649 \| 0.0 \| 0.9649 \| \| 0.499 \| 31.0 \| 620 \| 0.0805 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \| \| 0.1584 \| 32.0 \| 640 \| 0.0790 \| 0.4845 \| 0.9691 \| 0.9691 \| nan \| 0.9691 \| 0.0 \| 0.9691 \| \| 0.0689 \| 33.0 \| 660 \| 0.0785 \| 0.4845 \| 0.9690 \| 0.9690 \| nan \| 0.9690 \| 0.0 \| 0.9690 \| \| 1.3764 \| 34.0 \| 680 \| 0.0790 \| 0.4848 \| 0.9696 \| 0.9696 \| nan \| 0.9696 \| 0.0 \| 0.9696 \| \| 0.2597 \| 35.0 \| 700 \| 0.0808 \| 0.4875 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 1.0757 \| 36.0 \| 720 \| 0.0761 \| 0.4841 \| 0.9681 \| 0.9681 \| nan \| 0.9681 \| 0.0 \| 0.9681 \| \| 0.6112 \| 37.0 \| 740 \| 0.0779 \| 0.4825 \| 0.9650 \| 0.9650 \| nan \| 0.9650 \| 0.0 \| 0.9650 \| \| 0.2899 \| 38.0 \| 760 \| 0.0787 \| 0.4796 \| 0.9591 \| 0.9591 \| nan \| 0.9591 \| 0.0 \| 0.9591 \| \| 0.3402 \| 39.0 \| 780 \| 0.0777 \| 0.4838 \| 0.9676 \| 0.9676 \| nan \| 0.9676 \| 0.0 \| 0.9676 \| \| 0.0183 \| 40.0 \| 800 \| 0.0771 \| 0.4829 \| 0.9657 \| 0.9657 \| nan \| 0.9657 \| 0.0 \| 0.9657 \| \| 0.1407 \| 41.0 \| 820 \| 0.0774 \| 0.4809 \| 0.9617 \| 0.9617 \| nan \| 0.9617 \| 0.0 \| 0.9617 \| \| 0.4045 \| 42.0 \| 840 \| 0.0767 \| 0.4819 \| 0.9638 \| 0.9638 \| nan \| 0.9638 \| 0.0 \| 0.9638 \| \| 0.2159 \| 43.0 \| 860 \| 0.0780 \| 0.4850 \| 0.9699 \| 0.9699 \| nan \| 0.9699 \| 0.0 \| 0.9699 \| \| 0.0541 \| 44.0 \| 880 \| 0.0768 \| 0.4812 \| 0.9624 \| 0.9624 \| nan \| 0.9624 \| 0.0 \| 0.9624 \| \| 0.0638 \| 45.0 \| 900 \| 0.0774 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \| \| 0.0409 \| 46.0 \| 920 \| 0.0788 \| 0.4875 \| 0.9749 \| 0.9749 \| nan \| 0.9749 \| 0.0 \| 0.9749 \| \| 0.1662 \| 47.0 \| 940 \| 0.0774 \| 0.4871 \| 0.9743 \| 0.9743 \| nan \| 0.9743 \| 0.0 \| 0.9743 \| \| 0.1636 \| 48.0 \| 960 \| 0.0783 \| 0.4860 \| 0.9720 \| 0.9720 \| nan \| 0.9720 \| 0.0 \| 0.9720 \| \| 0.033 \| 49.0 \| 980 \| 0.0791 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \| \| 0.171 \| 50.0 \| 1000 \| 0.0772 \| 0.4863 \| 0.9725 \| 0.9725 \| nan \| 0.9725 \| 0.0 \| 0.9725 \| ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu115 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "circuit" ]
yijisuk/segformer-b1-finetuned-segments-ic-chip-sample	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b1-finetuned-segments-ic-chip-sample This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set: - Loss: 0.1227 - Mean Iou: 0.4744 - Mean Accuracy: 0.9489 - Overall Accuracy: 0.9489 - Accuracy Unlabeled: nan - Accuracy Circuit: 0.9489 - Iou Unlabeled: 0.0 - Iou Circuit: 0.9489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Circuit \| Iou Unlabeled \| Iou Circuit \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:-------------:\|:-----------:\| \| 0.4185 \| 1.0 \| 20 \| 0.5878 \| 0.3632 \| 0.7265 \| 0.7265 \| nan \| 0.7265 \| 0.0 \| 0.7265 \| \| 0.4477 \| 2.0 \| 40 \| 0.4288 \| 0.4894 \| 0.9788 \| 0.9788 \| nan \| 0.9788 \| 0.0 \| 0.9788 \| \| 0.9304 \| 3.0 \| 60 \| 0.2053 \| 0.4520 \| 0.9041 \| 0.9041 \| nan \| 0.9041 \| 0.0 \| 0.9041 \| \| 0.1409 \| 4.0 \| 80 \| 0.1817 \| 0.4738 \| 0.9477 \| 0.9477 \| nan \| 0.9477 \| 0.0 \| 0.9477 \| \| 0.392 \| 5.0 \| 100 \| 0.1824 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.1589 \| 6.0 \| 120 \| 0.1594 \| 0.4814 \| 0.9628 \| 0.9628 \| nan \| 0.9628 \| 0.0 \| 0.9628 \| \| 0.1848 \| 7.0 \| 140 \| 0.1551 \| 0.4625 \| 0.9251 \| 0.9251 \| nan \| 0.9251 \| 0.0 \| 0.9251 \| \| 0.0874 \| 8.0 \| 160 \| 0.1503 \| 0.4829 \| 0.9657 \| 0.9657 \| nan \| 0.9657 \| 0.0 \| 0.9657 \| \| 0.2172 \| 9.0 \| 180 \| 0.1558 \| 0.4591 \| 0.9182 \| 0.9182 \| nan \| 0.9182 \| 0.0 \| 0.9182 \| \| 0.9914 \| 10.0 \| 200 \| 0.1457 \| 0.4698 \| 0.9396 \| 0.9396 \| nan \| 0.9396 \| 0.0 \| 0.9396 \| \| 0.2387 \| 11.0 \| 220 \| 0.1494 \| 0.4709 \| 0.9419 \| 0.9419 \| nan \| 0.9419 \| 0.0 \| 0.9419 \| \| 0.1242 \| 12.0 \| 240 \| 0.1463 \| 0.4743 \| 0.9486 \| 0.9486 \| nan \| 0.9486 \| 0.0 \| 0.9486 \| \| 0.0819 \| 13.0 \| 260 \| 0.1492 \| 0.4757 \| 0.9515 \| 0.9515 \| nan \| 0.9515 \| 0.0 \| 0.9515 \| \| 0.6077 \| 14.0 \| 280 \| 0.1442 \| 0.4793 \| 0.9586 \| 0.9586 \| nan \| 0.9586 \| 0.0 \| 0.9586 \| \| 0.3156 \| 15.0 \| 300 \| 0.1430 \| 0.4813 \| 0.9627 \| 0.9627 \| nan \| 0.9627 \| 0.0 \| 0.9627 \| \| 0.2564 \| 16.0 \| 320 \| 0.1483 \| 0.4673 \| 0.9347 \| 0.9347 \| nan \| 0.9347 \| 0.0 \| 0.9347 \| \| 0.107 \| 17.0 \| 340 \| 0.1467 \| 0.4695 \| 0.9390 \| 0.9390 \| nan \| 0.9390 \| 0.0 \| 0.9390 \| \| 1.1592 \| 18.0 \| 360 \| 0.1437 \| 0.4814 \| 0.9628 \| 0.9628 \| nan \| 0.9628 \| 0.0 \| 0.9628 \| \| 0.0586 \| 19.0 \| 380 \| 0.1396 \| 0.4811 \| 0.9622 \| 0.9622 \| nan \| 0.9622 \| 0.0 \| 0.9622 \| \| 0.9815 \| 20.0 \| 400 \| 0.1399 \| 0.4812 \| 0.9624 \| 0.9624 \| nan \| 0.9624 \| 0.0 \| 0.9624 \| \| 0.3101 \| 21.0 \| 420 \| 0.1411 \| 0.4836 \| 0.9672 \| 0.9672 \| nan \| 0.9672 \| 0.0 \| 0.9672 \| \| 0.2325 \| 22.0 \| 440 \| 0.1395 \| 0.4672 \| 0.9344 \| 0.9344 \| nan \| 0.9344 \| 0.0 \| 0.9344 \| \| 0.1504 \| 23.0 \| 460 \| 0.1420 \| 0.4720 \| 0.9441 \| 0.9441 \| nan \| 0.9441 \| 0.0 \| 0.9441 \| \| 0.2831 \| 24.0 \| 480 \| 0.1393 \| 0.4697 \| 0.9395 \| 0.9395 \| nan \| 0.9395 \| 0.0 \| 0.9395 \| \| 0.0921 \| 25.0 \| 500 \| 0.1418 \| 0.4701 \| 0.9401 \| 0.9401 \| nan \| 0.9401 \| 0.0 \| 0.9401 \| \| 0.141 \| 26.0 \| 520 \| 0.1318 \| 0.4648 \| 0.9296 \| 0.9296 \| nan \| 0.9296 \| 0.0 \| 0.9296 \| \| 0.1381 \| 27.0 \| 540 \| 0.1316 \| 0.4697 \| 0.9395 \| 0.9395 \| nan \| 0.9395 \| 0.0 \| 0.9395 \| \| 1.1864 \| 28.0 \| 560 \| 0.1292 \| 0.4774 \| 0.9548 \| 0.9548 \| nan \| 0.9548 \| 0.0 \| 0.9548 \| \| 0.9492 \| 29.0 \| 580 \| 0.1290 \| 0.4709 \| 0.9418 \| 0.9418 \| nan \| 0.9418 \| 0.0 \| 0.9418 \| \| 0.3061 \| 30.0 \| 600 \| 0.1303 \| 0.4536 \| 0.9071 \| 0.9071 \| nan \| 0.9071 \| 0.0 \| 0.9071 \| \| 0.2511 \| 31.0 \| 620 \| 0.1318 \| 0.4725 \| 0.9451 \| 0.9451 \| nan \| 0.9451 \| 0.0 \| 0.9451 \| \| 0.2706 \| 32.0 \| 640 \| 0.1284 \| 0.4790 \| 0.9580 \| 0.9580 \| nan \| 0.9580 \| 0.0 \| 0.9580 \| \| 0.1508 \| 33.0 \| 660 \| 0.1264 \| 0.4698 \| 0.9396 \| 0.9396 \| nan \| 0.9396 \| 0.0 \| 0.9396 \| \| 0.2802 \| 34.0 \| 680 \| 0.1308 \| 0.4733 \| 0.9467 \| 0.9467 \| nan \| 0.9467 \| 0.0 \| 0.9467 \| \| 0.1897 \| 35.0 \| 700 \| 0.1315 \| 0.4681 \| 0.9361 \| 0.9361 \| nan \| 0.9361 \| 0.0 \| 0.9361 \| \| 0.1981 \| 36.0 \| 720 \| 0.1289 \| 0.4766 \| 0.9531 \| 0.9531 \| nan \| 0.9531 \| 0.0 \| 0.9531 \| \| 0.2742 \| 37.0 \| 740 \| 0.1284 \| 0.4818 \| 0.9635 \| 0.9635 \| nan \| 0.9635 \| 0.0 \| 0.9635 \| \| 0.0418 \| 38.0 \| 760 \| 0.1240 \| 0.4762 \| 0.9525 \| 0.9525 \| nan \| 0.9525 \| 0.0 \| 0.9525 \| \| 0.1946 \| 39.0 \| 780 \| 0.1253 \| 0.4750 \| 0.9500 \| 0.9500 \| nan \| 0.9500 \| 0.0 \| 0.9500 \| \| 0.1692 \| 40.0 \| 800 \| 0.1253 \| 0.4836 \| 0.9672 \| 0.9672 \| nan \| 0.9672 \| 0.0 \| 0.9672 \| \| 0.3071 \| 41.0 \| 820 \| 0.1227 \| 0.4751 \| 0.9503 \| 0.9503 \| nan \| 0.9503 \| 0.0 \| 0.9503 \| \| 0.2003 \| 42.0 \| 840 \| 0.1250 \| 0.4762 \| 0.9524 \| 0.9524 \| nan \| 0.9524 \| 0.0 \| 0.9524 \| \| 0.2099 \| 43.0 \| 860 \| 0.1235 \| 0.4740 \| 0.9480 \| 0.9480 \| nan \| 0.9480 \| 0.0 \| 0.9480 \| \| 0.1218 \| 44.0 \| 880 \| 0.1222 \| 0.4743 \| 0.9486 \| 0.9486 \| nan \| 0.9486 \| 0.0 \| 0.9486 \| \| 0.1583 \| 45.0 \| 900 \| 0.1226 \| 0.4708 \| 0.9415 \| 0.9415 \| nan \| 0.9415 \| 0.0 \| 0.9415 \| \| 0.1506 \| 46.0 \| 920 \| 0.1215 \| 0.4686 \| 0.9372 \| 0.9372 \| nan \| 0.9372 \| 0.0 \| 0.9372 \| \| 0.0643 \| 47.0 \| 940 \| 0.1234 \| 0.4779 \| 0.9559 \| 0.9559 \| nan \| 0.9559 \| 0.0 \| 0.9559 \| \| 0.2006 \| 48.0 \| 960 \| 0.1213 \| 0.4757 \| 0.9515 \| 0.9515 \| nan \| 0.9515 \| 0.0 \| 0.9515 \| \| 0.0783 \| 49.0 \| 980 \| 0.1241 \| 0.4726 \| 0.9452 \| 0.9452 \| nan \| 0.9452 \| 0.0 \| 0.9452 \| \| 0.0552 \| 50.0 \| 1000 \| 0.1227 \| 0.4744 \| 0.9489 \| 0.9489 \| nan \| 0.9489 \| 0.0 \| 0.9489 \| ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu115 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "unlabeled", "circuit" ]
Gregghe/segformer-finetuned-fingertip-10-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-fingertip-10-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 3.5204 - Mean Iou: 0.0132 - Mean Accuracy: 0.0688 - Overall Accuracy: 0.0956 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0201 - Accuracy Flat-sidewalk: 0.1177 - Accuracy Flat-crosswalk: 0.0014 - Accuracy Flat-cyclinglane: 0.4426 - Accuracy Flat-parkingdriveway: 0.0021 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0008 - Accuracy Human-person: 0.0010 - Accuracy Human-rider: 0.0077 - Accuracy Vehicle-car: 0.1566 - Accuracy Vehicle-truck: 0.0040 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.5317 - Accuracy Vehicle-bicycle: 0.0892 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: nan - Accuracy Construction-building: 0.0578 - Accuracy Construction-door: 0.0682 - Accuracy Construction-wall: 0.0002 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0055 - Accuracy Object-pole: 0.0162 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.3811 - Accuracy Nature-vegetation: 0.0756 - Accuracy Nature-terrain: 0.0010 - Accuracy Sky: 0.0038 - Accuracy Void-ground: 0.0400 - Accuracy Void-dynamic: 0.0002 - Accuracy Void-static: 0.0386 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.0193 - Iou Flat-sidewalk: 0.1141 - Iou Flat-crosswalk: 0.0013 - Iou Flat-cyclinglane: 0.0702 - Iou Flat-parkingdriveway: 0.0019 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0007 - Iou Human-person: 0.0005 - Iou Human-rider: 0.0001 - Iou Vehicle-car: 0.1087 - Iou Vehicle-truck: 0.0003 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0004 - Iou Vehicle-bicycle: 0.0085 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.0413 - Iou Construction-door: 0.0067 - Iou Construction-wall: 0.0002 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0021 - Iou Object-pole: 0.0036 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0001 - Iou Nature-vegetation: 0.0663 - Iou Nature-terrain: 0.0009 - Iou Sky: 0.0038 - Iou Void-ground: 0.0049 - Iou Void-dynamic: 0.0000 - Iou Void-static: 0.0046 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:------------------:\|:----------------------:\|:-----------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------:\|:---------------------:\|:--------------------:\|:--------------------:\|:----------------------:\|:--------------------:\|:--------------------------:\|:---------------------------:\|:------------------------:\|:------------------------:\|:---------------------------:\|:------------------------------:\|:--------------------------:\|:--------------------------:\|:------------------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:--------------------:\|:---------------------------:\|:----------------------------:\|:--------------------------:\|:-----------------------:\|:------------:\|:--------------------:\|:---------------------:\|:--------------------:\|:---------------------:\|:-------------:\|:-------------:\|:-----------------:\|:------------------:\|:--------------------:\|:------------------------:\|:------------------:\|:-------------:\|:----------------:\|:---------------:\|:---------------:\|:-----------------:\|:---------------:\|:---------------------:\|:----------------------:\|:-------------------:\|:-------------------:\|:----------------------:\|:-------------------------:\|:---------------------:\|:---------------------:\|:-------------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:---------------:\|:----------------------:\|:-----------------------:\|:---------------------:\|:------------------:\|:-------:\|:---------------:\|:----------------:\|:---------------:\|:----------------:\| \| No log \| 0.09 \| 10 \| 3.5204 \| 0.0132 \| 0.0688 \| 0.0956 \| nan \| 0.0201 \| 0.1177 \| 0.0014 \| 0.4426 \| 0.0021 \| nan \| 0.0008 \| 0.0010 \| 0.0077 \| 0.1566 \| 0.0040 \| 0.0 \| nan \| 0.5317 \| 0.0892 \| 0.0 \| nan \| 0.0578 \| 0.0682 \| 0.0002 \| 0.0000 \| 0.0 \| nan \| 0.0055 \| 0.0162 \| 0.0 \| 0.3811 \| 0.0756 \| 0.0010 \| 0.0038 \| 0.0400 \| 0.0002 \| 0.0386 \| 0.0 \| 0.0 \| 0.0193 \| 0.1141 \| 0.0013 \| 0.0702 \| 0.0019 \| 0.0 \| 0.0007 \| 0.0005 \| 0.0001 \| 0.1087 \| 0.0003 \| 0.0 \| 0.0 \| 0.0004 \| 0.0085 \| 0.0 \| 0.0 \| 0.0413 \| 0.0067 \| 0.0002 \| 0.0000 \| 0.0 \| 0.0 \| 0.0021 \| 0.0036 \| 0.0 \| 0.0001 \| 0.0663 \| 0.0009 \| 0.0038 \| 0.0049 \| 0.0000 \| 0.0046 \| 0.0 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.16.0 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
Gregghe/segformer-finetuned-fingertip-1000-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-fingertip-10-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 3.5204 - Mean Iou: 0.0132 - Mean Accuracy: 0.0688 - Overall Accuracy: 0.0956 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0201 - Accuracy Flat-sidewalk: 0.1177 - Accuracy Flat-crosswalk: 0.0014 - Accuracy Flat-cyclinglane: 0.4426 - Accuracy Flat-parkingdriveway: 0.0021 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0008 - Accuracy Human-person: 0.0010 - Accuracy Human-rider: 0.0077 - Accuracy Vehicle-car: 0.1566 - Accuracy Vehicle-truck: 0.0040 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.5317 - Accuracy Vehicle-bicycle: 0.0892 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: nan - Accuracy Construction-building: 0.0578 - Accuracy Construction-door: 0.0682 - Accuracy Construction-wall: 0.0002 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0055 - Accuracy Object-pole: 0.0162 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.3811 - Accuracy Nature-vegetation: 0.0756 - Accuracy Nature-terrain: 0.0010 - Accuracy Sky: 0.0038 - Accuracy Void-ground: 0.0400 - Accuracy Void-dynamic: 0.0002 - Accuracy Void-static: 0.0386 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.0193 - Iou Flat-sidewalk: 0.1141 - Iou Flat-crosswalk: 0.0013 - Iou Flat-cyclinglane: 0.0702 - Iou Flat-parkingdriveway: 0.0019 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0007 - Iou Human-person: 0.0005 - Iou Human-rider: 0.0001 - Iou Vehicle-car: 0.1087 - Iou Vehicle-truck: 0.0003 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0004 - Iou Vehicle-bicycle: 0.0085 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.0413 - Iou Construction-door: 0.0067 - Iou Construction-wall: 0.0002 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0021 - Iou Object-pole: 0.0036 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0001 - Iou Nature-vegetation: 0.0663 - Iou Nature-terrain: 0.0009 - Iou Sky: 0.0038 - Iou Void-ground: 0.0049 - Iou Void-dynamic: 0.0000 - Iou Void-static: 0.0046 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:------------------:\|:----------------------:\|:-----------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------:\|:---------------------:\|:--------------------:\|:--------------------:\|:----------------------:\|:--------------------:\|:--------------------------:\|:---------------------------:\|:------------------------:\|:------------------------:\|:---------------------------:\|:------------------------------:\|:--------------------------:\|:--------------------------:\|:------------------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:--------------------:\|:---------------------------:\|:----------------------------:\|:--------------------------:\|:-----------------------:\|:------------:\|:--------------------:\|:---------------------:\|:--------------------:\|:---------------------:\|:-------------:\|:-------------:\|:-----------------:\|:------------------:\|:--------------------:\|:------------------------:\|:------------------:\|:-------------:\|:----------------:\|:---------------:\|:---------------:\|:-----------------:\|:---------------:\|:---------------------:\|:----------------------:\|:-------------------:\|:-------------------:\|:----------------------:\|:-------------------------:\|:---------------------:\|:---------------------:\|:-------------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:---------------:\|:----------------------:\|:-----------------------:\|:---------------------:\|:------------------:\|:-------:\|:---------------:\|:----------------:\|:---------------:\|:----------------:\| \| No log \| 0.09 \| 10 \| 3.5204 \| 0.0132 \| 0.0688 \| 0.0956 \| nan \| 0.0201 \| 0.1177 \| 0.0014 \| 0.4426 \| 0.0021 \| nan \| 0.0008 \| 0.0010 \| 0.0077 \| 0.1566 \| 0.0040 \| 0.0 \| nan \| 0.5317 \| 0.0892 \| 0.0 \| nan \| 0.0578 \| 0.0682 \| 0.0002 \| 0.0000 \| 0.0 \| nan \| 0.0055 \| 0.0162 \| 0.0 \| 0.3811 \| 0.0756 \| 0.0010 \| 0.0038 \| 0.0400 \| 0.0002 \| 0.0386 \| 0.0 \| 0.0 \| 0.0193 \| 0.1141 \| 0.0013 \| 0.0702 \| 0.0019 \| 0.0 \| 0.0007 \| 0.0005 \| 0.0001 \| 0.1087 \| 0.0003 \| 0.0 \| 0.0 \| 0.0004 \| 0.0085 \| 0.0 \| 0.0 \| 0.0413 \| 0.0067 \| 0.0002 \| 0.0000 \| 0.0 \| 0.0 \| 0.0021 \| 0.0036 \| 0.0 \| 0.0001 \| 0.0663 \| 0.0009 \| 0.0038 \| 0.0049 \| 0.0000 \| 0.0046 \| 0.0 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.16.0 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
sawthiha/segformer-b0-finetuned-deprem-satellite	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-deprem-satellite This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the deprem-ml/deprem_satellite_semantic_whu_dataset dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0641 - eval_mean_iou: 0.9849 - eval_mean_accuracy: 0.9933 - eval_overall_accuracy: 0.9933 - eval_runtime: 94.2835 - eval_samples_per_second: 10.988 - eval_steps_per_second: 2.206 - epoch: 4.18 - step: 1980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 10 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "building" ]
johanhag/segformer-b0-winter_2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-winter_2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the johanhag/winter dataset. It achieves the following results on the evaluation set: - Loss: 0.0665 - Mean Iou: 0.7168 - Mean Accuracy: 0.7840 - Overall Accuracy: 0.9775 - Accuracy Unlabeled: nan - Accuracy Road: 0.9559 - Accuracy Side walk: 0.7693 - Accuracy Car: 0.8882 - Accuracy Pedestrian: 0.8345 - Accuracy Sign: 0.2653 - Accuracy Other: 0.9907 - Iou Unlabeled: nan - Iou Road: 0.9088 - Iou Side walk: 0.6583 - Iou Car: 0.7943 - Iou Pedestrian: 0.7201 - Iou Sign: 0.2393 - Iou Other: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Road \| Accuracy Side walk \| Accuracy Car \| Accuracy Pedestrian \| Accuracy Sign \| Accuracy Other \| Iou Unlabeled \| Iou Road \| Iou Side walk \| Iou Car \| Iou Pedestrian \| Iou Sign \| Iou Other \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:------------------:\|:------------:\|:-------------------:\|:-------------:\|:--------------:\|:-------------:\|:--------:\|:-------------:\|:-------:\|:--------------:\|:--------:\|:---------:\| \| 1.2678 \| 0.37 \| 20 \| 1.4585 \| 0.2824 \| 0.4123 \| 0.8374 \| nan \| 0.9932 \| 0.0 \| 0.6072 \| 0.0205 \| 0.0 \| 0.8528 \| nan \| 0.4383 \| 0.0 \| 0.3876 \| 0.0204 \| 0.0 \| 0.8480 \| \| 0.9377 \| 0.74 \| 40 \| 0.9219 \| 0.3461 \| 0.4345 \| 0.9161 \| nan \| 0.9666 \| 0.0013 \| 0.6904 \| 0.0 \| 0.0 \| 0.9490 \| nan \| 0.6449 \| 0.0013 \| 0.5036 \| 0.0 \| 0.0 \| 0.9265 \| \| 0.7819 \| 1.11 \| 60 \| 0.7375 \| 0.3622 \| 0.4469 \| 0.9248 \| nan \| 0.9687 \| 0.0098 \| 0.7451 \| 0.0 \| 0.0 \| 0.9576 \| nan \| 0.6748 \| 0.0095 \| 0.5520 \| 0.0 \| 0.0 \| 0.9367 \| \| 0.687 \| 1.48 \| 80 \| 0.6354 \| 0.3674 \| 0.4573 \| 0.9286 \| nan \| 0.9687 \| 0.0131 \| 0.8014 \| 0.0 \| 0.0 \| 0.9608 \| nan \| 0.6873 \| 0.0130 \| 0.5626 \| 0.0 \| 0.0 \| 0.9417 \| \| 0.552 \| 1.85 \| 100 \| 0.5199 \| 0.3753 \| 0.4700 \| 0.9360 \| nan \| 0.9564 \| 0.0246 \| 0.8691 \| 0.0 \| 0.0 \| 0.9697 \| nan \| 0.7283 \| 0.0246 \| 0.5492 \| 0.0 \| 0.0 \| 0.9497 \| \| 0.521 \| 2.22 \| 120 \| 0.4620 \| 0.3897 \| 0.4651 \| 0.9419 \| nan \| 0.9413 \| 0.0622 \| 0.8083 \| 0.0 \| 0.0 \| 0.9791 \| nan \| 0.7606 \| 0.0615 \| 0.5631 \| 0.0 \| 0.0 \| 0.9531 \| \| 0.3901 \| 2.59 \| 140 \| 0.4255 \| 0.4088 \| 0.5067 \| 0.9398 \| nan \| 0.9671 \| 0.1414 \| 0.8939 \| 0.0695 \| 0.0 \| 0.9684 \| nan \| 0.7369 \| 0.1358 \| 0.5570 \| 0.0694 \| 0.0 \| 0.9540 \| \| 0.4172 \| 2.96 \| 160 \| 0.3836 \| 0.4307 \| 0.5198 \| 0.9436 \| nan \| 0.9591 \| 0.2494 \| 0.8904 \| 0.0485 \| 0.0 \| 0.9714 \| nan \| 0.7552 \| 0.2212 \| 0.6043 \| 0.0485 \| 0.0 \| 0.9552 \| \| 0.2995 \| 3.33 \| 180 \| 0.3209 \| 0.4755 \| 0.5497 \| 0.9483 \| nan \| 0.9631 \| 0.1625 \| 0.8542 \| 0.3417 \| 0.0 \| 0.9768 \| nan \| 0.7656 \| 0.1585 \| 0.6356 \| 0.3340 \| 0.0 \| 0.9592 \| \| 1.6503 \| 3.7 \| 200 \| 0.2891 \| 0.4914 \| 0.5539 \| 0.9529 \| nan \| 0.9505 \| 0.4372 \| 0.7938 \| 0.1621 \| 0.0 \| 0.9797 \| nan \| 0.8021 \| 0.3827 \| 0.6457 \| 0.1592 \| 0.0 \| 0.9585 \| \| 0.2506 \| 4.07 \| 220 \| 0.2502 \| 0.4813 \| 0.5573 \| 0.9537 \| nan \| 0.9412 \| 0.3606 \| 0.8825 \| 0.1774 \| 0.0 \| 0.9820 \| nan \| 0.8068 \| 0.3260 \| 0.6168 \| 0.1758 \| 0.0 \| 0.9625 \| \| 0.2336 \| 4.44 \| 240 \| 0.2367 \| 0.5543 \| 0.6178 \| 0.9610 \| nan \| 0.9211 \| 0.4445 \| 0.8401 \| 0.5124 \| 0.0 \| 0.9889 \| nan \| 0.8354 \| 0.4044 \| 0.6477 \| 0.4713 \| 0.0 \| 0.9670 \| \| 0.1932 \| 4.81 \| 260 \| 0.2088 \| 0.5436 \| 0.5960 \| 0.9613 \| nan \| 0.9283 \| 0.4497 \| 0.8144 \| 0.3935 \| 0.0 \| 0.9898 \| nan \| 0.8360 \| 0.4188 \| 0.6606 \| 0.3794 \| 0.0 \| 0.9667 \| \| 1.8362 \| 5.19 \| 280 \| 0.1866 \| 0.5727 \| 0.6422 \| 0.9628 \| nan \| 0.9358 \| 0.4776 \| 0.8185 \| 0.6339 \| 0.0 \| 0.9876 \| nan \| 0.8474 \| 0.4320 \| 0.6612 \| 0.5279 \| 0.0 \| 0.9679 \| \| 0.2085 \| 5.56 \| 300 \| 0.1677 \| 0.5749 \| 0.6321 \| 0.9646 \| nan \| 0.9571 \| 0.4811 \| 0.7743 \| 0.5920 \| 0.0 \| 0.9881 \| nan \| 0.8488 \| 0.4438 \| 0.6761 \| 0.5099 \| 0.0 \| 0.9706 \| \| 0.1389 \| 5.93 \| 320 \| 0.1566 \| 0.5761 \| 0.6368 \| 0.9641 \| nan \| 0.9566 \| 0.5138 \| 0.8141 \| 0.5499 \| 0.0 \| 0.9862 \| nan \| 0.8483 \| 0.4661 \| 0.6803 \| 0.4929 \| 0.0 \| 0.9693 \| \| 0.1799 \| 6.3 \| 340 \| 0.1620 \| 0.5836 \| 0.6678 \| 0.9625 \| nan \| 0.9604 \| 0.5769 \| 0.8649 \| 0.6244 \| 0.0 \| 0.9802 \| nan \| 0.8437 \| 0.4753 \| 0.6798 \| 0.5350 \| 0.0 \| 0.9679 \| \| 0.1291 \| 6.67 \| 360 \| 0.1400 \| 0.5926 \| 0.6680 \| 0.9649 \| nan \| 0.9637 \| 0.5702 \| 0.8362 \| 0.6548 \| 0.0 \| 0.9831 \| nan \| 0.8505 \| 0.4971 \| 0.6900 \| 0.5478 \| 0.0 \| 0.9701 \| \| 0.1275 \| 7.04 \| 380 \| 0.1505 \| 0.5710 \| 0.6346 \| 0.9625 \| nan \| 0.9682 \| 0.4619 \| 0.7713 \| 0.6220 \| 0.0 \| 0.9843 \| nan \| 0.8387 \| 0.4057 \| 0.6858 \| 0.5268 \| 0.0 \| 0.9691 \| \| 0.0968 \| 7.41 \| 400 \| 0.1368 \| 0.5774 \| 0.6363 \| 0.9644 \| nan \| 0.9666 \| 0.4759 \| 0.8145 \| 0.5753 \| 0.0 \| 0.9859 \| nan \| 0.8407 \| 0.4348 \| 0.6960 \| 0.5216 \| 0.0 \| 0.9714 \| \| 0.1279 \| 7.78 \| 420 \| 0.1317 \| 0.5911 \| 0.6616 \| 0.9665 \| nan \| 0.9405 \| 0.5449 \| 0.9055 \| 0.5909 \| 0.0 \| 0.9880 \| nan \| 0.8677 \| 0.4742 \| 0.7002 \| 0.5328 \| 0.0 \| 0.9714 \| \| 0.0917 \| 8.15 \| 440 \| 0.1217 \| 0.6187 \| 0.7110 \| 0.9684 \| nan \| 0.9505 \| 0.7295 \| 0.8745 \| 0.7278 \| 0.0 \| 0.9834 \| nan \| 0.8802 \| 0.5694 \| 0.7065 \| 0.5843 \| 0.0 \| 0.9719 \| \| 0.0896 \| 8.52 \| 460 \| 0.1164 \| 0.6055 \| 0.6809 \| 0.9678 \| nan \| 0.9333 \| 0.7235 \| 0.8257 \| 0.6158 \| 0.0 \| 0.9873 \| nan \| 0.8787 \| 0.5338 \| 0.7053 \| 0.5428 \| 0.0 \| 0.9722 \| \| 0.0903 \| 8.89 \| 480 \| 0.1186 \| 0.6195 \| 0.6956 \| 0.9683 \| nan \| 0.9632 \| 0.6929 \| 0.8345 \| 0.6995 \| 0.0 \| 0.9836 \| nan \| 0.8645 \| 0.5528 \| 0.7308 \| 0.5960 \| 0.0 \| 0.9730 \| \| 0.0943 \| 9.26 \| 500 \| 0.1183 \| 0.6205 \| 0.7187 \| 0.9672 \| nan \| 0.9539 \| 0.7619 \| 0.8675 \| 0.7483 \| 0.0 \| 0.9805 \| nan \| 0.8773 \| 0.5444 \| 0.7161 \| 0.6142 \| 0.0 \| 0.9709 \| \| 0.0886 \| 9.63 \| 520 \| 0.1080 \| 0.6233 \| 0.7010 \| 0.9687 \| nan \| 0.9665 \| 0.6540 \| 0.8584 \| 0.7434 \| 0.0 \| 0.9838 \| nan \| 0.8689 \| 0.5572 \| 0.7272 \| 0.6140 \| 0.0 \| 0.9726 \| \| 0.1024 \| 10.0 \| 540 \| 0.1047 \| 0.6224 \| 0.6872 \| 0.9706 \| nan \| 0.9419 \| 0.6739 \| 0.8470 \| 0.6710 \| 0.0 \| 0.9898 \| nan \| 0.8803 \| 0.5702 \| 0.7278 \| 0.5818 \| 0.0 \| 0.9744 \| \| 0.0779 \| 10.37 \| 560 \| 0.1116 \| 0.6168 \| 0.6841 \| 0.9683 \| nan \| 0.9726 \| 0.5517 \| 0.8334 \| 0.7614 \| 0.0 \| 0.9856 \| nan \| 0.8627 \| 0.5211 \| 0.7301 \| 0.6145 \| 0.0 \| 0.9724 \| \| 0.1082 \| 10.74 \| 580 \| 0.1044 \| 0.6277 \| 0.7012 \| 0.9695 \| nan \| 0.9656 \| 0.6787 \| 0.8693 \| 0.7096 \| 0.0 \| 0.9843 \| nan \| 0.8712 \| 0.5607 \| 0.7441 \| 0.6166 \| 0.0 \| 0.9736 \| \| 0.0797 \| 11.11 \| 600 \| 0.1004 \| 0.6356 \| 0.7205 \| 0.9703 \| nan \| 0.9625 \| 0.7506 \| 0.8580 \| 0.7686 \| 0.0 \| 0.9834 \| nan \| 0.8826 \| 0.5894 \| 0.7393 \| 0.6292 \| 0.0 \| 0.9734 \| \| 0.0766 \| 11.48 \| 620 \| 0.0957 \| 0.6377 \| 0.7250 \| 0.9714 \| nan \| 0.9507 \| 0.7480 \| 0.8487 \| 0.8162 \| 0.0 \| 0.9862 \| nan \| 0.8869 \| 0.6181 \| 0.7356 \| 0.6112 \| 0.0 \| 0.9743 \| \| 0.1557 \| 11.85 \| 640 \| 0.0951 \| 0.6367 \| 0.7029 \| 0.9719 \| nan \| 0.9529 \| 0.7316 \| 0.8288 \| 0.7157 \| 0.0 \| 0.9883 \| nan \| 0.8896 \| 0.6117 \| 0.7317 \| 0.6125 \| 0.0 \| 0.9745 \| \| 0.0623 \| 12.22 \| 660 \| 0.0939 \| 0.6462 \| 0.7283 \| 0.9726 \| nan \| 0.9467 \| 0.7844 \| 0.8493 \| 0.8022 \| 0.0 \| 0.9873 \| nan \| 0.8923 \| 0.6157 \| 0.7518 \| 0.6416 \| 0.0 \| 0.9756 \| \| 0.0619 \| 12.59 \| 680 \| 0.0976 \| 0.6349 \| 0.7030 \| 0.9710 \| nan \| 0.9603 \| 0.7091 \| 0.8715 \| 0.6907 \| 0.0 \| 0.9861 \| nan \| 0.8802 \| 0.5868 \| 0.7498 \| 0.6182 \| 0.0 \| 0.9745 \| \| 0.0617 \| 12.96 \| 700 \| 0.0919 \| 0.6354 \| 0.7228 \| 0.9719 \| nan \| 0.9470 \| 0.7145 \| 0.8873 \| 0.8003 \| 0.0 \| 0.9874 \| nan \| 0.8887 \| 0.5929 \| 0.7426 \| 0.6128 \| 0.0 \| 0.9756 \| \| 0.0544 \| 13.33 \| 720 \| 0.0870 \| 0.6517 \| 0.7173 \| 0.9741 \| nan \| 0.9465 \| 0.7433 \| 0.8302 \| 0.7930 \| 0.0 \| 0.9907 \| nan \| 0.9005 \| 0.6367 \| 0.7452 \| 0.6518 \| 0.0 \| 0.9760 \| \| 0.1027 \| 13.7 \| 740 \| 0.0894 \| 0.6446 \| 0.7207 \| 0.9728 \| nan \| 0.9207 \| 0.7490 \| 0.8655 \| 0.7973 \| 0.0 \| 0.9917 \| nan \| 0.8926 \| 0.6210 \| 0.7383 \| 0.6398 \| 0.0 \| 0.9755 \| \| 0.0568 \| 14.07 \| 760 \| 0.0897 \| 0.6434 \| 0.7118 \| 0.9729 \| nan \| 0.9570 \| 0.7121 \| 0.8779 \| 0.7342 \| 0.0016 \| 0.9882 \| nan \| 0.8947 \| 0.6098 \| 0.7497 \| 0.6292 \| 0.0016 \| 0.9753 \| \| 0.0612 \| 14.44 \| 780 \| 0.0851 \| 0.6517 \| 0.7181 \| 0.9740 \| nan \| 0.9599 \| 0.7121 \| 0.8589 \| 0.7874 \| 0.0012 \| 0.9890 \| nan \| 0.8967 \| 0.6182 \| 0.7590 \| 0.6582 \| 0.0012 \| 0.9766 \| \| 0.076 \| 14.81 \| 800 \| 0.0859 \| 0.6485 \| 0.7126 \| 0.9735 \| nan \| 0.9589 \| 0.6878 \| 0.8535 \| 0.7830 \| 0.0033 \| 0.9894 \| nan \| 0.8925 \| 0.6028 \| 0.7595 \| 0.6563 \| 0.0033 \| 0.9765 \| \| 0.0489 \| 15.19 \| 820 \| 0.0815 \| 0.6528 \| 0.7188 \| 0.9744 \| nan \| 0.9484 \| 0.7477 \| 0.8180 \| 0.7988 \| 0.0090 \| 0.9909 \| nan \| 0.9011 \| 0.6294 \| 0.7421 \| 0.6584 \| 0.0090 \| 0.9771 \| \| 0.0859 \| 15.56 \| 840 \| 0.0828 \| 0.6555 \| 0.7296 \| 0.9738 \| nan \| 0.9508 \| 0.7726 \| 0.8563 \| 0.7974 \| 0.0118 \| 0.9884 \| nan \| 0.8961 \| 0.6231 \| 0.7577 \| 0.6674 \| 0.0118 \| 0.9768 \| \| 0.0661 \| 15.93 \| 860 \| 0.0840 \| 0.6555 \| 0.7190 \| 0.9741 \| nan \| 0.9603 \| 0.7187 \| 0.8370 \| 0.7849 \| 0.0234 \| 0.9894 \| nan \| 0.8993 \| 0.6216 \| 0.7473 \| 0.6652 \| 0.0234 \| 0.9766 \| \| 0.0654 \| 16.3 \| 880 \| 0.0840 \| 0.6584 \| 0.7227 \| 0.9742 \| nan \| 0.9564 \| 0.7158 \| 0.8614 \| 0.7876 \| 0.0257 \| 0.9895 \| nan \| 0.8970 \| 0.6175 \| 0.7638 \| 0.6699 \| 0.0257 \| 0.9767 \| \| 0.0607 \| 16.67 \| 900 \| 0.0822 \| 0.6559 \| 0.7390 \| 0.9732 \| nan \| 0.9545 \| 0.7766 \| 0.8700 \| 0.8213 \| 0.0253 \| 0.9865 \| nan \| 0.8920 \| 0.6220 \| 0.7645 \| 0.6554 \| 0.0253 \| 0.9763 \| \| 0.0497 \| 17.04 \| 920 \| 0.0817 \| 0.6673 \| 0.7313 \| 0.9748 \| nan \| 0.9606 \| 0.6869 \| 0.8915 \| 0.7878 \| 0.0713 \| 0.9897 \| nan \| 0.8995 \| 0.6138 \| 0.7722 \| 0.6698 \| 0.0711 \| 0.9773 \| \| 0.0497 \| 17.41 \| 940 \| 0.0841 \| 0.6523 \| 0.7202 \| 0.9731 \| nan \| 0.9672 \| 0.6538 \| 0.8894 \| 0.7861 \| 0.0371 \| 0.9878 \| nan \| 0.8868 \| 0.5868 \| 0.7649 \| 0.6617 \| 0.0371 \| 0.9766 \| \| 0.055 \| 17.78 \| 960 \| 0.0832 \| 0.6568 \| 0.7440 \| 0.9730 \| nan \| 0.9510 \| 0.8045 \| 0.8484 \| 0.8358 \| 0.0383 \| 0.9863 \| nan \| 0.8946 \| 0.6165 \| 0.7569 \| 0.6587 \| 0.0383 \| 0.9762 \| \| 0.0967 \| 18.15 \| 980 \| 0.0854 \| 0.6650 \| 0.7524 \| 0.9726 \| nan \| 0.9647 \| 0.7887 \| 0.8950 \| 0.7981 \| 0.0846 \| 0.9836 \| nan \| 0.8918 \| 0.6210 \| 0.7621 \| 0.6561 \| 0.0839 \| 0.9752 \| \| 0.0436 \| 18.52 \| 1000 \| 0.0814 \| 0.6623 \| 0.7454 \| 0.9739 \| nan \| 0.9593 \| 0.7287 \| 0.8957 \| 0.8436 \| 0.0579 \| 0.9871 \| nan \| 0.8986 \| 0.6138 \| 0.7595 \| 0.6672 \| 0.0577 \| 0.9767 \| \| 0.0465 \| 18.89 \| 1020 \| 0.0773 \| 0.6646 \| 0.7224 \| 0.9754 \| nan \| 0.9473 \| 0.7419 \| 0.8615 \| 0.7462 \| 0.0456 \| 0.9919 \| nan \| 0.9050 \| 0.6357 \| 0.7607 \| 0.6632 \| 0.0456 \| 0.9777 \| \| 0.0672 \| 19.26 \| 1040 \| 0.0778 \| 0.6713 \| 0.7454 \| 0.9751 \| nan \| 0.9464 \| 0.7716 \| 0.8746 \| 0.8165 \| 0.0736 \| 0.9898 \| nan \| 0.9037 \| 0.6316 \| 0.7748 \| 0.6671 \| 0.0730 \| 0.9776 \| \| 0.0592 \| 19.63 \| 1060 \| 0.0799 \| 0.6616 \| 0.7223 \| 0.9747 \| nan \| 0.9596 \| 0.6442 \| 0.8656 \| 0.8116 \| 0.0613 \| 0.9912 \| nan \| 0.8997 \| 0.5864 \| 0.7681 \| 0.6765 \| 0.0612 \| 0.9776 \| \| 0.0876 \| 20.0 \| 1080 \| 0.0794 \| 0.6712 \| 0.7386 \| 0.9745 \| nan \| 0.9608 \| 0.7370 \| 0.8057 \| 0.8434 \| 0.0954 \| 0.9893 \| nan \| 0.8993 \| 0.6468 \| 0.7364 \| 0.6735 \| 0.0943 \| 0.9767 \| \| 0.0483 \| 20.37 \| 1100 \| 0.0742 \| 0.6783 \| 0.7469 \| 0.9760 \| nan \| 0.9576 \| 0.7331 \| 0.8860 \| 0.8272 \| 0.0872 \| 0.9901 \| nan \| 0.9066 \| 0.6469 \| 0.7731 \| 0.6784 \| 0.0866 \| 0.9782 \| \| 0.0837 \| 20.74 \| 1120 \| 0.0746 \| 0.6756 \| 0.7378 \| 0.9762 \| nan \| 0.9491 \| 0.7350 \| 0.8654 \| 0.8146 \| 0.0709 \| 0.9920 \| nan \| 0.9067 \| 0.6411 \| 0.7718 \| 0.6849 \| 0.0705 \| 0.9785 \| \| 0.0685 \| 21.11 \| 1140 \| 0.0743 \| 0.6787 \| 0.7383 \| 0.9759 \| nan \| 0.9475 \| 0.7316 \| 0.8709 \| 0.7859 \| 0.1015 \| 0.9921 \| nan \| 0.9070 \| 0.6387 \| 0.7661 \| 0.6817 \| 0.1004 \| 0.9781 \| \| 0.0596 \| 21.48 \| 1160 \| 0.0751 \| 0.6762 \| 0.7418 \| 0.9753 \| nan \| 0.9499 \| 0.7516 \| 0.8336 \| 0.8182 \| 0.1066 \| 0.9909 \| nan \| 0.9056 \| 0.6307 \| 0.7558 \| 0.6827 \| 0.1051 \| 0.9776 \| \| 0.0608 \| 21.85 \| 1180 \| 0.0728 \| 0.6833 \| 0.7549 \| 0.9763 \| nan \| 0.9428 \| 0.7846 \| 0.8823 \| 0.8219 \| 0.1066 \| 0.9911 \| nan \| 0.9070 \| 0.6479 \| 0.7742 \| 0.6862 \| 0.1055 \| 0.9789 \| \| 0.0918 \| 22.22 \| 1200 \| 0.0731 \| 0.6846 \| 0.7457 \| 0.9763 \| nan \| 0.9597 \| 0.7567 \| 0.8516 \| 0.7984 \| 0.1174 \| 0.9904 \| nan \| 0.9049 \| 0.6518 \| 0.7691 \| 0.6875 \| 0.1153 \| 0.9787 \| \| 0.0539 \| 22.59 \| 1220 \| 0.0743 \| 0.6785 \| 0.7431 \| 0.9756 \| nan \| 0.9563 \| 0.7037 \| 0.8680 \| 0.8308 \| 0.1086 \| 0.9909 \| nan \| 0.9011 \| 0.6200 \| 0.7720 \| 0.6918 \| 0.1075 \| 0.9786 \| \| 0.112 \| 22.96 \| 1240 \| 0.0732 \| 0.6786 \| 0.7429 \| 0.9759 \| nan \| 0.9550 \| 0.7656 \| 0.8544 \| 0.8060 \| 0.0862 \| 0.9902 \| nan \| 0.9026 \| 0.6444 \| 0.7701 \| 0.6902 \| 0.0856 \| 0.9786 \| \| 0.0719 \| 23.33 \| 1260 \| 0.0752 \| 0.6812 \| 0.7419 \| 0.9752 \| nan \| 0.9556 \| 0.7059 \| 0.8454 \| 0.8174 \| 0.1363 \| 0.9910 \| nan \| 0.8975 \| 0.6136 \| 0.7681 \| 0.6947 \| 0.1347 \| 0.9784 \| \| 0.0669 \| 23.7 \| 1280 \| 0.0769 \| 0.6881 \| 0.7787 \| 0.9740 \| nan \| 0.9620 \| 0.7954 \| 0.9020 \| 0.8524 \| 0.1760 \| 0.9846 \| nan \| 0.8958 \| 0.6387 \| 0.7668 \| 0.6829 \| 0.1678 \| 0.9766 \| \| 0.0493 \| 24.07 \| 1300 \| 0.0759 \| 0.6778 \| 0.7518 \| 0.9748 \| nan \| 0.9568 \| 0.7398 \| 0.8994 \| 0.7977 \| 0.1284 \| 0.9885 \| nan \| 0.8995 \| 0.6076 \| 0.7751 \| 0.6805 \| 0.1260 \| 0.9783 \| \| 0.11 \| 24.44 \| 1320 \| 0.0752 \| 0.6879 \| 0.7625 \| 0.9751 \| nan \| 0.9636 \| 0.7402 \| 0.8775 \| 0.8339 \| 0.1720 \| 0.9879 \| nan \| 0.9000 \| 0.6242 \| 0.7765 \| 0.6846 \| 0.1644 \| 0.9779 \| \| 0.0631 \| 24.81 \| 1340 \| 0.0720 \| 0.6898 \| 0.7530 \| 0.9763 \| nan \| 0.9664 \| 0.7223 \| 0.8807 \| 0.8130 \| 0.1459 \| 0.9896 \| nan \| 0.9044 \| 0.6431 \| 0.7812 \| 0.6906 \| 0.1411 \| 0.9786 \| \| 0.038 \| 25.19 \| 1360 \| 0.0719 \| 0.6909 \| 0.7599 \| 0.9761 \| nan \| 0.9491 \| 0.7646 \| 0.8841 \| 0.8190 \| 0.1522 \| 0.9904 \| nan \| 0.9034 \| 0.6346 \| 0.7828 \| 0.6976 \| 0.1480 \| 0.9790 \| \| 0.0431 \| 25.56 \| 1380 \| 0.0721 \| 0.6925 \| 0.7596 \| 0.9760 \| nan \| 0.9651 \| 0.7530 \| 0.8864 \| 0.8031 \| 0.1614 \| 0.9886 \| nan \| 0.9033 \| 0.6418 \| 0.7848 \| 0.6910 \| 0.1558 \| 0.9785 \| \| 0.0289 \| 25.93 \| 1400 \| 0.0711 \| 0.6914 \| 0.7587 \| 0.9762 \| nan \| 0.9381 \| 0.7752 \| 0.8909 \| 0.7947 \| 0.1614 \| 0.9919 \| nan \| 0.9045 \| 0.6471 \| 0.7738 \| 0.6893 \| 0.1549 \| 0.9789 \| \| 0.0426 \| 26.3 \| 1420 \| 0.0706 \| 0.6950 \| 0.7523 \| 0.9768 \| nan \| 0.9640 \| 0.7361 \| 0.8359 \| 0.8120 \| 0.1746 \| 0.9911 \| nan \| 0.9078 \| 0.6530 \| 0.7639 \| 0.6987 \| 0.1673 \| 0.9791 \| \| 0.0467 \| 26.67 \| 1440 \| 0.0707 \| 0.6961 \| 0.7609 \| 0.9765 \| nan \| 0.9479 \| 0.7615 \| 0.9029 \| 0.7805 \| 0.1813 \| 0.9911 \| nan \| 0.9072 \| 0.6454 \| 0.7790 \| 0.6928 \| 0.1734 \| 0.9790 \| \| 0.0408 \| 27.04 \| 1460 \| 0.0709 \| 0.6920 \| 0.7549 \| 0.9765 \| nan \| 0.9492 \| 0.7672 \| 0.8323 \| 0.8389 \| 0.1500 \| 0.9918 \| nan \| 0.9080 \| 0.6569 \| 0.7628 \| 0.7006 \| 0.1450 \| 0.9786 \| \| 0.0615 \| 27.41 \| 1480 \| 0.0703 \| 0.6963 \| 0.7537 \| 0.9768 \| nan \| 0.9581 \| 0.7484 \| 0.8667 \| 0.7992 \| 0.1587 \| 0.9911 \| nan \| 0.9084 \| 0.6610 \| 0.7770 \| 0.7002 \| 0.1525 \| 0.9786 \| \| 0.0533 \| 27.78 \| 1500 \| 0.0706 \| 0.6946 \| 0.7582 \| 0.9764 \| nan \| 0.9456 \| 0.7452 \| 0.8519 \| 0.8340 \| 0.1799 \| 0.9924 \| nan \| 0.9061 \| 0.6408 \| 0.7720 \| 0.6988 \| 0.1708 \| 0.9789 \| \| 0.0711 \| 28.15 \| 1520 \| 0.0706 \| 0.6920 \| 0.7510 \| 0.9763 \| nan \| 0.9534 \| 0.7701 \| 0.8629 \| 0.7678 \| 0.1608 \| 0.9909 \| nan \| 0.9047 \| 0.6450 \| 0.7784 \| 0.6898 \| 0.1554 \| 0.9789 \| \| 0.0393 \| 28.52 \| 1540 \| 0.0682 \| 0.6969 \| 0.7657 \| 0.9768 \| nan \| 0.9478 \| 0.7615 \| 0.8731 \| 0.8445 \| 0.1756 \| 0.9915 \| nan \| 0.9068 \| 0.6512 \| 0.7821 \| 0.6940 \| 0.1677 \| 0.9795 \| \| 0.0554 \| 28.89 \| 1560 \| 0.0692 \| 0.6964 \| 0.7709 \| 0.9760 \| nan \| 0.9589 \| 0.7844 \| 0.8853 \| 0.8399 \| 0.1685 \| 0.9882 \| nan \| 0.9025 \| 0.6465 \| 0.7864 \| 0.7042 \| 0.1603 \| 0.9786 \| \| 0.0434 \| 29.26 \| 1580 \| 0.0701 \| 0.7004 \| 0.7726 \| 0.9764 \| nan \| 0.9626 \| 0.7718 \| 0.9017 \| 0.8279 \| 0.1836 \| 0.9883 \| nan \| 0.9042 \| 0.6550 \| 0.7841 \| 0.7068 \| 0.1736 \| 0.9789 \| \| 0.0291 \| 29.63 \| 1600 \| 0.0697 \| 0.6993 \| 0.7828 \| 0.9764 \| nan \| 0.9514 \| 0.7878 \| 0.9098 \| 0.8654 \| 0.1934 \| 0.9889 \| nan \| 0.9074 \| 0.6529 \| 0.7792 \| 0.6950 \| 0.1820 \| 0.9791 \| \| 2.6544 \| 30.0 \| 1620 \| 0.0684 \| 0.6924 \| 0.7548 \| 0.9766 \| nan \| 0.9521 \| 0.7645 \| 0.8669 \| 0.8127 \| 0.1414 \| 0.9912 \| nan \| 0.9047 \| 0.6439 \| 0.7817 \| 0.7072 \| 0.1378 \| 0.9794 \| \| 0.0575 \| 30.37 \| 1640 \| 0.0695 \| 0.7022 \| 0.7716 \| 0.9766 \| nan \| 0.9572 \| 0.7694 \| 0.8932 \| 0.8291 \| 0.1913 \| 0.9895 \| nan \| 0.9055 \| 0.6478 \| 0.7868 \| 0.7134 \| 0.1807 \| 0.9792 \| \| 0.0445 \| 30.74 \| 1660 \| 0.0695 \| 0.6967 \| 0.7644 \| 0.9766 \| nan \| 0.9469 \| 0.7587 \| 0.8831 \| 0.8402 \| 0.1661 \| 0.9914 \| nan \| 0.9087 \| 0.6416 \| 0.7843 \| 0.7072 \| 0.1596 \| 0.9789 \| \| 0.0487 \| 31.11 \| 1680 \| 0.0700 \| 0.7022 \| 0.7674 \| 0.9768 \| nan \| 0.9598 \| 0.7276 \| 0.8745 \| 0.8351 \| 0.2170 \| 0.9907 \| nan \| 0.9066 \| 0.6404 \| 0.7870 \| 0.7006 \| 0.1996 \| 0.9792 \| \| 0.0539 \| 31.48 \| 1700 \| 0.0700 \| 0.6978 \| 0.7634 \| 0.9764 \| nan \| 0.9489 \| 0.7680 \| 0.8586 \| 0.8232 \| 0.1903 \| 0.9912 \| nan \| 0.9054 \| 0.6365 \| 0.7813 \| 0.7053 \| 0.1791 \| 0.9792 \| \| 0.0617 \| 31.85 \| 1720 \| 0.0717 \| 0.7077 \| 0.7777 \| 0.9765 \| nan \| 0.9562 \| 0.7683 \| 0.8947 \| 0.8146 \| 0.2433 \| 0.9895 \| nan \| 0.9038 \| 0.6415 \| 0.7921 \| 0.7096 \| 0.2201 \| 0.9791 \| \| 0.0391 \| 32.22 \| 1740 \| 0.0690 \| 0.7007 \| 0.7676 \| 0.9766 \| nan \| 0.9557 \| 0.7667 \| 0.8654 \| 0.8402 \| 0.1874 \| 0.9902 \| nan \| 0.9044 \| 0.6447 \| 0.7851 \| 0.7129 \| 0.1775 \| 0.9793 \| \| 0.0834 \| 32.59 \| 1760 \| 0.0688 \| 0.7015 \| 0.7699 \| 0.9768 \| nan \| 0.9519 \| 0.7571 \| 0.8926 \| 0.8319 \| 0.1954 \| 0.9907 \| nan \| 0.9048 \| 0.6459 \| 0.7860 \| 0.7089 \| 0.1840 \| 0.9795 \| \| 0.0412 \| 32.96 \| 1780 \| 0.0694 \| 0.7080 \| 0.7786 \| 0.9768 \| nan \| 0.9560 \| 0.7600 \| 0.8859 \| 0.8386 \| 0.2410 \| 0.9901 \| nan \| 0.9077 \| 0.6464 \| 0.7894 \| 0.7049 \| 0.2203 \| 0.9791 \| \| 0.0476 \| 33.33 \| 1800 \| 0.0698 \| 0.7045 \| 0.7689 \| 0.9766 \| nan \| 0.9582 \| 0.7688 \| 0.8677 \| 0.8225 \| 0.2062 \| 0.9900 \| nan \| 0.9055 \| 0.6537 \| 0.7854 \| 0.7090 \| 0.1948 \| 0.9788 \| \| 0.0407 \| 33.7 \| 1820 \| 0.0684 \| 0.7103 \| 0.7824 \| 0.9770 \| nan \| 0.9535 \| 0.7700 \| 0.8809 \| 0.8575 \| 0.2423 \| 0.9904 \| nan \| 0.9077 \| 0.6555 \| 0.7885 \| 0.7073 \| 0.2233 \| 0.9794 \| \| 0.0307 \| 34.07 \| 1840 \| 0.0717 \| 0.7072 \| 0.7902 \| 0.9755 \| nan \| 0.9608 \| 0.7869 \| 0.8959 \| 0.8623 \| 0.2484 \| 0.9867 \| nan \| 0.9000 \| 0.6421 \| 0.7882 \| 0.7089 \| 0.2258 \| 0.9781 \| \| 0.0236 \| 34.44 \| 1860 \| 0.0700 \| 0.7104 \| 0.7889 \| 0.9765 \| nan \| 0.9502 \| 0.7737 \| 0.8754 \| 0.8807 \| 0.2639 \| 0.9900 \| nan \| 0.9041 \| 0.6532 \| 0.7850 \| 0.7041 \| 0.2370 \| 0.9791 \| \| 0.0752 \| 34.81 \| 1880 \| 0.0689 \| 0.7068 \| 0.7716 \| 0.9766 \| nan \| 0.9564 \| 0.7689 \| 0.8845 \| 0.8157 \| 0.2139 \| 0.9899 \| nan \| 0.9032 \| 0.6542 \| 0.7915 \| 0.7115 \| 0.2012 \| 0.9790 \| \| 0.0301 \| 35.19 \| 1900 \| 0.0701 \| 0.7021 \| 0.7862 \| 0.9761 \| nan \| 0.9427 \| 0.8005 \| 0.8935 \| 0.8702 \| 0.2209 \| 0.9896 \| nan \| 0.9046 \| 0.6342 \| 0.7870 \| 0.7013 \| 0.2064 \| 0.9793 \| \| 0.0457 \| 35.56 \| 1920 \| 0.0694 \| 0.7053 \| 0.7683 \| 0.9769 \| nan \| 0.9525 \| 0.7313 \| 0.8720 \| 0.8319 \| 0.2302 \| 0.9918 \| nan \| 0.9053 \| 0.6363 \| 0.7872 \| 0.7100 \| 0.2136 \| 0.9796 \| \| 0.033 \| 35.93 \| 1940 \| 0.0695 \| 0.7078 \| 0.7745 \| 0.9766 \| nan \| 0.9618 \| 0.7552 \| 0.8873 \| 0.8138 \| 0.2392 \| 0.9894 \| nan \| 0.9037 \| 0.6423 \| 0.7926 \| 0.7104 \| 0.2183 \| 0.9793 \| \| 0.0231 \| 36.3 \| 1960 \| 0.0687 \| 0.7060 \| 0.7741 \| 0.9769 \| nan \| 0.9493 \| 0.7574 \| 0.8883 \| 0.8302 \| 0.2284 \| 0.9912 \| nan \| 0.9052 \| 0.6403 \| 0.7898 \| 0.7092 \| 0.2118 \| 0.9798 \| \| 0.038 \| 36.67 \| 1980 \| 0.0685 \| 0.7082 \| 0.7775 \| 0.9767 \| nan \| 0.9640 \| 0.7617 \| 0.8762 \| 0.8311 \| 0.2431 \| 0.9891 \| nan \| 0.9042 \| 0.6457 \| 0.7927 \| 0.7064 \| 0.2206 \| 0.9795 \| \| 0.0421 \| 37.04 \| 2000 \| 0.0684 \| 0.7083 \| 0.7806 \| 0.9767 \| nan \| 0.9523 \| 0.7679 \| 0.8937 \| 0.8367 \| 0.2427 \| 0.9902 \| nan \| 0.9048 \| 0.6430 \| 0.7934 \| 0.7069 \| 0.2222 \| 0.9796 \| \| 0.0431 \| 37.41 \| 2020 \| 0.0700 \| 0.7097 \| 0.7792 \| 0.9763 \| nan \| 0.9580 \| 0.7520 \| 0.8796 \| 0.8316 \| 0.2641 \| 0.9896 \| nan \| 0.9019 \| 0.6332 \| 0.7909 \| 0.7144 \| 0.2385 \| 0.9793 \| \| 0.0384 \| 37.78 \| 2040 \| 0.0671 \| 0.7123 \| 0.7770 \| 0.9774 \| nan \| 0.9580 \| 0.7566 \| 0.8859 \| 0.8268 \| 0.2441 \| 0.9908 \| nan \| 0.9085 \| 0.6542 \| 0.7907 \| 0.7163 \| 0.2242 \| 0.9799 \| \| 0.0405 \| 38.15 \| 2060 \| 0.0672 \| 0.7141 \| 0.7884 \| 0.9772 \| nan \| 0.9559 \| 0.7886 \| 0.8953 \| 0.8413 \| 0.2596 \| 0.9895 \| nan \| 0.9092 \| 0.6515 \| 0.7949 \| 0.7149 \| 0.2345 \| 0.9797 \| \| 0.0474 \| 38.52 \| 2080 \| 0.0677 \| 0.7122 \| 0.7779 \| 0.9773 \| nan \| 0.9611 \| 0.7538 \| 0.8965 \| 0.8228 \| 0.2431 \| 0.9901 \| nan \| 0.9072 \| 0.6535 \| 0.7946 \| 0.7159 \| 0.2224 \| 0.9798 \| \| 0.0862 \| 38.89 \| 2100 \| 0.0682 \| 0.7115 \| 0.7787 \| 0.9772 \| nan \| 0.9539 \| 0.7524 \| 0.8878 \| 0.8352 \| 0.2516 \| 0.9910 \| nan \| 0.9073 \| 0.6464 \| 0.7929 \| 0.7136 \| 0.2290 \| 0.9798 \| \| 0.0354 \| 39.26 \| 2120 \| 0.0677 \| 0.7085 \| 0.7713 \| 0.9772 \| nan \| 0.9562 \| 0.7498 \| 0.8778 \| 0.8236 \| 0.2290 \| 0.9912 \| nan \| 0.9084 \| 0.6464 \| 0.7894 \| 0.7146 \| 0.2127 \| 0.9796 \| \| 0.0549 \| 39.63 \| 2140 \| 0.0686 \| 0.7079 \| 0.7702 \| 0.9768 \| nan \| 0.9573 \| 0.7483 \| 0.8772 \| 0.8090 \| 0.2386 \| 0.9907 \| nan \| 0.9060 \| 0.6389 \| 0.7896 \| 0.7132 \| 0.2204 \| 0.9794 \| \| 0.0355 \| 40.0 \| 2160 \| 0.0688 \| 0.7131 \| 0.7845 \| 0.9768 \| nan \| 0.9595 \| 0.7532 \| 0.8882 \| 0.8417 \| 0.2747 \| 0.9896 \| nan \| 0.9049 \| 0.6461 \| 0.7936 \| 0.7110 \| 0.2441 \| 0.9793 \| \| 2.1231 \| 40.37 \| 2180 \| 0.0676 \| 0.7069 \| 0.7721 \| 0.9769 \| nan \| 0.9521 \| 0.7665 \| 0.8752 \| 0.8309 \| 0.2170 \| 0.9910 \| nan \| 0.9060 \| 0.6463 \| 0.7894 \| 0.7156 \| 0.2044 \| 0.9796 \| \| 0.0577 \| 40.74 \| 2200 \| 0.0669 \| 0.7129 \| 0.7797 \| 0.9774 \| nan \| 0.9582 \| 0.7679 \| 0.8834 \| 0.8338 \| 0.2445 \| 0.9904 \| nan \| 0.9097 \| 0.6600 \| 0.7920 \| 0.7108 \| 0.2256 \| 0.9796 \| \| 0.0318 \| 41.11 \| 2220 \| 0.0676 \| 0.7131 \| 0.7817 \| 0.9771 \| nan \| 0.9615 \| 0.7636 \| 0.8916 \| 0.8335 \| 0.2504 \| 0.9895 \| nan \| 0.9067 \| 0.6543 \| 0.7943 \| 0.7147 \| 0.2292 \| 0.9795 \| \| 0.0356 \| 41.48 \| 2240 \| 0.0674 \| 0.7167 \| 0.7861 \| 0.9773 \| nan \| 0.9582 \| 0.7643 \| 0.8956 \| 0.8287 \| 0.2800 \| 0.9900 \| nan \| 0.9076 \| 0.6530 \| 0.7965 \| 0.7154 \| 0.2479 \| 0.9797 \| \| 0.0816 \| 41.85 \| 2260 \| 0.0676 \| 0.7126 \| 0.7728 \| 0.9774 \| nan \| 0.9568 \| 0.7636 \| 0.8714 \| 0.8100 \| 0.2439 \| 0.9912 \| nan \| 0.9090 \| 0.6544 \| 0.7924 \| 0.7146 \| 0.2254 \| 0.9797 \| \| 0.0561 \| 42.22 \| 2280 \| 0.0666 \| 0.7130 \| 0.7759 \| 0.9775 \| nan \| 0.9537 \| 0.7694 \| 0.8763 \| 0.8244 \| 0.2400 \| 0.9914 \| nan \| 0.9092 \| 0.6624 \| 0.7912 \| 0.7133 \| 0.2222 \| 0.9797 \| \| 0.0336 \| 42.59 \| 2300 \| 0.0669 \| 0.7173 \| 0.7858 \| 0.9776 \| nan \| 0.9553 \| 0.7755 \| 0.8866 \| 0.8381 \| 0.2685 \| 0.9907 \| nan \| 0.9095 \| 0.6654 \| 0.7936 \| 0.7138 \| 0.2419 \| 0.9798 \| \| 0.0313 \| 42.96 \| 2320 \| 0.0669 \| 0.7119 \| 0.7773 \| 0.9775 \| nan \| 0.9560 \| 0.7609 \| 0.8968 \| 0.8289 \| 0.2304 \| 0.9907 \| nan \| 0.9084 \| 0.6581 \| 0.7945 \| 0.7150 \| 0.2154 \| 0.9798 \| \| 0.0611 \| 43.33 \| 2340 \| 0.0670 \| 0.7090 \| 0.7756 \| 0.9772 \| nan \| 0.9547 \| 0.7711 \| 0.8810 \| 0.8347 \| 0.2215 \| 0.9907 \| nan \| 0.9071 \| 0.6520 \| 0.7914 \| 0.7153 \| 0.2082 \| 0.9798 \| \| 0.0329 \| 43.7 \| 2360 \| 0.0669 \| 0.7146 \| 0.7803 \| 0.9775 \| nan \| 0.9577 \| 0.7647 \| 0.8782 \| 0.8346 \| 0.2555 \| 0.9908 \| nan \| 0.9091 \| 0.6560 \| 0.7926 \| 0.7162 \| 0.2337 \| 0.9800 \| \| 0.0528 \| 44.07 \| 2380 \| 0.0668 \| 0.7131 \| 0.7721 \| 0.9776 \| nan \| 0.9594 \| 0.7513 \| 0.8762 \| 0.8097 \| 0.2449 \| 0.9913 \| nan \| 0.9087 \| 0.6565 \| 0.7923 \| 0.7147 \| 0.2262 \| 0.9799 \| \| 0.0729 \| 44.44 \| 2400 \| 0.0671 \| 0.7131 \| 0.7748 \| 0.9775 \| nan \| 0.9578 \| 0.7573 \| 0.8777 \| 0.8187 \| 0.2461 \| 0.9911 \| nan \| 0.9085 \| 0.6566 \| 0.7912 \| 0.7152 \| 0.2272 \| 0.9798 \| \| 0.0383 \| 44.81 \| 2420 \| 0.0675 \| 0.7108 \| 0.7809 \| 0.9771 \| nan \| 0.9506 \| 0.7742 \| 0.8796 \| 0.8459 \| 0.2443 \| 0.9909 \| nan \| 0.9072 \| 0.6499 \| 0.7903 \| 0.7116 \| 0.2262 \| 0.9798 \| \| 0.0565 \| 45.19 \| 2440 \| 0.0674 \| 0.7154 \| 0.7821 \| 0.9774 \| nan \| 0.9590 \| 0.7609 \| 0.8808 \| 0.8296 \| 0.2720 \| 0.9905 \| nan \| 0.9086 \| 0.6546 \| 0.7931 \| 0.7129 \| 0.2436 \| 0.9797 \| \| 0.0417 \| 45.56 \| 2460 \| 0.0671 \| 0.7149 \| 0.7809 \| 0.9776 \| nan \| 0.9563 \| 0.7631 \| 0.8843 \| 0.8383 \| 0.2524 \| 0.9909 \| nan \| 0.9095 \| 0.6570 \| 0.7939 \| 0.7174 \| 0.2314 \| 0.9799 \| \| 0.024 \| 45.93 \| 2480 \| 0.0678 \| 0.7159 \| 0.7872 \| 0.9772 \| nan \| 0.9564 \| 0.7709 \| 0.8828 \| 0.8458 \| 0.2769 \| 0.9902 \| nan \| 0.9074 \| 0.6549 \| 0.7927 \| 0.7148 \| 0.2458 \| 0.9797 \| \| 0.0496 \| 46.3 \| 2500 \| 0.0668 \| 0.7148 \| 0.7836 \| 0.9775 \| nan \| 0.9554 \| 0.7648 \| 0.8905 \| 0.8390 \| 0.2614 \| 0.9907 \| nan \| 0.9092 \| 0.6567 \| 0.7931 \| 0.7131 \| 0.2368 \| 0.9799 \| \| 0.0428 \| 46.67 \| 2520 \| 0.0668 \| 0.7150 \| 0.7857 \| 0.9773 \| nan \| 0.9571 \| 0.7641 \| 0.8969 \| 0.8438 \| 0.2624 \| 0.9902 \| nan \| 0.9083 \| 0.6559 \| 0.7941 \| 0.7144 \| 0.2372 \| 0.9798 \| \| 0.0458 \| 47.04 \| 2540 \| 0.0665 \| 0.7141 \| 0.7811 \| 0.9775 \| nan \| 0.9540 \| 0.7665 \| 0.8957 \| 0.8351 \| 0.2443 \| 0.9908 \| nan \| 0.9088 \| 0.6565 \| 0.7946 \| 0.7192 \| 0.2257 \| 0.9799 \| \| 0.0429 \| 47.41 \| 2560 \| 0.0673 \| 0.7131 \| 0.7791 \| 0.9773 \| nan \| 0.9604 \| 0.7621 \| 0.8836 \| 0.8325 \| 0.2457 \| 0.9901 \| nan \| 0.9073 \| 0.6536 \| 0.7936 \| 0.7180 \| 0.2266 \| 0.9797 \| \| 0.0449 \| 47.78 \| 2580 \| 0.0666 \| 0.7144 \| 0.7786 \| 0.9775 \| nan \| 0.9571 \| 0.7630 \| 0.8873 \| 0.8248 \| 0.2484 \| 0.9908 \| nan \| 0.9089 \| 0.6565 \| 0.7944 \| 0.7188 \| 0.2281 \| 0.9799 \| \| 0.0302 \| 48.15 \| 2600 \| 0.0665 \| 0.7155 \| 0.7800 \| 0.9776 \| nan \| 0.9552 \| 0.7637 \| 0.8904 \| 0.8251 \| 0.2545 \| 0.9911 \| nan \| 0.9094 \| 0.6580 \| 0.7936 \| 0.7196 \| 0.2326 \| 0.9799 \| \| 0.029 \| 48.52 \| 2620 \| 0.0675 \| 0.7176 \| 0.7877 \| 0.9774 \| nan \| 0.9588 \| 0.7640 \| 0.8947 \| 0.8445 \| 0.2744 \| 0.9901 \| nan \| 0.9081 \| 0.6575 \| 0.7948 \| 0.7204 \| 0.2447 \| 0.9798 \| \| 1.9447 \| 48.89 \| 2640 \| 0.0662 \| 0.7130 \| 0.7803 \| 0.9774 \| nan \| 0.9529 \| 0.7688 \| 0.8720 \| 0.8497 \| 0.2471 \| 0.9912 \| nan \| 0.9085 \| 0.6550 \| 0.7904 \| 0.7170 \| 0.2272 \| 0.9799 \| \| 0.0247 \| 49.26 \| 2660 \| 0.0674 \| 0.7163 \| 0.7870 \| 0.9773 \| nan \| 0.9593 \| 0.7720 \| 0.8815 \| 0.8486 \| 0.2704 \| 0.9899 \| nan \| 0.9077 \| 0.6575 \| 0.7928 \| 0.7181 \| 0.2422 \| 0.9797 \| \| 0.0369 \| 49.63 \| 2680 \| 0.0662 \| 0.7154 \| 0.7839 \| 0.9775 \| nan \| 0.9547 \| 0.7711 \| 0.8850 \| 0.8430 \| 0.2588 \| 0.9907 \| nan \| 0.9087 \| 0.6571 \| 0.7934 \| 0.7181 \| 0.2354 \| 0.9799 \| \| 0.0317 \| 50.0 \| 2700 \| 0.0665 \| 0.7168 \| 0.7840 \| 0.9775 \| nan \| 0.9559 \| 0.7693 \| 0.8882 \| 0.8345 \| 0.2653 \| 0.9907 \| nan \| 0.9088 \| 0.6583 \| 0.7943 \| 0.7201 \| 0.2393 \| 0.9799 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "road", "side walk", "car", "pedestrian", "sign", "other" ]
DrWasabiii/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.5644 - Mean Iou: 0.3202 - Mean Accuracy: 0.3861 - Overall Accuracy: 0.8525 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8598 - Accuracy Flat-sidewalk: 0.9440 - Accuracy Flat-crosswalk: 0.6837 - Accuracy Flat-cyclinglane: 0.8295 - Accuracy Flat-parkingdriveway: 0.5586 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.6015 - Accuracy Human-person: 0.6019 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9395 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.6060 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8961 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.5249 - Accuracy Construction-fenceguardrail: 0.4892 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3733 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9376 - Accuracy Nature-terrain: 0.9033 - Accuracy Sky: 0.9668 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.2540 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7395 - Iou Flat-sidewalk: 0.8717 - Iou Flat-crosswalk: 0.5750 - Iou Flat-cyclinglane: 0.6481 - Iou Flat-parkingdriveway: 0.4281 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.4534 - Iou Human-person: 0.4648 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.8127 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.3687 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7090 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3987 - Iou Construction-fenceguardrail: 0.4429 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.2772 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8463 - Iou Nature-terrain: 0.7719 - Iou Sky: 0.9189 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.2004 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| 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\| 1.462 \| 0.1 \| 20 \| 1.2595 \| 0.1419 \| 0.1947 \| 0.7120 \| nan \| 0.7582 \| 0.9092 \| 0.0 \| 0.0660 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9133 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8346 \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9263 \| 0.7278 \| 0.9005 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4874 \| 0.7290 \| 0.0 \| 0.0657 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5744 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5093 \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7362 \| 0.5259 \| 0.7708 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3298 \| 0.2 \| 40 \| 1.1696 \| 0.1552 \| 0.2048 \| 0.7307 \| nan \| 0.8291 \| 0.9109 \| 0.0 \| 0.2907 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9111 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8254 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9266 \| 0.7259 \| 0.9291 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5114 \| 0.7458 \| 0.0 \| 0.2725 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6361 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5295 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7486 \| 0.5861 \| 0.7800 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8454 \| 0.3 \| 60 \| 1.1409 \| 0.1623 \| 0.2113 \| 0.7445 \| nan \| 0.8024 \| 0.9346 \| 0.0 \| 0.4023 \| 0.0079 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8823 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8139 \| 0.0 \| 0.0016 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9372 \| 0.8410 \| 0.9269 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5516 \| 0.7597 \| 0.0 \| 0.3598 \| 0.0078 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6808 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5406 \| 0.0 \| 0.0016 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7382 \| 0.5945 \| 0.7969 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9115 \| 0.4 \| 80 \| 1.0588 \| 0.1639 \| 0.2118 \| 0.7400 \| nan \| 0.8770 \| 0.8928 \| 0.0 \| 0.4710 \| 0.0030 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9011 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8552 \| 0.0 \| 0.0003 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9351 \| 0.6907 \| 0.9398 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5108 \| 0.7636 \| 0.0 \| 0.4068 \| 0.0030 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6580 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5332 \| 0.0 \| 0.0003 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7594 \| 0.6421 \| 0.8024 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.011 \| 0.5 \| 100 \| 1.0279 \| 0.1646 \| 0.2179 \| 0.7466 \| nan \| 0.8087 \| 0.9090 \| 0.0 \| 0.5313 \| 0.0041 \| nan \| 0.0166 \| 0.0 \| 0.0 \| 0.9286 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8570 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9349 \| 0.8582 \| 0.9038 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5617 \| 0.7613 \| 0.0 \| 0.4246 \| 0.0041 \| nan \| 0.0163 \| 0.0 \| 0.0 \| 0.6325 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5354 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7643 \| 0.5952 \| 0.8043 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3211 \| 0.6 \| 120 \| 1.0123 \| 0.1692 \| 0.2180 \| 0.7496 \| nan \| 0.7381 \| 0.9409 \| 0.0 \| 0.5419 \| 0.0436 \| nan \| 0.1004 \| 0.0 \| 0.0 \| 0.9234 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8649 \| 0.0 \| 0.0082 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9434 \| 0.7166 \| 0.9375 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5590 \| 0.7596 \| 0.0 \| 0.3963 \| 0.0422 \| nan \| 0.0943 \| 0.0 \| 0.0 \| 0.6632 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5399 \| 0.0 \| 0.0081 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7656 \| 0.5951 \| 0.8222 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.0065 \| 0.7 \| 140 \| 0.9791 \| 0.1689 \| 0.2176 \| 0.7502 \| nan \| 0.8268 \| 0.9187 \| 0.0 \| 0.5011 \| 0.0677 \| nan \| 0.0773 \| 0.0 \| 0.0 \| 0.9338 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9045 \| 0.0 \| 0.0077 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9298 \| 0.6508 \| 0.9265 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5600 \| 0.7809 \| 0.0 \| 0.4371 \| 0.0637 \| nan \| 0.0721 \| 0.0 \| 0.0 \| 0.6222 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5215 \| 0.0 \| 0.0075 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7620 \| 0.5671 \| 0.8406 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8046 \| 0.8 \| 160 \| 0.9695 \| 0.1703 \| 0.2147 \| 0.7442 \| nan \| 0.6653 \| 0.9719 \| 0.0 \| 0.4870 \| 0.0490 \| nan \| 0.0337 \| 0.0 \| 0.0 \| 0.8754 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7818 \| 0.0 \| 0.0510 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| 0.9269 \| 0.8824 \| 0.9306 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5548 \| 0.7164 \| 0.0 \| 0.3614 \| 0.0463 \| nan \| 0.0332 \| 0.0 \| 0.0 \| 0.7196 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5634 \| 0.0 \| 0.0487 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| 0.7624 \| 0.6451 \| 0.8265 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8299 \| 0.9 \| 180 \| 0.9008 \| 0.1791 \| 0.2264 \| 0.7632 \| nan \| 0.7556 \| 0.9611 \| 0.0 \| 0.5157 \| 0.0804 \| nan \| 0.0962 \| 0.0 \| 0.0 \| 0.9573 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8870 \| 0.0 \| 0.0491 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.9050 \| 0.8751 \| 0.9354 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6112 \| 0.7531 \| 0.0 \| 0.4358 \| 0.0736 \| nan \| 0.0907 \| 0.0 \| 0.0 \| 0.6175 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5662 \| 0.0 \| 0.0461 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.7988 \| 0.7005 \| 0.8590 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.7382 \| 1.0 \| 200 \| 0.9204 \| 0.1770 \| 0.2246 \| 0.7550 \| nan \| 0.7011 \| 0.9563 \| 0.0 \| 0.5585 \| 0.0606 \| nan \| 0.0821 \| 0.0 \| 0.0 \| 0.9182 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8852 \| 0.0 \| 0.0592 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.9046 \| 0.9074 \| 0.9303 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5664 \| 0.7432 \| 0.0 \| 0.4047 \| 0.0573 \| nan \| 0.0758 \| 0.0 \| 0.0 \| 0.7003 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5667 \| 0.0 \| 0.0544 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.7864 \| 0.6709 \| 0.8622 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9823 \| 1.1 \| 220 \| 0.8596 \| 0.1868 \| 0.2319 \| 0.7714 \| nan \| 0.8631 \| 0.9325 \| 0.0 \| 0.4702 \| 0.0986 \| nan \| 0.1854 \| 0.0 \| 0.0 \| 0.9024 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8971 \| 0.0 \| 0.0805 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0076 \| 0.0 \| 0.0 \| 0.9362 \| 0.8675 \| 0.9474 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.5915 \| 0.7841 \| 0.0 \| 0.4126 \| 0.0936 \| nan \| 0.1616 \| 0.0 \| 0.0 \| 0.7255 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5662 \| 0.0 \| 0.0702 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0076 \| 0.0 \| 0.0 \| 0.7982 \| 0.7233 \| 0.8577 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 0.7915 \| 1.2 \| 240 \| 0.8611 \| 0.1979 \| 0.2482 \| 0.7736 \| nan \| 0.8241 \| 0.9115 \| 0.0 \| 0.6181 \| 0.2810 \| nan \| 0.2094 \| 0.0 \| 0.0 \| 0.9181 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8534 \| 0.0 \| 0.3040 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0363 \| 0.0 \| 0.0 \| 0.9445 \| 0.8380 \| 0.9548 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5898 \| 0.7872 \| 0.0 \| 0.4598 \| 0.2158 \| nan \| 0.1784 \| 0.0 \| 0.0 \| 0.7214 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5774 \| 0.0 \| 0.2155 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0358 \| 0.0 \| 0.0 \| 0.7978 \| 0.7230 \| 0.8327 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9356 \| 1.3 \| 260 \| 0.8942 \| 0.1878 \| 0.2379 \| 0.7599 \| nan \| 0.6265 \| 0.9610 \| 0.0 \| 0.4888 \| 0.2483 \| nan \| 0.3971 \| 0.0 \| 0.0 \| 0.9394 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9111 \| 0.0 \| 0.0591 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0127 \| 0.0 \| 0.0 \| 0.9038 \| 0.8992 \| 0.9293 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5615 \| 0.7544 \| 0.0 \| 0.3832 \| 0.1988 \| nan \| 0.2752 \| 0.0 \| 0.0 \| 0.6594 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5589 \| 0.0 \| 0.0521 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0126 \| 0.0 \| 0.0 \| 0.8008 \| 0.6830 \| 0.8813 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8097 \| 1.4 \| 280 \| 0.8333 \| 0.1992 \| 0.2498 \| 0.7752 \| nan \| 0.8669 \| 0.9017 \| 0.0 \| 0.5555 \| 0.3048 \| nan \| 0.3696 \| 0.0 \| 0.0 \| 0.9208 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8507 \| 0.0 \| 0.1704 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0472 \| 0.0 \| 0.0 \| 0.9403 \| 0.8713 \| 0.9429 \| 0.0 \| 0.0 \| 0.0010 \| 0.0 \| nan \| 0.5646 \| 0.8035 \| 0.0 \| 0.4533 \| 0.2374 \| nan \| 0.2820 \| 0.0 \| 0.0 \| 0.6974 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5820 \| 0.0 \| 0.1490 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0466 \| 0.0 \| 0.0 \| 0.8006 \| 0.7220 \| 0.8348 \| 0.0 \| 0.0 \| 0.0010 \| 0.0 \| \| 0.9072 \| 1.5 \| 300 \| 0.7929 \| 0.2056 \| 0.2588 \| 0.7819 \| nan \| 0.8537 \| 0.9209 \| 0.0 \| 0.5869 \| 0.3037 \| nan \| 0.2640 \| 0.0 \| 0.0 \| 0.9100 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8098 \| 0.0 \| 0.5714 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0396 \| 0.0 \| 0.0 \| 0.9337 \| 0.8841 \| 0.9436 \| 0.0 \| 0.0 \| 0.0002 \| 0.0 \| nan \| 0.6061 \| 0.8024 \| 0.0 \| 0.4735 \| 0.2292 \| nan \| 0.2244 \| 0.0 \| 0.0 \| 0.7509 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5947 \| 0.0 \| 0.2729 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0390 \| 0.0 \| 0.0 \| 0.8104 \| 0.7065 \| 0.8620 \| 0.0 \| 0.0 \| 0.0002 \| 0.0 \| \| 1.0461 \| 1.6 \| 320 \| 0.7737 \| 0.2131 \| 0.2593 \| 0.7938 \| nan \| 0.8158 \| 0.9467 \| 0.0 \| 0.6523 \| 0.3113 \| nan \| 0.3152 \| 0.0048 \| 0.0 \| 0.9263 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8790 \| 0.0 \| 0.3751 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0794 \| 0.0 \| 0.0 \| 0.9515 \| 0.8307 \| 0.9473 \| 0.0 \| 0.0 \| 0.0014 \| 0.0 \| nan \| 0.6374 \| 0.7992 \| 0.0 \| 0.5435 \| 0.2390 \| nan \| 0.2686 \| 0.0048 \| 0.0 \| 0.7307 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6365 \| 0.0 \| 0.2649 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0758 \| 0.0 \| 0.0 \| 0.7983 \| 0.7205 \| 0.8855 \| 0.0 \| 0.0 \| 0.0014 \| 0.0 \| \| 0.9489 \| 1.7 \| 340 \| 0.8072 \| 0.2036 \| 0.2463 \| 0.7800 \| nan \| 0.7780 \| 0.9572 \| 0.0 \| 0.4694 \| 0.1562 \| nan \| 0.3721 \| 0.0209 \| 0.0 \| 0.8907 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9140 \| 0.0 \| 0.2420 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0765 \| 0.0 \| 0.0 \| 0.9421 \| 0.8619 \| 0.9524 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| nan \| 0.6090 \| 0.7697 \| 0.0 \| 0.4229 \| 0.1445 \| nan \| 0.2903 \| 0.0208 \| 0.0 \| 0.7654 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5966 \| 0.0 \| 0.1859 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0727 \| 0.0 \| 0.0 \| 0.8061 \| 0.7399 \| 0.8871 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| \| 0.9753 \| 1.8 \| 360 \| 0.7746 \| 0.2153 \| 0.2641 \| 0.7867 \| nan \| 0.8023 \| 0.9353 \| 0.0 \| 0.6272 \| 0.2777 \| nan \| 0.4336 \| 0.1660 \| 0.0 \| 0.9215 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.8746 \| 0.0 \| 0.3293 \| 0.0026 \| 0.0 \| nan \| 0.0 \| 0.1379 \| 0.0 \| 0.0 \| 0.9597 \| 0.7740 \| 0.9401 \| 0.0 \| 0.0 \| 0.0042 \| 0.0 \| nan \| 0.6029 \| 0.8027 \| 0.0 \| 0.4873 \| 0.2297 \| nan \| 0.3194 \| 0.1551 \| 0.0 \| 0.7449 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.6383 \| 0.0 \| 0.2494 \| 0.0026 \| 0.0 \| nan \| 0.0 \| 0.1280 \| 0.0 \| 0.0 \| 0.7825 \| 0.6509 \| 0.8777 \| 0.0 \| 0.0 \| 0.0042 \| 0.0 \| \| 0.4749 \| 1.9 \| 380 \| 0.7667 \| 0.2100 \| 0.2617 \| 0.7877 \| nan \| 0.8307 \| 0.9286 \| 0.0 \| 0.6598 \| 0.2633 \| nan \| 0.4358 \| 0.0341 \| 0.0 \| 0.9003 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9166 \| 0.0 \| 0.3043 \| 0.0007 \| 0.0 \| nan \| 0.0 \| 0.0742 \| 0.0 \| 0.0 \| 0.9062 \| 0.9248 \| 0.9341 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| nan \| 0.6254 \| 0.8110 \| 0.0 \| 0.5224 \| 0.2188 \| nan \| 0.3221 \| 0.0340 \| 0.0 \| 0.7415 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5945 \| 0.0 \| 0.2276 \| 0.0007 \| 0.0 \| nan \| 0.0 \| 0.0714 \| 0.0 \| 0.0 \| 0.7916 \| 0.6625 \| 0.8869 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| \| 0.8488 \| 2.0 \| 400 \| 0.7579 \| 0.2130 \| 0.2610 \| 0.7868 \| nan \| 0.7601 \| 0.9616 \| 0.0 \| 0.6160 \| 0.1515 \| nan \| 0.4506 \| 0.0924 \| 0.0 \| 0.9517 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8802 \| 0.0 \| 0.2621 \| 0.0050 \| 0.0 \| nan \| 0.0 \| 0.1955 \| 0.0 \| 0.0 \| 0.9306 \| 0.8694 \| 0.9588 \| 0.0 \| 0.0 \| 0.0059 \| 0.0 \| nan \| 0.6188 \| 0.7790 \| 0.0 \| 0.4692 \| 0.1373 \| nan \| 0.3326 \| 0.0905 \| 0.0 \| 0.6748 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6297 \| 0.0 \| 0.1994 \| 0.0050 \| 0.0 \| nan \| 0.0 \| 0.1755 \| 0.0 \| 0.0 \| 0.8279 \| 0.7703 \| 0.8878 \| 0.0 \| 0.0 \| 0.0058 \| 0.0 \| \| 0.7258 \| 2.1 \| 420 \| 0.7628 \| 0.2109 \| 0.2612 \| 0.7874 \| nan \| 0.7680 \| 0.9575 \| 0.0 \| 0.6261 \| 0.1983 \| nan \| 0.4499 \| 0.0235 \| 0.0 \| 0.9443 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9318 \| 0.0 \| 0.2420 \| 0.0014 \| 0.0 \| nan \| 0.0 \| 0.1949 \| 0.0 \| 0.0 \| 0.8929 \| 0.9162 \| 0.9480 \| 0.0 \| 0.0 \| 0.0033 \| 0.0 \| nan \| 0.6260 \| 0.7908 \| 0.0 \| 0.4894 \| 0.1734 \| nan \| 0.3339 \| 0.0234 \| 0.0 \| 0.7108 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6110 \| 0.0 \| 0.1859 \| 0.0014 \| 0.0 \| nan \| 0.0 \| 0.1688 \| 0.0 \| 0.0 \| 0.8185 \| 0.7066 \| 0.8943 \| 0.0 \| 0.0 \| 0.0032 \| 0.0 \| \| 0.6387 \| 2.2 \| 440 \| 0.7467 \| 0.2259 \| 0.2790 \| 0.7961 \| nan \| 0.8881 \| 0.9214 \| 0.0 \| 0.6555 \| 0.3252 \| nan \| 0.4215 \| 0.2331 \| 0.0 \| 0.9267 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8443 \| 0.0 \| 0.3404 \| 0.0093 \| 0.0 \| nan \| 0.0 \| 0.2579 \| 0.0 \| 0.0 \| 0.9271 \| 0.9100 \| 0.9659 \| 0.0 \| 0.0 \| 0.0232 \| 0.0 \| nan \| 0.6131 \| 0.8291 \| 0.0 \| 0.5515 \| 0.2656 \| nan \| 0.3047 \| 0.2144 \| 0.0 \| 0.7371 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6369 \| 0.0 \| 0.2684 \| 0.0093 \| 0.0 \| nan \| 0.0 \| 0.2159 \| 0.0 \| 0.0 \| 0.7852 \| 0.6973 \| 0.8499 \| 0.0 \| 0.0 \| 0.0230 \| 0.0 \| \| 0.8595 \| 2.3 \| 460 \| 0.7549 \| 0.2211 \| 0.2763 \| 0.7854 \| nan \| 0.6418 \| 0.9541 \| 0.0000 \| 0.7728 \| 0.3492 \| nan \| 0.3529 \| 0.1397 \| 0.0 \| 0.9007 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9039 \| 0.0 \| 0.4719 \| 0.0047 \| 0.0 \| nan \| 0.0 \| 0.2823 \| 0.0 \| 0.0 \| 0.9262 \| 0.9046 \| 0.9483 \| 0.0 \| 0.0 \| 0.0109 \| 0.0 \| nan \| 0.5582 \| 0.7974 \| 0.0000 \| 0.4679 \| 0.2678 \| nan \| 0.2545 \| 0.1292 \| 0.0 \| 0.7601 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6389 \| 0.0 \| 0.3016 \| 0.0047 \| 0.0 \| nan \| 0.0 \| 0.2292 \| 0.0 \| 0.0 \| 0.8247 \| 0.7112 \| 0.8969 \| 0.0 \| 0.0 \| 0.0106 \| 0.0 \| \| 0.6146 \| 2.4 \| 480 \| 0.7211 \| 0.2221 \| 0.2704 \| 0.7963 \| nan \| 0.8419 \| 0.9430 \| 0.0000 \| 0.6053 \| 0.2553 \| nan \| 0.3931 \| 0.1885 \| 0.0 \| 0.9484 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9034 \| 0.0 \| 0.3059 \| 0.0073 \| 0.0 \| nan \| 0.0 \| 0.2091 \| 0.0 \| 0.0 \| 0.9321 \| 0.8916 \| 0.9507 \| 0.0 \| 0.0 \| 0.0068 \| 0.0 \| nan \| 0.6385 \| 0.7998 \| 0.0000 \| 0.5424 \| 0.2125 \| nan \| 0.3006 \| 0.1679 \| 0.0 \| 0.7083 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6353 \| 0.0 \| 0.2292 \| 0.0073 \| 0.0 \| nan \| 0.0 \| 0.1913 \| 0.0 \| 0.0 \| 0.8244 \| 0.7278 \| 0.8938 \| 0.0 \| 0.0 \| 0.0067 \| 0.0 \| \| 0.787 \| 2.5 \| 500 \| 0.7471 \| 0.2210 \| 0.2765 \| 0.7875 \| nan \| 0.8141 \| 0.9058 \| 0.0017 \| 0.7987 \| 0.3047 \| nan \| 0.4503 \| 0.2588 \| 0.0 \| 0.9095 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| 0.9374 \| 0.0 \| 0.2298 \| 0.0048 \| 0.0 \| nan \| 0.0 \| 0.2382 \| 0.0 \| 0.0 \| 0.9250 \| 0.8154 \| 0.9670 \| 0.0 \| 0.0 \| 0.0106 \| 0.0 \| nan \| 0.6225 \| 0.8069 \| 0.0017 \| 0.4804 \| 0.2544 \| nan \| 0.3447 \| 0.1973 \| 0.0 \| 0.7497 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| 0.5931 \| 0.0 \| 0.1708 \| 0.0048 \| 0.0 \| nan \| 0.0 \| 0.2098 \| 0.0 \| 0.0 \| 0.8123 \| 0.7113 \| 0.8818 \| 0.0 \| 0.0 \| 0.0104 \| 0.0 \| \| 0.6147 \| 2.6 \| 520 \| 0.7091 \| 0.2294 \| 0.2750 \| 0.8011 \| nan \| 0.8416 \| 0.9589 \| 0.0020 \| 0.5932 \| 0.2965 \| nan \| 0.3216 \| 0.2599 \| 0.0 \| 0.9252 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9012 \| 0.0 \| 0.4304 \| 0.0045 \| 0.0 \| nan \| 0.0 \| 0.2349 \| 0.0 \| 0.0 \| 0.9295 \| 0.8463 \| 0.9644 \| 0.0 \| 0.0 \| 0.0146 \| 0.0 \| nan \| 0.6358 \| 0.8040 \| 0.0020 \| 0.5333 \| 0.2447 \| nan \| 0.2753 \| 0.2214 \| 0.0 \| 0.7491 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6417 \| 0.0 \| 0.2980 \| 0.0045 \| 0.0 \| nan \| 0.0 \| 0.2090 \| 0.0 \| 0.0 \| 0.8240 \| 0.7611 \| 0.8937 \| 0.0 \| 0.0 \| 0.0144 \| 0.0 \| \| 0.777 \| 2.7 \| 540 \| 0.7115 \| 0.2351 \| 0.2943 \| 0.7979 \| nan \| 0.8442 \| 0.9297 \| 0.0043 \| 0.7113 \| 0.3582 \| nan \| 0.4900 \| 0.3847 \| 0.0 \| 0.9173 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0011 \| 0.0 \| 0.0 \| 0.8410 \| 0.0 \| 0.4747 \| 0.0086 \| 0.0 \| nan \| 0.0 \| 0.3288 \| 0.0 \| 0.0 \| 0.9013 \| 0.9356 \| 0.9526 \| 0.0 \| 0.0 \| 0.0385 \| 0.0 \| nan \| 0.6368 \| 0.8246 \| 0.0043 \| 0.5395 \| 0.2822 \| nan \| 0.3695 \| 0.3024 \| 0.0 \| 0.7647 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0011 \| 0.0 \| 0.0 \| 0.6615 \| 0.0 \| 0.3152 \| 0.0086 \| 0.0 \| nan \| 0.0 \| 0.2424 \| 0.0 \| 0.0 \| 0.7731 \| 0.6266 \| 0.8999 \| 0.0 \| 0.0 \| 0.0370 \| 0.0 \| \| 1.2945 \| 2.8 \| 560 \| 0.6830 \| 0.2393 \| 0.2901 \| 0.8066 \| nan \| 0.8630 \| 0.9487 \| 0.0763 \| 0.6376 \| 0.3071 \| nan \| 0.4487 \| 0.4212 \| 0.0 \| 0.9518 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.8668 \| 0.0 \| 0.3683 \| 0.0116 \| 0.0 \| nan \| 0.0 \| 0.2616 \| 0.0 \| 0.0 \| 0.9417 \| 0.8390 \| 0.9567 \| 0.0 \| 0.0 \| 0.0921 \| 0.0 \| nan \| 0.6674 \| 0.8206 \| 0.0762 \| 0.5492 \| 0.2547 \| nan \| 0.3654 \| 0.3311 \| 0.0 \| 0.6964 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.6301 \| 0.0 \| 0.2466 \| 0.0115 \| 0.0 \| nan \| 0.0 \| 0.2259 \| 0.0 \| 0.0 \| 0.8271 \| 0.7508 \| 0.8847 \| 0.0 \| 0.0 \| 0.0794 \| 0.0 \| \| 0.4699 \| 2.9 \| 580 \| 0.6701 \| 0.2548 \| 0.3078 \| 0.8155 \| nan \| 0.8274 \| 0.9381 \| 0.3424 \| 0.6896 \| 0.4323 \| nan \| 0.5397 \| 0.3759 \| 0.0 \| 0.9079 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0148 \| 0.0 \| 0.0 \| 0.8963 \| 0.0 \| 0.4932 \| 0.0072 \| 0.0 \| nan \| 0.0 \| 0.2208 \| 0.0 \| 0.0 \| 0.9471 \| 0.8648 \| 0.9568 \| 0.0 \| 0.0 \| 0.0882 \| 0.0 \| nan \| 0.6735 \| 0.8311 \| 0.3185 \| 0.5595 \| 0.3178 \| nan \| 0.3849 \| 0.2986 \| 0.0 \| 0.7825 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0148 \| 0.0 \| 0.0 \| 0.6547 \| 0.0 \| 0.3087 \| 0.0072 \| 0.0 \| nan \| 0.0 \| 0.1933 \| 0.0 \| 0.0 \| 0.8256 \| 0.7570 \| 0.8980 \| 0.0 \| 0.0 \| 0.0727 \| 0.0 \| \| 0.7277 \| 3.0 \| 600 \| 0.6626 \| 0.2524 \| 0.3047 \| 0.8166 \| nan \| 0.8627 \| 0.9347 \| 0.2669 \| 0.7073 \| 0.3747 \| nan \| 0.4831 \| 0.3256 \| 0.0 \| 0.9118 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0202 \| 0.0 \| 0.0 \| 0.9073 \| 0.0 \| 0.5294 \| 0.0219 \| 0.0 \| nan \| 0.0 \| 0.2649 \| 0.0 \| 0.0 \| 0.9429 \| 0.8806 \| 0.9577 \| 0.0 \| 0.0 \| 0.0553 \| 0.0 \| nan \| 0.6675 \| 0.8352 \| 0.2593 \| 0.5624 \| 0.2937 \| nan \| 0.3757 \| 0.2698 \| 0.0 \| 0.7718 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0202 \| 0.0 \| 0.0 \| 0.6614 \| 0.0 \| 0.3459 \| 0.0218 \| 0.0 \| nan \| 0.0 \| 0.2213 \| 0.0 \| 0.0 \| 0.8292 \| 0.7484 \| 0.8921 \| 0.0 \| 0.0 \| 0.0489 \| 0.0 \| \| 0.4261 \| 3.1 \| 620 \| 0.7159 \| 0.2492 \| 0.3077 \| 0.8015 \| nan \| 0.7873 \| 0.9264 \| 0.3010 \| 0.7709 \| 0.3242 \| nan \| 0.4667 \| 0.4816 \| 0.0 \| 0.9464 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0399 \| 0.0 \| 0.0 \| 0.8882 \| 0.0 \| 0.2940 \| 0.0648 \| 0.0 \| nan \| 0.0 \| 0.3657 \| 0.0 \| 0.0 \| 0.9219 \| 0.9102 \| 0.9562 \| 0.0 \| 0.0 \| 0.0926 \| 0.0 \| nan \| 0.6298 \| 0.8116 \| 0.2880 \| 0.4593 \| 0.2671 \| nan \| 0.3647 \| 0.3560 \| 0.0 \| 0.7478 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0398 \| 0.0 \| 0.0 \| 0.6769 \| 0.0 \| 0.2291 \| 0.0643 \| 0.0 \| nan \| 0.0 \| 0.2635 \| 0.0 \| 0.0 \| 0.8168 \| 0.7229 \| 0.9058 \| 0.0 \| 0.0 \| 0.0829 \| 0.0 \| \| 0.5901 \| 3.2 \| 640 \| 0.6688 \| 0.2576 \| 0.3153 \| 0.8124 \| nan \| 0.7619 \| 0.9540 \| 0.3024 \| 0.7631 \| 0.3767 \| nan \| 0.4635 \| 0.5193 \| 0.0 \| 0.9231 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0490 \| 0.0 \| 0.0 \| 0.9038 \| 0.0 \| 0.4962 \| 0.0420 \| 0.0 \| nan \| 0.0 \| 0.3598 \| 0.0 \| 0.0 \| 0.9221 \| 0.8992 \| 0.9603 \| 0.0 \| 0.0 \| 0.0767 \| 0.0 \| nan \| 0.6467 \| 0.8201 \| 0.2905 \| 0.5484 \| 0.2993 \| nan \| 0.3538 \| 0.3554 \| 0.0 \| 0.7821 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0488 \| 0.0 \| 0.0 \| 0.6677 \| 0.0 \| 0.3089 \| 0.0415 \| 0.0 \| nan \| 0.0 \| 0.2737 \| 0.0 \| 0.0 \| 0.8281 \| 0.7554 \| 0.8959 \| 0.0 \| 0.0 \| 0.0708 \| 0.0 \| \| 0.7962 \| 3.3 \| 660 \| 0.6800 \| 0.2658 \| 0.3266 \| 0.8155 \| nan \| 0.9044 \| 0.9098 \| 0.5540 \| 0.6474 \| 0.3950 \| nan \| 0.4505 \| 0.5812 \| 0.0 \| 0.9203 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0649 \| 0.0 \| 0.0 \| 0.8530 \| 0.0 \| 0.5437 \| 0.1232 \| 0.0 \| nan \| 0.0 \| 0.3232 \| 0.0 \| 0.0 \| 0.9429 \| 0.8957 \| 0.9629 \| 0.0 \| 0.0 \| 0.0535 \| 0.0 \| nan \| 0.6358 \| 0.8382 \| 0.4186 \| 0.5670 \| 0.3137 \| nan \| 0.3522 \| 0.3830 \| 0.0 \| 0.7748 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0648 \| 0.0 \| 0.0 \| 0.6735 \| 0.0 \| 0.3304 \| 0.1199 \| 0.0 \| nan \| 0.0 \| 0.2608 \| 0.0 \| 0.0 \| 0.8224 \| 0.7588 \| 0.8746 \| 0.0 \| 0.0 \| 0.0509 \| 0.0 \| \| 0.5779 \| 3.4 \| 680 \| 0.6590 \| 0.2691 \| 0.3225 \| 0.8177 \| nan \| 0.8129 \| 0.9519 \| 0.4768 \| 0.6884 \| 0.3156 \| nan \| 0.5264 \| 0.4963 \| 0.0 \| 0.9386 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1614 \| 0.0 \| 0.0 \| 0.9464 \| 0.0 \| 0.3499 \| 0.2275 \| 0.0 \| nan \| 0.0 \| 0.3370 \| 0.0 \| 0.0 \| 0.9184 \| 0.8585 \| 0.9358 \| 0.0 \| 0.0 \| 0.0560 \| 0.0 \| nan \| 0.6773 \| 0.8279 \| 0.4439 \| 0.5408 \| 0.2690 \| nan \| 0.3863 \| 0.3310 \| 0.0 \| 0.7642 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1581 \| 0.0 \| 0.0 \| 0.6273 \| 0.0 \| 0.2573 \| 0.2204 \| 0.0 \| nan \| 0.0 \| 0.2727 \| 0.0 \| 0.0 \| 0.8365 \| 0.7771 \| 0.8987 \| 0.0 \| 0.0 \| 0.0521 \| 0.0 \| \| 0.627 \| 3.5 \| 700 \| 0.6482 \| 0.2730 \| 0.3293 \| 0.8217 \| nan \| 0.8289 \| 0.9337 \| 0.4760 \| 0.7499 \| 0.4370 \| nan \| 0.4828 \| 0.5444 \| 0.0 \| 0.9094 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1504 \| 0.0 \| 0.0 \| 0.9126 \| 0.0 \| 0.3427 \| 0.2291 \| 0.0 \| nan \| 0.0 \| 0.3545 \| 0.0 \| 0.0 \| 0.9396 \| 0.9033 \| 0.9684 \| 0.0 \| 0.0 \| 0.0455 \| 0.0 \| nan \| 0.6682 \| 0.8365 \| 0.4425 \| 0.5625 \| 0.3519 \| nan \| 0.3731 \| 0.3747 \| 0.0 \| 0.7945 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1475 \| 0.0 \| 0.0 \| 0.6545 \| 0.0 \| 0.2565 \| 0.2234 \| 0.0 \| nan \| 0.0 \| 0.2659 \| 0.0 \| 0.0 \| 0.8243 \| 0.7500 \| 0.8927 \| 0.0 \| 0.0 \| 0.0442 \| 0.0 \| \| 1.043 \| 3.6 \| 720 \| 0.6670 \| 0.2690 \| 0.3303 \| 0.8121 \| nan \| 0.7414 \| 0.9510 \| 0.4527 \| 0.7293 \| 0.4232 \| nan \| 0.4919 \| 0.5529 \| 0.0 \| 0.9499 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2058 \| 0.0 \| 0.0 \| 0.7978 \| 0.0 \| 0.5494 \| 0.1643 \| 0.0 \| nan \| 0.0 \| 0.3267 \| 0.0 \| 0.0 \| 0.9457 \| 0.9190 \| 0.9663 \| 0.0 \| 0.0 \| 0.0709 \| 0.0 \| nan \| 0.6380 \| 0.8182 \| 0.4270 \| 0.5396 \| 0.3443 \| nan \| 0.3577 \| 0.3869 \| 0.0 \| 0.7453 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1965 \| 0.0 \| 0.0 \| 0.6520 \| 0.0 \| 0.3273 \| 0.1621 \| 0.0 \| nan \| 0.0 \| 0.2547 \| 0.0 \| 0.0 \| 0.8264 \| 0.7138 \| 0.8809 \| 0.0 \| 0.0 \| 0.0670 \| 0.0 \| \| 0.4814 \| 3.7 \| 740 \| 0.6550 \| 0.2747 \| 0.3324 \| 0.8234 \| nan \| 0.9006 \| 0.9283 \| 0.5611 \| 0.7474 \| 0.2894 \| nan \| 0.4573 \| 0.5845 \| 0.0 \| 0.9186 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1849 \| 0.0 \| 0.0 \| 0.8607 \| 0.0 \| 0.4995 \| 0.1799 \| 0.0 \| nan \| 0.0 \| 0.3080 \| 0.0 \| 0.0 \| 0.9508 \| 0.8698 \| 0.9653 \| 0.0 \| 0.0 \| 0.0970 \| 0.0 \| nan \| 0.6682 \| 0.8446 \| 0.4544 \| 0.6005 \| 0.2601 \| nan \| 0.3483 \| 0.3947 \| 0.0 \| 0.7874 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1805 \| 0.0 \| 0.0 \| 0.6645 \| 0.0 \| 0.3344 \| 0.1762 \| 0.0 \| nan \| 0.0 \| 0.2477 \| 0.0 \| 0.0 \| 0.8274 \| 0.7503 \| 0.8873 \| 0.0 \| 0.0 \| 0.0878 \| 0.0 \| \| 0.8735 \| 3.8 \| 760 \| 0.6511 \| 0.2686 \| 0.3329 \| 0.8143 \| nan \| 0.7260 \| 0.9541 \| 0.6209 \| 0.6746 \| 0.4355 \| nan \| 0.5429 \| 0.4623 \| 0.0 \| 0.9414 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2207 \| 0.0 \| 0.0 \| 0.8851 \| 0.0 \| 0.6008 \| 0.0698 \| 0.0 \| nan \| 0.0 \| 0.2883 \| 0.0 \| 0.0 \| 0.9114 \| 0.9274 \| 0.9592 \| 0.0 \| 0.0 \| 0.0980 \| 0.0 \| nan \| 0.6484 \| 0.8316 \| 0.4111 \| 0.5496 \| 0.3179 \| nan \| 0.3790 \| 0.3583 \| 0.0 \| 0.7645 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2136 \| 0.0 \| 0.0 \| 0.6712 \| 0.0 \| 0.3522 \| 0.0691 \| 0.0 \| nan \| 0.0 \| 0.2393 \| 0.0 \| 0.0 \| 0.8276 \| 0.7120 \| 0.8955 \| 0.0 \| 0.0 \| 0.0865 \| 0.0 \| \| 0.5682 \| 3.9 \| 780 \| 0.6575 \| 0.2699 \| 0.3248 \| 0.8181 \| nan \| 0.9116 \| 0.9023 \| 0.5169 \| 0.7581 \| 0.3574 \| nan \| 0.4132 \| 0.4557 \| 0.0 \| 0.9207 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2064 \| 0.0 \| 0.0 \| 0.8894 \| 0.0 \| 0.3918 \| 0.1926 \| 0.0 \| nan \| 0.0 \| 0.2659 \| 0.0 \| 0.0 \| 0.9597 \| 0.8699 \| 0.9541 \| 0.0 \| 0.0 \| 0.1026 \| 0.0 \| nan \| 0.6547 \| 0.8448 \| 0.4310 \| 0.5466 \| 0.2962 \| nan \| 0.3280 \| 0.3493 \| 0.0 \| 0.7781 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2014 \| 0.0 \| 0.0 \| 0.6549 \| 0.0 \| 0.3050 \| 0.1889 \| 0.0 \| nan \| 0.0 \| 0.2235 \| 0.0 \| 0.0 \| 0.8224 \| 0.7640 \| 0.8867 \| 0.0 \| 0.0 \| 0.0899 \| 0.0 \| \| 0.4817 \| 4.0 \| 800 \| 0.6374 \| 0.2842 \| 0.3351 \| 0.8286 \| nan \| 0.8555 \| 0.9524 \| 0.4450 \| 0.6285 \| 0.4133 \| nan \| 0.5067 \| 0.4467 \| 0.0 \| 0.9324 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3081 \| 0.0 \| 0.0 \| 0.9170 \| 0.0 \| 0.4322 \| 0.3224 \| 0.0 \| nan \| 0.0 \| 0.2858 \| 0.0 \| 0.0 \| 0.9309 \| 0.8960 \| 0.9586 \| 0.0 \| 0.0 \| 0.1573 \| 0.0 \| nan \| 0.7043 \| 0.8377 \| 0.4324 \| 0.5554 \| 0.3270 \| nan \| 0.3894 \| 0.3513 \| 0.0 \| 0.7776 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2928 \| 0.0 \| 0.0 \| 0.6627 \| 0.0 \| 0.3241 \| 0.3021 \| 0.0 \| nan \| 0.0 \| 0.2310 \| 0.0 \| 0.0 \| 0.8454 \| 0.7534 \| 0.9049 \| 0.0 \| 0.0 \| 0.1188 \| 0.0 \| \| 0.4642 \| 4.1 \| 820 \| 0.6287 \| 0.2868 \| 0.3408 \| 0.8310 \| nan \| 0.9123 \| 0.9252 \| 0.5041 \| 0.7334 \| 0.4165 \| nan \| 0.5144 \| 0.5003 \| 0.0 \| 0.9309 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3421 \| 0.0 \| 0.0 \| 0.9123 \| 0.0 \| 0.4394 \| 0.2869 \| 0.0 \| nan \| 0.0 \| 0.2655 \| 0.0 \| 0.0 \| 0.9400 \| 0.8675 \| 0.9662 \| 0.0 \| 0.0 \| 0.1094 \| 0.0 \| nan \| 0.6799 \| 0.8581 \| 0.4673 \| 0.5899 \| 0.3244 \| nan \| 0.3995 \| 0.3747 \| 0.0 \| 0.7891 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3087 \| 0.0 \| 0.0 \| 0.6594 \| 0.0 \| 0.3274 \| 0.2753 \| 0.0 \| nan \| 0.0 \| 0.2254 \| 0.0 \| 0.0 \| 0.8433 \| 0.7769 \| 0.8969 \| 0.0 \| 0.0 \| 0.0958 \| 0.0 \| \| 0.6249 \| 4.2 \| 840 \| 0.6191 \| 0.2936 \| 0.3534 \| 0.8297 \| nan \| 0.7971 \| 0.9443 \| 0.5810 \| 0.7279 \| 0.4803 \| nan \| 0.5396 \| 0.6216 \| 0.0 \| 0.9299 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3012 \| 0.0 \| 0.0 \| 0.8692 \| 0.0 \| 0.5200 \| 0.3452 \| 0.0 \| nan \| 0.0 \| 0.3316 \| 0.0 \| 0.0 \| 0.9482 \| 0.9134 \| 0.9529 \| 0.0 \| 0.0 \| 0.1506 \| 0.0 \| nan \| 0.6902 \| 0.8375 \| 0.5165 \| 0.5620 \| 0.3584 \| nan \| 0.4021 \| 0.4267 \| 0.0 \| 0.7910 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2716 \| 0.0 \| 0.0 \| 0.6951 \| 0.0 \| 0.3857 \| 0.3238 \| 0.0 \| nan \| 0.0 \| 0.2516 \| 0.0 \| 0.0 \| 0.8273 \| 0.7315 \| 0.9101 \| 0.0 \| 0.0 \| 0.1215 \| 0.0 \| \| 0.3472 \| 4.3 \| 860 \| 0.6095 \| 0.2955 \| 0.3495 \| 0.8351 \| nan \| 0.8528 \| 0.9530 \| 0.6076 \| 0.6775 \| 0.4019 \| nan \| 0.5564 \| 0.5145 \| 0.0 \| 0.9282 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3640 \| 0.0 \| 0.0 \| 0.9051 \| 0.0 \| 0.4762 \| 0.3858 \| 0.0 \| nan \| 0.0 \| 0.3110 \| 0.0 \| 0.0 \| 0.9446 \| 0.8605 \| 0.9565 \| 0.0 \| 0.0 \| 0.1394 \| 0.0 \| nan \| 0.7095 \| 0.8454 \| 0.4904 \| 0.5882 \| 0.3297 \| nan \| 0.4103 \| 0.3965 \| 0.0 \| 0.7975 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3230 \| 0.0 \| 0.0 \| 0.6836 \| 0.0 \| 0.3551 \| 0.3631 \| 0.0 \| nan \| 0.0 \| 0.2458 \| 0.0 \| 0.0 \| 0.8385 \| 0.7605 \| 0.9108 \| 0.0 \| 0.0 \| 0.1139 \| 0.0 \| \| 1.1638 \| 4.4 \| 880 \| 0.7640 \| 0.2662 \| 0.3295 \| 0.7799 \| nan \| 0.8622 \| 0.8015 \| 0.3356 \| 0.8659 \| 0.4153 \| nan \| 0.2301 \| 0.5091 \| 0.0 \| 0.9329 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3879 \| 0.0 \| 0.0 \| 0.9063 \| 0.0 \| 0.4288 \| 0.3297 \| 0.0 \| nan \| 0.0 \| 0.2614 \| 0.0 \| 0.0 \| 0.9372 \| 0.9031 \| 0.9668 \| 0.0 \| 0.0 \| 0.1393 \| 0.0 \| nan \| 0.6116 \| 0.7663 \| 0.3321 \| 0.3163 \| 0.3278 \| nan \| 0.2076 \| 0.3955 \| 0.0 \| 0.7845 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3343 \| 0.0 \| 0.0 \| 0.6859 \| 0.0 \| 0.3368 \| 0.3190 \| 0.0 \| nan \| 0.0 \| 0.2220 \| 0.0 \| 0.0 \| 0.8371 \| 0.7586 \| 0.9031 \| 0.0 \| 0.0 \| 0.1150 \| 0.0 \| \| 0.5639 \| 4.5 \| 900 \| 0.6138 \| 0.2912 \| 0.3495 \| 0.8329 \| nan \| 0.8329 \| 0.9475 \| 0.6199 \| 0.7317 \| 0.5188 \| nan \| 0.5028 \| 0.4681 \| 0.0 \| 0.9315 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3889 \| 0.0 \| 0.0 \| 0.9242 \| 0.0 \| 0.3445 \| 0.3785 \| 0.0 \| nan \| 0.0 \| 0.2967 \| 0.0 \| 0.0 \| 0.9146 \| 0.8915 \| 0.9640 \| 0.0 \| 0.0 \| 0.1801 \| 0.0 \| nan \| 0.6981 \| 0.8617 \| 0.4190 \| 0.5752 \| 0.3841 \| nan \| 0.3943 \| 0.3792 \| 0.0 \| 0.7822 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3336 \| 0.0 \| 0.0 \| 0.6600 \| 0.0 \| 0.2849 \| 0.3522 \| 0.0 \| nan \| 0.0 \| 0.2394 \| 0.0 \| 0.0 \| 0.8441 \| 0.7760 \| 0.9091 \| 0.0 \| 0.0 \| 0.1331 \| 0.0 \| \| 1.1424 \| 4.6 \| 920 \| 0.6094 \| 0.2951 \| 0.3564 \| 0.8335 \| nan \| 0.8276 \| 0.9425 \| 0.6345 \| 0.7938 \| 0.3963 \| nan \| 0.5233 \| 0.5383 \| 0.0 \| 0.9430 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3499 \| 0.0 \| 0.0 \| 0.8573 \| 0.0 \| 0.5273 \| 0.3810 \| 0.0 \| nan \| 0.0 \| 0.3033 \| 0.0 \| 0.0 \| 0.9501 \| 0.8880 \| 0.9582 \| 0.0 \| 0.0 \| 0.2327 \| 0.0 \| nan \| 0.6973 \| 0.8551 \| 0.4481 \| 0.5758 \| 0.3347 \| nan \| 0.4040 \| 0.4162 \| 0.0 \| 0.7766 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3041 \| 0.0 \| 0.0 \| 0.6977 \| 0.0 \| 0.3915 \| 0.3541 \| 0.0 \| nan \| 0.0 \| 0.2395 \| 0.0 \| 0.0 \| 0.8314 \| 0.7473 \| 0.9128 \| 0.0 \| 0.0 \| 0.1612 \| 0.0 \| \| 1.063 \| 4.7 \| 940 \| 0.6149 \| 0.2913 \| 0.3458 \| 0.8329 \| nan \| 0.8402 \| 0.9494 \| 0.5703 \| 0.7595 \| 0.4040 \| nan \| 0.4466 \| 0.4354 \| 0.0 \| 0.9334 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3930 \| 0.0 \| 0.0 \| 0.9198 \| 0.0 \| 0.4596 \| 0.3577 \| 0.0 \| nan \| 0.0 \| 0.3191 \| 0.0 \| 0.0 \| 0.9258 \| 0.9082 \| 0.9708 \| 0.0 \| 0.0 \| 0.1261 \| 0.0 \| nan \| 0.6985 \| 0.8471 \| 0.5053 \| 0.5807 \| 0.3370 \| nan \| 0.3472 \| 0.3580 \| 0.0 \| 0.7914 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3419 \| 0.0 \| 0.0 \| 0.6788 \| 0.0 \| 0.3541 \| 0.3363 \| 0.0 \| nan \| 0.0 \| 0.2502 \| 0.0 \| 0.0 \| 0.8367 \| 0.7601 \| 0.8995 \| 0.0 \| 0.0 \| 0.1069 \| 0.0 \| \| 0.4322 \| 4.8 \| 960 \| 0.6046 \| 0.2973 \| 0.3546 \| 0.8371 \| nan \| 0.8606 \| 0.9394 \| 0.6192 \| 0.7778 \| 0.3978 \| nan \| 0.5424 \| 0.5753 \| 0.0 \| 0.9320 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3721 \| 0.0 \| 0.0 \| 0.9029 \| 0.0 \| 0.4837 \| 0.3128 \| 0.0 \| nan \| 0.0 \| 0.3398 \| 0.0 \| 0.0 \| 0.9514 \| 0.8918 \| 0.9551 \| 0.0 \| 0.0 \| 0.1386 \| 0.0 \| nan \| 0.7099 \| 0.8546 \| 0.4951 \| 0.5780 \| 0.3377 \| nan \| 0.4132 \| 0.4390 \| 0.0 \| 0.7976 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3289 \| 0.0 \| 0.0 \| 0.6960 \| 0.0 \| 0.3689 \| 0.3023 \| 0.0 \| nan \| 0.0 \| 0.2638 \| 0.0 \| 0.0 \| 0.8282 \| 0.7678 \| 0.9131 \| 0.0 \| 0.0 \| 0.1212 \| 0.0 \| \| 0.8249 \| 4.9 \| 980 \| 0.5971 \| 0.3067 \| 0.3699 \| 0.8397 \| nan \| 0.8568 \| 0.9392 \| 0.5578 \| 0.7432 \| 0.5491 \| nan \| 0.5904 \| 0.6089 \| 0.0 \| 0.9391 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4445 \| 0.0 \| 0.0 \| 0.8718 \| 0.0 \| 0.5895 \| 0.4400 \| 0.0 \| nan \| 0.0 \| 0.3854 \| 0.0 \| 0.0 \| 0.9269 \| 0.9088 \| 0.9634 \| 0.0 \| 0.0 \| 0.1515 \| 0.0 \| nan \| 0.7231 \| 0.8558 \| 0.5109 \| 0.6045 \| 0.3935 \| nan \| 0.4213 \| 0.4557 \| 0.0 \| 0.7863 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3644 \| 0.0 \| 0.0 \| 0.6801 \| 0.0 \| 0.3959 \| 0.3773 \| 0.0 \| nan \| 0.0 \| 0.2890 \| 0.0 \| 0.0 \| 0.8416 \| 0.7634 \| 0.9103 \| 0.0 \| 0.0 \| 0.1337 \| 0.0 \| \| 0.4197 \| 5.0 \| 1000 \| 0.5949 \| 0.3031 \| 0.3663 \| 0.8403 \| nan \| 0.8410 \| 0.9410 \| 0.6234 \| 0.8261 \| 0.4882 \| nan \| 0.4796 \| 0.6131 \| 0.0 \| 0.9508 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4801 \| 0.0 \| 0.0 \| 0.9049 \| 0.0 \| 0.4725 \| 0.4507 \| 0.0 \| nan \| 0.0 \| 0.3670 \| 0.0 \| 0.0 \| 0.9397 \| 0.8838 \| 0.9650 \| 0.0 \| 0.0 \| 0.1289 \| 0.0 \| nan \| 0.7092 \| 0.8644 \| 0.5033 \| 0.5772 \| 0.3852 \| nan \| 0.3963 \| 0.4297 \| 0.0 \| 0.7706 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3633 \| 0.0 \| 0.0 \| 0.6818 \| 0.0 \| 0.3832 \| 0.4105 \| 0.0 \| nan \| 0.0 \| 0.2829 \| 0.0 \| 0.0 \| 0.8449 \| 0.7682 \| 0.9103 \| 0.0 \| 0.0 \| 0.1147 \| 0.0 \| \| 0.455 \| 5.1 \| 1020 \| 0.5954 \| 0.3023 \| 0.3596 \| 0.8400 \| nan \| 0.8863 \| 0.9341 \| 0.6041 \| 0.7294 \| 0.5180 \| nan \| 0.5288 \| 0.5182 \| 0.0 \| 0.9290 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5215 \| 0.0 \| 0.0 \| 0.9194 \| 0.0 \| 0.4468 \| 0.3474 \| 0.0 \| nan \| 0.0 \| 0.3495 \| 0.0 \| 0.0 \| 0.9427 \| 0.8947 \| 0.9523 \| 0.0 \| 0.0 \| 0.1259 \| 0.0 \| nan \| 0.7157 \| 0.8610 \| 0.5337 \| 0.5857 \| 0.3762 \| nan \| 0.4079 \| 0.4252 \| 0.0 \| 0.8000 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3841 \| 0.0 \| 0.0 \| 0.6899 \| 0.0 \| 0.3486 \| 0.3326 \| 0.0 \| nan \| 0.0 \| 0.2761 \| 0.0 \| 0.0 \| 0.8391 \| 0.7696 \| 0.9137 \| 0.0 \| 0.0 \| 0.1110 \| 0.0 \| \| 0.573 \| 5.2 \| 1040 \| 0.6142 \| 0.3010 \| 0.3602 \| 0.8352 \| nan \| 0.9199 \| 0.9149 \| 0.5815 \| 0.7589 \| 0.4252 \| nan \| 0.4792 \| 0.5478 \| 0.0 \| 0.9296 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5160 \| 0.0 \| 0.0 \| 0.9104 \| 0.0 \| 0.5451 \| 0.3589 \| 0.0 \| nan \| 0.0 \| 0.3316 \| 0.0 \| 0.0 \| 0.9391 \| 0.8918 \| 0.9667 \| 0.0 \| 0.0 \| 0.1497 \| 0.0 \| nan \| 0.6839 \| 0.8525 \| 0.5056 \| 0.5907 \| 0.3327 \| nan \| 0.3869 \| 0.4349 \| 0.0 \| 0.8018 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3767 \| 0.0 \| 0.0 \| 0.6990 \| 0.0 \| 0.4174 \| 0.3425 \| 0.0 \| nan \| 0.0 \| 0.2627 \| 0.0 \| 0.0 \| 0.8403 \| 0.7694 \| 0.9045 \| 0.0 \| 0.0 \| 0.1296 \| 0.0 \| \| 0.461 \| 5.3 \| 1060 \| 0.6007 \| 0.2976 \| 0.3587 \| 0.8365 \| nan \| 0.8484 \| 0.9428 \| 0.6427 \| 0.8153 \| 0.3290 \| nan \| 0.5091 \| 0.4475 \| 0.0 \| 0.9296 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5200 \| 0.0 \| 0.0 \| 0.8912 \| 0.0 \| 0.5687 \| 0.3954 \| 0.0 \| nan \| 0.0 \| 0.3056 \| 0.0 \| 0.0 \| 0.9431 \| 0.8985 \| 0.9695 \| 0.0 \| 0.0 \| 0.1626 \| 0.0 \| nan \| 0.7065 \| 0.8570 \| 0.5135 \| 0.5742 \| 0.2842 \| nan \| 0.4056 \| 0.3774 \| 0.0 \| 0.7905 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3762 \| 0.0 \| 0.0 \| 0.6970 \| 0.0 \| 0.4058 \| 0.3684 \| 0.0 \| nan \| 0.0 \| 0.2481 \| 0.0 \| 0.0 \| 0.8385 \| 0.7417 \| 0.9074 \| 0.0 \| 0.0 \| 0.1347 \| 0.0 \| \| 0.4084 \| 5.4 \| 1080 \| 0.5992 \| 0.2994 \| 0.3620 \| 0.8364 \| nan \| 0.8610 \| 0.9321 \| 0.5964 \| 0.8214 \| 0.4050 \| nan \| 0.5314 \| 0.5094 \| 0.0 \| 0.9453 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5343 \| 0.0 \| 0.0 \| 0.9199 \| 0.0 \| 0.5534 \| 0.3635 \| 0.0 \| nan \| 0.0 \| 0.2904 \| 0.0 \| 0.0 \| 0.9271 \| 0.8947 \| 0.9565 \| 0.0 \| 0.0 \| 0.1793 \| 0.0 \| nan \| 0.7074 \| 0.8568 \| 0.4895 \| 0.5576 \| 0.3418 \| nan \| 0.4125 \| 0.4074 \| 0.0 \| 0.7771 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3876 \| 0.0 \| 0.0 \| 0.6874 \| 0.0 \| 0.4108 \| 0.3432 \| 0.0 \| nan \| 0.0 \| 0.2438 \| 0.0 \| 0.0 \| 0.8474 \| 0.7488 \| 0.9139 \| 0.0 \| 0.0 \| 0.1477 \| 0.0 \| \| 0.3638 \| 5.5 \| 1100 \| 0.5961 \| 0.3008 \| 0.3578 \| 0.8397 \| nan \| 0.8651 \| 0.9565 \| 0.5715 \| 0.7040 \| 0.3987 \| nan \| 0.5602 \| 0.5043 \| 0.0 \| 0.9363 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4938 \| 0.0 \| 0.0 \| 0.9127 \| 0.0 \| 0.4815 \| 0.3983 \| 0.0 \| nan \| 0.0 \| 0.3482 \| 0.0 \| 0.0 \| 0.9282 \| 0.8948 \| 0.9682 \| 0.0 \| 0.0 \| 0.1683 \| 0.0 \| nan \| 0.7202 \| 0.8518 \| 0.5089 \| 0.5910 \| 0.3353 \| nan \| 0.4178 \| 0.3949 \| 0.0 \| 0.7993 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3728 \| 0.0 \| 0.0 \| 0.6855 \| 0.0 \| 0.3587 \| 0.3661 \| 0.0 \| nan \| 0.0 \| 0.2640 \| 0.0 \| 0.0 \| 0.8506 \| 0.7644 \| 0.9066 \| 0.0 \| 0.0 \| 0.1368 \| 0.0 \| \| 0.3183 \| 5.6 \| 1120 \| 0.5982 \| 0.2983 \| 0.3672 \| 0.8344 \| nan \| 0.8373 \| 0.9374 \| 0.6989 \| 0.7369 \| 0.4777 \| nan \| 0.5391 \| 0.6504 \| 0.0 \| 0.9233 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4965 \| 0.0 \| 0.0 \| 0.8986 \| 0.0 \| 0.4019 \| 0.4308 \| 0.0 \| nan \| 0.0 \| 0.3717 \| 0.0 \| 0.0 \| 0.9269 \| 0.9174 \| 0.9701 \| 0.0 \| 0.0 \| 0.1700 \| 0.0 \| nan \| 0.6888 \| 0.8680 \| 0.3857 \| 0.6007 \| 0.3648 \| nan \| 0.4233 \| 0.4603 \| 0.0 \| 0.8015 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3714 \| 0.0 \| 0.0 \| 0.6845 \| 0.0 \| 0.3295 \| 0.3990 \| 0.0 \| nan \| 0.0 \| 0.2687 \| 0.0 \| 0.0 \| 0.8401 \| 0.7341 \| 0.8831 \| 0.0 \| 0.0 \| 0.1432 \| 0.0 \| \| 0.3373 \| 5.7 \| 1140 \| 0.5926 \| 0.3036 \| 0.3668 \| 0.8390 \| nan \| 0.8819 \| 0.9257 \| 0.6487 \| 0.7322 \| 0.5088 \| nan \| 0.5627 \| 0.5298 \| 0.0 \| 0.9481 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5145 \| 0.0 \| 0.0 \| 0.8940 \| 0.0 \| 0.5565 \| 0.4014 \| 0.0 \| nan \| 0.0 \| 0.3247 \| 0.0 \| 0.0 \| 0.9419 \| 0.8932 \| 0.9479 \| 0.0 \| 0.0 \| 0.1594 \| 0.0 \| nan \| 0.6977 \| 0.8672 \| 0.4568 \| 0.6011 \| 0.3805 \| nan \| 0.4309 \| 0.4341 \| 0.0 \| 0.7790 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3900 \| 0.0 \| 0.0 \| 0.6910 \| 0.0 \| 0.4052 \| 0.3770 \| 0.0 \| nan \| 0.0 \| 0.2609 \| 0.0 \| 0.0 \| 0.8433 \| 0.7580 \| 0.9030 \| 0.0 \| 0.0 \| 0.1354 \| 0.0 \| \| 0.5786 \| 5.8 \| 1160 \| 0.5888 \| 0.3057 \| 0.3725 \| 0.8407 \| nan \| 0.8291 \| 0.9356 \| 0.6557 \| 0.8378 \| 0.5211 \| nan \| 0.5686 \| 0.6574 \| 0.0 \| 0.9386 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5123 \| 0.0 \| 0.0 \| 0.8987 \| 0.0 \| 0.5060 \| 0.3604 \| 0.0 \| nan \| 0.0 \| 0.3712 \| 0.0 \| 0.0 \| 0.9496 \| 0.8555 \| 0.9649 \| 0.0 \| 0.0 \| 0.1836 \| 0.0 \| nan \| 0.6966 \| 0.8668 \| 0.5092 \| 0.5926 \| 0.4044 \| nan \| 0.4317 \| 0.4607 \| 0.0 \| 0.7984 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3596 \| 0.0 \| 0.0 \| 0.6941 \| 0.0 \| 0.3737 \| 0.3438 \| 0.0 \| nan \| 0.0 \| 0.2733 \| 0.0 \| 0.0 \| 0.8431 \| 0.7703 \| 0.9097 \| 0.0 \| 0.0 \| 0.1501 \| 0.0 \| \| 0.4665 \| 5.9 \| 1180 \| 0.5788 \| 0.3083 \| 0.3673 \| 0.8455 \| nan \| 0.8570 \| 0.9491 \| 0.6249 \| 0.7949 \| 0.5169 \| nan \| 0.5641 \| 0.5620 \| 0.0 \| 0.9319 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5126 \| 0.0 \| 0.0 \| 0.9148 \| 0.0 \| 0.5272 \| 0.3540 \| 0.0 \| nan \| 0.0 \| 0.3429 \| 0.0 \| 0.0 \| 0.9331 \| 0.8915 \| 0.9621 \| 0.0 \| 0.0 \| 0.1459 \| 0.0 \| nan \| 0.7315 \| 0.8616 \| 0.5393 \| 0.6123 \| 0.4003 \| nan \| 0.4398 \| 0.4429 \| 0.0 \| 0.8090 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3793 \| 0.0 \| 0.0 \| 0.6951 \| 0.0 \| 0.3920 \| 0.3379 \| 0.0 \| nan \| 0.0 \| 0.2643 \| 0.0 \| 0.0 \| 0.8445 \| 0.7700 \| 0.9136 \| 0.0 \| 0.0 \| 0.1232 \| 0.0 \| \| 0.4062 \| 6.0 \| 1200 \| 0.5990 \| 0.3034 \| 0.3661 \| 0.8390 \| nan \| 0.8692 \| 0.9362 \| 0.5182 \| 0.8309 \| 0.4266 \| nan \| 0.5545 \| 0.5376 \| 0.0 \| 0.9458 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6000 \| 0.0 \| 0.0 \| 0.9145 \| 0.0 \| 0.4208 \| 0.4792 \| 0.0 \| nan \| 0.0 \| 0.3516 \| 0.0 \| 0.0 \| 0.9300 \| 0.8846 \| 0.9620 \| 0.0 \| 0.0 \| 0.1879 \| 0.0 \| nan \| 0.7062 \| 0.8574 \| 0.4890 \| 0.5916 \| 0.3444 \| nan \| 0.4258 \| 0.4307 \| 0.0 \| 0.7885 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3544 \| 0.0 \| 0.0 \| 0.6851 \| 0.0 \| 0.3325 \| 0.4280 \| 0.0 \| nan \| 0.0 \| 0.2681 \| 0.0 \| 0.0 \| 0.8506 \| 0.7829 \| 0.9171 \| 0.0 \| 0.0 \| 0.1526 \| 0.0 \| \| 0.3422 \| 6.1 \| 1220 \| 0.6136 \| 0.2994 \| 0.3547 \| 0.8386 \| nan \| 0.8994 \| 0.9470 \| 0.5071 \| 0.7181 \| 0.3467 \| nan \| 0.5558 \| 0.5191 \| 0.0 \| 0.9338 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5650 \| 0.0 \| 0.0 \| 0.9167 \| 0.0 \| 0.3571 \| 0.4699 \| 0.0 \| nan \| 0.0 \| 0.3153 \| 0.0 \| 0.0 \| 0.9401 \| 0.8957 \| 0.9657 \| 0.0 \| 0.0 \| 0.1437 \| 0.0 \| nan \| 0.7120 \| 0.8521 \| 0.4770 \| 0.6135 \| 0.2925 \| nan \| 0.4314 \| 0.4286 \| 0.0 \| 0.8057 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3728 \| 0.0 \| 0.0 \| 0.6836 \| 0.0 \| 0.2839 \| 0.4187 \| 0.0 \| nan \| 0.0 \| 0.2556 \| 0.0 \| 0.0 \| 0.8447 \| 0.7720 \| 0.9130 \| 0.0 \| 0.0 \| 0.1240 \| 0.0 \| \| 0.3667 \| 6.2 \| 1240 \| 0.5865 \| 0.3074 \| 0.3673 \| 0.8438 \| nan \| 0.8719 \| 0.9503 \| 0.6165 \| 0.7441 \| 0.4360 \| nan \| 0.5970 \| 0.5278 \| 0.0 \| 0.9347 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5660 \| 0.0 \| 0.0 \| 0.8997 \| 0.0 \| 0.4856 \| 0.4265 \| 0.0 \| nan \| 0.0 \| 0.3539 \| 0.0 \| 0.0 \| 0.9352 \| 0.9020 \| 0.9647 \| 0.0 \| 0.0 \| 0.1752 \| 0.0 \| nan \| 0.7281 \| 0.8557 \| 0.5315 \| 0.6217 \| 0.3452 \| nan \| 0.4397 \| 0.4263 \| 0.0 \| 0.8010 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3623 \| 0.0 \| 0.0 \| 0.6997 \| 0.0 \| 0.3732 \| 0.3967 \| 0.0 \| nan \| 0.0 \| 0.2729 \| 0.0 \| 0.0 \| 0.8474 \| 0.7669 \| 0.9133 \| 0.0 \| 0.0 \| 0.1475 \| 0.0 \| \| 0.3964 \| 6.3 \| 1260 \| 0.5919 \| 0.3052 \| 0.3715 \| 0.8415 \| nan \| 0.8636 \| 0.9456 \| 0.5274 \| 0.7548 \| 0.5466 \| nan \| 0.5639 \| 0.6438 \| 0.0 \| 0.9241 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6155 \| 0.0 \| 0.0 \| 0.9048 \| 0.0 \| 0.5285 \| 0.4056 \| 0.0 \| nan \| 0.0 \| 0.3135 \| 0.0 \| 0.0 \| 0.9171 \| 0.9077 \| 0.9684 \| 0.0 \| 0.0 \| 0.1849 \| 0.0 \| nan \| 0.7294 \| 0.8517 \| 0.4793 \| 0.6185 \| 0.4047 \| nan \| 0.4379 \| 0.4247 \| 0.0 \| 0.8134 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3556 \| 0.0 \| 0.0 \| 0.6892 \| 0.0 \| 0.3648 \| 0.3728 \| 0.0 \| nan \| 0.0 \| 0.2539 \| 0.0 \| 0.0 \| 0.8452 \| 0.7688 \| 0.8997 \| 0.0 \| 0.0 \| 0.1528 \| 0.0 \| \| 0.3974 \| 6.4 \| 1280 \| 0.6011 \| 0.3013 \| 0.3642 \| 0.8380 \| nan \| 0.8523 \| 0.9361 \| 0.6131 \| 0.8445 \| 0.4463 \| nan \| 0.5026 \| 0.5776 \| 0.0 \| 0.9477 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5712 \| 0.0 \| 0.0 \| 0.8948 \| 0.0 \| 0.4493 \| 0.3396 \| 0.0 \| nan \| 0.0 \| 0.3616 \| 0.0 \| 0.0 \| 0.9456 \| 0.8771 \| 0.9659 \| 0.0 \| 0.0 \| 0.1656 \| 0.0 \| nan \| 0.7006 \| 0.8617 \| 0.5345 \| 0.5834 \| 0.3578 \| nan \| 0.4094 \| 0.4542 \| 0.0 \| 0.7889 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3706 \| 0.0 \| 0.0 \| 0.6830 \| 0.0 \| 0.3222 \| 0.3219 \| 0.0 \| nan \| 0.0 \| 0.2803 \| 0.0 \| 0.0 \| 0.8424 \| 0.7820 \| 0.9040 \| 0.0 \| 0.0 \| 0.1447 \| 0.0 \| \| 0.3549 \| 6.5 \| 1300 \| 0.5949 \| 0.3054 \| 0.3741 \| 0.8391 \| nan \| 0.8498 \| 0.9302 \| 0.6762 \| 0.8403 \| 0.4753 \| nan \| 0.4975 \| 0.5990 \| 0.0 \| 0.9416 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5850 \| 0.0 \| 0.0 \| 0.8735 \| 0.0 \| 0.5281 \| 0.3714 \| 0.0 \| nan \| 0.0 \| 0.3628 \| 0.0 \| 0.0 \| 0.9433 \| 0.8961 \| 0.9669 \| 0.0 \| 0.0 \| 0.2600 \| 0.0 \| nan \| 0.6920 \| 0.8684 \| 0.4967 \| 0.5858 \| 0.3694 \| nan \| 0.4055 \| 0.4655 \| 0.0 \| 0.7965 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3631 \| 0.0 \| 0.0 \| 0.7006 \| 0.0 \| 0.3859 \| 0.3490 \| 0.0 \| nan \| 0.0 \| 0.2691 \| 0.0 \| 0.0 \| 0.8409 \| 0.7679 \| 0.9098 \| 0.0 \| 0.0 \| 0.2010 \| 0.0 \| \| 0.2758 \| 6.6 \| 1320 \| 0.5834 \| 0.3085 \| 0.3673 \| 0.8435 \| nan \| 0.8289 \| 0.9513 \| 0.6518 \| 0.7364 \| 0.5566 \| nan \| 0.5425 \| 0.4657 \| 0.0 \| 0.9327 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5677 \| 0.0 \| 0.0 \| 0.8959 \| 0.0 \| 0.5306 \| 0.4128 \| 0.0 \| nan \| 0.0 \| 0.3408 \| 0.0 \| 0.0 \| 0.9520 \| 0.8735 \| 0.9548 \| 0.0 \| 0.0 \| 0.1924 \| 0.0 \| nan \| 0.7241 \| 0.8578 \| 0.5561 \| 0.5779 \| 0.4191 \| nan \| 0.4193 \| 0.4042 \| 0.0 \| 0.8086 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3795 \| 0.0 \| 0.0 \| 0.7027 \| 0.0 \| 0.3963 \| 0.3815 \| 0.0 \| nan \| 0.0 \| 0.2673 \| 0.0 \| 0.0 \| 0.8369 \| 0.7604 \| 0.9187 \| 0.0 \| 0.0 \| 0.1534 \| 0.0 \| \| 0.4533 \| 6.7 \| 1340 \| 0.5830 \| 0.3052 \| 0.3725 \| 0.8390 \| nan \| 0.8076 \| 0.9474 \| 0.6777 \| 0.7975 \| 0.5173 \| nan \| 0.5476 \| 0.4662 \| 0.0 \| 0.9397 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5851 \| 0.0 \| 0.0 \| 0.8903 \| 0.0 \| 0.5785 \| 0.4557 \| 0.0 \| nan \| 0.0 \| 0.3384 \| 0.0 \| 0.0 \| 0.9171 \| 0.9185 \| 0.9545 \| 0.0 \| 0.0 \| 0.2086 \| 0.0 \| nan \| 0.6955 \| 0.8641 \| 0.4621 \| 0.5747 \| 0.4068 \| nan \| 0.4199 \| 0.4060 \| 0.0 \| 0.8046 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3753 \| 0.0 \| 0.0 \| 0.6958 \| 0.0 \| 0.4234 \| 0.4041 \| 0.0 \| nan \| 0.0 \| 0.2615 \| 0.0 \| 0.0 \| 0.8418 \| 0.7440 \| 0.9185 \| 0.0 \| 0.0 \| 0.1628 \| 0.0 \| \| 0.4431 \| 6.8 \| 1360 \| 0.5965 \| 0.3079 \| 0.3716 \| 0.8396 \| nan \| 0.9063 \| 0.9083 \| 0.6162 \| 0.8022 \| 0.4839 \| nan \| 0.5679 \| 0.4944 \| 0.0 \| 0.9397 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5788 \| 0.0 \| 0.0 \| 0.9175 \| 0.0 \| 0.5235 \| 0.4825 \| 0.0 \| nan \| 0.0 \| 0.3448 \| 0.0 \| 0.0 \| 0.9400 \| 0.8798 \| 0.9675 \| 0.0 \| 0.0 \| 0.1664 \| 0.0 \| nan \| 0.7017 \| 0.8573 \| 0.5427 \| 0.5755 \| 0.3769 \| nan \| 0.4289 \| 0.4170 \| 0.0 \| 0.8026 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3763 \| 0.0 \| 0.0 \| 0.6975 \| 0.0 \| 0.4024 \| 0.4249 \| 0.0 \| nan \| 0.0 \| 0.2638 \| 0.0 \| 0.0 \| 0.8461 \| 0.7712 \| 0.9157 \| 0.0 \| 0.0 \| 0.1431 \| 0.0 \| \| 0.4814 \| 6.9 \| 1380 \| 0.5858 \| 0.3091 \| 0.3683 \| 0.8447 \| nan \| 0.8749 \| 0.9559 \| 0.6054 \| 0.6724 \| 0.4716 \| nan \| 0.5793 \| 0.5114 \| 0.0 \| 0.9466 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5914 \| 0.0 \| 0.0 \| 0.9019 \| 0.0 \| 0.4914 \| 0.4580 \| 0.0 \| nan \| 0.0 \| 0.3607 \| 0.0 \| 0.0 \| 0.9281 \| 0.9113 \| 0.9668 \| 0.0 \| 0.0 \| 0.1902 \| 0.0 \| nan \| 0.7352 \| 0.8534 \| 0.5436 \| 0.5826 \| 0.3838 \| nan \| 0.4293 \| 0.4296 \| 0.0 \| 0.7971 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3719 \| 0.0 \| 0.0 \| 0.7038 \| 0.0 \| 0.3838 \| 0.4147 \| 0.0 \| nan \| 0.0 \| 0.2636 \| 0.0 \| 0.0 \| 0.8452 \| 0.7693 \| 0.9185 \| 0.0 \| 0.0 \| 0.1574 \| 0.0 \| \| 0.4765 \| 7.0 \| 1400 \| 0.5937 \| 0.3077 \| 0.3727 \| 0.8409 \| nan \| 0.8946 \| 0.9235 \| 0.6725 \| 0.7406 \| 0.4691 \| nan \| 0.6064 \| 0.5834 \| 0.0 \| 0.9342 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5602 \| 0.0 \| 0.0 \| 0.9083 \| 0.0 \| 0.5148 \| 0.3851 \| 0.0 \| nan \| 0.0 \| 0.3517 \| 0.0 \| 0.0 \| 0.9388 \| 0.8882 \| 0.9676 \| 0.0 \| 0.0 \| 0.2146 \| 0.0 \| nan \| 0.6928 \| 0.8682 \| 0.4465 \| 0.6082 \| 0.3684 \| nan \| 0.4403 \| 0.4549 \| 0.0 \| 0.8080 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3872 \| 0.0 \| 0.0 \| 0.6920 \| 0.0 \| 0.4152 \| 0.3672 \| 0.0 \| nan \| 0.0 \| 0.2658 \| 0.0 \| 0.0 \| 0.8474 \| 0.7841 \| 0.9152 \| 0.0 \| 0.0 \| 0.1761 \| 0.0 \| \| 0.3616 \| 7.1 \| 1420 \| 0.5733 \| 0.3132 \| 0.3776 \| 0.8461 \| nan \| 0.8448 \| 0.9405 \| 0.6132 \| 0.8331 \| 0.5368 \| nan \| 0.5895 \| 0.5248 \| 0.0 \| 0.9373 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5603 \| 0.0 \| 0.0 \| 0.8932 \| 0.0 \| 0.5813 \| 0.4069 \| 0.0 \| nan \| 0.0 \| 0.3559 \| 0.0 \| 0.0 \| 0.9324 \| 0.9075 \| 0.9655 \| 0.0 \| 0.0 \| 0.2818 \| 0.0 \| nan \| 0.7223 \| 0.8676 \| 0.5453 \| 0.5946 \| 0.4112 \| nan \| 0.4264 \| 0.4193 \| 0.0 \| 0.8046 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3849 \| 0.0 \| 0.0 \| 0.7001 \| 0.0 \| 0.4327 \| 0.3820 \| 0.0 \| nan \| 0.0 \| 0.2659 \| 0.0 \| 0.0 \| 0.8487 \| 0.7737 \| 0.9185 \| 0.0 \| 0.0 \| 0.2120 \| 0.0 \| \| 0.4933 \| 7.2 \| 1440 \| 0.5813 \| 0.3109 \| 0.3753 \| 0.8451 \| nan \| 0.8538 \| 0.9437 \| 0.5760 \| 0.8191 \| 0.5058 \| nan \| 0.5736 \| 0.6060 \| 0.0 \| 0.9314 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5896 \| 0.0 \| 0.0 \| 0.9101 \| 0.0 \| 0.4950 \| 0.4585 \| 0.0 \| nan \| 0.0 \| 0.3675 \| 0.0 \| 0.0 \| 0.9298 \| 0.9159 \| 0.9637 \| 0.0 \| 0.0 \| 0.1930 \| 0.0 \| nan \| 0.7222 \| 0.8639 \| 0.5238 \| 0.6014 \| 0.3986 \| nan \| 0.4346 \| 0.4511 \| 0.0 \| 0.8135 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3784 \| 0.0 \| 0.0 \| 0.6960 \| 0.0 \| 0.3766 \| 0.4129 \| 0.0 \| nan \| 0.0 \| 0.2718 \| 0.0 \| 0.0 \| 0.8480 \| 0.7611 \| 0.9201 \| 0.0 \| 0.0 \| 0.1638 \| 0.0 \| \| 0.4028 \| 7.3 \| 1460 \| 0.5729 \| 0.3121 \| 0.3725 \| 0.8482 \| nan \| 0.8882 \| 0.9510 \| 0.5887 \| 0.7435 \| 0.5139 \| nan \| 0.5499 \| 0.5656 \| 0.0 \| 0.9375 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6045 \| 0.0 \| 0.0 \| 0.9055 \| 0.0 \| 0.4169 \| 0.5068 \| 0.0 \| nan \| 0.0 \| 0.3830 \| 0.0 \| 0.0 \| 0.9357 \| 0.8909 \| 0.9639 \| 0.0 \| 0.0 \| 0.2030 \| 0.0 \| nan \| 0.7279 \| 0.8672 \| 0.5187 \| 0.6300 \| 0.4068 \| nan \| 0.4401 \| 0.4476 \| 0.0 \| 0.8015 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3645 \| 0.0 \| 0.0 \| 0.6940 \| 0.0 \| 0.3380 \| 0.4487 \| 0.0 \| nan \| 0.0 \| 0.2823 \| 0.0 \| 0.0 \| 0.8485 \| 0.7751 \| 0.9158 \| 0.0 \| 0.0 \| 0.1674 \| 0.0 \| \| 0.7888 \| 7.4 \| 1480 \| 0.5864 \| 0.3107 \| 0.3756 \| 0.8444 \| nan \| 0.8955 \| 0.9296 \| 0.6318 \| 0.7855 \| 0.5471 \| nan \| 0.5243 \| 0.5552 \| 0.0 \| 0.9379 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6029 \| 0.0 \| 0.0 \| 0.9031 \| 0.0 \| 0.4917 \| 0.4499 \| 0.0 \| nan \| 0.0 \| 0.3496 \| 0.0 \| 0.0 \| 0.9258 \| 0.9006 \| 0.9672 \| 0.0 \| 0.0 \| 0.2454 \| 0.0 \| nan \| 0.7057 \| 0.8712 \| 0.5006 \| 0.6259 \| 0.4010 \| nan \| 0.4206 \| 0.4426 \| 0.0 \| 0.7998 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3727 \| 0.0 \| 0.0 \| 0.6992 \| 0.0 \| 0.3834 \| 0.4140 \| 0.0 \| nan \| 0.0 \| 0.2712 \| 0.0 \| 0.0 \| 0.8454 \| 0.7705 \| 0.9131 \| 0.0 \| 0.0 \| 0.1953 \| 0.0 \| \| 0.4472 \| 7.5 \| 1500 \| 0.5824 \| 0.3101 \| 0.3722 \| 0.8445 \| nan \| 0.8338 \| 0.9448 \| 0.5706 \| 0.8580 \| 0.5130 \| nan \| 0.5545 \| 0.4821 \| 0.0 \| 0.9256 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5927 \| 0.0 \| 0.0 \| 0.9045 \| 0.0 \| 0.5369 \| 0.4377 \| 0.0 \| nan \| 0.0 \| 0.3506 \| 0.0 \| 0.0 \| 0.9366 \| 0.8973 \| 0.9610 \| 0.0 \| 0.0 \| 0.2392 \| 0.0 \| nan \| 0.7096 \| 0.8653 \| 0.5068 \| 0.5979 \| 0.4057 \| nan \| 0.4239 \| 0.4143 \| 0.0 \| 0.8112 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3738 \| 0.0 \| 0.0 \| 0.7033 \| 0.0 \| 0.4068 \| 0.4079 \| 0.0 \| nan \| 0.0 \| 0.2711 \| 0.0 \| 0.0 \| 0.8453 \| 0.7657 \| 0.9190 \| 0.0 \| 0.0 \| 0.1862 \| 0.0 \| \| 0.2959 \| 7.6 \| 1520 \| 0.5668 \| 0.3165 \| 0.3833 \| 0.8484 \| nan \| 0.8573 \| 0.9417 \| 0.6345 \| 0.8128 \| 0.5696 \| nan \| 0.5761 \| 0.5790 \| 0.0 \| 0.9427 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6075 \| 0.0 \| 0.0 \| 0.8679 \| 0.0 \| 0.5660 \| 0.4868 \| 0.0 \| nan \| 0.0 \| 0.3390 \| 0.0 \| 0.0 \| 0.9379 \| 0.9009 \| 0.9638 \| 0.0 \| 0.0 \| 0.2990 \| 0.0 \| nan \| 0.7247 \| 0.8693 \| 0.5410 \| 0.6352 \| 0.4291 \| nan \| 0.4455 \| 0.4492 \| 0.0 \| 0.8027 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3639 \| 0.0 \| 0.0 \| 0.7058 \| 0.0 \| 0.4061 \| 0.4309 \| 0.0 \| nan \| 0.0 \| 0.2674 \| 0.0 \| 0.0 \| 0.8437 \| 0.7651 \| 0.9195 \| 0.0 \| 0.0 \| 0.2130 \| 0.0 \| \| 0.6112 \| 7.7 \| 1540 \| 0.5789 \| 0.3128 \| 0.3782 \| 0.8459 \| nan \| 0.8626 \| 0.9375 \| 0.6726 \| 0.8403 \| 0.5117 \| nan \| 0.5372 \| 0.5917 \| 0.0 \| 0.9368 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5974 \| 0.0 \| 0.0 \| 0.9098 \| 0.0 \| 0.5040 \| 0.4817 \| 0.0 \| nan \| 0.0 \| 0.3231 \| 0.0 \| 0.0 \| 0.9241 \| 0.9213 \| 0.9645 \| 0.0 \| 0.0 \| 0.2064 \| 0.0 \| nan \| 0.7126 \| 0.8682 \| 0.5547 \| 0.6269 \| 0.4078 \| nan \| 0.4295 \| 0.4562 \| 0.0 \| 0.8116 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3813 \| 0.0 \| 0.0 \| 0.7024 \| 0.0 \| 0.3765 \| 0.4300 \| 0.0 \| nan \| 0.0 \| 0.2614 \| 0.0 \| 0.0 \| 0.8421 \| 0.7503 \| 0.9187 \| 0.0 \| 0.0 \| 0.1678 \| 0.0 \| \| 0.385 \| 7.8 \| 1560 \| 0.5831 \| 0.3131 \| 0.3791 \| 0.8448 \| nan \| 0.8485 \| 0.9346 \| 0.6713 \| 0.8758 \| 0.4915 \| nan \| 0.5322 \| 0.6046 \| 0.0 \| 0.9425 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5874 \| 0.0 \| 0.0 \| 0.8924 \| 0.0 \| 0.5252 \| 0.4491 \| 0.0 \| nan \| 0.0 \| 0.3385 \| 0.0 \| 0.0 \| 0.9377 \| 0.9091 \| 0.9658 \| 0.0 \| 0.0 \| 0.2456 \| 0.0 \| nan \| 0.7112 \| 0.8643 \| 0.5588 \| 0.5947 \| 0.4014 \| nan \| 0.4258 \| 0.4594 \| 0.0 \| 0.8089 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3855 \| 0.0 \| 0.0 \| 0.7088 \| 0.0 \| 0.3989 \| 0.4132 \| 0.0 \| nan \| 0.0 \| 0.2616 \| 0.0 \| 0.0 \| 0.8429 \| 0.7604 \| 0.9170 \| 0.0 \| 0.0 \| 0.1948 \| 0.0 \| \| 0.3023 \| 7.9 \| 1580 \| 0.5723 \| 0.3149 \| 0.3819 \| 0.8466 \| nan \| 0.8500 \| 0.9378 \| 0.7040 \| 0.8430 \| 0.5060 \| nan \| 0.5910 \| 0.5657 \| 0.0 \| 0.9353 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5876 \| 0.0 \| 0.0 \| 0.9079 \| 0.0 \| 0.5166 \| 0.5220 \| 0.0 \| nan \| 0.0 \| 0.3322 \| 0.0 \| 0.0 \| 0.9274 \| 0.8971 \| 0.9669 \| 0.0 \| 0.0 \| 0.2493 \| 0.0 \| nan \| 0.7181 \| 0.8674 \| 0.5304 \| 0.6254 \| 0.4013 \| nan \| 0.4403 \| 0.4496 \| 0.0 \| 0.8136 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3828 \| 0.0 \| 0.0 \| 0.6964 \| 0.0 \| 0.3900 \| 0.4504 \| 0.0 \| nan \| 0.0 \| 0.2613 \| 0.0 \| 0.0 \| 0.8497 \| 0.7753 \| 0.9145 \| 0.0 \| 0.0 \| 0.1965 \| 0.0 \| \| 0.2996 \| 8.0 \| 1600 \| 0.5833 \| 0.3115 \| 0.3742 \| 0.8446 \| nan \| 0.8598 \| 0.9405 \| 0.5576 \| 0.8611 \| 0.4879 \| nan \| 0.5030 \| 0.6505 \| 0.0 \| 0.9331 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5664 \| 0.0 \| 0.0 \| 0.8904 \| 0.0 \| 0.5266 \| 0.4377 \| 0.0 \| nan \| 0.0 \| 0.3461 \| 0.0 \| 0.0 \| 0.9471 \| 0.8733 \| 0.9674 \| 0.0 \| 0.0 \| 0.2518 \| 0.0 \| nan \| 0.7107 \| 0.8647 \| 0.5110 \| 0.5997 \| 0.3949 \| nan \| 0.4173 \| 0.4636 \| 0.0 \| 0.8112 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3853 \| 0.0 \| 0.0 \| 0.7031 \| 0.0 \| 0.4045 \| 0.4033 \| 0.0 \| nan \| 0.0 \| 0.2658 \| 0.0 \| 0.0 \| 0.8403 \| 0.7659 \| 0.9125 \| 0.0 \| 0.0 \| 0.2029 \| 0.0 \| \| 0.6193 \| 8.1 \| 1620 \| 0.5776 \| 0.3150 \| 0.3768 \| 0.8475 \| nan \| 0.8553 \| 0.9466 \| 0.5537 \| 0.8181 \| 0.5218 \| nan \| 0.5600 \| 0.6284 \| 0.0 \| 0.9381 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5590 \| 0.0 \| 0.0 \| 0.8879 \| 0.0 \| 0.5564 \| 0.4580 \| 0.0 \| nan \| 0.0 \| 0.3610 \| 0.0 \| 0.0 \| 0.9447 \| 0.8941 \| 0.9605 \| 0.0 \| 0.0 \| 0.2361 \| 0.0 \| nan \| 0.7149 \| 0.8665 \| 0.5031 \| 0.6349 \| 0.4079 \| nan \| 0.4338 \| 0.4728 \| 0.0 \| 0.8083 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3884 \| 0.0 \| 0.0 \| 0.7066 \| 0.0 \| 0.4185 \| 0.4150 \| 0.0 \| nan \| 0.0 \| 0.2718 \| 0.0 \| 0.0 \| 0.8427 \| 0.7671 \| 0.9200 \| 0.0 \| 0.0 \| 0.1932 \| 0.0 \| \| 0.3867 \| 8.2 \| 1640 \| 0.5783 \| 0.3154 \| 0.3788 \| 0.8485 \| nan \| 0.8492 \| 0.9482 \| 0.6227 \| 0.8445 \| 0.5045 \| nan \| 0.5666 \| 0.5861 \| 0.0 \| 0.9364 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5957 \| 0.0 \| 0.0 \| 0.8933 \| 0.0 \| 0.5372 \| 0.4904 \| 0.0 \| nan \| 0.0 \| 0.3774 \| 0.0 \| 0.0 \| 0.9419 \| 0.8839 \| 0.9666 \| 0.0 \| 0.0 \| 0.1988 \| 0.0 \| nan \| 0.7210 \| 0.8676 \| 0.5481 \| 0.6260 \| 0.4021 \| nan \| 0.4385 \| 0.4505 \| 0.0 \| 0.8097 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3775 \| 0.0 \| 0.0 \| 0.7023 \| 0.0 \| 0.4096 \| 0.4369 \| 0.0 \| nan \| 0.0 \| 0.2813 \| 0.0 \| 0.0 \| 0.8467 \| 0.7758 \| 0.9186 \| 0.0 \| 0.0 \| 0.1659 \| 0.0 \| \| 0.317 \| 8.3 \| 1660 \| 0.5688 \| 0.3161 \| 0.3812 \| 0.8494 \| nan \| 0.8745 \| 0.9402 \| 0.6804 \| 0.8045 \| 0.5513 \| nan \| 0.5835 \| 0.5725 \| 0.0 \| 0.9466 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5978 \| 0.0 \| 0.0 \| 0.8948 \| 0.0 \| 0.5215 \| 0.4635 \| 0.0 \| nan \| 0.0 \| 0.3691 \| 0.0 \| 0.0 \| 0.9312 \| 0.8965 \| 0.9691 \| 0.0 \| 0.0 \| 0.2206 \| 0.0 \| nan \| 0.7271 \| 0.8711 \| 0.5578 \| 0.6459 \| 0.4147 \| nan \| 0.4456 \| 0.4482 \| 0.0 \| 0.7997 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3772 \| 0.0 \| 0.0 \| 0.7015 \| 0.0 \| 0.4000 \| 0.4200 \| 0.0 \| nan \| 0.0 \| 0.2752 \| 0.0 \| 0.0 \| 0.8472 \| 0.7738 \| 0.9171 \| 0.0 \| 0.0 \| 0.1764 \| 0.0 \| \| 1.3707 \| 8.4 \| 1680 \| 0.5730 \| 0.3154 \| 0.3780 \| 0.8490 \| nan \| 0.8738 \| 0.9492 \| 0.6781 \| 0.7945 \| 0.4691 \| nan \| 0.5674 \| 0.6020 \| 0.0 \| 0.9409 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5897 \| 0.0 \| 0.0 \| 0.8936 \| 0.0 \| 0.5349 \| 0.4505 \| 0.0 \| nan \| 0.0 \| 0.3486 \| 0.0 \| 0.0 \| 0.9357 \| 0.8928 \| 0.9704 \| 0.0 \| 0.0 \| 0.2273 \| 0.0 \| nan \| 0.7278 \| 0.8678 \| 0.5609 \| 0.6440 \| 0.3773 \| nan \| 0.4447 \| 0.4656 \| 0.0 \| 0.8115 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3821 \| 0.0 \| 0.0 \| 0.7028 \| 0.0 \| 0.3953 \| 0.4097 \| 0.0 \| nan \| 0.0 \| 0.2698 \| 0.0 \| 0.0 \| 0.8456 \| 0.7769 \| 0.9117 \| 0.0 \| 0.0 \| 0.1825 \| 0.0 \| \| 0.3326 \| 8.5 \| 1700 \| 0.5753 \| 0.3159 \| 0.3826 \| 0.8462 \| nan \| 0.8597 \| 0.9398 \| 0.6403 \| 0.8407 \| 0.4915 \| nan \| 0.5517 \| 0.5957 \| 0.0 \| 0.9403 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5921 \| 0.0 \| 0.0 \| 0.8785 \| 0.0 \| 0.5797 \| 0.5061 \| 0.0 \| nan \| 0.0 \| 0.3642 \| 0.0 \| 0.0 \| 0.9300 \| 0.9048 \| 0.9683 \| 0.0 \| 0.0 \| 0.2772 \| 0.0 \| nan \| 0.7204 \| 0.8666 \| 0.5546 \| 0.6021 \| 0.3889 \| nan \| 0.4336 \| 0.4644 \| 0.0 \| 0.8123 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3759 \| 0.0 \| 0.0 \| 0.7026 \| 0.0 \| 0.4171 \| 0.4383 \| 0.0 \| nan \| 0.0 \| 0.2735 \| 0.0 \| 0.0 \| 0.8466 \| 0.7702 \| 0.9170 \| 0.0 \| 0.0 \| 0.2075 \| 0.0 \| \| 0.343 \| 8.6 \| 1720 \| 0.5699 \| 0.3183 \| 0.3813 \| 0.8500 \| nan \| 0.8720 \| 0.9460 \| 0.6340 \| 0.8164 \| 0.5137 \| nan \| 0.5698 \| 0.5619 \| 0.0 \| 0.9335 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5860 \| 0.0 \| 0.0 \| 0.8936 \| 0.0 \| 0.5409 \| 0.5306 \| 0.0 \| nan \| 0.0 \| 0.3632 \| 0.0 \| 0.0 \| 0.9313 \| 0.8939 \| 0.9638 \| 0.0 \| 0.0 \| 0.2693 \| 0.0 \| nan \| 0.7295 \| 0.8693 \| 0.5551 \| 0.6398 \| 0.4015 \| nan \| 0.4431 \| 0.4498 \| 0.0 \| 0.8168 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3764 \| 0.0 \| 0.0 \| 0.6999 \| 0.0 \| 0.4146 \| 0.4559 \| 0.0 \| nan \| 0.0 \| 0.2766 \| 0.0 \| 0.0 \| 0.8478 \| 0.7674 \| 0.9199 \| 0.0 \| 0.0 \| 0.2053 \| 0.0 \| \| 0.4636 \| 8.7 \| 1740 \| 0.5700 \| 0.3170 \| 0.3791 \| 0.8494 \| nan \| 0.8801 \| 0.9435 \| 0.6036 \| 0.8067 \| 0.5080 \| nan \| 0.5780 \| 0.5943 \| 0.0 \| 0.9338 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5831 \| 0.0 \| 0.0 \| 0.9054 \| 0.0 \| 0.5190 \| 0.4826 \| 0.0 \| nan \| 0.0 \| 0.3532 \| 0.0 \| 0.0 \| 0.9301 \| 0.9133 \| 0.9593 \| 0.0 \| 0.0 \| 0.2595 \| 0.0 \| nan \| 0.7261 \| 0.8706 \| 0.5399 \| 0.6449 \| 0.4018 \| nan \| 0.4455 \| 0.4644 \| 0.0 \| 0.8180 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3798 \| 0.0 \| 0.0 \| 0.7039 \| 0.0 \| 0.4047 \| 0.4369 \| 0.0 \| nan \| 0.0 \| 0.2706 \| 0.0 \| 0.0 \| 0.8422 \| 0.7544 \| 0.9215 \| 0.0 \| 0.0 \| 0.2013 \| 0.0 \| \| 0.4121 \| 8.8 \| 1760 \| 0.5690 \| 0.3166 \| 0.3770 \| 0.8504 \| nan \| 0.8629 \| 0.9514 \| 0.6429 \| 0.8294 \| 0.4950 \| nan \| 0.5736 \| 0.6034 \| 0.0 \| 0.9316 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5776 \| 0.0 \| 0.0 \| 0.9107 \| 0.0 \| 0.5116 \| 0.4527 \| 0.0 \| nan \| 0.0 \| 0.3313 \| 0.0 \| 0.0 \| 0.9344 \| 0.9031 \| 0.9622 \| 0.0 \| 0.0 \| 0.2129 \| 0.0 \| nan \| 0.7339 \| 0.8671 \| 0.5670 \| 0.6443 \| 0.4004 \| nan \| 0.4438 \| 0.4655 \| 0.0 \| 0.8183 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3821 \| 0.0 \| 0.0 \| 0.7003 \| 0.0 \| 0.3945 \| 0.4177 \| 0.0 \| nan \| 0.0 \| 0.2684 \| 0.0 \| 0.0 \| 0.8459 \| 0.7742 \| 0.9209 \| 0.0 \| 0.0 \| 0.1710 \| 0.0 \| \| 0.3737 \| 8.9 \| 1780 \| 0.5611 \| 0.3183 \| 0.3813 \| 0.8513 \| nan \| 0.8763 \| 0.9429 \| 0.6298 \| 0.8213 \| 0.5577 \| nan \| 0.5642 \| 0.6243 \| 0.0 \| 0.9447 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5873 \| 0.0 \| 0.0 \| 0.9152 \| 0.0 \| 0.5375 \| 0.4555 \| 0.0 \| nan \| 0.0 \| 0.3414 \| 0.0 \| 0.0 \| 0.9293 \| 0.8930 \| 0.9650 \| 0.0 \| 0.0 \| 0.2335 \| 0.0 \| nan \| 0.7321 \| 0.8707 \| 0.5613 \| 0.6495 \| 0.4244 \| nan \| 0.4409 \| 0.4685 \| 0.0 \| 0.8063 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3790 \| 0.0 \| 0.0 \| 0.7031 \| 0.0 \| 0.4053 \| 0.4191 \| 0.0 \| nan \| 0.0 \| 0.2713 \| 0.0 \| 0.0 \| 0.8502 \| 0.7808 \| 0.9205 \| 0.0 \| 0.0 \| 0.1839 \| 0.0 \| \| 0.4408 \| 9.0 \| 1800 \| 0.5686 \| 0.3186 \| 0.3868 \| 0.8498 \| nan \| 0.8582 \| 0.9370 \| 0.6738 \| 0.8667 \| 0.5464 \| nan \| 0.5771 \| 0.6496 \| 0.0 \| 0.9442 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5956 \| 0.0 \| 0.0 \| 0.8915 \| 0.0 \| 0.5419 \| 0.4507 \| 0.0 \| nan \| 0.0 \| 0.3632 \| 0.0 \| 0.0 \| 0.9340 \| 0.9020 \| 0.9687 \| 0.0 \| 0.0 \| 0.2900 \| 0.0 \| nan \| 0.7299 \| 0.8694 \| 0.5746 \| 0.6252 \| 0.4206 \| nan \| 0.4388 \| 0.4654 \| 0.0 \| 0.8066 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3746 \| 0.0 \| 0.0 \| 0.7127 \| 0.0 \| 0.4090 \| 0.4180 \| 0.0 \| nan \| 0.0 \| 0.2755 \| 0.0 \| 0.0 \| 0.8474 \| 0.7750 \| 0.9177 \| 0.0 \| 0.0 \| 0.2170 \| 0.0 \| \| 0.402 \| 9.1 \| 1820 \| 0.5630 \| 0.3196 \| 0.3841 \| 0.8521 \| nan \| 0.8595 \| 0.9464 \| 0.6812 \| 0.8413 \| 0.5551 \| nan \| 0.5696 \| 0.6260 \| 0.0 \| 0.9382 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5894 \| 0.0 \| 0.0 \| 0.8971 \| 0.0 \| 0.5589 \| 0.4594 \| 0.0 \| nan \| 0.0 \| 0.3541 \| 0.0 \| 0.0 \| 0.9363 \| 0.8953 \| 0.9632 \| 0.0 \| 0.0 \| 0.2347 \| 0.0 \| nan \| 0.7346 \| 0.8703 \| 0.5700 \| 0.6527 \| 0.4236 \| nan \| 0.4401 \| 0.4699 \| 0.0 \| 0.8127 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3778 \| 0.0 \| 0.0 \| 0.7121 \| 0.0 \| 0.4124 \| 0.4193 \| 0.0 \| nan \| 0.0 \| 0.2748 \| 0.0 \| 0.0 \| 0.8471 \| 0.7811 \| 0.9195 \| 0.0 \| 0.0 \| 0.1880 \| 0.0 \| \| 0.3377 \| 9.2 \| 1840 \| 0.5694 \| 0.3185 \| 0.3830 \| 0.8509 \| nan \| 0.8655 \| 0.9424 \| 0.6606 \| 0.8551 \| 0.5115 \| nan \| 0.5671 \| 0.6184 \| 0.0 \| 0.9379 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5804 \| 0.0 \| 0.0 \| 0.9100 \| 0.0 \| 0.5601 \| 0.4767 \| 0.0 \| nan \| 0.0 \| 0.3502 \| 0.0 \| 0.0 \| 0.9300 \| 0.9088 \| 0.9670 \| 0.0 \| 0.0 \| 0.2307 \| 0.0 \| nan \| 0.7342 \| 0.8710 \| 0.5740 \| 0.6386 \| 0.4059 \| nan \| 0.4415 \| 0.4724 \| 0.0 \| 0.8146 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3815 \| 0.0 \| 0.0 \| 0.7064 \| 0.0 \| 0.4131 \| 0.4315 \| 0.0 \| nan \| 0.0 \| 0.2729 \| 0.0 \| 0.0 \| 0.8475 \| 0.7661 \| 0.9172 \| 0.0 \| 0.0 \| 0.1850 \| 0.0 \| \| 0.4663 \| 9.3 \| 1860 \| 0.5681 \| 0.3192 \| 0.3833 \| 0.8511 \| nan \| 0.8702 \| 0.9435 \| 0.6699 \| 0.8311 \| 0.5039 \| nan \| 0.5762 \| 0.5998 \| 0.0 \| 0.9349 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5908 \| 0.0 \| 0.0 \| 0.8964 \| 0.0 \| 0.5619 \| 0.5139 \| 0.0 \| nan \| 0.0 \| 0.3556 \| 0.0 \| 0.0 \| 0.9378 \| 0.8993 \| 0.9672 \| 0.0 \| 0.0 \| 0.2300 \| 0.0 \| nan \| 0.7313 \| 0.8702 \| 0.5734 \| 0.6435 \| 0.3982 \| nan \| 0.4442 \| 0.4682 \| 0.0 \| 0.8179 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3761 \| 0.0 \| 0.0 \| 0.7091 \| 0.0 \| 0.4147 \| 0.4523 \| 0.0 \| nan \| 0.0 \| 0.2746 \| 0.0 \| 0.0 \| 0.8468 \| 0.7747 \| 0.9172 \| 0.0 \| 0.0 \| 0.1845 \| 0.0 \| \| 0.419 \| 9.4 \| 1880 \| 0.5703 \| 0.3185 \| 0.3821 \| 0.8510 \| nan \| 0.8632 \| 0.9463 \| 0.6844 \| 0.8301 \| 0.5153 \| nan \| 0.5890 \| 0.6125 \| 0.0 \| 0.9357 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5848 \| 0.0 \| 0.0 \| 0.9128 \| 0.0 \| 0.4967 \| 0.5189 \| 0.0 \| nan \| 0.0 \| 0.3516 \| 0.0 \| 0.0 \| 0.9318 \| 0.8924 \| 0.9689 \| 0.0 \| 0.0 \| 0.2100 \| 0.0 \| nan \| 0.7324 \| 0.8705 \| 0.5729 \| 0.6473 \| 0.4037 \| nan \| 0.4476 \| 0.4693 \| 0.0 \| 0.8170 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3798 \| 0.0 \| 0.0 \| 0.6988 \| 0.0 \| 0.3785 \| 0.4589 \| 0.0 \| nan \| 0.0 \| 0.2752 \| 0.0 \| 0.0 \| 0.8501 \| 0.7819 \| 0.9173 \| 0.0 \| 0.0 \| 0.1715 \| 0.0 \| \| 0.2796 \| 9.5 \| 1900 \| 0.5678 \| 0.3168 \| 0.3808 \| 0.8499 \| nan \| 0.8652 \| 0.9410 \| 0.6789 \| 0.8372 \| 0.5167 \| nan \| 0.5874 \| 0.5978 \| 0.0 \| 0.9425 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5874 \| 0.0 \| 0.0 \| 0.9008 \| 0.0 \| 0.4731 \| 0.5021 \| 0.0 \| nan \| 0.0 \| 0.3488 \| 0.0 \| 0.0 \| 0.9399 \| 0.9032 \| 0.9677 \| 0.0 \| 0.0 \| 0.2138 \| 0.0 \| nan \| 0.7294 \| 0.8708 \| 0.5725 \| 0.6440 \| 0.4041 \| nan \| 0.4481 \| 0.4663 \| 0.0 \| 0.8105 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3794 \| 0.0 \| 0.0 \| 0.7025 \| 0.0 \| 0.3640 \| 0.4497 \| 0.0 \| nan \| 0.0 \| 0.2714 \| 0.0 \| 0.0 \| 0.8454 \| 0.7704 \| 0.9170 \| 0.0 \| 0.0 \| 0.1765 \| 0.0 \| \| 0.3892 \| 9.6 \| 1920 \| 0.5666 \| 0.3184 \| 0.3817 \| 0.8511 \| nan \| 0.8720 \| 0.9433 \| 0.6665 \| 0.8238 \| 0.5257 \| nan \| 0.5873 \| 0.5904 \| 0.0 \| 0.9392 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5954 \| 0.0 \| 0.0 \| 0.9039 \| 0.0 \| 0.5012 \| 0.4987 \| 0.0 \| nan \| 0.0 \| 0.3520 \| 0.0 \| 0.0 \| 0.9354 \| 0.9017 \| 0.9666 \| 0.0 \| 0.0 \| 0.2303 \| 0.0 \| nan \| 0.7313 \| 0.8705 \| 0.5718 \| 0.6482 \| 0.4098 \| nan \| 0.4498 \| 0.4632 \| 0.0 \| 0.8137 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3740 \| 0.0 \| 0.0 \| 0.7050 \| 0.0 \| 0.3821 \| 0.4487 \| 0.0 \| nan \| 0.0 \| 0.2734 \| 0.0 \| 0.0 \| 0.8476 \| 0.7743 \| 0.9187 \| 0.0 \| 0.0 \| 0.1872 \| 0.0 \| \| 0.4762 \| 9.7 \| 1940 \| 0.5650 \| 0.3186 \| 0.3821 \| 0.8515 \| nan \| 0.8680 \| 0.9457 \| 0.6698 \| 0.8232 \| 0.5305 \| nan \| 0.5911 \| 0.5940 \| 0.0 \| 0.9374 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5999 \| 0.0 \| 0.0 \| 0.8884 \| 0.0 \| 0.4855 \| 0.4855 \| 0.0 \| nan \| 0.0 \| 0.3632 \| 0.0 \| 0.0 \| 0.9414 \| 0.9042 \| 0.9673 \| 0.0 \| 0.0 \| 0.2512 \| 0.0 \| nan \| 0.7347 \| 0.8701 \| 0.5730 \| 0.6501 \| 0.4141 \| nan \| 0.4509 \| 0.4629 \| 0.0 \| 0.8150 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3703 \| 0.0 \| 0.0 \| 0.7088 \| 0.0 \| 0.3807 \| 0.4421 \| 0.0 \| nan \| 0.0 \| 0.2731 \| 0.0 \| 0.0 \| 0.8434 \| 0.7698 \| 0.9171 \| 0.0 \| 0.0 \| 0.1997 \| 0.0 \| \| 0.2866 \| 9.8 \| 1960 \| 0.5644 \| 0.3189 \| 0.3818 \| 0.8522 \| nan \| 0.8639 \| 0.9487 \| 0.6788 \| 0.8166 \| 0.5314 \| nan \| 0.5863 \| 0.5961 \| 0.0 \| 0.9381 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6016 \| 0.0 \| 0.0 \| 0.9010 \| 0.0 \| 0.5091 \| 0.4735 \| 0.0 \| nan \| 0.0 \| 0.3538 \| 0.0 \| 0.0 \| 0.9399 \| 0.8958 \| 0.9638 \| 0.0 \| 0.0 \| 0.2383 \| 0.0 \| nan \| 0.7381 \| 0.8699 \| 0.5739 \| 0.6515 \| 0.4167 \| nan \| 0.4503 \| 0.4642 \| 0.0 \| 0.8143 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3707 \| 0.0 \| 0.0 \| 0.7074 \| 0.0 \| 0.3888 \| 0.4333 \| 0.0 \| nan \| 0.0 \| 0.2740 \| 0.0 \| 0.0 \| 0.8448 \| 0.7760 \| 0.9199 \| 0.0 \| 0.0 \| 0.1921 \| 0.0 \| \| 0.1857 \| 9.9 \| 1980 \| 0.5650 \| 0.3193 \| 0.3828 \| 0.8521 \| nan \| 0.8701 \| 0.9477 \| 0.6701 \| 0.8152 \| 0.5338 \| nan \| 0.5782 \| 0.5986 \| 0.0 \| 0.9404 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6036 \| 0.0 \| 0.0 \| 0.9080 \| 0.0 \| 0.5119 \| 0.4885 \| 0.0 \| nan \| 0.0 \| 0.3618 \| 0.0 \| 0.0 \| 0.9304 \| 0.9003 \| 0.9645 \| 0.0 \| 0.0 \| 0.2422 \| 0.0 \| nan \| 0.7346 \| 0.8706 \| 0.5727 \| 0.6517 \| 0.4164 \| nan \| 0.4489 \| 0.4635 \| 0.0 \| 0.8113 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3693 \| 0.0 \| 0.0 \| 0.7051 \| 0.0 \| 0.3929 \| 0.4436 \| 0.0 \| nan \| 0.0 \| 0.2765 \| 0.0 \| 0.0 \| 0.8485 \| 0.7771 \| 0.9204 \| 0.0 \| 0.0 \| 0.1940 \| 0.0 \| \| 0.3021 \| 10.0 \| 2000 \| 0.5644 \| 0.3202 \| 0.3861 \| 0.8525 \| nan \| 0.8598 \| 0.9440 \| 0.6837 \| 0.8295 \| 0.5586 \| nan \| 0.6015 \| 0.6019 \| 0.0 \| 0.9395 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6060 \| 0.0 \| 0.0 \| 0.8961 \| 0.0 \| 0.5249 \| 0.4892 \| 0.0 \| nan \| 0.0 \| 0.3733 \| 0.0 \| 0.0 \| 0.9376 \| 0.9033 \| 0.9668 \| 0.0 \| 0.0 \| 0.2540 \| 0.0 \| nan \| 0.7395 \| 0.8717 \| 0.5750 \| 0.6481 \| 0.4281 \| nan \| 0.4534 \| 0.4648 \| 0.0 \| 0.8127 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3687 \| 0.0 \| 0.0 \| 0.7090 \| 0.0 \| 0.3987 \| 0.4429 \| 0.0 \| nan \| 0.0 \| 0.2772 \| 0.0 \| 0.0 \| 0.8463 \| 0.7719 \| 0.9189 \| 0.0 \| 0.0 \| 0.2004 \| 0.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
ItDoBe/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.5881 - Mean Iou: 0.2988 - Mean Accuracy: 0.3613 - Overall Accuracy: 0.8463 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8649 - Accuracy Flat-sidewalk: 0.9502 - Accuracy Flat-crosswalk: 0.5791 - Accuracy Flat-cyclinglane: 0.7393 - Accuracy Flat-parkingdriveway: 0.4485 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.4859 - Accuracy Human-person: 0.7767 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9341 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.5642 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8944 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.4959 - Accuracy Construction-fenceguardrail: 0.4208 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3432 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9466 - Accuracy Nature-terrain: 0.8884 - Accuracy Sky: 0.9698 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.2614 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7372 - Iou Flat-sidewalk: 0.8540 - Iou Flat-crosswalk: 0.4322 - Iou Flat-cyclinglane: 0.6499 - Iou Flat-parkingdriveway: 0.3421 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.3932 - Iou Human-person: 0.5795 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.8033 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.3104 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7075 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3911 - Iou Construction-fenceguardrail: 0.3791 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.2524 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8559 - Iou Nature-terrain: 0.7644 - Iou Sky: 0.9219 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.1876 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| 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\| 2.6446 \| 0.1 \| 20 \| 2.9036 \| 0.0732 \| 0.1243 \| 0.5841 \| nan \| 0.1650 \| 0.9388 \| 0.0 \| 0.0110 \| 0.0046 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8412 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8181 \| 0.0 \| 0.0068 \| 0.0002 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9482 \| 0.0503 \| 0.1924 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.1551 \| 0.5858 \| 0.0 \| 0.0109 \| 0.0045 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4684 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4527 \| 0.0 \| 0.0065 \| 0.0002 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5709 \| 0.0419 \| 0.1923 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| \| 2.2236 \| 0.2 \| 40 \| 1.9829 \| 0.1087 \| 0.1536 \| 0.6546 \| nan \| 0.7316 \| 0.8821 \| 0.0 \| 0.0000 \| 0.0012 \| nan \| 0.0000 \| 0.0 \| 0.0 \| 0.7926 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8106 \| 0.0 \| 0.0032 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9636 \| 0.1494 \| 0.5799 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4221 \| 0.6904 \| 0.0 \| 0.0000 \| 0.0012 \| nan \| 0.0000 \| 0.0 \| 0.0 \| 0.5490 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4808 \| 0.0 \| 0.0032 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6243 \| 0.1322 \| 0.5746 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.6689 \| 0.3 \| 60 \| 1.6583 \| 0.1325 \| 0.1763 \| 0.6901 \| nan \| 0.6909 \| 0.9280 \| 0.0 \| 0.0 \| 0.0008 \| nan \| 0.0000 \| 0.0 \| 0.0 \| 0.8070 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8276 \| 0.0 \| 0.0110 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9101 \| 0.6704 \| 0.7960 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4339 \| 0.6869 \| 0.0 \| 0.0 \| 0.0008 \| nan \| 0.0000 \| 0.0 \| 0.0 \| 0.6051 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5031 \| 0.0 \| 0.0108 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7312 \| 0.5005 \| 0.7680 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.5794 \| 0.4 \| 80 \| 1.4876 \| 0.1366 \| 0.1825 \| 0.7038 \| nan \| 0.7097 \| 0.9350 \| 0.0 \| 0.0 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8456 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8642 \| 0.0 \| 0.0015 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9109 \| 0.7571 \| 0.8142 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4701 \| 0.7084 \| 0.0 \| 0.0 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6189 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5059 \| 0.0 \| 0.0015 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7397 \| 0.5460 \| 0.7797 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.5229 \| 0.5 \| 100 \| 1.4013 \| 0.1359 \| 0.1803 \| 0.7076 \| nan \| 0.6886 \| 0.9534 \| 0.0 \| 0.0347 \| 0.0016 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8892 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8217 \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9568 \| 0.5926 \| 0.8294 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5080 \| 0.7029 \| 0.0 \| 0.0346 \| 0.0016 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5598 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5472 \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6971 \| 0.5004 \| 0.7969 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.2003 \| 0.6 \| 120 \| 1.2425 \| 0.1474 \| 0.1965 \| 0.7285 \| nan \| 0.7966 \| 0.9182 \| 0.0 \| 0.1579 \| 0.0008 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8939 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8634 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9261 \| 0.8480 \| 0.8819 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5148 \| 0.7446 \| 0.0 \| 0.1559 \| 0.0008 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5954 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5441 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7506 \| 0.5912 \| 0.8198 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.2507 \| 0.7 \| 140 \| 1.2214 \| 0.1562 \| 0.2002 \| 0.7378 \| nan \| 0.8805 \| 0.8858 \| 0.0 \| 0.4127 \| 0.0019 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8466 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8672 \| 0.0 \| 0.0005 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9579 \| 0.6735 \| 0.8808 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5213 \| 0.7597 \| 0.0 \| 0.3764 \| 0.0019 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6704 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5638 \| 0.0 \| 0.0005 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7209 \| 0.5572 \| 0.8258 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.2322 \| 0.8 \| 160 \| 1.1794 \| 0.1537 \| 0.1993 \| 0.7361 \| nan \| 0.7339 \| 0.9511 \| 0.0 \| 0.3368 \| 0.0055 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.9218 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7810 \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9360 \| 0.7657 \| 0.9447 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5275 \| 0.7280 \| 0.0 \| 0.3161 \| 0.0055 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6021 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5550 \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7441 \| 0.6084 \| 0.8287 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4998 \| 0.9 \| 180 \| 1.0915 \| 0.1633 \| 0.2070 \| 0.7543 \| nan \| 0.8040 \| 0.9417 \| 0.0 \| 0.4433 \| 0.0024 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8499 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9059 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9153 \| 0.8368 \| 0.9238 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5594 \| 0.7627 \| 0.0 \| 0.3896 \| 0.0024 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.6526 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5509 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7779 \| 0.6755 \| 0.8528 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9777 \| 1.0 \| 200 \| 1.0430 \| 0.1674 \| 0.2097 \| 0.7623 \| nan \| 0.8292 \| 0.9417 \| 0.0 \| 0.5123 \| 0.0048 \| nan \| 0.0009 \| 0.0 \| 0.0 \| 0.8854 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8897 \| 0.0 \| 0.0131 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9389 \| 0.7870 \| 0.9084 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5751 \| 0.7711 \| 0.0 \| 0.4464 \| 0.0048 \| nan \| 0.0009 \| 0.0 \| 0.0 \| 0.6782 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5701 \| 0.0 \| 0.0131 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7720 \| 0.6698 \| 0.8562 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9326 \| 1.1 \| 220 \| 1.0643 \| 0.1551 \| 0.2051 \| 0.7343 \| nan \| 0.7976 \| 0.9512 \| 0.0 \| 0.4709 \| 0.0063 \| nan \| 0.0001 \| 0.0 \| 0.0 \| 0.9118 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8695 \| 0.0 \| 0.0038 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7454 \| 0.8821 \| 0.9252 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5785 \| 0.7469 \| 0.0 \| 0.4200 \| 0.0063 \| nan \| 0.0001 \| 0.0 \| 0.0 \| 0.6615 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5728 \| 0.0 \| 0.0038 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6639 \| 0.4500 \| 0.8600 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.1853 \| 1.2 \| 240 \| 1.0255 \| 0.1645 \| 0.2065 \| 0.7501 \| nan \| 0.6850 \| 0.9636 \| 0.0 \| 0.4379 \| 0.0078 \| nan \| 0.0006 \| 0.0 \| 0.0 \| 0.8839 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8852 \| 0.0 \| 0.0239 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9317 \| 0.8506 \| 0.9366 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5494 \| 0.7233 \| 0.0 \| 0.3777 \| 0.0077 \| nan \| 0.0006 \| 0.0 \| 0.0 \| 0.6827 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5818 \| 0.0 \| 0.0235 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7729 \| 0.6805 \| 0.8655 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.1844 \| 1.3 \| 260 \| 0.9715 \| 0.1677 \| 0.2091 \| 0.7599 \| nan \| 0.8100 \| 0.9497 \| 0.0 \| 0.4492 \| 0.0216 \| nan \| 0.0013 \| 0.0 \| 0.0 \| 0.8900 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8913 \| 0.0 \| 0.0401 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.9334 \| 0.7794 \| 0.9254 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.5640 \| 0.7634 \| 0.0 \| 0.4075 \| 0.0211 \| nan \| 0.0013 \| 0.0 \| 0.0 \| 0.6789 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5737 \| 0.0 \| 0.0388 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.7761 \| 0.6731 \| 0.8696 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 0.8037 \| 1.4 \| 280 \| 0.9345 \| 0.1694 \| 0.2120 \| 0.7620 \| nan \| 0.8153 \| 0.9338 \| 0.0 \| 0.5336 \| 0.0190 \| nan \| 0.0096 \| 0.0 \| 0.0 \| 0.8687 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8660 \| 0.0 \| 0.0239 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9518 \| 0.8266 \| 0.9353 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5748 \| 0.7621 \| 0.0 \| 0.4545 \| 0.0187 \| nan \| 0.0095 \| 0.0 \| 0.0 \| 0.7099 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5969 \| 0.0 \| 0.0234 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7515 \| 0.6518 \| 0.8682 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.046 \| 1.5 \| 300 \| 0.9362 \| 0.1665 \| 0.2063 \| 0.7602 \| nan \| 0.8074 \| 0.9552 \| 0.0 \| 0.4627 \| 0.0251 \| nan \| 0.0132 \| 0.0 \| 0.0 \| 0.8712 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9206 \| 0.0 \| 0.0295 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.9472 \| 0.6334 \| 0.9373 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.5877 \| 0.7633 \| 0.0 \| 0.4225 \| 0.0245 \| nan \| 0.0131 \| 0.0 \| 0.0 \| 0.6925 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5721 \| 0.0 \| 0.0285 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.7648 \| 0.5827 \| 0.8754 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8823 \| 1.6 \| 320 \| 0.9555 \| 0.1726 \| 0.2141 \| 0.7562 \| nan \| 0.6423 \| 0.9676 \| 0.0 \| 0.5346 \| 0.0428 \| nan \| 0.0608 \| 0.0 \| 0.0 \| 0.8740 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9115 \| 0.0 \| 0.1039 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9256 \| 0.8544 \| 0.9354 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.5514 \| 0.7267 \| 0.0 \| 0.4372 \| 0.0407 \| nan \| 0.0585 \| 0.0 \| 0.0 \| 0.6901 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5904 \| 0.0 \| 0.0878 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7883 \| 0.6758 \| 0.8769 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 0.7509 \| 1.7 \| 340 \| 0.9001 \| 0.1779 \| 0.2237 \| 0.7709 \| nan \| 0.9064 \| 0.9093 \| 0.0 \| 0.5323 \| 0.1030 \| nan \| 0.0284 \| 0.0 \| 0.0 \| 0.9033 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9072 \| 0.0 \| 0.1315 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.9072 \| 0.8914 \| 0.9371 \| 0.0 \| 0.0 \| 0.0002 \| 0.0 \| nan \| 0.5784 \| 0.7877 \| 0.0 \| 0.4640 \| 0.0934 \| nan \| 0.0277 \| 0.0 \| 0.0 \| 0.6997 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6068 \| 0.0 \| 0.1094 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.7947 \| 0.6547 \| 0.8762 \| 0.0 \| 0.0 \| 0.0002 \| 0.0 \| \| 1.4454 \| 1.8 \| 360 \| 0.8672 \| 0.1819 \| 0.2232 \| 0.7747 \| nan \| 0.8583 \| 0.9287 \| 0.0018 \| 0.5609 \| 0.1483 \| nan \| 0.0427 \| 0.0 \| 0.0 \| 0.8350 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9246 \| 0.0 \| 0.1830 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.9426 \| 0.7789 \| 0.9388 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.6006 \| 0.7921 \| 0.0018 \| 0.4665 \| 0.1287 \| nan \| 0.0418 \| 0.0 \| 0.0 \| 0.7032 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5769 \| 0.0 \| 0.1453 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| 0.7918 \| 0.6899 \| 0.8831 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.0715 \| 1.9 \| 380 \| 0.8731 \| 0.1822 \| 0.2251 \| 0.7731 \| nan \| 0.7780 \| 0.9396 \| 0.0 \| 0.6530 \| 0.1692 \| nan \| 0.0842 \| 0.0 \| 0.0 \| 0.8696 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8489 \| 0.0 \| 0.1946 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0007 \| 0.0 \| 0.0 \| 0.9709 \| 0.7592 \| 0.9323 \| 0.0 \| 0.0 \| 0.0022 \| 0.0 \| nan \| 0.6210 \| 0.7887 \| 0.0 \| 0.5005 \| 0.1439 \| nan \| 0.0808 \| 0.0 \| 0.0 \| 0.7147 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6286 \| 0.0 \| 0.1591 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0007 \| 0.0 \| 0.0 \| 0.7136 \| 0.5999 \| 0.8767 \| 0.0 \| 0.0 \| 0.0022 \| 0.0 \| \| 0.9872 \| 2.0 \| 400 \| 0.8644 \| 0.1814 \| 0.2287 \| 0.7679 \| nan \| 0.9228 \| 0.8901 \| 0.0 \| 0.5135 \| 0.1829 \| nan \| 0.0730 \| 0.0 \| 0.0 \| 0.9320 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8858 \| 0.0 \| 0.1816 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.8969 \| 0.8870 \| 0.9520 \| 0.0 \| 0.0 \| 0.0006 \| 0.0 \| nan \| 0.5509 \| 0.7875 \| 0.0 \| 0.4591 \| 0.1650 \| nan \| 0.0689 \| 0.0 \| 0.0 \| 0.6744 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6095 \| 0.0 \| 0.1509 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.7905 \| 0.6681 \| 0.8782 \| 0.0 \| 0.0 \| 0.0006 \| 0.0 \| \| 0.9402 \| 2.1 \| 420 \| 0.8389 \| 0.1881 \| 0.2315 \| 0.7818 \| nan \| 0.8322 \| 0.9390 \| 0.0 \| 0.5695 \| 0.2419 \| nan \| 0.2642 \| 0.0 \| 0.0 \| 0.9113 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8622 \| 0.0 \| 0.1379 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0076 \| 0.0 \| 0.0 \| 0.9714 \| 0.7160 \| 0.9485 \| 0.0 \| 0.0 \| 0.0075 \| 0.0 \| nan \| 0.6471 \| 0.7996 \| 0.0 \| 0.4905 \| 0.1948 \| nan \| 0.2126 \| 0.0 \| 0.0 \| 0.6854 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6175 \| 0.0 \| 0.1159 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0076 \| 0.0 \| 0.0 \| 0.7514 \| 0.6111 \| 0.8784 \| 0.0 \| 0.0 \| 0.0075 \| 0.0 \| \| 0.8086 \| 2.2 \| 440 \| 0.8111 \| 0.1975 \| 0.2430 \| 0.7900 \| nan \| 0.8340 \| 0.9349 \| 0.0 \| 0.6432 \| 0.2336 \| nan \| 0.3225 \| 0.0 \| 0.0 \| 0.8310 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9304 \| 0.0 \| 0.3000 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| 0.9186 \| 0.8714 \| 0.9526 \| 0.0 \| 0.0 \| 0.0019 \| 0.0 \| nan \| 0.6502 \| 0.8090 \| 0.0 \| 0.5334 \| 0.1844 \| nan \| 0.2397 \| 0.0 \| 0.0 \| 0.7169 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5935 \| 0.0 \| 0.2089 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| 0.8048 \| 0.6888 \| 0.8868 \| 0.0 \| 0.0 \| 0.0019 \| 0.0 \| \| 0.8764 \| 2.3 \| 460 \| 0.7955 \| 0.1956 \| 0.2410 \| 0.7898 \| nan \| 0.8731 \| 0.9284 \| 0.0 \| 0.5585 \| 0.2978 \| nan \| 0.2489 \| 0.0 \| 0.0 \| 0.9034 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8977 \| 0.0 \| 0.2272 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0092 \| 0.0 \| 0.0 \| 0.9343 \| 0.8924 \| 0.9346 \| 0.0 \| 0.0 \| 0.0051 \| 0.0 \| nan \| 0.6369 \| 0.8102 \| 0.0 \| 0.4941 \| 0.2258 \| nan \| 0.2154 \| 0.0 \| 0.0 \| 0.7013 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6180 \| 0.0 \| 0.1729 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0091 \| 0.0 \| 0.0 \| 0.7948 \| 0.6877 \| 0.8877 \| 0.0 \| 0.0 \| 0.0050 \| 0.0 \| \| 0.5838 \| 2.4 \| 480 \| 0.7779 \| 0.1984 \| 0.2443 \| 0.7934 \| nan \| 0.8579 \| 0.9338 \| 0.0219 \| 0.6356 \| 0.3655 \| nan \| 0.1993 \| 0.0000 \| 0.0 \| 0.9151 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9047 \| 0.0 \| 0.2618 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0106 \| 0.0 \| 0.0 \| 0.9247 \| 0.8154 \| 0.9695 \| 0.0 \| 0.0 \| 0.0029 \| 0.0 \| nan \| 0.6396 \| 0.8160 \| 0.0219 \| 0.5207 \| 0.2520 \| nan \| 0.1786 \| 0.0000 \| 0.0 \| 0.7051 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6203 \| 0.0 \| 0.1896 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0105 \| 0.0 \| 0.0 \| 0.8129 \| 0.7016 \| 0.8771 \| 0.0 \| 0.0 \| 0.0029 \| 0.0 \| \| 1.2875 \| 2.5 \| 500 \| 0.7613 \| 0.1991 \| 0.2422 \| 0.7947 \| nan \| 0.8127 \| 0.9518 \| 0.0246 \| 0.7146 \| 0.2126 \| nan \| 0.1842 \| 0.0028 \| 0.0 \| 0.8969 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8676 \| 0.0 \| 0.3247 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0138 \| 0.0 \| 0.0 \| 0.9529 \| 0.8308 \| 0.9494 \| 0.0 \| 0.0 \| 0.0126 \| 0.0 \| nan \| 0.6576 \| 0.8048 \| 0.0246 \| 0.5380 \| 0.1803 \| nan \| 0.1649 \| 0.0028 \| 0.0 \| 0.7277 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6375 \| 0.0 \| 0.2192 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0135 \| 0.0 \| 0.0 \| 0.7849 \| 0.7109 \| 0.8922 \| 0.0 \| 0.0 \| 0.0121 \| 0.0 \| \| 0.6721 \| 2.6 \| 520 \| 0.7535 \| 0.2130 \| 0.2636 \| 0.8017 \| nan \| 0.8805 \| 0.9166 \| 0.2113 \| 0.6640 \| 0.3623 \| nan \| 0.3572 \| 0.0017 \| 0.0 \| 0.9307 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8472 \| 0.0 \| 0.4867 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0110 \| 0.0 \| 0.0 \| 0.9472 \| 0.8436 \| 0.9570 \| 0.0 \| 0.0 \| 0.0187 \| 0.0 \| nan \| 0.6645 \| 0.8283 \| 0.2069 \| 0.5498 \| 0.2632 \| nan \| 0.2810 \| 0.0017 \| 0.0 \| 0.7005 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6432 \| 0.0 \| 0.2494 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0108 \| 0.0 \| 0.0 \| 0.8056 \| 0.7092 \| 0.8851 \| 0.0 \| 0.0 \| 0.0173 \| 0.0 \| \| 0.6638 \| 2.7 \| 540 \| 0.7462 \| 0.2072 \| 0.2495 \| 0.8018 \| nan \| 0.8340 \| 0.9531 \| 0.2099 \| 0.6785 \| 0.1671 \| nan \| 0.2453 \| 0.0013 \| 0.0 \| 0.9226 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9109 \| 0.0 \| 0.2672 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0214 \| 0.0 \| 0.0 \| 0.9429 \| 0.8541 \| 0.9533 \| 0.0 \| 0.0 \| 0.0238 \| 0.0 \| nan \| 0.6690 \| 0.8096 \| 0.2068 \| 0.5655 \| 0.1509 \| nan \| 0.2195 \| 0.0013 \| 0.0 \| 0.7109 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6292 \| 0.0 \| 0.1887 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0209 \| 0.0 \| 0.0 \| 0.8074 \| 0.7301 \| 0.8983 \| 0.0 \| 0.0 \| 0.0220 \| 0.0 \| \| 0.978 \| 2.8 \| 560 \| 0.7408 \| 0.2084 \| 0.2538 \| 0.7986 \| nan \| 0.8039 \| 0.9309 \| 0.2190 \| 0.8212 \| 0.2427 \| nan \| 0.2441 \| 0.0110 \| 0.0 \| 0.9215 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9296 \| 0.0 \| 0.2020 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0350 \| 0.0 \| 0.0 \| 0.9274 \| 0.8378 \| 0.9518 \| 0.0 \| 0.0 \| 0.0437 \| 0.0 \| nan \| 0.6568 \| 0.8125 \| 0.2120 \| 0.5344 \| 0.1964 \| nan \| 0.2175 \| 0.0110 \| 0.0 \| 0.7195 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6137 \| 0.0 \| 0.1500 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0338 \| 0.0 \| 0.0 \| 0.8339 \| 0.7423 \| 0.8955 \| 0.0 \| 0.0 \| 0.0410 \| 0.0 \| \| 0.654 \| 2.9 \| 580 \| 0.7562 \| 0.2148 \| 0.2672 \| 0.7950 \| nan \| 0.7972 \| 0.9253 \| 0.3007 \| 0.6560 \| 0.4779 \| nan \| 0.2650 \| 0.0224 \| 0.0 \| 0.9136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8899 \| 0.0 \| 0.4572 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0478 \| 0.0 \| 0.0 \| 0.8980 \| 0.9169 \| 0.9524 \| 0.0 \| 0.0 \| 0.0296 \| 0.0 \| nan \| 0.6365 \| 0.8171 \| 0.2613 \| 0.5745 \| 0.2708 \| nan \| 0.2223 \| 0.0224 \| 0.0 \| 0.7265 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6664 \| 0.0 \| 0.2762 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0453 \| 0.0 \| 0.0 \| 0.7805 \| 0.6445 \| 0.9008 \| 0.0 \| 0.0 \| 0.0281 \| 0.0 \| \| 0.427 \| 3.0 \| 600 \| 0.7294 \| 0.2180 \| 0.2615 \| 0.8057 \| nan \| 0.9073 \| 0.9191 \| 0.3864 \| 0.6607 \| 0.3389 \| nan \| 0.2097 \| 0.0261 \| 0.0 \| 0.8805 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9396 \| 0.0 \| 0.2776 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0553 \| 0.0 \| 0.0 \| 0.9380 \| 0.8637 \| 0.9350 \| 0.0 \| 0.0 \| 0.0303 \| 0.0 \| nan \| 0.6418 \| 0.8283 \| 0.3227 \| 0.5986 \| 0.2736 \| nan \| 0.1886 \| 0.0260 \| 0.0 \| 0.7477 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6196 \| 0.0 \| 0.2092 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0527 \| 0.0 \| 0.0 \| 0.8182 \| 0.7251 \| 0.8956 \| 0.0 \| 0.0 \| 0.0285 \| 0.0 \| \| 0.4829 \| 3.1 \| 620 \| 0.7174 \| 0.2226 \| 0.2714 \| 0.8035 \| nan \| 0.7341 \| 0.9489 \| 0.2510 \| 0.7072 \| 0.4433 \| nan \| 0.3186 \| 0.0785 \| 0.0 \| 0.9233 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8824 \| 0.0 \| 0.4427 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0811 \| 0.0 \| 0.0 \| 0.9422 \| 0.8826 \| 0.9675 \| 0.0 \| 0.0 \| 0.0825 \| 0.0 \| nan \| 0.6433 \| 0.8177 \| 0.2251 \| 0.5822 \| 0.2480 \| nan \| 0.2623 \| 0.0779 \| 0.0 \| 0.7399 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6700 \| 0.0 \| 0.2810 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0738 \| 0.0 \| 0.0 \| 0.8124 \| 0.7220 \| 0.8935 \| 0.0 \| 0.0 \| 0.0739 \| 0.0 \| \| 0.8156 \| 3.2 \| 640 \| 0.7023 \| 0.2292 \| 0.2758 \| 0.8133 \| nan \| 0.9020 \| 0.9250 \| 0.2563 \| 0.6719 \| 0.3992 \| nan \| 0.2941 \| 0.1677 \| 0.0 \| 0.9138 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| 0.9105 \| 0.0 \| 0.5084 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0722 \| 0.0 \| 0.0 \| 0.9385 \| 0.8559 \| 0.9473 \| 0.0 \| 0.0 \| 0.0624 \| 0.0 \| nan \| 0.6589 \| 0.8354 \| 0.2415 \| 0.5910 \| 0.3009 \| nan \| 0.2484 \| 0.1649 \| 0.0 \| 0.7474 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| 0.6694 \| 0.0 \| 0.2805 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0673 \| 0.0 \| 0.0 \| 0.8256 \| 0.7435 \| 0.9028 \| 0.0 \| 0.0 \| 0.0555 \| 0.0 \| \| 0.7138 \| 3.3 \| 660 \| 0.7069 \| 0.2279 \| 0.2749 \| 0.8092 \| nan \| 0.8287 \| 0.9482 \| 0.3510 \| 0.6702 \| 0.3620 \| nan \| 0.3297 \| 0.2351 \| 0.0 \| 0.9397 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0025 \| 0.0 \| 0.0 \| 0.9130 \| 0.0 \| 0.2962 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0865 \| 0.0 \| 0.0 \| 0.9161 \| 0.8831 \| 0.9583 \| 0.0 \| 0.0 \| 0.0772 \| 0.0 \| nan \| 0.6930 \| 0.8247 \| 0.2957 \| 0.5459 \| 0.2651 \| nan \| 0.2782 \| 0.2294 \| 0.0 \| 0.7291 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0025 \| 0.0 \| 0.0 \| 0.6440 \| 0.0 \| 0.2120 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0793 \| 0.0 \| 0.0 \| 0.8128 \| 0.7087 \| 0.9045 \| 0.0 \| 0.0 \| 0.0688 \| 0.0 \| \| 0.6917 \| 3.4 \| 680 \| 0.7028 \| 0.2269 \| 0.2739 \| 0.8077 \| nan \| 0.8149 \| 0.9433 \| 0.3073 \| 0.5962 \| 0.4292 \| nan \| 0.3537 \| 0.1186 \| 0.0 \| 0.9274 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| 0.9047 \| 0.0 \| 0.4330 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0956 \| 0.0 \| 0.0 \| 0.9498 \| 0.8324 \| 0.9623 \| 0.0 \| 0.0 \| 0.0971 \| 0.0 \| nan \| 0.6718 \| 0.8234 \| 0.2649 \| 0.5345 \| 0.2617 \| nan \| 0.2900 \| 0.1173 \| 0.0 \| 0.7382 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| 0.6571 \| 0.0 \| 0.2746 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0864 \| 0.0 \| 0.0 \| 0.8218 \| 0.7334 \| 0.8996 \| 0.0 \| 0.0 \| 0.0849 \| 0.0 \| \| 0.9325 \| 3.5 \| 700 \| 0.6855 \| 0.2374 \| 0.2848 \| 0.8158 \| nan \| 0.8325 \| 0.9446 \| 0.4574 \| 0.7255 \| 0.3362 \| nan \| 0.3317 \| 0.2539 \| 0.0 \| 0.9209 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0033 \| 0.0 \| 0.0 \| 0.8843 \| 0.0 \| 0.4345 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0910 \| 0.0 \| 0.0 \| 0.9536 \| 0.8368 \| 0.9573 \| 0.0 \| 0.0 \| 0.1510 \| 0.0 \| nan \| 0.6861 \| 0.8344 \| 0.3742 \| 0.5966 \| 0.2529 \| nan \| 0.2720 \| 0.2421 \| 0.0 \| 0.7385 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0033 \| 0.0 \| 0.0 \| 0.6538 \| 0.0 \| 0.2749 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0835 \| 0.0 \| 0.0 \| 0.8168 \| 0.7370 \| 0.9000 \| 0.0 \| 0.0 \| 0.1323 \| 0.0 \| \| 0.5989 \| 3.6 \| 720 \| 0.6805 \| 0.2479 \| 0.2980 \| 0.8191 \| nan \| 0.8635 \| 0.9373 \| 0.4433 \| 0.7377 \| 0.3153 \| nan \| 0.3770 \| 0.4409 \| 0.0 \| 0.8934 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0660 \| 0.0 \| 0.0 \| 0.8861 \| 0.0 \| 0.4853 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1566 \| 0.0 \| 0.0 \| 0.9363 \| 0.9031 \| 0.9487 \| 0.0 \| 0.0 \| 0.1443 \| 0.0 \| nan \| 0.6924 \| 0.8392 \| 0.3558 \| 0.6152 \| 0.2621 \| nan \| 0.3052 \| 0.4010 \| 0.0 \| 0.7701 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0637 \| 0.0 \| 0.0 \| 0.6449 \| 0.0 \| 0.2661 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1368 \| 0.0 \| 0.0 \| 0.8341 \| 0.7158 \| 0.9041 \| 0.0 \| 0.0 \| 0.1256 \| 0.0 \| \| 0.6463 \| 3.7 \| 740 \| 0.6961 \| 0.2427 \| 0.2911 \| 0.8170 \| nan \| 0.8652 \| 0.9428 \| 0.4865 \| 0.5954 \| 0.4087 \| nan \| 0.3786 \| 0.4660 \| 0.0 \| 0.9244 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0138 \| 0.0 \| 0.0 \| 0.9135 \| 0.0 \| 0.3219 \| 0.0001 \| 0.0 \| nan \| 0.0 \| 0.1248 \| 0.0 \| 0.0 \| 0.9401 \| 0.8902 \| 0.9563 \| 0.0 \| 0.0 \| 0.0872 \| 0.0 \| nan \| 0.6906 \| 0.8322 \| 0.3576 \| 0.5430 \| 0.3092 \| nan \| 0.2937 \| 0.4203 \| 0.0 \| 0.7447 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0131 \| 0.0 \| 0.0 \| 0.6471 \| 0.0 \| 0.2497 \| 0.0001 \| 0.0 \| nan \| 0.0 \| 0.1142 \| 0.0 \| 0.0 \| 0.8326 \| 0.7372 \| 0.9031 \| 0.0 \| 0.0 \| 0.0789 \| 0.0 \| \| 0.7591 \| 3.8 \| 760 \| 0.6612 \| 0.2580 \| 0.3138 \| 0.8244 \| nan \| 0.8508 \| 0.9313 \| 0.5719 \| 0.7780 \| 0.4338 \| nan \| 0.3375 \| 0.6091 \| 0.0 \| 0.9092 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1580 \| 0.0 \| 0.0 \| 0.9023 \| 0.0 \| 0.4899 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1601 \| 0.0 \| 0.0 \| 0.9426 \| 0.8625 \| 0.9558 \| 0.0 \| 0.0 \| 0.1474 \| 0.0 \| nan \| 0.7094 \| 0.8419 \| 0.3247 \| 0.6473 \| 0.3145 \| nan \| 0.2901 \| 0.5056 \| 0.0 \| 0.7800 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1394 \| 0.0 \| 0.0 \| 0.6658 \| 0.0 \| 0.2838 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1420 \| 0.0 \| 0.0 \| 0.8403 \| 0.7416 \| 0.9069 \| 0.0 \| 0.0 \| 0.1233 \| 0.0 \| \| 0.5534 \| 3.9 \| 780 \| 0.6732 \| 0.2532 \| 0.3063 \| 0.8181 \| nan \| 0.8214 \| 0.9461 \| 0.5042 \| 0.6612 \| 0.3922 \| nan \| 0.4129 \| 0.5771 \| 0.0 \| 0.9359 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0469 \| 0.0 \| 0.0 \| 0.8828 \| 0.0 \| 0.4341 \| 0.0002 \| 0.0 \| nan \| 0.0 \| 0.1264 \| 0.0 \| 0.0 \| 0.9313 \| 0.9053 \| 0.9573 \| 0.0 \| 0.0 \| 0.2661 \| 0.0 \| nan \| 0.6922 \| 0.8335 \| 0.4137 \| 0.5804 \| 0.2705 \| nan \| 0.3330 \| 0.4903 \| 0.0 \| 0.7471 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0450 \| 0.0 \| 0.0 \| 0.6712 \| 0.0 \| 0.2813 \| 0.0002 \| 0.0 \| nan \| 0.0 \| 0.1168 \| 0.0 \| 0.0 \| 0.8218 \| 0.7088 \| 0.9092 \| 0.0 \| 0.0 \| 0.1879 \| 0.0 \| \| 0.5253 \| 4.0 \| 800 \| 0.6851 \| 0.2526 \| 0.3064 \| 0.8152 \| nan \| 0.7825 \| 0.9559 \| 0.5397 \| 0.7223 \| 0.4111 \| nan \| 0.3937 \| 0.5704 \| 0.0 \| 0.9213 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0866 \| 0.0 \| 0.0 \| 0.9097 \| 0.0 \| 0.3959 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.1174 \| 0.0 \| 0.0 \| 0.8933 \| 0.8890 \| 0.9628 \| 0.0 \| 0.0 \| 0.2529 \| 0.0 \| nan \| 0.6829 \| 0.8305 \| 0.4174 \| 0.5845 \| 0.2797 \| nan \| 0.3243 \| 0.4921 \| 0.0 \| 0.7609 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0810 \| 0.0 \| 0.0 \| 0.6700 \| 0.0 \| 0.2668 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.1099 \| 0.0 \| 0.0 \| 0.8165 \| 0.6845 \| 0.9037 \| 0.0 \| 0.0 \| 0.1780 \| 0.0 \| \| 0.7711 \| 4.1 \| 820 \| 0.6676 \| 0.2542 \| 0.3083 \| 0.8201 \| nan \| 0.8437 \| 0.9316 \| 0.5681 \| 0.7326 \| 0.4147 \| nan \| 0.3457 \| 0.6161 \| 0.0 \| 0.9313 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1167 \| 0.0 \| 0.0 \| 0.8849 \| 0.0 \| 0.3741 \| 0.0008 \| 0.0 \| nan \| 0.0 \| 0.1325 \| 0.0 \| 0.0 \| 0.9571 \| 0.8486 \| 0.9574 \| 0.0 \| 0.0 \| 0.2110 \| 0.0 \| nan \| 0.6873 \| 0.8377 \| 0.3471 \| 0.5874 \| 0.3225 \| nan \| 0.2941 \| 0.5096 \| 0.0 \| 0.7620 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1004 \| 0.0 \| 0.0 \| 0.6810 \| 0.0 \| 0.2695 \| 0.0008 \| 0.0 \| nan \| 0.0 \| 0.1181 \| 0.0 \| 0.0 \| 0.8163 \| 0.7409 \| 0.9069 \| 0.0 \| 0.0 \| 0.1537 \| 0.0 \| \| 0.707 \| 4.2 \| 840 \| 0.6544 \| 0.2666 \| 0.3247 \| 0.8257 \| nan \| 0.8754 \| 0.9295 \| 0.4642 \| 0.7581 \| 0.4803 \| nan \| 0.3356 \| 0.6712 \| 0.0 \| 0.9103 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3022 \| 0.0 \| 0.0 \| 0.8921 \| 0.0 \| 0.5800 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2018 \| 0.0 \| 0.0 \| 0.9160 \| 0.9045 \| 0.9600 \| 0.0 \| 0.0 \| 0.2085 \| 0.0 \| nan \| 0.7049 \| 0.8479 \| 0.4035 \| 0.6244 \| 0.3176 \| nan \| 0.2962 \| 0.5501 \| 0.0 \| 0.7839 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2270 \| 0.0 \| 0.0 \| 0.6912 \| 0.0 \| 0.2901 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.1745 \| 0.0 \| 0.0 \| 0.8381 \| 0.7161 \| 0.9089 \| 0.0 \| 0.0 \| 0.1575 \| 0.0 \| \| 0.3871 \| 4.3 \| 860 \| 0.6769 \| 0.2557 \| 0.3102 \| 0.8184 \| nan \| 0.7837 \| 0.9498 \| 0.4837 \| 0.6756 \| 0.4045 \| nan \| 0.4306 \| 0.6623 \| 0.0 \| 0.9270 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0860 \| 0.0 \| 0.0 \| 0.8875 \| 0.0 \| 0.5056 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.1384 \| 0.0 \| 0.0 \| 0.9505 \| 0.8714 \| 0.9552 \| 0.0 \| 0.0 \| 0.2160 \| 0.0 \| nan \| 0.6800 \| 0.8332 \| 0.3980 \| 0.5833 \| 0.2693 \| nan \| 0.3201 \| 0.5166 \| 0.0 \| 0.7572 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0793 \| 0.0 \| 0.0 \| 0.6881 \| 0.0 \| 0.2960 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.1238 \| 0.0 \| 0.0 \| 0.8248 \| 0.7405 \| 0.9098 \| 0.0 \| 0.0 \| 0.1637 \| 0.0 \| \| 0.3875 \| 4.4 \| 880 \| 0.6636 \| 0.2572 \| 0.3122 \| 0.8214 \| nan \| 0.8858 \| 0.9330 \| 0.5198 \| 0.6597 \| 0.3446 \| nan \| 0.3893 \| 0.6406 \| 0.0 \| 0.9311 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0998 \| 0.0 \| 0.0 \| 0.9076 \| 0.0 \| 0.4959 \| 0.0013 \| 0.0 \| nan \| 0.0 \| 0.1958 \| 0.0 \| 0.0 \| 0.9191 \| 0.8930 \| 0.9695 \| 0.0 \| 0.0 \| 0.2060 \| 0.0 \| nan \| 0.6752 \| 0.8406 \| 0.3812 \| 0.5797 \| 0.2876 \| nan \| 0.3076 \| 0.5213 \| 0.0 \| 0.7672 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0920 \| 0.0 \| 0.0 \| 0.6889 \| 0.0 \| 0.3101 \| 0.0013 \| 0.0 \| nan \| 0.0 \| 0.1681 \| 0.0 \| 0.0 \| 0.8338 \| 0.7172 \| 0.9046 \| 0.0 \| 0.0 \| 0.1554 \| 0.0 \| \| 0.3193 \| 4.5 \| 900 \| 0.6542 \| 0.2597 \| 0.3132 \| 0.8219 \| nan \| 0.8326 \| 0.9582 \| 0.4788 \| 0.7010 \| 0.3630 \| nan \| 0.3619 \| 0.6338 \| 0.0 \| 0.9361 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1771 \| 0.0 \| 0.0 \| 0.9081 \| 0.0 \| 0.4641 \| 0.0076 \| 0.0 \| nan \| 0.0 \| 0.2136 \| 0.0 \| 0.0 \| 0.8987 \| 0.9015 \| 0.9614 \| 0.0 \| 0.0 \| 0.2243 \| 0.0 \| nan \| 0.7043 \| 0.8323 \| 0.4032 \| 0.5934 \| 0.2855 \| nan \| 0.3055 \| 0.5127 \| 0.0 \| 0.7645 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1567 \| 0.0 \| 0.0 \| 0.6876 \| 0.0 \| 0.2943 \| 0.0075 \| 0.0 \| nan \| 0.0 \| 0.1783 \| 0.0 \| 0.0 \| 0.8255 \| 0.6886 \| 0.9085 \| 0.0 \| 0.0 \| 0.1632 \| 0.0 \| \| 0.4406 \| 4.6 \| 920 \| 0.6569 \| 0.2614 \| 0.3241 \| 0.8187 \| nan \| 0.8827 \| 0.8931 \| 0.5627 \| 0.7492 \| 0.4866 \| nan \| 0.3742 \| 0.6700 \| 0.0 \| 0.9376 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1651 \| 0.0 \| 0.0 \| 0.8695 \| 0.0 \| 0.4994 \| 0.0318 \| 0.0 \| nan \| 0.0 \| 0.2074 \| 0.0 \| 0.0 \| 0.9504 \| 0.8615 \| 0.9666 \| 0.0 \| 0.0 \| 0.2622 \| 0.0 \| nan \| 0.6811 \| 0.8343 \| 0.3170 \| 0.6401 \| 0.3189 \| nan \| 0.2997 \| 0.5251 \| 0.0 \| 0.7588 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1441 \| 0.0 \| 0.0 \| 0.6967 \| 0.0 \| 0.3081 \| 0.0312 \| 0.0 \| nan \| 0.0 \| 0.1656 \| 0.0 \| 0.0 \| 0.8313 \| 0.7308 \| 0.9035 \| 0.0 \| 0.0 \| 0.1784 \| 0.0 \| \| 0.5358 \| 4.7 \| 940 \| 0.6500 \| 0.2597 \| 0.3110 \| 0.8236 \| nan \| 0.8691 \| 0.9456 \| 0.5509 \| 0.6797 \| 0.4115 \| nan \| 0.3701 \| 0.6042 \| 0.0 \| 0.8879 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1980 \| 0.0 \| 0.0 \| 0.9315 \| 0.0 \| 0.4259 \| 0.0054 \| 0.0 \| nan \| 0.0 \| 0.1871 \| 0.0 \| 0.0 \| 0.9234 \| 0.8543 \| 0.9585 \| 0.0 \| 0.0 \| 0.1503 \| 0.0 \| nan \| 0.6986 \| 0.8374 \| 0.3752 \| 0.6046 \| 0.3042 \| nan \| 0.3063 \| 0.4954 \| 0.0 \| 0.7844 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1822 \| 0.0 \| 0.0 \| 0.6668 \| 0.0 \| 0.2805 \| 0.0053 \| 0.0 \| nan \| 0.0 \| 0.1594 \| 0.0 \| 0.0 \| 0.8424 \| 0.7319 \| 0.9097 \| 0.0 \| 0.0 \| 0.1245 \| 0.0 \| \| 0.6313 \| 4.8 \| 960 \| 0.6320 \| 0.2673 \| 0.3266 \| 0.8278 \| nan \| 0.8247 \| 0.9394 \| 0.5275 \| 0.8027 \| 0.4413 \| nan \| 0.3894 \| 0.6899 \| 0.0 \| 0.9403 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2349 \| 0.0 \| 0.0 \| 0.8570 \| 0.0 \| 0.5248 \| 0.0266 \| 0.0 \| nan \| 0.0 \| 0.2259 \| 0.0 \| 0.0 \| 0.9468 \| 0.8795 \| 0.9636 \| 0.0 \| 0.0 \| 0.2359 \| 0.0 \| nan \| 0.7012 \| 0.8478 \| 0.4074 \| 0.6506 \| 0.3106 \| nan \| 0.3286 \| 0.5297 \| 0.0 \| 0.7582 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1792 \| 0.0 \| 0.0 \| 0.6927 \| 0.0 \| 0.2988 \| 0.0263 \| 0.0 \| nan \| 0.0 \| 0.1773 \| 0.0 \| 0.0 \| 0.8298 \| 0.7318 \| 0.9079 \| 0.0 \| 0.0 \| 0.1767 \| 0.0 \| \| 0.3536 \| 4.9 \| 980 \| 0.6347 \| 0.2695 \| 0.3225 \| 0.8290 \| nan \| 0.8067 \| 0.9585 \| 0.5452 \| 0.7794 \| 0.4020 \| nan \| 0.3660 \| 0.7025 \| 0.0 \| 0.9025 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2276 \| 0.0 \| 0.0 \| 0.9014 \| 0.0 \| 0.4827 \| 0.0392 \| 0.0 \| nan \| 0.0 \| 0.2068 \| 0.0 \| 0.0 \| 0.9458 \| 0.8488 \| 0.9537 \| 0.0 \| 0.0 \| 0.2515 \| 0.0 \| nan \| 0.7024 \| 0.8392 \| 0.4190 \| 0.6574 \| 0.3068 \| nan \| 0.3154 \| 0.5403 \| 0.0 \| 0.7839 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2015 \| 0.0 \| 0.0 \| 0.6764 \| 0.0 \| 0.2990 \| 0.0388 \| 0.0 \| nan \| 0.0 \| 0.1732 \| 0.0 \| 0.0 \| 0.8356 \| 0.7403 \| 0.9103 \| 0.0 \| 0.0 \| 0.1850 \| 0.0 \| \| 0.4556 \| 5.0 \| 1000 \| 0.6622 \| 0.2569 \| 0.3150 \| 0.8227 \| nan \| 0.8442 \| 0.9340 \| 0.6047 \| 0.7161 \| 0.4726 \| nan \| 0.4128 \| 0.7259 \| 0.0 \| 0.9344 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0922 \| 0.0 \| 0.0 \| 0.9187 \| 0.0 \| 0.3290 \| 0.0050 \| 0.0 \| nan \| 0.0 \| 0.1817 \| 0.0 \| 0.0 \| 0.9212 \| 0.8851 \| 0.9671 \| 0.0 \| 0.0 \| 0.1343 \| 0.0 \| nan \| 0.6994 \| 0.8458 \| 0.3676 \| 0.6354 \| 0.3216 \| nan \| 0.3289 \| 0.5432 \| 0.0 \| 0.7672 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0816 \| 0.0 \| 0.0 \| 0.6547 \| 0.0 \| 0.2310 \| 0.0050 \| 0.0 \| nan \| 0.0 \| 0.1578 \| 0.0 \| 0.0 \| 0.8300 \| 0.7204 \| 0.9092 \| 0.0 \| 0.0 \| 0.1206 \| 0.0 \| \| 0.4591 \| 5.1 \| 1020 \| 0.6507 \| 0.2648 \| 0.3244 \| 0.8256 \| nan \| 0.8822 \| 0.9379 \| 0.5695 \| 0.6632 \| 0.4411 \| nan \| 0.3617 \| 0.7320 \| 0.0 \| 0.9309 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1561 \| 0.0 \| 0.0 \| 0.8420 \| 0.0 \| 0.5086 \| 0.0280 \| 0.0 \| nan \| 0.0 \| 0.2619 \| 0.0 \| 0.0 \| 0.9492 \| 0.8699 \| 0.9623 \| 0.0 \| 0.0 \| 0.2847 \| 0.0 \| nan \| 0.6970 \| 0.8414 \| 0.3791 \| 0.6022 \| 0.3283 \| nan \| 0.2991 \| 0.5071 \| 0.0 \| 0.7714 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1242 \| 0.0 \| 0.0 \| 0.6887 \| 0.0 \| 0.2880 \| 0.0277 \| 0.0 \| nan \| 0.0 \| 0.2067 \| 0.0 \| 0.0 \| 0.8351 \| 0.7556 \| 0.9135 \| 0.0 \| 0.0 \| 0.2093 \| 0.0 \| \| 0.5285 \| 5.2 \| 1040 \| 0.6560 \| 0.2682 \| 0.3265 \| 0.8218 \| nan \| 0.7659 \| 0.9305 \| 0.5688 \| 0.7382 \| 0.5242 \| nan \| 0.4848 \| 0.6590 \| 0.0 \| 0.9092 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1483 \| 0.0 \| 0.0 \| 0.9157 \| 0.0 \| 0.4034 \| 0.0615 \| 0.0 \| nan \| 0.0 \| 0.2794 \| 0.0 \| 0.0 \| 0.9405 \| 0.9060 \| 0.9620 \| 0.0 \| 0.0 \| 0.2511 \| 0.0 \| nan \| 0.6776 \| 0.8350 \| 0.4324 \| 0.6042 \| 0.3074 \| nan \| 0.3654 \| 0.5356 \| 0.0 \| 0.7741 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1338 \| 0.0 \| 0.0 \| 0.6821 \| 0.0 \| 0.2853 \| 0.0607 \| 0.0 \| nan \| 0.0 \| 0.2136 \| 0.0 \| 0.0 \| 0.8388 \| 0.7444 \| 0.9136 \| 0.0 \| 0.0 \| 0.1794 \| 0.0 \| \| 0.4361 \| 5.3 \| 1060 \| 0.6255 \| 0.2711 \| 0.3246 \| 0.8323 \| nan \| 0.8628 \| 0.9391 \| 0.5433 \| 0.8105 \| 0.3499 \| nan \| 0.4237 \| 0.6427 \| 0.0 \| 0.9101 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1850 \| 0.0 \| 0.0 \| 0.9182 \| 0.0 \| 0.4471 \| 0.0439 \| 0.0 \| nan \| 0.0 \| 0.2448 \| 0.0 \| 0.0 \| 0.9329 \| 0.8739 \| 0.9652 \| 0.0 \| 0.0 \| 0.2933 \| 0.0 \| nan \| 0.7083 \| 0.8463 \| 0.4068 \| 0.6505 \| 0.2900 \| nan \| 0.3443 \| 0.5433 \| 0.0 \| 0.7810 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1652 \| 0.0 \| 0.0 \| 0.6823 \| 0.0 \| 0.3011 \| 0.0435 \| 0.0 \| nan \| 0.0 \| 0.1998 \| 0.0 \| 0.0 \| 0.8500 \| 0.7546 \| 0.9110 \| 0.0 \| 0.0 \| 0.1970 \| 0.0 \| \| 0.4952 \| 5.4 \| 1080 \| 0.6550 \| 0.2708 \| 0.3276 \| 0.8243 \| nan \| 0.7442 \| 0.9626 \| 0.5128 \| 0.7462 \| 0.4368 \| nan \| 0.4072 \| 0.7272 \| 0.0 \| 0.9251 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2467 \| 0.0 \| 0.0 \| 0.9016 \| 0.0 \| 0.5444 \| 0.0087 \| 0.0 \| nan \| 0.0 \| 0.2676 \| 0.0 \| 0.0 \| 0.9317 \| 0.8792 \| 0.9622 \| 0.0 \| 0.0 \| 0.2788 \| 0.0 \| nan \| 0.6666 \| 0.8202 \| 0.4120 \| 0.6328 \| 0.3133 \| nan \| 0.3450 \| 0.5479 \| 0.0 \| 0.7769 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1962 \| 0.0 \| 0.0 \| 0.6943 \| 0.0 \| 0.3191 \| 0.0087 \| 0.0 \| nan \| 0.0 \| 0.2106 \| 0.0 \| 0.0 \| 0.8527 \| 0.7557 \| 0.9140 \| 0.0 \| 0.0 \| 0.1996 \| 0.0 \| \| 0.3561 \| 5.5 \| 1100 \| 0.6307 \| 0.2710 \| 0.3310 \| 0.8307 \| nan \| 0.8767 \| 0.9299 \| 0.5615 \| 0.7294 \| 0.4652 \| nan \| 0.4155 \| 0.7589 \| 0.0 \| 0.9234 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2777 \| 0.0 \| 0.0 \| 0.8979 \| 0.0 \| 0.4442 \| 0.0240 \| 0.0 \| nan \| 0.0 \| 0.2477 \| 0.0 \| 0.0 \| 0.9423 \| 0.8935 \| 0.9645 \| 0.0 \| 0.0 \| 0.2384 \| 0.0 \| nan \| 0.7054 \| 0.8493 \| 0.4020 \| 0.6369 \| 0.3361 \| nan \| 0.3419 \| 0.5484 \| 0.0 \| 0.7772 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2010 \| 0.0 \| 0.0 \| 0.6856 \| 0.0 \| 0.3081 \| 0.0238 \| 0.0 \| nan \| 0.0 \| 0.1943 \| 0.0 \| 0.0 \| 0.8398 \| 0.7333 \| 0.9128 \| 0.0 \| 0.0 \| 0.1760 \| 0.0 \| \| 0.5354 \| 5.6 \| 1120 \| 0.6387 \| 0.2721 \| 0.3312 \| 0.8275 \| nan \| 0.7875 \| 0.9516 \| 0.5832 \| 0.7279 \| 0.4278 \| nan \| 0.4853 \| 0.7450 \| 0.0 \| 0.9340 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3487 \| 0.0 \| 0.0 \| 0.9019 \| 0.0 \| 0.5119 \| 0.0186 \| 0.0 \| nan \| 0.0 \| 0.2194 \| 0.0 \| 0.0 \| 0.9566 \| 0.8174 \| 0.9629 \| 0.0 \| 0.0 \| 0.2197 \| 0.0 \| nan \| 0.6898 \| 0.8340 \| 0.4223 \| 0.6318 \| 0.3037 \| nan \| 0.3741 \| 0.5537 \| 0.0 \| 0.7810 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2291 \| 0.0 \| 0.0 \| 0.6908 \| 0.0 \| 0.3200 \| 0.0184 \| 0.0 \| nan \| 0.0 \| 0.1828 \| 0.0 \| 0.0 \| 0.8404 \| 0.7435 \| 0.9153 \| 0.0 \| 0.0 \| 0.1769 \| 0.0 \| \| 0.3437 \| 5.7 \| 1140 \| 0.6174 \| 0.2792 \| 0.3423 \| 0.8346 \| nan \| 0.8410 \| 0.9443 \| 0.5442 \| 0.7439 \| 0.4972 \| nan \| 0.4534 \| 0.7617 \| 0.0 \| 0.9308 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4301 \| 0.0 \| 0.0 \| 0.8915 \| 0.0 \| 0.5431 \| 0.0521 \| 0.0 \| nan \| 0.0 \| 0.2596 \| 0.0 \| 0.0 \| 0.9355 \| 0.8947 \| 0.9595 \| 0.0 \| 0.0 \| 0.2720 \| 0.0 \| nan \| 0.7226 \| 0.8471 \| 0.4400 \| 0.6482 \| 0.3168 \| nan \| 0.3740 \| 0.5410 \| 0.0 \| 0.7879 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2574 \| 0.0 \| 0.0 \| 0.7063 \| 0.0 \| 0.3265 \| 0.0508 \| 0.0 \| nan \| 0.0 \| 0.2106 \| 0.0 \| 0.0 \| 0.8479 \| 0.7455 \| 0.9171 \| 0.0 \| 0.0 \| 0.1962 \| 0.0 \| \| 0.4298 \| 5.8 \| 1160 \| 0.6134 \| 0.2781 \| 0.3353 \| 0.8353 \| nan \| 0.8577 \| 0.9386 \| 0.5211 \| 0.7719 \| 0.4591 \| nan \| 0.4418 \| 0.7563 \| 0.0 \| 0.9277 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3321 \| 0.0 \| 0.0 \| 0.9252 \| 0.0 \| 0.4237 \| 0.0904 \| 0.0 \| nan \| 0.0 \| 0.2481 \| 0.0 \| 0.0 \| 0.9335 \| 0.8962 \| 0.9621 \| 0.0 \| 0.0 \| 0.2454 \| 0.0 \| nan \| 0.7176 \| 0.8498 \| 0.4310 \| 0.6682 \| 0.3271 \| nan \| 0.3581 \| 0.5549 \| 0.0 \| 0.7808 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2378 \| 0.0 \| 0.0 \| 0.6713 \| 0.0 \| 0.3008 \| 0.0883 \| 0.0 \| nan \| 0.0 \| 0.2080 \| 0.0 \| 0.0 \| 0.8506 \| 0.7543 \| 0.9174 \| 0.0 \| 0.0 \| 0.1841 \| 0.0 \| \| 0.6889 \| 5.9 \| 1180 \| 0.6136 \| 0.2788 \| 0.3369 \| 0.8355 \| nan \| 0.8318 \| 0.9517 \| 0.5924 \| 0.7928 \| 0.3679 \| nan \| 0.4446 \| 0.7324 \| 0.0 \| 0.9197 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3977 \| 0.0 \| 0.0 \| 0.9050 \| 0.0 \| 0.4513 \| 0.1394 \| 0.0 \| nan \| 0.0 \| 0.2672 \| 0.0 \| 0.0 \| 0.9468 \| 0.8603 \| 0.9680 \| 0.0 \| 0.0 \| 0.2134 \| 0.0 \| nan \| 0.7105 \| 0.8455 \| 0.4164 \| 0.6613 \| 0.3017 \| nan \| 0.3604 \| 0.5448 \| 0.0 \| 0.7887 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2619 \| 0.0 \| 0.0 \| 0.6877 \| 0.0 \| 0.3169 \| 0.1338 \| 0.0 \| nan \| 0.0 \| 0.2144 \| 0.0 \| 0.0 \| 0.8462 \| 0.7462 \| 0.9155 \| 0.0 \| 0.0 \| 0.1707 \| 0.0 \| \| 0.4294 \| 6.0 \| 1200 \| 0.6237 \| 0.2787 \| 0.3372 \| 0.8343 \| nan \| 0.8774 \| 0.9479 \| 0.5795 \| 0.7154 \| 0.3698 \| nan \| 0.3976 \| 0.7525 \| 0.0 \| 0.9183 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4412 \| 0.0 \| 0.0 \| 0.9200 \| 0.0 \| 0.4551 \| 0.1293 \| 0.0 \| nan \| 0.0 \| 0.3076 \| 0.0 \| 0.0 \| 0.9386 \| 0.8491 \| 0.9612 \| 0.0 \| 0.0 \| 0.2297 \| 0.0 \| nan \| 0.7117 \| 0.8500 \| 0.4300 \| 0.6318 \| 0.3026 \| nan \| 0.3341 \| 0.5519 \| 0.0 \| 0.7848 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2811 \| 0.0 \| 0.0 \| 0.6789 \| 0.0 \| 0.3069 \| 0.1241 \| 0.0 \| nan \| 0.0 \| 0.2370 \| 0.0 \| 0.0 \| 0.8482 \| 0.7534 \| 0.9164 \| 0.0 \| 0.0 \| 0.1750 \| 0.0 \| \| 0.3842 \| 6.1 \| 1220 \| 0.6013 \| 0.2861 \| 0.3502 \| 0.8391 \| nan \| 0.8607 \| 0.9402 \| 0.5867 \| 0.7663 \| 0.4641 \| nan \| 0.4457 \| 0.7530 \| 0.0 \| 0.9345 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4567 \| 0.0 \| 0.0 \| 0.8909 \| 0.0 \| 0.4718 \| 0.2415 \| 0.0 \| nan \| 0.0 \| 0.3472 \| 0.0 \| 0.0 \| 0.9365 \| 0.9007 \| 0.9691 \| 0.0 \| 0.0 \| 0.2402 \| 0.0 \| nan \| 0.7264 \| 0.8525 \| 0.4257 \| 0.6615 \| 0.3263 \| nan \| 0.3696 \| 0.5470 \| 0.0 \| 0.7799 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2428 \| 0.0 \| 0.0 \| 0.7000 \| 0.0 \| 0.3559 \| 0.2309 \| 0.0 \| nan \| 0.0 \| 0.2498 \| 0.0 \| 0.0 \| 0.8444 \| 0.7538 \| 0.9154 \| 0.0 \| 0.0 \| 0.1744 \| 0.0 \| \| 0.3414 \| 6.2 \| 1240 \| 0.6057 \| 0.2841 \| 0.3458 \| 0.8372 \| nan \| 0.8653 \| 0.9294 \| 0.5736 \| 0.8022 \| 0.4314 \| nan \| 0.4221 \| 0.7446 \| 0.0 \| 0.9128 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4779 \| 0.0 \| 0.0 \| 0.9221 \| 0.0 \| 0.4615 \| 0.2442 \| 0.0 \| nan \| 0.0 \| 0.3033 \| 0.0 \| 0.0 \| 0.9383 \| 0.8996 \| 0.9713 \| 0.0 \| 0.0 \| 0.1656 \| 0.0 \| nan \| 0.7125 \| 0.8481 \| 0.4153 \| 0.6647 \| 0.3241 \| nan \| 0.3588 \| 0.5569 \| 0.0 \| 0.7896 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2746 \| 0.0 \| 0.0 \| 0.6852 \| 0.0 \| 0.3387 \| 0.2334 \| 0.0 \| nan \| 0.0 \| 0.2254 \| 0.0 \| 0.0 \| 0.8512 \| 0.7521 \| 0.9160 \| 0.0 \| 0.0 \| 0.1452 \| 0.0 \| \| 0.2668 \| 6.3 \| 1260 \| 0.6107 \| 0.2826 \| 0.3450 \| 0.8359 \| nan \| 0.8583 \| 0.9382 \| 0.5687 \| 0.7657 \| 0.4032 \| nan \| 0.4406 \| 0.7619 \| 0.0 \| 0.9462 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4559 \| 0.0 \| 0.0 \| 0.9111 \| 0.0 \| 0.4298 \| 0.2429 \| 0.0 \| nan \| 0.0 \| 0.3298 \| 0.0 \| 0.0 \| 0.9344 \| 0.8866 \| 0.9681 \| 0.0 \| 0.0 \| 0.1990 \| 0.0 \| nan \| 0.7169 \| 0.8450 \| 0.4128 \| 0.6378 \| 0.3066 \| nan \| 0.3632 \| 0.5477 \| 0.0 \| 0.7759 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2558 \| 0.0 \| 0.0 \| 0.6840 \| 0.0 \| 0.3300 \| 0.2327 \| 0.0 \| nan \| 0.0 \| 0.2419 \| 0.0 \| 0.0 \| 0.8551 \| 0.7577 \| 0.9192 \| 0.0 \| 0.0 \| 0.1616 \| 0.0 \| \| 0.345 \| 6.4 \| 1280 \| 0.6021 \| 0.2856 \| 0.3476 \| 0.8376 \| nan \| 0.8565 \| 0.9475 \| 0.5464 \| 0.7295 \| 0.4149 \| nan \| 0.4622 \| 0.7741 \| 0.0 \| 0.9269 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4755 \| 0.0 \| 0.0 \| 0.8993 \| 0.0 \| 0.5527 \| 0.1694 \| 0.0 \| nan \| 0.0 \| 0.3497 \| 0.0 \| 0.0 \| 0.9417 \| 0.8722 \| 0.9643 \| 0.0 \| 0.0 \| 0.2404 \| 0.0 \| nan \| 0.7249 \| 0.8440 \| 0.4423 \| 0.6305 \| 0.3065 \| nan \| 0.3753 \| 0.5470 \| 0.0 \| 0.7960 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2836 \| 0.0 \| 0.0 \| 0.7073 \| 0.0 \| 0.3537 \| 0.1610 \| 0.0 \| nan \| 0.0 \| 0.2510 \| 0.0 \| 0.0 \| 0.8510 \| 0.7626 \| 0.9202 \| 0.0 \| 0.0 \| 0.1828 \| 0.0 \| \| 0.5035 \| 6.5 \| 1300 \| 0.6096 \| 0.2843 \| 0.3532 \| 0.8321 \| nan \| 0.8155 \| 0.9197 \| 0.6216 \| 0.8217 \| 0.4931 \| nan \| 0.4721 \| 0.7753 \| 0.0 \| 0.9274 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5045 \| 0.0 \| 0.0 \| 0.9012 \| 0.0 \| 0.5240 \| 0.1959 \| 0.0 \| nan \| 0.0 \| 0.3288 \| 0.0 \| 0.0 \| 0.9487 \| 0.8548 \| 0.9681 \| 0.0 \| 0.0 \| 0.2306 \| 0.0 \| nan \| 0.7139 \| 0.8361 \| 0.3180 \| 0.6709 \| 0.3177 \| nan \| 0.3877 \| 0.5664 \| 0.0 \| 0.7972 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2867 \| 0.0 \| 0.0 \| 0.7039 \| 0.0 \| 0.3595 \| 0.1869 \| 0.0 \| nan \| 0.0 \| 0.2409 \| 0.0 \| 0.0 \| 0.8500 \| 0.7600 \| 0.9195 \| 0.0 \| 0.0 \| 0.1806 \| 0.0 \| \| 0.4929 \| 6.6 \| 1320 \| 0.5924 \| 0.2927 \| 0.3533 \| 0.8429 \| nan \| 0.8576 \| 0.9476 \| 0.5682 \| 0.7956 \| 0.4425 \| nan \| 0.4412 \| 0.7597 \| 0.0 \| 0.8999 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4884 \| 0.0 \| 0.0 \| 0.9014 \| 0.0 \| 0.5541 \| 0.2743 \| 0.0 \| nan \| 0.0 \| 0.3219 \| 0.0 \| 0.0 \| 0.9345 \| 0.9021 \| 0.9714 \| 0.0 \| 0.0 \| 0.2466 \| 0.0 \| nan \| 0.7308 \| 0.8552 \| 0.4243 \| 0.6894 \| 0.3305 \| nan \| 0.3699 \| 0.5736 \| 0.0 \| 0.8015 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3291 \| 0.0 \| 0.0 \| 0.7071 \| 0.0 \| 0.3793 \| 0.2500 \| 0.0 \| nan \| 0.0 \| 0.2393 \| 0.0 \| 0.0 \| 0.8447 \| 0.7398 \| 0.9170 \| 0.0 \| 0.0 \| 0.1852 \| 0.0 \| \| 0.3436 \| 6.7 \| 1340 \| 0.5995 \| 0.2838 \| 0.3429 \| 0.8384 \| nan \| 0.8742 \| 0.9381 \| 0.5760 \| 0.7711 \| 0.4374 \| nan \| 0.4228 \| 0.7859 \| 0.0 \| 0.9308 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4498 \| 0.0 \| 0.0 \| 0.9159 \| 0.0 \| 0.4172 \| 0.1522 \| 0.0 \| nan \| 0.0 \| 0.2774 \| 0.0 \| 0.0 \| 0.9524 \| 0.8462 \| 0.9683 \| 0.0 \| 0.0 \| 0.2565 \| 0.0 \| nan \| 0.7204 \| 0.8513 \| 0.4114 \| 0.6822 \| 0.3266 \| nan \| 0.3601 \| 0.5441 \| 0.0 \| 0.7848 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3261 \| 0.0 \| 0.0 \| 0.6896 \| 0.0 \| 0.3091 \| 0.1470 \| 0.0 \| nan \| 0.0 \| 0.2188 \| 0.0 \| 0.0 \| 0.8459 \| 0.7607 \| 0.9183 \| 0.0 \| 0.0 \| 0.1861 \| 0.0 \| \| 0.7377 \| 6.8 \| 1360 \| 0.6134 \| 0.2833 \| 0.3463 \| 0.8354 \| nan \| 0.8362 \| 0.9553 \| 0.5929 \| 0.7248 \| 0.4510 \| nan \| 0.4497 \| 0.7811 \| 0.0 \| 0.9095 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5309 \| 0.0 \| 0.0 \| 0.9113 \| 0.0 \| 0.4732 \| 0.1200 \| 0.0 \| nan \| 0.0 \| 0.3126 \| 0.0 \| 0.0 \| 0.9227 \| 0.9004 \| 0.9666 \| 0.0 \| 0.0 \| 0.2439 \| 0.0 \| nan \| 0.7292 \| 0.8460 \| 0.4295 \| 0.6422 \| 0.3253 \| nan \| 0.3596 \| 0.5413 \| 0.0 \| 0.7990 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3584 \| 0.0 \| 0.0 \| 0.6972 \| 0.0 \| 0.3199 \| 0.1156 \| 0.0 \| nan \| 0.0 \| 0.2365 \| 0.0 \| 0.0 \| 0.8455 \| 0.7276 \| 0.9192 \| 0.0 \| 0.0 \| 0.1726 \| 0.0 \| \| 0.4128 \| 6.9 \| 1380 \| 0.6006 \| 0.2878 \| 0.3553 \| 0.8391 \| nan \| 0.8415 \| 0.9462 \| 0.6214 \| 0.7366 \| 0.4697 \| nan \| 0.4957 \| 0.7677 \| 0.0 \| 0.9305 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6027 \| 0.0 \| 0.0 \| 0.8901 \| 0.0 \| 0.5046 \| 0.2026 \| 0.0 \| nan \| 0.0 \| 0.2951 \| 0.0 \| 0.0 \| 0.9465 \| 0.8840 \| 0.9614 \| 0.0 \| 0.0 \| 0.2743 \| 0.0 \| nan \| 0.7322 \| 0.8519 \| 0.4002 \| 0.6451 \| 0.3300 \| nan \| 0.3886 \| 0.5512 \| 0.0 \| 0.7940 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3177 \| 0.0 \| 0.0 \| 0.7060 \| 0.0 \| 0.3487 \| 0.1921 \| 0.0 \| nan \| 0.0 \| 0.2287 \| 0.0 \| 0.0 \| 0.8469 \| 0.7604 \| 0.9208 \| 0.0 \| 0.0 \| 0.1936 \| 0.0 \| \| 0.4907 \| 7.0 \| 1400 \| 0.6117 \| 0.2858 \| 0.3455 \| 0.8376 \| nan \| 0.8634 \| 0.9474 \| 0.5965 \| 0.7312 \| 0.4186 \| nan \| 0.4155 \| 0.7470 \| 0.0 \| 0.9203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5297 \| 0.0 \| 0.0 \| 0.9243 \| 0.0 \| 0.4433 \| 0.2116 \| 0.0 \| nan \| 0.0 \| 0.2940 \| 0.0 \| 0.0 \| 0.9311 \| 0.8932 \| 0.9692 \| 0.0 \| 0.0 \| 0.2194 \| 0.0 \| nan \| 0.7268 \| 0.8486 \| 0.4166 \| 0.6316 \| 0.3267 \| nan \| 0.3552 \| 0.5937 \| 0.0 \| 0.8002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3162 \| 0.0 \| 0.0 \| 0.6794 \| 0.0 \| 0.3256 \| 0.1987 \| 0.0 \| nan \| 0.0 \| 0.2328 \| 0.0 \| 0.0 \| 0.8514 \| 0.7513 \| 0.9192 \| 0.0 \| 0.0 \| 0.1712 \| 0.0 \| \| 0.4294 \| 7.1 \| 1420 \| 0.6113 \| 0.2919 \| 0.3560 \| 0.8385 \| nan \| 0.8568 \| 0.9325 \| 0.6310 \| 0.7411 \| 0.4957 \| nan \| 0.4646 \| 0.7486 \| 0.0 \| 0.9336 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5143 \| 0.0 \| 0.0 \| 0.8965 \| 0.0 \| 0.4609 \| 0.3225 \| 0.0 \| nan \| 0.0 \| 0.3558 \| 0.0 \| 0.0 \| 0.9469 \| 0.8690 \| 0.9707 \| 0.0 \| 0.0 \| 0.2502 \| 0.0 \| nan \| 0.7284 \| 0.8455 \| 0.3658 \| 0.6479 \| 0.3335 \| nan \| 0.3784 \| 0.5995 \| 0.0 \| 0.7964 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3039 \| 0.0 \| 0.0 \| 0.6996 \| 0.0 \| 0.3631 \| 0.3026 \| 0.0 \| nan \| 0.0 \| 0.2534 \| 0.0 \| 0.0 \| 0.8554 \| 0.7669 \| 0.9180 \| 0.0 \| 0.0 \| 0.1809 \| 0.0 \| \| 0.3443 \| 7.2 \| 1440 \| 0.5995 \| 0.2913 \| 0.3533 \| 0.8400 \| nan \| 0.8612 \| 0.9478 \| 0.5732 \| 0.7560 \| 0.4347 \| nan \| 0.4262 \| 0.7650 \| 0.0 \| 0.9334 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5207 \| 0.0 \| 0.0 \| 0.9054 \| 0.0 \| 0.4590 \| 0.3551 \| 0.0 \| nan \| 0.0 \| 0.3457 \| 0.0 \| 0.0 \| 0.9247 \| 0.8845 \| 0.9716 \| 0.0 \| 0.0 \| 0.2411 \| 0.0 \| nan \| 0.7276 \| 0.8468 \| 0.4115 \| 0.6580 \| 0.3226 \| nan \| 0.3623 \| 0.5876 \| 0.0 \| 0.7949 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2886 \| 0.0 \| 0.0 \| 0.6991 \| 0.0 \| 0.3612 \| 0.3291 \| 0.0 \| nan \| 0.0 \| 0.2508 \| 0.0 \| 0.0 \| 0.8487 \| 0.7406 \| 0.9172 \| 0.0 \| 0.0 \| 0.1762 \| 0.0 \| \| 0.3951 \| 7.3 \| 1460 \| 0.5993 \| 0.2881 \| 0.3512 \| 0.8389 \| nan \| 0.8361 \| 0.9493 \| 0.5587 \| 0.7858 \| 0.4044 \| nan \| 0.4564 \| 0.7742 \| 0.0 \| 0.9285 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5710 \| 0.0 \| 0.0 \| 0.9064 \| 0.0 \| 0.4519 \| 0.2775 \| 0.0 \| nan \| 0.0 \| 0.3373 \| 0.0 \| 0.0 \| 0.9471 \| 0.8486 \| 0.9723 \| 0.0 \| 0.0 \| 0.2314 \| 0.0 \| nan \| 0.7132 \| 0.8495 \| 0.4238 \| 0.6330 \| 0.3179 \| nan \| 0.3756 \| 0.5680 \| 0.0 \| 0.7991 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2852 \| 0.0 \| 0.0 \| 0.6937 \| 0.0 \| 0.3453 \| 0.2636 \| 0.0 \| nan \| 0.0 \| 0.2459 \| 0.0 \| 0.0 \| 0.8534 \| 0.7632 \| 0.9170 \| 0.0 \| 0.0 \| 0.1723 \| 0.0 \| \| 0.2938 \| 7.4 \| 1480 \| 0.5979 \| 0.2917 \| 0.3518 \| 0.8414 \| nan \| 0.8454 \| 0.9501 \| 0.5647 \| 0.7701 \| 0.4146 \| nan \| 0.4512 \| 0.7719 \| 0.0 \| 0.9103 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5378 \| 0.0 \| 0.0 \| 0.9112 \| 0.0 \| 0.4758 \| 0.3094 \| 0.0 \| nan \| 0.0 \| 0.3269 \| 0.0 \| 0.0 \| 0.9511 \| 0.8692 \| 0.9649 \| 0.0 \| 0.0 \| 0.2323 \| 0.0 \| nan \| 0.7207 \| 0.8505 \| 0.4227 \| 0.6485 \| 0.3229 \| nan \| 0.3762 \| 0.5570 \| 0.0 \| 0.8052 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3362 \| 0.0 \| 0.0 \| 0.6955 \| 0.0 \| 0.3548 \| 0.2909 \| 0.0 \| nan \| 0.0 \| 0.2420 \| 0.0 \| 0.0 \| 0.8549 \| 0.7673 \| 0.9201 \| 0.0 \| 0.0 \| 0.1700 \| 0.0 \| \| 0.3063 \| 7.5 \| 1500 \| 0.6103 \| 0.2915 \| 0.3522 \| 0.8394 \| nan \| 0.8513 \| 0.9569 \| 0.5581 \| 0.7077 \| 0.4143 \| nan \| 0.3906 \| 0.7775 \| 0.0 \| 0.9367 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4860 \| 0.0 \| 0.0 \| 0.8950 \| 0.0 \| 0.5014 \| 0.3608 \| 0.0 \| nan \| 0.0 \| 0.3768 \| 0.0 \| 0.0 \| 0.9326 \| 0.8919 \| 0.9663 \| 0.0 \| 0.0 \| 0.2673 \| 0.0 \| nan \| 0.7188 \| 0.8446 \| 0.4269 \| 0.6124 \| 0.3294 \| nan \| 0.3401 \| 0.5628 \| 0.0 \| 0.7936 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3101 \| 0.0 \| 0.0 \| 0.7025 \| 0.0 \| 0.3740 \| 0.3336 \| 0.0 \| nan \| 0.0 \| 0.2626 \| 0.0 \| 0.0 \| 0.8565 \| 0.7535 \| 0.9210 \| 0.0 \| 0.0 \| 0.1848 \| 0.0 \| \| 0.4145 \| 7.6 \| 1520 \| 0.6139 \| 0.2870 \| 0.3462 \| 0.8375 \| nan \| 0.8695 \| 0.9446 \| 0.5545 \| 0.6713 \| 0.4503 \| nan \| 0.4578 \| 0.7690 \| 0.0 \| 0.9225 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4519 \| 0.0 \| 0.0 \| 0.9195 \| 0.0 \| 0.4797 \| 0.2639 \| 0.0 \| nan \| 0.0 \| 0.3073 \| 0.0 \| 0.0 \| 0.9363 \| 0.8959 \| 0.9690 \| 0.0 \| 0.0 \| 0.2163 \| 0.0 \| nan \| 0.7244 \| 0.8468 \| 0.4270 \| 0.5928 \| 0.3406 \| nan \| 0.3668 \| 0.5579 \| 0.0 \| 0.8011 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3160 \| 0.0 \| 0.0 \| 0.6902 \| 0.0 \| 0.3466 \| 0.2489 \| 0.0 \| nan \| 0.0 \| 0.2337 \| 0.0 \| 0.0 \| 0.8549 \| 0.7506 \| 0.9195 \| 0.0 \| 0.0 \| 0.1673 \| 0.0 \| \| 0.9153 \| 7.7 \| 1540 \| 0.6055 \| 0.2901 \| 0.3539 \| 0.8373 \| nan \| 0.8256 \| 0.9482 \| 0.5821 \| 0.7362 \| 0.4750 \| nan \| 0.4594 \| 0.7786 \| 0.0 \| 0.9368 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4499 \| 0.0 \| 0.0 \| 0.8786 \| 0.0 \| 0.5140 \| 0.2710 \| 0.0 \| nan \| 0.0 \| 0.3504 \| 0.0 \| 0.0 \| 0.9374 \| 0.8967 \| 0.9696 \| 0.0 \| 0.0 \| 0.3158 \| 0.0 \| nan \| 0.7159 \| 0.8463 \| 0.4322 \| 0.6072 \| 0.3435 \| nan \| 0.3800 \| 0.5541 \| 0.0 \| 0.7938 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3016 \| 0.0 \| 0.0 \| 0.7086 \| 0.0 \| 0.3710 \| 0.2589 \| 0.0 \| nan \| 0.0 \| 0.2577 \| 0.0 \| 0.0 \| 0.8505 \| 0.7452 \| 0.9191 \| 0.0 \| 0.0 \| 0.1964 \| 0.0 \| \| 0.3268 \| 7.8 \| 1560 \| 0.6009 \| 0.2911 \| 0.3532 \| 0.8407 \| nan \| 0.8675 \| 0.9404 \| 0.5684 \| 0.7250 \| 0.4900 \| nan \| 0.4666 \| 0.7657 \| 0.0 \| 0.9205 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5209 \| 0.0 \| 0.0 \| 0.9170 \| 0.0 \| 0.5335 \| 0.2576 \| 0.0 \| nan \| 0.0 \| 0.3168 \| 0.0 \| 0.0 \| 0.9390 \| 0.8789 \| 0.9671 \| 0.0 \| 0.0 \| 0.2280 \| 0.0 \| nan \| 0.7273 \| 0.8501 \| 0.4168 \| 0.6399 \| 0.3439 \| nan \| 0.3778 \| 0.5709 \| 0.0 \| 0.8027 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3164 \| 0.0 \| 0.0 \| 0.6985 \| 0.0 \| 0.3665 \| 0.2401 \| 0.0 \| nan \| 0.0 \| 0.2423 \| 0.0 \| 0.0 \| 0.8595 \| 0.7642 \| 0.9212 \| 0.0 \| 0.0 \| 0.1759 \| 0.0 \| \| 0.4042 \| 7.9 \| 1580 \| 0.5907 \| 0.2940 \| 0.3564 \| 0.8436 \| nan \| 0.8629 \| 0.9405 \| 0.5990 \| 0.7337 \| 0.4989 \| nan \| 0.4793 \| 0.7556 \| 0.0 \| 0.9333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4932 \| 0.0 \| 0.0 \| 0.9074 \| 0.0 \| 0.5340 \| 0.3461 \| 0.0 \| nan \| 0.0 \| 0.3155 \| 0.0 \| 0.0 \| 0.9464 \| 0.8863 \| 0.9658 \| 0.0 \| 0.0 \| 0.2071 \| 0.0 \| nan \| 0.7331 \| 0.8538 \| 0.4141 \| 0.6457 \| 0.3494 \| nan \| 0.3874 \| 0.5881 \| 0.0 \| 0.7962 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2864 \| 0.0 \| 0.0 \| 0.7063 \| 0.0 \| 0.3829 \| 0.3137 \| 0.0 \| nan \| 0.0 \| 0.2380 \| 0.0 \| 0.0 \| 0.8578 \| 0.7657 \| 0.9210 \| 0.0 \| 0.0 \| 0.1687 \| 0.0 \| \| 0.4915 \| 8.0 \| 1600 \| 0.5805 \| 0.2976 \| 0.3593 \| 0.8458 \| nan \| 0.8618 \| 0.9395 \| 0.5801 \| 0.7933 \| 0.4558 \| nan \| 0.4925 \| 0.7563 \| 0.0 \| 0.9391 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4860 \| 0.0 \| 0.0 \| 0.8979 \| 0.0 \| 0.5390 \| 0.4165 \| 0.0 \| nan \| 0.0 \| 0.3220 \| 0.0 \| 0.0 \| 0.9466 \| 0.8853 \| 0.9648 \| 0.0 \| 0.0 \| 0.2215 \| 0.0 \| nan \| 0.7311 \| 0.8544 \| 0.4294 \| 0.6731 \| 0.3421 \| nan \| 0.3934 \| 0.6052 \| 0.0 \| 0.7928 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2803 \| 0.0 \| 0.0 \| 0.7124 \| 0.0 \| 0.3900 \| 0.3612 \| 0.0 \| nan \| 0.0 \| 0.2420 \| 0.0 \| 0.0 \| 0.8564 \| 0.7633 \| 0.9208 \| 0.0 \| 0.0 \| 0.1752 \| 0.0 \| \| 0.4564 \| 8.1 \| 1620 \| 0.5909 \| 0.2975 \| 0.3610 \| 0.8440 \| nan \| 0.8619 \| 0.9410 \| 0.5701 \| 0.7384 \| 0.4921 \| nan \| 0.4827 \| 0.7838 \| 0.0 \| 0.9279 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5328 \| 0.0 \| 0.0 \| 0.9000 \| 0.0 \| 0.4814 \| 0.4299 \| 0.0 \| nan \| 0.0 \| 0.3525 \| 0.0 \| 0.0 \| 0.9441 \| 0.9024 \| 0.9651 \| 0.0 \| 0.0 \| 0.2475 \| 0.0 \| nan \| 0.7357 \| 0.8515 \| 0.4326 \| 0.6508 \| 0.3435 \| nan \| 0.3904 \| 0.5702 \| 0.0 \| 0.8007 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2983 \| 0.0 \| 0.0 \| 0.7060 \| 0.0 \| 0.3913 \| 0.3839 \| 0.0 \| nan \| 0.0 \| 0.2573 \| 0.0 \| 0.0 \| 0.8514 \| 0.7541 \| 0.9211 \| 0.0 \| 0.0 \| 0.1825 \| 0.0 \| \| 0.4293 \| 8.2 \| 1640 \| 0.6016 \| 0.2936 \| 0.3568 \| 0.8418 \| nan \| 0.8488 \| 0.9535 \| 0.5645 \| 0.7347 \| 0.4343 \| nan \| 0.4671 \| 0.7780 \| 0.0 \| 0.9347 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5543 \| 0.0 \| 0.0 \| 0.9067 \| 0.0 \| 0.4645 \| 0.3615 \| 0.0 \| nan \| 0.0 \| 0.3499 \| 0.0 \| 0.0 \| 0.9274 \| 0.9089 \| 0.9687 \| 0.0 \| 0.0 \| 0.2600 \| 0.0 \| nan \| 0.7298 \| 0.8479 \| 0.4386 \| 0.6442 \| 0.3341 \| nan \| 0.3834 \| 0.5715 \| 0.0 \| 0.7976 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2911 \| 0.0 \| 0.0 \| 0.6990 \| 0.0 \| 0.3703 \| 0.3328 \| 0.0 \| nan \| 0.0 \| 0.2537 \| 0.0 \| 0.0 \| 0.8524 \| 0.7378 \| 0.9213 \| 0.0 \| 0.0 \| 0.1892 \| 0.0 \| \| 0.2416 \| 8.3 \| 1660 \| 0.5930 \| 0.2960 \| 0.3567 \| 0.8451 \| nan \| 0.8627 \| 0.9523 \| 0.5585 \| 0.7374 \| 0.4508 \| nan \| 0.4778 \| 0.7690 \| 0.0 \| 0.9195 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5584 \| 0.0 \| 0.0 \| 0.8988 \| 0.0 \| 0.4868 \| 0.3511 \| 0.0 \| nan \| 0.0 \| 0.3384 \| 0.0 \| 0.0 \| 0.9519 \| 0.8790 \| 0.9689 \| 0.0 \| 0.0 \| 0.2518 \| 0.0 \| nan \| 0.7371 \| 0.8516 \| 0.4397 \| 0.6510 \| 0.3411 \| nan \| 0.3888 \| 0.5760 \| 0.0 \| 0.8089 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3068 \| 0.0 \| 0.0 \| 0.7035 \| 0.0 \| 0.3740 \| 0.3219 \| 0.0 \| nan \| 0.0 \| 0.2455 \| 0.0 \| 0.0 \| 0.8547 \| 0.7661 \| 0.9207 \| 0.0 \| 0.0 \| 0.1857 \| 0.0 \| \| 0.474 \| 8.4 \| 1680 \| 0.5887 \| 0.2968 \| 0.3623 \| 0.8446 \| nan \| 0.8520 \| 0.9483 \| 0.5958 \| 0.7393 \| 0.4958 \| nan \| 0.4858 \| 0.7934 \| 0.0 \| 0.9286 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5722 \| 0.0 \| 0.0 \| 0.8975 \| 0.0 \| 0.5263 \| 0.3477 \| 0.0 \| nan \| 0.0 \| 0.3560 \| 0.0 \| 0.0 \| 0.9466 \| 0.8638 \| 0.9745 \| 0.0 \| 0.0 \| 0.2685 \| 0.0 \| nan \| 0.7394 \| 0.8504 \| 0.4320 \| 0.6534 \| 0.3434 \| nan \| 0.3952 \| 0.5589 \| 0.0 \| 0.8037 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3123 \| 0.0 \| 0.0 \| 0.7126 \| 0.0 \| 0.3813 \| 0.3195 \| 0.0 \| nan \| 0.0 \| 0.2577 \| 0.0 \| 0.0 \| 0.8598 \| 0.7705 \| 0.9179 \| 0.0 \| 0.0 \| 0.1891 \| 0.0 \| \| 0.7499 \| 8.5 \| 1700 \| 0.5912 \| 0.2969 \| 0.3622 \| 0.8429 \| nan \| 0.8376 \| 0.9494 \| 0.5958 \| 0.7615 \| 0.4718 \| nan \| 0.4667 \| 0.7842 \| 0.0 \| 0.9141 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5775 \| 0.0 \| 0.0 \| 0.9007 \| 0.0 \| 0.5435 \| 0.3685 \| 0.0 \| nan \| 0.0 \| 0.3537 \| 0.0 \| 0.0 \| 0.9321 \| 0.9052 \| 0.9709 \| 0.0 \| 0.0 \| 0.2569 \| 0.0 \| nan \| 0.7311 \| 0.8501 \| 0.4380 \| 0.6496 \| 0.3393 \| nan \| 0.3869 \| 0.5726 \| 0.0 \| 0.8112 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3299 \| 0.0 \| 0.0 \| 0.7124 \| 0.0 \| 0.3851 \| 0.3354 \| 0.0 \| nan \| 0.0 \| 0.2580 \| 0.0 \| 0.0 \| 0.8530 \| 0.7413 \| 0.9208 \| 0.0 \| 0.0 \| 0.1863 \| 0.0 \| \| 0.3122 \| 8.6 \| 1720 \| 0.5893 \| 0.2950 \| 0.3559 \| 0.8435 \| nan \| 0.8625 \| 0.9424 \| 0.5925 \| 0.7487 \| 0.4691 \| nan \| 0.4895 \| 0.7853 \| 0.0 \| 0.9243 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5009 \| 0.0 \| 0.0 \| 0.9190 \| 0.0 \| 0.4775 \| 0.3233 \| 0.0 \| nan \| 0.0 \| 0.3342 \| 0.0 \| 0.0 \| 0.9466 \| 0.8685 \| 0.9681 \| 0.0 \| 0.0 \| 0.2382 \| 0.0 \| nan \| 0.7296 \| 0.8538 \| 0.4289 \| 0.6504 \| 0.3395 \| nan \| 0.3980 \| 0.5691 \| 0.0 \| 0.8038 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3235 \| 0.0 \| 0.0 \| 0.6949 \| 0.0 \| 0.3672 \| 0.3033 \| 0.0 \| nan \| 0.0 \| 0.2485 \| 0.0 \| 0.0 \| 0.8586 \| 0.7664 \| 0.9228 \| 0.0 \| 0.0 \| 0.1826 \| 0.0 \| \| 0.4643 \| 8.7 \| 1740 \| 0.5790 \| 0.2978 \| 0.3629 \| 0.8454 \| nan \| 0.8425 \| 0.9357 \| 0.5978 \| 0.8333 \| 0.4818 \| nan \| 0.5086 \| 0.7809 \| 0.0 \| 0.9420 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5146 \| 0.0 \| 0.0 \| 0.8946 \| 0.0 \| 0.5240 \| 0.3464 \| 0.0 \| nan \| 0.0 \| 0.3436 \| 0.0 \| 0.0 \| 0.9468 \| 0.8926 \| 0.9658 \| 0.0 \| 0.0 \| 0.2632 \| 0.0 \| nan \| 0.7326 \| 0.8559 \| 0.4279 \| 0.6736 \| 0.3453 \| nan \| 0.4089 \| 0.5846 \| 0.0 \| 0.7928 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3042 \| 0.0 \| 0.0 \| 0.7079 \| 0.0 \| 0.3830 \| 0.3168 \| 0.0 \| nan \| 0.0 \| 0.2545 \| 0.0 \| 0.0 \| 0.8582 \| 0.7638 \| 0.9225 \| 0.0 \| 0.0 \| 0.1969 \| 0.0 \| \| 0.4116 \| 8.8 \| 1760 \| 0.5820 \| 0.2977 \| 0.3611 \| 0.8465 \| nan \| 0.8533 \| 0.9489 \| 0.5951 \| 0.7959 \| 0.4441 \| nan \| 0.4867 \| 0.7812 \| 0.0 \| 0.9328 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5563 \| 0.0 \| 0.0 \| 0.9081 \| 0.0 \| 0.4951 \| 0.3623 \| 0.0 \| nan \| 0.0 \| 0.3574 \| 0.0 \| 0.0 \| 0.9371 \| 0.8924 \| 0.9654 \| 0.0 \| 0.0 \| 0.2426 \| 0.0 \| nan \| 0.7344 \| 0.8561 \| 0.4307 \| 0.6806 \| 0.3428 \| nan \| 0.3969 \| 0.5798 \| 0.0 \| 0.8034 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3147 \| 0.0 \| 0.0 \| 0.7025 \| 0.0 \| 0.3742 \| 0.3268 \| 0.0 \| nan \| 0.0 \| 0.2597 \| 0.0 \| 0.0 \| 0.8580 \| 0.7590 \| 0.9230 \| 0.0 \| 0.0 \| 0.1833 \| 0.0 \| \| 0.3642 \| 8.9 \| 1780 \| 0.5825 \| 0.2992 \| 0.3612 \| 0.8464 \| nan \| 0.8459 \| 0.9514 \| 0.5853 \| 0.7834 \| 0.4573 \| nan \| 0.4848 \| 0.7784 \| 0.0 \| 0.9270 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5579 \| 0.0 \| 0.0 \| 0.9058 \| 0.0 \| 0.4744 \| 0.3935 \| 0.0 \| nan \| 0.0 \| 0.3623 \| 0.0 \| 0.0 \| 0.9419 \| 0.8927 \| 0.9659 \| 0.0 \| 0.0 \| 0.2505 \| 0.0 \| nan \| 0.7352 \| 0.8545 \| 0.4346 \| 0.6736 \| 0.3430 \| nan \| 0.3968 \| 0.5806 \| 0.0 \| 0.8086 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3254 \| 0.0 \| 0.0 \| 0.7030 \| 0.0 \| 0.3787 \| 0.3601 \| 0.0 \| nan \| 0.0 \| 0.2615 \| 0.0 \| 0.0 \| 0.8548 \| 0.7584 \| 0.9230 \| 0.0 \| 0.0 \| 0.1835 \| 0.0 \| \| 0.3355 \| 9.0 \| 1800 \| 0.5826 \| 0.2994 \| 0.3636 \| 0.8459 \| nan \| 0.8603 \| 0.9413 \| 0.5917 \| 0.7590 \| 0.4987 \| nan \| 0.5007 \| 0.7845 \| 0.0 \| 0.9374 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5508 \| 0.0 \| 0.0 \| 0.8996 \| 0.0 \| 0.4774 \| 0.4227 \| 0.0 \| nan \| 0.0 \| 0.3510 \| 0.0 \| 0.0 \| 0.9467 \| 0.8837 \| 0.9653 \| 0.0 \| 0.0 \| 0.2642 \| 0.0 \| nan \| 0.7357 \| 0.8555 \| 0.4223 \| 0.6648 \| 0.3500 \| nan \| 0.4009 \| 0.5832 \| 0.0 \| 0.8002 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3057 \| 0.0 \| 0.0 \| 0.7050 \| 0.0 \| 0.3839 \| 0.3773 \| 0.0 \| nan \| 0.0 \| 0.2590 \| 0.0 \| 0.0 \| 0.8559 \| 0.7678 \| 0.9229 \| 0.0 \| 0.0 \| 0.1900 \| 0.0 \| \| 0.3793 \| 9.1 \| 1820 \| 0.5882 \| 0.2978 \| 0.3613 \| 0.8447 \| nan \| 0.8496 \| 0.9467 \| 0.5920 \| 0.7591 \| 0.4889 \| nan \| 0.5053 \| 0.7759 \| 0.0 \| 0.9374 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5241 \| 0.0 \| 0.0 \| 0.9129 \| 0.0 \| 0.4975 \| 0.3962 \| 0.0 \| nan \| 0.0 \| 0.3467 \| 0.0 \| 0.0 \| 0.9294 \| 0.8948 \| 0.9636 \| 0.0 \| 0.0 \| 0.2414 \| 0.0 \| nan \| 0.7405 \| 0.8545 \| 0.4316 \| 0.6590 \| 0.3488 \| nan \| 0.4008 \| 0.5891 \| 0.0 \| 0.7990 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3005 \| 0.0 \| 0.0 \| 0.7028 \| 0.0 \| 0.3844 \| 0.3574 \| 0.0 \| nan \| 0.0 \| 0.2570 \| 0.0 \| 0.0 \| 0.8520 \| 0.7453 \| 0.9236 \| 0.0 \| 0.0 \| 0.1823 \| 0.0 \| \| 0.2793 \| 9.2 \| 1840 \| 0.5878 \| 0.2974 \| 0.3616 \| 0.8446 \| nan \| 0.8513 \| 0.9463 \| 0.5973 \| 0.7588 \| 0.5000 \| nan \| 0.4986 \| 0.7865 \| 0.0 \| 0.9334 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5269 \| 0.0 \| 0.0 \| 0.9034 \| 0.0 \| 0.4835 \| 0.3733 \| 0.0 \| nan \| 0.0 \| 0.3521 \| 0.0 \| 0.0 \| 0.9342 \| 0.8937 \| 0.9654 \| 0.0 \| 0.0 \| 0.2658 \| 0.0 \| nan \| 0.7408 \| 0.8537 \| 0.4275 \| 0.6630 \| 0.3483 \| nan \| 0.4014 \| 0.5750 \| 0.0 \| 0.8024 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3071 \| 0.0 \| 0.0 \| 0.7035 \| 0.0 \| 0.3766 \| 0.3445 \| 0.0 \| nan \| 0.0 \| 0.2565 \| 0.0 \| 0.0 \| 0.8533 \| 0.7524 \| 0.9224 \| 0.0 \| 0.0 \| 0.1887 \| 0.0 \| \| 0.3716 \| 9.3 \| 1860 \| 0.5903 \| 0.2961 \| 0.3573 \| 0.8455 \| nan \| 0.8684 \| 0.9481 \| 0.5853 \| 0.7523 \| 0.4617 \| nan \| 0.4910 \| 0.7798 \| 0.0 \| 0.9249 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5365 \| 0.0 \| 0.0 \| 0.9166 \| 0.0 \| 0.4795 \| 0.3358 \| 0.0 \| nan \| 0.0 \| 0.3293 \| 0.0 \| 0.0 \| 0.9398 \| 0.8781 \| 0.9679 \| 0.0 \| 0.0 \| 0.2395 \| 0.0 \| nan \| 0.7364 \| 0.8548 \| 0.4289 \| 0.6600 \| 0.3465 \| nan \| 0.3954 \| 0.5684 \| 0.0 \| 0.8093 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3222 \| 0.0 \| 0.0 \| 0.6978 \| 0.0 \| 0.3660 \| 0.3115 \| 0.0 \| nan \| 0.0 \| 0.2477 \| 0.0 \| 0.0 \| 0.8597 \| 0.7694 \| 0.9220 \| 0.0 \| 0.0 \| 0.1779 \| 0.0 \| \| 0.3073 \| 9.4 \| 1880 \| 0.5829 \| 0.2971 \| 0.3572 \| 0.8471 \| nan \| 0.8683 \| 0.9506 \| 0.5674 \| 0.7611 \| 0.4517 \| nan \| 0.5018 \| 0.7762 \| 0.0 \| 0.9282 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5395 \| 0.0 \| 0.0 \| 0.9162 \| 0.0 \| 0.4836 \| 0.3563 \| 0.0 \| nan \| 0.0 \| 0.3141 \| 0.0 \| 0.0 \| 0.9427 \| 0.8766 \| 0.9691 \| 0.0 \| 0.0 \| 0.2265 \| 0.0 \| nan \| 0.7407 \| 0.8562 \| 0.4418 \| 0.6648 \| 0.3464 \| nan \| 0.3993 \| 0.5748 \| 0.0 \| 0.8072 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3174 \| 0.0 \| 0.0 \| 0.6974 \| 0.0 \| 0.3692 \| 0.3253 \| 0.0 \| nan \| 0.0 \| 0.2385 \| 0.0 \| 0.0 \| 0.8599 \| 0.7719 \| 0.9217 \| 0.0 \| 0.0 \| 0.1733 \| 0.0 \| \| 0.2746 \| 9.5 \| 1900 \| 0.5797 \| 0.2978 \| 0.3595 \| 0.8471 \| nan \| 0.8629 \| 0.9477 \| 0.5770 \| 0.7784 \| 0.4531 \| nan \| 0.4984 \| 0.7777 \| 0.0 \| 0.9258 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5575 \| 0.0 \| 0.0 \| 0.9103 \| 0.0 \| 0.4861 \| 0.3582 \| 0.0 \| nan \| 0.0 \| 0.3287 \| 0.0 \| 0.0 \| 0.9462 \| 0.8830 \| 0.9698 \| 0.0 \| 0.0 \| 0.2437 \| 0.0 \| nan \| 0.7410 \| 0.8565 \| 0.4411 \| 0.6698 \| 0.3466 \| nan \| 0.3982 \| 0.5803 \| 0.0 \| 0.8092 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3144 \| 0.0 \| 0.0 \| 0.7000 \| 0.0 \| 0.3715 \| 0.3289 \| 0.0 \| nan \| 0.0 \| 0.2446 \| 0.0 \| 0.0 \| 0.8575 \| 0.7704 \| 0.9218 \| 0.0 \| 0.0 \| 0.1790 \| 0.0 \| \| 0.3607 \| 9.6 \| 1920 \| 0.5941 \| 0.2970 \| 0.3575 \| 0.8456 \| nan \| 0.8571 \| 0.9580 \| 0.5710 \| 0.7238 \| 0.4492 \| nan \| 0.4884 \| 0.7706 \| 0.0 \| 0.9305 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5505 \| 0.0 \| 0.0 \| 0.9117 \| 0.0 \| 0.4975 \| 0.3714 \| 0.0 \| nan \| 0.0 \| 0.3271 \| 0.0 \| 0.0 \| 0.9358 \| 0.8878 \| 0.9665 \| 0.0 \| 0.0 \| 0.2429 \| 0.0 \| nan \| 0.7387 \| 0.8515 \| 0.4434 \| 0.6432 \| 0.3427 \| nan \| 0.3884 \| 0.5768 \| 0.0 \| 0.8068 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3156 \| 0.0 \| 0.0 \| 0.7027 \| 0.0 \| 0.3808 \| 0.3404 \| 0.0 \| nan \| 0.0 \| 0.2459 \| 0.0 \| 0.0 \| 0.8600 \| 0.7649 \| 0.9235 \| 0.0 \| 0.0 \| 0.1797 \| 0.0 \| \| 0.551 \| 9.7 \| 1940 \| 0.5854 \| 0.2979 \| 0.3612 \| 0.8452 \| nan \| 0.8724 \| 0.9397 \| 0.5686 \| 0.7450 \| 0.4752 \| nan \| 0.5028 \| 0.7750 \| 0.0 \| 0.9353 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5518 \| 0.0 \| 0.0 \| 0.9036 \| 0.0 \| 0.5075 \| 0.3871 \| 0.0 \| nan \| 0.0 \| 0.3322 \| 0.0 \| 0.0 \| 0.9442 \| 0.8904 \| 0.9713 \| 0.0 \| 0.0 \| 0.2548 \| 0.0 \| nan \| 0.7333 \| 0.8548 \| 0.4334 \| 0.6499 \| 0.3496 \| nan \| 0.3970 \| 0.5874 \| 0.0 \| 0.8014 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3017 \| 0.0 \| 0.0 \| 0.7046 \| 0.0 \| 0.3878 \| 0.3525 \| 0.0 \| nan \| 0.0 \| 0.2472 \| 0.0 \| 0.0 \| 0.8574 \| 0.7654 \| 0.9218 \| 0.0 \| 0.0 \| 0.1864 \| 0.0 \| \| 0.417 \| 9.8 \| 1960 \| 0.5867 \| 0.2993 \| 0.3616 \| 0.8462 \| nan \| 0.8696 \| 0.9473 \| 0.5746 \| 0.7423 \| 0.4598 \| nan \| 0.4883 \| 0.7754 \| 0.0 \| 0.9295 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5606 \| 0.0 \| 0.0 \| 0.9018 \| 0.0 \| 0.4964 \| 0.4181 \| 0.0 \| nan \| 0.0 \| 0.3385 \| 0.0 \| 0.0 \| 0.9421 \| 0.8914 \| 0.9685 \| 0.0 \| 0.0 \| 0.2674 \| 0.0 \| nan \| 0.7348 \| 0.8543 \| 0.4322 \| 0.6508 \| 0.3474 \| nan \| 0.3945 \| 0.5816 \| 0.0 \| 0.8074 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3141 \| 0.0 \| 0.0 \| 0.7062 \| 0.0 \| 0.3905 \| 0.3765 \| 0.0 \| nan \| 0.0 \| 0.2517 \| 0.0 \| 0.0 \| 0.8584 \| 0.7636 \| 0.9229 \| 0.0 \| 0.0 \| 0.1896 \| 0.0 \| \| 0.3938 \| 9.9 \| 1980 \| 0.5864 \| 0.2984 \| 0.3604 \| 0.8458 \| nan \| 0.8685 \| 0.9452 \| 0.5814 \| 0.7414 \| 0.4627 \| nan \| 0.4926 \| 0.7724 \| 0.0 \| 0.9271 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5601 \| 0.0 \| 0.0 \| 0.9028 \| 0.0 \| 0.4875 \| 0.4032 \| 0.0 \| nan \| 0.0 \| 0.3355 \| 0.0 \| 0.0 \| 0.9493 \| 0.8861 \| 0.9661 \| 0.0 \| 0.0 \| 0.2511 \| 0.0 \| nan \| 0.7348 \| 0.8543 \| 0.4299 \| 0.6507 \| 0.3471 \| nan \| 0.3960 \| 0.5810 \| 0.0 \| 0.8082 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3151 \| 0.0 \| 0.0 \| 0.7047 \| 0.0 \| 0.3855 \| 0.3669 \| 0.0 \| nan \| 0.0 \| 0.2480 \| 0.0 \| 0.0 \| 0.8549 \| 0.7663 \| 0.9226 \| 0.0 \| 0.0 \| 0.1842 \| 0.0 \| \| 0.3931 \| 10.0 \| 2000 \| 0.5881 \| 0.2988 \| 0.3613 \| 0.8463 \| nan \| 0.8649 \| 0.9502 \| 0.5791 \| 0.7393 \| 0.4485 \| nan \| 0.4859 \| 0.7767 \| 0.0 \| 0.9341 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5642 \| 0.0 \| 0.0 \| 0.8944 \| 0.0 \| 0.4959 \| 0.4208 \| 0.0 \| nan \| 0.0 \| 0.3432 \| 0.0 \| 0.0 \| 0.9466 \| 0.8884 \| 0.9698 \| 0.0 \| 0.0 \| 0.2614 \| 0.0 \| nan \| 0.7372 \| 0.8540 \| 0.4322 \| 0.6499 \| 0.3421 \| nan \| 0.3932 \| 0.5795 \| 0.0 \| 0.8033 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.3104 \| 0.0 \| 0.0 \| 0.7075 \| 0.0 \| 0.3911 \| 0.3791 \| 0.0 \| nan \| 0.0 \| 0.2524 \| 0.0 \| 0.0 \| 0.8559 \| 0.7644 \| 0.9219 \| 0.0 \| 0.0 \| 0.1876 \| 0.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
samitizerxu/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5925 - eval_mean_iou: 0.2753 - eval_mean_accuracy: 0.3327 - eval_overall_accuracy: 0.8401 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8405 - eval_accuracy_flat-sidewalk: 0.9533 - eval_accuracy_flat-crosswalk: 0.6601 - eval_accuracy_flat-cyclinglane: 0.7992 - eval_accuracy_flat-parkingdriveway: 0.5578 - eval_accuracy_flat-railtrack: nan - eval_accuracy_flat-curb: 0.4836 - eval_accuracy_human-person: 0.6161 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.9299 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: nan - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.0003 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.8840 - eval_accuracy_construction-door: 0.0 - eval_accuracy_construction-wall: 0.3660 - eval_accuracy_construction-fenceguardrail: 0.3076 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: 0.0 - eval_accuracy_construction-stairs: 0.0 - eval_accuracy_object-pole: 0.2707 - eval_accuracy_object-trafficsign: 0.0 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9456 - eval_accuracy_nature-terrain: 0.8426 - eval_accuracy_sky: 0.9610 - eval_accuracy_void-ground: 0.0 - eval_accuracy_void-dynamic: 0.0 - eval_accuracy_void-static: 0.2296 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.7077 - eval_iou_flat-sidewalk: 0.8656 - eval_iou_flat-crosswalk: 0.5379 - eval_iou_flat-cyclinglane: 0.7062 - eval_iou_flat-parkingdriveway: 0.4285 - eval_iou_flat-railtrack: nan - eval_iou_flat-curb: 0.3675 - eval_iou_human-person: 0.3194 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.7878 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: nan - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.0003 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.6784 - eval_iou_construction-door: 0.0 - eval_iou_construction-wall: 0.2711 - eval_iou_construction-fenceguardrail: 0.2716 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: 0.0 - eval_iou_construction-stairs: 0.0 - eval_iou_object-pole: 0.2059 - eval_iou_object-trafficsign: 0.0 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.8358 - eval_iou_nature-terrain: 0.7375 - eval_iou_sky: 0.9064 - eval_iou_void-ground: 0.0 - eval_iou_void-dynamic: 0.0 - eval_iou_void-static: 0.1826 - eval_iou_void-unclear: 0.0 - eval_runtime: 11.1228 - eval_samples_per_second: 17.981 - eval_steps_per_second: 2.248 - epoch: 17.4 - step: 1740 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-4ss1st3r_s3gs3m_24Jan-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan dataset. It achieves the following results on the evaluation set: - Loss: 0.1305 - Mean Iou: 0.6564 - Mean Accuracy: 0.8562 - Overall Accuracy: 0.9780 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9896 - Accuracy Fallo malla: 0.9270 - Accuracy Fallo adhesivo: 0.9478 - Accuracy Fallo burbuja: 0.5603 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9749 - Iou Fallo malla: 0.8458 - Iou Fallo adhesivo: 0.9324 - Iou Fallo burbuja: 0.5290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.3639 \| 1.0 \| 193 \| 0.1583 \| 0.6076 \| 0.8441 \| 0.9607 \| nan \| 0.9660 \| 0.9617 \| 0.9644 \| 0.4844 \| 0.0 \| 0.9553 \| 0.7294 \| 0.9301 \| 0.4231 \| \| 0.1148 \| 2.0 \| 386 \| 0.0991 \| 0.6189 \| 0.8025 \| 0.9754 \| nan \| 0.9912 \| 0.9045 \| 0.9417 \| 0.3725 \| 0.0 \| 0.9723 \| 0.8404 \| 0.9283 \| 0.3534 \| \| 0.0937 \| 3.0 \| 579 \| 0.1414 \| 0.5848 \| 0.8155 \| 0.9554 \| nan \| 0.9606 \| 0.9630 \| 0.9707 \| 0.3675 \| 0.0 \| 0.9487 \| 0.6791 \| 0.9442 \| 0.3519 \| \| 0.0827 \| 4.0 \| 772 \| 0.1028 \| 0.6390 \| 0.8484 \| 0.9747 \| nan \| 0.9831 \| 0.9530 \| 0.9640 \| 0.4936 \| 0.0 \| 0.9714 \| 0.8231 \| 0.9388 \| 0.4617 \| \| 0.0735 \| 5.0 \| 965 \| 0.0948 \| 0.6425 \| 0.8423 \| 0.9777 \| nan \| 0.9875 \| 0.9487 \| 0.9594 \| 0.4737 \| 0.0 \| 0.9745 \| 0.8484 \| 0.9415 \| 0.4479 \| \| 0.0716 \| 6.0 \| 1158 \| 0.0968 \| 0.6638 \| 0.8622 \| 0.9804 \| nan \| 0.9936 \| 0.8987 \| 0.9579 \| 0.5985 \| 0.0 \| 0.9777 \| 0.8654 \| 0.9403 \| 0.5355 \| \| 0.0692 \| 7.0 \| 1351 \| 0.1123 \| 0.6389 \| 0.8535 \| 0.9718 \| nan \| 0.9804 \| 0.9425 \| 0.9604 \| 0.5307 \| 0.0 \| 0.9678 \| 0.7878 \| 0.9403 \| 0.4984 \| \| 0.0718 \| 8.0 \| 1544 \| 0.1097 \| 0.6424 \| 0.8668 \| 0.9703 \| nan \| 0.9770 \| 0.9520 \| 0.9642 \| 0.5738 \| 0.0 \| 0.9663 \| 0.7792 \| 0.9423 \| 0.5243 \| \| 0.0613 \| 9.0 \| 1737 \| 0.1212 \| 0.6341 \| 0.8625 \| 0.9669 \| nan \| 0.9735 \| 0.9412 \| 0.9721 \| 0.5634 \| 0.0 \| 0.9621 \| 0.7447 \| 0.9430 \| 0.5208 \| \| 0.06 \| 10.0 \| 1930 \| 0.0983 \| 0.6724 \| 0.8945 \| 0.9793 \| nan \| 0.9875 \| 0.9335 \| 0.9682 \| 0.6889 \| 0.0 \| 0.9765 \| 0.8490 \| 0.9461 \| 0.5905 \| \| 0.0593 \| 11.0 \| 2123 \| 0.1104 \| 0.6577 \| 0.8803 \| 0.9743 \| nan \| 0.9830 \| 0.9249 \| 0.9670 \| 0.6462 \| 0.0 \| 0.9709 \| 0.8028 \| 0.9419 \| 0.5729 \| \| 0.056 \| 12.0 \| 2316 \| 0.1029 \| 0.6589 \| 0.8829 \| 0.9755 \| nan \| 0.9833 \| 0.9349 \| 0.9712 \| 0.6420 \| 0.0 \| 0.9721 \| 0.8170 \| 0.9399 \| 0.5655 \| \| 0.0547 \| 13.0 \| 2509 \| 0.1037 \| 0.6613 \| 0.8944 \| 0.9746 \| nan \| 0.9815 \| 0.9406 \| 0.9680 \| 0.6877 \| 0.0 \| 0.9712 \| 0.8089 \| 0.9434 \| 0.5832 \| \| 0.0538 \| 14.0 \| 2702 \| 0.1342 \| 0.6338 \| 0.8750 \| 0.9625 \| nan \| 0.9677 \| 0.9470 \| 0.9647 \| 0.6204 \| 0.0 \| 0.9570 \| 0.7080 \| 0.9412 \| 0.5627 \| \| 0.052 \| 15.0 \| 2895 \| 0.0961 \| 0.6525 \| 0.8507 \| 0.9787 \| nan \| 0.9894 \| 0.9292 \| 0.9656 \| 0.5187 \| 0.0 \| 0.9758 \| 0.8514 \| 0.9439 \| 0.4915 \| \| 0.0489 \| 16.0 \| 3088 \| 0.1093 \| 0.6464 \| 0.8626 \| 0.9725 \| nan \| 0.9812 \| 0.9345 \| 0.9639 \| 0.5708 \| 0.0 \| 0.9688 \| 0.7900 \| 0.9440 \| 0.5290 \| \| 0.0478 \| 17.0 \| 3281 \| 0.1053 \| 0.6503 \| 0.8574 \| 0.9760 \| nan \| 0.9858 \| 0.9300 \| 0.9673 \| 0.5465 \| 0.0 \| 0.9726 \| 0.8239 \| 0.9411 \| 0.5138 \| \| 0.048 \| 18.0 \| 3474 \| 0.1314 \| 0.6416 \| 0.8884 \| 0.9644 \| nan \| 0.9691 \| 0.9517 \| 0.9642 \| 0.6688 \| 0.0 \| 0.9591 \| 0.7232 \| 0.9415 \| 0.5842 \| \| 0.0474 \| 19.0 \| 3667 \| 0.1197 \| 0.6473 \| 0.8559 \| 0.9743 \| nan \| 0.9842 \| 0.9344 \| 0.9557 \| 0.5493 \| 0.0 \| 0.9707 \| 0.8067 \| 0.9394 \| 0.5196 \| \| 0.0456 \| 20.0 \| 3860 \| 0.1149 \| 0.6587 \| 0.8578 \| 0.9788 \| nan \| 0.9905 \| 0.9241 \| 0.9503 \| 0.5665 \| 0.0 \| 0.9759 \| 0.8513 \| 0.9344 \| 0.5321 \| \| 0.044 \| 21.0 \| 4053 \| 0.1183 \| 0.6574 \| 0.8612 \| 0.9774 \| nan \| 0.9885 \| 0.9280 \| 0.9487 \| 0.5794 \| 0.0 \| 0.9743 \| 0.8367 \| 0.9345 \| 0.5413 \| \| 0.0431 \| 22.0 \| 4246 \| 0.1326 \| 0.6425 \| 0.8599 \| 0.9711 \| nan \| 0.9795 \| 0.9405 \| 0.9595 \| 0.5601 \| 0.0 \| 0.9670 \| 0.7783 \| 0.9384 \| 0.5291 \| \| 0.0446 \| 23.0 \| 4439 \| 0.1253 \| 0.6535 \| 0.8678 \| 0.9743 \| nan \| 0.9833 \| 0.9309 \| 0.9635 \| 0.5933 \| 0.0 \| 0.9706 \| 0.8007 \| 0.9427 \| 0.5535 \| \| 0.0427 \| 24.0 \| 4632 \| 0.1075 \| 0.6568 \| 0.8602 \| 0.9771 \| nan \| 0.9882 \| 0.9229 \| 0.9543 \| 0.5755 \| 0.0 \| 0.9739 \| 0.8342 \| 0.9379 \| 0.5379 \| \| 0.0417 \| 25.0 \| 4825 \| 0.1250 \| 0.6443 \| 0.8559 \| 0.9723 \| nan \| 0.9820 \| 0.9337 \| 0.9542 \| 0.5539 \| 0.0 \| 0.9684 \| 0.7904 \| 0.9375 \| 0.5250 \| \| 0.0402 \| 26.0 \| 5018 \| 0.1206 \| 0.6518 \| 0.8497 \| 0.9775 \| nan \| 0.9892 \| 0.9236 \| 0.9536 \| 0.5324 \| 0.0 \| 0.9744 \| 0.8373 \| 0.9383 \| 0.5089 \| \| 0.0403 \| 27.0 \| 5211 \| 0.1164 \| 0.6565 \| 0.8688 \| 0.9755 \| nan \| 0.9848 \| 0.9382 \| 0.9531 \| 0.5991 \| 0.0 \| 0.9723 \| 0.8183 \| 0.9378 \| 0.5540 \| \| 0.0405 \| 28.0 \| 5404 \| 0.1091 \| 0.6586 \| 0.8505 \| 0.9799 \| nan \| 0.9926 \| 0.9177 \| 0.9530 \| 0.5389 \| 0.0 \| 0.9773 \| 0.8650 \| 0.9381 \| 0.5128 \| \| 0.0384 \| 29.0 \| 5597 \| 0.1304 \| 0.6504 \| 0.8470 \| 0.9781 \| nan \| 0.9893 \| 0.9365 \| 0.9508 \| 0.5112 \| 0.0 \| 0.9751 \| 0.8477 \| 0.9365 \| 0.4926 \| \| 0.0374 \| 30.0 \| 5790 \| 0.1095 \| 0.6585 \| 0.8605 \| 0.9783 \| nan \| 0.9891 \| 0.9323 \| 0.9507 \| 0.5698 \| 0.0 \| 0.9754 \| 0.8469 \| 0.9358 \| 0.5345 \| \| 0.0378 \| 31.0 \| 5983 \| 0.1245 \| 0.6558 \| 0.8553 \| 0.9780 \| nan \| 0.9896 \| 0.9237 \| 0.9539 \| 0.5540 \| 0.0 \| 0.9750 \| 0.8435 \| 0.9353 \| 0.5254 \| \| 0.0367 \| 32.0 \| 6176 \| 0.1288 \| 0.6504 \| 0.8637 \| 0.9737 \| nan \| 0.9828 \| 0.9386 \| 0.9555 \| 0.5778 \| 0.0 \| 0.9700 \| 0.8016 \| 0.9362 \| 0.5443 \| \| 0.037 \| 33.0 \| 6369 \| 0.1293 \| 0.6565 \| 0.8656 \| 0.9760 \| nan \| 0.9862 \| 0.9381 \| 0.9443 \| 0.5938 \| 0.0 \| 0.9726 \| 0.8273 \| 0.9314 \| 0.5512 \| \| 0.0363 \| 34.0 \| 6562 \| 0.1242 \| 0.6594 \| 0.8528 \| 0.9800 \| nan \| 0.9926 \| 0.9171 \| 0.9529 \| 0.5485 \| 0.0 \| 0.9773 \| 0.8632 \| 0.9378 \| 0.5188 \| \| 0.0361 \| 35.0 \| 6755 \| 0.1239 \| 0.6653 \| 0.8739 \| 0.9781 \| nan \| 0.9886 \| 0.9247 \| 0.9557 \| 0.6264 \| 0.0 \| 0.9752 \| 0.8420 \| 0.9374 \| 0.5718 \| \| 0.0371 \| 36.0 \| 6948 \| 0.1220 \| 0.6626 \| 0.8691 \| 0.9782 \| nan \| 0.9887 \| 0.9297 \| 0.9530 \| 0.6049 \| 0.0 \| 0.9751 \| 0.8418 \| 0.9375 \| 0.5585 \| \| 0.034 \| 37.0 \| 7141 \| 0.1694 \| 0.6300 \| 0.8685 \| 0.9609 \| nan \| 0.9666 \| 0.9453 \| 0.9602 \| 0.6020 \| 0.0 \| 0.9551 \| 0.6981 \| 0.9399 \| 0.5567 \| \| 0.0358 \| 38.0 \| 7334 \| 0.1251 \| 0.6513 \| 0.8534 \| 0.9764 \| nan \| 0.9878 \| 0.9270 \| 0.9492 \| 0.5497 \| 0.0 \| 0.9731 \| 0.8290 \| 0.9345 \| 0.5198 \| \| 0.033 \| 39.0 \| 7527 \| 0.1330 \| 0.6542 \| 0.8604 \| 0.9764 \| nan \| 0.9868 \| 0.9343 \| 0.9503 \| 0.5700 \| 0.0 \| 0.9731 \| 0.8292 \| 0.9351 \| 0.5336 \| \| 0.0327 \| 40.0 \| 7720 \| 0.1359 \| 0.6490 \| 0.8537 \| 0.9750 \| nan \| 0.9862 \| 0.9269 \| 0.9483 \| 0.5535 \| 0.0 \| 0.9716 \| 0.8183 \| 0.9330 \| 0.5221 \| \| 0.0336 \| 41.0 \| 7913 \| 0.1277 \| 0.6588 \| 0.8667 \| 0.9766 \| nan \| 0.9874 \| 0.9267 \| 0.9489 \| 0.6037 \| 0.0 \| 0.9734 \| 0.8288 \| 0.9341 \| 0.5577 \| \| 0.0312 \| 42.0 \| 8106 \| 0.1321 \| 0.6568 \| 0.8716 \| 0.9749 \| nan \| 0.9844 \| 0.9358 \| 0.9500 \| 0.6163 \| 0.0 \| 0.9714 \| 0.8132 \| 0.9344 \| 0.5650 \| \| 0.0321 \| 43.0 \| 8299 \| 0.1269 \| 0.6533 \| 0.8574 \| 0.9763 \| nan \| 0.9874 \| 0.9283 \| 0.9490 \| 0.5649 \| 0.0 \| 0.9730 \| 0.8285 \| 0.9335 \| 0.5316 \| \| 0.0306 \| 44.0 \| 8492 \| 0.1269 \| 0.6583 \| 0.8528 \| 0.9792 \| nan \| 0.9918 \| 0.9207 \| 0.9467 \| 0.5520 \| 0.0 \| 0.9764 \| 0.8593 \| 0.9324 \| 0.5236 \| \| 0.0306 \| 45.0 \| 8685 \| 0.1335 \| 0.6503 \| 0.8503 \| 0.9765 \| nan \| 0.9883 \| 0.9283 \| 0.9439 \| 0.5407 \| 0.0 \| 0.9733 \| 0.8345 \| 0.9295 \| 0.5144 \| \| 0.0324 \| 46.0 \| 8878 \| 0.1294 \| 0.6538 \| 0.8490 \| 0.9784 \| nan \| 0.9908 \| 0.9254 \| 0.9441 \| 0.5358 \| 0.0 \| 0.9754 \| 0.8525 \| 0.9303 \| 0.5107 \| \| 0.0318 \| 47.0 \| 9071 \| 0.1230 \| 0.6564 \| 0.8549 \| 0.9782 \| nan \| 0.9900 \| 0.9252 \| 0.9486 \| 0.5559 \| 0.0 \| 0.9752 \| 0.8477 \| 0.9335 \| 0.5255 \| \| 0.0319 \| 48.0 \| 9264 \| 0.1267 \| 0.6524 \| 0.8501 \| 0.9776 \| nan \| 0.9895 \| 0.9278 \| 0.9464 \| 0.5368 \| 0.0 \| 0.9745 \| 0.8438 \| 0.9322 \| 0.5117 \| \| 0.0312 \| 49.0 \| 9457 \| 0.1258 \| 0.6568 \| 0.8602 \| 0.9774 \| nan \| 0.9884 \| 0.9321 \| 0.9482 \| 0.5720 \| 0.0 \| 0.9743 \| 0.8399 \| 0.9327 \| 0.5373 \| \| 0.0311 \| 50.0 \| 9650 \| 0.1203 \| 0.6589 \| 0.8610 \| 0.9779 \| nan \| 0.9894 \| 0.9262 \| 0.9471 \| 0.5814 \| 0.0 \| 0.9749 \| 0.8444 \| 0.9319 \| 0.5435 \| \| 0.0327 \| 51.0 \| 9843 \| 0.1219 \| 0.6575 \| 0.8577 \| 0.9780 \| nan \| 0.9897 \| 0.9265 \| 0.9457 \| 0.5688 \| 0.0 \| 0.9750 \| 0.8462 \| 0.9314 \| 0.5348 \| \| 0.031 \| 51.81 \| 10000 \| 0.1305 \| 0.6564 \| 0.8562 \| 0.9780 \| nan \| 0.9896 \| 0.9270 \| 0.9478 \| 0.5603 \| 0.0 \| 0.9749 \| 0.8458 \| 0.9324 \| 0.5290 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3	[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]
krnl/segformer-b5-finetuned-human-parsing	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b5-finetuned-human-parsing This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2292 - Mean Iou: 0.6258 - Mean Accuracy: 0.7547 - Overall Accuracy: 0.8256 - Accuracy Background: nan - Accuracy Hat: 0.8561 - Accuracy Hair: 0.8974 - Accuracy Sunglasses: 0.7540 - Accuracy Upper-clothes: 0.8553 - Accuracy Skirt: 0.7026 - Accuracy Pants: 0.8913 - Accuracy Dress: 0.7525 - Accuracy Belt: 0.4251 - Accuracy Left-shoe: 0.6014 - Accuracy Right-shoe: 0.6374 - Accuracy Face: 0.9094 - Accuracy Left-leg: 0.8452 - Accuracy Right-leg: 0.8343 - Accuracy Left-arm: 0.8506 - Accuracy Right-arm: 0.8287 - Accuracy Bag: 0.8232 - Accuracy Scarf: 0.3662 - Iou Background: 0.0 - Iou Hat: 0.7625 - Iou Hair: 0.8171 - Iou Sunglasses: 0.6400 - Iou Upper-clothes: 0.7700 - Iou Skirt: 0.6211 - Iou Pants: 0.7788 - Iou Dress: 0.5512 - Iou Belt: 0.3564 - Iou Left-shoe: 0.5032 - Iou Right-shoe: 0.5381 - Iou Face: 0.8294 - Iou Left-leg: 0.7412 - Iou Right-leg: 0.7591 - Iou Left-arm: 0.7579 - Iou Right-arm: 0.7705 - Iou Bag: 0.7729 - Iou Scarf: 0.2956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Hat \| Accuracy Hair \| Accuracy Sunglasses \| Accuracy Upper-clothes \| Accuracy Skirt \| Accuracy Pants \| Accuracy Dress \| Accuracy Belt \| Accuracy Left-shoe \| Accuracy Right-shoe \| Accuracy Face \| Accuracy Left-leg \| Accuracy Right-leg \| Accuracy Left-arm \| Accuracy Right-arm \| Accuracy Bag \| Accuracy Scarf \| Iou Background \| Iou Hat \| Iou Hair \| Iou Sunglasses \| Iou Upper-clothes \| Iou Skirt \| Iou Pants \| Iou Dress \| Iou Belt \| Iou Left-shoe \| Iou Right-shoe \| Iou Face \| Iou Left-leg \| Iou Right-leg \| Iou Left-arm \| Iou Right-arm \| Iou Bag \| Iou Scarf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:------------:\|:-------------:\|:-------------------:\|:----------------------:\|:--------------:\|:--------------:\|:--------------:\|:-------------:\|:------------------:\|:-------------------:\|:-------------:\|:-----------------:\|:------------------:\|:-----------------:\|:------------------:\|:------------:\|:--------------:\|:--------------:\|:-------:\|:--------:\|:--------------:\|:-----------------:\|:---------:\|:---------:\|:---------:\|:--------:\|:-------------:\|:--------------:\|:--------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:-------:\|:---------:\| \| 1.1597 \| 0.04 \| 20 \| 1.5815 \| 0.1179 \| 0.1991 \| 0.4296 \| nan \| 0.0060 \| 0.6905 \| 0.0 \| 0.7657 \| 0.0108 \| 0.6431 \| 0.2946 \| 0.0 \| 0.0288 \| 0.0366 \| 0.1480 \| 0.0025 \| 0.5692 \| 0.0096 \| 0.0259 \| 0.1537 \| 0.0 \| 0.0 \| 0.0051 \| 0.4253 \| 0.0 \| 0.5199 \| 0.0103 \| 0.3388 \| 0.1700 \| 0.0 \| 0.0258 \| 0.0338 \| 0.0895 \| 0.0025 \| 0.3162 \| 0.0094 \| 0.0253 \| 0.1495 \| 0.0 \| \| 0.6963 \| 0.08 \| 40 \| 0.8073 \| 0.1759 \| 0.2719 \| 0.4628 \| nan \| 0.0015 \| 0.8699 \| 0.0 \| 0.4736 \| 0.4932 \| 0.5141 \| 0.6775 \| 0.0 \| 0.0062 \| 0.1038 \| 0.5301 \| 0.0916 \| 0.5071 \| 0.0092 \| 0.0549 \| 0.2889 \| 0.0 \| 0.0 \| 0.0015 \| 0.6169 \| 0.0 \| 0.4242 \| 0.2202 \| 0.3522 \| 0.2251 \| 0.0 \| 0.0062 \| 0.0904 \| 0.4914 \| 0.0852 \| 0.3160 \| 0.0092 \| 0.0541 \| 0.2731 \| 0.0 \| \| 0.5786 \| 0.12 \| 60 \| 0.6136 \| 0.2538 \| 0.3642 \| 0.4679 \| nan \| 0.0180 \| 0.8122 \| 0.0 \| 0.1998 \| 0.0000 \| 0.6621 \| 0.8592 \| 0.0 \| 0.1440 \| 0.2772 \| 0.8381 \| 0.4032 \| 0.6068 \| 0.4182 \| 0.3097 \| 0.6434 \| 0.0 \| 0.0 \| 0.0179 \| 0.6760 \| 0.0 \| 0.1951 \| 0.0000 \| 0.5471 \| 0.2218 \| 0.0 \| 0.1147 \| 0.2032 \| 0.6403 \| 0.3189 \| 0.4204 \| 0.3505 \| 0.2947 \| 0.5676 \| 0.0 \| \| 0.324 \| 0.16 \| 80 \| 0.4282 \| 0.2893 \| 0.4044 \| 0.6041 \| nan \| 0.0147 \| 0.7890 \| 0.0 \| 0.8222 \| 0.7984 \| 0.6646 \| 0.1038 \| 0.0 \| 0.0896 \| 0.3308 \| 0.8277 \| 0.4099 \| 0.6839 \| 0.2401 \| 0.5474 \| 0.5521 \| 0.0 \| 0.0 \| 0.0147 \| 0.6800 \| 0.0 \| 0.6159 \| 0.3049 \| 0.5913 \| 0.0938 \| 0.0 \| 0.0802 \| 0.2394 \| 0.6598 \| 0.3178 \| 0.4504 \| 0.2288 \| 0.4189 \| 0.5113 \| 0.0 \| \| 0.297 \| 0.2 \| 100 \| 0.4020 \| 0.3034 \| 0.4230 \| 0.6332 \| nan \| 0.0048 \| 0.8076 \| 0.0080 \| 0.9042 \| 0.6567 \| 0.8036 \| 0.0317 \| 0.0 \| 0.0481 \| 0.5298 \| 0.7728 \| 0.2589 \| 0.7232 \| 0.5941 \| 0.3839 \| 0.6643 \| 0.0 \| 0.0 \| 0.0048 \| 0.6708 \| 0.0080 \| 0.6300 \| 0.3836 \| 0.5929 \| 0.0314 \| 0.0 \| 0.0441 \| 0.3152 \| 0.6726 \| 0.2420 \| 0.4745 \| 0.4532 \| 0.3631 \| 0.5759 \| 0.0 \| \| 0.2608 \| 0.24 \| 120 \| 0.3538 \| 0.3444 \| 0.4554 \| 0.6504 \| nan \| 0.2922 \| 0.8078 \| 0.0753 \| 0.8472 \| 0.0425 \| 0.6961 \| 0.6197 \| 0.0 \| 0.2550 \| 0.3074 \| 0.8020 \| 0.5636 \| 0.6895 \| 0.3779 \| 0.6930 \| 0.6734 \| 0.0 \| 0.0 \| 0.2757 \| 0.6940 \| 0.0747 \| 0.6457 \| 0.0419 \| 0.6098 \| 0.3611 \| 0.0 \| 0.1849 \| 0.2412 \| 0.7038 \| 0.4513 \| 0.5038 \| 0.3439 \| 0.4760 \| 0.5915 \| 0.0 \| \| 0.3306 \| 0.28 \| 140 \| 0.3281 \| 0.3562 \| 0.4736 \| 0.6560 \| nan \| 0.4111 \| 0.8576 \| 0.1953 \| 0.8081 \| 0.6916 \| 0.7888 \| 0.3489 \| 0.0 \| 0.0809 \| 0.3612 \| 0.8132 \| 0.0622 \| 0.7078 \| 0.6328 \| 0.5437 \| 0.7482 \| 0.0 \| 0.0 \| 0.3895 \| 0.7227 \| 0.1857 \| 0.6777 \| 0.3750 \| 0.6015 \| 0.2749 \| 0.0 \| 0.0740 \| 0.2602 \| 0.7070 \| 0.0612 \| 0.4348 \| 0.5114 \| 0.4966 \| 0.6385 \| 0.0 \| \| 0.364 \| 0.32 \| 160 \| 0.3368 \| 0.3689 \| 0.4836 \| 0.6531 \| nan \| 0.3898 \| 0.8453 \| 0.1743 \| 0.9269 \| 0.2493 \| 0.7922 \| 0.0842 \| 0.0 \| 0.4874 \| 0.2384 \| 0.8116 \| 0.6226 \| 0.5731 \| 0.6049 \| 0.6620 \| 0.7597 \| 0.0 \| 0.0 \| 0.3746 \| 0.7246 \| 0.1690 \| 0.6015 \| 0.1998 \| 0.5942 \| 0.0786 \| 0.0 \| 0.2682 \| 0.1904 \| 0.7015 \| 0.4781 \| 0.4781 \| 0.5452 \| 0.5804 \| 0.6562 \| 0.0 \| \| 0.635 \| 0.36 \| 180 \| 0.3092 \| 0.3699 \| 0.4903 \| 0.6319 \| nan \| 0.4996 \| 0.8387 \| 0.2136 \| 0.6184 \| 0.0129 \| 0.7920 \| 0.8199 \| 0.0 \| 0.1895 \| 0.3028 \| 0.8307 \| 0.7258 \| 0.3386 \| 0.7480 \| 0.6543 \| 0.7511 \| 0.0 \| 0.0 \| 0.4613 \| 0.7126 \| 0.2042 \| 0.5589 \| 0.0128 \| 0.6658 \| 0.3529 \| 0.0 \| 0.1622 \| 0.2426 \| 0.7363 \| 0.4646 \| 0.3144 \| 0.5794 \| 0.5575 \| 0.6321 \| 0.0 \| \| 0.1464 \| 0.4 \| 200 \| 0.3306 \| 0.3809 \| 0.5041 \| 0.6544 \| nan \| 0.6110 \| 0.8337 \| 0.2420 \| 0.8913 \| 0.8862 \| 0.6492 \| 0.0004 \| 0.0 \| 0.2888 \| 0.2949 \| 0.8514 \| 0.4630 \| 0.7751 \| 0.7020 \| 0.5429 \| 0.5386 \| 0.0 \| 0.0 \| 0.5329 \| 0.7348 \| 0.2331 \| 0.6567 \| 0.3661 \| 0.5769 \| 0.0004 \| 0.0 \| 0.2221 \| 0.2333 \| 0.7431 \| 0.4133 \| 0.5478 \| 0.5718 \| 0.5125 \| 0.5107 \| 0.0 \| \| 0.2257 \| 0.44 \| 220 \| 0.2751 \| 0.4089 \| 0.5400 \| 0.6752 \| nan \| 0.6851 \| 0.8458 \| 0.4204 \| 0.7241 \| 0.1085 \| 0.7997 \| 0.7657 \| 0.0 \| 0.2458 \| 0.4039 \| 0.8858 \| 0.7863 \| 0.3199 \| 0.7405 \| 0.6974 \| 0.7508 \| 0.0 \| 0.0 \| 0.5815 \| 0.7437 \| 0.3776 \| 0.6458 \| 0.1033 \| 0.6526 \| 0.3966 \| 0.0 \| 0.2027 \| 0.3078 \| 0.7438 \| 0.4680 \| 0.2966 \| 0.6204 \| 0.5942 \| 0.6260 \| 0.0 \| \| 0.3069 \| 0.48 \| 240 \| 0.2614 \| 0.4163 \| 0.5499 \| 0.6868 \| nan \| 0.6246 \| 0.8571 \| 0.3130 \| 0.7765 \| 0.8266 \| 0.7786 \| 0.3212 \| 0.0 \| 0.3560 \| 0.3736 \| 0.8579 \| 0.1780 \| 0.8761 \| 0.7423 \| 0.7693 \| 0.6970 \| 0.0 \| 0.0 \| 0.5597 \| 0.7370 \| 0.2931 \| 0.6733 \| 0.4032 \| 0.6889 \| 0.2487 \| 0.0 \| 0.2662 \| 0.2901 \| 0.7425 \| 0.1724 \| 0.4957 \| 0.6373 \| 0.6376 \| 0.6470 \| 0.0 \| \| 0.1454 \| 0.52 \| 260 \| 0.2563 \| 0.4316 \| 0.5610 \| 0.6965 \| nan \| 0.6707 \| 0.8388 \| 0.5572 \| 0.7616 \| 0.3854 \| 0.7280 \| 0.7114 \| 0.0 \| 0.1934 \| 0.3621 \| 0.8718 \| 0.7860 \| 0.6140 \| 0.7403 \| 0.5340 \| 0.7820 \| 0.0 \| 0.0 \| 0.5710 \| 0.7446 \| 0.4497 \| 0.6637 \| 0.3125 \| 0.6624 \| 0.4219 \| 0.0 \| 0.1731 \| 0.2862 \| 0.7295 \| 0.5339 \| 0.5054 \| 0.5742 \| 0.4967 \| 0.6449 \| 0.0 \| \| 0.1522 \| 0.56 \| 280 \| 0.2521 \| 0.4327 \| 0.5567 \| 0.7138 \| nan \| 0.5098 \| 0.9135 \| 0.3399 \| 0.8898 \| 0.5537 \| 0.7508 \| 0.2922 \| 0.0 \| 0.3367 \| 0.2484 \| 0.8388 \| 0.7460 \| 0.7191 \| 0.7496 \| 0.7996 \| 0.7753 \| 0.0 \| 0.0 \| 0.4902 \| 0.7541 \| 0.3196 \| 0.6924 \| 0.3853 \| 0.6261 \| 0.2512 \| 0.0 \| 0.2575 \| 0.2171 \| 0.7393 \| 0.5563 \| 0.5633 \| 0.6403 \| 0.6335 \| 0.6621 \| 0.0 \| \| 0.1872 \| 0.6 \| 300 \| 0.2359 \| 0.4557 \| 0.5797 \| 0.7247 \| nan \| 0.6901 \| 0.8832 \| 0.5498 \| 0.8857 \| 0.6636 \| 0.7843 \| 0.3983 \| 0.0 \| 0.4549 \| 0.2292 \| 0.8147 \| 0.7126 \| 0.6223 \| 0.7467 \| 0.7014 \| 0.7185 \| 0.0 \| 0.0 \| 0.5881 \| 0.7556 \| 0.4621 \| 0.7131 \| 0.4264 \| 0.6506 \| 0.3311 \| 0.0 \| 0.3025 \| 0.1975 \| 0.7350 \| 0.5635 \| 0.5513 \| 0.6505 \| 0.6318 \| 0.6439 \| 0.0 \| \| 0.2512 \| 0.64 \| 320 \| 0.2601 \| 0.4363 \| 0.5599 \| 0.6801 \| nan \| 0.6470 \| 0.8482 \| 0.3819 \| 0.6317 \| 0.2525 \| 0.7437 \| 0.8755 \| 0.0 \| 0.1737 \| 0.5412 \| 0.8907 \| 0.5915 \| 0.7943 \| 0.7177 \| 0.7437 \| 0.6852 \| 0.0 \| 0.0 \| 0.5580 \| 0.7612 \| 0.3645 \| 0.5718 \| 0.2362 \| 0.6671 \| 0.3836 \| 0.0 \| 0.1621 \| 0.3641 \| 0.7486 \| 0.5132 \| 0.5865 \| 0.6472 \| 0.6479 \| 0.6409 \| 0.0 \| \| 0.6521 \| 0.68 \| 340 \| 0.2286 \| 0.4716 \| 0.6024 \| 0.7351 \| nan \| 0.6559 \| 0.8492 \| 0.3976 \| 0.7517 \| 0.5818 \| 0.7622 \| 0.7422 \| 0.0 \| 0.5277 \| 0.2673 \| 0.9098 \| 0.7514 \| 0.6903 \| 0.7853 \| 0.7795 \| 0.7896 \| 0.0 \| 0.0 \| 0.5839 \| 0.7531 \| 0.3761 \| 0.6829 \| 0.4643 \| 0.6722 \| 0.4762 \| 0.0 \| 0.3375 \| 0.2261 \| 0.7551 \| 0.5838 \| 0.5730 \| 0.6548 \| 0.6501 \| 0.6987 \| 0.0 \| \| 0.1734 \| 0.72 \| 360 \| 0.2511 \| 0.4464 \| 0.5731 \| 0.7074 \| nan \| 0.6326 \| 0.8339 \| 0.5953 \| 0.8987 \| 0.8731 \| 0.7575 \| 0.1617 \| 0.0 \| 0.2314 \| 0.5741 \| 0.8497 \| 0.6140 \| 0.6973 \| 0.5250 \| 0.7576 \| 0.7406 \| 0.0 \| 0.0 \| 0.5777 \| 0.7535 \| 0.4840 \| 0.6597 \| 0.5208 \| 0.6261 \| 0.1507 \| 0.0 \| 0.2110 \| 0.3920 \| 0.7579 \| 0.5436 \| 0.5825 \| 0.4959 \| 0.6090 \| 0.6702 \| 0.0 \| \| 0.1596 \| 0.76 \| 380 \| 0.2303 \| 0.4702 \| 0.5922 \| 0.7351 \| nan \| 0.6336 \| 0.8772 \| 0.4196 \| 0.8004 \| 0.4307 \| 0.7034 \| 0.7554 \| 0.0 \| 0.2914 \| 0.4563 \| 0.8930 \| 0.7517 \| 0.7146 \| 0.7649 \| 0.7420 \| 0.8336 \| 0.0 \| 0.0 \| 0.5854 \| 0.7635 \| 0.3937 \| 0.6838 \| 0.3957 \| 0.6295 \| 0.4857 \| 0.0 \| 0.2497 \| 0.3439 \| 0.7526 \| 0.6021 \| 0.6055 \| 0.6591 \| 0.6473 \| 0.6657 \| 0.0 \| \| 0.1359 \| 0.8 \| 400 \| 0.2332 \| 0.4592 \| 0.5773 \| 0.7182 \| nan \| 0.6759 \| 0.8564 \| 0.5305 \| 0.8221 \| 0.4710 \| 0.8463 \| 0.5733 \| 0.0 \| 0.2630 \| 0.5031 \| 0.8314 \| 0.7079 \| 0.7295 \| 0.7449 \| 0.7233 \| 0.5357 \| 0.0 \| 0.0 \| 0.6046 \| 0.7593 \| 0.4670 \| 0.7005 \| 0.3678 \| 0.5672 \| 0.4327 \| 0.0 \| 0.2265 \| 0.3566 \| 0.7593 \| 0.5975 \| 0.6161 \| 0.6456 \| 0.6470 \| 0.5178 \| 0.0 \| \| 0.2014 \| 0.84 \| 420 \| 0.2298 \| 0.4709 \| 0.5957 \| 0.7268 \| nan \| 0.6207 \| 0.8571 \| 0.5146 \| 0.7670 \| 0.6367 \| 0.6238 \| 0.7682 \| 0.0 \| 0.2470 \| 0.4863 \| 0.8939 \| 0.5487 \| 0.8665 \| 0.7735 \| 0.7827 \| 0.7405 \| 0.0 \| 0.0 \| 0.5729 \| 0.7737 \| 0.4637 \| 0.6774 \| 0.4588 \| 0.5840 \| 0.4656 \| 0.0 \| 0.2219 \| 0.3668 \| 0.7764 \| 0.5020 \| 0.5959 \| 0.6641 \| 0.6698 \| 0.6833 \| 0.0 \| \| 0.137 \| 0.88 \| 440 \| 0.2260 \| 0.4824 \| 0.6147 \| 0.7401 \| nan \| 0.7197 \| 0.8823 \| 0.6023 \| 0.8544 \| 0.9048 \| 0.7837 \| 0.3094 \| 0.0245 \| 0.2877 \| 0.4592 \| 0.8905 \| 0.6846 \| 0.8472 \| 0.7109 \| 0.7647 \| 0.7230 \| 0.0 \| 0.0 \| 0.6089 \| 0.7638 \| 0.5071 \| 0.7154 \| 0.5084 \| 0.6749 \| 0.2801 \| 0.0227 \| 0.2486 \| 0.3499 \| 0.7679 \| 0.6127 \| 0.6436 \| 0.6445 \| 0.6729 \| 0.6626 \| 0.0 \| \| 0.2494 \| 0.92 \| 460 \| 0.2275 \| 0.4721 \| 0.5997 \| 0.7321 \| nan \| 0.6265 \| 0.8452 \| 0.6870 \| 0.8116 \| 0.4266 \| 0.8250 \| 0.7026 \| 0.0498 \| 0.5651 \| 0.1855 \| 0.8745 \| 0.7293 \| 0.6387 \| 0.6783 \| 0.8080 \| 0.7416 \| 0.0 \| 0.0 \| 0.5764 \| 0.7653 \| 0.5312 \| 0.7021 \| 0.3635 \| 0.6488 \| 0.4468 \| 0.0475 \| 0.3420 \| 0.1695 \| 0.7764 \| 0.5981 \| 0.5821 \| 0.6149 \| 0.6599 \| 0.6740 \| 0.0 \| \| 0.2788 \| 0.96 \| 480 \| 0.2315 \| 0.4670 \| 0.5977 \| 0.7292 \| nan \| 0.7105 \| 0.8142 \| 0.4169 \| 0.8597 \| 0.8573 \| 0.7707 \| 0.3171 \| 0.0476 \| 0.2514 \| 0.4432 \| 0.9214 \| 0.7332 \| 0.6989 \| 0.8245 \| 0.6729 \| 0.7954 \| 0.0255 \| 0.0 \| 0.6051 \| 0.7556 \| 0.3986 \| 0.7114 \| 0.4588 \| 0.6423 \| 0.2727 \| 0.0438 \| 0.2215 \| 0.3468 \| 0.7650 \| 0.6030 \| 0.6007 \| 0.6432 \| 0.6180 \| 0.6945 \| 0.0253 \| \| 0.1254 \| 1.0 \| 500 \| 0.2176 \| 0.4955 \| 0.6287 \| 0.7450 \| nan \| 0.7081 \| 0.9094 \| 0.4628 \| 0.7437 \| 0.5938 \| 0.7126 \| 0.7410 \| 0.0560 \| 0.3971 \| 0.5239 \| 0.8992 \| 0.7446 \| 0.8258 \| 0.8028 \| 0.7613 \| 0.8041 \| 0.0014 \| 0.0 \| 0.6242 \| 0.7741 \| 0.4367 \| 0.6877 \| 0.4856 \| 0.6276 \| 0.4823 \| 0.0535 \| 0.3136 \| 0.3836 \| 0.7687 \| 0.6347 \| 0.6510 \| 0.6615 \| 0.6529 \| 0.6795 \| 0.0014 \| \| 0.2625 \| 1.04 \| 520 \| 0.2270 \| 0.5000 \| 0.6339 \| 0.7411 \| nan \| 0.7844 \| 0.8633 \| 0.6442 \| 0.8202 \| 0.3913 \| 0.6661 \| 0.7393 \| 0.0533 \| 0.4684 \| 0.5305 \| 0.8686 \| 0.6858 \| 0.8024 \| 0.7433 \| 0.7895 \| 0.8522 \| 0.0729 \| 0.0 \| 0.6637 \| 0.7685 \| 0.5378 \| 0.7150 \| 0.3328 \| 0.6169 \| 0.4502 \| 0.0468 \| 0.3310 \| 0.3732 \| 0.7769 \| 0.6214 \| 0.6602 \| 0.6614 \| 0.6823 \| 0.6913 \| 0.0714 \| \| 0.2871 \| 1.08 \| 540 \| 0.2072 \| 0.5091 \| 0.6337 \| 0.7630 \| nan \| 0.7427 \| 0.8718 \| 0.5674 \| 0.8080 \| 0.6131 \| 0.7855 \| 0.7672 \| 0.0584 \| 0.3031 \| 0.4535 \| 0.8750 \| 0.6763 \| 0.8457 \| 0.8027 \| 0.7491 \| 0.7710 \| 0.0820 \| 0.0 \| 0.6471 \| 0.7706 \| 0.5092 \| 0.7250 \| 0.4737 \| 0.6887 \| 0.5152 \| 0.0507 \| 0.2643 \| 0.3623 \| 0.7788 \| 0.6085 \| 0.6565 \| 0.6659 \| 0.6633 \| 0.7025 \| 0.0816 \| \| 0.1481 \| 1.12 \| 560 \| 0.2250 \| 0.4824 \| 0.5946 \| 0.7494 \| nan \| 0.6480 \| 0.8561 \| 0.5148 \| 0.8637 \| 0.5174 \| 0.7904 \| 0.6671 \| 0.0029 \| 0.3782 \| 0.2824 \| 0.8794 \| 0.6807 \| 0.7755 \| 0.6985 \| 0.7431 \| 0.8058 \| 0.0039 \| 0.0 \| 0.6099 \| 0.7755 \| 0.4755 \| 0.7081 \| 0.4454 \| 0.6483 \| 0.4567 \| 0.0029 \| 0.2864 \| 0.2456 \| 0.7798 \| 0.6012 \| 0.6408 \| 0.6339 \| 0.6715 \| 0.6978 \| 0.0039 \| \| 0.0995 \| 1.16 \| 580 \| 0.2084 \| 0.5218 \| 0.6570 \| 0.7698 \| nan \| 0.7706 \| 0.8532 \| 0.4978 \| 0.7874 \| 0.7761 \| 0.8102 \| 0.6761 \| 0.0 \| 0.4886 \| 0.4884 \| 0.9113 \| 0.7646 \| 0.8476 \| 0.8129 \| 0.7507 \| 0.7485 \| 0.1856 \| 0.0 \| 0.6194 \| 0.7661 \| 0.4641 \| 0.7200 \| 0.5843 \| 0.6730 \| 0.5042 \| 0.0 \| 0.3497 \| 0.3711 \| 0.7790 \| 0.6542 \| 0.6835 \| 0.6823 \| 0.6749 \| 0.6959 \| 0.1715 \| \| 0.2912 \| 1.2 \| 600 \| 0.2166 \| 0.5136 \| 0.6304 \| 0.7632 \| nan \| 0.6870 \| 0.8862 \| 0.4891 \| 0.7752 \| 0.6264 \| 0.8143 \| 0.8202 \| 0.0227 \| 0.2578 \| 0.4830 \| 0.8932 \| 0.7564 \| 0.7366 \| 0.7746 \| 0.7678 \| 0.7558 \| 0.1712 \| 0.0 \| 0.6243 \| 0.7791 \| 0.4579 \| 0.6983 \| 0.5530 \| 0.6676 \| 0.5248 \| 0.0222 \| 0.2333 \| 0.3786 \| 0.7820 \| 0.6438 \| 0.6651 \| 0.6773 \| 0.6749 \| 0.7018 \| 0.1610 \| \| 0.1874 \| 1.24 \| 620 \| 0.2280 \| 0.5053 \| 0.6296 \| 0.7525 \| nan \| 0.7070 \| 0.8689 \| 0.5348 \| 0.8275 \| 0.3452 \| 0.8441 \| 0.7748 \| 0.0643 \| 0.4207 \| 0.4251 \| 0.8840 \| 0.7698 \| 0.6921 \| 0.7271 \| 0.7322 \| 0.7544 \| 0.3318 \| 0.0 \| 0.6387 \| 0.7766 \| 0.4873 \| 0.7210 \| 0.3283 \| 0.6163 \| 0.5014 \| 0.0600 \| 0.3252 \| 0.3434 \| 0.7750 \| 0.6349 \| 0.6336 \| 0.6531 \| 0.6560 \| 0.6995 \| 0.2448 \| \| 0.1634 \| 1.28 \| 640 \| 0.2052 \| 0.5100 \| 0.6350 \| 0.7638 \| nan \| 0.7069 \| 0.8648 \| 0.6021 \| 0.8461 \| 0.6408 \| 0.8499 \| 0.6889 \| 0.0 \| 0.3772 \| 0.5718 \| 0.8730 \| 0.7073 \| 0.5765 \| 0.7283 \| 0.7600 \| 0.7655 \| 0.2362 \| 0.0 \| 0.6105 \| 0.7777 \| 0.5233 \| 0.7372 \| 0.5287 \| 0.6565 \| 0.5185 \| 0.0 \| 0.3063 \| 0.4062 \| 0.7743 \| 0.5688 \| 0.5413 \| 0.6500 \| 0.6721 \| 0.6956 \| 0.2133 \| \| 0.1894 \| 1.32 \| 660 \| 0.2169 \| 0.5104 \| 0.6553 \| 0.7601 \| nan \| 0.7721 \| 0.8902 \| 0.7412 \| 0.8309 \| 0.9266 \| 0.6974 \| 0.5353 \| 0.0008 \| 0.4742 \| 0.4474 \| 0.8442 \| 0.8150 \| 0.7319 \| 0.7803 \| 0.7767 \| 0.7605 \| 0.1160 \| 0.0 \| 0.6506 \| 0.7883 \| 0.5373 \| 0.7116 \| 0.5472 \| 0.6493 \| 0.4321 \| 0.0008 \| 0.3256 \| 0.3422 \| 0.7644 \| 0.6199 \| 0.6389 \| 0.6849 \| 0.6873 \| 0.6919 \| 0.1141 \| \| 0.0769 \| 1.36 \| 680 \| 0.1993 \| 0.5250 \| 0.6596 \| 0.7756 \| nan \| 0.8044 \| 0.8515 \| 0.6711 \| 0.8456 \| 0.6474 \| 0.7909 \| 0.7083 \| 0.0630 \| 0.3173 \| 0.6517 \| 0.9003 \| 0.6833 \| 0.8527 \| 0.8065 \| 0.8162 \| 0.7165 \| 0.0872 \| 0.0 \| 0.6318 \| 0.7755 \| 0.5553 \| 0.7207 \| 0.5836 \| 0.6787 \| 0.5292 \| 0.0625 \| 0.2650 \| 0.4130 \| 0.7788 \| 0.6131 \| 0.6713 \| 0.7043 \| 0.7024 \| 0.6790 \| 0.0866 \| \| 0.2145 \| 1.4 \| 700 \| 0.2052 \| 0.5114 \| 0.6438 \| 0.7630 \| nan \| 0.7518 \| 0.8628 \| 0.5961 \| 0.8763 \| 0.9097 \| 0.7924 \| 0.4449 \| 0.0273 \| 0.4497 \| 0.3682 \| 0.8965 \| 0.8234 \| 0.6068 \| 0.8065 \| 0.6396 \| 0.8432 \| 0.2497 \| 0.0 \| 0.6262 \| 0.7776 \| 0.5260 \| 0.7240 \| 0.6034 \| 0.6991 \| 0.4150 \| 0.0270 \| 0.3297 \| 0.3073 \| 0.7805 \| 0.6198 \| 0.5743 \| 0.6511 \| 0.6071 \| 0.7187 \| 0.2189 \| \| 0.2162 \| 1.44 \| 720 \| 0.2290 \| 0.5246 \| 0.6727 \| 0.7519 \| nan \| 0.8003 \| 0.8895 \| 0.7155 \| 0.7305 \| 0.4570 \| 0.8400 \| 0.8071 \| 0.0599 \| 0.2933 \| 0.5509 \| 0.8700 \| 0.7052 \| 0.8386 \| 0.7501 \| 0.8070 \| 0.8038 \| 0.5178 \| 0.0 \| 0.7009 \| 0.7910 \| 0.5717 \| 0.6618 \| 0.4195 \| 0.6924 \| 0.4677 \| 0.0587 \| 0.2536 \| 0.4050 \| 0.7823 \| 0.6246 \| 0.6658 \| 0.6718 \| 0.6930 \| 0.7369 \| 0.2463 \| \| 0.1751 \| 1.48 \| 740 \| 0.2073 \| 0.5376 \| 0.6734 \| 0.7847 \| nan \| 0.8054 \| 0.8711 \| 0.6506 \| 0.8714 \| 0.7615 \| 0.7720 \| 0.6263 \| 0.1874 \| 0.4293 \| 0.4568 \| 0.9023 \| 0.8568 \| 0.7712 \| 0.7206 \| 0.8132 \| 0.8180 \| 0.1342 \| 0.0 \| 0.7205 \| 0.7976 \| 0.5541 \| 0.7385 \| 0.5644 \| 0.6886 \| 0.4901 \| 0.1510 \| 0.3297 \| 0.3632 \| 0.7814 \| 0.6510 \| 0.6618 \| 0.6457 \| 0.6798 \| 0.7289 \| 0.1310 \| \| 0.1175 \| 1.52 \| 760 \| 0.2123 \| 0.5114 \| 0.6336 \| 0.7694 \| nan \| 0.6736 \| 0.8370 \| 0.6304 \| 0.8724 \| 0.7794 \| 0.7886 \| 0.6708 \| 0.0890 \| 0.2305 \| 0.6798 \| 0.9045 \| 0.5193 \| 0.7556 \| 0.7443 \| 0.7395 \| 0.7734 \| 0.0836 \| 0.0 \| 0.6314 \| 0.7744 \| 0.5497 \| 0.7306 \| 0.5991 \| 0.6411 \| 0.5151 \| 0.0705 \| 0.2169 \| 0.4380 \| 0.7806 \| 0.4983 \| 0.6112 \| 0.6768 \| 0.6739 \| 0.7150 \| 0.0824 \| \| 0.1317 \| 1.56 \| 780 \| 0.2097 \| 0.5035 \| 0.6318 \| 0.7713 \| nan \| 0.3686 \| 0.9006 \| 0.6208 \| 0.8267 \| 0.8135 \| 0.7586 \| 0.6929 \| 0.0769 \| 0.5944 \| 0.2629 \| 0.9171 \| 0.8806 \| 0.6178 \| 0.7197 \| 0.8212 \| 0.7743 \| 0.0936 \| 0.0 \| 0.3669 \| 0.7756 \| 0.5497 \| 0.7150 \| 0.6423 \| 0.6759 \| 0.5331 \| 0.0657 \| 0.3886 \| 0.2448 \| 0.7772 \| 0.6177 \| 0.5813 \| 0.6439 \| 0.6832 \| 0.7088 \| 0.0931 \| \| 0.5482 \| 1.6 \| 800 \| 0.2511 \| 0.5037 \| 0.6255 \| 0.7414 \| nan \| 0.7498 \| 0.8591 \| 0.6984 \| 0.7940 \| 0.4886 \| 0.7867 \| 0.8255 \| 0.0677 \| 0.3048 \| 0.4688 \| 0.8573 \| 0.6227 \| 0.7895 \| 0.7323 \| 0.7148 \| 0.6241 \| 0.2496 \| 0.0 \| 0.6732 \| 0.7722 \| 0.5691 \| 0.6858 \| 0.4315 \| 0.6297 \| 0.4643 \| 0.0620 \| 0.2595 \| 0.3642 \| 0.7790 \| 0.5765 \| 0.6519 \| 0.6686 \| 0.6574 \| 0.6024 \| 0.2200 \| \| 0.0895 \| 1.64 \| 820 \| 0.1973 \| 0.5191 \| 0.6552 \| 0.7673 \| nan \| 0.6921 \| 0.8844 \| 0.4919 \| 0.8472 \| 0.6608 \| 0.7842 \| 0.6546 \| 0.2664 \| 0.4594 \| 0.4925 \| 0.9224 \| 0.8699 \| 0.6010 \| 0.8740 \| 0.5805 \| 0.8105 \| 0.2468 \| 0.0 \| 0.6533 \| 0.7801 \| 0.4672 \| 0.7328 \| 0.5569 \| 0.6945 \| 0.5016 \| 0.2105 \| 0.3565 \| 0.3858 \| 0.7737 \| 0.6031 \| 0.5611 \| 0.5950 \| 0.5277 \| 0.7232 \| 0.2217 \| \| 0.1804 \| 1.68 \| 840 \| 0.2026 \| 0.5308 \| 0.6584 \| 0.7736 \| nan \| 0.7891 \| 0.8563 \| 0.6565 \| 0.8528 \| 0.6089 \| 0.7999 \| 0.7005 \| 0.0741 \| 0.2858 \| 0.6423 \| 0.9035 \| 0.7926 \| 0.7474 \| 0.7500 \| 0.7516 \| 0.7824 \| 0.1997 \| 0.0 \| 0.6848 \| 0.7815 \| 0.5627 \| 0.7391 \| 0.5112 \| 0.6788 \| 0.4969 \| 0.0673 \| 0.2622 \| 0.4456 \| 0.7909 \| 0.6476 \| 0.6569 \| 0.6618 \| 0.6640 \| 0.7139 \| 0.1888 \| \| 0.1271 \| 1.72 \| 860 \| 0.2134 \| 0.5227 \| 0.6505 \| 0.7693 \| nan \| 0.7656 \| 0.8645 \| 0.6724 \| 0.9055 \| 0.6357 \| 0.7807 \| 0.5336 \| 0.0838 \| 0.5199 \| 0.4139 \| 0.9021 \| 0.8526 \| 0.7135 \| 0.7894 \| 0.7737 \| 0.7407 \| 0.1103 \| 0.0 \| 0.6177 \| 0.7821 \| 0.5747 \| 0.7212 \| 0.5627 \| 0.6421 \| 0.4449 \| 0.0762 \| 0.3731 \| 0.3486 \| 0.7872 \| 0.6535 \| 0.6449 \| 0.6892 \| 0.6914 \| 0.6913 \| 0.1075 \| \| 0.1344 \| 1.76 \| 880 \| 0.2099 \| 0.5269 \| 0.6527 \| 0.7711 \| nan \| 0.7985 \| 0.8741 \| 0.6237 \| 0.8356 \| 0.5284 \| 0.8054 \| 0.7358 \| 0.0469 \| 0.3616 \| 0.5220 \| 0.9019 \| 0.6880 \| 0.8126 \| 0.8358 \| 0.7745 \| 0.7948 \| 0.1563 \| 0.0 \| 0.6922 \| 0.7886 \| 0.5563 \| 0.7230 \| 0.4772 \| 0.6475 \| 0.5068 \| 0.0436 \| 0.3041 \| 0.3968 \| 0.7794 \| 0.6338 \| 0.6822 \| 0.6952 \| 0.6896 \| 0.7182 \| 0.1498 \| \| 0.2751 \| 1.8 \| 900 \| 0.2006 \| 0.5334 \| 0.6488 \| 0.7806 \| nan \| 0.7400 \| 0.8550 \| 0.5263 \| 0.8847 \| 0.6187 \| 0.8032 \| 0.6862 \| 0.0858 \| 0.4811 \| 0.4224 \| 0.9162 \| 0.7803 \| 0.7360 \| 0.7854 \| 0.7527 \| 0.7963 \| 0.1590 \| 0.0 \| 0.6939 \| 0.7836 \| 0.4954 \| 0.7498 \| 0.5176 \| 0.6805 \| 0.5075 \| 0.0725 \| 0.3595 \| 0.3539 \| 0.7824 \| 0.6646 \| 0.6782 \| 0.6886 \| 0.6885 \| 0.7303 \| 0.1537 \| \| 0.2685 \| 1.84 \| 920 \| 0.2153 \| 0.5265 \| 0.6600 \| 0.7598 \| nan \| 0.7896 \| 0.8871 \| 0.4589 \| 0.8001 \| 0.3307 \| 0.8338 \| 0.7796 \| 0.2192 \| 0.4367 \| 0.4581 \| 0.9048 \| 0.8316 \| 0.7681 \| 0.8377 \| 0.7482 \| 0.7620 \| 0.3733 \| 0.0 \| 0.6760 \| 0.7832 \| 0.4428 \| 0.7128 \| 0.3263 \| 0.6632 \| 0.5039 \| 0.1754 \| 0.3545 \| 0.3853 \| 0.7758 \| 0.6553 \| 0.6665 \| 0.6908 \| 0.6811 \| 0.6933 \| 0.2913 \| \| 0.5729 \| 1.88 \| 940 \| 0.2057 \| 0.5343 \| 0.6737 \| 0.7641 \| nan \| 0.8351 \| 0.8681 \| 0.5588 \| 0.7823 \| 0.5378 \| 0.8769 \| 0.6844 \| 0.2025 \| 0.4204 \| 0.5736 \| 0.9000 \| 0.6586 \| 0.8633 \| 0.7572 \| 0.8501 \| 0.8008 \| 0.2837 \| 0.0 \| 0.6697 \| 0.7834 \| 0.5193 \| 0.7093 \| 0.4643 \| 0.6734 \| 0.4657 \| 0.1703 \| 0.3568 \| 0.4435 \| 0.7947 \| 0.5896 \| 0.6450 \| 0.6871 \| 0.7106 \| 0.6983 \| 0.2363 \| \| 0.0862 \| 1.92 \| 960 \| 0.2208 \| 0.5155 \| 0.6363 \| 0.7593 \| nan \| 0.6683 \| 0.8670 \| 0.6014 \| 0.8336 \| 0.9151 \| 0.7537 \| 0.6623 \| 0.0948 \| 0.5015 \| 0.4279 \| 0.8643 \| 0.5706 \| 0.5450 \| 0.7743 \| 0.7484 \| 0.7914 \| 0.1971 \| 0.0 \| 0.6179 \| 0.7799 \| 0.5443 \| 0.7211 \| 0.5925 \| 0.5910 \| 0.5189 \| 0.0850 \| 0.3944 \| 0.3678 \| 0.7979 \| 0.5061 \| 0.4893 \| 0.6992 \| 0.6875 \| 0.7094 \| 0.1772 \| \| 0.0793 \| 1.96 \| 980 \| 0.2003 \| 0.5467 \| 0.6820 \| 0.7890 \| nan \| 0.8868 \| 0.8560 \| 0.6296 \| 0.8753 \| 0.5087 \| 0.8319 \| 0.7202 \| 0.2101 \| 0.3960 \| 0.5827 \| 0.9140 \| 0.6879 \| 0.8781 \| 0.7924 \| 0.8308 \| 0.8216 \| 0.1713 \| 0.0 \| 0.7187 \| 0.7865 \| 0.5556 \| 0.7705 \| 0.4573 \| 0.6863 \| 0.5364 \| 0.1786 \| 0.3456 \| 0.4445 \| 0.7817 \| 0.6192 \| 0.6617 \| 0.7112 \| 0.7034 \| 0.7258 \| 0.1571 \| \| 0.0881 \| 2.0 \| 1000 \| 0.2026 \| 0.5430 \| 0.6696 \| 0.7758 \| nan \| 0.8055 \| 0.9034 \| 0.6463 \| 0.8711 \| 0.5281 \| 0.7951 \| 0.6777 \| 0.2288 \| 0.4737 \| 0.5184 \| 0.8716 \| 0.7001 \| 0.7681 \| 0.7600 \| 0.7842 \| 0.8391 \| 0.2115 \| 0.0 \| 0.7040 \| 0.7865 \| 0.5595 \| 0.7562 \| 0.4646 \| 0.6923 \| 0.4893 \| 0.1846 \| 0.3773 \| 0.4039 \| 0.7844 \| 0.6285 \| 0.6595 \| 0.6914 \| 0.6785 \| 0.7171 \| 0.1954 \| \| 0.164 \| 2.04 \| 1020 \| 0.1894 \| 0.5566 \| 0.6935 \| 0.7948 \| nan \| 0.8577 \| 0.8451 \| 0.6489 \| 0.8534 \| 0.6880 \| 0.8749 \| 0.7212 \| 0.2222 \| 0.3785 \| 0.5106 \| 0.8963 \| 0.6640 \| 0.8652 \| 0.8502 \| 0.7722 \| 0.8177 \| 0.3231 \| 0.0 \| 0.6855 \| 0.7756 \| 0.5673 \| 0.7557 \| 0.5883 \| 0.7037 \| 0.5489 \| 0.1773 \| 0.3318 \| 0.4129 \| 0.7889 \| 0.6126 \| 0.6769 \| 0.7042 \| 0.6960 \| 0.7334 \| 0.2598 \| \| 0.158 \| 2.08 \| 1040 \| 0.2104 \| 0.5418 \| 0.6749 \| 0.7757 \| nan \| 0.8476 \| 0.8642 \| 0.6307 \| 0.8318 \| 0.4601 \| 0.8279 \| 0.7790 \| 0.1855 \| 0.4350 \| 0.4771 \| 0.9067 \| 0.7989 \| 0.7718 \| 0.7935 \| 0.8241 \| 0.7621 \| 0.2772 \| 0.0 \| 0.6748 \| 0.7851 \| 0.5636 \| 0.7291 \| 0.4187 \| 0.6812 \| 0.5001 \| 0.1576 \| 0.3579 \| 0.3982 \| 0.7805 \| 0.6649 \| 0.6787 \| 0.7120 \| 0.7232 \| 0.7037 \| 0.2224 \| \| 0.0724 \| 2.12 \| 1060 \| 0.1818 \| 0.5813 \| 0.7159 \| 0.8143 \| nan \| 0.7836 \| 0.8837 \| 0.6726 \| 0.8872 \| 0.8685 \| 0.7996 \| 0.6764 \| 0.3285 \| 0.5280 \| 0.5486 \| 0.9054 \| 0.7857 \| 0.7742 \| 0.8468 \| 0.7965 \| 0.8587 \| 0.2266 \| 0.0 \| 0.7030 \| 0.8008 \| 0.5746 \| 0.7651 \| 0.7179 \| 0.6962 \| 0.5985 \| 0.2603 \| 0.3984 \| 0.4226 \| 0.7850 \| 0.6685 \| 0.6898 \| 0.7073 \| 0.7058 \| 0.7649 \| 0.2040 \| \| 0.0906 \| 2.16 \| 1080 \| 0.2052 \| 0.5553 \| 0.6875 \| 0.7897 \| nan \| 0.8313 \| 0.8956 \| 0.6559 \| 0.8275 \| 0.7173 \| 0.8116 \| 0.7495 \| 0.0605 \| 0.4663 \| 0.5563 \| 0.8829 \| 0.7184 \| 0.8017 \| 0.8431 \| 0.7774 \| 0.7582 \| 0.3341 \| 0.0 \| 0.7110 \| 0.7958 \| 0.5638 \| 0.7280 \| 0.6074 \| 0.6818 \| 0.5526 \| 0.0575 \| 0.3893 \| 0.4334 \| 0.7886 \| 0.6394 \| 0.6823 \| 0.6993 \| 0.6912 \| 0.7073 \| 0.2664 \| \| 0.119 \| 2.2 \| 1100 \| 0.1916 \| 0.5489 \| 0.6809 \| 0.7793 \| nan \| 0.8171 \| 0.8442 \| 0.7128 \| 0.8296 \| 0.7356 \| 0.8236 \| 0.6640 \| 0.1341 \| 0.5456 \| 0.3609 \| 0.8916 \| 0.8015 \| 0.8096 \| 0.7653 \| 0.7951 \| 0.8119 \| 0.2322 \| 0.0 \| 0.6733 \| 0.7745 \| 0.5803 \| 0.7182 \| 0.5975 \| 0.6999 \| 0.4996 \| 0.1222 \| 0.4036 \| 0.3207 \| 0.7971 \| 0.6684 \| 0.6861 \| 0.6890 \| 0.7078 \| 0.7365 \| 0.2050 \| \| 0.1114 \| 2.24 \| 1120 \| 0.1905 \| 0.5526 \| 0.6769 \| 0.7941 \| nan \| 0.7955 \| 0.8759 \| 0.6366 \| 0.8947 \| 0.7813 \| 0.7845 \| 0.6508 \| 0.0994 \| 0.3162 \| 0.6568 \| 0.9211 \| 0.7643 \| 0.7050 \| 0.7629 \| 0.8151 \| 0.7977 \| 0.2493 \| 0.0 \| 0.7142 \| 0.7945 \| 0.5553 \| 0.7484 \| 0.6236 \| 0.6722 \| 0.5467 \| 0.0970 \| 0.2894 \| 0.4530 \| 0.7868 \| 0.6591 \| 0.6571 \| 0.6901 \| 0.7167 \| 0.7352 \| 0.2069 \| \| 0.0757 \| 2.28 \| 1140 \| 0.2216 \| 0.5458 \| 0.6760 \| 0.7809 \| nan \| 0.7006 \| 0.8731 \| 0.6414 \| 0.7872 \| 0.6130 \| 0.8828 \| 0.8184 \| 0.1886 \| 0.5750 \| 0.4180 \| 0.9033 \| 0.8910 \| 0.6235 \| 0.7955 \| 0.7737 \| 0.7658 \| 0.2404 \| 0.0 \| 0.6697 \| 0.7931 \| 0.5672 \| 0.7090 \| 0.5666 \| 0.7073 \| 0.5092 \| 0.1636 \| 0.4196 \| 0.3611 \| 0.7910 \| 0.6391 \| 0.5870 \| 0.7086 \| 0.7117 \| 0.7096 \| 0.2109 \| \| 0.0551 \| 2.32 \| 1160 \| 0.2090 \| 0.5627 \| 0.7010 \| 0.7837 \| nan \| 0.8745 \| 0.9012 \| 0.6052 \| 0.7677 \| 0.6776 \| 0.8074 \| 0.8089 \| 0.2178 \| 0.4269 \| 0.6106 \| 0.8998 \| 0.7038 \| 0.8525 \| 0.8516 \| 0.7745 \| 0.8010 \| 0.3363 \| 0.0 \| 0.7143 \| 0.7934 \| 0.5484 \| 0.6976 \| 0.5691 \| 0.7044 \| 0.5204 \| 0.1995 \| 0.3665 \| 0.4422 \| 0.7764 \| 0.6567 \| 0.7059 \| 0.7163 \| 0.7137 \| 0.7323 \| 0.2721 \| \| 0.0936 \| 2.36 \| 1180 \| 0.2165 \| 0.5574 \| 0.6867 \| 0.7878 \| nan \| 0.7461 \| 0.8914 \| 0.5665 \| 0.8649 \| 0.6354 \| 0.7483 \| 0.7363 \| 0.1586 \| 0.5903 \| 0.3687 \| 0.9141 \| 0.8447 \| 0.7667 \| 0.8507 \| 0.7891 \| 0.7414 \| 0.4599 \| 0.0 \| 0.7022 \| 0.7991 \| 0.5241 \| 0.7179 \| 0.5839 \| 0.6630 \| 0.5364 \| 0.1484 \| 0.4303 \| 0.3375 \| 0.7782 \| 0.6839 \| 0.6968 \| 0.7225 \| 0.7219 \| 0.7012 \| 0.2853 \| \| 0.2249 \| 2.4 \| 1200 \| 0.1985 \| 0.5576 \| 0.6820 \| 0.7858 \| nan \| 0.7479 \| 0.8644 \| 0.6423 \| 0.8948 \| 0.5762 \| 0.7743 \| 0.6383 \| 0.2730 \| 0.4054 \| 0.5883 \| 0.9116 \| 0.8846 \| 0.7431 \| 0.8103 \| 0.7955 \| 0.8559 \| 0.1886 \| 0.0 \| 0.6950 \| 0.7889 \| 0.5656 \| 0.7334 \| 0.5212 \| 0.6682 \| 0.4872 \| 0.2366 \| 0.3478 \| 0.4438 \| 0.7828 \| 0.6917 \| 0.6892 \| 0.7223 \| 0.7322 \| 0.7515 \| 0.1796 \| \| 0.1143 \| 2.44 \| 1220 \| 0.2132 \| 0.5542 \| 0.6838 \| 0.7695 \| nan \| 0.7928 \| 0.8792 \| 0.6912 \| 0.7460 \| 0.6283 \| 0.8408 \| 0.7820 \| 0.2448 \| 0.4905 \| 0.4794 \| 0.8976 \| 0.7917 \| 0.8155 \| 0.8065 \| 0.8147 \| 0.7629 \| 0.1613 \| 0.0 \| 0.7049 \| 0.7851 \| 0.5858 \| 0.6884 \| 0.5133 \| 0.7170 \| 0.4776 \| 0.2212 \| 0.3929 \| 0.3954 \| 0.7848 \| 0.6841 \| 0.7051 \| 0.7079 \| 0.7274 \| 0.7252 \| 0.1587 \| \| 0.2771 \| 2.48 \| 1240 \| 0.2258 \| 0.5580 \| 0.6880 \| 0.7782 \| nan \| 0.8725 \| 0.8834 \| 0.7124 \| 0.8679 \| 0.4498 \| 0.7580 \| 0.7352 \| 0.2542 \| 0.4660 \| 0.5536 \| 0.8802 \| 0.8435 \| 0.7798 \| 0.8040 \| 0.7906 \| 0.7847 \| 0.2596 \| 0.0 \| 0.7281 \| 0.7984 \| 0.5992 \| 0.7209 \| 0.4263 \| 0.6651 \| 0.4859 \| 0.2339 \| 0.3893 \| 0.4403 \| 0.7874 \| 0.6971 \| 0.7040 \| 0.7042 \| 0.7258 \| 0.7388 \| 0.1998 \| \| 0.1988 \| 2.52 \| 1260 \| 0.2129 \| 0.5508 \| 0.6813 \| 0.7733 \| nan \| 0.8449 \| 0.8465 \| 0.6716 \| 0.7666 \| 0.7912 \| 0.7598 \| 0.7918 \| 0.1262 \| 0.4035 \| 0.5711 \| 0.9015 \| 0.7580 \| 0.8154 \| 0.7535 \| 0.8030 \| 0.7797 \| 0.1979 \| 0.0 \| 0.6909 \| 0.7793 \| 0.5838 \| 0.6927 \| 0.5898 \| 0.6771 \| 0.5041 \| 0.1199 \| 0.3521 \| 0.4372 \| 0.7785 \| 0.6728 \| 0.6947 \| 0.6964 \| 0.7248 \| 0.7309 \| 0.1903 \| \| 0.0679 \| 2.56 \| 1280 \| 0.1937 \| 0.5683 \| 0.6963 \| 0.7955 \| nan \| 0.7422 \| 0.9044 \| 0.6691 \| 0.8294 \| 0.6163 \| 0.8310 \| 0.7917 \| 0.3324 \| 0.6376 \| 0.3930 \| 0.9014 \| 0.8285 \| 0.7713 \| 0.8048 \| 0.8271 \| 0.7844 \| 0.1731 \| 0.0 \| 0.6872 \| 0.7986 \| 0.5810 \| 0.7385 \| 0.5481 \| 0.7185 \| 0.5452 \| 0.2847 \| 0.4488 \| 0.3568 \| 0.7849 \| 0.6938 \| 0.6977 \| 0.7127 \| 0.7261 \| 0.7367 \| 0.1710 \| \| 0.0815 \| 2.6 \| 1300 \| 0.1993 \| 0.5763 \| 0.7156 \| 0.7826 \| nan \| 0.7970 \| 0.8772 \| 0.7409 \| 0.7964 \| 0.5757 \| 0.8128 \| 0.8230 \| 0.3708 \| 0.4442 \| 0.6099 \| 0.8871 \| 0.7767 \| 0.7949 \| 0.8140 \| 0.8029 \| 0.7646 \| 0.4768 \| 0.0 \| 0.7016 \| 0.7953 \| 0.5979 \| 0.7150 \| 0.5049 \| 0.7142 \| 0.5156 \| 0.3068 \| 0.3891 \| 0.4637 \| 0.7945 \| 0.6853 \| 0.7042 \| 0.7075 \| 0.7254 \| 0.7177 \| 0.3345 \| \| 0.0705 \| 2.64 \| 1320 \| 0.1949 \| 0.5713 \| 0.7021 \| 0.7976 \| nan \| 0.7553 \| 0.8927 \| 0.5885 \| 0.8530 \| 0.6004 \| 0.8552 \| 0.7297 \| 0.4790 \| 0.6650 \| 0.3765 \| 0.9130 \| 0.8525 \| 0.7707 \| 0.8690 \| 0.7700 \| 0.7959 \| 0.1701 \| 0.0 \| 0.7081 \| 0.8047 \| 0.5361 \| 0.7416 \| 0.5569 \| 0.7221 \| 0.5327 \| 0.3904 \| 0.4541 \| 0.3455 \| 0.7975 \| 0.6906 \| 0.6955 \| 0.6991 \| 0.7043 \| 0.7369 \| 0.1673 \| \| 0.0785 \| 2.68 \| 1340 \| 0.2041 \| 0.5795 \| 0.7097 \| 0.7964 \| nan \| 0.8035 \| 0.9029 \| 0.6566 \| 0.8325 \| 0.5972 \| 0.8503 \| 0.7806 \| 0.4603 \| 0.4654 \| 0.6184 \| 0.8752 \| 0.8377 \| 0.6987 \| 0.7804 \| 0.8239 \| 0.8514 \| 0.2307 \| 0.0 \| 0.7323 \| 0.8038 \| 0.5765 \| 0.7379 \| 0.5414 \| 0.7289 \| 0.5339 \| 0.3700 \| 0.3978 \| 0.4754 \| 0.8028 \| 0.6705 \| 0.6539 \| 0.7093 \| 0.7269 \| 0.7478 \| 0.2222 \| \| 0.1076 \| 2.72 \| 1360 \| 0.1929 \| 0.5838 \| 0.7190 \| 0.8072 \| nan \| 0.6902 \| 0.8919 \| 0.6362 \| 0.8790 \| 0.7117 \| 0.8261 \| 0.6784 \| 0.4480 \| 0.6431 \| 0.4944 \| 0.9088 \| 0.8265 \| 0.7479 \| 0.8457 \| 0.8242 \| 0.8532 \| 0.3170 \| 0.0 \| 0.6566 \| 0.7966 \| 0.5662 \| 0.7594 \| 0.6488 \| 0.6928 \| 0.5625 \| 0.3419 \| 0.4482 \| 0.4200 \| 0.7937 \| 0.6759 \| 0.6761 \| 0.7319 \| 0.7342 \| 0.7486 \| 0.2551 \| \| 0.1436 \| 2.76 \| 1380 \| 0.2300 \| 0.5385 \| 0.6686 \| 0.7745 \| nan \| 0.7836 \| 0.9007 \| 0.6214 \| 0.7997 \| 0.6169 \| 0.8014 \| 0.8452 \| 0.1419 \| 0.3989 \| 0.6709 \| 0.8971 \| 0.4475 \| 0.9195 \| 0.7890 \| 0.8211 \| 0.6318 \| 0.2804 \| 0.0 \| 0.7015 \| 0.8036 \| 0.5603 \| 0.7213 \| 0.5128 \| 0.7149 \| 0.5244 \| 0.1311 \| 0.3619 \| 0.4863 \| 0.7927 \| 0.4435 \| 0.6090 \| 0.7179 \| 0.7295 \| 0.6180 \| 0.2642 \| \| 0.1067 \| 2.8 \| 1400 \| 0.1957 \| 0.5652 \| 0.7049 \| 0.7881 \| nan \| 0.8524 \| 0.8639 \| 0.7524 \| 0.8659 \| 0.6098 \| 0.8053 \| 0.6721 \| 0.2178 \| 0.4882 \| 0.5218 \| 0.8987 \| 0.8355 \| 0.7302 \| 0.8198 \| 0.8127 \| 0.8579 \| 0.3791 \| 0.0 \| 0.6970 \| 0.7926 \| 0.6030 \| 0.7407 \| 0.5254 \| 0.7052 \| 0.4947 \| 0.1865 \| 0.4082 \| 0.4450 \| 0.7945 \| 0.6695 \| 0.6767 \| 0.7171 \| 0.7172 \| 0.7042 \| 0.2960 \| \| 0.0719 \| 2.84 \| 1420 \| 0.2357 \| 0.5517 \| 0.6760 \| 0.7766 \| nan \| 0.8245 \| 0.9166 \| 0.6213 \| 0.8075 \| 0.5353 \| 0.7805 \| 0.8185 \| 0.1425 \| 0.4855 \| 0.5586 \| 0.8749 \| 0.7612 \| 0.7448 \| 0.8053 \| 0.8224 \| 0.7441 \| 0.2490 \| 0.0 \| 0.7198 \| 0.8016 \| 0.5629 \| 0.7240 \| 0.4794 \| 0.6637 \| 0.4917 \| 0.1346 \| 0.4047 \| 0.4592 \| 0.7837 \| 0.6594 \| 0.6719 \| 0.7211 \| 0.7227 \| 0.6954 \| 0.2352 \| \| 0.1557 \| 2.88 \| 1440 \| 0.2600 \| 0.5491 \| 0.6861 \| 0.7593 \| nan \| 0.8334 \| 0.8869 \| 0.6552 \| 0.7548 \| 0.4025 \| 0.7557 \| 0.8423 \| 0.1417 \| 0.5662 \| 0.5345 \| 0.9121 \| 0.8045 \| 0.7816 \| 0.8586 \| 0.7752 \| 0.7765 \| 0.3824 \| 0.0 \| 0.7396 \| 0.7964 \| 0.5758 \| 0.6832 \| 0.3855 \| 0.6560 \| 0.4511 \| 0.1362 \| 0.4422 \| 0.4574 \| 0.7852 \| 0.6753 \| 0.6876 \| 0.7167 \| 0.7123 \| 0.7135 \| 0.2702 \| \| 0.0667 \| 2.92 \| 1460 \| 0.2077 \| 0.5685 \| 0.7011 \| 0.7889 \| nan \| 0.8600 \| 0.9046 \| 0.7236 \| 0.8549 \| 0.4918 \| 0.7826 \| 0.7566 \| 0.2867 \| 0.5245 \| 0.5654 \| 0.8922 \| 0.7801 \| 0.8728 \| 0.7829 \| 0.8062 \| 0.7907 \| 0.2428 \| 0.0 \| 0.7300 \| 0.7920 \| 0.5978 \| 0.7410 \| 0.4591 \| 0.6792 \| 0.5235 \| 0.2351 \| 0.4322 \| 0.4714 \| 0.7826 \| 0.6802 \| 0.7142 \| 0.7103 \| 0.7247 \| 0.7266 \| 0.2322 \| \| 0.0599 \| 2.96 \| 1480 \| 0.1890 \| 0.5706 \| 0.7078 \| 0.7904 \| nan \| 0.8265 \| 0.8570 \| 0.7718 \| 0.8348 \| 0.7044 \| 0.8015 \| 0.7367 \| 0.2153 \| 0.3547 \| 0.6748 \| 0.8968 \| 0.8277 \| 0.7836 \| 0.8193 \| 0.7976 \| 0.7714 \| 0.3590 \| 0.0 \| 0.7146 \| 0.7867 \| 0.6005 \| 0.7420 \| 0.5740 \| 0.7028 \| 0.5212 \| 0.1847 \| 0.3318 \| 0.4857 \| 0.7959 \| 0.6741 \| 0.6980 \| 0.7252 \| 0.7153 \| 0.7289 \| 0.2884 \| \| 0.1253 \| 3.0 \| 1500 \| 0.2008 \| 0.5730 \| 0.7009 \| 0.7958 \| nan \| 0.7983 \| 0.9092 \| 0.6277 \| 0.8403 \| 0.5076 \| 0.8389 \| 0.8069 \| 0.2118 \| 0.5775 \| 0.5092 \| 0.8948 \| 0.8165 \| 0.7955 \| 0.8217 \| 0.7863 \| 0.7737 \| 0.3995 \| 0.0 \| 0.7192 \| 0.8033 \| 0.5639 \| 0.7514 \| 0.4760 \| 0.7203 \| 0.5251 \| 0.2002 \| 0.4440 \| 0.4414 \| 0.8016 \| 0.6836 \| 0.7215 \| 0.7145 \| 0.7223 \| 0.7263 \| 0.3001 \| \| 0.0902 \| 3.04 \| 1520 \| 0.2002 \| 0.5771 \| 0.7138 \| 0.7951 \| nan \| 0.8105 \| 0.8611 \| 0.6681 \| 0.8355 \| 0.6470 \| 0.8293 \| 0.7263 \| 0.2985 \| 0.5198 \| 0.6254 \| 0.9141 \| 0.8738 \| 0.7614 \| 0.8100 \| 0.8081 \| 0.8212 \| 0.3252 \| 0.0 \| 0.6917 \| 0.7907 \| 0.5827 \| 0.7466 \| 0.5515 \| 0.7143 \| 0.5243 \| 0.2417 \| 0.4221 \| 0.4795 \| 0.7980 \| 0.6762 \| 0.6961 \| 0.7176 \| 0.7377 \| 0.7402 \| 0.2770 \| \| 0.0793 \| 3.08 \| 1540 \| 0.2097 \| 0.5740 \| 0.6960 \| 0.7942 \| nan \| 0.7866 \| 0.8973 \| 0.5933 \| 0.8581 \| 0.5793 \| 0.8686 \| 0.7112 \| 0.2830 \| 0.5339 \| 0.5274 \| 0.9094 \| 0.7455 \| 0.8031 \| 0.8050 \| 0.7807 \| 0.7956 \| 0.3542 \| 0.0 \| 0.7338 \| 0.8013 \| 0.5453 \| 0.7551 \| 0.5228 \| 0.7043 \| 0.5216 \| 0.2388 \| 0.4344 \| 0.4474 \| 0.8099 \| 0.6517 \| 0.7028 \| 0.7118 \| 0.7277 \| 0.7302 \| 0.2923 \| \| 0.1151 \| 3.12 \| 1560 \| 0.2098 \| 0.5839 \| 0.7069 \| 0.8009 \| nan \| 0.7985 \| 0.8944 \| 0.6654 \| 0.8527 \| 0.5810 \| 0.8246 \| 0.7654 \| 0.2375 \| 0.5160 \| 0.5706 \| 0.9016 \| 0.7949 \| 0.8408 \| 0.8249 \| 0.8197 \| 0.8106 \| 0.3179 \| 0.0 \| 0.7424 \| 0.8056 \| 0.5829 \| 0.7540 \| 0.5176 \| 0.7170 \| 0.5205 \| 0.2154 \| 0.4395 \| 0.4741 \| 0.8142 \| 0.6893 \| 0.7262 \| 0.7221 \| 0.7486 \| 0.7490 \| 0.2920 \| \| 0.0521 \| 3.16 \| 1580 \| 0.2137 \| 0.5824 \| 0.7149 \| 0.7968 \| nan \| 0.8334 \| 0.8909 \| 0.7298 \| 0.8236 \| 0.5910 \| 0.8047 \| 0.7947 \| 0.2071 \| 0.6304 \| 0.5506 \| 0.8957 \| 0.7867 \| 0.8203 \| 0.8369 \| 0.8054 \| 0.8381 \| 0.3143 \| 0.0 \| 0.7455 \| 0.8052 \| 0.6026 \| 0.7391 \| 0.5304 \| 0.7111 \| 0.5198 \| 0.1846 \| 0.4810 \| 0.4572 \| 0.8158 \| 0.6975 \| 0.7236 \| 0.7188 \| 0.7349 \| 0.7416 \| 0.2748 \| \| 0.0974 \| 3.2 \| 1600 \| 0.2145 \| 0.5750 \| 0.7010 \| 0.7944 \| nan \| 0.8038 \| 0.8812 \| 0.5595 \| 0.8303 \| 0.5730 \| 0.8460 \| 0.7588 \| 0.2021 \| 0.5587 \| 0.5178 \| 0.9135 \| 0.8271 \| 0.8278 \| 0.8214 \| 0.8236 \| 0.7844 \| 0.3874 \| 0.0 \| 0.7228 \| 0.7942 \| 0.5250 \| 0.7341 \| 0.5138 \| 0.7236 \| 0.5115 \| 0.1847 \| 0.4478 \| 0.4420 \| 0.8113 \| 0.6973 \| 0.7192 \| 0.7389 \| 0.7447 \| 0.7376 \| 0.3004 \| \| 0.0535 \| 3.24 \| 1620 \| 0.1986 \| 0.5803 \| 0.7191 \| 0.8025 \| nan \| 0.8644 \| 0.8970 \| 0.7361 \| 0.8454 \| 0.7897 \| 0.7807 \| 0.7109 \| 0.1309 \| 0.5688 \| 0.5623 \| 0.8898 \| 0.8323 \| 0.7588 \| 0.8089 \| 0.8493 \| 0.8091 \| 0.3906 \| 0.0 \| 0.7053 \| 0.7997 \| 0.6082 \| 0.7580 \| 0.5811 \| 0.6955 \| 0.5477 \| 0.1201 \| 0.4516 \| 0.4580 \| 0.8204 \| 0.6752 \| 0.6925 \| 0.7320 \| 0.7468 \| 0.7445 \| 0.3084 \| \| 0.072 \| 3.28 \| 1640 \| 0.1961 \| 0.5782 \| 0.7140 \| 0.8020 \| nan \| 0.8503 \| 0.8473 \| 0.7632 \| 0.8730 \| 0.6862 \| 0.8199 \| 0.7197 \| 0.1571 \| 0.5890 \| 0.5258 \| 0.8817 \| 0.8225 \| 0.7920 \| 0.8056 \| 0.8447 \| 0.7937 \| 0.3655 \| 0.0 \| 0.7110 \| 0.7839 \| 0.6097 \| 0.7675 \| 0.5405 \| 0.7156 \| 0.5373 \| 0.1418 \| 0.4611 \| 0.4439 \| 0.8183 \| 0.6797 \| 0.7040 \| 0.7167 \| 0.7379 \| 0.7420 \| 0.2963 \| \| 0.0929 \| 3.32 \| 1660 \| 0.2014 \| 0.5753 \| 0.7005 \| 0.8059 \| nan \| 0.8069 \| 0.9063 \| 0.6184 \| 0.8649 \| 0.7200 \| 0.8257 \| 0.7258 \| 0.1376 \| 0.4722 \| 0.6033 \| 0.9044 \| 0.8147 \| 0.7698 \| 0.8141 \| 0.8313 \| 0.7646 \| 0.3288 \| 0.0 \| 0.7399 \| 0.8079 \| 0.5636 \| 0.7677 \| 0.5656 \| 0.7075 \| 0.5457 \| 0.1288 \| 0.4001 \| 0.4691 \| 0.8136 \| 0.6855 \| 0.6978 \| 0.7241 \| 0.7291 \| 0.7303 \| 0.2799 \| \| 0.0883 \| 3.36 \| 1680 \| 0.2020 \| 0.5681 \| 0.6953 \| 0.8032 \| nan \| 0.6691 \| 0.8898 \| 0.7268 \| 0.8802 \| 0.6571 \| 0.8365 \| 0.7110 \| 0.2121 \| 0.4769 \| 0.6395 \| 0.9106 \| 0.7869 \| 0.8422 \| 0.8619 \| 0.7270 \| 0.7651 \| 0.2267 \| 0.0 \| 0.6504 \| 0.7967 \| 0.6055 \| 0.7711 \| 0.5475 \| 0.7188 \| 0.5390 \| 0.1922 \| 0.4080 \| 0.4835 \| 0.8028 \| 0.6882 \| 0.7258 \| 0.6887 \| 0.6628 \| 0.7315 \| 0.2127 \| \| 0.0795 \| 3.4 \| 1700 \| 0.2029 \| 0.5795 \| 0.7073 \| 0.8019 \| nan \| 0.7438 \| 0.8881 \| 0.7527 \| 0.8645 \| 0.6712 \| 0.8071 \| 0.7493 \| 0.2007 \| 0.5927 \| 0.5268 \| 0.8968 \| 0.8016 \| 0.8051 \| 0.8163 \| 0.7442 \| 0.8168 \| 0.3461 \| 0.0 \| 0.7033 \| 0.8013 \| 0.6111 \| 0.7618 \| 0.5426 \| 0.7030 \| 0.5380 \| 0.1790 \| 0.4673 \| 0.4503 \| 0.8021 \| 0.7002 \| 0.7318 \| 0.6986 \| 0.6781 \| 0.7602 \| 0.3015 \| \| 0.0773 \| 3.44 \| 1720 \| 0.1989 \| 0.5812 \| 0.7117 \| 0.8050 \| nan \| 0.8647 \| 0.8811 \| 0.6571 \| 0.8473 \| 0.7655 \| 0.8081 \| 0.7508 \| 0.2 \| 0.5806 \| 0.5543 \| 0.8992 \| 0.8711 \| 0.7839 \| 0.8109 \| 0.7591 \| 0.7706 \| 0.2941 \| 0.0 \| 0.7410 \| 0.8039 \| 0.5819 \| 0.7591 \| 0.5928 \| 0.7198 \| 0.5427 \| 0.1720 \| 0.4644 \| 0.4652 \| 0.8028 \| 0.7036 \| 0.7189 \| 0.7045 \| 0.6864 \| 0.7301 \| 0.2720 \| \| 0.0627 \| 3.48 \| 1740 \| 0.1922 \| 0.5946 \| 0.7326 \| 0.8082 \| nan \| 0.8001 \| 0.9026 \| 0.7357 \| 0.8662 \| 0.7616 \| 0.7701 \| 0.7071 \| 0.3307 \| 0.5833 \| 0.5425 \| 0.8858 \| 0.8232 \| 0.8421 \| 0.8466 \| 0.7911 \| 0.8314 \| 0.4343 \| 0.0 \| 0.6934 \| 0.8002 \| 0.6045 \| 0.7542 \| 0.6357 \| 0.6823 \| 0.5510 \| 0.2671 \| 0.4622 \| 0.4621 \| 0.8064 \| 0.7109 \| 0.7429 \| 0.7272 \| 0.7122 \| 0.7564 \| 0.3336 \| \| 0.0901 \| 3.52 \| 1760 \| 0.1963 \| 0.5834 \| 0.7287 \| 0.8048 \| nan \| 0.7844 \| 0.8874 \| 0.7435 \| 0.8876 \| 0.7874 \| 0.7760 \| 0.6757 \| 0.5528 \| 0.4646 \| 0.6739 \| 0.9074 \| 0.8113 \| 0.7329 \| 0.8419 \| 0.7806 \| 0.8012 \| 0.2792 \| 0.0 \| 0.7058 \| 0.8024 \| 0.6052 \| 0.7672 \| 0.6304 \| 0.6697 \| 0.5552 \| 0.2771 \| 0.4063 \| 0.4888 \| 0.8120 \| 0.6742 \| 0.6810 \| 0.7174 \| 0.7099 \| 0.7422 \| 0.2573 \| \| 0.0945 \| 3.56 \| 1780 \| 0.2033 \| 0.5814 \| 0.7128 \| 0.8026 \| nan \| 0.7737 \| 0.8804 \| 0.7259 \| 0.8444 \| 0.7017 \| 0.7875 \| 0.7936 \| 0.2431 \| 0.5968 \| 0.5278 \| 0.8723 \| 0.8753 \| 0.7400 \| 0.8484 \| 0.7847 \| 0.8230 \| 0.2984 \| 0.0 \| 0.6802 \| 0.7959 \| 0.6064 \| 0.7510 \| 0.6027 \| 0.6942 \| 0.5622 \| 0.2123 \| 0.4806 \| 0.4601 \| 0.8139 \| 0.6697 \| 0.6731 \| 0.7165 \| 0.7166 \| 0.7550 \| 0.2749 \| \| 0.0621 \| 3.6 \| 1800 \| 0.2150 \| 0.5739 \| 0.7002 \| 0.7936 \| nan \| 0.7455 \| 0.8958 \| 0.6267 \| 0.8290 \| 0.4667 \| 0.8545 \| 0.7940 \| 0.2158 \| 0.5564 \| 0.5418 \| 0.9127 \| 0.7723 \| 0.8698 \| 0.8493 \| 0.8622 \| 0.7568 \| 0.3538 \| 0.0 \| 0.6987 \| 0.8035 \| 0.5693 \| 0.7468 \| 0.4405 \| 0.7120 \| 0.5078 \| 0.1976 \| 0.4466 \| 0.4550 \| 0.8211 \| 0.6854 \| 0.7335 \| 0.7358 \| 0.7513 \| 0.7141 \| 0.3113 \| \| 0.2598 \| 3.64 \| 1820 \| 0.2038 \| 0.5845 \| 0.7150 \| 0.7998 \| nan \| 0.7778 \| 0.8901 \| 0.7231 \| 0.8247 \| 0.6904 \| 0.7825 \| 0.8005 \| 0.2124 \| 0.6003 \| 0.5248 \| 0.8983 \| 0.8224 \| 0.8289 \| 0.8407 \| 0.8113 \| 0.7780 \| 0.3483 \| 0.0 \| 0.6945 \| 0.8005 \| 0.6083 \| 0.7402 \| 0.5578 \| 0.7011 \| 0.5394 \| 0.1800 \| 0.4688 \| 0.4444 \| 0.8234 \| 0.7047 \| 0.7372 \| 0.7436 \| 0.7471 \| 0.7255 \| 0.3038 \| \| 0.0579 \| 3.68 \| 1840 \| 0.2159 \| 0.5795 \| 0.7123 \| 0.7900 \| nan \| 0.8404 \| 0.8889 \| 0.6393 \| 0.8015 \| 0.7077 \| 0.7805 \| 0.7668 \| 0.2314 \| 0.5816 \| 0.5545 \| 0.9067 \| 0.7842 \| 0.8706 \| 0.8527 \| 0.7892 \| 0.7389 \| 0.3750 \| 0.0 \| 0.7195 \| 0.8002 \| 0.5789 \| 0.7270 \| 0.5200 \| 0.6983 \| 0.5056 \| 0.1995 \| 0.4739 \| 0.4656 \| 0.8146 \| 0.6987 \| 0.7419 \| 0.7428 \| 0.7333 \| 0.7015 \| 0.3093 \| \| 0.178 \| 3.72 \| 1860 \| 0.2051 \| 0.5807 \| 0.7160 \| 0.7960 \| nan \| 0.8871 \| 0.8580 \| 0.7123 \| 0.8313 \| 0.6923 \| 0.8340 \| 0.7196 \| 0.2726 \| 0.5479 \| 0.5092 \| 0.9005 \| 0.7924 \| 0.8467 \| 0.8338 \| 0.8060 \| 0.8261 \| 0.3020 \| 0.0 \| 0.7153 \| 0.7931 \| 0.6083 \| 0.7428 \| 0.5218 \| 0.6966 \| 0.5077 \| 0.2297 \| 0.4433 \| 0.4389 \| 0.8253 \| 0.7020 \| 0.7463 \| 0.7383 \| 0.7449 \| 0.7425 \| 0.2560 \| \| 0.0652 \| 3.76 \| 1880 \| 0.1907 \| 0.5881 \| 0.7163 \| 0.8079 \| nan \| 0.7918 \| 0.9170 \| 0.6292 \| 0.8550 \| 0.7548 \| 0.8023 \| 0.7000 \| 0.3581 \| 0.4405 \| 0.6354 \| 0.9116 \| 0.7540 \| 0.8829 \| 0.8521 \| 0.8078 \| 0.8326 \| 0.2512 \| 0.0 \| 0.7321 \| 0.8032 \| 0.5730 \| 0.7583 \| 0.6172 \| 0.6924 \| 0.5500 \| 0.2969 \| 0.3963 \| 0.4849 \| 0.8110 \| 0.6878 \| 0.7372 \| 0.7356 \| 0.7367 \| 0.7462 \| 0.2273 \| \| 0.0909 \| 3.8 \| 1900 \| 0.2130 \| 0.5773 \| 0.7080 \| 0.7958 \| nan \| 0.8645 \| 0.8670 \| 0.6606 \| 0.8539 \| 0.6938 \| 0.7894 \| 0.7331 \| 0.3307 \| 0.5893 \| 0.5149 \| 0.9035 \| 0.7804 \| 0.8136 \| 0.8174 \| 0.8344 \| 0.7693 \| 0.2204 \| 0.0 \| 0.7076 \| 0.7923 \| 0.5894 \| 0.7520 \| 0.5299 \| 0.6777 \| 0.5221 \| 0.2841 \| 0.4775 \| 0.4505 \| 0.8169 \| 0.6857 \| 0.7220 \| 0.7303 \| 0.7335 \| 0.7171 \| 0.2021 \| \| 0.0961 \| 3.84 \| 1920 \| 0.2318 \| 0.5699 \| 0.7032 \| 0.7850 \| nan \| 0.8585 \| 0.8814 \| 0.7262 \| 0.8201 \| 0.4649 \| 0.7840 \| 0.8384 \| 0.2209 \| 0.5943 \| 0.5827 \| 0.9001 \| 0.8247 \| 0.8078 \| 0.8476 \| 0.7852 \| 0.7388 \| 0.2795 \| 0.0 \| 0.7093 \| 0.8043 \| 0.6152 \| 0.7333 \| 0.4218 \| 0.6994 \| 0.5020 \| 0.2123 \| 0.4828 \| 0.4840 \| 0.8139 \| 0.6860 \| 0.7119 \| 0.7325 \| 0.7223 \| 0.6961 \| 0.2304 \| \| 0.0786 \| 3.88 \| 1940 \| 0.2128 \| 0.5738 \| 0.7009 \| 0.7950 \| nan \| 0.8275 \| 0.8709 \| 0.7395 \| 0.8610 \| 0.4952 \| 0.8088 \| 0.7840 \| 0.1940 \| 0.5403 \| 0.5177 \| 0.9014 \| 0.8490 \| 0.8128 \| 0.8428 \| 0.7945 \| 0.8037 \| 0.2714 \| 0.0 \| 0.7236 \| 0.8014 \| 0.6126 \| 0.7543 \| 0.4507 \| 0.7059 \| 0.5309 \| 0.1864 \| 0.4458 \| 0.4492 \| 0.8150 \| 0.6890 \| 0.7151 \| 0.7352 \| 0.7267 \| 0.7343 \| 0.2527 \| \| 0.0636 \| 3.92 \| 1960 \| 0.2003 \| 0.5833 \| 0.7262 \| 0.7998 \| nan \| 0.8600 \| 0.9029 \| 0.7461 \| 0.8230 \| 0.7772 \| 0.7915 \| 0.7836 \| 0.2790 \| 0.6805 \| 0.5158 \| 0.8845 \| 0.9083 \| 0.5324 \| 0.8158 \| 0.8110 \| 0.8367 \| 0.3969 \| 0.0 \| 0.7663 \| 0.8098 \| 0.6200 \| 0.7428 \| 0.6096 \| 0.7033 \| 0.5636 \| 0.2286 \| 0.5014 \| 0.4624 \| 0.8116 \| 0.6112 \| 0.5186 \| 0.7507 \| 0.7531 \| 0.7431 \| 0.3027 \| \| 0.0589 \| 3.96 \| 1980 \| 0.1973 \| 0.5911 \| 0.7251 \| 0.8140 \| nan \| 0.8461 \| 0.8977 \| 0.7605 \| 0.8543 \| 0.7629 \| 0.8871 \| 0.7317 \| 0.2834 \| 0.4014 \| 0.6770 \| 0.8954 \| 0.6367 \| 0.8749 \| 0.8640 \| 0.8419 \| 0.7840 \| 0.3271 \| 0.0 \| 0.7618 \| 0.8090 \| 0.6166 \| 0.7608 \| 0.6512 \| 0.7371 \| 0.5648 \| 0.2335 \| 0.3683 \| 0.4960 \| 0.8055 \| 0.6120 \| 0.6862 \| 0.7559 \| 0.7535 \| 0.7329 \| 0.2949 \| \| 0.0517 \| 4.0 \| 2000 \| 0.1941 \| 0.5891 \| 0.7204 \| 0.8118 \| nan \| 0.8225 \| 0.8958 \| 0.7789 \| 0.8578 \| 0.7435 \| 0.8709 \| 0.7380 \| 0.2309 \| 0.4791 \| 0.6296 \| 0.9095 \| 0.6344 \| 0.8537 \| 0.8293 \| 0.8547 \| 0.7902 \| 0.3280 \| 0.0 \| 0.7503 \| 0.8103 \| 0.6221 \| 0.7556 \| 0.6648 \| 0.7287 \| 0.5669 \| 0.1981 \| 0.4191 \| 0.4888 \| 0.8081 \| 0.6006 \| 0.6729 \| 0.7420 \| 0.7530 \| 0.7366 \| 0.2853 \| \| 0.0422 \| 4.04 \| 2020 \| 0.2032 \| 0.5913 \| 0.7192 \| 0.8093 \| nan \| 0.8231 \| 0.9095 \| 0.7218 \| 0.8538 \| 0.6817 \| 0.8363 \| 0.7633 \| 0.2917 \| 0.6304 \| 0.5313 \| 0.8938 \| 0.8721 \| 0.7443 \| 0.8296 \| 0.8005 \| 0.7821 \| 0.2613 \| 0.0 \| 0.7468 \| 0.8084 \| 0.6122 \| 0.7520 \| 0.6243 \| 0.7129 \| 0.5594 \| 0.2464 \| 0.4930 \| 0.4699 \| 0.8102 \| 0.6812 \| 0.6865 \| 0.7350 \| 0.7349 \| 0.7280 \| 0.2428 \| \| 0.0861 \| 4.08 \| 2040 \| 0.1949 \| 0.6051 \| 0.7314 \| 0.8172 \| nan \| 0.8581 \| 0.8851 \| 0.6496 \| 0.8789 \| 0.7340 \| 0.8261 \| 0.7216 \| 0.3758 \| 0.5946 \| 0.6085 \| 0.9150 \| 0.7740 \| 0.8651 \| 0.8405 \| 0.8456 \| 0.7810 \| 0.2805 \| 0.0 \| 0.7568 \| 0.8076 \| 0.5824 \| 0.7632 \| 0.6593 \| 0.7058 \| 0.5758 \| 0.3100 \| 0.4879 \| 0.5035 \| 0.8131 \| 0.6936 \| 0.7382 \| 0.7537 \| 0.7513 \| 0.7339 \| 0.2559 \| \| 0.1004 \| 4.12 \| 2060 \| 0.1849 \| 0.6016 \| 0.7348 \| 0.8130 \| nan \| 0.8772 \| 0.8872 \| 0.7109 \| 0.8664 \| 0.7335 \| 0.8124 \| 0.7212 \| 0.3150 \| 0.5658 \| 0.5974 \| 0.8978 \| 0.8494 \| 0.8242 \| 0.8454 \| 0.7727 \| 0.8369 \| 0.3779 \| 0.0 \| 0.7377 \| 0.8023 \| 0.6101 \| 0.7663 \| 0.6085 \| 0.7008 \| 0.5652 \| 0.2773 \| 0.4760 \| 0.5018 \| 0.8146 \| 0.7021 \| 0.7407 \| 0.7424 \| 0.7256 \| 0.7571 \| 0.2995 \| \| 0.0719 \| 4.16 \| 2080 \| 0.1912 \| 0.6032 \| 0.7298 \| 0.8135 \| nan \| 0.8346 \| 0.8875 \| 0.7111 \| 0.8822 \| 0.6972 \| 0.8197 \| 0.7397 \| 0.3803 \| 0.4940 \| 0.6159 \| 0.8967 \| 0.7861 \| 0.8263 \| 0.8352 \| 0.8367 \| 0.7981 \| 0.3657 \| 0.0 \| 0.7349 \| 0.8040 \| 0.6079 \| 0.7695 \| 0.6152 \| 0.7048 \| 0.5781 \| 0.3232 \| 0.4266 \| 0.4933 \| 0.8114 \| 0.7068 \| 0.7369 \| 0.7475 \| 0.7395 \| 0.7458 \| 0.3123 \| \| 0.224 \| 4.2 \| 2100 \| 0.1948 \| 0.6021 \| 0.7505 \| 0.8035 \| nan \| 0.8737 \| 0.8728 \| 0.7274 \| 0.8104 \| 0.6621 \| 0.8323 \| 0.7839 \| 0.3911 \| 0.5970 \| 0.5556 \| 0.9130 \| 0.8385 \| 0.8260 \| 0.8199 \| 0.8032 \| 0.8605 \| 0.5910 \| 0.0 \| 0.7477 \| 0.7968 \| 0.6154 \| 0.7411 \| 0.5771 \| 0.7240 \| 0.5406 \| 0.3181 \| 0.4838 \| 0.4865 \| 0.8125 \| 0.7163 \| 0.7370 \| 0.7422 \| 0.7408 \| 0.7673 \| 0.2905 \| \| 0.0466 \| 4.24 \| 2120 \| 0.2434 \| 0.5946 \| 0.7268 \| 0.7963 \| nan \| 0.8764 \| 0.9095 \| 0.6823 \| 0.8218 \| 0.4696 \| 0.7933 \| 0.8337 \| 0.3116 \| 0.5430 \| 0.6394 \| 0.8914 \| 0.8331 \| 0.8339 \| 0.8381 \| 0.8504 \| 0.7937 \| 0.4342 \| 0.0 \| 0.7687 \| 0.8141 \| 0.6015 \| 0.7469 \| 0.4352 \| 0.7082 \| 0.4933 \| 0.2877 \| 0.4605 \| 0.5188 \| 0.8125 \| 0.7242 \| 0.7473 \| 0.7572 \| 0.7617 \| 0.7461 \| 0.3184 \| \| 0.0539 \| 4.28 \| 2140 \| 0.1896 \| 0.6050 \| 0.7340 \| 0.8169 \| nan \| 0.8388 \| 0.9077 \| 0.6648 \| 0.8533 \| 0.6652 \| 0.8309 \| 0.7608 \| 0.3731 \| 0.5600 \| 0.6177 \| 0.9098 \| 0.8182 \| 0.8691 \| 0.8359 \| 0.8473 \| 0.8432 \| 0.2830 \| 0.0 \| 0.7588 \| 0.8153 \| 0.5957 \| 0.7674 \| 0.5456 \| 0.7155 \| 0.5559 \| 0.2860 \| 0.4728 \| 0.5089 \| 0.8094 \| 0.7313 \| 0.7640 \| 0.7594 \| 0.7571 \| 0.7767 \| 0.2705 \| \| 0.059 \| 4.32 \| 2160 \| 0.1847 \| 0.6109 \| 0.7484 \| 0.8196 \| nan \| 0.8403 \| 0.8857 \| 0.7280 \| 0.8728 \| 0.8208 \| 0.8416 \| 0.6962 \| 0.5104 \| 0.5866 \| 0.5678 \| 0.9112 \| 0.8835 \| 0.7349 \| 0.8507 \| 0.8213 \| 0.8171 \| 0.3544 \| 0.0 \| 0.7478 \| 0.8066 \| 0.6136 \| 0.7724 \| 0.6560 \| 0.7165 \| 0.5754 \| 0.3698 \| 0.4771 \| 0.4890 \| 0.8076 \| 0.6971 \| 0.6900 \| 0.7560 \| 0.7475 \| 0.7570 \| 0.3171 \| \| 0.1197 \| 4.36 \| 2180 \| 0.1876 \| 0.6047 \| 0.7393 \| 0.8149 \| nan \| 0.8044 \| 0.8933 \| 0.7389 \| 0.8448 \| 0.6778 \| 0.8412 \| 0.7902 \| 0.3160 \| 0.5677 \| 0.6155 \| 0.9092 \| 0.8380 \| 0.8052 \| 0.8398 \| 0.8433 \| 0.8004 \| 0.4429 \| 0.0 \| 0.7342 \| 0.8048 \| 0.6188 \| 0.7600 \| 0.6143 \| 0.7251 \| 0.5925 \| 0.2804 \| 0.4754 \| 0.5111 \| 0.7982 \| 0.7126 \| 0.7249 \| 0.7478 \| 0.7272 \| 0.7579 \| 0.2998 \| \| 0.0465 \| 4.4 \| 2200 \| 0.1911 \| 0.6054 \| 0.7314 \| 0.8183 \| nan \| 0.8259 \| 0.8737 \| 0.7163 \| 0.8957 \| 0.6328 \| 0.8381 \| 0.7482 \| 0.3173 \| 0.6658 \| 0.5310 \| 0.9127 \| 0.8460 \| 0.8064 \| 0.8194 \| 0.8513 \| 0.7996 \| 0.3532 \| 0.0 \| 0.7469 \| 0.8076 \| 0.6081 \| 0.7664 \| 0.5869 \| 0.7234 \| 0.5681 \| 0.2813 \| 0.5096 \| 0.4736 \| 0.8098 \| 0.7253 \| 0.7382 \| 0.7474 \| 0.7536 \| 0.7591 \| 0.2918 \| \| 0.0487 \| 4.44 \| 2220 \| 0.1965 \| 0.6013 \| 0.7315 \| 0.8136 \| nan \| 0.7788 \| 0.8986 \| 0.7435 \| 0.8289 \| 0.6291 \| 0.8804 \| 0.8127 \| 0.2571 \| 0.6768 \| 0.5054 \| 0.9008 \| 0.8404 \| 0.7823 \| 0.8593 \| 0.8272 \| 0.8242 \| 0.3893 \| 0.0 \| 0.7376 \| 0.8085 \| 0.6166 \| 0.7574 \| 0.5702 \| 0.7369 \| 0.5632 \| 0.2343 \| 0.5060 \| 0.4531 \| 0.8089 \| 0.7150 \| 0.7207 \| 0.7511 \| 0.7581 \| 0.7700 \| 0.3161 \| \| 0.1179 \| 4.48 \| 2240 \| 0.1887 \| 0.5990 \| 0.7318 \| 0.8132 \| nan \| 0.8549 \| 0.8896 \| 0.7258 \| 0.8461 \| 0.7417 \| 0.8238 \| 0.7644 \| 0.2442 \| 0.5182 \| 0.6746 \| 0.8932 \| 0.7816 \| 0.8325 \| 0.8215 \| 0.8220 \| 0.8357 \| 0.3714 \| 0.0 \| 0.7284 \| 0.8028 \| 0.6163 \| 0.7562 \| 0.6044 \| 0.7211 \| 0.5610 \| 0.2192 \| 0.4385 \| 0.5071 \| 0.8120 \| 0.7167 \| 0.7340 \| 0.7441 \| 0.7553 \| 0.7684 \| 0.2975 \| \| 0.0534 \| 4.52 \| 2260 \| 0.2019 \| 0.5961 \| 0.7289 \| 0.7971 \| nan \| 0.7750 \| 0.8870 \| 0.6503 \| 0.8138 \| 0.6035 \| 0.7839 \| 0.8024 \| 0.3672 \| 0.6275 \| 0.5181 \| 0.9029 \| 0.8397 \| 0.8378 \| 0.8522 \| 0.8529 \| 0.8193 \| 0.4577 \| 0.0 \| 0.7304 \| 0.8049 \| 0.5836 \| 0.7364 \| 0.4990 \| 0.6986 \| 0.5223 \| 0.3085 \| 0.4876 \| 0.4604 \| 0.8131 \| 0.7311 \| 0.7468 \| 0.7495 \| 0.7642 \| 0.7595 \| 0.3334 \| \| 0.0893 \| 4.56 \| 2280 \| 0.2143 \| 0.5987 \| 0.7251 \| 0.7975 \| nan \| 0.7382 \| 0.8807 \| 0.7100 \| 0.8223 \| 0.5582 \| 0.8206 \| 0.8070 \| 0.4127 \| 0.5589 \| 0.5561 \| 0.9109 \| 0.8181 \| 0.8803 \| 0.8305 \| 0.8518 \| 0.7708 \| 0.4003 \| 0.0 \| 0.6998 \| 0.7978 \| 0.6118 \| 0.7331 \| 0.5165 \| 0.7194 \| 0.5223 \| 0.3504 \| 0.4649 \| 0.4763 \| 0.8118 \| 0.7273 \| 0.7508 \| 0.7484 \| 0.7669 \| 0.7286 \| 0.3511 \| \| 0.0443 \| 4.6 \| 2300 \| 0.2107 \| 0.6088 \| 0.7451 \| 0.8079 \| nan \| 0.8517 \| 0.9117 \| 0.7681 \| 0.7944 \| 0.6344 \| 0.8661 \| 0.8579 \| 0.4570 \| 0.6486 \| 0.5666 \| 0.8783 \| 0.7970 \| 0.7842 \| 0.8453 \| 0.8396 \| 0.8066 \| 0.3593 \| 0.0 \| 0.7495 \| 0.8123 \| 0.6290 \| 0.7319 \| 0.5771 \| 0.7430 \| 0.5452 \| 0.3799 \| 0.5021 \| 0.4816 \| 0.8129 \| 0.6993 \| 0.7201 \| 0.7506 \| 0.7543 \| 0.7490 \| 0.3210 \| \| 0.0433 \| 4.64 \| 2320 \| 0.1912 \| 0.6129 \| 0.7392 \| 0.8284 \| nan \| 0.7361 \| 0.8932 \| 0.6950 \| 0.8864 \| 0.7410 \| 0.8718 \| 0.7345 \| 0.3977 \| 0.5824 \| 0.6076 \| 0.9056 \| 0.8344 \| 0.8280 \| 0.8070 \| 0.8582 \| 0.8404 \| 0.3467 \| 0.0 \| 0.6949 \| 0.8102 \| 0.6119 \| 0.7813 \| 0.6735 \| 0.7293 \| 0.6042 \| 0.3380 \| 0.4804 \| 0.4994 \| 0.8156 \| 0.7139 \| 0.7324 \| 0.7331 \| 0.7432 \| 0.7583 \| 0.3130 \| \| 0.0651 \| 4.68 \| 2340 \| 0.1955 \| 0.5984 \| 0.7331 \| 0.8145 \| nan \| 0.7781 \| 0.8997 \| 0.6854 \| 0.8605 \| 0.7560 \| 0.8705 \| 0.7164 \| 0.3448 \| 0.4754 \| 0.6191 \| 0.9088 \| 0.8595 \| 0.7697 \| 0.8969 \| 0.7732 \| 0.7595 \| 0.4887 \| 0.0 \| 0.7105 \| 0.8075 \| 0.6069 \| 0.7669 \| 0.6593 \| 0.7214 \| 0.5752 \| 0.3071 \| 0.4179 \| 0.4920 \| 0.8140 \| 0.6969 \| 0.6987 \| 0.7080 \| 0.7072 \| 0.7070 \| 0.3752 \| \| 0.047 \| 4.72 \| 2360 \| 0.1974 \| 0.6030 \| 0.7384 \| 0.8122 \| nan \| 0.8279 \| 0.8898 \| 0.7517 \| 0.8304 \| 0.7763 \| 0.8685 \| 0.7752 \| 0.3255 \| 0.6484 \| 0.5347 \| 0.9093 \| 0.8206 \| 0.7810 \| 0.7968 \| 0.7843 \| 0.7683 \| 0.4647 \| 0.0 \| 0.7301 \| 0.8074 \| 0.6251 \| 0.7479 \| 0.6533 \| 0.7407 \| 0.5601 \| 0.2951 \| 0.4976 \| 0.4673 \| 0.8199 \| 0.6923 \| 0.7070 \| 0.6976 \| 0.7268 \| 0.7155 \| 0.3707 \| \| 0.0631 \| 4.76 \| 2380 \| 0.1954 \| 0.5960 \| 0.7307 \| 0.8121 \| nan \| 0.8168 \| 0.8874 \| 0.7811 \| 0.8587 \| 0.6200 \| 0.8873 \| 0.7910 \| 0.3364 \| 0.5003 \| 0.5346 \| 0.8992 \| 0.7987 \| 0.8182 \| 0.8688 \| 0.7974 \| 0.7612 \| 0.4653 \| 0.0 \| 0.7194 \| 0.8007 \| 0.6240 \| 0.7744 \| 0.5822 \| 0.7310 \| 0.5696 \| 0.3095 \| 0.4293 \| 0.4671 \| 0.8212 \| 0.6866 \| 0.7134 \| 0.7140 \| 0.7220 \| 0.7142 \| 0.3493 \| \| 0.1457 \| 4.8 \| 2400 \| 0.1905 \| 0.6017 \| 0.7349 \| 0.8122 \| nan \| 0.8354 \| 0.8926 \| 0.6969 \| 0.8269 \| 0.7461 \| 0.8505 \| 0.8060 \| 0.2994 \| 0.5935 \| 0.6066 \| 0.9038 \| 0.7462 \| 0.7689 \| 0.8211 \| 0.8379 \| 0.8154 \| 0.4459 \| 0.0 \| 0.7296 \| 0.8007 \| 0.6096 \| 0.7530 \| 0.6517 \| 0.7186 \| 0.5810 \| 0.2758 \| 0.4734 \| 0.4956 \| 0.8201 \| 0.6783 \| 0.6944 \| 0.7232 \| 0.7516 \| 0.7421 \| 0.3313 \| \| 0.096 \| 4.84 \| 2420 \| 0.1839 \| 0.6017 \| 0.7401 \| 0.8149 \| nan \| 0.7803 \| 0.9063 \| 0.6768 \| 0.8360 \| 0.7918 \| 0.8266 \| 0.7649 \| 0.3966 \| 0.4742 \| 0.5942 \| 0.9145 \| 0.8588 \| 0.7922 \| 0.8473 \| 0.8116 \| 0.8137 \| 0.4953 \| 0.0 \| 0.7268 \| 0.8094 \| 0.6010 \| 0.7528 \| 0.6775 \| 0.7149 \| 0.5818 \| 0.3183 \| 0.4211 \| 0.4933 \| 0.8070 \| 0.7010 \| 0.7037 \| 0.7242 \| 0.7304 \| 0.7427 \| 0.3250 \| \| 0.1249 \| 4.88 \| 2440 \| 0.1843 \| 0.6077 \| 0.7428 \| 0.8183 \| nan \| 0.7991 \| 0.8892 \| 0.7353 \| 0.8558 \| 0.7765 \| 0.8298 \| 0.7553 \| 0.3874 \| 0.6074 \| 0.5186 \| 0.8991 \| 0.8337 \| 0.8211 \| 0.8479 \| 0.8120 \| 0.8344 \| 0.4250 \| 0.0 \| 0.7245 \| 0.8070 \| 0.6209 \| 0.7615 \| 0.6935 \| 0.7093 \| 0.5903 \| 0.3295 \| 0.4814 \| 0.4554 \| 0.8175 \| 0.7059 \| 0.7231 \| 0.7149 \| 0.7316 \| 0.7606 \| 0.3115 \| \| 0.0401 \| 4.92 \| 2460 \| 0.1957 \| 0.5969 \| 0.7328 \| 0.8131 \| nan \| 0.8428 \| 0.8672 \| 0.7114 \| 0.8465 \| 0.7447 \| 0.8701 \| 0.7830 \| 0.3845 \| 0.4677 \| 0.5891 \| 0.9165 \| 0.8389 \| 0.7478 \| 0.8294 \| 0.8362 \| 0.7763 \| 0.4048 \| 0.0 \| 0.7000 \| 0.7963 \| 0.6113 \| 0.7632 \| 0.6725 \| 0.7203 \| 0.5890 \| 0.3243 \| 0.4029 \| 0.4698 \| 0.8106 \| 0.6811 \| 0.6832 \| 0.7295 \| 0.7424 \| 0.7313 \| 0.3166 \| \| 0.0957 \| 4.96 \| 2480 \| 0.1954 \| 0.5970 \| 0.7327 \| 0.8071 \| nan \| 0.7625 \| 0.9070 \| 0.7343 \| 0.8071 \| 0.7656 \| 0.8036 \| 0.8320 \| 0.3442 \| 0.6363 \| 0.5262 \| 0.8882 \| 0.8642 \| 0.7561 \| 0.8266 \| 0.8168 \| 0.7836 \| 0.4017 \| 0.0 \| 0.7029 \| 0.8052 \| 0.6222 \| 0.7440 \| 0.6464 \| 0.7212 \| 0.5705 \| 0.2782 \| 0.4840 \| 0.4610 \| 0.8116 \| 0.6883 \| 0.6958 \| 0.7341 \| 0.7438 \| 0.7305 \| 0.3055 \| \| 0.0839 \| 5.0 \| 2500 \| 0.1927 \| 0.5988 \| 0.7278 \| 0.8200 \| nan \| 0.8173 \| 0.8802 \| 0.6631 \| 0.8705 \| 0.7154 \| 0.8477 \| 0.7848 \| 0.3481 \| 0.5379 \| 0.5904 \| 0.9287 \| 0.8472 \| 0.7912 \| 0.8622 \| 0.7959 \| 0.7803 \| 0.3110 \| 0.0 \| 0.7022 \| 0.7997 \| 0.5917 \| 0.7852 \| 0.6572 \| 0.7213 \| 0.6105 \| 0.3032 \| 0.4454 \| 0.4850 \| 0.8044 \| 0.6982 \| 0.7167 \| 0.7343 \| 0.7337 \| 0.7242 \| 0.2656 \| \| 0.0441 \| 5.04 \| 2520 \| 0.1921 \| 0.6079 \| 0.7363 \| 0.8239 \| nan \| 0.7877 \| 0.9069 \| 0.6990 \| 0.8730 \| 0.6964 \| 0.8387 \| 0.7980 \| 0.3814 \| 0.5382 \| 0.6421 \| 0.8931 \| 0.7990 \| 0.8441 \| 0.8362 \| 0.7911 \| 0.8295 \| 0.3623 \| 0.0 \| 0.7204 \| 0.8072 \| 0.6099 \| 0.7831 \| 0.6568 \| 0.7206 \| 0.6074 \| 0.3140 \| 0.4512 \| 0.5082 \| 0.8085 \| 0.7050 \| 0.7430 \| 0.7261 \| 0.7320 \| 0.7559 \| 0.2924 \| \| 0.0679 \| 5.08 \| 2540 \| 0.1925 \| 0.6096 \| 0.7439 \| 0.8232 \| nan \| 0.8173 \| 0.8865 \| 0.7275 \| 0.8745 \| 0.7319 \| 0.8373 \| 0.7785 \| 0.4078 \| 0.6311 \| 0.5003 \| 0.9109 \| 0.8146 \| 0.8512 \| 0.8179 \| 0.8481 \| 0.7909 \| 0.4206 \| 0.0 \| 0.7017 \| 0.7981 \| 0.6187 \| 0.7817 \| 0.6650 \| 0.7292 \| 0.6078 \| 0.3298 \| 0.4797 \| 0.4434 \| 0.8036 \| 0.7140 \| 0.7518 \| 0.7390 \| 0.7513 \| 0.7401 \| 0.3181 \| \| 0.0762 \| 5.12 \| 2560 \| 0.1966 \| 0.5916 \| 0.7179 \| 0.8151 \| nan \| 0.8240 \| 0.8840 \| 0.7232 \| 0.8917 \| 0.7024 \| 0.8440 \| 0.7422 \| 0.2452 \| 0.5112 \| 0.5410 \| 0.8962 \| 0.8666 \| 0.7568 \| 0.8129 \| 0.7840 \| 0.8040 \| 0.3755 \| 0.0 \| 0.7137 \| 0.8025 \| 0.6176 \| 0.7807 \| 0.6263 \| 0.7113 \| 0.5895 \| 0.2326 \| 0.4191 \| 0.4490 \| 0.8085 \| 0.6947 \| 0.6996 \| 0.7263 \| 0.7315 \| 0.7389 \| 0.3072 \| \| 0.0812 \| 5.16 \| 2580 \| 0.2026 \| 0.6001 \| 0.7320 \| 0.8174 \| nan \| 0.8212 \| 0.8860 \| 0.7650 \| 0.8756 \| 0.6950 \| 0.8321 \| 0.7693 \| 0.2653 \| 0.5681 \| 0.6025 \| 0.8839 \| 0.7922 \| 0.8607 \| 0.8671 \| 0.8006 \| 0.7756 \| 0.3841 \| 0.0 \| 0.7167 \| 0.8025 \| 0.6292 \| 0.7705 \| 0.6289 \| 0.7126 \| 0.5880 \| 0.2479 \| 0.4657 \| 0.4925 \| 0.8058 \| 0.7027 \| 0.7396 \| 0.7394 \| 0.7389 \| 0.7249 \| 0.2954 \| \| 0.0444 \| 5.2 \| 2600 \| 0.1922 \| 0.6073 \| 0.7353 \| 0.8240 \| nan \| 0.8309 \| 0.8819 \| 0.7276 \| 0.8846 \| 0.7038 \| 0.8672 \| 0.7492 \| 0.3170 \| 0.5291 \| 0.6013 \| 0.9216 \| 0.7891 \| 0.8462 \| 0.8551 \| 0.8235 \| 0.8115 \| 0.3607 \| 0.0 \| 0.7482 \| 0.8089 \| 0.6214 \| 0.7816 \| 0.6365 \| 0.7228 \| 0.5935 \| 0.2867 \| 0.4451 \| 0.4850 \| 0.8089 \| 0.7069 \| 0.7373 \| 0.7486 \| 0.7520 \| 0.7531 \| 0.2952 \| \| 0.0723 \| 5.24 \| 2620 \| 0.1927 \| 0.6121 \| 0.7415 \| 0.8252 \| nan \| 0.8155 \| 0.9101 \| 0.7610 \| 0.8830 \| 0.7283 \| 0.8631 \| 0.7346 \| 0.3833 \| 0.5606 \| 0.6069 \| 0.8925 \| 0.8546 \| 0.7885 \| 0.8422 \| 0.8172 \| 0.8209 \| 0.3426 \| 0.0 \| 0.7512 \| 0.8134 \| 0.6279 \| 0.7795 \| 0.6473 \| 0.7385 \| 0.5807 \| 0.3154 \| 0.4708 \| 0.5069 \| 0.8119 \| 0.7036 \| 0.7149 \| 0.7469 \| 0.7491 \| 0.7647 \| 0.2952 \| \| 0.038 \| 5.28 \| 2640 \| 0.2019 \| 0.6074 \| 0.7386 \| 0.8191 \| nan \| 0.8442 \| 0.8776 \| 0.6959 \| 0.8620 \| 0.6979 \| 0.8641 \| 0.7660 \| 0.3409 \| 0.5926 \| 0.5682 \| 0.9181 \| 0.8056 \| 0.8347 \| 0.8304 \| 0.8502 \| 0.7998 \| 0.4073 \| 0.0 \| 0.7348 \| 0.8067 \| 0.6034 \| 0.7649 \| 0.6384 \| 0.7362 \| 0.5644 \| 0.2843 \| 0.4902 \| 0.4999 \| 0.8107 \| 0.7004 \| 0.7260 \| 0.7447 \| 0.7476 \| 0.7535 \| 0.3277 \| \| 0.0545 \| 5.32 \| 2660 \| 0.2016 \| 0.6004 \| 0.7302 \| 0.8180 \| nan \| 0.8631 \| 0.8974 \| 0.7166 \| 0.8660 \| 0.7245 \| 0.8718 \| 0.7548 \| 0.2667 \| 0.5677 \| 0.6003 \| 0.8980 \| 0.8759 \| 0.6811 \| 0.8429 \| 0.8213 \| 0.8017 \| 0.3629 \| 0.0 \| 0.7388 \| 0.8079 \| 0.6163 \| 0.7700 \| 0.6512 \| 0.7397 \| 0.5754 \| 0.2448 \| 0.4684 \| 0.5051 \| 0.8088 \| 0.6704 \| 0.6398 \| 0.7464 \| 0.7525 \| 0.7572 \| 0.3150 \| \| 0.0644 \| 5.36 \| 2680 \| 0.1983 \| 0.6148 \| 0.7467 \| 0.8243 \| nan \| 0.8191 \| 0.9042 \| 0.7854 \| 0.8634 \| 0.7273 \| 0.8680 \| 0.7522 \| 0.3414 \| 0.5216 \| 0.6624 \| 0.9161 \| 0.8504 \| 0.7786 \| 0.8585 \| 0.8159 \| 0.8329 \| 0.3973 \| 0.0 \| 0.7561 \| 0.8158 \| 0.6249 \| 0.7688 \| 0.6561 \| 0.7506 \| 0.5695 \| 0.3067 \| 0.4492 \| 0.5257 \| 0.8140 \| 0.7002 \| 0.7042 \| 0.7520 \| 0.7539 \| 0.7789 \| 0.3397 \| \| 0.0814 \| 5.4 \| 2700 \| 0.2007 \| 0.6073 \| 0.7347 \| 0.8188 \| nan \| 0.8194 \| 0.8470 \| 0.7055 \| 0.8634 \| 0.7134 \| 0.8847 \| 0.7546 \| 0.3835 \| 0.5528 \| 0.6093 \| 0.9166 \| 0.8344 \| 0.8307 \| 0.8395 \| 0.8268 \| 0.8139 \| 0.2941 \| 0.0 \| 0.7186 \| 0.7897 \| 0.6077 \| 0.7645 \| 0.6524 \| 0.7458 \| 0.5671 \| 0.3165 \| 0.4585 \| 0.4999 \| 0.8149 \| 0.7195 \| 0.7399 \| 0.7455 \| 0.7569 \| 0.7621 \| 0.2722 \| \| 0.0437 \| 5.44 \| 2720 \| 0.1966 \| 0.5950 \| 0.7244 \| 0.8125 \| nan \| 0.8239 \| 0.9001 \| 0.7067 \| 0.8549 \| 0.6844 \| 0.8617 \| 0.7482 \| 0.3604 \| 0.3834 \| 0.7186 \| 0.8956 \| 0.7357 \| 0.8750 \| 0.8170 \| 0.8544 \| 0.8115 \| 0.2840 \| 0.0 \| 0.7154 \| 0.8097 \| 0.6147 \| 0.7641 \| 0.6176 \| 0.7480 \| 0.5478 \| 0.3101 \| 0.3551 \| 0.5117 \| 0.8145 \| 0.6718 \| 0.7199 \| 0.7336 \| 0.7494 \| 0.7618 \| 0.2643 \| \| 0.0536 \| 5.48 \| 2740 \| 0.2150 \| 0.5843 \| 0.7165 \| 0.7959 \| nan \| 0.7963 \| 0.8867 \| 0.7123 \| 0.8105 \| 0.5821 \| 0.8255 \| 0.8201 \| 0.1755 \| 0.6811 \| 0.4980 \| 0.9111 \| 0.8575 \| 0.7585 \| 0.8623 \| 0.7805 \| 0.7913 \| 0.4308 \| 0.0 \| 0.7054 \| 0.8026 \| 0.6155 \| 0.7356 \| 0.5335 \| 0.7356 \| 0.5247 \| 0.1583 \| 0.5013 \| 0.4513 \| 0.8245 \| 0.6981 \| 0.7006 \| 0.7356 \| 0.7260 \| 0.7410 \| 0.3277 \| \| 0.0715 \| 5.52 \| 2760 \| 0.2088 \| 0.6012 \| 0.7312 \| 0.8127 \| nan \| 0.8138 \| 0.9035 \| 0.7080 \| 0.8064 \| 0.6314 \| 0.8808 \| 0.8267 \| 0.2183 \| 0.5328 \| 0.6335 \| 0.9009 \| 0.8449 \| 0.8147 \| 0.8598 \| 0.8543 \| 0.8246 \| 0.3757 \| 0.0 \| 0.7301 \| 0.8171 \| 0.6143 \| 0.7385 \| 0.6004 \| 0.7536 \| 0.5403 \| 0.2052 \| 0.4497 \| 0.5130 \| 0.8184 \| 0.7211 \| 0.7285 \| 0.7539 \| 0.7559 \| 0.7625 \| 0.3198 \| \| 0.091 \| 5.56 \| 2780 \| 0.2041 \| 0.6034 \| 0.7423 \| 0.8110 \| nan \| 0.8689 \| 0.8771 \| 0.7109 \| 0.8244 \| 0.6929 \| 0.8958 \| 0.7644 \| 0.3538 \| 0.5529 \| 0.6557 \| 0.9130 \| 0.7627 \| 0.8228 \| 0.8463 \| 0.8492 \| 0.7885 \| 0.4405 \| 0.0 \| 0.7032 \| 0.8010 \| 0.6166 \| 0.7454 \| 0.6173 \| 0.7461 \| 0.5405 \| 0.2925 \| 0.4647 \| 0.5157 \| 0.8187 \| 0.6948 \| 0.7173 \| 0.7534 \| 0.7638 \| 0.7432 \| 0.3271 \| \| 0.0393 \| 5.6 \| 2800 \| 0.2226 \| 0.6045 \| 0.7396 \| 0.8036 \| nan \| 0.8102 \| 0.8913 \| 0.7321 \| 0.7656 \| 0.6763 \| 0.8691 \| 0.8480 \| 0.2555 \| 0.6074 \| 0.6081 \| 0.9126 \| 0.8450 \| 0.8172 \| 0.8242 \| 0.8333 \| 0.7906 \| 0.4864 \| 0.0 \| 0.7332 \| 0.8077 \| 0.6235 \| 0.7098 \| 0.6315 \| 0.7585 \| 0.5299 \| 0.2295 \| 0.4953 \| 0.5149 \| 0.8173 \| 0.7228 \| 0.7343 \| 0.7428 \| 0.7567 \| 0.7462 \| 0.3268 \| \| 0.0414 \| 5.64 \| 2820 \| 0.2097 \| 0.6077 \| 0.7418 \| 0.8162 \| nan \| 0.8296 \| 0.8941 \| 0.7577 \| 0.8453 \| 0.6405 \| 0.8695 \| 0.7839 \| 0.2478 \| 0.5981 \| 0.6425 \| 0.9007 \| 0.8282 \| 0.8185 \| 0.8512 \| 0.8185 \| 0.8026 \| 0.4819 \| 0.0 \| 0.7503 \| 0.8152 \| 0.6313 \| 0.7578 \| 0.6115 \| 0.7246 \| 0.5618 \| 0.2221 \| 0.4853 \| 0.5175 \| 0.8186 \| 0.7245 \| 0.7450 \| 0.7400 \| 0.7506 \| 0.7550 \| 0.3278 \| \| 0.1061 \| 5.68 \| 2840 \| 0.2079 \| 0.6085 \| 0.7396 \| 0.8139 \| nan \| 0.8036 \| 0.8906 \| 0.7229 \| 0.8454 \| 0.6381 \| 0.8610 \| 0.7658 \| 0.3532 \| 0.5529 \| 0.6432 \| 0.9175 \| 0.8227 \| 0.8296 \| 0.8678 \| 0.8142 \| 0.8296 \| 0.4152 \| 0.0 \| 0.7403 \| 0.8125 \| 0.6203 \| 0.7558 \| 0.6054 \| 0.7119 \| 0.5590 \| 0.2992 \| 0.4675 \| 0.5195 \| 0.8170 \| 0.7175 \| 0.7413 \| 0.7384 \| 0.7399 \| 0.7671 \| 0.3402 \| \| 0.044 \| 5.72 \| 2860 \| 0.2129 \| 0.5955 \| 0.7281 \| 0.8073 \| nan \| 0.8760 \| 0.8817 \| 0.7239 \| 0.8369 \| 0.6680 \| 0.8184 \| 0.7773 \| 0.1578 \| 0.5715 \| 0.6299 \| 0.8995 \| 0.8182 \| 0.8421 \| 0.8418 \| 0.8206 \| 0.7976 \| 0.4171 \| 0.0 \| 0.7150 \| 0.8070 \| 0.6213 \| 0.7422 \| 0.5868 \| 0.7250 \| 0.5270 \| 0.1529 \| 0.4774 \| 0.5211 \| 0.8188 \| 0.7080 \| 0.7371 \| 0.7410 \| 0.7502 \| 0.7567 \| 0.3316 \| \| 0.0832 \| 5.76 \| 2880 \| 0.2060 \| 0.6030 \| 0.7359 \| 0.8107 \| nan \| 0.8445 \| 0.8972 \| 0.7264 \| 0.8434 \| 0.6797 \| 0.8604 \| 0.7604 \| 0.3486 \| 0.6110 \| 0.5234 \| 0.9062 \| 0.8506 \| 0.7577 \| 0.8396 \| 0.7971 \| 0.8256 \| 0.4393 \| 0.0 \| 0.7388 \| 0.8133 \| 0.6226 \| 0.7478 \| 0.6102 \| 0.7332 \| 0.5351 \| 0.2822 \| 0.4757 \| 0.4633 \| 0.8212 \| 0.7096 \| 0.7075 \| 0.7428 \| 0.7475 \| 0.7665 \| 0.3362 \| \| 0.0458 \| 5.8 \| 2900 \| 0.2080 \| 0.6049 \| 0.7418 \| 0.8100 \| nan \| 0.8212 \| 0.8851 \| 0.7796 \| 0.8234 \| 0.6697 \| 0.8497 \| 0.7741 \| 0.3276 \| 0.5372 \| 0.6118 \| 0.9123 \| 0.8514 \| 0.8086 \| 0.8501 \| 0.8600 \| 0.8373 \| 0.4115 \| 0.0 \| 0.7295 \| 0.8059 \| 0.6272 \| 0.7408 \| 0.6093 \| 0.7425 \| 0.5317 \| 0.2670 \| 0.4552 \| 0.5113 \| 0.8183 \| 0.7212 \| 0.7323 \| 0.7519 \| 0.7597 \| 0.7728 \| 0.3115 \| \| 0.0429 \| 5.84 \| 2920 \| 0.2081 \| 0.6025 \| 0.7436 \| 0.8059 \| nan \| 0.8457 \| 0.8600 \| 0.7882 \| 0.8348 \| 0.7078 \| 0.8311 \| 0.7521 \| 0.3579 \| 0.6167 \| 0.6247 \| 0.9036 \| 0.8151 \| 0.8271 \| 0.8229 \| 0.8037 \| 0.8150 \| 0.4357 \| 0.0 \| 0.6773 \| 0.7914 \| 0.6201 \| 0.7399 \| 0.6141 \| 0.7345 \| 0.5253 \| 0.2743 \| 0.4960 \| 0.5209 \| 0.8174 \| 0.7162 \| 0.7385 \| 0.7401 \| 0.7466 \| 0.7628 \| 0.3298 \| \| 0.054 \| 5.88 \| 2940 \| 0.2204 \| 0.5966 \| 0.7370 \| 0.8051 \| nan \| 0.7878 \| 0.8990 \| 0.6791 \| 0.8110 \| 0.6541 \| 0.8227 \| 0.7956 \| 0.3752 \| 0.5920 \| 0.5842 \| 0.9142 \| 0.8651 \| 0.7815 \| 0.8965 \| 0.8251 \| 0.8181 \| 0.4275 \| 0.0 \| 0.7110 \| 0.8108 \| 0.5928 \| 0.7351 \| 0.5910 \| 0.7252 \| 0.5343 \| 0.2705 \| 0.4730 \| 0.4895 \| 0.8139 \| 0.7148 \| 0.7219 \| 0.7288 \| 0.7308 \| 0.7629 \| 0.3323 \| \| 0.0526 \| 5.92 \| 2960 \| 0.2271 \| 0.5921 \| 0.7137 \| 0.8074 \| nan \| 0.7305 \| 0.8986 \| 0.6791 \| 0.8207 \| 0.6357 \| 0.8230 \| 0.8263 \| 0.1921 \| 0.5933 \| 0.5993 \| 0.9000 \| 0.7991 \| 0.8748 \| 0.8311 \| 0.8310 \| 0.8173 \| 0.2809 \| 0.0 \| 0.7011 \| 0.8120 \| 0.5975 \| 0.7376 \| 0.5800 \| 0.7360 \| 0.5299 \| 0.1707 \| 0.4862 \| 0.5073 \| 0.8141 \| 0.7211 \| 0.7529 \| 0.7340 \| 0.7601 \| 0.7582 \| 0.2582 \| \| 0.0787 \| 5.96 \| 2980 \| 0.1975 \| 0.6112 \| 0.7550 \| 0.8154 \| nan \| 0.8712 \| 0.8549 \| 0.7621 \| 0.8253 \| 0.7788 \| 0.8781 \| 0.7424 \| 0.3999 \| 0.5312 \| 0.6642 \| 0.8947 \| 0.8300 \| 0.8439 \| 0.8626 \| 0.8529 \| 0.8194 \| 0.4234 \| 0.0 \| 0.7091 \| 0.7942 \| 0.6226 \| 0.7461 \| 0.6634 \| 0.7555 \| 0.5392 \| 0.2870 \| 0.4502 \| 0.5253 \| 0.8142 \| 0.7330 \| 0.7496 \| 0.7555 \| 0.7663 \| 0.7618 \| 0.3277 \| \| 0.077 \| 6.0 \| 3000 \| 0.1916 \| 0.6131 \| 0.7349 \| 0.8314 \| nan \| 0.7940 \| 0.9065 \| 0.6260 \| 0.9004 \| 0.8101 \| 0.8706 \| 0.6719 \| 0.3622 \| 0.5013 \| 0.6468 \| 0.9126 \| 0.8364 \| 0.8394 \| 0.8486 \| 0.8284 \| 0.8337 \| 0.3050 \| 0.0 \| 0.7254 \| 0.8176 \| 0.5706 \| 0.7962 \| 0.6645 \| 0.7580 \| 0.5719 \| 0.3001 \| 0.4435 \| 0.5239 \| 0.8170 \| 0.7344 \| 0.7520 \| 0.7461 \| 0.7545 \| 0.7748 \| 0.2853 \| \| 0.0346 \| 6.04 \| 3020 \| 0.2012 \| 0.6083 \| 0.7380 \| 0.8188 \| nan \| 0.7745 \| 0.9022 \| 0.7231 \| 0.8575 \| 0.8261 \| 0.8445 \| 0.6999 \| 0.3096 \| 0.6325 \| 0.5564 \| 0.9167 \| 0.8575 \| 0.7878 \| 0.8419 \| 0.8112 \| 0.8105 \| 0.3936 \| 0.0 \| 0.7344 \| 0.8100 \| 0.6167 \| 0.7698 \| 0.6416 \| 0.7527 \| 0.5464 \| 0.2616 \| 0.5020 \| 0.4926 \| 0.8105 \| 0.7176 \| 0.7237 \| 0.7446 \| 0.7461 \| 0.7632 \| 0.3165 \| \| 0.1209 \| 6.08 \| 3040 \| 0.1980 \| 0.6079 \| 0.7360 \| 0.8205 \| nan \| 0.7869 \| 0.9241 \| 0.7542 \| 0.8579 \| 0.8112 \| 0.8485 \| 0.7041 \| 0.2721 \| 0.5967 \| 0.5987 \| 0.8841 \| 0.8261 \| 0.8375 \| 0.7873 \| 0.8708 \| 0.8015 \| 0.3504 \| 0.0 \| 0.7410 \| 0.8122 \| 0.6182 \| 0.7677 \| 0.6410 \| 0.7500 \| 0.5438 \| 0.2397 \| 0.4894 \| 0.5063 \| 0.8078 \| 0.7253 \| 0.7425 \| 0.7332 \| 0.7581 \| 0.7582 \| 0.3084 \| \| 0.0416 \| 6.12 \| 3060 \| 0.1973 \| 0.6151 \| 0.7489 \| 0.8206 \| nan \| 0.8262 \| 0.8939 \| 0.7506 \| 0.8571 \| 0.8057 \| 0.8401 \| 0.7037 \| 0.3972 \| 0.5672 \| 0.6363 \| 0.9234 \| 0.8370 \| 0.8145 \| 0.8586 \| 0.8284 \| 0.8339 \| 0.3578 \| 0.0 \| 0.7489 \| 0.8125 \| 0.6259 \| 0.7666 \| 0.6410 \| 0.7477 \| 0.5412 \| 0.3161 \| 0.4825 \| 0.5207 \| 0.8096 \| 0.7224 \| 0.7329 \| 0.7565 \| 0.7594 \| 0.7769 \| 0.3103 \| \| 0.0439 \| 6.16 \| 3080 \| 0.1961 \| 0.6118 \| 0.7440 \| 0.8172 \| nan \| 0.7941 \| 0.9156 \| 0.7294 \| 0.8416 \| 0.8139 \| 0.8435 \| 0.7159 \| 0.4061 \| 0.5151 \| 0.6792 \| 0.8770 \| 0.8128 \| 0.8503 \| 0.8483 \| 0.8207 \| 0.8163 \| 0.3677 \| 0.0 \| 0.7441 \| 0.8094 \| 0.6217 \| 0.7562 \| 0.6516 \| 0.7470 \| 0.5382 \| 0.3008 \| 0.4542 \| 0.5300 \| 0.8056 \| 0.7234 \| 0.7458 \| 0.7501 \| 0.7595 \| 0.7641 \| 0.3111 \| \| 0.0467 \| 6.2 \| 3100 \| 0.1993 \| 0.6138 \| 0.7460 \| 0.8209 \| nan \| 0.8212 \| 0.8919 \| 0.6869 \| 0.8580 \| 0.8067 \| 0.8439 \| 0.7093 \| 0.4469 \| 0.6316 \| 0.5251 \| 0.9193 \| 0.8579 \| 0.8226 \| 0.8563 \| 0.8425 \| 0.8178 \| 0.3439 \| 0.0 \| 0.7509 \| 0.8102 \| 0.6060 \| 0.7639 \| 0.6560 \| 0.7453 \| 0.5469 \| 0.3408 \| 0.4853 \| 0.4688 \| 0.8130 \| 0.7281 \| 0.7425 \| 0.7528 \| 0.7666 \| 0.7698 \| 0.3011 \| \| 0.0639 \| 6.24 \| 3120 \| 0.1939 \| 0.6192 \| 0.7579 \| 0.8265 \| nan \| 0.8742 \| 0.8965 \| 0.7917 \| 0.8737 \| 0.8232 \| 0.8609 \| 0.6932 \| 0.4729 \| 0.6036 \| 0.5774 \| 0.8927 \| 0.8442 \| 0.8251 \| 0.8449 \| 0.8313 \| 0.8289 \| 0.3502 \| 0.0 \| 0.7351 \| 0.8098 \| 0.6319 \| 0.7757 \| 0.6619 \| 0.7524 \| 0.5602 \| 0.3486 \| 0.4854 \| 0.4994 \| 0.8139 \| 0.7307 \| 0.7493 \| 0.7511 \| 0.7647 \| 0.7767 \| 0.2985 \| \| 0.051 \| 6.28 \| 3140 \| 0.1950 \| 0.6220 \| 0.7483 \| 0.8331 \| nan \| 0.7659 \| 0.9159 \| 0.7107 \| 0.8991 \| 0.8370 \| 0.8677 \| 0.6572 \| 0.4390 \| 0.5374 \| 0.6454 \| 0.9061 \| 0.8271 \| 0.8608 \| 0.8353 \| 0.8573 \| 0.8176 \| 0.3415 \| 0.0 \| 0.7207 \| 0.8189 \| 0.6197 \| 0.7846 \| 0.6857 \| 0.7521 \| 0.5636 \| 0.3327 \| 0.4680 \| 0.5305 \| 0.8165 \| 0.7409 \| 0.7586 \| 0.7565 \| 0.7750 \| 0.7733 \| 0.2979 \| \| 0.0594 \| 6.32 \| 3160 \| 0.1999 \| 0.6118 \| 0.7459 \| 0.8225 \| nan \| 0.8510 \| 0.9022 \| 0.7170 \| 0.8657 \| 0.8162 \| 0.8436 \| 0.7059 \| 0.3287 \| 0.6434 \| 0.5611 \| 0.9120 \| 0.8567 \| 0.7685 \| 0.8446 \| 0.8253 \| 0.8281 \| 0.4108 \| 0.0 \| 0.7467 \| 0.8156 \| 0.6191 \| 0.7684 \| 0.6833 \| 0.7346 \| 0.5523 \| 0.2637 \| 0.4770 \| 0.4749 \| 0.8154 \| 0.7191 \| 0.7192 \| 0.7495 \| 0.7587 \| 0.7786 \| 0.3354 \| \| 0.0409 \| 6.36 \| 3180 \| 0.1963 \| 0.6192 \| 0.7575 \| 0.8254 \| nan \| 0.8534 \| 0.8970 \| 0.7831 \| 0.8714 \| 0.8042 \| 0.8622 \| 0.6865 \| 0.3991 \| 0.5463 \| 0.6052 \| 0.8979 \| 0.8318 \| 0.8446 \| 0.8594 \| 0.8434 \| 0.8354 \| 0.4559 \| 0.0 \| 0.7419 \| 0.8124 \| 0.6315 \| 0.7739 \| 0.6809 \| 0.7405 \| 0.5644 \| 0.3053 \| 0.4624 \| 0.5117 \| 0.8150 \| 0.7316 \| 0.7465 \| 0.7526 \| 0.7583 \| 0.7718 \| 0.3453 \| \| 0.0484 \| 6.4 \| 3200 \| 0.1987 \| 0.6111 \| 0.7424 \| 0.8209 \| nan \| 0.8281 \| 0.9024 \| 0.6257 \| 0.8620 \| 0.7811 \| 0.8600 \| 0.7204 \| 0.3944 \| 0.6037 \| 0.5542 \| 0.9109 \| 0.8334 \| 0.7980 \| 0.8483 \| 0.8021 \| 0.8282 \| 0.4678 \| 0.0 \| 0.7514 \| 0.8125 \| 0.5716 \| 0.7692 \| 0.6594 \| 0.7495 \| 0.5607 \| 0.3106 \| 0.4874 \| 0.4876 \| 0.8123 \| 0.7212 \| 0.7319 \| 0.7363 \| 0.7409 \| 0.7658 \| 0.3315 \| \| 0.0487 \| 6.44 \| 3220 \| 0.2036 \| 0.6149 \| 0.7462 \| 0.8220 \| nan \| 0.8514 \| 0.8878 \| 0.6876 \| 0.8779 \| 0.7743 \| 0.8645 \| 0.7127 \| 0.4503 \| 0.6008 \| 0.6170 \| 0.8930 \| 0.8475 \| 0.7827 \| 0.8425 \| 0.7648 \| 0.8298 \| 0.4014 \| 0.0 \| 0.7369 \| 0.8084 \| 0.6070 \| 0.7732 \| 0.6437 \| 0.7516 \| 0.5569 \| 0.3440 \| 0.4941 \| 0.5205 \| 0.8114 \| 0.7216 \| 0.7223 \| 0.7360 \| 0.7266 \| 0.7781 \| 0.3354 \| \| 0.0718 \| 6.48 \| 3240 \| 0.2046 \| 0.6133 \| 0.7454 \| 0.8194 \| nan \| 0.8476 \| 0.8742 \| 0.7364 \| 0.8526 \| 0.7709 \| 0.8615 \| 0.7423 \| 0.3505 \| 0.5951 \| 0.6241 \| 0.9139 \| 0.8710 \| 0.7696 \| 0.8505 \| 0.8137 \| 0.8146 \| 0.3840 \| 0.0 \| 0.7315 \| 0.8026 \| 0.6207 \| 0.7617 \| 0.6362 \| 0.7511 \| 0.5496 \| 0.2920 \| 0.4927 \| 0.5264 \| 0.8121 \| 0.7279 \| 0.7215 \| 0.7561 \| 0.7537 \| 0.7749 \| 0.3295 \| \| 0.0666 \| 6.52 \| 3260 \| 0.2018 \| 0.6103 \| 0.7363 \| 0.8237 \| nan \| 0.8582 \| 0.8781 \| 0.6627 \| 0.8734 \| 0.7564 \| 0.8542 \| 0.7235 \| 0.2790 \| 0.5671 \| 0.6371 \| 0.9218 \| 0.8576 \| 0.8130 \| 0.8377 \| 0.8483 \| 0.8246 \| 0.3251 \| 0.0 \| 0.7225 \| 0.8045 \| 0.5912 \| 0.7707 \| 0.6436 \| 0.7516 \| 0.5589 \| 0.2490 \| 0.4777 \| 0.5257 \| 0.8117 \| 0.7423 \| 0.7456 \| 0.7587 \| 0.7612 \| 0.7767 \| 0.2931 \| \| 0.055 \| 6.56 \| 3280 \| 0.1966 \| 0.6189 \| 0.7470 \| 0.8300 \| nan \| 0.8568 \| 0.8922 \| 0.7653 \| 0.8824 \| 0.7941 \| 0.8632 \| 0.7250 \| 0.2978 \| 0.5364 \| 0.6410 \| 0.9050 \| 0.8366 \| 0.8240 \| 0.8570 \| 0.8350 \| 0.8158 \| 0.3706 \| 0.0 \| 0.7414 \| 0.8117 \| 0.6337 \| 0.7806 \| 0.6754 \| 0.7565 \| 0.5760 \| 0.2545 \| 0.4678 \| 0.5268 \| 0.8145 \| 0.7382 \| 0.7441 \| 0.7636 \| 0.7652 \| 0.7718 \| 0.3177 \| \| 0.0586 \| 6.6 \| 3300 \| 0.1984 \| 0.6196 \| 0.7496 \| 0.8243 \| nan \| 0.8386 \| 0.8967 \| 0.7340 \| 0.8547 \| 0.7187 \| 0.8721 \| 0.7726 \| 0.3753 \| 0.5738 \| 0.6503 \| 0.8946 \| 0.8357 \| 0.8508 \| 0.8480 \| 0.8219 \| 0.8012 \| 0.4052 \| 0.0 \| 0.7402 \| 0.8120 \| 0.6273 \| 0.7598 \| 0.6449 \| 0.7617 \| 0.5580 \| 0.3214 \| 0.4871 \| 0.5336 \| 0.8142 \| 0.7362 \| 0.7574 \| 0.7550 \| 0.7624 \| 0.7641 \| 0.3178 \| \| 0.054 \| 6.64 \| 3320 \| 0.1945 \| 0.6176 \| 0.7460 \| 0.8294 \| nan \| 0.8253 \| 0.8945 \| 0.7383 \| 0.8571 \| 0.7803 \| 0.8766 \| 0.7617 \| 0.3174 \| 0.5509 \| 0.6698 \| 0.9057 \| 0.8345 \| 0.8537 \| 0.8654 \| 0.8013 \| 0.8261 \| 0.3228 \| 0.0 \| 0.7363 \| 0.8137 \| 0.6226 \| 0.7705 \| 0.6833 \| 0.7702 \| 0.5751 \| 0.2722 \| 0.4787 \| 0.5382 \| 0.8145 \| 0.7380 \| 0.7596 \| 0.7456 \| 0.7416 \| 0.7759 \| 0.2810 \| \| 0.0918 \| 6.68 \| 3340 \| 0.1977 \| 0.6178 \| 0.7486 \| 0.8289 \| nan \| 0.8353 \| 0.8952 \| 0.7535 \| 0.8468 \| 0.7779 \| 0.8714 \| 0.7888 \| 0.2920 \| 0.6281 \| 0.6273 \| 0.9077 \| 0.8633 \| 0.8127 \| 0.8589 \| 0.7952 \| 0.8256 \| 0.3471 \| 0.0 \| 0.7390 \| 0.8108 \| 0.6278 \| 0.7660 \| 0.6963 \| 0.7715 \| 0.5818 \| 0.2559 \| 0.5158 \| 0.5280 \| 0.8138 \| 0.7307 \| 0.7471 \| 0.7388 \| 0.7326 \| 0.7751 \| 0.2890 \| \| 0.0485 \| 6.72 \| 3360 \| 0.1935 \| 0.6151 \| 0.7438 \| 0.8303 \| nan \| 0.8458 \| 0.8932 \| 0.7397 \| 0.8770 \| 0.8135 \| 0.8903 \| 0.7165 \| 0.3140 \| 0.5889 \| 0.5917 \| 0.9073 \| 0.8082 \| 0.8552 \| 0.8702 \| 0.8096 \| 0.7906 \| 0.3327 \| 0.0 \| 0.7394 \| 0.8087 \| 0.6186 \| 0.7824 \| 0.6935 \| 0.7730 \| 0.5854 \| 0.2679 \| 0.4889 \| 0.5122 \| 0.8123 \| 0.7277 \| 0.7524 \| 0.7443 \| 0.7412 \| 0.7485 \| 0.2754 \| \| 0.0707 \| 6.76 \| 3380 \| 0.1956 \| 0.6135 \| 0.7441 \| 0.8298 \| nan \| 0.8640 \| 0.8860 \| 0.7093 \| 0.8739 \| 0.7835 \| 0.8753 \| 0.7555 \| 0.2561 \| 0.5847 \| 0.6261 \| 0.9060 \| 0.8611 \| 0.7900 \| 0.8629 \| 0.7686 \| 0.8284 \| 0.4190 \| 0.0 \| 0.7419 \| 0.8129 \| 0.6096 \| 0.7736 \| 0.6967 \| 0.7596 \| 0.5906 \| 0.2294 \| 0.4890 \| 0.5329 \| 0.8141 \| 0.7393 \| 0.7399 \| 0.7283 \| 0.7234 \| 0.7732 \| 0.2894 \| \| 0.0479 \| 6.8 \| 3400 \| 0.2023 \| 0.6173 \| 0.7511 \| 0.8284 \| nan \| 0.8060 \| 0.8994 \| 0.7386 \| 0.8636 \| 0.7464 \| 0.8888 \| 0.7598 \| 0.4561 \| 0.4873 \| 0.6764 \| 0.9131 \| 0.8034 \| 0.8738 \| 0.8118 \| 0.8396 \| 0.8131 \| 0.3915 \| 0.0 \| 0.7375 \| 0.8162 \| 0.6257 \| 0.7735 \| 0.6701 \| 0.7588 \| 0.5810 \| 0.3186 \| 0.4419 \| 0.5425 \| 0.8176 \| 0.7365 \| 0.7567 \| 0.7342 \| 0.7566 \| 0.7620 \| 0.2826 \| \| 0.0681 \| 6.84 \| 3420 \| 0.1910 \| 0.6193 \| 0.7555 \| 0.8259 \| nan \| 0.8415 \| 0.9146 \| 0.7535 \| 0.8504 \| 0.8153 \| 0.8551 \| 0.7497 \| 0.4178 \| 0.6103 \| 0.6438 \| 0.8894 \| 0.8482 \| 0.8158 \| 0.8471 \| 0.7817 \| 0.8164 \| 0.3937 \| 0.0 \| 0.7503 \| 0.8117 \| 0.6351 \| 0.7665 \| 0.6840 \| 0.7704 \| 0.5730 \| 0.3150 \| 0.4980 \| 0.5348 \| 0.8161 \| 0.7367 \| 0.7468 \| 0.7341 \| 0.7374 \| 0.7620 \| 0.2755 \| \| 0.0527 \| 6.88 \| 3440 \| 0.2485 \| 0.5992 \| 0.7385 \| 0.7956 \| nan \| 0.8583 \| 0.9012 \| 0.6970 \| 0.8161 \| 0.4887 \| 0.7942 \| 0.8130 \| 0.4627 \| 0.5456 \| 0.6360 \| 0.9041 \| 0.8325 \| 0.8547 \| 0.8560 \| 0.8318 \| 0.8158 \| 0.4471 \| 0.0 \| 0.7482 \| 0.8062 \| 0.6126 \| 0.7252 \| 0.4562 \| 0.7181 \| 0.5111 \| 0.3480 \| 0.4828 \| 0.5308 \| 0.8197 \| 0.7248 \| 0.7482 \| 0.7423 \| 0.7568 \| 0.7627 \| 0.2918 \| \| 0.0552 \| 6.92 \| 3460 \| 0.2340 \| 0.6095 \| 0.7488 \| 0.8068 \| nan \| 0.8834 \| 0.8760 \| 0.7525 \| 0.8465 \| 0.5447 \| 0.8179 \| 0.7830 \| 0.4823 \| 0.6262 \| 0.6107 \| 0.9227 \| 0.8547 \| 0.8231 \| 0.8487 \| 0.8352 \| 0.8306 \| 0.3909 \| 0.0 \| 0.7352 \| 0.8068 \| 0.6311 \| 0.7391 \| 0.5054 \| 0.7189 \| 0.5285 \| 0.3720 \| 0.5219 \| 0.5261 \| 0.8197 \| 0.7314 \| 0.7472 \| 0.7522 \| 0.7705 \| 0.7766 \| 0.2888 \| \| 0.0467 \| 6.96 \| 3480 \| 0.2203 \| 0.6069 \| 0.7367 \| 0.8145 \| nan \| 0.8406 \| 0.8867 \| 0.7886 \| 0.8672 \| 0.6168 \| 0.8327 \| 0.7707 \| 0.4004 \| 0.5789 \| 0.6595 \| 0.8940 \| 0.8545 \| 0.8098 \| 0.8402 \| 0.8144 \| 0.8331 \| 0.2364 \| 0.0 \| 0.7277 \| 0.8133 \| 0.6357 \| 0.7570 \| 0.5657 \| 0.7190 \| 0.5524 \| 0.3152 \| 0.5036 \| 0.5464 \| 0.8177 \| 0.7252 \| 0.7368 \| 0.7496 \| 0.7611 \| 0.7712 \| 0.2260 \| \| 0.0439 \| 7.0 \| 3500 \| 0.2275 \| 0.6051 \| 0.7303 \| 0.8110 \| nan \| 0.8470 \| 0.8943 \| 0.6942 \| 0.8549 \| 0.6545 \| 0.8176 \| 0.7530 \| 0.3131 \| 0.6298 \| 0.6255 \| 0.9062 \| 0.8398 \| 0.8313 \| 0.8247 \| 0.8425 \| 0.7976 \| 0.2892 \| 0.0 \| 0.7388 \| 0.8097 \| 0.6109 \| 0.7479 \| 0.5758 \| 0.7290 \| 0.5326 \| 0.2652 \| 0.5192 \| 0.5317 \| 0.8174 \| 0.7297 \| 0.7445 \| 0.7506 \| 0.7710 \| 0.7548 \| 0.2632 \| \| 0.0521 \| 7.04 \| 3520 \| 0.2251 \| 0.6088 \| 0.7420 \| 0.8137 \| nan \| 0.8732 \| 0.8794 \| 0.7645 \| 0.8523 \| 0.7060 \| 0.8313 \| 0.7457 \| 0.3072 \| 0.5490 \| 0.6194 \| 0.9096 \| 0.7940 \| 0.8878 \| 0.8470 \| 0.8630 \| 0.7804 \| 0.4046 \| 0.0 \| 0.7474 \| 0.8070 \| 0.6354 \| 0.7500 \| 0.6009 \| 0.7335 \| 0.5409 \| 0.2459 \| 0.4823 \| 0.5253 \| 0.8200 \| 0.7260 \| 0.7482 \| 0.7637 \| 0.7731 \| 0.7381 \| 0.3209 \| \| 0.0309 \| 7.08 \| 3540 \| 0.2404 \| 0.6004 \| 0.7310 \| 0.8055 \| nan \| 0.8285 \| 0.8969 \| 0.7766 \| 0.8295 \| 0.6323 \| 0.8245 \| 0.7835 \| 0.2006 \| 0.6407 \| 0.5665 \| 0.9032 \| 0.8318 \| 0.8426 \| 0.8452 \| 0.8229 \| 0.7760 \| 0.4249 \| 0.0 \| 0.7453 \| 0.8156 \| 0.6395 \| 0.7323 \| 0.5505 \| 0.7299 \| 0.5171 \| 0.1760 \| 0.5113 \| 0.4996 \| 0.8199 \| 0.7356 \| 0.7584 \| 0.7586 \| 0.7658 \| 0.7337 \| 0.3173 \| \| 0.041 \| 7.12 \| 3560 \| 0.2270 \| 0.6036 \| 0.7300 \| 0.8090 \| nan \| 0.8632 \| 0.8931 \| 0.6576 \| 0.8435 \| 0.6410 \| 0.8345 \| 0.7614 \| 0.2645 \| 0.5828 \| 0.6233 \| 0.9111 \| 0.8093 \| 0.8429 \| 0.8509 \| 0.8402 \| 0.7893 \| 0.4015 \| 0.0 \| 0.7552 \| 0.8082 \| 0.5947 \| 0.7412 \| 0.5704 \| 0.7325 \| 0.5289 \| 0.2354 \| 0.4928 \| 0.5234 \| 0.8194 \| 0.7224 \| 0.7475 \| 0.7582 \| 0.7656 \| 0.7445 \| 0.3243 \| \| 0.0516 \| 7.16 \| 3580 \| 0.2235 \| 0.6068 \| 0.7352 \| 0.8144 \| nan \| 0.8617 \| 0.8936 \| 0.6386 \| 0.8380 \| 0.7164 \| 0.8518 \| 0.7569 \| 0.3071 \| 0.5462 \| 0.6456 \| 0.9117 \| 0.8011 \| 0.8581 \| 0.8647 \| 0.8250 \| 0.7942 \| 0.3875 \| 0.0 \| 0.7505 \| 0.8082 \| 0.5835 \| 0.7456 \| 0.6204 \| 0.7463 \| 0.5376 \| 0.2546 \| 0.4747 \| 0.5257 \| 0.8182 \| 0.7202 \| 0.7438 \| 0.7604 \| 0.7633 \| 0.7480 \| 0.3215 \| \| 0.0444 \| 7.2 \| 3600 \| 0.2259 \| 0.6113 \| 0.7438 \| 0.8127 \| nan \| 0.8235 \| 0.8932 \| 0.7367 \| 0.8187 \| 0.6444 \| 0.8665 \| 0.8099 \| 0.3617 \| 0.6273 \| 0.6339 \| 0.9160 \| 0.8431 \| 0.8102 \| 0.8794 \| 0.7815 \| 0.8072 \| 0.3914 \| 0.0 \| 0.7410 \| 0.8093 \| 0.6299 \| 0.7389 \| 0.5965 \| 0.7561 \| 0.5393 \| 0.2970 \| 0.5100 \| 0.5292 \| 0.8202 \| 0.7342 \| 0.7461 \| 0.7426 \| 0.7365 \| 0.7587 \| 0.3185 \| \| 0.0443 \| 7.24 \| 3620 \| 0.2187 \| 0.6184 \| 0.7525 \| 0.8190 \| nan \| 0.8530 \| 0.8955 \| 0.7771 \| 0.8274 \| 0.6900 \| 0.8704 \| 0.8047 \| 0.4150 \| 0.5997 \| 0.6314 \| 0.9052 \| 0.8460 \| 0.8180 \| 0.8637 \| 0.8151 \| 0.8124 \| 0.3677 \| 0.0 \| 0.7525 \| 0.8098 \| 0.6386 \| 0.7475 \| 0.6305 \| 0.7658 \| 0.5575 \| 0.3230 \| 0.4993 \| 0.5302 \| 0.8209 \| 0.7393 \| 0.7484 \| 0.7495 \| 0.7537 \| 0.7600 \| 0.3047 \| \| 0.0773 \| 7.28 \| 3640 \| 0.2119 \| 0.6199 \| 0.7516 \| 0.8210 \| nan \| 0.8772 \| 0.9028 \| 0.7443 \| 0.8270 \| 0.7095 \| 0.8537 \| 0.8093 \| 0.3872 \| 0.6059 \| 0.6146 \| 0.9135 \| 0.8322 \| 0.8508 \| 0.8241 \| 0.8498 \| 0.8167 \| 0.3585 \| 0.0 \| 0.7556 \| 0.8096 \| 0.6315 \| 0.7483 \| 0.6378 \| 0.7640 \| 0.5590 \| 0.3153 \| 0.5032 \| 0.5233 \| 0.8201 \| 0.7369 \| 0.7544 \| 0.7562 \| 0.7717 \| 0.7639 \| 0.3068 \| \| 0.0381 \| 7.32 \| 3660 \| 0.2086 \| 0.6088 \| 0.7409 \| 0.8124 \| nan \| 0.8592 \| 0.8914 \| 0.7294 \| 0.8354 \| 0.7693 \| 0.8301 \| 0.7288 \| 0.3523 \| 0.5901 \| 0.6420 \| 0.9119 \| 0.8092 \| 0.8492 \| 0.8283 \| 0.8480 \| 0.8010 \| 0.3202 \| 0.0 \| 0.7428 \| 0.8083 \| 0.6265 \| 0.7509 \| 0.6160 \| 0.7504 \| 0.5294 \| 0.2703 \| 0.4945 \| 0.5303 \| 0.8220 \| 0.7244 \| 0.7482 \| 0.7515 \| 0.7664 \| 0.7466 \| 0.2808 \| \| 0.0464 \| 7.36 \| 3680 \| 0.2155 \| 0.6097 \| 0.7379 \| 0.8135 \| nan \| 0.8474 \| 0.8975 \| 0.7075 \| 0.8456 \| 0.7282 \| 0.8638 \| 0.7149 \| 0.3864 \| 0.5974 \| 0.5819 \| 0.9084 \| 0.8256 \| 0.8455 \| 0.8365 \| 0.8537 \| 0.7817 \| 0.3218 \| 0.0 \| 0.7408 \| 0.8088 \| 0.6178 \| 0.7546 \| 0.5922 \| 0.7590 \| 0.5152 \| 0.3069 \| 0.4931 \| 0.5063 \| 0.8208 \| 0.7335 \| 0.7535 \| 0.7598 \| 0.7728 \| 0.7445 \| 0.2955 \| \| 0.0614 \| 7.4 \| 3700 \| 0.2195 \| 0.6135 \| 0.7500 \| 0.8110 \| nan \| 0.8656 \| 0.8814 \| 0.7297 \| 0.8356 \| 0.7074 \| 0.8448 \| 0.7365 \| 0.4880 \| 0.5984 \| 0.5838 \| 0.9176 \| 0.8240 \| 0.8345 \| 0.8315 \| 0.8677 \| 0.8213 \| 0.3814 \| 0.0 \| 0.7419 \| 0.8070 \| 0.6230 \| 0.7469 \| 0.6014 \| 0.7525 \| 0.5221 \| 0.3509 \| 0.4926 \| 0.5007 \| 0.8195 \| 0.7314 \| 0.7503 \| 0.7538 \| 0.7739 \| 0.7641 \| 0.3112 \| \| 0.0387 \| 7.44 \| 3720 \| 0.2203 \| 0.6204 \| 0.7548 \| 0.8177 \| nan \| 0.8613 \| 0.8878 \| 0.7302 \| 0.8424 \| 0.7072 \| 0.8624 \| 0.7557 \| 0.4943 \| 0.6047 \| 0.6229 \| 0.8990 \| 0.8379 \| 0.8324 \| 0.8634 \| 0.8426 \| 0.8073 \| 0.3807 \| 0.0 \| 0.7446 \| 0.8094 \| 0.6272 \| 0.7534 \| 0.6154 \| 0.7665 \| 0.5337 \| 0.3675 \| 0.5036 \| 0.5203 \| 0.8211 \| 0.7351 \| 0.7515 \| 0.7663 \| 0.7764 \| 0.7584 \| 0.3162 \| \| 0.0756 \| 7.48 \| 3740 \| 0.2238 \| 0.6159 \| 0.7463 \| 0.8187 \| nan \| 0.8548 \| 0.8948 \| 0.7299 \| 0.8325 \| 0.7072 \| 0.8686 \| 0.7847 \| 0.3186 \| 0.5954 \| 0.6298 \| 0.9106 \| 0.8341 \| 0.8262 \| 0.8541 \| 0.8236 \| 0.8073 \| 0.4144 \| 0.0 \| 0.7526 \| 0.8121 \| 0.6257 \| 0.7509 \| 0.6249 \| 0.7643 \| 0.5441 \| 0.2633 \| 0.5015 \| 0.5210 \| 0.8226 \| 0.7318 \| 0.7506 \| 0.7612 \| 0.7694 \| 0.7608 \| 0.3291 \| \| 0.0442 \| 7.52 \| 3760 \| 0.2139 \| 0.6150 \| 0.7454 \| 0.8186 \| nan \| 0.8580 \| 0.9114 \| 0.7052 \| 0.8456 \| 0.6986 \| 0.8814 \| 0.7237 \| 0.3424 \| 0.6381 \| 0.5776 \| 0.9101 \| 0.8323 \| 0.8491 \| 0.8561 \| 0.8353 \| 0.8024 \| 0.4048 \| 0.0 \| 0.7541 \| 0.8137 \| 0.6187 \| 0.7590 \| 0.5916 \| 0.7691 \| 0.5252 \| 0.2908 \| 0.5133 \| 0.4968 \| 0.8226 \| 0.7350 \| 0.7548 \| 0.7660 \| 0.7775 \| 0.7583 \| 0.3228 \| \| 0.0539 \| 7.56 \| 3780 \| 0.2026 \| 0.6198 \| 0.7487 \| 0.8260 \| nan \| 0.8731 \| 0.8995 \| 0.7449 \| 0.8710 \| 0.7200 \| 0.8849 \| 0.7157 \| 0.3682 \| 0.6338 \| 0.6196 \| 0.9113 \| 0.7938 \| 0.8676 \| 0.8560 \| 0.8503 \| 0.8035 \| 0.3142 \| 0.0 \| 0.7541 \| 0.8119 \| 0.6326 \| 0.7762 \| 0.6140 \| 0.7747 \| 0.5466 \| 0.3147 \| 0.5172 \| 0.5266 \| 0.8235 \| 0.7179 \| 0.7479 \| 0.7624 \| 0.7809 \| 0.7632 \| 0.2924 \| \| 0.047 \| 7.6 \| 3800 \| 0.2024 \| 0.6212 \| 0.7475 \| 0.8296 \| nan \| 0.8603 \| 0.8992 \| 0.7020 \| 0.8756 \| 0.7303 \| 0.8862 \| 0.7304 \| 0.3776 \| 0.6126 \| 0.5955 \| 0.9141 \| 0.8511 \| 0.8107 \| 0.8579 \| 0.8407 \| 0.8378 \| 0.3254 \| 0.0 \| 0.7505 \| 0.8158 \| 0.6163 \| 0.7818 \| 0.6228 \| 0.7744 \| 0.5596 \| 0.3137 \| 0.5023 \| 0.5141 \| 0.8244 \| 0.7330 \| 0.7436 \| 0.7663 \| 0.7774 \| 0.7821 \| 0.3034 \| \| 0.0467 \| 7.64 \| 3820 \| 0.2028 \| 0.6129 \| 0.7373 \| 0.8199 \| nan \| 0.8027 \| 0.8986 \| 0.6825 \| 0.8596 \| 0.7446 \| 0.8921 \| 0.7136 \| 0.3488 \| 0.5648 \| 0.6284 \| 0.8980 \| 0.8360 \| 0.7928 \| 0.8416 \| 0.8354 \| 0.8113 \| 0.3827 \| 0.0 \| 0.7326 \| 0.8131 \| 0.6084 \| 0.7703 \| 0.6213 \| 0.7694 \| 0.5378 \| 0.2919 \| 0.4774 \| 0.5175 \| 0.8227 \| 0.7184 \| 0.7291 \| 0.7620 \| 0.7710 \| 0.7670 \| 0.3217 \| \| 0.0385 \| 7.68 \| 3840 \| 0.2022 \| 0.6145 \| 0.7447 \| 0.8220 \| nan \| 0.8519 \| 0.8908 \| 0.7347 \| 0.8602 \| 0.7322 \| 0.8846 \| 0.7235 \| 0.3646 \| 0.5684 \| 0.5708 \| 0.9190 \| 0.8485 \| 0.8332 \| 0.8654 \| 0.8127 \| 0.8194 \| 0.3794 \| 0.0 \| 0.7498 \| 0.8106 \| 0.6242 \| 0.7719 \| 0.6280 \| 0.7682 \| 0.5460 \| 0.3105 \| 0.4718 \| 0.4958 \| 0.8232 \| 0.7229 \| 0.7411 \| 0.7460 \| 0.7484 \| 0.7711 \| 0.3320 \| \| 0.082 \| 7.72 \| 3860 \| 0.2047 \| 0.6180 \| 0.7498 \| 0.8261 \| nan \| 0.8602 \| 0.9006 \| 0.7283 \| 0.8621 \| 0.7385 \| 0.9008 \| 0.7200 \| 0.3629 \| 0.6560 \| 0.5607 \| 0.9057 \| 0.8369 \| 0.8220 \| 0.8445 \| 0.8385 \| 0.8283 \| 0.3802 \| 0.0 \| 0.7502 \| 0.8141 \| 0.6240 \| 0.7764 \| 0.6321 \| 0.7699 \| 0.5491 \| 0.3007 \| 0.5019 \| 0.4872 \| 0.8222 \| 0.7280 \| 0.7474 \| 0.7510 \| 0.7594 \| 0.7764 \| 0.3344 \| \| 0.0452 \| 7.76 \| 3880 \| 0.2026 \| 0.6187 \| 0.7544 \| 0.8228 \| nan \| 0.8522 \| 0.8847 \| 0.7730 \| 0.8698 \| 0.7117 \| 0.8773 \| 0.7253 \| 0.4656 \| 0.5983 \| 0.6178 \| 0.9034 \| 0.8591 \| 0.8158 \| 0.8700 \| 0.7850 \| 0.8353 \| 0.3805 \| 0.0 \| 0.7390 \| 0.8077 \| 0.6320 \| 0.7788 \| 0.6067 \| 0.7798 \| 0.5474 \| 0.3492 \| 0.4907 \| 0.5241 \| 0.8225 \| 0.7265 \| 0.7491 \| 0.7399 \| 0.7383 \| 0.7763 \| 0.3284 \| \| 0.0709 \| 7.8 \| 3900 \| 0.2129 \| 0.6168 \| 0.7514 \| 0.8228 \| nan \| 0.8691 \| 0.8992 \| 0.7177 \| 0.8494 \| 0.7060 \| 0.8733 \| 0.7717 \| 0.3807 \| 0.6129 \| 0.5954 \| 0.8941 \| 0.8474 \| 0.8247 \| 0.8548 \| 0.8000 \| 0.8376 \| 0.4392 \| 0.0 \| 0.7570 \| 0.8118 \| 0.6217 \| 0.7666 \| 0.6198 \| 0.7782 \| 0.5532 \| 0.3023 \| 0.4885 \| 0.5096 \| 0.8215 \| 0.7249 \| 0.7523 \| 0.7414 \| 0.7392 \| 0.7806 \| 0.3340 \| \| 0.0445 \| 7.84 \| 3920 \| 0.2112 \| 0.6152 \| 0.7479 \| 0.8208 \| nan \| 0.8354 \| 0.8968 \| 0.7225 \| 0.8596 \| 0.7025 \| 0.8762 \| 0.7309 \| 0.4132 \| 0.5801 \| 0.6012 \| 0.9090 \| 0.8403 \| 0.8277 \| 0.8304 \| 0.8310 \| 0.8418 \| 0.4157 \| 0.0 \| 0.7408 \| 0.8104 \| 0.6227 \| 0.7721 \| 0.5956 \| 0.7748 \| 0.5390 \| 0.3149 \| 0.4730 \| 0.5026 \| 0.8231 \| 0.7298 \| 0.7588 \| 0.7489 \| 0.7601 \| 0.7798 \| 0.3279 \| \| 0.0684 \| 7.88 \| 3940 \| 0.2073 \| 0.6162 \| 0.7520 \| 0.8212 \| nan \| 0.8597 \| 0.8945 \| 0.7100 \| 0.8620 \| 0.7031 \| 0.8764 \| 0.7234 \| 0.4659 \| 0.6066 \| 0.6061 \| 0.9092 \| 0.8544 \| 0.8132 \| 0.8451 \| 0.8228 \| 0.8359 \| 0.3964 \| 0.0 \| 0.7482 \| 0.8133 \| 0.6172 \| 0.7733 \| 0.5902 \| 0.7734 \| 0.5329 \| 0.3347 \| 0.4877 \| 0.5064 \| 0.8232 \| 0.7360 \| 0.7534 \| 0.7511 \| 0.7568 \| 0.7794 \| 0.3151 \| \| 0.0461 \| 7.92 \| 3960 \| 0.2076 \| 0.6157 \| 0.7469 \| 0.8250 \| nan \| 0.8594 \| 0.8986 \| 0.6659 \| 0.8770 \| 0.7159 \| 0.8692 \| 0.7100 \| 0.4604 \| 0.5639 \| 0.5988 \| 0.9099 \| 0.8512 \| 0.8546 \| 0.8655 \| 0.8396 \| 0.8200 \| 0.3381 \| 0.0 \| 0.7589 \| 0.8132 \| 0.5961 \| 0.7836 \| 0.5913 \| 0.7689 \| 0.5409 \| 0.3507 \| 0.4729 \| 0.5124 \| 0.8228 \| 0.7347 \| 0.7600 \| 0.7532 \| 0.7589 \| 0.7715 \| 0.2926 \| \| 0.053 \| 7.96 \| 3980 \| 0.2174 \| 0.6106 \| 0.7430 \| 0.8218 \| nan \| 0.8738 \| 0.8890 \| 0.7214 \| 0.8702 \| 0.7303 \| 0.8785 \| 0.7177 \| 0.3748 \| 0.5630 \| 0.5626 \| 0.9187 \| 0.8729 \| 0.7903 \| 0.8706 \| 0.8151 \| 0.8118 \| 0.3701 \| 0.0 \| 0.7605 \| 0.8134 \| 0.6223 \| 0.7789 \| 0.6070 \| 0.7638 \| 0.5417 \| 0.3003 \| 0.4682 \| 0.4870 \| 0.8225 \| 0.7172 \| 0.7317 \| 0.7506 \| 0.7546 \| 0.7626 \| 0.3087 \| \| 0.063 \| 8.0 \| 4000 \| 0.2192 \| 0.6122 \| 0.7454 \| 0.8158 \| nan \| 0.8402 \| 0.9083 \| 0.7397 \| 0.8477 \| 0.6991 \| 0.8612 \| 0.7566 \| 0.4382 \| 0.5439 \| 0.5893 \| 0.8825 \| 0.8527 \| 0.8119 \| 0.8538 \| 0.8192 \| 0.8023 \| 0.4255 \| 0.0 \| 0.7532 \| 0.8109 \| 0.6301 \| 0.7640 \| 0.6155 \| 0.7572 \| 0.5442 \| 0.3352 \| 0.4609 \| 0.5009 \| 0.8170 \| 0.7196 \| 0.7373 \| 0.7428 \| 0.7510 \| 0.7598 \| 0.3202 \| \| 0.0696 \| 8.04 \| 4020 \| 0.2176 \| 0.6092 \| 0.7437 \| 0.8174 \| nan \| 0.8501 \| 0.8994 \| 0.7617 \| 0.8430 \| 0.7154 \| 0.8691 \| 0.7603 \| 0.3024 \| 0.5760 \| 0.6084 \| 0.9050 \| 0.7923 \| 0.8546 \| 0.8203 \| 0.8350 \| 0.8042 \| 0.4453 \| 0.0 \| 0.7502 \| 0.8153 \| 0.6318 \| 0.7643 \| 0.6147 \| 0.7635 \| 0.5416 \| 0.2431 \| 0.4767 \| 0.5068 \| 0.8239 \| 0.7155 \| 0.7448 \| 0.7377 \| 0.7574 \| 0.7632 \| 0.3147 \| \| 0.0716 \| 8.08 \| 4040 \| 0.2081 \| 0.6164 \| 0.7460 \| 0.8256 \| nan \| 0.8457 \| 0.8875 \| 0.7553 \| 0.8637 \| 0.7370 \| 0.8820 \| 0.7645 \| 0.3343 \| 0.6189 \| 0.5919 \| 0.9100 \| 0.8438 \| 0.8032 \| 0.8669 \| 0.7875 \| 0.8119 \| 0.3787 \| 0.0 \| 0.7475 \| 0.8098 \| 0.6328 \| 0.7769 \| 0.6563 \| 0.7717 \| 0.5780 \| 0.2759 \| 0.4884 \| 0.5018 \| 0.8258 \| 0.7273 \| 0.7430 \| 0.7411 \| 0.7404 \| 0.7682 \| 0.3097 \| \| 0.0476 \| 8.12 \| 4060 \| 0.2121 \| 0.6152 \| 0.7445 \| 0.8213 \| nan \| 0.8776 \| 0.8912 \| 0.7376 \| 0.8673 \| 0.7077 \| 0.8765 \| 0.7191 \| 0.3780 \| 0.5497 \| 0.6293 \| 0.9055 \| 0.8189 \| 0.8572 \| 0.8369 \| 0.8284 \| 0.8291 \| 0.3461 \| 0.0 \| 0.7476 \| 0.8102 \| 0.6290 \| 0.7731 \| 0.6066 \| 0.7692 \| 0.5422 \| 0.3104 \| 0.4700 \| 0.5254 \| 0.8235 \| 0.7342 \| 0.7590 \| 0.7401 \| 0.7570 \| 0.7771 \| 0.2989 \| \| 0.0282 \| 8.16 \| 4080 \| 0.2227 \| 0.6179 \| 0.7503 \| 0.8233 \| nan \| 0.8493 \| 0.8953 \| 0.7243 \| 0.8609 \| 0.7054 \| 0.8850 \| 0.7402 \| 0.4689 \| 0.5992 \| 0.6032 \| 0.9098 \| 0.8674 \| 0.8176 \| 0.8304 \| 0.8224 \| 0.8261 \| 0.3496 \| 0.0 \| 0.7464 \| 0.8135 \| 0.6221 \| 0.7722 \| 0.6173 \| 0.7711 \| 0.5455 \| 0.3437 \| 0.4873 \| 0.5114 \| 0.8242 \| 0.7384 \| 0.7545 \| 0.7427 \| 0.7597 \| 0.7755 \| 0.2974 \| \| 0.066 \| 8.2 \| 4100 \| 0.2196 \| 0.6178 \| 0.7515 \| 0.8234 \| nan \| 0.8605 \| 0.8867 \| 0.7520 \| 0.8588 \| 0.6987 \| 0.8821 \| 0.7467 \| 0.3982 \| 0.5296 \| 0.7016 \| 0.9118 \| 0.8113 \| 0.8563 \| 0.8405 \| 0.8277 \| 0.8350 \| 0.3778 \| 0.0 \| 0.7526 \| 0.8116 \| 0.6271 \| 0.7691 \| 0.6182 \| 0.7677 \| 0.5441 \| 0.3201 \| 0.4636 \| 0.5442 \| 0.8241 \| 0.7320 \| 0.7581 \| 0.7477 \| 0.7625 \| 0.7792 \| 0.2986 \| \| 0.0778 \| 8.24 \| 4120 \| 0.2207 \| 0.6158 \| 0.7519 \| 0.8196 \| nan \| 0.8662 \| 0.8984 \| 0.7375 \| 0.8417 \| 0.7046 \| 0.8827 \| 0.7453 \| 0.4193 \| 0.5823 \| 0.6251 \| 0.9098 \| 0.8391 \| 0.8376 \| 0.8596 \| 0.8218 \| 0.8180 \| 0.3927 \| 0.0 \| 0.7475 \| 0.8082 \| 0.6272 \| 0.7637 \| 0.6183 \| 0.7644 \| 0.5440 \| 0.3164 \| 0.4880 \| 0.5196 \| 0.8225 \| 0.7363 \| 0.7576 \| 0.7433 \| 0.7527 \| 0.7708 \| 0.3040 \| \| 0.0467 \| 8.28 \| 4140 \| 0.2348 \| 0.6066 \| 0.7388 \| 0.8137 \| nan \| 0.8778 \| 0.8864 \| 0.7390 \| 0.8421 \| 0.6967 \| 0.8328 \| 0.7716 \| 0.2713 \| 0.5635 \| 0.6553 \| 0.9114 \| 0.8487 \| 0.8293 \| 0.8234 \| 0.8276 \| 0.8004 \| 0.3817 \| 0.0 \| 0.7537 \| 0.8105 \| 0.6265 \| 0.7509 \| 0.5995 \| 0.7384 \| 0.5331 \| 0.2300 \| 0.4803 \| 0.5344 \| 0.8265 \| 0.7321 \| 0.7541 \| 0.7356 \| 0.7555 \| 0.7611 \| 0.2958 \| \| 0.0729 \| 8.32 \| 4160 \| 0.2377 \| 0.6016 \| 0.7342 \| 0.8092 \| nan \| 0.8659 \| 0.9055 \| 0.7306 \| 0.8257 \| 0.6606 \| 0.8228 \| 0.7871 \| 0.2538 \| 0.5477 \| 0.6017 \| 0.9047 \| 0.8374 \| 0.8502 \| 0.8598 \| 0.8237 \| 0.8064 \| 0.3972 \| 0.0 \| 0.7492 \| 0.8113 \| 0.6273 \| 0.7462 \| 0.5713 \| 0.7264 \| 0.5335 \| 0.2263 \| 0.4677 \| 0.5076 \| 0.8252 \| 0.7278 \| 0.7542 \| 0.7386 \| 0.7492 \| 0.7609 \| 0.3055 \| \| 0.0314 \| 8.36 \| 4180 \| 0.2331 \| 0.6053 \| 0.7385 \| 0.8127 \| nan \| 0.8451 \| 0.9049 \| 0.7290 \| 0.8338 \| 0.6926 \| 0.8417 \| 0.7630 \| 0.2421 \| 0.5876 \| 0.6142 \| 0.8928 \| 0.8661 \| 0.8179 \| 0.8467 \| 0.7945 \| 0.8287 \| 0.4544 \| 0.0 \| 0.7498 \| 0.8132 \| 0.6277 \| 0.7472 \| 0.6012 \| 0.7386 \| 0.5316 \| 0.2206 \| 0.4866 \| 0.5158 \| 0.8222 \| 0.7240 \| 0.7455 \| 0.7416 \| 0.7438 \| 0.7740 \| 0.3121 \| \| 0.0417 \| 8.4 \| 4200 \| 0.2297 \| 0.6117 \| 0.7444 \| 0.8154 \| nan \| 0.8657 \| 0.8996 \| 0.7438 \| 0.8327 \| 0.6827 \| 0.8573 \| 0.7638 \| 0.3149 \| 0.5832 \| 0.6047 \| 0.9116 \| 0.8488 \| 0.8428 \| 0.8714 \| 0.8068 \| 0.8253 \| 0.3989 \| 0.0 \| 0.7633 \| 0.8123 \| 0.6302 \| 0.7487 \| 0.6098 \| 0.7502 \| 0.5344 \| 0.2728 \| 0.4839 \| 0.5104 \| 0.8265 \| 0.7317 \| 0.7573 \| 0.7467 \| 0.7452 \| 0.7725 \| 0.3153 \| \| 0.05 \| 8.44 \| 4220 \| 0.2271 \| 0.6161 \| 0.7455 \| 0.8215 \| nan \| 0.8631 \| 0.9026 \| 0.7511 \| 0.8575 \| 0.6986 \| 0.8655 \| 0.7491 \| 0.3346 \| 0.5949 \| 0.6104 \| 0.8962 \| 0.8472 \| 0.8347 \| 0.8632 \| 0.8054 \| 0.8265 \| 0.3734 \| 0.0 \| 0.7625 \| 0.8138 \| 0.6335 \| 0.7622 \| 0.6207 \| 0.7495 \| 0.5443 \| 0.2922 \| 0.4888 \| 0.5166 \| 0.8246 \| 0.7395 \| 0.7607 \| 0.7435 \| 0.7497 \| 0.7785 \| 0.3096 \| \| 0.0541 \| 8.48 \| 4240 \| 0.2227 \| 0.6164 \| 0.7388 \| 0.8272 \| nan \| 0.8478 \| 0.9055 \| 0.7101 \| 0.8879 \| 0.6953 \| 0.8455 \| 0.7449 \| 0.2957 \| 0.6119 \| 0.6019 \| 0.9073 \| 0.8250 \| 0.8470 \| 0.8558 \| 0.8319 \| 0.8216 \| 0.3246 \| 0.0 \| 0.7695 \| 0.8174 \| 0.6182 \| 0.7706 \| 0.6331 \| 0.7388 \| 0.5698 \| 0.2683 \| 0.4955 \| 0.5107 \| 0.8253 \| 0.7389 \| 0.7609 \| 0.7467 \| 0.7556 \| 0.7774 \| 0.2987 \| \| 0.0361 \| 8.52 \| 4260 \| 0.2275 \| 0.6168 \| 0.7421 \| 0.8272 \| nan \| 0.8536 \| 0.8995 \| 0.7183 \| 0.8799 \| 0.7048 \| 0.8417 \| 0.7611 \| 0.3506 \| 0.5552 \| 0.6506 \| 0.9142 \| 0.8481 \| 0.8468 \| 0.8534 \| 0.8210 \| 0.8173 \| 0.2988 \| 0.0 \| 0.7541 \| 0.8131 \| 0.6184 \| 0.7697 \| 0.6383 \| 0.7402 \| 0.5791 \| 0.2963 \| 0.4795 \| 0.5348 \| 0.8234 \| 0.7439 \| 0.7606 \| 0.7444 \| 0.7527 \| 0.7714 \| 0.2818 \| \| 0.0389 \| 8.56 \| 4280 \| 0.2229 \| 0.6198 \| 0.7505 \| 0.8290 \| nan \| 0.8848 \| 0.8979 \| 0.7303 \| 0.8820 \| 0.6991 \| 0.8480 \| 0.7562 \| 0.4229 \| 0.6257 \| 0.6414 \| 0.9137 \| 0.8404 \| 0.8390 \| 0.8373 \| 0.8252 \| 0.8380 \| 0.2764 \| 0.0 \| 0.7545 \| 0.8152 \| 0.6212 \| 0.7709 \| 0.6410 \| 0.7464 \| 0.5771 \| 0.3138 \| 0.5133 \| 0.5295 \| 0.8244 \| 0.7366 \| 0.7597 \| 0.7498 \| 0.7575 \| 0.7848 \| 0.2609 \| \| 0.0558 \| 8.6 \| 4300 \| 0.2195 \| 0.6148 \| 0.7425 \| 0.8239 \| nan \| 0.8746 \| 0.8951 \| 0.7349 \| 0.8719 \| 0.7043 \| 0.8426 \| 0.7614 \| 0.3675 \| 0.5751 \| 0.6383 \| 0.9055 \| 0.8457 \| 0.8271 \| 0.8390 \| 0.8284 \| 0.8273 \| 0.2837 \| 0.0 \| 0.7525 \| 0.8152 \| 0.6213 \| 0.7658 \| 0.6393 \| 0.7416 \| 0.5705 \| 0.2937 \| 0.4901 \| 0.5255 \| 0.8239 \| 0.7295 \| 0.7502 \| 0.7522 \| 0.7592 \| 0.7772 \| 0.2592 \| \| 0.0518 \| 8.64 \| 4320 \| 0.2243 \| 0.6100 \| 0.7395 \| 0.8187 \| nan \| 0.8573 \| 0.8882 \| 0.6744 \| 0.8370 \| 0.7125 \| 0.8549 \| 0.7943 \| 0.3636 \| 0.5806 \| 0.6465 \| 0.9170 \| 0.8424 \| 0.8206 \| 0.8424 \| 0.8282 \| 0.8064 \| 0.3046 \| 0.0 \| 0.7494 \| 0.8109 \| 0.5947 \| 0.7505 \| 0.6412 \| 0.7541 \| 0.5576 \| 0.2835 \| 0.4915 \| 0.5322 \| 0.8231 \| 0.7243 \| 0.7405 \| 0.7508 \| 0.7564 \| 0.7662 \| 0.2525 \| \| 0.0494 \| 8.68 \| 4340 \| 0.2253 \| 0.6195 \| 0.7551 \| 0.8216 \| nan \| 0.8610 \| 0.8936 \| 0.7012 \| 0.8349 \| 0.7050 \| 0.8833 \| 0.7699 \| 0.4646 \| 0.6088 \| 0.6337 \| 0.9047 \| 0.8290 \| 0.8393 \| 0.8325 \| 0.8498 \| 0.8475 \| 0.3772 \| 0.0 \| 0.7665 \| 0.8150 \| 0.6144 \| 0.7542 \| 0.6231 \| 0.7692 \| 0.5457 \| 0.3459 \| 0.5041 \| 0.5377 \| 0.8237 \| 0.7256 \| 0.7486 \| 0.7537 \| 0.7631 \| 0.7836 \| 0.2767 \| \| 0.0383 \| 8.72 \| 4360 \| 0.2340 \| 0.6198 \| 0.7559 \| 0.8203 \| nan \| 0.8520 \| 0.9032 \| 0.6900 \| 0.8180 \| 0.7041 \| 0.8829 \| 0.7936 \| 0.4585 \| 0.5947 \| 0.6318 \| 0.9162 \| 0.8363 \| 0.8426 \| 0.8445 \| 0.8350 \| 0.8253 \| 0.4223 \| 0.0 \| 0.7709 \| 0.8145 \| 0.6086 \| 0.7455 \| 0.6360 \| 0.7683 \| 0.5511 \| 0.3486 \| 0.4953 \| 0.5325 \| 0.8247 \| 0.7336 \| 0.7517 \| 0.7556 \| 0.7600 \| 0.7761 \| 0.2841 \| \| 0.0375 \| 8.76 \| 4380 \| 0.2217 \| 0.6190 \| 0.7508 \| 0.8206 \| nan \| 0.8522 \| 0.8948 \| 0.7449 \| 0.8423 \| 0.7028 \| 0.8788 \| 0.7530 \| 0.3981 \| 0.5796 \| 0.6369 \| 0.9089 \| 0.8378 \| 0.8432 \| 0.8509 \| 0.8432 \| 0.8251 \| 0.3711 \| 0.0 \| 0.7654 \| 0.8135 \| 0.6285 \| 0.7568 \| 0.6153 \| 0.7698 \| 0.5412 \| 0.3309 \| 0.4905 \| 0.5324 \| 0.8261 \| 0.7353 \| 0.7564 \| 0.7579 \| 0.7658 \| 0.7739 \| 0.2821 \| \| 0.0367 \| 8.8 \| 4400 \| 0.2246 \| 0.6145 \| 0.7416 \| 0.8197 \| nan \| 0.8334 \| 0.9068 \| 0.7256 \| 0.8322 \| 0.7058 \| 0.8781 \| 0.7749 \| 0.3536 \| 0.6074 \| 0.5935 \| 0.9129 \| 0.8687 \| 0.8181 \| 0.8378 \| 0.8418 \| 0.8087 \| 0.3076 \| 0.0 \| 0.7557 \| 0.8191 \| 0.6192 \| 0.7533 \| 0.6236 \| 0.7658 \| 0.5453 \| 0.3045 \| 0.4992 \| 0.5132 \| 0.8244 \| 0.7292 \| 0.7469 \| 0.7588 \| 0.7727 \| 0.7672 \| 0.2636 \| \| 0.074 \| 8.84 \| 4420 \| 0.2326 \| 0.6138 \| 0.7419 \| 0.8193 \| nan \| 0.8483 \| 0.8924 \| 0.7183 \| 0.8400 \| 0.7080 \| 0.8732 \| 0.7632 \| 0.3716 \| 0.5848 \| 0.6264 \| 0.9149 \| 0.8326 \| 0.8520 \| 0.8216 \| 0.8525 \| 0.8141 \| 0.2978 \| 0.0 \| 0.7572 \| 0.8164 \| 0.6165 \| 0.7537 \| 0.6143 \| 0.7643 \| 0.5398 \| 0.2985 \| 0.4928 \| 0.5247 \| 0.8242 \| 0.7314 \| 0.7556 \| 0.7516 \| 0.7737 \| 0.7704 \| 0.2632 \| \| 0.038 \| 8.88 \| 4440 \| 0.2314 \| 0.6220 \| 0.7566 \| 0.8234 \| nan \| 0.8469 \| 0.9163 \| 0.7150 \| 0.8502 \| 0.6724 \| 0.8791 \| 0.7627 \| 0.5999 \| 0.6023 \| 0.6458 \| 0.9025 \| 0.8408 \| 0.8365 \| 0.8592 \| 0.8411 \| 0.8110 \| 0.2815 \| 0.0 \| 0.7603 \| 0.8159 \| 0.6218 \| 0.7619 \| 0.6140 \| 0.7485 \| 0.5529 \| 0.4128 \| 0.5023 \| 0.5385 \| 0.8229 \| 0.7336 \| 0.7566 \| 0.7688 \| 0.7776 \| 0.7600 \| 0.2481 \| \| 0.0486 \| 8.92 \| 4460 \| 0.2265 \| 0.6176 \| 0.7471 \| 0.8218 \| nan \| 0.8489 \| 0.8824 \| 0.7234 \| 0.8589 \| 0.6716 \| 0.8823 \| 0.7616 \| 0.4996 \| 0.5337 \| 0.6612 \| 0.9079 \| 0.8436 \| 0.8342 \| 0.8490 \| 0.8480 \| 0.8212 \| 0.2740 \| 0.0 \| 0.7505 \| 0.8106 \| 0.6248 \| 0.7667 \| 0.6140 \| 0.7534 \| 0.5585 \| 0.3704 \| 0.4673 \| 0.5368 \| 0.8259 \| 0.7315 \| 0.7565 \| 0.7639 \| 0.7729 \| 0.7609 \| 0.2529 \| \| 0.0589 \| 8.96 \| 4480 \| 0.2335 \| 0.6140 \| 0.7404 \| 0.8184 \| nan \| 0.8318 \| 0.9004 \| 0.7298 \| 0.8342 \| 0.6780 \| 0.8726 \| 0.7916 \| 0.3401 \| 0.5608 \| 0.6654 \| 0.9077 \| 0.8507 \| 0.8166 \| 0.8645 \| 0.8000 \| 0.8166 \| 0.3265 \| 0.0 \| 0.7470 \| 0.8131 \| 0.6265 \| 0.7503 \| 0.6308 \| 0.7604 \| 0.5579 \| 0.2867 \| 0.4791 \| 0.5382 \| 0.8258 \| 0.7323 \| 0.7529 \| 0.7567 \| 0.7570 \| 0.7584 \| 0.2784 \| \| 0.0928 \| 9.0 \| 4500 \| 0.2234 \| 0.6170 \| 0.7451 \| 0.8261 \| nan \| 0.8674 \| 0.8965 \| 0.7326 \| 0.8676 \| 0.6852 \| 0.8826 \| 0.7584 \| 0.2819 \| 0.6002 \| 0.6321 \| 0.9084 \| 0.8186 \| 0.8459 \| 0.8650 \| 0.8278 \| 0.8071 \| 0.3889 \| 0.0 \| 0.7652 \| 0.8184 \| 0.6286 \| 0.7709 \| 0.6255 \| 0.7565 \| 0.5666 \| 0.2459 \| 0.4963 \| 0.5297 \| 0.8279 \| 0.7332 \| 0.7606 \| 0.7590 \| 0.7643 \| 0.7585 \| 0.2994 \| \| 0.0573 \| 9.04 \| 4520 \| 0.2235 \| 0.6203 \| 0.7464 \| 0.8278 \| nan \| 0.8441 \| 0.9048 \| 0.7557 \| 0.8764 \| 0.6927 \| 0.8730 \| 0.7553 \| 0.3351 \| 0.5800 \| 0.6431 \| 0.8976 \| 0.8401 \| 0.8388 \| 0.8349 \| 0.8396 \| 0.8132 \| 0.3647 \| 0.0 \| 0.7572 \| 0.8168 \| 0.6361 \| 0.7754 \| 0.6289 \| 0.7582 \| 0.5684 \| 0.2740 \| 0.4915 \| 0.5384 \| 0.8271 \| 0.7325 \| 0.7539 \| 0.7550 \| 0.7734 \| 0.7672 \| 0.3111 \| \| 0.0523 \| 9.08 \| 4540 \| 0.2217 \| 0.6225 \| 0.7516 \| 0.8277 \| nan \| 0.8399 \| 0.8935 \| 0.7553 \| 0.8741 \| 0.6888 \| 0.8867 \| 0.7466 \| 0.4573 \| 0.5676 \| 0.6488 \| 0.9148 \| 0.8437 \| 0.8369 \| 0.8397 \| 0.8095 \| 0.8470 \| 0.3272 \| 0.0 \| 0.7538 \| 0.8140 \| 0.6335 \| 0.7796 \| 0.6226 \| 0.7658 \| 0.5625 \| 0.3275 \| 0.4876 \| 0.5414 \| 0.8296 \| 0.7301 \| 0.7501 \| 0.7519 \| 0.7637 \| 0.7855 \| 0.3064 \| \| 0.0372 \| 9.12 \| 4560 \| 0.2219 \| 0.6164 \| 0.7455 \| 0.8271 \| nan \| 0.8473 \| 0.9010 \| 0.7986 \| 0.8733 \| 0.7194 \| 0.8896 \| 0.7262 \| 0.2736 \| 0.6386 \| 0.5583 \| 0.9006 \| 0.8329 \| 0.8349 \| 0.8477 \| 0.8408 \| 0.8291 \| 0.3614 \| 0.0 \| 0.7641 \| 0.8176 \| 0.6378 \| 0.7793 \| 0.6240 \| 0.7654 \| 0.5526 \| 0.2364 \| 0.5000 \| 0.4922 \| 0.8266 \| 0.7301 \| 0.7500 \| 0.7622 \| 0.7750 \| 0.7774 \| 0.3053 \| \| 0.0338 \| 9.16 \| 4580 \| 0.2194 \| 0.6151 \| 0.7444 \| 0.8235 \| nan \| 0.8565 \| 0.9045 \| 0.7748 \| 0.8561 \| 0.6996 \| 0.8831 \| 0.7412 \| 0.2413 \| 0.5597 \| 0.6618 \| 0.9050 \| 0.8354 \| 0.8398 \| 0.8621 \| 0.8231 \| 0.8231 \| 0.3871 \| 0.0 \| 0.7564 \| 0.8160 \| 0.6372 \| 0.7708 \| 0.6168 \| 0.7642 \| 0.5501 \| 0.2192 \| 0.4849 \| 0.5388 \| 0.8262 \| 0.7314 \| 0.7486 \| 0.7605 \| 0.7652 \| 0.7703 \| 0.3151 \| \| 0.0257 \| 9.2 \| 4600 \| 0.2310 \| 0.6161 \| 0.7447 \| 0.8228 \| nan \| 0.8637 \| 0.9010 \| 0.7017 \| 0.8463 \| 0.6798 \| 0.8983 \| 0.7594 \| 0.3465 \| 0.5853 \| 0.6059 \| 0.9181 \| 0.8553 \| 0.8271 \| 0.8657 \| 0.8445 \| 0.8027 \| 0.3584 \| 0.0 \| 0.7591 \| 0.8155 \| 0.6141 \| 0.7665 \| 0.6172 \| 0.7607 \| 0.5539 \| 0.2841 \| 0.4879 \| 0.5133 \| 0.8245 \| 0.7351 \| 0.7456 \| 0.7654 \| 0.7720 \| 0.7611 \| 0.3139 \| \| 0.0361 \| 9.24 \| 4620 \| 0.2252 \| 0.6177 \| 0.7411 \| 0.8252 \| nan \| 0.8602 \| 0.8865 \| 0.7017 \| 0.8594 \| 0.6979 \| 0.8903 \| 0.7653 \| 0.2889 \| 0.5940 \| 0.6255 \| 0.9065 \| 0.8491 \| 0.8159 \| 0.8514 \| 0.8225 \| 0.8356 \| 0.3481 \| 0.0 \| 0.7640 \| 0.8156 \| 0.6161 \| 0.7685 \| 0.6218 \| 0.7700 \| 0.5514 \| 0.2470 \| 0.4947 \| 0.5280 \| 0.8262 \| 0.7354 \| 0.7478 \| 0.7640 \| 0.7766 \| 0.7753 \| 0.3158 \| \| 0.0347 \| 9.28 \| 4640 \| 0.2147 \| 0.6200 \| 0.7443 \| 0.8232 \| nan \| 0.8352 \| 0.9015 \| 0.6848 \| 0.8728 \| 0.6870 \| 0.8730 \| 0.7260 \| 0.4264 \| 0.5880 \| 0.6143 \| 0.9147 \| 0.8463 \| 0.8288 \| 0.8560 \| 0.8371 \| 0.8136 \| 0.3471 \| 0.0 \| 0.7601 \| 0.8139 \| 0.6094 \| 0.7736 \| 0.6179 \| 0.7552 \| 0.5541 \| 0.3273 \| 0.4945 \| 0.5242 \| 0.8248 \| 0.7396 \| 0.7573 \| 0.7674 \| 0.7783 \| 0.7595 \| 0.3036 \| \| 0.0448 \| 9.32 \| 4660 \| 0.2202 \| 0.6206 \| 0.7493 \| 0.8224 \| nan \| 0.8721 \| 0.8928 \| 0.7461 \| 0.8527 \| 0.6907 \| 0.8672 \| 0.7654 \| 0.3517 \| 0.5951 \| 0.6236 \| 0.9228 \| 0.8334 \| 0.8348 \| 0.8474 \| 0.8318 \| 0.8255 \| 0.3839 \| 0.0 \| 0.7636 \| 0.8125 \| 0.6291 \| 0.7633 \| 0.6285 \| 0.7545 \| 0.5555 \| 0.2887 \| 0.4987 \| 0.5293 \| 0.8243 \| 0.7380 \| 0.7602 \| 0.7629 \| 0.7738 \| 0.7699 \| 0.3173 \| \| 0.0461 \| 9.36 \| 4680 \| 0.2219 \| 0.6224 \| 0.7525 \| 0.8226 \| nan \| 0.8586 \| 0.8968 \| 0.7592 \| 0.8534 \| 0.6851 \| 0.8649 \| 0.7606 \| 0.4322 \| 0.5740 \| 0.6244 \| 0.9118 \| 0.8247 \| 0.8462 \| 0.8574 \| 0.8488 \| 0.8434 \| 0.3512 \| 0.0 \| 0.7621 \| 0.8184 \| 0.6386 \| 0.7572 \| 0.6231 \| 0.7441 \| 0.5521 \| 0.3460 \| 0.4897 \| 0.5255 \| 0.8259 \| 0.7370 \| 0.7613 \| 0.7627 \| 0.7814 \| 0.7692 \| 0.3093 \| \| 0.0426 \| 9.4 \| 4700 \| 0.2204 \| 0.6179 \| 0.7414 \| 0.8235 \| nan \| 0.8537 \| 0.8979 \| 0.7061 \| 0.8604 \| 0.7016 \| 0.8757 \| 0.7515 \| 0.3451 \| 0.5808 \| 0.6057 \| 0.9052 \| 0.8406 \| 0.8397 \| 0.8718 \| 0.8247 \| 0.8201 \| 0.3240 \| 0.0 \| 0.7597 \| 0.8162 \| 0.6196 \| 0.7614 \| 0.6139 \| 0.7480 \| 0.5505 \| 0.2937 \| 0.4912 \| 0.5175 \| 0.8257 \| 0.7415 \| 0.7624 \| 0.7689 \| 0.7782 \| 0.7715 \| 0.3032 \| \| 0.0298 \| 9.44 \| 4720 \| 0.2229 \| 0.6186 \| 0.7430 \| 0.8269 \| nan \| 0.8791 \| 0.8981 \| 0.6689 \| 0.8638 \| 0.6958 \| 0.8800 \| 0.7490 \| 0.3231 \| 0.5789 \| 0.6243 \| 0.9209 \| 0.8405 \| 0.8507 \| 0.8610 \| 0.8479 \| 0.8245 \| 0.3249 \| 0.0 \| 0.7687 \| 0.8212 \| 0.5974 \| 0.7684 \| 0.6145 \| 0.7596 \| 0.5552 \| 0.2785 \| 0.4865 \| 0.5197 \| 0.8278 \| 0.7435 \| 0.7616 \| 0.7728 \| 0.7830 \| 0.7728 \| 0.3035 \| \| 0.0396 \| 9.48 \| 4740 \| 0.2194 \| 0.6178 \| 0.7451 \| 0.8249 \| nan \| 0.8558 \| 0.8985 \| 0.7329 \| 0.8603 \| 0.6917 \| 0.8656 \| 0.7544 \| 0.2961 \| 0.5915 \| 0.6101 \| 0.9182 \| 0.8229 \| 0.8528 \| 0.8644 \| 0.8321 \| 0.8544 \| 0.3656 \| 0.0 \| 0.7584 \| 0.8118 \| 0.6254 \| 0.7689 \| 0.6216 \| 0.7592 \| 0.5630 \| 0.2490 \| 0.4927 \| 0.5189 \| 0.8270 \| 0.7312 \| 0.7581 \| 0.7654 \| 0.7742 \| 0.7813 \| 0.3152 \| \| 0.0376 \| 9.52 \| 4760 \| 0.2246 \| 0.6190 \| 0.7453 \| 0.8293 \| nan \| 0.8531 \| 0.9008 \| 0.7675 \| 0.8879 \| 0.6756 \| 0.8653 \| 0.7463 \| 0.3321 \| 0.5628 \| 0.6402 \| 0.9072 \| 0.8465 \| 0.8313 \| 0.8571 \| 0.8365 \| 0.8420 \| 0.3171 \| 0.0 \| 0.7661 \| 0.8215 \| 0.6346 \| 0.7783 \| 0.6147 \| 0.7502 \| 0.5700 \| 0.2799 \| 0.4806 \| 0.5286 \| 0.8280 \| 0.7331 \| 0.7529 \| 0.7601 \| 0.7741 \| 0.7820 \| 0.2880 \| \| 0.0352 \| 9.56 \| 4780 \| 0.2257 \| 0.6170 \| 0.7451 \| 0.8236 \| nan \| 0.8482 \| 0.8993 \| 0.7699 \| 0.8593 \| 0.6632 \| 0.8646 \| 0.7753 \| 0.3683 \| 0.5849 \| 0.6340 \| 0.9119 \| 0.8564 \| 0.8098 \| 0.8549 \| 0.8404 \| 0.8405 \| 0.2851 \| 0.0 \| 0.7635 \| 0.8183 \| 0.6363 \| 0.7651 \| 0.6148 \| 0.7525 \| 0.5637 \| 0.3001 \| 0.4872 \| 0.5264 \| 0.8264 \| 0.7286 \| 0.7449 \| 0.7577 \| 0.7713 \| 0.7812 \| 0.2675 \| \| 0.0542 \| 9.6 \| 4800 \| 0.2272 \| 0.6175 \| 0.7445 \| 0.8233 \| nan \| 0.8529 \| 0.9084 \| 0.7263 \| 0.8714 \| 0.6360 \| 0.8556 \| 0.7600 \| 0.4009 \| 0.5835 \| 0.6507 \| 0.9122 \| 0.8492 \| 0.8174 \| 0.8589 \| 0.8306 \| 0.8385 \| 0.3039 \| 0.0 \| 0.7682 \| 0.8175 \| 0.6278 \| 0.7656 \| 0.5900 \| 0.7477 \| 0.5562 \| 0.3268 \| 0.4917 \| 0.5386 \| 0.8276 \| 0.7303 \| 0.7475 \| 0.7591 \| 0.7672 \| 0.7832 \| 0.2697 \| \| 0.0597 \| 9.64 \| 4820 \| 0.2243 \| 0.6173 \| 0.7459 \| 0.8209 \| nan \| 0.8581 \| 0.8975 \| 0.7392 \| 0.8623 \| 0.6479 \| 0.8756 \| 0.7467 \| 0.4048 \| 0.5571 \| 0.6483 \| 0.9147 \| 0.8412 \| 0.8386 \| 0.8492 \| 0.8357 \| 0.8250 \| 0.3387 \| 0.0 \| 0.7632 \| 0.8103 \| 0.6294 \| 0.7655 \| 0.5914 \| 0.7578 \| 0.5482 \| 0.3298 \| 0.4798 \| 0.5331 \| 0.8269 \| 0.7378 \| 0.7562 \| 0.7535 \| 0.7642 \| 0.7758 \| 0.2888 \| \| 0.0855 \| 9.68 \| 4840 \| 0.2361 \| 0.6083 \| 0.7346 \| 0.8123 \| nan \| 0.8613 \| 0.8959 \| 0.7404 \| 0.8304 \| 0.6354 \| 0.8824 \| 0.7803 \| 0.2968 \| 0.5771 \| 0.6293 \| 0.9045 \| 0.8358 \| 0.8301 \| 0.8307 \| 0.8167 \| 0.8049 \| 0.3358 \| 0.0 \| 0.7646 \| 0.8139 \| 0.6307 \| 0.7473 \| 0.5705 \| 0.7638 \| 0.5246 \| 0.2614 \| 0.4867 \| 0.5266 \| 0.8259 \| 0.7308 \| 0.7483 \| 0.7451 \| 0.7598 \| 0.7597 \| 0.2898 \| \| 0.0563 \| 9.72 \| 4860 \| 0.2289 \| 0.6154 \| 0.7473 \| 0.8178 \| nan \| 0.8695 \| 0.9057 \| 0.7158 \| 0.8294 \| 0.6774 \| 0.8696 \| 0.7678 \| 0.3771 \| 0.6135 \| 0.5997 \| 0.9082 \| 0.8485 \| 0.8368 \| 0.8476 \| 0.8450 \| 0.8416 \| 0.3509 \| 0.0 \| 0.7647 \| 0.8150 \| 0.6206 \| 0.7501 \| 0.6023 \| 0.7646 \| 0.5346 \| 0.3071 \| 0.4981 \| 0.5153 \| 0.8249 \| 0.7351 \| 0.7524 \| 0.7541 \| 0.7661 \| 0.7764 \| 0.2950 \| \| 0.0694 \| 9.76 \| 4880 \| 0.2276 \| 0.6139 \| 0.7427 \| 0.8186 \| nan \| 0.8687 \| 0.9006 \| 0.7177 \| 0.8340 \| 0.7027 \| 0.8921 \| 0.7562 \| 0.3572 \| 0.6075 \| 0.5876 \| 0.9079 \| 0.8441 \| 0.8309 \| 0.8355 \| 0.8477 \| 0.8100 \| 0.3259 \| 0.0 \| 0.7621 \| 0.8153 \| 0.6212 \| 0.7563 \| 0.6080 \| 0.7695 \| 0.5397 \| 0.2979 \| 0.4909 \| 0.5043 \| 0.8247 \| 0.7374 \| 0.7541 \| 0.7512 \| 0.7699 \| 0.7609 \| 0.2868 \| \| 0.0377 \| 9.8 \| 4900 \| 0.2148 \| 0.6199 \| 0.7466 \| 0.8302 \| nan \| 0.8704 \| 0.8920 \| 0.7402 \| 0.8967 \| 0.7284 \| 0.8958 \| 0.6951 \| 0.3935 \| 0.5583 \| 0.6585 \| 0.9177 \| 0.8135 \| 0.8469 \| 0.8450 \| 0.8428 \| 0.8031 \| 0.2944 \| 0.0 \| 0.7683 \| 0.8178 \| 0.6249 \| 0.7924 \| 0.6244 \| 0.7652 \| 0.5623 \| 0.3204 \| 0.4778 \| 0.5333 \| 0.8260 \| 0.7276 \| 0.7532 \| 0.7546 \| 0.7684 \| 0.7598 \| 0.2823 \| \| 0.0534 \| 9.84 \| 4920 \| 0.2119 \| 0.6209 \| 0.7478 \| 0.8312 \| nan \| 0.8586 \| 0.9098 \| 0.7091 \| 0.8895 \| 0.7283 \| 0.8786 \| 0.7104 \| 0.4140 \| 0.6087 \| 0.6257 \| 0.9178 \| 0.8457 \| 0.8222 \| 0.8478 \| 0.8429 \| 0.8212 \| 0.2815 \| 0.0 \| 0.7662 \| 0.8146 \| 0.6182 \| 0.7906 \| 0.6364 \| 0.7656 \| 0.5698 \| 0.3339 \| 0.4983 \| 0.5214 \| 0.8250 \| 0.7333 \| 0.7485 \| 0.7513 \| 0.7668 \| 0.7670 \| 0.2699 \| \| 0.0596 \| 9.88 \| 4940 \| 0.2095 \| 0.6206 \| 0.7466 \| 0.8317 \| nan \| 0.8512 \| 0.9009 \| 0.7502 \| 0.8952 \| 0.7235 \| 0.8842 \| 0.7147 \| 0.3887 \| 0.5872 \| 0.6461 \| 0.8986 \| 0.8318 \| 0.8380 \| 0.8639 \| 0.8334 \| 0.8172 \| 0.2676 \| 0.0 \| 0.7584 \| 0.8165 \| 0.6355 \| 0.7917 \| 0.6305 \| 0.7697 \| 0.5682 \| 0.3181 \| 0.4964 \| 0.5364 \| 0.8242 \| 0.7291 \| 0.7487 \| 0.7529 \| 0.7690 \| 0.7622 \| 0.2628 \| \| 0.0443 \| 9.92 \| 4960 \| 0.2174 \| 0.6185 \| 0.7482 \| 0.8271 \| nan \| 0.8366 \| 0.8982 \| 0.7238 \| 0.8692 \| 0.6983 \| 0.8825 \| 0.7496 \| 0.4489 \| 0.5994 \| 0.6071 \| 0.9190 \| 0.8424 \| 0.8391 \| 0.8726 \| 0.8129 \| 0.8225 \| 0.2967 \| 0.0 \| 0.7460 \| 0.8164 \| 0.6239 \| 0.7827 \| 0.6206 \| 0.7661 \| 0.5694 \| 0.3445 \| 0.4996 \| 0.5204 \| 0.8233 \| 0.7275 \| 0.7472 \| 0.7485 \| 0.7583 \| 0.7631 \| 0.2748 \| \| 0.0366 \| 9.96 \| 4980 \| 0.2141 \| 0.6158 \| 0.7455 \| 0.8229 \| nan \| 0.8576 \| 0.8926 \| 0.7784 \| 0.8813 \| 0.7318 \| 0.8717 \| 0.7171 \| 0.3805 \| 0.5959 \| 0.6243 \| 0.8987 \| 0.8323 \| 0.8288 \| 0.8452 \| 0.7942 \| 0.7856 \| 0.3574 \| 0.0 \| 0.7498 \| 0.8127 \| 0.6371 \| 0.7817 \| 0.6057 \| 0.7667 \| 0.5537 \| 0.3075 \| 0.4946 \| 0.5219 \| 0.8239 \| 0.7303 \| 0.7494 \| 0.7416 \| 0.7523 \| 0.7455 \| 0.3103 \| \| 0.0378 \| 10.0 \| 5000 \| 0.2255 \| 0.6146 \| 0.7467 \| 0.8205 \| nan \| 0.8623 \| 0.8960 \| 0.7348 \| 0.8527 \| 0.7228 \| 0.8557 \| 0.7479 \| 0.3292 \| 0.6004 \| 0.6126 \| 0.9190 \| 0.8466 \| 0.8337 \| 0.8469 \| 0.8429 \| 0.7933 \| 0.3966 \| 0.0 \| 0.7603 \| 0.8111 \| 0.6260 \| 0.7673 \| 0.6062 \| 0.7592 \| 0.5469 \| 0.2756 \| 0.4944 \| 0.5214 \| 0.8248 \| 0.7310 \| 0.7492 \| 0.7548 \| 0.7687 \| 0.7511 \| 0.3148 \| \| 0.0412 \| 10.04 \| 5020 \| 0.2159 \| 0.6145 \| 0.7431 \| 0.8224 \| nan \| 0.8334 \| 0.9106 \| 0.7136 \| 0.8595 \| 0.7208 \| 0.8631 \| 0.7485 \| 0.3142 \| 0.5852 \| 0.6161 \| 0.9079 \| 0.8300 \| 0.8400 \| 0.8357 \| 0.8431 \| 0.7875 \| 0.4234 \| 0.0 \| 0.7569 \| 0.8186 \| 0.6197 \| 0.7697 \| 0.6115 \| 0.7634 \| 0.5506 \| 0.2716 \| 0.4897 \| 0.5235 \| 0.8262 \| 0.7264 \| 0.7453 \| 0.7509 \| 0.7665 \| 0.7481 \| 0.3230 \| \| 0.1179 \| 10.08 \| 5040 \| 0.2071 \| 0.6180 \| 0.7496 \| 0.8239 \| nan \| 0.8613 \| 0.9001 \| 0.7407 \| 0.8686 \| 0.7187 \| 0.8611 \| 0.7420 \| 0.3679 \| 0.5785 \| 0.6314 \| 0.9041 \| 0.8371 \| 0.8376 \| 0.8479 \| 0.8274 \| 0.8013 \| 0.4174 \| 0.0 \| 0.7594 \| 0.8119 \| 0.6309 \| 0.7763 \| 0.6182 \| 0.7628 \| 0.5625 \| 0.2990 \| 0.4925 \| 0.5347 \| 0.8260 \| 0.7251 \| 0.7413 \| 0.7540 \| 0.7641 \| 0.7537 \| 0.3123 \| \| 0.0377 \| 10.12 \| 5060 \| 0.2216 \| 0.6169 \| 0.7470 \| 0.8237 \| nan \| 0.8673 \| 0.8996 \| 0.6967 \| 0.8612 \| 0.7010 \| 0.8703 \| 0.7586 \| 0.3511 \| 0.6051 \| 0.6281 \| 0.9198 \| 0.8355 \| 0.8233 \| 0.8483 \| 0.8428 \| 0.7845 \| 0.4051 \| 0.0 \| 0.7629 \| 0.8188 \| 0.6104 \| 0.7721 \| 0.6135 \| 0.7628 \| 0.5605 \| 0.2929 \| 0.5016 \| 0.5304 \| 0.8264 \| 0.7279 \| 0.7409 \| 0.7580 \| 0.7689 \| 0.7454 \| 0.3103 \| \| 0.0445 \| 10.16 \| 5080 \| 0.2239 \| 0.6179 \| 0.7467 \| 0.8240 \| nan \| 0.8545 \| 0.8958 \| 0.7701 \| 0.8630 \| 0.7007 \| 0.8702 \| 0.7605 \| 0.3334 \| 0.6025 \| 0.6255 \| 0.9208 \| 0.8364 \| 0.8213 \| 0.8593 \| 0.8240 \| 0.7971 \| 0.3595 \| 0.0 \| 0.7640 \| 0.8170 \| 0.6357 \| 0.7734 \| 0.6157 \| 0.7643 \| 0.5647 \| 0.2889 \| 0.4980 \| 0.5305 \| 0.8266 \| 0.7283 \| 0.7441 \| 0.7552 \| 0.7653 \| 0.7550 \| 0.2959 \| \| 0.0397 \| 10.2 \| 5100 \| 0.2256 \| 0.6182 \| 0.7471 \| 0.8257 \| nan \| 0.8602 \| 0.8983 \| 0.7746 \| 0.8704 \| 0.7085 \| 0.8777 \| 0.7589 \| 0.3473 \| 0.6178 \| 0.5808 \| 0.9051 \| 0.8427 \| 0.8164 \| 0.8588 \| 0.8123 \| 0.7980 \| 0.3731 \| 0.0 \| 0.7645 \| 0.8199 \| 0.6395 \| 0.7753 \| 0.6197 \| 0.7651 \| 0.5659 \| 0.2990 \| 0.4985 \| 0.5049 \| 0.8273 \| 0.7302 \| 0.7465 \| 0.7551 \| 0.7643 \| 0.7549 \| 0.2975 \| \| 0.0335 \| 10.24 \| 5120 \| 0.2156 \| 0.6217 \| 0.7500 \| 0.8290 \| nan \| 0.8619 \| 0.8925 \| 0.7266 \| 0.8851 \| 0.7115 \| 0.8604 \| 0.7429 \| 0.4009 \| 0.6091 \| 0.6095 \| 0.9262 \| 0.8338 \| 0.8409 \| 0.8507 \| 0.8471 \| 0.8052 \| 0.3459 \| 0.0 \| 0.7638 \| 0.8206 \| 0.6234 \| 0.7827 \| 0.6255 \| 0.7603 \| 0.5746 \| 0.3377 \| 0.5021 \| 0.5211 \| 0.8260 \| 0.7300 \| 0.7501 \| 0.7550 \| 0.7683 \| 0.7577 \| 0.2923 \| \| 0.048 \| 10.28 \| 5140 \| 0.2195 \| 0.6206 \| 0.7457 \| 0.8280 \| nan \| 0.8531 \| 0.9014 \| 0.6939 \| 0.8842 \| 0.7175 \| 0.8655 \| 0.7404 \| 0.4071 \| 0.6059 \| 0.6196 \| 0.9035 \| 0.8571 \| 0.8085 \| 0.8516 \| 0.8397 \| 0.7988 \| 0.3285 \| 0.0 \| 0.7606 \| 0.8163 \| 0.6127 \| 0.7841 \| 0.6295 \| 0.7633 \| 0.5770 \| 0.3358 \| 0.5014 \| 0.5297 \| 0.8262 \| 0.7284 \| 0.7393 \| 0.7531 \| 0.7629 \| 0.7543 \| 0.2961 \| \| 0.0518 \| 10.32 \| 5160 \| 0.2107 \| 0.6254 \| 0.7515 \| 0.8318 \| nan \| 0.8493 \| 0.8991 \| 0.7409 \| 0.8845 \| 0.7223 \| 0.8681 \| 0.7451 \| 0.3741 \| 0.5943 \| 0.6483 \| 0.9188 \| 0.8404 \| 0.8384 \| 0.8459 \| 0.8475 \| 0.8103 \| 0.3483 \| 0.0 \| 0.7630 \| 0.8202 \| 0.6315 \| 0.7845 \| 0.6292 \| 0.7674 \| 0.5743 \| 0.3200 \| 0.5017 \| 0.5441 \| 0.8304 \| 0.7308 \| 0.7501 \| 0.7621 \| 0.7759 \| 0.7625 \| 0.3086 \| \| 0.0362 \| 10.36 \| 5180 \| 0.2180 \| 0.6204 \| 0.7522 \| 0.8271 \| nan \| 0.8772 \| 0.8998 \| 0.7279 \| 0.8720 \| 0.7247 \| 0.8705 \| 0.7520 \| 0.3289 \| 0.5850 \| 0.6485 \| 0.9086 \| 0.8084 \| 0.8411 \| 0.8353 \| 0.8401 \| 0.7921 \| 0.4761 \| 0.0 \| 0.7615 \| 0.8145 \| 0.6294 \| 0.7781 \| 0.6348 \| 0.7684 \| 0.5729 \| 0.2863 \| 0.4947 \| 0.5348 \| 0.8298 \| 0.7204 \| 0.7510 \| 0.7587 \| 0.7739 \| 0.7521 \| 0.3060 \| \| 0.053 \| 10.4 \| 5200 \| 0.2110 \| 0.6238 \| 0.7492 \| 0.8305 \| nan \| 0.8493 \| 0.9017 \| 0.7544 \| 0.8845 \| 0.7309 \| 0.8746 \| 0.7392 \| 0.3401 \| 0.5799 \| 0.6198 \| 0.9038 \| 0.8354 \| 0.8265 \| 0.8521 \| 0.8394 \| 0.8169 \| 0.3874 \| 0.0 \| 0.7631 \| 0.8175 \| 0.6393 \| 0.7849 \| 0.6409 \| 0.7671 \| 0.5779 \| 0.2954 \| 0.4893 \| 0.5222 \| 0.8297 \| 0.7329 \| 0.7528 \| 0.7644 \| 0.7747 \| 0.7652 \| 0.3116 \| \| 0.0626 \| 10.44 \| 5220 \| 0.2206 \| 0.6222 \| 0.7489 \| 0.8291 \| nan \| 0.8490 \| 0.9027 \| 0.7750 \| 0.8726 \| 0.7048 \| 0.8677 \| 0.7555 \| 0.2887 \| 0.5839 \| 0.6412 \| 0.9092 \| 0.8312 \| 0.8575 \| 0.8582 \| 0.8417 \| 0.8173 \| 0.3746 \| 0.0 \| 0.7695 \| 0.8209 \| 0.6442 \| 0.7780 \| 0.6199 \| 0.7659 \| 0.5642 \| 0.2613 \| 0.4979 \| 0.5377 \| 0.8304 \| 0.7308 \| 0.7592 \| 0.7671 \| 0.7764 \| 0.7675 \| 0.3092 \| \| 0.0327 \| 10.48 \| 5240 \| 0.2259 \| 0.6213 \| 0.7494 \| 0.8227 \| nan \| 0.8680 \| 0.8990 \| 0.7714 \| 0.8542 \| 0.6356 \| 0.8630 \| 0.8026 \| 0.3758 \| 0.5874 \| 0.6414 \| 0.9155 \| 0.8420 \| 0.8452 \| 0.8468 \| 0.8367 \| 0.7930 \| 0.3618 \| 0.0 \| 0.7686 \| 0.8182 \| 0.6428 \| 0.7656 \| 0.6033 \| 0.7590 \| 0.5645 \| 0.3215 \| 0.5024 \| 0.5408 \| 0.8284 \| 0.7342 \| 0.7579 \| 0.7564 \| 0.7711 \| 0.7505 \| 0.2988 \| \| 0.0502 \| 10.52 \| 5260 \| 0.2303 \| 0.6205 \| 0.7532 \| 0.8192 \| nan \| 0.8599 \| 0.9073 \| 0.7546 \| 0.8278 \| 0.6260 \| 0.8672 \| 0.8299 \| 0.4034 \| 0.6101 \| 0.6381 \| 0.9083 \| 0.8542 \| 0.8286 \| 0.8677 \| 0.8222 \| 0.7866 \| 0.4117 \| 0.0 \| 0.7654 \| 0.8201 \| 0.6399 \| 0.7542 \| 0.5932 \| 0.7602 \| 0.5554 \| 0.3344 \| 0.5066 \| 0.5406 \| 0.8287 \| 0.7359 \| 0.7540 \| 0.7616 \| 0.7674 \| 0.7443 \| 0.3081 \| \| 0.0261 \| 10.56 \| 5280 \| 0.2100 \| 0.6276 \| 0.7585 \| 0.8288 \| nan \| 0.8454 \| 0.8949 \| 0.7216 \| 0.8499 \| 0.7262 \| 0.8815 \| 0.7873 \| 0.4518 \| 0.5732 \| 0.6507 \| 0.9106 \| 0.8435 \| 0.8412 \| 0.8713 \| 0.8251 \| 0.8197 \| 0.4006 \| 0.0 \| 0.7584 \| 0.8135 \| 0.6246 \| 0.7698 \| 0.6606 \| 0.7626 \| 0.5794 \| 0.3609 \| 0.4907 \| 0.5444 \| 0.8275 \| 0.7360 \| 0.7576 \| 0.7629 \| 0.7689 \| 0.7684 \| 0.3100 \| \| 0.0367 \| 10.6 \| 5300 \| 0.2105 \| 0.6286 \| 0.7600 \| 0.8295 \| nan \| 0.8311 \| 0.9008 \| 0.7109 \| 0.8554 \| 0.7369 \| 0.8848 \| 0.7620 \| 0.4867 \| 0.6178 \| 0.6341 \| 0.9151 \| 0.8242 \| 0.8420 \| 0.8563 \| 0.8457 \| 0.8271 \| 0.3897 \| 0.0 \| 0.7560 \| 0.8176 \| 0.6200 \| 0.7720 \| 0.6602 \| 0.7608 \| 0.5730 \| 0.3741 \| 0.5059 \| 0.5350 \| 0.8270 \| 0.7342 \| 0.7568 \| 0.7657 \| 0.7747 \| 0.7741 \| 0.3087 \| \| 0.0494 \| 10.64 \| 5320 \| 0.2101 \| 0.6297 \| 0.7626 \| 0.8293 \| nan \| 0.8543 \| 0.8998 \| 0.7747 \| 0.8539 \| 0.7431 \| 0.8798 \| 0.7701 \| 0.4706 \| 0.5727 \| 0.6555 \| 0.9018 \| 0.8355 \| 0.8481 \| 0.8455 \| 0.8512 \| 0.8174 \| 0.3906 \| 0.0 \| 0.7598 \| 0.8179 \| 0.6454 \| 0.7712 \| 0.6626 \| 0.7614 \| 0.5729 \| 0.3685 \| 0.4892 \| 0.5435 \| 0.8264 \| 0.7346 \| 0.7580 \| 0.7642 \| 0.7773 \| 0.7698 \| 0.3125 \| \| 0.032 \| 10.68 \| 5340 \| 0.2092 \| 0.6256 \| 0.7525 \| 0.8288 \| nan \| 0.8394 \| 0.9041 \| 0.7387 \| 0.8624 \| 0.7439 \| 0.8801 \| 0.7531 \| 0.4074 \| 0.5851 \| 0.6353 \| 0.9099 \| 0.8306 \| 0.8395 \| 0.8385 \| 0.8394 \| 0.8221 \| 0.3637 \| 0.0 \| 0.7606 \| 0.8164 \| 0.6359 \| 0.7755 \| 0.6571 \| 0.7609 \| 0.5714 \| 0.3418 \| 0.4900 \| 0.5332 \| 0.8272 \| 0.7302 \| 0.7549 \| 0.7582 \| 0.7714 \| 0.7749 \| 0.3020 \| \| 0.0815 \| 10.72 \| 5360 \| 0.2096 \| 0.6262 \| 0.7539 \| 0.8322 \| nan \| 0.8455 \| 0.8997 \| 0.7519 \| 0.8693 \| 0.7435 \| 0.8937 \| 0.7511 \| 0.3924 \| 0.6073 \| 0.5934 \| 0.9148 \| 0.8577 \| 0.8196 \| 0.8589 \| 0.8448 \| 0.8204 \| 0.3518 \| 0.0 \| 0.7650 \| 0.8207 \| 0.6388 \| 0.7816 \| 0.6572 \| 0.7617 \| 0.5773 \| 0.3307 \| 0.4985 \| 0.5189 \| 0.8281 \| 0.7326 \| 0.7490 \| 0.7657 \| 0.7760 \| 0.7720 \| 0.2975 \| \| 0.0529 \| 10.76 \| 5380 \| 0.2130 \| 0.6261 \| 0.7532 \| 0.8287 \| nan \| 0.8682 \| 0.8954 \| 0.7341 \| 0.8578 \| 0.7345 \| 0.8630 \| 0.7909 \| 0.4212 \| 0.5998 \| 0.6059 \| 0.9135 \| 0.8545 \| 0.8225 \| 0.8491 \| 0.8459 \| 0.8118 \| 0.3364 \| 0.0 \| 0.7678 \| 0.8209 \| 0.6332 \| 0.7739 \| 0.6654 \| 0.7521 \| 0.5824 \| 0.3451 \| 0.4929 \| 0.5194 \| 0.8284 \| 0.7403 \| 0.7554 \| 0.7608 \| 0.7780 \| 0.7618 \| 0.2918 \| \| 0.0348 \| 10.8 \| 5400 \| 0.2174 \| 0.6254 \| 0.7535 \| 0.8270 \| nan \| 0.8603 \| 0.8988 \| 0.6850 \| 0.8541 \| 0.7227 \| 0.8434 \| 0.7972 \| 0.4412 \| 0.5788 \| 0.6221 \| 0.9185 \| 0.8593 \| 0.8188 \| 0.8499 \| 0.8463 \| 0.8332 \| 0.3798 \| 0.0 \| 0.7677 \| 0.8190 \| 0.6110 \| 0.7707 \| 0.6526 \| 0.7469 \| 0.5817 \| 0.3509 \| 0.4876 \| 0.5275 \| 0.8273 \| 0.7360 \| 0.7504 \| 0.7609 \| 0.7770 \| 0.7691 \| 0.3200 \| \| 0.0424 \| 10.84 \| 5420 \| 0.2128 \| 0.6286 \| 0.7594 \| 0.8323 \| nan \| 0.8541 \| 0.8904 \| 0.7261 \| 0.8671 \| 0.7348 \| 0.8700 \| 0.7783 \| 0.3729 \| 0.5723 \| 0.6372 \| 0.9230 \| 0.8561 \| 0.8202 \| 0.8570 \| 0.8394 \| 0.8370 \| 0.4743 \| 0.0 \| 0.7685 \| 0.8167 \| 0.6291 \| 0.7790 \| 0.6736 \| 0.7597 \| 0.5928 \| 0.3156 \| 0.4897 \| 0.5383 \| 0.8277 \| 0.7348 \| 0.7485 \| 0.7624 \| 0.7729 \| 0.7761 \| 0.3302 \| \| 0.0443 \| 10.88 \| 5440 \| 0.2127 \| 0.6280 \| 0.7551 \| 0.8318 \| nan \| 0.8285 \| 0.9077 \| 0.7122 \| 0.8713 \| 0.7365 \| 0.8675 \| 0.7681 \| 0.3680 \| 0.6071 \| 0.6288 \| 0.9094 \| 0.8384 \| 0.8312 \| 0.8476 \| 0.8344 \| 0.8200 \| 0.4596 \| 0.0 \| 0.7680 \| 0.8184 \| 0.6258 \| 0.7799 \| 0.6731 \| 0.7608 \| 0.5919 \| 0.3159 \| 0.5004 \| 0.5312 \| 0.8275 \| 0.7353 \| 0.7551 \| 0.7588 \| 0.7722 \| 0.7686 \| 0.3203 \| \| 0.0725 \| 10.92 \| 5460 \| 0.2146 \| 0.6300 \| 0.7579 \| 0.8328 \| nan \| 0.8521 \| 0.8993 \| 0.7243 \| 0.8694 \| 0.7051 \| 0.8731 \| 0.7830 \| 0.3837 \| 0.6255 \| 0.6155 \| 0.9131 \| 0.8557 \| 0.8331 \| 0.8509 \| 0.8339 \| 0.8381 \| 0.4290 \| 0.0 \| 0.7717 \| 0.8195 \| 0.6304 \| 0.7799 \| 0.6599 \| 0.7633 \| 0.5904 \| 0.3339 \| 0.5067 \| 0.5259 \| 0.8285 \| 0.7402 \| 0.7604 \| 0.7598 \| 0.7730 \| 0.7798 \| 0.3168 \| \| 0.0516 \| 10.96 \| 5480 \| 0.2144 \| 0.6324 \| 0.7604 \| 0.8355 \| nan \| 0.8486 \| 0.9148 \| 0.7692 \| 0.8722 \| 0.7175 \| 0.8743 \| 0.7817 \| 0.3988 \| 0.5847 \| 0.6573 \| 0.9090 \| 0.8564 \| 0.8315 \| 0.8598 \| 0.8257 \| 0.8307 \| 0.3950 \| 0.0 \| 0.7761 \| 0.8266 \| 0.6443 \| 0.7816 \| 0.6665 \| 0.7638 \| 0.5937 \| 0.3506 \| 0.4985 \| 0.5469 \| 0.8267 \| 0.7378 \| 0.7569 \| 0.7599 \| 0.7671 \| 0.7812 \| 0.3043 \| \| 0.0542 \| 11.0 \| 5500 \| 0.2133 \| 0.6298 \| 0.7572 \| 0.8331 \| nan \| 0.8333 \| 0.9010 \| 0.7820 \| 0.8751 \| 0.7279 \| 0.8879 \| 0.7564 \| 0.4034 \| 0.5703 \| 0.6596 \| 0.9128 \| 0.8320 \| 0.8518 \| 0.8707 \| 0.8125 \| 0.8182 \| 0.3773 \| 0.0 \| 0.7640 \| 0.8179 \| 0.6455 \| 0.7836 \| 0.6643 \| 0.7653 \| 0.5916 \| 0.3499 \| 0.4914 \| 0.5451 \| 0.8265 \| 0.7416 \| 0.7649 \| 0.7560 \| 0.7595 \| 0.7752 \| 0.2944 \| \| 0.0404 \| 11.04 \| 5520 \| 0.2140 \| 0.6325 \| 0.7596 \| 0.8365 \| nan \| 0.8663 \| 0.8999 \| 0.7718 \| 0.8849 \| 0.7341 \| 0.8879 \| 0.7504 \| 0.4153 \| 0.5794 \| 0.6610 \| 0.9106 \| 0.8534 \| 0.8390 \| 0.8535 \| 0.8246 \| 0.8299 \| 0.3507 \| 0.0 \| 0.7760 \| 0.8215 \| 0.6441 \| 0.7857 \| 0.6680 \| 0.7666 \| 0.5918 \| 0.3588 \| 0.4958 \| 0.5472 \| 0.8283 \| 0.7478 \| 0.7633 \| 0.7584 \| 0.7669 \| 0.7815 \| 0.2824 \| \| 0.0451 \| 11.08 \| 5540 \| 0.2135 \| 0.6290 \| 0.7568 \| 0.8327 \| nan \| 0.8767 \| 0.9029 \| 0.7418 \| 0.8730 \| 0.7319 \| 0.8768 \| 0.7688 \| 0.4331 \| 0.5868 \| 0.6178 \| 0.9193 \| 0.8594 \| 0.8246 \| 0.8577 \| 0.8235 \| 0.8097 \| 0.3623 \| 0.0 \| 0.7778 \| 0.8237 \| 0.6354 \| 0.7786 \| 0.6702 \| 0.7645 \| 0.5908 \| 0.3712 \| 0.4864 \| 0.5227 \| 0.8254 \| 0.7392 \| 0.7573 \| 0.7583 \| 0.7652 \| 0.7654 \| 0.2896 \| \| 0.0477 \| 11.12 \| 5560 \| 0.2149 \| 0.6277 \| 0.7547 \| 0.8314 \| nan \| 0.8641 \| 0.8961 \| 0.7527 \| 0.8823 \| 0.7351 \| 0.8723 \| 0.7534 \| 0.4310 \| 0.5857 \| 0.6364 \| 0.9045 \| 0.8509 \| 0.8218 \| 0.8473 \| 0.8141 \| 0.8222 \| 0.3595 \| 0.0 \| 0.7712 \| 0.8169 \| 0.6389 \| 0.7823 \| 0.6654 \| 0.7653 \| 0.5867 \| 0.3619 \| 0.4893 \| 0.5323 \| 0.8250 \| 0.7381 \| 0.7556 \| 0.7541 \| 0.7625 \| 0.7697 \| 0.2840 \| \| 0.0373 \| 11.16 \| 5580 \| 0.2104 \| 0.6275 \| 0.7564 \| 0.8315 \| nan \| 0.8625 \| 0.8935 \| 0.7551 \| 0.8727 \| 0.7091 \| 0.8845 \| 0.7628 \| 0.4085 \| 0.5838 \| 0.6345 \| 0.9158 \| 0.8496 \| 0.8422 \| 0.8586 \| 0.8232 \| 0.8257 \| 0.3760 \| 0.0 \| 0.7754 \| 0.8146 \| 0.6345 \| 0.7790 \| 0.6502 \| 0.7704 \| 0.5822 \| 0.3509 \| 0.4897 \| 0.5328 \| 0.8257 \| 0.7402 \| 0.7627 \| 0.7564 \| 0.7639 \| 0.7744 \| 0.2914 \| \| 0.0359 \| 11.2 \| 5600 \| 0.2093 \| 0.6279 \| 0.7592 \| 0.8310 \| nan \| 0.8657 \| 0.8938 \| 0.7299 \| 0.8674 \| 0.7094 \| 0.8825 \| 0.7700 \| 0.4404 \| 0.5819 \| 0.6408 \| 0.9095 \| 0.8462 \| 0.8487 \| 0.8587 \| 0.8144 \| 0.8360 \| 0.4117 \| 0.0 \| 0.7685 \| 0.8183 \| 0.6283 \| 0.7763 \| 0.6471 \| 0.7696 \| 0.5786 \| 0.3624 \| 0.4902 \| 0.5349 \| 0.8256 \| 0.7377 \| 0.7633 \| 0.7569 \| 0.7626 \| 0.7809 \| 0.3001 \| \| 0.0549 \| 11.24 \| 5620 \| 0.2080 \| 0.6231 \| 0.7515 \| 0.8279 \| nan \| 0.8614 \| 0.9001 \| 0.7025 \| 0.8620 \| 0.7041 \| 0.8777 \| 0.7664 \| 0.3873 \| 0.5975 \| 0.6150 \| 0.9117 \| 0.8499 \| 0.8352 \| 0.8384 \| 0.8368 \| 0.8320 \| 0.3974 \| 0.0 \| 0.7629 \| 0.8166 \| 0.6182 \| 0.7750 \| 0.6366 \| 0.7677 \| 0.5736 \| 0.3337 \| 0.4940 \| 0.5205 \| 0.8246 \| 0.7360 \| 0.7591 \| 0.7550 \| 0.7669 \| 0.7764 \| 0.2982 \| \| 0.0512 \| 11.28 \| 5640 \| 0.2108 \| 0.6251 \| 0.7541 \| 0.8314 \| nan \| 0.8632 \| 0.9005 \| 0.7296 \| 0.8754 \| 0.6961 \| 0.8890 \| 0.7573 \| 0.3910 \| 0.6054 \| 0.6270 \| 0.9131 \| 0.8557 \| 0.8332 \| 0.8487 \| 0.8269 \| 0.8212 \| 0.3856 \| 0.0 \| 0.7650 \| 0.8158 \| 0.6289 \| 0.7828 \| 0.6318 \| 0.7657 \| 0.5804 \| 0.3362 \| 0.4984 \| 0.5274 \| 0.8258 \| 0.7400 \| 0.7589 \| 0.7562 \| 0.7671 \| 0.7733 \| 0.2974 \| \| 0.0329 \| 11.32 \| 5660 \| 0.2129 \| 0.6243 \| 0.7561 \| 0.8279 \| nan \| 0.8647 \| 0.8870 \| 0.7665 \| 0.8815 \| 0.6828 \| 0.8908 \| 0.7336 \| 0.4715 \| 0.5886 \| 0.6307 \| 0.9125 \| 0.8491 \| 0.8272 \| 0.8533 \| 0.8241 \| 0.8292 \| 0.3601 \| 0.0 \| 0.7595 \| 0.8118 \| 0.6378 \| 0.7854 \| 0.6189 \| 0.7576 \| 0.5749 \| 0.3751 \| 0.4922 \| 0.5265 \| 0.8249 \| 0.7381 \| 0.7567 \| 0.7558 \| 0.7647 \| 0.7755 \| 0.2824 \| \| 0.0624 \| 11.36 \| 5680 \| 0.2176 \| 0.6276 \| 0.7592 \| 0.8322 \| nan \| 0.8666 \| 0.9038 \| 0.7484 \| 0.8798 \| 0.6784 \| 0.8929 \| 0.7404 \| 0.4572 \| 0.5800 \| 0.6500 \| 0.9101 \| 0.8476 \| 0.8426 \| 0.8579 \| 0.8419 \| 0.8432 \| 0.3653 \| 0.0 \| 0.7675 \| 0.8213 \| 0.6343 \| 0.7865 \| 0.6190 \| 0.7568 \| 0.5758 \| 0.3717 \| 0.4907 \| 0.5365 \| 0.8250 \| 0.7409 \| 0.7612 \| 0.7636 \| 0.7735 \| 0.7825 \| 0.2902 \| \| 0.0438 \| 11.4 \| 5700 \| 0.2145 \| 0.6197 \| 0.7470 \| 0.8276 \| nan \| 0.8583 \| 0.8946 \| 0.7420 \| 0.8716 \| 0.7013 \| 0.8881 \| 0.7458 \| 0.3413 \| 0.5784 \| 0.6286 \| 0.9070 \| 0.8443 \| 0.8230 \| 0.8482 \| 0.8484 \| 0.8255 \| 0.3524 \| 0.0 \| 0.7606 \| 0.8171 \| 0.6299 \| 0.7809 \| 0.6282 \| 0.7606 \| 0.5730 \| 0.2992 \| 0.4899 \| 0.5293 \| 0.8233 \| 0.7329 \| 0.7486 \| 0.7557 \| 0.7704 \| 0.7716 \| 0.2840 \| \| 0.0357 \| 11.44 \| 5720 \| 0.2165 \| 0.6224 \| 0.7510 \| 0.8290 \| nan \| 0.8549 \| 0.9050 \| 0.7354 \| 0.8677 \| 0.7007 \| 0.8893 \| 0.7488 \| 0.3664 \| 0.6073 \| 0.6288 \| 0.9029 \| 0.8525 \| 0.8212 \| 0.8488 \| 0.8396 \| 0.8376 \| 0.3607 \| 0.0 \| 0.7648 \| 0.8193 \| 0.6297 \| 0.7792 \| 0.6292 \| 0.7660 \| 0.5687 \| 0.3147 \| 0.5002 \| 0.5310 \| 0.8234 \| 0.7338 \| 0.7487 \| 0.7549 \| 0.7688 \| 0.7767 \| 0.2935 \| \| 0.0476 \| 11.48 \| 5740 \| 0.2198 \| 0.6246 \| 0.7552 \| 0.8284 \| nan \| 0.8697 \| 0.9019 \| 0.7674 \| 0.8673 \| 0.6945 \| 0.8877 \| 0.7538 \| 0.4138 \| 0.5647 \| 0.6587 \| 0.9082 \| 0.8418 \| 0.8337 \| 0.8504 \| 0.8338 \| 0.8250 \| 0.3658 \| 0.0 \| 0.7700 \| 0.8182 \| 0.6393 \| 0.7769 \| 0.6309 \| 0.7648 \| 0.5706 \| 0.3502 \| 0.4808 \| 0.5358 \| 0.8261 \| 0.7391 \| 0.7571 \| 0.7529 \| 0.7638 \| 0.7699 \| 0.2964 \| \| 0.0467 \| 11.52 \| 5760 \| 0.2185 \| 0.6216 \| 0.7517 \| 0.8282 \| nan \| 0.8730 \| 0.8975 \| 0.7627 \| 0.8608 \| 0.7052 \| 0.8941 \| 0.7595 \| 0.3479 \| 0.5988 \| 0.6071 \| 0.9060 \| 0.8564 \| 0.8250 \| 0.8546 \| 0.8256 \| 0.8349 \| 0.3691 \| 0.0 \| 0.7690 \| 0.8174 \| 0.6378 \| 0.7752 \| 0.6320 \| 0.7646 \| 0.5660 \| 0.3006 \| 0.4899 \| 0.5149 \| 0.8273 \| 0.7414 \| 0.7564 \| 0.7554 \| 0.7662 \| 0.7761 \| 0.2982 \| \| 0.0433 \| 11.56 \| 5780 \| 0.2111 \| 0.6208 \| 0.7480 \| 0.8278 \| nan \| 0.8661 \| 0.8986 \| 0.7574 \| 0.8706 \| 0.7196 \| 0.8946 \| 0.7398 \| 0.3789 \| 0.5838 \| 0.5903 \| 0.9081 \| 0.8516 \| 0.8256 \| 0.8466 \| 0.8307 \| 0.8173 \| 0.3367 \| 0.0 \| 0.7611 \| 0.8168 \| 0.6375 \| 0.7805 \| 0.6338 \| 0.7664 \| 0.5661 \| 0.3274 \| 0.4832 \| 0.5056 \| 0.8262 \| 0.7368 \| 0.7542 \| 0.7568 \| 0.7669 \| 0.7684 \| 0.2871 \| \| 0.0304 \| 11.6 \| 5800 \| 0.2154 \| 0.6191 \| 0.7436 \| 0.8277 \| nan \| 0.8618 \| 0.8979 \| 0.7209 \| 0.8716 \| 0.7157 \| 0.8946 \| 0.7407 \| 0.3319 \| 0.5662 \| 0.6205 \| 0.9098 \| 0.8442 \| 0.8295 \| 0.8469 \| 0.8375 \| 0.8093 \| 0.3416 \| 0.0 \| 0.7636 \| 0.8199 \| 0.6260 \| 0.7789 \| 0.6300 \| 0.7689 \| 0.5623 \| 0.2928 \| 0.4787 \| 0.5193 \| 0.8260 \| 0.7348 \| 0.7550 \| 0.7615 \| 0.7725 \| 0.7631 \| 0.2904 \| \| 0.0456 \| 11.64 \| 5820 \| 0.2215 \| 0.6230 \| 0.7521 \| 0.8266 \| nan \| 0.8554 \| 0.9081 \| 0.7498 \| 0.8582 \| 0.7027 \| 0.8900 \| 0.7501 \| 0.3789 \| 0.5846 \| 0.6315 \| 0.9070 \| 0.8620 \| 0.8103 \| 0.8540 \| 0.8338 \| 0.8297 \| 0.3791 \| 0.0 \| 0.7671 \| 0.8182 \| 0.6374 \| 0.7732 \| 0.6303 \| 0.7704 \| 0.5589 \| 0.3259 \| 0.4920 \| 0.5315 \| 0.8259 \| 0.7282 \| 0.7434 \| 0.7636 \| 0.7698 \| 0.7745 \| 0.3035 \| \| 0.0395 \| 11.68 \| 5840 \| 0.2156 \| 0.6267 \| 0.7571 \| 0.8288 \| nan \| 0.8395 \| 0.9011 \| 0.7391 \| 0.8638 \| 0.7053 \| 0.8887 \| 0.7445 \| 0.4443 \| 0.6131 \| 0.6206 \| 0.9123 \| 0.8457 \| 0.8456 \| 0.8584 \| 0.8299 \| 0.8410 \| 0.3778 \| 0.0 \| 0.7617 \| 0.8185 \| 0.6350 \| 0.7767 \| 0.6294 \| 0.7724 \| 0.5599 \| 0.3643 \| 0.5046 \| 0.5287 \| 0.8268 \| 0.7346 \| 0.7577 \| 0.7628 \| 0.7672 \| 0.7783 \| 0.3018 \| \| 0.0391 \| 11.72 \| 5860 \| 0.2238 \| 0.6280 \| 0.7600 \| 0.8292 \| nan \| 0.8536 \| 0.9013 \| 0.7666 \| 0.8641 \| 0.7047 \| 0.8927 \| 0.7430 \| 0.4564 \| 0.6096 \| 0.6249 \| 0.9133 \| 0.8358 \| 0.8507 \| 0.8643 \| 0.8406 \| 0.8269 \| 0.3712 \| 0.0 \| 0.7668 \| 0.8178 \| 0.6427 \| 0.7769 \| 0.6300 \| 0.7726 \| 0.5607 \| 0.3792 \| 0.5012 \| 0.5286 \| 0.8268 \| 0.7357 \| 0.7595 \| 0.7659 \| 0.7694 \| 0.7724 \| 0.2980 \| \| 0.0346 \| 11.76 \| 5880 \| 0.2191 \| 0.6239 \| 0.7533 \| 0.8262 \| nan \| 0.8605 \| 0.9009 \| 0.7339 \| 0.8621 \| 0.7032 \| 0.8922 \| 0.7436 \| 0.4405 \| 0.6021 \| 0.6003 \| 0.9150 \| 0.8546 \| 0.8185 \| 0.8389 \| 0.8408 \| 0.8256 \| 0.3724 \| 0.0 \| 0.7648 \| 0.8168 \| 0.6315 \| 0.7751 \| 0.6285 \| 0.7713 \| 0.5581 \| 0.3704 \| 0.4912 \| 0.5119 \| 0.8274 \| 0.7354 \| 0.7498 \| 0.7582 \| 0.7679 \| 0.7729 \| 0.2998 \| \| 0.0428 \| 11.8 \| 5900 \| 0.2230 \| 0.6226 \| 0.7544 \| 0.8254 \| nan \| 0.8661 \| 0.9018 \| 0.7239 \| 0.8575 \| 0.7139 \| 0.8850 \| 0.7410 \| 0.4345 \| 0.5995 \| 0.6129 \| 0.9190 \| 0.8587 \| 0.8062 \| 0.8652 \| 0.8263 \| 0.8313 \| 0.3829 \| 0.0 \| 0.7703 \| 0.8180 \| 0.6275 \| 0.7721 \| 0.6201 \| 0.7767 \| 0.5547 \| 0.3585 \| 0.4955 \| 0.5214 \| 0.8283 \| 0.7276 \| 0.7427 \| 0.7600 \| 0.7599 \| 0.7759 \| 0.2983 \| \| 0.0376 \| 11.84 \| 5920 \| 0.2248 \| 0.6199 \| 0.7497 \| 0.8238 \| nan \| 0.8727 \| 0.8903 \| 0.7110 \| 0.8552 \| 0.7067 \| 0.8816 \| 0.7558 \| 0.4013 \| 0.6114 \| 0.6214 \| 0.9192 \| 0.8479 \| 0.8203 \| 0.8553 \| 0.8205 \| 0.8192 \| 0.3547 \| 0.0 \| 0.7625 \| 0.8181 \| 0.6212 \| 0.7665 \| 0.6213 \| 0.7749 \| 0.5510 \| 0.3346 \| 0.5040 \| 0.5291 \| 0.8278 \| 0.7265 \| 0.7457 \| 0.7574 \| 0.7599 \| 0.7695 \| 0.2881 \| \| 0.0359 \| 11.88 \| 5940 \| 0.2267 \| 0.6204 \| 0.7485 \| 0.8251 \| nan \| 0.8528 \| 0.9020 \| 0.7108 \| 0.8586 \| 0.6962 \| 0.8801 \| 0.7549 \| 0.3779 \| 0.5934 \| 0.6442 \| 0.9122 \| 0.8469 \| 0.8411 \| 0.8614 \| 0.8156 \| 0.8121 \| 0.3645 \| 0.0 \| 0.7618 \| 0.8199 \| 0.6232 \| 0.7681 \| 0.6200 \| 0.7714 \| 0.5553 \| 0.3211 \| 0.4962 \| 0.5359 \| 0.8260 \| 0.7373 \| 0.7558 \| 0.7539 \| 0.7591 \| 0.7666 \| 0.2956 \| \| 0.0345 \| 11.92 \| 5960 \| 0.2250 \| 0.6230 \| 0.7505 \| 0.8262 \| nan \| 0.8300 \| 0.9085 \| 0.7175 \| 0.8635 \| 0.7003 \| 0.8771 \| 0.7477 \| 0.4255 \| 0.5867 \| 0.6400 \| 0.9049 \| 0.8476 \| 0.8444 \| 0.8481 \| 0.8222 \| 0.8292 \| 0.3651 \| 0.0 \| 0.7581 \| 0.8192 \| 0.6272 \| 0.7707 \| 0.6217 \| 0.7718 \| 0.5576 \| 0.3485 \| 0.4925 \| 0.5330 \| 0.8258 \| 0.7390 \| 0.7586 \| 0.7542 \| 0.7623 \| 0.7761 \| 0.2974 \| \| 0.0374 \| 11.96 \| 5980 \| 0.2186 \| 0.6238 \| 0.7521 \| 0.8256 \| nan \| 0.8399 \| 0.9082 \| 0.7109 \| 0.8621 \| 0.7026 \| 0.8810 \| 0.7414 \| 0.4934 \| 0.6159 \| 0.5969 \| 0.9150 \| 0.8529 \| 0.8370 \| 0.8347 \| 0.8388 \| 0.8225 \| 0.3330 \| 0.0 \| 0.7612 \| 0.8185 \| 0.6248 \| 0.7701 \| 0.6254 \| 0.7705 \| 0.5559 \| 0.3824 \| 0.5027 \| 0.5153 \| 0.8279 \| 0.7365 \| 0.7563 \| 0.7553 \| 0.7685 \| 0.7736 \| 0.2826 \| \| 0.055 \| 12.0 \| 6000 \| 0.2175 \| 0.6251 \| 0.7562 \| 0.8254 \| nan \| 0.8404 \| 0.9083 \| 0.6946 \| 0.8566 \| 0.7138 \| 0.8827 \| 0.7373 \| 0.4973 \| 0.5873 \| 0.6403 \| 0.9124 \| 0.8417 \| 0.8445 \| 0.8313 \| 0.8508 \| 0.8241 \| 0.3917 \| 0.0 \| 0.7637 \| 0.8192 \| 0.6178 \| 0.7687 \| 0.6261 \| 0.7699 \| 0.5516 \| 0.3656 \| 0.4957 \| 0.5332 \| 0.8288 \| 0.7396 \| 0.7604 \| 0.7583 \| 0.7731 \| 0.7739 \| 0.3052 \| \| 0.0336 \| 12.04 \| 6020 \| 0.2203 \| 0.6265 \| 0.7593 \| 0.8256 \| nan \| 0.8568 \| 0.8967 \| 0.7212 \| 0.8569 \| 0.7082 \| 0.8832 \| 0.7463 \| 0.4661 \| 0.6005 \| 0.6372 \| 0.9132 \| 0.8482 \| 0.8314 \| 0.8517 \| 0.8351 \| 0.8277 \| 0.4268 \| 0.0 \| 0.7665 \| 0.8147 \| 0.6297 \| 0.7679 \| 0.6285 \| 0.7707 \| 0.5547 \| 0.3604 \| 0.5009 \| 0.5349 \| 0.8294 \| 0.7401 \| 0.7577 \| 0.7606 \| 0.7723 \| 0.7758 \| 0.3115 \| \| 0.0286 \| 12.08 \| 6040 \| 0.2250 \| 0.6271 \| 0.7585 \| 0.8279 \| nan \| 0.8574 \| 0.8988 \| 0.7423 \| 0.8675 \| 0.7068 \| 0.8874 \| 0.7407 \| 0.4549 \| 0.6028 \| 0.6397 \| 0.9084 \| 0.8455 \| 0.8280 \| 0.8531 \| 0.8258 \| 0.8309 \| 0.4054 \| 0.0 \| 0.7684 \| 0.8179 \| 0.6376 \| 0.7737 \| 0.6257 \| 0.7723 \| 0.5564 \| 0.3632 \| 0.5019 \| 0.5374 \| 0.8298 \| 0.7358 \| 0.7559 \| 0.7609 \| 0.7717 \| 0.7777 \| 0.3018 \| \| 0.0309 \| 12.12 \| 6060 \| 0.2299 \| 0.6233 \| 0.7504 \| 0.8261 \| nan \| 0.8402 \| 0.9057 \| 0.6992 \| 0.8646 \| 0.6978 \| 0.8817 \| 0.7436 \| 0.4204 \| 0.5674 \| 0.6546 \| 0.9063 \| 0.8399 \| 0.8438 \| 0.8562 \| 0.8334 \| 0.8201 \| 0.3814 \| 0.0 \| 0.7621 \| 0.8182 \| 0.6200 \| 0.7726 \| 0.6221 \| 0.7671 \| 0.5575 \| 0.3449 \| 0.4860 \| 0.5407 \| 0.8272 \| 0.7355 \| 0.7586 \| 0.7605 \| 0.7725 \| 0.7678 \| 0.3055 \| \| 0.0335 \| 12.16 \| 6080 \| 0.2293 \| 0.6213 \| 0.7477 \| 0.8218 \| nan \| 0.8327 \| 0.8919 \| 0.7078 \| 0.8492 \| 0.6968 \| 0.8750 \| 0.7630 \| 0.4157 \| 0.5924 \| 0.6157 \| 0.9195 \| 0.8414 \| 0.8426 \| 0.8437 \| 0.8327 \| 0.8217 \| 0.3698 \| 0.0 \| 0.7595 \| 0.8158 \| 0.6204 \| 0.7626 \| 0.6262 \| 0.7656 \| 0.5507 \| 0.3408 \| 0.5001 \| 0.5262 \| 0.8277 \| 0.7348 \| 0.7566 \| 0.7532 \| 0.7695 \| 0.7708 \| 0.3035 \| \| 0.0334 \| 12.2 \| 6100 \| 0.2293 \| 0.6235 \| 0.7534 \| 0.8234 \| nan \| 0.8485 \| 0.9054 \| 0.7384 \| 0.8482 \| 0.7003 \| 0.8764 \| 0.7602 \| 0.4250 \| 0.6150 \| 0.6245 \| 0.9104 \| 0.8574 \| 0.8285 \| 0.8454 \| 0.8390 \| 0.8127 \| 0.3723 \| 0.0 \| 0.7650 \| 0.8190 \| 0.6351 \| 0.7630 \| 0.6232 \| 0.7688 \| 0.5482 \| 0.3464 \| 0.5120 \| 0.5339 \| 0.8277 \| 0.7326 \| 0.7495 \| 0.7546 \| 0.7702 \| 0.7689 \| 0.3058 \| \| 0.0413 \| 12.24 \| 6120 \| 0.2224 \| 0.6251 \| 0.7572 \| 0.8247 \| nan \| 0.8724 \| 0.9004 \| 0.7315 \| 0.8544 \| 0.7000 \| 0.8835 \| 0.7461 \| 0.4563 \| 0.5877 \| 0.6541 \| 0.9096 \| 0.8544 \| 0.8243 \| 0.8548 \| 0.8427 \| 0.8206 \| 0.3802 \| 0.0 \| 0.7680 \| 0.8162 \| 0.6332 \| 0.7667 \| 0.6181 \| 0.7706 \| 0.5492 \| 0.3710 \| 0.4992 \| 0.5461 \| 0.8275 \| 0.7315 \| 0.7495 \| 0.7578 \| 0.7706 \| 0.7733 \| 0.3027 \| \| 0.0317 \| 12.28 \| 6140 \| 0.2260 \| 0.6248 \| 0.7557 \| 0.8251 \| nan \| 0.8752 \| 0.8976 \| 0.7346 \| 0.8549 \| 0.7024 \| 0.8815 \| 0.7496 \| 0.4264 \| 0.6123 \| 0.6348 \| 0.9121 \| 0.8522 \| 0.8280 \| 0.8460 \| 0.8366 \| 0.8302 \| 0.3733 \| 0.0 \| 0.7699 \| 0.8190 \| 0.6336 \| 0.7651 \| 0.6137 \| 0.7748 \| 0.5445 \| 0.3573 \| 0.5082 \| 0.5388 \| 0.8289 \| 0.7329 \| 0.7518 \| 0.7561 \| 0.7711 \| 0.7785 \| 0.3026 \| \| 0.0357 \| 12.32 \| 6160 \| 0.2295 \| 0.6233 \| 0.7542 \| 0.8227 \| nan \| 0.8614 \| 0.9038 \| 0.7491 \| 0.8420 \| 0.7010 \| 0.8852 \| 0.7599 \| 0.4142 \| 0.5897 \| 0.6365 \| 0.9102 \| 0.8520 \| 0.8247 \| 0.8376 \| 0.8361 \| 0.8309 \| 0.3872 \| 0.0 \| 0.7686 \| 0.8170 \| 0.6399 \| 0.7610 \| 0.6167 \| 0.7771 \| 0.5468 \| 0.3442 \| 0.4971 \| 0.5352 \| 0.8287 \| 0.7332 \| 0.7538 \| 0.7543 \| 0.7677 \| 0.7790 \| 0.2991 \| \| 0.0362 \| 12.36 \| 6180 \| 0.2290 \| 0.6259 \| 0.7574 \| 0.8260 \| nan \| 0.8600 \| 0.9036 \| 0.7575 \| 0.8503 \| 0.6978 \| 0.8862 \| 0.7541 \| 0.3948 \| 0.6014 \| 0.6476 \| 0.9111 \| 0.8516 \| 0.8385 \| 0.8550 \| 0.8375 \| 0.8296 \| 0.3996 \| 0.0 \| 0.7734 \| 0.8207 \| 0.6423 \| 0.7641 \| 0.6160 \| 0.7747 \| 0.5464 \| 0.3382 \| 0.5043 \| 0.5414 \| 0.8295 \| 0.7401 \| 0.7631 \| 0.7609 \| 0.7712 \| 0.7805 \| 0.2992 \| \| 0.0484 \| 12.4 \| 6200 \| 0.2278 \| 0.6258 \| 0.7550 \| 0.8253 \| nan \| 0.8532 \| 0.9050 \| 0.7494 \| 0.8537 \| 0.6986 \| 0.8866 \| 0.7488 \| 0.4345 \| 0.5954 \| 0.6401 \| 0.9056 \| 0.8454 \| 0.8465 \| 0.8529 \| 0.8325 \| 0.8251 \| 0.3620 \| 0.0 \| 0.7676 \| 0.8179 \| 0.6411 \| 0.7666 \| 0.6182 \| 0.7709 \| 0.5495 \| 0.3644 \| 0.5026 \| 0.5401 \| 0.8280 \| 0.7389 \| 0.7637 \| 0.7584 \| 0.7688 \| 0.7777 \| 0.2906 \| \| 0.0353 \| 12.44 \| 6220 \| 0.2256 \| 0.6294 \| 0.7594 \| 0.8279 \| nan \| 0.8645 \| 0.9064 \| 0.7356 \| 0.8636 \| 0.7006 \| 0.8859 \| 0.7354 \| 0.4782 \| 0.6007 \| 0.6601 \| 0.9118 \| 0.8441 \| 0.8395 \| 0.8566 \| 0.8366 \| 0.8307 \| 0.3597 \| 0.0 \| 0.7718 \| 0.8221 \| 0.6358 \| 0.7727 \| 0.6177 \| 0.7719 \| 0.5516 \| 0.3942 \| 0.5053 \| 0.5472 \| 0.8294 \| 0.7407 \| 0.7645 \| 0.7622 \| 0.7718 \| 0.7794 \| 0.2904 \| \| 0.0361 \| 12.48 \| 6240 \| 0.2236 \| 0.6265 \| 0.7557 \| 0.8261 \| nan \| 0.8510 \| 0.9002 \| 0.7453 \| 0.8569 \| 0.6987 \| 0.8855 \| 0.7481 \| 0.4498 \| 0.5990 \| 0.6371 \| 0.9145 \| 0.8531 \| 0.8317 \| 0.8567 \| 0.8332 \| 0.8344 \| 0.3515 \| 0.0 \| 0.7644 \| 0.8185 \| 0.6377 \| 0.7703 \| 0.6220 \| 0.7704 \| 0.5536 \| 0.3703 \| 0.5037 \| 0.5392 \| 0.8296 \| 0.7372 \| 0.7584 \| 0.7628 \| 0.7706 \| 0.7792 \| 0.2882 \| \| 0.0511 \| 12.52 \| 6260 \| 0.2277 \| 0.6253 \| 0.7548 \| 0.8242 \| nan \| 0.8476 \| 0.8931 \| 0.7597 \| 0.8480 \| 0.7010 \| 0.8868 \| 0.7604 \| 0.4331 \| 0.6105 \| 0.6158 \| 0.9159 \| 0.8539 \| 0.8393 \| 0.8495 \| 0.8376 \| 0.8202 \| 0.3593 \| 0.0 \| 0.7640 \| 0.8184 \| 0.6402 \| 0.7621 \| 0.6255 \| 0.7707 \| 0.5468 \| 0.3641 \| 0.5030 \| 0.5265 \| 0.8306 \| 0.7393 \| 0.7614 \| 0.7625 \| 0.7736 \| 0.7731 \| 0.2929 \| \| 0.0586 \| 12.56 \| 6280 \| 0.2299 \| 0.6242 \| 0.7543 \| 0.8224 \| nan \| 0.8597 \| 0.8940 \| 0.7576 \| 0.8428 \| 0.7032 \| 0.8725 \| 0.7691 \| 0.4026 \| 0.6018 \| 0.6374 \| 0.9108 \| 0.8519 \| 0.8347 \| 0.8484 \| 0.8349 \| 0.8241 \| 0.3781 \| 0.0 \| 0.7651 \| 0.8190 \| 0.6397 \| 0.7572 \| 0.6239 \| 0.7710 \| 0.5454 \| 0.3396 \| 0.5044 \| 0.5380 \| 0.8308 \| 0.7388 \| 0.7568 \| 0.7571 \| 0.7711 \| 0.7736 \| 0.3037 \| \| 0.0261 \| 12.6 \| 6300 \| 0.2290 \| 0.6244 \| 0.7553 \| 0.8225 \| nan \| 0.8704 \| 0.8966 \| 0.7397 \| 0.8438 \| 0.7034 \| 0.8785 \| 0.7639 \| 0.4307 \| 0.5786 \| 0.6540 \| 0.9126 \| 0.8562 \| 0.8144 \| 0.8458 \| 0.8396 \| 0.8254 \| 0.3871 \| 0.0 \| 0.7687 \| 0.8189 \| 0.6341 \| 0.7586 \| 0.6231 \| 0.7710 \| 0.5491 \| 0.3626 \| 0.4951 \| 0.5432 \| 0.8300 \| 0.7328 \| 0.7460 \| 0.7561 \| 0.7715 \| 0.7739 \| 0.3039 \| \| 0.04 \| 12.64 \| 6320 \| 0.2329 \| 0.6277 \| 0.7594 \| 0.8252 \| nan \| 0.8835 \| 0.8976 \| 0.7452 \| 0.8447 \| 0.6999 \| 0.8810 \| 0.7746 \| 0.4545 \| 0.5671 \| 0.6589 \| 0.9143 \| 0.8449 \| 0.8405 \| 0.8459 \| 0.8404 \| 0.8280 \| 0.3883 \| 0.0 \| 0.7703 \| 0.8184 \| 0.6348 \| 0.7600 \| 0.6321 \| 0.7721 \| 0.5557 \| 0.3785 \| 0.4905 \| 0.5449 \| 0.8301 \| 0.7400 \| 0.7592 \| 0.7572 \| 0.7744 \| 0.7767 \| 0.3029 \| \| 0.0781 \| 12.68 \| 6340 \| 0.2256 \| 0.6255 \| 0.7551 \| 0.8252 \| nan \| 0.8670 \| 0.8966 \| 0.7042 \| 0.8548 \| 0.7049 \| 0.8694 \| 0.7625 \| 0.4641 \| 0.5711 \| 0.6488 \| 0.9170 \| 0.8511 \| 0.8333 \| 0.8529 \| 0.8267 \| 0.8376 \| 0.3752 \| 0.0 \| 0.7684 \| 0.8176 \| 0.6185 \| 0.7655 \| 0.6280 \| 0.7709 \| 0.5596 \| 0.3800 \| 0.4920 \| 0.5416 \| 0.8285 \| 0.7369 \| 0.7551 \| 0.7539 \| 0.7680 \| 0.7801 \| 0.2950 \| \| 0.0363 \| 12.72 \| 6360 \| 0.2250 \| 0.6249 \| 0.7525 \| 0.8253 \| nan \| 0.8345 \| 0.9020 \| 0.7121 \| 0.8564 \| 0.7004 \| 0.8744 \| 0.7617 \| 0.4431 \| 0.5829 \| 0.6374 \| 0.9145 \| 0.8497 \| 0.8377 \| 0.8500 \| 0.8310 \| 0.8220 \| 0.3834 \| 0.0 \| 0.7609 \| 0.8184 \| 0.6233 \| 0.7670 \| 0.6281 \| 0.7684 \| 0.5605 \| 0.3693 \| 0.4970 \| 0.5384 \| 0.8282 \| 0.7373 \| 0.7557 \| 0.7550 \| 0.7704 \| 0.7748 \| 0.2953 \| \| 0.0593 \| 12.76 \| 6380 \| 0.2211 \| 0.6265 \| 0.7593 \| 0.8254 \| nan \| 0.8452 \| 0.9015 \| 0.7707 \| 0.8558 \| 0.7028 \| 0.8872 \| 0.7524 \| 0.4794 \| 0.5794 \| 0.6445 \| 0.9071 \| 0.8471 \| 0.8278 \| 0.8500 \| 0.8447 \| 0.8158 \| 0.3967 \| 0.0 \| 0.7626 \| 0.8188 \| 0.6433 \| 0.7688 \| 0.6237 \| 0.7701 \| 0.5560 \| 0.3839 \| 0.4912 \| 0.5370 \| 0.8281 \| 0.7371 \| 0.7547 \| 0.7579 \| 0.7748 \| 0.7698 \| 0.2995 \| \| 0.0413 \| 12.8 \| 6400 \| 0.2294 \| 0.6243 \| 0.7537 \| 0.8258 \| nan \| 0.8638 \| 0.9019 \| 0.7530 \| 0.8542 \| 0.7002 \| 0.8839 \| 0.7623 \| 0.4083 \| 0.5835 \| 0.6451 \| 0.9085 \| 0.8509 \| 0.8287 \| 0.8441 \| 0.8519 \| 0.8128 \| 0.3597 \| 0.0 \| 0.7640 \| 0.8197 \| 0.6395 \| 0.7671 \| 0.6266 \| 0.7705 \| 0.5563 \| 0.3461 \| 0.4931 \| 0.5383 \| 0.8275 \| 0.7364 \| 0.7548 \| 0.7586 \| 0.7787 \| 0.7688 \| 0.2912 \| \| 0.0643 \| 12.84 \| 6420 \| 0.2268 \| 0.6205 \| 0.7460 \| 0.8258 \| nan \| 0.8655 \| 0.8930 \| 0.7164 \| 0.8635 \| 0.7005 \| 0.8766 \| 0.7589 \| 0.3734 \| 0.5981 \| 0.6227 \| 0.9131 \| 0.8497 \| 0.8373 \| 0.8601 \| 0.8369 \| 0.8054 \| 0.3110 \| 0.0 \| 0.7610 \| 0.8162 \| 0.6249 \| 0.7705 \| 0.6263 \| 0.7691 \| 0.5590 \| 0.3202 \| 0.4985 \| 0.5288 \| 0.8268 \| 0.7389 \| 0.7588 \| 0.7603 \| 0.7768 \| 0.7641 \| 0.2695 \| \| 0.0395 \| 12.88 \| 6440 \| 0.2283 \| 0.6245 \| 0.7517 \| 0.8281 \| nan \| 0.8563 \| 0.9066 \| 0.7141 \| 0.8563 \| 0.6995 \| 0.8924 \| 0.7603 \| 0.4074 \| 0.5881 \| 0.6474 \| 0.9126 \| 0.8516 \| 0.8405 \| 0.8606 \| 0.8364 \| 0.8136 \| 0.3343 \| 0.0 \| 0.7669 \| 0.8196 \| 0.6246 \| 0.7732 \| 0.6249 \| 0.7803 \| 0.5577 \| 0.3405 \| 0.4975 \| 0.5420 \| 0.8271 \| 0.7388 \| 0.7590 \| 0.7615 \| 0.7773 \| 0.7684 \| 0.2824 \| \| 0.0405 \| 12.92 \| 6460 \| 0.2262 \| 0.6250 \| 0.7532 \| 0.8276 \| nan \| 0.8608 \| 0.8982 \| 0.7534 \| 0.8616 \| 0.6956 \| 0.8992 \| 0.7566 \| 0.4366 \| 0.5871 \| 0.6325 \| 0.9096 \| 0.8538 \| 0.8318 \| 0.8533 \| 0.8305 \| 0.8112 \| 0.3330 \| 0.0 \| 0.7663 \| 0.8200 \| 0.6393 \| 0.7754 \| 0.6220 \| 0.7795 \| 0.5576 \| 0.3592 \| 0.4954 \| 0.5359 \| 0.8281 \| 0.7382 \| 0.7560 \| 0.7588 \| 0.7745 \| 0.7656 \| 0.2786 \| \| 0.0499 \| 12.96 \| 6480 \| 0.2250 \| 0.6244 \| 0.7539 \| 0.8266 \| nan \| 0.8606 \| 0.8939 \| 0.7448 \| 0.8603 \| 0.6943 \| 0.8968 \| 0.7500 \| 0.4342 \| 0.5886 \| 0.6393 \| 0.9134 \| 0.8534 \| 0.8320 \| 0.8542 \| 0.8384 \| 0.8159 \| 0.3458 \| 0.0 \| 0.7622 \| 0.8176 \| 0.6369 \| 0.7764 \| 0.6152 \| 0.7770 \| 0.5559 \| 0.3563 \| 0.4988 \| 0.5392 \| 0.8275 \| 0.7375 \| 0.7557 \| 0.7568 \| 0.7745 \| 0.7651 \| 0.2865 \| \| 0.0759 \| 13.0 \| 6500 \| 0.2264 \| 0.6232 \| 0.7504 \| 0.8292 \| nan \| 0.8655 \| 0.8962 \| 0.7145 \| 0.8670 \| 0.6965 \| 0.8953 \| 0.7496 \| 0.3597 \| 0.6238 \| 0.6121 \| 0.9137 \| 0.8443 \| 0.8349 \| 0.8655 \| 0.8427 \| 0.8243 \| 0.3505 \| 0.0 \| 0.7682 \| 0.8209 \| 0.6255 \| 0.7787 \| 0.6182 \| 0.7777 \| 0.5565 \| 0.3142 \| 0.5113 \| 0.5263 \| 0.8282 \| 0.7378 \| 0.7571 \| 0.7596 \| 0.7752 \| 0.7701 \| 0.2922 \| \| 0.0346 \| 13.04 \| 6520 \| 0.2275 \| 0.6243 \| 0.7520 \| 0.8272 \| nan \| 0.8592 \| 0.8995 \| 0.7354 \| 0.8599 \| 0.6961 \| 0.8982 \| 0.7528 \| 0.4045 \| 0.6214 \| 0.6191 \| 0.9039 \| 0.8312 \| 0.8476 \| 0.8599 \| 0.8302 \| 0.8213 \| 0.3431 \| 0.0 \| 0.7646 \| 0.8169 \| 0.6353 \| 0.7762 \| 0.6238 \| 0.7778 \| 0.5570 \| 0.3432 \| 0.5123 \| 0.5295 \| 0.8263 \| 0.7357 \| 0.7576 \| 0.7561 \| 0.7707 \| 0.7691 \| 0.2859 \| \| 0.0732 \| 13.08 \| 6540 \| 0.2215 \| 0.6250 \| 0.7543 \| 0.8273 \| nan \| 0.8583 \| 0.8961 \| 0.7646 \| 0.8582 \| 0.6937 \| 0.9009 \| 0.7533 \| 0.3897 \| 0.6029 \| 0.6417 \| 0.9080 \| 0.8392 \| 0.8441 \| 0.8565 \| 0.8248 \| 0.8273 \| 0.3642 \| 0.0 \| 0.7647 \| 0.8161 \| 0.6412 \| 0.7749 \| 0.6255 \| 0.7769 \| 0.5579 \| 0.3359 \| 0.5064 \| 0.5398 \| 0.8271 \| 0.7387 \| 0.7568 \| 0.7575 \| 0.7706 \| 0.7712 \| 0.2897 \| \| 0.0573 \| 13.12 \| 6560 \| 0.2277 \| 0.6239 \| 0.7521 \| 0.8278 \| nan \| 0.8549 \| 0.9003 \| 0.7490 \| 0.8600 \| 0.6969 \| 0.8982 \| 0.7494 \| 0.3698 \| 0.6079 \| 0.6389 \| 0.9136 \| 0.8442 \| 0.8399 \| 0.8518 \| 0.8394 \| 0.8175 \| 0.3537 \| 0.0 \| 0.7653 \| 0.8189 \| 0.6380 \| 0.7747 \| 0.6242 \| 0.7777 \| 0.5570 \| 0.3218 \| 0.5059 \| 0.5375 \| 0.8273 \| 0.7397 \| 0.7563 \| 0.7568 \| 0.7733 \| 0.7680 \| 0.2879 \| \| 0.0778 \| 13.16 \| 6580 \| 0.2248 \| 0.6238 \| 0.7522 \| 0.8273 \| nan \| 0.8608 \| 0.8912 \| 0.7534 \| 0.8637 \| 0.7025 \| 0.8966 \| 0.7488 \| 0.3889 \| 0.5965 \| 0.6424 \| 0.9154 \| 0.8425 \| 0.8367 \| 0.8508 \| 0.8299 \| 0.8165 \| 0.3509 \| 0.0 \| 0.7646 \| 0.8171 \| 0.6389 \| 0.7757 \| 0.6225 \| 0.7797 \| 0.5567 \| 0.3331 \| 0.4992 \| 0.5377 \| 0.8284 \| 0.7414 \| 0.7567 \| 0.7548 \| 0.7699 \| 0.7658 \| 0.2859 \| \| 0.0471 \| 13.2 \| 6600 \| 0.2250 \| 0.6262 \| 0.7549 \| 0.8281 \| nan \| 0.8596 \| 0.8987 \| 0.7459 \| 0.8625 \| 0.7008 \| 0.8925 \| 0.7580 \| 0.4383 \| 0.5730 \| 0.6607 \| 0.9067 \| 0.8400 \| 0.8446 \| 0.8613 \| 0.8206 \| 0.8170 \| 0.3530 \| 0.0 \| 0.7647 \| 0.8188 \| 0.6374 \| 0.7751 \| 0.6291 \| 0.7785 \| 0.5614 \| 0.3660 \| 0.4895 \| 0.5442 \| 0.8279 \| 0.7419 \| 0.7590 \| 0.7556 \| 0.7670 \| 0.7670 \| 0.2879 \| \| 0.0286 \| 13.24 \| 6620 \| 0.2275 \| 0.6251 \| 0.7541 \| 0.8264 \| nan \| 0.8571 \| 0.9003 \| 0.7318 \| 0.8554 \| 0.6998 \| 0.8911 \| 0.7582 \| 0.4283 \| 0.6010 \| 0.6385 \| 0.9097 \| 0.8407 \| 0.8339 \| 0.8515 \| 0.8390 \| 0.8200 \| 0.3629 \| 0.0 \| 0.7634 \| 0.8170 \| 0.6323 \| 0.7720 \| 0.6288 \| 0.7760 \| 0.5574 \| 0.3604 \| 0.4999 \| 0.5328 \| 0.8276 \| 0.7388 \| 0.7557 \| 0.7560 \| 0.7715 \| 0.7704 \| 0.2911 \| \| 0.0349 \| 13.28 \| 6640 \| 0.2300 \| 0.6261 \| 0.7560 \| 0.8281 \| nan \| 0.8669 \| 0.8957 \| 0.7340 \| 0.8597 \| 0.7003 \| 0.8930 \| 0.7522 \| 0.4288 \| 0.6015 \| 0.6413 \| 0.9139 \| 0.8436 \| 0.8380 \| 0.8504 \| 0.8430 \| 0.8348 \| 0.3542 \| 0.0 \| 0.7660 \| 0.8175 \| 0.6332 \| 0.7744 \| 0.6267 \| 0.7768 \| 0.5568 \| 0.3611 \| 0.5022 \| 0.5371 \| 0.8287 \| 0.7393 \| 0.7569 \| 0.7566 \| 0.7738 \| 0.7787 \| 0.2840 \| \| 0.0306 \| 13.32 \| 6660 \| 0.2307 \| 0.6266 \| 0.7564 \| 0.8283 \| nan \| 0.8701 \| 0.8999 \| 0.7348 \| 0.8608 \| 0.6980 \| 0.8911 \| 0.7512 \| 0.4234 \| 0.6036 \| 0.6437 \| 0.9131 \| 0.8350 \| 0.8476 \| 0.8492 \| 0.8432 \| 0.8303 \| 0.3638 \| 0.0 \| 0.7672 \| 0.8194 \| 0.6338 \| 0.7726 \| 0.6252 \| 0.7740 \| 0.5581 \| 0.3583 \| 0.5038 \| 0.5401 \| 0.8289 \| 0.7399 \| 0.7602 \| 0.7588 \| 0.7746 \| 0.7781 \| 0.2860 \| \| 0.0311 \| 13.36 \| 6680 \| 0.2294 \| 0.6266 \| 0.7570 \| 0.8274 \| nan \| 0.8626 \| 0.9014 \| 0.7207 \| 0.8600 \| 0.6993 \| 0.8897 \| 0.7452 \| 0.4604 \| 0.5968 \| 0.6393 \| 0.9114 \| 0.8394 \| 0.8461 \| 0.8619 \| 0.8449 \| 0.8236 \| 0.3669 \| 0.0 \| 0.7658 \| 0.8194 \| 0.6288 \| 0.7721 \| 0.6222 \| 0.7735 \| 0.5564 \| 0.3823 \| 0.4997 \| 0.5362 \| 0.8276 \| 0.7403 \| 0.7596 \| 0.7609 \| 0.7734 \| 0.7732 \| 0.2875 \| \| 0.0306 \| 13.4 \| 6700 \| 0.2276 \| 0.6260 \| 0.7567 \| 0.8260 \| nan \| 0.8548 \| 0.8992 \| 0.7393 \| 0.8555 \| 0.7020 \| 0.8857 \| 0.7528 \| 0.4614 \| 0.6084 \| 0.6323 \| 0.9099 \| 0.8456 \| 0.8381 \| 0.8640 \| 0.8356 \| 0.8217 \| 0.3570 \| 0.0 \| 0.7620 \| 0.8181 \| 0.6351 \| 0.7704 \| 0.6199 \| 0.7759 \| 0.5546 \| 0.3800 \| 0.5050 \| 0.5352 \| 0.8275 \| 0.7395 \| 0.7570 \| 0.7588 \| 0.7712 \| 0.7712 \| 0.2867 \| \| 0.039 \| 13.44 \| 6720 \| 0.2236 \| 0.6247 \| 0.7550 \| 0.8256 \| nan \| 0.8596 \| 0.8948 \| 0.7488 \| 0.8575 \| 0.7069 \| 0.8837 \| 0.7453 \| 0.4302 \| 0.6010 \| 0.6397 \| 0.9133 \| 0.8442 \| 0.8393 \| 0.8567 \| 0.8473 \| 0.8183 \| 0.3490 \| 0.0 \| 0.7612 \| 0.8165 \| 0.6382 \| 0.7708 \| 0.6181 \| 0.7748 \| 0.5527 \| 0.3618 \| 0.5029 \| 0.5384 \| 0.8277 \| 0.7398 \| 0.7570 \| 0.7584 \| 0.7745 \| 0.7701 \| 0.2819 \| \| 0.0432 \| 13.48 \| 6740 \| 0.2253 \| 0.6248 \| 0.7534 \| 0.8265 \| nan \| 0.8610 \| 0.8984 \| 0.7402 \| 0.8586 \| 0.7057 \| 0.8851 \| 0.7493 \| 0.4230 \| 0.5866 \| 0.6460 \| 0.9102 \| 0.8378 \| 0.8436 \| 0.8502 \| 0.8424 \| 0.8311 \| 0.3393 \| 0.0 \| 0.7616 \| 0.8183 \| 0.6364 \| 0.7708 \| 0.6213 \| 0.7751 \| 0.5551 \| 0.3581 \| 0.4975 \| 0.5416 \| 0.8284 \| 0.7396 \| 0.7572 \| 0.7562 \| 0.7728 \| 0.7769 \| 0.2802 \| \| 0.0561 \| 13.52 \| 6760 \| 0.2249 \| 0.6252 \| 0.7538 \| 0.8258 \| nan \| 0.8634 \| 0.8931 \| 0.7255 \| 0.8613 \| 0.7117 \| 0.8817 \| 0.7439 \| 0.4569 \| 0.5847 \| 0.6417 \| 0.9144 \| 0.8428 \| 0.8335 \| 0.8479 \| 0.8433 \| 0.8290 \| 0.3400 \| 0.0 \| 0.7629 \| 0.8170 \| 0.6301 \| 0.7711 \| 0.6232 \| 0.7737 \| 0.5549 \| 0.3799 \| 0.4953 \| 0.5382 \| 0.8287 \| 0.7379 \| 0.7536 \| 0.7570 \| 0.7743 \| 0.7745 \| 0.2810 \| \| 0.0274 \| 13.56 \| 6780 \| 0.2255 \| 0.6257 \| 0.7535 \| 0.8276 \| nan \| 0.8548 \| 0.9047 \| 0.7118 \| 0.8624 \| 0.7132 \| 0.8827 \| 0.7408 \| 0.4344 \| 0.5899 \| 0.6469 \| 0.9102 \| 0.8451 \| 0.8372 \| 0.8485 \| 0.8431 \| 0.8342 \| 0.3497 \| 0.0 \| 0.7653 \| 0.8200 \| 0.6253 \| 0.7725 \| 0.6241 \| 0.7734 \| 0.5553 \| 0.3660 \| 0.4977 \| 0.5403 \| 0.8283 \| 0.7404 \| 0.7565 \| 0.7594 \| 0.7761 \| 0.7769 \| 0.2846 \| \| 0.0351 \| 13.6 \| 6800 \| 0.2265 \| 0.6253 \| 0.7529 \| 0.8267 \| nan \| 0.8525 \| 0.8998 \| 0.7031 \| 0.8561 \| 0.7110 \| 0.8882 \| 0.7468 \| 0.4319 \| 0.6002 \| 0.6402 \| 0.9130 \| 0.8394 \| 0.8354 \| 0.8608 \| 0.8389 \| 0.8351 \| 0.3463 \| 0.0 \| 0.7637 \| 0.8186 \| 0.6208 \| 0.7706 \| 0.6258 \| 0.7751 \| 0.5545 \| 0.3662 \| 0.5019 \| 0.5370 \| 0.8282 \| 0.7395 \| 0.7572 \| 0.7591 \| 0.7740 \| 0.7785 \| 0.2853 \| \| 0.03 \| 13.64 \| 6820 \| 0.2249 \| 0.6277 \| 0.7571 \| 0.8275 \| nan \| 0.8542 \| 0.9011 \| 0.7257 \| 0.8581 \| 0.7129 \| 0.8850 \| 0.7441 \| 0.4520 \| 0.6132 \| 0.6366 \| 0.9075 \| 0.8438 \| 0.8373 \| 0.8605 \| 0.8409 \| 0.8409 \| 0.3568 \| 0.0 \| 0.7643 \| 0.8179 \| 0.6311 \| 0.7724 \| 0.6256 \| 0.7747 \| 0.5538 \| 0.3791 \| 0.5082 \| 0.5368 \| 0.8279 \| 0.7398 \| 0.7573 \| 0.7594 \| 0.7743 \| 0.7809 \| 0.2953 \| \| 0.054 \| 13.68 \| 6840 \| 0.2284 \| 0.6284 \| 0.7573 \| 0.8271 \| nan \| 0.8525 \| 0.9009 \| 0.7383 \| 0.8598 \| 0.7058 \| 0.8868 \| 0.7452 \| 0.4685 \| 0.6112 \| 0.6215 \| 0.9114 \| 0.8494 \| 0.8378 \| 0.8523 \| 0.8422 \| 0.8296 \| 0.3611 \| 0.0 \| 0.7645 \| 0.8183 \| 0.6366 \| 0.7723 \| 0.6244 \| 0.7750 \| 0.5526 \| 0.3927 \| 0.5061 \| 0.5297 \| 0.8286 \| 0.7404 \| 0.7574 \| 0.7601 \| 0.7754 \| 0.7771 \| 0.2994 \| \| 0.0289 \| 13.72 \| 6860 \| 0.2274 \| 0.6297 \| 0.7595 \| 0.8282 \| nan \| 0.8596 \| 0.9078 \| 0.7485 \| 0.8556 \| 0.6974 \| 0.8891 \| 0.7552 \| 0.4650 \| 0.6101 \| 0.6309 \| 0.9094 \| 0.8471 \| 0.8419 \| 0.8515 \| 0.8426 \| 0.8371 \| 0.3631 \| 0.0 \| 0.7676 \| 0.8199 \| 0.6401 \| 0.7709 \| 0.6265 \| 0.7744 \| 0.5559 \| 0.3916 \| 0.5079 \| 0.5359 \| 0.8284 \| 0.7406 \| 0.7582 \| 0.7617 \| 0.7749 \| 0.7807 \| 0.2995 \| \| 0.0428 \| 13.76 \| 6880 \| 0.2275 \| 0.6282 \| 0.7584 \| 0.8271 \| nan \| 0.8697 \| 0.8965 \| 0.7569 \| 0.8585 \| 0.7026 \| 0.8861 \| 0.7531 \| 0.4632 \| 0.5979 \| 0.6336 \| 0.9129 \| 0.8470 \| 0.8393 \| 0.8537 \| 0.8435 \| 0.8265 \| 0.3511 \| 0.0 \| 0.7661 \| 0.8186 \| 0.6414 \| 0.7708 \| 0.6242 \| 0.7755 \| 0.5540 \| 0.3873 \| 0.5025 \| 0.5358 \| 0.8288 \| 0.7410 \| 0.7598 \| 0.7606 \| 0.7745 \| 0.7759 \| 0.2906 \| \| 0.0262 \| 13.8 \| 6900 \| 0.2307 \| 0.6296 \| 0.7619 \| 0.8271 \| nan \| 0.8737 \| 0.9034 \| 0.7519 \| 0.8523 \| 0.7033 \| 0.8888 \| 0.7561 \| 0.4930 \| 0.5972 \| 0.6564 \| 0.9107 \| 0.8473 \| 0.8322 \| 0.8615 \| 0.8355 \| 0.8268 \| 0.3629 \| 0.0 \| 0.7664 \| 0.8191 \| 0.6413 \| 0.7697 \| 0.6252 \| 0.7771 \| 0.5545 \| 0.4006 \| 0.5030 \| 0.5446 \| 0.8283 \| 0.7406 \| 0.7594 \| 0.7598 \| 0.7721 \| 0.7765 \| 0.2948 \| \| 0.034 \| 13.84 \| 6920 \| 0.2306 \| 0.6306 \| 0.7642 \| 0.8280 \| nan \| 0.8723 \| 0.9016 \| 0.7533 \| 0.8544 \| 0.7030 \| 0.8925 \| 0.7515 \| 0.5148 \| 0.6102 \| 0.6373 \| 0.9143 \| 0.8519 \| 0.8318 \| 0.8573 \| 0.8446 \| 0.8296 \| 0.3707 \| 0.0 \| 0.7661 \| 0.8199 \| 0.6412 \| 0.7711 \| 0.6243 \| 0.7772 \| 0.5539 \| 0.4123 \| 0.5073 \| 0.5376 \| 0.8288 \| 0.7408 \| 0.7590 \| 0.7619 \| 0.7752 \| 0.7782 \| 0.2967 \| \| 0.0364 \| 13.88 \| 6940 \| 0.2279 \| 0.6298 \| 0.7626 \| 0.8268 \| nan \| 0.8683 \| 0.8940 \| 0.7468 \| 0.8531 \| 0.7034 \| 0.8918 \| 0.7561 \| 0.5135 \| 0.6046 \| 0.6460 \| 0.9170 \| 0.8475 \| 0.8329 \| 0.8522 \| 0.8421 \| 0.8244 \| 0.3708 \| 0.0 \| 0.7630 \| 0.8169 \| 0.6386 \| 0.7703 \| 0.6253 \| 0.7771 \| 0.5546 \| 0.4098 \| 0.5054 \| 0.5407 \| 0.8283 \| 0.7404 \| 0.7592 \| 0.7606 \| 0.7749 \| 0.7758 \| 0.2953 \| \| 0.0521 \| 13.92 \| 6960 \| 0.2275 \| 0.6300 \| 0.7623 \| 0.8271 \| nan \| 0.8659 \| 0.8991 \| 0.7397 \| 0.8542 \| 0.7023 \| 0.8863 \| 0.7566 \| 0.5049 \| 0.6100 \| 0.6424 \| 0.9156 \| 0.8527 \| 0.8341 \| 0.8587 \| 0.8405 \| 0.8202 \| 0.3765 \| 0.0 \| 0.7651 \| 0.8186 \| 0.6366 \| 0.7705 \| 0.6265 \| 0.7761 \| 0.5561 \| 0.4079 \| 0.5090 \| 0.5414 \| 0.8282 \| 0.7402 \| 0.7585 \| 0.7608 \| 0.7742 \| 0.7734 \| 0.2968 \| \| 0.0303 \| 13.96 \| 6980 \| 0.2276 \| 0.6298 \| 0.7618 \| 0.8277 \| nan \| 0.8509 \| 0.9057 \| 0.7412 \| 0.8548 \| 0.7029 \| 0.8858 \| 0.7557 \| 0.5095 \| 0.6060 \| 0.6347 \| 0.9150 \| 0.8610 \| 0.8288 \| 0.8581 \| 0.8385 \| 0.8270 \| 0.3748 \| 0.0 \| 0.7652 \| 0.8209 \| 0.6376 \| 0.7709 \| 0.6247 \| 0.7765 \| 0.5552 \| 0.4065 \| 0.5066 \| 0.5384 \| 0.8286 \| 0.7394 \| 0.7566 \| 0.7611 \| 0.7738 \| 0.7768 \| 0.2978 \| \| 0.0313 \| 14.0 \| 7000 \| 0.2287 \| 0.6287 \| 0.7593 \| 0.8268 \| nan \| 0.8446 \| 0.9004 \| 0.7461 \| 0.8603 \| 0.7037 \| 0.8831 \| 0.7461 \| 0.5012 \| 0.5885 \| 0.6483 \| 0.9121 \| 0.8581 \| 0.8307 \| 0.8587 \| 0.8352 \| 0.8259 \| 0.3648 \| 0.0 \| 0.7627 \| 0.8190 \| 0.6387 \| 0.7721 \| 0.6214 \| 0.7759 \| 0.5527 \| 0.4051 \| 0.5000 \| 0.5437 \| 0.8286 \| 0.7395 \| 0.7566 \| 0.7596 \| 0.7726 \| 0.7751 \| 0.2937 \| \| 0.0295 \| 14.04 \| 7020 \| 0.2245 \| 0.6275 \| 0.7567 \| 0.8264 \| nan \| 0.8466 \| 0.8983 \| 0.7551 \| 0.8597 \| 0.7003 \| 0.8906 \| 0.7474 \| 0.4686 \| 0.5751 \| 0.6514 \| 0.9114 \| 0.8521 \| 0.8331 \| 0.8520 \| 0.8400 \| 0.8209 \| 0.3621 \| 0.0 \| 0.7624 \| 0.8184 \| 0.6410 \| 0.7722 \| 0.6226 \| 0.7765 \| 0.5533 \| 0.3895 \| 0.4938 \| 0.5430 \| 0.8288 \| 0.7398 \| 0.7575 \| 0.7587 \| 0.7736 \| 0.7730 \| 0.2917 \| \| 0.0335 \| 14.08 \| 7040 \| 0.2252 \| 0.6275 \| 0.7575 \| 0.8272 \| nan \| 0.8498 \| 0.9024 \| 0.7555 \| 0.8566 \| 0.7038 \| 0.8934 \| 0.7482 \| 0.4464 \| 0.5949 \| 0.6440 \| 0.9122 \| 0.8530 \| 0.8304 \| 0.8594 \| 0.8340 \| 0.8248 \| 0.3678 \| 0.0 \| 0.7637 \| 0.8202 \| 0.6406 \| 0.7716 \| 0.6209 \| 0.7784 \| 0.5514 \| 0.3733 \| 0.5023 \| 0.5410 \| 0.8293 \| 0.7405 \| 0.7581 \| 0.7611 \| 0.7730 \| 0.7750 \| 0.2953 \| \| 0.0308 \| 14.12 \| 7060 \| 0.2311 \| 0.6268 \| 0.7569 \| 0.8266 \| nan \| 0.8595 \| 0.9011 \| 0.7570 \| 0.8525 \| 0.7014 \| 0.8929 \| 0.7559 \| 0.4232 \| 0.6013 \| 0.6404 \| 0.9101 \| 0.8513 \| 0.8356 \| 0.8590 \| 0.8357 \| 0.8193 \| 0.3719 \| 0.0 \| 0.7655 \| 0.8191 \| 0.6411 \| 0.7699 \| 0.6228 \| 0.7777 \| 0.5522 \| 0.3576 \| 0.5049 \| 0.5401 \| 0.8288 \| 0.7413 \| 0.7598 \| 0.7607 \| 0.7727 \| 0.7719 \| 0.2964 \| \| 0.0901 \| 14.16 \| 7080 \| 0.2238 \| 0.6248 \| 0.7521 \| 0.8257 \| nan \| 0.8481 \| 0.9002 \| 0.7428 \| 0.8561 \| 0.7035 \| 0.8920 \| 0.7512 \| 0.4234 \| 0.5920 \| 0.6396 \| 0.9113 \| 0.8482 \| 0.8325 \| 0.8501 \| 0.8319 \| 0.8177 \| 0.3453 \| 0.0 \| 0.7600 \| 0.8177 \| 0.6368 \| 0.7711 \| 0.6212 \| 0.7770 \| 0.5519 \| 0.3595 \| 0.5001 \| 0.5397 \| 0.8287 \| 0.7398 \| 0.7578 \| 0.7585 \| 0.7701 \| 0.7704 \| 0.2867 \| \| 0.0342 \| 14.2 \| 7100 \| 0.2280 \| 0.6261 \| 0.7537 \| 0.8276 \| nan \| 0.8545 \| 0.8969 \| 0.7266 \| 0.8636 \| 0.7020 \| 0.8895 \| 0.7496 \| 0.4283 \| 0.5876 \| 0.6378 \| 0.9157 \| 0.8480 \| 0.8372 \| 0.8573 \| 0.8374 \| 0.8164 \| 0.3642 \| 0.0 \| 0.7629 \| 0.8182 \| 0.6309 \| 0.7742 \| 0.6218 \| 0.7774 \| 0.5560 \| 0.3651 \| 0.4989 \| 0.5398 \| 0.8291 \| 0.7396 \| 0.7580 \| 0.7601 \| 0.7711 \| 0.7713 \| 0.2960 \| \| 0.0464 \| 14.24 \| 7120 \| 0.2264 \| 0.6276 \| 0.7568 \| 0.8275 \| nan \| 0.8558 \| 0.9002 \| 0.7335 \| 0.8599 \| 0.7014 \| 0.8886 \| 0.7512 \| 0.4561 \| 0.5957 \| 0.6327 \| 0.9154 \| 0.8503 \| 0.8383 \| 0.8605 \| 0.8382 \| 0.8179 \| 0.3697 \| 0.0 \| 0.7631 \| 0.8190 \| 0.6342 \| 0.7730 \| 0.6218 \| 0.7780 \| 0.5556 \| 0.3813 \| 0.5025 \| 0.5375 \| 0.8291 \| 0.7401 \| 0.7586 \| 0.7606 \| 0.7711 \| 0.7717 \| 0.2997 \| \| 0.035 \| 14.28 \| 7140 \| 0.2272 \| 0.6279 \| 0.7576 \| 0.8268 \| nan \| 0.8576 \| 0.8974 \| 0.7363 \| 0.8581 \| 0.7020 \| 0.8893 \| 0.7512 \| 0.4621 \| 0.5983 \| 0.6404 \| 0.9140 \| 0.8468 \| 0.8391 \| 0.8556 \| 0.8375 \| 0.8194 \| 0.3736 \| 0.0 \| 0.7628 \| 0.8179 \| 0.6349 \| 0.7721 \| 0.6215 \| 0.7788 \| 0.5543 \| 0.3825 \| 0.5040 \| 0.5402 \| 0.8294 \| 0.7404 \| 0.7596 \| 0.7597 \| 0.7714 \| 0.7721 \| 0.3005 \| \| 0.0607 \| 14.32 \| 7160 \| 0.2220 \| 0.6271 \| 0.7564 \| 0.8263 \| nan \| 0.8558 \| 0.9017 \| 0.7173 \| 0.8597 \| 0.7047 \| 0.8888 \| 0.7371 \| 0.4717 \| 0.6011 \| 0.6354 \| 0.9143 \| 0.8470 \| 0.8350 \| 0.8580 \| 0.8417 \| 0.8224 \| 0.3679 \| 0.0 \| 0.7621 \| 0.8178 \| 0.6282 \| 0.7734 \| 0.6183 \| 0.7781 \| 0.5518 \| 0.3886 \| 0.5048 \| 0.5376 \| 0.8283 \| 0.7403 \| 0.7586 \| 0.7595 \| 0.7711 \| 0.7723 \| 0.2973 \| \| 0.0385 \| 14.36 \| 7180 \| 0.2286 \| 0.6258 \| 0.7531 \| 0.8268 \| nan \| 0.8600 \| 0.9010 \| 0.7351 \| 0.8591 \| 0.7037 \| 0.8914 \| 0.7511 \| 0.4106 \| 0.5936 \| 0.6409 \| 0.9094 \| 0.8412 \| 0.8394 \| 0.8497 \| 0.8356 \| 0.8172 \| 0.3641 \| 0.0 \| 0.7641 \| 0.8184 \| 0.6351 \| 0.7720 \| 0.6222 \| 0.7784 \| 0.5543 \| 0.3494 \| 0.5018 \| 0.5404 \| 0.8295 \| 0.7407 \| 0.7608 \| 0.7580 \| 0.7723 \| 0.7711 \| 0.2966 \| \| 0.0249 \| 14.4 \| 7200 \| 0.2265 \| 0.6277 \| 0.7564 \| 0.8279 \| nan \| 0.8588 \| 0.9019 \| 0.7445 \| 0.8581 \| 0.7004 \| 0.8924 \| 0.7549 \| 0.4295 \| 0.5960 \| 0.6456 \| 0.9119 \| 0.8410 \| 0.8413 \| 0.8534 \| 0.8380 \| 0.8246 \| 0.3664 \| 0.0 \| 0.7653 \| 0.8200 \| 0.6387 \| 0.7719 \| 0.6239 \| 0.7791 \| 0.5555 \| 0.3618 \| 0.5031 \| 0.5431 \| 0.8301 \| 0.7412 \| 0.7613 \| 0.7592 \| 0.7726 \| 0.7750 \| 0.2969 \| \| 0.0696 \| 14.44 \| 7220 \| 0.2261 \| 0.6251 \| 0.7524 \| 0.8273 \| nan \| 0.8546 \| 0.9017 \| 0.7450 \| 0.8596 \| 0.7046 \| 0.8882 \| 0.7522 \| 0.3870 \| 0.5925 \| 0.6459 \| 0.9097 \| 0.8463 \| 0.8409 \| 0.8488 \| 0.8384 \| 0.8196 \| 0.3559 \| 0.0 \| 0.7635 \| 0.8198 \| 0.6386 \| 0.7722 \| 0.6209 \| 0.7789 \| 0.5533 \| 0.3322 \| 0.5011 \| 0.5428 \| 0.8294 \| 0.7415 \| 0.7607 \| 0.7582 \| 0.7736 \| 0.7719 \| 0.2937 \| \| 0.0275 \| 14.48 \| 7240 \| 0.2313 \| 0.6257 \| 0.7528 \| 0.8277 \| nan \| 0.8543 \| 0.9035 \| 0.7401 \| 0.8612 \| 0.7019 \| 0.8917 \| 0.7480 \| 0.3991 \| 0.5864 \| 0.6421 \| 0.9076 \| 0.8444 \| 0.8442 \| 0.8526 \| 0.8423 \| 0.8194 \| 0.3585 \| 0.0 \| 0.7642 \| 0.8200 \| 0.6380 \| 0.7729 \| 0.6208 \| 0.7786 \| 0.5533 \| 0.3423 \| 0.4979 \| 0.5409 \| 0.8290 \| 0.7413 \| 0.7612 \| 0.7601 \| 0.7746 \| 0.7720 \| 0.2953 \| \| 0.0275 \| 14.52 \| 7260 \| 0.2287 \| 0.6262 \| 0.7541 \| 0.8272 \| nan \| 0.8583 \| 0.8979 \| 0.7486 \| 0.8614 \| 0.7002 \| 0.8887 \| 0.7501 \| 0.4107 \| 0.6033 \| 0.6407 \| 0.9106 \| 0.8440 \| 0.8423 \| 0.8479 \| 0.8377 \| 0.8219 \| 0.3554 \| 0.0 \| 0.7642 \| 0.8185 \| 0.6394 \| 0.7725 \| 0.6219 \| 0.7782 \| 0.5540 \| 0.3498 \| 0.5049 \| 0.5400 \| 0.8294 \| 0.7409 \| 0.7605 \| 0.7583 \| 0.7735 \| 0.7729 \| 0.2929 \| \| 0.0289 \| 14.56 \| 7280 \| 0.2300 \| 0.6261 \| 0.7551 \| 0.8266 \| nan \| 0.8598 \| 0.9003 \| 0.7461 \| 0.8573 \| 0.7020 \| 0.8874 \| 0.7491 \| 0.4181 \| 0.5875 \| 0.6462 \| 0.9131 \| 0.8410 \| 0.8484 \| 0.8535 \| 0.8421 \| 0.8187 \| 0.3665 \| 0.0 \| 0.7650 \| 0.8185 \| 0.6386 \| 0.7712 \| 0.6206 \| 0.7776 \| 0.5530 \| 0.3536 \| 0.4989 \| 0.5423 \| 0.8287 \| 0.7406 \| 0.7610 \| 0.7598 \| 0.7737 \| 0.7712 \| 0.2960 \| \| 0.0344 \| 14.6 \| 7300 \| 0.2288 \| 0.6249 \| 0.7539 \| 0.8251 \| nan \| 0.8592 \| 0.8959 \| 0.7598 \| 0.8538 \| 0.7016 \| 0.8922 \| 0.7515 \| 0.4125 \| 0.5852 \| 0.6357 \| 0.9101 \| 0.8403 \| 0.8440 \| 0.8519 \| 0.8393 \| 0.8194 \| 0.3634 \| 0.0 \| 0.7626 \| 0.8163 \| 0.6421 \| 0.7700 \| 0.6202 \| 0.7781 \| 0.5519 \| 0.3484 \| 0.4966 \| 0.5370 \| 0.8285 \| 0.7408 \| 0.7605 \| 0.7584 \| 0.7721 \| 0.7707 \| 0.2945 \| \| 0.0346 \| 14.64 \| 7320 \| 0.2338 \| 0.6268 \| 0.7566 \| 0.8269 \| nan \| 0.8557 \| 0.9050 \| 0.7529 \| 0.8560 \| 0.7025 \| 0.8915 \| 0.7463 \| 0.4320 \| 0.5965 \| 0.6417 \| 0.9079 \| 0.8469 \| 0.8410 \| 0.8546 \| 0.8412 \| 0.8252 \| 0.3643 \| 0.0 \| 0.7642 \| 0.8193 \| 0.6412 \| 0.7712 \| 0.6191 \| 0.7787 \| 0.5509 \| 0.3614 \| 0.5017 \| 0.5405 \| 0.8284 \| 0.7421 \| 0.7605 \| 0.7596 \| 0.7733 \| 0.7743 \| 0.2955 \| \| 0.0307 \| 14.68 \| 7340 \| 0.2271 \| 0.6265 \| 0.7557 \| 0.8265 \| nan \| 0.8564 \| 0.9005 \| 0.7511 \| 0.8552 \| 0.7005 \| 0.8950 \| 0.7497 \| 0.4260 \| 0.5976 \| 0.6397 \| 0.9100 \| 0.8436 \| 0.8409 \| 0.8508 \| 0.8406 \| 0.8237 \| 0.3651 \| 0.0 \| 0.7637 \| 0.8184 \| 0.6400 \| 0.7708 \| 0.6203 \| 0.7788 \| 0.5515 \| 0.3581 \| 0.5018 \| 0.5393 \| 0.8290 \| 0.7419 \| 0.7607 \| 0.7594 \| 0.7733 \| 0.7741 \| 0.2952 \| \| 0.0336 \| 14.72 \| 7360 \| 0.2285 \| 0.6264 \| 0.7548 \| 0.8272 \| nan \| 0.8554 \| 0.9010 \| 0.7462 \| 0.8625 \| 0.7020 \| 0.8938 \| 0.7406 \| 0.4302 \| 0.5991 \| 0.6354 \| 0.9109 \| 0.8508 \| 0.8367 \| 0.8526 \| 0.8378 \| 0.8187 \| 0.3578 \| 0.0 \| 0.7643 \| 0.8186 \| 0.6385 \| 0.7732 \| 0.6186 \| 0.7790 \| 0.5506 \| 0.3647 \| 0.5021 \| 0.5378 \| 0.8292 \| 0.7416 \| 0.7589 \| 0.7596 \| 0.7736 \| 0.7720 \| 0.2933 \| \| 0.0341 \| 14.76 \| 7380 \| 0.2335 \| 0.6266 \| 0.7552 \| 0.8276 \| nan \| 0.8532 \| 0.9024 \| 0.7469 \| 0.8566 \| 0.6991 \| 0.8969 \| 0.7516 \| 0.4132 \| 0.5960 \| 0.6437 \| 0.9093 \| 0.8471 \| 0.8419 \| 0.8524 \| 0.8377 \| 0.8259 \| 0.3640 \| 0.0 \| 0.7633 \| 0.8190 \| 0.6388 \| 0.7714 \| 0.6215 \| 0.7792 \| 0.5524 \| 0.3511 \| 0.5017 \| 0.5419 \| 0.8290 \| 0.7430 \| 0.7612 \| 0.7601 \| 0.7740 \| 0.7756 \| 0.2950 \| \| 0.0321 \| 14.8 \| 7400 \| 0.2308 \| 0.6264 \| 0.7547 \| 0.8276 \| nan \| 0.8541 \| 0.9008 \| 0.7331 \| 0.8581 \| 0.7018 \| 0.8939 \| 0.7496 \| 0.4216 \| 0.5962 \| 0.6428 \| 0.9152 \| 0.8488 \| 0.8427 \| 0.8529 \| 0.8394 \| 0.8179 \| 0.3604 \| 0.0 \| 0.7644 \| 0.8196 \| 0.6338 \| 0.7715 \| 0.6202 \| 0.7798 \| 0.5516 \| 0.3569 \| 0.5020 \| 0.5416 \| 0.8294 \| 0.7426 \| 0.7612 \| 0.7601 \| 0.7747 \| 0.7718 \| 0.2938 \| \| 0.0783 \| 14.84 \| 7420 \| 0.2278 \| 0.6258 \| 0.7540 \| 0.8272 \| nan \| 0.8556 \| 0.9037 \| 0.7446 \| 0.8563 \| 0.7031 \| 0.8952 \| 0.7497 \| 0.3995 \| 0.6044 \| 0.6390 \| 0.9105 \| 0.8453 \| 0.8387 \| 0.8480 \| 0.8397 \| 0.8223 \| 0.3630 \| 0.0 \| 0.7644 \| 0.8195 \| 0.6383 \| 0.7710 \| 0.6198 \| 0.7802 \| 0.5510 \| 0.3419 \| 0.5047 \| 0.5393 \| 0.8294 \| 0.7428 \| 0.7609 \| 0.7590 \| 0.7742 \| 0.7735 \| 0.2945 \| \| 0.0259 \| 14.88 \| 7440 \| 0.2288 \| 0.6263 \| 0.7549 \| 0.8268 \| nan \| 0.8559 \| 0.8999 \| 0.7459 \| 0.8566 \| 0.7012 \| 0.8953 \| 0.7488 \| 0.4195 \| 0.6040 \| 0.6386 \| 0.9119 \| 0.8451 \| 0.8358 \| 0.8513 \| 0.8396 \| 0.8231 \| 0.3611 \| 0.0 \| 0.7638 \| 0.8185 \| 0.6384 \| 0.7710 \| 0.6206 \| 0.7792 \| 0.5513 \| 0.3552 \| 0.5042 \| 0.5389 \| 0.8296 \| 0.7422 \| 0.7600 \| 0.7592 \| 0.7734 \| 0.7737 \| 0.2937 \| \| 0.0353 \| 14.92 \| 7460 \| 0.2265 \| 0.6264 \| 0.7550 \| 0.8269 \| nan \| 0.8508 \| 0.9005 \| 0.7393 \| 0.8568 \| 0.7007 \| 0.8919 \| 0.7503 \| 0.4240 \| 0.6047 \| 0.6397 \| 0.9123 \| 0.8487 \| 0.8358 \| 0.8533 \| 0.8386 \| 0.8246 \| 0.3624 \| 0.0 \| 0.7618 \| 0.8188 \| 0.6362 \| 0.7709 \| 0.6215 \| 0.7790 \| 0.5519 \| 0.3581 \| 0.5051 \| 0.5396 \| 0.8293 \| 0.7418 \| 0.7596 \| 0.7598 \| 0.7734 \| 0.7745 \| 0.2943 \| \| 0.0386 \| 14.96 \| 7480 \| 0.2278 \| 0.6261 \| 0.7548 \| 0.8261 \| nan \| 0.8536 \| 0.8987 \| 0.7446 \| 0.8552 \| 0.7021 \| 0.8904 \| 0.7532 \| 0.4292 \| 0.6033 \| 0.6383 \| 0.9109 \| 0.8457 \| 0.8402 \| 0.8466 \| 0.8366 \| 0.8216 \| 0.3618 \| 0.0 \| 0.7620 \| 0.8177 \| 0.6378 \| 0.7700 \| 0.6215 \| 0.7788 \| 0.5515 \| 0.3583 \| 0.5046 \| 0.5391 \| 0.8293 \| 0.7418 \| 0.7605 \| 0.7575 \| 0.7728 \| 0.7725 \| 0.2940 \| \| 0.0279 \| 15.0 \| 7500 \| 0.2292 \| 0.6258 \| 0.7547 \| 0.8256 \| nan \| 0.8561 \| 0.8974 \| 0.7540 \| 0.8553 \| 0.7026 \| 0.8913 \| 0.7525 \| 0.4251 \| 0.6014 \| 0.6374 \| 0.9094 \| 0.8452 \| 0.8343 \| 0.8506 \| 0.8287 \| 0.8232 \| 0.3662 \| 0.0 \| 0.7625 \| 0.8171 \| 0.6400 \| 0.7700 \| 0.6211 \| 0.7788 \| 0.5512 \| 0.3564 \| 0.5032 \| 0.5381 \| 0.8294 \| 0.7412 \| 0.7591 \| 0.7579 \| 0.7705 \| 0.7729 \| 0.2956 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3	[ "background", "hat", "hair", "sunglasses", "upper-clothes", "skirt", "pants", "dress", "belt", "left-shoe", "right-shoe", "face", "left-leg", "right-leg", "left-arm", "right-arm", "bag", "scarf" ]
samitizerxu/segformer-b0-finetuned-kelp-segments-jan-18-10am	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-kelp-segments-jan-18-10am This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0197 - Mean Iou: 0.1778 - Mean Accuracy: 0.3556 - Overall Accuracy: 0.3556 - Accuracy Not Kelp: nan - Accuracy Kelp: 0.3556 - Iou Not Kelp: 0.0 - Iou Kelp: 0.3556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Not Kelp \| Accuracy Kelp \| Iou Not Kelp \| Iou Kelp \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------:\|:-------------:\|:------------:\|:--------:\| \| 0.3836 \| 0.05 \| 30 \| 0.3933 \| 0.0106 \| 0.0212 \| 0.0212 \| nan \| 0.0212 \| 0.0 \| 0.0212 \| \| 0.2552 \| 0.1 \| 60 \| 0.2404 \| 0.0002 \| 0.0003 \| 0.0003 \| nan \| 0.0003 \| 0.0 \| 0.0003 \| \| 0.2144 \| 0.14 \| 90 \| 0.1817 \| 0.0001 \| 0.0002 \| 0.0002 \| nan \| 0.0002 \| 0.0 \| 0.0002 \| \| 0.1172 \| 0.19 \| 120 \| 0.1304 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.1404 \| 0.24 \| 150 \| 0.0930 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0919 \| 0.29 \| 180 \| 0.0742 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0715 \| 0.34 \| 210 \| 0.0656 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0676 \| 0.38 \| 240 \| 0.0538 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.05 \| 0.43 \| 270 \| 0.0552 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.046 \| 0.48 \| 300 \| 0.0514 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0584 \| 0.53 \| 330 \| 0.0481 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0653 \| 0.58 \| 360 \| 0.0470 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0285 \| 0.62 \| 390 \| 0.0421 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0739 \| 0.67 \| 420 \| 0.0418 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0493 \| 0.72 \| 450 \| 0.0397 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0267 \| 0.77 \| 480 \| 0.0396 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0483 \| 0.82 \| 510 \| 0.0377 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0332 \| 0.86 \| 540 \| 0.0382 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0322 \| 0.91 \| 570 \| 0.0344 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0428 \| 0.96 \| 600 \| 0.0338 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0343 \| 1.01 \| 630 \| 0.0319 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.031 \| 1.06 \| 660 \| 0.0327 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| \| 0.0194 \| 1.1 \| 690 \| 0.0328 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \| \| 0.0451 \| 1.15 \| 720 \| 0.0347 \| 0.0012 \| 0.0025 \| 0.0025 \| nan \| 0.0025 \| 0.0 \| 0.0025 \| \| 0.0571 \| 1.2 \| 750 \| 0.0299 \| 0.0006 \| 0.0013 \| 0.0013 \| nan \| 0.0013 \| 0.0 \| 0.0013 \| \| 0.0572 \| 1.25 \| 780 \| 0.0301 \| 0.0086 \| 0.0172 \| 0.0172 \| nan \| 0.0172 \| 0.0 \| 0.0172 \| \| 0.0563 \| 1.3 \| 810 \| 0.0292 \| 0.0072 \| 0.0143 \| 0.0143 \| nan \| 0.0143 \| 0.0 \| 0.0143 \| \| 0.0183 \| 1.34 \| 840 \| 0.0294 \| 0.0005 \| 0.0010 \| 0.0010 \| nan \| 0.0010 \| 0.0 \| 0.0010 \| \| 0.0145 \| 1.39 \| 870 \| 0.0336 \| 0.0140 \| 0.0281 \| 0.0281 \| nan \| 0.0281 \| 0.0 \| 0.0281 \| \| 0.0275 \| 1.44 \| 900 \| 0.0276 \| 0.0015 \| 0.0029 \| 0.0029 \| nan \| 0.0029 \| 0.0 \| 0.0029 \| \| 0.0445 \| 1.49 \| 930 \| 0.0259 \| 0.0083 \| 0.0166 \| 0.0166 \| nan \| 0.0166 \| 0.0 \| 0.0166 \| \| 0.0101 \| 1.54 \| 960 \| 0.0255 \| 0.0462 \| 0.0923 \| 0.0923 \| nan \| 0.0923 \| 0.0 \| 0.0923 \| \| 0.0245 \| 1.58 \| 990 \| 0.0253 \| 0.0062 \| 0.0124 \| 0.0124 \| nan \| 0.0124 \| 0.0 \| 0.0124 \| \| 0.0146 \| 1.63 \| 1020 \| 0.0270 \| 0.0003 \| 0.0006 \| 0.0006 \| nan \| 0.0006 \| 0.0 \| 0.0006 \| \| 0.0244 \| 1.68 \| 1050 \| 0.0268 \| 0.0000 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \| \| 0.0338 \| 1.73 \| 1080 \| 0.0254 \| 0.0022 \| 0.0045 \| 0.0045 \| nan \| 0.0045 \| 0.0 \| 0.0045 \| \| 0.0232 \| 1.78 \| 1110 \| 0.0255 \| 0.0001 \| 0.0002 \| 0.0002 \| nan \| 0.0002 \| 0.0 \| 0.0002 \| \| 0.0176 \| 1.82 \| 1140 \| 0.0268 \| 0.0009 \| 0.0017 \| 0.0017 \| nan \| 0.0017 \| 0.0 \| 0.0017 \| \| 0.0127 \| 1.87 \| 1170 \| 0.0249 \| 0.0095 \| 0.0191 \| 0.0191 \| nan \| 0.0191 \| 0.0 \| 0.0191 \| \| 0.0327 \| 1.92 \| 1200 \| 0.0238 \| 0.0437 \| 0.0874 \| 0.0874 \| nan \| 0.0874 \| 0.0 \| 0.0874 \| \| 0.0375 \| 1.97 \| 1230 \| 0.0246 \| 0.0066 \| 0.0133 \| 0.0133 \| nan \| 0.0133 \| 0.0 \| 0.0133 \| \| 0.017 \| 2.02 \| 1260 \| 0.0245 \| 0.0085 \| 0.0169 \| 0.0169 \| nan \| 0.0169 \| 0.0 \| 0.0169 \| \| 0.0224 \| 2.06 \| 1290 \| 0.0251 \| 0.0028 \| 0.0057 \| 0.0057 \| nan \| 0.0057 \| 0.0 \| 0.0057 \| \| 0.0174 \| 2.11 \| 1320 \| 0.0240 \| 0.0687 \| 0.1374 \| 0.1374 \| nan \| 0.1374 \| 0.0 \| 0.1374 \| \| 0.0194 \| 2.16 \| 1350 \| 0.0239 \| 0.0193 \| 0.0386 \| 0.0386 \| nan \| 0.0386 \| 0.0 \| 0.0386 \| \| 0.1479 \| 2.21 \| 1380 \| 0.0244 \| 0.0073 \| 0.0147 \| 0.0147 \| nan \| 0.0147 \| 0.0 \| 0.0147 \| \| 0.0161 \| 2.26 \| 1410 \| 0.0229 \| 0.0586 \| 0.1173 \| 0.1173 \| nan \| 0.1173 \| 0.0 \| 0.1173 \| \| 0.0385 \| 2.3 \| 1440 \| 0.0231 \| 0.0295 \| 0.0590 \| 0.0590 \| nan \| 0.0590 \| 0.0 \| 0.0590 \| \| 0.0611 \| 2.35 \| 1470 \| 0.0235 \| 0.0199 \| 0.0399 \| 0.0399 \| nan \| 0.0399 \| 0.0 \| 0.0399 \| \| 0.0204 \| 2.4 \| 1500 \| 0.0225 \| 0.0497 \| 0.0994 \| 0.0994 \| nan \| 0.0994 \| 0.0 \| 0.0994 \| \| 0.0329 \| 2.45 \| 1530 \| 0.0230 \| 0.0164 \| 0.0327 \| 0.0327 \| nan \| 0.0327 \| 0.0 \| 0.0327 \| \| 0.025 \| 2.5 \| 1560 \| 0.0224 \| 0.0518 \| 0.1036 \| 0.1036 \| nan \| 0.1036 \| 0.0 \| 0.1036 \| \| 0.0104 \| 2.54 \| 1590 \| 0.0228 \| 0.0244 \| 0.0488 \| 0.0488 \| nan \| 0.0488 \| 0.0 \| 0.0488 \| \| 0.0096 \| 2.59 \| 1620 \| 0.0233 \| 0.1242 \| 0.2483 \| 0.2483 \| nan \| 0.2483 \| 0.0 \| 0.2483 \| \| 0.005 \| 2.64 \| 1650 \| 0.0221 \| 0.0206 \| 0.0411 \| 0.0411 \| nan \| 0.0411 \| 0.0 \| 0.0411 \| \| 0.0264 \| 2.69 \| 1680 \| 0.0236 \| 0.1290 \| 0.2581 \| 0.2581 \| nan \| 0.2581 \| 0.0 \| 0.2581 \| \| 0.0117 \| 2.74 \| 1710 \| 0.0215 \| 0.0665 \| 0.1330 \| 0.1330 \| nan \| 0.1330 \| 0.0 \| 0.1330 \| \| 0.0096 \| 2.78 \| 1740 \| 0.0229 \| 0.0154 \| 0.0307 \| 0.0307 \| nan \| 0.0307 \| 0.0 \| 0.0307 \| \| 0.0176 \| 2.83 \| 1770 \| 0.0222 \| 0.0348 \| 0.0696 \| 0.0696 \| nan \| 0.0696 \| 0.0 \| 0.0696 \| \| 0.0108 \| 2.88 \| 1800 \| 0.0219 \| 0.0528 \| 0.1056 \| 0.1056 \| nan \| 0.1056 \| 0.0 \| 0.1056 \| \| 0.0189 \| 2.93 \| 1830 \| 0.0217 \| 0.0405 \| 0.0811 \| 0.0811 \| nan \| 0.0811 \| 0.0 \| 0.0811 \| \| 0.0288 \| 2.98 \| 1860 \| 0.0218 \| 0.0392 \| 0.0784 \| 0.0784 \| nan \| 0.0784 \| 0.0 \| 0.0784 \| \| 0.0067 \| 3.02 \| 1890 \| 0.0213 \| 0.0399 \| 0.0799 \| 0.0799 \| nan \| 0.0799 \| 0.0 \| 0.0799 \| \| 0.0104 \| 3.07 \| 1920 \| 0.0225 \| 0.0948 \| 0.1896 \| 0.1896 \| nan \| 0.1896 \| 0.0 \| 0.1896 \| \| 0.0115 \| 3.12 \| 1950 \| 0.0214 \| 0.0755 \| 0.1510 \| 0.1510 \| nan \| 0.1510 \| 0.0 \| 0.1510 \| \| 0.0076 \| 3.17 \| 1980 \| 0.0222 \| 0.0823 \| 0.1646 \| 0.1646 \| nan \| 0.1646 \| 0.0 \| 0.1646 \| \| 0.0444 \| 3.22 \| 2010 \| 0.0213 \| 0.0394 \| 0.0788 \| 0.0788 \| nan \| 0.0788 \| 0.0 \| 0.0788 \| \| 0.021 \| 3.26 \| 2040 \| 0.0220 \| 0.0392 \| 0.0784 \| 0.0784 \| nan \| 0.0784 \| 0.0 \| 0.0784 \| \| 0.0188 \| 3.31 \| 2070 \| 0.0217 \| 0.1194 \| 0.2388 \| 0.2388 \| nan \| 0.2388 \| 0.0 \| 0.2388 \| \| 0.0235 \| 3.36 \| 2100 \| 0.0208 \| 0.0560 \| 0.1119 \| 0.1119 \| nan \| 0.1119 \| 0.0 \| 0.1119 \| \| 0.0112 \| 3.41 \| 2130 \| 0.0214 \| 0.0459 \| 0.0918 \| 0.0918 \| nan \| 0.0918 \| 0.0 \| 0.0918 \| \| 0.0109 \| 3.46 \| 2160 \| 0.0211 \| 0.0464 \| 0.0928 \| 0.0928 \| nan \| 0.0928 \| 0.0 \| 0.0928 \| \| 0.0136 \| 3.5 \| 2190 \| 0.0230 \| 0.0174 \| 0.0348 \| 0.0348 \| nan \| 0.0348 \| 0.0 \| 0.0348 \| \| 0.0194 \| 3.55 \| 2220 \| 0.0217 \| 0.0851 \| 0.1702 \| 0.1702 \| nan \| 0.1702 \| 0.0 \| 0.1702 \| \| 0.0117 \| 3.6 \| 2250 \| 0.0216 \| 0.0295 \| 0.0589 \| 0.0589 \| nan \| 0.0589 \| 0.0 \| 0.0589 \| \| 0.0079 \| 3.65 \| 2280 \| 0.0211 \| 0.0368 \| 0.0735 \| 0.0735 \| nan \| 0.0735 \| 0.0 \| 0.0735 \| \| 0.0074 \| 3.7 \| 2310 \| 0.0212 \| 0.0861 \| 0.1722 \| 0.1722 \| nan \| 0.1722 \| 0.0 \| 0.1722 \| \| 0.0467 \| 3.74 \| 2340 \| 0.0209 \| 0.0643 \| 0.1286 \| 0.1286 \| nan \| 0.1286 \| 0.0 \| 0.1286 \| \| 0.0526 \| 3.79 \| 2370 \| 0.0212 \| 0.1275 \| 0.2551 \| 0.2551 \| nan \| 0.2551 \| 0.0 \| 0.2551 \| \| 0.01 \| 3.84 \| 2400 \| 0.0213 \| 0.0363 \| 0.0725 \| 0.0725 \| nan \| 0.0725 \| 0.0 \| 0.0725 \| \| 0.0426 \| 3.89 \| 2430 \| 0.0212 \| 0.1367 \| 0.2733 \| 0.2733 \| nan \| 0.2733 \| 0.0 \| 0.2733 \| \| 0.0335 \| 3.94 \| 2460 \| 0.0220 \| 0.0235 \| 0.0470 \| 0.0470 \| nan \| 0.0470 \| 0.0 \| 0.0470 \| \| 0.014 \| 3.98 \| 2490 \| 0.0219 \| 0.0870 \| 0.1740 \| 0.1740 \| nan \| 0.1740 \| 0.0 \| 0.1740 \| \| 0.022 \| 4.03 \| 2520 \| 0.0212 \| 0.0388 \| 0.0775 \| 0.0775 \| nan \| 0.0775 \| 0.0 \| 0.0775 \| \| 0.0038 \| 4.08 \| 2550 \| 0.0211 \| 0.0691 \| 0.1382 \| 0.1382 \| nan \| 0.1382 \| 0.0 \| 0.1382 \| \| 0.0164 \| 4.13 \| 2580 \| 0.0210 \| 0.0848 \| 0.1697 \| 0.1697 \| nan \| 0.1697 \| 0.0 \| 0.1697 \| \| 0.0121 \| 4.18 \| 2610 \| 0.0215 \| 0.0824 \| 0.1648 \| 0.1648 \| nan \| 0.1648 \| 0.0 \| 0.1648 \| \| 0.0271 \| 4.22 \| 2640 \| 0.0211 \| 0.0877 \| 0.1754 \| 0.1754 \| nan \| 0.1754 \| 0.0 \| 0.1754 \| \| 0.0046 \| 4.27 \| 2670 \| 0.0200 \| 0.0812 \| 0.1625 \| 0.1625 \| nan \| 0.1625 \| 0.0 \| 0.1625 \| \| 0.02 \| 4.32 \| 2700 \| 0.0214 \| 0.0500 \| 0.1000 \| 0.1000 \| nan \| 0.1000 \| 0.0 \| 0.1000 \| \| 0.0148 \| 4.37 \| 2730 \| 0.0204 \| 0.0550 \| 0.1100 \| 0.1100 \| nan \| 0.1100 \| 0.0 \| 0.1100 \| \| 0.0103 \| 4.42 \| 2760 \| 0.0211 \| 0.1222 \| 0.2444 \| 0.2444 \| nan \| 0.2444 \| 0.0 \| 0.2444 \| \| 0.0687 \| 4.46 \| 2790 \| 0.0212 \| 0.1351 \| 0.2703 \| 0.2703 \| nan \| 0.2703 \| 0.0 \| 0.2703 \| \| 0.0186 \| 4.51 \| 2820 \| 0.0244 \| 0.1186 \| 0.2373 \| 0.2373 \| nan \| 0.2373 \| 0.0 \| 0.2373 \| \| 0.0174 \| 4.56 \| 2850 \| 0.0205 \| 0.0487 \| 0.0974 \| 0.0974 \| nan \| 0.0974 \| 0.0 \| 0.0974 \| \| 0.018 \| 4.61 \| 2880 \| 0.0204 \| 0.1213 \| 0.2426 \| 0.2426 \| nan \| 0.2426 \| 0.0 \| 0.2426 \| \| 0.0224 \| 4.66 \| 2910 \| 0.0201 \| 0.1220 \| 0.2439 \| 0.2439 \| nan \| 0.2439 \| 0.0 \| 0.2439 \| \| 0.0262 \| 4.7 \| 2940 \| 0.0202 \| 0.0943 \| 0.1885 \| 0.1885 \| nan \| 0.1885 \| 0.0 \| 0.1885 \| \| 0.0215 \| 4.75 \| 2970 \| 0.0197 \| 0.1166 \| 0.2333 \| 0.2333 \| nan \| 0.2333 \| 0.0 \| 0.2333 \| \| 0.0159 \| 4.8 \| 3000 \| 0.0203 \| 0.1080 \| 0.2159 \| 0.2159 \| nan \| 0.2159 \| 0.0 \| 0.2159 \| \| 0.018 \| 4.85 \| 3030 \| 0.0200 \| 0.1138 \| 0.2276 \| 0.2276 \| nan \| 0.2276 \| 0.0 \| 0.2276 \| \| 0.0112 \| 4.9 \| 3060 \| 0.0198 \| 0.0830 \| 0.1661 \| 0.1661 \| nan \| 0.1661 \| 0.0 \| 0.1661 \| \| 0.0038 \| 4.94 \| 3090 \| 0.0198 \| 0.0605 \| 0.1211 \| 0.1211 \| nan \| 0.1211 \| 0.0 \| 0.1211 \| \| 0.0076 \| 4.99 \| 3120 \| 0.0193 \| 0.1283 \| 0.2566 \| 0.2566 \| nan \| 0.2566 \| 0.0 \| 0.2566 \| \| 0.0076 \| 5.04 \| 3150 \| 0.0196 \| 0.1245 \| 0.2491 \| 0.2491 \| nan \| 0.2491 \| 0.0 \| 0.2491 \| \| 0.0145 \| 5.09 \| 3180 \| 0.0205 \| 0.1699 \| 0.3398 \| 0.3398 \| nan \| 0.3398 \| 0.0 \| 0.3398 \| \| 0.0079 \| 5.14 \| 3210 \| 0.0199 \| 0.1323 \| 0.2646 \| 0.2646 \| nan \| 0.2646 \| 0.0 \| 0.2646 \| \| 0.0144 \| 5.18 \| 3240 \| 0.0207 \| 0.1465 \| 0.2931 \| 0.2931 \| nan \| 0.2931 \| 0.0 \| 0.2931 \| \| 0.0114 \| 5.23 \| 3270 \| 0.0194 \| 0.1094 \| 0.2188 \| 0.2188 \| nan \| 0.2188 \| 0.0 \| 0.2188 \| \| 0.0255 \| 5.28 \| 3300 \| 0.0209 \| 0.1864 \| 0.3728 \| 0.3728 \| nan \| 0.3728 \| 0.0 \| 0.3728 \| \| 0.0214 \| 5.33 \| 3330 \| 0.0197 \| 0.0679 \| 0.1357 \| 0.1357 \| nan \| 0.1357 \| 0.0 \| 0.1357 \| \| 0.0124 \| 5.38 \| 3360 \| 0.0191 \| 0.0968 \| 0.1936 \| 0.1936 \| nan \| 0.1936 \| 0.0 \| 0.1936 \| \| 0.0301 \| 5.42 \| 3390 \| 0.0199 \| 0.0622 \| 0.1244 \| 0.1244 \| nan \| 0.1244 \| 0.0 \| 0.1244 \| \| 0.0042 \| 5.47 \| 3420 \| 0.0197 \| 0.1157 \| 0.2313 \| 0.2313 \| nan \| 0.2313 \| 0.0 \| 0.2313 \| \| 0.0254 \| 5.52 \| 3450 \| 0.0201 \| 0.1449 \| 0.2899 \| 0.2899 \| nan \| 0.2899 \| 0.0 \| 0.2899 \| \| 0.0112 \| 5.57 \| 3480 \| 0.0230 \| 0.2090 \| 0.4179 \| 0.4179 \| nan \| 0.4179 \| 0.0 \| 0.4179 \| \| 0.0064 \| 5.62 \| 3510 \| 0.0195 \| 0.1100 \| 0.2199 \| 0.2199 \| nan \| 0.2199 \| 0.0 \| 0.2199 \| \| 0.0177 \| 5.66 \| 3540 \| 0.0200 \| 0.1095 \| 0.2190 \| 0.2190 \| nan \| 0.2190 \| 0.0 \| 0.2190 \| \| 0.0204 \| 5.71 \| 3570 \| 0.0193 \| 0.1370 \| 0.2741 \| 0.2741 \| nan \| 0.2741 \| 0.0 \| 0.2741 \| \| 0.0101 \| 5.76 \| 3600 \| 0.0200 \| 0.1715 \| 0.3430 \| 0.3430 \| nan \| 0.3430 \| 0.0 \| 0.3430 \| \| 0.0179 \| 5.81 \| 3630 \| 0.0198 \| 0.1837 \| 0.3674 \| 0.3674 \| nan \| 0.3674 \| 0.0 \| 0.3674 \| \| 0.0398 \| 5.86 \| 3660 \| 0.0219 \| 0.1870 \| 0.3741 \| 0.3741 \| nan \| 0.3741 \| 0.0 \| 0.3741 \| \| 0.006 \| 5.9 \| 3690 \| 0.0206 \| 0.0590 \| 0.1179 \| 0.1179 \| nan \| 0.1179 \| 0.0 \| 0.1179 \| \| 0.0328 \| 5.95 \| 3720 \| 0.0193 \| 0.1281 \| 0.2562 \| 0.2562 \| nan \| 0.2562 \| 0.0 \| 0.2562 \| \| 0.0184 \| 6.0 \| 3750 \| 0.0192 \| 0.0955 \| 0.1910 \| 0.1910 \| nan \| 0.1910 \| 0.0 \| 0.1910 \| \| 0.018 \| 6.05 \| 3780 \| 0.0192 \| 0.1283 \| 0.2566 \| 0.2566 \| nan \| 0.2566 \| 0.0 \| 0.2566 \| \| 0.0074 \| 6.1 \| 3810 \| 0.0214 \| 0.0403 \| 0.0805 \| 0.0805 \| nan \| 0.0805 \| 0.0 \| 0.0805 \| \| 0.0098 \| 6.14 \| 3840 \| 0.0200 \| 0.0928 \| 0.1855 \| 0.1855 \| nan \| 0.1855 \| 0.0 \| 0.1855 \| \| 0.0154 \| 6.19 \| 3870 \| 0.0190 \| 0.1304 \| 0.2609 \| 0.2609 \| nan \| 0.2609 \| 0.0 \| 0.2609 \| \| 0.0197 \| 6.24 \| 3900 \| 0.0189 \| 0.1300 \| 0.2601 \| 0.2601 \| nan \| 0.2601 \| 0.0 \| 0.2601 \| \| 0.0063 \| 6.29 \| 3930 \| 0.0191 \| 0.1352 \| 0.2704 \| 0.2704 \| nan \| 0.2704 \| 0.0 \| 0.2704 \| \| 0.0215 \| 6.34 \| 3960 \| 0.0195 \| 0.1572 \| 0.3144 \| 0.3144 \| nan \| 0.3144 \| 0.0 \| 0.3144 \| \| 0.021 \| 6.38 \| 3990 \| 0.0188 \| 0.1590 \| 0.3179 \| 0.3179 \| nan \| 0.3179 \| 0.0 \| 0.3179 \| \| 0.0193 \| 6.43 \| 4020 \| 0.0195 \| 0.1676 \| 0.3352 \| 0.3352 \| nan \| 0.3352 \| 0.0 \| 0.3352 \| \| 0.0174 \| 6.48 \| 4050 \| 0.0202 \| 0.1943 \| 0.3886 \| 0.3886 \| nan \| 0.3886 \| 0.0 \| 0.3886 \| \| 0.0097 \| 6.53 \| 4080 \| 0.0196 \| 0.0611 \| 0.1223 \| 0.1223 \| nan \| 0.1223 \| 0.0 \| 0.1223 \| \| 0.0043 \| 6.58 \| 4110 \| 0.0196 \| 0.0700 \| 0.1399 \| 0.1399 \| nan \| 0.1399 \| 0.0 \| 0.1399 \| \| 0.0121 \| 6.62 \| 4140 \| 0.0190 \| 0.1047 \| 0.2093 \| 0.2093 \| nan \| 0.2093 \| 0.0 \| 0.2093 \| \| 0.0053 \| 6.67 \| 4170 \| 0.0190 \| 0.1374 \| 0.2748 \| 0.2748 \| nan \| 0.2748 \| 0.0 \| 0.2748 \| \| 0.0234 \| 6.72 \| 4200 \| 0.0197 \| 0.1250 \| 0.2500 \| 0.2500 \| nan \| 0.2500 \| 0.0 \| 0.2500 \| \| 0.0164 \| 6.77 \| 4230 \| 0.0187 \| 0.0911 \| 0.1822 \| 0.1822 \| nan \| 0.1822 \| 0.0 \| 0.1822 \| \| 0.0139 \| 6.82 \| 4260 \| 0.0190 \| 0.1242 \| 0.2485 \| 0.2485 \| nan \| 0.2485 \| 0.0 \| 0.2485 \| \| 0.0431 \| 6.86 \| 4290 \| 0.0194 \| 0.1405 \| 0.2809 \| 0.2809 \| nan \| 0.2809 \| 0.0 \| 0.2809 \| \| 0.0082 \| 6.91 \| 4320 \| 0.0189 \| 0.1354 \| 0.2707 \| 0.2707 \| nan \| 0.2707 \| 0.0 \| 0.2707 \| \| 0.0095 \| 6.96 \| 4350 \| 0.0193 \| 0.1231 \| 0.2462 \| 0.2462 \| nan \| 0.2462 \| 0.0 \| 0.2462 \| \| 0.0062 \| 7.01 \| 4380 \| 0.0195 \| 0.1617 \| 0.3233 \| 0.3233 \| nan \| 0.3233 \| 0.0 \| 0.3233 \| \| 0.0067 \| 7.06 \| 4410 \| 0.0206 \| 0.1945 \| 0.3890 \| 0.3890 \| nan \| 0.3890 \| 0.0 \| 0.3890 \| \| 0.0193 \| 7.1 \| 4440 \| 0.0193 \| 0.1563 \| 0.3126 \| 0.3126 \| nan \| 0.3126 \| 0.0 \| 0.3126 \| \| 0.0482 \| 7.15 \| 4470 \| 0.0197 \| 0.1142 \| 0.2284 \| 0.2284 \| nan \| 0.2284 \| 0.0 \| 0.2284 \| \| 0.6577 \| 7.2 \| 4500 \| 0.0188 \| 0.1033 \| 0.2066 \| 0.2066 \| nan \| 0.2066 \| 0.0 \| 0.2066 \| \| 0.0251 \| 7.25 \| 4530 \| 0.0201 \| 0.0845 \| 0.1689 \| 0.1689 \| nan \| 0.1689 \| 0.0 \| 0.1689 \| \| 0.0059 \| 7.3 \| 4560 \| 0.0191 \| 0.1534 \| 0.3068 \| 0.3068 \| nan \| 0.3068 \| 0.0 \| 0.3068 \| \| 0.0103 \| 7.34 \| 4590 \| 0.0192 \| 0.1793 \| 0.3586 \| 0.3586 \| nan \| 0.3586 \| 0.0 \| 0.3586 \| \| 0.0115 \| 7.39 \| 4620 \| 0.0191 \| 0.1000 \| 0.2001 \| 0.2001 \| nan \| 0.2001 \| 0.0 \| 0.2001 \| \| 0.0096 \| 7.44 \| 4650 \| 0.0194 \| 0.0976 \| 0.1952 \| 0.1952 \| nan \| 0.1952 \| 0.0 \| 0.1952 \| \| 0.0223 \| 7.49 \| 4680 \| 0.0185 \| 0.1023 \| 0.2046 \| 0.2046 \| nan \| 0.2046 \| 0.0 \| 0.2046 \| \| 0.0228 \| 7.54 \| 4710 \| 0.0190 \| 0.1669 \| 0.3339 \| 0.3339 \| nan \| 0.3339 \| 0.0 \| 0.3339 \| \| 0.0449 \| 7.58 \| 4740 \| 0.0183 \| 0.1302 \| 0.2603 \| 0.2603 \| nan \| 0.2603 \| 0.0 \| 0.2603 \| \| 0.0224 \| 7.63 \| 4770 \| 0.0193 \| 0.1061 \| 0.2122 \| 0.2122 \| nan \| 0.2122 \| 0.0 \| 0.2122 \| \| 0.0161 \| 7.68 \| 4800 \| 0.0188 \| 0.1251 \| 0.2502 \| 0.2502 \| nan \| 0.2502 \| 0.0 \| 0.2502 \| \| 0.0106 \| 7.73 \| 4830 \| 0.0194 \| 0.1002 \| 0.2005 \| 0.2005 \| nan \| 0.2005 \| 0.0 \| 0.2005 \| \| 0.0163 \| 7.78 \| 4860 \| 0.0186 \| 0.1264 \| 0.2528 \| 0.2528 \| nan \| 0.2528 \| 0.0 \| 0.2528 \| \| 0.0087 \| 7.82 \| 4890 \| 0.0184 \| 0.1355 \| 0.2709 \| 0.2709 \| nan \| 0.2709 \| 0.0 \| 0.2709 \| \| 0.0247 \| 7.87 \| 4920 \| 0.0195 \| 0.2030 \| 0.4060 \| 0.4060 \| nan \| 0.4060 \| 0.0 \| 0.4060 \| \| 0.0239 \| 7.92 \| 4950 \| 0.0188 \| 0.1153 \| 0.2306 \| 0.2306 \| nan \| 0.2306 \| 0.0 \| 0.2306 \| \| 0.0149 \| 7.97 \| 4980 \| 0.0192 \| 0.1265 \| 0.2531 \| 0.2531 \| nan \| 0.2531 \| 0.0 \| 0.2531 \| \| 0.0121 \| 8.02 \| 5010 \| 0.0191 \| 0.1640 \| 0.3280 \| 0.3280 \| nan \| 0.3280 \| 0.0 \| 0.3280 \| \| 0.0157 \| 8.06 \| 5040 \| 0.0192 \| 0.1459 \| 0.2917 \| 0.2917 \| nan \| 0.2917 \| 0.0 \| 0.2917 \| \| 0.0321 \| 8.11 \| 5070 \| 0.0194 \| 0.1636 \| 0.3273 \| 0.3273 \| nan \| 0.3273 \| 0.0 \| 0.3273 \| \| 0.0228 \| 8.16 \| 5100 \| 0.0185 \| 0.1471 \| 0.2943 \| 0.2943 \| nan \| 0.2943 \| 0.0 \| 0.2943 \| \| 0.0027 \| 8.21 \| 5130 \| 0.0185 \| 0.1940 \| 0.3881 \| 0.3881 \| nan \| 0.3881 \| 0.0 \| 0.3881 \| \| 0.0332 \| 8.26 \| 5160 \| 0.0183 \| 0.1219 \| 0.2439 \| 0.2439 \| nan \| 0.2439 \| 0.0 \| 0.2439 \| \| 0.0099 \| 8.3 \| 5190 \| 0.0183 \| 0.1310 \| 0.2621 \| 0.2621 \| nan \| 0.2621 \| 0.0 \| 0.2621 \| \| 0.0202 \| 8.35 \| 5220 \| 0.0186 \| 0.1128 \| 0.2257 \| 0.2257 \| nan \| 0.2257 \| 0.0 \| 0.2257 \| \| 0.0121 \| 8.4 \| 5250 \| 0.0192 \| 0.1444 \| 0.2888 \| 0.2888 \| nan \| 0.2888 \| 0.0 \| 0.2888 \| \| 0.0058 \| 8.45 \| 5280 \| 0.0201 \| 0.0798 \| 0.1597 \| 0.1597 \| nan \| 0.1597 \| 0.0 \| 0.1597 \| \| 0.014 \| 8.5 \| 5310 \| 0.0186 \| 0.1456 \| 0.2912 \| 0.2912 \| nan \| 0.2912 \| 0.0 \| 0.2912 \| \| 0.0325 \| 8.54 \| 5340 \| 0.0201 \| 0.2215 \| 0.4430 \| 0.4430 \| nan \| 0.4430 \| 0.0 \| 0.4430 \| \| 0.0084 \| 8.59 \| 5370 \| 0.0192 \| 0.1044 \| 0.2087 \| 0.2087 \| nan \| 0.2087 \| 0.0 \| 0.2087 \| \| 0.0125 \| 8.64 \| 5400 \| 0.0185 \| 0.1414 \| 0.2828 \| 0.2828 \| nan \| 0.2828 \| 0.0 \| 0.2828 \| \| 0.0246 \| 8.69 \| 5430 \| 0.0188 \| 0.1482 \| 0.2965 \| 0.2965 \| nan \| 0.2965 \| 0.0 \| 0.2965 \| \| 0.0306 \| 8.74 \| 5460 \| 0.0193 \| 0.1833 \| 0.3667 \| 0.3667 \| nan \| 0.3667 \| 0.0 \| 0.3667 \| \| 0.0145 \| 8.78 \| 5490 \| 0.0186 \| 0.1594 \| 0.3189 \| 0.3189 \| nan \| 0.3189 \| 0.0 \| 0.3189 \| \| 0.0149 \| 8.83 \| 5520 \| 0.0204 \| 0.2578 \| 0.5157 \| 0.5157 \| nan \| 0.5157 \| 0.0 \| 0.5157 \| \| 0.0095 \| 8.88 \| 5550 \| 0.0185 \| 0.1725 \| 0.3450 \| 0.3450 \| nan \| 0.3450 \| 0.0 \| 0.3450 \| \| 0.0157 \| 8.93 \| 5580 \| 0.0189 \| 0.1450 \| 0.2901 \| 0.2901 \| nan \| 0.2901 \| 0.0 \| 0.2901 \| \| 0.0135 \| 8.98 \| 5610 \| 0.0190 \| 0.1688 \| 0.3376 \| 0.3376 \| nan \| 0.3376 \| 0.0 \| 0.3376 \| \| 0.0099 \| 9.02 \| 5640 \| 0.0212 \| 0.2304 \| 0.4608 \| 0.4608 \| nan \| 0.4608 \| 0.0 \| 0.4608 \| \| 0.0232 \| 9.07 \| 5670 \| 0.0191 \| 0.2030 \| 0.4059 \| 0.4059 \| nan \| 0.4059 \| 0.0 \| 0.4059 \| \| 0.0163 \| 9.12 \| 5700 \| 0.0187 \| 0.1628 \| 0.3255 \| 0.3255 \| nan \| 0.3255 \| 0.0 \| 0.3255 \| \| 0.8412 \| 9.17 \| 5730 \| 0.0186 \| 0.1743 \| 0.3486 \| 0.3486 \| nan \| 0.3486 \| 0.0 \| 0.3486 \| \| 0.0189 \| 9.22 \| 5760 \| 0.0192 \| 0.1287 \| 0.2573 \| 0.2573 \| nan \| 0.2573 \| 0.0 \| 0.2573 \| \| 0.0148 \| 9.26 \| 5790 \| 0.0182 \| 0.1526 \| 0.3051 \| 0.3051 \| nan \| 0.3051 \| 0.0 \| 0.3051 \| \| 0.0211 \| 9.31 \| 5820 \| 0.0198 \| 0.2222 \| 0.4444 \| 0.4444 \| nan \| 0.4444 \| 0.0 \| 0.4444 \| \| 0.0185 \| 9.36 \| 5850 \| 0.0197 \| 0.2170 \| 0.4341 \| 0.4341 \| nan \| 0.4341 \| 0.0 \| 0.4341 \| \| 0.0098 \| 9.41 \| 5880 \| 0.0182 \| 0.1784 \| 0.3568 \| 0.3568 \| nan \| 0.3568 \| 0.0 \| 0.3568 \| \| 0.0135 \| 9.46 \| 5910 \| 0.0183 \| 0.1618 \| 0.3236 \| 0.3236 \| nan \| 0.3236 \| 0.0 \| 0.3236 \| \| 0.0123 \| 9.5 \| 5940 \| 0.0186 \| 0.1752 \| 0.3505 \| 0.3505 \| nan \| 0.3505 \| 0.0 \| 0.3505 \| \| 0.0105 \| 9.55 \| 5970 \| 0.0188 \| 0.1955 \| 0.3910 \| 0.3910 \| nan \| 0.3910 \| 0.0 \| 0.3910 \| \| 0.0147 \| 9.6 \| 6000 \| 0.0182 \| 0.1437 \| 0.2874 \| 0.2874 \| nan \| 0.2874 \| 0.0 \| 0.2874 \| \| 0.0181 \| 9.65 \| 6030 \| 0.0181 \| 0.1288 \| 0.2577 \| 0.2577 \| nan \| 0.2577 \| 0.0 \| 0.2577 \| \| 0.0153 \| 9.7 \| 6060 \| 0.0185 \| 0.1794 \| 0.3588 \| 0.3588 \| nan \| 0.3588 \| 0.0 \| 0.3588 \| \| 0.0331 \| 9.74 \| 6090 \| 0.0191 \| 0.1037 \| 0.2074 \| 0.2074 \| nan \| 0.2074 \| 0.0 \| 0.2074 \| \| 0.0105 \| 9.79 \| 6120 \| 0.0187 \| 0.1807 \| 0.3613 \| 0.3613 \| nan \| 0.3613 \| 0.0 \| 0.3613 \| \| 0.0212 \| 9.84 \| 6150 \| 0.0186 \| 0.1652 \| 0.3304 \| 0.3304 \| nan \| 0.3304 \| 0.0 \| 0.3304 \| \| 0.0191 \| 9.89 \| 6180 \| 0.0191 \| 0.1921 \| 0.3842 \| 0.3842 \| nan \| 0.3842 \| 0.0 \| 0.3842 \| \| 0.0163 \| 9.94 \| 6210 \| 0.0191 \| 0.1893 \| 0.3787 \| 0.3787 \| nan \| 0.3787 \| 0.0 \| 0.3787 \| \| 0.054 \| 9.98 \| 6240 \| 0.0190 \| 0.1888 \| 0.3777 \| 0.3777 \| nan \| 0.3777 \| 0.0 \| 0.3777 \| \| 0.0095 \| 10.03 \| 6270 \| 0.0193 \| 0.1441 \| 0.2881 \| 0.2881 \| nan \| 0.2881 \| 0.0 \| 0.2881 \| \| 0.0146 \| 10.08 \| 6300 \| 0.0194 \| 0.1614 \| 0.3228 \| 0.3228 \| nan \| 0.3228 \| 0.0 \| 0.3228 \| \| 0.0189 \| 10.13 \| 6330 \| 0.0189 \| 0.1799 \| 0.3598 \| 0.3598 \| nan \| 0.3598 \| 0.0 \| 0.3598 \| \| 0.01 \| 10.18 \| 6360 \| 0.0184 \| 0.2016 \| 0.4032 \| 0.4032 \| nan \| 0.4032 \| 0.0 \| 0.4032 \| \| 0.0165 \| 10.22 \| 6390 \| 0.0183 \| 0.1727 \| 0.3453 \| 0.3453 \| nan \| 0.3453 \| 0.0 \| 0.3453 \| \| 0.0114 \| 10.27 \| 6420 \| 0.0189 \| 0.1329 \| 0.2657 \| 0.2657 \| nan \| 0.2657 \| 0.0 \| 0.2657 \| \| 0.017 \| 10.32 \| 6450 \| 0.0186 \| 0.1636 \| 0.3272 \| 0.3272 \| nan \| 0.3272 \| 0.0 \| 0.3272 \| \| 0.0109 \| 10.37 \| 6480 \| 0.0184 \| 0.1718 \| 0.3436 \| 0.3436 \| nan \| 0.3436 \| 0.0 \| 0.3436 \| \| 0.0044 \| 10.42 \| 6510 \| 0.0188 \| 0.1326 \| 0.2652 \| 0.2652 \| nan \| 0.2652 \| 0.0 \| 0.2652 \| \| 0.0126 \| 10.46 \| 6540 \| 0.0188 \| 0.1786 \| 0.3573 \| 0.3573 \| nan \| 0.3573 \| 0.0 \| 0.3573 \| \| 0.0044 \| 10.51 \| 6570 \| 0.0191 \| 0.1379 \| 0.2758 \| 0.2758 \| nan \| 0.2758 \| 0.0 \| 0.2758 \| \| 0.0218 \| 10.56 \| 6600 \| 0.0188 \| 0.2023 \| 0.4046 \| 0.4046 \| nan \| 0.4046 \| 0.0 \| 0.4046 \| \| 0.0124 \| 10.61 \| 6630 \| 0.0189 \| 0.1895 \| 0.3791 \| 0.3791 \| nan \| 0.3791 \| 0.0 \| 0.3791 \| \| 0.0056 \| 10.66 \| 6660 \| 0.0189 \| 0.1573 \| 0.3145 \| 0.3145 \| nan \| 0.3145 \| 0.0 \| 0.3145 \| \| 0.0055 \| 10.7 \| 6690 \| 0.0187 \| 0.1269 \| 0.2538 \| 0.2538 \| nan \| 0.2538 \| 0.0 \| 0.2538 \| \| 0.0023 \| 10.75 \| 6720 \| 0.0185 \| 0.1908 \| 0.3815 \| 0.3815 \| nan \| 0.3815 \| 0.0 \| 0.3815 \| \| 0.0517 \| 10.8 \| 6750 \| 0.0181 \| 0.1342 \| 0.2684 \| 0.2684 \| nan \| 0.2684 \| 0.0 \| 0.2684 \| \| 0.032 \| 10.85 \| 6780 \| 0.0188 \| 0.1179 \| 0.2359 \| 0.2359 \| nan \| 0.2359 \| 0.0 \| 0.2359 \| \| 0.0111 \| 10.9 \| 6810 \| 0.0185 \| 0.1195 \| 0.2391 \| 0.2391 \| nan \| 0.2391 \| 0.0 \| 0.2391 \| \| 0.0354 \| 10.94 \| 6840 \| 0.0184 \| 0.1911 \| 0.3822 \| 0.3822 \| nan \| 0.3822 \| 0.0 \| 0.3822 \| \| 0.0051 \| 10.99 \| 6870 \| 0.0190 \| 0.1151 \| 0.2302 \| 0.2302 \| nan \| 0.2302 \| 0.0 \| 0.2302 \| \| 0.0104 \| 11.04 \| 6900 \| 0.0181 \| 0.1562 \| 0.3123 \| 0.3123 \| nan \| 0.3123 \| 0.0 \| 0.3123 \| \| 0.0145 \| 11.09 \| 6930 \| 0.0184 \| 0.1641 \| 0.3282 \| 0.3282 \| nan \| 0.3282 \| 0.0 \| 0.3282 \| \| 0.022 \| 11.14 \| 6960 \| 0.0192 \| 0.1011 \| 0.2023 \| 0.2023 \| nan \| 0.2023 \| 0.0 \| 0.2023 \| \| 0.014 \| 11.18 \| 6990 \| 0.0190 \| 0.0986 \| 0.1972 \| 0.1972 \| nan \| 0.1972 \| 0.0 \| 0.1972 \| \| 0.0195 \| 11.23 \| 7020 \| 0.0193 \| 0.1969 \| 0.3937 \| 0.3937 \| nan \| 0.3937 \| 0.0 \| 0.3937 \| \| 0.0084 \| 11.28 \| 7050 \| 0.0182 \| 0.1592 \| 0.3184 \| 0.3184 \| nan \| 0.3184 \| 0.0 \| 0.3184 \| \| 0.0099 \| 11.33 \| 7080 \| 0.0191 \| 0.1812 \| 0.3624 \| 0.3624 \| nan \| 0.3624 \| 0.0 \| 0.3624 \| \| 0.0165 \| 11.38 \| 7110 \| 0.0198 \| 0.1032 \| 0.2063 \| 0.2063 \| nan \| 0.2063 \| 0.0 \| 0.2063 \| \| 0.0175 \| 11.42 \| 7140 \| 0.0195 \| 0.1435 \| 0.2870 \| 0.2870 \| nan \| 0.2870 \| 0.0 \| 0.2870 \| \| 0.0146 \| 11.47 \| 7170 \| 0.0187 \| 0.1509 \| 0.3018 \| 0.3018 \| nan \| 0.3018 \| 0.0 \| 0.3018 \| \| 0.0106 \| 11.52 \| 7200 \| 0.0186 \| 0.1504 \| 0.3007 \| 0.3007 \| nan \| 0.3007 \| 0.0 \| 0.3007 \| \| 0.0195 \| 11.57 \| 7230 \| 0.0188 \| 0.1333 \| 0.2667 \| 0.2667 \| nan \| 0.2667 \| 0.0 \| 0.2667 \| \| 0.0162 \| 11.62 \| 7260 \| 0.0188 \| 0.1397 \| 0.2794 \| 0.2794 \| nan \| 0.2794 \| 0.0 \| 0.2794 \| \| 0.0198 \| 11.66 \| 7290 \| 0.0184 \| 0.1546 \| 0.3093 \| 0.3093 \| nan \| 0.3093 \| 0.0 \| 0.3093 \| \| 0.0161 \| 11.71 \| 7320 \| 0.0199 \| 0.2356 \| 0.4712 \| 0.4712 \| nan \| 0.4712 \| 0.0 \| 0.4712 \| \| 0.0252 \| 11.76 \| 7350 \| 0.0198 \| 0.1594 \| 0.3187 \| 0.3187 \| nan \| 0.3187 \| 0.0 \| 0.3187 \| \| 0.0122 \| 11.81 \| 7380 \| 0.0195 \| 0.1640 \| 0.3279 \| 0.3279 \| nan \| 0.3279 \| 0.0 \| 0.3279 \| \| 0.0045 \| 11.86 \| 7410 \| 0.0196 \| 0.1362 \| 0.2724 \| 0.2724 \| nan \| 0.2724 \| 0.0 \| 0.2724 \| \| 0.0154 \| 11.9 \| 7440 \| 0.0192 \| 0.1514 \| 0.3028 \| 0.3028 \| nan \| 0.3028 \| 0.0 \| 0.3028 \| \| 0.0153 \| 11.95 \| 7470 \| 0.0189 \| 0.1134 \| 0.2269 \| 0.2269 \| nan \| 0.2269 \| 0.0 \| 0.2269 \| \| 0.015 \| 12.0 \| 7500 \| 0.0189 \| 0.1524 \| 0.3049 \| 0.3049 \| nan \| 0.3049 \| 0.0 \| 0.3049 \| \| 0.011 \| 12.05 \| 7530 \| 0.0190 \| 0.1358 \| 0.2716 \| 0.2716 \| nan \| 0.2716 \| 0.0 \| 0.2716 \| \| 0.0105 \| 12.1 \| 7560 \| 0.0192 \| 0.1099 \| 0.2198 \| 0.2198 \| nan \| 0.2198 \| 0.0 \| 0.2198 \| \| 0.0028 \| 12.14 \| 7590 \| 0.0186 \| 0.1551 \| 0.3102 \| 0.3102 \| nan \| 0.3102 \| 0.0 \| 0.3102 \| \| 0.0088 \| 12.19 \| 7620 \| 0.0182 \| 0.1527 \| 0.3054 \| 0.3054 \| nan \| 0.3054 \| 0.0 \| 0.3054 \| \| 0.0122 \| 12.24 \| 7650 \| 0.0191 \| 0.2037 \| 0.4074 \| 0.4074 \| nan \| 0.4074 \| 0.0 \| 0.4074 \| \| 0.0188 \| 12.29 \| 7680 \| 0.0191 \| 0.1690 \| 0.3380 \| 0.3380 \| nan \| 0.3380 \| 0.0 \| 0.3380 \| \| 0.0108 \| 12.34 \| 7710 \| 0.0197 \| 0.1367 \| 0.2735 \| 0.2735 \| nan \| 0.2735 \| 0.0 \| 0.2735 \| \| 0.0054 \| 12.38 \| 7740 \| 0.0190 \| 0.1659 \| 0.3319 \| 0.3319 \| nan \| 0.3319 \| 0.0 \| 0.3319 \| \| 0.0105 \| 12.43 \| 7770 \| 0.0190 \| 0.1749 \| 0.3499 \| 0.3499 \| nan \| 0.3499 \| 0.0 \| 0.3499 \| \| 0.0131 \| 12.48 \| 7800 \| 0.0194 \| 0.1994 \| 0.3987 \| 0.3987 \| nan \| 0.3987 \| 0.0 \| 0.3987 \| \| 0.0127 \| 12.53 \| 7830 \| 0.0192 \| 0.2064 \| 0.4128 \| 0.4128 \| nan \| 0.4128 \| 0.0 \| 0.4128 \| \| 0.014 \| 12.58 \| 7860 \| 0.0187 \| 0.1686 \| 0.3372 \| 0.3372 \| nan \| 0.3372 \| 0.0 \| 0.3372 \| \| 0.0086 \| 12.62 \| 7890 \| 0.0192 \| 0.1871 \| 0.3742 \| 0.3742 \| nan \| 0.3742 \| 0.0 \| 0.3742 \| \| 0.0089 \| 12.67 \| 7920 \| 0.0187 \| 0.1386 \| 0.2772 \| 0.2772 \| nan \| 0.2772 \| 0.0 \| 0.2772 \| \| 0.0055 \| 12.72 \| 7950 \| 0.0187 \| 0.1513 \| 0.3026 \| 0.3026 \| nan \| 0.3026 \| 0.0 \| 0.3026 \| \| 0.0118 \| 12.77 \| 7980 \| 0.0186 \| 0.1484 \| 0.2968 \| 0.2968 \| nan \| 0.2968 \| 0.0 \| 0.2968 \| \| 0.0059 \| 12.82 \| 8010 \| 0.0185 \| 0.1568 \| 0.3137 \| 0.3137 \| nan \| 0.3137 \| 0.0 \| 0.3137 \| \| 0.0103 \| 12.86 \| 8040 \| 0.0182 \| 0.1728 \| 0.3456 \| 0.3456 \| nan \| 0.3456 \| 0.0 \| 0.3456 \| \| 0.0085 \| 12.91 \| 8070 \| 0.0187 \| 0.1903 \| 0.3806 \| 0.3806 \| nan \| 0.3806 \| 0.0 \| 0.3806 \| \| 0.0145 \| 12.96 \| 8100 \| 0.0185 \| 0.1887 \| 0.3773 \| 0.3773 \| nan \| 0.3773 \| 0.0 \| 0.3773 \| \| 0.0064 \| 13.01 \| 8130 \| 0.0191 \| 0.1963 \| 0.3925 \| 0.3925 \| nan \| 0.3925 \| 0.0 \| 0.3925 \| \| 0.0066 \| 13.06 \| 8160 \| 0.0186 \| 0.1888 \| 0.3777 \| 0.3777 \| nan \| 0.3777 \| 0.0 \| 0.3777 \| \| 0.0139 \| 13.1 \| 8190 \| 0.0183 \| 0.1427 \| 0.2854 \| 0.2854 \| nan \| 0.2854 \| 0.0 \| 0.2854 \| \| 0.0179 \| 13.15 \| 8220 \| 0.0185 \| 0.1460 \| 0.2921 \| 0.2921 \| nan \| 0.2921 \| 0.0 \| 0.2921 \| \| 0.0108 \| 13.2 \| 8250 \| 0.0190 \| 0.1738 \| 0.3476 \| 0.3476 \| nan \| 0.3476 \| 0.0 \| 0.3476 \| \| 0.0112 \| 13.25 \| 8280 \| 0.0196 \| 0.2183 \| 0.4365 \| 0.4365 \| nan \| 0.4365 \| 0.0 \| 0.4365 \| \| 0.0409 \| 13.3 \| 8310 \| 0.0191 \| 0.1923 \| 0.3846 \| 0.3846 \| nan \| 0.3846 \| 0.0 \| 0.3846 \| \| 0.0145 \| 13.34 \| 8340 \| 0.0194 \| 0.1946 \| 0.3892 \| 0.3892 \| nan \| 0.3892 \| 0.0 \| 0.3892 \| \| 0.0077 \| 13.39 \| 8370 \| 0.0195 \| 0.2010 \| 0.4020 \| 0.4020 \| nan \| 0.4020 \| 0.0 \| 0.4020 \| \| 0.0108 \| 13.44 \| 8400 \| 0.0194 \| 0.1367 \| 0.2734 \| 0.2734 \| nan \| 0.2734 \| 0.0 \| 0.2734 \| \| 0.0161 \| 13.49 \| 8430 \| 0.0187 \| 0.1856 \| 0.3711 \| 0.3711 \| nan \| 0.3711 \| 0.0 \| 0.3711 \| \| 0.0101 \| 13.54 \| 8460 \| 0.0185 \| 0.1812 \| 0.3623 \| 0.3623 \| nan \| 0.3623 \| 0.0 \| 0.3623 \| \| 0.0181 \| 13.58 \| 8490 \| 0.0192 \| 0.1788 \| 0.3576 \| 0.3576 \| nan \| 0.3576 \| 0.0 \| 0.3576 \| \| 0.0092 \| 13.63 \| 8520 \| 0.0188 \| 0.1516 \| 0.3032 \| 0.3032 \| nan \| 0.3032 \| 0.0 \| 0.3032 \| \| 0.028 \| 13.68 \| 8550 \| 0.0187 \| 0.1690 \| 0.3379 \| 0.3379 \| nan \| 0.3379 \| 0.0 \| 0.3379 \| \| 0.0176 \| 13.73 \| 8580 \| 0.0185 \| 0.1724 \| 0.3447 \| 0.3447 \| nan \| 0.3447 \| 0.0 \| 0.3447 \| \| 0.0075 \| 13.78 \| 8610 \| 0.0184 \| 0.1768 \| 0.3537 \| 0.3537 \| nan \| 0.3537 \| 0.0 \| 0.3537 \| \| 0.0108 \| 13.82 \| 8640 \| 0.0194 \| 0.1717 \| 0.3433 \| 0.3433 \| nan \| 0.3433 \| 0.0 \| 0.3433 \| \| 0.0209 \| 13.87 \| 8670 \| 0.0192 \| 0.1636 \| 0.3271 \| 0.3271 \| nan \| 0.3271 \| 0.0 \| 0.3271 \| \| 0.0088 \| 13.92 \| 8700 \| 0.0193 \| 0.2096 \| 0.4192 \| 0.4192 \| nan \| 0.4192 \| 0.0 \| 0.4192 \| \| 0.0099 \| 13.97 \| 8730 \| 0.0189 \| 0.1995 \| 0.3990 \| 0.3990 \| nan \| 0.3990 \| 0.0 \| 0.3990 \| \| 0.015 \| 14.02 \| 8760 \| 0.0186 \| 0.1647 \| 0.3295 \| 0.3295 \| nan \| 0.3295 \| 0.0 \| 0.3295 \| \| 0.011 \| 14.06 \| 8790 \| 0.0189 \| 0.1676 \| 0.3353 \| 0.3353 \| nan \| 0.3353 \| 0.0 \| 0.3353 \| \| 0.0081 \| 14.11 \| 8820 \| 0.0194 \| 0.1419 \| 0.2839 \| 0.2839 \| nan \| 0.2839 \| 0.0 \| 0.2839 \| \| 0.0164 \| 14.16 \| 8850 \| 0.0187 \| 0.1627 \| 0.3254 \| 0.3254 \| nan \| 0.3254 \| 0.0 \| 0.3254 \| \| 0.009 \| 14.21 \| 8880 \| 0.0183 \| 0.1909 \| 0.3817 \| 0.3817 \| nan \| 0.3817 \| 0.0 \| 0.3817 \| \| 0.0135 \| 14.26 \| 8910 \| 0.0186 \| 0.1640 \| 0.3280 \| 0.3280 \| nan \| 0.3280 \| 0.0 \| 0.3280 \| \| 0.0286 \| 14.3 \| 8940 \| 0.0188 \| 0.1691 \| 0.3383 \| 0.3383 \| nan \| 0.3383 \| 0.0 \| 0.3383 \| \| 0.0036 \| 14.35 \| 8970 \| 0.0189 \| 0.1616 \| 0.3231 \| 0.3231 \| nan \| 0.3231 \| 0.0 \| 0.3231 \| \| 0.0115 \| 14.4 \| 9000 \| 0.0193 \| 0.1359 \| 0.2718 \| 0.2718 \| nan \| 0.2718 \| 0.0 \| 0.2718 \| \| 0.0107 \| 14.45 \| 9030 \| 0.0189 \| 0.1498 \| 0.2996 \| 0.2996 \| nan \| 0.2996 \| 0.0 \| 0.2996 \| \| 0.0217 \| 14.5 \| 9060 \| 0.0194 \| 0.1486 \| 0.2972 \| 0.2972 \| nan \| 0.2972 \| 0.0 \| 0.2972 \| \| 0.0109 \| 14.54 \| 9090 \| 0.0194 \| 0.1554 \| 0.3109 \| 0.3109 \| nan \| 0.3109 \| 0.0 \| 0.3109 \| \| 0.0104 \| 14.59 \| 9120 \| 0.0195 \| 0.1661 \| 0.3321 \| 0.3321 \| nan \| 0.3321 \| 0.0 \| 0.3321 \| \| 0.0096 \| 14.64 \| 9150 \| 0.0187 \| 0.1850 \| 0.3700 \| 0.3700 \| nan \| 0.3700 \| 0.0 \| 0.3700 \| \| 0.018 \| 14.69 \| 9180 \| 0.0188 \| 0.1923 \| 0.3847 \| 0.3847 \| nan \| 0.3847 \| 0.0 \| 0.3847 \| \| 0.0072 \| 14.74 \| 9210 \| 0.0185 \| 0.1775 \| 0.3551 \| 0.3551 \| nan \| 0.3551 \| 0.0 \| 0.3551 \| \| 0.0106 \| 14.78 \| 9240 \| 0.0186 \| 0.1570 \| 0.3140 \| 0.3140 \| nan \| 0.3140 \| 0.0 \| 0.3140 \| \| 0.0209 \| 14.83 \| 9270 \| 0.0190 \| 0.1936 \| 0.3872 \| 0.3872 \| nan \| 0.3872 \| 0.0 \| 0.3872 \| \| 0.0174 \| 14.88 \| 9300 \| 0.0186 \| 0.1920 \| 0.3840 \| 0.3840 \| nan \| 0.3840 \| 0.0 \| 0.3840 \| \| 0.0109 \| 14.93 \| 9330 \| 0.0184 \| 0.1680 \| 0.3361 \| 0.3361 \| nan \| 0.3361 \| 0.0 \| 0.3361 \| \| 0.019 \| 14.98 \| 9360 \| 0.0189 \| 0.1962 \| 0.3924 \| 0.3924 \| nan \| 0.3924 \| 0.0 \| 0.3924 \| \| 0.0162 \| 15.02 \| 9390 \| 0.0195 \| 0.1523 \| 0.3045 \| 0.3045 \| nan \| 0.3045 \| 0.0 \| 0.3045 \| \| 0.013 \| 15.07 \| 9420 \| 0.0191 \| 0.1530 \| 0.3059 \| 0.3059 \| nan \| 0.3059 \| 0.0 \| 0.3059 \| \| 0.0091 \| 15.12 \| 9450 \| 0.0189 \| 0.1871 \| 0.3743 \| 0.3743 \| nan \| 0.3743 \| 0.0 \| 0.3743 \| \| 0.0022 \| 15.17 \| 9480 \| 0.0195 \| 0.1568 \| 0.3136 \| 0.3136 \| nan \| 0.3136 \| 0.0 \| 0.3136 \| \| 0.0092 \| 15.22 \| 9510 \| 0.0189 \| 0.2056 \| 0.4112 \| 0.4112 \| nan \| 0.4112 \| 0.0 \| 0.4112 \| \| 0.0269 \| 15.26 \| 9540 \| 0.0189 \| 0.1692 \| 0.3383 \| 0.3383 \| nan \| 0.3383 \| 0.0 \| 0.3383 \| \| 0.0194 \| 15.31 \| 9570 \| 0.0191 \| 0.1744 \| 0.3489 \| 0.3489 \| nan \| 0.3489 \| 0.0 \| 0.3489 \| \| 0.0063 \| 15.36 \| 9600 \| 0.0186 \| 0.1757 \| 0.3513 \| 0.3513 \| nan \| 0.3513 \| 0.0 \| 0.3513 \| \| 0.0283 \| 15.41 \| 9630 \| 0.0191 \| 0.1858 \| 0.3715 \| 0.3715 \| nan \| 0.3715 \| 0.0 \| 0.3715 \| \| 0.0051 \| 15.46 \| 9660 \| 0.0196 \| 0.1602 \| 0.3203 \| 0.3203 \| nan \| 0.3203 \| 0.0 \| 0.3203 \| \| 0.003 \| 15.5 \| 9690 \| 0.0195 \| 0.1377 \| 0.2753 \| 0.2753 \| nan \| 0.2753 \| 0.0 \| 0.2753 \| \| 0.0149 \| 15.55 \| 9720 \| 0.0189 \| 0.1799 \| 0.3597 \| 0.3597 \| nan \| 0.3597 \| 0.0 \| 0.3597 \| \| 0.0169 \| 15.6 \| 9750 \| 0.0189 \| 0.1772 \| 0.3544 \| 0.3544 \| nan \| 0.3544 \| 0.0 \| 0.3544 \| \| 0.0125 \| 15.65 \| 9780 \| 0.0193 \| 0.1436 \| 0.2872 \| 0.2872 \| nan \| 0.2872 \| 0.0 \| 0.2872 \| \| 0.0115 \| 15.7 \| 9810 \| 0.0192 \| 0.1560 \| 0.3121 \| 0.3121 \| nan \| 0.3121 \| 0.0 \| 0.3121 \| \| 0.0081 \| 15.74 \| 9840 \| 0.0186 \| 0.1693 \| 0.3386 \| 0.3386 \| nan \| 0.3386 \| 0.0 \| 0.3386 \| \| 0.0183 \| 15.79 \| 9870 \| 0.0192 \| 0.1414 \| 0.2828 \| 0.2828 \| nan \| 0.2828 \| 0.0 \| 0.2828 \| \| 0.0128 \| 15.84 \| 9900 \| 0.0189 \| 0.1484 \| 0.2967 \| 0.2967 \| nan \| 0.2967 \| 0.0 \| 0.2967 \| \| 0.0071 \| 15.89 \| 9930 \| 0.0192 \| 0.1309 \| 0.2618 \| 0.2618 \| nan \| 0.2618 \| 0.0 \| 0.2618 \| \| 0.0112 \| 15.94 \| 9960 \| 0.0194 \| 0.1479 \| 0.2957 \| 0.2957 \| nan \| 0.2957 \| 0.0 \| 0.2957 \| \| 0.002 \| 15.98 \| 9990 \| 0.0191 \| 0.1575 \| 0.3150 \| 0.3150 \| nan \| 0.3150 \| 0.0 \| 0.3150 \| \| 0.0201 \| 16.03 \| 10020 \| 0.0194 \| 0.1302 \| 0.2604 \| 0.2604 \| nan \| 0.2604 \| 0.0 \| 0.2604 \| \| 0.0078 \| 16.08 \| 10050 \| 0.0193 \| 0.1424 \| 0.2848 \| 0.2848 \| nan \| 0.2848 \| 0.0 \| 0.2848 \| \| 0.0288 \| 16.13 \| 10080 \| 0.0195 \| 0.1624 \| 0.3247 \| 0.3247 \| nan \| 0.3247 \| 0.0 \| 0.3247 \| \| 0.0229 \| 16.18 \| 10110 \| 0.0188 \| 0.1687 \| 0.3373 \| 0.3373 \| nan \| 0.3373 \| 0.0 \| 0.3373 \| \| 0.0124 \| 16.22 \| 10140 \| 0.0185 \| 0.1783 \| 0.3565 \| 0.3565 \| nan \| 0.3565 \| 0.0 \| 0.3565 \| \| 0.0098 \| 16.27 \| 10170 \| 0.0190 \| 0.1657 \| 0.3314 \| 0.3314 \| nan \| 0.3314 \| 0.0 \| 0.3314 \| \| 0.0117 \| 16.32 \| 10200 \| 0.0190 \| 0.1537 \| 0.3073 \| 0.3073 \| nan \| 0.3073 \| 0.0 \| 0.3073 \| \| 0.0104 \| 16.37 \| 10230 \| 0.0185 \| 0.1686 \| 0.3371 \| 0.3371 \| nan \| 0.3371 \| 0.0 \| 0.3371 \| \| 0.0131 \| 16.42 \| 10260 \| 0.0191 \| 0.1377 \| 0.2754 \| 0.2754 \| nan \| 0.2754 \| 0.0 \| 0.2754 \| \| 0.0087 \| 16.46 \| 10290 \| 0.0191 \| 0.1500 \| 0.3000 \| 0.3000 \| nan \| 0.3000 \| 0.0 \| 0.3000 \| \| 0.0023 \| 16.51 \| 10320 \| 0.0190 \| 0.1774 \| 0.3548 \| 0.3548 \| nan \| 0.3548 \| 0.0 \| 0.3548 \| \| 0.0132 \| 16.56 \| 10350 \| 0.0189 \| 0.1788 \| 0.3576 \| 0.3576 \| nan \| 0.3576 \| 0.0 \| 0.3576 \| \| 0.0061 \| 16.61 \| 10380 \| 0.0193 \| 0.1672 \| 0.3344 \| 0.3344 \| nan \| 0.3344 \| 0.0 \| 0.3344 \| \| 0.0054 \| 16.66 \| 10410 \| 0.0188 \| 0.1780 \| 0.3561 \| 0.3561 \| nan \| 0.3561 \| 0.0 \| 0.3561 \| \| 0.0228 \| 16.7 \| 10440 \| 0.0188 \| 0.1652 \| 0.3303 \| 0.3303 \| nan \| 0.3303 \| 0.0 \| 0.3303 \| \| 0.0195 \| 16.75 \| 10470 \| 0.0191 \| 0.1583 \| 0.3166 \| 0.3166 \| nan \| 0.3166 \| 0.0 \| 0.3166 \| \| 0.019 \| 16.8 \| 10500 \| 0.0193 \| 0.1583 \| 0.3166 \| 0.3166 \| nan \| 0.3166 \| 0.0 \| 0.3166 \| \| 0.0219 \| 16.85 \| 10530 \| 0.0187 \| 0.1671 \| 0.3342 \| 0.3342 \| nan \| 0.3342 \| 0.0 \| 0.3342 \| \| 0.0146 \| 16.9 \| 10560 \| 0.0189 \| 0.2074 \| 0.4148 \| 0.4148 \| nan \| 0.4148 \| 0.0 \| 0.4148 \| \| 0.0124 \| 16.94 \| 10590 \| 0.0192 \| 0.1798 \| 0.3597 \| 0.3597 \| nan \| 0.3597 \| 0.0 \| 0.3597 \| \| 0.0266 \| 16.99 \| 10620 \| 0.0187 \| 0.1631 \| 0.3262 \| 0.3262 \| nan \| 0.3262 \| 0.0 \| 0.3262 \| \| 0.0066 \| 17.04 \| 10650 \| 0.0194 \| 0.1399 \| 0.2799 \| 0.2799 \| nan \| 0.2799 \| 0.0 \| 0.2799 \| \| 0.0044 \| 17.09 \| 10680 \| 0.0188 \| 0.1624 \| 0.3248 \| 0.3248 \| nan \| 0.3248 \| 0.0 \| 0.3248 \| \| 0.0031 \| 17.14 \| 10710 \| 0.0193 \| 0.1989 \| 0.3979 \| 0.3979 \| nan \| 0.3979 \| 0.0 \| 0.3979 \| \| 0.0056 \| 17.18 \| 10740 \| 0.0188 \| 0.1898 \| 0.3795 \| 0.3795 \| nan \| 0.3795 \| 0.0 \| 0.3795 \| \| 0.0037 \| 17.23 \| 10770 \| 0.0189 \| 0.1557 \| 0.3114 \| 0.3114 \| nan \| 0.3114 \| 0.0 \| 0.3114 \| \| 0.0052 \| 17.28 \| 10800 \| 0.0194 \| 0.1391 \| 0.2781 \| 0.2781 \| nan \| 0.2781 \| 0.0 \| 0.2781 \| \| 0.0151 \| 17.33 \| 10830 \| 0.0191 \| 0.1553 \| 0.3106 \| 0.3106 \| nan \| 0.3106 \| 0.0 \| 0.3106 \| \| 0.0096 \| 17.38 \| 10860 \| 0.0190 \| 0.1524 \| 0.3047 \| 0.3047 \| nan \| 0.3047 \| 0.0 \| 0.3047 \| \| 0.008 \| 17.42 \| 10890 \| 0.0186 \| 0.1667 \| 0.3335 \| 0.3335 \| nan \| 0.3335 \| 0.0 \| 0.3335 \| \| 0.013 \| 17.47 \| 10920 \| 0.0186 \| 0.1708 \| 0.3416 \| 0.3416 \| nan \| 0.3416 \| 0.0 \| 0.3416 \| \| 0.0067 \| 17.52 \| 10950 \| 0.0184 \| 0.1808 \| 0.3616 \| 0.3616 \| nan \| 0.3616 \| 0.0 \| 0.3616 \| \| 0.0101 \| 17.57 \| 10980 \| 0.0187 \| 0.1626 \| 0.3252 \| 0.3252 \| nan \| 0.3252 \| 0.0 \| 0.3252 \| \| 0.0168 \| 17.62 \| 11010 \| 0.0188 \| 0.1724 \| 0.3447 \| 0.3447 \| nan \| 0.3447 \| 0.0 \| 0.3447 \| \| 0.0056 \| 17.66 \| 11040 \| 0.0191 \| 0.1582 \| 0.3165 \| 0.3165 \| nan \| 0.3165 \| 0.0 \| 0.3165 \| \| 0.0062 \| 17.71 \| 11070 \| 0.0194 \| 0.1439 \| 0.2877 \| 0.2877 \| nan \| 0.2877 \| 0.0 \| 0.2877 \| \| 0.0035 \| 17.76 \| 11100 \| 0.0189 \| 0.1584 \| 0.3168 \| 0.3168 \| nan \| 0.3168 \| 0.0 \| 0.3168 \| \| 0.0198 \| 17.81 \| 11130 \| 0.0191 \| 0.1671 \| 0.3343 \| 0.3343 \| nan \| 0.3343 \| 0.0 \| 0.3343 \| \| 0.0018 \| 17.86 \| 11160 \| 0.0195 \| 0.1473 \| 0.2945 \| 0.2945 \| nan \| 0.2945 \| 0.0 \| 0.2945 \| \| 0.0083 \| 17.9 \| 11190 \| 0.0189 \| 0.1866 \| 0.3732 \| 0.3732 \| nan \| 0.3732 \| 0.0 \| 0.3732 \| \| 0.0122 \| 17.95 \| 11220 \| 0.0192 \| 0.1647 \| 0.3294 \| 0.3294 \| nan \| 0.3294 \| 0.0 \| 0.3294 \| \| 0.0099 \| 18.0 \| 11250 \| 0.0192 \| 0.1858 \| 0.3716 \| 0.3716 \| nan \| 0.3716 \| 0.0 \| 0.3716 \| \| 0.0129 \| 18.05 \| 11280 \| 0.0191 \| 0.1828 \| 0.3656 \| 0.3656 \| nan \| 0.3656 \| 0.0 \| 0.3656 \| \| 0.026 \| 18.1 \| 11310 \| 0.0195 \| 0.1661 \| 0.3322 \| 0.3322 \| nan \| 0.3322 \| 0.0 \| 0.3322 \| \| 0.0353 \| 18.14 \| 11340 \| 0.0196 \| 0.1703 \| 0.3407 \| 0.3407 \| nan \| 0.3407 \| 0.0 \| 0.3407 \| \| 0.0104 \| 18.19 \| 11370 \| 0.0196 \| 0.1764 \| 0.3528 \| 0.3528 \| nan \| 0.3528 \| 0.0 \| 0.3528 \| \| 0.0184 \| 18.24 \| 11400 \| 0.0196 \| 0.1441 \| 0.2881 \| 0.2881 \| nan \| 0.2881 \| 0.0 \| 0.2881 \| \| 0.0071 \| 18.29 \| 11430 \| 0.0193 \| 0.1693 \| 0.3385 \| 0.3385 \| nan \| 0.3385 \| 0.0 \| 0.3385 \| \| 0.0359 \| 18.34 \| 11460 \| 0.0194 \| 0.1994 \| 0.3988 \| 0.3988 \| nan \| 0.3988 \| 0.0 \| 0.3988 \| \| 0.0338 \| 18.38 \| 11490 \| 0.0199 \| 0.1578 \| 0.3156 \| 0.3156 \| nan \| 0.3156 \| 0.0 \| 0.3156 \| \| 0.0137 \| 18.43 \| 11520 \| 0.0196 \| 0.1741 \| 0.3481 \| 0.3481 \| nan \| 0.3481 \| 0.0 \| 0.3481 \| \| 0.0098 \| 18.48 \| 11550 \| 0.0193 \| 0.1641 \| 0.3281 \| 0.3281 \| nan \| 0.3281 \| 0.0 \| 0.3281 \| \| 0.013 \| 18.53 \| 11580 \| 0.0193 \| 0.1508 \| 0.3016 \| 0.3016 \| nan \| 0.3016 \| 0.0 \| 0.3016 \| \| 0.008 \| 18.58 \| 11610 \| 0.0192 \| 0.1758 \| 0.3517 \| 0.3517 \| nan \| 0.3517 \| 0.0 \| 0.3517 \| \| 0.0089 \| 18.62 \| 11640 \| 0.0193 \| 0.1511 \| 0.3023 \| 0.3023 \| nan \| 0.3023 \| 0.0 \| 0.3023 \| \| 0.0176 \| 18.67 \| 11670 \| 0.0190 \| 0.1646 \| 0.3292 \| 0.3292 \| nan \| 0.3292 \| 0.0 \| 0.3292 \| \| 0.0091 \| 18.72 \| 11700 \| 0.0193 \| 0.1514 \| 0.3028 \| 0.3028 \| nan \| 0.3028 \| 0.0 \| 0.3028 \| \| 0.0075 \| 18.77 \| 11730 \| 0.0190 \| 0.1644 \| 0.3288 \| 0.3288 \| nan \| 0.3288 \| 0.0 \| 0.3288 \| \| 0.0057 \| 18.82 \| 11760 \| 0.0191 \| 0.1789 \| 0.3577 \| 0.3577 \| nan \| 0.3577 \| 0.0 \| 0.3577 \| \| 0.0063 \| 18.86 \| 11790 \| 0.0188 \| 0.1874 \| 0.3749 \| 0.3749 \| nan \| 0.3749 \| 0.0 \| 0.3749 \| \| 0.0119 \| 18.91 \| 11820 \| 0.0197 \| 0.1316 \| 0.2632 \| 0.2632 \| nan \| 0.2632 \| 0.0 \| 0.2632 \| \| 0.0068 \| 18.96 \| 11850 \| 0.0192 \| 0.1963 \| 0.3927 \| 0.3927 \| nan \| 0.3927 \| 0.0 \| 0.3927 \| \| 0.0179 \| 19.01 \| 11880 \| 0.0194 \| 0.1603 \| 0.3206 \| 0.3206 \| nan \| 0.3206 \| 0.0 \| 0.3206 \| \| 0.0107 \| 19.06 \| 11910 \| 0.0194 \| 0.1469 \| 0.2939 \| 0.2939 \| nan \| 0.2939 \| 0.0 \| 0.2939 \| \| 0.0131 \| 19.1 \| 11940 \| 0.0197 \| 0.1692 \| 0.3385 \| 0.3385 \| nan \| 0.3385 \| 0.0 \| 0.3385 \| \| 0.0093 \| 19.15 \| 11970 \| 0.0198 \| 0.1533 \| 0.3067 \| 0.3067 \| nan \| 0.3067 \| 0.0 \| 0.3067 \| \| 0.0022 \| 19.2 \| 12000 \| 0.0191 \| 0.1794 \| 0.3588 \| 0.3588 \| nan \| 0.3588 \| 0.0 \| 0.3588 \| \| 0.0095 \| 19.25 \| 12030 \| 0.0192 \| 0.1663 \| 0.3327 \| 0.3327 \| nan \| 0.3327 \| 0.0 \| 0.3327 \| \| 0.0129 \| 19.3 \| 12060 \| 0.0188 \| 0.1776 \| 0.3553 \| 0.3553 \| nan \| 0.3553 \| 0.0 \| 0.3553 \| \| 0.0132 \| 19.34 \| 12090 \| 0.0192 \| 0.1745 \| 0.3491 \| 0.3491 \| nan \| 0.3491 \| 0.0 \| 0.3491 \| \| 0.0114 \| 19.39 \| 12120 \| 0.0191 \| 0.1755 \| 0.3510 \| 0.3510 \| nan \| 0.3510 \| 0.0 \| 0.3510 \| \| 0.0117 \| 19.44 \| 12150 \| 0.0195 \| 0.1568 \| 0.3135 \| 0.3135 \| nan \| 0.3135 \| 0.0 \| 0.3135 \| \| 0.0152 \| 19.49 \| 12180 \| 0.0195 \| 0.1631 \| 0.3263 \| 0.3263 \| nan \| 0.3263 \| 0.0 \| 0.3263 \| \| 0.0102 \| 19.54 \| 12210 \| 0.0196 \| 0.1699 \| 0.3398 \| 0.3398 \| nan \| 0.3398 \| 0.0 \| 0.3398 \| \| 0.0424 \| 19.58 \| 12240 \| 0.0192 \| 0.1833 \| 0.3665 \| 0.3665 \| nan \| 0.3665 \| 0.0 \| 0.3665 \| \| 0.0051 \| 19.63 \| 12270 \| 0.0194 \| 0.1720 \| 0.3439 \| 0.3439 \| nan \| 0.3439 \| 0.0 \| 0.3439 \| \| 0.0087 \| 19.68 \| 12300 \| 0.0195 \| 0.1760 \| 0.3520 \| 0.3520 \| nan \| 0.3520 \| 0.0 \| 0.3520 \| \| 0.0116 \| 19.73 \| 12330 \| 0.0196 \| 0.1535 \| 0.3069 \| 0.3069 \| nan \| 0.3069 \| 0.0 \| 0.3069 \| \| 0.0159 \| 19.78 \| 12360 \| 0.0193 \| 0.1547 \| 0.3095 \| 0.3095 \| nan \| 0.3095 \| 0.0 \| 0.3095 \| \| 0.0155 \| 19.82 \| 12390 \| 0.0197 \| 0.1531 \| 0.3061 \| 0.3061 \| nan \| 0.3061 \| 0.0 \| 0.3061 \| \| 0.0193 \| 19.87 \| 12420 \| 0.0199 \| 0.1677 \| 0.3355 \| 0.3355 \| nan \| 0.3355 \| 0.0 \| 0.3355 \| \| 0.0045 \| 19.92 \| 12450 \| 0.0197 \| 0.1755 \| 0.3510 \| 0.3510 \| nan \| 0.3510 \| 0.0 \| 0.3510 \| \| 0.015 \| 19.97 \| 12480 \| 0.0192 \| 0.1873 \| 0.3747 \| 0.3747 \| nan \| 0.3747 \| 0.0 \| 0.3747 \| \| 0.0124 \| 20.02 \| 12510 \| 0.0198 \| 0.1526 \| 0.3053 \| 0.3053 \| nan \| 0.3053 \| 0.0 \| 0.3053 \| \| 0.009 \| 20.06 \| 12540 \| 0.0192 \| 0.1834 \| 0.3668 \| 0.3668 \| nan \| 0.3668 \| 0.0 \| 0.3668 \| \| 0.0152 \| 20.11 \| 12570 \| 0.0197 \| 0.1663 \| 0.3327 \| 0.3327 \| nan \| 0.3327 \| 0.0 \| 0.3327 \| \| 0.0089 \| 20.16 \| 12600 \| 0.0195 \| 0.1823 \| 0.3647 \| 0.3647 \| nan \| 0.3647 \| 0.0 \| 0.3647 \| \| 0.0057 \| 20.21 \| 12630 \| 0.0196 \| 0.1693 \| 0.3385 \| 0.3385 \| nan \| 0.3385 \| 0.0 \| 0.3385 \| \| 0.0091 \| 20.26 \| 12660 \| 0.0194 \| 0.1911 \| 0.3821 \| 0.3821 \| nan \| 0.3821 \| 0.0 \| 0.3821 \| \| 0.0094 \| 20.3 \| 12690 \| 0.0197 \| 0.1814 \| 0.3628 \| 0.3628 \| nan \| 0.3628 \| 0.0 \| 0.3628 \| \| 0.0132 \| 20.35 \| 12720 \| 0.0192 \| 0.1724 \| 0.3447 \| 0.3447 \| nan \| 0.3447 \| 0.0 \| 0.3447 \| \| 0.0136 \| 20.4 \| 12750 \| 0.0189 \| 0.1840 \| 0.3679 \| 0.3679 \| nan \| 0.3679 \| 0.0 \| 0.3679 \| \| 0.004 \| 20.45 \| 12780 \| 0.0191 \| 0.1719 \| 0.3437 \| 0.3437 \| nan \| 0.3437 \| 0.0 \| 0.3437 \| \| 0.001 \| 20.5 \| 12810 \| 0.0191 \| 0.1773 \| 0.3545 \| 0.3545 \| nan \| 0.3545 \| 0.0 \| 0.3545 \| \| 0.0148 \| 20.54 \| 12840 \| 0.0193 \| 0.1675 \| 0.3351 \| 0.3351 \| nan \| 0.3351 \| 0.0 \| 0.3351 \| \| 0.0136 \| 20.59 \| 12870 \| 0.0191 \| 0.1812 \| 0.3624 \| 0.3624 \| nan \| 0.3624 \| 0.0 \| 0.3624 \| \| 0.01 \| 20.64 \| 12900 \| 0.0192 \| 0.1713 \| 0.3426 \| 0.3426 \| nan \| 0.3426 \| 0.0 \| 0.3426 \| \| 0.0071 \| 20.69 \| 12930 \| 0.0193 \| 0.1588 \| 0.3175 \| 0.3175 \| nan \| 0.3175 \| 0.0 \| 0.3175 \| \| 0.0176 \| 20.74 \| 12960 \| 0.0192 \| 0.1752 \| 0.3504 \| 0.3504 \| nan \| 0.3504 \| 0.0 \| 0.3504 \| \| 0.0122 \| 20.78 \| 12990 \| 0.0195 \| 0.1603 \| 0.3205 \| 0.3205 \| nan \| 0.3205 \| 0.0 \| 0.3205 \| \| 0.0087 \| 20.83 \| 13020 \| 0.0196 \| 0.1670 \| 0.3340 \| 0.3340 \| nan \| 0.3340 \| 0.0 \| 0.3340 \| \| 0.0118 \| 20.88 \| 13050 \| 0.0190 \| 0.1727 \| 0.3454 \| 0.3454 \| nan \| 0.3454 \| 0.0 \| 0.3454 \| \| 0.0112 \| 20.93 \| 13080 \| 0.0192 \| 0.1669 \| 0.3337 \| 0.3337 \| nan \| 0.3337 \| 0.0 \| 0.3337 \| \| 0.0195 \| 20.98 \| 13110 \| 0.0191 \| 0.1936 \| 0.3872 \| 0.3872 \| nan \| 0.3872 \| 0.0 \| 0.3872 \| \| 0.0171 \| 21.02 \| 13140 \| 0.0196 \| 0.1571 \| 0.3142 \| 0.3142 \| nan \| 0.3142 \| 0.0 \| 0.3142 \| \| 0.0062 \| 21.07 \| 13170 \| 0.0192 \| 0.1928 \| 0.3856 \| 0.3856 \| nan \| 0.3856 \| 0.0 \| 0.3856 \| \| 0.016 \| 21.12 \| 13200 \| 0.0194 \| 0.1548 \| 0.3095 \| 0.3095 \| nan \| 0.3095 \| 0.0 \| 0.3095 \| \| 0.0103 \| 21.17 \| 13230 \| 0.0198 \| 0.1855 \| 0.3710 \| 0.3710 \| nan \| 0.3710 \| 0.0 \| 0.3710 \| \| 0.0127 \| 21.22 \| 13260 \| 0.0196 \| 0.1760 \| 0.3521 \| 0.3521 \| nan \| 0.3521 \| 0.0 \| 0.3521 \| \| 0.0076 \| 21.26 \| 13290 \| 0.0193 \| 0.1732 \| 0.3463 \| 0.3463 \| nan \| 0.3463 \| 0.0 \| 0.3463 \| \| 0.0178 \| 21.31 \| 13320 \| 0.0196 \| 0.1751 \| 0.3503 \| 0.3503 \| nan \| 0.3503 \| 0.0 \| 0.3503 \| \| 0.0127 \| 21.36 \| 13350 \| 0.0200 \| 0.1527 \| 0.3055 \| 0.3055 \| nan \| 0.3055 \| 0.0 \| 0.3055 \| \| 0.0128 \| 21.41 \| 13380 \| 0.0197 \| 0.1580 \| 0.3159 \| 0.3159 \| nan \| 0.3159 \| 0.0 \| 0.3159 \| \| 0.0081 \| 21.46 \| 13410 \| 0.0190 \| 0.1721 \| 0.3443 \| 0.3443 \| nan \| 0.3443 \| 0.0 \| 0.3443 \| \| 0.0159 \| 21.5 \| 13440 \| 0.0193 \| 0.1633 \| 0.3267 \| 0.3267 \| nan \| 0.3267 \| 0.0 \| 0.3267 \| \| 0.0084 \| 21.55 \| 13470 \| 0.0195 \| 0.1530 \| 0.3060 \| 0.3060 \| nan \| 0.3060 \| 0.0 \| 0.3060 \| \| 0.0227 \| 21.6 \| 13500 \| 0.0192 \| 0.1608 \| 0.3217 \| 0.3217 \| nan \| 0.3217 \| 0.0 \| 0.3217 \| \| 0.0086 \| 21.65 \| 13530 \| 0.0200 \| 0.1520 \| 0.3039 \| 0.3039 \| nan \| 0.3039 \| 0.0 \| 0.3039 \| \| 0.0087 \| 21.7 \| 13560 \| 0.0196 \| 0.1685 \| 0.3370 \| 0.3370 \| nan \| 0.3370 \| 0.0 \| 0.3370 \| \| 0.005 \| 21.74 \| 13590 \| 0.0192 \| 0.1762 \| 0.3523 \| 0.3523 \| nan \| 0.3523 \| 0.0 \| 0.3523 \| \| 0.0072 \| 21.79 \| 13620 \| 0.0196 \| 0.1577 \| 0.3153 \| 0.3153 \| nan \| 0.3153 \| 0.0 \| 0.3153 \| \| 0.0158 \| 21.84 \| 13650 \| 0.0194 \| 0.1881 \| 0.3762 \| 0.3762 \| nan \| 0.3762 \| 0.0 \| 0.3762 \| \| 0.0149 \| 21.89 \| 13680 \| 0.0195 \| 0.1565 \| 0.3130 \| 0.3130 \| nan \| 0.3130 \| 0.0 \| 0.3130 \| \| 0.0028 \| 21.94 \| 13710 \| 0.0190 \| 0.1885 \| 0.3770 \| 0.3770 \| nan \| 0.3770 \| 0.0 \| 0.3770 \| \| 0.0054 \| 21.98 \| 13740 \| 0.0192 \| 0.1741 \| 0.3481 \| 0.3481 \| nan \| 0.3481 \| 0.0 \| 0.3481 \| \| 0.0117 \| 22.03 \| 13770 \| 0.0191 \| 0.1779 \| 0.3559 \| 0.3559 \| nan \| 0.3559 \| 0.0 \| 0.3559 \| \| 0.008 \| 22.08 \| 13800 \| 0.0195 \| 0.1800 \| 0.3601 \| 0.3601 \| nan \| 0.3601 \| 0.0 \| 0.3601 \| \| 0.0072 \| 22.13 \| 13830 \| 0.0193 \| 0.1631 \| 0.3263 \| 0.3263 \| nan \| 0.3263 \| 0.0 \| 0.3263 \| \| 0.0101 \| 22.18 \| 13860 \| 0.0193 \| 0.1646 \| 0.3292 \| 0.3292 \| nan \| 0.3292 \| 0.0 \| 0.3292 \| \| 0.0054 \| 22.22 \| 13890 \| 0.0195 \| 0.1703 \| 0.3405 \| 0.3405 \| nan \| 0.3405 \| 0.0 \| 0.3405 \| \| 0.0091 \| 22.27 \| 13920 \| 0.0197 \| 0.1656 \| 0.3312 \| 0.3312 \| nan \| 0.3312 \| 0.0 \| 0.3312 \| \| 0.0137 \| 22.32 \| 13950 \| 0.0193 \| 0.1817 \| 0.3635 \| 0.3635 \| nan \| 0.3635 \| 0.0 \| 0.3635 \| \| 0.011 \| 22.37 \| 13980 \| 0.0194 \| 0.1695 \| 0.3391 \| 0.3391 \| nan \| 0.3391 \| 0.0 \| 0.3391 \| \| 0.0141 \| 22.42 \| 14010 \| 0.0193 \| 0.1778 \| 0.3556 \| 0.3556 \| nan \| 0.3556 \| 0.0 \| 0.3556 \| \| 0.0087 \| 22.46 \| 14040 \| 0.0193 \| 0.1806 \| 0.3613 \| 0.3613 \| nan \| 0.3613 \| 0.0 \| 0.3613 \| \| 0.037 \| 22.51 \| 14070 \| 0.0189 \| 0.1882 \| 0.3764 \| 0.3764 \| nan \| 0.3764 \| 0.0 \| 0.3764 \| \| 0.0815 \| 22.56 \| 14100 \| 0.0193 \| 0.1602 \| 0.3204 \| 0.3204 \| nan \| 0.3204 \| 0.0 \| 0.3204 \| \| 0.0132 \| 22.61 \| 14130 \| 0.0195 \| 0.1702 \| 0.3403 \| 0.3403 \| nan \| 0.3403 \| 0.0 \| 0.3403 \| \| 0.0042 \| 22.66 \| 14160 \| 0.0191 \| 0.1764 \| 0.3528 \| 0.3528 \| nan \| 0.3528 \| 0.0 \| 0.3528 \| \| 0.0057 \| 22.7 \| 14190 \| 0.0190 \| 0.1807 \| 0.3614 \| 0.3614 \| nan \| 0.3614 \| 0.0 \| 0.3614 \| \| 0.0199 \| 22.75 \| 14220 \| 0.0194 \| 0.1628 \| 0.3257 \| 0.3257 \| nan \| 0.3257 \| 0.0 \| 0.3257 \| \| 0.0103 \| 22.8 \| 14250 \| 0.0200 \| 0.1540 \| 0.3080 \| 0.3080 \| nan \| 0.3080 \| 0.0 \| 0.3080 \| \| 0.0087 \| 22.85 \| 14280 \| 0.0192 \| 0.1805 \| 0.3610 \| 0.3610 \| nan \| 0.3610 \| 0.0 \| 0.3610 \| \| 0.0082 \| 22.9 \| 14310 \| 0.0194 \| 0.1516 \| 0.3032 \| 0.3032 \| nan \| 0.3032 \| 0.0 \| 0.3032 \| \| 0.0186 \| 22.94 \| 14340 \| 0.0194 \| 0.1589 \| 0.3177 \| 0.3177 \| nan \| 0.3177 \| 0.0 \| 0.3177 \| \| 0.0034 \| 22.99 \| 14370 \| 0.0190 \| 0.1836 \| 0.3671 \| 0.3671 \| nan \| 0.3671 \| 0.0 \| 0.3671 \| \| 0.0031 \| 23.04 \| 14400 \| 0.0191 \| 0.1798 \| 0.3596 \| 0.3596 \| nan \| 0.3596 \| 0.0 \| 0.3596 \| \| 0.0171 \| 23.09 \| 14430 \| 0.0195 \| 0.1778 \| 0.3556 \| 0.3556 \| nan \| 0.3556 \| 0.0 \| 0.3556 \| \| 0.0073 \| 23.14 \| 14460 \| 0.0192 \| 0.1904 \| 0.3808 \| 0.3808 \| nan \| 0.3808 \| 0.0 \| 0.3808 \| \| 0.0026 \| 23.18 \| 14490 \| 0.0196 \| 0.1704 \| 0.3407 \| 0.3407 \| nan \| 0.3407 \| 0.0 \| 0.3407 \| \| 0.0067 \| 23.23 \| 14520 \| 0.0192 \| 0.1695 \| 0.3391 \| 0.3391 \| nan \| 0.3391 \| 0.0 \| 0.3391 \| \| 0.0183 \| 23.28 \| 14550 \| 0.0192 \| 0.1851 \| 0.3703 \| 0.3703 \| nan \| 0.3703 \| 0.0 \| 0.3703 \| \| 0.0025 \| 23.33 \| 14580 \| 0.0193 \| 0.1735 \| 0.3470 \| 0.3470 \| nan \| 0.3470 \| 0.0 \| 0.3470 \| \| 0.0055 \| 23.38 \| 14610 \| 0.0194 \| 0.1788 \| 0.3576 \| 0.3576 \| nan \| 0.3576 \| 0.0 \| 0.3576 \| \| 0.0018 \| 23.42 \| 14640 \| 0.0193 \| 0.1890 \| 0.3780 \| 0.3780 \| nan \| 0.3780 \| 0.0 \| 0.3780 \| \| 0.0044 \| 23.47 \| 14670 \| 0.0194 \| 0.1809 \| 0.3617 \| 0.3617 \| nan \| 0.3617 \| 0.0 \| 0.3617 \| \| 0.0209 \| 23.52 \| 14700 \| 0.0197 \| 0.1817 \| 0.3633 \| 0.3633 \| nan \| 0.3633 \| 0.0 \| 0.3633 \| \| 0.0116 \| 23.57 \| 14730 \| 0.0193 \| 0.1797 \| 0.3594 \| 0.3594 \| nan \| 0.3594 \| 0.0 \| 0.3594 \| \| 0.0134 \| 23.62 \| 14760 \| 0.0193 \| 0.1807 \| 0.3614 \| 0.3614 \| nan \| 0.3614 \| 0.0 \| 0.3614 \| \| 0.0067 \| 23.66 \| 14790 \| 0.0194 \| 0.1718 \| 0.3436 \| 0.3436 \| nan \| 0.3436 \| 0.0 \| 0.3436 \| \| 0.0057 \| 23.71 \| 14820 \| 0.0194 \| 0.1741 \| 0.3482 \| 0.3482 \| nan \| 0.3482 \| 0.0 \| 0.3482 \| \| 0.0034 \| 23.76 \| 14850 \| 0.0193 \| 0.1679 \| 0.3358 \| 0.3358 \| nan \| 0.3358 \| 0.0 \| 0.3358 \| \| 0.0176 \| 23.81 \| 14880 \| 0.0191 \| 0.2018 \| 0.4035 \| 0.4035 \| nan \| 0.4035 \| 0.0 \| 0.4035 \| \| 0.0071 \| 23.86 \| 14910 \| 0.0192 \| 0.1884 \| 0.3768 \| 0.3768 \| nan \| 0.3768 \| 0.0 \| 0.3768 \| \| 0.0065 \| 23.9 \| 14940 \| 0.0193 \| 0.1744 \| 0.3488 \| 0.3488 \| nan \| 0.3488 \| 0.0 \| 0.3488 \| \| 0.0188 \| 23.95 \| 14970 \| 0.0199 \| 0.1540 \| 0.3080 \| 0.3080 \| nan \| 0.3080 \| 0.0 \| 0.3080 \| \| 0.0014 \| 24.0 \| 15000 \| 0.0194 \| 0.2232 \| 0.4464 \| 0.4464 \| nan \| 0.4464 \| 0.0 \| 0.4464 \| \| 0.0192 \| 24.05 \| 15030 \| 0.0194 \| 0.1709 \| 0.3418 \| 0.3418 \| nan \| 0.3418 \| 0.0 \| 0.3418 \| \| 0.0064 \| 24.1 \| 15060 \| 0.0193 \| 0.1852 \| 0.3704 \| 0.3704 \| nan \| 0.3704 \| 0.0 \| 0.3704 \| \| 0.0067 \| 24.14 \| 15090 \| 0.0194 \| 0.1782 \| 0.3563 \| 0.3563 \| nan \| 0.3563 \| 0.0 \| 0.3563 \| \| 0.0153 \| 24.19 \| 15120 \| 0.0196 \| 0.1827 \| 0.3654 \| 0.3654 \| nan \| 0.3654 \| 0.0 \| 0.3654 \| \| 0.0072 \| 24.24 \| 15150 \| 0.0196 \| 0.1898 \| 0.3797 \| 0.3797 \| nan \| 0.3797 \| 0.0 \| 0.3797 \| \| 0.0217 \| 24.29 \| 15180 \| 0.0194 \| 0.1697 \| 0.3394 \| 0.3394 \| nan \| 0.3394 \| 0.0 \| 0.3394 \| \| 0.0098 \| 24.34 \| 15210 \| 0.0191 \| 0.1801 \| 0.3602 \| 0.3602 \| nan \| 0.3602 \| 0.0 \| 0.3602 \| \| 0.0031 \| 24.38 \| 15240 \| 0.0191 \| 0.1818 \| 0.3636 \| 0.3636 \| nan \| 0.3636 \| 0.0 \| 0.3636 \| \| 0.0064 \| 24.43 \| 15270 \| 0.0195 \| 0.1677 \| 0.3354 \| 0.3354 \| nan \| 0.3354 \| 0.0 \| 0.3354 \| \| 0.0135 \| 24.48 \| 15300 \| 0.0193 \| 0.1727 \| 0.3455 \| 0.3455 \| nan \| 0.3455 \| 0.0 \| 0.3455 \| \| 0.0048 \| 24.53 \| 15330 \| 0.0193 \| 0.1822 \| 0.3643 \| 0.3643 \| nan \| 0.3643 \| 0.0 \| 0.3643 \| \| 0.0155 \| 24.58 \| 15360 \| 0.0199 \| 0.1571 \| 0.3142 \| 0.3142 \| nan \| 0.3142 \| 0.0 \| 0.3142 \| \| 0.0034 \| 24.62 \| 15390 \| 0.0196 \| 0.1758 \| 0.3517 \| 0.3517 \| nan \| 0.3517 \| 0.0 \| 0.3517 \| \| 0.0057 \| 24.67 \| 15420 \| 0.0192 \| 0.1833 \| 0.3667 \| 0.3667 \| nan \| 0.3667 \| 0.0 \| 0.3667 \| \| 0.0176 \| 24.72 \| 15450 \| 0.0193 \| 0.1773 \| 0.3545 \| 0.3545 \| nan \| 0.3545 \| 0.0 \| 0.3545 \| \| 0.0095 \| 24.77 \| 15480 \| 0.0194 \| 0.1799 \| 0.3599 \| 0.3599 \| nan \| 0.3599 \| 0.0 \| 0.3599 \| \| 0.0166 \| 24.82 \| 15510 \| 0.0191 \| 0.1908 \| 0.3815 \| 0.3815 \| nan \| 0.3815 \| 0.0 \| 0.3815 \| \| 0.0172 \| 24.86 \| 15540 \| 0.0192 \| 0.1741 \| 0.3482 \| 0.3482 \| nan \| 0.3482 \| 0.0 \| 0.3482 \| \| 0.0111 \| 24.91 \| 15570 \| 0.0192 \| 0.1702 \| 0.3403 \| 0.3403 \| nan \| 0.3403 \| 0.0 \| 0.3403 \| \| 0.0075 \| 24.96 \| 15600 \| 0.0196 \| 0.1575 \| 0.3150 \| 0.3150 \| nan \| 0.3150 \| 0.0 \| 0.3150 \| \| 0.0095 \| 25.01 \| 15630 \| 0.0193 \| 0.1789 \| 0.3579 \| 0.3579 \| nan \| 0.3579 \| 0.0 \| 0.3579 \| \| 0.008 \| 25.06 \| 15660 \| 0.0196 \| 0.1579 \| 0.3158 \| 0.3158 \| nan \| 0.3158 \| 0.0 \| 0.3158 \| \| 0.0063 \| 25.1 \| 15690 \| 0.0195 \| 0.1660 \| 0.3319 \| 0.3319 \| nan \| 0.3319 \| 0.0 \| 0.3319 \| \| 0.0103 \| 25.15 \| 15720 \| 0.0195 \| 0.1689 \| 0.3379 \| 0.3379 \| nan \| 0.3379 \| 0.0 \| 0.3379 \| \| 0.0115 \| 25.2 \| 15750 \| 0.0194 \| 0.1729 \| 0.3457 \| 0.3457 \| nan \| 0.3457 \| 0.0 \| 0.3457 \| \| 0.0076 \| 25.25 \| 15780 \| 0.0195 \| 0.1731 \| 0.3461 \| 0.3461 \| nan \| 0.3461 \| 0.0 \| 0.3461 \| \| 0.0375 \| 25.3 \| 15810 \| 0.0196 \| 0.1754 \| 0.3508 \| 0.3508 \| nan \| 0.3508 \| 0.0 \| 0.3508 \| \| 0.0055 \| 25.34 \| 15840 \| 0.0198 \| 0.1738 \| 0.3475 \| 0.3475 \| nan \| 0.3475 \| 0.0 \| 0.3475 \| \| 0.0087 \| 25.39 \| 15870 \| 0.0196 \| 0.1677 \| 0.3353 \| 0.3353 \| nan \| 0.3353 \| 0.0 \| 0.3353 \| \| 0.0099 \| 25.44 \| 15900 \| 0.0193 \| 0.1882 \| 0.3763 \| 0.3763 \| nan \| 0.3763 \| 0.0 \| 0.3763 \| \| 0.0123 \| 25.49 \| 15930 \| 0.0195 \| 0.1744 \| 0.3488 \| 0.3488 \| nan \| 0.3488 \| 0.0 \| 0.3488 \| \| 0.0127 \| 25.54 \| 15960 \| 0.0195 \| 0.1818 \| 0.3636 \| 0.3636 \| nan \| 0.3636 \| 0.0 \| 0.3636 \| \| 0.0074 \| 25.58 \| 15990 \| 0.0196 \| 0.1656 \| 0.3311 \| 0.3311 \| nan \| 0.3311 \| 0.0 \| 0.3311 \| \| 0.0055 \| 25.63 \| 16020 \| 0.0194 \| 0.1756 \| 0.3511 \| 0.3511 \| nan \| 0.3511 \| 0.0 \| 0.3511 \| \| 0.0013 \| 25.68 \| 16050 \| 0.0196 \| 0.1834 \| 0.3667 \| 0.3667 \| nan \| 0.3667 \| 0.0 \| 0.3667 \| \| 0.0149 \| 25.73 \| 16080 \| 0.0197 \| 0.1668 \| 0.3336 \| 0.3336 \| nan \| 0.3336 \| 0.0 \| 0.3336 \| \| 0.0124 \| 25.78 \| 16110 \| 0.0196 \| 0.1748 \| 0.3497 \| 0.3497 \| nan \| 0.3497 \| 0.0 \| 0.3497 \| \| 0.0176 \| 25.82 \| 16140 \| 0.0193 \| 0.1926 \| 0.3851 \| 0.3851 \| nan \| 0.3851 \| 0.0 \| 0.3851 \| \| 0.002 \| 25.87 \| 16170 \| 0.0196 \| 0.1635 \| 0.3270 \| 0.3270 \| nan \| 0.3270 \| 0.0 \| 0.3270 \| \| 0.0074 \| 25.92 \| 16200 \| 0.0195 \| 0.1781 \| 0.3562 \| 0.3562 \| nan \| 0.3562 \| 0.0 \| 0.3562 \| \| 0.0103 \| 25.97 \| 16230 \| 0.0192 \| 0.1845 \| 0.3689 \| 0.3689 \| nan \| 0.3689 \| 0.0 \| 0.3689 \| \| 0.0209 \| 26.02 \| 16260 \| 0.0195 \| 0.1596 \| 0.3193 \| 0.3193 \| nan \| 0.3193 \| 0.0 \| 0.3193 \| \| 0.0499 \| 26.06 \| 16290 \| 0.0196 \| 0.1589 \| 0.3178 \| 0.3178 \| nan \| 0.3178 \| 0.0 \| 0.3178 \| \| 0.0123 \| 26.11 \| 16320 \| 0.0196 \| 0.1864 \| 0.3728 \| 0.3728 \| nan \| 0.3728 \| 0.0 \| 0.3728 \| \| 0.0067 \| 26.16 \| 16350 \| 0.0198 \| 0.1725 \| 0.3451 \| 0.3451 \| nan \| 0.3451 \| 0.0 \| 0.3451 \| \| 0.0081 \| 26.21 \| 16380 \| 0.0194 \| 0.1763 \| 0.3527 \| 0.3527 \| nan \| 0.3527 \| 0.0 \| 0.3527 \| \| 0.004 \| 26.26 \| 16410 \| 0.0196 \| 0.1746 \| 0.3491 \| 0.3491 \| nan \| 0.3491 \| 0.0 \| 0.3491 \| \| 0.0159 \| 26.3 \| 16440 \| 0.0198 \| 0.1775 \| 0.3551 \| 0.3551 \| nan \| 0.3551 \| 0.0 \| 0.3551 \| \| 0.0044 \| 26.35 \| 16470 \| 0.0200 \| 0.1773 \| 0.3546 \| 0.3546 \| nan \| 0.3546 \| 0.0 \| 0.3546 \| \| 0.006 \| 26.4 \| 16500 \| 0.0197 \| 0.1687 \| 0.3373 \| 0.3373 \| nan \| 0.3373 \| 0.0 \| 0.3373 \| \| 0.0115 \| 26.45 \| 16530 \| 0.0196 \| 0.1638 \| 0.3277 \| 0.3277 \| nan \| 0.3277 \| 0.0 \| 0.3277 \| \| 0.0166 \| 26.5 \| 16560 \| 0.0196 \| 0.1697 \| 0.3394 \| 0.3394 \| nan \| 0.3394 \| 0.0 \| 0.3394 \| \| 0.0113 \| 26.54 \| 16590 \| 0.0198 \| 0.1709 \| 0.3419 \| 0.3419 \| nan \| 0.3419 \| 0.0 \| 0.3419 \| \| 0.0092 \| 26.59 \| 16620 \| 0.0197 \| 0.1655 \| 0.3310 \| 0.3310 \| nan \| 0.3310 \| 0.0 \| 0.3310 \| \| 0.0028 \| 26.64 \| 16650 \| 0.0193 \| 0.1796 \| 0.3592 \| 0.3592 \| nan \| 0.3592 \| 0.0 \| 0.3592 \| \| 0.0057 \| 26.69 \| 16680 \| 0.0197 \| 0.1637 \| 0.3275 \| 0.3275 \| nan \| 0.3275 \| 0.0 \| 0.3275 \| \| 0.0041 \| 26.74 \| 16710 \| 0.0194 \| 0.1851 \| 0.3701 \| 0.3701 \| nan \| 0.3701 \| 0.0 \| 0.3701 \| \| 0.0157 \| 26.78 \| 16740 \| 0.0196 \| 0.1638 \| 0.3277 \| 0.3277 \| nan \| 0.3277 \| 0.0 \| 0.3277 \| \| 0.0061 \| 26.83 \| 16770 \| 0.0194 \| 0.1873 \| 0.3747 \| 0.3747 \| nan \| 0.3747 \| 0.0 \| 0.3747 \| \| 0.0068 \| 26.88 \| 16800 \| 0.0196 \| 0.1668 \| 0.3337 \| 0.3337 \| nan \| 0.3337 \| 0.0 \| 0.3337 \| \| 0.0113 \| 26.93 \| 16830 \| 0.0195 \| 0.1770 \| 0.3540 \| 0.3540 \| nan \| 0.3540 \| 0.0 \| 0.3540 \| \| 0.0054 \| 26.98 \| 16860 \| 0.0196 \| 0.1800 \| 0.3599 \| 0.3599 \| nan \| 0.3599 \| 0.0 \| 0.3599 \| \| 0.0091 \| 27.02 \| 16890 \| 0.0196 \| 0.1706 \| 0.3413 \| 0.3413 \| nan \| 0.3413 \| 0.0 \| 0.3413 \| \| 0.0071 \| 27.07 \| 16920 \| 0.0195 \| 0.1799 \| 0.3598 \| 0.3598 \| nan \| 0.3598 \| 0.0 \| 0.3598 \| \| 0.0205 \| 27.12 \| 16950 \| 0.0196 \| 0.1749 \| 0.3499 \| 0.3499 \| nan \| 0.3499 \| 0.0 \| 0.3499 \| \| 0.0092 \| 27.17 \| 16980 \| 0.0198 \| 0.1683 \| 0.3366 \| 0.3366 \| nan \| 0.3366 \| 0.0 \| 0.3366 \| \| 0.0074 \| 27.22 \| 17010 \| 0.0197 \| 0.1734 \| 0.3468 \| 0.3468 \| nan \| 0.3468 \| 0.0 \| 0.3468 \| \| 0.0182 \| 27.26 \| 17040 \| 0.0197 \| 0.1706 \| 0.3412 \| 0.3412 \| nan \| 0.3412 \| 0.0 \| 0.3412 \| \| 0.008 \| 27.31 \| 17070 \| 0.0196 \| 0.1755 \| 0.3510 \| 0.3510 \| nan \| 0.3510 \| 0.0 \| 0.3510 \| \| 0.0046 \| 27.36 \| 17100 \| 0.0195 \| 0.1864 \| 0.3727 \| 0.3727 \| nan \| 0.3727 \| 0.0 \| 0.3727 \| \| 0.0033 \| 27.41 \| 17130 \| 0.0196 \| 0.1913 \| 0.3826 \| 0.3826 \| nan \| 0.3826 \| 0.0 \| 0.3826 \| \| 0.0036 \| 27.46 \| 17160 \| 0.0199 \| 0.1749 \| 0.3498 \| 0.3498 \| nan \| 0.3498 \| 0.0 \| 0.3498 \| \| 0.0162 \| 27.5 \| 17190 \| 0.0195 \| 0.1833 \| 0.3666 \| 0.3666 \| nan \| 0.3666 \| 0.0 \| 0.3666 \| \| 0.0058 \| 27.55 \| 17220 \| 0.0195 \| 0.1798 \| 0.3596 \| 0.3596 \| nan \| 0.3596 \| 0.0 \| 0.3596 \| \| 0.0068 \| 27.6 \| 17250 \| 0.0196 \| 0.1746 \| 0.3492 \| 0.3492 \| nan \| 0.3492 \| 0.0 \| 0.3492 \| \| 0.0089 \| 27.65 \| 17280 \| 0.0196 \| 0.1827 \| 0.3654 \| 0.3654 \| nan \| 0.3654 \| 0.0 \| 0.3654 \| \| 0.014 \| 27.7 \| 17310 \| 0.0198 \| 0.1794 \| 0.3589 \| 0.3589 \| nan \| 0.3589 \| 0.0 \| 0.3589 \| \| 0.013 \| 27.74 \| 17340 \| 0.0197 \| 0.1886 \| 0.3772 \| 0.3772 \| nan \| 0.3772 \| 0.0 \| 0.3772 \| \| 0.005 \| 27.79 \| 17370 \| 0.0196 \| 0.1839 \| 0.3678 \| 0.3678 \| nan \| 0.3678 \| 0.0 \| 0.3678 \| \| 0.0043 \| 27.84 \| 17400 \| 0.0197 \| 0.1691 \| 0.3381 \| 0.3381 \| nan \| 0.3381 \| 0.0 \| 0.3381 \| \| 0.0095 \| 27.89 \| 17430 \| 0.0197 \| 0.1657 \| 0.3314 \| 0.3314 \| nan \| 0.3314 \| 0.0 \| 0.3314 \| \| 0.0061 \| 27.94 \| 17460 \| 0.0196 \| 0.1746 \| 0.3492 \| 0.3492 \| nan \| 0.3492 \| 0.0 \| 0.3492 \| \| 0.0049 \| 27.98 \| 17490 \| 0.0198 \| 0.1660 \| 0.3321 \| 0.3321 \| nan \| 0.3321 \| 0.0 \| 0.3321 \| \| 0.0116 \| 28.03 \| 17520 \| 0.0196 \| 0.1853 \| 0.3707 \| 0.3707 \| nan \| 0.3707 \| 0.0 \| 0.3707 \| \| 0.0172 \| 28.08 \| 17550 \| 0.0196 \| 0.1715 \| 0.3430 \| 0.3430 \| nan \| 0.3430 \| 0.0 \| 0.3430 \| \| 0.008 \| 28.13 \| 17580 \| 0.0198 \| 0.1725 \| 0.3450 \| 0.3450 \| nan \| 0.3450 \| 0.0 \| 0.3450 \| \| 0.0168 \| 28.18 \| 17610 \| 0.0197 \| 0.1784 \| 0.3569 \| 0.3569 \| nan \| 0.3569 \| 0.0 \| 0.3569 \| \| 0.01 \| 28.22 \| 17640 \| 0.0199 \| 0.1714 \| 0.3427 \| 0.3427 \| nan \| 0.3427 \| 0.0 \| 0.3427 \| \| 0.0263 \| 28.27 \| 17670 \| 0.0197 \| 0.1725 \| 0.3450 \| 0.3450 \| nan \| 0.3450 \| 0.0 \| 0.3450 \| \| 0.0035 \| 28.32 \| 17700 \| 0.0197 \| 0.1820 \| 0.3640 \| 0.3640 \| nan \| 0.3640 \| 0.0 \| 0.3640 \| \| 0.0192 \| 28.37 \| 17730 \| 0.0201 \| 0.1675 \| 0.3350 \| 0.3350 \| nan \| 0.3350 \| 0.0 \| 0.3350 \| \| 0.0174 \| 28.42 \| 17760 \| 0.0198 \| 0.1819 \| 0.3638 \| 0.3638 \| nan \| 0.3638 \| 0.0 \| 0.3638 \| \| 0.0049 \| 28.46 \| 17790 \| 0.0195 \| 0.1899 \| 0.3797 \| 0.3797 \| nan \| 0.3797 \| 0.0 \| 0.3797 \| \| 0.0134 \| 28.51 \| 17820 \| 0.0196 \| 0.1816 \| 0.3632 \| 0.3632 \| nan \| 0.3632 \| 0.0 \| 0.3632 \| \| 0.0056 \| 28.56 \| 17850 \| 0.0195 \| 0.1682 \| 0.3365 \| 0.3365 \| nan \| 0.3365 \| 0.0 \| 0.3365 \| \| 0.0017 \| 28.61 \| 17880 \| 0.0195 \| 0.1778 \| 0.3555 \| 0.3555 \| nan \| 0.3555 \| 0.0 \| 0.3555 \| \| 0.0129 \| 28.66 \| 17910 \| 0.0198 \| 0.1701 \| 0.3401 \| 0.3401 \| nan \| 0.3401 \| 0.0 \| 0.3401 \| \| 0.0039 \| 28.7 \| 17940 \| 0.0197 \| 0.1762 \| 0.3523 \| 0.3523 \| nan \| 0.3523 \| 0.0 \| 0.3523 \| \| 0.0089 \| 28.75 \| 17970 \| 0.0197 \| 0.1859 \| 0.3717 \| 0.3717 \| nan \| 0.3717 \| 0.0 \| 0.3717 \| \| 0.0017 \| 28.8 \| 18000 \| 0.0196 \| 0.1790 \| 0.3579 \| 0.3579 \| nan \| 0.3579 \| 0.0 \| 0.3579 \| \| 0.004 \| 28.85 \| 18030 \| 0.0199 \| 0.1749 \| 0.3498 \| 0.3498 \| nan \| 0.3498 \| 0.0 \| 0.3498 \| \| 0.0053 \| 28.9 \| 18060 \| 0.0196 \| 0.1767 \| 0.3534 \| 0.3534 \| nan \| 0.3534 \| 0.0 \| 0.3534 \| \| 0.0026 \| 28.94 \| 18090 \| 0.0198 \| 0.1740 \| 0.3480 \| 0.3480 \| nan \| 0.3480 \| 0.0 \| 0.3480 \| \| 0.0073 \| 28.99 \| 18120 \| 0.0197 \| 0.1784 \| 0.3568 \| 0.3568 \| nan \| 0.3568 \| 0.0 \| 0.3568 \| \| 0.0044 \| 29.04 \| 18150 \| 0.0197 \| 0.1825 \| 0.3649 \| 0.3649 \| nan \| 0.3649 \| 0.0 \| 0.3649 \| \| 0.0099 \| 29.09 \| 18180 \| 0.0197 \| 0.1771 \| 0.3542 \| 0.3542 \| nan \| 0.3542 \| 0.0 \| 0.3542 \| \| 0.0062 \| 29.14 \| 18210 \| 0.0198 \| 0.1785 \| 0.3570 \| 0.3570 \| nan \| 0.3570 \| 0.0 \| 0.3570 \| \| 0.0113 \| 29.18 \| 18240 \| 0.0198 \| 0.1730 \| 0.3459 \| 0.3459 \| nan \| 0.3459 \| 0.0 \| 0.3459 \| \| 0.0097 \| 29.23 \| 18270 \| 0.0197 \| 0.1728 \| 0.3456 \| 0.3456 \| nan \| 0.3456 \| 0.0 \| 0.3456 \| \| 0.0197 \| 29.28 \| 18300 \| 0.0197 \| 0.1799 \| 0.3599 \| 0.3599 \| nan \| 0.3599 \| 0.0 \| 0.3599 \| \| 0.0038 \| 29.33 \| 18330 \| 0.0198 \| 0.1745 \| 0.3491 \| 0.3491 \| nan \| 0.3491 \| 0.0 \| 0.3491 \| \| 0.0068 \| 29.38 \| 18360 \| 0.0196 \| 0.1750 \| 0.3500 \| 0.3500 \| nan \| 0.3500 \| 0.0 \| 0.3500 \| \| 0.0064 \| 29.42 \| 18390 \| 0.0197 \| 0.1793 \| 0.3587 \| 0.3587 \| nan \| 0.3587 \| 0.0 \| 0.3587 \| \| 0.002 \| 29.47 \| 18420 \| 0.0196 \| 0.1765 \| 0.3531 \| 0.3531 \| nan \| 0.3531 \| 0.0 \| 0.3531 \| \| 0.014 \| 29.52 \| 18450 \| 0.0198 \| 0.1732 \| 0.3463 \| 0.3463 \| nan \| 0.3463 \| 0.0 \| 0.3463 \| \| 0.0136 \| 29.57 \| 18480 \| 0.0197 \| 0.1743 \| 0.3486 \| 0.3486 \| nan \| 0.3486 \| 0.0 \| 0.3486 \| \| 0.0123 \| 29.62 \| 18510 \| 0.0197 \| 0.1785 \| 0.3570 \| 0.3570 \| nan \| 0.3570 \| 0.0 \| 0.3570 \| \| 0.0249 \| 29.66 \| 18540 \| 0.0197 \| 0.1726 \| 0.3452 \| 0.3452 \| nan \| 0.3452 \| 0.0 \| 0.3452 \| \| 0.0081 \| 29.71 \| 18570 \| 0.0196 \| 0.1818 \| 0.3635 \| 0.3635 \| nan \| 0.3635 \| 0.0 \| 0.3635 \| \| 0.0028 \| 29.76 \| 18600 \| 0.0197 \| 0.1826 \| 0.3652 \| 0.3652 \| nan \| 0.3652 \| 0.0 \| 0.3652 \| \| 0.0133 \| 29.81 \| 18630 \| 0.0197 \| 0.1751 \| 0.3502 \| 0.3502 \| nan \| 0.3502 \| 0.0 \| 0.3502 \| \| 0.0075 \| 29.86 \| 18660 \| 0.0196 \| 0.1764 \| 0.3527 \| 0.3527 \| nan \| 0.3527 \| 0.0 \| 0.3527 \| \| 0.0231 \| 29.9 \| 18690 \| 0.0197 \| 0.1734 \| 0.3469 \| 0.3469 \| nan \| 0.3469 \| 0.0 \| 0.3469 \| \| 0.0024 \| 29.95 \| 18720 \| 0.0197 \| 0.1766 \| 0.3531 \| 0.3531 \| nan \| 0.3531 \| 0.0 \| 0.3531 \| \| 0.0105 \| 30.0 \| 18750 \| 0.0197 \| 0.1778 \| 0.3556 \| 0.3556 \| nan \| 0.3556 \| 0.0 \| 0.3556 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "not_kelp", "kelp" ]
blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_gris-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-4ss1st3r_s3gs3m_24Jan_gris-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_gris dataset. It achieves the following results on the evaluation set: - Loss: 0.2539 - Mean Iou: 0.5001 - Mean Accuracy: 0.7682 - Overall Accuracy: 0.9671 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9929 - Accuracy Fallo malla: 0.5837 - Accuracy Fallo adhesivo: 0.8806 - Accuracy Fallo burbuja: 0.6154 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9663 - Iou Fallo malla: 0.5505 - Iou Fallo adhesivo: 0.4321 - Iou Fallo burbuja: 0.5515 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.1186 \| 1.0 \| 259 \| 0.1688 \| 0.5045 \| 0.6750 \| 0.9611 \| nan \| 0.9940 \| 0.5027 \| 0.8539 \| 0.3494 \| 0.0 \| 0.9600 \| 0.4717 \| 0.7563 \| 0.3344 \| \| 0.0669 \| 2.0 \| 518 \| 0.1603 \| 0.4270 \| 0.7755 \| 0.9501 \| nan \| 0.9685 \| 0.7091 \| 0.8964 \| 0.5282 \| 0.0 \| 0.9490 \| 0.5466 \| 0.1627 \| 0.4767 \| \| 0.0608 \| 3.0 \| 777 \| 0.1863 \| 0.4142 \| 0.7612 \| 0.9458 \| nan \| 0.9703 \| 0.5906 \| 0.9321 \| 0.5517 \| 0.0 \| 0.9467 \| 0.5366 \| 0.0891 \| 0.4985 \| \| 0.0551 \| 4.0 \| 1036 \| 0.1654 \| 0.4515 \| 0.7496 \| 0.9620 \| nan \| 0.9879 \| 0.5881 \| 0.8763 \| 0.5462 \| 0.0 \| 0.9620 \| 0.5560 \| 0.2349 \| 0.5043 \| \| 0.0462 \| 5.0 \| 1295 \| 0.2067 \| 0.4267 \| 0.7598 \| 0.9487 \| nan \| 0.9752 \| 0.5450 \| 0.8796 \| 0.6392 \| 0.0 \| 0.9502 \| 0.5377 \| 0.0838 \| 0.5620 \| \| 0.0445 \| 6.0 \| 1554 \| 0.1565 \| 0.4557 \| 0.7685 \| 0.9627 \| nan \| 0.9873 \| 0.5954 \| 0.8571 \| 0.6343 \| 0.0 \| 0.9636 \| 0.5689 \| 0.1837 \| 0.5623 \| \| 0.039 \| 7.0 \| 1813 \| 0.1523 \| 0.4576 \| 0.8005 \| 0.9609 \| nan \| 0.9817 \| 0.6535 \| 0.9036 \| 0.6630 \| 0.0 \| 0.9612 \| 0.5885 \| 0.1643 \| 0.5738 \| \| 0.0367 \| 8.0 \| 2072 \| 0.1954 \| 0.4573 \| 0.7462 \| 0.9614 \| nan \| 0.9917 \| 0.4963 \| 0.8762 \| 0.6206 \| 0.0 \| 0.9612 \| 0.4850 \| 0.2790 \| 0.5612 \| \| 0.0352 \| 9.0 \| 2331 \| 0.2244 \| 0.4757 \| 0.7542 \| 0.9636 \| nan \| 0.9932 \| 0.5098 \| 0.8867 \| 0.6269 \| 0.0 \| 0.9629 \| 0.5013 \| 0.3466 \| 0.5674 \| \| 0.0357 \| 10.0 \| 2590 \| 0.2119 \| 0.4687 \| 0.7394 \| 0.9645 \| nan \| 0.9934 \| 0.5378 \| 0.8710 \| 0.5552 \| 0.0 \| 0.9641 \| 0.5209 \| 0.3377 \| 0.5207 \| \| 0.0352 \| 11.0 \| 2849 \| 0.1957 \| 0.4469 \| 0.7903 \| 0.9584 \| nan \| 0.9791 \| 0.6656 \| 0.9237 \| 0.5927 \| 0.0 \| 0.9591 \| 0.5829 \| 0.1459 \| 0.5465 \| \| 0.032 \| 12.0 \| 3108 \| 0.1811 \| 0.4521 \| 0.8058 \| 0.9594 \| nan \| 0.9797 \| 0.6634 \| 0.9338 \| 0.6464 \| 0.0 \| 0.9608 \| 0.5929 \| 0.1397 \| 0.5671 \| \| 0.0299 \| 13.0 \| 3367 \| 0.2403 \| 0.4298 \| 0.7596 \| 0.9557 \| nan \| 0.9827 \| 0.5553 \| 0.9271 \| 0.5733 \| 0.0 \| 0.9572 \| 0.5336 \| 0.1272 \| 0.5311 \| \| 0.0292 \| 14.0 \| 3626 \| 0.2233 \| 0.4667 \| 0.7638 \| 0.9642 \| nan \| 0.9900 \| 0.5759 \| 0.8508 \| 0.6385 \| 0.0 \| 0.9638 \| 0.5475 \| 0.2511 \| 0.5709 \| \| 0.0264 \| 15.0 \| 3885 \| 0.2382 \| 0.4431 \| 0.7690 \| 0.9594 \| nan \| 0.9865 \| 0.5492 \| 0.9139 \| 0.6267 \| 0.0 \| 0.9602 \| 0.5326 \| 0.1568 \| 0.5658 \| \| 0.0273 \| 16.0 \| 4144 \| 0.2339 \| 0.4382 \| 0.7751 \| 0.9570 \| nan \| 0.9818 \| 0.5876 \| 0.9193 \| 0.6115 \| 0.0 \| 0.9584 \| 0.5419 \| 0.1352 \| 0.5554 \| \| 0.0249 \| 17.0 \| 4403 \| 0.2078 \| 0.4950 \| 0.7846 \| 0.9669 \| nan \| 0.9925 \| 0.5784 \| 0.9197 \| 0.6477 \| 0.0 \| 0.9663 \| 0.5508 \| 0.3921 \| 0.5658 \| \| 0.0242 \| 18.0 \| 4662 \| 0.2495 \| 0.4809 \| 0.7706 \| 0.9645 \| nan \| 0.9922 \| 0.5392 \| 0.9007 \| 0.6503 \| 0.0 \| 0.9640 \| 0.5147 \| 0.3577 \| 0.5682 \| \| 0.0241 \| 19.0 \| 4921 \| 0.2117 \| 0.4491 \| 0.7954 \| 0.9589 \| nan \| 0.9815 \| 0.6243 \| 0.9423 \| 0.6336 \| 0.0 \| 0.9597 \| 0.5703 \| 0.1508 \| 0.5647 \| \| 0.0243 \| 20.0 \| 5180 \| 0.1989 \| 0.4754 \| 0.8013 \| 0.9656 \| nan \| 0.9875 \| 0.6416 \| 0.9194 \| 0.6568 \| 0.0 \| 0.9658 \| 0.5879 \| 0.2482 \| 0.5751 \| \| 0.0251 \| 21.0 \| 5439 \| 0.2095 \| 0.4607 \| 0.7962 \| 0.9629 \| nan \| 0.9853 \| 0.6324 \| 0.9337 \| 0.6334 \| 0.0 \| 0.9634 \| 0.5732 \| 0.2073 \| 0.5598 \| \| 0.0238 \| 22.0 \| 5698 \| 0.2063 \| 0.4747 \| 0.7927 \| 0.9650 \| nan \| 0.9873 \| 0.6383 \| 0.9158 \| 0.6293 \| 0.0 \| 0.9645 \| 0.5779 \| 0.2744 \| 0.5569 \| \| 0.0225 \| 23.0 \| 5957 \| 0.2260 \| 0.4656 \| 0.7915 \| 0.9640 \| nan \| 0.9880 \| 0.6003 \| 0.9106 \| 0.6672 \| 0.0 \| 0.9642 \| 0.5647 \| 0.2241 \| 0.5752 \| \| 0.0231 \| 24.0 \| 6216 \| 0.2454 \| 0.4688 \| 0.7783 \| 0.9645 \| nan \| 0.9891 \| 0.6019 \| 0.9197 \| 0.6024 \| 0.0 \| 0.9643 \| 0.5591 \| 0.2766 \| 0.5442 \| \| 0.0218 \| 25.0 \| 6475 \| 0.2482 \| 0.5143 \| 0.7752 \| 0.9665 \| nan \| 0.9919 \| 0.5896 \| 0.9136 \| 0.6057 \| 0.0 \| 0.9655 \| 0.5433 \| 0.5236 \| 0.5390 \| \| 0.0223 \| 26.0 \| 6734 \| 0.2474 \| 0.4648 \| 0.7660 \| 0.9642 \| nan \| 0.9903 \| 0.5784 \| 0.9054 \| 0.5898 \| 0.0 \| 0.9639 \| 0.5502 \| 0.2768 \| 0.5334 \| \| 0.0238 \| 27.0 \| 6993 \| 0.2475 \| 0.4717 \| 0.7669 \| 0.9651 \| nan \| 0.9920 \| 0.5597 \| 0.9019 \| 0.6138 \| 0.0 \| 0.9648 \| 0.5379 \| 0.3087 \| 0.5470 \| \| 0.021 \| 28.0 \| 7252 \| 0.2490 \| 0.4740 \| 0.7708 \| 0.9649 \| nan \| 0.9919 \| 0.5573 \| 0.9116 \| 0.6222 \| 0.0 \| 0.9645 \| 0.5362 \| 0.3142 \| 0.5553 \| \| 0.0208 \| 29.0 \| 7511 \| 0.2369 \| 0.4633 \| 0.7669 \| 0.9653 \| nan \| 0.9896 \| 0.6134 \| 0.8846 \| 0.5799 \| 0.0 \| 0.9652 \| 0.5762 \| 0.2422 \| 0.5327 \| \| 0.0202 \| 30.0 \| 7770 \| 0.2498 \| 0.4863 \| 0.7654 \| 0.9655 \| nan \| 0.9930 \| 0.5488 \| 0.8931 \| 0.6267 \| 0.0 \| 0.9647 \| 0.5273 \| 0.3811 \| 0.5582 \| \| 0.021 \| 31.0 \| 8029 \| 0.2534 \| 0.4799 \| 0.7729 \| 0.9657 \| nan \| 0.9915 \| 0.5794 \| 0.9043 \| 0.6164 \| 0.0 \| 0.9652 \| 0.5474 \| 0.3368 \| 0.5502 \| \| 0.0202 \| 32.0 \| 8288 \| 0.2626 \| 0.4771 \| 0.7627 \| 0.9653 \| nan \| 0.9930 \| 0.5464 \| 0.9014 \| 0.6098 \| 0.0 \| 0.9647 \| 0.5272 \| 0.3464 \| 0.5474 \| \| 0.0201 \| 33.0 \| 8547 \| 0.2710 \| 0.4903 \| 0.7673 \| 0.9659 \| nan \| 0.9936 \| 0.5431 \| 0.8994 \| 0.6329 \| 0.0 \| 0.9653 \| 0.5221 \| 0.3997 \| 0.5645 \| \| 0.0195 \| 34.0 \| 8806 \| 0.2589 \| 0.4915 \| 0.7662 \| 0.9663 \| nan \| 0.9930 \| 0.5644 \| 0.8895 \| 0.6177 \| 0.0 \| 0.9656 \| 0.5368 \| 0.4014 \| 0.5537 \| \| 0.0194 \| 35.0 \| 9065 \| 0.2304 \| 0.5092 \| 0.7801 \| 0.9675 \| nan \| 0.9919 \| 0.6048 \| 0.8941 \| 0.6295 \| 0.0 \| 0.9667 \| 0.5615 \| 0.4576 \| 0.5603 \| \| 0.0188 \| 36.0 \| 9324 \| 0.2674 \| 0.5022 \| 0.7629 \| 0.9670 \| nan \| 0.9933 \| 0.5783 \| 0.8819 \| 0.5982 \| 0.0 \| 0.9662 \| 0.5461 \| 0.4567 \| 0.5418 \| \| 0.0188 \| 37.0 \| 9583 \| 0.2580 \| 0.4897 \| 0.7702 \| 0.9665 \| nan \| 0.9925 \| 0.5791 \| 0.8884 \| 0.6207 \| 0.0 \| 0.9660 \| 0.5485 \| 0.3793 \| 0.5548 \| \| 0.0192 \| 38.0 \| 9842 \| 0.2556 \| 0.5065 \| 0.7656 \| 0.9673 \| nan \| 0.9933 \| 0.5823 \| 0.8739 \| 0.6130 \| 0.0 \| 0.9665 \| 0.5494 \| 0.4667 \| 0.5500 \| \| 0.019 \| 38.61 \| 10000 \| 0.2539 \| 0.5001 \| 0.7682 \| 0.9671 \| nan \| 0.9929 \| 0.5837 \| 0.8806 \| 0.6154 \| 0.0 \| 0.9663 \| 0.5505 \| 0.4321 \| 0.5515 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3	[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]
samitizerxu/segformer-b1-kelp-rgb-agg-imgaug-jan-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b1-kelp-rgb-agg-imgaug-jan-22 This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set: - eval_accuracy_kelp: nan - eval_iou_kelp: 0.0 - eval_loss: 0.3223 - eval_mean_iou: 0.0205 - eval_mean_accuracy: 0.0410 - eval_overall_accuracy: 0.0410 - eval_runtime: 62.0057 - eval_samples_per_second: 27.272 - eval_steps_per_second: 3.419 - epoch: 1.16 - step: 570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 40 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "background", "kelp" ]
rohan8020/segformer-b0-finetuned-segments-dots	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-dots This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rohan8020/test dataset. It achieves the following results on the evaluation set: - Loss: 0.1328 - Mean Iou: 0.3201 - Mean Accuracy: 0.6402 - Overall Accuracy: 0.6402 - Accuracy Unlabeled: nan - Accuracy Dots: 0.6402 - Iou Unlabeled: 0.0 - Iou Dots: 0.6402 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dots \| Iou Unlabeled \| Iou Dots \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:-------------:\|:--------:\| \| 0.5642 \| 4.0 \| 20 \| 0.6209 \| 0.4838 \| 0.9677 \| 0.9677 \| nan \| 0.9677 \| 0.0 \| 0.9677 \| \| 0.4154 \| 8.0 \| 40 \| 0.4119 \| 0.2969 \| 0.5939 \| 0.5939 \| nan \| 0.5939 \| 0.0 \| 0.5939 \| \| 0.3246 \| 12.0 \| 60 \| 0.2900 \| 0.3123 \| 0.6246 \| 0.6246 \| nan \| 0.6246 \| 0.0 \| 0.6246 \| \| 0.2898 \| 16.0 \| 80 \| 0.3168 \| 0.4260 \| 0.8520 \| 0.8520 \| nan \| 0.8520 \| 0.0 \| 0.8520 \| \| 0.2419 \| 20.0 \| 100 \| 0.2201 \| 0.3446 \| 0.6892 \| 0.6892 \| nan \| 0.6892 \| 0.0 \| 0.6892 \| \| 0.2042 \| 24.0 \| 120 \| 0.2199 \| 0.3213 \| 0.6426 \| 0.6426 \| nan \| 0.6426 \| 0.0 \| 0.6426 \| \| 0.1662 \| 28.0 \| 140 \| 0.1797 \| 0.3002 \| 0.6005 \| 0.6005 \| nan \| 0.6005 \| 0.0 \| 0.6005 \| \| 0.1757 \| 32.0 \| 160 \| 0.1611 \| 0.2919 \| 0.5839 \| 0.5839 \| nan \| 0.5839 \| 0.0 \| 0.5839 \| \| 0.1473 \| 36.0 \| 180 \| 0.1477 \| 0.3219 \| 0.6439 \| 0.6439 \| nan \| 0.6439 \| 0.0 \| 0.6439 \| \| 0.1645 \| 40.0 \| 200 \| 0.1448 \| 0.3267 \| 0.6534 \| 0.6534 \| nan \| 0.6534 \| 0.0 \| 0.6534 \| \| 0.1576 \| 44.0 \| 220 \| 0.1389 \| 0.3377 \| 0.6754 \| 0.6754 \| nan \| 0.6754 \| 0.0 \| 0.6754 \| \| 0.1381 \| 48.0 \| 240 \| 0.1328 \| 0.3201 \| 0.6402 \| 0.6402 \| nan \| 0.6402 \| 0.0 \| 0.6402 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "dots" ]
rohan8020/segformer-b0-finetuned-segments-dots-1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-dots-1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rohan8020/test dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Mean Iou: 0.0 - Mean Accuracy: nan - Overall Accuracy: nan - Accuracy Unlabeled: nan - Accuracy Dots: nan - Iou Unlabeled: 0.0 - Iou Dots: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dots \| Iou Unlabeled \| Iou Dots \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:-------------:\|:--------:\| \| 0.0029 \| 4.0 \| 20 \| 0.0122 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0004 \| 8.0 \| 40 \| 0.0010 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0003 \| 12.0 \| 60 \| 0.0004 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0003 \| 16.0 \| 80 \| 0.0003 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0003 \| 20.0 \| 100 \| 0.0002 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0002 \| 24.0 \| 120 \| 0.0002 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 28.0 \| 140 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0002 \| 32.0 \| 160 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 36.0 \| 180 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 40.0 \| 200 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0002 \| 44.0 \| 220 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 48.0 \| 240 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 52.0 \| 260 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 56.0 \| 280 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 60.0 \| 300 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 64.0 \| 320 \| 0.0001 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 68.0 \| 340 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 72.0 \| 360 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 76.0 \| 380 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 80.0 \| 400 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 84.0 \| 420 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 88.0 \| 440 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0001 \| 92.0 \| 460 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 96.0 \| 480 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 100.0 \| 500 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 104.0 \| 520 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 108.0 \| 540 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 112.0 \| 560 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 116.0 \| 580 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 120.0 \| 600 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 124.0 \| 620 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 128.0 \| 640 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 132.0 \| 660 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 136.0 \| 680 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 140.0 \| 700 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 144.0 \| 720 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 148.0 \| 740 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 152.0 \| 760 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 156.0 \| 780 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 160.0 \| 800 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 164.0 \| 820 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 168.0 \| 840 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 172.0 \| 860 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 176.0 \| 880 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 180.0 \| 900 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 184.0 \| 920 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 188.0 \| 940 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 192.0 \| 960 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 196.0 \| 980 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 200.0 \| 1000 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 204.0 \| 1020 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 208.0 \| 1040 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 212.0 \| 1060 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 216.0 \| 1080 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 220.0 \| 1100 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 224.0 \| 1120 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 228.0 \| 1140 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 232.0 \| 1160 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 236.0 \| 1180 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 240.0 \| 1200 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 244.0 \| 1220 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.0 \| 248.0 \| 1240 \| 0.0000 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "dots" ]
mrkprc1/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuSegmentation2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3909 - Mean Iou: 0.3084 - Mean Accuracy: 0.9142 - Overall Accuracy: 0.9142 - Accuracy Empty-cell: nan - Accuracy Complete-cell: 0.9142 - Iou Empty-cell: 0.0 - Iou Complete-cell: 0.6169 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Empty-cell \| Accuracy Complete-cell \| Iou Empty-cell \| Iou Complete-cell \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:----------------------:\|:--------------:\|:-----------------:\| \| 0.3784 \| 10.0 \| 20 \| 0.4056 \| 0.3166 \| 0.9695 \| 0.9695 \| nan \| 0.9695 \| 0.0 \| 0.6333 \| \| 0.3037 \| 20.0 \| 40 \| 0.4253 \| 0.2816 \| 0.7997 \| 0.7997 \| nan \| 0.7997 \| 0.0 \| 0.5631 \| \| 0.3326 \| 30.0 \| 60 \| 0.4163 \| 0.2894 \| 0.8280 \| 0.8280 \| nan \| 0.8280 \| 0.0 \| 0.5789 \| \| 0.276 \| 40.0 \| 80 \| 0.4037 \| 0.2990 \| 0.8758 \| 0.8758 \| nan \| 0.8758 \| 0.0 \| 0.5979 \| \| 0.2616 \| 50.0 \| 100 \| 0.3909 \| 0.3084 \| 0.9142 \| 0.9142 \| nan \| 0.9142 \| 0.0 \| 0.6169 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "empty-cell", "complete-cell" ]
mrkprc1/segformer-b0-finetuned-sudoku	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-sudoku This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuBoundaries2 dataset. It achieves the following results on the evaluation set: - Loss: 1.5465 - Mean Iou: 0.2407 - Mean Accuracy: 0.5 - Overall Accuracy: 0.4814 - Accuracy Unlabelled: 1.0 - Accuracy Sudoku-boundary: 0.0 - Iou Unlabelled: 0.4814 - Iou Sudoku-boundary: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabelled \| Accuracy Sudoku-boundary \| Iou Unlabelled \| Iou Sudoku-boundary \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:------------------------:\|:--------------:\|:-------------------:\| \| 0.6257 \| 2.5 \| 20 \| 0.7024 \| 0.2992 \| 0.4856 \| 0.4769 \| 0.7186 \| 0.2525 \| 0.3981 \| 0.2002 \| \| 0.6194 \| 5.0 \| 40 \| 0.7513 \| 0.2593 \| 0.4960 \| 0.4797 \| 0.9332 \| 0.0588 \| 0.4633 \| 0.0553 \| \| 0.6134 \| 7.5 \| 60 \| 0.8649 \| 0.2428 \| 0.4993 \| 0.4809 \| 0.9921 \| 0.0065 \| 0.4792 \| 0.0065 \| \| 0.4962 \| 10.0 \| 80 \| 0.9245 \| 0.2434 \| 0.5006 \| 0.4822 \| 0.9949 \| 0.0063 \| 0.4805 \| 0.0063 \| \| 0.5552 \| 12.5 \| 100 \| 0.8606 \| 0.2442 \| 0.5009 \| 0.4826 \| 0.9939 \| 0.0080 \| 0.4804 \| 0.0079 \| \| 0.6282 \| 15.0 \| 120 \| 1.1507 \| 0.2407 \| 0.5000 \| 0.4814 \| 1.0000 \| 0.0000 \| 0.4813 \| 0.0000 \| \| 0.4042 \| 17.5 \| 140 \| 1.0916 \| 0.2408 \| 0.4997 \| 0.4811 \| 0.9988 \| 0.0007 \| 0.4810 \| 0.0007 \| \| 0.8174 \| 20.0 \| 160 \| 0.9731 \| 0.2424 \| 0.4991 \| 0.4807 \| 0.9926 \| 0.0056 \| 0.4792 \| 0.0055 \| \| 0.5353 \| 22.5 \| 180 \| 0.9754 \| 0.2409 \| 0.4991 \| 0.4805 \| 0.9964 \| 0.0017 \| 0.4801 \| 0.0017 \| \| 0.4792 \| 25.0 \| 200 \| 1.6835 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.4244 \| 27.5 \| 220 \| 1.5039 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.376 \| 30.0 \| 240 \| 2.2746 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.4129 \| 32.5 \| 260 \| 2.0116 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.4717 \| 35.0 \| 280 \| 1.8957 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.4229 \| 37.5 \| 300 \| 1.7574 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.5708 \| 40.0 \| 320 \| 2.0764 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.5826 \| 42.5 \| 340 \| 1.6177 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.3765 \| 45.0 \| 360 \| 1.8119 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 0.3704 \| 47.5 \| 380 \| 1.6863 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| \| 1.3265 \| 50.0 \| 400 \| 1.5465 \| 0.2407 \| 0.5 \| 0.4814 \| 1.0 \| 0.0 \| 0.4814 \| 0.0 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "unlabelled", "sudoku-boundary" ]
unreal-hug/segformer-b0-finetuned-segments-ECHO-jan-25-v2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-ECHO-jan-25-v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset. It achieves the following results on the evaluation set: - Loss: 0.4155 - Mean Iou: 0.3349 - Mean Accuracy: 0.3935 - Overall Accuracy: 0.5591 - Accuracy Unlabeled: nan - Accuracy Lv: 0.6815 - Accuracy Rv: 0.3865 - Accuracy Ra: 0.5805 - Accuracy La: 0.6544 - Accuracy Vs: 0.1155 - Accuracy As: nan - Accuracy Mk: 0.0497 - Accuracy Tk: nan - Accuracy Asd: 0.2779 - Accuracy Vsd: 0.3995 - Accuracy Ak: 0.3959 - Iou Unlabeled: 0.0 - Iou Lv: 0.6626 - Iou Rv: 0.3764 - Iou Ra: 0.5699 - Iou La: 0.6056 - Iou Vs: 0.1108 - Iou As: nan - Iou Mk: 0.0485 - Iou Tk: nan - Iou Asd: 0.2565 - Iou Vsd: 0.3465 - Iou Ak: 0.3718 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Lv \| Accuracy Rv \| Accuracy Ra \| Accuracy La \| Accuracy Vs \| Accuracy As \| Accuracy Mk \| Accuracy Tk \| Accuracy Asd \| Accuracy Vsd \| Accuracy Ak \| Iou Unlabeled \| Iou Lv \| Iou Rv \| Iou Ra \| Iou La \| Iou Vs \| Iou As \| Iou Mk \| Iou Tk \| Iou Asd \| Iou Vsd \| Iou Ak \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:------------:\|:------------:\|:-----------:\|:-------------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:-------:\|:-------:\|:------:\| \| 2.0322 \| 0.12 \| 20 \| 2.2124 \| 0.0954 \| 0.1885 \| 0.3033 \| nan \| 0.3903 \| 0.4680 \| 0.0850 \| 0.0173 \| 0.0 \| nan \| 0.0011 \| nan \| 0.0 \| 0.1087 \| 0.6263 \| 0.0 \| 0.2970 \| 0.2085 \| 0.0782 \| 0.0172 \| 0.0 \| nan \| 0.0011 \| 0.0 \| 0.0 \| 0.0823 \| 0.3647 \| \| 1.6027 \| 0.25 \| 40 \| 1.5649 \| 0.0789 \| 0.1168 \| 0.2640 \| nan \| 0.5149 \| 0.0061 \| 0.0264 \| 0.0839 \| 0.0 \| nan \| 0.0 \| nan \| 0.0001 \| 0.0014 \| 0.4180 \| 0.0 \| 0.3418 \| 0.0061 \| 0.0262 \| 0.0787 \| 0.0 \| nan \| 0.0 \| nan \| 0.0001 \| 0.0014 \| 0.3342 \| \| 1.2877 \| 0.38 \| 60 \| 1.2616 \| 0.0943 \| 0.1296 \| 0.2685 \| nan \| 0.4665 \| 0.0053 \| 0.0547 \| 0.2421 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0050 \| 0.3930 \| 0.0 \| 0.3612 \| 0.0053 \| 0.0529 \| 0.1877 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0050 \| 0.3312 \| \| 1.0981 \| 0.5 \| 80 \| 1.2208 \| 0.0967 \| 0.1552 \| 0.3898 \| nan \| 0.8151 \| 0.0079 \| 0.0082 \| 0.0794 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4863 \| 0.0 \| 0.4737 \| 0.0079 \| 0.0082 \| 0.0750 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4020 \| \| 1.0235 \| 0.62 \| 100 \| 0.9343 \| 0.1218 \| 0.1888 \| 0.4419 \| nan \| 0.8508 \| 0.0102 \| 0.0423 \| 0.3015 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4947 \| 0.0 \| 0.5319 \| 0.0101 \| 0.0418 \| 0.2283 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4059 \| \| 0.8977 \| 0.75 \| 120 \| 0.7806 \| 0.1592 \| 0.2227 \| 0.4764 \| nan \| 0.8124 \| 0.1787 \| 0.1188 \| 0.4178 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4763 \| 0.0 \| 0.6151 \| 0.1741 \| 0.1124 \| 0.2995 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3906 \| \| 0.6932 \| 0.88 \| 140 \| 0.6246 \| 0.1262 \| 0.1590 \| 0.3766 \| nan \| 0.6794 \| 0.2019 \| 0.1415 \| 0.2810 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.1276 \| 0.0 \| 0.5674 \| 0.1941 \| 0.1372 \| 0.2414 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.1217 \| \| 0.6168 \| 1.0 \| 160 \| 0.6124 \| 0.1752 \| 0.2277 \| 0.4717 \| nan \| 0.7500 \| 0.3261 \| 0.1491 \| 0.4375 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3864 \| 0.0 \| 0.6106 \| 0.2973 \| 0.1475 \| 0.3522 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3439 \| \| 0.5758 \| 1.12 \| 180 \| 0.5658 \| 0.2037 \| 0.2520 \| 0.4750 \| nan \| 0.6646 \| 0.3955 \| 0.3596 \| 0.4433 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4048 \| 0.0 \| 0.6048 \| 0.3705 \| 0.3133 \| 0.3865 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3623 \| \| 0.5081 \| 1.25 \| 200 \| 0.5116 \| 0.2316 \| 0.2993 \| 0.5280 \| nan \| 0.6460 \| 0.4867 \| 0.4741 \| 0.6477 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.4396 \| 0.0 \| 0.6098 \| 0.4523 \| 0.3961 \| 0.4611 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3965 \| \| 0.6351 \| 1.38 \| 220 \| 0.4879 \| 0.1127 \| 0.1324 \| 0.2609 \| nan \| 0.3749 \| 0.0902 \| 0.2601 \| 0.3883 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0783 \| 0.0 \| 0.3623 \| 0.0897 \| 0.2510 \| 0.3466 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0774 \| \| 0.6241 \| 1.5 \| 240 \| 0.4593 \| 0.2439 \| 0.3090 \| 0.5686 \| nan \| 0.7439 \| 0.4492 \| 0.5367 \| 0.6916 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3597 \| 0.0 \| 0.6995 \| 0.4322 \| 0.4400 \| 0.5265 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3410 \| \| 0.4315 \| 1.62 \| 260 \| 0.4082 \| 0.2175 \| 0.2611 \| 0.4948 \| nan \| 0.6811 \| 0.3535 \| 0.4253 \| 0.5871 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.3025 \| 0.0 \| 0.6398 \| 0.3459 \| 0.3952 \| 0.5052 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.2886 \| \| 0.5236 \| 1.75 \| 280 \| 0.4651 \| 0.1063 \| 0.1353 \| 0.2191 \| nan \| 0.2161 \| 0.0885 \| 0.3687 \| 0.4434 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.1015 \| 0.0 \| 0.2138 \| 0.0884 \| 0.3282 \| 0.3313 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.1012 \| \| 0.3688 \| 1.88 \| 300 \| 0.4279 \| 0.2796 \| 0.3459 \| 0.6382 \| nan \| 0.8529 \| 0.5705 \| 0.5493 \| 0.6449 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0051 \| 0.4903 \| 0.0 \| 0.7546 \| 0.5277 \| 0.5044 \| 0.5537 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0051 \| 0.4500 \| \| 0.3659 \| 2.0 \| 320 \| 0.3907 \| 0.1881 \| 0.2192 \| 0.4461 \| nan \| 0.7156 \| 0.1476 \| 0.3144 \| 0.4135 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0006 \| 0.3810 \| 0.0 \| 0.6851 \| 0.1461 \| 0.3012 \| 0.3919 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0006 \| 0.3560 \| \| 0.3243 \| 2.12 \| 340 \| 0.3846 \| 0.2737 \| 0.3272 \| 0.5846 \| nan \| 0.7313 \| 0.4747 \| 0.6435 \| 0.7038 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0010 \| 0.3904 \| 0.0 \| 0.7045 \| 0.4610 \| 0.5733 \| 0.6223 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0010 \| 0.3752 \| \| 0.4169 \| 2.25 \| 360 \| 0.4099 \| 0.1292 \| 0.1475 \| 0.2563 \| nan \| 0.3286 \| 0.0968 \| 0.3184 \| 0.3088 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0005 \| 0.2741 \| 0.0 \| 0.3241 \| 0.0965 \| 0.3061 \| 0.2960 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0005 \| 0.2685 \| \| 0.2951 \| 2.38 \| 380 \| 0.3583 \| 0.2277 \| 0.2701 \| 0.4962 \| nan \| 0.6695 \| 0.2136 \| 0.5730 \| 0.6784 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0442 \| 0.2519 \| 0.0 \| 0.6409 \| 0.2125 \| 0.5347 \| 0.5967 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0435 \| 0.2488 \| \| 0.3847 \| 2.5 \| 400 \| 0.3565 \| 0.2410 \| 0.2843 \| 0.5032 \| nan \| 0.6544 \| 0.3067 \| 0.5888 \| 0.6409 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.1089 \| 0.2594 \| 0.0 \| 0.6304 \| 0.3023 \| 0.5347 \| 0.5853 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.1033 \| 0.2535 \| \| 0.339 \| 2.62 \| 420 \| 0.3715 \| 0.3085 \| 0.3697 \| 0.6227 \| nan \| 0.7530 \| 0.5620 \| 0.6411 \| 0.6900 \| 0.0 \| nan \| 0.0 \| nan \| 0.0015 \| 0.1337 \| 0.5460 \| 0.0 \| 0.7083 \| 0.5347 \| 0.5722 \| 0.6160 \| 0.0 \| nan \| 0.0 \| nan \| 0.0015 \| 0.1261 \| 0.5260 \| \| 0.7318 \| 2.75 \| 440 \| 0.3574 \| 0.2478 \| 0.2950 \| 0.4525 \| nan \| 0.5247 \| 0.2338 \| 0.5171 \| 0.6926 \| 0.0 \| nan \| 0.0 \| nan \| 0.0097 \| 0.3424 \| 0.3350 \| 0.0 \| 0.5100 \| 0.2322 \| 0.4803 \| 0.6174 \| 0.0 \| nan \| 0.0 \| nan \| 0.0097 \| 0.3048 \| 0.3235 \| \| 0.2905 \| 2.88 \| 460 \| 0.3609 \| 0.1903 \| 0.2262 \| 0.3935 \| nan \| 0.4734 \| 0.1841 \| 0.5925 \| 0.5863 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0201 \| 0.1799 \| 0.0 \| 0.4671 \| 0.1834 \| 0.5348 \| 0.5192 \| 0.0 \| nan \| 0.0 \| nan \| 0.0 \| 0.0199 \| 0.1786 \| \| 0.3793 \| 3.0 \| 480 \| 0.3452 \| 0.2734 \| 0.3213 \| 0.5973 \| nan \| 0.8327 \| 0.4635 \| 0.5374 \| 0.6168 \| 0.0 \| nan \| 0.0 \| nan \| 0.0263 \| 0.0746 \| 0.3404 \| 0.0 \| 0.7723 \| 0.4512 \| 0.5139 \| 0.5700 \| 0.0 \| nan \| 0.0 \| nan \| 0.0260 \| 0.0734 \| 0.3270 \| \| 0.3922 \| 3.12 \| 500 \| 0.3695 \| 0.2151 \| 0.2604 \| 0.3659 \| nan \| 0.2751 \| 0.2847 \| 0.6318 \| 0.7206 \| 0.0 \| nan \| 0.0 \| nan \| 0.0045 \| 0.1409 \| 0.2863 \| 0.0 \| 0.2726 \| 0.2824 \| 0.5652 \| 0.6103 \| 0.0 \| nan \| 0.0 \| nan \| 0.0045 \| 0.1339 \| 0.2824 \| \| 0.3299 \| 3.25 \| 520 \| 0.3326 \| 0.3063 \| 0.3610 \| 0.6202 \| nan \| 0.8029 \| 0.5001 \| 0.5866 \| 0.6558 \| 0.0 \| nan \| 0.0 \| nan \| 0.0422 \| 0.1575 \| 0.5038 \| 0.0 \| 0.7639 \| 0.4932 \| 0.5461 \| 0.5948 \| 0.0 \| nan \| 0.0 \| nan \| 0.0416 \| 0.1472 \| 0.4762 \| \| 0.2547 \| 3.38 \| 540 \| 0.3323 \| 0.2650 \| 0.3121 \| 0.5052 \| nan \| 0.6042 \| 0.4311 \| 0.6044 \| 0.5282 \| 0.0 \| nan \| 0.0 \| nan \| 0.0813 \| 0.1438 \| 0.4164 \| 0.0 \| 0.5882 \| 0.4189 \| 0.5254 \| 0.5006 \| 0.0 \| nan \| 0.0 \| nan \| 0.0801 \| 0.1359 \| 0.4010 \| \| 0.2154 \| 3.5 \| 560 \| 0.3211 \| 0.2903 \| 0.3397 \| 0.5796 \| nan \| 0.7327 \| 0.4341 \| 0.6265 \| 0.6269 \| 0.0 \| nan \| 0.0 \| nan \| 0.0340 \| 0.1079 \| 0.4955 \| 0.0 \| 0.7034 \| 0.4304 \| 0.5828 \| 0.5874 \| 0.0 \| nan \| 0.0 \| nan \| 0.0337 \| 0.1022 \| 0.4634 \| \| 0.3146 \| 3.62 \| 580 \| 0.3642 \| 0.3096 \| 0.3854 \| 0.5967 \| nan \| 0.6732 \| 0.4518 \| 0.7254 \| 0.8100 \| 0.0 \| nan \| 0.0 \| nan \| 0.1293 \| 0.2673 \| 0.4116 \| 0.0 \| 0.6557 \| 0.4444 \| 0.5843 \| 0.6517 \| 0.0 \| nan \| 0.0 \| nan \| 0.1212 \| 0.2434 \| 0.3957 \| \| 0.2216 \| 3.75 \| 600 \| 0.3178 \| 0.3241 \| 0.3818 \| 0.5998 \| nan \| 0.7614 \| 0.4294 \| 0.5415 \| 0.7168 \| 0.0 \| nan \| 0.0 \| nan \| 0.1378 \| 0.4248 \| 0.4242 \| 0.0 \| 0.7254 \| 0.4212 \| 0.5274 \| 0.6520 \| 0.0 \| nan \| 0.0 \| nan \| 0.1338 \| 0.3687 \| 0.4125 \| \| 0.2973 \| 3.88 \| 620 \| 0.3199 \| 0.3486 \| 0.4127 \| 0.6217 \| nan \| 0.7369 \| 0.5178 \| 0.5558 \| 0.7739 \| 0.0 \| nan \| 0.0 \| nan \| 0.1965 \| 0.4456 \| 0.4876 \| 0.0 \| 0.7072 \| 0.4974 \| 0.5407 \| 0.7010 \| 0.0 \| nan \| 0.0 \| nan \| 0.1859 \| 0.3845 \| 0.4692 \| \| 0.2434 \| 4.0 \| 640 \| 0.3179 \| 0.3415 \| 0.4057 \| 0.6154 \| nan \| 0.7161 \| 0.4582 \| 0.6827 \| 0.7445 \| 0.0 \| nan \| 0.0 \| nan \| 0.1295 \| 0.3827 \| 0.5376 \| 0.0 \| 0.6869 \| 0.4483 \| 0.6280 \| 0.6769 \| 0.0 \| nan \| 0.0 \| nan \| 0.1254 \| 0.3360 \| 0.5134 \| \| 0.2283 \| 4.12 \| 660 \| 0.3310 \| 0.2584 \| 0.3073 \| 0.5076 \| nan \| 0.6237 \| 0.2267 \| 0.6673 \| 0.7014 \| 0.0 \| nan \| 0.0 \| nan \| 0.0718 \| 0.1288 \| 0.3464 \| 0.0 \| 0.6078 \| 0.2260 \| 0.5912 \| 0.6270 \| 0.0 \| nan \| 0.0 \| nan \| 0.0707 \| 0.1212 \| 0.3401 \| \| 0.6263 \| 4.25 \| 680 \| 0.3153 \| 0.2947 \| 0.3436 \| 0.5198 \| nan \| 0.6461 \| 0.2824 \| 0.5694 \| 0.6236 \| 0.0 \| nan \| 0.0 \| nan \| 0.1512 \| 0.3950 \| 0.4248 \| 0.0 \| 0.6244 \| 0.2811 \| 0.5498 \| 0.5852 \| 0.0 \| nan \| 0.0 \| nan \| 0.1449 \| 0.3479 \| 0.4140 \| \| 0.1708 \| 4.38 \| 700 \| 0.3173 \| 0.2957 \| 0.3435 \| 0.5834 \| nan \| 0.7531 \| 0.3902 \| 0.5853 \| 0.7153 \| 0.0 \| nan \| 0.0 \| nan \| 0.1331 \| 0.1239 \| 0.3908 \| 0.0 \| 0.7188 \| 0.3869 \| 0.5656 \| 0.6612 \| 0.0 \| nan \| 0.0 \| nan \| 0.1298 \| 0.1159 \| 0.3788 \| \| 0.246 \| 4.5 \| 720 \| 0.3138 \| 0.2570 \| 0.2955 \| 0.5052 \| nan \| 0.6686 \| 0.3447 \| 0.4552 \| 0.5989 \| 0.0 \| nan \| 0.0 \| nan \| 0.1237 \| 0.1230 \| 0.3459 \| 0.0 \| 0.6469 \| 0.3416 \| 0.4442 \| 0.5640 \| 0.0 \| nan \| 0.0 \| nan \| 0.1213 \| 0.1159 \| 0.3366 \| \| 0.3876 \| 4.62 \| 740 \| 0.3084 \| 0.3646 \| 0.4336 \| 0.6321 \| nan \| 0.7367 \| 0.4776 \| 0.6536 \| 0.7478 \| 0.0 \| nan \| 0.0 \| nan \| 0.2351 \| 0.4973 \| 0.5539 \| 0.0 \| 0.7108 \| 0.4700 \| 0.6166 \| 0.6824 \| 0.0 \| nan \| 0.0 \| nan \| 0.2208 \| 0.4179 \| 0.5274 \| \| 0.4766 \| 4.75 \| 760 \| 0.3251 \| 0.2509 \| 0.2894 \| 0.4716 \| nan \| 0.6095 \| 0.3498 \| 0.4348 \| 0.4989 \| 0.0 \| nan \| 0.0 \| nan \| 0.1119 \| 0.2020 \| 0.3972 \| 0.0 \| 0.5954 \| 0.3434 \| 0.4157 \| 0.4754 \| 0.0 \| nan \| 0.0 \| nan \| 0.1092 \| 0.1836 \| 0.3865 \| \| 0.4431 \| 4.88 \| 780 \| 0.3052 \| 0.3104 \| 0.3654 \| 0.5781 \| nan \| 0.7024 \| 0.4862 \| 0.5150 \| 0.7363 \| 0.0 \| nan \| 0.0 \| nan \| 0.2163 \| 0.2456 \| 0.3866 \| 0.0 \| 0.6801 \| 0.4736 \| 0.5039 \| 0.6461 \| 0.0 \| nan \| 0.0 \| nan \| 0.2021 \| 0.2209 \| 0.3771 \| \| 0.2319 \| 5.0 \| 800 \| 0.3104 \| 0.3316 \| 0.3938 \| 0.5875 \| nan \| 0.6790 \| 0.5433 \| 0.6711 \| 0.6572 \| 0.0 \| nan \| 0.0 \| nan \| 0.2908 \| 0.3349 \| 0.3675 \| 0.0 \| 0.6628 \| 0.5265 \| 0.5922 \| 0.6113 \| 0.0 \| nan \| 0.0 \| nan \| 0.2656 \| 0.2987 \| 0.3585 \| \| 0.3361 \| 5.12 \| 820 \| 0.3125 \| 0.3219 \| 0.3808 \| 0.5905 \| nan \| 0.7234 \| 0.3480 \| 0.6026 \| 0.7698 \| 0.0 \| nan \| 0.0 \| nan \| 0.1558 \| 0.3461 \| 0.4818 \| 0.0 \| 0.7028 \| 0.3453 \| 0.5677 \| 0.6877 \| 0.0 \| nan \| 0.0 \| nan \| 0.1498 \| 0.3077 \| 0.4584 \| \| 0.412 \| 5.25 \| 840 \| 0.3477 \| 0.2427 \| 0.2810 \| 0.4099 \| nan \| 0.3970 \| 0.2768 \| 0.5965 \| 0.6918 \| 0.0 \| nan \| 0.0 \| nan \| 0.1238 \| 0.1614 \| 0.2814 \| 0.0 \| 0.3899 \| 0.2752 \| 0.5703 \| 0.6446 \| 0.0 \| nan \| 0.0 \| nan \| 0.1208 \| 0.1494 \| 0.2767 \| \| 0.1799 \| 5.38 \| 860 \| 0.3132 \| 0.3444 \| 0.4035 \| 0.6161 \| nan \| 0.7651 \| 0.4804 \| 0.6609 \| 0.5953 \| 0.0 \| nan \| 0.0 \| nan \| 0.2167 \| 0.3901 \| 0.5227 \| 0.0 \| 0.7389 \| 0.4759 \| 0.6240 \| 0.5643 \| 0.0 \| nan \| 0.0 \| nan \| 0.2033 \| 0.3414 \| 0.4965 \| \| 0.1716 \| 5.5 \| 880 \| 0.3186 \| 0.2805 \| 0.3289 \| 0.4955 \| nan \| 0.5685 \| 0.3185 \| 0.5785 \| 0.6888 \| 0.0 \| nan \| 0.0 \| nan \| 0.1934 \| 0.2548 \| 0.3577 \| 0.0 \| 0.5592 \| 0.3165 \| 0.5487 \| 0.6271 \| 0.0 \| nan \| 0.0 \| nan \| 0.1798 \| 0.2295 \| 0.3441 \| \| 0.4388 \| 5.62 \| 900 \| 0.3171 \| 0.4098 \| 0.4914 \| 0.7348 \| nan \| 0.8788 \| 0.6109 \| 0.7475 \| 0.7979 \| 0.0 \| nan \| 0.0 \| nan \| 0.2607 \| 0.5234 \| 0.6032 \| 0.0 \| 0.8320 \| 0.5956 \| 0.6842 \| 0.7286 \| 0.0 \| nan \| 0.0 \| nan \| 0.2445 \| 0.4481 \| 0.5648 \| \| 0.2632 \| 5.75 \| 920 \| 0.3163 \| 0.2697 \| 0.3130 \| 0.4551 \| nan \| 0.5145 \| 0.3445 \| 0.4613 \| 0.6042 \| 0.0 \| nan \| 0.0 \| nan \| 0.1180 \| 0.3896 \| 0.3846 \| 0.0 \| 0.5045 \| 0.3404 \| 0.4568 \| 0.5718 \| 0.0 \| nan \| 0.0 \| nan \| 0.1141 \| 0.3416 \| 0.3677 \| \| 0.3024 \| 5.88 \| 940 \| 0.3063 \| 0.3883 \| 0.4645 \| 0.6758 \| nan \| 0.7778 \| 0.6624 \| 0.7137 \| 0.7013 \| 0.0 \| nan \| 0.0 \| nan \| 0.3253 \| 0.4950 \| 0.5054 \| 0.0 \| 0.7479 \| 0.6323 \| 0.6434 \| 0.6506 \| 0.0 \| nan \| 0.0 \| nan \| 0.2919 \| 0.4251 \| 0.4919 \| \| 0.2551 \| 6.0 \| 960 \| 0.3489 \| 0.2261 \| 0.2625 \| 0.4024 \| nan \| 0.5011 \| 0.1084 \| 0.4074 \| 0.6709 \| 0.0 \| nan \| 0.0 \| nan \| 0.1666 \| 0.2592 \| 0.2489 \| 0.0 \| 0.4903 \| 0.1081 \| 0.4042 \| 0.6181 \| 0.0 \| nan \| 0.0 \| nan \| 0.1562 \| 0.2369 \| 0.2470 \| \| 0.3281 \| 6.12 \| 980 \| 0.2939 \| 0.3635 \| 0.4275 \| 0.6116 \| nan \| 0.6803 \| 0.5665 \| 0.6418 \| 0.6806 \| 0.0 \| nan \| 0.0 \| nan \| 0.2545 \| 0.4660 \| 0.5579 \| 0.0 \| 0.6659 \| 0.5508 \| 0.6049 \| 0.6359 \| 0.0 \| nan \| 0.0 \| nan \| 0.2372 \| 0.4071 \| 0.5330 \| \| 0.1372 \| 6.25 \| 1000 \| 0.2998 \| 0.3755 \| 0.4413 \| 0.6450 \| nan \| 0.7530 \| 0.5417 \| 0.6673 \| 0.7023 \| 0.0 \| nan \| 0.0 \| nan \| 0.2979 \| 0.4450 \| 0.5648 \| 0.0 \| 0.7287 \| 0.5300 \| 0.6359 \| 0.6582 \| 0.0 \| nan \| 0.0 \| nan \| 0.2737 \| 0.3899 \| 0.5389 \| \| 0.3485 \| 6.38 \| 1020 \| 0.3398 \| 0.2557 \| 0.2941 \| 0.4515 \| nan \| 0.5305 \| 0.2957 \| 0.5158 \| 0.6263 \| 0.0 \| nan \| 0.0 \| nan \| 0.1937 \| 0.2043 \| 0.2811 \| 0.0 \| 0.5206 \| 0.2931 \| 0.5070 \| 0.5912 \| 0.0 \| nan \| 0.0 \| nan \| 0.1833 \| 0.1846 \| 0.2769 \| \| 0.3755 \| 6.5 \| 1040 \| 0.3034 \| 0.3526 \| 0.4160 \| 0.5795 \| nan \| 0.6346 \| 0.4564 \| 0.7050 \| 0.6986 \| 0.0 \| nan \| 0.0 \| nan \| 0.3010 \| 0.4881 \| 0.4598 \| 0.0 \| 0.6188 \| 0.4509 \| 0.6639 \| 0.6554 \| 0.0 \| nan \| 0.0 \| nan \| 0.2758 \| 0.4166 \| 0.4442 \| \| 0.2617 \| 6.62 \| 1060 \| 0.3166 \| 0.2905 \| 0.3384 \| 0.4820 \| nan \| 0.5401 \| 0.3225 \| 0.5981 \| 0.6153 \| 0.0 \| nan \| 0.0 \| nan \| 0.1880 \| 0.4060 \| 0.3756 \| 0.0 \| 0.5337 \| 0.3212 \| 0.5770 \| 0.5817 \| 0.0 \| nan \| 0.0 \| nan \| 0.1774 \| 0.3511 \| 0.3627 \| \| 0.2937 \| 6.75 \| 1080 \| 0.3090 \| 0.3864 \| 0.4585 \| 0.7031 \| nan \| 0.8093 \| 0.6687 \| 0.7189 \| 0.7808 \| 0.0 \| nan \| 0.0 \| nan \| 0.3271 \| 0.2123 \| 0.6092 \| 0.0 \| 0.7810 \| 0.6460 \| 0.6639 \| 0.7094 \| 0.0 \| nan \| 0.0 \| nan \| 0.2942 \| 0.1957 \| 0.5738 \| \| 0.3588 \| 6.88 \| 1100 \| 0.3011 \| 0.3653 \| 0.4310 \| 0.6482 \| nan \| 0.8132 \| 0.4360 \| 0.6549 \| 0.7523 \| 0.0 \| nan \| 0.0 \| nan \| 0.3123 \| 0.4840 \| 0.4267 \| 0.0 \| 0.7784 \| 0.4310 \| 0.6235 \| 0.6944 \| 0.0 \| nan \| 0.0 \| nan \| 0.2884 \| 0.4223 \| 0.4149 \| \| 0.1613 \| 7.0 \| 1120 \| 0.3302 \| 0.2838 \| 0.3344 \| 0.4622 \| nan \| 0.4687 \| 0.3133 \| 0.6402 \| 0.6782 \| 0.0 \| nan \| 0.0 \| nan \| 0.1601 \| 0.4001 \| 0.3490 \| 0.0 \| 0.4620 \| 0.3100 \| 0.5933 \| 0.6247 \| 0.0 \| nan \| 0.0 \| nan \| 0.1525 \| 0.3558 \| 0.3400 \| \| 0.4217 \| 7.12 \| 1140 \| 0.3087 \| 0.3723 \| 0.4451 \| 0.6405 \| nan \| 0.7463 \| 0.4361 \| 0.7284 \| 0.7778 \| 0.0 \| nan \| 0.0 \| nan \| 0.3229 \| 0.4981 \| 0.4960 \| 0.0 \| 0.7258 \| 0.4307 \| 0.6602 \| 0.7060 \| 0.0 \| nan \| 0.0 \| nan \| 0.2946 \| 0.4255 \| 0.4800 \| \| 0.1429 \| 7.25 \| 1160 \| 0.3227 \| 0.2794 \| 0.3221 \| 0.5335 \| nan \| 0.7381 \| 0.3377 \| 0.5059 \| 0.5342 \| 0.0 \| nan \| 0.0 \| nan \| 0.2719 \| 0.1916 \| 0.3198 \| 0.0 \| 0.7147 \| 0.3361 \| 0.4943 \| 0.5100 \| 0.0 \| nan \| 0.0 \| nan \| 0.2479 \| 0.1761 \| 0.3151 \| \| 0.227 \| 7.38 \| 1180 \| 0.3087 \| 0.3749 \| 0.4471 \| 0.6221 \| nan \| 0.6645 \| 0.5048 \| 0.7103 \| 0.7872 \| 0.0 \| nan \| 0.0 \| nan \| 0.3502 \| 0.4474 \| 0.5594 \| 0.0 \| 0.6499 \| 0.4966 \| 0.6631 \| 0.7065 \| 0.0 \| nan \| 0.0 \| nan \| 0.3168 \| 0.3915 \| 0.5250 \| \| 0.3733 \| 7.5 \| 1200 \| 0.3304 \| 0.2777 \| 0.3229 \| 0.4832 \| nan \| 0.5603 \| 0.3886 \| 0.5612 \| 0.5532 \| 0.0 \| nan \| 0.0 \| nan \| 0.1744 \| 0.2915 \| 0.3773 \| 0.0 \| 0.5501 \| 0.3824 \| 0.5434 \| 0.5248 \| 0.0 \| nan \| 0.0 \| nan \| 0.1655 \| 0.2561 \| 0.3542 \| \| 0.3148 \| 7.62 \| 1220 \| 0.3075 \| 0.3787 \| 0.4500 \| 0.6531 \| nan \| 0.7425 \| 0.5909 \| 0.7189 \| 0.7270 \| 0.0 \| nan \| 0.0 \| nan \| 0.3257 \| 0.4420 \| 0.5030 \| 0.0 \| 0.7152 \| 0.5687 \| 0.6673 \| 0.6700 \| 0.0 \| nan \| 0.0 \| nan \| 0.2976 \| 0.3823 \| 0.4863 \| \| 0.22 \| 7.75 \| 1240 \| 0.3156 \| 0.3340 \| 0.3934 \| 0.5589 \| nan \| 0.6127 \| 0.4262 \| 0.6387 \| 0.7554 \| 0.0 \| nan \| 0.0 \| nan \| 0.2170 \| 0.4800 \| 0.4108 \| 0.0 \| 0.6002 \| 0.4203 \| 0.6121 \| 0.6848 \| 0.0 \| nan \| 0.0 \| nan \| 0.2073 \| 0.4168 \| 0.3984 \| \| 0.499 \| 7.88 \| 1260 \| 0.3085 \| 0.3454 \| 0.4092 \| 0.6278 \| nan \| 0.7534 \| 0.4363 \| 0.7109 \| 0.7256 \| 0.0 \| nan \| 0.0 \| nan \| 0.2525 \| 0.2773 \| 0.5267 \| 0.0 \| 0.7296 \| 0.4311 \| 0.6556 \| 0.6668 \| 0.0 \| nan \| 0.0 \| nan \| 0.2355 \| 0.2512 \| 0.4848 \| \| 0.2604 \| 8.0 \| 1280 \| 0.3123 \| 0.3504 \| 0.4118 \| 0.6089 \| nan \| 0.7175 \| 0.4779 \| 0.6668 \| 0.6668 \| 0.0 \| nan \| 0.0 \| nan \| 0.2368 \| 0.4184 \| 0.5218 \| 0.0 \| 0.6949 \| 0.4708 \| 0.6402 \| 0.6225 \| 0.0 \| nan \| 0.0 \| nan \| 0.2192 \| 0.3649 \| 0.4915 \| \| 0.146 \| 8.12 \| 1300 \| 0.3274 \| 0.3036 \| 0.3526 \| 0.5368 \| nan \| 0.6413 \| 0.4378 \| 0.6158 \| 0.5674 \| 0.0 \| nan \| 0.0 \| nan \| 0.1757 \| 0.3165 \| 0.4191 \| 0.0 \| 0.6216 \| 0.4333 \| 0.5946 \| 0.5339 \| 0.0 \| nan \| 0.0 \| nan \| 0.1697 \| 0.2810 \| 0.4015 \| \| 0.1103 \| 8.25 \| 1320 \| 0.3339 \| 0.2738 \| 0.3155 \| 0.4762 \| nan \| 0.5556 \| 0.4026 \| 0.5015 \| 0.5408 \| 0.0 \| nan \| 0.0 \| nan \| 0.1370 \| 0.2938 \| 0.4084 \| 0.0 \| 0.5454 \| 0.3969 \| 0.4921 \| 0.5111 \| 0.0 \| nan \| 0.0 \| nan \| 0.1330 \| 0.2658 \| 0.3939 \| \| 0.1323 \| 8.38 \| 1340 \| 0.3179 \| 0.3304 \| 0.3865 \| 0.5986 \| nan \| 0.7334 \| 0.4769 \| 0.6287 \| 0.6839 \| 0.0 \| nan \| 0.0 \| nan \| 0.2152 \| 0.3473 \| 0.3933 \| 0.0 \| 0.7022 \| 0.4679 \| 0.6091 \| 0.6261 \| 0.0 \| nan \| 0.0 \| nan \| 0.2049 \| 0.3080 \| 0.3863 \| \| 0.1057 \| 8.5 \| 1360 \| 0.4118 \| 0.1949 \| 0.2242 \| 0.3076 \| nan \| 0.3007 \| 0.1559 \| 0.4492 \| 0.5135 \| 0.0 \| nan \| 0.0 \| nan \| 0.1282 \| 0.2228 \| 0.2472 \| 0.0 \| 0.2972 \| 0.1552 \| 0.4441 \| 0.4845 \| 0.0 \| nan \| 0.0 \| nan \| 0.1241 \| 0.2028 \| 0.2412 \| \| 0.1248 \| 8.62 \| 1380 \| 0.3228 \| 0.4155 \| 0.4991 \| 0.7269 \| nan \| 0.8415 \| 0.6462 \| 0.7622 \| 0.7778 \| 0.0 \| nan \| 0.0 \| nan \| 0.3683 \| 0.4851 \| 0.6106 \| 0.0 \| 0.8100 \| 0.6271 \| 0.6994 \| 0.7057 \| 0.0 \| nan \| 0.0 \| nan \| 0.3280 \| 0.4172 \| 0.5674 \| \| 0.1165 \| 8.75 \| 1400 \| 0.3307 \| 0.2995 \| 0.3485 \| 0.5194 \| nan \| 0.6149 \| 0.3102 \| 0.5592 \| 0.6835 \| 0.0 \| nan \| 0.0 \| nan \| 0.1857 \| 0.3513 \| 0.4318 \| 0.0 \| 0.5982 \| 0.3074 \| 0.5485 \| 0.6349 \| 0.0 \| nan \| 0.0 \| nan \| 0.1765 \| 0.3142 \| 0.4156 \| \| 0.2999 \| 8.88 \| 1420 \| 0.3766 \| 0.2329 \| 0.2673 \| 0.3927 \| nan \| 0.4223 \| 0.2954 \| 0.4437 \| 0.5620 \| 0.0 \| nan \| 0.0 \| nan \| 0.1641 \| 0.1857 \| 0.3327 \| 0.0 \| 0.4163 \| 0.2897 \| 0.4411 \| 0.5318 \| 0.0 \| nan \| 0.0 \| nan \| 0.1570 \| 0.1704 \| 0.3232 \| \| 0.2005 \| 9.0 \| 1440 \| 0.3224 \| 0.3457 \| 0.4100 \| 0.5800 \| nan \| 0.6510 \| 0.4458 \| 0.6701 \| 0.6765 \| 0.0 \| nan \| 0.0002 \| nan \| 0.2510 \| 0.4869 \| 0.5085 \| 0.0 \| 0.6327 \| 0.4381 \| 0.6441 \| 0.6348 \| 0.0 \| nan \| 0.0002 \| nan \| 0.2321 \| 0.4092 \| 0.4656 \| \| 0.0952 \| 9.12 \| 1460 \| 0.3368 \| 0.2986 \| 0.3475 \| 0.5230 \| nan \| 0.5872 \| 0.4686 \| 0.6328 \| 0.6070 \| 0.0 \| nan \| 0.0 \| nan \| 0.2227 \| 0.2252 \| 0.3843 \| 0.0 \| 0.5748 \| 0.4534 \| 0.5986 \| 0.5720 \| 0.0 \| nan \| 0.0 \| nan \| 0.2114 \| 0.2061 \| 0.3692 \| \| 0.3493 \| 9.25 \| 1480 \| 0.3637 \| 0.2527 \| 0.2909 \| 0.4285 \| nan \| 0.5072 \| 0.3061 \| 0.4793 \| 0.5032 \| 0.0 \| nan \| 0.0 \| nan \| 0.1539 \| 0.3356 \| 0.3325 \| 0.0 \| 0.4971 \| 0.3025 \| 0.4757 \| 0.4801 \| 0.0 \| nan \| 0.0 \| nan \| 0.1476 \| 0.2993 \| 0.3245 \| \| 0.6102 \| 9.38 \| 1500 \| 0.3302 \| 0.3325 \| 0.3885 \| 0.5757 \| nan \| 0.6527 \| 0.5490 \| 0.6148 \| 0.6472 \| 0.0 \| nan \| 0.0 \| nan \| 0.2155 \| 0.3649 \| 0.4522 \| 0.0 \| 0.6350 \| 0.5320 \| 0.5993 \| 0.6117 \| 0.0 \| nan \| 0.0 \| nan \| 0.2015 \| 0.3228 \| 0.4231 \| \| 0.1355 \| 9.5 \| 1520 \| 0.3136 \| 0.3397 \| 0.3985 \| 0.5917 \| nan \| 0.7078 \| 0.4385 \| 0.6197 \| 0.6754 \| 0.0 \| nan \| 0.0 \| nan \| 0.2411 \| 0.4128 \| 0.4908 \| 0.0 \| 0.6904 \| 0.4342 \| 0.6004 \| 0.6272 \| 0.0 \| nan \| 0.0 \| nan \| 0.2228 \| 0.3615 \| 0.4608 \| \| 0.2828 \| 9.62 \| 1540 \| 0.3214 \| 0.3632 \| 0.4329 \| 0.6220 \| nan \| 0.7412 \| 0.4203 \| 0.6106 \| 0.7371 \| 0.0 \| nan \| 0.0004 \| nan \| 0.3513 \| 0.4797 \| 0.5558 \| 0.0 \| 0.7149 \| 0.4099 \| 0.5925 \| 0.6724 \| 0.0 \| nan \| 0.0004 \| nan \| 0.3130 \| 0.4125 \| 0.5160 \| \| 0.2499 \| 9.75 \| 1560 \| 0.3470 \| 0.3178 \| 0.3744 \| 0.5370 \| nan \| 0.6135 \| 0.4310 \| 0.6613 \| 0.5944 \| 0.0 \| nan \| 0.0001 \| nan \| 0.3109 \| 0.3698 \| 0.3889 \| 0.0 \| 0.5963 \| 0.4247 \| 0.6161 \| 0.5602 \| 0.0 \| nan \| 0.0001 \| nan \| 0.2767 \| 0.3282 \| 0.3756 \| \| 0.3973 \| 9.88 \| 1580 \| 0.3292 \| 0.3557 \| 0.4222 \| 0.6036 \| nan \| 0.6854 \| 0.5253 \| 0.6598 \| 0.6929 \| 0.0 \| nan \| 0.0 \| nan \| 0.2791 \| 0.5040 \| 0.4535 \| 0.0 \| 0.6632 \| 0.5042 \| 0.6184 \| 0.6434 \| 0.0 \| nan \| 0.0 \| nan \| 0.2550 \| 0.4358 \| 0.4374 \| \| 0.1764 \| 10.0 \| 1600 \| 0.3317 \| 0.3493 \| 0.4150 \| 0.5984 \| nan \| 0.6897 \| 0.4370 \| 0.6896 \| 0.7262 \| 0.0 \| nan \| 0.0023 \| nan \| 0.2221 \| 0.5208 \| 0.4472 \| 0.0 \| 0.6702 \| 0.4293 \| 0.6340 \| 0.6649 \| 0.0 \| nan \| 0.0023 \| nan \| 0.2127 \| 0.4505 \| 0.4294 \| \| 0.3667 \| 10.12 \| 1620 \| 0.3224 \| 0.3385 \| 0.3978 \| 0.5951 \| nan \| 0.7262 \| 0.4129 \| 0.5865 \| 0.6864 \| 0.0 \| nan \| 0.0041 \| nan \| 0.2466 \| 0.4033 \| 0.5143 \| 0.0 \| 0.7033 \| 0.4053 \| 0.5673 \| 0.6383 \| 0.0 \| nan \| 0.0041 \| nan \| 0.2340 \| 0.3562 \| 0.4768 \| \| 0.2782 \| 10.25 \| 1640 \| 0.3243 \| 0.3675 \| 0.4383 \| 0.6355 \| nan \| 0.7304 \| 0.5449 \| 0.7001 \| 0.7129 \| 0.0 \| nan \| 0.0021 \| nan \| 0.2615 \| 0.5006 \| 0.4920 \| 0.0 \| 0.7054 \| 0.5218 \| 0.6534 \| 0.6531 \| 0.0 \| nan \| 0.0021 \| nan \| 0.2478 \| 0.4247 \| 0.4667 \| \| 0.1716 \| 10.38 \| 1660 \| 0.3199 \| 0.3531 \| 0.4131 \| 0.6228 \| nan \| 0.7347 \| 0.5687 \| 0.6019 \| 0.7168 \| 0.0 \| nan \| 0.0001 \| nan \| 0.2756 \| 0.3677 \| 0.4526 \| 0.0 \| 0.7107 \| 0.5562 \| 0.5817 \| 0.6609 \| 0.0 \| nan \| 0.0001 \| nan \| 0.2583 \| 0.3276 \| 0.4354 \| \| 0.1938 \| 10.5 \| 1680 \| 0.3304 \| 0.3369 \| 0.4038 \| 0.5768 \| nan \| 0.6403 \| 0.3871 \| 0.6906 \| 0.7228 \| 0.0 \| nan \| 0.0049 \| nan \| 0.2373 \| 0.4005 \| 0.5506 \| 0.0 \| 0.6248 \| 0.3827 \| 0.6360 \| 0.6713 \| 0.0 \| nan \| 0.0049 \| nan \| 0.2232 \| 0.3537 \| 0.4722 \| \| 0.0939 \| 10.62 \| 1700 \| 0.3178 \| 0.3848 \| 0.4610 \| 0.6472 \| nan \| 0.7358 \| 0.5401 \| 0.7080 \| 0.7452 \| 0.0 \| nan \| 0.0062 \| nan \| 0.3044 \| 0.6229 \| 0.4866 \| 0.0 \| 0.7137 \| 0.5308 \| 0.6585 \| 0.6909 \| 0.0 \| nan \| 0.0062 \| nan \| 0.2798 \| 0.5091 \| 0.4593 \| \| 0.1592 \| 10.75 \| 1720 \| 0.3323 \| 0.3312 \| 0.3861 \| 0.5834 \| nan \| 0.6894 \| 0.6042 \| 0.5845 \| 0.5805 \| 0.0 \| nan \| 0.0055 \| nan \| 0.2218 \| 0.3658 \| 0.4236 \| 0.0 \| 0.6704 \| 0.5786 \| 0.5710 \| 0.5451 \| 0.0 \| nan \| 0.0055 \| nan \| 0.2094 \| 0.3236 \| 0.4085 \| \| 0.186 \| 10.88 \| 1740 \| 0.3280 \| 0.3838 \| 0.4597 \| 0.6407 \| nan \| 0.7285 \| 0.4968 \| 0.6695 \| 0.7578 \| 0.0 \| nan \| 0.0145 \| nan \| 0.4074 \| 0.5172 \| 0.5460 \| 0.0 \| 0.7056 \| 0.4866 \| 0.6377 \| 0.6958 \| 0.0 \| nan \| 0.0144 \| nan \| 0.3540 \| 0.4358 \| 0.5086 \| \| 0.124 \| 11.0 \| 1760 \| 0.4089 \| 0.2396 \| 0.2751 \| 0.4128 \| nan \| 0.5165 \| 0.2682 \| 0.4594 \| 0.4520 \| 0.0 \| nan \| 0.0095 \| nan \| 0.2177 \| 0.2646 \| 0.2879 \| 0.0 \| 0.5080 \| 0.2658 \| 0.4487 \| 0.4315 \| 0.0 \| nan \| 0.0095 \| nan \| 0.2031 \| 0.2473 \| 0.2822 \| \| 0.1084 \| 11.12 \| 1780 \| 0.3512 \| 0.3283 \| 0.3864 \| 0.5415 \| nan \| 0.5910 \| 0.4464 \| 0.6008 \| 0.6724 \| 0.0 \| nan \| 0.0105 \| nan \| 0.2345 \| 0.4593 \| 0.4626 \| 0.0 \| 0.5774 \| 0.4313 \| 0.5885 \| 0.6218 \| 0.0 \| nan \| 0.0105 \| nan \| 0.2220 \| 0.3926 \| 0.4390 \| \| 0.3364 \| 11.25 \| 1800 \| 0.3514 \| 0.3245 \| 0.3806 \| 0.5425 \| nan \| 0.6046 \| 0.4578 \| 0.6162 \| 0.6777 \| 0.0 \| nan \| 0.0107 \| nan \| 0.2340 \| 0.4572 \| 0.3669 \| 0.0 \| 0.5876 \| 0.4430 \| 0.6018 \| 0.6301 \| 0.0 \| nan \| 0.0107 \| nan \| 0.2216 \| 0.3951 \| 0.3547 \| \| 0.186 \| 11.38 \| 1820 \| 0.3398 \| 0.3337 \| 0.3937 \| 0.5743 \| nan \| 0.6718 \| 0.3964 \| 0.6547 \| 0.6960 \| 0.0 \| nan \| 0.0095 \| nan \| 0.2726 \| 0.3967 \| 0.4452 \| 0.0 \| 0.6555 \| 0.3905 \| 0.6240 \| 0.6329 \| 0.0 \| nan \| 0.0095 \| nan \| 0.2543 \| 0.3500 \| 0.4209 \| \| 0.085 \| 11.5 \| 1840 \| 0.3395 \| 0.3541 \| 0.4172 \| 0.5969 \| nan \| 0.6834 \| 0.4750 \| 0.6591 \| 0.6675 \| 0.0029 \| nan \| 0.0163 \| nan \| 0.2791 \| 0.4399 \| 0.5321 \| 0.0 \| 0.6637 \| 0.4648 \| 0.6337 \| 0.6201 \| 0.0029 \| nan \| 0.0161 \| nan \| 0.2594 \| 0.3801 \| 0.5002 \| \| 0.2861 \| 11.62 \| 1860 \| 0.3575 \| 0.3064 \| 0.3548 \| 0.5451 \| nan \| 0.6531 \| 0.4069 \| 0.6701 \| 0.6340 \| 0.0000 \| nan \| 0.0111 \| nan \| 0.2071 \| 0.2802 \| 0.3303 \| 0.0 \| 0.6370 \| 0.4012 \| 0.6428 \| 0.5936 \| 0.0000 \| nan \| 0.0110 \| nan \| 0.1952 \| 0.2599 \| 0.3230 \| \| 0.2855 \| 11.75 \| 1880 \| 0.3932 \| 0.2415 \| 0.2791 \| 0.4276 \| nan \| 0.5475 \| 0.2912 \| 0.4359 \| 0.4478 \| 0.0 \| nan \| 0.0104 \| nan \| 0.1519 \| 0.3073 \| 0.3201 \| 0.0 \| 0.5356 \| 0.2855 \| 0.4203 \| 0.4307 \| 0.0 \| nan \| 0.0104 \| nan \| 0.1446 \| 0.2782 \| 0.3099 \| \| 0.1548 \| 11.88 \| 1900 \| 0.3398 \| 0.3513 \| 0.4167 \| 0.6004 \| nan \| 0.7097 \| 0.3884 \| 0.6305 \| 0.7354 \| 0.0002 \| nan \| 0.0138 \| nan \| 0.2828 \| 0.4755 \| 0.5141 \| 0.0 \| 0.6920 \| 0.3832 \| 0.6074 \| 0.6691 \| 0.0002 \| nan \| 0.0138 \| nan \| 0.2669 \| 0.4032 \| 0.4775 \| \| 0.123 \| 12.0 \| 1920 \| 0.3279 \| 0.3495 \| 0.4113 \| 0.6009 \| nan \| 0.7406 \| 0.4218 \| 0.5964 \| 0.6630 \| 0.0028 \| nan \| 0.0183 \| nan \| 0.2751 \| 0.5053 \| 0.4784 \| 0.0 \| 0.7190 \| 0.4153 \| 0.5867 \| 0.6196 \| 0.0028 \| nan \| 0.0181 \| nan \| 0.2542 \| 0.4223 \| 0.4568 \| \| 0.1138 \| 12.12 \| 1940 \| 0.3308 \| 0.3983 \| 0.4753 \| 0.6792 \| nan \| 0.7682 \| 0.5832 \| 0.7082 \| 0.8045 \| 0.0028 \| nan \| 0.0209 \| nan \| 0.3537 \| 0.4880 \| 0.5477 \| 0.0 \| 0.7424 \| 0.5667 \| 0.6711 \| 0.7307 \| 0.0028 \| nan \| 0.0207 \| nan \| 0.3242 \| 0.4171 \| 0.5076 \| \| 0.1582 \| 12.25 \| 1960 \| 0.3342 \| 0.3720 \| 0.4410 \| 0.6306 \| nan \| 0.7554 \| 0.4633 \| 0.6444 \| 0.7011 \| 0.0062 \| nan \| 0.0198 \| nan \| 0.3419 \| 0.5214 \| 0.5155 \| 0.0 \| 0.7309 \| 0.4556 \| 0.6269 \| 0.6504 \| 0.0062 \| nan \| 0.0196 \| nan \| 0.3087 \| 0.4391 \| 0.4823 \| \| 0.3449 \| 12.38 \| 1980 \| 0.3976 \| 0.2429 \| 0.2797 \| 0.4225 \| nan \| 0.5187 \| 0.2813 \| 0.4234 \| 0.5055 \| 0.0 \| nan \| 0.0111 \| nan \| 0.1548 \| 0.2818 \| 0.3408 \| 0.0 \| 0.5069 \| 0.2772 \| 0.4172 \| 0.4855 \| 0.0 \| nan \| 0.0111 \| nan \| 0.1490 \| 0.2573 \| 0.3246 \| \| 0.0296 \| 12.5 \| 2000 \| 0.3332 \| 0.3525 \| 0.4166 \| 0.5979 \| nan \| 0.6804 \| 0.4709 \| 0.6924 \| 0.6988 \| 0.0 \| nan \| 0.0172 \| nan \| 0.3403 \| 0.3831 \| 0.4662 \| 0.0 \| 0.6615 \| 0.4562 \| 0.6530 \| 0.6470 \| 0.0 \| nan \| 0.0171 \| nan \| 0.3043 \| 0.3389 \| 0.4466 \| \| 0.2308 \| 12.62 \| 2020 \| 0.3418 \| 0.3574 \| 0.4208 \| 0.6123 \| nan \| 0.7446 \| 0.3992 \| 0.6491 \| 0.7142 \| 0.0 \| nan \| 0.0130 \| nan \| 0.3085 \| 0.4940 \| 0.4650 \| 0.0 \| 0.7205 \| 0.3948 \| 0.6289 \| 0.6646 \| 0.0 \| nan \| 0.0129 \| nan \| 0.2849 \| 0.4238 \| 0.4435 \| \| 0.3632 \| 12.75 \| 2040 \| 0.3847 \| 0.2694 \| 0.3118 \| 0.4605 \| nan \| 0.5391 \| 0.3475 \| 0.5106 \| 0.5736 \| 0.0 \| nan \| 0.0090 \| nan \| 0.1930 \| 0.3284 \| 0.3053 \| 0.0 \| 0.5278 \| 0.3411 \| 0.5017 \| 0.5453 \| 0.0 \| nan \| 0.0089 \| nan \| 0.1846 \| 0.2916 \| 0.2930 \| \| 0.284 \| 12.88 \| 2060 \| 0.3425 \| 0.3836 \| 0.4597 \| 0.6559 \| nan \| 0.7536 \| 0.5838 \| 0.6914 \| 0.7272 \| 0.0094 \| nan \| 0.0148 \| nan \| 0.3443 \| 0.5142 \| 0.4986 \| 0.0 \| 0.7278 \| 0.5383 \| 0.6459 \| 0.6707 \| 0.0094 \| nan \| 0.0147 \| nan \| 0.3187 \| 0.4375 \| 0.4732 \| \| 0.1566 \| 13.0 \| 2080 \| 0.3586 \| 0.3093 \| 0.3628 \| 0.5439 \| nan \| 0.6643 \| 0.4048 \| 0.5981 \| 0.6177 \| 0.0075 \| nan \| 0.0124 \| nan \| 0.2639 \| 0.3493 \| 0.3475 \| 0.0 \| 0.6486 \| 0.3917 \| 0.5754 \| 0.5727 \| 0.0075 \| nan \| 0.0123 \| nan \| 0.2477 \| 0.3058 \| 0.3311 \| \| 0.1545 \| 13.12 \| 2100 \| 0.3630 \| 0.3109 \| 0.3644 \| 0.5329 \| nan \| 0.6530 \| 0.3620 \| 0.5186 \| 0.6261 \| 0.0122 \| nan \| 0.0107 \| nan \| 0.1922 \| 0.4837 \| 0.4209 \| 0.0 \| 0.6339 \| 0.3549 \| 0.5053 \| 0.5850 \| 0.0122 \| nan \| 0.0106 \| nan \| 0.1847 \| 0.4173 \| 0.4049 \| \| 0.1118 \| 13.25 \| 2120 \| 0.3435 \| 0.3579 \| 0.4228 \| 0.6077 \| nan \| 0.7231 \| 0.4451 \| 0.6276 \| 0.7012 \| 0.0175 \| nan \| 0.0249 \| nan \| 0.2535 \| 0.5218 \| 0.4904 \| 0.0 \| 0.7009 \| 0.4350 \| 0.6087 \| 0.6502 \| 0.0175 \| nan \| 0.0246 \| nan \| 0.2404 \| 0.4449 \| 0.4571 \| \| 0.0828 \| 13.38 \| 2140 \| 0.3544 \| 0.3361 \| 0.3956 \| 0.5662 \| nan \| 0.6733 \| 0.3762 \| 0.6572 \| 0.6600 \| 0.0125 \| nan \| 0.0328 \| nan \| 0.3596 \| 0.3828 \| 0.4058 \| 0.0 \| 0.6520 \| 0.3665 \| 0.6258 \| 0.6161 \| 0.0125 \| nan \| 0.0321 \| nan \| 0.3236 \| 0.3448 \| 0.3878 \| \| 0.2605 \| 13.5 \| 2160 \| 0.3451 \| 0.3732 \| 0.4421 \| 0.6309 \| nan \| 0.7398 \| 0.4876 \| 0.6322 \| 0.7386 \| 0.0182 \| nan \| 0.0378 \| nan \| 0.3453 \| 0.4635 \| 0.5161 \| 0.0 \| 0.7155 \| 0.4705 \| 0.6104 \| 0.6734 \| 0.0182 \| nan \| 0.0369 \| nan \| 0.3171 \| 0.4016 \| 0.4886 \| \| 0.0129 \| 13.62 \| 2180 \| 0.3919 \| 0.2765 \| 0.3196 \| 0.4836 \| nan \| 0.5955 \| 0.3326 \| 0.5530 \| 0.5408 \| 0.0179 \| nan \| 0.0140 \| nan \| 0.1846 \| 0.2991 \| 0.3392 \| 0.0 \| 0.5800 \| 0.3270 \| 0.5417 \| 0.5094 \| 0.0179 \| nan \| 0.0139 \| nan \| 0.1754 \| 0.2717 \| 0.3283 \| \| 0.1744 \| 13.75 \| 2200 \| 0.3543 \| 0.3287 \| 0.3864 \| 0.5730 \| nan \| 0.6835 \| 0.3695 \| 0.6680 \| 0.7169 \| 0.0132 \| nan \| 0.0120 \| nan \| 0.2276 \| 0.4016 \| 0.3853 \| 0.0 \| 0.6621 \| 0.3663 \| 0.6327 \| 0.6620 \| 0.0132 \| nan \| 0.0120 \| nan \| 0.2145 \| 0.3531 \| 0.3708 \| \| 0.0863 \| 13.88 \| 2220 \| 0.3536 \| 0.3503 \| 0.4130 \| 0.6052 \| nan \| 0.7206 \| 0.4645 \| 0.5874 \| 0.6916 \| 0.0115 \| nan \| 0.0200 \| nan \| 0.3492 \| 0.3373 \| 0.5352 \| 0.0 \| 0.6962 \| 0.4546 \| 0.5755 \| 0.6385 \| 0.0115 \| nan \| 0.0198 \| nan \| 0.3158 \| 0.2964 \| 0.4949 \| \| 0.2218 \| 14.0 \| 2240 \| 0.3552 \| 0.3527 \| 0.4186 \| 0.6185 \| nan \| 0.7443 \| 0.4727 \| 0.6913 \| 0.6287 \| 0.0118 \| nan \| 0.0181 \| nan \| 0.3010 \| 0.3621 \| 0.5372 \| 0.0 \| 0.7196 \| 0.4609 \| 0.6293 \| 0.5846 \| 0.0118 \| nan \| 0.0180 \| nan \| 0.2818 \| 0.3178 \| 0.5032 \| \| 0.1603 \| 14.12 \| 2260 \| 0.3853 \| 0.2835 \| 0.3305 \| 0.4804 \| nan \| 0.5713 \| 0.3329 \| 0.5072 \| 0.5800 \| 0.0061 \| nan \| 0.0185 \| nan \| 0.2341 \| 0.3506 \| 0.3738 \| 0.0 \| 0.5570 \| 0.3257 \| 0.4960 \| 0.5457 \| 0.0061 \| nan \| 0.0183 \| nan \| 0.2195 \| 0.3115 \| 0.3554 \| \| 0.1556 \| 14.25 \| 2280 \| 0.3580 \| 0.3469 \| 0.4112 \| 0.6041 \| nan \| 0.7349 \| 0.4383 \| 0.6388 \| 0.6923 \| 0.0131 \| nan \| 0.0196 \| nan \| 0.3261 \| 0.4219 \| 0.4161 \| 0.0 \| 0.7132 \| 0.4194 \| 0.6082 \| 0.6401 \| 0.0131 \| nan \| 0.0194 \| nan \| 0.2961 \| 0.3680 \| 0.3921 \| \| 0.2714 \| 14.38 \| 2300 \| 0.3716 \| 0.3215 \| 0.3763 \| 0.5454 \| nan \| 0.6469 \| 0.3780 \| 0.5739 \| 0.6474 \| 0.0130 \| nan \| 0.0128 \| nan \| 0.2433 \| 0.4166 \| 0.4552 \| 0.0 \| 0.6285 \| 0.3722 \| 0.5628 \| 0.6024 \| 0.0130 \| nan \| 0.0127 \| nan \| 0.2288 \| 0.3676 \| 0.4273 \| \| 0.2624 \| 14.5 \| 2320 \| 0.3524 \| 0.3357 \| 0.3931 \| 0.5833 \| nan \| 0.7198 \| 0.4113 \| 0.6229 \| 0.6339 \| 0.0164 \| nan \| 0.0153 \| nan \| 0.2559 \| 0.4207 \| 0.4414 \| 0.0 \| 0.6967 \| 0.4056 \| 0.6019 \| 0.5973 \| 0.0164 \| nan \| 0.0151 \| nan \| 0.2376 \| 0.3674 \| 0.4185 \| \| 0.2223 \| 14.62 \| 2340 \| 0.3570 \| 0.3148 \| 0.3663 \| 0.5565 \| nan \| 0.6820 \| 0.4156 \| 0.5894 \| 0.6286 \| 0.0155 \| nan \| 0.0149 \| nan \| 0.2473 \| 0.3015 \| 0.4022 \| 0.0 \| 0.6633 \| 0.4037 \| 0.5782 \| 0.5907 \| 0.0155 \| nan \| 0.0148 \| nan \| 0.2266 \| 0.2731 \| 0.3817 \| \| 0.1125 \| 14.75 \| 2360 \| 0.3766 \| 0.3027 \| 0.3526 \| 0.5064 \| nan \| 0.6058 \| 0.3030 \| 0.5083 \| 0.6290 \| 0.0217 \| nan \| 0.0107 \| nan \| 0.2239 \| 0.4108 \| 0.4599 \| 0.0 \| 0.5916 \| 0.2977 \| 0.5031 \| 0.5910 \| 0.0217 \| nan \| 0.0106 \| nan \| 0.2101 \| 0.3637 \| 0.4379 \| \| 0.1139 \| 14.88 \| 2380 \| 0.3541 \| 0.3752 \| 0.4445 \| 0.6352 \| nan \| 0.7230 \| 0.5186 \| 0.7029 \| 0.7593 \| 0.0249 \| nan \| 0.0174 \| nan \| 0.2855 \| 0.4914 \| 0.4778 \| 0.0 \| 0.7011 \| 0.4986 \| 0.6740 \| 0.6961 \| 0.0249 \| nan \| 0.0172 \| nan \| 0.2652 \| 0.4230 \| 0.4514 \| \| 0.1841 \| 15.0 \| 2400 \| 0.3596 \| 0.3337 \| 0.3915 \| 0.5678 \| nan \| 0.6749 \| 0.3898 \| 0.6395 \| 0.6779 \| 0.0267 \| nan \| 0.0162 \| nan \| 0.2609 \| 0.4275 \| 0.4104 \| 0.0 \| 0.6576 \| 0.3815 \| 0.6209 \| 0.6334 \| 0.0267 \| nan \| 0.0161 \| nan \| 0.2406 \| 0.3728 \| 0.3871 \| \| 0.1828 \| 15.12 \| 2420 \| 0.3641 \| 0.3060 \| 0.3564 \| 0.5373 \| nan \| 0.6464 \| 0.3564 \| 0.5524 \| 0.6620 \| 0.0132 \| nan \| 0.0079 \| nan \| 0.1817 \| 0.3260 \| 0.4615 \| 0.0 \| 0.6299 \| 0.3491 \| 0.5459 \| 0.6195 \| 0.0132 \| nan \| 0.0078 \| nan \| 0.1742 \| 0.2937 \| 0.4271 \| \| 0.0202 \| 15.25 \| 2440 \| 0.3637 \| 0.3310 \| 0.3910 \| 0.5513 \| nan \| 0.6071 \| 0.4158 \| 0.6488 \| 0.7365 \| 0.0339 \| nan \| 0.0181 \| nan \| 0.2819 \| 0.3906 \| 0.3863 \| 0.0 \| 0.5933 \| 0.3991 \| 0.6291 \| 0.6687 \| 0.0339 \| nan \| 0.0179 \| nan \| 0.2585 \| 0.3465 \| 0.3631 \| \| 0.3244 \| 15.38 \| 2460 \| 0.3752 \| 0.3290 \| 0.3885 \| 0.5348 \| nan \| 0.5915 \| 0.4283 \| 0.6163 \| 0.6207 \| 0.0442 \| nan \| 0.0320 \| nan \| 0.2746 \| 0.4170 \| 0.4722 \| 0.0 \| 0.5762 \| 0.4087 \| 0.5914 \| 0.5837 \| 0.0442 \| nan \| 0.0315 \| nan \| 0.2498 \| 0.3633 \| 0.4410 \| \| 0.0619 \| 15.5 \| 2480 \| 0.3794 \| 0.3052 \| 0.3560 \| 0.5207 \| nan \| 0.6266 \| 0.3283 \| 0.6050 \| 0.6392 \| 0.0387 \| nan \| 0.0303 \| nan \| 0.2255 \| 0.3605 \| 0.3501 \| 0.0 \| 0.6104 \| 0.3232 \| 0.5877 \| 0.5951 \| 0.0387 \| nan \| 0.0300 \| nan \| 0.2125 \| 0.3189 \| 0.3357 \| \| 0.0788 \| 15.62 \| 2500 \| 0.3641 \| 0.3564 \| 0.4204 \| 0.6062 \| nan \| 0.6924 \| 0.5027 \| 0.6264 \| 0.7243 \| 0.0330 \| nan \| 0.0246 \| nan \| 0.2279 \| 0.4379 \| 0.5142 \| 0.0 \| 0.6721 \| 0.4863 \| 0.6116 \| 0.6645 \| 0.0330 \| nan \| 0.0244 \| nan \| 0.2181 \| 0.3780 \| 0.4764 \| \| 0.1819 \| 15.75 \| 2520 \| 0.3730 \| 0.3330 \| 0.3919 \| 0.5496 \| nan \| 0.6366 \| 0.3864 \| 0.5794 \| 0.6584 \| 0.0296 \| nan \| 0.0193 \| nan \| 0.2730 \| 0.4636 \| 0.4804 \| 0.0 \| 0.6209 \| 0.3793 \| 0.5687 \| 0.6158 \| 0.0296 \| nan \| 0.0190 \| nan \| 0.2520 \| 0.3970 \| 0.4476 \| \| 0.1583 \| 15.88 \| 2540 \| 0.3707 \| 0.3437 \| 0.4053 \| 0.5775 \| nan \| 0.6838 \| 0.4042 \| 0.6664 \| 0.6669 \| 0.0270 \| nan \| 0.0380 \| nan \| 0.2653 \| 0.4832 \| 0.4127 \| 0.0 \| 0.6658 \| 0.3962 \| 0.6401 \| 0.6213 \| 0.0269 \| nan \| 0.0374 \| nan \| 0.2491 \| 0.4074 \| 0.3930 \| \| 0.0973 \| 16.0 \| 2560 \| 0.3789 \| 0.3102 \| 0.3645 \| 0.5251 \| nan \| 0.6202 \| 0.3226 \| 0.6377 \| 0.6612 \| 0.0240 \| nan \| 0.0420 \| nan \| 0.2656 \| 0.3646 \| 0.3432 \| 0.0 \| 0.6064 \| 0.3155 \| 0.6133 \| 0.6167 \| 0.0240 \| nan \| 0.0411 \| nan \| 0.2490 \| 0.3193 \| 0.3171 \| \| 0.188 \| 16.12 \| 2580 \| 0.3646 \| 0.3456 \| 0.4070 \| 0.5929 \| nan \| 0.7021 \| 0.4230 \| 0.6325 \| 0.7055 \| 0.0310 \| nan \| 0.0434 \| nan \| 0.3221 \| 0.3235 \| 0.4801 \| 0.0 \| 0.6798 \| 0.4127 \| 0.6135 \| 0.6518 \| 0.0309 \| nan \| 0.0423 \| nan \| 0.2943 \| 0.2847 \| 0.4458 \| \| 0.0581 \| 16.25 \| 2600 \| 0.4030 \| 0.2992 \| 0.3506 \| 0.4870 \| nan \| 0.5565 \| 0.3320 \| 0.6005 \| 0.6297 \| 0.0621 \| nan \| 0.0399 \| nan \| 0.2508 \| 0.4016 \| 0.2824 \| 0.0 \| 0.5438 \| 0.3243 \| 0.5851 \| 0.5852 \| 0.0610 \| nan \| 0.0391 \| nan \| 0.2336 \| 0.3470 \| 0.2729 \| \| 0.1891 \| 16.38 \| 2620 \| 0.3766 \| 0.3408 \| 0.4029 \| 0.5618 \| nan \| 0.6308 \| 0.4264 \| 0.6309 \| 0.6672 \| 0.0537 \| nan \| 0.0358 \| nan \| 0.2444 \| 0.4289 \| 0.5077 \| 0.0 \| 0.6150 \| 0.4147 \| 0.6043 \| 0.6202 \| 0.0528 \| nan \| 0.0353 \| nan \| 0.2302 \| 0.3673 \| 0.4679 \| \| 0.2495 \| 16.5 \| 2640 \| 0.3758 \| 0.3481 \| 0.4106 \| 0.5897 \| nan \| 0.7078 \| 0.4004 \| 0.6212 \| 0.6981 \| 0.0769 \| nan \| 0.0402 \| nan \| 0.2365 \| 0.4410 \| 0.4736 \| 0.0 \| 0.6873 \| 0.3937 \| 0.6026 \| 0.6416 \| 0.0749 \| nan \| 0.0391 \| nan \| 0.2227 \| 0.3776 \| 0.4418 \| \| 0.153 \| 16.62 \| 2660 \| 0.3899 \| 0.2935 \| 0.3435 \| 0.5074 \| nan \| 0.6166 \| 0.3835 \| 0.5905 \| 0.5641 \| 0.0402 \| nan \| 0.0290 \| nan \| 0.2244 \| 0.3407 \| 0.3025 \| 0.0 \| 0.6027 \| 0.3733 \| 0.5639 \| 0.5320 \| 0.0402 \| nan \| 0.0286 \| nan \| 0.2092 \| 0.2969 \| 0.2883 \| \| 0.083 \| 16.75 \| 2680 \| 0.3758 \| 0.3137 \| 0.3669 \| 0.5555 \| nan \| 0.6916 \| 0.3837 \| 0.5386 \| 0.6336 \| 0.0400 \| nan \| 0.0247 \| nan \| 0.2183 \| 0.3063 \| 0.4655 \| 0.0 \| 0.6742 \| 0.3761 \| 0.5322 \| 0.5845 \| 0.0400 \| nan \| 0.0243 \| nan \| 0.2066 \| 0.2715 \| 0.4276 \| \| 0.136 \| 16.88 \| 2700 \| 0.3570 \| 0.3606 \| 0.4264 \| 0.6044 \| nan \| 0.7070 \| 0.4583 \| 0.6819 \| 0.6820 \| 0.0525 \| nan \| 0.0404 \| nan \| 0.3045 \| 0.4473 \| 0.4636 \| 0.0 \| 0.6892 \| 0.4490 \| 0.6517 \| 0.6349 \| 0.0524 \| nan \| 0.0396 \| nan \| 0.2758 \| 0.3799 \| 0.4333 \| \| 0.0609 \| 17.0 \| 2720 \| 0.3657 \| 0.3182 \| 0.3719 \| 0.5547 \| nan \| 0.6883 \| 0.3931 \| 0.5851 \| 0.6371 \| 0.0482 \| nan \| 0.0290 \| nan \| 0.2633 \| 0.3435 \| 0.3598 \| 0.0 \| 0.6688 \| 0.3841 \| 0.5697 \| 0.5975 \| 0.0482 \| nan \| 0.0285 \| nan \| 0.2450 \| 0.3019 \| 0.3384 \| \| 0.1483 \| 17.12 \| 2740 \| 0.3847 \| 0.3068 \| 0.3620 \| 0.5117 \| nan \| 0.5805 \| 0.3386 \| 0.5781 \| 0.6593 \| 0.0445 \| nan \| 0.0343 \| nan \| 0.2763 \| 0.3060 \| 0.4403 \| 0.0 \| 0.5686 \| 0.3269 \| 0.5604 \| 0.6165 \| 0.0439 \| nan \| 0.0335 \| nan \| 0.2507 \| 0.2700 \| 0.3978 \| \| 0.2166 \| 17.25 \| 2760 \| 0.3986 \| 0.2853 \| 0.3307 \| 0.5154 \| nan \| 0.6550 \| 0.3083 \| 0.5705 \| 0.6325 \| 0.0442 \| nan \| 0.0205 \| nan \| 0.1784 \| 0.2618 \| 0.3047 \| 0.0 \| 0.6390 \| 0.3038 \| 0.5618 \| 0.5883 \| 0.0441 \| nan \| 0.0203 \| nan \| 0.1703 \| 0.2369 \| 0.2887 \| \| 0.096 \| 17.38 \| 2780 \| 0.4041 \| 0.2884 \| 0.3367 \| 0.4939 \| nan \| 0.6043 \| 0.2952 \| 0.5538 \| 0.6037 \| 0.0470 \| nan \| 0.0231 \| nan \| 0.2257 \| 0.3329 \| 0.3444 \| 0.0 \| 0.5912 \| 0.2905 \| 0.5458 \| 0.5617 \| 0.0470 \| nan \| 0.0228 \| nan \| 0.2106 \| 0.2930 \| 0.3217 \| \| 0.0839 \| 17.5 \| 2800 \| 0.3773 \| 0.3516 \| 0.4152 \| 0.6011 \| nan \| 0.7099 \| 0.4639 \| 0.6553 \| 0.7115 \| 0.0586 \| nan \| 0.0328 \| nan \| 0.3030 \| 0.3879 \| 0.4140 \| 0.0 \| 0.6884 \| 0.4484 \| 0.6341 \| 0.6528 \| 0.0586 \| nan \| 0.0324 \| nan \| 0.2784 \| 0.3349 \| 0.3877 \| \| 0.1185 \| 17.62 \| 2820 \| 0.3753 \| 0.3255 \| 0.3816 \| 0.5517 \| nan \| 0.6575 \| 0.4173 \| 0.6167 \| 0.6411 \| 0.0711 \| nan \| 0.0270 \| nan \| 0.2554 \| 0.3842 \| 0.3643 \| 0.0 \| 0.6400 \| 0.4044 \| 0.6019 \| 0.5946 \| 0.0708 \| nan \| 0.0267 \| nan \| 0.2403 \| 0.3319 \| 0.3448 \| \| 0.1155 \| 17.75 \| 2840 \| 0.3742 \| 0.3354 \| 0.3939 \| 0.5575 \| nan \| 0.6693 \| 0.3661 \| 0.6355 \| 0.6422 \| 0.0786 \| nan \| 0.0323 \| nan \| 0.2502 \| 0.4593 \| 0.4115 \| 0.0 \| 0.6510 \| 0.3595 \| 0.6193 \| 0.5977 \| 0.0776 \| nan \| 0.0320 \| nan \| 0.2347 \| 0.3894 \| 0.3928 \| \| 0.1311 \| 17.88 \| 2860 \| 0.3862 \| 0.3306 \| 0.3882 \| 0.5436 \| nan \| 0.6268 \| 0.3595 \| 0.6226 \| 0.6840 \| 0.0823 \| nan \| 0.0400 \| nan \| 0.2510 \| 0.4016 \| 0.4261 \| 0.0 \| 0.6125 \| 0.3525 \| 0.6087 \| 0.6301 \| 0.0816 \| nan \| 0.0394 \| nan \| 0.2348 \| 0.3472 \| 0.3995 \| \| 0.0059 \| 18.0 \| 2880 \| 0.3997 \| 0.2999 \| 0.3509 \| 0.5209 \| nan \| 0.6406 \| 0.3559 \| 0.6409 \| 0.5987 \| 0.0524 \| nan \| 0.0490 \| nan \| 0.2912 \| 0.2650 \| 0.2648 \| 0.0 \| 0.6221 \| 0.3495 \| 0.6051 \| 0.5625 \| 0.0523 \| nan \| 0.0474 \| nan \| 0.2684 \| 0.2353 \| 0.2563 \| \| 0.2505 \| 18.12 \| 2900 \| 0.3948 \| 0.3107 \| 0.3648 \| 0.5266 \| nan \| 0.6174 \| 0.3923 \| 0.5960 \| 0.6209 \| 0.0640 \| nan \| 0.0383 \| nan \| 0.2507 \| 0.3099 \| 0.3937 \| 0.0 \| 0.6028 \| 0.3777 \| 0.5739 \| 0.5786 \| 0.0634 \| nan \| 0.0375 \| nan \| 0.2350 \| 0.2722 \| 0.3660 \| \| 0.1181 \| 18.25 \| 2920 \| 0.3764 \| 0.3401 \| 0.3999 \| 0.5709 \| nan \| 0.6792 \| 0.4368 \| 0.6006 \| 0.6382 \| 0.0752 \| nan \| 0.0399 \| nan \| 0.2711 \| 0.3991 \| 0.4587 \| 0.0 \| 0.6604 \| 0.4226 \| 0.5815 \| 0.5978 \| 0.0736 \| nan \| 0.0392 \| nan \| 0.2526 \| 0.3440 \| 0.4290 \| \| 0.1169 \| 18.38 \| 2940 \| 0.3737 \| 0.3522 \| 0.4141 \| 0.5853 \| nan \| 0.6818 \| 0.4810 \| 0.6037 \| 0.6819 \| 0.1025 \| nan \| 0.0410 \| nan \| 0.2644 \| 0.4201 \| 0.4503 \| 0.0 \| 0.6615 \| 0.4636 \| 0.5918 \| 0.6288 \| 0.1004 \| nan \| 0.0400 \| nan \| 0.2486 \| 0.3615 \| 0.4256 \| \| 0.1075 \| 18.5 \| 2960 \| 0.3985 \| 0.3176 \| 0.3711 \| 0.5438 \| nan \| 0.6841 \| 0.3154 \| 0.5765 \| 0.6472 \| 0.0906 \| nan \| 0.0435 \| nan \| 0.2634 \| 0.3396 \| 0.3801 \| 0.0 \| 0.6643 \| 0.3104 \| 0.5673 \| 0.6015 \| 0.0897 \| nan \| 0.0423 \| nan \| 0.2444 \| 0.3020 \| 0.3537 \| \| 0.1468 \| 18.62 \| 2980 \| 0.3809 \| 0.3592 \| 0.4249 \| 0.5883 \| nan \| 0.6884 \| 0.4059 \| 0.6539 \| 0.6849 \| 0.0905 \| nan \| 0.0482 \| nan \| 0.2846 \| 0.4887 \| 0.4791 \| 0.0 \| 0.6685 \| 0.3996 \| 0.6290 \| 0.6350 \| 0.0881 \| nan \| 0.0469 \| nan \| 0.2632 \| 0.4144 \| 0.4469 \| \| 0.1438 \| 18.75 \| 3000 \| 0.4059 \| 0.3267 \| 0.3847 \| 0.5308 \| nan \| 0.6163 \| 0.4193 \| 0.5594 \| 0.6195 \| 0.1263 \| nan \| 0.0415 \| nan \| 0.2273 \| 0.4443 \| 0.4081 \| 0.0 \| 0.6014 \| 0.4035 \| 0.5470 \| 0.5813 \| 0.1224 \| nan \| 0.0406 \| nan \| 0.2127 \| 0.3774 \| 0.3810 \| \| 0.1021 \| 18.88 \| 3020 \| 0.3904 \| 0.3525 \| 0.4154 \| 0.5957 \| nan \| 0.7113 \| 0.4367 \| 0.6382 \| 0.7089 \| 0.1015 \| nan \| 0.0432 \| nan \| 0.2595 \| 0.4177 \| 0.4213 \| 0.0 \| 0.6907 \| 0.4264 \| 0.6212 \| 0.6528 \| 0.0988 \| nan \| 0.0422 \| nan \| 0.2428 \| 0.3582 \| 0.3923 \| \| 0.0308 \| 19.0 \| 3040 \| 0.3790 \| 0.3502 \| 0.4129 \| 0.5842 \| nan \| 0.6850 \| 0.4325 \| 0.6381 \| 0.6733 \| 0.0994 \| nan \| 0.0429 \| nan \| 0.2607 \| 0.4038 \| 0.4803 \| 0.0 \| 0.6662 \| 0.4228 \| 0.6189 \| 0.6261 \| 0.0967 \| nan \| 0.0420 \| nan \| 0.2443 \| 0.3470 \| 0.4380 \| \| 0.2127 \| 19.12 \| 3060 \| 0.3938 \| 0.3268 \| 0.3842 \| 0.5413 \| nan \| 0.6469 \| 0.3713 \| 0.5834 \| 0.6338 \| 0.0856 \| nan \| 0.0419 \| nan \| 0.2657 \| 0.4172 \| 0.4118 \| 0.0 \| 0.6307 \| 0.3641 \| 0.5711 \| 0.5914 \| 0.0838 \| nan \| 0.0410 \| nan \| 0.2465 \| 0.3570 \| 0.3822 \| \| 0.1228 \| 19.25 \| 3080 \| 0.3930 \| 0.3371 \| 0.3961 \| 0.5707 \| nan \| 0.6900 \| 0.3839 \| 0.5852 \| 0.6874 \| 0.0759 \| nan \| 0.0337 \| nan \| 0.2645 \| 0.4055 \| 0.4391 \| 0.0 \| 0.6701 \| 0.3767 \| 0.5733 \| 0.6383 \| 0.0745 \| nan \| 0.0331 \| nan \| 0.2462 \| 0.3494 \| 0.4094 \| \| 0.0882 \| 19.38 \| 3100 \| 0.3940 \| 0.3372 \| 0.3954 \| 0.5712 \| nan \| 0.6829 \| 0.4213 \| 0.6223 \| 0.6689 \| 0.0740 \| nan \| 0.0338 \| nan \| 0.2622 \| 0.3936 \| 0.3997 \| 0.0 \| 0.6636 \| 0.4108 \| 0.6046 \| 0.6226 \| 0.0729 \| nan \| 0.0332 \| nan \| 0.2445 \| 0.3419 \| 0.3776 \| \| 0.0798 \| 19.5 \| 3120 \| 0.4141 \| 0.3078 \| 0.3595 \| 0.5247 \| nan \| 0.6568 \| 0.2977 \| 0.5794 \| 0.6253 \| 0.0788 \| nan \| 0.0341 \| nan \| 0.2380 \| 0.3701 \| 0.3549 \| 0.0 \| 0.6388 \| 0.2941 \| 0.5651 \| 0.5852 \| 0.0775 \| nan \| 0.0332 \| nan \| 0.2237 \| 0.3256 \| 0.3351 \| \| 0.2337 \| 19.62 \| 3140 \| 0.3981 \| 0.3068 \| 0.3583 \| 0.5271 \| nan \| 0.6597 \| 0.3512 \| 0.5748 \| 0.5754 \| 0.0715 \| nan \| 0.0304 \| nan \| 0.2115 \| 0.3767 \| 0.3733 \| 0.0 \| 0.6439 \| 0.3439 \| 0.5569 \| 0.5412 \| 0.0701 \| nan \| 0.0299 \| nan \| 0.2004 \| 0.3301 \| 0.3515 \| \| 0.265 \| 19.75 \| 3160 \| 0.3878 \| 0.3441 \| 0.4051 \| 0.5883 \| nan \| 0.7216 \| 0.4309 \| 0.6112 \| 0.6456 \| 0.1042 \| nan \| 0.0316 \| nan \| 0.2539 \| 0.3974 \| 0.4499 \| 0.0 \| 0.6987 \| 0.4186 \| 0.5900 \| 0.6016 \| 0.1009 \| nan \| 0.0311 \| nan \| 0.2385 \| 0.3425 \| 0.4188 \| \| 0.1612 \| 19.88 \| 3180 \| 0.3921 \| 0.3399 \| 0.4015 \| 0.5622 \| nan \| 0.6680 \| 0.3758 \| 0.6031 \| 0.6745 \| 0.1219 \| nan \| 0.0368 \| nan \| 0.2783 \| 0.4113 \| 0.4439 \| 0.0 \| 0.6509 \| 0.3660 \| 0.5870 \| 0.6189 \| 0.1176 \| nan \| 0.0361 \| nan \| 0.2584 \| 0.3531 \| 0.4113 \| \| 0.119 \| 20.0 \| 3200 \| 0.3930 \| 0.3381 \| 0.3973 \| 0.5638 \| nan \| 0.6846 \| 0.3830 \| 0.6002 \| 0.6528 \| 0.1236 \| nan \| 0.0364 \| nan \| 0.2504 \| 0.4254 \| 0.4196 \| 0.0 \| 0.6659 \| 0.3747 \| 0.5862 \| 0.6027 \| 0.1201 \| nan \| 0.0357 \| nan \| 0.2355 \| 0.3642 \| 0.3958 \| \| 0.2127 \| 20.12 \| 3220 \| 0.4055 \| 0.3262 \| 0.3828 \| 0.5438 \| nan \| 0.6687 \| 0.3572 \| 0.5735 \| 0.6260 \| 0.1227 \| nan \| 0.0452 \| nan \| 0.2759 \| 0.3804 \| 0.3954 \| 0.0 \| 0.6509 \| 0.3480 \| 0.5618 \| 0.5802 \| 0.1193 \| nan \| 0.0442 \| nan \| 0.2554 \| 0.3293 \| 0.3729 \| \| 0.1082 \| 20.25 \| 3240 \| 0.4076 \| 0.3233 \| 0.3790 \| 0.5477 \| nan \| 0.6622 \| 0.3678 \| 0.6170 \| 0.6725 \| 0.1201 \| nan \| 0.0408 \| nan \| 0.2368 \| 0.3502 \| 0.3435 \| 0.0 \| 0.6445 \| 0.3588 \| 0.5987 \| 0.6188 \| 0.1167 \| nan \| 0.0400 \| nan \| 0.2242 \| 0.3062 \| 0.3253 \| \| 0.1413 \| 20.38 \| 3260 \| 0.3997 \| 0.3315 \| 0.3894 \| 0.5515 \| nan \| 0.6595 \| 0.3799 \| 0.5992 \| 0.6447 \| 0.1206 \| nan \| 0.0406 \| nan \| 0.2610 \| 0.3701 \| 0.4294 \| 0.0 \| 0.6426 \| 0.3715 \| 0.5838 \| 0.5996 \| 0.1176 \| nan \| 0.0399 \| nan \| 0.2422 \| 0.3200 \| 0.3973 \| \| 0.1308 \| 20.5 \| 3280 \| 0.4210 \| 0.3100 \| 0.3624 \| 0.5150 \| nan \| 0.6198 \| 0.3069 \| 0.5631 \| 0.6500 \| 0.1115 \| nan \| 0.0402 \| nan \| 0.2334 \| 0.3535 \| 0.3831 \| 0.0 \| 0.6049 \| 0.3025 \| 0.5540 \| 0.6033 \| 0.1102 \| nan \| 0.0393 \| nan \| 0.2189 \| 0.3098 \| 0.3574 \| \| 0.0599 \| 20.62 \| 3300 \| 0.3929 \| 0.3457 \| 0.4067 \| 0.5822 \| nan \| 0.6963 \| 0.4452 \| 0.6235 \| 0.6543 \| 0.1024 \| nan \| 0.0453 \| nan \| 0.2641 \| 0.3904 \| 0.4386 \| 0.0 \| 0.6756 \| 0.4337 \| 0.6062 \| 0.6087 \| 0.1002 \| nan \| 0.0443 \| nan \| 0.2460 \| 0.3365 \| 0.4061 \| \| 0.0948 \| 20.75 \| 3320 \| 0.3978 \| 0.3346 \| 0.3933 \| 0.5553 \| nan \| 0.6649 \| 0.4108 \| 0.6084 \| 0.6221 \| 0.1142 \| nan \| 0.0400 \| nan \| 0.2518 \| 0.4197 \| 0.4076 \| 0.0 \| 0.6479 \| 0.3990 \| 0.5918 \| 0.5812 \| 0.1109 \| nan \| 0.0393 \| nan \| 0.2337 \| 0.3621 \| 0.3800 \| \| 0.0418 \| 20.88 \| 3340 \| 0.3987 \| 0.3413 \| 0.4016 \| 0.5691 \| nan \| 0.6820 \| 0.4091 \| 0.6041 \| 0.6482 \| 0.1110 \| nan \| 0.0344 \| nan \| 0.2501 \| 0.4283 \| 0.4476 \| 0.0 \| 0.6632 \| 0.3996 \| 0.5894 \| 0.6026 \| 0.1079 \| nan \| 0.0337 \| nan \| 0.2342 \| 0.3677 \| 0.4142 \| \| 0.2251 \| 21.0 \| 3360 \| 0.4091 \| 0.3067 \| 0.3576 \| 0.5235 \| nan \| 0.6628 \| 0.3239 \| 0.5825 \| 0.6036 \| 0.1097 \| nan \| 0.0348 \| nan \| 0.2448 \| 0.3554 \| 0.3010 \| 0.0 \| 0.6446 \| 0.3177 \| 0.5663 \| 0.5655 \| 0.1064 \| nan \| 0.0340 \| nan \| 0.2288 \| 0.3149 \| 0.2890 \| \| 0.0887 \| 21.12 \| 3380 \| 0.3958 \| 0.3502 \| 0.4144 \| 0.5830 \| nan \| 0.6798 \| 0.4381 \| 0.6436 \| 0.7003 \| 0.0985 \| nan \| 0.0553 \| nan \| 0.3298 \| 0.3692 \| 0.4148 \| 0.0 \| 0.6608 \| 0.4252 \| 0.6204 \| 0.6454 \| 0.0957 \| nan \| 0.0537 \| nan \| 0.2975 \| 0.3220 \| 0.3809 \| \| 0.0981 \| 21.25 \| 3400 \| 0.4053 \| 0.3346 \| 0.3938 \| 0.5521 \| nan \| 0.6416 \| 0.4158 \| 0.6278 \| 0.6327 \| 0.1152 \| nan \| 0.0472 \| nan \| 0.2642 \| 0.3607 \| 0.4387 \| 0.0 \| 0.6267 \| 0.4034 \| 0.6096 \| 0.5906 \| 0.1116 \| nan \| 0.0460 \| nan \| 0.2430 \| 0.3153 \| 0.3998 \| \| 0.129 \| 21.38 \| 3420 \| 0.4044 \| 0.3195 \| 0.3724 \| 0.5357 \| nan \| 0.6534 \| 0.3715 \| 0.5931 \| 0.6088 \| 0.1166 \| nan \| 0.0411 \| nan \| 0.2463 \| 0.3438 \| 0.3765 \| 0.0 \| 0.6364 \| 0.3635 \| 0.5791 \| 0.5711 \| 0.1137 \| nan \| 0.0403 \| nan \| 0.2292 \| 0.3042 \| 0.3575 \| \| 0.0612 \| 21.5 \| 3440 \| 0.3903 \| 0.3486 \| 0.4101 \| 0.5821 \| nan \| 0.6849 \| 0.4416 \| 0.6240 \| 0.6569 \| 0.1031 \| nan \| 0.0424 \| nan \| 0.2770 \| 0.3685 \| 0.4925 \| 0.0 \| 0.6663 \| 0.4307 \| 0.6044 \| 0.6109 \| 0.0999 \| nan \| 0.0416 \| nan \| 0.2569 \| 0.3219 \| 0.4536 \| \| 0.1272 \| 21.62 \| 3460 \| 0.4190 \| 0.3084 \| 0.3600 \| 0.5155 \| nan \| 0.6333 \| 0.3347 \| 0.5558 \| 0.6046 \| 0.0905 \| nan \| 0.0351 \| nan \| 0.2328 \| 0.4021 \| 0.3511 \| 0.0 \| 0.6178 \| 0.3281 \| 0.5458 \| 0.5679 \| 0.0895 \| nan \| 0.0345 \| nan \| 0.2190 \| 0.3491 \| 0.3325 \| \| 0.0396 \| 21.75 \| 3480 \| 0.4083 \| 0.3243 \| 0.3801 \| 0.5463 \| nan \| 0.6695 \| 0.3636 \| 0.6050 \| 0.6290 \| 0.0988 \| nan \| 0.0423 \| nan \| 0.2516 \| 0.3892 \| 0.3716 \| 0.0 \| 0.6523 \| 0.3566 \| 0.5879 \| 0.5871 \| 0.0963 \| nan \| 0.0414 \| nan \| 0.2360 \| 0.3372 \| 0.3485 \| \| 0.1612 \| 21.88 \| 3500 \| 0.4034 \| 0.3276 \| 0.3836 \| 0.5571 \| nan \| 0.6817 \| 0.4096 \| 0.5779 \| 0.6470 \| 0.1009 \| nan \| 0.0430 \| nan \| 0.2657 \| 0.3577 \| 0.3690 \| 0.0 \| 0.6632 \| 0.3995 \| 0.5630 \| 0.6014 \| 0.0982 \| nan \| 0.0420 \| nan \| 0.2475 \| 0.3112 \| 0.3502 \| \| 0.168 \| 22.0 \| 3520 \| 0.3960 \| 0.3299 \| 0.3866 \| 0.5620 \| nan \| 0.6894 \| 0.3956 \| 0.5776 \| 0.6484 \| 0.0963 \| nan \| 0.0393 \| nan \| 0.2551 \| 0.3656 \| 0.4123 \| 0.0 \| 0.6689 \| 0.3867 \| 0.5646 \| 0.6040 \| 0.0935 \| nan \| 0.0384 \| nan \| 0.2394 \| 0.3177 \| 0.3853 \| \| 0.1447 \| 22.12 \| 3540 \| 0.4078 \| 0.3373 \| 0.3970 \| 0.5641 \| nan \| 0.6879 \| 0.3803 \| 0.5834 \| 0.6534 \| 0.0952 \| nan \| 0.0500 \| nan \| 0.2945 \| 0.4033 \| 0.4248 \| 0.0 \| 0.6674 \| 0.3724 \| 0.5691 \| 0.6079 \| 0.0923 \| nan \| 0.0486 \| nan \| 0.2704 \| 0.3478 \| 0.3974 \| \| 0.053 \| 22.25 \| 3560 \| 0.4011 \| 0.3369 \| 0.3956 \| 0.5696 \| nan \| 0.7026 \| 0.4088 \| 0.5707 \| 0.6348 \| 0.0921 \| nan \| 0.0507 \| nan \| 0.2935 \| 0.3820 \| 0.4256 \| 0.0 \| 0.6803 \| 0.3990 \| 0.5570 \| 0.5941 \| 0.0895 \| nan \| 0.0494 \| nan \| 0.2709 \| 0.3313 \| 0.3978 \| \| 0.1187 \| 22.38 \| 3580 \| 0.4053 \| 0.3330 \| 0.3913 \| 0.5555 \| nan \| 0.6709 \| 0.3798 \| 0.5832 \| 0.6476 \| 0.0971 \| nan \| 0.0440 \| nan \| 0.2728 \| 0.3996 \| 0.4266 \| 0.0 \| 0.6529 \| 0.3718 \| 0.5682 \| 0.6036 \| 0.0940 \| nan \| 0.0429 \| nan \| 0.2524 \| 0.3462 \| 0.3977 \| \| 0.1373 \| 22.5 \| 3600 \| 0.4094 \| 0.3264 \| 0.3829 \| 0.5411 \| nan \| 0.6504 \| 0.3713 \| 0.5818 \| 0.6355 \| 0.1050 \| nan \| 0.0402 \| nan \| 0.2590 \| 0.4045 \| 0.3984 \| 0.0 \| 0.6343 \| 0.3624 \| 0.5688 \| 0.5951 \| 0.1009 \| nan \| 0.0394 \| nan \| 0.2407 \| 0.3515 \| 0.3709 \| \| 0.144 \| 22.62 \| 3620 \| 0.4051 \| 0.3243 \| 0.3792 \| 0.5460 \| nan \| 0.6617 \| 0.3776 \| 0.6009 \| 0.6535 \| 0.1049 \| nan \| 0.0384 \| nan \| 0.2643 \| 0.3601 \| 0.3512 \| 0.0 \| 0.6448 \| 0.3688 \| 0.5872 \| 0.6082 \| 0.1005 \| nan \| 0.0377 \| nan \| 0.2463 \| 0.3173 \| 0.3320 \| \| 0.0716 \| 22.75 \| 3640 \| 0.4071 \| 0.3209 \| 0.3750 \| 0.5386 \| nan \| 0.6443 \| 0.3817 \| 0.5979 \| 0.6464 \| 0.1080 \| nan \| 0.0381 \| nan \| 0.2470 \| 0.3450 \| 0.3667 \| 0.0 \| 0.6288 \| 0.3727 \| 0.5835 \| 0.6006 \| 0.1032 \| nan \| 0.0374 \| nan \| 0.2315 \| 0.3057 \| 0.3453 \| \| 0.0869 \| 22.88 \| 3660 \| 0.4162 \| 0.3118 \| 0.3636 \| 0.5269 \| nan \| 0.6438 \| 0.3654 \| 0.5745 \| 0.6012 \| 0.1058 \| nan \| 0.0359 \| nan \| 0.2423 \| 0.3257 \| 0.3781 \| 0.0 \| 0.6281 \| 0.3557 \| 0.5610 \| 0.5643 \| 0.1014 \| nan \| 0.0353 \| nan \| 0.2266 \| 0.2902 \| 0.3554 \| \| 0.0846 \| 23.0 \| 3680 \| 0.4079 \| 0.3326 \| 0.3902 \| 0.5584 \| nan \| 0.6786 \| 0.3830 \| 0.5896 \| 0.6438 \| 0.1063 \| nan \| 0.0384 \| nan \| 0.2539 \| 0.3928 \| 0.4253 \| 0.0 \| 0.6605 \| 0.3742 \| 0.5765 \| 0.5994 \| 0.1019 \| nan \| 0.0376 \| nan \| 0.2376 \| 0.3425 \| 0.3957 \| \| 0.1137 \| 23.12 \| 3700 \| 0.4062 \| 0.3270 \| 0.3827 \| 0.5498 \| nan \| 0.6647 \| 0.3757 \| 0.6069 \| 0.6502 \| 0.1083 \| nan \| 0.0380 \| nan \| 0.2465 \| 0.3729 \| 0.3814 \| 0.0 \| 0.6474 \| 0.3675 \| 0.5903 \| 0.6042 \| 0.1038 \| nan \| 0.0372 \| nan \| 0.2317 \| 0.3290 \| 0.3588 \| \| 0.109 \| 23.25 \| 3720 \| 0.4160 \| 0.3217 \| 0.3761 \| 0.5445 \| nan \| 0.6679 \| 0.3698 \| 0.5816 \| 0.6386 \| 0.1013 \| nan \| 0.0424 \| nan \| 0.2568 \| 0.3538 \| 0.3723 \| 0.0 \| 0.6503 \| 0.3618 \| 0.5679 \| 0.5943 \| 0.0978 \| nan \| 0.0415 \| nan \| 0.2394 \| 0.3129 \| 0.3514 \| \| 0.0314 \| 23.38 \| 3740 \| 0.4166 \| 0.3156 \| 0.3685 \| 0.5342 \| nan \| 0.6615 \| 0.3651 \| 0.5650 \| 0.6126 \| 0.1027 \| nan \| 0.0433 \| nan \| 0.2581 \| 0.3479 \| 0.3605 \| 0.0 \| 0.6445 \| 0.3558 \| 0.5521 \| 0.5746 \| 0.0988 \| nan \| 0.0424 \| nan \| 0.2393 \| 0.3083 \| 0.3401 \| \| 0.1021 \| 23.5 \| 3760 \| 0.4215 \| 0.3218 \| 0.3774 \| 0.5350 \| nan \| 0.6494 \| 0.3625 \| 0.5961 \| 0.6217 \| 0.1038 \| nan \| 0.0448 \| nan \| 0.2675 \| 0.3971 \| 0.3539 \| 0.0 \| 0.6335 \| 0.3532 \| 0.5796 \| 0.5818 \| 0.0997 \| nan \| 0.0438 \| nan \| 0.2465 \| 0.3460 \| 0.3336 \| \| 0.0086 \| 23.62 \| 3780 \| 0.4121 \| 0.3224 \| 0.3779 \| 0.5430 \| nan \| 0.6630 \| 0.3837 \| 0.5842 \| 0.6257 \| 0.1028 \| nan \| 0.0432 \| nan \| 0.2651 \| 0.3702 \| 0.3634 \| 0.0 \| 0.6461 \| 0.3725 \| 0.5697 \| 0.5847 \| 0.0990 \| nan \| 0.0423 \| nan \| 0.2451 \| 0.3231 \| 0.3413 \| \| 0.1086 \| 23.75 \| 3800 \| 0.4078 \| 0.3309 \| 0.3892 \| 0.5521 \| nan \| 0.6630 \| 0.3944 \| 0.5991 \| 0.6368 \| 0.1092 \| nan \| 0.0444 \| nan \| 0.2721 \| 0.3831 \| 0.4007 \| 0.0 \| 0.6464 \| 0.3824 \| 0.5830 \| 0.5932 \| 0.1047 \| nan \| 0.0435 \| nan \| 0.2511 \| 0.3331 \| 0.3716 \| \| 0.1121 \| 23.88 \| 3820 \| 0.4119 \| 0.3221 \| 0.3780 \| 0.5369 \| nan \| 0.6487 \| 0.3695 \| 0.5765 \| 0.6271 \| 0.1155 \| nan \| 0.0431 \| nan \| 0.2546 \| 0.3730 \| 0.3941 \| 0.0 \| 0.6329 \| 0.3588 \| 0.5643 \| 0.5842 \| 0.1105 \| nan \| 0.0422 \| nan \| 0.2368 \| 0.3252 \| 0.3666 \| \| 0.1118 \| 24.0 \| 3840 \| 0.4161 \| 0.3239 \| 0.3799 \| 0.5430 \| nan \| 0.6645 \| 0.3623 \| 0.5649 \| 0.6499 \| 0.1168 \| nan \| 0.0422 \| nan \| 0.2492 \| 0.3927 \| 0.3769 \| 0.0 \| 0.6471 \| 0.3532 \| 0.5559 \| 0.6011 \| 0.1122 \| nan \| 0.0413 \| nan \| 0.2332 \| 0.3407 \| 0.3547 \| \| 0.0399 \| 24.12 \| 3860 \| 0.4107 \| 0.3313 \| 0.3888 \| 0.5557 \| nan \| 0.6795 \| 0.3847 \| 0.5899 \| 0.6415 \| 0.1208 \| nan \| 0.0444 \| nan \| 0.2652 \| 0.3843 \| 0.3891 \| 0.0 \| 0.6607 \| 0.3748 \| 0.5767 \| 0.5956 \| 0.1152 \| nan \| 0.0435 \| nan \| 0.2462 \| 0.3345 \| 0.3660 \| \| 0.0892 \| 24.25 \| 3880 \| 0.4233 \| 0.3200 \| 0.3743 \| 0.5371 \| nan \| 0.6571 \| 0.3608 \| 0.5803 \| 0.6290 \| 0.1144 \| nan \| 0.0414 \| nan \| 0.2556 \| 0.3617 \| 0.3685 \| 0.0 \| 0.6402 \| 0.3525 \| 0.5683 \| 0.5850 \| 0.1098 \| nan \| 0.0406 \| nan \| 0.2377 \| 0.3184 \| 0.3476 \| \| 0.0504 \| 24.38 \| 3900 \| 0.4126 \| 0.3272 \| 0.3838 \| 0.5469 \| nan \| 0.6627 \| 0.3726 \| 0.5902 \| 0.6472 \| 0.1122 \| nan \| 0.0434 \| nan \| 0.2626 \| 0.3820 \| 0.3810 \| 0.0 \| 0.6457 \| 0.3638 \| 0.5776 \| 0.5995 \| 0.1075 \| nan \| 0.0425 \| nan \| 0.2436 \| 0.3331 \| 0.3583 \| \| 0.1415 \| 24.5 \| 3920 \| 0.4125 \| 0.3313 \| 0.3890 \| 0.5544 \| nan \| 0.6739 \| 0.3827 \| 0.5877 \| 0.6506 \| 0.1154 \| nan \| 0.0461 \| nan \| 0.2685 \| 0.3880 \| 0.3885 \| 0.0 \| 0.6554 \| 0.3725 \| 0.5757 \| 0.6023 \| 0.1107 \| nan \| 0.0451 \| nan \| 0.2488 \| 0.3378 \| 0.3647 \| \| 0.0919 \| 24.62 \| 3940 \| 0.4155 \| 0.3205 \| 0.3752 \| 0.5383 \| nan \| 0.6617 \| 0.3538 \| 0.5750 \| 0.6351 \| 0.1112 \| nan \| 0.0444 \| nan \| 0.2523 \| 0.3764 \| 0.3666 \| 0.0 \| 0.6444 \| 0.3457 \| 0.5639 \| 0.5899 \| 0.1069 \| nan \| 0.0434 \| nan \| 0.2354 \| 0.3296 \| 0.3458 \| \| 0.0443 \| 24.75 \| 3960 \| 0.4101 \| 0.3270 \| 0.3837 \| 0.5447 \| nan \| 0.6616 \| 0.3704 \| 0.5699 \| 0.6538 \| 0.1158 \| nan \| 0.0460 \| nan \| 0.2670 \| 0.3933 \| 0.3753 \| 0.0 \| 0.6444 \| 0.3607 \| 0.5605 \| 0.6039 \| 0.1111 \| nan \| 0.0450 \| nan \| 0.2475 \| 0.3427 \| 0.3542 \| \| 0.1815 \| 24.88 \| 3980 \| 0.4182 \| 0.3204 \| 0.3750 \| 0.5340 \| nan \| 0.6499 \| 0.3529 \| 0.5731 \| 0.6316 \| 0.1129 \| nan \| 0.0422 \| nan \| 0.2545 \| 0.3768 \| 0.3816 \| 0.0 \| 0.6337 \| 0.3452 \| 0.5621 \| 0.5873 \| 0.1083 \| nan \| 0.0413 \| nan \| 0.2370 \| 0.3306 \| 0.3586 \| \| 0.005 \| 25.0 \| 4000 \| 0.4155 \| 0.3349 \| 0.3935 \| 0.5591 \| nan \| 0.6815 \| 0.3865 \| 0.5805 \| 0.6544 \| 0.1155 \| nan \| 0.0497 \| nan \| 0.2779 \| 0.3995 \| 0.3959 \| 0.0 \| 0.6626 \| 0.3764 \| 0.5699 \| 0.6056 \| 0.1108 \| nan \| 0.0485 \| nan \| 0.2565 \| 0.3465 \| 0.3718 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "unlabeled", "lv", "rv", "ra", "la", "vs", "as", "mk", "tk", "asd", "vsd", "ak" ]
mrkprc1/segformer-b1-finetuned-sudoku	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b1-finetuned-sudoku This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the mrkprc1/SudokuBoundaries2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9826 - Mean Iou: 0.2452 - Mean Accuracy: 0.4999 - Overall Accuracy: 0.4903 - Accuracy Unlabelled: 0.9996 - Accuracy Sudoku-boundary: 0.0001 - Iou Unlabelled: 0.4903 - Iou Sudoku-boundary: 0.0001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabelled \| Accuracy Sudoku-boundary \| Iou Unlabelled \| Iou Sudoku-boundary \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:------------------------:\|:--------------:\|:-------------------:\| \| 0.6034 \| 3.33 \| 20 \| 0.6951 \| 0.3427 \| 0.5173 \| 0.5149 \| 0.6432 \| 0.3914 \| 0.3940 \| 0.2913 \| \| 0.7796 \| 6.67 \| 40 \| 0.7150 \| 0.3049 \| 0.5083 \| 0.5021 \| 0.8309 \| 0.1857 \| 0.4501 \| 0.1597 \| \| 0.4378 \| 10.0 \| 60 \| 0.9772 \| 0.2452 \| 0.5 \| 0.4904 \| 1.0 \| 0.0 \| 0.4904 \| 0.0 \| \| 0.6804 \| 13.33 \| 80 \| 1.1605 \| 0.2452 \| 0.5 \| 0.4904 \| 1.0 \| 0.0 \| 0.4904 \| 0.0 \| \| 0.58 \| 16.67 \| 100 \| 0.9787 \| 0.2452 \| 0.5 \| 0.4904 \| 1.0 \| 0.0 \| 0.4904 \| 0.0 \| \| 0.6563 \| 20.0 \| 120 \| 1.1860 \| 0.2452 \| 0.5 \| 0.4904 \| 1.0 \| 0.0 \| 0.4904 \| 0.0 \| \| 0.5128 \| 23.33 \| 140 \| 0.8884 \| 0.2457 \| 0.5002 \| 0.4907 \| 0.9996 \| 0.0009 \| 0.4905 \| 0.0009 \| \| 0.5054 \| 26.67 \| 160 \| 0.8746 \| 0.2455 \| 0.5002 \| 0.4907 \| 0.9998 \| 0.0006 \| 0.4905 \| 0.0006 \| \| 0.5532 \| 30.0 \| 180 \| 0.9540 \| 0.2452 \| 0.5000 \| 0.4905 \| 1.0 \| 0.0000 \| 0.4905 \| 0.0000 \| \| 0.3238 \| 33.33 \| 200 \| 0.8916 \| 0.2470 \| 0.5009 \| 0.4914 \| 0.9984 \| 0.0035 \| 0.4905 \| 0.0035 \| \| 0.2964 \| 36.67 \| 220 \| 1.0162 \| 0.2453 \| 0.5000 \| 0.4905 \| 1.0000 \| 0.0000 \| 0.4905 \| 0.0000 \| \| 0.2102 \| 40.0 \| 240 \| 0.9650 \| 0.2452 \| 0.4998 \| 0.4903 \| 0.9996 \| 0.0001 \| 0.4903 \| 0.0001 \| \| 0.623 \| 43.33 \| 260 \| 0.9071 \| 0.2461 \| 0.5004 \| 0.4909 \| 0.9991 \| 0.0017 \| 0.4904 \| 0.0017 \| \| 0.3741 \| 46.67 \| 280 \| 0.9245 \| 0.2454 \| 0.5000 \| 0.4904 \| 0.9994 \| 0.0006 \| 0.4903 \| 0.0006 \| \| 0.5765 \| 50.0 \| 300 \| 0.9826 \| 0.2452 \| 0.4999 \| 0.4903 \| 0.9996 \| 0.0001 \| 0.4903 \| 0.0001 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "unlabelled", "sudoku-boundary" ]
samitizerxu/segformer-b2-kelp-rgb-agg-imgaug-jan-27	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b2-kelp-rgb-agg-imgaug-jan-27 This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set: - Accuracy Kelp: 0.4483 - Iou Kelp: 0.3982 - Accuracy Bg: 0.9991 - Iou Bg: 0.9954 - Loss: 0.0174 - Mean Iou: 0.6968 - Mean Accuracy: 0.7237 - Overall Accuracy: 0.9955 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Kelp \| Iou Kelp \| Accuracy Bg \| Iou Bg \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| \|:-------------:\|:-----:\|:-----:\|:-------------:\|:--------:\|:-----------:\|:------:\|:---------------:\|:--------:\|:-------------:\|:----------------:\| \| 0.7557 \| 0.06 \| 30 \| 0.5865 \| 0.0073 \| 0.4625 \| 0.4612 \| 0.7403 \| 0.2342 \| 0.5245 \| 0.4633 \| \| 0.7383 \| 0.12 \| 60 \| 0.5811 \| 0.0071 \| 0.4541 \| 0.4528 \| 0.7518 \| 0.2300 \| 0.5176 \| 0.4550 \| \| 0.7122 \| 0.18 \| 90 \| 0.5460 \| 0.0072 \| 0.4913 \| 0.4898 \| 0.7268 \| 0.2485 \| 0.5187 \| 0.4917 \| \| 0.6843 \| 0.24 \| 120 \| 0.5273 \| 0.0074 \| 0.5269 \| 0.5252 \| 0.7031 \| 0.2663 \| 0.5271 \| 0.5269 \| \| 0.6517 \| 0.3 \| 150 \| 0.5363 \| 0.0086 \| 0.5842 \| 0.5824 \| 0.6704 \| 0.2955 \| 0.5602 \| 0.5839 \| \| 0.5976 \| 0.37 \| 180 \| 0.4706 \| 0.0101 \| 0.6913 \| 0.6888 \| 0.5971 \| 0.3494 \| 0.5809 \| 0.6898 \| \| 0.5402 \| 0.43 \| 210 \| 0.4111 \| 0.0125 \| 0.7837 \| 0.7806 \| 0.5262 \| 0.3965 \| 0.5974 \| 0.7812 \| \| 0.4614 \| 0.49 \| 240 \| 0.3286 \| 0.0143 \| 0.8518 \| 0.8479 \| 0.4517 \| 0.4311 \| 0.5902 \| 0.8483 \| \| 0.4098 \| 0.55 \| 270 \| 0.2838 \| 0.0157 \| 0.8843 \| 0.8801 \| 0.4001 \| 0.4479 \| 0.5841 \| 0.8803 \| \| 0.3617 \| 0.61 \| 300 \| 0.1543 \| 0.0166 \| 0.9441 \| 0.9387 \| 0.3193 \| 0.4777 \| 0.5492 \| 0.9388 \| \| 0.2873 \| 0.67 \| 330 \| 0.0990 \| 0.0182 \| 0.9701 \| 0.9642 \| 0.2682 \| 0.4912 \| 0.5345 \| 0.9642 \| \| 0.3307 \| 0.73 \| 360 \| 0.0594 \| 0.0137 \| 0.9775 \| 0.9713 \| 0.2341 \| 0.4925 \| 0.5185 \| 0.9713 \| \| 0.2259 \| 0.79 \| 390 \| 0.0153 \| 0.0080 \| 0.9938 \| 0.9872 \| 0.1845 \| 0.4976 \| 0.5045 \| 0.9872 \| \| 0.3572 \| 0.85 \| 420 \| 0.0188 \| 0.0107 \| 0.9949 \| 0.9883 \| 0.1745 \| 0.4995 \| 0.5068 \| 0.9883 \| \| 0.216 \| 0.91 \| 450 \| 0.0039 \| 0.0033 \| 0.9989 \| 0.9922 \| 0.1289 \| 0.4978 \| 0.5014 \| 0.9922 \| \| 0.169 \| 0.97 \| 480 \| 0.0080 \| 0.0068 \| 0.9988 \| 0.9922 \| 0.1304 \| 0.4995 \| 0.5034 \| 0.9922 \| \| 0.1336 \| 1.03 \| 510 \| 0.0086 \| 0.0077 \| 0.9993 \| 0.9926 \| 0.1179 \| 0.5002 \| 0.5039 \| 0.9926 \| \| 0.1186 \| 1.1 \| 540 \| 0.0029 \| 0.0028 \| 0.9999 \| 0.9932 \| 0.0943 \| 0.4980 \| 0.5014 \| 0.9932 \| \| 0.1175 \| 1.16 \| 570 \| 0.0070 \| 0.0069 \| 0.9999 \| 0.9932 \| 0.0845 \| 0.5001 \| 0.5035 \| 0.9932 \| \| 0.0977 \| 1.22 \| 600 \| 0.0002 \| 0.0002 \| 1.0000 \| 0.9933 \| 0.0659 \| 0.4967 \| 0.5001 \| 0.9933 \| \| 0.073 \| 1.28 \| 630 \| 0.0319 \| 0.0307 \| 0.9997 \| 0.9932 \| 0.0739 \| 0.5119 \| 0.5158 \| 0.9932 \| \| 0.072 \| 1.34 \| 660 \| 0.0014 \| 0.0014 \| 1.0000 \| 0.9933 \| 0.0553 \| 0.4973 \| 0.5007 \| 0.9933 \| \| 0.0568 \| 1.4 \| 690 \| 0.0027 \| 0.0027 \| 1.0000 \| 0.9933 \| 0.0517 \| 0.4980 \| 0.5013 \| 0.9933 \| \| 0.0418 \| 1.46 \| 720 \| 0.0019 \| 0.0019 \| 1.0000 \| 0.9933 \| 0.0458 \| 0.4976 \| 0.5009 \| 0.9933 \| \| 0.0318 \| 1.52 \| 750 \| 0.0060 \| 0.0060 \| 1.0000 \| 0.9933 \| 0.0452 \| 0.4997 \| 0.5030 \| 0.9933 \| \| 0.0359 \| 1.58 \| 780 \| 0.0707 \| 0.0682 \| 0.9998 \| 0.9935 \| 0.0403 \| 0.5309 \| 0.5352 \| 0.9935 \| \| 0.0438 \| 1.64 \| 810 \| 0.1112 \| 0.1055 \| 0.9996 \| 0.9937 \| 0.0362 \| 0.5496 \| 0.5554 \| 0.9937 \| \| 0.0325 \| 1.7 \| 840 \| 0.1098 \| 0.1052 \| 0.9997 \| 0.9937 \| 0.0333 \| 0.5495 \| 0.5548 \| 0.9937 \| \| 0.022 \| 1.76 \| 870 \| 0.1215 \| 0.1173 \| 0.9998 \| 0.9939 \| 0.0318 \| 0.5556 \| 0.5606 \| 0.9939 \| \| 0.0264 \| 1.83 \| 900 \| 0.0352 \| 0.0350 \| 1.0000 \| 0.9935 \| 0.0316 \| 0.5143 \| 0.5176 \| 0.9935 \| \| 0.0191 \| 1.89 \| 930 \| 0.1969 \| 0.1849 \| 0.9996 \| 0.9942 \| 0.0289 \| 0.5895 \| 0.5982 \| 0.9942 \| \| 0.0268 \| 1.95 \| 960 \| 0.2421 \| 0.2221 \| 0.9994 \| 0.9943 \| 0.0284 \| 0.6082 \| 0.6207 \| 0.9943 \| \| 0.0164 \| 2.01 \| 990 \| 0.1332 \| 0.1298 \| 0.9998 \| 0.9940 \| 0.0273 \| 0.5619 \| 0.5665 \| 0.9940 \| \| 0.0261 \| 2.07 \| 1020 \| 0.3293 \| 0.2820 \| 0.9989 \| 0.9944 \| 0.0258 \| 0.6382 \| 0.6641 \| 0.9944 \| \| 0.0255 \| 2.13 \| 1050 \| 0.1026 \| 0.1010 \| 0.9999 \| 0.9939 \| 0.0268 \| 0.5474 \| 0.5512 \| 0.9939 \| \| 0.0178 \| 2.19 \| 1080 \| 0.3405 \| 0.2944 \| 0.9989 \| 0.9945 \| 0.0260 \| 0.6445 \| 0.6697 \| 0.9945 \| \| 0.0119 \| 2.25 \| 1110 \| 0.3838 \| 0.3184 \| 0.9986 \| 0.9945 \| 0.0238 \| 0.6564 \| 0.6912 \| 0.9945 \| \| 0.0253 \| 2.31 \| 1140 \| 0.3337 \| 0.2942 \| 0.9991 \| 0.9946 \| 0.0227 \| 0.6444 \| 0.6664 \| 0.9946 \| \| 0.0116 \| 2.37 \| 1170 \| 0.2765 \| 0.2549 \| 0.9994 \| 0.9946 \| 0.0224 \| 0.6247 \| 0.6380 \| 0.9946 \| \| 0.0145 \| 2.43 \| 1200 \| 0.1745 \| 0.1689 \| 0.9998 \| 0.9942 \| 0.0241 \| 0.5816 \| 0.5871 \| 0.9942 \| \| 0.0185 \| 2.49 \| 1230 \| 0.2434 \| 0.2293 \| 0.9996 \| 0.9945 \| 0.0223 \| 0.6119 \| 0.6215 \| 0.9945 \| \| 0.0248 \| 2.56 \| 1260 \| 0.2604 \| 0.2429 \| 0.9995 \| 0.9945 \| 0.0219 \| 0.6187 \| 0.6299 \| 0.9946 \| \| 0.0172 \| 2.62 \| 1290 \| 0.2145 \| 0.2048 \| 0.9997 \| 0.9944 \| 0.0221 \| 0.5996 \| 0.6071 \| 0.9944 \| \| 0.0125 \| 2.68 \| 1320 \| 0.3472 \| 0.3058 \| 0.9991 \| 0.9947 \| 0.0208 \| 0.6502 \| 0.6731 \| 0.9947 \| \| 0.0089 \| 2.74 \| 1350 \| 0.3391 \| 0.3003 \| 0.9991 \| 0.9947 \| 0.0209 \| 0.6475 \| 0.6691 \| 0.9947 \| \| 0.0061 \| 2.8 \| 1380 \| 0.3696 \| 0.3189 \| 0.9989 \| 0.9947 \| 0.0207 \| 0.6568 \| 0.6843 \| 0.9947 \| \| 0.0079 \| 2.86 \| 1410 \| 0.6068 \| 0.3737 \| 0.9958 \| 0.9931 \| 0.0252 \| 0.6834 \| 0.8013 \| 0.9932 \| \| 0.01 \| 2.92 \| 1440 \| 0.4511 \| 0.3647 \| 0.9984 \| 0.9947 \| 0.0205 \| 0.6797 \| 0.7248 \| 0.9947 \| \| 0.0112 \| 2.98 \| 1470 \| 0.3328 \| 0.2972 \| 0.9992 \| 0.9947 \| 0.0204 \| 0.6460 \| 0.6660 \| 0.9947 \| \| 0.007 \| 3.04 \| 1500 \| 0.3705 \| 0.3214 \| 0.9990 \| 0.9947 \| 0.0203 \| 0.6581 \| 0.6847 \| 0.9947 \| \| 0.0119 \| 3.1 \| 1530 \| 0.4911 \| 0.3717 \| 0.9978 \| 0.9944 \| 0.0207 \| 0.6831 \| 0.7445 \| 0.9944 \| \| 0.0086 \| 3.16 \| 1560 \| 0.2780 \| 0.2584 \| 0.9995 \| 0.9946 \| 0.0200 \| 0.6265 \| 0.6387 \| 0.9946 \| \| 0.0542 \| 3.23 \| 1590 \| 0.4441 \| 0.3631 \| 0.9985 \| 0.9948 \| 0.0196 \| 0.6789 \| 0.7213 \| 0.9948 \| \| 0.0096 \| 3.29 \| 1620 \| 0.4264 \| 0.3542 \| 0.9986 \| 0.9948 \| 0.0195 \| 0.6745 \| 0.7125 \| 0.9948 \| \| 0.0164 \| 3.35 \| 1650 \| 0.3326 \| 0.2997 \| 0.9993 \| 0.9948 \| 0.0195 \| 0.6472 \| 0.6659 \| 0.9948 \| \| 0.0076 \| 3.41 \| 1680 \| 0.4205 \| 0.3515 \| 0.9987 \| 0.9948 \| 0.0197 \| 0.6732 \| 0.7096 \| 0.9948 \| \| 0.0056 \| 3.47 \| 1710 \| 0.3465 \| 0.3085 \| 0.9992 \| 0.9948 \| 0.0194 \| 0.6516 \| 0.6728 \| 0.9948 \| \| 0.0047 \| 3.53 \| 1740 \| 0.4351 \| 0.3550 \| 0.9985 \| 0.9947 \| 0.0193 \| 0.6748 \| 0.7168 \| 0.9947 \| \| 0.0079 \| 3.59 \| 1770 \| 0.3970 \| 0.3436 \| 0.9989 \| 0.9949 \| 0.0190 \| 0.6693 \| 0.6980 \| 0.9949 \| \| 0.0175 \| 3.65 \| 1800 \| 0.3844 \| 0.3369 \| 0.9990 \| 0.9949 \| 0.0189 \| 0.6659 \| 0.6917 \| 0.9949 \| \| 0.004 \| 3.71 \| 1830 \| 0.3719 \| 0.3306 \| 0.9992 \| 0.9949 \| 0.0189 \| 0.6628 \| 0.6855 \| 0.9949 \| \| 0.0034 \| 3.77 \| 1860 \| 0.4341 \| 0.3590 \| 0.9986 \| 0.9948 \| 0.0189 \| 0.6769 \| 0.7164 \| 0.9948 \| \| 0.0139 \| 3.83 \| 1890 \| 0.3380 \| 0.3051 \| 0.9993 \| 0.9948 \| 0.0192 \| 0.6500 \| 0.6686 \| 0.9948 \| \| 0.0072 \| 3.89 \| 1920 \| 0.4258 \| 0.3585 \| 0.9987 \| 0.9949 \| 0.0188 \| 0.6767 \| 0.7123 \| 0.9949 \| \| 0.0269 \| 3.96 \| 1950 \| 0.2233 \| 0.2132 \| 0.9997 \| 0.9945 \| 0.0207 \| 0.6038 \| 0.6115 \| 0.9945 \| \| 0.006 \| 4.02 \| 1980 \| 0.3002 \| 0.2762 \| 0.9994 \| 0.9947 \| 0.0196 \| 0.6355 \| 0.6498 \| 0.9947 \| \| 0.0034 \| 4.08 \| 2010 \| 0.3036 \| 0.2785 \| 0.9994 \| 0.9947 \| 0.0194 \| 0.6366 \| 0.6515 \| 0.9947 \| \| 0.0039 \| 4.14 \| 2040 \| 0.2260 \| 0.2154 \| 0.9997 \| 0.9945 \| 0.0208 \| 0.6049 \| 0.6129 \| 0.9945 \| \| 0.0085 \| 4.2 \| 2070 \| 0.3807 \| 0.3353 \| 0.9991 \| 0.9949 \| 0.0187 \| 0.6651 \| 0.6899 \| 0.9949 \| \| 0.0064 \| 4.26 \| 2100 \| 0.4472 \| 0.3671 \| 0.9985 \| 0.9948 \| 0.0189 \| 0.6810 \| 0.7229 \| 0.9948 \| \| 0.0072 \| 4.32 \| 2130 \| 0.3930 \| 0.3431 \| 0.9990 \| 0.9949 \| 0.0188 \| 0.6690 \| 0.6960 \| 0.9949 \| \| 0.0285 \| 4.38 \| 2160 \| 0.3110 \| 0.2853 \| 0.9994 \| 0.9948 \| 0.0192 \| 0.6400 \| 0.6552 \| 0.9948 \| \| 0.0106 \| 4.44 \| 2190 \| 0.3427 \| 0.3094 \| 0.9993 \| 0.9949 \| 0.0190 \| 0.6521 \| 0.6710 \| 0.9949 \| \| 0.0139 \| 4.5 \| 2220 \| 0.3754 \| 0.3311 \| 0.9991 \| 0.9949 \| 0.0184 \| 0.6630 \| 0.6873 \| 0.9949 \| \| 0.0109 \| 4.56 \| 2250 \| 0.1866 \| 0.1809 \| 0.9998 \| 0.9943 \| 0.0202 \| 0.5876 \| 0.5932 \| 0.9943 \| \| 0.0143 \| 4.62 \| 2280 \| 0.3296 \| 0.3005 \| 0.9993 \| 0.9948 \| 0.0185 \| 0.6477 \| 0.6645 \| 0.9948 \| \| 0.0061 \| 4.69 \| 2310 \| 0.4646 \| 0.3774 \| 0.9984 \| 0.9948 \| 0.0185 \| 0.6861 \| 0.7315 \| 0.9949 \| \| 0.0039 \| 4.75 \| 2340 \| 0.3154 \| 0.2822 \| 0.9992 \| 0.9946 \| 0.0193 \| 0.6384 \| 0.6573 \| 0.9946 \| \| 0.0059 \| 4.81 \| 2370 \| 0.3287 \| 0.3011 \| 0.9994 \| 0.9949 \| 0.0185 \| 0.6480 \| 0.6640 \| 0.9949 \| \| 0.008 \| 4.87 \| 2400 \| 0.4402 \| 0.3721 \| 0.9988 \| 0.9950 \| 0.0182 \| 0.6835 \| 0.7195 \| 0.9950 \| \| 0.0123 \| 4.93 \| 2430 \| 0.3758 \| 0.3328 \| 0.9991 \| 0.9949 \| 0.0186 \| 0.6639 \| 0.6875 \| 0.9949 \| \| 0.0329 \| 4.99 \| 2460 \| 0.3555 \| 0.3223 \| 0.9993 \| 0.9950 \| 0.0183 \| 0.6586 \| 0.6774 \| 0.9950 \| \| 0.0051 \| 5.05 \| 2490 \| 0.4204 \| 0.3602 \| 0.9989 \| 0.9950 \| 0.0182 \| 0.6776 \| 0.7096 \| 0.9950 \| \| 0.0132 \| 5.11 \| 2520 \| 0.3182 \| 0.2873 \| 0.9993 \| 0.9947 \| 0.0190 \| 0.6410 \| 0.6587 \| 0.9947 \| \| 0.013 \| 5.17 \| 2550 \| 0.3397 \| 0.3094 \| 0.9993 \| 0.9949 \| 0.0183 \| 0.6522 \| 0.6695 \| 0.9949 \| \| 0.0014 \| 5.23 \| 2580 \| 0.2051 \| 0.1987 \| 0.9998 \| 0.9944 \| 0.0202 \| 0.5965 \| 0.6025 \| 0.9944 \| \| 0.0045 \| 5.29 \| 2610 \| 0.4802 \| 0.3916 \| 0.9985 \| 0.9950 \| 0.0185 \| 0.6933 \| 0.7393 \| 0.9950 \| \| 0.0085 \| 5.35 \| 2640 \| 0.3296 \| 0.3016 \| 0.9994 \| 0.9949 \| 0.0188 \| 0.6482 \| 0.6645 \| 0.9949 \| \| 0.0049 \| 5.42 \| 2670 \| 0.3292 \| 0.3010 \| 0.9994 \| 0.9949 \| 0.0184 \| 0.6479 \| 0.6643 \| 0.9949 \| \| 0.0181 \| 5.48 \| 2700 \| 0.3973 \| 0.3465 \| 0.9990 \| 0.9950 \| 0.0180 \| 0.6707 \| 0.6982 \| 0.9950 \| \| 0.0026 \| 5.54 \| 2730 \| 0.4025 \| 0.3520 \| 0.9990 \| 0.9950 \| 0.0179 \| 0.6735 \| 0.7007 \| 0.9950 \| \| 0.0037 \| 5.6 \| 2760 \| 0.2881 \| 0.2691 \| 0.9995 \| 0.9947 \| 0.0185 \| 0.6319 \| 0.6438 \| 0.9947 \| \| 0.0067 \| 5.66 \| 2790 \| 0.4916 \| 0.3854 \| 0.9981 \| 0.9947 \| 0.0188 \| 0.6901 \| 0.7449 \| 0.9947 \| \| 0.0045 \| 5.72 \| 2820 \| 0.4345 \| 0.3703 \| 0.9988 \| 0.9950 \| 0.0180 \| 0.6827 \| 0.7166 \| 0.9950 \| \| 0.0029 \| 5.78 \| 2850 \| 0.4856 \| 0.3766 \| 0.9980 \| 0.9946 \| 0.0191 \| 0.6856 \| 0.7418 \| 0.9946 \| \| 0.0058 \| 5.84 \| 2880 \| 0.3707 \| 0.3316 \| 0.9992 \| 0.9950 \| 0.0187 \| 0.6633 \| 0.6850 \| 0.9950 \| \| 0.0224 \| 5.9 \| 2910 \| 0.5015 \| 0.4023 \| 0.9983 \| 0.9950 \| 0.0183 \| 0.6986 \| 0.7499 \| 0.9950 \| \| 0.0161 \| 5.96 \| 2940 \| 0.4042 \| 0.3541 \| 0.9990 \| 0.9950 \| 0.0183 \| 0.6746 \| 0.7016 \| 0.9951 \| \| 0.0054 \| 6.02 \| 2970 \| 0.4196 \| 0.3623 \| 0.9989 \| 0.9950 \| 0.0183 \| 0.6787 \| 0.7093 \| 0.9950 \| \| 0.0118 \| 6.09 \| 3000 \| 0.4446 \| 0.3753 \| 0.9988 \| 0.9950 \| 0.0179 \| 0.6852 \| 0.7217 \| 0.9950 \| \| 0.0043 \| 6.15 \| 3030 \| 0.2735 \| 0.2587 \| 0.9996 \| 0.9947 \| 0.0192 \| 0.6267 \| 0.6366 \| 0.9947 \| \| 0.0058 \| 6.21 \| 3060 \| 0.2908 \| 0.2707 \| 0.9995 \| 0.9947 \| 0.0188 \| 0.6327 \| 0.6452 \| 0.9947 \| \| 0.0037 \| 6.27 \| 3090 \| 0.3848 \| 0.3405 \| 0.9991 \| 0.9950 \| 0.0179 \| 0.6677 \| 0.6920 \| 0.9950 \| \| 0.0038 \| 6.33 \| 3120 \| 0.3463 \| 0.3133 \| 0.9993 \| 0.9949 \| 0.0178 \| 0.6541 \| 0.6728 \| 0.9949 \| \| 0.0234 \| 6.39 \| 3150 \| 0.4518 \| 0.3825 \| 0.9988 \| 0.9951 \| 0.0176 \| 0.6888 \| 0.7253 \| 0.9951 \| \| 0.0066 \| 6.45 \| 3180 \| 0.3940 \| 0.3407 \| 0.9989 \| 0.9949 \| 0.0184 \| 0.6678 \| 0.6965 \| 0.9949 \| \| 0.0041 \| 6.51 \| 3210 \| 0.4930 \| 0.4010 \| 0.9985 \| 0.9950 \| 0.0175 \| 0.6980 \| 0.7457 \| 0.9951 \| \| 0.0139 \| 6.57 \| 3240 \| 0.5347 \| 0.4133 \| 0.9980 \| 0.9949 \| 0.0178 \| 0.7041 \| 0.7663 \| 0.9949 \| \| 0.0085 \| 6.63 \| 3270 \| 0.4837 \| 0.3867 \| 0.9983 \| 0.9948 \| 0.0182 \| 0.6908 \| 0.7410 \| 0.9949 \| \| 0.0024 \| 6.69 \| 3300 \| 0.3644 \| 0.3251 \| 0.9992 \| 0.9949 \| 0.0185 \| 0.6600 \| 0.6818 \| 0.9949 \| \| 0.0041 \| 6.75 \| 3330 \| 0.3855 \| 0.3450 \| 0.9992 \| 0.9951 \| 0.0177 \| 0.6700 \| 0.6923 \| 0.9951 \| \| 0.0021 \| 6.82 \| 3360 \| 0.3847 \| 0.3422 \| 0.9992 \| 0.9950 \| 0.0184 \| 0.6686 \| 0.6920 \| 0.9950 \| \| 0.0156 \| 6.88 \| 3390 \| 0.3380 \| 0.3129 \| 0.9995 \| 0.9950 \| 0.0179 \| 0.6540 \| 0.6687 \| 0.9950 \| \| 0.0042 \| 6.94 \| 3420 \| 0.5245 \| 0.4071 \| 0.9981 \| 0.9949 \| 0.0183 \| 0.7010 \| 0.7613 \| 0.9949 \| \| 0.0075 \| 7.0 \| 3450 \| 0.4134 \| 0.3642 \| 0.9991 \| 0.9951 \| 0.0177 \| 0.6797 \| 0.7062 \| 0.9952 \| \| 0.0036 \| 7.06 \| 3480 \| 0.3749 \| 0.3362 \| 0.9992 \| 0.9950 \| 0.0183 \| 0.6656 \| 0.6871 \| 0.9950 \| \| 0.0113 \| 7.12 \| 3510 \| 0.4672 \| 0.3887 \| 0.9986 \| 0.9951 \| 0.0178 \| 0.6919 \| 0.7329 \| 0.9951 \| \| 0.0385 \| 7.18 \| 3540 \| 0.2639 \| 0.2513 \| 0.9997 \| 0.9947 \| 0.0199 \| 0.6230 \| 0.6318 \| 0.9947 \| \| 0.0135 \| 7.24 \| 3570 \| 0.3820 \| 0.3435 \| 0.9992 \| 0.9951 \| 0.0178 \| 0.6693 \| 0.6906 \| 0.9951 \| \| 0.0032 \| 7.3 \| 3600 \| 0.3842 \| 0.3397 \| 0.9991 \| 0.9950 \| 0.0182 \| 0.6673 \| 0.6917 \| 0.9950 \| \| 0.0081 \| 7.36 \| 3630 \| 0.1317 \| 0.1292 \| 0.9999 \| 0.9940 \| 0.0235 \| 0.5616 \| 0.5658 \| 0.9940 \| \| 0.0029 \| 7.42 \| 3660 \| 0.3218 \| 0.2976 \| 0.9995 \| 0.9949 \| 0.0184 \| 0.6462 \| 0.6606 \| 0.9949 \| \| 0.0056 \| 7.48 \| 3690 \| 0.5155 \| 0.4111 \| 0.9983 \| 0.9950 \| 0.0181 \| 0.7031 \| 0.7569 \| 0.9950 \| \| 0.0212 \| 7.55 \| 3720 \| 0.4519 \| 0.3843 \| 0.9988 \| 0.9951 \| 0.0178 \| 0.6897 \| 0.7254 \| 0.9951 \| \| 0.0019 \| 7.61 \| 3750 \| 0.4913 \| 0.3989 \| 0.9984 \| 0.9950 \| 0.0176 \| 0.6970 \| 0.7449 \| 0.9950 \| \| 0.0049 \| 7.67 \| 3780 \| 0.3845 \| 0.3463 \| 0.9993 \| 0.9951 \| 0.0178 \| 0.6707 \| 0.6919 \| 0.9951 \| \| 0.0062 \| 7.73 \| 3810 \| 0.4232 \| 0.3606 \| 0.9988 \| 0.9949 \| 0.0202 \| 0.6778 \| 0.7110 \| 0.9950 \| \| 0.0036 \| 7.79 \| 3840 \| 0.3965 \| 0.3536 \| 0.9992 \| 0.9951 \| 0.0182 \| 0.6743 \| 0.6978 \| 0.9951 \| \| 0.008 \| 7.85 \| 3870 \| 0.3852 \| 0.3479 \| 0.9993 \| 0.9951 \| 0.0178 \| 0.6715 \| 0.6923 \| 0.9952 \| \| 0.0037 \| 7.91 \| 3900 \| 0.4829 \| 0.3891 \| 0.9984 \| 0.9949 \| 0.0182 \| 0.6920 \| 0.7406 \| 0.9949 \| \| 0.007 \| 7.97 \| 3930 \| 0.4051 \| 0.3582 \| 0.9991 \| 0.9951 \| 0.0181 \| 0.6767 \| 0.7021 \| 0.9951 \| \| 0.0151 \| 8.03 \| 3960 \| 0.3396 \| 0.3099 \| 0.9994 \| 0.9949 \| 0.0187 \| 0.6524 \| 0.6695 \| 0.9949 \| \| 0.0156 \| 8.09 \| 3990 \| 0.4679 \| 0.3832 \| 0.9985 \| 0.9949 \| 0.0183 \| 0.6891 \| 0.7332 \| 0.9949 \| \| 0.0114 \| 8.15 \| 4020 \| 0.4465 \| 0.3838 \| 0.9989 \| 0.9952 \| 0.0178 \| 0.6895 \| 0.7227 \| 0.9952 \| \| 0.0091 \| 8.22 \| 4050 \| 0.5417 \| 0.4095 \| 0.9978 \| 0.9947 \| 0.0181 \| 0.7021 \| 0.7698 \| 0.9948 \| \| 0.0058 \| 8.28 \| 4080 \| 0.5075 \| 0.4157 \| 0.9985 \| 0.9952 \| 0.0173 \| 0.7054 \| 0.7530 \| 0.9952 \| \| 0.0057 \| 8.34 \| 4110 \| 0.5139 \| 0.4156 \| 0.9984 \| 0.9951 \| 0.0171 \| 0.7054 \| 0.7562 \| 0.9951 \| \| 0.0196 \| 8.4 \| 4140 \| 0.3755 \| 0.3357 \| 0.9992 \| 0.9950 \| 0.0176 \| 0.6653 \| 0.6874 \| 0.9950 \| \| 0.0019 \| 8.46 \| 4170 \| 0.4160 \| 0.3638 \| 0.9990 \| 0.9951 \| 0.0175 \| 0.6794 \| 0.7075 \| 0.9951 \| \| 0.004 \| 8.52 \| 4200 \| 0.3155 \| 0.2948 \| 0.9995 \| 0.9949 \| 0.0179 \| 0.6449 \| 0.6575 \| 0.9949 \| \| 0.015 \| 8.58 \| 4230 \| 0.3264 \| 0.3041 \| 0.9995 \| 0.9950 \| 0.0180 \| 0.6495 \| 0.6630 \| 0.9950 \| \| 0.0024 \| 8.64 \| 4260 \| 0.3284 \| 0.3059 \| 0.9995 \| 0.9950 \| 0.0183 \| 0.6504 \| 0.6639 \| 0.9950 \| \| 0.0367 \| 8.7 \| 4290 \| 0.4347 \| 0.3764 \| 0.9990 \| 0.9952 \| 0.0172 \| 0.6858 \| 0.7168 \| 0.9952 \| \| 0.0126 \| 8.76 \| 4320 \| 0.4370 \| 0.3820 \| 0.9990 \| 0.9952 \| 0.0171 \| 0.6886 \| 0.7180 \| 0.9953 \| \| 0.0046 \| 8.82 \| 4350 \| 0.4161 \| 0.3627 \| 0.9990 \| 0.9951 \| 0.0176 \| 0.6789 \| 0.7076 \| 0.9951 \| \| 0.0041 \| 8.88 \| 4380 \| 0.5022 \| 0.4108 \| 0.9985 \| 0.9951 \| 0.0178 \| 0.7030 \| 0.7503 \| 0.9952 \| \| 0.0082 \| 8.95 \| 4410 \| 0.3560 \| 0.3239 \| 0.9993 \| 0.9950 \| 0.0184 \| 0.6594 \| 0.6776 \| 0.9950 \| \| 0.0072 \| 9.01 \| 4440 \| 0.5267 \| 0.4208 \| 0.9983 \| 0.9951 \| 0.0181 \| 0.7080 \| 0.7625 \| 0.9951 \| \| 0.017 \| 9.07 \| 4470 \| 0.3270 \| 0.3061 \| 0.9995 \| 0.9950 \| 0.0178 \| 0.6506 \| 0.6633 \| 0.9950 \| \| 0.019 \| 9.13 \| 4500 \| 0.4695 \| 0.4015 \| 0.9989 \| 0.9953 \| 0.0172 \| 0.6984 \| 0.7342 \| 0.9953 \| \| 0.0029 \| 9.19 \| 4530 \| 0.4155 \| 0.3695 \| 0.9992 \| 0.9952 \| 0.0167 \| 0.6824 \| 0.7073 \| 0.9952 \| \| 0.0077 \| 9.25 \| 4560 \| 0.4622 \| 0.3910 \| 0.9988 \| 0.9952 \| 0.0169 \| 0.6931 \| 0.7305 \| 0.9952 \| \| 0.0021 \| 9.31 \| 4590 \| 0.5527 \| 0.4208 \| 0.9979 \| 0.9949 \| 0.0176 \| 0.7079 \| 0.7753 \| 0.9949 \| \| 0.0076 \| 9.37 \| 4620 \| 0.3425 \| 0.3159 \| 0.9994 \| 0.9950 \| 0.0174 \| 0.6555 \| 0.6709 \| 0.9950 \| \| 0.0216 \| 9.43 \| 4650 \| 0.4100 \| 0.3655 \| 0.9992 \| 0.9952 \| 0.0174 \| 0.6804 \| 0.7046 \| 0.9952 \| \| 0.0013 \| 9.49 \| 4680 \| 0.3184 \| 0.2920 \| 0.9994 \| 0.9948 \| 0.0190 \| 0.6434 \| 0.6589 \| 0.9948 \| \| 0.0025 \| 9.55 \| 4710 \| 0.4960 \| 0.4125 \| 0.9986 \| 0.9952 \| 0.0170 \| 0.7039 \| 0.7473 \| 0.9953 \| \| 0.0139 \| 9.61 \| 4740 \| 0.4090 \| 0.3646 \| 0.9992 \| 0.9952 \| 0.0173 \| 0.6799 \| 0.7041 \| 0.9952 \| \| 0.0095 \| 9.68 \| 4770 \| 0.4806 \| 0.4038 \| 0.9987 \| 0.9952 \| 0.0173 \| 0.6995 \| 0.7397 \| 0.9952 \| \| 0.0167 \| 9.74 \| 4800 \| 0.6091 \| 0.4088 \| 0.9967 \| 0.9941 \| 0.0195 \| 0.7014 \| 0.8029 \| 0.9941 \| \| 0.0025 \| 9.8 \| 4830 \| 0.4785 \| 0.4023 \| 0.9987 \| 0.9952 \| 0.0170 \| 0.6987 \| 0.7386 \| 0.9952 \| \| 0.0054 \| 9.86 \| 4860 \| 0.2847 \| 0.2681 \| 0.9996 \| 0.9948 \| 0.0183 \| 0.6315 \| 0.6422 \| 0.9948 \| \| 0.0107 \| 9.92 \| 4890 \| 0.3816 \| 0.3482 \| 0.9994 \| 0.9952 \| 0.0178 \| 0.6717 \| 0.6905 \| 0.9952 \| \| 0.0039 \| 9.98 \| 4920 \| 0.3384 \| 0.3163 \| 0.9995 \| 0.9951 \| 0.0175 \| 0.6557 \| 0.6690 \| 0.9951 \| \| 0.0028 \| 10.04 \| 4950 \| 0.3742 \| 0.3350 \| 0.9992 \| 0.9950 \| 0.0182 \| 0.6650 \| 0.6867 \| 0.9950 \| \| 0.006 \| 10.1 \| 4980 \| 0.3311 \| 0.3071 \| 0.9995 \| 0.9950 \| 0.0187 \| 0.6510 \| 0.6653 \| 0.9950 \| \| 0.0077 \| 10.16 \| 5010 \| 0.3679 \| 0.3369 \| 0.9994 \| 0.9951 \| 0.0179 \| 0.6660 \| 0.6836 \| 0.9951 \| \| 0.0015 \| 10.22 \| 5040 \| 0.3777 \| 0.3462 \| 0.9994 \| 0.9952 \| 0.0177 \| 0.6707 \| 0.6885 \| 0.9952 \| \| 0.004 \| 10.28 \| 5070 \| 0.3744 \| 0.3442 \| 0.9994 \| 0.9952 \| 0.0178 \| 0.6697 \| 0.6869 \| 0.9952 \| \| 0.0071 \| 10.34 \| 5100 \| 0.5113 \| 0.4167 \| 0.9985 \| 0.9952 \| 0.0176 \| 0.7060 \| 0.7549 \| 0.9952 \| \| 0.0095 \| 10.41 \| 5130 \| 0.3498 \| 0.3191 \| 0.9993 \| 0.9950 \| 0.0182 \| 0.6570 \| 0.6746 \| 0.9950 \| \| 0.0031 \| 10.47 \| 5160 \| 0.4331 \| 0.3818 \| 0.9991 \| 0.9953 \| 0.0175 \| 0.6885 \| 0.7161 \| 0.9953 \| \| 0.0044 \| 10.53 \| 5190 \| 0.4276 \| 0.3762 \| 0.9991 \| 0.9952 \| 0.0173 \| 0.6857 \| 0.7133 \| 0.9952 \| \| 0.0041 \| 10.59 \| 5220 \| 0.4177 \| 0.3680 \| 0.9991 \| 0.9952 \| 0.0183 \| 0.6816 \| 0.7084 \| 0.9952 \| \| 0.0047 \| 10.65 \| 5250 \| 0.4054 \| 0.3648 \| 0.9992 \| 0.9952 \| 0.0175 \| 0.6800 \| 0.7023 \| 0.9953 \| \| 0.0072 \| 10.71 \| 5280 \| 0.4657 \| 0.4011 \| 0.9989 \| 0.9953 \| 0.0172 \| 0.6982 \| 0.7323 \| 0.9953 \| \| 0.0026 \| 10.77 \| 5310 \| 0.5030 \| 0.4209 \| 0.9987 \| 0.9953 \| 0.0172 \| 0.7081 \| 0.7509 \| 0.9954 \| \| 0.0025 \| 10.83 \| 5340 \| 0.5024 \| 0.4142 \| 0.9986 \| 0.9952 \| 0.0177 \| 0.7047 \| 0.7505 \| 0.9952 \| \| 0.0182 \| 10.89 \| 5370 \| 0.4298 \| 0.3824 \| 0.9992 \| 0.9953 \| 0.0174 \| 0.6889 \| 0.7145 \| 0.9953 \| \| 0.0019 \| 10.95 \| 5400 \| 0.4940 \| 0.4172 \| 0.9988 \| 0.9954 \| 0.0175 \| 0.7063 \| 0.7464 \| 0.9954 \| \| 0.0106 \| 11.01 \| 5430 \| 0.2958 \| 0.2791 \| 0.9996 \| 0.9949 \| 0.0188 \| 0.6370 \| 0.6477 \| 0.9949 \| \| 0.0053 \| 11.08 \| 5460 \| 0.5134 \| 0.4229 \| 0.9986 \| 0.9953 \| 0.0161 \| 0.7091 \| 0.7560 \| 0.9953 \| \| 0.0095 \| 11.14 \| 5490 \| 0.4817 \| 0.3820 \| 0.9982 \| 0.9948 \| 0.0181 \| 0.6884 \| 0.7400 \| 0.9948 \| \| 0.0074 \| 11.2 \| 5520 \| 0.3901 \| 0.3532 \| 0.9993 \| 0.9952 \| 0.0178 \| 0.6742 \| 0.6947 \| 0.9952 \| \| 0.006 \| 11.26 \| 5550 \| 0.4780 \| 0.4037 \| 0.9988 \| 0.9952 \| 0.0176 \| 0.6995 \| 0.7384 \| 0.9953 \| \| 0.0128 \| 11.32 \| 5580 \| 0.3996 \| 0.3579 \| 0.9992 \| 0.9952 \| 0.0162 \| 0.6765 \| 0.6994 \| 0.9952 \| \| 0.0129 \| 11.38 \| 5610 \| 0.5160 \| 0.4236 \| 0.9985 \| 0.9953 \| 0.0168 \| 0.7094 \| 0.7573 \| 0.9953 \| \| 0.0139 \| 11.44 \| 5640 \| 0.4689 \| 0.4012 \| 0.9989 \| 0.9953 \| 0.0168 \| 0.6982 \| 0.7339 \| 0.9953 \| \| 0.0115 \| 11.5 \| 5670 \| 0.3573 \| 0.3294 \| 0.9994 \| 0.9951 \| 0.0181 \| 0.6623 \| 0.6784 \| 0.9951 \| \| 0.0028 \| 11.56 \| 5700 \| 0.3349 \| 0.3133 \| 0.9995 \| 0.9951 \| 0.0178 \| 0.6542 \| 0.6672 \| 0.9951 \| \| 0.0058 \| 11.62 \| 5730 \| 0.5205 \| 0.4313 \| 0.9986 \| 0.9954 \| 0.0170 \| 0.7134 \| 0.7595 \| 0.9954 \| \| 0.0046 \| 11.68 \| 5760 \| 0.4486 \| 0.3942 \| 0.9991 \| 0.9954 \| 0.0170 \| 0.6948 \| 0.7238 \| 0.9954 \| \| 0.004 \| 11.74 \| 5790 \| 0.3965 \| 0.3591 \| 0.9993 \| 0.9952 \| 0.0172 \| 0.6772 \| 0.6979 \| 0.9952 \| \| 0.0222 \| 11.81 \| 5820 \| 0.4083 \| 0.3670 \| 0.9992 \| 0.9953 \| 0.0176 \| 0.6811 \| 0.7038 \| 0.9953 \| \| 0.0048 \| 11.87 \| 5850 \| 0.4489 \| 0.3904 \| 0.9990 \| 0.9953 \| 0.0175 \| 0.6929 \| 0.7239 \| 0.9953 \| \| 0.0025 \| 11.93 \| 5880 \| 0.3839 \| 0.3497 \| 0.9993 \| 0.9952 \| 0.0181 \| 0.6724 \| 0.6916 \| 0.9952 \| \| 0.0127 \| 11.99 \| 5910 \| 0.4677 \| 0.4063 \| 0.9990 \| 0.9954 \| 0.0174 \| 0.7008 \| 0.7334 \| 0.9954 \| \| 0.0201 \| 12.05 \| 5940 \| 0.4691 \| 0.4053 \| 0.9989 \| 0.9954 \| 0.0175 \| 0.7003 \| 0.7340 \| 0.9954 \| \| 0.0181 \| 12.11 \| 5970 \| 0.2999 \| 0.2851 \| 0.9996 \| 0.9949 \| 0.0201 \| 0.6400 \| 0.6498 \| 0.9950 \| \| 0.0043 \| 12.17 \| 6000 \| 0.3289 \| 0.3005 \| 0.9994 \| 0.9948 \| 0.0184 \| 0.6477 \| 0.6641 \| 0.9949 \| \| 0.0108 \| 12.23 \| 6030 \| 0.3651 \| 0.3365 \| 0.9994 \| 0.9952 \| 0.0170 \| 0.6658 \| 0.6823 \| 0.9952 \| \| 0.006 \| 12.29 \| 6060 \| 0.4369 \| 0.3833 \| 0.9991 \| 0.9953 \| 0.0171 \| 0.6893 \| 0.7180 \| 0.9953 \| \| 0.0041 \| 12.35 \| 6090 \| 0.3919 \| 0.3542 \| 0.9993 \| 0.9952 \| 0.0174 \| 0.6747 \| 0.6956 \| 0.9952 \| \| 0.0034 \| 12.41 \| 6120 \| 0.4418 \| 0.3914 \| 0.9991 \| 0.9954 \| 0.0168 \| 0.6934 \| 0.7205 \| 0.9954 \| \| 0.0007 \| 12.47 \| 6150 \| 0.4338 \| 0.3864 \| 0.9992 \| 0.9954 \| 0.0167 \| 0.6909 \| 0.7165 \| 0.9954 \| \| 0.0048 \| 12.54 \| 6180 \| 0.4303 \| 0.3839 \| 0.9992 \| 0.9954 \| 0.0174 \| 0.6896 \| 0.7147 \| 0.9954 \| \| 0.0078 \| 12.6 \| 6210 \| 0.3482 \| 0.3178 \| 0.9994 \| 0.9950 \| 0.0185 \| 0.6564 \| 0.6738 \| 0.9950 \| \| 0.0038 \| 12.66 \| 6240 \| 0.2892 \| 0.2742 \| 0.9996 \| 0.9949 \| 0.0184 \| 0.6345 \| 0.6444 \| 0.9949 \| \| 0.0078 \| 12.72 \| 6270 \| 0.4238 \| 0.3712 \| 0.9990 \| 0.9952 \| 0.0180 \| 0.6832 \| 0.7114 \| 0.9952 \| \| 0.0013 \| 12.78 \| 6300 \| 0.3916 \| 0.3584 \| 0.9994 \| 0.9953 \| 0.0175 \| 0.6768 \| 0.6955 \| 0.9953 \| \| 0.0025 \| 12.84 \| 6330 \| 0.3016 \| 0.2810 \| 0.9995 \| 0.9948 \| 0.0195 \| 0.6379 \| 0.6505 \| 0.9948 \| \| 0.0033 \| 12.9 \| 6360 \| 0.4662 \| 0.4071 \| 0.9990 \| 0.9954 \| 0.0168 \| 0.7013 \| 0.7326 \| 0.9954 \| \| 0.0225 \| 12.96 \| 6390 \| 0.5055 \| 0.4238 \| 0.9987 \| 0.9954 \| 0.0169 \| 0.7096 \| 0.7521 \| 0.9954 \| \| 0.0081 \| 13.02 \| 6420 \| 0.2816 \| 0.2664 \| 0.9996 \| 0.9948 \| 0.0184 \| 0.6306 \| 0.6406 \| 0.9948 \| \| 0.0032 \| 13.08 \| 6450 \| 0.4263 \| 0.3805 \| 0.9992 \| 0.9953 \| 0.0175 \| 0.6879 \| 0.7128 \| 0.9953 \| \| 0.0139 \| 13.14 \| 6480 \| 0.3732 \| 0.3428 \| 0.9994 \| 0.9952 \| 0.0157 \| 0.6690 \| 0.6863 \| 0.9952 \| \| 0.003 \| 13.2 \| 6510 \| 0.3906 \| 0.3565 \| 0.9994 \| 0.9953 \| 0.0162 \| 0.6759 \| 0.6950 \| 0.9953 \| \| 0.0098 \| 13.27 \| 6540 \| 0.4435 \| 0.3848 \| 0.9990 \| 0.9952 \| 0.0168 \| 0.6900 \| 0.7212 \| 0.9952 \| \| 0.0144 \| 13.33 \| 6570 \| 0.4259 \| 0.3729 \| 0.9990 \| 0.9952 \| 0.0167 \| 0.6840 \| 0.7125 \| 0.9952 \| \| 0.007 \| 13.39 \| 6600 \| 0.4175 \| 0.3731 \| 0.9992 \| 0.9953 \| 0.0165 \| 0.6842 \| 0.7084 \| 0.9953 \| \| 0.0012 \| 13.45 \| 6630 \| 0.4576 \| 0.4013 \| 0.9991 \| 0.9954 \| 0.0161 \| 0.6983 \| 0.7283 \| 0.9954 \| \| 0.0032 \| 13.51 \| 6660 \| 0.3585 \| 0.3310 \| 0.9994 \| 0.9951 \| 0.0174 \| 0.6631 \| 0.6790 \| 0.9951 \| \| 0.0077 \| 13.57 \| 6690 \| 0.4255 \| 0.3817 \| 0.9992 \| 0.9954 \| 0.0164 \| 0.6885 \| 0.7124 \| 0.9954 \| \| 0.002 \| 13.63 \| 6720 \| 0.3523 \| 0.3248 \| 0.9994 \| 0.9951 \| 0.0175 \| 0.6599 \| 0.6759 \| 0.9951 \| \| 0.0037 \| 13.69 \| 6750 \| 0.4441 \| 0.3920 \| 0.9991 \| 0.9954 \| 0.0164 \| 0.6937 \| 0.7216 \| 0.9954 \| \| 0.0043 \| 13.75 \| 6780 \| 0.4799 \| 0.4152 \| 0.9989 \| 0.9954 \| 0.0165 \| 0.7053 \| 0.7394 \| 0.9955 \| \| 0.008 \| 13.81 \| 6810 \| 0.4662 \| 0.3999 \| 0.9989 \| 0.9953 \| 0.0165 \| 0.6976 \| 0.7326 \| 0.9953 \| \| 0.0066 \| 13.87 \| 6840 \| 0.4061 \| 0.3643 \| 0.9992 \| 0.9952 \| 0.0167 \| 0.6798 \| 0.7027 \| 0.9952 \| \| 0.0061 \| 13.94 \| 6870 \| 0.3404 \| 0.3158 \| 0.9995 \| 0.9950 \| 0.0175 \| 0.6554 \| 0.6699 \| 0.9950 \| \| 0.0098 \| 14.0 \| 6900 \| 0.5015 \| 0.4226 \| 0.9987 \| 0.9954 \| 0.0166 \| 0.7090 \| 0.7501 \| 0.9954 \| \| 0.0067 \| 14.06 \| 6930 \| 0.4454 \| 0.3921 \| 0.9991 \| 0.9954 \| 0.0167 \| 0.6937 \| 0.7222 \| 0.9954 \| \| 0.0125 \| 14.12 \| 6960 \| 0.4326 \| 0.3792 \| 0.9990 \| 0.9952 \| 0.0166 \| 0.6872 \| 0.7158 \| 0.9952 \| \| 0.0032 \| 14.18 \| 6990 \| 0.4498 \| 0.3966 \| 0.9991 \| 0.9954 \| 0.0169 \| 0.6960 \| 0.7244 \| 0.9954 \| \| 0.0018 \| 14.24 \| 7020 \| 0.3867 \| 0.3527 \| 0.9993 \| 0.9952 \| 0.0174 \| 0.6740 \| 0.6930 \| 0.9952 \| \| 0.0022 \| 14.3 \| 7050 \| 0.5163 \| 0.4336 \| 0.9987 \| 0.9955 \| 0.0168 \| 0.7145 \| 0.7575 \| 0.9955 \| \| 0.0024 \| 14.36 \| 7080 \| 0.4913 \| 0.4189 \| 0.9988 \| 0.9954 \| 0.0168 \| 0.7071 \| 0.7451 \| 0.9954 \| \| 0.0037 \| 14.42 \| 7110 \| 0.4814 \| 0.4053 \| 0.9987 \| 0.9952 \| 0.0170 \| 0.7003 \| 0.7401 \| 0.9953 \| \| 0.0034 \| 14.48 \| 7140 \| 0.4541 \| 0.3577 \| 0.9982 \| 0.9945 \| 0.0196 \| 0.6761 \| 0.7262 \| 0.9945 \| \| 0.005 \| 14.54 \| 7170 \| 0.4347 \| 0.3862 \| 0.9992 \| 0.9953 \| 0.0170 \| 0.6908 \| 0.7169 \| 0.9954 \| \| 0.0019 \| 14.6 \| 7200 \| 0.4984 \| 0.4203 \| 0.9987 \| 0.9954 \| 0.0170 \| 0.7078 \| 0.7486 \| 0.9954 \| \| 0.0034 \| 14.67 \| 7230 \| 0.2893 \| 0.2756 \| 0.9997 \| 0.9949 \| 0.0194 \| 0.6353 \| 0.6445 \| 0.9949 \| \| 0.003 \| 14.73 \| 7260 \| 0.4117 \| 0.3725 \| 0.9993 \| 0.9953 \| 0.0171 \| 0.6839 \| 0.7055 \| 0.9953 \| \| 0.0081 \| 14.79 \| 7290 \| 0.4518 \| 0.3980 \| 0.9991 \| 0.9954 \| 0.0169 \| 0.6967 \| 0.7254 \| 0.9954 \| \| 0.0087 \| 14.85 \| 7320 \| 0.4720 \| 0.4094 \| 0.9990 \| 0.9954 \| 0.0170 \| 0.7024 \| 0.7355 \| 0.9954 \| \| 0.0051 \| 14.91 \| 7350 \| 0.3505 \| 0.3257 \| 0.9995 \| 0.9951 \| 0.0173 \| 0.6604 \| 0.6750 \| 0.9951 \| \| 0.0029 \| 14.97 \| 7380 \| 0.2699 \| 0.2504 \| 0.9995 \| 0.9946 \| 0.0183 \| 0.6225 \| 0.6347 \| 0.9946 \| \| 0.0014 \| 15.03 \| 7410 \| 0.4490 \| 0.3937 \| 0.9991 \| 0.9953 \| 0.0150 \| 0.6945 \| 0.7240 \| 0.9954 \| \| 0.0067 \| 15.09 \| 7440 \| 0.3776 \| 0.3474 \| 0.9994 \| 0.9952 \| 0.0162 \| 0.6713 \| 0.6885 \| 0.9952 \| \| 0.0013 \| 15.15 \| 7470 \| 0.2608 \| 0.2514 \| 0.9997 \| 0.9948 \| 0.0191 \| 0.6231 \| 0.6303 \| 0.9948 \| \| 0.0024 \| 15.21 \| 7500 \| 0.4044 \| 0.3670 \| 0.9993 \| 0.9953 \| 0.0172 \| 0.6811 \| 0.7018 \| 0.9953 \| \| 0.0255 \| 15.27 \| 7530 \| 0.4400 \| 0.3799 \| 0.9989 \| 0.9952 \| 0.0169 \| 0.6875 \| 0.7195 \| 0.9952 \| \| 0.0016 \| 15.33 \| 7560 \| 0.5440 \| 0.4439 \| 0.9985 \| 0.9954 \| 0.0168 \| 0.7196 \| 0.7712 \| 0.9954 \| \| 0.0153 \| 15.4 \| 7590 \| 0.5414 \| 0.4439 \| 0.9985 \| 0.9954 \| 0.0165 \| 0.7197 \| 0.7699 \| 0.9954 \| \| 0.015 \| 15.46 \| 7620 \| 0.3053 \| 0.2900 \| 0.9996 \| 0.9950 \| 0.0185 \| 0.6425 \| 0.6524 \| 0.9950 \| \| 0.0047 \| 15.52 \| 7650 \| 0.5346 \| 0.4336 \| 0.9984 \| 0.9953 \| 0.0170 \| 0.7144 \| 0.7665 \| 0.9953 \| \| 0.0086 \| 15.58 \| 7680 \| 0.5241 \| 0.4292 \| 0.9985 \| 0.9953 \| 0.0172 \| 0.7122 \| 0.7613 \| 0.9953 \| \| 0.0103 \| 15.64 \| 7710 \| 0.3444 \| 0.3208 \| 0.9995 \| 0.9951 \| 0.0177 \| 0.6580 \| 0.6719 \| 0.9951 \| \| 0.0091 \| 15.7 \| 7740 \| 0.3906 \| 0.3555 \| 0.9993 \| 0.9952 \| 0.0173 \| 0.6753 \| 0.6949 \| 0.9952 \| \| 0.0024 \| 15.76 \| 7770 \| 0.3728 \| 0.3452 \| 0.9995 \| 0.9952 \| 0.0182 \| 0.6702 \| 0.6862 \| 0.9953 \| \| 0.0061 \| 15.82 \| 7800 \| 0.4315 \| 0.3839 \| 0.9992 \| 0.9953 \| 0.0158 \| 0.6896 \| 0.7153 \| 0.9954 \| \| 0.0077 \| 15.88 \| 7830 \| 0.4274 \| 0.3831 \| 0.9992 \| 0.9954 \| 0.0160 \| 0.6892 \| 0.7133 \| 0.9954 \| \| 0.0072 \| 15.94 \| 7860 \| 0.4330 \| 0.3841 \| 0.9991 \| 0.9953 \| 0.0160 \| 0.6897 \| 0.7161 \| 0.9953 \| \| 0.009 \| 16.0 \| 7890 \| 0.3586 \| 0.3333 \| 0.9995 \| 0.9952 \| 0.0163 \| 0.6642 \| 0.6790 \| 0.9952 \| \| 0.0092 \| 16.06 \| 7920 \| 0.4129 \| 0.3706 \| 0.9992 \| 0.9953 \| 0.0161 \| 0.6829 \| 0.7060 \| 0.9953 \| \| 0.0032 \| 16.13 \| 7950 \| 0.4836 \| 0.4160 \| 0.9989 \| 0.9954 \| 0.0161 \| 0.7057 \| 0.7413 \| 0.9954 \| \| 0.0062 \| 16.19 \| 7980 \| 0.4115 \| 0.3322 \| 0.9984 \| 0.9944 \| 0.0206 \| 0.6633 \| 0.7049 \| 0.9944 \| \| 0.0093 \| 16.25 \| 8010 \| 0.3616 \| 0.3322 \| 0.9994 \| 0.9951 \| 0.0173 \| 0.6637 \| 0.6805 \| 0.9951 \| \| 0.001 \| 16.31 \| 8040 \| 0.4858 \| 0.4150 \| 0.9988 \| 0.9954 \| 0.0161 \| 0.7052 \| 0.7423 \| 0.9954 \| \| 0.0132 \| 16.37 \| 8070 \| 0.3209 \| 0.2952 \| 0.9994 \| 0.9948 \| 0.0188 \| 0.6450 \| 0.6602 \| 0.9949 \| \| 0.0016 \| 16.43 \| 8100 \| 0.4150 \| 0.3742 \| 0.9993 \| 0.9953 \| 0.0166 \| 0.6848 \| 0.7071 \| 0.9953 \| \| 0.0007 \| 16.49 \| 8130 \| 0.4981 \| 0.3988 \| 0.9983 \| 0.9949 \| 0.0185 \| 0.6969 \| 0.7482 \| 0.9950 \| \| 0.002 \| 16.55 \| 8160 \| 0.3659 \| 0.3385 \| 0.9995 \| 0.9952 \| 0.0184 \| 0.6668 \| 0.6827 \| 0.9952 \| \| 0.0074 \| 16.61 \| 8190 \| 0.4173 \| 0.3763 \| 0.9993 \| 0.9953 \| 0.0166 \| 0.6858 \| 0.7083 \| 0.9954 \| \| 0.0048 \| 16.67 \| 8220 \| 0.3306 \| 0.3108 \| 0.9996 \| 0.9951 \| 0.0179 \| 0.6529 \| 0.6651 \| 0.9951 \| \| 0.0028 \| 16.73 \| 8250 \| 0.4462 \| 0.3932 \| 0.9991 \| 0.9954 \| 0.0166 \| 0.6943 \| 0.7227 \| 0.9954 \| \| 0.0135 \| 16.8 \| 8280 \| 0.3967 \| 0.3617 \| 0.9993 \| 0.9953 \| 0.0172 \| 0.6785 \| 0.6980 \| 0.9953 \| \| 0.0138 \| 16.86 \| 8310 \| 0.3657 \| 0.3396 \| 0.9995 \| 0.9952 \| 0.0173 \| 0.6674 \| 0.6826 \| 0.9952 \| \| 0.0017 \| 16.92 \| 8340 \| 0.3303 \| 0.3061 \| 0.9995 \| 0.9950 \| 0.0191 \| 0.6505 \| 0.6649 \| 0.9950 \| \| 0.0071 \| 16.98 \| 8370 \| 0.3825 \| 0.3512 \| 0.9994 \| 0.9952 \| 0.0174 \| 0.6732 \| 0.6909 \| 0.9953 \| \| 0.0044 \| 17.04 \| 8400 \| 0.4303 \| 0.3849 \| 0.9992 \| 0.9954 \| 0.0171 \| 0.6901 \| 0.7148 \| 0.9954 \| \| 0.0101 \| 17.1 \| 8430 \| 0.4256 \| 0.3771 \| 0.9991 \| 0.9953 \| 0.0172 \| 0.6862 \| 0.7123 \| 0.9953 \| \| 0.0076 \| 17.16 \| 8460 \| 0.4438 \| 0.3939 \| 0.9991 \| 0.9954 \| 0.0168 \| 0.6947 \| 0.7215 \| 0.9954 \| \| 0.0073 \| 17.22 \| 8490 \| 0.5011 \| 0.4236 \| 0.9988 \| 0.9954 \| 0.0164 \| 0.7095 \| 0.7500 \| 0.9954 \| \| 0.0029 \| 17.28 \| 8520 \| 0.2741 \| 0.2627 \| 0.9997 \| 0.9948 \| 0.0177 \| 0.6288 \| 0.6369 \| 0.9948 \| \| 0.0109 \| 17.34 \| 8550 \| 0.4682 \| 0.4074 \| 0.9990 \| 0.9954 \| 0.0160 \| 0.7014 \| 0.7336 \| 0.9954 \| \| 0.0258 \| 17.4 \| 8580 \| 0.4508 \| 0.3901 \| 0.9989 \| 0.9953 \| 0.0166 \| 0.6927 \| 0.7249 \| 0.9953 \| \| 0.0181 \| 17.46 \| 8610 \| 0.4780 \| 0.4058 \| 0.9988 \| 0.9953 \| 0.0168 \| 0.7006 \| 0.7384 \| 0.9953 \| \| 0.0052 \| 17.53 \| 8640 \| 0.3767 \| 0.3447 \| 0.9994 \| 0.9952 \| 0.0177 \| 0.6699 \| 0.6880 \| 0.9952 \| \| 0.0069 \| 17.59 \| 8670 \| 0.3409 \| 0.3125 \| 0.9994 \| 0.9950 \| 0.0187 \| 0.6537 \| 0.6702 \| 0.9950 \| \| 0.004 \| 17.65 \| 8700 \| 0.3400 \| 0.3172 \| 0.9995 \| 0.9951 \| 0.0189 \| 0.6561 \| 0.6697 \| 0.9951 \| \| 0.0061 \| 17.71 \| 8730 \| 0.3931 \| 0.3594 \| 0.9994 \| 0.9953 \| 0.0183 \| 0.6773 \| 0.6962 \| 0.9953 \| \| 0.0038 \| 17.77 \| 8760 \| 0.2883 \| 0.2749 \| 0.9997 \| 0.9949 \| 0.0195 \| 0.6349 \| 0.6440 \| 0.9949 \| \| 0.0021 \| 17.83 \| 8790 \| 0.3568 \| 0.3300 \| 0.9995 \| 0.9951 \| 0.0182 \| 0.6626 \| 0.6781 \| 0.9951 \| \| 0.0019 \| 17.89 \| 8820 \| 0.5072 \| 0.4183 \| 0.9986 \| 0.9952 \| 0.0176 \| 0.7068 \| 0.7529 \| 0.9953 \| \| 0.0006 \| 17.95 \| 8850 \| 0.3592 \| 0.3334 \| 0.9995 \| 0.9952 \| 0.0182 \| 0.6643 \| 0.6793 \| 0.9952 \| \| 0.0031 \| 18.01 \| 8880 \| 0.3860 \| 0.3439 \| 0.9992 \| 0.9950 \| 0.0182 \| 0.6695 \| 0.6926 \| 0.9951 \| \| 0.003 \| 18.07 \| 8910 \| 0.2987 \| 0.2766 \| 0.9995 \| 0.9947 \| 0.0198 \| 0.6357 \| 0.6491 \| 0.9948 \| \| 0.0033 \| 18.13 \| 8940 \| 0.4374 \| 0.3876 \| 0.9991 \| 0.9953 \| 0.0173 \| 0.6915 \| 0.7182 \| 0.9954 \| \| 0.0067 \| 18.19 \| 8970 \| 0.5144 \| 0.4266 \| 0.9986 \| 0.9953 \| 0.0176 \| 0.7110 \| 0.7565 \| 0.9954 \| \| 0.0056 \| 18.26 \| 9000 \| 0.3742 \| 0.3451 \| 0.9994 \| 0.9952 \| 0.0179 \| 0.6701 \| 0.6868 \| 0.9952 \| \| 0.0008 \| 18.32 \| 9030 \| 0.3526 \| 0.3308 \| 0.9996 \| 0.9952 \| 0.0178 \| 0.6630 \| 0.6761 \| 0.9952 \| \| 0.0038 \| 18.38 \| 9060 \| 0.4611 \| 0.4051 \| 0.9991 \| 0.9954 \| 0.0176 \| 0.7003 \| 0.7301 \| 0.9955 \| \| 0.005 \| 18.44 \| 9090 \| 0.4017 \| 0.3636 \| 0.9993 \| 0.9953 \| 0.0183 \| 0.6794 \| 0.7005 \| 0.9953 \| \| 0.0028 \| 18.5 \| 9120 \| 0.4492 \| 0.3950 \| 0.9991 \| 0.9954 \| 0.0159 \| 0.6952 \| 0.7241 \| 0.9954 \| \| 0.0055 \| 18.56 \| 9150 \| 0.5008 \| 0.4227 \| 0.9988 \| 0.9954 \| 0.0154 \| 0.7090 \| 0.7498 \| 0.9954 \| \| 0.0057 \| 18.62 \| 9180 \| 0.3915 \| 0.3568 \| 0.9993 \| 0.9952 \| 0.0159 \| 0.6760 \| 0.6954 \| 0.9953 \| \| 0.0081 \| 18.68 \| 9210 \| 0.4314 \| 0.3832 \| 0.9991 \| 0.9953 \| 0.0159 \| 0.6892 \| 0.7153 \| 0.9953 \| \| 0.0051 \| 18.74 \| 9240 \| 0.3941 \| 0.3605 \| 0.9994 \| 0.9953 \| 0.0161 \| 0.6779 \| 0.6967 \| 0.9953 \| \| 0.0053 \| 18.8 \| 9270 \| 0.3960 \| 0.3606 \| 0.9993 \| 0.9953 \| 0.0165 \| 0.6779 \| 0.6977 \| 0.9953 \| \| 0.0171 \| 18.86 \| 9300 \| 0.4220 \| 0.3768 \| 0.9992 \| 0.9953 \| 0.0165 \| 0.6861 \| 0.7106 \| 0.9953 \| \| 0.0026 \| 18.92 \| 9330 \| 0.4337 \| 0.3876 \| 0.9992 \| 0.9954 \| 0.0166 \| 0.6915 \| 0.7165 \| 0.9954 \| \| 0.0188 \| 18.99 \| 9360 \| 0.4283 \| 0.3835 \| 0.9992 \| 0.9954 \| 0.0168 \| 0.6894 \| 0.7138 \| 0.9954 \| \| 0.0062 \| 19.05 \| 9390 \| 0.4129 \| 0.3726 \| 0.9993 \| 0.9953 \| 0.0166 \| 0.6840 \| 0.7061 \| 0.9953 \| \| 0.0136 \| 19.11 \| 9420 \| 0.4603 \| 0.4005 \| 0.9990 \| 0.9954 \| 0.0166 \| 0.6979 \| 0.7296 \| 0.9954 \| \| 0.005 \| 19.17 \| 9450 \| 0.3971 \| 0.3607 \| 0.9993 \| 0.9953 \| 0.0170 \| 0.6780 \| 0.6982 \| 0.9953 \| \| 0.0106 \| 19.23 \| 9480 \| 0.4597 \| 0.3994 \| 0.9990 \| 0.9953 \| 0.0163 \| 0.6974 \| 0.7293 \| 0.9954 \| \| 0.0045 \| 19.29 \| 9510 \| 0.3873 \| 0.3532 \| 0.9993 \| 0.9952 \| 0.0167 \| 0.6742 \| 0.6933 \| 0.9952 \| \| 0.0015 \| 19.35 \| 9540 \| 0.3104 \| 0.2948 \| 0.9996 \| 0.9950 \| 0.0175 \| 0.6449 \| 0.6550 \| 0.9950 \| \| 0.0049 \| 19.41 \| 9570 \| 0.4511 \| 0.3973 \| 0.9991 \| 0.9954 \| 0.0165 \| 0.6964 \| 0.7251 \| 0.9954 \| \| 0.0023 \| 19.47 \| 9600 \| 0.2654 \| 0.2552 \| 0.9997 \| 0.9948 \| 0.0187 \| 0.6250 \| 0.6326 \| 0.9948 \| \| 0.0079 \| 19.53 \| 9630 \| 0.3844 \| 0.3489 \| 0.9993 \| 0.9952 \| 0.0177 \| 0.6720 \| 0.6918 \| 0.9952 \| \| 0.0112 \| 19.59 \| 9660 \| 0.4717 \| 0.4130 \| 0.9990 \| 0.9955 \| 0.0166 \| 0.7042 \| 0.7353 \| 0.9955 \| \| 0.0021 \| 19.66 \| 9690 \| 0.3977 \| 0.3602 \| 0.9993 \| 0.9952 \| 0.0167 \| 0.6777 \| 0.6985 \| 0.9953 \| \| 0.0056 \| 19.72 \| 9720 \| 0.4466 \| 0.3951 \| 0.9991 \| 0.9954 \| 0.0169 \| 0.6952 \| 0.7229 \| 0.9954 \| \| 0.0089 \| 19.78 \| 9750 \| 0.3270 \| 0.3070 \| 0.9996 \| 0.9950 \| 0.0179 \| 0.6510 \| 0.6633 \| 0.9950 \| \| 0.0076 \| 19.84 \| 9780 \| 0.3947 \| 0.3586 \| 0.9993 \| 0.9952 \| 0.0181 \| 0.6769 \| 0.6970 \| 0.9953 \| \| 0.0012 \| 19.9 \| 9810 \| 0.5196 \| 0.4334 \| 0.9987 \| 0.9954 \| 0.0174 \| 0.7144 \| 0.7592 \| 0.9954 \| \| 0.0074 \| 19.96 \| 9840 \| 0.4607 \| 0.4064 \| 0.9991 \| 0.9955 \| 0.0178 \| 0.7009 \| 0.7299 \| 0.9955 \| \| 0.012 \| 20.02 \| 9870 \| 0.3824 \| 0.3508 \| 0.9994 \| 0.9952 \| 0.0186 \| 0.6730 \| 0.6909 \| 0.9952 \| \| 0.0034 \| 20.08 \| 9900 \| 0.4868 \| 0.4136 \| 0.9988 \| 0.9954 \| 0.0176 \| 0.7045 \| 0.7428 \| 0.9954 \| \| 0.0043 \| 20.14 \| 9930 \| 0.4499 \| 0.3988 \| 0.9991 \| 0.9954 \| 0.0181 \| 0.6971 \| 0.7245 \| 0.9954 \| \| 0.0021 \| 20.2 \| 9960 \| 0.3666 \| 0.3397 \| 0.9995 \| 0.9952 \| 0.0162 \| 0.6675 \| 0.6830 \| 0.9952 \| \| 0.0109 \| 20.26 \| 9990 \| 0.3804 \| 0.3483 \| 0.9994 \| 0.9952 \| 0.0156 \| 0.6718 \| 0.6899 \| 0.9952 \| \| 0.0045 \| 20.32 \| 10020 \| 0.4206 \| 0.3782 \| 0.9992 \| 0.9953 \| 0.0152 \| 0.6868 \| 0.7099 \| 0.9954 \| \| 0.0036 \| 20.39 \| 10050 \| 0.4397 \| 0.3924 \| 0.9992 \| 0.9954 \| 0.0157 \| 0.6939 \| 0.7194 \| 0.9954 \| \| 0.0029 \| 20.45 \| 10080 \| 0.4284 \| 0.3800 \| 0.9991 \| 0.9953 \| 0.0155 \| 0.6876 \| 0.7138 \| 0.9953 \| \| 0.0041 \| 20.51 \| 10110 \| 0.4948 \| 0.4209 \| 0.9988 \| 0.9954 \| 0.0159 \| 0.7082 \| 0.7468 \| 0.9954 \| \| 0.0017 \| 20.57 \| 10140 \| 0.4869 \| 0.4188 \| 0.9989 \| 0.9954 \| 0.0157 \| 0.7071 \| 0.7429 \| 0.9955 \| \| 0.0052 \| 20.63 \| 10170 \| 0.4236 \| 0.3800 \| 0.9992 \| 0.9953 \| 0.0160 \| 0.6877 \| 0.7114 \| 0.9954 \| \| 0.0096 \| 20.69 \| 10200 \| 0.3932 \| 0.3608 \| 0.9994 \| 0.9953 \| 0.0161 \| 0.6781 \| 0.6963 \| 0.9953 \| \| 0.003 \| 20.75 \| 10230 \| 0.4181 \| 0.3794 \| 0.9993 \| 0.9954 \| 0.0159 \| 0.6874 \| 0.7087 \| 0.9954 \| \| 0.0019 \| 20.81 \| 10260 \| 0.3584 \| 0.3341 \| 0.9995 \| 0.9952 \| 0.0162 \| 0.6646 \| 0.6790 \| 0.9952 \| \| 0.0041 \| 20.87 \| 10290 \| 0.3149 \| 0.2979 \| 0.9996 \| 0.9950 \| 0.0169 \| 0.6465 \| 0.6573 \| 0.9950 \| \| 0.0163 \| 20.93 \| 10320 \| 0.4067 \| 0.3711 \| 0.9994 \| 0.9954 \| 0.0160 \| 0.6833 \| 0.7030 \| 0.9954 \| \| 0.0083 \| 20.99 \| 10350 \| 0.4386 \| 0.3883 \| 0.9991 \| 0.9953 \| 0.0162 \| 0.6918 \| 0.7189 \| 0.9954 \| \| 0.0059 \| 21.05 \| 10380 \| 0.4596 \| 0.3989 \| 0.9990 \| 0.9953 \| 0.0161 \| 0.6971 \| 0.7293 \| 0.9954 \| \| 0.0136 \| 21.12 \| 10410 \| 0.4530 \| 0.4004 \| 0.9991 \| 0.9954 \| 0.0160 \| 0.6979 \| 0.7261 \| 0.9954 \| \| 0.0019 \| 21.18 \| 10440 \| 0.3820 \| 0.3496 \| 0.9994 \| 0.9952 \| 0.0168 \| 0.6724 \| 0.6907 \| 0.9952 \| \| 0.0034 \| 21.24 \| 10470 \| 0.5635 \| 0.4307 \| 0.9979 \| 0.9950 \| 0.0175 \| 0.7128 \| 0.7807 \| 0.9950 \| \| 0.0089 \| 21.3 \| 10500 \| 0.4849 \| 0.4173 \| 0.9989 \| 0.9954 \| 0.0164 \| 0.7064 \| 0.7419 \| 0.9955 \| \| 0.0067 \| 21.36 \| 10530 \| 0.3740 \| 0.3465 \| 0.9995 \| 0.9953 \| 0.0168 \| 0.6709 \| 0.6867 \| 0.9953 \| \| 0.0011 \| 21.42 \| 10560 \| 0.4060 \| 0.3705 \| 0.9994 \| 0.9954 \| 0.0166 \| 0.6829 \| 0.7027 \| 0.9954 \| \| 0.0051 \| 21.48 \| 10590 \| 0.4662 \| 0.4087 \| 0.9990 \| 0.9955 \| 0.0161 \| 0.7021 \| 0.7326 \| 0.9955 \| \| 0.0012 \| 21.54 \| 10620 \| 0.4241 \| 0.3834 \| 0.9993 \| 0.9954 \| 0.0157 \| 0.6894 \| 0.7117 \| 0.9954 \| \| 0.0175 \| 21.6 \| 10650 \| 0.3203 \| 0.3027 \| 0.9996 \| 0.9950 \| 0.0169 \| 0.6489 \| 0.6599 \| 0.9950 \| \| 0.0062 \| 21.66 \| 10680 \| 0.4179 \| 0.3777 \| 0.9993 \| 0.9954 \| 0.0164 \| 0.6865 \| 0.7086 \| 0.9954 \| \| 0.001 \| 21.72 \| 10710 \| 0.4611 \| 0.4031 \| 0.9990 \| 0.9954 \| 0.0162 \| 0.6993 \| 0.7301 \| 0.9954 \| \| 0.0007 \| 21.78 \| 10740 \| 0.4893 \| 0.4156 \| 0.9988 \| 0.9954 \| 0.0166 \| 0.7055 \| 0.7440 \| 0.9954 \| \| 0.0074 \| 21.85 \| 10770 \| 0.4030 \| 0.3601 \| 0.9992 \| 0.9952 \| 0.0169 \| 0.6776 \| 0.7011 \| 0.9952 \| \| 0.0064 \| 21.91 \| 10800 \| 0.5096 \| 0.4213 \| 0.9986 \| 0.9953 \| 0.0168 \| 0.7083 \| 0.7541 \| 0.9953 \| \| 0.0052 \| 21.97 \| 10830 \| 0.3540 \| 0.3284 \| 0.9995 \| 0.9951 \| 0.0176 \| 0.6618 \| 0.6767 \| 0.9951 \| \| 0.0075 \| 22.03 \| 10860 \| 0.3808 \| 0.3493 \| 0.9994 \| 0.9952 \| 0.0181 \| 0.6723 \| 0.6901 \| 0.9952 \| \| 0.0013 \| 22.09 \| 10890 \| 0.2467 \| 0.2382 \| 0.9998 \| 0.9947 \| 0.0198 \| 0.6164 \| 0.6232 \| 0.9947 \| \| 0.0055 \| 22.15 \| 10920 \| 0.4700 \| 0.4061 \| 0.9989 \| 0.9954 \| 0.0173 \| 0.7007 \| 0.7345 \| 0.9954 \| \| 0.0061 \| 22.21 \| 10950 \| 0.4901 \| 0.4211 \| 0.9989 \| 0.9955 \| 0.0168 \| 0.7083 \| 0.7445 \| 0.9955 \| \| 0.0004 \| 22.27 \| 10980 \| 0.4764 \| 0.4113 \| 0.9989 \| 0.9954 \| 0.0168 \| 0.7033 \| 0.7377 \| 0.9954 \| \| 0.0138 \| 22.33 \| 11010 \| 0.4446 \| 0.3937 \| 0.9991 \| 0.9954 \| 0.0168 \| 0.6945 \| 0.7219 \| 0.9954 \| \| 0.0094 \| 22.39 \| 11040 \| 0.4146 \| 0.3736 \| 0.9993 \| 0.9953 \| 0.0171 \| 0.6845 \| 0.7069 \| 0.9953 \| \| 0.0019 \| 22.45 \| 11070 \| 0.4256 \| 0.3792 \| 0.9992 \| 0.9953 \| 0.0177 \| 0.6873 \| 0.7124 \| 0.9953 \| \| 0.0061 \| 22.52 \| 11100 \| 0.4614 \| 0.4072 \| 0.9991 \| 0.9955 \| 0.0166 \| 0.7013 \| 0.7303 \| 0.9955 \| \| 0.0248 \| 22.58 \| 11130 \| 0.3462 \| 0.3249 \| 0.9996 \| 0.9952 \| 0.0173 \| 0.6600 \| 0.6729 \| 0.9952 \| \| 0.0038 \| 22.64 \| 11160 \| 0.2986 \| 0.2849 \| 0.9997 \| 0.9950 \| 0.0181 \| 0.6399 \| 0.6491 \| 0.9950 \| \| 0.0086 \| 22.7 \| 11190 \| 0.4815 \| 0.4106 \| 0.9988 \| 0.9953 \| 0.0177 \| 0.7030 \| 0.7402 \| 0.9954 \| \| 0.0024 \| 22.76 \| 11220 \| 0.4619 \| 0.4066 \| 0.9991 \| 0.9955 \| 0.0171 \| 0.7010 \| 0.7305 \| 0.9955 \| \| 0.5688 \| 22.82 \| 11250 \| 0.4511 \| 0.3964 \| 0.9991 \| 0.9954 \| 0.0171 \| 0.6959 \| 0.7251 \| 0.9954 \| \| 0.0245 \| 22.88 \| 11280 \| 0.3592 \| 0.3349 \| 0.9995 \| 0.9952 \| 0.0160 \| 0.6651 \| 0.6794 \| 0.9952 \| \| 0.0042 \| 22.94 \| 11310 \| 0.4520 \| 0.3982 \| 0.9991 \| 0.9954 \| 0.0152 \| 0.6968 \| 0.7256 \| 0.9954 \| \| 0.0071 \| 23.0 \| 11340 \| 0.4718 \| 0.3989 \| 0.9988 \| 0.9952 \| 0.0153 \| 0.6970 \| 0.7353 \| 0.9952 \| \| 0.0072 \| 23.06 \| 11370 \| 0.2602 \| 0.2505 \| 0.9997 \| 0.9948 \| 0.0174 \| 0.6226 \| 0.6300 \| 0.9948 \| \| 0.0018 \| 23.12 \| 11400 \| 0.3847 \| 0.3524 \| 0.9994 \| 0.9952 \| 0.0156 \| 0.6738 \| 0.6921 \| 0.9953 \| \| 0.0059 \| 23.18 \| 11430 \| 0.3958 \| 0.3630 \| 0.9994 \| 0.9953 \| 0.0160 \| 0.6792 \| 0.6976 \| 0.9953 \| \| 0.0061 \| 23.25 \| 11460 \| 0.4973 \| 0.4261 \| 0.9989 \| 0.9955 \| 0.0150 \| 0.7108 \| 0.7481 \| 0.9955 \| \| 0.0064 \| 23.31 \| 11490 \| 0.4901 \| 0.4158 \| 0.9988 \| 0.9954 \| 0.0157 \| 0.7056 \| 0.7444 \| 0.9954 \| \| 0.0038 \| 23.37 \| 11520 \| 0.4829 \| 0.4165 \| 0.9989 \| 0.9954 \| 0.0153 \| 0.7060 \| 0.7409 \| 0.9955 \| \| 0.0071 \| 23.43 \| 11550 \| 0.4206 \| 0.3773 \| 0.9992 \| 0.9953 \| 0.0157 \| 0.6863 \| 0.7099 \| 0.9953 \| \| 0.0038 \| 23.49 \| 11580 \| 0.4850 \| 0.4152 \| 0.9989 \| 0.9954 \| 0.0157 \| 0.7053 \| 0.7419 \| 0.9954 \| \| 0.0067 \| 23.55 \| 11610 \| 0.3487 \| 0.3226 \| 0.9995 \| 0.9951 \| 0.0167 \| 0.6588 \| 0.6741 \| 0.9951 \| \| 0.0153 \| 23.61 \| 11640 \| 0.3981 \| 0.3636 \| 0.9994 \| 0.9953 \| 0.0167 \| 0.6795 \| 0.6988 \| 0.9953 \| \| 0.0051 \| 23.67 \| 11670 \| 0.3383 \| 0.3170 \| 0.9995 \| 0.9951 \| 0.0169 \| 0.6560 \| 0.6689 \| 0.9951 \| \| 0.0046 \| 23.73 \| 11700 \| 0.3682 \| 0.3399 \| 0.9994 \| 0.9952 \| 0.0170 \| 0.6675 \| 0.6838 \| 0.9952 \| \| 0.0067 \| 23.79 \| 11730 \| 0.4416 \| 0.3886 \| 0.9991 \| 0.9953 \| 0.0165 \| 0.6920 \| 0.7203 \| 0.9953 \| \| 0.0064 \| 23.85 \| 11760 \| 0.4353 \| 0.3844 \| 0.9991 \| 0.9953 \| 0.0167 \| 0.6898 \| 0.7172 \| 0.9953 \| \| 0.0154 \| 23.91 \| 11790 \| 0.3648 \| 0.3389 \| 0.9995 \| 0.9952 \| 0.0167 \| 0.6670 \| 0.6821 \| 0.9952 \| \| 0.0024 \| 23.98 \| 11820 \| 0.3661 \| 0.3369 \| 0.9994 \| 0.9951 \| 0.0168 \| 0.6660 \| 0.6828 \| 0.9952 \| \| 0.0033 \| 24.04 \| 11850 \| 0.3889 \| 0.3486 \| 0.9992 \| 0.9951 \| 0.0164 \| 0.6719 \| 0.6941 \| 0.9951 \| \| 0.0008 \| 24.1 \| 11880 \| 0.3108 \| 0.2920 \| 0.9996 \| 0.9949 \| 0.0175 \| 0.6435 \| 0.6552 \| 0.9949 \| \| 0.0122 \| 24.16 \| 11910 \| 0.3765 \| 0.3477 \| 0.9994 \| 0.9952 \| 0.0165 \| 0.6715 \| 0.6880 \| 0.9953 \| \| 0.023 \| 24.22 \| 11940 \| 0.4958 \| 0.4147 \| 0.9987 \| 0.9953 \| 0.0159 \| 0.7050 \| 0.7472 \| 0.9953 \| \| 0.0144 \| 24.28 \| 11970 \| 0.4176 \| 0.3776 \| 0.9993 \| 0.9954 \| 0.0159 \| 0.6865 \| 0.7084 \| 0.9954 \| \| 0.0084 \| 24.34 \| 12000 \| 0.3756 \| 0.3454 \| 0.9994 \| 0.9952 \| 0.0162 \| 0.6703 \| 0.6875 \| 0.9952 \| \| 0.0026 \| 24.4 \| 12030 \| 0.4246 \| 0.3801 \| 0.9992 \| 0.9953 \| 0.0158 \| 0.6877 \| 0.7119 \| 0.9954 \| \| 0.003 \| 24.46 \| 12060 \| 0.4904 \| 0.4161 \| 0.9988 \| 0.9954 \| 0.0161 \| 0.7057 \| 0.7446 \| 0.9954 \| \| 0.0067 \| 24.52 \| 12090 \| 0.4726 \| 0.4060 \| 0.9989 \| 0.9953 \| 0.0163 \| 0.7007 \| 0.7357 \| 0.9954 \| \| 0.01 \| 24.58 \| 12120 \| 0.5012 \| 0.4232 \| 0.9988 \| 0.9954 \| 0.0163 \| 0.7093 \| 0.7500 \| 0.9954 \| \| 0.0047 \| 24.65 \| 12150 \| 0.4273 \| 0.3820 \| 0.9992 \| 0.9953 \| 0.0167 \| 0.6887 \| 0.7132 \| 0.9954 \| \| 0.008 \| 24.71 \| 12180 \| 0.4555 \| 0.3992 \| 0.9990 \| 0.9954 \| 0.0165 \| 0.6973 \| 0.7273 \| 0.9954 \| \| 0.004 \| 24.77 \| 12210 \| 0.4153 \| 0.3676 \| 0.9991 \| 0.9952 \| 0.0174 \| 0.6814 \| 0.7072 \| 0.9952 \| \| 0.0017 \| 24.83 \| 12240 \| 0.4730 \| 0.4066 \| 0.9989 \| 0.9954 \| 0.0158 \| 0.7010 \| 0.7360 \| 0.9954 \| \| 0.0141 \| 24.89 \| 12270 \| 0.4485 \| 0.3946 \| 0.9991 \| 0.9954 \| 0.0173 \| 0.6950 \| 0.7238 \| 0.9954 \| \| 0.018 \| 24.95 \| 12300 \| 0.4337 \| 0.3848 \| 0.9991 \| 0.9953 \| 0.0181 \| 0.6901 \| 0.7164 \| 0.9953 \| \| 0.004 \| 25.01 \| 12330 \| 0.5078 \| 0.4322 \| 0.9988 \| 0.9955 \| 0.0171 \| 0.7139 \| 0.7533 \| 0.9955 \| \| 0.0093 \| 25.07 \| 12360 \| 0.4210 \| 0.3783 \| 0.9992 \| 0.9953 \| 0.0172 \| 0.6868 \| 0.7101 \| 0.9954 \| \| 0.0054 \| 25.13 \| 12390 \| 0.4701 \| 0.4077 \| 0.9990 \| 0.9954 \| 0.0174 \| 0.7015 \| 0.7346 \| 0.9954 \| \| 0.0066 \| 25.19 \| 12420 \| 0.6023 \| 0.4358 \| 0.9974 \| 0.9947 \| 0.0189 \| 0.7153 \| 0.7999 \| 0.9948 \| \| 0.0017 \| 25.25 \| 12450 \| 0.4582 \| 0.4010 \| 0.9990 \| 0.9954 \| 0.0182 \| 0.6982 \| 0.7286 \| 0.9954 \| \| 0.0021 \| 25.31 \| 12480 \| 0.3981 \| 0.3630 \| 0.9993 \| 0.9953 \| 0.0184 \| 0.6791 \| 0.6987 \| 0.9953 \| \| 0.0017 \| 25.38 \| 12510 \| 0.4041 \| 0.3635 \| 0.9992 \| 0.9952 \| 0.0181 \| 0.6794 \| 0.7017 \| 0.9952 \| \| 0.0026 \| 25.44 \| 12540 \| 0.4181 \| 0.3758 \| 0.9992 \| 0.9953 \| 0.0184 \| 0.6856 \| 0.7087 \| 0.9953 \| \| 0.0029 \| 25.5 \| 12570 \| 0.3562 \| 0.3329 \| 0.9995 \| 0.9952 \| 0.0191 \| 0.6641 \| 0.6778 \| 0.9952 \| \| 0.0146 \| 25.56 \| 12600 \| 0.4022 \| 0.3673 \| 0.9994 \| 0.9953 \| 0.0185 \| 0.6813 \| 0.7008 \| 0.9953 \| \| 0.0015 \| 25.62 \| 12630 \| 0.3870 \| 0.3565 \| 0.9994 \| 0.9953 \| 0.0166 \| 0.6759 \| 0.6932 \| 0.9953 \| \| 0.0024 \| 25.68 \| 12660 \| 0.3898 \| 0.3570 \| 0.9994 \| 0.9953 \| 0.0171 \| 0.6762 \| 0.6946 \| 0.9953 \| \| 0.0022 \| 25.74 \| 12690 \| 0.4325 \| 0.3888 \| 0.9992 \| 0.9954 \| 0.0176 \| 0.6921 \| 0.7159 \| 0.9954 \| \| 0.004 \| 25.8 \| 12720 \| 0.4414 \| 0.3940 \| 0.9992 \| 0.9954 \| 0.0172 \| 0.6947 \| 0.7203 \| 0.9954 \| \| 0.0084 \| 25.86 \| 12750 \| 0.4358 \| 0.3912 \| 0.9992 \| 0.9954 \| 0.0165 \| 0.6933 \| 0.7175 \| 0.9954 \| \| 0.0065 \| 25.92 \| 12780 \| 0.3569 \| 0.3323 \| 0.9995 \| 0.9952 \| 0.0182 \| 0.6638 \| 0.6782 \| 0.9952 \| \| 0.0079 \| 25.98 \| 12810 \| 0.4055 \| 0.3689 \| 0.9993 \| 0.9953 \| 0.0172 \| 0.6821 \| 0.7024 \| 0.9953 \| \| 0.005 \| 26.04 \| 12840 \| 0.4212 \| 0.3794 \| 0.9993 \| 0.9954 \| 0.0172 \| 0.6874 \| 0.7102 \| 0.9954 \| \| 0.0007 \| 26.11 \| 12870 \| 0.4125 \| 0.3731 \| 0.9993 \| 0.9953 \| 0.0172 \| 0.6842 \| 0.7059 \| 0.9953 \| \| 0.01 \| 26.17 \| 12900 \| 0.3635 \| 0.3363 \| 0.9995 \| 0.9952 \| 0.0181 \| 0.6657 \| 0.6815 \| 0.9952 \| \| 0.0029 \| 26.23 \| 12930 \| 0.4441 \| 0.3959 \| 0.9992 \| 0.9954 \| 0.0172 \| 0.6957 \| 0.7216 \| 0.9955 \| \| 0.0028 \| 26.29 \| 12960 \| 0.3467 \| 0.3259 \| 0.9996 \| 0.9952 \| 0.0181 \| 0.6605 \| 0.6731 \| 0.9952 \| \| 0.01 \| 26.35 \| 12990 \| 0.3661 \| 0.3394 \| 0.9995 \| 0.9952 \| 0.0184 \| 0.6673 \| 0.6828 \| 0.9952 \| \| 0.0008 \| 26.41 \| 13020 \| 0.4187 \| 0.3783 \| 0.9993 \| 0.9954 \| 0.0176 \| 0.6868 \| 0.7090 \| 0.9954 \| \| 0.0046 \| 26.47 \| 13050 \| 0.3741 \| 0.3459 \| 0.9994 \| 0.9952 \| 0.0182 \| 0.6705 \| 0.6868 \| 0.9952 \| \| 0.0019 \| 26.53 \| 13080 \| 0.3689 \| 0.3407 \| 0.9994 \| 0.9952 \| 0.0184 \| 0.6680 \| 0.6842 \| 0.9952 \| \| 0.0143 \| 26.59 \| 13110 \| 0.3326 \| 0.3133 \| 0.9996 \| 0.9951 \| 0.0189 \| 0.6542 \| 0.6661 \| 0.9951 \| \| 0.0041 \| 26.65 \| 13140 \| 0.4335 \| 0.3869 \| 0.9992 \| 0.9954 \| 0.0177 \| 0.6911 \| 0.7163 \| 0.9954 \| \| 0.005 \| 26.71 \| 13170 \| 0.3742 \| 0.3444 \| 0.9994 \| 0.9952 \| 0.0187 \| 0.6698 \| 0.6868 \| 0.9952 \| \| 0.0019 \| 26.77 \| 13200 \| 0.3227 \| 0.3048 \| 0.9996 \| 0.9950 \| 0.0190 \| 0.6499 \| 0.6611 \| 0.9951 \| \| 0.0006 \| 26.84 \| 13230 \| 0.3938 \| 0.3619 \| 0.9994 \| 0.9953 \| 0.0157 \| 0.6786 \| 0.6966 \| 0.9953 \| \| 0.0096 \| 26.9 \| 13260 \| 0.4391 \| 0.3894 \| 0.9991 \| 0.9954 \| 0.0162 \| 0.6924 \| 0.7191 \| 0.9954 \| \| 0.0028 \| 26.96 \| 13290 \| 0.5010 \| 0.4275 \| 0.9988 \| 0.9955 \| 0.0169 \| 0.7115 \| 0.7499 \| 0.9955 \| \| 0.0067 \| 27.02 \| 13320 \| 0.4395 \| 0.3918 \| 0.9992 \| 0.9954 \| 0.0176 \| 0.6936 \| 0.7193 \| 0.9954 \| \| 0.0124 \| 27.08 \| 13350 \| 0.3646 \| 0.3392 \| 0.9995 \| 0.9952 \| 0.0181 \| 0.6672 \| 0.6820 \| 0.9952 \| \| 0.0016 \| 27.14 \| 13380 \| 0.4036 \| 0.3668 \| 0.9993 \| 0.9953 \| 0.0174 \| 0.6810 \| 0.7015 \| 0.9953 \| \| 0.0069 \| 27.2 \| 13410 \| 0.4628 \| 0.4058 \| 0.9990 \| 0.9954 \| 0.0172 \| 0.7006 \| 0.7309 \| 0.9954 \| \| 0.003 \| 27.26 \| 13440 \| 0.4313 \| 0.3868 \| 0.9992 \| 0.9954 \| 0.0162 \| 0.6911 \| 0.7153 \| 0.9954 \| \| 0.0015 \| 27.32 \| 13470 \| 0.4527 \| 0.3979 \| 0.9991 \| 0.9954 \| 0.0165 \| 0.6967 \| 0.7259 \| 0.9954 \| \| 0.0109 \| 27.38 \| 13500 \| 0.4257 \| 0.3809 \| 0.9992 \| 0.9953 \| 0.0169 \| 0.6881 \| 0.7124 \| 0.9954 \| \| 0.0053 \| 27.44 \| 13530 \| 0.4349 \| 0.3895 \| 0.9992 \| 0.9954 \| 0.0170 \| 0.6924 \| 0.7171 \| 0.9954 \| \| 0.0049 \| 27.51 \| 13560 \| 0.3426 \| 0.3183 \| 0.9995 \| 0.9951 \| 0.0175 \| 0.6567 \| 0.6711 \| 0.9951 \| \| 0.0023 \| 27.57 \| 13590 \| 0.3896 \| 0.3572 \| 0.9994 \| 0.9953 \| 0.0163 \| 0.6762 \| 0.6945 \| 0.9953 \| \| 0.0065 \| 27.63 \| 13620 \| 0.5389 \| 0.4445 \| 0.9986 \| 0.9955 \| 0.0162 \| 0.7200 \| 0.7687 \| 0.9955 \| \| 0.0009 \| 27.69 \| 13650 \| 0.4391 \| 0.3864 \| 0.9991 \| 0.9953 \| 0.0177 \| 0.6908 \| 0.7191 \| 0.9953 \| \| 0.0009 \| 27.75 \| 13680 \| 0.4727 \| 0.4134 \| 0.9990 \| 0.9955 \| 0.0177 \| 0.7044 \| 0.7358 \| 0.9955 \| \| 0.0056 \| 27.81 \| 13710 \| 0.4976 \| 0.4227 \| 0.9988 \| 0.9954 \| 0.0174 \| 0.7091 \| 0.7482 \| 0.9954 \| \| 0.0046 \| 27.87 \| 13740 \| 0.3521 \| 0.3289 \| 0.9995 \| 0.9952 \| 0.0183 \| 0.6620 \| 0.6758 \| 0.9952 \| \| 0.0053 \| 27.93 \| 13770 \| 0.4465 \| 0.3968 \| 0.9992 \| 0.9954 \| 0.0175 \| 0.6961 \| 0.7228 \| 0.9954 \| \| 0.0072 \| 27.99 \| 13800 \| 0.4351 \| 0.3902 \| 0.9992 \| 0.9954 \| 0.0177 \| 0.6928 \| 0.7171 \| 0.9954 \| \| 0.0067 \| 28.05 \| 13830 \| 0.3882 \| 0.3585 \| 0.9994 \| 0.9953 \| 0.0176 \| 0.6769 \| 0.6938 \| 0.9953 \| \| 0.0018 \| 28.11 \| 13860 \| 0.3821 \| 0.3486 \| 0.9993 \| 0.9952 \| 0.0177 \| 0.6719 \| 0.6907 \| 0.9952 \| \| 0.0044 \| 28.17 \| 13890 \| 0.4906 \| 0.4144 \| 0.9988 \| 0.9953 \| 0.0182 \| 0.7048 \| 0.7447 \| 0.9953 \| \| 0.0029 \| 28.24 \| 13920 \| 0.4622 \| 0.4051 \| 0.9990 \| 0.9954 \| 0.0173 \| 0.7003 \| 0.7306 \| 0.9954 \| \| 0.0037 \| 28.3 \| 13950 \| 0.3649 \| 0.3392 \| 0.9995 \| 0.9952 \| 0.0182 \| 0.6672 \| 0.6822 \| 0.9952 \| \| 0.0027 \| 28.36 \| 13980 \| 0.4789 \| 0.4142 \| 0.9989 \| 0.9954 \| 0.0176 \| 0.7048 \| 0.7389 \| 0.9955 \| \| 0.0035 \| 28.42 \| 14010 \| 0.4909 \| 0.4227 \| 0.9989 \| 0.9955 \| 0.0174 \| 0.7091 \| 0.7449 \| 0.9955 \| \| 0.0041 \| 28.48 \| 14040 \| 0.4197 \| 0.3795 \| 0.9993 \| 0.9954 \| 0.0179 \| 0.6874 \| 0.7095 \| 0.9954 \| \| 0.0034 \| 28.54 \| 14070 \| 0.4251 \| 0.3841 \| 0.9993 \| 0.9954 \| 0.0177 \| 0.6898 \| 0.7122 \| 0.9954 \| \| 0.012 \| 28.6 \| 14100 \| 0.4654 \| 0.4085 \| 0.9991 \| 0.9955 \| 0.0167 \| 0.7020 \| 0.7323 \| 0.9955 \| \| 0.0058 \| 28.66 \| 14130 \| 0.3843 \| 0.3532 \| 0.9994 \| 0.9953 \| 0.0157 \| 0.6742 \| 0.6919 \| 0.9953 \| \| 0.0018 \| 28.72 \| 14160 \| 0.4380 \| 0.3929 \| 0.9992 \| 0.9954 \| 0.0154 \| 0.6942 \| 0.7186 \| 0.9955 \| \| 0.0017 \| 28.78 \| 14190 \| 0.4560 \| 0.4016 \| 0.9991 \| 0.9954 \| 0.0158 \| 0.6985 \| 0.7276 \| 0.9954 \| \| 0.0037 \| 28.84 \| 14220 \| 0.4252 \| 0.3844 \| 0.9993 \| 0.9954 \| 0.0155 \| 0.6899 \| 0.7122 \| 0.9954 \| \| 0.0041 \| 28.9 \| 14250 \| 0.4537 \| 0.3999 \| 0.9991 \| 0.9954 \| 0.0157 \| 0.6977 \| 0.7264 \| 0.9954 \| \| 0.0079 \| 28.97 \| 14280 \| 0.3648 \| 0.3398 \| 0.9995 \| 0.9952 \| 0.0161 \| 0.6675 \| 0.6822 \| 0.9952 \| \| 0.0026 \| 29.03 \| 14310 \| 0.4027 \| 0.3670 \| 0.9993 \| 0.9953 \| 0.0158 \| 0.6811 \| 0.7010 \| 0.9953 \| \| 0.0071 \| 29.09 \| 14340 \| 0.4438 \| 0.3939 \| 0.9991 \| 0.9954 \| 0.0161 \| 0.6946 \| 0.7215 \| 0.9954 \| \| 0.0039 \| 29.15 \| 14370 \| 0.4662 \| 0.4061 \| 0.9990 \| 0.9954 \| 0.0161 \| 0.7007 \| 0.7326 \| 0.9954 \| \| 0.0037 \| 29.21 \| 14400 \| 0.3539 \| 0.3298 \| 0.9995 \| 0.9952 \| 0.0166 \| 0.6625 \| 0.6767 \| 0.9952 \| \| 0.0082 \| 29.27 \| 14430 \| 0.4226 \| 0.3833 \| 0.9993 \| 0.9954 \| 0.0161 \| 0.6894 \| 0.7109 \| 0.9954 \| \| 0.0014 \| 29.33 \| 14460 \| 0.4325 \| 0.3880 \| 0.9992 \| 0.9954 \| 0.0160 \| 0.6917 \| 0.7159 \| 0.9954 \| \| 0.0079 \| 29.39 \| 14490 \| 0.4482 \| 0.3975 \| 0.9991 \| 0.9954 \| 0.0161 \| 0.6965 \| 0.7237 \| 0.9954 \| \| 0.0008 \| 29.45 \| 14520 \| 0.3945 \| 0.3625 \| 0.9994 \| 0.9953 \| 0.0175 \| 0.6789 \| 0.6970 \| 0.9953 \| \| 0.0026 \| 29.51 \| 14550 \| 0.4091 \| 0.3721 \| 0.9993 \| 0.9954 \| 0.0174 \| 0.6837 \| 0.7042 \| 0.9954 \| \| 0.0034 \| 29.57 \| 14580 \| 0.4193 \| 0.3791 \| 0.9993 \| 0.9954 \| 0.0178 \| 0.6872 \| 0.7093 \| 0.9954 \| \| 0.0041 \| 29.63 \| 14610 \| 0.4392 \| 0.3892 \| 0.9991 \| 0.9954 \| 0.0180 \| 0.6923 \| 0.7192 \| 0.9954 \| \| 0.0136 \| 29.7 \| 14640 \| 0.4384 \| 0.3918 \| 0.9992 \| 0.9954 \| 0.0156 \| 0.6936 \| 0.7188 \| 0.9954 \| \| 0.004 \| 29.76 \| 14670 \| 0.4131 \| 0.3740 \| 0.9993 \| 0.9953 \| 0.0159 \| 0.6847 \| 0.7062 \| 0.9954 \| \| 0.0045 \| 29.82 \| 14700 \| 0.4490 \| 0.3985 \| 0.9991 \| 0.9954 \| 0.0155 \| 0.6969 \| 0.7241 \| 0.9954 \| \| 0.0013 \| 29.88 \| 14730 \| 0.5070 \| 0.4303 \| 0.9988 \| 0.9955 \| 0.0156 \| 0.7129 \| 0.7529 \| 0.9955 \| \| 0.0097 \| 29.94 \| 14760 \| 0.4140 \| 0.3752 \| 0.9993 \| 0.9954 \| 0.0161 \| 0.6853 \| 0.7066 \| 0.9954 \| \| 0.0175 \| 30.0 \| 14790 \| 0.3519 \| 0.3306 \| 0.9996 \| 0.9952 \| 0.0166 \| 0.6629 \| 0.6758 \| 0.9952 \| \| 0.0063 \| 30.06 \| 14820 \| 0.5193 \| 0.4337 \| 0.9987 \| 0.9954 \| 0.0163 \| 0.7146 \| 0.7590 \| 0.9954 \| \| 0.0017 \| 30.12 \| 14850 \| 0.4257 \| 0.3856 \| 0.9993 \| 0.9954 \| 0.0159 \| 0.6905 \| 0.7125 \| 0.9954 \| \| 0.0021 \| 30.18 \| 14880 \| 0.4579 \| 0.4069 \| 0.9992 \| 0.9955 \| 0.0163 \| 0.7012 \| 0.7285 \| 0.9955 \| \| 0.0119 \| 30.24 \| 14910 \| 0.4442 \| 0.3942 \| 0.9991 \| 0.9954 \| 0.0169 \| 0.6948 \| 0.7217 \| 0.9954 \| \| 0.0032 \| 30.3 \| 14940 \| 0.5246 \| 0.4310 \| 0.9985 \| 0.9953 \| 0.0179 \| 0.7132 \| 0.7616 \| 0.9954 \| \| 0.0001 \| 30.37 \| 14970 \| 0.4919 \| 0.4252 \| 0.9989 \| 0.9955 \| 0.0174 \| 0.7104 \| 0.7454 \| 0.9955 \| \| 0.0031 \| 30.43 \| 15000 \| 0.4361 \| 0.3913 \| 0.9992 \| 0.9954 \| 0.0193 \| 0.6933 \| 0.7177 \| 0.9954 \| \| 0.0195 \| 30.49 \| 15030 \| 0.4252 \| 0.3837 \| 0.9993 \| 0.9954 \| 0.0189 \| 0.6895 \| 0.7122 \| 0.9954 \| \| 0.0053 \| 30.55 \| 15060 \| 0.4938 \| 0.4235 \| 0.9989 \| 0.9955 \| 0.0192 \| 0.7095 \| 0.7464 \| 0.9955 \| \| 0.0077 \| 30.61 \| 15090 \| 0.4642 \| 0.4081 \| 0.9991 \| 0.9955 \| 0.0189 \| 0.7018 \| 0.7316 \| 0.9955 \| \| 0.0024 \| 30.67 \| 15120 \| 0.4152 \| 0.3775 \| 0.9993 \| 0.9954 \| 0.0185 \| 0.6865 \| 0.7073 \| 0.9954 \| \| 0.0148 \| 30.73 \| 15150 \| 0.4397 \| 0.3916 \| 0.9992 \| 0.9954 \| 0.0187 \| 0.6935 \| 0.7194 \| 0.9954 \| \| 0.0036 \| 30.79 \| 15180 \| 0.4450 \| 0.3959 \| 0.9992 \| 0.9954 \| 0.0185 \| 0.6957 \| 0.7221 \| 0.9954 \| \| 0.0029 \| 30.85 \| 15210 \| 0.4752 \| 0.4155 \| 0.9990 \| 0.9955 \| 0.0183 \| 0.7055 \| 0.7371 \| 0.9955 \| \| 0.0094 \| 30.91 \| 15240 \| 0.4414 \| 0.3928 \| 0.9992 \| 0.9954 \| 0.0183 \| 0.6941 \| 0.7203 \| 0.9954 \| \| 0.0027 \| 30.97 \| 15270 \| 0.4677 \| 0.4082 \| 0.9990 \| 0.9954 \| 0.0186 \| 0.7018 \| 0.7334 \| 0.9954 \| \| 0.003 \| 31.03 \| 15300 \| 0.5050 \| 0.4270 \| 0.9988 \| 0.9954 \| 0.0185 \| 0.7112 \| 0.7519 \| 0.9954 \| \| 0.0027 \| 31.1 \| 15330 \| 0.4934 \| 0.4153 \| 0.9987 \| 0.9953 \| 0.0190 \| 0.7053 \| 0.7460 \| 0.9953 \| \| 0.001 \| 31.16 \| 15360 \| 0.4425 \| 0.3920 \| 0.9991 \| 0.9954 \| 0.0185 \| 0.6937 \| 0.7208 \| 0.9954 \| \| 0.0116 \| 31.22 \| 15390 \| 0.4061 \| 0.3690 \| 0.9993 \| 0.9953 \| 0.0180 \| 0.6822 \| 0.7027 \| 0.9953 \| \| 0.0048 \| 31.28 \| 15420 \| 0.4669 \| 0.4056 \| 0.9990 \| 0.9954 \| 0.0170 \| 0.7005 \| 0.7329 \| 0.9954 \| \| 0.0024 \| 31.34 \| 15450 \| 0.4965 \| 0.4136 \| 0.9986 \| 0.9953 \| 0.0181 \| 0.7044 \| 0.7476 \| 0.9953 \| \| 0.0046 \| 31.4 \| 15480 \| 0.4865 \| 0.4205 \| 0.9989 \| 0.9955 \| 0.0168 \| 0.7080 \| 0.7427 \| 0.9955 \| \| 0.0022 \| 31.46 \| 15510 \| 0.4112 \| 0.3718 \| 0.9993 \| 0.9953 \| 0.0171 \| 0.6836 \| 0.7052 \| 0.9953 \| \| 0.0084 \| 31.52 \| 15540 \| 0.4468 \| 0.3923 \| 0.9991 \| 0.9953 \| 0.0178 \| 0.6938 \| 0.7229 \| 0.9954 \| \| 0.0048 \| 31.58 \| 15570 \| 0.3812 \| 0.3515 \| 0.9994 \| 0.9953 \| 0.0177 \| 0.6734 \| 0.6903 \| 0.9953 \| \| 0.0007 \| 31.64 \| 15600 \| 0.4963 \| 0.4164 \| 0.9987 \| 0.9953 \| 0.0177 \| 0.7058 \| 0.7475 \| 0.9953 \| \| 0.0036 \| 31.7 \| 15630 \| 0.4129 \| 0.3734 \| 0.9993 \| 0.9953 \| 0.0171 \| 0.6843 \| 0.7061 \| 0.9953 \| \| 0.0058 \| 31.76 \| 15660 \| 0.4910 \| 0.4203 \| 0.9989 \| 0.9954 \| 0.0168 \| 0.7079 \| 0.7449 \| 0.9955 \| \| 0.0061 \| 31.83 \| 15690 \| 0.4776 \| 0.4021 \| 0.9987 \| 0.9952 \| 0.0188 \| 0.6987 \| 0.7382 \| 0.9952 \| \| 0.0089 \| 31.89 \| 15720 \| 0.4732 \| 0.4122 \| 0.9990 \| 0.9955 \| 0.0175 \| 0.7038 \| 0.7361 \| 0.9955 \| \| 0.0039 \| 31.95 \| 15750 \| 0.4279 \| 0.3841 \| 0.9992 \| 0.9954 \| 0.0167 \| 0.6897 \| 0.7136 \| 0.9954 \| \| 0.0063 \| 32.01 \| 15780 \| 0.4312 \| 0.3876 \| 0.9992 \| 0.9954 \| 0.0169 \| 0.6915 \| 0.7152 \| 0.9954 \| \| 0.0045 \| 32.07 \| 15810 \| 0.4655 \| 0.4066 \| 0.9990 \| 0.9954 \| 0.0175 \| 0.7010 \| 0.7323 \| 0.9954 \| \| 0.0018 \| 32.13 \| 15840 \| 0.4271 \| 0.3829 \| 0.9992 \| 0.9954 \| 0.0178 \| 0.6892 \| 0.7132 \| 0.9954 \| \| 0.0182 \| 32.19 \| 15870 \| 0.4328 \| 0.3860 \| 0.9992 \| 0.9954 \| 0.0176 \| 0.6907 \| 0.7160 \| 0.9954 \| \| 0.0066 \| 32.25 \| 15900 \| 0.4436 \| 0.3935 \| 0.9991 \| 0.9954 \| 0.0182 \| 0.6944 \| 0.7214 \| 0.9954 \| \| 0.0006 \| 32.31 \| 15930 \| 0.4739 \| 0.4133 \| 0.9990 \| 0.9955 \| 0.0178 \| 0.7044 \| 0.7365 \| 0.9955 \| \| 0.0045 \| 32.37 \| 15960 \| 0.4645 \| 0.4079 \| 0.9991 \| 0.9955 \| 0.0166 \| 0.7017 \| 0.7318 \| 0.9955 \| \| 0.0109 \| 32.43 \| 15990 \| 0.4274 \| 0.3834 \| 0.9992 \| 0.9954 \| 0.0162 \| 0.6894 \| 0.7133 \| 0.9954 \| \| 0.0041 \| 32.49 \| 16020 \| 0.3816 \| 0.3520 \| 0.9994 \| 0.9953 \| 0.0166 \| 0.6736 \| 0.6905 \| 0.9953 \| \| 0.0069 \| 32.56 \| 16050 \| 0.4434 \| 0.3934 \| 0.9991 \| 0.9954 \| 0.0161 \| 0.6944 \| 0.7213 \| 0.9954 \| \| 0.006 \| 32.62 \| 16080 \| 0.4011 \| 0.3665 \| 0.9994 \| 0.9953 \| 0.0180 \| 0.6809 \| 0.7002 \| 0.9953 \| \| 0.0011 \| 32.68 \| 16110 \| 0.4647 \| 0.4076 \| 0.9991 \| 0.9955 \| 0.0180 \| 0.7015 \| 0.7319 \| 0.9955 \| \| 0.004 \| 32.74 \| 16140 \| 0.4268 \| 0.3837 \| 0.9992 \| 0.9954 \| 0.0186 \| 0.6895 \| 0.7130 \| 0.9954 \| \| 0.0035 \| 32.8 \| 16170 \| 0.4609 \| 0.4083 \| 0.9991 \| 0.9955 \| 0.0181 \| 0.7019 \| 0.7300 \| 0.9955 \| \| 0.0065 \| 32.86 \| 16200 \| 0.4633 \| 0.4065 \| 0.9991 \| 0.9954 \| 0.0177 \| 0.7010 \| 0.7312 \| 0.9955 \| \| 0.0076 \| 32.92 \| 16230 \| 0.5158 \| 0.4170 \| 0.9984 \| 0.9951 \| 0.0193 \| 0.7061 \| 0.7571 \| 0.9952 \| \| 0.0044 \| 32.98 \| 16260 \| 0.4693 \| 0.4100 \| 0.9990 \| 0.9955 \| 0.0178 \| 0.7027 \| 0.7342 \| 0.9955 \| \| 0.0089 \| 33.04 \| 16290 \| 0.4852 \| 0.4169 \| 0.9989 \| 0.9954 \| 0.0182 \| 0.7062 \| 0.7421 \| 0.9954 \| \| 0.0051 \| 33.1 \| 16320 \| 0.4001 \| 0.3625 \| 0.9993 \| 0.9953 \| 0.0195 \| 0.6789 \| 0.6997 \| 0.9953 \| \| 0.002 \| 33.16 \| 16350 \| 0.4790 \| 0.4157 \| 0.9990 \| 0.9955 \| 0.0182 \| 0.7056 \| 0.7390 \| 0.9955 \| \| 0.0036 \| 33.23 \| 16380 \| 0.4408 \| 0.3912 \| 0.9991 \| 0.9954 \| 0.0187 \| 0.6933 \| 0.7200 \| 0.9954 \| \| 0.0039 \| 33.29 \| 16410 \| 0.4106 \| 0.3733 \| 0.9993 \| 0.9954 \| 0.0185 \| 0.6843 \| 0.7050 \| 0.9954 \| \| 0.0047 \| 33.35 \| 16440 \| 0.4391 \| 0.3945 \| 0.9992 \| 0.9955 \| 0.0181 \| 0.6950 \| 0.7192 \| 0.9955 \| \| 0.0077 \| 33.41 \| 16470 \| 0.4937 \| 0.4278 \| 0.9990 \| 0.9956 \| 0.0178 \| 0.7117 \| 0.7464 \| 0.9956 \| \| 0.0047 \| 33.47 \| 16500 \| 0.4677 \| 0.4104 \| 0.9991 \| 0.9955 \| 0.0181 \| 0.7029 \| 0.7334 \| 0.9955 \| \| 0.0029 \| 33.53 \| 16530 \| 0.4279 \| 0.3858 \| 0.9993 \| 0.9954 \| 0.0182 \| 0.6906 \| 0.7136 \| 0.9954 \| \| 0.0019 \| 33.59 \| 16560 \| 0.4796 \| 0.4114 \| 0.9989 \| 0.9954 \| 0.0186 \| 0.7034 \| 0.7392 \| 0.9954 \| \| 0.0095 \| 33.65 \| 16590 \| 0.4710 \| 0.4125 \| 0.9990 \| 0.9955 \| 0.0179 \| 0.7040 \| 0.7350 \| 0.9955 \| \| 0.0025 \| 33.71 \| 16620 \| 0.4587 \| 0.4053 \| 0.9991 \| 0.9955 \| 0.0182 \| 0.7004 \| 0.7289 \| 0.9955 \| \| 0.0056 \| 33.77 \| 16650 \| 0.4961 \| 0.4237 \| 0.9988 \| 0.9955 \| 0.0194 \| 0.7096 \| 0.7475 \| 0.9955 \| \| 0.0026 \| 33.83 \| 16680 \| 0.4664 \| 0.4095 \| 0.9991 \| 0.9955 \| 0.0190 \| 0.7025 \| 0.7327 \| 0.9955 \| \| 0.0008 \| 33.89 \| 16710 \| 0.4138 \| 0.3757 \| 0.9993 \| 0.9954 \| 0.0188 \| 0.6855 \| 0.7066 \| 0.9954 \| \| 0.0093 \| 33.96 \| 16740 \| 0.4634 \| 0.4078 \| 0.9991 \| 0.9955 \| 0.0180 \| 0.7017 \| 0.7313 \| 0.9955 \| \| 0.0088 \| 34.02 \| 16770 \| 0.4480 \| 0.3977 \| 0.9991 \| 0.9954 \| 0.0182 \| 0.6966 \| 0.7236 \| 0.9954 \| \| 0.004 \| 34.08 \| 16800 \| 0.4497 \| 0.3994 \| 0.9991 \| 0.9954 \| 0.0186 \| 0.6974 \| 0.7244 \| 0.9955 \| \| 0.0074 \| 34.14 \| 16830 \| 0.4425 \| 0.3965 \| 0.9992 \| 0.9955 \| 0.0180 \| 0.6960 \| 0.7209 \| 0.9955 \| \| 0.0039 \| 34.2 \| 16860 \| 0.4196 \| 0.3796 \| 0.9993 \| 0.9954 \| 0.0182 \| 0.6875 \| 0.7095 \| 0.9954 \| \| 0.0008 \| 34.26 \| 16890 \| 0.5240 \| 0.4323 \| 0.9986 \| 0.9954 \| 0.0183 \| 0.7138 \| 0.7613 \| 0.9954 \| \| 0.006 \| 34.32 \| 16920 \| 0.4810 \| 0.4149 \| 0.9989 \| 0.9954 \| 0.0176 \| 0.7051 \| 0.7399 \| 0.9954 \| \| 0.0045 \| 34.38 \| 16950 \| 0.4393 \| 0.3918 \| 0.9992 \| 0.9954 \| 0.0181 \| 0.6936 \| 0.7192 \| 0.9954 \| \| 0.0022 \| 34.44 \| 16980 \| 0.4047 \| 0.3701 \| 0.9994 \| 0.9954 \| 0.0180 \| 0.6827 \| 0.7020 \| 0.9954 \| \| 0.0034 \| 34.5 \| 17010 \| 0.4488 \| 0.4005 \| 0.9992 \| 0.9955 \| 0.0179 \| 0.6980 \| 0.7240 \| 0.9955 \| \| 0.0045 \| 34.56 \| 17040 \| 0.4926 \| 0.4229 \| 0.9989 \| 0.9955 \| 0.0180 \| 0.7092 \| 0.7457 \| 0.9955 \| \| 0.0093 \| 34.62 \| 17070 \| 0.4787 \| 0.4095 \| 0.9989 \| 0.9953 \| 0.0189 \| 0.7024 \| 0.7388 \| 0.9954 \| \| 0.0104 \| 34.69 \| 17100 \| 0.4431 \| 0.3949 \| 0.9992 \| 0.9954 \| 0.0184 \| 0.6952 \| 0.7211 \| 0.9954 \| \| 0.0078 \| 34.75 \| 17130 \| 0.4445 \| 0.3946 \| 0.9991 \| 0.9954 \| 0.0185 \| 0.6950 \| 0.7218 \| 0.9954 \| \| 0.0687 \| 34.81 \| 17160 \| 0.4465 \| 0.3940 \| 0.9991 \| 0.9954 \| 0.0191 \| 0.6947 \| 0.7228 \| 0.9954 \| \| 0.0238 \| 34.87 \| 17190 \| 0.4192 \| 0.3795 \| 0.9993 \| 0.9954 \| 0.0187 \| 0.6875 \| 0.7093 \| 0.9954 \| \| 0.0037 \| 34.93 \| 17220 \| 0.4306 \| 0.3873 \| 0.9992 \| 0.9954 \| 0.0187 \| 0.6914 \| 0.7149 \| 0.9954 \| \| 0.0056 \| 34.99 \| 17250 \| 0.4309 \| 0.3861 \| 0.9992 \| 0.9954 \| 0.0186 \| 0.6908 \| 0.7151 \| 0.9954 \| \| 0.0067 \| 35.05 \| 17280 \| 0.4714 \| 0.4125 \| 0.9990 \| 0.9955 \| 0.0172 \| 0.7040 \| 0.7352 \| 0.9955 \| \| 0.0014 \| 35.11 \| 17310 \| 0.4805 \| 0.4171 \| 0.9990 \| 0.9955 \| 0.0163 \| 0.7063 \| 0.7397 \| 0.9955 \| \| 0.0095 \| 35.17 \| 17340 \| 0.4185 \| 0.3798 \| 0.9993 \| 0.9954 \| 0.0163 \| 0.6876 \| 0.7089 \| 0.9954 \| \| 0.0094 \| 35.23 \| 17370 \| 0.4587 \| 0.4050 \| 0.9991 \| 0.9955 \| 0.0164 \| 0.7002 \| 0.7289 \| 0.9955 \| \| 0.0004 \| 35.29 \| 17400 \| 0.4358 \| 0.3914 \| 0.9992 \| 0.9954 \| 0.0163 \| 0.6934 \| 0.7175 \| 0.9954 \| \| 0.0054 \| 35.35 \| 17430 \| 0.4985 \| 0.4226 \| 0.9988 \| 0.9954 \| 0.0163 \| 0.7090 \| 0.7486 \| 0.9954 \| \| 0.0026 \| 35.42 \| 17460 \| 0.4057 \| 0.3708 \| 0.9994 \| 0.9954 \| 0.0168 \| 0.6831 \| 0.7025 \| 0.9954 \| \| 0.0271 \| 35.48 \| 17490 \| 0.4883 \| 0.4182 \| 0.9989 \| 0.9954 \| 0.0173 \| 0.7068 \| 0.7436 \| 0.9954 \| \| 0.006 \| 35.54 \| 17520 \| 0.4228 \| 0.3823 \| 0.9993 \| 0.9954 \| 0.0170 \| 0.6889 \| 0.7110 \| 0.9954 \| \| 0.0114 \| 35.6 \| 17550 \| 0.4158 \| 0.3780 \| 0.9993 \| 0.9954 \| 0.0170 \| 0.6867 \| 0.7076 \| 0.9954 \| \| 0.0029 \| 35.66 \| 17580 \| 0.4267 \| 0.3848 \| 0.9993 \| 0.9954 \| 0.0167 \| 0.6901 \| 0.7130 \| 0.9954 \| \| 0.007 \| 35.72 \| 17610 \| 0.4375 \| 0.3900 \| 0.9992 \| 0.9954 \| 0.0170 \| 0.6927 \| 0.7183 \| 0.9954 \| \| 0.001 \| 35.78 \| 17640 \| 0.4386 \| 0.3920 \| 0.9992 \| 0.9954 \| 0.0169 \| 0.6937 \| 0.7189 \| 0.9954 \| \| 0.0022 \| 35.84 \| 17670 \| 0.4560 \| 0.4036 \| 0.9991 \| 0.9955 \| 0.0173 \| 0.6995 \| 0.7276 \| 0.9955 \| \| 0.0044 \| 35.9 \| 17700 \| 0.4911 \| 0.4245 \| 0.9989 \| 0.9955 \| 0.0168 \| 0.7100 \| 0.7450 \| 0.9955 \| \| 0.0024 \| 35.96 \| 17730 \| 0.4288 \| 0.3862 \| 0.9993 \| 0.9954 \| 0.0170 \| 0.6908 \| 0.7140 \| 0.9954 \| \| 0.0025 \| 36.02 \| 17760 \| 0.4409 \| 0.3935 \| 0.9992 \| 0.9954 \| 0.0177 \| 0.6945 \| 0.7200 \| 0.9954 \| \| 0.0007 \| 36.09 \| 17790 \| 0.4002 \| 0.3664 \| 0.9994 \| 0.9953 \| 0.0170 \| 0.6809 \| 0.6998 \| 0.9954 \| \| 0.0068 \| 36.15 \| 17820 \| 0.4613 \| 0.4075 \| 0.9991 \| 0.9955 \| 0.0167 \| 0.7015 \| 0.7302 \| 0.9955 \| \| 0.0016 \| 36.21 \| 17850 \| 0.4642 \| 0.4079 \| 0.9991 \| 0.9955 \| 0.0165 \| 0.7017 \| 0.7316 \| 0.9955 \| \| 0.0086 \| 36.27 \| 17880 \| 0.4357 \| 0.3909 \| 0.9992 \| 0.9954 \| 0.0167 \| 0.6932 \| 0.7175 \| 0.9954 \| \| 0.0327 \| 36.33 \| 17910 \| 0.4583 \| 0.4054 \| 0.9991 \| 0.9955 \| 0.0169 \| 0.7005 \| 0.7287 \| 0.9955 \| \| 0.0048 \| 36.39 \| 17940 \| 0.4437 \| 0.3969 \| 0.9992 \| 0.9955 \| 0.0167 \| 0.6962 \| 0.7215 \| 0.9955 \| \| 0.0033 \| 36.45 \| 17970 \| 0.4461 \| 0.3994 \| 0.9992 \| 0.9955 \| 0.0166 \| 0.6975 \| 0.7226 \| 0.9955 \| \| 0.0073 \| 36.51 \| 18000 \| 0.4410 \| 0.3950 \| 0.9992 \| 0.9955 \| 0.0162 \| 0.6952 \| 0.7201 \| 0.9955 \| \| 0.0195 \| 36.57 \| 18030 \| 0.4657 \| 0.4091 \| 0.9991 \| 0.9955 \| 0.0162 \| 0.7023 \| 0.7324 \| 0.9955 \| \| 0.0047 \| 36.63 \| 18060 \| 0.4303 \| 0.3871 \| 0.9992 \| 0.9954 \| 0.0167 \| 0.6913 \| 0.7148 \| 0.9954 \| \| 0.0023 \| 36.69 \| 18090 \| 0.5117 \| 0.4306 \| 0.9987 \| 0.9954 \| 0.0168 \| 0.7130 \| 0.7552 \| 0.9955 \| \| 0.0104 \| 36.75 \| 18120 \| 0.4503 \| 0.3998 \| 0.9991 \| 0.9954 \| 0.0166 \| 0.6976 \| 0.7247 \| 0.9955 \| \| 0.0044 \| 36.82 \| 18150 \| 0.4384 \| 0.3919 \| 0.9992 \| 0.9954 \| 0.0170 \| 0.6937 \| 0.7188 \| 0.9954 \| \| 0.0008 \| 36.88 \| 18180 \| 0.4641 \| 0.4074 \| 0.9991 \| 0.9955 \| 0.0167 \| 0.7014 \| 0.7316 \| 0.9955 \| \| 0.0076 \| 36.94 \| 18210 \| 0.4561 \| 0.4008 \| 0.9991 \| 0.9954 \| 0.0172 \| 0.6981 \| 0.7276 \| 0.9954 \| \| 0.0038 \| 37.0 \| 18240 \| 0.4060 \| 0.3706 \| 0.9994 \| 0.9954 \| 0.0171 \| 0.6830 \| 0.7027 \| 0.9954 \| \| 0.0049 \| 37.06 \| 18270 \| 0.4560 \| 0.4028 \| 0.9991 \| 0.9954 \| 0.0172 \| 0.6991 \| 0.7275 \| 0.9955 \| \| 0.009 \| 37.12 \| 18300 \| 0.4147 \| 0.3776 \| 0.9993 \| 0.9954 \| 0.0166 \| 0.6865 \| 0.7070 \| 0.9954 \| \| 0.0042 \| 37.18 \| 18330 \| 0.4605 \| 0.4069 \| 0.9991 \| 0.9955 \| 0.0162 \| 0.7012 \| 0.7298 \| 0.9955 \| \| 0.0058 \| 37.24 \| 18360 \| 0.4897 \| 0.4210 \| 0.9989 \| 0.9955 \| 0.0166 \| 0.7083 \| 0.7443 \| 0.9955 \| \| 0.0077 \| 37.3 \| 18390 \| 0.4272 \| 0.3848 \| 0.9993 \| 0.9954 \| 0.0167 \| 0.6901 \| 0.7132 \| 0.9954 \| \| 0.0033 \| 37.36 \| 18420 \| 0.4664 \| 0.4075 \| 0.9990 \| 0.9954 \| 0.0166 \| 0.7015 \| 0.7327 \| 0.9954 \| \| 0.0098 \| 37.42 \| 18450 \| 0.4841 \| 0.4172 \| 0.9989 \| 0.9954 \| 0.0166 \| 0.7063 \| 0.7415 \| 0.9955 \| \| 0.0062 \| 37.48 \| 18480 \| 0.4760 \| 0.4123 \| 0.9990 \| 0.9954 \| 0.0166 \| 0.7038 \| 0.7375 \| 0.9954 \| \| 0.0109 \| 37.55 \| 18510 \| 0.4307 \| 0.3856 \| 0.9992 \| 0.9954 \| 0.0167 \| 0.6905 \| 0.7150 \| 0.9954 \| \| 0.0015 \| 37.61 \| 18540 \| 0.4692 \| 0.4105 \| 0.9990 \| 0.9955 \| 0.0166 \| 0.7030 \| 0.7341 \| 0.9955 \| \| 0.001 \| 37.67 \| 18570 \| 0.4478 \| 0.3990 \| 0.9992 \| 0.9955 \| 0.0165 \| 0.6972 \| 0.7235 \| 0.9955 \| \| 0.004 \| 37.73 \| 18600 \| 0.4803 \| 0.4152 \| 0.9989 \| 0.9954 \| 0.0170 \| 0.7053 \| 0.7396 \| 0.9955 \| \| 0.0058 \| 37.79 \| 18630 \| 0.4536 \| 0.4029 \| 0.9991 \| 0.9955 \| 0.0166 \| 0.6992 \| 0.7264 \| 0.9955 \| \| 0.0049 \| 37.85 \| 18660 \| 0.4491 \| 0.3995 \| 0.9992 \| 0.9955 \| 0.0166 \| 0.6975 \| 0.7242 \| 0.9955 \| \| 0.0027 \| 37.91 \| 18690 \| 0.4636 \| 0.4083 \| 0.9991 \| 0.9955 \| 0.0165 \| 0.7019 \| 0.7314 \| 0.9955 \| \| 0.003 \| 37.97 \| 18720 \| 0.4497 \| 0.4002 \| 0.9992 \| 0.9955 \| 0.0166 \| 0.6978 \| 0.7244 \| 0.9955 \| \| 0.0064 \| 38.03 \| 18750 \| 0.4670 \| 0.4109 \| 0.9991 \| 0.9955 \| 0.0171 \| 0.7032 \| 0.7330 \| 0.9955 \| \| 0.0061 \| 38.09 \| 18780 \| 0.4513 \| 0.4016 \| 0.9992 \| 0.9955 \| 0.0172 \| 0.6985 \| 0.7253 \| 0.9955 \| \| 0.0028 \| 38.15 \| 18810 \| 0.4654 \| 0.4093 \| 0.9991 \| 0.9955 \| 0.0169 \| 0.7024 \| 0.7322 \| 0.9955 \| \| 0.0148 \| 38.22 \| 18840 \| 0.4696 \| 0.4111 \| 0.9990 \| 0.9955 \| 0.0172 \| 0.7033 \| 0.7343 \| 0.9955 \| \| 0.0022 \| 38.28 \| 18870 \| 0.4793 \| 0.4161 \| 0.9990 \| 0.9955 \| 0.0170 \| 0.7058 \| 0.7392 \| 0.9955 \| \| 0.0009 \| 38.34 \| 18900 \| 0.4668 \| 0.4085 \| 0.9990 \| 0.9954 \| 0.0172 \| 0.7020 \| 0.7329 \| 0.9955 \| \| 0.0955 \| 38.4 \| 18930 \| 0.4690 \| 0.4109 \| 0.9990 \| 0.9955 \| 0.0169 \| 0.7032 \| 0.7340 \| 0.9955 \| \| 0.0023 \| 38.46 \| 18960 \| 0.4585 \| 0.4047 \| 0.9991 \| 0.9955 \| 0.0170 \| 0.7001 \| 0.7288 \| 0.9955 \| \| 0.0066 \| 38.52 \| 18990 \| 0.4475 \| 0.3983 \| 0.9992 \| 0.9954 \| 0.0169 \| 0.6969 \| 0.7233 \| 0.9955 \| \| 0.0013 \| 38.58 \| 19020 \| 0.4621 \| 0.4073 \| 0.9991 \| 0.9955 \| 0.0169 \| 0.7014 \| 0.7306 \| 0.9955 \| \| 0.0062 \| 38.64 \| 19050 \| 0.4719 \| 0.4130 \| 0.9990 \| 0.9955 \| 0.0171 \| 0.7042 \| 0.7355 \| 0.9955 \| \| 0.0068 \| 38.7 \| 19080 \| 0.4541 \| 0.4032 \| 0.9991 \| 0.9955 \| 0.0168 \| 0.6994 \| 0.7266 \| 0.9955 \| \| 0.0039 \| 38.76 \| 19110 \| 0.4740 \| 0.4145 \| 0.9990 \| 0.9955 \| 0.0166 \| 0.7050 \| 0.7365 \| 0.9955 \| \| 0.0076 \| 38.82 \| 19140 \| 0.4421 \| 0.3953 \| 0.9992 \| 0.9954 \| 0.0170 \| 0.6954 \| 0.7206 \| 0.9955 \| \| 0.003 \| 38.88 \| 19170 \| 0.4267 \| 0.3849 \| 0.9993 \| 0.9954 \| 0.0172 \| 0.6901 \| 0.7130 \| 0.9954 \| \| 0.0044 \| 38.95 \| 19200 \| 0.4539 \| 0.4013 \| 0.9991 \| 0.9954 \| 0.0172 \| 0.6984 \| 0.7265 \| 0.9955 \| \| 0.005 \| 39.01 \| 19230 \| 0.4478 \| 0.3977 \| 0.9991 \| 0.9954 \| 0.0173 \| 0.6966 \| 0.7235 \| 0.9954 \| \| 0.0059 \| 39.07 \| 19260 \| 0.4546 \| 0.4024 \| 0.9991 \| 0.9955 \| 0.0173 \| 0.6989 \| 0.7269 \| 0.9955 \| \| 0.0134 \| 39.13 \| 19290 \| 0.4450 \| 0.3965 \| 0.9992 \| 0.9954 \| 0.0171 \| 0.6960 \| 0.7221 \| 0.9955 \| \| 0.0064 \| 39.19 \| 19320 \| 0.4387 \| 0.3928 \| 0.9992 \| 0.9954 \| 0.0172 \| 0.6941 \| 0.7189 \| 0.9954 \| \| 0.0034 \| 39.25 \| 19350 \| 0.4557 \| 0.4029 \| 0.9991 \| 0.9955 \| 0.0172 \| 0.6992 \| 0.7274 \| 0.9955 \| \| 0.0069 \| 39.31 \| 19380 \| 0.4393 \| 0.3931 \| 0.9992 \| 0.9954 \| 0.0172 \| 0.6943 \| 0.7193 \| 0.9954 \| \| 0.0037 \| 39.37 \| 19410 \| 0.4398 \| 0.3933 \| 0.9992 \| 0.9954 \| 0.0174 \| 0.6944 \| 0.7195 \| 0.9954 \| \| 0.0184 \| 39.43 \| 19440 \| 0.4530 \| 0.4012 \| 0.9991 \| 0.9954 \| 0.0174 \| 0.6983 \| 0.7261 \| 0.9955 \| \| 0.0044 \| 39.49 \| 19470 \| 0.4468 \| 0.3975 \| 0.9992 \| 0.9954 \| 0.0172 \| 0.6965 \| 0.7230 \| 0.9955 \| \| 0.0028 \| 39.55 \| 19500 \| 0.4435 \| 0.3953 \| 0.9992 \| 0.9954 \| 0.0173 \| 0.6954 \| 0.7213 \| 0.9954 \| \| 0.0099 \| 39.61 \| 19530 \| 0.4530 \| 0.4009 \| 0.9991 \| 0.9954 \| 0.0174 \| 0.6982 \| 0.7260 \| 0.9955 \| \| 0.0089 \| 39.68 \| 19560 \| 0.4471 \| 0.3977 \| 0.9992 \| 0.9954 \| 0.0174 \| 0.6965 \| 0.7231 \| 0.9955 \| \| 0.0027 \| 39.74 \| 19590 \| 0.4503 \| 0.3996 \| 0.9991 \| 0.9954 \| 0.0172 \| 0.6975 \| 0.7247 \| 0.9955 \| \| 0.0009 \| 39.8 \| 19620 \| 0.4504 \| 0.3996 \| 0.9991 \| 0.9954 \| 0.0173 \| 0.6975 \| 0.7248 \| 0.9955 \| \| 0.0046 \| 39.86 \| 19650 \| 0.4526 \| 0.4008 \| 0.9991 \| 0.9954 \| 0.0173 \| 0.6981 \| 0.7259 \| 0.9955 \| \| 0.0038 \| 39.92 \| 19680 \| 0.4547 \| 0.4021 \| 0.9991 \| 0.9954 \| 0.0174 \| 0.6988 \| 0.7269 \| 0.9955 \| \| 0.0057 \| 39.98 \| 19710 \| 0.4483 \| 0.3982 \| 0.9991 \| 0.9954 \| 0.0174 \| 0.6968 \| 0.7237 \| 0.9955 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "background", "kelp" ]
heroza/segformer-finetuned-biofilm	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-biofilm This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the heroza/biofilm dataset. It achieves the following results on the evaluation set: - Loss: 0.3028 - Mean Iou: 0.3319 - Mean Accuracy: 0.6638 - Overall Accuracy: 0.6638 - Accuracy Background: nan - Accuracy Biofilm: 0.6638 - Iou Background: 0.0 - Iou Biofilm: 0.6638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Biofilm \| Iou Background \| Iou Biofilm \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:----------------:\|:--------------:\|:-----------:\| \| No log \| 1.0 \| 17 \| 0.6476 \| 0.4955 \| 0.9910 \| 0.9910 \| nan \| 0.9910 \| 0.0 \| 0.9910 \| \| No log \| 2.0 \| 34 \| 0.5609 \| 0.4991 \| 0.9981 \| 0.9981 \| nan \| 0.9981 \| 0.0 \| 0.9981 \| \| No log \| 3.0 \| 51 \| 0.4363 \| 0.4869 \| 0.9738 \| 0.9738 \| nan \| 0.9738 \| 0.0 \| 0.9738 \| \| No log \| 4.0 \| 68 \| 0.3583 \| 0.3954 \| 0.7908 \| 0.7908 \| nan \| 0.7908 \| 0.0 \| 0.7908 \| \| No log \| 5.0 \| 85 \| 0.3015 \| 0.4016 \| 0.8033 \| 0.8033 \| nan \| 0.8033 \| 0.0 \| 0.8033 \| \| 0.4792 \| 6.0 \| 102 \| 0.2964 \| 0.0504 \| 0.1008 \| 0.1008 \| nan \| 0.1008 \| 0.0 \| 0.1008 \| \| 0.4792 \| 7.0 \| 119 \| 0.2995 \| 0.2103 \| 0.4206 \| 0.4206 \| nan \| 0.4206 \| 0.0 \| 0.4206 \| \| 0.4792 \| 8.0 \| 136 \| 0.2895 \| 0.0417 \| 0.0833 \| 0.0833 \| nan \| 0.0833 \| 0.0 \| 0.0833 \| \| 0.4792 \| 9.0 \| 153 \| 0.2499 \| 0.0562 \| 0.1123 \| 0.1123 \| nan \| 0.1123 \| 0.0 \| 0.1123 \| \| 0.4792 \| 10.0 \| 170 \| 0.2770 \| 0.0570 \| 0.1140 \| 0.1140 \| nan \| 0.1140 \| 0.0 \| 0.1140 \| \| 0.4792 \| 11.0 \| 187 \| 0.2268 \| 0.2041 \| 0.4082 \| 0.4082 \| nan \| 0.4082 \| 0.0 \| 0.4082 \| \| 0.3238 \| 12.0 \| 204 \| 0.2684 \| 0.1290 \| 0.2580 \| 0.2580 \| nan \| 0.2580 \| 0.0 \| 0.2580 \| \| 0.3238 \| 13.0 \| 221 \| 0.2584 \| 0.2469 \| 0.4938 \| 0.4938 \| nan \| 0.4938 \| 0.0 \| 0.4938 \| \| 0.3238 \| 14.0 \| 238 \| 0.2409 \| 0.1139 \| 0.2278 \| 0.2278 \| nan \| 0.2278 \| 0.0 \| 0.2278 \| \| 0.3238 \| 15.0 \| 255 \| 0.2800 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \| \| 0.3238 \| 16.0 \| 272 \| 0.2348 \| 0.0002 \| 0.0004 \| 0.0004 \| nan \| 0.0004 \| 0.0 \| 0.0004 \| \| 0.3238 \| 17.0 \| 289 \| 0.2290 \| 0.0009 \| 0.0018 \| 0.0018 \| nan \| 0.0018 \| 0.0 \| 0.0018 \| \| 0.289 \| 18.0 \| 306 \| 0.2077 \| 0.2453 \| 0.4906 \| 0.4906 \| nan \| 0.4906 \| 0.0 \| 0.4906 \| \| 0.289 \| 19.0 \| 323 \| 0.2471 \| 0.0117 \| 0.0233 \| 0.0233 \| nan \| 0.0233 \| 0.0 \| 0.0233 \| \| 0.289 \| 20.0 \| 340 \| 0.2226 \| 0.0346 \| 0.0692 \| 0.0692 \| nan \| 0.0692 \| 0.0 \| 0.0692 \| \| 0.289 \| 21.0 \| 357 \| 0.2539 \| 0.0044 \| 0.0088 \| 0.0088 \| nan \| 0.0088 \| 0.0 \| 0.0088 \| \| 0.289 \| 22.0 \| 374 \| 0.2323 \| 0.0029 \| 0.0058 \| 0.0058 \| nan \| 0.0058 \| 0.0 \| 0.0058 \| \| 0.289 \| 23.0 \| 391 \| 0.2700 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \| \| 0.2751 \| 24.0 \| 408 \| 0.2107 \| 0.2176 \| 0.4351 \| 0.4351 \| nan \| 0.4351 \| 0.0 \| 0.4351 \| \| 0.2751 \| 25.0 \| 425 \| 0.1984 \| 0.0114 \| 0.0228 \| 0.0228 \| nan \| 0.0228 \| 0.0 \| 0.0228 \| \| 0.2751 \| 26.0 \| 442 \| 0.2531 \| 0.1690 \| 0.3379 \| 0.3379 \| nan \| 0.3379 \| 0.0 \| 0.3379 \| \| 0.2751 \| 27.0 \| 459 \| 0.2402 \| 0.0000 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \| \| 0.2751 \| 28.0 \| 476 \| 0.2446 \| 0.1299 \| 0.2597 \| 0.2597 \| nan \| 0.2597 \| 0.0 \| 0.2597 \| \| 0.2751 \| 29.0 \| 493 \| 0.2327 \| 0.0069 \| 0.0139 \| 0.0139 \| nan \| 0.0139 \| 0.0 \| 0.0139 \| \| 0.2736 \| 30.0 \| 510 \| 0.2440 \| 0.0031 \| 0.0061 \| 0.0061 \| nan \| 0.0061 \| 0.0 \| 0.0061 \| \| 0.2736 \| 31.0 \| 527 \| 0.2527 \| 0.0060 \| 0.0119 \| 0.0119 \| nan \| 0.0119 \| 0.0 \| 0.0119 \| \| 0.2736 \| 32.0 \| 544 \| 0.2185 \| 0.0897 \| 0.1795 \| 0.1795 \| nan \| 0.1795 \| 0.0 \| 0.1795 \| \| 0.2736 \| 33.0 \| 561 \| 0.2256 \| 0.2468 \| 0.4936 \| 0.4936 \| nan \| 0.4936 \| 0.0 \| 0.4936 \| \| 0.2736 \| 34.0 \| 578 \| 0.2347 \| 0.0419 \| 0.0837 \| 0.0837 \| nan \| 0.0837 \| 0.0 \| 0.0837 \| \| 0.2736 \| 35.0 \| 595 \| 0.2034 \| 0.1546 \| 0.3091 \| 0.3091 \| nan \| 0.3091 \| 0.0 \| 0.3091 \| \| 0.2661 \| 36.0 \| 612 \| 0.2182 \| 0.0437 \| 0.0873 \| 0.0873 \| nan \| 0.0873 \| 0.0 \| 0.0873 \| \| 0.2661 \| 37.0 \| 629 \| 0.1937 \| 0.3058 \| 0.6116 \| 0.6116 \| nan \| 0.6116 \| 0.0 \| 0.6116 \| \| 0.2661 \| 38.0 \| 646 \| 0.2028 \| 0.0014 \| 0.0028 \| 0.0028 \| nan \| 0.0028 \| 0.0 \| 0.0028 \| \| 0.2661 \| 39.0 \| 663 \| 0.1853 \| 0.0649 \| 0.1298 \| 0.1298 \| nan \| 0.1298 \| 0.0 \| 0.1298 \| \| 0.2661 \| 40.0 \| 680 \| 0.2272 \| 0.3264 \| 0.6527 \| 0.6527 \| nan \| 0.6527 \| 0.0 \| 0.6527 \| \| 0.2661 \| 41.0 \| 697 \| 0.2654 \| 0.4513 \| 0.9027 \| 0.9027 \| nan \| 0.9027 \| 0.0 \| 0.9027 \| \| 0.256 \| 42.0 \| 714 \| 0.2408 \| 0.3147 \| 0.6295 \| 0.6295 \| nan \| 0.6295 \| 0.0 \| 0.6295 \| \| 0.256 \| 43.0 \| 731 \| 0.2253 \| 0.0708 \| 0.1415 \| 0.1415 \| nan \| 0.1415 \| 0.0 \| 0.1415 \| \| 0.256 \| 44.0 \| 748 \| 0.2025 \| 0.3538 \| 0.7076 \| 0.7076 \| nan \| 0.7076 \| 0.0 \| 0.7076 \| \| 0.256 \| 45.0 \| 765 \| 0.2271 \| 0.0238 \| 0.0476 \| 0.0476 \| nan \| 0.0476 \| 0.0 \| 0.0476 \| \| 0.256 \| 46.0 \| 782 \| 0.2171 \| 0.0911 \| 0.1821 \| 0.1821 \| nan \| 0.1821 \| 0.0 \| 0.1821 \| \| 0.256 \| 47.0 \| 799 \| 0.2525 \| 0.2226 \| 0.4453 \| 0.4453 \| nan \| 0.4453 \| 0.0 \| 0.4453 \| \| 0.2588 \| 48.0 \| 816 \| 0.2044 \| 0.3965 \| 0.7929 \| 0.7929 \| nan \| 0.7929 \| 0.0 \| 0.7929 \| \| 0.2588 \| 49.0 \| 833 \| 0.2328 \| 0.3985 \| 0.7969 \| 0.7969 \| nan \| 0.7969 \| 0.0 \| 0.7969 \| \| 0.2588 \| 50.0 \| 850 \| 0.2241 \| 0.4584 \| 0.9168 \| 0.9168 \| nan \| 0.9168 \| 0.0 \| 0.9168 \| \| 0.2588 \| 51.0 \| 867 \| 0.1806 \| 0.3473 \| 0.6946 \| 0.6946 \| nan \| 0.6946 \| 0.0 \| 0.6946 \| \| 0.2588 \| 52.0 \| 884 \| 0.2081 \| 0.3036 \| 0.6071 \| 0.6071 \| nan \| 0.6071 \| 0.0 \| 0.6071 \| \| 0.2462 \| 53.0 \| 901 \| 0.2037 \| 0.1534 \| 0.3068 \| 0.3068 \| nan \| 0.3068 \| 0.0 \| 0.3068 \| \| 0.2462 \| 54.0 \| 918 \| 0.2024 \| 0.2510 \| 0.5021 \| 0.5021 \| nan \| 0.5021 \| 0.0 \| 0.5021 \| \| 0.2462 \| 55.0 \| 935 \| 0.2100 \| 0.3440 \| 0.6880 \| 0.6880 \| nan \| 0.6880 \| 0.0 \| 0.6880 \| \| 0.2462 \| 56.0 \| 952 \| 0.1887 \| 0.3399 \| 0.6799 \| 0.6799 \| nan \| 0.6799 \| 0.0 \| 0.6799 \| \| 0.2462 \| 57.0 \| 969 \| 0.2333 \| 0.2049 \| 0.4097 \| 0.4097 \| nan \| 0.4097 \| 0.0 \| 0.4097 \| \| 0.2462 \| 58.0 \| 986 \| 0.2432 \| 0.0556 \| 0.1112 \| 0.1112 \| nan \| 0.1112 \| 0.0 \| 0.1112 \| \| 0.2414 \| 59.0 \| 1003 \| 0.2298 \| 0.0935 \| 0.1870 \| 0.1870 \| nan \| 0.1870 \| 0.0 \| 0.1870 \| \| 0.2414 \| 60.0 \| 1020 \| 0.2172 \| 0.1357 \| 0.2714 \| 0.2714 \| nan \| 0.2714 \| 0.0 \| 0.2714 \| \| 0.2414 \| 61.0 \| 1037 \| 0.1975 \| 0.2473 \| 0.4945 \| 0.4945 \| nan \| 0.4945 \| 0.0 \| 0.4945 \| \| 0.2414 \| 62.0 \| 1054 \| 0.2019 \| 0.3072 \| 0.6144 \| 0.6144 \| nan \| 0.6144 \| 0.0 \| 0.6144 \| \| 0.2414 \| 63.0 \| 1071 \| 0.2331 \| 0.2901 \| 0.5802 \| 0.5802 \| nan \| 0.5802 \| 0.0 \| 0.5802 \| \| 0.2414 \| 64.0 \| 1088 \| 0.2391 \| 0.0202 \| 0.0405 \| 0.0405 \| nan \| 0.0405 \| 0.0 \| 0.0405 \| \| 0.2244 \| 65.0 \| 1105 \| 0.2013 \| 0.2051 \| 0.4103 \| 0.4103 \| nan \| 0.4103 \| 0.0 \| 0.4103 \| \| 0.2244 \| 66.0 \| 1122 \| 0.2076 \| 0.2277 \| 0.4554 \| 0.4554 \| nan \| 0.4554 \| 0.0 \| 0.4554 \| \| 0.2244 \| 67.0 \| 1139 \| 0.2289 \| 0.2376 \| 0.4752 \| 0.4752 \| nan \| 0.4752 \| 0.0 \| 0.4752 \| \| 0.2244 \| 68.0 \| 1156 \| 0.2293 \| 0.2633 \| 0.5266 \| 0.5266 \| nan \| 0.5266 \| 0.0 \| 0.5266 \| \| 0.2244 \| 69.0 \| 1173 \| 0.1907 \| 0.2973 \| 0.5946 \| 0.5946 \| nan \| 0.5946 \| 0.0 \| 0.5946 \| \| 0.2244 \| 70.0 \| 1190 \| 0.2714 \| 0.4149 \| 0.8298 \| 0.8298 \| nan \| 0.8298 \| 0.0 \| 0.8298 \| \| 0.2067 \| 71.0 \| 1207 \| 0.2621 \| 0.1077 \| 0.2154 \| 0.2154 \| nan \| 0.2154 \| 0.0 \| 0.2154 \| \| 0.2067 \| 72.0 \| 1224 \| 0.2028 \| 0.3925 \| 0.7849 \| 0.7849 \| nan \| 0.7849 \| 0.0 \| 0.7849 \| \| 0.2067 \| 73.0 \| 1241 \| 0.2457 \| 0.3271 \| 0.6543 \| 0.6543 \| nan \| 0.6543 \| 0.0 \| 0.6543 \| \| 0.2067 \| 74.0 \| 1258 \| 0.2533 \| 0.2758 \| 0.5516 \| 0.5516 \| nan \| 0.5516 \| 0.0 \| 0.5516 \| \| 0.2067 \| 75.0 \| 1275 \| 0.2223 \| 0.2553 \| 0.5106 \| 0.5106 \| nan \| 0.5106 \| 0.0 \| 0.5106 \| \| 0.2067 \| 76.0 \| 1292 \| 0.2784 \| 0.1558 \| 0.3116 \| 0.3116 \| nan \| 0.3116 \| 0.0 \| 0.3116 \| \| 0.2111 \| 77.0 \| 1309 \| 0.2238 \| 0.2742 \| 0.5485 \| 0.5485 \| nan \| 0.5485 \| 0.0 \| 0.5485 \| \| 0.2111 \| 78.0 \| 1326 \| 0.2486 \| 0.3661 \| 0.7322 \| 0.7322 \| nan \| 0.7322 \| 0.0 \| 0.7322 \| \| 0.2111 \| 79.0 \| 1343 \| 0.2279 \| 0.2980 \| 0.5961 \| 0.5961 \| nan \| 0.5961 \| 0.0 \| 0.5961 \| \| 0.2111 \| 80.0 \| 1360 \| 0.2106 \| 0.4261 \| 0.8522 \| 0.8522 \| nan \| 0.8522 \| 0.0 \| 0.8522 \| \| 0.2111 \| 81.0 \| 1377 \| 0.2611 \| 0.1272 \| 0.2544 \| 0.2544 \| nan \| 0.2544 \| 0.0 \| 0.2544 \| \| 0.2111 \| 82.0 \| 1394 \| 0.2742 \| 0.2939 \| 0.5879 \| 0.5879 \| nan \| 0.5879 \| 0.0 \| 0.5879 \| \| 0.1945 \| 83.0 \| 1411 \| 0.2058 \| 0.2690 \| 0.5380 \| 0.5380 \| nan \| 0.5380 \| 0.0 \| 0.5380 \| \| 0.1945 \| 84.0 \| 1428 \| 0.2239 \| 0.2701 \| 0.5401 \| 0.5401 \| nan \| 0.5401 \| 0.0 \| 0.5401 \| \| 0.1945 \| 85.0 \| 1445 \| 0.3142 \| 0.3677 \| 0.7355 \| 0.7355 \| nan \| 0.7355 \| 0.0 \| 0.7355 \| \| 0.1945 \| 86.0 \| 1462 \| 0.3306 \| 0.1677 \| 0.3353 \| 0.3353 \| nan \| 0.3353 \| 0.0 \| 0.3353 \| \| 0.1945 \| 87.0 \| 1479 \| 0.2530 \| 0.2969 \| 0.5938 \| 0.5938 \| nan \| 0.5938 \| 0.0 \| 0.5938 \| \| 0.1945 \| 88.0 \| 1496 \| 0.2500 \| 0.2320 \| 0.4640 \| 0.4640 \| nan \| 0.4640 \| 0.0 \| 0.4640 \| \| 0.192 \| 89.0 \| 1513 \| 0.2924 \| 0.1946 \| 0.3891 \| 0.3891 \| nan \| 0.3891 \| 0.0 \| 0.3891 \| \| 0.192 \| 90.0 \| 1530 \| 0.3585 \| 0.0835 \| 0.1670 \| 0.1670 \| nan \| 0.1670 \| 0.0 \| 0.1670 \| \| 0.192 \| 91.0 \| 1547 \| 0.3411 \| 0.1406 \| 0.2813 \| 0.2813 \| nan \| 0.2813 \| 0.0 \| 0.2813 \| \| 0.192 \| 92.0 \| 1564 \| 0.2961 \| 0.2882 \| 0.5764 \| 0.5764 \| nan \| 0.5764 \| 0.0 \| 0.5764 \| \| 0.192 \| 93.0 \| 1581 \| 0.2800 \| 0.1694 \| 0.3387 \| 0.3387 \| nan \| 0.3387 \| 0.0 \| 0.3387 \| \| 0.192 \| 94.0 \| 1598 \| 0.3560 \| 0.3134 \| 0.6269 \| 0.6269 \| nan \| 0.6269 \| 0.0 \| 0.6269 \| \| 0.1913 \| 95.0 \| 1615 \| 0.3817 \| 0.2125 \| 0.4251 \| 0.4251 \| nan \| 0.4251 \| 0.0 \| 0.4251 \| \| 0.1913 \| 96.0 \| 1632 \| 0.3245 \| 0.0876 \| 0.1753 \| 0.1753 \| nan \| 0.1753 \| 0.0 \| 0.1753 \| \| 0.1913 \| 97.0 \| 1649 \| 0.3458 \| 0.1685 \| 0.3369 \| 0.3369 \| nan \| 0.3369 \| 0.0 \| 0.3369 \| \| 0.1913 \| 98.0 \| 1666 \| 0.3581 \| 0.2758 \| 0.5515 \| 0.5515 \| nan \| 0.5515 \| 0.0 \| 0.5515 \| \| 0.1913 \| 99.0 \| 1683 \| 0.3175 \| 0.2537 \| 0.5074 \| 0.5074 \| nan \| 0.5074 \| 0.0 \| 0.5074 \| \| 0.1731 \| 100.0 \| 1700 \| 0.2857 \| 0.2969 \| 0.5939 \| 0.5939 \| nan \| 0.5939 \| 0.0 \| 0.5939 \| \| 0.1731 \| 101.0 \| 1717 \| 0.3411 \| 0.3170 \| 0.6340 \| 0.6340 \| nan \| 0.6340 \| 0.0 \| 0.6340 \| \| 0.1731 \| 102.0 \| 1734 \| 0.3221 \| 0.2288 \| 0.4576 \| 0.4576 \| nan \| 0.4576 \| 0.0 \| 0.4576 \| \| 0.1731 \| 103.0 \| 1751 \| 0.2913 \| 0.3578 \| 0.7156 \| 0.7156 \| nan \| 0.7156 \| 0.0 \| 0.7156 \| \| 0.1731 \| 104.0 \| 1768 \| 0.2708 \| 0.3599 \| 0.7197 \| 0.7197 \| nan \| 0.7197 \| 0.0 \| 0.7197 \| \| 0.1731 \| 105.0 \| 1785 \| 0.3456 \| 0.2584 \| 0.5168 \| 0.5168 \| nan \| 0.5168 \| 0.0 \| 0.5168 \| \| 0.1581 \| 106.0 \| 1802 \| 0.2770 \| 0.3497 \| 0.6995 \| 0.6995 \| nan \| 0.6995 \| 0.0 \| 0.6995 \| \| 0.1581 \| 107.0 \| 1819 \| 0.2698 \| 0.3113 \| 0.6226 \| 0.6226 \| nan \| 0.6226 \| 0.0 \| 0.6226 \| \| 0.1581 \| 108.0 \| 1836 \| 0.2393 \| 0.3194 \| 0.6389 \| 0.6389 \| nan \| 0.6389 \| 0.0 \| 0.6389 \| \| 0.1581 \| 109.0 \| 1853 \| 0.3066 \| 0.2162 \| 0.4324 \| 0.4324 \| nan \| 0.4324 \| 0.0 \| 0.4324 \| \| 0.1581 \| 110.0 \| 1870 \| 0.2795 \| 0.3652 \| 0.7304 \| 0.7304 \| nan \| 0.7304 \| 0.0 \| 0.7304 \| \| 0.1581 \| 111.0 \| 1887 \| 0.2847 \| 0.3003 \| 0.6007 \| 0.6007 \| nan \| 0.6007 \| 0.0 \| 0.6007 \| \| 0.1441 \| 112.0 \| 1904 \| 0.2758 \| 0.3359 \| 0.6718 \| 0.6718 \| nan \| 0.6718 \| 0.0 \| 0.6718 \| \| 0.1441 \| 113.0 \| 1921 \| 0.3062 \| 0.3258 \| 0.6516 \| 0.6516 \| nan \| 0.6516 \| 0.0 \| 0.6516 \| \| 0.1441 \| 114.0 \| 1938 \| 0.2293 \| 0.3128 \| 0.6256 \| 0.6256 \| nan \| 0.6256 \| 0.0 \| 0.6256 \| \| 0.1441 \| 115.0 \| 1955 \| 0.2223 \| 0.3407 \| 0.6813 \| 0.6813 \| nan \| 0.6813 \| 0.0 \| 0.6813 \| \| 0.1441 \| 116.0 \| 1972 \| 0.3118 \| 0.4092 \| 0.8185 \| 0.8185 \| nan \| 0.8185 \| 0.0 \| 0.8185 \| \| 0.1441 \| 117.0 \| 1989 \| 0.2838 \| 0.2334 \| 0.4668 \| 0.4668 \| nan \| 0.4668 \| 0.0 \| 0.4668 \| \| 0.1393 \| 118.0 \| 2006 \| 0.3241 \| 0.2276 \| 0.4551 \| 0.4551 \| nan \| 0.4551 \| 0.0 \| 0.4551 \| \| 0.1393 \| 119.0 \| 2023 \| 0.2716 \| 0.3810 \| 0.7621 \| 0.7621 \| nan \| 0.7621 \| 0.0 \| 0.7621 \| \| 0.1393 \| 120.0 \| 2040 \| 0.3328 \| 0.3760 \| 0.7520 \| 0.7520 \| nan \| 0.7520 \| 0.0 \| 0.7520 \| \| 0.1393 \| 121.0 \| 2057 \| 0.3282 \| 0.2801 \| 0.5602 \| 0.5602 \| nan \| 0.5602 \| 0.0 \| 0.5602 \| \| 0.1393 \| 122.0 \| 2074 \| 0.3348 \| 0.3117 \| 0.6233 \| 0.6233 \| nan \| 0.6233 \| 0.0 \| 0.6233 \| \| 0.1393 \| 123.0 \| 2091 \| 0.3851 \| 0.2782 \| 0.5564 \| 0.5564 \| nan \| 0.5564 \| 0.0 \| 0.5564 \| \| 0.1449 \| 124.0 \| 2108 \| 0.3402 \| 0.3162 \| 0.6323 \| 0.6323 \| nan \| 0.6323 \| 0.0 \| 0.6323 \| \| 0.1449 \| 125.0 \| 2125 \| 0.3053 \| 0.2355 \| 0.4711 \| 0.4711 \| nan \| 0.4711 \| 0.0 \| 0.4711 \| \| 0.1449 \| 126.0 \| 2142 \| 0.3265 \| 0.2472 \| 0.4944 \| 0.4944 \| nan \| 0.4944 \| 0.0 \| 0.4944 \| \| 0.1449 \| 127.0 \| 2159 \| 0.4374 \| 0.1463 \| 0.2927 \| 0.2927 \| nan \| 0.2927 \| 0.0 \| 0.2927 \| \| 0.1449 \| 128.0 \| 2176 \| 0.3182 \| 0.2567 \| 0.5134 \| 0.5134 \| nan \| 0.5134 \| 0.0 \| 0.5134 \| \| 0.1449 \| 129.0 \| 2193 \| 0.3109 \| 0.3480 \| 0.6960 \| 0.6960 \| nan \| 0.6960 \| 0.0 \| 0.6960 \| \| 0.1245 \| 130.0 \| 2210 \| 0.3833 \| 0.1749 \| 0.3498 \| 0.3498 \| nan \| 0.3498 \| 0.0 \| 0.3498 \| \| 0.1245 \| 131.0 \| 2227 \| 0.3002 \| 0.2612 \| 0.5224 \| 0.5224 \| nan \| 0.5224 \| 0.0 \| 0.5224 \| \| 0.1245 \| 132.0 \| 2244 \| 0.3869 \| 0.3064 \| 0.6129 \| 0.6129 \| nan \| 0.6129 \| 0.0 \| 0.6129 \| \| 0.1245 \| 133.0 \| 2261 \| 0.3192 \| 0.3360 \| 0.6720 \| 0.6720 \| nan \| 0.6720 \| 0.0 \| 0.6720 \| \| 0.1245 \| 134.0 \| 2278 \| 0.2726 \| 0.3727 \| 0.7453 \| 0.7453 \| nan \| 0.7453 \| 0.0 \| 0.7453 \| \| 0.1245 \| 135.0 \| 2295 \| 0.2383 \| 0.3435 \| 0.6869 \| 0.6869 \| nan \| 0.6869 \| 0.0 \| 0.6869 \| \| 0.1235 \| 136.0 \| 2312 \| 0.2310 \| 0.3693 \| 0.7386 \| 0.7386 \| nan \| 0.7386 \| 0.0 \| 0.7386 \| \| 0.1235 \| 137.0 \| 2329 \| 0.3270 \| 0.3227 \| 0.6453 \| 0.6453 \| nan \| 0.6453 \| 0.0 \| 0.6453 \| \| 0.1235 \| 138.0 \| 2346 \| 0.2932 \| 0.3149 \| 0.6298 \| 0.6298 \| nan \| 0.6298 \| 0.0 \| 0.6298 \| \| 0.1235 \| 139.0 \| 2363 \| 0.2976 \| 0.3455 \| 0.6911 \| 0.6911 \| nan \| 0.6911 \| 0.0 \| 0.6911 \| \| 0.1235 \| 140.0 \| 2380 \| 0.3069 \| 0.3496 \| 0.6991 \| 0.6991 \| nan \| 0.6991 \| 0.0 \| 0.6991 \| \| 0.1235 \| 141.0 \| 2397 \| 0.2529 \| 0.2496 \| 0.4993 \| 0.4993 \| nan \| 0.4993 \| 0.0 \| 0.4993 \| \| 0.1128 \| 142.0 \| 2414 \| 0.2553 \| 0.2952 \| 0.5904 \| 0.5904 \| nan \| 0.5904 \| 0.0 \| 0.5904 \| \| 0.1128 \| 143.0 \| 2431 \| 0.2483 \| 0.3372 \| 0.6743 \| 0.6743 \| nan \| 0.6743 \| 0.0 \| 0.6743 \| \| 0.1128 \| 144.0 \| 2448 \| 0.2817 \| 0.3429 \| 0.6857 \| 0.6857 \| nan \| 0.6857 \| 0.0 \| 0.6857 \| \| 0.1128 \| 145.0 \| 2465 \| 0.2906 \| 0.1424 \| 0.2847 \| 0.2847 \| nan \| 0.2847 \| 0.0 \| 0.2847 \| \| 0.1128 \| 146.0 \| 2482 \| 0.2827 \| 0.2326 \| 0.4653 \| 0.4653 \| nan \| 0.4653 \| 0.0 \| 0.4653 \| \| 0.1128 \| 147.0 \| 2499 \| 0.2806 \| 0.2263 \| 0.4525 \| 0.4525 \| nan \| 0.4525 \| 0.0 \| 0.4525 \| \| 0.1086 \| 148.0 \| 2516 \| 0.3055 \| 0.2203 \| 0.4406 \| 0.4406 \| nan \| 0.4406 \| 0.0 \| 0.4406 \| \| 0.1086 \| 149.0 \| 2533 \| 0.2875 \| 0.2702 \| 0.5403 \| 0.5403 \| nan \| 0.5403 \| 0.0 \| 0.5403 \| \| 0.1086 \| 150.0 \| 2550 \| 0.4335 \| 0.2037 \| 0.4074 \| 0.4074 \| nan \| 0.4074 \| 0.0 \| 0.4074 \| \| 0.1086 \| 151.0 \| 2567 \| 0.3218 \| 0.2663 \| 0.5326 \| 0.5326 \| nan \| 0.5326 \| 0.0 \| 0.5326 \| \| 0.1086 \| 152.0 \| 2584 \| 0.2494 \| 0.3017 \| 0.6035 \| 0.6035 \| nan \| 0.6035 \| 0.0 \| 0.6035 \| \| 0.1059 \| 153.0 \| 2601 \| 0.2533 \| 0.3175 \| 0.6351 \| 0.6351 \| nan \| 0.6351 \| 0.0 \| 0.6351 \| \| 0.1059 \| 154.0 \| 2618 \| 0.3656 \| 0.2223 \| 0.4447 \| 0.4447 \| nan \| 0.4447 \| 0.0 \| 0.4447 \| \| 0.1059 \| 155.0 \| 2635 \| 0.3427 \| 0.2402 \| 0.4803 \| 0.4803 \| nan \| 0.4803 \| 0.0 \| 0.4803 \| \| 0.1059 \| 156.0 \| 2652 \| 0.3269 \| 0.2970 \| 0.5939 \| 0.5939 \| nan \| 0.5939 \| 0.0 \| 0.5939 \| \| 0.1059 \| 157.0 \| 2669 \| 0.3159 \| 0.2391 \| 0.4783 \| 0.4783 \| nan \| 0.4783 \| 0.0 \| 0.4783 \| \| 0.1059 \| 158.0 \| 2686 \| 0.3799 \| 0.3644 \| 0.7287 \| 0.7287 \| nan \| 0.7287 \| 0.0 \| 0.7287 \| \| 0.0998 \| 159.0 \| 2703 \| 0.3057 \| 0.2416 \| 0.4831 \| 0.4831 \| nan \| 0.4831 \| 0.0 \| 0.4831 \| \| 0.0998 \| 160.0 \| 2720 \| 0.3725 \| 0.2123 \| 0.4245 \| 0.4245 \| nan \| 0.4245 \| 0.0 \| 0.4245 \| \| 0.0998 \| 161.0 \| 2737 \| 0.4095 \| 0.2383 \| 0.4767 \| 0.4767 \| nan \| 0.4767 \| 0.0 \| 0.4767 \| \| 0.0998 \| 162.0 \| 2754 \| 0.3927 \| 0.3185 \| 0.6371 \| 0.6371 \| nan \| 0.6371 \| 0.0 \| 0.6371 \| \| 0.0998 \| 163.0 \| 2771 \| 0.3511 \| 0.3410 \| 0.6820 \| 0.6820 \| nan \| 0.6820 \| 0.0 \| 0.6820 \| \| 0.0998 \| 164.0 \| 2788 \| 0.3122 \| 0.2452 \| 0.4904 \| 0.4904 \| nan \| 0.4904 \| 0.0 \| 0.4904 \| \| 0.1008 \| 165.0 \| 2805 \| 0.4119 \| 0.2560 \| 0.5119 \| 0.5119 \| nan \| 0.5119 \| 0.0 \| 0.5119 \| \| 0.1008 \| 166.0 \| 2822 \| 0.4577 \| 0.2518 \| 0.5035 \| 0.5035 \| nan \| 0.5035 \| 0.0 \| 0.5035 \| \| 0.1008 \| 167.0 \| 2839 \| 0.3426 \| 0.2673 \| 0.5346 \| 0.5346 \| nan \| 0.5346 \| 0.0 \| 0.5346 \| \| 0.1008 \| 168.0 \| 2856 \| 0.3069 \| 0.3632 \| 0.7265 \| 0.7265 \| nan \| 0.7265 \| 0.0 \| 0.7265 \| \| 0.1008 \| 169.0 \| 2873 \| 0.4477 \| 0.2209 \| 0.4417 \| 0.4417 \| nan \| 0.4417 \| 0.0 \| 0.4417 \| \| 0.1008 \| 170.0 \| 2890 \| 0.3307 \| 0.2525 \| 0.5049 \| 0.5049 \| nan \| 0.5049 \| 0.0 \| 0.5049 \| \| 0.0998 \| 171.0 \| 2907 \| 0.3338 \| 0.2725 \| 0.5450 \| 0.5450 \| nan \| 0.5450 \| 0.0 \| 0.5450 \| \| 0.0998 \| 172.0 \| 2924 \| 0.3028 \| 0.3637 \| 0.7275 \| 0.7275 \| nan \| 0.7275 \| 0.0 \| 0.7275 \| \| 0.0998 \| 173.0 \| 2941 \| 0.3716 \| 0.2877 \| 0.5753 \| 0.5753 \| nan \| 0.5753 \| 0.0 \| 0.5753 \| \| 0.0998 \| 174.0 \| 2958 \| 0.4544 \| 0.3364 \| 0.6729 \| 0.6729 \| nan \| 0.6729 \| 0.0 \| 0.6729 \| \| 0.0998 \| 175.0 \| 2975 \| 0.3750 \| 0.2699 \| 0.5399 \| 0.5399 \| nan \| 0.5399 \| 0.0 \| 0.5399 \| \| 0.0998 \| 176.0 \| 2992 \| 0.3580 \| 0.3064 \| 0.6128 \| 0.6128 \| nan \| 0.6128 \| 0.0 \| 0.6128 \| \| 0.0944 \| 177.0 \| 3009 \| 0.3519 \| 0.2820 \| 0.5641 \| 0.5641 \| nan \| 0.5641 \| 0.0 \| 0.5641 \| \| 0.0944 \| 178.0 \| 3026 \| 0.4045 \| 0.2488 \| 0.4975 \| 0.4975 \| nan \| 0.4975 \| 0.0 \| 0.4975 \| \| 0.0944 \| 179.0 \| 3043 \| 0.4432 \| 0.2521 \| 0.5041 \| 0.5041 \| nan \| 0.5041 \| 0.0 \| 0.5041 \| \| 0.0944 \| 180.0 \| 3060 \| 0.4220 \| 0.1950 \| 0.3900 \| 0.3900 \| nan \| 0.3900 \| 0.0 \| 0.3900 \| \| 0.0944 \| 181.0 \| 3077 \| 0.3030 \| 0.3492 \| 0.6983 \| 0.6983 \| nan \| 0.6983 \| 0.0 \| 0.6983 \| \| 0.0944 \| 182.0 \| 3094 \| 0.3034 \| 0.2901 \| 0.5803 \| 0.5803 \| nan \| 0.5803 \| 0.0 \| 0.5803 \| \| 0.0934 \| 183.0 \| 3111 \| 0.3266 \| 0.2783 \| 0.5566 \| 0.5566 \| nan \| 0.5566 \| 0.0 \| 0.5566 \| \| 0.0934 \| 184.0 \| 3128 \| 0.3688 \| 0.2025 \| 0.4051 \| 0.4051 \| nan \| 0.4051 \| 0.0 \| 0.4051 \| \| 0.0934 \| 185.0 \| 3145 \| 0.3183 \| 0.3159 \| 0.6318 \| 0.6318 \| nan \| 0.6318 \| 0.0 \| 0.6318 \| \| 0.0934 \| 186.0 \| 3162 \| 0.2926 \| 0.3334 \| 0.6668 \| 0.6668 \| nan \| 0.6668 \| 0.0 \| 0.6668 \| \| 0.0934 \| 187.0 \| 3179 \| 0.3351 \| 0.2926 \| 0.5851 \| 0.5851 \| nan \| 0.5851 \| 0.0 \| 0.5851 \| \| 0.0934 \| 188.0 \| 3196 \| 0.3440 \| 0.3410 \| 0.6821 \| 0.6821 \| nan \| 0.6821 \| 0.0 \| 0.6821 \| \| 0.0835 \| 189.0 \| 3213 \| 0.2707 \| 0.3727 \| 0.7454 \| 0.7454 \| nan \| 0.7454 \| 0.0 \| 0.7454 \| \| 0.0835 \| 190.0 \| 3230 \| 0.2855 \| 0.3325 \| 0.6649 \| 0.6649 \| nan \| 0.6649 \| 0.0 \| 0.6649 \| \| 0.0835 \| 191.0 \| 3247 \| 0.3349 \| 0.3753 \| 0.7505 \| 0.7505 \| nan \| 0.7505 \| 0.0 \| 0.7505 \| \| 0.0835 \| 192.0 \| 3264 \| 0.3246 \| 0.3052 \| 0.6105 \| 0.6105 \| nan \| 0.6105 \| 0.0 \| 0.6105 \| \| 0.0835 \| 193.0 \| 3281 \| 0.3379 \| 0.3133 \| 0.6266 \| 0.6266 \| nan \| 0.6266 \| 0.0 \| 0.6266 \| \| 0.0835 \| 194.0 \| 3298 \| 0.2440 \| 0.3287 \| 0.6573 \| 0.6573 \| nan \| 0.6573 \| 0.0 \| 0.6573 \| \| 0.0886 \| 195.0 \| 3315 \| 0.2616 \| 0.3371 \| 0.6743 \| 0.6743 \| nan \| 0.6743 \| 0.0 \| 0.6743 \| \| 0.0886 \| 196.0 \| 3332 \| 0.3202 \| 0.3586 \| 0.7173 \| 0.7173 \| nan \| 0.7173 \| 0.0 \| 0.7173 \| \| 0.0886 \| 197.0 \| 3349 \| 0.3853 \| 0.3157 \| 0.6315 \| 0.6315 \| nan \| 0.6315 \| 0.0 \| 0.6315 \| \| 0.0886 \| 198.0 \| 3366 \| 0.2670 \| 0.3694 \| 0.7387 \| 0.7387 \| nan \| 0.7387 \| 0.0 \| 0.7387 \| \| 0.0886 \| 199.0 \| 3383 \| 0.2904 \| 0.3741 \| 0.7483 \| 0.7483 \| nan \| 0.7483 \| 0.0 \| 0.7483 \| \| 0.0852 \| 200.0 \| 3400 \| 0.2935 \| 0.3762 \| 0.7525 \| 0.7525 \| nan \| 0.7525 \| 0.0 \| 0.7525 \| \| 0.0852 \| 201.0 \| 3417 \| 0.2990 \| 0.2311 \| 0.4623 \| 0.4623 \| nan \| 0.4623 \| 0.0 \| 0.4623 \| \| 0.0852 \| 202.0 \| 3434 \| 0.4198 \| 0.2415 \| 0.4830 \| 0.4830 \| nan \| 0.4830 \| 0.0 \| 0.4830 \| \| 0.0852 \| 203.0 \| 3451 \| 0.2925 \| 0.3272 \| 0.6543 \| 0.6543 \| nan \| 0.6543 \| 0.0 \| 0.6543 \| \| 0.0852 \| 204.0 \| 3468 \| 0.3397 \| 0.3312 \| 0.6625 \| 0.6625 \| nan \| 0.6625 \| 0.0 \| 0.6625 \| \| 0.0852 \| 205.0 \| 3485 \| 0.3388 \| 0.3187 \| 0.6374 \| 0.6374 \| nan \| 0.6374 \| 0.0 \| 0.6374 \| \| 0.0799 \| 206.0 \| 3502 \| 0.4443 \| 0.2287 \| 0.4573 \| 0.4573 \| nan \| 0.4573 \| 0.0 \| 0.4573 \| \| 0.0799 \| 207.0 \| 3519 \| 0.3457 \| 0.2403 \| 0.4807 \| 0.4807 \| nan \| 0.4807 \| 0.0 \| 0.4807 \| \| 0.0799 \| 208.0 \| 3536 \| 0.2849 \| 0.3857 \| 0.7714 \| 0.7714 \| nan \| 0.7714 \| 0.0 \| 0.7714 \| \| 0.0799 \| 209.0 \| 3553 \| 0.2780 \| 0.3685 \| 0.7370 \| 0.7370 \| nan \| 0.7370 \| 0.0 \| 0.7370 \| \| 0.0799 \| 210.0 \| 3570 \| 0.3077 \| 0.4219 \| 0.8438 \| 0.8438 \| nan \| 0.8438 \| 0.0 \| 0.8438 \| \| 0.0799 \| 211.0 \| 3587 \| 0.3165 \| 0.3990 \| 0.7980 \| 0.7980 \| nan \| 0.7980 \| 0.0 \| 0.7980 \| \| 0.0814 \| 212.0 \| 3604 \| 0.2359 \| 0.3532 \| 0.7065 \| 0.7065 \| nan \| 0.7065 \| 0.0 \| 0.7065 \| \| 0.0814 \| 213.0 \| 3621 \| 0.2817 \| 0.3445 \| 0.6890 \| 0.6890 \| nan \| 0.6890 \| 0.0 \| 0.6890 \| \| 0.0814 \| 214.0 \| 3638 \| 0.2271 \| 0.3884 \| 0.7767 \| 0.7767 \| nan \| 0.7767 \| 0.0 \| 0.7767 \| \| 0.0814 \| 215.0 \| 3655 \| 0.2554 \| 0.3485 \| 0.6970 \| 0.6970 \| nan \| 0.6970 \| 0.0 \| 0.6970 \| \| 0.0814 \| 216.0 \| 3672 \| 0.3288 \| 0.2850 \| 0.5700 \| 0.5700 \| nan \| 0.5700 \| 0.0 \| 0.5700 \| \| 0.0814 \| 217.0 \| 3689 \| 0.3606 \| 0.2887 \| 0.5775 \| 0.5775 \| nan \| 0.5775 \| 0.0 \| 0.5775 \| \| 0.076 \| 218.0 \| 3706 \| 0.3455 \| 0.2865 \| 0.5730 \| 0.5730 \| nan \| 0.5730 \| 0.0 \| 0.5730 \| \| 0.076 \| 219.0 \| 3723 \| 0.2988 \| 0.2831 \| 0.5662 \| 0.5662 \| nan \| 0.5662 \| 0.0 \| 0.5662 \| \| 0.076 \| 220.0 \| 3740 \| 0.2780 \| 0.3819 \| 0.7639 \| 0.7639 \| nan \| 0.7639 \| 0.0 \| 0.7639 \| \| 0.076 \| 221.0 \| 3757 \| 0.3546 \| 0.3373 \| 0.6745 \| 0.6745 \| nan \| 0.6745 \| 0.0 \| 0.6745 \| \| 0.076 \| 222.0 \| 3774 \| 0.2463 \| 0.3351 \| 0.6701 \| 0.6701 \| nan \| 0.6701 \| 0.0 \| 0.6701 \| \| 0.076 \| 223.0 \| 3791 \| 0.2828 \| 0.3337 \| 0.6673 \| 0.6673 \| nan \| 0.6673 \| 0.0 \| 0.6673 \| \| 0.0695 \| 224.0 \| 3808 \| 0.2713 \| 0.3310 \| 0.6620 \| 0.6620 \| nan \| 0.6620 \| 0.0 \| 0.6620 \| \| 0.0695 \| 225.0 \| 3825 \| 0.2579 \| 0.3943 \| 0.7886 \| 0.7886 \| nan \| 0.7886 \| 0.0 \| 0.7886 \| \| 0.0695 \| 226.0 \| 3842 \| 0.2866 \| 0.3328 \| 0.6656 \| 0.6656 \| nan \| 0.6656 \| 0.0 \| 0.6656 \| \| 0.0695 \| 227.0 \| 3859 \| 0.2017 \| 0.3899 \| 0.7799 \| 0.7799 \| nan \| 0.7799 \| 0.0 \| 0.7799 \| \| 0.0695 \| 228.0 \| 3876 \| 0.2404 \| 0.3488 \| 0.6977 \| 0.6977 \| nan \| 0.6977 \| 0.0 \| 0.6977 \| \| 0.0695 \| 229.0 \| 3893 \| 0.2647 \| 0.3062 \| 0.6124 \| 0.6124 \| nan \| 0.6124 \| 0.0 \| 0.6124 \| \| 0.0711 \| 230.0 \| 3910 \| 0.3399 \| 0.3073 \| 0.6146 \| 0.6146 \| nan \| 0.6146 \| 0.0 \| 0.6146 \| \| 0.0711 \| 231.0 \| 3927 \| 0.2394 \| 0.2819 \| 0.5637 \| 0.5637 \| nan \| 0.5637 \| 0.0 \| 0.5637 \| \| 0.0711 \| 232.0 \| 3944 \| 0.2756 \| 0.2701 \| 0.5402 \| 0.5402 \| nan \| 0.5402 \| 0.0 \| 0.5402 \| \| 0.0711 \| 233.0 \| 3961 \| 0.3077 \| 0.1907 \| 0.3815 \| 0.3815 \| nan \| 0.3815 \| 0.0 \| 0.3815 \| \| 0.0711 \| 234.0 \| 3978 \| 0.2459 \| 0.3408 \| 0.6817 \| 0.6817 \| nan \| 0.6817 \| 0.0 \| 0.6817 \| \| 0.0711 \| 235.0 \| 3995 \| 0.2907 \| 0.3307 \| 0.6613 \| 0.6613 \| nan \| 0.6613 \| 0.0 \| 0.6613 \| \| 0.0708 \| 236.0 \| 4012 \| 0.2833 \| 0.3640 \| 0.7280 \| 0.7280 \| nan \| 0.7280 \| 0.0 \| 0.7280 \| \| 0.0708 \| 237.0 \| 4029 \| 0.3665 \| 0.2503 \| 0.5006 \| 0.5006 \| nan \| 0.5006 \| 0.0 \| 0.5006 \| \| 0.0708 \| 238.0 \| 4046 \| 0.3428 \| 0.2794 \| 0.5589 \| 0.5589 \| nan \| 0.5589 \| 0.0 \| 0.5589 \| \| 0.0708 \| 239.0 \| 4063 \| 0.3150 \| 0.2842 \| 0.5684 \| 0.5684 \| nan \| 0.5684 \| 0.0 \| 0.5684 \| \| 0.0708 \| 240.0 \| 4080 \| 0.2640 \| 0.3057 \| 0.6114 \| 0.6114 \| nan \| 0.6114 \| 0.0 \| 0.6114 \| \| 0.0708 \| 241.0 \| 4097 \| 0.2757 \| 0.3047 \| 0.6095 \| 0.6095 \| nan \| 0.6095 \| 0.0 \| 0.6095 \| \| 0.0723 \| 242.0 \| 4114 \| 0.3195 \| 0.2267 \| 0.4534 \| 0.4534 \| nan \| 0.4534 \| 0.0 \| 0.4534 \| \| 0.0723 \| 243.0 \| 4131 \| 0.2558 \| 0.3521 \| 0.7042 \| 0.7042 \| nan \| 0.7042 \| 0.0 \| 0.7042 \| \| 0.0723 \| 244.0 \| 4148 \| 0.2594 \| 0.3304 \| 0.6608 \| 0.6608 \| nan \| 0.6608 \| 0.0 \| 0.6608 \| \| 0.0723 \| 245.0 \| 4165 \| 0.3000 \| 0.2973 \| 0.5946 \| 0.5946 \| nan \| 0.5946 \| 0.0 \| 0.5946 \| \| 0.0723 \| 246.0 \| 4182 \| 0.2605 \| 0.2818 \| 0.5635 \| 0.5635 \| nan \| 0.5635 \| 0.0 \| 0.5635 \| \| 0.0723 \| 247.0 \| 4199 \| 0.4257 \| 0.2287 \| 0.4573 \| 0.4573 \| nan \| 0.4573 \| 0.0 \| 0.4573 \| \| 0.071 \| 248.0 \| 4216 \| 0.2179 \| 0.3327 \| 0.6654 \| 0.6654 \| nan \| 0.6654 \| 0.0 \| 0.6654 \| \| 0.071 \| 249.0 \| 4233 \| 0.2834 \| 0.3390 \| 0.6780 \| 0.6780 \| nan \| 0.6780 \| 0.0 \| 0.6780 \| \| 0.071 \| 250.0 \| 4250 \| 0.2524 \| 0.3294 \| 0.6587 \| 0.6587 \| nan \| 0.6587 \| 0.0 \| 0.6587 \| \| 0.071 \| 251.0 \| 4267 \| 0.2615 \| 0.3814 \| 0.7628 \| 0.7628 \| nan \| 0.7628 \| 0.0 \| 0.7628 \| \| 0.071 \| 252.0 \| 4284 \| 0.3364 \| 0.3251 \| 0.6502 \| 0.6502 \| nan \| 0.6502 \| 0.0 \| 0.6502 \| \| 0.0698 \| 253.0 \| 4301 \| 0.2771 \| 0.3535 \| 0.7069 \| 0.7069 \| nan \| 0.7069 \| 0.0 \| 0.7069 \| \| 0.0698 \| 254.0 \| 4318 \| 0.2185 \| 0.3216 \| 0.6432 \| 0.6432 \| nan \| 0.6432 \| 0.0 \| 0.6432 \| \| 0.0698 \| 255.0 \| 4335 \| 0.3342 \| 0.2726 \| 0.5452 \| 0.5452 \| nan \| 0.5452 \| 0.0 \| 0.5452 \| \| 0.0698 \| 256.0 \| 4352 \| 0.3268 \| 0.2718 \| 0.5436 \| 0.5436 \| nan \| 0.5436 \| 0.0 \| 0.5436 \| \| 0.0698 \| 257.0 \| 4369 \| 0.2967 \| 0.2964 \| 0.5929 \| 0.5929 \| nan \| 0.5929 \| 0.0 \| 0.5929 \| \| 0.0698 \| 258.0 \| 4386 \| 0.2684 \| 0.3280 \| 0.6559 \| 0.6559 \| nan \| 0.6559 \| 0.0 \| 0.6559 \| \| 0.0669 \| 259.0 \| 4403 \| 0.3126 \| 0.2626 \| 0.5251 \| 0.5251 \| nan \| 0.5251 \| 0.0 \| 0.5251 \| \| 0.0669 \| 260.0 \| 4420 \| 0.2726 \| 0.3176 \| 0.6353 \| 0.6353 \| nan \| 0.6353 \| 0.0 \| 0.6353 \| \| 0.0669 \| 261.0 \| 4437 \| 0.3289 \| 0.2687 \| 0.5374 \| 0.5374 \| nan \| 0.5374 \| 0.0 \| 0.5374 \| \| 0.0669 \| 262.0 \| 4454 \| 0.2902 \| 0.3025 \| 0.6050 \| 0.6050 \| nan \| 0.6050 \| 0.0 \| 0.6050 \| \| 0.0669 \| 263.0 \| 4471 \| 0.2427 \| 0.3462 \| 0.6925 \| 0.6925 \| nan \| 0.6925 \| 0.0 \| 0.6925 \| \| 0.0669 \| 264.0 \| 4488 \| 0.3117 \| 0.2864 \| 0.5727 \| 0.5727 \| nan \| 0.5727 \| 0.0 \| 0.5727 \| \| 0.062 \| 265.0 \| 4505 \| 0.2793 \| 0.3098 \| 0.6195 \| 0.6195 \| nan \| 0.6195 \| 0.0 \| 0.6195 \| \| 0.062 \| 266.0 \| 4522 \| 0.2447 \| 0.3196 \| 0.6391 \| 0.6391 \| nan \| 0.6391 \| 0.0 \| 0.6391 \| \| 0.062 \| 267.0 \| 4539 \| 0.2943 \| 0.2910 \| 0.5821 \| 0.5821 \| nan \| 0.5821 \| 0.0 \| 0.5821 \| \| 0.062 \| 268.0 \| 4556 \| 0.2506 \| 0.2764 \| 0.5527 \| 0.5527 \| nan \| 0.5527 \| 0.0 \| 0.5527 \| \| 0.062 \| 269.0 \| 4573 \| 0.2507 \| 0.3380 \| 0.6760 \| 0.6760 \| nan \| 0.6760 \| 0.0 \| 0.6760 \| \| 0.062 \| 270.0 \| 4590 \| 0.2783 \| 0.2740 \| 0.5480 \| 0.5480 \| nan \| 0.5480 \| 0.0 \| 0.5480 \| \| 0.0565 \| 271.0 \| 4607 \| 0.2896 \| 0.3476 \| 0.6952 \| 0.6952 \| nan \| 0.6952 \| 0.0 \| 0.6952 \| \| 0.0565 \| 272.0 \| 4624 \| 0.2855 \| 0.3333 \| 0.6665 \| 0.6665 \| nan \| 0.6665 \| 0.0 \| 0.6665 \| \| 0.0565 \| 273.0 \| 4641 \| 0.2183 \| 0.3395 \| 0.6790 \| 0.6790 \| nan \| 0.6790 \| 0.0 \| 0.6790 \| \| 0.0565 \| 274.0 \| 4658 \| 0.2132 \| 0.3567 \| 0.7134 \| 0.7134 \| nan \| 0.7134 \| 0.0 \| 0.7134 \| \| 0.0565 \| 275.0 \| 4675 \| 0.2632 \| 0.3130 \| 0.6260 \| 0.6260 \| nan \| 0.6260 \| 0.0 \| 0.6260 \| \| 0.0565 \| 276.0 \| 4692 \| 0.2548 \| 0.2878 \| 0.5756 \| 0.5756 \| nan \| 0.5756 \| 0.0 \| 0.5756 \| \| 0.0585 \| 277.0 \| 4709 \| 0.2379 \| 0.3666 \| 0.7333 \| 0.7333 \| nan \| 0.7333 \| 0.0 \| 0.7333 \| \| 0.0585 \| 278.0 \| 4726 \| 0.3023 \| 0.2528 \| 0.5056 \| 0.5056 \| nan \| 0.5056 \| 0.0 \| 0.5056 \| \| 0.0585 \| 279.0 \| 4743 \| 0.2598 \| 0.3065 \| 0.6130 \| 0.6130 \| nan \| 0.6130 \| 0.0 \| 0.6130 \| \| 0.0585 \| 280.0 \| 4760 \| 0.2836 \| 0.3839 \| 0.7679 \| 0.7679 \| nan \| 0.7679 \| 0.0 \| 0.7679 \| \| 0.0585 \| 281.0 \| 4777 \| 0.2904 \| 0.2997 \| 0.5994 \| 0.5994 \| nan \| 0.5994 \| 0.0 \| 0.5994 \| \| 0.0585 \| 282.0 \| 4794 \| 0.2363 \| 0.3810 \| 0.7621 \| 0.7621 \| nan \| 0.7621 \| 0.0 \| 0.7621 \| \| 0.0622 \| 283.0 \| 4811 \| 0.2126 \| 0.3643 \| 0.7286 \| 0.7286 \| nan \| 0.7286 \| 0.0 \| 0.7286 \| \| 0.0622 \| 284.0 \| 4828 \| 0.2295 \| 0.3264 \| 0.6528 \| 0.6528 \| nan \| 0.6528 \| 0.0 \| 0.6528 \| \| 0.0622 \| 285.0 \| 4845 \| 0.2663 \| 0.2939 \| 0.5879 \| 0.5879 \| nan \| 0.5879 \| 0.0 \| 0.5879 \| \| 0.0622 \| 286.0 \| 4862 \| 0.3011 \| 0.2987 \| 0.5974 \| 0.5974 \| nan \| 0.5974 \| 0.0 \| 0.5974 \| \| 0.0622 \| 287.0 \| 4879 \| 0.2626 \| 0.3025 \| 0.6051 \| 0.6051 \| nan \| 0.6051 \| 0.0 \| 0.6051 \| \| 0.0622 \| 288.0 \| 4896 \| 0.2773 \| 0.2663 \| 0.5326 \| 0.5326 \| nan \| 0.5326 \| 0.0 \| 0.5326 \| \| 0.0608 \| 289.0 \| 4913 \| 0.2915 \| 0.2532 \| 0.5064 \| 0.5064 \| nan \| 0.5064 \| 0.0 \| 0.5064 \| \| 0.0608 \| 290.0 \| 4930 \| 0.2915 \| 0.2240 \| 0.4480 \| 0.4480 \| nan \| 0.4480 \| 0.0 \| 0.4480 \| \| 0.0608 \| 291.0 \| 4947 \| 0.2605 \| 0.3051 \| 0.6103 \| 0.6103 \| nan \| 0.6103 \| 0.0 \| 0.6103 \| \| 0.0608 \| 292.0 \| 4964 \| 0.3391 \| 0.2572 \| 0.5145 \| 0.5145 \| nan \| 0.5145 \| 0.0 \| 0.5145 \| \| 0.0608 \| 293.0 \| 4981 \| 0.2621 \| 0.2815 \| 0.5629 \| 0.5629 \| nan \| 0.5629 \| 0.0 \| 0.5629 \| \| 0.0608 \| 294.0 \| 4998 \| 0.2932 \| 0.3072 \| 0.6144 \| 0.6144 \| nan \| 0.6144 \| 0.0 \| 0.6144 \| \| 0.0536 \| 295.0 \| 5015 \| 0.3546 \| 0.2762 \| 0.5524 \| 0.5524 \| nan \| 0.5524 \| 0.0 \| 0.5524 \| \| 0.0536 \| 296.0 \| 5032 \| 0.2652 \| 0.2772 \| 0.5545 \| 0.5545 \| nan \| 0.5545 \| 0.0 \| 0.5545 \| \| 0.0536 \| 297.0 \| 5049 \| 0.2781 \| 0.2515 \| 0.5030 \| 0.5030 \| nan \| 0.5030 \| 0.0 \| 0.5030 \| \| 0.0536 \| 298.0 \| 5066 \| 0.2777 \| 0.2666 \| 0.5333 \| 0.5333 \| nan \| 0.5333 \| 0.0 \| 0.5333 \| \| 0.0536 \| 299.0 \| 5083 \| 0.2573 \| 0.2863 \| 0.5726 \| 0.5726 \| nan \| 0.5726 \| 0.0 \| 0.5726 \| \| 0.0572 \| 300.0 \| 5100 \| 0.2443 \| 0.3174 \| 0.6348 \| 0.6348 \| nan \| 0.6348 \| 0.0 \| 0.6348 \| \| 0.0572 \| 301.0 \| 5117 \| 0.2724 \| 0.3107 \| 0.6214 \| 0.6214 \| nan \| 0.6214 \| 0.0 \| 0.6214 \| \| 0.0572 \| 302.0 \| 5134 \| 0.2443 \| 0.3254 \| 0.6509 \| 0.6509 \| nan \| 0.6509 \| 0.0 \| 0.6509 \| \| 0.0572 \| 303.0 \| 5151 \| 0.2556 \| 0.2854 \| 0.5708 \| 0.5708 \| nan \| 0.5708 \| 0.0 \| 0.5708 \| \| 0.0572 \| 304.0 \| 5168 \| 0.2798 \| 0.3086 \| 0.6171 \| 0.6171 \| nan \| 0.6171 \| 0.0 \| 0.6171 \| \| 0.0572 \| 305.0 \| 5185 \| 0.2805 \| 0.3136 \| 0.6271 \| 0.6271 \| nan \| 0.6271 \| 0.0 \| 0.6271 \| \| 0.057 \| 306.0 \| 5202 \| 0.1925 \| 0.3286 \| 0.6572 \| 0.6572 \| nan \| 0.6572 \| 0.0 \| 0.6572 \| \| 0.057 \| 307.0 \| 5219 \| 0.2125 \| 0.4029 \| 0.8058 \| 0.8058 \| nan \| 0.8058 \| 0.0 \| 0.8058 \| \| 0.057 \| 308.0 \| 5236 \| 0.2009 \| 0.3365 \| 0.6731 \| 0.6731 \| nan \| 0.6731 \| 0.0 \| 0.6731 \| \| 0.057 \| 309.0 \| 5253 \| 0.2251 \| 0.3493 \| 0.6986 \| 0.6986 \| nan \| 0.6986 \| 0.0 \| 0.6986 \| \| 0.057 \| 310.0 \| 5270 \| 0.2298 \| 0.3558 \| 0.7116 \| 0.7116 \| nan \| 0.7116 \| 0.0 \| 0.7116 \| \| 0.057 \| 311.0 \| 5287 \| 0.2192 \| 0.2883 \| 0.5767 \| 0.5767 \| nan \| 0.5767 \| 0.0 \| 0.5767 \| \| 0.0522 \| 312.0 \| 5304 \| 0.2349 \| 0.3551 \| 0.7102 \| 0.7102 \| nan \| 0.7102 \| 0.0 \| 0.7102 \| \| 0.0522 \| 313.0 \| 5321 \| 0.1832 \| 0.4190 \| 0.8379 \| 0.8379 \| nan \| 0.8379 \| 0.0 \| 0.8379 \| \| 0.0522 \| 314.0 \| 5338 \| 0.2119 \| 0.3985 \| 0.7971 \| 0.7971 \| nan \| 0.7971 \| 0.0 \| 0.7971 \| \| 0.0522 \| 315.0 \| 5355 \| 0.2038 \| 0.4374 \| 0.8747 \| 0.8747 \| nan \| 0.8747 \| 0.0 \| 0.8747 \| \| 0.0522 \| 316.0 \| 5372 \| 0.2124 \| 0.3713 \| 0.7427 \| 0.7427 \| nan \| 0.7427 \| 0.0 \| 0.7427 \| \| 0.0522 \| 317.0 \| 5389 \| 0.1980 \| 0.3498 \| 0.6995 \| 0.6995 \| nan \| 0.6995 \| 0.0 \| 0.6995 \| \| 0.0523 \| 318.0 \| 5406 \| 0.2483 \| 0.3521 \| 0.7042 \| 0.7042 \| nan \| 0.7042 \| 0.0 \| 0.7042 \| \| 0.0523 \| 319.0 \| 5423 \| 0.2873 \| 0.3058 \| 0.6116 \| 0.6116 \| nan \| 0.6116 \| 0.0 \| 0.6116 \| \| 0.0523 \| 320.0 \| 5440 \| 0.2366 \| 0.3530 \| 0.7060 \| 0.7060 \| nan \| 0.7060 \| 0.0 \| 0.7060 \| \| 0.0523 \| 321.0 \| 5457 \| 0.2613 \| 0.3360 \| 0.6720 \| 0.6720 \| nan \| 0.6720 \| 0.0 \| 0.6720 \| \| 0.0523 \| 322.0 \| 5474 \| 0.2392 \| 0.3401 \| 0.6802 \| 0.6802 \| nan \| 0.6802 \| 0.0 \| 0.6802 \| \| 0.0523 \| 323.0 \| 5491 \| 0.2279 \| 0.3464 \| 0.6928 \| 0.6928 \| nan \| 0.6928 \| 0.0 \| 0.6928 \| \| 0.05 \| 324.0 \| 5508 \| 0.2718 \| 0.2724 \| 0.5449 \| 0.5449 \| nan \| 0.5449 \| 0.0 \| 0.5449 \| \| 0.05 \| 325.0 \| 5525 \| 0.2367 \| 0.3879 \| 0.7758 \| 0.7758 \| nan \| 0.7758 \| 0.0 \| 0.7758 \| \| 0.05 \| 326.0 \| 5542 \| 0.2976 \| 0.3504 \| 0.7008 \| 0.7008 \| nan \| 0.7008 \| 0.0 \| 0.7008 \| \| 0.05 \| 327.0 \| 5559 \| 0.2424 \| 0.3214 \| 0.6429 \| 0.6429 \| nan \| 0.6429 \| 0.0 \| 0.6429 \| \| 0.05 \| 328.0 \| 5576 \| 0.2852 \| 0.3042 \| 0.6084 \| 0.6084 \| nan \| 0.6084 \| 0.0 \| 0.6084 \| \| 0.05 \| 329.0 \| 5593 \| 0.2473 \| 0.2739 \| 0.5479 \| 0.5479 \| nan \| 0.5479 \| 0.0 \| 0.5479 \| \| 0.0552 \| 330.0 \| 5610 \| 0.2461 \| 0.3971 \| 0.7943 \| 0.7943 \| nan \| 0.7943 \| 0.0 \| 0.7943 \| \| 0.0552 \| 331.0 \| 5627 \| 0.2122 \| 0.3252 \| 0.6503 \| 0.6503 \| nan \| 0.6503 \| 0.0 \| 0.6503 \| \| 0.0552 \| 332.0 \| 5644 \| 0.2318 \| 0.2996 \| 0.5993 \| 0.5993 \| nan \| 0.5993 \| 0.0 \| 0.5993 \| \| 0.0552 \| 333.0 \| 5661 \| 0.2186 \| 0.3774 \| 0.7547 \| 0.7547 \| nan \| 0.7547 \| 0.0 \| 0.7547 \| \| 0.0552 \| 334.0 \| 5678 \| 0.2682 \| 0.2615 \| 0.5230 \| 0.5230 \| nan \| 0.5230 \| 0.0 \| 0.5230 \| \| 0.0552 \| 335.0 \| 5695 \| 0.2490 \| 0.3225 \| 0.6451 \| 0.6451 \| nan \| 0.6451 \| 0.0 \| 0.6451 \| \| 0.0524 \| 336.0 \| 5712 \| 0.2334 \| 0.3464 \| 0.6927 \| 0.6927 \| nan \| 0.6927 \| 0.0 \| 0.6927 \| \| 0.0524 \| 337.0 \| 5729 \| 0.2234 \| 0.3504 \| 0.7008 \| 0.7008 \| nan \| 0.7008 \| 0.0 \| 0.7008 \| \| 0.0524 \| 338.0 \| 5746 \| 0.2413 \| 0.3737 \| 0.7473 \| 0.7473 \| nan \| 0.7473 \| 0.0 \| 0.7473 \| \| 0.0524 \| 339.0 \| 5763 \| 0.2368 \| 0.3699 \| 0.7399 \| 0.7399 \| nan \| 0.7399 \| 0.0 \| 0.7399 \| \| 0.0524 \| 340.0 \| 5780 \| 0.2442 \| 0.3809 \| 0.7619 \| 0.7619 \| nan \| 0.7619 \| 0.0 \| 0.7619 \| \| 0.0524 \| 341.0 \| 5797 \| 0.2208 \| 0.3740 \| 0.7481 \| 0.7481 \| nan \| 0.7481 \| 0.0 \| 0.7481 \| \| 0.0513 \| 342.0 \| 5814 \| 0.2509 \| 0.3849 \| 0.7698 \| 0.7698 \| nan \| 0.7698 \| 0.0 \| 0.7698 \| \| 0.0513 \| 343.0 \| 5831 \| 0.2194 \| 0.3553 \| 0.7106 \| 0.7106 \| nan \| 0.7106 \| 0.0 \| 0.7106 \| \| 0.0513 \| 344.0 \| 5848 \| 0.2536 \| 0.3990 \| 0.7980 \| 0.7980 \| nan \| 0.7980 \| 0.0 \| 0.7980 \| \| 0.0513 \| 345.0 \| 5865 \| 0.2803 \| 0.3740 \| 0.7479 \| 0.7479 \| nan \| 0.7479 \| 0.0 \| 0.7479 \| \| 0.0513 \| 346.0 \| 5882 \| 0.2732 \| 0.3944 \| 0.7889 \| 0.7889 \| nan \| 0.7889 \| 0.0 \| 0.7889 \| \| 0.0513 \| 347.0 \| 5899 \| 0.2759 \| 0.3588 \| 0.7176 \| 0.7176 \| nan \| 0.7176 \| 0.0 \| 0.7176 \| \| 0.0504 \| 348.0 \| 5916 \| 0.2941 \| 0.3472 \| 0.6945 \| 0.6945 \| nan \| 0.6945 \| 0.0 \| 0.6945 \| \| 0.0504 \| 349.0 \| 5933 \| 0.2170 \| 0.3646 \| 0.7292 \| 0.7292 \| nan \| 0.7292 \| 0.0 \| 0.7292 \| \| 0.0504 \| 350.0 \| 5950 \| 0.2464 \| 0.3842 \| 0.7685 \| 0.7685 \| nan \| 0.7685 \| 0.0 \| 0.7685 \| \| 0.0504 \| 351.0 \| 5967 \| 0.2394 \| 0.3697 \| 0.7395 \| 0.7395 \| nan \| 0.7395 \| 0.0 \| 0.7395 \| \| 0.0504 \| 352.0 \| 5984 \| 0.2645 \| 0.3719 \| 0.7438 \| 0.7438 \| nan \| 0.7438 \| 0.0 \| 0.7438 \| \| 0.0497 \| 353.0 \| 6001 \| 0.2226 \| 0.3932 \| 0.7865 \| 0.7865 \| nan \| 0.7865 \| 0.0 \| 0.7865 \| \| 0.0497 \| 354.0 \| 6018 \| 0.2579 \| 0.3718 \| 0.7436 \| 0.7436 \| nan \| 0.7436 \| 0.0 \| 0.7436 \| \| 0.0497 \| 355.0 \| 6035 \| 0.2149 \| 0.3905 \| 0.7810 \| 0.7810 \| nan \| 0.7810 \| 0.0 \| 0.7810 \| \| 0.0497 \| 356.0 \| 6052 \| 0.2202 \| 0.3803 \| 0.7606 \| 0.7606 \| nan \| 0.7606 \| 0.0 \| 0.7606 \| \| 0.0497 \| 357.0 \| 6069 \| 0.2281 \| 0.3398 \| 0.6795 \| 0.6795 \| nan \| 0.6795 \| 0.0 \| 0.6795 \| \| 0.0497 \| 358.0 \| 6086 \| 0.2857 \| 0.3210 \| 0.6419 \| 0.6419 \| nan \| 0.6419 \| 0.0 \| 0.6419 \| \| 0.0464 \| 359.0 \| 6103 \| 0.2393 \| 0.3573 \| 0.7146 \| 0.7146 \| nan \| 0.7146 \| 0.0 \| 0.7146 \| \| 0.0464 \| 360.0 \| 6120 \| 0.2901 \| 0.2873 \| 0.5747 \| 0.5747 \| nan \| 0.5747 \| 0.0 \| 0.5747 \| \| 0.0464 \| 361.0 \| 6137 \| 0.2689 \| 0.3335 \| 0.6669 \| 0.6669 \| nan \| 0.6669 \| 0.0 \| 0.6669 \| \| 0.0464 \| 362.0 \| 6154 \| 0.3531 \| 0.2828 \| 0.5655 \| 0.5655 \| nan \| 0.5655 \| 0.0 \| 0.5655 \| \| 0.0464 \| 363.0 \| 6171 \| 0.2432 \| 0.4020 \| 0.8039 \| 0.8039 \| nan \| 0.8039 \| 0.0 \| 0.8039 \| \| 0.0464 \| 364.0 \| 6188 \| 0.2345 \| 0.3927 \| 0.7855 \| 0.7855 \| nan \| 0.7855 \| 0.0 \| 0.7855 \| \| 0.0513 \| 365.0 \| 6205 \| 0.3075 \| 0.3593 \| 0.7185 \| 0.7185 \| nan \| 0.7185 \| 0.0 \| 0.7185 \| \| 0.0513 \| 366.0 \| 6222 \| 0.2853 \| 0.3391 \| 0.6782 \| 0.6782 \| nan \| 0.6782 \| 0.0 \| 0.6782 \| \| 0.0513 \| 367.0 \| 6239 \| 0.3308 \| 0.3121 \| 0.6243 \| 0.6243 \| nan \| 0.6243 \| 0.0 \| 0.6243 \| \| 0.0513 \| 368.0 \| 6256 \| 0.2364 \| 0.3805 \| 0.7610 \| 0.7610 \| nan \| 0.7610 \| 0.0 \| 0.7610 \| \| 0.0513 \| 369.0 \| 6273 \| 0.2420 \| 0.3628 \| 0.7257 \| 0.7257 \| nan \| 0.7257 \| 0.0 \| 0.7257 \| \| 0.0513 \| 370.0 \| 6290 \| 0.2562 \| 0.3500 \| 0.7000 \| 0.7000 \| nan \| 0.7000 \| 0.0 \| 0.7000 \| \| 0.0491 \| 371.0 \| 6307 \| 0.2455 \| 0.3431 \| 0.6863 \| 0.6863 \| nan \| 0.6863 \| 0.0 \| 0.6863 \| \| 0.0491 \| 372.0 \| 6324 \| 0.2793 \| 0.3599 \| 0.7198 \| 0.7198 \| nan \| 0.7198 \| 0.0 \| 0.7198 \| \| 0.0491 \| 373.0 \| 6341 \| 0.2232 \| 0.3733 \| 0.7466 \| 0.7466 \| nan \| 0.7466 \| 0.0 \| 0.7466 \| \| 0.0491 \| 374.0 \| 6358 \| 0.2899 \| 0.3353 \| 0.6706 \| 0.6706 \| nan \| 0.6706 \| 0.0 \| 0.6706 \| \| 0.0491 \| 375.0 \| 6375 \| 0.2351 \| 0.3562 \| 0.7124 \| 0.7124 \| nan \| 0.7124 \| 0.0 \| 0.7124 \| \| 0.0491 \| 376.0 \| 6392 \| 0.2447 \| 0.3252 \| 0.6504 \| 0.6504 \| nan \| 0.6504 \| 0.0 \| 0.6504 \| \| 0.0477 \| 377.0 \| 6409 \| 0.2346 \| 0.3344 \| 0.6689 \| 0.6689 \| nan \| 0.6689 \| 0.0 \| 0.6689 \| \| 0.0477 \| 378.0 \| 6426 \| 0.2468 \| 0.3630 \| 0.7259 \| 0.7259 \| nan \| 0.7259 \| 0.0 \| 0.7259 \| \| 0.0477 \| 379.0 \| 6443 \| 0.3616 \| 0.3322 \| 0.6645 \| 0.6645 \| nan \| 0.6645 \| 0.0 \| 0.6645 \| \| 0.0477 \| 380.0 \| 6460 \| 0.2846 \| 0.3387 \| 0.6775 \| 0.6775 \| nan \| 0.6775 \| 0.0 \| 0.6775 \| \| 0.0477 \| 381.0 \| 6477 \| 0.3733 \| 0.2779 \| 0.5558 \| 0.5558 \| nan \| 0.5558 \| 0.0 \| 0.5558 \| \| 0.0477 \| 382.0 \| 6494 \| 0.3140 \| 0.3178 \| 0.6356 \| 0.6356 \| nan \| 0.6356 \| 0.0 \| 0.6356 \| \| 0.0446 \| 383.0 \| 6511 \| 0.3926 \| 0.3009 \| 0.6018 \| 0.6018 \| nan \| 0.6018 \| 0.0 \| 0.6018 \| \| 0.0446 \| 384.0 \| 6528 \| 0.2901 \| 0.3070 \| 0.6140 \| 0.6140 \| nan \| 0.6140 \| 0.0 \| 0.6140 \| \| 0.0446 \| 385.0 \| 6545 \| 0.3247 \| 0.3514 \| 0.7027 \| 0.7027 \| nan \| 0.7027 \| 0.0 \| 0.7027 \| \| 0.0446 \| 386.0 \| 6562 \| 0.3177 \| 0.3340 \| 0.6681 \| 0.6681 \| nan \| 0.6681 \| 0.0 \| 0.6681 \| \| 0.0446 \| 387.0 \| 6579 \| 0.2939 \| 0.3542 \| 0.7084 \| 0.7084 \| nan \| 0.7084 \| 0.0 \| 0.7084 \| \| 0.0446 \| 388.0 \| 6596 \| 0.2585 \| 0.3410 \| 0.6820 \| 0.6820 \| nan \| 0.6820 \| 0.0 \| 0.6820 \| \| 0.0489 \| 389.0 \| 6613 \| 0.3240 \| 0.3161 \| 0.6321 \| 0.6321 \| nan \| 0.6321 \| 0.0 \| 0.6321 \| \| 0.0489 \| 390.0 \| 6630 \| 0.2895 \| 0.3698 \| 0.7396 \| 0.7396 \| nan \| 0.7396 \| 0.0 \| 0.7396 \| \| 0.0489 \| 391.0 \| 6647 \| 0.2994 \| 0.3214 \| 0.6428 \| 0.6428 \| nan \| 0.6428 \| 0.0 \| 0.6428 \| \| 0.0489 \| 392.0 \| 6664 \| 0.2779 \| 0.3668 \| 0.7336 \| 0.7336 \| nan \| 0.7336 \| 0.0 \| 0.7336 \| \| 0.0489 \| 393.0 \| 6681 \| 0.2841 \| 0.3098 \| 0.6197 \| 0.6197 \| nan \| 0.6197 \| 0.0 \| 0.6197 \| \| 0.0489 \| 394.0 \| 6698 \| 0.3211 \| 0.2982 \| 0.5964 \| 0.5964 \| nan \| 0.5964 \| 0.0 \| 0.5964 \| \| 0.0449 \| 395.0 \| 6715 \| 0.3262 \| 0.2988 \| 0.5977 \| 0.5977 \| nan \| 0.5977 \| 0.0 \| 0.5977 \| \| 0.0449 \| 396.0 \| 6732 \| 0.3133 \| 0.3101 \| 0.6202 \| 0.6202 \| nan \| 0.6202 \| 0.0 \| 0.6202 \| \| 0.0449 \| 397.0 \| 6749 \| 0.3241 \| 0.3381 \| 0.6762 \| 0.6762 \| nan \| 0.6762 \| 0.0 \| 0.6762 \| \| 0.0449 \| 398.0 \| 6766 \| 0.3566 \| 0.3000 \| 0.5999 \| 0.5999 \| nan \| 0.5999 \| 0.0 \| 0.5999 \| \| 0.0449 \| 399.0 \| 6783 \| 0.2994 \| 0.3600 \| 0.7199 \| 0.7199 \| nan \| 0.7199 \| 0.0 \| 0.7199 \| \| 0.0463 \| 400.0 \| 6800 \| 0.2672 \| 0.3268 \| 0.6536 \| 0.6536 \| nan \| 0.6536 \| 0.0 \| 0.6536 \| \| 0.0463 \| 401.0 \| 6817 \| 0.2945 \| 0.3565 \| 0.7131 \| 0.7131 \| nan \| 0.7131 \| 0.0 \| 0.7131 \| \| 0.0463 \| 402.0 \| 6834 \| 0.2970 \| 0.3397 \| 0.6794 \| 0.6794 \| nan \| 0.6794 \| 0.0 \| 0.6794 \| \| 0.0463 \| 403.0 \| 6851 \| 0.2687 \| 0.3577 \| 0.7154 \| 0.7154 \| nan \| 0.7154 \| 0.0 \| 0.7154 \| \| 0.0463 \| 404.0 \| 6868 \| 0.2877 \| 0.3387 \| 0.6774 \| 0.6774 \| nan \| 0.6774 \| 0.0 \| 0.6774 \| \| 0.0463 \| 405.0 \| 6885 \| 0.2600 \| 0.3489 \| 0.6977 \| 0.6977 \| nan \| 0.6977 \| 0.0 \| 0.6977 \| \| 0.0443 \| 406.0 \| 6902 \| 0.3077 \| 0.3165 \| 0.6330 \| 0.6330 \| nan \| 0.6330 \| 0.0 \| 0.6330 \| \| 0.0443 \| 407.0 \| 6919 \| 0.2634 \| 0.3436 \| 0.6873 \| 0.6873 \| nan \| 0.6873 \| 0.0 \| 0.6873 \| \| 0.0443 \| 408.0 \| 6936 \| 0.2766 \| 0.3393 \| 0.6786 \| 0.6786 \| nan \| 0.6786 \| 0.0 \| 0.6786 \| \| 0.0443 \| 409.0 \| 6953 \| 0.2781 \| 0.3096 \| 0.6191 \| 0.6191 \| nan \| 0.6191 \| 0.0 \| 0.6191 \| \| 0.0443 \| 410.0 \| 6970 \| 0.2749 \| 0.3357 \| 0.6714 \| 0.6714 \| nan \| 0.6714 \| 0.0 \| 0.6714 \| \| 0.0443 \| 411.0 \| 6987 \| 0.3368 \| 0.3182 \| 0.6364 \| 0.6364 \| nan \| 0.6364 \| 0.0 \| 0.6364 \| \| 0.0429 \| 412.0 \| 7004 \| 0.3096 \| 0.3016 \| 0.6032 \| 0.6032 \| nan \| 0.6032 \| 0.0 \| 0.6032 \| \| 0.0429 \| 413.0 \| 7021 \| 0.3003 \| 0.3402 \| 0.6804 \| 0.6804 \| nan \| 0.6804 \| 0.0 \| 0.6804 \| \| 0.0429 \| 414.0 \| 7038 \| 0.2831 \| 0.3491 \| 0.6983 \| 0.6983 \| nan \| 0.6983 \| 0.0 \| 0.6983 \| \| 0.0429 \| 415.0 \| 7055 \| 0.3294 \| 0.2715 \| 0.5430 \| 0.5430 \| nan \| 0.5430 \| 0.0 \| 0.5430 \| \| 0.0429 \| 416.0 \| 7072 \| 0.2549 \| 0.3333 \| 0.6665 \| 0.6665 \| nan \| 0.6665 \| 0.0 \| 0.6665 \| \| 0.0429 \| 417.0 \| 7089 \| 0.3011 \| 0.2956 \| 0.5911 \| 0.5911 \| nan \| 0.5911 \| 0.0 \| 0.5911 \| \| 0.0394 \| 418.0 \| 7106 \| 0.2614 \| 0.3257 \| 0.6514 \| 0.6514 \| nan \| 0.6514 \| 0.0 \| 0.6514 \| \| 0.0394 \| 419.0 \| 7123 \| 0.3040 \| 0.3185 \| 0.6369 \| 0.6369 \| nan \| 0.6369 \| 0.0 \| 0.6369 \| \| 0.0394 \| 420.0 \| 7140 \| 0.2870 \| 0.3443 \| 0.6885 \| 0.6885 \| nan \| 0.6885 \| 0.0 \| 0.6885 \| \| 0.0394 \| 421.0 \| 7157 \| 0.2593 \| 0.3536 \| 0.7072 \| 0.7072 \| nan \| 0.7072 \| 0.0 \| 0.7072 \| \| 0.0394 \| 422.0 \| 7174 \| 0.2808 \| 0.3458 \| 0.6915 \| 0.6915 \| nan \| 0.6915 \| 0.0 \| 0.6915 \| \| 0.0394 \| 423.0 \| 7191 \| 0.2958 \| 0.3261 \| 0.6522 \| 0.6522 \| nan \| 0.6522 \| 0.0 \| 0.6522 \| \| 0.0432 \| 424.0 \| 7208 \| 0.2818 \| 0.3377 \| 0.6753 \| 0.6753 \| nan \| 0.6753 \| 0.0 \| 0.6753 \| \| 0.0432 \| 425.0 \| 7225 \| 0.3045 \| 0.3385 \| 0.6769 \| 0.6769 \| nan \| 0.6769 \| 0.0 \| 0.6769 \| \| 0.0432 \| 426.0 \| 7242 \| 0.3077 \| 0.3070 \| 0.6139 \| 0.6139 \| nan \| 0.6139 \| 0.0 \| 0.6139 \| \| 0.0432 \| 427.0 \| 7259 \| 0.3225 \| 0.3227 \| 0.6455 \| 0.6455 \| nan \| 0.6455 \| 0.0 \| 0.6455 \| \| 0.0432 \| 428.0 \| 7276 \| 0.3013 \| 0.3387 \| 0.6774 \| 0.6774 \| nan \| 0.6774 \| 0.0 \| 0.6774 \| \| 0.0432 \| 429.0 \| 7293 \| 0.2909 \| 0.3348 \| 0.6697 \| 0.6697 \| nan \| 0.6697 \| 0.0 \| 0.6697 \| \| 0.0419 \| 430.0 \| 7310 \| 0.3022 \| 0.3250 \| 0.6501 \| 0.6501 \| nan \| 0.6501 \| 0.0 \| 0.6501 \| \| 0.0419 \| 431.0 \| 7327 \| 0.2724 \| 0.3467 \| 0.6934 \| 0.6934 \| nan \| 0.6934 \| 0.0 \| 0.6934 \| \| 0.0419 \| 432.0 \| 7344 \| 0.3426 \| 0.3265 \| 0.6529 \| 0.6529 \| nan \| 0.6529 \| 0.0 \| 0.6529 \| \| 0.0419 \| 433.0 \| 7361 \| 0.2962 \| 0.3380 \| 0.6760 \| 0.6760 \| nan \| 0.6760 \| 0.0 \| 0.6760 \| \| 0.0419 \| 434.0 \| 7378 \| 0.2787 \| 0.3242 \| 0.6484 \| 0.6484 \| nan \| 0.6484 \| 0.0 \| 0.6484 \| \| 0.0419 \| 435.0 \| 7395 \| 0.3095 \| 0.3365 \| 0.6730 \| 0.6730 \| nan \| 0.6730 \| 0.0 \| 0.6730 \| \| 0.0398 \| 436.0 \| 7412 \| 0.3047 \| 0.3122 \| 0.6243 \| 0.6243 \| nan \| 0.6243 \| 0.0 \| 0.6243 \| \| 0.0398 \| 437.0 \| 7429 \| 0.3225 \| 0.3024 \| 0.6048 \| 0.6048 \| nan \| 0.6048 \| 0.0 \| 0.6048 \| \| 0.0398 \| 438.0 \| 7446 \| 0.3274 \| 0.2866 \| 0.5731 \| 0.5731 \| nan \| 0.5731 \| 0.0 \| 0.5731 \| \| 0.0398 \| 439.0 \| 7463 \| 0.3423 \| 0.2367 \| 0.4735 \| 0.4735 \| nan \| 0.4735 \| 0.0 \| 0.4735 \| \| 0.0398 \| 440.0 \| 7480 \| 0.3609 \| 0.2558 \| 0.5117 \| 0.5117 \| nan \| 0.5117 \| 0.0 \| 0.5117 \| \| 0.0398 \| 441.0 \| 7497 \| 0.2948 \| 0.3113 \| 0.6227 \| 0.6227 \| nan \| 0.6227 \| 0.0 \| 0.6227 \| \| 0.0455 \| 442.0 \| 7514 \| 0.3299 \| 0.2566 \| 0.5132 \| 0.5132 \| nan \| 0.5132 \| 0.0 \| 0.5132 \| \| 0.0455 \| 443.0 \| 7531 \| 0.2974 \| 0.3005 \| 0.6011 \| 0.6011 \| nan \| 0.6011 \| 0.0 \| 0.6011 \| \| 0.0455 \| 444.0 \| 7548 \| 0.3208 \| 0.2764 \| 0.5529 \| 0.5529 \| nan \| 0.5529 \| 0.0 \| 0.5529 \| \| 0.0455 \| 445.0 \| 7565 \| 0.2894 \| 0.3427 \| 0.6854 \| 0.6854 \| nan \| 0.6854 \| 0.0 \| 0.6854 \| \| 0.0455 \| 446.0 \| 7582 \| 0.2981 \| 0.3060 \| 0.6119 \| 0.6119 \| nan \| 0.6119 \| 0.0 \| 0.6119 \| \| 0.0455 \| 447.0 \| 7599 \| 0.2535 \| 0.3309 \| 0.6618 \| 0.6618 \| nan \| 0.6618 \| 0.0 \| 0.6618 \| \| 0.0434 \| 448.0 \| 7616 \| 0.2865 \| 0.2696 \| 0.5392 \| 0.5392 \| nan \| 0.5392 \| 0.0 \| 0.5392 \| \| 0.0434 \| 449.0 \| 7633 \| 0.2940 \| 0.3166 \| 0.6332 \| 0.6332 \| nan \| 0.6332 \| 0.0 \| 0.6332 \| \| 0.0434 \| 450.0 \| 7650 \| 0.3000 \| 0.3000 \| 0.6001 \| 0.6001 \| nan \| 0.6001 \| 0.0 \| 0.6001 \| \| 0.0434 \| 451.0 \| 7667 \| 0.3164 \| 0.2847 \| 0.5694 \| 0.5694 \| nan \| 0.5694 \| 0.0 \| 0.5694 \| \| 0.0434 \| 452.0 \| 7684 \| 0.2465 \| 0.3287 \| 0.6575 \| 0.6575 \| nan \| 0.6575 \| 0.0 \| 0.6575 \| \| 0.0412 \| 453.0 \| 7701 \| 0.2836 \| 0.3172 \| 0.6344 \| 0.6344 \| nan \| 0.6344 \| 0.0 \| 0.6344 \| \| 0.0412 \| 454.0 \| 7718 \| 0.3480 \| 0.2801 \| 0.5603 \| 0.5603 \| nan \| 0.5603 \| 0.0 \| 0.5603 \| \| 0.0412 \| 455.0 \| 7735 \| 0.3047 \| 0.3197 \| 0.6394 \| 0.6394 \| nan \| 0.6394 \| 0.0 \| 0.6394 \| \| 0.0412 \| 456.0 \| 7752 \| 0.3430 \| 0.3214 \| 0.6428 \| 0.6428 \| nan \| 0.6428 \| 0.0 \| 0.6428 \| \| 0.0412 \| 457.0 \| 7769 \| 0.3213 \| 0.3358 \| 0.6717 \| 0.6717 \| nan \| 0.6717 \| 0.0 \| 0.6717 \| \| 0.0412 \| 458.0 \| 7786 \| 0.2413 \| 0.3538 \| 0.7076 \| 0.7076 \| nan \| 0.7076 \| 0.0 \| 0.7076 \| \| 0.0399 \| 459.0 \| 7803 \| 0.2633 \| 0.3588 \| 0.7176 \| 0.7176 \| nan \| 0.7176 \| 0.0 \| 0.7176 \| \| 0.0399 \| 460.0 \| 7820 \| 0.2632 \| 0.3204 \| 0.6409 \| 0.6409 \| nan \| 0.6409 \| 0.0 \| 0.6409 \| \| 0.0399 \| 461.0 \| 7837 \| 0.2530 \| 0.3445 \| 0.6890 \| 0.6890 \| nan \| 0.6890 \| 0.0 \| 0.6890 \| \| 0.0399 \| 462.0 \| 7854 \| 0.2841 \| 0.3305 \| 0.6610 \| 0.6610 \| nan \| 0.6610 \| 0.0 \| 0.6610 \| \| 0.0399 \| 463.0 \| 7871 \| 0.3084 \| 0.3473 \| 0.6945 \| 0.6945 \| nan \| 0.6945 \| 0.0 \| 0.6945 \| \| 0.0399 \| 464.0 \| 7888 \| 0.2717 \| 0.3447 \| 0.6893 \| 0.6893 \| nan \| 0.6893 \| 0.0 \| 0.6893 \| \| 0.0368 \| 465.0 \| 7905 \| 0.3134 \| 0.3144 \| 0.6288 \| 0.6288 \| nan \| 0.6288 \| 0.0 \| 0.6288 \| \| 0.0368 \| 466.0 \| 7922 \| 0.2957 \| 0.3273 \| 0.6546 \| 0.6546 \| nan \| 0.6546 \| 0.0 \| 0.6546 \| \| 0.0368 \| 467.0 \| 7939 \| 0.3295 \| 0.3207 \| 0.6413 \| 0.6413 \| nan \| 0.6413 \| 0.0 \| 0.6413 \| \| 0.0368 \| 468.0 \| 7956 \| 0.2908 \| 0.3230 \| 0.6461 \| 0.6461 \| nan \| 0.6461 \| 0.0 \| 0.6461 \| \| 0.0368 \| 469.0 \| 7973 \| 0.2739 \| 0.3202 \| 0.6405 \| 0.6405 \| nan \| 0.6405 \| 0.0 \| 0.6405 \| \| 0.0368 \| 470.0 \| 7990 \| 0.2764 \| 0.3203 \| 0.6406 \| 0.6406 \| nan \| 0.6406 \| 0.0 \| 0.6406 \| \| 0.0368 \| 471.0 \| 8007 \| 0.3048 \| 0.3294 \| 0.6587 \| 0.6587 \| nan \| 0.6587 \| 0.0 \| 0.6587 \| \| 0.0368 \| 472.0 \| 8024 \| 0.2829 \| 0.3251 \| 0.6502 \| 0.6502 \| nan \| 0.6502 \| 0.0 \| 0.6502 \| \| 0.0368 \| 473.0 \| 8041 \| 0.2717 \| 0.3276 \| 0.6551 \| 0.6551 \| nan \| 0.6551 \| 0.0 \| 0.6551 \| \| 0.0368 \| 474.0 \| 8058 \| 0.2902 \| 0.3323 \| 0.6647 \| 0.6647 \| nan \| 0.6647 \| 0.0 \| 0.6647 \| \| 0.0368 \| 475.0 \| 8075 \| 0.2730 \| 0.3443 \| 0.6886 \| 0.6886 \| nan \| 0.6886 \| 0.0 \| 0.6886 \| \| 0.0368 \| 476.0 \| 8092 \| 0.2629 \| 0.3312 \| 0.6624 \| 0.6624 \| nan \| 0.6624 \| 0.0 \| 0.6624 \| \| 0.0399 \| 477.0 \| 8109 \| 0.2836 \| 0.3352 \| 0.6704 \| 0.6704 \| nan \| 0.6704 \| 0.0 \| 0.6704 \| \| 0.0399 \| 478.0 \| 8126 \| 0.2831 \| 0.3203 \| 0.6405 \| 0.6405 \| nan \| 0.6405 \| 0.0 \| 0.6405 \| \| 0.0399 \| 479.0 \| 8143 \| 0.2949 \| 0.3243 \| 0.6487 \| 0.6487 \| nan \| 0.6487 \| 0.0 \| 0.6487 \| \| 0.0399 \| 480.0 \| 8160 \| 0.2918 \| 0.3236 \| 0.6471 \| 0.6471 \| nan \| 0.6471 \| 0.0 \| 0.6471 \| \| 0.0399 \| 481.0 \| 8177 \| 0.3012 \| 0.3402 \| 0.6805 \| 0.6805 \| nan \| 0.6805 \| 0.0 \| 0.6805 \| \| 0.0399 \| 482.0 \| 8194 \| 0.2733 \| 0.3648 \| 0.7297 \| 0.7297 \| nan \| 0.7297 \| 0.0 \| 0.7297 \| \| 0.0369 \| 483.0 \| 8211 \| 0.2584 \| 0.3431 \| 0.6862 \| 0.6862 \| nan \| 0.6862 \| 0.0 \| 0.6862 \| \| 0.0369 \| 484.0 \| 8228 \| 0.2849 \| 0.3199 \| 0.6397 \| 0.6397 \| nan \| 0.6397 \| 0.0 \| 0.6397 \| \| 0.0369 \| 485.0 \| 8245 \| 0.2702 \| 0.3411 \| 0.6822 \| 0.6822 \| nan \| 0.6822 \| 0.0 \| 0.6822 \| \| 0.0369 \| 486.0 \| 8262 \| 0.2605 \| 0.3625 \| 0.7251 \| 0.7251 \| nan \| 0.7251 \| 0.0 \| 0.7251 \| \| 0.0369 \| 487.0 \| 8279 \| 0.2785 \| 0.3134 \| 0.6269 \| 0.6269 \| nan \| 0.6269 \| 0.0 \| 0.6269 \| \| 0.0369 \| 488.0 \| 8296 \| 0.2773 \| 0.3261 \| 0.6521 \| 0.6521 \| nan \| 0.6521 \| 0.0 \| 0.6521 \| \| 0.0383 \| 489.0 \| 8313 \| 0.2727 \| 0.3387 \| 0.6774 \| 0.6774 \| nan \| 0.6774 \| 0.0 \| 0.6774 \| \| 0.0383 \| 490.0 \| 8330 \| 0.2823 \| 0.3219 \| 0.6438 \| 0.6438 \| nan \| 0.6438 \| 0.0 \| 0.6438 \| \| 0.0383 \| 491.0 \| 8347 \| 0.2764 \| 0.3363 \| 0.6727 \| 0.6727 \| nan \| 0.6727 \| 0.0 \| 0.6727 \| \| 0.0383 \| 492.0 \| 8364 \| 0.2678 \| 0.3299 \| 0.6598 \| 0.6598 \| nan \| 0.6598 \| 0.0 \| 0.6598 \| \| 0.0383 \| 493.0 \| 8381 \| 0.2692 \| 0.3311 \| 0.6621 \| 0.6621 \| nan \| 0.6621 \| 0.0 \| 0.6621 \| \| 0.0383 \| 494.0 \| 8398 \| 0.2727 \| 0.3456 \| 0.6911 \| 0.6911 \| nan \| 0.6911 \| 0.0 \| 0.6911 \| \| 0.0394 \| 495.0 \| 8415 \| 0.2802 \| 0.3402 \| 0.6804 \| 0.6804 \| nan \| 0.6804 \| 0.0 \| 0.6804 \| \| 0.0394 \| 496.0 \| 8432 \| 0.3122 \| 0.3183 \| 0.6367 \| 0.6367 \| nan \| 0.6367 \| 0.0 \| 0.6367 \| \| 0.0394 \| 497.0 \| 8449 \| 0.3404 \| 0.3141 \| 0.6281 \| 0.6281 \| nan \| 0.6281 \| 0.0 \| 0.6281 \| \| 0.0394 \| 498.0 \| 8466 \| 0.2993 \| 0.3081 \| 0.6162 \| 0.6162 \| nan \| 0.6162 \| 0.0 \| 0.6162 \| \| 0.0394 \| 499.0 \| 8483 \| 0.3233 \| 0.3339 \| 0.6679 \| 0.6679 \| nan \| 0.6679 \| 0.0 \| 0.6679 \| \| 0.0367 \| 500.0 \| 8500 \| 0.2986 \| 0.3229 \| 0.6458 \| 0.6458 \| nan \| 0.6458 \| 0.0 \| 0.6458 \| \| 0.0367 \| 501.0 \| 8517 \| 0.3184 \| 0.3021 \| 0.6042 \| 0.6042 \| nan \| 0.6042 \| 0.0 \| 0.6042 \| \| 0.0367 \| 502.0 \| 8534 \| 0.3128 \| 0.3209 \| 0.6419 \| 0.6419 \| nan \| 0.6419 \| 0.0 \| 0.6419 \| \| 0.0367 \| 503.0 \| 8551 \| 0.3056 \| 0.2974 \| 0.5949 \| 0.5949 \| nan \| 0.5949 \| 0.0 \| 0.5949 \| \| 0.0367 \| 504.0 \| 8568 \| 0.3157 \| 0.3028 \| 0.6056 \| 0.6056 \| nan \| 0.6056 \| 0.0 \| 0.6056 \| \| 0.0367 \| 505.0 \| 8585 \| 0.3014 \| 0.3058 \| 0.6115 \| 0.6115 \| nan \| 0.6115 \| 0.0 \| 0.6115 \| \| 0.0391 \| 506.0 \| 8602 \| 0.2977 \| 0.3321 \| 0.6643 \| 0.6643 \| nan \| 0.6643 \| 0.0 \| 0.6643 \| \| 0.0391 \| 507.0 \| 8619 \| 0.2992 \| 0.3053 \| 0.6105 \| 0.6105 \| nan \| 0.6105 \| 0.0 \| 0.6105 \| \| 0.0391 \| 508.0 \| 8636 \| 0.3318 \| 0.3268 \| 0.6536 \| 0.6536 \| nan \| 0.6536 \| 0.0 \| 0.6536 \| \| 0.0391 \| 509.0 \| 8653 \| 0.2701 \| 0.3549 \| 0.7097 \| 0.7097 \| nan \| 0.7097 \| 0.0 \| 0.7097 \| \| 0.0391 \| 510.0 \| 8670 \| 0.2745 \| 0.3606 \| 0.7212 \| 0.7212 \| nan \| 0.7212 \| 0.0 \| 0.7212 \| \| 0.0391 \| 511.0 \| 8687 \| 0.2766 \| 0.3344 \| 0.6687 \| 0.6687 \| nan \| 0.6687 \| 0.0 \| 0.6687 \| \| 0.0386 \| 512.0 \| 8704 \| 0.2634 \| 0.3547 \| 0.7094 \| 0.7094 \| nan \| 0.7094 \| 0.0 \| 0.7094 \| \| 0.0386 \| 513.0 \| 8721 \| 0.2668 \| 0.3444 \| 0.6888 \| 0.6888 \| nan \| 0.6888 \| 0.0 \| 0.6888 \| \| 0.0386 \| 514.0 \| 8738 \| 0.2762 \| 0.3647 \| 0.7295 \| 0.7295 \| nan \| 0.7295 \| 0.0 \| 0.7295 \| \| 0.0386 \| 515.0 \| 8755 \| 0.2736 \| 0.3652 \| 0.7304 \| 0.7304 \| nan \| 0.7304 \| 0.0 \| 0.7304 \| \| 0.0386 \| 516.0 \| 8772 \| 0.2756 \| 0.3618 \| 0.7236 \| 0.7236 \| nan \| 0.7236 \| 0.0 \| 0.7236 \| \| 0.0386 \| 517.0 \| 8789 \| 0.2841 \| 0.3720 \| 0.7440 \| 0.7440 \| nan \| 0.7440 \| 0.0 \| 0.7440 \| \| 0.0338 \| 518.0 \| 8806 \| 0.2861 \| 0.3610 \| 0.7220 \| 0.7220 \| nan \| 0.7220 \| 0.0 \| 0.7220 \| \| 0.0338 \| 519.0 \| 8823 \| 0.2724 \| 0.3430 \| 0.6861 \| 0.6861 \| nan \| 0.6861 \| 0.0 \| 0.6861 \| \| 0.0338 \| 520.0 \| 8840 \| 0.2911 \| 0.3522 \| 0.7044 \| 0.7044 \| nan \| 0.7044 \| 0.0 \| 0.7044 \| \| 0.0338 \| 521.0 \| 8857 \| 0.2740 \| 0.3668 \| 0.7335 \| 0.7335 \| nan \| 0.7335 \| 0.0 \| 0.7335 \| \| 0.0338 \| 522.0 \| 8874 \| 0.2657 \| 0.3511 \| 0.7023 \| 0.7023 \| nan \| 0.7023 \| 0.0 \| 0.7023 \| \| 0.0338 \| 523.0 \| 8891 \| 0.2856 \| 0.3520 \| 0.7041 \| 0.7041 \| nan \| 0.7041 \| 0.0 \| 0.7041 \| \| 0.0369 \| 524.0 \| 8908 \| 0.2662 \| 0.3424 \| 0.6848 \| 0.6848 \| nan \| 0.6848 \| 0.0 \| 0.6848 \| \| 0.0369 \| 525.0 \| 8925 \| 0.2772 \| 0.3398 \| 0.6796 \| 0.6796 \| nan \| 0.6796 \| 0.0 \| 0.6796 \| \| 0.0369 \| 526.0 \| 8942 \| 0.2844 \| 0.3388 \| 0.6776 \| 0.6776 \| nan \| 0.6776 \| 0.0 \| 0.6776 \| \| 0.0369 \| 527.0 \| 8959 \| 0.2786 \| 0.3548 \| 0.7096 \| 0.7096 \| nan \| 0.7096 \| 0.0 \| 0.7096 \| \| 0.0369 \| 528.0 \| 8976 \| 0.2524 \| 0.3508 \| 0.7016 \| 0.7016 \| nan \| 0.7016 \| 0.0 \| 0.7016 \| \| 0.0369 \| 529.0 \| 8993 \| 0.2647 \| 0.3509 \| 0.7017 \| 0.7017 \| nan \| 0.7017 \| 0.0 \| 0.7017 \| \| 0.0361 \| 530.0 \| 9010 \| 0.2829 \| 0.3421 \| 0.6842 \| 0.6842 \| nan \| 0.6842 \| 0.0 \| 0.6842 \| \| 0.0361 \| 531.0 \| 9027 \| 0.2837 \| 0.3338 \| 0.6676 \| 0.6676 \| nan \| 0.6676 \| 0.0 \| 0.6676 \| \| 0.0361 \| 532.0 \| 9044 \| 0.2799 \| 0.3444 \| 0.6888 \| 0.6888 \| nan \| 0.6888 \| 0.0 \| 0.6888 \| \| 0.0361 \| 533.0 \| 9061 \| 0.2871 \| 0.3425 \| 0.6850 \| 0.6850 \| nan \| 0.6850 \| 0.0 \| 0.6850 \| \| 0.0361 \| 534.0 \| 9078 \| 0.2889 \| 0.3486 \| 0.6971 \| 0.6971 \| nan \| 0.6971 \| 0.0 \| 0.6971 \| \| 0.0361 \| 535.0 \| 9095 \| 0.2979 \| 0.3557 \| 0.7115 \| 0.7115 \| nan \| 0.7115 \| 0.0 \| 0.7115 \| \| 0.0367 \| 536.0 \| 9112 \| 0.2985 \| 0.3555 \| 0.7109 \| 0.7109 \| nan \| 0.7109 \| 0.0 \| 0.7109 \| \| 0.0367 \| 537.0 \| 9129 \| 0.2993 \| 0.3417 \| 0.6835 \| 0.6835 \| nan \| 0.6835 \| 0.0 \| 0.6835 \| \| 0.0367 \| 538.0 \| 9146 \| 0.2988 \| 0.3512 \| 0.7023 \| 0.7023 \| nan \| 0.7023 \| 0.0 \| 0.7023 \| \| 0.0367 \| 539.0 \| 9163 \| 0.3023 \| 0.3459 \| 0.6918 \| 0.6918 \| nan \| 0.6918 \| 0.0 \| 0.6918 \| \| 0.0367 \| 540.0 \| 9180 \| 0.2876 \| 0.3139 \| 0.6277 \| 0.6277 \| nan \| 0.6277 \| 0.0 \| 0.6277 \| \| 0.0367 \| 541.0 \| 9197 \| 0.2854 \| 0.3197 \| 0.6394 \| 0.6394 \| nan \| 0.6394 \| 0.0 \| 0.6394 \| \| 0.0393 \| 542.0 \| 9214 \| 0.2806 \| 0.3452 \| 0.6904 \| 0.6904 \| nan \| 0.6904 \| 0.0 \| 0.6904 \| \| 0.0393 \| 543.0 \| 9231 \| 0.2664 \| 0.3571 \| 0.7142 \| 0.7142 \| nan \| 0.7142 \| 0.0 \| 0.7142 \| \| 0.0393 \| 544.0 \| 9248 \| 0.2923 \| 0.3503 \| 0.7006 \| 0.7006 \| nan \| 0.7006 \| 0.0 \| 0.7006 \| \| 0.0393 \| 545.0 \| 9265 \| 0.2719 \| 0.3553 \| 0.7105 \| 0.7105 \| nan \| 0.7105 \| 0.0 \| 0.7105 \| \| 0.0393 \| 546.0 \| 9282 \| 0.2820 \| 0.3617 \| 0.7234 \| 0.7234 \| nan \| 0.7234 \| 0.0 \| 0.7234 \| \| 0.0393 \| 547.0 \| 9299 \| 0.2697 \| 0.3491 \| 0.6982 \| 0.6982 \| nan \| 0.6982 \| 0.0 \| 0.6982 \| \| 0.0354 \| 548.0 \| 9316 \| 0.2908 \| 0.3322 \| 0.6645 \| 0.6645 \| nan \| 0.6645 \| 0.0 \| 0.6645 \| \| 0.0354 \| 549.0 \| 9333 \| 0.2798 \| 0.3340 \| 0.6680 \| 0.6680 \| nan \| 0.6680 \| 0.0 \| 0.6680 \| \| 0.0354 \| 550.0 \| 9350 \| 0.2844 \| 0.3518 \| 0.7037 \| 0.7037 \| nan \| 0.7037 \| 0.0 \| 0.7037 \| \| 0.0354 \| 551.0 \| 9367 \| 0.2983 \| 0.3524 \| 0.7047 \| 0.7047 \| nan \| 0.7047 \| 0.0 \| 0.7047 \| \| 0.0354 \| 552.0 \| 9384 \| 0.2837 \| 0.3202 \| 0.6405 \| 0.6405 \| nan \| 0.6405 \| 0.0 \| 0.6405 \| \| 0.0376 \| 553.0 \| 9401 \| 0.2840 \| 0.3301 \| 0.6603 \| 0.6603 \| nan \| 0.6603 \| 0.0 \| 0.6603 \| \| 0.0376 \| 554.0 \| 9418 \| 0.2836 \| 0.3496 \| 0.6993 \| 0.6993 \| nan \| 0.6993 \| 0.0 \| 0.6993 \| \| 0.0376 \| 555.0 \| 9435 \| 0.2829 \| 0.3432 \| 0.6864 \| 0.6864 \| nan \| 0.6864 \| 0.0 \| 0.6864 \| \| 0.0376 \| 556.0 \| 9452 \| 0.2956 \| 0.3330 \| 0.6659 \| 0.6659 \| nan \| 0.6659 \| 0.0 \| 0.6659 \| \| 0.0376 \| 557.0 \| 9469 \| 0.2928 \| 0.3373 \| 0.6745 \| 0.6745 \| nan \| 0.6745 \| 0.0 \| 0.6745 \| \| 0.0376 \| 558.0 \| 9486 \| 0.2903 \| 0.3444 \| 0.6888 \| 0.6888 \| nan \| 0.6888 \| 0.0 \| 0.6888 \| \| 0.0409 \| 559.0 \| 9503 \| 0.3086 \| 0.3323 \| 0.6646 \| 0.6646 \| nan \| 0.6646 \| 0.0 \| 0.6646 \| \| 0.0409 \| 560.0 \| 9520 \| 0.3257 \| 0.3188 \| 0.6377 \| 0.6377 \| nan \| 0.6377 \| 0.0 \| 0.6377 \| \| 0.0409 \| 561.0 \| 9537 \| 0.3098 \| 0.3307 \| 0.6614 \| 0.6614 \| nan \| 0.6614 \| 0.0 \| 0.6614 \| \| 0.0409 \| 562.0 \| 9554 \| 0.2905 \| 0.3315 \| 0.6631 \| 0.6631 \| nan \| 0.6631 \| 0.0 \| 0.6631 \| \| 0.0409 \| 563.0 \| 9571 \| 0.2886 \| 0.3368 \| 0.6736 \| 0.6736 \| nan \| 0.6736 \| 0.0 \| 0.6736 \| \| 0.0409 \| 564.0 \| 9588 \| 0.2952 \| 0.3354 \| 0.6709 \| 0.6709 \| nan \| 0.6709 \| 0.0 \| 0.6709 \| \| 0.0336 \| 565.0 \| 9605 \| 0.3089 \| 0.3303 \| 0.6605 \| 0.6605 \| nan \| 0.6605 \| 0.0 \| 0.6605 \| \| 0.0336 \| 566.0 \| 9622 \| 0.3004 \| 0.3305 \| 0.6611 \| 0.6611 \| nan \| 0.6611 \| 0.0 \| 0.6611 \| \| 0.0336 \| 567.0 \| 9639 \| 0.3005 \| 0.3417 \| 0.6835 \| 0.6835 \| nan \| 0.6835 \| 0.0 \| 0.6835 \| \| 0.0336 \| 568.0 \| 9656 \| 0.2944 \| 0.3374 \| 0.6748 \| 0.6748 \| nan \| 0.6748 \| 0.0 \| 0.6748 \| \| 0.0336 \| 569.0 \| 9673 \| 0.2885 \| 0.3375 \| 0.6750 \| 0.6750 \| nan \| 0.6750 \| 0.0 \| 0.6750 \| \| 0.0336 \| 570.0 \| 9690 \| 0.2940 \| 0.3365 \| 0.6731 \| 0.6731 \| nan \| 0.6731 \| 0.0 \| 0.6731 \| \| 0.0379 \| 571.0 \| 9707 \| 0.2970 \| 0.3249 \| 0.6498 \| 0.6498 \| nan \| 0.6498 \| 0.0 \| 0.6498 \| \| 0.0379 \| 572.0 \| 9724 \| 0.3117 \| 0.3362 \| 0.6723 \| 0.6723 \| nan \| 0.6723 \| 0.0 \| 0.6723 \| \| 0.0379 \| 573.0 \| 9741 \| 0.3093 \| 0.3406 \| 0.6813 \| 0.6813 \| nan \| 0.6813 \| 0.0 \| 0.6813 \| \| 0.0379 \| 574.0 \| 9758 \| 0.3059 \| 0.3450 \| 0.6901 \| 0.6901 \| nan \| 0.6901 \| 0.0 \| 0.6901 \| \| 0.0379 \| 575.0 \| 9775 \| 0.2997 \| 0.3425 \| 0.6849 \| 0.6849 \| nan \| 0.6849 \| 0.0 \| 0.6849 \| \| 0.0379 \| 576.0 \| 9792 \| 0.3000 \| 0.3420 \| 0.6840 \| 0.6840 \| nan \| 0.6840 \| 0.0 \| 0.6840 \| \| 0.0355 \| 577.0 \| 9809 \| 0.3034 \| 0.3334 \| 0.6669 \| 0.6669 \| nan \| 0.6669 \| 0.0 \| 0.6669 \| \| 0.0355 \| 578.0 \| 9826 \| 0.3058 \| 0.3415 \| 0.6831 \| 0.6831 \| nan \| 0.6831 \| 0.0 \| 0.6831 \| \| 0.0355 \| 579.0 \| 9843 \| 0.2928 \| 0.3354 \| 0.6708 \| 0.6708 \| nan \| 0.6708 \| 0.0 \| 0.6708 \| \| 0.0355 \| 580.0 \| 9860 \| 0.2982 \| 0.3291 \| 0.6582 \| 0.6582 \| nan \| 0.6582 \| 0.0 \| 0.6582 \| \| 0.0355 \| 581.0 \| 9877 \| 0.3069 \| 0.3263 \| 0.6526 \| 0.6526 \| nan \| 0.6526 \| 0.0 \| 0.6526 \| \| 0.0355 \| 582.0 \| 9894 \| 0.3015 \| 0.3348 \| 0.6696 \| 0.6696 \| nan \| 0.6696 \| 0.0 \| 0.6696 \| \| 0.0339 \| 583.0 \| 9911 \| 0.2990 \| 0.3225 \| 0.6450 \| 0.6450 \| nan \| 0.6450 \| 0.0 \| 0.6450 \| \| 0.0339 \| 584.0 \| 9928 \| 0.3005 \| 0.3259 \| 0.6518 \| 0.6518 \| nan \| 0.6518 \| 0.0 \| 0.6518 \| \| 0.0339 \| 585.0 \| 9945 \| 0.3164 \| 0.3330 \| 0.6661 \| 0.6661 \| nan \| 0.6661 \| 0.0 \| 0.6661 \| \| 0.0339 \| 586.0 \| 9962 \| 0.3049 \| 0.3255 \| 0.6510 \| 0.6510 \| nan \| 0.6510 \| 0.0 \| 0.6510 \| \| 0.0339 \| 587.0 \| 9979 \| 0.3079 \| 0.3267 \| 0.6534 \| 0.6534 \| nan \| 0.6534 \| 0.0 \| 0.6534 \| \| 0.0339 \| 588.0 \| 9996 \| 0.3019 \| 0.3333 \| 0.6667 \| 0.6667 \| nan \| 0.6667 \| 0.0 \| 0.6667 \| \| 0.0328 \| 588.24 \| 10000 \| 0.3028 \| 0.3319 \| 0.6638 \| 0.6638 \| nan \| 0.6638 \| 0.0 \| 0.6638 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.15.1	[ "background", "biofilm" ]
blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_rojo dataset. It achieves the following results on the evaluation set: - Loss: 1.2098 - Mean Iou: 0.3648 - Mean Accuracy: 0.6821 - Overall Accuracy: 0.6947 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.7354 - Accuracy Fallo malla: 0.6052 - Accuracy Fallo adhesivo: 0.9884 - Accuracy Fallo burbuja: 0.3995 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.5920 - Iou Fallo malla: 0.5774 - Iou Fallo adhesivo: 0.2950 - Iou Fallo burbuja: 0.3598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.5778 \| 1.0 \| 114 \| 0.8590 \| 0.2588 \| 0.5626 \| 0.6271 \| nan \| 0.3415 \| 0.9120 \| 0.9748 \| 0.0221 \| 0.0 \| 0.3339 \| 0.6211 \| 0.3168 \| 0.0220 \| \| 0.3326 \| 2.0 \| 228 \| 0.6845 \| 0.3570 \| 0.6755 \| 0.7131 \| nan \| 0.5232 \| 0.8953 \| 0.9911 \| 0.2921 \| 0.0 \| 0.5003 \| 0.6924 \| 0.3417 \| 0.2503 \| \| 0.2636 \| 3.0 \| 342 \| 0.6662 \| 0.3896 \| 0.7107 \| 0.7344 \| nan \| 0.7666 \| 0.6622 \| 0.9838 \| 0.4304 \| 0.0 \| 0.6088 \| 0.6188 \| 0.4014 \| 0.3191 \| \| 0.2505 \| 4.0 \| 456 \| 0.7666 \| 0.3732 \| 0.7408 \| 0.6807 \| nan \| 0.4141 \| 0.9417 \| 0.9778 \| 0.6297 \| 0.0 \| 0.4065 \| 0.6276 \| 0.4337 \| 0.3980 \| \| 0.2306 \| 5.0 \| 570 \| 0.4680 \| 0.4690 \| 0.7389 \| 0.8099 \| nan \| 0.7461 \| 0.8649 \| 0.9742 \| 0.3705 \| 0.0 \| 0.6711 \| 0.7095 \| 0.6349 \| 0.3294 \| \| 0.1998 \| 6.0 \| 684 \| 0.5711 \| 0.4449 \| 0.7494 \| 0.7824 \| nan \| 0.8528 \| 0.6732 \| 0.9865 \| 0.4850 \| 0.0 \| 0.6781 \| 0.6338 \| 0.5320 \| 0.3807 \| \| 0.2062 \| 7.0 \| 798 \| 0.6403 \| 0.4070 \| 0.7437 \| 0.7283 \| nan \| 0.5736 \| 0.8683 \| 0.9881 \| 0.5447 \| 0.0 \| 0.5300 \| 0.6452 \| 0.4613 \| 0.3987 \| \| 0.182 \| 8.0 \| 912 \| 0.5934 \| 0.4344 \| 0.7309 \| 0.7770 \| nan \| 0.8171 \| 0.7036 \| 0.9840 \| 0.4190 \| 0.0 \| 0.6640 \| 0.6485 \| 0.4916 \| 0.3681 \| \| 0.178 \| 9.0 \| 1026 \| 0.7158 \| 0.3811 \| 0.6915 \| 0.7313 \| nan \| 0.7292 \| 0.6984 \| 0.9913 \| 0.3472 \| 0.0 \| 0.6148 \| 0.6404 \| 0.3348 \| 0.3153 \| \| 0.1568 \| 10.0 \| 1140 \| 0.5892 \| 0.4169 \| 0.6970 \| 0.7873 \| nan \| 0.8088 \| 0.7398 \| 0.9855 \| 0.2538 \| 0.0 \| 0.6770 \| 0.6664 \| 0.5004 \| 0.2407 \| \| 0.1576 \| 11.0 \| 1254 \| 0.6419 \| 0.4228 \| 0.7177 \| 0.7652 \| nan \| 0.7970 \| 0.7001 \| 0.9805 \| 0.3931 \| 0.0 \| 0.6509 \| 0.6318 \| 0.4701 \| 0.3614 \| \| 0.1667 \| 12.0 \| 1368 \| 0.6563 \| 0.4060 \| 0.7369 \| 0.7605 \| nan \| 0.7409 \| 0.7517 \| 0.9871 \| 0.4681 \| 0.0 \| 0.6326 \| 0.6731 \| 0.4103 \| 0.3139 \| \| 0.1436 \| 13.0 \| 1482 \| 0.9148 \| 0.3864 \| 0.7079 \| 0.7187 \| nan \| 0.6666 \| 0.7400 \| 0.9900 \| 0.4352 \| 0.0 \| 0.6025 \| 0.6632 \| 0.2829 \| 0.3835 \| \| 0.1469 \| 14.0 \| 1596 \| 0.6680 \| 0.4166 \| 0.7216 \| 0.7689 \| nan \| 0.7843 \| 0.7225 \| 0.9861 \| 0.3936 \| 0.0 \| 0.6703 \| 0.6608 \| 0.3946 \| 0.3571 \| \| 0.1288 \| 15.0 \| 1710 \| 0.8170 \| 0.3765 \| 0.6849 \| 0.7164 \| nan \| 0.8269 \| 0.5509 \| 0.9859 \| 0.3759 \| 0.0 \| 0.6242 \| 0.5368 \| 0.3815 \| 0.3398 \| \| 0.1243 \| 16.0 \| 1824 \| 0.8197 \| 0.4034 \| 0.7169 \| 0.7375 \| nan \| 0.8456 \| 0.5776 \| 0.9842 \| 0.4602 \| 0.0 \| 0.6517 \| 0.5582 \| 0.4078 \| 0.3991 \| \| 0.1208 \| 17.0 \| 1938 \| 0.7927 \| 0.3848 \| 0.6774 \| 0.7295 \| nan \| 0.8592 \| 0.5460 \| 0.9810 \| 0.3233 \| 0.0 \| 0.6359 \| 0.5256 \| 0.4647 \| 0.2978 \| \| 0.115 \| 18.0 \| 2052 \| 1.1226 \| 0.3376 \| 0.6484 \| 0.6727 \| nan \| 0.7053 \| 0.5900 \| 0.9905 \| 0.3079 \| 0.0 \| 0.5659 \| 0.5688 \| 0.2673 \| 0.2860 \| \| 0.1138 \| 19.0 \| 2166 \| 0.8244 \| 0.4055 \| 0.7099 \| 0.7446 \| nan \| 0.8200 \| 0.6248 \| 0.9833 \| 0.4115 \| 0.0 \| 0.6364 \| 0.5964 \| 0.4287 \| 0.3659 \| \| 0.1144 \| 20.0 \| 2280 \| 0.5964 \| 0.4493 \| 0.7179 \| 0.8034 \| nan \| 0.8594 \| 0.7188 \| 0.9808 \| 0.3127 \| 0.0 \| 0.6995 \| 0.6608 \| 0.5990 \| 0.2873 \| \| 0.108 \| 21.0 \| 2394 \| 0.6545 \| 0.4418 \| 0.7348 \| 0.7902 \| nan \| 0.8263 \| 0.7241 \| 0.9835 \| 0.4053 \| 0.0 \| 0.6832 \| 0.6653 \| 0.5023 \| 0.3582 \| \| 0.109 \| 22.0 \| 2508 \| 0.9552 \| 0.3775 \| 0.6990 \| 0.7058 \| nan \| 0.6835 \| 0.6906 \| 0.9894 \| 0.4325 \| 0.0 \| 0.5907 \| 0.6391 \| 0.2756 \| 0.3819 \| \| 0.0987 \| 23.0 \| 2622 \| 0.7971 \| 0.3974 \| 0.7133 \| 0.7453 \| nan \| 0.7451 \| 0.7124 \| 0.9871 \| 0.4084 \| 0.0 \| 0.6281 \| 0.6560 \| 0.3577 \| 0.3452 \| \| 0.0977 \| 24.0 \| 2736 \| 0.9783 \| 0.3718 \| 0.6984 \| 0.7001 \| nan \| 0.5950 \| 0.7793 \| 0.9916 \| 0.4276 \| 0.0 \| 0.5491 \| 0.6786 \| 0.2620 \| 0.3692 \| \| 0.0954 \| 25.0 \| 2850 \| 0.9562 \| 0.3856 \| 0.6981 \| 0.7352 \| nan \| 0.7102 \| 0.7294 \| 0.9904 \| 0.3623 \| 0.0 \| 0.6355 \| 0.6603 \| 0.2988 \| 0.3332 \| \| 0.0928 \| 26.0 \| 2964 \| 0.9185 \| 0.3787 \| 0.6870 \| 0.7355 \| nan \| 0.7815 \| 0.6491 \| 0.9847 \| 0.3327 \| 0.0 \| 0.6569 \| 0.6184 \| 0.3151 \| 0.3028 \| \| 0.0918 \| 27.0 \| 3078 \| 0.9617 \| 0.3845 \| 0.6916 \| 0.7175 \| nan \| 0.8211 \| 0.5605 \| 0.9809 \| 0.4037 \| 0.0 \| 0.6123 \| 0.5462 \| 0.3994 \| 0.3648 \| \| 0.0801 \| 28.0 \| 3192 \| 1.1167 \| 0.3672 \| 0.6811 \| 0.7091 \| nan \| 0.7352 \| 0.6393 \| 0.9927 \| 0.3570 \| 0.0 \| 0.6151 \| 0.6141 \| 0.2816 \| 0.3250 \| \| 0.0852 \| 29.0 \| 3306 \| 0.8549 \| 0.4217 \| 0.7108 \| 0.7596 \| nan \| 0.8684 \| 0.6040 \| 0.9848 \| 0.3862 \| 0.0 \| 0.6576 \| 0.5808 \| 0.5146 \| 0.3553 \| \| 0.0816 \| 30.0 \| 3420 \| 0.9536 \| 0.3902 \| 0.7034 \| 0.7366 \| nan \| 0.7752 \| 0.6573 \| 0.9885 \| 0.3926 \| 0.0 \| 0.6415 \| 0.6301 \| 0.3274 \| 0.3517 \| \| 0.0876 \| 31.0 \| 3534 \| 1.0597 \| 0.3873 \| 0.7065 \| 0.7158 \| nan \| 0.7490 \| 0.6374 \| 0.9920 \| 0.4475 \| 0.0 \| 0.6117 \| 0.6160 \| 0.3051 \| 0.4035 \| \| 0.0811 \| 32.0 \| 3648 \| 0.8829 \| 0.4038 \| 0.7077 \| 0.7569 \| nan \| 0.7949 \| 0.6829 \| 0.9860 \| 0.3669 \| 0.0 \| 0.6442 \| 0.6498 \| 0.3943 \| 0.3304 \| \| 0.0789 \| 33.0 \| 3762 \| 0.9615 \| 0.4002 \| 0.7104 \| 0.7436 \| nan \| 0.7890 \| 0.6575 \| 0.9884 \| 0.4066 \| 0.0 \| 0.6344 \| 0.6308 \| 0.3702 \| 0.3658 \| \| 0.0752 \| 34.0 \| 3876 \| 0.7799 \| 0.4297 \| 0.7280 \| 0.7806 \| nan \| 0.8279 \| 0.6991 \| 0.9873 \| 0.3975 \| 0.0 \| 0.6787 \| 0.6605 \| 0.4458 \| 0.3634 \| \| 0.0731 \| 35.0 \| 3990 \| 0.9285 \| 0.4061 \| 0.7025 \| 0.7531 \| nan \| 0.8595 \| 0.5987 \| 0.9898 \| 0.3619 \| 0.0 \| 0.6579 \| 0.5797 \| 0.4600 \| 0.3330 \| \| 0.0752 \| 36.0 \| 4104 \| 0.9218 \| 0.4112 \| 0.7276 \| 0.7463 \| nan \| 0.7632 \| 0.6926 \| 0.9880 \| 0.4667 \| 0.0 \| 0.6393 \| 0.6507 \| 0.3462 \| 0.4200 \| \| 0.0701 \| 37.0 \| 4218 \| 0.8808 \| 0.4105 \| 0.7184 \| 0.7562 \| nan \| 0.8090 \| 0.6635 \| 0.9893 \| 0.4119 \| 0.0 \| 0.6569 \| 0.6342 \| 0.3876 \| 0.3740 \| \| 0.0717 \| 38.0 \| 4332 \| 1.1090 \| 0.3748 \| 0.6881 \| 0.7166 \| nan \| 0.7554 \| 0.6334 \| 0.9905 \| 0.3729 \| 0.0 \| 0.6272 \| 0.6069 \| 0.2969 \| 0.3433 \| \| 0.0716 \| 39.0 \| 4446 \| 0.9456 \| 0.4018 \| 0.7064 \| 0.7528 \| nan \| 0.8217 \| 0.6418 \| 0.9872 \| 0.3747 \| 0.0 \| 0.6638 \| 0.6131 \| 0.3863 \| 0.3456 \| \| 0.069 \| 40.0 \| 4560 \| 0.8462 \| 0.4157 \| 0.7038 \| 0.7697 \| nan \| 0.8656 \| 0.6316 \| 0.9856 \| 0.3324 \| 0.0 \| 0.6750 \| 0.6041 \| 0.4917 \| 0.3078 \| \| 0.07 \| 41.0 \| 4674 \| 0.9715 \| 0.3843 \| 0.6886 \| 0.7393 \| nan \| 0.8006 \| 0.6353 \| 0.9896 \| 0.3289 \| 0.0 \| 0.6420 \| 0.6104 \| 0.3633 \| 0.3056 \| \| 0.0649 \| 42.0 \| 4788 \| 0.9114 \| 0.3997 \| 0.7066 \| 0.7592 \| nan \| 0.7682 \| 0.7185 \| 0.9917 \| 0.3478 \| 0.0 \| 0.6613 \| 0.6728 \| 0.3449 \| 0.3196 \| \| 0.0665 \| 43.0 \| 4902 \| 1.1847 \| 0.3662 \| 0.6812 \| 0.6981 \| nan \| 0.7131 \| 0.6389 \| 0.9912 \| 0.3817 \| 0.0 \| 0.5853 \| 0.6122 \| 0.2832 \| 0.3504 \| \| 0.0646 \| 44.0 \| 5016 \| 1.1242 \| 0.3744 \| 0.6906 \| 0.7086 \| nan \| 0.6930 \| 0.6870 \| 0.9891 \| 0.3932 \| 0.0 \| 0.5902 \| 0.6495 \| 0.2770 \| 0.3555 \| \| 0.0662 \| 45.0 \| 5130 \| 1.1017 \| 0.3605 \| 0.6735 \| 0.7023 \| nan \| 0.7333 \| 0.6261 \| 0.9906 \| 0.3439 \| 0.0 \| 0.5997 \| 0.5996 \| 0.2906 \| 0.3126 \| \| 0.0644 \| 46.0 \| 5244 \| 1.2989 \| 0.3470 \| 0.6600 \| 0.6735 \| nan \| 0.6567 \| 0.6473 \| 0.9917 \| 0.3445 \| 0.0 \| 0.5607 \| 0.6182 \| 0.2377 \| 0.3185 \| \| 0.0595 \| 47.0 \| 5358 \| 1.0764 \| 0.3833 \| 0.6982 \| 0.7241 \| nan \| 0.7650 \| 0.6389 \| 0.9932 \| 0.3957 \| 0.0 \| 0.6345 \| 0.6134 \| 0.3071 \| 0.3618 \| \| 0.0603 \| 48.0 \| 5472 \| 1.0871 \| 0.3692 \| 0.6813 \| 0.7128 \| nan \| 0.7153 \| 0.6718 \| 0.9884 \| 0.3497 \| 0.0 \| 0.6079 \| 0.6388 \| 0.2797 \| 0.3197 \| \| 0.0591 \| 49.0 \| 5586 \| 1.1054 \| 0.3800 \| 0.6956 \| 0.7171 \| nan \| 0.7103 \| 0.6866 \| 0.9892 \| 0.3963 \| 0.0 \| 0.6116 \| 0.6458 \| 0.2816 \| 0.3609 \| \| 0.0612 \| 50.0 \| 5700 \| 1.1061 \| 0.3652 \| 0.6768 \| 0.7087 \| nan \| 0.7394 \| 0.6340 \| 0.9903 \| 0.3435 \| 0.0 \| 0.6027 \| 0.6074 \| 0.3009 \| 0.3147 \| \| 0.0609 \| 51.0 \| 5814 \| 0.9938 \| 0.3742 \| 0.6850 \| 0.7206 \| nan \| 0.7555 \| 0.6433 \| 0.9890 \| 0.3523 \| 0.0 \| 0.6210 \| 0.6121 \| 0.3143 \| 0.3235 \| \| 0.058 \| 52.0 \| 5928 \| 1.0391 \| 0.3745 \| 0.6836 \| 0.7248 \| nan \| 0.7691 \| 0.6374 \| 0.9901 \| 0.3379 \| 0.0 \| 0.6275 \| 0.6082 \| 0.3257 \| 0.3109 \| \| 0.0559 \| 53.0 \| 6042 \| 0.9916 \| 0.3922 \| 0.7033 \| 0.7373 \| nan \| 0.8044 \| 0.6249 \| 0.9902 \| 0.3938 \| 0.0 \| 0.6429 \| 0.6003 \| 0.3644 \| 0.3537 \| \| 0.0572 \| 54.0 \| 6156 \| 1.0124 \| 0.3801 \| 0.6907 \| 0.7262 \| nan \| 0.7721 \| 0.6371 \| 0.9885 \| 0.3650 \| 0.0 \| 0.6284 \| 0.6052 \| 0.3326 \| 0.3341 \| \| 0.0558 \| 55.0 \| 6270 \| 1.0856 \| 0.3692 \| 0.6823 \| 0.7120 \| nan \| 0.7232 \| 0.6604 \| 0.9894 \| 0.3565 \| 0.0 \| 0.6094 \| 0.6255 \| 0.2864 \| 0.3246 \| \| 0.058 \| 56.0 \| 6384 \| 1.0581 \| 0.3837 \| 0.6998 \| 0.7212 \| nan \| 0.7353 \| 0.6668 \| 0.9910 \| 0.4062 \| 0.0 \| 0.6126 \| 0.6269 \| 0.3125 \| 0.3666 \| \| 0.0518 \| 57.0 \| 6498 \| 1.0176 \| 0.3933 \| 0.7060 \| 0.7362 \| nan \| 0.7857 \| 0.6440 \| 0.9884 \| 0.4060 \| 0.0 \| 0.6395 \| 0.6127 \| 0.3489 \| 0.3655 \| \| 0.0537 \| 58.0 \| 6612 \| 1.2001 \| 0.3676 \| 0.6853 \| 0.6947 \| nan \| 0.7737 \| 0.5607 \| 0.9884 \| 0.4184 \| 0.0 \| 0.6003 \| 0.5391 \| 0.3221 \| 0.3764 \| \| 0.0552 \| 59.0 \| 6726 \| 0.9751 \| 0.3940 \| 0.7068 \| 0.7314 \| nan \| 0.8019 \| 0.6139 \| 0.9870 \| 0.4244 \| 0.0 \| 0.6353 \| 0.5871 \| 0.3662 \| 0.3816 \| \| 0.0538 \| 60.0 \| 6840 \| 1.0382 \| 0.3782 \| 0.6909 \| 0.7203 \| nan \| 0.7216 \| 0.6813 \| 0.9895 \| 0.3714 \| 0.0 \| 0.6093 \| 0.6389 \| 0.3011 \| 0.3418 \| \| 0.0528 \| 61.0 \| 6954 \| 1.1785 \| 0.3662 \| 0.6819 \| 0.7019 \| nan \| 0.7278 \| 0.6310 \| 0.9904 \| 0.3784 \| 0.0 \| 0.5966 \| 0.6013 \| 0.2914 \| 0.3419 \| \| 0.0531 \| 62.0 \| 7068 \| 1.1054 \| 0.3685 \| 0.6852 \| 0.7026 \| nan \| 0.7290 \| 0.6310 \| 0.9899 \| 0.3911 \| 0.0 \| 0.5969 \| 0.5981 \| 0.2961 \| 0.3514 \| \| 0.0522 \| 63.0 \| 7182 \| 1.1271 \| 0.3717 \| 0.6871 \| 0.7094 \| nan \| 0.7268 \| 0.6496 \| 0.9906 \| 0.3816 \| 0.0 \| 0.6069 \| 0.6148 \| 0.2905 \| 0.3460 \| \| 0.0507 \| 64.0 \| 7296 \| 1.0440 \| 0.3734 \| 0.6825 \| 0.7242 \| nan \| 0.7678 \| 0.6380 \| 0.9884 \| 0.3359 \| 0.0 \| 0.6279 \| 0.6043 \| 0.3272 \| 0.3076 \| \| 0.0519 \| 65.0 \| 7410 \| 1.1191 \| 0.3727 \| 0.6884 \| 0.7102 \| nan \| 0.7264 \| 0.6517 \| 0.9911 \| 0.3843 \| 0.0 \| 0.6028 \| 0.6156 \| 0.2978 \| 0.3472 \| \| 0.0502 \| 66.0 \| 7524 \| 1.0089 \| 0.3917 \| 0.7036 \| 0.7408 \| nan \| 0.7555 \| 0.6898 \| 0.9896 \| 0.3794 \| 0.0 \| 0.6413 \| 0.6472 \| 0.3261 \| 0.3437 \| \| 0.051 \| 67.0 \| 7638 \| 1.2112 \| 0.3672 \| 0.6806 \| 0.7083 \| nan \| 0.7352 \| 0.6378 \| 0.9899 \| 0.3593 \| 0.0 \| 0.6078 \| 0.6085 \| 0.2918 \| 0.3279 \| \| 0.0508 \| 68.0 \| 7752 \| 1.1584 \| 0.3702 \| 0.6860 \| 0.7052 \| nan \| 0.7202 \| 0.6477 \| 0.9888 \| 0.3875 \| 0.0 \| 0.5956 \| 0.6155 \| 0.2902 \| 0.3495 \| \| 0.048 \| 69.0 \| 7866 \| 1.1363 \| 0.3773 \| 0.6922 \| 0.7165 \| nan \| 0.7297 \| 0.6628 \| 0.9895 \| 0.3865 \| 0.0 \| 0.6158 \| 0.6289 \| 0.2901 \| 0.3518 \| \| 0.0483 \| 70.0 \| 7980 \| 1.1489 \| 0.3749 \| 0.6916 \| 0.7103 \| nan \| 0.7398 \| 0.6367 \| 0.9889 \| 0.4011 \| 0.0 \| 0.6074 \| 0.6080 \| 0.2994 \| 0.3598 \| \| 0.0495 \| 71.0 \| 8094 \| 1.1470 \| 0.3774 \| 0.6943 \| 0.7102 \| nan \| 0.7454 \| 0.6295 \| 0.9891 \| 0.4131 \| 0.0 \| 0.6059 \| 0.6032 \| 0.3053 \| 0.3724 \| \| 0.0472 \| 72.0 \| 8208 \| 1.2749 \| 0.3597 \| 0.6782 \| 0.6864 \| nan \| 0.7291 \| 0.5930 \| 0.9891 \| 0.4017 \| 0.0 \| 0.5899 \| 0.5704 \| 0.2771 \| 0.3612 \| \| 0.0486 \| 73.0 \| 8322 \| 1.1217 \| 0.3773 \| 0.6946 \| 0.7117 \| nan \| 0.7549 \| 0.6224 \| 0.9882 \| 0.4128 \| 0.0 \| 0.6094 \| 0.5946 \| 0.3150 \| 0.3678 \| \| 0.051 \| 74.0 \| 8436 \| 1.1895 \| 0.3724 \| 0.6889 \| 0.7069 \| nan \| 0.7432 \| 0.6247 \| 0.9888 \| 0.3990 \| 0.0 \| 0.6052 \| 0.5959 \| 0.3021 \| 0.3590 \| \| 0.0472 \| 75.0 \| 8550 \| 1.2084 \| 0.3677 \| 0.6847 \| 0.7009 \| nan \| 0.7179 \| 0.6399 \| 0.9905 \| 0.3904 \| 0.0 \| 0.5979 \| 0.6078 \| 0.2808 \| 0.3522 \| \| 0.0481 \| 76.0 \| 8664 \| 1.1778 \| 0.3688 \| 0.6841 \| 0.7049 \| nan \| 0.7395 \| 0.6244 \| 0.9899 \| 0.3824 \| 0.0 \| 0.6024 \| 0.5950 \| 0.2996 \| 0.3469 \| \| 0.0462 \| 77.0 \| 8778 \| 1.2409 \| 0.3693 \| 0.6863 \| 0.7015 \| nan \| 0.7278 \| 0.6297 \| 0.9900 \| 0.3975 \| 0.0 \| 0.5964 \| 0.5990 \| 0.2918 \| 0.3593 \| \| 0.0464 \| 78.0 \| 8892 \| 1.2724 \| 0.3606 \| 0.6792 \| 0.6877 \| nan \| 0.7119 \| 0.6158 \| 0.9905 \| 0.3986 \| 0.0 \| 0.5825 \| 0.5857 \| 0.2770 \| 0.3578 \| \| 0.0477 \| 79.0 \| 9006 \| 1.2107 \| 0.3629 \| 0.6797 \| 0.6936 \| nan \| 0.7322 \| 0.6063 \| 0.9898 \| 0.3905 \| 0.0 \| 0.5928 \| 0.5791 \| 0.2889 \| 0.3540 \| \| 0.0452 \| 80.0 \| 9120 \| 1.1745 \| 0.3721 \| 0.6889 \| 0.7059 \| nan \| 0.7548 \| 0.6087 \| 0.9899 \| 0.4022 \| 0.0 \| 0.6080 \| 0.5820 \| 0.3085 \| 0.3620 \| \| 0.0447 \| 81.0 \| 9234 \| 1.2787 \| 0.3599 \| 0.6776 \| 0.6876 \| nan \| 0.7199 \| 0.6063 \| 0.9902 \| 0.3938 \| 0.0 \| 0.5857 \| 0.5788 \| 0.2786 \| 0.3566 \| \| 0.0481 \| 82.0 \| 9348 \| 1.2049 \| 0.3658 \| 0.6836 \| 0.6947 \| nan \| 0.7515 \| 0.5865 \| 0.9887 \| 0.4078 \| 0.0 \| 0.5956 \| 0.5627 \| 0.3044 \| 0.3660 \| \| 0.0444 \| 83.0 \| 9462 \| 1.1427 \| 0.3746 \| 0.6930 \| 0.7051 \| nan \| 0.7520 \| 0.6100 \| 0.9883 \| 0.4215 \| 0.0 \| 0.6042 \| 0.5824 \| 0.3100 \| 0.3763 \| \| 0.0481 \| 84.0 \| 9576 \| 1.1876 \| 0.3669 \| 0.6848 \| 0.6968 \| nan \| 0.7358 \| 0.6094 \| 0.9895 \| 0.4046 \| 0.0 \| 0.5944 \| 0.5818 \| 0.2947 \| 0.3636 \| \| 0.046 \| 85.0 \| 9690 \| 1.2264 \| 0.3628 \| 0.6799 \| 0.6928 \| nan \| 0.7348 \| 0.6015 \| 0.9885 \| 0.3948 \| 0.0 \| 0.5906 \| 0.5746 \| 0.2930 \| 0.3560 \| \| 0.0472 \| 86.0 \| 9804 \| 1.2377 \| 0.3659 \| 0.6828 \| 0.6967 \| nan \| 0.7287 \| 0.6176 \| 0.9890 \| 0.3959 \| 0.0 \| 0.5926 \| 0.5876 \| 0.2913 \| 0.3577 \| \| 0.0465 \| 87.0 \| 9918 \| 1.2037 \| 0.3644 \| 0.6841 \| 0.6903 \| nan \| 0.7176 \| 0.6150 \| 0.9893 \| 0.4146 \| 0.0 \| 0.5859 \| 0.5856 \| 0.2808 \| 0.3697 \| \| 0.0475 \| 87.72 \| 10000 \| 1.2098 \| 0.3648 \| 0.6821 \| 0.6947 \| nan \| 0.7354 \| 0.6052 \| 0.9884 \| 0.3995 \| 0.0 \| 0.5920 \| 0.5774 \| 0.2950 \| 0.3598 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3	[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]
blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_negro-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-4ss1st3r_s3gs3m_24Jan_negro-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_negro dataset. It achieves the following results on the evaluation set: - Loss: 0.2142 - Mean Iou: 0.5724 - Mean Accuracy: 0.7571 - Overall Accuracy: 0.9468 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9826 - Accuracy Fallo malla: 0.7246 - Accuracy Fallo adhesivo: 0.9679 - Accuracy Fallo burbuja: 0.3533 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9368 - Iou Fallo malla: 0.6678 - Iou Fallo adhesivo: 0.9310 - Iou Fallo burbuja: 0.3263 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.1902 \| 1.0 \| 219 \| 0.2615 \| 0.5072 \| 0.7247 \| 0.9038 \| nan \| 0.9203 \| 0.7991 \| 0.9401 \| 0.2393 \| 0.0 \| 0.8853 \| 0.5556 \| 0.9006 \| 0.1944 \| \| 0.1367 \| 2.0 \| 438 \| 0.2067 \| 0.5492 \| 0.7602 \| 0.9293 \| nan \| 0.9599 \| 0.7487 \| 0.9317 \| 0.4004 \| 0.0 \| 0.9160 \| 0.6120 \| 0.8988 \| 0.3195 \| \| 0.1066 \| 3.0 \| 657 \| 0.1963 \| 0.5659 \| 0.7814 \| 0.9313 \| nan \| 0.9520 \| 0.8022 \| 0.9545 \| 0.4169 \| 0.0 \| 0.9175 \| 0.6270 \| 0.9267 \| 0.3584 \| \| 0.1102 \| 4.0 \| 876 \| 0.1595 \| 0.5782 \| 0.7756 \| 0.9444 \| nan \| 0.9727 \| 0.7669 \| 0.9693 \| 0.3934 \| 0.0 \| 0.9336 \| 0.6828 \| 0.9326 \| 0.3422 \| \| 0.1114 \| 5.0 \| 1095 \| 0.1678 \| 0.5772 \| 0.7950 \| 0.9378 \| nan \| 0.9619 \| 0.7778 \| 0.9756 \| 0.4648 \| 0.0 \| 0.9255 \| 0.6534 \| 0.9151 \| 0.3922 \| \| 0.0897 \| 6.0 \| 1314 \| 0.1726 \| 0.5811 \| 0.7976 \| 0.9420 \| nan \| 0.9701 \| 0.7598 \| 0.9723 \| 0.4881 \| 0.0 \| 0.9307 \| 0.6613 \| 0.9170 \| 0.3965 \| \| 0.0788 \| 7.0 \| 1533 \| 0.2096 \| 0.5491 \| 0.7253 \| 0.9342 \| nan \| 0.9898 \| 0.5936 \| 0.9381 \| 0.3797 \| 0.0 \| 0.9235 \| 0.5698 \| 0.9149 \| 0.3374 \| \| 0.0788 \| 8.0 \| 1752 \| 0.1574 \| 0.5774 \| 0.7733 \| 0.9465 \| nan \| 0.9726 \| 0.7858 \| 0.9675 \| 0.3673 \| 0.0 \| 0.9359 \| 0.6914 \| 0.9264 \| 0.3331 \| \| 0.0855 \| 9.0 \| 1971 \| 0.1970 \| 0.5406 \| 0.7141 \| 0.9380 \| nan \| 0.9866 \| 0.6305 \| 0.9708 \| 0.2687 \| 0.0 \| 0.9274 \| 0.5984 \| 0.9224 \| 0.2548 \| \| 0.0761 \| 10.0 \| 2190 \| 0.1903 \| 0.5564 \| 0.7479 \| 0.9382 \| nan \| 0.9746 \| 0.7050 \| 0.9737 \| 0.3383 \| 0.0 \| 0.9268 \| 0.6272 \| 0.9182 \| 0.3098 \| \| 0.0686 \| 11.0 \| 2409 \| 0.1910 \| 0.5562 \| 0.7435 \| 0.9393 \| nan \| 0.9827 \| 0.6605 \| 0.9738 \| 0.3572 \| 0.0 \| 0.9285 \| 0.6156 \| 0.9209 \| 0.3160 \| \| 0.062 \| 12.0 \| 2628 \| 0.2038 \| 0.5453 \| 0.7399 \| 0.9334 \| nan \| 0.9728 \| 0.6739 \| 0.9811 \| 0.3317 \| 0.0 \| 0.9214 \| 0.6013 \| 0.9035 \| 0.3001 \| \| 0.0586 \| 13.0 \| 2847 \| 0.1914 \| 0.5471 \| 0.7342 \| 0.9402 \| nan \| 0.9758 \| 0.7103 \| 0.9814 \| 0.2693 \| 0.0 \| 0.9290 \| 0.6397 \| 0.9150 \| 0.2517 \| \| 0.0531 \| 14.0 \| 3066 \| 0.1747 \| 0.5716 \| 0.7689 \| 0.9449 \| nan \| 0.9701 \| 0.7945 \| 0.9588 \| 0.3522 \| 0.0 \| 0.9339 \| 0.6815 \| 0.9280 \| 0.3147 \| \| 0.0522 \| 15.0 \| 3285 \| 0.1933 \| 0.5591 \| 0.7399 \| 0.9454 \| nan \| 0.9810 \| 0.7222 \| 0.9744 \| 0.2820 \| 0.0 \| 0.9351 \| 0.6603 \| 0.9355 \| 0.2645 \| \| 0.059 \| 16.0 \| 3504 \| 0.1897 \| 0.5691 \| 0.7878 \| 0.9384 \| nan \| 0.9499 \| 0.8594 \| 0.9809 \| 0.3608 \| 0.0 \| 0.9252 \| 0.6741 \| 0.9159 \| 0.3303 \| \| 0.0503 \| 17.0 \| 3723 \| 0.1895 \| 0.5652 \| 0.7795 \| 0.9365 \| nan \| 0.9588 \| 0.7866 \| 0.9808 \| 0.3917 \| 0.0 \| 0.9238 \| 0.6508 \| 0.9004 \| 0.3511 \| \| 0.0518 \| 18.0 \| 3942 \| 0.2131 \| 0.5533 \| 0.7332 \| 0.9402 \| nan \| 0.9807 \| 0.6877 \| 0.9645 \| 0.2998 \| 0.0 \| 0.9294 \| 0.6248 \| 0.9334 \| 0.2790 \| \| 0.0439 \| 19.0 \| 4161 \| 0.2168 \| 0.5565 \| 0.7411 \| 0.9388 \| nan \| 0.9801 \| 0.6828 \| 0.9567 \| 0.3448 \| 0.0 \| 0.9278 \| 0.6194 \| 0.9234 \| 0.3121 \| \| 0.0459 \| 20.0 \| 4380 \| 0.2688 \| 0.5266 \| 0.7127 \| 0.9266 \| nan \| 0.9824 \| 0.5567 \| 0.9841 \| 0.3277 \| 0.0 \| 0.9149 \| 0.5329 \| 0.8866 \| 0.2987 \| \| 0.043 \| 21.0 \| 4599 \| 0.2395 \| 0.5542 \| 0.7409 \| 0.9369 \| nan \| 0.9821 \| 0.6444 \| 0.9745 \| 0.3625 \| 0.0 \| 0.9258 \| 0.5974 \| 0.9228 \| 0.3248 \| \| 0.0436 \| 22.0 \| 4818 \| 0.1790 \| 0.5736 \| 0.7750 \| 0.9441 \| nan \| 0.9706 \| 0.7783 \| 0.9694 \| 0.3819 \| 0.0 \| 0.9331 \| 0.6772 \| 0.9143 \| 0.3433 \| \| 0.0443 \| 23.0 \| 5037 \| 0.1843 \| 0.5683 \| 0.7613 \| 0.9442 \| nan \| 0.9756 \| 0.7470 \| 0.9716 \| 0.3511 \| 0.0 \| 0.9335 \| 0.6684 \| 0.9177 \| 0.3219 \| \| 0.0402 \| 24.0 \| 5256 \| 0.2048 \| 0.5666 \| 0.7535 \| 0.9429 \| nan \| 0.9800 \| 0.7089 \| 0.9706 \| 0.3544 \| 0.0 \| 0.9324 \| 0.6457 \| 0.9302 \| 0.3246 \| \| 0.0399 \| 25.0 \| 5475 \| 0.2102 \| 0.5651 \| 0.7524 \| 0.9430 \| nan \| 0.9830 \| 0.6875 \| 0.9754 \| 0.3637 \| 0.0 \| 0.9327 \| 0.6412 \| 0.9231 \| 0.3287 \| \| 0.0404 \| 26.0 \| 5694 \| 0.1993 \| 0.5792 \| 0.7815 \| 0.9460 \| nan \| 0.9690 \| 0.8035 \| 0.9697 \| 0.3837 \| 0.0 \| 0.9351 \| 0.6876 \| 0.9289 \| 0.3443 \| \| 0.0388 \| 27.0 \| 5913 \| 0.2024 \| 0.5681 \| 0.7501 \| 0.9470 \| nan \| 0.9821 \| 0.7343 \| 0.9605 \| 0.3236 \| 0.0 \| 0.9370 \| 0.6715 \| 0.9322 \| 0.3001 \| \| 0.0369 \| 28.0 \| 6132 \| 0.1830 \| 0.5701 \| 0.7553 \| 0.9481 \| nan \| 0.9779 \| 0.7698 \| 0.9608 \| 0.3126 \| 0.0 \| 0.9379 \| 0.6871 \| 0.9323 \| 0.2931 \| \| 0.0373 \| 29.0 \| 6351 \| 0.2162 \| 0.5682 \| 0.7535 \| 0.9438 \| nan \| 0.9828 \| 0.7011 \| 0.9639 \| 0.3665 \| 0.0 \| 0.9335 \| 0.6482 \| 0.9239 \| 0.3352 \| \| 0.0348 \| 30.0 \| 6570 \| 0.2126 \| 0.5640 \| 0.7479 \| 0.9435 \| nan \| 0.9813 \| 0.7097 \| 0.9623 \| 0.3384 \| 0.0 \| 0.9330 \| 0.6537 \| 0.9197 \| 0.3135 \| \| 0.0354 \| 31.0 \| 6789 \| 0.2025 \| 0.5626 \| 0.7467 \| 0.9469 \| nan \| 0.9795 \| 0.7453 \| 0.9725 \| 0.2896 \| 0.0 \| 0.9368 \| 0.6762 \| 0.9285 \| 0.2716 \| \| 0.0344 \| 32.0 \| 7008 \| 0.1973 \| 0.5786 \| 0.7739 \| 0.9469 \| nan \| 0.9734 \| 0.7828 \| 0.9698 \| 0.3695 \| 0.0 \| 0.9364 \| 0.6853 \| 0.9326 \| 0.3389 \| \| 0.0333 \| 33.0 \| 7227 \| 0.2199 \| 0.5722 \| 0.7624 \| 0.9438 \| nan \| 0.9817 \| 0.7045 \| 0.9696 \| 0.3940 \| 0.0 \| 0.9334 \| 0.6481 \| 0.9287 \| 0.3510 \| \| 0.0345 \| 34.0 \| 7446 \| 0.2052 \| 0.5791 \| 0.7724 \| 0.9465 \| nan \| 0.9799 \| 0.7347 \| 0.9736 \| 0.4015 \| 0.0 \| 0.9363 \| 0.6698 \| 0.9311 \| 0.3582 \| \| 0.0326 \| 35.0 \| 7665 \| 0.2176 \| 0.5758 \| 0.7629 \| 0.9462 \| nan \| 0.9835 \| 0.7124 \| 0.9689 \| 0.3868 \| 0.0 \| 0.9362 \| 0.6595 \| 0.9345 \| 0.3490 \| \| 0.034 \| 36.0 \| 7884 \| 0.2247 \| 0.5717 \| 0.7557 \| 0.9453 \| nan \| 0.9841 \| 0.7033 \| 0.9661 \| 0.3694 \| 0.0 \| 0.9352 \| 0.6533 \| 0.9331 \| 0.3369 \| \| 0.0324 \| 37.0 \| 8103 \| 0.1957 \| 0.5797 \| 0.7736 \| 0.9490 \| nan \| 0.9763 \| 0.7801 \| 0.9725 \| 0.3657 \| 0.0 \| 0.9390 \| 0.6963 \| 0.9299 \| 0.3333 \| \| 0.0332 \| 38.0 \| 8322 \| 0.1996 \| 0.5770 \| 0.7644 \| 0.9478 \| nan \| 0.9826 \| 0.7310 \| 0.9696 \| 0.3743 \| 0.0 \| 0.9379 \| 0.6741 \| 0.9336 \| 0.3393 \| \| 0.0332 \| 39.0 \| 8541 \| 0.2129 \| 0.5638 \| 0.7423 \| 0.9449 \| nan \| 0.9845 \| 0.7021 \| 0.9616 \| 0.3212 \| 0.0 \| 0.9348 \| 0.6514 \| 0.9328 \| 0.3001 \| \| 0.03 \| 40.0 \| 8760 \| 0.2283 \| 0.5694 \| 0.7539 \| 0.9441 \| nan \| 0.9840 \| 0.6931 \| 0.9686 \| 0.3699 \| 0.0 \| 0.9339 \| 0.6464 \| 0.9277 \| 0.3387 \| \| 0.0319 \| 41.0 \| 8979 \| 0.2013 \| 0.5741 \| 0.7624 \| 0.9471 \| nan \| 0.9804 \| 0.7416 \| 0.9670 \| 0.3606 \| 0.0 \| 0.9370 \| 0.6760 \| 0.9277 \| 0.3300 \| \| 0.0361 \| 42.0 \| 9198 \| 0.2094 \| 0.5709 \| 0.7568 \| 0.9463 \| nan \| 0.9810 \| 0.7317 \| 0.9663 \| 0.3483 \| 0.0 \| 0.9362 \| 0.6689 \| 0.9279 \| 0.3216 \| \| 0.0304 \| 43.0 \| 9417 \| 0.2098 \| 0.5731 \| 0.7586 \| 0.9468 \| nan \| 0.9821 \| 0.7282 \| 0.9666 \| 0.3575 \| 0.0 \| 0.9368 \| 0.6700 \| 0.9295 \| 0.3293 \| \| 0.0303 \| 44.0 \| 9636 \| 0.2155 \| 0.5705 \| 0.7554 \| 0.9470 \| nan \| 0.9814 \| 0.7329 \| 0.9702 \| 0.3370 \| 0.0 \| 0.9369 \| 0.6718 \| 0.9301 \| 0.3137 \| \| 0.03 \| 45.0 \| 9855 \| 0.2183 \| 0.5703 \| 0.7541 \| 0.9464 \| nan \| 0.9825 \| 0.7229 \| 0.9677 \| 0.3435 \| 0.0 \| 0.9364 \| 0.6657 \| 0.9311 \| 0.3181 \| \| 0.0301 \| 45.66 \| 10000 \| 0.2142 \| 0.5724 \| 0.7571 \| 0.9468 \| nan \| 0.9826 \| 0.7246 \| 0.9679 \| 0.3533 \| 0.0 \| 0.9368 \| 0.6678 \| 0.9310 \| 0.3263 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3	[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]
Mohamad-Jaallouk/segformer-b0-finetuned-segments-sidewalk-oct-22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.8820 - eval_mean_iou: 0.1648 - eval_mean_accuracy: 0.2025 - eval_overall_accuracy: 0.7805 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8435 - eval_accuracy_flat-sidewalk: 0.9378 - eval_accuracy_flat-crosswalk: 0.0 - eval_accuracy_flat-cyclinglane: 0.5809 - eval_accuracy_flat-parkingdriveway: 0.0715 - eval_accuracy_flat-railtrack: 0.0 - eval_accuracy_flat-curb: 0.0041 - eval_accuracy_human-person: 0.0 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.8730 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: 0.0 - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.0 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.8780 - eval_accuracy_construction-door: 0.0 - eval_accuracy_construction-wall: 0.0000 - eval_accuracy_construction-fenceguardrail: 0.0 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: 0.0 - eval_accuracy_construction-stairs: 0.0 - eval_accuracy_object-pole: 0.0 - eval_accuracy_object-trafficsign: 0.0 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9399 - eval_accuracy_nature-terrain: 0.8232 - eval_accuracy_sky: 0.9347 - eval_accuracy_void-ground: 0.0 - eval_accuracy_void-dynamic: 0.0 - eval_accuracy_void-static: 0.0 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.5426 - eval_iou_flat-sidewalk: 0.8046 - eval_iou_flat-crosswalk: 0.0 - eval_iou_flat-cyclinglane: 0.5502 - eval_iou_flat-parkingdriveway: 0.0678 - eval_iou_flat-railtrack: 0.0 - eval_iou_flat-curb: 0.0041 - eval_iou_human-person: 0.0 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.6930 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: 0.0 - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.0 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.6055 - eval_iou_construction-door: 0.0 - eval_iou_construction-wall: 0.0000 - eval_iou_construction-fenceguardrail: 0.0 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: 0.0 - eval_iou_construction-stairs: 0.0 - eval_iou_object-pole: 0.0 - eval_iou_object-trafficsign: 0.0 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.7900 - eval_iou_nature-terrain: 0.7063 - eval_iou_sky: 0.8381 - eval_iou_void-ground: 0.0 - eval_iou_void-dynamic: 0.0 - eval_iou_void-static: 0.0 - eval_iou_void-unclear: 0.0 - eval_runtime: 21.9758 - eval_samples_per_second: 9.101 - eval_steps_per_second: 0.592 - epoch: 0.4 - step: 20 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
IrshadG/segformer-b0-finetuned-segments-sidewalk-123	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-123 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5603 - eval_mean_iou: 0.3194 - eval_mean_accuracy: 0.3797 - eval_overall_accuracy: 0.8649 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8652 - eval_accuracy_flat-sidewalk: 0.9642 - eval_accuracy_flat-crosswalk: 0.6083 - eval_accuracy_flat-cyclinglane: 0.7173 - eval_accuracy_flat-parkingdriveway: 0.5189 - eval_accuracy_flat-railtrack: 0.0 - eval_accuracy_flat-curb: 0.6089 - eval_accuracy_human-person: 0.8421 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.9347 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: 0.0 - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.7877 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.9288 - eval_accuracy_construction-door: 0.0851 - eval_accuracy_construction-wall: 0.4696 - eval_accuracy_construction-fenceguardrail: 0.4756 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: nan - eval_accuracy_construction-stairs: 0.0047 - eval_accuracy_object-pole: 0.3599 - eval_accuracy_object-trafficsign: 0.0028 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9419 - eval_accuracy_nature-terrain: 0.8921 - eval_accuracy_sky: 0.9773 - eval_accuracy_void-ground: 0.0003 - eval_accuracy_void-dynamic: 0.1852 - eval_accuracy_void-static: 0.3603 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.7774 - eval_iou_flat-sidewalk: 0.8659 - eval_iou_flat-crosswalk: 0.4889 - eval_iou_flat-cyclinglane: 0.6488 - eval_iou_flat-parkingdriveway: 0.4072 - eval_iou_flat-railtrack: 0.0 - eval_iou_flat-curb: 0.4944 - eval_iou_human-person: 0.6064 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.8283 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: 0.0 - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.5105 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.7458 - eval_iou_construction-door: 0.0760 - eval_iou_construction-wall: 0.4006 - eval_iou_construction-fenceguardrail: 0.3288 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: nan - eval_iou_construction-stairs: 0.0047 - eval_iou_object-pole: 0.2789 - eval_iou_object-trafficsign: 0.0027 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.8781 - eval_iou_nature-terrain: 0.8170 - eval_iou_sky: 0.9417 - eval_iou_void-ground: 0.0002 - eval_iou_void-dynamic: 0.1706 - eval_iou_void-static: 0.2686 - eval_iou_void-unclear: 0.0 - eval_runtime: 246.8492 - eval_samples_per_second: 0.81 - eval_steps_per_second: 0.405 - epoch: 15.45 - step: 6180 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0 - Pytorch 2.2.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1	[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]
sam1120/safety-utcustom-train-SF-RGBD-b5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0867 - Mean Iou: 0.7280 - Mean Accuracy: 0.7762 - Overall Accuracy: 0.9818 - Accuracy Unlabeled: nan - Accuracy Safe: 0.5578 - Accuracy Unsafe: 0.9947 - Iou Unlabeled: nan - Iou Safe: 0.4745 - Iou Unsafe: 0.9814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 0.789 \| 0.91 \| 10 \| 0.0203 \| nan \| 0.8957 \| 0.0095 \| 0.0 \| 0.8722 \| 0.9555 \| 0.4580 \| 0.2939 \| 0.8698 \| \| 0.7579 \| 1.82 \| 20 \| 0.0117 \| nan \| 0.9614 \| 0.0069 \| 0.0 \| 0.9338 \| 0.8322 \| 0.4866 \| 0.3136 \| 0.9334 \| \| 0.7103 \| 2.73 \| 30 \| 0.0051 \| nan \| 0.9893 \| 0.0043 \| 0.0 \| 0.9604 \| 0.6729 \| 0.4972 \| 0.3216 \| 0.9602 \| \| 0.676 \| 3.64 \| 40 \| 0.0021 \| nan \| 0.9969 \| 0.0020 \| 0.0 \| 0.9675 \| 0.5336 \| 0.4995 \| 0.3232 \| 0.9675 \| \| 0.5955 \| 4.55 \| 50 \| 0.0001 \| nan \| 0.9993 \| 0.0001 \| 0.0 \| 0.9698 \| 0.4440 \| 0.4997 \| 0.3233 \| 0.9698 \| \| 0.5691 \| 5.45 \| 60 \| 0.0000 \| nan \| 0.9997 \| 0.0000 \| 0.0 \| 0.9702 \| 0.3812 \| 0.4999 \| 0.3234 \| 0.9702 \| \| 0.5067 \| 6.36 \| 70 \| 0.0 \| nan \| 0.9996 \| 0.0 \| 0.0 \| 0.9701 \| 0.3590 \| 0.4998 \| 0.3234 \| 0.9701 \| \| 0.4656 \| 7.27 \| 80 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9703 \| 0.3247 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.4227 \| 8.18 \| 90 \| 0.0 \| nan \| 0.9998 \| 0.0 \| 0.0 \| 0.9702 \| 0.3171 \| 0.4999 \| 0.3234 \| 0.9702 \| \| 0.3898 \| 9.09 \| 100 \| 0.0004 \| nan \| 0.9996 \| 0.0004 \| 0.0 \| 0.9701 \| 0.3122 \| 0.5000 \| 0.3235 \| 0.9701 \| \| 0.3513 \| 10.0 \| 110 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9703 \| 0.2876 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.4157 \| 10.91 \| 120 \| 0.0000 \| nan \| 0.9998 \| 0.0000 \| 0.0 \| 0.9703 \| 0.2820 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.3317 \| 11.82 \| 130 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9703 \| 0.2693 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.321 \| 12.73 \| 140 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2647 \| 0.4999 \| 0.3235 \| 0.9704 \| \| 0.2887 \| 13.64 \| 150 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2539 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.3008 \| 14.55 \| 160 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2536 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2853 \| 15.45 \| 170 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2397 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2684 \| 16.36 \| 180 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2321 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2585 \| 17.27 \| 190 \| 0.0000 \| nan \| 0.9999 \| 0.0000 \| 0.0 \| 0.9704 \| 0.2208 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2088 \| 18.18 \| 200 \| 0.0084 \| nan \| 0.9997 \| 0.0083 \| 0.0 \| 0.9704 \| 0.2011 \| 0.5041 \| 0.3262 \| 0.9704 \| \| 0.2518 \| 19.09 \| 210 \| 0.0468 \| nan \| 0.9989 \| 0.0451 \| 0.0 \| 0.9707 \| 0.2026 \| 0.5228 \| 0.3386 \| 0.9707 \| \| 0.218 \| 20.0 \| 220 \| 0.0879 \| nan \| 0.9984 \| 0.0834 \| nan \| 0.9714 \| 0.1889 \| 0.5431 \| 0.5274 \| 0.9715 \| \| 0.2046 \| 20.91 \| 230 \| 0.1931 \| nan \| 0.9969 \| 0.1752 \| nan \| 0.9730 \| 0.1847 \| 0.5950 \| 0.5741 \| 0.9732 \| \| 0.2147 \| 21.82 \| 240 \| 0.2042 \| nan \| 0.9968 \| 0.1850 \| nan \| 0.9733 \| 0.1766 \| 0.6005 \| 0.5791 \| 0.9734 \| \| 0.188 \| 22.73 \| 250 \| 0.2020 \| nan \| 0.9972 \| 0.1849 \| nan \| 0.9735 \| 0.1726 \| 0.5996 \| 0.5792 \| 0.9737 \| \| 0.2175 \| 23.64 \| 260 \| 0.1898 \| nan \| 0.9974 \| 0.1748 \| nan \| 0.9734 \| 0.1706 \| 0.5936 \| 0.5741 \| 0.9735 \| \| 0.2059 \| 24.55 \| 270 \| 0.3006 \| nan \| 0.9962 \| 0.2670 \| nan \| 0.9754 \| 0.1689 \| 0.6484 \| 0.6212 \| 0.9756 \| \| 0.1776 \| 25.45 \| 280 \| 0.2870 \| nan \| 0.9967 \| 0.2587 \| nan \| 0.9755 \| 0.1612 \| 0.6418 \| 0.6171 \| 0.9757 \| \| 0.1585 \| 26.36 \| 290 \| 0.4254 \| nan \| 0.9944 \| 0.3593 \| nan \| 0.9773 \| 0.1537 \| 0.7099 \| 0.6683 \| 0.9776 \| \| 0.1588 \| 27.27 \| 300 \| 0.2798 \| nan \| 0.9970 \| 0.2548 \| nan \| 0.9756 \| 0.1527 \| 0.6384 \| 0.6152 \| 0.9758 \| \| 0.153 \| 28.18 \| 310 \| 0.4288 \| nan \| 0.9946 \| 0.3646 \| nan \| 0.9776 \| 0.1452 \| 0.7117 \| 0.6711 \| 0.9779 \| \| 0.1623 \| 29.09 \| 320 \| 0.4401 \| nan \| 0.9945 \| 0.3726 \| nan \| 0.9778 \| 0.1442 \| 0.7173 \| 0.6752 \| 0.9781 \| \| 0.1603 \| 30.0 \| 330 \| 0.4050 \| nan \| 0.9958 \| 0.3562 \| nan \| 0.9781 \| 0.1407 \| 0.7004 \| 0.6671 \| 0.9784 \| \| 0.1694 \| 30.91 \| 340 \| 0.4585 \| nan \| 0.9948 \| 0.3911 \| nan \| 0.9786 \| 0.1343 \| 0.7266 \| 0.6849 \| 0.9789 \| \| 0.1585 \| 31.82 \| 350 \| 0.3861 \| nan \| 0.9962 \| 0.3433 \| nan \| 0.9779 \| 0.1353 \| 0.6912 \| 0.6606 \| 0.9782 \| \| 0.1342 \| 32.73 \| 360 \| 0.4963 \| nan \| 0.9939 \| 0.4132 \| nan \| 0.9789 \| 0.1338 \| 0.7451 \| 0.6961 \| 0.9792 \| \| 0.1358 \| 33.64 \| 370 \| 0.5048 \| nan \| 0.9937 \| 0.4182 \| nan \| 0.9789 \| 0.1342 \| 0.7493 \| 0.6986 \| 0.9793 \| \| 0.1493 \| 34.55 \| 380 \| 0.4809 \| nan \| 0.9946 \| 0.4080 \| nan \| 0.9791 \| 0.1297 \| 0.7377 \| 0.6936 \| 0.9794 \| \| 0.1435 \| 35.45 \| 390 \| 0.5658 \| nan \| 0.9923 \| 0.4518 \| nan \| 0.9794 \| 0.1271 \| 0.7791 \| 0.7156 \| 0.9797 \| \| 0.1305 \| 36.36 \| 400 \| 0.4157 \| nan \| 0.9968 \| 0.3758 \| nan \| 0.9793 \| 0.1225 \| 0.7062 \| 0.6776 \| 0.9796 \| \| 0.1496 \| 37.27 \| 410 \| 0.5385 \| nan \| 0.9934 \| 0.4420 \| nan \| 0.9796 \| 0.1237 \| 0.7659 \| 0.7108 \| 0.9799 \| \| 0.1445 \| 38.18 \| 420 \| 0.5763 \| nan \| 0.9924 \| 0.4615 \| nan \| 0.9798 \| 0.1207 \| 0.7843 \| 0.7206 \| 0.9801 \| \| 0.1307 \| 39.09 \| 430 \| 0.4853 \| nan \| 0.9956 \| 0.4244 \| nan \| 0.9803 \| 0.1194 \| 0.7404 \| 0.7023 \| 0.9806 \| \| 0.1379 \| 40.0 \| 440 \| 0.5722 \| nan \| 0.9922 \| 0.4557 \| nan \| 0.9795 \| 0.1174 \| 0.7822 \| 0.7176 \| 0.9798 \| \| 0.1202 \| 40.91 \| 450 \| 0.5399 \| nan \| 0.9943 \| 0.4544 \| nan \| 0.9805 \| 0.1143 \| 0.7671 \| 0.7175 \| 0.9809 \| \| 0.1239 \| 41.82 \| 460 \| 0.5580 \| nan \| 0.9932 \| 0.4558 \| nan \| 0.9800 \| 0.1150 \| 0.7756 \| 0.7179 \| 0.9803 \| \| 0.1183 \| 42.73 \| 470 \| 0.4777 \| nan \| 0.9961 \| 0.4236 \| nan \| 0.9805 \| 0.1129 \| 0.7369 \| 0.7021 \| 0.9808 \| \| 0.1202 \| 43.64 \| 480 \| 0.5933 \| nan \| 0.9928 \| 0.4793 \| nan \| 0.9806 \| 0.1119 \| 0.7930 \| 0.7300 \| 0.9810 \| \| 0.1276 \| 44.55 \| 490 \| 0.5425 \| nan \| 0.9942 \| 0.4561 \| nan \| 0.9806 \| 0.1131 \| 0.7683 \| 0.7183 \| 0.9809 \| \| 0.1172 \| 45.45 \| 500 \| 0.6272 \| nan \| 0.9898 \| 0.4700 \| nan \| 0.9787 \| 0.1135 \| 0.8085 \| 0.7244 \| 0.9791 \| \| 0.1288 \| 46.36 \| 510 \| 0.4236 \| nan \| 0.9974 \| 0.3898 \| nan \| 0.9802 \| 0.1105 \| 0.7105 \| 0.6850 \| 0.9804 \| \| 0.1185 \| 47.27 \| 520 \| 0.6035 \| nan \| 0.9914 \| 0.4711 \| nan \| 0.9796 \| 0.1130 \| 0.7975 \| 0.7254 \| 0.9800 \| \| 0.1045 \| 48.18 \| 530 \| 0.5750 \| nan \| 0.9930 \| 0.4679 \| nan \| 0.9804 \| 0.1102 \| 0.7840 \| 0.7241 \| 0.9807 \| \| 0.1211 \| 49.09 \| 540 \| 0.5812 \| nan \| 0.9929 \| 0.4715 \| nan \| 0.9804 \| 0.1069 \| 0.7870 \| 0.7260 \| 0.9808 \| \| 0.1206 \| 50.0 \| 550 \| 0.5221 \| nan \| 0.9953 \| 0.4528 \| nan \| 0.9811 \| 0.1071 \| 0.7587 \| 0.7169 \| 0.9814 \| \| 0.1193 \| 50.91 \| 560 \| 0.4956 \| nan \| 0.9961 \| 0.4398 \| nan \| 0.9811 \| 0.1053 \| 0.7459 \| 0.7105 \| 0.9814 \| \| 0.1116 \| 51.82 \| 570 \| 0.5257 \| nan \| 0.9951 \| 0.4528 \| nan \| 0.9809 \| 0.1043 \| 0.7604 \| 0.7169 \| 0.9812 \| \| 0.1218 \| 52.73 \| 580 \| 0.5936 \| nan \| 0.9922 \| 0.4724 \| nan \| 0.9801 \| 0.1078 \| 0.7929 \| 0.7262 \| 0.9804 \| \| 0.1284 \| 53.64 \| 590 \| 0.5872 \| nan \| 0.9924 \| 0.4696 \| nan \| 0.9801 \| 0.1054 \| 0.7898 \| 0.7248 \| 0.9804 \| \| 0.096 \| 54.55 \| 600 \| 0.5451 \| nan \| 0.9942 \| 0.4580 \| nan \| 0.9806 \| 0.1028 \| 0.7697 \| 0.7193 \| 0.9809 \| \| 0.1091 \| 55.45 \| 610 \| 0.6014 \| nan \| 0.9917 \| 0.4725 \| nan \| 0.9798 \| 0.1022 \| 0.7965 \| 0.7261 \| 0.9802 \| \| 0.1068 \| 56.36 \| 620 \| 0.4926 \| nan \| 0.9962 \| 0.4374 \| nan \| 0.9810 \| 0.1015 \| 0.7444 \| 0.7092 \| 0.9813 \| \| 0.106 \| 57.27 \| 630 \| 0.5713 \| nan \| 0.9937 \| 0.4731 \| nan \| 0.9809 \| 0.1011 \| 0.7825 \| 0.7270 \| 0.9812 \| \| 0.1009 \| 58.18 \| 640 \| 0.4512 \| nan \| 0.9969 \| 0.4089 \| nan \| 0.9805 \| 0.1028 \| 0.7240 \| 0.6947 \| 0.9807 \| \| 0.1018 \| 59.09 \| 650 \| 0.6053 \| nan \| 0.9919 \| 0.4779 \| nan \| 0.9801 \| 0.1022 \| 0.7986 \| 0.7290 \| 0.9805 \| \| 0.1012 \| 60.0 \| 660 \| 0.5167 \| nan \| 0.9949 \| 0.4427 \| nan \| 0.9805 \| 0.1016 \| 0.7558 \| 0.7116 \| 0.9808 \| \| 0.1052 \| 60.91 \| 670 \| 0.5464 \| nan \| 0.9943 \| 0.4604 \| nan \| 0.9808 \| 0.0999 \| 0.7703 \| 0.7206 \| 0.9811 \| \| 0.1229 \| 61.82 \| 680 \| 0.5706 \| nan \| 0.9939 \| 0.4750 \| nan \| 0.9810 \| 0.0993 \| 0.7822 \| 0.7280 \| 0.9814 \| \| 0.0963 \| 62.73 \| 690 \| 0.5746 \| nan \| 0.9936 \| 0.4754 \| nan \| 0.9809 \| 0.0974 \| 0.7841 \| 0.7282 \| 0.9813 \| \| 0.1115 \| 63.64 \| 700 \| 0.5239 \| nan \| 0.9955 \| 0.4562 \| nan \| 0.9813 \| 0.0974 \| 0.7597 \| 0.7187 \| 0.9816 \| \| 0.1025 \| 64.55 \| 710 \| 0.5845 \| nan \| 0.9935 \| 0.4813 \| nan \| 0.9811 \| 0.0964 \| 0.7890 \| 0.7312 \| 0.9814 \| \| 0.0916 \| 65.45 \| 720 \| 0.5493 \| nan \| 0.9947 \| 0.4685 \| nan \| 0.9813 \| 0.0962 \| 0.7720 \| 0.7249 \| 0.9816 \| \| 0.1055 \| 66.36 \| 730 \| 0.5273 \| nan \| 0.9953 \| 0.4571 \| nan \| 0.9812 \| 0.0947 \| 0.7613 \| 0.7191 \| 0.9815 \| \| 0.1081 \| 67.27 \| 740 \| 0.6093 \| nan \| 0.9919 \| 0.4813 \| nan \| 0.9802 \| 0.0964 \| 0.8006 \| 0.7308 \| 0.9806 \| \| 0.1039 \| 68.18 \| 750 \| 0.5405 \| nan \| 0.9945 \| 0.4573 \| nan \| 0.9807 \| 0.0950 \| 0.7675 \| 0.7190 \| 0.9811 \| \| 0.106 \| 69.09 \| 760 \| 0.5564 \| nan \| 0.9943 \| 0.4682 \| nan \| 0.9810 \| 0.0939 \| 0.7753 \| 0.7246 \| 0.9813 \| \| 0.0912 \| 70.0 \| 770 \| 0.5377 \| nan \| 0.9949 \| 0.4612 \| nan \| 0.9811 \| 0.0936 \| 0.7663 \| 0.7212 \| 0.9814 \| \| 0.0951 \| 70.91 \| 780 \| 0.5600 \| nan \| 0.9941 \| 0.4689 \| nan \| 0.9809 \| 0.0938 \| 0.7771 \| 0.7249 \| 0.9813 \| \| 0.0998 \| 71.82 \| 790 \| 0.5573 \| nan \| 0.9944 \| 0.4705 \| nan \| 0.9812 \| 0.0928 \| 0.7759 \| 0.7258 \| 0.9815 \| \| 0.0889 \| 72.73 \| 800 \| 0.5398 \| nan \| 0.9949 \| 0.4628 \| nan \| 0.9812 \| 0.0931 \| 0.7674 \| 0.7220 \| 0.9815 \| \| 0.0906 \| 73.64 \| 810 \| 0.5151 \| nan \| 0.9958 \| 0.4528 \| nan \| 0.9813 \| 0.0928 \| 0.7555 \| 0.7171 \| 0.9816 \| \| 0.0911 \| 74.55 \| 820 \| 0.5682 \| nan \| 0.9938 \| 0.4722 \| nan \| 0.9809 \| 0.0924 \| 0.7810 \| 0.7265 \| 0.9812 \| \| 0.0907 \| 75.45 \| 830 \| 0.4864 \| nan \| 0.9965 \| 0.4365 \| nan \| 0.9812 \| 0.0929 \| 0.7415 \| 0.7089 \| 0.9815 \| \| 0.1117 \| 76.36 \| 840 \| 0.5239 \| nan \| 0.9956 \| 0.4576 \| nan \| 0.9814 \| 0.0934 \| 0.7598 \| 0.7195 \| 0.9817 \| \| 0.0812 \| 77.27 \| 850 \| 0.5279 \| nan \| 0.9956 \| 0.4605 \| nan \| 0.9814 \| 0.0915 \| 0.7617 \| 0.7210 \| 0.9817 \| \| 0.0888 \| 78.18 \| 860 \| 0.5615 \| nan \| 0.9942 \| 0.4720 \| nan \| 0.9811 \| 0.0915 \| 0.7778 \| 0.7266 \| 0.9814 \| \| 0.09 \| 79.09 \| 870 \| 0.5414 \| nan \| 0.9948 \| 0.4628 \| nan \| 0.9811 \| 0.0920 \| 0.7681 \| 0.7220 \| 0.9814 \| \| 0.1052 \| 80.0 \| 880 \| 0.5866 \| nan \| 0.9932 \| 0.4790 \| nan \| 0.9808 \| 0.0917 \| 0.7899 \| 0.7299 \| 0.9812 \| \| 0.0867 \| 80.91 \| 890 \| 0.5252 \| nan \| 0.9955 \| 0.4573 \| nan \| 0.9813 \| 0.0912 \| 0.7603 \| 0.7193 \| 0.9816 \| \| 0.0942 \| 81.82 \| 900 \| 0.5091 \| nan \| 0.9959 \| 0.4490 \| nan \| 0.9813 \| 0.0925 \| 0.7525 \| 0.7152 \| 0.9815 \| \| 0.0917 \| 82.73 \| 910 \| 0.5454 \| nan \| 0.9950 \| 0.4682 \| nan \| 0.9814 \| 0.0908 \| 0.7702 \| 0.7248 \| 0.9817 \| \| 0.103 \| 83.64 \| 920 \| 0.5452 \| nan \| 0.9949 \| 0.4672 \| nan \| 0.9813 \| 0.0912 \| 0.7701 \| 0.7243 \| 0.9816 \| \| 0.0939 \| 84.55 \| 930 \| 0.5539 \| nan \| 0.9947 \| 0.4717 \| nan \| 0.9814 \| 0.0900 \| 0.7743 \| 0.7265 \| 0.9817 \| \| 0.0892 \| 85.45 \| 940 \| 0.5330 \| nan \| 0.9954 \| 0.4635 \| nan \| 0.9815 \| 0.0900 \| 0.7642 \| 0.7225 \| 0.9818 \| \| 0.0899 \| 86.36 \| 950 \| 0.5756 \| nan \| 0.9938 \| 0.4778 \| nan \| 0.9811 \| 0.0905 \| 0.7847 \| 0.7295 \| 0.9814 \| \| 0.0877 \| 87.27 \| 960 \| 0.5771 \| nan \| 0.9937 \| 0.4787 \| nan \| 0.9811 \| 0.0893 \| 0.7854 \| 0.7299 \| 0.9814 \| \| 0.0851 \| 88.18 \| 970 \| 0.5087 \| nan \| 0.9961 \| 0.4512 \| nan \| 0.9814 \| 0.0897 \| 0.7524 \| 0.7163 \| 0.9817 \| \| 0.0857 \| 89.09 \| 980 \| 0.5363 \| nan \| 0.9953 \| 0.4644 \| nan \| 0.9814 \| 0.0894 \| 0.7658 \| 0.7229 \| 0.9817 \| \| 0.0821 \| 90.0 \| 990 \| 0.5333 \| nan \| 0.9953 \| 0.4623 \| nan \| 0.9814 \| 0.0895 \| 0.7643 \| 0.7218 \| 0.9817 \| \| 0.0931 \| 90.91 \| 1000 \| 0.5581 \| nan \| 0.9944 \| 0.4718 \| nan \| 0.9812 \| 0.0895 \| 0.7763 \| 0.7265 \| 0.9815 \| \| 0.0787 \| 91.82 \| 1010 \| 0.5525 \| nan \| 0.9946 \| 0.4689 \| nan \| 0.9812 \| 0.0889 \| 0.7735 \| 0.7251 \| 0.9815 \| \| 0.0865 \| 92.73 \| 1020 \| 0.5659 \| nan \| 0.9941 \| 0.4746 \| nan \| 0.9812 \| 0.0883 \| 0.7800 \| 0.7279 \| 0.9815 \| \| 0.0939 \| 93.64 \| 1030 \| 0.5583 \| nan \| 0.9945 \| 0.4723 \| nan \| 0.9813 \| 0.0891 \| 0.7764 \| 0.7268 \| 0.9816 \| \| 0.0874 \| 94.55 \| 1040 \| 0.5258 \| nan \| 0.9955 \| 0.4580 \| nan \| 0.9813 \| 0.0893 \| 0.7607 \| 0.7197 \| 0.9816 \| \| 0.0927 \| 95.45 \| 1050 \| 0.5319 \| nan \| 0.9953 \| 0.4608 \| nan \| 0.9813 \| 0.0894 \| 0.7636 \| 0.7211 \| 0.9816 \| \| 0.0808 \| 96.36 \| 1060 \| 0.5444 \| nan \| 0.9949 \| 0.4665 \| nan \| 0.9813 \| 0.0897 \| 0.7696 \| 0.7239 \| 0.9816 \| \| 0.0924 \| 97.27 \| 1070 \| 0.5445 \| nan \| 0.9950 \| 0.4671 \| nan \| 0.9814 \| 0.0892 \| 0.7697 \| 0.7243 \| 0.9817 \| \| 0.08 \| 98.18 \| 1080 \| 0.5522 \| nan \| 0.9947 \| 0.4703 \| nan \| 0.9813 \| 0.0884 \| 0.7735 \| 0.7258 \| 0.9816 \| \| 0.0798 \| 99.09 \| 1090 \| 0.0880 \| 0.7300 \| 0.7842 \| 0.9815 \| nan \| 0.5745 \| 0.9939 \| nan \| 0.4788 \| 0.9812 \| \| 0.0789 \| 100.0 \| 1100 \| 0.0877 \| 0.7231 \| 0.7668 \| 0.9817 \| nan \| 0.5383 \| 0.9952 \| nan \| 0.4647 \| 0.9814 \| \| 0.0801 \| 100.91 \| 1110 \| 0.0885 \| 0.7232 \| 0.7677 \| 0.9816 \| nan \| 0.5404 \| 0.9951 \| nan \| 0.4650 \| 0.9813 \| \| 0.1043 \| 101.82 \| 1120 \| 0.0891 \| 0.7242 \| 0.7697 \| 0.9816 \| nan \| 0.5445 \| 0.9950 \| nan \| 0.4670 \| 0.9813 \| \| 0.0893 \| 102.73 \| 1130 \| 0.0882 \| 0.7263 \| 0.7728 \| 0.9817 \| nan \| 0.5508 \| 0.9949 \| nan \| 0.4712 \| 0.9814 \| \| 0.0923 \| 103.64 \| 1140 \| 0.0892 \| 0.7134 \| 0.7504 \| 0.9815 \| nan \| 0.5048 \| 0.9960 \| nan \| 0.4457 \| 0.9812 \| \| 0.0915 \| 104.55 \| 1150 \| 0.0884 \| 0.7293 \| 0.7795 \| 0.9817 \| nan \| 0.5646 \| 0.9944 \| nan \| 0.4772 \| 0.9814 \| \| 0.0859 \| 105.45 \| 1160 \| 0.0880 \| 0.7340 \| 0.7941 \| 0.9815 \| nan \| 0.5949 \| 0.9932 \| nan \| 0.4869 \| 0.9811 \| \| 0.0872 \| 106.36 \| 1170 \| 0.0872 \| 0.7298 \| 0.7815 \| 0.9817 \| nan \| 0.5688 \| 0.9942 \| nan \| 0.4783 \| 0.9814 \| \| 0.0845 \| 107.27 \| 1180 \| 0.0881 \| 0.7310 \| 0.7843 \| 0.9817 \| nan \| 0.5746 \| 0.9940 \| nan \| 0.4806 \| 0.9813 \| \| 0.0842 \| 108.18 \| 1190 \| 0.0869 \| 0.7285 \| 0.7766 \| 0.9818 \| nan \| 0.5584 \| 0.9947 \| nan \| 0.4755 \| 0.9815 \| \| 0.0906 \| 109.09 \| 1200 \| 0.0875 \| 0.7277 \| 0.7754 \| 0.9818 \| nan \| 0.5560 \| 0.9947 \| nan \| 0.4740 \| 0.9815 \| \| 0.0953 \| 110.0 \| 1210 \| 0.0878 \| 0.7289 \| 0.7777 \| 0.9818 \| nan \| 0.5608 \| 0.9946 \| nan \| 0.4764 \| 0.9815 \| \| 0.0988 \| 110.91 \| 1220 \| 0.0880 \| 0.7303 \| 0.7809 \| 0.9818 \| nan \| 0.5674 \| 0.9944 \| nan \| 0.4790 \| 0.9815 \| \| 0.0894 \| 111.82 \| 1230 \| 0.0869 \| 0.7300 \| 0.7801 \| 0.9818 \| nan \| 0.5657 \| 0.9945 \| nan \| 0.4785 \| 0.9815 \| \| 0.0788 \| 112.73 \| 1240 \| 0.0868 \| 0.7283 \| 0.7758 \| 0.9818 \| nan \| 0.5569 \| 0.9948 \| nan \| 0.4750 \| 0.9815 \| \| 0.0793 \| 113.64 \| 1250 \| 0.0870 \| 0.7281 \| 0.7758 \| 0.9818 \| nan \| 0.5569 \| 0.9947 \| nan \| 0.4747 \| 0.9815 \| \| 0.084 \| 114.55 \| 1260 \| 0.0874 \| 0.7295 \| 0.7809 \| 0.9817 \| nan \| 0.5675 \| 0.9943 \| nan \| 0.4777 \| 0.9814 \| \| 0.0832 \| 115.45 \| 1270 \| 0.0875 \| 0.7277 \| 0.7760 \| 0.9817 \| nan \| 0.5574 \| 0.9946 \| nan \| 0.4739 \| 0.9814 \| \| 0.0833 \| 116.36 \| 1280 \| 0.0873 \| 0.7274 \| 0.7755 \| 0.9817 \| nan \| 0.5563 \| 0.9947 \| nan \| 0.4735 \| 0.9814 \| \| 0.0786 \| 117.27 \| 1290 \| 0.0867 \| 0.7277 \| 0.7754 \| 0.9818 \| nan \| 0.5561 \| 0.9947 \| nan \| 0.4740 \| 0.9815 \| \| 0.0839 \| 118.18 \| 1300 \| 0.0865 \| 0.7285 \| 0.7779 \| 0.9817 \| nan \| 0.5613 \| 0.9945 \| nan \| 0.4755 \| 0.9814 \| \| 0.0847 \| 119.09 \| 1310 \| 0.0877 \| 0.7293 \| 0.7816 \| 0.9816 \| nan \| 0.5691 \| 0.9941 \| nan \| 0.4773 \| 0.9813 \| \| 0.0933 \| 120.0 \| 1320 \| 0.0867 \| 0.7280 \| 0.7762 \| 0.9818 \| nan \| 0.5578 \| 0.9947 \| nan \| 0.4745 \| 0.9814 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/safety-utcustom-train-SF-RGB-b0	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF-RGB-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3221 - Mean Iou: 0.7557 - Mean Accuracy: 0.8092 - Overall Accuracy: 0.9835 - Accuracy Unlabeled: nan - Accuracy Safe: 0.6240 - Accuracy Unsafe: 0.9945 - Iou Unlabeled: nan - Iou Safe: 0.5281 - Iou Unsafe: 0.9832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Safe \| Accuracy Unsafe \| Iou Unlabeled \| Iou Safe \| Iou Unsafe \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------:\| \| 1.2069 \| 1.0 \| 10 \| 1.1287 \| 0.0406 \| 0.3613 \| 0.1117 \| nan \| 0.6267 \| 0.0960 \| 0.0 \| 0.0261 \| 0.0958 \| \| 1.196 \| 2.0 \| 20 \| 1.1408 \| 0.0465 \| 0.3971 \| 0.1274 \| nan \| 0.6837 \| 0.1105 \| 0.0 \| 0.0290 \| 0.1104 \| \| 1.1866 \| 3.0 \| 30 \| 1.1441 \| 0.0662 \| 0.4586 \| 0.1826 \| nan \| 0.7519 \| 0.1653 \| 0.0 \| 0.0335 \| 0.1652 \| \| 1.1701 \| 4.0 \| 40 \| 1.1350 \| 0.1016 \| 0.5469 \| 0.2805 \| nan \| 0.8301 \| 0.2638 \| 0.0 \| 0.0410 \| 0.2637 \| \| 1.1467 \| 5.0 \| 50 \| 1.1285 \| 0.1325 \| 0.6266 \| 0.3646 \| nan \| 0.9052 \| 0.3481 \| 0.0 \| 0.0496 \| 0.3481 \| \| 1.1126 \| 6.0 \| 60 \| 1.0914 \| 0.1933 \| 0.7318 \| 0.5257 \| nan \| 0.9508 \| 0.5128 \| 0.0 \| 0.0673 \| 0.5127 \| \| 1.0735 \| 7.0 \| 70 \| 1.0392 \| 0.2462 \| 0.8075 \| 0.6582 \| nan \| 0.9662 \| 0.6489 \| 0.0 \| 0.0900 \| 0.6487 \| \| 1.0335 \| 8.0 \| 80 \| 1.0015 \| 0.2783 \| 0.8456 \| 0.7301 \| nan \| 0.9683 \| 0.7228 \| 0.0 \| 0.1122 \| 0.7226 \| \| 1.0088 \| 9.0 \| 90 \| 0.9502 \| 0.3061 \| 0.8736 \| 0.7884 \| nan \| 0.9643 \| 0.7830 \| 0.0 \| 0.1359 \| 0.7825 \| \| 0.9993 \| 10.0 \| 100 \| 0.9158 \| 0.3246 \| 0.8886 \| 0.8232 \| nan \| 0.9581 \| 0.8191 \| 0.0 \| 0.1556 \| 0.8183 \| \| 0.9114 \| 11.0 \| 110 \| 0.8472 \| 0.3562 \| 0.9061 \| 0.8732 \| nan \| 0.9411 \| 0.8711 \| 0.0 \| 0.1990 \| 0.8697 \| \| 0.9027 \| 12.0 \| 120 \| 0.8073 \| 0.3687 \| 0.9085 \| 0.8909 \| nan \| 0.9271 \| 0.8898 \| 0.0 \| 0.2182 \| 0.8881 \| \| 0.8775 \| 13.0 \| 130 \| 0.7756 \| 0.3819 \| 0.9011 \| 0.9086 \| nan \| 0.8931 \| 0.9090 \| 0.0 \| 0.2394 \| 0.9062 \| \| 0.8532 \| 14.0 \| 140 \| 0.7544 \| 0.3883 \| 0.9005 \| 0.9156 \| nan \| 0.8844 \| 0.9166 \| 0.0 \| 0.2513 \| 0.9135 \| \| 0.7509 \| 15.0 \| 150 \| 0.7137 \| 0.4039 \| 0.8965 \| 0.9311 \| nan \| 0.8597 \| 0.9333 \| 0.0 \| 0.2824 \| 0.9294 \| \| 0.7711 \| 16.0 \| 160 \| 0.6837 \| 0.4131 \| 0.8959 \| 0.9394 \| nan \| 0.8497 \| 0.9422 \| 0.0 \| 0.3014 \| 0.9379 \| \| 0.7163 \| 17.0 \| 170 \| 0.6573 \| 0.4230 \| 0.8859 \| 0.9467 \| nan \| 0.8212 \| 0.9505 \| 0.0 \| 0.3234 \| 0.9454 \| \| 0.6609 \| 18.0 \| 180 \| 0.6698 \| 0.4200 \| 0.8889 \| 0.9449 \| nan \| 0.8294 \| 0.9484 \| 0.0 \| 0.3163 \| 0.9436 \| \| 0.7237 \| 19.0 \| 190 \| 0.6465 \| 0.4236 \| 0.8821 \| 0.9479 \| nan \| 0.8121 \| 0.9520 \| 0.0 \| 0.3241 \| 0.9467 \| \| 0.6264 \| 20.0 \| 200 \| 0.6300 \| 0.4293 \| 0.8776 \| 0.9520 \| nan \| 0.7985 \| 0.9566 \| 0.0 \| 0.3372 \| 0.9508 \| \| 0.6711 \| 21.0 \| 210 \| 0.6050 \| 0.4391 \| 0.8731 \| 0.9576 \| nan \| 0.7833 \| 0.9630 \| 0.0 \| 0.3605 \| 0.9567 \| \| 0.626 \| 22.0 \| 220 \| 0.5855 \| 0.4409 \| 0.8742 \| 0.9585 \| nan \| 0.7846 \| 0.9637 \| 0.0 \| 0.3653 \| 0.9575 \| \| 0.6103 \| 23.0 \| 230 \| 0.5651 \| 0.4474 \| 0.8671 \| 0.9623 \| nan \| 0.7658 \| 0.9683 \| 0.0 \| 0.3807 \| 0.9615 \| \| 0.6462 \| 24.0 \| 240 \| 0.5621 \| 0.4489 \| 0.8643 \| 0.9631 \| nan \| 0.7592 \| 0.9693 \| 0.0 \| 0.3844 \| 0.9623 \| \| 0.5442 \| 25.0 \| 250 \| 0.5460 \| 0.4563 \| 0.8592 \| 0.9668 \| nan \| 0.7450 \| 0.9735 \| 0.0 \| 0.4028 \| 0.9660 \| \| 0.6764 \| 26.0 \| 260 \| 0.5673 \| 0.4544 \| 0.8646 \| 0.9657 \| nan \| 0.7571 \| 0.9721 \| 0.0 \| 0.3983 \| 0.9650 \| \| 0.6471 \| 27.0 \| 270 \| 0.5412 \| 0.4586 \| 0.8561 \| 0.9679 \| nan \| 0.7374 \| 0.9749 \| 0.0 \| 0.4087 \| 0.9672 \| \| 0.5589 \| 28.0 \| 280 \| 0.5427 \| 0.4573 \| 0.8601 \| 0.9671 \| nan \| 0.7465 \| 0.9738 \| 0.0 \| 0.4057 \| 0.9663 \| \| 0.6512 \| 29.0 \| 290 \| 0.5264 \| 0.4600 \| 0.8567 \| 0.9681 \| nan \| 0.7384 \| 0.9751 \| 0.0 \| 0.4126 \| 0.9674 \| \| 0.6146 \| 30.0 \| 300 \| 0.5321 \| 0.4616 \| 0.8619 \| 0.9688 \| nan \| 0.7482 \| 0.9755 \| 0.0 \| 0.4167 \| 0.9681 \| \| 0.4938 \| 31.0 \| 310 \| 0.5025 \| 0.4751 \| 0.8475 \| 0.9744 \| nan \| 0.7127 \| 0.9823 \| 0.0 \| 0.4515 \| 0.9738 \| \| 0.4868 \| 32.0 \| 320 \| 0.4836 \| 0.4781 \| 0.8342 \| 0.9761 \| nan \| 0.6833 \| 0.9851 \| 0.0 \| 0.4586 \| 0.9756 \| \| 0.6315 \| 33.0 \| 330 \| 0.4918 \| 0.4739 \| 0.8479 \| 0.9740 \| nan \| 0.7139 \| 0.9819 \| 0.0 \| 0.4483 \| 0.9735 \| \| 0.5529 \| 34.0 \| 340 \| 0.4879 \| 0.4753 \| 0.8414 \| 0.9749 \| nan \| 0.6995 \| 0.9832 \| 0.0 \| 0.4516 \| 0.9743 \| \| 0.4592 \| 35.0 \| 350 \| 0.4826 \| 0.4764 \| 0.8364 \| 0.9754 \| nan \| 0.6887 \| 0.9842 \| 0.0 \| 0.4542 \| 0.9749 \| \| 0.5904 \| 36.0 \| 360 \| 0.4611 \| 0.4859 \| 0.8159 \| 0.9793 \| nan \| 0.6423 \| 0.9896 \| 0.0 \| 0.4789 \| 0.9789 \| \| 0.4804 \| 37.0 \| 370 \| 0.4654 \| 0.4796 \| 0.8359 \| 0.9764 \| nan \| 0.6865 \| 0.9853 \| 0.0 \| 0.4627 \| 0.9760 \| \| 0.4701 \| 38.0 \| 380 \| 0.4625 \| 0.4846 \| 0.8251 \| 0.9784 \| nan \| 0.6623 \| 0.9880 \| 0.0 \| 0.4758 \| 0.9779 \| \| 0.4729 \| 39.0 \| 390 \| 0.4536 \| 0.4838 \| 0.8231 \| 0.9783 \| nan \| 0.6582 \| 0.9881 \| 0.0 \| 0.4736 \| 0.9779 \| \| 0.4219 \| 40.0 \| 400 \| 0.4514 \| 0.4838 \| 0.8305 \| 0.9779 \| nan \| 0.6738 \| 0.9872 \| 0.0 \| 0.4739 \| 0.9775 \| \| 0.6494 \| 41.0 \| 410 \| 0.4425 \| 0.4892 \| 0.8162 \| 0.9801 \| nan \| 0.6420 \| 0.9904 \| 0.0 \| 0.4878 \| 0.9797 \| \| 0.4616 \| 42.0 \| 420 \| 0.4390 \| 0.7316 \| 0.8225 \| 0.9794 \| nan \| 0.6558 \| 0.9892 \| nan \| 0.4842 \| 0.9790 \| \| 0.4408 \| 43.0 \| 430 \| 0.4419 \| 0.7358 \| 0.8272 \| 0.9797 \| nan \| 0.6652 \| 0.9893 \| nan \| 0.4923 \| 0.9793 \| \| 0.4532 \| 44.0 \| 440 \| 0.4371 \| 0.7375 \| 0.8274 \| 0.9800 \| nan \| 0.6651 \| 0.9896 \| nan \| 0.4954 \| 0.9796 \| \| 0.5015 \| 45.0 \| 450 \| 0.4376 \| 0.7364 \| 0.8276 \| 0.9798 \| nan \| 0.6659 \| 0.9894 \| nan \| 0.4933 \| 0.9794 \| \| 0.4965 \| 46.0 \| 460 \| 0.4201 \| 0.7405 \| 0.8137 \| 0.9812 \| nan \| 0.6357 \| 0.9918 \| nan \| 0.5002 \| 0.9809 \| \| 0.4837 \| 47.0 \| 470 \| 0.4281 \| 0.7378 \| 0.8279 \| 0.9800 \| nan \| 0.6662 \| 0.9896 \| nan \| 0.4961 \| 0.9796 \| \| 0.4221 \| 48.0 \| 480 \| 0.4288 \| 0.7371 \| 0.8227 \| 0.9802 \| nan \| 0.6553 \| 0.9901 \| nan \| 0.4944 \| 0.9798 \| \| 0.4491 \| 49.0 \| 490 \| 0.4152 \| 0.7371 \| 0.8074 \| 0.9811 \| nan \| 0.6228 \| 0.9920 \| nan \| 0.4935 \| 0.9808 \| \| 0.4121 \| 50.0 \| 500 \| 0.4159 \| 0.7367 \| 0.8063 \| 0.9811 \| nan \| 0.6205 \| 0.9921 \| nan \| 0.4927 \| 0.9808 \| \| 0.4727 \| 51.0 \| 510 \| 0.4199 \| 0.7354 \| 0.8095 \| 0.9807 \| nan \| 0.6274 \| 0.9915 \| nan \| 0.4905 \| 0.9804 \| \| 0.5323 \| 52.0 \| 520 \| 0.4079 \| 0.7383 \| 0.8074 \| 0.9813 \| nan \| 0.6227 \| 0.9922 \| nan \| 0.4957 \| 0.9809 \| \| 0.409 \| 53.0 \| 530 \| 0.4103 \| 0.7392 \| 0.8161 \| 0.9809 \| nan \| 0.6409 \| 0.9913 \| nan \| 0.4978 \| 0.9805 \| \| 0.6391 \| 54.0 \| 540 \| 0.4063 \| 0.7406 \| 0.8133 \| 0.9813 \| nan \| 0.6349 \| 0.9918 \| nan \| 0.5003 \| 0.9809 \| \| 0.3905 \| 55.0 \| 550 \| 0.4000 \| 0.7409 \| 0.8122 \| 0.9814 \| nan \| 0.6325 \| 0.9920 \| nan \| 0.5007 \| 0.9810 \| \| 0.4138 \| 56.0 \| 560 \| 0.4028 \| 0.7398 \| 0.8183 \| 0.9809 \| nan \| 0.6455 \| 0.9911 \| nan \| 0.4990 \| 0.9805 \| \| 0.3977 \| 57.0 \| 570 \| 0.3865 \| 0.7372 \| 0.7912 \| 0.9821 \| nan \| 0.5884 \| 0.9941 \| nan \| 0.4926 \| 0.9818 \| \| 0.4186 \| 58.0 \| 580 \| 0.3845 \| 0.7416 \| 0.7994 \| 0.9822 \| nan \| 0.6050 \| 0.9937 \| nan \| 0.5014 \| 0.9819 \| \| 0.4921 \| 59.0 \| 590 \| 0.3881 \| 0.7427 \| 0.8102 \| 0.9817 \| nan \| 0.6278 \| 0.9925 \| nan \| 0.5039 \| 0.9814 \| \| 0.3953 \| 60.0 \| 600 \| 0.3823 \| 0.7429 \| 0.8027 \| 0.9822 \| nan \| 0.6119 \| 0.9935 \| nan \| 0.5039 \| 0.9819 \| \| 0.4263 \| 61.0 \| 610 \| 0.3841 \| 0.7420 \| 0.8075 \| 0.9818 \| nan \| 0.6222 \| 0.9928 \| nan \| 0.5026 \| 0.9815 \| \| 0.3798 \| 62.0 \| 620 \| 0.3763 \| 0.7446 \| 0.8054 \| 0.9823 \| nan \| 0.6174 \| 0.9934 \| nan \| 0.5072 \| 0.9820 \| \| 0.4208 \| 63.0 \| 630 \| 0.3724 \| 0.7437 \| 0.7919 \| 0.9829 \| nan \| 0.5888 \| 0.9949 \| nan \| 0.5047 \| 0.9826 \| \| 0.3627 \| 64.0 \| 640 \| 0.3760 \| 0.7466 \| 0.8111 \| 0.9822 \| nan \| 0.6292 \| 0.9930 \| nan \| 0.5112 \| 0.9819 \| \| 0.4156 \| 65.0 \| 650 \| 0.3669 \| 0.7478 \| 0.8018 \| 0.9829 \| nan \| 0.6092 \| 0.9943 \| nan \| 0.5130 \| 0.9826 \| \| 0.468 \| 66.0 \| 660 \| 0.3706 \| 0.7508 \| 0.8145 \| 0.9826 \| nan \| 0.6359 \| 0.9932 \| nan \| 0.5193 \| 0.9823 \| \| 0.4547 \| 67.0 \| 670 \| 0.3692 \| 0.7512 \| 0.8189 \| 0.9824 \| nan \| 0.6451 \| 0.9927 \| nan \| 0.5204 \| 0.9821 \| \| 0.3604 \| 68.0 \| 680 \| 0.3691 \| 0.7520 \| 0.8152 \| 0.9827 \| nan \| 0.6371 \| 0.9933 \| nan \| 0.5215 \| 0.9824 \| \| 0.4476 \| 69.0 \| 690 \| 0.3679 \| 0.7516 \| 0.8195 \| 0.9825 \| nan \| 0.6463 \| 0.9927 \| nan \| 0.5210 \| 0.9821 \| \| 0.3535 \| 70.0 \| 700 \| 0.3589 \| 0.7522 \| 0.8097 \| 0.9831 \| nan \| 0.6255 \| 0.9939 \| nan \| 0.5217 \| 0.9827 \| \| 0.3539 \| 71.0 \| 710 \| 0.3572 \| 0.7526 \| 0.8091 \| 0.9831 \| nan \| 0.6242 \| 0.9941 \| nan \| 0.5224 \| 0.9828 \| \| 0.3675 \| 72.0 \| 720 \| 0.3589 \| 0.7518 \| 0.8100 \| 0.9830 \| nan \| 0.6261 \| 0.9939 \| nan \| 0.5209 \| 0.9827 \| \| 0.4148 \| 73.0 \| 730 \| 0.3536 \| 0.7504 \| 0.8093 \| 0.9828 \| nan \| 0.6249 \| 0.9937 \| nan \| 0.5182 \| 0.9825 \| \| 0.3941 \| 74.0 \| 740 \| 0.3538 \| 0.7497 \| 0.8099 \| 0.9827 \| nan \| 0.6263 \| 0.9936 \| nan \| 0.5169 \| 0.9824 \| \| 0.4264 \| 75.0 \| 750 \| 0.3595 \| 0.7469 \| 0.8197 \| 0.9818 \| nan \| 0.6473 \| 0.9920 \| nan \| 0.5123 \| 0.9814 \| \| 0.3815 \| 76.0 \| 760 \| 0.3525 \| 0.7492 \| 0.8097 \| 0.9827 \| nan \| 0.6258 \| 0.9935 \| nan \| 0.5162 \| 0.9823 \| \| 0.3459 \| 77.0 \| 770 \| 0.3443 \| 0.7452 \| 0.7926 \| 0.9831 \| nan \| 0.5901 \| 0.9951 \| nan \| 0.5076 \| 0.9828 \| \| 0.3794 \| 78.0 \| 780 \| 0.3538 \| 0.7501 \| 0.8154 \| 0.9825 \| nan \| 0.6377 \| 0.9930 \| nan \| 0.5180 \| 0.9821 \| \| 0.3761 \| 79.0 \| 790 \| 0.3525 \| 0.7483 \| 0.8169 \| 0.9821 \| nan \| 0.6412 \| 0.9925 \| nan \| 0.5147 \| 0.9818 \| \| 0.3612 \| 80.0 \| 800 \| 0.3495 \| 0.7513 \| 0.8128 \| 0.9828 \| nan \| 0.6321 \| 0.9934 \| nan \| 0.5201 \| 0.9824 \| \| 0.405 \| 81.0 \| 810 \| 0.3466 \| 0.7502 \| 0.8148 \| 0.9825 \| nan \| 0.6365 \| 0.9931 \| nan \| 0.5182 \| 0.9822 \| \| 0.4289 \| 82.0 \| 820 \| 0.3458 \| 0.7498 \| 0.8092 \| 0.9828 \| nan \| 0.6247 \| 0.9937 \| nan \| 0.5171 \| 0.9824 \| \| 0.3523 \| 83.0 \| 830 \| 0.3435 \| 0.7503 \| 0.8112 \| 0.9827 \| nan \| 0.6288 \| 0.9935 \| nan \| 0.5183 \| 0.9824 \| \| 0.4254 \| 84.0 \| 840 \| 0.3403 \| 0.7495 \| 0.8000 \| 0.9832 \| nan \| 0.6052 \| 0.9947 \| nan \| 0.5160 \| 0.9829 \| \| 0.3399 \| 85.0 \| 850 \| 0.3355 \| 0.7492 \| 0.8003 \| 0.9832 \| nan \| 0.6059 \| 0.9947 \| nan \| 0.5155 \| 0.9829 \| \| 0.3251 \| 86.0 \| 860 \| 0.3395 \| 0.7503 \| 0.8028 \| 0.9832 \| nan \| 0.6111 \| 0.9945 \| nan \| 0.5178 \| 0.9829 \| \| 0.3748 \| 87.0 \| 870 \| 0.3400 \| 0.7502 \| 0.8117 \| 0.9827 \| nan \| 0.6299 \| 0.9934 \| nan \| 0.5181 \| 0.9824 \| \| 0.4398 \| 88.0 \| 880 \| 0.3450 \| 0.7527 \| 0.8197 \| 0.9826 \| nan \| 0.6466 \| 0.9928 \| nan \| 0.5231 \| 0.9822 \| \| 0.3782 \| 89.0 \| 890 \| 0.3454 \| 0.7547 \| 0.8180 \| 0.9829 \| nan \| 0.6426 \| 0.9933 \| nan \| 0.5268 \| 0.9826 \| \| 0.4318 \| 90.0 \| 900 \| 0.3424 \| 0.7541 \| 0.8162 \| 0.9830 \| nan \| 0.6390 \| 0.9934 \| nan \| 0.5255 \| 0.9826 \| \| 0.3428 \| 91.0 \| 910 \| 0.3327 \| 0.7541 \| 0.8124 \| 0.9832 \| nan \| 0.6309 \| 0.9939 \| nan \| 0.5253 \| 0.9828 \| \| 0.4303 \| 92.0 \| 920 \| 0.3364 \| 0.7525 \| 0.8108 \| 0.9830 \| nan \| 0.6277 \| 0.9939 \| nan \| 0.5223 \| 0.9827 \| \| 0.3624 \| 93.0 \| 930 \| 0.3277 \| 0.7531 \| 0.8063 \| 0.9834 \| nan \| 0.6182 \| 0.9945 \| nan \| 0.5231 \| 0.9830 \| \| 0.3418 \| 94.0 \| 940 \| 0.3315 \| 0.7548 \| 0.8125 \| 0.9833 \| nan \| 0.6311 \| 0.9940 \| nan \| 0.5267 \| 0.9829 \| \| 0.321 \| 95.0 \| 950 \| 0.3266 \| 0.7541 \| 0.8070 \| 0.9835 \| nan \| 0.6195 \| 0.9945 \| nan \| 0.5251 \| 0.9831 \| \| 0.3152 \| 96.0 \| 960 \| 0.3265 \| 0.7531 \| 0.8025 \| 0.9836 \| nan \| 0.6101 \| 0.9949 \| nan \| 0.5230 \| 0.9833 \| \| 0.3153 \| 97.0 \| 970 \| 0.3263 \| 0.7537 \| 0.8048 \| 0.9835 \| nan \| 0.6149 \| 0.9947 \| nan \| 0.5243 \| 0.9832 \| \| 0.3158 \| 98.0 \| 980 \| 0.3299 \| 0.7553 \| 0.8139 \| 0.9832 \| nan \| 0.6340 \| 0.9939 \| nan \| 0.5278 \| 0.9829 \| \| 0.3162 \| 99.0 \| 990 \| 0.3248 \| 0.7546 \| 0.8076 \| 0.9835 \| nan \| 0.6207 \| 0.9945 \| nan \| 0.5260 \| 0.9832 \| \| 0.3748 \| 100.0 \| 1000 \| 0.3238 \| 0.7553 \| 0.8077 \| 0.9836 \| nan \| 0.6208 \| 0.9946 \| nan \| 0.5274 \| 0.9833 \| \| 0.3598 \| 101.0 \| 1010 \| 0.3221 \| 0.7544 \| 0.8096 \| 0.9833 \| nan \| 0.6250 \| 0.9943 \| nan \| 0.5257 \| 0.9830 \| \| 0.3245 \| 102.0 \| 1020 \| 0.3247 \| 0.7527 \| 0.8156 \| 0.9828 \| nan \| 0.6380 \| 0.9933 \| nan \| 0.5228 \| 0.9825 \| \| 0.3527 \| 103.0 \| 1030 \| 0.3275 \| 0.7537 \| 0.8193 \| 0.9827 \| nan \| 0.6456 \| 0.9930 \| nan \| 0.5250 \| 0.9824 \| \| 0.5087 \| 104.0 \| 1040 \| 0.3221 \| 0.7559 \| 0.8105 \| 0.9835 \| nan \| 0.6266 \| 0.9944 \| nan \| 0.5287 \| 0.9832 \| \| 0.3331 \| 105.0 \| 1050 \| 0.3183 \| 0.7560 \| 0.8064 \| 0.9837 \| nan \| 0.6180 \| 0.9948 \| nan \| 0.5285 \| 0.9834 \| \| 0.324 \| 106.0 \| 1060 \| 0.3198 \| 0.7561 \| 0.8090 \| 0.9836 \| nan \| 0.6235 \| 0.9946 \| nan \| 0.5289 \| 0.9833 \| \| 0.3512 \| 107.0 \| 1070 \| 0.3194 \| 0.7549 \| 0.8052 \| 0.9836 \| nan \| 0.6155 \| 0.9949 \| nan \| 0.5265 \| 0.9833 \| \| 0.3274 \| 108.0 \| 1080 \| 0.3185 \| 0.7569 \| 0.8122 \| 0.9835 \| nan \| 0.6301 \| 0.9943 \| nan \| 0.5306 \| 0.9832 \| \| 0.335 \| 109.0 \| 1090 \| 0.3177 \| 0.7554 \| 0.8081 \| 0.9836 \| nan \| 0.6217 \| 0.9946 \| nan \| 0.5276 \| 0.9832 \| \| 0.3581 \| 110.0 \| 1100 \| 0.3204 \| 0.7568 \| 0.8146 \| 0.9834 \| nan \| 0.6352 \| 0.9940 \| nan \| 0.5306 \| 0.9831 \| \| 0.3307 \| 111.0 \| 1110 \| 0.3216 \| 0.7571 \| 0.8138 \| 0.9835 \| nan \| 0.6335 \| 0.9941 \| nan \| 0.5310 \| 0.9832 \| \| 0.3162 \| 112.0 \| 1120 \| 0.3227 \| 0.7575 \| 0.8181 \| 0.9833 \| nan \| 0.6425 \| 0.9937 \| nan \| 0.5320 \| 0.9830 \| \| 0.3687 \| 113.0 \| 1130 \| 0.3188 \| 0.7567 \| 0.8124 \| 0.9835 \| nan \| 0.6306 \| 0.9942 \| nan \| 0.5302 \| 0.9832 \| \| 0.4099 \| 114.0 \| 1140 \| 0.3151 \| 0.7550 \| 0.8063 \| 0.9836 \| nan \| 0.6178 \| 0.9947 \| nan \| 0.5266 \| 0.9833 \| \| 0.3283 \| 115.0 \| 1150 \| 0.3152 \| 0.7557 \| 0.8088 \| 0.9836 \| nan \| 0.6232 \| 0.9945 \| nan \| 0.5281 \| 0.9832 \| \| 0.3118 \| 116.0 \| 1160 \| 0.3180 \| 0.7556 \| 0.8097 \| 0.9835 \| nan \| 0.6249 \| 0.9944 \| nan \| 0.5280 \| 0.9832 \| \| 0.3233 \| 117.0 \| 1170 \| 0.3164 \| 0.7551 \| 0.8070 \| 0.9836 \| nan \| 0.6192 \| 0.9947 \| nan \| 0.5269 \| 0.9833 \| \| 0.3401 \| 118.0 \| 1180 \| 0.3192 \| 0.7562 \| 0.8122 \| 0.9834 \| nan \| 0.6303 \| 0.9942 \| nan \| 0.5292 \| 0.9831 \| \| 0.3867 \| 119.0 \| 1190 \| 0.3199 \| 0.7566 \| 0.8160 \| 0.9833 \| nan \| 0.6382 \| 0.9938 \| nan \| 0.5302 \| 0.9830 \| \| 0.3217 \| 120.0 \| 1200 \| 0.3221 \| 0.7557 \| 0.8092 \| 0.9835 \| nan \| 0.6240 \| 0.9945 \| nan \| 0.5281 \| 0.9832 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_all dataset. It achieves the following results on the evaluation set: - Loss: 0.3095 - Mean Iou: 0.5513 - Mean Accuracy: 0.7874 - Overall Accuracy: 0.9260 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9668 - Accuracy Fallo malla: 0.6808 - Accuracy Fallo adhesivo: 0.9727 - Accuracy Fallo burbuja: 0.5291 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9167 - Iou Fallo malla: 0.6189 - Iou Fallo adhesivo: 0.7307 - Iou Fallo burbuja: 0.4903 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.1378 \| 1.0 \| 783 \| 0.2677 \| 0.4895 \| 0.7143 \| 0.9122 \| nan \| 0.9724 \| 0.5531 \| 0.9663 \| 0.3654 \| 0.0 \| 0.9038 \| 0.5327 \| 0.6757 \| 0.3351 \| \| 0.1117 \| 2.0 \| 1566 \| 0.2305 \| 0.5289 \| 0.7978 \| 0.9246 \| nan \| 0.9507 \| 0.7727 \| 0.9705 \| 0.4974 \| 0.0 \| 0.9214 \| 0.6808 \| 0.5876 \| 0.4549 \| \| 0.0881 \| 3.0 \| 2349 \| 0.2041 \| 0.5556 \| 0.7867 \| 0.9354 \| nan \| 0.9712 \| 0.7391 \| 0.9389 \| 0.4975 \| 0.0 \| 0.9273 \| 0.6790 \| 0.7323 \| 0.4394 \| \| 0.0878 \| 4.0 \| 3132 \| 0.1984 \| 0.5584 \| 0.8003 \| 0.9346 \| nan \| 0.9556 \| 0.8247 \| 0.9602 \| 0.4606 \| 0.0 \| 0.9261 \| 0.6935 \| 0.7373 \| 0.4352 \| \| 0.0895 \| 5.0 \| 3915 \| 0.2841 \| 0.5246 \| 0.8086 \| 0.9088 \| nan \| 0.9137 \| 0.8834 \| 0.9719 \| 0.4652 \| 0.0 \| 0.8964 \| 0.6309 \| 0.6593 \| 0.4365 \| \| 0.0773 \| 6.0 \| 4698 \| 0.2547 \| 0.5652 \| 0.7823 \| 0.9336 \| nan \| 0.9775 \| 0.6843 \| 0.9384 \| 0.5291 \| 0.0 \| 0.9251 \| 0.6378 \| 0.7820 \| 0.4813 \| \| 0.0667 \| 7.0 \| 5481 \| 0.2726 \| 0.5609 \| 0.7932 \| 0.9295 \| nan \| 0.9741 \| 0.6609 \| 0.9689 \| 0.5689 \| 0.0 \| 0.9203 \| 0.6202 \| 0.7548 \| 0.5093 \| \| 0.0678 \| 8.0 \| 6264 \| 0.2950 \| 0.5276 \| 0.8002 \| 0.9175 \| nan \| 0.9443 \| 0.7561 \| 0.9713 \| 0.5292 \| 0.0 \| 0.9089 \| 0.6570 \| 0.5900 \| 0.4822 \| \| 0.0653 \| 9.0 \| 7047 \| 0.2712 \| 0.5467 \| 0.7682 \| 0.9288 \| nan \| 0.9690 \| 0.6971 \| 0.9641 \| 0.4425 \| 0.0 \| 0.9189 \| 0.6330 \| 0.7588 \| 0.4228 \| \| 0.0646 \| 10.0 \| 7830 \| 0.2841 \| 0.5499 \| 0.7819 \| 0.9272 \| nan \| 0.9681 \| 0.6840 \| 0.9688 \| 0.5068 \| 0.0 \| 0.9178 \| 0.6243 \| 0.7345 \| 0.4728 \| \| 0.057 \| 11.0 \| 8613 \| 0.3373 \| 0.5257 \| 0.7782 \| 0.9166 \| nan \| 0.9593 \| 0.6555 \| 0.9739 \| 0.5242 \| 0.0 \| 0.9075 \| 0.6040 \| 0.6319 \| 0.4848 \| \| 0.0591 \| 12.0 \| 9396 \| 0.3082 \| 0.5504 \| 0.7900 \| 0.9247 \| nan \| 0.9656 \| 0.6776 \| 0.9705 \| 0.5463 \| 0.0 \| 0.9148 \| 0.6172 \| 0.7182 \| 0.5019 \| \| 0.053 \| 12.77 \| 10000 \| 0.3095 \| 0.5513 \| 0.7874 \| 0.9260 \| nan \| 0.9668 \| 0.6808 \| 0.9727 \| 0.5291 \| 0.0 \| 0.9167 \| 0.6189 \| 0.7307 \| 0.4903 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3	[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]
PostsDesert/segformer-b5-finetuned-segments-instryde-foot-test	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b5-finetuned-segments-instryde-foot-test This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 - Mean Iou: 0.4800 - Mean Accuracy: 0.9599 - Overall Accuracy: 0.9599 - Per Category Iou: [0.0, 0.9599216842864238] - Per Category Accuracy: [nan, 0.9599216842864238] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------------:\|:-------------------------:\| \| 0.1024 \| 0.27 \| 20 \| 0.2085 \| 0.4534 \| 0.9067 \| 0.9067 \| [0.0, 0.9067344993758137] \| [nan, 0.9067344993758137] \| \| 0.0431 \| 0.53 \| 40 \| 0.0487 \| 0.4604 \| 0.9207 \| 0.9207 \| [0.0, 0.9207331455341442] \| [nan, 0.9207331455341442] \| \| 0.0354 \| 0.8 \| 60 \| 0.0319 \| 0.4577 \| 0.9155 \| 0.9155 \| [0.0, 0.9154662028576415] \| [nan, 0.9154662028576415] \| \| 0.0389 \| 1.07 \| 80 \| 0.0276 \| 0.4629 \| 0.9257 \| 0.9257 \| [0.0, 0.9257162800419576] \| [nan, 0.9257162800419576] \| \| 0.0208 \| 1.33 \| 100 \| 0.0244 \| 0.4702 \| 0.9404 \| 0.9404 \| [0.0, 0.9403945317069335] \| [nan, 0.9403945317069335] \| \| 0.0241 \| 1.6 \| 120 \| 0.0212 \| 0.4703 \| 0.9406 \| 0.9406 \| [0.0, 0.9406131407017349] \| [nan, 0.9406131407017349] \| \| 0.0167 \| 1.87 \| 140 \| 0.0208 \| 0.4761 \| 0.9521 \| 0.9521 \| [0.0, 0.9521215619420916] \| [nan, 0.9521215619420916] \| \| 0.0156 \| 2.13 \| 160 \| 0.0205 \| 0.4612 \| 0.9224 \| 0.9224 \| [0.0, 0.9224359945462809] \| [nan, 0.9224359945462809] \| \| 0.0156 \| 2.4 \| 180 \| 0.0208 \| 0.4734 \| 0.9468 \| 0.9468 \| [0.0, 0.9467575875538612] \| [nan, 0.9467575875538612] \| \| 0.0167 \| 2.67 \| 200 \| 0.0182 \| 0.4833 \| 0.9667 \| 0.9667 \| [0.0, 0.9666659635383208] \| [nan, 0.9666659635383208] \| \| 0.0145 \| 2.93 \| 220 \| 0.0243 \| 0.4351 \| 0.8702 \| 0.8702 \| [0.0, 0.8702122233110058] \| [nan, 0.8702122233110058] \| \| 0.0114 \| 3.2 \| 240 \| 0.0176 \| 0.4686 \| 0.9373 \| 0.9373 \| [0.0, 0.93726765603217] \| [nan, 0.93726765603217] \| \| 0.0155 \| 3.47 \| 260 \| 0.0161 \| 0.4770 \| 0.9541 \| 0.9541 \| [0.0, 0.9540767701096305] \| [nan, 0.9540767701096305] \| \| 0.0158 \| 3.73 \| 280 \| 0.0169 \| 0.4684 \| 0.9368 \| 0.9368 \| [0.0, 0.9368239181251786] \| [nan, 0.9368239181251786] \| \| 0.0114 \| 4.0 \| 300 \| 0.0162 \| 0.4777 \| 0.9554 \| 0.9554 \| [0.0, 0.9554348305492647] \| [nan, 0.9554348305492647] \| \| 0.0112 \| 4.27 \| 320 \| 0.0159 \| 0.4839 \| 0.9678 \| 0.9678 \| [0.0, 0.9677532556440432] \| [nan, 0.9677532556440432] \| \| 0.0131 \| 4.53 \| 340 \| 0.0154 \| 0.4811 \| 0.9622 \| 0.9622 \| [0.0, 0.9622032718479555] \| [nan, 0.9622032718479555] \| \| 0.0101 \| 4.8 \| 360 \| 0.0156 \| 0.4683 \| 0.9367 \| 0.9367 \| [0.0, 0.9366846987126999] \| [nan, 0.9366846987126999] \| \| 0.0102 \| 5.07 \| 380 \| 0.0152 \| 0.4758 \| 0.9517 \| 0.9517 \| [0.0, 0.9516509773164403] \| [nan, 0.9516509773164403] \| \| 0.0101 \| 5.33 \| 400 \| 0.0169 \| 0.4884 \| 0.9768 \| 0.9768 \| [0.0, 0.9768393358121804] \| [nan, 0.9768393358121804] \| \| 0.0082 \| 5.6 \| 420 \| 0.0150 \| 0.4761 \| 0.9522 \| 0.9522 \| [0.0, 0.9522462074215836] \| [nan, 0.9522462074215836] \| \| 0.01 \| 5.87 \| 440 \| 0.0152 \| 0.4788 \| 0.9576 \| 0.9576 \| [0.0, 0.9575745140264517] \| [nan, 0.9575745140264517] \| \| 0.0098 \| 6.13 \| 460 \| 0.0148 \| 0.4783 \| 0.9565 \| 0.9565 \| [0.0, 0.9565489693736469] \| [nan, 0.9565489693736469] \| \| 0.0088 \| 6.4 \| 480 \| 0.0153 \| 0.4795 \| 0.9591 \| 0.9591 \| [0.0, 0.959051850601846] \| [nan, 0.959051850601846] \| \| 0.0091 \| 6.67 \| 500 \| 0.0152 \| 0.4828 \| 0.9656 \| 0.9656 \| [0.0, 0.965590177169167] \| [nan, 0.965590177169167] \| \| 0.0102 \| 6.93 \| 520 \| 0.0149 \| 0.4800 \| 0.9599 \| 0.9599 \| [0.0, 0.9599216842864238] \| [nan, 0.9599216842864238] \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "unlabeled", "foot" ]
sam1120/safety-utcustom-train-SF-RGB-b5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2236 - Mean Iou: 0.5051 - Mean Accuracy: 0.8441 - Overall Accuracy: 0.9820 - Accuracy Unlabeled: nan - Accuracy Safe: 0.6977 - Accuracy Unsafe: 0.9906 - Iou Unlabeled: 0.0 - Iou Safe: 0.5337 - Iou Unsafe: 0.9816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 130 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 1.2239 \| 0.91 \| 10 \| 0.3992 \| nan \| 0.2951 \| 0.0314 \| 0.0 \| 0.2939 \| 1.1103 \| 0.3472 \| 0.1084 \| 0.2982 \| \| 1.1948 \| 1.82 \| 20 \| 0.5219 \| nan \| 0.3705 \| 0.0440 \| 0.0 \| 0.3689 \| 1.0963 \| 0.4462 \| 0.1376 \| 0.3750 \| \| 1.1661 \| 2.73 \| 30 \| 0.5863 \| nan \| 0.4988 \| 0.0647 \| 0.0 \| 0.4961 \| 1.0516 \| 0.5426 \| 0.1870 \| 0.5014 \| \| 1.1112 \| 3.64 \| 40 \| 0.5459 \| nan \| 0.5794 \| 0.0900 \| 0.0 \| 0.5754 \| 1.0048 \| 0.5626 \| 0.2218 \| 0.5784 \| \| 1.0907 \| 4.55 \| 50 \| 0.5993 \| nan \| 0.6367 \| 0.1094 \| 0.0 \| 0.6321 \| 0.9690 \| 0.6180 \| 0.2472 \| 0.6356 \| \| 1.047 \| 5.45 \| 60 \| 0.6692 \| nan \| 0.6699 \| 0.1159 \| 0.0 \| 0.6656 \| 0.9437 \| 0.6695 \| 0.2605 \| 0.6699 \| \| 1.0112 \| 6.36 \| 70 \| 0.6673 \| nan \| 0.7189 \| 0.1349 \| 0.0 \| 0.7137 \| 0.9084 \| 0.6931 \| 0.2829 \| 0.7173 \| \| 0.9925 \| 7.27 \| 80 \| 0.6842 \| nan \| 0.7665 \| 0.1452 \| 0.0 \| 0.7605 \| 0.8647 \| 0.7254 \| 0.3019 \| 0.7641 \| \| 0.9395 \| 8.18 \| 90 \| 0.6818 \| nan \| 0.7921 \| 0.1620 \| 0.0 \| 0.7856 \| 0.8319 \| 0.7369 \| 0.3159 \| 0.7888 \| \| 0.8902 \| 9.09 \| 100 \| 0.6806 \| nan \| 0.8142 \| 0.1770 \| 0.0 \| 0.8072 \| 0.8014 \| 0.7474 \| 0.3281 \| 0.8102 \| \| 0.9057 \| 10.0 \| 110 \| 0.6984 \| nan \| 0.8179 \| 0.1733 \| 0.0 \| 0.8109 \| 0.7867 \| 0.7581 \| 0.3281 \| 0.8143 \| \| 0.8321 \| 10.91 \| 120 \| 0.6744 \| nan \| 0.8494 \| 0.1862 \| 0.0 \| 0.8413 \| 0.7440 \| 0.7619 \| 0.3425 \| 0.8442 \| \| 0.8152 \| 11.82 \| 130 \| 0.6688 \| nan \| 0.8590 \| 0.2006 \| 0.0 \| 0.8507 \| 0.7270 \| 0.7639 \| 0.3504 \| 0.8534 \| \| 0.7929 \| 12.73 \| 140 \| 0.6660 \| nan \| 0.8657 \| 0.2085 \| 0.0 \| 0.8572 \| 0.7045 \| 0.7658 \| 0.3553 \| 0.8598 \| \| 0.7568 \| 13.64 \| 150 \| 0.6571 \| nan \| 0.8838 \| 0.2185 \| 0.0 \| 0.8748 \| 0.6744 \| 0.7704 \| 0.3644 \| 0.8771 \| \| 0.7085 \| 14.55 \| 160 \| 0.6519 \| nan \| 0.8934 \| 0.2260 \| 0.0 \| 0.8842 \| 0.6556 \| 0.7727 \| 0.3701 \| 0.8863 \| \| 0.7147 \| 15.45 \| 170 \| 0.6561 \| nan \| 0.8964 \| 0.2283 \| 0.0 \| 0.8872 \| 0.6509 \| 0.7762 \| 0.3718 \| 0.8893 \| \| 0.6991 \| 16.36 \| 180 \| 0.6620 \| nan \| 0.8964 \| 0.2267 \| 0.0 \| 0.8874 \| 0.6502 \| 0.7792 \| 0.3714 \| 0.8895 \| \| 0.6357 \| 17.27 \| 190 \| 0.6612 \| nan \| 0.9051 \| 0.2411 \| 0.0 \| 0.8960 \| 0.6230 \| 0.7831 \| 0.3790 \| 0.8979 \| \| 0.6815 \| 18.18 \| 200 \| 0.6484 \| nan \| 0.9178 \| 0.2594 \| 0.0 \| 0.9082 \| 0.5993 \| 0.7831 \| 0.3892 \| 0.9098 \| \| 0.6398 \| 19.09 \| 210 \| 0.6414 \| nan \| 0.9258 \| 0.2682 \| 0.0 \| 0.9159 \| 0.5785 \| 0.7836 \| 0.3947 \| 0.9174 \| \| 0.5845 \| 20.0 \| 220 \| 0.6426 \| nan \| 0.9286 \| 0.2698 \| 0.0 \| 0.9187 \| 0.5641 \| 0.7856 \| 0.3962 \| 0.9202 \| \| 0.6062 \| 20.91 \| 230 \| 0.6520 \| nan \| 0.9252 \| 0.2641 \| 0.0 \| 0.9156 \| 0.5693 \| 0.7886 \| 0.3932 \| 0.9171 \| \| 0.6071 \| 21.82 \| 240 \| 0.6592 \| nan \| 0.9283 \| 0.2675 \| 0.0 \| 0.9188 \| 0.5627 \| 0.7937 \| 0.3955 \| 0.9203 \| \| 0.6209 \| 22.73 \| 250 \| 0.6619 \| nan \| 0.9300 \| 0.2724 \| 0.0 \| 0.9205 \| 0.5632 \| 0.7959 \| 0.3977 \| 0.9220 \| \| 0.5609 \| 23.64 \| 260 \| 0.6505 \| nan \| 0.9379 \| 0.2868 \| 0.0 \| 0.9281 \| 0.5416 \| 0.7942 \| 0.4050 \| 0.9294 \| \| 0.5752 \| 24.55 \| 270 \| 0.6412 \| nan \| 0.9451 \| 0.2983 \| 0.0 \| 0.9350 \| 0.5141 \| 0.7932 \| 0.4111 \| 0.9362 \| \| 0.6004 \| 25.45 \| 280 \| 0.6492 \| nan \| 0.9412 \| 0.2907 \| 0.0 \| 0.9313 \| 0.5255 \| 0.7952 \| 0.4073 \| 0.9326 \| \| 0.5524 \| 26.36 \| 290 \| 0.6588 \| nan \| 0.9387 \| 0.2868 \| 0.0 \| 0.9291 \| 0.5314 \| 0.7987 \| 0.4053 \| 0.9304 \| \| 0.5758 \| 27.27 \| 300 \| 0.6544 \| nan \| 0.9423 \| 0.2913 \| 0.0 \| 0.9326 \| 0.5268 \| 0.7984 \| 0.4080 \| 0.9338 \| \| 0.5598 \| 28.18 \| 310 \| 0.6605 \| nan \| 0.9408 \| 0.2897 \| 0.0 \| 0.9312 \| 0.5240 \| 0.8006 \| 0.4070 \| 0.9325 \| \| 0.5505 \| 29.09 \| 320 \| 0.6582 \| nan \| 0.9421 \| 0.2959 \| 0.0 \| 0.9324 \| 0.5165 \| 0.8002 \| 0.4094 \| 0.9337 \| \| 0.5754 \| 30.0 \| 330 \| 0.6578 \| nan \| 0.9433 \| 0.2959 \| 0.0 \| 0.9336 \| 0.5145 \| 0.8005 \| 0.4098 \| 0.9348 \| \| 0.5284 \| 30.91 \| 340 \| 0.6719 \| nan \| 0.9411 \| 0.2941 \| 0.0 \| 0.9318 \| 0.5175 \| 0.8065 \| 0.4086 \| 0.9331 \| \| 0.5463 \| 31.82 \| 350 \| 0.6684 \| nan \| 0.9448 \| 0.3020 \| 0.0 \| 0.9354 \| 0.5016 \| 0.8066 \| 0.4125 \| 0.9367 \| \| 0.4923 \| 32.73 \| 360 \| 0.6688 \| nan \| 0.9463 \| 0.3066 \| 0.0 \| 0.9369 \| 0.4947 \| 0.8075 \| 0.4145 \| 0.9381 \| \| 0.4922 \| 33.64 \| 370 \| 0.6685 \| nan \| 0.9504 \| 0.3165 \| 0.0 \| 0.9409 \| 0.4738 \| 0.8094 \| 0.4191 \| 0.9420 \| \| 0.4976 \| 34.55 \| 380 \| 0.6748 \| nan \| 0.9535 \| 0.3233 \| 0.0 \| 0.9443 \| 0.4663 \| 0.8142 \| 0.4225 \| 0.9453 \| \| 0.4922 \| 35.45 \| 390 \| 0.6509 \| nan \| 0.9653 \| 0.3484 \| 0.0 \| 0.9552 \| 0.4295 \| 0.8081 \| 0.4345 \| 0.9560 \| \| 0.4608 \| 36.36 \| 400 \| 0.6580 \| nan \| 0.9637 \| 0.3507 \| 0.0 \| 0.9538 \| 0.4434 \| 0.8109 \| 0.4348 \| 0.9547 \| \| 0.4836 \| 37.27 \| 410 \| 0.6522 \| nan \| 0.9662 \| 0.3588 \| 0.0 \| 0.9561 \| 0.4328 \| 0.8092 \| 0.4383 \| 0.9569 \| \| 0.459 \| 38.18 \| 420 \| 0.6477 \| nan \| 0.9691 \| 0.3632 \| 0.0 \| 0.9588 \| 0.4211 \| 0.8084 \| 0.4407 \| 0.9596 \| \| 0.4528 \| 39.09 \| 430 \| 0.6593 \| nan \| 0.9668 \| 0.3574 \| 0.0 \| 0.9569 \| 0.4239 \| 0.8131 \| 0.4381 \| 0.9577 \| \| 0.4202 \| 40.0 \| 440 \| 0.6572 \| nan \| 0.9689 \| 0.3650 \| 0.0 \| 0.9590 \| 0.4141 \| 0.8130 \| 0.4413 \| 0.9597 \| \| 0.4805 \| 40.91 \| 450 \| 0.6470 \| nan \| 0.9724 \| 0.3754 \| 0.0 \| 0.9621 \| 0.4012 \| 0.8097 \| 0.4458 \| 0.9628 \| \| 0.4611 \| 41.82 \| 460 \| 0.6525 \| nan \| 0.9718 \| 0.3716 \| 0.0 \| 0.9617 \| 0.4025 \| 0.8122 \| 0.4444 \| 0.9624 \| \| 0.4339 \| 42.73 \| 470 \| 0.6487 \| nan \| 0.9726 \| 0.3744 \| 0.0 \| 0.9624 \| 0.3951 \| 0.8107 \| 0.4456 \| 0.9631 \| \| 0.4361 \| 43.64 \| 480 \| 0.6448 \| nan \| 0.9740 \| 0.3769 \| 0.0 \| 0.9636 \| 0.3946 \| 0.8094 \| 0.4468 \| 0.9643 \| \| 0.4416 \| 44.55 \| 490 \| 0.6447 \| nan \| 0.9746 \| 0.3783 \| 0.0 \| 0.9642 \| 0.3871 \| 0.8097 \| 0.4475 \| 0.9649 \| \| 0.4524 \| 45.45 \| 500 \| 0.6589 \| nan \| 0.9712 \| 0.3701 \| 0.0 \| 0.9612 \| 0.4025 \| 0.8151 \| 0.4438 \| 0.9620 \| \| 0.4319 \| 46.36 \| 510 \| 0.6730 \| nan \| 0.9673 \| 0.3594 \| 0.0 \| 0.9578 \| 0.4169 \| 0.8202 \| 0.4391 \| 0.9586 \| \| 0.4224 \| 47.27 \| 520 \| 0.6603 \| nan \| 0.9712 \| 0.3716 \| 0.0 \| 0.9613 \| 0.3986 \| 0.8158 \| 0.4443 \| 0.9620 \| \| 0.4333 \| 48.18 \| 530 \| 0.6650 \| nan \| 0.9703 \| 0.3724 \| 0.0 \| 0.9605 \| 0.4038 \| 0.8176 \| 0.4443 \| 0.9612 \| \| 0.3916 \| 49.09 \| 540 \| 0.6624 \| nan \| 0.9724 \| 0.3781 \| 0.0 \| 0.9626 \| 0.3968 \| 0.8174 \| 0.4469 \| 0.9633 \| \| 0.4803 \| 50.0 \| 550 \| 0.6680 \| nan \| 0.9726 \| 0.3809 \| 0.0 \| 0.9629 \| 0.3942 \| 0.8203 \| 0.4479 \| 0.9636 \| \| 0.3543 \| 50.91 \| 560 \| 0.6473 \| nan \| 0.9777 \| 0.3952 \| 0.0 \| 0.9673 \| 0.3697 \| 0.8125 \| 0.4542 \| 0.9680 \| \| 0.3684 \| 51.82 \| 570 \| 0.6515 \| nan \| 0.9772 \| 0.3951 \| 0.0 \| 0.9670 \| 0.3708 \| 0.8143 \| 0.4540 \| 0.9676 \| \| 0.4004 \| 52.73 \| 580 \| 0.6437 \| nan \| 0.9793 \| 0.4014 \| 0.0 \| 0.9688 \| 0.3585 \| 0.8115 \| 0.4567 \| 0.9694 \| \| 0.3656 \| 53.64 \| 590 \| 0.6559 \| nan \| 0.9780 \| 0.4010 \| 0.0 \| 0.9679 \| 0.3654 \| 0.8169 \| 0.4563 \| 0.9685 \| \| 0.3918 \| 54.55 \| 600 \| 0.6432 \| nan \| 0.9809 \| 0.4115 \| 0.0 \| 0.9704 \| 0.3527 \| 0.8121 \| 0.4606 \| 0.9709 \| \| 0.3741 \| 55.45 \| 610 \| 0.6393 \| nan \| 0.9827 \| 0.4185 \| 0.0 \| 0.9720 \| 0.3361 \| 0.8110 \| 0.4635 \| 0.9726 \| \| 0.3656 \| 56.36 \| 620 \| 0.6540 \| nan \| 0.9807 \| 0.4147 \| 0.0 \| 0.9705 \| 0.3473 \| 0.8174 \| 0.4617 \| 0.9710 \| \| 0.3341 \| 57.27 \| 630 \| 0.6258 \| nan \| 0.9845 \| 0.4247 \| 0.0 \| 0.9734 \| 0.3335 \| 0.8052 \| 0.4660 \| 0.9739 \| \| 0.3669 \| 58.18 \| 640 \| 0.6495 \| nan \| 0.9815 \| 0.4190 \| 0.0 \| 0.9712 \| 0.3395 \| 0.8155 \| 0.4634 \| 0.9717 \| \| 0.3347 \| 59.09 \| 650 \| 0.6612 \| nan \| 0.9800 \| 0.4174 \| 0.0 \| 0.9700 \| 0.3416 \| 0.8206 \| 0.4625 \| 0.9706 \| \| 0.4287 \| 60.0 \| 660 \| 0.6673 \| nan \| 0.9797 \| 0.4185 \| 0.0 \| 0.9699 \| 0.3419 \| 0.8235 \| 0.4628 \| 0.9705 \| \| 0.3838 \| 60.91 \| 670 \| 0.6611 \| nan \| 0.9812 \| 0.4227 \| 0.0 \| 0.9712 \| 0.3381 \| 0.8211 \| 0.4646 \| 0.9718 \| \| 0.352 \| 61.82 \| 680 \| 0.6407 \| nan \| 0.9845 \| 0.4318 \| 0.0 \| 0.9738 \| 0.3216 \| 0.8126 \| 0.4685 \| 0.9743 \| \| 0.3343 \| 62.73 \| 690 \| 0.6499 \| nan \| 0.9837 \| 0.4311 \| 0.0 \| 0.9733 \| 0.3275 \| 0.8168 \| 0.4681 \| 0.9738 \| \| 0.3443 \| 63.64 \| 700 \| 0.6528 \| nan \| 0.9836 \| 0.4324 \| 0.0 \| 0.9733 \| 0.3273 \| 0.8182 \| 0.4686 \| 0.9738 \| \| 0.3183 \| 64.55 \| 710 \| 0.6456 \| nan \| 0.9848 \| 0.4367 \| 0.0 \| 0.9743 \| 0.3155 \| 0.8152 \| 0.4703 \| 0.9748 \| \| 0.3346 \| 65.45 \| 720 \| 0.6517 \| nan \| 0.9841 \| 0.4356 \| 0.0 \| 0.9738 \| 0.3212 \| 0.8179 \| 0.4698 \| 0.9743 \| \| 0.3225 \| 66.36 \| 730 \| 0.6367 \| nan \| 0.9863 \| 0.4432 \| 0.0 \| 0.9755 \| 0.3052 \| 0.8115 \| 0.4729 \| 0.9759 \| \| 0.3792 \| 67.27 \| 740 \| 0.6381 \| nan \| 0.9861 \| 0.4429 \| 0.0 \| 0.9753 \| 0.3037 \| 0.8121 \| 0.4728 \| 0.9758 \| \| 0.3177 \| 68.18 \| 750 \| 0.6345 \| nan \| 0.9865 \| 0.4446 \| 0.0 \| 0.9756 \| 0.2989 \| 0.8105 \| 0.4734 \| 0.9761 \| \| 0.3295 \| 69.09 \| 760 \| 0.6404 \| nan \| 0.9859 \| 0.4426 \| 0.0 \| 0.9752 \| 0.3064 \| 0.8131 \| 0.4726 \| 0.9757 \| \| 0.3847 \| 70.0 \| 770 \| 0.6429 \| nan \| 0.9857 \| 0.4439 \| 0.0 \| 0.9751 \| 0.3054 \| 0.8143 \| 0.4730 \| 0.9756 \| \| 0.3406 \| 70.91 \| 780 \| 0.6443 \| nan \| 0.9862 \| 0.4476 \| 0.0 \| 0.9756 \| 0.3075 \| 0.8152 \| 0.4744 \| 0.9761 \| \| 0.3847 \| 71.82 \| 790 \| 0.6343 \| nan \| 0.9877 \| 0.4546 \| 0.0 \| 0.9769 \| 0.2911 \| 0.8110 \| 0.4772 \| 0.9773 \| \| 0.3292 \| 72.73 \| 800 \| 0.6328 \| nan \| 0.9881 \| 0.4567 \| 0.0 \| 0.9771 \| 0.2905 \| 0.8105 \| 0.4779 \| 0.9776 \| \| 0.3156 \| 73.64 \| 810 \| 0.6318 \| nan \| 0.9882 \| 0.4579 \| 0.0 \| 0.9773 \| 0.2865 \| 0.8100 \| 0.4784 \| 0.9777 \| \| 0.3106 \| 74.55 \| 820 \| 0.6333 \| nan \| 0.9884 \| 0.4600 \| 0.0 \| 0.9775 \| 0.2812 \| 0.8109 \| 0.4792 \| 0.9779 \| \| 0.3004 \| 75.45 \| 830 \| 0.6232 \| nan \| 0.9893 \| 0.4632 \| 0.0 \| 0.9781 \| 0.2798 \| 0.8063 \| 0.4804 \| 0.9785 \| \| 0.3336 \| 76.36 \| 840 \| 0.6485 \| nan \| 0.9872 \| 0.4593 \| 0.0 \| 0.9768 \| 0.2954 \| 0.8178 \| 0.4787 \| 0.9772 \| \| 0.299 \| 77.27 \| 850 \| 0.6490 \| nan \| 0.9874 \| 0.4613 \| 0.0 \| 0.9769 \| 0.2909 \| 0.8182 \| 0.4794 \| 0.9774 \| \| 0.292 \| 78.18 \| 860 \| 0.6497 \| nan \| 0.9875 \| 0.4629 \| 0.0 \| 0.9771 \| 0.2853 \| 0.8186 \| 0.4800 \| 0.9775 \| \| 0.2922 \| 79.09 \| 870 \| 0.6586 \| nan \| 0.9866 \| 0.4601 \| 0.0 \| 0.9765 \| 0.2917 \| 0.8226 \| 0.4789 \| 0.9770 \| \| 0.3583 \| 80.0 \| 880 \| 0.6515 \| nan \| 0.9876 \| 0.4644 \| 0.0 \| 0.9772 \| 0.2876 \| 0.8195 \| 0.4805 \| 0.9776 \| \| 0.293 \| 80.91 \| 890 \| 0.6465 \| nan \| 0.9882 \| 0.4674 \| 0.0 \| 0.9777 \| 0.2767 \| 0.8173 \| 0.4817 \| 0.9781 \| \| 0.3287 \| 81.82 \| 900 \| 0.6518 \| nan \| 0.9876 \| 0.4652 \| 0.0 \| 0.9773 \| 0.2858 \| 0.8197 \| 0.4808 \| 0.9777 \| \| 0.3067 \| 82.73 \| 910 \| 0.6528 \| nan \| 0.9875 \| 0.4654 \| 0.0 \| 0.9772 \| 0.2861 \| 0.8202 \| 0.4809 \| 0.9776 \| \| 0.3374 \| 83.64 \| 920 \| 0.6577 \| nan \| 0.9870 \| 0.4631 \| 0.0 \| 0.9768 \| 0.2869 \| 0.8224 \| 0.4800 \| 0.9773 \| \| 0.3171 \| 84.55 \| 930 \| 0.6442 \| nan \| 0.9887 \| 0.4714 \| 0.0 \| 0.9781 \| 0.2706 \| 0.8164 \| 0.4832 \| 0.9785 \| \| 0.3156 \| 85.45 \| 940 \| 0.6321 \| nan \| 0.9899 \| 0.4757 \| 0.0 \| 0.9789 \| 0.2708 \| 0.8110 \| 0.4849 \| 0.9793 \| \| 0.2749 \| 86.36 \| 950 \| 0.6518 \| nan \| 0.9887 \| 0.4765 \| 0.0 \| 0.9783 \| 0.2760 \| 0.8202 \| 0.4850 \| 0.9787 \| \| 0.2725 \| 87.27 \| 960 \| 0.6681 \| nan \| 0.9876 \| 0.4765 \| 0.0 \| 0.9777 \| 0.2780 \| 0.8279 \| 0.4847 \| 0.9782 \| \| 0.2948 \| 88.18 \| 970 \| 0.6565 \| nan \| 0.9891 \| 0.4845 \| 0.0 \| 0.9788 \| 0.2636 \| 0.8228 \| 0.4878 \| 0.9793 \| \| 0.2972 \| 89.09 \| 980 \| 0.6722 \| nan \| 0.9879 \| 0.4829 \| 0.0 \| 0.9782 \| 0.2770 \| 0.8301 \| 0.4870 \| 0.9786 \| \| 0.3101 \| 90.0 \| 990 \| 0.6711 \| nan \| 0.9882 \| 0.4859 \| 0.0 \| 0.9784 \| 0.2765 \| 0.8297 \| 0.4881 \| 0.9788 \| \| 0.2874 \| 90.91 \| 1000 \| 0.6689 \| nan \| 0.9888 \| 0.4899 \| 0.0 \| 0.9789 \| 0.2690 \| 0.8288 \| 0.4896 \| 0.9793 \| \| 0.275 \| 91.82 \| 1010 \| 0.6542 \| nan \| 0.9901 \| 0.4947 \| 0.0 \| 0.9798 \| 0.2593 \| 0.8221 \| 0.4915 \| 0.9802 \| \| 0.2711 \| 92.73 \| 1020 \| 0.6673 \| nan \| 0.9893 \| 0.4957 \| 0.0 \| 0.9794 \| 0.2608 \| 0.8283 \| 0.4917 \| 0.9798 \| \| 0.2691 \| 93.64 \| 1030 \| 0.6819 \| nan \| 0.9884 \| 0.4954 \| 0.0 \| 0.9789 \| 0.2609 \| 0.8352 \| 0.4915 \| 0.9794 \| \| 0.274 \| 94.55 \| 1040 \| 0.6722 \| nan \| 0.9895 \| 0.5007 \| 0.0 \| 0.9797 \| 0.2542 \| 0.8309 \| 0.4935 \| 0.9801 \| \| 0.27 \| 95.45 \| 1050 \| 0.6436 \| nan \| 0.9919 \| 0.5087 \| 0.0 \| 0.9812 \| 0.2357 \| 0.8177 \| 0.4966 \| 0.9816 \| \| 0.255 \| 96.36 \| 1060 \| 0.6671 \| nan \| 0.9903 \| 0.5074 \| 0.0 \| 0.9804 \| 0.2460 \| 0.8287 \| 0.4959 \| 0.9808 \| \| 0.2756 \| 97.27 \| 1070 \| 0.6634 \| nan \| 0.9909 \| 0.5113 \| 0.0 \| 0.9809 \| 0.2411 \| 0.8271 \| 0.4974 \| 0.9812 \| \| 0.2473 \| 98.18 \| 1080 \| 0.6724 \| nan \| 0.9904 \| 0.5116 \| 0.0 \| 0.9806 \| 0.2447 \| 0.8314 \| 0.4974 \| 0.9810 \| \| 0.266 \| 99.09 \| 1090 \| 0.6778 \| nan \| 0.9901 \| 0.5118 \| 0.0 \| 0.9804 \| 0.2455 \| 0.8339 \| 0.4974 \| 0.9808 \| \| 0.2682 \| 100.0 \| 1100 \| 0.6651 \| nan \| 0.9911 \| 0.5156 \| 0.0 \| 0.9811 \| 0.2359 \| 0.8281 \| 0.4989 \| 0.9815 \| \| 0.2607 \| 100.91 \| 1110 \| 0.6739 \| nan \| 0.9905 \| 0.5144 \| 0.0 \| 0.9808 \| 0.2376 \| 0.8322 \| 0.4984 \| 0.9812 \| \| 0.2506 \| 101.82 \| 1120 \| 0.6727 \| nan \| 0.9907 \| 0.5153 \| 0.0 \| 0.9809 \| 0.2380 \| 0.8317 \| 0.4987 \| 0.9813 \| \| 0.2729 \| 102.73 \| 1130 \| 0.6802 \| nan \| 0.9902 \| 0.5146 \| 0.0 \| 0.9806 \| 0.2375 \| 0.8352 \| 0.4984 \| 0.9810 \| \| 0.2348 \| 103.64 \| 1140 \| 0.6731 \| nan \| 0.9908 \| 0.5172 \| 0.0 \| 0.9810 \| 0.2335 \| 0.8319 \| 0.4994 \| 0.9814 \| \| 0.2409 \| 104.55 \| 1150 \| 0.6781 \| nan \| 0.9904 \| 0.5164 \| 0.0 \| 0.9808 \| 0.2385 \| 0.8343 \| 0.4991 \| 0.9812 \| \| 0.2737 \| 105.45 \| 1160 \| 0.6774 \| nan \| 0.9906 \| 0.5177 \| 0.0 \| 0.9809 \| 0.2357 \| 0.8340 \| 0.4995 \| 0.9813 \| \| 0.2857 \| 106.36 \| 1170 \| 0.6659 \| nan \| 0.9915 \| 0.5203 \| 0.0 \| 0.9815 \| 0.2304 \| 0.8287 \| 0.5006 \| 0.9818 \| \| 0.2503 \| 107.27 \| 1180 \| 0.6822 \| nan \| 0.9902 \| 0.5172 \| 0.0 \| 0.9807 \| 0.2397 \| 0.8362 \| 0.4993 \| 0.9811 \| \| 0.2524 \| 108.18 \| 1190 \| 0.6741 \| nan \| 0.9909 \| 0.5199 \| 0.0 \| 0.9812 \| 0.2294 \| 0.8325 \| 0.5004 \| 0.9816 \| \| 0.2722 \| 109.09 \| 1200 \| 0.6928 \| nan \| 0.9894 \| 0.5145 \| 0.0 \| 0.9802 \| 0.2461 \| 0.8411 \| 0.4982 \| 0.9806 \| \| 0.2761 \| 110.0 \| 1210 \| 0.2530 \| 0.4979 \| 0.8435 \| 0.9804 \| nan \| 0.6981 \| 0.9889 \| 0.0 \| 0.5136 \| 0.9799 \| \| 0.2595 \| 110.91 \| 1220 \| 0.2386 \| 0.5002 \| 0.8395 \| 0.9811 \| nan \| 0.6891 \| 0.9900 \| 0.0 \| 0.5197 \| 0.9808 \| \| 0.2877 \| 111.82 \| 1230 \| 0.2260 \| 0.5022 \| 0.8345 \| 0.9819 \| nan \| 0.6779 \| 0.9911 \| 0.0 \| 0.5251 \| 0.9815 \| \| 0.2307 \| 112.73 \| 1240 \| 0.2219 \| 0.5031 \| 0.8317 \| 0.9822 \| nan \| 0.6717 \| 0.9916 \| 0.0 \| 0.5275 \| 0.9818 \| \| 0.2559 \| 113.64 \| 1250 \| 0.2382 \| 0.5011 \| 0.8413 \| 0.9812 \| nan \| 0.6927 \| 0.9900 \| 0.0 \| 0.5224 \| 0.9808 \| \| 0.2668 \| 114.55 \| 1260 \| 0.2275 \| 0.5024 \| 0.8395 \| 0.9816 \| nan \| 0.6883 \| 0.9906 \| 0.0 \| 0.5258 \| 0.9813 \| \| 0.2269 \| 115.45 \| 1270 \| 0.2212 \| 0.5038 \| 0.8356 \| 0.9821 \| nan \| 0.6798 \| 0.9913 \| 0.0 \| 0.5296 \| 0.9818 \| \| 0.2713 \| 116.36 \| 1280 \| 0.2265 \| 0.5041 \| 0.8365 \| 0.9821 \| nan \| 0.6817 \| 0.9913 \| 0.0 \| 0.5305 \| 0.9818 \| \| 0.2351 \| 117.27 \| 1290 \| 0.2113 \| 0.5064 \| 0.8255 \| 0.9832 \| nan \| 0.6580 \| 0.9931 \| 0.0 \| 0.5363 \| 0.9828 \| \| 0.2306 \| 118.18 \| 1300 \| 0.2258 \| 0.5053 \| 0.8354 \| 0.9824 \| nan \| 0.6791 \| 0.9917 \| 0.0 \| 0.5337 \| 0.9821 \| \| 0.2371 \| 119.09 \| 1310 \| 0.2234 \| 0.5057 \| 0.8342 \| 0.9826 \| nan \| 0.6765 \| 0.9919 \| 0.0 \| 0.5348 \| 0.9822 \| \| 0.277 \| 120.0 \| 1320 \| 0.2263 \| 0.5061 \| 0.8340 \| 0.9827 \| nan \| 0.6760 \| 0.9920 \| 0.0 \| 0.5359 \| 0.9823 \| \| 0.2121 \| 120.91 \| 1330 \| 0.2191 \| 0.5065 \| 0.8337 \| 0.9828 \| nan \| 0.6752 \| 0.9921 \| 0.0 \| 0.5370 \| 0.9824 \| \| 0.2426 \| 121.82 \| 1340 \| 0.2237 \| 0.5055 \| 0.8400 \| 0.9822 \| nan \| 0.6889 \| 0.9912 \| 0.0 \| 0.5345 \| 0.9819 \| \| 0.2429 \| 122.73 \| 1350 \| 0.2106 \| 0.5069 \| 0.8340 \| 0.9829 \| nan \| 0.6757 \| 0.9922 \| 0.0 \| 0.5383 \| 0.9825 \| \| 0.244 \| 123.64 \| 1360 \| 0.2133 \| 0.5062 \| 0.8395 \| 0.9824 \| nan \| 0.6876 \| 0.9914 \| 0.0 \| 0.5364 \| 0.9821 \| \| 0.2287 \| 124.55 \| 1370 \| 0.2126 \| 0.5068 \| 0.8371 \| 0.9827 \| nan \| 0.6823 \| 0.9918 \| 0.0 \| 0.5381 \| 0.9823 \| \| 0.2223 \| 125.45 \| 1380 \| 0.1988 \| 0.5083 \| 0.8305 \| 0.9833 \| nan \| 0.6681 \| 0.9929 \| 0.0 \| 0.5420 \| 0.9830 \| \| 0.2275 \| 126.36 \| 1390 \| 0.2164 \| 0.5061 \| 0.8409 \| 0.9823 \| nan \| 0.6906 \| 0.9912 \| 0.0 \| 0.5363 \| 0.9820 \| \| 0.2378 \| 127.27 \| 1400 \| 0.2201 \| 0.5062 \| 0.8405 \| 0.9824 \| nan \| 0.6898 \| 0.9913 \| 0.0 \| 0.5365 \| 0.9820 \| \| 0.2385 \| 128.18 \| 1410 \| 0.2286 \| 0.5046 \| 0.8455 \| 0.9818 \| nan \| 0.7006 \| 0.9903 \| 0.0 \| 0.5324 \| 0.9814 \| \| 0.2787 \| 129.09 \| 1420 \| 0.2157 \| 0.5063 \| 0.8405 \| 0.9824 \| nan \| 0.6896 \| 0.9913 \| 0.0 \| 0.5369 \| 0.9820 \| \| 0.2377 \| 130.0 \| 1430 \| 0.2236 \| 0.5051 \| 0.8441 \| 0.9820 \| nan \| 0.6977 \| 0.9906 \| 0.0 \| 0.5337 \| 0.9816 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/safety-utcustom-train-SF-RGBD-b0	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF-RGBD-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1043 - Mean Iou: 0.7188 - Mean Accuracy: 0.7607 - Overall Accuracy: 0.9815 - Accuracy Unlabeled: nan - Accuracy Safe: 0.5261 - Accuracy Unsafe: 0.9953 - Iou Unlabeled: nan - Iou Safe: 0.4564 - Iou Unsafe: 0.9812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 130 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 1.0084 \| 1.0 \| 10 \| 0.0368 \| nan \| 0.7845 \| 0.0163 \| 0.0 \| 0.7666 \| 1.0688 \| 0.4107 \| 0.2610 \| 0.7625 \| \| 0.8483 \| 2.0 \| 20 \| 0.0002 \| nan \| 0.9980 \| 0.0002 \| 0.0 \| 0.9687 \| 0.8740 \| 0.4991 \| 0.3230 \| 0.9686 \| \| 0.7058 \| 3.0 \| 30 \| 0.0009 \| nan \| 0.9930 \| 0.0009 \| 0.0 \| 0.9641 \| 0.7416 \| 0.4969 \| 0.3217 \| 0.9637 \| \| 0.578 \| 4.0 \| 40 \| 0.0007 \| nan \| 0.9953 \| 0.0007 \| 0.0 \| 0.9662 \| 0.5969 \| 0.4980 \| 0.3223 \| 0.9659 \| \| 0.5531 \| 5.0 \| 50 \| 0.0061 \| nan \| 0.9974 \| 0.0059 \| 0.0 \| 0.9682 \| 0.5068 \| 0.5018 \| 0.3247 \| 0.9681 \| \| 0.4786 \| 6.0 \| 60 \| 0.0097 \| nan \| 0.9961 \| 0.0092 \| 0.0 \| 0.9671 \| 0.4575 \| 0.5029 \| 0.3254 \| 0.9670 \| \| 0.4681 \| 7.0 \| 70 \| 0.0067 \| nan \| 0.9983 \| 0.0064 \| 0.0 \| 0.9690 \| 0.4382 \| 0.5025 \| 0.3251 \| 0.9690 \| \| 0.4139 \| 8.0 \| 80 \| 0.0017 \| nan \| 0.9980 \| 0.0016 \| 0.0 \| 0.9686 \| 0.3973 \| 0.4998 \| 0.3234 \| 0.9686 \| \| 0.4275 \| 9.0 \| 90 \| 0.0077 \| nan \| 0.9994 \| 0.0076 \| nan \| 0.9701 \| 0.3983 \| 0.5036 \| 0.4888 \| 0.9701 \| \| 0.3975 \| 10.0 \| 100 \| 0.0008 \| nan \| 0.9998 \| 0.0008 \| 0.0 \| 0.9702 \| 0.3398 \| 0.5003 \| 0.3237 \| 0.9702 \| \| 0.4325 \| 11.0 \| 110 \| 0.0941 \| nan \| 0.9993 \| 0.0919 \| 0.0 \| 0.9725 \| 0.3785 \| 0.5467 \| 0.3548 \| 0.9725 \| \| 0.3239 \| 12.0 \| 120 \| 0.0772 \| nan \| 0.9995 \| 0.0759 \| 0.0 \| 0.9722 \| 0.3338 \| 0.5383 \| 0.3493 \| 0.9722 \| \| 0.3733 \| 13.0 \| 130 \| 0.0763 \| nan \| 0.9995 \| 0.0751 \| nan \| 0.9722 \| 0.3013 \| 0.5379 \| 0.5236 \| 0.9722 \| \| 0.3165 \| 14.0 \| 140 \| 0.0800 \| nan \| 0.9994 \| 0.0786 \| nan \| 0.9722 \| 0.2849 \| 0.5397 \| 0.5254 \| 0.9723 \| \| 0.3329 \| 15.0 \| 150 \| 0.1118 \| nan \| 0.9990 \| 0.1083 \| nan \| 0.9727 \| 0.3002 \| 0.5554 \| 0.5405 \| 0.9728 \| \| 0.3214 \| 16.0 \| 160 \| 0.0908 \| nan \| 0.9995 \| 0.0892 \| nan \| 0.9726 \| 0.2725 \| 0.5451 \| 0.5309 \| 0.9726 \| \| 0.2744 \| 17.0 \| 170 \| 0.1573 \| nan \| 0.9986 \| 0.1503 \| nan \| 0.9736 \| 0.2896 \| 0.5780 \| 0.5620 \| 0.9737 \| \| 0.2948 \| 18.0 \| 180 \| 0.1330 \| nan \| 0.9989 \| 0.1282 \| nan \| 0.9732 \| 0.2564 \| 0.5659 \| 0.5507 \| 0.9733 \| \| 0.2653 \| 19.0 \| 190 \| 0.1732 \| nan \| 0.9987 \| 0.1660 \| nan \| 0.9742 \| 0.2518 \| 0.5860 \| 0.5701 \| 0.9743 \| \| 0.3026 \| 20.0 \| 200 \| 0.1408 \| nan \| 0.9990 \| 0.1364 \| nan \| 0.9735 \| 0.2531 \| 0.5699 \| 0.5550 \| 0.9737 \| \| 0.2649 \| 21.0 \| 210 \| 0.1802 \| nan \| 0.9986 \| 0.1722 \| nan \| 0.9743 \| 0.2384 \| 0.5894 \| 0.5732 \| 0.9744 \| \| 0.2431 \| 22.0 \| 220 \| 0.1993 \| nan \| 0.9983 \| 0.1890 \| nan \| 0.9746 \| 0.2390 \| 0.5988 \| 0.5818 \| 0.9747 \| \| 0.2608 \| 23.0 \| 230 \| 0.2317 \| nan \| 0.9981 \| 0.2181 \| nan \| 0.9753 \| 0.2355 \| 0.6149 \| 0.5967 \| 0.9755 \| \| 0.223 \| 24.0 \| 240 \| 0.1697 \| nan \| 0.9989 \| 0.1637 \| nan \| 0.9743 \| 0.2290 \| 0.5843 \| 0.5690 \| 0.9744 \| \| 0.2448 \| 25.0 \| 250 \| 0.2141 \| nan \| 0.9985 \| 0.2037 \| nan \| 0.9751 \| 0.2262 \| 0.6063 \| 0.5894 \| 0.9753 \| \| 0.2547 \| 26.0 \| 260 \| 0.2737 \| nan \| 0.9978 \| 0.2555 \| nan \| 0.9763 \| 0.2281 \| 0.6357 \| 0.6159 \| 0.9764 \| \| 0.2266 \| 27.0 \| 270 \| 0.2391 \| nan \| 0.9981 \| 0.2252 \| nan \| 0.9755 \| 0.2191 \| 0.6186 \| 0.6004 \| 0.9757 \| \| 0.2357 \| 28.0 \| 280 \| 0.2227 \| nan \| 0.9985 \| 0.2122 \| nan \| 0.9754 \| 0.2218 \| 0.6106 \| 0.5938 \| 0.9756 \| \| 0.2563 \| 29.0 \| 290 \| 0.1852 \| nan \| 0.9988 \| 0.1782 \| nan \| 0.9746 \| 0.2096 \| 0.5920 \| 0.5764 \| 0.9748 \| \| 0.226 \| 30.0 \| 300 \| 0.2844 \| nan \| 0.9977 \| 0.2643 \| nan \| 0.9764 \| 0.2121 \| 0.6410 \| 0.6203 \| 0.9766 \| \| 0.2221 \| 31.0 \| 310 \| 0.2718 \| nan \| 0.9978 \| 0.2533 \| nan \| 0.9761 \| 0.2016 \| 0.6348 \| 0.6147 \| 0.9763 \| \| 0.2317 \| 32.0 \| 320 \| 0.2649 \| nan \| 0.9982 \| 0.2499 \| nan \| 0.9763 \| 0.2008 \| 0.6315 \| 0.6131 \| 0.9765 \| \| 0.2643 \| 33.0 \| 330 \| 0.3254 \| nan \| 0.9976 \| 0.3014 \| nan \| 0.9775 \| 0.1989 \| 0.6615 \| 0.6394 \| 0.9777 \| \| 0.2118 \| 34.0 \| 340 \| 0.3347 \| nan \| 0.9977 \| 0.3117 \| nan \| 0.9779 \| 0.1901 \| 0.6662 \| 0.6448 \| 0.9782 \| \| 0.2133 \| 35.0 \| 350 \| 0.3619 \| nan \| 0.9976 \| 0.3350 \| nan \| 0.9785 \| 0.1917 \| 0.6797 \| 0.6568 \| 0.9788 \| \| 0.2064 \| 36.0 \| 360 \| 0.3401 \| nan \| 0.9978 \| 0.3174 \| nan \| 0.9782 \| 0.1860 \| 0.6690 \| 0.6478 \| 0.9784 \| \| 0.2341 \| 37.0 \| 370 \| 0.2704 \| nan \| 0.9983 \| 0.2557 \| nan \| 0.9766 \| 0.1775 \| 0.6343 \| 0.6162 \| 0.9768 \| \| 0.2093 \| 38.0 \| 380 \| 0.3552 \| nan \| 0.9928 \| 0.2874 \| nan \| 0.9737 \| 0.1934 \| 0.6740 \| 0.6306 \| 0.9740 \| \| 0.1958 \| 39.0 \| 390 \| 0.3001 \| nan \| 0.9980 \| 0.2818 \| nan \| 0.9772 \| 0.1755 \| 0.6491 \| 0.6295 \| 0.9774 \| \| 0.1886 \| 40.0 \| 400 \| 0.3881 \| nan \| 0.9969 \| 0.3522 \| nan \| 0.9787 \| 0.1768 \| 0.6925 \| 0.6654 \| 0.9789 \| \| 0.1734 \| 41.0 \| 410 \| 0.3948 \| nan \| 0.9973 \| 0.3626 \| nan \| 0.9793 \| 0.1745 \| 0.6960 \| 0.6709 \| 0.9795 \| \| 0.1795 \| 42.0 \| 420 \| 0.4168 \| nan \| 0.9970 \| 0.3789 \| nan \| 0.9796 \| 0.1710 \| 0.7069 \| 0.6793 \| 0.9798 \| \| 0.222 \| 43.0 \| 430 \| 0.4041 \| nan \| 0.9972 \| 0.3700 \| nan \| 0.9794 \| 0.1706 \| 0.7007 \| 0.6747 \| 0.9797 \| \| 0.1831 \| 44.0 \| 440 \| 0.4044 \| nan \| 0.9972 \| 0.3708 \| nan \| 0.9795 \| 0.1687 \| 0.7008 \| 0.6752 \| 0.9797 \| \| 0.1935 \| 45.0 \| 450 \| 0.4347 \| nan \| 0.9964 \| 0.3889 \| nan \| 0.9796 \| 0.1711 \| 0.7155 \| 0.6842 \| 0.9798 \| \| 0.1728 \| 46.0 \| 460 \| 0.4208 \| nan \| 0.9969 \| 0.3819 \| nan \| 0.9796 \| 0.1714 \| 0.7088 \| 0.6808 \| 0.9799 \| \| 0.1742 \| 47.0 \| 470 \| 0.3898 \| nan \| 0.9974 \| 0.3590 \| nan \| 0.9792 \| 0.1670 \| 0.6936 \| 0.6691 \| 0.9794 \| \| 0.2064 \| 48.0 \| 480 \| 0.4209 \| nan \| 0.9970 \| 0.3827 \| nan \| 0.9797 \| 0.1683 \| 0.7089 \| 0.6812 \| 0.9799 \| \| 0.1946 \| 49.0 \| 490 \| 0.3746 \| nan \| 0.9976 \| 0.3471 \| nan \| 0.9790 \| 0.1659 \| 0.6861 \| 0.6630 \| 0.9792 \| \| 0.1836 \| 50.0 \| 500 \| 0.4487 \| nan \| 0.9965 \| 0.4020 \| nan \| 0.9800 \| 0.1618 \| 0.7226 \| 0.6910 \| 0.9803 \| \| 0.1786 \| 51.0 \| 510 \| 0.4327 \| nan \| 0.9966 \| 0.3896 \| nan \| 0.9797 \| 0.1595 \| 0.7147 \| 0.6846 \| 0.9800 \| \| 0.1867 \| 52.0 \| 520 \| 0.4540 \| nan \| 0.9966 \| 0.4083 \| nan \| 0.9803 \| 0.1555 \| 0.7253 \| 0.6943 \| 0.9806 \| \| 0.1824 \| 53.0 \| 530 \| 0.4386 \| nan \| 0.9966 \| 0.3942 \| nan \| 0.9798 \| 0.1564 \| 0.7176 \| 0.6870 \| 0.9801 \| \| 0.1494 \| 54.0 \| 540 \| 0.4920 \| nan \| 0.9956 \| 0.4299 \| nan \| 0.9804 \| 0.1540 \| 0.7438 \| 0.7052 \| 0.9807 \| \| 0.1583 \| 55.0 \| 550 \| 0.4558 \| nan \| 0.9964 \| 0.4075 \| nan \| 0.9802 \| 0.1502 \| 0.7261 \| 0.6939 \| 0.9804 \| \| 0.1648 \| 56.0 \| 560 \| 0.4791 \| nan \| 0.9958 \| 0.4208 \| nan \| 0.9802 \| 0.1523 \| 0.7374 \| 0.7005 \| 0.9805 \| \| 0.1993 \| 57.0 \| 570 \| 0.4586 \| nan \| 0.9964 \| 0.4103 \| nan \| 0.9803 \| 0.1502 \| 0.7275 \| 0.6953 \| 0.9805 \| \| 0.2243 \| 58.0 \| 580 \| 0.3920 \| nan \| 0.9973 \| 0.3599 \| nan \| 0.9792 \| 0.1474 \| 0.6946 \| 0.6695 \| 0.9794 \| \| 0.1551 \| 59.0 \| 590 \| 0.4687 \| nan \| 0.9961 \| 0.4157 \| nan \| 0.9803 \| 0.1445 \| 0.7324 \| 0.6980 \| 0.9805 \| \| 0.1666 \| 60.0 \| 600 \| 0.4460 \| nan \| 0.9964 \| 0.3986 \| nan \| 0.9799 \| 0.1444 \| 0.7212 \| 0.6892 \| 0.9801 \| \| 0.1632 \| 61.0 \| 610 \| 0.5120 \| nan \| 0.9951 \| 0.4411 \| nan \| 0.9805 \| 0.1504 \| 0.7535 \| 0.7108 \| 0.9808 \| \| 0.1589 \| 62.0 \| 620 \| 0.4059 \| nan \| 0.9971 \| 0.3704 \| nan \| 0.9794 \| 0.1430 \| 0.7015 \| 0.6749 \| 0.9796 \| \| 0.1454 \| 63.0 \| 630 \| 0.4835 \| nan \| 0.9959 \| 0.4260 \| nan \| 0.9805 \| 0.1423 \| 0.7397 \| 0.7032 \| 0.9808 \| \| 0.1635 \| 64.0 \| 640 \| 0.4902 \| nan \| 0.9957 \| 0.4299 \| nan \| 0.9805 \| 0.1424 \| 0.7430 \| 0.7052 \| 0.9808 \| \| 0.1515 \| 65.0 \| 650 \| 0.4775 \| nan \| 0.9962 \| 0.4239 \| nan \| 0.9806 \| 0.1422 \| 0.7368 \| 0.7022 \| 0.9808 \| \| 0.151 \| 66.0 \| 660 \| 0.4718 \| nan \| 0.9962 \| 0.4195 \| nan \| 0.9804 \| 0.1423 \| 0.7340 \| 0.7000 \| 0.9807 \| \| 0.166 \| 67.0 \| 670 \| 0.4721 \| nan \| 0.9963 \| 0.4208 \| nan \| 0.9805 \| 0.1427 \| 0.7342 \| 0.7007 \| 0.9808 \| \| 0.1561 \| 68.0 \| 680 \| 0.4916 \| nan \| 0.9959 \| 0.4332 \| nan \| 0.9807 \| 0.1420 \| 0.7437 \| 0.7070 \| 0.9810 \| \| 0.1501 \| 69.0 \| 690 \| 0.4906 \| nan \| 0.9958 \| 0.4311 \| nan \| 0.9806 \| 0.1437 \| 0.7432 \| 0.7058 \| 0.9809 \| \| 0.1598 \| 70.0 \| 700 \| 0.3445 \| nan \| 0.9977 \| 0.3204 \| nan \| 0.9782 \| 0.1379 \| 0.6711 \| 0.6493 \| 0.9784 \| \| 0.1431 \| 71.0 \| 710 \| 0.4898 \| nan \| 0.9960 \| 0.4325 \| nan \| 0.9807 \| 0.1400 \| 0.7429 \| 0.7066 \| 0.9810 \| \| 0.164 \| 72.0 \| 720 \| 0.4698 \| nan \| 0.9964 \| 0.4196 \| nan \| 0.9805 \| 0.1347 \| 0.7331 \| 0.7001 \| 0.9808 \| \| 0.1555 \| 73.0 \| 730 \| 0.5271 \| nan \| 0.9937 \| 0.4364 \| nan \| 0.9796 \| 0.1368 \| 0.7604 \| 0.7080 \| 0.9799 \| \| 0.1924 \| 74.0 \| 740 \| 0.4638 \| nan \| 0.9965 \| 0.4159 \| nan \| 0.9805 \| 0.1312 \| 0.7301 \| 0.6982 \| 0.9808 \| \| 0.1612 \| 75.0 \| 750 \| 0.5052 \| nan \| 0.9956 \| 0.4409 \| nan \| 0.9808 \| 0.1340 \| 0.7504 \| 0.7108 \| 0.9811 \| \| 0.1234 \| 76.0 \| 760 \| 0.5301 \| nan \| 0.9946 \| 0.4501 \| nan \| 0.9806 \| 0.1354 \| 0.7624 \| 0.7153 \| 0.9809 \| \| 0.1679 \| 77.0 \| 770 \| 0.4644 \| nan \| 0.9964 \| 0.4156 \| nan \| 0.9804 \| 0.1323 \| 0.7304 \| 0.6980 \| 0.9807 \| \| 0.1375 \| 78.0 \| 780 \| 0.4804 \| nan \| 0.9961 \| 0.4263 \| nan \| 0.9806 \| 0.1355 \| 0.7383 \| 0.7035 \| 0.9809 \| \| 0.1839 \| 79.0 \| 790 \| 0.5070 \| nan \| 0.9955 \| 0.4422 \| nan \| 0.9808 \| 0.1319 \| 0.7512 \| 0.7115 \| 0.9811 \| \| 0.155 \| 80.0 \| 800 \| 0.4846 \| nan \| 0.9961 \| 0.4295 \| nan \| 0.9807 \| 0.1298 \| 0.7403 \| 0.7051 \| 0.9810 \| \| 0.1219 \| 81.0 \| 810 \| 0.4671 \| nan \| 0.9963 \| 0.4167 \| nan \| 0.9804 \| 0.1302 \| 0.7317 \| 0.6986 \| 0.9807 \| \| 0.1218 \| 82.0 \| 820 \| 0.4864 \| nan \| 0.9960 \| 0.4300 \| nan \| 0.9807 \| 0.1313 \| 0.7412 \| 0.7054 \| 0.9810 \| \| 0.138 \| 83.0 \| 830 \| 0.5097 \| nan \| 0.9955 \| 0.4445 \| nan \| 0.9809 \| 0.1318 \| 0.7526 \| 0.7127 \| 0.9812 \| \| 0.1399 \| 84.0 \| 840 \| 0.5067 \| nan \| 0.9957 \| 0.4441 \| nan \| 0.9810 \| 0.1290 \| 0.7512 \| 0.7126 \| 0.9813 \| \| 0.1455 \| 85.0 \| 850 \| 0.5024 \| nan \| 0.9957 \| 0.4404 \| nan \| 0.9809 \| 0.1277 \| 0.7491 \| 0.7106 \| 0.9811 \| \| 0.1466 \| 86.0 \| 860 \| 0.4920 \| nan \| 0.9959 \| 0.4341 \| nan \| 0.9808 \| 0.1243 \| 0.7440 \| 0.7074 \| 0.9811 \| \| 0.1769 \| 87.0 \| 870 \| 0.5737 \| nan \| 0.9924 \| 0.4592 \| nan \| 0.9797 \| 0.1317 \| 0.7831 \| 0.7194 \| 0.9800 \| \| 0.1453 \| 88.0 \| 880 \| 0.3341 \| nan \| 0.9978 \| 0.3115 \| nan \| 0.9780 \| 0.1254 \| 0.6659 \| 0.6447 \| 0.9782 \| \| 0.133 \| 89.0 \| 890 \| 0.5257 \| nan \| 0.9950 \| 0.4518 \| nan \| 0.9809 \| 0.1283 \| 0.7603 \| 0.7163 \| 0.9812 \| \| 0.1288 \| 90.0 \| 900 \| 0.5049 \| nan \| 0.9957 \| 0.4420 \| nan \| 0.9809 \| 0.1221 \| 0.7503 \| 0.7115 \| 0.9812 \| \| 0.1318 \| 91.0 \| 910 \| 0.4838 \| nan \| 0.9961 \| 0.4290 \| nan \| 0.9807 \| 0.1219 \| 0.7400 \| 0.7049 \| 0.9810 \| \| 0.1211 \| 92.0 \| 920 \| 0.5355 \| nan \| 0.9950 \| 0.4596 \| nan \| 0.9811 \| 0.1242 \| 0.7652 \| 0.7203 \| 0.9814 \| \| 0.1137 \| 93.0 \| 930 \| 0.5135 \| nan \| 0.9958 \| 0.4517 \| nan \| 0.9813 \| 0.1181 \| 0.7547 \| 0.7165 \| 0.9816 \| \| 0.1312 \| 94.0 \| 940 \| 0.4775 \| nan \| 0.9963 \| 0.4262 \| nan \| 0.9807 \| 0.1199 \| 0.7369 \| 0.7035 \| 0.9810 \| \| 0.1591 \| 95.0 \| 950 \| 0.5115 \| nan \| 0.9956 \| 0.4473 \| nan \| 0.9810 \| 0.1182 \| 0.7536 \| 0.7142 \| 0.9813 \| \| 0.1207 \| 96.0 \| 960 \| 0.5206 \| nan \| 0.9956 \| 0.4544 \| nan \| 0.9812 \| 0.1156 \| 0.7581 \| 0.7178 \| 0.9815 \| \| 0.1203 \| 97.0 \| 970 \| 0.5054 \| nan \| 0.9958 \| 0.4439 \| nan \| 0.9810 \| 0.1165 \| 0.7506 \| 0.7124 \| 0.9813 \| \| 0.1196 \| 98.0 \| 980 \| 0.5296 \| nan \| 0.9953 \| 0.4585 \| nan \| 0.9812 \| 0.1131 \| 0.7624 \| 0.7199 \| 0.9815 \| \| 0.1304 \| 99.0 \| 990 \| 0.5269 \| nan \| 0.9953 \| 0.4568 \| nan \| 0.9812 \| 0.1155 \| 0.7611 \| 0.7190 \| 0.9815 \| \| 0.1058 \| 100.0 \| 1000 \| 0.5163 \| nan \| 0.9955 \| 0.4496 \| nan \| 0.9810 \| 0.1144 \| 0.7559 \| 0.7153 \| 0.9813 \| \| 0.1135 \| 101.0 \| 1010 \| 0.4934 \| nan \| 0.9961 \| 0.4368 \| nan \| 0.9809 \| 0.1113 \| 0.7447 \| 0.7089 \| 0.9812 \| \| 0.1116 \| 102.0 \| 1020 \| 0.5878 \| nan \| 0.9932 \| 0.4799 \| nan \| 0.9808 \| 0.1128 \| 0.7905 \| 0.7304 \| 0.9812 \| \| 0.1036 \| 103.0 \| 1030 \| 0.4826 \| nan \| 0.9963 \| 0.4304 \| nan \| 0.9809 \| 0.1078 \| 0.7394 \| 0.7056 \| 0.9811 \| \| 0.1195 \| 104.0 \| 1040 \| 0.4364 \| nan \| 0.9966 \| 0.3930 \| nan \| 0.9798 \| 0.1110 \| 0.7165 \| 0.6864 \| 0.9801 \| \| 0.1205 \| 105.0 \| 1050 \| 0.5793 \| nan \| 0.9934 \| 0.4762 \| nan \| 0.9808 \| 0.1120 \| 0.7864 \| 0.7285 \| 0.9812 \| \| 0.1453 \| 106.0 \| 1060 \| 0.4707 \| nan \| 0.9964 \| 0.4205 \| nan \| 0.9806 \| 0.1110 \| 0.7336 \| 0.7005 \| 0.9808 \| \| 0.0965 \| 107.0 \| 1070 \| 0.5638 \| nan \| 0.9941 \| 0.4723 \| nan \| 0.9811 \| 0.1091 \| 0.7789 \| 0.7267 \| 0.9814 \| \| 0.1058 \| 108.0 \| 1080 \| 0.4881 \| nan \| 0.9962 \| 0.4337 \| nan \| 0.9809 \| 0.1085 \| 0.7422 \| 0.7073 \| 0.9812 \| \| 0.1163 \| 109.0 \| 1090 \| 0.5128 \| nan \| 0.9957 \| 0.4493 \| nan \| 0.9811 \| 0.1077 \| 0.7542 \| 0.7152 \| 0.9814 \| \| 0.1145 \| 110.0 \| 1100 \| 0.5228 \| nan \| 0.9954 \| 0.4547 \| nan \| 0.9812 \| 0.1081 \| 0.7591 \| 0.7179 \| 0.9815 \| \| 0.1031 \| 111.0 \| 1110 \| 0.5522 \| nan \| 0.9945 \| 0.4673 \| nan \| 0.9811 \| 0.1073 \| 0.7733 \| 0.7242 \| 0.9814 \| \| 0.1042 \| 112.0 \| 1120 \| 0.5490 \| nan \| 0.9947 \| 0.4669 \| nan \| 0.9812 \| 0.1064 \| 0.7718 \| 0.7241 \| 0.9815 \| \| 0.1119 \| 113.0 \| 1130 \| 0.5064 \| nan \| 0.9958 \| 0.4449 \| nan \| 0.9811 \| 0.1063 \| 0.7511 \| 0.7130 \| 0.9813 \| \| 0.1116 \| 114.0 \| 1140 \| 0.5172 \| nan \| 0.9956 \| 0.4520 \| nan \| 0.9812 \| 0.1074 \| 0.7564 \| 0.7166 \| 0.9815 \| \| 0.1063 \| 115.0 \| 1150 \| 0.5163 \| nan \| 0.9956 \| 0.4511 \| nan \| 0.9812 \| 0.1072 \| 0.7560 \| 0.7161 \| 0.9814 \| \| 0.1054 \| 116.0 \| 1160 \| 0.4994 \| nan \| 0.9960 \| 0.4408 \| nan \| 0.9810 \| 0.1065 \| 0.7477 \| 0.7109 \| 0.9813 \| \| 0.1613 \| 117.0 \| 1170 \| 0.5251 \| nan \| 0.9955 \| 0.4576 \| nan \| 0.9813 \| 0.1060 \| 0.7603 \| 0.7195 \| 0.9816 \| \| 0.1542 \| 118.0 \| 1180 \| 0.5454 \| nan \| 0.9947 \| 0.4649 \| nan \| 0.9812 \| 0.1058 \| 0.7701 \| 0.7230 \| 0.9815 \| \| 0.1226 \| 119.0 \| 1190 \| 0.5469 \| nan \| 0.9947 \| 0.4658 \| nan \| 0.9812 \| 0.1064 \| 0.7708 \| 0.7235 \| 0.9815 \| \| 0.1295 \| 120.0 \| 1200 \| 0.5437 \| nan \| 0.9948 \| 0.4646 \| nan \| 0.9812 \| 0.1060 \| 0.7693 \| 0.7229 \| 0.9815 \| \| 0.1438 \| 121.0 \| 1210 \| 0.1076 \| 0.7084 \| 0.7435 \| 0.9812 \| nan \| 0.4909 \| 0.9962 \| nan \| 0.4358 \| 0.9809 \| \| 0.1391 \| 122.0 \| 1220 \| 0.1081 \| 0.7221 \| 0.7683 \| 0.9814 \| nan \| 0.5417 \| 0.9948 \| nan \| 0.4630 \| 0.9811 \| \| 0.1756 \| 123.0 \| 1230 \| 0.1041 \| 0.7233 \| 0.7710 \| 0.9814 \| nan \| 0.5473 \| 0.9947 \| nan \| 0.4655 \| 0.9811 \| \| 0.1174 \| 124.0 \| 1240 \| 0.1029 \| 0.7189 \| 0.7614 \| 0.9815 \| nan \| 0.5275 \| 0.9953 \| nan \| 0.4566 \| 0.9812 \| \| 0.1025 \| 125.0 \| 1250 \| 0.1043 \| 0.7100 \| 0.7470 \| 0.9812 \| nan \| 0.4980 \| 0.9959 \| nan \| 0.4391 \| 0.9809 \| \| 0.0997 \| 126.0 \| 1260 \| 0.1038 \| 0.7211 \| 0.7638 \| 0.9816 \| nan \| 0.5323 \| 0.9953 \| nan \| 0.4609 \| 0.9813 \| \| 0.1768 \| 127.0 \| 1270 \| 0.1037 \| 0.7204 \| 0.7617 \| 0.9817 \| nan \| 0.5279 \| 0.9955 \| nan \| 0.4594 \| 0.9814 \| \| 0.1527 \| 128.0 \| 1280 \| 0.1027 \| 0.7167 \| 0.7564 \| 0.9815 \| nan \| 0.5171 \| 0.9956 \| nan \| 0.4522 \| 0.9812 \| \| 0.1269 \| 129.0 \| 1290 \| 0.1041 \| 0.7178 \| 0.7583 \| 0.9815 \| nan \| 0.5211 \| 0.9955 \| nan \| 0.4543 \| 0.9812 \| \| 0.0968 \| 130.0 \| 1300 \| 0.1043 \| 0.7188 \| 0.7607 \| 0.9815 \| nan \| 0.5261 \| 0.9953 \| nan \| 0.4564 \| 0.9812 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0472 - Mean Iou: 0.8728 - Mean Accuracy: 0.9195 - Overall Accuracy: 0.9919 - Accuracy Unlabeled: nan - Accuracy Safe: 0.8426 - Accuracy Unsafe: 0.9964 - Iou Unlabeled: nan - Iou Safe: 0.7540 - Iou Unsafe: 0.9917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Safe \| Accuracy Unsafe \| Iou Unlabeled \| Iou Safe \| Iou Unsafe \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------:\| \| 1.2199 \| 2.0 \| 20 \| 1.1474 \| 0.0765 \| 0.4399 \| 0.2168 \| nan \| 0.6770 \| 0.2028 \| 0.0 \| 0.0279 \| 0.2014 \| \| 1.1542 \| 4.0 \| 40 \| 1.0616 \| 0.1558 \| 0.6082 \| 0.4365 \| nan \| 0.7908 \| 0.4257 \| 0.0 \| 0.0436 \| 0.4237 \| \| 1.0324 \| 6.0 \| 60 \| 0.9140 \| 0.2569 \| 0.6886 \| 0.7091 \| nan \| 0.6667 \| 0.7104 \| 0.0 \| 0.0672 \| 0.7036 \| \| 0.8058 \| 8.0 \| 80 \| 0.7629 \| 0.2970 \| 0.6780 \| 0.8115 \| nan \| 0.5360 \| 0.8199 \| 0.0 \| 0.0823 \| 0.8086 \| \| 0.681 \| 10.0 \| 100 \| 0.5545 \| 0.3510 \| 0.6964 \| 0.9135 \| nan \| 0.4657 \| 0.9271 \| 0.0 \| 0.1407 \| 0.9123 \| \| 0.5248 \| 12.0 \| 120 \| 0.4153 \| 0.3738 \| 0.6747 \| 0.9462 \| nan \| 0.3861 \| 0.9633 \| 0.0 \| 0.1757 \| 0.9456 \| \| 0.3372 \| 14.0 \| 140 \| 0.3050 \| 0.3896 \| 0.6548 \| 0.9628 \| nan \| 0.3276 \| 0.9821 \| 0.0 \| 0.2064 \| 0.9624 \| \| 0.2818 \| 16.0 \| 160 \| 0.2346 \| 0.4313 \| 0.7331 \| 0.9703 \| nan \| 0.4810 \| 0.9852 \| 0.0 \| 0.3239 \| 0.9699 \| \| 0.2081 \| 18.0 \| 180 \| 0.1802 \| 0.4762 \| 0.7973 \| 0.9782 \| nan \| 0.6050 \| 0.9896 \| 0.0 \| 0.4509 \| 0.9778 \| \| 0.141 \| 20.0 \| 200 \| 0.1362 \| 0.4968 \| 0.7945 \| 0.9830 \| nan \| 0.5943 \| 0.9948 \| 0.0 \| 0.5078 \| 0.9827 \| \| 0.0963 \| 22.0 \| 220 \| 0.1032 \| 0.7409 \| 0.7652 \| 0.9841 \| nan \| 0.5326 \| 0.9979 \| nan \| 0.4978 \| 0.9839 \| \| 0.0842 \| 24.0 \| 240 \| 0.0770 \| 0.7840 \| 0.8200 \| 0.9863 \| nan \| 0.6432 \| 0.9968 \| nan \| 0.5818 \| 0.9861 \| \| 0.0702 \| 26.0 \| 260 \| 0.0669 \| 0.7836 \| 0.8193 \| 0.9863 \| nan \| 0.6417 \| 0.9968 \| nan \| 0.5812 \| 0.9861 \| \| 0.0706 \| 28.0 \| 280 \| 0.0671 \| 0.8065 \| 0.8593 \| 0.9872 \| nan \| 0.7234 \| 0.9953 \| nan \| 0.6261 \| 0.9870 \| \| 0.0747 \| 30.0 \| 300 \| 0.0551 \| 0.7808 \| 0.7980 \| 0.9870 \| nan \| 0.5971 \| 0.9988 \| nan \| 0.5748 \| 0.9867 \| \| 0.057 \| 32.0 \| 320 \| 0.0492 \| 0.8267 \| 0.8736 \| 0.9888 \| nan \| 0.7511 \| 0.9961 \| nan \| 0.6648 \| 0.9886 \| \| 0.0435 \| 34.0 \| 340 \| 0.0507 \| 0.7956 \| 0.8134 \| 0.9878 \| nan \| 0.6280 \| 0.9988 \| nan \| 0.6035 \| 0.9876 \| \| 0.0326 \| 36.0 \| 360 \| 0.0418 \| 0.8422 \| 0.8895 \| 0.9898 \| nan \| 0.7830 \| 0.9961 \| nan \| 0.6947 \| 0.9896 \| \| 0.0262 \| 38.0 \| 380 \| 0.0420 \| 0.8280 \| 0.8550 \| 0.9895 \| nan \| 0.7120 \| 0.9979 \| nan \| 0.6667 \| 0.9893 \| \| 0.0268 \| 40.0 \| 400 \| 0.0392 \| 0.8407 \| 0.8822 \| 0.9899 \| nan \| 0.7676 \| 0.9967 \| nan \| 0.6918 \| 0.9897 \| \| 0.0395 \| 42.0 \| 420 \| 0.0466 \| 0.5436 \| 0.8370 \| 0.9889 \| nan \| 0.6755 \| 0.9984 \| 0.0 \| 0.6422 \| 0.9887 \| \| 0.0279 \| 44.0 \| 440 \| 0.0439 \| 0.8321 \| 0.8946 \| 0.9887 \| nan \| 0.7945 \| 0.9947 \| nan \| 0.6758 \| 0.9885 \| \| 0.0468 \| 46.0 \| 460 \| 0.0360 \| 0.8480 \| 0.8894 \| 0.9904 \| nan \| 0.7822 \| 0.9967 \| nan \| 0.7059 \| 0.9901 \| \| 0.0233 \| 48.0 \| 480 \| 0.0376 \| 0.8507 \| 0.8962 \| 0.9905 \| nan \| 0.7960 \| 0.9964 \| nan \| 0.7113 \| 0.9902 \| \| 0.0288 \| 50.0 \| 500 \| 0.0386 \| 0.8404 \| 0.8845 \| 0.9898 \| nan \| 0.7725 \| 0.9964 \| nan \| 0.6913 \| 0.9896 \| \| 0.0266 \| 52.0 \| 520 \| 0.0361 \| 0.8455 \| 0.8768 \| 0.9905 \| nan \| 0.7560 \| 0.9976 \| nan \| 0.7008 \| 0.9902 \| \| 0.0241 \| 54.0 \| 540 \| 0.0367 \| 0.8504 \| 0.9039 \| 0.9902 \| nan \| 0.8122 \| 0.9957 \| nan \| 0.7109 \| 0.9900 \| \| 0.0239 \| 56.0 \| 560 \| 0.0414 \| 0.8401 \| 0.8809 \| 0.9899 \| nan \| 0.7650 \| 0.9967 \| nan \| 0.6906 \| 0.9896 \| \| 0.0221 \| 58.0 \| 580 \| 0.0375 \| 0.8536 \| 0.8971 \| 0.9907 \| nan \| 0.7977 \| 0.9966 \| nan \| 0.7167 \| 0.9905 \| \| 0.0324 \| 60.0 \| 600 \| 0.0390 \| 0.8566 \| 0.9017 \| 0.9908 \| nan \| 0.8069 \| 0.9964 \| nan \| 0.7226 \| 0.9906 \| \| 0.0264 \| 62.0 \| 620 \| 0.0405 \| 0.8506 \| 0.9088 \| 0.9901 \| nan \| 0.8224 \| 0.9952 \| nan \| 0.7114 \| 0.9899 \| \| 0.0146 \| 64.0 \| 640 \| 0.0356 \| 0.8627 \| 0.9158 \| 0.9911 \| nan \| 0.8358 \| 0.9958 \| nan \| 0.7346 \| 0.9909 \| \| 0.0252 \| 66.0 \| 660 \| 0.0310 \| 0.8667 \| 0.9010 \| 0.9917 \| nan \| 0.8046 \| 0.9974 \| nan \| 0.7418 \| 0.9915 \| \| 0.0155 \| 68.0 \| 680 \| 0.0359 \| 0.8567 \| 0.9056 \| 0.9908 \| nan \| 0.8152 \| 0.9961 \| nan \| 0.7229 \| 0.9905 \| \| 0.0169 \| 70.0 \| 700 \| 0.0490 \| 0.8476 \| 0.9182 \| 0.9896 \| nan \| 0.8422 \| 0.9941 \| nan \| 0.7058 \| 0.9894 \| \| 0.0142 \| 72.0 \| 720 \| 0.0357 \| 0.8442 \| 0.8771 \| 0.9903 \| nan \| 0.7568 \| 0.9974 \| nan \| 0.6982 \| 0.9901 \| \| 0.0244 \| 74.0 \| 740 \| 0.0400 \| 0.8523 \| 0.9070 \| 0.9903 \| nan \| 0.8183 \| 0.9956 \| nan \| 0.7146 \| 0.9901 \| \| 0.016 \| 76.0 \| 760 \| 0.0302 \| 0.8644 \| 0.9037 \| 0.9915 \| nan \| 0.8105 \| 0.9970 \| nan \| 0.7376 \| 0.9913 \| \| 0.0137 \| 78.0 \| 780 \| 0.0325 \| 0.8664 \| 0.9118 \| 0.9915 \| nan \| 0.8271 \| 0.9965 \| nan \| 0.7415 \| 0.9913 \| \| 0.0115 \| 80.0 \| 800 \| 0.0347 \| 0.8678 \| 0.9162 \| 0.9915 \| nan \| 0.8362 \| 0.9962 \| nan \| 0.7443 \| 0.9913 \| \| 0.0117 \| 82.0 \| 820 \| 0.0320 \| 0.8697 \| 0.9084 \| 0.9918 \| nan \| 0.8197 \| 0.9971 \| nan \| 0.7478 \| 0.9916 \| \| 0.0108 \| 84.0 \| 840 \| 0.0348 \| 0.8691 \| 0.9192 \| 0.9916 \| nan \| 0.8423 \| 0.9961 \| nan \| 0.7468 \| 0.9913 \| \| 0.0101 \| 86.0 \| 860 \| 0.0342 \| 0.8683 \| 0.9081 \| 0.9917 \| nan \| 0.8192 \| 0.9970 \| nan \| 0.7452 \| 0.9915 \| \| 0.0085 \| 88.0 \| 880 \| 0.0441 \| 0.8639 \| 0.9214 \| 0.9911 \| nan \| 0.8474 \| 0.9954 \| nan \| 0.7370 \| 0.9908 \| \| 0.009 \| 90.0 \| 900 \| 0.0428 \| 0.8619 \| 0.9086 \| 0.9912 \| nan \| 0.8209 \| 0.9963 \| nan \| 0.7330 \| 0.9909 \| \| 0.009 \| 92.0 \| 920 \| 0.0444 \| 0.8620 \| 0.9089 \| 0.9912 \| nan \| 0.8215 \| 0.9963 \| nan \| 0.7331 \| 0.9909 \| \| 0.0089 \| 94.0 \| 940 \| 0.0410 \| 0.8645 \| 0.9147 \| 0.9913 \| nan \| 0.8334 \| 0.9961 \| nan \| 0.7380 \| 0.9910 \| \| 0.0091 \| 96.0 \| 960 \| 0.0418 \| 0.8663 \| 0.9155 \| 0.9914 \| nan \| 0.8349 \| 0.9962 \| nan \| 0.7413 \| 0.9912 \| \| 0.0079 \| 98.0 \| 980 \| 0.0398 \| 0.8629 \| 0.9085 \| 0.9913 \| nan \| 0.8205 \| 0.9965 \| nan \| 0.7348 \| 0.9910 \| \| 0.0084 \| 100.0 \| 1000 \| 0.0497 \| 0.8553 \| 0.9109 \| 0.9905 \| nan \| 0.8262 \| 0.9955 \| nan \| 0.7204 \| 0.9903 \| \| 0.0088 \| 102.0 \| 1020 \| 0.0399 \| 0.8558 \| 0.9058 \| 0.9907 \| nan \| 0.8156 \| 0.9960 \| nan \| 0.7212 \| 0.9905 \| \| 0.0089 \| 104.0 \| 1040 \| 0.0388 \| 0.8678 \| 0.9225 \| 0.9914 \| nan \| 0.8494 \| 0.9957 \| nan \| 0.7444 \| 0.9912 \| \| 0.008 \| 106.0 \| 1060 \| 0.0449 \| 0.8622 \| 0.9225 \| 0.9909 \| nan \| 0.8498 \| 0.9952 \| nan \| 0.7337 \| 0.9907 \| \| 0.0084 \| 108.0 \| 1080 \| 0.0429 \| 0.8687 \| 0.9233 \| 0.9914 \| nan \| 0.8510 \| 0.9957 \| nan \| 0.7462 \| 0.9912 \| \| 0.0084 \| 110.0 \| 1100 \| 0.0405 \| 0.8687 \| 0.9169 \| 0.9916 \| nan \| 0.8375 \| 0.9963 \| nan \| 0.7460 \| 0.9914 \| \| 0.007 \| 112.0 \| 1120 \| 0.0544 \| 0.8620 \| 0.9180 \| 0.9910 \| nan \| 0.8404 \| 0.9956 \| nan \| 0.7333 \| 0.9907 \| \| 0.0079 \| 114.0 \| 1140 \| 0.0501 \| 0.8602 \| 0.9176 \| 0.9908 \| nan \| 0.8399 \| 0.9954 \| nan \| 0.7299 \| 0.9906 \| \| 0.0084 \| 116.0 \| 1160 \| 0.0508 \| 0.8605 \| 0.9212 \| 0.9908 \| nan \| 0.8473 \| 0.9951 \| nan \| 0.7304 \| 0.9905 \| \| 0.0113 \| 118.0 \| 1180 \| 0.0511 \| 0.8601 \| 0.9228 \| 0.9907 \| nan \| 0.8507 \| 0.9950 \| nan \| 0.7298 \| 0.9905 \| \| 0.0076 \| 120.0 \| 1200 \| 0.0556 \| 0.8602 \| 0.9264 \| 0.9906 \| nan \| 0.8582 \| 0.9947 \| nan \| 0.7299 \| 0.9904 \| \| 0.0081 \| 122.0 \| 1220 \| 0.0471 \| 0.8665 \| 0.9256 \| 0.9912 \| nan \| 0.8559 \| 0.9953 \| nan \| 0.7420 \| 0.9910 \| \| 0.0054 \| 124.0 \| 1240 \| 0.0504 \| 0.8652 \| 0.9174 \| 0.9913 \| nan \| 0.8389 \| 0.9959 \| nan \| 0.7394 \| 0.9910 \| \| 0.0054 \| 126.0 \| 1260 \| 0.0502 \| 0.8666 \| 0.9209 \| 0.9913 \| nan \| 0.8461 \| 0.9957 \| nan \| 0.7420 \| 0.9911 \| \| 0.0092 \| 128.0 \| 1280 \| 0.0540 \| 0.8642 \| 0.9223 \| 0.9911 \| nan \| 0.8492 \| 0.9954 \| nan \| 0.7376 \| 0.9908 \| \| 0.007 \| 130.0 \| 1300 \| 0.0533 \| 0.8637 \| 0.9207 \| 0.9911 \| nan \| 0.8459 \| 0.9955 \| nan \| 0.7366 \| 0.9908 \| \| 0.0063 \| 132.0 \| 1320 \| 0.0526 \| 0.8644 \| 0.9259 \| 0.9910 \| nan \| 0.8567 \| 0.9951 \| nan \| 0.7380 \| 0.9908 \| \| 0.0101 \| 134.0 \| 1340 \| 0.0462 \| 0.8653 \| 0.9067 \| 0.9915 \| nan \| 0.8166 \| 0.9968 \| nan \| 0.7393 \| 0.9913 \| \| 0.0183 \| 136.0 \| 1360 \| 0.0516 \| 0.5675 \| 0.9024 \| 0.9904 \| nan \| 0.8089 \| 0.9959 \| 0.0 \| 0.7123 \| 0.9901 \| \| 0.0102 \| 138.0 \| 1380 \| 0.0388 \| 0.8366 \| 0.8716 \| 0.9898 \| nan \| 0.7460 \| 0.9972 \| nan \| 0.6837 \| 0.9896 \| \| 0.0277 \| 140.0 \| 1400 \| 0.0649 \| 0.8159 \| 0.9590 \| 0.9850 \| nan \| 0.9313 \| 0.9866 \| nan \| 0.6472 \| 0.9846 \| \| 0.0169 \| 142.0 \| 1420 \| 0.0340 \| 0.8444 \| 0.9148 \| 0.9894 \| nan \| 0.8355 \| 0.9941 \| nan \| 0.6996 \| 0.9891 \| \| 0.0359 \| 144.0 \| 1440 \| 0.0314 \| 0.8667 \| 0.8987 \| 0.9918 \| nan \| 0.7997 \| 0.9976 \| nan \| 0.7419 \| 0.9916 \| \| 0.0117 \| 146.0 \| 1460 \| 0.0307 \| 0.8517 \| 0.8869 \| 0.9908 \| nan \| 0.7765 \| 0.9973 \| nan \| 0.7129 \| 0.9905 \| \| 0.0097 \| 148.0 \| 1480 \| 0.0323 \| 0.8757 \| 0.9070 \| 0.9924 \| nan \| 0.8162 \| 0.9977 \| nan \| 0.7593 \| 0.9922 \| \| 0.0063 \| 150.0 \| 1500 \| 0.0302 \| 0.8808 \| 0.9155 \| 0.9926 \| nan \| 0.8335 \| 0.9975 \| nan \| 0.7692 \| 0.9924 \| \| 0.0085 \| 152.0 \| 1520 \| 0.0352 \| 0.8697 \| 0.9113 \| 0.9918 \| nan \| 0.8258 \| 0.9968 \| nan \| 0.7478 \| 0.9916 \| \| 0.0078 \| 154.0 \| 1540 \| 0.0428 \| 0.8649 \| 0.9190 \| 0.9912 \| nan \| 0.8423 \| 0.9957 \| nan \| 0.7388 \| 0.9910 \| \| 0.0056 \| 156.0 \| 1560 \| 0.0340 \| 0.8709 \| 0.9170 \| 0.9918 \| nan \| 0.8376 \| 0.9965 \| nan \| 0.7502 \| 0.9916 \| \| 0.0063 \| 158.0 \| 1580 \| 0.0359 \| 0.8661 \| 0.9201 \| 0.9913 \| nan \| 0.8445 \| 0.9958 \| nan \| 0.7412 \| 0.9911 \| \| 0.0083 \| 160.0 \| 1600 \| 0.0375 \| 0.8684 \| 0.9186 \| 0.9915 \| nan \| 0.8410 \| 0.9961 \| nan \| 0.7456 \| 0.9913 \| \| 0.0065 \| 162.0 \| 1620 \| 0.0370 \| 0.8699 \| 0.9210 \| 0.9916 \| nan \| 0.8459 \| 0.9960 \| nan \| 0.7484 \| 0.9914 \| \| 0.0063 \| 164.0 \| 1640 \| 0.0388 \| 0.8699 \| 0.9228 \| 0.9916 \| nan \| 0.8498 \| 0.9959 \| nan \| 0.7484 \| 0.9913 \| \| 0.0056 \| 166.0 \| 1660 \| 0.0386 \| 0.8702 \| 0.9238 \| 0.9916 \| nan \| 0.8517 \| 0.9958 \| nan \| 0.7491 \| 0.9914 \| \| 0.0049 \| 168.0 \| 1680 \| 0.0394 \| 0.8703 \| 0.9199 \| 0.9917 \| nan \| 0.8436 \| 0.9962 \| nan \| 0.7491 \| 0.9914 \| \| 0.0054 \| 170.0 \| 1700 \| 0.0400 \| 0.8704 \| 0.9195 \| 0.9917 \| nan \| 0.8428 \| 0.9962 \| nan \| 0.7494 \| 0.9915 \| \| 0.0046 \| 172.0 \| 1720 \| 0.0398 \| 0.8728 \| 0.9187 \| 0.9919 \| nan \| 0.8410 \| 0.9965 \| nan \| 0.7539 \| 0.9917 \| \| 0.0058 \| 174.0 \| 1740 \| 0.0402 \| 0.8711 \| 0.9166 \| 0.9918 \| nan \| 0.8367 \| 0.9965 \| nan \| 0.7507 \| 0.9916 \| \| 0.005 \| 176.0 \| 1760 \| 0.0400 \| 0.8720 \| 0.9196 \| 0.9918 \| nan \| 0.8428 \| 0.9963 \| nan \| 0.7525 \| 0.9916 \| \| 0.0061 \| 178.0 \| 1780 \| 0.0417 \| 0.8714 \| 0.9226 \| 0.9917 \| nan \| 0.8492 \| 0.9960 \| nan \| 0.7513 \| 0.9915 \| \| 0.0061 \| 180.0 \| 1800 \| 0.0407 \| 0.8731 \| 0.9249 \| 0.9918 \| nan \| 0.8538 \| 0.9960 \| nan \| 0.7545 \| 0.9916 \| \| 0.0065 \| 182.0 \| 1820 \| 0.0420 \| 0.8712 \| 0.9235 \| 0.9917 \| nan \| 0.8511 \| 0.9959 \| nan \| 0.7509 \| 0.9914 \| \| 0.0045 \| 184.0 \| 1840 \| 0.0421 \| 0.8718 \| 0.9250 \| 0.9917 \| nan \| 0.8541 \| 0.9959 \| nan \| 0.7522 \| 0.9915 \| \| 0.0056 \| 186.0 \| 1860 \| 0.0435 \| 0.8703 \| 0.9157 \| 0.9917 \| nan \| 0.8349 \| 0.9965 \| nan \| 0.7490 \| 0.9915 \| \| 0.0059 \| 188.0 \| 1880 \| 0.0436 \| 0.8707 \| 0.9191 \| 0.9917 \| nan \| 0.8419 \| 0.9963 \| nan \| 0.7499 \| 0.9915 \| \| 0.0042 \| 190.0 \| 1900 \| 0.0436 \| 0.8707 \| 0.9210 \| 0.9917 \| nan \| 0.8458 \| 0.9961 \| nan \| 0.7499 \| 0.9915 \| \| 0.006 \| 192.0 \| 1920 \| 0.0426 \| 0.8697 \| 0.9193 \| 0.9916 \| nan \| 0.8425 \| 0.9962 \| nan \| 0.7480 \| 0.9914 \| \| 0.0053 \| 194.0 \| 1940 \| 0.0447 \| 0.8697 \| 0.9199 \| 0.9916 \| nan \| 0.8437 \| 0.9961 \| nan \| 0.7480 \| 0.9914 \| \| 0.0044 \| 196.0 \| 1960 \| 0.0441 \| 0.8710 \| 0.9238 \| 0.9916 \| nan \| 0.8516 \| 0.9959 \| nan \| 0.7505 \| 0.9914 \| \| 0.0049 \| 198.0 \| 1980 \| 0.0453 \| 0.8693 \| 0.9219 \| 0.9915 \| nan \| 0.8479 \| 0.9959 \| nan \| 0.7473 \| 0.9913 \| \| 0.0059 \| 200.0 \| 2000 \| 0.0444 \| 0.8726 \| 0.9233 \| 0.9918 \| nan \| 0.8506 \| 0.9961 \| nan \| 0.7537 \| 0.9916 \| \| 0.005 \| 202.0 \| 2020 \| 0.0447 \| 0.8717 \| 0.9256 \| 0.9917 \| nan \| 0.8555 \| 0.9958 \| nan \| 0.7519 \| 0.9914 \| \| 0.005 \| 204.0 \| 2040 \| 0.0451 \| 0.8711 \| 0.9227 \| 0.9917 \| nan \| 0.8494 \| 0.9960 \| nan \| 0.7507 \| 0.9915 \| \| 0.0043 \| 206.0 \| 2060 \| 0.0458 \| 0.8707 \| 0.9220 \| 0.9916 \| nan \| 0.8480 \| 0.9960 \| nan \| 0.7499 \| 0.9914 \| \| 0.0043 \| 208.0 \| 2080 \| 0.0462 \| 0.8696 \| 0.9221 \| 0.9916 \| nan \| 0.8482 \| 0.9959 \| nan \| 0.7479 \| 0.9913 \| \| 0.0062 \| 210.0 \| 2100 \| 0.0451 \| 0.8715 \| 0.9211 \| 0.9917 \| nan \| 0.8461 \| 0.9962 \| nan \| 0.7515 \| 0.9915 \| \| 0.0056 \| 212.0 \| 2120 \| 0.0470 \| 0.8706 \| 0.9213 \| 0.9917 \| nan \| 0.8466 \| 0.9961 \| nan \| 0.7498 \| 0.9914 \| \| 0.0049 \| 214.0 \| 2140 \| 0.0480 \| 0.8679 \| 0.9229 \| 0.9914 \| nan \| 0.8500 \| 0.9957 \| nan \| 0.7447 \| 0.9912 \| \| 0.0038 \| 216.0 \| 2160 \| 0.0474 \| 0.8700 \| 0.9194 \| 0.9916 \| nan \| 0.8427 \| 0.9962 \| nan \| 0.7486 \| 0.9914 \| \| 0.0043 \| 218.0 \| 2180 \| 0.0472 \| 0.8693 \| 0.9231 \| 0.9915 \| nan \| 0.8503 \| 0.9958 \| nan \| 0.7474 \| 0.9913 \| \| 0.005 \| 220.0 \| 2200 \| 0.0471 \| 0.8695 \| 0.9156 \| 0.9917 \| nan \| 0.8348 \| 0.9964 \| nan \| 0.7475 \| 0.9915 \| \| 0.0041 \| 222.0 \| 2220 \| 0.0472 \| 0.8719 \| 0.9187 \| 0.9918 \| nan \| 0.8411 \| 0.9964 \| nan \| 0.7522 \| 0.9916 \| \| 0.0041 \| 224.0 \| 2240 \| 0.0471 \| 0.8720 \| 0.9219 \| 0.9918 \| nan \| 0.8477 \| 0.9962 \| nan \| 0.7525 \| 0.9916 \| \| 0.005 \| 226.0 \| 2260 \| 0.0479 \| 0.8720 \| 0.9191 \| 0.9918 \| nan \| 0.8418 \| 0.9964 \| nan \| 0.7524 \| 0.9916 \| \| 0.0041 \| 228.0 \| 2280 \| 0.0489 \| 0.8706 \| 0.9183 \| 0.9917 \| nan \| 0.8403 \| 0.9963 \| nan \| 0.7498 \| 0.9915 \| \| 0.005 \| 230.0 \| 2300 \| 0.0472 \| 0.8728 \| 0.9195 \| 0.9919 \| nan \| 0.8426 \| 0.9964 \| nan \| 0.7540 \| 0.9917 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0506 - Mean Iou: 0.8519 - Mean Accuracy: 0.9125 - Overall Accuracy: 0.9902 - Accuracy Unlabeled: nan - Accuracy Safe: 0.8300 - Accuracy Unsafe: 0.9950 - Iou Unlabeled: nan - Iou Safe: 0.7138 - Iou Unsafe: 0.9899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Safe \| Accuracy Unsafe \| Iou Unlabeled \| Iou Safe \| Iou Unsafe \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------:\| \| 1.4012 \| 2.0 \| 20 \| 1.1653 \| 0.0379 \| 0.0923 \| 0.0926 \| nan \| 0.0920 \| 0.0926 \| 0.0 \| 0.0214 \| 0.0923 \| \| 1.2461 \| 4.0 \| 40 \| 0.9899 \| 0.2255 \| 0.3670 \| 0.6307 \| nan \| 0.0868 \| 0.6473 \| 0.0 \| 0.0379 \| 0.6386 \| \| 1.0596 \| 6.0 \| 60 \| 0.7738 \| 0.2703 \| 0.4188 \| 0.7941 \| nan \| 0.0199 \| 0.8177 \| 0.0 \| 0.0143 \| 0.7967 \| \| 0.8267 \| 8.0 \| 80 \| 0.6767 \| 0.2902 \| 0.4512 \| 0.8507 \| nan \| 0.0265 \| 0.8758 \| 0.0 \| 0.0183 \| 0.8522 \| \| 0.7282 \| 10.0 \| 100 \| 0.5637 \| 0.3086 \| 0.4776 \| 0.9098 \| nan \| 0.0183 \| 0.9370 \| 0.0 \| 0.0149 \| 0.9110 \| \| 0.5124 \| 12.0 \| 120 \| 0.4667 \| 0.3254 \| 0.5053 \| 0.9512 \| nan \| 0.0314 \| 0.9792 \| 0.0 \| 0.0247 \| 0.9516 \| \| 0.3126 \| 14.0 \| 140 \| 0.3585 \| 0.3325 \| 0.5147 \| 0.9662 \| nan \| 0.0349 \| 0.9945 \| 0.0 \| 0.0313 \| 0.9663 \| \| 0.2862 \| 16.0 \| 160 \| 0.2890 \| 0.3346 \| 0.5168 \| 0.9703 \| nan \| 0.0349 \| 0.9988 \| 0.0 \| 0.0336 \| 0.9703 \| \| 0.2374 \| 18.0 \| 180 \| 0.2102 \| 0.3647 \| 0.5637 \| 0.9725 \| nan \| 0.1291 \| 0.9982 \| 0.0 \| 0.1218 \| 0.9724 \| \| 0.1583 \| 20.0 \| 200 \| 0.1730 \| 0.6293 \| 0.6574 \| 0.9761 \| nan \| 0.3186 \| 0.9961 \| nan \| 0.2827 \| 0.9759 \| \| 0.1082 \| 22.0 \| 220 \| 0.1317 \| 0.6306 \| 0.6566 \| 0.9765 \| nan \| 0.3166 \| 0.9966 \| nan \| 0.2849 \| 0.9763 \| \| 0.1025 \| 24.0 \| 240 \| 0.1116 \| 0.6494 \| 0.6766 \| 0.9777 \| nan \| 0.3565 \| 0.9967 \| nan \| 0.3212 \| 0.9775 \| \| 0.1158 \| 26.0 \| 260 \| 0.0965 \| 0.7200 \| 0.7978 \| 0.9791 \| nan \| 0.6051 \| 0.9905 \| nan \| 0.4612 \| 0.9787 \| \| 0.0882 \| 28.0 \| 280 \| 0.0857 \| 0.7356 \| 0.7857 \| 0.9822 \| nan \| 0.5769 \| 0.9946 \| nan \| 0.4893 \| 0.9819 \| \| 0.07 \| 30.0 \| 300 \| 0.0829 \| 0.6717 \| 0.6934 \| 0.9799 \| nan \| 0.3890 \| 0.9979 \| nan \| 0.3637 \| 0.9797 \| \| 0.0911 \| 32.0 \| 320 \| 0.0677 \| 0.7680 \| 0.8244 \| 0.9843 \| nan \| 0.6545 \| 0.9944 \| nan \| 0.5521 \| 0.9840 \| \| 0.0807 \| 34.0 \| 340 \| 0.0696 \| 0.7779 \| 0.8716 \| 0.9834 \| nan \| 0.7528 \| 0.9904 \| nan \| 0.5727 \| 0.9830 \| \| 0.0531 \| 36.0 \| 360 \| 0.0611 \| 0.7761 \| 0.8781 \| 0.9829 \| nan \| 0.7668 \| 0.9895 \| nan \| 0.5698 \| 0.9825 \| \| 0.0407 \| 38.0 \| 380 \| 0.0567 \| 0.7828 \| 0.8396 \| 0.9854 \| nan \| 0.6846 \| 0.9945 \| nan \| 0.5805 \| 0.9851 \| \| 0.0449 \| 40.0 \| 400 \| 0.0639 \| 0.7725 \| 0.8200 \| 0.9851 \| nan \| 0.6446 \| 0.9954 \| nan \| 0.5602 \| 0.9848 \| \| 0.0932 \| 42.0 \| 420 \| 0.0503 \| 0.7726 \| 0.7983 \| 0.9861 \| nan \| 0.5987 \| 0.9979 \| nan \| 0.5593 \| 0.9858 \| \| 0.0362 \| 44.0 \| 440 \| 0.0634 \| 0.7553 \| 0.8670 \| 0.9805 \| nan \| 0.7464 \| 0.9876 \| nan \| 0.5306 \| 0.9801 \| \| 0.0324 \| 46.0 \| 460 \| 0.0501 \| 0.8024 \| 0.8615 \| 0.9867 \| nan \| 0.7284 \| 0.9946 \| nan \| 0.6184 \| 0.9864 \| \| 0.036 \| 48.0 \| 480 \| 0.0454 \| 0.8010 \| 0.8454 \| 0.9872 \| nan \| 0.6947 \| 0.9961 \| nan \| 0.6151 \| 0.9869 \| \| 0.0356 \| 50.0 \| 500 \| 0.0495 \| 0.8061 \| 0.8760 \| 0.9866 \| nan \| 0.7585 \| 0.9936 \| nan \| 0.6260 \| 0.9863 \| \| 0.0333 \| 52.0 \| 520 \| 0.0483 \| 0.7743 \| 0.8128 \| 0.9856 \| nan \| 0.6292 \| 0.9964 \| nan \| 0.5632 \| 0.9853 \| \| 0.0277 \| 54.0 \| 540 \| 0.0445 \| 0.7714 \| 0.7932 \| 0.9862 \| nan \| 0.5880 \| 0.9983 \| nan \| 0.5569 \| 0.9859 \| \| 0.0298 \| 56.0 \| 560 \| 0.0460 \| 0.8034 \| 0.8518 \| 0.9872 \| nan \| 0.7078 \| 0.9957 \| nan \| 0.6198 \| 0.9869 \| \| 0.0256 \| 58.0 \| 580 \| 0.0416 \| 0.8181 \| 0.8548 \| 0.9886 \| nan \| 0.7126 \| 0.9970 \| nan \| 0.6479 \| 0.9883 \| \| 0.0336 \| 60.0 \| 600 \| 0.0442 \| 0.7957 \| 0.8168 \| 0.9877 \| nan \| 0.6351 \| 0.9984 \| nan \| 0.6039 \| 0.9875 \| \| 0.0283 \| 62.0 \| 620 \| 0.0425 \| 0.8141 \| 0.8812 \| 0.9873 \| nan \| 0.7684 \| 0.9940 \| nan \| 0.6413 \| 0.9870 \| \| 0.0198 \| 64.0 \| 640 \| 0.0455 \| 0.8059 \| 0.8401 \| 0.9879 \| nan \| 0.6830 \| 0.9971 \| nan \| 0.6242 \| 0.9876 \| \| 0.0181 \| 66.0 \| 660 \| 0.0444 \| 0.8144 \| 0.8733 \| 0.9876 \| nan \| 0.7519 \| 0.9948 \| nan \| 0.6415 \| 0.9873 \| \| 0.0188 \| 68.0 \| 680 \| 0.0456 \| 0.8179 \| 0.8696 \| 0.9881 \| nan \| 0.7436 \| 0.9955 \| nan \| 0.6479 \| 0.9878 \| \| 0.0165 \| 70.0 \| 700 \| 0.0431 \| 0.8208 \| 0.8985 \| 0.9875 \| nan \| 0.8040 \| 0.9930 \| nan \| 0.6544 \| 0.9872 \| \| 0.0184 \| 72.0 \| 720 \| 0.0421 \| 0.8165 \| 0.8785 \| 0.9876 \| nan \| 0.7625 \| 0.9945 \| nan \| 0.6457 \| 0.9874 \| \| 0.0336 \| 74.0 \| 740 \| 0.0441 \| 0.8081 \| 0.8792 \| 0.9867 \| nan \| 0.7650 \| 0.9935 \| nan \| 0.6298 \| 0.9864 \| \| 0.0165 \| 76.0 \| 760 \| 0.0374 \| 0.8200 \| 0.8555 \| 0.9887 \| nan \| 0.7139 \| 0.9971 \| nan \| 0.6515 \| 0.9885 \| \| 0.0127 \| 78.0 \| 780 \| 0.0402 \| 0.8222 \| 0.8780 \| 0.9882 \| nan \| 0.7608 \| 0.9952 \| nan \| 0.6563 \| 0.9880 \| \| 0.0152 \| 80.0 \| 800 \| 0.0430 \| 0.8230 \| 0.8687 \| 0.9886 \| nan \| 0.7413 \| 0.9961 \| nan \| 0.6576 \| 0.9883 \| \| 0.0143 \| 82.0 \| 820 \| 0.0410 \| 0.8087 \| 0.8422 \| 0.9881 \| nan \| 0.6873 \| 0.9972 \| nan \| 0.6297 \| 0.9878 \| \| 0.0134 \| 84.0 \| 840 \| 0.0335 \| 0.8429 \| 0.8893 \| 0.9899 \| nan \| 0.7823 \| 0.9962 \| nan \| 0.6962 \| 0.9897 \| \| 0.0122 \| 86.0 \| 860 \| 0.0396 \| 0.8312 \| 0.8749 \| 0.9892 \| nan \| 0.7534 \| 0.9964 \| nan \| 0.6734 \| 0.9890 \| \| 0.0126 \| 88.0 \| 880 \| 0.0405 \| 0.8341 \| 0.8805 \| 0.9893 \| nan \| 0.7649 \| 0.9962 \| nan \| 0.6791 \| 0.9891 \| \| 0.0121 \| 90.0 \| 900 \| 0.0400 \| 0.8390 \| 0.8810 \| 0.9898 \| nan \| 0.7654 \| 0.9966 \| nan \| 0.6884 \| 0.9895 \| \| 0.0104 \| 92.0 \| 920 \| 0.0372 \| 0.8453 \| 0.8990 \| 0.9899 \| nan \| 0.8024 \| 0.9956 \| nan \| 0.7010 \| 0.9896 \| \| 0.0128 \| 94.0 \| 940 \| 0.0394 \| 0.8411 \| 0.8893 \| 0.9897 \| nan \| 0.7825 \| 0.9961 \| nan \| 0.6927 \| 0.9895 \| \| 0.0124 \| 96.0 \| 960 \| 0.0409 \| 0.8395 \| 0.8948 \| 0.9895 \| nan \| 0.7943 \| 0.9954 \| nan \| 0.6899 \| 0.9892 \| \| 0.0095 \| 98.0 \| 980 \| 0.0413 \| 0.8258 \| 0.8903 \| 0.9882 \| nan \| 0.7863 \| 0.9944 \| nan \| 0.6637 \| 0.9880 \| \| 0.0147 \| 100.0 \| 1000 \| 0.0468 \| 0.8181 \| 0.9044 \| 0.9870 \| nan \| 0.8167 \| 0.9922 \| nan \| 0.6496 \| 0.9867 \| \| 0.0125 \| 102.0 \| 1020 \| 0.0379 \| 0.8213 \| 0.8961 \| 0.9876 \| nan \| 0.7989 \| 0.9933 \| nan \| 0.6553 \| 0.9873 \| \| 0.0142 \| 104.0 \| 1040 \| 0.0328 \| 0.8449 \| 0.9154 \| 0.9894 \| nan \| 0.8366 \| 0.9941 \| nan \| 0.7006 \| 0.9892 \| \| 0.0101 \| 106.0 \| 1060 \| 0.0428 \| 0.8407 \| 0.9144 \| 0.9891 \| nan \| 0.8351 \| 0.9937 \| nan \| 0.6927 \| 0.9888 \| \| 0.0097 \| 108.0 \| 1080 \| 0.0397 \| 0.8296 \| 0.8847 \| 0.9888 \| nan \| 0.7740 \| 0.9953 \| nan \| 0.6707 \| 0.9885 \| \| 0.01 \| 110.0 \| 1100 \| 0.0384 \| 0.8457 \| 0.8935 \| 0.9901 \| nan \| 0.7910 \| 0.9961 \| nan \| 0.7016 \| 0.9898 \| \| 0.0084 \| 112.0 \| 1120 \| 0.0385 \| 0.8421 \| 0.8874 \| 0.9899 \| nan \| 0.7784 \| 0.9963 \| nan \| 0.6945 \| 0.9896 \| \| 0.0086 \| 114.0 \| 1140 \| 0.0413 \| 0.8488 \| 0.8882 \| 0.9905 \| nan \| 0.7795 \| 0.9969 \| nan \| 0.7074 \| 0.9903 \| \| 0.0112 \| 116.0 \| 1160 \| 0.0427 \| 0.8459 \| 0.8942 \| 0.9901 \| nan \| 0.7924 \| 0.9961 \| nan \| 0.7020 \| 0.9898 \| \| 0.0132 \| 118.0 \| 1180 \| 0.0407 \| 0.8510 \| 0.9011 \| 0.9904 \| nan \| 0.8062 \| 0.9960 \| nan \| 0.7118 \| 0.9901 \| \| 0.0084 \| 120.0 \| 1200 \| 0.0432 \| 0.8510 \| 0.9015 \| 0.9903 \| nan \| 0.8071 \| 0.9959 \| nan \| 0.7118 \| 0.9901 \| \| 0.008 \| 122.0 \| 1220 \| 0.0431 \| 0.8504 \| 0.9077 \| 0.9901 \| nan \| 0.8202 \| 0.9953 \| nan \| 0.7109 \| 0.9899 \| \| 0.0069 \| 124.0 \| 1240 \| 0.0424 \| 0.8522 \| 0.8982 \| 0.9905 \| nan \| 0.8001 \| 0.9963 \| nan \| 0.7141 \| 0.9903 \| \| 0.006 \| 126.0 \| 1260 \| 0.0447 \| 0.8537 \| 0.9114 \| 0.9904 \| nan \| 0.8275 \| 0.9953 \| nan \| 0.7173 \| 0.9901 \| \| 0.0123 \| 128.0 \| 1280 \| 0.0464 \| 0.8529 \| 0.9102 \| 0.9903 \| nan \| 0.8250 \| 0.9954 \| nan \| 0.7157 \| 0.9901 \| \| 0.0073 \| 130.0 \| 1300 \| 0.0441 \| 0.8520 \| 0.9025 \| 0.9904 \| nan \| 0.8090 \| 0.9959 \| nan \| 0.7139 \| 0.9902 \| \| 0.0066 \| 132.0 \| 1320 \| 0.0447 \| 0.8524 \| 0.9086 \| 0.9903 \| nan \| 0.8217 \| 0.9954 \| nan \| 0.7148 \| 0.9901 \| \| 0.0063 \| 134.0 \| 1340 \| 0.0434 \| 0.8546 \| 0.9077 \| 0.9905 \| nan \| 0.8197 \| 0.9957 \| nan \| 0.7189 \| 0.9903 \| \| 0.0068 \| 136.0 \| 1360 \| 0.0475 \| 0.8518 \| 0.9090 \| 0.9902 \| nan \| 0.8226 \| 0.9953 \| nan \| 0.7135 \| 0.9900 \| \| 0.0056 \| 138.0 \| 1380 \| 0.0458 \| 0.8549 \| 0.9122 \| 0.9905 \| nan \| 0.8291 \| 0.9954 \| nan \| 0.7195 \| 0.9902 \| \| 0.007 \| 140.0 \| 1400 \| 0.0455 \| 0.8554 \| 0.9126 \| 0.9905 \| nan \| 0.8298 \| 0.9954 \| nan \| 0.7205 \| 0.9903 \| \| 0.0064 \| 142.0 \| 1420 \| 0.0476 \| 0.8542 \| 0.9047 \| 0.9906 \| nan \| 0.8133 \| 0.9960 \| nan \| 0.7180 \| 0.9903 \| \| 0.0065 \| 144.0 \| 1440 \| 0.0437 \| 0.8556 \| 0.9107 \| 0.9906 \| nan \| 0.8258 \| 0.9956 \| nan \| 0.7210 \| 0.9903 \| \| 0.005 \| 146.0 \| 1460 \| 0.0455 \| 0.8551 \| 0.9098 \| 0.9905 \| nan \| 0.8239 \| 0.9956 \| nan \| 0.7198 \| 0.9903 \| \| 0.005 \| 148.0 \| 1480 \| 0.0458 \| 0.8539 \| 0.9084 \| 0.9905 \| nan \| 0.8212 \| 0.9956 \| nan \| 0.7175 \| 0.9902 \| \| 0.0048 \| 150.0 \| 1500 \| 0.0462 \| 0.8558 \| 0.9041 \| 0.9907 \| nan \| 0.8121 \| 0.9962 \| nan \| 0.7211 \| 0.9905 \| \| 0.0063 \| 152.0 \| 1520 \| 0.0453 \| 0.8560 \| 0.9175 \| 0.9904 \| nan \| 0.8400 \| 0.9950 \| nan \| 0.7217 \| 0.9902 \| \| 0.006 \| 154.0 \| 1540 \| 0.0473 \| 0.8531 \| 0.9073 \| 0.9904 \| nan \| 0.8190 \| 0.9956 \| nan \| 0.7160 \| 0.9902 \| \| 0.0043 \| 156.0 \| 1560 \| 0.0448 \| 0.8562 \| 0.9100 \| 0.9906 \| nan \| 0.8243 \| 0.9957 \| nan \| 0.7220 \| 0.9904 \| \| 0.0049 \| 158.0 \| 1580 \| 0.0480 \| 0.8518 \| 0.9137 \| 0.9901 \| nan \| 0.8324 \| 0.9949 \| nan \| 0.7138 \| 0.9899 \| \| 0.0065 \| 160.0 \| 1600 \| 0.0475 \| 0.8556 \| 0.9095 \| 0.9906 \| nan \| 0.8233 \| 0.9957 \| nan \| 0.7209 \| 0.9903 \| \| 0.0052 \| 162.0 \| 1620 \| 0.0479 \| 0.8531 \| 0.9087 \| 0.9904 \| nan \| 0.8218 \| 0.9955 \| nan \| 0.7161 \| 0.9901 \| \| 0.0063 \| 164.0 \| 1640 \| 0.0488 \| 0.8571 \| 0.9115 \| 0.9907 \| nan \| 0.8273 \| 0.9956 \| nan \| 0.7238 \| 0.9904 \| \| 0.0053 \| 166.0 \| 1660 \| 0.0514 \| 0.8515 \| 0.9152 \| 0.9901 \| nan \| 0.8357 \| 0.9948 \| nan \| 0.7132 \| 0.9898 \| \| 0.0046 \| 168.0 \| 1680 \| 0.0476 \| 0.8540 \| 0.9040 \| 0.9906 \| nan \| 0.8119 \| 0.9960 \| nan \| 0.7177 \| 0.9903 \| \| 0.0039 \| 170.0 \| 1700 \| 0.0483 \| 0.5699 \| 0.9121 \| 0.9905 \| nan \| 0.8289 \| 0.9954 \| 0.0 \| 0.7195 \| 0.9902 \| \| 0.0044 \| 172.0 \| 1720 \| 0.0494 \| 0.8550 \| 0.9114 \| 0.9905 \| nan \| 0.8273 \| 0.9954 \| nan \| 0.7197 \| 0.9902 \| \| 0.0051 \| 174.0 \| 1740 \| 0.0503 \| 0.8556 \| 0.9103 \| 0.9906 \| nan \| 0.8250 \| 0.9956 \| nan \| 0.7208 \| 0.9903 \| \| 0.0041 \| 176.0 \| 1760 \| 0.0499 \| 0.8545 \| 0.9118 \| 0.9904 \| nan \| 0.8283 \| 0.9954 \| nan \| 0.7188 \| 0.9902 \| \| 0.0049 \| 178.0 \| 1780 \| 0.0525 \| 0.8541 \| 0.9066 \| 0.9905 \| nan \| 0.8174 \| 0.9958 \| nan \| 0.7179 \| 0.9903 \| \| 0.0048 \| 180.0 \| 1800 \| 0.0496 \| 0.8556 \| 0.9165 \| 0.9904 \| nan \| 0.8380 \| 0.9951 \| nan \| 0.7210 \| 0.9902 \| \| 0.008 \| 182.0 \| 1820 \| 0.0487 \| 0.8528 \| 0.9085 \| 0.9904 \| nan \| 0.8215 \| 0.9955 \| nan \| 0.7155 \| 0.9901 \| \| 0.0041 \| 184.0 \| 1840 \| 0.0506 \| 0.8519 \| 0.9125 \| 0.9902 \| nan \| 0.8300 \| 0.9950 \| nan \| 0.7138 \| 0.9899 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/safety-utcustom-train-SF30-RGB-b0	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF30-RGB-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Loss: 0.7492 - Mean Iou: 0.3878 - Mean Accuracy: 0.8431 - Overall Accuracy: 0.9233 - Accuracy Unlabeled: nan - Accuracy Safe: 0.7575 - Accuracy Unsafe: 0.9287 - Iou Unlabeled: 0.0 - Iou Safe: 0.2418 - Iou Unsafe: 0.9214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Safe \| Accuracy Unsafe \| Iou Unlabeled \| Iou Safe \| Iou Unsafe \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------:\| \| 1.1527 \| 5.0 \| 10 \| 1.1085 \| 0.0590 \| 0.4585 \| 0.1664 \| nan \| 0.7704 \| 0.1465 \| 0.0 \| 0.0307 \| 0.1464 \| \| 1.1326 \| 10.0 \| 20 \| 1.1091 \| 0.0963 \| 0.6082 \| 0.2699 \| nan \| 0.9695 \| 0.2470 \| 0.0 \| 0.0419 \| 0.2470 \| \| 1.0981 \| 15.0 \| 30 \| 1.0980 \| 0.1530 \| 0.6989 \| 0.4242 \| nan \| 0.9922 \| 0.4055 \| 0.0 \| 0.0535 \| 0.4055 \| \| 1.086 \| 20.0 \| 40 \| 1.0822 \| 0.1916 \| 0.7515 \| 0.5256 \| nan \| 0.9927 \| 0.5103 \| 0.0 \| 0.0644 \| 0.5103 \| \| 1.0466 \| 25.0 \| 50 \| 1.0541 \| 0.2226 \| 0.7909 \| 0.6043 \| nan \| 0.9902 \| 0.5917 \| 0.0 \| 0.0761 \| 0.5917 \| \| 1.0533 \| 30.0 \| 60 \| 1.0249 \| 0.2444 \| 0.8167 \| 0.6580 \| nan \| 0.9863 \| 0.6472 \| 0.0 \| 0.0861 \| 0.6471 \| \| 0.9779 \| 35.0 \| 70 \| 1.0010 \| 0.2607 \| 0.8322 \| 0.6966 \| nan \| 0.9771 \| 0.6874 \| 0.0 \| 0.0951 \| 0.6871 \| \| 0.9161 \| 40.0 \| 80 \| 0.9695 \| 0.2808 \| 0.8487 \| 0.7412 \| nan \| 0.9635 \| 0.7339 \| 0.0 \| 0.1091 \| 0.7334 \| \| 0.9843 \| 45.0 \| 90 \| 0.9403 \| 0.3004 \| 0.8631 \| 0.7823 \| nan \| 0.9494 \| 0.7768 \| 0.0 \| 0.1254 \| 0.7759 \| \| 0.9568 \| 50.0 \| 100 \| 0.9071 \| 0.3176 \| 0.8663 \| 0.8169 \| nan \| 0.9191 \| 0.8135 \| 0.0 \| 0.1412 \| 0.8117 \| \| 0.8443 \| 55.0 \| 110 \| 0.8627 \| 0.3403 \| 0.8656 \| 0.8576 \| nan \| 0.8742 \| 0.8570 \| 0.0 \| 0.1672 \| 0.8537 \| \| 0.8765 \| 60.0 \| 120 \| 0.8488 \| 0.3450 \| 0.8625 \| 0.8657 \| nan \| 0.8591 \| 0.8659 \| 0.0 \| 0.1729 \| 0.8620 \| \| 0.899 \| 65.0 \| 130 \| 0.8429 \| 0.3481 \| 0.8629 \| 0.8705 \| nan \| 0.8548 \| 0.8710 \| 0.0 \| 0.1772 \| 0.8669 \| \| 0.7713 \| 70.0 \| 140 \| 0.8085 \| 0.3632 \| 0.8497 \| 0.8939 \| nan \| 0.8026 \| 0.8969 \| 0.0 \| 0.1983 \| 0.8912 \| \| 0.8505 \| 75.0 \| 150 \| 0.7821 \| 0.3762 \| 0.8465 \| 0.9102 \| nan \| 0.7786 \| 0.9145 \| 0.0 \| 0.2208 \| 0.9079 \| \| 0.7352 \| 80.0 \| 160 \| 0.7841 \| 0.3819 \| 0.8392 \| 0.9173 \| nan \| 0.7557 \| 0.9226 \| 0.0 \| 0.2304 \| 0.9153 \| \| 0.7205 \| 85.0 \| 170 \| 0.7502 \| 0.3974 \| 0.8400 \| 0.9325 \| nan \| 0.7413 \| 0.9388 \| 0.0 \| 0.2613 \| 0.9309 \| \| 0.711 \| 90.0 \| 180 \| 0.7417 \| 0.3962 \| 0.8428 \| 0.9313 \| nan \| 0.7484 \| 0.9373 \| 0.0 \| 0.2591 \| 0.9296 \| \| 0.7855 \| 95.0 \| 190 \| 0.7281 \| 0.4003 \| 0.8439 \| 0.9343 \| nan \| 0.7473 \| 0.9404 \| 0.0 \| 0.2683 \| 0.9327 \| \| 0.7632 \| 100.0 \| 200 \| 0.7494 \| 0.3883 \| 0.8419 \| 0.9237 \| nan \| 0.7545 \| 0.9293 \| 0.0 \| 0.2430 \| 0.9219 \| \| 0.8145 \| 105.0 \| 210 \| 0.7495 \| 0.3862 \| 0.8412 \| 0.9219 \| nan \| 0.7551 \| 0.9274 \| 0.0 \| 0.2387 \| 0.9201 \| \| 0.8217 \| 110.0 \| 220 \| 0.7355 \| 0.3933 \| 0.8422 \| 0.9282 \| nan \| 0.7502 \| 0.9341 \| 0.0 \| 0.2533 \| 0.9265 \| \| 0.7784 \| 115.0 \| 230 \| 0.7258 \| 0.4088 \| 0.8411 \| 0.9413 \| nan \| 0.7340 \| 0.9481 \| 0.0 \| 0.2864 \| 0.9400 \| \| 0.8349 \| 120.0 \| 240 \| 0.7492 \| 0.3878 \| 0.8431 \| 0.9233 \| nan \| 0.7575 \| 0.9287 \| 0.0 \| 0.2418 \| 0.9214 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/safety-utcustom-train-SF30-RGB-b5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF30-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Accuracy Safe: 0.8299 - Accuracy Unlabeled: nan - Accuracy Unsafe: 0.9036 - Iou Safe: 0.3480 - Iou Unlabeled: 0.0 - Iou Unsafe: 0.8996 - Loss: 0.5783 - Mean Accuracy: 0.8668 - Mean Iou: 0.4158 - Overall Accuracy: 0.9013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 1.0614 \| 5.0 \| 10 \| 0.1904 \| nan \| 0.5439 \| 0.0682 \| 0.0 \| 0.5350 \| 1.0385 \| 0.3672 \| 0.2011 \| 0.5327 \| \| 1.0269 \| 10.0 \| 20 \| 0.4801 \| nan \| 0.5773 \| 0.1795 \| 0.0 \| 0.5719 \| 0.9975 \| 0.5287 \| 0.2505 \| 0.5742 \| \| 1.0005 \| 15.0 \| 30 \| 0.6270 \| nan \| 0.6316 \| 0.2261 \| 0.0 \| 0.6269 \| 0.9428 \| 0.6293 \| 0.2843 \| 0.6315 \| \| 0.9716 \| 20.0 \| 40 \| 0.6870 \| nan \| 0.6802 \| 0.2529 \| 0.0 \| 0.6756 \| 0.8918 \| 0.6836 \| 0.3095 \| 0.6804 \| \| 0.9255 \| 25.0 \| 50 \| 0.7339 \| nan \| 0.7081 \| 0.2805 \| 0.0 \| 0.7037 \| 0.8542 \| 0.7210 \| 0.3281 \| 0.7089 \| \| 0.9256 \| 30.0 \| 60 \| 0.7705 \| nan \| 0.7229 \| 0.2781 \| 0.0 \| 0.7189 \| 0.8330 \| 0.7467 \| 0.3324 \| 0.7244 \| \| 0.8167 \| 35.0 \| 70 \| 0.7622 \| nan \| 0.7349 \| 0.3004 \| 0.0 \| 0.7311 \| 0.8114 \| 0.7485 \| 0.3438 \| 0.7358 \| \| 0.7927 \| 40.0 \| 80 \| 0.7776 \| nan \| 0.7594 \| 0.3154 \| 0.0 \| 0.7559 \| 0.7793 \| 0.7685 \| 0.3571 \| 0.7600 \| \| 0.8227 \| 45.0 \| 90 \| 0.8020 \| nan \| 0.7821 \| 0.3152 \| 0.0 \| 0.7789 \| 0.7574 \| 0.7920 \| 0.3647 \| 0.7827 \| \| 0.81 \| 50.0 \| 100 \| 0.8114 \| nan \| 0.7983 \| 0.3140 \| 0.0 \| 0.7955 \| 0.7370 \| 0.8049 \| 0.3698 \| 0.7987 \| \| 0.7198 \| 55.0 \| 110 \| 0.8002 \| nan \| 0.8194 \| 0.3303 \| 0.0 \| 0.8162 \| 0.7118 \| 0.8098 \| 0.3822 \| 0.8188 \| \| 0.7523 \| 60.0 \| 120 \| 0.7877 \| nan \| 0.8482 \| 0.3457 \| 0.0 \| 0.8443 \| 0.6832 \| 0.8179 \| 0.3967 \| 0.8462 \| \| 0.7239 \| 65.0 \| 130 \| 0.8112 \| nan \| 0.8485 \| 0.3197 \| 0.0 \| 0.8453 \| 0.6745 \| 0.8298 \| 0.3883 \| 0.8473 \| \| 0.6235 \| 70.0 \| 140 \| 0.7906 \| nan \| 0.8686 \| 0.3507 \| 0.0 \| 0.8649 \| 0.6419 \| 0.8296 \| 0.4052 \| 0.8662 \| \| 0.6887 \| 75.0 \| 150 \| 0.7951 \| nan \| 0.8758 \| 0.3568 \| 0.0 \| 0.8720 \| 0.6302 \| 0.8354 \| 0.4096 \| 0.8732 \| \| 0.6079 \| 80.0 \| 160 \| 0.8069 \| nan \| 0.8879 \| 0.3561 \| 0.0 \| 0.8841 \| 0.6120 \| 0.8474 \| 0.4134 \| 0.8853 \| \| 0.6022 \| 85.0 \| 170 \| 0.8126 \| nan \| 0.9062 \| 0.3699 \| 0.0 \| 0.9020 \| 0.5849 \| 0.8594 \| 0.4240 \| 0.9032 \| \| 0.5748 \| 90.0 \| 180 \| 0.8053 \| nan \| 0.9047 \| 0.3793 \| 0.0 \| 0.9005 \| 0.5802 \| 0.8550 \| 0.4266 \| 0.9016 \| \| 0.6228 \| 95.0 \| 190 \| 0.8164 \| nan \| 0.9050 \| 0.3624 \| 0.0 \| 0.9007 \| 0.5793 \| 0.8607 \| 0.4210 \| 0.9022 \| \| 0.5332 \| 100.0 \| 200 \| 0.8214 \| nan \| 0.9134 \| 0.3623 \| 0.0 \| 0.9091 \| 0.5616 \| 0.8674 \| 0.4238 \| 0.9105 \| \| 0.6655 \| 105.0 \| 210 \| 0.8262 \| nan \| 0.9072 \| 0.3572 \| 0.0 \| 0.9031 \| 0.5688 \| 0.8667 \| 0.4201 \| 0.9046 \| \| 0.5835 \| 110.0 \| 220 \| 0.8233 \| nan \| 0.9092 \| 0.3599 \| 0.0 \| 0.9050 \| 0.5653 \| 0.8662 \| 0.4216 \| 0.9064 \| \| 0.5764 \| 115.0 \| 230 \| 0.8099 \| nan \| 0.9165 \| 0.3783 \| 0.0 \| 0.9120 \| 0.5460 \| 0.8632 \| 0.4301 \| 0.9131 \| \| 0.5621 \| 120.0 \| 240 \| 0.8299 \| nan \| 0.9036 \| 0.3480 \| 0.0 \| 0.8996 \| 0.5783 \| 0.8668 \| 0.4158 \| 0.9013 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/safety-utcustom-train-SF30-RGBD-b5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF30-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Mean Iou: 0.6486 - Mean Accuracy: 0.7199 - Overall Accuracy: 0.9704 - Accuracy Unlabeled: nan - Accuracy Safe: 0.4523 - Accuracy Unsafe: 0.9874 - Iou Unlabeled: nan - Iou Safe: 0.3271 - Iou Unsafe: 0.9700 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Safe \| Accuracy Unsafe \| Iou Unlabeled \| Iou Safe \| Iou Unsafe \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------:\| \| 0.8758 \| 5.0 \| 10 \| 0.9831 \| 0.3415 \| 0.6100 \| 0.9154 \| nan \| 0.2839 \| 0.9362 \| 0.0 \| 0.1099 \| 0.9147 \| \| 0.7637 \| 10.0 \| 20 \| 0.7236 \| 0.3771 \| 0.6275 \| 0.9582 \| nan \| 0.2745 \| 0.9806 \| 0.0 \| 0.1735 \| 0.9578 \| \| 0.6698 \| 15.0 \| 30 \| 0.5510 \| 0.3789 \| 0.6286 \| 0.9593 \| nan \| 0.2755 \| 0.9818 \| 0.0 \| 0.1776 \| 0.9590 \| \| 0.5935 \| 20.0 \| 40 \| 0.4632 \| 0.3822 \| 0.6388 \| 0.9591 \| nan \| 0.2967 \| 0.9809 \| 0.0 \| 0.1877 \| 0.9588 \| \| 0.5108 \| 25.0 \| 50 \| 0.4239 \| 0.3814 \| 0.6492 \| 0.9560 \| nan \| 0.3214 \| 0.9769 \| 0.0 \| 0.1887 \| 0.9556 \| \| 0.4597 \| 30.0 \| 60 \| 0.4134 \| 0.3845 \| 0.6422 \| 0.9596 \| nan \| 0.3034 \| 0.9811 \| 0.0 \| 0.1943 \| 0.9592 \| \| 0.4307 \| 35.0 \| 70 \| 0.3918 \| 0.3900 \| 0.6516 \| 0.9594 \| nan \| 0.3229 \| 0.9803 \| 0.0 \| 0.2111 \| 0.9590 \| \| 0.367 \| 40.0 \| 80 \| 0.3578 \| 0.3885 \| 0.6600 \| 0.9582 \| nan \| 0.3415 \| 0.9784 \| 0.0 \| 0.2077 \| 0.9577 \| \| 0.3249 \| 45.0 \| 90 \| 0.3395 \| 0.3921 \| 0.6587 \| 0.9607 \| nan \| 0.3360 \| 0.9813 \| 0.0 \| 0.2161 \| 0.9603 \| \| 0.292 \| 50.0 \| 100 \| 0.3124 \| 0.3969 \| 0.6622 \| 0.9633 \| nan \| 0.3408 \| 0.9837 \| 0.0 \| 0.2280 \| 0.9629 \| \| 0.2766 \| 55.0 \| 110 \| 0.2820 \| 0.4078 \| 0.6878 \| 0.9644 \| nan \| 0.3925 \| 0.9831 \| 0.0 \| 0.2594 \| 0.9639 \| \| 0.2347 \| 60.0 \| 120 \| 0.2673 \| 0.6169 \| 0.7000 \| 0.9641 \| nan \| 0.4181 \| 0.9820 \| nan \| 0.2701 \| 0.9636 \| \| 0.226 \| 65.0 \| 130 \| 0.2350 \| 0.6280 \| 0.6854 \| 0.9698 \| nan \| 0.3818 \| 0.9891 \| nan \| 0.2865 \| 0.9694 \| \| 0.3262 \| 70.0 \| 140 \| 0.2354 \| 0.6338 \| 0.7125 \| 0.9674 \| nan \| 0.4402 \| 0.9848 \| nan \| 0.3006 \| 0.9670 \| \| 0.1991 \| 75.0 \| 150 \| 0.2231 \| 0.6363 \| 0.7169 \| 0.9676 \| nan \| 0.4492 \| 0.9846 \| nan \| 0.3056 \| 0.9671 \| \| 0.2106 \| 80.0 \| 160 \| 0.2089 \| 0.6399 \| 0.7152 \| 0.9688 \| nan \| 0.4444 \| 0.9860 \| nan \| 0.3114 \| 0.9683 \| \| 0.1995 \| 85.0 \| 170 \| 0.1969 \| 0.6493 \| 0.7179 \| 0.9709 \| nan \| 0.4478 \| 0.9880 \| nan \| 0.3281 \| 0.9704 \| \| 0.1981 \| 90.0 \| 180 \| 0.1909 \| 0.6503 \| 0.7136 \| 0.9716 \| nan \| 0.4381 \| 0.9892 \| nan \| 0.3293 \| 0.9712 \| \| 0.1875 \| 95.0 \| 190 \| 0.1965 \| 0.6473 \| 0.7231 \| 0.9697 \| nan \| 0.4598 \| 0.9864 \| nan \| 0.3254 \| 0.9692 \| \| 0.2088 \| 100.0 \| 200 \| 0.1952 \| 0.6486 \| 0.7199 \| 0.9704 \| nan \| 0.4523 \| 0.9874 \| nan \| 0.3271 \| 0.9700 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/safety-utcustom-train-SF30-RGBD-b0	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF30-RGBD-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Loss: 0.3227 - Mean Iou: 0.5786 - Mean Accuracy: 0.6222 - Overall Accuracy: 0.9658 - Accuracy Unlabeled: nan - Accuracy Safe: 0.2552 - Accuracy Unsafe: 0.9891 - Iou Unlabeled: nan - Iou Safe: 0.1917 - Iou Unsafe: 0.9655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Safe \| Accuracy Unsafe \| Iou Unlabeled \| Iou Safe \| Iou Unsafe \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:----------:\| \| 0.9925 \| 5.0 \| 10 \| 1.0612 \| 0.3101 \| 0.5355 \| 0.8847 \| nan \| 0.1625 \| 0.9085 \| 0.0 \| 0.0462 \| 0.8841 \| \| 0.8589 \| 10.0 \| 20 \| 0.9441 \| 0.3303 \| 0.5181 \| 0.9537 \| nan \| 0.0529 \| 0.9833 \| 0.0 \| 0.0373 \| 0.9537 \| \| 0.7016 \| 15.0 \| 30 \| 0.7764 \| 0.3274 \| 0.5069 \| 0.9654 \| nan \| 0.0172 \| 0.9965 \| 0.0 \| 0.0169 \| 0.9654 \| \| 0.6093 \| 20.0 \| 40 \| 0.6213 \| 0.3339 \| 0.5219 \| 0.9603 \| nan \| 0.0538 \| 0.9901 \| 0.0 \| 0.0415 \| 0.9603 \| \| 0.5281 \| 25.0 \| 50 \| 0.5431 \| 0.3355 \| 0.5213 \| 0.9650 \| nan \| 0.0476 \| 0.9951 \| 0.0 \| 0.0417 \| 0.9649 \| \| 0.5077 \| 30.0 \| 60 \| 0.5043 \| 0.3361 \| 0.5231 \| 0.9638 \| nan \| 0.0524 \| 0.9938 \| 0.0 \| 0.0444 \| 0.9638 \| \| 0.5197 \| 35.0 \| 70 \| 0.4579 \| 0.3379 \| 0.5249 \| 0.9657 \| nan \| 0.0543 \| 0.9956 \| 0.0 \| 0.0481 \| 0.9656 \| \| 0.4477 \| 40.0 \| 80 \| 0.4340 \| 0.3395 \| 0.5271 \| 0.9662 \| nan \| 0.0583 \| 0.9960 \| 0.0 \| 0.0523 \| 0.9661 \| \| 0.4371 \| 45.0 \| 90 \| 0.4033 \| 0.3407 \| 0.5287 \| 0.9669 \| nan \| 0.0607 \| 0.9967 \| 0.0 \| 0.0553 \| 0.9669 \| \| 0.3972 \| 50.0 \| 100 \| 0.3975 \| 0.3420 \| 0.5292 \| 0.9686 \| nan \| 0.0600 \| 0.9985 \| 0.0 \| 0.0574 \| 0.9686 \| \| 0.4101 \| 55.0 \| 110 \| 0.3777 \| 0.5215 \| 0.5381 \| 0.9691 \| nan \| 0.0778 \| 0.9983 \| nan \| 0.0740 \| 0.9690 \| \| 0.3528 \| 60.0 \| 120 \| 0.3625 \| 0.5360 \| 0.5587 \| 0.9668 \| nan \| 0.1229 \| 0.9945 \| nan \| 0.1054 \| 0.9667 \| \| 0.3552 \| 65.0 \| 130 \| 0.3733 \| 0.5550 \| 0.5829 \| 0.9671 \| nan \| 0.1726 \| 0.9932 \| nan \| 0.1430 \| 0.9669 \| \| 0.3798 \| 70.0 \| 140 \| 0.3444 \| 0.5598 \| 0.5753 \| 0.9722 \| nan \| 0.1515 \| 0.9991 \| nan \| 0.1476 \| 0.9720 \| \| 0.3235 \| 75.0 \| 150 \| 0.3461 \| 0.5651 \| 0.6041 \| 0.9650 \| nan \| 0.2187 \| 0.9895 \| nan \| 0.1656 \| 0.9647 \| \| 0.3457 \| 80.0 \| 160 \| 0.3335 \| 0.5638 \| 0.5880 \| 0.9695 \| nan \| 0.1806 \| 0.9954 \| nan \| 0.1582 \| 0.9693 \| \| 0.318 \| 85.0 \| 170 \| 0.3334 \| 0.5739 \| 0.6114 \| 0.9667 \| nan \| 0.2321 \| 0.9908 \| nan \| 0.1814 \| 0.9665 \| \| 0.32 \| 90.0 \| 180 \| 0.3307 \| 0.5779 \| 0.6112 \| 0.9684 \| nan \| 0.2299 \| 0.9926 \| nan \| 0.1877 \| 0.9681 \| \| 0.3122 \| 95.0 \| 190 \| 0.3263 \| 0.5778 \| 0.6175 \| 0.9667 \| nan \| 0.2447 \| 0.9904 \| nan \| 0.1891 \| 0.9664 \| \| 0.3554 \| 100.0 \| 200 \| 0.3227 \| 0.5786 \| 0.6222 \| 0.9658 \| nan \| 0.2552 \| 0.9891 \| nan \| 0.1917 \| 0.9655 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "safe", "unsafe" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.5626 - Mean Iou: 0.4261 - Mean Accuracy: 0.7046 - Overall Accuracy: 0.9598 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4247 - Accuracy Undropoff: 0.9846 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3192 - Iou Undropoff: 0.9590 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.1029 \| 3.33 \| 10 \| 1.0852 \| 0.1637 \| 0.3955 \| 0.4522 \| nan \| 0.3333 \| 0.4577 \| 0.0 \| 0.0410 \| 0.4501 \| \| 1.0856 \| 6.67 \| 20 \| 1.0764 \| 0.1911 \| 0.5086 \| 0.5025 \| nan \| 0.5153 \| 0.5019 \| 0.0 \| 0.0761 \| 0.4972 \| \| 1.0755 \| 10.0 \| 30 \| 1.0611 \| 0.2252 \| 0.6367 \| 0.5749 \| nan \| 0.7045 \| 0.5688 \| 0.0 \| 0.1104 \| 0.5652 \| \| 1.0285 \| 13.33 \| 40 \| 1.0382 \| 0.2622 \| 0.7487 \| 0.6568 \| nan \| 0.8494 \| 0.6479 \| 0.0 \| 0.1420 \| 0.6445 \| \| 0.9935 \| 16.67 \| 50 \| 1.0151 \| 0.2893 \| 0.7814 \| 0.7201 \| nan \| 0.8486 \| 0.7141 \| 0.0 \| 0.1580 \| 0.7099 \| \| 0.9927 \| 20.0 \| 60 \| 0.9834 \| 0.3160 \| 0.7963 \| 0.7816 \| nan \| 0.8124 \| 0.7801 \| 0.0 \| 0.1735 \| 0.7744 \| \| 0.938 \| 23.33 \| 70 \| 0.9585 \| 0.3308 \| 0.8084 \| 0.8127 \| nan \| 0.8036 \| 0.8131 \| 0.0 \| 0.1860 \| 0.8065 \| \| 0.9169 \| 26.67 \| 80 \| 0.9376 \| 0.3457 \| 0.8169 \| 0.8376 \| nan \| 0.7943 \| 0.8396 \| 0.0 \| 0.2048 \| 0.8324 \| \| 0.8814 \| 30.0 \| 90 \| 0.9003 \| 0.3624 \| 0.8086 \| 0.8691 \| nan \| 0.7421 \| 0.8750 \| 0.0 \| 0.2220 \| 0.8651 \| \| 0.8618 \| 33.33 \| 100 \| 0.8894 \| 0.3669 \| 0.8184 \| 0.8761 \| nan \| 0.7550 \| 0.8817 \| 0.0 \| 0.2287 \| 0.8720 \| \| 0.8388 \| 36.67 \| 110 \| 0.8618 \| 0.3774 \| 0.8096 \| 0.8926 \| nan \| 0.7187 \| 0.9006 \| 0.0 \| 0.2431 \| 0.8892 \| \| 0.8878 \| 40.0 \| 120 \| 0.8269 \| 0.3929 \| 0.7937 \| 0.9140 \| nan \| 0.6618 \| 0.9257 \| 0.0 \| 0.2671 \| 0.9116 \| \| 0.8066 \| 43.33 \| 130 \| 0.8074 \| 0.4014 \| 0.7955 \| 0.9225 \| nan \| 0.6562 \| 0.9348 \| 0.0 \| 0.2839 \| 0.9202 \| \| 0.8084 \| 46.67 \| 140 \| 0.7919 \| 0.4023 \| 0.7932 \| 0.9248 \| nan \| 0.6487 \| 0.9376 \| 0.0 \| 0.2844 \| 0.9226 \| \| 0.7415 \| 50.0 \| 150 \| 0.7707 \| 0.4068 \| 0.7850 \| 0.9309 \| nan \| 0.6249 \| 0.9451 \| 0.0 \| 0.2913 \| 0.9290 \| \| 0.7508 \| 53.33 \| 160 \| 0.7326 \| 0.4154 \| 0.7660 \| 0.9415 \| nan \| 0.5735 \| 0.9585 \| 0.0 \| 0.3063 \| 0.9400 \| \| 0.7312 \| 56.67 \| 170 \| 0.7126 \| 0.4196 \| 0.7636 \| 0.9449 \| nan \| 0.5646 \| 0.9625 \| 0.0 \| 0.3155 \| 0.9435 \| \| 0.6442 \| 60.0 \| 180 \| 0.6869 \| 0.4255 \| 0.7500 \| 0.9509 \| nan \| 0.5296 \| 0.9704 \| 0.0 \| 0.3268 \| 0.9497 \| \| 0.6633 \| 63.33 \| 190 \| 0.6765 \| 0.4286 \| 0.7524 \| 0.9525 \| nan \| 0.5328 \| 0.9719 \| 0.0 \| 0.3343 \| 0.9513 \| \| 0.7247 \| 66.67 \| 200 \| 0.6557 \| 0.4307 \| 0.7335 \| 0.9568 \| nan \| 0.4886 \| 0.9785 \| 0.0 \| 0.3364 \| 0.9558 \| \| 0.6133 \| 70.0 \| 210 \| 0.6369 \| 0.4298 \| 0.7279 \| 0.9573 \| nan \| 0.4761 \| 0.9796 \| 0.0 \| 0.3330 \| 0.9564 \| \| 0.6309 \| 73.33 \| 220 \| 0.6309 \| 0.4298 \| 0.7437 \| 0.9547 \| nan \| 0.5123 \| 0.9752 \| 0.0 \| 0.3356 \| 0.9536 \| \| 0.6373 \| 76.67 \| 230 \| 0.6094 \| 0.4276 \| 0.7197 \| 0.9577 \| nan \| 0.4585 \| 0.9808 \| 0.0 \| 0.3262 \| 0.9568 \| \| 0.8436 \| 80.0 \| 240 \| 0.6195 \| 0.4341 \| 0.7438 \| 0.9569 \| nan \| 0.5101 \| 0.9776 \| 0.0 \| 0.3463 \| 0.9559 \| \| 0.6172 \| 83.33 \| 250 \| 0.6207 \| 0.4323 \| 0.7384 \| 0.9570 \| nan \| 0.4987 \| 0.9782 \| 0.0 \| 0.3409 \| 0.9560 \| \| 0.6048 \| 86.67 \| 260 \| 0.5949 \| 0.4272 \| 0.7136 \| 0.9586 \| nan \| 0.4449 \| 0.9824 \| 0.0 \| 0.3237 \| 0.9578 \| \| 0.7887 \| 90.0 \| 270 \| 0.6007 \| 0.4308 \| 0.7282 \| 0.9580 \| nan \| 0.4760 \| 0.9803 \| 0.0 \| 0.3353 \| 0.9571 \| \| 0.605 \| 93.33 \| 280 \| 0.5883 \| 0.4284 \| 0.7157 \| 0.9589 \| nan \| 0.4489 \| 0.9825 \| 0.0 \| 0.3271 \| 0.9581 \| \| 0.5964 \| 96.67 \| 290 \| 0.5872 \| 0.4277 \| 0.7134 \| 0.9590 \| nan \| 0.4439 \| 0.9828 \| 0.0 \| 0.3251 \| 0.9581 \| \| 0.6097 \| 100.0 \| 300 \| 0.5903 \| 0.4300 \| 0.7240 \| 0.9582 \| nan \| 0.4669 \| 0.9810 \| 0.0 \| 0.3325 \| 0.9573 \| \| 0.5886 \| 103.33 \| 310 \| 0.5710 \| 0.4250 \| 0.7035 \| 0.9594 \| nan \| 0.4227 \| 0.9843 \| 0.0 \| 0.3162 \| 0.9586 \| \| 0.6079 \| 106.67 \| 320 \| 0.5695 \| 0.4277 \| 0.7112 \| 0.9594 \| nan \| 0.4390 \| 0.9835 \| 0.0 \| 0.3245 \| 0.9586 \| \| 0.8054 \| 110.0 \| 330 \| 0.5746 \| 0.4308 \| 0.7237 \| 0.9588 \| nan \| 0.4657 \| 0.9816 \| 0.0 \| 0.3344 \| 0.9579 \| \| 0.5496 \| 113.33 \| 340 \| 0.5631 \| 0.4285 \| 0.7129 \| 0.9595 \| nan \| 0.4424 \| 0.9835 \| 0.0 \| 0.3269 \| 0.9587 \| \| 0.6271 \| 116.67 \| 350 \| 0.5761 \| 0.4302 \| 0.7214 \| 0.9589 \| nan \| 0.4608 \| 0.9819 \| 0.0 \| 0.3326 \| 0.9580 \| \| 0.5511 \| 120.0 \| 360 \| 0.5626 \| 0.4261 \| 0.7046 \| 0.9598 \| nan \| 0.4247 \| 0.9846 \| 0.0 \| 0.3192 \| 0.9590 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.6222 - Mean Iou: 0.4086 - Mean Accuracy: 0.6638 - Overall Accuracy: 0.9583 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3408 - Accuracy Undropoff: 0.9869 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.2682 - Iou Undropoff: 0.9576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.1857 \| 3.33 \| 10 \| 1.1215 \| 0.0852 \| 0.2039 \| 0.0995 \| nan \| 0.3183 \| 0.0894 \| 0.0 \| 0.1663 \| 0.0893 \| \| 1.1597 \| 6.67 \| 20 \| 1.1165 \| 0.1437 \| 0.3568 \| 0.2326 \| nan \| 0.4930 \| 0.2206 \| 0.0 \| 0.2108 \| 0.2203 \| \| 1.1528 \| 10.0 \| 30 \| 1.1040 \| 0.1938 \| 0.5140 \| 0.3464 \| nan \| 0.6978 \| 0.3301 \| 0.0 \| 0.2517 \| 0.3297 \| \| 1.0852 \| 13.33 \| 40 \| 1.0896 \| 0.2243 \| 0.6289 \| 0.4219 \| nan \| 0.8561 \| 0.4018 \| 0.0 \| 0.2717 \| 0.4011 \| \| 1.0388 \| 16.67 \| 50 \| 1.0511 \| 0.2700 \| 0.6748 \| 0.5498 \| nan \| 0.8120 \| 0.5377 \| 0.0 \| 0.2744 \| 0.5357 \| \| 1.0426 \| 20.0 \| 60 \| 1.0089 \| 0.3147 \| 0.6787 \| 0.6640 \| nan \| 0.6949 \| 0.6625 \| 0.0 \| 0.2857 \| 0.6583 \| \| 0.9621 \| 23.33 \| 70 \| 0.9921 \| 0.3374 \| 0.7060 \| 0.7392 \| nan \| 0.6695 \| 0.7424 \| 0.0 \| 0.2760 \| 0.7361 \| \| 0.925 \| 26.67 \| 80 \| 0.9464 \| 0.3591 \| 0.7031 \| 0.7964 \| nan \| 0.6007 \| 0.8054 \| 0.0 \| 0.2807 \| 0.7965 \| \| 0.8872 \| 30.0 \| 90 \| 0.8993 \| 0.3858 \| 0.7074 \| 0.8676 \| nan \| 0.5316 \| 0.8831 \| 0.0 \| 0.2888 \| 0.8686 \| \| 0.8751 \| 33.33 \| 100 \| 0.8974 \| 0.3896 \| 0.7177 \| 0.8817 \| nan \| 0.5379 \| 0.8976 \| 0.0 \| 0.2866 \| 0.8822 \| \| 0.8571 \| 36.67 \| 110 \| 0.8501 \| 0.4028 \| 0.7162 \| 0.9122 \| nan \| 0.5011 \| 0.9312 \| 0.0 \| 0.2953 \| 0.9131 \| \| 0.8866 \| 40.0 \| 120 \| 0.8434 \| 0.4072 \| 0.7240 \| 0.9252 \| nan \| 0.5032 \| 0.9448 \| 0.0 \| 0.2963 \| 0.9254 \| \| 0.8127 \| 43.33 \| 130 \| 0.7922 \| 0.4142 \| 0.7089 \| 0.9404 \| nan \| 0.4548 \| 0.9629 \| 0.0 \| 0.3025 \| 0.9402 \| \| 0.8062 \| 46.67 \| 140 \| 0.7917 \| 0.4123 \| 0.7103 \| 0.9432 \| nan \| 0.4548 \| 0.9658 \| 0.0 \| 0.2943 \| 0.9425 \| \| 0.7512 \| 50.0 \| 150 \| 0.7646 \| 0.4142 \| 0.7059 \| 0.9478 \| nan \| 0.4404 \| 0.9713 \| 0.0 \| 0.2955 \| 0.9470 \| \| 0.7554 \| 53.33 \| 160 \| 0.7497 \| 0.4161 \| 0.7001 \| 0.9510 \| nan \| 0.4248 \| 0.9754 \| 0.0 \| 0.2981 \| 0.9502 \| \| 0.7468 \| 56.67 \| 170 \| 0.7326 \| 0.4177 \| 0.6989 \| 0.9535 \| nan \| 0.4195 \| 0.9782 \| 0.0 \| 0.3005 \| 0.9527 \| \| 0.6506 \| 60.0 \| 180 \| 0.7184 \| 0.4173 \| 0.6992 \| 0.9541 \| nan \| 0.4196 \| 0.9789 \| 0.0 \| 0.2987 \| 0.9533 \| \| 0.6761 \| 63.33 \| 190 \| 0.7037 \| 0.4142 \| 0.6884 \| 0.9546 \| nan \| 0.3964 \| 0.9805 \| 0.0 \| 0.2886 \| 0.9539 \| \| 0.7245 \| 66.67 \| 200 \| 0.6960 \| 0.4122 \| 0.6821 \| 0.9553 \| nan \| 0.3824 \| 0.9818 \| 0.0 \| 0.2820 \| 0.9545 \| \| 0.6514 \| 70.0 \| 210 \| 0.6755 \| 0.4104 \| 0.6705 \| 0.9573 \| nan \| 0.3559 \| 0.9852 \| 0.0 \| 0.2746 \| 0.9566 \| \| 0.6433 \| 73.33 \| 220 \| 0.6804 \| 0.4180 \| 0.6954 \| 0.9556 \| nan \| 0.4100 \| 0.9809 \| 0.0 \| 0.2991 \| 0.9548 \| \| 0.6686 \| 76.67 \| 230 \| 0.6608 \| 0.4107 \| 0.6694 \| 0.9578 \| nan \| 0.3531 \| 0.9858 \| 0.0 \| 0.2749 \| 0.9571 \| \| 0.9091 \| 80.0 \| 240 \| 0.6701 \| 0.4160 \| 0.6922 \| 0.9557 \| nan \| 0.4031 \| 0.9813 \| 0.0 \| 0.2930 \| 0.9549 \| \| 0.6346 \| 83.33 \| 250 \| 0.6725 \| 0.4166 \| 0.6904 \| 0.9563 \| nan \| 0.3987 \| 0.9821 \| 0.0 \| 0.2944 \| 0.9555 \| \| 0.6303 \| 86.67 \| 260 \| 0.6460 \| 0.4090 \| 0.6670 \| 0.9576 \| nan \| 0.3481 \| 0.9858 \| 0.0 \| 0.2702 \| 0.9569 \| \| 0.8923 \| 90.0 \| 270 \| 0.6550 \| 0.4131 \| 0.6799 \| 0.9568 \| nan \| 0.3760 \| 0.9837 \| 0.0 \| 0.2832 \| 0.9561 \| \| 0.6334 \| 93.33 \| 280 \| 0.6468 \| 0.4100 \| 0.6708 \| 0.9572 \| nan \| 0.3566 \| 0.9851 \| 0.0 \| 0.2734 \| 0.9565 \| \| 0.6242 \| 96.67 \| 290 \| 0.6483 \| 0.4106 \| 0.6728 \| 0.9572 \| nan \| 0.3607 \| 0.9848 \| 0.0 \| 0.2754 \| 0.9565 \| \| 0.7401 \| 100.0 \| 300 \| 0.6470 \| 0.4129 \| 0.6796 \| 0.9569 \| nan \| 0.3755 \| 0.9838 \| 0.0 \| 0.2825 \| 0.9561 \| \| 0.6148 \| 103.33 \| 310 \| 0.6242 \| 0.4081 \| 0.6633 \| 0.9582 \| nan \| 0.3397 \| 0.9868 \| 0.0 \| 0.2668 \| 0.9575 \| \| 0.6345 \| 106.67 \| 320 \| 0.6287 \| 0.4093 \| 0.6670 \| 0.9579 \| nan \| 0.3478 \| 0.9862 \| 0.0 \| 0.2708 \| 0.9573 \| \| 0.8711 \| 110.0 \| 330 \| 0.6396 \| 0.4130 \| 0.6782 \| 0.9572 \| nan \| 0.3720 \| 0.9843 \| 0.0 \| 0.2826 \| 0.9565 \| \| 0.5812 \| 113.33 \| 340 \| 0.6266 \| 0.4101 \| 0.6689 \| 0.9580 \| nan \| 0.3517 \| 0.9861 \| 0.0 \| 0.2731 \| 0.9573 \| \| 0.6503 \| 116.67 \| 350 \| 0.6384 \| 0.4130 \| 0.6775 \| 0.9573 \| nan \| 0.3706 \| 0.9845 \| 0.0 \| 0.2824 \| 0.9566 \| \| 0.5923 \| 120.0 \| 360 \| 0.6222 \| 0.4086 \| 0.6638 \| 0.9583 \| nan \| 0.3408 \| 0.9869 \| 0.0 \| 0.2682 \| 0.9576 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_3 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3958 - Mean Iou: 0.6134 - Mean Accuracy: 0.6480 - Overall Accuracy: 0.9627 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3026 - Accuracy Undropoff: 0.9933 - Iou Unlabeled: nan - Iou Dropoff: 0.2645 - Iou Undropoff: 0.9622 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.1015 \| 3.33 \| 10 \| 1.0990 \| 0.1184 \| 0.4572 \| 0.3294 \| nan \| 0.5975 \| 0.3170 \| 0.0 \| 0.0427 \| 0.3124 \| \| 1.0478 \| 6.67 \| 20 \| 1.0756 \| 0.2121 \| 0.7082 \| 0.5654 \| nan \| 0.8648 \| 0.5515 \| 0.0 \| 0.0879 \| 0.5482 \| \| 1.0451 \| 10.0 \| 30 \| 1.0269 \| 0.2846 \| 0.8053 \| 0.7334 \| nan \| 0.8842 \| 0.7264 \| 0.0 \| 0.1313 \| 0.7226 \| \| 0.9095 \| 13.33 \| 40 \| 0.9476 \| 0.3360 \| 0.7905 \| 0.8411 \| nan \| 0.7349 \| 0.8460 \| 0.0 \| 0.1723 \| 0.8358 \| \| 0.8091 \| 16.67 \| 50 \| 0.8425 \| 0.3858 \| 0.7645 \| 0.9167 \| nan \| 0.5975 \| 0.9315 \| 0.0 \| 0.2429 \| 0.9145 \| \| 0.8094 \| 20.0 \| 60 \| 0.7489 \| 0.4090 \| 0.7445 \| 0.9417 \| nan \| 0.5281 \| 0.9608 \| 0.0 \| 0.2866 \| 0.9403 \| \| 0.6945 \| 23.33 \| 70 \| 0.7005 \| 0.4148 \| 0.7472 \| 0.9453 \| nan \| 0.5298 \| 0.9646 \| 0.0 \| 0.3004 \| 0.9440 \| \| 0.6337 \| 26.67 \| 80 \| 0.6331 \| 0.6267 \| 0.7334 \| 0.9499 \| nan \| 0.4958 \| 0.9709 \| nan \| 0.3047 \| 0.9488 \| \| 0.603 \| 30.0 \| 90 \| 0.5726 \| 0.6222 \| 0.6935 \| 0.9559 \| nan \| 0.4057 \| 0.9814 \| nan \| 0.2894 \| 0.9551 \| \| 0.5903 \| 33.33 \| 100 \| 0.5841 \| 0.6248 \| 0.7151 \| 0.9526 \| nan \| 0.4546 \| 0.9757 \| nan \| 0.2980 \| 0.9516 \| \| 0.5514 \| 36.67 \| 110 \| 0.5157 \| 0.6227 \| 0.6818 \| 0.9585 \| nan \| 0.3781 \| 0.9854 \| nan \| 0.2875 \| 0.9578 \| \| 0.6464 \| 40.0 \| 120 \| 0.5141 \| 0.6240 \| 0.6889 \| 0.9575 \| nan \| 0.3941 \| 0.9836 \| nan \| 0.2912 \| 0.9568 \| \| 0.5198 \| 43.33 \| 130 \| 0.4890 \| 0.4141 \| 0.6762 \| 0.9591 \| nan \| 0.3657 \| 0.9866 \| 0.0 \| 0.2838 \| 0.9585 \| \| 0.5077 \| 46.67 \| 140 \| 0.4855 \| 0.4118 \| 0.6719 \| 0.9588 \| nan \| 0.3572 \| 0.9866 \| 0.0 \| 0.2773 \| 0.9581 \| \| 0.4817 \| 50.0 \| 150 \| 0.4710 \| 0.6182 \| 0.6733 \| 0.9587 \| nan \| 0.3602 \| 0.9864 \| nan \| 0.2784 \| 0.9580 \| \| 0.4713 \| 53.33 \| 160 \| 0.4669 \| 0.6196 \| 0.6683 \| 0.9603 \| nan \| 0.3479 \| 0.9887 \| nan \| 0.2795 \| 0.9597 \| \| 0.4516 \| 56.67 \| 170 \| 0.4486 \| 0.4107 \| 0.6586 \| 0.9612 \| nan \| 0.3265 \| 0.9906 \| 0.0 \| 0.2715 \| 0.9606 \| \| 0.4059 \| 60.0 \| 180 \| 0.4361 \| 0.6136 \| 0.6548 \| 0.9612 \| nan \| 0.3187 \| 0.9909 \| nan \| 0.2665 \| 0.9606 \| \| 0.4142 \| 63.33 \| 190 \| 0.4267 \| 0.6115 \| 0.6503 \| 0.9615 \| nan \| 0.3089 \| 0.9917 \| nan \| 0.2621 \| 0.9610 \| \| 0.4393 \| 66.67 \| 200 \| 0.4188 \| 0.6035 \| 0.6354 \| 0.9623 \| nan \| 0.2768 \| 0.9940 \| nan \| 0.2452 \| 0.9618 \| \| 0.4071 \| 70.0 \| 210 \| 0.4224 \| 0.6137 \| 0.6528 \| 0.9617 \| nan \| 0.3138 \| 0.9917 \| nan \| 0.2663 \| 0.9612 \| \| 0.4009 \| 73.33 \| 220 \| 0.4205 \| 0.6136 \| 0.6540 \| 0.9614 \| nan \| 0.3167 \| 0.9912 \| nan \| 0.2664 \| 0.9608 \| \| 0.4043 \| 76.67 \| 230 \| 0.4148 \| 0.6132 \| 0.6514 \| 0.9619 \| nan \| 0.3108 \| 0.9920 \| nan \| 0.2651 \| 0.9613 \| \| 0.6302 \| 80.0 \| 240 \| 0.4116 \| 0.6133 \| 0.6513 \| 0.9619 \| nan \| 0.3105 \| 0.9921 \| nan \| 0.2653 \| 0.9614 \| \| 0.3859 \| 83.33 \| 250 \| 0.4113 \| 0.6141 \| 0.6543 \| 0.9615 \| nan \| 0.3174 \| 0.9913 \| nan \| 0.2673 \| 0.9609 \| \| 0.3791 \| 86.67 \| 260 \| 0.4033 \| 0.6042 \| 0.6361 \| 0.9623 \| nan \| 0.2782 \| 0.9940 \| nan \| 0.2465 \| 0.9619 \| \| 0.5716 \| 90.0 \| 270 \| 0.4088 \| 0.6168 \| 0.6575 \| 0.9617 \| nan \| 0.3237 \| 0.9913 \| nan \| 0.2724 \| 0.9612 \| \| 0.3803 \| 93.33 \| 280 \| 0.4024 \| 0.6171 \| 0.6565 \| 0.9621 \| nan \| 0.3211 \| 0.9918 \| nan \| 0.2727 \| 0.9615 \| \| 0.371 \| 96.67 \| 290 \| 0.3979 \| 0.6166 \| 0.6539 \| 0.9625 \| nan \| 0.3154 \| 0.9925 \| nan \| 0.2713 \| 0.9620 \| \| 0.3656 \| 100.0 \| 300 \| 0.3992 \| 0.6204 \| 0.6615 \| 0.9621 \| nan \| 0.3316 \| 0.9913 \| nan \| 0.2793 \| 0.9615 \| \| 0.3674 \| 103.33 \| 310 \| 0.3930 \| 0.6110 \| 0.6433 \| 0.9630 \| nan \| 0.2925 \| 0.9941 \| nan \| 0.2594 \| 0.9625 \| \| 0.378 \| 106.67 \| 320 \| 0.3925 \| 0.6124 \| 0.6459 \| 0.9629 \| nan \| 0.2981 \| 0.9937 \| nan \| 0.2623 \| 0.9624 \| \| 0.5766 \| 110.0 \| 330 \| 0.3965 \| 0.6192 \| 0.6594 \| 0.9621 \| nan \| 0.3272 \| 0.9916 \| nan \| 0.2768 \| 0.9616 \| \| 0.3513 \| 113.33 \| 340 \| 0.3927 \| 0.6161 \| 0.6523 \| 0.9627 \| nan \| 0.3118 \| 0.9928 \| nan \| 0.2701 \| 0.9622 \| \| 0.3731 \| 116.67 \| 350 \| 0.3975 \| 0.6200 \| 0.6613 \| 0.9620 \| nan \| 0.3315 \| 0.9912 \| nan \| 0.2785 \| 0.9614 \| \| 0.3489 \| 120.0 \| 360 \| 0.3958 \| 0.6134 \| 0.6480 \| 0.9627 \| nan \| 0.3026 \| 0.9933 \| nan \| 0.2645 \| 0.9622 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_4 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3032 - Mean Iou: 0.6301 - Mean Accuracy: 0.6710 - Overall Accuracy: 0.9634 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3502 - Accuracy Undropoff: 0.9918 - Iou Unlabeled: nan - Iou Dropoff: 0.2973 - Iou Undropoff: 0.9628 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0311 \| 3.33 \| 10 \| 1.0742 \| 0.2063 \| 0.6373 \| 0.5492 \| nan \| 0.7339 \| 0.5406 \| 0.0 \| 0.0848 \| 0.5342 \| \| 0.9741 \| 6.67 \| 20 \| 1.0151 \| 0.3072 \| 0.8067 \| 0.7686 \| nan \| 0.8485 \| 0.7649 \| 0.0 \| 0.1619 \| 0.7596 \| \| 0.9441 \| 10.0 \| 30 \| 0.9345 \| 0.3432 \| 0.8327 \| 0.8408 \| nan \| 0.8239 \| 0.8416 \| 0.0 \| 0.1947 \| 0.8348 \| \| 0.8222 \| 13.33 \| 40 \| 0.8358 \| 0.3643 \| 0.8236 \| 0.8773 \| nan \| 0.7646 \| 0.8825 \| 0.0 \| 0.2199 \| 0.8731 \| \| 0.7243 \| 16.67 \| 50 \| 0.7135 \| 0.3924 \| 0.7838 \| 0.9194 \| nan \| 0.6350 \| 0.9325 \| 0.0 \| 0.2603 \| 0.9170 \| \| 0.7213 \| 20.0 \| 60 \| 0.6358 \| 0.4054 \| 0.7528 \| 0.9374 \| nan \| 0.5502 \| 0.9554 \| 0.0 \| 0.2805 \| 0.9359 \| \| 0.5836 \| 23.33 \| 70 \| 0.5604 \| 0.4211 \| 0.7412 \| 0.9505 \| nan \| 0.5115 \| 0.9708 \| 0.0 \| 0.3139 \| 0.9493 \| \| 0.5285 \| 26.67 \| 80 \| 0.5227 \| 0.4281 \| 0.7570 \| 0.9519 \| nan \| 0.5432 \| 0.9708 \| 0.0 \| 0.3335 \| 0.9507 \| \| 0.4955 \| 30.0 \| 90 \| 0.4478 \| 0.4191 \| 0.6945 \| 0.9581 \| nan \| 0.4052 \| 0.9837 \| 0.0 \| 0.2999 \| 0.9573 \| \| 0.4646 \| 33.33 \| 100 \| 0.4537 \| 0.4215 \| 0.6998 \| 0.9584 \| nan \| 0.4161 \| 0.9835 \| 0.0 \| 0.3069 \| 0.9576 \| \| 0.4356 \| 36.67 \| 110 \| 0.4454 \| 0.4224 \| 0.7105 \| 0.9569 \| nan \| 0.4402 \| 0.9808 \| 0.0 \| 0.3112 \| 0.9560 \| \| 0.4829 \| 40.0 \| 120 \| 0.4099 \| 0.4196 \| 0.6901 \| 0.9593 \| nan \| 0.3947 \| 0.9854 \| 0.0 \| 0.3002 \| 0.9585 \| \| 0.4051 \| 43.33 \| 130 \| 0.3911 \| 0.6267 \| 0.6784 \| 0.9607 \| nan \| 0.3687 \| 0.9881 \| nan \| 0.2933 \| 0.9600 \| \| 0.3916 \| 46.67 \| 140 \| 0.3841 \| 0.4183 \| 0.6897 \| 0.9586 \| nan \| 0.3946 \| 0.9847 \| 0.0 \| 0.2969 \| 0.9579 \| \| 0.3713 \| 50.0 \| 150 \| 0.3788 \| 0.4248 \| 0.7001 \| 0.9600 \| nan \| 0.4149 \| 0.9853 \| 0.0 \| 0.3150 \| 0.9593 \| \| 0.359 \| 53.33 \| 160 \| 0.3719 \| 0.6254 \| 0.6761 \| 0.9607 \| nan \| 0.3639 \| 0.9883 \| nan \| 0.2908 \| 0.9601 \| \| 0.3459 \| 56.67 \| 170 \| 0.3610 \| 0.6245 \| 0.6774 \| 0.9601 \| nan \| 0.3673 \| 0.9876 \| nan \| 0.2895 \| 0.9594 \| \| 0.3099 \| 60.0 \| 180 \| 0.3455 \| 0.6246 \| 0.6687 \| 0.9620 \| nan \| 0.3468 \| 0.9905 \| nan \| 0.2879 \| 0.9614 \| \| 0.3124 \| 63.33 \| 190 \| 0.3436 \| 0.6277 \| 0.6763 \| 0.9615 \| nan \| 0.3634 \| 0.9892 \| nan \| 0.2946 \| 0.9608 \| \| 0.3283 \| 66.67 \| 200 \| 0.3344 \| 0.6237 \| 0.6607 \| 0.9634 \| nan \| 0.3286 \| 0.9928 \| nan \| 0.2845 \| 0.9629 \| \| 0.2974 \| 70.0 \| 210 \| 0.3412 \| 0.6312 \| 0.6817 \| 0.9616 \| nan \| 0.3746 \| 0.9888 \| nan \| 0.3014 \| 0.9609 \| \| 0.3003 \| 73.33 \| 220 \| 0.3322 \| 0.6320 \| 0.6877 \| 0.9607 \| nan \| 0.3881 \| 0.9872 \| nan \| 0.3041 \| 0.9600 \| \| 0.2968 \| 76.67 \| 230 \| 0.3289 \| 0.6344 \| 0.6807 \| 0.9628 \| nan \| 0.3712 \| 0.9902 \| nan \| 0.3066 \| 0.9622 \| \| 0.4415 \| 80.0 \| 240 \| 0.3333 \| 0.6320 \| 0.6800 \| 0.9622 \| nan \| 0.3705 \| 0.9896 \| nan \| 0.3024 \| 0.9615 \| \| 0.2836 \| 83.33 \| 250 \| 0.3271 \| 0.6287 \| 0.6757 \| 0.9619 \| nan \| 0.3617 \| 0.9897 \| nan \| 0.2960 \| 0.9613 \| \| 0.2762 \| 86.67 \| 260 \| 0.3203 \| 0.6263 \| 0.6673 \| 0.9629 \| nan \| 0.3429 \| 0.9916 \| nan \| 0.2903 \| 0.9623 \| \| 0.3901 \| 90.0 \| 270 \| 0.3186 \| 0.6290 \| 0.6787 \| 0.9614 \| nan \| 0.3685 \| 0.9889 \| nan \| 0.2971 \| 0.9608 \| \| 0.2755 \| 93.33 \| 280 \| 0.3086 \| 0.6283 \| 0.6693 \| 0.9631 \| nan \| 0.3468 \| 0.9917 \| nan \| 0.2940 \| 0.9625 \| \| 0.2652 \| 96.67 \| 290 \| 0.3099 \| 0.6302 \| 0.6779 \| 0.9620 \| nan \| 0.3661 \| 0.9896 \| nan \| 0.2991 \| 0.9614 \| \| 0.2627 \| 100.0 \| 300 \| 0.3056 \| 0.6294 \| 0.6728 \| 0.9627 \| nan \| 0.3548 \| 0.9909 \| nan \| 0.2966 \| 0.9622 \| \| 0.2647 \| 103.33 \| 310 \| 0.3036 \| 0.6292 \| 0.6689 \| 0.9635 \| nan \| 0.3458 \| 0.9921 \| nan \| 0.2954 \| 0.9629 \| \| 0.2697 \| 106.67 \| 320 \| 0.3043 \| 0.6298 \| 0.6713 \| 0.9632 \| nan \| 0.3510 \| 0.9916 \| nan \| 0.2970 \| 0.9626 \| \| 0.3878 \| 110.0 \| 330 \| 0.3037 \| 0.6297 \| 0.6740 \| 0.9626 \| nan \| 0.3573 \| 0.9907 \| nan \| 0.2973 \| 0.9620 \| \| 0.2521 \| 113.33 \| 340 \| 0.3013 \| 0.6300 \| 0.6714 \| 0.9633 \| nan \| 0.3513 \| 0.9916 \| nan \| 0.2974 \| 0.9627 \| \| 0.2663 \| 116.67 \| 350 \| 0.3060 \| 0.6298 \| 0.6766 \| 0.9621 \| nan \| 0.3634 \| 0.9899 \| nan \| 0.2981 \| 0.9615 \| \| 0.2507 \| 120.0 \| 360 \| 0.3032 \| 0.6301 \| 0.6710 \| 0.9634 \| nan \| 0.3502 \| 0.9918 \| nan \| 0.2973 \| 0.9628 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_5 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2543 - Mean Iou: 0.6541 - Mean Accuracy: 0.6937 - Overall Accuracy: 0.9665 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3944 - Accuracy Undropoff: 0.9930 - Iou Unlabeled: nan - Iou Dropoff: 0.3424 - Iou Undropoff: 0.9659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.2123 \| 3.33 \| 10 \| 1.1206 \| 0.0793 \| 0.1898 \| 0.1888 \| nan \| 0.1908 \| 0.1887 \| 0.0 \| 0.0494 \| 0.1886 \| \| 1.0927 \| 6.67 \| 20 \| 1.0985 \| 0.2196 \| 0.5875 \| 0.5351 \| nan \| 0.6450 \| 0.5300 \| 0.0 \| 0.1290 \| 0.5298 \| \| 1.0578 \| 10.0 \| 30 \| 0.9786 \| 0.3662 \| 0.7562 \| 0.8622 \| nan \| 0.6400 \| 0.8725 \| 0.0 \| 0.2367 \| 0.8621 \| \| 0.788 \| 13.33 \| 40 \| 0.7940 \| 0.4289 \| 0.7505 \| 0.9456 \| nan \| 0.5365 \| 0.9646 \| 0.0 \| 0.3398 \| 0.9468 \| \| 0.6353 \| 16.67 \| 50 \| 0.6206 \| 0.4182 \| 0.6840 \| 0.9583 \| nan \| 0.3830 \| 0.9850 \| 0.0 \| 0.2966 \| 0.9581 \| \| 0.6944 \| 20.0 \| 60 \| 0.5213 \| 0.4211 \| 0.6766 \| 0.9623 \| nan \| 0.3631 \| 0.9901 \| 0.0 \| 0.3014 \| 0.9620 \| \| 0.5046 \| 23.33 \| 70 \| 0.4765 \| 0.4239 \| 0.6796 \| 0.9634 \| nan \| 0.3683 \| 0.9910 \| 0.0 \| 0.3090 \| 0.9628 \| \| 0.4684 \| 26.67 \| 80 \| 0.4643 \| 0.3982 \| 0.6347 \| 0.9598 \| nan \| 0.2779 \| 0.9914 \| 0.0 \| 0.2352 \| 0.9593 \| \| 0.4401 \| 30.0 \| 90 \| 0.4483 \| 0.4110 \| 0.6507 \| 0.9632 \| nan \| 0.3077 \| 0.9936 \| 0.0 \| 0.2703 \| 0.9627 \| \| 0.4268 \| 33.33 \| 100 \| 0.4366 \| 0.6489 \| 0.7001 \| 0.9638 \| nan \| 0.4108 \| 0.9895 \| nan \| 0.3347 \| 0.9632 \| \| 0.3939 \| 36.67 \| 110 \| 0.4027 \| 0.4272 \| 0.6798 \| 0.9650 \| nan \| 0.3670 \| 0.9927 \| 0.0 \| 0.3171 \| 0.9644 \| \| 0.4472 \| 40.0 \| 120 \| 0.4159 \| 0.6428 \| 0.6896 \| 0.9638 \| nan \| 0.3887 \| 0.9905 \| nan \| 0.3225 \| 0.9632 \| \| 0.3618 \| 43.33 \| 130 \| 0.3765 \| 0.6325 \| 0.6671 \| 0.9650 \| nan \| 0.3402 \| 0.9939 \| nan \| 0.3006 \| 0.9644 \| \| 0.3456 \| 46.67 \| 140 \| 0.3671 \| 0.6395 \| 0.6816 \| 0.9643 \| nan \| 0.3715 \| 0.9917 \| nan \| 0.3153 \| 0.9637 \| \| 0.3352 \| 50.0 \| 150 \| 0.3572 \| 0.6431 \| 0.6839 \| 0.9650 \| nan \| 0.3755 \| 0.9923 \| nan \| 0.3218 \| 0.9644 \| \| 0.3143 \| 53.33 \| 160 \| 0.3451 \| 0.6351 \| 0.6702 \| 0.9651 \| nan \| 0.3467 \| 0.9938 \| nan \| 0.3056 \| 0.9646 \| \| 0.3009 \| 56.67 \| 170 \| 0.3357 \| 0.6449 \| 0.6941 \| 0.9636 \| nan \| 0.3984 \| 0.9898 \| nan \| 0.3267 \| 0.9630 \| \| 0.2765 \| 60.0 \| 180 \| 0.3188 \| 0.6458 \| 0.6934 \| 0.9641 \| nan \| 0.3965 \| 0.9903 \| nan \| 0.3282 \| 0.9634 \| \| 0.2703 \| 63.33 \| 190 \| 0.3179 \| 0.6385 \| 0.6732 \| 0.9656 \| nan \| 0.3525 \| 0.9940 \| nan \| 0.3119 \| 0.9650 \| \| 0.2746 \| 66.67 \| 200 \| 0.3067 \| 0.6385 \| 0.6702 \| 0.9662 \| nan \| 0.3456 \| 0.9949 \| nan \| 0.3113 \| 0.9656 \| \| 0.2516 \| 70.0 \| 210 \| 0.2992 \| 0.6569 \| 0.6968 \| 0.9667 \| nan \| 0.4008 \| 0.9929 \| nan \| 0.3477 \| 0.9661 \| \| 0.2503 \| 73.33 \| 220 \| 0.2999 \| 0.6671 \| 0.7198 \| 0.9659 \| nan \| 0.4497 \| 0.9899 \| nan \| 0.3689 \| 0.9652 \| \| 0.2443 \| 76.67 \| 230 \| 0.2816 \| 0.6439 \| 0.6750 \| 0.9668 \| nan \| 0.3547 \| 0.9952 \| nan \| 0.3215 \| 0.9663 \| \| 0.3757 \| 80.0 \| 240 \| 0.2907 \| 0.6593 \| 0.7063 \| 0.9659 \| nan \| 0.4215 \| 0.9911 \| nan \| 0.3535 \| 0.9652 \| \| 0.2306 \| 83.33 \| 250 \| 0.2767 \| 0.6439 \| 0.6807 \| 0.9658 \| nan \| 0.3680 \| 0.9935 \| nan \| 0.3226 \| 0.9652 \| \| 0.2216 \| 86.67 \| 260 \| 0.2792 \| 0.6583 \| 0.7018 \| 0.9663 \| nan \| 0.4115 \| 0.9920 \| nan \| 0.3509 \| 0.9657 \| \| 0.3202 \| 90.0 \| 270 \| 0.2681 \| 0.6425 \| 0.6789 \| 0.9657 \| nan \| 0.3642 \| 0.9936 \| nan \| 0.3199 \| 0.9652 \| \| 0.2174 \| 93.33 \| 280 \| 0.2633 \| 0.6467 \| 0.6860 \| 0.9657 \| nan \| 0.3791 \| 0.9928 \| nan \| 0.3284 \| 0.9651 \| \| 0.2086 \| 96.67 \| 290 \| 0.2658 \| 0.6476 \| 0.6900 \| 0.9652 \| nan \| 0.3880 \| 0.9920 \| nan \| 0.3306 \| 0.9646 \| \| 0.2042 \| 100.0 \| 300 \| 0.2651 \| 0.6486 \| 0.6898 \| 0.9655 \| nan \| 0.3873 \| 0.9923 \| nan \| 0.3322 \| 0.9649 \| \| 0.2071 \| 103.33 \| 310 \| 0.2597 \| 0.6445 \| 0.6792 \| 0.9662 \| nan \| 0.3643 \| 0.9941 \| nan \| 0.3233 \| 0.9657 \| \| 0.2097 \| 106.67 \| 320 \| 0.2596 \| 0.6615 \| 0.7062 \| 0.9665 \| nan \| 0.4206 \| 0.9918 \| nan \| 0.3571 \| 0.9658 \| \| 0.3118 \| 110.0 \| 330 \| 0.2557 \| 0.6516 \| 0.6928 \| 0.9659 \| nan \| 0.3931 \| 0.9924 \| nan \| 0.3380 \| 0.9653 \| \| 0.1956 \| 113.33 \| 340 \| 0.2517 \| 0.6494 \| 0.6865 \| 0.9664 \| nan \| 0.3794 \| 0.9936 \| nan \| 0.3331 \| 0.9658 \| \| 0.201 \| 116.67 \| 350 \| 0.2570 \| 0.6573 \| 0.7032 \| 0.9658 \| nan \| 0.4151 \| 0.9913 \| nan \| 0.3494 \| 0.9651 \| \| 0.1952 \| 120.0 \| 360 \| 0.2543 \| 0.6541 \| 0.6937 \| 0.9665 \| nan \| 0.3944 \| 0.9930 \| nan \| 0.3424 \| 0.9659 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_6	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_6 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1833 - Mean Iou: 0.6595 - Mean Accuracy: 0.7018 - Overall Accuracy: 0.9666 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4113 - Accuracy Undropoff: 0.9924 - Iou Unlabeled: nan - Iou Dropoff: 0.3531 - Iou Undropoff: 0.9660 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.1234 \| 3.33 \| 10 \| 1.0973 \| 0.1779 \| 0.5629 \| 0.3723 \| nan \| 0.7720 \| 0.3538 \| 0.0 \| 0.1801 \| 0.3536 \| \| 0.975 \| 6.67 \| 20 \| 1.0260 \| 0.3499 \| 0.8180 \| 0.8109 \| nan \| 0.8259 \| 0.8102 \| 0.0 \| 0.2428 \| 0.8069 \| \| 0.9464 \| 10.0 \| 30 \| 0.8130 \| 0.4297 \| 0.7456 \| 0.9502 \| nan \| 0.5212 \| 0.9700 \| 0.0 \| 0.3385 \| 0.9507 \| \| 0.6167 \| 13.33 \| 40 \| 0.6001 \| 0.4451 \| 0.7438 \| 0.9617 \| nan \| 0.5048 \| 0.9829 \| 0.0 \| 0.3743 \| 0.9610 \| \| 0.4818 \| 16.67 \| 50 \| 0.4629 \| 0.4491 \| 0.7237 \| 0.9666 \| nan \| 0.4573 \| 0.9902 \| 0.0 \| 0.3815 \| 0.9659 \| \| 0.4733 \| 20.0 \| 60 \| 0.4379 \| 0.4335 \| 0.7067 \| 0.9630 \| nan \| 0.4256 \| 0.9879 \| 0.0 \| 0.3383 \| 0.9623 \| \| 0.3843 \| 23.33 \| 70 \| 0.4073 \| 0.4310 \| 0.6872 \| 0.9652 \| nan \| 0.3821 \| 0.9922 \| 0.0 \| 0.3283 \| 0.9646 \| \| 0.3579 \| 26.67 \| 80 \| 0.3731 \| 0.4354 \| 0.6999 \| 0.9651 \| nan \| 0.4090 \| 0.9908 \| 0.0 \| 0.3418 \| 0.9644 \| \| 0.3212 \| 30.0 \| 90 \| 0.3655 \| 0.6589 \| 0.7129 \| 0.9647 \| nan \| 0.4366 \| 0.9892 \| nan \| 0.3538 \| 0.9640 \| \| 0.3088 \| 33.33 \| 100 \| 0.3306 \| 0.6310 \| 0.6689 \| 0.9641 \| nan \| 0.3451 \| 0.9928 \| nan \| 0.2985 \| 0.9635 \| \| 0.2825 \| 36.67 \| 110 \| 0.3253 \| 0.6633 \| 0.7103 \| 0.9663 \| nan \| 0.4293 \| 0.9912 \| nan \| 0.3609 \| 0.9657 \| \| 0.3029 \| 40.0 \| 120 \| 0.3130 \| 0.6556 \| 0.7079 \| 0.9645 \| nan \| 0.4264 \| 0.9895 \| nan \| 0.3474 \| 0.9638 \| \| 0.252 \| 43.33 \| 130 \| 0.2898 \| 0.6703 \| 0.7310 \| 0.9652 \| nan \| 0.4740 \| 0.9880 \| nan \| 0.3762 \| 0.9645 \| \| 0.2395 \| 46.67 \| 140 \| 0.2843 \| 0.6587 \| 0.7088 \| 0.9653 \| nan \| 0.4275 \| 0.9902 \| nan \| 0.3527 \| 0.9646 \| \| 0.2308 \| 50.0 \| 150 \| 0.2744 \| 0.6481 \| 0.6870 \| 0.9659 \| nan \| 0.3811 \| 0.9930 \| nan \| 0.3309 \| 0.9653 \| \| 0.2125 \| 53.33 \| 160 \| 0.2579 \| 0.6555 \| 0.7028 \| 0.9653 \| nan \| 0.4147 \| 0.9909 \| nan \| 0.3464 \| 0.9647 \| \| 0.1953 \| 56.67 \| 170 \| 0.2551 \| 0.6549 \| 0.7054 \| 0.9647 \| nan \| 0.4209 \| 0.9899 \| nan \| 0.3458 \| 0.9641 \| \| 0.1743 \| 60.0 \| 180 \| 0.2377 \| 0.6393 \| 0.6768 \| 0.9651 \| nan \| 0.3605 \| 0.9931 \| nan \| 0.3140 \| 0.9646 \| \| 0.17 \| 63.33 \| 190 \| 0.2342 \| 0.6564 \| 0.7002 \| 0.9660 \| nan \| 0.4086 \| 0.9918 \| nan \| 0.3474 \| 0.9654 \| \| 0.173 \| 66.67 \| 200 \| 0.2296 \| 0.6629 \| 0.7095 \| 0.9664 \| nan \| 0.4277 \| 0.9913 \| nan \| 0.3602 \| 0.9657 \| \| 0.1487 \| 70.0 \| 210 \| 0.2152 \| 0.6525 \| 0.6861 \| 0.9673 \| nan \| 0.3777 \| 0.9946 \| nan \| 0.3383 \| 0.9667 \| \| 0.1501 \| 73.33 \| 220 \| 0.2179 \| 0.6593 \| 0.7019 \| 0.9665 \| nan \| 0.4116 \| 0.9923 \| nan \| 0.3527 \| 0.9659 \| \| 0.1419 \| 76.67 \| 230 \| 0.2055 \| 0.6605 \| 0.7057 \| 0.9663 \| nan \| 0.4199 \| 0.9916 \| nan \| 0.3553 \| 0.9656 \| \| 0.2049 \| 80.0 \| 240 \| 0.2060 \| 0.6563 \| 0.7004 \| 0.9659 \| nan \| 0.4091 \| 0.9917 \| nan \| 0.3472 \| 0.9653 \| \| 0.1339 \| 83.33 \| 250 \| 0.2006 \| 0.6514 \| 0.6921 \| 0.9660 \| nan \| 0.3916 \| 0.9926 \| nan \| 0.3375 \| 0.9654 \| \| 0.1262 \| 86.67 \| 260 \| 0.1963 \| 0.6559 \| 0.7033 \| 0.9654 \| nan \| 0.4158 \| 0.9908 \| nan \| 0.3470 \| 0.9647 \| \| 0.179 \| 90.0 \| 270 \| 0.1907 \| 0.6549 \| 0.6976 \| 0.9660 \| nan \| 0.4032 \| 0.9921 \| nan \| 0.3445 \| 0.9654 \| \| 0.1216 \| 93.33 \| 280 \| 0.1901 \| 0.6561 \| 0.6994 \| 0.9661 \| nan \| 0.4068 \| 0.9920 \| nan \| 0.3468 \| 0.9655 \| \| 0.1144 \| 96.67 \| 290 \| 0.1917 \| 0.6565 \| 0.7017 \| 0.9658 \| nan \| 0.4119 \| 0.9915 \| nan \| 0.3478 \| 0.9652 \| \| 0.1095 \| 100.0 \| 300 \| 0.1900 \| 0.6621 \| 0.7108 \| 0.9659 \| nan \| 0.4309 \| 0.9907 \| nan \| 0.3590 \| 0.9653 \| \| 0.1144 \| 103.33 \| 310 \| 0.1848 \| 0.6595 \| 0.6994 \| 0.9670 \| nan \| 0.4058 \| 0.9930 \| nan \| 0.3526 \| 0.9664 \| \| 0.1144 \| 106.67 \| 320 \| 0.1849 \| 0.6585 \| 0.7011 \| 0.9665 \| nan \| 0.4100 \| 0.9922 \| nan \| 0.3512 \| 0.9658 \| \| 0.1574 \| 110.0 \| 330 \| 0.1852 \| 0.6592 \| 0.7025 \| 0.9664 \| nan \| 0.4128 \| 0.9921 \| nan \| 0.3526 \| 0.9658 \| \| 0.1085 \| 113.33 \| 340 \| 0.1819 \| 0.6595 \| 0.7016 \| 0.9667 \| nan \| 0.4108 \| 0.9924 \| nan \| 0.3530 \| 0.9660 \| \| 0.1099 \| 116.67 \| 350 \| 0.1856 \| 0.6602 \| 0.7057 \| 0.9662 \| nan \| 0.4198 \| 0.9915 \| nan \| 0.3548 \| 0.9656 \| \| 0.1048 \| 120.0 \| 360 \| 0.1833 \| 0.6595 \| 0.7018 \| 0.9666 \| nan \| 0.4113 \| 0.9924 \| nan \| 0.3531 \| 0.9660 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b0_7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b0_7 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1457 - Mean Iou: 0.6795 - Mean Accuracy: 0.7207 - Overall Accuracy: 0.9691 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4481 - Accuracy Undropoff: 0.9932 - Iou Unlabeled: nan - Iou Dropoff: 0.3907 - Iou Undropoff: 0.9684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.1505 \| 3.33 \| 10 \| 1.1103 \| 0.1106 \| 0.6036 \| 0.2919 \| nan \| 0.9456 \| 0.2616 \| 0.0 \| 0.0703 \| 0.2616 \| \| 0.9635 \| 6.67 \| 20 \| 1.0114 \| 0.3710 \| 0.8470 \| 0.8737 \| nan \| 0.8177 \| 0.8763 \| 0.0 \| 0.2435 \| 0.8694 \| \| 0.9358 \| 10.0 \| 30 \| 0.8242 \| 0.4206 \| 0.7727 \| 0.9440 \| nan \| 0.5848 \| 0.9606 \| 0.0 \| 0.3194 \| 0.9425 \| \| 0.579 \| 13.33 \| 40 \| 0.5703 \| 0.4525 \| 0.7615 \| 0.9633 \| nan \| 0.5402 \| 0.9829 \| 0.0 \| 0.3951 \| 0.9624 \| \| 0.4411 \| 16.67 \| 50 \| 0.4166 \| 0.4529 \| 0.7380 \| 0.9667 \| nan \| 0.4872 \| 0.9889 \| 0.0 \| 0.3928 \| 0.9659 \| \| 0.4311 \| 20.0 \| 60 \| 0.3843 \| 0.6678 \| 0.7156 \| 0.9667 \| nan \| 0.4400 \| 0.9911 \| nan \| 0.3695 \| 0.9661 \| \| 0.3437 \| 23.33 \| 70 \| 0.3590 \| 0.4347 \| 0.6956 \| 0.9655 \| nan \| 0.3995 \| 0.9918 \| 0.0 \| 0.3392 \| 0.9649 \| \| 0.3136 \| 26.67 \| 80 \| 0.3198 \| 0.6259 \| 0.6622 \| 0.9638 \| nan \| 0.3312 \| 0.9931 \| nan \| 0.2885 \| 0.9633 \| \| 0.2682 \| 30.0 \| 90 \| 0.2919 \| 0.6187 \| 0.6470 \| 0.9648 \| nan \| 0.2984 \| 0.9957 \| nan \| 0.2730 \| 0.9643 \| \| 0.2521 \| 33.33 \| 100 \| 0.2957 \| 0.6448 \| 0.6845 \| 0.9653 \| nan \| 0.3764 \| 0.9926 \| nan \| 0.3248 \| 0.9648 \| \| 0.2287 \| 36.67 \| 110 \| 0.2747 \| 0.6800 \| 0.7256 \| 0.9685 \| nan \| 0.4591 \| 0.9921 \| nan \| 0.3922 \| 0.9678 \| \| 0.2203 \| 40.0 \| 120 \| 0.2537 \| 0.7108 \| 0.7687 \| 0.9706 \| nan \| 0.5472 \| 0.9902 \| nan \| 0.4517 \| 0.9699 \| \| 0.1964 \| 43.33 \| 130 \| 0.2356 \| 0.6689 \| 0.7054 \| 0.9686 \| nan \| 0.4167 \| 0.9941 \| nan \| 0.3699 \| 0.9680 \| \| 0.1776 \| 46.67 \| 140 \| 0.2205 \| 0.6729 \| 0.7137 \| 0.9684 \| nan \| 0.4343 \| 0.9931 \| nan \| 0.3780 \| 0.9677 \| \| 0.1675 \| 50.0 \| 150 \| 0.2061 \| 0.6809 \| 0.7244 \| 0.9689 \| nan \| 0.4562 \| 0.9926 \| nan \| 0.3936 \| 0.9682 \| \| 0.148 \| 53.33 \| 160 \| 0.1954 \| 0.6924 \| 0.7418 \| 0.9694 \| nan \| 0.4920 \| 0.9915 \| nan \| 0.4160 \| 0.9687 \| \| 0.1364 \| 56.67 \| 170 \| 0.1915 \| 0.6869 \| 0.7415 \| 0.9681 \| nan \| 0.4928 \| 0.9902 \| nan \| 0.4064 \| 0.9674 \| \| 0.1171 \| 60.0 \| 180 \| 0.1776 \| 0.7206 \| 0.7816 \| 0.9714 \| nan \| 0.5734 \| 0.9899 \| nan \| 0.4706 \| 0.9707 \| \| 0.1169 \| 63.33 \| 190 \| 0.1754 \| 0.6580 \| 0.6853 \| 0.9689 \| nan \| 0.3741 \| 0.9965 \| nan \| 0.3476 \| 0.9684 \| \| 0.1178 \| 66.67 \| 200 \| 0.1676 \| 0.6783 \| 0.7233 \| 0.9684 \| nan \| 0.4545 \| 0.9922 \| nan \| 0.3888 \| 0.9677 \| \| 0.1016 \| 70.0 \| 210 \| 0.1670 \| 0.6633 \| 0.6985 \| 0.9682 \| nan \| 0.4025 \| 0.9944 \| nan \| 0.3590 \| 0.9676 \| \| 0.1025 \| 73.33 \| 220 \| 0.1648 \| 0.6789 \| 0.7154 \| 0.9696 \| nan \| 0.4366 \| 0.9943 \| nan \| 0.3888 \| 0.9690 \| \| 0.0956 \| 76.67 \| 230 \| 0.1607 \| 0.6684 \| 0.7103 \| 0.9677 \| nan \| 0.4279 \| 0.9927 \| nan \| 0.3697 \| 0.9671 \| \| 0.1443 \| 80.0 \| 240 \| 0.1611 \| 0.6747 \| 0.7134 \| 0.9688 \| nan \| 0.4332 \| 0.9937 \| nan \| 0.3811 \| 0.9682 \| \| 0.0902 \| 83.33 \| 250 \| 0.1600 \| 0.6713 \| 0.7060 \| 0.9691 \| nan \| 0.4174 \| 0.9946 \| nan \| 0.3740 \| 0.9685 \| \| 0.0846 \| 86.67 \| 260 \| 0.1559 \| 0.6772 \| 0.7263 \| 0.9677 \| nan \| 0.4613 \| 0.9912 \| nan \| 0.3874 \| 0.9670 \| \| 0.1166 \| 90.0 \| 270 \| 0.1587 \| 0.6615 \| 0.6984 \| 0.9677 \| nan \| 0.4030 \| 0.9939 \| nan \| 0.3559 \| 0.9671 \| \| 0.0825 \| 93.33 \| 280 \| 0.1538 \| 0.6684 \| 0.7068 \| 0.9682 \| nan \| 0.4199 \| 0.9936 \| nan \| 0.3692 \| 0.9676 \| \| 0.0769 \| 96.67 \| 290 \| 0.1527 \| 0.6649 \| 0.7033 \| 0.9679 \| nan \| 0.4130 \| 0.9936 \| nan \| 0.3626 \| 0.9673 \| \| 0.0722 \| 100.0 \| 300 \| 0.1473 \| 0.6832 \| 0.7247 \| 0.9694 \| nan \| 0.4563 \| 0.9932 \| nan \| 0.3976 \| 0.9688 \| \| 0.0779 \| 103.33 \| 310 \| 0.1465 \| 0.6809 \| 0.7200 \| 0.9695 \| nan \| 0.4462 \| 0.9937 \| nan \| 0.3930 \| 0.9689 \| \| 0.0771 \| 106.67 \| 320 \| 0.1494 \| 0.6673 \| 0.7052 \| 0.9682 \| nan \| 0.4167 \| 0.9937 \| nan \| 0.3670 \| 0.9676 \| \| 0.1082 \| 110.0 \| 330 \| 0.1479 \| 0.6753 \| 0.7182 \| 0.9683 \| nan \| 0.4438 \| 0.9926 \| nan \| 0.3830 \| 0.9677 \| \| 0.0726 \| 113.33 \| 340 \| 0.1451 \| 0.6765 \| 0.7159 \| 0.9689 \| nan \| 0.4384 \| 0.9935 \| nan \| 0.3846 \| 0.9683 \| \| 0.0743 \| 116.67 \| 350 \| 0.1469 \| 0.6814 \| 0.7249 \| 0.9689 \| nan \| 0.4571 \| 0.9927 \| nan \| 0.3946 \| 0.9683 \| \| 0.0703 \| 120.0 \| 360 \| 0.1457 \| 0.6795 \| 0.7207 \| 0.9691 \| nan \| 0.4481 \| 0.9932 \| nan \| 0.3907 \| 0.9684 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4979 - Mean Iou: 0.4170 - Mean Accuracy: 0.6846 - Overall Accuracy: 0.9603 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3839 - Accuracy Undropoff: 0.9853 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.2914 - Iou Undropoff: 0.9597 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0495 \| 5.0 \| 10 \| 1.0890 \| 0.1852 \| 0.3572 \| 0.4990 \| nan \| 0.2026 \| 0.5119 \| 0.0 \| 0.0474 \| 0.5081 \| \| 0.9941 \| 10.0 \| 20 \| 1.0479 \| 0.3452 \| 0.8357 \| 0.8479 \| nan \| 0.8225 \| 0.8490 \| 0.0 \| 0.1931 \| 0.8425 \| \| 0.9448 \| 15.0 \| 30 \| 0.9839 \| 0.3790 \| 0.8217 \| 0.9010 \| nan \| 0.7351 \| 0.9082 \| 0.0 \| 0.2390 \| 0.8980 \| \| 0.8912 \| 20.0 \| 40 \| 0.9041 \| 0.3845 \| 0.7150 \| 0.9247 \| nan \| 0.4863 \| 0.9437 \| 0.0 \| 0.2303 \| 0.9233 \| \| 0.8458 \| 25.0 \| 50 \| 0.7997 \| 0.3835 \| 0.6687 \| 0.9326 \| nan \| 0.3808 \| 0.9565 \| 0.0 \| 0.2188 \| 0.9316 \| \| 0.8299 \| 30.0 \| 60 \| 0.7387 \| 0.3751 \| 0.6333 \| 0.9326 \| nan \| 0.3068 \| 0.9597 \| 0.0 \| 0.1934 \| 0.9318 \| \| 0.7518 \| 35.0 \| 70 \| 0.6810 \| 0.3791 \| 0.6322 \| 0.9404 \| nan \| 0.2961 \| 0.9683 \| 0.0 \| 0.1975 \| 0.9397 \| \| 0.6943 \| 40.0 \| 80 \| 0.6322 \| 0.3703 \| 0.6069 \| 0.9422 \| nan \| 0.2411 \| 0.9726 \| 0.0 \| 0.1691 \| 0.9417 \| \| 0.6617 \| 45.0 \| 90 \| 0.6071 \| 0.3780 \| 0.6240 \| 0.9454 \| nan \| 0.2734 \| 0.9746 \| 0.0 \| 0.1892 \| 0.9449 \| \| 0.634 \| 50.0 \| 100 \| 0.5932 \| 0.3765 \| 0.6106 \| 0.9497 \| nan \| 0.2407 \| 0.9805 \| 0.0 \| 0.1802 \| 0.9494 \| \| 0.6157 \| 55.0 \| 110 \| 0.5829 \| 0.3982 \| 0.6538 \| 0.9524 \| nan \| 0.3281 \| 0.9795 \| 0.0 \| 0.2425 \| 0.9520 \| \| 0.5814 \| 60.0 \| 120 \| 0.5708 \| 0.4038 \| 0.6699 \| 0.9533 \| nan \| 0.3608 \| 0.9790 \| 0.0 \| 0.2586 \| 0.9528 \| \| 0.5988 \| 65.0 \| 130 \| 0.5575 \| 0.3974 \| 0.6456 \| 0.9569 \| nan \| 0.3061 \| 0.9851 \| 0.0 \| 0.2357 \| 0.9564 \| \| 0.5583 \| 70.0 \| 140 \| 0.5530 \| 0.4224 \| 0.7075 \| 0.9576 \| nan \| 0.4346 \| 0.9803 \| 0.0 \| 0.3103 \| 0.9570 \| \| 0.5596 \| 75.0 \| 150 \| 0.5264 \| 0.4034 \| 0.6522 \| 0.9598 \| nan \| 0.3167 \| 0.9877 \| 0.0 \| 0.2510 \| 0.9593 \| \| 0.5524 \| 80.0 \| 160 \| 0.5392 \| 0.4208 \| 0.7109 \| 0.9567 \| nan \| 0.4429 \| 0.9790 \| 0.0 \| 0.3065 \| 0.9560 \| \| 0.5294 \| 85.0 \| 170 \| 0.5257 \| 0.4161 \| 0.6913 \| 0.9582 \| nan \| 0.4002 \| 0.9824 \| 0.0 \| 0.2909 \| 0.9576 \| \| 0.5477 \| 90.0 \| 180 \| 0.5178 \| 0.4207 \| 0.6962 \| 0.9591 \| nan \| 0.4095 \| 0.9829 \| 0.0 \| 0.3035 \| 0.9584 \| \| 0.528 \| 95.0 \| 190 \| 0.5185 \| 0.4183 \| 0.6939 \| 0.9590 \| nan \| 0.4047 \| 0.9831 \| 0.0 \| 0.2965 \| 0.9584 \| \| 0.5144 \| 100.0 \| 200 \| 0.5004 \| 0.4153 \| 0.6788 \| 0.9604 \| nan \| 0.3716 \| 0.9860 \| 0.0 \| 0.2859 \| 0.9599 \| \| 0.5313 \| 105.0 \| 210 \| 0.5032 \| 0.4199 \| 0.7005 \| 0.9585 \| nan \| 0.4191 \| 0.9819 \| 0.0 \| 0.3020 \| 0.9578 \| \| 0.5172 \| 110.0 \| 220 \| 0.4993 \| 0.4188 \| 0.6931 \| 0.9591 \| nan \| 0.4030 \| 0.9832 \| 0.0 \| 0.2978 \| 0.9585 \| \| 0.5124 \| 115.0 \| 230 \| 0.4999 \| 0.4167 \| 0.6828 \| 0.9606 \| nan \| 0.3799 \| 0.9858 \| 0.0 \| 0.2901 \| 0.9600 \| \| 0.5025 \| 120.0 \| 240 \| 0.4979 \| 0.4170 \| 0.6846 \| 0.9603 \| nan \| 0.3839 \| 0.9853 \| 0.0 \| 0.2914 \| 0.9597 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4274 - Mean Iou: 0.6102 - Mean Accuracy: 0.6603 - Overall Accuracy: 0.9607 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3326 - Accuracy Undropoff: 0.9879 - Iou Unlabeled: nan - Iou Dropoff: 0.2602 - Iou Undropoff: 0.9601 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0555 \| 5.0 \| 10 \| 1.0734 \| 0.2254 \| 0.4211 \| 0.6018 \| nan \| 0.2240 \| 0.6182 \| 0.0 \| 0.0622 \| 0.6140 \| \| 0.9825 \| 10.0 \| 20 \| 1.0261 \| 0.2992 \| 0.6380 \| 0.7780 \| nan \| 0.4852 \| 0.7907 \| 0.0 \| 0.1170 \| 0.7807 \| \| 0.8991 \| 15.0 \| 30 \| 0.8985 \| 0.3231 \| 0.5517 \| 0.8892 \| nan \| 0.1836 \| 0.9198 \| 0.0 \| 0.0776 \| 0.8917 \| \| 0.8191 \| 20.0 \| 40 \| 0.7413 \| 0.3270 \| 0.5262 \| 0.9299 \| nan \| 0.0858 \| 0.9665 \| 0.0 \| 0.0513 \| 0.9296 \| \| 0.7562 \| 25.0 \| 50 \| 0.6268 \| 0.3259 \| 0.5130 \| 0.9436 \| nan \| 0.0433 \| 0.9826 \| 0.0 \| 0.0343 \| 0.9435 \| \| 0.7395 \| 30.0 \| 60 \| 0.5872 \| 0.3235 \| 0.5073 \| 0.9498 \| nan \| 0.0246 \| 0.9900 \| 0.0 \| 0.0206 \| 0.9498 \| \| 0.7272 \| 35.0 \| 70 \| 0.5820 \| 0.3379 \| 0.5415 \| 0.9411 \| nan \| 0.1055 \| 0.9774 \| 0.0 \| 0.0729 \| 0.9409 \| \| 0.6525 \| 40.0 \| 80 \| 0.5571 \| 0.3445 \| 0.5451 \| 0.9498 \| nan \| 0.1036 \| 0.9865 \| 0.0 \| 0.0839 \| 0.9496 \| \| 0.6161 \| 45.0 \| 90 \| 0.5465 \| 0.3480 \| 0.5480 \| 0.9528 \| nan \| 0.1064 \| 0.9895 \| 0.0 \| 0.0914 \| 0.9526 \| \| 0.6131 \| 50.0 \| 100 \| 0.5379 \| 0.3712 \| 0.5917 \| 0.9555 \| nan \| 0.1949 \| 0.9885 \| 0.0 \| 0.1584 \| 0.9551 \| \| 0.579 \| 55.0 \| 110 \| 0.5229 \| 0.3892 \| 0.6411 \| 0.9536 \| nan \| 0.3002 \| 0.9819 \| 0.0 \| 0.2146 \| 0.9530 \| \| 0.5133 \| 60.0 \| 120 \| 0.5113 \| 0.3962 \| 0.6596 \| 0.9541 \| nan \| 0.3384 \| 0.9808 \| 0.0 \| 0.2352 \| 0.9535 \| \| 0.535 \| 65.0 \| 130 \| 0.4925 \| 0.3981 \| 0.6566 \| 0.9561 \| nan \| 0.3299 \| 0.9833 \| 0.0 \| 0.2386 \| 0.9555 \| \| 0.4866 \| 70.0 \| 140 \| 0.4717 \| 0.5993 \| 0.6516 \| 0.9584 \| nan \| 0.3169 \| 0.9863 \| nan \| 0.2407 \| 0.9579 \| \| 0.5119 \| 75.0 \| 150 \| 0.4712 \| 0.5976 \| 0.6513 \| 0.9578 \| nan \| 0.3171 \| 0.9856 \| nan \| 0.2380 \| 0.9572 \| \| 0.5034 \| 80.0 \| 160 \| 0.4737 \| 0.6120 \| 0.6840 \| 0.9562 \| nan \| 0.3872 \| 0.9808 \| nan \| 0.2686 \| 0.9554 \| \| 0.4503 \| 85.0 \| 170 \| 0.4496 \| 0.6103 \| 0.6618 \| 0.9604 \| nan \| 0.3361 \| 0.9875 \| nan \| 0.2607 \| 0.9598 \| \| 0.4653 \| 90.0 \| 180 \| 0.4617 \| 0.6201 \| 0.6907 \| 0.9580 \| nan \| 0.3992 \| 0.9822 \| nan \| 0.2830 \| 0.9572 \| \| 0.4375 \| 95.0 \| 190 \| 0.4412 \| 0.6090 \| 0.6592 \| 0.9605 \| nan \| 0.3305 \| 0.9878 \| nan \| 0.2580 \| 0.9599 \| \| 0.4306 \| 100.0 \| 200 \| 0.4355 \| 0.6120 \| 0.6653 \| 0.9602 \| nan \| 0.3436 \| 0.9870 \| nan \| 0.2643 \| 0.9597 \| \| 0.4456 \| 105.0 \| 210 \| 0.4414 \| 0.6178 \| 0.6756 \| 0.9601 \| nan \| 0.3653 \| 0.9860 \| nan \| 0.2760 \| 0.9595 \| \| 0.4435 \| 110.0 \| 220 \| 0.4387 \| 0.6150 \| 0.6681 \| 0.9608 \| nan \| 0.3489 \| 0.9873 \| nan \| 0.2699 \| 0.9602 \| \| 0.4263 \| 115.0 \| 230 \| 0.4348 \| 0.6156 \| 0.6692 \| 0.9607 \| nan \| 0.3512 \| 0.9872 \| nan \| 0.2711 \| 0.9602 \| \| 0.4123 \| 120.0 \| 240 \| 0.4274 \| 0.6102 \| 0.6603 \| 0.9607 \| nan \| 0.3326 \| 0.9879 \| nan \| 0.2602 \| 0.9601 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_3 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3666 - Mean Iou: 0.6400 - Mean Accuracy: 0.7120 - Overall Accuracy: 0.9610 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4404 - Accuracy Undropoff: 0.9836 - Iou Unlabeled: nan - Iou Dropoff: 0.3196 - Iou Undropoff: 0.9603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0352 \| 5.0 \| 10 \| 1.0676 \| 0.2560 \| 0.5776 \| 0.7142 \| nan \| 0.4286 \| 0.7266 \| 0.0 \| 0.0589 \| 0.7090 \| \| 0.9564 \| 10.0 \| 20 \| 0.9743 \| 0.3355 \| 0.5576 \| 0.9248 \| nan \| 0.1571 \| 0.9581 \| 0.0 \| 0.0822 \| 0.9243 \| \| 0.8577 \| 15.0 \| 30 \| 0.8504 \| 0.3318 \| 0.5283 \| 0.9409 \| nan \| 0.0782 \| 0.9784 \| 0.0 \| 0.0545 \| 0.9407 \| \| 0.7512 \| 20.0 \| 40 \| 0.6972 \| 0.3270 \| 0.5122 \| 0.9527 \| nan \| 0.0318 \| 0.9926 \| 0.0 \| 0.0283 \| 0.9526 \| \| 0.6955 \| 25.0 \| 50 \| 0.5761 \| 0.3259 \| 0.5099 \| 0.9545 \| nan \| 0.0250 \| 0.9948 \| 0.0 \| 0.0234 \| 0.9544 \| \| 0.6691 \| 30.0 \| 60 \| 0.5209 \| 0.3360 \| 0.5271 \| 0.9525 \| nan \| 0.0632 \| 0.9911 \| 0.0 \| 0.0557 \| 0.9524 \| \| 0.626 \| 35.0 \| 70 \| 0.5297 \| 0.3408 \| 0.5362 \| 0.9505 \| nan \| 0.0844 \| 0.9881 \| 0.0 \| 0.0719 \| 0.9503 \| \| 0.5544 \| 40.0 \| 80 \| 0.5263 \| 0.3616 \| 0.5757 \| 0.9521 \| nan \| 0.1652 \| 0.9862 \| 0.0 \| 0.1330 \| 0.9518 \| \| 0.5316 \| 45.0 \| 90 \| 0.4825 \| 0.3836 \| 0.6353 \| 0.9506 \| nan \| 0.2915 \| 0.9792 \| 0.0 \| 0.2009 \| 0.9500 \| \| 0.4929 \| 50.0 \| 100 \| 0.4763 \| 0.3958 \| 0.6588 \| 0.9530 \| nan \| 0.3378 \| 0.9797 \| 0.0 \| 0.2352 \| 0.9524 \| \| 0.468 \| 55.0 \| 110 \| 0.4583 \| 0.4077 \| 0.6974 \| 0.9528 \| nan \| 0.4188 \| 0.9759 \| 0.0 \| 0.2713 \| 0.9519 \| \| 0.429 \| 60.0 \| 120 \| 0.4268 \| 0.3985 \| 0.6526 \| 0.9575 \| nan \| 0.3199 \| 0.9852 \| 0.0 \| 0.2386 \| 0.9569 \| \| 0.4211 \| 65.0 \| 130 \| 0.3988 \| 0.3951 \| 0.6406 \| 0.9584 \| nan \| 0.2939 \| 0.9872 \| 0.0 \| 0.2275 \| 0.9578 \| \| 0.3926 \| 70.0 \| 140 \| 0.4085 \| 0.4102 \| 0.6780 \| 0.9587 \| nan \| 0.3718 \| 0.9842 \| 0.0 \| 0.2726 \| 0.9581 \| \| 0.4006 \| 75.0 \| 150 \| 0.3944 \| 0.6077 \| 0.6574 \| 0.9604 \| nan \| 0.3269 \| 0.9879 \| nan \| 0.2555 \| 0.9599 \| \| 0.3978 \| 80.0 \| 160 \| 0.3881 \| 0.6216 \| 0.6875 \| 0.9591 \| nan \| 0.3912 \| 0.9838 \| nan \| 0.2848 \| 0.9585 \| \| 0.3553 \| 85.0 \| 170 \| 0.3877 \| 0.6333 \| 0.7077 \| 0.9595 \| nan \| 0.4329 \| 0.9824 \| nan \| 0.3079 \| 0.9588 \| \| 0.3637 \| 90.0 \| 180 \| 0.4004 \| 0.6428 \| 0.7273 \| 0.9594 \| nan \| 0.4741 \| 0.9805 \| nan \| 0.3270 \| 0.9586 \| \| 0.3416 \| 95.0 \| 190 \| 0.3835 \| 0.6403 \| 0.7166 \| 0.9604 \| nan \| 0.4507 \| 0.9825 \| nan \| 0.3210 \| 0.9596 \| \| 0.342 \| 100.0 \| 200 \| 0.3634 \| 0.6371 \| 0.7061 \| 0.9611 \| nan \| 0.4279 \| 0.9842 \| nan \| 0.3137 \| 0.9604 \| \| 0.3393 \| 105.0 \| 210 \| 0.3740 \| 0.6429 \| 0.7217 \| 0.9604 \| nan \| 0.4614 \| 0.9820 \| nan \| 0.3262 \| 0.9596 \| \| 0.3535 \| 110.0 \| 220 \| 0.3771 \| 0.6423 \| 0.7199 \| 0.9605 \| nan \| 0.4575 \| 0.9823 \| nan \| 0.3249 \| 0.9597 \| \| 0.3159 \| 115.0 \| 230 \| 0.3710 \| 0.6423 \| 0.7167 \| 0.9610 \| nan \| 0.4502 \| 0.9832 \| nan \| 0.3243 \| 0.9603 \| \| 0.3278 \| 120.0 \| 240 \| 0.3666 \| 0.6400 \| 0.7120 \| 0.9610 \| nan \| 0.4404 \| 0.9836 \| nan \| 0.3196 \| 0.9603 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_4 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3688 - Mean Iou: 0.3485 - Mean Accuracy: 0.5433 - Overall Accuracy: 0.9606 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0881 - Accuracy Undropoff: 0.9984 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.0851 - Iou Undropoff: 0.9604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.2008 \| 5.0 \| 10 \| 1.0960 \| 0.1205 \| 0.4461 \| 0.2825 \| nan \| 0.6246 \| 0.2677 \| 0.0 \| 0.0943 \| 0.2671 \| \| 1.0485 \| 10.0 \| 20 \| 1.0952 \| 0.1603 \| 0.6272 \| 0.4049 \| nan \| 0.8696 \| 0.3848 \| 0.0 \| 0.0965 \| 0.3843 \| \| 0.9156 \| 15.0 \| 30 \| 1.0312 \| 0.3080 \| 0.5963 \| 0.8333 \| nan \| 0.3377 \| 0.8548 \| 0.0 \| 0.0924 \| 0.8317 \| \| 0.7435 \| 20.0 \| 40 \| 0.9448 \| 0.3221 \| 0.5508 \| 0.8937 \| nan \| 0.1769 \| 0.9248 \| 0.0 \| 0.0733 \| 0.8930 \| \| 0.7336 \| 25.0 \| 50 \| 0.7446 \| 0.3191 \| 0.4998 \| 0.9461 \| nan \| 0.0129 \| 0.9866 \| 0.0 \| 0.0113 \| 0.9461 \| \| 0.6585 \| 30.0 \| 60 \| 0.6397 \| 0.3183 \| 0.4981 \| 0.9534 \| nan \| 0.0014 \| 0.9948 \| 0.0 \| 0.0013 \| 0.9534 \| \| 0.583 \| 35.0 \| 70 \| 0.5785 \| 0.3181 \| 0.4978 \| 0.9537 \| nan \| 0.0006 \| 0.9951 \| 0.0 \| 0.0005 \| 0.9537 \| \| 0.5324 \| 40.0 \| 80 \| 0.5458 \| 0.3182 \| 0.4980 \| 0.9545 \| nan \| 0.0002 \| 0.9958 \| 0.0 \| 0.0002 \| 0.9545 \| \| 0.5155 \| 45.0 \| 90 \| 0.5347 \| 0.3186 \| 0.4987 \| 0.9558 \| nan \| 0.0001 \| 0.9973 \| 0.0 \| 0.0001 \| 0.9558 \| \| 0.4874 \| 50.0 \| 100 \| 0.4954 \| 0.3179 \| 0.4976 \| 0.9537 \| nan \| 0.0 \| 0.9951 \| 0.0 \| 0.0 \| 0.9537 \| \| 0.4716 \| 55.0 \| 110 \| 0.4646 \| 0.3185 \| 0.4985 \| 0.9555 \| nan \| 0.0 \| 0.9969 \| 0.0 \| 0.0 \| 0.9555 \| \| 0.4441 \| 60.0 \| 120 \| 0.4426 \| 0.3185 \| 0.4985 \| 0.9555 \| nan \| 0.0 \| 0.9970 \| 0.0 \| 0.0 \| 0.9555 \| \| 0.4659 \| 65.0 \| 130 \| 0.4345 \| 0.3189 \| 0.4991 \| 0.9567 \| nan \| 0.0 \| 0.9982 \| 0.0 \| 0.0 \| 0.9567 \| \| 0.4758 \| 70.0 \| 140 \| 0.4221 \| 0.3181 \| 0.4978 \| 0.9543 \| nan \| 0.0 \| 0.9957 \| 0.0 \| 0.0 \| 0.9543 \| \| 0.4208 \| 75.0 \| 150 \| 0.4029 \| 0.3190 \| 0.4993 \| 0.9571 \| nan \| 0.0 \| 0.9987 \| 0.0 \| 0.0 \| 0.9571 \| \| 0.4395 \| 80.0 \| 160 \| 0.4170 \| 0.3207 \| 0.5016 \| 0.9559 \| nan \| 0.0062 \| 0.9971 \| 0.0 \| 0.0062 \| 0.9559 \| \| 0.3981 \| 85.0 \| 170 \| 0.3992 \| 0.3214 \| 0.5027 \| 0.9574 \| nan \| 0.0067 \| 0.9987 \| 0.0 \| 0.0066 \| 0.9574 \| \| 0.3983 \| 90.0 \| 180 \| 0.3965 \| 0.3282 \| 0.5125 \| 0.9560 \| nan \| 0.0288 \| 0.9963 \| 0.0 \| 0.0285 \| 0.9560 \| \| 0.398 \| 95.0 \| 190 \| 0.3747 \| 0.3272 \| 0.5112 \| 0.9569 \| nan \| 0.0251 \| 0.9973 \| 0.0 \| 0.0249 \| 0.9568 \| \| 0.3767 \| 100.0 \| 200 \| 0.3722 \| 0.3301 \| 0.5155 \| 0.9574 \| nan \| 0.0336 \| 0.9975 \| 0.0 \| 0.0330 \| 0.9573 \| \| 0.3797 \| 105.0 \| 210 \| 0.3781 \| 0.3334 \| 0.5204 \| 0.9583 \| nan \| 0.0429 \| 0.9980 \| 0.0 \| 0.0420 \| 0.9582 \| \| 0.373 \| 110.0 \| 220 \| 0.3744 \| 0.3409 \| 0.5317 \| 0.9593 \| nan \| 0.0654 \| 0.9980 \| 0.0 \| 0.0636 \| 0.9591 \| \| 0.372 \| 115.0 \| 230 \| 0.3700 \| 0.3440 \| 0.5364 \| 0.9599 \| nan \| 0.0746 \| 0.9983 \| 0.0 \| 0.0723 \| 0.9598 \| \| 0.3629 \| 120.0 \| 240 \| 0.3688 \| 0.3485 \| 0.5433 \| 0.9606 \| nan \| 0.0881 \| 0.9984 \| 0.0 \| 0.0851 \| 0.9604 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_5 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2608 - Mean Iou: 0.6161 - Mean Accuracy: 0.6630 - Overall Accuracy: 0.9623 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3365 - Accuracy Undropoff: 0.9894 - Iou Unlabeled: nan - Iou Dropoff: 0.2705 - Iou Undropoff: 0.9617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9263 \| 5.0 \| 10 \| 1.0370 \| 0.2869 \| 0.7147 \| 0.7632 \| nan \| 0.6618 \| 0.7675 \| 0.0 \| 0.1042 \| 0.7565 \| \| 0.8069 \| 10.0 \| 20 \| 0.8622 \| 0.4857 \| 0.5062 \| 0.9589 \| nan \| 0.0125 \| 0.9999 \| nan \| 0.0124 \| 0.9589 \| \| 0.6851 \| 15.0 \| 30 \| 0.6490 \| 0.4876 \| 0.5081 \| 0.9586 \| nan \| 0.0167 \| 0.9995 \| nan \| 0.0165 \| 0.9586 \| \| 0.5882 \| 20.0 \| 40 \| 0.4739 \| 0.3253 \| 0.5085 \| 0.9586 \| nan \| 0.0177 \| 0.9994 \| 0.0 \| 0.0174 \| 0.9585 \| \| 0.53 \| 25.0 \| 50 \| 0.4153 \| 0.3375 \| 0.5274 \| 0.9584 \| nan \| 0.0573 \| 0.9975 \| 0.0 \| 0.0542 \| 0.9583 \| \| 0.5009 \| 30.0 \| 60 \| 0.4275 \| 0.3835 \| 0.6488 \| 0.9475 \| nan \| 0.3230 \| 0.9746 \| 0.0 \| 0.2037 \| 0.9468 \| \| 0.4699 \| 35.0 \| 70 \| 0.3819 \| 0.4158 \| 0.6985 \| 0.9578 \| nan \| 0.4157 \| 0.9813 \| 0.0 \| 0.2904 \| 0.9570 \| \| 0.3946 \| 40.0 \| 80 \| 0.3563 \| 0.6183 \| 0.6844 \| 0.9585 \| nan \| 0.3854 \| 0.9834 \| nan \| 0.2787 \| 0.9579 \| \| 0.3788 \| 45.0 \| 90 \| 0.3259 \| 0.6292 \| 0.7011 \| 0.9593 \| nan \| 0.4196 \| 0.9827 \| nan \| 0.2998 \| 0.9585 \| \| 0.3412 \| 50.0 \| 100 \| 0.3392 \| 0.6170 \| 0.6933 \| 0.9562 \| nan \| 0.4066 \| 0.9801 \| nan \| 0.2785 \| 0.9555 \| \| 0.3326 \| 55.0 \| 110 \| 0.3214 \| 0.6279 \| 0.6914 \| 0.9606 \| nan \| 0.3977 \| 0.9851 \| nan \| 0.2958 \| 0.9600 \| \| 0.2954 \| 60.0 \| 120 \| 0.3119 \| 0.6261 \| 0.6847 \| 0.9613 \| nan \| 0.3831 \| 0.9864 \| nan \| 0.2915 \| 0.9607 \| \| 0.3006 \| 65.0 \| 130 \| 0.2853 \| 0.5900 \| 0.6223 \| 0.9625 \| nan \| 0.2513 \| 0.9934 \| nan \| 0.2180 \| 0.9621 \| \| 0.2715 \| 70.0 \| 140 \| 0.3021 \| 0.6314 \| 0.6903 \| 0.9620 \| nan \| 0.3938 \| 0.9867 \| nan \| 0.3014 \| 0.9614 \| \| 0.276 \| 75.0 \| 150 \| 0.2950 \| 0.6243 \| 0.6783 \| 0.9619 \| nan \| 0.3690 \| 0.9877 \| nan \| 0.2873 \| 0.9613 \| \| 0.2622 \| 80.0 \| 160 \| 0.2843 \| 0.6134 \| 0.6651 \| 0.9608 \| nan \| 0.3426 \| 0.9876 \| nan \| 0.2665 \| 0.9602 \| \| 0.2395 \| 85.0 \| 170 \| 0.2752 \| 0.6050 \| 0.6495 \| 0.9613 \| nan \| 0.3094 \| 0.9895 \| nan \| 0.2493 \| 0.9608 \| \| 0.2597 \| 90.0 \| 180 \| 0.2813 \| 0.6296 \| 0.6874 \| 0.9620 \| nan \| 0.3879 \| 0.9869 \| nan \| 0.2979 \| 0.9614 \| \| 0.2294 \| 95.0 \| 190 \| 0.2747 \| 0.6106 \| 0.6575 \| 0.9615 \| nan \| 0.3259 \| 0.9890 \| nan \| 0.2602 \| 0.9609 \| \| 0.2303 \| 100.0 \| 200 \| 0.2606 \| 0.6040 \| 0.6462 \| 0.9616 \| nan \| 0.3023 \| 0.9902 \| nan \| 0.2468 \| 0.9611 \| \| 0.2335 \| 105.0 \| 210 \| 0.2606 \| 0.6080 \| 0.6515 \| 0.9619 \| nan \| 0.3130 \| 0.9901 \| nan \| 0.2547 \| 0.9614 \| \| 0.2322 \| 110.0 \| 220 \| 0.2619 \| 0.6167 \| 0.6631 \| 0.9624 \| nan \| 0.3366 \| 0.9896 \| nan \| 0.2715 \| 0.9619 \| \| 0.2116 \| 115.0 \| 230 \| 0.2618 \| 0.6183 \| 0.6660 \| 0.9624 \| nan \| 0.3427 \| 0.9893 \| nan \| 0.2747 \| 0.9618 \| \| 0.2099 \| 120.0 \| 240 \| 0.2608 \| 0.6161 \| 0.6630 \| 0.9623 \| nan \| 0.3365 \| 0.9894 \| nan \| 0.2705 \| 0.9617 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_6	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_6 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2353 - Mean Iou: 0.6539 - Mean Accuracy: 0.7065 - Overall Accuracy: 0.9662 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4233 - Accuracy Undropoff: 0.9897 - Iou Unlabeled: nan - Iou Dropoff: 0.3423 - Iou Undropoff: 0.9656 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9975 \| 5.0 \| 10 \| 1.0470 \| 0.2819 \| 0.6747 \| 0.7186 \| nan \| 0.6267 \| 0.7226 \| 0.0 \| 0.1290 \| 0.7167 \| \| 0.8329 \| 10.0 \| 20 \| 0.8435 \| 0.3211 \| 0.5026 \| 0.9526 \| nan \| 0.0117 \| 0.9934 \| 0.0 \| 0.0106 \| 0.9526 \| \| 0.6857 \| 15.0 \| 30 \| 0.6184 \| 0.3191 \| 0.4994 \| 0.9567 \| nan \| 0.0006 \| 0.9981 \| 0.0 \| 0.0006 \| 0.9567 \| \| 0.5913 \| 20.0 \| 40 \| 0.4793 \| 0.3193 \| 0.4997 \| 0.9573 \| nan \| 0.0005 \| 0.9988 \| 0.0 \| 0.0005 \| 0.9573 \| \| 0.5299 \| 25.0 \| 50 \| 0.4529 \| 0.3488 \| 0.5442 \| 0.9596 \| nan \| 0.0911 \| 0.9973 \| 0.0 \| 0.0869 \| 0.9595 \| \| 0.4922 \| 30.0 \| 60 \| 0.4037 \| 0.4352 \| 0.6983 \| 0.9671 \| nan \| 0.4051 \| 0.9915 \| 0.0 \| 0.3390 \| 0.9666 \| \| 0.4769 \| 35.0 \| 70 \| 0.4161 \| 0.4090 \| 0.7560 \| 0.9426 \| nan \| 0.5524 \| 0.9595 \| 0.0 \| 0.2858 \| 0.9412 \| \| 0.3916 \| 40.0 \| 80 \| 0.3343 \| 0.6320 \| 0.6946 \| 0.9614 \| nan \| 0.4036 \| 0.9856 \| nan \| 0.3033 \| 0.9608 \| \| 0.3567 \| 45.0 \| 90 \| 0.3372 \| 0.6374 \| 0.7140 \| 0.9598 \| nan \| 0.4458 \| 0.9821 \| nan \| 0.3157 \| 0.9591 \| \| 0.3234 \| 50.0 \| 100 \| 0.3074 \| 0.6402 \| 0.6883 \| 0.9652 \| nan \| 0.3863 \| 0.9903 \| nan \| 0.3157 \| 0.9646 \| \| 0.3181 \| 55.0 \| 110 \| 0.3043 \| 0.6396 \| 0.7138 \| 0.9606 \| nan \| 0.4446 \| 0.9830 \| nan \| 0.3194 \| 0.9599 \| \| 0.2584 \| 60.0 \| 120 \| 0.3069 \| 0.6450 \| 0.7204 \| 0.9613 \| nan \| 0.4576 \| 0.9831 \| nan \| 0.3294 \| 0.9605 \| \| 0.2566 \| 65.0 \| 130 \| 0.2824 \| 0.6431 \| 0.7063 \| 0.9630 \| nan \| 0.4263 \| 0.9863 \| nan \| 0.3239 \| 0.9623 \| \| 0.2353 \| 70.0 \| 140 \| 0.2763 \| 0.6470 \| 0.7046 \| 0.9645 \| nan \| 0.4212 \| 0.9880 \| nan \| 0.3301 \| 0.9638 \| \| 0.2368 \| 75.0 \| 150 \| 0.2644 \| 0.6474 \| 0.6973 \| 0.9658 \| nan \| 0.4044 \| 0.9902 \| nan \| 0.3296 \| 0.9652 \| \| 0.2225 \| 80.0 \| 160 \| 0.2673 \| 0.6462 \| 0.7089 \| 0.9635 \| nan \| 0.4313 \| 0.9866 \| nan \| 0.3296 \| 0.9629 \| \| 0.1976 \| 85.0 \| 170 \| 0.2568 \| 0.6449 \| 0.7057 \| 0.9637 \| nan \| 0.4244 \| 0.9870 \| nan \| 0.3268 \| 0.9630 \| \| 0.1981 \| 90.0 \| 180 \| 0.2572 \| 0.6444 \| 0.7110 \| 0.9626 \| nan \| 0.4365 \| 0.9855 \| nan \| 0.3269 \| 0.9619 \| \| 0.1857 \| 95.0 \| 190 \| 0.2503 \| 0.6504 \| 0.7027 \| 0.9658 \| nan \| 0.4157 \| 0.9897 \| nan \| 0.3356 \| 0.9652 \| \| 0.1826 \| 100.0 \| 200 \| 0.2345 \| 0.6509 \| 0.6984 \| 0.9666 \| nan \| 0.4059 \| 0.9909 \| nan \| 0.3357 \| 0.9660 \| \| 0.1818 \| 105.0 \| 210 \| 0.2484 \| 0.6506 \| 0.7160 \| 0.9637 \| nan \| 0.4458 \| 0.9862 \| nan \| 0.3381 \| 0.9630 \| \| 0.1919 \| 110.0 \| 220 \| 0.2343 \| 0.6526 \| 0.6996 \| 0.9669 \| nan \| 0.4080 \| 0.9912 \| nan \| 0.3389 \| 0.9663 \| \| 0.17 \| 115.0 \| 230 \| 0.2377 \| 0.6535 \| 0.7065 \| 0.9661 \| nan \| 0.4235 \| 0.9896 \| nan \| 0.3416 \| 0.9655 \| \| 0.1739 \| 120.0 \| 240 \| 0.2353 \| 0.6539 \| 0.7065 \| 0.9662 \| nan \| 0.4233 \| 0.9897 \| nan \| 0.3423 \| 0.9656 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b0_7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b0_7 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2075 - Mean Iou: 0.6372 - Mean Accuracy: 0.6861 - Overall Accuracy: 0.9647 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3822 - Accuracy Undropoff: 0.9900 - Iou Unlabeled: nan - Iou Dropoff: 0.3104 - Iou Undropoff: 0.9641 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9508 \| 5.0 \| 10 \| 1.0263 \| 0.3104 \| 0.5474 \| 0.8717 \| nan \| 0.1937 \| 0.9011 \| 0.0 \| 0.0605 \| 0.8706 \| \| 0.7814 \| 10.0 \| 20 \| 0.7568 \| 0.4971 \| 0.5339 \| 0.9361 \| nan \| 0.0952 \| 0.9726 \| nan \| 0.0584 \| 0.9359 \| \| 0.642 \| 15.0 \| 30 \| 0.5907 \| 0.5134 \| 0.5443 \| 0.9494 \| nan \| 0.1026 \| 0.9861 \| nan \| 0.0777 \| 0.9492 \| \| 0.5118 \| 20.0 \| 40 \| 0.4804 \| 0.3658 \| 0.5923 \| 0.9513 \| nan \| 0.2006 \| 0.9839 \| 0.0 \| 0.1464 \| 0.9509 \| \| 0.4581 \| 25.0 \| 50 \| 0.4405 \| 0.3715 \| 0.5915 \| 0.9569 \| nan \| 0.1930 \| 0.9900 \| 0.0 \| 0.1578 \| 0.9565 \| \| 0.4213 \| 30.0 \| 60 \| 0.4146 \| 0.3828 \| 0.6136 \| 0.9580 \| nan \| 0.2379 \| 0.9892 \| 0.0 \| 0.1910 \| 0.9575 \| \| 0.3571 \| 35.0 \| 70 \| 0.3750 \| 0.3846 \| 0.6180 \| 0.9578 \| nan \| 0.2474 \| 0.9887 \| 0.0 \| 0.1963 \| 0.9574 \| \| 0.3205 \| 40.0 \| 80 \| 0.3478 \| 0.5777 \| 0.6202 \| 0.9576 \| nan \| 0.2522 \| 0.9882 \| nan \| 0.1982 \| 0.9571 \| \| 0.3114 \| 45.0 \| 90 \| 0.3461 \| 0.3895 \| 0.6423 \| 0.9541 \| nan \| 0.3022 \| 0.9824 \| 0.0 \| 0.2150 \| 0.9535 \| \| 0.2747 \| 50.0 \| 100 \| 0.3253 \| 0.5875 \| 0.6357 \| 0.9575 \| nan \| 0.2847 \| 0.9867 \| nan \| 0.2180 \| 0.9570 \| \| 0.2593 \| 55.0 \| 110 \| 0.3083 \| 0.5967 \| 0.6599 \| 0.9552 \| nan \| 0.3377 \| 0.9820 \| nan \| 0.2387 \| 0.9546 \| \| 0.2293 \| 60.0 \| 120 \| 0.2762 \| 0.5966 \| 0.6389 \| 0.9606 \| nan \| 0.2880 \| 0.9898 \| nan \| 0.2331 \| 0.9601 \| \| 0.2306 \| 65.0 \| 130 \| 0.2655 \| 0.6016 \| 0.6587 \| 0.9577 \| nan \| 0.3326 \| 0.9848 \| nan \| 0.2462 \| 0.9571 \| \| 0.2118 \| 70.0 \| 140 \| 0.2446 \| 0.6039 \| 0.6509 \| 0.9605 \| nan \| 0.3133 \| 0.9886 \| nan \| 0.2479 \| 0.9600 \| \| 0.2038 \| 75.0 \| 150 \| 0.2395 \| 0.6164 \| 0.6708 \| 0.9607 \| nan \| 0.3547 \| 0.9870 \| nan \| 0.2727 \| 0.9601 \| \| 0.1895 \| 80.0 \| 160 \| 0.2196 \| 0.6254 \| 0.6721 \| 0.9636 \| nan \| 0.3542 \| 0.9900 \| nan \| 0.2878 \| 0.9630 \| \| 0.1681 \| 85.0 \| 170 \| 0.2176 \| 0.6302 \| 0.6829 \| 0.9630 \| nan \| 0.3773 \| 0.9884 \| nan \| 0.2979 \| 0.9624 \| \| 0.1612 \| 90.0 \| 180 \| 0.2175 \| 0.6334 \| 0.6870 \| 0.9633 \| nan \| 0.3857 \| 0.9884 \| nan \| 0.3042 \| 0.9627 \| \| 0.1545 \| 95.0 \| 190 \| 0.2140 \| 0.6337 \| 0.6816 \| 0.9644 \| nan \| 0.3732 \| 0.9900 \| nan \| 0.3035 \| 0.9638 \| \| 0.1551 \| 100.0 \| 200 \| 0.2134 \| 0.6357 \| 0.6891 \| 0.9637 \| nan \| 0.3896 \| 0.9886 \| nan \| 0.3083 \| 0.9631 \| \| 0.1508 \| 105.0 \| 210 \| 0.2090 \| 0.6359 \| 0.6865 \| 0.9642 \| nan \| 0.3837 \| 0.9894 \| nan \| 0.3083 \| 0.9636 \| \| 0.1536 \| 110.0 \| 220 \| 0.2057 \| 0.6346 \| 0.6801 \| 0.9650 \| nan \| 0.3694 \| 0.9908 \| nan \| 0.3048 \| 0.9644 \| \| 0.1392 \| 115.0 \| 230 \| 0.2083 \| 0.6387 \| 0.6890 \| 0.9646 \| nan \| 0.3883 \| 0.9896 \| nan \| 0.3133 \| 0.9640 \| \| 0.1446 \| 120.0 \| 240 \| 0.2075 \| 0.6372 \| 0.6861 \| 0.9647 \| nan \| 0.3822 \| 0.9900 \| nan \| 0.3104 \| 0.9641 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3428 - Mean Iou: 0.4792 - Mean Accuracy: 0.5000 - Overall Accuracy: 0.9583 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0001 - Accuracy Undropoff: 0.9999 - Iou Unlabeled: nan - Iou Dropoff: 0.0001 - Iou Undropoff: 0.9583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.8047 \| 5.0 \| 10 \| 0.9867 \| 0.2744 \| 0.6315 \| 0.7475 \| nan \| 0.5049 \| 0.7581 \| 0.0 \| 0.0812 \| 0.7422 \| \| 0.7528 \| 10.0 \| 20 \| 0.8526 \| 0.3461 \| 0.5957 \| 0.9213 \| nan \| 0.2406 \| 0.9508 \| 0.0 \| 0.1178 \| 0.9205 \| \| 0.7087 \| 15.0 \| 30 \| 0.7023 \| 0.3450 \| 0.5533 \| 0.9467 \| nan \| 0.1243 \| 0.9824 \| 0.0 \| 0.0887 \| 0.9464 \| \| 0.6601 \| 20.0 \| 40 \| 0.6251 \| 0.3381 \| 0.5390 \| 0.9462 \| nan \| 0.0948 \| 0.9832 \| 0.0 \| 0.0684 \| 0.9460 \| \| 0.6274 \| 25.0 \| 50 \| 0.5828 \| 0.3286 \| 0.5178 \| 0.9486 \| nan \| 0.0479 \| 0.9876 \| 0.0 \| 0.0374 \| 0.9485 \| \| 0.5929 \| 30.0 \| 60 \| 0.5478 \| 0.3257 \| 0.5122 \| 0.9488 \| nan \| 0.0359 \| 0.9884 \| 0.0 \| 0.0284 \| 0.9487 \| \| 0.5672 \| 35.0 \| 70 \| 0.5237 \| 0.3240 \| 0.5088 \| 0.9494 \| nan \| 0.0283 \| 0.9893 \| 0.0 \| 0.0227 \| 0.9493 \| \| 0.5454 \| 40.0 \| 80 \| 0.4966 \| 0.4856 \| 0.5072 \| 0.9529 \| nan \| 0.0212 \| 0.9933 \| nan \| 0.0183 \| 0.9528 \| \| 0.5261 \| 45.0 \| 90 \| 0.4700 \| 0.3234 \| 0.5062 \| 0.9553 \| nan \| 0.0163 \| 0.9960 \| 0.0 \| 0.0149 \| 0.9552 \| \| 0.5012 \| 50.0 \| 100 \| 0.4576 \| 0.4832 \| 0.5041 \| 0.9563 \| nan \| 0.0107 \| 0.9974 \| nan \| 0.0101 \| 0.9563 \| \| 0.4875 \| 55.0 \| 110 \| 0.4430 \| 0.4811 \| 0.5018 \| 0.9566 \| nan \| 0.0058 \| 0.9978 \| nan \| 0.0056 \| 0.9565 \| \| 0.4622 \| 60.0 \| 120 \| 0.4328 \| 0.4800 \| 0.5007 \| 0.9570 \| nan \| 0.0031 \| 0.9983 \| nan \| 0.0030 \| 0.9570 \| \| 0.4394 \| 65.0 \| 130 \| 0.4179 \| 0.4796 \| 0.5004 \| 0.9572 \| nan \| 0.0021 \| 0.9986 \| nan \| 0.0021 \| 0.9572 \| \| 0.4352 \| 70.0 \| 140 \| 0.4048 \| 0.4795 \| 0.5002 \| 0.9573 \| nan \| 0.0016 \| 0.9988 \| nan \| 0.0016 \| 0.9573 \| \| 0.426 \| 75.0 \| 150 \| 0.3881 \| 0.4796 \| 0.5003 \| 0.9577 \| nan \| 0.0015 \| 0.9992 \| nan \| 0.0014 \| 0.9577 \| \| 0.4175 \| 80.0 \| 160 \| 0.3794 \| 0.4797 \| 0.5004 \| 0.9579 \| nan \| 0.0014 \| 0.9994 \| nan \| 0.0014 \| 0.9579 \| \| 0.4087 \| 85.0 \| 170 \| 0.3742 \| 0.3196 \| 0.5002 \| 0.9577 \| nan \| 0.0012 \| 0.9992 \| 0.0 \| 0.0012 \| 0.9577 \| \| 0.3887 \| 90.0 \| 180 \| 0.3645 \| 0.4792 \| 0.4999 \| 0.9581 \| nan \| 0.0003 \| 0.9996 \| nan \| 0.0003 \| 0.9581 \| \| 0.3799 \| 95.0 \| 190 \| 0.3540 \| 0.4791 \| 0.4999 \| 0.9581 \| nan \| 0.0001 \| 0.9997 \| nan \| 0.0001 \| 0.9581 \| \| 0.376 \| 100.0 \| 200 \| 0.3511 \| 0.4792 \| 0.4999 \| 0.9582 \| nan \| 0.0001 \| 0.9998 \| nan \| 0.0001 \| 0.9582 \| \| 0.3677 \| 105.0 \| 210 \| 0.3452 \| 0.4792 \| 0.4999 \| 0.9582 \| nan \| 0.0001 \| 0.9998 \| nan \| 0.0001 \| 0.9582 \| \| 0.358 \| 110.0 \| 220 \| 0.3437 \| 0.4792 \| 0.4999 \| 0.9582 \| nan \| 0.0001 \| 0.9998 \| nan \| 0.0001 \| 0.9582 \| \| 0.3997 \| 115.0 \| 230 \| 0.3434 \| 0.4792 \| 0.5000 \| 0.9583 \| nan \| 0.0001 \| 0.9999 \| nan \| 0.0001 \| 0.9583 \| \| 0.3769 \| 120.0 \| 240 \| 0.3428 \| 0.4792 \| 0.5000 \| 0.9583 \| nan \| 0.0001 \| 0.9999 \| nan \| 0.0001 \| 0.9583 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_2 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4198 - Mean Iou: 0.3194 - Mean Accuracy: 0.4998 - Overall Accuracy: 0.9558 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0023 - Accuracy Undropoff: 0.9972 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.0022 - Iou Undropoff: 0.9558 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.989 \| 5.0 \| 10 \| 1.0190 \| 0.2162 \| 0.5831 \| 0.5879 \| nan \| 0.5779 \| 0.5883 \| 0.0 \| 0.0657 \| 0.5829 \| \| 0.9092 \| 10.0 \| 20 \| 0.8686 \| 0.3164 \| 0.5199 \| 0.8922 \| nan \| 0.1137 \| 0.9260 \| 0.0 \| 0.0539 \| 0.8953 \| \| 0.8483 \| 15.0 \| 30 \| 0.7438 \| 0.3256 \| 0.5234 \| 0.9219 \| nan \| 0.0888 \| 0.9581 \| 0.0 \| 0.0545 \| 0.9224 \| \| 0.7856 \| 20.0 \| 40 \| 0.6571 \| 0.3182 \| 0.5013 \| 0.9336 \| nan \| 0.0297 \| 0.9728 \| 0.0 \| 0.0210 \| 0.9335 \| \| 0.7459 \| 25.0 \| 50 \| 0.6144 \| 0.3164 \| 0.4980 \| 0.9324 \| nan \| 0.0242 \| 0.9718 \| 0.0 \| 0.0168 \| 0.9324 \| \| 0.7027 \| 30.0 \| 60 \| 0.5861 \| 0.3168 \| 0.4975 \| 0.9351 \| nan \| 0.0202 \| 0.9748 \| 0.0 \| 0.0151 \| 0.9353 \| \| 0.6827 \| 35.0 \| 70 \| 0.5568 \| 0.3171 \| 0.4975 \| 0.9391 \| nan \| 0.0159 \| 0.9791 \| 0.0 \| 0.0122 \| 0.9391 \| \| 0.6362 \| 40.0 \| 80 \| 0.5405 \| 0.3179 \| 0.4982 \| 0.9424 \| nan \| 0.0138 \| 0.9827 \| 0.0 \| 0.0112 \| 0.9425 \| \| 0.6098 \| 45.0 \| 90 \| 0.5192 \| 0.3174 \| 0.4971 \| 0.9449 \| nan \| 0.0087 \| 0.9855 \| 0.0 \| 0.0073 \| 0.9449 \| \| 0.5946 \| 50.0 \| 100 \| 0.5025 \| 0.3179 \| 0.4978 \| 0.9475 \| nan \| 0.0072 \| 0.9883 \| 0.0 \| 0.0062 \| 0.9477 \| \| 0.5868 \| 55.0 \| 110 \| 0.4943 \| 0.3179 \| 0.4976 \| 0.9490 \| nan \| 0.0052 \| 0.9900 \| 0.0 \| 0.0046 \| 0.9491 \| \| 0.5557 \| 60.0 \| 120 \| 0.4798 \| 0.3184 \| 0.4983 \| 0.9505 \| nan \| 0.0051 \| 0.9915 \| 0.0 \| 0.0045 \| 0.9506 \| \| 0.5327 \| 65.0 \| 130 \| 0.4736 \| 0.3184 \| 0.4983 \| 0.9514 \| nan \| 0.0041 \| 0.9925 \| 0.0 \| 0.0038 \| 0.9514 \| \| 0.525 \| 70.0 \| 140 \| 0.4657 \| 0.3187 \| 0.4987 \| 0.9526 \| nan \| 0.0038 \| 0.9937 \| 0.0 \| 0.0035 \| 0.9526 \| \| 0.5266 \| 75.0 \| 150 \| 0.4528 \| 0.3190 \| 0.4992 \| 0.9534 \| nan \| 0.0037 \| 0.9946 \| 0.0 \| 0.0034 \| 0.9535 \| \| 0.5139 \| 80.0 \| 160 \| 0.4538 \| 0.3189 \| 0.4991 \| 0.9533 \| nan \| 0.0037 \| 0.9945 \| 0.0 \| 0.0035 \| 0.9534 \| \| 0.5128 \| 85.0 \| 170 \| 0.4460 \| 0.3192 \| 0.4995 \| 0.9543 \| nan \| 0.0033 \| 0.9956 \| 0.0 \| 0.0031 \| 0.9543 \| \| 0.4901 \| 90.0 \| 180 \| 0.4371 \| 0.3192 \| 0.4995 \| 0.9548 \| nan \| 0.0029 \| 0.9961 \| 0.0 \| 0.0027 \| 0.9548 \| \| 0.4767 \| 95.0 \| 190 \| 0.4325 \| 0.3193 \| 0.4997 \| 0.9552 \| nan \| 0.0029 \| 0.9965 \| 0.0 \| 0.0027 \| 0.9552 \| \| 0.4692 \| 100.0 \| 200 \| 0.4272 \| 0.3193 \| 0.4997 \| 0.9556 \| nan \| 0.0024 \| 0.9970 \| 0.0 \| 0.0023 \| 0.9556 \| \| 0.4632 \| 105.0 \| 210 \| 0.4251 \| 0.3193 \| 0.4996 \| 0.9556 \| nan \| 0.0023 \| 0.9969 \| 0.0 \| 0.0023 \| 0.9556 \| \| 0.4626 \| 110.0 \| 220 \| 0.4236 \| 0.3193 \| 0.4997 \| 0.9556 \| nan \| 0.0024 \| 0.9970 \| 0.0 \| 0.0024 \| 0.9556 \| \| 0.4837 \| 115.0 \| 230 \| 0.4216 \| 0.3194 \| 0.4998 \| 0.9558 \| nan \| 0.0023 \| 0.9972 \| 0.0 \| 0.0023 \| 0.9558 \| \| 0.4809 \| 120.0 \| 240 \| 0.4198 \| 0.3194 \| 0.4998 \| 0.9558 \| nan \| 0.0023 \| 0.9972 \| 0.0 \| 0.0022 \| 0.9558 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_3 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2768 - Mean Iou: 0.3194 - Mean Accuracy: 0.4999 - Overall Accuracy: 0.9578 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0006 - Accuracy Undropoff: 0.9993 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.0006 - Iou Undropoff: 0.9578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0992 \| 5.0 \| 10 \| 1.0599 \| 0.1938 \| 0.4241 \| 0.5281 \| nan \| 0.3106 \| 0.5376 \| 0.0 \| 0.0540 \| 0.5273 \| \| 1.0188 \| 10.0 \| 20 \| 0.9493 \| 0.2781 \| 0.4808 \| 0.7846 \| nan \| 0.1494 \| 0.8122 \| 0.0 \| 0.0476 \| 0.7868 \| \| 0.9218 \| 15.0 \| 30 \| 0.8130 \| 0.3074 \| 0.4913 \| 0.8851 \| nan \| 0.0618 \| 0.9209 \| 0.0 \| 0.0364 \| 0.8858 \| \| 0.8411 \| 20.0 \| 40 \| 0.7253 \| 0.3089 \| 0.4866 \| 0.9038 \| nan \| 0.0315 \| 0.9416 \| 0.0 \| 0.0221 \| 0.9047 \| \| 0.7583 \| 25.0 \| 50 \| 0.6719 \| 0.3097 \| 0.4890 \| 0.9069 \| nan \| 0.0331 \| 0.9448 \| 0.0 \| 0.0216 \| 0.9076 \| \| 0.688 \| 30.0 \| 60 \| 0.6303 \| 0.3109 \| 0.4883 \| 0.9170 \| nan \| 0.0207 \| 0.9559 \| 0.0 \| 0.0149 \| 0.9179 \| \| 0.6279 \| 35.0 \| 70 \| 0.5919 \| 0.3139 \| 0.4918 \| 0.9276 \| nan \| 0.0164 \| 0.9671 \| 0.0 \| 0.0133 \| 0.9283 \| \| 0.5533 \| 40.0 \| 80 \| 0.5375 \| 0.3168 \| 0.4961 \| 0.9377 \| nan \| 0.0144 \| 0.9777 \| 0.0 \| 0.0125 \| 0.9380 \| \| 0.5116 \| 45.0 \| 90 \| 0.5111 \| 0.3176 \| 0.4970 \| 0.9442 \| nan \| 0.0093 \| 0.9847 \| 0.0 \| 0.0083 \| 0.9445 \| \| 0.4801 \| 50.0 \| 100 \| 0.4696 \| 0.3183 \| 0.4981 \| 0.9492 \| nan \| 0.0062 \| 0.9901 \| 0.0 \| 0.0057 \| 0.9492 \| \| 0.4744 \| 55.0 \| 110 \| 0.4317 \| 0.3187 \| 0.4987 \| 0.9543 \| nan \| 0.0018 \| 0.9956 \| 0.0 \| 0.0017 \| 0.9543 \| \| 0.4494 \| 60.0 \| 120 \| 0.3991 \| 0.3189 \| 0.4991 \| 0.9555 \| nan \| 0.0013 \| 0.9969 \| 0.0 \| 0.0012 \| 0.9555 \| \| 0.386 \| 65.0 \| 130 \| 0.3737 \| 0.3189 \| 0.4990 \| 0.9565 \| nan \| 0.0000 \| 0.9980 \| 0.0 \| 0.0000 \| 0.9565 \| \| 0.3674 \| 70.0 \| 140 \| 0.3538 \| 0.3191 \| 0.4994 \| 0.9567 \| nan \| 0.0007 \| 0.9981 \| 0.0 \| 0.0007 \| 0.9567 \| \| 0.3601 \| 75.0 \| 150 \| 0.3413 \| 0.3192 \| 0.4995 \| 0.9573 \| nan \| 0.0002 \| 0.9988 \| 0.0 \| 0.0002 \| 0.9573 \| \| 0.3626 \| 80.0 \| 160 \| 0.3225 \| 0.3193 \| 0.4996 \| 0.9569 \| nan \| 0.0009 \| 0.9984 \| 0.0 \| 0.0009 \| 0.9569 \| \| 0.3331 \| 85.0 \| 170 \| 0.3163 \| 0.3195 \| 0.5000 \| 0.9576 \| nan \| 0.0009 \| 0.9991 \| 0.0 \| 0.0009 \| 0.9576 \| \| 0.319 \| 90.0 \| 180 \| 0.3004 \| 0.3200 \| 0.5008 \| 0.9577 \| nan \| 0.0024 \| 0.9991 \| 0.0 \| 0.0024 \| 0.9577 \| \| 0.3163 \| 95.0 \| 190 \| 0.2931 \| 0.3198 \| 0.5004 \| 0.9575 \| nan \| 0.0020 \| 0.9989 \| 0.0 \| 0.0020 \| 0.9575 \| \| 0.3185 \| 100.0 \| 200 \| 0.2920 \| 0.3194 \| 0.4999 \| 0.9577 \| nan \| 0.0006 \| 0.9992 \| 0.0 \| 0.0006 \| 0.9577 \| \| 0.3122 \| 105.0 \| 210 \| 0.2831 \| 0.3194 \| 0.4999 \| 0.9578 \| nan \| 0.0005 \| 0.9994 \| 0.0 \| 0.0005 \| 0.9578 \| \| 0.3218 \| 110.0 \| 220 \| 0.2788 \| 0.3195 \| 0.5000 \| 0.9576 \| nan \| 0.0009 \| 0.9991 \| 0.0 \| 0.0009 \| 0.9576 \| \| 0.3037 \| 115.0 \| 230 \| 0.2752 \| 0.3194 \| 0.4999 \| 0.9577 \| nan \| 0.0006 \| 0.9992 \| 0.0 \| 0.0006 \| 0.9577 \| \| 0.3319 \| 120.0 \| 240 \| 0.2768 \| 0.3194 \| 0.4999 \| 0.9578 \| nan \| 0.0006 \| 0.9993 \| 0.0 \| 0.0006 \| 0.9578 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_4 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2351 - Mean Iou: 0.4792 - Mean Accuracy: 0.5 - Overall Accuracy: 0.9584 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0 - Accuracy Undropoff: 1.0 - Iou Unlabeled: nan - Iou Dropoff: 0.0 - Iou Undropoff: 0.9584 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0114 \| 5.0 \| 10 \| 1.0037 \| 0.2459 \| 0.4345 \| 0.7074 \| nan \| 0.1368 \| 0.7322 \| 0.0 \| 0.0286 \| 0.7089 \| \| 0.9088 \| 10.0 \| 20 \| 0.8245 \| 0.3119 \| 0.5046 \| 0.8887 \| nan \| 0.0857 \| 0.9235 \| 0.0 \| 0.0460 \| 0.8897 \| \| 0.8029 \| 15.0 \| 30 \| 0.6620 \| 0.3157 \| 0.4998 \| 0.9214 \| nan \| 0.0399 \| 0.9596 \| 0.0 \| 0.0253 \| 0.9219 \| \| 0.6935 \| 20.0 \| 40 \| 0.5662 \| 0.3154 \| 0.4959 \| 0.9309 \| nan \| 0.0214 \| 0.9704 \| 0.0 \| 0.0151 \| 0.9311 \| \| 0.635 \| 25.0 \| 50 \| 0.5018 \| 0.3175 \| 0.4978 \| 0.9401 \| nan \| 0.0153 \| 0.9803 \| 0.0 \| 0.0121 \| 0.9404 \| \| 0.5579 \| 30.0 \| 60 \| 0.4701 \| 0.3178 \| 0.4978 \| 0.9422 \| nan \| 0.0131 \| 0.9825 \| 0.0 \| 0.0111 \| 0.9423 \| \| 0.5086 \| 35.0 \| 70 \| 0.4403 \| 0.3181 \| 0.4977 \| 0.9459 \| nan \| 0.0088 \| 0.9866 \| 0.0 \| 0.0080 \| 0.9461 \| \| 0.472 \| 40.0 \| 80 \| 0.4328 \| 0.3177 \| 0.4971 \| 0.9471 \| nan \| 0.0063 \| 0.9879 \| 0.0 \| 0.0059 \| 0.9473 \| \| 0.4484 \| 45.0 \| 90 \| 0.4136 \| 0.3184 \| 0.4981 \| 0.9506 \| nan \| 0.0046 \| 0.9916 \| 0.0 \| 0.0044 \| 0.9508 \| \| 0.4026 \| 50.0 \| 100 \| 0.4013 \| 0.3186 \| 0.4985 \| 0.9516 \| nan \| 0.0043 \| 0.9926 \| 0.0 \| 0.0042 \| 0.9517 \| \| 0.3873 \| 55.0 \| 110 \| 0.3621 \| 0.3189 \| 0.4991 \| 0.9557 \| nan \| 0.0010 \| 0.9971 \| 0.0 \| 0.0009 \| 0.9557 \| \| 0.3549 \| 60.0 \| 120 \| 0.3479 \| 0.3189 \| 0.4992 \| 0.9564 \| nan \| 0.0004 \| 0.9979 \| 0.0 \| 0.0004 \| 0.9564 \| \| 0.3358 \| 65.0 \| 130 \| 0.3282 \| 0.3191 \| 0.4994 \| 0.9571 \| nan \| 0.0001 \| 0.9986 \| 0.0 \| 0.0001 \| 0.9571 \| \| 0.3146 \| 70.0 \| 140 \| 0.3141 \| 0.3193 \| 0.4996 \| 0.9577 \| nan \| 0.0000 \| 0.9993 \| 0.0 \| 0.0000 \| 0.9577 \| \| 0.3116 \| 75.0 \| 150 \| 0.2941 \| 0.3194 \| 0.4999 \| 0.9582 \| nan \| 0.0 \| 0.9998 \| 0.0 \| 0.0 \| 0.9582 \| \| 0.3151 \| 80.0 \| 160 \| 0.2809 \| 0.3195 \| 0.5000 \| 0.9584 \| nan \| 0.0 \| 0.9999 \| 0.0 \| 0.0 \| 0.9584 \| \| 0.2778 \| 85.0 \| 170 \| 0.2750 \| 0.3195 \| 0.5000 \| 0.9584 \| nan \| 0.0 \| 1.0000 \| 0.0 \| 0.0 \| 0.9584 \| \| 0.2753 \| 90.0 \| 180 \| 0.2615 \| 0.3195 \| 0.5000 \| 0.9584 \| nan \| 0.0 \| 1.0000 \| 0.0 \| 0.0 \| 0.9584 \| \| 0.2809 \| 95.0 \| 190 \| 0.2547 \| 0.4792 \| 0.5 \| 0.9584 \| nan \| 0.0 \| 1.0 \| nan \| 0.0 \| 0.9584 \| \| 0.2606 \| 100.0 \| 200 \| 0.2464 \| 0.4792 \| 0.5 \| 0.9584 \| nan \| 0.0 \| 1.0 \| nan \| 0.0 \| 0.9584 \| \| 0.2563 \| 105.0 \| 210 \| 0.2459 \| 0.4792 \| 0.5 \| 0.9584 \| nan \| 0.0 \| 1.0 \| nan \| 0.0 \| 0.9584 \| \| 0.2454 \| 110.0 \| 220 \| 0.2393 \| 0.4792 \| 0.5 \| 0.9584 \| nan \| 0.0 \| 1.0 \| nan \| 0.0 \| 0.9584 \| \| 0.2707 \| 115.0 \| 230 \| 0.2368 \| 0.4792 \| 0.5 \| 0.9584 \| nan \| 0.0 \| 1.0 \| nan \| 0.0 \| 0.9584 \| \| 0.2433 \| 120.0 \| 240 \| 0.2351 \| 0.4792 \| 0.5 \| 0.9584 \| nan \| 0.0 \| 1.0 \| nan \| 0.0 \| 0.9584 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2636 - Mean Iou: 0.4256 - Mean Accuracy: 0.6832 - Overall Accuracy: 0.9656 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3752 - Accuracy Undropoff: 0.9912 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3118 - Iou Undropoff: 0.9650 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.1487 \| 5.0 \| 10 \| 1.0250 \| 0.2562 \| 0.6276 \| 0.6778 \| nan \| 0.5730 \| 0.6823 \| 0.0 \| 0.0971 \| 0.6714 \| \| 1.0128 \| 10.0 \| 20 \| 0.9030 \| 0.3142 \| 0.6730 \| 0.8268 \| nan \| 0.5053 \| 0.8407 \| 0.0 \| 0.1195 \| 0.8231 \| \| 0.8561 \| 15.0 \| 30 \| 0.7359 \| 0.3520 \| 0.6913 \| 0.8949 \| nan \| 0.4692 \| 0.9133 \| 0.0 \| 0.1632 \| 0.8928 \| \| 0.7551 \| 20.0 \| 40 \| 0.6534 \| 0.3634 \| 0.6999 \| 0.9090 \| nan \| 0.4719 \| 0.9280 \| 0.0 \| 0.1829 \| 0.9072 \| \| 0.6236 \| 25.0 \| 50 \| 0.5938 \| 0.3710 \| 0.7001 \| 0.9189 \| nan \| 0.4614 \| 0.9388 \| 0.0 \| 0.1955 \| 0.9173 \| \| 0.4977 \| 30.0 \| 60 \| 0.5293 \| 0.3850 \| 0.6987 \| 0.9341 \| nan \| 0.4420 \| 0.9555 \| 0.0 \| 0.2222 \| 0.9329 \| \| 0.4188 \| 35.0 \| 70 \| 0.4859 \| 0.3935 \| 0.6941 \| 0.9425 \| nan \| 0.4231 \| 0.9650 \| 0.0 \| 0.2390 \| 0.9415 \| \| 0.3532 \| 40.0 \| 80 \| 0.4278 \| 0.4019 \| 0.6823 \| 0.9519 \| nan \| 0.3881 \| 0.9764 \| 0.0 \| 0.2547 \| 0.9511 \| \| 0.3187 \| 45.0 \| 90 \| 0.3914 \| 0.4098 \| 0.6873 \| 0.9560 \| nan \| 0.3942 \| 0.9804 \| 0.0 \| 0.2742 \| 0.9553 \| \| 0.2631 \| 50.0 \| 100 \| 0.3647 \| 0.4134 \| 0.6918 \| 0.9575 \| nan \| 0.4020 \| 0.9815 \| 0.0 \| 0.2835 \| 0.9567 \| \| 0.2565 \| 55.0 \| 110 \| 0.3424 \| 0.4141 \| 0.6895 \| 0.9585 \| nan \| 0.3962 \| 0.9829 \| 0.0 \| 0.2846 \| 0.9578 \| \| 0.2259 \| 60.0 \| 120 \| 0.3127 \| 0.4178 \| 0.6853 \| 0.9613 \| nan \| 0.3843 \| 0.9863 \| 0.0 \| 0.2926 \| 0.9607 \| \| 0.2263 \| 65.0 \| 130 \| 0.2920 \| 0.4202 \| 0.6822 \| 0.9632 \| nan \| 0.3757 \| 0.9886 \| 0.0 \| 0.2981 \| 0.9626 \| \| 0.1961 \| 70.0 \| 140 \| 0.2755 \| 0.4218 \| 0.6769 \| 0.9649 \| nan \| 0.3627 \| 0.9911 \| 0.0 \| 0.3009 \| 0.9644 \| \| 0.1897 \| 75.0 \| 150 \| 0.2726 \| 0.4232 \| 0.6803 \| 0.9650 \| nan \| 0.3698 \| 0.9908 \| 0.0 \| 0.3052 \| 0.9645 \| \| 0.1863 \| 80.0 \| 160 \| 0.2762 \| 0.4241 \| 0.6830 \| 0.9649 \| nan \| 0.3756 \| 0.9904 \| 0.0 \| 0.3079 \| 0.9643 \| \| 0.1656 \| 85.0 \| 170 \| 0.2730 \| 0.4241 \| 0.6809 \| 0.9653 \| nan \| 0.3708 \| 0.9911 \| 0.0 \| 0.3076 \| 0.9648 \| \| 0.1745 \| 90.0 \| 180 \| 0.2740 \| 0.4241 \| 0.6821 \| 0.9651 \| nan \| 0.3736 \| 0.9907 \| 0.0 \| 0.3079 \| 0.9645 \| \| 0.1726 \| 95.0 \| 190 \| 0.2779 \| 0.4242 \| 0.6854 \| 0.9645 \| nan \| 0.3809 \| 0.9898 \| 0.0 \| 0.3085 \| 0.9639 \| \| 0.158 \| 100.0 \| 200 \| 0.2661 \| 0.4248 \| 0.6808 \| 0.9656 \| nan \| 0.3701 \| 0.9915 \| 0.0 \| 0.3094 \| 0.9651 \| \| 0.19 \| 105.0 \| 210 \| 0.2667 \| 0.4240 \| 0.6790 \| 0.9656 \| nan \| 0.3664 \| 0.9916 \| 0.0 \| 0.3070 \| 0.9651 \| \| 0.1533 \| 110.0 \| 220 \| 0.2696 \| 0.4258 \| 0.6843 \| 0.9655 \| nan \| 0.3777 \| 0.9910 \| 0.0 \| 0.3126 \| 0.9649 \| \| 0.1644 \| 115.0 \| 230 \| 0.2690 \| 0.4261 \| 0.6855 \| 0.9654 \| nan \| 0.3803 \| 0.9908 \| 0.0 \| 0.3136 \| 0.9648 \| \| 0.1594 \| 120.0 \| 240 \| 0.2636 \| 0.4256 \| 0.6832 \| 0.9656 \| nan \| 0.3752 \| 0.9912 \| 0.0 \| 0.3118 \| 0.9650 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_6	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_6 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1429 - Mean Iou: 0.6443 - Mean Accuracy: 0.6853 - Overall Accuracy: 0.9669 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3782 - Accuracy Undropoff: 0.9925 - Iou Unlabeled: nan - Iou Dropoff: 0.3223 - Iou Undropoff: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.159 \| 5.0 \| 10 \| 1.0040 \| 0.2283 \| 0.5676 \| 0.6267 \| nan \| 0.5031 \| 0.6321 \| 0.0 \| 0.0644 \| 0.6203 \| \| 0.8345 \| 10.0 \| 20 \| 0.7480 \| 0.3236 \| 0.5320 \| 0.9158 \| nan \| 0.1134 \| 0.9506 \| 0.0 \| 0.0555 \| 0.9154 \| \| 0.5406 \| 15.0 \| 30 \| 0.5477 \| 0.3223 \| 0.5049 \| 0.9513 \| nan \| 0.0179 \| 0.9918 \| 0.0 \| 0.0157 \| 0.9513 \| \| 0.3695 \| 20.0 \| 40 \| 0.4590 \| 0.3215 \| 0.5036 \| 0.9519 \| nan \| 0.0146 \| 0.9926 \| 0.0 \| 0.0125 \| 0.9519 \| \| 0.3053 \| 25.0 \| 50 \| 0.3790 \| 0.3196 \| 0.5001 \| 0.9565 \| nan \| 0.0023 \| 0.9979 \| 0.0 \| 0.0022 \| 0.9565 \| \| 0.2436 \| 30.0 \| 60 \| 0.3303 \| 0.4812 \| 0.5020 \| 0.9568 \| nan \| 0.0059 \| 0.9981 \| nan \| 0.0056 \| 0.9568 \| \| 0.2148 \| 35.0 \| 70 \| 0.2739 \| 0.4794 \| 0.5002 \| 0.9580 \| nan \| 0.0008 \| 0.9996 \| nan \| 0.0008 \| 0.9580 \| \| 0.1983 \| 40.0 \| 80 \| 0.2348 \| 0.5079 \| 0.5284 \| 0.9595 \| nan \| 0.0582 \| 0.9986 \| nan \| 0.0564 \| 0.9594 \| \| 0.1784 \| 45.0 \| 90 \| 0.2178 \| 0.6064 \| 0.6440 \| 0.9631 \| nan \| 0.2960 \| 0.9920 \| nan \| 0.2501 \| 0.9626 \| \| 0.1631 \| 50.0 \| 100 \| 0.1943 \| 0.6223 \| 0.6811 \| 0.9607 \| nan \| 0.3760 \| 0.9861 \| nan \| 0.2846 \| 0.9601 \| \| 0.1468 \| 55.0 \| 110 \| 0.1759 \| 0.6206 \| 0.6731 \| 0.9617 \| nan \| 0.3583 \| 0.9879 \| nan \| 0.2801 \| 0.9611 \| \| 0.1353 \| 60.0 \| 120 \| 0.1657 \| 0.6014 \| 0.6335 \| 0.9639 \| nan \| 0.2731 \| 0.9939 \| nan \| 0.2393 \| 0.9635 \| \| 0.1474 \| 65.0 \| 130 \| 0.1590 \| 0.5943 \| 0.6228 \| 0.9641 \| nan \| 0.2505 \| 0.9951 \| nan \| 0.2249 \| 0.9637 \| \| 0.1172 \| 70.0 \| 140 \| 0.1562 \| 0.6272 \| 0.6662 \| 0.9653 \| nan \| 0.3400 \| 0.9924 \| nan \| 0.2896 \| 0.9648 \| \| 0.1169 \| 75.0 \| 150 \| 0.1538 \| 0.6302 \| 0.6696 \| 0.9656 \| nan \| 0.3467 \| 0.9925 \| nan \| 0.2954 \| 0.9651 \| \| 0.1263 \| 80.0 \| 160 \| 0.1540 \| 0.6372 \| 0.6784 \| 0.9661 \| nan \| 0.3645 \| 0.9922 \| nan \| 0.3089 \| 0.9656 \| \| 0.1028 \| 85.0 \| 170 \| 0.1512 \| 0.6462 \| 0.6948 \| 0.9659 \| nan \| 0.3992 \| 0.9904 \| nan \| 0.3271 \| 0.9653 \| \| 0.1163 \| 90.0 \| 180 \| 0.1493 \| 0.6469 \| 0.6932 \| 0.9663 \| nan \| 0.3953 \| 0.9911 \| nan \| 0.3280 \| 0.9658 \| \| 0.0998 \| 95.0 \| 190 \| 0.1481 \| 0.6457 \| 0.6894 \| 0.9666 \| nan \| 0.3869 \| 0.9918 \| nan \| 0.3253 \| 0.9661 \| \| 0.0997 \| 100.0 \| 200 \| 0.1465 \| 0.6454 \| 0.6893 \| 0.9665 \| nan \| 0.3869 \| 0.9917 \| nan \| 0.3247 \| 0.9660 \| \| 0.0998 \| 105.0 \| 210 \| 0.1473 \| 0.6488 \| 0.6937 \| 0.9668 \| nan \| 0.3958 \| 0.9916 \| nan \| 0.3313 \| 0.9662 \| \| 0.1003 \| 110.0 \| 220 \| 0.1437 \| 0.6401 \| 0.6774 \| 0.9671 \| nan \| 0.3614 \| 0.9934 \| nan \| 0.3136 \| 0.9666 \| \| 0.0932 \| 115.0 \| 230 \| 0.1434 \| 0.6469 \| 0.6898 \| 0.9669 \| nan \| 0.3876 \| 0.9920 \| nan \| 0.3275 \| 0.9664 \| \| 0.0942 \| 120.0 \| 240 \| 0.1429 \| 0.6443 \| 0.6853 \| 0.9669 \| nan \| 0.3782 \| 0.9925 \| nan \| 0.3223 \| 0.9664 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGBD-b5_7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_7 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1296 - Mean Iou: 0.6242 - Mean Accuracy: 0.6623 - Overall Accuracy: 0.9652 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3319 - Accuracy Undropoff: 0.9926 - Iou Unlabeled: nan - Iou Dropoff: 0.2838 - Iou Undropoff: 0.9647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9278 \| 5.0 \| 10 \| 0.8454 \| 0.3197 \| 0.5545 \| 0.8788 \| nan \| 0.2009 \| 0.9082 \| 0.0 \| 0.0807 \| 0.8785 \| \| 0.5551 \| 10.0 \| 20 \| 0.4668 \| 0.3221 \| 0.5042 \| 0.9540 \| nan \| 0.0135 \| 0.9948 \| 0.0 \| 0.0122 \| 0.9540 \| \| 0.3667 \| 15.0 \| 30 \| 0.3354 \| 0.3218 \| 0.5035 \| 0.9570 \| nan \| 0.0088 \| 0.9982 \| 0.0 \| 0.0085 \| 0.9570 \| \| 0.2402 \| 20.0 \| 40 \| 0.2678 \| 0.5985 \| 0.6492 \| 0.9587 \| nan \| 0.3116 \| 0.9868 \| nan \| 0.2388 \| 0.9582 \| \| 0.1562 \| 25.0 \| 50 \| 0.2101 \| 0.6240 \| 0.6719 \| 0.9631 \| nan \| 0.3544 \| 0.9895 \| nan \| 0.2854 \| 0.9625 \| \| 0.1159 \| 30.0 \| 60 \| 0.1704 \| 0.6262 \| 0.6641 \| 0.9654 \| nan \| 0.3353 \| 0.9928 \| nan \| 0.2875 \| 0.9650 \| \| 0.0869 \| 35.0 \| 70 \| 0.1443 \| 0.6380 \| 0.6817 \| 0.9657 \| nan \| 0.3720 \| 0.9915 \| nan \| 0.3108 \| 0.9652 \| \| 0.079 \| 40.0 \| 80 \| 0.1350 \| 0.6072 \| 0.6360 \| 0.9654 \| nan \| 0.2766 \| 0.9953 \| nan \| 0.2494 \| 0.9650 \| \| 0.0647 \| 45.0 \| 90 \| 0.1370 \| 0.5800 \| 0.6031 \| 0.9643 \| nan \| 0.2090 \| 0.9971 \| nan \| 0.1959 \| 0.9640 \| \| 0.0587 \| 50.0 \| 100 \| 0.1336 \| 0.6276 \| 0.6796 \| 0.9628 \| nan \| 0.3707 \| 0.9885 \| nan \| 0.2929 \| 0.9622 \| \| 0.0575 \| 55.0 \| 110 \| 0.1313 \| 0.6189 \| 0.6531 \| 0.9654 \| nan \| 0.3126 \| 0.9937 \| nan \| 0.2729 \| 0.9649 \| \| 0.0527 \| 60.0 \| 120 \| 0.1298 \| 0.6252 \| 0.6655 \| 0.9648 \| nan \| 0.3391 \| 0.9920 \| nan \| 0.2860 \| 0.9643 \| \| 0.0491 \| 65.0 \| 130 \| 0.1313 \| 0.6110 \| 0.6492 \| 0.9635 \| nan \| 0.3063 \| 0.9920 \| nan \| 0.2589 \| 0.9631 \| \| 0.0441 \| 70.0 \| 140 \| 0.1295 \| 0.6103 \| 0.6429 \| 0.9648 \| nan \| 0.2919 \| 0.9939 \| nan \| 0.2562 \| 0.9643 \| \| 0.0426 \| 75.0 \| 150 \| 0.1233 \| 0.6271 \| 0.6633 \| 0.9659 \| nan \| 0.3333 \| 0.9933 \| nan \| 0.2887 \| 0.9654 \| \| 0.0477 \| 80.0 \| 160 \| 0.1286 \| 0.6255 \| 0.6629 \| 0.9655 \| nan \| 0.3328 \| 0.9929 \| nan \| 0.2861 \| 0.9650 \| \| 0.039 \| 85.0 \| 170 \| 0.1265 \| 0.6380 \| 0.6824 \| 0.9656 \| nan \| 0.3735 \| 0.9913 \| nan \| 0.3109 \| 0.9650 \| \| 0.0378 \| 90.0 \| 180 \| 0.1309 \| 0.6185 \| 0.6543 \| 0.9650 \| nan \| 0.3154 \| 0.9932 \| nan \| 0.2725 \| 0.9645 \| \| 0.0362 \| 95.0 \| 190 \| 0.1266 \| 0.6311 \| 0.6715 \| 0.9655 \| nan \| 0.3508 \| 0.9922 \| nan \| 0.2973 \| 0.9650 \| \| 0.0394 \| 100.0 \| 200 \| 0.1307 \| 0.6274 \| 0.6635 \| 0.9659 \| nan \| 0.3337 \| 0.9934 \| nan \| 0.2894 \| 0.9655 \| \| 0.0362 \| 105.0 \| 210 \| 0.1271 \| 0.6366 \| 0.6789 \| 0.9658 \| nan \| 0.3661 \| 0.9918 \| nan \| 0.3080 \| 0.9653 \| \| 0.0361 \| 110.0 \| 220 \| 0.1274 \| 0.6317 \| 0.6736 \| 0.9653 \| nan \| 0.3554 \| 0.9918 \| nan \| 0.2987 \| 0.9648 \| \| 0.0353 \| 115.0 \| 230 \| 0.1290 \| 0.6216 \| 0.6579 \| 0.9652 \| nan \| 0.3228 \| 0.9931 \| nan \| 0.2784 \| 0.9647 \| \| 0.0344 \| 120.0 \| 240 \| 0.1296 \| 0.6242 \| 0.6623 \| 0.9652 \| nan \| 0.3319 \| 0.9926 \| nan \| 0.2838 \| 0.9647 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b5_1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.6279 - Mean Iou: 0.4054 - Mean Accuracy: 0.7471 - Overall Accuracy: 0.8860 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5956 - Accuracy Undropoff: 0.8986 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3318 - Iou Undropoff: 0.8843 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0071 \| 5.0 \| 10 \| 1.0206 \| 0.1745 \| 0.2748 \| 0.5034 \| nan \| 0.0255 \| 0.5241 \| 0.0 \| 0.0147 \| 0.5087 \| \| 0.9688 \| 10.0 \| 20 \| 0.9873 \| 0.2140 \| 0.3486 \| 0.5771 \| nan \| 0.0992 \| 0.5979 \| 0.0 \| 0.0582 \| 0.5838 \| \| 0.9406 \| 15.0 \| 30 \| 0.9313 \| 0.2613 \| 0.4446 \| 0.6655 \| nan \| 0.2038 \| 0.6855 \| 0.0 \| 0.1135 \| 0.6705 \| \| 0.9278 \| 20.0 \| 40 \| 0.8851 \| 0.2930 \| 0.5149 \| 0.7111 \| nan \| 0.3009 \| 0.7289 \| 0.0 \| 0.1648 \| 0.7142 \| \| 0.8956 \| 25.0 \| 50 \| 0.8563 \| 0.3118 \| 0.5642 \| 0.7358 \| nan \| 0.3770 \| 0.7514 \| 0.0 \| 0.1985 \| 0.7370 \| \| 0.8674 \| 30.0 \| 60 \| 0.8260 \| 0.3303 \| 0.6086 \| 0.7664 \| nan \| 0.4366 \| 0.7807 \| 0.0 \| 0.2246 \| 0.7664 \| \| 0.8438 \| 35.0 \| 70 \| 0.8149 \| 0.3347 \| 0.6355 \| 0.7671 \| nan \| 0.4921 \| 0.7790 \| 0.0 \| 0.2381 \| 0.7660 \| \| 0.8309 \| 40.0 \| 80 \| 0.7881 \| 0.3459 \| 0.6472 \| 0.7847 \| nan \| 0.4972 \| 0.7972 \| 0.0 \| 0.2539 \| 0.7839 \| \| 0.8069 \| 45.0 \| 90 \| 0.7640 \| 0.3567 \| 0.6617 \| 0.8041 \| nan \| 0.5063 \| 0.8170 \| 0.0 \| 0.2668 \| 0.8033 \| \| 0.7779 \| 50.0 \| 100 \| 0.7486 \| 0.3637 \| 0.6792 \| 0.8145 \| nan \| 0.5316 \| 0.8268 \| 0.0 \| 0.2778 \| 0.8132 \| \| 0.7695 \| 55.0 \| 110 \| 0.7354 \| 0.3684 \| 0.6936 \| 0.8214 \| nan \| 0.5542 \| 0.8329 \| 0.0 \| 0.2858 \| 0.8195 \| \| 0.7568 \| 60.0 \| 120 \| 0.7164 \| 0.3757 \| 0.7032 \| 0.8365 \| nan \| 0.5577 \| 0.8486 \| 0.0 \| 0.2924 \| 0.8347 \| \| 0.7285 \| 65.0 \| 130 \| 0.6976 \| 0.3836 \| 0.7119 \| 0.8484 \| nan \| 0.5630 \| 0.8608 \| 0.0 \| 0.3042 \| 0.8467 \| \| 0.7217 \| 70.0 \| 140 \| 0.6922 \| 0.3857 \| 0.7217 \| 0.8499 \| nan \| 0.5817 \| 0.8616 \| 0.0 \| 0.3091 \| 0.8480 \| \| 0.7095 \| 75.0 \| 150 \| 0.6708 \| 0.3926 \| 0.7287 \| 0.8624 \| nan \| 0.5828 \| 0.8745 \| 0.0 \| 0.3172 \| 0.8605 \| \| 0.6944 \| 80.0 \| 160 \| 0.6637 \| 0.3951 \| 0.7320 \| 0.8660 \| nan \| 0.5858 \| 0.8781 \| 0.0 \| 0.3212 \| 0.8641 \| \| 0.6878 \| 85.0 \| 170 \| 0.6632 \| 0.3942 \| 0.7397 \| 0.8673 \| nan \| 0.6005 \| 0.8788 \| 0.0 \| 0.3175 \| 0.8652 \| \| 0.6868 \| 90.0 \| 180 \| 0.6468 \| 0.3998 \| 0.7391 \| 0.8756 \| nan \| 0.5902 \| 0.8880 \| 0.0 \| 0.3257 \| 0.8739 \| \| 0.6581 \| 95.0 \| 190 \| 0.6444 \| 0.4003 \| 0.7421 \| 0.8776 \| nan \| 0.5942 \| 0.8899 \| 0.0 \| 0.3249 \| 0.8759 \| \| 0.6587 \| 100.0 \| 200 \| 0.6383 \| 0.4026 \| 0.7427 \| 0.8814 \| nan \| 0.5914 \| 0.8940 \| 0.0 \| 0.3281 \| 0.8797 \| \| 0.6525 \| 105.0 \| 210 \| 0.6334 \| 0.4032 \| 0.7434 \| 0.8825 \| nan \| 0.5918 \| 0.8951 \| 0.0 \| 0.3289 \| 0.8808 \| \| 0.658 \| 110.0 \| 220 \| 0.6345 \| 0.4026 \| 0.7451 \| 0.8811 \| nan \| 0.5968 \| 0.8934 \| 0.0 \| 0.3285 \| 0.8793 \| \| 0.6575 \| 115.0 \| 230 \| 0.6300 \| 0.4050 \| 0.7463 \| 0.8851 \| nan \| 0.5948 \| 0.8977 \| 0.0 \| 0.3314 \| 0.8835 \| \| 0.6625 \| 120.0 \| 240 \| 0.6279 \| 0.4054 \| 0.7471 \| 0.8860 \| nan \| 0.5956 \| 0.8986 \| 0.0 \| 0.3318 \| 0.8843 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b5_2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_2 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4848 - Mean Iou: 0.4257 - Mean Accuracy: 0.7972 - Overall Accuracy: 0.9466 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.6343 - Accuracy Undropoff: 0.9601 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3321 - Iou Undropoff: 0.9451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0108 \| 5.0 \| 10 \| 1.0721 \| 0.1514 \| 0.5401 \| 0.4205 \| nan \| 0.6706 \| 0.4096 \| 0.0 \| 0.0494 \| 0.4047 \| \| 0.9654 \| 10.0 \| 20 \| 0.9802 \| 0.2190 \| 0.6570 \| 0.5944 \| nan \| 0.7253 \| 0.5887 \| 0.0 \| 0.0745 \| 0.5826 \| \| 0.9175 \| 15.0 \| 30 \| 0.9047 \| 0.2553 \| 0.7350 \| 0.6792 \| nan \| 0.7960 \| 0.6741 \| 0.0 \| 0.0973 \| 0.6686 \| \| 0.9052 \| 20.0 \| 40 \| 0.8427 \| 0.2812 \| 0.7661 \| 0.7377 \| nan \| 0.7971 \| 0.7351 \| 0.0 \| 0.1146 \| 0.7290 \| \| 0.8555 \| 25.0 \| 50 \| 0.7970 \| 0.3063 \| 0.7827 \| 0.7900 \| nan \| 0.7748 \| 0.7906 \| 0.0 \| 0.1357 \| 0.7832 \| \| 0.8291 \| 30.0 \| 60 \| 0.7543 \| 0.3289 \| 0.7891 \| 0.8332 \| nan \| 0.7410 \| 0.8372 \| 0.0 \| 0.1586 \| 0.8282 \| \| 0.7923 \| 35.0 \| 70 \| 0.7327 \| 0.3375 \| 0.7961 \| 0.8471 \| nan \| 0.7405 \| 0.8517 \| 0.0 \| 0.1701 \| 0.8425 \| \| 0.7724 \| 40.0 \| 80 \| 0.6994 \| 0.3529 \| 0.7968 \| 0.8719 \| nan \| 0.7149 \| 0.8787 \| 0.0 \| 0.1906 \| 0.8682 \| \| 0.7215 \| 45.0 \| 90 \| 0.6675 \| 0.3694 \| 0.7935 \| 0.8954 \| nan \| 0.6824 \| 0.9047 \| 0.0 \| 0.2157 \| 0.8926 \| \| 0.6907 \| 50.0 \| 100 \| 0.6521 \| 0.3742 \| 0.7998 \| 0.9000 \| nan \| 0.6904 \| 0.9091 \| 0.0 \| 0.2252 \| 0.8973 \| \| 0.6768 \| 55.0 \| 110 \| 0.6260 \| 0.3850 \| 0.8022 \| 0.9118 \| nan \| 0.6827 \| 0.9217 \| 0.0 \| 0.2455 \| 0.9094 \| \| 0.659 \| 60.0 \| 120 \| 0.6010 \| 0.3965 \| 0.7973 \| 0.9244 \| nan \| 0.6586 \| 0.9359 \| 0.0 \| 0.2671 \| 0.9224 \| \| 0.6265 \| 65.0 \| 130 \| 0.5847 \| 0.4005 \| 0.7992 \| 0.9276 \| nan \| 0.6592 \| 0.9393 \| 0.0 \| 0.2757 \| 0.9258 \| \| 0.6134 \| 70.0 \| 140 \| 0.5673 \| 0.4060 \| 0.8022 \| 0.9316 \| nan \| 0.6611 \| 0.9433 \| 0.0 \| 0.2881 \| 0.9297 \| \| 0.5864 \| 75.0 \| 150 \| 0.5401 \| 0.4132 \| 0.7961 \| 0.9383 \| nan \| 0.6410 \| 0.9511 \| 0.0 \| 0.3029 \| 0.9366 \| \| 0.5686 \| 80.0 \| 160 \| 0.5289 \| 0.4153 \| 0.7974 \| 0.9395 \| nan \| 0.6424 \| 0.9524 \| 0.0 \| 0.3080 \| 0.9379 \| \| 0.5597 \| 85.0 \| 170 \| 0.5386 \| 0.4114 \| 0.8079 \| 0.9350 \| nan \| 0.6692 \| 0.9465 \| 0.0 \| 0.3011 \| 0.9331 \| \| 0.5718 \| 90.0 \| 180 \| 0.5080 \| 0.4210 \| 0.7947 \| 0.9438 \| nan \| 0.6321 \| 0.9573 \| 0.0 \| 0.3208 \| 0.9423 \| \| 0.517 \| 95.0 \| 190 \| 0.5026 \| 0.4222 \| 0.7956 \| 0.9445 \| nan \| 0.6332 \| 0.9580 \| 0.0 \| 0.3236 \| 0.9430 \| \| 0.5252 \| 100.0 \| 200 \| 0.4990 \| 0.4232 \| 0.7969 \| 0.9450 \| nan \| 0.6354 \| 0.9584 \| 0.0 \| 0.3261 \| 0.9435 \| \| 0.5174 \| 105.0 \| 210 \| 0.4951 \| 0.4223 \| 0.8012 \| 0.9437 \| nan \| 0.6457 \| 0.9567 \| 0.0 \| 0.3249 \| 0.9422 \| \| 0.5217 \| 110.0 \| 220 \| 0.4882 \| 0.4238 \| 0.7993 \| 0.9450 \| nan \| 0.6404 \| 0.9582 \| 0.0 \| 0.3280 \| 0.9435 \| \| 0.5224 \| 115.0 \| 230 \| 0.4846 \| 0.4258 \| 0.7968 \| 0.9467 \| nan \| 0.6333 \| 0.9603 \| 0.0 \| 0.3321 \| 0.9452 \| \| 0.5399 \| 120.0 \| 240 \| 0.4848 \| 0.4257 \| 0.7972 \| 0.9466 \| nan \| 0.6343 \| 0.9601 \| 0.0 \| 0.3321 \| 0.9451 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b5_3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_3 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3770 - Mean Iou: 0.4572 - Mean Accuracy: 0.7822 - Overall Accuracy: 0.9640 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5839 - Accuracy Undropoff: 0.9805 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4086 - Iou Undropoff: 0.9631 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.3135 \| 5.0 \| 10 \| 1.2008 \| 0.0546 \| 0.2586 \| 0.1227 \| nan \| 0.4069 \| 0.1103 \| 0.0 \| 0.0535 \| 0.1102 \| \| 1.2309 \| 10.0 \| 20 \| 1.1294 \| 0.1176 \| 0.3397 \| 0.2490 \| nan \| 0.4388 \| 0.2407 \| 0.0 \| 0.1129 \| 0.2400 \| \| 1.1346 \| 15.0 \| 30 \| 1.0395 \| 0.2171 \| 0.4865 \| 0.5022 \| nan \| 0.4694 \| 0.5036 \| 0.0 \| 0.1524 \| 0.4989 \| \| 1.1088 \| 20.0 \| 40 \| 0.9755 \| 0.2608 \| 0.5521 \| 0.6176 \| nan \| 0.4808 \| 0.6235 \| 0.0 \| 0.1661 \| 0.6163 \| \| 1.007 \| 25.0 \| 50 \| 0.9197 \| 0.2895 \| 0.5959 \| 0.6775 \| nan \| 0.5068 \| 0.6849 \| 0.0 \| 0.1923 \| 0.6763 \| \| 0.9145 \| 30.0 \| 60 \| 0.8635 \| 0.3162 \| 0.6299 \| 0.7335 \| nan \| 0.5168 \| 0.7429 \| 0.0 \| 0.2156 \| 0.7329 \| \| 0.8745 \| 35.0 \| 70 \| 0.8070 \| 0.3398 \| 0.6784 \| 0.7808 \| nan \| 0.5667 \| 0.7901 \| 0.0 \| 0.2404 \| 0.7791 \| \| 0.8088 \| 40.0 \| 80 \| 0.7442 \| 0.3667 \| 0.7191 \| 0.8290 \| nan \| 0.5993 \| 0.8389 \| 0.0 \| 0.2730 \| 0.8272 \| \| 0.7184 \| 45.0 \| 90 \| 0.6956 \| 0.3832 \| 0.7513 \| 0.8603 \| nan \| 0.6323 \| 0.8702 \| 0.0 \| 0.2915 \| 0.8580 \| \| 0.6908 \| 50.0 \| 100 \| 0.6751 \| 0.3931 \| 0.7592 \| 0.8748 \| nan \| 0.6332 \| 0.8853 \| 0.0 \| 0.3067 \| 0.8728 \| \| 0.643 \| 55.0 \| 110 \| 0.6101 \| 0.4134 \| 0.7714 \| 0.9108 \| nan \| 0.6194 \| 0.9234 \| 0.0 \| 0.3308 \| 0.9094 \| \| 0.6014 \| 60.0 \| 120 \| 0.5971 \| 0.4166 \| 0.7826 \| 0.9189 \| nan \| 0.6339 \| 0.9313 \| 0.0 \| 0.3324 \| 0.9175 \| \| 0.5685 \| 65.0 \| 130 \| 0.5595 \| 0.4304 \| 0.7946 \| 0.9328 \| nan \| 0.6439 \| 0.9453 \| 0.0 \| 0.3599 \| 0.9314 \| \| 0.5172 \| 70.0 \| 140 \| 0.5344 \| 0.4373 \| 0.8010 \| 0.9406 \| nan \| 0.6488 \| 0.9532 \| 0.0 \| 0.3727 \| 0.9393 \| \| 0.4757 \| 75.0 \| 150 \| 0.4963 \| 0.4434 \| 0.7997 \| 0.9490 \| nan \| 0.6368 \| 0.9626 \| 0.0 \| 0.3822 \| 0.9479 \| \| 0.4288 \| 80.0 \| 160 \| 0.4599 \| 0.4488 \| 0.7936 \| 0.9556 \| nan \| 0.6169 \| 0.9702 \| 0.0 \| 0.3918 \| 0.9546 \| \| 0.4124 \| 85.0 \| 170 \| 0.4710 \| 0.4469 \| 0.7989 \| 0.9540 \| nan \| 0.6296 \| 0.9681 \| 0.0 \| 0.3876 \| 0.9529 \| \| 0.4995 \| 90.0 \| 180 \| 0.4209 \| 0.4537 \| 0.7883 \| 0.9606 \| nan \| 0.6004 \| 0.9762 \| 0.0 \| 0.4015 \| 0.9597 \| \| 0.3815 \| 95.0 \| 190 \| 0.4287 \| 0.4524 \| 0.7919 \| 0.9595 \| nan \| 0.6090 \| 0.9748 \| 0.0 \| 0.3988 \| 0.9586 \| \| 0.3764 \| 100.0 \| 200 \| 0.4245 \| 0.4529 \| 0.7913 \| 0.9600 \| nan \| 0.6073 \| 0.9753 \| 0.0 \| 0.3998 \| 0.9590 \| \| 0.4074 \| 105.0 \| 210 \| 0.4096 \| 0.4542 \| 0.7894 \| 0.9613 \| nan \| 0.6018 \| 0.9769 \| 0.0 \| 0.4021 \| 0.9603 \| \| 0.3975 \| 110.0 \| 220 \| 0.4107 \| 0.4538 \| 0.7905 \| 0.9610 \| nan \| 0.6045 \| 0.9765 \| 0.0 \| 0.4013 \| 0.9601 \| \| 0.3598 \| 115.0 \| 230 \| 0.3918 \| 0.4558 \| 0.7863 \| 0.9627 \| nan \| 0.5939 \| 0.9787 \| 0.0 \| 0.4057 \| 0.9618 \| \| 0.3709 \| 120.0 \| 240 \| 0.3770 \| 0.4572 \| 0.7822 \| 0.9640 \| nan \| 0.5839 \| 0.9805 \| 0.0 \| 0.4086 \| 0.9631 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b5_4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_4 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2242 - Mean Iou: 0.4568 - Mean Accuracy: 0.7402 - Overall Accuracy: 0.9696 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4899 - Accuracy Undropoff: 0.9904 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4016 - Iou Undropoff: 0.9690 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9465 \| 5.0 \| 10 \| 0.9974 \| 0.2695 \| 0.5001 \| 0.6771 \| nan \| 0.3071 \| 0.6931 \| 0.0 \| 0.1261 \| 0.6824 \| \| 0.8558 \| 10.0 \| 20 \| 0.8237 \| 0.3822 \| 0.7119 \| 0.8664 \| nan \| 0.5434 \| 0.8804 \| 0.0 \| 0.2787 \| 0.8678 \| \| 0.7585 \| 15.0 \| 30 \| 0.6801 \| 0.4232 \| 0.7487 \| 0.9194 \| nan \| 0.5625 \| 0.9349 \| 0.0 \| 0.3494 \| 0.9202 \| \| 0.715 \| 20.0 \| 40 \| 0.6076 \| 0.4298 \| 0.7663 \| 0.9232 \| nan \| 0.5952 \| 0.9375 \| 0.0 \| 0.3661 \| 0.9233 \| \| 0.6145 \| 25.0 \| 50 \| 0.5298 \| 0.4398 \| 0.7760 \| 0.9380 \| nan \| 0.5994 \| 0.9527 \| 0.0 \| 0.3819 \| 0.9375 \| \| 0.5355 \| 30.0 \| 60 \| 0.4821 \| 0.4426 \| 0.7749 \| 0.9428 \| nan \| 0.5918 \| 0.9581 \| 0.0 \| 0.3857 \| 0.9422 \| \| 0.4619 \| 35.0 \| 70 \| 0.4266 \| 0.4493 \| 0.7716 \| 0.9524 \| nan \| 0.5743 \| 0.9688 \| 0.0 \| 0.3962 \| 0.9517 \| \| 0.4367 \| 40.0 \| 80 \| 0.3941 \| 0.4519 \| 0.7738 \| 0.9568 \| nan \| 0.5742 \| 0.9734 \| 0.0 \| 0.3997 \| 0.9559 \| \| 0.3839 \| 45.0 \| 90 \| 0.3801 \| 0.4528 \| 0.7796 \| 0.9577 \| nan \| 0.5853 \| 0.9738 \| 0.0 \| 0.4017 \| 0.9567 \| \| 0.3164 \| 50.0 \| 100 \| 0.3549 \| 0.4543 \| 0.7785 \| 0.9608 \| nan \| 0.5797 \| 0.9773 \| 0.0 \| 0.4030 \| 0.9599 \| \| 0.3018 \| 55.0 \| 110 \| 0.3327 \| 0.4573 \| 0.7731 \| 0.9639 \| nan \| 0.5650 \| 0.9812 \| 0.0 \| 0.4087 \| 0.9631 \| \| 0.2646 \| 60.0 \| 120 \| 0.3127 \| 0.4590 \| 0.7703 \| 0.9658 \| nan \| 0.5571 \| 0.9835 \| 0.0 \| 0.4121 \| 0.9650 \| \| 0.2378 \| 65.0 \| 130 \| 0.2958 \| 0.4628 \| 0.7728 \| 0.9673 \| nan \| 0.5607 \| 0.9850 \| 0.0 \| 0.4217 \| 0.9666 \| \| 0.2076 \| 70.0 \| 140 \| 0.2778 \| 0.4675 \| 0.7729 \| 0.9693 \| nan \| 0.5586 \| 0.9871 \| 0.0 \| 0.4340 \| 0.9686 \| \| 0.1951 \| 75.0 \| 150 \| 0.2648 \| 0.4666 \| 0.7719 \| 0.9692 \| nan \| 0.5567 \| 0.9871 \| 0.0 \| 0.4314 \| 0.9685 \| \| 0.1734 \| 80.0 \| 160 \| 0.2522 \| 0.4673 \| 0.7643 \| 0.9703 \| nan \| 0.5397 \| 0.9890 \| 0.0 \| 0.4322 \| 0.9696 \| \| 0.1569 \| 85.0 \| 170 \| 0.2436 \| 0.4660 \| 0.7603 \| 0.9703 \| nan \| 0.5312 \| 0.9894 \| 0.0 \| 0.4282 \| 0.9697 \| \| 0.1691 \| 90.0 \| 180 \| 0.2411 \| 0.4647 \| 0.7624 \| 0.9697 \| nan \| 0.5363 \| 0.9885 \| 0.0 \| 0.4250 \| 0.9690 \| \| 0.1498 \| 95.0 \| 190 \| 0.2335 \| 0.4623 \| 0.7537 \| 0.9699 \| nan \| 0.5179 \| 0.9895 \| 0.0 \| 0.4176 \| 0.9692 \| \| 0.1478 \| 100.0 \| 200 \| 0.2281 \| 0.4585 \| 0.7420 \| 0.9700 \| nan \| 0.4934 \| 0.9906 \| 0.0 \| 0.4062 \| 0.9693 \| \| 0.1407 \| 105.0 \| 210 \| 0.2278 \| 0.4615 \| 0.7501 \| 0.9701 \| nan \| 0.5102 \| 0.9900 \| 0.0 \| 0.4151 \| 0.9694 \| \| 0.1397 \| 110.0 \| 220 \| 0.2305 \| 0.4610 \| 0.7512 \| 0.9698 \| nan \| 0.5129 \| 0.9896 \| 0.0 \| 0.4140 \| 0.9691 \| \| 0.1317 \| 115.0 \| 230 \| 0.2265 \| 0.4576 \| 0.7430 \| 0.9695 \| nan \| 0.4959 \| 0.9901 \| 0.0 \| 0.4038 \| 0.9689 \| \| 0.1548 \| 120.0 \| 240 \| 0.2242 \| 0.4568 \| 0.7402 \| 0.9696 \| nan \| 0.4899 \| 0.9904 \| 0.0 \| 0.4016 \| 0.9690 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b5_6	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_6 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Mean Iou: 0.6980 - Mean Accuracy: 0.7503 - Overall Accuracy: 0.9714 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5091 - Accuracy Undropoff: 0.9915 - Iou Unlabeled: nan - Iou Dropoff: 0.4253 - Iou Undropoff: 0.9708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0694 \| 5.0 \| 10 \| 1.0190 \| 0.2533 \| 0.6371 \| 0.6676 \| nan \| 0.6038 \| 0.6703 \| 0.0 \| 0.0976 \| 0.6624 \| \| 0.8457 \| 10.0 \| 20 \| 0.7681 \| 0.4126 \| 0.7662 \| 0.9307 \| nan \| 0.5867 \| 0.9457 \| 0.0 \| 0.3078 \| 0.9300 \| \| 0.6049 \| 15.0 \| 30 \| 0.5718 \| 0.4362 \| 0.7527 \| 0.9568 \| nan \| 0.5301 \| 0.9753 \| 0.0 \| 0.3527 \| 0.9561 \| \| 0.5206 \| 20.0 \| 40 \| 0.4181 \| 0.4522 \| 0.7468 \| 0.9662 \| nan \| 0.5076 \| 0.9861 \| 0.0 \| 0.3909 \| 0.9656 \| \| 0.3478 \| 25.0 \| 50 \| 0.3144 \| 0.4603 \| 0.7376 \| 0.9709 \| nan \| 0.4832 \| 0.9920 \| 0.0 \| 0.4105 \| 0.9705 \| \| 0.2023 \| 30.0 \| 60 \| 0.2893 \| 0.4654 \| 0.7612 \| 0.9701 \| nan \| 0.5332 \| 0.9891 \| 0.0 \| 0.4267 \| 0.9695 \| \| 0.1367 \| 35.0 \| 70 \| 0.2351 \| 0.6813 \| 0.7176 \| 0.9715 \| nan \| 0.4407 \| 0.9946 \| nan \| 0.3916 \| 0.9710 \| \| 0.1272 \| 40.0 \| 80 \| 0.2364 \| 0.6824 \| 0.7217 \| 0.9713 \| nan \| 0.4495 \| 0.9939 \| nan \| 0.3941 \| 0.9707 \| \| 0.0929 \| 45.0 \| 90 \| 0.2536 \| 0.4704 \| 0.7617 \| 0.9718 \| nan \| 0.5326 \| 0.9909 \| 0.0 \| 0.4401 \| 0.9712 \| \| 0.0756 \| 50.0 \| 100 \| 0.2253 \| 0.6950 \| 0.7479 \| 0.9710 \| nan \| 0.5045 \| 0.9912 \| nan \| 0.4197 \| 0.9704 \| \| 0.0756 \| 55.0 \| 110 \| 0.2305 \| 0.7043 \| 0.7606 \| 0.9716 \| nan \| 0.5305 \| 0.9908 \| nan \| 0.4375 \| 0.9710 \| \| 0.0721 \| 60.0 \| 120 \| 0.2213 \| 0.6964 \| 0.7448 \| 0.9716 \| nan \| 0.4974 \| 0.9922 \| nan \| 0.4218 \| 0.9711 \| \| 0.0683 \| 65.0 \| 130 \| 0.2338 \| 0.7047 \| 0.7631 \| 0.9715 \| nan \| 0.5359 \| 0.9904 \| nan \| 0.4385 \| 0.9708 \| \| 0.0642 \| 70.0 \| 140 \| 0.2314 \| 0.7046 \| 0.7637 \| 0.9714 \| nan \| 0.5373 \| 0.9902 \| nan \| 0.4385 \| 0.9707 \| \| 0.0623 \| 75.0 \| 150 \| 0.2205 \| 0.7013 \| 0.7565 \| 0.9714 \| nan \| 0.5222 \| 0.9909 \| nan \| 0.4317 \| 0.9708 \| \| 0.0601 \| 80.0 \| 160 \| 0.2209 \| 0.6983 \| 0.7496 \| 0.9715 \| nan \| 0.5075 \| 0.9917 \| nan \| 0.4257 \| 0.9709 \| \| 0.0557 \| 85.0 \| 170 \| 0.2067 \| 0.6982 \| 0.7463 \| 0.9719 \| nan \| 0.5003 \| 0.9923 \| nan \| 0.4252 \| 0.9713 \| \| 0.0571 \| 90.0 \| 180 \| 0.2354 \| 0.7022 \| 0.7603 \| 0.9712 \| nan \| 0.5302 \| 0.9904 \| nan \| 0.4339 \| 0.9706 \| \| 0.0544 \| 95.0 \| 190 \| 0.2240 \| 0.7010 \| 0.7562 \| 0.9714 \| nan \| 0.5215 \| 0.9909 \| nan \| 0.4311 \| 0.9708 \| \| 0.0553 \| 100.0 \| 200 \| 0.2204 \| 0.6968 \| 0.7454 \| 0.9717 \| nan \| 0.4987 \| 0.9922 \| nan \| 0.4225 \| 0.9711 \| \| 0.0525 \| 105.0 \| 210 \| 0.2332 \| 0.7050 \| 0.7625 \| 0.9716 \| nan \| 0.5344 \| 0.9906 \| nan \| 0.4390 \| 0.9710 \| \| 0.0524 \| 110.0 \| 220 \| 0.2371 \| 0.7033 \| 0.7605 \| 0.9715 \| nan \| 0.5304 \| 0.9906 \| nan \| 0.4359 \| 0.9708 \| \| 0.0513 \| 115.0 \| 230 \| 0.2333 \| 0.6987 \| 0.7519 \| 0.9714 \| nan \| 0.5125 \| 0.9913 \| nan \| 0.4267 \| 0.9707 \| \| 0.0537 \| 120.0 \| 240 \| 0.2315 \| 0.6980 \| 0.7503 \| 0.9714 \| nan \| 0.5091 \| 0.9915 \| nan \| 0.4253 \| 0.9708 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]
sam1120/dropoff-utcustom-train-SF-RGB-b5_7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_7 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1841 - Mean Iou: 0.7025 - Mean Accuracy: 0.7532 - Overall Accuracy: 0.9721 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5145 - Accuracy Undropoff: 0.9919 - Iou Unlabeled: nan - Iou Dropoff: 0.4336 - Iou Undropoff: 0.9715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.8255 \| 5.0 \| 10 \| 0.7949 \| 0.4128 \| 0.7856 \| 0.9393 \| nan \| 0.6179 \| 0.9533 \| 0.0 \| 0.3007 \| 0.9377 \| \| 0.4434 \| 10.0 \| 20 \| 0.4247 \| 0.4471 \| 0.7066 \| 0.9705 \| nan \| 0.4187 \| 0.9944 \| 0.0 \| 0.3714 \| 0.9700 \| \| 0.2107 \| 15.0 \| 30 \| 0.2726 \| 0.6711 \| 0.7003 \| 0.9715 \| nan \| 0.4046 \| 0.9961 \| nan \| 0.3713 \| 0.9710 \| \| 0.1678 \| 20.0 \| 40 \| 0.2388 \| 0.6801 \| 0.7343 \| 0.9691 \| nan \| 0.4782 \| 0.9904 \| nan \| 0.3917 \| 0.9685 \| \| 0.0972 \| 25.0 \| 50 \| 0.1849 \| 0.6764 \| 0.7096 \| 0.9715 \| nan \| 0.4241 \| 0.9952 \| nan \| 0.3818 \| 0.9709 \| \| 0.0604 \| 30.0 \| 60 \| 0.2019 \| 0.4644 \| 0.7568 \| 0.9704 \| nan \| 0.5239 \| 0.9897 \| 0.0 \| 0.4236 \| 0.9697 \| \| 0.0497 \| 35.0 \| 70 \| 0.1793 \| 0.6838 \| 0.7345 \| 0.9700 \| nan \| 0.4775 \| 0.9914 \| nan \| 0.3983 \| 0.9694 \| \| 0.0492 \| 40.0 \| 80 \| 0.2000 \| 0.4639 \| 0.7567 \| 0.9702 \| nan \| 0.5239 \| 0.9896 \| 0.0 \| 0.4223 \| 0.9695 \| \| 0.0409 \| 45.0 \| 90 \| 0.1893 \| 0.7030 \| 0.7778 \| 0.9696 \| nan \| 0.5687 \| 0.9869 \| nan \| 0.4372 \| 0.9688 \| \| 0.0328 \| 50.0 \| 100 \| 0.1842 \| 0.7040 \| 0.7715 \| 0.9704 \| nan \| 0.5545 \| 0.9885 \| nan \| 0.4382 \| 0.9697 \| \| 0.0332 \| 55.0 \| 110 \| 0.1781 \| 0.7015 \| 0.7563 \| 0.9715 \| nan \| 0.5216 \| 0.9910 \| nan \| 0.4322 \| 0.9709 \| \| 0.0314 \| 60.0 \| 120 \| 0.1732 \| 0.6890 \| 0.7305 \| 0.9717 \| nan \| 0.4675 \| 0.9935 \| nan \| 0.4068 \| 0.9711 \| \| 0.0318 \| 65.0 \| 130 \| 0.1786 \| 0.6971 \| 0.7477 \| 0.9715 \| nan \| 0.5037 \| 0.9918 \| nan \| 0.4233 \| 0.9709 \| \| 0.0291 \| 70.0 \| 140 \| 0.1814 \| 0.7119 \| 0.7687 \| 0.9725 \| nan \| 0.5466 \| 0.9909 \| nan \| 0.4521 \| 0.9718 \| \| 0.0273 \| 75.0 \| 150 \| 0.1755 \| 0.7101 \| 0.7677 \| 0.9722 \| nan \| 0.5446 \| 0.9907 \| nan \| 0.4487 \| 0.9715 \| \| 0.0274 \| 80.0 \| 160 \| 0.1786 \| 0.7006 \| 0.7494 \| 0.9720 \| nan \| 0.5066 \| 0.9922 \| nan \| 0.4297 \| 0.9714 \| \| 0.0248 \| 85.0 \| 170 \| 0.1741 \| 0.7029 \| 0.7526 \| 0.9722 \| nan \| 0.5131 \| 0.9921 \| nan \| 0.4341 \| 0.9716 \| \| 0.0248 \| 90.0 \| 180 \| 0.1832 \| 0.7050 \| 0.7595 \| 0.9719 \| nan \| 0.5278 \| 0.9912 \| nan \| 0.4387 \| 0.9713 \| \| 0.0242 \| 95.0 \| 190 \| 0.1808 \| 0.7028 \| 0.7539 \| 0.9720 \| nan \| 0.5160 \| 0.9918 \| nan \| 0.4341 \| 0.9714 \| \| 0.024 \| 100.0 \| 200 \| 0.1796 \| 0.7022 \| 0.7501 \| 0.9723 \| nan \| 0.5077 \| 0.9925 \| nan \| 0.4327 \| 0.9717 \| \| 0.0231 \| 105.0 \| 210 \| 0.1835 \| 0.7137 \| 0.7731 \| 0.9724 \| nan \| 0.5557 \| 0.9905 \| nan \| 0.4556 \| 0.9717 \| \| 0.0238 \| 110.0 \| 220 \| 0.1823 \| 0.7046 \| 0.7565 \| 0.9721 \| nan \| 0.5214 \| 0.9917 \| nan \| 0.4376 \| 0.9715 \| \| 0.0228 \| 115.0 \| 230 \| 0.1833 \| 0.7009 \| 0.7504 \| 0.9720 \| nan \| 0.5088 \| 0.9921 \| nan \| 0.4305 \| 0.9714 \| \| 0.0255 \| 120.0 \| 240 \| 0.1841 \| 0.7025 \| 0.7532 \| 0.9721 \| nan \| 0.5145 \| 0.9919 \| nan \| 0.4336 \| 0.9715 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "unlabeled", "dropoff", "undropoff" ]

giuseppemartino/model2

# model2 This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the giuseppemartino/isaid_sam_predicted dataset. It achieves the following results on the evaluation set: - Loss: 0.2318 - Mean Iou: 0.2504 - Mean Accuracy: 0.3019 - Overall Accuracy: 0.4542 - Accuracy Background: nan - Accuracy Ship: 0.6330 - Accuracy Small-vehicle: 0.4644 - Accuracy Tennis-court: 0.0280 - Accuracy Helicopter: nan - Accuracy Basketball-court: 0.0 - Accuracy Ground-track-field: 0.6010 - Accuracy Swimming-pool: nan - Accuracy Harbor: 0.4575 - Accuracy Soccer-ball-field: 0.7776 - Accuracy Plane: nan - Accuracy Storage-tank: 0.0 - Accuracy Baseball-diamond: nan - Accuracy Large-vehicle: 0.3594 - Accuracy Bridge: 0.0 - Accuracy Roundabout: 0.0 - Iou Background: 0.0 - Iou Ship: 0.5194 - Iou Small-vehicle: 0.4368 - Iou Tennis-court: 0.0280 - Iou Helicopter: nan - Iou Basketball-court: 0.0 - Iou Ground-track-field: 0.5492 - Iou Swimming-pool: nan - Iou Harbor: 0.3611 - Iou Soccer-ball-field: 0.7592 - Iou Plane: nan - Iou Storage-tank: 0.0 - Iou Baseball-diamond: nan - Iou Large-vehicle: 0.3508 - Iou Bridge: 0.0 - Iou Roundabout: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 1345 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Ship | Accuracy Small-vehicle | Accuracy Tennis-court | Accuracy Helicopter | Accuracy Basketball-court | Accuracy Ground-track-field | Accuracy Swimming-pool | Accuracy Harbor | Accuracy Soccer-ball-field | Accuracy Plane | Accuracy Storage-tank | Accuracy Baseball-diamond | Accuracy Large-vehicle | Accuracy Bridge | Accuracy Roundabout | Iou Background | Iou Ship | Iou Small-vehicle | Iou Tennis-court | Iou Helicopter | Iou Basketball-court | Iou Ground-track-field | Iou Swimming-pool | Iou Harbor | Iou Soccer-ball-field | Iou Plane | Iou Storage-tank | Iou Baseball-diamond | Iou Large-vehicle | Iou Bridge | Iou Roundabout | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------------:|:----------------------:|:---------------:|:--------------------------:|:--------------:|:---------------------:|:-------------------------:|:----------------------:|:---------------:|:-------------------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------------------:|:----------------------:|:-----------------:|:----------:|:---------------------:|:---------:|:----------------:|:--------------------:|:-----------------:|:----------:|:--------------:| | 1.1413 | 1.0 | 113 | 0.5054 | 0.0431 | 0.0841 | 0.0445 | nan | 0.0611 | 0.0179 | 0.0079 | nan | 0.0 | 0.0 | nan | 0.8374 | 0.0 | nan | 0.0 | nan | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0592 | 0.0178 | 0.0079 | nan | 0.0 | 0.0 | nan | 0.4319 | 0.0 | nan | 0.0 | nan | 0.0010 | 0.0 | 0.0 | | 0.326 | 2.0 | 226 | 0.3240 | 0.0756 | 0.1192 | 0.2144 | nan | 0.0433 | 0.1690 | 0.0 | nan | 0.0 | 0.0 | nan | 0.8062 | 0.0 | nan | 0.0 | nan | 0.2926 | 0.0 | 0.0 | 0.0 | 0.0430 | 0.1614 | 0.0 | nan | 0.0 | 0.0 | nan | 0.4129 | 0.0 | nan | 0.0 | nan | 0.2904 | 0.0 | 0.0 | | 0.1849 | 3.0 | 339 | 0.2807 | 0.1589 | 0.2164 | 0.3238 | nan | 0.3520 | 0.3125 | 0.0 | nan | 0.0 | 0.3563 | nan | 0.6252 | 0.4509 | nan | 0.0 | nan | 0.2835 | 0.0 | 0.0 | 0.0 | 0.3236 | 0.2894 | 0.0 | nan | 0.0 | 0.3265 | 0.0 | 0.3954 | 0.4506 | nan | 0.0 | nan | 0.2807 | 0.0 | 0.0 | | 0.1341 | 4.0 | 452 | 0.2694 | 0.1618 | 0.2309 | 0.3055 | nan | 0.2089 | 0.3628 | 0.0188 | nan | 0.0 | 0.4866 | nan | 0.7552 | 0.5206 | nan | 0.0 | nan | 0.1866 | 0.0 | 0.0 | 0.0 | 0.2004 | 0.3303 | 0.0188 | nan | 0.0 | 0.4268 | 0.0 | 0.4221 | 0.5205 | nan | 0.0 | nan | 0.1840 | 0.0 | 0.0 | | 0.1282 | 5.0 | 565 | 0.2631 | 0.2057 | 0.2726 | 0.3396 | nan | 0.4061 | 0.3347 | 0.0292 | nan | 0.0 | 0.6126 | nan | 0.6152 | 0.8252 | nan | 0.0 | nan | 0.1751 | 0.0 | 0.0 | 0.0 | 0.3667 | 0.3169 | 0.0292 | nan | 0.0 | 0.4767 | nan | 0.3995 | 0.7049 | nan | 0.0 | nan | 0.1745 | 0.0 | 0.0 | | 0.1138 | 6.0 | 678 | 0.2418 | 0.1949 | 0.2558 | 0.3865 | nan | 0.2362 | 0.3709 | 0.0122 | nan | 0.0 | 0.6128 | nan | 0.6627 | 0.5823 | nan | 0.0 | nan | 0.3365 | 0.0 | 0.0 | 0.0 | 0.2249 | 0.3444 | 0.0122 | nan | 0.0 | 0.4625 | nan | 0.3921 | 0.5725 | nan | 0.0 | nan | 0.3301 | 0.0 | 0.0 | | 0.1049 | 7.0 | 791 | 0.2345 | 0.2013 | 0.2623 | 0.4725 | nan | 0.3186 | 0.4071 | 0.0827 | nan | 0.0 | 0.1697 | nan | 0.7809 | 0.6140 | nan | 0.0 | nan | 0.5118 | 0.0 | 0.0 | 0.0 | 0.2927 | 0.3851 | 0.0827 | nan | 0.0 | 0.1679 | nan | 0.4702 | 0.5212 | nan | 0.0 | nan | 0.4961 | 0.0 | 0.0 | | 0.0829 | 8.0 | 904 | 0.2351 | 0.2194 | 0.2818 | 0.4348 | nan | 0.1689 | 0.4289 | 0.0980 | nan | 0.0 | 0.5547 | nan | 0.7050 | 0.7860 | nan | 0.0 | nan | 0.3580 | 0.0 | 0.0 | 0.0 | 0.1619 | 0.4048 | 0.0980 | nan | 0.0 | 0.5205 | nan | 0.3967 | 0.7023 | nan | 0.0 | nan | 0.3490 | 0.0 | 0.0 | | 0.0922 | 9.0 | 1017 | 0.2350 | 0.2549 | 0.3103 | 0.5060 | nan | 0.4729 | 0.4726 | 0.0572 | nan | 0.0 | 0.5679 | nan | 0.5794 | 0.7942 | nan | 0.0 | nan | 0.4690 | 0.0 | 0.0 | 0.0 | 0.4143 | 0.4398 | 0.0572 | nan | 0.0 | 0.5293 | nan | 0.4010 | 0.7613 | nan | 0.0 | nan | 0.4563 | 0.0 | 0.0 | | 0.0717 | 10.0 | 1130 | 0.2399 | 0.2344 | 0.2871 | 0.4150 | nan | 0.4512 | 0.4155 | 0.0169 | nan | 0.0 | 0.5706 | nan | 0.6279 | 0.7676 | nan | 0.0 | nan | 0.3089 | 0.0 | 0.0 | 0.0 | 0.3995 | 0.3949 | 0.0169 | nan | 0.0 | 0.5351 | nan | 0.4246 | 0.7393 | nan | 0.0 | nan | 0.3023 | 0.0 | 0.0 | | 0.0787 | 11.0 | 1243 | 0.2228 | 0.2578 | 0.3105 | 0.4726 | nan | 0.6679 | 0.4378 | 0.0666 | nan | 0.0 | 0.5865 | nan | 0.4684 | 0.7796 | nan | 0.0 | nan | 0.4087 | 0.0 | 0.0 | 0.0 | 0.5359 | 0.4172 | 0.0666 | nan | 0.0 | 0.5456 | nan | 0.3785 | 0.7528 | nan | 0.0 | nan | 0.3975 | 0.0 | 0.0 | | 0.0787 | 11.9 | 1345 | 0.2318 | 0.2504 | 0.3019 | 0.4542 | nan | 0.6330 | 0.4644 | 0.0280 | nan | 0.0 | 0.6010 | nan | 0.4575 | 0.7776 | nan | 0.0 | nan | 0.3594 | 0.0 | 0.0 | 0.0 | 0.5194 | 0.4368 | 0.0280 | nan | 0.0 | 0.5492 | nan | 0.3611 | 0.7592 | nan | 0.0 | nan | 0.3508 | 0.0 | 0.0 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "background", "ship", "small-vehicle", "tennis-court", "helicopter", "basketball-court", "ground-track-field", "swimming-pool", "harbor", "soccer-ball-field", "plane", "storage-tank", "baseball-diamond", "large-vehicle", "bridge", "roundabout" ]

magitz/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7960 - eval_mean_iou: 0.2053 - eval_mean_accuracy: 0.2533 - eval_overall_accuracy: 0.7980 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8959 - eval_accuracy_flat-sidewalk: 0.9420 - eval_accuracy_flat-crosswalk: 0.0 - eval_accuracy_flat-cyclinglane: 0.4535 - eval_accuracy_flat-parkingdriveway: 0.2273 - eval_accuracy_flat-railtrack: nan - eval_accuracy_flat-curb: 0.3520 - eval_accuracy_human-person: 0.0 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.9346 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: nan - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.0 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.8792 - eval_accuracy_construction-door: 0.0 - eval_accuracy_construction-wall: 0.3746 - eval_accuracy_construction-fenceguardrail: 0.0 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: nan - eval_accuracy_construction-stairs: 0.0 - eval_accuracy_object-pole: 0.0065 - eval_accuracy_object-trafficsign: 0.0 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9279 - eval_accuracy_nature-terrain: 0.8991 - eval_accuracy_sky: 0.9585 - eval_accuracy_void-ground: 0.0 - eval_accuracy_void-dynamic: 0.0 - eval_accuracy_void-static: 0.0014 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.6699 - eval_iou_flat-sidewalk: 0.7999 - eval_iou_flat-crosswalk: 0.0 - eval_iou_flat-cyclinglane: 0.4306 - eval_iou_flat-parkingdriveway: 0.1874 - eval_iou_flat-railtrack: nan - eval_iou_flat-curb: 0.2837 - eval_iou_human-person: 0.0 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.6757 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: nan - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.0 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.6298 - eval_iou_construction-door: 0.0 - eval_iou_construction-wall: 0.2766 - eval_iou_construction-fenceguardrail: 0.0 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: nan - eval_iou_construction-stairs: 0.0 - eval_iou_object-pole: 0.0065 - eval_iou_object-trafficsign: 0.0 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.8333 - eval_iou_nature-terrain: 0.6916 - eval_iou_sky: 0.8777 - eval_iou_void-ground: 0.0 - eval_iou_void-dynamic: 0.0 - eval_iou_void-static: 0.0014 - eval_iou_void-unclear: 0.0 - eval_runtime: 34.3451 - eval_samples_per_second: 5.823 - eval_steps_per_second: 2.912 - epoch: 2.05 - step: 820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

wang1215/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

jericojr23/segformer-b0-finetuned-segments-sidewalk-2

# segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.6325 - Mean Iou: 0.0535 - Mean Accuracy: 0.0868 - Overall Accuracy: 0.5176 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0813 - Accuracy Flat-sidewalk: 0.9451 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.0 - Accuracy Flat-parkingdriveway: 0.0 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.0017 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.2409 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: 0.0 - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9096 - Accuracy Nature-terrain: 0.0349 - Accuracy Sky: 0.5635 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.0721 - Iou Flat-sidewalk: 0.4933 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.0 - Iou Flat-parkingdriveway: 0.0 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.0017 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.1927 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.5226 - Iou Nature-terrain: 0.0332 - Iou Sky: 0.3971 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | 2.1406 | 0.2 | 20 | 3.3169 | 0.0250 | 0.0555 | 0.4250 | nan | 0.0 | 0.8293 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9246 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.4257 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0208 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3535 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.856 | 0.4 | 40 | 1.9761 | 0.0295 | 0.0611 | 0.4511 | nan | 0.0 | 0.8624 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.1301 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9616 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.4700 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.1012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3725 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9981 | 0.6 | 60 | 2.2395 | 0.0259 | 0.0508 | 0.4085 | nan | 0.0 | 0.9972 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4062 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2213 | 0.0000 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.4001 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.2248 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2034 | 0.0000 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.1244 | 0.8 | 80 | 1.7573 | 0.0401 | 0.0707 | 0.4917 | nan | 0.0004 | 0.9494 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.2756 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9149 | 0.0540 | 0.0670 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0004 | 0.4783 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.1872 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5061 | 0.0472 | 0.0636 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8822 | 1.0 | 100 | 1.6325 | 0.0535 | 0.0868 | 0.5176 | nan | 0.0813 | 0.9451 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.2409 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9096 | 0.0349 | 0.5635 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0721 | 0.4933 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.1927 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5226 | 0.0332 | 0.3971 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

wang1215/segformer-b3

# segformer-b3 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.7826 - Mean Iou: 0.3995 - Mean Accuracy: 0.4977 - Overall Accuracy: 0.8759 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.9069 - Accuracy Flat-sidewalk: 0.9471 - Accuracy Flat-crosswalk: 0.5043 - Accuracy Flat-cyclinglane: 0.8684 - Accuracy Flat-parkingdriveway: 0.5057 - Accuracy Flat-railtrack: 0.0 - Accuracy Flat-curb: 0.7351 - Accuracy Human-person: 0.8662 - Accuracy Human-rider: 0.2599 - Accuracy Vehicle-car: 0.9494 - Accuracy Vehicle-truck: 0.1607 - Accuracy Vehicle-bus: 0.0044 - Accuracy Vehicle-tramtrain: 0.1992 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.7913 - Accuracy Vehicle-caravan: 0.4628 - Accuracy Vehicle-cartrailer: 0.0106 - Accuracy Construction-building: 0.9117 - Accuracy Construction-door: 0.2679 - Accuracy Construction-wall: 0.6351 - Accuracy Construction-fenceguardrail: 0.5893 - Accuracy Construction-bridge: 0.5639 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.4246 - Accuracy Object-pole: 0.6323 - Accuracy Object-trafficsign: 0.4266 - Accuracy Object-trafficlight: 0.2431 - Accuracy Nature-vegetation: 0.9540 - Accuracy Nature-terrain: 0.8819 - Accuracy Sky: 0.9827 - Accuracy Void-ground: 0.0045 - Accuracy Void-dynamic: 0.2006 - Accuracy Void-static: 0.5328 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.7947 - Iou Flat-sidewalk: 0.8656 - Iou Flat-crosswalk: 0.4529 - Iou Flat-cyclinglane: 0.6876 - Iou Flat-parkingdriveway: 0.4461 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.5989 - Iou Human-person: 0.6127 - Iou Human-rider: 0.2346 - Iou Vehicle-car: 0.8877 - Iou Vehicle-truck: 0.0662 - Iou Vehicle-bus: 0.0044 - Iou Vehicle-tramtrain: 0.1985 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.5765 - Iou Vehicle-caravan: 0.1495 - Iou Vehicle-cartrailer: 0.0106 - Iou Construction-building: 0.8060 - Iou Construction-door: 0.2190 - Iou Construction-wall: 0.5015 - Iou Construction-fenceguardrail: 0.4923 - Iou Construction-bridge: 0.3467 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.3908 - Iou Object-pole: 0.4693 - Iou Object-trafficsign: 0.3698 - Iou Object-trafficlight: 0.2052 - Iou Nature-vegetation: 0.8832 - Iou Nature-terrain: 0.7906 - Iou Sky: 0.9519 - Iou Void-ground: 0.0038 - Iou Void-dynamic: 0.1774 - Iou Void-static: 0.3885 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | 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| 0.7275 | 2.5 | 500 | 0.5765 | 0.3050 | 0.3654 | 0.8441 | nan | 0.9561 | 0.9153 | 0.3719 | 0.7164 | 0.4360 | 0.0 | 0.3475 | 0.8270 | 0.0 | 0.9318 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6843 | 0.0 | 0.0 | 0.9160 | 0.0667 | 0.3893 | 0.6512 | 0.0 | nan | 0.0 | 0.5447 | 0.0525 | 0.0 | 0.9581 | 0.8185 | 0.9737 | 0.0 | 0.0262 | 0.4752 | 0.0 | nan | 0.7208 | 0.8407 | 0.3582 | 0.6393 | 0.3693 | 0.0 | 0.2705 | 0.5291 | 0.0 | 0.8548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5158 | 0.0 | 0.0 | 0.7684 | 0.0638 | 0.3606 | 0.4620 | 0.0 | nan | 0.0 | 0.3805 | 0.0522 | 0.0 | 0.8572 | 0.7657 | 0.9225 | 0.0 | 0.0256 | 0.3078 | 0.0 | | 0.3654 | 5.0 | 1000 | 0.5265 | 0.3531 | 0.4276 | 0.8622 | nan | 0.9116 | 0.9476 | 0.4986 | 0.8194 | 0.4632 | 0.0 | 0.5613 | 0.8672 | 0.1407 | 0.9399 | 0.2129 | 0.0 | 0.0 | 0.0 | 0.7640 | 0.0 | 0.0 | 0.8915 | 0.1468 | 0.5813 | 0.5719 | 0.0 | nan | 0.3336 | 0.5583 | 0.4068 | 0.0 | 0.9470 | 0.8512 | 0.9780 | 0.0004 | 0.1935 | 0.5228 | 0.0 | nan | 0.7904 | 0.8491 | 0.4455 | 0.6935 | 0.4013 | 0.0 | 0.4607 | 0.5435 | 0.1227 | 0.8663 | 0.1019 | 0.0 | 0.0 | 0.0 | 0.5464 | 0.0 | 0.0 | 0.7740 | 0.1198 | 0.4746 | 0.4587 | 0.0 | nan | 0.2403 | 0.4036 | 0.2949 | 0.0 | 0.8663 | 0.7614 | 0.9334 | 0.0003 | 0.1492 | 0.3544 | 0.0 | | 0.2359 | 7.5 | 1500 | 0.5790 | 0.3584 | 0.4296 | 0.8649 | nan | 0.8646 | 0.9501 | 0.4466 | 0.8506 | 0.5513 | 0.0 | 0.7099 | 0.8317 | 0.2099 | 0.9442 | 0.2546 | 0.0 | 0.0 | 0.0 | 0.7862 | 0.0087 | 0.0 | 0.9079 | 0.1046 | 0.6479 | 0.5239 | 0.0 | nan | 0.1543 | 0.5674 | 0.3864 | 0.0 | 0.9461 | 0.8753 | 0.9776 | 0.0000 | 0.1974 | 0.4793 | 0.0 | nan | 0.7917 | 0.8414 | 0.4214 | 0.7012 | 0.4494 | 0.0 | 0.5333 | 0.5888 | 0.1897 | 0.8663 | 0.1009 | 0.0 | 0.0 | 0.0 | 0.5427 | 0.0042 | 0.0 | 0.7817 | 0.0945 | 0.4696 | 0.4312 | 0.0 | nan | 0.1477 | 0.4314 | 0.3152 | 0.0 | 0.8749 | 0.7719 | 0.9409 | 0.0000 | 0.1780 | 0.3593 | 0.0 | | 0.1708 | 10.0 | 2000 | 0.6066 | 0.3684 | 0.4479 | 0.8666 | nan | 0.8819 | 0.9466 | 0.5609 | 0.8324 | 0.4835 | 0.0 | 0.7200 | 0.8575 | 0.1404 | 0.9422 | 0.2656 | 0.0 | 0.0590 | 0.0 | 0.7505 | 0.2619 | 0.0 | 0.8906 | 0.2203 | 0.6425 | 0.5323 | 0.0 | nan | 0.3455 | 0.5923 | 0.4085 | 0.0 | 0.9552 | 0.8844 | 0.9791 | 0.0024 | 0.0951 | 0.5293 | 0.0 | nan | 0.7930 | 0.8537 | 0.4562 | 0.6519 | 0.4311 | 0.0 | 0.5478 | 0.5960 | 0.1301 | 0.8728 | 0.1051 | 0.0 | 0.0590 | 0.0 | 0.5390 | 0.0916 | 0.0 | 0.7864 | 0.1779 | 0.4949 | 0.4467 | 0.0 | nan | 0.3107 | 0.4336 | 0.3289 | 0.0 | 0.8757 | 0.7694 | 0.9416 | 0.0019 | 0.0893 | 0.3741 | 0.0 | | 0.1326 | 12.5 | 2500 | 0.5934 | 0.3969 | 0.4877 | 0.8753 | nan | 0.9227 | 0.9490 | 0.4762 | 0.8499 | 0.5255 | 0.0 | 0.6941 | 0.8115 | 0.3960 | 0.9430 | 0.3828 | 0.0266 | 0.0998 | 0.0 | 0.7963 | 0.6565 | 0.0003 | 0.8988 | 0.2880 | 0.6352 | 0.5442 | 0.2746 | nan | 0.3048 | 0.6133 | 0.4269 | 0.0 | 0.9483 | 0.9064 | 0.9838 | 0.0025 | 0.2297 | 0.5070 | 0.0 | nan | 0.8032 | 0.8631 | 0.4521 | 0.7678 | 0.4440 | 0.0 | 0.5593 | 0.6096 | 0.2994 | 0.8777 | 0.1308 | 0.0266 | 0.0998 | 0.0 | 0.5663 | 0.2016 | 0.0003 | 0.7931 | 0.1957 | 0.4838 | 0.4475 | 0.2094 | nan | 0.2849 | 0.4530 | 0.3424 | 0.0 | 0.8798 | 0.7777 | 0.9441 | 0.0023 | 0.1979 | 0.3857 | 0.0 | | 0.1116 | 15.0 | 3000 | 0.6267 | 0.3978 | 0.4820 | 0.8734 | nan | 0.9155 | 0.9431 | 0.5145 | 0.8423 | 0.4973 | 0.0 | 0.7284 | 0.8513 | 0.3146 | 0.9492 | 0.0753 | 0.0 | 0.2565 | 0.0 | 0.7845 | 0.3853 | 0.0220 | 0.8998 | 0.2497 | 0.6306 | 0.5571 | 0.3186 | nan | 0.4912 | 0.5902 | 0.4795 | 0.0 | 0.9501 | 0.9004 | 0.9843 | 0.0038 | 0.2017 | 0.5677 | 0.0 | nan | 0.8037 | 0.8591 | 0.4665 | 0.7147 | 0.4311 | 0.0 | 0.5698 | 0.5996 | 0.2693 | 0.8800 | 0.0366 | 0.0 | 0.2554 | 0.0 | 0.5526 | 0.1200 | 0.0216 | 0.7935 | 0.1949 | 0.4853 | 0.4704 | 0.2116 | nan | 0.3980 | 0.4450 | 0.3691 | 0.0 | 0.8815 | 0.7857 | 0.9439 | 0.0031 | 0.1751 | 0.3899 | 0.0 | | 0.098 | 17.5 | 3500 | 0.6334 | 0.3922 | 0.5006 | 0.8729 | nan | 0.8961 | 0.9419 | 0.5747 | 0.8862 | 0.4977 | 0.0 | 0.7428 | 0.8491 | 0.3477 | 0.9464 | 0.0952 | 0.0 | 0.2937 | 0.0 | 0.7908 | 0.7738 | 0.0 | 0.8934 | 0.2479 | 0.6445 | 0.6108 | 0.4273 | nan | 0.4435 | 0.6190 | 0.4308 | 0.0015 | 0.9486 | 0.9026 | 0.9818 | 0.0099 | 0.2216 | 0.4994 | 0.0 | 0.0 | 0.7961 | 0.8651 | 0.5005 | 0.6765 | 0.4413 | 0.0 | 0.5751 | 0.6176 | 0.2944 | 0.8811 | 0.0373 | 0.0 | 0.2919 | 0.0 | 0.5578 | 0.2307 | 0.0 | 0.7961 | 0.1835 | 0.4901 | 0.4814 | 0.2506 | nan | 0.3771 | 0.4560 | 0.3562 | 0.0015 | 0.8810 | 0.7806 | 0.9474 | 0.0077 | 0.1823 | 0.3784 | 0.0 | | 0.0894 | 20.0 | 4000 | 0.6973 | 0.3988 | 0.4923 | 0.8722 | nan | 0.8952 | 0.9456 | 0.5309 | 0.8357 | 0.4777 | 0.0 | 0.7630 | 0.8291 | 0.2785 | 0.9467 | 0.1712 | 0.0047 | 0.1377 | 0.0 | 0.7854 | 0.8237 | 0.0 | 0.9282 | 0.1899 | 0.5904 | 0.6020 | 0.4761 | nan | 0.3323 | 0.6192 | 0.4047 | 0.1381 | 0.9522 | 0.8851 | 0.9767 | 0.0050 | 0.1916 | 0.5277 | 0.0 | nan | 0.8062 | 0.8543 | 0.4692 | 0.6675 | 0.4204 | 0.0 | 0.5710 | 0.6083 | 0.2366 | 0.8855 | 0.0677 | 0.0046 | 0.1374 | 0.0 | 0.5492 | 0.2453 | 0.0 | 0.7996 | 0.1571 | 0.4887 | 0.4847 | 0.2656 | nan | 0.3148 | 0.4670 | 0.3491 | 0.1264 | 0.8815 | 0.7829 | 0.9496 | 0.0043 | 0.1718 | 0.3936 | 0.0 | | 0.0819 | 22.5 | 4500 | 0.6867 | 0.4098 | 0.5001 | 0.8778 | nan | 0.9344 | 0.9410 | 0.5690 | 0.8783 | 0.4856 | 0.0 | 0.7065 | 0.8495 | 0.2085 | 0.9415 | 0.1530 | 0.0018 | 0.2354 | 0.0 | 0.7829 | 0.7796 | 0.0 | 0.9044 | 0.2261 | 0.6171 | 0.6045 | 0.4780 | nan | 0.4156 | 0.6265 | 0.4288 | 0.1457 | 0.9563 | 0.8877 | 0.9804 | 0.0064 | 0.2136 | 0.5447 | 0.0 | nan | 0.8016 | 0.8702 | 0.4902 | 0.7597 | 0.4279 | 0.0 | 0.5780 | 0.6123 | 0.1998 | 0.8889 | 0.0577 | 0.0018 | 0.2348 | 0.0 | 0.5898 | 0.2436 | 0.0 | 0.7992 | 0.1842 | 0.4829 | 0.4918 | 0.2855 | nan | 0.3732 | 0.4658 | 0.3650 | 0.1297 | 0.8823 | 0.7837 | 0.9500 | 0.0053 | 0.1841 | 0.3839 | 0.0 | | 0.0767 | 25.0 | 5000 | 0.7377 | 0.4096 | 0.5109 | 0.8720 | nan | 0.8599 | 0.9464 | 0.5724 | 0.9354 | 0.4838 | 0.0 | 0.7392 | 0.8475 | 0.2679 | 0.9530 | 0.2438 | 0.0 | 0.2405 | 0.0 | 0.7879 | 0.8364 | 0.0 | 0.9155 | 0.2107 | 0.5924 | 0.5901 | 0.5525 | nan | 0.3980 | 0.6229 | 0.4648 | 0.2165 | 0.9550 | 0.8865 | 0.9823 | 0.0047 | 0.1970 | 0.5557 | 0.0 | nan | 0.7881 | 0.8643 | 0.5042 | 0.6317 | 0.4280 | 0.0 | 0.5817 | 0.6075 | 0.2397 | 0.8857 | 0.1052 | 0.0 | 0.2384 | 0.0 | 0.5664 | 0.2501 | 0.0 | 0.8056 | 0.1813 | 0.4878 | 0.4863 | 0.2871 | nan | 0.3652 | 0.4725 | 0.3883 | 0.1660 | 0.8804 | 0.7905 | 0.9503 | 0.0040 | 0.1704 | 0.3891 | 0.0 | | 0.0725 | 27.5 | 5500 | 0.7085 | 0.3977 | 0.5056 | 0.8782 | nan | 0.9177 | 0.9482 | 0.4916 | 0.8966 | 0.4989 | 0.0 | 0.7119 | 0.8469 | 0.2483 | 0.9512 | 0.2387 | 0.0440 | 0.1287 | 0.0 | 0.7947 | 0.8184 | 0.0 | 0.9152 | 0.2257 | 0.6472 | 0.5963 | 0.5426 | nan | 0.3951 | 0.6422 | 0.4369 | 0.2195 | 0.9499 | 0.8824 | 0.9821 | 0.0036 | 0.1824 | 0.5266 | 0.0 | 0.0 | 0.8109 | 0.8638 | 0.4498 | 0.7314 | 0.4437 | 0.0 | 0.5797 | 0.6047 | 0.2215 | 0.8861 | 0.0855 | 0.0430 | 0.1284 | 0.0 | 0.5657 | 0.2395 | 0.0 | 0.8058 | 0.1939 | 0.5113 | 0.4913 | 0.2943 | nan | 0.3732 | 0.4773 | 0.3770 | 0.1643 | 0.8836 | 0.7864 | 0.9509 | 0.0029 | 0.1639 | 0.3905 | 0.0 | | 0.0685 | 30.0 | 6000 | 0.7388 | 0.4115 | 0.5051 | 0.8738 | nan | 0.9135 | 0.9420 | 0.5290 | 0.8405 | 0.4909 | 0.0 | 0.7408 | 0.8566 | 0.3161 | 0.9461 | 0.1138 | 0.0003 | 0.1616 | 0.0 | 0.8061 | 0.7486 | 0.0001 | 0.9074 | 0.2986 | 0.6418 | 0.5669 | 0.4769 | nan | 0.4607 | 0.6454 | 0.4717 | 0.2320 | 0.9531 | 0.8849 | 0.9802 | 0.0037 | 0.1983 | 0.5417 | 0.0 | nan | 0.7911 | 0.8647 | 0.4671 | 0.6651 | 0.4361 | 0.0 | 0.5848 | 0.6127 | 0.2642 | 0.8885 | 0.0453 | 0.0003 | 0.1613 | 0.0 | 0.5455 | 0.2421 | 0.0001 | 0.8022 | 0.2382 | 0.4975 | 0.4741 | 0.3279 | nan | 0.4050 | 0.4789 | 0.3937 | 0.1921 | 0.8825 | 0.7873 | 0.9516 | 0.0032 | 0.1809 | 0.3970 | 0.0 | | 0.0654 | 32.5 | 6500 | 0.7246 | 0.4128 | 0.5034 | 0.8789 | nan | 0.9247 | 0.9424 | 0.5865 | 0.8579 | 0.5105 | 0.0 | 0.7409 | 0.8799 | 0.2449 | 0.9462 | 0.0922 | 0.0 | 0.1728 | 0.0 | 0.7762 | 0.7085 | 0.0 | 0.9151 | 0.2459 | 0.6278 | 0.6088 | 0.5426 | nan | 0.4260 | 0.6444 | 0.4471 | 0.2230 | 0.9530 | 0.8839 | 0.9833 | 0.0040 | 0.1978 | 0.5251 | 0.0 | nan | 0.8010 | 0.8705 | 0.5132 | 0.7193 | 0.4466 | 0.0 | 0.5906 | 0.5971 | 0.2204 | 0.8884 | 0.0419 | 0.0 | 0.1724 | 0.0 | 0.5623 | 0.2184 | 0.0 | 0.8044 | 0.2015 | 0.5037 | 0.4964 | 0.3206 | nan | 0.4032 | 0.4828 | 0.3859 | 0.1802 | 0.8828 | 0.7909 | 0.9505 | 0.0033 | 0.1792 | 0.3959 | 0.0 | | 0.0629 | 35.0 | 7000 | 0.7655 | 0.4168 | 0.5105 | 0.8741 | nan | 0.8961 | 0.9470 | 0.5214 | 0.8906 | 0.4982 | 0.0 | 0.7542 | 0.8631 | 0.2754 | 0.9512 | 0.1882 | 0.0015 | 0.3457 | 0.0 | 0.7778 | 0.6418 | 0.0144 | 0.8908 | 0.2816 | 0.6612 | 0.5910 | 0.5330 | nan | 0.4434 | 0.6305 | 0.4273 | 0.2421 | 0.9516 | 0.8805 | 0.9821 | 0.0036 | 0.2172 | 0.5444 | 0.0 | nan | 0.7981 | 0.8672 | 0.4665 | 0.6765 | 0.4364 | 0.0 | 0.5934 | 0.6114 | 0.2489 | 0.8877 | 0.0831 | 0.0013 | 0.3436 | 0.0 | 0.5668 | 0.2017 | 0.0140 | 0.7928 | 0.2283 | 0.4708 | 0.4904 | 0.3458 | nan | 0.4011 | 0.4722 | 0.3699 | 0.1843 | 0.8836 | 0.7898 | 0.9516 | 0.0030 | 0.1851 | 0.3897 | 0.0 | | 0.0607 | 37.5 | 7500 | 0.7668 | 0.4180 | 0.5139 | 0.8751 | nan | 0.8948 | 0.9480 | 0.5612 | 0.8579 | 0.4903 | 0.0 | 0.7432 | 0.8676 | 0.2619 | 0.9495 | 0.1718 | 0.0165 | 0.3359 | 0.0010 | 0.7738 | 0.7077 | 0.0304 | 0.9104 | 0.2826 | 0.6353 | 0.6045 | 0.5609 | nan | 0.4406 | 0.6293 | 0.4355 | 0.2376 | 0.9511 | 0.8940 | 0.9818 | 0.0033 | 0.2277 | 0.5530 | 0.0 | nan | 0.7933 | 0.8676 | 0.4914 | 0.6562 | 0.4327 | 0.0 | 0.5956 | 0.6059 | 0.2348 | 0.8875 | 0.0739 | 0.0161 | 0.3343 | 0.0010 | 0.5696 | 0.2086 | 0.0295 | 0.8084 | 0.2268 | 0.5014 | 0.4962 | 0.3297 | nan | 0.3948 | 0.4702 | 0.3754 | 0.1918 | 0.8836 | 0.7857 | 0.9519 | 0.0029 | 0.1886 | 0.3900 | 0.0 | | 0.0582 | 40.0 | 8000 | 0.7562 | 0.4049 | 0.5074 | 0.8780 | nan | 0.9204 | 0.9463 | 0.5052 | 0.8643 | 0.5082 | 0.0 | 0.7360 | 0.8650 | 0.2462 | 0.9501 | 0.1893 | 0.0024 | 0.2403 | 0.0 | 0.7814 | 0.6631 | 0.0242 | 0.9169 | 0.2821 | 0.6356 | 0.5984 | 0.5609 | nan | 0.4218 | 0.6299 | 0.4414 | 0.2421 | 0.9504 | 0.8835 | 0.9796 | 0.0043 | 0.2138 | 0.5395 | 0.0 | 0.0 | 0.8004 | 0.8683 | 0.4553 | 0.7073 | 0.4478 | 0.0 | 0.6007 | 0.6132 | 0.2291 | 0.8882 | 0.0839 | 0.0024 | 0.2391 | 0.0 | 0.5777 | 0.2020 | 0.0237 | 0.8060 | 0.2276 | 0.5058 | 0.4990 | 0.3415 | nan | 0.3905 | 0.4699 | 0.3815 | 0.1952 | 0.8837 | 0.7908 | 0.9522 | 0.0036 | 0.1849 | 0.3941 | 0.0 | | 0.0565 | 42.5 | 8500 | 0.7834 | 0.4004 | 0.5024 | 0.8762 | nan | 0.8960 | 0.9474 | 0.5430 | 0.8894 | 0.4937 | 0.0 | 0.7492 | 0.8696 | 0.2727 | 0.9482 | 0.1505 | 0.0006 | 0.1868 | 0.0 | 0.7945 | 0.5042 | 0.0051 | 0.9155 | 0.2834 | 0.6369 | 0.5958 | 0.5811 | nan | 0.4136 | 0.6419 | 0.4457 | 0.2481 | 0.9510 | 0.8887 | 0.9822 | 0.0041 | 0.2054 | 0.5358 | 0.0 | 0.0 | 0.7955 | 0.8676 | 0.4613 | 0.6877 | 0.4390 | 0.0 | 0.6023 | 0.6092 | 0.2503 | 0.8879 | 0.0626 | 0.0006 | 0.1860 | 0.0 | 0.5802 | 0.1628 | 0.0051 | 0.8061 | 0.2307 | 0.5003 | 0.4961 | 0.3290 | nan | 0.3820 | 0.4752 | 0.3819 | 0.2070 | 0.8838 | 0.7914 | 0.9520 | 0.0034 | 0.1813 | 0.3944 | 0.0 | | 0.0562 | 45.0 | 9000 | 0.7812 | 0.4015 | 0.5008 | 0.8772 | nan | 0.9064 | 0.9466 | 0.5058 | 0.8872 | 0.5059 | 0.0 | 0.7482 | 0.8642 | 0.2957 | 0.9489 | 0.1494 | 0.0059 | 0.1674 | 0.0003 | 0.8079 | 0.4484 | 0.0122 | 0.9134 | 0.2785 | 0.6303 | 0.6007 | 0.5723 | nan | 0.4337 | 0.6286 | 0.4291 | 0.2541 | 0.9521 | 0.8902 | 0.9821 | 0.0054 | 0.2097 | 0.5444 | 0.0 | 0.0 | 0.7954 | 0.8685 | 0.4597 | 0.7046 | 0.4471 | 0.0 | 0.6024 | 0.6174 | 0.2611 | 0.8881 | 0.0630 | 0.0057 | 0.1667 | 0.0003 | 0.5844 | 0.1441 | 0.0120 | 0.8059 | 0.2279 | 0.5021 | 0.4963 | 0.3439 | nan | 0.3981 | 0.4694 | 0.3714 | 0.2134 | 0.8841 | 0.7883 | 0.9522 | 0.0044 | 0.1836 | 0.3913 | 0.0 | | 0.0547 | 47.5 | 9500 | 0.7899 | 0.3997 | 0.4971 | 0.8759 | nan | 0.9053 | 0.9472 | 0.4999 | 0.8752 | 0.5002 | 0.0 | 0.7334 | 0.8557 | 0.2947 | 0.9505 | 0.1326 | 0.0 | 0.1843 | 0.0 | 0.8065 | 0.3995 | 0.0184 | 0.9146 | 0.2650 | 0.6301 | 0.6056 | 0.5749 | nan | 0.4294 | 0.6299 | 0.4450 | 0.2461 | 0.9515 | 0.8854 | 0.9825 | 0.0044 | 0.2045 | 0.5311 | 0.0000 | 0.0 | 0.7939 | 0.8655 | 0.4530 | 0.6865 | 0.4427 | 0.0 | 0.5983 | 0.6206 | 0.2592 | 0.8881 | 0.0597 | 0.0 | 0.1837 | 0.0 | 0.5769 | 0.1272 | 0.0183 | 0.8055 | 0.2174 | 0.5004 | 0.4960 | 0.3457 | nan | 0.3926 | 0.4724 | 0.3800 | 0.2072 | 0.8841 | 0.7912 | 0.9522 | 0.0037 | 0.1789 | 0.3895 | 0.0000 | | 0.0543 | 50.0 | 10000 | 0.7826 | 0.3995 | 0.4977 | 0.8759 | nan | 0.9069 | 0.9471 | 0.5043 | 0.8684 | 0.5057 | 0.0 | 0.7351 | 0.8662 | 0.2599 | 0.9494 | 0.1607 | 0.0044 | 0.1992 | 0.0 | 0.7913 | 0.4628 | 0.0106 | 0.9117 | 0.2679 | 0.6351 | 0.5893 | 0.5639 | nan | 0.4246 | 0.6323 | 0.4266 | 0.2431 | 0.9540 | 0.8819 | 0.9827 | 0.0045 | 0.2006 | 0.5328 | 0.0 | 0.0 | 0.7947 | 0.8656 | 0.4529 | 0.6876 | 0.4461 | 0.0 | 0.5989 | 0.6127 | 0.2346 | 0.8877 | 0.0662 | 0.0044 | 0.1985 | 0.0 | 0.5765 | 0.1495 | 0.0106 | 0.8060 | 0.2190 | 0.5015 | 0.4923 | 0.3467 | nan | 0.3908 | 0.4693 | 0.3698 | 0.2052 | 0.8832 | 0.7906 | 0.9519 | 0.0038 | 0.1774 | 0.3885 | 0.0 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

SatwikKambham/segformer-b0-finetuned-suim

# segformer-b0-finetuned-suim This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the SatwikKambham/suim dataset. It achieves the following results on the evaluation set: - Loss: 0.5044 - Mean Iou: 0.6138 - Mean Accuracy: 0.7713 - Overall Accuracy: 0.8213 - Accuracy Background (waterbody): nan - Accuracy Human divers: 0.9139 - Accuracy Aquatic plants and sea-grass: 0.2842 - Accuracy Wrecks and ruins: 0.8156 - Accuracy Robots (auvs/rovs/instruments): 0.8117 - Accuracy Reefs and invertebrates: 0.9098 - Accuracy Fish and vertebrates: 0.8540 - Accuracy Sea-floor and rocks: 0.8096 - Iou Background (waterbody): 0.0 - Iou Human divers: 0.8428 - Iou Aquatic plants and sea-grass: 0.2638 - Iou Wrecks and ruins: 0.7560 - Iou Robots (auvs/rovs/instruments): 0.7896 - Iou Reefs and invertebrates: 0.7482 - Iou Fish and vertebrates: 0.7927 - Iou Sea-floor and rocks: 0.7176 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background (waterbody) | Accuracy Human divers | Accuracy Aquatic plants and sea-grass | Accuracy Wrecks and ruins | Accuracy Robots (auvs/rovs/instruments) | Accuracy Reefs and invertebrates | Accuracy Fish and vertebrates | Accuracy Sea-floor and rocks | Iou Background (waterbody) | Iou Human divers | Iou Aquatic plants and sea-grass | Iou Wrecks and ruins | Iou Robots (auvs/rovs/instruments) | Iou Reefs and invertebrates | Iou Fish and vertebrates | Iou Sea-floor and rocks | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------:|:---------------------:|:-------------------------------------:|:-------------------------:|:---------------------------------------:|:--------------------------------:|:-----------------------------:|:----------------------------:|:--------------------------:|:----------------:|:--------------------------------:|:--------------------:|:----------------------------------:|:---------------------------:|:------------------------:|:-----------------------:| | 1.1911 | 0.54 | 100 | 0.9270 | 0.3052 | 0.4434 | 0.6401 | nan | 0.2785 | 0.0 | 0.6857 | 0.0 | 0.7643 | 0.7044 | 0.6709 | 0.0 | 0.2555 | 0.0 | 0.4605 | 0.0 | 0.6111 | 0.5394 | 0.5753 | | 0.6852 | 1.08 | 200 | 0.7299 | 0.3457 | 0.4967 | 0.7065 | nan | 0.3429 | 0.0 | 0.8033 | 0.0 | 0.7829 | 0.7653 | 0.7826 | 0.0 | 0.3126 | 0.0 | 0.5458 | 0.0 | 0.6336 | 0.5961 | 0.6776 | | 0.854 | 1.61 | 300 | 0.7453 | 0.3420 | 0.4937 | 0.6506 | nan | 0.5488 | 0.0 | 0.7778 | 0.0 | 0.6528 | 0.7659 | 0.7103 | 0.0 | 0.4867 | 0.0 | 0.5156 | 0.0 | 0.5619 | 0.6180 | 0.5536 | | 0.5111 | 2.15 | 400 | 0.6174 | 0.3920 | 0.5455 | 0.7207 | nan | 0.7475 | 0.0 | 0.6988 | 0.0 | 0.8364 | 0.7907 | 0.7449 | 0.0 | 0.5704 | 0.0 | 0.5986 | 0.0 | 0.6422 | 0.6796 | 0.6451 | | 0.5311 | 2.69 | 500 | 0.5811 | 0.4084 | 0.5606 | 0.7368 | nan | 0.7697 | 0.0 | 0.7729 | 0.0 | 0.8658 | 0.7824 | 0.7333 | 0.0 | 0.6522 | 0.0 | 0.6000 | 0.0 | 0.6551 | 0.7045 | 0.6555 | | 0.5595 | 3.23 | 600 | 0.5591 | 0.4165 | 0.5686 | 0.7420 | nan | 0.8291 | 0.0 | 0.7557 | 0.0 | 0.8848 | 0.7805 | 0.7299 | 0.0 | 0.6524 | 0.0 | 0.6536 | 0.0 | 0.6760 | 0.7011 | 0.6489 | | 0.6847 | 3.76 | 700 | 0.5657 | 0.4068 | 0.5740 | 0.7336 | nan | 0.8233 | 0.0 | 0.8490 | 0.0 | 0.8649 | 0.8157 | 0.6648 | 0.0 | 0.6575 | 0.0 | 0.5982 | 0.0 | 0.6532 | 0.7179 | 0.6277 | | 0.3272 | 4.3 | 800 | 0.5065 | 0.4309 | 0.5838 | 0.7573 | nan | 0.7906 | 0.0 | 0.8117 | 0.0467 | 0.8729 | 0.8021 | 0.7629 | 0.0 | 0.6564 | 0.0 | 0.6608 | 0.0467 | 0.6666 | 0.7242 | 0.6925 | | 0.5065 | 4.84 | 900 | 0.4745 | 0.4420 | 0.5978 | 0.7675 | nan | 0.7969 | 0.0000 | 0.8235 | 0.0879 | 0.8301 | 0.8187 | 0.8273 | 0.0 | 0.6668 | 0.0000 | 0.6834 | 0.0879 | 0.6847 | 0.7247 | 0.6882 | | 0.3712 | 5.38 | 1000 | 0.4567 | 0.4691 | 0.6296 | 0.7824 | nan | 0.8468 | 0.0004 | 0.7933 | 0.2299 | 0.9005 | 0.8431 | 0.7929 | 0.0 | 0.6829 | 0.0004 | 0.7036 | 0.2298 | 0.6852 | 0.7381 | 0.7125 | | 0.6866 | 5.91 | 1100 | 0.4453 | 0.5352 | 0.6932 | 0.7843 | nan | 0.8487 | 0.0236 | 0.8462 | 0.6450 | 0.8373 | 0.8198 | 0.8317 | 0.0 | 0.7810 | 0.0227 | 0.7122 | 0.6437 | 0.6768 | 0.7454 | 0.7001 | | 0.4374 | 6.45 | 1200 | 0.4806 | 0.5279 | 0.6836 | 0.7705 | nan | 0.8392 | 0.1452 | 0.7508 | 0.5700 | 0.8615 | 0.8452 | 0.7732 | 0.0 | 0.7349 | 0.1403 | 0.6883 | 0.5638 | 0.6990 | 0.7439 | 0.6532 | | 0.5409 | 6.99 | 1300 | 0.4671 | 0.5403 | 0.6987 | 0.7768 | nan | 0.8636 | 0.0181 | 0.7956 | 0.7149 | 0.8739 | 0.8502 | 0.7746 | 0.0 | 0.7666 | 0.0179 | 0.7256 | 0.7072 | 0.6748 | 0.7473 | 0.6834 | | 0.3526 | 7.53 | 1400 | 0.4691 | 0.5517 | 0.7168 | 0.7811 | nan | 0.8670 | 0.2005 | 0.8345 | 0.6704 | 0.8837 | 0.8260 | 0.7353 | 0.0 | 0.7791 | 0.1891 | 0.6723 | 0.6687 | 0.6845 | 0.7447 | 0.6752 | | 0.2883 | 8.06 | 1500 | 0.4418 | 0.5744 | 0.7341 | 0.8013 | nan | 0.8526 | 0.2870 | 0.7820 | 0.6996 | 0.8971 | 0.8211 | 0.7993 | 0.0 | 0.7654 | 0.2764 | 0.7106 | 0.6901 | 0.6891 | 0.7557 | 0.7081 | | 0.645 | 8.6 | 1600 | 0.4597 | 0.5730 | 0.7375 | 0.7945 | nan | 0.8874 | 0.1758 | 0.8621 | 0.7514 | 0.8888 | 0.8442 | 0.7530 | 0.0 | 0.8018 | 0.1683 | 0.7251 | 0.7413 | 0.7048 | 0.7556 | 0.6869 | | 0.2953 | 9.14 | 1700 | 0.4279 | 0.5967 | 0.7596 | 0.8068 | nan | 0.8210 | 0.4181 | 0.8465 | 0.7328 | 0.8293 | 0.8410 | 0.8285 | 0.0 | 0.7607 | 0.3735 | 0.7601 | 0.7259 | 0.7098 | 0.7495 | 0.6944 | | 0.3441 | 9.68 | 1800 | 0.4727 | 0.5662 | 0.7387 | 0.7701 | nan | 0.8660 | 0.2776 | 0.8603 | 0.7675 | 0.6688 | 0.8444 | 0.8867 | 0.0 | 0.7965 | 0.2577 | 0.7043 | 0.7602 | 0.6180 | 0.7374 | 0.6556 | | 0.4354 | 10.22 | 1900 | 0.4499 | 0.5868 | 0.7466 | 0.8000 | nan | 0.7931 | 0.3488 | 0.8160 | 0.7521 | 0.8689 | 0.8599 | 0.7876 | 0.0 | 0.7399 | 0.3263 | 0.7469 | 0.7342 | 0.7090 | 0.7542 | 0.6836 | | 0.3476 | 10.75 | 2000 | 0.4732 | 0.5468 | 0.7053 | 0.7802 | nan | 0.8976 | 0.0416 | 0.8285 | 0.6882 | 0.8939 | 0.8447 | 0.7426 | 0.0 | 0.8055 | 0.0402 | 0.7180 | 0.6837 | 0.6855 | 0.7632 | 0.6781 | | 0.3416 | 11.29 | 2100 | 0.4553 | 0.5745 | 0.7342 | 0.7949 | nan | 0.8803 | 0.1754 | 0.7792 | 0.7736 | 0.9134 | 0.8551 | 0.7625 | 0.0 | 0.8009 | 0.1632 | 0.7275 | 0.7484 | 0.6939 | 0.7627 | 0.6996 | | 0.157 | 11.83 | 2200 | 0.4684 | 0.5814 | 0.7432 | 0.7927 | nan | 0.8915 | 0.2169 | 0.8390 | 0.7693 | 0.8894 | 0.8532 | 0.7434 | 0.0 | 0.8147 | 0.2091 | 0.7275 | 0.7518 | 0.7105 | 0.7707 | 0.6666 | | 0.1665 | 12.37 | 2300 | 0.4369 | 0.6031 | 0.7710 | 0.8076 | nan | 0.9014 | 0.3852 | 0.8488 | 0.7526 | 0.8590 | 0.8721 | 0.7780 | 0.0 | 0.8264 | 0.3500 | 0.7249 | 0.7403 | 0.7166 | 0.7678 | 0.6990 | | 0.3426 | 12.9 | 2400 | 0.4458 | 0.5814 | 0.7495 | 0.7925 | nan | 0.8842 | 0.2608 | 0.8393 | 0.7845 | 0.8458 | 0.8491 | 0.7826 | 0.0 | 0.8201 | 0.2433 | 0.6979 | 0.7533 | 0.6987 | 0.7597 | 0.6779 | | 0.2268 | 13.44 | 2500 | 0.4612 | 0.5776 | 0.7355 | 0.7929 | nan | 0.8722 | 0.1872 | 0.7989 | 0.7789 | 0.8675 | 0.8461 | 0.7978 | 0.0 | 0.7964 | 0.1755 | 0.7478 | 0.7551 | 0.7139 | 0.7654 | 0.6665 | | 0.3108 | 13.98 | 2600 | 0.4449 | 0.5926 | 0.7490 | 0.8112 | nan | 0.8756 | 0.2111 | 0.8021 | 0.7897 | 0.8921 | 0.8473 | 0.8256 | 0.0 | 0.8106 | 0.2007 | 0.7520 | 0.7736 | 0.7287 | 0.7737 | 0.7017 | | 0.1832 | 14.52 | 2700 | 0.4271 | 0.6254 | 0.7965 | 0.8277 | nan | 0.8930 | 0.5784 | 0.7930 | 0.7627 | 0.8712 | 0.8446 | 0.8323 | 0.0 | 0.8085 | 0.4667 | 0.7385 | 0.7489 | 0.7402 | 0.7689 | 0.7312 | | 0.1784 | 15.05 | 2800 | 0.4531 | 0.5858 | 0.7448 | 0.7987 | nan | 0.9073 | 0.2227 | 0.7956 | 0.7764 | 0.8722 | 0.8323 | 0.8069 | 0.0 | 0.8232 | 0.2077 | 0.7337 | 0.7556 | 0.7050 | 0.7734 | 0.6880 | | 0.2532 | 15.59 | 2900 | 0.4925 | 0.5712 | 0.7273 | 0.7948 | nan | 0.9059 | 0.0955 | 0.8082 | 0.7507 | 0.9018 | 0.8558 | 0.7736 | 0.0 | 0.8223 | 0.0914 | 0.7457 | 0.7283 | 0.7327 | 0.7813 | 0.6678 | | 0.2804 | 16.13 | 3000 | 0.4406 | 0.6236 | 0.7967 | 0.8169 | nan | 0.9282 | 0.5433 | 0.8292 | 0.7638 | 0.8782 | 0.8735 | 0.7609 | 0.0 | 0.8340 | 0.4822 | 0.7338 | 0.7438 | 0.7286 | 0.7710 | 0.6958 | | 0.2874 | 16.67 | 3100 | 0.4576 | 0.5876 | 0.7474 | 0.8073 | nan | 0.8891 | 0.1620 | 0.8209 | 0.7999 | 0.8955 | 0.8642 | 0.8001 | 0.0 | 0.8199 | 0.1544 | 0.7478 | 0.7678 | 0.7430 | 0.7774 | 0.6904 | | 0.2731 | 17.2 | 3200 | 0.4212 | 0.6263 | 0.7914 | 0.8341 | nan | 0.9025 | 0.5544 | 0.7921 | 0.7143 | 0.9193 | 0.8514 | 0.8057 | 0.0 | 0.8120 | 0.4920 | 0.7327 | 0.6956 | 0.7547 | 0.7783 | 0.7447 | | 0.1974 | 17.74 | 3300 | 0.4423 | 0.6215 | 0.7846 | 0.8138 | nan | 0.8722 | 0.4908 | 0.8068 | 0.8134 | 0.8748 | 0.8356 | 0.7988 | 0.0 | 0.8076 | 0.4451 | 0.7482 | 0.7737 | 0.7350 | 0.7671 | 0.6953 | | 0.1833 | 18.28 | 3400 | 0.4207 | 0.6284 | 0.7944 | 0.8250 | nan | 0.8809 | 0.4986 | 0.8445 | 0.7943 | 0.8829 | 0.8643 | 0.7955 | 0.0 | 0.8206 | 0.4449 | 0.7446 | 0.7776 | 0.7421 | 0.7729 | 0.7244 | | 0.1611 | 18.82 | 3500 | 0.4327 | 0.6085 | 0.7733 | 0.8244 | nan | 0.8563 | 0.4147 | 0.7980 | 0.7706 | 0.9201 | 0.8495 | 0.8041 | 0.0 | 0.7886 | 0.3657 | 0.7429 | 0.7326 | 0.7438 | 0.7712 | 0.7230 | | 0.1339 | 19.35 | 3600 | 0.4795 | 0.5796 | 0.7369 | 0.7978 | nan | 0.8572 | 0.1929 | 0.7776 | 0.7929 | 0.9016 | 0.8480 | 0.7882 | 0.0 | 0.7934 | 0.1840 | 0.7334 | 0.7498 | 0.7296 | 0.7615 | 0.6848 | | 0.1805 | 19.89 | 3700 | 0.4722 | 0.6137 | 0.7739 | 0.8134 | nan | 0.8924 | 0.3871 | 0.8223 | 0.7928 | 0.8949 | 0.8441 | 0.7836 | 0.0 | 0.8249 | 0.3603 | 0.7521 | 0.7557 | 0.7451 | 0.7728 | 0.6990 | | 0.173 | 20.43 | 3800 | 0.4495 | 0.6170 | 0.7843 | 0.8230 | nan | 0.9220 | 0.3516 | 0.8189 | 0.8353 | 0.9090 | 0.8546 | 0.7988 | 0.0 | 0.8318 | 0.3314 | 0.7584 | 0.7729 | 0.7453 | 0.7761 | 0.7202 | | 0.2476 | 20.97 | 3900 | 0.4426 | 0.6363 | 0.8007 | 0.8331 | nan | 0.9142 | 0.4724 | 0.8094 | 0.8448 | 0.9178 | 0.8330 | 0.8136 | 0.0 | 0.8485 | 0.4259 | 0.7481 | 0.8003 | 0.7454 | 0.7840 | 0.7380 | | 0.3163 | 21.51 | 4000 | 0.4550 | 0.6337 | 0.8023 | 0.8273 | nan | 0.8808 | 0.5716 | 0.8209 | 0.8055 | 0.9199 | 0.8518 | 0.7655 | 0.0 | 0.8129 | 0.4923 | 0.7503 | 0.7815 | 0.7402 | 0.7748 | 0.7180 | | 0.12 | 22.04 | 4100 | 0.4396 | 0.6142 | 0.7772 | 0.8249 | nan | 0.9254 | 0.2952 | 0.8118 | 0.8303 | 0.9143 | 0.8460 | 0.8174 | 0.0 | 0.8363 | 0.2724 | 0.7579 | 0.7875 | 0.7491 | 0.7787 | 0.7316 | | 0.2351 | 22.58 | 4200 | 0.4622 | 0.6020 | 0.7612 | 0.8087 | nan | 0.8992 | 0.2520 | 0.8300 | 0.8150 | 0.8647 | 0.8478 | 0.8200 | 0.0 | 0.8366 | 0.2309 | 0.7655 | 0.7912 | 0.7260 | 0.7714 | 0.6945 | | 0.106 | 23.12 | 4300 | 0.4570 | 0.6001 | 0.7624 | 0.8163 | nan | 0.8731 | 0.2566 | 0.7931 | 0.8358 | 0.8961 | 0.8514 | 0.8306 | 0.0 | 0.8110 | 0.2326 | 0.7431 | 0.7773 | 0.7413 | 0.7771 | 0.7182 | | 0.2408 | 23.66 | 4400 | 0.4795 | 0.5944 | 0.7501 | 0.8138 | nan | 0.9040 | 0.1740 | 0.8437 | 0.7707 | 0.8980 | 0.8486 | 0.8115 | 0.0 | 0.8454 | 0.1631 | 0.7700 | 0.7579 | 0.7352 | 0.7742 | 0.7091 | | 0.1285 | 24.19 | 4500 | 0.4311 | 0.6377 | 0.8055 | 0.8339 | nan | 0.9053 | 0.5473 | 0.8025 | 0.8113 | 0.9093 | 0.8567 | 0.8060 | 0.0 | 0.8279 | 0.4738 | 0.7405 | 0.7775 | 0.7588 | 0.7855 | 0.7380 | | 0.2967 | 24.73 | 4600 | 0.4509 | 0.6273 | 0.7913 | 0.8209 | nan | 0.8998 | 0.4427 | 0.8456 | 0.8264 | 0.8666 | 0.8433 | 0.8146 | 0.0 | 0.8437 | 0.4058 | 0.7616 | 0.7873 | 0.7382 | 0.7723 | 0.7098 | | 0.2716 | 25.27 | 4700 | 0.4708 | 0.6149 | 0.7815 | 0.8199 | nan | 0.8998 | 0.3343 | 0.8176 | 0.8528 | 0.9088 | 0.8668 | 0.7906 | 0.0 | 0.8345 | 0.3116 | 0.7546 | 0.7633 | 0.7474 | 0.7928 | 0.7152 | | 0.2685 | 25.81 | 4800 | 0.4688 | 0.6166 | 0.7778 | 0.8135 | nan | 0.9177 | 0.3472 | 0.8437 | 0.8130 | 0.9059 | 0.8572 | 0.7598 | 0.0 | 0.8452 | 0.3205 | 0.7512 | 0.7864 | 0.7475 | 0.7879 | 0.6937 | | 0.1678 | 26.34 | 4900 | 0.4833 | 0.6056 | 0.7701 | 0.8111 | nan | 0.9324 | 0.2775 | 0.8162 | 0.8232 | 0.8679 | 0.8571 | 0.8162 | 0.0 | 0.8521 | 0.2610 | 0.7384 | 0.7874 | 0.7107 | 0.7817 | 0.7138 | | 0.1904 | 26.88 | 5000 | 0.4423 | 0.6367 | 0.8050 | 0.8313 | nan | 0.9204 | 0.5078 | 0.8254 | 0.8175 | 0.8890 | 0.8661 | 0.8091 | 0.0 | 0.8452 | 0.4385 | 0.7538 | 0.7841 | 0.7505 | 0.7884 | 0.7333 | | 0.202 | 27.42 | 5100 | 0.4582 | 0.6143 | 0.7755 | 0.8206 | nan | 0.8995 | 0.2772 | 0.8510 | 0.8392 | 0.8871 | 0.8583 | 0.8159 | 0.0 | 0.8469 | 0.2557 | 0.7571 | 0.8076 | 0.7329 | 0.7852 | 0.7286 | | 0.1242 | 27.96 | 5200 | 0.4692 | 0.6143 | 0.7766 | 0.8192 | nan | 0.8887 | 0.3377 | 0.8350 | 0.8156 | 0.8718 | 0.8649 | 0.8222 | 0.0 | 0.8302 | 0.3055 | 0.7600 | 0.7944 | 0.7255 | 0.7826 | 0.7164 | | 0.1594 | 28.49 | 5300 | 0.4588 | 0.6187 | 0.7860 | 0.8190 | nan | 0.9347 | 0.3829 | 0.8410 | 0.8051 | 0.8479 | 0.8606 | 0.8297 | 0.0 | 0.8549 | 0.3390 | 0.7618 | 0.7801 | 0.7272 | 0.7862 | 0.7004 | | 0.1414 | 29.03 | 5400 | 0.4591 | 0.6293 | 0.7923 | 0.8344 | nan | 0.9064 | 0.5202 | 0.8183 | 0.7327 | 0.9143 | 0.8469 | 0.8073 | 0.0 | 0.8323 | 0.4441 | 0.7449 | 0.7207 | 0.7524 | 0.7906 | 0.7496 | | 0.1208 | 29.57 | 5500 | 0.4611 | 0.6370 | 0.8006 | 0.8280 | nan | 0.9187 | 0.5037 | 0.7983 | 0.8194 | 0.9176 | 0.8641 | 0.7823 | 0.0 | 0.8407 | 0.4436 | 0.7389 | 0.7951 | 0.7609 | 0.7936 | 0.7233 | | 0.227 | 30.11 | 5600 | 0.4488 | 0.6304 | 0.7893 | 0.8314 | nan | 0.8984 | 0.4428 | 0.8197 | 0.7898 | 0.9108 | 0.8502 | 0.8135 | 0.0 | 0.8355 | 0.3967 | 0.7621 | 0.7707 | 0.7496 | 0.7875 | 0.7410 | | 0.2627 | 30.65 | 5700 | 0.4513 | 0.6384 | 0.8022 | 0.8295 | nan | 0.8818 | 0.5132 | 0.8376 | 0.8269 | 0.9009 | 0.8641 | 0.7907 | 0.0 | 0.8304 | 0.4484 | 0.7652 | 0.8068 | 0.7548 | 0.7772 | 0.7245 | | 0.1512 | 31.18 | 5800 | 0.4655 | 0.6151 | 0.7736 | 0.8222 | nan | 0.9195 | 0.3265 | 0.7978 | 0.8000 | 0.9074 | 0.8461 | 0.8181 | 0.0 | 0.8427 | 0.2993 | 0.7452 | 0.7803 | 0.7358 | 0.7824 | 0.7347 | | 0.0861 | 31.72 | 5900 | 0.4754 | 0.6258 | 0.7850 | 0.8212 | nan | 0.9167 | 0.3799 | 0.8171 | 0.8288 | 0.9081 | 0.8549 | 0.7897 | 0.0 | 0.8467 | 0.3493 | 0.7586 | 0.8013 | 0.7459 | 0.7807 | 0.7235 | | 0.1755 | 32.26 | 6000 | 0.5160 | 0.5938 | 0.7507 | 0.8166 | nan | 0.9229 | 0.1614 | 0.8288 | 0.7708 | 0.9159 | 0.8458 | 0.8096 | 0.0 | 0.8410 | 0.1522 | 0.7619 | 0.7540 | 0.7284 | 0.7849 | 0.7280 | | 0.1556 | 32.8 | 6100 | 0.4894 | 0.6129 | 0.7734 | 0.8141 | nan | 0.8895 | 0.3034 | 0.8242 | 0.8482 | 0.8989 | 0.8603 | 0.7893 | 0.0 | 0.8294 | 0.2825 | 0.7532 | 0.8083 | 0.7317 | 0.7820 | 0.7158 | | 0.0927 | 33.33 | 6200 | 0.4981 | 0.6298 | 0.7908 | 0.8216 | nan | 0.9017 | 0.4726 | 0.8020 | 0.8062 | 0.9026 | 0.8678 | 0.7829 | 0.0 | 0.8340 | 0.4196 | 0.7415 | 0.7908 | 0.7476 | 0.7910 | 0.7138 | | 0.1746 | 33.87 | 6300 | 0.4925 | 0.6243 | 0.7844 | 0.8169 | nan | 0.9056 | 0.4036 | 0.8058 | 0.8331 | 0.8925 | 0.8574 | 0.7928 | 0.0 | 0.8421 | 0.3654 | 0.7480 | 0.8046 | 0.7419 | 0.7887 | 0.7037 | | 0.2803 | 34.41 | 6400 | 0.5036 | 0.6192 | 0.7785 | 0.8179 | nan | 0.9037 | 0.4145 | 0.8096 | 0.7774 | 0.9187 | 0.8604 | 0.7655 | 0.0 | 0.8204 | 0.3805 | 0.7519 | 0.7603 | 0.7509 | 0.7855 | 0.7038 | | 0.1777 | 34.95 | 6500 | 0.4886 | 0.6197 | 0.7787 | 0.8209 | nan | 0.9173 | 0.3337 | 0.8366 | 0.8071 | 0.9066 | 0.8615 | 0.7883 | 0.0 | 0.8405 | 0.3112 | 0.7654 | 0.7895 | 0.7486 | 0.7875 | 0.7149 | | 0.1073 | 35.48 | 6600 | 0.4839 | 0.6271 | 0.7868 | 0.8245 | nan | 0.9297 | 0.3696 | 0.8335 | 0.8214 | 0.9102 | 0.8503 | 0.7930 | 0.0 | 0.8519 | 0.3424 | 0.7647 | 0.7957 | 0.7553 | 0.7908 | 0.7156 | | 0.0958 | 36.02 | 6700 | 0.5011 | 0.6186 | 0.7744 | 0.8177 | nan | 0.9017 | 0.3398 | 0.8211 | 0.8092 | 0.8996 | 0.8509 | 0.7981 | 0.0 | 0.8383 | 0.3189 | 0.7575 | 0.7915 | 0.7508 | 0.7882 | 0.7037 | | 0.1107 | 36.56 | 6800 | 0.4854 | 0.6291 | 0.7880 | 0.8229 | nan | 0.9108 | 0.3956 | 0.8209 | 0.8310 | 0.9093 | 0.8608 | 0.7876 | 0.0 | 0.8423 | 0.3649 | 0.7552 | 0.8064 | 0.7546 | 0.7949 | 0.7143 | | 0.1541 | 37.1 | 6900 | 0.4965 | 0.6126 | 0.7684 | 0.8160 | nan | 0.9126 | 0.2968 | 0.7884 | 0.8303 | 0.9209 | 0.8291 | 0.8006 | 0.0 | 0.8398 | 0.2769 | 0.7381 | 0.8048 | 0.7358 | 0.7817 | 0.7240 | | 0.445 | 37.63 | 7000 | 0.5060 | 0.6189 | 0.7769 | 0.8191 | nan | 0.9039 | 0.3292 | 0.8121 | 0.8313 | 0.9127 | 0.8582 | 0.7910 | 0.0 | 0.8355 | 0.3081 | 0.7533 | 0.8065 | 0.7455 | 0.7894 | 0.7132 | | 0.2018 | 38.17 | 7100 | 0.5000 | 0.6144 | 0.7723 | 0.8270 | nan | 0.9077 | 0.2693 | 0.8312 | 0.8125 | 0.9103 | 0.8466 | 0.8282 | 0.0 | 0.8408 | 0.2498 | 0.7595 | 0.7957 | 0.7438 | 0.7901 | 0.7351 | | 0.3123 | 38.71 | 7200 | 0.5074 | 0.6111 | 0.7691 | 0.8184 | nan | 0.9025 | 0.2503 | 0.8243 | 0.8337 | 0.8975 | 0.8623 | 0.8133 | 0.0 | 0.8378 | 0.2337 | 0.7602 | 0.8090 | 0.7412 | 0.7912 | 0.7153 | | 0.1877 | 39.25 | 7300 | 0.5227 | 0.6147 | 0.7722 | 0.8159 | nan | 0.9113 | 0.3006 | 0.8216 | 0.8232 | 0.9182 | 0.8541 | 0.7766 | 0.0 | 0.8393 | 0.2827 | 0.7559 | 0.7979 | 0.7479 | 0.7922 | 0.7018 | | 0.1139 | 39.78 | 7400 | 0.5134 | 0.6146 | 0.7727 | 0.8208 | nan | 0.9234 | 0.2839 | 0.8203 | 0.8118 | 0.9109 | 0.8565 | 0.8021 | 0.0 | 0.8449 | 0.2659 | 0.7558 | 0.7906 | 0.7470 | 0.7941 | 0.7181 | | 0.2875 | 40.32 | 7500 | 0.4953 | 0.6309 | 0.7919 | 0.8246 | nan | 0.9165 | 0.4221 | 0.8274 | 0.8228 | 0.9026 | 0.8602 | 0.7917 | 0.0 | 0.8437 | 0.3822 | 0.7642 | 0.7975 | 0.7566 | 0.7930 | 0.7096 | | 0.1543 | 40.86 | 7600 | 0.5131 | 0.6227 | 0.7814 | 0.8225 | nan | 0.9159 | 0.3537 | 0.8144 | 0.8157 | 0.9106 | 0.8651 | 0.7943 | 0.0 | 0.8452 | 0.3252 | 0.7587 | 0.7914 | 0.7537 | 0.7956 | 0.7120 | | 0.0935 | 41.4 | 7700 | 0.4870 | 0.6333 | 0.7958 | 0.8326 | nan | 0.9115 | 0.4304 | 0.8214 | 0.8199 | 0.9069 | 0.8677 | 0.8127 | 0.0 | 0.8401 | 0.3865 | 0.7595 | 0.7926 | 0.7518 | 0.7931 | 0.7424 | | 0.1449 | 41.94 | 7800 | 0.5064 | 0.6131 | 0.7718 | 0.8177 | nan | 0.9140 | 0.2785 | 0.8342 | 0.8206 | 0.9070 | 0.8563 | 0.7919 | 0.0 | 0.8467 | 0.2571 | 0.7621 | 0.7934 | 0.7425 | 0.7947 | 0.7085 | | 0.196 | 42.47 | 7900 | 0.4914 | 0.6158 | 0.7747 | 0.8250 | nan | 0.9065 | 0.3182 | 0.8195 | 0.7971 | 0.9159 | 0.8592 | 0.8066 | 0.0 | 0.8378 | 0.2900 | 0.7554 | 0.7732 | 0.7429 | 0.7941 | 0.7334 | | 0.0902 | 43.01 | 8000 | 0.5049 | 0.6092 | 0.7678 | 0.8156 | nan | 0.9207 | 0.2618 | 0.8027 | 0.8255 | 0.9075 | 0.8543 | 0.8020 | 0.0 | 0.8510 | 0.2407 | 0.7415 | 0.7976 | 0.7388 | 0.7934 | 0.7108 | | 0.0981 | 43.55 | 8100 | 0.5101 | 0.6028 | 0.7602 | 0.8140 | nan | 0.9289 | 0.2040 | 0.8124 | 0.8145 | 0.9034 | 0.8485 | 0.8094 | 0.0 | 0.8519 | 0.1903 | 0.7504 | 0.7913 | 0.7421 | 0.7891 | 0.7070 | | 0.0804 | 44.09 | 8200 | 0.5136 | 0.6187 | 0.7777 | 0.8179 | nan | 0.9224 | 0.3260 | 0.8225 | 0.8244 | 0.9141 | 0.8543 | 0.7800 | 0.0 | 0.8514 | 0.3008 | 0.7543 | 0.7957 | 0.7521 | 0.7924 | 0.7033 | | 0.125 | 44.62 | 8300 | 0.5089 | 0.6182 | 0.7770 | 0.8184 | nan | 0.9165 | 0.3390 | 0.8121 | 0.8162 | 0.9054 | 0.8558 | 0.7939 | 0.0 | 0.8459 | 0.3079 | 0.7522 | 0.7915 | 0.7521 | 0.7904 | 0.7052 | | 0.1567 | 45.16 | 8400 | 0.5128 | 0.6093 | 0.7676 | 0.8178 | nan | 0.9259 | 0.2550 | 0.8216 | 0.8056 | 0.9131 | 0.8573 | 0.7945 | 0.0 | 0.8460 | 0.2413 | 0.7548 | 0.7802 | 0.7473 | 0.7921 | 0.7124 | | 0.1533 | 45.7 | 8500 | 0.5073 | 0.6144 | 0.7719 | 0.8183 | nan | 0.9250 | 0.3017 | 0.8238 | 0.7985 | 0.9106 | 0.8532 | 0.7905 | 0.0 | 0.8477 | 0.2807 | 0.7582 | 0.7781 | 0.7505 | 0.7924 | 0.7075 | | 0.1264 | 46.24 | 8600 | 0.5117 | 0.6175 | 0.7771 | 0.8281 | nan | 0.9186 | 0.3045 | 0.8219 | 0.8005 | 0.9130 | 0.8633 | 0.8178 | 0.0 | 0.8426 | 0.2823 | 0.7565 | 0.7777 | 0.7496 | 0.7957 | 0.7358 | | 0.196 | 46.77 | 8700 | 0.5079 | 0.6199 | 0.7785 | 0.8230 | nan | 0.9060 | 0.3527 | 0.8219 | 0.8023 | 0.9128 | 0.8590 | 0.7949 | 0.0 | 0.8362 | 0.3234 | 0.7564 | 0.7803 | 0.7515 | 0.7915 | 0.7200 | | 0.1576 | 47.31 | 8800 | 0.5046 | 0.6192 | 0.7778 | 0.8236 | nan | 0.9132 | 0.3347 | 0.8159 | 0.8091 | 0.9072 | 0.8549 | 0.8096 | 0.0 | 0.8410 | 0.3079 | 0.7548 | 0.7871 | 0.7504 | 0.7915 | 0.7211 | | 0.1362 | 47.85 | 8900 | 0.5086 | 0.6226 | 0.7815 | 0.8228 | nan | 0.9132 | 0.3548 | 0.8207 | 0.8199 | 0.9185 | 0.8555 | 0.7881 | 0.0 | 0.8427 | 0.3250 | 0.7574 | 0.7954 | 0.7543 | 0.7932 | 0.7124 | | 0.1221 | 48.39 | 9000 | 0.5128 | 0.6172 | 0.7760 | 0.8201 | nan | 0.9197 | 0.3168 | 0.8285 | 0.8051 | 0.9096 | 0.8635 | 0.7887 | 0.0 | 0.8438 | 0.2946 | 0.7603 | 0.7838 | 0.7511 | 0.7937 | 0.7101 | | 0.1495 | 48.92 | 9100 | 0.5083 | 0.6219 | 0.7808 | 0.8212 | nan | 0.9145 | 0.3516 | 0.8284 | 0.8180 | 0.9109 | 0.8546 | 0.7879 | 0.0 | 0.8451 | 0.3218 | 0.7593 | 0.7943 | 0.7525 | 0.7924 | 0.7096 | | 0.2303 | 49.46 | 9200 | 0.5041 | 0.6165 | 0.7747 | 0.8238 | nan | 0.9155 | 0.3038 | 0.8206 | 0.8039 | 0.9129 | 0.8603 | 0.8058 | 0.0 | 0.8434 | 0.2804 | 0.7582 | 0.7845 | 0.7494 | 0.7953 | 0.7211 | | 0.1028 | 50.0 | 9300 | 0.5044 | 0.6138 | 0.7713 | 0.8213 | nan | 0.9139 | 0.2842 | 0.8156 | 0.8117 | 0.9098 | 0.8540 | 0.8096 | 0.0 | 0.8428 | 0.2638 | 0.7560 | 0.7896 | 0.7482 | 0.7927 | 0.7176 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

[ "background (waterbody)", "human divers", "aquatic plants and sea-grass", "wrecks and ruins", "robots (auvs/rovs/instruments)", "reefs and invertebrates", "fish and vertebrates", "sea-floor and rocks" ]

rishitunu/FINAL_ecc_segformer

# FINAL_ecc_segformer This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset_exhaustive dataset. It achieves the following results on the evaluation set: - Loss: 0.0749 - Mean Iou: 0.1968 - Mean Accuracy: 0.3939 - Overall Accuracy: 0.3939 - Accuracy Background: nan - Accuracy Crack: 0.3939 - Iou Background: 0.0 - Iou Crack: 0.3936 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| | 0.0534 | 1.0 | 548 | 0.0614 | 0.1368 | 0.2750 | 0.2750 | nan | 0.2750 | 0.0 | 0.2736 | | 0.058 | 2.0 | 1096 | 0.1018 | 0.2093 | 0.4238 | 0.4238 | nan | 0.4238 | 0.0 | 0.4186 | | 0.0482 | 3.0 | 1644 | 0.0508 | 0.1791 | 0.4315 | 0.4315 | nan | 0.4315 | 0.0 | 0.3582 | | 0.0338 | 4.0 | 2192 | 0.0569 | 0.1849 | 0.3716 | 0.3716 | nan | 0.3716 | 0.0 | 0.3698 | | 0.0395 | 5.0 | 2740 | 0.0597 | 0.1745 | 0.3506 | 0.3506 | nan | 0.3506 | 0.0 | 0.3490 | | 0.0372 | 6.0 | 3288 | 0.0509 | 0.2298 | 0.4635 | 0.4635 | nan | 0.4635 | 0.0 | 0.4597 | | 0.0402 | 7.0 | 3836 | 0.0620 | 0.1751 | 0.3507 | 0.3507 | nan | 0.3507 | 0.0 | 0.3503 | | 0.038 | 8.0 | 4384 | 0.0681 | 0.1905 | 0.3815 | 0.3815 | nan | 0.3815 | 0.0 | 0.3810 | | 0.0393 | 9.0 | 4932 | 0.0685 | 0.2213 | 0.4433 | 0.4433 | nan | 0.4433 | 0.0 | 0.4425 | | 0.0376 | 10.0 | 5480 | 0.0590 | 0.1962 | 0.3929 | 0.3929 | nan | 0.3929 | 0.0 | 0.3924 | | 0.0381 | 11.0 | 6028 | 0.0626 | 0.1891 | 0.3801 | 0.3801 | nan | 0.3801 | 0.0 | 0.3783 | | 0.034 | 12.0 | 6576 | 0.0623 | 0.2061 | 0.4162 | 0.4162 | nan | 0.4162 | 0.0 | 0.4122 | | 0.0301 | 13.0 | 7124 | 0.0831 | 0.1832 | 0.3669 | 0.3669 | nan | 0.3669 | 0.0 | 0.3664 | | 0.034 | 14.0 | 7672 | 0.0636 | 0.2059 | 0.4119 | 0.4119 | nan | 0.4119 | 0.0 | 0.4118 | | 0.0303 | 15.0 | 8220 | 0.0705 | 0.1931 | 0.3864 | 0.3864 | nan | 0.3864 | 0.0 | 0.3862 | | 0.0338 | 16.0 | 8768 | 0.0685 | 0.2101 | 0.4206 | 0.4206 | nan | 0.4206 | 0.0 | 0.4202 | | 0.0229 | 17.0 | 9316 | 0.0706 | 0.2099 | 0.4204 | 0.4204 | nan | 0.4204 | 0.0 | 0.4197 | | 0.0337 | 18.0 | 9864 | 0.0742 | 0.1982 | 0.3968 | 0.3968 | nan | 0.3968 | 0.0 | 0.3965 | | 0.0257 | 18.25 | 10000 | 0.0749 | 0.1968 | 0.3939 | 0.3939 | nan | 0.3939 | 0.0 | 0.3936 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1

[ "background", "crack" ]

bvaibhav83/segformer-b0-finetuned-segments-sidewalk-3

# SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic. SegFormer model fine-tuned on [Segments.ai](https://segments.ai) [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic). It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. ### How to use Here is how to use this model to classify an image of the sidewalk dataset: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") model = SegformerForSemanticSegmentation.from_pretrained("segments-tobias/segformer-b0-finetuned-segments-sidewalk") url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) ```

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

peldrak/segformer_finetuned_coasts

# segformer_finetuned_coasts This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coast dataset. It achieves the following results on the evaluation set: - Loss: 0.3304 - Mean Iou: 0.4794 - Mean Accuracy: 0.6650 - Overall Accuracy: 0.9144 - Accuracy Water: nan - Accuracy Whitewater: 0.4315 - Accuracy Sediment: 0.8895 - Accuracy Other Natural Terrain: 0.0 - Accuracy Vegetation: 0.8740 - Accuracy Development: 0.8271 - Accuracy Unknown: 0.9678 - Iou Water: 0.0 - Iou Whitewater: 0.2745 - Iou Sediment: 0.7784 - Iou Other Natural Terrain: 0.0 - Iou Vegetation: 0.7930 - Iou Development: 0.5438 - Iou Unknown: 0.9658 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:| | 1.5565 | 0.01 | 20 | 1.3796 | 0.2430 | 0.3551 | 0.7951 | nan | 0.0097 | 0.1349 | 0.0623 | 0.8407 | 0.1406 | 0.9421 | 0.0 | 0.0051 | 0.1067 | 0.0232 | 0.5193 | 0.1049 | 0.9414 | | 1.7203 | 0.02 | 40 | 1.0580 | 0.2474 | 0.3601 | 0.8312 | nan | 0.0000 | 0.1687 | 0.0012 | 0.9812 | 0.0552 | 0.9541 | 0.0 | 0.0000 | 0.1594 | 0.0010 | 0.5662 | 0.0518 | 0.9534 | | 1.5577 | 0.04 | 60 | 0.9417 | 0.2218 | 0.3312 | 0.8179 | nan | 0.0000 | 0.0330 | 0.0001 | 0.9915 | 0.0100 | 0.9525 | 0.0 | 0.0000 | 0.0323 | 0.0001 | 0.5585 | 0.0099 | 0.9517 | | 0.8823 | 0.05 | 80 | 0.7899 | 0.2293 | 0.3345 | 0.8217 | nan | 0.0000 | 0.0599 | 0.0 | 0.9906 | 0.0011 | 0.9553 | 0.0 | 0.0000 | 0.0596 | 0.0 | 0.5903 | 0.0011 | 0.9538 | | 1.2586 | 0.06 | 100 | 0.6372 | 0.2532 | 0.3440 | 0.8300 | nan | 0.0000 | 0.0981 | 0.0000 | 0.9697 | 0.0284 | 0.9679 | 0.0 | 0.0000 | 0.0971 | 0.0000 | 0.6818 | 0.0284 | 0.9653 | | 1.5022 | 0.07 | 120 | 0.6110 | 0.2431 | 0.3372 | 0.8258 | nan | 0.0 | 0.0759 | 0.0 | 0.9823 | 0.0029 | 0.9619 | 0.0 | 0.0 | 0.0757 | 0.0 | 0.6633 | 0.0029 | 0.9599 | | 0.7693 | 0.08 | 140 | 0.5468 | 0.2613 | 0.3416 | 0.8125 | nan | 0.0 | 0.2451 | 0.0 | 0.8389 | 0.0027 | 0.9628 | 0.0 | 0.0 | 0.2372 | 0.0 | 0.6285 | 0.0027 | 0.9606 | | 1.6587 | 0.1 | 160 | 0.5876 | 0.2717 | 0.3736 | 0.8444 | nan | 0.0 | 0.3158 | 0.0 | 0.9600 | 0.0029 | 0.9628 | 0.0 | 0.0 | 0.3106 | 0.0 | 0.6272 | 0.0029 | 0.9609 | | 1.259 | 0.11 | 180 | 0.5015 | 0.2883 | 0.3752 | 0.8292 | nan | 0.0 | 0.4727 | 0.0 | 0.7987 | 0.0120 | 0.9679 | 0.0 | 0.0 | 0.4317 | 0.0 | 0.6105 | 0.0120 | 0.9642 | | 1.1834 | 0.12 | 200 | 0.5206 | 0.3021 | 0.4047 | 0.8628 | nan | 0.0 | 0.4986 | 0.0 | 0.9598 | 0.0051 | 0.9646 | 0.0 | 0.0 | 0.4515 | 0.0 | 0.6958 | 0.0051 | 0.9620 | | 1.1998 | 0.13 | 220 | 0.5969 | 0.3095 | 0.4198 | 0.8740 | nan | 0.0 | 0.5619 | 0.0 | 0.9853 | 0.0075 | 0.9642 | 0.0 | 0.0 | 0.5167 | 0.0 | 0.6803 | 0.0075 | 0.9620 | | 1.2329 | 0.15 | 240 | 0.4667 | 0.3146 | 0.4176 | 0.8629 | nan | 0.0 | 0.5946 | 0.0 | 0.9102 | 0.0359 | 0.9649 | 0.0 | 0.0 | 0.5137 | 0.0 | 0.6915 | 0.0348 | 0.9622 | | 0.4256 | 0.16 | 260 | 0.4695 | 0.3513 | 0.4674 | 0.8878 | nan | 0.0 | 0.7239 | 0.0 | 0.9465 | 0.1678 | 0.9660 | 0.0 | 0.0 | 0.6265 | 0.0 | 0.7136 | 0.1548 | 0.9639 | | 0.6354 | 0.17 | 280 | 0.4582 | 0.3651 | 0.4836 | 0.8773 | nan | 0.0 | 0.6636 | 0.0 | 0.8840 | 0.3866 | 0.9674 | 0.0 | 0.0 | 0.5882 | 0.0 | 0.6976 | 0.3052 | 0.9648 | | 0.7103 | 0.18 | 300 | 0.4466 | 0.3736 | 0.5041 | 0.8699 | nan | 0.0 | 0.7149 | 0.0 | 0.8020 | 0.5409 | 0.9668 | 0.0 | 0.0 | 0.6068 | 0.0 | 0.6750 | 0.3693 | 0.9639 | | 0.7022 | 0.19 | 320 | 0.4621 | 0.3560 | 0.4756 | 0.8796 | nan | 0.0 | 0.6003 | 0.0 | 0.9246 | 0.3601 | 0.9685 | 0.0 | 0.0 | 0.5370 | 0.0 | 0.7056 | 0.2843 | 0.9653 | | 0.8337 | 0.21 | 340 | 0.4500 | 0.3678 | 0.4897 | 0.8754 | nan | 0.0 | 0.6673 | 0.0 | 0.8694 | 0.4355 | 0.9660 | 0.0 | 0.0 | 0.5735 | 0.0 | 0.7075 | 0.3300 | 0.9634 | | 0.3512 | 0.22 | 360 | 0.4664 | 0.3630 | 0.4910 | 0.8783 | nan | 0.0 | 0.5888 | 0.0 | 0.8998 | 0.4880 | 0.9696 | 0.0 | 0.0 | 0.5302 | 0.0 | 0.7033 | 0.3433 | 0.9645 | | 1.3383 | 0.23 | 380 | 0.5411 | 0.3447 | 0.4592 | 0.8802 | nan | 0.0 | 0.5990 | 0.0 | 0.9552 | 0.2349 | 0.9663 | 0.0 | 0.0 | 0.5463 | 0.0 | 0.7003 | 0.2027 | 0.9633 | | 1.421 | 0.24 | 400 | 0.4386 | 0.3646 | 0.4884 | 0.8661 | nan | 0.0 | 0.6447 | 0.0 | 0.8233 | 0.4958 | 0.9668 | 0.0 | 0.0 | 0.5597 | 0.0 | 0.6861 | 0.3435 | 0.9631 | | 0.5563 | 0.25 | 420 | 0.4313 | 0.3801 | 0.5625 | 0.8797 | nan | 0.0 | 0.7973 | 0.0 | 0.7520 | 0.8561 | 0.9697 | 0.0 | 0.0 | 0.6663 | 0.0 | 0.6742 | 0.3552 | 0.9652 | | 0.6055 | 0.27 | 440 | 0.4203 | 0.3731 | 0.5480 | 0.8733 | nan | 0.0 | 0.7083 | 0.0 | 0.7656 | 0.8454 | 0.9690 | 0.0 | 0.0 | 0.6071 | 0.0 | 0.6947 | 0.3457 | 0.9645 | | 1.0955 | 0.28 | 460 | 0.4412 | 0.3664 | 0.5119 | 0.8665 | nan | 0.0 | 0.6004 | 0.0 | 0.8418 | 0.6706 | 0.9587 | 0.0 | 0.0 | 0.5408 | 0.0 | 0.7123 | 0.3561 | 0.9554 | | 0.9308 | 0.29 | 480 | 0.4208 | 0.3845 | 0.5381 | 0.8852 | nan | 0.0 | 0.7242 | 0.0 | 0.8578 | 0.6826 | 0.9637 | 0.0 | 0.0 | 0.6415 | 0.0 | 0.7161 | 0.3737 | 0.9600 | | 0.3463 | 0.3 | 500 | 0.4321 | 0.3713 | 0.5160 | 0.8621 | nan | 0.0 | 0.6749 | 0.0 | 0.7632 | 0.6930 | 0.9652 | 0.0 | 0.0 | 0.5896 | 0.0 | 0.6635 | 0.3833 | 0.9627 | | 0.8166 | 0.32 | 520 | 0.4851 | 0.3999 | 0.5399 | 0.8955 | nan | 0.0 | 0.7876 | 0.0 | 0.8940 | 0.5942 | 0.9633 | 0.0 | 0.0 | 0.7062 | 0.0 | 0.7290 | 0.4032 | 0.9609 | | 0.5054 | 0.33 | 540 | 0.4328 | 0.3951 | 0.5256 | 0.8936 | nan | 0.0 | 0.7356 | 0.0 | 0.9243 | 0.5325 | 0.9614 | 0.0 | 0.0 | 0.6860 | 0.0 | 0.7262 | 0.3955 | 0.9579 | | 0.316 | 0.34 | 560 | 0.3850 | 0.3985 | 0.5660 | 0.8957 | nan | 0.0 | 0.8014 | 0.0 | 0.8400 | 0.7855 | 0.9691 | 0.0 | 0.0 | 0.7180 | 0.0 | 0.7286 | 0.3782 | 0.9647 | | 0.2616 | 0.35 | 580 | 0.3974 | 0.3831 | 0.5087 | 0.8852 | nan | 0.0 | 0.6365 | 0.0 | 0.9089 | 0.5391 | 0.9677 | 0.0 | 0.0 | 0.5653 | 0.0 | 0.7531 | 0.3995 | 0.9640 | | 0.4969 | 0.36 | 600 | 0.4115 | 0.3849 | 0.5209 | 0.8738 | nan | 0.0 | 0.7301 | 0.0 | 0.8064 | 0.6238 | 0.9650 | 0.0 | 0.0 | 0.6141 | 0.0 | 0.7030 | 0.4155 | 0.9618 | | 0.6554 | 0.38 | 620 | 0.3927 | 0.3997 | 0.5634 | 0.8927 | nan | 0.0 | 0.8489 | 0.0 | 0.8074 | 0.7544 | 0.9698 | 0.0 | 0.0 | 0.7254 | 0.0 | 0.7077 | 0.4004 | 0.9641 | | 0.5096 | 0.39 | 640 | 0.4265 | 0.3833 | 0.5554 | 0.8772 | nan | 0.0 | 0.8562 | 0.0 | 0.7283 | 0.7792 | 0.9688 | 0.0 | 0.0 | 0.6714 | 0.0 | 0.6556 | 0.3905 | 0.9655 | | 0.5453 | 0.4 | 660 | 0.4163 | 0.3830 | 0.5356 | 0.8763 | nan | 0.0 | 0.7352 | 0.0 | 0.8036 | 0.7106 | 0.9644 | 0.0 | 0.0 | 0.6194 | 0.0 | 0.6953 | 0.4040 | 0.9625 | | 0.8522 | 0.41 | 680 | 0.3850 | 0.3859 | 0.5522 | 0.8779 | nan | 0.0 | 0.8238 | 0.0 | 0.7533 | 0.7690 | 0.9672 | 0.0 | 0.0 | 0.6587 | 0.0 | 0.6785 | 0.4009 | 0.9634 | | 0.324 | 0.42 | 700 | 0.3980 | 0.3957 | 0.5386 | 0.8933 | nan | 0.0 | 0.7033 | 0.0 | 0.8982 | 0.6630 | 0.9674 | 0.0 | 0.0 | 0.6302 | 0.0 | 0.7372 | 0.4384 | 0.9644 | | 0.6783 | 0.44 | 720 | 0.4155 | 0.3798 | 0.5332 | 0.8771 | nan | 0.0 | 0.6873 | 0.0 | 0.8122 | 0.7309 | 0.9689 | 0.0 | 0.0 | 0.5793 | 0.0 | 0.7058 | 0.4079 | 0.9656 | | 0.6283 | 0.45 | 740 | 0.4053 | 0.3875 | 0.5179 | 0.8917 | nan | 0.0 | 0.6808 | 0.0 | 0.9207 | 0.5386 | 0.9674 | 0.0 | 0.0 | 0.6077 | 0.0 | 0.7409 | 0.3986 | 0.9649 | | 0.831 | 0.46 | 760 | 0.3984 | 0.3850 | 0.5178 | 0.8838 | nan | 0.0 | 0.7712 | 0.0 | 0.8476 | 0.5216 | 0.9664 | 0.0 | 0.0 | 0.5900 | 0.0 | 0.7380 | 0.4044 | 0.9629 | | 0.5993 | 0.47 | 780 | 0.4069 | 0.3891 | 0.5129 | 0.8961 | nan | 0.0 | 0.7348 | 0.0 | 0.9287 | 0.4453 | 0.9687 | 0.0 | 0.0 | 0.6417 | 0.0 | 0.7532 | 0.3632 | 0.9658 | | 0.719 | 0.49 | 800 | 0.3856 | 0.4053 | 0.5597 | 0.9006 | nan | 0.0 | 0.8377 | 0.0 | 0.8641 | 0.6875 | 0.9688 | 0.0 | 0.0 | 0.7007 | 0.0 | 0.7423 | 0.4286 | 0.9657 | | 0.4896 | 0.5 | 820 | 0.3741 | 0.4033 | 0.5511 | 0.8982 | nan | 0.0 | 0.7926 | 0.0 | 0.8730 | 0.6715 | 0.9697 | 0.0 | 0.0 | 0.6861 | 0.0 | 0.7405 | 0.4302 | 0.9662 | | 0.5632 | 0.51 | 840 | 0.4503 | 0.4085 | 0.5607 | 0.9069 | nan | 0.0 | 0.8602 | 0.0 | 0.8788 | 0.6530 | 0.9724 | 0.0 | 0.0 | 0.7133 | 0.0 | 0.7441 | 0.4355 | 0.9664 | | 0.2878 | 0.52 | 860 | 0.4594 | 0.3772 | 0.4928 | 0.8637 | nan | 0.0 | 0.5859 | 0.0 | 0.8246 | 0.5792 | 0.9668 | 0.0 | 0.0 | 0.5066 | 0.0 | 0.7162 | 0.4538 | 0.9639 | | 0.5433 | 0.53 | 880 | 0.3916 | 0.3899 | 0.5519 | 0.8832 | nan | 0.0 | 0.7655 | 0.0 | 0.8304 | 0.7561 | 0.9596 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.7277 | 0.4197 | 0.9571 | | 1.1254 | 0.55 | 900 | 0.3724 | 0.4012 | 0.5588 | 0.8989 | nan | 0.0 | 0.8798 | 0.0 | 0.8445 | 0.6612 | 0.9676 | 0.0 | 0.0 | 0.6859 | 0.0 | 0.7355 | 0.4222 | 0.9645 | | 0.3224 | 0.56 | 920 | 0.3896 | 0.3832 | 0.5236 | 0.8848 | nan | 0.0 | 0.6800 | 0.0 | 0.8722 | 0.6211 | 0.9682 | 0.0 | 0.0 | 0.5748 | 0.0 | 0.7403 | 0.4029 | 0.9642 | | 0.5332 | 0.57 | 940 | 0.4089 | 0.3939 | 0.5357 | 0.9015 | nan | 0.0 | 0.7886 | 0.0 | 0.9190 | 0.5394 | 0.9673 | 0.0 | 0.0 | 0.6730 | 0.0 | 0.7391 | 0.3803 | 0.9647 | | 0.5894 | 0.58 | 960 | 0.3700 | 0.3927 | 0.5405 | 0.8839 | nan | 0.0 | 0.7708 | 0.0 | 0.8235 | 0.6824 | 0.9660 | 0.0 | 0.0 | 0.6556 | 0.0 | 0.7161 | 0.4163 | 0.9612 | | 1.036 | 0.59 | 980 | 0.3671 | 0.3944 | 0.5432 | 0.8837 | nan | 0.0 | 0.7763 | 0.0 | 0.8179 | 0.6990 | 0.9658 | 0.0 | 0.0 | 0.6802 | 0.0 | 0.7127 | 0.4058 | 0.9623 | | 1.5145 | 0.61 | 1000 | 0.3916 | 0.3965 | 0.5456 | 0.8958 | nan | 0.0 | 0.7867 | 0.0 | 0.8846 | 0.6375 | 0.9647 | 0.0 | 0.0 | 0.7018 | 0.0 | 0.7190 | 0.3925 | 0.9624 | | 0.4625 | 0.62 | 1020 | 0.3603 | 0.4067 | 0.5496 | 0.9010 | nan | 0.0 | 0.8180 | 0.0 | 0.8992 | 0.6160 | 0.9646 | 0.0 | 0.0 | 0.7095 | 0.0 | 0.7525 | 0.4239 | 0.9608 | | 0.3804 | 0.63 | 1040 | 0.4219 | 0.3928 | 0.5328 | 0.8789 | nan | 0.0 | 0.8239 | 0.0 | 0.7882 | 0.6196 | 0.9652 | 0.0 | 0.0 | 0.6778 | 0.0 | 0.6963 | 0.4115 | 0.9637 | | 0.1372 | 0.64 | 1060 | 0.3774 | 0.4034 | 0.5582 | 0.8978 | nan | 0.0 | 0.8729 | 0.0 | 0.8348 | 0.6721 | 0.9696 | 0.0 | 0.0 | 0.7169 | 0.0 | 0.7231 | 0.4186 | 0.9653 | | 0.4438 | 0.65 | 1080 | 0.3409 | 0.4042 | 0.5501 | 0.8971 | nan | 0.0 | 0.7877 | 0.0 | 0.8704 | 0.6733 | 0.9694 | 0.0 | 0.0 | 0.6900 | 0.0 | 0.7509 | 0.4241 | 0.9648 | | 0.3661 | 0.67 | 1100 | 0.3662 | 0.3994 | 0.5728 | 0.8936 | nan | 0.0039 | 0.8665 | 0.0 | 0.7975 | 0.7998 | 0.9693 | 0.0 | 0.0039 | 0.7029 | 0.0 | 0.7202 | 0.4038 | 0.9651 | | 0.3783 | 0.68 | 1120 | 0.3625 | 0.4003 | 0.5673 | 0.8893 | nan | 0.0 | 0.8860 | 0.0 | 0.7856 | 0.7669 | 0.9654 | 0.0 | 0.0 | 0.7155 | 0.0 | 0.7166 | 0.4074 | 0.9628 | | 0.3344 | 0.69 | 1140 | 0.3889 | 0.4142 | 0.5535 | 0.9062 | nan | 0.0 | 0.8531 | 0.0 | 0.9107 | 0.5919 | 0.9651 | 0.0 | 0.0 | 0.7437 | 0.0 | 0.7598 | 0.4330 | 0.9628 | | 0.448 | 0.7 | 1160 | 0.3701 | 0.3875 | 0.5226 | 0.8774 | nan | 0.0 | 0.6806 | 0.0 | 0.8287 | 0.6578 | 0.9688 | 0.0 | 0.0 | 0.5996 | 0.0 | 0.7207 | 0.4273 | 0.9648 | | 0.5724 | 0.72 | 1180 | 0.3466 | 0.4074 | 0.5587 | 0.8998 | nan | 0.0000 | 0.8195 | 0.0 | 0.8653 | 0.6982 | 0.9692 | 0.0 | 0.0000 | 0.7289 | 0.0 | 0.7380 | 0.4198 | 0.9648 | | 0.2868 | 0.73 | 1200 | 0.3369 | 0.4088 | 0.5653 | 0.8956 | nan | 0.0005 | 0.8380 | 0.0 | 0.8254 | 0.7584 | 0.9697 | 0.0 | 0.0005 | 0.7145 | 0.0 | 0.7399 | 0.4414 | 0.9652 | | 1.2485 | 0.74 | 1220 | 0.3480 | 0.4120 | 0.5639 | 0.9023 | nan | 0.0079 | 0.8643 | 0.0 | 0.8543 | 0.6865 | 0.9706 | 0.0 | 0.0079 | 0.7086 | 0.0 | 0.7548 | 0.4469 | 0.9659 | | 0.6551 | 0.75 | 1240 | 0.3791 | 0.4219 | 0.5591 | 0.9145 | nan | 0.0048 | 0.8977 | 0.0 | 0.9139 | 0.5662 | 0.9718 | 0.0 | 0.0048 | 0.7367 | 0.0 | 0.7844 | 0.4620 | 0.9651 | | 0.2599 | 0.76 | 1260 | 0.4596 | 0.3694 | 0.5736 | 0.8698 | nan | 0.0001 | 0.9234 | 0.0 | 0.6502 | 0.9018 | 0.9664 | 0.0 | 0.0001 | 0.6060 | 0.0 | 0.6179 | 0.3974 | 0.9644 | | 0.4005 | 0.78 | 1280 | 0.3520 | 0.4018 | 0.5566 | 0.8965 | nan | 0.0076 | 0.7654 | 0.0 | 0.8698 | 0.7278 | 0.9690 | 0.0 | 0.0076 | 0.6936 | 0.0 | 0.7161 | 0.4306 | 0.9648 | | 0.4001 | 0.79 | 1300 | 0.3456 | 0.4070 | 0.5543 | 0.8977 | nan | 0.0001 | 0.7549 | 0.0 | 0.8813 | 0.7203 | 0.9692 | 0.0 | 0.0001 | 0.6933 | 0.0 | 0.7277 | 0.4620 | 0.9658 | | 0.9039 | 0.8 | 1320 | 0.3889 | 0.4112 | 0.5790 | 0.8984 | nan | 0.0006 | 0.8918 | 0.0 | 0.8236 | 0.7933 | 0.9650 | 0.0 | 0.0006 | 0.7323 | 0.0 | 0.7349 | 0.4479 | 0.9626 | | 0.6388 | 0.81 | 1340 | 0.4108 | 0.4121 | 0.5752 | 0.9051 | nan | 0.0001 | 0.8354 | 0.0 | 0.8712 | 0.7752 | 0.9692 | 0.0 | 0.0001 | 0.7325 | 0.0 | 0.7351 | 0.4512 | 0.9655 | | 0.3616 | 0.82 | 1360 | 0.4138 | 0.4221 | 0.5638 | 0.9099 | nan | 0.0 | 0.8630 | 0.0 | 0.9119 | 0.6414 | 0.9664 | 0.0 | 0.0 | 0.7581 | 0.0 | 0.7689 | 0.4631 | 0.9646 | | 0.2287 | 0.84 | 1380 | 0.3833 | 0.4229 | 0.5762 | 0.9055 | nan | 0.0000 | 0.8940 | 0.0 | 0.8637 | 0.7335 | 0.9658 | 0.0 | 0.0000 | 0.7658 | 0.0 | 0.7545 | 0.4779 | 0.9620 | | 0.2687 | 0.85 | 1400 | 0.3732 | 0.4287 | 0.5666 | 0.9118 | nan | 0.0000 | 0.8972 | 0.0 | 0.9157 | 0.6226 | 0.9641 | 0.0 | 0.0000 | 0.7764 | 0.0 | 0.7815 | 0.4814 | 0.9618 | | 0.3827 | 0.86 | 1420 | 0.3344 | 0.4176 | 0.5816 | 0.9030 | nan | 0.0 | 0.8511 | 0.0 | 0.8537 | 0.8177 | 0.9671 | 0.0 | 0.0 | 0.7452 | 0.0 | 0.7485 | 0.4653 | 0.9641 | | 1.1798 | 0.87 | 1440 | 0.3485 | 0.4198 | 0.5742 | 0.9089 | nan | 0.0002 | 0.8352 | 0.0 | 0.8893 | 0.7500 | 0.9705 | 0.0 | 0.0002 | 0.7491 | 0.0 | 0.7514 | 0.4729 | 0.9653 | | 0.5062 | 0.89 | 1460 | 0.3882 | 0.4145 | 0.5766 | 0.9028 | nan | 0.0013 | 0.8817 | 0.0 | 0.8337 | 0.7717 | 0.9712 | 0.0 | 0.0013 | 0.7656 | 0.0 | 0.7303 | 0.4389 | 0.9656 | | 0.2002 | 0.9 | 1480 | 0.3677 | 0.4167 | 0.5785 | 0.9005 | nan | 0.0001 | 0.8674 | 0.0 | 0.8450 | 0.7934 | 0.9649 | 0.0 | 0.0001 | 0.7513 | 0.0 | 0.7391 | 0.4645 | 0.9620 | | 0.1993 | 0.91 | 1500 | 0.3801 | 0.4240 | 0.5584 | 0.9134 | nan | 0.0000 | 0.8673 | 0.0 | 0.9311 | 0.5842 | 0.9680 | 0.0 | 0.0000 | 0.7626 | 0.0 | 0.7714 | 0.4687 | 0.9653 | | 0.2609 | 0.92 | 1520 | 0.3489 | 0.4199 | 0.5475 | 0.9084 | nan | 0.0001 | 0.8222 | 0.0 | 0.9304 | 0.5642 | 0.9682 | 0.0 | 0.0001 | 0.7412 | 0.0 | 0.7755 | 0.4568 | 0.9657 | | 0.4571 | 0.93 | 1540 | 0.3767 | 0.4218 | 0.5832 | 0.9040 | nan | 0.0372 | 0.8298 | 0.0 | 0.8774 | 0.7894 | 0.9651 | 0.0 | 0.0365 | 0.7505 | 0.0 | 0.7451 | 0.4583 | 0.9621 | | 0.5643 | 0.95 | 1560 | 0.3707 | 0.4220 | 0.5848 | 0.9027 | nan | 0.0187 | 0.8455 | 0.0 | 0.8446 | 0.8307 | 0.9695 | 0.0 | 0.0186 | 0.7506 | 0.0 | 0.7459 | 0.4715 | 0.9670 | | 0.2607 | 0.96 | 1580 | 0.3601 | 0.4304 | 0.5782 | 0.9099 | nan | 0.0119 | 0.8665 | 0.0 | 0.8921 | 0.7312 | 0.9675 | 0.0 | 0.0118 | 0.7565 | 0.0 | 0.7761 | 0.5029 | 0.9652 | | 0.2481 | 0.97 | 1600 | 0.3817 | 0.4281 | 0.5952 | 0.9043 | nan | 0.0346 | 0.8954 | 0.0 | 0.8582 | 0.8225 | 0.9604 | 0.0 | 0.0342 | 0.7426 | 0.0 | 0.7691 | 0.4917 | 0.9589 | | 0.1986 | 0.98 | 1620 | 0.3817 | 0.4475 | 0.5988 | 0.9135 | nan | 0.1260 | 0.8537 | 0.0 | 0.9194 | 0.7288 | 0.9651 | 0.0 | 0.1246 | 0.7532 | 0.0 | 0.7756 | 0.5166 | 0.9626 | | 0.455 | 0.99 | 1640 | 0.3812 | 0.4526 | 0.5958 | 0.9144 | nan | 0.1518 | 0.8918 | 0.0 | 0.9239 | 0.6439 | 0.9636 | 0.0 | 0.1467 | 0.7665 | 0.0 | 0.7888 | 0.5058 | 0.9602 | | 0.4941 | 1.01 | 1660 | 0.3751 | 0.4389 | 0.6153 | 0.9094 | nan | 0.1142 | 0.9261 | 0.0 | 0.8391 | 0.8440 | 0.9682 | 0.0 | 0.1110 | 0.7601 | 0.0 | 0.7621 | 0.4737 | 0.9652 | | 0.2446 | 1.02 | 1680 | 0.3794 | 0.4389 | 0.5817 | 0.9136 | nan | 0.0160 | 0.9137 | 0.0 | 0.8883 | 0.7034 | 0.9689 | 0.0 | 0.0160 | 0.7765 | 0.0 | 0.7870 | 0.5264 | 0.9664 | | 0.3996 | 1.03 | 1700 | 0.3408 | 0.4193 | 0.5478 | 0.9004 | nan | 0.0060 | 0.7699 | 0.0 | 0.8999 | 0.6419 | 0.9691 | 0.0 | 0.0060 | 0.6761 | 0.0 | 0.7813 | 0.5054 | 0.9660 | | 0.6762 | 1.04 | 1720 | 0.3653 | 0.4203 | 0.5646 | 0.8983 | nan | 0.0554 | 0.7069 | 0.0 | 0.8982 | 0.7581 | 0.9691 | 0.0 | 0.0550 | 0.6118 | 0.0 | 0.7905 | 0.5192 | 0.9658 | | 0.3445 | 1.06 | 1740 | 0.3179 | 0.4460 | 0.6063 | 0.9131 | nan | 0.1284 | 0.8453 | 0.0 | 0.8960 | 0.7983 | 0.9697 | 0.0 | 0.1275 | 0.7422 | 0.0 | 0.7797 | 0.5068 | 0.9658 | | 0.5238 | 1.07 | 1760 | 0.3420 | 0.4456 | 0.6022 | 0.9178 | nan | 0.0626 | 0.9129 | 0.0 | 0.8955 | 0.7732 | 0.9692 | 0.0 | 0.0616 | 0.7810 | 0.0 | 0.7931 | 0.5180 | 0.9658 | | 0.4666 | 1.08 | 1780 | 0.3410 | 0.4379 | 0.5994 | 0.9099 | nan | 0.0695 | 0.8674 | 0.0 | 0.8796 | 0.8136 | 0.9666 | 0.0 | 0.0678 | 0.7510 | 0.0 | 0.7786 | 0.5035 | 0.9645 | | 0.1126 | 1.09 | 1800 | 0.3464 | 0.4383 | 0.6157 | 0.9101 | nan | 0.1208 | 0.8865 | 0.0 | 0.8516 | 0.8653 | 0.9700 | 0.0 | 0.1180 | 0.7454 | 0.0 | 0.7670 | 0.4711 | 0.9664 | | 0.7935 | 1.1 | 1820 | 0.3999 | 0.4382 | 0.5737 | 0.9179 | nan | 0.0529 | 0.8790 | 0.0 | 0.9439 | 0.5984 | 0.9679 | 0.0 | 0.0528 | 0.7719 | 0.0 | 0.7874 | 0.4906 | 0.9650 | | 0.6014 | 1.12 | 1840 | 0.3176 | 0.4707 | 0.6222 | 0.9193 | nan | 0.2149 | 0.8795 | 0.0 | 0.9128 | 0.7558 | 0.9702 | 0.0 | 0.2033 | 0.7731 | 0.0 | 0.7938 | 0.5591 | 0.9653 | | 1.1728 | 1.13 | 1860 | 0.3165 | 0.4715 | 0.6205 | 0.9205 | nan | 0.2344 | 0.8965 | 0.0 | 0.9165 | 0.7044 | 0.9709 | 0.0 | 0.2151 | 0.7453 | 0.0 | 0.8097 | 0.5650 | 0.9654 | | 0.1275 | 1.14 | 1880 | 0.3705 | 0.4586 | 0.5965 | 0.9140 | nan | 0.2139 | 0.8187 | 0.0 | 0.9455 | 0.6344 | 0.9666 | 0.0 | 0.2070 | 0.7223 | 0.0 | 0.7887 | 0.5277 | 0.9649 | | 0.2179 | 1.15 | 1900 | 0.3268 | 0.4608 | 0.6240 | 0.9126 | nan | 0.2176 | 0.8507 | 0.0 | 0.8910 | 0.8165 | 0.9679 | 0.0 | 0.2054 | 0.7202 | 0.0 | 0.7888 | 0.5454 | 0.9656 | | 0.1725 | 1.16 | 1920 | 0.3277 | 0.4655 | 0.6146 | 0.9203 | nan | 0.1809 | 0.8656 | 0.0 | 0.9297 | 0.7422 | 0.9693 | 0.0 | 0.1753 | 0.7456 | 0.0 | 0.8099 | 0.5610 | 0.9667 | | 0.3003 | 1.18 | 1940 | 0.3347 | 0.4741 | 0.6311 | 0.9203 | nan | 0.2239 | 0.8903 | 0.0 | 0.9079 | 0.7951 | 0.9696 | 0.0 | 0.2144 | 0.7493 | 0.0 | 0.8067 | 0.5815 | 0.9669 | | 0.4764 | 1.19 | 1960 | 0.3413 | 0.4478 | 0.6279 | 0.9079 | nan | 0.1854 | 0.9016 | 0.0 | 0.8339 | 0.8779 | 0.9687 | 0.0 | 0.1690 | 0.7441 | 0.0 | 0.7655 | 0.4897 | 0.9663 | | 0.1679 | 1.2 | 1980 | 0.3516 | 0.4473 | 0.5798 | 0.9191 | nan | 0.0754 | 0.8674 | 0.0 | 0.9466 | 0.6204 | 0.9692 | 0.0 | 0.0724 | 0.7752 | 0.0 | 0.7993 | 0.5181 | 0.9660 | | 0.1999 | 1.21 | 2000 | 0.3341 | 0.4721 | 0.6457 | 0.9175 | nan | 0.2688 | 0.8973 | 0.0 | 0.8822 | 0.8568 | 0.9688 | 0.0 | 0.2286 | 0.7781 | 0.0 | 0.7962 | 0.5359 | 0.9662 | | 0.8992 | 1.22 | 2020 | 0.3121 | 0.4757 | 0.6409 | 0.9179 | nan | 0.2915 | 0.8910 | 0.0 | 0.8935 | 0.8000 | 0.9695 | 0.0 | 0.2634 | 0.7798 | 0.0 | 0.7856 | 0.5347 | 0.9661 | | 0.7007 | 1.24 | 2040 | 0.3041 | 0.4618 | 0.6127 | 0.9128 | nan | 0.1999 | 0.8653 | 0.0 | 0.8950 | 0.7474 | 0.9685 | 0.0 | 0.1702 | 0.7512 | 0.0 | 0.7931 | 0.5532 | 0.9647 | | 0.5711 | 1.25 | 2060 | 0.3104 | 0.4758 | 0.6277 | 0.9190 | nan | 0.2760 | 0.8877 | 0.0 | 0.9128 | 0.7196 | 0.9699 | 0.0 | 0.2357 | 0.7700 | 0.0 | 0.8006 | 0.5579 | 0.9666 | | 0.7925 | 1.26 | 2080 | 0.3465 | 0.4605 | 0.6418 | 0.9091 | nan | 0.2878 | 0.8570 | 0.0 | 0.8532 | 0.8831 | 0.9696 | 0.0 | 0.2437 | 0.7217 | 0.0 | 0.7779 | 0.5136 | 0.9670 | | 0.1759 | 1.27 | 2100 | 0.3406 | 0.4619 | 0.6235 | 0.9078 | nan | 0.2492 | 0.8230 | 0.0 | 0.8719 | 0.8272 | 0.9698 | 0.0 | 0.2157 | 0.6976 | 0.0 | 0.7884 | 0.5655 | 0.9663 | | 0.6914 | 1.29 | 2120 | 0.3209 | 0.4694 | 0.6229 | 0.9168 | nan | 0.2340 | 0.8629 | 0.0 | 0.9024 | 0.7665 | 0.9713 | 0.0 | 0.2126 | 0.7390 | 0.0 | 0.8017 | 0.5650 | 0.9672 | | 0.1292 | 1.3 | 2140 | 0.3151 | 0.4735 | 0.6349 | 0.9147 | nan | 0.2670 | 0.8724 | 0.0 | 0.8983 | 0.8062 | 0.9657 | 0.0 | 0.2358 | 0.7487 | 0.0 | 0.8011 | 0.5662 | 0.9625 | | 0.5439 | 1.31 | 2160 | 0.3343 | 0.4617 | 0.6058 | 0.9133 | nan | 0.2228 | 0.7989 | 0.0 | 0.9340 | 0.7110 | 0.9681 | 0.0 | 0.2091 | 0.7154 | 0.0 | 0.7958 | 0.5458 | 0.9660 | | 0.5949 | 1.32 | 2180 | 0.3260 | 0.4561 | 0.6419 | 0.9072 | nan | 0.3080 | 0.8721 | 0.0 | 0.8355 | 0.8650 | 0.9709 | 0.0 | 0.2166 | 0.7527 | 0.0 | 0.7632 | 0.4930 | 0.9670 | | 0.9366 | 1.33 | 2200 | 0.3182 | 0.4748 | 0.6430 | 0.9190 | nan | 0.2967 | 0.9100 | 0.0 | 0.8864 | 0.7941 | 0.9710 | 0.0 | 0.2381 | 0.7813 | 0.0 | 0.7926 | 0.5446 | 0.9669 | | 0.4478 | 1.35 | 2220 | 0.3531 | 0.4596 | 0.5997 | 0.9223 | nan | 0.1622 | 0.8843 | 0.0 | 0.9456 | 0.6361 | 0.9703 | 0.0 | 0.1565 | 0.7919 | 0.0 | 0.7921 | 0.5099 | 0.9670 | | 0.2858 | 1.36 | 2240 | 0.3627 | 0.4607 | 0.6228 | 0.9173 | nan | 0.1578 | 0.8976 | 0.0 | 0.8883 | 0.8239 | 0.9694 | 0.0 | 0.1500 | 0.7902 | 0.0 | 0.7738 | 0.5443 | 0.9666 | | 0.4923 | 1.37 | 2260 | 0.3367 | 0.4498 | 0.6047 | 0.9082 | nan | 0.1380 | 0.8348 | 0.0 | 0.8814 | 0.8063 | 0.9678 | 0.0 | 0.1299 | 0.7364 | 0.0 | 0.7740 | 0.5442 | 0.9638 | | 0.1323 | 1.38 | 2280 | 0.3380 | 0.4515 | 0.6301 | 0.9084 | nan | 0.1971 | 0.8926 | 0.0 | 0.8345 | 0.8863 | 0.9700 | 0.0 | 0.1751 | 0.7494 | 0.0 | 0.7577 | 0.5118 | 0.9668 | | 0.3126 | 1.39 | 2300 | 0.3519 | 0.4753 | 0.6187 | 0.9234 | nan | 0.2345 | 0.8849 | 0.0 | 0.9471 | 0.6770 | 0.9686 | 0.0 | 0.2226 | 0.7876 | 0.0 | 0.7971 | 0.5533 | 0.9662 | | 1.8741 | 1.41 | 2320 | 0.3483 | 0.4766 | 0.6352 | 0.9194 | nan | 0.2550 | 0.8999 | 0.0 | 0.9013 | 0.7856 | 0.9691 | 0.0 | 0.2242 | 0.7747 | 0.0 | 0.7957 | 0.5761 | 0.9656 | | 0.3519 | 1.42 | 2340 | 0.3390 | 0.4794 | 0.6391 | 0.9205 | nan | 0.2594 | 0.8966 | 0.0 | 0.9017 | 0.8068 | 0.9700 | 0.0 | 0.2378 | 0.7732 | 0.0 | 0.7951 | 0.5829 | 0.9671 | | 0.4777 | 1.43 | 2360 | 0.3234 | 0.4707 | 0.6410 | 0.9149 | nan | 0.2605 | 0.8687 | 0.0 | 0.8787 | 0.8681 | 0.9698 | 0.0 | 0.2422 | 0.7715 | 0.0 | 0.7784 | 0.5358 | 0.9670 | | 0.7156 | 1.44 | 2380 | 0.3451 | 0.4794 | 0.6418 | 0.9201 | nan | 0.2929 | 0.8798 | 0.0 | 0.9094 | 0.7995 | 0.9694 | 0.0 | 0.2575 | 0.7733 | 0.0 | 0.7918 | 0.5663 | 0.9670 | | 0.3765 | 1.46 | 2400 | 0.3339 | 0.4639 | 0.6238 | 0.9133 | nan | 0.2550 | 0.8921 | 0.0 | 0.8805 | 0.7455 | 0.9696 | 0.0 | 0.1751 | 0.7569 | 0.0 | 0.7919 | 0.5564 | 0.9669 | | 0.4343 | 1.47 | 2420 | 0.3374 | 0.4630 | 0.6168 | 0.9135 | nan | 0.2179 | 0.8541 | 0.0 | 0.9035 | 0.7576 | 0.9678 | 0.0 | 0.1775 | 0.7531 | 0.0 | 0.7934 | 0.5515 | 0.9658 | | 0.2178 | 1.48 | 2440 | 0.3254 | 0.4825 | 0.6499 | 0.9219 | nan | 0.3424 | 0.8816 | 0.0 | 0.9140 | 0.7912 | 0.9704 | 0.0 | 0.2639 | 0.7733 | 0.0 | 0.8027 | 0.5707 | 0.9669 | | 0.1439 | 1.49 | 2460 | 0.3176 | 0.4752 | 0.6409 | 0.9175 | nan | 0.2942 | 0.8787 | 0.0 | 0.8935 | 0.8089 | 0.9702 | 0.0 | 0.2160 | 0.7786 | 0.0 | 0.7978 | 0.5673 | 0.9664 | | 0.2481 | 1.5 | 2480 | 0.3195 | 0.4773 | 0.6265 | 0.9190 | nan | 0.2645 | 0.8625 | 0.0 | 0.9263 | 0.7376 | 0.9683 | 0.0 | 0.2089 | 0.7689 | 0.0 | 0.8083 | 0.5890 | 0.9658 | | 0.9347 | 1.52 | 2500 | 0.3430 | 0.4789 | 0.6464 | 0.9207 | nan | 0.3009 | 0.8885 | 0.0 | 0.9029 | 0.8159 | 0.9702 | 0.0 | 0.2626 | 0.7724 | 0.0 | 0.7975 | 0.5528 | 0.9670 | | 0.1827 | 1.53 | 2520 | 0.3459 | 0.4726 | 0.6328 | 0.9181 | nan | 0.2484 | 0.9093 | 0.0 | 0.8921 | 0.7775 | 0.9692 | 0.0 | 0.2042 | 0.7741 | 0.0 | 0.7998 | 0.5643 | 0.9662 | | 0.3971 | 1.54 | 2540 | 0.3276 | 0.4661 | 0.6353 | 0.9144 | nan | 0.2600 | 0.8791 | 0.0 | 0.8777 | 0.8247 | 0.9700 | 0.0 | 0.2198 | 0.7730 | 0.0 | 0.7785 | 0.5249 | 0.9663 | | 0.173 | 1.55 | 2560 | 0.3109 | 0.4591 | 0.6419 | 0.9093 | nan | 0.2986 | 0.8694 | 0.0 | 0.8536 | 0.8611 | 0.9690 | 0.0 | 0.2176 | 0.7731 | 0.0 | 0.7626 | 0.4941 | 0.9662 | | 0.2705 | 1.56 | 2580 | 0.3112 | 0.4632 | 0.6178 | 0.9101 | nan | 0.2942 | 0.8141 | 0.0 | 0.9057 | 0.7240 | 0.9687 | 0.0 | 0.2203 | 0.7423 | 0.0 | 0.7818 | 0.5318 | 0.9661 | | 0.2656 | 1.58 | 2600 | 0.3331 | 0.4806 | 0.6427 | 0.9202 | nan | 0.3186 | 0.8837 | 0.0 | 0.9166 | 0.7691 | 0.9680 | 0.0 | 0.2634 | 0.7761 | 0.0 | 0.7986 | 0.5600 | 0.9661 | | 0.9206 | 1.59 | 2620 | 0.3247 | 0.4688 | 0.6290 | 0.9162 | nan | 0.3530 | 0.8646 | 0.0 | 0.9130 | 0.6725 | 0.9706 | 0.0 | 0.2167 | 0.7747 | 0.0 | 0.7877 | 0.5357 | 0.9667 | | 0.6181 | 1.6 | 2640 | 0.4032 | 0.4888 | 0.6638 | 0.9239 | nan | 0.3722 | 0.9134 | 0.0 | 0.9041 | 0.8231 | 0.9701 | 0.0 | 0.3080 | 0.7774 | 0.0 | 0.8039 | 0.5652 | 0.9668 | | 0.3185 | 1.61 | 2660 | 0.3383 | 0.4705 | 0.6598 | 0.9145 | nan | 0.3341 | 0.9161 | 0.0 | 0.8448 | 0.8926 | 0.9711 | 0.0 | 0.2860 | 0.7681 | 0.0 | 0.7732 | 0.4997 | 0.9667 | | 0.2155 | 1.63 | 2680 | 0.3389 | 0.4639 | 0.6447 | 0.9109 | nan | 0.3037 | 0.8741 | 0.0 | 0.8593 | 0.8625 | 0.9689 | 0.0 | 0.2608 | 0.7573 | 0.0 | 0.7696 | 0.4933 | 0.9664 | | 0.2003 | 1.64 | 2700 | 0.3230 | 0.4649 | 0.6588 | 0.9112 | nan | 0.3442 | 0.8983 | 0.0 | 0.8350 | 0.9045 | 0.9710 | 0.0 | 0.2818 | 0.7704 | 0.0 | 0.7626 | 0.4723 | 0.9672 | | 0.1279 | 1.65 | 2720 | 0.3241 | 0.4747 | 0.6489 | 0.9163 | nan | 0.3240 | 0.8760 | 0.0 | 0.8863 | 0.8379 | 0.9693 | 0.0 | 0.2713 | 0.7729 | 0.0 | 0.7876 | 0.5243 | 0.9666 | | 1.5163 | 1.66 | 2740 | 0.3286 | 0.4822 | 0.6409 | 0.9182 | nan | 0.3422 | 0.8431 | 0.0 | 0.9203 | 0.7703 | 0.9694 | 0.0 | 0.2859 | 0.7533 | 0.0 | 0.7992 | 0.5702 | 0.9667 | | 0.5542 | 1.67 | 2760 | 0.3147 | 0.4774 | 0.6336 | 0.9157 | nan | 0.3563 | 0.8646 | 0.0 | 0.9065 | 0.7039 | 0.9702 | 0.0 | 0.2631 | 0.7649 | 0.0 | 0.7932 | 0.5544 | 0.9664 | | 0.343 | 1.69 | 2780 | 0.3632 | 0.4858 | 0.6494 | 0.9212 | nan | 0.3345 | 0.8857 | 0.0 | 0.9134 | 0.7937 | 0.9689 | 0.0 | 0.2855 | 0.7758 | 0.0 | 0.7975 | 0.5756 | 0.9665 | | 0.4835 | 1.7 | 2800 | 0.3339 | 0.4753 | 0.6437 | 0.9181 | nan | 0.2891 | 0.8746 | 0.0 | 0.8945 | 0.8339 | 0.9703 | 0.0 | 0.2399 | 0.7766 | 0.0 | 0.7894 | 0.5538 | 0.9670 | | 0.1818 | 1.71 | 2820 | 0.3292 | 0.4655 | 0.6247 | 0.9133 | nan | 0.2390 | 0.8420 | 0.0 | 0.8972 | 0.8010 | 0.9688 | 0.0 | 0.1741 | 0.7599 | 0.0 | 0.7941 | 0.5642 | 0.9664 | | 0.9569 | 1.72 | 2840 | 0.3529 | 0.4755 | 0.6446 | 0.9220 | nan | 0.2926 | 0.8872 | 0.0 | 0.9090 | 0.8079 | 0.9709 | 0.0 | 0.2297 | 0.7690 | 0.0 | 0.8052 | 0.5572 | 0.9672 | | 0.1522 | 1.73 | 2860 | 0.3493 | 0.4606 | 0.6375 | 0.9130 | nan | 0.2946 | 0.8384 | 0.0 | 0.8860 | 0.8360 | 0.9702 | 0.0 | 0.2283 | 0.7457 | 0.0 | 0.7715 | 0.5112 | 0.9675 | | 0.1661 | 1.75 | 2880 | 0.3477 | 0.4546 | 0.6333 | 0.9095 | nan | 0.2724 | 0.8481 | 0.0 | 0.8671 | 0.8428 | 0.9693 | 0.0 | 0.2106 | 0.7487 | 0.0 | 0.7711 | 0.4853 | 0.9667 | | 0.2484 | 1.76 | 2900 | 0.3435 | 0.4515 | 0.6292 | 0.9050 | nan | 0.2757 | 0.8926 | 0.0 | 0.8347 | 0.8039 | 0.9683 | 0.0 | 0.1935 | 0.7419 | 0.0 | 0.7619 | 0.4975 | 0.9658 | | 0.1391 | 1.77 | 2920 | 0.3083 | 0.4621 | 0.6332 | 0.9138 | nan | 0.3116 | 0.8488 | 0.0 | 0.8965 | 0.7730 | 0.9696 | 0.0 | 0.2033 | 0.7516 | 0.0 | 0.7920 | 0.5213 | 0.9666 | | 0.1363 | 1.78 | 2940 | 0.3371 | 0.4638 | 0.6463 | 0.9110 | nan | 0.3354 | 0.8500 | 0.0 | 0.8745 | 0.8501 | 0.9679 | 0.0 | 0.2511 | 0.7327 | 0.0 | 0.7852 | 0.5121 | 0.9656 | | 0.3563 | 1.8 | 2960 | 0.3271 | 0.4584 | 0.6325 | 0.9096 | nan | 0.2815 | 0.8248 | 0.0 | 0.8714 | 0.8465 | 0.9711 | 0.0 | 0.2268 | 0.7214 | 0.0 | 0.7805 | 0.5133 | 0.9670 | | 0.7689 | 1.81 | 2980 | 0.3054 | 0.4593 | 0.6192 | 0.9096 | nan | 0.2350 | 0.8486 | 0.0 | 0.8729 | 0.7878 | 0.9708 | 0.0 | 0.1854 | 0.7343 | 0.0 | 0.7904 | 0.5387 | 0.9664 | | 0.333 | 1.82 | 3000 | 0.3318 | 0.4715 | 0.6438 | 0.9178 | nan | 0.2849 | 0.8976 | 0.0 | 0.8858 | 0.8248 | 0.9698 | 0.0 | 0.2529 | 0.7470 | 0.0 | 0.7938 | 0.5400 | 0.9669 | | 0.0664 | 1.83 | 3020 | 0.3174 | 0.4616 | 0.6548 | 0.9101 | nan | 0.3318 | 0.8963 | 0.0 | 0.8294 | 0.8993 | 0.9718 | 0.0 | 0.2594 | 0.7513 | 0.0 | 0.7654 | 0.4884 | 0.9670 | | 0.5802 | 1.84 | 3040 | 0.3003 | 0.4694 | 0.6533 | 0.9138 | nan | 0.3512 | 0.8930 | 0.0 | 0.8604 | 0.8446 | 0.9707 | 0.0 | 0.2368 | 0.7722 | 0.0 | 0.7820 | 0.5277 | 0.9668 | | 0.158 | 1.86 | 3060 | 0.2986 | 0.4762 | 0.6403 | 0.9197 | nan | 0.3481 | 0.8854 | 0.0 | 0.9133 | 0.7247 | 0.9701 | 0.0 | 0.2633 | 0.7703 | 0.0 | 0.7969 | 0.5362 | 0.9669 | | 0.1517 | 1.87 | 3080 | 0.3548 | 0.4843 | 0.6427 | 0.9240 | nan | 0.4025 | 0.8833 | 0.0 | 0.9451 | 0.6554 | 0.9699 | 0.0 | 0.3066 | 0.7910 | 0.0 | 0.7953 | 0.5295 | 0.9675 | | 0.5864 | 1.88 | 3100 | 0.3026 | 0.4797 | 0.6438 | 0.9193 | nan | 0.3682 | 0.8682 | 0.0 | 0.9130 | 0.7426 | 0.9709 | 0.0 | 0.2850 | 0.7875 | 0.0 | 0.7846 | 0.5333 | 0.9676 | | 0.4852 | 1.89 | 3120 | 0.3118 | 0.4811 | 0.6588 | 0.9188 | nan | 0.3660 | 0.8840 | 0.0 | 0.8897 | 0.8429 | 0.9703 | 0.0 | 0.2981 | 0.7871 | 0.0 | 0.7857 | 0.5299 | 0.9672 | | 0.2932 | 1.9 | 3140 | 0.2926 | 0.4920 | 0.6502 | 0.9245 | nan | 0.3744 | 0.8931 | 0.0 | 0.9253 | 0.7370 | 0.9717 | 0.0 | 0.3025 | 0.7904 | 0.0 | 0.8108 | 0.5731 | 0.9675 | | 0.2021 | 1.92 | 3160 | 0.3038 | 0.4779 | 0.6211 | 0.9178 | nan | 0.3510 | 0.8491 | 0.0 | 0.9417 | 0.6159 | 0.9689 | 0.0 | 0.2831 | 0.7536 | 0.0 | 0.8076 | 0.5351 | 0.9663 | | 0.3806 | 1.93 | 3180 | 0.3182 | 0.4760 | 0.6492 | 0.9141 | nan | 0.3445 | 0.8570 | 0.0 | 0.8832 | 0.8411 | 0.9692 | 0.0 | 0.2641 | 0.7392 | 0.0 | 0.7967 | 0.5651 | 0.9670 | | 0.1496 | 1.94 | 3200 | 0.3361 | 0.4753 | 0.6422 | 0.9156 | nan | 0.3138 | 0.9011 | 0.0 | 0.8849 | 0.7859 | 0.9678 | 0.0 | 0.2435 | 0.7803 | 0.0 | 0.7946 | 0.5434 | 0.9651 | | 0.0671 | 1.95 | 3220 | 0.3265 | 0.4742 | 0.6578 | 0.9147 | nan | 0.3656 | 0.8798 | 0.0 | 0.8741 | 0.8584 | 0.9687 | 0.0 | 0.2816 | 0.7842 | 0.0 | 0.7832 | 0.5041 | 0.9659 | | 0.15 | 1.96 | 3240 | 0.3244 | 0.4739 | 0.6427 | 0.9158 | nan | 0.3541 | 0.8804 | 0.0 | 0.8972 | 0.7564 | 0.9682 | 0.0 | 0.2528 | 0.7590 | 0.0 | 0.7978 | 0.5417 | 0.9660 | | 0.1933 | 1.98 | 3260 | 0.3516 | 0.4773 | 0.6542 | 0.9174 | nan | 0.3953 | 0.9202 | 0.0 | 0.8821 | 0.7583 | 0.9693 | 0.0 | 0.2658 | 0.7510 | 0.0 | 0.8026 | 0.5550 | 0.9665 | | 0.1333 | 1.99 | 3280 | 0.3080 | 0.4830 | 0.6444 | 0.9189 | nan | 0.3587 | 0.9054 | 0.0 | 0.9009 | 0.7318 | 0.9695 | 0.0 | 0.3008 | 0.7536 | 0.0 | 0.8027 | 0.5574 | 0.9667 | | 0.1016 | 2.0 | 3300 | 0.3048 | 0.4824 | 0.6479 | 0.9180 | nan | 0.3595 | 0.8686 | 0.0 | 0.9005 | 0.7879 | 0.9707 | 0.0 | 0.3022 | 0.7380 | 0.0 | 0.8046 | 0.5642 | 0.9675 | | 0.3692 | 2.01 | 3320 | 0.3021 | 0.4761 | 0.6468 | 0.9144 | nan | 0.3537 | 0.8509 | 0.0 | 0.8868 | 0.8186 | 0.9706 | 0.0 | 0.2802 | 0.7268 | 0.0 | 0.8011 | 0.5570 | 0.9672 | | 0.8706 | 2.03 | 3340 | 0.3320 | 0.4796 | 0.6430 | 0.9199 | nan | 0.3116 | 0.9048 | 0.0 | 0.8987 | 0.7727 | 0.9701 | 0.0 | 0.2747 | 0.7327 | 0.0 | 0.8107 | 0.5718 | 0.9674 | | 0.3265 | 2.04 | 3360 | 0.3169 | 0.4765 | 0.6552 | 0.9168 | nan | 0.3639 | 0.9043 | 0.0 | 0.8762 | 0.8171 | 0.9698 | 0.0 | 0.2622 | 0.7622 | 0.0 | 0.7963 | 0.5480 | 0.9671 | | 0.1013 | 2.05 | 3380 | 0.3117 | 0.4774 | 0.6759 | 0.9165 | nan | 0.4382 | 0.9000 | 0.0 | 0.8647 | 0.8830 | 0.9696 | 0.0 | 0.2732 | 0.7770 | 0.0 | 0.7877 | 0.5370 | 0.9668 | | 0.2655 | 2.06 | 3400 | 0.3157 | 0.4852 | 0.6580 | 0.9201 | nan | 0.3775 | 0.8841 | 0.0 | 0.9011 | 0.8152 | 0.9698 | 0.0 | 0.3075 | 0.7750 | 0.0 | 0.7963 | 0.5505 | 0.9673 | | 0.321 | 2.07 | 3420 | 0.2988 | 0.4807 | 0.6601 | 0.9180 | nan | 0.3850 | 0.8639 | 0.0 | 0.8911 | 0.8497 | 0.9709 | 0.0 | 0.2850 | 0.7833 | 0.0 | 0.7893 | 0.5400 | 0.9673 | | 0.1012 | 2.09 | 3440 | 0.3125 | 0.4939 | 0.6538 | 0.9251 | nan | 0.3966 | 0.8959 | 0.0 | 0.9308 | 0.7287 | 0.9706 | 0.0 | 0.3223 | 0.7911 | 0.0 | 0.8092 | 0.5669 | 0.9676 | | 0.412 | 2.1 | 3460 | 0.3296 | 0.4815 | 0.6585 | 0.9182 | nan | 0.3481 | 0.9184 | 0.0 | 0.8726 | 0.8417 | 0.9701 | 0.0 | 0.2909 | 0.7612 | 0.0 | 0.7971 | 0.5547 | 0.9666 | | 0.1383 | 2.11 | 3480 | 0.3082 | 0.4884 | 0.6540 | 0.9225 | nan | 0.3569 | 0.9140 | 0.0 | 0.9056 | 0.7776 | 0.9699 | 0.0 | 0.2999 | 0.7737 | 0.0 | 0.8086 | 0.5698 | 0.9665 | | 0.1925 | 2.12 | 3500 | 0.3206 | 0.4821 | 0.6536 | 0.9182 | nan | 0.3449 | 0.9109 | 0.0 | 0.8844 | 0.8124 | 0.9688 | 0.0 | 0.2829 | 0.7721 | 0.0 | 0.7977 | 0.5564 | 0.9659 | | 0.6483 | 2.13 | 3520 | 0.3155 | 0.4756 | 0.6540 | 0.9136 | nan | 0.3536 | 0.8712 | 0.0 | 0.8698 | 0.8595 | 0.9697 | 0.0 | 0.2857 | 0.7718 | 0.0 | 0.7790 | 0.5269 | 0.9660 | | 0.4534 | 2.15 | 3540 | 0.3218 | 0.4792 | 0.6513 | 0.9170 | nan | 0.3516 | 0.9113 | 0.0 | 0.8804 | 0.7955 | 0.9690 | 0.0 | 0.2800 | 0.7792 | 0.0 | 0.7901 | 0.5392 | 0.9657 | | 0.1899 | 2.16 | 3560 | 0.3131 | 0.4741 | 0.6607 | 0.9143 | nan | 0.3562 | 0.9017 | 0.0 | 0.8519 | 0.8837 | 0.9708 | 0.0 | 0.2942 | 0.7720 | 0.0 | 0.7761 | 0.5095 | 0.9666 | | 0.6685 | 2.17 | 3580 | 0.3230 | 0.4836 | 0.6603 | 0.9195 | nan | 0.3811 | 0.9106 | 0.0 | 0.8880 | 0.8125 | 0.9694 | 0.0 | 0.3074 | 0.7791 | 0.0 | 0.7905 | 0.5417 | 0.9664 | | 0.1743 | 2.18 | 3600 | 0.3060 | 0.4764 | 0.6571 | 0.9157 | nan | 0.3722 | 0.9087 | 0.0 | 0.8655 | 0.8258 | 0.9704 | 0.0 | 0.2614 | 0.7796 | 0.0 | 0.7862 | 0.5412 | 0.9661 | | 0.2988 | 2.2 | 3620 | 0.3150 | 0.4883 | 0.6569 | 0.9234 | nan | 0.3869 | 0.8954 | 0.0 | 0.9154 | 0.7728 | 0.9709 | 0.0 | 0.2894 | 0.7913 | 0.0 | 0.8052 | 0.5647 | 0.9671 | | 0.199 | 2.21 | 3640 | 0.3073 | 0.4799 | 0.6563 | 0.9205 | nan | 0.3823 | 0.9094 | 0.0 | 0.8966 | 0.7794 | 0.9700 | 0.0 | 0.2462 | 0.7752 | 0.0 | 0.8058 | 0.5652 | 0.9668 | | 0.356 | 2.22 | 3660 | 0.3088 | 0.4787 | 0.6539 | 0.9202 | nan | 0.3759 | 0.8945 | 0.0 | 0.9014 | 0.7813 | 0.9701 | 0.0 | 0.2406 | 0.7759 | 0.0 | 0.8051 | 0.5631 | 0.9663 | | 1.2003 | 2.23 | 3680 | 0.3037 | 0.4829 | 0.6551 | 0.9212 | nan | 0.4053 | 0.8855 | 0.0 | 0.9139 | 0.7558 | 0.9701 | 0.0 | 0.2744 | 0.7755 | 0.0 | 0.8075 | 0.5561 | 0.9666 | | 0.1801 | 2.24 | 3700 | 0.3155 | 0.4837 | 0.6570 | 0.9202 | nan | 0.4074 | 0.8924 | 0.0 | 0.9082 | 0.7655 | 0.9688 | 0.0 | 0.3066 | 0.7867 | 0.0 | 0.7995 | 0.5269 | 0.9664 | | 0.2767 | 2.26 | 3720 | 0.3365 | 0.4783 | 0.6767 | 0.9163 | nan | 0.4490 | 0.9065 | 0.0000 | 0.8610 | 0.8735 | 0.9699 | 0.0 | 0.2931 | 0.7931 | 0.0000 | 0.7863 | 0.5086 | 0.9666 | | 0.2641 | 2.27 | 3740 | 0.3103 | 0.4783 | 0.6719 | 0.9153 | nan | 0.4315 | 0.8901 | 0.0 | 0.8631 | 0.8765 | 0.9700 | 0.0 | 0.2993 | 0.7883 | 0.0 | 0.7826 | 0.5114 | 0.9668 | | 0.7382 | 2.28 | 3760 | 0.3684 | 0.4819 | 0.6608 | 0.9156 | nan | 0.3913 | 0.8903 | 0.0 | 0.8832 | 0.8335 | 0.9667 | 0.0 | 0.3122 | 0.7635 | 0.0 | 0.7916 | 0.5410 | 0.9651 | | 0.1887 | 2.29 | 3780 | 0.3380 | 0.4850 | 0.6672 | 0.9190 | nan | 0.3872 | 0.9062 | 0.0 | 0.8795 | 0.8607 | 0.9693 | 0.0 | 0.3182 | 0.7798 | 0.0 | 0.7920 | 0.5385 | 0.9668 | | 0.4301 | 2.3 | 3800 | 0.3126 | 0.4902 | 0.6655 | 0.9219 | nan | 0.4399 | 0.8957 | 0.0 | 0.9040 | 0.7823 | 0.9711 | 0.0 | 0.3320 | 0.7915 | 0.0 | 0.7986 | 0.5423 | 0.9673 | | 0.0796 | 2.32 | 3820 | 0.3078 | 0.4931 | 0.6700 | 0.9231 | nan | 0.4574 | 0.8945 | 0.0 | 0.9082 | 0.7887 | 0.9712 | 0.0 | 0.3244 | 0.7902 | 0.0 | 0.8045 | 0.5654 | 0.9673 | | 0.5856 | 2.33 | 3840 | 0.3280 | 0.4860 | 0.6739 | 0.9186 | nan | 0.4448 | 0.8992 | 0.0 | 0.8803 | 0.8492 | 0.9697 | 0.0 | 0.3209 | 0.7944 | 0.0 | 0.7874 | 0.5328 | 0.9665 | | 0.3133 | 2.34 | 3860 | 0.3306 | 0.4901 | 0.6625 | 0.9220 | nan | 0.4158 | 0.8912 | 0.0 | 0.9104 | 0.7877 | 0.9698 | 0.0 | 0.3321 | 0.7919 | 0.0 | 0.7933 | 0.5461 | 0.9669 | | 0.1759 | 2.35 | 3880 | 0.3318 | 0.4893 | 0.6707 | 0.9209 | nan | 0.4327 | 0.9019 | 0.0 | 0.8910 | 0.8278 | 0.9705 | 0.0 | 0.3309 | 0.7941 | 0.0 | 0.7933 | 0.5402 | 0.9669 | | 0.2173 | 2.37 | 3900 | 0.3227 | 0.4842 | 0.6738 | 0.9186 | nan | 0.4241 | 0.9066 | 0.0 | 0.8627 | 0.8765 | 0.9731 | 0.0 | 0.3222 | 0.7925 | 0.0 | 0.7836 | 0.5238 | 0.9670 | | 0.3338 | 2.38 | 3920 | 0.3146 | 0.4892 | 0.6725 | 0.9208 | nan | 0.4330 | 0.8966 | 0.0 | 0.8909 | 0.8443 | 0.9704 | 0.0 | 0.3364 | 0.7922 | 0.0 | 0.7910 | 0.5381 | 0.9671 | | 0.1098 | 2.39 | 3940 | 0.3051 | 0.4850 | 0.6648 | 0.9186 | nan | 0.4086 | 0.9006 | 0.0 | 0.8823 | 0.8271 | 0.9703 | 0.0 | 0.2986 | 0.7912 | 0.0 | 0.7915 | 0.5466 | 0.9668 | | 0.2892 | 2.4 | 3960 | 0.3277 | 0.4914 | 0.6582 | 0.9235 | nan | 0.4131 | 0.9019 | 0.0 | 0.9224 | 0.7429 | 0.9691 | 0.0 | 0.3010 | 0.7897 | 0.0 | 0.8109 | 0.5718 | 0.9666 | | 0.7496 | 2.41 | 3980 | 0.3239 | 0.4799 | 0.6699 | 0.9166 | nan | 0.4203 | 0.9044 | 0.0000 | 0.8701 | 0.8556 | 0.9690 | 0.0 | 0.2835 | 0.7871 | 0.0000 | 0.7897 | 0.5325 | 0.9666 | | 0.1267 | 2.43 | 4000 | 0.3030 | 0.4849 | 0.6719 | 0.9188 | nan | 0.4334 | 0.8752 | 0.0 | 0.8889 | 0.8636 | 0.9700 | 0.0 | 0.3219 | 0.7904 | 0.0 | 0.7879 | 0.5268 | 0.9670 | | 0.1651 | 2.44 | 4020 | 0.3301 | 0.4801 | 0.6728 | 0.9161 | nan | 0.4176 | 0.8973 | 0.0 | 0.8652 | 0.8877 | 0.9692 | 0.0 | 0.2969 | 0.7938 | 0.0 | 0.7830 | 0.5201 | 0.9669 | | 0.3399 | 2.45 | 4040 | 0.3104 | 0.4728 | 0.6705 | 0.9147 | nan | 0.4008 | 0.8929 | 0.0000 | 0.8517 | 0.9064 | 0.9712 | 0.0 | 0.2694 | 0.7927 | 0.0000 | 0.7785 | 0.5023 | 0.9669 | | 0.399 | 2.46 | 4060 | 0.2932 | 0.4825 | 0.6653 | 0.9207 | nan | 0.4182 | 0.8889 | 0.0000 | 0.8964 | 0.8174 | 0.9711 | 0.0 | 0.2890 | 0.7885 | 0.0000 | 0.7953 | 0.5376 | 0.9672 | | 0.1291 | 2.47 | 4080 | 0.3093 | 0.4793 | 0.6726 | 0.9178 | nan | 0.4242 | 0.8888 | 0.0 | 0.8791 | 0.8745 | 0.9691 | 0.0 | 0.2900 | 0.7911 | 0.0 | 0.7879 | 0.5195 | 0.9669 | | 0.0833 | 2.49 | 4100 | 0.3038 | 0.4784 | 0.6705 | 0.9162 | nan | 0.4206 | 0.8873 | 0.0000 | 0.8697 | 0.8757 | 0.9699 | 0.0 | 0.2837 | 0.7942 | 0.0000 | 0.7854 | 0.5195 | 0.9664 | | 0.4351 | 2.5 | 4120 | 0.3084 | 0.4812 | 0.6710 | 0.9181 | nan | 0.4290 | 0.8976 | 0.0 | 0.8771 | 0.8520 | 0.9702 | 0.0 | 0.2869 | 0.7811 | 0.0 | 0.7942 | 0.5398 | 0.9666 | | 0.6208 | 2.51 | 4140 | 0.3039 | 0.4776 | 0.6523 | 0.9168 | nan | 0.3598 | 0.8827 | 0.0 | 0.8828 | 0.8175 | 0.9710 | 0.0 | 0.2525 | 0.7696 | 0.0 | 0.7989 | 0.5560 | 0.9663 | | 0.3642 | 2.52 | 4160 | 0.3249 | 0.4892 | 0.6676 | 0.9218 | nan | 0.4016 | 0.9010 | 0.0 | 0.8974 | 0.8360 | 0.9699 | 0.0 | 0.3151 | 0.7717 | 0.0 | 0.8056 | 0.5650 | 0.9670 | | 0.6865 | 2.53 | 4180 | 0.3203 | 0.4934 | 0.6659 | 0.9243 | nan | 0.4052 | 0.8954 | 0.0 | 0.9134 | 0.8108 | 0.9708 | 0.0 | 0.3057 | 0.7841 | 0.0 | 0.8130 | 0.5842 | 0.9672 | | 1.0893 | 2.55 | 4200 | 0.2951 | 0.4863 | 0.6613 | 0.9193 | nan | 0.4150 | 0.8548 | 0.0000 | 0.9060 | 0.8212 | 0.9707 | 0.0 | 0.2905 | 0.7699 | 0.0000 | 0.8057 | 0.5707 | 0.9670 | | 0.4914 | 2.56 | 4220 | 0.2988 | 0.4951 | 0.6731 | 0.9224 | nan | 0.4553 | 0.8745 | 0.0000 | 0.9001 | 0.8352 | 0.9734 | 0.0 | 0.3491 | 0.7804 | 0.0000 | 0.8038 | 0.5630 | 0.9690 | | 0.2864 | 2.57 | 4240 | 0.3029 | 0.4946 | 0.6670 | 0.9232 | nan | 0.4448 | 0.8753 | 0.0 | 0.9182 | 0.7926 | 0.9710 | 0.0 | 0.3187 | 0.7765 | 0.0 | 0.8127 | 0.5870 | 0.9673 | | 0.1695 | 2.58 | 4260 | 0.3356 | 0.5014 | 0.6749 | 0.9263 | nan | 0.4545 | 0.8932 | 0.0 | 0.9249 | 0.8066 | 0.9702 | 0.0 | 0.3657 | 0.7799 | 0.0 | 0.8119 | 0.5847 | 0.9676 | | 0.5746 | 2.6 | 4280 | 0.3106 | 0.5024 | 0.6754 | 0.9260 | nan | 0.4610 | 0.8796 | 0.0 | 0.9206 | 0.8187 | 0.9723 | 0.0 | 0.3690 | 0.7837 | 0.0 | 0.8137 | 0.5818 | 0.9683 | | 0.2629 | 2.61 | 4300 | 0.3153 | 0.5020 | 0.6771 | 0.9269 | nan | 0.4768 | 0.9000 | 0.0 | 0.9266 | 0.7890 | 0.9702 | 0.0 | 0.3420 | 0.7826 | 0.0 | 0.8218 | 0.6005 | 0.9671 | | 0.4151 | 2.62 | 4320 | 0.2932 | 0.4957 | 0.6677 | 0.9229 | nan | 0.4382 | 0.8798 | 0.0 | 0.9142 | 0.8031 | 0.9706 | 0.0 | 0.3086 | 0.7840 | 0.0 | 0.8159 | 0.5944 | 0.9668 | | 0.1169 | 2.63 | 4340 | 0.2940 | 0.4995 | 0.6683 | 0.9244 | nan | 0.4620 | 0.8738 | 0.0 | 0.9295 | 0.7744 | 0.9701 | 0.0 | 0.3315 | 0.7860 | 0.0 | 0.8161 | 0.5960 | 0.9668 | | 0.1379 | 2.64 | 4360 | 0.2958 | 0.5047 | 0.6750 | 0.9264 | nan | 0.4636 | 0.8776 | 0.0000 | 0.9138 | 0.8195 | 0.9754 | 0.0 | 0.3667 | 0.7920 | 0.0000 | 0.8160 | 0.5891 | 0.9689 | | 0.1106 | 2.66 | 4380 | 0.3271 | 0.5063 | 0.6866 | 0.9278 | nan | 0.5061 | 0.9005 | 0.0 | 0.9251 | 0.8175 | 0.9702 | 0.0 | 0.3773 | 0.7976 | 0.0 | 0.8143 | 0.5878 | 0.9673 | | 0.6561 | 2.67 | 4400 | 0.3072 | 0.4820 | 0.6689 | 0.9179 | nan | 0.4274 | 0.9043 | 0.0001 | 0.8736 | 0.8372 | 0.9707 | 0.0 | 0.2704 | 0.7896 | 0.0001 | 0.7980 | 0.5493 | 0.9665 | | 1.0281 | 2.68 | 4420 | 0.3102 | 0.4815 | 0.6878 | 0.9179 | nan | 0.4964 | 0.9069 | 0.0000 | 0.8569 | 0.8942 | 0.9721 | 0.0 | 0.3072 | 0.7945 | 0.0000 | 0.7899 | 0.5115 | 0.9676 | | 0.2507 | 2.69 | 4440 | 0.3143 | 0.4839 | 0.6758 | 0.9200 | nan | 0.4543 | 0.9103 | 0.0 | 0.8806 | 0.8390 | 0.9704 | 0.0 | 0.2914 | 0.7904 | 0.0 | 0.8009 | 0.5380 | 0.9668 | | 1.8848 | 2.7 | 4460 | 0.3204 | 0.4827 | 0.6763 | 0.9186 | nan | 0.4533 | 0.9111 | 0.0000 | 0.8766 | 0.8475 | 0.9690 | 0.0 | 0.2813 | 0.7838 | 0.0000 | 0.8023 | 0.5453 | 0.9664 | | 0.2177 | 2.72 | 4480 | 0.3152 | 0.4841 | 0.6658 | 0.9196 | nan | 0.4292 | 0.8986 | 0.0 | 0.8922 | 0.8050 | 0.9699 | 0.0 | 0.2702 | 0.7881 | 0.0 | 0.8063 | 0.5579 | 0.9666 | | 0.2683 | 2.73 | 4500 | 0.3165 | 0.4810 | 0.6710 | 0.9176 | nan | 0.4378 | 0.9003 | 0.0 | 0.8797 | 0.8395 | 0.9687 | 0.0 | 0.2755 | 0.7847 | 0.0 | 0.7996 | 0.5408 | 0.9661 | | 0.4168 | 2.74 | 4520 | 0.3347 | 0.4920 | 0.6748 | 0.9233 | nan | 0.4527 | 0.9122 | 0.0 | 0.9021 | 0.8121 | 0.9698 | 0.0 | 0.3271 | 0.7776 | 0.0 | 0.8105 | 0.5616 | 0.9670 | | 0.89 | 2.75 | 4540 | 0.3345 | 0.4875 | 0.6709 | 0.9211 | nan | 0.4383 | 0.9006 | 0.0 | 0.8985 | 0.8189 | 0.9690 | 0.0 | 0.2903 | 0.7895 | 0.0 | 0.8071 | 0.5591 | 0.9666 | | 0.3392 | 2.77 | 4560 | 0.3020 | 0.4852 | 0.6730 | 0.9197 | nan | 0.4509 | 0.8977 | 0.0 | 0.8886 | 0.8312 | 0.9698 | 0.0 | 0.2847 | 0.7921 | 0.0 | 0.8024 | 0.5506 | 0.9665 | | 0.4607 | 2.78 | 4580 | 0.3230 | 0.4935 | 0.6757 | 0.9238 | nan | 0.4640 | 0.9097 | 0.0 | 0.9075 | 0.8036 | 0.9696 | 0.0 | 0.3195 | 0.7866 | 0.0 | 0.8116 | 0.5697 | 0.9670 | | 0.3654 | 2.79 | 4600 | 0.3109 | 0.4928 | 0.6672 | 0.9231 | nan | 0.4508 | 0.8907 | 0.0 | 0.9169 | 0.7750 | 0.9698 | 0.0 | 0.3078 | 0.7942 | 0.0 | 0.8102 | 0.5704 | 0.9670 | | 0.4694 | 2.8 | 4620 | 0.3250 | 0.4919 | 0.6806 | 0.9221 | nan | 0.4773 | 0.9033 | 0.0 | 0.8870 | 0.8435 | 0.9722 | 0.0 | 0.3328 | 0.7919 | 0.0 | 0.8015 | 0.5489 | 0.9679 | | 0.7987 | 2.81 | 4640 | 0.3292 | 0.4954 | 0.6686 | 0.9240 | nan | 0.4267 | 0.8898 | 0.0 | 0.9178 | 0.8078 | 0.9696 | 0.0 | 0.3340 | 0.7982 | 0.0 | 0.8063 | 0.5622 | 0.9672 | | 0.1422 | 2.83 | 4660 | 0.3098 | 0.4980 | 0.6793 | 0.9252 | nan | 0.4912 | 0.9051 | 0.0 | 0.9137 | 0.7954 | 0.9706 | 0.0 | 0.3358 | 0.7895 | 0.0 | 0.8146 | 0.5789 | 0.9674 | | 0.2764 | 2.84 | 4680 | 0.2950 | 0.4955 | 0.6780 | 0.9236 | nan | 0.4777 | 0.8876 | 0.0 | 0.9065 | 0.8249 | 0.9715 | 0.0 | 0.3347 | 0.7929 | 0.0 | 0.8095 | 0.5634 | 0.9678 | | 0.7027 | 2.85 | 4700 | 0.3178 | 0.4956 | 0.6811 | 0.9237 | nan | 0.4798 | 0.9043 | 0.0 | 0.9009 | 0.8310 | 0.9708 | 0.0 | 0.3432 | 0.7879 | 0.0 | 0.8083 | 0.5622 | 0.9676 | | 0.1181 | 2.86 | 4720 | 0.3131 | 0.4926 | 0.6812 | 0.9229 | nan | 0.4841 | 0.9019 | 0.0 | 0.8987 | 0.8317 | 0.9705 | 0.0 | 0.3250 | 0.7894 | 0.0 | 0.8072 | 0.5593 | 0.9673 | | 0.1929 | 2.87 | 4740 | 0.3345 | 0.4983 | 0.6796 | 0.9246 | nan | 0.4671 | 0.9066 | 0.0 | 0.9087 | 0.8258 | 0.9697 | 0.0 | 0.3638 | 0.7872 | 0.0 | 0.8075 | 0.5620 | 0.9673 | | 0.1227 | 2.89 | 4760 | 0.3008 | 0.4923 | 0.6766 | 0.9215 | nan | 0.4699 | 0.8828 | 0.0 | 0.8972 | 0.8386 | 0.9714 | 0.0 | 0.3415 | 0.7942 | 0.0 | 0.8013 | 0.5408 | 0.9681 | | 0.2807 | 2.9 | 4780 | 0.3065 | 0.4867 | 0.6674 | 0.9185 | nan | 0.4280 | 0.8885 | 0.0 | 0.8864 | 0.8315 | 0.9701 | 0.0 | 0.3093 | 0.7890 | 0.0 | 0.7973 | 0.5438 | 0.9672 | | 0.1685 | 2.91 | 4800 | 0.3054 | 0.4890 | 0.6712 | 0.9206 | nan | 0.4508 | 0.8940 | 0.0 | 0.8947 | 0.8176 | 0.9704 | 0.0 | 0.3112 | 0.7901 | 0.0 | 0.8020 | 0.5527 | 0.9672 | | 0.2884 | 2.92 | 4820 | 0.3072 | 0.4896 | 0.6770 | 0.9201 | nan | 0.4773 | 0.8843 | 0.0 | 0.8932 | 0.8372 | 0.9703 | 0.0 | 0.3251 | 0.7914 | 0.0 | 0.7997 | 0.5440 | 0.9673 | | 0.6886 | 2.94 | 4840 | 0.3077 | 0.4936 | 0.6643 | 0.9225 | nan | 0.4469 | 0.8662 | 0.0 | 0.9228 | 0.7796 | 0.9704 | 0.0 | 0.3339 | 0.7868 | 0.0 | 0.8057 | 0.5614 | 0.9676 | | 0.3344 | 2.95 | 4860 | 0.3017 | 0.4943 | 0.6733 | 0.9225 | nan | 0.4660 | 0.8746 | 0.0 | 0.9098 | 0.8184 | 0.9711 | 0.0 | 0.3488 | 0.7900 | 0.0 | 0.8032 | 0.5498 | 0.9681 | | 0.1385 | 2.96 | 4880 | 0.3149 | 0.4846 | 0.6688 | 0.9180 | nan | 0.4436 | 0.8811 | 0.0000 | 0.8905 | 0.8288 | 0.9692 | 0.0 | 0.2956 | 0.7873 | 0.0000 | 0.7986 | 0.5441 | 0.9667 | | 0.559 | 2.97 | 4900 | 0.3012 | 0.4827 | 0.6598 | 0.9172 | nan | 0.4204 | 0.8584 | 0.0000 | 0.8997 | 0.8108 | 0.9693 | 0.0 | 0.2854 | 0.7789 | 0.0000 | 0.7992 | 0.5489 | 0.9666 | | 0.0576 | 2.98 | 4920 | 0.3116 | 0.4880 | 0.6794 | 0.9200 | nan | 0.4841 | 0.8852 | 0.0 | 0.8866 | 0.8489 | 0.9714 | 0.0 | 0.3229 | 0.7898 | 0.0 | 0.7986 | 0.5372 | 0.9678 | | 0.1294 | 3.0 | 4940 | 0.3304 | 0.4794 | 0.6650 | 0.9144 | nan | 0.4315 | 0.8895 | 0.0 | 0.8740 | 0.8271 | 0.9678 | 0.0 | 0.2745 | 0.7784 | 0.0 | 0.7930 | 0.5438 | 0.9658 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]

Aassemtkt/segformer-b0-finetuned-drugs-in-bins-nov-23

# segformer-b0-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Aassemtkt/v0.1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "unlabeled", "drug-blister" ]

Aassemtkt/segformer-b3-finetuned-drugs-in-bins-nov-23

# segformer-b3-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the Aassemtkt/v0.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.0494 - Mean Iou: 0.4900 - Mean Accuracy: 0.9799 - Overall Accuracy: 0.9799 - Accuracy Unlabeled: nan - Accuracy Drug-blister: 0.9799 - Iou Unlabeled: 0.0 - Iou Drug-blister: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Drug-blister | Iou Unlabeled | Iou Drug-blister | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------------:|:-------------:|:----------------:| | 0.4816 | 0.14 | 20 | 0.2387 | 0.4744 | 0.9488 | 0.9488 | nan | 0.9488 | 0.0 | 0.9488 | | 0.1071 | 0.29 | 40 | 0.0969 | 0.4766 | 0.9532 | 0.9532 | nan | 0.9532 | 0.0 | 0.9532 | | 0.102 | 0.43 | 60 | 0.0701 | 0.4799 | 0.9599 | 0.9599 | nan | 0.9599 | 0.0 | 0.9599 | | 0.0828 | 0.58 | 80 | 0.0748 | 0.4865 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 | | 0.2944 | 0.72 | 100 | 0.0517 | 0.4816 | 0.9633 | 0.9633 | nan | 0.9633 | 0.0 | 0.9633 | | 0.0308 | 0.86 | 120 | 0.0493 | 0.4854 | 0.9709 | 0.9709 | nan | 0.9709 | 0.0 | 0.9709 | | 0.0247 | 1.01 | 140 | 0.0488 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 | | 0.0194 | 1.15 | 160 | 0.0447 | 0.4864 | 0.9728 | 0.9728 | nan | 0.9728 | 0.0 | 0.9728 | | 0.1873 | 1.29 | 180 | 0.0496 | 0.4789 | 0.9579 | 0.9579 | nan | 0.9579 | 0.0 | 0.9579 | | 0.0984 | 1.44 | 200 | 0.0442 | 0.4838 | 0.9676 | 0.9676 | nan | 0.9676 | 0.0 | 0.9676 | | 0.4066 | 1.58 | 220 | 0.0384 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0197 | 1.73 | 240 | 0.0567 | 0.4809 | 0.9619 | 0.9619 | nan | 0.9619 | 0.0 | 0.9619 | | 0.068 | 1.87 | 260 | 0.0389 | 0.4849 | 0.9698 | 0.9698 | nan | 0.9698 | 0.0 | 0.9698 | | 0.029 | 2.01 | 280 | 0.0351 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 | | 0.016 | 2.16 | 300 | 0.0373 | 0.4821 | 0.9642 | 0.9642 | nan | 0.9642 | 0.0 | 0.9642 | | 0.0146 | 2.3 | 320 | 0.0367 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0123 | 2.45 | 340 | 0.0388 | 0.4872 | 0.9745 | 0.9745 | nan | 0.9745 | 0.0 | 0.9745 | | 0.1359 | 2.59 | 360 | 0.0360 | 0.4858 | 0.9715 | 0.9715 | nan | 0.9715 | 0.0 | 0.9715 | | 0.0142 | 2.73 | 380 | 0.0337 | 0.4882 | 0.9765 | 0.9765 | nan | 0.9765 | 0.0 | 0.9765 | | 0.012 | 2.88 | 400 | 0.0357 | 0.4865 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 | | 0.0101 | 3.02 | 420 | 0.0370 | 0.4864 | 0.9728 | 0.9728 | nan | 0.9728 | 0.0 | 0.9728 | | 0.0098 | 3.17 | 440 | 0.0361 | 0.4870 | 0.9740 | 0.9740 | nan | 0.9740 | 0.0 | 0.9740 | | 0.0226 | 3.31 | 460 | 0.0349 | 0.4895 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0157 | 3.45 | 480 | 0.0362 | 0.4856 | 0.9712 | 0.9712 | nan | 0.9712 | 0.0 | 0.9712 | | 0.0145 | 3.6 | 500 | 0.0468 | 0.4816 | 0.9632 | 0.9632 | nan | 0.9632 | 0.0 | 0.9632 | | 0.1801 | 3.74 | 520 | 0.0324 | 0.4906 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0129 | 3.88 | 540 | 0.0314 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0159 | 4.03 | 560 | 0.0310 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0132 | 4.17 | 580 | 0.0321 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0126 | 4.32 | 600 | 0.0329 | 0.4874 | 0.9747 | 0.9747 | nan | 0.9747 | 0.0 | 0.9747 | | 0.0156 | 4.46 | 620 | 0.0381 | 0.4876 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 0.0147 | 4.6 | 640 | 0.0322 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0174 | 4.75 | 660 | 0.0344 | 0.4886 | 0.9772 | 0.9772 | nan | 0.9772 | 0.0 | 0.9772 | | 0.1191 | 4.89 | 680 | 0.0378 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 | | 0.0117 | 5.04 | 700 | 0.0386 | 0.4873 | 0.9745 | 0.9745 | nan | 0.9745 | 0.0 | 0.9745 | | 0.0193 | 5.18 | 720 | 0.0361 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0214 | 5.32 | 740 | 0.0360 | 0.4886 | 0.9772 | 0.9772 | nan | 0.9772 | 0.0 | 0.9772 | | 0.0184 | 5.47 | 760 | 0.0322 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0262 | 5.61 | 780 | 0.0357 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0115 | 5.76 | 800 | 0.0386 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 | | 0.0145 | 5.9 | 820 | 0.0394 | 0.4879 | 0.9759 | 0.9759 | nan | 0.9759 | 0.0 | 0.9759 | | 0.0097 | 6.04 | 840 | 0.0322 | 0.4889 | 0.9777 | 0.9777 | nan | 0.9777 | 0.0 | 0.9777 | | 0.0101 | 6.19 | 860 | 0.0313 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0099 | 6.33 | 880 | 0.0336 | 0.4876 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 0.0092 | 6.47 | 900 | 0.0342 | 0.4894 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0087 | 6.62 | 920 | 0.0352 | 0.4913 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.019 | 6.76 | 940 | 0.0516 | 0.4871 | 0.9742 | 0.9742 | nan | 0.9742 | 0.0 | 0.9742 | | 0.0104 | 6.91 | 960 | 0.0364 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 | | 0.0079 | 7.05 | 980 | 0.0300 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.0107 | 7.19 | 1000 | 0.0327 | 0.4939 | 0.9878 | 0.9878 | nan | 0.9878 | 0.0 | 0.9878 | | 0.0097 | 7.34 | 1020 | 0.0294 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0359 | 7.48 | 1040 | 0.0321 | 0.4908 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0674 | 7.63 | 1060 | 0.0321 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.1484 | 7.77 | 1080 | 0.0428 | 0.4868 | 0.9737 | 0.9737 | nan | 0.9737 | 0.0 | 0.9737 | | 0.188 | 7.91 | 1100 | 0.0338 | 0.4945 | 0.9890 | 0.9890 | nan | 0.9890 | 0.0 | 0.9890 | | 0.0124 | 8.06 | 1120 | 0.0345 | 0.4873 | 0.9746 | 0.9746 | nan | 0.9746 | 0.0 | 0.9746 | | 0.011 | 8.2 | 1140 | 0.0350 | 0.4913 | 0.9827 | 0.9827 | nan | 0.9827 | 0.0 | 0.9827 | | 0.0076 | 8.35 | 1160 | 0.0373 | 0.4884 | 0.9767 | 0.9767 | nan | 0.9767 | 0.0 | 0.9767 | | 0.0074 | 8.49 | 1180 | 0.0378 | 0.4931 | 0.9862 | 0.9862 | nan | 0.9862 | 0.0 | 0.9862 | | 0.0757 | 8.63 | 1200 | 0.0364 | 0.4880 | 0.9761 | 0.9761 | nan | 0.9761 | 0.0 | 0.9761 | | 0.0276 | 8.78 | 1220 | 0.0297 | 0.4906 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0072 | 8.92 | 1240 | 0.0308 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0061 | 9.06 | 1260 | 0.0308 | 0.4912 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.0063 | 9.21 | 1280 | 0.0323 | 0.4894 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0088 | 9.35 | 1300 | 0.0308 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0129 | 9.5 | 1320 | 0.0295 | 0.4911 | 0.9823 | 0.9823 | nan | 0.9823 | 0.0 | 0.9823 | | 0.0277 | 9.64 | 1340 | 0.0388 | 0.4876 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 0.0115 | 9.78 | 1360 | 0.0345 | 0.4894 | 0.9787 | 0.9787 | nan | 0.9787 | 0.0 | 0.9787 | | 0.0129 | 9.93 | 1380 | 0.0394 | 0.4879 | 0.9758 | 0.9758 | nan | 0.9758 | 0.0 | 0.9758 | | 0.0092 | 10.07 | 1400 | 0.0335 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.0107 | 10.22 | 1420 | 0.0348 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0072 | 10.36 | 1440 | 0.0334 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0081 | 10.5 | 1460 | 0.0409 | 0.4886 | 0.9772 | 0.9772 | nan | 0.9772 | 0.0 | 0.9772 | | 0.0158 | 10.65 | 1480 | 0.0337 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0058 | 10.79 | 1500 | 0.0364 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.0102 | 10.94 | 1520 | 0.0354 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.0098 | 11.08 | 1540 | 0.0515 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 | | 0.0063 | 11.22 | 1560 | 0.0337 | 0.4882 | 0.9763 | 0.9763 | nan | 0.9763 | 0.0 | 0.9763 | | 0.0151 | 11.37 | 1580 | 0.0313 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0197 | 11.51 | 1600 | 0.0384 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0093 | 11.65 | 1620 | 0.0328 | 0.4910 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.2493 | 11.8 | 1640 | 0.0413 | 0.4880 | 0.9759 | 0.9759 | nan | 0.9759 | 0.0 | 0.9759 | | 0.0133 | 11.94 | 1660 | 0.0385 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 | | 0.0484 | 12.09 | 1680 | 0.0364 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0074 | 12.23 | 1700 | 0.0334 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.0202 | 12.37 | 1720 | 0.0409 | 0.4876 | 0.9752 | 0.9752 | nan | 0.9752 | 0.0 | 0.9752 | | 0.006 | 12.52 | 1740 | 0.0540 | 0.4860 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 | | 0.0059 | 12.66 | 1760 | 0.0601 | 0.4857 | 0.9714 | 0.9714 | nan | 0.9714 | 0.0 | 0.9714 | | 0.0083 | 12.81 | 1780 | 0.0348 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.011 | 12.95 | 1800 | 0.0402 | 0.4885 | 0.9770 | 0.9770 | nan | 0.9770 | 0.0 | 0.9770 | | 0.045 | 13.09 | 1820 | 0.0322 | 0.4911 | 0.9822 | 0.9822 | nan | 0.9822 | 0.0 | 0.9822 | | 0.043 | 13.24 | 1840 | 0.0331 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0061 | 13.38 | 1860 | 0.0314 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 | | 0.0062 | 13.53 | 1880 | 0.0358 | 0.4890 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0087 | 13.67 | 1900 | 0.0334 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0106 | 13.81 | 1920 | 0.0341 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0554 | 13.96 | 1940 | 0.0359 | 0.4881 | 0.9762 | 0.9762 | nan | 0.9762 | 0.0 | 0.9762 | | 0.009 | 14.1 | 1960 | 0.0424 | 0.4865 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 | | 0.0078 | 14.24 | 1980 | 0.0329 | 0.4885 | 0.9770 | 0.9770 | nan | 0.9770 | 0.0 | 0.9770 | | 0.012 | 14.39 | 2000 | 0.0346 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0064 | 14.53 | 2020 | 0.0362 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0345 | 14.68 | 2040 | 0.0309 | 0.4919 | 0.9838 | 0.9838 | nan | 0.9838 | 0.0 | 0.9838 | | 0.0075 | 14.82 | 2060 | 0.0389 | 0.4884 | 0.9768 | 0.9768 | nan | 0.9768 | 0.0 | 0.9768 | | 0.0066 | 14.96 | 2080 | 0.0337 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.0081 | 15.11 | 2100 | 0.0365 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0071 | 15.25 | 2120 | 0.0349 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0054 | 15.4 | 2140 | 0.0388 | 0.4885 | 0.9769 | 0.9769 | nan | 0.9769 | 0.0 | 0.9769 | | 0.4004 | 15.54 | 2160 | 0.0339 | 0.4909 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | | 0.008 | 15.68 | 2180 | 0.0422 | 0.4896 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0365 | 15.83 | 2200 | 0.0468 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 | | 0.0067 | 15.97 | 2220 | 0.0416 | 0.4890 | 0.9780 | 0.9780 | nan | 0.9780 | 0.0 | 0.9780 | | 0.0079 | 16.12 | 2240 | 0.0377 | 0.4908 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0075 | 16.26 | 2260 | 0.0420 | 0.4889 | 0.9779 | 0.9779 | nan | 0.9779 | 0.0 | 0.9779 | | 0.0063 | 16.4 | 2280 | 0.0422 | 0.4889 | 0.9777 | 0.9777 | nan | 0.9777 | 0.0 | 0.9777 | | 0.0062 | 16.55 | 2300 | 0.0338 | 0.4912 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.0413 | 16.69 | 2320 | 0.0345 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0411 | 16.83 | 2340 | 0.0387 | 0.4891 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0548 | 16.98 | 2360 | 0.0333 | 0.4936 | 0.9872 | 0.9872 | nan | 0.9872 | 0.0 | 0.9872 | | 0.0431 | 17.12 | 2380 | 0.0352 | 0.4887 | 0.9773 | 0.9773 | nan | 0.9773 | 0.0 | 0.9773 | | 0.0069 | 17.27 | 2400 | 0.0327 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0059 | 17.41 | 2420 | 0.0406 | 0.4881 | 0.9763 | 0.9763 | nan | 0.9763 | 0.0 | 0.9763 | | 0.0062 | 17.55 | 2440 | 0.0434 | 0.4875 | 0.9750 | 0.9750 | nan | 0.9750 | 0.0 | 0.9750 | | 0.0064 | 17.7 | 2460 | 0.0350 | 0.4910 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0077 | 17.84 | 2480 | 0.0390 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 | | 0.0061 | 17.99 | 2500 | 0.0395 | 0.4906 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0073 | 18.13 | 2520 | 0.0370 | 0.4911 | 0.9822 | 0.9822 | nan | 0.9822 | 0.0 | 0.9822 | | 0.0038 | 18.27 | 2540 | 0.0383 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0066 | 18.42 | 2560 | 0.0394 | 0.4888 | 0.9776 | 0.9776 | nan | 0.9776 | 0.0 | 0.9776 | | 0.0232 | 18.56 | 2580 | 0.0384 | 0.4891 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0066 | 18.71 | 2600 | 0.0408 | 0.4887 | 0.9773 | 0.9773 | nan | 0.9773 | 0.0 | 0.9773 | | 0.0355 | 18.85 | 2620 | 0.0367 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0048 | 18.99 | 2640 | 0.0366 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0083 | 19.14 | 2660 | 0.0422 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0215 | 19.28 | 2680 | 0.0376 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0315 | 19.42 | 2700 | 0.0370 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0061 | 19.57 | 2720 | 0.0380 | 0.4909 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0048 | 19.71 | 2740 | 0.0371 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0058 | 19.86 | 2760 | 0.0389 | 0.4892 | 0.9785 | 0.9785 | nan | 0.9785 | 0.0 | 0.9785 | | 0.01 | 20.0 | 2780 | 0.0354 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.0051 | 20.14 | 2800 | 0.0380 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0053 | 20.29 | 2820 | 0.0426 | 0.4889 | 0.9779 | 0.9779 | nan | 0.9779 | 0.0 | 0.9779 | | 0.0062 | 20.43 | 2840 | 0.0359 | 0.4913 | 0.9827 | 0.9827 | nan | 0.9827 | 0.0 | 0.9827 | | 0.0573 | 20.58 | 2860 | 0.0370 | 0.4909 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | | 0.0087 | 20.72 | 2880 | 0.0418 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0144 | 20.86 | 2900 | 0.0398 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0157 | 21.01 | 2920 | 0.0463 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0063 | 21.15 | 2940 | 0.0357 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0079 | 21.29 | 2960 | 0.0339 | 0.4928 | 0.9856 | 0.9856 | nan | 0.9856 | 0.0 | 0.9856 | | 0.0052 | 21.44 | 2980 | 0.0419 | 0.4888 | 0.9775 | 0.9775 | nan | 0.9775 | 0.0 | 0.9775 | | 0.0068 | 21.58 | 3000 | 0.0370 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0109 | 21.73 | 3020 | 0.0350 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0048 | 21.87 | 3040 | 0.0353 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0053 | 22.01 | 3060 | 0.0369 | 0.4911 | 0.9823 | 0.9823 | nan | 0.9823 | 0.0 | 0.9823 | | 0.006 | 22.16 | 3080 | 0.0339 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0336 | 22.3 | 3100 | 0.0339 | 0.4914 | 0.9828 | 0.9828 | nan | 0.9828 | 0.0 | 0.9828 | | 0.0222 | 22.45 | 3120 | 0.0513 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.0072 | 22.59 | 3140 | 0.0328 | 0.4920 | 0.9840 | 0.9840 | nan | 0.9840 | 0.0 | 0.9840 | | 0.0046 | 22.73 | 3160 | 0.0334 | 0.4907 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.0039 | 22.88 | 3180 | 0.0352 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0059 | 23.02 | 3200 | 0.0359 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0049 | 23.17 | 3220 | 0.0425 | 0.4881 | 0.9762 | 0.9762 | nan | 0.9762 | 0.0 | 0.9762 | | 0.0244 | 23.31 | 3240 | 0.0351 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0047 | 23.45 | 3260 | 0.0339 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0074 | 23.6 | 3280 | 0.0382 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0062 | 23.74 | 3300 | 0.0366 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0339 | 23.88 | 3320 | 0.0378 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.005 | 24.03 | 3340 | 0.0395 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0038 | 24.17 | 3360 | 0.0455 | 0.4887 | 0.9773 | 0.9773 | nan | 0.9773 | 0.0 | 0.9773 | | 0.008 | 24.32 | 3380 | 0.0389 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0071 | 24.46 | 3400 | 0.0367 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0308 | 24.6 | 3420 | 0.0390 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0062 | 24.75 | 3440 | 0.0368 | 0.4918 | 0.9837 | 0.9837 | nan | 0.9837 | 0.0 | 0.9837 | | 0.0062 | 24.89 | 3460 | 0.0378 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.006 | 25.04 | 3480 | 0.0413 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0057 | 25.18 | 3500 | 0.0383 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0149 | 25.32 | 3520 | 0.0367 | 0.4911 | 0.9822 | 0.9822 | nan | 0.9822 | 0.0 | 0.9822 | | 0.0185 | 25.47 | 3540 | 0.0409 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0057 | 25.61 | 3560 | 0.0390 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.005 | 25.76 | 3580 | 0.0383 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0109 | 25.9 | 3600 | 0.0379 | 0.4909 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | | 0.0055 | 26.04 | 3620 | 0.0471 | 0.4883 | 0.9767 | 0.9767 | nan | 0.9767 | 0.0 | 0.9767 | | 0.042 | 26.19 | 3640 | 0.0481 | 0.4877 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 | | 0.0226 | 26.33 | 3660 | 0.0383 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0143 | 26.47 | 3680 | 0.0402 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.008 | 26.62 | 3700 | 0.0381 | 0.4908 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0241 | 26.76 | 3720 | 0.0411 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0055 | 26.91 | 3740 | 0.0386 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0109 | 27.05 | 3760 | 0.0376 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 | | 0.0072 | 27.19 | 3780 | 0.0457 | 0.4890 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0048 | 27.34 | 3800 | 0.0512 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.006 | 27.48 | 3820 | 0.0430 | 0.4891 | 0.9783 | 0.9783 | nan | 0.9783 | 0.0 | 0.9783 | | 0.0161 | 27.63 | 3840 | 0.0404 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0169 | 27.77 | 3860 | 0.0386 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0041 | 27.91 | 3880 | 0.0375 | 0.4917 | 0.9835 | 0.9835 | nan | 0.9835 | 0.0 | 0.9835 | | 0.0068 | 28.06 | 3900 | 0.0381 | 0.4917 | 0.9834 | 0.9834 | nan | 0.9834 | 0.0 | 0.9834 | | 0.0122 | 28.2 | 3920 | 0.0463 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0055 | 28.35 | 3940 | 0.0456 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 | | 0.0048 | 28.49 | 3960 | 0.0398 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0126 | 28.63 | 3980 | 0.0401 | 0.4917 | 0.9834 | 0.9834 | nan | 0.9834 | 0.0 | 0.9834 | | 0.0134 | 28.78 | 4000 | 0.0404 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0109 | 28.92 | 4020 | 0.0414 | 0.4894 | 0.9787 | 0.9787 | nan | 0.9787 | 0.0 | 0.9787 | | 0.0479 | 29.06 | 4040 | 0.0409 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0061 | 29.21 | 4060 | 0.0422 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0278 | 29.35 | 4080 | 0.0416 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0049 | 29.5 | 4100 | 0.0400 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0438 | 29.64 | 4120 | 0.0390 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0041 | 29.78 | 4140 | 0.0425 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0078 | 29.93 | 4160 | 0.0411 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0057 | 30.07 | 4180 | 0.0375 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.0053 | 30.22 | 4200 | 0.0423 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0133 | 30.36 | 4220 | 0.0429 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0045 | 30.5 | 4240 | 0.0454 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0044 | 30.65 | 4260 | 0.0415 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.006 | 30.79 | 4280 | 0.0420 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0043 | 30.94 | 4300 | 0.0428 | 0.4899 | 0.9797 | 0.9797 | nan | 0.9797 | 0.0 | 0.9797 | | 0.017 | 31.08 | 4320 | 0.0421 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0043 | 31.22 | 4340 | 0.0400 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0061 | 31.37 | 4360 | 0.0383 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0378 | 31.51 | 4380 | 0.0371 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 | | 0.0052 | 31.65 | 4400 | 0.0382 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0046 | 31.8 | 4420 | 0.0398 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0076 | 31.94 | 4440 | 0.0400 | 0.4904 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0062 | 32.09 | 4460 | 0.0396 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0152 | 32.23 | 4480 | 0.0399 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0044 | 32.37 | 4500 | 0.0426 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0104 | 32.52 | 4520 | 0.0431 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0041 | 32.66 | 4540 | 0.0458 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0084 | 32.81 | 4560 | 0.0457 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0046 | 32.95 | 4580 | 0.0465 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0038 | 33.09 | 4600 | 0.0422 | 0.4907 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.0039 | 33.24 | 4620 | 0.0410 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.004 | 33.38 | 4640 | 0.0427 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.006 | 33.53 | 4660 | 0.0458 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0039 | 33.67 | 4680 | 0.0484 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0065 | 33.81 | 4700 | 0.0516 | 0.4894 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0065 | 33.96 | 4720 | 0.0525 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0041 | 34.1 | 4740 | 0.0462 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0031 | 34.24 | 4760 | 0.0458 | 0.4909 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0039 | 34.39 | 4780 | 0.0493 | 0.4895 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0125 | 34.53 | 4800 | 0.0467 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0038 | 34.68 | 4820 | 0.0456 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0043 | 34.82 | 4840 | 0.0484 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0098 | 34.96 | 4860 | 0.0460 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.004 | 35.11 | 4880 | 0.0475 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0087 | 35.25 | 4900 | 0.0460 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0093 | 35.4 | 4920 | 0.0455 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0052 | 35.54 | 4940 | 0.0500 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0045 | 35.68 | 4960 | 0.0482 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0036 | 35.83 | 4980 | 0.0443 | 0.4906 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0034 | 35.97 | 5000 | 0.0426 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0041 | 36.12 | 5020 | 0.0415 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0043 | 36.26 | 5040 | 0.0450 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.007 | 36.4 | 5060 | 0.0467 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.006 | 36.55 | 5080 | 0.0463 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.006 | 36.69 | 5100 | 0.0468 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0043 | 36.83 | 5120 | 0.0428 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0073 | 36.98 | 5140 | 0.0417 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0188 | 37.12 | 5160 | 0.0418 | 0.4908 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.0052 | 37.27 | 5180 | 0.0450 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0089 | 37.41 | 5200 | 0.0476 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0041 | 37.55 | 5220 | 0.0505 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0062 | 37.7 | 5240 | 0.0478 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0035 | 37.84 | 5260 | 0.0463 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0163 | 37.99 | 5280 | 0.0453 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0054 | 38.13 | 5300 | 0.0462 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0132 | 38.27 | 5320 | 0.0481 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.0056 | 38.42 | 5340 | 0.0460 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0054 | 38.56 | 5360 | 0.0449 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0037 | 38.71 | 5380 | 0.0432 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0049 | 38.85 | 5400 | 0.0449 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0044 | 38.99 | 5420 | 0.0448 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0037 | 39.14 | 5440 | 0.0462 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0079 | 39.28 | 5460 | 0.0490 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0033 | 39.42 | 5480 | 0.0494 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0066 | 39.57 | 5500 | 0.0458 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0053 | 39.71 | 5520 | 0.0482 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0044 | 39.86 | 5540 | 0.0483 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0044 | 40.0 | 5560 | 0.0497 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0062 | 40.14 | 5580 | 0.0476 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 | | 0.0047 | 40.29 | 5600 | 0.0467 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.006 | 40.43 | 5620 | 0.0444 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0041 | 40.58 | 5640 | 0.0459 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0098 | 40.72 | 5660 | 0.0447 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0026 | 40.86 | 5680 | 0.0439 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0043 | 41.01 | 5700 | 0.0466 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0044 | 41.15 | 5720 | 0.0444 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0041 | 41.29 | 5740 | 0.0452 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0043 | 41.44 | 5760 | 0.0468 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0071 | 41.58 | 5780 | 0.0482 | 0.4897 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0187 | 41.73 | 5800 | 0.0463 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0034 | 41.87 | 5820 | 0.0456 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0238 | 42.01 | 5840 | 0.0450 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0048 | 42.16 | 5860 | 0.0464 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0116 | 42.3 | 5880 | 0.0475 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0039 | 42.45 | 5900 | 0.0475 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0042 | 42.59 | 5920 | 0.0446 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0069 | 42.73 | 5940 | 0.0441 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0045 | 42.88 | 5960 | 0.0460 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0038 | 43.02 | 5980 | 0.0501 | 0.4896 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0123 | 43.17 | 6000 | 0.0490 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0079 | 43.31 | 6020 | 0.0471 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.004 | 43.45 | 6040 | 0.0453 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0145 | 43.6 | 6060 | 0.0439 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0038 | 43.74 | 6080 | 0.0466 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.004 | 43.88 | 6100 | 0.0467 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0044 | 44.03 | 6120 | 0.0480 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0193 | 44.17 | 6140 | 0.0458 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0036 | 44.32 | 6160 | 0.0470 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0042 | 44.46 | 6180 | 0.0456 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0031 | 44.6 | 6200 | 0.0454 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0117 | 44.75 | 6220 | 0.0478 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0036 | 44.89 | 6240 | 0.0482 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0036 | 45.04 | 6260 | 0.0506 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0052 | 45.18 | 6280 | 0.0485 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0035 | 45.32 | 6300 | 0.0496 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0056 | 45.47 | 6320 | 0.0494 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0172 | 45.61 | 6340 | 0.0482 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0041 | 45.76 | 6360 | 0.0484 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0034 | 45.9 | 6380 | 0.0492 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0108 | 46.04 | 6400 | 0.0481 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0054 | 46.19 | 6420 | 0.0474 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0102 | 46.33 | 6440 | 0.0483 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0036 | 46.47 | 6460 | 0.0493 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0057 | 46.62 | 6480 | 0.0496 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.003 | 46.76 | 6500 | 0.0504 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0057 | 46.91 | 6520 | 0.0492 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0048 | 47.05 | 6540 | 0.0524 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.004 | 47.19 | 6560 | 0.0500 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0218 | 47.34 | 6580 | 0.0502 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0038 | 47.48 | 6600 | 0.0532 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0029 | 47.63 | 6620 | 0.0496 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0035 | 47.77 | 6640 | 0.0508 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0049 | 47.91 | 6660 | 0.0501 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0056 | 48.06 | 6680 | 0.0488 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0182 | 48.2 | 6700 | 0.0482 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0056 | 48.35 | 6720 | 0.0494 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0029 | 48.49 | 6740 | 0.0501 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0076 | 48.63 | 6760 | 0.0480 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0042 | 48.78 | 6780 | 0.0514 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0069 | 48.92 | 6800 | 0.0483 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0144 | 49.06 | 6820 | 0.0472 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0041 | 49.21 | 6840 | 0.0491 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0034 | 49.35 | 6860 | 0.0481 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0117 | 49.5 | 6880 | 0.0482 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0042 | 49.64 | 6900 | 0.0499 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0057 | 49.78 | 6920 | 0.0507 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0183 | 49.93 | 6940 | 0.0494 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "unlabeled", "drug-blister" ]

gdurkin/segformer-b0-finetuned-segments-floods-S2

# segformer-b0-finetuned-segments-floods-S2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the gdurkin/flood_dataset_S2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu121 - Datasets 2.11.0 - Tokenizers 0.13.3

[ "invalid", "not water", "water" ]

peldrak/segformer-finetuned-coastal

# segformer-finetuned-coastal This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coastal3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7456 - Mean Iou: 0.0841 - Mean Accuracy: 0.1629 - Overall Accuracy: 0.3309 - Accuracy Water: 0.2728 - Accuracy Whitewater: 0.0 - Accuracy Sediment: 0.0194 - Accuracy Other Natural Terrain: 0.0 - Accuracy Vegetation: 0.7325 - Accuracy Development: 0.0055 - Accuracy Unknown: 0.1102 - Iou Water: 0.1767 - Iou Whitewater: 0.0 - Iou Sediment: 0.0157 - Iou Other Natural Terrain: 0.0 - Iou Vegetation: 0.3002 - Iou Development: 0.0052 - Iou Unknown: 0.0908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:| | 1.8429 | 0.12 | 20 | 1.8742 | 0.0623 | 0.1432 | 0.2198 | 0.5172 | 0.0044 | 0.3137 | 0.0050 | 0.1165 | 0.0019 | 0.0435 | 0.2514 | 0.0037 | 0.0521 | 0.0034 | 0.0889 | 0.0019 | 0.0348 | | 1.6623 | 0.25 | 40 | 1.8297 | 0.0731 | 0.1572 | 0.2536 | 0.4349 | 0.0007 | 0.3336 | 0.0010 | 0.3158 | 0.0011 | 0.0135 | 0.2462 | 0.0006 | 0.0569 | 0.0009 | 0.1940 | 0.0011 | 0.0120 | | 1.6173 | 0.37 | 60 | 1.7856 | 0.0812 | 0.1585 | 0.3080 | 0.3151 | 0.0007 | 0.1179 | 0.0005 | 0.6339 | 0.0012 | 0.0405 | 0.2056 | 0.0007 | 0.0357 | 0.0005 | 0.2859 | 0.0012 | 0.0388 | | 1.5078 | 0.49 | 80 | 1.7805 | 0.0764 | 0.1537 | 0.3038 | 0.2792 | 0.0 | 0.1036 | 0.0000 | 0.6639 | 0.0003 | 0.0290 | 0.1839 | 0.0 | 0.0338 | 0.0000 | 0.2895 | 0.0003 | 0.0274 | | 1.3927 | 0.62 | 100 | 1.7549 | 0.0725 | 0.1521 | 0.3227 | 0.2589 | 0.0 | 0.0222 | 0.0001 | 0.7643 | 0.0005 | 0.0187 | 0.1703 | 0.0 | 0.0133 | 0.0001 | 0.3055 | 0.0005 | 0.0179 | | 1.6024 | 0.74 | 120 | 1.7348 | 0.0835 | 0.1598 | 0.3271 | 0.3514 | 0.0 | 0.0204 | 0.0001 | 0.6538 | 0.0005 | 0.0926 | 0.2021 | 0.0 | 0.0147 | 0.0001 | 0.2873 | 0.0005 | 0.0796 | | 1.3287 | 0.86 | 140 | 1.7352 | 0.0776 | 0.1556 | 0.3267 | 0.3309 | 0.0 | 0.0083 | 0.0 | 0.6910 | 0.0000 | 0.0591 | 0.1935 | 0.0 | 0.0076 | 0.0 | 0.2849 | 0.0000 | 0.0573 | | 1.4153 | 0.99 | 160 | 1.7024 | 0.0746 | 0.1567 | 0.3356 | 0.2947 | 0.0 | 0.0011 | 0.0 | 0.7683 | 0.0006 | 0.0320 | 0.1879 | 0.0 | 0.0011 | 0.0 | 0.3021 | 0.0006 | 0.0309 | | 1.3334 | 1.11 | 180 | 1.7262 | 0.0744 | 0.1567 | 0.3374 | 0.3130 | 0.0 | 0.0018 | 0.0 | 0.7620 | 0.0001 | 0.0203 | 0.1988 | 0.0 | 0.0017 | 0.0 | 0.3004 | 0.0001 | 0.0198 | | 1.3956 | 1.23 | 200 | 1.7304 | 0.0858 | 0.1622 | 0.3326 | 0.4838 | 0.0 | 0.0127 | 0.0 | 0.5432 | 0.0015 | 0.0944 | 0.2373 | 0.0 | 0.0112 | 0.0 | 0.2731 | 0.0015 | 0.0777 | | 1.5776 | 1.36 | 220 | 1.7300 | 0.0791 | 0.1622 | 0.3411 | 0.2581 | 0.0 | 0.0010 | 0.0 | 0.8012 | 0.0003 | 0.0748 | 0.1734 | 0.0 | 0.0010 | 0.0 | 0.3144 | 0.0003 | 0.0647 | | 1.1656 | 1.48 | 240 | 1.7248 | 0.0831 | 0.1657 | 0.3440 | 0.2687 | 0.0 | 0.0026 | 0.0 | 0.7879 | 0.0014 | 0.0995 | 0.1775 | 0.0 | 0.0025 | 0.0 | 0.3183 | 0.0014 | 0.0822 | | 1.4429 | 1.6 | 260 | 1.7308 | 0.0764 | 0.1616 | 0.3408 | 0.2091 | 0.0 | 0.0029 | 0.0 | 0.8518 | 0.0037 | 0.0637 | 0.1507 | 0.0 | 0.0028 | 0.0 | 0.3200 | 0.0036 | 0.0578 | | 1.6649 | 1.73 | 280 | 1.7282 | 0.0743 | 0.1564 | 0.3372 | 0.3261 | 0.0 | 0.0033 | 0.0 | 0.7514 | 0.0015 | 0.0128 | 0.1994 | 0.0 | 0.0031 | 0.0 | 0.3037 | 0.0015 | 0.0125 | | 1.3634 | 1.85 | 300 | 1.7216 | 0.0847 | 0.1653 | 0.3413 | 0.3196 | 0.0 | 0.0273 | 0.0 | 0.7402 | 0.0036 | 0.0665 | 0.2012 | 0.0 | 0.0204 | 0.0 | 0.3101 | 0.0035 | 0.0579 | | 1.5224 | 1.98 | 320 | 1.7343 | 0.0822 | 0.1626 | 0.3410 | 0.3793 | 0.0 | 0.0277 | 0.0 | 0.6985 | 0.0017 | 0.0311 | 0.2236 | 0.0 | 0.0210 | 0.0 | 0.3000 | 0.0016 | 0.0293 | | 1.2527 | 2.1 | 340 | 1.7149 | 0.0759 | 0.1559 | 0.3344 | 0.4108 | 0.0 | 0.0119 | 0.0 | 0.6629 | 0.0021 | 0.0034 | 0.2244 | 0.0 | 0.0105 | 0.0 | 0.2913 | 0.0021 | 0.0033 | | 1.5931 | 2.22 | 360 | 1.7170 | 0.0838 | 0.1619 | 0.3365 | 0.4061 | 0.0 | 0.0098 | 0.0 | 0.6414 | 0.0028 | 0.0730 | 0.2228 | 0.0 | 0.0090 | 0.0 | 0.2882 | 0.0027 | 0.0641 | | 1.2434 | 2.35 | 380 | 1.7437 | 0.0844 | 0.1619 | 0.3364 | 0.4734 | 0.0 | 0.0118 | 0.0 | 0.5785 | 0.0028 | 0.0667 | 0.2391 | 0.0 | 0.0105 | 0.0 | 0.2777 | 0.0027 | 0.0607 | | 1.4071 | 2.47 | 400 | 1.7316 | 0.0823 | 0.1639 | 0.3433 | 0.3054 | 0.0 | 0.0053 | 0.0 | 0.7633 | 0.0057 | 0.0674 | 0.1933 | 0.0 | 0.0051 | 0.0 | 0.3125 | 0.0054 | 0.0596 | | 1.2177 | 2.59 | 420 | 1.7195 | 0.0848 | 0.1657 | 0.3459 | 0.3442 | 0.0 | 0.0030 | 0.0 | 0.7315 | 0.0110 | 0.0705 | 0.2084 | 0.0 | 0.0029 | 0.0 | 0.3111 | 0.0102 | 0.0613 | | 1.3724 | 2.72 | 440 | 1.7359 | 0.0843 | 0.1660 | 0.3455 | 0.3091 | 0.0 | 0.0089 | 0.0 | 0.7620 | 0.0057 | 0.0761 | 0.1946 | 0.0 | 0.0084 | 0.0 | 0.3147 | 0.0054 | 0.0669 | | 1.3973 | 2.84 | 460 | 1.7469 | 0.0827 | 0.1617 | 0.3352 | 0.3153 | 0.0 | 0.0101 | 0.0 | 0.7231 | 0.0109 | 0.0724 | 0.1922 | 0.0 | 0.0088 | 0.0 | 0.3045 | 0.0099 | 0.0638 | | 1.3098 | 2.96 | 480 | 1.7193 | 0.0852 | 0.1658 | 0.3447 | 0.3412 | 0.0 | 0.0032 | 0.0 | 0.7240 | 0.0038 | 0.0887 | 0.2039 | 0.0 | 0.0031 | 0.0 | 0.3076 | 0.0037 | 0.0779 | | 0.9545 | 3.09 | 500 | 1.7256 | 0.0840 | 0.1627 | 0.3359 | 0.3366 | 0.0 | 0.0026 | 0.0 | 0.6959 | 0.0077 | 0.0960 | 0.2007 | 0.0 | 0.0025 | 0.0 | 0.2969 | 0.0072 | 0.0808 | | 1.176 | 3.21 | 520 | 1.7334 | 0.0827 | 0.1616 | 0.3357 | 0.3396 | 0.0 | 0.0024 | 0.0 | 0.6963 | 0.0016 | 0.0915 | 0.1999 | 0.0 | 0.0024 | 0.0 | 0.2966 | 0.0015 | 0.0785 | | 1.5622 | 3.33 | 540 | 1.7790 | 0.0689 | 0.1528 | 0.3311 | 0.2393 | 0.0 | 0.0015 | 0.0 | 0.8186 | 0.0009 | 0.0091 | 0.1638 | 0.0 | 0.0015 | 0.0 | 0.3073 | 0.0009 | 0.0089 | | 1.2673 | 3.46 | 560 | 1.7339 | 0.0803 | 0.1585 | 0.3302 | 0.3228 | 0.0 | 0.0113 | 0.0 | 0.7035 | 0.0036 | 0.0686 | 0.1930 | 0.0 | 0.0104 | 0.0 | 0.2942 | 0.0034 | 0.0613 | | 1.418 | 3.58 | 580 | 1.7648 | 0.0760 | 0.1563 | 0.3325 | 0.3074 | 0.0 | 0.0022 | 0.0 | 0.7416 | 0.0041 | 0.0386 | 0.1902 | 0.0 | 0.0022 | 0.0 | 0.2994 | 0.0038 | 0.0363 | | 1.3578 | 3.7 | 600 | 1.7338 | 0.0845 | 0.1619 | 0.3327 | 0.3548 | 0.0 | 0.0119 | 0.0 | 0.6693 | 0.0076 | 0.0898 | 0.2039 | 0.0 | 0.0108 | 0.0 | 0.2940 | 0.0070 | 0.0758 | | 1.1991 | 3.83 | 620 | 1.7711 | 0.0761 | 0.1546 | 0.3285 | 0.3473 | 0.0 | 0.0049 | 0.0 | 0.6906 | 0.0018 | 0.0379 | 0.1995 | 0.0 | 0.0046 | 0.0 | 0.2912 | 0.0017 | 0.0356 | | 1.3699 | 3.95 | 640 | 1.7421 | 0.0829 | 0.1595 | 0.3312 | 0.4290 | 0.0 | 0.0048 | 0.0 | 0.5994 | 0.0032 | 0.0804 | 0.2223 | 0.0 | 0.0045 | 0.0 | 0.2819 | 0.0031 | 0.0687 | | 1.308 | 4.07 | 660 | 1.7769 | 0.0709 | 0.1518 | 0.3250 | 0.2512 | 0.0 | 0.0035 | 0.0 | 0.7784 | 0.0034 | 0.0260 | 0.1656 | 0.0 | 0.0033 | 0.0 | 0.2996 | 0.0033 | 0.0245 | | 1.3746 | 4.2 | 680 | 1.7811 | 0.0749 | 0.1538 | 0.3283 | 0.3497 | 0.0 | 0.0077 | 0.0 | 0.6953 | 0.0037 | 0.0200 | 0.2022 | 0.0 | 0.0070 | 0.0 | 0.2928 | 0.0035 | 0.0191 | | 1.2085 | 4.32 | 700 | 1.7401 | 0.0825 | 0.1600 | 0.3319 | 0.3632 | 0.0 | 0.0106 | 0.0 | 0.6663 | 0.0042 | 0.0759 | 0.2064 | 0.0 | 0.0096 | 0.0 | 0.2912 | 0.0040 | 0.0664 | | 0.8119 | 4.44 | 720 | 1.7638 | 0.0755 | 0.1539 | 0.3284 | 0.3660 | 0.0 | 0.0060 | 0.0 | 0.6784 | 0.0034 | 0.0237 | 0.2073 | 0.0 | 0.0053 | 0.0 | 0.2902 | 0.0033 | 0.0226 | | 1.1547 | 4.57 | 740 | 1.7581 | 0.0795 | 0.1573 | 0.3289 | 0.3410 | 0.0 | 0.0140 | 0.0 | 0.6879 | 0.0030 | 0.0550 | 0.1996 | 0.0 | 0.0116 | 0.0 | 0.2929 | 0.0029 | 0.0497 | | 1.2229 | 4.69 | 760 | 1.7817 | 0.0730 | 0.1550 | 0.3243 | 0.1861 | 0.0 | 0.0124 | 0.0 | 0.8198 | 0.0036 | 0.0631 | 0.1365 | 0.0 | 0.0103 | 0.0 | 0.3050 | 0.0034 | 0.0557 | | 1.3332 | 4.81 | 780 | 1.7580 | 0.0769 | 0.1563 | 0.3276 | 0.2656 | 0.0 | 0.0044 | 0.0 | 0.7524 | 0.0042 | 0.0677 | 0.1721 | 0.0 | 0.0041 | 0.0 | 0.2998 | 0.0040 | 0.0581 | | 1.1668 | 4.94 | 800 | 1.7456 | 0.0841 | 0.1629 | 0.3309 | 0.2728 | 0.0 | 0.0194 | 0.0 | 0.7325 | 0.0055 | 0.1102 | 0.1767 | 0.0 | 0.0157 | 0.0 | 0.3002 | 0.0052 | 0.0908 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]

rwood-97/test_os_counties

# test_os_counties This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rwood-97/os_counties dataset. It achieves the following results on the evaluation set: - Loss: 1.8997 - Mean Iou: 0.1075 - Mean Accuracy: 0.4992 - Overall Accuracy: 0.2118 - Accuracy Non-map: 0.9899 - Accuracy Map: 0.0084 - Iou Non-map: 0.2065 - Iou Map: 0.0084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Non-map | Accuracy Map | Iou Non-map | Iou Map | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:------------:|:-----------:|:-------:| | 0.4081 | 0.41 | 20 | 0.8267 | 0.2101 | 0.4981 | 0.3478 | 0.7548 | 0.2414 | 0.1934 | 0.2268 | | 0.3939 | 0.82 | 40 | 0.8190 | 0.2387 | 0.4978 | 0.3898 | 0.6821 | 0.3134 | 0.1881 | 0.2894 | | 0.3319 | 1.22 | 60 | 0.9487 | 0.1579 | 0.4976 | 0.2759 | 0.8762 | 0.1191 | 0.2005 | 0.1153 | | 0.3234 | 1.63 | 80 | 0.9275 | 0.1847 | 0.4974 | 0.3120 | 0.8138 | 0.1809 | 0.1969 | 0.1725 | | 0.3932 | 2.04 | 100 | 0.9053 | 0.1892 | 0.4974 | 0.3183 | 0.8031 | 0.1916 | 0.1962 | 0.1822 | | 0.3412 | 2.45 | 120 | 0.8265 | 0.2385 | 0.4973 | 0.3895 | 0.6814 | 0.3132 | 0.1878 | 0.2891 | | 0.2976 | 2.86 | 140 | 1.3713 | 0.1127 | 0.4987 | 0.2182 | 0.9778 | 0.0197 | 0.2058 | 0.0195 | | 0.2803 | 3.27 | 160 | 1.6436 | 0.1095 | 0.4993 | 0.2143 | 0.9860 | 0.0126 | 0.2064 | 0.0125 | | 0.3686 | 3.67 | 180 | 1.2379 | 0.1221 | 0.4982 | 0.2298 | 0.9564 | 0.0399 | 0.2047 | 0.0395 | | 0.3434 | 4.08 | 200 | 1.1857 | 0.1385 | 0.4978 | 0.2507 | 0.9197 | 0.0758 | 0.2028 | 0.0743 | | 0.2951 | 4.49 | 220 | 1.3947 | 0.1160 | 0.4986 | 0.2223 | 0.9704 | 0.0268 | 0.2054 | 0.0266 | | 0.2333 | 4.9 | 240 | 1.5480 | 0.1170 | 0.4988 | 0.2236 | 0.9687 | 0.0288 | 0.2054 | 0.0286 | | 0.2491 | 5.31 | 260 | 1.6563 | 0.1136 | 0.4990 | 0.2194 | 0.9764 | 0.0215 | 0.2058 | 0.0214 | | 0.2706 | 5.71 | 280 | 1.9766 | 0.1058 | 0.4995 | 0.2098 | 0.9942 | 0.0048 | 0.2068 | 0.0048 | | 0.2171 | 6.12 | 300 | 1.6191 | 0.1117 | 0.4989 | 0.2170 | 0.9804 | 0.0175 | 0.2060 | 0.0174 | | 0.2352 | 6.53 | 320 | 1.8075 | 0.1102 | 0.4992 | 0.2151 | 0.9842 | 0.0141 | 0.2062 | 0.0141 | | 0.2953 | 6.94 | 340 | 1.4709 | 0.1178 | 0.4986 | 0.2245 | 0.9667 | 0.0305 | 0.2053 | 0.0303 | | 0.2799 | 7.35 | 360 | 1.3843 | 0.1300 | 0.4982 | 0.2399 | 0.9392 | 0.0571 | 0.2038 | 0.0562 | | 0.2285 | 7.76 | 380 | 1.7799 | 0.1101 | 0.4991 | 0.2150 | 0.9842 | 0.0140 | 0.2062 | 0.0139 | | 0.3461 | 8.16 | 400 | 1.7282 | 0.1106 | 0.4989 | 0.2157 | 0.9826 | 0.0153 | 0.2061 | 0.0152 | | 0.1867 | 8.57 | 420 | 2.3356 | 0.1042 | 0.4997 | 0.2079 | 0.9981 | 0.0014 | 0.2070 | 0.0014 | | 0.1731 | 8.98 | 440 | 2.1465 | 0.1050 | 0.4996 | 0.2089 | 0.9959 | 0.0032 | 0.2069 | 0.0032 | | 0.224 | 9.39 | 460 | 2.3467 | 0.1047 | 0.4996 | 0.2084 | 0.9969 | 0.0024 | 0.2070 | 0.0024 | | 0.2199 | 9.8 | 480 | 1.8997 | 0.1075 | 0.4992 | 0.2118 | 0.9899 | 0.0084 | 0.2065 | 0.0084 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0 - Datasets 2.14.5 - Tokenizers 0.14.1

[ "non-map", "map" ]

rishitunu/new_ecc_segformer

# new_ecc_segformer This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ECC_crackdataset_withsplit dataset. It achieves the following results on the evaluation set: - Loss: 0.0663 - Mean Iou: 0.1943 - Mean Accuracy: 0.3915 - Overall Accuracy: 0.3915 - Accuracy Background: nan - Accuracy Crack: 0.3915 - Iou Background: 0.0 - Iou Crack: 0.3887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| | 0.0489 | 1.0 | 438 | 0.0634 | 0.1464 | 0.2933 | 0.2933 | nan | 0.2933 | 0.0 | 0.2929 | | 0.0542 | 2.0 | 876 | 0.0439 | 0.1956 | 0.3917 | 0.3917 | nan | 0.3917 | 0.0 | 0.3912 | | 0.0484 | 3.0 | 1314 | 0.0434 | 0.1719 | 0.3551 | 0.3551 | nan | 0.3551 | 0.0 | 0.3439 | | 0.0539 | 4.0 | 1752 | 0.0447 | 0.1871 | 0.3820 | 0.3820 | nan | 0.3820 | 0.0 | 0.3741 | | 0.0565 | 5.0 | 2190 | 0.0435 | 0.1888 | 0.3937 | 0.3937 | nan | 0.3937 | 0.0 | 0.3777 | | 0.0544 | 6.0 | 2628 | 0.0442 | 0.1904 | 0.3930 | 0.3930 | nan | 0.3930 | 0.0 | 0.3808 | | 0.0421 | 7.0 | 3066 | 0.0449 | 0.2256 | 0.4651 | 0.4651 | nan | 0.4651 | 0.0 | 0.4513 | | 0.0352 | 8.0 | 3504 | 0.0587 | 0.1569 | 0.3165 | 0.3165 | nan | 0.3165 | 0.0 | 0.3138 | | 0.0394 | 9.0 | 3942 | 0.0442 | 0.1842 | 0.3710 | 0.3710 | nan | 0.3710 | 0.0 | 0.3684 | | 0.0445 | 10.0 | 4380 | 0.0609 | 0.1167 | 0.4173 | 0.4173 | nan | 0.4173 | 0.0 | 0.2334 | | 0.0503 | 11.0 | 4818 | 0.0504 | 0.1702 | 0.3714 | 0.3714 | nan | 0.3714 | 0.0 | 0.3403 | | 0.0379 | 12.0 | 5256 | 0.0460 | 0.1903 | 0.3869 | 0.3869 | nan | 0.3869 | 0.0 | 0.3807 | | 0.0405 | 13.0 | 5694 | 0.0452 | 0.2017 | 0.4084 | 0.4084 | nan | 0.4084 | 0.0 | 0.4034 | | 0.0367 | 14.0 | 6132 | 0.0477 | 0.1995 | 0.4060 | 0.4060 | nan | 0.4060 | 0.0 | 0.3990 | | 0.0315 | 15.0 | 6570 | 0.0498 | 0.2073 | 0.4208 | 0.4208 | nan | 0.4208 | 0.0 | 0.4147 | | 0.0244 | 16.0 | 7008 | 0.0486 | 0.1963 | 0.4029 | 0.4029 | nan | 0.4029 | 0.0 | 0.3926 | | 0.031 | 17.0 | 7446 | 0.0568 | 0.1927 | 0.3892 | 0.3892 | nan | 0.3892 | 0.0 | 0.3855 | | 0.0288 | 18.0 | 7884 | 0.0560 | 0.2033 | 0.4092 | 0.4092 | nan | 0.4092 | 0.0 | 0.4067 | | 0.0354 | 19.0 | 8322 | 0.0613 | 0.2007 | 0.4056 | 0.4056 | nan | 0.4056 | 0.0 | 0.4013 | | 0.0315 | 20.0 | 8760 | 0.0605 | 0.1865 | 0.3752 | 0.3752 | nan | 0.3752 | 0.0 | 0.3731 | | 0.0343 | 21.0 | 9198 | 0.0653 | 0.1991 | 0.4019 | 0.4019 | nan | 0.4019 | 0.0 | 0.3981 | | 0.0327 | 22.0 | 9636 | 0.0660 | 0.1945 | 0.3924 | 0.3924 | nan | 0.3924 | 0.0 | 0.3891 | | 0.0252 | 22.83 | 10000 | 0.0663 | 0.1943 | 0.3915 | 0.3915 | nan | 0.3915 | 0.0 | 0.3887 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1

[ "background", "crack" ]

peldrak/segformer-finetuned-coastalDataset

# segformer-finetuned-coastalDataset This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coastal_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.6091 - Mean Iou: 0.6876 - Mean Accuracy: 0.7945 - Overall Accuracy: 0.8704 - Accuracy Water: 0.9332 - Accuracy Whitewater: 0.7904 - Accuracy Sediment: 0.8591 - Accuracy Other Natural Terrain: 0.4778 - Accuracy Vegetation: 0.9017 - Accuracy Development: 0.8549 - Accuracy Unknown: 0.7443 - Iou Water: 0.8671 - Iou Whitewater: 0.6713 - Iou Sediment: 0.7452 - Iou Other Natural Terrain: 0.3782 - Iou Vegetation: 0.7799 - Iou Development: 0.6736 - Iou Unknown: 0.6978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:| | 1.7886 | 0.05 | 20 | 1.6491 | 0.1498 | 0.2460 | 0.4112 | 0.4687 | 0.0287 | 0.0048 | 0.0144 | 0.6736 | 0.4392 | 0.0923 | 0.3518 | 0.0098 | 0.0048 | 0.0058 | 0.3662 | 0.2469 | 0.0632 | | 1.7534 | 0.11 | 40 | 1.4097 | 0.2254 | 0.3388 | 0.5545 | 0.7079 | 0.0074 | 0.0185 | 0.0066 | 0.7428 | 0.5723 | 0.3159 | 0.4839 | 0.0069 | 0.0184 | 0.0047 | 0.4440 | 0.3124 | 0.3073 | | 1.3223 | 0.16 | 60 | 1.3142 | 0.2096 | 0.3198 | 0.5687 | 0.7425 | 0.0013 | 0.0487 | 0.0054 | 0.8736 | 0.5420 | 0.0248 | 0.5201 | 0.0013 | 0.0468 | 0.0041 | 0.4890 | 0.3818 | 0.0242 | | 1.2429 | 0.22 | 80 | 1.1734 | 0.2616 | 0.3747 | 0.6300 | 0.8271 | 0.0011 | 0.0271 | 0.0000 | 0.8500 | 0.6156 | 0.3021 | 0.5954 | 0.0011 | 0.0265 | 0.0000 | 0.5267 | 0.3806 | 0.3011 | | 1.3556 | 0.27 | 100 | 1.1141 | 0.2878 | 0.4016 | 0.6536 | 0.8384 | 0.0114 | 0.0710 | 0.0000 | 0.8670 | 0.6799 | 0.3432 | 0.6140 | 0.0113 | 0.0655 | 0.0000 | 0.5632 | 0.4226 | 0.3381 | | 0.8995 | 0.32 | 120 | 1.0387 | 0.3096 | 0.4245 | 0.6724 | 0.8439 | 0.0148 | 0.1242 | 0.0 | 0.8742 | 0.7178 | 0.3966 | 0.6438 | 0.0147 | 0.1147 | 0.0 | 0.5733 | 0.4289 | 0.3921 | | 1.0435 | 0.38 | 140 | 1.0234 | 0.3135 | 0.4294 | 0.6615 | 0.7801 | 0.0023 | 0.1835 | 0.0000 | 0.8794 | 0.7321 | 0.4284 | 0.6033 | 0.0023 | 0.1685 | 0.0000 | 0.5538 | 0.4573 | 0.4094 | | 1.7516 | 0.43 | 160 | 1.0258 | 0.3287 | 0.4534 | 0.6665 | 0.7874 | 0.0122 | 0.3467 | 0.0 | 0.8209 | 0.7619 | 0.4449 | 0.6094 | 0.0121 | 0.3020 | 0.0 | 0.5660 | 0.4476 | 0.3638 | | 1.1561 | 0.49 | 180 | 0.9637 | 0.3662 | 0.4967 | 0.6989 | 0.8001 | 0.0003 | 0.5674 | 0.0 | 0.8247 | 0.8164 | 0.4682 | 0.6765 | 0.0003 | 0.4476 | 0.0 | 0.5738 | 0.4173 | 0.4477 | | 1.2007 | 0.54 | 200 | 0.8883 | 0.3867 | 0.5175 | 0.7210 | 0.8696 | 0.0006 | 0.6747 | 0.0 | 0.7787 | 0.8218 | 0.4771 | 0.6801 | 0.0006 | 0.4996 | 0.0 | 0.6154 | 0.4621 | 0.4495 | | 0.8308 | 0.59 | 220 | 0.8686 | 0.3936 | 0.5091 | 0.7279 | 0.8239 | 0.0035 | 0.6613 | 0.0 | 0.8950 | 0.7065 | 0.4738 | 0.6718 | 0.0035 | 0.4924 | 0.0 | 0.6390 | 0.5353 | 0.4134 | | 1.3709 | 0.65 | 240 | 0.8437 | 0.3847 | 0.5166 | 0.7198 | 0.8709 | 0.0081 | 0.6150 | 0.0 | 0.7759 | 0.8590 | 0.4872 | 0.6789 | 0.0081 | 0.4558 | 0.0 | 0.6192 | 0.4765 | 0.4545 | | 1.0652 | 0.7 | 260 | 0.8299 | 0.3842 | 0.4930 | 0.7246 | 0.8817 | 0.0420 | 0.4268 | 0.0 | 0.8581 | 0.7229 | 0.5196 | 0.6779 | 0.0418 | 0.3285 | 0.0 | 0.6367 | 0.5153 | 0.4892 | | 0.8973 | 0.76 | 280 | 0.8115 | 0.4030 | 0.5232 | 0.7436 | 0.8562 | 0.0126 | 0.6910 | 0.0 | 0.8836 | 0.7093 | 0.5098 | 0.7304 | 0.0126 | 0.4955 | 0.0 | 0.6481 | 0.4535 | 0.4811 | | 0.6368 | 0.81 | 300 | 0.8043 | 0.4291 | 0.5447 | 0.7516 | 0.8700 | 0.1096 | 0.6775 | 0.0 | 0.8668 | 0.7688 | 0.5198 | 0.7217 | 0.1079 | 0.5182 | 0.0 | 0.6438 | 0.5261 | 0.4860 | | 1.946 | 0.86 | 320 | 0.7983 | 0.4245 | 0.5350 | 0.7481 | 0.8194 | 0.0592 | 0.6388 | 0.0 | 0.9216 | 0.7385 | 0.5671 | 0.7116 | 0.0582 | 0.5212 | 0.0 | 0.6329 | 0.5278 | 0.5199 | | 0.9624 | 0.92 | 340 | 0.8263 | 0.4067 | 0.5372 | 0.7345 | 0.8064 | 0.0394 | 0.7976 | 0.0 | 0.8713 | 0.7507 | 0.4953 | 0.6855 | 0.0391 | 0.4348 | 0.0 | 0.6591 | 0.5461 | 0.4821 | | 0.7984 | 0.97 | 360 | 0.7752 | 0.4175 | 0.5430 | 0.7456 | 0.8623 | 0.0198 | 0.7372 | 0.0 | 0.8328 | 0.8482 | 0.5007 | 0.7137 | 0.0198 | 0.5537 | 0.0 | 0.6271 | 0.5259 | 0.4821 | | 0.7808 | 1.03 | 380 | 0.7499 | 0.4329 | 0.5442 | 0.7642 | 0.8970 | 0.0194 | 0.7168 | 0.0 | 0.8745 | 0.8007 | 0.5010 | 0.7389 | 0.0193 | 0.5562 | 0.0 | 0.6543 | 0.5754 | 0.4860 | | 0.9687 | 1.08 | 400 | 0.7386 | 0.4288 | 0.5348 | 0.7645 | 0.8957 | 0.0001 | 0.7287 | 0.0 | 0.8980 | 0.7178 | 0.5034 | 0.7365 | 0.0001 | 0.5451 | 0.0 | 0.6627 | 0.5687 | 0.4884 | | 0.7036 | 1.14 | 420 | 0.7221 | 0.4424 | 0.5739 | 0.7627 | 0.8876 | 0.1237 | 0.8011 | 0.0 | 0.8096 | 0.8442 | 0.5511 | 0.7576 | 0.1223 | 0.5346 | 0.0 | 0.6532 | 0.5191 | 0.5103 | | 0.5789 | 1.19 | 440 | 0.7387 | 0.4409 | 0.5588 | 0.7688 | 0.9018 | 0.0959 | 0.8107 | 0.0 | 0.8672 | 0.7453 | 0.4904 | 0.7527 | 0.0945 | 0.5342 | 0.0 | 0.6773 | 0.5507 | 0.4773 | | 0.5338 | 1.24 | 460 | 0.6946 | 0.4416 | 0.5639 | 0.7645 | 0.9112 | 0.1294 | 0.7190 | 0.0 | 0.8238 | 0.8345 | 0.5296 | 0.7535 | 0.1236 | 0.5391 | 0.0 | 0.6670 | 0.5441 | 0.4639 | | 0.7953 | 1.3 | 480 | 0.7493 | 0.4686 | 0.5872 | 0.7794 | 0.9053 | 0.2310 | 0.8043 | 0.0 | 0.8665 | 0.8091 | 0.4944 | 0.7708 | 0.2220 | 0.5434 | 0.0 | 0.6828 | 0.5814 | 0.4798 | | 1.0133 | 1.35 | 500 | 0.7158 | 0.4634 | 0.5757 | 0.7767 | 0.9061 | 0.1974 | 0.7773 | 0.0 | 0.8755 | 0.7682 | 0.5051 | 0.7564 | 0.1901 | 0.5548 | 0.0 | 0.6792 | 0.5743 | 0.4893 | | 0.6369 | 1.41 | 520 | 0.7021 | 0.4645 | 0.5829 | 0.7781 | 0.8936 | 0.2029 | 0.7963 | 0.0 | 0.8787 | 0.8036 | 0.5054 | 0.7735 | 0.1920 | 0.5585 | 0.0 | 0.6762 | 0.5605 | 0.4907 | | 0.5932 | 1.46 | 540 | 0.6935 | 0.4591 | 0.5654 | 0.7807 | 0.9025 | 0.1195 | 0.7795 | 0.0 | 0.9085 | 0.7415 | 0.5061 | 0.7705 | 0.1135 | 0.5608 | 0.0 | 0.6785 | 0.5978 | 0.4927 | | 0.7677 | 1.51 | 560 | 0.6552 | 0.4872 | 0.6003 | 0.7875 | 0.9044 | 0.3022 | 0.7881 | 0.0006 | 0.8814 | 0.7990 | 0.5267 | 0.7754 | 0.2845 | 0.5843 | 0.0006 | 0.6876 | 0.5780 | 0.4998 | | 0.5607 | 1.57 | 580 | 0.6682 | 0.4871 | 0.5980 | 0.7867 | 0.8976 | 0.3117 | 0.7955 | 0.0 | 0.8992 | 0.7793 | 0.5028 | 0.7697 | 0.2911 | 0.5902 | 0.0 | 0.6878 | 0.5807 | 0.4901 | | 0.7269 | 1.62 | 600 | 0.6849 | 0.4823 | 0.5975 | 0.7853 | 0.9091 | 0.2905 | 0.8256 | 0.0003 | 0.8768 | 0.7944 | 0.4861 | 0.7729 | 0.2647 | 0.5936 | 0.0003 | 0.6846 | 0.5836 | 0.4761 | | 0.4449 | 1.68 | 620 | 0.6690 | 0.4930 | 0.6175 | 0.7831 | 0.8831 | 0.3929 | 0.8275 | 0.0009 | 0.8606 | 0.8298 | 0.5280 | 0.7744 | 0.3556 | 0.5759 | 0.0009 | 0.6753 | 0.5654 | 0.5040 | | 0.6588 | 1.73 | 640 | 0.6417 | 0.5054 | 0.6210 | 0.7914 | 0.9059 | 0.4289 | 0.7604 | 0.0 | 0.8640 | 0.8192 | 0.5685 | 0.7932 | 0.3960 | 0.5968 | 0.0 | 0.6779 | 0.5516 | 0.5226 | | 0.8525 | 1.78 | 660 | 0.6499 | 0.4952 | 0.6075 | 0.7905 | 0.8953 | 0.3919 | 0.7930 | 0.0 | 0.9060 | 0.7254 | 0.5409 | 0.7884 | 0.3341 | 0.5840 | 0.0 | 0.6871 | 0.5555 | 0.5175 | | 0.7697 | 1.84 | 680 | 0.6378 | 0.5138 | 0.6339 | 0.7969 | 0.8922 | 0.4613 | 0.8212 | 0.0004 | 0.8760 | 0.8083 | 0.5777 | 0.7860 | 0.3819 | 0.6104 | 0.0004 | 0.6916 | 0.5844 | 0.5424 | | 0.5325 | 1.89 | 700 | 0.6448 | 0.5178 | 0.6301 | 0.8011 | 0.8963 | 0.4397 | 0.8236 | 0.0072 | 0.8988 | 0.7742 | 0.5709 | 0.7955 | 0.3818 | 0.6057 | 0.0072 | 0.6929 | 0.5915 | 0.5502 | | 0.7487 | 1.95 | 720 | 0.5989 | 0.5416 | 0.6563 | 0.8108 | 0.8980 | 0.5287 | 0.8194 | 0.0077 | 0.8714 | 0.7934 | 0.6758 | 0.7995 | 0.4509 | 0.6198 | 0.0077 | 0.6996 | 0.6079 | 0.6059 | | 1.8711 | 2.0 | 740 | 0.6323 | 0.5173 | 0.6399 | 0.7992 | 0.9035 | 0.5213 | 0.7845 | 0.0088 | 0.8817 | 0.8414 | 0.5378 | 0.7977 | 0.4148 | 0.6028 | 0.0088 | 0.6987 | 0.5864 | 0.5123 | | 0.4823 | 2.05 | 760 | 0.6463 | 0.5050 | 0.6120 | 0.7958 | 0.9088 | 0.4049 | 0.7904 | 0.0078 | 0.9076 | 0.7275 | 0.5368 | 0.7895 | 0.3503 | 0.5980 | 0.0078 | 0.6907 | 0.5788 | 0.5196 | | 0.6854 | 2.11 | 780 | 0.6507 | 0.5066 | 0.6351 | 0.7889 | 0.9002 | 0.5056 | 0.8252 | 0.0130 | 0.8507 | 0.8364 | 0.5145 | 0.7809 | 0.4048 | 0.6104 | 0.0130 | 0.6831 | 0.5572 | 0.4964 | | 0.5534 | 2.16 | 800 | 0.6499 | 0.4977 | 0.6310 | 0.7870 | 0.8875 | 0.4178 | 0.8700 | 0.0133 | 0.8414 | 0.8439 | 0.5428 | 0.7735 | 0.3444 | 0.5597 | 0.0133 | 0.6990 | 0.5860 | 0.5082 | | 1.6573 | 2.22 | 820 | 0.6379 | 0.5041 | 0.6137 | 0.7944 | 0.9105 | 0.4303 | 0.7719 | 0.0039 | 0.9020 | 0.7513 | 0.5256 | 0.7882 | 0.3374 | 0.6124 | 0.0039 | 0.6883 | 0.6030 | 0.4955 | | 0.422 | 2.27 | 840 | 0.6730 | 0.4999 | 0.6418 | 0.7800 | 0.8509 | 0.5520 | 0.8265 | 0.0241 | 0.8649 | 0.8507 | 0.5237 | 0.7707 | 0.4076 | 0.5670 | 0.0240 | 0.6808 | 0.5550 | 0.4941 | | 0.7256 | 2.32 | 860 | 0.6374 | 0.5134 | 0.6478 | 0.7898 | 0.8793 | 0.5758 | 0.8032 | 0.0284 | 0.8657 | 0.8556 | 0.5266 | 0.7931 | 0.4553 | 0.5835 | 0.0276 | 0.6888 | 0.5477 | 0.4980 | | 0.3261 | 2.38 | 880 | 0.6084 | 0.5307 | 0.6678 | 0.8026 | 0.8987 | 0.7237 | 0.7705 | 0.0299 | 0.8830 | 0.8401 | 0.5288 | 0.8012 | 0.4855 | 0.6110 | 0.0299 | 0.7123 | 0.5715 | 0.5037 | | 0.9935 | 2.43 | 900 | 0.6262 | 0.5298 | 0.6550 | 0.8025 | 0.8743 | 0.5612 | 0.8547 | 0.0157 | 0.8966 | 0.8177 | 0.5645 | 0.7868 | 0.4467 | 0.6069 | 0.0157 | 0.7097 | 0.6212 | 0.5217 | | 1.2977 | 2.49 | 920 | 0.6661 | 0.5119 | 0.6417 | 0.7924 | 0.8757 | 0.5019 | 0.8213 | 0.0103 | 0.8746 | 0.8719 | 0.5365 | 0.7896 | 0.4260 | 0.5785 | 0.0103 | 0.6917 | 0.5665 | 0.5211 | | 0.4984 | 2.54 | 940 | 0.5994 | 0.5452 | 0.6678 | 0.8086 | 0.9115 | 0.6257 | 0.8113 | 0.0159 | 0.8524 | 0.8568 | 0.6009 | 0.8123 | 0.5189 | 0.6264 | 0.0159 | 0.6904 | 0.6049 | 0.5473 | | 0.6221 | 2.59 | 960 | 0.6465 | 0.5342 | 0.6660 | 0.7959 | 0.9048 | 0.6706 | 0.8602 | 0.0221 | 0.8192 | 0.7986 | 0.5866 | 0.7858 | 0.5098 | 0.5983 | 0.0221 | 0.6700 | 0.6201 | 0.5335 | | 0.3674 | 2.65 | 980 | 0.6477 | 0.5323 | 0.6529 | 0.8017 | 0.8813 | 0.6333 | 0.8210 | 0.0130 | 0.9148 | 0.7782 | 0.5286 | 0.8052 | 0.5073 | 0.6330 | 0.0130 | 0.6904 | 0.5653 | 0.5121 | | 0.4939 | 2.7 | 1000 | 0.6064 | 0.5424 | 0.6765 | 0.8087 | 0.9061 | 0.6742 | 0.8566 | 0.0241 | 0.8567 | 0.8603 | 0.5579 | 0.8141 | 0.5124 | 0.6231 | 0.0241 | 0.7060 | 0.5928 | 0.5246 | | 0.4825 | 2.76 | 1020 | 0.6061 | 0.5371 | 0.6714 | 0.8050 | 0.9102 | 0.6962 | 0.8545 | 0.0245 | 0.8540 | 0.8060 | 0.5546 | 0.8184 | 0.5311 | 0.6143 | 0.0245 | 0.7033 | 0.5607 | 0.5077 | | 0.2858 | 2.81 | 1040 | 0.6032 | 0.5408 | 0.6806 | 0.8019 | 0.8807 | 0.7392 | 0.8252 | 0.0246 | 0.8606 | 0.8663 | 0.5675 | 0.8098 | 0.5620 | 0.6315 | 0.0245 | 0.6985 | 0.5374 | 0.5220 | | 0.6248 | 2.86 | 1060 | 0.6321 | 0.5317 | 0.6489 | 0.8059 | 0.9103 | 0.6419 | 0.7859 | 0.0122 | 0.9188 | 0.7799 | 0.4933 | 0.8025 | 0.4770 | 0.6247 | 0.0122 | 0.7102 | 0.6242 | 0.4709 | | 0.5328 | 2.92 | 1080 | 0.5913 | 0.5412 | 0.6815 | 0.8091 | 0.9023 | 0.7421 | 0.8144 | 0.0280 | 0.8715 | 0.8700 | 0.5422 | 0.8102 | 0.4764 | 0.6364 | 0.0279 | 0.7111 | 0.6099 | 0.5161 | | 0.3748 | 2.97 | 1100 | 0.6328 | 0.5279 | 0.6739 | 0.7984 | 0.9168 | 0.7236 | 0.8730 | 0.0248 | 0.8217 | 0.8466 | 0.5111 | 0.8111 | 0.5002 | 0.6253 | 0.0247 | 0.6869 | 0.5492 | 0.4976 | | 0.5079 | 3.03 | 1120 | 0.6226 | 0.5361 | 0.6568 | 0.8084 | 0.9069 | 0.6303 | 0.8483 | 0.0226 | 0.9101 | 0.7806 | 0.4986 | 0.8151 | 0.4983 | 0.6206 | 0.0225 | 0.7104 | 0.5992 | 0.4862 | | 1.1987 | 3.08 | 1140 | 0.5712 | 0.5714 | 0.6993 | 0.8244 | 0.8928 | 0.7590 | 0.8263 | 0.0269 | 0.8931 | 0.8668 | 0.6305 | 0.8213 | 0.5669 | 0.6592 | 0.0268 | 0.7241 | 0.6041 | 0.5972 | | 0.7254 | 3.14 | 1160 | 0.6330 | 0.5416 | 0.6696 | 0.8088 | 0.8986 | 0.6701 | 0.8139 | 0.0237 | 0.8958 | 0.8757 | 0.5094 | 0.8103 | 0.5280 | 0.6353 | 0.0236 | 0.7146 | 0.5816 | 0.4980 | | 0.4003 | 3.19 | 1180 | 0.6043 | 0.5385 | 0.6659 | 0.8092 | 0.9123 | 0.6244 | 0.8237 | 0.0240 | 0.8793 | 0.8897 | 0.5081 | 0.8140 | 0.4894 | 0.6426 | 0.0239 | 0.7104 | 0.5954 | 0.4938 | | 0.3748 | 3.24 | 1200 | 0.5919 | 0.5438 | 0.6621 | 0.8102 | 0.9296 | 0.5921 | 0.8031 | 0.0300 | 0.8633 | 0.8873 | 0.5289 | 0.8136 | 0.5033 | 0.6481 | 0.0299 | 0.6993 | 0.6011 | 0.5113 | | 0.6813 | 3.3 | 1220 | 0.5742 | 0.5589 | 0.6683 | 0.8179 | 0.9073 | 0.5921 | 0.8173 | 0.0311 | 0.8975 | 0.8354 | 0.5973 | 0.8079 | 0.5053 | 0.6427 | 0.0309 | 0.7055 | 0.6439 | 0.5764 | | 0.3824 | 3.35 | 1240 | 0.5503 | 0.5679 | 0.6963 | 0.8207 | 0.9115 | 0.7776 | 0.8276 | 0.0345 | 0.8744 | 0.8579 | 0.5906 | 0.8203 | 0.5693 | 0.6410 | 0.0342 | 0.7103 | 0.6310 | 0.5693 | | 0.7603 | 3.41 | 1260 | 0.5712 | 0.5638 | 0.6821 | 0.8174 | 0.8986 | 0.7138 | 0.8280 | 0.0439 | 0.9093 | 0.8306 | 0.5506 | 0.8160 | 0.5587 | 0.6624 | 0.0433 | 0.7065 | 0.6286 | 0.5310 | | 0.7926 | 3.46 | 1280 | 0.5728 | 0.5673 | 0.6926 | 0.8185 | 0.9123 | 0.6875 | 0.8869 | 0.0490 | 0.8531 | 0.8686 | 0.5909 | 0.8117 | 0.5581 | 0.6424 | 0.0483 | 0.7068 | 0.6355 | 0.5681 | | 0.5149 | 3.51 | 1300 | 0.5715 | 0.5679 | 0.6958 | 0.8194 | 0.9052 | 0.6954 | 0.8254 | 0.0548 | 0.8556 | 0.9054 | 0.6290 | 0.8223 | 0.5488 | 0.6569 | 0.0544 | 0.7048 | 0.5916 | 0.5967 | | 0.623 | 3.57 | 1320 | 0.5723 | 0.5752 | 0.6931 | 0.8245 | 0.9129 | 0.6938 | 0.8519 | 0.0379 | 0.8771 | 0.8737 | 0.6042 | 0.8195 | 0.5656 | 0.6468 | 0.0371 | 0.7112 | 0.6614 | 0.5844 | | 0.5331 | 3.62 | 1340 | 0.5802 | 0.5694 | 0.6758 | 0.8221 | 0.9283 | 0.6637 | 0.8086 | 0.0427 | 0.8874 | 0.7865 | 0.6136 | 0.8052 | 0.5426 | 0.6474 | 0.0418 | 0.7151 | 0.6460 | 0.5877 | | 0.4966 | 3.68 | 1360 | 0.5776 | 0.5653 | 0.6944 | 0.8189 | 0.9041 | 0.7890 | 0.8019 | 0.0577 | 0.8948 | 0.8461 | 0.5674 | 0.8222 | 0.5665 | 0.6527 | 0.0565 | 0.7171 | 0.5995 | 0.5425 | | 0.7875 | 3.73 | 1380 | 0.5500 | 0.5825 | 0.7075 | 0.8249 | 0.9192 | 0.7436 | 0.8403 | 0.1109 | 0.8625 | 0.8702 | 0.6056 | 0.8167 | 0.5658 | 0.6609 | 0.1036 | 0.7230 | 0.6398 | 0.5679 | | 0.4906 | 3.78 | 1400 | 0.5681 | 0.5805 | 0.7036 | 0.8191 | 0.8903 | 0.6878 | 0.7936 | 0.1968 | 0.8973 | 0.8741 | 0.5855 | 0.8242 | 0.5382 | 0.6658 | 0.1750 | 0.7098 | 0.6022 | 0.5484 | | 0.3565 | 3.84 | 1420 | 0.6125 | 0.5640 | 0.6908 | 0.8155 | 0.9136 | 0.7074 | 0.8146 | 0.1141 | 0.8803 | 0.8820 | 0.5234 | 0.8172 | 0.5596 | 0.6229 | 0.1071 | 0.7207 | 0.6137 | 0.5068 | | 1.3393 | 3.89 | 1440 | 0.5608 | 0.5915 | 0.7116 | 0.8272 | 0.8975 | 0.7789 | 0.8623 | 0.1120 | 0.8902 | 0.7977 | 0.6430 | 0.8131 | 0.6059 | 0.6366 | 0.1083 | 0.7236 | 0.6506 | 0.6027 | | 0.864 | 3.95 | 1460 | 0.5728 | 0.5831 | 0.7010 | 0.8272 | 0.9050 | 0.7358 | 0.7942 | 0.0735 | 0.8930 | 0.8686 | 0.6372 | 0.8239 | 0.5722 | 0.6571 | 0.0716 | 0.7173 | 0.6471 | 0.5923 | | 0.4925 | 4.0 | 1480 | 0.5538 | 0.5883 | 0.6994 | 0.8313 | 0.9121 | 0.6628 | 0.8424 | 0.0866 | 0.8914 | 0.8586 | 0.6421 | 0.8283 | 0.5531 | 0.6734 | 0.0811 | 0.7206 | 0.6540 | 0.6076 | | 0.4559 | 4.05 | 1500 | 0.5789 | 0.5725 | 0.6901 | 0.8233 | 0.9219 | 0.6579 | 0.8358 | 0.0760 | 0.8747 | 0.8848 | 0.5795 | 0.8222 | 0.5401 | 0.6579 | 0.0735 | 0.7155 | 0.6462 | 0.5521 | | 0.4295 | 4.11 | 1520 | 0.6088 | 0.5705 | 0.6949 | 0.8165 | 0.9330 | 0.6590 | 0.8607 | 0.0876 | 0.8127 | 0.9066 | 0.6045 | 0.7917 | 0.5603 | 0.6283 | 0.0844 | 0.7159 | 0.6390 | 0.5737 | | 0.5591 | 4.16 | 1540 | 0.5385 | 0.6038 | 0.7106 | 0.8364 | 0.9136 | 0.6978 | 0.7744 | 0.1493 | 0.8992 | 0.8430 | 0.6971 | 0.8240 | 0.5594 | 0.6594 | 0.1425 | 0.7283 | 0.6668 | 0.6462 | | 0.536 | 4.22 | 1560 | 0.6030 | 0.5814 | 0.7128 | 0.8229 | 0.8963 | 0.7285 | 0.8589 | 0.1084 | 0.8596 | 0.9171 | 0.6206 | 0.8224 | 0.5719 | 0.6460 | 0.1042 | 0.7128 | 0.6234 | 0.5890 | | 1.0689 | 4.27 | 1580 | 0.5964 | 0.5930 | 0.7215 | 0.8262 | 0.9036 | 0.7625 | 0.8329 | 0.1609 | 0.8663 | 0.9100 | 0.6144 | 0.8191 | 0.5807 | 0.6749 | 0.1523 | 0.7192 | 0.6189 | 0.5861 | | 0.2286 | 4.32 | 1600 | 0.5731 | 0.6002 | 0.7168 | 0.8319 | 0.9089 | 0.7182 | 0.8832 | 0.1414 | 0.8752 | 0.8408 | 0.6502 | 0.8171 | 0.5893 | 0.6404 | 0.1364 | 0.7279 | 0.6738 | 0.6166 | | 0.4283 | 4.38 | 1620 | 0.5744 | 0.6147 | 0.7181 | 0.8372 | 0.8998 | 0.6260 | 0.8260 | 0.2497 | 0.9142 | 0.8480 | 0.6631 | 0.8316 | 0.5335 | 0.6976 | 0.2223 | 0.7171 | 0.6660 | 0.6351 | | 0.4222 | 4.43 | 1640 | 0.5881 | 0.6014 | 0.6982 | 0.8337 | 0.9133 | 0.6604 | 0.8085 | 0.1416 | 0.9237 | 0.8171 | 0.6224 | 0.8244 | 0.5747 | 0.6702 | 0.1357 | 0.7182 | 0.6822 | 0.6041 | | 0.7353 | 4.49 | 1660 | 0.5441 | 0.6086 | 0.7291 | 0.8298 | 0.9064 | 0.7696 | 0.8084 | 0.2185 | 0.8724 | 0.8933 | 0.6352 | 0.8266 | 0.6096 | 0.6655 | 0.2029 | 0.7105 | 0.6564 | 0.5887 | | 0.3957 | 4.54 | 1680 | 0.6038 | 0.5958 | 0.7275 | 0.8192 | 0.8897 | 0.7436 | 0.8966 | 0.2666 | 0.8646 | 0.8630 | 0.5688 | 0.8015 | 0.5760 | 0.6355 | 0.2409 | 0.7136 | 0.6558 | 0.5474 | | 0.2487 | 4.59 | 1700 | 0.5658 | 0.6187 | 0.7409 | 0.8320 | 0.9057 | 0.7238 | 0.8779 | 0.3069 | 0.8613 | 0.8739 | 0.6365 | 0.8163 | 0.6037 | 0.6622 | 0.2484 | 0.7229 | 0.6738 | 0.6033 | | 0.3008 | 4.65 | 1720 | 0.5535 | 0.6035 | 0.7189 | 0.8289 | 0.9121 | 0.7453 | 0.7740 | 0.2170 | 0.8902 | 0.8903 | 0.6032 | 0.8225 | 0.5837 | 0.6813 | 0.1916 | 0.7119 | 0.6491 | 0.5841 | | 0.6365 | 4.7 | 1740 | 0.5208 | 0.6136 | 0.7279 | 0.8371 | 0.9263 | 0.7461 | 0.8308 | 0.1831 | 0.8638 | 0.8811 | 0.6639 | 0.8202 | 0.6155 | 0.6586 | 0.1628 | 0.7354 | 0.6755 | 0.6269 | | 0.4279 | 4.76 | 1760 | 0.6134 | 0.5704 | 0.6952 | 0.8183 | 0.9179 | 0.6845 | 0.8761 | 0.1342 | 0.8760 | 0.8668 | 0.5109 | 0.8247 | 0.5764 | 0.6442 | 0.1245 | 0.7225 | 0.5997 | 0.5010 | | 0.6908 | 4.81 | 1780 | 0.5553 | 0.6059 | 0.7304 | 0.8273 | 0.9250 | 0.7461 | 0.8168 | 0.2998 | 0.8635 | 0.9004 | 0.5613 | 0.8287 | 0.6168 | 0.6874 | 0.2536 | 0.7293 | 0.5799 | 0.5453 | | 0.5609 | 4.86 | 1800 | 0.5144 | 0.6264 | 0.7445 | 0.8389 | 0.9163 | 0.7618 | 0.8524 | 0.3185 | 0.8861 | 0.8515 | 0.6247 | 0.8349 | 0.6154 | 0.7107 | 0.2581 | 0.7332 | 0.6332 | 0.5991 | | 1.0128 | 4.92 | 1820 | 0.5699 | 0.6074 | 0.7300 | 0.8298 | 0.9006 | 0.7923 | 0.8483 | 0.2638 | 0.9048 | 0.8210 | 0.5789 | 0.8302 | 0.6199 | 0.6829 | 0.2303 | 0.7278 | 0.5999 | 0.5610 | | 0.516 | 4.97 | 1840 | 0.5578 | 0.6022 | 0.7351 | 0.8286 | 0.9112 | 0.7915 | 0.8450 | 0.2377 | 0.8619 | 0.9054 | 0.5928 | 0.8346 | 0.6146 | 0.6743 | 0.1882 | 0.7268 | 0.6007 | 0.5763 | | 0.3054 | 5.03 | 1860 | 0.5368 | 0.6154 | 0.7584 | 0.8301 | 0.9143 | 0.7967 | 0.8678 | 0.4105 | 0.8507 | 0.8955 | 0.5728 | 0.8363 | 0.6110 | 0.6948 | 0.2822 | 0.7338 | 0.5973 | 0.5523 | | 0.6332 | 5.08 | 1880 | 0.5266 | 0.6299 | 0.7423 | 0.8414 | 0.9295 | 0.7338 | 0.8080 | 0.3166 | 0.8789 | 0.8939 | 0.6356 | 0.8371 | 0.6080 | 0.6962 | 0.2749 | 0.7321 | 0.6483 | 0.6128 | | 0.4124 | 5.14 | 1900 | 0.5191 | 0.6227 | 0.7240 | 0.8400 | 0.9238 | 0.6855 | 0.7834 | 0.2553 | 0.8945 | 0.8449 | 0.6803 | 0.8282 | 0.5864 | 0.6731 | 0.2227 | 0.7297 | 0.6979 | 0.6208 | | 0.3046 | 5.19 | 1920 | 0.5244 | 0.6272 | 0.7438 | 0.8437 | 0.9192 | 0.7810 | 0.8585 | 0.2385 | 0.8838 | 0.8707 | 0.6552 | 0.8372 | 0.6193 | 0.6793 | 0.1982 | 0.7389 | 0.6853 | 0.6323 | | 0.5471 | 5.24 | 1940 | 0.4855 | 0.6292 | 0.7565 | 0.8412 | 0.8930 | 0.8240 | 0.8479 | 0.2628 | 0.8820 | 0.9020 | 0.6839 | 0.8266 | 0.6157 | 0.6929 | 0.2276 | 0.7393 | 0.6698 | 0.6326 | | 0.2924 | 5.3 | 1960 | 0.5475 | 0.6145 | 0.7243 | 0.8377 | 0.9260 | 0.6746 | 0.8684 | 0.2452 | 0.8921 | 0.8872 | 0.5767 | 0.8220 | 0.5689 | 0.6825 | 0.2239 | 0.7409 | 0.6992 | 0.5639 | | 0.7133 | 5.35 | 1980 | 0.4943 | 0.6401 | 0.7533 | 0.8468 | 0.9030 | 0.7853 | 0.8514 | 0.2992 | 0.9117 | 0.8727 | 0.6501 | 0.8375 | 0.6315 | 0.7148 | 0.2638 | 0.7475 | 0.6811 | 0.6041 | | 0.3775 | 5.41 | 2000 | 0.5023 | 0.6105 | 0.7239 | 0.8412 | 0.9024 | 0.7428 | 0.8539 | 0.1445 | 0.9132 | 0.8469 | 0.6636 | 0.8286 | 0.6235 | 0.6728 | 0.1400 | 0.7618 | 0.6342 | 0.6125 | | 0.8475 | 5.46 | 2020 | 0.5247 | 0.6125 | 0.7384 | 0.8312 | 0.8990 | 0.7531 | 0.8370 | 0.2787 | 0.8786 | 0.9103 | 0.6121 | 0.8195 | 0.6126 | 0.7011 | 0.2521 | 0.7470 | 0.5952 | 0.5598 | | 0.2725 | 5.51 | 2040 | 0.5264 | 0.6263 | 0.7294 | 0.8445 | 0.9250 | 0.7440 | 0.8590 | 0.2303 | 0.9168 | 0.8122 | 0.6183 | 0.8321 | 0.6192 | 0.6931 | 0.2099 | 0.7482 | 0.6815 | 0.5999 | | 0.5317 | 5.57 | 2060 | 0.5177 | 0.6208 | 0.7396 | 0.8372 | 0.9330 | 0.7005 | 0.8587 | 0.2808 | 0.8339 | 0.8877 | 0.6823 | 0.8095 | 0.5866 | 0.6802 | 0.2380 | 0.7451 | 0.6555 | 0.6306 | | 0.6395 | 5.62 | 2080 | 0.5292 | 0.6252 | 0.7410 | 0.8365 | 0.8861 | 0.7238 | 0.8425 | 0.3135 | 0.9041 | 0.8495 | 0.6674 | 0.8211 | 0.6161 | 0.6870 | 0.2707 | 0.7388 | 0.6309 | 0.6120 | | 0.3586 | 5.68 | 2100 | 0.4814 | 0.6412 | 0.7579 | 0.8492 | 0.9053 | 0.8220 | 0.8115 | 0.2991 | 0.9051 | 0.8471 | 0.7149 | 0.8402 | 0.6208 | 0.6996 | 0.2540 | 0.7490 | 0.6618 | 0.6634 | | 0.3127 | 5.73 | 2120 | 0.4579 | 0.6571 | 0.7653 | 0.8566 | 0.9160 | 0.8202 | 0.8085 | 0.3566 | 0.9232 | 0.8290 | 0.7034 | 0.8397 | 0.6271 | 0.7127 | 0.2927 | 0.7648 | 0.6997 | 0.6628 | | 0.6651 | 5.78 | 2140 | 0.5767 | 0.6326 | 0.7389 | 0.8437 | 0.9129 | 0.7090 | 0.8142 | 0.3383 | 0.9263 | 0.8663 | 0.6056 | 0.8355 | 0.5889 | 0.7222 | 0.2958 | 0.7460 | 0.6529 | 0.5868 | | 0.2858 | 5.84 | 2160 | 0.5401 | 0.6216 | 0.7492 | 0.8346 | 0.9231 | 0.6579 | 0.8866 | 0.3958 | 0.8346 | 0.9036 | 0.6429 | 0.8273 | 0.5633 | 0.6981 | 0.3002 | 0.7283 | 0.6183 | 0.6156 | | 0.2566 | 5.89 | 2180 | 0.5070 | 0.6388 | 0.7608 | 0.8433 | 0.9055 | 0.7782 | 0.8674 | 0.3469 | 0.8775 | 0.8787 | 0.6715 | 0.8361 | 0.6347 | 0.7050 | 0.2857 | 0.7392 | 0.6408 | 0.6301 | | 0.4278 | 5.95 | 2200 | 0.5319 | 0.6431 | 0.7485 | 0.8506 | 0.8914 | 0.7504 | 0.8633 | 0.2503 | 0.9239 | 0.8485 | 0.7118 | 0.8342 | 0.6068 | 0.7183 | 0.2295 | 0.7408 | 0.6970 | 0.6752 | | 0.3374 | 6.0 | 2220 | 0.5220 | 0.6446 | 0.7587 | 0.8504 | 0.9149 | 0.7802 | 0.8598 | 0.3481 | 0.9145 | 0.8635 | 0.6300 | 0.8448 | 0.6248 | 0.7057 | 0.3009 | 0.7573 | 0.6588 | 0.6198 | | 0.4162 | 6.05 | 2240 | 0.5373 | 0.6312 | 0.7413 | 0.8445 | 0.9082 | 0.7615 | 0.8502 | 0.2900 | 0.9255 | 0.8356 | 0.6185 | 0.8390 | 0.6241 | 0.7017 | 0.2609 | 0.7497 | 0.6446 | 0.5982 | | 0.3659 | 6.11 | 2260 | 0.5100 | 0.6503 | 0.7555 | 0.8558 | 0.9271 | 0.7673 | 0.8519 | 0.3182 | 0.9162 | 0.8447 | 0.6630 | 0.8468 | 0.6269 | 0.7024 | 0.2766 | 0.7626 | 0.6856 | 0.6513 | | 0.3019 | 6.16 | 2280 | 0.5076 | 0.6437 | 0.7600 | 0.8502 | 0.9256 | 0.7714 | 0.8497 | 0.3067 | 0.8644 | 0.8754 | 0.7266 | 0.8413 | 0.6254 | 0.6728 | 0.2581 | 0.7460 | 0.6784 | 0.6838 | | 0.419 | 6.22 | 2300 | 0.5132 | 0.6403 | 0.7579 | 0.8483 | 0.9121 | 0.7802 | 0.8602 | 0.3012 | 0.8819 | 0.8600 | 0.7096 | 0.8394 | 0.6169 | 0.6742 | 0.2599 | 0.7437 | 0.6698 | 0.6782 | | 0.3698 | 6.27 | 2320 | 0.5154 | 0.6424 | 0.7488 | 0.8505 | 0.9401 | 0.7680 | 0.8024 | 0.3329 | 0.9022 | 0.8523 | 0.6434 | 0.8390 | 0.6141 | 0.6964 | 0.2846 | 0.7549 | 0.6732 | 0.6348 | | 0.3336 | 6.32 | 2340 | 0.5687 | 0.6079 | 0.7314 | 0.8337 | 0.9302 | 0.7395 | 0.8798 | 0.2330 | 0.8582 | 0.8943 | 0.5849 | 0.8411 | 0.6169 | 0.6405 | 0.2012 | 0.7290 | 0.6507 | 0.5757 | | 0.3567 | 6.38 | 2360 | 0.5166 | 0.6322 | 0.7639 | 0.8435 | 0.9016 | 0.8075 | 0.8436 | 0.3007 | 0.8602 | 0.9100 | 0.7236 | 0.8399 | 0.6166 | 0.6671 | 0.2515 | 0.7433 | 0.6095 | 0.6972 | | 0.4141 | 6.43 | 2380 | 0.4746 | 0.6610 | 0.7868 | 0.8559 | 0.9129 | 0.8145 | 0.8264 | 0.4202 | 0.8670 | 0.9008 | 0.7660 | 0.8423 | 0.6173 | 0.6954 | 0.3593 | 0.7663 | 0.6171 | 0.7296 | | 1.0648 | 6.49 | 2400 | 0.4916 | 0.6551 | 0.7625 | 0.8531 | 0.9221 | 0.7768 | 0.8518 | 0.3922 | 0.9069 | 0.7967 | 0.6909 | 0.8407 | 0.6314 | 0.7027 | 0.3444 | 0.7561 | 0.6446 | 0.6659 | | 0.3123 | 6.54 | 2420 | 0.4354 | 0.6802 | 0.7896 | 0.8663 | 0.8954 | 0.7670 | 0.8441 | 0.4503 | 0.9090 | 0.8037 | 0.8579 | 0.8356 | 0.6152 | 0.7331 | 0.3740 | 0.7816 | 0.6607 | 0.7611 | | 0.3032 | 6.59 | 2440 | 0.4324 | 0.6820 | 0.7943 | 0.8670 | 0.9154 | 0.7967 | 0.8428 | 0.4614 | 0.8942 | 0.8423 | 0.8070 | 0.8411 | 0.6355 | 0.7143 | 0.3655 | 0.7800 | 0.6673 | 0.7703 | | 0.4145 | 6.65 | 2460 | 0.5008 | 0.6531 | 0.7604 | 0.8555 | 0.9128 | 0.7721 | 0.8558 | 0.3218 | 0.9130 | 0.8410 | 0.7061 | 0.8412 | 0.6222 | 0.7058 | 0.2820 | 0.7575 | 0.6801 | 0.6831 | | 0.241 | 6.7 | 2480 | 0.5001 | 0.6470 | 0.7684 | 0.8514 | 0.9097 | 0.8170 | 0.8686 | 0.3234 | 0.8854 | 0.8659 | 0.7087 | 0.8413 | 0.6228 | 0.7038 | 0.2625 | 0.7491 | 0.6655 | 0.6840 | | 0.5881 | 6.76 | 2500 | 0.4669 | 0.6416 | 0.7549 | 0.8518 | 0.9329 | 0.8055 | 0.8571 | 0.2522 | 0.8717 | 0.8462 | 0.7189 | 0.8367 | 0.6359 | 0.6855 | 0.2267 | 0.7549 | 0.6625 | 0.6889 | | 0.6851 | 6.81 | 2520 | 0.5335 | 0.6332 | 0.7561 | 0.8480 | 0.9172 | 0.8463 | 0.8290 | 0.2526 | 0.8877 | 0.8747 | 0.6852 | 0.8404 | 0.6112 | 0.6837 | 0.2164 | 0.7485 | 0.6639 | 0.6686 | | 0.3773 | 6.86 | 2540 | 0.5191 | 0.6317 | 0.7559 | 0.8453 | 0.9217 | 0.7878 | 0.8746 | 0.2958 | 0.8702 | 0.8793 | 0.6617 | 0.8440 | 0.6182 | 0.6861 | 0.2415 | 0.7457 | 0.6392 | 0.6474 | | 0.5092 | 6.92 | 2560 | 0.4745 | 0.6505 | 0.7763 | 0.8521 | 0.9234 | 0.8219 | 0.8182 | 0.3888 | 0.8668 | 0.8939 | 0.7211 | 0.8481 | 0.6088 | 0.6894 | 0.3175 | 0.7488 | 0.6412 | 0.6999 | | 0.3652 | 6.97 | 2580 | 0.4222 | 0.6749 | 0.7796 | 0.8686 | 0.9326 | 0.7960 | 0.8283 | 0.3546 | 0.8916 | 0.8440 | 0.8104 | 0.8442 | 0.6262 | 0.7074 | 0.3021 | 0.7813 | 0.6852 | 0.7777 | | 0.3016 | 7.03 | 2600 | 0.4632 | 0.6570 | 0.7702 | 0.8602 | 0.9343 | 0.8038 | 0.8335 | 0.3145 | 0.8827 | 0.8947 | 0.7276 | 0.8449 | 0.6266 | 0.6963 | 0.2810 | 0.7739 | 0.6594 | 0.7169 | | 0.9354 | 7.08 | 2620 | 0.4494 | 0.6551 | 0.7692 | 0.8545 | 0.9277 | 0.7874 | 0.8065 | 0.3745 | 0.8844 | 0.8996 | 0.7043 | 0.8435 | 0.6262 | 0.7053 | 0.3194 | 0.7577 | 0.6523 | 0.6814 | | 0.8554 | 7.14 | 2640 | 0.4588 | 0.6492 | 0.7579 | 0.8527 | 0.9130 | 0.7053 | 0.8017 | 0.4151 | 0.9105 | 0.8226 | 0.7374 | 0.8395 | 0.5736 | 0.7068 | 0.3074 | 0.7560 | 0.6831 | 0.6783 | | 0.1009 | 7.19 | 2660 | 0.4967 | 0.6422 | 0.7690 | 0.8439 | 0.9088 | 0.8026 | 0.8285 | 0.3227 | 0.8242 | 0.8732 | 0.8231 | 0.8260 | 0.6197 | 0.6833 | 0.2669 | 0.7236 | 0.6736 | 0.7024 | | 0.5878 | 7.24 | 2680 | 0.4940 | 0.6499 | 0.7592 | 0.8555 | 0.9238 | 0.7939 | 0.8104 | 0.3196 | 0.9069 | 0.8481 | 0.7119 | 0.8408 | 0.6032 | 0.6907 | 0.2835 | 0.7620 | 0.6881 | 0.6809 | | 0.3527 | 7.3 | 2700 | 0.4924 | 0.6378 | 0.7595 | 0.8482 | 0.9264 | 0.8187 | 0.8311 | 0.2991 | 0.8702 | 0.8720 | 0.6992 | 0.8383 | 0.6033 | 0.6794 | 0.2624 | 0.7467 | 0.6595 | 0.6750 | | 0.3733 | 7.35 | 2720 | 0.5420 | 0.6321 | 0.7384 | 0.8446 | 0.9223 | 0.7199 | 0.8052 | 0.3394 | 0.9110 | 0.8042 | 0.6671 | 0.8367 | 0.5826 | 0.6743 | 0.2822 | 0.7412 | 0.6629 | 0.6450 | | 0.2743 | 7.41 | 2740 | 0.5190 | 0.6463 | 0.7655 | 0.8483 | 0.9182 | 0.7957 | 0.8177 | 0.3926 | 0.8902 | 0.8732 | 0.6707 | 0.8416 | 0.6282 | 0.7044 | 0.3097 | 0.7491 | 0.6528 | 0.6385 | | 0.6321 | 7.46 | 2760 | 0.5105 | 0.6427 | 0.7447 | 0.8529 | 0.9358 | 0.7341 | 0.8344 | 0.2998 | 0.9051 | 0.8230 | 0.6807 | 0.8437 | 0.6225 | 0.6947 | 0.2592 | 0.7593 | 0.6632 | 0.6567 | | 0.7369 | 7.51 | 2780 | 0.5156 | 0.6364 | 0.7543 | 0.8434 | 0.9160 | 0.7795 | 0.8035 | 0.3590 | 0.8889 | 0.8646 | 0.6685 | 0.8289 | 0.6335 | 0.6704 | 0.3000 | 0.7561 | 0.6414 | 0.6245 | | 0.2981 | 7.57 | 2800 | 0.5989 | 0.6172 | 0.7461 | 0.8338 | 0.8977 | 0.6911 | 0.8723 | 0.3880 | 0.8857 | 0.8817 | 0.6059 | 0.8271 | 0.5719 | 0.6510 | 0.3053 | 0.7454 | 0.6301 | 0.5892 | | 0.4012 | 7.62 | 2820 | 0.5105 | 0.6539 | 0.7702 | 0.8487 | 0.9265 | 0.8119 | 0.7934 | 0.4344 | 0.8756 | 0.8344 | 0.7152 | 0.8218 | 0.6226 | 0.6768 | 0.3363 | 0.7478 | 0.6871 | 0.6846 | | 0.3812 | 7.68 | 2840 | 0.5101 | 0.6602 | 0.7640 | 0.8547 | 0.9132 | 0.7615 | 0.7728 | 0.4581 | 0.9319 | 0.7872 | 0.7229 | 0.8344 | 0.6247 | 0.6867 | 0.3336 | 0.7610 | 0.6983 | 0.6824 | | 2.1456 | 7.73 | 2860 | 0.5119 | 0.6437 | 0.7641 | 0.8458 | 0.9168 | 0.7538 | 0.8622 | 0.3724 | 0.8550 | 0.8562 | 0.7322 | 0.8331 | 0.6207 | 0.6904 | 0.3005 | 0.7397 | 0.6531 | 0.6685 | | 0.3138 | 7.78 | 2880 | 0.5153 | 0.6446 | 0.7572 | 0.8519 | 0.9389 | 0.7837 | 0.8490 | 0.3067 | 0.8751 | 0.8660 | 0.6808 | 0.8358 | 0.6323 | 0.6872 | 0.2656 | 0.7627 | 0.6731 | 0.6553 | | 0.3975 | 7.84 | 2900 | 0.4575 | 0.6640 | 0.7735 | 0.8570 | 0.9125 | 0.7619 | 0.8285 | 0.3969 | 0.8894 | 0.8522 | 0.7731 | 0.8417 | 0.6410 | 0.7047 | 0.3399 | 0.7579 | 0.6468 | 0.7160 | | 0.138 | 7.89 | 2920 | 0.4832 | 0.6531 | 0.7794 | 0.8500 | 0.9183 | 0.8233 | 0.8272 | 0.4431 | 0.8725 | 0.8669 | 0.7047 | 0.8386 | 0.6239 | 0.6978 | 0.3327 | 0.7489 | 0.6565 | 0.6735 | | 1.2288 | 7.95 | 2940 | 0.5300 | 0.6530 | 0.7646 | 0.8526 | 0.9186 | 0.8154 | 0.7786 | 0.3941 | 0.9151 | 0.8382 | 0.6919 | 0.8355 | 0.6158 | 0.6870 | 0.3192 | 0.7584 | 0.6955 | 0.6596 | | 0.2467 | 8.0 | 2960 | 0.4991 | 0.6630 | 0.7906 | 0.8548 | 0.9189 | 0.8304 | 0.8237 | 0.4906 | 0.8798 | 0.8931 | 0.6975 | 0.8475 | 0.6185 | 0.7209 | 0.3837 | 0.7599 | 0.6494 | 0.6610 | | 0.4246 | 8.05 | 2980 | 0.5756 | 0.6489 | 0.7639 | 0.8513 | 0.9129 | 0.7837 | 0.8474 | 0.3609 | 0.8986 | 0.8498 | 0.6938 | 0.8375 | 0.6280 | 0.6816 | 0.2919 | 0.7553 | 0.6776 | 0.6706 | | 0.6102 | 8.11 | 3000 | 0.5444 | 0.6359 | 0.7444 | 0.8520 | 0.9323 | 0.7862 | 0.8062 | 0.2210 | 0.8929 | 0.8628 | 0.7094 | 0.8397 | 0.6343 | 0.6563 | 0.2024 | 0.7613 | 0.6760 | 0.6813 | | 0.5901 | 8.16 | 3020 | 0.5296 | 0.6434 | 0.7519 | 0.8538 | 0.9221 | 0.8084 | 0.8229 | 0.2617 | 0.9094 | 0.8298 | 0.7092 | 0.8441 | 0.6404 | 0.6818 | 0.2361 | 0.7613 | 0.6664 | 0.6739 | | 0.7706 | 8.22 | 3040 | 0.5710 | 0.6410 | 0.7500 | 0.8467 | 0.9227 | 0.7546 | 0.8326 | 0.3445 | 0.8991 | 0.8377 | 0.6586 | 0.8363 | 0.6332 | 0.6955 | 0.2874 | 0.7468 | 0.6499 | 0.6377 | | 0.2613 | 8.27 | 3060 | 0.4850 | 0.6706 | 0.7869 | 0.8569 | 0.9173 | 0.7762 | 0.8292 | 0.5361 | 0.8933 | 0.8203 | 0.7362 | 0.8406 | 0.6317 | 0.7262 | 0.3816 | 0.7588 | 0.6762 | 0.6791 | | 0.3787 | 8.32 | 3080 | 0.5123 | 0.6724 | 0.7794 | 0.8608 | 0.9303 | 0.7850 | 0.8282 | 0.4586 | 0.9080 | 0.8630 | 0.6827 | 0.8453 | 0.6354 | 0.7207 | 0.3831 | 0.7686 | 0.6930 | 0.6607 | | 0.2027 | 8.38 | 3100 | 0.5467 | 0.6577 | 0.7658 | 0.8547 | 0.9104 | 0.7148 | 0.8596 | 0.4194 | 0.9114 | 0.8503 | 0.6949 | 0.8470 | 0.5904 | 0.7081 | 0.3613 | 0.7506 | 0.6764 | 0.6699 | | 0.2585 | 8.43 | 3120 | 0.5285 | 0.6540 | 0.7727 | 0.8509 | 0.9137 | 0.7336 | 0.8450 | 0.4543 | 0.8851 | 0.8879 | 0.6896 | 0.8468 | 0.6090 | 0.6951 | 0.3664 | 0.7487 | 0.6517 | 0.6603 | | 1.3011 | 8.49 | 3140 | 0.5199 | 0.6615 | 0.7847 | 0.8541 | 0.9062 | 0.8103 | 0.8696 | 0.4618 | 0.8940 | 0.8486 | 0.7024 | 0.8446 | 0.6277 | 0.7115 | 0.3608 | 0.7552 | 0.6605 | 0.6703 | | 0.4755 | 8.54 | 3160 | 0.4897 | 0.6631 | 0.7860 | 0.8533 | 0.9125 | 0.8131 | 0.8255 | 0.4825 | 0.8793 | 0.8535 | 0.7360 | 0.8390 | 0.6284 | 0.7110 | 0.3724 | 0.7533 | 0.6619 | 0.6759 | | 1.0989 | 8.59 | 3180 | 0.5838 | 0.6376 | 0.7531 | 0.8451 | 0.9081 | 0.7302 | 0.8004 | 0.4229 | 0.9152 | 0.8258 | 0.6692 | 0.8425 | 0.5774 | 0.7018 | 0.3407 | 0.7489 | 0.6215 | 0.6307 | | 0.2332 | 8.65 | 3200 | 0.4615 | 0.6701 | 0.7911 | 0.8600 | 0.9218 | 0.7880 | 0.8669 | 0.4674 | 0.8654 | 0.8479 | 0.7801 | 0.8409 | 0.6291 | 0.7166 | 0.3506 | 0.7689 | 0.6726 | 0.7122 | | 0.8864 | 8.7 | 3220 | 0.5110 | 0.6600 | 0.7710 | 0.8540 | 0.9048 | 0.7695 | 0.8619 | 0.4088 | 0.9046 | 0.8200 | 0.7270 | 0.8333 | 0.6345 | 0.6866 | 0.3386 | 0.7581 | 0.6790 | 0.6897 | | 0.1611 | 8.76 | 3240 | 0.4700 | 0.6711 | 0.7814 | 0.8612 | 0.9116 | 0.7684 | 0.8589 | 0.4298 | 0.9013 | 0.8437 | 0.7561 | 0.8420 | 0.6227 | 0.7142 | 0.3619 | 0.7678 | 0.6764 | 0.7128 | | 0.4228 | 8.81 | 3260 | 0.4747 | 0.6717 | 0.7968 | 0.8588 | 0.9180 | 0.8397 | 0.8264 | 0.4786 | 0.8719 | 0.8969 | 0.7463 | 0.8504 | 0.6326 | 0.7152 | 0.3934 | 0.7592 | 0.6400 | 0.7109 | | 0.2511 | 8.86 | 3280 | 0.5220 | 0.6704 | 0.7833 | 0.8600 | 0.9261 | 0.8208 | 0.8527 | 0.4542 | 0.9033 | 0.8320 | 0.6943 | 0.8505 | 0.6246 | 0.7248 | 0.3880 | 0.7629 | 0.6744 | 0.6678 | | 0.2103 | 8.92 | 3300 | 0.4626 | 0.6784 | 0.7827 | 0.8642 | 0.9136 | 0.7540 | 0.8653 | 0.4562 | 0.9063 | 0.7931 | 0.7904 | 0.8421 | 0.6237 | 0.7221 | 0.3783 | 0.7716 | 0.6970 | 0.7139 | | 0.3643 | 8.97 | 3320 | 0.4803 | 0.6726 | 0.7760 | 0.8625 | 0.9124 | 0.7542 | 0.8562 | 0.3939 | 0.9034 | 0.8287 | 0.7829 | 0.8432 | 0.6269 | 0.7278 | 0.3554 | 0.7672 | 0.6791 | 0.7084 | | 0.2641 | 9.03 | 3340 | 0.4765 | 0.6783 | 0.8001 | 0.8637 | 0.9147 | 0.8312 | 0.8584 | 0.4151 | 0.8551 | 0.9011 | 0.8251 | 0.8528 | 0.6453 | 0.7192 | 0.3693 | 0.7606 | 0.6432 | 0.7575 | | 0.2143 | 9.08 | 3360 | 0.5518 | 0.6610 | 0.7748 | 0.8540 | 0.9158 | 0.7887 | 0.8516 | 0.4334 | 0.9013 | 0.8551 | 0.6778 | 0.8508 | 0.6459 | 0.7176 | 0.3734 | 0.7556 | 0.6398 | 0.6439 | | 0.3505 | 9.14 | 3380 | 0.5120 | 0.6642 | 0.7757 | 0.8588 | 0.9292 | 0.7881 | 0.8391 | 0.4038 | 0.8775 | 0.8161 | 0.7764 | 0.8389 | 0.6293 | 0.7024 | 0.3511 | 0.7709 | 0.6456 | 0.7114 | | 0.358 | 9.19 | 3400 | 0.5251 | 0.6604 | 0.7720 | 0.8579 | 0.9340 | 0.7911 | 0.8394 | 0.4064 | 0.8972 | 0.8521 | 0.6837 | 0.8526 | 0.6403 | 0.7061 | 0.3513 | 0.7708 | 0.6472 | 0.6547 | | 0.5739 | 9.24 | 3420 | 0.4954 | 0.6654 | 0.7883 | 0.8615 | 0.9251 | 0.8190 | 0.8580 | 0.4015 | 0.8678 | 0.8745 | 0.7726 | 0.8513 | 0.6423 | 0.6940 | 0.3262 | 0.7799 | 0.6355 | 0.7285 | | 0.3859 | 9.3 | 3440 | 0.5409 | 0.6563 | 0.7677 | 0.8556 | 0.9375 | 0.7684 | 0.8450 | 0.4151 | 0.8911 | 0.8357 | 0.6808 | 0.8483 | 0.6370 | 0.7071 | 0.3394 | 0.7688 | 0.6456 | 0.6483 | | 0.2072 | 9.35 | 3460 | 0.4515 | 0.6701 | 0.7792 | 0.8644 | 0.9235 | 0.7975 | 0.8361 | 0.3910 | 0.9044 | 0.8436 | 0.7580 | 0.8473 | 0.6406 | 0.7065 | 0.3286 | 0.7822 | 0.6679 | 0.7177 | | 0.2763 | 9.41 | 3480 | 0.4903 | 0.6651 | 0.7767 | 0.8597 | 0.9285 | 0.8032 | 0.8346 | 0.4003 | 0.8937 | 0.8548 | 0.7218 | 0.8446 | 0.6381 | 0.7099 | 0.3249 | 0.7675 | 0.6788 | 0.6920 | | 0.4056 | 9.46 | 3500 | 0.4582 | 0.6714 | 0.7913 | 0.8648 | 0.9196 | 0.8358 | 0.8390 | 0.3835 | 0.8786 | 0.8930 | 0.7896 | 0.8485 | 0.6269 | 0.7106 | 0.3245 | 0.7766 | 0.6624 | 0.7503 | | 0.2243 | 9.51 | 3520 | 0.5132 | 0.6559 | 0.7656 | 0.8591 | 0.9275 | 0.7915 | 0.8485 | 0.3016 | 0.8904 | 0.8661 | 0.7339 | 0.8451 | 0.6310 | 0.7043 | 0.2617 | 0.7657 | 0.6894 | 0.6945 | | 0.7284 | 9.57 | 3540 | 0.4846 | 0.6554 | 0.7593 | 0.8617 | 0.9298 | 0.7302 | 0.8479 | 0.2907 | 0.8971 | 0.8715 | 0.7482 | 0.8547 | 0.5976 | 0.7054 | 0.2533 | 0.7619 | 0.7019 | 0.7130 | | 0.3212 | 9.62 | 3560 | 0.4803 | 0.6572 | 0.7699 | 0.8608 | 0.9212 | 0.7555 | 0.8622 | 0.3283 | 0.8883 | 0.8770 | 0.7572 | 0.8590 | 0.5987 | 0.7006 | 0.2688 | 0.7570 | 0.6950 | 0.7215 | | 0.6291 | 9.68 | 3580 | 0.4862 | 0.6426 | 0.7535 | 0.8544 | 0.9147 | 0.7223 | 0.8381 | 0.2837 | 0.8865 | 0.8410 | 0.7880 | 0.8526 | 0.5934 | 0.6839 | 0.2294 | 0.7511 | 0.6794 | 0.7087 | | 0.3655 | 9.73 | 3600 | 0.4545 | 0.6705 | 0.7891 | 0.8621 | 0.8971 | 0.8076 | 0.8626 | 0.3888 | 0.8789 | 0.8207 | 0.8681 | 0.8355 | 0.6397 | 0.6816 | 0.3088 | 0.7717 | 0.6847 | 0.7717 | | 0.2223 | 9.78 | 3620 | 0.4530 | 0.6666 | 0.7888 | 0.8596 | 0.9038 | 0.8096 | 0.8744 | 0.4158 | 0.8844 | 0.8551 | 0.7787 | 0.8421 | 0.6465 | 0.6691 | 0.3365 | 0.7757 | 0.6604 | 0.7361 | | 0.2464 | 9.84 | 3640 | 0.5492 | 0.6501 | 0.7693 | 0.8513 | 0.9188 | 0.7944 | 0.8741 | 0.3862 | 0.8928 | 0.8657 | 0.6531 | 0.8540 | 0.6479 | 0.7151 | 0.3299 | 0.7584 | 0.6197 | 0.6259 | | 0.3132 | 9.89 | 3660 | 0.4718 | 0.6706 | 0.7842 | 0.8630 | 0.9278 | 0.8222 | 0.8293 | 0.3615 | 0.8638 | 0.8602 | 0.8244 | 0.8415 | 0.6531 | 0.7033 | 0.3108 | 0.7700 | 0.6474 | 0.7678 | | 0.3324 | 9.95 | 3680 | 0.4550 | 0.6790 | 0.7803 | 0.8673 | 0.9217 | 0.8007 | 0.8257 | 0.4419 | 0.9299 | 0.7736 | 0.7688 | 0.8515 | 0.6404 | 0.7304 | 0.3462 | 0.7750 | 0.6775 | 0.7321 | | 0.2938 | 10.0 | 3700 | 0.4770 | 0.6700 | 0.8004 | 0.8599 | 0.9264 | 0.8356 | 0.8404 | 0.4655 | 0.8441 | 0.9064 | 0.7846 | 0.8502 | 0.6429 | 0.7106 | 0.3299 | 0.7600 | 0.6462 | 0.7503 | | 0.1729 | 10.05 | 3720 | 0.5693 | 0.6432 | 0.7511 | 0.8483 | 0.9290 | 0.7376 | 0.8531 | 0.3331 | 0.8826 | 0.8375 | 0.6846 | 0.8417 | 0.6330 | 0.6959 | 0.2686 | 0.7390 | 0.6639 | 0.6601 | | 0.2335 | 10.11 | 3740 | 0.5438 | 0.6523 | 0.7610 | 0.8543 | 0.9426 | 0.7742 | 0.8817 | 0.3151 | 0.8659 | 0.8432 | 0.7042 | 0.8419 | 0.6442 | 0.6913 | 0.2620 | 0.7497 | 0.6928 | 0.6843 | | 0.2832 | 10.16 | 3760 | 0.5138 | 0.6525 | 0.7706 | 0.8527 | 0.9438 | 0.8356 | 0.7781 | 0.3821 | 0.8624 | 0.8700 | 0.7221 | 0.8392 | 0.6126 | 0.6875 | 0.3230 | 0.7502 | 0.6561 | 0.6986 | | 0.8137 | 10.22 | 3780 | 0.5498 | 0.6492 | 0.7599 | 0.8514 | 0.9200 | 0.7925 | 0.8534 | 0.3559 | 0.9091 | 0.8304 | 0.6579 | 0.8508 | 0.6445 | 0.7176 | 0.3250 | 0.7553 | 0.6220 | 0.6292 | | 0.3137 | 10.27 | 3800 | 0.5088 | 0.6593 | 0.7719 | 0.8540 | 0.9410 | 0.8104 | 0.8620 | 0.4019 | 0.8722 | 0.8296 | 0.6862 | 0.8435 | 0.6484 | 0.7093 | 0.3554 | 0.7551 | 0.6471 | 0.6563 | | 0.2471 | 10.32 | 3820 | 0.5060 | 0.6601 | 0.7856 | 0.8546 | 0.9305 | 0.8251 | 0.8578 | 0.4243 | 0.8544 | 0.8923 | 0.7148 | 0.8498 | 0.6390 | 0.7031 | 0.3558 | 0.7563 | 0.6367 | 0.6798 | | 0.2507 | 10.38 | 3840 | 0.5004 | 0.6596 | 0.7779 | 0.8572 | 0.9356 | 0.8184 | 0.8547 | 0.4060 | 0.8776 | 0.8508 | 0.7022 | 0.8528 | 0.6355 | 0.7072 | 0.3361 | 0.7627 | 0.6493 | 0.6736 | | 0.2734 | 10.43 | 3860 | 0.5179 | 0.6519 | 0.7642 | 0.8567 | 0.9220 | 0.8118 | 0.8682 | 0.2989 | 0.8972 | 0.8345 | 0.7170 | 0.8491 | 0.6421 | 0.7026 | 0.2487 | 0.7605 | 0.6748 | 0.6852 | | 0.9895 | 10.49 | 3880 | 0.4962 | 0.6658 | 0.7818 | 0.8615 | 0.9164 | 0.8324 | 0.8711 | 0.3679 | 0.8991 | 0.8569 | 0.7290 | 0.8500 | 0.6467 | 0.7104 | 0.2938 | 0.7691 | 0.6931 | 0.6973 | | 0.1867 | 10.54 | 3900 | 0.4848 | 0.6646 | 0.7732 | 0.8621 | 0.9189 | 0.7909 | 0.8550 | 0.3523 | 0.9082 | 0.8533 | 0.7337 | 0.8511 | 0.6524 | 0.7081 | 0.2877 | 0.7739 | 0.6864 | 0.6926 | | 0.2285 | 10.59 | 3920 | 0.4964 | 0.6639 | 0.7690 | 0.8601 | 0.9294 | 0.7393 | 0.8069 | 0.4343 | 0.9073 | 0.8280 | 0.7377 | 0.8440 | 0.6155 | 0.7073 | 0.3567 | 0.7764 | 0.6515 | 0.6956 | | 0.236 | 10.65 | 3940 | 0.5185 | 0.6739 | 0.7955 | 0.8625 | 0.9266 | 0.8106 | 0.8428 | 0.4827 | 0.8763 | 0.8845 | 0.7446 | 0.8541 | 0.6558 | 0.7205 | 0.3622 | 0.7763 | 0.6455 | 0.7026 | | 0.1239 | 10.7 | 3960 | 0.5523 | 0.6651 | 0.7828 | 0.8568 | 0.9286 | 0.7897 | 0.8435 | 0.4805 | 0.8873 | 0.8628 | 0.6874 | 0.8569 | 0.6587 | 0.7208 | 0.3616 | 0.7617 | 0.6427 | 0.6534 | | 0.2032 | 10.76 | 3980 | 0.5506 | 0.6614 | 0.7776 | 0.8553 | 0.9275 | 0.7965 | 0.8530 | 0.4918 | 0.9019 | 0.7928 | 0.6798 | 0.8537 | 0.6437 | 0.7292 | 0.3746 | 0.7632 | 0.6240 | 0.6416 | | 0.226 | 10.81 | 4000 | 0.4977 | 0.6652 | 0.7906 | 0.8569 | 0.9294 | 0.7910 | 0.8634 | 0.5039 | 0.8687 | 0.8779 | 0.7002 | 0.8540 | 0.6425 | 0.7169 | 0.3600 | 0.7622 | 0.6504 | 0.6706 | | 0.2067 | 10.86 | 4020 | 0.5152 | 0.6573 | 0.7743 | 0.8513 | 0.9192 | 0.7888 | 0.8503 | 0.4305 | 0.8782 | 0.8423 | 0.7110 | 0.8364 | 0.6244 | 0.7229 | 0.3532 | 0.7514 | 0.6666 | 0.6465 | | 0.4254 | 10.92 | 4040 | 0.4596 | 0.6791 | 0.8022 | 0.8614 | 0.9125 | 0.8033 | 0.8455 | 0.5592 | 0.8885 | 0.8584 | 0.7476 | 0.8421 | 0.6433 | 0.7377 | 0.3891 | 0.7717 | 0.6739 | 0.6958 | | 1.076 | 10.97 | 4060 | 0.4386 | 0.6890 | 0.7967 | 0.8683 | 0.9162 | 0.7783 | 0.8235 | 0.5060 | 0.8973 | 0.8320 | 0.8238 | 0.8371 | 0.6368 | 0.7309 | 0.3817 | 0.7783 | 0.7000 | 0.7585 | | 0.4622 | 11.03 | 4080 | 0.4688 | 0.6831 | 0.7961 | 0.8663 | 0.9228 | 0.7979 | 0.8488 | 0.4770 | 0.8826 | 0.8460 | 0.7975 | 0.8440 | 0.6515 | 0.7273 | 0.3719 | 0.7770 | 0.6573 | 0.7529 | | 0.2557 | 11.08 | 4100 | 0.5033 | 0.6658 | 0.7796 | 0.8615 | 0.9334 | 0.8058 | 0.8427 | 0.4212 | 0.8923 | 0.8325 | 0.7292 | 0.8470 | 0.6434 | 0.7070 | 0.3484 | 0.7860 | 0.6294 | 0.6995 | | 0.2339 | 11.14 | 4120 | 0.4985 | 0.6627 | 0.7835 | 0.8575 | 0.9314 | 0.8093 | 0.8638 | 0.4633 | 0.8814 | 0.8293 | 0.7056 | 0.8550 | 0.6525 | 0.7165 | 0.3499 | 0.7696 | 0.6218 | 0.6737 | | 0.293 | 11.19 | 4140 | 0.5416 | 0.6486 | 0.7653 | 0.8508 | 0.9307 | 0.8219 | 0.8263 | 0.3641 | 0.8809 | 0.8411 | 0.6920 | 0.8436 | 0.6564 | 0.7103 | 0.3014 | 0.7591 | 0.6107 | 0.6587 | | 0.1347 | 11.24 | 4160 | 0.4919 | 0.6628 | 0.7802 | 0.8581 | 0.9256 | 0.8205 | 0.8425 | 0.4267 | 0.8878 | 0.8205 | 0.7377 | 0.8457 | 0.6563 | 0.7114 | 0.3152 | 0.7709 | 0.6407 | 0.6992 | | 0.3583 | 11.3 | 4180 | 0.4759 | 0.6615 | 0.7760 | 0.8565 | 0.9060 | 0.8063 | 0.8523 | 0.4017 | 0.9049 | 0.8172 | 0.7439 | 0.8435 | 0.6547 | 0.7142 | 0.3172 | 0.7643 | 0.6492 | 0.6876 | | 0.4382 | 11.35 | 4200 | 0.6038 | 0.6373 | 0.7558 | 0.8421 | 0.9127 | 0.7383 | 0.8616 | 0.4178 | 0.8920 | 0.8279 | 0.6399 | 0.8332 | 0.6320 | 0.6805 | 0.3458 | 0.7562 | 0.6042 | 0.6096 | | 0.3586 | 11.41 | 4220 | 0.5314 | 0.6627 | 0.7782 | 0.8553 | 0.9249 | 0.8193 | 0.8616 | 0.4531 | 0.8962 | 0.8018 | 0.6907 | 0.8483 | 0.6569 | 0.7091 | 0.3746 | 0.7638 | 0.6338 | 0.6521 | | 0.2528 | 11.46 | 4240 | 0.5731 | 0.6546 | 0.7763 | 0.8495 | 0.9296 | 0.8141 | 0.8520 | 0.4677 | 0.8737 | 0.8220 | 0.6754 | 0.8366 | 0.6431 | 0.7088 | 0.3676 | 0.7566 | 0.6234 | 0.6460 | | 0.4401 | 11.51 | 4260 | 0.5289 | 0.6641 | 0.7897 | 0.8533 | 0.9213 | 0.8162 | 0.8542 | 0.5106 | 0.8659 | 0.8265 | 0.7332 | 0.8398 | 0.6423 | 0.7196 | 0.3737 | 0.7539 | 0.6285 | 0.6911 | | 0.1793 | 11.57 | 4280 | 0.5084 | 0.6584 | 0.7837 | 0.8504 | 0.9197 | 0.7662 | 0.8610 | 0.5191 | 0.8659 | 0.8455 | 0.7083 | 0.8411 | 0.6381 | 0.7032 | 0.3661 | 0.7500 | 0.6359 | 0.6740 | | 0.4799 | 11.62 | 4300 | 0.5034 | 0.6746 | 0.8053 | 0.8576 | 0.9170 | 0.7807 | 0.8470 | 0.5406 | 0.8247 | 0.8734 | 0.8540 | 0.8432 | 0.6430 | 0.7224 | 0.3701 | 0.7474 | 0.6522 | 0.7442 | | 0.1624 | 11.68 | 4320 | 0.4906 | 0.6740 | 0.7886 | 0.8598 | 0.9088 | 0.7923 | 0.8441 | 0.4827 | 0.8826 | 0.7731 | 0.8367 | 0.8399 | 0.6449 | 0.7167 | 0.3352 | 0.7557 | 0.6873 | 0.7381 | | 0.8102 | 11.73 | 4340 | 0.5751 | 0.6489 | 0.7797 | 0.8484 | 0.9225 | 0.8260 | 0.8834 | 0.4228 | 0.8568 | 0.8642 | 0.6826 | 0.8541 | 0.6489 | 0.7003 | 0.3127 | 0.7414 | 0.6291 | 0.6557 | | 0.276 | 11.78 | 4360 | 0.5836 | 0.6572 | 0.7753 | 0.8508 | 0.9281 | 0.7902 | 0.8354 | 0.5140 | 0.8948 | 0.8049 | 0.6599 | 0.8430 | 0.6425 | 0.7164 | 0.3771 | 0.7560 | 0.6329 | 0.6327 | | 0.1517 | 11.84 | 4380 | 0.5328 | 0.6633 | 0.7821 | 0.8551 | 0.9274 | 0.8103 | 0.8505 | 0.4561 | 0.8747 | 0.8357 | 0.7202 | 0.8449 | 0.6465 | 0.7077 | 0.3596 | 0.7565 | 0.6448 | 0.6831 | | 0.3974 | 11.89 | 4400 | 0.5615 | 0.6587 | 0.7745 | 0.8535 | 0.9407 | 0.8102 | 0.8532 | 0.4042 | 0.8589 | 0.8444 | 0.7100 | 0.8412 | 0.6467 | 0.7079 | 0.3426 | 0.7524 | 0.6433 | 0.6766 | | 0.2477 | 11.95 | 4420 | 0.5036 | 0.6828 | 0.7966 | 0.8658 | 0.9258 | 0.8122 | 0.8544 | 0.4738 | 0.8794 | 0.8440 | 0.7865 | 0.8471 | 0.6419 | 0.7226 | 0.3875 | 0.7715 | 0.6759 | 0.7329 | | 0.1876 | 12.0 | 4440 | 0.5161 | 0.6774 | 0.8080 | 0.8608 | 0.9253 | 0.8386 | 0.8543 | 0.5258 | 0.8468 | 0.8885 | 0.7765 | 0.8491 | 0.6436 | 0.7152 | 0.3906 | 0.7604 | 0.6463 | 0.7364 | | 0.3552 | 12.05 | 4460 | 0.5186 | 0.6651 | 0.7778 | 0.8574 | 0.9117 | 0.7749 | 0.8590 | 0.4480 | 0.9008 | 0.8023 | 0.7483 | 0.8461 | 0.6384 | 0.7082 | 0.3417 | 0.7580 | 0.6591 | 0.7042 | | 0.3803 | 12.11 | 4480 | 0.4803 | 0.6761 | 0.7897 | 0.8669 | 0.9237 | 0.8025 | 0.8526 | 0.4206 | 0.8850 | 0.8320 | 0.8112 | 0.8475 | 0.6370 | 0.7145 | 0.3249 | 0.7813 | 0.6757 | 0.7520 | | 0.3355 | 12.16 | 4500 | 0.5312 | 0.6579 | 0.7699 | 0.8559 | 0.9315 | 0.7728 | 0.8423 | 0.3943 | 0.8790 | 0.8367 | 0.7328 | 0.8388 | 0.6299 | 0.7106 | 0.2987 | 0.7625 | 0.6824 | 0.6820 | | 0.2417 | 12.22 | 4520 | 0.5646 | 0.6554 | 0.7789 | 0.8537 | 0.9142 | 0.8015 | 0.8827 | 0.4214 | 0.8845 | 0.8463 | 0.7018 | 0.8546 | 0.6449 | 0.7107 | 0.3123 | 0.7550 | 0.6418 | 0.6685 | | 0.5121 | 12.27 | 4540 | 0.5256 | 0.6580 | 0.7809 | 0.8532 | 0.9073 | 0.7828 | 0.8720 | 0.4260 | 0.8725 | 0.8535 | 0.7526 | 0.8491 | 0.6504 | 0.7196 | 0.3252 | 0.7559 | 0.6362 | 0.6699 | | 0.2514 | 12.32 | 4560 | 0.5088 | 0.6662 | 0.7933 | 0.8565 | 0.9103 | 0.8052 | 0.8638 | 0.4647 | 0.8684 | 0.8983 | 0.7422 | 0.8549 | 0.6506 | 0.7166 | 0.3664 | 0.7602 | 0.6327 | 0.6825 | | 0.1052 | 12.38 | 4580 | 0.4722 | 0.6896 | 0.8055 | 0.8706 | 0.9056 | 0.8071 | 0.8511 | 0.4913 | 0.8924 | 0.8311 | 0.8599 | 0.8479 | 0.6509 | 0.7136 | 0.3808 | 0.7866 | 0.6746 | 0.7729 | | 0.1715 | 12.43 | 4600 | 0.5683 | 0.6530 | 0.7754 | 0.8539 | 0.9251 | 0.8011 | 0.8864 | 0.3906 | 0.8757 | 0.8612 | 0.6881 | 0.8552 | 0.6512 | 0.7004 | 0.2957 | 0.7584 | 0.6524 | 0.6581 | | 0.3604 | 12.49 | 4620 | 0.5680 | 0.6696 | 0.7885 | 0.8616 | 0.9308 | 0.8331 | 0.8639 | 0.4597 | 0.8946 | 0.8443 | 0.6931 | 0.8578 | 0.6440 | 0.7312 | 0.3553 | 0.7723 | 0.6691 | 0.6575 | | 0.3714 | 12.54 | 4640 | 0.5835 | 0.6659 | 0.7884 | 0.8562 | 0.9224 | 0.8000 | 0.8512 | 0.4935 | 0.8794 | 0.8691 | 0.7033 | 0.8538 | 0.6472 | 0.7285 | 0.3717 | 0.7599 | 0.6454 | 0.6545 | | 0.2263 | 12.59 | 4660 | 0.4759 | 0.6725 | 0.7893 | 0.8652 | 0.9259 | 0.7661 | 0.8610 | 0.4304 | 0.8656 | 0.8497 | 0.8266 | 0.8538 | 0.6425 | 0.7132 | 0.3036 | 0.7750 | 0.6634 | 0.7561 | | 0.5225 | 12.65 | 4680 | 0.4786 | 0.6851 | 0.8043 | 0.8688 | 0.9180 | 0.7921 | 0.8680 | 0.4940 | 0.8808 | 0.8818 | 0.7950 | 0.8538 | 0.6470 | 0.7295 | 0.3847 | 0.7884 | 0.6532 | 0.7388 | | 0.3626 | 12.7 | 4700 | 0.5585 | 0.6550 | 0.7612 | 0.8589 | 0.9307 | 0.6914 | 0.8602 | 0.3850 | 0.9027 | 0.8599 | 0.6985 | 0.8588 | 0.5957 | 0.7259 | 0.3158 | 0.7659 | 0.6579 | 0.6651 | | 0.3352 | 12.76 | 4720 | 0.5811 | 0.6610 | 0.7714 | 0.8576 | 0.9286 | 0.6931 | 0.8372 | 0.4718 | 0.8974 | 0.8828 | 0.6887 | 0.8534 | 0.5920 | 0.7401 | 0.3678 | 0.7630 | 0.6525 | 0.6585 | | 0.1431 | 12.81 | 4740 | 0.5148 | 0.6730 | 0.7917 | 0.8621 | 0.9251 | 0.8162 | 0.8604 | 0.5046 | 0.8997 | 0.8249 | 0.7111 | 0.8538 | 0.6535 | 0.7287 | 0.3494 | 0.7734 | 0.6723 | 0.6799 | | 0.2278 | 12.86 | 4760 | 0.5648 | 0.6589 | 0.7866 | 0.8529 | 0.9235 | 0.7984 | 0.8661 | 0.4670 | 0.8616 | 0.8986 | 0.6910 | 0.8515 | 0.6571 | 0.7189 | 0.3448 | 0.7587 | 0.6227 | 0.6589 | | 0.2733 | 12.92 | 4780 | 0.5890 | 0.6732 | 0.7910 | 0.8599 | 0.9260 | 0.8214 | 0.8534 | 0.5203 | 0.9015 | 0.8293 | 0.6848 | 0.8503 | 0.6511 | 0.7426 | 0.3901 | 0.7709 | 0.6560 | 0.6512 | | 0.2281 | 12.97 | 4800 | 0.5588 | 0.6596 | 0.7686 | 0.8568 | 0.9229 | 0.7174 | 0.8451 | 0.4202 | 0.8968 | 0.8708 | 0.7069 | 0.8519 | 0.6191 | 0.7290 | 0.3404 | 0.7612 | 0.6532 | 0.6625 | | 0.183 | 13.03 | 4820 | 0.5286 | 0.6607 | 0.7743 | 0.8578 | 0.9355 | 0.7726 | 0.8733 | 0.4067 | 0.8809 | 0.8563 | 0.6949 | 0.8523 | 0.6487 | 0.7186 | 0.3214 | 0.7671 | 0.6575 | 0.6593 | | 0.2967 | 13.08 | 4840 | 0.5679 | 0.6615 | 0.7843 | 0.8564 | 0.9251 | 0.8120 | 0.8757 | 0.4203 | 0.8735 | 0.8922 | 0.6910 | 0.8546 | 0.6581 | 0.7157 | 0.3374 | 0.7639 | 0.6437 | 0.6574 | | 0.2665 | 13.14 | 4860 | 0.5360 | 0.6636 | 0.7856 | 0.8584 | 0.9333 | 0.8339 | 0.8712 | 0.4552 | 0.8844 | 0.8341 | 0.6873 | 0.8544 | 0.6576 | 0.7082 | 0.3394 | 0.7732 | 0.6580 | 0.6542 | | 0.1838 | 13.19 | 4880 | 0.5524 | 0.6748 | 0.7894 | 0.8623 | 0.9380 | 0.8202 | 0.8343 | 0.5153 | 0.9034 | 0.8439 | 0.6706 | 0.8562 | 0.6601 | 0.7390 | 0.3860 | 0.7759 | 0.6547 | 0.6517 | | 0.4542 | 13.24 | 4900 | 0.4713 | 0.6811 | 0.7893 | 0.8673 | 0.9307 | 0.8204 | 0.8339 | 0.4459 | 0.9051 | 0.8505 | 0.7387 | 0.8562 | 0.6606 | 0.7316 | 0.3687 | 0.7802 | 0.6740 | 0.6964 | | 0.1641 | 13.3 | 4920 | 0.4515 | 0.6862 | 0.8015 | 0.8679 | 0.9280 | 0.8041 | 0.8644 | 0.4573 | 0.8587 | 0.8722 | 0.8255 | 0.8481 | 0.6610 | 0.7254 | 0.3649 | 0.7742 | 0.6789 | 0.7509 | | 0.3412 | 13.35 | 4940 | 0.5416 | 0.6607 | 0.7873 | 0.8550 | 0.9109 | 0.8503 | 0.8744 | 0.4282 | 0.8865 | 0.8621 | 0.6985 | 0.8449 | 0.6412 | 0.7099 | 0.3406 | 0.7618 | 0.6590 | 0.6675 | | 0.4612 | 13.41 | 4960 | 0.5605 | 0.6679 | 0.7859 | 0.8566 | 0.9213 | 0.8035 | 0.8725 | 0.4759 | 0.8875 | 0.8487 | 0.6918 | 0.8472 | 0.6569 | 0.7228 | 0.3761 | 0.7624 | 0.6588 | 0.6510 | | 0.2196 | 13.46 | 4980 | 0.5417 | 0.6690 | 0.7952 | 0.8569 | 0.9264 | 0.7930 | 0.8688 | 0.5372 | 0.8706 | 0.8781 | 0.6920 | 0.8500 | 0.6441 | 0.7425 | 0.3886 | 0.7678 | 0.6499 | 0.6399 | | 0.7025 | 13.51 | 5000 | 0.5168 | 0.6712 | 0.7874 | 0.8581 | 0.9288 | 0.8091 | 0.8454 | 0.5175 | 0.8924 | 0.8165 | 0.7019 | 0.8416 | 0.6440 | 0.7388 | 0.3998 | 0.7765 | 0.6671 | 0.6308 | | 0.1238 | 13.57 | 5020 | 0.5405 | 0.6739 | 0.7896 | 0.8613 | 0.9134 | 0.7864 | 0.8344 | 0.4747 | 0.8913 | 0.8576 | 0.7696 | 0.8390 | 0.6454 | 0.7264 | 0.3818 | 0.7845 | 0.6485 | 0.6919 | | 0.1813 | 13.62 | 5040 | 0.6150 | 0.6523 | 0.7701 | 0.8504 | 0.9111 | 0.7670 | 0.8743 | 0.4085 | 0.8852 | 0.8403 | 0.7043 | 0.8415 | 0.6351 | 0.7191 | 0.3253 | 0.7541 | 0.6522 | 0.6387 | | 0.5263 | 13.68 | 5060 | 0.5872 | 0.6592 | 0.7730 | 0.8501 | 0.9338 | 0.7673 | 0.8176 | 0.4907 | 0.8729 | 0.8338 | 0.6946 | 0.8341 | 0.6418 | 0.7174 | 0.3718 | 0.7489 | 0.6506 | 0.6496 | | 0.3174 | 13.73 | 5080 | 0.5468 | 0.6728 | 0.7925 | 0.8599 | 0.9251 | 0.7621 | 0.8633 | 0.5261 | 0.8801 | 0.8816 | 0.7095 | 0.8532 | 0.6392 | 0.7456 | 0.4023 | 0.7725 | 0.6278 | 0.6690 | | 0.6582 | 13.78 | 5100 | 0.5299 | 0.6786 | 0.7957 | 0.8638 | 0.9292 | 0.7859 | 0.8641 | 0.5234 | 0.8881 | 0.8596 | 0.7194 | 0.8553 | 0.6478 | 0.7414 | 0.3962 | 0.7765 | 0.6479 | 0.6848 | | 0.8889 | 13.84 | 5120 | 0.5259 | 0.6615 | 0.7793 | 0.8560 | 0.9346 | 0.7669 | 0.8527 | 0.4365 | 0.8553 | 0.8574 | 0.7519 | 0.8439 | 0.6393 | 0.7169 | 0.3305 | 0.7599 | 0.6424 | 0.6978 | | 0.2472 | 13.89 | 5140 | 0.5258 | 0.6621 | 0.7770 | 0.8572 | 0.9363 | 0.7882 | 0.8225 | 0.4469 | 0.8850 | 0.8630 | 0.6967 | 0.8522 | 0.6564 | 0.7134 | 0.3403 | 0.7683 | 0.6416 | 0.6626 | | 0.1984 | 13.95 | 5160 | 0.5231 | 0.6743 | 0.7961 | 0.8606 | 0.9256 | 0.8202 | 0.8650 | 0.5210 | 0.8900 | 0.8627 | 0.6882 | 0.8602 | 0.6571 | 0.7492 | 0.3873 | 0.7665 | 0.6384 | 0.6611 | | 0.293 | 14.0 | 5180 | 0.5337 | 0.6850 | 0.7922 | 0.8675 | 0.9405 | 0.7842 | 0.8660 | 0.5240 | 0.9062 | 0.8357 | 0.6889 | 0.8591 | 0.6501 | 0.7540 | 0.4165 | 0.7804 | 0.6725 | 0.6622 | | 0.0932 | 14.05 | 5200 | 0.5744 | 0.6686 | 0.8039 | 0.8546 | 0.9212 | 0.8140 | 0.8778 | 0.5835 | 0.8628 | 0.8955 | 0.6726 | 0.8531 | 0.6446 | 0.7479 | 0.4070 | 0.7589 | 0.6220 | 0.6466 | | 1.7961 | 14.11 | 5220 | 0.5400 | 0.6665 | 0.7940 | 0.8557 | 0.9216 | 0.7976 | 0.8808 | 0.5004 | 0.8603 | 0.8823 | 0.7155 | 0.8542 | 0.6424 | 0.7432 | 0.3609 | 0.7505 | 0.6406 | 0.6741 | | 0.2783 | 14.16 | 5240 | 0.6033 | 0.6572 | 0.7874 | 0.8493 | 0.9334 | 0.7866 | 0.8922 | 0.5057 | 0.8404 | 0.8819 | 0.6718 | 0.8494 | 0.6407 | 0.7336 | 0.3738 | 0.7428 | 0.6120 | 0.6477 | | 0.3782 | 14.22 | 5260 | 0.5731 | 0.6678 | 0.7849 | 0.8557 | 0.9300 | 0.7987 | 0.8899 | 0.4775 | 0.8734 | 0.8322 | 0.6927 | 0.8434 | 0.6574 | 0.7411 | 0.3627 | 0.7539 | 0.6534 | 0.6625 | | 0.0964 | 14.27 | 5280 | 0.5934 | 0.6485 | 0.7755 | 0.8480 | 0.9251 | 0.7968 | 0.8610 | 0.4235 | 0.8593 | 0.8934 | 0.6696 | 0.8509 | 0.6531 | 0.7278 | 0.2966 | 0.7442 | 0.6246 | 0.6419 | | 0.2305 | 14.32 | 5300 | 0.4892 | 0.6904 | 0.7988 | 0.8707 | 0.9198 | 0.7801 | 0.8533 | 0.5252 | 0.9124 | 0.8244 | 0.7762 | 0.8525 | 0.6445 | 0.7501 | 0.3770 | 0.7856 | 0.7124 | 0.7106 | | 0.3662 | 14.38 | 5320 | 0.4420 | 0.7077 | 0.8096 | 0.8819 | 0.9296 | 0.7715 | 0.8475 | 0.5408 | 0.9138 | 0.8296 | 0.8347 | 0.8577 | 0.6444 | 0.7556 | 0.4044 | 0.8066 | 0.7094 | 0.7760 | | 0.1456 | 14.43 | 5340 | 0.4490 | 0.7042 | 0.8103 | 0.8782 | 0.9334 | 0.7755 | 0.8288 | 0.5435 | 0.8972 | 0.8859 | 0.8080 | 0.8624 | 0.6527 | 0.7505 | 0.4182 | 0.7931 | 0.6729 | 0.7792 | | 0.2907 | 14.49 | 5360 | 0.4646 | 0.6876 | 0.8047 | 0.8699 | 0.9373 | 0.7725 | 0.8833 | 0.5180 | 0.8584 | 0.8532 | 0.8101 | 0.8522 | 0.6453 | 0.7350 | 0.3536 | 0.7792 | 0.6911 | 0.7568 | | 0.2382 | 14.54 | 5380 | 0.5256 | 0.6661 | 0.7936 | 0.8540 | 0.9247 | 0.7901 | 0.8871 | 0.5412 | 0.8609 | 0.8475 | 0.7034 | 0.8390 | 0.6473 | 0.7149 | 0.3565 | 0.7585 | 0.6907 | 0.6558 | | 0.5997 | 14.59 | 5400 | 0.5506 | 0.6638 | 0.7806 | 0.8569 | 0.9306 | 0.7989 | 0.8488 | 0.4217 | 0.8792 | 0.9043 | 0.6806 | 0.8595 | 0.6680 | 0.7178 | 0.3565 | 0.7619 | 0.6256 | 0.6575 | | 0.2169 | 14.65 | 5420 | 0.4596 | 0.6812 | 0.7941 | 0.8719 | 0.9319 | 0.8269 | 0.8532 | 0.3951 | 0.8856 | 0.8563 | 0.8099 | 0.8611 | 0.6533 | 0.7260 | 0.3334 | 0.7942 | 0.6397 | 0.7606 | | 0.4158 | 14.7 | 5440 | 0.4825 | 0.6757 | 0.7914 | 0.8663 | 0.9302 | 0.8297 | 0.8431 | 0.4104 | 0.8852 | 0.8899 | 0.7515 | 0.8589 | 0.6603 | 0.7279 | 0.3425 | 0.7823 | 0.6390 | 0.7190 | | 0.2731 | 14.76 | 5460 | 0.4921 | 0.6693 | 0.7758 | 0.8654 | 0.9363 | 0.7729 | 0.8511 | 0.3669 | 0.8939 | 0.8740 | 0.7355 | 0.8595 | 0.6582 | 0.7365 | 0.3036 | 0.7804 | 0.6435 | 0.7032 | | 0.2632 | 14.81 | 5480 | 0.5184 | 0.6608 | 0.7736 | 0.8576 | 0.9230 | 0.7621 | 0.8457 | 0.4050 | 0.8904 | 0.8685 | 0.7206 | 0.8540 | 0.6451 | 0.7336 | 0.3116 | 0.7644 | 0.6550 | 0.6616 | | 0.251 | 14.86 | 5500 | 0.5260 | 0.6573 | 0.7853 | 0.8539 | 0.9166 | 0.8257 | 0.8636 | 0.4380 | 0.8732 | 0.8650 | 0.7147 | 0.8556 | 0.6489 | 0.7266 | 0.3015 | 0.7549 | 0.6576 | 0.6559 | | 1.2515 | 14.92 | 5520 | 0.5372 | 0.6612 | 0.7934 | 0.8557 | 0.9341 | 0.8601 | 0.8507 | 0.4872 | 0.8631 | 0.8660 | 0.6925 | 0.8518 | 0.6443 | 0.7224 | 0.3354 | 0.7645 | 0.6509 | 0.6593 | | 0.3045 | 14.97 | 5540 | 0.5962 | 0.6381 | 0.7591 | 0.8482 | 0.9409 | 0.7581 | 0.8722 | 0.3525 | 0.8610 | 0.8802 | 0.6490 | 0.8545 | 0.6279 | 0.7132 | 0.2629 | 0.7500 | 0.6308 | 0.6273 | | 0.3704 | 15.03 | 5560 | 0.5266 | 0.6592 | 0.7633 | 0.8630 | 0.9323 | 0.7273 | 0.8722 | 0.3523 | 0.9088 | 0.8266 | 0.7236 | 0.8633 | 0.6164 | 0.7280 | 0.2874 | 0.7715 | 0.6555 | 0.6927 | | 0.2794 | 15.08 | 5580 | 0.5012 | 0.6792 | 0.7915 | 0.8685 | 0.9195 | 0.7160 | 0.8471 | 0.4489 | 0.8742 | 0.8946 | 0.8405 | 0.8594 | 0.6204 | 0.7412 | 0.3444 | 0.7757 | 0.6530 | 0.7606 | | 0.2056 | 15.14 | 5600 | 0.5639 | 0.6621 | 0.7691 | 0.8613 | 0.9277 | 0.7933 | 0.8774 | 0.3491 | 0.9046 | 0.8036 | 0.7281 | 0.8611 | 0.6590 | 0.7253 | 0.2813 | 0.7655 | 0.6626 | 0.6801 | | 0.2394 | 15.19 | 5620 | 0.5071 | 0.6802 | 0.8040 | 0.8661 | 0.9105 | 0.8203 | 0.8734 | 0.4962 | 0.8814 | 0.8298 | 0.8163 | 0.8554 | 0.6479 | 0.7288 | 0.3451 | 0.7744 | 0.6686 | 0.7416 | | 0.1558 | 15.24 | 5640 | 0.4703 | 0.6941 | 0.8137 | 0.8737 | 0.9194 | 0.8197 | 0.8654 | 0.5217 | 0.8806 | 0.8437 | 0.8452 | 0.8587 | 0.6512 | 0.7389 | 0.3639 | 0.7848 | 0.6852 | 0.7759 | | 0.4394 | 15.3 | 5660 | 0.4602 | 0.7047 | 0.8105 | 0.8796 | 0.9243 | 0.7421 | 0.8635 | 0.5609 | 0.8994 | 0.8189 | 0.8641 | 0.8556 | 0.6480 | 0.7427 | 0.3821 | 0.7983 | 0.7087 | 0.7976 | | 0.1494 | 15.35 | 5680 | 0.5335 | 0.6770 | 0.8024 | 0.8617 | 0.9188 | 0.7986 | 0.8514 | 0.5649 | 0.8821 | 0.8571 | 0.7442 | 0.8588 | 0.6418 | 0.7489 | 0.3658 | 0.7623 | 0.6771 | 0.6846 | | 0.1822 | 15.41 | 5700 | 0.5092 | 0.6811 | 0.7989 | 0.8656 | 0.9255 | 0.8020 | 0.8331 | 0.5376 | 0.8932 | 0.8421 | 0.7585 | 0.8585 | 0.6473 | 0.7361 | 0.3856 | 0.7764 | 0.6664 | 0.6972 | | 0.3058 | 15.46 | 5720 | 0.4918 | 0.6802 | 0.7954 | 0.8706 | 0.9194 | 0.7899 | 0.8618 | 0.4001 | 0.8777 | 0.8769 | 0.8417 | 0.8589 | 0.6568 | 0.7226 | 0.3124 | 0.7887 | 0.6539 | 0.7681 | | 0.3935 | 15.51 | 5740 | 0.4652 | 0.6979 | 0.8067 | 0.8774 | 0.9232 | 0.8056 | 0.8448 | 0.4917 | 0.9065 | 0.8527 | 0.8223 | 0.8610 | 0.6478 | 0.7408 | 0.4001 | 0.8026 | 0.6846 | 0.7481 | | 0.2512 | 15.57 | 5760 | 0.4909 | 0.6962 | 0.8121 | 0.8750 | 0.9275 | 0.8334 | 0.8500 | 0.5508 | 0.9036 | 0.8298 | 0.7896 | 0.8591 | 0.6628 | 0.7455 | 0.4008 | 0.8035 | 0.6663 | 0.7356 | | 0.2733 | 15.62 | 5780 | 0.5788 | 0.6696 | 0.7811 | 0.8604 | 0.9341 | 0.7313 | 0.8732 | 0.5224 | 0.9025 | 0.8375 | 0.6670 | 0.8598 | 0.6275 | 0.7424 | 0.3998 | 0.7718 | 0.6556 | 0.6304 | | 0.169 | 15.68 | 5800 | 0.5864 | 0.6691 | 0.7881 | 0.8567 | 0.9258 | 0.7840 | 0.8879 | 0.4849 | 0.8772 | 0.8845 | 0.6727 | 0.8595 | 0.6625 | 0.7433 | 0.3931 | 0.7578 | 0.6336 | 0.6342 | | 0.1417 | 15.73 | 5820 | 0.5768 | 0.6779 | 0.7897 | 0.8648 | 0.9303 | 0.7753 | 0.8797 | 0.4615 | 0.8854 | 0.8617 | 0.7343 | 0.8603 | 0.6483 | 0.7433 | 0.3798 | 0.7725 | 0.6672 | 0.6742 | | 0.167 | 15.78 | 5840 | 0.5437 | 0.6705 | 0.7821 | 0.8620 | 0.9320 | 0.7385 | 0.8467 | 0.4932 | 0.8902 | 0.8329 | 0.7414 | 0.8533 | 0.6186 | 0.7282 | 0.3730 | 0.7755 | 0.6743 | 0.6702 | | 0.1848 | 15.84 | 5860 | 0.5523 | 0.6718 | 0.7834 | 0.8637 | 0.9479 | 0.7919 | 0.8362 | 0.4667 | 0.8878 | 0.8478 | 0.7055 | 0.8525 | 0.6592 | 0.7111 | 0.3435 | 0.7841 | 0.6711 | 0.6813 | | 0.2633 | 15.89 | 5880 | 0.5593 | 0.6719 | 0.7908 | 0.8619 | 0.9277 | 0.8281 | 0.8055 | 0.5113 | 0.9051 | 0.8474 | 0.7102 | 0.8546 | 0.6527 | 0.7220 | 0.3589 | 0.7781 | 0.6629 | 0.6743 | | 0.4253 | 15.95 | 5900 | 0.5592 | 0.6702 | 0.7819 | 0.8634 | 0.9234 | 0.7766 | 0.8696 | 0.4417 | 0.9088 | 0.8397 | 0.7135 | 0.8611 | 0.6580 | 0.7278 | 0.3209 | 0.7759 | 0.6673 | 0.6801 | | 0.2215 | 16.0 | 5920 | 0.5557 | 0.6733 | 0.7792 | 0.8650 | 0.9244 | 0.7379 | 0.8344 | 0.4831 | 0.9254 | 0.8374 | 0.7120 | 0.8621 | 0.6246 | 0.7419 | 0.3600 | 0.7752 | 0.6696 | 0.6797 | | 0.1312 | 16.05 | 5940 | 0.5140 | 0.6661 | 0.7905 | 0.8577 | 0.9235 | 0.8223 | 0.8654 | 0.4607 | 0.8732 | 0.8718 | 0.7166 | 0.8513 | 0.6364 | 0.7172 | 0.3534 | 0.7601 | 0.6666 | 0.6773 | | 0.2601 | 16.11 | 5960 | 0.5589 | 0.6644 | 0.7804 | 0.8577 | 0.9311 | 0.7976 | 0.8697 | 0.4033 | 0.8717 | 0.8812 | 0.7081 | 0.8468 | 0.6647 | 0.7076 | 0.3282 | 0.7644 | 0.6586 | 0.6807 | | 0.2015 | 16.16 | 5980 | 0.5697 | 0.6602 | 0.7921 | 0.8577 | 0.9164 | 0.8626 | 0.8408 | 0.4628 | 0.8911 | 0.8656 | 0.7056 | 0.8520 | 0.6123 | 0.7242 | 0.3098 | 0.7704 | 0.6842 | 0.6689 | | 0.7605 | 16.22 | 6000 | 0.5931 | 0.6614 | 0.7788 | 0.8597 | 0.9300 | 0.7898 | 0.8712 | 0.3997 | 0.8861 | 0.8765 | 0.6981 | 0.8554 | 0.6396 | 0.7276 | 0.2938 | 0.7699 | 0.6805 | 0.6631 | | 0.2138 | 16.27 | 6020 | 0.5283 | 0.6785 | 0.7962 | 0.8628 | 0.9194 | 0.7919 | 0.8577 | 0.5364 | 0.9008 | 0.8538 | 0.7131 | 0.8529 | 0.6356 | 0.7431 | 0.4098 | 0.7757 | 0.6747 | 0.6581 | | 0.1078 | 16.32 | 6040 | 0.5326 | 0.6731 | 0.7963 | 0.8601 | 0.9164 | 0.8080 | 0.8588 | 0.5105 | 0.8852 | 0.8675 | 0.7278 | 0.8514 | 0.6393 | 0.7371 | 0.3786 | 0.7677 | 0.6686 | 0.6692 | | 0.2353 | 16.38 | 6060 | 0.6190 | 0.6729 | 0.7833 | 0.8617 | 0.9301 | 0.7918 | 0.8480 | 0.4871 | 0.9120 | 0.8401 | 0.6740 | 0.8497 | 0.6474 | 0.7350 | 0.3857 | 0.7782 | 0.6665 | 0.6477 | | 0.2366 | 16.43 | 6080 | 0.5435 | 0.6764 | 0.7883 | 0.8650 | 0.9354 | 0.8085 | 0.8590 | 0.4732 | 0.9044 | 0.8483 | 0.6893 | 0.8548 | 0.6452 | 0.7389 | 0.3762 | 0.7820 | 0.6741 | 0.6633 | | 0.2295 | 16.49 | 6100 | 0.5347 | 0.6782 | 0.7865 | 0.8673 | 0.9395 | 0.8152 | 0.8355 | 0.4706 | 0.9174 | 0.8422 | 0.6852 | 0.8580 | 0.6427 | 0.7344 | 0.3860 | 0.7892 | 0.6746 | 0.6629 | | 0.1413 | 16.54 | 6120 | 0.5431 | 0.6763 | 0.7882 | 0.8669 | 0.9277 | 0.8083 | 0.8725 | 0.4629 | 0.9205 | 0.8386 | 0.6871 | 0.8616 | 0.6501 | 0.7270 | 0.3681 | 0.7885 | 0.6747 | 0.6642 | | 0.967 | 16.59 | 6140 | 0.5356 | 0.6685 | 0.7755 | 0.8641 | 0.9359 | 0.7922 | 0.8503 | 0.3939 | 0.9090 | 0.8533 | 0.6940 | 0.8561 | 0.6626 | 0.7212 | 0.3244 | 0.7854 | 0.6656 | 0.6646 | | 0.1501 | 16.65 | 6160 | 0.5186 | 0.6858 | 0.7926 | 0.8682 | 0.9315 | 0.8167 | 0.8330 | 0.4982 | 0.9193 | 0.8465 | 0.7030 | 0.8566 | 0.6663 | 0.7399 | 0.4115 | 0.7886 | 0.6676 | 0.6700 | | 0.1527 | 16.7 | 6180 | 0.4952 | 0.6802 | 0.7988 | 0.8623 | 0.9312 | 0.8229 | 0.8458 | 0.4948 | 0.8677 | 0.8908 | 0.7386 | 0.8549 | 0.6666 | 0.7339 | 0.3974 | 0.7634 | 0.6400 | 0.7050 | | 0.2878 | 16.76 | 6200 | 0.4871 | 0.6759 | 0.7870 | 0.8634 | 0.9276 | 0.7620 | 0.8634 | 0.4550 | 0.8841 | 0.8775 | 0.7392 | 0.8604 | 0.6593 | 0.7302 | 0.3735 | 0.7686 | 0.6468 | 0.6926 | | 0.3001 | 16.81 | 6220 | 0.4844 | 0.6884 | 0.8021 | 0.8665 | 0.9231 | 0.7721 | 0.8516 | 0.5451 | 0.8885 | 0.8888 | 0.7458 | 0.8591 | 0.6591 | 0.7514 | 0.4375 | 0.7779 | 0.6351 | 0.6984 | | 2.424 | 16.86 | 6240 | 0.5032 | 0.6759 | 0.7760 | 0.8693 | 0.9355 | 0.7415 | 0.8712 | 0.3962 | 0.9116 | 0.8358 | 0.7404 | 0.8582 | 0.6502 | 0.7282 | 0.3402 | 0.7949 | 0.6654 | 0.6946 | | 0.1105 | 16.92 | 6260 | 0.4684 | 0.6984 | 0.8042 | 0.8785 | 0.9209 | 0.8012 | 0.8217 | 0.4430 | 0.8964 | 0.8652 | 0.8811 | 0.8552 | 0.6593 | 0.7190 | 0.3668 | 0.8010 | 0.6967 | 0.7907 | | 0.3355 | 16.97 | 6280 | 0.4982 | 0.6788 | 0.7843 | 0.8656 | 0.9386 | 0.7810 | 0.8146 | 0.4848 | 0.9087 | 0.8580 | 0.7045 | 0.8520 | 0.6508 | 0.7215 | 0.3874 | 0.7825 | 0.6807 | 0.6768 | | 0.1191 | 17.03 | 6300 | 0.4687 | 0.6812 | 0.7999 | 0.8670 | 0.9303 | 0.8004 | 0.8551 | 0.5189 | 0.8870 | 0.8598 | 0.7475 | 0.8538 | 0.6336 | 0.7457 | 0.3761 | 0.7847 | 0.6798 | 0.6946 | | 0.2914 | 17.08 | 6320 | 0.4668 | 0.6869 | 0.8088 | 0.8695 | 0.9212 | 0.7948 | 0.8863 | 0.5264 | 0.8773 | 0.8666 | 0.7893 | 0.8570 | 0.6411 | 0.7525 | 0.3733 | 0.7843 | 0.6786 | 0.7214 | | 0.1232 | 17.14 | 6340 | 0.4774 | 0.6801 | 0.7939 | 0.8704 | 0.9307 | 0.7988 | 0.8856 | 0.4509 | 0.8972 | 0.8307 | 0.7631 | 0.8620 | 0.6321 | 0.7414 | 0.3314 | 0.7876 | 0.6911 | 0.7151 | | 0.2338 | 17.19 | 6360 | 0.5762 | 0.6572 | 0.7692 | 0.8579 | 0.9362 | 0.8172 | 0.8860 | 0.3874 | 0.9167 | 0.8342 | 0.6068 | 0.8522 | 0.6441 | 0.7254 | 0.3293 | 0.7787 | 0.6821 | 0.5886 | | 0.6755 | 17.24 | 6380 | 0.5811 | 0.6644 | 0.7687 | 0.8618 | 0.9434 | 0.7529 | 0.8458 | 0.4083 | 0.9060 | 0.8501 | 0.6745 | 0.8556 | 0.6378 | 0.7335 | 0.3314 | 0.7767 | 0.6644 | 0.6515 | | 1.194 | 17.3 | 6400 | 0.5682 | 0.6739 | 0.7769 | 0.8665 | 0.9349 | 0.7822 | 0.8431 | 0.4116 | 0.9165 | 0.8349 | 0.7150 | 0.8586 | 0.6539 | 0.7349 | 0.3380 | 0.7835 | 0.6700 | 0.6782 | | 0.1116 | 17.35 | 6420 | 0.5574 | 0.6875 | 0.7958 | 0.8693 | 0.9329 | 0.8225 | 0.8437 | 0.4951 | 0.9105 | 0.8437 | 0.7219 | 0.8620 | 0.6686 | 0.7506 | 0.3975 | 0.7825 | 0.6649 | 0.6864 | | 0.5987 | 17.41 | 6440 | 0.5782 | 0.6833 | 0.7990 | 0.8666 | 0.9311 | 0.8397 | 0.8033 | 0.5404 | 0.9096 | 0.8529 | 0.7161 | 0.8603 | 0.6482 | 0.7287 | 0.4138 | 0.7792 | 0.6665 | 0.6864 | | 0.5289 | 17.46 | 6460 | 0.5611 | 0.6843 | 0.7995 | 0.8665 | 0.9344 | 0.8108 | 0.8677 | 0.5384 | 0.8972 | 0.8494 | 0.6990 | 0.8619 | 0.6737 | 0.7491 | 0.3982 | 0.7796 | 0.6570 | 0.6708 | | 0.1055 | 17.51 | 6480 | 0.5299 | 0.6816 | 0.8016 | 0.8634 | 0.9276 | 0.7722 | 0.8812 | 0.5540 | 0.8755 | 0.8793 | 0.7215 | 0.8606 | 0.6600 | 0.7496 | 0.4094 | 0.7708 | 0.6508 | 0.6698 | | 0.1996 | 17.57 | 6500 | 0.5186 | 0.6846 | 0.8096 | 0.8669 | 0.9139 | 0.8119 | 0.8522 | 0.5283 | 0.8727 | 0.8726 | 0.8154 | 0.8536 | 0.6580 | 0.7417 | 0.3706 | 0.7813 | 0.6467 | 0.7401 | | 0.8667 | 17.62 | 6520 | 0.4990 | 0.6866 | 0.8012 | 0.8694 | 0.9243 | 0.7851 | 0.8510 | 0.4927 | 0.8820 | 0.8749 | 0.7984 | 0.8600 | 0.6594 | 0.7428 | 0.3791 | 0.7827 | 0.6482 | 0.7342 | | 0.241 | 17.68 | 6540 | 0.5401 | 0.6882 | 0.8099 | 0.8681 | 0.9116 | 0.8045 | 0.8465 | 0.5455 | 0.8792 | 0.8454 | 0.8365 | 0.8513 | 0.6577 | 0.7394 | 0.4010 | 0.7885 | 0.6561 | 0.7231 | | 0.2098 | 17.73 | 6560 | 0.5664 | 0.6702 | 0.7946 | 0.8594 | 0.9256 | 0.7894 | 0.8642 | 0.5095 | 0.8676 | 0.8668 | 0.7389 | 0.8593 | 0.6539 | 0.7301 | 0.3559 | 0.7628 | 0.6490 | 0.6806 | | 0.5638 | 17.78 | 6580 | 0.5683 | 0.6764 | 0.7958 | 0.8628 | 0.9150 | 0.8131 | 0.8525 | 0.4853 | 0.8941 | 0.8735 | 0.7370 | 0.8587 | 0.6467 | 0.7469 | 0.3802 | 0.7699 | 0.6521 | 0.6804 | | 0.2553 | 17.84 | 6600 | 0.6075 | 0.6721 | 0.7817 | 0.8644 | 0.9213 | 0.7710 | 0.8738 | 0.4099 | 0.9017 | 0.8573 | 0.7372 | 0.8562 | 0.6562 | 0.7240 | 0.3281 | 0.7796 | 0.6819 | 0.6784 | | 0.4949 | 17.89 | 6620 | 0.5938 | 0.6702 | 0.7857 | 0.8627 | 0.9278 | 0.7916 | 0.8809 | 0.4344 | 0.8916 | 0.8662 | 0.7078 | 0.8599 | 0.6583 | 0.7253 | 0.3382 | 0.7766 | 0.6735 | 0.6599 | | 0.3578 | 17.95 | 6640 | 0.6084 | 0.6767 | 0.7912 | 0.8650 | 0.9183 | 0.8099 | 0.8671 | 0.4311 | 0.8997 | 0.8854 | 0.7265 | 0.8619 | 0.6534 | 0.7510 | 0.3597 | 0.7749 | 0.6652 | 0.6705 | | 0.2771 | 18.0 | 6660 | 0.5365 | 0.6951 | 0.8192 | 0.8708 | 0.9139 | 0.8194 | 0.8766 | 0.5758 | 0.8843 | 0.8783 | 0.7862 | 0.8577 | 0.6546 | 0.7497 | 0.4320 | 0.7889 | 0.6627 | 0.7200 | | 0.1865 | 18.05 | 6680 | 0.5234 | 0.6917 | 0.7939 | 0.8786 | 0.9192 | 0.8008 | 0.8629 | 0.3839 | 0.9192 | 0.8337 | 0.8374 | 0.8585 | 0.6700 | 0.7384 | 0.3171 | 0.8105 | 0.6856 | 0.7621 | | 0.3439 | 18.11 | 6700 | 0.5187 | 0.7022 | 0.8172 | 0.8768 | 0.9080 | 0.8281 | 0.8201 | 0.5452 | 0.9090 | 0.8610 | 0.8493 | 0.8541 | 0.6545 | 0.7509 | 0.4222 | 0.8047 | 0.6654 | 0.7633 | | 0.4716 | 18.16 | 6720 | 0.5480 | 0.6704 | 0.7856 | 0.8639 | 0.9208 | 0.7829 | 0.8962 | 0.4060 | 0.8920 | 0.8730 | 0.7283 | 0.8575 | 0.6527 | 0.7335 | 0.3266 | 0.7814 | 0.6738 | 0.6677 | | 0.2285 | 18.22 | 6740 | 0.5800 | 0.6786 | 0.7927 | 0.8665 | 0.9309 | 0.8055 | 0.8851 | 0.4608 | 0.8981 | 0.8685 | 0.7001 | 0.8623 | 0.6682 | 0.7501 | 0.3433 | 0.7805 | 0.6806 | 0.6655 | | 0.2874 | 18.27 | 6760 | 0.5568 | 0.6854 | 0.7961 | 0.8692 | 0.9238 | 0.8069 | 0.8568 | 0.5064 | 0.9231 | 0.8517 | 0.7040 | 0.8607 | 0.6629 | 0.7494 | 0.3803 | 0.7894 | 0.6884 | 0.6670 | | 0.2325 | 18.32 | 6780 | 0.5335 | 0.6784 | 0.7921 | 0.8678 | 0.9300 | 0.8023 | 0.8615 | 0.4633 | 0.9001 | 0.8499 | 0.7378 | 0.8611 | 0.6612 | 0.7392 | 0.3237 | 0.7850 | 0.6803 | 0.6981 | | 0.4917 | 18.38 | 6800 | 0.5157 | 0.6791 | 0.7932 | 0.8678 | 0.9295 | 0.7975 | 0.8441 | 0.4491 | 0.8877 | 0.8765 | 0.7678 | 0.8603 | 0.6539 | 0.7362 | 0.3412 | 0.7829 | 0.6722 | 0.7069 | | 0.2626 | 18.43 | 6820 | 0.5124 | 0.6862 | 0.8043 | 0.8704 | 0.9235 | 0.8027 | 0.8406 | 0.4731 | 0.8755 | 0.9020 | 0.8128 | 0.8581 | 0.6539 | 0.7425 | 0.3670 | 0.7898 | 0.6569 | 0.7354 | | 0.2628 | 18.49 | 6840 | 0.5533 | 0.6837 | 0.7922 | 0.8687 | 0.9340 | 0.7953 | 0.8593 | 0.4783 | 0.9067 | 0.8609 | 0.7109 | 0.8617 | 0.6657 | 0.7457 | 0.3766 | 0.7853 | 0.6769 | 0.6740 | | 0.6479 | 18.54 | 6860 | 0.6086 | 0.6772 | 0.7953 | 0.8621 | 0.9180 | 0.7915 | 0.8750 | 0.5212 | 0.8993 | 0.8629 | 0.6995 | 0.8614 | 0.6602 | 0.7503 | 0.3844 | 0.7669 | 0.6552 | 0.6620 | | 0.1974 | 18.59 | 6880 | 0.5783 | 0.6766 | 0.7891 | 0.8652 | 0.9379 | 0.8097 | 0.8820 | 0.4458 | 0.8880 | 0.8511 | 0.7092 | 0.8621 | 0.6774 | 0.7359 | 0.3479 | 0.7762 | 0.6608 | 0.6759 | | 0.3145 | 18.65 | 6900 | 0.5640 | 0.6844 | 0.7992 | 0.8661 | 0.9178 | 0.7956 | 0.8842 | 0.5153 | 0.8987 | 0.8417 | 0.7411 | 0.8573 | 0.6348 | 0.7569 | 0.4158 | 0.7746 | 0.6763 | 0.6749 | | 0.1216 | 18.7 | 6920 | 0.4967 | 0.6987 | 0.8231 | 0.8751 | 0.9126 | 0.8145 | 0.8771 | 0.5640 | 0.8812 | 0.8753 | 0.8371 | 0.8588 | 0.6335 | 0.7599 | 0.4134 | 0.7943 | 0.6746 | 0.7565 | | 0.1664 | 18.76 | 6940 | 0.5310 | 0.6900 | 0.8060 | 0.8679 | 0.9170 | 0.7942 | 0.8529 | 0.5591 | 0.8988 | 0.8622 | 0.7575 | 0.8591 | 0.6545 | 0.7631 | 0.4149 | 0.7766 | 0.6683 | 0.6936 | | 0.2653 | 18.81 | 6960 | 0.5674 | 0.6819 | 0.7923 | 0.8660 | 0.9224 | 0.7480 | 0.8707 | 0.5060 | 0.8992 | 0.8637 | 0.7362 | 0.8612 | 0.6425 | 0.7551 | 0.3906 | 0.7732 | 0.6745 | 0.6761 | | 0.1461 | 18.86 | 6980 | 0.5890 | 0.6794 | 0.7804 | 0.8693 | 0.9328 | 0.7151 | 0.8666 | 0.4832 | 0.9264 | 0.8316 | 0.7068 | 0.8629 | 0.6129 | 0.7558 | 0.3803 | 0.7851 | 0.6818 | 0.6770 | | 0.2221 | 18.92 | 7000 | 0.5067 | 0.6993 | 0.8129 | 0.8735 | 0.9338 | 0.7829 | 0.8681 | 0.5879 | 0.8860 | 0.8581 | 0.7738 | 0.8635 | 0.6556 | 0.7693 | 0.4326 | 0.7850 | 0.6695 | 0.7195 | | 0.2125 | 18.97 | 7020 | 0.5190 | 0.7000 | 0.8024 | 0.8755 | 0.9392 | 0.7639 | 0.8447 | 0.5359 | 0.9017 | 0.8645 | 0.7666 | 0.8657 | 0.6558 | 0.7583 | 0.4329 | 0.7883 | 0.6770 | 0.7221 | | 0.1904 | 19.03 | 7040 | 0.5813 | 0.6759 | 0.7899 | 0.8618 | 0.9322 | 0.7705 | 0.8519 | 0.4998 | 0.8815 | 0.8776 | 0.7160 | 0.8604 | 0.6552 | 0.7470 | 0.3914 | 0.7667 | 0.6452 | 0.6655 | | 0.2587 | 19.08 | 7060 | 0.5817 | 0.6675 | 0.7925 | 0.8587 | 0.9359 | 0.8122 | 0.8658 | 0.5069 | 0.8690 | 0.8550 | 0.7027 | 0.8628 | 0.6479 | 0.7377 | 0.3555 | 0.7601 | 0.6446 | 0.6638 | | 0.1773 | 19.14 | 7080 | 0.6150 | 0.6701 | 0.7901 | 0.8583 | 0.9266 | 0.8001 | 0.8581 | 0.5196 | 0.8907 | 0.8551 | 0.6805 | 0.8601 | 0.6697 | 0.7471 | 0.3655 | 0.7629 | 0.6369 | 0.6487 | | 0.32 | 19.19 | 7100 | 0.5867 | 0.6727 | 0.7812 | 0.8661 | 0.9307 | 0.7993 | 0.8512 | 0.3972 | 0.9027 | 0.8471 | 0.7406 | 0.8645 | 0.6655 | 0.7247 | 0.2904 | 0.7725 | 0.6829 | 0.7082 | | 0.1643 | 19.24 | 7120 | 0.5770 | 0.6743 | 0.7900 | 0.8651 | 0.9329 | 0.8131 | 0.8479 | 0.4639 | 0.8926 | 0.8417 | 0.7377 | 0.8608 | 0.6356 | 0.7377 | 0.3433 | 0.7742 | 0.6839 | 0.6846 | | 0.2318 | 19.3 | 7140 | 0.6071 | 0.6768 | 0.7972 | 0.8651 | 0.9316 | 0.8415 | 0.8710 | 0.4708 | 0.8879 | 0.8668 | 0.7106 | 0.8631 | 0.6620 | 0.7394 | 0.3453 | 0.7732 | 0.6689 | 0.6858 | | 0.2215 | 19.35 | 7160 | 0.5905 | 0.6844 | 0.7986 | 0.8671 | 0.9299 | 0.8322 | 0.8591 | 0.4789 | 0.8946 | 0.8795 | 0.7163 | 0.8611 | 0.6652 | 0.7432 | 0.3924 | 0.7757 | 0.6682 | 0.6850 | | 0.1181 | 19.41 | 7180 | 0.5889 | 0.6876 | 0.7907 | 0.8691 | 0.9314 | 0.7910 | 0.8487 | 0.4678 | 0.9082 | 0.8539 | 0.7337 | 0.8629 | 0.6766 | 0.7420 | 0.3876 | 0.7752 | 0.6697 | 0.6993 | | 0.2127 | 19.46 | 7200 | 0.6140 | 0.6876 | 0.7902 | 0.8686 | 0.9233 | 0.7681 | 0.8369 | 0.5085 | 0.9208 | 0.8268 | 0.7468 | 0.8609 | 0.6536 | 0.7440 | 0.4109 | 0.7763 | 0.6677 | 0.6997 | | 0.1767 | 19.51 | 7220 | 0.5347 | 0.6865 | 0.8045 | 0.8655 | 0.9298 | 0.8068 | 0.8362 | 0.5574 | 0.8796 | 0.8711 | 0.7504 | 0.8545 | 0.6559 | 0.7433 | 0.4151 | 0.7713 | 0.6658 | 0.6998 | | 0.1436 | 19.57 | 7240 | 0.5328 | 0.6897 | 0.8028 | 0.8693 | 0.9276 | 0.7944 | 0.8557 | 0.5430 | 0.8988 | 0.8528 | 0.7470 | 0.8604 | 0.6553 | 0.7435 | 0.4161 | 0.7815 | 0.6687 | 0.7023 | | 0.3826 | 19.62 | 7260 | 0.5324 | 0.6842 | 0.7933 | 0.8691 | 0.9348 | 0.7950 | 0.8648 | 0.4719 | 0.8966 | 0.8462 | 0.7434 | 0.8652 | 0.6548 | 0.7315 | 0.3897 | 0.7796 | 0.6686 | 0.7003 | | 0.1237 | 19.68 | 7280 | 0.5873 | 0.6829 | 0.7902 | 0.8679 | 0.9212 | 0.7642 | 0.8508 | 0.4830 | 0.9112 | 0.8521 | 0.7490 | 0.8627 | 0.6414 | 0.7475 | 0.3882 | 0.7758 | 0.6633 | 0.7012 | | 0.3202 | 19.73 | 7300 | 0.5673 | 0.6791 | 0.7928 | 0.8647 | 0.9204 | 0.7484 | 0.8683 | 0.5007 | 0.8901 | 0.8702 | 0.7515 | 0.8628 | 0.6275 | 0.7545 | 0.3923 | 0.7672 | 0.6612 | 0.6880 | | 0.151 | 19.78 | 7320 | 0.5385 | 0.6796 | 0.7916 | 0.8648 | 0.9233 | 0.7913 | 0.8560 | 0.4815 | 0.8975 | 0.8501 | 0.7412 | 0.8572 | 0.6437 | 0.7462 | 0.3911 | 0.7732 | 0.6667 | 0.6787 | | 1.8943 | 19.84 | 7340 | 0.5848 | 0.6687 | 0.7813 | 0.8632 | 0.9277 | 0.8039 | 0.8795 | 0.3675 | 0.8863 | 0.8732 | 0.7314 | 0.8603 | 0.6622 | 0.7305 | 0.3137 | 0.7705 | 0.6628 | 0.6812 | | 2.0602 | 19.89 | 7360 | 0.6545 | 0.6648 | 0.7714 | 0.8624 | 0.9365 | 0.7130 | 0.8754 | 0.4220 | 0.9027 | 0.8739 | 0.6764 | 0.8619 | 0.6179 | 0.7413 | 0.3474 | 0.7738 | 0.6558 | 0.6558 | | 0.1774 | 19.95 | 7380 | 0.5291 | 0.6811 | 0.7850 | 0.8668 | 0.9319 | 0.7039 | 0.8638 | 0.4985 | 0.8997 | 0.8678 | 0.7296 | 0.8574 | 0.6120 | 0.7519 | 0.4044 | 0.7726 | 0.6769 | 0.6924 | | 0.4491 | 20.0 | 7400 | 0.5457 | 0.6885 | 0.8092 | 0.8668 | 0.9288 | 0.7781 | 0.8755 | 0.5600 | 0.8597 | 0.8784 | 0.7842 | 0.8537 | 0.6507 | 0.7424 | 0.4132 | 0.7719 | 0.6517 | 0.7357 | | 0.2327 | 20.05 | 7420 | 0.4986 | 0.6951 | 0.8138 | 0.8718 | 0.9203 | 0.7875 | 0.8774 | 0.5437 | 0.8700 | 0.8639 | 0.8341 | 0.8540 | 0.6482 | 0.7439 | 0.4032 | 0.7815 | 0.6722 | 0.7627 | | 0.174 | 20.11 | 7440 | 0.5225 | 0.6850 | 0.7940 | 0.8693 | 0.9285 | 0.7177 | 0.8441 | 0.5245 | 0.8854 | 0.8430 | 0.8149 | 0.8542 | 0.6089 | 0.7398 | 0.3890 | 0.7762 | 0.6749 | 0.7516 | | 0.223 | 20.16 | 7460 | 0.5617 | 0.6735 | 0.7814 | 0.8641 | 0.9207 | 0.7170 | 0.8325 | 0.4816 | 0.9066 | 0.8579 | 0.7533 | 0.8607 | 0.6110 | 0.7362 | 0.3626 | 0.7656 | 0.6743 | 0.7039 | | 0.1366 | 20.22 | 7480 | 0.5322 | 0.6753 | 0.7865 | 0.8674 | 0.9287 | 0.7346 | 0.8706 | 0.4498 | 0.8929 | 0.8790 | 0.7499 | 0.8666 | 0.6240 | 0.7352 | 0.3376 | 0.7741 | 0.6781 | 0.7117 | | 0.1194 | 20.27 | 7500 | 0.5123 | 0.6821 | 0.7900 | 0.8729 | 0.9274 | 0.7172 | 0.8794 | 0.4368 | 0.8893 | 0.8580 | 0.8217 | 0.8618 | 0.6174 | 0.7346 | 0.3412 | 0.7914 | 0.6706 | 0.7576 | | 0.4355 | 20.32 | 7520 | 0.5022 | 0.6883 | 0.7942 | 0.8700 | 0.9291 | 0.7423 | 0.8283 | 0.5127 | 0.8953 | 0.8612 | 0.7909 | 0.8556 | 0.6275 | 0.7459 | 0.4057 | 0.7788 | 0.6703 | 0.7344 | | 0.2599 | 20.38 | 7540 | 0.5213 | 0.6843 | 0.7884 | 0.8699 | 0.9255 | 0.7675 | 0.8745 | 0.4440 | 0.9038 | 0.8239 | 0.7798 | 0.8591 | 0.6403 | 0.7475 | 0.3666 | 0.7765 | 0.6735 | 0.7268 | | 0.1313 | 20.43 | 7560 | 0.5307 | 0.6870 | 0.7970 | 0.8715 | 0.9309 | 0.8263 | 0.8690 | 0.4340 | 0.8996 | 0.8745 | 0.7449 | 0.8690 | 0.6708 | 0.7400 | 0.3684 | 0.7822 | 0.6607 | 0.7177 | | 0.2357 | 20.49 | 7580 | 0.5712 | 0.6687 | 0.7818 | 0.8625 | 0.9381 | 0.7413 | 0.8777 | 0.4756 | 0.8956 | 0.8785 | 0.6658 | 0.8660 | 0.6395 | 0.7327 | 0.3683 | 0.7771 | 0.6526 | 0.6443 | | 0.0972 | 20.54 | 7600 | 0.5153 | 0.6886 | 0.8072 | 0.8707 | 0.9321 | 0.8320 | 0.8792 | 0.5420 | 0.8992 | 0.8391 | 0.7266 | 0.8704 | 0.6725 | 0.7292 | 0.3949 | 0.7864 | 0.6665 | 0.7001 | | 0.1029 | 20.59 | 7620 | 0.5067 | 0.6902 | 0.7927 | 0.8727 | 0.9382 | 0.7702 | 0.8274 | 0.4957 | 0.9044 | 0.8297 | 0.7829 | 0.8599 | 0.6495 | 0.7447 | 0.3951 | 0.7903 | 0.6682 | 0.7236 | | 0.5238 | 20.65 | 7640 | 0.5782 | 0.6799 | 0.7993 | 0.8650 | 0.9307 | 0.8141 | 0.8524 | 0.4980 | 0.8774 | 0.8783 | 0.7441 | 0.8642 | 0.6526 | 0.7503 | 0.3906 | 0.7734 | 0.6402 | 0.6883 | | 1.6773 | 20.7 | 7660 | 0.5609 | 0.6822 | 0.8005 | 0.8664 | 0.9312 | 0.8056 | 0.8660 | 0.5051 | 0.8798 | 0.8725 | 0.7432 | 0.8641 | 0.6569 | 0.7514 | 0.3921 | 0.7781 | 0.6436 | 0.6894 | | 0.1892 | 20.76 | 7680 | 0.5891 | 0.6798 | 0.7864 | 0.8684 | 0.9357 | 0.7842 | 0.8568 | 0.4363 | 0.9010 | 0.8586 | 0.7325 | 0.8671 | 0.6601 | 0.7397 | 0.3563 | 0.7793 | 0.6701 | 0.6859 | | 0.8756 | 20.81 | 7700 | 0.6172 | 0.6740 | 0.7825 | 0.8664 | 0.9333 | 0.8033 | 0.8448 | 0.3998 | 0.9053 | 0.8823 | 0.7090 | 0.8657 | 0.6639 | 0.7382 | 0.3314 | 0.7780 | 0.6583 | 0.6828 | | 0.2574 | 20.86 | 7720 | 0.5685 | 0.6730 | 0.7848 | 0.8657 | 0.9321 | 0.8291 | 0.8601 | 0.3877 | 0.9004 | 0.8768 | 0.7071 | 0.8641 | 0.6729 | 0.7365 | 0.3251 | 0.7792 | 0.6542 | 0.6789 | | 0.2701 | 20.92 | 7740 | 0.5149 | 0.6892 | 0.8032 | 0.8752 | 0.9224 | 0.8254 | 0.8539 | 0.4344 | 0.8943 | 0.8636 | 0.8288 | 0.8631 | 0.6578 | 0.7363 | 0.3458 | 0.7994 | 0.6619 | 0.7604 | | 0.5788 | 20.97 | 7760 | 0.5510 | 0.6740 | 0.7774 | 0.8681 | 0.9259 | 0.7662 | 0.8814 | 0.3868 | 0.9117 | 0.8002 | 0.7697 | 0.8639 | 0.6620 | 0.7312 | 0.3048 | 0.7825 | 0.6629 | 0.7104 | | 0.3711 | 21.03 | 7780 | 0.5231 | 0.6849 | 0.7949 | 0.8718 | 0.9327 | 0.7757 | 0.8866 | 0.4610 | 0.8855 | 0.8102 | 0.8124 | 0.8622 | 0.6629 | 0.7369 | 0.3397 | 0.7892 | 0.6596 | 0.7439 | | 0.2244 | 21.08 | 7800 | 0.5435 | 0.6822 | 0.8026 | 0.8675 | 0.9217 | 0.7945 | 0.8675 | 0.4906 | 0.8746 | 0.8746 | 0.7946 | 0.8641 | 0.6620 | 0.7449 | 0.3363 | 0.7738 | 0.6658 | 0.7286 | | 0.2711 | 21.14 | 7820 | 0.6044 | 0.6836 | 0.7940 | 0.8682 | 0.9306 | 0.8000 | 0.8592 | 0.5011 | 0.9066 | 0.8271 | 0.7334 | 0.8685 | 0.6619 | 0.7554 | 0.3755 | 0.7750 | 0.6568 | 0.6922 | | 0.1413 | 21.19 | 7840 | 0.5306 | 0.6918 | 0.8031 | 0.8733 | 0.9320 | 0.7765 | 0.8831 | 0.4888 | 0.8802 | 0.8552 | 0.8056 | 0.8615 | 0.6591 | 0.7465 | 0.3789 | 0.7903 | 0.6675 | 0.7387 | | 0.2264 | 21.24 | 7860 | 0.5182 | 0.6880 | 0.8088 | 0.8700 | 0.9337 | 0.8223 | 0.8892 | 0.5332 | 0.8845 | 0.8725 | 0.7264 | 0.8712 | 0.6752 | 0.7285 | 0.3943 | 0.7840 | 0.6610 | 0.7017 | | 0.0717 | 21.3 | 7880 | 0.5174 | 0.6903 | 0.8026 | 0.8729 | 0.9256 | 0.8152 | 0.8579 | 0.4825 | 0.9021 | 0.8608 | 0.7740 | 0.8675 | 0.6609 | 0.7434 | 0.3772 | 0.7865 | 0.6670 | 0.7296 | | 0.2197 | 21.35 | 7900 | 0.5787 | 0.6807 | 0.7905 | 0.8691 | 0.9341 | 0.8186 | 0.8830 | 0.4429 | 0.9089 | 0.8356 | 0.7103 | 0.8681 | 0.6762 | 0.7340 | 0.3512 | 0.7846 | 0.6679 | 0.6827 | | 0.316 | 21.41 | 7920 | 0.5727 | 0.6830 | 0.7970 | 0.8686 | 0.9316 | 0.8351 | 0.8586 | 0.4615 | 0.8985 | 0.8697 | 0.7243 | 0.8680 | 0.6698 | 0.7498 | 0.3609 | 0.7777 | 0.6645 | 0.6907 | | 0.2365 | 21.46 | 7940 | 0.5699 | 0.6770 | 0.7762 | 0.8693 | 0.9408 | 0.7563 | 0.8860 | 0.3834 | 0.9086 | 0.8411 | 0.7171 | 0.8658 | 0.6654 | 0.7459 | 0.3222 | 0.7818 | 0.6689 | 0.6889 | | 0.2017 | 21.51 | 7960 | 0.5048 | 0.6899 | 0.7966 | 0.8736 | 0.9328 | 0.8088 | 0.8570 | 0.4467 | 0.9042 | 0.8644 | 0.7625 | 0.8659 | 0.6671 | 0.7526 | 0.3645 | 0.7899 | 0.6704 | 0.7190 | | 0.1801 | 21.57 | 7980 | 0.5304 | 0.6847 | 0.7873 | 0.8729 | 0.9377 | 0.7963 | 0.8375 | 0.4198 | 0.9089 | 0.8376 | 0.7732 | 0.8617 | 0.6613 | 0.7328 | 0.3441 | 0.7944 | 0.6750 | 0.7235 | | 0.1469 | 21.62 | 8000 | 0.5277 | 0.6891 | 0.8025 | 0.8738 | 0.9288 | 0.8013 | 0.8589 | 0.4444 | 0.8796 | 0.8819 | 0.8228 | 0.8600 | 0.6624 | 0.7352 | 0.3509 | 0.7964 | 0.6605 | 0.7580 | | 0.1851 | 21.68 | 8020 | 0.5037 | 0.6917 | 0.8026 | 0.8752 | 0.9236 | 0.7849 | 0.8607 | 0.4541 | 0.8874 | 0.8659 | 0.8417 | 0.8595 | 0.6612 | 0.7352 | 0.3574 | 0.7997 | 0.6688 | 0.7600 | | 0.2767 | 21.73 | 8040 | 0.5497 | 0.6743 | 0.7824 | 0.8654 | 0.9295 | 0.7648 | 0.8667 | 0.4335 | 0.9000 | 0.8474 | 0.7347 | 0.8657 | 0.6586 | 0.7309 | 0.3443 | 0.7752 | 0.6644 | 0.6809 | | 0.4517 | 21.78 | 8060 | 0.5216 | 0.6863 | 0.8001 | 0.8694 | 0.9328 | 0.7981 | 0.8670 | 0.4973 | 0.8863 | 0.8636 | 0.7556 | 0.8637 | 0.6652 | 0.7368 | 0.3809 | 0.7812 | 0.6670 | 0.7091 | | 0.1079 | 21.84 | 8080 | 0.5996 | 0.6822 | 0.7995 | 0.8670 | 0.9256 | 0.8301 | 0.8706 | 0.4783 | 0.8941 | 0.8730 | 0.7251 | 0.8654 | 0.6743 | 0.7266 | 0.3776 | 0.7783 | 0.6619 | 0.6910 | | 0.1717 | 21.89 | 8100 | 0.5482 | 0.6819 | 0.7917 | 0.8708 | 0.9316 | 0.8331 | 0.8817 | 0.4027 | 0.9023 | 0.8436 | 0.7470 | 0.8700 | 0.6738 | 0.7327 | 0.3401 | 0.7833 | 0.6612 | 0.7121 | | 0.3239 | 21.95 | 8120 | 0.5427 | 0.6794 | 0.7976 | 0.8651 | 0.8876 | 0.7914 | 0.8811 | 0.4234 | 0.8909 | 0.8623 | 0.8468 | 0.8398 | 0.6644 | 0.7446 | 0.3468 | 0.7923 | 0.6618 | 0.7062 | | 0.1983 | 22.0 | 8140 | 0.6081 | 0.6780 | 0.7984 | 0.8627 | 0.8777 | 0.7846 | 0.8739 | 0.4317 | 0.8891 | 0.8768 | 0.8552 | 0.8323 | 0.6606 | 0.7464 | 0.3587 | 0.7932 | 0.6566 | 0.6980 | | 0.1484 | 22.05 | 8160 | 0.5349 | 0.6790 | 0.7993 | 0.8633 | 0.8994 | 0.8141 | 0.8562 | 0.4765 | 0.8940 | 0.8652 | 0.7899 | 0.8491 | 0.6716 | 0.7440 | 0.3733 | 0.7850 | 0.6449 | 0.6849 | | 0.1894 | 22.11 | 8180 | 0.5522 | 0.6756 | 0.7889 | 0.8596 | 0.9088 | 0.7743 | 0.8673 | 0.4937 | 0.8993 | 0.8464 | 0.7326 | 0.8393 | 0.6720 | 0.7426 | 0.3832 | 0.7810 | 0.6632 | 0.6477 | | 0.3145 | 22.16 | 8200 | 0.5572 | 0.6822 | 0.7993 | 0.8665 | 0.9307 | 0.8412 | 0.8772 | 0.4812 | 0.8923 | 0.8619 | 0.7103 | 0.8636 | 0.6720 | 0.7410 | 0.3873 | 0.7780 | 0.6549 | 0.6784 | | 0.2407 | 22.22 | 8220 | 0.5461 | 0.6860 | 0.7994 | 0.8673 | 0.9205 | 0.8052 | 0.8697 | 0.4979 | 0.8992 | 0.8660 | 0.7374 | 0.8618 | 0.6721 | 0.7515 | 0.3905 | 0.7765 | 0.6633 | 0.6864 | | 0.2316 | 22.27 | 8240 | 0.5117 | 0.6899 | 0.8124 | 0.8722 | 0.9228 | 0.8234 | 0.8740 | 0.5053 | 0.8763 | 0.8768 | 0.8084 | 0.8632 | 0.6560 | 0.7452 | 0.3687 | 0.7897 | 0.6626 | 0.7437 | | 0.1487 | 22.32 | 8260 | 0.5589 | 0.6807 | 0.7897 | 0.8691 | 0.9372 | 0.7995 | 0.8774 | 0.4483 | 0.9010 | 0.8372 | 0.7271 | 0.8645 | 0.6632 | 0.7359 | 0.3582 | 0.7870 | 0.6715 | 0.6844 | | 0.5139 | 22.38 | 8280 | 0.6136 | 0.6741 | 0.7883 | 0.8648 | 0.9278 | 0.8190 | 0.8847 | 0.4439 | 0.9060 | 0.8414 | 0.6949 | 0.8632 | 0.6656 | 0.7263 | 0.3530 | 0.7808 | 0.6715 | 0.6581 | | 0.1885 | 22.43 | 8300 | 0.5952 | 0.6781 | 0.7890 | 0.8660 | 0.9278 | 0.8208 | 0.8729 | 0.4469 | 0.9098 | 0.8419 | 0.7027 | 0.8622 | 0.6664 | 0.7447 | 0.3691 | 0.7802 | 0.6591 | 0.6653 | | 0.1655 | 22.49 | 8320 | 0.5607 | 0.6828 | 0.7937 | 0.8679 | 0.9396 | 0.8111 | 0.8783 | 0.4798 | 0.8965 | 0.8448 | 0.7055 | 0.8655 | 0.6729 | 0.7423 | 0.3851 | 0.7813 | 0.6571 | 0.6754 | | 0.2207 | 22.54 | 8340 | 0.5650 | 0.6836 | 0.7969 | 0.8673 | 0.9392 | 0.8329 | 0.8358 | 0.5001 | 0.8973 | 0.8690 | 0.7041 | 0.8633 | 0.6696 | 0.7397 | 0.3907 | 0.7790 | 0.6637 | 0.6792 | | 0.1021 | 22.59 | 8360 | 0.5846 | 0.6821 | 0.7936 | 0.8673 | 0.9382 | 0.8232 | 0.8513 | 0.4894 | 0.9030 | 0.8514 | 0.6990 | 0.8629 | 0.6707 | 0.7395 | 0.3838 | 0.7821 | 0.6650 | 0.6711 | | 0.2844 | 22.65 | 8380 | 0.5945 | 0.6737 | 0.7886 | 0.8641 | 0.9414 | 0.8176 | 0.8566 | 0.4632 | 0.8908 | 0.8607 | 0.6901 | 0.8643 | 0.6701 | 0.7218 | 0.3506 | 0.7753 | 0.6670 | 0.6671 | | 0.2364 | 22.7 | 8400 | 0.5675 | 0.6828 | 0.7980 | 0.8668 | 0.9386 | 0.8129 | 0.8429 | 0.5250 | 0.8927 | 0.8625 | 0.7115 | 0.8599 | 0.6651 | 0.7359 | 0.3909 | 0.7825 | 0.6661 | 0.6791 | | 0.0931 | 22.76 | 8420 | 0.5758 | 0.6814 | 0.7965 | 0.8677 | 0.9280 | 0.8355 | 0.8542 | 0.4923 | 0.9146 | 0.8567 | 0.6943 | 0.8600 | 0.6565 | 0.7365 | 0.3779 | 0.7881 | 0.6878 | 0.6632 | | 1.9535 | 22.81 | 8440 | 0.5744 | 0.6708 | 0.7846 | 0.8615 | 0.9414 | 0.8052 | 0.8589 | 0.4741 | 0.8864 | 0.8198 | 0.7064 | 0.8527 | 0.6636 | 0.7285 | 0.3274 | 0.7738 | 0.6879 | 0.6616 | | 0.089 | 22.86 | 8460 | 0.5871 | 0.6750 | 0.7902 | 0.8621 | 0.9236 | 0.8017 | 0.8604 | 0.4905 | 0.8994 | 0.8477 | 0.7080 | 0.8547 | 0.6580 | 0.7415 | 0.3755 | 0.7767 | 0.6754 | 0.6433 | | 0.4797 | 22.92 | 8480 | 0.5729 | 0.6792 | 0.7963 | 0.8640 | 0.9317 | 0.7961 | 0.8674 | 0.5229 | 0.8887 | 0.8601 | 0.7072 | 0.8603 | 0.6589 | 0.7385 | 0.3936 | 0.7750 | 0.6674 | 0.6609 | | 0.3645 | 22.97 | 8500 | 0.5924 | 0.6621 | 0.7854 | 0.8574 | 0.9327 | 0.8002 | 0.8649 | 0.4443 | 0.8653 | 0.8807 | 0.7099 | 0.8588 | 0.6602 | 0.7243 | 0.3064 | 0.7636 | 0.6646 | 0.6571 | | 1.9274 | 23.03 | 8520 | 0.5832 | 0.6716 | 0.7923 | 0.8619 | 0.9191 | 0.8040 | 0.8636 | 0.4716 | 0.8842 | 0.8479 | 0.7558 | 0.8588 | 0.6626 | 0.7375 | 0.3162 | 0.7693 | 0.6642 | 0.6928 | | 0.4846 | 23.08 | 8540 | 0.5686 | 0.6763 | 0.8021 | 0.8606 | 0.9225 | 0.8182 | 0.8364 | 0.5516 | 0.8752 | 0.8683 | 0.7425 | 0.8571 | 0.6578 | 0.7466 | 0.3835 | 0.7664 | 0.6399 | 0.6830 | | 0.238 | 23.14 | 8560 | 0.5864 | 0.6750 | 0.7964 | 0.8601 | 0.9281 | 0.8056 | 0.8387 | 0.5468 | 0.8832 | 0.8638 | 0.7090 | 0.8589 | 0.6643 | 0.7471 | 0.3803 | 0.7640 | 0.6465 | 0.6642 | | 0.1989 | 23.19 | 8580 | 0.5883 | 0.6800 | 0.7955 | 0.8633 | 0.9314 | 0.8008 | 0.8531 | 0.5379 | 0.8912 | 0.8374 | 0.7170 | 0.8609 | 0.6653 | 0.7501 | 0.3932 | 0.7695 | 0.6599 | 0.6609 | | 0.1894 | 23.24 | 8600 | 0.6118 | 0.6843 | 0.7944 | 0.8675 | 0.9321 | 0.8160 | 0.8606 | 0.4999 | 0.9119 | 0.8475 | 0.6927 | 0.8617 | 0.6717 | 0.7498 | 0.3885 | 0.7821 | 0.6766 | 0.6594 | | 0.2921 | 23.3 | 8620 | 0.6391 | 0.6731 | 0.7798 | 0.8643 | 0.9367 | 0.7823 | 0.8612 | 0.4541 | 0.9168 | 0.8444 | 0.6629 | 0.8619 | 0.6692 | 0.7416 | 0.3576 | 0.7836 | 0.6656 | 0.6324 | | 0.1991 | 23.35 | 8640 | 0.5604 | 0.6692 | 0.7986 | 0.8594 | 0.9283 | 0.8299 | 0.8807 | 0.4897 | 0.8591 | 0.8730 | 0.7292 | 0.8630 | 0.6569 | 0.7372 | 0.3387 | 0.7609 | 0.6585 | 0.6690 | | 2.1941 | 23.41 | 8660 | 0.6231 | 0.6726 | 0.7960 | 0.8598 | 0.9252 | 0.7916 | 0.8704 | 0.5168 | 0.8739 | 0.8811 | 0.7128 | 0.8600 | 0.6560 | 0.7499 | 0.3834 | 0.7690 | 0.6361 | 0.6540 | | 1.0263 | 23.46 | 8680 | 0.5725 | 0.6812 | 0.7969 | 0.8661 | 0.9317 | 0.7970 | 0.8727 | 0.4957 | 0.8853 | 0.8666 | 0.7292 | 0.8650 | 0.6602 | 0.7414 | 0.3854 | 0.7752 | 0.6646 | 0.6764 | | 0.3965 | 23.51 | 8700 | 0.6046 | 0.6701 | 0.7845 | 0.8601 | 0.9275 | 0.7791 | 0.8538 | 0.4748 | 0.8911 | 0.8554 | 0.7099 | 0.8609 | 0.6647 | 0.7444 | 0.3495 | 0.7669 | 0.6535 | 0.6511 | | 0.3083 | 23.57 | 8720 | 0.5639 | 0.6899 | 0.8086 | 0.8714 | 0.9230 | 0.8020 | 0.8668 | 0.5128 | 0.8775 | 0.8573 | 0.8208 | 0.8613 | 0.6656 | 0.7423 | 0.3584 | 0.7845 | 0.6696 | 0.7476 | | 0.348 | 23.62 | 8740 | 0.5833 | 0.6772 | 0.8023 | 0.8639 | 0.9258 | 0.8096 | 0.8594 | 0.5035 | 0.8651 | 0.8836 | 0.7693 | 0.8626 | 0.6613 | 0.7440 | 0.3460 | 0.7708 | 0.6550 | 0.7010 | | 0.2902 | 23.68 | 8760 | 0.6245 | 0.6601 | 0.7887 | 0.8543 | 0.9189 | 0.8138 | 0.8727 | 0.4692 | 0.8708 | 0.8761 | 0.6994 | 0.8602 | 0.6599 | 0.7390 | 0.3228 | 0.7571 | 0.6401 | 0.6419 | | 0.1974 | 23.73 | 8780 | 0.5838 | 0.6695 | 0.7817 | 0.8603 | 0.9269 | 0.7834 | 0.8570 | 0.4585 | 0.8937 | 0.8266 | 0.7255 | 0.8591 | 0.6629 | 0.7404 | 0.3419 | 0.7653 | 0.6539 | 0.6629 | | 0.206 | 23.78 | 8800 | 0.5512 | 0.6790 | 0.7919 | 0.8634 | 0.9296 | 0.7790 | 0.8484 | 0.5005 | 0.8879 | 0.8753 | 0.7230 | 0.8614 | 0.6625 | 0.7393 | 0.3936 | 0.7701 | 0.6631 | 0.6630 | | 0.0565 | 23.84 | 8820 | 0.5502 | 0.6865 | 0.8006 | 0.8691 | 0.9266 | 0.8180 | 0.8576 | 0.4931 | 0.8943 | 0.8524 | 0.7625 | 0.8607 | 0.6622 | 0.7458 | 0.3915 | 0.7857 | 0.6619 | 0.6976 | | 0.1328 | 23.89 | 8840 | 0.5634 | 0.6827 | 0.7948 | 0.8668 | 0.9207 | 0.7875 | 0.8474 | 0.5038 | 0.9039 | 0.8439 | 0.7562 | 0.8580 | 0.6475 | 0.7534 | 0.3939 | 0.7817 | 0.6573 | 0.6872 | | 0.1884 | 23.95 | 8860 | 0.6300 | 0.6704 | 0.7872 | 0.8606 | 0.9145 | 0.8043 | 0.8633 | 0.4493 | 0.9001 | 0.8627 | 0.7165 | 0.8571 | 0.6550 | 0.7494 | 0.3593 | 0.7726 | 0.6456 | 0.6540 | | 0.2707 | 24.0 | 8880 | 0.6569 | 0.6586 | 0.7709 | 0.8575 | 0.9294 | 0.7927 | 0.8687 | 0.3950 | 0.9010 | 0.8286 | 0.6807 | 0.8605 | 0.6619 | 0.7332 | 0.3093 | 0.7674 | 0.6457 | 0.6322 | | 0.3871 | 24.05 | 8900 | 0.6504 | 0.6578 | 0.7787 | 0.8564 | 0.9356 | 0.8423 | 0.8347 | 0.4222 | 0.8869 | 0.8495 | 0.6795 | 0.8592 | 0.6567 | 0.7318 | 0.3005 | 0.7635 | 0.6441 | 0.6486 | | 0.3144 | 24.11 | 8920 | 0.6706 | 0.6548 | 0.7795 | 0.8536 | 0.9291 | 0.8228 | 0.8620 | 0.4337 | 0.8836 | 0.8777 | 0.6475 | 0.8604 | 0.6646 | 0.7307 | 0.3255 | 0.7670 | 0.6234 | 0.6122 | | 0.0824 | 24.16 | 8940 | 0.6211 | 0.6640 | 0.7859 | 0.8568 | 0.9224 | 0.7709 | 0.8858 | 0.4967 | 0.8808 | 0.8346 | 0.7101 | 0.8536 | 0.6410 | 0.7348 | 0.3514 | 0.7663 | 0.6524 | 0.6482 | | 0.147 | 24.22 | 8960 | 0.6018 | 0.6663 | 0.7807 | 0.8599 | 0.9246 | 0.6985 | 0.8578 | 0.5418 | 0.9017 | 0.8264 | 0.7142 | 0.8596 | 0.6068 | 0.7480 | 0.3659 | 0.7694 | 0.6577 | 0.6565 | | 0.2046 | 24.27 | 8980 | 0.5952 | 0.6760 | 0.7914 | 0.8632 | 0.9212 | 0.7512 | 0.8642 | 0.5368 | 0.8980 | 0.8256 | 0.7427 | 0.8591 | 0.6417 | 0.7422 | 0.3755 | 0.7736 | 0.6646 | 0.6755 | | 0.2014 | 24.32 | 9000 | 0.5944 | 0.6783 | 0.7980 | 0.8641 | 0.9273 | 0.8022 | 0.8638 | 0.5247 | 0.8844 | 0.8371 | 0.7463 | 0.8614 | 0.6586 | 0.7374 | 0.3680 | 0.7722 | 0.6679 | 0.6826 | | 0.0533 | 24.38 | 9020 | 0.6587 | 0.6734 | 0.7872 | 0.8619 | 0.9203 | 0.7801 | 0.8606 | 0.4876 | 0.9016 | 0.8332 | 0.7268 | 0.8595 | 0.6487 | 0.7451 | 0.3628 | 0.7684 | 0.6648 | 0.6641 | | 0.2093 | 24.43 | 9040 | 0.6173 | 0.6763 | 0.7903 | 0.8648 | 0.9328 | 0.8158 | 0.8373 | 0.4630 | 0.8884 | 0.8442 | 0.7507 | 0.8599 | 0.6587 | 0.7291 | 0.3529 | 0.7738 | 0.6708 | 0.6886 | | 0.1713 | 24.49 | 9060 | 0.5820 | 0.6747 | 0.7958 | 0.8626 | 0.9317 | 0.7998 | 0.8558 | 0.4790 | 0.8616 | 0.8773 | 0.7652 | 0.8635 | 0.6673 | 0.7396 | 0.3206 | 0.7610 | 0.6686 | 0.7023 | | 0.1691 | 24.54 | 9080 | 0.5721 | 0.6751 | 0.7924 | 0.8631 | 0.9324 | 0.8007 | 0.8585 | 0.4802 | 0.8762 | 0.8532 | 0.7460 | 0.8643 | 0.6645 | 0.7445 | 0.3347 | 0.7627 | 0.6669 | 0.6883 | | 0.1106 | 24.59 | 9100 | 0.5679 | 0.6808 | 0.7961 | 0.8660 | 0.9313 | 0.8225 | 0.8635 | 0.4743 | 0.8893 | 0.8673 | 0.7246 | 0.8669 | 0.6724 | 0.7418 | 0.3643 | 0.7698 | 0.6645 | 0.6859 | | 0.1896 | 24.65 | 9120 | 0.5785 | 0.6783 | 0.7910 | 0.8649 | 0.9234 | 0.7689 | 0.8535 | 0.4665 | 0.8878 | 0.8821 | 0.7548 | 0.8615 | 0.6394 | 0.7505 | 0.3741 | 0.7690 | 0.6665 | 0.6871 | | 0.5134 | 24.7 | 9140 | 0.5299 | 0.6942 | 0.8100 | 0.8722 | 0.9225 | 0.8044 | 0.8590 | 0.5211 | 0.8837 | 0.8726 | 0.8068 | 0.8600 | 0.6590 | 0.7510 | 0.4038 | 0.7866 | 0.6650 | 0.7339 | | 0.4509 | 24.76 | 9160 | 0.5697 | 0.6858 | 0.8003 | 0.8677 | 0.9174 | 0.8081 | 0.8697 | 0.5085 | 0.9011 | 0.8324 | 0.7648 | 0.8602 | 0.6603 | 0.7489 | 0.3869 | 0.7777 | 0.6692 | 0.6972 | | 0.1456 | 24.81 | 9180 | 0.6313 | 0.6818 | 0.7974 | 0.8656 | 0.9252 | 0.8321 | 0.8474 | 0.5258 | 0.9130 | 0.8379 | 0.7006 | 0.8625 | 0.6669 | 0.7485 | 0.3889 | 0.7775 | 0.6627 | 0.6657 | | 0.3161 | 24.86 | 9200 | 0.5902 | 0.6805 | 0.7989 | 0.8655 | 0.9239 | 0.8146 | 0.8581 | 0.5054 | 0.8938 | 0.8657 | 0.7306 | 0.8655 | 0.6603 | 0.7480 | 0.3758 | 0.7729 | 0.6618 | 0.6794 | | 0.1412 | 24.92 | 9220 | 0.6194 | 0.6728 | 0.7906 | 0.8624 | 0.9236 | 0.8272 | 0.8365 | 0.4650 | 0.8953 | 0.8559 | 0.7307 | 0.8560 | 0.6528 | 0.7384 | 0.3507 | 0.7740 | 0.6678 | 0.6698 | | 0.169 | 24.97 | 9240 | 0.5721 | 0.6742 | 0.7854 | 0.8649 | 0.9276 | 0.8090 | 0.8546 | 0.4162 | 0.8956 | 0.8470 | 0.7475 | 0.8569 | 0.6604 | 0.7383 | 0.3288 | 0.7776 | 0.6754 | 0.6818 | | 0.1168 | 25.03 | 9260 | 0.5408 | 0.6758 | 0.7857 | 0.8653 | 0.9315 | 0.8146 | 0.8371 | 0.4369 | 0.8992 | 0.8365 | 0.7444 | 0.8570 | 0.6611 | 0.7355 | 0.3402 | 0.7776 | 0.6756 | 0.6834 | | 0.1236 | 25.08 | 9280 | 0.5989 | 0.6796 | 0.7947 | 0.8637 | 0.9337 | 0.8156 | 0.8602 | 0.5125 | 0.8951 | 0.8586 | 0.6868 | 0.8574 | 0.6684 | 0.7470 | 0.3954 | 0.7758 | 0.6597 | 0.6533 | | 0.1402 | 25.14 | 9300 | 0.5337 | 0.6828 | 0.7902 | 0.8682 | 0.9369 | 0.8239 | 0.8565 | 0.4630 | 0.9081 | 0.8272 | 0.7161 | 0.8596 | 0.6726 | 0.7423 | 0.3713 | 0.7842 | 0.6732 | 0.6764 | | 0.0659 | 25.19 | 9320 | 0.5782 | 0.6736 | 0.7954 | 0.8614 | 0.9302 | 0.8341 | 0.8540 | 0.4856 | 0.8799 | 0.8762 | 0.7081 | 0.8572 | 0.6569 | 0.7373 | 0.3741 | 0.7711 | 0.6594 | 0.6595 | | 0.3301 | 25.24 | 9340 | 0.6045 | 0.6715 | 0.7866 | 0.8620 | 0.9276 | 0.7994 | 0.8715 | 0.4603 | 0.8958 | 0.8467 | 0.7047 | 0.8590 | 0.6512 | 0.7431 | 0.3611 | 0.7735 | 0.6582 | 0.6542 | | 0.2523 | 25.3 | 9360 | 0.5964 | 0.6754 | 0.7884 | 0.8641 | 0.9323 | 0.8082 | 0.8618 | 0.4527 | 0.8947 | 0.8632 | 0.7063 | 0.8602 | 0.6700 | 0.7370 | 0.3532 | 0.7760 | 0.6639 | 0.6673 | | 0.2483 | 25.35 | 9380 | 0.5981 | 0.6707 | 0.7816 | 0.8634 | 0.9321 | 0.7652 | 0.8546 | 0.4422 | 0.8953 | 0.8623 | 0.7191 | 0.8631 | 0.6526 | 0.7336 | 0.3334 | 0.7730 | 0.6677 | 0.6716 | | 0.1621 | 25.41 | 9400 | 0.5591 | 0.6763 | 0.7884 | 0.8664 | 0.9338 | 0.7969 | 0.8472 | 0.4417 | 0.8910 | 0.8637 | 0.7442 | 0.8621 | 0.6397 | 0.7300 | 0.3486 | 0.7741 | 0.6882 | 0.6915 | | 0.1596 | 25.46 | 9420 | 0.6054 | 0.6797 | 0.7894 | 0.8674 | 0.9307 | 0.8010 | 0.8502 | 0.4750 | 0.9109 | 0.8343 | 0.7235 | 0.8612 | 0.6403 | 0.7309 | 0.3735 | 0.7782 | 0.6834 | 0.6904 | | 0.3227 | 25.51 | 9440 | 0.5901 | 0.6776 | 0.7910 | 0.8658 | 0.9212 | 0.7572 | 0.8674 | 0.4964 | 0.9057 | 0.8652 | 0.7235 | 0.8660 | 0.6386 | 0.7388 | 0.3695 | 0.7733 | 0.6707 | 0.6865 | | 0.2153 | 25.57 | 9460 | 0.5758 | 0.6730 | 0.7875 | 0.8648 | 0.9244 | 0.7906 | 0.8777 | 0.4487 | 0.9020 | 0.8477 | 0.7211 | 0.8647 | 0.6502 | 0.7290 | 0.3387 | 0.7741 | 0.6667 | 0.6879 | | 0.3868 | 25.62 | 9480 | 0.5591 | 0.6795 | 0.7912 | 0.8671 | 0.9252 | 0.8109 | 0.8717 | 0.4495 | 0.9042 | 0.8442 | 0.7330 | 0.8641 | 0.6580 | 0.7303 | 0.3576 | 0.7743 | 0.6755 | 0.6967 | | 0.2817 | 25.68 | 9500 | 0.5215 | 0.6878 | 0.7959 | 0.8738 | 0.9336 | 0.8251 | 0.8433 | 0.4297 | 0.9011 | 0.8513 | 0.7875 | 0.8663 | 0.6666 | 0.7304 | 0.3412 | 0.7889 | 0.6801 | 0.7408 | | 0.2824 | 25.73 | 9520 | 0.5074 | 0.6917 | 0.8034 | 0.8757 | 0.9264 | 0.8140 | 0.8525 | 0.4297 | 0.8823 | 0.8657 | 0.8532 | 0.8594 | 0.6615 | 0.7304 | 0.3441 | 0.7954 | 0.6741 | 0.7768 | | 0.1215 | 25.78 | 9540 | 0.5113 | 0.6956 | 0.8073 | 0.8768 | 0.9241 | 0.8304 | 0.8311 | 0.4732 | 0.8996 | 0.8544 | 0.8380 | 0.8596 | 0.6533 | 0.7364 | 0.3791 | 0.8016 | 0.6742 | 0.7651 | | 0.2846 | 25.84 | 9560 | 0.5507 | 0.6867 | 0.7933 | 0.8711 | 0.9280 | 0.8186 | 0.8677 | 0.4674 | 0.9230 | 0.8247 | 0.7234 | 0.8678 | 0.6740 | 0.7375 | 0.3711 | 0.7854 | 0.6774 | 0.6935 | | 0.4948 | 25.89 | 9580 | 0.5195 | 0.6907 | 0.7986 | 0.8729 | 0.9382 | 0.8081 | 0.8478 | 0.4941 | 0.9044 | 0.8507 | 0.7467 | 0.8678 | 0.6684 | 0.7407 | 0.3734 | 0.7855 | 0.6878 | 0.7113 | | 0.3136 | 25.95 | 9600 | 0.5447 | 0.6889 | 0.7947 | 0.8721 | 0.9387 | 0.8271 | 0.8304 | 0.4608 | 0.9087 | 0.8594 | 0.7375 | 0.8647 | 0.6735 | 0.7420 | 0.3706 | 0.7852 | 0.6748 | 0.7117 | | 0.1665 | 26.0 | 9620 | 0.5755 | 0.6885 | 0.7950 | 0.8711 | 0.9373 | 0.8130 | 0.8596 | 0.4627 | 0.9026 | 0.8602 | 0.7300 | 0.8679 | 0.6796 | 0.7482 | 0.3707 | 0.7794 | 0.6681 | 0.7055 | | 0.0865 | 26.05 | 9640 | 0.5778 | 0.6861 | 0.7970 | 0.8684 | 0.9328 | 0.7874 | 0.8652 | 0.4998 | 0.8920 | 0.8654 | 0.7365 | 0.8684 | 0.6677 | 0.7499 | 0.3870 | 0.7731 | 0.6623 | 0.6941 | | 0.1036 | 26.11 | 9660 | 0.5929 | 0.6828 | 0.7923 | 0.8666 | 0.9384 | 0.7990 | 0.8381 | 0.4884 | 0.8910 | 0.8574 | 0.7335 | 0.8626 | 0.6687 | 0.7389 | 0.3858 | 0.7744 | 0.6637 | 0.6854 | | 0.2062 | 26.16 | 9680 | 0.6145 | 0.6766 | 0.7881 | 0.8658 | 0.9364 | 0.8016 | 0.8480 | 0.4344 | 0.8859 | 0.8719 | 0.7387 | 0.8637 | 0.6647 | 0.7360 | 0.3431 | 0.7736 | 0.6675 | 0.6879 | | 0.2066 | 26.22 | 9700 | 0.6302 | 0.6749 | 0.7899 | 0.8650 | 0.9310 | 0.7961 | 0.8787 | 0.4181 | 0.8760 | 0.8837 | 0.7460 | 0.8641 | 0.6654 | 0.7349 | 0.3311 | 0.7713 | 0.6681 | 0.6891 | | 0.3122 | 26.27 | 9720 | 0.5919 | 0.6790 | 0.7833 | 0.8695 | 0.9322 | 0.7807 | 0.8695 | 0.3970 | 0.9022 | 0.8424 | 0.7595 | 0.8660 | 0.6666 | 0.7363 | 0.3249 | 0.7820 | 0.6801 | 0.6968 | | 0.2804 | 26.32 | 9740 | 0.5657 | 0.6853 | 0.7938 | 0.8715 | 0.9325 | 0.8105 | 0.8793 | 0.4290 | 0.8968 | 0.8425 | 0.7658 | 0.8653 | 0.6692 | 0.7429 | 0.3469 | 0.7857 | 0.6797 | 0.7073 | | 0.3325 | 26.38 | 9760 | 0.5954 | 0.6892 | 0.8015 | 0.8691 | 0.9321 | 0.8292 | 0.8377 | 0.5225 | 0.9006 | 0.8510 | 0.7375 | 0.8623 | 0.6595 | 0.7504 | 0.4156 | 0.7805 | 0.6716 | 0.6847 | | 0.6275 | 26.43 | 9780 | 0.5847 | 0.6860 | 0.7970 | 0.8684 | 0.9342 | 0.8112 | 0.8649 | 0.4878 | 0.8961 | 0.8643 | 0.7204 | 0.8665 | 0.6660 | 0.7513 | 0.3979 | 0.7769 | 0.6660 | 0.6776 | | 0.3794 | 26.49 | 9800 | 0.5874 | 0.6800 | 0.7975 | 0.8655 | 0.9268 | 0.8132 | 0.8603 | 0.4910 | 0.8874 | 0.8596 | 0.7441 | 0.8640 | 0.6618 | 0.7541 | 0.3529 | 0.7702 | 0.6686 | 0.6883 | | 0.3265 | 26.54 | 9820 | 0.5431 | 0.6868 | 0.7962 | 0.8708 | 0.9312 | 0.8130 | 0.8648 | 0.4739 | 0.9030 | 0.8283 | 0.7593 | 0.8628 | 0.6600 | 0.7500 | 0.3676 | 0.7845 | 0.6808 | 0.7021 | | 0.2163 | 26.59 | 9840 | 0.5910 | 0.6866 | 0.7966 | 0.8696 | 0.9273 | 0.8196 | 0.8565 | 0.4767 | 0.9066 | 0.8479 | 0.7419 | 0.8645 | 0.6613 | 0.7493 | 0.3847 | 0.7799 | 0.6765 | 0.6901 | | 0.2469 | 26.65 | 9860 | 0.6472 | 0.6810 | 0.7872 | 0.8665 | 0.9281 | 0.8071 | 0.8293 | 0.4536 | 0.9103 | 0.8488 | 0.7333 | 0.8567 | 0.6656 | 0.7254 | 0.3819 | 0.7806 | 0.6762 | 0.6803 | | 0.2078 | 26.7 | 9880 | 0.5434 | 0.6868 | 0.7946 | 0.8705 | 0.9330 | 0.8069 | 0.8265 | 0.4754 | 0.8997 | 0.8422 | 0.7785 | 0.8561 | 0.6640 | 0.7201 | 0.3839 | 0.7911 | 0.6748 | 0.7178 | | 0.2536 | 26.76 | 9900 | 0.5317 | 0.6955 | 0.8006 | 0.8779 | 0.9326 | 0.8000 | 0.8603 | 0.4675 | 0.9055 | 0.8162 | 0.8222 | 0.8635 | 0.6658 | 0.7390 | 0.3615 | 0.8027 | 0.6718 | 0.7643 | | 0.1666 | 26.81 | 9920 | 0.5297 | 0.6903 | 0.7972 | 0.8721 | 0.9321 | 0.7830 | 0.8653 | 0.4971 | 0.9042 | 0.8388 | 0.7595 | 0.8693 | 0.6671 | 0.7499 | 0.3844 | 0.7824 | 0.6657 | 0.7136 | | 0.3875 | 26.86 | 9940 | 0.6049 | 0.6835 | 0.7915 | 0.8701 | 0.9437 | 0.8191 | 0.8704 | 0.4413 | 0.8953 | 0.8433 | 0.7278 | 0.8678 | 0.6791 | 0.7416 | 0.3449 | 0.7801 | 0.6683 | 0.7030 | | 0.1804 | 26.92 | 9960 | 0.5804 | 0.6904 | 0.7902 | 0.8738 | 0.9399 | 0.8023 | 0.8600 | 0.4524 | 0.9183 | 0.8191 | 0.7393 | 0.8686 | 0.6755 | 0.7466 | 0.3702 | 0.7885 | 0.6844 | 0.6988 | | 0.5588 | 26.97 | 9980 | 0.5892 | 0.6911 | 0.7966 | 0.8728 | 0.9391 | 0.8084 | 0.8569 | 0.4798 | 0.9074 | 0.8526 | 0.7320 | 0.8699 | 0.6801 | 0.7471 | 0.3797 | 0.7849 | 0.6690 | 0.7068 | | 0.2821 | 27.03 | 10000 | 0.6022 | 0.6881 | 0.7945 | 0.8725 | 0.9408 | 0.8337 | 0.8606 | 0.4497 | 0.9088 | 0.8421 | 0.7255 | 0.8673 | 0.6786 | 0.7456 | 0.3603 | 0.7874 | 0.6745 | 0.7029 | | 0.3519 | 27.08 | 10020 | 0.5578 | 0.6892 | 0.7975 | 0.8707 | 0.9388 | 0.8167 | 0.8569 | 0.4875 | 0.8977 | 0.8465 | 0.7385 | 0.8657 | 0.6727 | 0.7516 | 0.3869 | 0.7808 | 0.6718 | 0.6952 | | 0.1704 | 27.14 | 10040 | 0.5708 | 0.6861 | 0.7956 | 0.8689 | 0.9361 | 0.8084 | 0.8535 | 0.4892 | 0.8961 | 0.8440 | 0.7416 | 0.8657 | 0.6736 | 0.7454 | 0.3780 | 0.7770 | 0.6687 | 0.6943 | | 0.3597 | 27.19 | 10060 | 0.5695 | 0.6876 | 0.7976 | 0.8695 | 0.9339 | 0.7896 | 0.8656 | 0.4934 | 0.8918 | 0.8648 | 0.7440 | 0.8703 | 0.6722 | 0.7468 | 0.3854 | 0.7742 | 0.6671 | 0.6975 | | 0.3913 | 27.24 | 10080 | 0.5464 | 0.6787 | 0.7917 | 0.8608 | 0.9082 | 0.8047 | 0.8496 | 0.4676 | 0.8936 | 0.8591 | 0.7592 | 0.8406 | 0.6733 | 0.7477 | 0.3879 | 0.7782 | 0.6667 | 0.6563 | | 0.3059 | 27.3 | 10100 | 0.5639 | 0.6874 | 0.7933 | 0.8717 | 0.9390 | 0.8386 | 0.8589 | 0.4450 | 0.9122 | 0.8430 | 0.7161 | 0.8675 | 0.6804 | 0.7375 | 0.3737 | 0.7863 | 0.6743 | 0.6922 | | 0.2716 | 27.35 | 10120 | 0.5449 | 0.6894 | 0.7902 | 0.8746 | 0.9436 | 0.8183 | 0.8533 | 0.4371 | 0.9208 | 0.8367 | 0.7217 | 0.8678 | 0.6694 | 0.7321 | 0.3694 | 0.7947 | 0.6974 | 0.6953 | | 0.6047 | 27.41 | 10140 | 0.5346 | 0.6943 | 0.7947 | 0.8758 | 0.9383 | 0.8108 | 0.8538 | 0.4517 | 0.9177 | 0.8405 | 0.7500 | 0.8708 | 0.6828 | 0.7374 | 0.3747 | 0.7913 | 0.6854 | 0.7174 | | 0.151 | 27.46 | 10160 | 0.5572 | 0.6890 | 0.7934 | 0.8717 | 0.9355 | 0.8047 | 0.8545 | 0.4600 | 0.9073 | 0.8513 | 0.7409 | 0.8698 | 0.6781 | 0.7413 | 0.3766 | 0.7808 | 0.6734 | 0.7026 | | 0.1925 | 27.51 | 10180 | 0.5290 | 0.6971 | 0.8094 | 0.8775 | 0.9312 | 0.8126 | 0.8643 | 0.4669 | 0.8834 | 0.8820 | 0.8253 | 0.8688 | 0.6679 | 0.7391 | 0.3718 | 0.7955 | 0.6638 | 0.7726 | | 0.2092 | 27.57 | 10200 | 0.5235 | 0.6899 | 0.7992 | 0.8743 | 0.9368 | 0.8169 | 0.8616 | 0.4508 | 0.8914 | 0.8400 | 0.7967 | 0.8674 | 0.6691 | 0.7364 | 0.3548 | 0.7895 | 0.6677 | 0.7446 | | 0.1342 | 27.62 | 10220 | 0.5449 | 0.6871 | 0.7969 | 0.8709 | 0.9362 | 0.8072 | 0.8619 | 0.4596 | 0.8907 | 0.8692 | 0.7537 | 0.8680 | 0.6829 | 0.7420 | 0.3525 | 0.7800 | 0.6626 | 0.7218 | | 0.2301 | 27.68 | 10240 | 0.5375 | 0.6890 | 0.8043 | 0.8713 | 0.9345 | 0.8359 | 0.8612 | 0.4908 | 0.8915 | 0.8715 | 0.7447 | 0.8686 | 0.6741 | 0.7481 | 0.3662 | 0.7806 | 0.6661 | 0.7193 | | 0.1528 | 27.73 | 10260 | 0.5133 | 0.6878 | 0.7960 | 0.8723 | 0.9392 | 0.8050 | 0.8680 | 0.4585 | 0.8929 | 0.8435 | 0.7649 | 0.8703 | 0.6833 | 0.7429 | 0.3345 | 0.7807 | 0.6716 | 0.7310 | | 0.1313 | 27.78 | 10280 | 0.5783 | 0.6771 | 0.7860 | 0.8671 | 0.9423 | 0.8021 | 0.8589 | 0.4187 | 0.8882 | 0.8644 | 0.7276 | 0.8679 | 0.6851 | 0.7331 | 0.3114 | 0.7731 | 0.6649 | 0.7039 | | 0.0945 | 27.84 | 10300 | 0.5292 | 0.6898 | 0.7973 | 0.8734 | 0.9356 | 0.8261 | 0.8556 | 0.4516 | 0.9036 | 0.8470 | 0.7613 | 0.8689 | 0.6769 | 0.7382 | 0.3610 | 0.7861 | 0.6681 | 0.7293 | | 0.1894 | 27.89 | 10320 | 0.5126 | 0.6927 | 0.8039 | 0.8739 | 0.9350 | 0.8048 | 0.8570 | 0.4898 | 0.8885 | 0.8658 | 0.7861 | 0.8686 | 0.6668 | 0.7428 | 0.3762 | 0.7854 | 0.6741 | 0.7351 | | 0.494 | 27.95 | 10340 | 0.5499 | 0.6927 | 0.8089 | 0.8733 | 0.9358 | 0.7857 | 0.8717 | 0.5165 | 0.8665 | 0.8672 | 0.8185 | 0.8648 | 0.6660 | 0.7457 | 0.3595 | 0.7839 | 0.6729 | 0.7561 | | 0.1046 | 28.0 | 10360 | 0.5496 | 0.6962 | 0.8084 | 0.8731 | 0.9251 | 0.7879 | 0.8410 | 0.5496 | 0.8945 | 0.8596 | 0.8012 | 0.8606 | 0.6626 | 0.7457 | 0.4125 | 0.7902 | 0.6745 | 0.7276 | | 0.196 | 28.05 | 10380 | 0.6126 | 0.6778 | 0.7928 | 0.8640 | 0.9287 | 0.8225 | 0.8503 | 0.5001 | 0.9049 | 0.8465 | 0.6965 | 0.8619 | 0.6751 | 0.7386 | 0.3830 | 0.7784 | 0.6479 | 0.6597 | | 0.5045 | 28.11 | 10400 | 0.5943 | 0.6726 | 0.7889 | 0.8614 | 0.9384 | 0.8178 | 0.8455 | 0.5025 | 0.8972 | 0.8583 | 0.6626 | 0.8610 | 0.6741 | 0.7375 | 0.3788 | 0.7771 | 0.6459 | 0.6336 | | 0.1568 | 28.16 | 10420 | 0.5737 | 0.6751 | 0.7883 | 0.8654 | 0.9394 | 0.8093 | 0.8596 | 0.4498 | 0.8935 | 0.8680 | 0.6983 | 0.8628 | 0.6736 | 0.7297 | 0.3467 | 0.7823 | 0.6611 | 0.6698 | | 0.2164 | 28.22 | 10440 | 0.5116 | 0.6823 | 0.7975 | 0.8708 | 0.9333 | 0.8081 | 0.8632 | 0.4418 | 0.8809 | 0.8672 | 0.7880 | 0.8710 | 0.6666 | 0.7358 | 0.3057 | 0.7778 | 0.6720 | 0.7472 | | 0.2651 | 28.27 | 10460 | 0.5385 | 0.6866 | 0.7999 | 0.8708 | 0.9318 | 0.8073 | 0.8674 | 0.4666 | 0.8846 | 0.8628 | 0.7789 | 0.8699 | 0.6695 | 0.7466 | 0.3412 | 0.7748 | 0.6752 | 0.7286 | | 0.1581 | 28.32 | 10480 | 0.5258 | 0.7004 | 0.8087 | 0.8776 | 0.9289 | 0.7974 | 0.8575 | 0.4937 | 0.8935 | 0.8703 | 0.8197 | 0.8649 | 0.6687 | 0.7515 | 0.3923 | 0.7967 | 0.6763 | 0.7522 | | 0.161 | 28.38 | 10500 | 0.5039 | 0.6967 | 0.8080 | 0.8749 | 0.9339 | 0.7980 | 0.8717 | 0.5228 | 0.8884 | 0.8471 | 0.7941 | 0.8685 | 0.6661 | 0.7482 | 0.3973 | 0.7868 | 0.6734 | 0.7366 | | 0.2177 | 28.43 | 10520 | 0.5610 | 0.6881 | 0.8035 | 0.8698 | 0.9305 | 0.8322 | 0.8692 | 0.5056 | 0.8966 | 0.8588 | 0.7318 | 0.8702 | 0.6773 | 0.7482 | 0.3806 | 0.7772 | 0.6648 | 0.6985 | | 0.1825 | 28.49 | 10540 | 0.5600 | 0.6819 | 0.7925 | 0.8669 | 0.9374 | 0.8129 | 0.8308 | 0.4943 | 0.8953 | 0.8302 | 0.7467 | 0.8687 | 0.6701 | 0.7466 | 0.3572 | 0.7675 | 0.6661 | 0.6971 | | 0.3412 | 28.54 | 10560 | 0.5749 | 0.6811 | 0.7952 | 0.8669 | 0.9277 | 0.8007 | 0.8613 | 0.4673 | 0.8851 | 0.8556 | 0.7684 | 0.8639 | 0.6714 | 0.7460 | 0.3381 | 0.7720 | 0.6707 | 0.7060 | | 0.1687 | 28.59 | 10580 | 0.5676 | 0.6847 | 0.8014 | 0.8683 | 0.9322 | 0.7985 | 0.8455 | 0.5046 | 0.8758 | 0.8706 | 0.7828 | 0.8638 | 0.6689 | 0.7457 | 0.3469 | 0.7749 | 0.6728 | 0.7199 | | 0.2223 | 28.65 | 10600 | 0.5701 | 0.6774 | 0.7878 | 0.8648 | 0.9342 | 0.8000 | 0.8401 | 0.4748 | 0.8960 | 0.8328 | 0.7369 | 0.8625 | 0.6639 | 0.7426 | 0.3513 | 0.7699 | 0.6685 | 0.6830 | | 0.1686 | 28.7 | 10620 | 0.5562 | 0.6936 | 0.8015 | 0.8732 | 0.9310 | 0.7666 | 0.8577 | 0.5019 | 0.8869 | 0.8590 | 0.8072 | 0.8607 | 0.6638 | 0.7470 | 0.3897 | 0.7890 | 0.6721 | 0.7326 | | 0.1468 | 28.76 | 10640 | 0.5762 | 0.6844 | 0.7940 | 0.8680 | 0.9349 | 0.7952 | 0.8749 | 0.4870 | 0.8933 | 0.8312 | 0.7411 | 0.8633 | 0.6666 | 0.7430 | 0.3805 | 0.7766 | 0.6717 | 0.6888 | | 0.2564 | 28.81 | 10660 | 0.5444 | 0.7031 | 0.8104 | 0.8793 | 0.9220 | 0.7982 | 0.8617 | 0.4997 | 0.9015 | 0.8416 | 0.8484 | 0.8618 | 0.6682 | 0.7459 | 0.3963 | 0.8024 | 0.6746 | 0.7727 | | 0.1094 | 28.86 | 10680 | 0.5362 | 0.6983 | 0.8109 | 0.8771 | 0.9224 | 0.7900 | 0.8721 | 0.5041 | 0.8903 | 0.8609 | 0.8362 | 0.8627 | 0.6654 | 0.7475 | 0.3752 | 0.7983 | 0.6734 | 0.7654 | | 0.2855 | 28.92 | 10700 | 0.5108 | 0.6925 | 0.8033 | 0.8755 | 0.9335 | 0.7491 | 0.8749 | 0.4847 | 0.8740 | 0.8681 | 0.8390 | 0.8638 | 0.6534 | 0.7505 | 0.3432 | 0.7913 | 0.6772 | 0.7683 | | 0.121 | 28.97 | 10720 | 0.5407 | 0.6888 | 0.8004 | 0.8717 | 0.9329 | 0.8138 | 0.8687 | 0.4840 | 0.8944 | 0.8419 | 0.7670 | 0.8660 | 0.6689 | 0.7485 | 0.3754 | 0.7861 | 0.6645 | 0.7124 | | 0.5389 | 29.03 | 10740 | 0.5872 | 0.6803 | 0.7784 | 0.8701 | 0.9412 | 0.7895 | 0.8622 | 0.3998 | 0.9191 | 0.8209 | 0.7163 | 0.8677 | 0.6793 | 0.7382 | 0.3383 | 0.7835 | 0.6663 | 0.6887 | | 0.2407 | 29.08 | 10760 | 0.5927 | 0.6761 | 0.7843 | 0.8656 | 0.9352 | 0.8173 | 0.8767 | 0.4338 | 0.9091 | 0.8260 | 0.6917 | 0.8661 | 0.6843 | 0.7311 | 0.3475 | 0.7762 | 0.6627 | 0.6652 | | 0.0844 | 29.14 | 10780 | 0.5656 | 0.6827 | 0.7954 | 0.8672 | 0.9407 | 0.8211 | 0.8714 | 0.4765 | 0.8884 | 0.8615 | 0.7078 | 0.8616 | 0.6856 | 0.7376 | 0.3630 | 0.7793 | 0.6693 | 0.6826 | | 0.3771 | 29.19 | 10800 | 0.5372 | 0.6878 | 0.7945 | 0.8720 | 0.9441 | 0.8078 | 0.8704 | 0.4439 | 0.8889 | 0.8554 | 0.7510 | 0.8645 | 0.6810 | 0.7412 | 0.3544 | 0.7860 | 0.6720 | 0.7154 | | 1.0276 | 29.24 | 10820 | 0.5559 | 0.6891 | 0.8020 | 0.8731 | 0.9295 | 0.7942 | 0.8719 | 0.4453 | 0.8755 | 0.8764 | 0.8212 | 0.8604 | 0.6669 | 0.7405 | 0.3507 | 0.7921 | 0.6630 | 0.7501 | | 0.2003 | 29.3 | 10840 | 0.6174 | 0.6756 | 0.7818 | 0.8656 | 0.9368 | 0.8194 | 0.8466 | 0.4171 | 0.9106 | 0.8493 | 0.6932 | 0.8639 | 0.6785 | 0.7393 | 0.3413 | 0.7760 | 0.6667 | 0.6636 | | 0.0898 | 29.35 | 10860 | 0.6115 | 0.6727 | 0.7790 | 0.8647 | 0.9395 | 0.7981 | 0.8618 | 0.4070 | 0.9027 | 0.8527 | 0.6911 | 0.8636 | 0.6768 | 0.7372 | 0.3321 | 0.7756 | 0.6629 | 0.6608 | | 0.2334 | 29.41 | 10880 | 0.6047 | 0.6724 | 0.7800 | 0.8653 | 0.9387 | 0.8099 | 0.8712 | 0.3895 | 0.9004 | 0.8511 | 0.6989 | 0.8659 | 0.6842 | 0.7330 | 0.3197 | 0.7772 | 0.6620 | 0.6649 | | 0.0804 | 29.46 | 10900 | 0.5706 | 0.6802 | 0.7888 | 0.8679 | 0.9416 | 0.7941 | 0.8703 | 0.4362 | 0.8853 | 0.8550 | 0.7389 | 0.8680 | 0.6767 | 0.7409 | 0.3356 | 0.7729 | 0.6741 | 0.6933 | | 0.1789 | 29.51 | 10920 | 0.5623 | 0.6849 | 0.7960 | 0.8688 | 0.9329 | 0.8047 | 0.8644 | 0.4843 | 0.8936 | 0.8412 | 0.7512 | 0.8691 | 0.6729 | 0.7498 | 0.3684 | 0.7749 | 0.6640 | 0.6956 | | 0.1435 | 29.57 | 10940 | 0.5850 | 0.6812 | 0.7944 | 0.8671 | 0.9321 | 0.8034 | 0.8794 | 0.4527 | 0.8837 | 0.8672 | 0.7426 | 0.8679 | 0.6755 | 0.7446 | 0.3683 | 0.7766 | 0.6464 | 0.6892 | | 0.2155 | 29.62 | 10960 | 0.6244 | 0.6839 | 0.7895 | 0.8685 | 0.9388 | 0.7877 | 0.8621 | 0.4645 | 0.9001 | 0.8552 | 0.7180 | 0.8682 | 0.6809 | 0.7439 | 0.3787 | 0.7786 | 0.6521 | 0.6850 | | 0.2224 | 29.68 | 10980 | 0.6111 | 0.6886 | 0.7970 | 0.8692 | 0.9322 | 0.7919 | 0.8653 | 0.5128 | 0.9025 | 0.8449 | 0.7296 | 0.8696 | 0.6832 | 0.7454 | 0.3983 | 0.7768 | 0.6573 | 0.6897 | | 0.1149 | 29.73 | 11000 | 0.5952 | 0.6806 | 0.7983 | 0.8665 | 0.9384 | 0.8183 | 0.8729 | 0.4753 | 0.8747 | 0.8806 | 0.7282 | 0.8712 | 0.6840 | 0.7324 | 0.3515 | 0.7705 | 0.6608 | 0.6935 | | 0.1818 | 29.78 | 11020 | 0.5987 | 0.6814 | 0.7965 | 0.8671 | 0.9375 | 0.8148 | 0.8667 | 0.4696 | 0.8807 | 0.8742 | 0.7321 | 0.8725 | 0.6840 | 0.7402 | 0.3450 | 0.7688 | 0.6630 | 0.6959 | | 0.1756 | 29.84 | 11040 | 0.5670 | 0.6774 | 0.7905 | 0.8668 | 0.9377 | 0.7966 | 0.8599 | 0.4413 | 0.8791 | 0.8631 | 0.7559 | 0.8693 | 0.6710 | 0.7368 | 0.3171 | 0.7699 | 0.6703 | 0.7072 | | 0.1492 | 29.89 | 11060 | 0.5367 | 0.6823 | 0.7962 | 0.8708 | 0.9361 | 0.8053 | 0.8733 | 0.4252 | 0.8708 | 0.8506 | 0.8121 | 0.8690 | 0.6733 | 0.7339 | 0.2975 | 0.7764 | 0.6697 | 0.7562 | | 0.1751 | 29.95 | 11080 | 0.5206 | 0.6859 | 0.7955 | 0.8723 | 0.9391 | 0.8241 | 0.8631 | 0.4284 | 0.8892 | 0.8497 | 0.7746 | 0.8672 | 0.6816 | 0.7384 | 0.3159 | 0.7823 | 0.6747 | 0.7412 | | 1.3064 | 30.0 | 11100 | 0.5996 | 0.6743 | 0.7801 | 0.8670 | 0.9437 | 0.8023 | 0.8694 | 0.4123 | 0.9054 | 0.8260 | 0.7016 | 0.8651 | 0.6754 | 0.7186 | 0.3279 | 0.7822 | 0.6724 | 0.6786 | | 0.3603 | 30.05 | 11120 | 0.6017 | 0.6762 | 0.7899 | 0.8670 | 0.9342 | 0.8223 | 0.8725 | 0.4273 | 0.8954 | 0.8571 | 0.7206 | 0.8687 | 0.6756 | 0.7215 | 0.3337 | 0.7795 | 0.6623 | 0.6918 | | 0.2207 | 30.11 | 11140 | 0.5756 | 0.6827 | 0.7851 | 0.8719 | 0.9386 | 0.8051 | 0.8549 | 0.4086 | 0.9150 | 0.8366 | 0.7368 | 0.8692 | 0.6788 | 0.7276 | 0.3323 | 0.7877 | 0.6754 | 0.7082 | | 0.3663 | 30.16 | 11160 | 0.5422 | 0.6897 | 0.7993 | 0.8723 | 0.9384 | 0.8169 | 0.8639 | 0.4655 | 0.8914 | 0.8623 | 0.7569 | 0.8694 | 0.6825 | 0.7404 | 0.3611 | 0.7819 | 0.6684 | 0.7244 | | 0.1134 | 30.22 | 11180 | 0.5514 | 0.6917 | 0.8059 | 0.8723 | 0.9295 | 0.8160 | 0.8783 | 0.5033 | 0.8933 | 0.8629 | 0.7581 | 0.8684 | 0.6767 | 0.7455 | 0.3758 | 0.7831 | 0.6704 | 0.7219 | | 0.1779 | 30.27 | 11200 | 0.6142 | 0.6754 | 0.7894 | 0.8651 | 0.9376 | 0.8027 | 0.8878 | 0.4476 | 0.8880 | 0.8635 | 0.6985 | 0.8634 | 0.6782 | 0.7282 | 0.3454 | 0.7792 | 0.6618 | 0.6716 | | 0.1774 | 30.32 | 11220 | 0.6221 | 0.6834 | 0.7954 | 0.8677 | 0.9317 | 0.8304 | 0.8640 | 0.4816 | 0.9048 | 0.8428 | 0.7128 | 0.8604 | 0.6786 | 0.7377 | 0.3705 | 0.7826 | 0.6754 | 0.6785 | | 0.0996 | 30.38 | 11240 | 0.5661 | 0.6912 | 0.7990 | 0.8715 | 0.9347 | 0.8160 | 0.8392 | 0.4970 | 0.9044 | 0.8581 | 0.7438 | 0.8625 | 0.6807 | 0.7443 | 0.3849 | 0.7860 | 0.6744 | 0.7056 | | 0.1338 | 30.43 | 11260 | 0.5457 | 0.6903 | 0.7959 | 0.8731 | 0.9344 | 0.8023 | 0.8667 | 0.4449 | 0.8995 | 0.8613 | 0.7622 | 0.8651 | 0.6802 | 0.7445 | 0.3611 | 0.7879 | 0.6738 | 0.7193 | | 0.1228 | 30.49 | 11280 | 0.5527 | 0.6908 | 0.7976 | 0.8727 | 0.9291 | 0.7971 | 0.8688 | 0.4670 | 0.9044 | 0.8569 | 0.7600 | 0.8664 | 0.6797 | 0.7432 | 0.3680 | 0.7855 | 0.6748 | 0.7183 | | 0.1422 | 30.54 | 11300 | 0.5595 | 0.6907 | 0.8010 | 0.8712 | 0.9351 | 0.8085 | 0.8636 | 0.4905 | 0.8920 | 0.8701 | 0.7472 | 0.8652 | 0.6841 | 0.7470 | 0.3814 | 0.7839 | 0.6653 | 0.7078 | | 0.1277 | 30.59 | 11320 | 0.5841 | 0.6835 | 0.7978 | 0.8687 | 0.9369 | 0.7791 | 0.8686 | 0.5062 | 0.8874 | 0.8764 | 0.7298 | 0.8653 | 0.6621 | 0.7434 | 0.3664 | 0.7820 | 0.6648 | 0.7002 | | 0.3679 | 30.65 | 11340 | 0.5968 | 0.6760 | 0.7887 | 0.8655 | 0.9362 | 0.8030 | 0.8731 | 0.4540 | 0.8889 | 0.8327 | 0.7330 | 0.8673 | 0.6802 | 0.7292 | 0.3118 | 0.7687 | 0.6675 | 0.7075 | | 0.1493 | 30.7 | 11360 | 0.5796 | 0.6791 | 0.7888 | 0.8671 | 0.9404 | 0.8160 | 0.8517 | 0.4411 | 0.8890 | 0.8368 | 0.7467 | 0.8671 | 0.6856 | 0.7335 | 0.3129 | 0.7696 | 0.6674 | 0.7174 | | 0.1479 | 30.76 | 11380 | 0.5848 | 0.6839 | 0.7953 | 0.8691 | 0.9403 | 0.8206 | 0.8591 | 0.4617 | 0.8880 | 0.8564 | 0.7407 | 0.8673 | 0.6859 | 0.7356 | 0.3426 | 0.7764 | 0.6680 | 0.7117 | | 0.1328 | 30.81 | 11400 | 0.5847 | 0.6846 | 0.7904 | 0.8705 | 0.9383 | 0.8054 | 0.8517 | 0.4391 | 0.9000 | 0.8512 | 0.7471 | 0.8684 | 0.6805 | 0.7387 | 0.3422 | 0.7781 | 0.6691 | 0.7154 | | 0.2385 | 30.86 | 11420 | 0.5694 | 0.6856 | 0.7946 | 0.8709 | 0.9389 | 0.8115 | 0.8643 | 0.4473 | 0.8931 | 0.8599 | 0.7470 | 0.8689 | 0.6834 | 0.7410 | 0.3447 | 0.7813 | 0.6652 | 0.7147 | | 0.3155 | 30.92 | 11440 | 0.5510 | 0.6868 | 0.7946 | 0.8707 | 0.9316 | 0.7732 | 0.8801 | 0.4731 | 0.8983 | 0.8581 | 0.7477 | 0.8668 | 0.6722 | 0.7457 | 0.3686 | 0.7833 | 0.6634 | 0.7075 | | 0.1535 | 30.97 | 11460 | 0.5864 | 0.6868 | 0.7928 | 0.8701 | 0.9311 | 0.7825 | 0.8671 | 0.4806 | 0.9058 | 0.8358 | 0.7467 | 0.8659 | 0.6644 | 0.7488 | 0.3787 | 0.7789 | 0.6664 | 0.7042 | | 0.097 | 31.03 | 11480 | 0.5224 | 0.6941 | 0.7999 | 0.8750 | 0.9330 | 0.7874 | 0.8563 | 0.4869 | 0.8984 | 0.8317 | 0.8057 | 0.8648 | 0.6619 | 0.7487 | 0.3868 | 0.7914 | 0.6641 | 0.7413 | | 0.3133 | 31.08 | 11500 | 0.5746 | 0.6834 | 0.7888 | 0.8705 | 0.9364 | 0.8264 | 0.8609 | 0.3981 | 0.9002 | 0.8511 | 0.7486 | 0.8646 | 0.6800 | 0.7402 | 0.3364 | 0.7805 | 0.6718 | 0.7100 | | 0.0855 | 31.14 | 11520 | 0.5645 | 0.6903 | 0.7952 | 0.8723 | 0.9404 | 0.8164 | 0.8666 | 0.4516 | 0.8986 | 0.8471 | 0.7453 | 0.8673 | 0.6853 | 0.7445 | 0.3686 | 0.7816 | 0.6715 | 0.7131 | | 0.0744 | 31.19 | 11540 | 0.5626 | 0.6893 | 0.7952 | 0.8718 | 0.9387 | 0.8155 | 0.8578 | 0.4492 | 0.8961 | 0.8562 | 0.7528 | 0.8696 | 0.6861 | 0.7414 | 0.3639 | 0.7785 | 0.6722 | 0.7132 | | 0.2231 | 31.24 | 11560 | 0.5724 | 0.6874 | 0.7940 | 0.8699 | 0.9358 | 0.8015 | 0.8501 | 0.4769 | 0.8988 | 0.8444 | 0.7507 | 0.8685 | 0.6683 | 0.7460 | 0.3839 | 0.7751 | 0.6713 | 0.6982 | | 0.1109 | 31.3 | 11580 | 0.6260 | 0.6867 | 0.7934 | 0.8701 | 0.9380 | 0.7974 | 0.8640 | 0.4617 | 0.8965 | 0.8635 | 0.7329 | 0.8675 | 0.6758 | 0.7450 | 0.3734 | 0.7776 | 0.6658 | 0.7019 | | 1.0078 | 31.35 | 11600 | 0.6212 | 0.6856 | 0.7888 | 0.8703 | 0.9424 | 0.7945 | 0.8673 | 0.4464 | 0.8997 | 0.8385 | 0.7329 | 0.8675 | 0.6846 | 0.7420 | 0.3578 | 0.7787 | 0.6654 | 0.7027 | | 0.1772 | 31.41 | 11620 | 0.6059 | 0.6872 | 0.7913 | 0.8718 | 0.9407 | 0.8118 | 0.8641 | 0.4315 | 0.9000 | 0.8440 | 0.7466 | 0.8688 | 0.6868 | 0.7420 | 0.3489 | 0.7805 | 0.6684 | 0.7152 | | 0.1711 | 31.46 | 11640 | 0.6070 | 0.6863 | 0.7947 | 0.8700 | 0.9350 | 0.8271 | 0.8735 | 0.4440 | 0.8963 | 0.8411 | 0.7457 | 0.8645 | 0.6850 | 0.7380 | 0.3600 | 0.7794 | 0.6686 | 0.7084 | | 0.1414 | 31.51 | 11660 | 0.5673 | 0.6927 | 0.7979 | 0.8724 | 0.9349 | 0.8041 | 0.8693 | 0.4685 | 0.8992 | 0.8598 | 0.7493 | 0.8678 | 0.6843 | 0.7474 | 0.3852 | 0.7800 | 0.6699 | 0.7143 | | 0.2769 | 31.57 | 11680 | 0.5566 | 0.6923 | 0.7990 | 0.8727 | 0.9313 | 0.8129 | 0.8601 | 0.4634 | 0.9009 | 0.8642 | 0.7603 | 0.8669 | 0.6812 | 0.7465 | 0.3811 | 0.7832 | 0.6681 | 0.7188 | | 0.1607 | 31.62 | 11700 | 0.5462 | 0.6854 | 0.7928 | 0.8716 | 0.9336 | 0.8169 | 0.8736 | 0.4127 | 0.8973 | 0.8578 | 0.7578 | 0.8671 | 0.6828 | 0.7397 | 0.3381 | 0.7831 | 0.6684 | 0.7189 | | 0.4752 | 31.68 | 11720 | 0.5840 | 0.6771 | 0.7857 | 0.8678 | 0.9354 | 0.8028 | 0.8720 | 0.4040 | 0.8940 | 0.8490 | 0.7424 | 0.8708 | 0.6820 | 0.7388 | 0.2953 | 0.7704 | 0.6695 | 0.7128 | | 0.2187 | 31.73 | 11740 | 0.5990 | 0.6799 | 0.7890 | 0.8683 | 0.9355 | 0.8061 | 0.8600 | 0.4239 | 0.8928 | 0.8570 | 0.7475 | 0.8693 | 0.6805 | 0.7430 | 0.3093 | 0.7710 | 0.6734 | 0.7130 | | 0.2686 | 31.78 | 11760 | 0.6126 | 0.6797 | 0.7898 | 0.8681 | 0.9337 | 0.8209 | 0.8567 | 0.4304 | 0.8976 | 0.8441 | 0.7454 | 0.8700 | 0.6787 | 0.7415 | 0.3112 | 0.7701 | 0.6702 | 0.7161 | | 0.1204 | 31.84 | 11780 | 0.6013 | 0.6757 | 0.7905 | 0.8657 | 0.9353 | 0.8212 | 0.8707 | 0.4238 | 0.8796 | 0.8572 | 0.7459 | 0.8702 | 0.6781 | 0.7393 | 0.2985 | 0.7638 | 0.6724 | 0.7075 | | 0.1468 | 31.89 | 11800 | 0.6401 | 0.6777 | 0.7915 | 0.8665 | 0.9385 | 0.8270 | 0.8623 | 0.4304 | 0.8835 | 0.8695 | 0.7291 | 0.8679 | 0.6817 | 0.7453 | 0.3132 | 0.7690 | 0.6639 | 0.7032 | | 0.232 | 31.95 | 11820 | 0.6002 | 0.6923 | 0.8004 | 0.8712 | 0.9326 | 0.8146 | 0.8547 | 0.5084 | 0.9039 | 0.8416 | 0.7468 | 0.8673 | 0.6807 | 0.7504 | 0.3936 | 0.7786 | 0.6689 | 0.7065 | | 0.1691 | 32.0 | 11840 | 0.5936 | 0.6901 | 0.8007 | 0.8695 | 0.9308 | 0.8029 | 0.8417 | 0.5180 | 0.8970 | 0.8671 | 0.7476 | 0.8663 | 0.6722 | 0.7471 | 0.4006 | 0.7754 | 0.6637 | 0.7055 | | 0.2323 | 32.05 | 11860 | 0.6082 | 0.6801 | 0.7981 | 0.8658 | 0.9372 | 0.8277 | 0.8615 | 0.4913 | 0.8800 | 0.8580 | 0.7308 | 0.8672 | 0.6782 | 0.7418 | 0.3405 | 0.7674 | 0.6603 | 0.7052 | | 2.4548 | 32.11 | 11880 | 0.6021 | 0.6788 | 0.7962 | 0.8651 | 0.9351 | 0.8213 | 0.8721 | 0.4859 | 0.8830 | 0.8548 | 0.7208 | 0.8633 | 0.6791 | 0.7419 | 0.3397 | 0.7707 | 0.6677 | 0.6894 | | 0.2158 | 32.16 | 11900 | 0.5693 | 0.6823 | 0.7965 | 0.8679 | 0.9374 | 0.8008 | 0.8740 | 0.4830 | 0.8836 | 0.8587 | 0.7379 | 0.8674 | 0.6773 | 0.7381 | 0.3467 | 0.7750 | 0.6658 | 0.7058 | | 0.1927 | 32.22 | 11920 | 0.5284 | 0.6910 | 0.8004 | 0.8726 | 0.9366 | 0.8123 | 0.8556 | 0.4924 | 0.8981 | 0.8492 | 0.7589 | 0.8697 | 0.6735 | 0.7471 | 0.3741 | 0.7822 | 0.6657 | 0.7245 | | 0.2838 | 32.27 | 11940 | 0.5619 | 0.6909 | 0.8074 | 0.8702 | 0.9284 | 0.8075 | 0.8623 | 0.5499 | 0.8952 | 0.8694 | 0.7393 | 0.8664 | 0.6686 | 0.7547 | 0.4031 | 0.7818 | 0.6570 | 0.7049 | | 0.2922 | 32.32 | 11960 | 0.5656 | 0.6856 | 0.8001 | 0.8685 | 0.9310 | 0.8307 | 0.8601 | 0.4979 | 0.9003 | 0.8625 | 0.7179 | 0.8621 | 0.6717 | 0.7440 | 0.3858 | 0.7831 | 0.6659 | 0.6866 | | 0.1937 | 32.38 | 11980 | 0.5836 | 0.6818 | 0.7950 | 0.8690 | 0.9286 | 0.8243 | 0.8696 | 0.4326 | 0.8948 | 0.8685 | 0.7468 | 0.8660 | 0.6764 | 0.7286 | 0.3389 | 0.7805 | 0.6751 | 0.7068 | | 0.7299 | 32.43 | 12000 | 0.5913 | 0.6810 | 0.7865 | 0.8692 | 0.9312 | 0.8205 | 0.8379 | 0.4219 | 0.9128 | 0.8312 | 0.7500 | 0.8647 | 0.6747 | 0.7242 | 0.3392 | 0.7809 | 0.6781 | 0.7049 | | 0.1485 | 32.49 | 12020 | 0.5512 | 0.6889 | 0.7972 | 0.8717 | 0.9347 | 0.8325 | 0.8413 | 0.4624 | 0.9041 | 0.8560 | 0.7495 | 0.8646 | 0.6797 | 0.7385 | 0.3651 | 0.7850 | 0.6777 | 0.7117 | | 0.2181 | 32.54 | 12040 | 0.5926 | 0.6830 | 0.7958 | 0.8697 | 0.9364 | 0.8359 | 0.8676 | 0.4369 | 0.8932 | 0.8717 | 0.7292 | 0.8626 | 0.6799 | 0.7326 | 0.3465 | 0.7872 | 0.6722 | 0.6997 | | 0.1291 | 32.59 | 12060 | 0.5869 | 0.6820 | 0.7940 | 0.8692 | 0.9371 | 0.8218 | 0.8700 | 0.4356 | 0.8916 | 0.8759 | 0.7264 | 0.8644 | 0.6830 | 0.7377 | 0.3383 | 0.7844 | 0.6696 | 0.6964 | | 0.1469 | 32.65 | 12080 | 0.5852 | 0.6802 | 0.7831 | 0.8701 | 0.9389 | 0.7942 | 0.8545 | 0.3930 | 0.9051 | 0.8628 | 0.7331 | 0.8666 | 0.6800 | 0.7414 | 0.3209 | 0.7831 | 0.6715 | 0.6982 | | 0.263 | 32.7 | 12100 | 0.5941 | 0.6803 | 0.7892 | 0.8696 | 0.9372 | 0.8100 | 0.8631 | 0.4090 | 0.8958 | 0.8766 | 0.7331 | 0.8664 | 0.6774 | 0.7397 | 0.3267 | 0.7838 | 0.6699 | 0.6981 | | 1.5095 | 32.76 | 12120 | 0.5723 | 0.6848 | 0.7946 | 0.8715 | 0.9285 | 0.8299 | 0.8757 | 0.4208 | 0.9067 | 0.8592 | 0.7414 | 0.8675 | 0.6780 | 0.7456 | 0.3376 | 0.7858 | 0.6736 | 0.7052 | | 0.1808 | 32.81 | 12140 | 0.5699 | 0.6850 | 0.7913 | 0.8717 | 0.9332 | 0.8255 | 0.8600 | 0.4157 | 0.9094 | 0.8537 | 0.7413 | 0.8668 | 0.6807 | 0.7435 | 0.3382 | 0.7862 | 0.6737 | 0.7063 | | 0.235 | 32.86 | 12160 | 0.5363 | 0.6912 | 0.7951 | 0.8751 | 0.9378 | 0.7900 | 0.8752 | 0.4286 | 0.8944 | 0.8517 | 0.7878 | 0.8678 | 0.6817 | 0.7446 | 0.3422 | 0.7900 | 0.6728 | 0.7392 | | 1.1158 | 32.92 | 12180 | 0.5303 | 0.6872 | 0.8004 | 0.8718 | 0.9367 | 0.8376 | 0.8672 | 0.4419 | 0.8821 | 0.8578 | 0.7795 | 0.8657 | 0.6833 | 0.7439 | 0.3319 | 0.7831 | 0.6705 | 0.7317 | | 0.1304 | 32.97 | 12200 | 0.5607 | 0.6809 | 0.7900 | 0.8689 | 0.9395 | 0.8156 | 0.8663 | 0.4354 | 0.8962 | 0.8458 | 0.7310 | 0.8681 | 0.6881 | 0.7349 | 0.3345 | 0.7811 | 0.6597 | 0.7001 | | 0.2281 | 33.03 | 12220 | 0.5931 | 0.6836 | 0.7949 | 0.8671 | 0.9305 | 0.8126 | 0.8581 | 0.4859 | 0.8990 | 0.8518 | 0.7267 | 0.8656 | 0.6799 | 0.7423 | 0.3775 | 0.7750 | 0.6616 | 0.6833 | | 0.1557 | 33.08 | 12240 | 0.5808 | 0.6903 | 0.7965 | 0.8705 | 0.9294 | 0.8090 | 0.8536 | 0.4897 | 0.9086 | 0.8380 | 0.7475 | 0.8660 | 0.6826 | 0.7468 | 0.3837 | 0.7779 | 0.6734 | 0.7018 | | 0.6017 | 33.14 | 12260 | 0.5959 | 0.6844 | 0.7954 | 0.8695 | 0.9363 | 0.8391 | 0.8658 | 0.4471 | 0.8974 | 0.8538 | 0.7281 | 0.8639 | 0.6804 | 0.7448 | 0.3591 | 0.7825 | 0.6646 | 0.6957 | | 0.2142 | 33.19 | 12280 | 0.6027 | 0.6835 | 0.7967 | 0.8679 | 0.9284 | 0.8333 | 0.8645 | 0.4702 | 0.9038 | 0.8604 | 0.7161 | 0.8653 | 0.6795 | 0.7515 | 0.3699 | 0.7802 | 0.6537 | 0.6843 | | 0.1474 | 33.24 | 12300 | 0.5862 | 0.6777 | 0.7958 | 0.8650 | 0.9330 | 0.8353 | 0.8693 | 0.4818 | 0.8901 | 0.8462 | 0.7150 | 0.8669 | 0.6789 | 0.7468 | 0.3554 | 0.7752 | 0.6441 | 0.6768 | | 0.1682 | 33.3 | 12320 | 0.5766 | 0.6800 | 0.7929 | 0.8660 | 0.9408 | 0.7946 | 0.8645 | 0.4906 | 0.8837 | 0.8518 | 0.7241 | 0.8663 | 0.6744 | 0.7485 | 0.3706 | 0.7762 | 0.6440 | 0.6800 | | 0.1448 | 33.35 | 12340 | 0.5752 | 0.6824 | 0.7950 | 0.8667 | 0.9330 | 0.8126 | 0.8599 | 0.4810 | 0.8927 | 0.8587 | 0.7270 | 0.8643 | 0.6770 | 0.7509 | 0.3804 | 0.7793 | 0.6449 | 0.6800 | | 0.1857 | 33.41 | 12360 | 0.6157 | 0.6804 | 0.7852 | 0.8665 | 0.9366 | 0.7806 | 0.8492 | 0.4680 | 0.9048 | 0.8367 | 0.7202 | 0.8631 | 0.6720 | 0.7459 | 0.3743 | 0.7777 | 0.6549 | 0.6750 | | 0.1125 | 33.46 | 12380 | 0.5973 | 0.6810 | 0.7933 | 0.8663 | 0.9359 | 0.8104 | 0.8734 | 0.4840 | 0.8948 | 0.8440 | 0.7105 | 0.8599 | 0.6790 | 0.7413 | 0.3753 | 0.7828 | 0.6546 | 0.6740 | | 0.2302 | 33.51 | 12400 | 0.5912 | 0.6799 | 0.7960 | 0.8649 | 0.9247 | 0.8093 | 0.8589 | 0.5027 | 0.8975 | 0.8561 | 0.7231 | 0.8639 | 0.6605 | 0.7535 | 0.3885 | 0.7751 | 0.6447 | 0.6734 | | 0.2708 | 33.57 | 12420 | 0.5855 | 0.6800 | 0.7918 | 0.8669 | 0.9294 | 0.7983 | 0.8735 | 0.4647 | 0.8996 | 0.8533 | 0.7234 | 0.8654 | 0.6682 | 0.7515 | 0.3720 | 0.7817 | 0.6398 | 0.6815 | | 0.2264 | 33.62 | 12440 | 0.5963 | 0.6825 | 0.7979 | 0.8658 | 0.9302 | 0.8181 | 0.8566 | 0.5026 | 0.8944 | 0.8739 | 0.7095 | 0.8654 | 0.6781 | 0.7531 | 0.3897 | 0.7770 | 0.6388 | 0.6756 | | 0.206 | 33.68 | 12460 | 0.5587 | 0.6852 | 0.7922 | 0.8695 | 0.9287 | 0.7938 | 0.8625 | 0.4779 | 0.9121 | 0.8362 | 0.7343 | 0.8677 | 0.6742 | 0.7522 | 0.3769 | 0.7820 | 0.6499 | 0.6936 | | 0.1734 | 33.73 | 12480 | 0.5269 | 0.6910 | 0.7995 | 0.8734 | 0.9357 | 0.8040 | 0.8626 | 0.4864 | 0.9002 | 0.8428 | 0.7648 | 0.8704 | 0.6659 | 0.7543 | 0.3811 | 0.7869 | 0.6629 | 0.7156 | | 0.1171 | 33.78 | 12500 | 0.5463 | 0.6876 | 0.7964 | 0.8715 | 0.9350 | 0.7956 | 0.8662 | 0.4893 | 0.9022 | 0.8371 | 0.7495 | 0.8707 | 0.6670 | 0.7534 | 0.3737 | 0.7832 | 0.6624 | 0.7030 | | 0.2482 | 33.84 | 12520 | 0.5667 | 0.6851 | 0.7959 | 0.8694 | 0.9250 | 0.8061 | 0.8682 | 0.4704 | 0.9049 | 0.8508 | 0.7461 | 0.8671 | 0.6662 | 0.7539 | 0.3678 | 0.7798 | 0.6688 | 0.6923 | | 0.162 | 33.89 | 12540 | 0.5679 | 0.6802 | 0.7933 | 0.8679 | 0.9215 | 0.8198 | 0.8724 | 0.4372 | 0.9024 | 0.8493 | 0.7507 | 0.8641 | 0.6646 | 0.7526 | 0.3456 | 0.7807 | 0.6640 | 0.6899 | | 0.3138 | 33.95 | 12560 | 0.5669 | 0.6866 | 0.7918 | 0.8715 | 0.9281 | 0.7997 | 0.8617 | 0.4423 | 0.9106 | 0.8405 | 0.7597 | 0.8649 | 0.6689 | 0.7510 | 0.3551 | 0.7857 | 0.6824 | 0.6984 | | 0.1161 | 34.0 | 12580 | 0.5417 | 0.6924 | 0.7947 | 0.8745 | 0.9312 | 0.7930 | 0.8545 | 0.4611 | 0.9144 | 0.8362 | 0.7722 | 0.8672 | 0.6676 | 0.7518 | 0.3713 | 0.7895 | 0.6849 | 0.7144 | | 0.2788 | 34.05 | 12600 | 0.5523 | 0.6907 | 0.7943 | 0.8741 | 0.9342 | 0.8215 | 0.8531 | 0.4377 | 0.9138 | 0.8457 | 0.7540 | 0.8668 | 0.6792 | 0.7494 | 0.3577 | 0.7906 | 0.6810 | 0.7101 | | 0.3315 | 34.11 | 12620 | 0.5840 | 0.6890 | 0.7910 | 0.8734 | 0.9300 | 0.8029 | 0.8621 | 0.4280 | 0.9186 | 0.8452 | 0.7498 | 0.8672 | 0.6793 | 0.7465 | 0.3509 | 0.7883 | 0.6815 | 0.7095 | | 0.2396 | 34.16 | 12640 | 0.5434 | 0.6875 | 0.8004 | 0.8720 | 0.9292 | 0.8108 | 0.8679 | 0.4665 | 0.8898 | 0.8431 | 0.7956 | 0.8667 | 0.6651 | 0.7541 | 0.3331 | 0.7814 | 0.6751 | 0.7367 | | 0.1696 | 34.22 | 12660 | 0.5481 | 0.6887 | 0.8029 | 0.8734 | 0.9292 | 0.7997 | 0.8789 | 0.4458 | 0.8764 | 0.8718 | 0.8184 | 0.8659 | 0.6684 | 0.7514 | 0.3227 | 0.7849 | 0.6672 | 0.7601 | | 0.1219 | 34.27 | 12680 | 0.5375 | 0.6820 | 0.7902 | 0.8727 | 0.9330 | 0.7890 | 0.8734 | 0.3810 | 0.8846 | 0.8562 | 0.8140 | 0.8677 | 0.6714 | 0.7417 | 0.2785 | 0.7840 | 0.6740 | 0.7569 | | 0.1933 | 34.32 | 12700 | 0.5553 | 0.6836 | 0.7926 | 0.8707 | 0.9335 | 0.8171 | 0.8641 | 0.4131 | 0.8941 | 0.8681 | 0.7581 | 0.8672 | 0.6821 | 0.7432 | 0.3223 | 0.7801 | 0.6697 | 0.7203 | | 0.1571 | 34.38 | 12720 | 0.5774 | 0.6904 | 0.7963 | 0.8726 | 0.9342 | 0.8166 | 0.8529 | 0.4615 | 0.9076 | 0.8535 | 0.7476 | 0.8703 | 0.6839 | 0.7477 | 0.3588 | 0.7805 | 0.6729 | 0.7185 | | 0.0608 | 34.43 | 12740 | 0.5362 | 0.6865 | 0.8022 | 0.8711 | 0.9334 | 0.8206 | 0.8708 | 0.4746 | 0.8821 | 0.8506 | 0.7833 | 0.8711 | 0.6691 | 0.7450 | 0.3349 | 0.7752 | 0.6730 | 0.7373 | | 0.2141 | 34.49 | 12760 | 0.5540 | 0.6939 | 0.8000 | 0.8760 | 0.9406 | 0.7782 | 0.8619 | 0.4693 | 0.8877 | 0.8645 | 0.7978 | 0.8699 | 0.6696 | 0.7435 | 0.3612 | 0.7894 | 0.6749 | 0.7489 | | 0.1735 | 34.54 | 12780 | 0.5424 | 0.6879 | 0.7928 | 0.8738 | 0.9372 | 0.7928 | 0.8580 | 0.4286 | 0.8995 | 0.8605 | 0.7729 | 0.8702 | 0.6691 | 0.7420 | 0.3386 | 0.7852 | 0.6812 | 0.7288 | | 0.1217 | 34.59 | 12800 | 0.5753 | 0.6933 | 0.7980 | 0.8744 | 0.9404 | 0.8221 | 0.8491 | 0.4777 | 0.9107 | 0.8456 | 0.7403 | 0.8691 | 0.6794 | 0.7448 | 0.3714 | 0.7865 | 0.6836 | 0.7180 | | 0.1423 | 34.65 | 12820 | 0.5989 | 0.6918 | 0.7979 | 0.8728 | 0.9357 | 0.8113 | 0.8612 | 0.4718 | 0.9046 | 0.8596 | 0.7411 | 0.8699 | 0.6801 | 0.7434 | 0.3774 | 0.7808 | 0.6737 | 0.7173 | | 0.1074 | 34.7 | 12840 | 0.5823 | 0.6906 | 0.7938 | 0.8728 | 0.9386 | 0.8146 | 0.8467 | 0.4384 | 0.9038 | 0.8714 | 0.7433 | 0.8685 | 0.6807 | 0.7451 | 0.3731 | 0.7820 | 0.6715 | 0.7132 | | 0.1764 | 34.76 | 12860 | 0.6007 | 0.6936 | 0.7986 | 0.8733 | 0.9357 | 0.8238 | 0.8581 | 0.4647 | 0.9060 | 0.8608 | 0.7413 | 0.8687 | 0.6820 | 0.7520 | 0.3894 | 0.7832 | 0.6704 | 0.7095 | | 0.1833 | 34.81 | 12880 | 0.5795 | 0.6925 | 0.7981 | 0.8732 | 0.9340 | 0.8254 | 0.8625 | 0.4631 | 0.9084 | 0.8488 | 0.7446 | 0.8684 | 0.6820 | 0.7482 | 0.3796 | 0.7840 | 0.6739 | 0.7114 | | 0.1584 | 34.86 | 12900 | 0.6025 | 0.6867 | 0.7946 | 0.8703 | 0.9357 | 0.8123 | 0.8721 | 0.4592 | 0.8965 | 0.8376 | 0.7491 | 0.8684 | 0.6769 | 0.7440 | 0.3667 | 0.7765 | 0.6677 | 0.7068 | | 0.129 | 34.92 | 12920 | 0.5499 | 0.6906 | 0.7959 | 0.8721 | 0.9341 | 0.7971 | 0.8729 | 0.4661 | 0.9007 | 0.8455 | 0.7552 | 0.8663 | 0.6828 | 0.7443 | 0.3728 | 0.7828 | 0.6686 | 0.7164 | | 0.1255 | 34.97 | 12940 | 0.5757 | 0.6861 | 0.7956 | 0.8704 | 0.9365 | 0.8204 | 0.8667 | 0.4516 | 0.8970 | 0.8656 | 0.7316 | 0.8661 | 0.6835 | 0.7382 | 0.3643 | 0.7840 | 0.6617 | 0.7050 | | 0.2092 | 35.03 | 12960 | 0.5626 | 0.6938 | 0.8046 | 0.8723 | 0.9337 | 0.8250 | 0.8536 | 0.5120 | 0.8993 | 0.8605 | 0.7481 | 0.8663 | 0.6784 | 0.7466 | 0.4004 | 0.7841 | 0.6658 | 0.7152 | | 0.1811 | 35.08 | 12980 | 0.5657 | 0.6919 | 0.7958 | 0.8721 | 0.9384 | 0.7799 | 0.8454 | 0.4902 | 0.9006 | 0.8713 | 0.7447 | 0.8670 | 0.6760 | 0.7436 | 0.3916 | 0.7810 | 0.6680 | 0.7164 | | 0.2131 | 35.14 | 13000 | 0.5284 | 0.6957 | 0.8061 | 0.8744 | 0.9357 | 0.8163 | 0.8580 | 0.5018 | 0.8940 | 0.8721 | 0.7649 | 0.8698 | 0.6769 | 0.7495 | 0.3824 | 0.7836 | 0.6734 | 0.7345 | | 0.1875 | 35.19 | 13020 | 0.5665 | 0.6945 | 0.8023 | 0.8731 | 0.9382 | 0.8077 | 0.8550 | 0.5006 | 0.8965 | 0.8716 | 0.7464 | 0.8685 | 0.6763 | 0.7515 | 0.3943 | 0.7814 | 0.6722 | 0.7171 | | 0.1252 | 35.24 | 13040 | 0.5460 | 0.6942 | 0.8021 | 0.8724 | 0.9346 | 0.8044 | 0.8655 | 0.5060 | 0.8979 | 0.8550 | 0.7510 | 0.8685 | 0.6785 | 0.7549 | 0.3956 | 0.7792 | 0.6715 | 0.7116 | | 0.9007 | 35.3 | 13060 | 0.5500 | 0.6894 | 0.7948 | 0.8715 | 0.9381 | 0.7895 | 0.8665 | 0.4685 | 0.8925 | 0.8409 | 0.7677 | 0.8652 | 0.6714 | 0.7466 | 0.3765 | 0.7801 | 0.6676 | 0.7184 | | 0.4496 | 35.35 | 13080 | 0.5447 | 0.6946 | 0.8039 | 0.8742 | 0.9275 | 0.8035 | 0.8622 | 0.4909 | 0.8957 | 0.8432 | 0.8040 | 0.8646 | 0.6690 | 0.7453 | 0.3884 | 0.7882 | 0.6636 | 0.7428 | | 0.2982 | 35.41 | 13100 | 0.5485 | 0.6925 | 0.7993 | 0.8724 | 0.9335 | 0.7963 | 0.8511 | 0.4910 | 0.8967 | 0.8507 | 0.7760 | 0.8675 | 0.6710 | 0.7460 | 0.3931 | 0.7811 | 0.6651 | 0.7237 | | 0.0562 | 35.46 | 13120 | 0.5580 | 0.6918 | 0.8072 | 0.8716 | 0.9344 | 0.8218 | 0.8702 | 0.5000 | 0.8812 | 0.8844 | 0.7582 | 0.8686 | 0.6842 | 0.7443 | 0.3822 | 0.7813 | 0.6536 | 0.7282 | | 0.4671 | 35.51 | 13140 | 0.5683 | 0.6872 | 0.8005 | 0.8702 | 0.9335 | 0.8228 | 0.8739 | 0.4743 | 0.8906 | 0.8659 | 0.7425 | 0.8683 | 0.6857 | 0.7382 | 0.3660 | 0.7808 | 0.6611 | 0.7104 | | 0.1878 | 35.57 | 13160 | 0.6551 | 0.6788 | 0.7907 | 0.8655 | 0.9340 | 0.8225 | 0.8581 | 0.4803 | 0.9071 | 0.8440 | 0.6887 | 0.8648 | 0.6817 | 0.7419 | 0.3751 | 0.7795 | 0.6427 | 0.6659 | | 0.0784 | 35.62 | 13180 | 0.5837 | 0.6837 | 0.7996 | 0.8671 | 0.9367 | 0.8309 | 0.8542 | 0.5127 | 0.8948 | 0.8661 | 0.7021 | 0.8665 | 0.6833 | 0.7454 | 0.3876 | 0.7812 | 0.6468 | 0.6753 | | 0.15 | 35.68 | 13200 | 0.5714 | 0.6864 | 0.7937 | 0.8705 | 0.9408 | 0.8253 | 0.8461 | 0.4674 | 0.9046 | 0.8472 | 0.7247 | 0.8672 | 0.6855 | 0.7445 | 0.3744 | 0.7863 | 0.6517 | 0.6954 | | 0.113 | 35.73 | 13220 | 0.5633 | 0.6879 | 0.7957 | 0.8714 | 0.9385 | 0.8247 | 0.8559 | 0.4693 | 0.9055 | 0.8475 | 0.7285 | 0.8687 | 0.6867 | 0.7479 | 0.3715 | 0.7866 | 0.6522 | 0.7017 | | 0.2453 | 35.78 | 13240 | 0.5593 | 0.6869 | 0.8005 | 0.8715 | 0.9345 | 0.8308 | 0.8694 | 0.4542 | 0.8902 | 0.8711 | 0.7532 | 0.8706 | 0.6846 | 0.7427 | 0.3448 | 0.7827 | 0.6578 | 0.7253 | | 0.1704 | 35.84 | 13260 | 0.5220 | 0.6978 | 0.8067 | 0.8764 | 0.9364 | 0.8005 | 0.8554 | 0.5021 | 0.8921 | 0.8680 | 0.7924 | 0.8708 | 0.6704 | 0.7485 | 0.3915 | 0.7905 | 0.6676 | 0.7454 | | 0.3966 | 35.89 | 13280 | 0.5495 | 0.6943 | 0.7998 | 0.8733 | 0.9351 | 0.7967 | 0.8509 | 0.5202 | 0.9086 | 0.8287 | 0.7588 | 0.8712 | 0.6678 | 0.7512 | 0.4067 | 0.7818 | 0.6676 | 0.7140 | | 0.2348 | 35.95 | 13300 | 0.5630 | 0.6864 | 0.7984 | 0.8706 | 0.9355 | 0.8080 | 0.8558 | 0.4874 | 0.8900 | 0.8395 | 0.7726 | 0.8714 | 0.6699 | 0.7451 | 0.3509 | 0.7740 | 0.6662 | 0.7275 | | 0.1803 | 36.0 | 13320 | 0.5926 | 0.6799 | 0.7877 | 0.8672 | 0.9423 | 0.7878 | 0.8591 | 0.4682 | 0.8944 | 0.8373 | 0.7245 | 0.8678 | 0.6819 | 0.7356 | 0.3364 | 0.7728 | 0.6653 | 0.6993 | | 0.1647 | 36.05 | 13340 | 0.5567 | 0.6929 | 0.8017 | 0.8723 | 0.9345 | 0.8153 | 0.8514 | 0.5048 | 0.9030 | 0.8593 | 0.7435 | 0.8706 | 0.6842 | 0.7461 | 0.3918 | 0.7819 | 0.6626 | 0.7130 | | 0.1053 | 36.11 | 13360 | 0.5655 | 0.6904 | 0.7969 | 0.8721 | 0.9346 | 0.8230 | 0.8569 | 0.4639 | 0.9070 | 0.8573 | 0.7358 | 0.8699 | 0.6844 | 0.7485 | 0.3822 | 0.7836 | 0.6575 | 0.7065 | | 0.1856 | 36.16 | 13380 | 0.5469 | 0.6876 | 0.7972 | 0.8718 | 0.9382 | 0.8268 | 0.8705 | 0.4523 | 0.8964 | 0.8550 | 0.7413 | 0.8701 | 0.6858 | 0.7415 | 0.3572 | 0.7832 | 0.6617 | 0.7138 | | 0.228 | 36.22 | 13400 | 0.5294 | 0.7021 | 0.8100 | 0.8801 | 0.9366 | 0.8260 | 0.8586 | 0.4713 | 0.8916 | 0.8604 | 0.8254 | 0.8714 | 0.6841 | 0.7491 | 0.3654 | 0.7966 | 0.6686 | 0.7798 | | 0.2333 | 36.27 | 13420 | 0.5561 | 0.6968 | 0.8054 | 0.8753 | 0.9340 | 0.8265 | 0.8667 | 0.4814 | 0.8983 | 0.8607 | 0.7703 | 0.8710 | 0.6830 | 0.7528 | 0.3852 | 0.7864 | 0.6664 | 0.7329 | | 0.157 | 36.32 | 13440 | 0.5574 | 0.6951 | 0.8006 | 0.8743 | 0.9385 | 0.7941 | 0.8679 | 0.4821 | 0.8959 | 0.8671 | 0.7583 | 0.8694 | 0.6798 | 0.7521 | 0.3887 | 0.7854 | 0.6646 | 0.7260 | | 0.3018 | 36.38 | 13460 | 0.5771 | 0.6896 | 0.7940 | 0.8713 | 0.9288 | 0.7818 | 0.8672 | 0.4633 | 0.9064 | 0.8647 | 0.7459 | 0.8672 | 0.6786 | 0.7469 | 0.3785 | 0.7809 | 0.6670 | 0.7079 | | 0.2048 | 36.43 | 13480 | 0.5862 | 0.6917 | 0.7952 | 0.8727 | 0.9341 | 0.7953 | 0.8683 | 0.4584 | 0.9043 | 0.8582 | 0.7481 | 0.8689 | 0.6857 | 0.7472 | 0.3734 | 0.7810 | 0.6718 | 0.7141 | | 0.0848 | 36.49 | 13500 | 0.5357 | 0.6944 | 0.8027 | 0.8735 | 0.9390 | 0.8168 | 0.8557 | 0.4818 | 0.8887 | 0.8676 | 0.7695 | 0.8701 | 0.6841 | 0.7480 | 0.3829 | 0.7812 | 0.6706 | 0.7237 | | 0.1358 | 36.54 | 13520 | 0.5912 | 0.6908 | 0.8002 | 0.8717 | 0.9312 | 0.8253 | 0.8684 | 0.4716 | 0.9010 | 0.8579 | 0.7459 | 0.8702 | 0.6827 | 0.7495 | 0.3764 | 0.7786 | 0.6677 | 0.7105 | | 0.2787 | 36.59 | 13540 | 0.5802 | 0.6898 | 0.8003 | 0.8712 | 0.9346 | 0.8261 | 0.8675 | 0.4779 | 0.8946 | 0.8488 | 0.7525 | 0.8693 | 0.6790 | 0.7451 | 0.3759 | 0.7783 | 0.6728 | 0.7078 | | 0.1792 | 36.65 | 13560 | 0.5838 | 0.6875 | 0.7981 | 0.8701 | 0.9352 | 0.7941 | 0.8721 | 0.4879 | 0.8900 | 0.8588 | 0.7483 | 0.8684 | 0.6681 | 0.7488 | 0.3779 | 0.7769 | 0.6712 | 0.7011 | | 0.31 | 36.7 | 13580 | 0.5766 | 0.6867 | 0.8050 | 0.8679 | 0.9249 | 0.8010 | 0.8752 | 0.5418 | 0.8880 | 0.8490 | 0.7552 | 0.8660 | 0.6669 | 0.7520 | 0.3778 | 0.7732 | 0.6712 | 0.6998 | | 0.1493 | 36.76 | 13600 | 0.5914 | 0.6910 | 0.7988 | 0.8716 | 0.9309 | 0.8183 | 0.8561 | 0.4823 | 0.9065 | 0.8535 | 0.7443 | 0.8686 | 0.6843 | 0.7493 | 0.3789 | 0.7808 | 0.6680 | 0.7072 | | 0.1263 | 36.81 | 13620 | 0.5895 | 0.6931 | 0.7997 | 0.8721 | 0.9355 | 0.8179 | 0.8394 | 0.5075 | 0.9067 | 0.8425 | 0.7487 | 0.8692 | 0.6775 | 0.7478 | 0.3973 | 0.7796 | 0.6723 | 0.7078 | | 0.1087 | 36.86 | 13640 | 0.5736 | 0.6880 | 0.7967 | 0.8705 | 0.9381 | 0.7949 | 0.8681 | 0.4828 | 0.8910 | 0.8527 | 0.7495 | 0.8685 | 0.6745 | 0.7450 | 0.3713 | 0.7769 | 0.6742 | 0.7057 | | 0.1361 | 36.92 | 13660 | 0.5874 | 0.6901 | 0.7968 | 0.8720 | 0.9342 | 0.7967 | 0.8643 | 0.4772 | 0.9022 | 0.8556 | 0.7475 | 0.8682 | 0.6808 | 0.7457 | 0.3701 | 0.7827 | 0.6734 | 0.7100 | | 0.2133 | 36.97 | 13680 | 0.6268 | 0.6845 | 0.7971 | 0.8679 | 0.9323 | 0.8224 | 0.8718 | 0.4855 | 0.8983 | 0.8508 | 0.7184 | 0.8622 | 0.6796 | 0.7454 | 0.3773 | 0.7818 | 0.6636 | 0.6816 | | 0.118 | 37.03 | 13700 | 0.6365 | 0.6843 | 0.7959 | 0.8669 | 0.9297 | 0.8242 | 0.8565 | 0.4941 | 0.9071 | 0.8601 | 0.6995 | 0.8618 | 0.6778 | 0.7497 | 0.3953 | 0.7813 | 0.6603 | 0.6637 | | 0.2593 | 37.08 | 13720 | 0.5960 | 0.6911 | 0.8020 | 0.8703 | 0.9289 | 0.8211 | 0.8531 | 0.4929 | 0.8975 | 0.8760 | 0.7444 | 0.8647 | 0.6801 | 0.7497 | 0.3965 | 0.7801 | 0.6667 | 0.7002 | | 0.0827 | 37.14 | 13740 | 0.5955 | 0.6934 | 0.8019 | 0.8715 | 0.9332 | 0.8024 | 0.8570 | 0.5219 | 0.9006 | 0.8523 | 0.7461 | 0.8647 | 0.6788 | 0.7485 | 0.3990 | 0.7816 | 0.6778 | 0.7032 | | 0.0814 | 37.19 | 13760 | 0.5991 | 0.6895 | 0.8009 | 0.8703 | 0.9325 | 0.8252 | 0.8515 | 0.4945 | 0.8987 | 0.8662 | 0.7377 | 0.8637 | 0.6799 | 0.7460 | 0.3810 | 0.7821 | 0.6734 | 0.7003 | | 0.1256 | 37.24 | 13780 | 0.6215 | 0.6886 | 0.7992 | 0.8704 | 0.9319 | 0.8285 | 0.8542 | 0.4980 | 0.9101 | 0.8535 | 0.7182 | 0.8646 | 0.6778 | 0.7464 | 0.3842 | 0.7848 | 0.6722 | 0.6903 | | 0.1831 | 37.3 | 13800 | 0.5965 | 0.6921 | 0.8039 | 0.8722 | 0.9328 | 0.8406 | 0.8499 | 0.5007 | 0.9045 | 0.8615 | 0.7375 | 0.8694 | 0.6795 | 0.7478 | 0.3885 | 0.7837 | 0.6690 | 0.7066 | | 0.1468 | 37.35 | 13820 | 0.6292 | 0.6810 | 0.7915 | 0.8674 | 0.9363 | 0.8282 | 0.8616 | 0.4482 | 0.9026 | 0.8689 | 0.6948 | 0.8663 | 0.6815 | 0.7447 | 0.3665 | 0.7804 | 0.6584 | 0.6689 | | 0.2897 | 37.41 | 13840 | 0.6377 | 0.6749 | 0.7849 | 0.8648 | 0.9336 | 0.8309 | 0.8658 | 0.4189 | 0.9080 | 0.8592 | 0.6781 | 0.8650 | 0.6811 | 0.7412 | 0.3519 | 0.7772 | 0.6566 | 0.6512 | | 0.1732 | 37.46 | 13860 | 0.6302 | 0.6813 | 0.7866 | 0.8683 | 0.9395 | 0.8019 | 0.8601 | 0.4386 | 0.9064 | 0.8596 | 0.6999 | 0.8660 | 0.6827 | 0.7401 | 0.3569 | 0.7809 | 0.6685 | 0.6740 | | 0.3542 | 37.51 | 13880 | 0.6502 | 0.6835 | 0.7933 | 0.8673 | 0.9379 | 0.8145 | 0.8609 | 0.4908 | 0.9034 | 0.8516 | 0.6937 | 0.8639 | 0.6811 | 0.7438 | 0.3849 | 0.7800 | 0.6610 | 0.6694 | | 0.1036 | 37.57 | 13900 | 0.6033 | 0.6828 | 0.7972 | 0.8675 | 0.9271 | 0.8177 | 0.8730 | 0.4687 | 0.8950 | 0.8699 | 0.7291 | 0.8645 | 0.6811 | 0.7420 | 0.3650 | 0.7798 | 0.6560 | 0.6914 | | 0.409 | 37.62 | 13920 | 0.6042 | 0.6853 | 0.7915 | 0.8705 | 0.9315 | 0.7980 | 0.8674 | 0.4327 | 0.9030 | 0.8662 | 0.7415 | 0.8660 | 0.6811 | 0.7433 | 0.3500 | 0.7820 | 0.6694 | 0.7054 | | 0.1406 | 37.68 | 13940 | 0.6144 | 0.6794 | 0.7867 | 0.8687 | 0.9320 | 0.8037 | 0.8743 | 0.3970 | 0.9010 | 0.8697 | 0.7289 | 0.8639 | 0.6812 | 0.7400 | 0.3293 | 0.7831 | 0.6650 | 0.6934 | | 1.313 | 37.73 | 13960 | 0.6022 | 0.6820 | 0.7912 | 0.8691 | 0.9333 | 0.8145 | 0.8752 | 0.4238 | 0.8973 | 0.8589 | 0.7354 | 0.8657 | 0.6834 | 0.7369 | 0.3427 | 0.7814 | 0.6650 | 0.6992 | | 0.1975 | 37.78 | 13980 | 0.6037 | 0.6833 | 0.7923 | 0.8701 | 0.9341 | 0.8190 | 0.8761 | 0.4237 | 0.8992 | 0.8615 | 0.7326 | 0.8679 | 0.6853 | 0.7358 | 0.3423 | 0.7827 | 0.6690 | 0.6998 | | 0.2584 | 37.84 | 14000 | 0.5807 | 0.6901 | 0.7957 | 0.8727 | 0.9347 | 0.8231 | 0.8562 | 0.4460 | 0.9069 | 0.8583 | 0.7448 | 0.8675 | 0.6826 | 0.7458 | 0.3671 | 0.7856 | 0.6739 | 0.7084 | | 0.2032 | 37.89 | 14020 | 0.5551 | 0.6926 | 0.7968 | 0.8737 | 0.9390 | 0.8200 | 0.8492 | 0.4610 | 0.9058 | 0.8541 | 0.7482 | 0.8674 | 0.6827 | 0.7500 | 0.3784 | 0.7873 | 0.6681 | 0.7144 | | 0.1317 | 37.95 | 14040 | 0.5509 | 0.6899 | 0.7980 | 0.8715 | 0.9342 | 0.8237 | 0.8539 | 0.4756 | 0.9026 | 0.8470 | 0.7486 | 0.8657 | 0.6836 | 0.7505 | 0.3788 | 0.7849 | 0.6588 | 0.7072 | | 0.3604 | 38.0 | 14060 | 0.5349 | 0.6912 | 0.7985 | 0.8731 | 0.9346 | 0.7841 | 0.8664 | 0.4658 | 0.8895 | 0.8672 | 0.7822 | 0.8660 | 0.6780 | 0.7456 | 0.3673 | 0.7874 | 0.6647 | 0.7294 | | 0.2222 | 38.05 | 14080 | 0.5328 | 0.6934 | 0.8066 | 0.8727 | 0.9309 | 0.8128 | 0.8685 | 0.4980 | 0.8868 | 0.8755 | 0.7734 | 0.8666 | 0.6802 | 0.7526 | 0.3808 | 0.7849 | 0.6649 | 0.7238 | | 0.2449 | 38.11 | 14100 | 0.5987 | 0.6862 | 0.7949 | 0.8699 | 0.9331 | 0.8197 | 0.8593 | 0.4756 | 0.9094 | 0.8469 | 0.7204 | 0.8669 | 0.6817 | 0.7497 | 0.3721 | 0.7831 | 0.6583 | 0.6917 | | 0.1192 | 38.16 | 14120 | 0.5685 | 0.6854 | 0.7936 | 0.8716 | 0.9370 | 0.8237 | 0.8656 | 0.4263 | 0.9006 | 0.8654 | 0.7364 | 0.8683 | 0.6840 | 0.7460 | 0.3447 | 0.7864 | 0.6622 | 0.7064 | | 0.1802 | 38.22 | 14140 | 0.5572 | 0.6864 | 0.7974 | 0.8706 | 0.9384 | 0.8201 | 0.8507 | 0.4702 | 0.8938 | 0.8650 | 0.7433 | 0.8693 | 0.6860 | 0.7460 | 0.3442 | 0.7802 | 0.6655 | 0.7138 | | 0.1099 | 38.27 | 14160 | 0.5273 | 0.6947 | 0.8008 | 0.8757 | 0.9340 | 0.8020 | 0.8591 | 0.4683 | 0.8998 | 0.8542 | 0.7885 | 0.8683 | 0.6787 | 0.7498 | 0.3568 | 0.7892 | 0.6763 | 0.7435 | | 0.1271 | 38.32 | 14180 | 0.5254 | 0.6958 | 0.8033 | 0.8761 | 0.9346 | 0.8124 | 0.8634 | 0.4681 | 0.8967 | 0.8600 | 0.7880 | 0.8691 | 0.6809 | 0.7508 | 0.3583 | 0.7892 | 0.6761 | 0.7461 | | 0.2621 | 38.38 | 14200 | 0.5331 | 0.6914 | 0.8033 | 0.8727 | 0.9300 | 0.8093 | 0.8749 | 0.4784 | 0.8887 | 0.8572 | 0.7849 | 0.8678 | 0.6814 | 0.7501 | 0.3479 | 0.7809 | 0.6761 | 0.7354 | | 0.1324 | 38.43 | 14220 | 0.5733 | 0.6884 | 0.7913 | 0.8725 | 0.9396 | 0.7890 | 0.8555 | 0.4533 | 0.9036 | 0.8445 | 0.7535 | 0.8690 | 0.6752 | 0.7446 | 0.3556 | 0.7818 | 0.6776 | 0.7145 | | 0.2456 | 38.49 | 14240 | 0.5537 | 0.6811 | 0.7909 | 0.8691 | 0.9311 | 0.7956 | 0.8796 | 0.4251 | 0.8921 | 0.8550 | 0.7580 | 0.8669 | 0.6773 | 0.7350 | 0.3256 | 0.7791 | 0.6777 | 0.7062 | | 0.1776 | 38.54 | 14260 | 0.5484 | 0.6846 | 0.7967 | 0.8702 | 0.9346 | 0.8246 | 0.8577 | 0.4512 | 0.8922 | 0.8647 | 0.7521 | 0.8666 | 0.6817 | 0.7389 | 0.3411 | 0.7820 | 0.6697 | 0.7125 | | 0.0734 | 38.59 | 14280 | 0.5084 | 0.6870 | 0.8004 | 0.8719 | 0.9307 | 0.8319 | 0.8716 | 0.4560 | 0.8947 | 0.8509 | 0.7672 | 0.8691 | 0.6835 | 0.7401 | 0.3239 | 0.7826 | 0.6831 | 0.7267 | | 0.148 | 38.65 | 14300 | 0.5462 | 0.6909 | 0.7990 | 0.8734 | 0.9311 | 0.8299 | 0.8548 | 0.4524 | 0.9069 | 0.8641 | 0.7541 | 0.8685 | 0.6792 | 0.7504 | 0.3577 | 0.7858 | 0.6809 | 0.7136 | | 0.2257 | 38.7 | 14320 | 0.5883 | 0.6896 | 0.7977 | 0.8735 | 0.9362 | 0.8310 | 0.8678 | 0.4374 | 0.9021 | 0.8645 | 0.7452 | 0.8677 | 0.6782 | 0.7464 | 0.3489 | 0.7870 | 0.6866 | 0.7121 | | 0.2438 | 38.76 | 14340 | 0.5733 | 0.6899 | 0.7938 | 0.8737 | 0.9393 | 0.8069 | 0.8720 | 0.4422 | 0.9046 | 0.8429 | 0.7489 | 0.8688 | 0.6787 | 0.7507 | 0.3463 | 0.7847 | 0.6856 | 0.7149 | | 0.3243 | 38.81 | 14360 | 0.5874 | 0.6858 | 0.7993 | 0.8701 | 0.9368 | 0.8230 | 0.8774 | 0.4547 | 0.8836 | 0.8765 | 0.7430 | 0.8699 | 0.6823 | 0.7501 | 0.3330 | 0.7733 | 0.6795 | 0.7122 | | 1.404 | 38.86 | 14380 | 0.6100 | 0.6864 | 0.7991 | 0.8700 | 0.9364 | 0.8289 | 0.8609 | 0.4629 | 0.8904 | 0.8808 | 0.7335 | 0.8682 | 0.6790 | 0.7495 | 0.3527 | 0.7767 | 0.6739 | 0.7045 | | 0.1701 | 38.92 | 14400 | 0.5984 | 0.6881 | 0.7996 | 0.8700 | 0.9313 | 0.8191 | 0.8529 | 0.4826 | 0.8977 | 0.8748 | 0.7388 | 0.8657 | 0.6784 | 0.7495 | 0.3718 | 0.7800 | 0.6704 | 0.7010 | | 0.1059 | 38.97 | 14420 | 0.5984 | 0.6861 | 0.7914 | 0.8710 | 0.9374 | 0.8123 | 0.8498 | 0.4406 | 0.9055 | 0.8613 | 0.7328 | 0.8665 | 0.6823 | 0.7450 | 0.3535 | 0.7830 | 0.6716 | 0.7012 | | 0.12 | 39.03 | 14440 | 0.6271 | 0.6814 | 0.7880 | 0.8693 | 0.9408 | 0.8146 | 0.8609 | 0.4222 | 0.9020 | 0.8635 | 0.7120 | 0.8637 | 0.6826 | 0.7393 | 0.3380 | 0.7841 | 0.6756 | 0.6866 | | 0.1683 | 39.08 | 14460 | 0.5926 | 0.6871 | 0.7967 | 0.8710 | 0.9393 | 0.8177 | 0.8623 | 0.4680 | 0.8966 | 0.8592 | 0.7340 | 0.8662 | 0.6814 | 0.7366 | 0.3594 | 0.7843 | 0.6762 | 0.7055 | | 0.2943 | 39.14 | 14480 | 0.5866 | 0.6918 | 0.8027 | 0.8716 | 0.9340 | 0.8184 | 0.8565 | 0.5067 | 0.8983 | 0.8610 | 0.7440 | 0.8657 | 0.6794 | 0.7444 | 0.3851 | 0.7827 | 0.6765 | 0.7092 | | 0.1408 | 39.19 | 14500 | 0.5564 | 0.6949 | 0.8101 | 0.8720 | 0.9309 | 0.8207 | 0.8574 | 0.5468 | 0.8937 | 0.8708 | 0.7504 | 0.8662 | 0.6776 | 0.7480 | 0.3995 | 0.7821 | 0.6761 | 0.7151 | | 0.1082 | 39.24 | 14520 | 0.5643 | 0.6929 | 0.8061 | 0.8712 | 0.9298 | 0.8192 | 0.8561 | 0.5210 | 0.8954 | 0.8730 | 0.7483 | 0.8654 | 0.6766 | 0.7484 | 0.3964 | 0.7805 | 0.6728 | 0.7101 | | 0.0968 | 39.3 | 14540 | 0.5755 | 0.6852 | 0.8011 | 0.8686 | 0.9351 | 0.8134 | 0.8756 | 0.4924 | 0.8807 | 0.8623 | 0.7483 | 0.8651 | 0.6733 | 0.7371 | 0.3614 | 0.7761 | 0.6791 | 0.7044 | | 0.2545 | 39.35 | 14560 | 0.5966 | 0.6834 | 0.7974 | 0.8674 | 0.9279 | 0.8092 | 0.8527 | 0.4868 | 0.8930 | 0.8675 | 0.7449 | 0.8660 | 0.6733 | 0.7499 | 0.3469 | 0.7702 | 0.6733 | 0.7040 | | 0.1329 | 39.41 | 14580 | 0.5686 | 0.6871 | 0.7964 | 0.8703 | 0.9331 | 0.8087 | 0.8529 | 0.4747 | 0.8990 | 0.8579 | 0.7486 | 0.8675 | 0.6719 | 0.7497 | 0.3583 | 0.7761 | 0.6762 | 0.7100 | | 0.2681 | 39.46 | 14600 | 0.5751 | 0.6901 | 0.7980 | 0.8719 | 0.9340 | 0.8132 | 0.8697 | 0.4691 | 0.8993 | 0.8493 | 0.7515 | 0.8660 | 0.6751 | 0.7442 | 0.3771 | 0.7826 | 0.6776 | 0.7084 | | 0.2 | 39.51 | 14620 | 0.5701 | 0.6920 | 0.8018 | 0.8713 | 0.9335 | 0.8146 | 0.8519 | 0.4990 | 0.8949 | 0.8640 | 0.7546 | 0.8642 | 0.6691 | 0.7492 | 0.3978 | 0.7803 | 0.6759 | 0.7074 | | 0.2699 | 39.57 | 14640 | 0.5471 | 0.6912 | 0.7975 | 0.8729 | 0.9307 | 0.7951 | 0.8601 | 0.4642 | 0.8994 | 0.8551 | 0.7780 | 0.8644 | 0.6666 | 0.7483 | 0.3761 | 0.7850 | 0.6755 | 0.7225 | | 0.3498 | 39.62 | 14660 | 0.5512 | 0.6915 | 0.7995 | 0.8733 | 0.9313 | 0.8081 | 0.8764 | 0.4658 | 0.9028 | 0.8590 | 0.7531 | 0.8672 | 0.6789 | 0.7416 | 0.3679 | 0.7869 | 0.6813 | 0.7165 | | 0.2973 | 39.68 | 14680 | 0.5437 | 0.6932 | 0.8031 | 0.8732 | 0.9291 | 0.8143 | 0.8607 | 0.4818 | 0.8990 | 0.8706 | 0.7660 | 0.8667 | 0.6742 | 0.7480 | 0.3785 | 0.7840 | 0.6784 | 0.7226 | | 0.1419 | 39.73 | 14700 | 0.5355 | 0.6952 | 0.8051 | 0.8753 | 0.9312 | 0.8024 | 0.8720 | 0.4789 | 0.8898 | 0.8601 | 0.8017 | 0.8662 | 0.6684 | 0.7473 | 0.3741 | 0.7893 | 0.6771 | 0.7443 | | 0.0852 | 39.78 | 14720 | 0.5293 | 0.6959 | 0.8066 | 0.8756 | 0.9318 | 0.8143 | 0.8604 | 0.4872 | 0.8932 | 0.8656 | 0.7939 | 0.8680 | 0.6670 | 0.7500 | 0.3789 | 0.7895 | 0.6785 | 0.7393 | | 0.2802 | 39.84 | 14740 | 0.5564 | 0.6967 | 0.8063 | 0.8765 | 0.9361 | 0.8137 | 0.8491 | 0.4888 | 0.8909 | 0.8590 | 0.8065 | 0.8668 | 0.6649 | 0.7473 | 0.3778 | 0.7922 | 0.6751 | 0.7525 | | 0.2943 | 39.89 | 14760 | 0.5502 | 0.6917 | 0.8007 | 0.8729 | 0.9309 | 0.8013 | 0.8420 | 0.4998 | 0.9022 | 0.8526 | 0.7759 | 0.8675 | 0.6637 | 0.7483 | 0.3821 | 0.7843 | 0.6731 | 0.7233 | | 1.6794 | 39.95 | 14780 | 0.5218 | 0.6937 | 0.8051 | 0.8750 | 0.9322 | 0.8053 | 0.8602 | 0.4909 | 0.8909 | 0.8536 | 0.8028 | 0.8683 | 0.6670 | 0.7508 | 0.3589 | 0.7873 | 0.6775 | 0.7463 | | 0.1365 | 40.0 | 14800 | 0.5598 | 0.6912 | 0.8028 | 0.8714 | 0.9296 | 0.8137 | 0.8529 | 0.5126 | 0.9004 | 0.8539 | 0.7566 | 0.8667 | 0.6711 | 0.7530 | 0.3841 | 0.7810 | 0.6735 | 0.7090 | | 0.2468 | 40.05 | 14820 | 0.5624 | 0.6893 | 0.8013 | 0.8703 | 0.9332 | 0.7991 | 0.8578 | 0.5107 | 0.8911 | 0.8601 | 0.7568 | 0.8657 | 0.6739 | 0.7522 | 0.3772 | 0.7799 | 0.6658 | 0.7102 | | 0.1003 | 40.11 | 14840 | 0.5568 | 0.6938 | 0.8027 | 0.8731 | 0.9307 | 0.8190 | 0.8441 | 0.4911 | 0.9014 | 0.8655 | 0.7668 | 0.8659 | 0.6782 | 0.7516 | 0.3845 | 0.7851 | 0.6677 | 0.7235 | | 0.1876 | 40.16 | 14860 | 0.5569 | 0.6948 | 0.8014 | 0.8735 | 0.9317 | 0.8163 | 0.8593 | 0.4999 | 0.9088 | 0.8370 | 0.7569 | 0.8669 | 0.6796 | 0.7488 | 0.3910 | 0.7847 | 0.6742 | 0.7186 | | 0.1911 | 40.22 | 14880 | 0.5425 | 0.6941 | 0.8083 | 0.8738 | 0.9302 | 0.8237 | 0.8678 | 0.4900 | 0.8875 | 0.8798 | 0.7791 | 0.8681 | 0.6750 | 0.7483 | 0.3762 | 0.7852 | 0.6759 | 0.7304 | | 0.2452 | 40.27 | 14900 | 0.5416 | 0.6949 | 0.8067 | 0.8761 | 0.9335 | 0.8207 | 0.8548 | 0.4748 | 0.8882 | 0.8637 | 0.8113 | 0.8696 | 0.6707 | 0.7449 | 0.3573 | 0.7889 | 0.6750 | 0.7582 | | 0.1584 | 40.32 | 14920 | 0.5212 | 0.6915 | 0.8043 | 0.8746 | 0.9345 | 0.8157 | 0.8682 | 0.4560 | 0.8813 | 0.8720 | 0.8024 | 0.8679 | 0.6698 | 0.7462 | 0.3467 | 0.7874 | 0.6759 | 0.7467 | | 0.1308 | 40.38 | 14940 | 0.5571 | 0.6847 | 0.7996 | 0.8701 | 0.9315 | 0.8329 | 0.8717 | 0.4544 | 0.8927 | 0.8726 | 0.7413 | 0.8677 | 0.6802 | 0.7430 | 0.3440 | 0.7825 | 0.6649 | 0.7103 | | 0.1618 | 40.43 | 14960 | 0.5995 | 0.6829 | 0.7980 | 0.8685 | 0.9332 | 0.8245 | 0.8659 | 0.4842 | 0.8970 | 0.8592 | 0.7217 | 0.8683 | 0.6802 | 0.7435 | 0.3530 | 0.7801 | 0.6599 | 0.6956 | | 0.1534 | 40.49 | 14980 | 0.5638 | 0.6891 | 0.7991 | 0.8720 | 0.9338 | 0.8280 | 0.8527 | 0.4667 | 0.9008 | 0.8612 | 0.7509 | 0.8675 | 0.6793 | 0.7472 | 0.3558 | 0.7829 | 0.6735 | 0.7177 | | 0.2106 | 40.54 | 15000 | 0.5425 | 0.6857 | 0.7977 | 0.8703 | 0.9336 | 0.8019 | 0.8692 | 0.4726 | 0.8889 | 0.8523 | 0.7654 | 0.8677 | 0.6708 | 0.7480 | 0.3436 | 0.7780 | 0.6774 | 0.7144 | | 0.354 | 40.59 | 15020 | 0.5624 | 0.6866 | 0.8001 | 0.8704 | 0.9353 | 0.8050 | 0.8576 | 0.4813 | 0.8845 | 0.8732 | 0.7634 | 0.8688 | 0.6659 | 0.7478 | 0.3570 | 0.7772 | 0.6741 | 0.7150 | | 0.2076 | 40.65 | 15040 | 0.5313 | 0.6878 | 0.7973 | 0.8718 | 0.9335 | 0.7936 | 0.8647 | 0.4629 | 0.8919 | 0.8648 | 0.7696 | 0.8685 | 0.6698 | 0.7495 | 0.3525 | 0.7810 | 0.6749 | 0.7187 | | 0.2259 | 40.7 | 15060 | 0.5863 | 0.6810 | 0.7920 | 0.8680 | 0.9389 | 0.7749 | 0.8614 | 0.4676 | 0.8837 | 0.8760 | 0.7416 | 0.8690 | 0.6671 | 0.7470 | 0.3448 | 0.7737 | 0.6645 | 0.7009 | | 0.1089 | 40.76 | 15080 | 0.5667 | 0.6846 | 0.7934 | 0.8700 | 0.9306 | 0.7920 | 0.8619 | 0.4550 | 0.8983 | 0.8608 | 0.7552 | 0.8689 | 0.6689 | 0.7460 | 0.3516 | 0.7774 | 0.6731 | 0.7064 | | 0.1985 | 40.81 | 15100 | 0.5746 | 0.6852 | 0.7922 | 0.8710 | 0.9395 | 0.8048 | 0.8625 | 0.4457 | 0.9008 | 0.8620 | 0.7298 | 0.8702 | 0.6842 | 0.7426 | 0.3464 | 0.7827 | 0.6672 | 0.7032 | | 0.2077 | 40.86 | 15120 | 0.6194 | 0.6860 | 0.7942 | 0.8708 | 0.9381 | 0.8099 | 0.8644 | 0.4613 | 0.9026 | 0.8608 | 0.7223 | 0.8702 | 0.6848 | 0.7435 | 0.3577 | 0.7832 | 0.6654 | 0.6969 | | 0.3211 | 40.92 | 15140 | 0.5721 | 0.6908 | 0.8018 | 0.8724 | 0.9329 | 0.7984 | 0.8731 | 0.4909 | 0.8950 | 0.8706 | 0.7519 | 0.8700 | 0.6818 | 0.7414 | 0.3629 | 0.7829 | 0.6786 | 0.7181 | | 0.1905 | 40.97 | 15160 | 0.6226 | 0.6913 | 0.8014 | 0.8726 | 0.9336 | 0.8158 | 0.8653 | 0.4835 | 0.8994 | 0.8663 | 0.7461 | 0.8706 | 0.6841 | 0.7474 | 0.3639 | 0.7819 | 0.6761 | 0.7149 | | 0.1425 | 41.03 | 15180 | 0.6056 | 0.6930 | 0.7992 | 0.8737 | 0.9356 | 0.8152 | 0.8504 | 0.4829 | 0.9080 | 0.8536 | 0.7489 | 0.8702 | 0.6834 | 0.7472 | 0.3659 | 0.7834 | 0.6829 | 0.7179 | | 0.1168 | 41.08 | 15200 | 0.5751 | 0.6940 | 0.8069 | 0.8735 | 0.9348 | 0.8303 | 0.8649 | 0.5107 | 0.8983 | 0.8643 | 0.7448 | 0.8706 | 0.6811 | 0.7456 | 0.3795 | 0.7845 | 0.6799 | 0.7166 | | 0.086 | 41.14 | 15220 | 0.5493 | 0.6947 | 0.8031 | 0.8743 | 0.9343 | 0.8231 | 0.8562 | 0.4884 | 0.9041 | 0.8623 | 0.7533 | 0.8703 | 0.6848 | 0.7461 | 0.3760 | 0.7863 | 0.6785 | 0.7208 | | 0.1855 | 41.19 | 15240 | 0.5724 | 0.6924 | 0.7966 | 0.8740 | 0.9333 | 0.8292 | 0.8535 | 0.4596 | 0.9158 | 0.8325 | 0.7525 | 0.8691 | 0.6858 | 0.7451 | 0.3649 | 0.7859 | 0.6784 | 0.7174 | | 0.0492 | 41.24 | 15260 | 0.6137 | 0.6902 | 0.7969 | 0.8727 | 0.9405 | 0.8323 | 0.8365 | 0.4716 | 0.9080 | 0.8575 | 0.7321 | 0.8679 | 0.6820 | 0.7423 | 0.3731 | 0.7871 | 0.6706 | 0.7081 | | 0.1612 | 41.3 | 15280 | 0.5675 | 0.6914 | 0.8014 | 0.8733 | 0.9367 | 0.8226 | 0.8679 | 0.4751 | 0.8979 | 0.8623 | 0.7475 | 0.8702 | 0.6836 | 0.7426 | 0.3659 | 0.7861 | 0.6748 | 0.7164 | | 0.1317 | 41.35 | 15300 | 0.6373 | 0.6892 | 0.8021 | 0.8703 | 0.9340 | 0.8263 | 0.8559 | 0.5042 | 0.8989 | 0.8713 | 0.7244 | 0.8683 | 0.6790 | 0.7479 | 0.3902 | 0.7821 | 0.6606 | 0.6961 | | 0.2445 | 41.41 | 15320 | 0.5977 | 0.6866 | 0.8012 | 0.8681 | 0.9275 | 0.8100 | 0.8435 | 0.5090 | 0.8927 | 0.8800 | 0.7459 | 0.8665 | 0.6677 | 0.7469 | 0.3924 | 0.7757 | 0.6653 | 0.6918 | | 0.1636 | 41.46 | 15340 | 0.5728 | 0.6885 | 0.8013 | 0.8701 | 0.9312 | 0.7956 | 0.8557 | 0.5054 | 0.8891 | 0.8701 | 0.7623 | 0.8667 | 0.6663 | 0.7506 | 0.3795 | 0.7786 | 0.6739 | 0.7043 | | 0.1409 | 41.51 | 15360 | 0.6116 | 0.6890 | 0.7989 | 0.8703 | 0.9343 | 0.8016 | 0.8489 | 0.5056 | 0.8979 | 0.8582 | 0.7456 | 0.8677 | 0.6651 | 0.7539 | 0.3910 | 0.7786 | 0.6701 | 0.6964 | | 0.1588 | 41.57 | 15380 | 0.5694 | 0.6898 | 0.7994 | 0.8714 | 0.9346 | 0.7957 | 0.8644 | 0.4974 | 0.8945 | 0.8505 | 0.7590 | 0.8684 | 0.6684 | 0.7535 | 0.3769 | 0.7800 | 0.6750 | 0.7063 | | 0.1663 | 41.62 | 15400 | 0.5666 | 0.6901 | 0.8053 | 0.8707 | 0.9302 | 0.8135 | 0.8689 | 0.5097 | 0.8881 | 0.8691 | 0.7574 | 0.8670 | 0.6729 | 0.7533 | 0.3769 | 0.7791 | 0.6741 | 0.7077 | | 0.1314 | 41.68 | 15420 | 0.5718 | 0.6937 | 0.8029 | 0.8727 | 0.9314 | 0.8171 | 0.8418 | 0.5087 | 0.9047 | 0.8625 | 0.7544 | 0.8666 | 0.6782 | 0.7517 | 0.3845 | 0.7834 | 0.6774 | 0.7139 | | 0.2941 | 41.73 | 15440 | 0.5796 | 0.6922 | 0.8003 | 0.8725 | 0.9328 | 0.8158 | 0.8496 | 0.4832 | 0.9020 | 0.8643 | 0.7542 | 0.8663 | 0.6794 | 0.7494 | 0.3763 | 0.7830 | 0.6768 | 0.7143 | | 0.1006 | 41.78 | 15460 | 0.5848 | 0.6879 | 0.7983 | 0.8705 | 0.9349 | 0.8157 | 0.8510 | 0.4731 | 0.8927 | 0.8676 | 0.7532 | 0.8661 | 0.6816 | 0.7458 | 0.3534 | 0.7778 | 0.6742 | 0.7163 | | 0.083 | 41.84 | 15480 | 0.5681 | 0.6853 | 0.8023 | 0.8688 | 0.9312 | 0.8305 | 0.8626 | 0.4771 | 0.8814 | 0.8734 | 0.7595 | 0.8661 | 0.6839 | 0.7471 | 0.3324 | 0.7733 | 0.6750 | 0.7195 | | 0.133 | 41.89 | 15500 | 0.5777 | 0.6856 | 0.7990 | 0.8691 | 0.9364 | 0.8230 | 0.8427 | 0.4706 | 0.8831 | 0.8800 | 0.7569 | 0.8654 | 0.6831 | 0.7483 | 0.3344 | 0.7736 | 0.6757 | 0.7190 | | 0.2027 | 41.95 | 15520 | 0.5768 | 0.6863 | 0.8004 | 0.8686 | 0.9301 | 0.8058 | 0.8600 | 0.5054 | 0.8898 | 0.8587 | 0.7532 | 0.8669 | 0.6841 | 0.7468 | 0.3422 | 0.7715 | 0.6754 | 0.7173 | | 0.2747 | 42.0 | 15540 | 0.5677 | 0.6844 | 0.7915 | 0.8694 | 0.9351 | 0.7751 | 0.8569 | 0.4625 | 0.8912 | 0.8537 | 0.7657 | 0.8663 | 0.6772 | 0.7467 | 0.3274 | 0.7734 | 0.6753 | 0.7242 | | 0.1922 | 42.05 | 15560 | 0.5835 | 0.6852 | 0.7977 | 0.8685 | 0.9404 | 0.8020 | 0.8617 | 0.4933 | 0.8807 | 0.8569 | 0.7491 | 0.8654 | 0.6851 | 0.7424 | 0.3382 | 0.7725 | 0.6742 | 0.7185 | | 0.0675 | 42.11 | 15580 | 0.6323 | 0.6829 | 0.7929 | 0.8682 | 0.9452 | 0.8078 | 0.8576 | 0.4661 | 0.8825 | 0.8532 | 0.7377 | 0.8639 | 0.6849 | 0.7410 | 0.3311 | 0.7731 | 0.6738 | 0.7128 | | 0.344 | 42.16 | 15600 | 0.5709 | 0.6870 | 0.7964 | 0.8701 | 0.9394 | 0.7835 | 0.8659 | 0.4898 | 0.8870 | 0.8564 | 0.7524 | 0.8661 | 0.6786 | 0.7474 | 0.3460 | 0.7764 | 0.6774 | 0.7173 | | 0.125 | 42.22 | 15620 | 0.5475 | 0.6960 | 0.8002 | 0.8737 | 0.9331 | 0.7964 | 0.8468 | 0.5046 | 0.9077 | 0.8527 | 0.7598 | 0.8662 | 0.6806 | 0.7539 | 0.3933 | 0.7843 | 0.6768 | 0.7171 | | 0.1713 | 42.27 | 15640 | 0.5458 | 0.6953 | 0.8001 | 0.8743 | 0.9405 | 0.8079 | 0.8563 | 0.4827 | 0.8983 | 0.8567 | 0.7584 | 0.8664 | 0.6831 | 0.7485 | 0.3822 | 0.7861 | 0.6786 | 0.7221 | | 1.7043 | 42.32 | 15660 | 0.5768 | 0.6964 | 0.8020 | 0.8740 | 0.9388 | 0.8151 | 0.8449 | 0.4936 | 0.9001 | 0.8707 | 0.7506 | 0.8667 | 0.6813 | 0.7503 | 0.3968 | 0.7842 | 0.6772 | 0.7182 | | 0.2095 | 42.38 | 15680 | 0.5594 | 0.6969 | 0.8035 | 0.8747 | 0.9433 | 0.8076 | 0.8542 | 0.5005 | 0.8914 | 0.8655 | 0.7617 | 0.8656 | 0.6815 | 0.7481 | 0.3907 | 0.7876 | 0.6779 | 0.7273 | | 0.2733 | 42.43 | 15700 | 0.5576 | 0.6942 | 0.8026 | 0.8739 | 0.9435 | 0.8113 | 0.8656 | 0.4881 | 0.8876 | 0.8656 | 0.7568 | 0.8680 | 0.6844 | 0.7441 | 0.3746 | 0.7846 | 0.6772 | 0.7261 | | 0.1815 | 42.49 | 15720 | 0.5334 | 0.6928 | 0.8016 | 0.8730 | 0.9367 | 0.8150 | 0.8411 | 0.4963 | 0.8961 | 0.8553 | 0.7707 | 0.8689 | 0.6745 | 0.7472 | 0.3774 | 0.7808 | 0.6770 | 0.7239 | | 0.6893 | 42.54 | 15740 | 0.5713 | 0.6868 | 0.8000 | 0.8697 | 0.9306 | 0.7965 | 0.8702 | 0.4901 | 0.8868 | 0.8600 | 0.7657 | 0.8692 | 0.6693 | 0.7456 | 0.3598 | 0.7730 | 0.6764 | 0.7146 | | 0.2895 | 42.59 | 15760 | 0.5742 | 0.6908 | 0.8016 | 0.8712 | 0.9326 | 0.8033 | 0.8497 | 0.5158 | 0.8978 | 0.8543 | 0.7574 | 0.8693 | 0.6655 | 0.7480 | 0.3885 | 0.7770 | 0.6769 | 0.7103 | | 0.1651 | 42.65 | 15780 | 0.5621 | 0.6913 | 0.8010 | 0.8726 | 0.9350 | 0.7999 | 0.8561 | 0.5020 | 0.8951 | 0.8461 | 0.7726 | 0.8686 | 0.6651 | 0.7467 | 0.3770 | 0.7808 | 0.6783 | 0.7230 | | 0.449 | 42.7 | 15800 | 0.5858 | 0.6869 | 0.8018 | 0.8699 | 0.9356 | 0.8069 | 0.8563 | 0.5081 | 0.8846 | 0.8587 | 0.7623 | 0.8693 | 0.6651 | 0.7477 | 0.3575 | 0.7732 | 0.6769 | 0.7184 | | 0.1698 | 42.76 | 15820 | 0.5816 | 0.6848 | 0.7945 | 0.8693 | 0.9375 | 0.7910 | 0.8581 | 0.4779 | 0.8894 | 0.8547 | 0.7528 | 0.8690 | 0.6786 | 0.7444 | 0.3352 | 0.7715 | 0.6771 | 0.7180 | | 0.2006 | 42.81 | 15840 | 0.6001 | 0.6854 | 0.7968 | 0.8691 | 0.9409 | 0.8045 | 0.8469 | 0.4956 | 0.8878 | 0.8578 | 0.7444 | 0.8687 | 0.6803 | 0.7442 | 0.3423 | 0.7725 | 0.6735 | 0.7161 | | 0.1577 | 42.86 | 15860 | 0.5450 | 0.6840 | 0.7929 | 0.8694 | 0.9403 | 0.7646 | 0.8591 | 0.4852 | 0.8863 | 0.8611 | 0.7537 | 0.8685 | 0.6691 | 0.7435 | 0.3371 | 0.7739 | 0.6774 | 0.7185 | | 0.1936 | 42.92 | 15880 | 0.5545 | 0.6879 | 0.7945 | 0.8718 | 0.9361 | 0.7968 | 0.8605 | 0.4570 | 0.8976 | 0.8518 | 0.7614 | 0.8684 | 0.6781 | 0.7421 | 0.3501 | 0.7804 | 0.6758 | 0.7205 | | 0.187 | 42.97 | 15900 | 0.5604 | 0.6892 | 0.8003 | 0.8719 | 0.9387 | 0.8140 | 0.8597 | 0.4801 | 0.8886 | 0.8594 | 0.7619 | 0.8692 | 0.6803 | 0.7403 | 0.3501 | 0.7794 | 0.6754 | 0.7298 | | 0.1292 | 43.03 | 15920 | 0.5918 | 0.6899 | 0.7987 | 0.8723 | 0.9343 | 0.8061 | 0.8655 | 0.4822 | 0.9014 | 0.8520 | 0.7494 | 0.8705 | 0.6816 | 0.7414 | 0.3591 | 0.7811 | 0.6760 | 0.7198 | | 0.2654 | 43.08 | 15940 | 0.5506 | 0.6924 | 0.8040 | 0.8733 | 0.9316 | 0.8047 | 0.8668 | 0.5038 | 0.8963 | 0.8558 | 0.7690 | 0.8702 | 0.6782 | 0.7449 | 0.3624 | 0.7832 | 0.6761 | 0.7316 | | 0.1696 | 43.14 | 15960 | 0.5600 | 0.6910 | 0.8012 | 0.8727 | 0.9401 | 0.7865 | 0.8614 | 0.4967 | 0.8849 | 0.8721 | 0.7668 | 0.8702 | 0.6720 | 0.7463 | 0.3690 | 0.7820 | 0.6737 | 0.7236 | | 0.2917 | 43.19 | 15980 | 0.5430 | 0.6926 | 0.7988 | 0.8744 | 0.9379 | 0.7929 | 0.8628 | 0.4769 | 0.8968 | 0.8486 | 0.7756 | 0.8706 | 0.6744 | 0.7458 | 0.3646 | 0.7850 | 0.6774 | 0.7302 | | 0.0827 | 43.24 | 16000 | 0.5455 | 0.6914 | 0.7944 | 0.8734 | 0.9381 | 0.7788 | 0.8395 | 0.4787 | 0.9044 | 0.8579 | 0.7635 | 0.8699 | 0.6717 | 0.7451 | 0.3722 | 0.7834 | 0.6767 | 0.7209 | | 0.2404 | 43.3 | 16020 | 0.5516 | 0.6906 | 0.7971 | 0.8732 | 0.9376 | 0.7851 | 0.8611 | 0.4708 | 0.8933 | 0.8598 | 0.7722 | 0.8706 | 0.6717 | 0.7443 | 0.3645 | 0.7817 | 0.6772 | 0.7243 | | 0.1556 | 43.35 | 16040 | 0.5462 | 0.6925 | 0.8005 | 0.8740 | 0.9348 | 0.8013 | 0.8643 | 0.4726 | 0.8955 | 0.8622 | 0.7731 | 0.8710 | 0.6781 | 0.7439 | 0.3634 | 0.7834 | 0.6764 | 0.7314 | | 0.1845 | 43.41 | 16060 | 0.5758 | 0.6900 | 0.7977 | 0.8726 | 0.9373 | 0.8043 | 0.8549 | 0.4728 | 0.8985 | 0.8616 | 0.7542 | 0.8716 | 0.6811 | 0.7436 | 0.3533 | 0.7796 | 0.6770 | 0.7236 | | 0.0959 | 43.46 | 16080 | 0.5496 | 0.6879 | 0.7945 | 0.8715 | 0.9385 | 0.7898 | 0.8393 | 0.4796 | 0.8961 | 0.8472 | 0.7711 | 0.8710 | 0.6724 | 0.7427 | 0.3525 | 0.7764 | 0.6759 | 0.7241 | | 0.1834 | 43.51 | 16100 | 0.5518 | 0.6892 | 0.7975 | 0.8726 | 0.9407 | 0.7930 | 0.8585 | 0.4830 | 0.8892 | 0.8362 | 0.7816 | 0.8708 | 0.6710 | 0.7472 | 0.3442 | 0.7781 | 0.6783 | 0.7348 | | 0.2178 | 43.57 | 16120 | 0.5328 | 0.6921 | 0.8030 | 0.8734 | 0.9350 | 0.8053 | 0.8563 | 0.5076 | 0.8924 | 0.8315 | 0.7927 | 0.8709 | 0.6699 | 0.7490 | 0.3533 | 0.7788 | 0.6799 | 0.7427 | | 0.1988 | 43.62 | 16140 | 0.5387 | 0.6941 | 0.8093 | 0.8742 | 0.9337 | 0.8106 | 0.8586 | 0.5149 | 0.8813 | 0.8615 | 0.8042 | 0.8718 | 0.6710 | 0.7514 | 0.3541 | 0.7794 | 0.6751 | 0.7559 | | 0.3371 | 43.68 | 16160 | 0.5451 | 0.6948 | 0.8107 | 0.8745 | 0.9361 | 0.8125 | 0.8608 | 0.5162 | 0.8753 | 0.8655 | 0.8087 | 0.8707 | 0.6722 | 0.7527 | 0.3544 | 0.7810 | 0.6747 | 0.7576 | | 1.6466 | 43.73 | 16180 | 0.5541 | 0.6939 | 0.8030 | 0.8738 | 0.9353 | 0.8098 | 0.8611 | 0.4924 | 0.8941 | 0.8530 | 0.7751 | 0.8704 | 0.6773 | 0.7537 | 0.3695 | 0.7808 | 0.6745 | 0.7312 | | 0.2617 | 43.78 | 16200 | 0.5834 | 0.6951 | 0.8019 | 0.8739 | 0.9381 | 0.8178 | 0.8479 | 0.4975 | 0.9021 | 0.8580 | 0.7523 | 0.8700 | 0.6792 | 0.7536 | 0.3892 | 0.7828 | 0.6701 | 0.7209 | | 0.2101 | 43.84 | 16220 | 0.5582 | 0.6940 | 0.8025 | 0.8735 | 0.9336 | 0.8114 | 0.8511 | 0.4866 | 0.8968 | 0.8705 | 0.7675 | 0.8678 | 0.6763 | 0.7535 | 0.3827 | 0.7837 | 0.6701 | 0.7243 | | 0.248 | 43.89 | 16240 | 0.5753 | 0.6913 | 0.8026 | 0.8719 | 0.9363 | 0.8156 | 0.8578 | 0.4772 | 0.8858 | 0.8880 | 0.7575 | 0.8660 | 0.6777 | 0.7501 | 0.3793 | 0.7831 | 0.6637 | 0.7189 | | 0.0976 | 43.95 | 16260 | 0.5836 | 0.6927 | 0.7998 | 0.8728 | 0.9350 | 0.8089 | 0.8572 | 0.4870 | 0.9019 | 0.8582 | 0.7504 | 0.8679 | 0.6799 | 0.7492 | 0.3808 | 0.7828 | 0.6723 | 0.7159 | | 0.1359 | 44.0 | 16280 | 0.5986 | 0.6928 | 0.8023 | 0.8729 | 0.9355 | 0.8258 | 0.8575 | 0.4942 | 0.9022 | 0.8580 | 0.7430 | 0.8687 | 0.6795 | 0.7498 | 0.3827 | 0.7839 | 0.6713 | 0.7136 | | 0.218 | 44.05 | 16300 | 0.5782 | 0.6934 | 0.8002 | 0.8732 | 0.9368 | 0.8057 | 0.8505 | 0.4946 | 0.9012 | 0.8595 | 0.7528 | 0.8682 | 0.6810 | 0.7469 | 0.3838 | 0.7839 | 0.6711 | 0.7190 | | 0.0846 | 44.11 | 16320 | 0.5503 | 0.6929 | 0.8037 | 0.8732 | 0.9361 | 0.8267 | 0.8657 | 0.4897 | 0.8946 | 0.8532 | 0.7598 | 0.8685 | 0.6821 | 0.7442 | 0.3739 | 0.7838 | 0.6730 | 0.7250 | | 0.0841 | 44.16 | 16340 | 0.5649 | 0.6924 | 0.8015 | 0.8730 | 0.9346 | 0.8188 | 0.8560 | 0.4862 | 0.8994 | 0.8579 | 0.7576 | 0.8685 | 0.6774 | 0.7467 | 0.3787 | 0.7833 | 0.6699 | 0.7220 | | 0.138 | 44.22 | 16360 | 0.5352 | 0.6917 | 0.8020 | 0.8722 | 0.9318 | 0.7998 | 0.8597 | 0.5036 | 0.8948 | 0.8518 | 0.7726 | 0.8673 | 0.6706 | 0.7498 | 0.3805 | 0.7817 | 0.6724 | 0.7198 | | 0.2396 | 44.27 | 16380 | 0.5898 | 0.6893 | 0.7971 | 0.8713 | 0.9356 | 0.7989 | 0.8599 | 0.4779 | 0.8967 | 0.8606 | 0.7503 | 0.8678 | 0.6759 | 0.7459 | 0.3729 | 0.7798 | 0.6699 | 0.7128 | | 0.1535 | 44.32 | 16400 | 0.5528 | 0.6914 | 0.8003 | 0.8720 | 0.9319 | 0.8086 | 0.8525 | 0.4884 | 0.8988 | 0.8591 | 0.7626 | 0.8666 | 0.6793 | 0.7472 | 0.3750 | 0.7816 | 0.6698 | 0.7205 | | 0.1736 | 44.38 | 16420 | 0.5528 | 0.6919 | 0.7969 | 0.8735 | 0.9377 | 0.8168 | 0.8477 | 0.4540 | 0.9000 | 0.8561 | 0.7660 | 0.8655 | 0.6819 | 0.7432 | 0.3698 | 0.7864 | 0.6703 | 0.7263 | | 0.1992 | 44.43 | 16440 | 0.5871 | 0.6912 | 0.7981 | 0.8723 | 0.9371 | 0.8070 | 0.8657 | 0.4709 | 0.8975 | 0.8629 | 0.7452 | 0.8669 | 0.6839 | 0.7436 | 0.3786 | 0.7839 | 0.6676 | 0.7137 | | 0.2313 | 44.49 | 16460 | 0.5677 | 0.6935 | 0.8018 | 0.8732 | 0.9371 | 0.8135 | 0.8642 | 0.4868 | 0.8965 | 0.8653 | 0.7494 | 0.8665 | 0.6815 | 0.7453 | 0.3865 | 0.7861 | 0.6714 | 0.7173 | | 0.124 | 44.54 | 16480 | 0.6107 | 0.6914 | 0.8017 | 0.8711 | 0.9335 | 0.8205 | 0.8562 | 0.5112 | 0.9042 | 0.8585 | 0.7275 | 0.8683 | 0.6794 | 0.7500 | 0.3988 | 0.7815 | 0.6621 | 0.6997 | | 0.1359 | 44.59 | 16500 | 0.5790 | 0.6935 | 0.8042 | 0.8727 | 0.9342 | 0.8224 | 0.8697 | 0.5065 | 0.8998 | 0.8527 | 0.7441 | 0.8671 | 0.6789 | 0.7465 | 0.3922 | 0.7851 | 0.6728 | 0.7117 | | 0.2572 | 44.65 | 16520 | 0.5907 | 0.6919 | 0.8025 | 0.8724 | 0.9346 | 0.8322 | 0.8607 | 0.4982 | 0.9031 | 0.8458 | 0.7428 | 0.8675 | 0.6778 | 0.7443 | 0.3847 | 0.7843 | 0.6715 | 0.7128 | | 0.1618 | 44.7 | 16540 | 0.5507 | 0.6949 | 0.8049 | 0.8737 | 0.9316 | 0.8115 | 0.8589 | 0.5026 | 0.8969 | 0.8590 | 0.7734 | 0.8672 | 0.6752 | 0.7483 | 0.3889 | 0.7855 | 0.6723 | 0.7270 | | 0.1022 | 44.76 | 16560 | 0.5530 | 0.6934 | 0.8010 | 0.8735 | 0.9352 | 0.7987 | 0.8503 | 0.4847 | 0.8931 | 0.8648 | 0.7801 | 0.8665 | 0.6719 | 0.7473 | 0.3849 | 0.7856 | 0.6711 | 0.7265 | | 0.0975 | 44.81 | 16580 | 0.5464 | 0.6923 | 0.7998 | 0.8733 | 0.9321 | 0.8051 | 0.8521 | 0.4701 | 0.8961 | 0.8606 | 0.7827 | 0.8662 | 0.6718 | 0.7458 | 0.3799 | 0.7855 | 0.6720 | 0.7252 | | 0.1967 | 44.86 | 16600 | 0.5835 | 0.6893 | 0.8004 | 0.8704 | 0.9298 | 0.8013 | 0.8634 | 0.5021 | 0.8976 | 0.8577 | 0.7508 | 0.8662 | 0.6693 | 0.7460 | 0.3878 | 0.7799 | 0.6712 | 0.7049 | | 0.1643 | 44.92 | 16620 | 0.5923 | 0.6886 | 0.7946 | 0.8704 | 0.9351 | 0.7896 | 0.8547 | 0.4878 | 0.9003 | 0.8447 | 0.7501 | 0.8662 | 0.6708 | 0.7443 | 0.3847 | 0.7786 | 0.6743 | 0.7012 | | 0.2977 | 44.97 | 16640 | 0.5870 | 0.6887 | 0.7951 | 0.8709 | 0.9369 | 0.7886 | 0.8553 | 0.4781 | 0.8971 | 0.8644 | 0.7453 | 0.8680 | 0.6709 | 0.7468 | 0.3824 | 0.7792 | 0.6728 | 0.7007 | | 0.1541 | 45.03 | 16660 | 0.5820 | 0.6859 | 0.7942 | 0.8699 | 0.9342 | 0.7986 | 0.8597 | 0.4528 | 0.8924 | 0.8690 | 0.7524 | 0.8667 | 0.6706 | 0.7448 | 0.3690 | 0.7776 | 0.6711 | 0.7018 | | 0.1896 | 45.08 | 16680 | 0.5527 | 0.6892 | 0.7964 | 0.8718 | 0.9328 | 0.8019 | 0.8546 | 0.4620 | 0.8974 | 0.8574 | 0.7687 | 0.8670 | 0.6703 | 0.7458 | 0.3736 | 0.7821 | 0.6740 | 0.7117 | | 1.3552 | 45.14 | 16700 | 0.5711 | 0.6900 | 0.8013 | 0.8716 | 0.9352 | 0.8069 | 0.8606 | 0.4923 | 0.8914 | 0.8660 | 0.7568 | 0.8686 | 0.6672 | 0.7452 | 0.3851 | 0.7815 | 0.6740 | 0.7088 | | 0.2574 | 45.19 | 16720 | 0.5742 | 0.6898 | 0.8009 | 0.8712 | 0.9328 | 0.8003 | 0.8590 | 0.4953 | 0.8929 | 0.8698 | 0.7563 | 0.8690 | 0.6677 | 0.7465 | 0.3846 | 0.7797 | 0.6740 | 0.7068 | | 0.0881 | 45.24 | 16740 | 0.5674 | 0.6899 | 0.7971 | 0.8714 | 0.9339 | 0.7903 | 0.8497 | 0.4926 | 0.8995 | 0.8543 | 0.7595 | 0.8684 | 0.6678 | 0.7465 | 0.3848 | 0.7800 | 0.6756 | 0.7059 | | 0.1921 | 45.3 | 16760 | 0.5661 | 0.6893 | 0.7965 | 0.8721 | 0.9360 | 0.7912 | 0.8675 | 0.4678 | 0.8936 | 0.8550 | 0.7646 | 0.8683 | 0.6688 | 0.7459 | 0.3729 | 0.7818 | 0.6767 | 0.7106 | | 0.2151 | 45.35 | 16780 | 0.5675 | 0.6882 | 0.7980 | 0.8706 | 0.9294 | 0.8043 | 0.8658 | 0.4780 | 0.8980 | 0.8512 | 0.7594 | 0.8679 | 0.6692 | 0.7463 | 0.3759 | 0.7789 | 0.6771 | 0.7020 | | 0.2561 | 45.41 | 16800 | 0.5671 | 0.6863 | 0.7922 | 0.8707 | 0.9321 | 0.7896 | 0.8597 | 0.4527 | 0.9011 | 0.8521 | 0.7581 | 0.8678 | 0.6706 | 0.7433 | 0.3635 | 0.7798 | 0.6768 | 0.7022 | | 0.2134 | 45.46 | 16820 | 0.5914 | 0.6850 | 0.7933 | 0.8697 | 0.9293 | 0.7952 | 0.8649 | 0.4569 | 0.9003 | 0.8540 | 0.7524 | 0.8677 | 0.6685 | 0.7414 | 0.3652 | 0.7775 | 0.6756 | 0.6994 | | 0.1302 | 45.51 | 16840 | 0.5701 | 0.6869 | 0.7950 | 0.8701 | 0.9313 | 0.7960 | 0.8615 | 0.4770 | 0.8996 | 0.8421 | 0.7574 | 0.8668 | 0.6689 | 0.7441 | 0.3744 | 0.7788 | 0.6752 | 0.7002 | | 0.1511 | 45.57 | 16860 | 0.5676 | 0.6885 | 0.7989 | 0.8704 | 0.9319 | 0.7977 | 0.8592 | 0.4974 | 0.8958 | 0.8547 | 0.7556 | 0.8666 | 0.6675 | 0.7457 | 0.3834 | 0.7801 | 0.6762 | 0.7004 | | 0.107 | 45.62 | 16880 | 0.5839 | 0.6881 | 0.7957 | 0.8710 | 0.9380 | 0.7988 | 0.8588 | 0.4721 | 0.8950 | 0.8595 | 0.7477 | 0.8681 | 0.6709 | 0.7444 | 0.3747 | 0.7800 | 0.6769 | 0.7015 | | 0.1537 | 45.68 | 16900 | 0.5476 | 0.6871 | 0.7989 | 0.8701 | 0.9338 | 0.7988 | 0.8663 | 0.4889 | 0.8887 | 0.8529 | 0.7627 | 0.8672 | 0.6707 | 0.7451 | 0.3635 | 0.7779 | 0.6774 | 0.7080 | | 0.2827 | 45.73 | 16920 | 0.5664 | 0.6887 | 0.7985 | 0.8711 | 0.9355 | 0.8167 | 0.8571 | 0.4762 | 0.8957 | 0.8578 | 0.7505 | 0.8672 | 0.6798 | 0.7419 | 0.3660 | 0.7808 | 0.6775 | 0.7077 | | 0.1563 | 45.78 | 16940 | 0.5821 | 0.6871 | 0.7969 | 0.8700 | 0.9316 | 0.7993 | 0.8576 | 0.4792 | 0.8967 | 0.8643 | 0.7493 | 0.8675 | 0.6746 | 0.7431 | 0.3696 | 0.7782 | 0.6743 | 0.7022 | | 0.2185 | 45.84 | 16960 | 0.5599 | 0.6836 | 0.7929 | 0.8692 | 0.9370 | 0.7951 | 0.8652 | 0.4628 | 0.8910 | 0.8473 | 0.7516 | 0.8663 | 0.6747 | 0.7374 | 0.3487 | 0.7781 | 0.6768 | 0.7032 | | 0.222 | 45.89 | 16980 | 0.6002 | 0.6813 | 0.7913 | 0.8685 | 0.9413 | 0.7986 | 0.8563 | 0.4529 | 0.8878 | 0.8653 | 0.7372 | 0.8660 | 0.6771 | 0.7349 | 0.3389 | 0.7782 | 0.6722 | 0.7019 | | 0.1177 | 45.95 | 17000 | 0.5830 | 0.6806 | 0.7892 | 0.8682 | 0.9355 | 0.7860 | 0.8612 | 0.4440 | 0.8911 | 0.8532 | 0.7537 | 0.8660 | 0.6708 | 0.7367 | 0.3370 | 0.7763 | 0.6765 | 0.7010 | | 0.1475 | 46.0 | 17020 | 0.6041 | 0.6798 | 0.7864 | 0.8690 | 0.9365 | 0.7879 | 0.8659 | 0.4130 | 0.8942 | 0.8586 | 0.7485 | 0.8657 | 0.6742 | 0.7364 | 0.3253 | 0.7791 | 0.6752 | 0.7028 | | 0.1955 | 46.05 | 17040 | 0.5875 | 0.6802 | 0.7932 | 0.8670 | 0.9361 | 0.7959 | 0.8552 | 0.4665 | 0.8825 | 0.8654 | 0.7506 | 0.8647 | 0.6700 | 0.7418 | 0.3382 | 0.7731 | 0.6742 | 0.6997 | | 0.1266 | 46.11 | 17060 | 0.5758 | 0.6832 | 0.7978 | 0.8680 | 0.9279 | 0.8001 | 0.8576 | 0.4831 | 0.8856 | 0.8557 | 0.7744 | 0.8660 | 0.6712 | 0.7465 | 0.3394 | 0.7728 | 0.6729 | 0.7134 | | 0.1401 | 46.16 | 17080 | 0.5915 | 0.6821 | 0.7904 | 0.8681 | 0.9319 | 0.7868 | 0.8599 | 0.4571 | 0.8955 | 0.8477 | 0.7542 | 0.8658 | 0.6716 | 0.7430 | 0.3456 | 0.7749 | 0.6748 | 0.6988 | | 0.1667 | 46.22 | 17100 | 0.6235 | 0.6830 | 0.7912 | 0.8685 | 0.9305 | 0.7992 | 0.8465 | 0.4527 | 0.8998 | 0.8588 | 0.7507 | 0.8658 | 0.6710 | 0.7446 | 0.3528 | 0.7758 | 0.6744 | 0.6963 | | 0.3457 | 46.27 | 17120 | 0.5880 | 0.6838 | 0.7940 | 0.8684 | 0.9266 | 0.7901 | 0.8612 | 0.4706 | 0.8981 | 0.8572 | 0.7539 | 0.8662 | 0.6699 | 0.7492 | 0.3538 | 0.7748 | 0.6751 | 0.6976 | | 0.1532 | 46.32 | 17140 | 0.5769 | 0.6822 | 0.7960 | 0.8676 | 0.9343 | 0.8066 | 0.8563 | 0.4731 | 0.8835 | 0.8635 | 0.7543 | 0.8653 | 0.6705 | 0.7443 | 0.3464 | 0.7732 | 0.6757 | 0.6998 | | 0.0841 | 46.38 | 17160 | 0.5844 | 0.6853 | 0.7975 | 0.8691 | 0.9308 | 0.8124 | 0.8582 | 0.4786 | 0.8940 | 0.8537 | 0.7544 | 0.8657 | 0.6693 | 0.7440 | 0.3639 | 0.7776 | 0.6767 | 0.6997 | | 0.1693 | 46.43 | 17180 | 0.5783 | 0.6858 | 0.7952 | 0.8695 | 0.9338 | 0.8027 | 0.8494 | 0.4747 | 0.8957 | 0.8583 | 0.7518 | 0.8658 | 0.6697 | 0.7443 | 0.3679 | 0.7785 | 0.6771 | 0.6971 | | 0.301 | 46.49 | 17200 | 0.5805 | 0.6854 | 0.7922 | 0.8697 | 0.9351 | 0.8009 | 0.8429 | 0.4677 | 0.9017 | 0.8494 | 0.7479 | 0.8657 | 0.6696 | 0.7444 | 0.3678 | 0.7798 | 0.6750 | 0.6954 | | 0.2313 | 46.54 | 17220 | 0.6168 | 0.6833 | 0.7940 | 0.8685 | 0.9329 | 0.8082 | 0.8601 | 0.4705 | 0.8977 | 0.8502 | 0.7383 | 0.8659 | 0.6692 | 0.7442 | 0.3659 | 0.7786 | 0.6701 | 0.6893 | | 0.0797 | 46.59 | 17240 | 0.5888 | 0.6855 | 0.7917 | 0.8697 | 0.9337 | 0.7843 | 0.8561 | 0.4666 | 0.9007 | 0.8532 | 0.7472 | 0.8663 | 0.6702 | 0.7459 | 0.3682 | 0.7795 | 0.6745 | 0.6941 | | 0.1081 | 46.65 | 17260 | 0.5985 | 0.6837 | 0.7910 | 0.8697 | 0.9352 | 0.8008 | 0.8645 | 0.4397 | 0.8965 | 0.8499 | 0.7502 | 0.8659 | 0.6712 | 0.7410 | 0.3553 | 0.7799 | 0.6765 | 0.6965 | | 0.1764 | 46.7 | 17280 | 0.5906 | 0.6859 | 0.7944 | 0.8698 | 0.9319 | 0.7982 | 0.8652 | 0.4635 | 0.8976 | 0.8563 | 0.7484 | 0.8669 | 0.6721 | 0.7459 | 0.3668 | 0.7787 | 0.6742 | 0.6965 | | 0.2976 | 46.76 | 17300 | 0.5939 | 0.6864 | 0.7952 | 0.8698 | 0.9328 | 0.7964 | 0.8673 | 0.4718 | 0.8958 | 0.8514 | 0.7507 | 0.8668 | 0.6716 | 0.7455 | 0.3705 | 0.7785 | 0.6752 | 0.6967 | | 0.1649 | 46.81 | 17320 | 0.6012 | 0.6851 | 0.7915 | 0.8698 | 0.9349 | 0.8003 | 0.8531 | 0.4458 | 0.8986 | 0.8610 | 0.7467 | 0.8662 | 0.6728 | 0.7438 | 0.3637 | 0.7792 | 0.6741 | 0.6957 | | 0.3159 | 46.86 | 17340 | 0.5776 | 0.6861 | 0.7962 | 0.8695 | 0.9327 | 0.7969 | 0.8685 | 0.4762 | 0.8924 | 0.8533 | 0.7535 | 0.8661 | 0.6711 | 0.7428 | 0.3709 | 0.7787 | 0.6760 | 0.6975 | | 0.0889 | 46.92 | 17360 | 0.5593 | 0.6858 | 0.7952 | 0.8695 | 0.9327 | 0.7738 | 0.8665 | 0.4856 | 0.8908 | 0.8584 | 0.7587 | 0.8658 | 0.6671 | 0.7409 | 0.3702 | 0.7788 | 0.6769 | 0.7009 | | 0.1136 | 46.97 | 17380 | 0.5620 | 0.6860 | 0.7935 | 0.8697 | 0.9372 | 0.7844 | 0.8580 | 0.4767 | 0.8920 | 0.8514 | 0.7551 | 0.8647 | 0.6699 | 0.7409 | 0.3701 | 0.7796 | 0.6774 | 0.6998 | | 0.0898 | 47.03 | 17400 | 0.6127 | 0.6875 | 0.7960 | 0.8705 | 0.9371 | 0.8083 | 0.8603 | 0.4688 | 0.8956 | 0.8601 | 0.7417 | 0.8651 | 0.6765 | 0.7419 | 0.3717 | 0.7821 | 0.6742 | 0.7007 | | 0.2659 | 47.08 | 17420 | 0.5871 | 0.6894 | 0.7979 | 0.8712 | 0.9316 | 0.8010 | 0.8654 | 0.4770 | 0.8997 | 0.8614 | 0.7489 | 0.8665 | 0.6771 | 0.7450 | 0.3736 | 0.7817 | 0.6756 | 0.7062 | | 0.1993 | 47.14 | 17440 | 0.6110 | 0.6910 | 0.7991 | 0.8719 | 0.9334 | 0.8201 | 0.8481 | 0.4852 | 0.9056 | 0.8585 | 0.7429 | 0.8666 | 0.6801 | 0.7452 | 0.3807 | 0.7836 | 0.6729 | 0.7081 | | 0.6759 | 47.19 | 17460 | 0.6108 | 0.6898 | 0.7991 | 0.8715 | 0.9319 | 0.8170 | 0.8590 | 0.4770 | 0.9020 | 0.8624 | 0.7440 | 0.8666 | 0.6792 | 0.7455 | 0.3756 | 0.7828 | 0.6720 | 0.7072 | | 0.3493 | 47.24 | 17480 | 0.5807 | 0.6884 | 0.7973 | 0.8715 | 0.9325 | 0.8143 | 0.8716 | 0.4591 | 0.8995 | 0.8553 | 0.7490 | 0.8666 | 0.6794 | 0.7428 | 0.3630 | 0.7836 | 0.6768 | 0.7065 | | 1.1969 | 47.3 | 17500 | 0.5830 | 0.6894 | 0.8009 | 0.8716 | 0.9343 | 0.8273 | 0.8659 | 0.4762 | 0.8958 | 0.8589 | 0.7475 | 0.8660 | 0.6793 | 0.7426 | 0.3681 | 0.7841 | 0.6766 | 0.7088 | | 0.2925 | 47.35 | 17520 | 0.5777 | 0.6890 | 0.7957 | 0.8710 | 0.9350 | 0.7949 | 0.8508 | 0.4839 | 0.9005 | 0.8514 | 0.7531 | 0.8664 | 0.6765 | 0.7452 | 0.3728 | 0.7812 | 0.6753 | 0.7056 | | 0.1624 | 47.41 | 17540 | 0.5718 | 0.6887 | 0.7983 | 0.8709 | 0.9360 | 0.8097 | 0.8584 | 0.4825 | 0.8944 | 0.8563 | 0.7509 | 0.8659 | 0.6795 | 0.7429 | 0.3686 | 0.7816 | 0.6760 | 0.7063 | | 0.1431 | 47.46 | 17560 | 0.5648 | 0.6864 | 0.7947 | 0.8703 | 0.9367 | 0.8056 | 0.8620 | 0.4635 | 0.8951 | 0.8503 | 0.7495 | 0.8654 | 0.6778 | 0.7402 | 0.3609 | 0.7816 | 0.6755 | 0.7032 | | 0.3399 | 47.51 | 17580 | 0.5932 | 0.6888 | 0.7999 | 0.8708 | 0.9332 | 0.8139 | 0.8631 | 0.4861 | 0.8959 | 0.8598 | 0.7474 | 0.8665 | 0.6779 | 0.7434 | 0.3730 | 0.7818 | 0.6751 | 0.7040 | | 0.2807 | 47.57 | 17600 | 0.6147 | 0.6890 | 0.7964 | 0.8713 | 0.9327 | 0.8105 | 0.8611 | 0.4676 | 0.9040 | 0.8567 | 0.7421 | 0.8670 | 0.6795 | 0.7461 | 0.3726 | 0.7826 | 0.6718 | 0.7032 | | 0.1749 | 47.62 | 17620 | 0.5876 | 0.6866 | 0.7996 | 0.8696 | 0.9365 | 0.8126 | 0.8634 | 0.4794 | 0.8840 | 0.8724 | 0.7493 | 0.8651 | 0.6754 | 0.7383 | 0.3706 | 0.7801 | 0.6764 | 0.7002 | | 0.2923 | 47.68 | 17640 | 0.6136 | 0.6892 | 0.7968 | 0.8711 | 0.9332 | 0.8137 | 0.8570 | 0.4787 | 0.9054 | 0.8508 | 0.7390 | 0.8666 | 0.6781 | 0.7455 | 0.3797 | 0.7821 | 0.6700 | 0.7020 | | 0.11 | 47.73 | 17660 | 0.6215 | 0.6886 | 0.7977 | 0.8710 | 0.9334 | 0.8180 | 0.8694 | 0.4789 | 0.9041 | 0.8476 | 0.7327 | 0.8668 | 0.6787 | 0.7454 | 0.3778 | 0.7833 | 0.6690 | 0.6992 | | 0.1884 | 47.78 | 17680 | 0.5969 | 0.6886 | 0.7962 | 0.8707 | 0.9348 | 0.8053 | 0.8524 | 0.4755 | 0.8989 | 0.8604 | 0.7464 | 0.8665 | 0.6752 | 0.7465 | 0.3785 | 0.7804 | 0.6724 | 0.7005 | | 0.2251 | 47.84 | 17700 | 0.5911 | 0.6846 | 0.7895 | 0.8701 | 0.9340 | 0.8002 | 0.8500 | 0.4295 | 0.9015 | 0.8602 | 0.7509 | 0.8664 | 0.6723 | 0.7441 | 0.3568 | 0.7790 | 0.6746 | 0.6986 | | 0.2057 | 47.89 | 17720 | 0.5940 | 0.6863 | 0.7949 | 0.8699 | 0.9365 | 0.8072 | 0.8506 | 0.4683 | 0.8947 | 0.8569 | 0.7498 | 0.8658 | 0.6692 | 0.7432 | 0.3744 | 0.7794 | 0.6750 | 0.6972 | | 0.1724 | 47.95 | 17740 | 0.5831 | 0.6872 | 0.7950 | 0.8701 | 0.9323 | 0.8035 | 0.8605 | 0.4827 | 0.9031 | 0.8323 | 0.7508 | 0.8667 | 0.6696 | 0.7456 | 0.3780 | 0.7792 | 0.6732 | 0.6980 | | 0.1173 | 48.0 | 17760 | 0.6074 | 0.6874 | 0.7990 | 0.8698 | 0.9319 | 0.8008 | 0.8714 | 0.4907 | 0.8939 | 0.8605 | 0.7435 | 0.8673 | 0.6719 | 0.7454 | 0.3795 | 0.7793 | 0.6723 | 0.6961 | | 0.8592 | 48.05 | 17780 | 0.5757 | 0.6849 | 0.7921 | 0.8699 | 0.9348 | 0.8001 | 0.8577 | 0.4457 | 0.8958 | 0.8550 | 0.7555 | 0.8655 | 0.6690 | 0.7424 | 0.3621 | 0.7799 | 0.6757 | 0.6995 | | 0.1753 | 48.11 | 17800 | 0.5664 | 0.6867 | 0.7929 | 0.8704 | 0.9332 | 0.7859 | 0.8596 | 0.4656 | 0.8998 | 0.8509 | 0.7554 | 0.8666 | 0.6685 | 0.7448 | 0.3710 | 0.7806 | 0.6761 | 0.6991 | | 0.1293 | 48.16 | 17820 | 0.5801 | 0.6847 | 0.7894 | 0.8702 | 0.9362 | 0.7863 | 0.8680 | 0.4540 | 0.9021 | 0.8296 | 0.7498 | 0.8659 | 0.6696 | 0.7414 | 0.3637 | 0.7818 | 0.6726 | 0.6980 | | 0.1466 | 48.22 | 17840 | 0.6018 | 0.6865 | 0.7967 | 0.8699 | 0.9352 | 0.8069 | 0.8598 | 0.4755 | 0.8944 | 0.8620 | 0.7429 | 0.8661 | 0.6711 | 0.7429 | 0.3759 | 0.7812 | 0.6732 | 0.6951 | | 0.1305 | 48.27 | 17860 | 0.5926 | 0.6869 | 0.7944 | 0.8700 | 0.9330 | 0.7882 | 0.8580 | 0.4770 | 0.8985 | 0.8558 | 0.7502 | 0.8668 | 0.6682 | 0.7448 | 0.3776 | 0.7791 | 0.6749 | 0.6969 | | 0.252 | 48.32 | 17880 | 0.6012 | 0.6852 | 0.7916 | 0.8692 | 0.9317 | 0.7725 | 0.8558 | 0.4697 | 0.8987 | 0.8640 | 0.7490 | 0.8667 | 0.6671 | 0.7437 | 0.3736 | 0.7775 | 0.6720 | 0.6959 | | 0.176 | 48.38 | 17900 | 0.5816 | 0.6857 | 0.7896 | 0.8704 | 0.9382 | 0.7821 | 0.8497 | 0.4503 | 0.8985 | 0.8567 | 0.7516 | 0.8653 | 0.6686 | 0.7437 | 0.3667 | 0.7814 | 0.6756 | 0.6984 | | 0.1354 | 48.43 | 17920 | 0.5885 | 0.6866 | 0.7965 | 0.8698 | 0.9383 | 0.7939 | 0.8655 | 0.4795 | 0.8873 | 0.8632 | 0.7481 | 0.8653 | 0.6703 | 0.7420 | 0.3758 | 0.7805 | 0.6744 | 0.6981 | | 0.0773 | 48.49 | 17940 | 0.5890 | 0.6846 | 0.7979 | 0.8692 | 0.9380 | 0.8073 | 0.8778 | 0.4728 | 0.8817 | 0.8590 | 0.7486 | 0.8643 | 0.6719 | 0.7348 | 0.3646 | 0.7812 | 0.6761 | 0.6993 | | 0.1057 | 48.54 | 17960 | 0.5929 | 0.6855 | 0.7928 | 0.8703 | 0.9382 | 0.8082 | 0.8636 | 0.4427 | 0.8942 | 0.8564 | 0.7460 | 0.8652 | 0.6748 | 0.7406 | 0.3605 | 0.7815 | 0.6761 | 0.6998 | | 0.1428 | 48.59 | 17980 | 0.5724 | 0.6871 | 0.7995 | 0.8699 | 0.9311 | 0.8045 | 0.8725 | 0.4847 | 0.8916 | 0.8622 | 0.7498 | 0.8666 | 0.6723 | 0.7415 | 0.3745 | 0.7800 | 0.6748 | 0.6997 | | 0.1607 | 48.65 | 18000 | 0.5906 | 0.6874 | 0.7968 | 0.8705 | 0.9340 | 0.8172 | 0.8630 | 0.4680 | 0.8982 | 0.8524 | 0.7449 | 0.8655 | 0.6750 | 0.7424 | 0.3728 | 0.7821 | 0.6742 | 0.6998 | | 0.159 | 48.7 | 18020 | 0.6010 | 0.6882 | 0.8004 | 0.8701 | 0.9354 | 0.8119 | 0.8650 | 0.4949 | 0.8912 | 0.8634 | 0.7409 | 0.8658 | 0.6757 | 0.7414 | 0.3813 | 0.7810 | 0.6721 | 0.6999 | | 0.8972 | 48.76 | 18040 | 0.5830 | 0.6852 | 0.7904 | 0.8705 | 0.9348 | 0.8012 | 0.8621 | 0.4320 | 0.9007 | 0.8526 | 0.7492 | 0.8659 | 0.6747 | 0.7424 | 0.3570 | 0.7810 | 0.6744 | 0.7008 | | 0.1339 | 48.81 | 18060 | 0.5907 | 0.6863 | 0.7959 | 0.8700 | 0.9335 | 0.7957 | 0.8694 | 0.4691 | 0.8929 | 0.8591 | 0.7513 | 0.8662 | 0.6717 | 0.7408 | 0.3699 | 0.7802 | 0.6751 | 0.7005 | | 0.1247 | 48.86 | 18080 | 0.5731 | 0.6889 | 0.7992 | 0.8706 | 0.9324 | 0.8044 | 0.8637 | 0.4928 | 0.8965 | 0.8554 | 0.7496 | 0.8665 | 0.6725 | 0.7444 | 0.3816 | 0.7807 | 0.6754 | 0.7010 | | 0.2777 | 48.92 | 18100 | 0.5914 | 0.6881 | 0.7955 | 0.8706 | 0.9306 | 0.7950 | 0.8613 | 0.4777 | 0.9030 | 0.8501 | 0.7509 | 0.8671 | 0.6716 | 0.7448 | 0.3763 | 0.7797 | 0.6759 | 0.7013 | | 0.1174 | 48.97 | 18120 | 0.5918 | 0.6879 | 0.7934 | 0.8710 | 0.9356 | 0.8026 | 0.8500 | 0.4661 | 0.9041 | 0.8506 | 0.7451 | 0.8664 | 0.6750 | 0.7432 | 0.3734 | 0.7818 | 0.6741 | 0.7012 | | 0.1679 | 49.03 | 18140 | 0.6104 | 0.6886 | 0.7963 | 0.8708 | 0.9340 | 0.8055 | 0.8518 | 0.4844 | 0.9041 | 0.8542 | 0.7403 | 0.8664 | 0.6750 | 0.7451 | 0.3803 | 0.7825 | 0.6717 | 0.6993 | | 0.1887 | 49.08 | 18160 | 0.6064 | 0.6856 | 0.7934 | 0.8696 | 0.9332 | 0.7898 | 0.8612 | 0.4606 | 0.8952 | 0.8644 | 0.7493 | 0.8668 | 0.6719 | 0.7422 | 0.3693 | 0.7783 | 0.6729 | 0.6981 | | 0.2318 | 49.14 | 18180 | 0.5992 | 0.6876 | 0.7973 | 0.8701 | 0.9329 | 0.8117 | 0.8590 | 0.4754 | 0.8966 | 0.8567 | 0.7490 | 0.8664 | 0.6727 | 0.7442 | 0.3777 | 0.7794 | 0.6744 | 0.6986 | | 0.298 | 49.19 | 18200 | 0.5877 | 0.6846 | 0.7942 | 0.8695 | 0.9321 | 0.8075 | 0.8668 | 0.4455 | 0.8935 | 0.8625 | 0.7517 | 0.8663 | 0.6718 | 0.7405 | 0.3614 | 0.7788 | 0.6750 | 0.6985 | | 0.0678 | 49.24 | 18220 | 0.5772 | 0.6867 | 0.7934 | 0.8705 | 0.9371 | 0.8053 | 0.8553 | 0.4609 | 0.8981 | 0.8475 | 0.7496 | 0.8654 | 0.6719 | 0.7419 | 0.3718 | 0.7813 | 0.6760 | 0.6989 | | 0.156 | 49.3 | 18240 | 0.5871 | 0.6862 | 0.7925 | 0.8702 | 0.9372 | 0.8002 | 0.8573 | 0.4599 | 0.8979 | 0.8466 | 0.7483 | 0.8657 | 0.6720 | 0.7417 | 0.3707 | 0.7802 | 0.6745 | 0.6984 | | 0.1281 | 49.35 | 18260 | 0.5737 | 0.6857 | 0.7913 | 0.8702 | 0.9357 | 0.7840 | 0.8605 | 0.4548 | 0.8979 | 0.8570 | 0.7494 | 0.8660 | 0.6711 | 0.7417 | 0.3669 | 0.7811 | 0.6754 | 0.6980 | | 0.1969 | 49.41 | 18280 | 0.5778 | 0.6881 | 0.7983 | 0.8701 | 0.9323 | 0.7933 | 0.8588 | 0.4953 | 0.8948 | 0.8609 | 0.7525 | 0.8669 | 0.6693 | 0.7445 | 0.3819 | 0.7794 | 0.6754 | 0.6992 | | 0.6113 | 49.46 | 18300 | 0.5947 | 0.6868 | 0.7978 | 0.8696 | 0.9324 | 0.7983 | 0.8647 | 0.4854 | 0.8932 | 0.8637 | 0.7466 | 0.8669 | 0.6712 | 0.7427 | 0.3775 | 0.7788 | 0.6726 | 0.6976 | | 0.1182 | 49.51 | 18320 | 0.6038 | 0.6865 | 0.7949 | 0.8700 | 0.9366 | 0.8004 | 0.8610 | 0.4717 | 0.8948 | 0.8539 | 0.7455 | 0.8660 | 0.6720 | 0.7418 | 0.3743 | 0.7801 | 0.6738 | 0.6977 | | 0.2847 | 49.57 | 18340 | 0.5843 | 0.6879 | 0.7966 | 0.8701 | 0.9318 | 0.7919 | 0.8567 | 0.4863 | 0.8970 | 0.8583 | 0.7545 | 0.8663 | 0.6683 | 0.7453 | 0.3815 | 0.7793 | 0.6757 | 0.6989 | | 0.1221 | 49.62 | 18360 | 0.5991 | 0.6883 | 0.7973 | 0.8702 | 0.9357 | 0.8075 | 0.8487 | 0.4922 | 0.8977 | 0.8538 | 0.7456 | 0.8660 | 0.6707 | 0.7450 | 0.3860 | 0.7800 | 0.6730 | 0.6975 | | 0.3461 | 49.68 | 18380 | 0.6064 | 0.6875 | 0.7930 | 0.8702 | 0.9378 | 0.7899 | 0.8451 | 0.4777 | 0.8990 | 0.8562 | 0.7456 | 0.8659 | 0.6714 | 0.7432 | 0.3807 | 0.7798 | 0.6736 | 0.6977 | | 0.3113 | 49.73 | 18400 | 0.5789 | 0.6872 | 0.7957 | 0.8701 | 0.9312 | 0.8022 | 0.8621 | 0.4767 | 0.9000 | 0.8423 | 0.7555 | 0.8663 | 0.6698 | 0.7435 | 0.3764 | 0.7796 | 0.6755 | 0.6991 | | 0.143 | 49.78 | 18420 | 0.5968 | 0.6831 | 0.7882 | 0.8699 | 0.9359 | 0.7844 | 0.8689 | 0.4298 | 0.8975 | 0.8514 | 0.7497 | 0.8661 | 0.6701 | 0.7386 | 0.3526 | 0.7804 | 0.6758 | 0.6983 | | 0.1059 | 49.84 | 18440 | 0.6216 | 0.6848 | 0.7897 | 0.8704 | 0.9392 | 0.7951 | 0.8648 | 0.4421 | 0.9001 | 0.8480 | 0.7386 | 0.8656 | 0.6749 | 0.7390 | 0.3603 | 0.7832 | 0.6731 | 0.6977 | | 0.1687 | 49.89 | 18460 | 0.5976 | 0.6875 | 0.7969 | 0.8700 | 0.9327 | 0.8021 | 0.8562 | 0.4814 | 0.8967 | 0.8616 | 0.7479 | 0.8665 | 0.6708 | 0.7448 | 0.3803 | 0.7790 | 0.6739 | 0.6973 | | 0.1931 | 49.95 | 18480 | 0.6122 | 0.6876 | 0.7979 | 0.8699 | 0.9317 | 0.7972 | 0.8658 | 0.4922 | 0.8967 | 0.8553 | 0.7464 | 0.8671 | 0.6702 | 0.7440 | 0.3812 | 0.7790 | 0.6737 | 0.6980 | | 0.1159 | 50.0 | 18500 | 0.6091 | 0.6876 | 0.7945 | 0.8704 | 0.9332 | 0.7904 | 0.8591 | 0.4778 | 0.9017 | 0.8549 | 0.7443 | 0.8671 | 0.6713 | 0.7452 | 0.3782 | 0.7799 | 0.6736 | 0.6978 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]

peldrak/segformer-finetuned-coasts-final

# segformer-finetuned-coasts-final This model is a fine-tuned version of [peldrak/segformer-finetuned-coastalDataset](https://huggingface.co/peldrak/segformer-finetuned-coastalDataset) on the peldrak/coastal2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Mean Iou: 0.5765 - Mean Accuracy: 0.7934 - Overall Accuracy: 0.8942 - Accuracy Water: 0.9204 - Accuracy Whitewater: 0.6450 - Accuracy Sediment: 0.8936 - Accuracy Other Natural Terrain: 0.5526 - Accuracy Vegetation: 0.9077 - Accuracy Development: 0.8415 - Accuracy Unknown: nan - Iou Water: 0.8847 - Iou Whitewater: 0.4614 - Iou Sediment: 0.7695 - Iou Other Natural Terrain: 0.4632 - Iou Vegetation: 0.8233 - Iou Development: 0.6331 - Iou Unknown: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:| | 0.6692 | 0.01 | 20 | 0.4614 | 0.4642 | 0.6595 | 0.8193 | 0.8313 | 0.4104 | 0.7626 | 0.3112 | 0.9007 | 0.7410 | nan | 0.7682 | 0.3175 | 0.6041 | 0.2766 | 0.7352 | 0.5478 | 0.0 | | 0.6263 | 0.02 | 40 | 0.4235 | 0.4592 | 0.6471 | 0.8295 | 0.9136 | 0.3421 | 0.7720 | 0.2776 | 0.8233 | 0.7542 | nan | 0.8087 | 0.2990 | 0.6199 | 0.2471 | 0.7340 | 0.5055 | 0.0 | | 1.3017 | 0.04 | 60 | 0.4394 | 0.4653 | 0.6590 | 0.8330 | 0.8610 | 0.3290 | 0.9077 | 0.2547 | 0.8425 | 0.7591 | nan | 0.8012 | 0.2886 | 0.6560 | 0.2308 | 0.7409 | 0.5394 | 0.0 | | 0.4226 | 0.05 | 80 | 0.4617 | 0.4769 | 0.6540 | 0.8418 | 0.9305 | 0.4493 | 0.7371 | 0.2628 | 0.8645 | 0.6799 | nan | 0.8198 | 0.3649 | 0.6261 | 0.2443 | 0.7488 | 0.5347 | 0.0 | | 0.8408 | 0.06 | 100 | 0.4295 | 0.4505 | 0.6816 | 0.8087 | 0.8050 | 0.4237 | 0.8763 | 0.2882 | 0.8330 | 0.8634 | nan | 0.7757 | 0.3119 | 0.6338 | 0.2439 | 0.7017 | 0.4861 | 0.0 | | 1.474 | 0.07 | 120 | 0.4297 | 0.4708 | 0.6926 | 0.8290 | 0.8725 | 0.5252 | 0.8807 | 0.2439 | 0.8128 | 0.8206 | nan | 0.8183 | 0.3891 | 0.6635 | 0.2246 | 0.7283 | 0.4714 | 0.0 | | 0.5739 | 0.08 | 140 | 0.3918 | 0.4891 | 0.6964 | 0.8398 | 0.8682 | 0.4291 | 0.8989 | 0.3777 | 0.8434 | 0.7609 | nan | 0.8206 | 0.3611 | 0.6719 | 0.3271 | 0.7499 | 0.4930 | 0.0 | | 1.8433 | 0.1 | 160 | 0.4038 | 0.4801 | 0.7087 | 0.8276 | 0.8590 | 0.4685 | 0.9100 | 0.3518 | 0.7974 | 0.8656 | nan | 0.8192 | 0.3831 | 0.6661 | 0.3156 | 0.7184 | 0.4585 | 0.0 | | 0.3492 | 0.11 | 180 | 0.3186 | 0.5025 | 0.7143 | 0.8507 | 0.9192 | 0.5610 | 0.8231 | 0.3873 | 0.8404 | 0.7547 | nan | 0.8615 | 0.3949 | 0.7214 | 0.3523 | 0.7304 | 0.4573 | 0.0 | | 1.4639 | 0.12 | 200 | 0.4100 | 0.4907 | 0.7152 | 0.8368 | 0.8950 | 0.5659 | 0.8607 | 0.3800 | 0.8092 | 0.7800 | nan | 0.8302 | 0.4098 | 0.6733 | 0.3275 | 0.7245 | 0.4700 | 0.0 | | 0.4228 | 0.13 | 220 | 0.4499 | 0.4825 | 0.7167 | 0.8290 | 0.8528 | 0.6444 | 0.9074 | 0.3113 | 0.8265 | 0.7581 | nan | 0.8071 | 0.4126 | 0.6496 | 0.2843 | 0.7270 | 0.4967 | 0.0 | | 0.4334 | 0.15 | 240 | 0.3982 | 0.4789 | 0.7042 | 0.8384 | 0.8739 | 0.6612 | 0.8529 | 0.2549 | 0.8633 | 0.7188 | nan | 0.8264 | 0.3878 | 0.6766 | 0.2461 | 0.7383 | 0.4771 | 0.0 | | 0.2844 | 0.16 | 260 | 0.4778 | 0.4594 | 0.6958 | 0.8264 | 0.8630 | 0.6156 | 0.8865 | 0.2021 | 0.8209 | 0.7869 | nan | 0.8210 | 0.3735 | 0.6681 | 0.1918 | 0.7157 | 0.4459 | 0.0 | | 0.4044 | 0.17 | 280 | 0.4410 | 0.4585 | 0.6861 | 0.8289 | 0.8873 | 0.5803 | 0.8906 | 0.1865 | 0.8000 | 0.7719 | nan | 0.8319 | 0.3753 | 0.6804 | 0.1753 | 0.7113 | 0.4354 | 0.0 | | 0.3748 | 0.18 | 300 | 0.4839 | 0.4393 | 0.6829 | 0.7970 | 0.8379 | 0.5714 | 0.9223 | 0.2301 | 0.7465 | 0.7892 | nan | 0.7826 | 0.3613 | 0.5881 | 0.1983 | 0.6774 | 0.4673 | 0.0 | | 0.4755 | 0.19 | 320 | 0.3532 | 0.4889 | 0.7063 | 0.8552 | 0.8869 | 0.5985 | 0.8407 | 0.2399 | 0.8972 | 0.7746 | nan | 0.8519 | 0.3580 | 0.7004 | 0.2270 | 0.7688 | 0.5159 | 0.0 | | 0.1647 | 0.21 | 340 | 0.4263 | 0.4482 | 0.6750 | 0.8275 | 0.8739 | 0.4728 | 0.8689 | 0.1326 | 0.8123 | 0.8897 | nan | 0.8275 | 0.3375 | 0.6691 | 0.1279 | 0.7168 | 0.4589 | 0.0 | | 0.1324 | 0.22 | 360 | 0.3186 | 0.4722 | 0.7102 | 0.8414 | 0.9062 | 0.6599 | 0.8066 | 0.1877 | 0.8335 | 0.8674 | nan | 0.8473 | 0.3452 | 0.6860 | 0.1817 | 0.7490 | 0.4958 | 0.0 | | 1.2721 | 0.23 | 380 | 0.4527 | 0.4562 | 0.6524 | 0.8415 | 0.9224 | 0.5242 | 0.7128 | 0.0855 | 0.8813 | 0.7885 | nan | 0.8233 | 0.3347 | 0.6008 | 0.0845 | 0.7683 | 0.5818 | 0.0 | | 1.5569 | 0.24 | 400 | 0.3610 | 0.4686 | 0.7072 | 0.8468 | 0.8868 | 0.6755 | 0.8329 | 0.1835 | 0.8760 | 0.7884 | nan | 0.8391 | 0.3119 | 0.6641 | 0.1672 | 0.7827 | 0.5152 | 0.0 | | 0.3027 | 0.25 | 420 | 0.3637 | 0.4659 | 0.7499 | 0.8159 | 0.8104 | 0.7363 | 0.8482 | 0.4076 | 0.8415 | 0.8554 | nan | 0.7855 | 0.2747 | 0.7003 | 0.3160 | 0.7191 | 0.4658 | 0.0 | | 0.2337 | 0.27 | 440 | 0.3187 | 0.4821 | 0.7265 | 0.8479 | 0.9054 | 0.5871 | 0.8284 | 0.3716 | 0.8357 | 0.8309 | nan | 0.8486 | 0.2510 | 0.7033 | 0.3185 | 0.7569 | 0.4964 | 0.0 | | 0.9241 | 0.28 | 460 | 0.3566 | 0.4981 | 0.6725 | 0.8624 | 0.9099 | 0.4308 | 0.7761 | 0.3893 | 0.9400 | 0.5890 | nan | 0.8466 | 0.3307 | 0.6713 | 0.3407 | 0.7924 | 0.5048 | 0.0 | | 0.3494 | 0.29 | 480 | 0.3679 | 0.5070 | 0.7207 | 0.8460 | 0.8849 | 0.5082 | 0.7560 | 0.4808 | 0.8855 | 0.8088 | nan | 0.8284 | 0.3782 | 0.6844 | 0.4029 | 0.7387 | 0.5163 | 0.0 | | 0.1691 | 0.3 | 500 | 0.3436 | 0.5198 | 0.7262 | 0.8638 | 0.9274 | 0.5366 | 0.7800 | 0.4651 | 0.8808 | 0.7675 | nan | 0.8564 | 0.3869 | 0.6920 | 0.4037 | 0.7766 | 0.5232 | 0.0 | | 0.5782 | 0.32 | 520 | 0.4702 | 0.5067 | 0.7430 | 0.8398 | 0.8207 | 0.5963 | 0.8942 | 0.4582 | 0.8883 | 0.8005 | nan | 0.7941 | 0.3921 | 0.6395 | 0.4094 | 0.7838 | 0.5280 | 0.0 | | 0.7752 | 0.33 | 540 | 0.6535 | 0.4591 | 0.6686 | 0.7923 | 0.7106 | 0.5371 | 0.8396 | 0.3616 | 0.9534 | 0.6090 | nan | 0.6940 | 0.4164 | 0.5151 | 0.3090 | 0.7622 | 0.5171 | 0.0 | | 0.2089 | 0.34 | 560 | 0.3457 | 0.5121 | 0.7382 | 0.8549 | 0.8789 | 0.6058 | 0.8199 | 0.4140 | 0.8932 | 0.8171 | nan | 0.8497 | 0.3949 | 0.7054 | 0.3535 | 0.7516 | 0.5294 | 0.0 | | 0.1504 | 0.35 | 580 | 0.3299 | 0.5208 | 0.7438 | 0.8605 | 0.8912 | 0.6374 | 0.8511 | 0.3967 | 0.8831 | 0.8036 | nan | 0.8542 | 0.4207 | 0.7280 | 0.3507 | 0.7619 | 0.5301 | 0.0 | | 0.1052 | 0.36 | 600 | 0.3514 | 0.5118 | 0.7143 | 0.8557 | 0.9408 | 0.5685 | 0.8064 | 0.3304 | 0.8314 | 0.8086 | nan | 0.8346 | 0.4004 | 0.7211 | 0.3147 | 0.7433 | 0.5682 | 0.0 | | 0.3436 | 0.38 | 620 | 0.3174 | 0.5293 | 0.7225 | 0.8702 | 0.9170 | 0.5454 | 0.8581 | 0.3863 | 0.8901 | 0.7380 | nan | 0.8482 | 0.3866 | 0.7412 | 0.3614 | 0.7797 | 0.5882 | 0.0 | | 0.2951 | 0.39 | 640 | 0.3557 | 0.5297 | 0.7490 | 0.8605 | 0.8547 | 0.6049 | 0.8916 | 0.4654 | 0.9130 | 0.7643 | nan | 0.8272 | 0.4138 | 0.7145 | 0.3913 | 0.7777 | 0.5834 | 0.0 | | 0.3383 | 0.4 | 660 | 0.3802 | 0.5108 | 0.7373 | 0.8460 | 0.9167 | 0.5489 | 0.8977 | 0.4706 | 0.7794 | 0.8104 | nan | 0.8249 | 0.3975 | 0.7184 | 0.3596 | 0.7186 | 0.5569 | 0.0 | | 0.4272 | 0.41 | 680 | 0.2963 | 0.5330 | 0.7518 | 0.8669 | 0.9489 | 0.6026 | 0.7575 | 0.5282 | 0.8565 | 0.8174 | nan | 0.8580 | 0.4109 | 0.7019 | 0.4198 | 0.7806 | 0.5601 | 0.0 | | 0.3004 | 0.42 | 700 | 0.3287 | 0.5242 | 0.7068 | 0.8728 | 0.9485 | 0.4648 | 0.7569 | 0.4087 | 0.8993 | 0.7628 | nan | 0.8661 | 0.3948 | 0.6820 | 0.3687 | 0.7975 | 0.5607 | 0.0 | | 0.368 | 0.44 | 720 | 0.3581 | 0.5145 | 0.7082 | 0.8678 | 0.8976 | 0.4916 | 0.9031 | 0.3196 | 0.8945 | 0.7427 | nan | 0.8625 | 0.4132 | 0.6936 | 0.2926 | 0.7928 | 0.5464 | 0.0 | | 0.5273 | 0.45 | 740 | 0.3392 | 0.5172 | 0.7461 | 0.8659 | 0.8987 | 0.6086 | 0.8829 | 0.3754 | 0.8723 | 0.8384 | nan | 0.8710 | 0.3943 | 0.7320 | 0.3510 | 0.7704 | 0.5018 | 0.0 | | 0.8596 | 0.46 | 760 | 0.2994 | 0.5144 | 0.7075 | 0.8733 | 0.9402 | 0.5261 | 0.8305 | 0.2849 | 0.8849 | 0.7781 | nan | 0.8820 | 0.4074 | 0.7324 | 0.2753 | 0.7808 | 0.5227 | 0.0 | | 0.3141 | 0.47 | 780 | 0.2862 | 0.5220 | 0.7148 | 0.8762 | 0.9304 | 0.6100 | 0.8318 | 0.2835 | 0.9128 | 0.7206 | nan | 0.8776 | 0.4229 | 0.7512 | 0.2734 | 0.7838 | 0.5453 | 0.0 | | 0.4501 | 0.49 | 800 | 0.3087 | 0.5179 | 0.7518 | 0.8713 | 0.9077 | 0.7417 | 0.8486 | 0.3256 | 0.9016 | 0.7858 | nan | 0.8720 | 0.3803 | 0.7325 | 0.3019 | 0.7876 | 0.5509 | 0.0 | | 0.5333 | 0.5 | 820 | 0.3242 | 0.5222 | 0.7365 | 0.8736 | 0.9122 | 0.6724 | 0.8373 | 0.2926 | 0.9118 | 0.7930 | nan | 0.8753 | 0.4221 | 0.7275 | 0.2756 | 0.7903 | 0.5645 | 0.0 | | 0.4082 | 0.51 | 840 | 0.3147 | 0.5108 | 0.7301 | 0.8651 | 0.8951 | 0.6418 | 0.8918 | 0.2432 | 0.8836 | 0.8253 | nan | 0.8640 | 0.4205 | 0.7213 | 0.2295 | 0.7843 | 0.5561 | 0.0 | | 0.1804 | 0.52 | 860 | 0.3430 | 0.5104 | 0.7248 | 0.8642 | 0.8920 | 0.6020 | 0.8910 | 0.2571 | 0.8860 | 0.8208 | nan | 0.8544 | 0.4244 | 0.7103 | 0.2378 | 0.7834 | 0.5628 | 0.0 | | 0.2636 | 0.53 | 880 | 0.2953 | 0.5190 | 0.7434 | 0.8647 | 0.9241 | 0.6135 | 0.8433 | 0.3692 | 0.8507 | 0.8594 | nan | 0.8685 | 0.4108 | 0.7447 | 0.3300 | 0.7575 | 0.5215 | 0.0 | | 0.9171 | 0.55 | 900 | 0.2782 | 0.5373 | 0.7328 | 0.8752 | 0.9118 | 0.5616 | 0.8318 | 0.4369 | 0.9192 | 0.7355 | nan | 0.8710 | 0.4169 | 0.7506 | 0.3880 | 0.7798 | 0.5546 | 0.0 | | 0.1809 | 0.56 | 920 | 0.2736 | 0.5392 | 0.7376 | 0.8749 | 0.9108 | 0.5501 | 0.8474 | 0.4279 | 0.9061 | 0.7835 | nan | 0.8691 | 0.4246 | 0.7473 | 0.3769 | 0.7837 | 0.5728 | 0.0 | | 0.3095 | 0.57 | 940 | 0.2762 | 0.5405 | 0.7460 | 0.8771 | 0.9084 | 0.5879 | 0.8590 | 0.4068 | 0.9076 | 0.8066 | nan | 0.8716 | 0.4199 | 0.7538 | 0.3667 | 0.7874 | 0.5842 | 0.0 | | 0.6095 | 0.58 | 960 | 0.2831 | 0.5220 | 0.7405 | 0.8655 | 0.9084 | 0.6134 | 0.8646 | 0.3468 | 0.8689 | 0.8412 | nan | 0.8645 | 0.4026 | 0.7262 | 0.3220 | 0.7696 | 0.5695 | 0.0 | | 0.6369 | 0.59 | 980 | 0.3259 | 0.5190 | 0.7511 | 0.8612 | 0.8955 | 0.6528 | 0.9060 | 0.3588 | 0.8511 | 0.8425 | nan | 0.8545 | 0.4057 | 0.7171 | 0.3390 | 0.7648 | 0.5522 | 0.0 | | 1.711 | 0.61 | 1000 | 0.3439 | 0.5268 | 0.7580 | 0.8615 | 0.8617 | 0.6757 | 0.8944 | 0.4164 | 0.9026 | 0.7975 | nan | 0.8434 | 0.4338 | 0.7032 | 0.3844 | 0.7818 | 0.5411 | 0.0 | | 0.2443 | 0.62 | 1020 | 0.2789 | 0.5429 | 0.7415 | 0.8809 | 0.8993 | 0.6534 | 0.8807 | 0.4180 | 0.9425 | 0.6553 | nan | 0.8714 | 0.4214 | 0.7501 | 0.3893 | 0.8031 | 0.5651 | 0.0 | | 0.1833 | 0.63 | 1040 | 0.3325 | 0.5356 | 0.7394 | 0.8787 | 0.9275 | 0.6594 | 0.8686 | 0.3051 | 0.8942 | 0.7819 | nan | 0.8675 | 0.4338 | 0.7404 | 0.2979 | 0.7905 | 0.6189 | 0.0 | | 0.0592 | 0.64 | 1060 | 0.3044 | 0.5287 | 0.7271 | 0.8714 | 0.9078 | 0.6267 | 0.8723 | 0.2993 | 0.9020 | 0.7541 | nan | 0.8564 | 0.4381 | 0.7202 | 0.2867 | 0.7899 | 0.6094 | 0.0 | | 0.5377 | 0.65 | 1080 | 0.2896 | 0.5328 | 0.7307 | 0.8780 | 0.9091 | 0.5475 | 0.8544 | 0.3355 | 0.9168 | 0.8209 | nan | 0.8693 | 0.4136 | 0.7217 | 0.3155 | 0.8081 | 0.6012 | 0.0 | | 0.25 | 0.67 | 1100 | 0.3252 | 0.5161 | 0.7578 | 0.8583 | 0.8770 | 0.6339 | 0.9036 | 0.3850 | 0.8583 | 0.8890 | nan | 0.8596 | 0.4195 | 0.7235 | 0.3470 | 0.7676 | 0.4955 | 0.0 | | 0.3291 | 0.68 | 1120 | 0.3295 | 0.5100 | 0.7740 | 0.8550 | 0.8794 | 0.6870 | 0.9169 | 0.4707 | 0.8352 | 0.8547 | nan | 0.8526 | 0.3684 | 0.7032 | 0.3674 | 0.7645 | 0.5141 | 0.0 | | 0.1965 | 0.69 | 1140 | 0.2703 | 0.5336 | 0.7583 | 0.8752 | 0.9154 | 0.6437 | 0.8906 | 0.4315 | 0.8769 | 0.7919 | nan | 0.8785 | 0.4010 | 0.7532 | 0.3803 | 0.7833 | 0.5385 | 0.0 | | 0.129 | 0.7 | 1160 | 0.2486 | 0.5496 | 0.7453 | 0.8864 | 0.9272 | 0.5909 | 0.8937 | 0.4061 | 0.9048 | 0.7493 | nan | 0.8838 | 0.4231 | 0.7696 | 0.3737 | 0.7984 | 0.5990 | 0.0 | | 0.5261 | 0.72 | 1180 | 0.2643 | 0.5498 | 0.7565 | 0.8759 | 0.9306 | 0.6428 | 0.8678 | 0.4760 | 0.8724 | 0.7493 | nan | 0.8654 | 0.4343 | 0.7636 | 0.4222 | 0.7719 | 0.5914 | 0.0 | | 0.1468 | 0.73 | 1200 | 0.2658 | 0.5414 | 0.7664 | 0.8706 | 0.9212 | 0.6083 | 0.9012 | 0.5039 | 0.8395 | 0.8244 | nan | 0.8717 | 0.4338 | 0.7665 | 0.4223 | 0.7572 | 0.5382 | 0.0 | | 1.0941 | 0.74 | 1220 | 0.2715 | 0.5423 | 0.7611 | 0.8718 | 0.9043 | 0.5967 | 0.8793 | 0.4764 | 0.8766 | 0.8333 | nan | 0.8692 | 0.4257 | 0.7705 | 0.4168 | 0.7701 | 0.5438 | 0.0 | | 0.418 | 0.75 | 1240 | 0.2988 | 0.5385 | 0.7575 | 0.8631 | 0.8481 | 0.6181 | 0.8973 | 0.4993 | 0.9246 | 0.7573 | nan | 0.8263 | 0.4090 | 0.6882 | 0.4314 | 0.8081 | 0.6062 | 0.0 | | 0.173 | 0.76 | 1260 | 0.2824 | 0.5602 | 0.7812 | 0.8816 | 0.8983 | 0.6148 | 0.9039 | 0.5562 | 0.8982 | 0.8157 | nan | 0.8658 | 0.4415 | 0.7486 | 0.4542 | 0.8029 | 0.6083 | 0.0 | | 0.2501 | 0.78 | 1280 | 0.2893 | 0.5345 | 0.7801 | 0.8579 | 0.8856 | 0.6174 | 0.8920 | 0.5393 | 0.8337 | 0.9126 | nan | 0.8537 | 0.4336 | 0.7198 | 0.4689 | 0.7618 | 0.5034 | 0.0 | | 0.2213 | 0.79 | 1300 | 0.2579 | 0.5536 | 0.7501 | 0.8840 | 0.9223 | 0.5794 | 0.8305 | 0.4354 | 0.9198 | 0.8130 | nan | 0.8825 | 0.4336 | 0.7614 | 0.4142 | 0.7924 | 0.5915 | 0.0 | | 0.6845 | 0.8 | 1320 | 0.2766 | 0.5574 | 0.7593 | 0.8816 | 0.9103 | 0.5735 | 0.8930 | 0.4722 | 0.8971 | 0.8099 | nan | 0.8684 | 0.4448 | 0.7728 | 0.4200 | 0.7871 | 0.6083 | 0.0 | | 0.3139 | 0.81 | 1340 | 0.2591 | 0.5600 | 0.7617 | 0.8861 | 0.9095 | 0.5474 | 0.9057 | 0.4920 | 0.9052 | 0.8103 | nan | 0.8801 | 0.4356 | 0.7602 | 0.4273 | 0.8075 | 0.6096 | 0.0 | | 0.2207 | 0.82 | 1360 | 0.3023 | 0.5519 | 0.7398 | 0.8842 | 0.9065 | 0.4921 | 0.8773 | 0.4597 | 0.9261 | 0.7774 | nan | 0.8717 | 0.4113 | 0.7559 | 0.4242 | 0.8022 | 0.5979 | 0.0 | | 0.055 | 0.84 | 1380 | 0.2899 | 0.5583 | 0.7640 | 0.8801 | 0.9062 | 0.6091 | 0.8915 | 0.4858 | 0.8992 | 0.7923 | nan | 0.8653 | 0.4651 | 0.7603 | 0.4424 | 0.7933 | 0.5815 | 0.0 | | 0.1648 | 0.85 | 1400 | 0.3026 | 0.5493 | 0.7529 | 0.8830 | 0.9021 | 0.6004 | 0.9090 | 0.4134 | 0.9147 | 0.7780 | nan | 0.8722 | 0.4553 | 0.7478 | 0.3737 | 0.8087 | 0.5877 | 0.0 | | 0.1717 | 0.86 | 1420 | 0.2719 | 0.5577 | 0.7945 | 0.8802 | 0.9051 | 0.6636 | 0.8980 | 0.5765 | 0.8797 | 0.8441 | nan | 0.8782 | 0.4527 | 0.7591 | 0.4554 | 0.7931 | 0.5656 | 0.0 | | 1.2901 | 0.87 | 1440 | 0.2583 | 0.5666 | 0.7906 | 0.8837 | 0.9008 | 0.6385 | 0.8771 | 0.5761 | 0.9061 | 0.8453 | nan | 0.8801 | 0.4551 | 0.7863 | 0.4752 | 0.7929 | 0.5766 | 0.0 | | 0.3958 | 0.89 | 1460 | 0.2567 | 0.5636 | 0.7722 | 0.8856 | 0.9209 | 0.5674 | 0.8749 | 0.5567 | 0.8950 | 0.8186 | nan | 0.8795 | 0.4382 | 0.7648 | 0.4645 | 0.8065 | 0.5913 | 0.0 | | 0.132 | 0.9 | 1480 | 0.3065 | 0.5529 | 0.7824 | 0.8769 | 0.8965 | 0.6280 | 0.8995 | 0.5739 | 0.8871 | 0.8096 | nan | 0.8637 | 0.4402 | 0.7403 | 0.4606 | 0.8000 | 0.5655 | 0.0 | | 0.1086 | 0.91 | 1500 | 0.3508 | 0.5322 | 0.7658 | 0.8560 | 0.8395 | 0.6086 | 0.9102 | 0.5617 | 0.9003 | 0.7743 | nan | 0.8166 | 0.4316 | 0.6703 | 0.4462 | 0.7977 | 0.5629 | 0.0 | | 0.1127 | 0.92 | 1520 | 0.3589 | 0.5310 | 0.7720 | 0.8529 | 0.8399 | 0.6515 | 0.9186 | 0.5537 | 0.8843 | 0.7840 | nan | 0.8100 | 0.4318 | 0.6527 | 0.4291 | 0.7970 | 0.5963 | 0.0 | | 0.3807 | 0.93 | 1540 | 0.2715 | 0.5554 | 0.7927 | 0.8792 | 0.8975 | 0.6704 | 0.8713 | 0.5857 | 0.8995 | 0.8319 | nan | 0.8742 | 0.4380 | 0.7658 | 0.4537 | 0.7898 | 0.5663 | 0.0 | | 0.3847 | 0.95 | 1560 | 0.2537 | 0.5589 | 0.7988 | 0.8847 | 0.9184 | 0.6811 | 0.8729 | 0.5827 | 0.8849 | 0.8530 | nan | 0.8863 | 0.4253 | 0.7846 | 0.4582 | 0.7933 | 0.5645 | 0.0 | | 0.2076 | 0.96 | 1580 | 0.2669 | 0.5517 | 0.7704 | 0.8865 | 0.9249 | 0.6891 | 0.8583 | 0.4244 | 0.9084 | 0.8171 | nan | 0.8811 | 0.4304 | 0.7516 | 0.3920 | 0.8123 | 0.5943 | 0.0 | | 0.1031 | 0.97 | 1600 | 0.2806 | 0.5474 | 0.7524 | 0.8853 | 0.9163 | 0.6332 | 0.8666 | 0.3833 | 0.9215 | 0.7932 | nan | 0.8782 | 0.4432 | 0.7448 | 0.3527 | 0.8112 | 0.6020 | 0.0 | | 0.2727 | 0.98 | 1620 | 0.3023 | 0.5370 | 0.7711 | 0.8710 | 0.8763 | 0.6717 | 0.9048 | 0.4370 | 0.8998 | 0.8370 | nan | 0.8557 | 0.4296 | 0.7258 | 0.3803 | 0.7962 | 0.5714 | 0.0 | | 0.2601 | 0.99 | 1640 | 0.3792 | 0.5187 | 0.7649 | 0.8554 | 0.8331 | 0.7026 | 0.9221 | 0.4155 | 0.9061 | 0.8097 | nan | 0.8181 | 0.3941 | 0.6737 | 0.3592 | 0.7989 | 0.5868 | 0.0 | | 0.239 | 1.01 | 1660 | 0.3111 | 0.5377 | 0.7876 | 0.8674 | 0.8753 | 0.6883 | 0.9208 | 0.5076 | 0.8718 | 0.8621 | nan | 0.8552 | 0.4240 | 0.7277 | 0.4169 | 0.7868 | 0.5533 | 0.0 | | 0.2939 | 1.02 | 1680 | 0.2581 | 0.5698 | 0.7874 | 0.8910 | 0.9218 | 0.6397 | 0.9075 | 0.5464 | 0.8947 | 0.8144 | nan | 0.8834 | 0.4488 | 0.7785 | 0.4475 | 0.8142 | 0.6161 | 0.0 | | 0.1292 | 1.03 | 1700 | 0.2495 | 0.5694 | 0.7724 | 0.8886 | 0.9291 | 0.6174 | 0.8777 | 0.5010 | 0.8964 | 0.8129 | nan | 0.8768 | 0.4587 | 0.7841 | 0.4434 | 0.8067 | 0.6160 | 0.0 | | 0.4733 | 1.04 | 1720 | 0.2527 | 0.5662 | 0.7743 | 0.8881 | 0.9246 | 0.6255 | 0.8860 | 0.4842 | 0.8958 | 0.8297 | nan | 0.8793 | 0.4605 | 0.7759 | 0.4335 | 0.8041 | 0.6104 | 0.0 | | 0.1835 | 1.06 | 1740 | 0.2607 | 0.5558 | 0.7705 | 0.8842 | 0.8955 | 0.6777 | 0.8876 | 0.4263 | 0.9280 | 0.8080 | nan | 0.8712 | 0.4401 | 0.7581 | 0.4051 | 0.8096 | 0.6061 | 0.0 | | 0.4408 | 1.07 | 1760 | 0.2765 | 0.5536 | 0.7802 | 0.8793 | 0.8830 | 0.6812 | 0.8959 | 0.4971 | 0.9197 | 0.8046 | nan | 0.8569 | 0.4205 | 0.7288 | 0.4439 | 0.8175 | 0.6073 | 0.0 | | 0.1692 | 1.08 | 1780 | 0.2987 | 0.5517 | 0.7740 | 0.8819 | 0.8844 | 0.6921 | 0.9004 | 0.4412 | 0.9298 | 0.7961 | nan | 0.8635 | 0.4247 | 0.7442 | 0.4119 | 0.8119 | 0.6054 | 0.0 | | 0.0829 | 1.09 | 1800 | 0.2804 | 0.5394 | 0.7624 | 0.8797 | 0.9094 | 0.7067 | 0.8563 | 0.3608 | 0.9121 | 0.8289 | nan | 0.8706 | 0.4162 | 0.7470 | 0.3457 | 0.8019 | 0.5943 | 0.0 | | 0.6807 | 1.1 | 1820 | 0.3080 | 0.5373 | 0.7638 | 0.8729 | 0.8821 | 0.6748 | 0.9134 | 0.4184 | 0.9030 | 0.7915 | nan | 0.8532 | 0.4092 | 0.7250 | 0.3750 | 0.7992 | 0.5993 | 0.0 | | 0.2518 | 1.12 | 1840 | 0.2835 | 0.5332 | 0.7744 | 0.8669 | 0.8880 | 0.6827 | 0.9181 | 0.4495 | 0.8632 | 0.8447 | nan | 0.8575 | 0.4012 | 0.7213 | 0.3993 | 0.7805 | 0.5729 | 0.0 | | 0.7864 | 1.13 | 1860 | 0.2637 | 0.5532 | 0.7737 | 0.8879 | 0.9086 | 0.6829 | 0.9120 | 0.4406 | 0.9137 | 0.7842 | nan | 0.8832 | 0.4071 | 0.7702 | 0.3902 | 0.8153 | 0.6065 | 0.0 | | 0.0924 | 1.14 | 1880 | 0.2864 | 0.5621 | 0.7705 | 0.8949 | 0.9200 | 0.6569 | 0.8994 | 0.4515 | 0.9284 | 0.7665 | nan | 0.8908 | 0.4259 | 0.7806 | 0.4054 | 0.8209 | 0.6114 | 0.0 | | 0.1686 | 1.15 | 1900 | 0.2654 | 0.5637 | 0.7854 | 0.8912 | 0.9187 | 0.6555 | 0.8953 | 0.4942 | 0.9044 | 0.8445 | nan | 0.8892 | 0.4345 | 0.7787 | 0.4371 | 0.8110 | 0.5950 | 0.0 | | 0.1225 | 1.16 | 1920 | 0.2667 | 0.5587 | 0.7855 | 0.8852 | 0.9104 | 0.6479 | 0.9008 | 0.5066 | 0.8918 | 0.8552 | nan | 0.8832 | 0.4434 | 0.7668 | 0.4407 | 0.8031 | 0.5738 | 0.0 | | 0.1435 | 1.18 | 1940 | 0.2704 | 0.5619 | 0.7807 | 0.8860 | 0.8959 | 0.6520 | 0.8953 | 0.5080 | 0.9226 | 0.8107 | nan | 0.8726 | 0.4417 | 0.7566 | 0.4386 | 0.8158 | 0.6082 | 0.0 | | 0.2853 | 1.19 | 1960 | 0.2792 | 0.5523 | 0.7959 | 0.8822 | 0.9074 | 0.7239 | 0.8853 | 0.5060 | 0.8900 | 0.8628 | nan | 0.8801 | 0.4154 | 0.7708 | 0.4392 | 0.7978 | 0.5625 | 0.0 | | 0.1169 | 1.2 | 1980 | 0.2879 | 0.5629 | 0.7787 | 0.8900 | 0.9085 | 0.6766 | 0.8683 | 0.4738 | 0.9324 | 0.8124 | nan | 0.8797 | 0.4266 | 0.7629 | 0.4292 | 0.8164 | 0.6251 | 0.0 | | 0.1685 | 1.21 | 2000 | 0.2960 | 0.5645 | 0.7948 | 0.8899 | 0.9075 | 0.6827 | 0.8744 | 0.5332 | 0.9189 | 0.8520 | nan | 0.8793 | 0.4256 | 0.7577 | 0.4546 | 0.8224 | 0.6117 | 0.0 | | 0.7038 | 1.22 | 2020 | 0.2923 | 0.5641 | 0.7853 | 0.8874 | 0.8949 | 0.6525 | 0.8828 | 0.5251 | 0.9296 | 0.8266 | nan | 0.8714 | 0.4343 | 0.7489 | 0.4541 | 0.8219 | 0.6178 | 0.0 | | 0.6504 | 1.24 | 2040 | 0.2727 | 0.5656 | 0.7799 | 0.8906 | 0.9181 | 0.6237 | 0.9048 | 0.5272 | 0.9039 | 0.8017 | nan | 0.8771 | 0.4327 | 0.7616 | 0.4410 | 0.8245 | 0.6224 | 0.0 | | 0.3215 | 1.25 | 2060 | 0.2697 | 0.5692 | 0.7798 | 0.8921 | 0.9109 | 0.6537 | 0.9027 | 0.4922 | 0.9214 | 0.7980 | nan | 0.8794 | 0.4591 | 0.7621 | 0.4293 | 0.8272 | 0.6271 | 0.0 | | 0.7559 | 1.26 | 2080 | 0.2841 | 0.5703 | 0.7909 | 0.8938 | 0.9121 | 0.6957 | 0.8969 | 0.4812 | 0.9210 | 0.8386 | nan | 0.8861 | 0.4526 | 0.7802 | 0.4258 | 0.8202 | 0.6275 | 0.0 | | 0.0888 | 1.27 | 2100 | 0.2607 | 0.5670 | 0.7677 | 0.8935 | 0.9342 | 0.6483 | 0.8855 | 0.4075 | 0.9070 | 0.8234 | nan | 0.8861 | 0.4785 | 0.7798 | 0.3772 | 0.8168 | 0.6305 | 0.0 | | 0.4145 | 1.29 | 2120 | 0.2646 | 0.5615 | 0.7702 | 0.8867 | 0.9221 | 0.6530 | 0.8885 | 0.4237 | 0.8969 | 0.8367 | nan | 0.8752 | 0.4703 | 0.7690 | 0.3822 | 0.8086 | 0.6253 | 0.0 | | 0.059 | 1.3 | 2140 | 0.2673 | 0.5619 | 0.7662 | 0.8893 | 0.9142 | 0.6207 | 0.9007 | 0.4401 | 0.9140 | 0.8076 | nan | 0.8776 | 0.4666 | 0.7564 | 0.3822 | 0.8222 | 0.6286 | 0.0 | | 0.2973 | 1.31 | 2160 | 0.2817 | 0.5660 | 0.7736 | 0.8912 | 0.9093 | 0.6434 | 0.8961 | 0.4758 | 0.9269 | 0.7901 | nan | 0.8798 | 0.4608 | 0.7590 | 0.4140 | 0.8216 | 0.6265 | 0.0 | | 0.4088 | 1.32 | 2180 | 0.2859 | 0.5479 | 0.7895 | 0.8790 | 0.9071 | 0.6934 | 0.9058 | 0.4804 | 0.8722 | 0.8782 | nan | 0.8749 | 0.4309 | 0.7583 | 0.4013 | 0.8000 | 0.5703 | 0.0 | | 0.6954 | 1.33 | 2200 | 0.2905 | 0.5541 | 0.7861 | 0.8764 | 0.8848 | 0.6835 | 0.8968 | 0.5118 | 0.9013 | 0.8386 | nan | 0.8572 | 0.4295 | 0.7496 | 0.4346 | 0.8012 | 0.6064 | 0.0 | | 0.5832 | 1.35 | 2220 | 0.3119 | 0.5508 | 0.7683 | 0.8747 | 0.8692 | 0.6771 | 0.8894 | 0.4628 | 0.9345 | 0.7771 | nan | 0.8459 | 0.4351 | 0.7185 | 0.4202 | 0.8111 | 0.6249 | 0.0 | | 0.0921 | 1.36 | 2240 | 0.3298 | 0.5461 | 0.7714 | 0.8718 | 0.8627 | 0.7034 | 0.8801 | 0.4590 | 0.9362 | 0.7869 | nan | 0.8396 | 0.4272 | 0.6938 | 0.4216 | 0.8149 | 0.6257 | 0.0 | | 0.3714 | 1.37 | 2260 | 0.2859 | 0.5629 | 0.7798 | 0.8866 | 0.9126 | 0.6515 | 0.8707 | 0.4921 | 0.9104 | 0.8414 | nan | 0.8783 | 0.4474 | 0.7555 | 0.4428 | 0.8119 | 0.6045 | 0.0 | | 0.1119 | 1.38 | 2280 | 0.2804 | 0.5521 | 0.7694 | 0.8821 | 0.9107 | 0.6123 | 0.8938 | 0.4466 | 0.8905 | 0.8629 | nan | 0.8753 | 0.4630 | 0.7505 | 0.3926 | 0.8054 | 0.5777 | 0.0 | | 0.256 | 1.39 | 2300 | 0.2871 | 0.5641 | 0.7823 | 0.8843 | 0.8895 | 0.6507 | 0.8879 | 0.5237 | 0.9270 | 0.8150 | nan | 0.8622 | 0.4488 | 0.7299 | 0.4582 | 0.8236 | 0.6261 | 0.0 | | 1.856 | 1.41 | 2320 | 0.3132 | 0.5445 | 0.7815 | 0.8742 | 0.8851 | 0.6856 | 0.9064 | 0.4533 | 0.8902 | 0.8684 | nan | 0.8555 | 0.4349 | 0.7115 | 0.4077 | 0.8106 | 0.5913 | 0.0 | | 0.2095 | 1.42 | 2340 | 0.2954 | 0.5624 | 0.7891 | 0.8844 | 0.8890 | 0.6993 | 0.8840 | 0.4935 | 0.9264 | 0.8427 | nan | 0.8638 | 0.4331 | 0.7320 | 0.4606 | 0.8214 | 0.6255 | 0.0 | | 0.4009 | 1.43 | 2360 | 0.2798 | 0.5565 | 0.8003 | 0.8818 | 0.9017 | 0.7110 | 0.9015 | 0.5252 | 0.8853 | 0.8769 | nan | 0.8714 | 0.4175 | 0.7538 | 0.4672 | 0.8064 | 0.5789 | 0.0 | | 0.324 | 1.44 | 2380 | 0.2871 | 0.5622 | 0.7878 | 0.8838 | 0.8877 | 0.6549 | 0.9034 | 0.5290 | 0.9171 | 0.8345 | nan | 0.8595 | 0.4275 | 0.7316 | 0.4622 | 0.8243 | 0.6302 | 0.0 | | 0.1967 | 1.46 | 2400 | 0.2866 | 0.5607 | 0.7792 | 0.8862 | 0.8918 | 0.6889 | 0.9070 | 0.5223 | 0.9336 | 0.7318 | nan | 0.8663 | 0.4122 | 0.7426 | 0.4698 | 0.8234 | 0.6108 | 0.0 | | 0.3549 | 1.47 | 2420 | 0.2846 | 0.5661 | 0.7802 | 0.8907 | 0.9066 | 0.6648 | 0.8892 | 0.4930 | 0.9283 | 0.7996 | nan | 0.8749 | 0.4221 | 0.7503 | 0.4583 | 0.8237 | 0.6336 | 0.0 | | 0.1298 | 1.48 | 2440 | 0.2927 | 0.5576 | 0.7861 | 0.8844 | 0.8865 | 0.7135 | 0.9022 | 0.4713 | 0.9275 | 0.8154 | nan | 0.8638 | 0.4191 | 0.7356 | 0.4254 | 0.8226 | 0.6368 | 0.0 | | 0.1442 | 1.49 | 2460 | 0.2714 | 0.5632 | 0.7953 | 0.8880 | 0.9137 | 0.6747 | 0.8955 | 0.5360 | 0.8946 | 0.8574 | nan | 0.8757 | 0.4123 | 0.7575 | 0.4568 | 0.8186 | 0.6218 | 0.0 | | 0.1179 | 1.5 | 2480 | 0.2873 | 0.5739 | 0.7854 | 0.8936 | 0.9142 | 0.6517 | 0.8819 | 0.5194 | 0.9252 | 0.8201 | nan | 0.8803 | 0.4460 | 0.7597 | 0.4696 | 0.8249 | 0.6369 | 0.0 | | 0.8153 | 1.52 | 2500 | 0.3244 | 0.5628 | 0.7819 | 0.8828 | 0.8855 | 0.6434 | 0.8990 | 0.4903 | 0.9196 | 0.8538 | nan | 0.8601 | 0.4594 | 0.7278 | 0.4458 | 0.8205 | 0.6262 | 0.0 | | 0.1386 | 1.53 | 2520 | 0.3220 | 0.5548 | 0.7648 | 0.8788 | 0.8843 | 0.6161 | 0.9062 | 0.4492 | 0.9163 | 0.8168 | nan | 0.8494 | 0.4596 | 0.7033 | 0.4101 | 0.8261 | 0.6350 | 0.0 | | 0.3631 | 1.54 | 2540 | 0.2927 | 0.5611 | 0.7734 | 0.8852 | 0.9010 | 0.6518 | 0.8930 | 0.4558 | 0.9176 | 0.8214 | nan | 0.8666 | 0.4574 | 0.7316 | 0.4180 | 0.8232 | 0.6306 | 0.0 | | 0.1358 | 1.55 | 2560 | 0.2741 | 0.5618 | 0.7824 | 0.8875 | 0.9088 | 0.6661 | 0.8972 | 0.4611 | 0.9061 | 0.8551 | nan | 0.8760 | 0.4523 | 0.7497 | 0.4236 | 0.8184 | 0.6127 | 0.0 | | 0.1636 | 1.56 | 2580 | 0.2639 | 0.5716 | 0.7802 | 0.8943 | 0.9340 | 0.6282 | 0.8823 | 0.4880 | 0.9004 | 0.8484 | nan | 0.8878 | 0.4554 | 0.7736 | 0.4482 | 0.8186 | 0.6174 | 0.0 | | 0.218 | 1.58 | 2600 | 0.2771 | 0.5722 | 0.7804 | 0.8914 | 0.9113 | 0.6134 | 0.8914 | 0.4984 | 0.9160 | 0.8517 | nan | 0.8823 | 0.4693 | 0.7641 | 0.4576 | 0.8154 | 0.6165 | 0.0 | | 0.6271 | 1.59 | 2620 | 0.2715 | 0.5713 | 0.7803 | 0.8871 | 0.8985 | 0.6390 | 0.8907 | 0.5256 | 0.9244 | 0.8038 | nan | 0.8682 | 0.4587 | 0.7446 | 0.4759 | 0.8199 | 0.6316 | 0.0 | | 0.307 | 1.6 | 2640 | 0.3499 | 0.5620 | 0.7852 | 0.8789 | 0.8686 | 0.6577 | 0.9099 | 0.5258 | 0.9260 | 0.8234 | nan | 0.8479 | 0.4454 | 0.7133 | 0.4692 | 0.8217 | 0.6363 | 0.0 | | 0.2482 | 1.61 | 2660 | 0.2980 | 0.5654 | 0.7890 | 0.8830 | 0.8834 | 0.6598 | 0.9082 | 0.5263 | 0.9173 | 0.8390 | nan | 0.8598 | 0.4448 | 0.7340 | 0.4643 | 0.8210 | 0.6338 | 0.0 | | 0.1496 | 1.63 | 2680 | 0.2893 | 0.5750 | 0.7881 | 0.8922 | 0.9088 | 0.6366 | 0.8930 | 0.5268 | 0.9191 | 0.8445 | nan | 0.8799 | 0.4588 | 0.7638 | 0.4720 | 0.8201 | 0.6304 | 0.0 | | 0.2405 | 1.64 | 2700 | 0.2712 | 0.5738 | 0.7958 | 0.8884 | 0.8965 | 0.6660 | 0.8927 | 0.5553 | 0.9199 | 0.8441 | nan | 0.8731 | 0.4552 | 0.7597 | 0.4861 | 0.8167 | 0.6260 | 0.0 | | 0.0689 | 1.65 | 2720 | 0.2699 | 0.5773 | 0.8004 | 0.8916 | 0.9093 | 0.6645 | 0.8839 | 0.5675 | 0.9127 | 0.8645 | nan | 0.8818 | 0.4697 | 0.7649 | 0.4878 | 0.8179 | 0.6192 | 0.0 | | 1.3023 | 1.66 | 2740 | 0.2776 | 0.5739 | 0.8043 | 0.8902 | 0.9055 | 0.6846 | 0.8874 | 0.5705 | 0.9107 | 0.8673 | nan | 0.8798 | 0.4549 | 0.7620 | 0.4845 | 0.8171 | 0.6193 | 0.0 | | 0.401 | 1.67 | 2760 | 0.2705 | 0.5721 | 0.7931 | 0.8883 | 0.9074 | 0.6542 | 0.8896 | 0.5540 | 0.9072 | 0.8462 | nan | 0.8721 | 0.4551 | 0.7475 | 0.4782 | 0.8221 | 0.6295 | 0.0 | | 0.2472 | 1.69 | 2780 | 0.3258 | 0.5705 | 0.7939 | 0.8852 | 0.8866 | 0.6562 | 0.9042 | 0.5490 | 0.9180 | 0.8494 | nan | 0.8631 | 0.4681 | 0.7336 | 0.4728 | 0.8223 | 0.6338 | 0.0 | | 0.4908 | 1.7 | 2800 | 0.3074 | 0.5651 | 0.7893 | 0.8807 | 0.8882 | 0.6278 | 0.9025 | 0.5567 | 0.9016 | 0.8588 | nan | 0.8577 | 0.4620 | 0.7193 | 0.4756 | 0.8178 | 0.6234 | 0.0 | | 0.1247 | 1.71 | 2820 | 0.2845 | 0.5739 | 0.7789 | 0.8909 | 0.9063 | 0.6080 | 0.8883 | 0.5422 | 0.9268 | 0.8014 | nan | 0.8726 | 0.4572 | 0.7445 | 0.4782 | 0.8260 | 0.6386 | 0.0 | | 0.9431 | 1.72 | 2840 | 0.2788 | 0.5753 | 0.7823 | 0.8900 | 0.8967 | 0.6380 | 0.8976 | 0.5355 | 0.9333 | 0.7929 | nan | 0.8721 | 0.4694 | 0.7523 | 0.4729 | 0.8222 | 0.6386 | 0.0 | | 0.1346 | 1.73 | 2860 | 0.2942 | 0.5749 | 0.7819 | 0.8907 | 0.8988 | 0.6226 | 0.9048 | 0.5251 | 0.9280 | 0.8121 | nan | 0.8736 | 0.4707 | 0.7525 | 0.4630 | 0.8226 | 0.6417 | 0.0 | | 0.1469 | 1.75 | 2880 | 0.2782 | 0.5762 | 0.7808 | 0.8933 | 0.9093 | 0.6225 | 0.8923 | 0.5319 | 0.9289 | 0.8001 | nan | 0.8793 | 0.4653 | 0.7595 | 0.4657 | 0.8239 | 0.6395 | 0.0 | | 0.1829 | 1.76 | 2900 | 0.2748 | 0.5727 | 0.7899 | 0.8908 | 0.9105 | 0.6307 | 0.9093 | 0.5512 | 0.9050 | 0.8326 | nan | 0.8795 | 0.4647 | 0.7606 | 0.4629 | 0.8195 | 0.6217 | 0.0 | | 0.1476 | 1.77 | 2920 | 0.2591 | 0.5729 | 0.7936 | 0.8911 | 0.9130 | 0.6489 | 0.8955 | 0.5441 | 0.9053 | 0.8549 | nan | 0.8828 | 0.4653 | 0.7657 | 0.4666 | 0.8169 | 0.6131 | 0.0 | | 0.09 | 1.78 | 2940 | 0.2802 | 0.5692 | 0.7887 | 0.8892 | 0.9127 | 0.6333 | 0.9030 | 0.5209 | 0.8974 | 0.8651 | nan | 0.8830 | 0.4768 | 0.7639 | 0.4502 | 0.8116 | 0.5989 | 0.0 | | 0.2123 | 1.8 | 2960 | 0.2605 | 0.5653 | 0.7857 | 0.8868 | 0.9139 | 0.6362 | 0.8964 | 0.5169 | 0.8930 | 0.8581 | nan | 0.8819 | 0.4653 | 0.7632 | 0.4462 | 0.8091 | 0.5913 | 0.0 | | 0.4904 | 1.81 | 2980 | 0.2484 | 0.5707 | 0.7754 | 0.8939 | 0.9314 | 0.5966 | 0.8876 | 0.5218 | 0.9042 | 0.8109 | nan | 0.8847 | 0.4367 | 0.7715 | 0.4485 | 0.8230 | 0.6308 | 0.0 | | 0.3414 | 1.82 | 3000 | 0.2569 | 0.5722 | 0.7811 | 0.8929 | 0.9166 | 0.6386 | 0.8934 | 0.5163 | 0.9180 | 0.8038 | nan | 0.8820 | 0.4596 | 0.7572 | 0.4440 | 0.8246 | 0.6380 | 0.0 | | 0.0498 | 1.83 | 3020 | 0.2547 | 0.5738 | 0.7870 | 0.8923 | 0.9148 | 0.6430 | 0.8918 | 0.5312 | 0.9144 | 0.8268 | nan | 0.8817 | 0.4658 | 0.7594 | 0.4496 | 0.8235 | 0.6365 | 0.0 | | 0.3362 | 1.84 | 3040 | 0.2595 | 0.5756 | 0.7856 | 0.8937 | 0.9173 | 0.6259 | 0.8873 | 0.5339 | 0.9167 | 0.8325 | nan | 0.8833 | 0.4694 | 0.7628 | 0.4537 | 0.8248 | 0.6352 | 0.0 | | 0.1219 | 1.86 | 3060 | 0.2537 | 0.5750 | 0.7841 | 0.8947 | 0.9217 | 0.6318 | 0.8915 | 0.5239 | 0.9145 | 0.8211 | nan | 0.8846 | 0.4633 | 0.7649 | 0.4475 | 0.8255 | 0.6395 | 0.0 | | 0.123 | 1.87 | 3080 | 0.2808 | 0.5758 | 0.7945 | 0.8934 | 0.9069 | 0.6636 | 0.9036 | 0.5391 | 0.9199 | 0.8339 | nan | 0.8822 | 0.4694 | 0.7664 | 0.4539 | 0.8238 | 0.6346 | 0.0 | | 0.3721 | 1.88 | 3100 | 0.2523 | 0.5777 | 0.7885 | 0.8954 | 0.9207 | 0.6431 | 0.8826 | 0.5526 | 0.9209 | 0.8111 | nan | 0.8858 | 0.4599 | 0.7679 | 0.4677 | 0.8257 | 0.6367 | 0.0 | | 0.284 | 1.89 | 3120 | 0.2545 | 0.5786 | 0.7927 | 0.8948 | 0.9151 | 0.6464 | 0.8905 | 0.5516 | 0.9188 | 0.8336 | nan | 0.8854 | 0.4687 | 0.7707 | 0.4664 | 0.8241 | 0.6350 | 0.0 | | 0.5131 | 1.9 | 3140 | 0.2502 | 0.5768 | 0.7875 | 0.8948 | 0.9234 | 0.6250 | 0.8978 | 0.5439 | 0.9072 | 0.8275 | nan | 0.8856 | 0.4619 | 0.7722 | 0.4582 | 0.8243 | 0.6357 | 0.0 | | 0.1381 | 1.92 | 3160 | 0.2580 | 0.5760 | 0.7772 | 0.8954 | 0.9194 | 0.6006 | 0.9047 | 0.5318 | 0.9194 | 0.7870 | nan | 0.8853 | 0.4661 | 0.7702 | 0.4505 | 0.8260 | 0.6338 | 0.0 | | 0.3162 | 1.93 | 3180 | 0.2739 | 0.5775 | 0.7918 | 0.8935 | 0.9066 | 0.6439 | 0.9006 | 0.5423 | 0.9222 | 0.8350 | nan | 0.8822 | 0.4750 | 0.7676 | 0.4609 | 0.8230 | 0.6338 | 0.0 | | 0.1153 | 1.94 | 3200 | 0.2678 | 0.5704 | 0.7631 | 0.8953 | 0.9273 | 0.5727 | 0.8881 | 0.5334 | 0.9260 | 0.7312 | nan | 0.8843 | 0.4380 | 0.7696 | 0.4600 | 0.8251 | 0.6161 | 0.0 | | 0.0417 | 1.95 | 3220 | 0.2650 | 0.5775 | 0.7906 | 0.8950 | 0.9137 | 0.6581 | 0.8944 | 0.5503 | 0.9244 | 0.8028 | nan | 0.8844 | 0.4627 | 0.7687 | 0.4657 | 0.8250 | 0.6359 | 0.0 | | 0.1364 | 1.96 | 3240 | 0.2696 | 0.5771 | 0.7861 | 0.8953 | 0.9133 | 0.6531 | 0.8970 | 0.5482 | 0.9295 | 0.7756 | nan | 0.8844 | 0.4652 | 0.7692 | 0.4642 | 0.8254 | 0.6317 | 0.0 | | 0.0806 | 1.98 | 3260 | 0.2718 | 0.5771 | 0.7957 | 0.8945 | 0.9151 | 0.6654 | 0.9009 | 0.5405 | 0.9130 | 0.8391 | nan | 0.8844 | 0.4683 | 0.7680 | 0.4590 | 0.8247 | 0.6356 | 0.0 | | 0.1196 | 1.99 | 3280 | 0.2563 | 0.5765 | 0.7934 | 0.8942 | 0.9204 | 0.6450 | 0.8936 | 0.5526 | 0.9077 | 0.8415 | nan | 0.8847 | 0.4614 | 0.7695 | 0.4632 | 0.8233 | 0.6331 | 0.0 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

[ "water", "whitewater", "sediment", "other_natural_terrain", "vegetation", "development", "unknown" ]

vigneshgs7/segformer-b0-finetuned-segments-docboundary-nov-13

# segformer-b0-finetuned-segments-docboundary-nov-13 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the vigneshgs7/doc-boundary dataset. It achieves the following results on the evaluation set: - Loss: 0.2806 - Mean Iou: 0.4886 - Mean Accuracy: 0.9771 - Overall Accuracy: 0.9771 - Accuracy Page: nan - Accuracy Surface: 0.9771 - Iou Page: 0.0 - Iou Surface: 0.9771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Page | Accuracy Surface | Iou Page | Iou Surface | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:----------------:|:--------:|:-----------:| | 0.4191 | 2.22 | 20 | 0.5643 | 0.4651 | 0.9301 | 0.9301 | nan | 0.9301 | 0.0 | 0.9301 | | 0.3141 | 4.44 | 40 | 0.3959 | 0.4866 | 0.9733 | 0.9733 | nan | 0.9733 | 0.0 | 0.9733 | | 0.2865 | 6.67 | 60 | 0.2889 | 0.4870 | 0.9740 | 0.9740 | nan | 0.9740 | 0.0 | 0.9740 | | 0.3955 | 8.89 | 80 | 0.2806 | 0.4886 | 0.9771 | 0.9771 | nan | 0.9771 | 0.0 | 0.9771 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "page", "surface" ]

piecurus/segformer-finetuned-segments-opit

# segformer-finetuned-segments-opit This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.3483 - Mean Iou: 0.1474 - Mean Accuracy: 0.1958 - Overall Accuracy: 0.7227 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.7892 - Accuracy Flat-sidewalk: 0.9075 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.1029 - Accuracy Flat-parkingdriveway: 0.0001 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.8749 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8943 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9118 - Accuracy Nature-terrain: 0.6546 - Accuracy Sky: 0.9352 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.4282 - Iou Flat-sidewalk: 0.7768 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.1021 - Iou Flat-parkingdriveway: 0.0001 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.6372 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.5530 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.7392 - Iou Nature-terrain: 0.5009 - Iou Sky: 0.8328 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | 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| 2.8103 | 0.06 | 25 | 3.0462 | 0.0835 | 0.1351 | 0.5790 | nan | 0.1892 | 0.9330 | 0.0 | 0.0048 | 0.0006 | nan | 0.0005 | 0.0002 | 0.0 | 0.5793 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8849 | 0.0 | 0.0000 | 0.0002 | 0.0 | nan | 0.0003 | 0.0009 | 0.0 | 0.0 | 0.6599 | 0.4398 | 0.4779 | 0.0004 | 0.0 | 0.0151 | 0.0 | 0.0 | 0.1368 | 0.6091 | 0.0 | 0.0046 | 0.0006 | 0.0 | 0.0005 | 0.0002 | 0.0 | 0.4916 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3537 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.0 | 0.0003 | 0.0008 | 0.0 | 0.0 | 0.5851 | 0.2698 | 0.4572 | 0.0004 | 0.0 | 0.0123 | 0.0 | | 2.3833 | 0.12 | 50 | 2.3708 | 0.1025 | 0.1539 | 0.6295 | nan | 0.6185 | 0.8261 | 0.0 | 0.0007 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.7749 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8688 | 0.0568 | 0.6944 | 0.0 | 0.0 | 0.0016 | 0.0 | nan | 0.3347 | 0.6672 | 0.0 | 0.0007 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.5428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4369 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6274 | 0.0451 | 0.6247 | 0.0 | 0.0 | 0.0015 | 0.0 | | 2.1946 | 0.19 | 75 | 1.9680 | 0.1145 | 0.1612 | 0.6615 | nan | 0.6725 | 0.8726 | 0.0 | 0.0011 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.7496 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9185 | 0.0333 | 0.8355 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.3738 | 0.7037 | 0.0 | 0.0011 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5385 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4836 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6493 | 0.0311 | 0.7684 | 0.0 | 0.0 | 0.0000 | 0.0 | | 1.959 | 0.25 | 100 | 1.8828 | 0.1179 | 0.1636 | 0.6727 | nan | 0.6801 | 0.8928 | 0.0 | 0.0006 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.7736 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8784 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9482 | 0.0389 | 0.8584 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.3884 | 0.7151 | 0.0 | 0.0006 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.5705 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5160 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6418 | 0.0360 | 0.7878 | 0.0 | 0.0 | 0.0000 | 0.0 | | 1.8759 | 0.31 | 125 | 1.7092 | 0.1260 | 0.1758 | 0.6862 | nan | 0.7247 | 0.8976 | 0.0 | 0.0000 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.9094 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8479 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9024 | 0.2625 | 0.9054 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3977 | 0.7307 | 0.0 | 0.0000 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5237 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5329 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6911 | 0.2245 | 0.8065 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.0333 | 0.38 | 150 | 1.5558 | 0.1267 | 0.1751 | 0.6898 | nan | 0.7565 | 0.8952 | 0.0 | 0.0007 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8792 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8874 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9104 | 0.1928 | 0.9055 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4083 | 0.7455 | 0.0 | 0.0007 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5928 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5267 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6820 | 0.1637 | 0.8069 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8985 | 0.44 | 175 | 1.5370 | 0.1277 | 0.1752 | 0.6939 | nan | 0.7438 | 0.9025 | 0.0 | 0.0068 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8237 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8984 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9307 | 0.1976 | 0.9266 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4223 | 0.7553 | 0.0 | 0.0068 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.6077 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5131 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6724 | 0.1691 | 0.8133 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7908 | 0.5 | 200 | 1.4854 | 0.1339 | 0.1843 | 0.7020 | nan | 0.7612 | 0.9068 | 0.0 | 0.0012 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.9215 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8381 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9078 | 0.4480 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4095 | 0.7591 | 0.0 | 0.0012 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.5471 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5386 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7168 | 0.3673 | 0.8119 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4371 | 0.56 | 225 | 1.4176 | 0.1367 | 0.1830 | 0.7079 | nan | 0.7035 | 0.9303 | 0.0 | 0.0255 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8848 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9287 | 0.4230 | 0.9065 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4233 | 0.7534 | 0.0 | 0.0255 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5331 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7049 | 0.3427 | 0.8312 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4506 | 0.62 | 250 | 1.4011 | 0.1350 | 0.1827 | 0.7079 | nan | 0.6936 | 0.9362 | 0.0 | 0.0364 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8955 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8612 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9299 | 0.3720 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4251 | 0.7533 | 0.0 | 0.0363 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5961 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5472 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6990 | 0.3026 | 0.8250 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3095 | 0.69 | 275 | 1.4039 | 0.1398 | 0.1861 | 0.7134 | nan | 0.6873 | 0.9430 | 0.0 | 0.0260 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8807 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8960 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9156 | 0.5055 | 0.9137 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4187 | 0.7531 | 0.0 | 0.0260 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.6172 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5457 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7261 | 0.4143 | 0.8343 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4218 | 0.75 | 300 | 1.3735 | 0.1370 | 0.1856 | 0.7082 | nan | 0.7701 | 0.8980 | 0.0 | 0.0524 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8776 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8762 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9444 | 0.4052 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4267 | 0.7668 | 0.0 | 0.0522 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.6148 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5481 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6931 | 0.3263 | 0.8172 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3986 | 0.81 | 325 | 1.3224 | 0.1451 | 0.1917 | 0.7223 | nan | 0.7354 | 0.9296 | 0.0 | 0.1261 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.8747 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8946 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9269 | 0.5292 | 0.9261 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4389 | 0.7700 | 0.0 | 0.1253 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.6395 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5541 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7200 | 0.4083 | 0.8409 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3335 | 0.88 | 350 | 1.2909 | 0.1454 | 0.1921 | 0.7230 | nan | 0.7157 | 0.9388 | 0.0 | 0.1024 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8896 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8932 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9207 | 0.5667 | 0.9276 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4347 | 0.7657 | 0.0 | 0.1020 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.6232 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5533 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7300 | 0.4607 | 0.8376 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6376 | 0.94 | 375 | 1.3109 | 0.1476 | 0.1964 | 0.7253 | nan | 0.7617 | 0.9215 | 0.0 | 0.1225 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.9008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8842 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9118 | 0.6529 | 0.9335 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4357 | 0.7750 | 0.0 | 0.1216 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.6139 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5572 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7387 | 0.5011 | 0.8333 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4354 | 1.0 | 400 | 1.3483 | 0.1474 | 0.1958 | 0.7227 | nan | 0.7892 | 0.9075 | 0.0 | 0.1029 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8749 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8943 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9118 | 0.6546 | 0.9352 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4282 | 0.7768 | 0.0 | 0.1021 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.6372 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5530 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7392 | 0.5009 | 0.8328 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

jfdpastor/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.0243 - Mean Iou: 0.9582 - Mean Accuracy: 0.9792 - Overall Accuracy: 0.9965 - Accuracy Unlabeled: 0.9981 - Accuracy Numero: 0.9603 - Iou Unlabeled: 0.9963 - Iou Numero: 0.9200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Numero | Iou Unlabeled | Iou Numero | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:----------:| | 0.1406 | 5.0 | 20 | 0.1672 | 0.7389 | 0.7497 | 0.9790 | 1.0000 | 0.4994 | 0.9785 | 0.4993 | | 0.045 | 10.0 | 40 | 0.0498 | 0.9398 | 0.9476 | 0.9951 | 0.9994 | 0.8958 | 0.9949 | 0.8846 | | 0.0361 | 15.0 | 60 | 0.0296 | 0.9575 | 0.9811 | 0.9964 | 0.9978 | 0.9643 | 0.9963 | 0.9187 | | 0.026 | 20.0 | 80 | 0.0243 | 0.9582 | 0.9792 | 0.9965 | 0.9981 | 0.9603 | 0.9963 | 0.9200 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "numero" ]

peldrak/segformer-finetuned-riviera

# segformer-finetuned-riviera This model is a fine-tuned version of [peldrak/segformer-finetuned-coasts-final](https://huggingface.co/peldrak/segformer-finetuned-coasts-final) on the peldrak/riviera_labeled_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1898 - Mean Iou: 0.5029 - Mean Accuracy: 0.6197 - Overall Accuracy: 0.9030 - Accuracy Water: 0.9790 - Accuracy Whitewater: 0.0 - Accuracy Sand: 0.8826 - Accuracy Rocky Terrain: 0.8951 - Accuracy Agricultural: 0.8809 - Accuracy Vegetation: 0.9396 - Accuracy Road: 0.6302 - Accuracy Building: 0.5410 - Accuracy Soil: 0.4486 - Accuracy Boat: nan - Accuracy Development: 0.0 - Accuracy Unknown: nan - Iou Water: 0.9539 - Iou Whitewater: 0.0 - Iou Sand: 0.8064 - Iou Rocky Terrain: 0.8292 - Iou Agricultural: 0.8190 - Iou Vegetation: 0.7979 - Iou Road: 0.5135 - Iou Building: 0.4346 - Iou Soil: 0.3769 - Iou Boat: nan - Iou Development: 0.0 - Iou Unknown: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sand | Accuracy Rocky Terrain | Accuracy Agricultural | Accuracy Vegetation | Accuracy Road | Accuracy Building | Accuracy Soil | Accuracy Boat | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sand | Iou Rocky Terrain | Iou Agricultural | Iou Vegetation | Iou Road | Iou Building | Iou Soil | Iou Boat | Iou Development | Iou Unknown | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------:|:-----------------:|:-------------:|:-------------:|:--------------------:|:----------------:|:---------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------:|:------------:|:--------:|:--------:|:---------------:|:-----------:| | 1.908 | 0.43 | 20 | 1.5006 | 0.1386 | 0.2716 | 0.5245 | 0.5673 | 0.0015 | 0.1051 | 0.7783 | 0.0605 | 0.9687 | 0.0004 | 0.2337 | 0.0003 | nan | 0.0 | nan | 0.5526 | 0.0002 | 0.0967 | 0.5020 | 0.0515 | 0.4225 | 0.0004 | 0.0371 | 0.0003 | 0.0 | 0.0 | 0.0 | | 1.4398 | 0.87 | 40 | 0.9640 | 0.2528 | 0.3806 | 0.7203 | 0.9531 | 0.0 | 0.8145 | 0.9034 | 0.1560 | 0.9690 | 0.0 | 0.0075 | 0.0027 | nan | 0.0 | nan | 0.9207 | 0.0 | 0.7481 | 0.6936 | 0.1539 | 0.5106 | 0.0 | 0.0046 | 0.0027 | 0.0 | 0.0 | 0.0 | | 1.0307 | 1.3 | 60 | 0.7848 | 0.2878 | 0.3859 | 0.7291 | 0.9583 | 0.0 | 0.8569 | 0.8883 | 0.1763 | 0.9791 | 0.0 | 0.0 | 0.0000 | nan | 0.0 | nan | 0.9395 | 0.0 | 0.8017 | 0.7539 | 0.1747 | 0.4960 | 0.0 | 0.0 | 0.0000 | nan | 0.0 | 0.0 | | 0.8326 | 1.74 | 80 | 0.6153 | 0.2889 | 0.3878 | 0.7374 | 0.9785 | 0.0 | 0.8393 | 0.8917 | 0.1999 | 0.9681 | 0.0 | 0.0 | 0.0000 | nan | 0.0 | nan | 0.9483 | 0.0 | 0.8011 | 0.7193 | 0.1976 | 0.5110 | 0.0 | 0.0 | 0.0000 | nan | 0.0 | 0.0 | | 0.8073 | 2.17 | 100 | 0.4691 | 0.3205 | 0.4189 | 0.8037 | 0.9808 | 0.0 | 0.8498 | 0.8368 | 0.5480 | 0.9713 | 0.0 | 0.0 | 0.0022 | nan | 0.0 | nan | 0.9307 | 0.0 | 0.6741 | 0.7811 | 0.5204 | 0.6174 | 0.0 | 0.0 | 0.0022 | nan | 0.0 | 0.0 | | 0.704 | 2.61 | 120 | 0.4258 | 0.3332 | 0.4358 | 0.8194 | 0.9702 | 0.0 | 0.8729 | 0.8962 | 0.6086 | 0.9639 | 0.0 | 0.0 | 0.0457 | nan | 0.0 | nan | 0.9486 | 0.0 | 0.6774 | 0.7899 | 0.5665 | 0.6374 | 0.0 | 0.0 | 0.0455 | nan | 0.0 | 0.0 | | 0.6908 | 3.04 | 140 | 0.3838 | 0.3652 | 0.4545 | 0.8613 | 0.9801 | 0.0 | 0.8479 | 0.8894 | 0.8547 | 0.9289 | 0.0 | 0.0 | 0.0435 | nan | 0.0 | nan | 0.9418 | 0.0 | 0.8003 | 0.7730 | 0.7470 | 0.7114 | 0.0 | 0.0 | 0.0434 | nan | 0.0 | 0.0 | | 0.8322 | 3.48 | 160 | 0.3320 | 0.3723 | 0.4625 | 0.8673 | 0.9825 | 0.0 | 0.8877 | 0.8569 | 0.8891 | 0.9189 | 0.0 | 0.0 | 0.0898 | nan | 0.0 | nan | 0.9352 | 0.0 | 0.7833 | 0.7893 | 0.7687 | 0.7353 | 0.0 | 0.0 | 0.0832 | nan | 0.0 | 0.0 | | 0.4966 | 3.91 | 180 | 0.2892 | 0.3848 | 0.4793 | 0.8732 | 0.9715 | 0.0 | 0.8962 | 0.8724 | 0.8856 | 0.9257 | 0.0 | 0.0 | 0.2418 | nan | 0.0 | nan | 0.9447 | 0.0 | 0.7618 | 0.7963 | 0.7712 | 0.7571 | 0.0 | 0.0 | 0.2014 | nan | 0.0 | 0.0 | | 0.5418 | 4.35 | 200 | 0.2968 | 0.3658 | 0.4564 | 0.8647 | 0.9697 | 0.0 | 0.8268 | 0.8930 | 0.8605 | 0.9519 | 0.0 | 0.0 | 0.0625 | nan | 0.0 | nan | 0.9464 | 0.0 | 0.7798 | 0.7485 | 0.7633 | 0.7261 | 0.0 | 0.0 | 0.0601 | nan | 0.0 | 0.0 | | 0.6316 | 4.78 | 220 | 0.2704 | 0.3803 | 0.4742 | 0.8606 | 0.9748 | 0.0 | 0.8969 | 0.9011 | 0.7804 | 0.9430 | 0.0 | 0.0 | 0.2453 | nan | 0.0 | nan | 0.9473 | 0.0 | 0.7903 | 0.8128 | 0.7240 | 0.7156 | 0.0 | 0.0 | 0.1931 | nan | 0.0 | 0.0 | | 0.6079 | 5.22 | 240 | 0.2406 | 0.3930 | 0.4851 | 0.8765 | 0.9742 | 0.0 | 0.8936 | 0.8915 | 0.9013 | 0.9088 | 0.0 | 0.0 | 0.2812 | nan | 0.0 | nan | 0.9356 | 0.0 | 0.7964 | 0.8103 | 0.7827 | 0.7700 | 0.0 | 0.0 | 0.2281 | nan | 0.0 | 0.0 | | 0.3053 | 5.65 | 260 | 0.2500 | 0.3926 | 0.4892 | 0.8681 | 0.9674 | 0.0 | 0.8905 | 0.9148 | 0.9038 | 0.8597 | 0.0 | 0.0 | 0.3563 | nan | 0.0 | nan | 0.9246 | 0.0 | 0.8017 | 0.8123 | 0.7713 | 0.7575 | 0.0 | 0.0 | 0.2513 | nan | 0.0 | 0.0 | | 0.2995 | 6.09 | 280 | 0.2654 | 0.3756 | 0.4619 | 0.8622 | 0.9758 | 0.0 | 0.8820 | 0.8759 | 0.8019 | 0.9671 | 0.0 | 0.0 | 0.1161 | nan | 0.0 | nan | 0.9458 | 0.0 | 0.8107 | 0.8151 | 0.7566 | 0.6975 | 0.0 | 0.0 | 0.1056 | nan | 0.0 | 0.0 | | 0.2772 | 6.52 | 300 | 0.2242 | 0.4013 | 0.4916 | 0.8844 | 0.9834 | 0.0 | 0.8714 | 0.8650 | 0.9075 | 0.9231 | 0.0 | 0.0 | 0.3653 | nan | 0.0 | nan | 0.9429 | 0.0 | 0.8099 | 0.7963 | 0.8043 | 0.7682 | 0.0 | 0.0 | 0.2922 | nan | 0.0 | 0.0 | | 0.4586 | 6.96 | 320 | 0.2596 | 0.3783 | 0.4670 | 0.8555 | 0.9771 | 0.0 | 0.8875 | 0.8424 | 0.7514 | 0.9690 | 0.0 | 0.0 | 0.2426 | nan | 0.0 | nan | 0.9414 | 0.0 | 0.8145 | 0.7988 | 0.7052 | 0.6870 | 0.0 | 0.0 | 0.2148 | nan | 0.0 | 0.0 | | 0.4574 | 7.39 | 340 | 0.2196 | 0.4007 | 0.4989 | 0.8832 | 0.9701 | 0.0 | 0.8993 | 0.9037 | 0.9086 | 0.9088 | 0.0 | 0.0 | 0.3981 | nan | 0.0 | nan | 0.9464 | 0.0 | 0.7823 | 0.8113 | 0.8016 | 0.7711 | 0.0 | 0.0 | 0.2950 | nan | 0.0 | 0.0 | | 0.5272 | 7.83 | 360 | 0.2229 | 0.3997 | 0.4979 | 0.8804 | 0.9830 | 0.0 | 0.9013 | 0.8904 | 0.8637 | 0.9164 | 0.0 | 0.0 | 0.4245 | nan | 0.0 | nan | 0.9426 | 0.0 | 0.7984 | 0.8113 | 0.7873 | 0.7681 | 0.0 | 0.0 | 0.2895 | nan | 0.0 | 0.0 | | 0.3955 | 8.26 | 380 | 0.2135 | 0.4035 | 0.5020 | 0.8833 | 0.9786 | 0.0 | 0.8921 | 0.8926 | 0.9247 | 0.8807 | 0.0 | 0.0 | 0.4515 | nan | 0.0 | nan | 0.9452 | 0.0 | 0.7945 | 0.8264 | 0.7946 | 0.7726 | 0.0 | 0.0 | 0.3048 | nan | 0.0 | 0.0 | | 0.5612 | 8.7 | 400 | 0.2228 | 0.4009 | 0.4919 | 0.8849 | 0.9892 | 0.0 | 0.8790 | 0.8097 | 0.8977 | 0.9389 | 0.0 | 0.0 | 0.4042 | nan | 0.0 | nan | 0.9419 | 0.0 | 0.8002 | 0.7755 | 0.8134 | 0.7698 | 0.0 | 0.0 | 0.3086 | nan | 0.0 | 0.0 | | 0.1692 | 9.13 | 420 | 0.2094 | 0.4046 | 0.5041 | 0.8853 | 0.9658 | 0.0 | 0.9087 | 0.9039 | 0.8833 | 0.9330 | 0.0 | 0.0 | 0.4467 | nan | 0.0 | nan | 0.9479 | 0.0 | 0.7794 | 0.8138 | 0.8039 | 0.7686 | 0.0 | 0.0 | 0.3368 | nan | 0.0 | 0.0 | | 0.4642 | 9.57 | 440 | 0.2251 | 0.3966 | 0.4857 | 0.8790 | 0.9910 | 0.0 | 0.8899 | 0.8028 | 0.8914 | 0.9269 | 0.0 | 0.0 | 0.3552 | nan | 0.0 | nan | 0.9288 | 0.0 | 0.8090 | 0.7718 | 0.7912 | 0.7577 | 0.0 | 0.0 | 0.3036 | nan | 0.0 | 0.0 | | 0.2049 | 10.0 | 460 | 0.1939 | 0.4101 | 0.5068 | 0.8893 | 0.9763 | 0.0 | 0.8918 | 0.8915 | 0.8887 | 0.9321 | 0.0061 | 0.0 | 0.4817 | nan | 0.0 | nan | 0.9501 | 0.0 | 0.8010 | 0.8156 | 0.8126 | 0.7766 | 0.0061 | 0.0 | 0.3493 | nan | 0.0 | 0.0 | | 0.2509 | 10.43 | 480 | 0.2103 | 0.4012 | 0.4950 | 0.8773 | 0.9847 | 0.0 | 0.8763 | 0.8441 | 0.8569 | 0.9243 | 0.0069 | 0.0 | 0.4566 | nan | 0.0 | nan | 0.9417 | 0.0 | 0.8099 | 0.7984 | 0.7737 | 0.7506 | 0.0069 | 0.0 | 0.3319 | nan | 0.0 | 0.0 | | 0.2327 | 10.87 | 500 | 0.2140 | 0.4026 | 0.4977 | 0.8841 | 0.9813 | 0.0 | 0.8971 | 0.8575 | 0.8907 | 0.9270 | 0.0105 | 0.0 | 0.4126 | nan | 0.0 | nan | 0.9486 | 0.0 | 0.7858 | 0.8034 | 0.7947 | 0.7634 | 0.0105 | 0.0 | 0.3218 | nan | 0.0 | 0.0 | | 0.2991 | 11.3 | 520 | 0.2111 | 0.3995 | 0.4917 | 0.8787 | 0.9828 | 0.0 | 0.8797 | 0.8606 | 0.8767 | 0.9223 | 0.0151 | 0.0 | 0.3794 | nan | 0.0 | nan | 0.9402 | 0.0 | 0.8020 | 0.7913 | 0.7812 | 0.7541 | 0.0151 | 0.0 | 0.3105 | nan | 0.0 | 0.0 | | 0.2296 | 11.74 | 540 | 0.2011 | 0.4054 | 0.4998 | 0.8862 | 0.9755 | 0.0 | 0.8746 | 0.8879 | 0.8974 | 0.9281 | 0.0060 | 0.0 | 0.4286 | nan | 0.0 | nan | 0.9508 | 0.0 | 0.8014 | 0.8092 | 0.8019 | 0.7688 | 0.0060 | 0.0 | 0.3209 | nan | 0.0 | 0.0 | | 1.0788 | 12.17 | 560 | 0.1957 | 0.4251 | 0.5366 | 0.8836 | 0.9853 | 0.0 | 0.9076 | 0.8684 | 0.8856 | 0.8582 | 0.1839 | 0.0 | 0.6768 | nan | 0.0 | nan | 0.9360 | 0.0 | 0.7905 | 0.8101 | 0.8059 | 0.7701 | 0.1835 | 0.0 | 0.3802 | nan | 0.0 | 0.0 | | 0.2605 | 12.61 | 580 | 0.1892 | 0.4191 | 0.5156 | 0.8896 | 0.9833 | 0.0 | 0.8800 | 0.8779 | 0.8794 | 0.9283 | 0.0863 | 0.0121 | 0.5091 | nan | 0.0 | nan | 0.9490 | 0.0 | 0.7897 | 0.8130 | 0.8217 | 0.7759 | 0.0862 | 0.0121 | 0.3627 | nan | 0.0 | 0.0 | | 0.0813 | 13.04 | 600 | 0.1897 | 0.4230 | 0.5230 | 0.8907 | 0.9813 | 0.0 | 0.8939 | 0.8806 | 0.8869 | 0.9180 | 0.1234 | 0.0078 | 0.5375 | nan | 0.0 | nan | 0.9482 | 0.0 | 0.7897 | 0.8206 | 0.8134 | 0.7772 | 0.1234 | 0.0078 | 0.3729 | nan | 0.0 | 0.0 | | 0.1048 | 13.48 | 620 | 0.1922 | 0.4193 | 0.5126 | 0.8877 | 0.9825 | 0.0 | 0.8779 | 0.8872 | 0.8628 | 0.9418 | 0.1405 | 0.0 | 0.4337 | nan | 0.0 | nan | 0.9486 | 0.0 | 0.7928 | 0.8189 | 0.7970 | 0.7656 | 0.1403 | 0.0 | 0.3486 | nan | 0.0 | 0.0 | | 0.5566 | 13.91 | 640 | 0.1864 | 0.4260 | 0.5238 | 0.8922 | 0.9771 | 0.0 | 0.9038 | 0.8912 | 0.9021 | 0.9222 | 0.1904 | 0.0 | 0.4507 | nan | 0.0 | nan | 0.9487 | 0.0 | 0.7827 | 0.8262 | 0.8114 | 0.7797 | 0.1894 | 0.0 | 0.3479 | nan | 0.0 | 0.0 | | 0.1057 | 14.35 | 660 | 0.1946 | 0.4181 | 0.5078 | 0.8910 | 0.9873 | 0.0 | 0.8601 | 0.8752 | 0.8917 | 0.9425 | 0.1355 | 0.0 | 0.3856 | nan | 0.0 | nan | 0.9483 | 0.0 | 0.7937 | 0.8148 | 0.8133 | 0.7715 | 0.1349 | 0.0 | 0.3226 | nan | 0.0 | 0.0 | | 0.1439 | 14.78 | 680 | 0.1795 | 0.4398 | 0.5410 | 0.8934 | 0.9836 | 0.0 | 0.8812 | 0.8729 | 0.9105 | 0.8974 | 0.3216 | 0.0 | 0.5430 | nan | 0.0 | nan | 0.9457 | 0.0 | 0.7858 | 0.8205 | 0.8126 | 0.7930 | 0.3162 | 0.0 | 0.3637 | nan | 0.0 | 0.0 | | 0.2824 | 15.22 | 700 | 0.1861 | 0.4336 | 0.5321 | 0.8931 | 0.9800 | 0.0 | 0.8835 | 0.9116 | 0.8853 | 0.9227 | 0.2573 | 0.0090 | 0.4711 | nan | 0.0 | nan | 0.9529 | 0.0 | 0.7882 | 0.8265 | 0.8065 | 0.7834 | 0.2549 | 0.0090 | 0.3478 | nan | 0.0 | 0.0 | | 0.1526 | 15.65 | 720 | 0.1906 | 0.4393 | 0.5373 | 0.8937 | 0.9825 | 0.0 | 0.9053 | 0.8791 | 0.9102 | 0.9116 | 0.3373 | 0.0175 | 0.4292 | nan | 0.0 | nan | 0.9494 | 0.0 | 0.7948 | 0.8218 | 0.7996 | 0.7924 | 0.3272 | 0.0175 | 0.3300 | nan | 0.0 | 0.0 | | 0.2005 | 16.09 | 740 | 0.2003 | 0.4357 | 0.5370 | 0.8935 | 0.9823 | 0.0 | 0.9005 | 0.8834 | 0.8703 | 0.9318 | 0.2638 | 0.0170 | 0.5209 | nan | 0.0 | nan | 0.9518 | 0.0 | 0.7887 | 0.8264 | 0.8059 | 0.7930 | 0.2606 | 0.0168 | 0.3492 | nan | 0.0 | 0.0 | | 0.2036 | 16.52 | 760 | 0.1838 | 0.4435 | 0.5415 | 0.8945 | 0.9871 | 0.0 | 0.8860 | 0.8864 | 0.9123 | 0.9028 | 0.3602 | 0.0335 | 0.4468 | nan | 0.0 | nan | 0.9537 | 0.0 | 0.7910 | 0.8258 | 0.8032 | 0.7884 | 0.3456 | 0.0334 | 0.3371 | nan | 0.0 | 0.0 | | 0.2055 | 16.96 | 780 | 0.1869 | 0.4504 | 0.5608 | 0.8918 | 0.9767 | 0.0 | 0.9097 | 0.9289 | 0.8528 | 0.9078 | 0.4082 | 0.0591 | 0.5649 | nan | 0.0 | nan | 0.9562 | 0.0 | 0.8009 | 0.8215 | 0.7875 | 0.7943 | 0.3743 | 0.0567 | 0.3631 | nan | 0.0 | 0.0 | | 0.3388 | 17.39 | 800 | 0.1877 | 0.4429 | 0.5406 | 0.8969 | 0.9833 | 0.0 | 0.9198 | 0.8840 | 0.9019 | 0.9237 | 0.3276 | 0.0213 | 0.4441 | nan | 0.0 | nan | 0.9538 | 0.0 | 0.8057 | 0.8268 | 0.8104 | 0.7897 | 0.3181 | 0.0213 | 0.3467 | nan | 0.0 | 0.0 | | 0.2979 | 17.83 | 820 | 0.1659 | 0.4508 | 0.5498 | 0.8986 | 0.9788 | 0.0 | 0.9080 | 0.8993 | 0.9090 | 0.9164 | 0.4007 | 0.0071 | 0.4783 | nan | 0.0 | nan | 0.9526 | 0.0 | 0.8108 | 0.8275 | 0.8099 | 0.7985 | 0.3902 | 0.0071 | 0.3619 | nan | 0.0 | 0.0 | | 0.2011 | 18.26 | 840 | 0.1894 | 0.4475 | 0.5465 | 0.8967 | 0.9815 | 0.0 | 0.9013 | 0.8978 | 0.8898 | 0.9281 | 0.3918 | 0.0271 | 0.4472 | nan | 0.0 | nan | 0.9541 | 0.0 | 0.8001 | 0.8326 | 0.8058 | 0.7907 | 0.3727 | 0.0268 | 0.3394 | nan | 0.0 | 0.0 | | 0.2616 | 18.7 | 860 | 0.1768 | 0.4599 | 0.5629 | 0.9003 | 0.9787 | 0.0 | 0.8992 | 0.9177 | 0.9072 | 0.9140 | 0.4964 | 0.0517 | 0.4638 | nan | 0.0 | nan | 0.9548 | 0.0 | 0.7989 | 0.8413 | 0.8094 | 0.7996 | 0.4472 | 0.0507 | 0.3566 | nan | 0.0 | 0.0 | | 0.1374 | 19.13 | 880 | 0.1818 | 0.4574 | 0.5580 | 0.8986 | 0.9885 | 0.0 | 0.8604 | 0.8578 | 0.8910 | 0.9292 | 0.5430 | 0.0049 | 0.5051 | nan | 0.0 | nan | 0.9514 | 0.0 | 0.8067 | 0.8207 | 0.8126 | 0.7994 | 0.4787 | 0.0049 | 0.3568 | nan | 0.0 | 0.0 | | 0.4279 | 19.57 | 900 | 0.1698 | 0.4761 | 0.5908 | 0.8989 | 0.9739 | 0.0 | 0.9051 | 0.9403 | 0.9099 | 0.8918 | 0.5825 | 0.2412 | 0.4630 | nan | 0.0 | nan | 0.9536 | 0.0 | 0.8065 | 0.8213 | 0.8168 | 0.8022 | 0.4816 | 0.2106 | 0.3449 | nan | 0.0 | 0.0 | | 0.1616 | 20.0 | 920 | 0.1756 | 0.4670 | 0.5709 | 0.8988 | 0.9765 | 0.0 | 0.8972 | 0.9157 | 0.9099 | 0.9152 | 0.4892 | 0.1875 | 0.4178 | nan | 0.0 | nan | 0.9540 | 0.0 | 0.7955 | 0.8372 | 0.8129 | 0.7971 | 0.4375 | 0.1709 | 0.3319 | nan | 0.0 | 0.0 | | 0.1919 | 20.43 | 940 | 0.1604 | 0.4676 | 0.5748 | 0.9024 | 0.9800 | 0.0 | 0.8835 | 0.9098 | 0.9301 | 0.8918 | 0.5902 | 0.0423 | 0.5207 | nan | 0.0 | nan | 0.9535 | 0.0 | 0.8118 | 0.8462 | 0.8284 | 0.8078 | 0.4979 | 0.0412 | 0.3564 | nan | 0.0 | 0.0 | | 0.2451 | 20.87 | 960 | 0.1640 | 0.4733 | 0.5909 | 0.8996 | 0.9784 | 0.0 | 0.8962 | 0.9061 | 0.9132 | 0.8778 | 0.6155 | 0.1235 | 0.5980 | nan | 0.0 | nan | 0.9502 | 0.0 | 0.8104 | 0.8375 | 0.8307 | 0.8073 | 0.4862 | 0.1120 | 0.3723 | nan | 0.0 | 0.0 | | 0.3232 | 21.3 | 980 | 0.1749 | 0.4743 | 0.5760 | 0.9007 | 0.9839 | 0.0 | 0.8839 | 0.8859 | 0.9054 | 0.9281 | 0.5201 | 0.2357 | 0.4168 | nan | 0.0 | nan | 0.9558 | 0.0 | 0.8040 | 0.8320 | 0.8197 | 0.7934 | 0.4538 | 0.2167 | 0.3415 | nan | 0.0 | 0.0 | | 0.1096 | 21.74 | 1000 | 0.1663 | 0.4741 | 0.5870 | 0.8996 | 0.9817 | 0.0 | 0.8991 | 0.8940 | 0.8923 | 0.9046 | 0.5485 | 0.1900 | 0.5595 | nan | 0.0 | nan | 0.9524 | 0.0 | 0.8085 | 0.8322 | 0.8204 | 0.8010 | 0.4463 | 0.1727 | 0.3818 | nan | 0.0 | 0.0 | | 0.0849 | 22.17 | 1020 | 0.1842 | 0.4726 | 0.5788 | 0.9019 | 0.9792 | 0.0 | 0.8916 | 0.9083 | 0.9011 | 0.9196 | 0.5674 | 0.1386 | 0.4818 | nan | 0.0 | nan | 0.9548 | 0.0 | 0.8071 | 0.8381 | 0.8157 | 0.8027 | 0.4867 | 0.1327 | 0.3609 | nan | 0.0 | 0.0 | | 0.28 | 22.61 | 1040 | 0.1841 | 0.4814 | 0.5925 | 0.9031 | 0.9753 | 0.0 | 0.9142 | 0.9124 | 0.9188 | 0.9145 | 0.5855 | 0.2764 | 0.4275 | nan | 0.0 | nan | 0.9542 | 0.0 | 0.8005 | 0.8341 | 0.8146 | 0.8048 | 0.4811 | 0.2472 | 0.3589 | nan | 0.0 | 0.0 | | 0.3515 | 23.04 | 1060 | 0.1875 | 0.4773 | 0.5847 | 0.8997 | 0.9851 | 0.0 | 0.8938 | 0.8555 | 0.9027 | 0.9324 | 0.5401 | 0.3279 | 0.4096 | nan | 0.0 | nan | 0.9494 | 0.0 | 0.7907 | 0.8135 | 0.8221 | 0.7940 | 0.4402 | 0.2804 | 0.3601 | nan | 0.0 | 0.0 | | 0.2499 | 23.48 | 1080 | 0.1807 | 0.4796 | 0.5940 | 0.8965 | 0.9705 | 0.0 | 0.8706 | 0.9246 | 0.8663 | 0.9262 | 0.5712 | 0.3009 | 0.5100 | nan | 0.0 | nan | 0.9511 | 0.0 | 0.7995 | 0.8286 | 0.8060 | 0.8026 | 0.4593 | 0.2677 | 0.3605 | nan | 0.0 | 0.0 | | 0.1632 | 23.91 | 1100 | 0.1788 | 0.4839 | 0.5988 | 0.8990 | 0.9779 | 0.0 | 0.9129 | 0.9039 | 0.8835 | 0.9191 | 0.6179 | 0.3242 | 0.4487 | nan | 0.0 | nan | 0.9497 | 0.0 | 0.8102 | 0.8378 | 0.8036 | 0.8011 | 0.4888 | 0.2808 | 0.3514 | nan | 0.0 | 0.0 | | 0.099 | 24.35 | 1120 | 0.1757 | 0.4872 | 0.6020 | 0.9007 | 0.9820 | 0.0 | 0.8959 | 0.9089 | 0.8962 | 0.9048 | 0.6043 | 0.3368 | 0.4915 | nan | 0.0 | nan | 0.9517 | 0.0 | 0.8099 | 0.8424 | 0.8083 | 0.8020 | 0.5038 | 0.2829 | 0.3583 | nan | 0.0 | 0.0 | | 0.4929 | 24.78 | 1140 | 0.1815 | 0.4739 | 0.5880 | 0.8978 | 0.9747 | 0.0 | 0.8931 | 0.9110 | 0.9018 | 0.8914 | 0.6130 | 0.1439 | 0.5510 | nan | 0.0 | nan | 0.9482 | 0.0 | 0.7966 | 0.8351 | 0.8117 | 0.7985 | 0.5134 | 0.1375 | 0.3723 | nan | 0.0 | 0.0 | | 0.1068 | 25.22 | 1160 | 0.1735 | 0.4870 | 0.6002 | 0.9025 | 0.9788 | 0.0 | 0.9070 | 0.9055 | 0.9000 | 0.9107 | 0.6189 | 0.2777 | 0.5030 | nan | 0.0 | nan | 0.9528 | 0.0 | 0.8086 | 0.8388 | 0.8150 | 0.8055 | 0.5062 | 0.2559 | 0.3739 | nan | 0.0 | 0.0 | | 0.1619 | 25.65 | 1180 | 0.2112 | 0.4802 | 0.5975 | 0.8927 | 0.9747 | 0.0 | 0.8930 | 0.9126 | 0.8258 | 0.9279 | 0.5779 | 0.2974 | 0.5657 | nan | 0.0 | nan | 0.9543 | 0.0 | 0.8057 | 0.8380 | 0.7781 | 0.7911 | 0.4740 | 0.2635 | 0.3775 | nan | 0.0 | 0.0 | | 0.1147 | 26.09 | 1200 | 0.1840 | 0.4895 | 0.6056 | 0.8986 | 0.9826 | 0.0 | 0.8927 | 0.8858 | 0.8902 | 0.9032 | 0.6027 | 0.3710 | 0.5275 | nan | 0.0 | nan | 0.9497 | 0.0 | 0.8161 | 0.8296 | 0.8054 | 0.8056 | 0.5088 | 0.2954 | 0.3736 | nan | 0.0 | 0.0 | | 0.1106 | 26.52 | 1220 | 0.2082 | 0.4780 | 0.5868 | 0.8931 | 0.9814 | 0.0 | 0.8777 | 0.8927 | 0.8463 | 0.9429 | 0.5490 | 0.3677 | 0.4107 | nan | 0.0 | nan | 0.9539 | 0.0 | 0.8048 | 0.8265 | 0.7864 | 0.7734 | 0.4618 | 0.3047 | 0.3467 | nan | 0.0 | 0.0 | | 0.1019 | 26.96 | 1240 | 0.1741 | 0.4906 | 0.6002 | 0.9021 | 0.9802 | 0.0 | 0.8946 | 0.8912 | 0.9047 | 0.9228 | 0.5771 | 0.3889 | 0.4420 | nan | 0.0 | nan | 0.9546 | 0.0 | 0.8079 | 0.8331 | 0.8130 | 0.8005 | 0.4971 | 0.3282 | 0.3624 | nan | 0.0 | 0.0 | | 0.1566 | 27.39 | 1260 | 0.1982 | 0.4930 | 0.6067 | 0.8989 | 0.9807 | 0.0 | 0.8996 | 0.8879 | 0.8647 | 0.9430 | 0.5618 | 0.5036 | 0.4259 | nan | 0.0 | nan | 0.9546 | 0.0 | 0.8068 | 0.8311 | 0.8025 | 0.7808 | 0.4765 | 0.4065 | 0.3646 | nan | 0.0 | 0.0 | | 0.1307 | 27.83 | 1280 | 0.1957 | 0.4988 | 0.6173 | 0.8993 | 0.9837 | 0.0 | 0.8992 | 0.8860 | 0.8612 | 0.9329 | 0.6201 | 0.5265 | 0.4633 | nan | 0.0 | nan | 0.9533 | 0.0 | 0.8102 | 0.8357 | 0.7989 | 0.7835 | 0.5001 | 0.4210 | 0.3846 | nan | 0.0 | 0.0 | | 0.3709 | 28.26 | 1300 | 0.1761 | 0.4998 | 0.6132 | 0.9042 | 0.9854 | 0.0 | 0.8862 | 0.8905 | 0.8969 | 0.9201 | 0.6066 | 0.4549 | 0.4919 | nan | 0.0 | nan | 0.9519 | 0.0 | 0.8211 | 0.8331 | 0.8191 | 0.8051 | 0.5097 | 0.3766 | 0.3807 | nan | 0.0 | 0.0 | | 0.1188 | 28.7 | 1320 | 0.1788 | 0.4940 | 0.6100 | 0.8998 | 0.9852 | 0.0 | 0.8999 | 0.8697 | 0.8697 | 0.9414 | 0.6017 | 0.5108 | 0.4218 | nan | 0.0 | nan | 0.9493 | 0.0 | 0.8232 | 0.8243 | 0.8091 | 0.7932 | 0.4950 | 0.3705 | 0.3695 | nan | 0.0 | 0.0 | | 0.1173 | 29.13 | 1340 | 0.1626 | 0.5031 | 0.6225 | 0.9073 | 0.9794 | 0.0 | 0.9253 | 0.9190 | 0.9075 | 0.9157 | 0.6297 | 0.4858 | 0.4621 | nan | 0.0 | nan | 0.9551 | 0.0 | 0.8261 | 0.8429 | 0.8273 | 0.8112 | 0.4861 | 0.4026 | 0.3823 | nan | 0.0 | 0.0 | | 0.113 | 29.57 | 1360 | 0.1880 | 0.5000 | 0.6237 | 0.9023 | 0.9809 | 0.0 | 0.9132 | 0.9085 | 0.8644 | 0.9310 | 0.6079 | 0.5466 | 0.4847 | nan | 0.0 | nan | 0.9543 | 0.0 | 0.8245 | 0.8391 | 0.8084 | 0.8025 | 0.4856 | 0.3998 | 0.3854 | nan | 0.0 | 0.0 | | 0.1002 | 30.0 | 1380 | 0.1767 | 0.5000 | 0.6287 | 0.9011 | 0.9794 | 0.0 | 0.9151 | 0.8999 | 0.8781 | 0.9104 | 0.6512 | 0.5282 | 0.5245 | nan | 0.0 | nan | 0.9525 | 0.0 | 0.8241 | 0.8392 | 0.8049 | 0.8057 | 0.5064 | 0.3795 | 0.3881 | nan | 0.0 | 0.0 | | 0.1108 | 30.43 | 1400 | 0.1767 | 0.5017 | 0.6256 | 0.9023 | 0.9836 | 0.0 | 0.9006 | 0.9011 | 0.8708 | 0.9210 | 0.6497 | 0.5210 | 0.5086 | nan | 0.0 | nan | 0.9544 | 0.0 | 0.8193 | 0.8402 | 0.8055 | 0.8035 | 0.5106 | 0.3919 | 0.3932 | nan | 0.0 | 0.0 | | 0.3149 | 30.87 | 1420 | 0.1749 | 0.4987 | 0.6121 | 0.9019 | 0.9800 | 0.0 | 0.8933 | 0.8955 | 0.8844 | 0.9353 | 0.6261 | 0.4876 | 0.4187 | nan | 0.0 | nan | 0.9521 | 0.0 | 0.8120 | 0.8369 | 0.8133 | 0.8002 | 0.4998 | 0.4099 | 0.3621 | nan | 0.0 | 0.0 | | 0.0742 | 31.3 | 1440 | 0.1684 | 0.4996 | 0.6206 | 0.9022 | 0.9802 | 0.0 | 0.8933 | 0.9001 | 0.8860 | 0.9096 | 0.6485 | 0.4379 | 0.5506 | nan | 0.0 | nan | 0.9509 | 0.0 | 0.8178 | 0.8327 | 0.8114 | 0.8110 | 0.5176 | 0.3538 | 0.4006 | nan | 0.0 | 0.0 | | 0.1199 | 31.74 | 1460 | 0.1784 | 0.4989 | 0.6242 | 0.9008 | 0.9795 | 0.0 | 0.9031 | 0.8940 | 0.8800 | 0.9248 | 0.6414 | 0.5655 | 0.4540 | nan | 0.0 | nan | 0.9517 | 0.0 | 0.8162 | 0.8322 | 0.8076 | 0.8021 | 0.5085 | 0.3951 | 0.3748 | nan | 0.0 | 0.0 | | 0.0889 | 32.17 | 1480 | 0.1994 | 0.4930 | 0.6116 | 0.8949 | 0.9809 | 0.0 | 0.9031 | 0.8725 | 0.8473 | 0.9406 | 0.6060 | 0.5287 | 0.4367 | nan | 0.0 | nan | 0.9497 | 0.0 | 0.8134 | 0.8244 | 0.7915 | 0.7827 | 0.4829 | 0.4020 | 0.3765 | nan | 0.0 | 0.0 | | 0.1348 | 32.61 | 1500 | 0.1751 | 0.4897 | 0.6067 | 0.8997 | 0.9801 | 0.0 | 0.9140 | 0.8786 | 0.8865 | 0.9283 | 0.6049 | 0.4488 | 0.4262 | nan | 0.0 | nan | 0.9516 | 0.0 | 0.8119 | 0.8287 | 0.8064 | 0.8012 | 0.4851 | 0.3438 | 0.3585 | nan | 0.0 | 0.0 | | 0.0824 | 33.04 | 1520 | 0.2008 | 0.4911 | 0.6102 | 0.8963 | 0.9818 | 0.0 | 0.8937 | 0.8893 | 0.8384 | 0.9462 | 0.5993 | 0.5037 | 0.4493 | nan | 0.0 | nan | 0.9547 | 0.0 | 0.8114 | 0.8321 | 0.7839 | 0.7881 | 0.4963 | 0.3691 | 0.3666 | nan | 0.0 | 0.0 | | 0.2206 | 33.48 | 1540 | 0.1816 | 0.4997 | 0.6190 | 0.9049 | 0.9793 | 0.0 | 0.9133 | 0.9083 | 0.9027 | 0.9249 | 0.6156 | 0.5286 | 0.4172 | nan | 0.0 | nan | 0.9560 | 0.0 | 0.8168 | 0.8359 | 0.8192 | 0.8045 | 0.4901 | 0.4187 | 0.3556 | nan | 0.0 | 0.0 | | 0.2564 | 33.91 | 1560 | 0.1816 | 0.5030 | 0.6190 | 0.9055 | 0.9793 | 0.0 | 0.8910 | 0.9106 | 0.8974 | 0.9267 | 0.6259 | 0.4937 | 0.4653 | nan | 0.0 | nan | 0.9575 | 0.0 | 0.8166 | 0.8366 | 0.8212 | 0.8020 | 0.5080 | 0.4151 | 0.3756 | nan | 0.0 | 0.0 | | 0.7781 | 34.35 | 1580 | 0.1789 | 0.5015 | 0.6268 | 0.9026 | 0.9822 | 0.0 | 0.9084 | 0.8930 | 0.8861 | 0.9127 | 0.6613 | 0.5154 | 0.5090 | nan | 0.0 | nan | 0.9517 | 0.0 | 0.8238 | 0.8372 | 0.8176 | 0.8066 | 0.4996 | 0.3958 | 0.3838 | nan | 0.0 | 0.0 | | 0.1307 | 34.78 | 1600 | 0.1819 | 0.4971 | 0.6269 | 0.9005 | 0.9814 | 0.0 | 0.9058 | 0.8986 | 0.8647 | 0.9231 | 0.6565 | 0.5438 | 0.4953 | nan | 0.0 | nan | 0.9520 | 0.0 | 0.8230 | 0.8387 | 0.8107 | 0.8020 | 0.4825 | 0.3801 | 0.3787 | nan | 0.0 | 0.0 | | 0.1537 | 35.22 | 1620 | 0.1882 | 0.4975 | 0.6133 | 0.9003 | 0.9849 | 0.0 | 0.8850 | 0.8805 | 0.8719 | 0.9344 | 0.6117 | 0.5106 | 0.4542 | nan | 0.0 | nan | 0.9539 | 0.0 | 0.8155 | 0.8300 | 0.8080 | 0.7942 | 0.4903 | 0.4111 | 0.3691 | nan | 0.0 | 0.0 | | 0.3649 | 35.65 | 1640 | 0.1842 | 0.4998 | 0.6161 | 0.9028 | 0.9850 | 0.0 | 0.8915 | 0.8816 | 0.8819 | 0.9255 | 0.6284 | 0.4610 | 0.5064 | nan | 0.0 | nan | 0.9535 | 0.0 | 0.8140 | 0.8306 | 0.8151 | 0.7999 | 0.5073 | 0.3892 | 0.3882 | nan | 0.0 | 0.0 | | 0.1117 | 36.09 | 1660 | 0.1835 | 0.4993 | 0.6205 | 0.9012 | 0.9844 | 0.0 | 0.8968 | 0.8939 | 0.8717 | 0.9304 | 0.6331 | 0.5485 | 0.4457 | nan | 0.0 | nan | 0.9548 | 0.0 | 0.8168 | 0.8367 | 0.8063 | 0.7964 | 0.4972 | 0.4147 | 0.3689 | nan | 0.0 | 0.0 | | 0.3235 | 36.52 | 1680 | 0.1805 | 0.5016 | 0.6294 | 0.9012 | 0.9789 | 0.0 | 0.8928 | 0.9158 | 0.8713 | 0.9192 | 0.6496 | 0.5691 | 0.4976 | nan | 0.0 | nan | 0.9543 | 0.0 | 0.8171 | 0.8398 | 0.8126 | 0.8037 | 0.4998 | 0.4098 | 0.3807 | nan | 0.0 | 0.0 | | 0.1136 | 36.96 | 1700 | 0.1833 | 0.5018 | 0.6181 | 0.9033 | 0.9837 | 0.0 | 0.8949 | 0.9012 | 0.8819 | 0.9230 | 0.6126 | 0.4889 | 0.4946 | nan | 0.0 | nan | 0.9564 | 0.0 | 0.8155 | 0.8392 | 0.8128 | 0.8028 | 0.5070 | 0.4067 | 0.3794 | nan | 0.0 | 0.0 | | 0.0901 | 37.39 | 1720 | 0.1862 | 0.4974 | 0.6077 | 0.9026 | 0.9816 | 0.0 | 0.8790 | 0.8881 | 0.8917 | 0.9380 | 0.5725 | 0.4988 | 0.4273 | nan | 0.0 | nan | 0.9552 | 0.0 | 0.8102 | 0.8308 | 0.8218 | 0.7934 | 0.4915 | 0.4088 | 0.3594 | nan | 0.0 | 0.0 | | 0.2317 | 37.83 | 1740 | 0.1824 | 0.5044 | 0.6259 | 0.9042 | 0.9829 | 0.0 | 0.8811 | 0.9026 | 0.8869 | 0.9203 | 0.6555 | 0.5244 | 0.5049 | nan | 0.0 | nan | 0.9548 | 0.0 | 0.8146 | 0.8359 | 0.8189 | 0.8042 | 0.5142 | 0.4207 | 0.3854 | nan | 0.0 | 0.0 | | 0.0893 | 38.26 | 1760 | 0.1893 | 0.5002 | 0.6251 | 0.9036 | 0.9810 | 0.0 | 0.8931 | 0.9047 | 0.8649 | 0.9285 | 0.6431 | 0.4835 | 0.5527 | nan | 0.0 | nan | 0.9565 | 0.0 | 0.8097 | 0.8389 | 0.8180 | 0.8079 | 0.4992 | 0.3713 | 0.4004 | nan | 0.0 | 0.0 | | 0.2952 | 38.7 | 1780 | 0.1832 | 0.4990 | 0.6201 | 0.9018 | 0.9811 | 0.0 | 0.8979 | 0.8879 | 0.8947 | 0.9246 | 0.6275 | 0.5634 | 0.4235 | nan | 0.0 | nan | 0.9522 | 0.0 | 0.8077 | 0.8323 | 0.8147 | 0.8033 | 0.5077 | 0.4146 | 0.3562 | nan | 0.0 | 0.0 | | 0.1111 | 39.13 | 1800 | 0.1769 | 0.5015 | 0.6316 | 0.9018 | 0.9818 | 0.0 | 0.9004 | 0.8883 | 0.8842 | 0.9185 | 0.6708 | 0.5974 | 0.4743 | nan | 0.0 | nan | 0.9525 | 0.0 | 0.8107 | 0.8304 | 0.8132 | 0.8045 | 0.5118 | 0.4135 | 0.3799 | nan | 0.0 | 0.0 | | 0.1 | 39.57 | 1820 | 0.1738 | 0.5041 | 0.6271 | 0.9042 | 0.9794 | 0.0 | 0.8911 | 0.8970 | 0.8915 | 0.9162 | 0.6539 | 0.5031 | 0.5385 | nan | 0.0 | nan | 0.9555 | 0.0 | 0.8088 | 0.8301 | 0.8191 | 0.8064 | 0.5209 | 0.4062 | 0.3979 | nan | 0.0 | 0.0 | | 0.2137 | 40.0 | 1840 | 0.1747 | 0.5048 | 0.6263 | 0.9047 | 0.9846 | 0.0 | 0.8822 | 0.8788 | 0.9000 | 0.9183 | 0.6654 | 0.5311 | 0.5028 | nan | 0.0 | nan | 0.9527 | 0.0 | 0.8094 | 0.8257 | 0.8270 | 0.8071 | 0.5224 | 0.4186 | 0.3901 | nan | 0.0 | 0.0 | | 0.0432 | 40.43 | 1860 | 0.1944 | 0.4978 | 0.6195 | 0.9009 | 0.9813 | 0.0 | 0.8832 | 0.8845 | 0.8725 | 0.9384 | 0.6305 | 0.5560 | 0.4488 | nan | 0.0 | nan | 0.9550 | 0.0 | 0.8099 | 0.8251 | 0.8098 | 0.7980 | 0.5071 | 0.4025 | 0.3683 | nan | 0.0 | 0.0 | | 0.0705 | 40.87 | 1880 | 0.1915 | 0.4989 | 0.6289 | 0.8996 | 0.9818 | 0.0 | 0.8845 | 0.8985 | 0.8555 | 0.9340 | 0.6647 | 0.6050 | 0.4652 | nan | 0.0 | nan | 0.9559 | 0.0 | 0.8117 | 0.8332 | 0.8002 | 0.7950 | 0.5122 | 0.4033 | 0.3761 | nan | 0.0 | 0.0 | | 0.1875 | 41.3 | 1900 | 0.1808 | 0.5035 | 0.6284 | 0.9014 | 0.9784 | 0.0 | 0.8915 | 0.8955 | 0.8781 | 0.9244 | 0.6737 | 0.5559 | 0.4863 | nan | 0.0 | nan | 0.9538 | 0.0 | 0.8075 | 0.8298 | 0.8104 | 0.7990 | 0.5307 | 0.4133 | 0.3934 | nan | 0.0 | 0.0 | | 0.041 | 41.74 | 1920 | 0.1772 | 0.5040 | 0.6316 | 0.9030 | 0.9790 | 0.0 | 0.8964 | 0.9038 | 0.8777 | 0.9238 | 0.6675 | 0.5689 | 0.4989 | nan | 0.0 | nan | 0.9555 | 0.0 | 0.8123 | 0.8319 | 0.8131 | 0.8050 | 0.5199 | 0.4136 | 0.3931 | nan | 0.0 | 0.0 | | 0.1552 | 42.17 | 1940 | 0.1809 | 0.5044 | 0.6265 | 0.9025 | 0.9807 | 0.0 | 0.8953 | 0.8909 | 0.8869 | 0.9270 | 0.6385 | 0.5922 | 0.4534 | nan | 0.0 | nan | 0.9529 | 0.0 | 0.8134 | 0.8321 | 0.8114 | 0.8023 | 0.5260 | 0.4335 | 0.3768 | nan | 0.0 | 0.0 | | 0.5015 | 42.61 | 1960 | 0.1770 | 0.5030 | 0.6263 | 0.9025 | 0.9782 | 0.0 | 0.8934 | 0.8864 | 0.8939 | 0.9262 | 0.6580 | 0.5736 | 0.4532 | nan | 0.0 | nan | 0.9517 | 0.0 | 0.8116 | 0.8269 | 0.8184 | 0.8047 | 0.5204 | 0.4251 | 0.3737 | nan | 0.0 | 0.0 | | 0.1057 | 43.04 | 1980 | 0.1809 | 0.4991 | 0.6197 | 0.9006 | 0.9812 | 0.0 | 0.8849 | 0.8871 | 0.8775 | 0.9361 | 0.6232 | 0.5822 | 0.4248 | nan | 0.0 | nan | 0.9531 | 0.0 | 0.8106 | 0.8272 | 0.8123 | 0.7967 | 0.5061 | 0.4258 | 0.3585 | nan | 0.0 | 0.0 | | 0.0832 | 43.48 | 2000 | 0.1802 | 0.5042 | 0.6240 | 0.9032 | 0.9776 | 0.0 | 0.8946 | 0.8932 | 0.8916 | 0.9287 | 0.6458 | 0.5483 | 0.4599 | nan | 0.0 | nan | 0.9519 | 0.0 | 0.8075 | 0.8274 | 0.8211 | 0.8033 | 0.5229 | 0.4312 | 0.3810 | nan | 0.0 | 0.0 | | 0.0851 | 43.91 | 2020 | 0.1826 | 0.5033 | 0.6257 | 0.9037 | 0.9795 | 0.0 | 0.9015 | 0.8938 | 0.8928 | 0.9311 | 0.6515 | 0.5854 | 0.4217 | nan | 0.0 | nan | 0.9539 | 0.0 | 0.8098 | 0.8285 | 0.8192 | 0.8015 | 0.5246 | 0.4349 | 0.3637 | nan | 0.0 | 0.0 | | 0.3671 | 44.35 | 2040 | 0.1850 | 0.5028 | 0.6233 | 0.9007 | 0.9792 | 0.0 | 0.8862 | 0.8917 | 0.8732 | 0.9317 | 0.6350 | 0.5619 | 0.4743 | nan | 0.0 | nan | 0.9503 | 0.0 | 0.8076 | 0.8289 | 0.8138 | 0.7987 | 0.5204 | 0.4218 | 0.3889 | nan | 0.0 | 0.0 | | 0.0769 | 44.78 | 2060 | 0.1957 | 0.5002 | 0.6240 | 0.9016 | 0.9768 | 0.0 | 0.8906 | 0.8940 | 0.8726 | 0.9344 | 0.6404 | 0.5415 | 0.4901 | nan | 0.0 | nan | 0.9531 | 0.0 | 0.8011 | 0.8250 | 0.8149 | 0.7996 | 0.5125 | 0.4037 | 0.3928 | nan | 0.0 | 0.0 | | 0.0754 | 45.22 | 2080 | 0.1943 | 0.4992 | 0.6205 | 0.9007 | 0.9772 | 0.0 | 0.8831 | 0.8980 | 0.8669 | 0.9415 | 0.6323 | 0.5524 | 0.4536 | nan | 0.0 | nan | 0.9544 | 0.0 | 0.8029 | 0.8287 | 0.8108 | 0.7929 | 0.5148 | 0.4063 | 0.3805 | nan | 0.0 | 0.0 | | 0.1296 | 45.65 | 2100 | 0.1879 | 0.5014 | 0.6278 | 0.9008 | 0.9763 | 0.0 | 0.8937 | 0.9025 | 0.8724 | 0.9312 | 0.6504 | 0.5896 | 0.4622 | nan | 0.0 | nan | 0.9534 | 0.0 | 0.8029 | 0.8333 | 0.8086 | 0.7965 | 0.5233 | 0.4111 | 0.3863 | nan | 0.0 | 0.0 | | 0.1158 | 46.09 | 2120 | 0.1835 | 0.5033 | 0.6266 | 0.9021 | 0.9833 | 0.0 | 0.8874 | 0.8859 | 0.8737 | 0.9294 | 0.6400 | 0.5732 | 0.4934 | nan | 0.0 | nan | 0.9531 | 0.0 | 0.8069 | 0.8320 | 0.8101 | 0.8015 | 0.5208 | 0.4155 | 0.3966 | nan | 0.0 | 0.0 | | 0.0872 | 46.52 | 2140 | 0.1891 | 0.5017 | 0.6269 | 0.9026 | 0.9845 | 0.0 | 0.8937 | 0.8879 | 0.8760 | 0.9310 | 0.6513 | 0.5860 | 0.4583 | nan | 0.0 | nan | 0.9537 | 0.0 | 0.8097 | 0.8323 | 0.8113 | 0.8016 | 0.5129 | 0.4153 | 0.3815 | nan | 0.0 | 0.0 | | 0.0851 | 46.96 | 2160 | 0.1803 | 0.5039 | 0.6309 | 0.9024 | 0.9799 | 0.0 | 0.8952 | 0.9048 | 0.8685 | 0.9292 | 0.6604 | 0.5796 | 0.4910 | nan | 0.0 | nan | 0.9533 | 0.0 | 0.8096 | 0.8372 | 0.8117 | 0.8031 | 0.5144 | 0.4165 | 0.3975 | nan | 0.0 | 0.0 | | 0.061 | 47.39 | 2180 | 0.1820 | 0.5061 | 0.6296 | 0.9045 | 0.9792 | 0.0 | 0.9011 | 0.9064 | 0.8802 | 0.9305 | 0.6656 | 0.5608 | 0.4717 | nan | 0.0 | nan | 0.9546 | 0.0 | 0.8103 | 0.8377 | 0.8172 | 0.8049 | 0.5148 | 0.4388 | 0.3886 | nan | 0.0 | 0.0 | | 0.094 | 47.83 | 2200 | 0.1817 | 0.5077 | 0.6281 | 0.9056 | 0.9821 | 0.0 | 0.8850 | 0.8979 | 0.8903 | 0.9287 | 0.6542 | 0.5599 | 0.4831 | nan | 0.0 | nan | 0.9555 | 0.0 | 0.8091 | 0.8353 | 0.8209 | 0.8058 | 0.5259 | 0.4400 | 0.3926 | nan | 0.0 | 0.0 | | 0.0832 | 48.26 | 2220 | 0.1822 | 0.5050 | 0.6208 | 0.9050 | 0.9810 | 0.0 | 0.8805 | 0.9008 | 0.8954 | 0.9313 | 0.6348 | 0.5361 | 0.4480 | nan | 0.0 | nan | 0.9550 | 0.0 | 0.8100 | 0.8339 | 0.8193 | 0.8038 | 0.5261 | 0.4307 | 0.3757 | nan | 0.0 | 0.0 | | 0.4708 | 48.7 | 2240 | 0.1810 | 0.5065 | 0.6273 | 0.9048 | 0.9828 | 0.0 | 0.8856 | 0.8951 | 0.8892 | 0.9279 | 0.6487 | 0.5682 | 0.4758 | nan | 0.0 | nan | 0.9544 | 0.0 | 0.8085 | 0.8336 | 0.8189 | 0.8051 | 0.5249 | 0.4356 | 0.3900 | nan | 0.0 | 0.0 | | 0.0847 | 49.13 | 2260 | 0.1841 | 0.5043 | 0.6209 | 0.9036 | 0.9783 | 0.0 | 0.8842 | 0.8957 | 0.8915 | 0.9332 | 0.6351 | 0.5396 | 0.4515 | nan | 0.0 | nan | 0.9531 | 0.0 | 0.8072 | 0.8299 | 0.8201 | 0.8014 | 0.5221 | 0.4353 | 0.3777 | nan | 0.0 | 0.0 | | 0.0518 | 49.57 | 2280 | 0.1782 | 0.5066 | 0.6298 | 0.9046 | 0.9814 | 0.0 | 0.8872 | 0.8958 | 0.8905 | 0.9256 | 0.6696 | 0.5721 | 0.4752 | nan | 0.0 | nan | 0.9532 | 0.0 | 0.8083 | 0.8334 | 0.8209 | 0.8067 | 0.5232 | 0.4383 | 0.3889 | nan | 0.0 | 0.0 | | 0.109 | 50.0 | 2300 | 0.1898 | 0.5029 | 0.6197 | 0.9030 | 0.9790 | 0.0 | 0.8826 | 0.8951 | 0.8809 | 0.9396 | 0.6302 | 0.5410 | 0.4486 | nan | 0.0 | nan | 0.9539 | 0.0 | 0.8064 | 0.8292 | 0.8190 | 0.7979 | 0.5135 | 0.4346 | 0.3769 | nan | 0.0 | 0.0 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1

[ "water", "whitewater", "sand", "rocky_terrain", "agricultural", "vegetation", "road", "building", "soil", "boat", "development", "unknown" ]

maratuly/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the maratuly/Pseudo-echo dataset. It achieves the following results on the evaluation set: - Loss: 0.3463 - Mean Iou: 0.7622 - Mean Accuracy: 0.9528 - Overall Accuracy: 0.9581 - Accuracy Unlabeled: nan - Accuracy Lv: 0.9931 - Accuracy Rv: 0.9354 - Accuracy La: 0.9533 - Accuracy Ra: 0.9293 - Iou Unlabeled: 0.0 - Iou Lv: 0.9931 - Iou Rv: 0.9354 - Iou La: 0.9533 - Iou Ra: 0.9293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Rv | Accuracy La | Accuracy Ra | Iou Unlabeled | Iou Lv | Iou Rv | Iou La | Iou Ra | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:| | 1.0046 | 5.0 | 20 | 1.3540 | 0.4931 | 0.7263 | 0.7606 | nan | 0.9742 | 0.6789 | 0.5862 | 0.6657 | 0.0 | 0.6545 | 0.6789 | 0.5540 | 0.5783 | | 0.7304 | 10.0 | 40 | 0.8262 | 0.6901 | 0.8908 | 0.9017 | nan | 0.9906 | 0.8147 | 0.9192 | 0.8387 | 0.0 | 0.9028 | 0.8145 | 0.9046 | 0.8287 | | 0.5957 | 15.0 | 60 | 0.5683 | 0.7395 | 0.9277 | 0.9367 | nan | 0.9896 | 0.9141 | 0.9225 | 0.8845 | 0.0 | 0.9766 | 0.9141 | 0.9225 | 0.8845 | | 0.5094 | 20.0 | 80 | 0.4881 | 0.7541 | 0.9426 | 0.9499 | nan | 0.9909 | 0.9368 | 0.9312 | 0.9116 | 0.0 | 0.9909 | 0.9368 | 0.9312 | 0.9116 | | 0.4681 | 25.0 | 100 | 0.4384 | 0.7689 | 0.9612 | 0.9653 | nan | 0.9942 | 0.9434 | 0.9650 | 0.9421 | 0.0 | 0.9942 | 0.9434 | 0.9650 | 0.9421 | | 0.4045 | 30.0 | 120 | 0.4078 | 0.7639 | 0.9549 | 0.9602 | nan | 0.9933 | 0.9418 | 0.9519 | 0.9328 | 0.0 | 0.9933 | 0.9418 | 0.9519 | 0.9328 | | 0.3956 | 35.0 | 140 | 0.3844 | 0.7664 | 0.9580 | 0.9625 | nan | 0.9939 | 0.9406 | 0.9551 | 0.9425 | 0.0 | 0.9939 | 0.9406 | 0.9551 | 0.9425 | | 0.3736 | 40.0 | 160 | 0.3736 | 0.7687 | 0.9609 | 0.9652 | nan | 0.9961 | 0.9409 | 0.9631 | 0.9436 | 0.0 | 0.9961 | 0.9409 | 0.9631 | 0.9436 | | 0.3431 | 45.0 | 180 | 0.3528 | 0.7622 | 0.9528 | 0.9577 | nan | 0.9923 | 0.9321 | 0.9539 | 0.9327 | 0.0 | 0.9923 | 0.9321 | 0.9539 | 0.9327 | | 0.3428 | 50.0 | 200 | 0.3463 | 0.7622 | 0.9528 | 0.9581 | nan | 0.9931 | 0.9354 | 0.9533 | 0.9293 | 0.0 | 0.9931 | 0.9354 | 0.9533 | 0.9293 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "lv", "rv", "la", "ra" ]

khalidddd/dinat_l_oneformer_coco

# OneFormer OneFormer model trained on the COCO dataset (large-sized version, Dinat backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer). ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_teaser.png) ## Model description OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset. ### How to use Here is how to use this model: ```python from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.jpeg" image = Image.open(requests.get(url, stream=True).raw) # Loading a single model for all three tasks processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_coco_dinat_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_dinat_large") # Semantic Segmentation semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt") semantic_outputs = model(**semantic_inputs) # pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # Instance Segmentation instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt") instance_outputs = model(**instance_inputs) # pass through image_processor for postprocessing predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] # Panoptic Segmentation panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt") panoptic_outputs = model(**panoptic_inputs) # pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] ``` For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer). ### Citation ```bibtex @article{jain2022oneformer, title={{OneFormer: One Transformer to Rule Universal Image Segmentation}}, author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi}, journal={arXiv}, year={2022} } ```

[ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "bridge", "cardboard", "counter", "curtain", "door-stuff", "floor-wood", "flower", "fruit", "gravel", "house", "light", "mirror-stuff", "net", "pillow", "platform", "playingfield", "railroad", "river", "road", "roof", "sand", "sea", "shelf", "snow", "stairs", "tent", "towel", "wall-brick", "wall-stone", "wall-tile", "wall-wood", "water-other", "window-blind", "window-other", "tree-merged", "fence-merged", "ceiling-merged", "sky-other-merged", "cabinet-merged", "table-merged", "floor-other-merged", "pavement-merged", "mountain-merged", "grass-merged", "dirt-merged", "paper-merged", "food-other-merged", "building-other-merged", "rock-merged", "wall-other-merged", "rug-merged" ]

peldrak/segformer-finetuned-riviera2

# segformer-finetuned-riviera2 This model is a fine-tuned version of [peldrak/segformer-finetuned-coasts-final](https://huggingface.co/peldrak/segformer-finetuned-coasts-final) on the peldrak/riviera_labeled_split2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2172 - Mean Iou: 0.5684 - Mean Accuracy: 0.7041 - Overall Accuracy: 0.9037 - Accuracy Water: 0.9782 - Accuracy Whitewater: 0.0031 - Accuracy Sand: 0.9694 - Accuracy Rocky Terrain: 0.8474 - Accuracy Agricultural: 0.8818 - Accuracy Vegetation: 0.9453 - Accuracy Road: 0.5085 - Accuracy Development: 0.7910 - Accuracy Other Natural Terrain: 0.4118 - Accuracy Unknown: nan - Iou Water: 0.9541 - Iou Whitewater: 0.0031 - Iou Sand: 0.8472 - Iou Rocky Terrain: 0.7939 - Iou Agricultural: 0.7881 - Iou Vegetation: 0.8610 - Iou Road: 0.4506 - Iou Development: 0.6761 - Iou Other Natural Terrain: 0.3104 - Iou Unknown: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sand | Accuracy Rocky Terrain | Accuracy Agricultural | Accuracy Vegetation | Accuracy Road | Accuracy Development | Accuracy Other Natural Terrain | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sand | Iou Rocky Terrain | Iou Agricultural | Iou Vegetation | Iou Road | Iou Development | Iou Other Natural Terrain | Iou Unknown | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------:|:--------------------:|:------------------------------:|:----------------:|:---------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------:|:---------------:|:-------------------------:|:-----------:| | 1.6151 | 0.24 | 20 | 1.4156 | 0.0850 | 0.1749 | 0.3514 | 0.9391 | 0.0113 | 0.0 | 0.1553 | 0.0248 | 0.1658 | 0.0035 | 0.1890 | 0.0857 | nan | 0.3854 | 0.0012 | 0.0 | 0.1345 | 0.0110 | 0.1615 | 0.0017 | 0.1034 | 0.0518 | 0.0 | | 1.3149 | 0.49 | 40 | 1.0378 | 0.2239 | 0.3363 | 0.6043 | 0.9571 | 0.0318 | 0.2679 | 0.5472 | 0.0248 | 0.6780 | 0.0 | 0.4390 | 0.0811 | nan | 0.6123 | 0.0184 | 0.2520 | 0.5089 | 0.0141 | 0.5632 | 0.0 | 0.2247 | 0.0456 | 0.0 | | 1.1554 | 0.73 | 60 | 0.7989 | 0.3289 | 0.4401 | 0.7722 | 0.9818 | 0.0 | 0.8295 | 0.6482 | 0.0581 | 0.9606 | 0.0008 | 0.4676 | 0.0146 | nan | 0.8261 | 0.0 | 0.7142 | 0.6253 | 0.0523 | 0.7241 | 0.0008 | 0.3334 | 0.0130 | 0.0 | | 1.0181 | 0.98 | 80 | 0.6544 | 0.3592 | 0.4747 | 0.8043 | 0.9692 | 0.0 | 0.8957 | 0.7580 | 0.3678 | 0.9693 | 0.0001 | 0.3088 | 0.0032 | nan | 0.9121 | 0.0 | 0.6664 | 0.7171 | 0.2835 | 0.7605 | 0.0001 | 0.2489 | 0.0031 | 0.0 | | 1.119 | 1.22 | 100 | 0.5360 | 0.3739 | 0.4880 | 0.8252 | 0.9764 | 0.0 | 0.9123 | 0.7932 | 0.5894 | 0.9776 | 0.0 | 0.1422 | 0.0008 | nan | 0.9300 | 0.0 | 0.7082 | 0.7339 | 0.4630 | 0.7700 | 0.0 | 0.1329 | 0.0008 | 0.0 | | 0.8191 | 1.46 | 120 | 0.4732 | 0.4115 | 0.5270 | 0.8487 | 0.9860 | 0.0 | 0.8930 | 0.8134 | 0.7485 | 0.9750 | 0.0 | 0.3256 | 0.0019 | nan | 0.9360 | 0.0 | 0.7421 | 0.7490 | 0.6024 | 0.8003 | 0.0 | 0.2832 | 0.0018 | 0.0 | | 0.7274 | 1.71 | 140 | 0.4744 | 0.4010 | 0.5172 | 0.8459 | 0.9847 | 0.0 | 0.8945 | 0.7845 | 0.7891 | 0.9754 | 0.0 | 0.2258 | 0.0011 | nan | 0.9485 | 0.0 | 0.7668 | 0.7512 | 0.5327 | 0.8076 | 0.0 | 0.2025 | 0.0011 | 0.0 | | 0.3963 | 1.95 | 160 | 0.4212 | 0.4143 | 0.5289 | 0.8491 | 0.9835 | 0.0 | 0.9249 | 0.8669 | 0.6654 | 0.9796 | 0.0 | 0.3312 | 0.0084 | nan | 0.9514 | 0.0 | 0.7786 | 0.7736 | 0.5481 | 0.7861 | 0.0 | 0.2971 | 0.0082 | 0.0 | | 0.8763 | 2.2 | 180 | 0.3832 | 0.4210 | 0.5390 | 0.8587 | 0.9798 | 0.0 | 0.9471 | 0.7914 | 0.9032 | 0.9688 | 0.0 | 0.2596 | 0.0012 | nan | 0.9483 | 0.0 | 0.8401 | 0.7517 | 0.6183 | 0.8151 | 0.0 | 0.2348 | 0.0012 | 0.0 | | 0.868 | 2.44 | 200 | 0.3764 | 0.4061 | 0.5216 | 0.8472 | 0.9793 | 0.0 | 0.9666 | 0.8200 | 0.7151 | 0.9764 | 0.0 | 0.2267 | 0.0103 | nan | 0.9514 | 0.0 | 0.6926 | 0.7764 | 0.6398 | 0.7901 | 0.0 | 0.2007 | 0.0102 | 0.0 | | 0.7492 | 2.68 | 220 | 0.3502 | 0.4267 | 0.5626 | 0.8629 | 0.9742 | 0.0 | 0.9717 | 0.8473 | 0.9721 | 0.9408 | 0.0 | 0.3376 | 0.0200 | nan | 0.9493 | 0.0 | 0.7071 | 0.7902 | 0.6701 | 0.8511 | 0.0 | 0.2796 | 0.0192 | 0.0 | | 0.9957 | 2.93 | 240 | 0.3382 | 0.4572 | 0.5778 | 0.8688 | 0.9842 | 0.0 | 0.9534 | 0.8633 | 0.7888 | 0.9593 | 0.0 | 0.5910 | 0.0602 | nan | 0.9527 | 0.0 | 0.8231 | 0.8012 | 0.6499 | 0.8112 | 0.0 | 0.4790 | 0.0545 | 0.0 | | 0.416 | 3.17 | 260 | 0.3426 | 0.4475 | 0.5700 | 0.8617 | 0.9725 | 0.0 | 0.9648 | 0.8810 | 0.7076 | 0.9640 | 0.0 | 0.5665 | 0.0738 | nan | 0.9499 | 0.0 | 0.7928 | 0.7900 | 0.6095 | 0.8113 | 0.0 | 0.4584 | 0.0628 | 0.0 | | 0.3574 | 3.41 | 280 | 0.3294 | 0.4534 | 0.5701 | 0.8659 | 0.9835 | 0.0 | 0.9498 | 0.7931 | 0.7716 | 0.9674 | 0.0 | 0.6177 | 0.0481 | nan | 0.9441 | 0.0 | 0.8365 | 0.7578 | 0.6388 | 0.8089 | 0.0 | 0.5038 | 0.0438 | 0.0 | | 0.2504 | 3.66 | 300 | 0.3045 | 0.4381 | 0.5590 | 0.8596 | 0.9787 | 0.0 | 0.9376 | 0.8223 | 0.8269 | 0.9575 | 0.0 | 0.4316 | 0.0765 | nan | 0.9492 | 0.0 | 0.7856 | 0.7767 | 0.6163 | 0.8214 | 0.0 | 0.3681 | 0.0637 | 0.0 | | 0.3342 | 3.9 | 320 | 0.3037 | 0.4675 | 0.5986 | 0.8712 | 0.9712 | 0.0 | 0.9625 | 0.9100 | 0.7707 | 0.9492 | 0.0 | 0.7293 | 0.0942 | nan | 0.9492 | 0.0 | 0.8752 | 0.7814 | 0.6541 | 0.8159 | 0.0 | 0.5153 | 0.0836 | 0.0 | | 0.7272 | 4.15 | 340 | 0.3025 | 0.4617 | 0.5795 | 0.8694 | 0.9742 | 0.0 | 0.9665 | 0.8396 | 0.7763 | 0.9678 | 0.0 | 0.6223 | 0.0689 | nan | 0.9486 | 0.0 | 0.8327 | 0.7980 | 0.6451 | 0.8157 | 0.0 | 0.5163 | 0.0605 | 0.0 | | 0.452 | 4.39 | 360 | 0.2799 | 0.4589 | 0.5891 | 0.8688 | 0.9723 | 0.0 | 0.9796 | 0.8554 | 0.8382 | 0.9439 | 0.0 | 0.5294 | 0.1836 | nan | 0.9510 | 0.0 | 0.7637 | 0.7978 | 0.6793 | 0.8367 | 0.0 | 0.4202 | 0.1405 | 0.0 | | 0.2372 | 4.63 | 380 | 0.2749 | 0.4642 | 0.5891 | 0.8765 | 0.9755 | 0.0 | 0.9684 | 0.8436 | 0.9190 | 0.9569 | 0.0 | 0.5282 | 0.1100 | nan | 0.9493 | 0.0 | 0.7809 | 0.7960 | 0.7737 | 0.8389 | 0.0 | 0.4060 | 0.0969 | 0.0 | | 1.3141 | 4.88 | 400 | 0.2875 | 0.4723 | 0.6004 | 0.8726 | 0.9700 | 0.0 | 0.9573 | 0.8714 | 0.7878 | 0.9529 | 0.0 | 0.7587 | 0.1054 | nan | 0.9449 | 0.0 | 0.8715 | 0.7964 | 0.6752 | 0.8177 | 0.0 | 0.5243 | 0.0929 | 0.0 | | 0.605 | 5.12 | 420 | 0.2752 | 0.4653 | 0.6031 | 0.8722 | 0.9752 | 0.0 | 0.9356 | 0.8359 | 0.9082 | 0.9332 | 0.0010 | 0.7404 | 0.0986 | nan | 0.9462 | 0.0 | 0.8710 | 0.7879 | 0.6188 | 0.8482 | 0.0010 | 0.4905 | 0.0892 | 0.0 | | 0.3456 | 5.37 | 440 | 0.2907 | 0.4721 | 0.5955 | 0.8717 | 0.9761 | 0.0 | 0.9655 | 0.8499 | 0.7721 | 0.9535 | 0.0 | 0.6785 | 0.1641 | nan | 0.9531 | 0.0 | 0.8718 | 0.7986 | 0.6161 | 0.8207 | 0.0 | 0.5316 | 0.1291 | 0.0 | | 0.4065 | 5.61 | 460 | 0.2588 | 0.4707 | 0.6035 | 0.8751 | 0.9735 | 0.0 | 0.9728 | 0.8768 | 0.8025 | 0.9485 | 0.0 | 0.5470 | 0.3100 | nan | 0.9528 | 0.0 | 0.7724 | 0.8075 | 0.6647 | 0.8490 | 0.0 | 0.4260 | 0.2346 | 0.0 | | 0.5515 | 5.85 | 480 | 0.2509 | 0.4817 | 0.6075 | 0.8803 | 0.9607 | 0.0 | 0.9654 | 0.8643 | 0.8910 | 0.9597 | 0.0000 | 0.6962 | 0.1303 | nan | 0.9444 | 0.0 | 0.8502 | 0.7854 | 0.7400 | 0.8362 | 0.0000 | 0.5515 | 0.1096 | 0.0 | | 0.7913 | 6.1 | 500 | 0.2392 | 0.4835 | 0.6074 | 0.8854 | 0.9772 | 0.0 | 0.9578 | 0.8821 | 0.9384 | 0.9594 | 0.0244 | 0.6102 | 0.1169 | nan | 0.9511 | 0.0 | 0.8279 | 0.8049 | 0.7936 | 0.8365 | 0.0244 | 0.4897 | 0.1072 | 0.0 | | 0.3186 | 6.34 | 520 | 0.2556 | 0.4736 | 0.6009 | 0.8775 | 0.9769 | 0.0 | 0.9765 | 0.8601 | 0.9007 | 0.9478 | 0.0386 | 0.5438 | 0.1636 | nan | 0.9521 | 0.0 | 0.7805 | 0.8029 | 0.7483 | 0.8328 | 0.0385 | 0.4392 | 0.1420 | 0.0 | | 0.2549 | 6.59 | 540 | 0.2342 | 0.5138 | 0.6555 | 0.8909 | 0.9748 | 0.0 | 0.9533 | 0.8514 | 0.8781 | 0.9276 | 0.0280 | 0.7772 | 0.5094 | nan | 0.9512 | 0.0 | 0.8886 | 0.7891 | 0.7547 | 0.8588 | 0.0279 | 0.5303 | 0.3371 | 0.0 | | 0.3034 | 6.83 | 560 | 0.2574 | 0.4892 | 0.6058 | 0.8794 | 0.9819 | 0.0 | 0.9563 | 0.7993 | 0.7777 | 0.9666 | 0.0356 | 0.7305 | 0.2041 | nan | 0.9505 | 0.0 | 0.8925 | 0.7697 | 0.6993 | 0.8226 | 0.0355 | 0.5656 | 0.1561 | 0.0 | | 0.2759 | 7.07 | 580 | 0.2417 | 0.5055 | 0.6355 | 0.8899 | 0.9735 | 0.0 | 0.9789 | 0.8397 | 0.9129 | 0.9483 | 0.0845 | 0.6354 | 0.3465 | nan | 0.9530 | 0.0 | 0.8171 | 0.7897 | 0.7572 | 0.8530 | 0.0840 | 0.5311 | 0.2698 | 0.0 | | 0.4661 | 7.32 | 600 | 0.2272 | 0.5198 | 0.6647 | 0.8944 | 0.9787 | 0.0 | 0.9780 | 0.8505 | 0.8440 | 0.9286 | 0.0697 | 0.6809 | 0.6515 | nan | 0.9552 | 0.0 | 0.8088 | 0.7960 | 0.7438 | 0.8682 | 0.0691 | 0.5247 | 0.4328 | 0.0 | | 0.1629 | 7.56 | 620 | 0.2331 | 0.5248 | 0.6639 | 0.8916 | 0.9782 | 0.0 | 0.9591 | 0.8574 | 0.8627 | 0.9262 | 0.1268 | 0.7364 | 0.5286 | nan | 0.9539 | 0.0 | 0.8723 | 0.8011 | 0.6613 | 0.8493 | 0.1252 | 0.5734 | 0.4116 | 0.0 | | 0.1212 | 7.8 | 640 | 0.2431 | 0.5138 | 0.6604 | 0.8841 | 0.9727 | 0.0 | 0.9811 | 0.8664 | 0.7576 | 0.9201 | 0.1052 | 0.7484 | 0.5919 | nan | 0.9543 | 0.0 | 0.8160 | 0.7970 | 0.6137 | 0.8406 | 0.1039 | 0.5809 | 0.4314 | 0.0 | | 0.4444 | 8.05 | 660 | 0.2277 | 0.5174 | 0.6470 | 0.8938 | 0.9807 | 0.0 | 0.9616 | 0.8141 | 0.9088 | 0.9461 | 0.0907 | 0.7961 | 0.3247 | nan | 0.9518 | 0.0 | 0.8687 | 0.7832 | 0.7780 | 0.8490 | 0.0901 | 0.5943 | 0.2586 | 0.0 | | 0.2176 | 8.29 | 680 | 0.2123 | 0.5327 | 0.6734 | 0.9001 | 0.9774 | 0.0 | 0.9701 | 0.8768 | 0.8724 | 0.9365 | 0.1111 | 0.8156 | 0.5004 | nan | 0.9541 | 0.0 | 0.8617 | 0.8040 | 0.7823 | 0.8657 | 0.1092 | 0.5785 | 0.3715 | 0.0 | | 0.4515 | 8.54 | 700 | 0.2545 | 0.5198 | 0.6586 | 0.8886 | 0.9852 | 0.0 | 0.9652 | 0.8208 | 0.9443 | 0.9214 | 0.2593 | 0.7064 | 0.3252 | nan | 0.9517 | 0.0 | 0.8370 | 0.7896 | 0.6680 | 0.8531 | 0.2441 | 0.5716 | 0.2833 | 0.0 | | 0.2276 | 8.78 | 720 | 0.2427 | 0.5161 | 0.6488 | 0.8869 | 0.9730 | 0.0 | 0.9757 | 0.8662 | 0.8668 | 0.9466 | 0.2834 | 0.7261 | 0.2011 | nan | 0.9521 | 0.0 | 0.8300 | 0.8106 | 0.7177 | 0.8432 | 0.2632 | 0.5765 | 0.1681 | 0.0 | | 0.1664 | 9.02 | 740 | 0.2403 | 0.5174 | 0.6477 | 0.8924 | 0.9731 | 0.0 | 0.9722 | 0.8425 | 0.8760 | 0.9551 | 0.1686 | 0.7817 | 0.2597 | nan | 0.9516 | 0.0 | 0.8776 | 0.7910 | 0.7536 | 0.8527 | 0.1648 | 0.5767 | 0.2062 | 0.0 | | 0.1256 | 9.27 | 760 | 0.2232 | 0.5293 | 0.6719 | 0.8913 | 0.9753 | 0.0 | 0.9694 | 0.8536 | 0.8344 | 0.9224 | 0.1563 | 0.7175 | 0.6187 | nan | 0.9520 | 0.0 | 0.8567 | 0.7925 | 0.7405 | 0.8617 | 0.1530 | 0.5946 | 0.3420 | 0.0 | | 0.336 | 9.51 | 780 | 0.2125 | 0.5451 | 0.6873 | 0.9017 | 0.9794 | 0.0 | 0.9741 | 0.8796 | 0.8795 | 0.9323 | 0.2422 | 0.7836 | 0.5153 | nan | 0.9522 | 0.0 | 0.8579 | 0.7945 | 0.7699 | 0.8705 | 0.2256 | 0.5934 | 0.3870 | 0.0 | | 0.2018 | 9.76 | 800 | 0.2224 | 0.5404 | 0.6681 | 0.8984 | 0.9822 | 0.0 | 0.9652 | 0.8555 | 0.8950 | 0.9519 | 0.3185 | 0.7221 | 0.3220 | nan | 0.9531 | 0.0 | 0.8535 | 0.8067 | 0.7567 | 0.8584 | 0.2949 | 0.6183 | 0.2621 | 0.0 | | 0.1682 | 10.0 | 820 | 0.2187 | 0.5320 | 0.6820 | 0.8924 | 0.9718 | 0.0 | 0.9814 | 0.8696 | 0.8334 | 0.9177 | 0.1967 | 0.8444 | 0.5228 | nan | 0.9521 | 0.0 | 0.8742 | 0.7917 | 0.7417 | 0.8515 | 0.1852 | 0.5529 | 0.3707 | 0.0 | | 0.176 | 10.24 | 840 | 0.2228 | 0.5335 | 0.6621 | 0.8964 | 0.9811 | 0.0 | 0.9741 | 0.8623 | 0.8578 | 0.9513 | 0.2482 | 0.7291 | 0.3547 | nan | 0.9553 | 0.0 | 0.8520 | 0.8082 | 0.7474 | 0.8587 | 0.2369 | 0.6102 | 0.2662 | 0.0 | | 0.4021 | 10.49 | 860 | 0.2221 | 0.5370 | 0.6793 | 0.8973 | 0.9742 | 0.0 | 0.9634 | 0.8676 | 0.9133 | 0.9356 | 0.2970 | 0.7645 | 0.3982 | nan | 0.9533 | 0.0 | 0.8641 | 0.7969 | 0.7135 | 0.8725 | 0.2745 | 0.5827 | 0.3125 | 0.0 | | 0.2189 | 10.73 | 880 | 0.2594 | 0.5157 | 0.6489 | 0.8857 | 0.9846 | 0.0 | 0.9684 | 0.8699 | 0.7198 | 0.9467 | 0.2024 | 0.8028 | 0.3458 | nan | 0.9562 | 0.0 | 0.8654 | 0.8160 | 0.6462 | 0.8367 | 0.1928 | 0.5791 | 0.2648 | 0.0 | | 0.218 | 10.98 | 900 | 0.2445 | 0.5208 | 0.6623 | 0.8853 | 0.9734 | 0.0 | 0.9834 | 0.8650 | 0.7271 | 0.9380 | 0.2457 | 0.7985 | 0.4296 | nan | 0.9561 | 0.0 | 0.8233 | 0.8043 | 0.6560 | 0.8406 | 0.2276 | 0.5827 | 0.3177 | 0.0 | | 0.3402 | 11.22 | 920 | 0.2789 | 0.5181 | 0.6438 | 0.8821 | 0.9728 | 0.0 | 0.9665 | 0.8725 | 0.6840 | 0.9599 | 0.2502 | 0.7863 | 0.3024 | nan | 0.9507 | 0.0 | 0.8598 | 0.8023 | 0.6413 | 0.8265 | 0.2387 | 0.6396 | 0.2225 | 0.0 | | 0.2232 | 11.46 | 940 | 0.2219 | 0.5387 | 0.6810 | 0.8920 | 0.9705 | 0.0 | 0.9498 | 0.9055 | 0.7311 | 0.9404 | 0.2678 | 0.7632 | 0.6004 | nan | 0.9490 | 0.0 | 0.8674 | 0.7808 | 0.6552 | 0.8561 | 0.2553 | 0.6268 | 0.3965 | 0.0 | | 0.1727 | 11.71 | 960 | 0.2741 | 0.5328 | 0.6593 | 0.8880 | 0.9812 | 0.0 | 0.9682 | 0.8166 | 0.7600 | 0.9648 | 0.4642 | 0.7672 | 0.2112 | nan | 0.9554 | 0.0 | 0.8701 | 0.7751 | 0.6763 | 0.8300 | 0.4070 | 0.6312 | 0.1824 | 0.0 | | 0.3027 | 11.95 | 980 | 0.2126 | 0.5477 | 0.6784 | 0.9005 | 0.9774 | 0.0 | 0.9702 | 0.8705 | 0.8597 | 0.9570 | 0.3690 | 0.7887 | 0.3127 | nan | 0.9542 | 0.0 | 0.8645 | 0.8041 | 0.7646 | 0.8582 | 0.3354 | 0.6361 | 0.2600 | 0.0 | | 0.2245 | 12.2 | 1000 | 0.2490 | 0.5254 | 0.6482 | 0.8899 | 0.9833 | 0.0 | 0.9638 | 0.8135 | 0.7925 | 0.9626 | 0.2752 | 0.8146 | 0.2286 | nan | 0.9548 | 0.0 | 0.8727 | 0.7852 | 0.7239 | 0.8352 | 0.2619 | 0.6371 | 0.1834 | 0.0 | | 0.1551 | 12.44 | 1020 | 0.2332 | 0.5364 | 0.6875 | 0.8924 | 0.9766 | 0.0 | 0.9856 | 0.8760 | 0.8783 | 0.9118 | 0.3322 | 0.6270 | 0.5995 | nan | 0.9571 | 0.0 | 0.7598 | 0.8028 | 0.7604 | 0.8562 | 0.2982 | 0.5043 | 0.4247 | 0.0 | | 0.3828 | 12.68 | 1040 | 0.2138 | 0.5437 | 0.6753 | 0.8979 | 0.9799 | 0.0 | 0.9477 | 0.8984 | 0.8723 | 0.9574 | 0.4473 | 0.6534 | 0.3209 | nan | 0.9542 | 0.0 | 0.8187 | 0.8072 | 0.7887 | 0.8580 | 0.3793 | 0.5674 | 0.2640 | 0.0 | | 0.0929 | 12.93 | 1060 | 0.2544 | 0.5186 | 0.6501 | 0.8855 | 0.9783 | 0.0 | 0.9709 | 0.8634 | 0.7491 | 0.9542 | 0.3067 | 0.6526 | 0.3757 | nan | 0.9549 | 0.0 | 0.8048 | 0.8069 | 0.6771 | 0.8418 | 0.2878 | 0.5185 | 0.2948 | 0.0 | | 0.2362 | 13.17 | 1080 | 0.2353 | 0.5278 | 0.6661 | 0.8877 | 0.9763 | 0.0 | 0.9807 | 0.8577 | 0.8281 | 0.9374 | 0.3786 | 0.6547 | 0.3816 | nan | 0.9531 | 0.0 | 0.7907 | 0.8045 | 0.7260 | 0.8447 | 0.3395 | 0.5196 | 0.3001 | 0.0 | | 0.1954 | 13.41 | 1100 | 0.2073 | 0.5580 | 0.7009 | 0.9019 | 0.9762 | 0.0 | 0.9681 | 0.8902 | 0.8184 | 0.9348 | 0.3367 | 0.7731 | 0.6111 | nan | 0.9548 | 0.0 | 0.8642 | 0.8120 | 0.7373 | 0.8679 | 0.3120 | 0.6404 | 0.3918 | 0.0 | | 0.2412 | 13.66 | 1120 | 0.2144 | 0.5520 | 0.6946 | 0.9022 | 0.9808 | 0.0 | 0.9764 | 0.8460 | 0.8418 | 0.9352 | 0.3048 | 0.7065 | 0.6605 | nan | 0.9554 | 0.0 | 0.8265 | 0.7963 | 0.7536 | 0.8773 | 0.2868 | 0.6078 | 0.4162 | 0.0 | | 0.2167 | 13.9 | 1140 | 0.2111 | 0.5577 | 0.6947 | 0.9005 | 0.9753 | 0.0 | 0.9744 | 0.8242 | 0.8303 | 0.9442 | 0.3808 | 0.7631 | 0.5597 | nan | 0.9516 | 0.0 | 0.8487 | 0.7825 | 0.7616 | 0.8716 | 0.3440 | 0.6640 | 0.3534 | 0.0 | | 0.0952 | 14.15 | 1160 | 0.2637 | 0.5279 | 0.6724 | 0.8863 | 0.9740 | 0.0 | 0.9860 | 0.8767 | 0.7933 | 0.9334 | 0.4219 | 0.5904 | 0.4754 | nan | 0.9531 | 0.0 | 0.7480 | 0.8056 | 0.7181 | 0.8535 | 0.3615 | 0.4894 | 0.3497 | 0.0 | | 0.1032 | 14.39 | 1180 | 0.2484 | 0.5403 | 0.6730 | 0.8921 | 0.9769 | 0.0 | 0.9724 | 0.8537 | 0.7452 | 0.9591 | 0.4251 | 0.8313 | 0.2934 | nan | 0.9553 | 0.0 | 0.8613 | 0.7993 | 0.6934 | 0.8405 | 0.3786 | 0.6417 | 0.2331 | 0.0 | | 0.2301 | 14.63 | 1200 | 0.2167 | 0.5450 | 0.6843 | 0.8969 | 0.9752 | 0.0 | 0.9785 | 0.8525 | 0.9088 | 0.9402 | 0.4511 | 0.6742 | 0.3782 | nan | 0.9534 | 0.0 | 0.8015 | 0.7952 | 0.7726 | 0.8610 | 0.3911 | 0.5705 | 0.3044 | 0.0 | | 0.3602 | 14.88 | 1220 | 0.2154 | 0.5522 | 0.7015 | 0.8960 | 0.9738 | 0.0 | 0.9770 | 0.8642 | 0.7977 | 0.9202 | 0.3336 | 0.7938 | 0.6533 | nan | 0.9495 | 0.0 | 0.8433 | 0.7947 | 0.7288 | 0.8638 | 0.3085 | 0.6520 | 0.3817 | 0.0 | | 0.1081 | 15.12 | 1240 | 0.2512 | 0.5321 | 0.6634 | 0.8872 | 0.9717 | 0.0 | 0.9707 | 0.8631 | 0.7590 | 0.9627 | 0.4839 | 0.6768 | 0.2828 | nan | 0.9508 | 0.0 | 0.8181 | 0.7973 | 0.6834 | 0.8401 | 0.4158 | 0.5872 | 0.2285 | 0.0 | | 0.138 | 15.37 | 1260 | 0.1995 | 0.5660 | 0.7144 | 0.9028 | 0.9747 | 0.0 | 0.9687 | 0.8627 | 0.8566 | 0.9240 | 0.3978 | 0.8400 | 0.6048 | nan | 0.9511 | 0.0 | 0.8700 | 0.7965 | 0.7700 | 0.8698 | 0.3606 | 0.6542 | 0.3876 | 0.0 | | 0.1254 | 15.61 | 1280 | 0.2302 | 0.5517 | 0.6866 | 0.8994 | 0.9652 | 0.0 | 0.9730 | 0.8691 | 0.9000 | 0.9510 | 0.4196 | 0.7253 | 0.3762 | nan | 0.9483 | 0.0 | 0.8413 | 0.7915 | 0.7688 | 0.8603 | 0.3770 | 0.6323 | 0.2976 | 0.0 | | 0.0939 | 15.85 | 1300 | 0.2252 | 0.5506 | 0.6884 | 0.8930 | 0.9853 | 0.0 | 0.9585 | 0.8488 | 0.7720 | 0.9348 | 0.4332 | 0.7397 | 0.5235 | nan | 0.9494 | 0.0 | 0.8510 | 0.8047 | 0.6791 | 0.8478 | 0.3833 | 0.6327 | 0.3582 | 0.0 | | 0.079 | 16.1 | 1320 | 0.2439 | 0.5381 | 0.6686 | 0.8888 | 0.9764 | 0.0 | 0.9686 | 0.8422 | 0.7464 | 0.9577 | 0.4522 | 0.6807 | 0.3930 | nan | 0.9544 | 0.0 | 0.8271 | 0.7930 | 0.6805 | 0.8475 | 0.3958 | 0.6125 | 0.2706 | 0.0 | | 0.1409 | 16.34 | 1340 | 0.2244 | 0.5567 | 0.7105 | 0.8928 | 0.9660 | 0.0 | 0.9787 | 0.8779 | 0.7602 | 0.9191 | 0.4474 | 0.7951 | 0.6504 | nan | 0.9501 | 0.0 | 0.8409 | 0.7902 | 0.6957 | 0.8539 | 0.3926 | 0.6588 | 0.3851 | 0.0 | | 0.3714 | 16.59 | 1360 | 0.2480 | 0.5451 | 0.6732 | 0.8965 | 0.9827 | 0.0 | 0.9692 | 0.8453 | 0.9122 | 0.9530 | 0.4942 | 0.7125 | 0.1900 | nan | 0.9553 | 0.0 | 0.8439 | 0.8058 | 0.7744 | 0.8417 | 0.4262 | 0.6336 | 0.1704 | 0.0 | | 0.2288 | 16.83 | 1380 | 0.2100 | 0.5634 | 0.7029 | 0.9011 | 0.9767 | 0.0 | 0.9704 | 0.8758 | 0.8995 | 0.9382 | 0.5412 | 0.7601 | 0.3645 | nan | 0.9520 | 0.0 | 0.8497 | 0.8046 | 0.7688 | 0.8513 | 0.4448 | 0.6575 | 0.3057 | 0.0 | | 0.2297 | 17.07 | 1400 | 0.2083 | 0.5629 | 0.7053 | 0.9023 | 0.9720 | 0.0 | 0.9721 | 0.8785 | 0.9438 | 0.9305 | 0.4819 | 0.7221 | 0.4466 | nan | 0.9514 | 0.0 | 0.8451 | 0.7942 | 0.7728 | 0.8596 | 0.4149 | 0.6409 | 0.3503 | 0.0 | | 0.1961 | 17.32 | 1420 | 0.2102 | 0.5588 | 0.6962 | 0.9023 | 0.9768 | 0.0 | 0.9704 | 0.8353 | 0.8819 | 0.9435 | 0.4188 | 0.7559 | 0.4831 | nan | 0.9528 | 0.0 | 0.8311 | 0.7837 | 0.7888 | 0.8673 | 0.3779 | 0.6372 | 0.3493 | 0.0 | | 0.1938 | 17.56 | 1440 | 0.2151 | 0.5554 | 0.6917 | 0.8999 | 0.9775 | 0.0 | 0.9638 | 0.8859 | 0.8447 | 0.9448 | 0.4199 | 0.8143 | 0.3742 | nan | 0.9525 | 0.0 | 0.8517 | 0.8075 | 0.7688 | 0.8574 | 0.3760 | 0.6574 | 0.2831 | 0.0 | | 0.283 | 17.8 | 1460 | 0.2285 | 0.5502 | 0.6886 | 0.8971 | 0.9752 | 0.0 | 0.9658 | 0.8285 | 0.7796 | 0.9470 | 0.3540 | 0.8388 | 0.5087 | nan | 0.9553 | 0.0 | 0.8671 | 0.7779 | 0.7223 | 0.8636 | 0.3317 | 0.6615 | 0.3227 | 0.0 | | 0.2744 | 18.05 | 1480 | 0.2301 | 0.5532 | 0.6929 | 0.8974 | 0.9741 | 0.0 | 0.9831 | 0.8644 | 0.7817 | 0.9473 | 0.4503 | 0.7601 | 0.4751 | nan | 0.9550 | 0.0 | 0.8186 | 0.7977 | 0.7268 | 0.8598 | 0.3918 | 0.6399 | 0.3421 | 0.0 | | 0.0956 | 18.29 | 1500 | 0.2140 | 0.5637 | 0.7149 | 0.9026 | 0.9717 | 0.0 | 0.9813 | 0.8585 | 0.9085 | 0.9223 | 0.4672 | 0.7189 | 0.6055 | nan | 0.9535 | 0.0 | 0.8169 | 0.7947 | 0.7597 | 0.8727 | 0.3985 | 0.6146 | 0.4264 | 0.0 | | 0.0477 | 18.54 | 1520 | 0.2209 | 0.5551 | 0.6878 | 0.8975 | 0.9692 | 0.0 | 0.9696 | 0.8742 | 0.8338 | 0.9573 | 0.5019 | 0.7648 | 0.3190 | nan | 0.9495 | 0.0 | 0.8549 | 0.7934 | 0.7518 | 0.8579 | 0.4426 | 0.6592 | 0.2416 | 0.0 | | 0.3598 | 18.78 | 1540 | 0.2430 | 0.5330 | 0.6704 | 0.8890 | 0.9762 | 0.0 | 0.9813 | 0.8502 | 0.8685 | 0.9383 | 0.4623 | 0.5791 | 0.3782 | nan | 0.9527 | 0.0 | 0.7785 | 0.7953 | 0.7300 | 0.8520 | 0.4042 | 0.5312 | 0.2861 | 0.0 | | 0.1007 | 19.02 | 1560 | 0.2182 | 0.5626 | 0.7106 | 0.8983 | 0.9772 | 0.0 | 0.9776 | 0.8621 | 0.8359 | 0.9232 | 0.4950 | 0.7717 | 0.5527 | nan | 0.9560 | 0.0 | 0.8457 | 0.7943 | 0.7189 | 0.8602 | 0.4321 | 0.6575 | 0.3613 | 0.0 | | 0.1631 | 19.27 | 1580 | 0.2335 | 0.5543 | 0.6887 | 0.8983 | 0.9767 | 0.0 | 0.9741 | 0.8444 | 0.8350 | 0.9533 | 0.5018 | 0.6999 | 0.4135 | nan | 0.9555 | 0.0 | 0.8328 | 0.7923 | 0.7482 | 0.8629 | 0.4291 | 0.6299 | 0.2924 | 0.0 | | 0.4344 | 19.51 | 1600 | 0.2087 | 0.5602 | 0.6987 | 0.9025 | 0.9797 | 0.0 | 0.9657 | 0.8446 | 0.8997 | 0.9404 | 0.4632 | 0.7297 | 0.4654 | nan | 0.9533 | 0.0 | 0.8205 | 0.7975 | 0.7871 | 0.8685 | 0.4160 | 0.6058 | 0.3531 | 0.0 | | 0.0956 | 19.76 | 1620 | 0.2287 | 0.5574 | 0.6894 | 0.9007 | 0.9774 | 0.0 | 0.9667 | 0.8465 | 0.8975 | 0.9511 | 0.4834 | 0.8058 | 0.2760 | nan | 0.9546 | 0.0 | 0.8582 | 0.7967 | 0.7856 | 0.8517 | 0.4333 | 0.6646 | 0.2291 | 0.0 | | 0.1634 | 20.0 | 1640 | 0.2126 | 0.5576 | 0.6997 | 0.9003 | 0.9725 | 0.0 | 0.9783 | 0.8538 | 0.8832 | 0.9374 | 0.4600 | 0.7096 | 0.5020 | nan | 0.9523 | 0.0 | 0.8042 | 0.7903 | 0.7842 | 0.8664 | 0.4073 | 0.5994 | 0.3719 | 0.0 | | 0.1048 | 20.24 | 1660 | 0.2254 | 0.5588 | 0.6831 | 0.9017 | 0.9784 | 0.0 | 0.9590 | 0.8543 | 0.8564 | 0.9646 | 0.4542 | 0.7924 | 0.2885 | nan | 0.9534 | 0.0 | 0.8616 | 0.7995 | 0.7886 | 0.8552 | 0.4168 | 0.6842 | 0.2289 | 0.0 | | 0.1636 | 20.49 | 1680 | 0.2063 | 0.5713 | 0.7097 | 0.9046 | 0.9746 | 0.0 | 0.9741 | 0.8597 | 0.8250 | 0.9483 | 0.5023 | 0.7629 | 0.5409 | nan | 0.9537 | 0.0 | 0.8468 | 0.7972 | 0.7660 | 0.8728 | 0.4351 | 0.6817 | 0.3597 | 0.0 | | 0.0753 | 20.73 | 1700 | 0.2005 | 0.5738 | 0.7122 | 0.9053 | 0.9742 | 0.0 | 0.9762 | 0.8687 | 0.8586 | 0.9470 | 0.5485 | 0.7920 | 0.4442 | nan | 0.9536 | 0.0 | 0.8456 | 0.8049 | 0.7854 | 0.8662 | 0.4710 | 0.6828 | 0.3289 | 0.0 | | 0.1346 | 20.98 | 1720 | 0.1977 | 0.5762 | 0.7246 | 0.9065 | 0.9759 | 0.0 | 0.9775 | 0.8651 | 0.8993 | 0.9239 | 0.5007 | 0.7995 | 0.5794 | nan | 0.9548 | 0.0 | 0.8379 | 0.7961 | 0.7913 | 0.8679 | 0.4392 | 0.6660 | 0.4089 | 0.0 | | 0.1527 | 21.22 | 1740 | 0.2123 | 0.5662 | 0.7087 | 0.9025 | 0.9759 | 0.0002 | 0.9621 | 0.8524 | 0.9264 | 0.9353 | 0.5505 | 0.7677 | 0.4078 | nan | 0.9542 | 0.0002 | 0.8533 | 0.7906 | 0.7497 | 0.8610 | 0.4724 | 0.6514 | 0.3290 | 0.0 | | 0.1149 | 21.46 | 1760 | 0.2262 | 0.5623 | 0.6926 | 0.9003 | 0.9734 | 0.0 | 0.9632 | 0.8380 | 0.8228 | 0.9623 | 0.5167 | 0.7898 | 0.3669 | nan | 0.9523 | 0.0 | 0.8536 | 0.7847 | 0.7638 | 0.8602 | 0.4552 | 0.6804 | 0.2725 | 0.0 | | 0.3336 | 21.71 | 1780 | 0.2176 | 0.5647 | 0.7053 | 0.9023 | 0.9713 | 0.0003 | 0.9803 | 0.8546 | 0.8731 | 0.9426 | 0.4990 | 0.7902 | 0.4361 | nan | 0.9543 | 0.0003 | 0.8273 | 0.7953 | 0.7841 | 0.8632 | 0.4397 | 0.6565 | 0.3267 | 0.0 | | 0.0924 | 21.95 | 1800 | 0.2271 | 0.5608 | 0.6933 | 0.9011 | 0.9834 | 0.0 | 0.9709 | 0.8338 | 0.8657 | 0.9472 | 0.4902 | 0.7430 | 0.4059 | nan | 0.9577 | 0.0 | 0.8354 | 0.7946 | 0.7788 | 0.8591 | 0.4359 | 0.6520 | 0.2945 | 0.0 | | 0.256 | 22.2 | 1820 | 0.2140 | 0.5659 | 0.7010 | 0.9038 | 0.9820 | 0.0 | 0.9697 | 0.8401 | 0.9119 | 0.9435 | 0.5189 | 0.7867 | 0.3560 | nan | 0.9560 | 0.0 | 0.8462 | 0.7987 | 0.8078 | 0.8582 | 0.4515 | 0.6594 | 0.2817 | 0.0 | | 0.202 | 22.44 | 1840 | 0.2358 | 0.5589 | 0.6907 | 0.9001 | 0.9766 | 0.0 | 0.9760 | 0.8359 | 0.8945 | 0.9526 | 0.5406 | 0.7420 | 0.2981 | nan | 0.9553 | 0.0 | 0.8391 | 0.7915 | 0.7924 | 0.8522 | 0.4572 | 0.6613 | 0.2397 | 0.0 | | 0.1456 | 22.68 | 1860 | 0.2115 | 0.5669 | 0.7125 | 0.9016 | 0.9762 | 0.0 | 0.9801 | 0.8582 | 0.8871 | 0.9267 | 0.5130 | 0.8054 | 0.4657 | nan | 0.9556 | 0.0 | 0.8310 | 0.8008 | 0.7886 | 0.8585 | 0.4425 | 0.6588 | 0.3335 | 0.0 | | 0.1198 | 22.93 | 1880 | 0.2233 | 0.5631 | 0.6997 | 0.9020 | 0.9745 | 0.0 | 0.9731 | 0.8563 | 0.9047 | 0.9484 | 0.5580 | 0.7858 | 0.2967 | nan | 0.9529 | 0.0 | 0.8405 | 0.8011 | 0.7914 | 0.8561 | 0.4715 | 0.6652 | 0.2520 | 0.0 | | 0.0804 | 23.17 | 1900 | 0.2075 | 0.5719 | 0.7090 | 0.9047 | 0.9791 | 0.0 | 0.9686 | 0.8542 | 0.8664 | 0.9434 | 0.5174 | 0.7723 | 0.4800 | nan | 0.9552 | 0.0 | 0.8456 | 0.7995 | 0.7842 | 0.8636 | 0.4512 | 0.6765 | 0.3429 | 0.0 | | 0.0779 | 23.41 | 1920 | 0.2217 | 0.5630 | 0.7025 | 0.9007 | 0.9736 | 0.0001 | 0.9774 | 0.8596 | 0.8837 | 0.9422 | 0.5470 | 0.7558 | 0.3832 | nan | 0.9544 | 0.0001 | 0.8258 | 0.7991 | 0.7705 | 0.8559 | 0.4602 | 0.6611 | 0.3032 | 0.0 | | 0.1159 | 23.66 | 1940 | 0.2122 | 0.5690 | 0.7134 | 0.9030 | 0.9772 | 0.0002 | 0.9767 | 0.8574 | 0.8913 | 0.9275 | 0.4955 | 0.7959 | 0.4990 | nan | 0.9538 | 0.0002 | 0.8307 | 0.8004 | 0.7766 | 0.8612 | 0.4343 | 0.6650 | 0.3683 | 0.0 | | 0.1 | 23.9 | 1960 | 0.1988 | 0.5762 | 0.7116 | 0.9078 | 0.9834 | 0.0 | 0.9523 | 0.8439 | 0.8919 | 0.9463 | 0.5231 | 0.8031 | 0.4608 | nan | 0.9543 | 0.0 | 0.8620 | 0.7983 | 0.7991 | 0.8662 | 0.4611 | 0.6719 | 0.3492 | 0.0 | | 0.1052 | 24.15 | 1980 | 0.2147 | 0.5672 | 0.7012 | 0.9048 | 0.9778 | 0.0 | 0.9704 | 0.8441 | 0.8915 | 0.9499 | 0.5004 | 0.7622 | 0.4146 | nan | 0.9550 | 0.0 | 0.8396 | 0.7916 | 0.8009 | 0.8624 | 0.4403 | 0.6608 | 0.3219 | 0.0 | | 0.1478 | 24.39 | 2000 | 0.2206 | 0.5638 | 0.6950 | 0.9040 | 0.9815 | 0.0 | 0.9657 | 0.8313 | 0.8904 | 0.9530 | 0.4869 | 0.7536 | 0.3929 | nan | 0.9557 | 0.0 | 0.8433 | 0.7884 | 0.7959 | 0.8606 | 0.4347 | 0.6515 | 0.3081 | 0.0 | | 0.1752 | 24.63 | 2020 | 0.2048 | 0.5700 | 0.7085 | 0.9058 | 0.9793 | 0.0 | 0.9668 | 0.8566 | 0.9095 | 0.9430 | 0.5319 | 0.7519 | 0.4372 | nan | 0.9535 | 0.0 | 0.8458 | 0.7987 | 0.7801 | 0.8670 | 0.4597 | 0.6460 | 0.3492 | 0.0 | | 0.0451 | 24.88 | 2040 | 0.2171 | 0.5666 | 0.7053 | 0.9025 | 0.9799 | 0.0 | 0.9748 | 0.8552 | 0.8761 | 0.9405 | 0.5371 | 0.7711 | 0.4131 | nan | 0.9545 | 0.0 | 0.8309 | 0.8008 | 0.7878 | 0.8586 | 0.4534 | 0.6644 | 0.3154 | 0.0 | | 0.4243 | 25.12 | 2060 | 0.2341 | 0.5595 | 0.6916 | 0.8999 | 0.9746 | 0.0 | 0.9764 | 0.8444 | 0.8887 | 0.9491 | 0.4996 | 0.7703 | 0.3211 | nan | 0.9515 | 0.0 | 0.8327 | 0.7956 | 0.7984 | 0.8537 | 0.4397 | 0.6689 | 0.2545 | 0.0 | | 0.0585 | 25.37 | 2080 | 0.2213 | 0.5660 | 0.7007 | 0.9033 | 0.9820 | 0.0 | 0.9652 | 0.8551 | 0.9015 | 0.9435 | 0.5187 | 0.7915 | 0.3485 | nan | 0.9542 | 0.0 | 0.8466 | 0.8024 | 0.7984 | 0.8564 | 0.4574 | 0.6699 | 0.2748 | 0.0 | | 0.2001 | 25.61 | 2100 | 0.2246 | 0.5644 | 0.7026 | 0.9009 | 0.9783 | 0.0 | 0.9724 | 0.8691 | 0.8753 | 0.9397 | 0.5349 | 0.7871 | 0.3667 | nan | 0.9539 | 0.0 | 0.8358 | 0.8023 | 0.7863 | 0.8541 | 0.4617 | 0.6681 | 0.2817 | 0.0 | | 0.0681 | 25.85 | 2120 | 0.2243 | 0.5653 | 0.7001 | 0.9033 | 0.9774 | 0.0071 | 0.9602 | 0.8607 | 0.8942 | 0.9492 | 0.5104 | 0.8085 | 0.3328 | nan | 0.9538 | 0.0071 | 0.8526 | 0.7975 | 0.7950 | 0.8573 | 0.4517 | 0.6706 | 0.2679 | 0.0 | | 0.3011 | 26.1 | 2140 | 0.2142 | 0.5695 | 0.7099 | 0.9029 | 0.9755 | 0.0022 | 0.9730 | 0.8624 | 0.8863 | 0.9383 | 0.5417 | 0.7982 | 0.4115 | nan | 0.9541 | 0.0021 | 0.8424 | 0.8006 | 0.7913 | 0.8590 | 0.4633 | 0.6755 | 0.3067 | 0.0 | | 0.1124 | 26.34 | 2160 | 0.2134 | 0.5678 | 0.7069 | 0.9014 | 0.9740 | 0.0097 | 0.9692 | 0.8475 | 0.8710 | 0.9399 | 0.5086 | 0.8119 | 0.4309 | nan | 0.9526 | 0.0097 | 0.8497 | 0.7935 | 0.7773 | 0.8598 | 0.4480 | 0.6745 | 0.3125 | 0.0 | | 0.2631 | 26.59 | 2180 | 0.2171 | 0.5685 | 0.7043 | 0.9042 | 0.9764 | 0.0165 | 0.9626 | 0.8486 | 0.8985 | 0.9481 | 0.5077 | 0.8124 | 0.3682 | nan | 0.9540 | 0.0164 | 0.8554 | 0.7937 | 0.7880 | 0.8606 | 0.4499 | 0.6734 | 0.2935 | 0.0 | | 0.0783 | 26.83 | 2200 | 0.2094 | 0.5699 | 0.7120 | 0.9031 | 0.9788 | 0.0 | 0.9649 | 0.8464 | 0.9089 | 0.9335 | 0.5525 | 0.8184 | 0.4045 | nan | 0.9525 | 0.0 | 0.8521 | 0.7966 | 0.7837 | 0.8604 | 0.4736 | 0.6675 | 0.3123 | 0.0 | | 0.0412 | 27.07 | 2220 | 0.2115 | 0.5688 | 0.7096 | 0.9041 | 0.9772 | 0.0057 | 0.9679 | 0.8448 | 0.8925 | 0.9363 | 0.4727 | 0.8228 | 0.4667 | nan | 0.9545 | 0.0057 | 0.8544 | 0.7909 | 0.7903 | 0.8633 | 0.4230 | 0.6677 | 0.3380 | 0.0 | | 0.096 | 27.32 | 2240 | 0.2224 | 0.5626 | 0.6939 | 0.9029 | 0.9788 | 0.0 | 0.9699 | 0.8440 | 0.8945 | 0.9502 | 0.4758 | 0.7877 | 0.3443 | nan | 0.9551 | 0.0 | 0.8461 | 0.7956 | 0.7958 | 0.8576 | 0.4303 | 0.6747 | 0.2704 | 0.0 | | 0.1542 | 27.56 | 2260 | 0.2251 | 0.5646 | 0.6958 | 0.9027 | 0.9755 | 0.0040 | 0.9674 | 0.8465 | 0.9004 | 0.9548 | 0.5310 | 0.7762 | 0.3064 | nan | 0.9531 | 0.0040 | 0.8489 | 0.7935 | 0.7997 | 0.8566 | 0.4623 | 0.6790 | 0.2493 | 0.0 | | 0.2322 | 27.8 | 2280 | 0.2243 | 0.5641 | 0.6943 | 0.9029 | 0.9739 | 0.0024 | 0.9733 | 0.8549 | 0.8847 | 0.9570 | 0.5096 | 0.7569 | 0.3360 | nan | 0.9533 | 0.0024 | 0.8427 | 0.7961 | 0.7998 | 0.8584 | 0.4475 | 0.6741 | 0.2668 | 0.0 | | 0.1025 | 28.05 | 2300 | 0.2217 | 0.5665 | 0.7022 | 0.9034 | 0.9726 | 0.0077 | 0.9750 | 0.8541 | 0.9116 | 0.9503 | 0.5514 | 0.7723 | 0.3243 | nan | 0.9519 | 0.0077 | 0.8415 | 0.7960 | 0.7986 | 0.8598 | 0.4700 | 0.6711 | 0.2689 | 0.0 | | 0.1405 | 28.29 | 2320 | 0.2244 | 0.5680 | 0.7009 | 0.9042 | 0.9761 | 0.0087 | 0.9650 | 0.8600 | 0.8864 | 0.9543 | 0.5317 | 0.7668 | 0.3590 | nan | 0.9539 | 0.0087 | 0.8512 | 0.7963 | 0.7881 | 0.8603 | 0.4623 | 0.6746 | 0.2846 | 0.0 | | 0.1002 | 28.54 | 2340 | 0.2165 | 0.5682 | 0.7088 | 0.9031 | 0.9742 | 0.0035 | 0.9769 | 0.8598 | 0.8865 | 0.9408 | 0.5402 | 0.7832 | 0.4139 | nan | 0.9535 | 0.0035 | 0.8365 | 0.7943 | 0.7853 | 0.8610 | 0.4612 | 0.6691 | 0.3179 | 0.0 | | 0.0803 | 28.78 | 2360 | 0.2233 | 0.5655 | 0.7000 | 0.9019 | 0.9759 | 0.0059 | 0.9706 | 0.8529 | 0.8577 | 0.9497 | 0.5112 | 0.7560 | 0.4199 | nan | 0.9541 | 0.0059 | 0.8452 | 0.7923 | 0.7727 | 0.8604 | 0.4468 | 0.6716 | 0.3062 | 0.0 | | 0.1149 | 29.02 | 2380 | 0.2215 | 0.5663 | 0.7008 | 0.9027 | 0.9749 | 0.0078 | 0.9712 | 0.8512 | 0.8789 | 0.9503 | 0.5218 | 0.7721 | 0.3787 | nan | 0.9534 | 0.0078 | 0.8440 | 0.7935 | 0.7823 | 0.8596 | 0.4542 | 0.6740 | 0.2941 | 0.0 | | 0.0773 | 29.27 | 2400 | 0.2228 | 0.5675 | 0.7068 | 0.9027 | 0.9762 | 0.0070 | 0.9763 | 0.8517 | 0.8750 | 0.9405 | 0.5117 | 0.7937 | 0.4288 | nan | 0.9547 | 0.0070 | 0.8381 | 0.7930 | 0.7838 | 0.8606 | 0.4480 | 0.6735 | 0.3166 | 0.0 | | 0.1085 | 29.51 | 2420 | 0.2225 | 0.5650 | 0.6974 | 0.9033 | 0.9811 | 0.0 | 0.9693 | 0.8404 | 0.8858 | 0.9493 | 0.5061 | 0.7632 | 0.3816 | nan | 0.9550 | 0.0 | 0.8423 | 0.7940 | 0.7873 | 0.8597 | 0.4464 | 0.6708 | 0.2944 | 0.0 | | 0.0874 | 29.76 | 2440 | 0.2147 | 0.5680 | 0.7047 | 0.9033 | 0.9776 | 0.0012 | 0.9709 | 0.8514 | 0.8785 | 0.9432 | 0.5078 | 0.7886 | 0.4235 | nan | 0.9536 | 0.0012 | 0.8450 | 0.7948 | 0.7865 | 0.8610 | 0.4482 | 0.6751 | 0.3149 | 0.0 | | 0.1308 | 30.0 | 2460 | 0.2172 | 0.5684 | 0.7041 | 0.9037 | 0.9782 | 0.0031 | 0.9694 | 0.8474 | 0.8818 | 0.9453 | 0.5085 | 0.7910 | 0.4118 | nan | 0.9541 | 0.0031 | 0.8472 | 0.7939 | 0.7881 | 0.8610 | 0.4506 | 0.6761 | 0.3104 | 0.0 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1

[ "water", "whitewater", "sand", "rocky_terrain", "agricultural", "vegetation", "road", "development", "other_natural_terrain", "unknown" ]

unreal-hug/segformer-b0-finetuned-segments-ECHO-dev-01-v1

# segformer-b0-finetuned-segments-ECHO-dev-01-v1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the unreal-hug/SYNTH_DATASET_SEG dataset. It achieves the following results on the evaluation set: - Loss: 0.1147 - Mean Iou: 0.7718 - Mean Accuracy: 0.9647 - Overall Accuracy: 0.9651 - Accuracy Unlabeled: nan - Accuracy Lv: 0.9592 - Accuracy Lr: 0.9722 - Accuracy Ra: 0.9540 - Accuracy La: 0.9733 - Iou Unlabeled: 0.0 - Iou Lv: 0.9592 - Iou Lr: 0.9722 - Iou Ra: 0.9540 - Iou La: 0.9733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Lr | Accuracy Ra | Accuracy La | Iou Unlabeled | Iou Lv | Iou Lr | Iou Ra | Iou La | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:| | 0.9325 | 2.5 | 20 | 1.3095 | 0.4524 | 0.7068 | 0.7433 | nan | 0.9170 | 0.8666 | 0.2895 | 0.7542 | 0.0 | 0.7498 | 0.6467 | 0.2538 | 0.6116 | | 0.6633 | 5.0 | 40 | 0.7431 | 0.6359 | 0.8411 | 0.8600 | nan | 0.9559 | 0.9196 | 0.6304 | 0.8583 | 0.0 | 0.8810 | 0.8526 | 0.6270 | 0.8191 | | 0.5492 | 7.5 | 60 | 0.4682 | 0.7583 | 0.9515 | 0.9548 | nan | 0.9702 | 0.9610 | 0.9452 | 0.9295 | 0.0 | 0.9630 | 0.9610 | 0.9380 | 0.9295 | | 0.5418 | 10.0 | 80 | 0.3925 | 0.7453 | 0.9323 | 0.9367 | nan | 0.9520 | 0.9515 | 0.9050 | 0.9206 | 0.0 | 0.9509 | 0.9515 | 0.9036 | 0.9206 | | 0.3586 | 12.5 | 100 | 0.3216 | 0.7612 | 0.9515 | 0.9561 | nan | 0.9594 | 0.9767 | 0.9359 | 0.9340 | 0.0 | 0.9594 | 0.9767 | 0.9359 | 0.9340 | | 0.2943 | 15.0 | 120 | 0.2719 | 0.7615 | 0.9518 | 0.9541 | nan | 0.9480 | 0.9691 | 0.9413 | 0.9490 | 0.0 | 0.9480 | 0.9691 | 0.9413 | 0.9490 | | 0.3039 | 17.5 | 140 | 0.2505 | 0.7765 | 0.9763 | 0.9743 | nan | 0.9742 | 0.9650 | 0.9799 | 0.9861 | 0.0 | 0.9742 | 0.9650 | 0.9572 | 0.9861 | | 0.2639 | 20.0 | 160 | 0.2208 | 0.7743 | 0.9679 | 0.9690 | nan | 0.9676 | 0.9784 | 0.9477 | 0.9778 | 0.0 | 0.9676 | 0.9784 | 0.9477 | 0.9778 | | 0.2252 | 22.5 | 180 | 0.1928 | 0.7710 | 0.9637 | 0.9655 | nan | 0.9618 | 0.9784 | 0.9483 | 0.9664 | 0.0 | 0.9618 | 0.9784 | 0.9483 | 0.9664 | | 0.1816 | 25.0 | 200 | 0.1756 | 0.7690 | 0.9612 | 0.9619 | nan | 0.9574 | 0.9696 | 0.9501 | 0.9679 | 0.0 | 0.9574 | 0.9696 | 0.9501 | 0.9679 | | 0.1676 | 27.5 | 220 | 0.1556 | 0.7610 | 0.9513 | 0.9541 | nan | 0.9483 | 0.9721 | 0.9387 | 0.9460 | 0.0 | 0.9483 | 0.9721 | 0.9387 | 0.9460 | | 0.1833 | 30.0 | 240 | 0.1468 | 0.7786 | 0.9733 | 0.9742 | nan | 0.9669 | 0.9837 | 0.9639 | 0.9786 | 0.0 | 0.9669 | 0.9837 | 0.9639 | 0.9786 | | 0.1487 | 32.5 | 260 | 0.1367 | 0.7708 | 0.9635 | 0.9649 | nan | 0.9580 | 0.9776 | 0.9479 | 0.9705 | 0.0 | 0.9580 | 0.9776 | 0.9479 | 0.9705 | | 0.1482 | 35.0 | 280 | 0.1320 | 0.7712 | 0.9641 | 0.9655 | nan | 0.9576 | 0.9779 | 0.9555 | 0.9653 | 0.0 | 0.9576 | 0.9779 | 0.9555 | 0.9653 | | 0.1412 | 37.5 | 300 | 0.1241 | 0.7763 | 0.9704 | 0.9706 | nan | 0.9696 | 0.9738 | 0.9590 | 0.9791 | 0.0 | 0.9696 | 0.9738 | 0.9590 | 0.9791 | | 0.1282 | 40.0 | 320 | 0.1213 | 0.7724 | 0.9655 | 0.9665 | nan | 0.9588 | 0.9778 | 0.9512 | 0.9742 | 0.0 | 0.9588 | 0.9778 | 0.9512 | 0.9742 | | 0.133 | 42.5 | 340 | 0.1155 | 0.7745 | 0.9681 | 0.9686 | nan | 0.9640 | 0.9752 | 0.9580 | 0.9751 | 0.0 | 0.9640 | 0.9752 | 0.9580 | 0.9751 | | 0.1172 | 45.0 | 360 | 0.1178 | 0.7673 | 0.9603 | 0.9607 | nan | 0.9562 | 0.9665 | 0.9510 | 0.9676 | 0.0 | 0.9562 | 0.9665 | 0.9460 | 0.9676 | | 0.1469 | 47.5 | 380 | 0.1138 | 0.7734 | 0.9668 | 0.9673 | nan | 0.9629 | 0.9743 | 0.9545 | 0.9753 | 0.0 | 0.9629 | 0.9743 | 0.9545 | 0.9753 | | 0.1247 | 50.0 | 400 | 0.1147 | 0.7718 | 0.9647 | 0.9651 | nan | 0.9592 | 0.9722 | 0.9540 | 0.9733 | 0.0 | 0.9592 | 0.9722 | 0.9540 | 0.9733 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "lv", "lr", "ra", "la" ]

jinhong426/test

# Mask2Former Mask2Former model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on COCO panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-coco-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-coco-panoptic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).

[ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "bridge", "cardboard", "counter", "curtain", "door-stuff", "floor-wood", "flower", "fruit", "gravel", "house", "light", "mirror-stuff", "net", "pillow", "platform", "playingfield", "railroad", "river", "road", "roof", "sand", "sea", "shelf", "snow", "stairs", "tent", "towel", "wall-brick", "wall-stone", "wall-tile", "wall-wood", "water-other", "window-blind", "window-other", "tree-merged", "fence-merged", "ceiling-merged", "sky-other-merged", "cabinet-merged", "table-merged", "floor-other-merged", "pavement-merged", "mountain-merged", "grass-merged", "dirt-merged", "paper-merged", "food-other-merged", "building-other-merged", "rock-merged", "wall-other-merged", "rug-merged" ]

unreal-hug/segformer-b0-finetuned-segments-ECHO-dev-05-v1

# segformer-b0-finetuned-segments-ECHO-dev-05-v1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the unreal-hug/REAL_DATASET_SEG dataset. It achieves the following results on the evaluation set: - Loss: 0.4592 - Mean Iou: 0.3826 - Mean Accuracy: 0.5892 - Overall Accuracy: 0.5467 - Accuracy Unlabeled: nan - Accuracy Lv: 0.7143 - Accuracy Lr: 0.4323 - Accuracy Ra: 0.7629 - Accuracy La: 0.4472 - Iou Unlabeled: 0.0 - Iou Lv: 0.7065 - Iou Lr: 0.4317 - Iou Ra: 0.4223 - Iou La: 0.3527 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Lr | Accuracy Ra | Accuracy La | Iou Unlabeled | Iou Lv | Iou Lr | Iou Ra | Iou La | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:| | 1.1252 | 2.86 | 20 | 1.3259 | 0.1971 | 0.3379 | 0.4375 | nan | 0.0 | 0.6365 | 0.5291 | 0.1860 | 0.0 | 0.0 | 0.4923 | 0.3492 | 0.1439 | | 0.9104 | 5.71 | 40 | 0.9589 | 0.1818 | 0.3421 | 0.3596 | nan | 0.0145 | 0.3590 | 0.7644 | 0.2304 | 0.0 | 0.0144 | 0.3436 | 0.3778 | 0.1731 | | 0.7567 | 8.57 | 60 | 0.7761 | 0.2203 | 0.3739 | 0.3852 | nan | 0.0808 | 0.3882 | 0.6422 | 0.3844 | 0.0 | 0.0803 | 0.3778 | 0.4073 | 0.2360 | | 0.7035 | 11.43 | 80 | 0.7442 | 0.2729 | 0.4718 | 0.4941 | nan | 0.2145 | 0.5077 | 0.8370 | 0.3279 | 0.0 | 0.2134 | 0.4817 | 0.4073 | 0.2619 | | 0.5781 | 14.29 | 100 | 0.6260 | 0.2876 | 0.4446 | 0.4279 | nan | 0.4235 | 0.3777 | 0.5787 | 0.3986 | 0.0 | 0.3683 | 0.3761 | 0.4063 | 0.2873 | | 0.5438 | 17.14 | 120 | 0.5559 | 0.3877 | 0.5412 | 0.5761 | nan | 0.5803 | 0.6504 | 0.5190 | 0.4149 | 0.0 | 0.5671 | 0.6193 | 0.4171 | 0.3352 | | 0.5198 | 20.0 | 140 | 0.5617 | 0.3724 | 0.5617 | 0.5335 | nan | 0.6661 | 0.4532 | 0.7059 | 0.4216 | 0.0 | 0.6419 | 0.4532 | 0.4129 | 0.3540 | | 0.4435 | 22.86 | 160 | 0.5393 | 0.4160 | 0.6198 | 0.6126 | nan | 0.7555 | 0.5832 | 0.6962 | 0.4442 | 0.0 | 0.7000 | 0.5705 | 0.4873 | 0.3221 | | 0.5002 | 25.71 | 180 | 0.5126 | 0.4094 | 0.6080 | 0.6043 | nan | 0.6854 | 0.5833 | 0.6945 | 0.4687 | 0.0 | 0.6771 | 0.5761 | 0.4762 | 0.3176 | | 0.4142 | 28.57 | 200 | 0.4874 | 0.3503 | 0.5361 | 0.4949 | nan | 0.6967 | 0.3895 | 0.6436 | 0.4147 | 0.0 | 0.6287 | 0.3895 | 0.4106 | 0.3228 | | 0.3092 | 31.43 | 220 | 0.4819 | 0.3857 | 0.6001 | 0.5534 | nan | 0.7296 | 0.4267 | 0.8020 | 0.4423 | 0.0 | 0.7157 | 0.4267 | 0.4266 | 0.3595 | | 0.2895 | 34.29 | 240 | 0.4969 | 0.3983 | 0.6220 | 0.5809 | nan | 0.7353 | 0.4689 | 0.8050 | 0.4787 | 0.0 | 0.7265 | 0.4677 | 0.4474 | 0.3498 | | 0.3046 | 37.14 | 260 | 0.4767 | 0.4248 | 0.6412 | 0.6115 | nan | 0.7853 | 0.5270 | 0.7814 | 0.4711 | 0.0 | 0.7712 | 0.5199 | 0.4587 | 0.3742 | | 0.3514 | 40.0 | 280 | 0.4531 | 0.3978 | 0.5989 | 0.5767 | nan | 0.7112 | 0.5082 | 0.7478 | 0.4282 | 0.0 | 0.6979 | 0.5024 | 0.4353 | 0.3537 | | 0.2891 | 42.86 | 300 | 0.4629 | 0.3842 | 0.5885 | 0.5488 | nan | 0.7046 | 0.4397 | 0.7693 | 0.4403 | 0.0 | 0.6982 | 0.4366 | 0.4237 | 0.3623 | | 0.2512 | 45.71 | 320 | 0.4584 | 0.3783 | 0.5794 | 0.5357 | nan | 0.7144 | 0.4199 | 0.7390 | 0.4443 | 0.0 | 0.7016 | 0.4196 | 0.4134 | 0.3568 | | 0.2695 | 48.57 | 340 | 0.4592 | 0.3826 | 0.5892 | 0.5467 | nan | 0.7143 | 0.4323 | 0.7629 | 0.4472 | 0.0 | 0.7065 | 0.4317 | 0.4223 | 0.3527 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "lv", "lr", "ra", "la" ]

yolo12138/segformer-b2-human-parse-24

# segformer-b2-human-parse-24 This model is a fine-tuned version of [mattmdjaga/segformer_b2_clothes](https://huggingface.co/mattmdjaga/segformer_b2_clothes) on the human_parsing_29_mix dataset. It achieves the following results on the evaluation set: - Loss: 0.0818 - Mean Iou: 0.6023 - Mean Accuracy: 0.6321 - Overall Accuracy: 0.9780 - Accuracy Background: 0.9969 - Accuracy Hat: nan - Accuracy Hair: 0.9646 - Accuracy Glove: 0.0 - Accuracy Glasses: 0.0 - Accuracy Upper Only Torso Region: 0.9747 - Accuracy Dresses Only Torso Region: 0.4939 - Accuracy Coat Only Torso Region: 0.0039 - Accuracy Socks: 0.0 - Accuracy Left Pants: 0.9604 - Accuracy Right Patns: 0.9646 - Accuracy Skin Around Neck Region: 0.9585 - Accuracy Scarf: nan - Accuracy Skirts: 0.8904 - Accuracy Face: 0.9796 - Accuracy Left Arm: 0.9703 - Accuracy Right Arm: 0.9700 - Accuracy Left Leg: 0.9267 - Accuracy Right Leg: 0.9297 - Accuracy Left Shoe: 0.0 - Accuracy Right Shoe: 0.0 - Accuracy Left Sleeve For Upper: 0.9462 - Accuracy Right Sleeve For Upper: 0.9517 - Accuracy Bag: 0.0234 - Iou Background: 0.9941 - Iou Hat: nan - Iou Hair: 0.9268 - Iou Glove: 0.0 - Iou Glasses: 0.0 - Iou Upper Only Torso Region: 0.9351 - Iou Dresses Only Torso Region: 0.4059 - Iou Coat Only Torso Region: 0.0035 - Iou Socks: 0.0 - Iou Left Pants: 0.9232 - Iou Right Patns: 0.9217 - Iou Skin Around Neck Region: 0.9227 - Iou Scarf: nan - Iou Skirts: 0.7887 - Iou Face: 0.9582 - Iou Left Arm: 0.9436 - Iou Right Arm: 0.9426 - Iou Left Leg: 0.8836 - Iou Right Leg: 0.8767 - Iou Left Shoe: 0.0 - Iou Right Shoe: 0.0 - Iou Left Sleeve For Upper: 0.9005 - Iou Right Sleeve For Upper: 0.9012 - Iou Bag: 0.0232 ## Model description More information needed ``` "id2label": { "0": "background", "1": "hat", "2": "hair", "3": "glove", "4": "glasses", "5": "upper_only_torso_region", "6": "dresses_only_torso_region", "7": "coat_only_torso_region", "8": "socks", "9": "left_pants", "10": "right_patns", "11": "skin_around_neck_region", "12": "scarf", "13": "skirts", "14": "face", "15": "left_arm", "16": "right_arm", "17": "left_leg", "18": "right_leg", "19": "left_shoe", "20": "right_shoe", "21": "left_sleeve_for_upper", "22": "right_sleeve_for_upper", "23": "bag" } ``` ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Glove | Accuracy Glasses | Accuracy Upper Only Torso Region | Accuracy Dresses Only Torso Region | Accuracy Coat Only Torso Region | Accuracy Socks | Accuracy Left Pants | Accuracy Right Patns | Accuracy Skin Around Neck Region | Accuracy Scarf | Accuracy Skirts | Accuracy Face | Accuracy Left Arm | Accuracy Right Arm | Accuracy Left Leg | Accuracy Right Leg | Accuracy Left Shoe | Accuracy Right Shoe | Accuracy Left Sleeve For Upper | Accuracy Right Sleeve For Upper | Accuracy Bag | Iou Background | Iou Hat | Iou Hair | Iou Glove | Iou Glasses | Iou Upper Only Torso Region | Iou Dresses Only Torso Region | Iou Coat Only Torso Region | Iou Socks | Iou Left Pants | Iou Right Patns | Iou Skin Around Neck Region | Iou Scarf | Iou Skirts | Iou Face | Iou Left Arm | Iou Right Arm | Iou Left Leg | Iou Right Leg | Iou Left Shoe | Iou Right Shoe | Iou Left Sleeve For Upper | Iou Right Sleeve For Upper | Iou Bag | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:--------------:|:----------------:|:--------------------------------:|:----------------------------------:|:-------------------------------:|:--------------:|:-------------------:|:--------------------:|:--------------------------------:|:--------------:|:---------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------------:|:-------------------:|:------------------------------:|:-------------------------------:|:------------:|:--------------:|:-------:|:--------:|:---------:|:-----------:|:---------------------------:|:-----------------------------:|:--------------------------:|:---------:|:--------------:|:---------------:|:---------------------------:|:---------:|:----------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------------------------:|:--------------------------:|:-------:| | 0.0652 | 1.62 | 1000 | 0.0802 | 0.5857 | 0.6166 | 0.9737 | 0.9963 | nan | 0.9490 | 0.0 | 0.0 | 0.9801 | 0.4034 | 0.0 | 0.0 | 0.9487 | 0.9574 | 0.9272 | nan | 0.8783 | 0.9782 | 0.9628 | 0.9534 | 0.8874 | 0.9012 | 0.0 | 0.0 | 0.9227 | 0.9197 | 0.0 | 0.9926 | nan | 0.9117 | 0.0 | 0.0 | 0.9217 | 0.3541 | 0.0 | 0.0 | 0.9084 | 0.9073 | 0.8963 | nan | 0.7766 | 0.9455 | 0.9210 | 0.9191 | 0.8405 | 0.8496 | 0.0 | 0.0 | 0.8673 | 0.8728 | 0.0 | | 0.061 | 3.23 | 2000 | 0.0843 | 0.5977 | 0.6335 | 0.9747 | 0.9967 | nan | 0.9580 | 0.0 | 0.0 | 0.9657 | 0.5733 | 0.1504 | 0.0 | 0.9591 | 0.9600 | 0.9497 | nan | 0.8169 | 0.9789 | 0.9667 | 0.9645 | 0.8906 | 0.9165 | 0.0 | 0.0 | 0.9444 | 0.9445 | 0.0003 | 0.9935 | nan | 0.9199 | 0.0 | 0.0 | 0.9273 | 0.4058 | 0.1206 | 0.0 | 0.9131 | 0.9082 | 0.9128 | nan | 0.7330 | 0.9527 | 0.9355 | 0.9343 | 0.8534 | 0.8651 | 0.0 | 0.0 | 0.8860 | 0.8879 | 0.0003 | | 0.0653 | 4.85 | 3000 | 0.0823 | 0.6000 | 0.6295 | 0.9775 | 0.9967 | nan | 0.9621 | 0.0 | 0.0 | 0.9780 | 0.4991 | 0.0044 | 0.0 | 0.9587 | 0.9649 | 0.9562 | nan | 0.8842 | 0.9769 | 0.9692 | 0.9651 | 0.9198 | 0.9273 | 0.0 | 0.0 | 0.9422 | 0.9415 | 0.0037 | 0.9939 | nan | 0.9247 | 0.0 | 0.0 | 0.9341 | 0.4136 | 0.0042 | 0.0 | 0.9202 | 0.9193 | 0.9193 | nan | 0.7899 | 0.9563 | 0.9403 | 0.9388 | 0.8745 | 0.8741 | 0.0 | 0.0 | 0.8963 | 0.8970 | 0.0037 | | 0.0402 | 6.46 | 4000 | 0.0818 | 0.6023 | 0.6321 | 0.9780 | 0.9969 | nan | 0.9646 | 0.0 | 0.0 | 0.9747 | 0.4939 | 0.0039 | 0.0 | 0.9604 | 0.9646 | 0.9585 | nan | 0.8904 | 0.9796 | 0.9703 | 0.9700 | 0.9267 | 0.9297 | 0.0 | 0.0 | 0.9462 | 0.9517 | 0.0234 | 0.9941 | nan | 0.9268 | 0.0 | 0.0 | 0.9351 | 0.4059 | 0.0035 | 0.0 | 0.9232 | 0.9217 | 0.9227 | nan | 0.7887 | 0.9582 | 0.9436 | 0.9426 | 0.8836 | 0.8767 | 0.0 | 0.0 | 0.9005 | 0.9012 | 0.0232 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "background", "hat", "hair", "glove", "glasses", "upper_only_torso_region", "dresses_only_torso_region", "coat_only_torso_region", "socks", "left_pants", "right_patns", "skin_around_neck_region", "scarf", "skirts", "face", "left_arm", "right_arm", "left_leg", "right_leg", "left_shoe", "right_shoe", "left_sleeve_for_upper", "right_sleeve_for_upper", "bag" ]

sam1120/safety-utcustom-terrain

# safety-utcustom-terrain-final This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-terrain-jackal-full-391 dataset. It achieves the following results on the evaluation set: - Loss: 0.0892 - Mean Iou: 0.7759 - Mean Accuracy: 0.8195 - Overall Accuracy: 0.9811 - Accuracy Unlabeled: nan - Accuracy Nat: 0.9942 - Accuracy Concrete: 0.9592 - Accuracy Grass: 0.9021 - Accuracy Speedway bricks: 0.9902 - Accuracy Steel: 0.9315 - Accuracy Rough concrete: 0.6330 - Accuracy Dark bricks: 0.8554 - Accuracy Road: 0.9821 - Accuracy Rough red sidewalk: 0.7662 - Accuracy Tiles: 0.2392 - Accuracy Red bricks: 0.9438 - Accuracy Concrete tiles: 0.9536 - Accuracy Rest: 0.5024 - Iou Unlabeled: nan - Iou Nat: 0.9848 - Iou Concrete: 0.8976 - Iou Grass: 0.8486 - Iou Speedway bricks: 0.9809 - Iou Steel: 0.8822 - Iou Rough concrete: 0.6278 - Iou Dark bricks: 0.8418 - Iou Road: 0.9618 - Iou Rough red sidewalk: 0.6046 - Iou Tiles: 0.2381 - Iou Red bricks: 0.9093 - Iou Concrete tiles: 0.8939 - Iou Rest: 0.4153 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Accuracy Nat | Accuracy Rest | Accuracy Concrete | Accuracy Concrete tiles | Accuracy Dark bricks | Accuracy Grass | Accuracy Red bricks | Accuracy Road | Accuracy Rough concrete | Accuracy Rough red sidewalk | Accuracy Speedway bricks | Accuracy Steel | Accuracy Tiles | Accuracy Unlabeled | Iou Nat | Iou Rest | Iou Concrete | Iou Concrete tiles | Iou Dark bricks | Iou Grass | Iou Red bricks | Iou Road | Iou Rough concrete | Iou Rough red sidewalk | Iou Speedway bricks | Iou Steel | Iou Tiles | Iou Unlabeled | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |:-------------:|:------:|:----:|:------------:|:-------------:|:-----------------:|:-----------------------:|:--------------------:|:--------------:|:-------------------:|:-------------:|:-----------------------:|:---------------------------:|:------------------------:|:--------------:|:--------------:|:------------------:|:-------:|:--------:|:------------:|:------------------:|:---------------:|:---------:|:--------------:|:--------:|:------------------:|:----------------------:|:-------------------:|:---------:|:---------:|:-------------:|:---------------:|:-------------:|:--------:|:----------------:| | 2.51 | 1.18 | 20 | 0.0902 | 0.1772 | 0.2184 | 0.1090 | 0.0127 | 0.0271 | 0.0004 | 0.0071 | 0.0027 | 0.0 | 0.6742 | 0.0008 | 0.2799 | nan | 0.0901 | 0.0000 | 0.0797 | 0.0012 | 0.0031 | 0.0192 | 0.0000 | 0.0068 | 0.0001 | 0.0 | 0.4633 | 0.0007 | 0.0032 | 0.0 | 2.5071 | 0.1230 | 0.0477 | 0.2016 | | 1.5504 | 2.35 | 40 | 0.7602 | 0.0 | 0.3387 | 0.0079 | 0.0014 | 0.0745 | 0.0 | 0.3332 | 0.0010 | 0.0 | 0.9880 | 0.0 | 0.0088 | nan | 0.7577 | 0.0 | 0.2038 | 0.0003 | 0.0010 | 0.0682 | 0.0 | 0.3127 | 0.0002 | 0.0 | 0.5711 | 0.0 | 0.0008 | 0.0 | 1.8408 | 0.1934 | 0.1368 | 0.6936 | | 0.6271 | 3.53 | 60 | 0.9551 | 0.0 | 0.8127 | 0.0 | 0.0 | 0.2590 | 0.0 | 0.8979 | 0.0 | 0.0 | 0.9793 | 0.0 | 0.0000 | nan | 0.9405 | 0.0 | 0.5259 | 0.0 | 0.0 | 0.2392 | 0.0 | 0.6993 | 0.0 | 0.0 | 0.8263 | 0.0 | 0.0000 | nan | 0.8083 | 0.3003 | 0.2486 | 0.8894 | | 0.3963 | 4.71 | 80 | 0.9698 | 0.0 | 0.7973 | 0.0443 | 0.0 | 0.7826 | 0.0 | 0.9651 | 0.0 | 0.0 | 0.9777 | 0.0004 | 0.0 | nan | 0.9532 | 0.0 | 0.6901 | 0.0443 | 0.0 | 0.5632 | 0.0 | 0.7280 | 0.0 | 0.0 | 0.8641 | 0.0004 | 0.0 | nan | 0.4204 | 0.3490 | 0.2956 | 0.9186 | | 0.3558 | 5.88 | 100 | 0.9806 | 0.0 | 0.9025 | 0.3050 | 0.0 | 0.7816 | 0.0 | 0.9135 | 0.0031 | 0.0 | 0.9632 | 0.2874 | 0.0 | nan | 0.9618 | 0.0 | 0.6868 | 0.3027 | 0.0 | 0.6345 | 0.0 | 0.8305 | 0.0031 | 0.0 | 0.8915 | 0.2804 | 0.0 | nan | 0.3091 | 0.3952 | 0.3532 | 0.9322 | | 0.2654 | 7.06 | 120 | 0.9793 | 0.0 | 0.8161 | 0.3875 | 0.0 | 0.8487 | 0.0 | 0.9769 | 0.0002 | 0.0009 | 0.9862 | 0.4149 | 0.0 | nan | 0.9636 | 0.0 | 0.7301 | 0.3046 | 0.0 | 0.6491 | 0.0 | 0.7846 | 0.0002 | 0.0009 | 0.9014 | 0.4015 | 0.0 | nan | 0.2554 | 0.4162 | 0.3643 | 0.9363 | | 0.1938 | 8.24 | 140 | 0.9830 | 0.0 | 0.9010 | 0.6649 | 0.0 | 0.8719 | 0.0 | 0.9517 | 0.1663 | 0.0374 | 0.9779 | 0.6445 | 0.0 | nan | 0.9681 | 0.0 | 0.7740 | 0.1887 | 0.0 | 0.6642 | 0.0 | 0.8746 | 0.1660 | 0.0374 | 0.9354 | 0.5996 | 0.0 | nan | 0.2059 | 0.4768 | 0.4006 | 0.9478 | | 0.202 | 9.41 | 160 | 0.9873 | 0.0 | 0.8667 | 0.7499 | 0.0282 | 0.8553 | 0.0 | 0.9539 | 0.5565 | 0.1631 | 0.9878 | 0.6794 | 0.0172 | nan | 0.9705 | 0.0 | 0.7775 | 0.2007 | 0.0282 | 0.7107 | 0.0 | 0.9073 | 0.4995 | 0.1493 | 0.9308 | 0.6268 | 0.0166 | nan | 0.1785 | 0.5266 | 0.4475 | 0.9524 | | 0.174 | 10.59 | 180 | 0.9880 | 0.0 | 0.9067 | 0.7555 | 0.0684 | 0.8559 | 0.0881 | 0.9470 | 0.5194 | 0.4768 | 0.9831 | 0.7462 | 0.0 | nan | 0.9711 | 0.0 | 0.7934 | 0.2110 | 0.0680 | 0.7406 | 0.0881 | 0.9141 | 0.5091 | 0.3846 | 0.9482 | 0.6883 | 0.0 | nan | 0.1571 | 0.5642 | 0.4859 | 0.9572 | | 0.1317 | 11.76 | 200 | 0.9865 | 0.0 | 0.9250 | 0.7600 | 0.7836 | 0.8949 | 0.1046 | 0.9492 | 0.5791 | 0.5757 | 0.9715 | 0.7851 | 0.0 | nan | 0.9720 | 0.0 | 0.8087 | 0.6147 | 0.7372 | 0.7440 | 0.1044 | 0.9256 | 0.5756 | 0.4397 | 0.9522 | 0.7135 | 0.0 | nan | 0.1391 | 0.6396 | 0.5837 | 0.9632 | | 0.0915 | 12.94 | 220 | 0.9860 | 0.0 | 0.9262 | 0.7639 | 0.7600 | 0.9003 | 0.5878 | 0.9337 | 0.6388 | 0.5331 | 0.9805 | 0.7937 | 0.0115 | nan | 0.9733 | 0.0 | 0.8116 | 0.6127 | 0.7370 | 0.7638 | 0.5787 | 0.9065 | 0.5673 | 0.4644 | 0.9561 | 0.7260 | 0.0114 | nan | 0.1252 | 0.6781 | 0.6238 | 0.9644 | | 0.1027 | 14.12 | 240 | 0.9917 | 0.0 | 0.9190 | 0.7523 | 0.7272 | 0.8350 | 0.6341 | 0.9736 | 0.6123 | 0.5560 | 0.9814 | 0.7842 | 0.0590 | nan | 0.9741 | 0.0 | 0.8297 | 0.6982 | 0.7163 | 0.7690 | 0.6251 | 0.9232 | 0.5949 | 0.4768 | 0.9583 | 0.7348 | 0.0588 | nan | 0.1175 | 0.6789 | 0.6430 | 0.9672 | | 0.1055 | 15.29 | 260 | 0.9881 | 0.0 | 0.9170 | 0.6841 | 0.7566 | 0.8499 | 0.6607 | 0.9807 | 0.6126 | 0.6318 | 0.9881 | 0.7435 | 0.0304 | nan | 0.9746 | 0.0 | 0.8274 | 0.6670 | 0.7390 | 0.7777 | 0.6508 | 0.9133 | 0.5831 | 0.5124 | 0.9579 | 0.7130 | 0.0301 | nan | 0.1139 | 0.6803 | 0.6420 | 0.9669 | | 0.089 | 16.47 | 280 | 0.9883 | 0.0 | 0.9266 | 0.7338 | 0.7679 | 0.8684 | 0.7259 | 0.9741 | 0.6241 | 0.7053 | 0.9872 | 0.7572 | 0.0675 | nan | 0.9757 | 0.0 | 0.8339 | 0.6869 | 0.7546 | 0.7875 | 0.7145 | 0.9367 | 0.5637 | 0.5270 | 0.9598 | 0.7243 | 0.0665 | nan | 0.1049 | 0.7020 | 0.6562 | 0.9687 | | 0.0982 | 17.65 | 300 | 0.9932 | 0.0 | 0.9315 | 0.7460 | 0.8016 | 0.8161 | 0.8457 | 0.9702 | 0.6211 | 0.6153 | 0.9799 | 0.8352 | 0.0917 | nan | 0.9753 | 0.0 | 0.8406 | 0.7205 | 0.7828 | 0.7713 | 0.8009 | 0.9369 | 0.5512 | 0.5013 | 0.9641 | 0.7745 | 0.0881 | nan | 0.1024 | 0.7113 | 0.6698 | 0.9696 | | 0.0787 | 18.82 | 320 | 0.9902 | 0.0 | 0.9380 | 0.7392 | 0.8253 | 0.8625 | 0.8736 | 0.9765 | 0.6109 | 0.7008 | 0.9773 | 0.8889 | 0.1198 | nan | 0.9772 | 0.0 | 0.8491 | 0.6967 | 0.8024 | 0.7964 | 0.8242 | 0.9342 | 0.5884 | 0.5514 | 0.9625 | 0.7733 | 0.1164 | nan | 0.1005 | 0.7310 | 0.6825 | 0.9711 | | 0.0659 | 20.0 | 340 | 0.9924 | 0.0 | 0.9213 | 0.8308 | 0.8275 | 0.8675 | 0.8678 | 0.9701 | 0.6168 | 0.7045 | 0.9838 | 0.8907 | 0.1436 | nan | 0.9771 | 0.0 | 0.8576 | 0.7540 | 0.8017 | 0.8037 | 0.8045 | 0.9477 | 0.5888 | 0.5288 | 0.9634 | 0.7818 | 0.1388 | nan | 0.0948 | 0.7398 | 0.6883 | 0.9722 | | 0.0615 | 21.18 | 360 | 0.9914 | 0.0 | 0.9523 | 0.7668 | 0.8173 | 0.8567 | 0.7849 | 0.9601 | 0.6088 | 0.6383 | 0.9756 | 0.8970 | 0.0394 | nan | 0.9766 | 0.0 | 0.8440 | 0.7240 | 0.8007 | 0.7904 | 0.7613 | 0.9455 | 0.6013 | 0.5302 | 0.9648 | 0.7870 | 0.0386 | nan | 0.0998 | 0.7145 | 0.6742 | 0.9711 | | 0.0671 | 22.35 | 380 | 0.9918 | 0.0 | 0.9463 | 0.7548 | 0.8054 | 0.8489 | 0.8876 | 0.9673 | 0.6129 | 0.7477 | 0.9791 | 0.8704 | 0.1060 | nan | 0.9774 | 0.0 | 0.8497 | 0.7045 | 0.7850 | 0.7915 | 0.8271 | 0.9405 | 0.5940 | 0.5749 | 0.9671 | 0.7935 | 0.1040 | nan | 0.0957 | 0.7322 | 0.6853 | 0.9719 | | 0.0917 | 23.53 | 400 | 0.9909 | 0.0 | 0.9336 | 0.7850 | 0.8096 | 0.8687 | 0.8738 | 0.9780 | 0.6158 | 0.6849 | 0.9868 | 0.8362 | 0.1046 | nan | 0.9777 | 0.0 | 0.8545 | 0.7320 | 0.7838 | 0.8004 | 0.8380 | 0.9417 | 0.5996 | 0.5411 | 0.9665 | 0.7895 | 0.1043 | nan | 0.0935 | 0.7283 | 0.6869 | 0.9725 | | 0.0649 | 24.71 | 420 | 0.9925 | 0.0 | 0.9283 | 0.8514 | 0.8465 | 0.8542 | 0.9041 | 0.9788 | 0.6238 | 0.6471 | 0.9883 | 0.8563 | 0.1356 | nan | 0.9783 | 0.0 | 0.8609 | 0.7683 | 0.8168 | 0.8034 | 0.8423 | 0.9410 | 0.6038 | 0.5446 | 0.9664 | 0.7955 | 0.1335 | nan | 0.0880 | 0.7390 | 0.6965 | 0.9733 | | 0.0648 | 25.88 | 440 | 0.9920 | 0.0 | 0.9411 | 0.8344 | 0.8282 | 0.8705 | 0.8488 | 0.9759 | 0.6163 | 0.7026 | 0.9870 | 0.8342 | 0.1031 | nan | 0.9789 | 0.0 | 0.8594 | 0.7905 | 0.8074 | 0.8149 | 0.8212 | 0.9523 | 0.5988 | 0.5236 | 0.9685 | 0.7926 | 0.1028 | nan | 0.0886 | 0.7334 | 0.6931 | 0.9739 | | 0.0633 | 27.06 | 460 | 0.9913 | 0.0 | 0.9298 | 0.8931 | 0.8600 | 0.8680 | 0.9230 | 0.9770 | 0.6187 | 0.7837 | 0.9905 | 0.8313 | 0.1321 | nan | 0.9785 | 0.0 | 0.8658 | 0.7637 | 0.8113 | 0.8027 | 0.8376 | 0.9457 | 0.6000 | 0.6097 | 0.9674 | 0.7922 | 0.1316 | nan | 0.0885 | 0.7537 | 0.7005 | 0.9739 | | 0.0504 | 28.24 | 480 | 0.9911 | 0.0 | 0.9332 | 0.8848 | 0.8469 | 0.8925 | 0.9045 | 0.9779 | 0.6205 | 0.7196 | 0.9882 | 0.8626 | 0.1751 | nan | 0.9790 | 0.0 | 0.8676 | 0.7879 | 0.8161 | 0.8156 | 0.8464 | 0.9503 | 0.5961 | 0.5367 | 0.9697 | 0.8085 | 0.1739 | nan | 0.0862 | 0.7536 | 0.7037 | 0.9746 | | 0.0603 | 29.41 | 500 | 0.9932 | 0.0 | 0.9429 | 0.8191 | 0.8274 | 0.8523 | 0.8900 | 0.9719 | 0.6164 | 0.7170 | 0.9858 | 0.8697 | 0.1381 | nan | 0.9783 | 0.0 | 0.8635 | 0.7952 | 0.8105 | 0.8027 | 0.8526 | 0.9460 | 0.6043 | 0.5907 | 0.9711 | 0.8125 | 0.1370 | nan | 0.0885 | 0.7403 | 0.7050 | 0.9743 | | 0.0447 | 30.59 | 520 | 0.9902 | 0.0 | 0.9484 | 0.8591 | 0.8759 | 0.8866 | 0.9197 | 0.9788 | 0.6183 | 0.7369 | 0.9854 | 0.8896 | 0.2666 | nan | 0.9795 | 0.0 | 0.8699 | 0.8193 | 0.8352 | 0.8187 | 0.8540 | 0.9470 | 0.6008 | 0.5913 | 0.9714 | 0.8157 | 0.2453 | nan | 0.0824 | 0.7658 | 0.7191 | 0.9753 | | 0.0517 | 31.76 | 540 | 0.9910 | 0.0 | 0.9372 | 0.8937 | 0.8920 | 0.8815 | 0.9331 | 0.9784 | 0.6245 | 0.8062 | 0.9904 | 0.8431 | 0.3040 | nan | 0.9799 | 0.0 | 0.8732 | 0.8217 | 0.8341 | 0.8191 | 0.8354 | 0.9495 | 0.6024 | 0.5985 | 0.9702 | 0.8045 | 0.2819 | nan | 0.0807 | 0.7750 | 0.7208 | 0.9755 | | 0.0512 | 32.94 | 560 | 0.9934 | 0.0 | 0.9411 | 0.8627 | 0.7835 | 0.8619 | 0.8360 | 0.9789 | 0.6166 | 0.7804 | 0.9853 | 0.8938 | 0.1282 | nan | 0.9793 | 0.0 | 0.8740 | 0.8110 | 0.7669 | 0.8082 | 0.8223 | 0.9476 | 0.6077 | 0.6209 | 0.9704 | 0.8202 | 0.1262 | nan | 0.0856 | 0.7432 | 0.7042 | 0.9752 | | 0.0474 | 34.12 | 580 | 0.9925 | 0.0 | 0.9511 | 0.8677 | 0.8661 | 0.8697 | 0.8806 | 0.9743 | 0.6254 | 0.7290 | 0.9838 | 0.8987 | 0.2127 | nan | 0.9795 | 0.0 | 0.8724 | 0.8249 | 0.8305 | 0.8163 | 0.8461 | 0.9532 | 0.5900 | 0.6061 | 0.9724 | 0.8224 | 0.2101 | nan | 0.0796 | 0.7578 | 0.7172 | 0.9758 | | 0.0471 | 35.29 | 600 | 0.9922 | 0.0 | 0.9352 | 0.9168 | 0.8880 | 0.8596 | 0.9220 | 0.9797 | 0.6205 | 0.8090 | 0.9876 | 0.8911 | 0.4564 | nan | 0.9789 | 0.0 | 0.8785 | 0.8174 | 0.8203 | 0.8034 | 0.8567 | 0.9530 | 0.6048 | 0.5828 | 0.9728 | 0.8208 | 0.4101 | nan | 0.0815 | 0.7891 | 0.7307 | 0.9757 | | 0.0522 | 36.47 | 620 | 0.9943 | 0.0 | 0.9353 | 0.8650 | 0.8185 | 0.8543 | 0.8783 | 0.9783 | 0.6238 | 0.7479 | 0.9850 | 0.9008 | 0.1075 | nan | 0.9786 | 0.0 | 0.8728 | 0.8187 | 0.7978 | 0.8036 | 0.8519 | 0.9475 | 0.6092 | 0.6031 | 0.9727 | 0.8254 | 0.1075 | nan | 0.0846 | 0.7453 | 0.7068 | 0.9752 | | 0.043 | 37.65 | 640 | 0.9938 | 0.0 | 0.9391 | 0.9101 | 0.8167 | 0.8672 | 0.9004 | 0.9800 | 0.6179 | 0.7609 | 0.9853 | 0.9130 | 0.1707 | nan | 0.9796 | 0.0 | 0.8763 | 0.8325 | 0.7965 | 0.8145 | 0.8582 | 0.9520 | 0.6101 | 0.6211 | 0.9728 | 0.8251 | 0.1693 | nan | 0.0813 | 0.7581 | 0.7160 | 0.9761 | | 0.0429 | 38.82 | 660 | 0.9931 | 0.0 | 0.9416 | 0.8665 | 0.8163 | 0.8894 | 0.9095 | 0.9792 | 0.6195 | 0.7703 | 0.9876 | 0.8956 | 0.1931 | nan | 0.9804 | 0.0 | 0.8790 | 0.8424 | 0.7970 | 0.8264 | 0.8589 | 0.9544 | 0.6086 | 0.6180 | 0.9729 | 0.8282 | 0.1904 | nan | 0.0792 | 0.7586 | 0.7197 | 0.9768 | | 0.0506 | 40.0 | 680 | 0.9916 | 0.0 | 0.9474 | 0.8906 | 0.8488 | 0.8903 | 0.8936 | 0.9796 | 0.6164 | 0.7507 | 0.9894 | 0.8788 | 0.2110 | nan | 0.9807 | 0.0 | 0.8770 | 0.8471 | 0.8198 | 0.8269 | 0.8523 | 0.9516 | 0.6061 | 0.6140 | 0.9726 | 0.8269 | 0.2069 | nan | 0.0794 | 0.7606 | 0.7217 | 0.9767 | | 0.0426 | 41.18 | 700 | 0.9921 | 0.0 | 0.9513 | 0.9062 | 0.8437 | 0.8701 | 0.9256 | 0.9788 | 0.6143 | 0.7620 | 0.9887 | 0.8859 | 0.2141 | nan | 0.9804 | 0.0 | 0.8763 | 0.8417 | 0.8206 | 0.8174 | 0.8579 | 0.9524 | 0.6066 | 0.6145 | 0.9741 | 0.8318 | 0.2104 | nan | 0.0813 | 0.7641 | 0.7219 | 0.9766 | | 0.043 | 42.35 | 720 | 0.9924 | 0.0 | 0.9485 | 0.8467 | 0.8488 | 0.8918 | 0.9142 | 0.9799 | 0.6665 | 0.7487 | 0.9883 | 0.8804 | 0.1911 | nan | 0.9810 | 0.0 | 0.8794 | 0.8212 | 0.8281 | 0.8290 | 0.8633 | 0.9536 | 0.6521 | 0.6152 | 0.9744 | 0.8307 | 0.1892 | nan | 0.0758 | 0.7613 | 0.7244 | 0.9773 | | 0.0369 | 43.53 | 740 | 0.9928 | 0.0 | 0.9526 | 0.8837 | 0.8391 | 0.8761 | 0.9155 | 0.9745 | 0.6171 | 0.7684 | 0.9882 | 0.8889 | 0.2243 | nan | 0.9808 | 0.0 | 0.8761 | 0.8354 | 0.8189 | 0.8243 | 0.8648 | 0.9536 | 0.6082 | 0.6259 | 0.9747 | 0.8328 | 0.2171 | nan | 0.0800 | 0.7632 | 0.7241 | 0.9770 | | 0.0373 | 44.71 | 760 | 0.9928 | 0.0 | 0.9444 | 0.8855 | 0.8561 | 0.8601 | 0.8866 | 0.9836 | 0.6149 | 0.7557 | 0.9886 | 0.8776 | 0.2033 | nan | 0.9803 | 0.0 | 0.8743 | 0.8405 | 0.8347 | 0.8131 | 0.8579 | 0.9458 | 0.6082 | 0.6064 | 0.9741 | 0.8309 | 0.1965 | nan | 0.0832 | 0.7576 | 0.7202 | 0.9762 | | 0.0332 | 45.88 | 780 | 0.9923 | 0.0 | 0.9401 | 0.8656 | 0.8542 | 0.8879 | 0.8911 | 0.9791 | 0.6248 | 0.8070 | 0.9859 | 0.9102 | 0.2295 | nan | 0.9807 | 0.0 | 0.8770 | 0.8293 | 0.8286 | 0.8240 | 0.8598 | 0.9451 | 0.5887 | 0.5983 | 0.9746 | 0.8353 | 0.2259 | nan | 0.0818 | 0.7668 | 0.7205 | 0.9765 | | 0.0386 | 47.06 | 800 | 0.9926 | 0.0325 | 0.9593 | 0.8440 | 0.8593 | 0.8725 | 0.8980 | 0.9703 | 0.6201 | 0.7499 | 0.9851 | 0.9043 | 0.1994 | nan | 0.9810 | 0.0325 | 0.8732 | 0.8141 | 0.8340 | 0.8222 | 0.8616 | 0.9529 | 0.5787 | 0.5951 | 0.9753 | 0.8392 | 0.1945 | nan | 0.0835 | 0.7606 | 0.7196 | 0.9767 | | 0.0457 | 48.24 | 820 | 0.9933 | 0.0114 | 0.9413 | 0.9301 | 0.8847 | 0.8748 | 0.9043 | 0.9777 | 0.6238 | 0.7846 | 0.9887 | 0.8896 | 0.2452 | nan | 0.9810 | 0.0114 | 0.8789 | 0.8180 | 0.8470 | 0.8198 | 0.8569 | 0.9515 | 0.6110 | 0.6043 | 0.9751 | 0.8365 | 0.2376 | nan | 0.0799 | 0.7730 | 0.7253 | 0.9771 | | 0.045 | 49.41 | 840 | 0.9927 | 0.0 | 0.9411 | 0.8221 | 0.8123 | 0.8807 | 0.8406 | 0.9732 | 0.6200 | 0.7598 | 0.9920 | 0.8704 | 0.1768 | nan | 0.9807 | 0.0 | 0.8722 | 0.7926 | 0.7954 | 0.8200 | 0.8261 | 0.9524 | 0.6057 | 0.6041 | 0.9720 | 0.8282 | 0.1756 | nan | 0.0856 | 0.7447 | 0.7096 | 0.9762 | | 0.0414 | 50.59 | 860 | 0.9926 | 0.1220 | 0.9469 | 0.8722 | 0.8333 | 0.8965 | 0.8889 | 0.9775 | 0.6201 | 0.7202 | 0.9903 | 0.8716 | 0.2122 | nan | 0.9814 | 0.1218 | 0.8780 | 0.8280 | 0.8171 | 0.8290 | 0.8561 | 0.9514 | 0.6120 | 0.5965 | 0.9745 | 0.8299 | 0.2088 | nan | 0.0802 | 0.7649 | 0.7296 | 0.9772 | | 0.038 | 51.76 | 880 | 0.9934 | 0.0341 | 0.9430 | 0.8840 | 0.8400 | 0.8810 | 0.8959 | 0.9795 | 0.6205 | 0.7668 | 0.9909 | 0.8688 | 0.1805 | nan | 0.9812 | 0.0341 | 0.8798 | 0.8270 | 0.8212 | 0.8243 | 0.8576 | 0.9539 | 0.6108 | 0.6118 | 0.9746 | 0.8286 | 0.1792 | nan | 0.0805 | 0.7599 | 0.7219 | 0.9773 | | 0.0348 | 52.94 | 900 | 0.9889 | 0.3008 | 0.9530 | 0.9012 | 0.8565 | 0.9122 | 0.9022 | 0.9801 | 0.6242 | 0.8273 | 0.9888 | 0.8903 | 0.2021 | nan | 0.9810 | 0.2998 | 0.8773 | 0.8303 | 0.8292 | 0.8277 | 0.8710 | 0.9463 | 0.5850 | 0.6260 | 0.9757 | 0.8368 | 0.1981 | nan | 0.0810 | 0.7944 | 0.7449 | 0.9768 | | 0.0386 | 54.12 | 920 | 0.9938 | 0.0537 | 0.9512 | 0.8835 | 0.8350 | 0.8486 | 0.9054 | 0.9783 | 0.6164 | 0.7138 | 0.9896 | 0.8807 | 0.1991 | nan | 0.9803 | 0.0537 | 0.8779 | 0.8351 | 0.8194 | 0.8058 | 0.8772 | 0.9517 | 0.6106 | 0.6058 | 0.9751 | 0.8357 | 0.1963 | nan | 0.0854 | 0.7576 | 0.7250 | 0.9766 | | 0.0387 | 55.29 | 940 | 0.9936 | 0.0829 | 0.9422 | 0.8882 | 0.8625 | 0.8795 | 0.8723 | 0.9796 | 0.6231 | 0.8037 | 0.9910 | 0.8757 | 0.2241 | nan | 0.9811 | 0.0828 | 0.8820 | 0.8280 | 0.8400 | 0.8260 | 0.8475 | 0.9557 | 0.6126 | 0.6468 | 0.9754 | 0.8335 | 0.2217 | nan | 0.0794 | 0.7706 | 0.7333 | 0.9777 | | 0.0283 | 56.47 | 960 | 0.9940 | 0.1073 | 0.9472 | 0.8996 | 0.8596 | 0.8739 | 0.9006 | 0.9807 | 0.6203 | 0.7863 | 0.9864 | 0.8994 | 0.2271 | nan | 0.9810 | 0.1070 | 0.8836 | 0.8254 | 0.8396 | 0.8187 | 0.8586 | 0.9543 | 0.6134 | 0.6249 | 0.9763 | 0.8406 | 0.2245 | nan | 0.0813 | 0.7756 | 0.7345 | 0.9776 | | 0.0384 | 57.65 | 980 | 0.9925 | 0.1577 | 0.9499 | 0.9201 | 0.8493 | 0.8903 | 0.8871 | 0.9788 | 0.6250 | 0.7860 | 0.9886 | 0.8914 | 0.2491 | nan | 0.9817 | 0.1575 | 0.8825 | 0.8383 | 0.8280 | 0.8280 | 0.8572 | 0.9502 | 0.5993 | 0.6357 | 0.9760 | 0.8399 | 0.2445 | nan | 0.0800 | 0.7820 | 0.7399 | 0.9777 | | 0.0284 | 58.82 | 1000 | 0.9922 | 0.4341 | 0.9423 | 0.8988 | 0.8606 | 0.8817 | 0.8903 | 0.9850 | 0.6222 | 0.7714 | 0.9912 | 0.8787 | 0.2188 | nan | 0.9813 | 0.4070 | 0.8806 | 0.8373 | 0.8310 | 0.8225 | 0.8639 | 0.9464 | 0.6076 | 0.6204 | 0.9753 | 0.8363 | 0.2159 | nan | 0.0827 | 0.7975 | 0.7558 | 0.9772 | | 0.0282 | 60.0 | 1020 | 0.9918 | 0.4016 | 0.9464 | 0.9251 | 0.8827 | 0.8950 | 0.8991 | 0.9836 | 0.6233 | 0.7761 | 0.9871 | 0.9167 | 0.2432 | nan | 0.9815 | 0.3927 | 0.8841 | 0.8398 | 0.8436 | 0.8236 | 0.8689 | 0.9497 | 0.6084 | 0.6328 | 0.9760 | 0.8398 | 0.2396 | nan | 0.0809 | 0.8055 | 0.7600 | 0.9777 | | 0.0327 | 61.18 | 1040 | 0.9918 | 0.4114 | 0.9499 | 0.9102 | 0.8241 | 0.9009 | 0.8756 | 0.9790 | 0.6216 | 0.7633 | 0.9883 | 0.9068 | 0.2295 | nan | 0.9817 | 0.4081 | 0.8796 | 0.8338 | 0.8084 | 0.8279 | 0.8530 | 0.9510 | 0.6062 | 0.6306 | 0.9756 | 0.8438 | 0.2259 | nan | 0.0825 | 0.7963 | 0.7558 | 0.9775 | | 0.033 | 62.35 | 1060 | 0.9924 | 0.3024 | 0.9527 | 0.8912 | 0.8402 | 0.8845 | 0.9115 | 0.9813 | 0.6220 | 0.7280 | 0.9887 | 0.9029 | 0.2298 | nan | 0.9816 | 0.2995 | 0.8806 | 0.8413 | 0.8209 | 0.8232 | 0.8705 | 0.9529 | 0.6107 | 0.6132 | 0.9762 | 0.8435 | 0.2262 | nan | 0.0824 | 0.7868 | 0.7493 | 0.9776 | | 0.0368 | 63.53 | 1080 | 0.9934 | 0.1106 | 0.9447 | 0.8896 | 0.8379 | 0.8801 | 0.9108 | 0.9836 | 0.6245 | 0.7779 | 0.9905 | 0.8842 | 0.2147 | nan | 0.9817 | 0.1106 | 0.8837 | 0.8379 | 0.8201 | 0.8246 | 0.8741 | 0.9522 | 0.6121 | 0.6342 | 0.9760 | 0.8399 | 0.2128 | nan | 0.0803 | 0.7725 | 0.7354 | 0.9779 | | 0.0395 | 64.71 | 1100 | 0.9933 | 0.0553 | 0.9391 | 0.9201 | 0.8681 | 0.8875 | 0.9200 | 0.9820 | 0.6582 | 0.7844 | 0.9909 | 0.8916 | 0.2336 | nan | 0.9816 | 0.0553 | 0.8854 | 0.8479 | 0.8407 | 0.8273 | 0.8752 | 0.9526 | 0.6441 | 0.6288 | 0.9760 | 0.8418 | 0.2306 | nan | 0.0766 | 0.7788 | 0.7375 | 0.9782 | | 0.0355 | 65.88 | 1120 | 0.9935 | 0.1577 | 0.9472 | 0.9118 | 0.8493 | 0.8630 | 0.9045 | 0.9832 | 0.6617 | 0.7930 | 0.9891 | 0.8981 | 0.2304 | nan | 0.9811 | 0.1575 | 0.8863 | 0.8400 | 0.8324 | 0.8154 | 0.8784 | 0.9513 | 0.6451 | 0.6217 | 0.9769 | 0.8456 | 0.2256 | nan | 0.0792 | 0.7833 | 0.7429 | 0.9778 | | 0.0326 | 67.06 | 1140 | 0.9919 | 0.1919 | 0.9547 | 0.8761 | 0.8499 | 0.8893 | 0.9099 | 0.9822 | 0.6300 | 0.7588 | 0.9882 | 0.9022 | 0.1801 | nan | 0.9820 | 0.1919 | 0.8805 | 0.8213 | 0.8275 | 0.8292 | 0.8831 | 0.9492 | 0.6199 | 0.6253 | 0.9767 | 0.8458 | 0.1768 | nan | 0.0798 | 0.7773 | 0.7392 | 0.9778 | | 0.03 | 68.24 | 1160 | 0.9935 | 0.3057 | 0.9438 | 0.9097 | 0.8682 | 0.8853 | 0.9112 | 0.9814 | 0.7520 | 0.7964 | 0.9891 | 0.9062 | 0.2278 | nan | 0.9820 | 0.2998 | 0.8900 | 0.8445 | 0.8443 | 0.8281 | 0.8803 | 0.9556 | 0.7380 | 0.6151 | 0.9768 | 0.8482 | 0.2242 | nan | 0.0735 | 0.8054 | 0.7636 | 0.9789 | | 0.0304 | 69.41 | 1180 | 0.9931 | 0.2634 | 0.9452 | 0.9119 | 0.8890 | 0.8894 | 0.9082 | 0.9829 | 0.6426 | 0.7216 | 0.9899 | 0.9068 | 0.2100 | nan | 0.9820 | 0.2613 | 0.8847 | 0.8445 | 0.8600 | 0.8291 | 0.8779 | 0.9486 | 0.6299 | 0.6227 | 0.9780 | 0.8495 | 0.2039 | nan | 0.0749 | 0.7888 | 0.7517 | 0.9784 | | 0.0366 | 70.59 | 1200 | 0.9927 | 0.0472 | 0.9582 | 0.8675 | 0.8251 | 0.8619 | 0.9024 | 0.9774 | 0.6224 | 0.8008 | 0.9870 | 0.9060 | 0.1424 | nan | 0.9819 | 0.0471 | 0.8767 | 0.8272 | 0.8106 | 0.8130 | 0.8767 | 0.9599 | 0.6055 | 0.5239 | 0.9777 | 0.8490 | 0.1354 | nan | 0.0830 | 0.7609 | 0.7142 | 0.9771 | | 0.0296 | 71.76 | 1220 | 0.9916 | 0.1431 | 0.9524 | 0.9023 | 0.8712 | 0.9014 | 0.8903 | 0.9791 | 0.6243 | 0.7556 | 0.9916 | 0.8860 | 0.1754 | nan | 0.9819 | 0.1431 | 0.8808 | 0.8397 | 0.8471 | 0.8321 | 0.8665 | 0.9586 | 0.6103 | 0.6133 | 0.9772 | 0.8449 | 0.1710 | nan | 0.0791 | 0.7742 | 0.7359 | 0.9782 | | 0.0313 | 72.94 | 1240 | 0.9927 | 0.0894 | 0.9468 | 0.9155 | 0.8566 | 0.8993 | 0.8841 | 0.9796 | 0.6253 | 0.7843 | 0.9926 | 0.8653 | 0.2090 | nan | 0.9821 | 0.0894 | 0.8860 | 0.8653 | 0.8393 | 0.8309 | 0.8594 | 0.9601 | 0.6150 | 0.6127 | 0.9755 | 0.8342 | 0.2065 | nan | 0.0774 | 0.7724 | 0.7351 | 0.9784 | | 0.0272 | 74.12 | 1260 | 0.9942 | 0.2228 | 0.9498 | 0.9141 | 0.8849 | 0.8712 | 0.9235 | 0.9781 | 0.6261 | 0.7730 | 0.9907 | 0.8943 | 0.2644 | nan | 0.9817 | 0.2164 | 0.8902 | 0.8554 | 0.8587 | 0.8241 | 0.8853 | 0.9617 | 0.6170 | 0.6001 | 0.9772 | 0.8477 | 0.2536 | nan | 0.0761 | 0.7913 | 0.7515 | 0.9787 | | 0.0329 | 75.29 | 1280 | 0.9928 | 0.1902 | 0.9563 | 0.8935 | 0.8582 | 0.8855 | 0.8926 | 0.9794 | 0.6249 | 0.7687 | 0.9893 | 0.8970 | 0.2109 | nan | 0.9821 | 0.1896 | 0.8856 | 0.8492 | 0.8390 | 0.8261 | 0.8720 | 0.9610 | 0.6129 | 0.5996 | 0.9775 | 0.8492 | 0.2071 | nan | 0.0783 | 0.7800 | 0.7424 | 0.9785 | | 0.0297 | 76.47 | 1300 | 0.9925 | 0.2602 | 0.9522 | 0.9211 | 0.8558 | 0.9005 | 0.8835 | 0.9813 | 0.6233 | 0.7873 | 0.9882 | 0.9161 | 0.2259 | nan | 0.9823 | 0.2581 | 0.8877 | 0.8522 | 0.8369 | 0.8292 | 0.8674 | 0.9597 | 0.6142 | 0.6165 | 0.9772 | 0.8511 | 0.2211 | nan | 0.0783 | 0.7914 | 0.7503 | 0.9787 | | 0.0316 | 77.65 | 1320 | 0.9950 | 0.0683 | 0.9489 | 0.9051 | 0.8369 | 0.8689 | 0.8564 | 0.9788 | 0.6222 | 0.7373 | 0.9890 | 0.9112 | 0.2202 | nan | 0.9813 | 0.0681 | 0.8873 | 0.8526 | 0.8250 | 0.8239 | 0.8418 | 0.9599 | 0.6145 | 0.6000 | 0.9773 | 0.8518 | 0.2182 | nan | 0.0802 | 0.7645 | 0.7309 | 0.9783 | | 0.0295 | 78.82 | 1340 | 0.9931 | 0.2309 | 0.9575 | 0.8918 | 0.8445 | 0.8918 | 0.8780 | 0.9784 | 0.6227 | 0.7670 | 0.9876 | 0.9198 | 0.2125 | nan | 0.9825 | 0.2276 | 0.8858 | 0.8543 | 0.8294 | 0.8329 | 0.8619 | 0.9603 | 0.6136 | 0.6130 | 0.9771 | 0.8528 | 0.2104 | nan | 0.0793 | 0.7827 | 0.7463 | 0.9787 | | 0.0363 | 80.0 | 1360 | 0.9935 | 0.2293 | 0.9455 | 0.9084 | 0.8572 | 0.8976 | 0.8853 | 0.9827 | 0.6365 | 0.7868 | 0.9908 | 0.8975 | 0.2276 | nan | 0.9824 | 0.2267 | 0.8881 | 0.8519 | 0.8387 | 0.8352 | 0.8649 | 0.9615 | 0.6243 | 0.6143 | 0.9772 | 0.8497 | 0.2253 | nan | 0.0758 | 0.7876 | 0.7493 | 0.9790 | | 0.0278 | 81.18 | 1380 | 0.9933 | 0.1659 | 0.9558 | 0.9068 | 0.8466 | 0.8911 | 0.8826 | 0.9817 | 0.6237 | 0.7761 | 0.9879 | 0.8980 | 0.2160 | nan | 0.9825 | 0.1656 | 0.8856 | 0.8531 | 0.8324 | 0.8328 | 0.8661 | 0.9615 | 0.6169 | 0.6027 | 0.9773 | 0.8501 | 0.2138 | nan | 0.0779 | 0.7789 | 0.7416 | 0.9787 | | 0.0349 | 82.35 | 1400 | 0.9919 | 0.2244 | 0.9584 | 0.8933 | 0.8434 | 0.9011 | 0.8807 | 0.9809 | 0.6226 | 0.7732 | 0.9893 | 0.8945 | 0.2060 | nan | 0.9826 | 0.2240 | 0.8839 | 0.8456 | 0.8265 | 0.8310 | 0.8619 | 0.9604 | 0.6152 | 0.6070 | 0.9772 | 0.8500 | 0.2038 | nan | 0.0789 | 0.7815 | 0.7438 | 0.9785 | | 0.0334 | 83.53 | 1420 | 0.9938 | 0.2081 | 0.9553 | 0.9087 | 0.8442 | 0.8773 | 0.8931 | 0.9789 | 0.6275 | 0.7603 | 0.9896 | 0.8964 | 0.2237 | nan | 0.9822 | 0.2055 | 0.8865 | 0.8505 | 0.8299 | 0.8272 | 0.8748 | 0.9599 | 0.6198 | 0.6036 | 0.9772 | 0.8502 | 0.2211 | nan | 0.0789 | 0.7813 | 0.7452 | 0.9786 | | 0.0271 | 84.71 | 1440 | 0.9929 | 0.3496 | 0.9541 | 0.9276 | 0.8700 | 0.8941 | 0.8969 | 0.9810 | 0.6276 | 0.7772 | 0.9882 | 0.9110 | 0.2436 | nan | 0.9825 | 0.3418 | 0.8882 | 0.8426 | 0.8466 | 0.8327 | 0.8717 | 0.9594 | 0.6172 | 0.6108 | 0.9775 | 0.8514 | 0.2407 | nan | 0.0779 | 0.8011 | 0.7587 | 0.9789 | | 0.0269 | 85.88 | 1460 | 0.9929 | 0.2959 | 0.9507 | 0.9229 | 0.8474 | 0.8995 | 0.9106 | 0.9824 | 0.6299 | 0.7745 | 0.9895 | 0.9106 | 0.2364 | nan | 0.9826 | 0.2940 | 0.8884 | 0.8451 | 0.8318 | 0.8361 | 0.8758 | 0.9576 | 0.6199 | 0.6215 | 0.9774 | 0.8516 | 0.2340 | nan | 0.0778 | 0.7956 | 0.7551 | 0.9790 | | 0.0273 | 87.06 | 1480 | 0.9937 | 0.0894 | 0.9506 | 0.9190 | 0.8680 | 0.8860 | 0.9251 | 0.9807 | 0.6401 | 0.7765 | 0.9898 | 0.9120 | 0.2603 | nan | 0.9826 | 0.0894 | 0.8905 | 0.8519 | 0.8466 | 0.8315 | 0.8874 | 0.9592 | 0.6285 | 0.6164 | 0.9774 | 0.8540 | 0.2562 | nan | 0.0758 | 0.7839 | 0.7440 | 0.9791 | | 0.0288 | 88.24 | 1500 | 0.9932 | 0.1854 | 0.9524 | 0.9075 | 0.8556 | 0.8900 | 0.8910 | 0.9808 | 0.6311 | 0.7878 | 0.9903 | 0.9093 | 0.2340 | nan | 0.9828 | 0.1848 | 0.8889 | 0.8508 | 0.8370 | 0.8321 | 0.8751 | 0.9591 | 0.6224 | 0.6146 | 0.9774 | 0.8555 | 0.2317 | nan | 0.0780 | 0.7852 | 0.7471 | 0.9790 | | 0.0258 | 89.41 | 1520 | 0.9924 | 0.3057 | 0.9572 | 0.9354 | 0.8573 | 0.8933 | 0.8876 | 0.9823 | 0.6272 | 0.7935 | 0.9876 | 0.9146 | 0.2306 | nan | 0.9826 | 0.3003 | 0.8866 | 0.8465 | 0.8400 | 0.8322 | 0.8710 | 0.9575 | 0.6192 | 0.6181 | 0.9778 | 0.8540 | 0.2259 | nan | 0.0794 | 0.7973 | 0.7548 | 0.9788 | | 0.023 | 90.59 | 1540 | 0.9927 | 0.3252 | 0.9555 | 0.9297 | 0.8655 | 0.8867 | 0.9231 | 0.9818 | 0.6278 | 0.7999 | 0.9899 | 0.9057 | 0.2339 | nan | 0.9828 | 0.3135 | 0.8881 | 0.8506 | 0.8438 | 0.8323 | 0.8904 | 0.9598 | 0.6192 | 0.6107 | 0.9778 | 0.8548 | 0.2281 | nan | 0.0783 | 0.8014 | 0.7578 | 0.9790 | | 0.0263 | 91.76 | 1560 | 0.9935 | 0.2634 | 0.9549 | 0.9192 | 0.8576 | 0.8835 | 0.9122 | 0.9809 | 0.6293 | 0.7828 | 0.9882 | 0.9137 | 0.2440 | nan | 0.9826 | 0.2576 | 0.8877 | 0.8496 | 0.8401 | 0.8323 | 0.8877 | 0.9592 | 0.6229 | 0.6039 | 0.9777 | 0.8543 | 0.2387 | nan | 0.0788 | 0.7941 | 0.7534 | 0.9789 | | 0.0308 | 92.94 | 1580 | 0.9933 | 0.2195 | 0.9518 | 0.9267 | 0.8570 | 0.8981 | 0.8983 | 0.9813 | 0.6285 | 0.7817 | 0.9884 | 0.9184 | 0.2340 | nan | 0.9828 | 0.2181 | 0.8883 | 0.8554 | 0.8391 | 0.8335 | 0.8765 | 0.9603 | 0.6199 | 0.6176 | 0.9778 | 0.8563 | 0.2311 | nan | 0.0786 | 0.7905 | 0.7505 | 0.9791 | | 0.0255 | 94.12 | 1600 | 0.9936 | 0.2065 | 0.9601 | 0.9187 | 0.8598 | 0.8876 | 0.8846 | 0.9775 | 0.6275 | 0.7880 | 0.9860 | 0.9239 | 0.2229 | nan | 0.9829 | 0.2058 | 0.8874 | 0.8559 | 0.8419 | 0.8333 | 0.8670 | 0.9607 | 0.6197 | 0.6095 | 0.9777 | 0.8581 | 0.2203 | nan | 0.0799 | 0.7874 | 0.7477 | 0.9791 | | 0.0288 | 95.29 | 1620 | 0.9932 | 0.2878 | 0.9565 | 0.9128 | 0.8604 | 0.8750 | 0.9047 | 0.9825 | 0.6288 | 0.7858 | 0.9910 | 0.8878 | 0.2249 | nan | 0.9828 | 0.2846 | 0.8870 | 0.8520 | 0.8408 | 0.8290 | 0.8824 | 0.9594 | 0.6188 | 0.6095 | 0.9775 | 0.8493 | 0.2224 | nan | 0.0808 | 0.7916 | 0.7535 | 0.9789 | | 0.0278 | 96.47 | 1640 | 0.9944 | 0.1593 | 0.9515 | 0.9112 | 0.8437 | 0.8814 | 0.8830 | 0.9805 | 0.6292 | 0.7454 | 0.9892 | 0.9099 | 0.2248 | nan | 0.9825 | 0.1593 | 0.8880 | 0.8545 | 0.8285 | 0.8316 | 0.8652 | 0.9586 | 0.6228 | 0.5959 | 0.9778 | 0.8548 | 0.2228 | nan | 0.0802 | 0.7772 | 0.7417 | 0.9789 | | 0.0266 | 97.65 | 1660 | 0.9933 | 0.2033 | 0.9569 | 0.9173 | 0.8506 | 0.8898 | 0.8823 | 0.9815 | 0.6297 | 0.7677 | 0.9875 | 0.9221 | 0.2112 | nan | 0.9829 | 0.2029 | 0.8874 | 0.8550 | 0.8348 | 0.8321 | 0.8654 | 0.9597 | 0.6215 | 0.6081 | 0.9780 | 0.8558 | 0.2093 | nan | 0.0801 | 0.7841 | 0.7456 | 0.9790 | | 0.025 | 98.82 | 1680 | 0.9930 | 0.2081 | 0.9597 | 0.8978 | 0.8492 | 0.9013 | 0.8684 | 0.9799 | 0.6333 | 0.7604 | 0.9864 | 0.9254 | 0.2005 | nan | 0.9832 | 0.2075 | 0.8871 | 0.8506 | 0.8329 | 0.8398 | 0.8552 | 0.9606 | 0.6287 | 0.6087 | 0.9772 | 0.8545 | 0.1986 | nan | 0.0787 | 0.7818 | 0.7450 | 0.9792 | | 0.0268 | 100.0 | 1700 | 0.9931 | 0.1870 | 0.9549 | 0.9206 | 0.8659 | 0.8962 | 0.8848 | 0.9826 | 0.6367 | 0.7757 | 0.9882 | 0.9147 | 0.2171 | nan | 0.9830 | 0.1861 | 0.8891 | 0.8483 | 0.8460 | 0.8333 | 0.8671 | 0.9596 | 0.6281 | 0.6210 | 0.9784 | 0.8557 | 0.2148 | nan | 0.0788 | 0.7860 | 0.7470 | 0.9793 | | 0.0256 | 101.18 | 1720 | 0.9942 | 0.1902 | 0.9516 | 0.9301 | 0.8468 | 0.8849 | 0.9161 | 0.9808 | 0.6354 | 0.7714 | 0.9889 | 0.9183 | 0.2331 | nan | 0.9826 | 0.1896 | 0.8895 | 0.8524 | 0.8318 | 0.8335 | 0.8921 | 0.9605 | 0.6271 | 0.6191 | 0.9780 | 0.8559 | 0.2303 | nan | 0.0785 | 0.7878 | 0.7494 | 0.9792 | | 0.0256 | 102.35 | 1740 | 0.9930 | 0.3220 | 0.9563 | 0.9174 | 0.8542 | 0.8969 | 0.9228 | 0.9798 | 0.6242 | 0.7796 | 0.9893 | 0.9139 | 0.2347 | nan | 0.9831 | 0.3163 | 0.8872 | 0.8580 | 0.8359 | 0.8384 | 0.8959 | 0.9590 | 0.6187 | 0.6221 | 0.9785 | 0.8583 | 0.2265 | nan | 0.0795 | 0.7988 | 0.7598 | 0.9793 | | 0.0243 | 103.53 | 1760 | 0.9933 | 0.1967 | 0.9556 | 0.9194 | 0.8592 | 0.9019 | 0.9078 | 0.9791 | 0.6303 | 0.7629 | 0.9883 | 0.9218 | 0.2304 | nan | 0.9830 | 0.1961 | 0.8895 | 0.8594 | 0.8390 | 0.8391 | 0.8899 | 0.9602 | 0.6230 | 0.6181 | 0.9782 | 0.8581 | 0.2273 | nan | 0.0781 | 0.7882 | 0.7509 | 0.9794 | | 0.0299 | 104.71 | 1780 | 0.9931 | 0.2033 | 0.9584 | 0.9250 | 0.8485 | 0.9021 | 0.8709 | 0.9783 | 0.6225 | 0.7794 | 0.9884 | 0.9216 | 0.2051 | nan | 0.9831 | 0.2026 | 0.8884 | 0.8548 | 0.8335 | 0.8379 | 0.8556 | 0.9612 | 0.6179 | 0.6252 | 0.9788 | 0.8587 | 0.2033 | nan | 0.0785 | 0.7843 | 0.7462 | 0.9794 | | 0.0238 | 105.88 | 1800 | 0.9939 | 0.1789 | 0.9530 | 0.9209 | 0.8513 | 0.8998 | 0.8795 | 0.9815 | 0.6352 | 0.7671 | 0.9888 | 0.9116 | 0.2369 | nan | 0.9830 | 0.1789 | 0.8912 | 0.8586 | 0.8351 | 0.8372 | 0.8654 | 0.9615 | 0.6288 | 0.6171 | 0.9785 | 0.8580 | 0.2351 | nan | 0.0770 | 0.7845 | 0.7483 | 0.9796 | | 0.0283 | 107.06 | 1820 | 0.9930 | 0.2358 | 0.9605 | 0.9187 | 0.8514 | 0.8910 | 0.9089 | 0.9780 | 0.6273 | 0.8059 | 0.9888 | 0.9097 | 0.2182 | nan | 0.9829 | 0.2342 | 0.8885 | 0.8595 | 0.8352 | 0.8344 | 0.8892 | 0.9608 | 0.6210 | 0.6203 | 0.9786 | 0.8585 | 0.2165 | nan | 0.0792 | 0.7913 | 0.7523 | 0.9792 | | 0.0236 | 108.24 | 1840 | 0.9934 | 0.3024 | 0.9579 | 0.9291 | 0.8560 | 0.8895 | 0.9201 | 0.9805 | 0.6352 | 0.7655 | 0.9884 | 0.9079 | 0.2409 | nan | 0.9828 | 0.2981 | 0.8888 | 0.8550 | 0.8388 | 0.8342 | 0.8929 | 0.9592 | 0.6231 | 0.6139 | 0.9783 | 0.8570 | 0.2367 | nan | 0.0789 | 0.7974 | 0.7584 | 0.9792 | | 0.0248 | 109.41 | 1860 | 0.9936 | 0.2927 | 0.9553 | 0.9286 | 0.8521 | 0.8933 | 0.9070 | 0.9819 | 0.6299 | 0.7726 | 0.9893 | 0.9061 | 0.2390 | nan | 0.9830 | 0.2885 | 0.8901 | 0.8463 | 0.8344 | 0.8375 | 0.8853 | 0.9592 | 0.6220 | 0.6224 | 0.9784 | 0.8568 | 0.2365 | nan | 0.0784 | 0.7955 | 0.7570 | 0.9794 | | 0.0296 | 110.59 | 1880 | 0.9933 | 0.1561 | 0.9523 | 0.9349 | 0.8549 | 0.9022 | 0.8833 | 0.9830 | 0.6291 | 0.7902 | 0.9888 | 0.9162 | 0.2348 | nan | 0.9831 | 0.1561 | 0.8907 | 0.8562 | 0.8386 | 0.8375 | 0.8652 | 0.9607 | 0.6205 | 0.6305 | 0.9783 | 0.8588 | 0.2331 | nan | 0.0777 | 0.7861 | 0.7469 | 0.9795 | | 0.0234 | 111.76 | 1900 | 0.9937 | 0.2764 | 0.9536 | 0.9374 | 0.8559 | 0.8959 | 0.9211 | 0.9791 | 0.6245 | 0.7706 | 0.9896 | 0.9138 | 0.2421 | nan | 0.9828 | 0.2742 | 0.8901 | 0.8577 | 0.8386 | 0.8378 | 0.8941 | 0.9596 | 0.6196 | 0.6235 | 0.9785 | 0.8596 | 0.2400 | nan | 0.0799 | 0.7964 | 0.7582 | 0.9794 | | 0.0256 | 112.94 | 1920 | 0.9931 | 0.3415 | 0.9587 | 0.9101 | 0.8473 | 0.8977 | 0.9016 | 0.9780 | 0.6255 | 0.7751 | 0.9892 | 0.9108 | 0.2290 | nan | 0.9830 | 0.3371 | 0.8884 | 0.8634 | 0.8312 | 0.8370 | 0.8852 | 0.9599 | 0.6191 | 0.6203 | 0.9783 | 0.8605 | 0.2273 | nan | 0.0801 | 0.7967 | 0.7608 | 0.9793 | | 0.022 | 114.12 | 1940 | 0.9933 | 0.3789 | 0.9556 | 0.9383 | 0.8550 | 0.8948 | 0.9147 | 0.9789 | 0.6288 | 0.7992 | 0.9892 | 0.9181 | 0.2431 | nan | 0.9830 | 0.3612 | 0.8901 | 0.8594 | 0.8375 | 0.8357 | 0.8922 | 0.9607 | 0.6224 | 0.6209 | 0.9785 | 0.8616 | 0.2407 | nan | 0.0786 | 0.8068 | 0.7649 | 0.9794 | | 0.0235 | 115.29 | 1960 | 0.9932 | 0.3480 | 0.9560 | 0.9251 | 0.8506 | 0.8939 | 0.9053 | 0.9799 | 0.6274 | 0.7836 | 0.9905 | 0.9063 | 0.2364 | nan | 0.9830 | 0.3424 | 0.8901 | 0.8615 | 0.8324 | 0.8352 | 0.8878 | 0.9598 | 0.6207 | 0.6235 | 0.9783 | 0.8597 | 0.2343 | nan | 0.0790 | 0.7997 | 0.7622 | 0.9794 | | 0.0292 | 116.47 | 1980 | 0.9939 | 0.3545 | 0.9550 | 0.9267 | 0.8588 | 0.8872 | 0.9186 | 0.9809 | 0.6221 | 0.7914 | 0.9883 | 0.9215 | 0.2433 | nan | 0.9829 | 0.3455 | 0.8907 | 0.8650 | 0.8402 | 0.8341 | 0.8967 | 0.9594 | 0.6182 | 0.6286 | 0.9787 | 0.8614 | 0.2409 | nan | 0.0803 | 0.8032 | 0.7648 | 0.9795 | | 0.0252 | 117.65 | 2000 | 0.9940 | 0.3480 | 0.9533 | 0.9367 | 0.8492 | 0.8846 | 0.9283 | 0.9816 | 0.6248 | 0.7847 | 0.9894 | 0.9175 | 0.2436 | nan | 0.9829 | 0.3386 | 0.8908 | 0.8639 | 0.8326 | 0.8315 | 0.8974 | 0.9589 | 0.6189 | 0.6293 | 0.9788 | 0.8600 | 0.2391 | nan | 0.0801 | 0.8027 | 0.7633 | 0.9794 | | 0.0266 | 118.82 | 2020 | 0.9924 | 0.4439 | 0.9605 | 0.9216 | 0.8813 | 0.9003 | 0.9196 | 0.9826 | 0.6329 | 0.7891 | 0.9875 | 0.9215 | 0.1686 | nan | 0.9834 | 0.4326 | 0.8888 | 0.8657 | 0.8562 | 0.8397 | 0.8957 | 0.9589 | 0.6227 | 0.6283 | 0.9782 | 0.8601 | 0.1679 | nan | 0.0790 | 0.8078 | 0.7676 | 0.9795 | | 0.0257 | 120.0 | 2040 | 0.9935 | 0.3317 | 0.9584 | 0.9307 | 0.8576 | 0.8875 | 0.9198 | 0.9791 | 0.6243 | 0.8030 | 0.9882 | 0.9262 | 0.1780 | nan | 0.9831 | 0.3259 | 0.8903 | 0.8655 | 0.8413 | 0.8344 | 0.8958 | 0.9607 | 0.6169 | 0.6193 | 0.9788 | 0.8634 | 0.1772 | nan | 0.0813 | 0.7983 | 0.7579 | 0.9795 | | 0.0246 | 121.18 | 2060 | 0.9937 | 0.2504 | 0.9544 | 0.9303 | 0.8725 | 0.8938 | 0.9228 | 0.9800 | 0.6265 | 0.7641 | 0.9910 | 0.9045 | 0.2586 | nan | 0.9833 | 0.2476 | 0.8922 | 0.8671 | 0.8509 | 0.8386 | 0.9003 | 0.9603 | 0.6180 | 0.6211 | 0.9782 | 0.8594 | 0.2520 | nan | 0.0773 | 0.7956 | 0.7592 | 0.9797 | | 0.0273 | 122.35 | 2080 | 0.9939 | 0.1935 | 0.9529 | 0.9174 | 0.8531 | 0.8845 | 0.9048 | 0.9814 | 0.6249 | 0.7741 | 0.9913 | 0.9008 | 0.2092 | nan | 0.9832 | 0.1926 | 0.8896 | 0.8640 | 0.8383 | 0.8356 | 0.8907 | 0.9592 | 0.6192 | 0.6205 | 0.9774 | 0.8581 | 0.2077 | nan | 0.0812 | 0.7832 | 0.7489 | 0.9794 | | 0.0209 | 123.53 | 2100 | 0.9933 | 0.2455 | 0.9573 | 0.9207 | 0.8545 | 0.9000 | 0.9154 | 0.9801 | 0.6232 | 0.7778 | 0.9897 | 0.9137 | 0.2016 | nan | 0.9834 | 0.2439 | 0.8904 | 0.8679 | 0.8394 | 0.8419 | 0.8981 | 0.9600 | 0.6177 | 0.6289 | 0.9784 | 0.8627 | 0.2002 | nan | 0.0805 | 0.7902 | 0.7548 | 0.9797 | | 0.0233 | 124.71 | 2120 | 0.9930 | 0.2423 | 0.9551 | 0.9354 | 0.8548 | 0.9022 | 0.9183 | 0.9807 | 0.6224 | 0.7888 | 0.9906 | 0.9106 | 0.2290 | nan | 0.9834 | 0.2407 | 0.8903 | 0.8648 | 0.8388 | 0.8394 | 0.8962 | 0.9600 | 0.6161 | 0.6274 | 0.9785 | 0.8623 | 0.2271 | nan | 0.0803 | 0.7941 | 0.7558 | 0.9797 | | 0.0206 | 125.88 | 2140 | 0.9938 | 0.3089 | 0.9518 | 0.9177 | 0.8557 | 0.8886 | 0.9249 | 0.9814 | 0.6287 | 0.7948 | 0.9914 | 0.9048 | 0.2116 | nan | 0.9832 | 0.3035 | 0.8902 | 0.8658 | 0.8395 | 0.8396 | 0.9013 | 0.9591 | 0.6217 | 0.6244 | 0.9783 | 0.8598 | 0.2103 | nan | 0.0804 | 0.7965 | 0.7598 | 0.9796 | | 0.0252 | 127.06 | 2160 | 0.9933 | 0.3285 | 0.9544 | 0.9381 | 0.8706 | 0.8985 | 0.9303 | 0.9813 | 0.6272 | 0.7816 | 0.9902 | 0.9175 | 0.2381 | nan | 0.9834 | 0.3211 | 0.8908 | 0.8686 | 0.8502 | 0.8415 | 0.9023 | 0.9597 | 0.6207 | 0.6264 | 0.9788 | 0.8633 | 0.2337 | nan | 0.0794 | 0.8038 | 0.7646 | 0.9798 | | 0.0274 | 128.24 | 2180 | 0.9935 | 0.3366 | 0.9562 | 0.9205 | 0.8627 | 0.8976 | 0.9170 | 0.9812 | 0.6234 | 0.7904 | 0.9889 | 0.9198 | 0.2291 | nan | 0.9833 | 0.3312 | 0.8904 | 0.8680 | 0.8469 | 0.8408 | 0.8969 | 0.9593 | 0.6176 | 0.6297 | 0.9789 | 0.8639 | 0.2272 | nan | 0.0801 | 0.8013 | 0.7642 | 0.9797 | | 0.0254 | 129.41 | 2200 | 0.9936 | 0.2537 | 0.9551 | 0.9270 | 0.8635 | 0.8960 | 0.9123 | 0.9819 | 0.6250 | 0.7929 | 0.9886 | 0.9260 | 0.2223 | nan | 0.9833 | 0.2512 | 0.8911 | 0.8658 | 0.8461 | 0.8386 | 0.8925 | 0.9590 | 0.6188 | 0.6304 | 0.9792 | 0.8636 | 0.2206 | nan | 0.0796 | 0.7952 | 0.7569 | 0.9797 | | 0.024 | 130.59 | 2220 | 0.9936 | 0.1951 | 0.9572 | 0.9302 | 0.8622 | 0.8970 | 0.9229 | 0.9811 | 0.6275 | 0.7900 | 0.9893 | 0.9206 | 0.2262 | nan | 0.9835 | 0.1942 | 0.8927 | 0.8688 | 0.8467 | 0.8410 | 0.8989 | 0.9612 | 0.6217 | 0.6272 | 0.9791 | 0.8650 | 0.2247 | nan | 0.0783 | 0.7918 | 0.7542 | 0.9800 | | 0.027 | 131.76 | 2240 | 0.9933 | 0.2813 | 0.9517 | 0.9241 | 0.8712 | 0.9027 | 0.9345 | 0.9833 | 0.6333 | 0.7869 | 0.9902 | 0.9146 | 0.2275 | nan | 0.9834 | 0.2777 | 0.8926 | 0.8661 | 0.8497 | 0.8405 | 0.9018 | 0.9597 | 0.6237 | 0.6262 | 0.9790 | 0.8631 | 0.2257 | nan | 0.0786 | 0.7996 | 0.7607 | 0.9799 | | 0.0208 | 132.94 | 2260 | 0.9935 | 0.2358 | 0.9551 | 0.9257 | 0.8583 | 0.8920 | 0.9265 | 0.9821 | 0.6282 | 0.8028 | 0.9903 | 0.9136 | 0.2308 | nan | 0.9833 | 0.2339 | 0.8921 | 0.8694 | 0.8426 | 0.8365 | 0.8995 | 0.9604 | 0.6216 | 0.6271 | 0.9791 | 0.8638 | 0.2291 | nan | 0.0801 | 0.7950 | 0.7568 | 0.9798 | | 0.0251 | 134.12 | 2280 | 0.9937 | 0.2894 | 0.9541 | 0.9327 | 0.8687 | 0.8883 | 0.9182 | 0.9822 | 0.6234 | 0.7846 | 0.9905 | 0.9137 | 0.2341 | nan | 0.9833 | 0.2848 | 0.8915 | 0.8688 | 0.8491 | 0.8362 | 0.8970 | 0.9603 | 0.6178 | 0.6223 | 0.9790 | 0.8634 | 0.2323 | nan | 0.0810 | 0.7980 | 0.7604 | 0.9797 | | 0.0217 | 135.29 | 2300 | 0.9942 | 0.3285 | 0.9531 | 0.9385 | 0.8568 | 0.8883 | 0.9183 | 0.9803 | 0.6244 | 0.7928 | 0.9904 | 0.9173 | 0.2393 | nan | 0.9833 | 0.3201 | 0.8922 | 0.8765 | 0.8403 | 0.8374 | 0.8983 | 0.9603 | 0.6188 | 0.6297 | 0.9789 | 0.8644 | 0.2366 | nan | 0.0809 | 0.8017 | 0.7644 | 0.9798 | | 0.0249 | 136.47 | 2320 | 0.9937 | 0.2976 | 0.9560 | 0.9356 | 0.8588 | 0.8969 | 0.8910 | 0.9818 | 0.6262 | 0.7744 | 0.9887 | 0.9167 | 0.2345 | nan | 0.9833 | 0.2928 | 0.8913 | 0.8725 | 0.8419 | 0.8377 | 0.8746 | 0.9608 | 0.6198 | 0.6203 | 0.9789 | 0.8643 | 0.2324 | nan | 0.0812 | 0.7963 | 0.7593 | 0.9797 | | 0.0229 | 137.65 | 2340 | 0.9940 | 0.3398 | 0.9522 | 0.9262 | 0.8622 | 0.8958 | 0.9253 | 0.9809 | 0.6251 | 0.7975 | 0.9905 | 0.9102 | 0.2338 | nan | 0.9834 | 0.3333 | 0.8922 | 0.8732 | 0.8430 | 0.8405 | 0.9013 | 0.9609 | 0.6189 | 0.6253 | 0.9788 | 0.8638 | 0.2321 | nan | 0.0800 | 0.8026 | 0.7651 | 0.9799 | | 0.0255 | 138.82 | 2360 | 0.9942 | 0.3220 | 0.9547 | 0.9288 | 0.8616 | 0.8916 | 0.9138 | 0.9814 | 0.6246 | 0.7646 | 0.9898 | 0.9134 | 0.2408 | nan | 0.9832 | 0.3153 | 0.8923 | 0.8761 | 0.8445 | 0.8383 | 0.8958 | 0.9602 | 0.6180 | 0.6198 | 0.9792 | 0.8644 | 0.2385 | nan | 0.0807 | 0.7986 | 0.7635 | 0.9798 | | 0.025 | 140.0 | 2380 | 0.9932 | 0.3626 | 0.9544 | 0.9285 | 0.8567 | 0.8978 | 0.9276 | 0.9807 | 0.6266 | 0.7963 | 0.9910 | 0.9159 | 0.2400 | nan | 0.9836 | 0.3551 | 0.8904 | 0.8750 | 0.8394 | 0.8405 | 0.9008 | 0.9601 | 0.6177 | 0.6291 | 0.9785 | 0.8647 | 0.2366 | nan | 0.0811 | 0.8055 | 0.7670 | 0.9798 | | 0.0203 | 141.18 | 2400 | 0.9937 | 0.3220 | 0.9577 | 0.9230 | 0.8747 | 0.8940 | 0.9281 | 0.9797 | 0.6233 | 0.7675 | 0.9893 | 0.9159 | 0.2408 | nan | 0.9834 | 0.3163 | 0.8907 | 0.8699 | 0.8540 | 0.8388 | 0.8999 | 0.9594 | 0.6165 | 0.6187 | 0.9794 | 0.8643 | 0.2345 | nan | 0.0815 | 0.8007 | 0.7635 | 0.9798 | | 0.0245 | 142.35 | 2420 | 0.9939 | 0.3122 | 0.9541 | 0.9387 | 0.8693 | 0.8924 | 0.9261 | 0.9827 | 0.6251 | 0.7980 | 0.9883 | 0.9334 | 0.2442 | nan | 0.9834 | 0.3077 | 0.8928 | 0.8696 | 0.8511 | 0.8393 | 0.9012 | 0.9599 | 0.6196 | 0.6327 | 0.9793 | 0.8639 | 0.2411 | nan | 0.0800 | 0.8045 | 0.7647 | 0.9800 | | 0.021 | 143.53 | 2440 | 0.9934 | 0.3626 | 0.9557 | 0.9449 | 0.8650 | 0.8963 | 0.9220 | 0.9824 | 0.6284 | 0.7961 | 0.9896 | 0.9210 | 0.2369 | nan | 0.9836 | 0.3562 | 0.8920 | 0.8759 | 0.8457 | 0.8395 | 0.8999 | 0.9591 | 0.6215 | 0.6309 | 0.9794 | 0.8647 | 0.2342 | nan | 0.0797 | 0.8073 | 0.7679 | 0.9799 | | 0.0235 | 144.71 | 2460 | 0.9943 | 0.2911 | 0.9581 | 0.9411 | 0.8603 | 0.8844 | 0.9149 | 0.9794 | 0.6232 | 0.7764 | 0.9884 | 0.9207 | 0.2400 | nan | 0.9832 | 0.2869 | 0.8910 | 0.8680 | 0.8450 | 0.8354 | 0.8936 | 0.9600 | 0.6179 | 0.6198 | 0.9793 | 0.8658 | 0.2369 | nan | 0.0825 | 0.7979 | 0.7602 | 0.9797 | | 0.0254 | 145.88 | 2480 | 0.9941 | 0.2846 | 0.9532 | 0.9409 | 0.8680 | 0.8930 | 0.9247 | 0.9798 | 0.6242 | 0.7814 | 0.9903 | 0.9162 | 0.2461 | nan | 0.9833 | 0.2809 | 0.8925 | 0.8708 | 0.8467 | 0.8382 | 0.9010 | 0.9607 | 0.6183 | 0.6232 | 0.9792 | 0.8653 | 0.2431 | nan | 0.0812 | 0.7997 | 0.7618 | 0.9799 | | 0.0216 | 147.06 | 2500 | 0.9935 | 0.2927 | 0.9526 | 0.9281 | 0.8561 | 0.8995 | 0.9277 | 0.9810 | 0.6235 | 0.7848 | 0.9913 | 0.9075 | 0.2323 | nan | 0.9834 | 0.2903 | 0.8914 | 0.8714 | 0.8393 | 0.8375 | 0.9033 | 0.9601 | 0.6180 | 0.6270 | 0.9788 | 0.8628 | 0.2305 | nan | 0.0810 | 0.7977 | 0.7611 | 0.9798 | | 0.0229 | 148.24 | 2520 | 0.9936 | 0.3642 | 0.9542 | 0.9384 | 0.8601 | 0.8935 | 0.9304 | 0.9833 | 0.6248 | 0.7977 | 0.9898 | 0.9155 | 0.2459 | nan | 0.9835 | 0.3544 | 0.8921 | 0.8637 | 0.8429 | 0.8380 | 0.8966 | 0.9593 | 0.6182 | 0.6257 | 0.9792 | 0.8650 | 0.2430 | nan | 0.0810 | 0.8070 | 0.7663 | 0.9798 | | 0.022 | 149.41 | 2540 | 0.9934 | 0.3138 | 0.9572 | 0.9460 | 0.8611 | 0.8924 | 0.9241 | 0.9815 | 0.6235 | 0.7865 | 0.9897 | 0.9176 | 0.2397 | nan | 0.9835 | 0.3030 | 0.8909 | 0.8671 | 0.8420 | 0.8378 | 0.8953 | 0.9597 | 0.6182 | 0.6212 | 0.9792 | 0.8652 | 0.2377 | nan | 0.0817 | 0.8020 | 0.7616 | 0.9798 | | 0.0235 | 150.59 | 2560 | 0.9938 | 0.2667 | 0.9539 | 0.9397 | 0.8590 | 0.8940 | 0.9200 | 0.9818 | 0.6240 | 0.7736 | 0.9897 | 0.9229 | 0.2428 | nan | 0.9834 | 0.2641 | 0.8916 | 0.8688 | 0.8425 | 0.8378 | 0.8992 | 0.9601 | 0.6176 | 0.6177 | 0.9790 | 0.8660 | 0.2402 | nan | 0.0817 | 0.7971 | 0.7591 | 0.9798 | | 0.0224 | 151.76 | 2580 | 0.9938 | 0.3561 | 0.9555 | 0.9403 | 0.8661 | 0.8892 | 0.9244 | 0.9814 | 0.6238 | 0.7724 | 0.9899 | 0.9214 | 0.2453 | nan | 0.9834 | 0.3471 | 0.8912 | 0.8688 | 0.8470 | 0.8374 | 0.8984 | 0.9600 | 0.6176 | 0.6209 | 0.9793 | 0.8658 | 0.2398 | nan | 0.0820 | 0.8046 | 0.7659 | 0.9798 | | 0.0205 | 152.94 | 2600 | 0.9939 | 0.2943 | 0.9546 | 0.9375 | 0.8547 | 0.8966 | 0.9193 | 0.9801 | 0.6246 | 0.7738 | 0.9904 | 0.9152 | 0.2378 | nan | 0.9835 | 0.2896 | 0.8918 | 0.8715 | 0.8392 | 0.8383 | 0.8984 | 0.9611 | 0.6185 | 0.6206 | 0.9793 | 0.8657 | 0.2359 | nan | 0.0810 | 0.7979 | 0.7610 | 0.9799 | | 0.0197 | 154.12 | 2620 | 0.9929 | 0.3382 | 0.9570 | 0.9462 | 0.8656 | 0.9036 | 0.9324 | 0.9828 | 0.6258 | 0.7921 | 0.9891 | 0.9219 | 0.2389 | nan | 0.9836 | 0.3323 | 0.8919 | 0.8674 | 0.8475 | 0.8405 | 0.9058 | 0.9604 | 0.6171 | 0.6255 | 0.9794 | 0.8656 | 0.2358 | nan | 0.0808 | 0.8067 | 0.7656 | 0.9799 | | 0.0209 | 155.29 | 2640 | 0.9943 | 0.2423 | 0.9555 | 0.9340 | 0.8575 | 0.8939 | 0.9254 | 0.9817 | 0.6240 | 0.7638 | 0.9886 | 0.9252 | 0.2443 | nan | 0.9835 | 0.2399 | 0.8926 | 0.8723 | 0.8421 | 0.8398 | 0.9027 | 0.9610 | 0.6175 | 0.6173 | 0.9793 | 0.8657 | 0.2419 | nan | 0.0812 | 0.7946 | 0.7581 | 0.9800 | | 0.0203 | 156.47 | 2660 | 0.9940 | 0.3496 | 0.9558 | 0.9387 | 0.8629 | 0.8939 | 0.9251 | 0.9808 | 0.6245 | 0.7735 | 0.9892 | 0.9231 | 0.2499 | nan | 0.9834 | 0.3429 | 0.8926 | 0.8695 | 0.8463 | 0.8400 | 0.9050 | 0.9608 | 0.6177 | 0.6220 | 0.9796 | 0.8664 | 0.2460 | nan | 0.0809 | 0.8047 | 0.7671 | 0.9800 | | 0.0225 | 157.65 | 2680 | 0.9938 | 0.3203 | 0.9571 | 0.9292 | 0.8643 | 0.8980 | 0.9265 | 0.9810 | 0.6220 | 0.7662 | 0.9885 | 0.9237 | 0.2427 | nan | 0.9836 | 0.3152 | 0.8912 | 0.8653 | 0.8467 | 0.8419 | 0.9053 | 0.9602 | 0.6169 | 0.6222 | 0.9794 | 0.8654 | 0.2388 | nan | 0.0808 | 0.8010 | 0.7640 | 0.9800 | | 0.0269 | 158.82 | 2700 | 0.9943 | 0.2553 | 0.9558 | 0.9247 | 0.8603 | 0.8894 | 0.9212 | 0.9807 | 0.6250 | 0.7761 | 0.9899 | 0.9177 | 0.2444 | nan | 0.9834 | 0.2516 | 0.8928 | 0.8697 | 0.8439 | 0.8390 | 0.9014 | 0.9609 | 0.6184 | 0.6272 | 0.9795 | 0.8653 | 0.2418 | nan | 0.0806 | 0.7950 | 0.7596 | 0.9800 | | 0.0204 | 160.0 | 2720 | 0.9940 | 0.3480 | 0.9543 | 0.9310 | 0.8668 | 0.8949 | 0.9281 | 0.9816 | 0.6257 | 0.7981 | 0.9892 | 0.9248 | 0.2527 | nan | 0.9835 | 0.3391 | 0.8932 | 0.8665 | 0.8497 | 0.8404 | 0.9049 | 0.9605 | 0.6187 | 0.6308 | 0.9796 | 0.8656 | 0.2475 | nan | 0.0805 | 0.8069 | 0.7677 | 0.9801 | | 0.0188 | 161.18 | 2740 | 0.9934 | 0.3659 | 0.9579 | 0.9386 | 0.8677 | 0.8992 | 0.9274 | 0.9823 | 0.6255 | 0.7694 | 0.9896 | 0.9168 | 0.2531 | nan | 0.9837 | 0.3571 | 0.8920 | 0.8709 | 0.8494 | 0.8420 | 0.9019 | 0.9608 | 0.6191 | 0.6213 | 0.9794 | 0.8654 | 0.2484 | nan | 0.0801 | 0.8067 | 0.7686 | 0.9801 | | 0.0218 | 162.35 | 2760 | 0.9936 | 0.3577 | 0.9578 | 0.9321 | 0.8665 | 0.9000 | 0.9290 | 0.9800 | 0.6247 | 0.7756 | 0.9894 | 0.9216 | 0.2515 | nan | 0.9837 | 0.3514 | 0.8919 | 0.8732 | 0.8478 | 0.8421 | 0.9041 | 0.9609 | 0.6183 | 0.6240 | 0.9795 | 0.8674 | 0.2469 | nan | 0.0805 | 0.8061 | 0.7686 | 0.9801 | | 0.0203 | 163.53 | 2780 | 0.9931 | 0.3268 | 0.9580 | 0.9177 | 0.8569 | 0.9023 | 0.9230 | 0.9822 | 0.6227 | 0.7698 | 0.9898 | 0.9155 | 0.2411 | nan | 0.9836 | 0.3216 | 0.8910 | 0.8713 | 0.8405 | 0.8406 | 0.9018 | 0.9599 | 0.6167 | 0.6242 | 0.9795 | 0.8664 | 0.2384 | nan | 0.0815 | 0.7999 | 0.7643 | 0.9799 | | 0.0229 | 164.71 | 2800 | 0.9933 | 0.3301 | 0.9548 | 0.9399 | 0.8574 | 0.9024 | 0.9303 | 0.9801 | 0.6251 | 0.7891 | 0.9912 | 0.9162 | 0.2512 | nan | 0.9836 | 0.3248 | 0.8924 | 0.8793 | 0.8401 | 0.8410 | 0.9061 | 0.9611 | 0.6190 | 0.6316 | 0.9792 | 0.8669 | 0.2481 | nan | 0.0805 | 0.8047 | 0.7672 | 0.9801 | | 0.0189 | 165.88 | 2820 | 0.9940 | 0.3350 | 0.9556 | 0.9351 | 0.8622 | 0.8921 | 0.9278 | 0.9821 | 0.6248 | 0.7816 | 0.9890 | 0.9258 | 0.2524 | nan | 0.9835 | 0.3285 | 0.8930 | 0.8755 | 0.8450 | 0.8390 | 0.9041 | 0.9605 | 0.6187 | 0.6255 | 0.9795 | 0.8665 | 0.2472 | nan | 0.0816 | 0.8044 | 0.7667 | 0.9800 | | 0.0235 | 167.06 | 2840 | 0.9933 | 0.3772 | 0.9555 | 0.9379 | 0.8622 | 0.9032 | 0.9265 | 0.9822 | 0.6231 | 0.7864 | 0.9899 | 0.9224 | 0.2539 | nan | 0.9837 | 0.3700 | 0.8925 | 0.8772 | 0.8443 | 0.8412 | 0.9049 | 0.9603 | 0.6176 | 0.6272 | 0.9795 | 0.8669 | 0.2486 | nan | 0.0809 | 0.8087 | 0.7703 | 0.9801 | | 0.0218 | 168.24 | 2860 | 0.9935 | 0.3350 | 0.9582 | 0.9314 | 0.8572 | 0.8961 | 0.9207 | 0.9809 | 0.6220 | 0.7770 | 0.9906 | 0.9125 | 0.2426 | nan | 0.9837 | 0.3291 | 0.8914 | 0.8749 | 0.8395 | 0.8412 | 0.9003 | 0.9613 | 0.6170 | 0.6243 | 0.9792 | 0.8658 | 0.2400 | nan | 0.0825 | 0.8013 | 0.7652 | 0.9800 | | 0.0225 | 169.41 | 2880 | 0.9941 | 0.3089 | 0.9553 | 0.9303 | 0.8597 | 0.8913 | 0.9232 | 0.9819 | 0.6236 | 0.7837 | 0.9896 | 0.9214 | 0.2450 | nan | 0.9835 | 0.304 | 0.8928 | 0.8759 | 0.8426 | 0.8392 | 0.9029 | 0.9611 | 0.6185 | 0.6286 | 0.9794 | 0.8665 | 0.2414 | nan | 0.0818 | 0.8006 | 0.7643 | 0.9800 | | 0.0204 | 170.59 | 2900 | 0.9937 | 0.3870 | 0.9557 | 0.9338 | 0.8589 | 0.8986 | 0.9322 | 0.9815 | 0.6253 | 0.7736 | 0.9902 | 0.9196 | 0.2531 | nan | 0.9837 | 0.3766 | 0.8928 | 0.8770 | 0.8424 | 0.8412 | 0.9061 | 0.9611 | 0.6192 | 0.6238 | 0.9793 | 0.8667 | 0.2497 | nan | 0.0808 | 0.8079 | 0.7707 | 0.9801 | | 0.0184 | 171.76 | 2920 | 0.9939 | 0.3577 | 0.9575 | 0.9346 | 0.8621 | 0.8910 | 0.9337 | 0.9813 | 0.6253 | 0.7793 | 0.9892 | 0.9239 | 0.2545 | nan | 0.9836 | 0.3487 | 0.8927 | 0.8715 | 0.8450 | 0.8385 | 0.9022 | 0.9612 | 0.6189 | 0.6238 | 0.9795 | 0.8668 | 0.2488 | nan | 0.0817 | 0.8065 | 0.7678 | 0.9800 | | 0.0234 | 172.94 | 2940 | 0.9938 | 0.3301 | 0.9576 | 0.9343 | 0.8640 | 0.8945 | 0.9314 | 0.9801 | 0.6259 | 0.7919 | 0.9890 | 0.9268 | 0.2530 | nan | 0.9837 | 0.3232 | 0.8928 | 0.8738 | 0.8453 | 0.8400 | 0.9008 | 0.9615 | 0.6194 | 0.6259 | 0.9794 | 0.8671 | 0.2474 | nan | 0.0814 | 0.8056 | 0.7662 | 0.9801 | | 0.0213 | 174.12 | 2960 | 0.9934 | 0.3528 | 0.9570 | 0.9312 | 0.8599 | 0.9020 | 0.9337 | 0.9818 | 0.6259 | 0.7751 | 0.9894 | 0.9218 | 0.2475 | nan | 0.9837 | 0.3461 | 0.8921 | 0.8755 | 0.8432 | 0.8416 | 0.9035 | 0.9609 | 0.6192 | 0.6199 | 0.9796 | 0.8672 | 0.2440 | nan | 0.0815 | 0.8055 | 0.7674 | 0.9801 | | 0.0182 | 175.29 | 2980 | 0.9934 | 0.3610 | 0.9574 | 0.9413 | 0.8613 | 0.8957 | 0.9366 | 0.9802 | 0.6249 | 0.7775 | 0.9902 | 0.9251 | 0.2460 | nan | 0.9836 | 0.3535 | 0.8918 | 0.8758 | 0.8435 | 0.8402 | 0.9023 | 0.9612 | 0.6179 | 0.6216 | 0.9794 | 0.8677 | 0.2437 | nan | 0.0820 | 0.8070 | 0.7679 | 0.9800 | | 0.0203 | 176.47 | 3000 | 0.9934 | 0.3236 | 0.9573 | 0.9337 | 0.8583 | 0.8992 | 0.9322 | 0.9811 | 0.6247 | 0.7840 | 0.9893 | 0.9246 | 0.2318 | nan | 0.9836 | 0.3184 | 0.8917 | 0.8729 | 0.8421 | 0.8399 | 0.9037 | 0.9607 | 0.6183 | 0.6227 | 0.9795 | 0.8674 | 0.2303 | nan | 0.0825 | 0.8026 | 0.7639 | 0.9800 | | 0.024 | 177.65 | 3020 | 0.9938 | 0.3236 | 0.9539 | 0.9346 | 0.8584 | 0.8971 | 0.9300 | 0.9825 | 0.6259 | 0.7842 | 0.9899 | 0.9241 | 0.2416 | nan | 0.9837 | 0.3179 | 0.8926 | 0.8711 | 0.8415 | 0.8404 | 0.9034 | 0.9607 | 0.6197 | 0.6244 | 0.9794 | 0.8673 | 0.2400 | nan | 0.0821 | 0.8030 | 0.7648 | 0.9800 | | 0.0182 | 178.82 | 3040 | 0.9933 | 0.3740 | 0.9574 | 0.9400 | 0.8569 | 0.9011 | 0.9271 | 0.9818 | 0.6261 | 0.7775 | 0.9900 | 0.9205 | 0.2384 | nan | 0.9838 | 0.3668 | 0.8916 | 0.8713 | 0.8399 | 0.8421 | 0.9023 | 0.9607 | 0.6196 | 0.6247 | 0.9794 | 0.8675 | 0.2366 | nan | 0.0814 | 0.8065 | 0.7682 | 0.9801 | | 0.0227 | 180.0 | 3060 | 0.9932 | 0.3447 | 0.9574 | 0.9368 | 0.8584 | 0.9025 | 0.9287 | 0.9817 | 0.6249 | 0.7810 | 0.9896 | 0.9228 | 0.2408 | nan | 0.9838 | 0.3387 | 0.8918 | 0.8728 | 0.8409 | 0.8411 | 0.9050 | 0.9605 | 0.6186 | 0.6261 | 0.9795 | 0.8672 | 0.2391 | nan | 0.0813 | 0.8048 | 0.7665 | 0.9800 | | 0.0198 | 181.18 | 3080 | 0.9940 | 0.3593 | 0.9564 | 0.9417 | 0.8583 | 0.8938 | 0.9340 | 0.9814 | 0.6248 | 0.7771 | 0.9889 | 0.9282 | 0.2478 | nan | 0.9837 | 0.3508 | 0.8918 | 0.8712 | 0.8429 | 0.8406 | 0.9037 | 0.9606 | 0.6177 | 0.6240 | 0.9794 | 0.8673 | 0.2456 | nan | 0.0816 | 0.8066 | 0.7676 | 0.9800 | | 0.0249 | 182.35 | 3100 | 0.9939 | 0.3122 | 0.9550 | 0.9416 | 0.8634 | 0.8990 | 0.9334 | 0.9814 | 0.6256 | 0.7774 | 0.9899 | 0.9186 | 0.2534 | nan | 0.9838 | 0.3062 | 0.8929 | 0.8700 | 0.8448 | 0.8416 | 0.9050 | 0.9611 | 0.6180 | 0.6212 | 0.9795 | 0.8677 | 0.2501 | nan | 0.0812 | 0.8034 | 0.7648 | 0.9801 | | 0.0201 | 183.53 | 3120 | 0.9934 | 0.3008 | 0.9578 | 0.9378 | 0.8544 | 0.8992 | 0.9277 | 0.9808 | 0.6233 | 0.7769 | 0.9899 | 0.9199 | 0.2465 | nan | 0.9838 | 0.2955 | 0.8920 | 0.8724 | 0.8382 | 0.8405 | 0.9022 | 0.9609 | 0.6166 | 0.6208 | 0.9794 | 0.8678 | 0.2438 | nan | 0.0822 | 0.8007 | 0.7626 | 0.9800 | | 0.0262 | 184.71 | 3140 | 0.9936 | 0.3382 | 0.9554 | 0.9305 | 0.8554 | 0.8991 | 0.9308 | 0.9814 | 0.6254 | 0.7831 | 0.9905 | 0.9193 | 0.2475 | nan | 0.9838 | 0.3323 | 0.8927 | 0.8766 | 0.8390 | 0.8408 | 0.9037 | 0.9610 | 0.6180 | 0.6240 | 0.9794 | 0.8674 | 0.2455 | nan | 0.0807 | 0.8039 | 0.7665 | 0.9801 | | 0.0217 | 185.88 | 3160 | 0.9937 | 0.3740 | 0.9571 | 0.9372 | 0.8574 | 0.8971 | 0.9322 | 0.9816 | 0.6257 | 0.7808 | 0.9894 | 0.9236 | 0.2459 | nan | 0.9838 | 0.3651 | 0.8924 | 0.8744 | 0.8419 | 0.8408 | 0.9036 | 0.9608 | 0.6183 | 0.6234 | 0.9796 | 0.8684 | 0.2437 | nan | 0.0813 | 0.8073 | 0.7689 | 0.9801 | | 0.0193 | 187.06 | 3180 | 0.9937 | 0.3854 | 0.9561 | 0.9362 | 0.8592 | 0.8967 | 0.9305 | 0.9825 | 0.6256 | 0.7832 | 0.9895 | 0.9242 | 0.2482 | nan | 0.9837 | 0.375 | 0.8928 | 0.8725 | 0.8426 | 0.8408 | 0.9029 | 0.9606 | 0.6183 | 0.6244 | 0.9796 | 0.8681 | 0.2457 | nan | 0.0813 | 0.8085 | 0.7698 | 0.9801 | | 0.0252 | 188.24 | 3200 | 0.9938 | 0.3691 | 0.9568 | 0.9326 | 0.8565 | 0.8948 | 0.9250 | 0.9813 | 0.6246 | 0.7735 | 0.9899 | 0.9238 | 0.2452 | nan | 0.9837 | 0.3603 | 0.8923 | 0.8751 | 0.8404 | 0.8403 | 0.9006 | 0.9608 | 0.6178 | 0.6210 | 0.9794 | 0.8683 | 0.2428 | nan | 0.0817 | 0.8051 | 0.7679 | 0.9801 | | 0.0204 | 189.41 | 3220 | 0.9938 | 0.3626 | 0.9558 | 0.9326 | 0.8602 | 0.8968 | 0.9294 | 0.9805 | 0.6239 | 0.7823 | 0.9902 | 0.9197 | 0.2490 | nan | 0.9837 | 0.3557 | 0.8927 | 0.8750 | 0.8431 | 0.8403 | 0.9029 | 0.9610 | 0.6175 | 0.6246 | 0.9795 | 0.8682 | 0.2456 | nan | 0.0819 | 0.8059 | 0.7684 | 0.9801 | | 0.0249 | 190.59 | 3240 | 0.9936 | 0.3366 | 0.9576 | 0.9339 | 0.8609 | 0.8992 | 0.9281 | 0.9814 | 0.6233 | 0.7887 | 0.9894 | 0.9215 | 0.2500 | nan | 0.9838 | 0.3307 | 0.8927 | 0.8725 | 0.8438 | 0.8409 | 0.9025 | 0.9609 | 0.6170 | 0.6282 | 0.9796 | 0.8679 | 0.2464 | nan | 0.0822 | 0.8049 | 0.7667 | 0.9801 | | 0.0215 | 191.76 | 3260 | 0.9939 | 0.3463 | 0.9557 | 0.9361 | 0.8647 | 0.8962 | 0.9289 | 0.9821 | 0.6244 | 0.7839 | 0.9894 | 0.9245 | 0.2526 | nan | 0.9837 | 0.3397 | 0.8932 | 0.8727 | 0.8466 | 0.8406 | 0.9027 | 0.9609 | 0.6172 | 0.6255 | 0.9796 | 0.8678 | 0.2487 | nan | 0.0820 | 0.8060 | 0.7676 | 0.9801 | | 0.0225 | 192.94 | 3280 | 0.9937 | 0.3821 | 0.9572 | 0.9380 | 0.8615 | 0.8938 | 0.9264 | 0.9820 | 0.6240 | 0.7830 | 0.9897 | 0.9226 | 0.2461 | nan | 0.9838 | 0.3718 | 0.8921 | 0.8718 | 0.8438 | 0.8402 | 0.8987 | 0.9607 | 0.6166 | 0.6233 | 0.9795 | 0.8679 | 0.2435 | nan | 0.0824 | 0.8077 | 0.7687 | 0.9801 | | 0.0221 | 194.12 | 3300 | 0.9937 | 0.3675 | 0.9554 | 0.9395 | 0.8611 | 0.8971 | 0.9268 | 0.9819 | 0.6245 | 0.7911 | 0.9899 | 0.9238 | 0.2495 | nan | 0.9838 | 0.3604 | 0.8930 | 0.8720 | 0.8434 | 0.8408 | 0.9010 | 0.9610 | 0.6170 | 0.6262 | 0.9795 | 0.8679 | 0.2472 | nan | 0.0816 | 0.8078 | 0.7687 | 0.9801 | | 0.0198 | 195.29 | 3320 | 0.9939 | 0.3561 | 0.9560 | 0.9346 | 0.8574 | 0.8955 | 0.9280 | 0.9815 | 0.6237 | 0.7885 | 0.9897 | 0.9187 | 0.2494 | nan | 0.9837 | 0.3493 | 0.8926 | 0.8724 | 0.8411 | 0.8404 | 0.9010 | 0.9609 | 0.6173 | 0.6257 | 0.9796 | 0.8679 | 0.2463 | nan | 0.0821 | 0.8056 | 0.7675 | 0.9801 | | 0.0182 | 196.47 | 3340 | 0.9936 | 0.3545 | 0.9579 | 0.9313 | 0.8589 | 0.8976 | 0.9259 | 0.9813 | 0.6236 | 0.7862 | 0.9891 | 0.9238 | 0.2451 | nan | 0.9838 | 0.3482 | 0.8920 | 0.8730 | 0.8417 | 0.8409 | 0.9003 | 0.9609 | 0.6171 | 0.6250 | 0.9795 | 0.8682 | 0.2426 | nan | 0.0824 | 0.8053 | 0.7672 | 0.9801 | | 0.0186 | 197.65 | 3360 | 0.9936 | 0.3593 | 0.9573 | 0.9342 | 0.8621 | 0.8982 | 0.9247 | 0.9811 | 0.6239 | 0.7856 | 0.9894 | 0.9232 | 0.2462 | nan | 0.9838 | 0.3530 | 0.8923 | 0.8729 | 0.8439 | 0.8413 | 0.9004 | 0.9610 | 0.6173 | 0.6248 | 0.9796 | 0.8684 | 0.2439 | nan | 0.0821 | 0.8061 | 0.7679 | 0.9801 | | 0.0241 | 198.82 | 3380 | 0.9940 | 0.3431 | 0.9557 | 0.9396 | 0.8677 | 0.8904 | 0.9302 | 0.9817 | 0.6260 | 0.7848 | 0.9899 | 0.9173 | 0.2559 | nan | 0.9836 | 0.3344 | 0.8929 | 0.8736 | 0.8493 | 0.8387 | 0.9028 | 0.9606 | 0.6195 | 0.6250 | 0.9796 | 0.8669 | 0.2493 | nan | 0.0823 | 0.8059 | 0.7674 | 0.9801 | | 0.0212 | 200.0 | 3400 | 0.9945 | 0.3252 | 0.9566 | 0.9141 | 0.8484 | 0.8866 | 0.9209 | 0.9818 | 0.6251 | 0.7763 | 0.9894 | 0.9187 | 0.2221 | nan | 0.9835 | 0.3175 | 0.8928 | 0.8665 | 0.8352 | 0.8386 | 0.8999 | 0.9615 | 0.6194 | 0.6165 | 0.9793 | 0.8673 | 0.2204 | nan | 0.0827 | 0.7969 | 0.7614 | 0.9800 | | 0.0204 | 201.18 | 3420 | 0.9936 | 0.3870 | 0.9568 | 0.9381 | 0.8583 | 0.9025 | 0.9272 | 0.9818 | 0.6253 | 0.7838 | 0.9885 | 0.9281 | 0.2431 | nan | 0.9839 | 0.3802 | 0.8919 | 0.8633 | 0.8419 | 0.8438 | 0.9017 | 0.9610 | 0.6190 | 0.6216 | 0.9794 | 0.8688 | 0.2403 | nan | 0.0817 | 0.8088 | 0.7690 | 0.9801 | | 0.0216 | 202.35 | 3440 | 0.9938 | 0.3919 | 0.9606 | 0.9362 | 0.8438 | 0.8912 | 0.9277 | 0.9803 | 0.6227 | 0.7608 | 0.9890 | 0.9203 | 0.2388 | nan | 0.9837 | 0.3813 | 0.8904 | 0.8701 | 0.8296 | 0.8408 | 0.9007 | 0.9610 | 0.6166 | 0.6171 | 0.9791 | 0.8668 | 0.2372 | nan | 0.0829 | 0.8044 | 0.7673 | 0.9799 | | 0.0192 | 203.53 | 3460 | 0.9938 | 0.3951 | 0.9581 | 0.9246 | 0.8510 | 0.8967 | 0.9139 | 0.9803 | 0.6228 | 0.7711 | 0.9886 | 0.9360 | 0.2317 | nan | 0.9839 | 0.3857 | 0.8910 | 0.8700 | 0.8356 | 0.8436 | 0.8956 | 0.9600 | 0.6176 | 0.6196 | 0.9792 | 0.8666 | 0.2299 | nan | 0.0827 | 0.8049 | 0.7676 | 0.9800 | | 0.0189 | 204.71 | 3480 | 0.9939 | 0.3561 | 0.9578 | 0.9363 | 0.8616 | 0.8993 | 0.9235 | 0.9806 | 0.6256 | 0.7930 | 0.9867 | 0.9351 | 0.2401 | nan | 0.9837 | 0.3471 | 0.8919 | 0.8699 | 0.8441 | 0.8427 | 0.8997 | 0.9608 | 0.6185 | 0.6242 | 0.9791 | 0.8657 | 0.2386 | nan | 0.0822 | 0.8069 | 0.7666 | 0.9800 | | 0.0226 | 205.88 | 3500 | 0.9929 | 0.3024 | 0.9535 | 0.9352 | 0.8572 | 0.9105 | 0.9212 | 0.9821 | 0.6284 | 0.7859 | 0.9912 | 0.9195 | 0.2203 | nan | 0.9840 | 0.2948 | 0.8917 | 0.8777 | 0.8367 | 0.8440 | 0.8974 | 0.9606 | 0.6222 | 0.6266 | 0.9788 | 0.8678 | 0.2190 | nan | 0.0807 | 0.8000 | 0.7616 | 0.9801 | | 0.019 | 207.06 | 3520 | 0.9932 | 0.2927 | 0.9541 | 0.9493 | 0.8537 | 0.9081 | 0.9246 | 0.9808 | 0.6292 | 0.7824 | 0.9918 | 0.9102 | 0.2333 | nan | 0.9840 | 0.2894 | 0.8920 | 0.8771 | 0.8380 | 0.8451 | 0.9021 | 0.9604 | 0.6222 | 0.6305 | 0.9791 | 0.8668 | 0.2318 | nan | 0.0816 | 0.8003 | 0.7630 | 0.9802 | | 0.0192 | 208.24 | 3540 | 0.9940 | 0.3691 | 0.9518 | 0.9420 | 0.8676 | 0.8984 | 0.9314 | 0.9820 | 0.6263 | 0.7955 | 0.9912 | 0.9161 | 0.2501 | nan | 0.9838 | 0.3586 | 0.8937 | 0.8717 | 0.8469 | 0.8416 | 0.9064 | 0.9613 | 0.6197 | 0.6279 | 0.9798 | 0.8693 | 0.2475 | nan | 0.0819 | 0.8089 | 0.7699 | 0.9803 | | 0.0183 | 209.41 | 3560 | 0.9934 | 0.3593 | 0.9579 | 0.9417 | 0.8505 | 0.9046 | 0.9235 | 0.9808 | 0.6222 | 0.7652 | 0.9898 | 0.9218 | 0.2416 | nan | 0.9839 | 0.3469 | 0.8911 | 0.8719 | 0.8347 | 0.8452 | 0.9017 | 0.9597 | 0.6160 | 0.6251 | 0.9797 | 0.8693 | 0.2332 | nan | 0.0829 | 0.8040 | 0.7660 | 0.9801 | | 0.0203 | 210.59 | 3580 | 0.9936 | 0.2732 | 0.9575 | 0.9368 | 0.8535 | 0.8987 | 0.9253 | 0.9809 | 0.6303 | 0.8001 | 0.9898 | 0.9163 | 0.2412 | nan | 0.9838 | 0.2701 | 0.8924 | 0.8761 | 0.8377 | 0.8418 | 0.9035 | 0.9605 | 0.6226 | 0.6354 | 0.9795 | 0.8694 | 0.2368 | nan | 0.0813 | 0.7998 | 0.7623 | 0.9801 | | 0.0202 | 211.76 | 3600 | 0.9940 | 0.4033 | 0.9544 | 0.9563 | 0.8578 | 0.8902 | 0.9383 | 0.9820 | 0.6282 | 0.7908 | 0.9894 | 0.9287 | 0.2551 | nan | 0.9837 | 0.3899 | 0.8925 | 0.8662 | 0.8412 | 0.8385 | 0.9049 | 0.9599 | 0.6211 | 0.6218 | 0.9795 | 0.8693 | 0.2502 | nan | 0.0827 | 0.8130 | 0.7707 | 0.9800 | | 0.0166 | 212.94 | 3620 | 0.9944 | 0.2878 | 0.9547 | 0.9338 | 0.8550 | 0.8885 | 0.9356 | 0.9824 | 0.6238 | 0.7580 | 0.9892 | 0.9279 | 0.2508 | nan | 0.9836 | 0.2823 | 0.8923 | 0.8738 | 0.8384 | 0.8381 | 0.9061 | 0.9597 | 0.6176 | 0.6064 | 0.9797 | 0.8700 | 0.2483 | nan | 0.0835 | 0.7986 | 0.7612 | 0.9799 | | 0.0211 | 214.12 | 3640 | 0.9942 | 0.3252 | 0.9542 | 0.9483 | 0.8648 | 0.8890 | 0.9359 | 0.9806 | 0.6263 | 0.7818 | 0.9906 | 0.9218 | 0.2551 | nan | 0.9836 | 0.3180 | 0.8936 | 0.8714 | 0.8477 | 0.8378 | 0.9079 | 0.9612 | 0.6199 | 0.6162 | 0.9796 | 0.8705 | 0.2513 | nan | 0.0829 | 0.8052 | 0.7661 | 0.9801 | | 0.0217 | 215.29 | 3660 | 0.9938 | 0.4683 | 0.9548 | 0.9303 | 0.8702 | 0.8987 | 0.9307 | 0.9829 | 0.6275 | 0.7809 | 0.9910 | 0.9140 | 0.2478 | nan | 0.9841 | 0.4486 | 0.8930 | 0.8733 | 0.8507 | 0.8446 | 0.9020 | 0.9605 | 0.6205 | 0.6242 | 0.9797 | 0.8681 | 0.2431 | nan | 0.0806 | 0.8147 | 0.7763 | 0.9804 | | 0.0191 | 216.47 | 3680 | 0.9941 | 0.3772 | 0.9546 | 0.9322 | 0.8528 | 0.8988 | 0.9296 | 0.9824 | 0.6285 | 0.7986 | 0.9893 | 0.9253 | 0.2534 | nan | 0.9839 | 0.3694 | 0.8941 | 0.8726 | 0.8372 | 0.8427 | 0.9067 | 0.9605 | 0.6208 | 0.6240 | 0.9797 | 0.8702 | 0.2514 | nan | 0.0811 | 0.8090 | 0.7703 | 0.9803 | | 0.0202 | 217.65 | 3700 | 0.9936 | 0.3724 | 0.9585 | 0.9363 | 0.8553 | 0.9000 | 0.9307 | 0.9813 | 0.6279 | 0.7648 | 0.9895 | 0.9255 | 0.2396 | nan | 0.9840 | 0.3658 | 0.8923 | 0.8793 | 0.8386 | 0.8432 | 0.9073 | 0.9599 | 0.6208 | 0.6136 | 0.9798 | 0.8708 | 0.2372 | nan | 0.0823 | 0.8058 | 0.7687 | 0.9802 | | 0.0192 | 218.82 | 3720 | 0.9938 | 0.3886 | 0.9556 | 0.9488 | 0.8705 | 0.8988 | 0.9315 | 0.9813 | 0.6290 | 0.7774 | 0.9897 | 0.9304 | 0.2487 | nan | 0.9840 | 0.3794 | 0.8944 | 0.8817 | 0.8481 | 0.8429 | 0.9072 | 0.9610 | 0.6228 | 0.6140 | 0.9797 | 0.8704 | 0.2451 | nan | 0.0816 | 0.8111 | 0.7716 | 0.9803 | | 0.0211 | 220.0 | 3740 | 0.9943 | 0.3447 | 0.9568 | 0.9372 | 0.8628 | 0.8932 | 0.9251 | 0.9793 | 0.6242 | 0.7699 | 0.9897 | 0.9231 | 0.2349 | nan | 0.9839 | 0.3387 | 0.8930 | 0.8822 | 0.8455 | 0.8413 | 0.9037 | 0.9600 | 0.6190 | 0.6111 | 0.9799 | 0.8703 | 0.2297 | nan | 0.0837 | 0.8027 | 0.7660 | 0.9802 | | 0.0169 | 221.18 | 3760 | 0.9934 | 0.4260 | 0.9574 | 0.9449 | 0.8633 | 0.9003 | 0.9379 | 0.9820 | 0.6276 | 0.7915 | 0.9893 | 0.9292 | 0.2544 | nan | 0.9840 | 0.4031 | 0.8935 | 0.8798 | 0.8463 | 0.8396 | 0.9068 | 0.9605 | 0.6209 | 0.6207 | 0.9801 | 0.8713 | 0.2485 | nan | 0.0826 | 0.8152 | 0.7735 | 0.9802 | | 0.0179 | 222.35 | 3780 | 0.9943 | 0.4455 | 0.9546 | 0.9557 | 0.8707 | 0.8969 | 0.9343 | 0.9812 | 0.6284 | 0.7858 | 0.9895 | 0.9230 | 0.2580 | nan | 0.9838 | 0.4295 | 0.8944 | 0.8720 | 0.8492 | 0.8404 | 0.9090 | 0.9619 | 0.6224 | 0.6242 | 0.9801 | 0.8724 | 0.2549 | nan | 0.0818 | 0.8168 | 0.7765 | 0.9804 | | 0.0174 | 223.53 | 3800 | 0.9941 | 0.2683 | 0.9578 | 0.9291 | 0.8487 | 0.8960 | 0.9115 | 0.9813 | 0.6218 | 0.7664 | 0.9891 | 0.9271 | 0.2324 | nan | 0.9839 | 0.2657 | 0.8924 | 0.8757 | 0.8345 | 0.8414 | 0.8948 | 0.9607 | 0.6180 | 0.6167 | 0.9797 | 0.8718 | 0.2309 | nan | 0.0845 | 0.7941 | 0.7589 | 0.9801 | | 0.0177 | 224.71 | 3820 | 0.9939 | 0.3512 | 0.9566 | 0.9368 | 0.8628 | 0.8967 | 0.9329 | 0.9826 | 0.6266 | 0.7956 | 0.9894 | 0.9307 | 0.2566 | nan | 0.9840 | 0.3445 | 0.8948 | 0.8795 | 0.8452 | 0.8426 | 0.9045 | 0.9618 | 0.6203 | 0.6299 | 0.9799 | 0.8718 | 0.2531 | nan | 0.0815 | 0.8086 | 0.7701 | 0.9805 | | 0.0207 | 225.88 | 3840 | 0.9934 | 0.4341 | 0.9566 | 0.9327 | 0.8625 | 0.8968 | 0.9374 | 0.9825 | 0.6270 | 0.7652 | 0.9907 | 0.9247 | 0.2518 | nan | 0.9840 | 0.4205 | 0.8920 | 0.8749 | 0.8435 | 0.8427 | 0.8982 | 0.9601 | 0.6198 | 0.6150 | 0.9796 | 0.8708 | 0.2468 | nan | 0.0825 | 0.8120 | 0.7729 | 0.9802 | | 0.0186 | 227.06 | 3860 | 0.9944 | 0.3593 | 0.9568 | 0.9338 | 0.8586 | 0.8876 | 0.9340 | 0.9812 | 0.6258 | 0.7708 | 0.9903 | 0.9223 | 0.2453 | nan | 0.9839 | 0.3491 | 0.8931 | 0.8787 | 0.8429 | 0.8409 | 0.8995 | 0.9609 | 0.6192 | 0.6172 | 0.9798 | 0.8716 | 0.2401 | nan | 0.0832 | 0.8046 | 0.7675 | 0.9802 | | 0.0215 | 228.24 | 3880 | 0.9938 | 0.4341 | 0.9571 | 0.9345 | 0.8621 | 0.8989 | 0.9379 | 0.9819 | 0.6253 | 0.7557 | 0.9900 | 0.9258 | 0.2545 | nan | 0.9839 | 0.4192 | 0.8930 | 0.8791 | 0.8442 | 0.8447 | 0.9060 | 0.9598 | 0.6205 | 0.6098 | 0.9800 | 0.8715 | 0.2500 | nan | 0.0826 | 0.8117 | 0.7740 | 0.9803 | | 0.0191 | 229.41 | 3900 | 0.9935 | 0.4228 | 0.9609 | 0.9298 | 0.8530 | 0.8971 | 0.9220 | 0.9810 | 0.6242 | 0.7614 | 0.9892 | 0.9239 | 0.2450 | nan | 0.9840 | 0.4107 | 0.8910 | 0.8748 | 0.8368 | 0.8431 | 0.8969 | 0.9596 | 0.6181 | 0.6114 | 0.9798 | 0.8712 | 0.2416 | nan | 0.0839 | 0.8080 | 0.7707 | 0.9801 | | 0.0194 | 230.59 | 3920 | 0.9935 | 0.4114 | 0.9572 | 0.9463 | 0.8608 | 0.9017 | 0.9329 | 0.9809 | 0.6252 | 0.7815 | 0.9901 | 0.9263 | 0.2493 | nan | 0.9841 | 0.4003 | 0.8930 | 0.8789 | 0.8421 | 0.8443 | 0.9048 | 0.9610 | 0.6189 | 0.6224 | 0.9797 | 0.8721 | 0.2465 | nan | 0.0825 | 0.8121 | 0.7729 | 0.9803 | | 0.0181 | 231.76 | 3940 | 0.9935 | 0.3984 | 0.9567 | 0.9417 | 0.8638 | 0.9068 | 0.9288 | 0.9793 | 0.6239 | 0.7886 | 0.9905 | 0.9218 | 0.2480 | nan | 0.9841 | 0.3907 | 0.8936 | 0.8811 | 0.8454 | 0.8443 | 0.9052 | 0.9609 | 0.6184 | 0.6328 | 0.9800 | 0.8721 | 0.2439 | nan | 0.0813 | 0.8109 | 0.7733 | 0.9804 | | 0.0219 | 232.94 | 3960 | 0.9933 | 0.4797 | 0.9603 | 0.9400 | 0.8654 | 0.9075 | 0.9241 | 0.9802 | 0.6246 | 0.7859 | 0.9884 | 0.9301 | 0.2488 | nan | 0.9841 | 0.4531 | 0.8924 | 0.8777 | 0.8482 | 0.8453 | 0.9005 | 0.9612 | 0.6176 | 0.6328 | 0.9800 | 0.8722 | 0.2445 | nan | 0.0816 | 0.8176 | 0.7777 | 0.9804 | | 0.017 | 234.12 | 3980 | 0.9943 | 0.3984 | 0.9591 | 0.9360 | 0.8552 | 0.8959 | 0.9249 | 0.9810 | 0.6254 | 0.7733 | 0.9886 | 0.9256 | 0.2392 | nan | 0.9840 | 0.3834 | 0.8924 | 0.8772 | 0.8409 | 0.8454 | 0.9022 | 0.9609 | 0.6199 | 0.6260 | 0.9798 | 0.8721 | 0.2357 | nan | 0.0822 | 0.8075 | 0.7708 | 0.9803 | | 0.0178 | 235.29 | 4000 | 0.9938 | 0.4504 | 0.9568 | 0.9284 | 0.8601 | 0.8952 | 0.9263 | 0.9842 | 0.6245 | 0.7851 | 0.9895 | 0.9196 | 0.2356 | nan | 0.9841 | 0.4321 | 0.8926 | 0.8717 | 0.8432 | 0.8439 | 0.9021 | 0.9587 | 0.6172 | 0.6180 | 0.9801 | 0.8699 | 0.2261 | nan | 0.0836 | 0.8115 | 0.7723 | 0.9802 | | 0.0198 | 236.47 | 4020 | 0.9939 | 0.3886 | 0.9591 | 0.9406 | 0.8569 | 0.8932 | 0.9280 | 0.9807 | 0.6237 | 0.7595 | 0.9902 | 0.9249 | 0.2407 | nan | 0.9842 | 0.3776 | 0.8928 | 0.8755 | 0.8403 | 0.8432 | 0.9005 | 0.9603 | 0.6186 | 0.6093 | 0.9797 | 0.8717 | 0.2346 | nan | 0.0836 | 0.8062 | 0.7683 | 0.9803 | | 0.017 | 237.65 | 4040 | 0.9941 | 0.4065 | 0.9591 | 0.9322 | 0.8549 | 0.8945 | 0.9281 | 0.9806 | 0.6263 | 0.7652 | 0.9907 | 0.9183 | 0.2460 | nan | 0.9842 | 0.3937 | 0.8937 | 0.8821 | 0.8395 | 0.8443 | 0.9029 | 0.9611 | 0.6205 | 0.6150 | 0.9798 | 0.8723 | 0.2421 | nan | 0.0826 | 0.8074 | 0.7716 | 0.9804 | | 0.0195 | 238.82 | 4060 | 0.9942 | 0.3805 | 0.9595 | 0.9244 | 0.8475 | 0.8913 | 0.9235 | 0.9799 | 0.6239 | 0.7692 | 0.9901 | 0.9209 | 0.2439 | nan | 0.9840 | 0.3685 | 0.8931 | 0.8770 | 0.8345 | 0.8419 | 0.9021 | 0.9607 | 0.6195 | 0.6139 | 0.9798 | 0.8725 | 0.2412 | nan | 0.0848 | 0.8037 | 0.7684 | 0.9803 | | 0.0195 | 240.0 | 4080 | 0.9939 | 0.3740 | 0.9576 | 0.9336 | 0.8518 | 0.9005 | 0.9294 | 0.9802 | 0.6219 | 0.7682 | 0.9900 | 0.9267 | 0.2434 | nan | 0.9841 | 0.3668 | 0.8932 | 0.8810 | 0.8362 | 0.8436 | 0.9046 | 0.9609 | 0.6179 | 0.6148 | 0.9798 | 0.8731 | 0.2409 | nan | 0.0841 | 0.8055 | 0.7690 | 0.9803 | | 0.019 | 241.18 | 4100 | 0.9934 | 0.4130 | 0.9557 | 0.9383 | 0.8568 | 0.9091 | 0.9265 | 0.9816 | 0.6237 | 0.7526 | 0.9907 | 0.9207 | 0.2527 | nan | 0.9842 | 0.4032 | 0.8923 | 0.8823 | 0.8388 | 0.8452 | 0.9044 | 0.9596 | 0.6181 | 0.6075 | 0.9801 | 0.8726 | 0.2473 | nan | 0.0839 | 0.8088 | 0.7720 | 0.9803 | | 0.0155 | 242.35 | 4120 | 0.9937 | 0.4146 | 0.9588 | 0.9401 | 0.8506 | 0.8980 | 0.9292 | 0.9800 | 0.6262 | 0.7814 | 0.9903 | 0.9263 | 0.2435 | nan | 0.9841 | 0.3966 | 0.8933 | 0.8803 | 0.8337 | 0.8436 | 0.9036 | 0.9615 | 0.6203 | 0.6218 | 0.9796 | 0.8736 | 0.2413 | nan | 0.0827 | 0.8102 | 0.7718 | 0.9803 | | 0.0185 | 243.53 | 4140 | 0.9933 | 0.4407 | 0.9586 | 0.9358 | 0.8511 | 0.9064 | 0.9302 | 0.9809 | 0.6269 | 0.7668 | 0.9902 | 0.9265 | 0.2348 | nan | 0.9842 | 0.4274 | 0.8925 | 0.8811 | 0.8337 | 0.8460 | 0.9038 | 0.9601 | 0.6209 | 0.6185 | 0.9797 | 0.8737 | 0.2329 | nan | 0.0827 | 0.8109 | 0.7734 | 0.9803 | | 0.0197 | 244.71 | 4160 | 0.9941 | 0.4016 | 0.9574 | 0.9368 | 0.8594 | 0.8953 | 0.9265 | 0.9816 | 0.6265 | 0.7806 | 0.9889 | 0.9315 | 0.2460 | nan | 0.9840 | 0.3914 | 0.8931 | 0.8748 | 0.8421 | 0.8429 | 0.9029 | 0.9601 | 0.6198 | 0.6194 | 0.9800 | 0.8737 | 0.2438 | nan | 0.0838 | 0.8097 | 0.7714 | 0.9803 | | 0.0178 | 245.88 | 4180 | 0.9942 | 0.4211 | 0.9549 | 0.9475 | 0.8526 | 0.8957 | 0.9281 | 0.9834 | 0.6257 | 0.7645 | 0.9887 | 0.9300 | 0.2505 | nan | 0.9838 | 0.4098 | 0.8933 | 0.8740 | 0.8387 | 0.8414 | 0.9039 | 0.9585 | 0.6200 | 0.6099 | 0.9801 | 0.8723 | 0.2476 | nan | 0.0851 | 0.8105 | 0.7718 | 0.9802 | | 0.0181 | 247.06 | 4200 | 0.9934 | 0.4797 | 0.9565 | 0.9473 | 0.8639 | 0.9014 | 0.9278 | 0.9820 | 0.6290 | 0.7834 | 0.9903 | 0.9272 | 0.2492 | nan | 0.9841 | 0.4631 | 0.8940 | 0.8791 | 0.8450 | 0.8423 | 0.9042 | 0.9606 | 0.6226 | 0.6200 | 0.9799 | 0.8731 | 0.2465 | nan | 0.0819 | 0.8178 | 0.7780 | 0.9804 | | 0.0222 | 248.24 | 4220 | 0.9938 | 0.4049 | 0.9570 | 0.9411 | 0.8531 | 0.9025 | 0.9344 | 0.9807 | 0.6242 | 0.7708 | 0.9900 | 0.9230 | 0.2420 | nan | 0.9841 | 0.3934 | 0.8930 | 0.8778 | 0.8377 | 0.8430 | 0.9060 | 0.9607 | 0.6193 | 0.6136 | 0.9800 | 0.8723 | 0.2393 | nan | 0.0837 | 0.8090 | 0.7708 | 0.9803 | | 0.0197 | 249.41 | 4240 | 0.9936 | 0.3740 | 0.9589 | 0.9474 | 0.8535 | 0.9008 | 0.9293 | 0.9819 | 0.6252 | 0.7759 | 0.9897 | 0.9225 | 0.2403 | nan | 0.9841 | 0.3633 | 0.8932 | 0.8757 | 0.8384 | 0.8433 | 0.9037 | 0.9607 | 0.6193 | 0.6239 | 0.9800 | 0.8733 | 0.2382 | nan | 0.0835 | 0.8072 | 0.7690 | 0.9803 | | 0.0198 | 250.59 | 4260 | 0.9939 | 0.3854 | 0.9596 | 0.9399 | 0.8664 | 0.8952 | 0.9290 | 0.9808 | 0.6238 | 0.7739 | 0.9886 | 0.9305 | 0.2437 | nan | 0.9840 | 0.3652 | 0.8927 | 0.8747 | 0.8457 | 0.8428 | 0.9030 | 0.9609 | 0.6167 | 0.6180 | 0.9799 | 0.8736 | 0.2415 | nan | 0.0848 | 0.8085 | 0.7691 | 0.9803 | | 0.0194 | 251.76 | 4280 | 0.9936 | 0.4553 | 0.9588 | 0.9327 | 0.8496 | 0.9022 | 0.9293 | 0.9809 | 0.6247 | 0.7580 | 0.9897 | 0.9296 | 0.2382 | nan | 0.9842 | 0.4355 | 0.8918 | 0.8756 | 0.8360 | 0.8450 | 0.9026 | 0.9606 | 0.6185 | 0.6111 | 0.9799 | 0.8738 | 0.2361 | nan | 0.0835 | 0.8110 | 0.7731 | 0.9803 | | 0.0181 | 252.94 | 4300 | 0.9935 | 0.4602 | 0.9567 | 0.9443 | 0.8640 | 0.9062 | 0.9366 | 0.9819 | 0.6251 | 0.7717 | 0.9900 | 0.9281 | 0.2511 | nan | 0.9842 | 0.4388 | 0.8939 | 0.8769 | 0.8460 | 0.8454 | 0.9043 | 0.9610 | 0.6190 | 0.6160 | 0.9800 | 0.8732 | 0.2487 | nan | 0.0834 | 0.8161 | 0.7760 | 0.9805 | | 0.024 | 254.12 | 4320 | 0.9944 | 0.3919 | 0.9566 | 0.9335 | 0.8590 | 0.8940 | 0.9317 | 0.9804 | 0.6241 | 0.7724 | 0.9891 | 0.9283 | 0.2530 | nan | 0.9839 | 0.3813 | 0.8934 | 0.8777 | 0.8436 | 0.8412 | 0.9047 | 0.9602 | 0.6196 | 0.6129 | 0.9800 | 0.8729 | 0.2507 | nan | 0.0847 | 0.8083 | 0.7709 | 0.9803 | | 0.0186 | 255.29 | 4340 | 0.9942 | 0.4455 | 0.9557 | 0.9383 | 0.8635 | 0.9019 | 0.9373 | 0.9813 | 0.6262 | 0.7775 | 0.9895 | 0.9279 | 0.2574 | nan | 0.9842 | 0.4281 | 0.8947 | 0.8749 | 0.8463 | 0.8457 | 0.9051 | 0.9605 | 0.6209 | 0.6189 | 0.9801 | 0.8731 | 0.2546 | nan | 0.0830 | 0.8151 | 0.7759 | 0.9805 | | 0.0222 | 256.47 | 4360 | 0.9940 | 0.4325 | 0.9572 | 0.9444 | 0.8710 | 0.8992 | 0.9341 | 0.9807 | 0.6243 | 0.7862 | 0.9897 | 0.9272 | 0.2564 | nan | 0.9841 | 0.4130 | 0.8945 | 0.8743 | 0.8524 | 0.8440 | 0.9022 | 0.9603 | 0.6192 | 0.6287 | 0.9804 | 0.8735 | 0.2526 | nan | 0.0845 | 0.8152 | 0.7753 | 0.9805 | | 0.0218 | 257.65 | 4380 | 0.9937 | 0.4309 | 0.9583 | 0.9415 | 0.8610 | 0.8997 | 0.9356 | 0.9813 | 0.6261 | 0.7693 | 0.9900 | 0.9263 | 0.2509 | nan | 0.9842 | 0.4033 | 0.8936 | 0.8816 | 0.8437 | 0.8442 | 0.9018 | 0.9600 | 0.6198 | 0.6182 | 0.9800 | 0.8737 | 0.2489 | nan | 0.0837 | 0.8127 | 0.7733 | 0.9804 | | 0.0192 | 258.82 | 4400 | 0.9938 | 0.4650 | 0.9569 | 0.9477 | 0.8656 | 0.8980 | 0.9341 | 0.9821 | 0.6272 | 0.7713 | 0.9900 | 0.9276 | 0.2486 | nan | 0.9841 | 0.4269 | 0.8939 | 0.8792 | 0.8465 | 0.8449 | 0.9027 | 0.9603 | 0.6197 | 0.6161 | 0.9799 | 0.8731 | 0.2468 | nan | 0.0834 | 0.8160 | 0.7749 | 0.9804 | | 0.0184 | 260.0 | 4420 | 0.9941 | 0.4472 | 0.9547 | 0.9486 | 0.8584 | 0.8966 | 0.9336 | 0.9813 | 0.6281 | 0.7855 | 0.9907 | 0.9238 | 0.2488 | nan | 0.9841 | 0.4224 | 0.8939 | 0.8808 | 0.8417 | 0.8439 | 0.9044 | 0.9600 | 0.6214 | 0.6226 | 0.9801 | 0.8740 | 0.2469 | nan | 0.0842 | 0.8147 | 0.7751 | 0.9804 | | 0.022 | 261.18 | 4440 | 0.9941 | 0.3902 | 0.9557 | 0.9395 | 0.8583 | 0.8976 | 0.9382 | 0.9808 | 0.6246 | 0.7736 | 0.9910 | 0.9207 | 0.2465 | nan | 0.9841 | 0.3756 | 0.8935 | 0.8816 | 0.8425 | 0.8451 | 0.9033 | 0.9606 | 0.6192 | 0.6170 | 0.9798 | 0.8739 | 0.2445 | nan | 0.0853 | 0.8085 | 0.7708 | 0.9804 | | 0.0171 | 262.35 | 4460 | 0.9938 | 0.3902 | 0.9571 | 0.9364 | 0.8589 | 0.9032 | 0.9348 | 0.9815 | 0.6240 | 0.7598 | 0.9902 | 0.9245 | 0.2466 | nan | 0.9842 | 0.3785 | 0.8933 | 0.8816 | 0.8419 | 0.8455 | 0.9045 | 0.9605 | 0.6179 | 0.6089 | 0.9801 | 0.8745 | 0.2446 | nan | 0.0846 | 0.8078 | 0.7705 | 0.9804 | | 0.0186 | 263.53 | 4480 | 0.9938 | 0.4049 | 0.9589 | 0.9366 | 0.8536 | 0.8974 | 0.9305 | 0.9810 | 0.6224 | 0.7809 | 0.9894 | 0.9279 | 0.2440 | nan | 0.9841 | 0.3915 | 0.8926 | 0.8789 | 0.8384 | 0.8423 | 0.9034 | 0.9601 | 0.6177 | 0.6193 | 0.9802 | 0.8740 | 0.2417 | nan | 0.0860 | 0.8093 | 0.7711 | 0.9803 | | 0.0161 | 264.71 | 4500 | 0.9936 | 0.4325 | 0.9587 | 0.9336 | 0.8584 | 0.9029 | 0.9324 | 0.9817 | 0.6250 | 0.7839 | 0.9893 | 0.9286 | 0.2442 | nan | 0.9842 | 0.4111 | 0.8937 | 0.8809 | 0.8413 | 0.8443 | 0.9049 | 0.9605 | 0.6189 | 0.6239 | 0.9801 | 0.8744 | 0.2420 | nan | 0.0846 | 0.8127 | 0.7739 | 0.9804 | | 0.0159 | 265.88 | 4520 | 0.9938 | 0.4163 | 0.9579 | 0.9452 | 0.8556 | 0.9034 | 0.9332 | 0.9812 | 0.6251 | 0.7862 | 0.9897 | 0.9289 | 0.2462 | nan | 0.9842 | 0.4 | 0.8942 | 0.8814 | 0.8398 | 0.8449 | 0.9062 | 0.9611 | 0.6194 | 0.6278 | 0.9802 | 0.8750 | 0.2443 | nan | 0.0841 | 0.8125 | 0.7737 | 0.9805 | | 0.0195 | 267.06 | 4540 | 0.9940 | 0.4407 | 0.9593 | 0.9412 | 0.8514 | 0.8952 | 0.9284 | 0.9814 | 0.6239 | 0.7695 | 0.9897 | 0.9249 | 0.2377 | nan | 0.9842 | 0.4215 | 0.8929 | 0.8807 | 0.8358 | 0.8433 | 0.9019 | 0.9603 | 0.6183 | 0.6176 | 0.9800 | 0.8746 | 0.2360 | nan | 0.0858 | 0.8105 | 0.7729 | 0.9803 | | 0.0172 | 268.24 | 4560 | 0.9934 | 0.4634 | 0.9586 | 0.9494 | 0.8576 | 0.9058 | 0.9289 | 0.9817 | 0.6260 | 0.7862 | 0.9899 | 0.9261 | 0.2372 | nan | 0.9843 | 0.4286 | 0.8937 | 0.8813 | 0.8384 | 0.8461 | 0.9010 | 0.9609 | 0.6199 | 0.6251 | 0.9800 | 0.8740 | 0.2354 | nan | 0.0835 | 0.8157 | 0.7745 | 0.9805 | | 0.019 | 269.41 | 4580 | 0.9940 | 0.4081 | 0.9559 | 0.9444 | 0.8570 | 0.9055 | 0.9306 | 0.9818 | 0.6256 | 0.7708 | 0.9897 | 0.9284 | 0.2432 | nan | 0.9843 | 0.3885 | 0.8943 | 0.8814 | 0.8404 | 0.8466 | 0.9033 | 0.9613 | 0.6203 | 0.6223 | 0.9800 | 0.8742 | 0.2415 | nan | 0.0834 | 0.8104 | 0.7722 | 0.9806 | | 0.018 | 270.59 | 4600 | 0.9938 | 0.4293 | 0.9568 | 0.9400 | 0.8524 | 0.9018 | 0.9287 | 0.9821 | 0.6257 | 0.7869 | 0.9904 | 0.9227 | 0.2394 | nan | 0.9843 | 0.3923 | 0.8938 | 0.8821 | 0.8364 | 0.8462 | 0.9013 | 0.9614 | 0.6200 | 0.6244 | 0.9799 | 0.8736 | 0.2377 | nan | 0.0835 | 0.8115 | 0.7718 | 0.9805 | | 0.0223 | 271.76 | 4620 | 0.9937 | 0.4081 | 0.9571 | 0.9406 | 0.8528 | 0.9023 | 0.9291 | 0.9811 | 0.6254 | 0.7921 | 0.9903 | 0.9257 | 0.2447 | nan | 0.9843 | 0.3769 | 0.8937 | 0.8822 | 0.8364 | 0.8446 | 0.9017 | 0.9610 | 0.6194 | 0.6266 | 0.9799 | 0.8734 | 0.2425 | nan | 0.0839 | 0.8110 | 0.7710 | 0.9805 | | 0.0227 | 272.94 | 4640 | 0.9940 | 0.4 | 0.9574 | 0.9357 | 0.8553 | 0.8968 | 0.9276 | 0.9807 | 0.6246 | 0.7851 | 0.9906 | 0.9236 | 0.2449 | nan | 0.9842 | 0.3814 | 0.8941 | 0.8830 | 0.8386 | 0.8435 | 0.9026 | 0.9612 | 0.6195 | 0.6246 | 0.9801 | 0.8743 | 0.2430 | nan | 0.0845 | 0.8089 | 0.7715 | 0.9805 | | 0.0165 | 274.12 | 4660 | 0.9940 | 0.4033 | 0.9576 | 0.9453 | 0.8601 | 0.8980 | 0.9318 | 0.9805 | 0.6254 | 0.7957 | 0.9900 | 0.9278 | 0.2541 | nan | 0.9842 | 0.3758 | 0.8946 | 0.8774 | 0.8433 | 0.8438 | 0.9025 | 0.9615 | 0.6199 | 0.6288 | 0.9802 | 0.8743 | 0.2517 | nan | 0.0850 | 0.8126 | 0.7722 | 0.9806 | | 0.0192 | 275.29 | 4680 | 0.9941 | 0.4033 | 0.9571 | 0.9376 | 0.8547 | 0.8986 | 0.9298 | 0.9805 | 0.6271 | 0.7904 | 0.9903 | 0.9243 | 0.2444 | nan | 0.9842 | 0.3887 | 0.8946 | 0.8788 | 0.8394 | 0.8444 | 0.9025 | 0.9617 | 0.6214 | 0.6275 | 0.9801 | 0.8747 | 0.2426 | nan | 0.0836 | 0.8102 | 0.7724 | 0.9806 | | 0.0172 | 276.47 | 4700 | 0.9940 | 0.4049 | 0.9573 | 0.9333 | 0.8525 | 0.8995 | 0.9312 | 0.9818 | 0.6278 | 0.7866 | 0.9894 | 0.9315 | 0.2391 | nan | 0.9842 | 0.3879 | 0.8943 | 0.8779 | 0.8361 | 0.8443 | 0.9031 | 0.9609 | 0.6212 | 0.6245 | 0.9798 | 0.8740 | 0.2374 | nan | 0.0843 | 0.8099 | 0.7712 | 0.9804 | | 0.0173 | 277.65 | 4720 | 0.9942 | 0.3984 | 0.9576 | 0.9424 | 0.8548 | 0.8968 | 0.9321 | 0.9812 | 0.6268 | 0.7832 | 0.9892 | 0.9319 | 0.2483 | nan | 0.9842 | 0.3834 | 0.8948 | 0.8782 | 0.8391 | 0.8436 | 0.9037 | 0.9609 | 0.6215 | 0.6230 | 0.9800 | 0.8738 | 0.2464 | nan | 0.0851 | 0.8105 | 0.7717 | 0.9805 | | 0.0183 | 278.82 | 4740 | 0.9941 | 0.3951 | 0.9570 | 0.9430 | 0.8546 | 0.8972 | 0.9328 | 0.9814 | 0.6268 | 0.7811 | 0.9901 | 0.9249 | 0.2505 | nan | 0.9842 | 0.3767 | 0.8947 | 0.8781 | 0.8386 | 0.8437 | 0.9043 | 0.9609 | 0.6208 | 0.6215 | 0.9799 | 0.8737 | 0.2485 | nan | 0.0844 | 0.8099 | 0.7712 | 0.9805 | | 0.0193 | 280.0 | 4760 | 0.9941 | 0.4179 | 0.9565 | 0.9445 | 0.8497 | 0.8988 | 0.9308 | 0.9818 | 0.6264 | 0.7781 | 0.9901 | 0.9254 | 0.2521 | nan | 0.9842 | 0.3984 | 0.8945 | 0.8806 | 0.8353 | 0.8440 | 0.9032 | 0.9607 | 0.6205 | 0.6244 | 0.9799 | 0.8737 | 0.2500 | nan | 0.0838 | 0.8113 | 0.7730 | 0.9805 | | 0.0207 | 281.18 | 4780 | 0.9939 | 0.4081 | 0.9582 | 0.9366 | 0.8501 | 0.9014 | 0.9304 | 0.9805 | 0.6262 | 0.7810 | 0.9905 | 0.9217 | 0.2484 | nan | 0.9843 | 0.3928 | 0.8940 | 0.8790 | 0.8351 | 0.8462 | 0.9038 | 0.9610 | 0.6204 | 0.6277 | 0.9800 | 0.8740 | 0.2463 | nan | 0.0842 | 0.8098 | 0.7727 | 0.9805 | | 0.0166 | 282.35 | 4800 | 0.9942 | 0.4228 | 0.9571 | 0.9356 | 0.8498 | 0.8966 | 0.9317 | 0.9815 | 0.6259 | 0.7806 | 0.9900 | 0.9290 | 0.2540 | nan | 0.9842 | 0.4075 | 0.8946 | 0.8781 | 0.8356 | 0.8444 | 0.9057 | 0.9609 | 0.6203 | 0.6242 | 0.9800 | 0.8741 | 0.2518 | nan | 0.0846 | 0.8114 | 0.7740 | 0.9805 | | 0.0203 | 283.53 | 4820 | 0.9941 | 0.4244 | 0.9571 | 0.9421 | 0.8525 | 0.8984 | 0.9327 | 0.9813 | 0.6270 | 0.7913 | 0.9901 | 0.9257 | 0.2532 | nan | 0.9842 | 0.4078 | 0.8948 | 0.8788 | 0.8370 | 0.8445 | 0.9065 | 0.9613 | 0.6204 | 0.6273 | 0.9801 | 0.8745 | 0.2510 | nan | 0.0843 | 0.8131 | 0.7745 | 0.9805 | | 0.0153 | 284.71 | 4840 | 0.9939 | 0.4 | 0.9586 | 0.9311 | 0.8477 | 0.9007 | 0.9293 | 0.9805 | 0.6264 | 0.7916 | 0.9900 | 0.9249 | 0.2477 | nan | 0.9842 | 0.3862 | 0.8942 | 0.8752 | 0.8333 | 0.8451 | 0.9043 | 0.9614 | 0.6202 | 0.6283 | 0.9799 | 0.8742 | 0.2458 | nan | 0.0842 | 0.8094 | 0.7717 | 0.9805 | | 0.017 | 285.88 | 4860 | 0.9938 | 0.4049 | 0.9583 | 0.9444 | 0.8532 | 0.9027 | 0.9308 | 0.9802 | 0.6260 | 0.7857 | 0.9899 | 0.9276 | 0.2542 | nan | 0.9843 | 0.3921 | 0.8941 | 0.8785 | 0.8372 | 0.8460 | 0.9050 | 0.9611 | 0.6199 | 0.6283 | 0.9800 | 0.8744 | 0.2516 | nan | 0.0843 | 0.8117 | 0.7733 | 0.9805 | | 0.0192 | 287.06 | 4880 | 0.9941 | 0.4065 | 0.9575 | 0.9453 | 0.8526 | 0.8979 | 0.9343 | 0.9813 | 0.6260 | 0.7883 | 0.9896 | 0.9279 | 0.2535 | nan | 0.9842 | 0.3925 | 0.8945 | 0.8776 | 0.8372 | 0.8446 | 0.9069 | 0.9612 | 0.6202 | 0.6266 | 0.9801 | 0.8744 | 0.2513 | nan | 0.0848 | 0.8119 | 0.7732 | 0.9805 | | 0.0173 | 288.24 | 4900 | 0.9941 | 0.4098 | 0.9586 | 0.9355 | 0.8501 | 0.8983 | 0.9294 | 0.9805 | 0.6255 | 0.7836 | 0.9899 | 0.9256 | 0.2480 | nan | 0.9842 | 0.3962 | 0.8940 | 0.8788 | 0.8350 | 0.8452 | 0.9048 | 0.9611 | 0.6197 | 0.6256 | 0.9800 | 0.8744 | 0.2459 | nan | 0.0854 | 0.8099 | 0.7727 | 0.9805 | | 0.0162 | 289.41 | 4920 | 0.9940 | 0.4390 | 0.9584 | 0.9386 | 0.8496 | 0.9000 | 0.9311 | 0.9803 | 0.6253 | 0.7781 | 0.9900 | 0.9283 | 0.2452 | nan | 0.9843 | 0.4225 | 0.8938 | 0.8789 | 0.8341 | 0.8458 | 0.9055 | 0.9611 | 0.6198 | 0.6240 | 0.9800 | 0.8746 | 0.2433 | nan | 0.0848 | 0.8121 | 0.7744 | 0.9805 | | 0.0181 | 290.59 | 4940 | 0.9940 | 0.4244 | 0.9578 | 0.9367 | 0.8518 | 0.8986 | 0.9304 | 0.9817 | 0.6253 | 0.7813 | 0.9900 | 0.9262 | 0.2500 | nan | 0.9842 | 0.4085 | 0.8941 | 0.8775 | 0.8356 | 0.8452 | 0.9054 | 0.9608 | 0.6197 | 0.6246 | 0.9801 | 0.8743 | 0.2476 | nan | 0.0856 | 0.8114 | 0.7737 | 0.9805 | | 0.02 | 291.76 | 4960 | 0.9938 | 0.4293 | 0.9577 | 0.9439 | 0.8527 | 0.9013 | 0.9346 | 0.9820 | 0.6250 | 0.7868 | 0.9899 | 0.9269 | 0.2499 | nan | 0.9843 | 0.4106 | 0.8941 | 0.8791 | 0.8363 | 0.8456 | 0.9060 | 0.9609 | 0.6197 | 0.6259 | 0.9801 | 0.8745 | 0.2476 | nan | 0.0854 | 0.8134 | 0.7742 | 0.9805 | | 0.0202 | 292.94 | 4980 | 0.9939 | 0.4244 | 0.9585 | 0.9413 | 0.8516 | 0.9000 | 0.9309 | 0.9804 | 0.6256 | 0.7881 | 0.9902 | 0.9257 | 0.2459 | nan | 0.9843 | 0.4078 | 0.8940 | 0.8797 | 0.8353 | 0.8456 | 0.9054 | 0.9615 | 0.6198 | 0.6272 | 0.9801 | 0.8748 | 0.2440 | nan | 0.0845 | 0.8120 | 0.7738 | 0.9805 | | 0.0179 | 294.12 | 5000 | 0.9939 | 0.4423 | 0.9586 | 0.9298 | 0.8508 | 0.9009 | 0.9313 | 0.9810 | 0.6256 | 0.7896 | 0.9899 | 0.9268 | 0.2483 | nan | 0.9843 | 0.4243 | 0.8940 | 0.8800 | 0.8353 | 0.8459 | 0.9060 | 0.9614 | 0.6199 | 0.6280 | 0.9801 | 0.8749 | 0.2463 | nan | 0.0847 | 0.8130 | 0.7754 | 0.9805 | | 0.0163 | 295.29 | 5020 | 0.9938 | 0.4472 | 0.9584 | 0.9438 | 0.8520 | 0.9005 | 0.9351 | 0.9814 | 0.6257 | 0.7885 | 0.9902 | 0.9256 | 0.2510 | nan | 0.9843 | 0.4198 | 0.8940 | 0.8804 | 0.8358 | 0.8459 | 0.9061 | 0.9613 | 0.6200 | 0.6274 | 0.9801 | 0.8747 | 0.2488 | nan | 0.0845 | 0.8149 | 0.7753 | 0.9805 | | 0.0172 | 296.47 | 5040 | 0.9939 | 0.4423 | 0.9577 | 0.9471 | 0.8509 | 0.9005 | 0.9342 | 0.9814 | 0.6260 | 0.7869 | 0.9902 | 0.9259 | 0.2548 | nan | 0.9843 | 0.4146 | 0.8944 | 0.8798 | 0.8355 | 0.8456 | 0.9066 | 0.9613 | 0.6198 | 0.6271 | 0.9801 | 0.8747 | 0.2524 | nan | 0.0843 | 0.8148 | 0.7751 | 0.9806 | | 0.0203 | 297.65 | 5060 | 0.0854 | 0.7753 | 0.8134 | 0.9804 | nan | 0.9945 | 0.9549 | 0.8916 | 0.9913 | 0.9137 | 0.6245 | 0.8581 | 0.9801 | 0.7858 | 0.2508 | 0.9343 | 0.9540 | 0.4407 | nan | 0.9839 | 0.8947 | 0.8421 | 0.9799 | 0.8705 | 0.6197 | 0.8416 | 0.9614 | 0.6280 | 0.2472 | 0.9054 | 0.8846 | 0.4195 | | 0.0205 | 298.82 | 5080 | 0.0871 | 0.7626 | 0.8020 | 0.9801 | nan | 0.9931 | 0.9611 | 0.9024 | 0.9895 | 0.9241 | 0.6243 | 0.8558 | 0.9790 | 0.7904 | 0.2323 | 0.9215 | 0.9416 | 0.3106 | nan | 0.9840 | 0.8921 | 0.8393 | 0.9800 | 0.8719 | 0.6151 | 0.8377 | 0.9609 | 0.6236 | 0.2302 | 0.9017 | 0.8872 | 0.2898 | | 0.0188 | 300.0 | 5100 | 0.0845 | 0.7689 | 0.8070 | 0.9804 | nan | 0.9940 | 0.9574 | 0.8962 | 0.9902 | 0.9197 | 0.6269 | 0.8422 | 0.9827 | 0.7988 | 0.2310 | 0.9154 | 0.9435 | 0.3935 | nan | 0.9842 | 0.8939 | 0.8437 | 0.9794 | 0.8726 | 0.6211 | 0.8274 | 0.9616 | 0.6333 | 0.2295 | 0.8987 | 0.8783 | 0.3717 | | 0.0206 | 301.18 | 5120 | 0.0832 | 0.7768 | 0.8159 | 0.9805 | nan | 0.9936 | 0.9556 | 0.9060 | 0.9911 | 0.9237 | 0.6286 | 0.8542 | 0.9815 | 0.7855 | 0.2610 | 0.9257 | 0.9445 | 0.4553 | nan | 0.9844 | 0.8934 | 0.8474 | 0.9795 | 0.8737 | 0.6225 | 0.8381 | 0.9608 | 0.6310 | 0.2587 | 0.9043 | 0.8865 | 0.4179 | | 0.0193 | 302.35 | 5140 | 0.0867 | 0.7780 | 0.8209 | 0.9801 | nan | 0.9937 | 0.9592 | 0.8986 | 0.9880 | 0.9259 | 0.6240 | 0.8541 | 0.9825 | 0.7898 | 0.2379 | 0.9193 | 0.9365 | 0.5626 | nan | 0.9841 | 0.8905 | 0.8436 | 0.9801 | 0.8725 | 0.6174 | 0.8376 | 0.9586 | 0.6280 | 0.2327 | 0.8946 | 0.8769 | 0.4971 | | 0.0208 | 303.53 | 5160 | 0.0848 | 0.7725 | 0.8100 | 0.9805 | nan | 0.9938 | 0.9579 | 0.8993 | 0.9905 | 0.9238 | 0.6241 | 0.8569 | 0.9811 | 0.7872 | 0.2388 | 0.9215 | 0.9274 | 0.4276 | nan | 0.9844 | 0.8929 | 0.8451 | 0.9798 | 0.8744 | 0.6190 | 0.8393 | 0.9609 | 0.6293 | 0.2325 | 0.8954 | 0.8792 | 0.4103 | | 0.0217 | 304.71 | 5180 | 0.0816 | 0.7778 | 0.8182 | 0.9806 | nan | 0.9930 | 0.9544 | 0.9174 | 0.9897 | 0.9243 | 0.6281 | 0.8733 | 0.9847 | 0.7901 | 0.2384 | 0.9351 | 0.9500 | 0.4585 | nan | 0.9844 | 0.8931 | 0.8497 | 0.9803 | 0.8737 | 0.6188 | 0.8540 | 0.9584 | 0.6310 | 0.2370 | 0.9060 | 0.8821 | 0.4434 | | 0.02 | 305.88 | 5200 | 0.0823 | 0.7771 | 0.8183 | 0.9805 | nan | 0.9935 | 0.9587 | 0.9048 | 0.9891 | 0.9334 | 0.6309 | 0.8489 | 0.9818 | 0.7880 | 0.2463 | 0.9414 | 0.9495 | 0.4715 | nan | 0.9844 | 0.8931 | 0.8467 | 0.9798 | 0.8730 | 0.6252 | 0.8337 | 0.9604 | 0.6265 | 0.2440 | 0.9043 | 0.8854 | 0.4462 | | 0.0216 | 307.06 | 5220 | 0.0838 | 0.7722 | 0.8099 | 0.9804 | nan | 0.9936 | 0.9595 | 0.8957 | 0.9918 | 0.9096 | 0.6247 | 0.8559 | 0.9791 | 0.8019 | 0.2321 | 0.9240 | 0.9352 | 0.4260 | nan | 0.9843 | 0.8921 | 0.8419 | 0.9799 | 0.8723 | 0.6176 | 0.8409 | 0.9613 | 0.6354 | 0.2293 | 0.8969 | 0.8748 | 0.4119 | | 0.0181 | 308.24 | 5240 | 0.0851 | 0.7724 | 0.8131 | 0.9804 | nan | 0.9938 | 0.9602 | 0.8987 | 0.9875 | 0.9376 | 0.6258 | 0.8607 | 0.9818 | 0.7931 | 0.2409 | 0.9377 | 0.9427 | 0.4098 | nan | 0.9843 | 0.8927 | 0.8436 | 0.9799 | 0.8725 | 0.6198 | 0.8439 | 0.9611 | 0.6237 | 0.2393 | 0.9071 | 0.8850 | 0.3877 | | 0.0206 | 309.41 | 5260 | 0.0842 | 0.7819 | 0.8284 | 0.9806 | nan | 0.9933 | 0.9567 | 0.9048 | 0.9903 | 0.9186 | 0.6272 | 0.8752 | 0.9835 | 0.8066 | 0.2561 | 0.9462 | 0.9472 | 0.5642 | nan | 0.9843 | 0.8949 | 0.8457 | 0.9805 | 0.8736 | 0.6186 | 0.8517 | 0.9598 | 0.6297 | 0.2533 | 0.9011 | 0.8833 | 0.4880 | | 0.0168 | 310.59 | 5280 | 0.0851 | 0.7785 | 0.8190 | 0.9805 | nan | 0.9944 | 0.9578 | 0.8907 | 0.9885 | 0.9279 | 0.6256 | 0.8771 | 0.9826 | 0.7949 | 0.2443 | 0.9371 | 0.9498 | 0.4764 | nan | 0.9841 | 0.8934 | 0.8397 | 0.9808 | 0.8769 | 0.6197 | 0.8586 | 0.9610 | 0.6243 | 0.2429 | 0.9081 | 0.8825 | 0.4480 | | 0.0153 | 311.76 | 5300 | 0.0753 | 0.7878 | 0.8268 | 0.9809 | nan | 0.9948 | 0.9401 | 0.8909 | 0.9905 | 0.9115 | 0.8866 | 0.8532 | 0.9862 | 0.8041 | 0.2192 | 0.9210 | 0.9295 | 0.4211 | nan | 0.9841 | 0.8982 | 0.8439 | 0.9787 | 0.8682 | 0.8549 | 0.8367 | 0.9515 | 0.6267 | 0.2180 | 0.9013 | 0.8796 | 0.3991 | | 0.0199 | 312.94 | 5320 | 0.0868 | 0.7613 | 0.7949 | 0.9792 | nan | 0.9942 | 0.9650 | 0.8893 | 0.9877 | 0.9383 | 0.6267 | 0.8545 | 0.9722 | 0.5985 | 0.2032 | 0.9396 | 0.9374 | 0.4276 | nan | 0.9840 | 0.8846 | 0.8418 | 0.9790 | 0.8723 | 0.6225 | 0.8339 | 0.9520 | 0.5141 | 0.2010 | 0.9139 | 0.8863 | 0.4116 | | 0.0178 | 314.12 | 5340 | 0.0850 | 0.7697 | 0.8068 | 0.9805 | nan | 0.9939 | 0.9599 | 0.9062 | 0.9907 | 0.9161 | 0.6306 | 0.8499 | 0.9758 | 0.7884 | 0.2163 | 0.9081 | 0.9348 | 0.4179 | nan | 0.9842 | 0.8935 | 0.8489 | 0.9799 | 0.8708 | 0.6261 | 0.8356 | 0.9646 | 0.6107 | 0.2151 | 0.8895 | 0.8810 | 0.4060 | | 0.017 | 315.29 | 5360 | 0.0872 | 0.7739 | 0.8119 | 0.9804 | nan | 0.9942 | 0.9653 | 0.8954 | 0.9886 | 0.9236 | 0.6239 | 0.8803 | 0.9739 | 0.7395 | 0.2294 | 0.9410 | 0.9470 | 0.4520 | nan | 0.9838 | 0.8924 | 0.8439 | 0.9810 | 0.8726 | 0.6182 | 0.8629 | 0.9623 | 0.6111 | 0.2177 | 0.9017 | 0.8837 | 0.4297 | | 0.0214 | 316.47 | 5380 | 0.0780 | 0.7554 | 0.7923 | 0.9805 | nan | 0.9944 | 0.9553 | 0.8832 | 0.9898 | 0.9253 | 0.7320 | 0.8456 | 0.9838 | 0.7827 | 0.1308 | 0.9238 | 0.9396 | 0.2130 | nan | 0.9835 | 0.8995 | 0.8368 | 0.9787 | 0.8746 | 0.7223 | 0.8282 | 0.9635 | 0.6154 | 0.1301 | 0.8980 | 0.8782 | 0.2120 | | 0.0192 | 317.65 | 5400 | 0.0794 | 0.7672 | 0.8048 | 0.9801 | nan | 0.9931 | 0.9617 | 0.9034 | 0.9888 | 0.9378 | 0.7786 | 0.8047 | 0.9713 | 0.7534 | 0.1816 | 0.9131 | 0.9216 | 0.3528 | nan | 0.9841 | 0.8940 | 0.8434 | 0.9781 | 0.8732 | 0.7684 | 0.7948 | 0.9531 | 0.5930 | 0.1806 | 0.8918 | 0.8682 | 0.3506 | | 0.0199 | 318.82 | 5420 | 0.0806 | 0.7680 | 0.7966 | 0.9800 | nan | 0.9934 | 0.9598 | 0.9019 | 0.9903 | 0.9157 | 0.7492 | 0.8477 | 0.9818 | 0.5849 | 0.1813 | 0.8985 | 0.9140 | 0.4374 | nan | 0.9841 | 0.8917 | 0.8419 | 0.9800 | 0.8735 | 0.7441 | 0.8360 | 0.9487 | 0.5177 | 0.1794 | 0.8826 | 0.8756 | 0.4290 | | 0.0522 | 320.0 | 5440 | 0.0767 | 0.7813 | 0.8402 | 0.9796 | nan | 0.9939 | 0.9494 | 0.9045 | 0.9897 | 0.9328 | 0.8251 | 0.8745 | 0.9511 | 0.8380 | 0.2580 | 0.9385 | 0.9516 | 0.5154 | nan | 0.9835 | 0.9003 | 0.8501 | 0.9807 | 0.8752 | 0.8062 | 0.8536 | 0.9432 | 0.4502 | 0.2562 | 0.9162 | 0.8516 | 0.4900 | | 0.0183 | 321.18 | 5460 | 0.0721 | 0.7875 | 0.8286 | 0.9813 | nan | 0.9938 | 0.9601 | 0.9028 | 0.9891 | 0.9246 | 0.8399 | 0.8574 | 0.9798 | 0.7226 | 0.1786 | 0.9456 | 0.9131 | 0.5642 | nan | 0.9842 | 0.9066 | 0.8467 | 0.9800 | 0.8756 | 0.8286 | 0.8383 | 0.9568 | 0.5555 | 0.1776 | 0.9117 | 0.8482 | 0.5282 | | 0.0224 | 322.35 | 5480 | 0.0707 | 0.7849 | 0.8216 | 0.9813 | nan | 0.9940 | 0.9585 | 0.9074 | 0.9907 | 0.9264 | 0.7408 | 0.8514 | 0.9840 | 0.6871 | 0.2202 | 0.9366 | 0.9440 | 0.5398 | nan | 0.9843 | 0.9055 | 0.8501 | 0.9803 | 0.8770 | 0.7348 | 0.8349 | 0.9602 | 0.5580 | 0.2191 | 0.9116 | 0.8847 | 0.5038 | | 0.0145 | 323.53 | 5500 | 0.0739 | 0.7783 | 0.8199 | 0.9812 | nan | 0.9939 | 0.9629 | 0.8979 | 0.9902 | 0.9209 | 0.6621 | 0.8560 | 0.9834 | 0.8037 | 0.1874 | 0.9307 | 0.9302 | 0.5398 | nan | 0.9844 | 0.9003 | 0.8474 | 0.9802 | 0.8753 | 0.6562 | 0.8379 | 0.9712 | 0.5973 | 0.1862 | 0.9012 | 0.8796 | 0.5 | | 0.0167 | 324.71 | 5520 | 0.0751 | 0.7809 | 0.8219 | 0.9811 | nan | 0.9940 | 0.9595 | 0.9052 | 0.9896 | 0.9284 | 0.6530 | 0.8600 | 0.9835 | 0.7705 | 0.2407 | 0.9443 | 0.9504 | 0.5057 | nan | 0.9842 | 0.9006 | 0.8473 | 0.9804 | 0.8766 | 0.6480 | 0.8405 | 0.9679 | 0.5969 | 0.2393 | 0.9117 | 0.8859 | 0.4726 | | 0.0167 | 325.88 | 5540 | 0.0757 | 0.7792 | 0.8172 | 0.9810 | nan | 0.9943 | 0.9591 | 0.9005 | 0.9897 | 0.9241 | 0.6657 | 0.8523 | 0.9829 | 0.7736 | 0.2321 | 0.9343 | 0.9331 | 0.4813 | nan | 0.9841 | 0.9011 | 0.8447 | 0.9802 | 0.8778 | 0.6593 | 0.8347 | 0.9667 | 0.5967 | 0.2306 | 0.9053 | 0.8844 | 0.4639 | | 0.018 | 327.06 | 5560 | 0.0733 | 0.7836 | 0.8218 | 0.9815 | nan | 0.9938 | 0.9587 | 0.9098 | 0.9896 | 0.9323 | 0.7120 | 0.8470 | 0.9847 | 0.7838 | 0.2247 | 0.9277 | 0.9430 | 0.4764 | nan | 0.9845 | 0.9039 | 0.8514 | 0.9800 | 0.8768 | 0.7052 | 0.8317 | 0.9655 | 0.6138 | 0.2237 | 0.9037 | 0.8878 | 0.4585 | | 0.0168 | 328.24 | 5580 | 0.0745 | 0.7829 | 0.8221 | 0.9813 | nan | 0.9936 | 0.9593 | 0.9110 | 0.9905 | 0.9306 | 0.7000 | 0.8505 | 0.9825 | 0.7681 | 0.2374 | 0.9332 | 0.9366 | 0.4943 | nan | 0.9845 | 0.9028 | 0.8487 | 0.9801 | 0.8782 | 0.6912 | 0.8345 | 0.9664 | 0.5946 | 0.2362 | 0.9035 | 0.8832 | 0.4735 | | 0.0175 | 329.41 | 5600 | 0.0757 | 0.7841 | 0.8254 | 0.9811 | nan | 0.9938 | 0.9617 | 0.9038 | 0.9894 | 0.9297 | 0.6870 | 0.8482 | 0.9825 | 0.7651 | 0.2317 | 0.9259 | 0.9476 | 0.5642 | nan | 0.9845 | 0.9005 | 0.8476 | 0.9800 | 0.8785 | 0.6805 | 0.8328 | 0.9647 | 0.5972 | 0.2304 | 0.9003 | 0.8830 | 0.5133 | | 0.0164 | 330.59 | 5620 | 0.0787 | 0.7808 | 0.8214 | 0.9808 | nan | 0.9940 | 0.9603 | 0.9011 | 0.9895 | 0.9351 | 0.6607 | 0.8453 | 0.9814 | 0.7521 | 0.2425 | 0.9304 | 0.9317 | 0.5545 | nan | 0.9843 | 0.8983 | 0.8475 | 0.9798 | 0.8765 | 0.6549 | 0.8299 | 0.9633 | 0.5923 | 0.2410 | 0.9038 | 0.8818 | 0.4964 | | 0.0179 | 331.76 | 5640 | 0.0778 | 0.7818 | 0.8227 | 0.9810 | nan | 0.9938 | 0.9612 | 0.9026 | 0.9895 | 0.9322 | 0.6636 | 0.8488 | 0.9825 | 0.7723 | 0.2528 | 0.9212 | 0.9530 | 0.5220 | nan | 0.9844 | 0.8995 | 0.8489 | 0.9797 | 0.8779 | 0.6562 | 0.8323 | 0.9637 | 0.6118 | 0.2507 | 0.9000 | 0.8859 | 0.4728 | | 0.019 | 332.94 | 5660 | 0.0804 | 0.7753 | 0.8132 | 0.9807 | nan | 0.9940 | 0.9583 | 0.8986 | 0.9906 | 0.9298 | 0.6477 | 0.8399 | 0.9824 | 0.7718 | 0.2279 | 0.9242 | 0.9402 | 0.4667 | nan | 0.9841 | 0.8987 | 0.8444 | 0.9797 | 0.8781 | 0.6414 | 0.8257 | 0.9641 | 0.6016 | 0.2267 | 0.8997 | 0.8870 | 0.4477 | | 0.0126 | 334.12 | 5680 | 0.0798 | 0.7735 | 0.8106 | 0.9809 | nan | 0.9941 | 0.9575 | 0.9043 | 0.9906 | 0.9254 | 0.6521 | 0.8461 | 0.9820 | 0.7726 | 0.2260 | 0.9259 | 0.9372 | 0.4244 | nan | 0.9843 | 0.8987 | 0.8474 | 0.9800 | 0.8780 | 0.6472 | 0.8309 | 0.9637 | 0.6034 | 0.2248 | 0.9020 | 0.8843 | 0.4104 | | 0.0167 | 335.29 | 5700 | 0.0788 | 0.7771 | 0.8177 | 0.9809 | nan | 0.9941 | 0.9593 | 0.9045 | 0.9896 | 0.9284 | 0.6608 | 0.8483 | 0.9817 | 0.7561 | 0.2539 | 0.9400 | 0.9584 | 0.4553 | nan | 0.9844 | 0.8990 | 0.8486 | 0.9801 | 0.8784 | 0.6551 | 0.8324 | 0.9629 | 0.5952 | 0.2523 | 0.9068 | 0.8795 | 0.4281 | | 0.0199 | 336.47 | 5720 | 0.0800 | 0.7829 | 0.8225 | 0.9809 | nan | 0.9933 | 0.9610 | 0.9132 | 0.9895 | 0.9275 | 0.6461 | 0.8501 | 0.9814 | 0.7706 | 0.2532 | 0.9303 | 0.9451 | 0.5317 | nan | 0.9843 | 0.8977 | 0.8488 | 0.9802 | 0.8786 | 0.6404 | 0.8345 | 0.9625 | 0.6099 | 0.2511 | 0.9028 | 0.8930 | 0.4940 | | 0.0148 | 337.65 | 5740 | 0.0814 | 0.7736 | 0.8127 | 0.9808 | nan | 0.9946 | 0.9606 | 0.8911 | 0.9900 | 0.9238 | 0.6395 | 0.8454 | 0.9811 | 0.7812 | 0.2523 | 0.9277 | 0.9406 | 0.4374 | nan | 0.9841 | 0.8981 | 0.8436 | 0.9801 | 0.8773 | 0.6347 | 0.8311 | 0.9651 | 0.6175 | 0.2502 | 0.9010 | 0.8881 | 0.3854 | | 0.016 | 338.82 | 5760 | 0.0792 | 0.7829 | 0.8301 | 0.9810 | nan | 0.9931 | 0.9610 | 0.9094 | 0.9907 | 0.9274 | 0.6476 | 0.8547 | 0.9813 | 0.8063 | 0.2462 | 0.9472 | 0.9573 | 0.5691 | nan | 0.9845 | 0.8977 | 0.8480 | 0.9801 | 0.8788 | 0.6406 | 0.8354 | 0.9653 | 0.6268 | 0.2439 | 0.9063 | 0.8886 | 0.4821 | | 0.0199 | 340.0 | 5780 | 0.0800 | 0.7762 | 0.8147 | 0.9810 | nan | 0.9941 | 0.9587 | 0.9087 | 0.9896 | 0.9298 | 0.6454 | 0.8490 | 0.9809 | 0.7833 | 0.2335 | 0.9356 | 0.9332 | 0.4488 | nan | 0.9844 | 0.8985 | 0.8488 | 0.9802 | 0.8788 | 0.6408 | 0.8330 | 0.9650 | 0.6192 | 0.2319 | 0.9081 | 0.8861 | 0.4157 | | 0.0174 | 341.18 | 5800 | 0.0804 | 0.7764 | 0.8164 | 0.9809 | nan | 0.9939 | 0.9600 | 0.9025 | 0.9896 | 0.9298 | 0.6367 | 0.8582 | 0.9819 | 0.7819 | 0.2368 | 0.9369 | 0.9449 | 0.4602 | nan | 0.9843 | 0.8980 | 0.8454 | 0.9805 | 0.8793 | 0.6314 | 0.8407 | 0.9639 | 0.6147 | 0.2351 | 0.9070 | 0.8868 | 0.4256 | | 0.0154 | 342.35 | 5820 | 0.0806 | 0.7798 | 0.8230 | 0.9809 | nan | 0.9937 | 0.9583 | 0.9049 | 0.9910 | 0.9229 | 0.6410 | 0.8561 | 0.9834 | 0.7681 | 0.2487 | 0.9395 | 0.9550 | 0.5366 | nan | 0.9846 | 0.8978 | 0.8499 | 0.9803 | 0.8778 | 0.6348 | 0.8378 | 0.9621 | 0.6047 | 0.2465 | 0.9055 | 0.8833 | 0.4728 | | 0.0183 | 343.53 | 5840 | 0.0813 | 0.7791 | 0.8207 | 0.9809 | nan | 0.9940 | 0.9606 | 0.9000 | 0.9899 | 0.9282 | 0.6387 | 0.8518 | 0.9822 | 0.7755 | 0.2453 | 0.9338 | 0.9375 | 0.5317 | nan | 0.9844 | 0.8975 | 0.8469 | 0.9802 | 0.8789 | 0.6325 | 0.8372 | 0.9643 | 0.6081 | 0.2434 | 0.9069 | 0.8862 | 0.4619 | | 0.016 | 344.71 | 5860 | 0.0784 | 0.7812 | 0.8259 | 0.9811 | nan | 0.9939 | 0.9609 | 0.9019 | 0.9902 | 0.9314 | 0.6573 | 0.8590 | 0.9817 | 0.7669 | 0.2522 | 0.9553 | 0.9484 | 0.5382 | nan | 0.9844 | 0.8996 | 0.8478 | 0.9804 | 0.8786 | 0.6501 | 0.8423 | 0.9639 | 0.6110 | 0.2502 | 0.8980 | 0.8922 | 0.4566 | | 0.0149 | 345.88 | 5880 | 0.0794 | 0.7759 | 0.8167 | 0.9811 | nan | 0.9938 | 0.9576 | 0.9067 | 0.9907 | 0.9308 | 0.6571 | 0.8498 | 0.9834 | 0.7812 | 0.2183 | 0.9416 | 0.9471 | 0.4585 | nan | 0.9846 | 0.8996 | 0.8499 | 0.9802 | 0.8787 | 0.6509 | 0.8342 | 0.9627 | 0.6134 | 0.2174 | 0.9059 | 0.8921 | 0.4165 | | 0.0163 | 347.06 | 5900 | 0.0789 | 0.7814 | 0.8240 | 0.9811 | nan | 0.9939 | 0.9579 | 0.9034 | 0.9911 | 0.9261 | 0.6744 | 0.8515 | 0.9834 | 0.7660 | 0.2308 | 0.9402 | 0.9386 | 0.5545 | nan | 0.9845 | 0.9000 | 0.8485 | 0.9800 | 0.8791 | 0.6665 | 0.8360 | 0.9634 | 0.6086 | 0.2294 | 0.9029 | 0.8884 | 0.4703 | | 0.0145 | 348.24 | 5920 | 0.0813 | 0.7778 | 0.8159 | 0.9809 | nan | 0.9945 | 0.9596 | 0.8964 | 0.9898 | 0.9303 | 0.6516 | 0.8508 | 0.9812 | 0.7580 | 0.2286 | 0.9312 | 0.9455 | 0.4894 | nan | 0.9843 | 0.8992 | 0.8457 | 0.9802 | 0.8800 | 0.6468 | 0.8360 | 0.9636 | 0.6023 | 0.2273 | 0.9038 | 0.8864 | 0.4561 | | 0.0131 | 349.41 | 5940 | 0.0812 | 0.7794 | 0.8175 | 0.9809 | nan | 0.9942 | 0.9605 | 0.8982 | 0.9905 | 0.9285 | 0.6388 | 0.8540 | 0.9813 | 0.7538 | 0.2441 | 0.9348 | 0.9416 | 0.5073 | nan | 0.9844 | 0.8984 | 0.8468 | 0.9805 | 0.8793 | 0.6339 | 0.8375 | 0.9627 | 0.6020 | 0.2424 | 0.9083 | 0.8903 | 0.4650 | | 0.0161 | 350.59 | 5960 | 0.0798 | 0.7816 | 0.8190 | 0.9811 | nan | 0.9941 | 0.9602 | 0.9037 | 0.9905 | 0.9309 | 0.6560 | 0.8494 | 0.9810 | 0.7680 | 0.2377 | 0.9310 | 0.9369 | 0.5073 | nan | 0.9846 | 0.8992 | 0.8494 | 0.9802 | 0.8792 | 0.6491 | 0.8345 | 0.9637 | 0.6106 | 0.2360 | 0.9081 | 0.8880 | 0.4778 | | 0.0155 | 351.76 | 5980 | 0.0787 | 0.7809 | 0.8176 | 0.9813 | nan | 0.9937 | 0.9589 | 0.9104 | 0.9918 | 0.9208 | 0.6665 | 0.8498 | 0.9817 | 0.7653 | 0.2358 | 0.9344 | 0.9451 | 0.4748 | nan | 0.9847 | 0.8998 | 0.8526 | 0.9797 | 0.8781 | 0.6597 | 0.8339 | 0.9647 | 0.6131 | 0.2344 | 0.9110 | 0.8917 | 0.4485 | | 0.0143 | 352.94 | 6000 | 0.0802 | 0.7834 | 0.8233 | 0.9811 | nan | 0.9939 | 0.9601 | 0.9068 | 0.9892 | 0.9346 | 0.6400 | 0.8534 | 0.9825 | 0.7932 | 0.2468 | 0.9422 | 0.9463 | 0.5138 | nan | 0.9845 | 0.8987 | 0.8502 | 0.9801 | 0.8784 | 0.6352 | 0.8373 | 0.9650 | 0.6210 | 0.2448 | 0.9135 | 0.8887 | 0.4869 | | 0.0142 | 354.12 | 6020 | 0.0830 | 0.7801 | 0.8185 | 0.9809 | nan | 0.9942 | 0.9580 | 0.8996 | 0.9906 | 0.9292 | 0.6345 | 0.8529 | 0.9820 | 0.7825 | 0.2270 | 0.9368 | 0.9447 | 0.5089 | nan | 0.9844 | 0.8980 | 0.8473 | 0.9803 | 0.8797 | 0.6287 | 0.8374 | 0.9634 | 0.6151 | 0.2255 | 0.9101 | 0.8900 | 0.4808 | | 0.0176 | 355.29 | 6040 | 0.0822 | 0.7770 | 0.8179 | 0.9809 | nan | 0.9938 | 0.9582 | 0.9086 | 0.9904 | 0.9291 | 0.6323 | 0.8537 | 0.9827 | 0.7620 | 0.2408 | 0.9336 | 0.9404 | 0.5073 | nan | 0.9845 | 0.8972 | 0.8504 | 0.9802 | 0.8788 | 0.6268 | 0.8372 | 0.9625 | 0.6082 | 0.2393 | 0.9082 | 0.8906 | 0.4370 | | 0.0155 | 356.47 | 6060 | 0.0822 | 0.7834 | 0.8251 | 0.9809 | nan | 0.9940 | 0.9606 | 0.9042 | 0.9901 | 0.9284 | 0.6369 | 0.8456 | 0.9803 | 0.7700 | 0.2431 | 0.9318 | 0.9489 | 0.5919 | nan | 0.9845 | 0.8967 | 0.8493 | 0.9804 | 0.8797 | 0.6313 | 0.8330 | 0.9631 | 0.6089 | 0.2417 | 0.9055 | 0.8927 | 0.5170 | | 0.0157 | 357.65 | 6080 | 0.0823 | 0.7775 | 0.8172 | 0.9808 | nan | 0.9941 | 0.9604 | 0.8986 | 0.9902 | 0.9303 | 0.6334 | 0.8564 | 0.9814 | 0.7666 | 0.2261 | 0.9380 | 0.9454 | 0.5024 | nan | 0.9845 | 0.8965 | 0.8475 | 0.9803 | 0.8801 | 0.6285 | 0.8363 | 0.9628 | 0.6066 | 0.2250 | 0.9051 | 0.8907 | 0.4640 | | 0.0183 | 358.82 | 6100 | 0.0830 | 0.7770 | 0.8150 | 0.9807 | nan | 0.9941 | 0.9597 | 0.9015 | 0.9898 | 0.9287 | 0.6385 | 0.8428 | 0.9826 | 0.7569 | 0.2303 | 0.9378 | 0.9431 | 0.4894 | nan | 0.9844 | 0.8967 | 0.8470 | 0.9803 | 0.8796 | 0.6318 | 0.8298 | 0.9619 | 0.6016 | 0.2294 | 0.9082 | 0.8910 | 0.4588 | | 0.0193 | 360.0 | 6120 | 0.0827 | 0.7779 | 0.8179 | 0.9808 | nan | 0.9942 | 0.9596 | 0.8990 | 0.9909 | 0.9198 | 0.6361 | 0.8494 | 0.9809 | 0.7656 | 0.2428 | 0.9407 | 0.9479 | 0.5057 | nan | 0.9844 | 0.8968 | 0.8472 | 0.9802 | 0.8780 | 0.6311 | 0.8345 | 0.9629 | 0.6084 | 0.2412 | 0.9063 | 0.8930 | 0.4481 | | 0.0175 | 361.18 | 6140 | 0.0824 | 0.7758 | 0.8156 | 0.9808 | nan | 0.9941 | 0.9581 | 0.9025 | 0.9905 | 0.9268 | 0.6345 | 0.8485 | 0.9820 | 0.7693 | 0.2345 | 0.9352 | 0.9371 | 0.4894 | nan | 0.9844 | 0.8971 | 0.8471 | 0.9804 | 0.8793 | 0.6291 | 0.8344 | 0.9623 | 0.6060 | 0.2334 | 0.9054 | 0.8897 | 0.4362 | | 0.0188 | 362.35 | 6160 | 0.0828 | 0.7759 | 0.8161 | 0.9809 | nan | 0.9940 | 0.9591 | 0.9031 | 0.9909 | 0.9237 | 0.6369 | 0.8463 | 0.9817 | 0.7660 | 0.2411 | 0.9363 | 0.9467 | 0.4829 | nan | 0.9844 | 0.8975 | 0.8469 | 0.9803 | 0.8798 | 0.6303 | 0.8325 | 0.9628 | 0.6088 | 0.2396 | 0.9072 | 0.8898 | 0.4267 | | 0.018 | 363.53 | 6180 | 0.0823 | 0.7776 | 0.8196 | 0.9810 | nan | 0.9942 | 0.9583 | 0.9025 | 0.9905 | 0.9247 | 0.6379 | 0.8545 | 0.9819 | 0.7668 | 0.2543 | 0.9499 | 0.9477 | 0.4911 | nan | 0.9845 | 0.8980 | 0.8489 | 0.9804 | 0.8785 | 0.6320 | 0.8376 | 0.9624 | 0.6121 | 0.2526 | 0.9085 | 0.8856 | 0.4272 | | 0.0182 | 364.71 | 6200 | 0.0805 | 0.7784 | 0.8179 | 0.9811 | nan | 0.9938 | 0.9591 | 0.9102 | 0.9906 | 0.9244 | 0.6425 | 0.8608 | 0.9818 | 0.7789 | 0.2407 | 0.9394 | 0.9405 | 0.4699 | nan | 0.9846 | 0.8986 | 0.8499 | 0.9806 | 0.8794 | 0.6369 | 0.8443 | 0.9626 | 0.6202 | 0.2393 | 0.9092 | 0.8896 | 0.4244 | | 0.0134 | 365.88 | 6220 | 0.0818 | 0.7802 | 0.8213 | 0.9810 | nan | 0.9939 | 0.9608 | 0.9063 | 0.9895 | 0.9316 | 0.6353 | 0.8563 | 0.9826 | 0.7626 | 0.2332 | 0.9301 | 0.9447 | 0.5496 | nan | 0.9846 | 0.8969 | 0.8505 | 0.9805 | 0.8805 | 0.6308 | 0.8411 | 0.9620 | 0.6115 | 0.2321 | 0.9018 | 0.8917 | 0.4788 | | 0.0151 | 367.06 | 6240 | 0.0829 | 0.7799 | 0.8221 | 0.9809 | nan | 0.9937 | 0.9595 | 0.9031 | 0.9901 | 0.9348 | 0.6398 | 0.8571 | 0.9827 | 0.7771 | 0.2305 | 0.9308 | 0.9515 | 0.5366 | nan | 0.9846 | 0.8973 | 0.8482 | 0.9802 | 0.8796 | 0.6346 | 0.8409 | 0.9620 | 0.6127 | 0.2292 | 0.9017 | 0.8930 | 0.4741 | | 0.0191 | 368.24 | 6260 | 0.0838 | 0.7805 | 0.8252 | 0.9808 | nan | 0.9936 | 0.9591 | 0.9048 | 0.9901 | 0.9324 | 0.6411 | 0.8566 | 0.9825 | 0.7689 | 0.2388 | 0.9461 | 0.9518 | 0.5626 | nan | 0.9845 | 0.8968 | 0.8469 | 0.9803 | 0.8801 | 0.6341 | 0.8403 | 0.9610 | 0.6096 | 0.2375 | 0.9042 | 0.8904 | 0.4806 | | 0.0149 | 369.41 | 6280 | 0.0830 | 0.7762 | 0.8152 | 0.9809 | nan | 0.9942 | 0.9584 | 0.9036 | 0.9899 | 0.9309 | 0.6430 | 0.8511 | 0.9819 | 0.7671 | 0.2318 | 0.9366 | 0.9493 | 0.4602 | nan | 0.9845 | 0.8981 | 0.8485 | 0.9802 | 0.8802 | 0.6382 | 0.8359 | 0.9618 | 0.6090 | 0.2306 | 0.9093 | 0.8882 | 0.4256 | | 0.0159 | 370.59 | 6300 | 0.0831 | 0.7828 | 0.8245 | 0.9811 | nan | 0.9940 | 0.9601 | 0.9058 | 0.9898 | 0.9324 | 0.6392 | 0.8581 | 0.9819 | 0.7696 | 0.2508 | 0.9445 | 0.9494 | 0.5431 | nan | 0.9847 | 0.8975 | 0.8497 | 0.9805 | 0.8806 | 0.6338 | 0.8429 | 0.9625 | 0.6132 | 0.2492 | 0.9053 | 0.8870 | 0.4890 | | 0.0168 | 371.76 | 6320 | 0.0826 | 0.7797 | 0.8191 | 0.9810 | nan | 0.9946 | 0.9582 | 0.8949 | 0.9904 | 0.9307 | 0.6373 | 0.8570 | 0.9820 | 0.7708 | 0.2460 | 0.9435 | 0.9518 | 0.4911 | nan | 0.9844 | 0.8986 | 0.8460 | 0.9806 | 0.8804 | 0.6325 | 0.8417 | 0.9624 | 0.6131 | 0.2448 | 0.9056 | 0.8869 | 0.4590 | | 0.017 | 372.94 | 6340 | 0.0827 | 0.7824 | 0.8220 | 0.9810 | nan | 0.9938 | 0.9587 | 0.9091 | 0.9897 | 0.9373 | 0.6388 | 0.8545 | 0.9818 | 0.7691 | 0.2442 | 0.9389 | 0.9406 | 0.5301 | nan | 0.9847 | 0.8976 | 0.8496 | 0.9805 | 0.8796 | 0.6334 | 0.8399 | 0.9615 | 0.6071 | 0.2429 | 0.9090 | 0.8898 | 0.4962 | | 0.0165 | 374.12 | 6360 | 0.0834 | 0.7825 | 0.8236 | 0.9810 | nan | 0.9938 | 0.9597 | 0.9054 | 0.9905 | 0.9282 | 0.6359 | 0.8550 | 0.9821 | 0.7719 | 0.2534 | 0.9416 | 0.9532 | 0.5366 | nan | 0.9847 | 0.8976 | 0.8496 | 0.9806 | 0.8804 | 0.6301 | 0.8408 | 0.9617 | 0.6117 | 0.2517 | 0.9045 | 0.8926 | 0.4867 | | 0.0159 | 375.29 | 6380 | 0.0834 | 0.7815 | 0.8244 | 0.9810 | nan | 0.9942 | 0.9586 | 0.8998 | 0.9900 | 0.9282 | 0.6385 | 0.8632 | 0.9827 | 0.7744 | 0.2471 | 0.9433 | 0.9503 | 0.5463 | nan | 0.9845 | 0.8980 | 0.8473 | 0.9809 | 0.8802 | 0.6320 | 0.8461 | 0.9613 | 0.6087 | 0.2457 | 0.9106 | 0.8898 | 0.4739 | | 0.0138 | 376.47 | 6400 | 0.0830 | 0.7878 | 0.8311 | 0.9809 | nan | 0.9932 | 0.9580 | 0.9116 | 0.9912 | 0.9286 | 0.6430 | 0.8590 | 0.9831 | 0.7723 | 0.2505 | 0.9405 | 0.9516 | 0.6211 | nan | 0.9847 | 0.8970 | 0.8497 | 0.9804 | 0.8802 | 0.6352 | 0.8422 | 0.9605 | 0.6079 | 0.2486 | 0.9064 | 0.8916 | 0.5569 | | 0.0122 | 377.65 | 6420 | 0.0841 | 0.7819 | 0.8203 | 0.9809 | nan | 0.9942 | 0.9597 | 0.8999 | 0.9896 | 0.9349 | 0.6352 | 0.8539 | 0.9814 | 0.7718 | 0.2446 | 0.9339 | 0.9450 | 0.5203 | nan | 0.9845 | 0.8973 | 0.8475 | 0.9807 | 0.8809 | 0.6299 | 0.8395 | 0.9616 | 0.6135 | 0.2431 | 0.9070 | 0.8900 | 0.4893 | | 0.0182 | 378.82 | 6440 | 0.0838 | 0.7803 | 0.8197 | 0.9809 | nan | 0.9940 | 0.9587 | 0.9042 | 0.9905 | 0.9276 | 0.6341 | 0.8534 | 0.9815 | 0.7783 | 0.2354 | 0.9376 | 0.9534 | 0.5073 | nan | 0.9845 | 0.8976 | 0.8480 | 0.9806 | 0.8795 | 0.6292 | 0.8388 | 0.9614 | 0.6157 | 0.2341 | 0.9103 | 0.8939 | 0.4699 | | 0.0177 | 380.0 | 6460 | 0.0830 | 0.7854 | 0.8328 | 0.9810 | nan | 0.9931 | 0.9604 | 0.9114 | 0.9906 | 0.9274 | 0.6435 | 0.8592 | 0.9823 | 0.7762 | 0.2498 | 0.9456 | 0.9515 | 0.6358 | nan | 0.9847 | 0.8973 | 0.8493 | 0.9805 | 0.8794 | 0.6360 | 0.8425 | 0.9613 | 0.6131 | 0.2480 | 0.9113 | 0.8956 | 0.5118 | | 0.0144 | 381.18 | 6480 | 0.0834 | 0.7799 | 0.8235 | 0.9810 | nan | 0.9941 | 0.9593 | 0.8995 | 0.9904 | 0.9250 | 0.6398 | 0.8570 | 0.9831 | 0.7834 | 0.2409 | 0.9433 | 0.9473 | 0.5431 | nan | 0.9846 | 0.8980 | 0.8474 | 0.9807 | 0.8801 | 0.6341 | 0.8414 | 0.9621 | 0.6104 | 0.2397 | 0.9097 | 0.8909 | 0.4601 | | 0.0158 | 382.35 | 6500 | 0.0878 | 0.7685 | 0.8145 | 0.9806 | nan | 0.9942 | 0.9590 | 0.8857 | 0.9900 | 0.9321 | 0.6415 | 0.8506 | 0.9834 | 0.7825 | 0.2307 | 0.9454 | 0.9544 | 0.4390 | nan | 0.9843 | 0.8984 | 0.8365 | 0.9803 | 0.8799 | 0.6354 | 0.8363 | 0.9624 | 0.5821 | 0.2293 | 0.9056 | 0.8859 | 0.3734 | | 0.0166 | 383.53 | 6520 | 0.0841 | 0.7749 | 0.8166 | 0.9809 | nan | 0.9941 | 0.9565 | 0.8992 | 0.9913 | 0.9226 | 0.6385 | 0.8618 | 0.9833 | 0.7698 | 0.2447 | 0.9405 | 0.9533 | 0.4602 | nan | 0.9845 | 0.8983 | 0.8447 | 0.9805 | 0.8796 | 0.6329 | 0.8449 | 0.9621 | 0.6013 | 0.2433 | 0.9085 | 0.8908 | 0.4026 | | 0.0175 | 384.71 | 6540 | 0.0854 | 0.7701 | 0.8109 | 0.9809 | nan | 0.9941 | 0.9587 | 0.9005 | 0.9906 | 0.9271 | 0.6375 | 0.8499 | 0.9820 | 0.7882 | 0.2268 | 0.9342 | 0.9389 | 0.4130 | nan | 0.9845 | 0.8986 | 0.8455 | 0.9806 | 0.8808 | 0.6315 | 0.8366 | 0.9631 | 0.6051 | 0.2258 | 0.9090 | 0.8870 | 0.3629 | | 0.0183 | 385.88 | 6560 | 0.0850 | 0.7753 | 0.8173 | 0.9808 | nan | 0.9937 | 0.9593 | 0.9063 | 0.9908 | 0.9280 | 0.6389 | 0.8468 | 0.9820 | 0.7561 | 0.2393 | 0.9362 | 0.9504 | 0.4976 | nan | 0.9847 | 0.8974 | 0.8464 | 0.9804 | 0.8799 | 0.6340 | 0.8340 | 0.9614 | 0.5945 | 0.2379 | 0.9073 | 0.8919 | 0.4286 | | 0.0132 | 387.06 | 6580 | 0.0858 | 0.7780 | 0.8173 | 0.9808 | nan | 0.9945 | 0.9573 | 0.8936 | 0.9905 | 0.9336 | 0.6400 | 0.8506 | 0.9823 | 0.7634 | 0.2441 | 0.9468 | 0.9480 | 0.4797 | nan | 0.9844 | 0.8981 | 0.8433 | 0.9806 | 0.8810 | 0.6353 | 0.8376 | 0.9619 | 0.5958 | 0.2427 | 0.9079 | 0.8894 | 0.4560 | | 0.0164 | 388.24 | 6600 | 0.0835 | 0.7774 | 0.8197 | 0.9810 | nan | 0.9942 | 0.9572 | 0.8993 | 0.9910 | 0.9280 | 0.6455 | 0.8550 | 0.9830 | 0.7761 | 0.2508 | 0.9418 | 0.9484 | 0.4862 | nan | 0.9846 | 0.8988 | 0.8451 | 0.9807 | 0.8811 | 0.6402 | 0.8416 | 0.9633 | 0.6048 | 0.2493 | 0.9116 | 0.8885 | 0.4164 | | 0.0169 | 389.41 | 6620 | 0.0826 | 0.7791 | 0.8170 | 0.9812 | nan | 0.9944 | 0.9582 | 0.8922 | 0.9910 | 0.9285 | 0.6970 | 0.8513 | 0.9819 | 0.7809 | 0.2323 | 0.9360 | 0.9385 | 0.4390 | nan | 0.9844 | 0.9009 | 0.8434 | 0.9806 | 0.8812 | 0.6911 | 0.8371 | 0.9622 | 0.6115 | 0.2314 | 0.9040 | 0.8857 | 0.4147 | | 0.0174 | 390.59 | 6640 | 0.0850 | 0.7770 | 0.8154 | 0.9809 | nan | 0.9943 | 0.9611 | 0.8892 | 0.9902 | 0.9333 | 0.6636 | 0.8497 | 0.9806 | 0.7781 | 0.2386 | 0.9328 | 0.9409 | 0.4472 | nan | 0.9844 | 0.8983 | 0.8408 | 0.9805 | 0.8816 | 0.6591 | 0.8373 | 0.9629 | 0.6062 | 0.2376 | 0.9021 | 0.8848 | 0.4257 | | 0.0136 | 391.76 | 6660 | 0.0832 | 0.7800 | 0.8234 | 0.9811 | nan | 0.9939 | 0.9601 | 0.8985 | 0.9905 | 0.9343 | 0.6811 | 0.8530 | 0.9808 | 0.7674 | 0.2534 | 0.9437 | 0.9554 | 0.4927 | nan | 0.9846 | 0.8998 | 0.8440 | 0.9806 | 0.8804 | 0.6752 | 0.8382 | 0.9619 | 0.6016 | 0.2518 | 0.9071 | 0.8898 | 0.4256 | | 0.0154 | 392.94 | 6680 | 0.0839 | 0.7790 | 0.8178 | 0.9810 | nan | 0.9942 | 0.9600 | 0.8961 | 0.9901 | 0.9288 | 0.6741 | 0.8511 | 0.9816 | 0.7804 | 0.2303 | 0.9383 | 0.9465 | 0.4602 | nan | 0.9845 | 0.8989 | 0.8447 | 0.9807 | 0.8813 | 0.6691 | 0.8380 | 0.9618 | 0.6088 | 0.2293 | 0.9087 | 0.8900 | 0.4307 | | 0.017 | 394.12 | 6700 | 0.0831 | 0.7782 | 0.8191 | 0.9811 | nan | 0.9941 | 0.9573 | 0.8999 | 0.9906 | 0.9314 | 0.6706 | 0.8559 | 0.9826 | 0.7835 | 0.2388 | 0.9406 | 0.9473 | 0.4553 | nan | 0.9845 | 0.8998 | 0.8445 | 0.9806 | 0.8809 | 0.6654 | 0.8407 | 0.9622 | 0.6109 | 0.2378 | 0.9084 | 0.8928 | 0.4076 | | 0.0167 | 395.29 | 6720 | 0.0841 | 0.7793 | 0.8205 | 0.9810 | nan | 0.9944 | 0.9576 | 0.8970 | 0.9897 | 0.9379 | 0.6649 | 0.8526 | 0.9817 | 0.7850 | 0.2475 | 0.9416 | 0.9481 | 0.4683 | nan | 0.9844 | 0.8996 | 0.8437 | 0.9807 | 0.8800 | 0.6595 | 0.8390 | 0.9622 | 0.6118 | 0.2463 | 0.9091 | 0.8902 | 0.4248 | | 0.0158 | 396.47 | 6740 | 0.0833 | 0.7808 | 0.8225 | 0.9812 | nan | 0.9938 | 0.9576 | 0.9064 | 0.9907 | 0.9314 | 0.6662 | 0.8514 | 0.9828 | 0.7837 | 0.2379 | 0.9403 | 0.9485 | 0.5024 | nan | 0.9847 | 0.8993 | 0.8481 | 0.9806 | 0.8806 | 0.6605 | 0.8369 | 0.9623 | 0.6130 | 0.2367 | 0.9103 | 0.8936 | 0.4433 | | 0.0183 | 397.65 | 6760 | 0.0830 | 0.7765 | 0.8210 | 0.9811 | nan | 0.9939 | 0.9580 | 0.9014 | 0.9905 | 0.9318 | 0.6794 | 0.8495 | 0.9832 | 0.7843 | 0.2060 | 0.9401 | 0.9481 | 0.5073 | nan | 0.9846 | 0.9003 | 0.8466 | 0.9797 | 0.8786 | 0.6724 | 0.8336 | 0.9628 | 0.6114 | 0.2051 | 0.9017 | 0.8936 | 0.4245 | | 0.0155 | 398.82 | 6780 | 0.0842 | 0.7763 | 0.8177 | 0.9811 | nan | 0.9944 | 0.9598 | 0.8961 | 0.9898 | 0.9287 | 0.6737 | 0.8478 | 0.9829 | 0.7680 | 0.2345 | 0.9411 | 0.9471 | 0.4667 | nan | 0.9846 | 0.9004 | 0.8456 | 0.9802 | 0.8793 | 0.6689 | 0.8338 | 0.9628 | 0.6017 | 0.2334 | 0.9042 | 0.8903 | 0.4065 | | 0.0149 | 400.0 | 6800 | 0.0845 | 0.7825 | 0.8292 | 0.9811 | nan | 0.9939 | 0.9578 | 0.9039 | 0.9903 | 0.9277 | 0.6656 | 0.8527 | 0.9839 | 0.7760 | 0.2570 | 0.9447 | 0.9540 | 0.5724 | nan | 0.9847 | 0.8996 | 0.8469 | 0.9805 | 0.8805 | 0.6588 | 0.8382 | 0.9619 | 0.6028 | 0.2550 | 0.9095 | 0.8910 | 0.4632 | | 0.0184 | 401.18 | 6820 | 0.0845 | 0.7789 | 0.8230 | 0.9810 | nan | 0.9941 | 0.9596 | 0.8991 | 0.9900 | 0.9340 | 0.6628 | 0.8511 | 0.9818 | 0.7708 | 0.2505 | 0.9413 | 0.9530 | 0.5106 | nan | 0.9845 | 0.8991 | 0.8445 | 0.9807 | 0.8808 | 0.6573 | 0.8382 | 0.9632 | 0.6002 | 0.2489 | 0.9130 | 0.8904 | 0.4249 | | 0.0162 | 402.35 | 6840 | 0.0857 | 0.7757 | 0.8189 | 0.9810 | nan | 0.9938 | 0.9589 | 0.9035 | 0.9899 | 0.9319 | 0.6466 | 0.8547 | 0.9838 | 0.7756 | 0.2385 | 0.9432 | 0.9419 | 0.4829 | nan | 0.9846 | 0.8982 | 0.8459 | 0.9806 | 0.8811 | 0.6418 | 0.8401 | 0.9627 | 0.6004 | 0.2374 | 0.9112 | 0.8876 | 0.4119 | | 0.0157 | 403.53 | 6860 | 0.0855 | 0.7766 | 0.8192 | 0.9810 | nan | 0.9942 | 0.9586 | 0.8980 | 0.9901 | 0.9347 | 0.6456 | 0.8568 | 0.9830 | 0.7649 | 0.2415 | 0.9432 | 0.9477 | 0.4911 | nan | 0.9847 | 0.8980 | 0.8453 | 0.9807 | 0.8806 | 0.6403 | 0.8419 | 0.9628 | 0.5964 | 0.2402 | 0.9092 | 0.8897 | 0.4260 | | 0.0167 | 404.71 | 6880 | 0.0847 | 0.7781 | 0.8199 | 0.9811 | nan | 0.9942 | 0.9586 | 0.9025 | 0.9902 | 0.9307 | 0.6442 | 0.8535 | 0.9831 | 0.7756 | 0.2390 | 0.9388 | 0.9479 | 0.5008 | nan | 0.9847 | 0.8988 | 0.8471 | 0.9807 | 0.8811 | 0.6389 | 0.8401 | 0.9631 | 0.6059 | 0.2378 | 0.9088 | 0.8913 | 0.4369 | | 0.0145 | 405.88 | 6900 | 0.0845 | 0.7806 | 0.8236 | 0.9811 | nan | 0.9942 | 0.9569 | 0.9016 | 0.9908 | 0.9292 | 0.6595 | 0.8572 | 0.9826 | 0.7626 | 0.2518 | 0.9434 | 0.9532 | 0.5236 | nan | 0.9846 | 0.8990 | 0.8467 | 0.9806 | 0.8809 | 0.6513 | 0.8423 | 0.9626 | 0.5981 | 0.2502 | 0.9122 | 0.8929 | 0.4466 | | 0.0154 | 407.06 | 6920 | 0.0846 | 0.7780 | 0.8198 | 0.9810 | nan | 0.9940 | 0.9580 | 0.9016 | 0.9909 | 0.9292 | 0.6557 | 0.8550 | 0.9827 | 0.7667 | 0.2377 | 0.9383 | 0.9445 | 0.5024 | nan | 0.9846 | 0.8985 | 0.8464 | 0.9807 | 0.8818 | 0.6496 | 0.8409 | 0.9625 | 0.5998 | 0.2364 | 0.9099 | 0.8916 | 0.4316 | | 0.0159 | 408.24 | 6940 | 0.0859 | 0.7788 | 0.8219 | 0.9811 | nan | 0.9942 | 0.9589 | 0.9006 | 0.9906 | 0.9286 | 0.6487 | 0.8535 | 0.9823 | 0.7609 | 0.2400 | 0.9457 | 0.9549 | 0.5252 | nan | 0.9847 | 0.8986 | 0.8475 | 0.9807 | 0.8805 | 0.6431 | 0.8395 | 0.9621 | 0.5973 | 0.2386 | 0.9077 | 0.8927 | 0.4511 | | 0.0152 | 409.41 | 6960 | 0.0859 | 0.7766 | 0.8179 | 0.9809 | nan | 0.9945 | 0.9577 | 0.8985 | 0.9903 | 0.9332 | 0.6384 | 0.8520 | 0.9815 | 0.7575 | 0.2407 | 0.9382 | 0.9442 | 0.5057 | nan | 0.9845 | 0.8982 | 0.8462 | 0.9807 | 0.8808 | 0.6337 | 0.8390 | 0.9615 | 0.5976 | 0.2394 | 0.9080 | 0.8938 | 0.4319 | | 0.014 | 410.59 | 6980 | 0.0862 | 0.7784 | 0.8199 | 0.9810 | nan | 0.9943 | 0.9577 | 0.9012 | 0.9907 | 0.9288 | 0.6393 | 0.8494 | 0.9817 | 0.7704 | 0.2373 | 0.9383 | 0.9504 | 0.5187 | nan | 0.9847 | 0.8976 | 0.8476 | 0.9807 | 0.8812 | 0.6343 | 0.8363 | 0.9617 | 0.6033 | 0.2360 | 0.9078 | 0.8940 | 0.4538 | | 0.0155 | 411.76 | 7000 | 0.0866 | 0.7776 | 0.8204 | 0.9809 | nan | 0.9941 | 0.9595 | 0.8999 | 0.9903 | 0.9335 | 0.6357 | 0.8580 | 0.9816 | 0.7542 | 0.2504 | 0.9424 | 0.9521 | 0.5138 | nan | 0.9847 | 0.8972 | 0.8465 | 0.9808 | 0.8807 | 0.6306 | 0.8432 | 0.9614 | 0.5978 | 0.2488 | 0.9089 | 0.8948 | 0.4335 | | 0.0165 | 412.94 | 7020 | 0.0861 | 0.7777 | 0.8232 | 0.9810 | nan | 0.9939 | 0.9580 | 0.9050 | 0.9909 | 0.9307 | 0.6354 | 0.8595 | 0.9819 | 0.7652 | 0.2393 | 0.9467 | 0.9501 | 0.5447 | nan | 0.9848 | 0.8973 | 0.8482 | 0.9808 | 0.8813 | 0.6300 | 0.8442 | 0.9620 | 0.5964 | 0.2380 | 0.9081 | 0.8922 | 0.4473 | | 0.0147 | 414.12 | 7040 | 0.0868 | 0.7762 | 0.8173 | 0.9809 | nan | 0.9943 | 0.9576 | 0.9003 | 0.9908 | 0.9307 | 0.6357 | 0.8572 | 0.9813 | 0.7540 | 0.2318 | 0.9396 | 0.9442 | 0.5073 | nan | 0.9846 | 0.8969 | 0.8462 | 0.9807 | 0.8819 | 0.6305 | 0.8419 | 0.9614 | 0.5952 | 0.2306 | 0.9080 | 0.8922 | 0.4401 | | 0.0147 | 415.29 | 7060 | 0.0859 | 0.7789 | 0.8220 | 0.9810 | nan | 0.9942 | 0.9590 | 0.9003 | 0.9901 | 0.9344 | 0.6386 | 0.8597 | 0.9813 | 0.7823 | 0.2422 | 0.9429 | 0.9514 | 0.5089 | nan | 0.9847 | 0.8983 | 0.8453 | 0.9809 | 0.8819 | 0.6343 | 0.8453 | 0.9627 | 0.6063 | 0.2409 | 0.9091 | 0.8923 | 0.4433 | | 0.0158 | 416.47 | 7080 | 0.0858 | 0.7813 | 0.8262 | 0.9811 | nan | 0.9939 | 0.9586 | 0.9062 | 0.9911 | 0.9282 | 0.6393 | 0.8540 | 0.9814 | 0.7755 | 0.2555 | 0.9404 | 0.9571 | 0.5593 | nan | 0.9849 | 0.8979 | 0.8486 | 0.9807 | 0.8821 | 0.6345 | 0.8402 | 0.9629 | 0.6057 | 0.2538 | 0.9084 | 0.8935 | 0.4642 | | 0.0151 | 417.65 | 7100 | 0.0854 | 0.7789 | 0.8210 | 0.9811 | nan | 0.9943 | 0.9591 | 0.9029 | 0.9899 | 0.9325 | 0.6451 | 0.8519 | 0.9822 | 0.7760 | 0.2511 | 0.9344 | 0.9516 | 0.5024 | nan | 0.9848 | 0.8983 | 0.8483 | 0.9809 | 0.8823 | 0.6402 | 0.8389 | 0.9633 | 0.6066 | 0.2495 | 0.9069 | 0.8937 | 0.4322 | | 0.0163 | 418.82 | 7120 | 0.0861 | 0.7775 | 0.8202 | 0.9811 | nan | 0.9940 | 0.9587 | 0.9058 | 0.9903 | 0.9305 | 0.6370 | 0.8553 | 0.9830 | 0.7598 | 0.2476 | 0.9382 | 0.9567 | 0.5057 | nan | 0.9848 | 0.8978 | 0.8482 | 0.9809 | 0.8820 | 0.6311 | 0.8414 | 0.9618 | 0.5989 | 0.2462 | 0.9092 | 0.8933 | 0.4319 | | 0.0171 | 420.0 | 7140 | 0.0856 | 0.7772 | 0.8200 | 0.9811 | nan | 0.9939 | 0.9580 | 0.9083 | 0.9903 | 0.9342 | 0.6387 | 0.8617 | 0.9827 | 0.7644 | 0.2490 | 0.9373 | 0.9507 | 0.4911 | nan | 0.9848 | 0.8985 | 0.8481 | 0.9808 | 0.8814 | 0.6331 | 0.8453 | 0.9621 | 0.6018 | 0.2476 | 0.9088 | 0.8942 | 0.4177 | | 0.0154 | 421.18 | 7160 | 0.0854 | 0.7797 | 0.8232 | 0.9811 | nan | 0.9940 | 0.9592 | 0.9080 | 0.9899 | 0.9359 | 0.6397 | 0.8581 | 0.9822 | 0.7639 | 0.2410 | 0.9424 | 0.9495 | 0.5382 | nan | 0.9848 | 0.8983 | 0.8497 | 0.9808 | 0.8815 | 0.6349 | 0.8422 | 0.9623 | 0.6027 | 0.2399 | 0.9120 | 0.8955 | 0.4510 | | 0.0132 | 422.35 | 7180 | 0.0857 | 0.7810 | 0.8238 | 0.9811 | nan | 0.9942 | 0.9590 | 0.9042 | 0.9902 | 0.9314 | 0.6393 | 0.8514 | 0.9818 | 0.7735 | 0.2489 | 0.9383 | 0.9541 | 0.5431 | nan | 0.9848 | 0.8983 | 0.8485 | 0.9806 | 0.8806 | 0.6338 | 0.8375 | 0.9625 | 0.6084 | 0.2473 | 0.9103 | 0.8953 | 0.4645 | | 0.0146 | 423.53 | 7200 | 0.0858 | 0.7792 | 0.8235 | 0.9811 | nan | 0.9940 | 0.9591 | 0.9052 | 0.9900 | 0.9371 | 0.6385 | 0.8489 | 0.9822 | 0.7739 | 0.2488 | 0.9457 | 0.9539 | 0.5285 | nan | 0.9848 | 0.8983 | 0.8487 | 0.9805 | 0.8800 | 0.6331 | 0.8356 | 0.9627 | 0.6083 | 0.2472 | 0.9115 | 0.8948 | 0.4440 | | 0.0172 | 424.71 | 7220 | 0.0854 | 0.7790 | 0.8231 | 0.9811 | nan | 0.9940 | 0.9597 | 0.9055 | 0.9893 | 0.9388 | 0.6390 | 0.8526 | 0.9825 | 0.7776 | 0.2414 | 0.9422 | 0.9490 | 0.5285 | nan | 0.9848 | 0.8983 | 0.8488 | 0.9804 | 0.8796 | 0.6333 | 0.8386 | 0.9629 | 0.6116 | 0.2400 | 0.9114 | 0.8941 | 0.4434 | | 0.0169 | 425.88 | 7240 | 0.0869 | 0.7769 | 0.8230 | 0.9810 | nan | 0.9942 | 0.9600 | 0.9021 | 0.9894 | 0.9341 | 0.6328 | 0.8560 | 0.9824 | 0.7743 | 0.2400 | 0.9456 | 0.9583 | 0.5301 | nan | 0.9847 | 0.8978 | 0.8481 | 0.9806 | 0.8801 | 0.6279 | 0.8419 | 0.9623 | 0.6093 | 0.2387 | 0.9108 | 0.8897 | 0.4278 | | 0.0159 | 427.06 | 7260 | 0.0870 | 0.7777 | 0.8192 | 0.9811 | nan | 0.9943 | 0.9567 | 0.9060 | 0.9904 | 0.9334 | 0.6365 | 0.8581 | 0.9830 | 0.7613 | 0.2368 | 0.9406 | 0.9463 | 0.5057 | nan | 0.9847 | 0.8981 | 0.8493 | 0.9808 | 0.8816 | 0.6315 | 0.8431 | 0.9620 | 0.6033 | 0.2358 | 0.9098 | 0.8946 | 0.4350 | | 0.0171 | 428.24 | 7280 | 0.0865 | 0.7808 | 0.8271 | 0.9811 | nan | 0.9939 | 0.9589 | 0.9037 | 0.9912 | 0.9259 | 0.6339 | 0.8552 | 0.9822 | 0.7752 | 0.2475 | 0.9462 | 0.9557 | 0.5821 | nan | 0.9848 | 0.8977 | 0.8483 | 0.9807 | 0.8815 | 0.6286 | 0.8404 | 0.9625 | 0.6075 | 0.2459 | 0.9050 | 0.8970 | 0.4711 | | 0.0131 | 429.41 | 7300 | 0.0865 | 0.7791 | 0.8237 | 0.9811 | nan | 0.9938 | 0.9621 | 0.9062 | 0.9896 | 0.9333 | 0.6360 | 0.8605 | 0.9818 | 0.7703 | 0.2459 | 0.9388 | 0.9502 | 0.5398 | nan | 0.9849 | 0.8970 | 0.8497 | 0.9809 | 0.8819 | 0.6299 | 0.8469 | 0.9627 | 0.6100 | 0.2445 | 0.9079 | 0.8959 | 0.4363 | | 0.0164 | 430.59 | 7320 | 0.0864 | 0.7792 | 0.8261 | 0.9810 | nan | 0.9938 | 0.9598 | 0.9064 | 0.9904 | 0.9271 | 0.6358 | 0.8581 | 0.9822 | 0.7538 | 0.2504 | 0.9422 | 0.9560 | 0.5837 | nan | 0.9849 | 0.8967 | 0.8489 | 0.9807 | 0.8810 | 0.6313 | 0.8426 | 0.9610 | 0.5967 | 0.2490 | 0.9051 | 0.8957 | 0.4556 | | 0.0132 | 431.76 | 7340 | 0.0873 | 0.7747 | 0.8162 | 0.9811 | nan | 0.9939 | 0.9582 | 0.9055 | 0.9910 | 0.9262 | 0.6361 | 0.8753 | 0.9827 | 0.7511 | 0.2465 | 0.9367 | 0.9509 | 0.4569 | nan | 0.9848 | 0.8975 | 0.8471 | 0.9813 | 0.8818 | 0.6313 | 0.8594 | 0.9605 | 0.5933 | 0.2454 | 0.9084 | 0.8957 | 0.3844 | | 0.0127 | 432.94 | 7360 | 0.0863 | 0.7789 | 0.8217 | 0.9811 | nan | 0.9941 | 0.9588 | 0.9027 | 0.9909 | 0.9249 | 0.6431 | 0.8660 | 0.9820 | 0.7604 | 0.2497 | 0.9388 | 0.9556 | 0.5154 | nan | 0.9848 | 0.8984 | 0.8481 | 0.9809 | 0.8808 | 0.6372 | 0.8503 | 0.9609 | 0.6001 | 0.2484 | 0.9068 | 0.8972 | 0.4319 | | 0.0152 | 434.12 | 7380 | 0.0877 | 0.7794 | 0.8228 | 0.9810 | nan | 0.9944 | 0.9600 | 0.8963 | 0.9905 | 0.9251 | 0.6386 | 0.8668 | 0.9813 | 0.7583 | 0.2581 | 0.9397 | 0.9525 | 0.5350 | nan | 0.9848 | 0.8974 | 0.8471 | 0.9810 | 0.8809 | 0.6328 | 0.8512 | 0.9611 | 0.5993 | 0.2562 | 0.9069 | 0.8945 | 0.4393 | | 0.018 | 435.29 | 7400 | 0.0881 | 0.7752 | 0.8171 | 0.9810 | nan | 0.9940 | 0.9590 | 0.9032 | 0.9912 | 0.9265 | 0.6346 | 0.8597 | 0.9815 | 0.7511 | 0.2454 | 0.9358 | 0.9511 | 0.4894 | nan | 0.9848 | 0.8968 | 0.8484 | 0.9808 | 0.8807 | 0.6296 | 0.8455 | 0.9607 | 0.5962 | 0.2441 | 0.9066 | 0.8951 | 0.4079 | | 0.0148 | 436.47 | 7420 | 0.0879 | 0.7788 | 0.8208 | 0.9810 | nan | 0.9942 | 0.9594 | 0.8996 | 0.9906 | 0.9274 | 0.6353 | 0.8614 | 0.9816 | 0.7678 | 0.2475 | 0.9396 | 0.9505 | 0.5154 | nan | 0.9848 | 0.8972 | 0.8474 | 0.9810 | 0.8812 | 0.6303 | 0.8471 | 0.9617 | 0.6016 | 0.2462 | 0.9093 | 0.8943 | 0.4421 | | 0.0144 | 437.65 | 7440 | 0.0879 | 0.7763 | 0.8174 | 0.9810 | nan | 0.9945 | 0.9593 | 0.8979 | 0.9901 | 0.9302 | 0.6345 | 0.8570 | 0.9816 | 0.7623 | 0.2468 | 0.9385 | 0.9495 | 0.4846 | nan | 0.9847 | 0.8974 | 0.8472 | 0.9808 | 0.8812 | 0.6298 | 0.8431 | 0.9615 | 0.6031 | 0.2455 | 0.9074 | 0.8924 | 0.4180 | | 0.0135 | 438.82 | 7460 | 0.0870 | 0.7788 | 0.8227 | 0.9810 | nan | 0.9938 | 0.9582 | 0.9080 | 0.9908 | 0.9288 | 0.6338 | 0.8619 | 0.9823 | 0.7745 | 0.2426 | 0.9383 | 0.9475 | 0.5350 | nan | 0.9849 | 0.8974 | 0.8482 | 0.9809 | 0.8813 | 0.6276 | 0.8461 | 0.9613 | 0.6064 | 0.2414 | 0.9074 | 0.8957 | 0.4452 | | 0.0163 | 440.0 | 7480 | 0.0866 | 0.7789 | 0.8234 | 0.9810 | nan | 0.9938 | 0.9589 | 0.9074 | 0.9913 | 0.9244 | 0.6372 | 0.8584 | 0.9818 | 0.7663 | 0.2434 | 0.9421 | 0.9501 | 0.5496 | nan | 0.9848 | 0.8973 | 0.8492 | 0.9807 | 0.8805 | 0.6311 | 0.8438 | 0.9616 | 0.6055 | 0.2423 | 0.9108 | 0.8945 | 0.4436 | | 0.0163 | 441.18 | 7500 | 0.0880 | 0.7785 | 0.8228 | 0.9810 | nan | 0.9942 | 0.9596 | 0.9017 | 0.9901 | 0.9297 | 0.6381 | 0.8557 | 0.9820 | 0.7674 | 0.2384 | 0.9387 | 0.9451 | 0.5561 | nan | 0.9847 | 0.8976 | 0.8473 | 0.9808 | 0.8806 | 0.6324 | 0.8425 | 0.9619 | 0.6035 | 0.2372 | 0.9088 | 0.8933 | 0.4494 | | 0.0161 | 442.35 | 7520 | 0.0883 | 0.7755 | 0.8213 | 0.9809 | nan | 0.9941 | 0.9604 | 0.9010 | 0.9900 | 0.9294 | 0.6327 | 0.8547 | 0.9819 | 0.7662 | 0.2359 | 0.9429 | 0.9515 | 0.5366 | nan | 0.9847 | 0.8970 | 0.8475 | 0.9808 | 0.8810 | 0.6276 | 0.8416 | 0.9614 | 0.6022 | 0.2348 | 0.9081 | 0.8918 | 0.4231 | | 0.0139 | 443.53 | 7540 | 0.0880 | 0.7763 | 0.8199 | 0.9810 | nan | 0.9941 | 0.9591 | 0.9030 | 0.9903 | 0.9309 | 0.6363 | 0.8543 | 0.9827 | 0.7530 | 0.2365 | 0.9432 | 0.9456 | 0.5301 | nan | 0.9848 | 0.8972 | 0.8486 | 0.9809 | 0.8813 | 0.6311 | 0.8415 | 0.9612 | 0.5959 | 0.2355 | 0.9104 | 0.8932 | 0.4301 | | 0.0131 | 444.71 | 7560 | 0.0876 | 0.7763 | 0.8197 | 0.9810 | nan | 0.9942 | 0.9587 | 0.8992 | 0.9910 | 0.9280 | 0.6356 | 0.8559 | 0.9820 | 0.7614 | 0.2425 | 0.9400 | 0.9472 | 0.5203 | nan | 0.9848 | 0.8972 | 0.8478 | 0.9807 | 0.8809 | 0.6303 | 0.8410 | 0.9610 | 0.6026 | 0.2412 | 0.9092 | 0.8955 | 0.4194 | | 0.0159 | 445.88 | 7580 | 0.0872 | 0.7756 | 0.8196 | 0.9810 | nan | 0.9939 | 0.9604 | 0.9046 | 0.9901 | 0.9297 | 0.6339 | 0.8545 | 0.9825 | 0.7557 | 0.2425 | 0.9373 | 0.9463 | 0.5236 | nan | 0.9848 | 0.8969 | 0.8490 | 0.9808 | 0.8810 | 0.6285 | 0.8404 | 0.9610 | 0.5993 | 0.2411 | 0.9099 | 0.8946 | 0.4149 | | 0.0148 | 447.06 | 7600 | 0.0879 | 0.7775 | 0.8218 | 0.9810 | nan | 0.9941 | 0.9594 | 0.9045 | 0.9906 | 0.9247 | 0.6363 | 0.8524 | 0.9819 | 0.7584 | 0.2471 | 0.9341 | 0.9502 | 0.5496 | nan | 0.9848 | 0.8969 | 0.8491 | 0.9808 | 0.8802 | 0.6306 | 0.8393 | 0.9613 | 0.6004 | 0.2453 | 0.9100 | 0.8955 | 0.4339 | | 0.0134 | 448.24 | 7620 | 0.0879 | 0.7771 | 0.8228 | 0.9810 | nan | 0.9941 | 0.9592 | 0.9036 | 0.9909 | 0.9285 | 0.6344 | 0.8575 | 0.9804 | 0.7698 | 0.2428 | 0.9385 | 0.9567 | 0.5398 | nan | 0.9848 | 0.8967 | 0.8490 | 0.9809 | 0.8812 | 0.6284 | 0.8430 | 0.9617 | 0.6081 | 0.2415 | 0.9081 | 0.8957 | 0.4235 | | 0.0147 | 449.41 | 7640 | 0.0888 | 0.7747 | 0.8171 | 0.9810 | nan | 0.9946 | 0.9583 | 0.8969 | 0.9905 | 0.9287 | 0.6332 | 0.8589 | 0.9821 | 0.7671 | 0.2446 | 0.9396 | 0.9494 | 0.4780 | nan | 0.9847 | 0.8973 | 0.8473 | 0.9810 | 0.8814 | 0.6283 | 0.8446 | 0.9620 | 0.6066 | 0.2435 | 0.9079 | 0.8920 | 0.3941 | | 0.0166 | 450.59 | 7660 | 0.0864 | 0.7770 | 0.8206 | 0.9811 | nan | 0.9942 | 0.9587 | 0.9036 | 0.9910 | 0.9256 | 0.6334 | 0.8584 | 0.9817 | 0.7694 | 0.2435 | 0.9328 | 0.9501 | 0.5252 | nan | 0.9848 | 0.8971 | 0.8498 | 0.9809 | 0.8809 | 0.6286 | 0.8440 | 0.9622 | 0.6110 | 0.2423 | 0.9077 | 0.8956 | 0.4157 | | 0.0154 | 451.76 | 7680 | 0.0869 | 0.7774 | 0.8202 | 0.9811 | nan | 0.9942 | 0.9594 | 0.9022 | 0.9907 | 0.9295 | 0.6352 | 0.8584 | 0.9813 | 0.7750 | 0.2382 | 0.9341 | 0.9468 | 0.5171 | nan | 0.9848 | 0.8973 | 0.8489 | 0.9809 | 0.8814 | 0.6299 | 0.8439 | 0.9619 | 0.6129 | 0.2371 | 0.9071 | 0.8967 | 0.4229 | | 0.0117 | 452.94 | 7700 | 0.0869 | 0.7781 | 0.8221 | 0.9811 | nan | 0.9942 | 0.9585 | 0.9022 | 0.9906 | 0.9303 | 0.6360 | 0.8611 | 0.9819 | 0.7771 | 0.2489 | 0.9394 | 0.9513 | 0.5154 | nan | 0.9848 | 0.8981 | 0.8487 | 0.9809 | 0.8816 | 0.6306 | 0.8460 | 0.9621 | 0.6115 | 0.2475 | 0.9078 | 0.8934 | 0.4221 | | 0.0128 | 454.12 | 7720 | 0.0884 | 0.7798 | 0.8240 | 0.9811 | nan | 0.9940 | 0.9583 | 0.9037 | 0.9907 | 0.9270 | 0.6335 | 0.8599 | 0.9833 | 0.7809 | 0.2439 | 0.9433 | 0.9506 | 0.5431 | nan | 0.9848 | 0.8976 | 0.8489 | 0.9810 | 0.8810 | 0.6280 | 0.8452 | 0.9620 | 0.6110 | 0.2427 | 0.9089 | 0.8950 | 0.4507 | | 0.0139 | 455.29 | 7740 | 0.0883 | 0.7783 | 0.8237 | 0.9810 | nan | 0.9938 | 0.9600 | 0.9046 | 0.9906 | 0.9306 | 0.6308 | 0.8584 | 0.9814 | 0.7676 | 0.2412 | 0.9439 | 0.9537 | 0.5512 | nan | 0.9848 | 0.8965 | 0.8491 | 0.9807 | 0.8813 | 0.6252 | 0.8438 | 0.9618 | 0.6057 | 0.2398 | 0.9089 | 0.8956 | 0.4443 | | 0.0134 | 456.47 | 7760 | 0.0879 | 0.7775 | 0.8208 | 0.9810 | nan | 0.9941 | 0.9589 | 0.9037 | 0.9906 | 0.9296 | 0.6326 | 0.8550 | 0.9823 | 0.7643 | 0.2371 | 0.9394 | 0.9466 | 0.5366 | nan | 0.9849 | 0.8969 | 0.8496 | 0.9807 | 0.8809 | 0.6271 | 0.8410 | 0.9618 | 0.6053 | 0.2356 | 0.9073 | 0.8953 | 0.4412 | | 0.0115 | 457.65 | 7780 | 0.0886 | 0.7789 | 0.8224 | 0.9811 | nan | 0.9941 | 0.9591 | 0.9027 | 0.9908 | 0.9295 | 0.6308 | 0.8579 | 0.9818 | 0.7714 | 0.2381 | 0.9397 | 0.9502 | 0.5447 | nan | 0.9849 | 0.8970 | 0.8495 | 0.9808 | 0.8814 | 0.6263 | 0.8435 | 0.9618 | 0.6085 | 0.2369 | 0.9082 | 0.8955 | 0.4521 | | 0.0123 | 458.82 | 7800 | 0.0888 | 0.7774 | 0.8198 | 0.9810 | nan | 0.9940 | 0.9594 | 0.9044 | 0.9908 | 0.9286 | 0.6295 | 0.8554 | 0.9819 | 0.7670 | 0.2368 | 0.9354 | 0.9530 | 0.5220 | nan | 0.9848 | 0.8965 | 0.8496 | 0.9808 | 0.8814 | 0.6249 | 0.8415 | 0.9615 | 0.6062 | 0.2356 | 0.9062 | 0.8941 | 0.4428 | | 0.0149 | 460.0 | 7820 | 0.0879 | 0.7799 | 0.8228 | 0.9811 | nan | 0.9941 | 0.9588 | 0.9047 | 0.9907 | 0.9300 | 0.6342 | 0.8599 | 0.9815 | 0.7671 | 0.2414 | 0.9422 | 0.9521 | 0.5398 | nan | 0.9849 | 0.8971 | 0.8501 | 0.9808 | 0.8813 | 0.6289 | 0.8448 | 0.9619 | 0.6067 | 0.2401 | 0.9084 | 0.8938 | 0.4598 | | 0.0139 | 461.18 | 7840 | 0.0878 | 0.7785 | 0.8218 | 0.9811 | nan | 0.9941 | 0.9592 | 0.9047 | 0.9904 | 0.9328 | 0.6346 | 0.8575 | 0.9819 | 0.7639 | 0.2464 | 0.9443 | 0.9531 | 0.5203 | nan | 0.9848 | 0.8975 | 0.8496 | 0.9809 | 0.8814 | 0.6294 | 0.8430 | 0.9619 | 0.6070 | 0.2451 | 0.9101 | 0.8956 | 0.4348 | | 0.0143 | 462.35 | 7860 | 0.0888 | 0.7793 | 0.8223 | 0.9810 | nan | 0.9942 | 0.9596 | 0.9033 | 0.9901 | 0.9313 | 0.6324 | 0.8589 | 0.9813 | 0.7688 | 0.2494 | 0.9387 | 0.9505 | 0.5317 | nan | 0.9848 | 0.8970 | 0.8490 | 0.9809 | 0.8815 | 0.6275 | 0.8452 | 0.9617 | 0.6075 | 0.2479 | 0.9063 | 0.8942 | 0.4479 | | 0.0155 | 463.53 | 7880 | 0.0878 | 0.7807 | 0.8242 | 0.9812 | nan | 0.9941 | 0.9584 | 0.9066 | 0.9905 | 0.9315 | 0.6383 | 0.8593 | 0.9817 | 0.7721 | 0.2556 | 0.9436 | 0.9568 | 0.5268 | nan | 0.9848 | 0.8979 | 0.8499 | 0.9810 | 0.8818 | 0.6327 | 0.8452 | 0.9620 | 0.6103 | 0.2540 | 0.9084 | 0.8965 | 0.4451 | | 0.0152 | 464.71 | 7900 | 0.0876 | 0.7804 | 0.8217 | 0.9811 | nan | 0.9942 | 0.9595 | 0.9025 | 0.9901 | 0.9319 | 0.6367 | 0.8621 | 0.9825 | 0.7643 | 0.2459 | 0.9366 | 0.9553 | 0.5203 | nan | 0.9847 | 0.8978 | 0.8482 | 0.9812 | 0.8817 | 0.6314 | 0.8486 | 0.9623 | 0.6089 | 0.2446 | 0.9103 | 0.8950 | 0.4501 | | 0.0137 | 465.88 | 7920 | 0.0880 | 0.7804 | 0.8231 | 0.9812 | nan | 0.9942 | 0.9587 | 0.9029 | 0.9906 | 0.9292 | 0.6363 | 0.8634 | 0.9827 | 0.7728 | 0.2493 | 0.9413 | 0.9501 | 0.5285 | nan | 0.9848 | 0.8981 | 0.8488 | 0.9811 | 0.8818 | 0.6312 | 0.8486 | 0.9624 | 0.6111 | 0.2480 | 0.9110 | 0.8958 | 0.4422 | | 0.0164 | 467.06 | 7940 | 0.0887 | 0.7780 | 0.8214 | 0.9811 | nan | 0.9942 | 0.9599 | 0.9015 | 0.9902 | 0.9318 | 0.6311 | 0.8595 | 0.9821 | 0.7624 | 0.2502 | 0.9410 | 0.9508 | 0.5236 | nan | 0.9848 | 0.8972 | 0.8484 | 0.9811 | 0.8819 | 0.6262 | 0.8456 | 0.9620 | 0.6046 | 0.2487 | 0.9097 | 0.8948 | 0.4293 | | 0.0142 | 468.24 | 7960 | 0.0887 | 0.7770 | 0.8194 | 0.9810 | nan | 0.9941 | 0.9595 | 0.9015 | 0.9906 | 0.9295 | 0.6287 | 0.8614 | 0.9816 | 0.7750 | 0.2461 | 0.9390 | 0.9504 | 0.4943 | nan | 0.9847 | 0.8974 | 0.8472 | 0.9811 | 0.8820 | 0.6239 | 0.8472 | 0.9623 | 0.6098 | 0.2449 | 0.9106 | 0.8934 | 0.4164 | | 0.0152 | 469.41 | 7980 | 0.0887 | 0.7778 | 0.8212 | 0.9811 | nan | 0.9943 | 0.9591 | 0.9029 | 0.9903 | 0.9306 | 0.6300 | 0.8576 | 0.9814 | 0.7729 | 0.2470 | 0.9437 | 0.9521 | 0.5138 | nan | 0.9847 | 0.8978 | 0.8485 | 0.9810 | 0.8822 | 0.6249 | 0.8437 | 0.9624 | 0.6101 | 0.2457 | 0.9109 | 0.8944 | 0.4247 | | 0.0134 | 470.59 | 8000 | 0.0885 | 0.7757 | 0.8181 | 0.9810 | nan | 0.9944 | 0.9594 | 0.8983 | 0.9904 | 0.9307 | 0.6323 | 0.8549 | 0.9819 | 0.7692 | 0.2428 | 0.9365 | 0.9536 | 0.4911 | nan | 0.9847 | 0.8979 | 0.8465 | 0.9808 | 0.8819 | 0.6273 | 0.8411 | 0.9621 | 0.6099 | 0.2417 | 0.9091 | 0.8937 | 0.4070 | | 0.0132 | 471.76 | 8020 | 0.0883 | 0.7795 | 0.8240 | 0.9811 | nan | 0.9942 | 0.9598 | 0.9034 | 0.9904 | 0.9288 | 0.6340 | 0.8547 | 0.9808 | 0.7711 | 0.2508 | 0.9408 | 0.9569 | 0.5463 | nan | 0.9848 | 0.8978 | 0.8489 | 0.9808 | 0.8822 | 0.6286 | 0.8408 | 0.9620 | 0.6114 | 0.2492 | 0.9090 | 0.8932 | 0.4450 | | 0.0136 | 472.94 | 8040 | 0.0882 | 0.7783 | 0.8230 | 0.9810 | nan | 0.9941 | 0.9588 | 0.9048 | 0.9906 | 0.9312 | 0.6301 | 0.8533 | 0.9820 | 0.7679 | 0.2477 | 0.9404 | 0.9491 | 0.5496 | nan | 0.9848 | 0.8974 | 0.8494 | 0.9808 | 0.8819 | 0.6249 | 0.8391 | 0.9617 | 0.6097 | 0.2462 | 0.9087 | 0.8952 | 0.4390 | | 0.0149 | 474.12 | 8060 | 0.0882 | 0.7777 | 0.8217 | 0.9811 | nan | 0.9942 | 0.9596 | 0.9020 | 0.9904 | 0.9313 | 0.6301 | 0.8545 | 0.9815 | 0.7732 | 0.2458 | 0.9380 | 0.9464 | 0.5350 | nan | 0.9848 | 0.8974 | 0.8488 | 0.9808 | 0.8822 | 0.6250 | 0.8401 | 0.9621 | 0.6110 | 0.2444 | 0.9086 | 0.8937 | 0.4306 | | 0.0139 | 475.29 | 8080 | 0.0883 | 0.7786 | 0.8224 | 0.9811 | nan | 0.9940 | 0.9599 | 0.9042 | 0.9908 | 0.9271 | 0.6312 | 0.8554 | 0.9822 | 0.7699 | 0.2503 | 0.9405 | 0.9458 | 0.5398 | nan | 0.9849 | 0.8973 | 0.8495 | 0.9809 | 0.8817 | 0.6257 | 0.8413 | 0.9620 | 0.6087 | 0.2488 | 0.9095 | 0.8944 | 0.4374 | | 0.0164 | 476.47 | 8100 | 0.0886 | 0.7773 | 0.8202 | 0.9810 | nan | 0.9943 | 0.9591 | 0.9030 | 0.9903 | 0.9299 | 0.6293 | 0.8524 | 0.9818 | 0.7669 | 0.2504 | 0.9393 | 0.9485 | 0.5171 | nan | 0.9848 | 0.8974 | 0.8490 | 0.9809 | 0.8820 | 0.6246 | 0.8395 | 0.9618 | 0.6073 | 0.2491 | 0.9096 | 0.8938 | 0.4251 | | 0.0168 | 477.65 | 8120 | 0.0890 | 0.7771 | 0.8195 | 0.9810 | nan | 0.9942 | 0.9592 | 0.9030 | 0.9903 | 0.9321 | 0.6308 | 0.8556 | 0.9819 | 0.7576 | 0.2493 | 0.9401 | 0.9501 | 0.5089 | nan | 0.9848 | 0.8974 | 0.8489 | 0.9809 | 0.8823 | 0.6258 | 0.8412 | 0.9614 | 0.6026 | 0.2479 | 0.9096 | 0.8945 | 0.4247 | | 0.0148 | 478.82 | 8140 | 0.0888 | 0.7784 | 0.8230 | 0.9811 | nan | 0.9942 | 0.9589 | 0.9036 | 0.9904 | 0.9303 | 0.6333 | 0.8583 | 0.9821 | 0.7736 | 0.2514 | 0.9442 | 0.9536 | 0.5252 | nan | 0.9848 | 0.8978 | 0.8492 | 0.9809 | 0.8824 | 0.6278 | 0.8434 | 0.9618 | 0.6089 | 0.2499 | 0.9104 | 0.8933 | 0.4284 | | 0.0141 | 480.0 | 8160 | 0.0887 | 0.7777 | 0.8221 | 0.9811 | nan | 0.9942 | 0.9586 | 0.9028 | 0.9907 | 0.9298 | 0.6325 | 0.8558 | 0.9816 | 0.7710 | 0.2474 | 0.9437 | 0.9552 | 0.5236 | nan | 0.9848 | 0.8973 | 0.8490 | 0.9809 | 0.8823 | 0.6271 | 0.8415 | 0.9616 | 0.6069 | 0.2459 | 0.9086 | 0.8943 | 0.4299 | | 0.0175 | 481.18 | 8180 | 0.0880 | 0.7778 | 0.8223 | 0.9811 | nan | 0.9942 | 0.9593 | 0.9033 | 0.9904 | 0.9292 | 0.6337 | 0.8569 | 0.9818 | 0.7735 | 0.2448 | 0.9433 | 0.9524 | 0.5268 | nan | 0.9848 | 0.8976 | 0.8491 | 0.9809 | 0.8822 | 0.6289 | 0.8424 | 0.9619 | 0.6098 | 0.2434 | 0.9090 | 0.8949 | 0.4269 | | 0.0141 | 482.35 | 8200 | 0.0883 | 0.7767 | 0.8201 | 0.9811 | nan | 0.9942 | 0.9591 | 0.9027 | 0.9905 | 0.9316 | 0.6338 | 0.8564 | 0.9816 | 0.7668 | 0.2413 | 0.9422 | 0.9524 | 0.5089 | nan | 0.9848 | 0.8976 | 0.8489 | 0.9808 | 0.8822 | 0.6285 | 0.8417 | 0.9619 | 0.6074 | 0.2401 | 0.9085 | 0.8960 | 0.4184 | | 0.0141 | 483.53 | 8220 | 0.0890 | 0.7767 | 0.8201 | 0.9811 | nan | 0.9942 | 0.9594 | 0.9033 | 0.9905 | 0.9297 | 0.6310 | 0.8546 | 0.9821 | 0.7660 | 0.2430 | 0.9413 | 0.9527 | 0.5138 | nan | 0.9848 | 0.8974 | 0.8490 | 0.9809 | 0.8819 | 0.6262 | 0.8404 | 0.9617 | 0.6068 | 0.2416 | 0.9091 | 0.8956 | 0.4213 | | 0.0132 | 484.71 | 8240 | 0.0887 | 0.7767 | 0.8211 | 0.9810 | nan | 0.9940 | 0.9598 | 0.9033 | 0.9906 | 0.9322 | 0.6318 | 0.8543 | 0.9818 | 0.7626 | 0.2416 | 0.9427 | 0.9556 | 0.5236 | nan | 0.9848 | 0.8973 | 0.8491 | 0.9808 | 0.8820 | 0.6268 | 0.8403 | 0.9616 | 0.6045 | 0.2402 | 0.9091 | 0.8956 | 0.4248 | | 0.0148 | 485.88 | 8260 | 0.0885 | 0.7773 | 0.8210 | 0.9810 | nan | 0.9941 | 0.9599 | 0.9028 | 0.9906 | 0.9291 | 0.6326 | 0.8543 | 0.9812 | 0.7682 | 0.2418 | 0.9413 | 0.9500 | 0.5268 | nan | 0.9848 | 0.8972 | 0.8490 | 0.9808 | 0.8822 | 0.6273 | 0.8407 | 0.9617 | 0.6073 | 0.2404 | 0.9091 | 0.8946 | 0.4297 | | 0.0138 | 487.06 | 8280 | 0.0884 | 0.7781 | 0.8226 | 0.9811 | nan | 0.9942 | 0.9592 | 0.9034 | 0.9903 | 0.9320 | 0.6341 | 0.8603 | 0.9815 | 0.7740 | 0.2397 | 0.9443 | 0.9528 | 0.5285 | nan | 0.9848 | 0.8977 | 0.8492 | 0.9809 | 0.8825 | 0.6285 | 0.8451 | 0.9619 | 0.6098 | 0.2385 | 0.9103 | 0.8943 | 0.4316 | | 0.0122 | 488.24 | 8300 | 0.0885 | 0.7777 | 0.8220 | 0.9811 | nan | 0.9941 | 0.9592 | 0.9034 | 0.9906 | 0.9321 | 0.6326 | 0.8576 | 0.9816 | 0.7695 | 0.2422 | 0.9447 | 0.9484 | 0.5301 | nan | 0.9848 | 0.8976 | 0.8488 | 0.9809 | 0.8823 | 0.6273 | 0.8428 | 0.9617 | 0.6080 | 0.2408 | 0.9102 | 0.8965 | 0.4289 | | 0.0138 | 489.41 | 8320 | 0.0889 | 0.7774 | 0.8212 | 0.9811 | nan | 0.9942 | 0.9594 | 0.9023 | 0.9906 | 0.9290 | 0.6331 | 0.8563 | 0.9818 | 0.7680 | 0.2399 | 0.9424 | 0.9547 | 0.5236 | nan | 0.9848 | 0.8976 | 0.8486 | 0.9809 | 0.8821 | 0.6278 | 0.8425 | 0.9617 | 0.6062 | 0.2387 | 0.9091 | 0.8949 | 0.4311 | | 0.0136 | 490.59 | 8340 | 0.0885 | 0.7769 | 0.8208 | 0.9811 | nan | 0.9942 | 0.9585 | 0.9028 | 0.9907 | 0.9299 | 0.6321 | 0.8574 | 0.9820 | 0.7626 | 0.2412 | 0.9436 | 0.9551 | 0.5203 | nan | 0.9848 | 0.8977 | 0.8485 | 0.9809 | 0.8821 | 0.6270 | 0.8432 | 0.9615 | 0.6035 | 0.2400 | 0.9091 | 0.8956 | 0.4261 | | 0.0162 | 491.76 | 8360 | 0.0891 | 0.7772 | 0.8214 | 0.9810 | nan | 0.9942 | 0.9593 | 0.9029 | 0.9906 | 0.9295 | 0.6303 | 0.8574 | 0.9814 | 0.7630 | 0.2443 | 0.9431 | 0.9567 | 0.5252 | nan | 0.9848 | 0.8972 | 0.8486 | 0.9809 | 0.8822 | 0.6255 | 0.8432 | 0.9615 | 0.6032 | 0.2429 | 0.9089 | 0.8944 | 0.4301 | | 0.0159 | 492.94 | 8380 | 0.0889 | 0.7770 | 0.8215 | 0.9811 | nan | 0.9942 | 0.9589 | 0.9030 | 0.9904 | 0.9309 | 0.6332 | 0.8590 | 0.9819 | 0.7676 | 0.2435 | 0.9446 | 0.9580 | 0.5138 | nan | 0.9848 | 0.8978 | 0.8487 | 0.9809 | 0.8821 | 0.6279 | 0.8444 | 0.9618 | 0.6055 | 0.2423 | 0.9100 | 0.8933 | 0.4219 | | 0.0154 | 494.12 | 8400 | 0.0900 | 0.7764 | 0.8206 | 0.9810 | nan | 0.9941 | 0.9602 | 0.9023 | 0.9905 | 0.9297 | 0.6290 | 0.8555 | 0.9816 | 0.7684 | 0.2415 | 0.9439 | 0.9562 | 0.5154 | nan | 0.9848 | 0.8970 | 0.8487 | 0.9808 | 0.8821 | 0.6248 | 0.8417 | 0.9618 | 0.6054 | 0.2402 | 0.9085 | 0.8940 | 0.4238 | | 0.0135 | 495.29 | 8420 | 0.0890 | 0.7765 | 0.8210 | 0.9810 | nan | 0.9941 | 0.9596 | 0.9028 | 0.9905 | 0.9307 | 0.6315 | 0.8555 | 0.9819 | 0.7657 | 0.2423 | 0.9442 | 0.9567 | 0.5171 | nan | 0.9848 | 0.8974 | 0.8488 | 0.9808 | 0.8821 | 0.6266 | 0.8415 | 0.9617 | 0.6040 | 0.2410 | 0.9091 | 0.8943 | 0.4229 | | 0.0148 | 496.47 | 8440 | 0.0886 | 0.7767 | 0.8206 | 0.9811 | nan | 0.9942 | 0.9591 | 0.9022 | 0.9906 | 0.9283 | 0.6328 | 0.8574 | 0.9821 | 0.7661 | 0.2425 | 0.9424 | 0.9525 | 0.5171 | nan | 0.9848 | 0.8976 | 0.8487 | 0.9809 | 0.8820 | 0.6277 | 0.8427 | 0.9617 | 0.6046 | 0.2412 | 0.9089 | 0.8946 | 0.4218 | | 0.0165 | 497.65 | 8460 | 0.0885 | 0.7771 | 0.8214 | 0.9811 | nan | 0.9941 | 0.9591 | 0.9037 | 0.9905 | 0.9306 | 0.6345 | 0.8564 | 0.9823 | 0.7623 | 0.2447 | 0.9447 | 0.9545 | 0.5203 | nan | 0.9848 | 0.8977 | 0.8490 | 0.9809 | 0.8821 | 0.6287 | 0.8422 | 0.9615 | 0.6034 | 0.2433 | 0.9095 | 0.8950 | 0.4238 | | 0.0138 | 498.82 | 8480 | 0.0890 | 0.7763 | 0.8202 | 0.9810 | nan | 0.9941 | 0.9594 | 0.9033 | 0.9905 | 0.9302 | 0.6320 | 0.8534 | 0.9822 | 0.7664 | 0.2426 | 0.9419 | 0.9531 | 0.5138 | nan | 0.9848 | 0.8974 | 0.8489 | 0.9809 | 0.8821 | 0.6269 | 0.8401 | 0.9617 | 0.6046 | 0.2413 | 0.9087 | 0.8949 | 0.4191 | | 0.0162 | 500.0 | 8500 | 0.0892 | 0.7759 | 0.8195 | 0.9811 | nan | 0.9942 | 0.9592 | 0.9021 | 0.9902 | 0.9315 | 0.6330 | 0.8554 | 0.9821 | 0.7662 | 0.2392 | 0.9438 | 0.9536 | 0.5024 | nan | 0.9848 | 0.8976 | 0.8486 | 0.9809 | 0.8822 | 0.6278 | 0.8418 | 0.9618 | 0.6046 | 0.2381 | 0.9093 | 0.8939 | 0.4153 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "nat", "concrete", "grass", "speedway bricks", "steel", "rough concrete", "dark bricks", "road", "rough red sidewalk", "tiles", "red bricks", "concrete tiles", "rest" ]

fshala/segformer-cloud

# segformer-cloud This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

johanhag/segformer-b0-winter

# segformer-b0-winter This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the johanhag/winter-test dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - Mean Iou: 0.8861 - Mean Accuracy: 0.9456 - Overall Accuracy: 0.9660 - Accuracy Unlabeled: nan - Accuracy Object: nan - Accuracy Road: 0.9769 - Accuracy Side walk: 0.8930 - Accuracy Car: 0.9347 - Accuracy Pedestrian: nan - Accuracy Other: 0.9779 - Iou Unlabeled: nan - Iou Object: nan - Iou Road: 0.9197 - Iou Side walk: 0.8250 - Iou Car: 0.8319 - Iou Pedestrian: nan - Iou Other: 0.9678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Object | Accuracy Road | Accuracy Side walk | Accuracy Car | Accuracy Pedestrian | Accuracy Other | Iou Unlabeled | Iou Object | Iou Road | Iou Side walk | Iou Car | Iou Pedestrian | Iou Other | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:------------------:|:------------:|:-------------------:|:--------------:|:-------------:|:----------:|:--------:|:-------------:|:-------:|:--------------:|:---------:| | 0.2207 | 4.0 | 20 | 0.2300 | 0.8576 | 0.9439 | 0.9583 | nan | nan | 0.9738 | 0.8767 | 0.9564 | nan | 0.9686 | nan | nan | 0.9018 | 0.8000 | 0.7670 | nan | 0.9617 | | 0.1792 | 8.0 | 40 | 0.2126 | 0.8696 | 0.9457 | 0.9614 | nan | nan | 0.9768 | 0.8911 | 0.9444 | nan | 0.9706 | nan | nan | 0.9106 | 0.8122 | 0.7924 | nan | 0.9633 | | 0.1527 | 12.0 | 60 | 0.1869 | 0.8769 | 0.9470 | 0.9634 | nan | nan | 0.9776 | 0.9023 | 0.9364 | nan | 0.9718 | nan | nan | 0.9180 | 0.8165 | 0.8085 | nan | 0.9647 | | 0.1329 | 16.0 | 80 | 0.1787 | 0.8783 | 0.9429 | 0.9634 | nan | nan | 0.9772 | 0.8880 | 0.9314 | nan | 0.9749 | nan | nan | 0.9126 | 0.8117 | 0.8229 | nan | 0.9661 | | 0.1746 | 20.0 | 100 | 0.1651 | 0.8864 | 0.9511 | 0.9668 | nan | nan | 0.9771 | 0.9126 | 0.9395 | nan | 0.9751 | nan | nan | 0.9258 | 0.8369 | 0.8157 | nan | 0.9671 | | 0.1218 | 24.0 | 120 | 0.1652 | 0.8798 | 0.9444 | 0.9643 | nan | nan | 0.9791 | 0.8858 | 0.9370 | nan | 0.9757 | nan | nan | 0.9140 | 0.8156 | 0.8224 | nan | 0.9673 | | 0.0816 | 28.0 | 140 | 0.1473 | 0.8921 | 0.9521 | 0.9684 | nan | nan | 0.9723 | 0.9199 | 0.9383 | nan | 0.9780 | nan | nan | 0.9299 | 0.8478 | 0.8231 | nan | 0.9676 | | 0.0893 | 32.0 | 160 | 0.1490 | 0.8892 | 0.9502 | 0.9672 | nan | nan | 0.9749 | 0.9140 | 0.9354 | nan | 0.9766 | nan | nan | 0.9260 | 0.8384 | 0.825 | nan | 0.9673 | | 0.0849 | 36.0 | 180 | 0.1517 | 0.8861 | 0.9476 | 0.9660 | nan | nan | 0.9791 | 0.8987 | 0.9367 | nan | 0.9760 | nan | nan | 0.9205 | 0.8258 | 0.8308 | nan | 0.9674 | | 0.1625 | 40.0 | 200 | 0.1519 | 0.8843 | 0.9468 | 0.9654 | nan | nan | 0.9777 | 0.8938 | 0.9394 | nan | 0.9763 | nan | nan | 0.9176 | 0.8235 | 0.8289 | nan | 0.9673 | | 0.1396 | 44.0 | 220 | 0.1500 | 0.8850 | 0.9476 | 0.9655 | nan | nan | 0.9791 | 0.8949 | 0.9408 | nan | 0.9757 | nan | nan | 0.9187 | 0.8223 | 0.8317 | nan | 0.9674 | | 0.0931 | 48.0 | 240 | 0.1441 | 0.8861 | 0.9456 | 0.9660 | nan | nan | 0.9769 | 0.8930 | 0.9347 | nan | 0.9779 | nan | nan | 0.9197 | 0.8250 | 0.8319 | nan | 0.9678 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "object", "road", "side walk", "car", "pedestrian", "other" ]

yolo12138/segformer-b2-cloth-parse-9

# segformer-b2-cloth-parse-9 This model is a fine-tuned version of [mattmdjaga/segformer_b2_clothes](https://huggingface.co/mattmdjaga/segformer_b2_clothes) on the cloth_parsing_mix dataset. It achieves the following results on the evaluation set: - Loss: 0.0433 - Mean Iou: 0.8611 - Mean Accuracy: 0.9107 - Overall Accuracy: 0.9846 - Accuracy Background: 0.9964 - Accuracy Upper Torso: 0.9857 - Accuracy Left Pants: 0.9654 - Accuracy Right Patns: 0.9664 - Accuracy Skirts: 0.9065 - Accuracy Left Sleeve: 0.9591 - Accuracy Right Sleeve: 0.9662 - Accuracy Outer Collar: 0.6491 - Accuracy Inner Collar: 0.8015 - Iou Background: 0.9923 - Iou Upper Torso: 0.9655 - Iou Left Pants: 0.9017 - Iou Right Patns: 0.9085 - Iou Skirts: 0.8749 - Iou Left Sleeve: 0.9223 - Iou Right Sleeve: 0.9289 - Iou Outer Collar: 0.5394 - Iou Inner Collar: 0.7160 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Upper Torso | Accuracy Left Pants | Accuracy Right Patns | Accuracy Skirts | Accuracy Left Sleeve | Accuracy Right Sleeve | Accuracy Outer Collar | Accuracy Inner Collar | Iou Background | Iou Upper Torso | Iou Left Pants | Iou Right Patns | Iou Skirts | Iou Left Sleeve | Iou Right Sleeve | Iou Outer Collar | Iou Inner Collar | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------------:|:-------------------:|:--------------------:|:---------------:|:--------------------:|:---------------------:|:---------------------:|:---------------------:|:--------------:|:---------------:|:--------------:|:---------------:|:----------:|:---------------:|:----------------:|:----------------:|:----------------:| | 0.1054 | 0.11 | 500 | 0.1180 | 0.7305 | 0.7971 | 0.9670 | 0.9902 | 0.9720 | 0.9654 | 0.9756 | 0.8036 | 0.9226 | 0.9289 | 0.0716 | 0.5444 | 0.9830 | 0.9234 | 0.8752 | 0.8765 | 0.7370 | 0.8236 | 0.8232 | 0.0703 | 0.4628 | | 0.1033 | 0.22 | 1000 | 0.0851 | 0.7862 | 0.8418 | 0.9746 | 0.9924 | 0.9829 | 0.9665 | 0.9653 | 0.8491 | 0.9145 | 0.9226 | 0.3219 | 0.6608 | 0.9866 | 0.9424 | 0.8858 | 0.8875 | 0.8105 | 0.8538 | 0.8614 | 0.2833 | 0.5642 | | 0.0944 | 0.32 | 1500 | 0.0713 | 0.8077 | 0.8595 | 0.9773 | 0.9941 | 0.9833 | 0.9566 | 0.9625 | 0.8924 | 0.9094 | 0.9181 | 0.4414 | 0.6774 | 0.9880 | 0.9481 | 0.8937 | 0.8950 | 0.8437 | 0.8668 | 0.8751 | 0.3629 | 0.5958 | | 0.0746 | 0.43 | 2000 | 0.0683 | 0.8190 | 0.8770 | 0.9783 | 0.9941 | 0.9796 | 0.9652 | 0.9722 | 0.8656 | 0.9480 | 0.9562 | 0.4882 | 0.7236 | 0.9888 | 0.9497 | 0.9070 | 0.9127 | 0.8306 | 0.8790 | 0.8870 | 0.3945 | 0.6218 | | 0.0548 | 0.54 | 2500 | 0.0666 | 0.8187 | 0.8713 | 0.9787 | 0.9951 | 0.9831 | 0.9580 | 0.9606 | 0.8651 | 0.9215 | 0.9453 | 0.4839 | 0.7293 | 0.9893 | 0.9514 | 0.8939 | 0.9006 | 0.8245 | 0.8812 | 0.8964 | 0.4010 | 0.6298 | | 0.0728 | 0.65 | 3000 | 0.0591 | 0.8271 | 0.8806 | 0.9804 | 0.9945 | 0.9839 | 0.9624 | 0.9659 | 0.8982 | 0.9399 | 0.9430 | 0.4884 | 0.7493 | 0.9900 | 0.9551 | 0.8940 | 0.8966 | 0.8583 | 0.8930 | 0.9011 | 0.4100 | 0.6458 | | 0.0505 | 0.75 | 3500 | 0.0648 | 0.8218 | 0.8745 | 0.9797 | 0.9947 | 0.9847 | 0.9858 | 0.9905 | 0.8402 | 0.9500 | 0.9587 | 0.4480 | 0.7178 | 0.9900 | 0.9534 | 0.9022 | 0.9037 | 0.8223 | 0.8944 | 0.9017 | 0.3881 | 0.6402 | | 0.0601 | 0.86 | 4000 | 0.0568 | 0.8415 | 0.8951 | 0.9817 | 0.9952 | 0.9817 | 0.9632 | 0.9640 | 0.9170 | 0.9521 | 0.9541 | 0.5781 | 0.7508 | 0.9903 | 0.9576 | 0.9138 | 0.9199 | 0.8716 | 0.9010 | 0.9106 | 0.4562 | 0.6529 | | 0.0438 | 0.97 | 4500 | 0.0569 | 0.8431 | 0.8925 | 0.9815 | 0.9947 | 0.9844 | 0.9764 | 0.9838 | 0.8870 | 0.9492 | 0.9595 | 0.5561 | 0.7416 | 0.9903 | 0.9560 | 0.9287 | 0.9370 | 0.8585 | 0.9000 | 0.9089 | 0.4524 | 0.6559 | | 0.0617 | 1.08 | 5000 | 0.0529 | 0.8417 | 0.8933 | 0.9816 | 0.9952 | 0.9841 | 0.9602 | 0.9631 | 0.8922 | 0.9475 | 0.9533 | 0.5797 | 0.7642 | 0.9907 | 0.9571 | 0.9097 | 0.9126 | 0.8488 | 0.9044 | 0.9158 | 0.4687 | 0.6678 | | 0.0452 | 1.19 | 5500 | 0.0557 | 0.8351 | 0.8935 | 0.9812 | 0.9949 | 0.9842 | 0.9644 | 0.9667 | 0.8781 | 0.9494 | 0.9604 | 0.5961 | 0.7471 | 0.9906 | 0.9588 | 0.8803 | 0.8885 | 0.8349 | 0.9069 | 0.9169 | 0.4743 | 0.6645 | | 0.0571 | 1.29 | 6000 | 0.0551 | 0.8351 | 0.8934 | 0.9810 | 0.9957 | 0.9831 | 0.9652 | 0.9693 | 0.8562 | 0.9593 | 0.9569 | 0.5959 | 0.7586 | 0.9910 | 0.9579 | 0.8842 | 0.8879 | 0.8188 | 0.9084 | 0.9155 | 0.4774 | 0.6749 | | 0.0778 | 1.4 | 6500 | 0.0537 | 0.8430 | 0.8994 | 0.9818 | 0.9948 | 0.9839 | 0.9872 | 0.9921 | 0.8702 | 0.9587 | 0.9635 | 0.5790 | 0.7656 | 0.9911 | 0.9579 | 0.9044 | 0.9093 | 0.8458 | 0.9060 | 0.9157 | 0.4760 | 0.6808 | | 0.0392 | 1.51 | 7000 | 0.0491 | 0.8503 | 0.9069 | 0.9830 | 0.9954 | 0.9823 | 0.9645 | 0.9666 | 0.9205 | 0.9534 | 0.9599 | 0.6214 | 0.7984 | 0.9916 | 0.9607 | 0.9123 | 0.9139 | 0.8755 | 0.9072 | 0.9180 | 0.4907 | 0.6830 | | 0.0376 | 1.62 | 7500 | 0.0514 | 0.8442 | 0.9010 | 0.9819 | 0.9954 | 0.9832 | 0.9652 | 0.9660 | 0.8850 | 0.9525 | 0.9598 | 0.6257 | 0.7762 | 0.9914 | 0.9586 | 0.8944 | 0.9053 | 0.8355 | 0.9104 | 0.9215 | 0.4965 | 0.6838 | | 0.0391 | 1.73 | 8000 | 0.0492 | 0.8422 | 0.8993 | 0.9819 | 0.9958 | 0.9836 | 0.9641 | 0.9671 | 0.8692 | 0.9561 | 0.9661 | 0.6159 | 0.7756 | 0.9916 | 0.9596 | 0.8882 | 0.8930 | 0.8338 | 0.9103 | 0.9189 | 0.4982 | 0.6860 | | 0.0446 | 1.83 | 8500 | 0.0491 | 0.8515 | 0.9079 | 0.9829 | 0.9960 | 0.9836 | 0.9890 | 0.9913 | 0.8770 | 0.9505 | 0.9631 | 0.6458 | 0.7751 | 0.9916 | 0.9603 | 0.9114 | 0.9161 | 0.8559 | 0.9100 | 0.9217 | 0.5096 | 0.6867 | | 0.041 | 1.94 | 9000 | 0.0482 | 0.8464 | 0.8978 | 0.9825 | 0.9958 | 0.9848 | 0.9619 | 0.9668 | 0.8822 | 0.9569 | 0.9659 | 0.5961 | 0.7703 | 0.9916 | 0.9602 | 0.8958 | 0.9018 | 0.8438 | 0.9148 | 0.9231 | 0.4966 | 0.6899 | | 0.0744 | 2.05 | 9500 | 0.0474 | 0.8523 | 0.9018 | 0.9834 | 0.9961 | 0.9840 | 0.9598 | 0.9633 | 0.9195 | 0.9471 | 0.9644 | 0.6055 | 0.7766 | 0.9919 | 0.9619 | 0.9095 | 0.9125 | 0.8697 | 0.9113 | 0.9238 | 0.5010 | 0.6889 | | 0.0433 | 2.16 | 10000 | 0.0471 | 0.8581 | 0.9103 | 0.9842 | 0.9951 | 0.9843 | 0.9617 | 0.9646 | 0.9416 | 0.9549 | 0.9718 | 0.6305 | 0.7879 | 0.9915 | 0.9644 | 0.9100 | 0.9155 | 0.8976 | 0.9145 | 0.9245 | 0.5127 | 0.6920 | | 0.0412 | 2.26 | 10500 | 0.0468 | 0.8574 | 0.9042 | 0.9835 | 0.9956 | 0.9848 | 0.9628 | 0.9669 | 0.9023 | 0.9615 | 0.9677 | 0.6115 | 0.7847 | 0.9918 | 0.9601 | 0.9248 | 0.9286 | 0.8656 | 0.9177 | 0.9245 | 0.5073 | 0.6964 | | 0.0489 | 2.37 | 11000 | 0.0496 | 0.8511 | 0.9029 | 0.9832 | 0.9956 | 0.9858 | 0.9905 | 0.9948 | 0.8694 | 0.9574 | 0.9654 | 0.5748 | 0.7926 | 0.9921 | 0.9604 | 0.9066 | 0.9086 | 0.8615 | 0.9167 | 0.9228 | 0.4913 | 0.7004 | | 0.0388 | 2.48 | 11500 | 0.0450 | 0.8594 | 0.9036 | 0.9849 | 0.9957 | 0.9857 | 0.9621 | 0.9648 | 0.9620 | 0.9493 | 0.9604 | 0.5733 | 0.7793 | 0.9922 | 0.9649 | 0.9155 | 0.9205 | 0.9076 | 0.9138 | 0.9257 | 0.4941 | 0.7002 | | 0.0409 | 2.59 | 12000 | 0.0493 | 0.8579 | 0.9124 | 0.9844 | 0.9955 | 0.9853 | 0.9928 | 0.9929 | 0.9083 | 0.9573 | 0.9671 | 0.6288 | 0.7832 | 0.9921 | 0.9651 | 0.9046 | 0.9086 | 0.8842 | 0.9196 | 0.9267 | 0.5175 | 0.7026 | | 0.0477 | 2.7 | 12500 | 0.0436 | 0.8610 | 0.9051 | 0.9848 | 0.9957 | 0.9868 | 0.9639 | 0.9675 | 0.9478 | 0.9445 | 0.9590 | 0.5972 | 0.7831 | 0.9919 | 0.9654 | 0.9187 | 0.9251 | 0.9029 | 0.9126 | 0.9253 | 0.5035 | 0.7034 | | 0.0488 | 2.8 | 13000 | 0.0450 | 0.8577 | 0.9076 | 0.9842 | 0.9963 | 0.9848 | 0.9712 | 0.9695 | 0.9132 | 0.9493 | 0.9621 | 0.6188 | 0.8026 | 0.9924 | 0.9635 | 0.9095 | 0.9124 | 0.8742 | 0.9172 | 0.9276 | 0.5157 | 0.7065 | | 0.0879 | 2.91 | 13500 | 0.0516 | 0.8453 | 0.8949 | 0.9819 | 0.9960 | 0.9867 | 0.9631 | 0.9665 | 0.8325 | 0.9618 | 0.9678 | 0.6033 | 0.7763 | 0.9919 | 0.9574 | 0.8955 | 0.9007 | 0.8088 | 0.9206 | 0.9245 | 0.5069 | 0.7013 | | 0.0525 | 3.02 | 14000 | 0.0474 | 0.8521 | 0.9053 | 0.9830 | 0.9959 | 0.9849 | 0.9850 | 0.9925 | 0.8703 | 0.9481 | 0.9597 | 0.6076 | 0.8038 | 0.9923 | 0.9600 | 0.9050 | 0.9099 | 0.8420 | 0.9143 | 0.9263 | 0.5148 | 0.7044 | | 0.0455 | 3.13 | 14500 | 0.0435 | 0.8579 | 0.9111 | 0.9842 | 0.9953 | 0.9852 | 0.9646 | 0.9672 | 0.9255 | 0.9569 | 0.9654 | 0.6514 | 0.7888 | 0.9923 | 0.9642 | 0.8971 | 0.9055 | 0.8780 | 0.9182 | 0.9284 | 0.5327 | 0.7046 | | 0.0454 | 3.24 | 15000 | 0.0451 | 0.8599 | 0.9161 | 0.9844 | 0.9953 | 0.9858 | 0.9895 | 0.9907 | 0.8944 | 0.9635 | 0.9692 | 0.6643 | 0.7925 | 0.9924 | 0.9645 | 0.9061 | 0.9107 | 0.8803 | 0.9202 | 0.9236 | 0.5356 | 0.7058 | | 0.0687 | 3.34 | 15500 | 0.0496 | 0.8482 | 0.9017 | 0.9827 | 0.9959 | 0.9869 | 0.9715 | 0.9676 | 0.8483 | 0.9616 | 0.9672 | 0.6235 | 0.7932 | 0.9922 | 0.9614 | 0.8904 | 0.8909 | 0.8269 | 0.9187 | 0.9218 | 0.5249 | 0.7069 | | 0.0555 | 3.45 | 16000 | 0.0445 | 0.8568 | 0.9081 | 0.9838 | 0.9964 | 0.9858 | 0.9649 | 0.9681 | 0.8880 | 0.9585 | 0.9610 | 0.6510 | 0.7995 | 0.9922 | 0.9635 | 0.8996 | 0.9073 | 0.8582 | 0.9230 | 0.9257 | 0.5328 | 0.7093 | | 0.0528 | 3.56 | 16500 | 0.0477 | 0.8549 | 0.9053 | 0.9833 | 0.9958 | 0.9875 | 0.9668 | 0.9677 | 0.8740 | 0.9512 | 0.9631 | 0.6512 | 0.7902 | 0.9920 | 0.9618 | 0.9021 | 0.9036 | 0.8486 | 0.9185 | 0.9254 | 0.5348 | 0.7070 | | 0.043 | 3.67 | 17000 | 0.0439 | 0.8633 | 0.9173 | 0.9849 | 0.9960 | 0.9851 | 0.9860 | 0.9893 | 0.9114 | 0.9555 | 0.9656 | 0.6623 | 0.8046 | 0.9921 | 0.9666 | 0.9083 | 0.9158 | 0.8910 | 0.9197 | 0.9262 | 0.5391 | 0.7111 | | 0.0372 | 3.77 | 17500 | 0.0474 | 0.8555 | 0.9039 | 0.9836 | 0.9959 | 0.9876 | 0.9626 | 0.9647 | 0.8818 | 0.9556 | 0.9623 | 0.6393 | 0.7858 | 0.9921 | 0.9623 | 0.8999 | 0.9065 | 0.8526 | 0.9218 | 0.9264 | 0.5299 | 0.7082 | | 0.0614 | 3.88 | 18000 | 0.0463 | 0.8564 | 0.9088 | 0.9839 | 0.9959 | 0.9853 | 0.9644 | 0.9662 | 0.9035 | 0.9569 | 0.9638 | 0.6413 | 0.8025 | 0.9921 | 0.9643 | 0.8967 | 0.9020 | 0.8607 | 0.9202 | 0.9276 | 0.5330 | 0.7111 | | 0.0413 | 3.99 | 18500 | 0.0453 | 0.8579 | 0.9123 | 0.9841 | 0.9963 | 0.9848 | 0.9794 | 0.9828 | 0.8865 | 0.9613 | 0.9695 | 0.6526 | 0.7977 | 0.9922 | 0.9648 | 0.8991 | 0.9047 | 0.8629 | 0.9221 | 0.9274 | 0.5369 | 0.7112 | | 0.0386 | 4.1 | 19000 | 0.0438 | 0.8578 | 0.9109 | 0.9842 | 0.9959 | 0.9844 | 0.9649 | 0.9667 | 0.9154 | 0.9580 | 0.9662 | 0.6408 | 0.8062 | 0.9924 | 0.9644 | 0.8973 | 0.9025 | 0.8683 | 0.9196 | 0.9279 | 0.5340 | 0.7134 | | 0.0541 | 4.21 | 19500 | 0.0443 | 0.8577 | 0.9118 | 0.9840 | 0.9957 | 0.9847 | 0.9829 | 0.9872 | 0.8935 | 0.9594 | 0.9686 | 0.6265 | 0.8077 | 0.9921 | 0.9641 | 0.9017 | 0.9079 | 0.8621 | 0.9203 | 0.9277 | 0.5298 | 0.7133 | | 0.0409 | 4.31 | 20000 | 0.0433 | 0.8560 | 0.9083 | 0.9840 | 0.9959 | 0.9860 | 0.9670 | 0.9687 | 0.9020 | 0.9578 | 0.9632 | 0.6421 | 0.7918 | 0.9922 | 0.9652 | 0.8921 | 0.8966 | 0.8633 | 0.9206 | 0.9278 | 0.5349 | 0.7117 | | 0.0398 | 4.42 | 20500 | 0.0451 | 0.8581 | 0.9102 | 0.9840 | 0.9960 | 0.9859 | 0.9687 | 0.9685 | 0.8885 | 0.9597 | 0.9684 | 0.6554 | 0.8004 | 0.9922 | 0.9638 | 0.9000 | 0.9042 | 0.8595 | 0.9232 | 0.9266 | 0.5395 | 0.7144 | | 0.038 | 4.53 | 21000 | 0.0464 | 0.8608 | 0.9123 | 0.9843 | 0.9959 | 0.9866 | 0.9885 | 0.9907 | 0.8739 | 0.9616 | 0.9678 | 0.6398 | 0.8056 | 0.9921 | 0.9639 | 0.9088 | 0.9160 | 0.8657 | 0.9238 | 0.9273 | 0.5347 | 0.7150 | | 0.0295 | 4.64 | 21500 | 0.0433 | 0.8596 | 0.9094 | 0.9840 | 0.9960 | 0.9864 | 0.9641 | 0.9664 | 0.8985 | 0.9535 | 0.9582 | 0.6581 | 0.8033 | 0.9922 | 0.9633 | 0.9056 | 0.9102 | 0.8619 | 0.9195 | 0.9276 | 0.5408 | 0.7151 | | 0.0318 | 4.75 | 22000 | 0.0439 | 0.8600 | 0.9127 | 0.9842 | 0.9964 | 0.9848 | 0.9665 | 0.9676 | 0.8929 | 0.9627 | 0.9689 | 0.6656 | 0.8089 | 0.9923 | 0.9643 | 0.9007 | 0.9080 | 0.8645 | 0.9223 | 0.9283 | 0.5444 | 0.7156 | | 0.0377 | 4.85 | 22500 | 0.0429 | 0.8619 | 0.9125 | 0.9846 | 0.9963 | 0.9849 | 0.9633 | 0.9666 | 0.9115 | 0.9609 | 0.9689 | 0.6527 | 0.8069 | 0.9923 | 0.9654 | 0.9052 | 0.9104 | 0.8762 | 0.9217 | 0.9288 | 0.5407 | 0.7166 | | 0.0419 | 4.96 | 23000 | 0.0433 | 0.8611 | 0.9107 | 0.9846 | 0.9964 | 0.9857 | 0.9654 | 0.9664 | 0.9065 | 0.9591 | 0.9662 | 0.6491 | 0.8015 | 0.9923 | 0.9655 | 0.9017 | 0.9085 | 0.8749 | 0.9223 | 0.9289 | 0.5394 | 0.7160 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "background", "upper_torso", "left_pants", "right_patns", "skirts", "left_sleeve", "right_sleeve", "outer_collar", "inner_collar" ]

fshala/segformer-finetuned-sidewalk-10k-steps

# segformer-finetuned-sidewalk-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.3528 - Mean Iou: 0.0705 - Mean Accuracy: 0.1115 - Overall Accuracy: 0.5632 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.5861 - Accuracy Flat-sidewalk: 0.8653 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.0136 - Accuracy Flat-parkingdriveway: 0.0078 - Accuracy Flat-railtrack: 0.0 - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.0737 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.9444 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0012 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.8841 - Accuracy Nature-terrain: 0.2830 - Accuracy Sky: 0.0216 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.3993 - Iou Flat-sidewalk: 0.6672 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.0135 - Iou Flat-parkingdriveway: 0.0076 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.0712 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.2455 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0011 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.6484 - Iou Nature-terrain: 0.2515 - Iou Sky: 0.0216 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | 2.8114 | 0.93 | 100 | 2.3528 | 0.0705 | 0.1115 | 0.5632 | nan | 0.5861 | 0.8653 | 0.0 | 0.0136 | 0.0078 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0737 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9444 | 0.0 | 0.0012 | 0.0000 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8841 | 0.2830 | 0.0216 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3993 | 0.6672 | 0.0 | 0.0135 | 0.0076 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2455 | 0.0 | 0.0011 | 0.0000 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6484 | 0.2515 | 0.0216 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.0.0+cpu - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

vigneshgs7/segformer-b0-finetuned-segments-pageboundet-finaltry

# segformer-b0-finetuned-segments-pageboundet-finaltry This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the vigneshgs7/Boundary_detection_onelasttry dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - Mean Iou: nan - Mean Accuracy: nan - Overall Accuracy: nan - Accuracy Object: nan - Accuracy Surface: nan - Iou Object: nan - Iou Surface: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Object | Accuracy Surface | Iou Object | Iou Surface | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------:|:----------------:|:----------:|:-----------:| | 0.4616 | 2.0 | 20 | 0.4828 | nan | nan | nan | nan | nan | nan | nan | | 0.3359 | 4.0 | 40 | 0.3351 | nan | nan | nan | nan | nan | nan | nan | | 0.2898 | 6.0 | 60 | 0.2885 | nan | nan | nan | nan | nan | nan | nan | | 0.3041 | 8.0 | 80 | 0.2671 | nan | nan | nan | nan | nan | nan | nan | | 0.2429 | 10.0 | 100 | 0.2569 | nan | nan | nan | nan | nan | nan | nan | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1

[ "object", "surface" ]

eungitoto/segformer-b0-finetuned-segments-sidewalk-2

# segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.6351 - Mean Iou: 0.2834 - Mean Accuracy: 0.3374 - Overall Accuracy: 0.8443 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.9045 - Accuracy Flat-sidewalk: 0.9530 - Accuracy Flat-crosswalk: 0.6075 - Accuracy Flat-cyclinglane: 0.7798 - Accuracy Flat-parkingdriveway: 0.3400 - Accuracy Flat-railtrack: 0.0 - Accuracy Flat-curb: 0.5121 - Accuracy Human-person: 0.6170 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9458 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.6956 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.9045 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.4037 - Accuracy Construction-fenceguardrail: 0.3503 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: 0.0 - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3679 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9274 - Accuracy Nature-terrain: 0.8889 - Accuracy Sky: 0.9722 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0119 - Accuracy Void-static: 0.2879 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7249 - Iou Flat-sidewalk: 0.8500 - Iou Flat-crosswalk: 0.5367 - Iou Flat-cyclinglane: 0.7300 - Iou Flat-parkingdriveway: 0.2828 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.4008 - Iou Human-person: 0.4039 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.8291 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.5268 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7142 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3242 - Iou Construction-fenceguardrail: 0.2450 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.3031 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8429 - Iou Nature-terrain: 0.7587 - Iou Sky: 0.9308 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0117 - Iou Void-static: 0.2194 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | 2.6363 | 0.4 | 20 | 3.0746 | 0.0958 | 0.1387 | 0.6097 | nan | 0.4923 | 0.9140 | 0.0023 | 0.0115 | 0.0163 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.7446 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9002 | 0.0 | 0.0074 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8330 | 0.0676 | 0.7219 | 0.0 | 0.0 | 0.0033 | 0.0 | nan | 0.3615 | 0.6272 | 0.0023 | 0.0115 | 0.0138 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.4781 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3994 | 0.0 | 0.0072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6074 | 0.0646 | 0.6789 | 0.0 | 0.0 | 0.0033 | 0.0 | | 2.1728 | 0.8 | 40 | 2.1331 | 0.1057 | 0.1504 | 0.6493 | nan | 0.7183 | 0.9087 | 0.0 | 0.0011 | 0.0031 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8617 | 0.0 | 0.0025 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9374 | 0.0054 | 0.8823 | 0.0 | 0.0 | 0.0004 | 0.0 | nan | 0.4086 | 0.6905 | 0.0 | 0.0011 | 0.0030 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.5711 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4771 | 0.0 | 0.0025 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6142 | 0.0053 | 0.8190 | 0.0 | 0.0 | 0.0004 | 0.0 | | 1.8992 | 1.2 | 60 | 1.7800 | 0.1102 | 0.1549 | 0.6621 | nan | 0.6924 | 0.9401 | 0.0 | 0.0144 | 0.0013 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8730 | 0.0 | 0.0008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9193 | 0.0615 | 0.9003 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4255 | 0.6860 | 0.0 | 0.0144 | 0.0013 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.5819 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4994 | 0.0 | 0.0008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6468 | 0.0608 | 0.8313 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8919 | 1.6 | 80 | 1.6335 | 0.1201 | 0.1629 | 0.6809 | nan | 0.7313 | 0.9424 | 0.0 | 0.0501 | 0.0008 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8569 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8876 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9397 | 0.2331 | 0.8972 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4467 | 0.7033 | 0.0 | 0.0501 | 0.0008 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6179 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5237 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6657 | 0.2237 | 0.8501 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.63 | 2.0 | 100 | 1.4813 | 0.1342 | 0.1756 | 0.7089 | nan | 0.8218 | 0.9389 | 0.0 | 0.2047 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8384 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8802 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9479 | 0.4267 | 0.9098 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4844 | 0.7364 | 0.0 | 0.2025 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6453 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5371 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6948 | 0.4056 | 0.8548 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3523 | 2.4 | 120 | 1.3727 | 0.1467 | 0.1884 | 0.7335 | nan | 0.8524 | 0.9382 | 0.0 | 0.4096 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8653 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8659 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9450 | 0.5996 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5341 | 0.7489 | 0.0 | 0.4011 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6377 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5535 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7173 | 0.5393 | 0.8549 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2951 | 2.8 | 140 | 1.2957 | 0.1511 | 0.1932 | 0.7407 | nan | 0.8465 | 0.9414 | 0.0 | 0.4159 | 0.0029 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8775 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9327 | 0.7282 | 0.9275 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5327 | 0.7570 | 0.0 | 0.4072 | 0.0029 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6595 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5523 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7399 | 0.6208 | 0.8658 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3024 | 3.2 | 160 | 1.2389 | 0.1536 | 0.1941 | 0.7464 | nan | 0.8844 | 0.9356 | 0.0 | 0.5074 | 0.0044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8589 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8984 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9380 | 0.6688 | 0.9035 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5543 | 0.7672 | 0.0 | 0.4812 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5517 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7351 | 0.5888 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3291 | 3.6 | 180 | 1.1719 | 0.1582 | 0.1970 | 0.7516 | nan | 0.7975 | 0.9658 | 0.0001 | 0.5346 | 0.0113 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8905 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9044 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9127 | 0.7531 | 0.9290 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5791 | 0.7373 | 0.0001 | 0.5120 | 0.0111 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6910 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5522 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7708 | 0.6519 | 0.8725 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.171 | 4.0 | 200 | 1.1129 | 0.1583 | 0.1982 | 0.7564 | nan | 0.8861 | 0.9498 | 0.0 | 0.5324 | 0.0266 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8648 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9478 | 0.7150 | 0.9343 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5772 | 0.7708 | 0.0 | 0.5090 | 0.0259 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6945 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5713 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7383 | 0.6220 | 0.8744 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2553 | 4.4 | 220 | 1.0982 | 0.1597 | 0.2022 | 0.7524 | nan | 0.9524 | 0.8937 | 0.0 | 0.5760 | 0.0539 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8626 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9018 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9364 | 0.7568 | 0.9400 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5187 | 0.7967 | 0.0 | 0.5249 | 0.0509 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6982 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5609 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7580 | 0.6483 | 0.8736 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9428 | 4.8 | 240 | 1.0357 | 0.1662 | 0.2074 | 0.7694 | nan | 0.9300 | 0.9359 | 0.0074 | 0.6225 | 0.0780 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.8966 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9012 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9207 | 0.8155 | 0.9398 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5795 | 0.7982 | 0.0074 | 0.5725 | 0.0727 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.7107 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5673 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7794 | 0.6826 | 0.8785 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9843 | 5.2 | 260 | 0.9881 | 0.1689 | 0.2087 | 0.7706 | nan | 0.8682 | 0.9574 | 0.0704 | 0.6251 | 0.0810 | 0.0 | 0.0020 | 0.0 | 0.0 | 0.9139 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8783 | 0.0 | 0.0051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9202 | 0.8331 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6115 | 0.7686 | 0.0704 | 0.5919 | 0.0731 | 0.0 | 0.0020 | 0.0 | 0.0 | 0.7004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5797 | 0.0 | 0.0050 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7766 | 0.6863 | 0.8784 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9274 | 5.6 | 280 | 0.9701 | 0.1738 | 0.2166 | 0.7794 | nan | 0.9171 | 0.9276 | 0.1062 | 0.7376 | 0.1365 | 0.0 | 0.0097 | 0.0 | 0.0 | 0.9144 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8870 | 0.0 | 0.0102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9297 | 0.8383 | 0.9493 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6165 | 0.8027 | 0.1059 | 0.6193 | 0.1201 | 0.0 | 0.0096 | 0.0 | 0.0 | 0.6958 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5848 | 0.0 | 0.0101 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7777 | 0.6882 | 0.8794 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8762 | 6.0 | 300 | 0.9369 | 0.1850 | 0.2257 | 0.7864 | nan | 0.9056 | 0.9452 | 0.4152 | 0.6530 | 0.1945 | 0.0 | 0.0313 | 0.0 | 0.0 | 0.9224 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8770 | 0.0 | 0.0269 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9266 | 0.8372 | 0.9387 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6408 | 0.8042 | 0.3904 | 0.6126 | 0.1521 | 0.0 | 0.0310 | 0.0 | 0.0 | 0.6888 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5874 | 0.0 | 0.0257 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7821 | 0.6954 | 0.8809 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8044 | 6.4 | 320 | 0.9197 | 0.1903 | 0.2308 | 0.7896 | nan | 0.9174 | 0.9437 | 0.5151 | 0.6212 | 0.2095 | 0.0 | 0.0733 | 0.0 | 0.0 | 0.9091 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8953 | 0.0 | 0.0558 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9174 | 0.8372 | 0.9511 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6400 | 0.8072 | 0.4651 | 0.5881 | 0.1706 | 0.0 | 0.0714 | 0.0 | 0.0 | 0.7168 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5855 | 0.0 | 0.0512 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7905 | 0.7010 | 0.8817 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.7438 | 6.8 | 340 | 0.8776 | 0.1934 | 0.2344 | 0.7940 | nan | 0.9134 | 0.9414 | 0.5616 | 0.6671 | 0.2014 | 0.0 | 0.0966 | 0.0 | 0.0 | 0.9162 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9011 | 0.0 | 0.0716 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9231 | 0.8347 | 0.9422 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6545 | 0.8114 | 0.4818 | 0.6313 | 0.1657 | 0.0 | 0.0933 | 0.0 | 0.0 | 0.7053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5874 | 0.0 | 0.0639 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7945 | 0.6990 | 0.8886 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9671 | 7.2 | 360 | 0.8704 | 0.1975 | 0.2399 | 0.7955 | nan | 0.8871 | 0.9408 | 0.5863 | 0.6940 | 0.2363 | 0.0 | 0.1439 | 0.0 | 0.0 | 0.9029 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8684 | 0.0 | 0.1670 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9291 | 0.8532 | 0.9471 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6532 | 0.8126 | 0.4632 | 0.6345 | 0.1855 | 0.0 | 0.1361 | 0.0 | 0.0 | 0.7283 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5977 | 0.0 | 0.1406 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7862 | 0.6935 | 0.8840 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8191 | 7.6 | 380 | 0.8422 | 0.2048 | 0.2442 | 0.8033 | nan | 0.9159 | 0.9441 | 0.5265 | 0.7238 | 0.2684 | 0.0 | 0.1784 | 0.0003 | 0.0 | 0.8799 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8603 | 0.0 | 0.2762 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9402 | 0.8318 | 0.9551 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.6939 | 0.8112 | 0.4965 | 0.6614 | 0.2087 | 0.0 | 0.1667 | 0.0003 | 0.0 | 0.7490 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6063 | 0.0 | 0.2037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7830 | 0.7000 | 0.8823 | 0.0 | 0.0 | 0.0005 | 0.0 | | 0.8005 | 8.0 | 400 | 0.8414 | 0.1996 | 0.2372 | 0.7972 | nan | 0.9003 | 0.9492 | 0.5198 | 0.6659 | 0.1888 | 0.0 | 0.2424 | 0.0000 | 0.0 | 0.9048 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9291 | 0.0 | 0.1105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9216 | 0.7883 | 0.9434 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.6719 | 0.8049 | 0.4991 | 0.6372 | 0.1537 | 0.0 | 0.2152 | 0.0000 | 0.0 | 0.7254 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5841 | 0.0 | 0.0940 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8047 | 0.7032 | 0.8927 | 0.0 | 0.0 | 0.0001 | 0.0 | | 0.9294 | 8.4 | 420 | 0.8182 | 0.2036 | 0.2430 | 0.8029 | nan | 0.9269 | 0.9482 | 0.5270 | 0.6550 | 0.2482 | 0.0 | 0.2108 | 0.0002 | 0.0 | 0.9167 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8894 | 0.0 | 0.2316 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.9295 | 0.8273 | 0.9512 | 0.0 | 0.0 | 0.0007 | 0.0 | nan | 0.6841 | 0.8133 | 0.4924 | 0.6336 | 0.1979 | 0.0 | 0.1916 | 0.0002 | 0.0 | 0.7197 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6065 | 0.0 | 0.1800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.8014 | 0.7111 | 0.8901 | 0.0 | 0.0 | 0.0007 | 0.0 | | 0.7301 | 8.8 | 440 | 0.8036 | 0.2086 | 0.2527 | 0.8061 | nan | 0.9223 | 0.9419 | 0.5814 | 0.6931 | 0.2747 | 0.0 | 0.2847 | 0.0053 | 0.0 | 0.9361 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8273 | 0.0 | 0.3837 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9300 | 0.8531 | 0.9561 | 0.0 | 0.0 | 0.0008 | 0.0 | nan | 0.6964 | 0.8218 | 0.5116 | 0.6603 | 0.2195 | 0.0 | 0.2440 | 0.0053 | 0.0 | 0.6847 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6037 | 0.0 | 0.2526 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.7979 | 0.7074 | 0.8852 | 0.0 | 0.0 | 0.0008 | 0.0 | | 0.6754 | 9.2 | 460 | 0.7868 | 0.2083 | 0.2482 | 0.8070 | nan | 0.9098 | 0.9432 | 0.5608 | 0.7545 | 0.2089 | 0.0 | 0.2884 | 0.0073 | 0.0 | 0.9000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8991 | 0.0 | 0.2473 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9193 | 0.8499 | 0.9478 | 0.0 | 0.0 | 0.0023 | 0.0 | nan | 0.6748 | 0.8194 | 0.5305 | 0.6745 | 0.1787 | 0.0 | 0.2438 | 0.0073 | 0.0 | 0.7486 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6100 | 0.0 | 0.1831 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8067 | 0.7069 | 0.8942 | 0.0 | 0.0 | 0.0023 | 0.0 | | 0.7499 | 9.6 | 480 | 0.7756 | 0.2112 | 0.2511 | 0.8109 | nan | 0.9109 | 0.9429 | 0.5288 | 0.7550 | 0.2927 | 0.0 | 0.2838 | 0.0126 | 0.0 | 0.9117 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9054 | 0.0 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.9263 | 0.8295 | 0.9522 | 0.0 | 0.0 | 0.0039 | 0.0 | nan | 0.7098 | 0.8209 | 0.4936 | 0.7004 | 0.2259 | 0.0 | 0.2461 | 0.0126 | 0.0 | 0.7399 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6058 | 0.0 | 0.1980 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.8077 | 0.7226 | 0.8942 | 0.0 | 0.0 | 0.0039 | 0.0 | | 0.5878 | 10.0 | 500 | 0.7564 | 0.2131 | 0.2568 | 0.8123 | nan | 0.9099 | 0.9388 | 0.5922 | 0.7452 | 0.3135 | 0.0 | 0.2995 | 0.0235 | 0.0 | 0.9165 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8776 | 0.0 | 0.3744 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0008 | 0.0 | 0.0 | 0.9301 | 0.8555 | 0.9495 | 0.0 | 0.0 | 0.0047 | 0.0 | nan | 0.6945 | 0.8339 | 0.4657 | 0.6925 | 0.2436 | 0.0 | 0.2602 | 0.0235 | 0.0 | 0.7350 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6171 | 0.0 | 0.2630 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0008 | 0.0 | 0.0 | 0.7991 | 0.7150 | 0.8956 | 0.0 | 0.0 | 0.0046 | 0.0 | | 0.874 | 10.4 | 520 | 0.7601 | 0.2169 | 0.2584 | 0.8133 | nan | 0.9144 | 0.9378 | 0.5474 | 0.7449 | 0.3350 | 0.0 | 0.3237 | 0.0738 | 0.0 | 0.9292 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8918 | 0.0 | 0.3508 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.9232 | 0.8450 | 0.9579 | 0.0 | 0.0 | 0.0086 | 0.0 | nan | 0.7030 | 0.8214 | 0.5211 | 0.6962 | 0.2497 | 0.0 | 0.2740 | 0.0735 | 0.0 | 0.7398 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6252 | 0.0 | 0.2396 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.8120 | 0.7134 | 0.8942 | 0.0 | 0.0 | 0.0084 | 0.0 | | 0.5957 | 10.8 | 540 | 0.7557 | 0.2115 | 0.2534 | 0.8114 | nan | 0.8494 | 0.9518 | 0.6034 | 0.7916 | 0.2467 | 0.0 | 0.3290 | 0.0558 | 0.0 | 0.9140 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9268 | 0.0 | 0.2114 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0043 | 0.0 | 0.0 | 0.9189 | 0.8608 | 0.9472 | 0.0 | 0.0 | 0.0041 | 0.0 | nan | 0.7069 | 0.8234 | 0.4749 | 0.6717 | 0.2108 | 0.0 | 0.2916 | 0.0557 | 0.0 | 0.7444 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6064 | 0.0 | 0.1714 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0043 | 0.0 | 0.0 | 0.8108 | 0.7131 | 0.9005 | 0.0 | 0.0 | 0.0040 | 0.0 | | 0.5981 | 11.2 | 560 | 0.7396 | 0.2223 | 0.2641 | 0.8176 | nan | 0.8798 | 0.9418 | 0.5376 | 0.8258 | 0.2915 | 0.0 | 0.3517 | 0.1215 | 0.0 | 0.9240 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8754 | 0.0 | 0.4492 | 0.0029 | 0.0 | 0.0 | 0.0 | 0.0095 | 0.0 | 0.0 | 0.9420 | 0.8361 | 0.9583 | 0.0 | 0.0 | 0.0309 | 0.0 | nan | 0.7188 | 0.8224 | 0.5170 | 0.7030 | 0.2402 | 0.0 | 0.2981 | 0.1202 | 0.0 | 0.7507 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6314 | 0.0 | 0.3022 | 0.0028 | 0.0 | 0.0 | 0.0 | 0.0095 | 0.0 | 0.0 | 0.8028 | 0.7141 | 0.8963 | 0.0 | 0.0 | 0.0295 | 0.0 | | 0.5177 | 11.6 | 580 | 0.7403 | 0.2227 | 0.2665 | 0.8144 | nan | 0.9070 | 0.9314 | 0.6047 | 0.7349 | 0.3815 | 0.0 | 0.3395 | 0.1778 | 0.0 | 0.9082 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8715 | 0.0 | 0.4334 | 0.0040 | 0.0 | 0.0 | 0.0 | 0.0100 | 0.0 | 0.0 | 0.9464 | 0.8180 | 0.9571 | 0.0 | 0.0 | 0.0353 | 0.0 | nan | 0.6983 | 0.8321 | 0.4714 | 0.6965 | 0.2653 | 0.0 | 0.2899 | 0.1731 | 0.0 | 0.7622 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6327 | 0.0 | 0.2963 | 0.0038 | 0.0 | 0.0 | 0.0 | 0.0100 | 0.0 | 0.0 | 0.7972 | 0.7113 | 0.8992 | 0.0 | 0.0 | 0.0334 | 0.0 | | 0.5689 | 12.0 | 600 | 0.7382 | 0.2237 | 0.2642 | 0.8176 | nan | 0.9339 | 0.9440 | 0.5055 | 0.7365 | 0.3006 | 0.0 | 0.3610 | 0.1763 | 0.0 | 0.9328 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8772 | 0.0 | 0.4224 | 0.0153 | 0.0 | 0.0 | 0.0 | 0.0108 | 0.0 | 0.0 | 0.9320 | 0.8401 | 0.9553 | 0.0 | 0.0 | 0.0391 | 0.0 | nan | 0.7077 | 0.8244 | 0.4863 | 0.7017 | 0.2403 | 0.0 | 0.3038 | 0.1720 | 0.0 | 0.7422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6333 | 0.0 | 0.2928 | 0.0142 | 0.0 | 0.0 | 0.0 | 0.0108 | 0.0 | 0.0 | 0.8130 | 0.7274 | 0.8999 | 0.0 | 0.0 | 0.0366 | 0.0 | | 0.6379 | 12.4 | 620 | 0.7284 | 0.2260 | 0.2674 | 0.8171 | nan | 0.8613 | 0.9554 | 0.5848 | 0.7909 | 0.2547 | 0.0 | 0.3498 | 0.2235 | 0.0 | 0.9240 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.0 | 0.0 | 0.8959 | 0.0 | 0.4200 | 0.0197 | 0.0 | 0.0 | 0.0 | 0.0173 | 0.0 | 0.0 | 0.9153 | 0.8660 | 0.9535 | 0.0 | 0.0 | 0.0598 | 0.0 | nan | 0.7209 | 0.8117 | 0.5001 | 0.7144 | 0.2080 | 0.0 | 0.3020 | 0.2141 | 0.0 | 0.7682 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.0 | 0.0 | 0.6390 | 0.0 | 0.2812 | 0.0188 | 0.0 | 0.0 | 0.0 | 0.0172 | 0.0 | 0.0 | 0.8154 | 0.7159 | 0.9029 | 0.0 | 0.0 | 0.0546 | 0.0 | | 0.5772 | 12.8 | 640 | 0.7187 | 0.2317 | 0.2755 | 0.8213 | nan | 0.9200 | 0.9479 | 0.5902 | 0.7480 | 0.2776 | 0.0 | 0.3621 | 0.3136 | 0.0 | 0.9321 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.8618 | 0.0 | 0.4991 | 0.0422 | 0.0 | 0.0 | 0.0 | 0.0430 | 0.0 | 0.0 | 0.9282 | 0.8676 | 0.9574 | 0.0 | 0.0 | 0.0742 | 0.0 | nan | 0.7049 | 0.8337 | 0.4961 | 0.7056 | 0.2336 | 0.0 | 0.3085 | 0.2828 | 0.0 | 0.7610 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.6438 | 0.0 | 0.3241 | 0.0375 | 0.0 | 0.0 | 0.0 | 0.0427 | 0.0 | 0.0 | 0.8137 | 0.7203 | 0.9018 | 0.0 | 0.0 | 0.0667 | 0.0 | | 0.6958 | 13.2 | 660 | 0.6981 | 0.2346 | 0.2795 | 0.8243 | nan | 0.9034 | 0.9373 | 0.6079 | 0.8121 | 0.3564 | 0.0 | 0.3662 | 0.3246 | 0.0 | 0.9333 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0022 | 0.0 | 0.0 | 0.8780 | 0.0 | 0.4743 | 0.0491 | 0.0 | 0.0 | 0.0 | 0.0335 | 0.0 | 0.0 | 0.9305 | 0.8584 | 0.9573 | 0.0 | 0.0 | 0.0779 | 0.0 | nan | 0.7277 | 0.8370 | 0.4877 | 0.7263 | 0.2627 | 0.0 | 0.3095 | 0.2965 | 0.0 | 0.7655 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0022 | 0.0 | 0.0 | 0.6538 | 0.0 | 0.3213 | 0.0434 | 0.0 | 0.0 | 0.0 | 0.0333 | 0.0 | 0.0 | 0.8126 | 0.7263 | 0.9021 | 0.0 | 0.0 | 0.0692 | 0.0 | | 0.6015 | 13.6 | 680 | 0.7058 | 0.2382 | 0.2808 | 0.8256 | nan | 0.9422 | 0.9404 | 0.5065 | 0.7814 | 0.3153 | 0.0 | 0.3763 | 0.3868 | 0.0 | 0.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0052 | 0.0 | 0.0 | 0.9027 | 0.0 | 0.4368 | 0.1332 | 0.0 | 0.0 | 0.0 | 0.0725 | 0.0 | 0.0 | 0.9198 | 0.8596 | 0.9627 | 0.0 | 0.0 | 0.0749 | 0.0 | nan | 0.7195 | 0.8358 | 0.4934 | 0.7243 | 0.2502 | 0.0 | 0.3068 | 0.3232 | 0.0 | 0.7693 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0052 | 0.0 | 0.0 | 0.6496 | 0.0 | 0.3222 | 0.1099 | 0.0 | 0.0 | 0.0 | 0.0713 | 0.0 | 0.0 | 0.8216 | 0.7279 | 0.9005 | 0.0 | 0.0 | 0.0679 | 0.0 | | 0.5698 | 14.0 | 700 | 0.6973 | 0.2374 | 0.2806 | 0.8240 | nan | 0.9140 | 0.9468 | 0.5788 | 0.7557 | 0.3021 | 0.0 | 0.3873 | 0.3988 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0113 | 0.0 | 0.0 | 0.9088 | 0.0 | 0.3731 | 0.1032 | 0.0 | 0.0 | 0.0 | 0.0816 | 0.0 | 0.0 | 0.9156 | 0.8763 | 0.9563 | 0.0 | 0.0 | 0.0937 | 0.0 | nan | 0.7136 | 0.8338 | 0.4892 | 0.7176 | 0.2411 | 0.0 | 0.3250 | 0.3327 | 0.0 | 0.7658 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0113 | 0.0 | 0.0 | 0.6525 | 0.0 | 0.3009 | 0.0915 | 0.0 | 0.0 | 0.0 | 0.0804 | 0.0 | 0.0 | 0.8157 | 0.7135 | 0.9056 | 0.0 | 0.0 | 0.0825 | 0.0 | | 0.6438 | 14.4 | 720 | 0.6984 | 0.2417 | 0.2849 | 0.8258 | nan | 0.9185 | 0.9511 | 0.5585 | 0.7586 | 0.3002 | 0.0 | 0.3864 | 0.4324 | 0.0 | 0.9399 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0168 | 0.0 | 0.0 | 0.8707 | 0.0 | 0.4506 | 0.1372 | 0.0 | 0.0 | 0.0 | 0.0670 | 0.0 | 0.0 | 0.9270 | 0.8638 | 0.9586 | 0.0 | 0.0 | 0.1479 | 0.0 | nan | 0.7374 | 0.8238 | 0.5275 | 0.7116 | 0.2414 | 0.0 | 0.3184 | 0.3475 | 0.0 | 0.7622 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0167 | 0.0 | 0.0 | 0.6636 | 0.0 | 0.3196 | 0.1092 | 0.0 | 0.0 | 0.0 | 0.0661 | 0.0 | 0.0 | 0.8193 | 0.7246 | 0.9037 | 0.0 | 0.0 | 0.1249 | 0.0 | | 0.4882 | 14.8 | 740 | 0.6937 | 0.2419 | 0.2838 | 0.8280 | nan | 0.9320 | 0.9493 | 0.5035 | 0.7776 | 0.2815 | 0.0 | 0.4022 | 0.4547 | 0.0 | 0.9293 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0157 | 0.0 | 0.0 | 0.9167 | 0.0 | 0.3765 | 0.1535 | 0.0 | 0.0 | 0.0 | 0.0903 | 0.0 | 0.0 | 0.9199 | 0.8638 | 0.9618 | 0.0 | 0.0 | 0.1214 | 0.0 | nan | 0.7364 | 0.8293 | 0.4922 | 0.7276 | 0.2350 | 0.0 | 0.3321 | 0.3528 | 0.0 | 0.7749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0157 | 0.0 | 0.0 | 0.6534 | 0.0 | 0.2906 | 0.1250 | 0.0 | 0.0 | 0.0 | 0.0889 | 0.0 | 0.0 | 0.8270 | 0.7322 | 0.9064 | 0.0 | 0.0 | 0.1054 | 0.0 | | 0.4693 | 15.2 | 760 | 0.6931 | 0.2437 | 0.2883 | 0.8248 | nan | 0.9276 | 0.9436 | 0.5805 | 0.7401 | 0.3161 | 0.0 | 0.4023 | 0.4731 | 0.0 | 0.9225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0212 | 0.0 | 0.0 | 0.8888 | 0.0 | 0.4353 | 0.1921 | 0.0 | 0.0 | 0.0 | 0.0994 | 0.0 | 0.0 | 0.9260 | 0.8468 | 0.9584 | 0.0 | 0.0 | 0.1281 | 0.0 | nan | 0.7061 | 0.8324 | 0.5132 | 0.7048 | 0.2545 | 0.0 | 0.3326 | 0.3620 | 0.0 | 0.7827 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0212 | 0.0 | 0.0 | 0.6548 | 0.0 | 0.3164 | 0.1357 | 0.0 | 0.0 | 0.0 | 0.0970 | 0.0 | 0.0 | 0.8211 | 0.7338 | 0.9070 | 0.0 | 0.0 | 0.1113 | 0.0 | | 0.5884 | 15.6 | 780 | 0.6870 | 0.2460 | 0.2919 | 0.8280 | nan | 0.8965 | 0.9478 | 0.5938 | 0.7779 | 0.3113 | 0.0 | 0.4136 | 0.5281 | 0.0 | 0.9223 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0379 | 0.0 | 0.0 | 0.9103 | 0.0 | 0.3960 | 0.1587 | 0.0 | 0.0 | 0.0 | 0.1163 | 0.0 | 0.0 | 0.9203 | 0.8756 | 0.9641 | 0.0 | 0.0 | 0.1551 | 0.0 | nan | 0.7291 | 0.8308 | 0.5095 | 0.7130 | 0.2456 | 0.0 | 0.3393 | 0.3730 | 0.0 | 0.7832 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0378 | 0.0 | 0.0 | 0.6676 | 0.0 | 0.3097 | 0.1280 | 0.0 | 0.0 | 0.0 | 0.1127 | 0.0 | 0.0 | 0.8267 | 0.7249 | 0.9050 | 0.0 | 0.0 | 0.1296 | 0.0 | | 0.4822 | 16.0 | 800 | 0.6822 | 0.2490 | 0.2929 | 0.8277 | nan | 0.8592 | 0.9535 | 0.5867 | 0.8150 | 0.2845 | 0.0 | 0.4140 | 0.5151 | 0.0 | 0.9363 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0563 | 0.0 | 0.0 | 0.8924 | 0.0 | 0.3955 | 0.2003 | 0.0 | 0.0 | 0.0 | 0.1470 | 0.0 | 0.0 | 0.9358 | 0.8603 | 0.9569 | 0.0 | 0.0 | 0.1481 | 0.0 | nan | 0.7180 | 0.8218 | 0.5548 | 0.7288 | 0.2424 | 0.0 | 0.3294 | 0.3797 | 0.0 | 0.7752 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0562 | 0.0 | 0.0 | 0.6685 | 0.0 | 0.3157 | 0.1505 | 0.0 | 0.0 | 0.0 | 0.1400 | 0.0 | 0.0 | 0.8201 | 0.7296 | 0.9095 | 0.0 | 0.0 | 0.1267 | 0.0 | | 0.4741 | 16.4 | 820 | 0.6827 | 0.2488 | 0.2926 | 0.8283 | nan | 0.8902 | 0.9524 | 0.5983 | 0.7742 | 0.3112 | 0.0 | 0.4137 | 0.4934 | 0.0 | 0.9314 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0730 | 0.0 | 0.0 | 0.8856 | 0.0 | 0.4433 | 0.1811 | 0.0 | 0.0 | 0.0 | 0.1122 | 0.0 | 0.0 | 0.9397 | 0.8325 | 0.9600 | 0.0 | 0.0 | 0.1564 | 0.0 | nan | 0.7279 | 0.8282 | 0.5455 | 0.7332 | 0.2494 | 0.0 | 0.3343 | 0.3734 | 0.0 | 0.7841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0729 | 0.0 | 0.0 | 0.6686 | 0.0 | 0.3175 | 0.1309 | 0.0 | 0.0 | 0.0 | 0.1090 | 0.0 | 0.0 | 0.8181 | 0.7275 | 0.9094 | 0.0 | 0.0 | 0.1289 | 0.0 | | 0.5164 | 16.8 | 840 | 0.6803 | 0.2488 | 0.2928 | 0.8309 | nan | 0.9067 | 0.9498 | 0.5655 | 0.8037 | 0.3081 | 0.0 | 0.4061 | 0.5493 | 0.0 | 0.9276 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0686 | 0.0 | 0.0 | 0.9156 | 0.0 | 0.3162 | 0.1933 | 0.0 | 0.0 | 0.0 | 0.1543 | 0.0 | 0.0 | 0.9273 | 0.8698 | 0.9604 | 0.0 | 0.0 | 0.1324 | 0.0 | nan | 0.7404 | 0.8309 | 0.5307 | 0.7191 | 0.2548 | 0.0 | 0.3371 | 0.3740 | 0.0 | 0.7897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0685 | 0.0 | 0.0 | 0.6642 | 0.0 | 0.2698 | 0.1508 | 0.0 | 0.0 | 0.0 | 0.1469 | 0.0 | 0.0 | 0.8258 | 0.7322 | 0.9105 | 0.0 | 0.0 | 0.1132 | 0.0 | | 0.4384 | 17.2 | 860 | 0.6711 | 0.2512 | 0.2978 | 0.8313 | nan | 0.9294 | 0.9412 | 0.5986 | 0.7805 | 0.3288 | 0.0 | 0.4120 | 0.5585 | 0.0 | 0.9350 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0621 | 0.0 | 0.0 | 0.8890 | 0.0 | 0.4228 | 0.1557 | 0.0 | 0.0 | 0.0 | 0.1642 | 0.0 | 0.0 | 0.9272 | 0.8743 | 0.9600 | 0.0 | 0.0 | 0.1852 | 0.0 | nan | 0.7282 | 0.8363 | 0.5214 | 0.7324 | 0.2651 | 0.0 | 0.3417 | 0.3806 | 0.0 | 0.7894 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0620 | 0.0 | 0.0 | 0.6730 | 0.0 | 0.3112 | 0.1257 | 0.0 | 0.0 | 0.0 | 0.1559 | 0.0 | 0.0 | 0.8215 | 0.7310 | 0.9109 | 0.0 | 0.0 | 0.1533 | 0.0 | | 0.4316 | 17.6 | 880 | 0.6659 | 0.2556 | 0.3047 | 0.8338 | nan | 0.9368 | 0.9390 | 0.5718 | 0.8247 | 0.3282 | 0.0 | 0.4395 | 0.5804 | 0.0 | 0.9423 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0993 | 0.0 | 0.0 | 0.8502 | 0.0 | 0.4347 | 0.2537 | 0.0 | 0.0 | 0.0 | 0.2004 | 0.0 | 0.0 | 0.9375 | 0.8574 | 0.9656 | 0.0 | 0.0 | 0.1969 | 0.0 | nan | 0.7368 | 0.8437 | 0.5323 | 0.7437 | 0.2656 | 0.0 | 0.3588 | 0.3788 | 0.0 | 0.7690 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0989 | 0.0 | 0.0 | 0.6726 | 0.0 | 0.3185 | 0.1661 | 0.0 | 0.0 | 0.0 | 0.1845 | 0.0 | 0.0 | 0.8227 | 0.7293 | 0.9084 | 0.0 | 0.0 | 0.1607 | 0.0 | | 0.4505 | 18.0 | 900 | 0.6758 | 0.2542 | 0.2989 | 0.8298 | nan | 0.9136 | 0.9505 | 0.5574 | 0.7432 | 0.2982 | 0.0 | 0.4386 | 0.5360 | 0.0 | 0.9437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1767 | 0.0 | 0.0 | 0.9093 | 0.0 | 0.4136 | 0.2006 | 0.0 | 0.0 | 0.0 | 0.1824 | 0.0 | 0.0 | 0.9305 | 0.8321 | 0.9575 | 0.0 | 0.0 | 0.1794 | 0.0 | nan | 0.7194 | 0.8331 | 0.5212 | 0.7112 | 0.2453 | 0.0 | 0.3458 | 0.3758 | 0.0 | 0.7782 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1753 | 0.0 | 0.0 | 0.6756 | 0.0 | 0.3193 | 0.1549 | 0.0 | 0.0 | 0.0 | 0.1715 | 0.0 | 0.0 | 0.8259 | 0.7307 | 0.9115 | 0.0 | 0.0 | 0.1480 | 0.0 | | 0.4388 | 18.4 | 920 | 0.6661 | 0.2565 | 0.3072 | 0.8297 | nan | 0.8822 | 0.9428 | 0.6160 | 0.8143 | 0.3081 | 0.0 | 0.4454 | 0.5414 | 0.0 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2064 | 0.0 | 0.0 | 0.8709 | 0.0 | 0.4429 | 0.2824 | 0.0 | 0.0 | 0.0 | 0.1963 | 0.0 | 0.0 | 0.9268 | 0.8822 | 0.9626 | 0.0 | 0.0 | 0.1975 | 0.0 | nan | 0.7157 | 0.8393 | 0.4520 | 0.7274 | 0.2555 | 0.0 | 0.3630 | 0.3766 | 0.0 | 0.8002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2041 | 0.0 | 0.0 | 0.6728 | 0.0 | 0.3199 | 0.1873 | 0.0 | 0.0 | 0.0 | 0.1827 | 0.0 | 0.0 | 0.8220 | 0.7298 | 0.9113 | 0.0 | 0.0 | 0.1622 | 0.0 | | 0.361 | 18.8 | 940 | 0.6628 | 0.2605 | 0.3096 | 0.8323 | nan | 0.8951 | 0.9452 | 0.5626 | 0.8170 | 0.3278 | 0.0 | 0.4493 | 0.5721 | 0.0 | 0.9407 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2403 | 0.0 | 0.0 | 0.8579 | 0.0 | 0.4486 | 0.2506 | 0.0 | 0.0 | 0.0 | 0.1961 | 0.0 | 0.0 | 0.9287 | 0.8857 | 0.9658 | 0.0 | 0.0 | 0.2412 | 0.0 | nan | 0.7357 | 0.8323 | 0.5378 | 0.7362 | 0.2625 | 0.0 | 0.3491 | 0.3683 | 0.0 | 0.7867 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2364 | 0.0 | 0.0 | 0.6806 | 0.0 | 0.3224 | 0.1758 | 0.0 | 0.0 | 0.0 | 0.1821 | 0.0 | 0.0 | 0.8223 | 0.7321 | 0.9113 | 0.0 | 0.0 | 0.1866 | 0.0 | | 0.3828 | 19.2 | 960 | 0.6739 | 0.2605 | 0.3097 | 0.8287 | nan | 0.8388 | 0.9556 | 0.5834 | 0.8373 | 0.2736 | 0.0 | 0.4234 | 0.6044 | 0.0 | 0.9343 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2668 | 0.0 | 0.0 | 0.8799 | 0.0 | 0.4288 | 0.2738 | 0.0 | 0.0 | 0.0 | 0.2310 | 0.0 | 0.0 | 0.9154 | 0.8914 | 0.9607 | 0.0 | 0.0 | 0.2327 | 0.0 | nan | 0.7156 | 0.8243 | 0.5543 | 0.6957 | 0.2336 | 0.0 | 0.3482 | 0.3805 | 0.0 | 0.7961 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2613 | 0.0 | 0.0 | 0.6838 | 0.0 | 0.3288 | 0.1820 | 0.0 | 0.0 | 0.0 | 0.2107 | 0.0 | 0.0 | 0.8227 | 0.7190 | 0.9134 | 0.0 | 0.0 | 0.1857 | 0.0 | | 0.6092 | 19.6 | 980 | 0.6651 | 0.2619 | 0.3102 | 0.8331 | nan | 0.9454 | 0.9455 | 0.5339 | 0.7741 | 0.3170 | 0.0 | 0.4542 | 0.6123 | 0.0 | 0.9329 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2972 | 0.0 | 0.0 | 0.9051 | 0.0 | 0.3950 | 0.2431 | 0.0 | 0.0 | 0.0 | 0.2317 | 0.0 | 0.0 | 0.9022 | 0.8711 | 0.9631 | 0.0 | 0.0 | 0.2214 | 0.0 | nan | 0.7209 | 0.8382 | 0.5155 | 0.7300 | 0.2616 | 0.0 | 0.3551 | 0.3647 | 0.0 | 0.7986 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2900 | 0.0 | 0.0 | 0.6842 | 0.0 | 0.3113 | 0.1817 | 0.0 | 0.0 | 0.0 | 0.2123 | 0.0 | 0.0 | 0.8260 | 0.7207 | 0.9135 | 0.0 | 0.0 | 0.1808 | 0.0 | | 0.4141 | 20.0 | 1000 | 0.6641 | 0.2647 | 0.3139 | 0.8316 | nan | 0.9264 | 0.9481 | 0.5341 | 0.7649 | 0.3226 | 0.0 | 0.4334 | 0.5668 | 0.0 | 0.9405 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3605 | 0.0 | 0.0 | 0.8534 | 0.0 | 0.4698 | 0.2992 | 0.0 | 0.0 | 0.0 | 0.2259 | 0.0 | 0.0 | 0.9166 | 0.8909 | 0.9625 | 0.0 | 0.0 | 0.2556 | 0.0 | nan | 0.7208 | 0.8365 | 0.5058 | 0.7259 | 0.2609 | 0.0 | 0.3563 | 0.3898 | 0.0 | 0.7969 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3435 | 0.0 | 0.0 | 0.6834 | 0.0 | 0.3347 | 0.1917 | 0.0 | 0.0 | 0.0 | 0.2066 | 0.0 | 0.0 | 0.8200 | 0.7153 | 0.9160 | 0.0 | 0.0 | 0.1970 | 0.0 | | 0.4172 | 20.4 | 1020 | 0.6580 | 0.2638 | 0.3124 | 0.8320 | nan | 0.8716 | 0.9580 | 0.6106 | 0.7824 | 0.2688 | 0.0 | 0.4657 | 0.5675 | 0.0 | 0.9364 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3450 | 0.0 | 0.0 | 0.8929 | 0.0 | 0.4323 | 0.3108 | 0.0 | 0.0 | 0.0 | 0.2097 | 0.0 | 0.0 | 0.9226 | 0.8574 | 0.9667 | 0.0 | 0.0 | 0.2240 | 0.0 | nan | 0.7318 | 0.8241 | 0.5236 | 0.7430 | 0.2310 | 0.0 | 0.3535 | 0.3745 | 0.0 | 0.8019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3327 | 0.0 | 0.0 | 0.6804 | 0.0 | 0.3154 | 0.2001 | 0.0 | 0.0 | 0.0 | 0.1933 | 0.0 | 0.0 | 0.8345 | 0.7404 | 0.9137 | 0.0 | 0.0 | 0.1768 | 0.0 | | 0.4116 | 20.8 | 1040 | 0.6727 | 0.2634 | 0.3088 | 0.8344 | nan | 0.8931 | 0.9598 | 0.6058 | 0.7902 | 0.2737 | 0.0 | 0.4378 | 0.5670 | 0.0 | 0.9385 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3090 | 0.0 | 0.0 | 0.8957 | 0.0 | 0.3829 | 0.2471 | 0.0 | 0.0 | 0.0 | 0.2405 | 0.0 | 0.0 | 0.9260 | 0.8616 | 0.9612 | 0.0 | 0.0 | 0.2085 | 0.0 | nan | 0.7426 | 0.8232 | 0.5412 | 0.7417 | 0.2388 | 0.0 | 0.3514 | 0.3729 | 0.0 | 0.7984 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2989 | 0.0 | 0.0 | 0.6842 | 0.0 | 0.3072 | 0.1826 | 0.0 | 0.0 | 0.0 | 0.2174 | 0.0 | 0.0 | 0.8303 | 0.7357 | 0.9163 | 0.0 | 0.0 | 0.1736 | 0.0 | | 0.422 | 21.2 | 1060 | 0.6688 | 0.2681 | 0.3141 | 0.8333 | nan | 0.8560 | 0.9509 | 0.5973 | 0.8216 | 0.3325 | 0.0 | 0.4396 | 0.5635 | 0.0 | 0.9370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4310 | 0.0 | 0.0 | 0.9214 | 0.0 | 0.3744 | 0.2274 | 0.0 | 0.0 | 0.0 | 0.2551 | 0.0 | 0.0 | 0.9186 | 0.8724 | 0.9636 | 0.0 | 0.0 | 0.2176 | 0.0 | nan | 0.7133 | 0.8257 | 0.5657 | 0.7377 | 0.2744 | 0.0 | 0.3530 | 0.3860 | 0.0 | 0.8069 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4029 | 0.0 | 0.0 | 0.6819 | 0.0 | 0.2941 | 0.1799 | 0.0 | 0.0 | 0.0 | 0.2286 | 0.0 | 0.0 | 0.8336 | 0.7400 | 0.9167 | 0.0 | 0.0 | 0.1740 | 0.0 | | 0.3686 | 21.6 | 1080 | 0.6515 | 0.2695 | 0.3171 | 0.8381 | nan | 0.9222 | 0.9530 | 0.6073 | 0.7765 | 0.3097 | 0.0 | 0.4667 | 0.6143 | 0.0 | 0.9413 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4369 | 0.0 | 0.0 | 0.9051 | 0.0 | 0.3810 | 0.2432 | 0.0 | 0.0 | 0.0 | 0.2461 | 0.0 | 0.0 | 0.9233 | 0.8697 | 0.9598 | 0.0 | 0.0 | 0.2258 | 0.0 | nan | 0.7351 | 0.8391 | 0.5477 | 0.7423 | 0.2621 | 0.0 | 0.3677 | 0.3792 | 0.0 | 0.7985 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4059 | 0.0 | 0.0 | 0.6838 | 0.0 | 0.3067 | 0.1912 | 0.0 | 0.0 | 0.0 | 0.2229 | 0.0 | 0.0 | 0.8352 | 0.7458 | 0.9177 | 0.0 | 0.0 | 0.1822 | 0.0 | | 0.3979 | 22.0 | 1100 | 0.6568 | 0.2693 | 0.3208 | 0.8330 | nan | 0.8615 | 0.9551 | 0.6160 | 0.7842 | 0.3245 | 0.0 | 0.4468 | 0.5568 | 0.0 | 0.9365 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4700 | 0.0 | 0.0 | 0.8862 | 0.0 | 0.4119 | 0.3784 | 0.0 | 0.0 | 0.0 | 0.2737 | 0.0 | 0.0 | 0.9224 | 0.8753 | 0.9673 | 0.0 | 0.0 | 0.2389 | 0.0 | nan | 0.7164 | 0.8363 | 0.4831 | 0.7147 | 0.2654 | 0.0 | 0.3574 | 0.3850 | 0.0 | 0.8012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4289 | 0.0 | 0.0 | 0.6866 | 0.0 | 0.3288 | 0.2315 | 0.0 | 0.0 | 0.0 | 0.2423 | 0.0 | 0.0 | 0.8331 | 0.7420 | 0.9164 | 0.0 | 0.0 | 0.1857 | 0.0 | | 0.4441 | 22.4 | 1120 | 0.6608 | 0.2695 | 0.3167 | 0.8365 | nan | 0.9346 | 0.9529 | 0.5573 | 0.7570 | 0.2894 | 0.0 | 0.4630 | 0.5627 | 0.0 | 0.9363 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4252 | 0.0 | 0.0 | 0.8931 | 0.0 | 0.4402 | 0.3039 | 0.0 | 0.0 | 0.0 | 0.2724 | 0.0 | 0.0 | 0.9202 | 0.8783 | 0.9636 | 0.0 | 0.0 | 0.2175 | 0.0 | nan | 0.7278 | 0.8391 | 0.5251 | 0.7202 | 0.2519 | 0.0 | 0.3607 | 0.3867 | 0.0 | 0.8006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3984 | 0.0 | 0.0 | 0.6891 | 0.0 | 0.3342 | 0.2181 | 0.0 | 0.0 | 0.0 | 0.2424 | 0.0 | 0.0 | 0.8331 | 0.7397 | 0.9175 | 0.0 | 0.0 | 0.1767 | 0.0 | | 0.4551 | 22.8 | 1140 | 0.6578 | 0.2728 | 0.3240 | 0.8349 | nan | 0.9032 | 0.9512 | 0.5769 | 0.7527 | 0.3419 | 0.0 | 0.4811 | 0.6010 | 0.0 | 0.9427 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5591 | 0.0 | 0.0 | 0.9050 | 0.0 | 0.4264 | 0.3202 | 0.0 | 0.0 | 0.0 | 0.2784 | 0.0 | 0.0 | 0.9086 | 0.8781 | 0.9624 | 0.0 | 0.0 | 0.2286 | 0.0 | nan | 0.7093 | 0.8423 | 0.5256 | 0.7125 | 0.2784 | 0.0 | 0.3686 | 0.3908 | 0.0 | 0.8046 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4909 | 0.0 | 0.0 | 0.6934 | 0.0 | 0.3288 | 0.2186 | 0.0 | 0.0 | 0.0 | 0.2458 | 0.0 | 0.0 | 0.8315 | 0.7326 | 0.9183 | 0.0 | 0.0 | 0.1832 | 0.0 | | 0.4407 | 23.2 | 1160 | 0.6450 | 0.2690 | 0.3193 | 0.8365 | nan | 0.9042 | 0.9432 | 0.6099 | 0.7794 | 0.3788 | 0.0 | 0.4657 | 0.6034 | 0.0 | 0.9444 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4627 | 0.0 | 0.0 | 0.9219 | 0.0 | 0.3212 | 0.2968 | 0.0 | 0.0 | 0.0 | 0.2736 | 0.0 | 0.0 | 0.9216 | 0.8775 | 0.9650 | 0.0 | 0.0 | 0.1872 | 0.0 | nan | 0.7256 | 0.8449 | 0.5092 | 0.7254 | 0.2939 | 0.0 | 0.3773 | 0.3916 | 0.0 | 0.7977 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4162 | 0.0 | 0.0 | 0.6820 | 0.0 | 0.2634 | 0.2125 | 0.0 | 0.0 | 0.0 | 0.2438 | 0.0 | 0.0 | 0.8376 | 0.7448 | 0.9180 | 0.0 | 0.0 | 0.1607 | 0.0 | | 0.4254 | 23.6 | 1180 | 0.6625 | 0.2688 | 0.3188 | 0.8334 | nan | 0.9195 | 0.9530 | 0.6182 | 0.6861 | 0.3235 | 0.0 | 0.4638 | 0.5651 | 0.0 | 0.9376 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5024 | 0.0 | 0.0 | 0.8938 | 0.0 | 0.3684 | 0.3308 | 0.0 | 0.0 | 0.0 | 0.2762 | 0.0 | 0.0 | 0.9336 | 0.8559 | 0.9677 | 0.0 | 0.0 | 0.2428 | 0.0 | nan | 0.6894 | 0.8508 | 0.4903 | 0.6638 | 0.2781 | 0.0 | 0.3752 | 0.3880 | 0.0 | 0.8096 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4447 | 0.0 | 0.0 | 0.6880 | 0.0 | 0.3020 | 0.2205 | 0.0 | 0.0 | 0.0 | 0.2448 | 0.0 | 0.0 | 0.8333 | 0.7482 | 0.9180 | 0.0 | 0.0 | 0.1936 | 0.0 | | 0.3237 | 24.0 | 1200 | 0.6369 | 0.2716 | 0.3205 | 0.8382 | nan | 0.9325 | 0.9453 | 0.5367 | 0.7718 | 0.3618 | 0.0 | 0.4612 | 0.6096 | 0.0 | 0.9404 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4799 | 0.0 | 0.0 | 0.8972 | 0.0 | 0.4341 | 0.2409 | 0.0 | 0.0 | 0.0 | 0.2640 | 0.0 | 0.0 | 0.9304 | 0.8631 | 0.9614 | 0.0 | 0.0 | 0.2649 | 0.0 | nan | 0.7146 | 0.8491 | 0.5225 | 0.7157 | 0.2907 | 0.0 | 0.3681 | 0.3802 | 0.0 | 0.8074 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4313 | 0.0 | 0.0 | 0.6964 | 0.0 | 0.3215 | 0.1884 | 0.0 | 0.0 | 0.0 | 0.2369 | 0.0 | 0.0 | 0.8355 | 0.7482 | 0.9192 | 0.0 | 0.0 | 0.2078 | 0.0 | | 0.365 | 24.4 | 1220 | 0.6506 | 0.2730 | 0.3214 | 0.8385 | nan | 0.9277 | 0.9542 | 0.5852 | 0.7626 | 0.2932 | 0.0 | 0.4719 | 0.5930 | 0.0 | 0.9376 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5711 | 0.0 | 0.0 | 0.9051 | 0.0 | 0.4031 | 0.2609 | 0.0 | 0.0 | 0.0 | 0.2769 | 0.0 | 0.0 | 0.9232 | 0.8704 | 0.9705 | 0.0 | 0.0 | 0.2199 | 0.0 | nan | 0.7176 | 0.8458 | 0.5448 | 0.7225 | 0.2537 | 0.0 | 0.3714 | 0.3947 | 0.0 | 0.8089 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4881 | 0.0 | 0.0 | 0.6931 | 0.0 | 0.3242 | 0.1968 | 0.0 | 0.0 | 0.0 | 0.2446 | 0.0 | 0.0 | 0.8333 | 0.7446 | 0.9172 | 0.0 | 0.0 | 0.1814 | 0.0 | | 0.4789 | 24.8 | 1240 | 0.6483 | 0.2759 | 0.3270 | 0.8389 | nan | 0.9107 | 0.9543 | 0.6023 | 0.7839 | 0.2900 | 0.0 | 0.4549 | 0.5867 | 0.0 | 0.9412 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6129 | 0.0 | 0.0 | 0.8747 | 0.0 | 0.4464 | 0.3362 | 0.0 | 0.0 | 0.0 | 0.2824 | 0.0 | 0.0 | 0.9350 | 0.8723 | 0.9630 | 0.0 | 0.0 | 0.2702 | 0.0 | nan | 0.7231 | 0.8439 | 0.5571 | 0.7132 | 0.2529 | 0.0 | 0.3650 | 0.3984 | 0.0 | 0.8099 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5013 | 0.0 | 0.0 | 0.7005 | 0.0 | 0.3384 | 0.2177 | 0.0 | 0.0 | 0.0 | 0.2474 | 0.0 | 0.0 | 0.8330 | 0.7510 | 0.9199 | 0.0 | 0.0 | 0.2094 | 0.0 | | 0.5162 | 25.2 | 1260 | 0.6464 | 0.2760 | 0.3278 | 0.8383 | nan | 0.9063 | 0.9529 | 0.6199 | 0.7501 | 0.3129 | 0.0 | 0.4830 | 0.6013 | 0.0 | 0.9348 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6178 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.4529 | 0.2548 | 0.0 | 0.0 | 0.0 | 0.3034 | 0.0 | 0.0 | 0.9285 | 0.8805 | 0.9658 | 0.0 | 0.0 | 0.2889 | 0.0 | nan | 0.7113 | 0.8456 | 0.5127 | 0.7169 | 0.2679 | 0.0 | 0.3825 | 0.3913 | 0.0 | 0.8162 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5243 | 0.0 | 0.0 | 0.7037 | 0.0 | 0.3354 | 0.1923 | 0.0 | 0.0 | 0.0 | 0.2616 | 0.0 | 0.0 | 0.8367 | 0.7464 | 0.9201 | 0.0 | 0.0 | 0.2177 | 0.0 | | 0.3914 | 25.6 | 1280 | 0.6455 | 0.2748 | 0.3256 | 0.8379 | nan | 0.9405 | 0.9451 | 0.5647 | 0.7560 | 0.3129 | 0.0 | 0.4521 | 0.5869 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6490 | 0.0 | 0.0 | 0.8990 | 0.0 | 0.3980 | 0.3299 | 0.0 | 0.0 | 0.0 | 0.3075 | 0.0 | 0.0 | 0.9318 | 0.8666 | 0.9697 | 0.0 | 0.0 | 0.2156 | 0.0 | nan | 0.7086 | 0.8472 | 0.5238 | 0.7070 | 0.2627 | 0.0 | 0.3769 | 0.4007 | 0.0 | 0.8069 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5155 | 0.0 | 0.0 | 0.6933 | 0.0 | 0.3223 | 0.2192 | 0.0 | 0.0 | 0.0 | 0.2646 | 0.0 | 0.0 | 0.8382 | 0.7529 | 0.9194 | 0.0 | 0.0 | 0.1835 | 0.0 | | 0.3738 | 26.0 | 1300 | 0.6373 | 0.2726 | 0.3267 | 0.8361 | nan | 0.8998 | 0.9382 | 0.5957 | 0.7798 | 0.3951 | 0.0 | 0.5195 | 0.5915 | 0.0 | 0.9516 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5415 | 0.0 | 0.0 | 0.8902 | 0.0 | 0.4308 | 0.2702 | 0.0 | 0.0 | 0.0 | 0.2883 | 0.0 | 0.0 | 0.9131 | 0.9061 | 0.9652 | 0.0 | 0.0 | 0.2309 | 0.0 | nan | 0.7155 | 0.8478 | 0.5211 | 0.7238 | 0.2994 | 0.0 | 0.3901 | 0.3975 | 0.0 | 0.7894 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4424 | 0.0 | 0.0 | 0.6989 | 0.0 | 0.3224 | 0.2038 | 0.0 | 0.0 | 0.0 | 0.2529 | 0.0 | 0.0 | 0.8274 | 0.7245 | 0.9210 | 0.0 | 0.0 | 0.1919 | 0.0 | | 0.3512 | 26.4 | 1320 | 0.6418 | 0.2763 | 0.3321 | 0.8362 | nan | 0.9184 | 0.9437 | 0.6087 | 0.7657 | 0.3338 | 0.0 | 0.4730 | 0.5723 | 0.0 | 0.9370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6904 | 0.0 | 0.0 | 0.8705 | 0.0 | 0.4566 | 0.3468 | 0.0 | 0.0 | 0.0 | 0.3086 | 0.0 | 0.0 | 0.9188 | 0.8848 | 0.9680 | 0.0 | 0.0 | 0.2939 | 0.0 | nan | 0.7141 | 0.8468 | 0.5263 | 0.6994 | 0.2719 | 0.0 | 0.3787 | 0.4026 | 0.0 | 0.8163 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5194 | 0.0 | 0.0 | 0.6977 | 0.0 | 0.3261 | 0.2179 | 0.0 | 0.0 | 0.0 | 0.2645 | 0.0 | 0.0 | 0.8347 | 0.7424 | 0.9208 | 0.0 | 0.0 | 0.2151 | 0.0 | | 0.4588 | 26.8 | 1340 | 0.6435 | 0.2748 | 0.3252 | 0.8395 | nan | 0.8778 | 0.9551 | 0.6037 | 0.8296 | 0.2888 | 0.0 | 0.4736 | 0.6074 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5804 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.4158 | 0.2494 | 0.0 | 0.0 | 0.0 | 0.2931 | 0.0 | 0.0 | 0.9314 | 0.8776 | 0.9691 | 0.0 | 0.0 | 0.2711 | 0.0 | nan | 0.7259 | 0.8395 | 0.5360 | 0.7373 | 0.2532 | 0.0 | 0.3753 | 0.3901 | 0.0 | 0.8111 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4937 | 0.0 | 0.0 | 0.7002 | 0.0 | 0.3175 | 0.1880 | 0.0 | 0.0 | 0.0 | 0.2560 | 0.0 | 0.0 | 0.8377 | 0.7488 | 0.9216 | 0.0 | 0.0 | 0.2116 | 0.0 | | 0.3339 | 27.2 | 1360 | 0.6434 | 0.2732 | 0.3252 | 0.8375 | nan | 0.9120 | 0.9427 | 0.6126 | 0.7717 | 0.3484 | 0.0 | 0.4983 | 0.5931 | 0.0 | 0.9441 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6112 | 0.0 | 0.0 | 0.9134 | 0.0 | 0.3259 | 0.2733 | 0.0 | 0.0 | 0.0 | 0.2995 | 0.0 | 0.0 | 0.9217 | 0.8908 | 0.9681 | 0.0 | 0.0 | 0.2298 | 0.0 | nan | 0.7060 | 0.8513 | 0.5062 | 0.7258 | 0.2805 | 0.0 | 0.3963 | 0.3985 | 0.0 | 0.8093 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4980 | 0.0 | 0.0 | 0.6925 | 0.0 | 0.2695 | 0.2074 | 0.0 | 0.0 | 0.0 | 0.2605 | 0.0 | 0.0 | 0.8341 | 0.7418 | 0.9222 | 0.0 | 0.0 | 0.1907 | 0.0 | | 0.3599 | 27.6 | 1380 | 0.6405 | 0.2755 | 0.3266 | 0.8384 | nan | 0.9207 | 0.9422 | 0.6103 | 0.7769 | 0.3508 | 0.0 | 0.4741 | 0.5936 | 0.0 | 0.9373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6317 | 0.0 | 0.0 | 0.9056 | 0.0 | 0.3968 | 0.2528 | 0.0 | 0.0 | 0.0 | 0.3090 | 0.0 | 0.0 | 0.9369 | 0.8342 | 0.9656 | 0.0 | 0.0 | 0.2645 | 0.0 | nan | 0.7140 | 0.8470 | 0.5370 | 0.7255 | 0.2798 | 0.0 | 0.3807 | 0.3961 | 0.0 | 0.8127 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4994 | 0.0 | 0.0 | 0.6990 | 0.0 | 0.3122 | 0.1956 | 0.0 | 0.0 | 0.0 | 0.2654 | 0.0 | 0.0 | 0.8335 | 0.7405 | 0.9227 | 0.0 | 0.0 | 0.2059 | 0.0 | | 0.4722 | 28.0 | 1400 | 0.6423 | 0.2776 | 0.3307 | 0.8391 | nan | 0.8828 | 0.9548 | 0.6152 | 0.7934 | 0.3135 | 0.0 | 0.4904 | 0.5925 | 0.0 | 0.9444 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6486 | 0.0 | 0.0 | 0.8909 | 0.0 | 0.4174 | 0.3296 | 0.0 | 0.0 | 0.0 | 0.3117 | 0.0 | 0.0 | 0.9236 | 0.8846 | 0.9647 | 0.0 | 0.0 | 0.2867 | 0.0 | nan | 0.7319 | 0.8373 | 0.5339 | 0.7168 | 0.2670 | 0.0 | 0.3887 | 0.3930 | 0.0 | 0.8085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5058 | 0.0 | 0.0 | 0.7043 | 0.0 | 0.3277 | 0.2334 | 0.0 | 0.0 | 0.0 | 0.2652 | 0.0 | 0.0 | 0.8358 | 0.7486 | 0.9243 | 0.0 | 0.0 | 0.2164 | 0.0 | | 0.3999 | 28.4 | 1420 | 0.6573 | 0.2758 | 0.3279 | 0.8344 | nan | 0.9074 | 0.9564 | 0.6044 | 0.6972 | 0.3170 | 0.0 | 0.4638 | 0.5768 | 0.0 | 0.9393 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6698 | 0.0 | 0.0 | 0.8872 | 0.0 | 0.4259 | 0.3229 | 0.0 | 0.0 | 0.0 | 0.3066 | 0.0 | 0.0 | 0.9183 | 0.8809 | 0.9685 | 0.0 | 0.0 | 0.3048 | 0.0 | nan | 0.7038 | 0.8367 | 0.5552 | 0.6687 | 0.2655 | 0.0 | 0.3707 | 0.3920 | 0.0 | 0.8207 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5299 | 0.0 | 0.0 | 0.7058 | 0.0 | 0.3154 | 0.2230 | 0.0 | 0.0 | 0.0 | 0.2644 | 0.0 | 0.0 | 0.8368 | 0.7440 | 0.9234 | 0.0 | 0.0 | 0.2197 | 0.0 | | 0.3251 | 28.8 | 1440 | 0.6375 | 0.2770 | 0.3291 | 0.8408 | nan | 0.9199 | 0.9489 | 0.6086 | 0.7769 | 0.3191 | 0.0 | 0.4958 | 0.5862 | 0.0 | 0.9461 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6409 | 0.0 | 0.0 | 0.8994 | 0.0 | 0.4118 | 0.2941 | 0.0 | 0.0 | 0.0 | 0.3050 | 0.0 | 0.0 | 0.9265 | 0.8786 | 0.9683 | 0.0 | 0.0 | 0.2619 | 0.0 | nan | 0.7236 | 0.8482 | 0.5358 | 0.7290 | 0.2666 | 0.0 | 0.3854 | 0.3929 | 0.0 | 0.8090 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5036 | 0.0 | 0.0 | 0.7042 | 0.0 | 0.3149 | 0.2207 | 0.0 | 0.0 | 0.0 | 0.2649 | 0.0 | 0.0 | 0.8387 | 0.7519 | 0.9235 | 0.0 | 0.0 | 0.2062 | 0.0 | | 0.3289 | 29.2 | 1460 | 0.6556 | 0.2768 | 0.3296 | 0.8362 | nan | 0.9273 | 0.9485 | 0.5861 | 0.6879 | 0.3439 | 0.0 | 0.4798 | 0.6059 | 0.0 | 0.9416 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6749 | 0.0 | 0.0 | 0.8971 | 0.0 | 0.4096 | 0.3502 | 0.0 | 0.0 | 0.0 | 0.3222 | 0.0 | 0.0 | 0.9308 | 0.8716 | 0.9684 | 0.0 | 0.0 | 0.2596 | 0.0 | nan | 0.6921 | 0.8489 | 0.5571 | 0.6606 | 0.2774 | 0.0 | 0.3801 | 0.3953 | 0.0 | 0.8177 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5192 | 0.0 | 0.0 | 0.7043 | 0.0 | 0.3281 | 0.2382 | 0.0 | 0.0 | 0.0 | 0.2725 | 0.0 | 0.0 | 0.8351 | 0.7541 | 0.9241 | 0.0 | 0.0 | 0.2073 | 0.0 | | 0.3227 | 29.6 | 1480 | 0.6274 | 0.2803 | 0.3334 | 0.8430 | nan | 0.9079 | 0.9456 | 0.6035 | 0.8309 | 0.3371 | 0.0 | 0.4808 | 0.5824 | 0.0 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6828 | 0.0 | 0.0 | 0.8969 | 0.0 | 0.4624 | 0.3205 | 0.0 | 0.0 | 0.0 | 0.3089 | 0.0 | 0.0 | 0.9276 | 0.8832 | 0.9688 | 0.0 | 0.0 | 0.2577 | 0.0 | nan | 0.7452 | 0.8459 | 0.5544 | 0.7339 | 0.2753 | 0.0 | 0.3877 | 0.3953 | 0.0 | 0.8192 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5186 | 0.0 | 0.0 | 0.7039 | 0.0 | 0.3355 | 0.2291 | 0.0 | 0.0 | 0.0 | 0.2654 | 0.0 | 0.0 | 0.8382 | 0.7490 | 0.9239 | 0.0 | 0.0 | 0.2088 | 0.0 | | 0.4271 | 30.0 | 1500 | 0.6412 | 0.2769 | 0.3306 | 0.8382 | nan | 0.9016 | 0.9544 | 0.6153 | 0.7497 | 0.3307 | 0.0 | 0.4845 | 0.5851 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6663 | 0.0 | 0.0 | 0.9019 | 0.0 | 0.4165 | 0.3538 | 0.0 | 0.0 | 0.0 | 0.3151 | 0.0 | 0.0 | 0.9151 | 0.8806 | 0.9701 | 0.0 | 0.0 | 0.2557 | 0.0 | nan | 0.7166 | 0.8431 | 0.5148 | 0.7143 | 0.2745 | 0.0 | 0.3858 | 0.3937 | 0.0 | 0.8110 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5152 | 0.0 | 0.0 | 0.7040 | 0.0 | 0.3288 | 0.2387 | 0.0 | 0.0 | 0.0 | 0.2693 | 0.0 | 0.0 | 0.8363 | 0.7431 | 0.9235 | 0.0 | 0.0 | 0.2023 | 0.0 | | 0.2996 | 30.4 | 1520 | 0.6320 | 0.2803 | 0.3323 | 0.8430 | nan | 0.9176 | 0.9528 | 0.5870 | 0.8010 | 0.3246 | 0.0 | 0.4829 | 0.5975 | 0.0 | 0.9428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6519 | 0.0 | 0.0 | 0.8888 | 0.0 | 0.4324 | 0.3396 | 0.0 | 0.0 | 0.0 | 0.3139 | 0.0 | 0.0 | 0.9282 | 0.8780 | 0.9651 | 0.0 | 0.0 | 0.2940 | 0.0 | nan | 0.7387 | 0.8443 | 0.5328 | 0.7418 | 0.2720 | 0.0 | 0.3946 | 0.3908 | 0.0 | 0.8167 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5156 | 0.0 | 0.0 | 0.7091 | 0.0 | 0.3351 | 0.2379 | 0.0 | 0.0 | 0.0 | 0.2681 | 0.0 | 0.0 | 0.8362 | 0.7528 | 0.9250 | 0.0 | 0.0 | 0.2202 | 0.0 | | 0.3392 | 30.8 | 1540 | 0.6255 | 0.2797 | 0.3345 | 0.8414 | nan | 0.8964 | 0.9477 | 0.6256 | 0.7851 | 0.3788 | 0.0 | 0.4961 | 0.5916 | 0.0 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6616 | 0.0 | 0.0 | 0.8897 | 0.0 | 0.4526 | 0.3530 | 0.0 | 0.0 | 0.0 | 0.3145 | 0.0 | 0.0 | 0.9310 | 0.8777 | 0.9663 | 0.0 | 0.0 | 0.2673 | 0.0 | nan | 0.7268 | 0.8503 | 0.4849 | 0.7355 | 0.3015 | 0.0 | 0.3984 | 0.3966 | 0.0 | 0.8222 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5235 | 0.0 | 0.0 | 0.7054 | 0.0 | 0.3339 | 0.2396 | 0.0 | 0.0 | 0.0 | 0.2685 | 0.0 | 0.0 | 0.8372 | 0.7534 | 0.9251 | 0.0 | 0.0 | 0.2059 | 0.0 | | 0.2965 | 31.2 | 1560 | 0.6367 | 0.2789 | 0.3312 | 0.8415 | nan | 0.9269 | 0.9523 | 0.5961 | 0.7622 | 0.3118 | 0.0 | 0.4969 | 0.6140 | 0.0 | 0.9472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6352 | 0.0 | 0.0 | 0.8980 | 0.0 | 0.4180 | 0.3364 | 0.0 | 0.0 | 0.0 | 0.3069 | 0.0 | 0.0 | 0.9211 | 0.8850 | 0.9695 | 0.0 | 0.0 | 0.2831 | 0.0 | nan | 0.7329 | 0.8441 | 0.5433 | 0.7287 | 0.2676 | 0.0 | 0.3809 | 0.3910 | 0.0 | 0.8149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5191 | 0.0 | 0.0 | 0.7090 | 0.0 | 0.3280 | 0.2362 | 0.0 | 0.0 | 0.0 | 0.2642 | 0.0 | 0.0 | 0.8376 | 0.7476 | 0.9244 | 0.0 | 0.0 | 0.2140 | 0.0 | | 0.3831 | 31.6 | 1580 | 0.6300 | 0.2812 | 0.3332 | 0.8429 | nan | 0.9094 | 0.9529 | 0.5967 | 0.7851 | 0.3383 | 0.0 | 0.4985 | 0.5956 | 0.0 | 0.9411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6727 | 0.0 | 0.0 | 0.9011 | 0.0 | 0.4275 | 0.3208 | 0.0 | 0.0 | 0.0 | 0.3346 | 0.0 | 0.0 | 0.9285 | 0.8705 | 0.9694 | 0.0 | 0.0 | 0.2851 | 0.0 | nan | 0.7337 | 0.8445 | 0.5445 | 0.7378 | 0.2787 | 0.0 | 0.3846 | 0.3957 | 0.0 | 0.8222 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5248 | 0.0 | 0.0 | 0.7076 | 0.0 | 0.3366 | 0.2305 | 0.0 | 0.0 | 0.0 | 0.2797 | 0.0 | 0.0 | 0.8416 | 0.7545 | 0.9260 | 0.0 | 0.0 | 0.2181 | 0.0 | | 0.3493 | 32.0 | 1600 | 0.6407 | 0.2785 | 0.3356 | 0.8400 | nan | 0.9012 | 0.9423 | 0.6217 | 0.7873 | 0.3749 | 0.0 | 0.4880 | 0.5983 | 0.0 | 0.9465 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6982 | 0.0 | 0.0 | 0.8913 | 0.0 | 0.4313 | 0.3725 | 0.0 | 0.0 | 0.0 | 0.3320 | 0.0 | 0.0 | 0.9243 | 0.8959 | 0.9695 | 0.0 | 0.0 | 0.2344 | 0.0 | nan | 0.7114 | 0.8536 | 0.5136 | 0.7126 | 0.2994 | 0.0 | 0.3924 | 0.3971 | 0.0 | 0.8130 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5163 | 0.0 | 0.0 | 0.7068 | 0.0 | 0.3303 | 0.2404 | 0.0 | 0.0 | 0.0 | 0.2795 | 0.0 | 0.0 | 0.8369 | 0.7481 | 0.9259 | 0.0 | 0.0 | 0.1920 | 0.0 | | 0.3436 | 32.4 | 1620 | 0.6411 | 0.2797 | 0.3326 | 0.8412 | nan | 0.9267 | 0.9499 | 0.5514 | 0.7637 | 0.3525 | 0.0 | 0.5014 | 0.6383 | 0.0 | 0.9393 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6488 | 0.0 | 0.0 | 0.8973 | 0.0 | 0.4156 | 0.3836 | 0.0 | 0.0 | 0.0 | 0.3289 | 0.0 | 0.0 | 0.9348 | 0.8434 | 0.9690 | 0.0 | 0.0 | 0.2623 | 0.0 | nan | 0.7220 | 0.8468 | 0.5165 | 0.7270 | 0.2840 | 0.0 | 0.3934 | 0.3914 | 0.0 | 0.8211 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5100 | 0.0 | 0.0 | 0.7055 | 0.0 | 0.3275 | 0.2602 | 0.0 | 0.0 | 0.0 | 0.2775 | 0.0 | 0.0 | 0.8389 | 0.7518 | 0.9259 | 0.0 | 0.0 | 0.2099 | 0.0 | | 0.3519 | 32.8 | 1640 | 0.6279 | 0.2803 | 0.3326 | 0.8424 | nan | 0.9006 | 0.9528 | 0.5944 | 0.8234 | 0.3076 | 0.0 | 0.4970 | 0.5845 | 0.0 | 0.9431 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6655 | 0.0 | 0.0 | 0.8913 | 0.0 | 0.4559 | 0.3274 | 0.0 | 0.0 | 0.0 | 0.3196 | 0.0 | 0.0 | 0.9211 | 0.8900 | 0.9693 | 0.0 | 0.0 | 0.2651 | 0.0 | nan | 0.7445 | 0.8418 | 0.5386 | 0.7479 | 0.2611 | 0.0 | 0.3900 | 0.4008 | 0.0 | 0.8198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5284 | 0.0 | 0.0 | 0.7091 | 0.0 | 0.3360 | 0.2321 | 0.0 | 0.0 | 0.0 | 0.2734 | 0.0 | 0.0 | 0.8349 | 0.7426 | 0.9262 | 0.0 | 0.0 | 0.2026 | 0.0 | | 0.4359 | 33.2 | 1660 | 0.6426 | 0.2794 | 0.3331 | 0.8414 | nan | 0.9304 | 0.9499 | 0.6014 | 0.7611 | 0.3188 | 0.0 | 0.4850 | 0.6366 | 0.0 | 0.9443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6564 | 0.0 | 0.0 | 0.8743 | 0.0 | 0.4532 | 0.3225 | 0.0 | 0.0 | 0.0 | 0.3332 | 0.0 | 0.0 | 0.9382 | 0.8668 | 0.9680 | 0.0 | 0.0 | 0.2871 | 0.0 | nan | 0.7254 | 0.8473 | 0.5447 | 0.7218 | 0.2699 | 0.0 | 0.3871 | 0.3876 | 0.0 | 0.8191 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5169 | 0.0 | 0.0 | 0.7093 | 0.0 | 0.3314 | 0.2280 | 0.0 | 0.0 | 0.0 | 0.2796 | 0.0 | 0.0 | 0.8370 | 0.7538 | 0.9272 | 0.0 | 0.0 | 0.2141 | 0.0 | | 0.2864 | 33.6 | 1680 | 0.6615 | 0.2758 | 0.3289 | 0.8372 | nan | 0.9209 | 0.9509 | 0.6063 | 0.6941 | 0.3526 | 0.0 | 0.5022 | 0.6105 | 0.0 | 0.9401 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6613 | 0.0 | 0.0 | 0.9223 | 0.0 | 0.3529 | 0.3426 | 0.0 | 0.0 | 0.0 | 0.3352 | 0.0 | 0.0 | 0.9148 | 0.8885 | 0.9698 | 0.0 | 0.0 | 0.2183 | 0.0 | nan | 0.7027 | 0.8478 | 0.5329 | 0.6720 | 0.2889 | 0.0 | 0.3945 | 0.3956 | 0.0 | 0.8200 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5033 | 0.0 | 0.0 | 0.6960 | 0.0 | 0.2995 | 0.2472 | 0.0 | 0.0 | 0.0 | 0.2831 | 0.0 | 0.0 | 0.8392 | 0.7452 | 0.9262 | 0.0 | 0.0 | 0.1834 | 0.0 | | 0.2795 | 34.0 | 1700 | 0.6369 | 0.2810 | 0.3350 | 0.8426 | nan | 0.9120 | 0.9520 | 0.6168 | 0.7708 | 0.3537 | 0.0 | 0.4978 | 0.5937 | 0.0 | 0.9442 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6760 | 0.0 | 0.0 | 0.8977 | 0.0 | 0.4292 | 0.3539 | 0.0 | 0.0 | 0.0 | 0.3445 | 0.0 | 0.0 | 0.9238 | 0.8757 | 0.9703 | 0.0 | 0.0 | 0.2783 | 0.0 | nan | 0.7260 | 0.8481 | 0.5226 | 0.7362 | 0.2894 | 0.0 | 0.3964 | 0.3960 | 0.0 | 0.8202 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5116 | 0.0 | 0.0 | 0.7095 | 0.0 | 0.3331 | 0.2424 | 0.0 | 0.0 | 0.0 | 0.2883 | 0.0 | 0.0 | 0.8397 | 0.7539 | 0.9264 | 0.0 | 0.0 | 0.2139 | 0.0 | | 0.2809 | 34.4 | 1720 | 0.6342 | 0.2803 | 0.3341 | 0.8432 | nan | 0.9144 | 0.9513 | 0.5907 | 0.7914 | 0.3386 | 0.0 | 0.5002 | 0.6073 | 0.0 | 0.9444 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6870 | 0.0 | 0.0 | 0.8900 | 0.0 | 0.4229 | 0.3337 | 0.0 | 0.0 | 0.0 | 0.3399 | 0.0 | 0.0 | 0.9301 | 0.8865 | 0.9689 | 0.0 | 0.0 | 0.2613 | 0.0 | nan | 0.7315 | 0.8487 | 0.5274 | 0.7364 | 0.2840 | 0.0 | 0.3930 | 0.3953 | 0.0 | 0.8166 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5111 | 0.0 | 0.0 | 0.7087 | 0.0 | 0.3336 | 0.2379 | 0.0 | 0.0 | 0.0 | 0.2834 | 0.0 | 0.0 | 0.8369 | 0.7519 | 0.9277 | 0.0 | 0.0 | 0.2047 | 0.0 | | 0.2926 | 34.8 | 1740 | 0.6361 | 0.2805 | 0.3333 | 0.8417 | nan | 0.9156 | 0.9475 | 0.6039 | 0.7719 | 0.3567 | 0.0 | 0.5087 | 0.5719 | 0.0 | 0.9427 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6924 | 0.0 | 0.0 | 0.9058 | 0.0 | 0.3941 | 0.3460 | 0.0 | 0.0 | 0.0 | 0.3368 | 0.0 | 0.0 | 0.9269 | 0.8690 | 0.9715 | 0.0 | 0.0 | 0.2698 | 0.0 | nan | 0.7200 | 0.8492 | 0.5450 | 0.7109 | 0.2920 | 0.0 | 0.3954 | 0.4026 | 0.0 | 0.8197 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5170 | 0.0 | 0.0 | 0.7056 | 0.0 | 0.3175 | 0.2451 | 0.0 | 0.0 | 0.0 | 0.2846 | 0.0 | 0.0 | 0.8415 | 0.7558 | 0.9275 | 0.0 | 0.0 | 0.2081 | 0.0 | | 0.3475 | 35.2 | 1760 | 0.6425 | 0.2810 | 0.3359 | 0.8420 | nan | 0.9072 | 0.9492 | 0.6142 | 0.7957 | 0.3298 | 0.0 | 0.4928 | 0.5961 | 0.0 | 0.9413 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7089 | 0.0 | 0.0 | 0.8849 | 0.0 | 0.4386 | 0.3497 | 0.0 | 0.0 | 0.0 | 0.3410 | 0.0 | 0.0 | 0.9280 | 0.8849 | 0.9715 | 0.0 | 0.0 | 0.2870 | 0.0 | nan | 0.7278 | 0.8464 | 0.5424 | 0.7240 | 0.2750 | 0.0 | 0.3918 | 0.3997 | 0.0 | 0.8241 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5166 | 0.0 | 0.0 | 0.7098 | 0.0 | 0.3390 | 0.2400 | 0.0 | 0.0 | 0.0 | 0.2865 | 0.0 | 0.0 | 0.8361 | 0.7514 | 0.9274 | 0.0 | 0.0 | 0.2172 | 0.0 | | 0.3079 | 35.6 | 1780 | 0.6318 | 0.2815 | 0.3353 | 0.8421 | nan | 0.8986 | 0.9491 | 0.6034 | 0.7912 | 0.3674 | 0.0 | 0.5018 | 0.5996 | 0.0 | 0.9460 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6846 | 0.0 | 0.0 | 0.8921 | 0.0 | 0.4470 | 0.3286 | 0.0 | 0.0 | 0.0 | 0.3315 | 0.0 | 0.0 | 0.9285 | 0.8754 | 0.9703 | 0.0 | 0.0 | 0.2843 | 0.0 | nan | 0.7219 | 0.8490 | 0.5500 | 0.7212 | 0.2938 | 0.0 | 0.3942 | 0.3987 | 0.0 | 0.8225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5247 | 0.0 | 0.0 | 0.7107 | 0.0 | 0.3360 | 0.2305 | 0.0 | 0.0 | 0.0 | 0.2817 | 0.0 | 0.0 | 0.8399 | 0.7553 | 0.9282 | 0.0 | 0.0 | 0.2137 | 0.0 | | 0.3387 | 36.0 | 1800 | 0.6398 | 0.2806 | 0.3351 | 0.8414 | nan | 0.9147 | 0.9487 | 0.6065 | 0.7587 | 0.3486 | 0.0 | 0.5085 | 0.6111 | 0.0 | 0.9467 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6779 | 0.0 | 0.0 | 0.8984 | 0.0 | 0.3925 | 0.3676 | 0.0 | 0.0 | 0.0 | 0.3387 | 0.0 | 0.0 | 0.9234 | 0.8892 | 0.9718 | 0.0 | 0.0 | 0.2900 | 0.0 | nan | 0.7073 | 0.8535 | 0.5247 | 0.7137 | 0.2883 | 0.0 | 0.4003 | 0.4007 | 0.0 | 0.8207 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5182 | 0.0 | 0.0 | 0.7085 | 0.0 | 0.3295 | 0.2525 | 0.0 | 0.0 | 0.0 | 0.2841 | 0.0 | 0.0 | 0.8374 | 0.7504 | 0.9273 | 0.0 | 0.0 | 0.2232 | 0.0 | | 0.353 | 36.4 | 1820 | 0.6366 | 0.2812 | 0.3340 | 0.8429 | nan | 0.8990 | 0.9528 | 0.5851 | 0.7833 | 0.3531 | 0.0 | 0.5036 | 0.6333 | 0.0 | 0.9468 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6623 | 0.0 | 0.0 | 0.9047 | 0.0 | 0.3893 | 0.3411 | 0.0 | 0.0 | 0.0 | 0.3444 | 0.0 | 0.0 | 0.9330 | 0.8748 | 0.9680 | 0.0 | 0.0 | 0.2812 | 0.0 | nan | 0.7231 | 0.8457 | 0.5366 | 0.7293 | 0.2918 | 0.0 | 0.3941 | 0.3964 | 0.0 | 0.8177 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5151 | 0.0 | 0.0 | 0.7096 | 0.0 | 0.3197 | 0.2451 | 0.0 | 0.0 | 0.0 | 0.2880 | 0.0 | 0.0 | 0.8415 | 0.7584 | 0.9291 | 0.0 | 0.0 | 0.2203 | 0.0 | | 0.3651 | 36.8 | 1840 | 0.6441 | 0.2809 | 0.3325 | 0.8417 | nan | 0.9112 | 0.9519 | 0.6114 | 0.7513 | 0.3439 | 0.0 | 0.5112 | 0.5639 | 0.0 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6871 | 0.0 | 0.0 | 0.9136 | 0.0 | 0.4066 | 0.3437 | 0.0 | 0.0 | 0.0 | 0.3372 | 0.0 | 0.0 | 0.9259 | 0.8716 | 0.9707 | 0.0 | 0.0 | 0.2644 | 0.0 | nan | 0.7087 | 0.8503 | 0.5294 | 0.7131 | 0.2885 | 0.0 | 0.4001 | 0.4039 | 0.0 | 0.8261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5293 | 0.0 | 0.0 | 0.7053 | 0.0 | 0.3313 | 0.2438 | 0.0 | 0.0 | 0.0 | 0.2856 | 0.0 | 0.0 | 0.8430 | 0.7564 | 0.9282 | 0.0 | 0.0 | 0.2072 | 0.0 | | 0.3073 | 37.2 | 1860 | 0.6468 | 0.2810 | 0.3346 | 0.8406 | nan | 0.9223 | 0.9433 | 0.6039 | 0.7368 | 0.3718 | 0.0 | 0.5138 | 0.6017 | 0.0 | 0.9489 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6509 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.4196 | 0.3052 | 0.0 | 0.0 | 0.0 | 0.3519 | 0.0 | 0.0 | 0.9278 | 0.8895 | 0.9719 | 0.0 | 0.0 | 0.3263 | 0.0 | nan | 0.7052 | 0.8516 | 0.5366 | 0.7012 | 0.2969 | 0.0 | 0.4010 | 0.4007 | 0.0 | 0.8187 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5200 | 0.0 | 0.0 | 0.7125 | 0.0 | 0.3332 | 0.2251 | 0.0 | 0.0 | 0.0 | 0.2916 | 0.0 | 0.0 | 0.8387 | 0.7544 | 0.9276 | 0.0 | 0.0 | 0.2398 | 0.0 | | 0.4331 | 37.6 | 1880 | 0.6398 | 0.2816 | 0.3362 | 0.8424 | nan | 0.9012 | 0.9534 | 0.6108 | 0.7906 | 0.3207 | 0.0 | 0.5132 | 0.5980 | 0.0 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7046 | 0.0 | 0.0 | 0.8988 | 0.0 | 0.4445 | 0.3527 | 0.0 | 0.0 | 0.0 | 0.3542 | 0.0 | 0.0 | 0.9206 | 0.8769 | 0.9700 | 0.0 | 0.0 | 0.2780 | 0.0 | nan | 0.7220 | 0.8473 | 0.5391 | 0.7306 | 0.2707 | 0.0 | 0.3963 | 0.4018 | 0.0 | 0.8242 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5264 | 0.0 | 0.0 | 0.7122 | 0.0 | 0.3363 | 0.2354 | 0.0 | 0.0 | 0.0 | 0.2949 | 0.0 | 0.0 | 0.8415 | 0.7557 | 0.9286 | 0.0 | 0.0 | 0.2124 | 0.0 | | 0.3336 | 38.0 | 1900 | 0.6402 | 0.2813 | 0.3366 | 0.8419 | nan | 0.9057 | 0.9474 | 0.6130 | 0.7764 | 0.3629 | 0.0 | 0.4953 | 0.6091 | 0.0 | 0.9453 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7066 | 0.0 | 0.0 | 0.8942 | 0.0 | 0.4472 | 0.3294 | 0.0 | 0.0 | 0.0 | 0.3447 | 0.0 | 0.0 | 0.9256 | 0.8864 | 0.9735 | 0.0 | 0.0 | 0.2811 | 0.0 | nan | 0.7148 | 0.8503 | 0.5302 | 0.7213 | 0.2942 | 0.0 | 0.3992 | 0.4010 | 0.0 | 0.8215 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5246 | 0.0 | 0.0 | 0.7111 | 0.0 | 0.3400 | 0.2281 | 0.0 | 0.0 | 0.0 | 0.2888 | 0.0 | 0.0 | 0.8405 | 0.7575 | 0.9275 | 0.0 | 0.0 | 0.2128 | 0.0 | | 0.3628 | 38.4 | 1920 | 0.6451 | 0.2803 | 0.3339 | 0.8419 | nan | 0.9157 | 0.9506 | 0.6013 | 0.7584 | 0.3272 | 0.0 | 0.5187 | 0.6031 | 0.0 | 0.9455 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6922 | 0.0 | 0.0 | 0.9004 | 0.0 | 0.4047 | 0.3112 | 0.0 | 0.0 | 0.0 | 0.3390 | 0.0 | 0.0 | 0.9255 | 0.8911 | 0.9729 | 0.0 | 0.0000 | 0.2961 | 0.0 | nan | 0.7134 | 0.8508 | 0.5294 | 0.7156 | 0.2770 | 0.0 | 0.4017 | 0.4011 | 0.0 | 0.8237 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5241 | 0.0 | 0.0 | 0.7121 | 0.0 | 0.3262 | 0.2266 | 0.0 | 0.0 | 0.0 | 0.2849 | 0.0 | 0.0 | 0.8403 | 0.7520 | 0.9276 | 0.0 | 0.0000 | 0.2235 | 0.0 | | 0.3378 | 38.8 | 1940 | 0.6329 | 0.2818 | 0.3348 | 0.8434 | nan | 0.9127 | 0.9465 | 0.5858 | 0.7964 | 0.3750 | 0.0 | 0.5024 | 0.6098 | 0.0 | 0.9438 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6808 | 0.0 | 0.0 | 0.8976 | 0.0 | 0.4174 | 0.3276 | 0.0 | 0.0 | 0.0 | 0.3451 | 0.0 | 0.0 | 0.9327 | 0.8730 | 0.9706 | 0.0 | 0.0005 | 0.2661 | 0.0 | nan | 0.7267 | 0.8480 | 0.5227 | 0.7435 | 0.2949 | 0.0 | 0.4038 | 0.4030 | 0.0 | 0.8264 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5204 | 0.0 | 0.0 | 0.7092 | 0.0 | 0.3240 | 0.2280 | 0.0 | 0.0 | 0.0 | 0.2899 | 0.0 | 0.0 | 0.8409 | 0.7582 | 0.9292 | 0.0 | 0.0005 | 0.2104 | 0.0 | | 0.3523 | 39.2 | 1960 | 0.6349 | 0.2824 | 0.3353 | 0.8436 | nan | 0.9112 | 0.9520 | 0.5920 | 0.7852 | 0.3394 | 0.0 | 0.5104 | 0.6062 | 0.0 | 0.9452 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6808 | 0.0 | 0.0 | 0.8983 | 0.0 | 0.4215 | 0.3371 | 0.0 | 0.0 | 0.0 | 0.3531 | 0.0 | 0.0 | 0.9274 | 0.8815 | 0.9701 | 0.0 | 0.0000 | 0.2872 | 0.0 | nan | 0.7310 | 0.8457 | 0.5393 | 0.7385 | 0.2799 | 0.0 | 0.3992 | 0.4024 | 0.0 | 0.8248 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5245 | 0.0 | 0.0 | 0.7115 | 0.0 | 0.3345 | 0.2331 | 0.0 | 0.0 | 0.0 | 0.2944 | 0.0 | 0.0 | 0.8416 | 0.7565 | 0.9298 | 0.0 | 0.0000 | 0.2159 | 0.0 | | 0.3157 | 39.6 | 1980 | 0.6250 | 0.2842 | 0.3374 | 0.8460 | nan | 0.9196 | 0.9467 | 0.5922 | 0.8099 | 0.3500 | 0.0 | 0.5136 | 0.6095 | 0.0 | 0.9401 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6812 | 0.0 | 0.0 | 0.8994 | 0.0 | 0.4322 | 0.3447 | 0.0 | 0.0 | 0.0 | 0.3562 | 0.0 | 0.0 | 0.9348 | 0.8775 | 0.9712 | 0.0 | 0.0002 | 0.2918 | 0.0 | nan | 0.7379 | 0.8502 | 0.5435 | 0.7448 | 0.2894 | 0.0 | 0.4010 | 0.4024 | 0.0 | 0.8284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5241 | 0.0 | 0.0 | 0.7128 | 0.0 | 0.3439 | 0.2390 | 0.0 | 0.0 | 0.0 | 0.2957 | 0.0 | 0.0 | 0.8413 | 0.7601 | 0.9295 | 0.0 | 0.0001 | 0.2202 | 0.0 | | 0.3091 | 40.0 | 2000 | 0.6428 | 0.2819 | 0.3352 | 0.8439 | nan | 0.9164 | 0.9545 | 0.5811 | 0.7845 | 0.3218 | 0.0 | 0.5088 | 0.6127 | 0.0 | 0.9482 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6690 | 0.0 | 0.0 | 0.9004 | 0.0 | 0.4326 | 0.3583 | 0.0 | 0.0 | 0.0 | 0.3424 | 0.0 | 0.0 | 0.9202 | 0.8846 | 0.9727 | 0.0 | 0.0004 | 0.2870 | 0.0 | nan | 0.7315 | 0.8476 | 0.5387 | 0.7317 | 0.2720 | 0.0 | 0.3982 | 0.3976 | 0.0 | 0.8193 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5167 | 0.0 | 0.0 | 0.7140 | 0.0 | 0.3426 | 0.2420 | 0.0 | 0.0 | 0.0 | 0.2883 | 0.0 | 0.0 | 0.8415 | 0.7529 | 0.9287 | 0.0 | 0.0004 | 0.2197 | 0.0 | | 0.3324 | 40.4 | 2020 | 0.6305 | 0.2832 | 0.3382 | 0.8453 | nan | 0.9081 | 0.9501 | 0.6112 | 0.8081 | 0.3387 | 0.0 | 0.5193 | 0.6078 | 0.0 | 0.9427 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6990 | 0.0 | 0.0 | 0.9021 | 0.0 | 0.4415 | 0.3691 | 0.0 | 0.0 | 0.0 | 0.3462 | 0.0 | 0.0 | 0.9252 | 0.8865 | 0.9728 | 0.0 | 0.0017 | 0.2685 | 0.0 | nan | 0.7342 | 0.8523 | 0.5291 | 0.7446 | 0.2823 | 0.0 | 0.4023 | 0.4058 | 0.0 | 0.8258 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5201 | 0.0 | 0.0 | 0.7108 | 0.0 | 0.3504 | 0.2445 | 0.0 | 0.0 | 0.0 | 0.2902 | 0.0 | 0.0 | 0.8413 | 0.7568 | 0.9290 | 0.0 | 0.0017 | 0.2092 | 0.0 | | 0.446 | 40.8 | 2040 | 0.6335 | 0.2823 | 0.3354 | 0.8436 | nan | 0.9158 | 0.9466 | 0.6004 | 0.7753 | 0.3634 | 0.0 | 0.5087 | 0.6236 | 0.0 | 0.9416 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6766 | 0.0 | 0.0 | 0.9036 | 0.0 | 0.3974 | 0.3363 | 0.0 | 0.0 | 0.0 | 0.3486 | 0.0 | 0.0 | 0.9336 | 0.8864 | 0.9729 | 0.0 | 0.0003 | 0.2717 | 0.0 | nan | 0.7228 | 0.8505 | 0.5378 | 0.7275 | 0.2928 | 0.0 | 0.3973 | 0.4040 | 0.0 | 0.8271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5219 | 0.0 | 0.0 | 0.7113 | 0.0 | 0.3299 | 0.2440 | 0.0 | 0.0 | 0.0 | 0.2917 | 0.0 | 0.0 | 0.8392 | 0.7599 | 0.9290 | 0.0 | 0.0003 | 0.2106 | 0.0 | | 0.2991 | 41.2 | 2060 | 0.6400 | 0.2819 | 0.3346 | 0.8430 | nan | 0.9021 | 0.9569 | 0.6020 | 0.7808 | 0.3141 | 0.0 | 0.5107 | 0.6066 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6738 | 0.0 | 0.0 | 0.8978 | 0.0 | 0.4379 | 0.3122 | 0.0 | 0.0 | 0.0 | 0.3561 | 0.0 | 0.0 | 0.9229 | 0.8815 | 0.9712 | 0.0 | 0.0002 | 0.3062 | 0.0 | nan | 0.7281 | 0.8460 | 0.5370 | 0.7257 | 0.2681 | 0.0 | 0.3941 | 0.4008 | 0.0 | 0.8270 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5334 | 0.0 | 0.0 | 0.7140 | 0.0 | 0.3370 | 0.2272 | 0.0 | 0.0 | 0.0 | 0.2963 | 0.0 | 0.0 | 0.8422 | 0.7558 | 0.9299 | 0.0 | 0.0002 | 0.2222 | 0.0 | | 0.2577 | 41.6 | 2080 | 0.6273 | 0.2827 | 0.3367 | 0.8433 | nan | 0.9083 | 0.9458 | 0.6065 | 0.7727 | 0.3782 | 0.0 | 0.5244 | 0.5984 | 0.0 | 0.9455 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6865 | 0.0 | 0.0 | 0.9076 | 0.0 | 0.4176 | 0.3493 | 0.0 | 0.0 | 0.0 | 0.3545 | 0.0 | 0.0 | 0.9272 | 0.8876 | 0.9708 | 0.0 | 0.0003 | 0.2670 | 0.0 | nan | 0.7190 | 0.8537 | 0.5356 | 0.7125 | 0.3026 | 0.0 | 0.4038 | 0.4057 | 0.0 | 0.8259 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5295 | 0.0 | 0.0 | 0.7117 | 0.0 | 0.3325 | 0.2436 | 0.0 | 0.0 | 0.0 | 0.2964 | 0.0 | 0.0 | 0.8426 | 0.7589 | 0.9302 | 0.0 | 0.0003 | 0.2075 | 0.0 | | 0.2437 | 42.0 | 2100 | 0.6321 | 0.2827 | 0.3357 | 0.8439 | nan | 0.9057 | 0.9521 | 0.6023 | 0.7836 | 0.3366 | 0.0 | 0.5205 | 0.6125 | 0.0 | 0.9438 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6864 | 0.0 | 0.0 | 0.9087 | 0.0 | 0.4109 | 0.3239 | 0.0 | 0.0 | 0.0 | 0.3558 | 0.0 | 0.0 | 0.9261 | 0.8823 | 0.9705 | 0.0 | 0.0011 | 0.2920 | 0.0 | nan | 0.7288 | 0.8478 | 0.5416 | 0.7320 | 0.2805 | 0.0 | 0.3960 | 0.4063 | 0.0 | 0.8286 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5299 | 0.0 | 0.0 | 0.7129 | 0.0 | 0.3234 | 0.2309 | 0.0 | 0.0 | 0.0 | 0.2972 | 0.0 | 0.0 | 0.8438 | 0.7595 | 0.9304 | 0.0 | 0.0011 | 0.2199 | 0.0 | | 0.3548 | 42.4 | 2120 | 0.6317 | 0.2831 | 0.3370 | 0.8445 | nan | 0.9133 | 0.9517 | 0.6028 | 0.7868 | 0.3391 | 0.0 | 0.5153 | 0.5879 | 0.0 | 0.9435 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7178 | 0.0 | 0.0 | 0.9042 | 0.0 | 0.3861 | 0.3665 | 0.0 | 0.0 | 0.0 | 0.3643 | 0.0 | 0.0 | 0.9244 | 0.8901 | 0.9723 | 0.0 | 0.0010 | 0.2925 | 0.0 | nan | 0.7290 | 0.8519 | 0.5360 | 0.7372 | 0.2841 | 0.0 | 0.3997 | 0.4093 | 0.0 | 0.8285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5271 | 0.0 | 0.0 | 0.7117 | 0.0 | 0.3179 | 0.2480 | 0.0 | 0.0 | 0.0 | 0.3012 | 0.0 | 0.0 | 0.8393 | 0.7536 | 0.9296 | 0.0 | 0.0010 | 0.2212 | 0.0 | | 0.2519 | 42.8 | 2140 | 0.6322 | 0.2827 | 0.3354 | 0.8445 | nan | 0.9146 | 0.9501 | 0.6036 | 0.7848 | 0.3492 | 0.0 | 0.5133 | 0.6009 | 0.0 | 0.9451 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6921 | 0.0 | 0.0 | 0.9111 | 0.0 | 0.3731 | 0.3440 | 0.0 | 0.0 | 0.0 | 0.3634 | 0.0 | 0.0 | 0.9296 | 0.8763 | 0.9724 | 0.0 | 0.0010 | 0.2798 | 0.0 | nan | 0.7241 | 0.8515 | 0.5364 | 0.7345 | 0.2894 | 0.0 | 0.4034 | 0.4073 | 0.0 | 0.8253 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5263 | 0.0 | 0.0 | 0.7099 | 0.0 | 0.3078 | 0.2443 | 0.0 | 0.0 | 0.0 | 0.3013 | 0.0 | 0.0 | 0.8437 | 0.7608 | 0.9298 | 0.0 | 0.0010 | 0.2159 | 0.0 | | 0.275 | 43.2 | 2160 | 0.6368 | 0.2833 | 0.3374 | 0.8434 | nan | 0.9058 | 0.9532 | 0.6054 | 0.7740 | 0.3447 | 0.0 | 0.5067 | 0.5989 | 0.0 | 0.9488 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7074 | 0.0 | 0.0 | 0.9007 | 0.0 | 0.4272 | 0.3609 | 0.0 | 0.0 | 0.0 | 0.3579 | 0.0 | 0.0 | 0.9222 | 0.8823 | 0.9725 | 0.0 | 0.0051 | 0.2991 | 0.0 | nan | 0.7228 | 0.8475 | 0.5442 | 0.7262 | 0.2855 | 0.0 | 0.3968 | 0.4046 | 0.0 | 0.8230 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5262 | 0.0 | 0.0 | 0.7152 | 0.0 | 0.3375 | 0.2463 | 0.0 | 0.0 | 0.0 | 0.2984 | 0.0 | 0.0 | 0.8432 | 0.7559 | 0.9298 | 0.0 | 0.0051 | 0.2229 | 0.0 | | 0.357 | 43.6 | 2180 | 0.6373 | 0.2837 | 0.3380 | 0.8440 | nan | 0.9100 | 0.9519 | 0.5992 | 0.7804 | 0.3476 | 0.0 | 0.5014 | 0.6046 | 0.0 | 0.9414 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7171 | 0.0 | 0.0 | 0.8995 | 0.0 | 0.4276 | 0.3742 | 0.0 | 0.0 | 0.0 | 0.3613 | 0.0 | 0.0 | 0.9259 | 0.8901 | 0.9719 | 0.0 | 0.0077 | 0.2811 | 0.0 | nan | 0.7280 | 0.8485 | 0.5382 | 0.7278 | 0.2877 | 0.0 | 0.3975 | 0.4056 | 0.0 | 0.8300 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5271 | 0.0 | 0.0 | 0.7146 | 0.0 | 0.3410 | 0.2515 | 0.0 | 0.0 | 0.0 | 0.2997 | 0.0 | 0.0 | 0.8392 | 0.7543 | 0.9303 | 0.0 | 0.0076 | 0.2168 | 0.0 | | 0.2715 | 44.0 | 2200 | 0.6300 | 0.2841 | 0.3390 | 0.8446 | nan | 0.9019 | 0.9507 | 0.6033 | 0.7953 | 0.3516 | 0.0 | 0.5189 | 0.6222 | 0.0 | 0.9445 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7024 | 0.0 | 0.0 | 0.8993 | 0.0 | 0.4278 | 0.3749 | 0.0 | 0.0 | 0.0 | 0.3613 | 0.0 | 0.0 | 0.9300 | 0.8815 | 0.9718 | 0.0 | 0.0074 | 0.2824 | 0.0 | nan | 0.7301 | 0.8489 | 0.5301 | 0.7339 | 0.2892 | 0.0 | 0.4044 | 0.4043 | 0.0 | 0.8268 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5254 | 0.0 | 0.0 | 0.7149 | 0.0 | 0.3419 | 0.2532 | 0.0 | 0.0 | 0.0 | 0.2997 | 0.0 | 0.0 | 0.8414 | 0.7608 | 0.9304 | 0.0 | 0.0073 | 0.2169 | 0.0 | | 0.296 | 44.4 | 2220 | 0.6370 | 0.2836 | 0.3366 | 0.8448 | nan | 0.9177 | 0.9524 | 0.5981 | 0.7768 | 0.3251 | 0.0 | 0.5173 | 0.6082 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7014 | 0.0 | 0.0 | 0.9090 | 0.0 | 0.3956 | 0.3633 | 0.0 | 0.0 | 0.0 | 0.3607 | 0.0 | 0.0 | 0.9299 | 0.8809 | 0.9714 | 0.0 | 0.0099 | 0.2825 | 0.0 | nan | 0.7277 | 0.8500 | 0.5310 | 0.7310 | 0.2768 | 0.0 | 0.4025 | 0.4067 | 0.0 | 0.8290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5305 | 0.0 | 0.0 | 0.7134 | 0.0 | 0.3306 | 0.2546 | 0.0 | 0.0 | 0.0 | 0.2995 | 0.0 | 0.0 | 0.8416 | 0.7599 | 0.9306 | 0.0 | 0.0098 | 0.2172 | 0.0 | | 0.2938 | 44.8 | 2240 | 0.6356 | 0.2838 | 0.3381 | 0.8450 | nan | 0.9084 | 0.9523 | 0.5857 | 0.7985 | 0.3389 | 0.0 | 0.5128 | 0.6084 | 0.0 | 0.9490 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7049 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.4398 | 0.3642 | 0.0 | 0.0 | 0.0 | 0.3571 | 0.0 | 0.0 | 0.9269 | 0.8862 | 0.9726 | 0.0 | 0.0085 | 0.2834 | 0.0 | nan | 0.7298 | 0.8499 | 0.5354 | 0.7345 | 0.2844 | 0.0 | 0.4013 | 0.4050 | 0.0 | 0.8243 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5253 | 0.0 | 0.0 | 0.7163 | 0.0 | 0.3436 | 0.2459 | 0.0 | 0.0 | 0.0 | 0.2973 | 0.0 | 0.0 | 0.8423 | 0.7578 | 0.9302 | 0.0 | 0.0084 | 0.2171 | 0.0 | | 0.3444 | 45.2 | 2260 | 0.6378 | 0.2838 | 0.3366 | 0.8450 | nan | 0.9139 | 0.9506 | 0.5811 | 0.7960 | 0.3489 | 0.0 | 0.5043 | 0.6117 | 0.0 | 0.9429 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6932 | 0.0 | 0.0 | 0.9158 | 0.0 | 0.3896 | 0.3592 | 0.0 | 0.0 | 0.0 | 0.3710 | 0.0 | 0.0 | 0.9246 | 0.8836 | 0.9719 | 0.0 | 0.0115 | 0.2739 | 0.0 | nan | 0.7284 | 0.8503 | 0.5353 | 0.7357 | 0.2867 | 0.0 | 0.3994 | 0.4063 | 0.0 | 0.8309 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5304 | 0.0 | 0.0 | 0.7117 | 0.0 | 0.3196 | 0.2487 | 0.0 | 0.0 | 0.0 | 0.3047 | 0.0 | 0.0 | 0.8438 | 0.7605 | 0.9305 | 0.0 | 0.0114 | 0.2156 | 0.0 | | 0.3901 | 45.6 | 2280 | 0.6350 | 0.2841 | 0.3381 | 0.8447 | nan | 0.9130 | 0.9486 | 0.5974 | 0.7909 | 0.3553 | 0.0 | 0.5118 | 0.6193 | 0.0 | 0.9459 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6916 | 0.0 | 0.0 | 0.8993 | 0.0 | 0.3965 | 0.3624 | 0.0 | 0.0 | 0.0 | 0.3728 | 0.0 | 0.0 | 0.9318 | 0.8800 | 0.9722 | 0.0 | 0.0117 | 0.2949 | 0.0 | nan | 0.7220 | 0.8523 | 0.5333 | 0.7371 | 0.2906 | 0.0 | 0.4042 | 0.4047 | 0.0 | 0.8284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5288 | 0.0 | 0.0 | 0.7136 | 0.0 | 0.3227 | 0.2481 | 0.0 | 0.0 | 0.0 | 0.3042 | 0.0 | 0.0 | 0.8416 | 0.7623 | 0.9306 | 0.0 | 0.0115 | 0.2240 | 0.0 | | 0.2613 | 46.0 | 2300 | 0.6456 | 0.2825 | 0.3352 | 0.8438 | nan | 0.9255 | 0.9513 | 0.5876 | 0.7639 | 0.3329 | 0.0 | 0.5091 | 0.6051 | 0.0 | 0.9460 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6952 | 0.0 | 0.0 | 0.9115 | 0.0 | 0.3870 | 0.3567 | 0.0 | 0.0 | 0.0 | 0.3589 | 0.0 | 0.0 | 0.9250 | 0.8831 | 0.9721 | 0.0 | 0.0129 | 0.2745 | 0.0 | nan | 0.7192 | 0.8508 | 0.5345 | 0.7288 | 0.2799 | 0.0 | 0.3986 | 0.4038 | 0.0 | 0.8275 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5291 | 0.0 | 0.0 | 0.7122 | 0.0 | 0.3142 | 0.2470 | 0.0 | 0.0 | 0.0 | 0.2997 | 0.0 | 0.0 | 0.8435 | 0.7592 | 0.9305 | 0.0 | 0.0127 | 0.2152 | 0.0 | | 0.2849 | 46.4 | 2320 | 0.6370 | 0.2838 | 0.3371 | 0.8447 | nan | 0.9062 | 0.9524 | 0.6058 | 0.7879 | 0.3519 | 0.0 | 0.5141 | 0.6140 | 0.0 | 0.9449 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6878 | 0.0 | 0.0 | 0.9100 | 0.0 | 0.3961 | 0.3550 | 0.0 | 0.0 | 0.0 | 0.3628 | 0.0 | 0.0 | 0.9247 | 0.8827 | 0.9722 | 0.0 | 0.0138 | 0.2791 | 0.0 | nan | 0.7274 | 0.8498 | 0.5370 | 0.7366 | 0.2880 | 0.0 | 0.4025 | 0.4042 | 0.0 | 0.8285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5304 | 0.0 | 0.0 | 0.7132 | 0.0 | 0.3211 | 0.2469 | 0.0 | 0.0 | 0.0 | 0.3011 | 0.0 | 0.0 | 0.8429 | 0.7607 | 0.9306 | 0.0 | 0.0136 | 0.2164 | 0.0 | | 0.2599 | 46.8 | 2340 | 0.6385 | 0.2839 | 0.3383 | 0.8446 | nan | 0.9125 | 0.9515 | 0.5988 | 0.7867 | 0.3383 | 0.0 | 0.5109 | 0.6167 | 0.0 | 0.9444 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6999 | 0.0 | 0.0 | 0.8998 | 0.0 | 0.4217 | 0.3582 | 0.0 | 0.0 | 0.0 | 0.3618 | 0.0 | 0.0 | 0.9241 | 0.8944 | 0.9728 | 0.0 | 0.0140 | 0.2960 | 0.0 | nan | 0.7253 | 0.8508 | 0.5379 | 0.7364 | 0.2833 | 0.0 | 0.4010 | 0.4031 | 0.0 | 0.8290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5276 | 0.0 | 0.0 | 0.7151 | 0.0 | 0.3357 | 0.2444 | 0.0 | 0.0 | 0.0 | 0.2996 | 0.0 | 0.0 | 0.8416 | 0.7563 | 0.9304 | 0.0 | 0.0138 | 0.2217 | 0.0 | | 0.3051 | 47.2 | 2360 | 0.6322 | 0.2843 | 0.3384 | 0.8449 | nan | 0.9056 | 0.9501 | 0.6024 | 0.7992 | 0.3467 | 0.0 | 0.5156 | 0.6115 | 0.0 | 0.9455 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7007 | 0.0 | 0.0 | 0.9005 | 0.0 | 0.4145 | 0.3529 | 0.0 | 0.0 | 0.0 | 0.3639 | 0.0 | 0.0 | 0.9305 | 0.8834 | 0.9705 | 0.0 | 0.0145 | 0.2967 | 0.0 | nan | 0.7256 | 0.8512 | 0.5395 | 0.7363 | 0.2865 | 0.0 | 0.4030 | 0.4059 | 0.0 | 0.8285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5279 | 0.0 | 0.0 | 0.7145 | 0.0 | 0.3315 | 0.2443 | 0.0 | 0.0 | 0.0 | 0.3009 | 0.0 | 0.0 | 0.8422 | 0.7621 | 0.9314 | 0.0 | 0.0143 | 0.2224 | 0.0 | | 0.3295 | 47.6 | 2380 | 0.6335 | 0.2843 | 0.3385 | 0.8448 | nan | 0.9076 | 0.9511 | 0.5946 | 0.7985 | 0.3416 | 0.0 | 0.5124 | 0.6087 | 0.0 | 0.9466 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7039 | 0.0 | 0.0 | 0.8969 | 0.0 | 0.4223 | 0.3597 | 0.0 | 0.0 | 0.0 | 0.3662 | 0.0 | 0.0 | 0.9273 | 0.8890 | 0.9726 | 0.0 | 0.0139 | 0.2955 | 0.0 | nan | 0.7261 | 0.8504 | 0.5418 | 0.7353 | 0.2840 | 0.0 | 0.3997 | 0.4052 | 0.0 | 0.8276 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5280 | 0.0 | 0.0 | 0.7159 | 0.0 | 0.3352 | 0.2454 | 0.0 | 0.0 | 0.0 | 0.3018 | 0.0 | 0.0 | 0.8429 | 0.7597 | 0.9307 | 0.0 | 0.0137 | 0.2220 | 0.0 | | 0.3076 | 48.0 | 2400 | 0.6354 | 0.2839 | 0.3371 | 0.8449 | nan | 0.9013 | 0.9540 | 0.5975 | 0.8009 | 0.3376 | 0.0 | 0.5096 | 0.6170 | 0.0 | 0.9443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6890 | 0.0 | 0.0 | 0.9068 | 0.0 | 0.4008 | 0.3547 | 0.0 | 0.0 | 0.0 | 0.3703 | 0.0 | 0.0 | 0.9272 | 0.8803 | 0.9721 | 0.0 | 0.0159 | 0.2833 | 0.0 | nan | 0.7289 | 0.8493 | 0.5403 | 0.7362 | 0.2811 | 0.0 | 0.3987 | 0.4042 | 0.0 | 0.8300 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5282 | 0.0 | 0.0 | 0.7131 | 0.0 | 0.3234 | 0.2461 | 0.0 | 0.0 | 0.0 | 0.3041 | 0.0 | 0.0 | 0.8440 | 0.7614 | 0.9308 | 0.0 | 0.0155 | 0.2175 | 0.0 | | 0.306 | 48.4 | 2420 | 0.6358 | 0.2836 | 0.3364 | 0.8446 | nan | 0.9125 | 0.9514 | 0.6005 | 0.7840 | 0.3395 | 0.0 | 0.5120 | 0.6077 | 0.0 | 0.9452 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6902 | 0.0 | 0.0 | 0.9049 | 0.0 | 0.4001 | 0.3406 | 0.0 | 0.0 | 0.0 | 0.3646 | 0.0 | 0.0 | 0.9304 | 0.8802 | 0.9712 | 0.0 | 0.0157 | 0.2882 | 0.0 | nan | 0.7236 | 0.8512 | 0.5370 | 0.7335 | 0.2827 | 0.0 | 0.4008 | 0.4065 | 0.0 | 0.8291 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5290 | 0.0 | 0.0 | 0.7132 | 0.0 | 0.3206 | 0.2414 | 0.0 | 0.0 | 0.0 | 0.3014 | 0.0 | 0.0 | 0.8427 | 0.7619 | 0.9312 | 0.0 | 0.0154 | 0.2195 | 0.0 | | 0.2623 | 48.8 | 2440 | 0.6342 | 0.2839 | 0.3381 | 0.8444 | nan | 0.9127 | 0.9487 | 0.6027 | 0.7876 | 0.3482 | 0.0 | 0.5157 | 0.6140 | 0.0 | 0.9431 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6961 | 0.0 | 0.0 | 0.9053 | 0.0 | 0.4075 | 0.3583 | 0.0 | 0.0 | 0.0 | 0.3654 | 0.0 | 0.0 | 0.9251 | 0.8914 | 0.9738 | 0.0 | 0.0143 | 0.2854 | 0.0 | nan | 0.7237 | 0.8518 | 0.5387 | 0.7316 | 0.2869 | 0.0 | 0.4022 | 0.4066 | 0.0 | 0.8309 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5283 | 0.0 | 0.0 | 0.7137 | 0.0 | 0.3254 | 0.2469 | 0.0 | 0.0 | 0.0 | 0.3021 | 0.0 | 0.0 | 0.8428 | 0.7587 | 0.9298 | 0.0 | 0.0140 | 0.2182 | 0.0 | | 0.2494 | 49.2 | 2460 | 0.6372 | 0.2839 | 0.3377 | 0.8441 | nan | 0.9121 | 0.9506 | 0.6043 | 0.7734 | 0.3497 | 0.0 | 0.5153 | 0.6076 | 0.0 | 0.9462 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6948 | 0.0 | 0.0 | 0.9019 | 0.0 | 0.4125 | 0.3576 | 0.0 | 0.0 | 0.0 | 0.3646 | 0.0 | 0.0 | 0.9270 | 0.8869 | 0.9730 | 0.0 | 0.0143 | 0.2903 | 0.0 | nan | 0.7210 | 0.8511 | 0.5373 | 0.7297 | 0.2867 | 0.0 | 0.4017 | 0.4059 | 0.0 | 0.8284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5309 | 0.0 | 0.0 | 0.7149 | 0.0 | 0.3281 | 0.2462 | 0.0 | 0.0 | 0.0 | 0.3017 | 0.0 | 0.0 | 0.8432 | 0.7605 | 0.9304 | 0.0 | 0.0141 | 0.2201 | 0.0 | | 0.2411 | 49.6 | 2480 | 0.6353 | 0.2838 | 0.3373 | 0.8443 | nan | 0.9128 | 0.9496 | 0.6019 | 0.7793 | 0.3468 | 0.0 | 0.5182 | 0.6107 | 0.0 | 0.9421 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6947 | 0.0 | 0.0 | 0.9064 | 0.0 | 0.4011 | 0.3651 | 0.0 | 0.0 | 0.0 | 0.3610 | 0.0 | 0.0 | 0.9318 | 0.8792 | 0.9705 | 0.0 | 0.0149 | 0.2827 | 0.0 | nan | 0.7221 | 0.8517 | 0.5390 | 0.7298 | 0.2859 | 0.0 | 0.4017 | 0.4064 | 0.0 | 0.8315 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5277 | 0.0 | 0.0 | 0.7128 | 0.0 | 0.3236 | 0.2505 | 0.0 | 0.0 | 0.0 | 0.3001 | 0.0 | 0.0 | 0.8418 | 0.7627 | 0.9313 | 0.0 | 0.0147 | 0.2176 | 0.0 | | 0.2768 | 50.0 | 2500 | 0.6351 | 0.2834 | 0.3374 | 0.8443 | nan | 0.9045 | 0.9530 | 0.6075 | 0.7798 | 0.3400 | 0.0 | 0.5121 | 0.6170 | 0.0 | 0.9458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6956 | 0.0 | 0.0 | 0.9045 | 0.0 | 0.4037 | 0.3503 | 0.0 | 0.0 | 0.0 | 0.3679 | 0.0 | 0.0 | 0.9274 | 0.8889 | 0.9722 | 0.0 | 0.0119 | 0.2879 | 0.0 | nan | 0.7249 | 0.8500 | 0.5367 | 0.7300 | 0.2828 | 0.0 | 0.4008 | 0.4039 | 0.0 | 0.8291 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5268 | 0.0 | 0.0 | 0.7142 | 0.0 | 0.3242 | 0.2450 | 0.0 | 0.0 | 0.0 | 0.3031 | 0.0 | 0.0 | 0.8429 | 0.7587 | 0.9308 | 0.0 | 0.0117 | 0.2194 | 0.0 | ### Framework versions - Transformers 4.34.1 - Pytorch 1.11.0+cu113 - Datasets 2.15.0 - Tokenizers 0.14.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

yijisuk/segformer-b0-finetuned-segments-ic-chip-sample

# segformer-b0-finetuned-segments-ic-chip-sample This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Mean Iou: 0.4863 - Mean Accuracy: 0.9725 - Overall Accuracy: 0.9725 - Accuracy Unlabeled: nan - Accuracy Circuit: 0.9725 - Iou Unlabeled: 0.0 - Iou Circuit: 0.9725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Circuit | Iou Unlabeled | Iou Circuit | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:| | 0.0885 | 1.0 | 20 | 0.0858 | 0.4907 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.1764 | 2.0 | 40 | 0.0854 | 0.4734 | 0.9469 | 0.9469 | nan | 0.9469 | 0.0 | 0.9469 | | 0.0569 | 3.0 | 60 | 0.0854 | 0.4702 | 0.9404 | 0.9404 | nan | 0.9404 | 0.0 | 0.9404 | | 0.0959 | 4.0 | 80 | 0.0851 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.2969 | 5.0 | 100 | 0.0825 | 0.4863 | 0.9727 | 0.9727 | nan | 0.9727 | 0.0 | 0.9727 | | 0.1979 | 6.0 | 120 | 0.0824 | 0.4873 | 0.9746 | 0.9746 | nan | 0.9746 | 0.0 | 0.9746 | | 0.0906 | 7.0 | 140 | 0.0824 | 0.4740 | 0.9480 | 0.9480 | nan | 0.9480 | 0.0 | 0.9480 | | 0.2879 | 8.0 | 160 | 0.0821 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.1366 | 9.0 | 180 | 0.0807 | 0.4833 | 0.9666 | 0.9666 | nan | 0.9666 | 0.0 | 0.9666 | | 0.1664 | 10.0 | 200 | 0.0813 | 0.4860 | 0.9720 | 0.9720 | nan | 0.9720 | 0.0 | 0.9720 | | 0.1521 | 11.0 | 220 | 0.0831 | 0.4830 | 0.9660 | 0.9660 | nan | 0.9660 | 0.0 | 0.9660 | | 0.2004 | 12.0 | 240 | 0.0795 | 0.4825 | 0.9651 | 0.9651 | nan | 0.9651 | 0.0 | 0.9651 | | 0.1547 | 13.0 | 260 | 0.0793 | 0.4812 | 0.9625 | 0.9625 | nan | 0.9625 | 0.0 | 0.9625 | | 0.4191 | 14.0 | 280 | 0.0788 | 0.4830 | 0.9659 | 0.9659 | nan | 0.9659 | 0.0 | 0.9659 | | 0.0431 | 15.0 | 300 | 0.0782 | 0.4815 | 0.9630 | 0.9630 | nan | 0.9630 | 0.0 | 0.9630 | | 1.3911 | 16.0 | 320 | 0.0793 | 0.4820 | 0.9640 | 0.9640 | nan | 0.9640 | 0.0 | 0.9640 | | 0.0217 | 17.0 | 340 | 0.0814 | 0.4836 | 0.9671 | 0.9671 | nan | 0.9671 | 0.0 | 0.9671 | | 0.1116 | 18.0 | 360 | 0.0789 | 0.4839 | 0.9678 | 0.9678 | nan | 0.9678 | 0.0 | 0.9678 | | 0.3295 | 19.0 | 380 | 0.0791 | 0.4763 | 0.9526 | 0.9526 | nan | 0.9526 | 0.0 | 0.9526 | | 0.0327 | 20.0 | 400 | 0.0792 | 0.4829 | 0.9658 | 0.9658 | nan | 0.9658 | 0.0 | 0.9658 | | 0.2542 | 21.0 | 420 | 0.0787 | 0.4861 | 0.9722 | 0.9722 | nan | 0.9722 | 0.0 | 0.9722 | | 0.1587 | 22.0 | 440 | 0.0783 | 0.4772 | 0.9543 | 0.9543 | nan | 0.9543 | 0.0 | 0.9543 | | 0.2721 | 23.0 | 460 | 0.0804 | 0.4913 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.0505 | 24.0 | 480 | 0.0781 | 0.4827 | 0.9655 | 0.9655 | nan | 0.9655 | 0.0 | 0.9655 | | 0.1417 | 25.0 | 500 | 0.0801 | 0.4834 | 0.9669 | 0.9669 | nan | 0.9669 | 0.0 | 0.9669 | | 0.1371 | 26.0 | 520 | 0.0777 | 0.4838 | 0.9676 | 0.9676 | nan | 0.9676 | 0.0 | 0.9676 | | 0.1282 | 27.0 | 540 | 0.0773 | 0.4807 | 0.9613 | 0.9613 | nan | 0.9613 | 0.0 | 0.9613 | | 0.057 | 28.0 | 560 | 0.0772 | 0.4829 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | | 0.2592 | 29.0 | 580 | 0.0807 | 0.4872 | 0.9744 | 0.9744 | nan | 0.9744 | 0.0 | 0.9744 | | 0.1687 | 30.0 | 600 | 0.0794 | 0.4825 | 0.9649 | 0.9649 | nan | 0.9649 | 0.0 | 0.9649 | | 0.499 | 31.0 | 620 | 0.0805 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 | | 0.1584 | 32.0 | 640 | 0.0790 | 0.4845 | 0.9691 | 0.9691 | nan | 0.9691 | 0.0 | 0.9691 | | 0.0689 | 33.0 | 660 | 0.0785 | 0.4845 | 0.9690 | 0.9690 | nan | 0.9690 | 0.0 | 0.9690 | | 1.3764 | 34.0 | 680 | 0.0790 | 0.4848 | 0.9696 | 0.9696 | nan | 0.9696 | 0.0 | 0.9696 | | 0.2597 | 35.0 | 700 | 0.0808 | 0.4875 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 1.0757 | 36.0 | 720 | 0.0761 | 0.4841 | 0.9681 | 0.9681 | nan | 0.9681 | 0.0 | 0.9681 | | 0.6112 | 37.0 | 740 | 0.0779 | 0.4825 | 0.9650 | 0.9650 | nan | 0.9650 | 0.0 | 0.9650 | | 0.2899 | 38.0 | 760 | 0.0787 | 0.4796 | 0.9591 | 0.9591 | nan | 0.9591 | 0.0 | 0.9591 | | 0.3402 | 39.0 | 780 | 0.0777 | 0.4838 | 0.9676 | 0.9676 | nan | 0.9676 | 0.0 | 0.9676 | | 0.0183 | 40.0 | 800 | 0.0771 | 0.4829 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | | 0.1407 | 41.0 | 820 | 0.0774 | 0.4809 | 0.9617 | 0.9617 | nan | 0.9617 | 0.0 | 0.9617 | | 0.4045 | 42.0 | 840 | 0.0767 | 0.4819 | 0.9638 | 0.9638 | nan | 0.9638 | 0.0 | 0.9638 | | 0.2159 | 43.0 | 860 | 0.0780 | 0.4850 | 0.9699 | 0.9699 | nan | 0.9699 | 0.0 | 0.9699 | | 0.0541 | 44.0 | 880 | 0.0768 | 0.4812 | 0.9624 | 0.9624 | nan | 0.9624 | 0.0 | 0.9624 | | 0.0638 | 45.0 | 900 | 0.0774 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 | | 0.0409 | 46.0 | 920 | 0.0788 | 0.4875 | 0.9749 | 0.9749 | nan | 0.9749 | 0.0 | 0.9749 | | 0.1662 | 47.0 | 940 | 0.0774 | 0.4871 | 0.9743 | 0.9743 | nan | 0.9743 | 0.0 | 0.9743 | | 0.1636 | 48.0 | 960 | 0.0783 | 0.4860 | 0.9720 | 0.9720 | nan | 0.9720 | 0.0 | 0.9720 | | 0.033 | 49.0 | 980 | 0.0791 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.171 | 50.0 | 1000 | 0.0772 | 0.4863 | 0.9725 | 0.9725 | nan | 0.9725 | 0.0 | 0.9725 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu115 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "circuit" ]

yijisuk/segformer-b1-finetuned-segments-ic-chip-sample

# segformer-b1-finetuned-segments-ic-chip-sample This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set: - Loss: 0.1227 - Mean Iou: 0.4744 - Mean Accuracy: 0.9489 - Overall Accuracy: 0.9489 - Accuracy Unlabeled: nan - Accuracy Circuit: 0.9489 - Iou Unlabeled: 0.0 - Iou Circuit: 0.9489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Circuit | Iou Unlabeled | Iou Circuit | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:| | 0.4185 | 1.0 | 20 | 0.5878 | 0.3632 | 0.7265 | 0.7265 | nan | 0.7265 | 0.0 | 0.7265 | | 0.4477 | 2.0 | 40 | 0.4288 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 | | 0.9304 | 3.0 | 60 | 0.2053 | 0.4520 | 0.9041 | 0.9041 | nan | 0.9041 | 0.0 | 0.9041 | | 0.1409 | 4.0 | 80 | 0.1817 | 0.4738 | 0.9477 | 0.9477 | nan | 0.9477 | 0.0 | 0.9477 | | 0.392 | 5.0 | 100 | 0.1824 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.1589 | 6.0 | 120 | 0.1594 | 0.4814 | 0.9628 | 0.9628 | nan | 0.9628 | 0.0 | 0.9628 | | 0.1848 | 7.0 | 140 | 0.1551 | 0.4625 | 0.9251 | 0.9251 | nan | 0.9251 | 0.0 | 0.9251 | | 0.0874 | 8.0 | 160 | 0.1503 | 0.4829 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | | 0.2172 | 9.0 | 180 | 0.1558 | 0.4591 | 0.9182 | 0.9182 | nan | 0.9182 | 0.0 | 0.9182 | | 0.9914 | 10.0 | 200 | 0.1457 | 0.4698 | 0.9396 | 0.9396 | nan | 0.9396 | 0.0 | 0.9396 | | 0.2387 | 11.0 | 220 | 0.1494 | 0.4709 | 0.9419 | 0.9419 | nan | 0.9419 | 0.0 | 0.9419 | | 0.1242 | 12.0 | 240 | 0.1463 | 0.4743 | 0.9486 | 0.9486 | nan | 0.9486 | 0.0 | 0.9486 | | 0.0819 | 13.0 | 260 | 0.1492 | 0.4757 | 0.9515 | 0.9515 | nan | 0.9515 | 0.0 | 0.9515 | | 0.6077 | 14.0 | 280 | 0.1442 | 0.4793 | 0.9586 | 0.9586 | nan | 0.9586 | 0.0 | 0.9586 | | 0.3156 | 15.0 | 300 | 0.1430 | 0.4813 | 0.9627 | 0.9627 | nan | 0.9627 | 0.0 | 0.9627 | | 0.2564 | 16.0 | 320 | 0.1483 | 0.4673 | 0.9347 | 0.9347 | nan | 0.9347 | 0.0 | 0.9347 | | 0.107 | 17.0 | 340 | 0.1467 | 0.4695 | 0.9390 | 0.9390 | nan | 0.9390 | 0.0 | 0.9390 | | 1.1592 | 18.0 | 360 | 0.1437 | 0.4814 | 0.9628 | 0.9628 | nan | 0.9628 | 0.0 | 0.9628 | | 0.0586 | 19.0 | 380 | 0.1396 | 0.4811 | 0.9622 | 0.9622 | nan | 0.9622 | 0.0 | 0.9622 | | 0.9815 | 20.0 | 400 | 0.1399 | 0.4812 | 0.9624 | 0.9624 | nan | 0.9624 | 0.0 | 0.9624 | | 0.3101 | 21.0 | 420 | 0.1411 | 0.4836 | 0.9672 | 0.9672 | nan | 0.9672 | 0.0 | 0.9672 | | 0.2325 | 22.0 | 440 | 0.1395 | 0.4672 | 0.9344 | 0.9344 | nan | 0.9344 | 0.0 | 0.9344 | | 0.1504 | 23.0 | 460 | 0.1420 | 0.4720 | 0.9441 | 0.9441 | nan | 0.9441 | 0.0 | 0.9441 | | 0.2831 | 24.0 | 480 | 0.1393 | 0.4697 | 0.9395 | 0.9395 | nan | 0.9395 | 0.0 | 0.9395 | | 0.0921 | 25.0 | 500 | 0.1418 | 0.4701 | 0.9401 | 0.9401 | nan | 0.9401 | 0.0 | 0.9401 | | 0.141 | 26.0 | 520 | 0.1318 | 0.4648 | 0.9296 | 0.9296 | nan | 0.9296 | 0.0 | 0.9296 | | 0.1381 | 27.0 | 540 | 0.1316 | 0.4697 | 0.9395 | 0.9395 | nan | 0.9395 | 0.0 | 0.9395 | | 1.1864 | 28.0 | 560 | 0.1292 | 0.4774 | 0.9548 | 0.9548 | nan | 0.9548 | 0.0 | 0.9548 | | 0.9492 | 29.0 | 580 | 0.1290 | 0.4709 | 0.9418 | 0.9418 | nan | 0.9418 | 0.0 | 0.9418 | | 0.3061 | 30.0 | 600 | 0.1303 | 0.4536 | 0.9071 | 0.9071 | nan | 0.9071 | 0.0 | 0.9071 | | 0.2511 | 31.0 | 620 | 0.1318 | 0.4725 | 0.9451 | 0.9451 | nan | 0.9451 | 0.0 | 0.9451 | | 0.2706 | 32.0 | 640 | 0.1284 | 0.4790 | 0.9580 | 0.9580 | nan | 0.9580 | 0.0 | 0.9580 | | 0.1508 | 33.0 | 660 | 0.1264 | 0.4698 | 0.9396 | 0.9396 | nan | 0.9396 | 0.0 | 0.9396 | | 0.2802 | 34.0 | 680 | 0.1308 | 0.4733 | 0.9467 | 0.9467 | nan | 0.9467 | 0.0 | 0.9467 | | 0.1897 | 35.0 | 700 | 0.1315 | 0.4681 | 0.9361 | 0.9361 | nan | 0.9361 | 0.0 | 0.9361 | | 0.1981 | 36.0 | 720 | 0.1289 | 0.4766 | 0.9531 | 0.9531 | nan | 0.9531 | 0.0 | 0.9531 | | 0.2742 | 37.0 | 740 | 0.1284 | 0.4818 | 0.9635 | 0.9635 | nan | 0.9635 | 0.0 | 0.9635 | | 0.0418 | 38.0 | 760 | 0.1240 | 0.4762 | 0.9525 | 0.9525 | nan | 0.9525 | 0.0 | 0.9525 | | 0.1946 | 39.0 | 780 | 0.1253 | 0.4750 | 0.9500 | 0.9500 | nan | 0.9500 | 0.0 | 0.9500 | | 0.1692 | 40.0 | 800 | 0.1253 | 0.4836 | 0.9672 | 0.9672 | nan | 0.9672 | 0.0 | 0.9672 | | 0.3071 | 41.0 | 820 | 0.1227 | 0.4751 | 0.9503 | 0.9503 | nan | 0.9503 | 0.0 | 0.9503 | | 0.2003 | 42.0 | 840 | 0.1250 | 0.4762 | 0.9524 | 0.9524 | nan | 0.9524 | 0.0 | 0.9524 | | 0.2099 | 43.0 | 860 | 0.1235 | 0.4740 | 0.9480 | 0.9480 | nan | 0.9480 | 0.0 | 0.9480 | | 0.1218 | 44.0 | 880 | 0.1222 | 0.4743 | 0.9486 | 0.9486 | nan | 0.9486 | 0.0 | 0.9486 | | 0.1583 | 45.0 | 900 | 0.1226 | 0.4708 | 0.9415 | 0.9415 | nan | 0.9415 | 0.0 | 0.9415 | | 0.1506 | 46.0 | 920 | 0.1215 | 0.4686 | 0.9372 | 0.9372 | nan | 0.9372 | 0.0 | 0.9372 | | 0.0643 | 47.0 | 940 | 0.1234 | 0.4779 | 0.9559 | 0.9559 | nan | 0.9559 | 0.0 | 0.9559 | | 0.2006 | 48.0 | 960 | 0.1213 | 0.4757 | 0.9515 | 0.9515 | nan | 0.9515 | 0.0 | 0.9515 | | 0.0783 | 49.0 | 980 | 0.1241 | 0.4726 | 0.9452 | 0.9452 | nan | 0.9452 | 0.0 | 0.9452 | | 0.0552 | 50.0 | 1000 | 0.1227 | 0.4744 | 0.9489 | 0.9489 | nan | 0.9489 | 0.0 | 0.9489 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu115 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "unlabeled", "circuit" ]

Gregghe/segformer-finetuned-fingertip-10-steps

# segformer-finetuned-fingertip-10-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 3.5204 - Mean Iou: 0.0132 - Mean Accuracy: 0.0688 - Overall Accuracy: 0.0956 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0201 - Accuracy Flat-sidewalk: 0.1177 - Accuracy Flat-crosswalk: 0.0014 - Accuracy Flat-cyclinglane: 0.4426 - Accuracy Flat-parkingdriveway: 0.0021 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0008 - Accuracy Human-person: 0.0010 - Accuracy Human-rider: 0.0077 - Accuracy Vehicle-car: 0.1566 - Accuracy Vehicle-truck: 0.0040 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.5317 - Accuracy Vehicle-bicycle: 0.0892 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: nan - Accuracy Construction-building: 0.0578 - Accuracy Construction-door: 0.0682 - Accuracy Construction-wall: 0.0002 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0055 - Accuracy Object-pole: 0.0162 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.3811 - Accuracy Nature-vegetation: 0.0756 - Accuracy Nature-terrain: 0.0010 - Accuracy Sky: 0.0038 - Accuracy Void-ground: 0.0400 - Accuracy Void-dynamic: 0.0002 - Accuracy Void-static: 0.0386 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.0193 - Iou Flat-sidewalk: 0.1141 - Iou Flat-crosswalk: 0.0013 - Iou Flat-cyclinglane: 0.0702 - Iou Flat-parkingdriveway: 0.0019 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0007 - Iou Human-person: 0.0005 - Iou Human-rider: 0.0001 - Iou Vehicle-car: 0.1087 - Iou Vehicle-truck: 0.0003 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0004 - Iou Vehicle-bicycle: 0.0085 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.0413 - Iou Construction-door: 0.0067 - Iou Construction-wall: 0.0002 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0021 - Iou Object-pole: 0.0036 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0001 - Iou Nature-vegetation: 0.0663 - Iou Nature-terrain: 0.0009 - Iou Sky: 0.0038 - Iou Void-ground: 0.0049 - Iou Void-dynamic: 0.0000 - Iou Void-static: 0.0046 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | No log | 0.09 | 10 | 3.5204 | 0.0132 | 0.0688 | 0.0956 | nan | 0.0201 | 0.1177 | 0.0014 | 0.4426 | 0.0021 | nan | 0.0008 | 0.0010 | 0.0077 | 0.1566 | 0.0040 | 0.0 | nan | 0.5317 | 0.0892 | 0.0 | nan | 0.0578 | 0.0682 | 0.0002 | 0.0000 | 0.0 | nan | 0.0055 | 0.0162 | 0.0 | 0.3811 | 0.0756 | 0.0010 | 0.0038 | 0.0400 | 0.0002 | 0.0386 | 0.0 | 0.0 | 0.0193 | 0.1141 | 0.0013 | 0.0702 | 0.0019 | 0.0 | 0.0007 | 0.0005 | 0.0001 | 0.1087 | 0.0003 | 0.0 | 0.0 | 0.0004 | 0.0085 | 0.0 | 0.0 | 0.0413 | 0.0067 | 0.0002 | 0.0000 | 0.0 | 0.0 | 0.0021 | 0.0036 | 0.0 | 0.0001 | 0.0663 | 0.0009 | 0.0038 | 0.0049 | 0.0000 | 0.0046 | 0.0 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.16.0 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

Gregghe/segformer-finetuned-fingertip-1000-steps

# segformer-finetuned-fingertip-10-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 3.5204 - Mean Iou: 0.0132 - Mean Accuracy: 0.0688 - Overall Accuracy: 0.0956 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0201 - Accuracy Flat-sidewalk: 0.1177 - Accuracy Flat-crosswalk: 0.0014 - Accuracy Flat-cyclinglane: 0.4426 - Accuracy Flat-parkingdriveway: 0.0021 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0008 - Accuracy Human-person: 0.0010 - Accuracy Human-rider: 0.0077 - Accuracy Vehicle-car: 0.1566 - Accuracy Vehicle-truck: 0.0040 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.5317 - Accuracy Vehicle-bicycle: 0.0892 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: nan - Accuracy Construction-building: 0.0578 - Accuracy Construction-door: 0.0682 - Accuracy Construction-wall: 0.0002 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0055 - Accuracy Object-pole: 0.0162 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.3811 - Accuracy Nature-vegetation: 0.0756 - Accuracy Nature-terrain: 0.0010 - Accuracy Sky: 0.0038 - Accuracy Void-ground: 0.0400 - Accuracy Void-dynamic: 0.0002 - Accuracy Void-static: 0.0386 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.0193 - Iou Flat-sidewalk: 0.1141 - Iou Flat-crosswalk: 0.0013 - Iou Flat-cyclinglane: 0.0702 - Iou Flat-parkingdriveway: 0.0019 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0007 - Iou Human-person: 0.0005 - Iou Human-rider: 0.0001 - Iou Vehicle-car: 0.1087 - Iou Vehicle-truck: 0.0003 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0004 - Iou Vehicle-bicycle: 0.0085 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.0413 - Iou Construction-door: 0.0067 - Iou Construction-wall: 0.0002 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0021 - Iou Object-pole: 0.0036 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0001 - Iou Nature-vegetation: 0.0663 - Iou Nature-terrain: 0.0009 - Iou Sky: 0.0038 - Iou Void-ground: 0.0049 - Iou Void-dynamic: 0.0000 - Iou Void-static: 0.0046 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | No log | 0.09 | 10 | 3.5204 | 0.0132 | 0.0688 | 0.0956 | nan | 0.0201 | 0.1177 | 0.0014 | 0.4426 | 0.0021 | nan | 0.0008 | 0.0010 | 0.0077 | 0.1566 | 0.0040 | 0.0 | nan | 0.5317 | 0.0892 | 0.0 | nan | 0.0578 | 0.0682 | 0.0002 | 0.0000 | 0.0 | nan | 0.0055 | 0.0162 | 0.0 | 0.3811 | 0.0756 | 0.0010 | 0.0038 | 0.0400 | 0.0002 | 0.0386 | 0.0 | 0.0 | 0.0193 | 0.1141 | 0.0013 | 0.0702 | 0.0019 | 0.0 | 0.0007 | 0.0005 | 0.0001 | 0.1087 | 0.0003 | 0.0 | 0.0 | 0.0004 | 0.0085 | 0.0 | 0.0 | 0.0413 | 0.0067 | 0.0002 | 0.0000 | 0.0 | 0.0 | 0.0021 | 0.0036 | 0.0 | 0.0001 | 0.0663 | 0.0009 | 0.0038 | 0.0049 | 0.0000 | 0.0046 | 0.0 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.16.0 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

sawthiha/segformer-b0-finetuned-deprem-satellite

# segformer-b0-finetuned-deprem-satellite This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the deprem-ml/deprem_satellite_semantic_whu_dataset dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0641 - eval_mean_iou: 0.9849 - eval_mean_accuracy: 0.9933 - eval_overall_accuracy: 0.9933 - eval_runtime: 94.2835 - eval_samples_per_second: 10.988 - eval_steps_per_second: 2.206 - epoch: 4.18 - step: 1980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 10 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "building" ]

johanhag/segformer-b0-winter_2

# segformer-b0-winter_2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the johanhag/winter dataset. It achieves the following results on the evaluation set: - Loss: 0.0665 - Mean Iou: 0.7168 - Mean Accuracy: 0.7840 - Overall Accuracy: 0.9775 - Accuracy Unlabeled: nan - Accuracy Road: 0.9559 - Accuracy Side walk: 0.7693 - Accuracy Car: 0.8882 - Accuracy Pedestrian: 0.8345 - Accuracy Sign: 0.2653 - Accuracy Other: 0.9907 - Iou Unlabeled: nan - Iou Road: 0.9088 - Iou Side walk: 0.6583 - Iou Car: 0.7943 - Iou Pedestrian: 0.7201 - Iou Sign: 0.2393 - Iou Other: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Road | Accuracy Side walk | Accuracy Car | Accuracy Pedestrian | Accuracy Sign | Accuracy Other | Iou Unlabeled | Iou Road | Iou Side walk | Iou Car | Iou Pedestrian | Iou Sign | Iou Other | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:------------------:|:------------:|:-------------------:|:-------------:|:--------------:|:-------------:|:--------:|:-------------:|:-------:|:--------------:|:--------:|:---------:| | 1.2678 | 0.37 | 20 | 1.4585 | 0.2824 | 0.4123 | 0.8374 | nan | 0.9932 | 0.0 | 0.6072 | 0.0205 | 0.0 | 0.8528 | nan | 0.4383 | 0.0 | 0.3876 | 0.0204 | 0.0 | 0.8480 | | 0.9377 | 0.74 | 40 | 0.9219 | 0.3461 | 0.4345 | 0.9161 | nan | 0.9666 | 0.0013 | 0.6904 | 0.0 | 0.0 | 0.9490 | nan | 0.6449 | 0.0013 | 0.5036 | 0.0 | 0.0 | 0.9265 | | 0.7819 | 1.11 | 60 | 0.7375 | 0.3622 | 0.4469 | 0.9248 | nan | 0.9687 | 0.0098 | 0.7451 | 0.0 | 0.0 | 0.9576 | nan | 0.6748 | 0.0095 | 0.5520 | 0.0 | 0.0 | 0.9367 | | 0.687 | 1.48 | 80 | 0.6354 | 0.3674 | 0.4573 | 0.9286 | nan | 0.9687 | 0.0131 | 0.8014 | 0.0 | 0.0 | 0.9608 | nan | 0.6873 | 0.0130 | 0.5626 | 0.0 | 0.0 | 0.9417 | | 0.552 | 1.85 | 100 | 0.5199 | 0.3753 | 0.4700 | 0.9360 | nan | 0.9564 | 0.0246 | 0.8691 | 0.0 | 0.0 | 0.9697 | nan | 0.7283 | 0.0246 | 0.5492 | 0.0 | 0.0 | 0.9497 | | 0.521 | 2.22 | 120 | 0.4620 | 0.3897 | 0.4651 | 0.9419 | nan | 0.9413 | 0.0622 | 0.8083 | 0.0 | 0.0 | 0.9791 | nan | 0.7606 | 0.0615 | 0.5631 | 0.0 | 0.0 | 0.9531 | | 0.3901 | 2.59 | 140 | 0.4255 | 0.4088 | 0.5067 | 0.9398 | nan | 0.9671 | 0.1414 | 0.8939 | 0.0695 | 0.0 | 0.9684 | nan | 0.7369 | 0.1358 | 0.5570 | 0.0694 | 0.0 | 0.9540 | | 0.4172 | 2.96 | 160 | 0.3836 | 0.4307 | 0.5198 | 0.9436 | nan | 0.9591 | 0.2494 | 0.8904 | 0.0485 | 0.0 | 0.9714 | nan | 0.7552 | 0.2212 | 0.6043 | 0.0485 | 0.0 | 0.9552 | | 0.2995 | 3.33 | 180 | 0.3209 | 0.4755 | 0.5497 | 0.9483 | nan | 0.9631 | 0.1625 | 0.8542 | 0.3417 | 0.0 | 0.9768 | nan | 0.7656 | 0.1585 | 0.6356 | 0.3340 | 0.0 | 0.9592 | | 1.6503 | 3.7 | 200 | 0.2891 | 0.4914 | 0.5539 | 0.9529 | nan | 0.9505 | 0.4372 | 0.7938 | 0.1621 | 0.0 | 0.9797 | nan | 0.8021 | 0.3827 | 0.6457 | 0.1592 | 0.0 | 0.9585 | | 0.2506 | 4.07 | 220 | 0.2502 | 0.4813 | 0.5573 | 0.9537 | nan | 0.9412 | 0.3606 | 0.8825 | 0.1774 | 0.0 | 0.9820 | nan | 0.8068 | 0.3260 | 0.6168 | 0.1758 | 0.0 | 0.9625 | | 0.2336 | 4.44 | 240 | 0.2367 | 0.5543 | 0.6178 | 0.9610 | nan | 0.9211 | 0.4445 | 0.8401 | 0.5124 | 0.0 | 0.9889 | nan | 0.8354 | 0.4044 | 0.6477 | 0.4713 | 0.0 | 0.9670 | | 0.1932 | 4.81 | 260 | 0.2088 | 0.5436 | 0.5960 | 0.9613 | nan | 0.9283 | 0.4497 | 0.8144 | 0.3935 | 0.0 | 0.9898 | nan | 0.8360 | 0.4188 | 0.6606 | 0.3794 | 0.0 | 0.9667 | | 1.8362 | 5.19 | 280 | 0.1866 | 0.5727 | 0.6422 | 0.9628 | nan | 0.9358 | 0.4776 | 0.8185 | 0.6339 | 0.0 | 0.9876 | nan | 0.8474 | 0.4320 | 0.6612 | 0.5279 | 0.0 | 0.9679 | | 0.2085 | 5.56 | 300 | 0.1677 | 0.5749 | 0.6321 | 0.9646 | nan | 0.9571 | 0.4811 | 0.7743 | 0.5920 | 0.0 | 0.9881 | nan | 0.8488 | 0.4438 | 0.6761 | 0.5099 | 0.0 | 0.9706 | | 0.1389 | 5.93 | 320 | 0.1566 | 0.5761 | 0.6368 | 0.9641 | nan | 0.9566 | 0.5138 | 0.8141 | 0.5499 | 0.0 | 0.9862 | nan | 0.8483 | 0.4661 | 0.6803 | 0.4929 | 0.0 | 0.9693 | | 0.1799 | 6.3 | 340 | 0.1620 | 0.5836 | 0.6678 | 0.9625 | nan | 0.9604 | 0.5769 | 0.8649 | 0.6244 | 0.0 | 0.9802 | nan | 0.8437 | 0.4753 | 0.6798 | 0.5350 | 0.0 | 0.9679 | | 0.1291 | 6.67 | 360 | 0.1400 | 0.5926 | 0.6680 | 0.9649 | nan | 0.9637 | 0.5702 | 0.8362 | 0.6548 | 0.0 | 0.9831 | nan | 0.8505 | 0.4971 | 0.6900 | 0.5478 | 0.0 | 0.9701 | | 0.1275 | 7.04 | 380 | 0.1505 | 0.5710 | 0.6346 | 0.9625 | nan | 0.9682 | 0.4619 | 0.7713 | 0.6220 | 0.0 | 0.9843 | nan | 0.8387 | 0.4057 | 0.6858 | 0.5268 | 0.0 | 0.9691 | | 0.0968 | 7.41 | 400 | 0.1368 | 0.5774 | 0.6363 | 0.9644 | nan | 0.9666 | 0.4759 | 0.8145 | 0.5753 | 0.0 | 0.9859 | nan | 0.8407 | 0.4348 | 0.6960 | 0.5216 | 0.0 | 0.9714 | | 0.1279 | 7.78 | 420 | 0.1317 | 0.5911 | 0.6616 | 0.9665 | nan | 0.9405 | 0.5449 | 0.9055 | 0.5909 | 0.0 | 0.9880 | nan | 0.8677 | 0.4742 | 0.7002 | 0.5328 | 0.0 | 0.9714 | | 0.0917 | 8.15 | 440 | 0.1217 | 0.6187 | 0.7110 | 0.9684 | nan | 0.9505 | 0.7295 | 0.8745 | 0.7278 | 0.0 | 0.9834 | nan | 0.8802 | 0.5694 | 0.7065 | 0.5843 | 0.0 | 0.9719 | | 0.0896 | 8.52 | 460 | 0.1164 | 0.6055 | 0.6809 | 0.9678 | nan | 0.9333 | 0.7235 | 0.8257 | 0.6158 | 0.0 | 0.9873 | nan | 0.8787 | 0.5338 | 0.7053 | 0.5428 | 0.0 | 0.9722 | | 0.0903 | 8.89 | 480 | 0.1186 | 0.6195 | 0.6956 | 0.9683 | nan | 0.9632 | 0.6929 | 0.8345 | 0.6995 | 0.0 | 0.9836 | nan | 0.8645 | 0.5528 | 0.7308 | 0.5960 | 0.0 | 0.9730 | | 0.0943 | 9.26 | 500 | 0.1183 | 0.6205 | 0.7187 | 0.9672 | nan | 0.9539 | 0.7619 | 0.8675 | 0.7483 | 0.0 | 0.9805 | nan | 0.8773 | 0.5444 | 0.7161 | 0.6142 | 0.0 | 0.9709 | | 0.0886 | 9.63 | 520 | 0.1080 | 0.6233 | 0.7010 | 0.9687 | nan | 0.9665 | 0.6540 | 0.8584 | 0.7434 | 0.0 | 0.9838 | nan | 0.8689 | 0.5572 | 0.7272 | 0.6140 | 0.0 | 0.9726 | | 0.1024 | 10.0 | 540 | 0.1047 | 0.6224 | 0.6872 | 0.9706 | nan | 0.9419 | 0.6739 | 0.8470 | 0.6710 | 0.0 | 0.9898 | nan | 0.8803 | 0.5702 | 0.7278 | 0.5818 | 0.0 | 0.9744 | | 0.0779 | 10.37 | 560 | 0.1116 | 0.6168 | 0.6841 | 0.9683 | nan | 0.9726 | 0.5517 | 0.8334 | 0.7614 | 0.0 | 0.9856 | nan | 0.8627 | 0.5211 | 0.7301 | 0.6145 | 0.0 | 0.9724 | | 0.1082 | 10.74 | 580 | 0.1044 | 0.6277 | 0.7012 | 0.9695 | nan | 0.9656 | 0.6787 | 0.8693 | 0.7096 | 0.0 | 0.9843 | nan | 0.8712 | 0.5607 | 0.7441 | 0.6166 | 0.0 | 0.9736 | | 0.0797 | 11.11 | 600 | 0.1004 | 0.6356 | 0.7205 | 0.9703 | nan | 0.9625 | 0.7506 | 0.8580 | 0.7686 | 0.0 | 0.9834 | nan | 0.8826 | 0.5894 | 0.7393 | 0.6292 | 0.0 | 0.9734 | | 0.0766 | 11.48 | 620 | 0.0957 | 0.6377 | 0.7250 | 0.9714 | nan | 0.9507 | 0.7480 | 0.8487 | 0.8162 | 0.0 | 0.9862 | nan | 0.8869 | 0.6181 | 0.7356 | 0.6112 | 0.0 | 0.9743 | | 0.1557 | 11.85 | 640 | 0.0951 | 0.6367 | 0.7029 | 0.9719 | nan | 0.9529 | 0.7316 | 0.8288 | 0.7157 | 0.0 | 0.9883 | nan | 0.8896 | 0.6117 | 0.7317 | 0.6125 | 0.0 | 0.9745 | | 0.0623 | 12.22 | 660 | 0.0939 | 0.6462 | 0.7283 | 0.9726 | nan | 0.9467 | 0.7844 | 0.8493 | 0.8022 | 0.0 | 0.9873 | nan | 0.8923 | 0.6157 | 0.7518 | 0.6416 | 0.0 | 0.9756 | | 0.0619 | 12.59 | 680 | 0.0976 | 0.6349 | 0.7030 | 0.9710 | nan | 0.9603 | 0.7091 | 0.8715 | 0.6907 | 0.0 | 0.9861 | nan | 0.8802 | 0.5868 | 0.7498 | 0.6182 | 0.0 | 0.9745 | | 0.0617 | 12.96 | 700 | 0.0919 | 0.6354 | 0.7228 | 0.9719 | nan | 0.9470 | 0.7145 | 0.8873 | 0.8003 | 0.0 | 0.9874 | nan | 0.8887 | 0.5929 | 0.7426 | 0.6128 | 0.0 | 0.9756 | | 0.0544 | 13.33 | 720 | 0.0870 | 0.6517 | 0.7173 | 0.9741 | nan | 0.9465 | 0.7433 | 0.8302 | 0.7930 | 0.0 | 0.9907 | nan | 0.9005 | 0.6367 | 0.7452 | 0.6518 | 0.0 | 0.9760 | | 0.1027 | 13.7 | 740 | 0.0894 | 0.6446 | 0.7207 | 0.9728 | nan | 0.9207 | 0.7490 | 0.8655 | 0.7973 | 0.0 | 0.9917 | nan | 0.8926 | 0.6210 | 0.7383 | 0.6398 | 0.0 | 0.9755 | | 0.0568 | 14.07 | 760 | 0.0897 | 0.6434 | 0.7118 | 0.9729 | nan | 0.9570 | 0.7121 | 0.8779 | 0.7342 | 0.0016 | 0.9882 | nan | 0.8947 | 0.6098 | 0.7497 | 0.6292 | 0.0016 | 0.9753 | | 0.0612 | 14.44 | 780 | 0.0851 | 0.6517 | 0.7181 | 0.9740 | nan | 0.9599 | 0.7121 | 0.8589 | 0.7874 | 0.0012 | 0.9890 | nan | 0.8967 | 0.6182 | 0.7590 | 0.6582 | 0.0012 | 0.9766 | | 0.076 | 14.81 | 800 | 0.0859 | 0.6485 | 0.7126 | 0.9735 | nan | 0.9589 | 0.6878 | 0.8535 | 0.7830 | 0.0033 | 0.9894 | nan | 0.8925 | 0.6028 | 0.7595 | 0.6563 | 0.0033 | 0.9765 | | 0.0489 | 15.19 | 820 | 0.0815 | 0.6528 | 0.7188 | 0.9744 | nan | 0.9484 | 0.7477 | 0.8180 | 0.7988 | 0.0090 | 0.9909 | nan | 0.9011 | 0.6294 | 0.7421 | 0.6584 | 0.0090 | 0.9771 | | 0.0859 | 15.56 | 840 | 0.0828 | 0.6555 | 0.7296 | 0.9738 | nan | 0.9508 | 0.7726 | 0.8563 | 0.7974 | 0.0118 | 0.9884 | nan | 0.8961 | 0.6231 | 0.7577 | 0.6674 | 0.0118 | 0.9768 | | 0.0661 | 15.93 | 860 | 0.0840 | 0.6555 | 0.7190 | 0.9741 | nan | 0.9603 | 0.7187 | 0.8370 | 0.7849 | 0.0234 | 0.9894 | nan | 0.8993 | 0.6216 | 0.7473 | 0.6652 | 0.0234 | 0.9766 | | 0.0654 | 16.3 | 880 | 0.0840 | 0.6584 | 0.7227 | 0.9742 | nan | 0.9564 | 0.7158 | 0.8614 | 0.7876 | 0.0257 | 0.9895 | nan | 0.8970 | 0.6175 | 0.7638 | 0.6699 | 0.0257 | 0.9767 | | 0.0607 | 16.67 | 900 | 0.0822 | 0.6559 | 0.7390 | 0.9732 | nan | 0.9545 | 0.7766 | 0.8700 | 0.8213 | 0.0253 | 0.9865 | nan | 0.8920 | 0.6220 | 0.7645 | 0.6554 | 0.0253 | 0.9763 | | 0.0497 | 17.04 | 920 | 0.0817 | 0.6673 | 0.7313 | 0.9748 | nan | 0.9606 | 0.6869 | 0.8915 | 0.7878 | 0.0713 | 0.9897 | nan | 0.8995 | 0.6138 | 0.7722 | 0.6698 | 0.0711 | 0.9773 | | 0.0497 | 17.41 | 940 | 0.0841 | 0.6523 | 0.7202 | 0.9731 | nan | 0.9672 | 0.6538 | 0.8894 | 0.7861 | 0.0371 | 0.9878 | nan | 0.8868 | 0.5868 | 0.7649 | 0.6617 | 0.0371 | 0.9766 | | 0.055 | 17.78 | 960 | 0.0832 | 0.6568 | 0.7440 | 0.9730 | nan | 0.9510 | 0.8045 | 0.8484 | 0.8358 | 0.0383 | 0.9863 | nan | 0.8946 | 0.6165 | 0.7569 | 0.6587 | 0.0383 | 0.9762 | | 0.0967 | 18.15 | 980 | 0.0854 | 0.6650 | 0.7524 | 0.9726 | nan | 0.9647 | 0.7887 | 0.8950 | 0.7981 | 0.0846 | 0.9836 | nan | 0.8918 | 0.6210 | 0.7621 | 0.6561 | 0.0839 | 0.9752 | | 0.0436 | 18.52 | 1000 | 0.0814 | 0.6623 | 0.7454 | 0.9739 | nan | 0.9593 | 0.7287 | 0.8957 | 0.8436 | 0.0579 | 0.9871 | nan | 0.8986 | 0.6138 | 0.7595 | 0.6672 | 0.0577 | 0.9767 | | 0.0465 | 18.89 | 1020 | 0.0773 | 0.6646 | 0.7224 | 0.9754 | nan | 0.9473 | 0.7419 | 0.8615 | 0.7462 | 0.0456 | 0.9919 | nan | 0.9050 | 0.6357 | 0.7607 | 0.6632 | 0.0456 | 0.9777 | | 0.0672 | 19.26 | 1040 | 0.0778 | 0.6713 | 0.7454 | 0.9751 | nan | 0.9464 | 0.7716 | 0.8746 | 0.8165 | 0.0736 | 0.9898 | nan | 0.9037 | 0.6316 | 0.7748 | 0.6671 | 0.0730 | 0.9776 | | 0.0592 | 19.63 | 1060 | 0.0799 | 0.6616 | 0.7223 | 0.9747 | nan | 0.9596 | 0.6442 | 0.8656 | 0.8116 | 0.0613 | 0.9912 | nan | 0.8997 | 0.5864 | 0.7681 | 0.6765 | 0.0612 | 0.9776 | | 0.0876 | 20.0 | 1080 | 0.0794 | 0.6712 | 0.7386 | 0.9745 | nan | 0.9608 | 0.7370 | 0.8057 | 0.8434 | 0.0954 | 0.9893 | nan | 0.8993 | 0.6468 | 0.7364 | 0.6735 | 0.0943 | 0.9767 | | 0.0483 | 20.37 | 1100 | 0.0742 | 0.6783 | 0.7469 | 0.9760 | nan | 0.9576 | 0.7331 | 0.8860 | 0.8272 | 0.0872 | 0.9901 | nan | 0.9066 | 0.6469 | 0.7731 | 0.6784 | 0.0866 | 0.9782 | | 0.0837 | 20.74 | 1120 | 0.0746 | 0.6756 | 0.7378 | 0.9762 | nan | 0.9491 | 0.7350 | 0.8654 | 0.8146 | 0.0709 | 0.9920 | nan | 0.9067 | 0.6411 | 0.7718 | 0.6849 | 0.0705 | 0.9785 | | 0.0685 | 21.11 | 1140 | 0.0743 | 0.6787 | 0.7383 | 0.9759 | nan | 0.9475 | 0.7316 | 0.8709 | 0.7859 | 0.1015 | 0.9921 | nan | 0.9070 | 0.6387 | 0.7661 | 0.6817 | 0.1004 | 0.9781 | | 0.0596 | 21.48 | 1160 | 0.0751 | 0.6762 | 0.7418 | 0.9753 | nan | 0.9499 | 0.7516 | 0.8336 | 0.8182 | 0.1066 | 0.9909 | nan | 0.9056 | 0.6307 | 0.7558 | 0.6827 | 0.1051 | 0.9776 | | 0.0608 | 21.85 | 1180 | 0.0728 | 0.6833 | 0.7549 | 0.9763 | nan | 0.9428 | 0.7846 | 0.8823 | 0.8219 | 0.1066 | 0.9911 | nan | 0.9070 | 0.6479 | 0.7742 | 0.6862 | 0.1055 | 0.9789 | | 0.0918 | 22.22 | 1200 | 0.0731 | 0.6846 | 0.7457 | 0.9763 | nan | 0.9597 | 0.7567 | 0.8516 | 0.7984 | 0.1174 | 0.9904 | nan | 0.9049 | 0.6518 | 0.7691 | 0.6875 | 0.1153 | 0.9787 | | 0.0539 | 22.59 | 1220 | 0.0743 | 0.6785 | 0.7431 | 0.9756 | nan | 0.9563 | 0.7037 | 0.8680 | 0.8308 | 0.1086 | 0.9909 | nan | 0.9011 | 0.6200 | 0.7720 | 0.6918 | 0.1075 | 0.9786 | | 0.112 | 22.96 | 1240 | 0.0732 | 0.6786 | 0.7429 | 0.9759 | nan | 0.9550 | 0.7656 | 0.8544 | 0.8060 | 0.0862 | 0.9902 | nan | 0.9026 | 0.6444 | 0.7701 | 0.6902 | 0.0856 | 0.9786 | | 0.0719 | 23.33 | 1260 | 0.0752 | 0.6812 | 0.7419 | 0.9752 | nan | 0.9556 | 0.7059 | 0.8454 | 0.8174 | 0.1363 | 0.9910 | nan | 0.8975 | 0.6136 | 0.7681 | 0.6947 | 0.1347 | 0.9784 | | 0.0669 | 23.7 | 1280 | 0.0769 | 0.6881 | 0.7787 | 0.9740 | nan | 0.9620 | 0.7954 | 0.9020 | 0.8524 | 0.1760 | 0.9846 | nan | 0.8958 | 0.6387 | 0.7668 | 0.6829 | 0.1678 | 0.9766 | | 0.0493 | 24.07 | 1300 | 0.0759 | 0.6778 | 0.7518 | 0.9748 | nan | 0.9568 | 0.7398 | 0.8994 | 0.7977 | 0.1284 | 0.9885 | nan | 0.8995 | 0.6076 | 0.7751 | 0.6805 | 0.1260 | 0.9783 | | 0.11 | 24.44 | 1320 | 0.0752 | 0.6879 | 0.7625 | 0.9751 | nan | 0.9636 | 0.7402 | 0.8775 | 0.8339 | 0.1720 | 0.9879 | nan | 0.9000 | 0.6242 | 0.7765 | 0.6846 | 0.1644 | 0.9779 | | 0.0631 | 24.81 | 1340 | 0.0720 | 0.6898 | 0.7530 | 0.9763 | nan | 0.9664 | 0.7223 | 0.8807 | 0.8130 | 0.1459 | 0.9896 | nan | 0.9044 | 0.6431 | 0.7812 | 0.6906 | 0.1411 | 0.9786 | | 0.038 | 25.19 | 1360 | 0.0719 | 0.6909 | 0.7599 | 0.9761 | nan | 0.9491 | 0.7646 | 0.8841 | 0.8190 | 0.1522 | 0.9904 | nan | 0.9034 | 0.6346 | 0.7828 | 0.6976 | 0.1480 | 0.9790 | | 0.0431 | 25.56 | 1380 | 0.0721 | 0.6925 | 0.7596 | 0.9760 | nan | 0.9651 | 0.7530 | 0.8864 | 0.8031 | 0.1614 | 0.9886 | nan | 0.9033 | 0.6418 | 0.7848 | 0.6910 | 0.1558 | 0.9785 | | 0.0289 | 25.93 | 1400 | 0.0711 | 0.6914 | 0.7587 | 0.9762 | nan | 0.9381 | 0.7752 | 0.8909 | 0.7947 | 0.1614 | 0.9919 | nan | 0.9045 | 0.6471 | 0.7738 | 0.6893 | 0.1549 | 0.9789 | | 0.0426 | 26.3 | 1420 | 0.0706 | 0.6950 | 0.7523 | 0.9768 | nan | 0.9640 | 0.7361 | 0.8359 | 0.8120 | 0.1746 | 0.9911 | nan | 0.9078 | 0.6530 | 0.7639 | 0.6987 | 0.1673 | 0.9791 | | 0.0467 | 26.67 | 1440 | 0.0707 | 0.6961 | 0.7609 | 0.9765 | nan | 0.9479 | 0.7615 | 0.9029 | 0.7805 | 0.1813 | 0.9911 | nan | 0.9072 | 0.6454 | 0.7790 | 0.6928 | 0.1734 | 0.9790 | | 0.0408 | 27.04 | 1460 | 0.0709 | 0.6920 | 0.7549 | 0.9765 | nan | 0.9492 | 0.7672 | 0.8323 | 0.8389 | 0.1500 | 0.9918 | nan | 0.9080 | 0.6569 | 0.7628 | 0.7006 | 0.1450 | 0.9786 | | 0.0615 | 27.41 | 1480 | 0.0703 | 0.6963 | 0.7537 | 0.9768 | nan | 0.9581 | 0.7484 | 0.8667 | 0.7992 | 0.1587 | 0.9911 | nan | 0.9084 | 0.6610 | 0.7770 | 0.7002 | 0.1525 | 0.9786 | | 0.0533 | 27.78 | 1500 | 0.0706 | 0.6946 | 0.7582 | 0.9764 | nan | 0.9456 | 0.7452 | 0.8519 | 0.8340 | 0.1799 | 0.9924 | nan | 0.9061 | 0.6408 | 0.7720 | 0.6988 | 0.1708 | 0.9789 | | 0.0711 | 28.15 | 1520 | 0.0706 | 0.6920 | 0.7510 | 0.9763 | nan | 0.9534 | 0.7701 | 0.8629 | 0.7678 | 0.1608 | 0.9909 | nan | 0.9047 | 0.6450 | 0.7784 | 0.6898 | 0.1554 | 0.9789 | | 0.0393 | 28.52 | 1540 | 0.0682 | 0.6969 | 0.7657 | 0.9768 | nan | 0.9478 | 0.7615 | 0.8731 | 0.8445 | 0.1756 | 0.9915 | nan | 0.9068 | 0.6512 | 0.7821 | 0.6940 | 0.1677 | 0.9795 | | 0.0554 | 28.89 | 1560 | 0.0692 | 0.6964 | 0.7709 | 0.9760 | nan | 0.9589 | 0.7844 | 0.8853 | 0.8399 | 0.1685 | 0.9882 | nan | 0.9025 | 0.6465 | 0.7864 | 0.7042 | 0.1603 | 0.9786 | | 0.0434 | 29.26 | 1580 | 0.0701 | 0.7004 | 0.7726 | 0.9764 | nan | 0.9626 | 0.7718 | 0.9017 | 0.8279 | 0.1836 | 0.9883 | nan | 0.9042 | 0.6550 | 0.7841 | 0.7068 | 0.1736 | 0.9789 | | 0.0291 | 29.63 | 1600 | 0.0697 | 0.6993 | 0.7828 | 0.9764 | nan | 0.9514 | 0.7878 | 0.9098 | 0.8654 | 0.1934 | 0.9889 | nan | 0.9074 | 0.6529 | 0.7792 | 0.6950 | 0.1820 | 0.9791 | | 2.6544 | 30.0 | 1620 | 0.0684 | 0.6924 | 0.7548 | 0.9766 | nan | 0.9521 | 0.7645 | 0.8669 | 0.8127 | 0.1414 | 0.9912 | nan | 0.9047 | 0.6439 | 0.7817 | 0.7072 | 0.1378 | 0.9794 | | 0.0575 | 30.37 | 1640 | 0.0695 | 0.7022 | 0.7716 | 0.9766 | nan | 0.9572 | 0.7694 | 0.8932 | 0.8291 | 0.1913 | 0.9895 | nan | 0.9055 | 0.6478 | 0.7868 | 0.7134 | 0.1807 | 0.9792 | | 0.0445 | 30.74 | 1660 | 0.0695 | 0.6967 | 0.7644 | 0.9766 | nan | 0.9469 | 0.7587 | 0.8831 | 0.8402 | 0.1661 | 0.9914 | nan | 0.9087 | 0.6416 | 0.7843 | 0.7072 | 0.1596 | 0.9789 | | 0.0487 | 31.11 | 1680 | 0.0700 | 0.7022 | 0.7674 | 0.9768 | nan | 0.9598 | 0.7276 | 0.8745 | 0.8351 | 0.2170 | 0.9907 | nan | 0.9066 | 0.6404 | 0.7870 | 0.7006 | 0.1996 | 0.9792 | | 0.0539 | 31.48 | 1700 | 0.0700 | 0.6978 | 0.7634 | 0.9764 | nan | 0.9489 | 0.7680 | 0.8586 | 0.8232 | 0.1903 | 0.9912 | nan | 0.9054 | 0.6365 | 0.7813 | 0.7053 | 0.1791 | 0.9792 | | 0.0617 | 31.85 | 1720 | 0.0717 | 0.7077 | 0.7777 | 0.9765 | nan | 0.9562 | 0.7683 | 0.8947 | 0.8146 | 0.2433 | 0.9895 | nan | 0.9038 | 0.6415 | 0.7921 | 0.7096 | 0.2201 | 0.9791 | | 0.0391 | 32.22 | 1740 | 0.0690 | 0.7007 | 0.7676 | 0.9766 | nan | 0.9557 | 0.7667 | 0.8654 | 0.8402 | 0.1874 | 0.9902 | nan | 0.9044 | 0.6447 | 0.7851 | 0.7129 | 0.1775 | 0.9793 | | 0.0834 | 32.59 | 1760 | 0.0688 | 0.7015 | 0.7699 | 0.9768 | nan | 0.9519 | 0.7571 | 0.8926 | 0.8319 | 0.1954 | 0.9907 | nan | 0.9048 | 0.6459 | 0.7860 | 0.7089 | 0.1840 | 0.9795 | | 0.0412 | 32.96 | 1780 | 0.0694 | 0.7080 | 0.7786 | 0.9768 | nan | 0.9560 | 0.7600 | 0.8859 | 0.8386 | 0.2410 | 0.9901 | nan | 0.9077 | 0.6464 | 0.7894 | 0.7049 | 0.2203 | 0.9791 | | 0.0476 | 33.33 | 1800 | 0.0698 | 0.7045 | 0.7689 | 0.9766 | nan | 0.9582 | 0.7688 | 0.8677 | 0.8225 | 0.2062 | 0.9900 | nan | 0.9055 | 0.6537 | 0.7854 | 0.7090 | 0.1948 | 0.9788 | | 0.0407 | 33.7 | 1820 | 0.0684 | 0.7103 | 0.7824 | 0.9770 | nan | 0.9535 | 0.7700 | 0.8809 | 0.8575 | 0.2423 | 0.9904 | nan | 0.9077 | 0.6555 | 0.7885 | 0.7073 | 0.2233 | 0.9794 | | 0.0307 | 34.07 | 1840 | 0.0717 | 0.7072 | 0.7902 | 0.9755 | nan | 0.9608 | 0.7869 | 0.8959 | 0.8623 | 0.2484 | 0.9867 | nan | 0.9000 | 0.6421 | 0.7882 | 0.7089 | 0.2258 | 0.9781 | | 0.0236 | 34.44 | 1860 | 0.0700 | 0.7104 | 0.7889 | 0.9765 | nan | 0.9502 | 0.7737 | 0.8754 | 0.8807 | 0.2639 | 0.9900 | nan | 0.9041 | 0.6532 | 0.7850 | 0.7041 | 0.2370 | 0.9791 | | 0.0752 | 34.81 | 1880 | 0.0689 | 0.7068 | 0.7716 | 0.9766 | nan | 0.9564 | 0.7689 | 0.8845 | 0.8157 | 0.2139 | 0.9899 | nan | 0.9032 | 0.6542 | 0.7915 | 0.7115 | 0.2012 | 0.9790 | | 0.0301 | 35.19 | 1900 | 0.0701 | 0.7021 | 0.7862 | 0.9761 | nan | 0.9427 | 0.8005 | 0.8935 | 0.8702 | 0.2209 | 0.9896 | nan | 0.9046 | 0.6342 | 0.7870 | 0.7013 | 0.2064 | 0.9793 | | 0.0457 | 35.56 | 1920 | 0.0694 | 0.7053 | 0.7683 | 0.9769 | nan | 0.9525 | 0.7313 | 0.8720 | 0.8319 | 0.2302 | 0.9918 | nan | 0.9053 | 0.6363 | 0.7872 | 0.7100 | 0.2136 | 0.9796 | | 0.033 | 35.93 | 1940 | 0.0695 | 0.7078 | 0.7745 | 0.9766 | nan | 0.9618 | 0.7552 | 0.8873 | 0.8138 | 0.2392 | 0.9894 | nan | 0.9037 | 0.6423 | 0.7926 | 0.7104 | 0.2183 | 0.9793 | | 0.0231 | 36.3 | 1960 | 0.0687 | 0.7060 | 0.7741 | 0.9769 | nan | 0.9493 | 0.7574 | 0.8883 | 0.8302 | 0.2284 | 0.9912 | nan | 0.9052 | 0.6403 | 0.7898 | 0.7092 | 0.2118 | 0.9798 | | 0.038 | 36.67 | 1980 | 0.0685 | 0.7082 | 0.7775 | 0.9767 | nan | 0.9640 | 0.7617 | 0.8762 | 0.8311 | 0.2431 | 0.9891 | nan | 0.9042 | 0.6457 | 0.7927 | 0.7064 | 0.2206 | 0.9795 | | 0.0421 | 37.04 | 2000 | 0.0684 | 0.7083 | 0.7806 | 0.9767 | nan | 0.9523 | 0.7679 | 0.8937 | 0.8367 | 0.2427 | 0.9902 | nan | 0.9048 | 0.6430 | 0.7934 | 0.7069 | 0.2222 | 0.9796 | | 0.0431 | 37.41 | 2020 | 0.0700 | 0.7097 | 0.7792 | 0.9763 | nan | 0.9580 | 0.7520 | 0.8796 | 0.8316 | 0.2641 | 0.9896 | nan | 0.9019 | 0.6332 | 0.7909 | 0.7144 | 0.2385 | 0.9793 | | 0.0384 | 37.78 | 2040 | 0.0671 | 0.7123 | 0.7770 | 0.9774 | nan | 0.9580 | 0.7566 | 0.8859 | 0.8268 | 0.2441 | 0.9908 | nan | 0.9085 | 0.6542 | 0.7907 | 0.7163 | 0.2242 | 0.9799 | | 0.0405 | 38.15 | 2060 | 0.0672 | 0.7141 | 0.7884 | 0.9772 | nan | 0.9559 | 0.7886 | 0.8953 | 0.8413 | 0.2596 | 0.9895 | nan | 0.9092 | 0.6515 | 0.7949 | 0.7149 | 0.2345 | 0.9797 | | 0.0474 | 38.52 | 2080 | 0.0677 | 0.7122 | 0.7779 | 0.9773 | nan | 0.9611 | 0.7538 | 0.8965 | 0.8228 | 0.2431 | 0.9901 | nan | 0.9072 | 0.6535 | 0.7946 | 0.7159 | 0.2224 | 0.9798 | | 0.0862 | 38.89 | 2100 | 0.0682 | 0.7115 | 0.7787 | 0.9772 | nan | 0.9539 | 0.7524 | 0.8878 | 0.8352 | 0.2516 | 0.9910 | nan | 0.9073 | 0.6464 | 0.7929 | 0.7136 | 0.2290 | 0.9798 | | 0.0354 | 39.26 | 2120 | 0.0677 | 0.7085 | 0.7713 | 0.9772 | nan | 0.9562 | 0.7498 | 0.8778 | 0.8236 | 0.2290 | 0.9912 | nan | 0.9084 | 0.6464 | 0.7894 | 0.7146 | 0.2127 | 0.9796 | | 0.0549 | 39.63 | 2140 | 0.0686 | 0.7079 | 0.7702 | 0.9768 | nan | 0.9573 | 0.7483 | 0.8772 | 0.8090 | 0.2386 | 0.9907 | nan | 0.9060 | 0.6389 | 0.7896 | 0.7132 | 0.2204 | 0.9794 | | 0.0355 | 40.0 | 2160 | 0.0688 | 0.7131 | 0.7845 | 0.9768 | nan | 0.9595 | 0.7532 | 0.8882 | 0.8417 | 0.2747 | 0.9896 | nan | 0.9049 | 0.6461 | 0.7936 | 0.7110 | 0.2441 | 0.9793 | | 2.1231 | 40.37 | 2180 | 0.0676 | 0.7069 | 0.7721 | 0.9769 | nan | 0.9521 | 0.7665 | 0.8752 | 0.8309 | 0.2170 | 0.9910 | nan | 0.9060 | 0.6463 | 0.7894 | 0.7156 | 0.2044 | 0.9796 | | 0.0577 | 40.74 | 2200 | 0.0669 | 0.7129 | 0.7797 | 0.9774 | nan | 0.9582 | 0.7679 | 0.8834 | 0.8338 | 0.2445 | 0.9904 | nan | 0.9097 | 0.6600 | 0.7920 | 0.7108 | 0.2256 | 0.9796 | | 0.0318 | 41.11 | 2220 | 0.0676 | 0.7131 | 0.7817 | 0.9771 | nan | 0.9615 | 0.7636 | 0.8916 | 0.8335 | 0.2504 | 0.9895 | nan | 0.9067 | 0.6543 | 0.7943 | 0.7147 | 0.2292 | 0.9795 | | 0.0356 | 41.48 | 2240 | 0.0674 | 0.7167 | 0.7861 | 0.9773 | nan | 0.9582 | 0.7643 | 0.8956 | 0.8287 | 0.2800 | 0.9900 | nan | 0.9076 | 0.6530 | 0.7965 | 0.7154 | 0.2479 | 0.9797 | | 0.0816 | 41.85 | 2260 | 0.0676 | 0.7126 | 0.7728 | 0.9774 | nan | 0.9568 | 0.7636 | 0.8714 | 0.8100 | 0.2439 | 0.9912 | nan | 0.9090 | 0.6544 | 0.7924 | 0.7146 | 0.2254 | 0.9797 | | 0.0561 | 42.22 | 2280 | 0.0666 | 0.7130 | 0.7759 | 0.9775 | nan | 0.9537 | 0.7694 | 0.8763 | 0.8244 | 0.2400 | 0.9914 | nan | 0.9092 | 0.6624 | 0.7912 | 0.7133 | 0.2222 | 0.9797 | | 0.0336 | 42.59 | 2300 | 0.0669 | 0.7173 | 0.7858 | 0.9776 | nan | 0.9553 | 0.7755 | 0.8866 | 0.8381 | 0.2685 | 0.9907 | nan | 0.9095 | 0.6654 | 0.7936 | 0.7138 | 0.2419 | 0.9798 | | 0.0313 | 42.96 | 2320 | 0.0669 | 0.7119 | 0.7773 | 0.9775 | nan | 0.9560 | 0.7609 | 0.8968 | 0.8289 | 0.2304 | 0.9907 | nan | 0.9084 | 0.6581 | 0.7945 | 0.7150 | 0.2154 | 0.9798 | | 0.0611 | 43.33 | 2340 | 0.0670 | 0.7090 | 0.7756 | 0.9772 | nan | 0.9547 | 0.7711 | 0.8810 | 0.8347 | 0.2215 | 0.9907 | nan | 0.9071 | 0.6520 | 0.7914 | 0.7153 | 0.2082 | 0.9798 | | 0.0329 | 43.7 | 2360 | 0.0669 | 0.7146 | 0.7803 | 0.9775 | nan | 0.9577 | 0.7647 | 0.8782 | 0.8346 | 0.2555 | 0.9908 | nan | 0.9091 | 0.6560 | 0.7926 | 0.7162 | 0.2337 | 0.9800 | | 0.0528 | 44.07 | 2380 | 0.0668 | 0.7131 | 0.7721 | 0.9776 | nan | 0.9594 | 0.7513 | 0.8762 | 0.8097 | 0.2449 | 0.9913 | nan | 0.9087 | 0.6565 | 0.7923 | 0.7147 | 0.2262 | 0.9799 | | 0.0729 | 44.44 | 2400 | 0.0671 | 0.7131 | 0.7748 | 0.9775 | nan | 0.9578 | 0.7573 | 0.8777 | 0.8187 | 0.2461 | 0.9911 | nan | 0.9085 | 0.6566 | 0.7912 | 0.7152 | 0.2272 | 0.9798 | | 0.0383 | 44.81 | 2420 | 0.0675 | 0.7108 | 0.7809 | 0.9771 | nan | 0.9506 | 0.7742 | 0.8796 | 0.8459 | 0.2443 | 0.9909 | nan | 0.9072 | 0.6499 | 0.7903 | 0.7116 | 0.2262 | 0.9798 | | 0.0565 | 45.19 | 2440 | 0.0674 | 0.7154 | 0.7821 | 0.9774 | nan | 0.9590 | 0.7609 | 0.8808 | 0.8296 | 0.2720 | 0.9905 | nan | 0.9086 | 0.6546 | 0.7931 | 0.7129 | 0.2436 | 0.9797 | | 0.0417 | 45.56 | 2460 | 0.0671 | 0.7149 | 0.7809 | 0.9776 | nan | 0.9563 | 0.7631 | 0.8843 | 0.8383 | 0.2524 | 0.9909 | nan | 0.9095 | 0.6570 | 0.7939 | 0.7174 | 0.2314 | 0.9799 | | 0.024 | 45.93 | 2480 | 0.0678 | 0.7159 | 0.7872 | 0.9772 | nan | 0.9564 | 0.7709 | 0.8828 | 0.8458 | 0.2769 | 0.9902 | nan | 0.9074 | 0.6549 | 0.7927 | 0.7148 | 0.2458 | 0.9797 | | 0.0496 | 46.3 | 2500 | 0.0668 | 0.7148 | 0.7836 | 0.9775 | nan | 0.9554 | 0.7648 | 0.8905 | 0.8390 | 0.2614 | 0.9907 | nan | 0.9092 | 0.6567 | 0.7931 | 0.7131 | 0.2368 | 0.9799 | | 0.0428 | 46.67 | 2520 | 0.0668 | 0.7150 | 0.7857 | 0.9773 | nan | 0.9571 | 0.7641 | 0.8969 | 0.8438 | 0.2624 | 0.9902 | nan | 0.9083 | 0.6559 | 0.7941 | 0.7144 | 0.2372 | 0.9798 | | 0.0458 | 47.04 | 2540 | 0.0665 | 0.7141 | 0.7811 | 0.9775 | nan | 0.9540 | 0.7665 | 0.8957 | 0.8351 | 0.2443 | 0.9908 | nan | 0.9088 | 0.6565 | 0.7946 | 0.7192 | 0.2257 | 0.9799 | | 0.0429 | 47.41 | 2560 | 0.0673 | 0.7131 | 0.7791 | 0.9773 | nan | 0.9604 | 0.7621 | 0.8836 | 0.8325 | 0.2457 | 0.9901 | nan | 0.9073 | 0.6536 | 0.7936 | 0.7180 | 0.2266 | 0.9797 | | 0.0449 | 47.78 | 2580 | 0.0666 | 0.7144 | 0.7786 | 0.9775 | nan | 0.9571 | 0.7630 | 0.8873 | 0.8248 | 0.2484 | 0.9908 | nan | 0.9089 | 0.6565 | 0.7944 | 0.7188 | 0.2281 | 0.9799 | | 0.0302 | 48.15 | 2600 | 0.0665 | 0.7155 | 0.7800 | 0.9776 | nan | 0.9552 | 0.7637 | 0.8904 | 0.8251 | 0.2545 | 0.9911 | nan | 0.9094 | 0.6580 | 0.7936 | 0.7196 | 0.2326 | 0.9799 | | 0.029 | 48.52 | 2620 | 0.0675 | 0.7176 | 0.7877 | 0.9774 | nan | 0.9588 | 0.7640 | 0.8947 | 0.8445 | 0.2744 | 0.9901 | nan | 0.9081 | 0.6575 | 0.7948 | 0.7204 | 0.2447 | 0.9798 | | 1.9447 | 48.89 | 2640 | 0.0662 | 0.7130 | 0.7803 | 0.9774 | nan | 0.9529 | 0.7688 | 0.8720 | 0.8497 | 0.2471 | 0.9912 | nan | 0.9085 | 0.6550 | 0.7904 | 0.7170 | 0.2272 | 0.9799 | | 0.0247 | 49.26 | 2660 | 0.0674 | 0.7163 | 0.7870 | 0.9773 | nan | 0.9593 | 0.7720 | 0.8815 | 0.8486 | 0.2704 | 0.9899 | nan | 0.9077 | 0.6575 | 0.7928 | 0.7181 | 0.2422 | 0.9797 | | 0.0369 | 49.63 | 2680 | 0.0662 | 0.7154 | 0.7839 | 0.9775 | nan | 0.9547 | 0.7711 | 0.8850 | 0.8430 | 0.2588 | 0.9907 | nan | 0.9087 | 0.6571 | 0.7934 | 0.7181 | 0.2354 | 0.9799 | | 0.0317 | 50.0 | 2700 | 0.0665 | 0.7168 | 0.7840 | 0.9775 | nan | 0.9559 | 0.7693 | 0.8882 | 0.8345 | 0.2653 | 0.9907 | nan | 0.9088 | 0.6583 | 0.7943 | 0.7201 | 0.2393 | 0.9799 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "road", "side walk", "car", "pedestrian", "sign", "other" ]

DrWasabiii/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.5644 - Mean Iou: 0.3202 - Mean Accuracy: 0.3861 - Overall Accuracy: 0.8525 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8598 - Accuracy Flat-sidewalk: 0.9440 - Accuracy Flat-crosswalk: 0.6837 - Accuracy Flat-cyclinglane: 0.8295 - Accuracy Flat-parkingdriveway: 0.5586 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.6015 - Accuracy Human-person: 0.6019 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9395 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.6060 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8961 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.5249 - Accuracy Construction-fenceguardrail: 0.4892 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3733 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9376 - Accuracy Nature-terrain: 0.9033 - Accuracy Sky: 0.9668 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.2540 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7395 - Iou Flat-sidewalk: 0.8717 - Iou Flat-crosswalk: 0.5750 - Iou Flat-cyclinglane: 0.6481 - Iou Flat-parkingdriveway: 0.4281 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.4534 - Iou Human-person: 0.4648 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.8127 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.3687 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7090 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3987 - Iou Construction-fenceguardrail: 0.4429 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.2772 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8463 - Iou Nature-terrain: 0.7719 - Iou Sky: 0.9189 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.2004 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | 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| 1.462 | 0.1 | 20 | 1.2595 | 0.1419 | 0.1947 | 0.7120 | nan | 0.7582 | 0.9092 | 0.0 | 0.0660 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.9133 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8346 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9263 | 0.7278 | 0.9005 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4874 | 0.7290 | 0.0 | 0.0657 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5744 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5093 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7362 | 0.5259 | 0.7708 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3298 | 0.2 | 40 | 1.1696 | 0.1552 | 0.2048 | 0.7307 | nan | 0.8291 | 0.9109 | 0.0 | 0.2907 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.9111 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8254 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9266 | 0.7259 | 0.9291 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5114 | 0.7458 | 0.0 | 0.2725 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.6361 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5295 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7486 | 0.5861 | 0.7800 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8454 | 0.3 | 60 | 1.1409 | 0.1623 | 0.2113 | 0.7445 | nan | 0.8024 | 0.9346 | 0.0 | 0.4023 | 0.0079 | nan | 0.0 | 0.0 | 0.0 | 0.8823 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8139 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9372 | 0.8410 | 0.9269 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5516 | 0.7597 | 0.0 | 0.3598 | 0.0078 | nan | 0.0 | 0.0 | 0.0 | 0.6808 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5406 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7382 | 0.5945 | 0.7969 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9115 | 0.4 | 80 | 1.0588 | 0.1639 | 0.2118 | 0.7400 | nan | 0.8770 | 0.8928 | 0.0 | 0.4710 | 0.0030 | nan | 0.0 | 0.0 | 0.0 | 0.9011 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8552 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9351 | 0.6907 | 0.9398 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5108 | 0.7636 | 0.0 | 0.4068 | 0.0030 | nan | 0.0 | 0.0 | 0.0 | 0.6580 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5332 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7594 | 0.6421 | 0.8024 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.011 | 0.5 | 100 | 1.0279 | 0.1646 | 0.2179 | 0.7466 | nan | 0.8087 | 0.9090 | 0.0 | 0.5313 | 0.0041 | nan | 0.0166 | 0.0 | 0.0 | 0.9286 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8570 | 0.0 | 0.0019 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9349 | 0.8582 | 0.9038 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5617 | 0.7613 | 0.0 | 0.4246 | 0.0041 | nan | 0.0163 | 0.0 | 0.0 | 0.6325 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5354 | 0.0 | 0.0019 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7643 | 0.5952 | 0.8043 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3211 | 0.6 | 120 | 1.0123 | 0.1692 | 0.2180 | 0.7496 | nan | 0.7381 | 0.9409 | 0.0 | 0.5419 | 0.0436 | nan | 0.1004 | 0.0 | 0.0 | 0.9234 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8649 | 0.0 | 0.0082 | 0.0000 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9434 | 0.7166 | 0.9375 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5590 | 0.7596 | 0.0 | 0.3963 | 0.0422 | nan | 0.0943 | 0.0 | 0.0 | 0.6632 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5399 | 0.0 | 0.0081 | 0.0000 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7656 | 0.5951 | 0.8222 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0065 | 0.7 | 140 | 0.9791 | 0.1689 | 0.2176 | 0.7502 | nan | 0.8268 | 0.9187 | 0.0 | 0.5011 | 0.0677 | nan | 0.0773 | 0.0 | 0.0 | 0.9338 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9045 | 0.0 | 0.0077 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9298 | 0.6508 | 0.9265 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5600 | 0.7809 | 0.0 | 0.4371 | 0.0637 | nan | 0.0721 | 0.0 | 0.0 | 0.6222 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5215 | 0.0 | 0.0075 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7620 | 0.5671 | 0.8406 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8046 | 0.8 | 160 | 0.9695 | 0.1703 | 0.2147 | 0.7442 | nan | 0.6653 | 0.9719 | 0.0 | 0.4870 | 0.0490 | nan | 0.0337 | 0.0 | 0.0 | 0.8754 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7818 | 0.0 | 0.0510 | 0.0 | 0.0 | nan | 0.0 | 0.0006 | 0.0 | 0.0 | 0.9269 | 0.8824 | 0.9306 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5548 | 0.7164 | 0.0 | 0.3614 | 0.0463 | nan | 0.0332 | 0.0 | 0.0 | 0.7196 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5634 | 0.0 | 0.0487 | 0.0 | 0.0 | nan | 0.0 | 0.0006 | 0.0 | 0.0 | 0.7624 | 0.6451 | 0.8265 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8299 | 0.9 | 180 | 0.9008 | 0.1791 | 0.2264 | 0.7632 | nan | 0.7556 | 0.9611 | 0.0 | 0.5157 | 0.0804 | nan | 0.0962 | 0.0 | 0.0 | 0.9573 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8870 | 0.0 | 0.0491 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9050 | 0.8751 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6112 | 0.7531 | 0.0 | 0.4358 | 0.0736 | nan | 0.0907 | 0.0 | 0.0 | 0.6175 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5662 | 0.0 | 0.0461 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.7988 | 0.7005 | 0.8590 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7382 | 1.0 | 200 | 0.9204 | 0.1770 | 0.2246 | 0.7550 | nan | 0.7011 | 0.9563 | 0.0 | 0.5585 | 0.0606 | nan | 0.0821 | 0.0 | 0.0 | 0.9182 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8852 | 0.0 | 0.0592 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9046 | 0.9074 | 0.9303 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5664 | 0.7432 | 0.0 | 0.4047 | 0.0573 | nan | 0.0758 | 0.0 | 0.0 | 0.7003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5667 | 0.0 | 0.0544 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.7864 | 0.6709 | 0.8622 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9823 | 1.1 | 220 | 0.8596 | 0.1868 | 0.2319 | 0.7714 | nan | 0.8631 | 0.9325 | 0.0 | 0.4702 | 0.0986 | nan | 0.1854 | 0.0 | 0.0 | 0.9024 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8971 | 0.0 | 0.0805 | 0.0 | 0.0 | nan | 0.0 | 0.0076 | 0.0 | 0.0 | 0.9362 | 0.8675 | 0.9474 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5915 | 0.7841 | 0.0 | 0.4126 | 0.0936 | nan | 0.1616 | 0.0 | 0.0 | 0.7255 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5662 | 0.0 | 0.0702 | 0.0 | 0.0 | nan | 0.0 | 0.0076 | 0.0 | 0.0 | 0.7982 | 0.7233 | 0.8577 | 0.0 | 0.0 | 0.0000 | 0.0 | | 0.7915 | 1.2 | 240 | 0.8611 | 0.1979 | 0.2482 | 0.7736 | nan | 0.8241 | 0.9115 | 0.0 | 0.6181 | 0.2810 | nan | 0.2094 | 0.0 | 0.0 | 0.9181 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8534 | 0.0 | 0.3040 | 0.0 | 0.0 | nan | 0.0 | 0.0363 | 0.0 | 0.0 | 0.9445 | 0.8380 | 0.9548 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5898 | 0.7872 | 0.0 | 0.4598 | 0.2158 | nan | 0.1784 | 0.0 | 0.0 | 0.7214 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5774 | 0.0 | 0.2155 | 0.0 | 0.0 | nan | 0.0 | 0.0358 | 0.0 | 0.0 | 0.7978 | 0.7230 | 0.8327 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9356 | 1.3 | 260 | 0.8942 | 0.1878 | 0.2379 | 0.7599 | nan | 0.6265 | 0.9610 | 0.0 | 0.4888 | 0.2483 | nan | 0.3971 | 0.0 | 0.0 | 0.9394 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9111 | 0.0 | 0.0591 | 0.0 | 0.0 | nan | 0.0 | 0.0127 | 0.0 | 0.0 | 0.9038 | 0.8992 | 0.9293 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5615 | 0.7544 | 0.0 | 0.3832 | 0.1988 | nan | 0.2752 | 0.0 | 0.0 | 0.6594 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5589 | 0.0 | 0.0521 | 0.0 | 0.0 | nan | 0.0 | 0.0126 | 0.0 | 0.0 | 0.8008 | 0.6830 | 0.8813 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8097 | 1.4 | 280 | 0.8333 | 0.1992 | 0.2498 | 0.7752 | nan | 0.8669 | 0.9017 | 0.0 | 0.5555 | 0.3048 | nan | 0.3696 | 0.0 | 0.0 | 0.9208 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8507 | 0.0 | 0.1704 | 0.0 | 0.0 | nan | 0.0 | 0.0472 | 0.0 | 0.0 | 0.9403 | 0.8713 | 0.9429 | 0.0 | 0.0 | 0.0010 | 0.0 | nan | 0.5646 | 0.8035 | 0.0 | 0.4533 | 0.2374 | nan | 0.2820 | 0.0 | 0.0 | 0.6974 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5820 | 0.0 | 0.1490 | 0.0 | 0.0 | nan | 0.0 | 0.0466 | 0.0 | 0.0 | 0.8006 | 0.7220 | 0.8348 | 0.0 | 0.0 | 0.0010 | 0.0 | | 0.9072 | 1.5 | 300 | 0.7929 | 0.2056 | 0.2588 | 0.7819 | nan | 0.8537 | 0.9209 | 0.0 | 0.5869 | 0.3037 | nan | 0.2640 | 0.0 | 0.0 | 0.9100 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8098 | 0.0 | 0.5714 | 0.0 | 0.0 | nan | 0.0 | 0.0396 | 0.0 | 0.0 | 0.9337 | 0.8841 | 0.9436 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.6061 | 0.8024 | 0.0 | 0.4735 | 0.2292 | nan | 0.2244 | 0.0 | 0.0 | 0.7509 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5947 | 0.0 | 0.2729 | 0.0 | 0.0 | nan | 0.0 | 0.0390 | 0.0 | 0.0 | 0.8104 | 0.7065 | 0.8620 | 0.0 | 0.0 | 0.0002 | 0.0 | | 1.0461 | 1.6 | 320 | 0.7737 | 0.2131 | 0.2593 | 0.7938 | nan | 0.8158 | 0.9467 | 0.0 | 0.6523 | 0.3113 | nan | 0.3152 | 0.0048 | 0.0 | 0.9263 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8790 | 0.0 | 0.3751 | 0.0 | 0.0 | nan | 0.0 | 0.0794 | 0.0 | 0.0 | 0.9515 | 0.8307 | 0.9473 | 0.0 | 0.0 | 0.0014 | 0.0 | nan | 0.6374 | 0.7992 | 0.0 | 0.5435 | 0.2390 | nan | 0.2686 | 0.0048 | 0.0 | 0.7307 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6365 | 0.0 | 0.2649 | 0.0 | 0.0 | nan | 0.0 | 0.0758 | 0.0 | 0.0 | 0.7983 | 0.7205 | 0.8855 | 0.0 | 0.0 | 0.0014 | 0.0 | | 0.9489 | 1.7 | 340 | 0.8072 | 0.2036 | 0.2463 | 0.7800 | nan | 0.7780 | 0.9572 | 0.0 | 0.4694 | 0.1562 | nan | 0.3721 | 0.0209 | 0.0 | 0.8907 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9140 | 0.0 | 0.2420 | 0.0000 | 0.0 | nan | 0.0 | 0.0765 | 0.0 | 0.0 | 0.9421 | 0.8619 | 0.9524 | 0.0 | 0.0 | 0.0008 | 0.0 | nan | 0.6090 | 0.7697 | 0.0 | 0.4229 | 0.1445 | nan | 0.2903 | 0.0208 | 0.0 | 0.7654 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5966 | 0.0 | 0.1859 | 0.0000 | 0.0 | nan | 0.0 | 0.0727 | 0.0 | 0.0 | 0.8061 | 0.7399 | 0.8871 | 0.0 | 0.0 | 0.0008 | 0.0 | | 0.9753 | 1.8 | 360 | 0.7746 | 0.2153 | 0.2641 | 0.7867 | nan | 0.8023 | 0.9353 | 0.0 | 0.6272 | 0.2777 | nan | 0.4336 | 0.1660 | 0.0 | 0.9215 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8746 | 0.0 | 0.3293 | 0.0026 | 0.0 | nan | 0.0 | 0.1379 | 0.0 | 0.0 | 0.9597 | 0.7740 | 0.9401 | 0.0 | 0.0 | 0.0042 | 0.0 | nan | 0.6029 | 0.8027 | 0.0 | 0.4873 | 0.2297 | nan | 0.3194 | 0.1551 | 0.0 | 0.7449 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6383 | 0.0 | 0.2494 | 0.0026 | 0.0 | nan | 0.0 | 0.1280 | 0.0 | 0.0 | 0.7825 | 0.6509 | 0.8777 | 0.0 | 0.0 | 0.0042 | 0.0 | | 0.4749 | 1.9 | 380 | 0.7667 | 0.2100 | 0.2617 | 0.7877 | nan | 0.8307 | 0.9286 | 0.0 | 0.6598 | 0.2633 | nan | 0.4358 | 0.0341 | 0.0 | 0.9003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9166 | 0.0 | 0.3043 | 0.0007 | 0.0 | nan | 0.0 | 0.0742 | 0.0 | 0.0 | 0.9062 | 0.9248 | 0.9341 | 0.0 | 0.0 | 0.0004 | 0.0 | nan | 0.6254 | 0.8110 | 0.0 | 0.5224 | 0.2188 | nan | 0.3221 | 0.0340 | 0.0 | 0.7415 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5945 | 0.0 | 0.2276 | 0.0007 | 0.0 | nan | 0.0 | 0.0714 | 0.0 | 0.0 | 0.7916 | 0.6625 | 0.8869 | 0.0 | 0.0 | 0.0004 | 0.0 | | 0.8488 | 2.0 | 400 | 0.7579 | 0.2130 | 0.2610 | 0.7868 | nan | 0.7601 | 0.9616 | 0.0 | 0.6160 | 0.1515 | nan | 0.4506 | 0.0924 | 0.0 | 0.9517 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8802 | 0.0 | 0.2621 | 0.0050 | 0.0 | nan | 0.0 | 0.1955 | 0.0 | 0.0 | 0.9306 | 0.8694 | 0.9588 | 0.0 | 0.0 | 0.0059 | 0.0 | nan | 0.6188 | 0.7790 | 0.0 | 0.4692 | 0.1373 | nan | 0.3326 | 0.0905 | 0.0 | 0.6748 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6297 | 0.0 | 0.1994 | 0.0050 | 0.0 | nan | 0.0 | 0.1755 | 0.0 | 0.0 | 0.8279 | 0.7703 | 0.8878 | 0.0 | 0.0 | 0.0058 | 0.0 | | 0.7258 | 2.1 | 420 | 0.7628 | 0.2109 | 0.2612 | 0.7874 | nan | 0.7680 | 0.9575 | 0.0 | 0.6261 | 0.1983 | nan | 0.4499 | 0.0235 | 0.0 | 0.9443 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.0 | 0.2420 | 0.0014 | 0.0 | nan | 0.0 | 0.1949 | 0.0 | 0.0 | 0.8929 | 0.9162 | 0.9480 | 0.0 | 0.0 | 0.0033 | 0.0 | nan | 0.6260 | 0.7908 | 0.0 | 0.4894 | 0.1734 | nan | 0.3339 | 0.0234 | 0.0 | 0.7108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6110 | 0.0 | 0.1859 | 0.0014 | 0.0 | nan | 0.0 | 0.1688 | 0.0 | 0.0 | 0.8185 | 0.7066 | 0.8943 | 0.0 | 0.0 | 0.0032 | 0.0 | | 0.6387 | 2.2 | 440 | 0.7467 | 0.2259 | 0.2790 | 0.7961 | nan | 0.8881 | 0.9214 | 0.0 | 0.6555 | 0.3252 | nan | 0.4215 | 0.2331 | 0.0 | 0.9267 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8443 | 0.0 | 0.3404 | 0.0093 | 0.0 | nan | 0.0 | 0.2579 | 0.0 | 0.0 | 0.9271 | 0.9100 | 0.9659 | 0.0 | 0.0 | 0.0232 | 0.0 | nan | 0.6131 | 0.8291 | 0.0 | 0.5515 | 0.2656 | nan | 0.3047 | 0.2144 | 0.0 | 0.7371 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6369 | 0.0 | 0.2684 | 0.0093 | 0.0 | nan | 0.0 | 0.2159 | 0.0 | 0.0 | 0.7852 | 0.6973 | 0.8499 | 0.0 | 0.0 | 0.0230 | 0.0 | | 0.8595 | 2.3 | 460 | 0.7549 | 0.2211 | 0.2763 | 0.7854 | nan | 0.6418 | 0.9541 | 0.0000 | 0.7728 | 0.3492 | nan | 0.3529 | 0.1397 | 0.0 | 0.9007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9039 | 0.0 | 0.4719 | 0.0047 | 0.0 | nan | 0.0 | 0.2823 | 0.0 | 0.0 | 0.9262 | 0.9046 | 0.9483 | 0.0 | 0.0 | 0.0109 | 0.0 | nan | 0.5582 | 0.7974 | 0.0000 | 0.4679 | 0.2678 | nan | 0.2545 | 0.1292 | 0.0 | 0.7601 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6389 | 0.0 | 0.3016 | 0.0047 | 0.0 | nan | 0.0 | 0.2292 | 0.0 | 0.0 | 0.8247 | 0.7112 | 0.8969 | 0.0 | 0.0 | 0.0106 | 0.0 | | 0.6146 | 2.4 | 480 | 0.7211 | 0.2221 | 0.2704 | 0.7963 | nan | 0.8419 | 0.9430 | 0.0000 | 0.6053 | 0.2553 | nan | 0.3931 | 0.1885 | 0.0 | 0.9484 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9034 | 0.0 | 0.3059 | 0.0073 | 0.0 | nan | 0.0 | 0.2091 | 0.0 | 0.0 | 0.9321 | 0.8916 | 0.9507 | 0.0 | 0.0 | 0.0068 | 0.0 | nan | 0.6385 | 0.7998 | 0.0000 | 0.5424 | 0.2125 | nan | 0.3006 | 0.1679 | 0.0 | 0.7083 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6353 | 0.0 | 0.2292 | 0.0073 | 0.0 | nan | 0.0 | 0.1913 | 0.0 | 0.0 | 0.8244 | 0.7278 | 0.8938 | 0.0 | 0.0 | 0.0067 | 0.0 | | 0.787 | 2.5 | 500 | 0.7471 | 0.2210 | 0.2765 | 0.7875 | nan | 0.8141 | 0.9058 | 0.0017 | 0.7987 | 0.3047 | nan | 0.4503 | 0.2588 | 0.0 | 0.9095 | 0.0 | 0.0 | nan | 0.0 | 0.0004 | 0.0 | 0.0 | 0.9374 | 0.0 | 0.2298 | 0.0048 | 0.0 | nan | 0.0 | 0.2382 | 0.0 | 0.0 | 0.9250 | 0.8154 | 0.9670 | 0.0 | 0.0 | 0.0106 | 0.0 | nan | 0.6225 | 0.8069 | 0.0017 | 0.4804 | 0.2544 | nan | 0.3447 | 0.1973 | 0.0 | 0.7497 | 0.0 | 0.0 | nan | 0.0 | 0.0004 | 0.0 | 0.0 | 0.5931 | 0.0 | 0.1708 | 0.0048 | 0.0 | nan | 0.0 | 0.2098 | 0.0 | 0.0 | 0.8123 | 0.7113 | 0.8818 | 0.0 | 0.0 | 0.0104 | 0.0 | | 0.6147 | 2.6 | 520 | 0.7091 | 0.2294 | 0.2750 | 0.8011 | nan | 0.8416 | 0.9589 | 0.0020 | 0.5932 | 0.2965 | nan | 0.3216 | 0.2599 | 0.0 | 0.9252 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9012 | 0.0 | 0.4304 | 0.0045 | 0.0 | nan | 0.0 | 0.2349 | 0.0 | 0.0 | 0.9295 | 0.8463 | 0.9644 | 0.0 | 0.0 | 0.0146 | 0.0 | nan | 0.6358 | 0.8040 | 0.0020 | 0.5333 | 0.2447 | nan | 0.2753 | 0.2214 | 0.0 | 0.7491 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6417 | 0.0 | 0.2980 | 0.0045 | 0.0 | nan | 0.0 | 0.2090 | 0.0 | 0.0 | 0.8240 | 0.7611 | 0.8937 | 0.0 | 0.0 | 0.0144 | 0.0 | | 0.777 | 2.7 | 540 | 0.7115 | 0.2351 | 0.2943 | 0.7979 | nan | 0.8442 | 0.9297 | 0.0043 | 0.7113 | 0.3582 | nan | 0.4900 | 0.3847 | 0.0 | 0.9173 | 0.0 | 0.0 | nan | 0.0 | 0.0011 | 0.0 | 0.0 | 0.8410 | 0.0 | 0.4747 | 0.0086 | 0.0 | nan | 0.0 | 0.3288 | 0.0 | 0.0 | 0.9013 | 0.9356 | 0.9526 | 0.0 | 0.0 | 0.0385 | 0.0 | nan | 0.6368 | 0.8246 | 0.0043 | 0.5395 | 0.2822 | nan | 0.3695 | 0.3024 | 0.0 | 0.7647 | 0.0 | 0.0 | nan | 0.0 | 0.0011 | 0.0 | 0.0 | 0.6615 | 0.0 | 0.3152 | 0.0086 | 0.0 | nan | 0.0 | 0.2424 | 0.0 | 0.0 | 0.7731 | 0.6266 | 0.8999 | 0.0 | 0.0 | 0.0370 | 0.0 | | 1.2945 | 2.8 | 560 | 0.6830 | 0.2393 | 0.2901 | 0.8066 | nan | 0.8630 | 0.9487 | 0.0763 | 0.6376 | 0.3071 | nan | 0.4487 | 0.4212 | 0.0 | 0.9518 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8668 | 0.0 | 0.3683 | 0.0116 | 0.0 | nan | 0.0 | 0.2616 | 0.0 | 0.0 | 0.9417 | 0.8390 | 0.9567 | 0.0 | 0.0 | 0.0921 | 0.0 | nan | 0.6674 | 0.8206 | 0.0762 | 0.5492 | 0.2547 | nan | 0.3654 | 0.3311 | 0.0 | 0.6964 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6301 | 0.0 | 0.2466 | 0.0115 | 0.0 | nan | 0.0 | 0.2259 | 0.0 | 0.0 | 0.8271 | 0.7508 | 0.8847 | 0.0 | 0.0 | 0.0794 | 0.0 | | 0.4699 | 2.9 | 580 | 0.6701 | 0.2548 | 0.3078 | 0.8155 | nan | 0.8274 | 0.9381 | 0.3424 | 0.6896 | 0.4323 | nan | 0.5397 | 0.3759 | 0.0 | 0.9079 | 0.0 | 0.0 | nan | 0.0 | 0.0148 | 0.0 | 0.0 | 0.8963 | 0.0 | 0.4932 | 0.0072 | 0.0 | nan | 0.0 | 0.2208 | 0.0 | 0.0 | 0.9471 | 0.8648 | 0.9568 | 0.0 | 0.0 | 0.0882 | 0.0 | nan | 0.6735 | 0.8311 | 0.3185 | 0.5595 | 0.3178 | nan | 0.3849 | 0.2986 | 0.0 | 0.7825 | 0.0 | 0.0 | nan | 0.0 | 0.0148 | 0.0 | 0.0 | 0.6547 | 0.0 | 0.3087 | 0.0072 | 0.0 | nan | 0.0 | 0.1933 | 0.0 | 0.0 | 0.8256 | 0.7570 | 0.8980 | 0.0 | 0.0 | 0.0727 | 0.0 | | 0.7277 | 3.0 | 600 | 0.6626 | 0.2524 | 0.3047 | 0.8166 | nan | 0.8627 | 0.9347 | 0.2669 | 0.7073 | 0.3747 | nan | 0.4831 | 0.3256 | 0.0 | 0.9118 | 0.0 | 0.0 | nan | 0.0 | 0.0202 | 0.0 | 0.0 | 0.9073 | 0.0 | 0.5294 | 0.0219 | 0.0 | nan | 0.0 | 0.2649 | 0.0 | 0.0 | 0.9429 | 0.8806 | 0.9577 | 0.0 | 0.0 | 0.0553 | 0.0 | nan | 0.6675 | 0.8352 | 0.2593 | 0.5624 | 0.2937 | nan | 0.3757 | 0.2698 | 0.0 | 0.7718 | 0.0 | 0.0 | nan | 0.0 | 0.0202 | 0.0 | 0.0 | 0.6614 | 0.0 | 0.3459 | 0.0218 | 0.0 | nan | 0.0 | 0.2213 | 0.0 | 0.0 | 0.8292 | 0.7484 | 0.8921 | 0.0 | 0.0 | 0.0489 | 0.0 | | 0.4261 | 3.1 | 620 | 0.7159 | 0.2492 | 0.3077 | 0.8015 | nan | 0.7873 | 0.9264 | 0.3010 | 0.7709 | 0.3242 | nan | 0.4667 | 0.4816 | 0.0 | 0.9464 | 0.0 | 0.0 | nan | 0.0 | 0.0399 | 0.0 | 0.0 | 0.8882 | 0.0 | 0.2940 | 0.0648 | 0.0 | nan | 0.0 | 0.3657 | 0.0 | 0.0 | 0.9219 | 0.9102 | 0.9562 | 0.0 | 0.0 | 0.0926 | 0.0 | nan | 0.6298 | 0.8116 | 0.2880 | 0.4593 | 0.2671 | nan | 0.3647 | 0.3560 | 0.0 | 0.7478 | 0.0 | 0.0 | nan | 0.0 | 0.0398 | 0.0 | 0.0 | 0.6769 | 0.0 | 0.2291 | 0.0643 | 0.0 | nan | 0.0 | 0.2635 | 0.0 | 0.0 | 0.8168 | 0.7229 | 0.9058 | 0.0 | 0.0 | 0.0829 | 0.0 | | 0.5901 | 3.2 | 640 | 0.6688 | 0.2576 | 0.3153 | 0.8124 | nan | 0.7619 | 0.9540 | 0.3024 | 0.7631 | 0.3767 | nan | 0.4635 | 0.5193 | 0.0 | 0.9231 | 0.0 | 0.0 | nan | 0.0 | 0.0490 | 0.0 | 0.0 | 0.9038 | 0.0 | 0.4962 | 0.0420 | 0.0 | nan | 0.0 | 0.3598 | 0.0 | 0.0 | 0.9221 | 0.8992 | 0.9603 | 0.0 | 0.0 | 0.0767 | 0.0 | nan | 0.6467 | 0.8201 | 0.2905 | 0.5484 | 0.2993 | nan | 0.3538 | 0.3554 | 0.0 | 0.7821 | 0.0 | 0.0 | nan | 0.0 | 0.0488 | 0.0 | 0.0 | 0.6677 | 0.0 | 0.3089 | 0.0415 | 0.0 | nan | 0.0 | 0.2737 | 0.0 | 0.0 | 0.8281 | 0.7554 | 0.8959 | 0.0 | 0.0 | 0.0708 | 0.0 | | 0.7962 | 3.3 | 660 | 0.6800 | 0.2658 | 0.3266 | 0.8155 | nan | 0.9044 | 0.9098 | 0.5540 | 0.6474 | 0.3950 | nan | 0.4505 | 0.5812 | 0.0 | 0.9203 | 0.0 | 0.0 | nan | 0.0 | 0.0649 | 0.0 | 0.0 | 0.8530 | 0.0 | 0.5437 | 0.1232 | 0.0 | nan | 0.0 | 0.3232 | 0.0 | 0.0 | 0.9429 | 0.8957 | 0.9629 | 0.0 | 0.0 | 0.0535 | 0.0 | nan | 0.6358 | 0.8382 | 0.4186 | 0.5670 | 0.3137 | nan | 0.3522 | 0.3830 | 0.0 | 0.7748 | 0.0 | 0.0 | nan | 0.0 | 0.0648 | 0.0 | 0.0 | 0.6735 | 0.0 | 0.3304 | 0.1199 | 0.0 | nan | 0.0 | 0.2608 | 0.0 | 0.0 | 0.8224 | 0.7588 | 0.8746 | 0.0 | 0.0 | 0.0509 | 0.0 | | 0.5779 | 3.4 | 680 | 0.6590 | 0.2691 | 0.3225 | 0.8177 | nan | 0.8129 | 0.9519 | 0.4768 | 0.6884 | 0.3156 | nan | 0.5264 | 0.4963 | 0.0 | 0.9386 | 0.0 | 0.0 | nan | 0.0 | 0.1614 | 0.0 | 0.0 | 0.9464 | 0.0 | 0.3499 | 0.2275 | 0.0 | nan | 0.0 | 0.3370 | 0.0 | 0.0 | 0.9184 | 0.8585 | 0.9358 | 0.0 | 0.0 | 0.0560 | 0.0 | nan | 0.6773 | 0.8279 | 0.4439 | 0.5408 | 0.2690 | nan | 0.3863 | 0.3310 | 0.0 | 0.7642 | 0.0 | 0.0 | nan | 0.0 | 0.1581 | 0.0 | 0.0 | 0.6273 | 0.0 | 0.2573 | 0.2204 | 0.0 | nan | 0.0 | 0.2727 | 0.0 | 0.0 | 0.8365 | 0.7771 | 0.8987 | 0.0 | 0.0 | 0.0521 | 0.0 | | 0.627 | 3.5 | 700 | 0.6482 | 0.2730 | 0.3293 | 0.8217 | nan | 0.8289 | 0.9337 | 0.4760 | 0.7499 | 0.4370 | nan | 0.4828 | 0.5444 | 0.0 | 0.9094 | 0.0 | 0.0 | nan | 0.0 | 0.1504 | 0.0 | 0.0 | 0.9126 | 0.0 | 0.3427 | 0.2291 | 0.0 | nan | 0.0 | 0.3545 | 0.0 | 0.0 | 0.9396 | 0.9033 | 0.9684 | 0.0 | 0.0 | 0.0455 | 0.0 | nan | 0.6682 | 0.8365 | 0.4425 | 0.5625 | 0.3519 | nan | 0.3731 | 0.3747 | 0.0 | 0.7945 | 0.0 | 0.0 | nan | 0.0 | 0.1475 | 0.0 | 0.0 | 0.6545 | 0.0 | 0.2565 | 0.2234 | 0.0 | nan | 0.0 | 0.2659 | 0.0 | 0.0 | 0.8243 | 0.7500 | 0.8927 | 0.0 | 0.0 | 0.0442 | 0.0 | | 1.043 | 3.6 | 720 | 0.6670 | 0.2690 | 0.3303 | 0.8121 | nan | 0.7414 | 0.9510 | 0.4527 | 0.7293 | 0.4232 | nan | 0.4919 | 0.5529 | 0.0 | 0.9499 | 0.0 | 0.0 | nan | 0.0 | 0.2058 | 0.0 | 0.0 | 0.7978 | 0.0 | 0.5494 | 0.1643 | 0.0 | nan | 0.0 | 0.3267 | 0.0 | 0.0 | 0.9457 | 0.9190 | 0.9663 | 0.0 | 0.0 | 0.0709 | 0.0 | nan | 0.6380 | 0.8182 | 0.4270 | 0.5396 | 0.3443 | nan | 0.3577 | 0.3869 | 0.0 | 0.7453 | 0.0 | 0.0 | nan | 0.0 | 0.1965 | 0.0 | 0.0 | 0.6520 | 0.0 | 0.3273 | 0.1621 | 0.0 | nan | 0.0 | 0.2547 | 0.0 | 0.0 | 0.8264 | 0.7138 | 0.8809 | 0.0 | 0.0 | 0.0670 | 0.0 | | 0.4814 | 3.7 | 740 | 0.6550 | 0.2747 | 0.3324 | 0.8234 | nan | 0.9006 | 0.9283 | 0.5611 | 0.7474 | 0.2894 | nan | 0.4573 | 0.5845 | 0.0 | 0.9186 | 0.0 | 0.0 | nan | 0.0 | 0.1849 | 0.0 | 0.0 | 0.8607 | 0.0 | 0.4995 | 0.1799 | 0.0 | nan | 0.0 | 0.3080 | 0.0 | 0.0 | 0.9508 | 0.8698 | 0.9653 | 0.0 | 0.0 | 0.0970 | 0.0 | nan | 0.6682 | 0.8446 | 0.4544 | 0.6005 | 0.2601 | nan | 0.3483 | 0.3947 | 0.0 | 0.7874 | 0.0 | 0.0 | nan | 0.0 | 0.1805 | 0.0 | 0.0 | 0.6645 | 0.0 | 0.3344 | 0.1762 | 0.0 | nan | 0.0 | 0.2477 | 0.0 | 0.0 | 0.8274 | 0.7503 | 0.8873 | 0.0 | 0.0 | 0.0878 | 0.0 | | 0.8735 | 3.8 | 760 | 0.6511 | 0.2686 | 0.3329 | 0.8143 | nan | 0.7260 | 0.9541 | 0.6209 | 0.6746 | 0.4355 | nan | 0.5429 | 0.4623 | 0.0 | 0.9414 | 0.0 | 0.0 | nan | 0.0 | 0.2207 | 0.0 | 0.0 | 0.8851 | 0.0 | 0.6008 | 0.0698 | 0.0 | nan | 0.0 | 0.2883 | 0.0 | 0.0 | 0.9114 | 0.9274 | 0.9592 | 0.0 | 0.0 | 0.0980 | 0.0 | nan | 0.6484 | 0.8316 | 0.4111 | 0.5496 | 0.3179 | nan | 0.3790 | 0.3583 | 0.0 | 0.7645 | 0.0 | 0.0 | nan | 0.0 | 0.2136 | 0.0 | 0.0 | 0.6712 | 0.0 | 0.3522 | 0.0691 | 0.0 | nan | 0.0 | 0.2393 | 0.0 | 0.0 | 0.8276 | 0.7120 | 0.8955 | 0.0 | 0.0 | 0.0865 | 0.0 | | 0.5682 | 3.9 | 780 | 0.6575 | 0.2699 | 0.3248 | 0.8181 | nan | 0.9116 | 0.9023 | 0.5169 | 0.7581 | 0.3574 | nan | 0.4132 | 0.4557 | 0.0 | 0.9207 | 0.0 | 0.0 | nan | 0.0 | 0.2064 | 0.0 | 0.0 | 0.8894 | 0.0 | 0.3918 | 0.1926 | 0.0 | nan | 0.0 | 0.2659 | 0.0 | 0.0 | 0.9597 | 0.8699 | 0.9541 | 0.0 | 0.0 | 0.1026 | 0.0 | nan | 0.6547 | 0.8448 | 0.4310 | 0.5466 | 0.2962 | nan | 0.3280 | 0.3493 | 0.0 | 0.7781 | 0.0 | 0.0 | nan | 0.0 | 0.2014 | 0.0 | 0.0 | 0.6549 | 0.0 | 0.3050 | 0.1889 | 0.0 | nan | 0.0 | 0.2235 | 0.0 | 0.0 | 0.8224 | 0.7640 | 0.8867 | 0.0 | 0.0 | 0.0899 | 0.0 | | 0.4817 | 4.0 | 800 | 0.6374 | 0.2842 | 0.3351 | 0.8286 | nan | 0.8555 | 0.9524 | 0.4450 | 0.6285 | 0.4133 | nan | 0.5067 | 0.4467 | 0.0 | 0.9324 | 0.0 | 0.0 | nan | 0.0 | 0.3081 | 0.0 | 0.0 | 0.9170 | 0.0 | 0.4322 | 0.3224 | 0.0 | nan | 0.0 | 0.2858 | 0.0 | 0.0 | 0.9309 | 0.8960 | 0.9586 | 0.0 | 0.0 | 0.1573 | 0.0 | nan | 0.7043 | 0.8377 | 0.4324 | 0.5554 | 0.3270 | nan | 0.3894 | 0.3513 | 0.0 | 0.7776 | 0.0 | 0.0 | nan | 0.0 | 0.2928 | 0.0 | 0.0 | 0.6627 | 0.0 | 0.3241 | 0.3021 | 0.0 | nan | 0.0 | 0.2310 | 0.0 | 0.0 | 0.8454 | 0.7534 | 0.9049 | 0.0 | 0.0 | 0.1188 | 0.0 | | 0.4642 | 4.1 | 820 | 0.6287 | 0.2868 | 0.3408 | 0.8310 | nan | 0.9123 | 0.9252 | 0.5041 | 0.7334 | 0.4165 | nan | 0.5144 | 0.5003 | 0.0 | 0.9309 | 0.0 | 0.0 | nan | 0.0 | 0.3421 | 0.0 | 0.0 | 0.9123 | 0.0 | 0.4394 | 0.2869 | 0.0 | nan | 0.0 | 0.2655 | 0.0 | 0.0 | 0.9400 | 0.8675 | 0.9662 | 0.0 | 0.0 | 0.1094 | 0.0 | nan | 0.6799 | 0.8581 | 0.4673 | 0.5899 | 0.3244 | nan | 0.3995 | 0.3747 | 0.0 | 0.7891 | 0.0 | 0.0 | nan | 0.0 | 0.3087 | 0.0 | 0.0 | 0.6594 | 0.0 | 0.3274 | 0.2753 | 0.0 | nan | 0.0 | 0.2254 | 0.0 | 0.0 | 0.8433 | 0.7769 | 0.8969 | 0.0 | 0.0 | 0.0958 | 0.0 | | 0.6249 | 4.2 | 840 | 0.6191 | 0.2936 | 0.3534 | 0.8297 | nan | 0.7971 | 0.9443 | 0.5810 | 0.7279 | 0.4803 | nan | 0.5396 | 0.6216 | 0.0 | 0.9299 | 0.0 | 0.0 | nan | 0.0 | 0.3012 | 0.0 | 0.0 | 0.8692 | 0.0 | 0.5200 | 0.3452 | 0.0 | nan | 0.0 | 0.3316 | 0.0 | 0.0 | 0.9482 | 0.9134 | 0.9529 | 0.0 | 0.0 | 0.1506 | 0.0 | nan | 0.6902 | 0.8375 | 0.5165 | 0.5620 | 0.3584 | nan | 0.4021 | 0.4267 | 0.0 | 0.7910 | 0.0 | 0.0 | nan | 0.0 | 0.2716 | 0.0 | 0.0 | 0.6951 | 0.0 | 0.3857 | 0.3238 | 0.0 | nan | 0.0 | 0.2516 | 0.0 | 0.0 | 0.8273 | 0.7315 | 0.9101 | 0.0 | 0.0 | 0.1215 | 0.0 | | 0.3472 | 4.3 | 860 | 0.6095 | 0.2955 | 0.3495 | 0.8351 | nan | 0.8528 | 0.9530 | 0.6076 | 0.6775 | 0.4019 | nan | 0.5564 | 0.5145 | 0.0 | 0.9282 | 0.0 | 0.0 | nan | 0.0 | 0.3640 | 0.0 | 0.0 | 0.9051 | 0.0 | 0.4762 | 0.3858 | 0.0 | nan | 0.0 | 0.3110 | 0.0 | 0.0 | 0.9446 | 0.8605 | 0.9565 | 0.0 | 0.0 | 0.1394 | 0.0 | nan | 0.7095 | 0.8454 | 0.4904 | 0.5882 | 0.3297 | nan | 0.4103 | 0.3965 | 0.0 | 0.7975 | 0.0 | 0.0 | nan | 0.0 | 0.3230 | 0.0 | 0.0 | 0.6836 | 0.0 | 0.3551 | 0.3631 | 0.0 | nan | 0.0 | 0.2458 | 0.0 | 0.0 | 0.8385 | 0.7605 | 0.9108 | 0.0 | 0.0 | 0.1139 | 0.0 | | 1.1638 | 4.4 | 880 | 0.7640 | 0.2662 | 0.3295 | 0.7799 | nan | 0.8622 | 0.8015 | 0.3356 | 0.8659 | 0.4153 | nan | 0.2301 | 0.5091 | 0.0 | 0.9329 | 0.0 | 0.0 | nan | 0.0 | 0.3879 | 0.0 | 0.0 | 0.9063 | 0.0 | 0.4288 | 0.3297 | 0.0 | nan | 0.0 | 0.2614 | 0.0 | 0.0 | 0.9372 | 0.9031 | 0.9668 | 0.0 | 0.0 | 0.1393 | 0.0 | nan | 0.6116 | 0.7663 | 0.3321 | 0.3163 | 0.3278 | nan | 0.2076 | 0.3955 | 0.0 | 0.7845 | 0.0 | 0.0 | nan | 0.0 | 0.3343 | 0.0 | 0.0 | 0.6859 | 0.0 | 0.3368 | 0.3190 | 0.0 | nan | 0.0 | 0.2220 | 0.0 | 0.0 | 0.8371 | 0.7586 | 0.9031 | 0.0 | 0.0 | 0.1150 | 0.0 | | 0.5639 | 4.5 | 900 | 0.6138 | 0.2912 | 0.3495 | 0.8329 | nan | 0.8329 | 0.9475 | 0.6199 | 0.7317 | 0.5188 | nan | 0.5028 | 0.4681 | 0.0 | 0.9315 | 0.0 | 0.0 | nan | 0.0 | 0.3889 | 0.0 | 0.0 | 0.9242 | 0.0 | 0.3445 | 0.3785 | 0.0 | nan | 0.0 | 0.2967 | 0.0 | 0.0 | 0.9146 | 0.8915 | 0.9640 | 0.0 | 0.0 | 0.1801 | 0.0 | nan | 0.6981 | 0.8617 | 0.4190 | 0.5752 | 0.3841 | nan | 0.3943 | 0.3792 | 0.0 | 0.7822 | 0.0 | 0.0 | nan | 0.0 | 0.3336 | 0.0 | 0.0 | 0.6600 | 0.0 | 0.2849 | 0.3522 | 0.0 | nan | 0.0 | 0.2394 | 0.0 | 0.0 | 0.8441 | 0.7760 | 0.9091 | 0.0 | 0.0 | 0.1331 | 0.0 | | 1.1424 | 4.6 | 920 | 0.6094 | 0.2951 | 0.3564 | 0.8335 | nan | 0.8276 | 0.9425 | 0.6345 | 0.7938 | 0.3963 | nan | 0.5233 | 0.5383 | 0.0 | 0.9430 | 0.0 | 0.0 | nan | 0.0 | 0.3499 | 0.0 | 0.0 | 0.8573 | 0.0 | 0.5273 | 0.3810 | 0.0 | nan | 0.0 | 0.3033 | 0.0 | 0.0 | 0.9501 | 0.8880 | 0.9582 | 0.0 | 0.0 | 0.2327 | 0.0 | nan | 0.6973 | 0.8551 | 0.4481 | 0.5758 | 0.3347 | nan | 0.4040 | 0.4162 | 0.0 | 0.7766 | 0.0 | 0.0 | nan | 0.0 | 0.3041 | 0.0 | 0.0 | 0.6977 | 0.0 | 0.3915 | 0.3541 | 0.0 | nan | 0.0 | 0.2395 | 0.0 | 0.0 | 0.8314 | 0.7473 | 0.9128 | 0.0 | 0.0 | 0.1612 | 0.0 | | 1.063 | 4.7 | 940 | 0.6149 | 0.2913 | 0.3458 | 0.8329 | nan | 0.8402 | 0.9494 | 0.5703 | 0.7595 | 0.4040 | nan | 0.4466 | 0.4354 | 0.0 | 0.9334 | 0.0 | 0.0 | nan | 0.0 | 0.3930 | 0.0 | 0.0 | 0.9198 | 0.0 | 0.4596 | 0.3577 | 0.0 | nan | 0.0 | 0.3191 | 0.0 | 0.0 | 0.9258 | 0.9082 | 0.9708 | 0.0 | 0.0 | 0.1261 | 0.0 | nan | 0.6985 | 0.8471 | 0.5053 | 0.5807 | 0.3370 | nan | 0.3472 | 0.3580 | 0.0 | 0.7914 | 0.0 | 0.0 | nan | 0.0 | 0.3419 | 0.0 | 0.0 | 0.6788 | 0.0 | 0.3541 | 0.3363 | 0.0 | nan | 0.0 | 0.2502 | 0.0 | 0.0 | 0.8367 | 0.7601 | 0.8995 | 0.0 | 0.0 | 0.1069 | 0.0 | | 0.4322 | 4.8 | 960 | 0.6046 | 0.2973 | 0.3546 | 0.8371 | nan | 0.8606 | 0.9394 | 0.6192 | 0.7778 | 0.3978 | nan | 0.5424 | 0.5753 | 0.0 | 0.9320 | 0.0 | 0.0 | nan | 0.0 | 0.3721 | 0.0 | 0.0 | 0.9029 | 0.0 | 0.4837 | 0.3128 | 0.0 | nan | 0.0 | 0.3398 | 0.0 | 0.0 | 0.9514 | 0.8918 | 0.9551 | 0.0 | 0.0 | 0.1386 | 0.0 | nan | 0.7099 | 0.8546 | 0.4951 | 0.5780 | 0.3377 | nan | 0.4132 | 0.4390 | 0.0 | 0.7976 | 0.0 | 0.0 | nan | 0.0 | 0.3289 | 0.0 | 0.0 | 0.6960 | 0.0 | 0.3689 | 0.3023 | 0.0 | nan | 0.0 | 0.2638 | 0.0 | 0.0 | 0.8282 | 0.7678 | 0.9131 | 0.0 | 0.0 | 0.1212 | 0.0 | | 0.8249 | 4.9 | 980 | 0.5971 | 0.3067 | 0.3699 | 0.8397 | nan | 0.8568 | 0.9392 | 0.5578 | 0.7432 | 0.5491 | nan | 0.5904 | 0.6089 | 0.0 | 0.9391 | 0.0 | 0.0 | nan | 0.0 | 0.4445 | 0.0 | 0.0 | 0.8718 | 0.0 | 0.5895 | 0.4400 | 0.0 | nan | 0.0 | 0.3854 | 0.0 | 0.0 | 0.9269 | 0.9088 | 0.9634 | 0.0 | 0.0 | 0.1515 | 0.0 | nan | 0.7231 | 0.8558 | 0.5109 | 0.6045 | 0.3935 | nan | 0.4213 | 0.4557 | 0.0 | 0.7863 | 0.0 | 0.0 | nan | 0.0 | 0.3644 | 0.0 | 0.0 | 0.6801 | 0.0 | 0.3959 | 0.3773 | 0.0 | nan | 0.0 | 0.2890 | 0.0 | 0.0 | 0.8416 | 0.7634 | 0.9103 | 0.0 | 0.0 | 0.1337 | 0.0 | | 0.4197 | 5.0 | 1000 | 0.5949 | 0.3031 | 0.3663 | 0.8403 | nan | 0.8410 | 0.9410 | 0.6234 | 0.8261 | 0.4882 | nan | 0.4796 | 0.6131 | 0.0 | 0.9508 | 0.0 | 0.0 | nan | 0.0 | 0.4801 | 0.0 | 0.0 | 0.9049 | 0.0 | 0.4725 | 0.4507 | 0.0 | nan | 0.0 | 0.3670 | 0.0 | 0.0 | 0.9397 | 0.8838 | 0.9650 | 0.0 | 0.0 | 0.1289 | 0.0 | nan | 0.7092 | 0.8644 | 0.5033 | 0.5772 | 0.3852 | nan | 0.3963 | 0.4297 | 0.0 | 0.7706 | 0.0 | 0.0 | nan | 0.0 | 0.3633 | 0.0 | 0.0 | 0.6818 | 0.0 | 0.3832 | 0.4105 | 0.0 | nan | 0.0 | 0.2829 | 0.0 | 0.0 | 0.8449 | 0.7682 | 0.9103 | 0.0 | 0.0 | 0.1147 | 0.0 | | 0.455 | 5.1 | 1020 | 0.5954 | 0.3023 | 0.3596 | 0.8400 | nan | 0.8863 | 0.9341 | 0.6041 | 0.7294 | 0.5180 | nan | 0.5288 | 0.5182 | 0.0 | 0.9290 | 0.0 | 0.0 | nan | 0.0 | 0.5215 | 0.0 | 0.0 | 0.9194 | 0.0 | 0.4468 | 0.3474 | 0.0 | nan | 0.0 | 0.3495 | 0.0 | 0.0 | 0.9427 | 0.8947 | 0.9523 | 0.0 | 0.0 | 0.1259 | 0.0 | nan | 0.7157 | 0.8610 | 0.5337 | 0.5857 | 0.3762 | nan | 0.4079 | 0.4252 | 0.0 | 0.8000 | 0.0 | 0.0 | nan | 0.0 | 0.3841 | 0.0 | 0.0 | 0.6899 | 0.0 | 0.3486 | 0.3326 | 0.0 | nan | 0.0 | 0.2761 | 0.0 | 0.0 | 0.8391 | 0.7696 | 0.9137 | 0.0 | 0.0 | 0.1110 | 0.0 | | 0.573 | 5.2 | 1040 | 0.6142 | 0.3010 | 0.3602 | 0.8352 | nan | 0.9199 | 0.9149 | 0.5815 | 0.7589 | 0.4252 | nan | 0.4792 | 0.5478 | 0.0 | 0.9296 | 0.0 | 0.0 | nan | 0.0 | 0.5160 | 0.0 | 0.0 | 0.9104 | 0.0 | 0.5451 | 0.3589 | 0.0 | nan | 0.0 | 0.3316 | 0.0 | 0.0 | 0.9391 | 0.8918 | 0.9667 | 0.0 | 0.0 | 0.1497 | 0.0 | nan | 0.6839 | 0.8525 | 0.5056 | 0.5907 | 0.3327 | nan | 0.3869 | 0.4349 | 0.0 | 0.8018 | 0.0 | 0.0 | nan | 0.0 | 0.3767 | 0.0 | 0.0 | 0.6990 | 0.0 | 0.4174 | 0.3425 | 0.0 | nan | 0.0 | 0.2627 | 0.0 | 0.0 | 0.8403 | 0.7694 | 0.9045 | 0.0 | 0.0 | 0.1296 | 0.0 | | 0.461 | 5.3 | 1060 | 0.6007 | 0.2976 | 0.3587 | 0.8365 | nan | 0.8484 | 0.9428 | 0.6427 | 0.8153 | 0.3290 | nan | 0.5091 | 0.4475 | 0.0 | 0.9296 | 0.0 | 0.0 | nan | 0.0 | 0.5200 | 0.0 | 0.0 | 0.8912 | 0.0 | 0.5687 | 0.3954 | 0.0 | nan | 0.0 | 0.3056 | 0.0 | 0.0 | 0.9431 | 0.8985 | 0.9695 | 0.0 | 0.0 | 0.1626 | 0.0 | nan | 0.7065 | 0.8570 | 0.5135 | 0.5742 | 0.2842 | nan | 0.4056 | 0.3774 | 0.0 | 0.7905 | 0.0 | 0.0 | nan | 0.0 | 0.3762 | 0.0 | 0.0 | 0.6970 | 0.0 | 0.4058 | 0.3684 | 0.0 | nan | 0.0 | 0.2481 | 0.0 | 0.0 | 0.8385 | 0.7417 | 0.9074 | 0.0 | 0.0 | 0.1347 | 0.0 | | 0.4084 | 5.4 | 1080 | 0.5992 | 0.2994 | 0.3620 | 0.8364 | nan | 0.8610 | 0.9321 | 0.5964 | 0.8214 | 0.4050 | nan | 0.5314 | 0.5094 | 0.0 | 0.9453 | 0.0 | 0.0 | nan | 0.0 | 0.5343 | 0.0 | 0.0 | 0.9199 | 0.0 | 0.5534 | 0.3635 | 0.0 | nan | 0.0 | 0.2904 | 0.0 | 0.0 | 0.9271 | 0.8947 | 0.9565 | 0.0 | 0.0 | 0.1793 | 0.0 | nan | 0.7074 | 0.8568 | 0.4895 | 0.5576 | 0.3418 | nan | 0.4125 | 0.4074 | 0.0 | 0.7771 | 0.0 | 0.0 | nan | 0.0 | 0.3876 | 0.0 | 0.0 | 0.6874 | 0.0 | 0.4108 | 0.3432 | 0.0 | nan | 0.0 | 0.2438 | 0.0 | 0.0 | 0.8474 | 0.7488 | 0.9139 | 0.0 | 0.0 | 0.1477 | 0.0 | | 0.3638 | 5.5 | 1100 | 0.5961 | 0.3008 | 0.3578 | 0.8397 | nan | 0.8651 | 0.9565 | 0.5715 | 0.7040 | 0.3987 | nan | 0.5602 | 0.5043 | 0.0 | 0.9363 | 0.0 | 0.0 | nan | 0.0 | 0.4938 | 0.0 | 0.0 | 0.9127 | 0.0 | 0.4815 | 0.3983 | 0.0 | nan | 0.0 | 0.3482 | 0.0 | 0.0 | 0.9282 | 0.8948 | 0.9682 | 0.0 | 0.0 | 0.1683 | 0.0 | nan | 0.7202 | 0.8518 | 0.5089 | 0.5910 | 0.3353 | nan | 0.4178 | 0.3949 | 0.0 | 0.7993 | 0.0 | 0.0 | nan | 0.0 | 0.3728 | 0.0 | 0.0 | 0.6855 | 0.0 | 0.3587 | 0.3661 | 0.0 | nan | 0.0 | 0.2640 | 0.0 | 0.0 | 0.8506 | 0.7644 | 0.9066 | 0.0 | 0.0 | 0.1368 | 0.0 | | 0.3183 | 5.6 | 1120 | 0.5982 | 0.2983 | 0.3672 | 0.8344 | nan | 0.8373 | 0.9374 | 0.6989 | 0.7369 | 0.4777 | nan | 0.5391 | 0.6504 | 0.0 | 0.9233 | 0.0 | 0.0 | nan | 0.0 | 0.4965 | 0.0 | 0.0 | 0.8986 | 0.0 | 0.4019 | 0.4308 | 0.0 | nan | 0.0 | 0.3717 | 0.0 | 0.0 | 0.9269 | 0.9174 | 0.9701 | 0.0 | 0.0 | 0.1700 | 0.0 | nan | 0.6888 | 0.8680 | 0.3857 | 0.6007 | 0.3648 | nan | 0.4233 | 0.4603 | 0.0 | 0.8015 | 0.0 | 0.0 | nan | 0.0 | 0.3714 | 0.0 | 0.0 | 0.6845 | 0.0 | 0.3295 | 0.3990 | 0.0 | nan | 0.0 | 0.2687 | 0.0 | 0.0 | 0.8401 | 0.7341 | 0.8831 | 0.0 | 0.0 | 0.1432 | 0.0 | | 0.3373 | 5.7 | 1140 | 0.5926 | 0.3036 | 0.3668 | 0.8390 | nan | 0.8819 | 0.9257 | 0.6487 | 0.7322 | 0.5088 | nan | 0.5627 | 0.5298 | 0.0 | 0.9481 | 0.0 | 0.0 | nan | 0.0 | 0.5145 | 0.0 | 0.0 | 0.8940 | 0.0 | 0.5565 | 0.4014 | 0.0 | nan | 0.0 | 0.3247 | 0.0 | 0.0 | 0.9419 | 0.8932 | 0.9479 | 0.0 | 0.0 | 0.1594 | 0.0 | nan | 0.6977 | 0.8672 | 0.4568 | 0.6011 | 0.3805 | nan | 0.4309 | 0.4341 | 0.0 | 0.7790 | 0.0 | 0.0 | nan | 0.0 | 0.3900 | 0.0 | 0.0 | 0.6910 | 0.0 | 0.4052 | 0.3770 | 0.0 | nan | 0.0 | 0.2609 | 0.0 | 0.0 | 0.8433 | 0.7580 | 0.9030 | 0.0 | 0.0 | 0.1354 | 0.0 | | 0.5786 | 5.8 | 1160 | 0.5888 | 0.3057 | 0.3725 | 0.8407 | nan | 0.8291 | 0.9356 | 0.6557 | 0.8378 | 0.5211 | nan | 0.5686 | 0.6574 | 0.0 | 0.9386 | 0.0 | 0.0 | nan | 0.0 | 0.5123 | 0.0 | 0.0 | 0.8987 | 0.0 | 0.5060 | 0.3604 | 0.0 | nan | 0.0 | 0.3712 | 0.0 | 0.0 | 0.9496 | 0.8555 | 0.9649 | 0.0 | 0.0 | 0.1836 | 0.0 | nan | 0.6966 | 0.8668 | 0.5092 | 0.5926 | 0.4044 | nan | 0.4317 | 0.4607 | 0.0 | 0.7984 | 0.0 | 0.0 | nan | 0.0 | 0.3596 | 0.0 | 0.0 | 0.6941 | 0.0 | 0.3737 | 0.3438 | 0.0 | nan | 0.0 | 0.2733 | 0.0 | 0.0 | 0.8431 | 0.7703 | 0.9097 | 0.0 | 0.0 | 0.1501 | 0.0 | | 0.4665 | 5.9 | 1180 | 0.5788 | 0.3083 | 0.3673 | 0.8455 | nan | 0.8570 | 0.9491 | 0.6249 | 0.7949 | 0.5169 | nan | 0.5641 | 0.5620 | 0.0 | 0.9319 | 0.0 | 0.0 | nan | 0.0 | 0.5126 | 0.0 | 0.0 | 0.9148 | 0.0 | 0.5272 | 0.3540 | 0.0 | nan | 0.0 | 0.3429 | 0.0 | 0.0 | 0.9331 | 0.8915 | 0.9621 | 0.0 | 0.0 | 0.1459 | 0.0 | nan | 0.7315 | 0.8616 | 0.5393 | 0.6123 | 0.4003 | nan | 0.4398 | 0.4429 | 0.0 | 0.8090 | 0.0 | 0.0 | nan | 0.0 | 0.3793 | 0.0 | 0.0 | 0.6951 | 0.0 | 0.3920 | 0.3379 | 0.0 | nan | 0.0 | 0.2643 | 0.0 | 0.0 | 0.8445 | 0.7700 | 0.9136 | 0.0 | 0.0 | 0.1232 | 0.0 | | 0.4062 | 6.0 | 1200 | 0.5990 | 0.3034 | 0.3661 | 0.8390 | nan | 0.8692 | 0.9362 | 0.5182 | 0.8309 | 0.4266 | nan | 0.5545 | 0.5376 | 0.0 | 0.9458 | 0.0 | 0.0 | nan | 0.0 | 0.6000 | 0.0 | 0.0 | 0.9145 | 0.0 | 0.4208 | 0.4792 | 0.0 | nan | 0.0 | 0.3516 | 0.0 | 0.0 | 0.9300 | 0.8846 | 0.9620 | 0.0 | 0.0 | 0.1879 | 0.0 | nan | 0.7062 | 0.8574 | 0.4890 | 0.5916 | 0.3444 | nan | 0.4258 | 0.4307 | 0.0 | 0.7885 | 0.0 | 0.0 | nan | 0.0 | 0.3544 | 0.0 | 0.0 | 0.6851 | 0.0 | 0.3325 | 0.4280 | 0.0 | nan | 0.0 | 0.2681 | 0.0 | 0.0 | 0.8506 | 0.7829 | 0.9171 | 0.0 | 0.0 | 0.1526 | 0.0 | | 0.3422 | 6.1 | 1220 | 0.6136 | 0.2994 | 0.3547 | 0.8386 | nan | 0.8994 | 0.9470 | 0.5071 | 0.7181 | 0.3467 | nan | 0.5558 | 0.5191 | 0.0 | 0.9338 | 0.0 | 0.0 | nan | 0.0 | 0.5650 | 0.0 | 0.0 | 0.9167 | 0.0 | 0.3571 | 0.4699 | 0.0 | nan | 0.0 | 0.3153 | 0.0 | 0.0 | 0.9401 | 0.8957 | 0.9657 | 0.0 | 0.0 | 0.1437 | 0.0 | nan | 0.7120 | 0.8521 | 0.4770 | 0.6135 | 0.2925 | nan | 0.4314 | 0.4286 | 0.0 | 0.8057 | 0.0 | 0.0 | nan | 0.0 | 0.3728 | 0.0 | 0.0 | 0.6836 | 0.0 | 0.2839 | 0.4187 | 0.0 | nan | 0.0 | 0.2556 | 0.0 | 0.0 | 0.8447 | 0.7720 | 0.9130 | 0.0 | 0.0 | 0.1240 | 0.0 | | 0.3667 | 6.2 | 1240 | 0.5865 | 0.3074 | 0.3673 | 0.8438 | nan | 0.8719 | 0.9503 | 0.6165 | 0.7441 | 0.4360 | nan | 0.5970 | 0.5278 | 0.0 | 0.9347 | 0.0 | 0.0 | nan | 0.0 | 0.5660 | 0.0 | 0.0 | 0.8997 | 0.0 | 0.4856 | 0.4265 | 0.0 | nan | 0.0 | 0.3539 | 0.0 | 0.0 | 0.9352 | 0.9020 | 0.9647 | 0.0 | 0.0 | 0.1752 | 0.0 | nan | 0.7281 | 0.8557 | 0.5315 | 0.6217 | 0.3452 | nan | 0.4397 | 0.4263 | 0.0 | 0.8010 | 0.0 | 0.0 | nan | 0.0 | 0.3623 | 0.0 | 0.0 | 0.6997 | 0.0 | 0.3732 | 0.3967 | 0.0 | nan | 0.0 | 0.2729 | 0.0 | 0.0 | 0.8474 | 0.7669 | 0.9133 | 0.0 | 0.0 | 0.1475 | 0.0 | | 0.3964 | 6.3 | 1260 | 0.5919 | 0.3052 | 0.3715 | 0.8415 | nan | 0.8636 | 0.9456 | 0.5274 | 0.7548 | 0.5466 | nan | 0.5639 | 0.6438 | 0.0 | 0.9241 | 0.0 | 0.0 | nan | 0.0 | 0.6155 | 0.0 | 0.0 | 0.9048 | 0.0 | 0.5285 | 0.4056 | 0.0 | nan | 0.0 | 0.3135 | 0.0 | 0.0 | 0.9171 | 0.9077 | 0.9684 | 0.0 | 0.0 | 0.1849 | 0.0 | nan | 0.7294 | 0.8517 | 0.4793 | 0.6185 | 0.4047 | nan | 0.4379 | 0.4247 | 0.0 | 0.8134 | 0.0 | 0.0 | nan | 0.0 | 0.3556 | 0.0 | 0.0 | 0.6892 | 0.0 | 0.3648 | 0.3728 | 0.0 | nan | 0.0 | 0.2539 | 0.0 | 0.0 | 0.8452 | 0.7688 | 0.8997 | 0.0 | 0.0 | 0.1528 | 0.0 | | 0.3974 | 6.4 | 1280 | 0.6011 | 0.3013 | 0.3642 | 0.8380 | nan | 0.8523 | 0.9361 | 0.6131 | 0.8445 | 0.4463 | nan | 0.5026 | 0.5776 | 0.0 | 0.9477 | 0.0 | 0.0 | nan | 0.0 | 0.5712 | 0.0 | 0.0 | 0.8948 | 0.0 | 0.4493 | 0.3396 | 0.0 | nan | 0.0 | 0.3616 | 0.0 | 0.0 | 0.9456 | 0.8771 | 0.9659 | 0.0 | 0.0 | 0.1656 | 0.0 | nan | 0.7006 | 0.8617 | 0.5345 | 0.5834 | 0.3578 | nan | 0.4094 | 0.4542 | 0.0 | 0.7889 | 0.0 | 0.0 | nan | 0.0 | 0.3706 | 0.0 | 0.0 | 0.6830 | 0.0 | 0.3222 | 0.3219 | 0.0 | nan | 0.0 | 0.2803 | 0.0 | 0.0 | 0.8424 | 0.7820 | 0.9040 | 0.0 | 0.0 | 0.1447 | 0.0 | | 0.3549 | 6.5 | 1300 | 0.5949 | 0.3054 | 0.3741 | 0.8391 | nan | 0.8498 | 0.9302 | 0.6762 | 0.8403 | 0.4753 | nan | 0.4975 | 0.5990 | 0.0 | 0.9416 | 0.0 | 0.0 | nan | 0.0 | 0.5850 | 0.0 | 0.0 | 0.8735 | 0.0 | 0.5281 | 0.3714 | 0.0 | nan | 0.0 | 0.3628 | 0.0 | 0.0 | 0.9433 | 0.8961 | 0.9669 | 0.0 | 0.0 | 0.2600 | 0.0 | nan | 0.6920 | 0.8684 | 0.4967 | 0.5858 | 0.3694 | nan | 0.4055 | 0.4655 | 0.0 | 0.7965 | 0.0 | 0.0 | nan | 0.0 | 0.3631 | 0.0 | 0.0 | 0.7006 | 0.0 | 0.3859 | 0.3490 | 0.0 | nan | 0.0 | 0.2691 | 0.0 | 0.0 | 0.8409 | 0.7679 | 0.9098 | 0.0 | 0.0 | 0.2010 | 0.0 | | 0.2758 | 6.6 | 1320 | 0.5834 | 0.3085 | 0.3673 | 0.8435 | nan | 0.8289 | 0.9513 | 0.6518 | 0.7364 | 0.5566 | nan | 0.5425 | 0.4657 | 0.0 | 0.9327 | 0.0 | 0.0 | nan | 0.0 | 0.5677 | 0.0 | 0.0 | 0.8959 | 0.0 | 0.5306 | 0.4128 | 0.0 | nan | 0.0 | 0.3408 | 0.0 | 0.0 | 0.9520 | 0.8735 | 0.9548 | 0.0 | 0.0 | 0.1924 | 0.0 | nan | 0.7241 | 0.8578 | 0.5561 | 0.5779 | 0.4191 | nan | 0.4193 | 0.4042 | 0.0 | 0.8086 | 0.0 | 0.0 | nan | 0.0 | 0.3795 | 0.0 | 0.0 | 0.7027 | 0.0 | 0.3963 | 0.3815 | 0.0 | nan | 0.0 | 0.2673 | 0.0 | 0.0 | 0.8369 | 0.7604 | 0.9187 | 0.0 | 0.0 | 0.1534 | 0.0 | | 0.4533 | 6.7 | 1340 | 0.5830 | 0.3052 | 0.3725 | 0.8390 | nan | 0.8076 | 0.9474 | 0.6777 | 0.7975 | 0.5173 | nan | 0.5476 | 0.4662 | 0.0 | 0.9397 | 0.0 | 0.0 | nan | 0.0 | 0.5851 | 0.0 | 0.0 | 0.8903 | 0.0 | 0.5785 | 0.4557 | 0.0 | nan | 0.0 | 0.3384 | 0.0 | 0.0 | 0.9171 | 0.9185 | 0.9545 | 0.0 | 0.0 | 0.2086 | 0.0 | nan | 0.6955 | 0.8641 | 0.4621 | 0.5747 | 0.4068 | nan | 0.4199 | 0.4060 | 0.0 | 0.8046 | 0.0 | 0.0 | nan | 0.0 | 0.3753 | 0.0 | 0.0 | 0.6958 | 0.0 | 0.4234 | 0.4041 | 0.0 | nan | 0.0 | 0.2615 | 0.0 | 0.0 | 0.8418 | 0.7440 | 0.9185 | 0.0 | 0.0 | 0.1628 | 0.0 | | 0.4431 | 6.8 | 1360 | 0.5965 | 0.3079 | 0.3716 | 0.8396 | nan | 0.9063 | 0.9083 | 0.6162 | 0.8022 | 0.4839 | nan | 0.5679 | 0.4944 | 0.0 | 0.9397 | 0.0 | 0.0 | nan | 0.0 | 0.5788 | 0.0 | 0.0 | 0.9175 | 0.0 | 0.5235 | 0.4825 | 0.0 | nan | 0.0 | 0.3448 | 0.0 | 0.0 | 0.9400 | 0.8798 | 0.9675 | 0.0 | 0.0 | 0.1664 | 0.0 | nan | 0.7017 | 0.8573 | 0.5427 | 0.5755 | 0.3769 | nan | 0.4289 | 0.4170 | 0.0 | 0.8026 | 0.0 | 0.0 | nan | 0.0 | 0.3763 | 0.0 | 0.0 | 0.6975 | 0.0 | 0.4024 | 0.4249 | 0.0 | nan | 0.0 | 0.2638 | 0.0 | 0.0 | 0.8461 | 0.7712 | 0.9157 | 0.0 | 0.0 | 0.1431 | 0.0 | | 0.4814 | 6.9 | 1380 | 0.5858 | 0.3091 | 0.3683 | 0.8447 | nan | 0.8749 | 0.9559 | 0.6054 | 0.6724 | 0.4716 | nan | 0.5793 | 0.5114 | 0.0 | 0.9466 | 0.0 | 0.0 | nan | 0.0 | 0.5914 | 0.0 | 0.0 | 0.9019 | 0.0 | 0.4914 | 0.4580 | 0.0 | nan | 0.0 | 0.3607 | 0.0 | 0.0 | 0.9281 | 0.9113 | 0.9668 | 0.0 | 0.0 | 0.1902 | 0.0 | nan | 0.7352 | 0.8534 | 0.5436 | 0.5826 | 0.3838 | nan | 0.4293 | 0.4296 | 0.0 | 0.7971 | 0.0 | 0.0 | nan | 0.0 | 0.3719 | 0.0 | 0.0 | 0.7038 | 0.0 | 0.3838 | 0.4147 | 0.0 | nan | 0.0 | 0.2636 | 0.0 | 0.0 | 0.8452 | 0.7693 | 0.9185 | 0.0 | 0.0 | 0.1574 | 0.0 | | 0.4765 | 7.0 | 1400 | 0.5937 | 0.3077 | 0.3727 | 0.8409 | nan | 0.8946 | 0.9235 | 0.6725 | 0.7406 | 0.4691 | nan | 0.6064 | 0.5834 | 0.0 | 0.9342 | 0.0 | 0.0 | nan | 0.0 | 0.5602 | 0.0 | 0.0 | 0.9083 | 0.0 | 0.5148 | 0.3851 | 0.0 | nan | 0.0 | 0.3517 | 0.0 | 0.0 | 0.9388 | 0.8882 | 0.9676 | 0.0 | 0.0 | 0.2146 | 0.0 | nan | 0.6928 | 0.8682 | 0.4465 | 0.6082 | 0.3684 | nan | 0.4403 | 0.4549 | 0.0 | 0.8080 | 0.0 | 0.0 | nan | 0.0 | 0.3872 | 0.0 | 0.0 | 0.6920 | 0.0 | 0.4152 | 0.3672 | 0.0 | nan | 0.0 | 0.2658 | 0.0 | 0.0 | 0.8474 | 0.7841 | 0.9152 | 0.0 | 0.0 | 0.1761 | 0.0 | | 0.3616 | 7.1 | 1420 | 0.5733 | 0.3132 | 0.3776 | 0.8461 | nan | 0.8448 | 0.9405 | 0.6132 | 0.8331 | 0.5368 | nan | 0.5895 | 0.5248 | 0.0 | 0.9373 | 0.0 | 0.0 | nan | 0.0 | 0.5603 | 0.0 | 0.0 | 0.8932 | 0.0 | 0.5813 | 0.4069 | 0.0 | nan | 0.0 | 0.3559 | 0.0 | 0.0 | 0.9324 | 0.9075 | 0.9655 | 0.0 | 0.0 | 0.2818 | 0.0 | nan | 0.7223 | 0.8676 | 0.5453 | 0.5946 | 0.4112 | nan | 0.4264 | 0.4193 | 0.0 | 0.8046 | 0.0 | 0.0 | nan | 0.0 | 0.3849 | 0.0 | 0.0 | 0.7001 | 0.0 | 0.4327 | 0.3820 | 0.0 | nan | 0.0 | 0.2659 | 0.0 | 0.0 | 0.8487 | 0.7737 | 0.9185 | 0.0 | 0.0 | 0.2120 | 0.0 | | 0.4933 | 7.2 | 1440 | 0.5813 | 0.3109 | 0.3753 | 0.8451 | nan | 0.8538 | 0.9437 | 0.5760 | 0.8191 | 0.5058 | nan | 0.5736 | 0.6060 | 0.0 | 0.9314 | 0.0 | 0.0 | nan | 0.0 | 0.5896 | 0.0 | 0.0 | 0.9101 | 0.0 | 0.4950 | 0.4585 | 0.0 | nan | 0.0 | 0.3675 | 0.0 | 0.0 | 0.9298 | 0.9159 | 0.9637 | 0.0 | 0.0 | 0.1930 | 0.0 | nan | 0.7222 | 0.8639 | 0.5238 | 0.6014 | 0.3986 | nan | 0.4346 | 0.4511 | 0.0 | 0.8135 | 0.0 | 0.0 | nan | 0.0 | 0.3784 | 0.0 | 0.0 | 0.6960 | 0.0 | 0.3766 | 0.4129 | 0.0 | nan | 0.0 | 0.2718 | 0.0 | 0.0 | 0.8480 | 0.7611 | 0.9201 | 0.0 | 0.0 | 0.1638 | 0.0 | | 0.4028 | 7.3 | 1460 | 0.5729 | 0.3121 | 0.3725 | 0.8482 | nan | 0.8882 | 0.9510 | 0.5887 | 0.7435 | 0.5139 | nan | 0.5499 | 0.5656 | 0.0 | 0.9375 | 0.0 | 0.0 | nan | 0.0 | 0.6045 | 0.0 | 0.0 | 0.9055 | 0.0 | 0.4169 | 0.5068 | 0.0 | nan | 0.0 | 0.3830 | 0.0 | 0.0 | 0.9357 | 0.8909 | 0.9639 | 0.0 | 0.0 | 0.2030 | 0.0 | nan | 0.7279 | 0.8672 | 0.5187 | 0.6300 | 0.4068 | nan | 0.4401 | 0.4476 | 0.0 | 0.8015 | 0.0 | 0.0 | nan | 0.0 | 0.3645 | 0.0 | 0.0 | 0.6940 | 0.0 | 0.3380 | 0.4487 | 0.0 | nan | 0.0 | 0.2823 | 0.0 | 0.0 | 0.8485 | 0.7751 | 0.9158 | 0.0 | 0.0 | 0.1674 | 0.0 | | 0.7888 | 7.4 | 1480 | 0.5864 | 0.3107 | 0.3756 | 0.8444 | nan | 0.8955 | 0.9296 | 0.6318 | 0.7855 | 0.5471 | nan | 0.5243 | 0.5552 | 0.0 | 0.9379 | 0.0 | 0.0 | nan | 0.0 | 0.6029 | 0.0 | 0.0 | 0.9031 | 0.0 | 0.4917 | 0.4499 | 0.0 | nan | 0.0 | 0.3496 | 0.0 | 0.0 | 0.9258 | 0.9006 | 0.9672 | 0.0 | 0.0 | 0.2454 | 0.0 | nan | 0.7057 | 0.8712 | 0.5006 | 0.6259 | 0.4010 | nan | 0.4206 | 0.4426 | 0.0 | 0.7998 | 0.0 | 0.0 | nan | 0.0 | 0.3727 | 0.0 | 0.0 | 0.6992 | 0.0 | 0.3834 | 0.4140 | 0.0 | nan | 0.0 | 0.2712 | 0.0 | 0.0 | 0.8454 | 0.7705 | 0.9131 | 0.0 | 0.0 | 0.1953 | 0.0 | | 0.4472 | 7.5 | 1500 | 0.5824 | 0.3101 | 0.3722 | 0.8445 | nan | 0.8338 | 0.9448 | 0.5706 | 0.8580 | 0.5130 | nan | 0.5545 | 0.4821 | 0.0 | 0.9256 | 0.0 | 0.0 | nan | 0.0 | 0.5927 | 0.0 | 0.0 | 0.9045 | 0.0 | 0.5369 | 0.4377 | 0.0 | nan | 0.0 | 0.3506 | 0.0 | 0.0 | 0.9366 | 0.8973 | 0.9610 | 0.0 | 0.0 | 0.2392 | 0.0 | nan | 0.7096 | 0.8653 | 0.5068 | 0.5979 | 0.4057 | nan | 0.4239 | 0.4143 | 0.0 | 0.8112 | 0.0 | 0.0 | nan | 0.0 | 0.3738 | 0.0 | 0.0 | 0.7033 | 0.0 | 0.4068 | 0.4079 | 0.0 | nan | 0.0 | 0.2711 | 0.0 | 0.0 | 0.8453 | 0.7657 | 0.9190 | 0.0 | 0.0 | 0.1862 | 0.0 | | 0.2959 | 7.6 | 1520 | 0.5668 | 0.3165 | 0.3833 | 0.8484 | nan | 0.8573 | 0.9417 | 0.6345 | 0.8128 | 0.5696 | nan | 0.5761 | 0.5790 | 0.0 | 0.9427 | 0.0 | 0.0 | nan | 0.0 | 0.6075 | 0.0 | 0.0 | 0.8679 | 0.0 | 0.5660 | 0.4868 | 0.0 | nan | 0.0 | 0.3390 | 0.0 | 0.0 | 0.9379 | 0.9009 | 0.9638 | 0.0 | 0.0 | 0.2990 | 0.0 | nan | 0.7247 | 0.8693 | 0.5410 | 0.6352 | 0.4291 | nan | 0.4455 | 0.4492 | 0.0 | 0.8027 | 0.0 | 0.0 | nan | 0.0 | 0.3639 | 0.0 | 0.0 | 0.7058 | 0.0 | 0.4061 | 0.4309 | 0.0 | nan | 0.0 | 0.2674 | 0.0 | 0.0 | 0.8437 | 0.7651 | 0.9195 | 0.0 | 0.0 | 0.2130 | 0.0 | | 0.6112 | 7.7 | 1540 | 0.5789 | 0.3128 | 0.3782 | 0.8459 | nan | 0.8626 | 0.9375 | 0.6726 | 0.8403 | 0.5117 | nan | 0.5372 | 0.5917 | 0.0 | 0.9368 | 0.0 | 0.0 | nan | 0.0 | 0.5974 | 0.0 | 0.0 | 0.9098 | 0.0 | 0.5040 | 0.4817 | 0.0 | nan | 0.0 | 0.3231 | 0.0 | 0.0 | 0.9241 | 0.9213 | 0.9645 | 0.0 | 0.0 | 0.2064 | 0.0 | nan | 0.7126 | 0.8682 | 0.5547 | 0.6269 | 0.4078 | nan | 0.4295 | 0.4562 | 0.0 | 0.8116 | 0.0 | 0.0 | nan | 0.0 | 0.3813 | 0.0 | 0.0 | 0.7024 | 0.0 | 0.3765 | 0.4300 | 0.0 | nan | 0.0 | 0.2614 | 0.0 | 0.0 | 0.8421 | 0.7503 | 0.9187 | 0.0 | 0.0 | 0.1678 | 0.0 | | 0.385 | 7.8 | 1560 | 0.5831 | 0.3131 | 0.3791 | 0.8448 | nan | 0.8485 | 0.9346 | 0.6713 | 0.8758 | 0.4915 | nan | 0.5322 | 0.6046 | 0.0 | 0.9425 | 0.0 | 0.0 | nan | 0.0 | 0.5874 | 0.0 | 0.0 | 0.8924 | 0.0 | 0.5252 | 0.4491 | 0.0 | nan | 0.0 | 0.3385 | 0.0 | 0.0 | 0.9377 | 0.9091 | 0.9658 | 0.0 | 0.0 | 0.2456 | 0.0 | nan | 0.7112 | 0.8643 | 0.5588 | 0.5947 | 0.4014 | nan | 0.4258 | 0.4594 | 0.0 | 0.8089 | 0.0 | 0.0 | nan | 0.0 | 0.3855 | 0.0 | 0.0 | 0.7088 | 0.0 | 0.3989 | 0.4132 | 0.0 | nan | 0.0 | 0.2616 | 0.0 | 0.0 | 0.8429 | 0.7604 | 0.9170 | 0.0 | 0.0 | 0.1948 | 0.0 | | 0.3023 | 7.9 | 1580 | 0.5723 | 0.3149 | 0.3819 | 0.8466 | nan | 0.8500 | 0.9378 | 0.7040 | 0.8430 | 0.5060 | nan | 0.5910 | 0.5657 | 0.0 | 0.9353 | 0.0 | 0.0 | nan | 0.0 | 0.5876 | 0.0 | 0.0 | 0.9079 | 0.0 | 0.5166 | 0.5220 | 0.0 | nan | 0.0 | 0.3322 | 0.0 | 0.0 | 0.9274 | 0.8971 | 0.9669 | 0.0 | 0.0 | 0.2493 | 0.0 | nan | 0.7181 | 0.8674 | 0.5304 | 0.6254 | 0.4013 | nan | 0.4403 | 0.4496 | 0.0 | 0.8136 | 0.0 | 0.0 | nan | 0.0 | 0.3828 | 0.0 | 0.0 | 0.6964 | 0.0 | 0.3900 | 0.4504 | 0.0 | nan | 0.0 | 0.2613 | 0.0 | 0.0 | 0.8497 | 0.7753 | 0.9145 | 0.0 | 0.0 | 0.1965 | 0.0 | | 0.2996 | 8.0 | 1600 | 0.5833 | 0.3115 | 0.3742 | 0.8446 | nan | 0.8598 | 0.9405 | 0.5576 | 0.8611 | 0.4879 | nan | 0.5030 | 0.6505 | 0.0 | 0.9331 | 0.0 | 0.0 | nan | 0.0 | 0.5664 | 0.0 | 0.0 | 0.8904 | 0.0 | 0.5266 | 0.4377 | 0.0 | nan | 0.0 | 0.3461 | 0.0 | 0.0 | 0.9471 | 0.8733 | 0.9674 | 0.0 | 0.0 | 0.2518 | 0.0 | nan | 0.7107 | 0.8647 | 0.5110 | 0.5997 | 0.3949 | nan | 0.4173 | 0.4636 | 0.0 | 0.8112 | 0.0 | 0.0 | nan | 0.0 | 0.3853 | 0.0 | 0.0 | 0.7031 | 0.0 | 0.4045 | 0.4033 | 0.0 | nan | 0.0 | 0.2658 | 0.0 | 0.0 | 0.8403 | 0.7659 | 0.9125 | 0.0 | 0.0 | 0.2029 | 0.0 | | 0.6193 | 8.1 | 1620 | 0.5776 | 0.3150 | 0.3768 | 0.8475 | nan | 0.8553 | 0.9466 | 0.5537 | 0.8181 | 0.5218 | nan | 0.5600 | 0.6284 | 0.0 | 0.9381 | 0.0 | 0.0 | nan | 0.0 | 0.5590 | 0.0 | 0.0 | 0.8879 | 0.0 | 0.5564 | 0.4580 | 0.0 | nan | 0.0 | 0.3610 | 0.0 | 0.0 | 0.9447 | 0.8941 | 0.9605 | 0.0 | 0.0 | 0.2361 | 0.0 | nan | 0.7149 | 0.8665 | 0.5031 | 0.6349 | 0.4079 | nan | 0.4338 | 0.4728 | 0.0 | 0.8083 | 0.0 | 0.0 | nan | 0.0 | 0.3884 | 0.0 | 0.0 | 0.7066 | 0.0 | 0.4185 | 0.4150 | 0.0 | nan | 0.0 | 0.2718 | 0.0 | 0.0 | 0.8427 | 0.7671 | 0.9200 | 0.0 | 0.0 | 0.1932 | 0.0 | | 0.3867 | 8.2 | 1640 | 0.5783 | 0.3154 | 0.3788 | 0.8485 | nan | 0.8492 | 0.9482 | 0.6227 | 0.8445 | 0.5045 | nan | 0.5666 | 0.5861 | 0.0 | 0.9364 | 0.0 | 0.0 | nan | 0.0 | 0.5957 | 0.0 | 0.0 | 0.8933 | 0.0 | 0.5372 | 0.4904 | 0.0 | nan | 0.0 | 0.3774 | 0.0 | 0.0 | 0.9419 | 0.8839 | 0.9666 | 0.0 | 0.0 | 0.1988 | 0.0 | nan | 0.7210 | 0.8676 | 0.5481 | 0.6260 | 0.4021 | nan | 0.4385 | 0.4505 | 0.0 | 0.8097 | 0.0 | 0.0 | nan | 0.0 | 0.3775 | 0.0 | 0.0 | 0.7023 | 0.0 | 0.4096 | 0.4369 | 0.0 | nan | 0.0 | 0.2813 | 0.0 | 0.0 | 0.8467 | 0.7758 | 0.9186 | 0.0 | 0.0 | 0.1659 | 0.0 | | 0.317 | 8.3 | 1660 | 0.5688 | 0.3161 | 0.3812 | 0.8494 | nan | 0.8745 | 0.9402 | 0.6804 | 0.8045 | 0.5513 | nan | 0.5835 | 0.5725 | 0.0 | 0.9466 | 0.0 | 0.0 | nan | 0.0 | 0.5978 | 0.0 | 0.0 | 0.8948 | 0.0 | 0.5215 | 0.4635 | 0.0 | nan | 0.0 | 0.3691 | 0.0 | 0.0 | 0.9312 | 0.8965 | 0.9691 | 0.0 | 0.0 | 0.2206 | 0.0 | nan | 0.7271 | 0.8711 | 0.5578 | 0.6459 | 0.4147 | nan | 0.4456 | 0.4482 | 0.0 | 0.7997 | 0.0 | 0.0 | nan | 0.0 | 0.3772 | 0.0 | 0.0 | 0.7015 | 0.0 | 0.4000 | 0.4200 | 0.0 | nan | 0.0 | 0.2752 | 0.0 | 0.0 | 0.8472 | 0.7738 | 0.9171 | 0.0 | 0.0 | 0.1764 | 0.0 | | 1.3707 | 8.4 | 1680 | 0.5730 | 0.3154 | 0.3780 | 0.8490 | nan | 0.8738 | 0.9492 | 0.6781 | 0.7945 | 0.4691 | nan | 0.5674 | 0.6020 | 0.0 | 0.9409 | 0.0 | 0.0 | nan | 0.0 | 0.5897 | 0.0 | 0.0 | 0.8936 | 0.0 | 0.5349 | 0.4505 | 0.0 | nan | 0.0 | 0.3486 | 0.0 | 0.0 | 0.9357 | 0.8928 | 0.9704 | 0.0 | 0.0 | 0.2273 | 0.0 | nan | 0.7278 | 0.8678 | 0.5609 | 0.6440 | 0.3773 | nan | 0.4447 | 0.4656 | 0.0 | 0.8115 | 0.0 | 0.0 | nan | 0.0 | 0.3821 | 0.0 | 0.0 | 0.7028 | 0.0 | 0.3953 | 0.4097 | 0.0 | nan | 0.0 | 0.2698 | 0.0 | 0.0 | 0.8456 | 0.7769 | 0.9117 | 0.0 | 0.0 | 0.1825 | 0.0 | | 0.3326 | 8.5 | 1700 | 0.5753 | 0.3159 | 0.3826 | 0.8462 | nan | 0.8597 | 0.9398 | 0.6403 | 0.8407 | 0.4915 | nan | 0.5517 | 0.5957 | 0.0 | 0.9403 | 0.0 | 0.0 | nan | 0.0 | 0.5921 | 0.0 | 0.0 | 0.8785 | 0.0 | 0.5797 | 0.5061 | 0.0 | nan | 0.0 | 0.3642 | 0.0 | 0.0 | 0.9300 | 0.9048 | 0.9683 | 0.0 | 0.0 | 0.2772 | 0.0 | nan | 0.7204 | 0.8666 | 0.5546 | 0.6021 | 0.3889 | nan | 0.4336 | 0.4644 | 0.0 | 0.8123 | 0.0 | 0.0 | nan | 0.0 | 0.3759 | 0.0 | 0.0 | 0.7026 | 0.0 | 0.4171 | 0.4383 | 0.0 | nan | 0.0 | 0.2735 | 0.0 | 0.0 | 0.8466 | 0.7702 | 0.9170 | 0.0 | 0.0 | 0.2075 | 0.0 | | 0.343 | 8.6 | 1720 | 0.5699 | 0.3183 | 0.3813 | 0.8500 | nan | 0.8720 | 0.9460 | 0.6340 | 0.8164 | 0.5137 | nan | 0.5698 | 0.5619 | 0.0 | 0.9335 | 0.0 | 0.0 | nan | 0.0 | 0.5860 | 0.0 | 0.0 | 0.8936 | 0.0 | 0.5409 | 0.5306 | 0.0 | nan | 0.0 | 0.3632 | 0.0 | 0.0 | 0.9313 | 0.8939 | 0.9638 | 0.0 | 0.0 | 0.2693 | 0.0 | nan | 0.7295 | 0.8693 | 0.5551 | 0.6398 | 0.4015 | nan | 0.4431 | 0.4498 | 0.0 | 0.8168 | 0.0 | 0.0 | nan | 0.0 | 0.3764 | 0.0 | 0.0 | 0.6999 | 0.0 | 0.4146 | 0.4559 | 0.0 | nan | 0.0 | 0.2766 | 0.0 | 0.0 | 0.8478 | 0.7674 | 0.9199 | 0.0 | 0.0 | 0.2053 | 0.0 | | 0.4636 | 8.7 | 1740 | 0.5700 | 0.3170 | 0.3791 | 0.8494 | nan | 0.8801 | 0.9435 | 0.6036 | 0.8067 | 0.5080 | nan | 0.5780 | 0.5943 | 0.0 | 0.9338 | 0.0 | 0.0 | nan | 0.0 | 0.5831 | 0.0 | 0.0 | 0.9054 | 0.0 | 0.5190 | 0.4826 | 0.0 | nan | 0.0 | 0.3532 | 0.0 | 0.0 | 0.9301 | 0.9133 | 0.9593 | 0.0 | 0.0 | 0.2595 | 0.0 | nan | 0.7261 | 0.8706 | 0.5399 | 0.6449 | 0.4018 | nan | 0.4455 | 0.4644 | 0.0 | 0.8180 | 0.0 | 0.0 | nan | 0.0 | 0.3798 | 0.0 | 0.0 | 0.7039 | 0.0 | 0.4047 | 0.4369 | 0.0 | nan | 0.0 | 0.2706 | 0.0 | 0.0 | 0.8422 | 0.7544 | 0.9215 | 0.0 | 0.0 | 0.2013 | 0.0 | | 0.4121 | 8.8 | 1760 | 0.5690 | 0.3166 | 0.3770 | 0.8504 | nan | 0.8629 | 0.9514 | 0.6429 | 0.8294 | 0.4950 | nan | 0.5736 | 0.6034 | 0.0 | 0.9316 | 0.0 | 0.0 | nan | 0.0 | 0.5776 | 0.0 | 0.0 | 0.9107 | 0.0 | 0.5116 | 0.4527 | 0.0 | nan | 0.0 | 0.3313 | 0.0 | 0.0 | 0.9344 | 0.9031 | 0.9622 | 0.0 | 0.0 | 0.2129 | 0.0 | nan | 0.7339 | 0.8671 | 0.5670 | 0.6443 | 0.4004 | nan | 0.4438 | 0.4655 | 0.0 | 0.8183 | 0.0 | 0.0 | nan | 0.0 | 0.3821 | 0.0 | 0.0 | 0.7003 | 0.0 | 0.3945 | 0.4177 | 0.0 | nan | 0.0 | 0.2684 | 0.0 | 0.0 | 0.8459 | 0.7742 | 0.9209 | 0.0 | 0.0 | 0.1710 | 0.0 | | 0.3737 | 8.9 | 1780 | 0.5611 | 0.3183 | 0.3813 | 0.8513 | nan | 0.8763 | 0.9429 | 0.6298 | 0.8213 | 0.5577 | nan | 0.5642 | 0.6243 | 0.0 | 0.9447 | 0.0 | 0.0 | nan | 0.0 | 0.5873 | 0.0 | 0.0 | 0.9152 | 0.0 | 0.5375 | 0.4555 | 0.0 | nan | 0.0 | 0.3414 | 0.0 | 0.0 | 0.9293 | 0.8930 | 0.9650 | 0.0 | 0.0 | 0.2335 | 0.0 | nan | 0.7321 | 0.8707 | 0.5613 | 0.6495 | 0.4244 | nan | 0.4409 | 0.4685 | 0.0 | 0.8063 | 0.0 | 0.0 | nan | 0.0 | 0.3790 | 0.0 | 0.0 | 0.7031 | 0.0 | 0.4053 | 0.4191 | 0.0 | nan | 0.0 | 0.2713 | 0.0 | 0.0 | 0.8502 | 0.7808 | 0.9205 | 0.0 | 0.0 | 0.1839 | 0.0 | | 0.4408 | 9.0 | 1800 | 0.5686 | 0.3186 | 0.3868 | 0.8498 | nan | 0.8582 | 0.9370 | 0.6738 | 0.8667 | 0.5464 | nan | 0.5771 | 0.6496 | 0.0 | 0.9442 | 0.0 | 0.0 | nan | 0.0 | 0.5956 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.5419 | 0.4507 | 0.0 | nan | 0.0 | 0.3632 | 0.0 | 0.0 | 0.9340 | 0.9020 | 0.9687 | 0.0 | 0.0 | 0.2900 | 0.0 | nan | 0.7299 | 0.8694 | 0.5746 | 0.6252 | 0.4206 | nan | 0.4388 | 0.4654 | 0.0 | 0.8066 | 0.0 | 0.0 | nan | 0.0 | 0.3746 | 0.0 | 0.0 | 0.7127 | 0.0 | 0.4090 | 0.4180 | 0.0 | nan | 0.0 | 0.2755 | 0.0 | 0.0 | 0.8474 | 0.7750 | 0.9177 | 0.0 | 0.0 | 0.2170 | 0.0 | | 0.402 | 9.1 | 1820 | 0.5630 | 0.3196 | 0.3841 | 0.8521 | nan | 0.8595 | 0.9464 | 0.6812 | 0.8413 | 0.5551 | nan | 0.5696 | 0.6260 | 0.0 | 0.9382 | 0.0 | 0.0 | nan | 0.0 | 0.5894 | 0.0 | 0.0 | 0.8971 | 0.0 | 0.5589 | 0.4594 | 0.0 | nan | 0.0 | 0.3541 | 0.0 | 0.0 | 0.9363 | 0.8953 | 0.9632 | 0.0 | 0.0 | 0.2347 | 0.0 | nan | 0.7346 | 0.8703 | 0.5700 | 0.6527 | 0.4236 | nan | 0.4401 | 0.4699 | 0.0 | 0.8127 | 0.0 | 0.0 | nan | 0.0 | 0.3778 | 0.0 | 0.0 | 0.7121 | 0.0 | 0.4124 | 0.4193 | 0.0 | nan | 0.0 | 0.2748 | 0.0 | 0.0 | 0.8471 | 0.7811 | 0.9195 | 0.0 | 0.0 | 0.1880 | 0.0 | | 0.3377 | 9.2 | 1840 | 0.5694 | 0.3185 | 0.3830 | 0.8509 | nan | 0.8655 | 0.9424 | 0.6606 | 0.8551 | 0.5115 | nan | 0.5671 | 0.6184 | 0.0 | 0.9379 | 0.0 | 0.0 | nan | 0.0 | 0.5804 | 0.0 | 0.0 | 0.9100 | 0.0 | 0.5601 | 0.4767 | 0.0 | nan | 0.0 | 0.3502 | 0.0 | 0.0 | 0.9300 | 0.9088 | 0.9670 | 0.0 | 0.0 | 0.2307 | 0.0 | nan | 0.7342 | 0.8710 | 0.5740 | 0.6386 | 0.4059 | nan | 0.4415 | 0.4724 | 0.0 | 0.8146 | 0.0 | 0.0 | nan | 0.0 | 0.3815 | 0.0 | 0.0 | 0.7064 | 0.0 | 0.4131 | 0.4315 | 0.0 | nan | 0.0 | 0.2729 | 0.0 | 0.0 | 0.8475 | 0.7661 | 0.9172 | 0.0 | 0.0 | 0.1850 | 0.0 | | 0.4663 | 9.3 | 1860 | 0.5681 | 0.3192 | 0.3833 | 0.8511 | nan | 0.8702 | 0.9435 | 0.6699 | 0.8311 | 0.5039 | nan | 0.5762 | 0.5998 | 0.0 | 0.9349 | 0.0 | 0.0 | nan | 0.0 | 0.5908 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.5619 | 0.5139 | 0.0 | nan | 0.0 | 0.3556 | 0.0 | 0.0 | 0.9378 | 0.8993 | 0.9672 | 0.0 | 0.0 | 0.2300 | 0.0 | nan | 0.7313 | 0.8702 | 0.5734 | 0.6435 | 0.3982 | nan | 0.4442 | 0.4682 | 0.0 | 0.8179 | 0.0 | 0.0 | nan | 0.0 | 0.3761 | 0.0 | 0.0 | 0.7091 | 0.0 | 0.4147 | 0.4523 | 0.0 | nan | 0.0 | 0.2746 | 0.0 | 0.0 | 0.8468 | 0.7747 | 0.9172 | 0.0 | 0.0 | 0.1845 | 0.0 | | 0.419 | 9.4 | 1880 | 0.5703 | 0.3185 | 0.3821 | 0.8510 | nan | 0.8632 | 0.9463 | 0.6844 | 0.8301 | 0.5153 | nan | 0.5890 | 0.6125 | 0.0 | 0.9357 | 0.0 | 0.0 | nan | 0.0 | 0.5848 | 0.0 | 0.0 | 0.9128 | 0.0 | 0.4967 | 0.5189 | 0.0 | nan | 0.0 | 0.3516 | 0.0 | 0.0 | 0.9318 | 0.8924 | 0.9689 | 0.0 | 0.0 | 0.2100 | 0.0 | nan | 0.7324 | 0.8705 | 0.5729 | 0.6473 | 0.4037 | nan | 0.4476 | 0.4693 | 0.0 | 0.8170 | 0.0 | 0.0 | nan | 0.0 | 0.3798 | 0.0 | 0.0 | 0.6988 | 0.0 | 0.3785 | 0.4589 | 0.0 | nan | 0.0 | 0.2752 | 0.0 | 0.0 | 0.8501 | 0.7819 | 0.9173 | 0.0 | 0.0 | 0.1715 | 0.0 | | 0.2796 | 9.5 | 1900 | 0.5678 | 0.3168 | 0.3808 | 0.8499 | nan | 0.8652 | 0.9410 | 0.6789 | 0.8372 | 0.5167 | nan | 0.5874 | 0.5978 | 0.0 | 0.9425 | 0.0 | 0.0 | nan | 0.0 | 0.5874 | 0.0 | 0.0 | 0.9008 | 0.0 | 0.4731 | 0.5021 | 0.0 | nan | 0.0 | 0.3488 | 0.0 | 0.0 | 0.9399 | 0.9032 | 0.9677 | 0.0 | 0.0 | 0.2138 | 0.0 | nan | 0.7294 | 0.8708 | 0.5725 | 0.6440 | 0.4041 | nan | 0.4481 | 0.4663 | 0.0 | 0.8105 | 0.0 | 0.0 | nan | 0.0 | 0.3794 | 0.0 | 0.0 | 0.7025 | 0.0 | 0.3640 | 0.4497 | 0.0 | nan | 0.0 | 0.2714 | 0.0 | 0.0 | 0.8454 | 0.7704 | 0.9170 | 0.0 | 0.0 | 0.1765 | 0.0 | | 0.3892 | 9.6 | 1920 | 0.5666 | 0.3184 | 0.3817 | 0.8511 | nan | 0.8720 | 0.9433 | 0.6665 | 0.8238 | 0.5257 | nan | 0.5873 | 0.5904 | 0.0 | 0.9392 | 0.0 | 0.0 | nan | 0.0 | 0.5954 | 0.0 | 0.0 | 0.9039 | 0.0 | 0.5012 | 0.4987 | 0.0 | nan | 0.0 | 0.3520 | 0.0 | 0.0 | 0.9354 | 0.9017 | 0.9666 | 0.0 | 0.0 | 0.2303 | 0.0 | nan | 0.7313 | 0.8705 | 0.5718 | 0.6482 | 0.4098 | nan | 0.4498 | 0.4632 | 0.0 | 0.8137 | 0.0 | 0.0 | nan | 0.0 | 0.3740 | 0.0 | 0.0 | 0.7050 | 0.0 | 0.3821 | 0.4487 | 0.0 | nan | 0.0 | 0.2734 | 0.0 | 0.0 | 0.8476 | 0.7743 | 0.9187 | 0.0 | 0.0 | 0.1872 | 0.0 | | 0.4762 | 9.7 | 1940 | 0.5650 | 0.3186 | 0.3821 | 0.8515 | nan | 0.8680 | 0.9457 | 0.6698 | 0.8232 | 0.5305 | nan | 0.5911 | 0.5940 | 0.0 | 0.9374 | 0.0 | 0.0 | nan | 0.0 | 0.5999 | 0.0 | 0.0 | 0.8884 | 0.0 | 0.4855 | 0.4855 | 0.0 | nan | 0.0 | 0.3632 | 0.0 | 0.0 | 0.9414 | 0.9042 | 0.9673 | 0.0 | 0.0 | 0.2512 | 0.0 | nan | 0.7347 | 0.8701 | 0.5730 | 0.6501 | 0.4141 | nan | 0.4509 | 0.4629 | 0.0 | 0.8150 | 0.0 | 0.0 | nan | 0.0 | 0.3703 | 0.0 | 0.0 | 0.7088 | 0.0 | 0.3807 | 0.4421 | 0.0 | nan | 0.0 | 0.2731 | 0.0 | 0.0 | 0.8434 | 0.7698 | 0.9171 | 0.0 | 0.0 | 0.1997 | 0.0 | | 0.2866 | 9.8 | 1960 | 0.5644 | 0.3189 | 0.3818 | 0.8522 | nan | 0.8639 | 0.9487 | 0.6788 | 0.8166 | 0.5314 | nan | 0.5863 | 0.5961 | 0.0 | 0.9381 | 0.0 | 0.0 | nan | 0.0 | 0.6016 | 0.0 | 0.0 | 0.9010 | 0.0 | 0.5091 | 0.4735 | 0.0 | nan | 0.0 | 0.3538 | 0.0 | 0.0 | 0.9399 | 0.8958 | 0.9638 | 0.0 | 0.0 | 0.2383 | 0.0 | nan | 0.7381 | 0.8699 | 0.5739 | 0.6515 | 0.4167 | nan | 0.4503 | 0.4642 | 0.0 | 0.8143 | 0.0 | 0.0 | nan | 0.0 | 0.3707 | 0.0 | 0.0 | 0.7074 | 0.0 | 0.3888 | 0.4333 | 0.0 | nan | 0.0 | 0.2740 | 0.0 | 0.0 | 0.8448 | 0.7760 | 0.9199 | 0.0 | 0.0 | 0.1921 | 0.0 | | 0.1857 | 9.9 | 1980 | 0.5650 | 0.3193 | 0.3828 | 0.8521 | nan | 0.8701 | 0.9477 | 0.6701 | 0.8152 | 0.5338 | nan | 0.5782 | 0.5986 | 0.0 | 0.9404 | 0.0 | 0.0 | nan | 0.0 | 0.6036 | 0.0 | 0.0 | 0.9080 | 0.0 | 0.5119 | 0.4885 | 0.0 | nan | 0.0 | 0.3618 | 0.0 | 0.0 | 0.9304 | 0.9003 | 0.9645 | 0.0 | 0.0 | 0.2422 | 0.0 | nan | 0.7346 | 0.8706 | 0.5727 | 0.6517 | 0.4164 | nan | 0.4489 | 0.4635 | 0.0 | 0.8113 | 0.0 | 0.0 | nan | 0.0 | 0.3693 | 0.0 | 0.0 | 0.7051 | 0.0 | 0.3929 | 0.4436 | 0.0 | nan | 0.0 | 0.2765 | 0.0 | 0.0 | 0.8485 | 0.7771 | 0.9204 | 0.0 | 0.0 | 0.1940 | 0.0 | | 0.3021 | 10.0 | 2000 | 0.5644 | 0.3202 | 0.3861 | 0.8525 | nan | 0.8598 | 0.9440 | 0.6837 | 0.8295 | 0.5586 | nan | 0.6015 | 0.6019 | 0.0 | 0.9395 | 0.0 | 0.0 | nan | 0.0 | 0.6060 | 0.0 | 0.0 | 0.8961 | 0.0 | 0.5249 | 0.4892 | 0.0 | nan | 0.0 | 0.3733 | 0.0 | 0.0 | 0.9376 | 0.9033 | 0.9668 | 0.0 | 0.0 | 0.2540 | 0.0 | nan | 0.7395 | 0.8717 | 0.5750 | 0.6481 | 0.4281 | nan | 0.4534 | 0.4648 | 0.0 | 0.8127 | 0.0 | 0.0 | nan | 0.0 | 0.3687 | 0.0 | 0.0 | 0.7090 | 0.0 | 0.3987 | 0.4429 | 0.0 | nan | 0.0 | 0.2772 | 0.0 | 0.0 | 0.8463 | 0.7719 | 0.9189 | 0.0 | 0.0 | 0.2004 | 0.0 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

ItDoBe/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.5881 - Mean Iou: 0.2988 - Mean Accuracy: 0.3613 - Overall Accuracy: 0.8463 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8649 - Accuracy Flat-sidewalk: 0.9502 - Accuracy Flat-crosswalk: 0.5791 - Accuracy Flat-cyclinglane: 0.7393 - Accuracy Flat-parkingdriveway: 0.4485 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.4859 - Accuracy Human-person: 0.7767 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9341 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.5642 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8944 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.4959 - Accuracy Construction-fenceguardrail: 0.4208 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3432 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9466 - Accuracy Nature-terrain: 0.8884 - Accuracy Sky: 0.9698 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.2614 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7372 - Iou Flat-sidewalk: 0.8540 - Iou Flat-crosswalk: 0.4322 - Iou Flat-cyclinglane: 0.6499 - Iou Flat-parkingdriveway: 0.3421 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.3932 - Iou Human-person: 0.5795 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.8033 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.3104 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7075 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3911 - Iou Construction-fenceguardrail: 0.3791 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.2524 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8559 - Iou Nature-terrain: 0.7644 - Iou Sky: 0.9219 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.1876 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | 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| 2.6446 | 0.1 | 20 | 2.9036 | 0.0732 | 0.1243 | 0.5841 | nan | 0.1650 | 0.9388 | 0.0 | 0.0110 | 0.0046 | nan | 0.0 | 0.0 | 0.0 | 0.8412 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8181 | 0.0 | 0.0068 | 0.0002 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9482 | 0.0503 | 0.1924 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.1551 | 0.5858 | 0.0 | 0.0109 | 0.0045 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4684 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4527 | 0.0 | 0.0065 | 0.0002 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5709 | 0.0419 | 0.1923 | 0.0 | 0.0 | 0.0001 | 0.0 | | 2.2236 | 0.2 | 40 | 1.9829 | 0.1087 | 0.1536 | 0.6546 | nan | 0.7316 | 0.8821 | 0.0 | 0.0000 | 0.0012 | nan | 0.0000 | 0.0 | 0.0 | 0.7926 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8106 | 0.0 | 0.0032 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9636 | 0.1494 | 0.5799 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4221 | 0.6904 | 0.0 | 0.0000 | 0.0012 | nan | 0.0000 | 0.0 | 0.0 | 0.5490 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4808 | 0.0 | 0.0032 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6243 | 0.1322 | 0.5746 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6689 | 0.3 | 60 | 1.6583 | 0.1325 | 0.1763 | 0.6901 | nan | 0.6909 | 0.9280 | 0.0 | 0.0 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.8070 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8276 | 0.0 | 0.0110 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9101 | 0.6704 | 0.7960 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4339 | 0.6869 | 0.0 | 0.0 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.6051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5031 | 0.0 | 0.0108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7312 | 0.5005 | 0.7680 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5794 | 0.4 | 80 | 1.4876 | 0.1366 | 0.1825 | 0.7038 | nan | 0.7097 | 0.9350 | 0.0 | 0.0 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8456 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8642 | 0.0 | 0.0015 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9109 | 0.7571 | 0.8142 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4701 | 0.7084 | 0.0 | 0.0 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.6189 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5059 | 0.0 | 0.0015 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7397 | 0.5460 | 0.7797 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5229 | 0.5 | 100 | 1.4013 | 0.1359 | 0.1803 | 0.7076 | nan | 0.6886 | 0.9534 | 0.0 | 0.0347 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.8892 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8217 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9568 | 0.5926 | 0.8294 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5080 | 0.7029 | 0.0 | 0.0346 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.5598 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5472 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6971 | 0.5004 | 0.7969 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2003 | 0.6 | 120 | 1.2425 | 0.1474 | 0.1965 | 0.7285 | nan | 0.7966 | 0.9182 | 0.0 | 0.1579 | 0.0008 | nan | 0.0 | 0.0 | 0.0 | 0.8939 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8634 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9261 | 0.8480 | 0.8819 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5148 | 0.7446 | 0.0 | 0.1559 | 0.0008 | nan | 0.0 | 0.0 | 0.0 | 0.5954 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5441 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7506 | 0.5912 | 0.8198 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2507 | 0.7 | 140 | 1.2214 | 0.1562 | 0.2002 | 0.7378 | nan | 0.8805 | 0.8858 | 0.0 | 0.4127 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.8466 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8672 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9579 | 0.6735 | 0.8808 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5213 | 0.7597 | 0.0 | 0.3764 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.6704 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5638 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7209 | 0.5572 | 0.8258 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2322 | 0.8 | 160 | 1.1794 | 0.1537 | 0.1993 | 0.7361 | nan | 0.7339 | 0.9511 | 0.0 | 0.3368 | 0.0055 | nan | 0.0 | 0.0 | 0.0 | 0.9218 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7810 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9360 | 0.7657 | 0.9447 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5275 | 0.7280 | 0.0 | 0.3161 | 0.0055 | nan | 0.0 | 0.0 | 0.0 | 0.6021 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5550 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7441 | 0.6084 | 0.8287 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4998 | 0.9 | 180 | 1.0915 | 0.1633 | 0.2070 | 0.7543 | nan | 0.8040 | 0.9417 | 0.0 | 0.4433 | 0.0024 | nan | 0.0 | 0.0 | 0.0 | 0.8499 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9059 | 0.0 | 0.0009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9153 | 0.8368 | 0.9238 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5594 | 0.7627 | 0.0 | 0.3896 | 0.0024 | nan | 0.0 | 0.0 | 0.0 | 0.6526 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5509 | 0.0 | 0.0009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7779 | 0.6755 | 0.8528 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9777 | 1.0 | 200 | 1.0430 | 0.1674 | 0.2097 | 0.7623 | nan | 0.8292 | 0.9417 | 0.0 | 0.5123 | 0.0048 | nan | 0.0009 | 0.0 | 0.0 | 0.8854 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8897 | 0.0 | 0.0131 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9389 | 0.7870 | 0.9084 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5751 | 0.7711 | 0.0 | 0.4464 | 0.0048 | nan | 0.0009 | 0.0 | 0.0 | 0.6782 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5701 | 0.0 | 0.0131 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7720 | 0.6698 | 0.8562 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9326 | 1.1 | 220 | 1.0643 | 0.1551 | 0.2051 | 0.7343 | nan | 0.7976 | 0.9512 | 0.0 | 0.4709 | 0.0063 | nan | 0.0001 | 0.0 | 0.0 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.0038 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7454 | 0.8821 | 0.9252 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5785 | 0.7469 | 0.0 | 0.4200 | 0.0063 | nan | 0.0001 | 0.0 | 0.0 | 0.6615 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5728 | 0.0 | 0.0038 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6639 | 0.4500 | 0.8600 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1853 | 1.2 | 240 | 1.0255 | 0.1645 | 0.2065 | 0.7501 | nan | 0.6850 | 0.9636 | 0.0 | 0.4379 | 0.0078 | nan | 0.0006 | 0.0 | 0.0 | 0.8839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8852 | 0.0 | 0.0239 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9317 | 0.8506 | 0.9366 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5494 | 0.7233 | 0.0 | 0.3777 | 0.0077 | nan | 0.0006 | 0.0 | 0.0 | 0.6827 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5818 | 0.0 | 0.0235 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7729 | 0.6805 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1844 | 1.3 | 260 | 0.9715 | 0.1677 | 0.2091 | 0.7599 | nan | 0.8100 | 0.9497 | 0.0 | 0.4492 | 0.0216 | nan | 0.0013 | 0.0 | 0.0 | 0.8900 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8913 | 0.0 | 0.0401 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9334 | 0.7794 | 0.9254 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5640 | 0.7634 | 0.0 | 0.4075 | 0.0211 | nan | 0.0013 | 0.0 | 0.0 | 0.6789 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5737 | 0.0 | 0.0388 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7761 | 0.6731 | 0.8696 | 0.0 | 0.0 | 0.0000 | 0.0 | | 0.8037 | 1.4 | 280 | 0.9345 | 0.1694 | 0.2120 | 0.7620 | nan | 0.8153 | 0.9338 | 0.0 | 0.5336 | 0.0190 | nan | 0.0096 | 0.0 | 0.0 | 0.8687 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8660 | 0.0 | 0.0239 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9518 | 0.8266 | 0.9353 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5748 | 0.7621 | 0.0 | 0.4545 | 0.0187 | nan | 0.0095 | 0.0 | 0.0 | 0.7099 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5969 | 0.0 | 0.0234 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7515 | 0.6518 | 0.8682 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.046 | 1.5 | 300 | 0.9362 | 0.1665 | 0.2063 | 0.7602 | nan | 0.8074 | 0.9552 | 0.0 | 0.4627 | 0.0251 | nan | 0.0132 | 0.0 | 0.0 | 0.8712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9206 | 0.0 | 0.0295 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9472 | 0.6334 | 0.9373 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5877 | 0.7633 | 0.0 | 0.4225 | 0.0245 | nan | 0.0131 | 0.0 | 0.0 | 0.6925 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5721 | 0.0 | 0.0285 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7648 | 0.5827 | 0.8754 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8823 | 1.6 | 320 | 0.9555 | 0.1726 | 0.2141 | 0.7562 | nan | 0.6423 | 0.9676 | 0.0 | 0.5346 | 0.0428 | nan | 0.0608 | 0.0 | 0.0 | 0.8740 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9115 | 0.0 | 0.1039 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9256 | 0.8544 | 0.9354 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5514 | 0.7267 | 0.0 | 0.4372 | 0.0407 | nan | 0.0585 | 0.0 | 0.0 | 0.6901 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5904 | 0.0 | 0.0878 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7883 | 0.6758 | 0.8769 | 0.0 | 0.0 | 0.0000 | 0.0 | | 0.7509 | 1.7 | 340 | 0.9001 | 0.1779 | 0.2237 | 0.7709 | nan | 0.9064 | 0.9093 | 0.0 | 0.5323 | 0.1030 | nan | 0.0284 | 0.0 | 0.0 | 0.9033 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9072 | 0.0 | 0.1315 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9072 | 0.8914 | 0.9371 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.5784 | 0.7877 | 0.0 | 0.4640 | 0.0934 | nan | 0.0277 | 0.0 | 0.0 | 0.6997 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6068 | 0.0 | 0.1094 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7947 | 0.6547 | 0.8762 | 0.0 | 0.0 | 0.0002 | 0.0 | | 1.4454 | 1.8 | 360 | 0.8672 | 0.1819 | 0.2232 | 0.7747 | nan | 0.8583 | 0.9287 | 0.0018 | 0.5609 | 0.1483 | nan | 0.0427 | 0.0 | 0.0 | 0.8350 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9246 | 0.0 | 0.1830 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9426 | 0.7789 | 0.9388 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6006 | 0.7921 | 0.0018 | 0.4665 | 0.1287 | nan | 0.0418 | 0.0 | 0.0 | 0.7032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5769 | 0.0 | 0.1453 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7918 | 0.6899 | 0.8831 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0715 | 1.9 | 380 | 0.8731 | 0.1822 | 0.2251 | 0.7731 | nan | 0.7780 | 0.9396 | 0.0 | 0.6530 | 0.1692 | nan | 0.0842 | 0.0 | 0.0 | 0.8696 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8489 | 0.0 | 0.1946 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.9709 | 0.7592 | 0.9323 | 0.0 | 0.0 | 0.0022 | 0.0 | nan | 0.6210 | 0.7887 | 0.0 | 0.5005 | 0.1439 | nan | 0.0808 | 0.0 | 0.0 | 0.7147 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6286 | 0.0 | 0.1591 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.7136 | 0.5999 | 0.8767 | 0.0 | 0.0 | 0.0022 | 0.0 | | 0.9872 | 2.0 | 400 | 0.8644 | 0.1814 | 0.2287 | 0.7679 | nan | 0.9228 | 0.8901 | 0.0 | 0.5135 | 0.1829 | nan | 0.0730 | 0.0 | 0.0 | 0.9320 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8858 | 0.0 | 0.1816 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8969 | 0.8870 | 0.9520 | 0.0 | 0.0 | 0.0006 | 0.0 | nan | 0.5509 | 0.7875 | 0.0 | 0.4591 | 0.1650 | nan | 0.0689 | 0.0 | 0.0 | 0.6744 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6095 | 0.0 | 0.1509 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.7905 | 0.6681 | 0.8782 | 0.0 | 0.0 | 0.0006 | 0.0 | | 0.9402 | 2.1 | 420 | 0.8389 | 0.1881 | 0.2315 | 0.7818 | nan | 0.8322 | 0.9390 | 0.0 | 0.5695 | 0.2419 | nan | 0.2642 | 0.0 | 0.0 | 0.9113 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8622 | 0.0 | 0.1379 | 0.0 | 0.0 | nan | 0.0 | 0.0076 | 0.0 | 0.0 | 0.9714 | 0.7160 | 0.9485 | 0.0 | 0.0 | 0.0075 | 0.0 | nan | 0.6471 | 0.7996 | 0.0 | 0.4905 | 0.1948 | nan | 0.2126 | 0.0 | 0.0 | 0.6854 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6175 | 0.0 | 0.1159 | 0.0 | 0.0 | nan | 0.0 | 0.0076 | 0.0 | 0.0 | 0.7514 | 0.6111 | 0.8784 | 0.0 | 0.0 | 0.0075 | 0.0 | | 0.8086 | 2.2 | 440 | 0.8111 | 0.1975 | 0.2430 | 0.7900 | nan | 0.8340 | 0.9349 | 0.0 | 0.6432 | 0.2336 | nan | 0.3225 | 0.0 | 0.0 | 0.8310 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9304 | 0.0 | 0.3000 | 0.0 | 0.0 | nan | 0.0 | 0.0017 | 0.0 | 0.0 | 0.9186 | 0.8714 | 0.9526 | 0.0 | 0.0 | 0.0019 | 0.0 | nan | 0.6502 | 0.8090 | 0.0 | 0.5334 | 0.1844 | nan | 0.2397 | 0.0 | 0.0 | 0.7169 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5935 | 0.0 | 0.2089 | 0.0 | 0.0 | nan | 0.0 | 0.0017 | 0.0 | 0.0 | 0.8048 | 0.6888 | 0.8868 | 0.0 | 0.0 | 0.0019 | 0.0 | | 0.8764 | 2.3 | 460 | 0.7955 | 0.1956 | 0.2410 | 0.7898 | nan | 0.8731 | 0.9284 | 0.0 | 0.5585 | 0.2978 | nan | 0.2489 | 0.0 | 0.0 | 0.9034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8977 | 0.0 | 0.2272 | 0.0 | 0.0 | nan | 0.0 | 0.0092 | 0.0 | 0.0 | 0.9343 | 0.8924 | 0.9346 | 0.0 | 0.0 | 0.0051 | 0.0 | nan | 0.6369 | 0.8102 | 0.0 | 0.4941 | 0.2258 | nan | 0.2154 | 0.0 | 0.0 | 0.7013 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6180 | 0.0 | 0.1729 | 0.0 | 0.0 | nan | 0.0 | 0.0091 | 0.0 | 0.0 | 0.7948 | 0.6877 | 0.8877 | 0.0 | 0.0 | 0.0050 | 0.0 | | 0.5838 | 2.4 | 480 | 0.7779 | 0.1984 | 0.2443 | 0.7934 | nan | 0.8579 | 0.9338 | 0.0219 | 0.6356 | 0.3655 | nan | 0.1993 | 0.0000 | 0.0 | 0.9151 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9047 | 0.0 | 0.2618 | 0.0 | 0.0 | nan | 0.0 | 0.0106 | 0.0 | 0.0 | 0.9247 | 0.8154 | 0.9695 | 0.0 | 0.0 | 0.0029 | 0.0 | nan | 0.6396 | 0.8160 | 0.0219 | 0.5207 | 0.2520 | nan | 0.1786 | 0.0000 | 0.0 | 0.7051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6203 | 0.0 | 0.1896 | 0.0 | 0.0 | nan | 0.0 | 0.0105 | 0.0 | 0.0 | 0.8129 | 0.7016 | 0.8771 | 0.0 | 0.0 | 0.0029 | 0.0 | | 1.2875 | 2.5 | 500 | 0.7613 | 0.1991 | 0.2422 | 0.7947 | nan | 0.8127 | 0.9518 | 0.0246 | 0.7146 | 0.2126 | nan | 0.1842 | 0.0028 | 0.0 | 0.8969 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8676 | 0.0 | 0.3247 | 0.0 | 0.0 | nan | 0.0 | 0.0138 | 0.0 | 0.0 | 0.9529 | 0.8308 | 0.9494 | 0.0 | 0.0 | 0.0126 | 0.0 | nan | 0.6576 | 0.8048 | 0.0246 | 0.5380 | 0.1803 | nan | 0.1649 | 0.0028 | 0.0 | 0.7277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6375 | 0.0 | 0.2192 | 0.0 | 0.0 | nan | 0.0 | 0.0135 | 0.0 | 0.0 | 0.7849 | 0.7109 | 0.8922 | 0.0 | 0.0 | 0.0121 | 0.0 | | 0.6721 | 2.6 | 520 | 0.7535 | 0.2130 | 0.2636 | 0.8017 | nan | 0.8805 | 0.9166 | 0.2113 | 0.6640 | 0.3623 | nan | 0.3572 | 0.0017 | 0.0 | 0.9307 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8472 | 0.0 | 0.4867 | 0.0 | 0.0 | nan | 0.0 | 0.0110 | 0.0 | 0.0 | 0.9472 | 0.8436 | 0.9570 | 0.0 | 0.0 | 0.0187 | 0.0 | nan | 0.6645 | 0.8283 | 0.2069 | 0.5498 | 0.2632 | nan | 0.2810 | 0.0017 | 0.0 | 0.7005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6432 | 0.0 | 0.2494 | 0.0 | 0.0 | nan | 0.0 | 0.0108 | 0.0 | 0.0 | 0.8056 | 0.7092 | 0.8851 | 0.0 | 0.0 | 0.0173 | 0.0 | | 0.6638 | 2.7 | 540 | 0.7462 | 0.2072 | 0.2495 | 0.8018 | nan | 0.8340 | 0.9531 | 0.2099 | 0.6785 | 0.1671 | nan | 0.2453 | 0.0013 | 0.0 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9109 | 0.0 | 0.2672 | 0.0 | 0.0 | nan | 0.0 | 0.0214 | 0.0 | 0.0 | 0.9429 | 0.8541 | 0.9533 | 0.0 | 0.0 | 0.0238 | 0.0 | nan | 0.6690 | 0.8096 | 0.2068 | 0.5655 | 0.1509 | nan | 0.2195 | 0.0013 | 0.0 | 0.7109 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6292 | 0.0 | 0.1887 | 0.0 | 0.0 | nan | 0.0 | 0.0209 | 0.0 | 0.0 | 0.8074 | 0.7301 | 0.8983 | 0.0 | 0.0 | 0.0220 | 0.0 | | 0.978 | 2.8 | 560 | 0.7408 | 0.2084 | 0.2538 | 0.7986 | nan | 0.8039 | 0.9309 | 0.2190 | 0.8212 | 0.2427 | nan | 0.2441 | 0.0110 | 0.0 | 0.9215 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9296 | 0.0 | 0.2020 | 0.0 | 0.0 | nan | 0.0 | 0.0350 | 0.0 | 0.0 | 0.9274 | 0.8378 | 0.9518 | 0.0 | 0.0 | 0.0437 | 0.0 | nan | 0.6568 | 0.8125 | 0.2120 | 0.5344 | 0.1964 | nan | 0.2175 | 0.0110 | 0.0 | 0.7195 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6137 | 0.0 | 0.1500 | 0.0 | 0.0 | nan | 0.0 | 0.0338 | 0.0 | 0.0 | 0.8339 | 0.7423 | 0.8955 | 0.0 | 0.0 | 0.0410 | 0.0 | | 0.654 | 2.9 | 580 | 0.7562 | 0.2148 | 0.2672 | 0.7950 | nan | 0.7972 | 0.9253 | 0.3007 | 0.6560 | 0.4779 | nan | 0.2650 | 0.0224 | 0.0 | 0.9136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8899 | 0.0 | 0.4572 | 0.0 | 0.0 | nan | 0.0 | 0.0478 | 0.0 | 0.0 | 0.8980 | 0.9169 | 0.9524 | 0.0 | 0.0 | 0.0296 | 0.0 | nan | 0.6365 | 0.8171 | 0.2613 | 0.5745 | 0.2708 | nan | 0.2223 | 0.0224 | 0.0 | 0.7265 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6664 | 0.0 | 0.2762 | 0.0 | 0.0 | nan | 0.0 | 0.0453 | 0.0 | 0.0 | 0.7805 | 0.6445 | 0.9008 | 0.0 | 0.0 | 0.0281 | 0.0 | | 0.427 | 3.0 | 600 | 0.7294 | 0.2180 | 0.2615 | 0.8057 | nan | 0.9073 | 0.9191 | 0.3864 | 0.6607 | 0.3389 | nan | 0.2097 | 0.0261 | 0.0 | 0.8805 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9396 | 0.0 | 0.2776 | 0.0 | 0.0 | nan | 0.0 | 0.0553 | 0.0 | 0.0 | 0.9380 | 0.8637 | 0.9350 | 0.0 | 0.0 | 0.0303 | 0.0 | nan | 0.6418 | 0.8283 | 0.3227 | 0.5986 | 0.2736 | nan | 0.1886 | 0.0260 | 0.0 | 0.7477 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6196 | 0.0 | 0.2092 | 0.0 | 0.0 | nan | 0.0 | 0.0527 | 0.0 | 0.0 | 0.8182 | 0.7251 | 0.8956 | 0.0 | 0.0 | 0.0285 | 0.0 | | 0.4829 | 3.1 | 620 | 0.7174 | 0.2226 | 0.2714 | 0.8035 | nan | 0.7341 | 0.9489 | 0.2510 | 0.7072 | 0.4433 | nan | 0.3186 | 0.0785 | 0.0 | 0.9233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8824 | 0.0 | 0.4427 | 0.0 | 0.0 | nan | 0.0 | 0.0811 | 0.0 | 0.0 | 0.9422 | 0.8826 | 0.9675 | 0.0 | 0.0 | 0.0825 | 0.0 | nan | 0.6433 | 0.8177 | 0.2251 | 0.5822 | 0.2480 | nan | 0.2623 | 0.0779 | 0.0 | 0.7399 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6700 | 0.0 | 0.2810 | 0.0 | 0.0 | nan | 0.0 | 0.0738 | 0.0 | 0.0 | 0.8124 | 0.7220 | 0.8935 | 0.0 | 0.0 | 0.0739 | 0.0 | | 0.8156 | 3.2 | 640 | 0.7023 | 0.2292 | 0.2758 | 0.8133 | nan | 0.9020 | 0.9250 | 0.2563 | 0.6719 | 0.3992 | nan | 0.2941 | 0.1677 | 0.0 | 0.9138 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.9105 | 0.0 | 0.5084 | 0.0 | 0.0 | nan | 0.0 | 0.0722 | 0.0 | 0.0 | 0.9385 | 0.8559 | 0.9473 | 0.0 | 0.0 | 0.0624 | 0.0 | nan | 0.6589 | 0.8354 | 0.2415 | 0.5910 | 0.3009 | nan | 0.2484 | 0.1649 | 0.0 | 0.7474 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.6694 | 0.0 | 0.2805 | 0.0 | 0.0 | nan | 0.0 | 0.0673 | 0.0 | 0.0 | 0.8256 | 0.7435 | 0.9028 | 0.0 | 0.0 | 0.0555 | 0.0 | | 0.7138 | 3.3 | 660 | 0.7069 | 0.2279 | 0.2749 | 0.8092 | nan | 0.8287 | 0.9482 | 0.3510 | 0.6702 | 0.3620 | nan | 0.3297 | 0.2351 | 0.0 | 0.9397 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0025 | 0.0 | 0.0 | 0.9130 | 0.0 | 0.2962 | 0.0000 | 0.0 | nan | 0.0 | 0.0865 | 0.0 | 0.0 | 0.9161 | 0.8831 | 0.9583 | 0.0 | 0.0 | 0.0772 | 0.0 | nan | 0.6930 | 0.8247 | 0.2957 | 0.5459 | 0.2651 | nan | 0.2782 | 0.2294 | 0.0 | 0.7291 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0025 | 0.0 | 0.0 | 0.6440 | 0.0 | 0.2120 | 0.0000 | 0.0 | nan | 0.0 | 0.0793 | 0.0 | 0.0 | 0.8128 | 0.7087 | 0.9045 | 0.0 | 0.0 | 0.0688 | 0.0 | | 0.6917 | 3.4 | 680 | 0.7028 | 0.2269 | 0.2739 | 0.8077 | nan | 0.8149 | 0.9433 | 0.3073 | 0.5962 | 0.4292 | nan | 0.3537 | 0.1186 | 0.0 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.9047 | 0.0 | 0.4330 | 0.0 | 0.0 | nan | 0.0 | 0.0956 | 0.0 | 0.0 | 0.9498 | 0.8324 | 0.9623 | 0.0 | 0.0 | 0.0971 | 0.0 | nan | 0.6718 | 0.8234 | 0.2649 | 0.5345 | 0.2617 | nan | 0.2900 | 0.1173 | 0.0 | 0.7382 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.6571 | 0.0 | 0.2746 | 0.0 | 0.0 | nan | 0.0 | 0.0864 | 0.0 | 0.0 | 0.8218 | 0.7334 | 0.8996 | 0.0 | 0.0 | 0.0849 | 0.0 | | 0.9325 | 3.5 | 700 | 0.6855 | 0.2374 | 0.2848 | 0.8158 | nan | 0.8325 | 0.9446 | 0.4574 | 0.7255 | 0.3362 | nan | 0.3317 | 0.2539 | 0.0 | 0.9209 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0033 | 0.0 | 0.0 | 0.8843 | 0.0 | 0.4345 | 0.0 | 0.0 | nan | 0.0 | 0.0910 | 0.0 | 0.0 | 0.9536 | 0.8368 | 0.9573 | 0.0 | 0.0 | 0.1510 | 0.0 | nan | 0.6861 | 0.8344 | 0.3742 | 0.5966 | 0.2529 | nan | 0.2720 | 0.2421 | 0.0 | 0.7385 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0033 | 0.0 | 0.0 | 0.6538 | 0.0 | 0.2749 | 0.0 | 0.0 | nan | 0.0 | 0.0835 | 0.0 | 0.0 | 0.8168 | 0.7370 | 0.9000 | 0.0 | 0.0 | 0.1323 | 0.0 | | 0.5989 | 3.6 | 720 | 0.6805 | 0.2479 | 0.2980 | 0.8191 | nan | 0.8635 | 0.9373 | 0.4433 | 0.7377 | 0.3153 | nan | 0.3770 | 0.4409 | 0.0 | 0.8934 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0660 | 0.0 | 0.0 | 0.8861 | 0.0 | 0.4853 | 0.0 | 0.0 | nan | 0.0 | 0.1566 | 0.0 | 0.0 | 0.9363 | 0.9031 | 0.9487 | 0.0 | 0.0 | 0.1443 | 0.0 | nan | 0.6924 | 0.8392 | 0.3558 | 0.6152 | 0.2621 | nan | 0.3052 | 0.4010 | 0.0 | 0.7701 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0637 | 0.0 | 0.0 | 0.6449 | 0.0 | 0.2661 | 0.0 | 0.0 | nan | 0.0 | 0.1368 | 0.0 | 0.0 | 0.8341 | 0.7158 | 0.9041 | 0.0 | 0.0 | 0.1256 | 0.0 | | 0.6463 | 3.7 | 740 | 0.6961 | 0.2427 | 0.2911 | 0.8170 | nan | 0.8652 | 0.9428 | 0.4865 | 0.5954 | 0.4087 | nan | 0.3786 | 0.4660 | 0.0 | 0.9244 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0138 | 0.0 | 0.0 | 0.9135 | 0.0 | 0.3219 | 0.0001 | 0.0 | nan | 0.0 | 0.1248 | 0.0 | 0.0 | 0.9401 | 0.8902 | 0.9563 | 0.0 | 0.0 | 0.0872 | 0.0 | nan | 0.6906 | 0.8322 | 0.3576 | 0.5430 | 0.3092 | nan | 0.2937 | 0.4203 | 0.0 | 0.7447 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0131 | 0.0 | 0.0 | 0.6471 | 0.0 | 0.2497 | 0.0001 | 0.0 | nan | 0.0 | 0.1142 | 0.0 | 0.0 | 0.8326 | 0.7372 | 0.9031 | 0.0 | 0.0 | 0.0789 | 0.0 | | 0.7591 | 3.8 | 760 | 0.6612 | 0.2580 | 0.3138 | 0.8244 | nan | 0.8508 | 0.9313 | 0.5719 | 0.7780 | 0.4338 | nan | 0.3375 | 0.6091 | 0.0 | 0.9092 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1580 | 0.0 | 0.0 | 0.9023 | 0.0 | 0.4899 | 0.0 | 0.0 | nan | 0.0 | 0.1601 | 0.0 | 0.0 | 0.9426 | 0.8625 | 0.9558 | 0.0 | 0.0 | 0.1474 | 0.0 | nan | 0.7094 | 0.8419 | 0.3247 | 0.6473 | 0.3145 | nan | 0.2901 | 0.5056 | 0.0 | 0.7800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1394 | 0.0 | 0.0 | 0.6658 | 0.0 | 0.2838 | 0.0 | 0.0 | nan | 0.0 | 0.1420 | 0.0 | 0.0 | 0.8403 | 0.7416 | 0.9069 | 0.0 | 0.0 | 0.1233 | 0.0 | | 0.5534 | 3.9 | 780 | 0.6732 | 0.2532 | 0.3063 | 0.8181 | nan | 0.8214 | 0.9461 | 0.5042 | 0.6612 | 0.3922 | nan | 0.4129 | 0.5771 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0469 | 0.0 | 0.0 | 0.8828 | 0.0 | 0.4341 | 0.0002 | 0.0 | nan | 0.0 | 0.1264 | 0.0 | 0.0 | 0.9313 | 0.9053 | 0.9573 | 0.0 | 0.0 | 0.2661 | 0.0 | nan | 0.6922 | 0.8335 | 0.4137 | 0.5804 | 0.2705 | nan | 0.3330 | 0.4903 | 0.0 | 0.7471 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0450 | 0.0 | 0.0 | 0.6712 | 0.0 | 0.2813 | 0.0002 | 0.0 | nan | 0.0 | 0.1168 | 0.0 | 0.0 | 0.8218 | 0.7088 | 0.9092 | 0.0 | 0.0 | 0.1879 | 0.0 | | 0.5253 | 4.0 | 800 | 0.6851 | 0.2526 | 0.3064 | 0.8152 | nan | 0.7825 | 0.9559 | 0.5397 | 0.7223 | 0.4111 | nan | 0.3937 | 0.5704 | 0.0 | 0.9213 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0866 | 0.0 | 0.0 | 0.9097 | 0.0 | 0.3959 | 0.0000 | 0.0 | nan | 0.0 | 0.1174 | 0.0 | 0.0 | 0.8933 | 0.8890 | 0.9628 | 0.0 | 0.0 | 0.2529 | 0.0 | nan | 0.6829 | 0.8305 | 0.4174 | 0.5845 | 0.2797 | nan | 0.3243 | 0.4921 | 0.0 | 0.7609 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0810 | 0.0 | 0.0 | 0.6700 | 0.0 | 0.2668 | 0.0000 | 0.0 | nan | 0.0 | 0.1099 | 0.0 | 0.0 | 0.8165 | 0.6845 | 0.9037 | 0.0 | 0.0 | 0.1780 | 0.0 | | 0.7711 | 4.1 | 820 | 0.6676 | 0.2542 | 0.3083 | 0.8201 | nan | 0.8437 | 0.9316 | 0.5681 | 0.7326 | 0.4147 | nan | 0.3457 | 0.6161 | 0.0 | 0.9313 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1167 | 0.0 | 0.0 | 0.8849 | 0.0 | 0.3741 | 0.0008 | 0.0 | nan | 0.0 | 0.1325 | 0.0 | 0.0 | 0.9571 | 0.8486 | 0.9574 | 0.0 | 0.0 | 0.2110 | 0.0 | nan | 0.6873 | 0.8377 | 0.3471 | 0.5874 | 0.3225 | nan | 0.2941 | 0.5096 | 0.0 | 0.7620 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1004 | 0.0 | 0.0 | 0.6810 | 0.0 | 0.2695 | 0.0008 | 0.0 | nan | 0.0 | 0.1181 | 0.0 | 0.0 | 0.8163 | 0.7409 | 0.9069 | 0.0 | 0.0 | 0.1537 | 0.0 | | 0.707 | 4.2 | 840 | 0.6544 | 0.2666 | 0.3247 | 0.8257 | nan | 0.8754 | 0.9295 | 0.4642 | 0.7581 | 0.4803 | nan | 0.3356 | 0.6712 | 0.0 | 0.9103 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3022 | 0.0 | 0.0 | 0.8921 | 0.0 | 0.5800 | 0.0 | 0.0 | nan | 0.0 | 0.2018 | 0.0 | 0.0 | 0.9160 | 0.9045 | 0.9600 | 0.0 | 0.0 | 0.2085 | 0.0 | nan | 0.7049 | 0.8479 | 0.4035 | 0.6244 | 0.3176 | nan | 0.2962 | 0.5501 | 0.0 | 0.7839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2270 | 0.0 | 0.0 | 0.6912 | 0.0 | 0.2901 | 0.0 | 0.0 | nan | 0.0 | 0.1745 | 0.0 | 0.0 | 0.8381 | 0.7161 | 0.9089 | 0.0 | 0.0 | 0.1575 | 0.0 | | 0.3871 | 4.3 | 860 | 0.6769 | 0.2557 | 0.3102 | 0.8184 | nan | 0.7837 | 0.9498 | 0.4837 | 0.6756 | 0.4045 | nan | 0.4306 | 0.6623 | 0.0 | 0.9270 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0860 | 0.0 | 0.0 | 0.8875 | 0.0 | 0.5056 | 0.0000 | 0.0 | nan | 0.0 | 0.1384 | 0.0 | 0.0 | 0.9505 | 0.8714 | 0.9552 | 0.0 | 0.0 | 0.2160 | 0.0 | nan | 0.6800 | 0.8332 | 0.3980 | 0.5833 | 0.2693 | nan | 0.3201 | 0.5166 | 0.0 | 0.7572 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0793 | 0.0 | 0.0 | 0.6881 | 0.0 | 0.2960 | 0.0000 | 0.0 | nan | 0.0 | 0.1238 | 0.0 | 0.0 | 0.8248 | 0.7405 | 0.9098 | 0.0 | 0.0 | 0.1637 | 0.0 | | 0.3875 | 4.4 | 880 | 0.6636 | 0.2572 | 0.3122 | 0.8214 | nan | 0.8858 | 0.9330 | 0.5198 | 0.6597 | 0.3446 | nan | 0.3893 | 0.6406 | 0.0 | 0.9311 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0998 | 0.0 | 0.0 | 0.9076 | 0.0 | 0.4959 | 0.0013 | 0.0 | nan | 0.0 | 0.1958 | 0.0 | 0.0 | 0.9191 | 0.8930 | 0.9695 | 0.0 | 0.0 | 0.2060 | 0.0 | nan | 0.6752 | 0.8406 | 0.3812 | 0.5797 | 0.2876 | nan | 0.3076 | 0.5213 | 0.0 | 0.7672 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0920 | 0.0 | 0.0 | 0.6889 | 0.0 | 0.3101 | 0.0013 | 0.0 | nan | 0.0 | 0.1681 | 0.0 | 0.0 | 0.8338 | 0.7172 | 0.9046 | 0.0 | 0.0 | 0.1554 | 0.0 | | 0.3193 | 4.5 | 900 | 0.6542 | 0.2597 | 0.3132 | 0.8219 | nan | 0.8326 | 0.9582 | 0.4788 | 0.7010 | 0.3630 | nan | 0.3619 | 0.6338 | 0.0 | 0.9361 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1771 | 0.0 | 0.0 | 0.9081 | 0.0 | 0.4641 | 0.0076 | 0.0 | nan | 0.0 | 0.2136 | 0.0 | 0.0 | 0.8987 | 0.9015 | 0.9614 | 0.0 | 0.0 | 0.2243 | 0.0 | nan | 0.7043 | 0.8323 | 0.4032 | 0.5934 | 0.2855 | nan | 0.3055 | 0.5127 | 0.0 | 0.7645 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1567 | 0.0 | 0.0 | 0.6876 | 0.0 | 0.2943 | 0.0075 | 0.0 | nan | 0.0 | 0.1783 | 0.0 | 0.0 | 0.8255 | 0.6886 | 0.9085 | 0.0 | 0.0 | 0.1632 | 0.0 | | 0.4406 | 4.6 | 920 | 0.6569 | 0.2614 | 0.3241 | 0.8187 | nan | 0.8827 | 0.8931 | 0.5627 | 0.7492 | 0.4866 | nan | 0.3742 | 0.6700 | 0.0 | 0.9376 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1651 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.4994 | 0.0318 | 0.0 | nan | 0.0 | 0.2074 | 0.0 | 0.0 | 0.9504 | 0.8615 | 0.9666 | 0.0 | 0.0 | 0.2622 | 0.0 | nan | 0.6811 | 0.8343 | 0.3170 | 0.6401 | 0.3189 | nan | 0.2997 | 0.5251 | 0.0 | 0.7588 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1441 | 0.0 | 0.0 | 0.6967 | 0.0 | 0.3081 | 0.0312 | 0.0 | nan | 0.0 | 0.1656 | 0.0 | 0.0 | 0.8313 | 0.7308 | 0.9035 | 0.0 | 0.0 | 0.1784 | 0.0 | | 0.5358 | 4.7 | 940 | 0.6500 | 0.2597 | 0.3110 | 0.8236 | nan | 0.8691 | 0.9456 | 0.5509 | 0.6797 | 0.4115 | nan | 0.3701 | 0.6042 | 0.0 | 0.8879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1980 | 0.0 | 0.0 | 0.9315 | 0.0 | 0.4259 | 0.0054 | 0.0 | nan | 0.0 | 0.1871 | 0.0 | 0.0 | 0.9234 | 0.8543 | 0.9585 | 0.0 | 0.0 | 0.1503 | 0.0 | nan | 0.6986 | 0.8374 | 0.3752 | 0.6046 | 0.3042 | nan | 0.3063 | 0.4954 | 0.0 | 0.7844 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1822 | 0.0 | 0.0 | 0.6668 | 0.0 | 0.2805 | 0.0053 | 0.0 | nan | 0.0 | 0.1594 | 0.0 | 0.0 | 0.8424 | 0.7319 | 0.9097 | 0.0 | 0.0 | 0.1245 | 0.0 | | 0.6313 | 4.8 | 960 | 0.6320 | 0.2673 | 0.3266 | 0.8278 | nan | 0.8247 | 0.9394 | 0.5275 | 0.8027 | 0.4413 | nan | 0.3894 | 0.6899 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2349 | 0.0 | 0.0 | 0.8570 | 0.0 | 0.5248 | 0.0266 | 0.0 | nan | 0.0 | 0.2259 | 0.0 | 0.0 | 0.9468 | 0.8795 | 0.9636 | 0.0 | 0.0 | 0.2359 | 0.0 | nan | 0.7012 | 0.8478 | 0.4074 | 0.6506 | 0.3106 | nan | 0.3286 | 0.5297 | 0.0 | 0.7582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1792 | 0.0 | 0.0 | 0.6927 | 0.0 | 0.2988 | 0.0263 | 0.0 | nan | 0.0 | 0.1773 | 0.0 | 0.0 | 0.8298 | 0.7318 | 0.9079 | 0.0 | 0.0 | 0.1767 | 0.0 | | 0.3536 | 4.9 | 980 | 0.6347 | 0.2695 | 0.3225 | 0.8290 | nan | 0.8067 | 0.9585 | 0.5452 | 0.7794 | 0.4020 | nan | 0.3660 | 0.7025 | 0.0 | 0.9025 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2276 | 0.0 | 0.0 | 0.9014 | 0.0 | 0.4827 | 0.0392 | 0.0 | nan | 0.0 | 0.2068 | 0.0 | 0.0 | 0.9458 | 0.8488 | 0.9537 | 0.0 | 0.0 | 0.2515 | 0.0 | nan | 0.7024 | 0.8392 | 0.4190 | 0.6574 | 0.3068 | nan | 0.3154 | 0.5403 | 0.0 | 0.7839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2015 | 0.0 | 0.0 | 0.6764 | 0.0 | 0.2990 | 0.0388 | 0.0 | nan | 0.0 | 0.1732 | 0.0 | 0.0 | 0.8356 | 0.7403 | 0.9103 | 0.0 | 0.0 | 0.1850 | 0.0 | | 0.4556 | 5.0 | 1000 | 0.6622 | 0.2569 | 0.3150 | 0.8227 | nan | 0.8442 | 0.9340 | 0.6047 | 0.7161 | 0.4726 | nan | 0.4128 | 0.7259 | 0.0 | 0.9344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0922 | 0.0 | 0.0 | 0.9187 | 0.0 | 0.3290 | 0.0050 | 0.0 | nan | 0.0 | 0.1817 | 0.0 | 0.0 | 0.9212 | 0.8851 | 0.9671 | 0.0 | 0.0 | 0.1343 | 0.0 | nan | 0.6994 | 0.8458 | 0.3676 | 0.6354 | 0.3216 | nan | 0.3289 | 0.5432 | 0.0 | 0.7672 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0816 | 0.0 | 0.0 | 0.6547 | 0.0 | 0.2310 | 0.0050 | 0.0 | nan | 0.0 | 0.1578 | 0.0 | 0.0 | 0.8300 | 0.7204 | 0.9092 | 0.0 | 0.0 | 0.1206 | 0.0 | | 0.4591 | 5.1 | 1020 | 0.6507 | 0.2648 | 0.3244 | 0.8256 | nan | 0.8822 | 0.9379 | 0.5695 | 0.6632 | 0.4411 | nan | 0.3617 | 0.7320 | 0.0 | 0.9309 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1561 | 0.0 | 0.0 | 0.8420 | 0.0 | 0.5086 | 0.0280 | 0.0 | nan | 0.0 | 0.2619 | 0.0 | 0.0 | 0.9492 | 0.8699 | 0.9623 | 0.0 | 0.0 | 0.2847 | 0.0 | nan | 0.6970 | 0.8414 | 0.3791 | 0.6022 | 0.3283 | nan | 0.2991 | 0.5071 | 0.0 | 0.7714 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1242 | 0.0 | 0.0 | 0.6887 | 0.0 | 0.2880 | 0.0277 | 0.0 | nan | 0.0 | 0.2067 | 0.0 | 0.0 | 0.8351 | 0.7556 | 0.9135 | 0.0 | 0.0 | 0.2093 | 0.0 | | 0.5285 | 5.2 | 1040 | 0.6560 | 0.2682 | 0.3265 | 0.8218 | nan | 0.7659 | 0.9305 | 0.5688 | 0.7382 | 0.5242 | nan | 0.4848 | 0.6590 | 0.0 | 0.9092 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1483 | 0.0 | 0.0 | 0.9157 | 0.0 | 0.4034 | 0.0615 | 0.0 | nan | 0.0 | 0.2794 | 0.0 | 0.0 | 0.9405 | 0.9060 | 0.9620 | 0.0 | 0.0 | 0.2511 | 0.0 | nan | 0.6776 | 0.8350 | 0.4324 | 0.6042 | 0.3074 | nan | 0.3654 | 0.5356 | 0.0 | 0.7741 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1338 | 0.0 | 0.0 | 0.6821 | 0.0 | 0.2853 | 0.0607 | 0.0 | nan | 0.0 | 0.2136 | 0.0 | 0.0 | 0.8388 | 0.7444 | 0.9136 | 0.0 | 0.0 | 0.1794 | 0.0 | | 0.4361 | 5.3 | 1060 | 0.6255 | 0.2711 | 0.3246 | 0.8323 | nan | 0.8628 | 0.9391 | 0.5433 | 0.8105 | 0.3499 | nan | 0.4237 | 0.6427 | 0.0 | 0.9101 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1850 | 0.0 | 0.0 | 0.9182 | 0.0 | 0.4471 | 0.0439 | 0.0 | nan | 0.0 | 0.2448 | 0.0 | 0.0 | 0.9329 | 0.8739 | 0.9652 | 0.0 | 0.0 | 0.2933 | 0.0 | nan | 0.7083 | 0.8463 | 0.4068 | 0.6505 | 0.2900 | nan | 0.3443 | 0.5433 | 0.0 | 0.7810 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1652 | 0.0 | 0.0 | 0.6823 | 0.0 | 0.3011 | 0.0435 | 0.0 | nan | 0.0 | 0.1998 | 0.0 | 0.0 | 0.8500 | 0.7546 | 0.9110 | 0.0 | 0.0 | 0.1970 | 0.0 | | 0.4952 | 5.4 | 1080 | 0.6550 | 0.2708 | 0.3276 | 0.8243 | nan | 0.7442 | 0.9626 | 0.5128 | 0.7462 | 0.4368 | nan | 0.4072 | 0.7272 | 0.0 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2467 | 0.0 | 0.0 | 0.9016 | 0.0 | 0.5444 | 0.0087 | 0.0 | nan | 0.0 | 0.2676 | 0.0 | 0.0 | 0.9317 | 0.8792 | 0.9622 | 0.0 | 0.0 | 0.2788 | 0.0 | nan | 0.6666 | 0.8202 | 0.4120 | 0.6328 | 0.3133 | nan | 0.3450 | 0.5479 | 0.0 | 0.7769 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1962 | 0.0 | 0.0 | 0.6943 | 0.0 | 0.3191 | 0.0087 | 0.0 | nan | 0.0 | 0.2106 | 0.0 | 0.0 | 0.8527 | 0.7557 | 0.9140 | 0.0 | 0.0 | 0.1996 | 0.0 | | 0.3561 | 5.5 | 1100 | 0.6307 | 0.2710 | 0.3310 | 0.8307 | nan | 0.8767 | 0.9299 | 0.5615 | 0.7294 | 0.4652 | nan | 0.4155 | 0.7589 | 0.0 | 0.9234 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2777 | 0.0 | 0.0 | 0.8979 | 0.0 | 0.4442 | 0.0240 | 0.0 | nan | 0.0 | 0.2477 | 0.0 | 0.0 | 0.9423 | 0.8935 | 0.9645 | 0.0 | 0.0 | 0.2384 | 0.0 | nan | 0.7054 | 0.8493 | 0.4020 | 0.6369 | 0.3361 | nan | 0.3419 | 0.5484 | 0.0 | 0.7772 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2010 | 0.0 | 0.0 | 0.6856 | 0.0 | 0.3081 | 0.0238 | 0.0 | nan | 0.0 | 0.1943 | 0.0 | 0.0 | 0.8398 | 0.7333 | 0.9128 | 0.0 | 0.0 | 0.1760 | 0.0 | | 0.5354 | 5.6 | 1120 | 0.6387 | 0.2721 | 0.3312 | 0.8275 | nan | 0.7875 | 0.9516 | 0.5832 | 0.7279 | 0.4278 | nan | 0.4853 | 0.7450 | 0.0 | 0.9340 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3487 | 0.0 | 0.0 | 0.9019 | 0.0 | 0.5119 | 0.0186 | 0.0 | nan | 0.0 | 0.2194 | 0.0 | 0.0 | 0.9566 | 0.8174 | 0.9629 | 0.0 | 0.0 | 0.2197 | 0.0 | nan | 0.6898 | 0.8340 | 0.4223 | 0.6318 | 0.3037 | nan | 0.3741 | 0.5537 | 0.0 | 0.7810 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2291 | 0.0 | 0.0 | 0.6908 | 0.0 | 0.3200 | 0.0184 | 0.0 | nan | 0.0 | 0.1828 | 0.0 | 0.0 | 0.8404 | 0.7435 | 0.9153 | 0.0 | 0.0 | 0.1769 | 0.0 | | 0.3437 | 5.7 | 1140 | 0.6174 | 0.2792 | 0.3423 | 0.8346 | nan | 0.8410 | 0.9443 | 0.5442 | 0.7439 | 0.4972 | nan | 0.4534 | 0.7617 | 0.0 | 0.9308 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4301 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.5431 | 0.0521 | 0.0 | nan | 0.0 | 0.2596 | 0.0 | 0.0 | 0.9355 | 0.8947 | 0.9595 | 0.0 | 0.0 | 0.2720 | 0.0 | nan | 0.7226 | 0.8471 | 0.4400 | 0.6482 | 0.3168 | nan | 0.3740 | 0.5410 | 0.0 | 0.7879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2574 | 0.0 | 0.0 | 0.7063 | 0.0 | 0.3265 | 0.0508 | 0.0 | nan | 0.0 | 0.2106 | 0.0 | 0.0 | 0.8479 | 0.7455 | 0.9171 | 0.0 | 0.0 | 0.1962 | 0.0 | | 0.4298 | 5.8 | 1160 | 0.6134 | 0.2781 | 0.3353 | 0.8353 | nan | 0.8577 | 0.9386 | 0.5211 | 0.7719 | 0.4591 | nan | 0.4418 | 0.7563 | 0.0 | 0.9277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3321 | 0.0 | 0.0 | 0.9252 | 0.0 | 0.4237 | 0.0904 | 0.0 | nan | 0.0 | 0.2481 | 0.0 | 0.0 | 0.9335 | 0.8962 | 0.9621 | 0.0 | 0.0 | 0.2454 | 0.0 | nan | 0.7176 | 0.8498 | 0.4310 | 0.6682 | 0.3271 | nan | 0.3581 | 0.5549 | 0.0 | 0.7808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2378 | 0.0 | 0.0 | 0.6713 | 0.0 | 0.3008 | 0.0883 | 0.0 | nan | 0.0 | 0.2080 | 0.0 | 0.0 | 0.8506 | 0.7543 | 0.9174 | 0.0 | 0.0 | 0.1841 | 0.0 | | 0.6889 | 5.9 | 1180 | 0.6136 | 0.2788 | 0.3369 | 0.8355 | nan | 0.8318 | 0.9517 | 0.5924 | 0.7928 | 0.3679 | nan | 0.4446 | 0.7324 | 0.0 | 0.9197 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3977 | 0.0 | 0.0 | 0.9050 | 0.0 | 0.4513 | 0.1394 | 0.0 | nan | 0.0 | 0.2672 | 0.0 | 0.0 | 0.9468 | 0.8603 | 0.9680 | 0.0 | 0.0 | 0.2134 | 0.0 | nan | 0.7105 | 0.8455 | 0.4164 | 0.6613 | 0.3017 | nan | 0.3604 | 0.5448 | 0.0 | 0.7887 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2619 | 0.0 | 0.0 | 0.6877 | 0.0 | 0.3169 | 0.1338 | 0.0 | nan | 0.0 | 0.2144 | 0.0 | 0.0 | 0.8462 | 0.7462 | 0.9155 | 0.0 | 0.0 | 0.1707 | 0.0 | | 0.4294 | 6.0 | 1200 | 0.6237 | 0.2787 | 0.3372 | 0.8343 | nan | 0.8774 | 0.9479 | 0.5795 | 0.7154 | 0.3698 | nan | 0.3976 | 0.7525 | 0.0 | 0.9183 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4412 | 0.0 | 0.0 | 0.9200 | 0.0 | 0.4551 | 0.1293 | 0.0 | nan | 0.0 | 0.3076 | 0.0 | 0.0 | 0.9386 | 0.8491 | 0.9612 | 0.0 | 0.0 | 0.2297 | 0.0 | nan | 0.7117 | 0.8500 | 0.4300 | 0.6318 | 0.3026 | nan | 0.3341 | 0.5519 | 0.0 | 0.7848 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2811 | 0.0 | 0.0 | 0.6789 | 0.0 | 0.3069 | 0.1241 | 0.0 | nan | 0.0 | 0.2370 | 0.0 | 0.0 | 0.8482 | 0.7534 | 0.9164 | 0.0 | 0.0 | 0.1750 | 0.0 | | 0.3842 | 6.1 | 1220 | 0.6013 | 0.2861 | 0.3502 | 0.8391 | nan | 0.8607 | 0.9402 | 0.5867 | 0.7663 | 0.4641 | nan | 0.4457 | 0.7530 | 0.0 | 0.9345 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4567 | 0.0 | 0.0 | 0.8909 | 0.0 | 0.4718 | 0.2415 | 0.0 | nan | 0.0 | 0.3472 | 0.0 | 0.0 | 0.9365 | 0.9007 | 0.9691 | 0.0 | 0.0 | 0.2402 | 0.0 | nan | 0.7264 | 0.8525 | 0.4257 | 0.6615 | 0.3263 | nan | 0.3696 | 0.5470 | 0.0 | 0.7799 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2428 | 0.0 | 0.0 | 0.7000 | 0.0 | 0.3559 | 0.2309 | 0.0 | nan | 0.0 | 0.2498 | 0.0 | 0.0 | 0.8444 | 0.7538 | 0.9154 | 0.0 | 0.0 | 0.1744 | 0.0 | | 0.3414 | 6.2 | 1240 | 0.6057 | 0.2841 | 0.3458 | 0.8372 | nan | 0.8653 | 0.9294 | 0.5736 | 0.8022 | 0.4314 | nan | 0.4221 | 0.7446 | 0.0 | 0.9128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4779 | 0.0 | 0.0 | 0.9221 | 0.0 | 0.4615 | 0.2442 | 0.0 | nan | 0.0 | 0.3033 | 0.0 | 0.0 | 0.9383 | 0.8996 | 0.9713 | 0.0 | 0.0 | 0.1656 | 0.0 | nan | 0.7125 | 0.8481 | 0.4153 | 0.6647 | 0.3241 | nan | 0.3588 | 0.5569 | 0.0 | 0.7896 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2746 | 0.0 | 0.0 | 0.6852 | 0.0 | 0.3387 | 0.2334 | 0.0 | nan | 0.0 | 0.2254 | 0.0 | 0.0 | 0.8512 | 0.7521 | 0.9160 | 0.0 | 0.0 | 0.1452 | 0.0 | | 0.2668 | 6.3 | 1260 | 0.6107 | 0.2826 | 0.3450 | 0.8359 | nan | 0.8583 | 0.9382 | 0.5687 | 0.7657 | 0.4032 | nan | 0.4406 | 0.7619 | 0.0 | 0.9462 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4559 | 0.0 | 0.0 | 0.9111 | 0.0 | 0.4298 | 0.2429 | 0.0 | nan | 0.0 | 0.3298 | 0.0 | 0.0 | 0.9344 | 0.8866 | 0.9681 | 0.0 | 0.0 | 0.1990 | 0.0 | nan | 0.7169 | 0.8450 | 0.4128 | 0.6378 | 0.3066 | nan | 0.3632 | 0.5477 | 0.0 | 0.7759 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2558 | 0.0 | 0.0 | 0.6840 | 0.0 | 0.3300 | 0.2327 | 0.0 | nan | 0.0 | 0.2419 | 0.0 | 0.0 | 0.8551 | 0.7577 | 0.9192 | 0.0 | 0.0 | 0.1616 | 0.0 | | 0.345 | 6.4 | 1280 | 0.6021 | 0.2856 | 0.3476 | 0.8376 | nan | 0.8565 | 0.9475 | 0.5464 | 0.7295 | 0.4149 | nan | 0.4622 | 0.7741 | 0.0 | 0.9269 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4755 | 0.0 | 0.0 | 0.8993 | 0.0 | 0.5527 | 0.1694 | 0.0 | nan | 0.0 | 0.3497 | 0.0 | 0.0 | 0.9417 | 0.8722 | 0.9643 | 0.0 | 0.0 | 0.2404 | 0.0 | nan | 0.7249 | 0.8440 | 0.4423 | 0.6305 | 0.3065 | nan | 0.3753 | 0.5470 | 0.0 | 0.7960 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2836 | 0.0 | 0.0 | 0.7073 | 0.0 | 0.3537 | 0.1610 | 0.0 | nan | 0.0 | 0.2510 | 0.0 | 0.0 | 0.8510 | 0.7626 | 0.9202 | 0.0 | 0.0 | 0.1828 | 0.0 | | 0.5035 | 6.5 | 1300 | 0.6096 | 0.2843 | 0.3532 | 0.8321 | nan | 0.8155 | 0.9197 | 0.6216 | 0.8217 | 0.4931 | nan | 0.4721 | 0.7753 | 0.0 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5045 | 0.0 | 0.0 | 0.9012 | 0.0 | 0.5240 | 0.1959 | 0.0 | nan | 0.0 | 0.3288 | 0.0 | 0.0 | 0.9487 | 0.8548 | 0.9681 | 0.0 | 0.0 | 0.2306 | 0.0 | nan | 0.7139 | 0.8361 | 0.3180 | 0.6709 | 0.3177 | nan | 0.3877 | 0.5664 | 0.0 | 0.7972 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2867 | 0.0 | 0.0 | 0.7039 | 0.0 | 0.3595 | 0.1869 | 0.0 | nan | 0.0 | 0.2409 | 0.0 | 0.0 | 0.8500 | 0.7600 | 0.9195 | 0.0 | 0.0 | 0.1806 | 0.0 | | 0.4929 | 6.6 | 1320 | 0.5924 | 0.2927 | 0.3533 | 0.8429 | nan | 0.8576 | 0.9476 | 0.5682 | 0.7956 | 0.4425 | nan | 0.4412 | 0.7597 | 0.0 | 0.8999 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4884 | 0.0 | 0.0 | 0.9014 | 0.0 | 0.5541 | 0.2743 | 0.0 | nan | 0.0 | 0.3219 | 0.0 | 0.0 | 0.9345 | 0.9021 | 0.9714 | 0.0 | 0.0 | 0.2466 | 0.0 | nan | 0.7308 | 0.8552 | 0.4243 | 0.6894 | 0.3305 | nan | 0.3699 | 0.5736 | 0.0 | 0.8015 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3291 | 0.0 | 0.0 | 0.7071 | 0.0 | 0.3793 | 0.2500 | 0.0 | nan | 0.0 | 0.2393 | 0.0 | 0.0 | 0.8447 | 0.7398 | 0.9170 | 0.0 | 0.0 | 0.1852 | 0.0 | | 0.3436 | 6.7 | 1340 | 0.5995 | 0.2838 | 0.3429 | 0.8384 | nan | 0.8742 | 0.9381 | 0.5760 | 0.7711 | 0.4374 | nan | 0.4228 | 0.7859 | 0.0 | 0.9308 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4498 | 0.0 | 0.0 | 0.9159 | 0.0 | 0.4172 | 0.1522 | 0.0 | nan | 0.0 | 0.2774 | 0.0 | 0.0 | 0.9524 | 0.8462 | 0.9683 | 0.0 | 0.0 | 0.2565 | 0.0 | nan | 0.7204 | 0.8513 | 0.4114 | 0.6822 | 0.3266 | nan | 0.3601 | 0.5441 | 0.0 | 0.7848 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3261 | 0.0 | 0.0 | 0.6896 | 0.0 | 0.3091 | 0.1470 | 0.0 | nan | 0.0 | 0.2188 | 0.0 | 0.0 | 0.8459 | 0.7607 | 0.9183 | 0.0 | 0.0 | 0.1861 | 0.0 | | 0.7377 | 6.8 | 1360 | 0.6134 | 0.2833 | 0.3463 | 0.8354 | nan | 0.8362 | 0.9553 | 0.5929 | 0.7248 | 0.4510 | nan | 0.4497 | 0.7811 | 0.0 | 0.9095 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5309 | 0.0 | 0.0 | 0.9113 | 0.0 | 0.4732 | 0.1200 | 0.0 | nan | 0.0 | 0.3126 | 0.0 | 0.0 | 0.9227 | 0.9004 | 0.9666 | 0.0 | 0.0 | 0.2439 | 0.0 | nan | 0.7292 | 0.8460 | 0.4295 | 0.6422 | 0.3253 | nan | 0.3596 | 0.5413 | 0.0 | 0.7990 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3584 | 0.0 | 0.0 | 0.6972 | 0.0 | 0.3199 | 0.1156 | 0.0 | nan | 0.0 | 0.2365 | 0.0 | 0.0 | 0.8455 | 0.7276 | 0.9192 | 0.0 | 0.0 | 0.1726 | 0.0 | | 0.4128 | 6.9 | 1380 | 0.6006 | 0.2878 | 0.3553 | 0.8391 | nan | 0.8415 | 0.9462 | 0.6214 | 0.7366 | 0.4697 | nan | 0.4957 | 0.7677 | 0.0 | 0.9305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6027 | 0.0 | 0.0 | 0.8901 | 0.0 | 0.5046 | 0.2026 | 0.0 | nan | 0.0 | 0.2951 | 0.0 | 0.0 | 0.9465 | 0.8840 | 0.9614 | 0.0 | 0.0 | 0.2743 | 0.0 | nan | 0.7322 | 0.8519 | 0.4002 | 0.6451 | 0.3300 | nan | 0.3886 | 0.5512 | 0.0 | 0.7940 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3177 | 0.0 | 0.0 | 0.7060 | 0.0 | 0.3487 | 0.1921 | 0.0 | nan | 0.0 | 0.2287 | 0.0 | 0.0 | 0.8469 | 0.7604 | 0.9208 | 0.0 | 0.0 | 0.1936 | 0.0 | | 0.4907 | 7.0 | 1400 | 0.6117 | 0.2858 | 0.3455 | 0.8376 | nan | 0.8634 | 0.9474 | 0.5965 | 0.7312 | 0.4186 | nan | 0.4155 | 0.7470 | 0.0 | 0.9203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5297 | 0.0 | 0.0 | 0.9243 | 0.0 | 0.4433 | 0.2116 | 0.0 | nan | 0.0 | 0.2940 | 0.0 | 0.0 | 0.9311 | 0.8932 | 0.9692 | 0.0 | 0.0 | 0.2194 | 0.0 | nan | 0.7268 | 0.8486 | 0.4166 | 0.6316 | 0.3267 | nan | 0.3552 | 0.5937 | 0.0 | 0.8002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3162 | 0.0 | 0.0 | 0.6794 | 0.0 | 0.3256 | 0.1987 | 0.0 | nan | 0.0 | 0.2328 | 0.0 | 0.0 | 0.8514 | 0.7513 | 0.9192 | 0.0 | 0.0 | 0.1712 | 0.0 | | 0.4294 | 7.1 | 1420 | 0.6113 | 0.2919 | 0.3560 | 0.8385 | nan | 0.8568 | 0.9325 | 0.6310 | 0.7411 | 0.4957 | nan | 0.4646 | 0.7486 | 0.0 | 0.9336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5143 | 0.0 | 0.0 | 0.8965 | 0.0 | 0.4609 | 0.3225 | 0.0 | nan | 0.0 | 0.3558 | 0.0 | 0.0 | 0.9469 | 0.8690 | 0.9707 | 0.0 | 0.0 | 0.2502 | 0.0 | nan | 0.7284 | 0.8455 | 0.3658 | 0.6479 | 0.3335 | nan | 0.3784 | 0.5995 | 0.0 | 0.7964 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3039 | 0.0 | 0.0 | 0.6996 | 0.0 | 0.3631 | 0.3026 | 0.0 | nan | 0.0 | 0.2534 | 0.0 | 0.0 | 0.8554 | 0.7669 | 0.9180 | 0.0 | 0.0 | 0.1809 | 0.0 | | 0.3443 | 7.2 | 1440 | 0.5995 | 0.2913 | 0.3533 | 0.8400 | nan | 0.8612 | 0.9478 | 0.5732 | 0.7560 | 0.4347 | nan | 0.4262 | 0.7650 | 0.0 | 0.9334 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5207 | 0.0 | 0.0 | 0.9054 | 0.0 | 0.4590 | 0.3551 | 0.0 | nan | 0.0 | 0.3457 | 0.0 | 0.0 | 0.9247 | 0.8845 | 0.9716 | 0.0 | 0.0 | 0.2411 | 0.0 | nan | 0.7276 | 0.8468 | 0.4115 | 0.6580 | 0.3226 | nan | 0.3623 | 0.5876 | 0.0 | 0.7949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2886 | 0.0 | 0.0 | 0.6991 | 0.0 | 0.3612 | 0.3291 | 0.0 | nan | 0.0 | 0.2508 | 0.0 | 0.0 | 0.8487 | 0.7406 | 0.9172 | 0.0 | 0.0 | 0.1762 | 0.0 | | 0.3951 | 7.3 | 1460 | 0.5993 | 0.2881 | 0.3512 | 0.8389 | nan | 0.8361 | 0.9493 | 0.5587 | 0.7858 | 0.4044 | nan | 0.4564 | 0.7742 | 0.0 | 0.9285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5710 | 0.0 | 0.0 | 0.9064 | 0.0 | 0.4519 | 0.2775 | 0.0 | nan | 0.0 | 0.3373 | 0.0 | 0.0 | 0.9471 | 0.8486 | 0.9723 | 0.0 | 0.0 | 0.2314 | 0.0 | nan | 0.7132 | 0.8495 | 0.4238 | 0.6330 | 0.3179 | nan | 0.3756 | 0.5680 | 0.0 | 0.7991 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2852 | 0.0 | 0.0 | 0.6937 | 0.0 | 0.3453 | 0.2636 | 0.0 | nan | 0.0 | 0.2459 | 0.0 | 0.0 | 0.8534 | 0.7632 | 0.9170 | 0.0 | 0.0 | 0.1723 | 0.0 | | 0.2938 | 7.4 | 1480 | 0.5979 | 0.2917 | 0.3518 | 0.8414 | nan | 0.8454 | 0.9501 | 0.5647 | 0.7701 | 0.4146 | nan | 0.4512 | 0.7719 | 0.0 | 0.9103 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5378 | 0.0 | 0.0 | 0.9112 | 0.0 | 0.4758 | 0.3094 | 0.0 | nan | 0.0 | 0.3269 | 0.0 | 0.0 | 0.9511 | 0.8692 | 0.9649 | 0.0 | 0.0 | 0.2323 | 0.0 | nan | 0.7207 | 0.8505 | 0.4227 | 0.6485 | 0.3229 | nan | 0.3762 | 0.5570 | 0.0 | 0.8052 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3362 | 0.0 | 0.0 | 0.6955 | 0.0 | 0.3548 | 0.2909 | 0.0 | nan | 0.0 | 0.2420 | 0.0 | 0.0 | 0.8549 | 0.7673 | 0.9201 | 0.0 | 0.0 | 0.1700 | 0.0 | | 0.3063 | 7.5 | 1500 | 0.6103 | 0.2915 | 0.3522 | 0.8394 | nan | 0.8513 | 0.9569 | 0.5581 | 0.7077 | 0.4143 | nan | 0.3906 | 0.7775 | 0.0 | 0.9367 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4860 | 0.0 | 0.0 | 0.8950 | 0.0 | 0.5014 | 0.3608 | 0.0 | nan | 0.0 | 0.3768 | 0.0 | 0.0 | 0.9326 | 0.8919 | 0.9663 | 0.0 | 0.0 | 0.2673 | 0.0 | nan | 0.7188 | 0.8446 | 0.4269 | 0.6124 | 0.3294 | nan | 0.3401 | 0.5628 | 0.0 | 0.7936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3101 | 0.0 | 0.0 | 0.7025 | 0.0 | 0.3740 | 0.3336 | 0.0 | nan | 0.0 | 0.2626 | 0.0 | 0.0 | 0.8565 | 0.7535 | 0.9210 | 0.0 | 0.0 | 0.1848 | 0.0 | | 0.4145 | 7.6 | 1520 | 0.6139 | 0.2870 | 0.3462 | 0.8375 | nan | 0.8695 | 0.9446 | 0.5545 | 0.6713 | 0.4503 | nan | 0.4578 | 0.7690 | 0.0 | 0.9225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4519 | 0.0 | 0.0 | 0.9195 | 0.0 | 0.4797 | 0.2639 | 0.0 | nan | 0.0 | 0.3073 | 0.0 | 0.0 | 0.9363 | 0.8959 | 0.9690 | 0.0 | 0.0 | 0.2163 | 0.0 | nan | 0.7244 | 0.8468 | 0.4270 | 0.5928 | 0.3406 | nan | 0.3668 | 0.5579 | 0.0 | 0.8011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3160 | 0.0 | 0.0 | 0.6902 | 0.0 | 0.3466 | 0.2489 | 0.0 | nan | 0.0 | 0.2337 | 0.0 | 0.0 | 0.8549 | 0.7506 | 0.9195 | 0.0 | 0.0 | 0.1673 | 0.0 | | 0.9153 | 7.7 | 1540 | 0.6055 | 0.2901 | 0.3539 | 0.8373 | nan | 0.8256 | 0.9482 | 0.5821 | 0.7362 | 0.4750 | nan | 0.4594 | 0.7786 | 0.0 | 0.9368 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4499 | 0.0 | 0.0 | 0.8786 | 0.0 | 0.5140 | 0.2710 | 0.0 | nan | 0.0 | 0.3504 | 0.0 | 0.0 | 0.9374 | 0.8967 | 0.9696 | 0.0 | 0.0 | 0.3158 | 0.0 | nan | 0.7159 | 0.8463 | 0.4322 | 0.6072 | 0.3435 | nan | 0.3800 | 0.5541 | 0.0 | 0.7938 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3016 | 0.0 | 0.0 | 0.7086 | 0.0 | 0.3710 | 0.2589 | 0.0 | nan | 0.0 | 0.2577 | 0.0 | 0.0 | 0.8505 | 0.7452 | 0.9191 | 0.0 | 0.0 | 0.1964 | 0.0 | | 0.3268 | 7.8 | 1560 | 0.6009 | 0.2911 | 0.3532 | 0.8407 | nan | 0.8675 | 0.9404 | 0.5684 | 0.7250 | 0.4900 | nan | 0.4666 | 0.7657 | 0.0 | 0.9205 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5209 | 0.0 | 0.0 | 0.9170 | 0.0 | 0.5335 | 0.2576 | 0.0 | nan | 0.0 | 0.3168 | 0.0 | 0.0 | 0.9390 | 0.8789 | 0.9671 | 0.0 | 0.0 | 0.2280 | 0.0 | nan | 0.7273 | 0.8501 | 0.4168 | 0.6399 | 0.3439 | nan | 0.3778 | 0.5709 | 0.0 | 0.8027 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3164 | 0.0 | 0.0 | 0.6985 | 0.0 | 0.3665 | 0.2401 | 0.0 | nan | 0.0 | 0.2423 | 0.0 | 0.0 | 0.8595 | 0.7642 | 0.9212 | 0.0 | 0.0 | 0.1759 | 0.0 | | 0.4042 | 7.9 | 1580 | 0.5907 | 0.2940 | 0.3564 | 0.8436 | nan | 0.8629 | 0.9405 | 0.5990 | 0.7337 | 0.4989 | nan | 0.4793 | 0.7556 | 0.0 | 0.9333 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4932 | 0.0 | 0.0 | 0.9074 | 0.0 | 0.5340 | 0.3461 | 0.0 | nan | 0.0 | 0.3155 | 0.0 | 0.0 | 0.9464 | 0.8863 | 0.9658 | 0.0 | 0.0 | 0.2071 | 0.0 | nan | 0.7331 | 0.8538 | 0.4141 | 0.6457 | 0.3494 | nan | 0.3874 | 0.5881 | 0.0 | 0.7962 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2864 | 0.0 | 0.0 | 0.7063 | 0.0 | 0.3829 | 0.3137 | 0.0 | nan | 0.0 | 0.2380 | 0.0 | 0.0 | 0.8578 | 0.7657 | 0.9210 | 0.0 | 0.0 | 0.1687 | 0.0 | | 0.4915 | 8.0 | 1600 | 0.5805 | 0.2976 | 0.3593 | 0.8458 | nan | 0.8618 | 0.9395 | 0.5801 | 0.7933 | 0.4558 | nan | 0.4925 | 0.7563 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4860 | 0.0 | 0.0 | 0.8979 | 0.0 | 0.5390 | 0.4165 | 0.0 | nan | 0.0 | 0.3220 | 0.0 | 0.0 | 0.9466 | 0.8853 | 0.9648 | 0.0 | 0.0 | 0.2215 | 0.0 | nan | 0.7311 | 0.8544 | 0.4294 | 0.6731 | 0.3421 | nan | 0.3934 | 0.6052 | 0.0 | 0.7928 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2803 | 0.0 | 0.0 | 0.7124 | 0.0 | 0.3900 | 0.3612 | 0.0 | nan | 0.0 | 0.2420 | 0.0 | 0.0 | 0.8564 | 0.7633 | 0.9208 | 0.0 | 0.0 | 0.1752 | 0.0 | | 0.4564 | 8.1 | 1620 | 0.5909 | 0.2975 | 0.3610 | 0.8440 | nan | 0.8619 | 0.9410 | 0.5701 | 0.7384 | 0.4921 | nan | 0.4827 | 0.7838 | 0.0 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5328 | 0.0 | 0.0 | 0.9000 | 0.0 | 0.4814 | 0.4299 | 0.0 | nan | 0.0 | 0.3525 | 0.0 | 0.0 | 0.9441 | 0.9024 | 0.9651 | 0.0 | 0.0 | 0.2475 | 0.0 | nan | 0.7357 | 0.8515 | 0.4326 | 0.6508 | 0.3435 | nan | 0.3904 | 0.5702 | 0.0 | 0.8007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2983 | 0.0 | 0.0 | 0.7060 | 0.0 | 0.3913 | 0.3839 | 0.0 | nan | 0.0 | 0.2573 | 0.0 | 0.0 | 0.8514 | 0.7541 | 0.9211 | 0.0 | 0.0 | 0.1825 | 0.0 | | 0.4293 | 8.2 | 1640 | 0.6016 | 0.2936 | 0.3568 | 0.8418 | nan | 0.8488 | 0.9535 | 0.5645 | 0.7347 | 0.4343 | nan | 0.4671 | 0.7780 | 0.0 | 0.9347 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5543 | 0.0 | 0.0 | 0.9067 | 0.0 | 0.4645 | 0.3615 | 0.0 | nan | 0.0 | 0.3499 | 0.0 | 0.0 | 0.9274 | 0.9089 | 0.9687 | 0.0 | 0.0 | 0.2600 | 0.0 | nan | 0.7298 | 0.8479 | 0.4386 | 0.6442 | 0.3341 | nan | 0.3834 | 0.5715 | 0.0 | 0.7976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2911 | 0.0 | 0.0 | 0.6990 | 0.0 | 0.3703 | 0.3328 | 0.0 | nan | 0.0 | 0.2537 | 0.0 | 0.0 | 0.8524 | 0.7378 | 0.9213 | 0.0 | 0.0 | 0.1892 | 0.0 | | 0.2416 | 8.3 | 1660 | 0.5930 | 0.2960 | 0.3567 | 0.8451 | nan | 0.8627 | 0.9523 | 0.5585 | 0.7374 | 0.4508 | nan | 0.4778 | 0.7690 | 0.0 | 0.9195 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5584 | 0.0 | 0.0 | 0.8988 | 0.0 | 0.4868 | 0.3511 | 0.0 | nan | 0.0 | 0.3384 | 0.0 | 0.0 | 0.9519 | 0.8790 | 0.9689 | 0.0 | 0.0 | 0.2518 | 0.0 | nan | 0.7371 | 0.8516 | 0.4397 | 0.6510 | 0.3411 | nan | 0.3888 | 0.5760 | 0.0 | 0.8089 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3068 | 0.0 | 0.0 | 0.7035 | 0.0 | 0.3740 | 0.3219 | 0.0 | nan | 0.0 | 0.2455 | 0.0 | 0.0 | 0.8547 | 0.7661 | 0.9207 | 0.0 | 0.0 | 0.1857 | 0.0 | | 0.474 | 8.4 | 1680 | 0.5887 | 0.2968 | 0.3623 | 0.8446 | nan | 0.8520 | 0.9483 | 0.5958 | 0.7393 | 0.4958 | nan | 0.4858 | 0.7934 | 0.0 | 0.9286 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5722 | 0.0 | 0.0 | 0.8975 | 0.0 | 0.5263 | 0.3477 | 0.0 | nan | 0.0 | 0.3560 | 0.0 | 0.0 | 0.9466 | 0.8638 | 0.9745 | 0.0 | 0.0 | 0.2685 | 0.0 | nan | 0.7394 | 0.8504 | 0.4320 | 0.6534 | 0.3434 | nan | 0.3952 | 0.5589 | 0.0 | 0.8037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3123 | 0.0 | 0.0 | 0.7126 | 0.0 | 0.3813 | 0.3195 | 0.0 | nan | 0.0 | 0.2577 | 0.0 | 0.0 | 0.8598 | 0.7705 | 0.9179 | 0.0 | 0.0 | 0.1891 | 0.0 | | 0.7499 | 8.5 | 1700 | 0.5912 | 0.2969 | 0.3622 | 0.8429 | nan | 0.8376 | 0.9494 | 0.5958 | 0.7615 | 0.4718 | nan | 0.4667 | 0.7842 | 0.0 | 0.9141 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5775 | 0.0 | 0.0 | 0.9007 | 0.0 | 0.5435 | 0.3685 | 0.0 | nan | 0.0 | 0.3537 | 0.0 | 0.0 | 0.9321 | 0.9052 | 0.9709 | 0.0 | 0.0 | 0.2569 | 0.0 | nan | 0.7311 | 0.8501 | 0.4380 | 0.6496 | 0.3393 | nan | 0.3869 | 0.5726 | 0.0 | 0.8112 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3299 | 0.0 | 0.0 | 0.7124 | 0.0 | 0.3851 | 0.3354 | 0.0 | nan | 0.0 | 0.2580 | 0.0 | 0.0 | 0.8530 | 0.7413 | 0.9208 | 0.0 | 0.0 | 0.1863 | 0.0 | | 0.3122 | 8.6 | 1720 | 0.5893 | 0.2950 | 0.3559 | 0.8435 | nan | 0.8625 | 0.9424 | 0.5925 | 0.7487 | 0.4691 | nan | 0.4895 | 0.7853 | 0.0 | 0.9243 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5009 | 0.0 | 0.0 | 0.9190 | 0.0 | 0.4775 | 0.3233 | 0.0 | nan | 0.0 | 0.3342 | 0.0 | 0.0 | 0.9466 | 0.8685 | 0.9681 | 0.0 | 0.0 | 0.2382 | 0.0 | nan | 0.7296 | 0.8538 | 0.4289 | 0.6504 | 0.3395 | nan | 0.3980 | 0.5691 | 0.0 | 0.8038 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3235 | 0.0 | 0.0 | 0.6949 | 0.0 | 0.3672 | 0.3033 | 0.0 | nan | 0.0 | 0.2485 | 0.0 | 0.0 | 0.8586 | 0.7664 | 0.9228 | 0.0 | 0.0 | 0.1826 | 0.0 | | 0.4643 | 8.7 | 1740 | 0.5790 | 0.2978 | 0.3629 | 0.8454 | nan | 0.8425 | 0.9357 | 0.5978 | 0.8333 | 0.4818 | nan | 0.5086 | 0.7809 | 0.0 | 0.9420 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5146 | 0.0 | 0.0 | 0.8946 | 0.0 | 0.5240 | 0.3464 | 0.0 | nan | 0.0 | 0.3436 | 0.0 | 0.0 | 0.9468 | 0.8926 | 0.9658 | 0.0 | 0.0 | 0.2632 | 0.0 | nan | 0.7326 | 0.8559 | 0.4279 | 0.6736 | 0.3453 | nan | 0.4089 | 0.5846 | 0.0 | 0.7928 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3042 | 0.0 | 0.0 | 0.7079 | 0.0 | 0.3830 | 0.3168 | 0.0 | nan | 0.0 | 0.2545 | 0.0 | 0.0 | 0.8582 | 0.7638 | 0.9225 | 0.0 | 0.0 | 0.1969 | 0.0 | | 0.4116 | 8.8 | 1760 | 0.5820 | 0.2977 | 0.3611 | 0.8465 | nan | 0.8533 | 0.9489 | 0.5951 | 0.7959 | 0.4441 | nan | 0.4867 | 0.7812 | 0.0 | 0.9328 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5563 | 0.0 | 0.0 | 0.9081 | 0.0 | 0.4951 | 0.3623 | 0.0 | nan | 0.0 | 0.3574 | 0.0 | 0.0 | 0.9371 | 0.8924 | 0.9654 | 0.0 | 0.0 | 0.2426 | 0.0 | nan | 0.7344 | 0.8561 | 0.4307 | 0.6806 | 0.3428 | nan | 0.3969 | 0.5798 | 0.0 | 0.8034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3147 | 0.0 | 0.0 | 0.7025 | 0.0 | 0.3742 | 0.3268 | 0.0 | nan | 0.0 | 0.2597 | 0.0 | 0.0 | 0.8580 | 0.7590 | 0.9230 | 0.0 | 0.0 | 0.1833 | 0.0 | | 0.3642 | 8.9 | 1780 | 0.5825 | 0.2992 | 0.3612 | 0.8464 | nan | 0.8459 | 0.9514 | 0.5853 | 0.7834 | 0.4573 | nan | 0.4848 | 0.7784 | 0.0 | 0.9270 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5579 | 0.0 | 0.0 | 0.9058 | 0.0 | 0.4744 | 0.3935 | 0.0 | nan | 0.0 | 0.3623 | 0.0 | 0.0 | 0.9419 | 0.8927 | 0.9659 | 0.0 | 0.0 | 0.2505 | 0.0 | nan | 0.7352 | 0.8545 | 0.4346 | 0.6736 | 0.3430 | nan | 0.3968 | 0.5806 | 0.0 | 0.8086 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3254 | 0.0 | 0.0 | 0.7030 | 0.0 | 0.3787 | 0.3601 | 0.0 | nan | 0.0 | 0.2615 | 0.0 | 0.0 | 0.8548 | 0.7584 | 0.9230 | 0.0 | 0.0 | 0.1835 | 0.0 | | 0.3355 | 9.0 | 1800 | 0.5826 | 0.2994 | 0.3636 | 0.8459 | nan | 0.8603 | 0.9413 | 0.5917 | 0.7590 | 0.4987 | nan | 0.5007 | 0.7845 | 0.0 | 0.9374 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5508 | 0.0 | 0.0 | 0.8996 | 0.0 | 0.4774 | 0.4227 | 0.0 | nan | 0.0 | 0.3510 | 0.0 | 0.0 | 0.9467 | 0.8837 | 0.9653 | 0.0 | 0.0 | 0.2642 | 0.0 | nan | 0.7357 | 0.8555 | 0.4223 | 0.6648 | 0.3500 | nan | 0.4009 | 0.5832 | 0.0 | 0.8002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3057 | 0.0 | 0.0 | 0.7050 | 0.0 | 0.3839 | 0.3773 | 0.0 | nan | 0.0 | 0.2590 | 0.0 | 0.0 | 0.8559 | 0.7678 | 0.9229 | 0.0 | 0.0 | 0.1900 | 0.0 | | 0.3793 | 9.1 | 1820 | 0.5882 | 0.2978 | 0.3613 | 0.8447 | nan | 0.8496 | 0.9467 | 0.5920 | 0.7591 | 0.4889 | nan | 0.5053 | 0.7759 | 0.0 | 0.9374 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5241 | 0.0 | 0.0 | 0.9129 | 0.0 | 0.4975 | 0.3962 | 0.0 | nan | 0.0 | 0.3467 | 0.0 | 0.0 | 0.9294 | 0.8948 | 0.9636 | 0.0 | 0.0 | 0.2414 | 0.0 | nan | 0.7405 | 0.8545 | 0.4316 | 0.6590 | 0.3488 | nan | 0.4008 | 0.5891 | 0.0 | 0.7990 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3005 | 0.0 | 0.0 | 0.7028 | 0.0 | 0.3844 | 0.3574 | 0.0 | nan | 0.0 | 0.2570 | 0.0 | 0.0 | 0.8520 | 0.7453 | 0.9236 | 0.0 | 0.0 | 0.1823 | 0.0 | | 0.2793 | 9.2 | 1840 | 0.5878 | 0.2974 | 0.3616 | 0.8446 | nan | 0.8513 | 0.9463 | 0.5973 | 0.7588 | 0.5000 | nan | 0.4986 | 0.7865 | 0.0 | 0.9334 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5269 | 0.0 | 0.0 | 0.9034 | 0.0 | 0.4835 | 0.3733 | 0.0 | nan | 0.0 | 0.3521 | 0.0 | 0.0 | 0.9342 | 0.8937 | 0.9654 | 0.0 | 0.0 | 0.2658 | 0.0 | nan | 0.7408 | 0.8537 | 0.4275 | 0.6630 | 0.3483 | nan | 0.4014 | 0.5750 | 0.0 | 0.8024 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3071 | 0.0 | 0.0 | 0.7035 | 0.0 | 0.3766 | 0.3445 | 0.0 | nan | 0.0 | 0.2565 | 0.0 | 0.0 | 0.8533 | 0.7524 | 0.9224 | 0.0 | 0.0 | 0.1887 | 0.0 | | 0.3716 | 9.3 | 1860 | 0.5903 | 0.2961 | 0.3573 | 0.8455 | nan | 0.8684 | 0.9481 | 0.5853 | 0.7523 | 0.4617 | nan | 0.4910 | 0.7798 | 0.0 | 0.9249 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5365 | 0.0 | 0.0 | 0.9166 | 0.0 | 0.4795 | 0.3358 | 0.0 | nan | 0.0 | 0.3293 | 0.0 | 0.0 | 0.9398 | 0.8781 | 0.9679 | 0.0 | 0.0 | 0.2395 | 0.0 | nan | 0.7364 | 0.8548 | 0.4289 | 0.6600 | 0.3465 | nan | 0.3954 | 0.5684 | 0.0 | 0.8093 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3222 | 0.0 | 0.0 | 0.6978 | 0.0 | 0.3660 | 0.3115 | 0.0 | nan | 0.0 | 0.2477 | 0.0 | 0.0 | 0.8597 | 0.7694 | 0.9220 | 0.0 | 0.0 | 0.1779 | 0.0 | | 0.3073 | 9.4 | 1880 | 0.5829 | 0.2971 | 0.3572 | 0.8471 | nan | 0.8683 | 0.9506 | 0.5674 | 0.7611 | 0.4517 | nan | 0.5018 | 0.7762 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5395 | 0.0 | 0.0 | 0.9162 | 0.0 | 0.4836 | 0.3563 | 0.0 | nan | 0.0 | 0.3141 | 0.0 | 0.0 | 0.9427 | 0.8766 | 0.9691 | 0.0 | 0.0 | 0.2265 | 0.0 | nan | 0.7407 | 0.8562 | 0.4418 | 0.6648 | 0.3464 | nan | 0.3993 | 0.5748 | 0.0 | 0.8072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3174 | 0.0 | 0.0 | 0.6974 | 0.0 | 0.3692 | 0.3253 | 0.0 | nan | 0.0 | 0.2385 | 0.0 | 0.0 | 0.8599 | 0.7719 | 0.9217 | 0.0 | 0.0 | 0.1733 | 0.0 | | 0.2746 | 9.5 | 1900 | 0.5797 | 0.2978 | 0.3595 | 0.8471 | nan | 0.8629 | 0.9477 | 0.5770 | 0.7784 | 0.4531 | nan | 0.4984 | 0.7777 | 0.0 | 0.9258 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5575 | 0.0 | 0.0 | 0.9103 | 0.0 | 0.4861 | 0.3582 | 0.0 | nan | 0.0 | 0.3287 | 0.0 | 0.0 | 0.9462 | 0.8830 | 0.9698 | 0.0 | 0.0 | 0.2437 | 0.0 | nan | 0.7410 | 0.8565 | 0.4411 | 0.6698 | 0.3466 | nan | 0.3982 | 0.5803 | 0.0 | 0.8092 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3144 | 0.0 | 0.0 | 0.7000 | 0.0 | 0.3715 | 0.3289 | 0.0 | nan | 0.0 | 0.2446 | 0.0 | 0.0 | 0.8575 | 0.7704 | 0.9218 | 0.0 | 0.0 | 0.1790 | 0.0 | | 0.3607 | 9.6 | 1920 | 0.5941 | 0.2970 | 0.3575 | 0.8456 | nan | 0.8571 | 0.9580 | 0.5710 | 0.7238 | 0.4492 | nan | 0.4884 | 0.7706 | 0.0 | 0.9305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5505 | 0.0 | 0.0 | 0.9117 | 0.0 | 0.4975 | 0.3714 | 0.0 | nan | 0.0 | 0.3271 | 0.0 | 0.0 | 0.9358 | 0.8878 | 0.9665 | 0.0 | 0.0 | 0.2429 | 0.0 | nan | 0.7387 | 0.8515 | 0.4434 | 0.6432 | 0.3427 | nan | 0.3884 | 0.5768 | 0.0 | 0.8068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3156 | 0.0 | 0.0 | 0.7027 | 0.0 | 0.3808 | 0.3404 | 0.0 | nan | 0.0 | 0.2459 | 0.0 | 0.0 | 0.8600 | 0.7649 | 0.9235 | 0.0 | 0.0 | 0.1797 | 0.0 | | 0.551 | 9.7 | 1940 | 0.5854 | 0.2979 | 0.3612 | 0.8452 | nan | 0.8724 | 0.9397 | 0.5686 | 0.7450 | 0.4752 | nan | 0.5028 | 0.7750 | 0.0 | 0.9353 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5518 | 0.0 | 0.0 | 0.9036 | 0.0 | 0.5075 | 0.3871 | 0.0 | nan | 0.0 | 0.3322 | 0.0 | 0.0 | 0.9442 | 0.8904 | 0.9713 | 0.0 | 0.0 | 0.2548 | 0.0 | nan | 0.7333 | 0.8548 | 0.4334 | 0.6499 | 0.3496 | nan | 0.3970 | 0.5874 | 0.0 | 0.8014 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3017 | 0.0 | 0.0 | 0.7046 | 0.0 | 0.3878 | 0.3525 | 0.0 | nan | 0.0 | 0.2472 | 0.0 | 0.0 | 0.8574 | 0.7654 | 0.9218 | 0.0 | 0.0 | 0.1864 | 0.0 | | 0.417 | 9.8 | 1960 | 0.5867 | 0.2993 | 0.3616 | 0.8462 | nan | 0.8696 | 0.9473 | 0.5746 | 0.7423 | 0.4598 | nan | 0.4883 | 0.7754 | 0.0 | 0.9295 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5606 | 0.0 | 0.0 | 0.9018 | 0.0 | 0.4964 | 0.4181 | 0.0 | nan | 0.0 | 0.3385 | 0.0 | 0.0 | 0.9421 | 0.8914 | 0.9685 | 0.0 | 0.0 | 0.2674 | 0.0 | nan | 0.7348 | 0.8543 | 0.4322 | 0.6508 | 0.3474 | nan | 0.3945 | 0.5816 | 0.0 | 0.8074 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3141 | 0.0 | 0.0 | 0.7062 | 0.0 | 0.3905 | 0.3765 | 0.0 | nan | 0.0 | 0.2517 | 0.0 | 0.0 | 0.8584 | 0.7636 | 0.9229 | 0.0 | 0.0 | 0.1896 | 0.0 | | 0.3938 | 9.9 | 1980 | 0.5864 | 0.2984 | 0.3604 | 0.8458 | nan | 0.8685 | 0.9452 | 0.5814 | 0.7414 | 0.4627 | nan | 0.4926 | 0.7724 | 0.0 | 0.9271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5601 | 0.0 | 0.0 | 0.9028 | 0.0 | 0.4875 | 0.4032 | 0.0 | nan | 0.0 | 0.3355 | 0.0 | 0.0 | 0.9493 | 0.8861 | 0.9661 | 0.0 | 0.0 | 0.2511 | 0.0 | nan | 0.7348 | 0.8543 | 0.4299 | 0.6507 | 0.3471 | nan | 0.3960 | 0.5810 | 0.0 | 0.8082 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3151 | 0.0 | 0.0 | 0.7047 | 0.0 | 0.3855 | 0.3669 | 0.0 | nan | 0.0 | 0.2480 | 0.0 | 0.0 | 0.8549 | 0.7663 | 0.9226 | 0.0 | 0.0 | 0.1842 | 0.0 | | 0.3931 | 10.0 | 2000 | 0.5881 | 0.2988 | 0.3613 | 0.8463 | nan | 0.8649 | 0.9502 | 0.5791 | 0.7393 | 0.4485 | nan | 0.4859 | 0.7767 | 0.0 | 0.9341 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5642 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.4959 | 0.4208 | 0.0 | nan | 0.0 | 0.3432 | 0.0 | 0.0 | 0.9466 | 0.8884 | 0.9698 | 0.0 | 0.0 | 0.2614 | 0.0 | nan | 0.7372 | 0.8540 | 0.4322 | 0.6499 | 0.3421 | nan | 0.3932 | 0.5795 | 0.0 | 0.8033 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3104 | 0.0 | 0.0 | 0.7075 | 0.0 | 0.3911 | 0.3791 | 0.0 | nan | 0.0 | 0.2524 | 0.0 | 0.0 | 0.8559 | 0.7644 | 0.9219 | 0.0 | 0.0 | 0.1876 | 0.0 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

samitizerxu/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5925 - eval_mean_iou: 0.2753 - eval_mean_accuracy: 0.3327 - eval_overall_accuracy: 0.8401 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8405 - eval_accuracy_flat-sidewalk: 0.9533 - eval_accuracy_flat-crosswalk: 0.6601 - eval_accuracy_flat-cyclinglane: 0.7992 - eval_accuracy_flat-parkingdriveway: 0.5578 - eval_accuracy_flat-railtrack: nan - eval_accuracy_flat-curb: 0.4836 - eval_accuracy_human-person: 0.6161 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.9299 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: nan - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.0003 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.8840 - eval_accuracy_construction-door: 0.0 - eval_accuracy_construction-wall: 0.3660 - eval_accuracy_construction-fenceguardrail: 0.3076 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: 0.0 - eval_accuracy_construction-stairs: 0.0 - eval_accuracy_object-pole: 0.2707 - eval_accuracy_object-trafficsign: 0.0 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9456 - eval_accuracy_nature-terrain: 0.8426 - eval_accuracy_sky: 0.9610 - eval_accuracy_void-ground: 0.0 - eval_accuracy_void-dynamic: 0.0 - eval_accuracy_void-static: 0.2296 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.7077 - eval_iou_flat-sidewalk: 0.8656 - eval_iou_flat-crosswalk: 0.5379 - eval_iou_flat-cyclinglane: 0.7062 - eval_iou_flat-parkingdriveway: 0.4285 - eval_iou_flat-railtrack: nan - eval_iou_flat-curb: 0.3675 - eval_iou_human-person: 0.3194 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.7878 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: nan - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.0003 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.6784 - eval_iou_construction-door: 0.0 - eval_iou_construction-wall: 0.2711 - eval_iou_construction-fenceguardrail: 0.2716 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: 0.0 - eval_iou_construction-stairs: 0.0 - eval_iou_object-pole: 0.2059 - eval_iou_object-trafficsign: 0.0 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.8358 - eval_iou_nature-terrain: 0.7375 - eval_iou_sky: 0.9064 - eval_iou_void-ground: 0.0 - eval_iou_void-dynamic: 0.0 - eval_iou_void-static: 0.1826 - eval_iou_void-unclear: 0.0 - eval_runtime: 11.1228 - eval_samples_per_second: 17.981 - eval_steps_per_second: 2.248 - epoch: 17.4 - step: 1740 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan-10k-steps

# segformer-finetuned-4ss1st3r_s3gs3m_24Jan-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan dataset. It achieves the following results on the evaluation set: - Loss: 0.1305 - Mean Iou: 0.6564 - Mean Accuracy: 0.8562 - Overall Accuracy: 0.9780 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9896 - Accuracy Fallo malla: 0.9270 - Accuracy Fallo adhesivo: 0.9478 - Accuracy Fallo burbuja: 0.5603 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9749 - Iou Fallo malla: 0.8458 - Iou Fallo adhesivo: 0.9324 - Iou Fallo burbuja: 0.5290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.3639 | 1.0 | 193 | 0.1583 | 0.6076 | 0.8441 | 0.9607 | nan | 0.9660 | 0.9617 | 0.9644 | 0.4844 | 0.0 | 0.9553 | 0.7294 | 0.9301 | 0.4231 | | 0.1148 | 2.0 | 386 | 0.0991 | 0.6189 | 0.8025 | 0.9754 | nan | 0.9912 | 0.9045 | 0.9417 | 0.3725 | 0.0 | 0.9723 | 0.8404 | 0.9283 | 0.3534 | | 0.0937 | 3.0 | 579 | 0.1414 | 0.5848 | 0.8155 | 0.9554 | nan | 0.9606 | 0.9630 | 0.9707 | 0.3675 | 0.0 | 0.9487 | 0.6791 | 0.9442 | 0.3519 | | 0.0827 | 4.0 | 772 | 0.1028 | 0.6390 | 0.8484 | 0.9747 | nan | 0.9831 | 0.9530 | 0.9640 | 0.4936 | 0.0 | 0.9714 | 0.8231 | 0.9388 | 0.4617 | | 0.0735 | 5.0 | 965 | 0.0948 | 0.6425 | 0.8423 | 0.9777 | nan | 0.9875 | 0.9487 | 0.9594 | 0.4737 | 0.0 | 0.9745 | 0.8484 | 0.9415 | 0.4479 | | 0.0716 | 6.0 | 1158 | 0.0968 | 0.6638 | 0.8622 | 0.9804 | nan | 0.9936 | 0.8987 | 0.9579 | 0.5985 | 0.0 | 0.9777 | 0.8654 | 0.9403 | 0.5355 | | 0.0692 | 7.0 | 1351 | 0.1123 | 0.6389 | 0.8535 | 0.9718 | nan | 0.9804 | 0.9425 | 0.9604 | 0.5307 | 0.0 | 0.9678 | 0.7878 | 0.9403 | 0.4984 | | 0.0718 | 8.0 | 1544 | 0.1097 | 0.6424 | 0.8668 | 0.9703 | nan | 0.9770 | 0.9520 | 0.9642 | 0.5738 | 0.0 | 0.9663 | 0.7792 | 0.9423 | 0.5243 | | 0.0613 | 9.0 | 1737 | 0.1212 | 0.6341 | 0.8625 | 0.9669 | nan | 0.9735 | 0.9412 | 0.9721 | 0.5634 | 0.0 | 0.9621 | 0.7447 | 0.9430 | 0.5208 | | 0.06 | 10.0 | 1930 | 0.0983 | 0.6724 | 0.8945 | 0.9793 | nan | 0.9875 | 0.9335 | 0.9682 | 0.6889 | 0.0 | 0.9765 | 0.8490 | 0.9461 | 0.5905 | | 0.0593 | 11.0 | 2123 | 0.1104 | 0.6577 | 0.8803 | 0.9743 | nan | 0.9830 | 0.9249 | 0.9670 | 0.6462 | 0.0 | 0.9709 | 0.8028 | 0.9419 | 0.5729 | | 0.056 | 12.0 | 2316 | 0.1029 | 0.6589 | 0.8829 | 0.9755 | nan | 0.9833 | 0.9349 | 0.9712 | 0.6420 | 0.0 | 0.9721 | 0.8170 | 0.9399 | 0.5655 | | 0.0547 | 13.0 | 2509 | 0.1037 | 0.6613 | 0.8944 | 0.9746 | nan | 0.9815 | 0.9406 | 0.9680 | 0.6877 | 0.0 | 0.9712 | 0.8089 | 0.9434 | 0.5832 | | 0.0538 | 14.0 | 2702 | 0.1342 | 0.6338 | 0.8750 | 0.9625 | nan | 0.9677 | 0.9470 | 0.9647 | 0.6204 | 0.0 | 0.9570 | 0.7080 | 0.9412 | 0.5627 | | 0.052 | 15.0 | 2895 | 0.0961 | 0.6525 | 0.8507 | 0.9787 | nan | 0.9894 | 0.9292 | 0.9656 | 0.5187 | 0.0 | 0.9758 | 0.8514 | 0.9439 | 0.4915 | | 0.0489 | 16.0 | 3088 | 0.1093 | 0.6464 | 0.8626 | 0.9725 | nan | 0.9812 | 0.9345 | 0.9639 | 0.5708 | 0.0 | 0.9688 | 0.7900 | 0.9440 | 0.5290 | | 0.0478 | 17.0 | 3281 | 0.1053 | 0.6503 | 0.8574 | 0.9760 | nan | 0.9858 | 0.9300 | 0.9673 | 0.5465 | 0.0 | 0.9726 | 0.8239 | 0.9411 | 0.5138 | | 0.048 | 18.0 | 3474 | 0.1314 | 0.6416 | 0.8884 | 0.9644 | nan | 0.9691 | 0.9517 | 0.9642 | 0.6688 | 0.0 | 0.9591 | 0.7232 | 0.9415 | 0.5842 | | 0.0474 | 19.0 | 3667 | 0.1197 | 0.6473 | 0.8559 | 0.9743 | nan | 0.9842 | 0.9344 | 0.9557 | 0.5493 | 0.0 | 0.9707 | 0.8067 | 0.9394 | 0.5196 | | 0.0456 | 20.0 | 3860 | 0.1149 | 0.6587 | 0.8578 | 0.9788 | nan | 0.9905 | 0.9241 | 0.9503 | 0.5665 | 0.0 | 0.9759 | 0.8513 | 0.9344 | 0.5321 | | 0.044 | 21.0 | 4053 | 0.1183 | 0.6574 | 0.8612 | 0.9774 | nan | 0.9885 | 0.9280 | 0.9487 | 0.5794 | 0.0 | 0.9743 | 0.8367 | 0.9345 | 0.5413 | | 0.0431 | 22.0 | 4246 | 0.1326 | 0.6425 | 0.8599 | 0.9711 | nan | 0.9795 | 0.9405 | 0.9595 | 0.5601 | 0.0 | 0.9670 | 0.7783 | 0.9384 | 0.5291 | | 0.0446 | 23.0 | 4439 | 0.1253 | 0.6535 | 0.8678 | 0.9743 | nan | 0.9833 | 0.9309 | 0.9635 | 0.5933 | 0.0 | 0.9706 | 0.8007 | 0.9427 | 0.5535 | | 0.0427 | 24.0 | 4632 | 0.1075 | 0.6568 | 0.8602 | 0.9771 | nan | 0.9882 | 0.9229 | 0.9543 | 0.5755 | 0.0 | 0.9739 | 0.8342 | 0.9379 | 0.5379 | | 0.0417 | 25.0 | 4825 | 0.1250 | 0.6443 | 0.8559 | 0.9723 | nan | 0.9820 | 0.9337 | 0.9542 | 0.5539 | 0.0 | 0.9684 | 0.7904 | 0.9375 | 0.5250 | | 0.0402 | 26.0 | 5018 | 0.1206 | 0.6518 | 0.8497 | 0.9775 | nan | 0.9892 | 0.9236 | 0.9536 | 0.5324 | 0.0 | 0.9744 | 0.8373 | 0.9383 | 0.5089 | | 0.0403 | 27.0 | 5211 | 0.1164 | 0.6565 | 0.8688 | 0.9755 | nan | 0.9848 | 0.9382 | 0.9531 | 0.5991 | 0.0 | 0.9723 | 0.8183 | 0.9378 | 0.5540 | | 0.0405 | 28.0 | 5404 | 0.1091 | 0.6586 | 0.8505 | 0.9799 | nan | 0.9926 | 0.9177 | 0.9530 | 0.5389 | 0.0 | 0.9773 | 0.8650 | 0.9381 | 0.5128 | | 0.0384 | 29.0 | 5597 | 0.1304 | 0.6504 | 0.8470 | 0.9781 | nan | 0.9893 | 0.9365 | 0.9508 | 0.5112 | 0.0 | 0.9751 | 0.8477 | 0.9365 | 0.4926 | | 0.0374 | 30.0 | 5790 | 0.1095 | 0.6585 | 0.8605 | 0.9783 | nan | 0.9891 | 0.9323 | 0.9507 | 0.5698 | 0.0 | 0.9754 | 0.8469 | 0.9358 | 0.5345 | | 0.0378 | 31.0 | 5983 | 0.1245 | 0.6558 | 0.8553 | 0.9780 | nan | 0.9896 | 0.9237 | 0.9539 | 0.5540 | 0.0 | 0.9750 | 0.8435 | 0.9353 | 0.5254 | | 0.0367 | 32.0 | 6176 | 0.1288 | 0.6504 | 0.8637 | 0.9737 | nan | 0.9828 | 0.9386 | 0.9555 | 0.5778 | 0.0 | 0.9700 | 0.8016 | 0.9362 | 0.5443 | | 0.037 | 33.0 | 6369 | 0.1293 | 0.6565 | 0.8656 | 0.9760 | nan | 0.9862 | 0.9381 | 0.9443 | 0.5938 | 0.0 | 0.9726 | 0.8273 | 0.9314 | 0.5512 | | 0.0363 | 34.0 | 6562 | 0.1242 | 0.6594 | 0.8528 | 0.9800 | nan | 0.9926 | 0.9171 | 0.9529 | 0.5485 | 0.0 | 0.9773 | 0.8632 | 0.9378 | 0.5188 | | 0.0361 | 35.0 | 6755 | 0.1239 | 0.6653 | 0.8739 | 0.9781 | nan | 0.9886 | 0.9247 | 0.9557 | 0.6264 | 0.0 | 0.9752 | 0.8420 | 0.9374 | 0.5718 | | 0.0371 | 36.0 | 6948 | 0.1220 | 0.6626 | 0.8691 | 0.9782 | nan | 0.9887 | 0.9297 | 0.9530 | 0.6049 | 0.0 | 0.9751 | 0.8418 | 0.9375 | 0.5585 | | 0.034 | 37.0 | 7141 | 0.1694 | 0.6300 | 0.8685 | 0.9609 | nan | 0.9666 | 0.9453 | 0.9602 | 0.6020 | 0.0 | 0.9551 | 0.6981 | 0.9399 | 0.5567 | | 0.0358 | 38.0 | 7334 | 0.1251 | 0.6513 | 0.8534 | 0.9764 | nan | 0.9878 | 0.9270 | 0.9492 | 0.5497 | 0.0 | 0.9731 | 0.8290 | 0.9345 | 0.5198 | | 0.033 | 39.0 | 7527 | 0.1330 | 0.6542 | 0.8604 | 0.9764 | nan | 0.9868 | 0.9343 | 0.9503 | 0.5700 | 0.0 | 0.9731 | 0.8292 | 0.9351 | 0.5336 | | 0.0327 | 40.0 | 7720 | 0.1359 | 0.6490 | 0.8537 | 0.9750 | nan | 0.9862 | 0.9269 | 0.9483 | 0.5535 | 0.0 | 0.9716 | 0.8183 | 0.9330 | 0.5221 | | 0.0336 | 41.0 | 7913 | 0.1277 | 0.6588 | 0.8667 | 0.9766 | nan | 0.9874 | 0.9267 | 0.9489 | 0.6037 | 0.0 | 0.9734 | 0.8288 | 0.9341 | 0.5577 | | 0.0312 | 42.0 | 8106 | 0.1321 | 0.6568 | 0.8716 | 0.9749 | nan | 0.9844 | 0.9358 | 0.9500 | 0.6163 | 0.0 | 0.9714 | 0.8132 | 0.9344 | 0.5650 | | 0.0321 | 43.0 | 8299 | 0.1269 | 0.6533 | 0.8574 | 0.9763 | nan | 0.9874 | 0.9283 | 0.9490 | 0.5649 | 0.0 | 0.9730 | 0.8285 | 0.9335 | 0.5316 | | 0.0306 | 44.0 | 8492 | 0.1269 | 0.6583 | 0.8528 | 0.9792 | nan | 0.9918 | 0.9207 | 0.9467 | 0.5520 | 0.0 | 0.9764 | 0.8593 | 0.9324 | 0.5236 | | 0.0306 | 45.0 | 8685 | 0.1335 | 0.6503 | 0.8503 | 0.9765 | nan | 0.9883 | 0.9283 | 0.9439 | 0.5407 | 0.0 | 0.9733 | 0.8345 | 0.9295 | 0.5144 | | 0.0324 | 46.0 | 8878 | 0.1294 | 0.6538 | 0.8490 | 0.9784 | nan | 0.9908 | 0.9254 | 0.9441 | 0.5358 | 0.0 | 0.9754 | 0.8525 | 0.9303 | 0.5107 | | 0.0318 | 47.0 | 9071 | 0.1230 | 0.6564 | 0.8549 | 0.9782 | nan | 0.9900 | 0.9252 | 0.9486 | 0.5559 | 0.0 | 0.9752 | 0.8477 | 0.9335 | 0.5255 | | 0.0319 | 48.0 | 9264 | 0.1267 | 0.6524 | 0.8501 | 0.9776 | nan | 0.9895 | 0.9278 | 0.9464 | 0.5368 | 0.0 | 0.9745 | 0.8438 | 0.9322 | 0.5117 | | 0.0312 | 49.0 | 9457 | 0.1258 | 0.6568 | 0.8602 | 0.9774 | nan | 0.9884 | 0.9321 | 0.9482 | 0.5720 | 0.0 | 0.9743 | 0.8399 | 0.9327 | 0.5373 | | 0.0311 | 50.0 | 9650 | 0.1203 | 0.6589 | 0.8610 | 0.9779 | nan | 0.9894 | 0.9262 | 0.9471 | 0.5814 | 0.0 | 0.9749 | 0.8444 | 0.9319 | 0.5435 | | 0.0327 | 51.0 | 9843 | 0.1219 | 0.6575 | 0.8577 | 0.9780 | nan | 0.9897 | 0.9265 | 0.9457 | 0.5688 | 0.0 | 0.9750 | 0.8462 | 0.9314 | 0.5348 | | 0.031 | 51.81 | 10000 | 0.1305 | 0.6564 | 0.8562 | 0.9780 | nan | 0.9896 | 0.9270 | 0.9478 | 0.5603 | 0.0 | 0.9749 | 0.8458 | 0.9324 | 0.5290 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]

krnl/segformer-b5-finetuned-human-parsing

# segformer-b5-finetuned-human-parsing This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2292 - Mean Iou: 0.6258 - Mean Accuracy: 0.7547 - Overall Accuracy: 0.8256 - Accuracy Background: nan - Accuracy Hat: 0.8561 - Accuracy Hair: 0.8974 - Accuracy Sunglasses: 0.7540 - Accuracy Upper-clothes: 0.8553 - Accuracy Skirt: 0.7026 - Accuracy Pants: 0.8913 - Accuracy Dress: 0.7525 - Accuracy Belt: 0.4251 - Accuracy Left-shoe: 0.6014 - Accuracy Right-shoe: 0.6374 - Accuracy Face: 0.9094 - Accuracy Left-leg: 0.8452 - Accuracy Right-leg: 0.8343 - Accuracy Left-arm: 0.8506 - Accuracy Right-arm: 0.8287 - Accuracy Bag: 0.8232 - Accuracy Scarf: 0.3662 - Iou Background: 0.0 - Iou Hat: 0.7625 - Iou Hair: 0.8171 - Iou Sunglasses: 0.6400 - Iou Upper-clothes: 0.7700 - Iou Skirt: 0.6211 - Iou Pants: 0.7788 - Iou Dress: 0.5512 - Iou Belt: 0.3564 - Iou Left-shoe: 0.5032 - Iou Right-shoe: 0.5381 - Iou Face: 0.8294 - Iou Left-leg: 0.7412 - Iou Right-leg: 0.7591 - Iou Left-arm: 0.7579 - Iou Right-arm: 0.7705 - Iou Bag: 0.7729 - Iou Scarf: 0.2956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Sunglasses | Accuracy Upper-clothes | Accuracy Skirt | Accuracy Pants | Accuracy Dress | Accuracy Belt | Accuracy Left-shoe | Accuracy Right-shoe | Accuracy Face | Accuracy Left-leg | Accuracy Right-leg | Accuracy Left-arm | Accuracy Right-arm | Accuracy Bag | Accuracy Scarf | Iou Background | Iou Hat | Iou Hair | Iou Sunglasses | Iou Upper-clothes | Iou Skirt | Iou Pants | Iou Dress | Iou Belt | Iou Left-shoe | Iou Right-shoe | Iou Face | Iou Left-leg | Iou Right-leg | Iou Left-arm | Iou Right-arm | Iou Bag | Iou Scarf | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:| | 1.1597 | 0.04 | 20 | 1.5815 | 0.1179 | 0.1991 | 0.4296 | nan | 0.0060 | 0.6905 | 0.0 | 0.7657 | 0.0108 | 0.6431 | 0.2946 | 0.0 | 0.0288 | 0.0366 | 0.1480 | 0.0025 | 0.5692 | 0.0096 | 0.0259 | 0.1537 | 0.0 | 0.0 | 0.0051 | 0.4253 | 0.0 | 0.5199 | 0.0103 | 0.3388 | 0.1700 | 0.0 | 0.0258 | 0.0338 | 0.0895 | 0.0025 | 0.3162 | 0.0094 | 0.0253 | 0.1495 | 0.0 | | 0.6963 | 0.08 | 40 | 0.8073 | 0.1759 | 0.2719 | 0.4628 | nan | 0.0015 | 0.8699 | 0.0 | 0.4736 | 0.4932 | 0.5141 | 0.6775 | 0.0 | 0.0062 | 0.1038 | 0.5301 | 0.0916 | 0.5071 | 0.0092 | 0.0549 | 0.2889 | 0.0 | 0.0 | 0.0015 | 0.6169 | 0.0 | 0.4242 | 0.2202 | 0.3522 | 0.2251 | 0.0 | 0.0062 | 0.0904 | 0.4914 | 0.0852 | 0.3160 | 0.0092 | 0.0541 | 0.2731 | 0.0 | | 0.5786 | 0.12 | 60 | 0.6136 | 0.2538 | 0.3642 | 0.4679 | nan | 0.0180 | 0.8122 | 0.0 | 0.1998 | 0.0000 | 0.6621 | 0.8592 | 0.0 | 0.1440 | 0.2772 | 0.8381 | 0.4032 | 0.6068 | 0.4182 | 0.3097 | 0.6434 | 0.0 | 0.0 | 0.0179 | 0.6760 | 0.0 | 0.1951 | 0.0000 | 0.5471 | 0.2218 | 0.0 | 0.1147 | 0.2032 | 0.6403 | 0.3189 | 0.4204 | 0.3505 | 0.2947 | 0.5676 | 0.0 | | 0.324 | 0.16 | 80 | 0.4282 | 0.2893 | 0.4044 | 0.6041 | nan | 0.0147 | 0.7890 | 0.0 | 0.8222 | 0.7984 | 0.6646 | 0.1038 | 0.0 | 0.0896 | 0.3308 | 0.8277 | 0.4099 | 0.6839 | 0.2401 | 0.5474 | 0.5521 | 0.0 | 0.0 | 0.0147 | 0.6800 | 0.0 | 0.6159 | 0.3049 | 0.5913 | 0.0938 | 0.0 | 0.0802 | 0.2394 | 0.6598 | 0.3178 | 0.4504 | 0.2288 | 0.4189 | 0.5113 | 0.0 | | 0.297 | 0.2 | 100 | 0.4020 | 0.3034 | 0.4230 | 0.6332 | nan | 0.0048 | 0.8076 | 0.0080 | 0.9042 | 0.6567 | 0.8036 | 0.0317 | 0.0 | 0.0481 | 0.5298 | 0.7728 | 0.2589 | 0.7232 | 0.5941 | 0.3839 | 0.6643 | 0.0 | 0.0 | 0.0048 | 0.6708 | 0.0080 | 0.6300 | 0.3836 | 0.5929 | 0.0314 | 0.0 | 0.0441 | 0.3152 | 0.6726 | 0.2420 | 0.4745 | 0.4532 | 0.3631 | 0.5759 | 0.0 | | 0.2608 | 0.24 | 120 | 0.3538 | 0.3444 | 0.4554 | 0.6504 | nan | 0.2922 | 0.8078 | 0.0753 | 0.8472 | 0.0425 | 0.6961 | 0.6197 | 0.0 | 0.2550 | 0.3074 | 0.8020 | 0.5636 | 0.6895 | 0.3779 | 0.6930 | 0.6734 | 0.0 | 0.0 | 0.2757 | 0.6940 | 0.0747 | 0.6457 | 0.0419 | 0.6098 | 0.3611 | 0.0 | 0.1849 | 0.2412 | 0.7038 | 0.4513 | 0.5038 | 0.3439 | 0.4760 | 0.5915 | 0.0 | | 0.3306 | 0.28 | 140 | 0.3281 | 0.3562 | 0.4736 | 0.6560 | nan | 0.4111 | 0.8576 | 0.1953 | 0.8081 | 0.6916 | 0.7888 | 0.3489 | 0.0 | 0.0809 | 0.3612 | 0.8132 | 0.0622 | 0.7078 | 0.6328 | 0.5437 | 0.7482 | 0.0 | 0.0 | 0.3895 | 0.7227 | 0.1857 | 0.6777 | 0.3750 | 0.6015 | 0.2749 | 0.0 | 0.0740 | 0.2602 | 0.7070 | 0.0612 | 0.4348 | 0.5114 | 0.4966 | 0.6385 | 0.0 | | 0.364 | 0.32 | 160 | 0.3368 | 0.3689 | 0.4836 | 0.6531 | nan | 0.3898 | 0.8453 | 0.1743 | 0.9269 | 0.2493 | 0.7922 | 0.0842 | 0.0 | 0.4874 | 0.2384 | 0.8116 | 0.6226 | 0.5731 | 0.6049 | 0.6620 | 0.7597 | 0.0 | 0.0 | 0.3746 | 0.7246 | 0.1690 | 0.6015 | 0.1998 | 0.5942 | 0.0786 | 0.0 | 0.2682 | 0.1904 | 0.7015 | 0.4781 | 0.4781 | 0.5452 | 0.5804 | 0.6562 | 0.0 | | 0.635 | 0.36 | 180 | 0.3092 | 0.3699 | 0.4903 | 0.6319 | nan | 0.4996 | 0.8387 | 0.2136 | 0.6184 | 0.0129 | 0.7920 | 0.8199 | 0.0 | 0.1895 | 0.3028 | 0.8307 | 0.7258 | 0.3386 | 0.7480 | 0.6543 | 0.7511 | 0.0 | 0.0 | 0.4613 | 0.7126 | 0.2042 | 0.5589 | 0.0128 | 0.6658 | 0.3529 | 0.0 | 0.1622 | 0.2426 | 0.7363 | 0.4646 | 0.3144 | 0.5794 | 0.5575 | 0.6321 | 0.0 | | 0.1464 | 0.4 | 200 | 0.3306 | 0.3809 | 0.5041 | 0.6544 | nan | 0.6110 | 0.8337 | 0.2420 | 0.8913 | 0.8862 | 0.6492 | 0.0004 | 0.0 | 0.2888 | 0.2949 | 0.8514 | 0.4630 | 0.7751 | 0.7020 | 0.5429 | 0.5386 | 0.0 | 0.0 | 0.5329 | 0.7348 | 0.2331 | 0.6567 | 0.3661 | 0.5769 | 0.0004 | 0.0 | 0.2221 | 0.2333 | 0.7431 | 0.4133 | 0.5478 | 0.5718 | 0.5125 | 0.5107 | 0.0 | | 0.2257 | 0.44 | 220 | 0.2751 | 0.4089 | 0.5400 | 0.6752 | nan | 0.6851 | 0.8458 | 0.4204 | 0.7241 | 0.1085 | 0.7997 | 0.7657 | 0.0 | 0.2458 | 0.4039 | 0.8858 | 0.7863 | 0.3199 | 0.7405 | 0.6974 | 0.7508 | 0.0 | 0.0 | 0.5815 | 0.7437 | 0.3776 | 0.6458 | 0.1033 | 0.6526 | 0.3966 | 0.0 | 0.2027 | 0.3078 | 0.7438 | 0.4680 | 0.2966 | 0.6204 | 0.5942 | 0.6260 | 0.0 | | 0.3069 | 0.48 | 240 | 0.2614 | 0.4163 | 0.5499 | 0.6868 | nan | 0.6246 | 0.8571 | 0.3130 | 0.7765 | 0.8266 | 0.7786 | 0.3212 | 0.0 | 0.3560 | 0.3736 | 0.8579 | 0.1780 | 0.8761 | 0.7423 | 0.7693 | 0.6970 | 0.0 | 0.0 | 0.5597 | 0.7370 | 0.2931 | 0.6733 | 0.4032 | 0.6889 | 0.2487 | 0.0 | 0.2662 | 0.2901 | 0.7425 | 0.1724 | 0.4957 | 0.6373 | 0.6376 | 0.6470 | 0.0 | | 0.1454 | 0.52 | 260 | 0.2563 | 0.4316 | 0.5610 | 0.6965 | nan | 0.6707 | 0.8388 | 0.5572 | 0.7616 | 0.3854 | 0.7280 | 0.7114 | 0.0 | 0.1934 | 0.3621 | 0.8718 | 0.7860 | 0.6140 | 0.7403 | 0.5340 | 0.7820 | 0.0 | 0.0 | 0.5710 | 0.7446 | 0.4497 | 0.6637 | 0.3125 | 0.6624 | 0.4219 | 0.0 | 0.1731 | 0.2862 | 0.7295 | 0.5339 | 0.5054 | 0.5742 | 0.4967 | 0.6449 | 0.0 | | 0.1522 | 0.56 | 280 | 0.2521 | 0.4327 | 0.5567 | 0.7138 | nan | 0.5098 | 0.9135 | 0.3399 | 0.8898 | 0.5537 | 0.7508 | 0.2922 | 0.0 | 0.3367 | 0.2484 | 0.8388 | 0.7460 | 0.7191 | 0.7496 | 0.7996 | 0.7753 | 0.0 | 0.0 | 0.4902 | 0.7541 | 0.3196 | 0.6924 | 0.3853 | 0.6261 | 0.2512 | 0.0 | 0.2575 | 0.2171 | 0.7393 | 0.5563 | 0.5633 | 0.6403 | 0.6335 | 0.6621 | 0.0 | | 0.1872 | 0.6 | 300 | 0.2359 | 0.4557 | 0.5797 | 0.7247 | nan | 0.6901 | 0.8832 | 0.5498 | 0.8857 | 0.6636 | 0.7843 | 0.3983 | 0.0 | 0.4549 | 0.2292 | 0.8147 | 0.7126 | 0.6223 | 0.7467 | 0.7014 | 0.7185 | 0.0 | 0.0 | 0.5881 | 0.7556 | 0.4621 | 0.7131 | 0.4264 | 0.6506 | 0.3311 | 0.0 | 0.3025 | 0.1975 | 0.7350 | 0.5635 | 0.5513 | 0.6505 | 0.6318 | 0.6439 | 0.0 | | 0.2512 | 0.64 | 320 | 0.2601 | 0.4363 | 0.5599 | 0.6801 | nan | 0.6470 | 0.8482 | 0.3819 | 0.6317 | 0.2525 | 0.7437 | 0.8755 | 0.0 | 0.1737 | 0.5412 | 0.8907 | 0.5915 | 0.7943 | 0.7177 | 0.7437 | 0.6852 | 0.0 | 0.0 | 0.5580 | 0.7612 | 0.3645 | 0.5718 | 0.2362 | 0.6671 | 0.3836 | 0.0 | 0.1621 | 0.3641 | 0.7486 | 0.5132 | 0.5865 | 0.6472 | 0.6479 | 0.6409 | 0.0 | | 0.6521 | 0.68 | 340 | 0.2286 | 0.4716 | 0.6024 | 0.7351 | nan | 0.6559 | 0.8492 | 0.3976 | 0.7517 | 0.5818 | 0.7622 | 0.7422 | 0.0 | 0.5277 | 0.2673 | 0.9098 | 0.7514 | 0.6903 | 0.7853 | 0.7795 | 0.7896 | 0.0 | 0.0 | 0.5839 | 0.7531 | 0.3761 | 0.6829 | 0.4643 | 0.6722 | 0.4762 | 0.0 | 0.3375 | 0.2261 | 0.7551 | 0.5838 | 0.5730 | 0.6548 | 0.6501 | 0.6987 | 0.0 | | 0.1734 | 0.72 | 360 | 0.2511 | 0.4464 | 0.5731 | 0.7074 | nan | 0.6326 | 0.8339 | 0.5953 | 0.8987 | 0.8731 | 0.7575 | 0.1617 | 0.0 | 0.2314 | 0.5741 | 0.8497 | 0.6140 | 0.6973 | 0.5250 | 0.7576 | 0.7406 | 0.0 | 0.0 | 0.5777 | 0.7535 | 0.4840 | 0.6597 | 0.5208 | 0.6261 | 0.1507 | 0.0 | 0.2110 | 0.3920 | 0.7579 | 0.5436 | 0.5825 | 0.4959 | 0.6090 | 0.6702 | 0.0 | | 0.1596 | 0.76 | 380 | 0.2303 | 0.4702 | 0.5922 | 0.7351 | nan | 0.6336 | 0.8772 | 0.4196 | 0.8004 | 0.4307 | 0.7034 | 0.7554 | 0.0 | 0.2914 | 0.4563 | 0.8930 | 0.7517 | 0.7146 | 0.7649 | 0.7420 | 0.8336 | 0.0 | 0.0 | 0.5854 | 0.7635 | 0.3937 | 0.6838 | 0.3957 | 0.6295 | 0.4857 | 0.0 | 0.2497 | 0.3439 | 0.7526 | 0.6021 | 0.6055 | 0.6591 | 0.6473 | 0.6657 | 0.0 | | 0.1359 | 0.8 | 400 | 0.2332 | 0.4592 | 0.5773 | 0.7182 | nan | 0.6759 | 0.8564 | 0.5305 | 0.8221 | 0.4710 | 0.8463 | 0.5733 | 0.0 | 0.2630 | 0.5031 | 0.8314 | 0.7079 | 0.7295 | 0.7449 | 0.7233 | 0.5357 | 0.0 | 0.0 | 0.6046 | 0.7593 | 0.4670 | 0.7005 | 0.3678 | 0.5672 | 0.4327 | 0.0 | 0.2265 | 0.3566 | 0.7593 | 0.5975 | 0.6161 | 0.6456 | 0.6470 | 0.5178 | 0.0 | | 0.2014 | 0.84 | 420 | 0.2298 | 0.4709 | 0.5957 | 0.7268 | nan | 0.6207 | 0.8571 | 0.5146 | 0.7670 | 0.6367 | 0.6238 | 0.7682 | 0.0 | 0.2470 | 0.4863 | 0.8939 | 0.5487 | 0.8665 | 0.7735 | 0.7827 | 0.7405 | 0.0 | 0.0 | 0.5729 | 0.7737 | 0.4637 | 0.6774 | 0.4588 | 0.5840 | 0.4656 | 0.0 | 0.2219 | 0.3668 | 0.7764 | 0.5020 | 0.5959 | 0.6641 | 0.6698 | 0.6833 | 0.0 | | 0.137 | 0.88 | 440 | 0.2260 | 0.4824 | 0.6147 | 0.7401 | nan | 0.7197 | 0.8823 | 0.6023 | 0.8544 | 0.9048 | 0.7837 | 0.3094 | 0.0245 | 0.2877 | 0.4592 | 0.8905 | 0.6846 | 0.8472 | 0.7109 | 0.7647 | 0.7230 | 0.0 | 0.0 | 0.6089 | 0.7638 | 0.5071 | 0.7154 | 0.5084 | 0.6749 | 0.2801 | 0.0227 | 0.2486 | 0.3499 | 0.7679 | 0.6127 | 0.6436 | 0.6445 | 0.6729 | 0.6626 | 0.0 | | 0.2494 | 0.92 | 460 | 0.2275 | 0.4721 | 0.5997 | 0.7321 | nan | 0.6265 | 0.8452 | 0.6870 | 0.8116 | 0.4266 | 0.8250 | 0.7026 | 0.0498 | 0.5651 | 0.1855 | 0.8745 | 0.7293 | 0.6387 | 0.6783 | 0.8080 | 0.7416 | 0.0 | 0.0 | 0.5764 | 0.7653 | 0.5312 | 0.7021 | 0.3635 | 0.6488 | 0.4468 | 0.0475 | 0.3420 | 0.1695 | 0.7764 | 0.5981 | 0.5821 | 0.6149 | 0.6599 | 0.6740 | 0.0 | | 0.2788 | 0.96 | 480 | 0.2315 | 0.4670 | 0.5977 | 0.7292 | nan | 0.7105 | 0.8142 | 0.4169 | 0.8597 | 0.8573 | 0.7707 | 0.3171 | 0.0476 | 0.2514 | 0.4432 | 0.9214 | 0.7332 | 0.6989 | 0.8245 | 0.6729 | 0.7954 | 0.0255 | 0.0 | 0.6051 | 0.7556 | 0.3986 | 0.7114 | 0.4588 | 0.6423 | 0.2727 | 0.0438 | 0.2215 | 0.3468 | 0.7650 | 0.6030 | 0.6007 | 0.6432 | 0.6180 | 0.6945 | 0.0253 | | 0.1254 | 1.0 | 500 | 0.2176 | 0.4955 | 0.6287 | 0.7450 | nan | 0.7081 | 0.9094 | 0.4628 | 0.7437 | 0.5938 | 0.7126 | 0.7410 | 0.0560 | 0.3971 | 0.5239 | 0.8992 | 0.7446 | 0.8258 | 0.8028 | 0.7613 | 0.8041 | 0.0014 | 0.0 | 0.6242 | 0.7741 | 0.4367 | 0.6877 | 0.4856 | 0.6276 | 0.4823 | 0.0535 | 0.3136 | 0.3836 | 0.7687 | 0.6347 | 0.6510 | 0.6615 | 0.6529 | 0.6795 | 0.0014 | | 0.2625 | 1.04 | 520 | 0.2270 | 0.5000 | 0.6339 | 0.7411 | nan | 0.7844 | 0.8633 | 0.6442 | 0.8202 | 0.3913 | 0.6661 | 0.7393 | 0.0533 | 0.4684 | 0.5305 | 0.8686 | 0.6858 | 0.8024 | 0.7433 | 0.7895 | 0.8522 | 0.0729 | 0.0 | 0.6637 | 0.7685 | 0.5378 | 0.7150 | 0.3328 | 0.6169 | 0.4502 | 0.0468 | 0.3310 | 0.3732 | 0.7769 | 0.6214 | 0.6602 | 0.6614 | 0.6823 | 0.6913 | 0.0714 | | 0.2871 | 1.08 | 540 | 0.2072 | 0.5091 | 0.6337 | 0.7630 | nan | 0.7427 | 0.8718 | 0.5674 | 0.8080 | 0.6131 | 0.7855 | 0.7672 | 0.0584 | 0.3031 | 0.4535 | 0.8750 | 0.6763 | 0.8457 | 0.8027 | 0.7491 | 0.7710 | 0.0820 | 0.0 | 0.6471 | 0.7706 | 0.5092 | 0.7250 | 0.4737 | 0.6887 | 0.5152 | 0.0507 | 0.2643 | 0.3623 | 0.7788 | 0.6085 | 0.6565 | 0.6659 | 0.6633 | 0.7025 | 0.0816 | | 0.1481 | 1.12 | 560 | 0.2250 | 0.4824 | 0.5946 | 0.7494 | nan | 0.6480 | 0.8561 | 0.5148 | 0.8637 | 0.5174 | 0.7904 | 0.6671 | 0.0029 | 0.3782 | 0.2824 | 0.8794 | 0.6807 | 0.7755 | 0.6985 | 0.7431 | 0.8058 | 0.0039 | 0.0 | 0.6099 | 0.7755 | 0.4755 | 0.7081 | 0.4454 | 0.6483 | 0.4567 | 0.0029 | 0.2864 | 0.2456 | 0.7798 | 0.6012 | 0.6408 | 0.6339 | 0.6715 | 0.6978 | 0.0039 | | 0.0995 | 1.16 | 580 | 0.2084 | 0.5218 | 0.6570 | 0.7698 | nan | 0.7706 | 0.8532 | 0.4978 | 0.7874 | 0.7761 | 0.8102 | 0.6761 | 0.0 | 0.4886 | 0.4884 | 0.9113 | 0.7646 | 0.8476 | 0.8129 | 0.7507 | 0.7485 | 0.1856 | 0.0 | 0.6194 | 0.7661 | 0.4641 | 0.7200 | 0.5843 | 0.6730 | 0.5042 | 0.0 | 0.3497 | 0.3711 | 0.7790 | 0.6542 | 0.6835 | 0.6823 | 0.6749 | 0.6959 | 0.1715 | | 0.2912 | 1.2 | 600 | 0.2166 | 0.5136 | 0.6304 | 0.7632 | nan | 0.6870 | 0.8862 | 0.4891 | 0.7752 | 0.6264 | 0.8143 | 0.8202 | 0.0227 | 0.2578 | 0.4830 | 0.8932 | 0.7564 | 0.7366 | 0.7746 | 0.7678 | 0.7558 | 0.1712 | 0.0 | 0.6243 | 0.7791 | 0.4579 | 0.6983 | 0.5530 | 0.6676 | 0.5248 | 0.0222 | 0.2333 | 0.3786 | 0.7820 | 0.6438 | 0.6651 | 0.6773 | 0.6749 | 0.7018 | 0.1610 | | 0.1874 | 1.24 | 620 | 0.2280 | 0.5053 | 0.6296 | 0.7525 | nan | 0.7070 | 0.8689 | 0.5348 | 0.8275 | 0.3452 | 0.8441 | 0.7748 | 0.0643 | 0.4207 | 0.4251 | 0.8840 | 0.7698 | 0.6921 | 0.7271 | 0.7322 | 0.7544 | 0.3318 | 0.0 | 0.6387 | 0.7766 | 0.4873 | 0.7210 | 0.3283 | 0.6163 | 0.5014 | 0.0600 | 0.3252 | 0.3434 | 0.7750 | 0.6349 | 0.6336 | 0.6531 | 0.6560 | 0.6995 | 0.2448 | | 0.1634 | 1.28 | 640 | 0.2052 | 0.5100 | 0.6350 | 0.7638 | nan | 0.7069 | 0.8648 | 0.6021 | 0.8461 | 0.6408 | 0.8499 | 0.6889 | 0.0 | 0.3772 | 0.5718 | 0.8730 | 0.7073 | 0.5765 | 0.7283 | 0.7600 | 0.7655 | 0.2362 | 0.0 | 0.6105 | 0.7777 | 0.5233 | 0.7372 | 0.5287 | 0.6565 | 0.5185 | 0.0 | 0.3063 | 0.4062 | 0.7743 | 0.5688 | 0.5413 | 0.6500 | 0.6721 | 0.6956 | 0.2133 | | 0.1894 | 1.32 | 660 | 0.2169 | 0.5104 | 0.6553 | 0.7601 | nan | 0.7721 | 0.8902 | 0.7412 | 0.8309 | 0.9266 | 0.6974 | 0.5353 | 0.0008 | 0.4742 | 0.4474 | 0.8442 | 0.8150 | 0.7319 | 0.7803 | 0.7767 | 0.7605 | 0.1160 | 0.0 | 0.6506 | 0.7883 | 0.5373 | 0.7116 | 0.5472 | 0.6493 | 0.4321 | 0.0008 | 0.3256 | 0.3422 | 0.7644 | 0.6199 | 0.6389 | 0.6849 | 0.6873 | 0.6919 | 0.1141 | | 0.0769 | 1.36 | 680 | 0.1993 | 0.5250 | 0.6596 | 0.7756 | nan | 0.8044 | 0.8515 | 0.6711 | 0.8456 | 0.6474 | 0.7909 | 0.7083 | 0.0630 | 0.3173 | 0.6517 | 0.9003 | 0.6833 | 0.8527 | 0.8065 | 0.8162 | 0.7165 | 0.0872 | 0.0 | 0.6318 | 0.7755 | 0.5553 | 0.7207 | 0.5836 | 0.6787 | 0.5292 | 0.0625 | 0.2650 | 0.4130 | 0.7788 | 0.6131 | 0.6713 | 0.7043 | 0.7024 | 0.6790 | 0.0866 | | 0.2145 | 1.4 | 700 | 0.2052 | 0.5114 | 0.6438 | 0.7630 | nan | 0.7518 | 0.8628 | 0.5961 | 0.8763 | 0.9097 | 0.7924 | 0.4449 | 0.0273 | 0.4497 | 0.3682 | 0.8965 | 0.8234 | 0.6068 | 0.8065 | 0.6396 | 0.8432 | 0.2497 | 0.0 | 0.6262 | 0.7776 | 0.5260 | 0.7240 | 0.6034 | 0.6991 | 0.4150 | 0.0270 | 0.3297 | 0.3073 | 0.7805 | 0.6198 | 0.5743 | 0.6511 | 0.6071 | 0.7187 | 0.2189 | | 0.2162 | 1.44 | 720 | 0.2290 | 0.5246 | 0.6727 | 0.7519 | nan | 0.8003 | 0.8895 | 0.7155 | 0.7305 | 0.4570 | 0.8400 | 0.8071 | 0.0599 | 0.2933 | 0.5509 | 0.8700 | 0.7052 | 0.8386 | 0.7501 | 0.8070 | 0.8038 | 0.5178 | 0.0 | 0.7009 | 0.7910 | 0.5717 | 0.6618 | 0.4195 | 0.6924 | 0.4677 | 0.0587 | 0.2536 | 0.4050 | 0.7823 | 0.6246 | 0.6658 | 0.6718 | 0.6930 | 0.7369 | 0.2463 | | 0.1751 | 1.48 | 740 | 0.2073 | 0.5376 | 0.6734 | 0.7847 | nan | 0.8054 | 0.8711 | 0.6506 | 0.8714 | 0.7615 | 0.7720 | 0.6263 | 0.1874 | 0.4293 | 0.4568 | 0.9023 | 0.8568 | 0.7712 | 0.7206 | 0.8132 | 0.8180 | 0.1342 | 0.0 | 0.7205 | 0.7976 | 0.5541 | 0.7385 | 0.5644 | 0.6886 | 0.4901 | 0.1510 | 0.3297 | 0.3632 | 0.7814 | 0.6510 | 0.6618 | 0.6457 | 0.6798 | 0.7289 | 0.1310 | | 0.1175 | 1.52 | 760 | 0.2123 | 0.5114 | 0.6336 | 0.7694 | nan | 0.6736 | 0.8370 | 0.6304 | 0.8724 | 0.7794 | 0.7886 | 0.6708 | 0.0890 | 0.2305 | 0.6798 | 0.9045 | 0.5193 | 0.7556 | 0.7443 | 0.7395 | 0.7734 | 0.0836 | 0.0 | 0.6314 | 0.7744 | 0.5497 | 0.7306 | 0.5991 | 0.6411 | 0.5151 | 0.0705 | 0.2169 | 0.4380 | 0.7806 | 0.4983 | 0.6112 | 0.6768 | 0.6739 | 0.7150 | 0.0824 | | 0.1317 | 1.56 | 780 | 0.2097 | 0.5035 | 0.6318 | 0.7713 | nan | 0.3686 | 0.9006 | 0.6208 | 0.8267 | 0.8135 | 0.7586 | 0.6929 | 0.0769 | 0.5944 | 0.2629 | 0.9171 | 0.8806 | 0.6178 | 0.7197 | 0.8212 | 0.7743 | 0.0936 | 0.0 | 0.3669 | 0.7756 | 0.5497 | 0.7150 | 0.6423 | 0.6759 | 0.5331 | 0.0657 | 0.3886 | 0.2448 | 0.7772 | 0.6177 | 0.5813 | 0.6439 | 0.6832 | 0.7088 | 0.0931 | | 0.5482 | 1.6 | 800 | 0.2511 | 0.5037 | 0.6255 | 0.7414 | nan | 0.7498 | 0.8591 | 0.6984 | 0.7940 | 0.4886 | 0.7867 | 0.8255 | 0.0677 | 0.3048 | 0.4688 | 0.8573 | 0.6227 | 0.7895 | 0.7323 | 0.7148 | 0.6241 | 0.2496 | 0.0 | 0.6732 | 0.7722 | 0.5691 | 0.6858 | 0.4315 | 0.6297 | 0.4643 | 0.0620 | 0.2595 | 0.3642 | 0.7790 | 0.5765 | 0.6519 | 0.6686 | 0.6574 | 0.6024 | 0.2200 | | 0.0895 | 1.64 | 820 | 0.1973 | 0.5191 | 0.6552 | 0.7673 | nan | 0.6921 | 0.8844 | 0.4919 | 0.8472 | 0.6608 | 0.7842 | 0.6546 | 0.2664 | 0.4594 | 0.4925 | 0.9224 | 0.8699 | 0.6010 | 0.8740 | 0.5805 | 0.8105 | 0.2468 | 0.0 | 0.6533 | 0.7801 | 0.4672 | 0.7328 | 0.5569 | 0.6945 | 0.5016 | 0.2105 | 0.3565 | 0.3858 | 0.7737 | 0.6031 | 0.5611 | 0.5950 | 0.5277 | 0.7232 | 0.2217 | | 0.1804 | 1.68 | 840 | 0.2026 | 0.5308 | 0.6584 | 0.7736 | nan | 0.7891 | 0.8563 | 0.6565 | 0.8528 | 0.6089 | 0.7999 | 0.7005 | 0.0741 | 0.2858 | 0.6423 | 0.9035 | 0.7926 | 0.7474 | 0.7500 | 0.7516 | 0.7824 | 0.1997 | 0.0 | 0.6848 | 0.7815 | 0.5627 | 0.7391 | 0.5112 | 0.6788 | 0.4969 | 0.0673 | 0.2622 | 0.4456 | 0.7909 | 0.6476 | 0.6569 | 0.6618 | 0.6640 | 0.7139 | 0.1888 | | 0.1271 | 1.72 | 860 | 0.2134 | 0.5227 | 0.6505 | 0.7693 | nan | 0.7656 | 0.8645 | 0.6724 | 0.9055 | 0.6357 | 0.7807 | 0.5336 | 0.0838 | 0.5199 | 0.4139 | 0.9021 | 0.8526 | 0.7135 | 0.7894 | 0.7737 | 0.7407 | 0.1103 | 0.0 | 0.6177 | 0.7821 | 0.5747 | 0.7212 | 0.5627 | 0.6421 | 0.4449 | 0.0762 | 0.3731 | 0.3486 | 0.7872 | 0.6535 | 0.6449 | 0.6892 | 0.6914 | 0.6913 | 0.1075 | | 0.1344 | 1.76 | 880 | 0.2099 | 0.5269 | 0.6527 | 0.7711 | nan | 0.7985 | 0.8741 | 0.6237 | 0.8356 | 0.5284 | 0.8054 | 0.7358 | 0.0469 | 0.3616 | 0.5220 | 0.9019 | 0.6880 | 0.8126 | 0.8358 | 0.7745 | 0.7948 | 0.1563 | 0.0 | 0.6922 | 0.7886 | 0.5563 | 0.7230 | 0.4772 | 0.6475 | 0.5068 | 0.0436 | 0.3041 | 0.3968 | 0.7794 | 0.6338 | 0.6822 | 0.6952 | 0.6896 | 0.7182 | 0.1498 | | 0.2751 | 1.8 | 900 | 0.2006 | 0.5334 | 0.6488 | 0.7806 | nan | 0.7400 | 0.8550 | 0.5263 | 0.8847 | 0.6187 | 0.8032 | 0.6862 | 0.0858 | 0.4811 | 0.4224 | 0.9162 | 0.7803 | 0.7360 | 0.7854 | 0.7527 | 0.7963 | 0.1590 | 0.0 | 0.6939 | 0.7836 | 0.4954 | 0.7498 | 0.5176 | 0.6805 | 0.5075 | 0.0725 | 0.3595 | 0.3539 | 0.7824 | 0.6646 | 0.6782 | 0.6886 | 0.6885 | 0.7303 | 0.1537 | | 0.2685 | 1.84 | 920 | 0.2153 | 0.5265 | 0.6600 | 0.7598 | nan | 0.7896 | 0.8871 | 0.4589 | 0.8001 | 0.3307 | 0.8338 | 0.7796 | 0.2192 | 0.4367 | 0.4581 | 0.9048 | 0.8316 | 0.7681 | 0.8377 | 0.7482 | 0.7620 | 0.3733 | 0.0 | 0.6760 | 0.7832 | 0.4428 | 0.7128 | 0.3263 | 0.6632 | 0.5039 | 0.1754 | 0.3545 | 0.3853 | 0.7758 | 0.6553 | 0.6665 | 0.6908 | 0.6811 | 0.6933 | 0.2913 | | 0.5729 | 1.88 | 940 | 0.2057 | 0.5343 | 0.6737 | 0.7641 | nan | 0.8351 | 0.8681 | 0.5588 | 0.7823 | 0.5378 | 0.8769 | 0.6844 | 0.2025 | 0.4204 | 0.5736 | 0.9000 | 0.6586 | 0.8633 | 0.7572 | 0.8501 | 0.8008 | 0.2837 | 0.0 | 0.6697 | 0.7834 | 0.5193 | 0.7093 | 0.4643 | 0.6734 | 0.4657 | 0.1703 | 0.3568 | 0.4435 | 0.7947 | 0.5896 | 0.6450 | 0.6871 | 0.7106 | 0.6983 | 0.2363 | | 0.0862 | 1.92 | 960 | 0.2208 | 0.5155 | 0.6363 | 0.7593 | nan | 0.6683 | 0.8670 | 0.6014 | 0.8336 | 0.9151 | 0.7537 | 0.6623 | 0.0948 | 0.5015 | 0.4279 | 0.8643 | 0.5706 | 0.5450 | 0.7743 | 0.7484 | 0.7914 | 0.1971 | 0.0 | 0.6179 | 0.7799 | 0.5443 | 0.7211 | 0.5925 | 0.5910 | 0.5189 | 0.0850 | 0.3944 | 0.3678 | 0.7979 | 0.5061 | 0.4893 | 0.6992 | 0.6875 | 0.7094 | 0.1772 | | 0.0793 | 1.96 | 980 | 0.2003 | 0.5467 | 0.6820 | 0.7890 | nan | 0.8868 | 0.8560 | 0.6296 | 0.8753 | 0.5087 | 0.8319 | 0.7202 | 0.2101 | 0.3960 | 0.5827 | 0.9140 | 0.6879 | 0.8781 | 0.7924 | 0.8308 | 0.8216 | 0.1713 | 0.0 | 0.7187 | 0.7865 | 0.5556 | 0.7705 | 0.4573 | 0.6863 | 0.5364 | 0.1786 | 0.3456 | 0.4445 | 0.7817 | 0.6192 | 0.6617 | 0.7112 | 0.7034 | 0.7258 | 0.1571 | | 0.0881 | 2.0 | 1000 | 0.2026 | 0.5430 | 0.6696 | 0.7758 | nan | 0.8055 | 0.9034 | 0.6463 | 0.8711 | 0.5281 | 0.7951 | 0.6777 | 0.2288 | 0.4737 | 0.5184 | 0.8716 | 0.7001 | 0.7681 | 0.7600 | 0.7842 | 0.8391 | 0.2115 | 0.0 | 0.7040 | 0.7865 | 0.5595 | 0.7562 | 0.4646 | 0.6923 | 0.4893 | 0.1846 | 0.3773 | 0.4039 | 0.7844 | 0.6285 | 0.6595 | 0.6914 | 0.6785 | 0.7171 | 0.1954 | | 0.164 | 2.04 | 1020 | 0.1894 | 0.5566 | 0.6935 | 0.7948 | nan | 0.8577 | 0.8451 | 0.6489 | 0.8534 | 0.6880 | 0.8749 | 0.7212 | 0.2222 | 0.3785 | 0.5106 | 0.8963 | 0.6640 | 0.8652 | 0.8502 | 0.7722 | 0.8177 | 0.3231 | 0.0 | 0.6855 | 0.7756 | 0.5673 | 0.7557 | 0.5883 | 0.7037 | 0.5489 | 0.1773 | 0.3318 | 0.4129 | 0.7889 | 0.6126 | 0.6769 | 0.7042 | 0.6960 | 0.7334 | 0.2598 | | 0.158 | 2.08 | 1040 | 0.2104 | 0.5418 | 0.6749 | 0.7757 | nan | 0.8476 | 0.8642 | 0.6307 | 0.8318 | 0.4601 | 0.8279 | 0.7790 | 0.1855 | 0.4350 | 0.4771 | 0.9067 | 0.7989 | 0.7718 | 0.7935 | 0.8241 | 0.7621 | 0.2772 | 0.0 | 0.6748 | 0.7851 | 0.5636 | 0.7291 | 0.4187 | 0.6812 | 0.5001 | 0.1576 | 0.3579 | 0.3982 | 0.7805 | 0.6649 | 0.6787 | 0.7120 | 0.7232 | 0.7037 | 0.2224 | | 0.0724 | 2.12 | 1060 | 0.1818 | 0.5813 | 0.7159 | 0.8143 | nan | 0.7836 | 0.8837 | 0.6726 | 0.8872 | 0.8685 | 0.7996 | 0.6764 | 0.3285 | 0.5280 | 0.5486 | 0.9054 | 0.7857 | 0.7742 | 0.8468 | 0.7965 | 0.8587 | 0.2266 | 0.0 | 0.7030 | 0.8008 | 0.5746 | 0.7651 | 0.7179 | 0.6962 | 0.5985 | 0.2603 | 0.3984 | 0.4226 | 0.7850 | 0.6685 | 0.6898 | 0.7073 | 0.7058 | 0.7649 | 0.2040 | | 0.0906 | 2.16 | 1080 | 0.2052 | 0.5553 | 0.6875 | 0.7897 | nan | 0.8313 | 0.8956 | 0.6559 | 0.8275 | 0.7173 | 0.8116 | 0.7495 | 0.0605 | 0.4663 | 0.5563 | 0.8829 | 0.7184 | 0.8017 | 0.8431 | 0.7774 | 0.7582 | 0.3341 | 0.0 | 0.7110 | 0.7958 | 0.5638 | 0.7280 | 0.6074 | 0.6818 | 0.5526 | 0.0575 | 0.3893 | 0.4334 | 0.7886 | 0.6394 | 0.6823 | 0.6993 | 0.6912 | 0.7073 | 0.2664 | | 0.119 | 2.2 | 1100 | 0.1916 | 0.5489 | 0.6809 | 0.7793 | nan | 0.8171 | 0.8442 | 0.7128 | 0.8296 | 0.7356 | 0.8236 | 0.6640 | 0.1341 | 0.5456 | 0.3609 | 0.8916 | 0.8015 | 0.8096 | 0.7653 | 0.7951 | 0.8119 | 0.2322 | 0.0 | 0.6733 | 0.7745 | 0.5803 | 0.7182 | 0.5975 | 0.6999 | 0.4996 | 0.1222 | 0.4036 | 0.3207 | 0.7971 | 0.6684 | 0.6861 | 0.6890 | 0.7078 | 0.7365 | 0.2050 | | 0.1114 | 2.24 | 1120 | 0.1905 | 0.5526 | 0.6769 | 0.7941 | nan | 0.7955 | 0.8759 | 0.6366 | 0.8947 | 0.7813 | 0.7845 | 0.6508 | 0.0994 | 0.3162 | 0.6568 | 0.9211 | 0.7643 | 0.7050 | 0.7629 | 0.8151 | 0.7977 | 0.2493 | 0.0 | 0.7142 | 0.7945 | 0.5553 | 0.7484 | 0.6236 | 0.6722 | 0.5467 | 0.0970 | 0.2894 | 0.4530 | 0.7868 | 0.6591 | 0.6571 | 0.6901 | 0.7167 | 0.7352 | 0.2069 | | 0.0757 | 2.28 | 1140 | 0.2216 | 0.5458 | 0.6760 | 0.7809 | nan | 0.7006 | 0.8731 | 0.6414 | 0.7872 | 0.6130 | 0.8828 | 0.8184 | 0.1886 | 0.5750 | 0.4180 | 0.9033 | 0.8910 | 0.6235 | 0.7955 | 0.7737 | 0.7658 | 0.2404 | 0.0 | 0.6697 | 0.7931 | 0.5672 | 0.7090 | 0.5666 | 0.7073 | 0.5092 | 0.1636 | 0.4196 | 0.3611 | 0.7910 | 0.6391 | 0.5870 | 0.7086 | 0.7117 | 0.7096 | 0.2109 | | 0.0551 | 2.32 | 1160 | 0.2090 | 0.5627 | 0.7010 | 0.7837 | nan | 0.8745 | 0.9012 | 0.6052 | 0.7677 | 0.6776 | 0.8074 | 0.8089 | 0.2178 | 0.4269 | 0.6106 | 0.8998 | 0.7038 | 0.8525 | 0.8516 | 0.7745 | 0.8010 | 0.3363 | 0.0 | 0.7143 | 0.7934 | 0.5484 | 0.6976 | 0.5691 | 0.7044 | 0.5204 | 0.1995 | 0.3665 | 0.4422 | 0.7764 | 0.6567 | 0.7059 | 0.7163 | 0.7137 | 0.7323 | 0.2721 | | 0.0936 | 2.36 | 1180 | 0.2165 | 0.5574 | 0.6867 | 0.7878 | nan | 0.7461 | 0.8914 | 0.5665 | 0.8649 | 0.6354 | 0.7483 | 0.7363 | 0.1586 | 0.5903 | 0.3687 | 0.9141 | 0.8447 | 0.7667 | 0.8507 | 0.7891 | 0.7414 | 0.4599 | 0.0 | 0.7022 | 0.7991 | 0.5241 | 0.7179 | 0.5839 | 0.6630 | 0.5364 | 0.1484 | 0.4303 | 0.3375 | 0.7782 | 0.6839 | 0.6968 | 0.7225 | 0.7219 | 0.7012 | 0.2853 | | 0.2249 | 2.4 | 1200 | 0.1985 | 0.5576 | 0.6820 | 0.7858 | nan | 0.7479 | 0.8644 | 0.6423 | 0.8948 | 0.5762 | 0.7743 | 0.6383 | 0.2730 | 0.4054 | 0.5883 | 0.9116 | 0.8846 | 0.7431 | 0.8103 | 0.7955 | 0.8559 | 0.1886 | 0.0 | 0.6950 | 0.7889 | 0.5656 | 0.7334 | 0.5212 | 0.6682 | 0.4872 | 0.2366 | 0.3478 | 0.4438 | 0.7828 | 0.6917 | 0.6892 | 0.7223 | 0.7322 | 0.7515 | 0.1796 | | 0.1143 | 2.44 | 1220 | 0.2132 | 0.5542 | 0.6838 | 0.7695 | nan | 0.7928 | 0.8792 | 0.6912 | 0.7460 | 0.6283 | 0.8408 | 0.7820 | 0.2448 | 0.4905 | 0.4794 | 0.8976 | 0.7917 | 0.8155 | 0.8065 | 0.8147 | 0.7629 | 0.1613 | 0.0 | 0.7049 | 0.7851 | 0.5858 | 0.6884 | 0.5133 | 0.7170 | 0.4776 | 0.2212 | 0.3929 | 0.3954 | 0.7848 | 0.6841 | 0.7051 | 0.7079 | 0.7274 | 0.7252 | 0.1587 | | 0.2771 | 2.48 | 1240 | 0.2258 | 0.5580 | 0.6880 | 0.7782 | nan | 0.8725 | 0.8834 | 0.7124 | 0.8679 | 0.4498 | 0.7580 | 0.7352 | 0.2542 | 0.4660 | 0.5536 | 0.8802 | 0.8435 | 0.7798 | 0.8040 | 0.7906 | 0.7847 | 0.2596 | 0.0 | 0.7281 | 0.7984 | 0.5992 | 0.7209 | 0.4263 | 0.6651 | 0.4859 | 0.2339 | 0.3893 | 0.4403 | 0.7874 | 0.6971 | 0.7040 | 0.7042 | 0.7258 | 0.7388 | 0.1998 | | 0.1988 | 2.52 | 1260 | 0.2129 | 0.5508 | 0.6813 | 0.7733 | nan | 0.8449 | 0.8465 | 0.6716 | 0.7666 | 0.7912 | 0.7598 | 0.7918 | 0.1262 | 0.4035 | 0.5711 | 0.9015 | 0.7580 | 0.8154 | 0.7535 | 0.8030 | 0.7797 | 0.1979 | 0.0 | 0.6909 | 0.7793 | 0.5838 | 0.6927 | 0.5898 | 0.6771 | 0.5041 | 0.1199 | 0.3521 | 0.4372 | 0.7785 | 0.6728 | 0.6947 | 0.6964 | 0.7248 | 0.7309 | 0.1903 | | 0.0679 | 2.56 | 1280 | 0.1937 | 0.5683 | 0.6963 | 0.7955 | nan | 0.7422 | 0.9044 | 0.6691 | 0.8294 | 0.6163 | 0.8310 | 0.7917 | 0.3324 | 0.6376 | 0.3930 | 0.9014 | 0.8285 | 0.7713 | 0.8048 | 0.8271 | 0.7844 | 0.1731 | 0.0 | 0.6872 | 0.7986 | 0.5810 | 0.7385 | 0.5481 | 0.7185 | 0.5452 | 0.2847 | 0.4488 | 0.3568 | 0.7849 | 0.6938 | 0.6977 | 0.7127 | 0.7261 | 0.7367 | 0.1710 | | 0.0815 | 2.6 | 1300 | 0.1993 | 0.5763 | 0.7156 | 0.7826 | nan | 0.7970 | 0.8772 | 0.7409 | 0.7964 | 0.5757 | 0.8128 | 0.8230 | 0.3708 | 0.4442 | 0.6099 | 0.8871 | 0.7767 | 0.7949 | 0.8140 | 0.8029 | 0.7646 | 0.4768 | 0.0 | 0.7016 | 0.7953 | 0.5979 | 0.7150 | 0.5049 | 0.7142 | 0.5156 | 0.3068 | 0.3891 | 0.4637 | 0.7945 | 0.6853 | 0.7042 | 0.7075 | 0.7254 | 0.7177 | 0.3345 | | 0.0705 | 2.64 | 1320 | 0.1949 | 0.5713 | 0.7021 | 0.7976 | nan | 0.7553 | 0.8927 | 0.5885 | 0.8530 | 0.6004 | 0.8552 | 0.7297 | 0.4790 | 0.6650 | 0.3765 | 0.9130 | 0.8525 | 0.7707 | 0.8690 | 0.7700 | 0.7959 | 0.1701 | 0.0 | 0.7081 | 0.8047 | 0.5361 | 0.7416 | 0.5569 | 0.7221 | 0.5327 | 0.3904 | 0.4541 | 0.3455 | 0.7975 | 0.6906 | 0.6955 | 0.6991 | 0.7043 | 0.7369 | 0.1673 | | 0.0785 | 2.68 | 1340 | 0.2041 | 0.5795 | 0.7097 | 0.7964 | nan | 0.8035 | 0.9029 | 0.6566 | 0.8325 | 0.5972 | 0.8503 | 0.7806 | 0.4603 | 0.4654 | 0.6184 | 0.8752 | 0.8377 | 0.6987 | 0.7804 | 0.8239 | 0.8514 | 0.2307 | 0.0 | 0.7323 | 0.8038 | 0.5765 | 0.7379 | 0.5414 | 0.7289 | 0.5339 | 0.3700 | 0.3978 | 0.4754 | 0.8028 | 0.6705 | 0.6539 | 0.7093 | 0.7269 | 0.7478 | 0.2222 | | 0.1076 | 2.72 | 1360 | 0.1929 | 0.5838 | 0.7190 | 0.8072 | nan | 0.6902 | 0.8919 | 0.6362 | 0.8790 | 0.7117 | 0.8261 | 0.6784 | 0.4480 | 0.6431 | 0.4944 | 0.9088 | 0.8265 | 0.7479 | 0.8457 | 0.8242 | 0.8532 | 0.3170 | 0.0 | 0.6566 | 0.7966 | 0.5662 | 0.7594 | 0.6488 | 0.6928 | 0.5625 | 0.3419 | 0.4482 | 0.4200 | 0.7937 | 0.6759 | 0.6761 | 0.7319 | 0.7342 | 0.7486 | 0.2551 | | 0.1436 | 2.76 | 1380 | 0.2300 | 0.5385 | 0.6686 | 0.7745 | nan | 0.7836 | 0.9007 | 0.6214 | 0.7997 | 0.6169 | 0.8014 | 0.8452 | 0.1419 | 0.3989 | 0.6709 | 0.8971 | 0.4475 | 0.9195 | 0.7890 | 0.8211 | 0.6318 | 0.2804 | 0.0 | 0.7015 | 0.8036 | 0.5603 | 0.7213 | 0.5128 | 0.7149 | 0.5244 | 0.1311 | 0.3619 | 0.4863 | 0.7927 | 0.4435 | 0.6090 | 0.7179 | 0.7295 | 0.6180 | 0.2642 | | 0.1067 | 2.8 | 1400 | 0.1957 | 0.5652 | 0.7049 | 0.7881 | nan | 0.8524 | 0.8639 | 0.7524 | 0.8659 | 0.6098 | 0.8053 | 0.6721 | 0.2178 | 0.4882 | 0.5218 | 0.8987 | 0.8355 | 0.7302 | 0.8198 | 0.8127 | 0.8579 | 0.3791 | 0.0 | 0.6970 | 0.7926 | 0.6030 | 0.7407 | 0.5254 | 0.7052 | 0.4947 | 0.1865 | 0.4082 | 0.4450 | 0.7945 | 0.6695 | 0.6767 | 0.7171 | 0.7172 | 0.7042 | 0.2960 | | 0.0719 | 2.84 | 1420 | 0.2357 | 0.5517 | 0.6760 | 0.7766 | nan | 0.8245 | 0.9166 | 0.6213 | 0.8075 | 0.5353 | 0.7805 | 0.8185 | 0.1425 | 0.4855 | 0.5586 | 0.8749 | 0.7612 | 0.7448 | 0.8053 | 0.8224 | 0.7441 | 0.2490 | 0.0 | 0.7198 | 0.8016 | 0.5629 | 0.7240 | 0.4794 | 0.6637 | 0.4917 | 0.1346 | 0.4047 | 0.4592 | 0.7837 | 0.6594 | 0.6719 | 0.7211 | 0.7227 | 0.6954 | 0.2352 | | 0.1557 | 2.88 | 1440 | 0.2600 | 0.5491 | 0.6861 | 0.7593 | nan | 0.8334 | 0.8869 | 0.6552 | 0.7548 | 0.4025 | 0.7557 | 0.8423 | 0.1417 | 0.5662 | 0.5345 | 0.9121 | 0.8045 | 0.7816 | 0.8586 | 0.7752 | 0.7765 | 0.3824 | 0.0 | 0.7396 | 0.7964 | 0.5758 | 0.6832 | 0.3855 | 0.6560 | 0.4511 | 0.1362 | 0.4422 | 0.4574 | 0.7852 | 0.6753 | 0.6876 | 0.7167 | 0.7123 | 0.7135 | 0.2702 | | 0.0667 | 2.92 | 1460 | 0.2077 | 0.5685 | 0.7011 | 0.7889 | nan | 0.8600 | 0.9046 | 0.7236 | 0.8549 | 0.4918 | 0.7826 | 0.7566 | 0.2867 | 0.5245 | 0.5654 | 0.8922 | 0.7801 | 0.8728 | 0.7829 | 0.8062 | 0.7907 | 0.2428 | 0.0 | 0.7300 | 0.7920 | 0.5978 | 0.7410 | 0.4591 | 0.6792 | 0.5235 | 0.2351 | 0.4322 | 0.4714 | 0.7826 | 0.6802 | 0.7142 | 0.7103 | 0.7247 | 0.7266 | 0.2322 | | 0.0599 | 2.96 | 1480 | 0.1890 | 0.5706 | 0.7078 | 0.7904 | nan | 0.8265 | 0.8570 | 0.7718 | 0.8348 | 0.7044 | 0.8015 | 0.7367 | 0.2153 | 0.3547 | 0.6748 | 0.8968 | 0.8277 | 0.7836 | 0.8193 | 0.7976 | 0.7714 | 0.3590 | 0.0 | 0.7146 | 0.7867 | 0.6005 | 0.7420 | 0.5740 | 0.7028 | 0.5212 | 0.1847 | 0.3318 | 0.4857 | 0.7959 | 0.6741 | 0.6980 | 0.7252 | 0.7153 | 0.7289 | 0.2884 | | 0.1253 | 3.0 | 1500 | 0.2008 | 0.5730 | 0.7009 | 0.7958 | nan | 0.7983 | 0.9092 | 0.6277 | 0.8403 | 0.5076 | 0.8389 | 0.8069 | 0.2118 | 0.5775 | 0.5092 | 0.8948 | 0.8165 | 0.7955 | 0.8217 | 0.7863 | 0.7737 | 0.3995 | 0.0 | 0.7192 | 0.8033 | 0.5639 | 0.7514 | 0.4760 | 0.7203 | 0.5251 | 0.2002 | 0.4440 | 0.4414 | 0.8016 | 0.6836 | 0.7215 | 0.7145 | 0.7223 | 0.7263 | 0.3001 | | 0.0902 | 3.04 | 1520 | 0.2002 | 0.5771 | 0.7138 | 0.7951 | nan | 0.8105 | 0.8611 | 0.6681 | 0.8355 | 0.6470 | 0.8293 | 0.7263 | 0.2985 | 0.5198 | 0.6254 | 0.9141 | 0.8738 | 0.7614 | 0.8100 | 0.8081 | 0.8212 | 0.3252 | 0.0 | 0.6917 | 0.7907 | 0.5827 | 0.7466 | 0.5515 | 0.7143 | 0.5243 | 0.2417 | 0.4221 | 0.4795 | 0.7980 | 0.6762 | 0.6961 | 0.7176 | 0.7377 | 0.7402 | 0.2770 | | 0.0793 | 3.08 | 1540 | 0.2097 | 0.5740 | 0.6960 | 0.7942 | nan | 0.7866 | 0.8973 | 0.5933 | 0.8581 | 0.5793 | 0.8686 | 0.7112 | 0.2830 | 0.5339 | 0.5274 | 0.9094 | 0.7455 | 0.8031 | 0.8050 | 0.7807 | 0.7956 | 0.3542 | 0.0 | 0.7338 | 0.8013 | 0.5453 | 0.7551 | 0.5228 | 0.7043 | 0.5216 | 0.2388 | 0.4344 | 0.4474 | 0.8099 | 0.6517 | 0.7028 | 0.7118 | 0.7277 | 0.7302 | 0.2923 | | 0.1151 | 3.12 | 1560 | 0.2098 | 0.5839 | 0.7069 | 0.8009 | nan | 0.7985 | 0.8944 | 0.6654 | 0.8527 | 0.5810 | 0.8246 | 0.7654 | 0.2375 | 0.5160 | 0.5706 | 0.9016 | 0.7949 | 0.8408 | 0.8249 | 0.8197 | 0.8106 | 0.3179 | 0.0 | 0.7424 | 0.8056 | 0.5829 | 0.7540 | 0.5176 | 0.7170 | 0.5205 | 0.2154 | 0.4395 | 0.4741 | 0.8142 | 0.6893 | 0.7262 | 0.7221 | 0.7486 | 0.7490 | 0.2920 | | 0.0521 | 3.16 | 1580 | 0.2137 | 0.5824 | 0.7149 | 0.7968 | nan | 0.8334 | 0.8909 | 0.7298 | 0.8236 | 0.5910 | 0.8047 | 0.7947 | 0.2071 | 0.6304 | 0.5506 | 0.8957 | 0.7867 | 0.8203 | 0.8369 | 0.8054 | 0.8381 | 0.3143 | 0.0 | 0.7455 | 0.8052 | 0.6026 | 0.7391 | 0.5304 | 0.7111 | 0.5198 | 0.1846 | 0.4810 | 0.4572 | 0.8158 | 0.6975 | 0.7236 | 0.7188 | 0.7349 | 0.7416 | 0.2748 | | 0.0974 | 3.2 | 1600 | 0.2145 | 0.5750 | 0.7010 | 0.7944 | nan | 0.8038 | 0.8812 | 0.5595 | 0.8303 | 0.5730 | 0.8460 | 0.7588 | 0.2021 | 0.5587 | 0.5178 | 0.9135 | 0.8271 | 0.8278 | 0.8214 | 0.8236 | 0.7844 | 0.3874 | 0.0 | 0.7228 | 0.7942 | 0.5250 | 0.7341 | 0.5138 | 0.7236 | 0.5115 | 0.1847 | 0.4478 | 0.4420 | 0.8113 | 0.6973 | 0.7192 | 0.7389 | 0.7447 | 0.7376 | 0.3004 | | 0.0535 | 3.24 | 1620 | 0.1986 | 0.5803 | 0.7191 | 0.8025 | nan | 0.8644 | 0.8970 | 0.7361 | 0.8454 | 0.7897 | 0.7807 | 0.7109 | 0.1309 | 0.5688 | 0.5623 | 0.8898 | 0.8323 | 0.7588 | 0.8089 | 0.8493 | 0.8091 | 0.3906 | 0.0 | 0.7053 | 0.7997 | 0.6082 | 0.7580 | 0.5811 | 0.6955 | 0.5477 | 0.1201 | 0.4516 | 0.4580 | 0.8204 | 0.6752 | 0.6925 | 0.7320 | 0.7468 | 0.7445 | 0.3084 | | 0.072 | 3.28 | 1640 | 0.1961 | 0.5782 | 0.7140 | 0.8020 | nan | 0.8503 | 0.8473 | 0.7632 | 0.8730 | 0.6862 | 0.8199 | 0.7197 | 0.1571 | 0.5890 | 0.5258 | 0.8817 | 0.8225 | 0.7920 | 0.8056 | 0.8447 | 0.7937 | 0.3655 | 0.0 | 0.7110 | 0.7839 | 0.6097 | 0.7675 | 0.5405 | 0.7156 | 0.5373 | 0.1418 | 0.4611 | 0.4439 | 0.8183 | 0.6797 | 0.7040 | 0.7167 | 0.7379 | 0.7420 | 0.2963 | | 0.0929 | 3.32 | 1660 | 0.2014 | 0.5753 | 0.7005 | 0.8059 | nan | 0.8069 | 0.9063 | 0.6184 | 0.8649 | 0.7200 | 0.8257 | 0.7258 | 0.1376 | 0.4722 | 0.6033 | 0.9044 | 0.8147 | 0.7698 | 0.8141 | 0.8313 | 0.7646 | 0.3288 | 0.0 | 0.7399 | 0.8079 | 0.5636 | 0.7677 | 0.5656 | 0.7075 | 0.5457 | 0.1288 | 0.4001 | 0.4691 | 0.8136 | 0.6855 | 0.6978 | 0.7241 | 0.7291 | 0.7303 | 0.2799 | | 0.0883 | 3.36 | 1680 | 0.2020 | 0.5681 | 0.6953 | 0.8032 | nan | 0.6691 | 0.8898 | 0.7268 | 0.8802 | 0.6571 | 0.8365 | 0.7110 | 0.2121 | 0.4769 | 0.6395 | 0.9106 | 0.7869 | 0.8422 | 0.8619 | 0.7270 | 0.7651 | 0.2267 | 0.0 | 0.6504 | 0.7967 | 0.6055 | 0.7711 | 0.5475 | 0.7188 | 0.5390 | 0.1922 | 0.4080 | 0.4835 | 0.8028 | 0.6882 | 0.7258 | 0.6887 | 0.6628 | 0.7315 | 0.2127 | | 0.0795 | 3.4 | 1700 | 0.2029 | 0.5795 | 0.7073 | 0.8019 | nan | 0.7438 | 0.8881 | 0.7527 | 0.8645 | 0.6712 | 0.8071 | 0.7493 | 0.2007 | 0.5927 | 0.5268 | 0.8968 | 0.8016 | 0.8051 | 0.8163 | 0.7442 | 0.8168 | 0.3461 | 0.0 | 0.7033 | 0.8013 | 0.6111 | 0.7618 | 0.5426 | 0.7030 | 0.5380 | 0.1790 | 0.4673 | 0.4503 | 0.8021 | 0.7002 | 0.7318 | 0.6986 | 0.6781 | 0.7602 | 0.3015 | | 0.0773 | 3.44 | 1720 | 0.1989 | 0.5812 | 0.7117 | 0.8050 | nan | 0.8647 | 0.8811 | 0.6571 | 0.8473 | 0.7655 | 0.8081 | 0.7508 | 0.2 | 0.5806 | 0.5543 | 0.8992 | 0.8711 | 0.7839 | 0.8109 | 0.7591 | 0.7706 | 0.2941 | 0.0 | 0.7410 | 0.8039 | 0.5819 | 0.7591 | 0.5928 | 0.7198 | 0.5427 | 0.1720 | 0.4644 | 0.4652 | 0.8028 | 0.7036 | 0.7189 | 0.7045 | 0.6864 | 0.7301 | 0.2720 | | 0.0627 | 3.48 | 1740 | 0.1922 | 0.5946 | 0.7326 | 0.8082 | nan | 0.8001 | 0.9026 | 0.7357 | 0.8662 | 0.7616 | 0.7701 | 0.7071 | 0.3307 | 0.5833 | 0.5425 | 0.8858 | 0.8232 | 0.8421 | 0.8466 | 0.7911 | 0.8314 | 0.4343 | 0.0 | 0.6934 | 0.8002 | 0.6045 | 0.7542 | 0.6357 | 0.6823 | 0.5510 | 0.2671 | 0.4622 | 0.4621 | 0.8064 | 0.7109 | 0.7429 | 0.7272 | 0.7122 | 0.7564 | 0.3336 | | 0.0901 | 3.52 | 1760 | 0.1963 | 0.5834 | 0.7287 | 0.8048 | nan | 0.7844 | 0.8874 | 0.7435 | 0.8876 | 0.7874 | 0.7760 | 0.6757 | 0.5528 | 0.4646 | 0.6739 | 0.9074 | 0.8113 | 0.7329 | 0.8419 | 0.7806 | 0.8012 | 0.2792 | 0.0 | 0.7058 | 0.8024 | 0.6052 | 0.7672 | 0.6304 | 0.6697 | 0.5552 | 0.2771 | 0.4063 | 0.4888 | 0.8120 | 0.6742 | 0.6810 | 0.7174 | 0.7099 | 0.7422 | 0.2573 | | 0.0945 | 3.56 | 1780 | 0.2033 | 0.5814 | 0.7128 | 0.8026 | nan | 0.7737 | 0.8804 | 0.7259 | 0.8444 | 0.7017 | 0.7875 | 0.7936 | 0.2431 | 0.5968 | 0.5278 | 0.8723 | 0.8753 | 0.7400 | 0.8484 | 0.7847 | 0.8230 | 0.2984 | 0.0 | 0.6802 | 0.7959 | 0.6064 | 0.7510 | 0.6027 | 0.6942 | 0.5622 | 0.2123 | 0.4806 | 0.4601 | 0.8139 | 0.6697 | 0.6731 | 0.7165 | 0.7166 | 0.7550 | 0.2749 | | 0.0621 | 3.6 | 1800 | 0.2150 | 0.5739 | 0.7002 | 0.7936 | nan | 0.7455 | 0.8958 | 0.6267 | 0.8290 | 0.4667 | 0.8545 | 0.7940 | 0.2158 | 0.5564 | 0.5418 | 0.9127 | 0.7723 | 0.8698 | 0.8493 | 0.8622 | 0.7568 | 0.3538 | 0.0 | 0.6987 | 0.8035 | 0.5693 | 0.7468 | 0.4405 | 0.7120 | 0.5078 | 0.1976 | 0.4466 | 0.4550 | 0.8211 | 0.6854 | 0.7335 | 0.7358 | 0.7513 | 0.7141 | 0.3113 | | 0.2598 | 3.64 | 1820 | 0.2038 | 0.5845 | 0.7150 | 0.7998 | nan | 0.7778 | 0.8901 | 0.7231 | 0.8247 | 0.6904 | 0.7825 | 0.8005 | 0.2124 | 0.6003 | 0.5248 | 0.8983 | 0.8224 | 0.8289 | 0.8407 | 0.8113 | 0.7780 | 0.3483 | 0.0 | 0.6945 | 0.8005 | 0.6083 | 0.7402 | 0.5578 | 0.7011 | 0.5394 | 0.1800 | 0.4688 | 0.4444 | 0.8234 | 0.7047 | 0.7372 | 0.7436 | 0.7471 | 0.7255 | 0.3038 | | 0.0579 | 3.68 | 1840 | 0.2159 | 0.5795 | 0.7123 | 0.7900 | nan | 0.8404 | 0.8889 | 0.6393 | 0.8015 | 0.7077 | 0.7805 | 0.7668 | 0.2314 | 0.5816 | 0.5545 | 0.9067 | 0.7842 | 0.8706 | 0.8527 | 0.7892 | 0.7389 | 0.3750 | 0.0 | 0.7195 | 0.8002 | 0.5789 | 0.7270 | 0.5200 | 0.6983 | 0.5056 | 0.1995 | 0.4739 | 0.4656 | 0.8146 | 0.6987 | 0.7419 | 0.7428 | 0.7333 | 0.7015 | 0.3093 | | 0.178 | 3.72 | 1860 | 0.2051 | 0.5807 | 0.7160 | 0.7960 | nan | 0.8871 | 0.8580 | 0.7123 | 0.8313 | 0.6923 | 0.8340 | 0.7196 | 0.2726 | 0.5479 | 0.5092 | 0.9005 | 0.7924 | 0.8467 | 0.8338 | 0.8060 | 0.8261 | 0.3020 | 0.0 | 0.7153 | 0.7931 | 0.6083 | 0.7428 | 0.5218 | 0.6966 | 0.5077 | 0.2297 | 0.4433 | 0.4389 | 0.8253 | 0.7020 | 0.7463 | 0.7383 | 0.7449 | 0.7425 | 0.2560 | | 0.0652 | 3.76 | 1880 | 0.1907 | 0.5881 | 0.7163 | 0.8079 | nan | 0.7918 | 0.9170 | 0.6292 | 0.8550 | 0.7548 | 0.8023 | 0.7000 | 0.3581 | 0.4405 | 0.6354 | 0.9116 | 0.7540 | 0.8829 | 0.8521 | 0.8078 | 0.8326 | 0.2512 | 0.0 | 0.7321 | 0.8032 | 0.5730 | 0.7583 | 0.6172 | 0.6924 | 0.5500 | 0.2969 | 0.3963 | 0.4849 | 0.8110 | 0.6878 | 0.7372 | 0.7356 | 0.7367 | 0.7462 | 0.2273 | | 0.0909 | 3.8 | 1900 | 0.2130 | 0.5773 | 0.7080 | 0.7958 | nan | 0.8645 | 0.8670 | 0.6606 | 0.8539 | 0.6938 | 0.7894 | 0.7331 | 0.3307 | 0.5893 | 0.5149 | 0.9035 | 0.7804 | 0.8136 | 0.8174 | 0.8344 | 0.7693 | 0.2204 | 0.0 | 0.7076 | 0.7923 | 0.5894 | 0.7520 | 0.5299 | 0.6777 | 0.5221 | 0.2841 | 0.4775 | 0.4505 | 0.8169 | 0.6857 | 0.7220 | 0.7303 | 0.7335 | 0.7171 | 0.2021 | | 0.0961 | 3.84 | 1920 | 0.2318 | 0.5699 | 0.7032 | 0.7850 | nan | 0.8585 | 0.8814 | 0.7262 | 0.8201 | 0.4649 | 0.7840 | 0.8384 | 0.2209 | 0.5943 | 0.5827 | 0.9001 | 0.8247 | 0.8078 | 0.8476 | 0.7852 | 0.7388 | 0.2795 | 0.0 | 0.7093 | 0.8043 | 0.6152 | 0.7333 | 0.4218 | 0.6994 | 0.5020 | 0.2123 | 0.4828 | 0.4840 | 0.8139 | 0.6860 | 0.7119 | 0.7325 | 0.7223 | 0.6961 | 0.2304 | | 0.0786 | 3.88 | 1940 | 0.2128 | 0.5738 | 0.7009 | 0.7950 | nan | 0.8275 | 0.8709 | 0.7395 | 0.8610 | 0.4952 | 0.8088 | 0.7840 | 0.1940 | 0.5403 | 0.5177 | 0.9014 | 0.8490 | 0.8128 | 0.8428 | 0.7945 | 0.8037 | 0.2714 | 0.0 | 0.7236 | 0.8014 | 0.6126 | 0.7543 | 0.4507 | 0.7059 | 0.5309 | 0.1864 | 0.4458 | 0.4492 | 0.8150 | 0.6890 | 0.7151 | 0.7352 | 0.7267 | 0.7343 | 0.2527 | | 0.0636 | 3.92 | 1960 | 0.2003 | 0.5833 | 0.7262 | 0.7998 | nan | 0.8600 | 0.9029 | 0.7461 | 0.8230 | 0.7772 | 0.7915 | 0.7836 | 0.2790 | 0.6805 | 0.5158 | 0.8845 | 0.9083 | 0.5324 | 0.8158 | 0.8110 | 0.8367 | 0.3969 | 0.0 | 0.7663 | 0.8098 | 0.6200 | 0.7428 | 0.6096 | 0.7033 | 0.5636 | 0.2286 | 0.5014 | 0.4624 | 0.8116 | 0.6112 | 0.5186 | 0.7507 | 0.7531 | 0.7431 | 0.3027 | | 0.0589 | 3.96 | 1980 | 0.1973 | 0.5911 | 0.7251 | 0.8140 | nan | 0.8461 | 0.8977 | 0.7605 | 0.8543 | 0.7629 | 0.8871 | 0.7317 | 0.2834 | 0.4014 | 0.6770 | 0.8954 | 0.6367 | 0.8749 | 0.8640 | 0.8419 | 0.7840 | 0.3271 | 0.0 | 0.7618 | 0.8090 | 0.6166 | 0.7608 | 0.6512 | 0.7371 | 0.5648 | 0.2335 | 0.3683 | 0.4960 | 0.8055 | 0.6120 | 0.6862 | 0.7559 | 0.7535 | 0.7329 | 0.2949 | | 0.0517 | 4.0 | 2000 | 0.1941 | 0.5891 | 0.7204 | 0.8118 | nan | 0.8225 | 0.8958 | 0.7789 | 0.8578 | 0.7435 | 0.8709 | 0.7380 | 0.2309 | 0.4791 | 0.6296 | 0.9095 | 0.6344 | 0.8537 | 0.8293 | 0.8547 | 0.7902 | 0.3280 | 0.0 | 0.7503 | 0.8103 | 0.6221 | 0.7556 | 0.6648 | 0.7287 | 0.5669 | 0.1981 | 0.4191 | 0.4888 | 0.8081 | 0.6006 | 0.6729 | 0.7420 | 0.7530 | 0.7366 | 0.2853 | | 0.0422 | 4.04 | 2020 | 0.2032 | 0.5913 | 0.7192 | 0.8093 | nan | 0.8231 | 0.9095 | 0.7218 | 0.8538 | 0.6817 | 0.8363 | 0.7633 | 0.2917 | 0.6304 | 0.5313 | 0.8938 | 0.8721 | 0.7443 | 0.8296 | 0.8005 | 0.7821 | 0.2613 | 0.0 | 0.7468 | 0.8084 | 0.6122 | 0.7520 | 0.6243 | 0.7129 | 0.5594 | 0.2464 | 0.4930 | 0.4699 | 0.8102 | 0.6812 | 0.6865 | 0.7350 | 0.7349 | 0.7280 | 0.2428 | | 0.0861 | 4.08 | 2040 | 0.1949 | 0.6051 | 0.7314 | 0.8172 | nan | 0.8581 | 0.8851 | 0.6496 | 0.8789 | 0.7340 | 0.8261 | 0.7216 | 0.3758 | 0.5946 | 0.6085 | 0.9150 | 0.7740 | 0.8651 | 0.8405 | 0.8456 | 0.7810 | 0.2805 | 0.0 | 0.7568 | 0.8076 | 0.5824 | 0.7632 | 0.6593 | 0.7058 | 0.5758 | 0.3100 | 0.4879 | 0.5035 | 0.8131 | 0.6936 | 0.7382 | 0.7537 | 0.7513 | 0.7339 | 0.2559 | | 0.1004 | 4.12 | 2060 | 0.1849 | 0.6016 | 0.7348 | 0.8130 | nan | 0.8772 | 0.8872 | 0.7109 | 0.8664 | 0.7335 | 0.8124 | 0.7212 | 0.3150 | 0.5658 | 0.5974 | 0.8978 | 0.8494 | 0.8242 | 0.8454 | 0.7727 | 0.8369 | 0.3779 | 0.0 | 0.7377 | 0.8023 | 0.6101 | 0.7663 | 0.6085 | 0.7008 | 0.5652 | 0.2773 | 0.4760 | 0.5018 | 0.8146 | 0.7021 | 0.7407 | 0.7424 | 0.7256 | 0.7571 | 0.2995 | | 0.0719 | 4.16 | 2080 | 0.1912 | 0.6032 | 0.7298 | 0.8135 | nan | 0.8346 | 0.8875 | 0.7111 | 0.8822 | 0.6972 | 0.8197 | 0.7397 | 0.3803 | 0.4940 | 0.6159 | 0.8967 | 0.7861 | 0.8263 | 0.8352 | 0.8367 | 0.7981 | 0.3657 | 0.0 | 0.7349 | 0.8040 | 0.6079 | 0.7695 | 0.6152 | 0.7048 | 0.5781 | 0.3232 | 0.4266 | 0.4933 | 0.8114 | 0.7068 | 0.7369 | 0.7475 | 0.7395 | 0.7458 | 0.3123 | | 0.224 | 4.2 | 2100 | 0.1948 | 0.6021 | 0.7505 | 0.8035 | nan | 0.8737 | 0.8728 | 0.7274 | 0.8104 | 0.6621 | 0.8323 | 0.7839 | 0.3911 | 0.5970 | 0.5556 | 0.9130 | 0.8385 | 0.8260 | 0.8199 | 0.8032 | 0.8605 | 0.5910 | 0.0 | 0.7477 | 0.7968 | 0.6154 | 0.7411 | 0.5771 | 0.7240 | 0.5406 | 0.3181 | 0.4838 | 0.4865 | 0.8125 | 0.7163 | 0.7370 | 0.7422 | 0.7408 | 0.7673 | 0.2905 | | 0.0466 | 4.24 | 2120 | 0.2434 | 0.5946 | 0.7268 | 0.7963 | nan | 0.8764 | 0.9095 | 0.6823 | 0.8218 | 0.4696 | 0.7933 | 0.8337 | 0.3116 | 0.5430 | 0.6394 | 0.8914 | 0.8331 | 0.8339 | 0.8381 | 0.8504 | 0.7937 | 0.4342 | 0.0 | 0.7687 | 0.8141 | 0.6015 | 0.7469 | 0.4352 | 0.7082 | 0.4933 | 0.2877 | 0.4605 | 0.5188 | 0.8125 | 0.7242 | 0.7473 | 0.7572 | 0.7617 | 0.7461 | 0.3184 | | 0.0539 | 4.28 | 2140 | 0.1896 | 0.6050 | 0.7340 | 0.8169 | nan | 0.8388 | 0.9077 | 0.6648 | 0.8533 | 0.6652 | 0.8309 | 0.7608 | 0.3731 | 0.5600 | 0.6177 | 0.9098 | 0.8182 | 0.8691 | 0.8359 | 0.8473 | 0.8432 | 0.2830 | 0.0 | 0.7588 | 0.8153 | 0.5957 | 0.7674 | 0.5456 | 0.7155 | 0.5559 | 0.2860 | 0.4728 | 0.5089 | 0.8094 | 0.7313 | 0.7640 | 0.7594 | 0.7571 | 0.7767 | 0.2705 | | 0.059 | 4.32 | 2160 | 0.1847 | 0.6109 | 0.7484 | 0.8196 | nan | 0.8403 | 0.8857 | 0.7280 | 0.8728 | 0.8208 | 0.8416 | 0.6962 | 0.5104 | 0.5866 | 0.5678 | 0.9112 | 0.8835 | 0.7349 | 0.8507 | 0.8213 | 0.8171 | 0.3544 | 0.0 | 0.7478 | 0.8066 | 0.6136 | 0.7724 | 0.6560 | 0.7165 | 0.5754 | 0.3698 | 0.4771 | 0.4890 | 0.8076 | 0.6971 | 0.6900 | 0.7560 | 0.7475 | 0.7570 | 0.3171 | | 0.1197 | 4.36 | 2180 | 0.1876 | 0.6047 | 0.7393 | 0.8149 | nan | 0.8044 | 0.8933 | 0.7389 | 0.8448 | 0.6778 | 0.8412 | 0.7902 | 0.3160 | 0.5677 | 0.6155 | 0.9092 | 0.8380 | 0.8052 | 0.8398 | 0.8433 | 0.8004 | 0.4429 | 0.0 | 0.7342 | 0.8048 | 0.6188 | 0.7600 | 0.6143 | 0.7251 | 0.5925 | 0.2804 | 0.4754 | 0.5111 | 0.7982 | 0.7126 | 0.7249 | 0.7478 | 0.7272 | 0.7579 | 0.2998 | | 0.0465 | 4.4 | 2200 | 0.1911 | 0.6054 | 0.7314 | 0.8183 | nan | 0.8259 | 0.8737 | 0.7163 | 0.8957 | 0.6328 | 0.8381 | 0.7482 | 0.3173 | 0.6658 | 0.5310 | 0.9127 | 0.8460 | 0.8064 | 0.8194 | 0.8513 | 0.7996 | 0.3532 | 0.0 | 0.7469 | 0.8076 | 0.6081 | 0.7664 | 0.5869 | 0.7234 | 0.5681 | 0.2813 | 0.5096 | 0.4736 | 0.8098 | 0.7253 | 0.7382 | 0.7474 | 0.7536 | 0.7591 | 0.2918 | | 0.0487 | 4.44 | 2220 | 0.1965 | 0.6013 | 0.7315 | 0.8136 | nan | 0.7788 | 0.8986 | 0.7435 | 0.8289 | 0.6291 | 0.8804 | 0.8127 | 0.2571 | 0.6768 | 0.5054 | 0.9008 | 0.8404 | 0.7823 | 0.8593 | 0.8272 | 0.8242 | 0.3893 | 0.0 | 0.7376 | 0.8085 | 0.6166 | 0.7574 | 0.5702 | 0.7369 | 0.5632 | 0.2343 | 0.5060 | 0.4531 | 0.8089 | 0.7150 | 0.7207 | 0.7511 | 0.7581 | 0.7700 | 0.3161 | | 0.1179 | 4.48 | 2240 | 0.1887 | 0.5990 | 0.7318 | 0.8132 | nan | 0.8549 | 0.8896 | 0.7258 | 0.8461 | 0.7417 | 0.8238 | 0.7644 | 0.2442 | 0.5182 | 0.6746 | 0.8932 | 0.7816 | 0.8325 | 0.8215 | 0.8220 | 0.8357 | 0.3714 | 0.0 | 0.7284 | 0.8028 | 0.6163 | 0.7562 | 0.6044 | 0.7211 | 0.5610 | 0.2192 | 0.4385 | 0.5071 | 0.8120 | 0.7167 | 0.7340 | 0.7441 | 0.7553 | 0.7684 | 0.2975 | | 0.0534 | 4.52 | 2260 | 0.2019 | 0.5961 | 0.7289 | 0.7971 | nan | 0.7750 | 0.8870 | 0.6503 | 0.8138 | 0.6035 | 0.7839 | 0.8024 | 0.3672 | 0.6275 | 0.5181 | 0.9029 | 0.8397 | 0.8378 | 0.8522 | 0.8529 | 0.8193 | 0.4577 | 0.0 | 0.7304 | 0.8049 | 0.5836 | 0.7364 | 0.4990 | 0.6986 | 0.5223 | 0.3085 | 0.4876 | 0.4604 | 0.8131 | 0.7311 | 0.7468 | 0.7495 | 0.7642 | 0.7595 | 0.3334 | | 0.0893 | 4.56 | 2280 | 0.2143 | 0.5987 | 0.7251 | 0.7975 | nan | 0.7382 | 0.8807 | 0.7100 | 0.8223 | 0.5582 | 0.8206 | 0.8070 | 0.4127 | 0.5589 | 0.5561 | 0.9109 | 0.8181 | 0.8803 | 0.8305 | 0.8518 | 0.7708 | 0.4003 | 0.0 | 0.6998 | 0.7978 | 0.6118 | 0.7331 | 0.5165 | 0.7194 | 0.5223 | 0.3504 | 0.4649 | 0.4763 | 0.8118 | 0.7273 | 0.7508 | 0.7484 | 0.7669 | 0.7286 | 0.3511 | | 0.0443 | 4.6 | 2300 | 0.2107 | 0.6088 | 0.7451 | 0.8079 | nan | 0.8517 | 0.9117 | 0.7681 | 0.7944 | 0.6344 | 0.8661 | 0.8579 | 0.4570 | 0.6486 | 0.5666 | 0.8783 | 0.7970 | 0.7842 | 0.8453 | 0.8396 | 0.8066 | 0.3593 | 0.0 | 0.7495 | 0.8123 | 0.6290 | 0.7319 | 0.5771 | 0.7430 | 0.5452 | 0.3799 | 0.5021 | 0.4816 | 0.8129 | 0.6993 | 0.7201 | 0.7506 | 0.7543 | 0.7490 | 0.3210 | | 0.0433 | 4.64 | 2320 | 0.1912 | 0.6129 | 0.7392 | 0.8284 | nan | 0.7361 | 0.8932 | 0.6950 | 0.8864 | 0.7410 | 0.8718 | 0.7345 | 0.3977 | 0.5824 | 0.6076 | 0.9056 | 0.8344 | 0.8280 | 0.8070 | 0.8582 | 0.8404 | 0.3467 | 0.0 | 0.6949 | 0.8102 | 0.6119 | 0.7813 | 0.6735 | 0.7293 | 0.6042 | 0.3380 | 0.4804 | 0.4994 | 0.8156 | 0.7139 | 0.7324 | 0.7331 | 0.7432 | 0.7583 | 0.3130 | | 0.0651 | 4.68 | 2340 | 0.1955 | 0.5984 | 0.7331 | 0.8145 | nan | 0.7781 | 0.8997 | 0.6854 | 0.8605 | 0.7560 | 0.8705 | 0.7164 | 0.3448 | 0.4754 | 0.6191 | 0.9088 | 0.8595 | 0.7697 | 0.8969 | 0.7732 | 0.7595 | 0.4887 | 0.0 | 0.7105 | 0.8075 | 0.6069 | 0.7669 | 0.6593 | 0.7214 | 0.5752 | 0.3071 | 0.4179 | 0.4920 | 0.8140 | 0.6969 | 0.6987 | 0.7080 | 0.7072 | 0.7070 | 0.3752 | | 0.047 | 4.72 | 2360 | 0.1974 | 0.6030 | 0.7384 | 0.8122 | nan | 0.8279 | 0.8898 | 0.7517 | 0.8304 | 0.7763 | 0.8685 | 0.7752 | 0.3255 | 0.6484 | 0.5347 | 0.9093 | 0.8206 | 0.7810 | 0.7968 | 0.7843 | 0.7683 | 0.4647 | 0.0 | 0.7301 | 0.8074 | 0.6251 | 0.7479 | 0.6533 | 0.7407 | 0.5601 | 0.2951 | 0.4976 | 0.4673 | 0.8199 | 0.6923 | 0.7070 | 0.6976 | 0.7268 | 0.7155 | 0.3707 | | 0.0631 | 4.76 | 2380 | 0.1954 | 0.5960 | 0.7307 | 0.8121 | nan | 0.8168 | 0.8874 | 0.7811 | 0.8587 | 0.6200 | 0.8873 | 0.7910 | 0.3364 | 0.5003 | 0.5346 | 0.8992 | 0.7987 | 0.8182 | 0.8688 | 0.7974 | 0.7612 | 0.4653 | 0.0 | 0.7194 | 0.8007 | 0.6240 | 0.7744 | 0.5822 | 0.7310 | 0.5696 | 0.3095 | 0.4293 | 0.4671 | 0.8212 | 0.6866 | 0.7134 | 0.7140 | 0.7220 | 0.7142 | 0.3493 | | 0.1457 | 4.8 | 2400 | 0.1905 | 0.6017 | 0.7349 | 0.8122 | nan | 0.8354 | 0.8926 | 0.6969 | 0.8269 | 0.7461 | 0.8505 | 0.8060 | 0.2994 | 0.5935 | 0.6066 | 0.9038 | 0.7462 | 0.7689 | 0.8211 | 0.8379 | 0.8154 | 0.4459 | 0.0 | 0.7296 | 0.8007 | 0.6096 | 0.7530 | 0.6517 | 0.7186 | 0.5810 | 0.2758 | 0.4734 | 0.4956 | 0.8201 | 0.6783 | 0.6944 | 0.7232 | 0.7516 | 0.7421 | 0.3313 | | 0.096 | 4.84 | 2420 | 0.1839 | 0.6017 | 0.7401 | 0.8149 | nan | 0.7803 | 0.9063 | 0.6768 | 0.8360 | 0.7918 | 0.8266 | 0.7649 | 0.3966 | 0.4742 | 0.5942 | 0.9145 | 0.8588 | 0.7922 | 0.8473 | 0.8116 | 0.8137 | 0.4953 | 0.0 | 0.7268 | 0.8094 | 0.6010 | 0.7528 | 0.6775 | 0.7149 | 0.5818 | 0.3183 | 0.4211 | 0.4933 | 0.8070 | 0.7010 | 0.7037 | 0.7242 | 0.7304 | 0.7427 | 0.3250 | | 0.1249 | 4.88 | 2440 | 0.1843 | 0.6077 | 0.7428 | 0.8183 | nan | 0.7991 | 0.8892 | 0.7353 | 0.8558 | 0.7765 | 0.8298 | 0.7553 | 0.3874 | 0.6074 | 0.5186 | 0.8991 | 0.8337 | 0.8211 | 0.8479 | 0.8120 | 0.8344 | 0.4250 | 0.0 | 0.7245 | 0.8070 | 0.6209 | 0.7615 | 0.6935 | 0.7093 | 0.5903 | 0.3295 | 0.4814 | 0.4554 | 0.8175 | 0.7059 | 0.7231 | 0.7149 | 0.7316 | 0.7606 | 0.3115 | | 0.0401 | 4.92 | 2460 | 0.1957 | 0.5969 | 0.7328 | 0.8131 | nan | 0.8428 | 0.8672 | 0.7114 | 0.8465 | 0.7447 | 0.8701 | 0.7830 | 0.3845 | 0.4677 | 0.5891 | 0.9165 | 0.8389 | 0.7478 | 0.8294 | 0.8362 | 0.7763 | 0.4048 | 0.0 | 0.7000 | 0.7963 | 0.6113 | 0.7632 | 0.6725 | 0.7203 | 0.5890 | 0.3243 | 0.4029 | 0.4698 | 0.8106 | 0.6811 | 0.6832 | 0.7295 | 0.7424 | 0.7313 | 0.3166 | | 0.0957 | 4.96 | 2480 | 0.1954 | 0.5970 | 0.7327 | 0.8071 | nan | 0.7625 | 0.9070 | 0.7343 | 0.8071 | 0.7656 | 0.8036 | 0.8320 | 0.3442 | 0.6363 | 0.5262 | 0.8882 | 0.8642 | 0.7561 | 0.8266 | 0.8168 | 0.7836 | 0.4017 | 0.0 | 0.7029 | 0.8052 | 0.6222 | 0.7440 | 0.6464 | 0.7212 | 0.5705 | 0.2782 | 0.4840 | 0.4610 | 0.8116 | 0.6883 | 0.6958 | 0.7341 | 0.7438 | 0.7305 | 0.3055 | | 0.0839 | 5.0 | 2500 | 0.1927 | 0.5988 | 0.7278 | 0.8200 | nan | 0.8173 | 0.8802 | 0.6631 | 0.8705 | 0.7154 | 0.8477 | 0.7848 | 0.3481 | 0.5379 | 0.5904 | 0.9287 | 0.8472 | 0.7912 | 0.8622 | 0.7959 | 0.7803 | 0.3110 | 0.0 | 0.7022 | 0.7997 | 0.5917 | 0.7852 | 0.6572 | 0.7213 | 0.6105 | 0.3032 | 0.4454 | 0.4850 | 0.8044 | 0.6982 | 0.7167 | 0.7343 | 0.7337 | 0.7242 | 0.2656 | | 0.0441 | 5.04 | 2520 | 0.1921 | 0.6079 | 0.7363 | 0.8239 | nan | 0.7877 | 0.9069 | 0.6990 | 0.8730 | 0.6964 | 0.8387 | 0.7980 | 0.3814 | 0.5382 | 0.6421 | 0.8931 | 0.7990 | 0.8441 | 0.8362 | 0.7911 | 0.8295 | 0.3623 | 0.0 | 0.7204 | 0.8072 | 0.6099 | 0.7831 | 0.6568 | 0.7206 | 0.6074 | 0.3140 | 0.4512 | 0.5082 | 0.8085 | 0.7050 | 0.7430 | 0.7261 | 0.7320 | 0.7559 | 0.2924 | | 0.0679 | 5.08 | 2540 | 0.1925 | 0.6096 | 0.7439 | 0.8232 | nan | 0.8173 | 0.8865 | 0.7275 | 0.8745 | 0.7319 | 0.8373 | 0.7785 | 0.4078 | 0.6311 | 0.5003 | 0.9109 | 0.8146 | 0.8512 | 0.8179 | 0.8481 | 0.7909 | 0.4206 | 0.0 | 0.7017 | 0.7981 | 0.6187 | 0.7817 | 0.6650 | 0.7292 | 0.6078 | 0.3298 | 0.4797 | 0.4434 | 0.8036 | 0.7140 | 0.7518 | 0.7390 | 0.7513 | 0.7401 | 0.3181 | | 0.0762 | 5.12 | 2560 | 0.1966 | 0.5916 | 0.7179 | 0.8151 | nan | 0.8240 | 0.8840 | 0.7232 | 0.8917 | 0.7024 | 0.8440 | 0.7422 | 0.2452 | 0.5112 | 0.5410 | 0.8962 | 0.8666 | 0.7568 | 0.8129 | 0.7840 | 0.8040 | 0.3755 | 0.0 | 0.7137 | 0.8025 | 0.6176 | 0.7807 | 0.6263 | 0.7113 | 0.5895 | 0.2326 | 0.4191 | 0.4490 | 0.8085 | 0.6947 | 0.6996 | 0.7263 | 0.7315 | 0.7389 | 0.3072 | | 0.0812 | 5.16 | 2580 | 0.2026 | 0.6001 | 0.7320 | 0.8174 | nan | 0.8212 | 0.8860 | 0.7650 | 0.8756 | 0.6950 | 0.8321 | 0.7693 | 0.2653 | 0.5681 | 0.6025 | 0.8839 | 0.7922 | 0.8607 | 0.8671 | 0.8006 | 0.7756 | 0.3841 | 0.0 | 0.7167 | 0.8025 | 0.6292 | 0.7705 | 0.6289 | 0.7126 | 0.5880 | 0.2479 | 0.4657 | 0.4925 | 0.8058 | 0.7027 | 0.7396 | 0.7394 | 0.7389 | 0.7249 | 0.2954 | | 0.0444 | 5.2 | 2600 | 0.1922 | 0.6073 | 0.7353 | 0.8240 | nan | 0.8309 | 0.8819 | 0.7276 | 0.8846 | 0.7038 | 0.8672 | 0.7492 | 0.3170 | 0.5291 | 0.6013 | 0.9216 | 0.7891 | 0.8462 | 0.8551 | 0.8235 | 0.8115 | 0.3607 | 0.0 | 0.7482 | 0.8089 | 0.6214 | 0.7816 | 0.6365 | 0.7228 | 0.5935 | 0.2867 | 0.4451 | 0.4850 | 0.8089 | 0.7069 | 0.7373 | 0.7486 | 0.7520 | 0.7531 | 0.2952 | | 0.0723 | 5.24 | 2620 | 0.1927 | 0.6121 | 0.7415 | 0.8252 | nan | 0.8155 | 0.9101 | 0.7610 | 0.8830 | 0.7283 | 0.8631 | 0.7346 | 0.3833 | 0.5606 | 0.6069 | 0.8925 | 0.8546 | 0.7885 | 0.8422 | 0.8172 | 0.8209 | 0.3426 | 0.0 | 0.7512 | 0.8134 | 0.6279 | 0.7795 | 0.6473 | 0.7385 | 0.5807 | 0.3154 | 0.4708 | 0.5069 | 0.8119 | 0.7036 | 0.7149 | 0.7469 | 0.7491 | 0.7647 | 0.2952 | | 0.038 | 5.28 | 2640 | 0.2019 | 0.6074 | 0.7386 | 0.8191 | nan | 0.8442 | 0.8776 | 0.6959 | 0.8620 | 0.6979 | 0.8641 | 0.7660 | 0.3409 | 0.5926 | 0.5682 | 0.9181 | 0.8056 | 0.8347 | 0.8304 | 0.8502 | 0.7998 | 0.4073 | 0.0 | 0.7348 | 0.8067 | 0.6034 | 0.7649 | 0.6384 | 0.7362 | 0.5644 | 0.2843 | 0.4902 | 0.4999 | 0.8107 | 0.7004 | 0.7260 | 0.7447 | 0.7476 | 0.7535 | 0.3277 | | 0.0545 | 5.32 | 2660 | 0.2016 | 0.6004 | 0.7302 | 0.8180 | nan | 0.8631 | 0.8974 | 0.7166 | 0.8660 | 0.7245 | 0.8718 | 0.7548 | 0.2667 | 0.5677 | 0.6003 | 0.8980 | 0.8759 | 0.6811 | 0.8429 | 0.8213 | 0.8017 | 0.3629 | 0.0 | 0.7388 | 0.8079 | 0.6163 | 0.7700 | 0.6512 | 0.7397 | 0.5754 | 0.2448 | 0.4684 | 0.5051 | 0.8088 | 0.6704 | 0.6398 | 0.7464 | 0.7525 | 0.7572 | 0.3150 | | 0.0644 | 5.36 | 2680 | 0.1983 | 0.6148 | 0.7467 | 0.8243 | nan | 0.8191 | 0.9042 | 0.7854 | 0.8634 | 0.7273 | 0.8680 | 0.7522 | 0.3414 | 0.5216 | 0.6624 | 0.9161 | 0.8504 | 0.7786 | 0.8585 | 0.8159 | 0.8329 | 0.3973 | 0.0 | 0.7561 | 0.8158 | 0.6249 | 0.7688 | 0.6561 | 0.7506 | 0.5695 | 0.3067 | 0.4492 | 0.5257 | 0.8140 | 0.7002 | 0.7042 | 0.7520 | 0.7539 | 0.7789 | 0.3397 | | 0.0814 | 5.4 | 2700 | 0.2007 | 0.6073 | 0.7347 | 0.8188 | nan | 0.8194 | 0.8470 | 0.7055 | 0.8634 | 0.7134 | 0.8847 | 0.7546 | 0.3835 | 0.5528 | 0.6093 | 0.9166 | 0.8344 | 0.8307 | 0.8395 | 0.8268 | 0.8139 | 0.2941 | 0.0 | 0.7186 | 0.7897 | 0.6077 | 0.7645 | 0.6524 | 0.7458 | 0.5671 | 0.3165 | 0.4585 | 0.4999 | 0.8149 | 0.7195 | 0.7399 | 0.7455 | 0.7569 | 0.7621 | 0.2722 | | 0.0437 | 5.44 | 2720 | 0.1966 | 0.5950 | 0.7244 | 0.8125 | nan | 0.8239 | 0.9001 | 0.7067 | 0.8549 | 0.6844 | 0.8617 | 0.7482 | 0.3604 | 0.3834 | 0.7186 | 0.8956 | 0.7357 | 0.8750 | 0.8170 | 0.8544 | 0.8115 | 0.2840 | 0.0 | 0.7154 | 0.8097 | 0.6147 | 0.7641 | 0.6176 | 0.7480 | 0.5478 | 0.3101 | 0.3551 | 0.5117 | 0.8145 | 0.6718 | 0.7199 | 0.7336 | 0.7494 | 0.7618 | 0.2643 | | 0.0536 | 5.48 | 2740 | 0.2150 | 0.5843 | 0.7165 | 0.7959 | nan | 0.7963 | 0.8867 | 0.7123 | 0.8105 | 0.5821 | 0.8255 | 0.8201 | 0.1755 | 0.6811 | 0.4980 | 0.9111 | 0.8575 | 0.7585 | 0.8623 | 0.7805 | 0.7913 | 0.4308 | 0.0 | 0.7054 | 0.8026 | 0.6155 | 0.7356 | 0.5335 | 0.7356 | 0.5247 | 0.1583 | 0.5013 | 0.4513 | 0.8245 | 0.6981 | 0.7006 | 0.7356 | 0.7260 | 0.7410 | 0.3277 | | 0.0715 | 5.52 | 2760 | 0.2088 | 0.6012 | 0.7312 | 0.8127 | nan | 0.8138 | 0.9035 | 0.7080 | 0.8064 | 0.6314 | 0.8808 | 0.8267 | 0.2183 | 0.5328 | 0.6335 | 0.9009 | 0.8449 | 0.8147 | 0.8598 | 0.8543 | 0.8246 | 0.3757 | 0.0 | 0.7301 | 0.8171 | 0.6143 | 0.7385 | 0.6004 | 0.7536 | 0.5403 | 0.2052 | 0.4497 | 0.5130 | 0.8184 | 0.7211 | 0.7285 | 0.7539 | 0.7559 | 0.7625 | 0.3198 | | 0.091 | 5.56 | 2780 | 0.2041 | 0.6034 | 0.7423 | 0.8110 | nan | 0.8689 | 0.8771 | 0.7109 | 0.8244 | 0.6929 | 0.8958 | 0.7644 | 0.3538 | 0.5529 | 0.6557 | 0.9130 | 0.7627 | 0.8228 | 0.8463 | 0.8492 | 0.7885 | 0.4405 | 0.0 | 0.7032 | 0.8010 | 0.6166 | 0.7454 | 0.6173 | 0.7461 | 0.5405 | 0.2925 | 0.4647 | 0.5157 | 0.8187 | 0.6948 | 0.7173 | 0.7534 | 0.7638 | 0.7432 | 0.3271 | | 0.0393 | 5.6 | 2800 | 0.2226 | 0.6045 | 0.7396 | 0.8036 | nan | 0.8102 | 0.8913 | 0.7321 | 0.7656 | 0.6763 | 0.8691 | 0.8480 | 0.2555 | 0.6074 | 0.6081 | 0.9126 | 0.8450 | 0.8172 | 0.8242 | 0.8333 | 0.7906 | 0.4864 | 0.0 | 0.7332 | 0.8077 | 0.6235 | 0.7098 | 0.6315 | 0.7585 | 0.5299 | 0.2295 | 0.4953 | 0.5149 | 0.8173 | 0.7228 | 0.7343 | 0.7428 | 0.7567 | 0.7462 | 0.3268 | | 0.0414 | 5.64 | 2820 | 0.2097 | 0.6077 | 0.7418 | 0.8162 | nan | 0.8296 | 0.8941 | 0.7577 | 0.8453 | 0.6405 | 0.8695 | 0.7839 | 0.2478 | 0.5981 | 0.6425 | 0.9007 | 0.8282 | 0.8185 | 0.8512 | 0.8185 | 0.8026 | 0.4819 | 0.0 | 0.7503 | 0.8152 | 0.6313 | 0.7578 | 0.6115 | 0.7246 | 0.5618 | 0.2221 | 0.4853 | 0.5175 | 0.8186 | 0.7245 | 0.7450 | 0.7400 | 0.7506 | 0.7550 | 0.3278 | | 0.1061 | 5.68 | 2840 | 0.2079 | 0.6085 | 0.7396 | 0.8139 | nan | 0.8036 | 0.8906 | 0.7229 | 0.8454 | 0.6381 | 0.8610 | 0.7658 | 0.3532 | 0.5529 | 0.6432 | 0.9175 | 0.8227 | 0.8296 | 0.8678 | 0.8142 | 0.8296 | 0.4152 | 0.0 | 0.7403 | 0.8125 | 0.6203 | 0.7558 | 0.6054 | 0.7119 | 0.5590 | 0.2992 | 0.4675 | 0.5195 | 0.8170 | 0.7175 | 0.7413 | 0.7384 | 0.7399 | 0.7671 | 0.3402 | | 0.044 | 5.72 | 2860 | 0.2129 | 0.5955 | 0.7281 | 0.8073 | nan | 0.8760 | 0.8817 | 0.7239 | 0.8369 | 0.6680 | 0.8184 | 0.7773 | 0.1578 | 0.5715 | 0.6299 | 0.8995 | 0.8182 | 0.8421 | 0.8418 | 0.8206 | 0.7976 | 0.4171 | 0.0 | 0.7150 | 0.8070 | 0.6213 | 0.7422 | 0.5868 | 0.7250 | 0.5270 | 0.1529 | 0.4774 | 0.5211 | 0.8188 | 0.7080 | 0.7371 | 0.7410 | 0.7502 | 0.7567 | 0.3316 | | 0.0832 | 5.76 | 2880 | 0.2060 | 0.6030 | 0.7359 | 0.8107 | nan | 0.8445 | 0.8972 | 0.7264 | 0.8434 | 0.6797 | 0.8604 | 0.7604 | 0.3486 | 0.6110 | 0.5234 | 0.9062 | 0.8506 | 0.7577 | 0.8396 | 0.7971 | 0.8256 | 0.4393 | 0.0 | 0.7388 | 0.8133 | 0.6226 | 0.7478 | 0.6102 | 0.7332 | 0.5351 | 0.2822 | 0.4757 | 0.4633 | 0.8212 | 0.7096 | 0.7075 | 0.7428 | 0.7475 | 0.7665 | 0.3362 | | 0.0458 | 5.8 | 2900 | 0.2080 | 0.6049 | 0.7418 | 0.8100 | nan | 0.8212 | 0.8851 | 0.7796 | 0.8234 | 0.6697 | 0.8497 | 0.7741 | 0.3276 | 0.5372 | 0.6118 | 0.9123 | 0.8514 | 0.8086 | 0.8501 | 0.8600 | 0.8373 | 0.4115 | 0.0 | 0.7295 | 0.8059 | 0.6272 | 0.7408 | 0.6093 | 0.7425 | 0.5317 | 0.2670 | 0.4552 | 0.5113 | 0.8183 | 0.7212 | 0.7323 | 0.7519 | 0.7597 | 0.7728 | 0.3115 | | 0.0429 | 5.84 | 2920 | 0.2081 | 0.6025 | 0.7436 | 0.8059 | nan | 0.8457 | 0.8600 | 0.7882 | 0.8348 | 0.7078 | 0.8311 | 0.7521 | 0.3579 | 0.6167 | 0.6247 | 0.9036 | 0.8151 | 0.8271 | 0.8229 | 0.8037 | 0.8150 | 0.4357 | 0.0 | 0.6773 | 0.7914 | 0.6201 | 0.7399 | 0.6141 | 0.7345 | 0.5253 | 0.2743 | 0.4960 | 0.5209 | 0.8174 | 0.7162 | 0.7385 | 0.7401 | 0.7466 | 0.7628 | 0.3298 | | 0.054 | 5.88 | 2940 | 0.2204 | 0.5966 | 0.7370 | 0.8051 | nan | 0.7878 | 0.8990 | 0.6791 | 0.8110 | 0.6541 | 0.8227 | 0.7956 | 0.3752 | 0.5920 | 0.5842 | 0.9142 | 0.8651 | 0.7815 | 0.8965 | 0.8251 | 0.8181 | 0.4275 | 0.0 | 0.7110 | 0.8108 | 0.5928 | 0.7351 | 0.5910 | 0.7252 | 0.5343 | 0.2705 | 0.4730 | 0.4895 | 0.8139 | 0.7148 | 0.7219 | 0.7288 | 0.7308 | 0.7629 | 0.3323 | | 0.0526 | 5.92 | 2960 | 0.2271 | 0.5921 | 0.7137 | 0.8074 | nan | 0.7305 | 0.8986 | 0.6791 | 0.8207 | 0.6357 | 0.8230 | 0.8263 | 0.1921 | 0.5933 | 0.5993 | 0.9000 | 0.7991 | 0.8748 | 0.8311 | 0.8310 | 0.8173 | 0.2809 | 0.0 | 0.7011 | 0.8120 | 0.5975 | 0.7376 | 0.5800 | 0.7360 | 0.5299 | 0.1707 | 0.4862 | 0.5073 | 0.8141 | 0.7211 | 0.7529 | 0.7340 | 0.7601 | 0.7582 | 0.2582 | | 0.0787 | 5.96 | 2980 | 0.1975 | 0.6112 | 0.7550 | 0.8154 | nan | 0.8712 | 0.8549 | 0.7621 | 0.8253 | 0.7788 | 0.8781 | 0.7424 | 0.3999 | 0.5312 | 0.6642 | 0.8947 | 0.8300 | 0.8439 | 0.8626 | 0.8529 | 0.8194 | 0.4234 | 0.0 | 0.7091 | 0.7942 | 0.6226 | 0.7461 | 0.6634 | 0.7555 | 0.5392 | 0.2870 | 0.4502 | 0.5253 | 0.8142 | 0.7330 | 0.7496 | 0.7555 | 0.7663 | 0.7618 | 0.3277 | | 0.077 | 6.0 | 3000 | 0.1916 | 0.6131 | 0.7349 | 0.8314 | nan | 0.7940 | 0.9065 | 0.6260 | 0.9004 | 0.8101 | 0.8706 | 0.6719 | 0.3622 | 0.5013 | 0.6468 | 0.9126 | 0.8364 | 0.8394 | 0.8486 | 0.8284 | 0.8337 | 0.3050 | 0.0 | 0.7254 | 0.8176 | 0.5706 | 0.7962 | 0.6645 | 0.7580 | 0.5719 | 0.3001 | 0.4435 | 0.5239 | 0.8170 | 0.7344 | 0.7520 | 0.7461 | 0.7545 | 0.7748 | 0.2853 | | 0.0346 | 6.04 | 3020 | 0.2012 | 0.6083 | 0.7380 | 0.8188 | nan | 0.7745 | 0.9022 | 0.7231 | 0.8575 | 0.8261 | 0.8445 | 0.6999 | 0.3096 | 0.6325 | 0.5564 | 0.9167 | 0.8575 | 0.7878 | 0.8419 | 0.8112 | 0.8105 | 0.3936 | 0.0 | 0.7344 | 0.8100 | 0.6167 | 0.7698 | 0.6416 | 0.7527 | 0.5464 | 0.2616 | 0.5020 | 0.4926 | 0.8105 | 0.7176 | 0.7237 | 0.7446 | 0.7461 | 0.7632 | 0.3165 | | 0.1209 | 6.08 | 3040 | 0.1980 | 0.6079 | 0.7360 | 0.8205 | nan | 0.7869 | 0.9241 | 0.7542 | 0.8579 | 0.8112 | 0.8485 | 0.7041 | 0.2721 | 0.5967 | 0.5987 | 0.8841 | 0.8261 | 0.8375 | 0.7873 | 0.8708 | 0.8015 | 0.3504 | 0.0 | 0.7410 | 0.8122 | 0.6182 | 0.7677 | 0.6410 | 0.7500 | 0.5438 | 0.2397 | 0.4894 | 0.5063 | 0.8078 | 0.7253 | 0.7425 | 0.7332 | 0.7581 | 0.7582 | 0.3084 | | 0.0416 | 6.12 | 3060 | 0.1973 | 0.6151 | 0.7489 | 0.8206 | nan | 0.8262 | 0.8939 | 0.7506 | 0.8571 | 0.8057 | 0.8401 | 0.7037 | 0.3972 | 0.5672 | 0.6363 | 0.9234 | 0.8370 | 0.8145 | 0.8586 | 0.8284 | 0.8339 | 0.3578 | 0.0 | 0.7489 | 0.8125 | 0.6259 | 0.7666 | 0.6410 | 0.7477 | 0.5412 | 0.3161 | 0.4825 | 0.5207 | 0.8096 | 0.7224 | 0.7329 | 0.7565 | 0.7594 | 0.7769 | 0.3103 | | 0.0439 | 6.16 | 3080 | 0.1961 | 0.6118 | 0.7440 | 0.8172 | nan | 0.7941 | 0.9156 | 0.7294 | 0.8416 | 0.8139 | 0.8435 | 0.7159 | 0.4061 | 0.5151 | 0.6792 | 0.8770 | 0.8128 | 0.8503 | 0.8483 | 0.8207 | 0.8163 | 0.3677 | 0.0 | 0.7441 | 0.8094 | 0.6217 | 0.7562 | 0.6516 | 0.7470 | 0.5382 | 0.3008 | 0.4542 | 0.5300 | 0.8056 | 0.7234 | 0.7458 | 0.7501 | 0.7595 | 0.7641 | 0.3111 | | 0.0467 | 6.2 | 3100 | 0.1993 | 0.6138 | 0.7460 | 0.8209 | nan | 0.8212 | 0.8919 | 0.6869 | 0.8580 | 0.8067 | 0.8439 | 0.7093 | 0.4469 | 0.6316 | 0.5251 | 0.9193 | 0.8579 | 0.8226 | 0.8563 | 0.8425 | 0.8178 | 0.3439 | 0.0 | 0.7509 | 0.8102 | 0.6060 | 0.7639 | 0.6560 | 0.7453 | 0.5469 | 0.3408 | 0.4853 | 0.4688 | 0.8130 | 0.7281 | 0.7425 | 0.7528 | 0.7666 | 0.7698 | 0.3011 | | 0.0639 | 6.24 | 3120 | 0.1939 | 0.6192 | 0.7579 | 0.8265 | nan | 0.8742 | 0.8965 | 0.7917 | 0.8737 | 0.8232 | 0.8609 | 0.6932 | 0.4729 | 0.6036 | 0.5774 | 0.8927 | 0.8442 | 0.8251 | 0.8449 | 0.8313 | 0.8289 | 0.3502 | 0.0 | 0.7351 | 0.8098 | 0.6319 | 0.7757 | 0.6619 | 0.7524 | 0.5602 | 0.3486 | 0.4854 | 0.4994 | 0.8139 | 0.7307 | 0.7493 | 0.7511 | 0.7647 | 0.7767 | 0.2985 | | 0.051 | 6.28 | 3140 | 0.1950 | 0.6220 | 0.7483 | 0.8331 | nan | 0.7659 | 0.9159 | 0.7107 | 0.8991 | 0.8370 | 0.8677 | 0.6572 | 0.4390 | 0.5374 | 0.6454 | 0.9061 | 0.8271 | 0.8608 | 0.8353 | 0.8573 | 0.8176 | 0.3415 | 0.0 | 0.7207 | 0.8189 | 0.6197 | 0.7846 | 0.6857 | 0.7521 | 0.5636 | 0.3327 | 0.4680 | 0.5305 | 0.8165 | 0.7409 | 0.7586 | 0.7565 | 0.7750 | 0.7733 | 0.2979 | | 0.0594 | 6.32 | 3160 | 0.1999 | 0.6118 | 0.7459 | 0.8225 | nan | 0.8510 | 0.9022 | 0.7170 | 0.8657 | 0.8162 | 0.8436 | 0.7059 | 0.3287 | 0.6434 | 0.5611 | 0.9120 | 0.8567 | 0.7685 | 0.8446 | 0.8253 | 0.8281 | 0.4108 | 0.0 | 0.7467 | 0.8156 | 0.6191 | 0.7684 | 0.6833 | 0.7346 | 0.5523 | 0.2637 | 0.4770 | 0.4749 | 0.8154 | 0.7191 | 0.7192 | 0.7495 | 0.7587 | 0.7786 | 0.3354 | | 0.0409 | 6.36 | 3180 | 0.1963 | 0.6192 | 0.7575 | 0.8254 | nan | 0.8534 | 0.8970 | 0.7831 | 0.8714 | 0.8042 | 0.8622 | 0.6865 | 0.3991 | 0.5463 | 0.6052 | 0.8979 | 0.8318 | 0.8446 | 0.8594 | 0.8434 | 0.8354 | 0.4559 | 0.0 | 0.7419 | 0.8124 | 0.6315 | 0.7739 | 0.6809 | 0.7405 | 0.5644 | 0.3053 | 0.4624 | 0.5117 | 0.8150 | 0.7316 | 0.7465 | 0.7526 | 0.7583 | 0.7718 | 0.3453 | | 0.0484 | 6.4 | 3200 | 0.1987 | 0.6111 | 0.7424 | 0.8209 | nan | 0.8281 | 0.9024 | 0.6257 | 0.8620 | 0.7811 | 0.8600 | 0.7204 | 0.3944 | 0.6037 | 0.5542 | 0.9109 | 0.8334 | 0.7980 | 0.8483 | 0.8021 | 0.8282 | 0.4678 | 0.0 | 0.7514 | 0.8125 | 0.5716 | 0.7692 | 0.6594 | 0.7495 | 0.5607 | 0.3106 | 0.4874 | 0.4876 | 0.8123 | 0.7212 | 0.7319 | 0.7363 | 0.7409 | 0.7658 | 0.3315 | | 0.0487 | 6.44 | 3220 | 0.2036 | 0.6149 | 0.7462 | 0.8220 | nan | 0.8514 | 0.8878 | 0.6876 | 0.8779 | 0.7743 | 0.8645 | 0.7127 | 0.4503 | 0.6008 | 0.6170 | 0.8930 | 0.8475 | 0.7827 | 0.8425 | 0.7648 | 0.8298 | 0.4014 | 0.0 | 0.7369 | 0.8084 | 0.6070 | 0.7732 | 0.6437 | 0.7516 | 0.5569 | 0.3440 | 0.4941 | 0.5205 | 0.8114 | 0.7216 | 0.7223 | 0.7360 | 0.7266 | 0.7781 | 0.3354 | | 0.0718 | 6.48 | 3240 | 0.2046 | 0.6133 | 0.7454 | 0.8194 | nan | 0.8476 | 0.8742 | 0.7364 | 0.8526 | 0.7709 | 0.8615 | 0.7423 | 0.3505 | 0.5951 | 0.6241 | 0.9139 | 0.8710 | 0.7696 | 0.8505 | 0.8137 | 0.8146 | 0.3840 | 0.0 | 0.7315 | 0.8026 | 0.6207 | 0.7617 | 0.6362 | 0.7511 | 0.5496 | 0.2920 | 0.4927 | 0.5264 | 0.8121 | 0.7279 | 0.7215 | 0.7561 | 0.7537 | 0.7749 | 0.3295 | | 0.0666 | 6.52 | 3260 | 0.2018 | 0.6103 | 0.7363 | 0.8237 | nan | 0.8582 | 0.8781 | 0.6627 | 0.8734 | 0.7564 | 0.8542 | 0.7235 | 0.2790 | 0.5671 | 0.6371 | 0.9218 | 0.8576 | 0.8130 | 0.8377 | 0.8483 | 0.8246 | 0.3251 | 0.0 | 0.7225 | 0.8045 | 0.5912 | 0.7707 | 0.6436 | 0.7516 | 0.5589 | 0.2490 | 0.4777 | 0.5257 | 0.8117 | 0.7423 | 0.7456 | 0.7587 | 0.7612 | 0.7767 | 0.2931 | | 0.055 | 6.56 | 3280 | 0.1966 | 0.6189 | 0.7470 | 0.8300 | nan | 0.8568 | 0.8922 | 0.7653 | 0.8824 | 0.7941 | 0.8632 | 0.7250 | 0.2978 | 0.5364 | 0.6410 | 0.9050 | 0.8366 | 0.8240 | 0.8570 | 0.8350 | 0.8158 | 0.3706 | 0.0 | 0.7414 | 0.8117 | 0.6337 | 0.7806 | 0.6754 | 0.7565 | 0.5760 | 0.2545 | 0.4678 | 0.5268 | 0.8145 | 0.7382 | 0.7441 | 0.7636 | 0.7652 | 0.7718 | 0.3177 | | 0.0586 | 6.6 | 3300 | 0.1984 | 0.6196 | 0.7496 | 0.8243 | nan | 0.8386 | 0.8967 | 0.7340 | 0.8547 | 0.7187 | 0.8721 | 0.7726 | 0.3753 | 0.5738 | 0.6503 | 0.8946 | 0.8357 | 0.8508 | 0.8480 | 0.8219 | 0.8012 | 0.4052 | 0.0 | 0.7402 | 0.8120 | 0.6273 | 0.7598 | 0.6449 | 0.7617 | 0.5580 | 0.3214 | 0.4871 | 0.5336 | 0.8142 | 0.7362 | 0.7574 | 0.7550 | 0.7624 | 0.7641 | 0.3178 | | 0.054 | 6.64 | 3320 | 0.1945 | 0.6176 | 0.7460 | 0.8294 | nan | 0.8253 | 0.8945 | 0.7383 | 0.8571 | 0.7803 | 0.8766 | 0.7617 | 0.3174 | 0.5509 | 0.6698 | 0.9057 | 0.8345 | 0.8537 | 0.8654 | 0.8013 | 0.8261 | 0.3228 | 0.0 | 0.7363 | 0.8137 | 0.6226 | 0.7705 | 0.6833 | 0.7702 | 0.5751 | 0.2722 | 0.4787 | 0.5382 | 0.8145 | 0.7380 | 0.7596 | 0.7456 | 0.7416 | 0.7759 | 0.2810 | | 0.0918 | 6.68 | 3340 | 0.1977 | 0.6178 | 0.7486 | 0.8289 | nan | 0.8353 | 0.8952 | 0.7535 | 0.8468 | 0.7779 | 0.8714 | 0.7888 | 0.2920 | 0.6281 | 0.6273 | 0.9077 | 0.8633 | 0.8127 | 0.8589 | 0.7952 | 0.8256 | 0.3471 | 0.0 | 0.7390 | 0.8108 | 0.6278 | 0.7660 | 0.6963 | 0.7715 | 0.5818 | 0.2559 | 0.5158 | 0.5280 | 0.8138 | 0.7307 | 0.7471 | 0.7388 | 0.7326 | 0.7751 | 0.2890 | | 0.0485 | 6.72 | 3360 | 0.1935 | 0.6151 | 0.7438 | 0.8303 | nan | 0.8458 | 0.8932 | 0.7397 | 0.8770 | 0.8135 | 0.8903 | 0.7165 | 0.3140 | 0.5889 | 0.5917 | 0.9073 | 0.8082 | 0.8552 | 0.8702 | 0.8096 | 0.7906 | 0.3327 | 0.0 | 0.7394 | 0.8087 | 0.6186 | 0.7824 | 0.6935 | 0.7730 | 0.5854 | 0.2679 | 0.4889 | 0.5122 | 0.8123 | 0.7277 | 0.7524 | 0.7443 | 0.7412 | 0.7485 | 0.2754 | | 0.0707 | 6.76 | 3380 | 0.1956 | 0.6135 | 0.7441 | 0.8298 | nan | 0.8640 | 0.8860 | 0.7093 | 0.8739 | 0.7835 | 0.8753 | 0.7555 | 0.2561 | 0.5847 | 0.6261 | 0.9060 | 0.8611 | 0.7900 | 0.8629 | 0.7686 | 0.8284 | 0.4190 | 0.0 | 0.7419 | 0.8129 | 0.6096 | 0.7736 | 0.6967 | 0.7596 | 0.5906 | 0.2294 | 0.4890 | 0.5329 | 0.8141 | 0.7393 | 0.7399 | 0.7283 | 0.7234 | 0.7732 | 0.2894 | | 0.0479 | 6.8 | 3400 | 0.2023 | 0.6173 | 0.7511 | 0.8284 | nan | 0.8060 | 0.8994 | 0.7386 | 0.8636 | 0.7464 | 0.8888 | 0.7598 | 0.4561 | 0.4873 | 0.6764 | 0.9131 | 0.8034 | 0.8738 | 0.8118 | 0.8396 | 0.8131 | 0.3915 | 0.0 | 0.7375 | 0.8162 | 0.6257 | 0.7735 | 0.6701 | 0.7588 | 0.5810 | 0.3186 | 0.4419 | 0.5425 | 0.8176 | 0.7365 | 0.7567 | 0.7342 | 0.7566 | 0.7620 | 0.2826 | | 0.0681 | 6.84 | 3420 | 0.1910 | 0.6193 | 0.7555 | 0.8259 | nan | 0.8415 | 0.9146 | 0.7535 | 0.8504 | 0.8153 | 0.8551 | 0.7497 | 0.4178 | 0.6103 | 0.6438 | 0.8894 | 0.8482 | 0.8158 | 0.8471 | 0.7817 | 0.8164 | 0.3937 | 0.0 | 0.7503 | 0.8117 | 0.6351 | 0.7665 | 0.6840 | 0.7704 | 0.5730 | 0.3150 | 0.4980 | 0.5348 | 0.8161 | 0.7367 | 0.7468 | 0.7341 | 0.7374 | 0.7620 | 0.2755 | | 0.0527 | 6.88 | 3440 | 0.2485 | 0.5992 | 0.7385 | 0.7956 | nan | 0.8583 | 0.9012 | 0.6970 | 0.8161 | 0.4887 | 0.7942 | 0.8130 | 0.4627 | 0.5456 | 0.6360 | 0.9041 | 0.8325 | 0.8547 | 0.8560 | 0.8318 | 0.8158 | 0.4471 | 0.0 | 0.7482 | 0.8062 | 0.6126 | 0.7252 | 0.4562 | 0.7181 | 0.5111 | 0.3480 | 0.4828 | 0.5308 | 0.8197 | 0.7248 | 0.7482 | 0.7423 | 0.7568 | 0.7627 | 0.2918 | | 0.0552 | 6.92 | 3460 | 0.2340 | 0.6095 | 0.7488 | 0.8068 | nan | 0.8834 | 0.8760 | 0.7525 | 0.8465 | 0.5447 | 0.8179 | 0.7830 | 0.4823 | 0.6262 | 0.6107 | 0.9227 | 0.8547 | 0.8231 | 0.8487 | 0.8352 | 0.8306 | 0.3909 | 0.0 | 0.7352 | 0.8068 | 0.6311 | 0.7391 | 0.5054 | 0.7189 | 0.5285 | 0.3720 | 0.5219 | 0.5261 | 0.8197 | 0.7314 | 0.7472 | 0.7522 | 0.7705 | 0.7766 | 0.2888 | | 0.0467 | 6.96 | 3480 | 0.2203 | 0.6069 | 0.7367 | 0.8145 | nan | 0.8406 | 0.8867 | 0.7886 | 0.8672 | 0.6168 | 0.8327 | 0.7707 | 0.4004 | 0.5789 | 0.6595 | 0.8940 | 0.8545 | 0.8098 | 0.8402 | 0.8144 | 0.8331 | 0.2364 | 0.0 | 0.7277 | 0.8133 | 0.6357 | 0.7570 | 0.5657 | 0.7190 | 0.5524 | 0.3152 | 0.5036 | 0.5464 | 0.8177 | 0.7252 | 0.7368 | 0.7496 | 0.7611 | 0.7712 | 0.2260 | | 0.0439 | 7.0 | 3500 | 0.2275 | 0.6051 | 0.7303 | 0.8110 | nan | 0.8470 | 0.8943 | 0.6942 | 0.8549 | 0.6545 | 0.8176 | 0.7530 | 0.3131 | 0.6298 | 0.6255 | 0.9062 | 0.8398 | 0.8313 | 0.8247 | 0.8425 | 0.7976 | 0.2892 | 0.0 | 0.7388 | 0.8097 | 0.6109 | 0.7479 | 0.5758 | 0.7290 | 0.5326 | 0.2652 | 0.5192 | 0.5317 | 0.8174 | 0.7297 | 0.7445 | 0.7506 | 0.7710 | 0.7548 | 0.2632 | | 0.0521 | 7.04 | 3520 | 0.2251 | 0.6088 | 0.7420 | 0.8137 | nan | 0.8732 | 0.8794 | 0.7645 | 0.8523 | 0.7060 | 0.8313 | 0.7457 | 0.3072 | 0.5490 | 0.6194 | 0.9096 | 0.7940 | 0.8878 | 0.8470 | 0.8630 | 0.7804 | 0.4046 | 0.0 | 0.7474 | 0.8070 | 0.6354 | 0.7500 | 0.6009 | 0.7335 | 0.5409 | 0.2459 | 0.4823 | 0.5253 | 0.8200 | 0.7260 | 0.7482 | 0.7637 | 0.7731 | 0.7381 | 0.3209 | | 0.0309 | 7.08 | 3540 | 0.2404 | 0.6004 | 0.7310 | 0.8055 | nan | 0.8285 | 0.8969 | 0.7766 | 0.8295 | 0.6323 | 0.8245 | 0.7835 | 0.2006 | 0.6407 | 0.5665 | 0.9032 | 0.8318 | 0.8426 | 0.8452 | 0.8229 | 0.7760 | 0.4249 | 0.0 | 0.7453 | 0.8156 | 0.6395 | 0.7323 | 0.5505 | 0.7299 | 0.5171 | 0.1760 | 0.5113 | 0.4996 | 0.8199 | 0.7356 | 0.7584 | 0.7586 | 0.7658 | 0.7337 | 0.3173 | | 0.041 | 7.12 | 3560 | 0.2270 | 0.6036 | 0.7300 | 0.8090 | nan | 0.8632 | 0.8931 | 0.6576 | 0.8435 | 0.6410 | 0.8345 | 0.7614 | 0.2645 | 0.5828 | 0.6233 | 0.9111 | 0.8093 | 0.8429 | 0.8509 | 0.8402 | 0.7893 | 0.4015 | 0.0 | 0.7552 | 0.8082 | 0.5947 | 0.7412 | 0.5704 | 0.7325 | 0.5289 | 0.2354 | 0.4928 | 0.5234 | 0.8194 | 0.7224 | 0.7475 | 0.7582 | 0.7656 | 0.7445 | 0.3243 | | 0.0516 | 7.16 | 3580 | 0.2235 | 0.6068 | 0.7352 | 0.8144 | nan | 0.8617 | 0.8936 | 0.6386 | 0.8380 | 0.7164 | 0.8518 | 0.7569 | 0.3071 | 0.5462 | 0.6456 | 0.9117 | 0.8011 | 0.8581 | 0.8647 | 0.8250 | 0.7942 | 0.3875 | 0.0 | 0.7505 | 0.8082 | 0.5835 | 0.7456 | 0.6204 | 0.7463 | 0.5376 | 0.2546 | 0.4747 | 0.5257 | 0.8182 | 0.7202 | 0.7438 | 0.7604 | 0.7633 | 0.7480 | 0.3215 | | 0.0444 | 7.2 | 3600 | 0.2259 | 0.6113 | 0.7438 | 0.8127 | nan | 0.8235 | 0.8932 | 0.7367 | 0.8187 | 0.6444 | 0.8665 | 0.8099 | 0.3617 | 0.6273 | 0.6339 | 0.9160 | 0.8431 | 0.8102 | 0.8794 | 0.7815 | 0.8072 | 0.3914 | 0.0 | 0.7410 | 0.8093 | 0.6299 | 0.7389 | 0.5965 | 0.7561 | 0.5393 | 0.2970 | 0.5100 | 0.5292 | 0.8202 | 0.7342 | 0.7461 | 0.7426 | 0.7365 | 0.7587 | 0.3185 | | 0.0443 | 7.24 | 3620 | 0.2187 | 0.6184 | 0.7525 | 0.8190 | nan | 0.8530 | 0.8955 | 0.7771 | 0.8274 | 0.6900 | 0.8704 | 0.8047 | 0.4150 | 0.5997 | 0.6314 | 0.9052 | 0.8460 | 0.8180 | 0.8637 | 0.8151 | 0.8124 | 0.3677 | 0.0 | 0.7525 | 0.8098 | 0.6386 | 0.7475 | 0.6305 | 0.7658 | 0.5575 | 0.3230 | 0.4993 | 0.5302 | 0.8209 | 0.7393 | 0.7484 | 0.7495 | 0.7537 | 0.7600 | 0.3047 | | 0.0773 | 7.28 | 3640 | 0.2119 | 0.6199 | 0.7516 | 0.8210 | nan | 0.8772 | 0.9028 | 0.7443 | 0.8270 | 0.7095 | 0.8537 | 0.8093 | 0.3872 | 0.6059 | 0.6146 | 0.9135 | 0.8322 | 0.8508 | 0.8241 | 0.8498 | 0.8167 | 0.3585 | 0.0 | 0.7556 | 0.8096 | 0.6315 | 0.7483 | 0.6378 | 0.7640 | 0.5590 | 0.3153 | 0.5032 | 0.5233 | 0.8201 | 0.7369 | 0.7544 | 0.7562 | 0.7717 | 0.7639 | 0.3068 | | 0.0381 | 7.32 | 3660 | 0.2086 | 0.6088 | 0.7409 | 0.8124 | nan | 0.8592 | 0.8914 | 0.7294 | 0.8354 | 0.7693 | 0.8301 | 0.7288 | 0.3523 | 0.5901 | 0.6420 | 0.9119 | 0.8092 | 0.8492 | 0.8283 | 0.8480 | 0.8010 | 0.3202 | 0.0 | 0.7428 | 0.8083 | 0.6265 | 0.7509 | 0.6160 | 0.7504 | 0.5294 | 0.2703 | 0.4945 | 0.5303 | 0.8220 | 0.7244 | 0.7482 | 0.7515 | 0.7664 | 0.7466 | 0.2808 | | 0.0464 | 7.36 | 3680 | 0.2155 | 0.6097 | 0.7379 | 0.8135 | nan | 0.8474 | 0.8975 | 0.7075 | 0.8456 | 0.7282 | 0.8638 | 0.7149 | 0.3864 | 0.5974 | 0.5819 | 0.9084 | 0.8256 | 0.8455 | 0.8365 | 0.8537 | 0.7817 | 0.3218 | 0.0 | 0.7408 | 0.8088 | 0.6178 | 0.7546 | 0.5922 | 0.7590 | 0.5152 | 0.3069 | 0.4931 | 0.5063 | 0.8208 | 0.7335 | 0.7535 | 0.7598 | 0.7728 | 0.7445 | 0.2955 | | 0.0614 | 7.4 | 3700 | 0.2195 | 0.6135 | 0.7500 | 0.8110 | nan | 0.8656 | 0.8814 | 0.7297 | 0.8356 | 0.7074 | 0.8448 | 0.7365 | 0.4880 | 0.5984 | 0.5838 | 0.9176 | 0.8240 | 0.8345 | 0.8315 | 0.8677 | 0.8213 | 0.3814 | 0.0 | 0.7419 | 0.8070 | 0.6230 | 0.7469 | 0.6014 | 0.7525 | 0.5221 | 0.3509 | 0.4926 | 0.5007 | 0.8195 | 0.7314 | 0.7503 | 0.7538 | 0.7739 | 0.7641 | 0.3112 | | 0.0387 | 7.44 | 3720 | 0.2203 | 0.6204 | 0.7548 | 0.8177 | nan | 0.8613 | 0.8878 | 0.7302 | 0.8424 | 0.7072 | 0.8624 | 0.7557 | 0.4943 | 0.6047 | 0.6229 | 0.8990 | 0.8379 | 0.8324 | 0.8634 | 0.8426 | 0.8073 | 0.3807 | 0.0 | 0.7446 | 0.8094 | 0.6272 | 0.7534 | 0.6154 | 0.7665 | 0.5337 | 0.3675 | 0.5036 | 0.5203 | 0.8211 | 0.7351 | 0.7515 | 0.7663 | 0.7764 | 0.7584 | 0.3162 | | 0.0756 | 7.48 | 3740 | 0.2238 | 0.6159 | 0.7463 | 0.8187 | nan | 0.8548 | 0.8948 | 0.7299 | 0.8325 | 0.7072 | 0.8686 | 0.7847 | 0.3186 | 0.5954 | 0.6298 | 0.9106 | 0.8341 | 0.8262 | 0.8541 | 0.8236 | 0.8073 | 0.4144 | 0.0 | 0.7526 | 0.8121 | 0.6257 | 0.7509 | 0.6249 | 0.7643 | 0.5441 | 0.2633 | 0.5015 | 0.5210 | 0.8226 | 0.7318 | 0.7506 | 0.7612 | 0.7694 | 0.7608 | 0.3291 | | 0.0442 | 7.52 | 3760 | 0.2139 | 0.6150 | 0.7454 | 0.8186 | nan | 0.8580 | 0.9114 | 0.7052 | 0.8456 | 0.6986 | 0.8814 | 0.7237 | 0.3424 | 0.6381 | 0.5776 | 0.9101 | 0.8323 | 0.8491 | 0.8561 | 0.8353 | 0.8024 | 0.4048 | 0.0 | 0.7541 | 0.8137 | 0.6187 | 0.7590 | 0.5916 | 0.7691 | 0.5252 | 0.2908 | 0.5133 | 0.4968 | 0.8226 | 0.7350 | 0.7548 | 0.7660 | 0.7775 | 0.7583 | 0.3228 | | 0.0539 | 7.56 | 3780 | 0.2026 | 0.6198 | 0.7487 | 0.8260 | nan | 0.8731 | 0.8995 | 0.7449 | 0.8710 | 0.7200 | 0.8849 | 0.7157 | 0.3682 | 0.6338 | 0.6196 | 0.9113 | 0.7938 | 0.8676 | 0.8560 | 0.8503 | 0.8035 | 0.3142 | 0.0 | 0.7541 | 0.8119 | 0.6326 | 0.7762 | 0.6140 | 0.7747 | 0.5466 | 0.3147 | 0.5172 | 0.5266 | 0.8235 | 0.7179 | 0.7479 | 0.7624 | 0.7809 | 0.7632 | 0.2924 | | 0.047 | 7.6 | 3800 | 0.2024 | 0.6212 | 0.7475 | 0.8296 | nan | 0.8603 | 0.8992 | 0.7020 | 0.8756 | 0.7303 | 0.8862 | 0.7304 | 0.3776 | 0.6126 | 0.5955 | 0.9141 | 0.8511 | 0.8107 | 0.8579 | 0.8407 | 0.8378 | 0.3254 | 0.0 | 0.7505 | 0.8158 | 0.6163 | 0.7818 | 0.6228 | 0.7744 | 0.5596 | 0.3137 | 0.5023 | 0.5141 | 0.8244 | 0.7330 | 0.7436 | 0.7663 | 0.7774 | 0.7821 | 0.3034 | | 0.0467 | 7.64 | 3820 | 0.2028 | 0.6129 | 0.7373 | 0.8199 | nan | 0.8027 | 0.8986 | 0.6825 | 0.8596 | 0.7446 | 0.8921 | 0.7136 | 0.3488 | 0.5648 | 0.6284 | 0.8980 | 0.8360 | 0.7928 | 0.8416 | 0.8354 | 0.8113 | 0.3827 | 0.0 | 0.7326 | 0.8131 | 0.6084 | 0.7703 | 0.6213 | 0.7694 | 0.5378 | 0.2919 | 0.4774 | 0.5175 | 0.8227 | 0.7184 | 0.7291 | 0.7620 | 0.7710 | 0.7670 | 0.3217 | | 0.0385 | 7.68 | 3840 | 0.2022 | 0.6145 | 0.7447 | 0.8220 | nan | 0.8519 | 0.8908 | 0.7347 | 0.8602 | 0.7322 | 0.8846 | 0.7235 | 0.3646 | 0.5684 | 0.5708 | 0.9190 | 0.8485 | 0.8332 | 0.8654 | 0.8127 | 0.8194 | 0.3794 | 0.0 | 0.7498 | 0.8106 | 0.6242 | 0.7719 | 0.6280 | 0.7682 | 0.5460 | 0.3105 | 0.4718 | 0.4958 | 0.8232 | 0.7229 | 0.7411 | 0.7460 | 0.7484 | 0.7711 | 0.3320 | | 0.082 | 7.72 | 3860 | 0.2047 | 0.6180 | 0.7498 | 0.8261 | nan | 0.8602 | 0.9006 | 0.7283 | 0.8621 | 0.7385 | 0.9008 | 0.7200 | 0.3629 | 0.6560 | 0.5607 | 0.9057 | 0.8369 | 0.8220 | 0.8445 | 0.8385 | 0.8283 | 0.3802 | 0.0 | 0.7502 | 0.8141 | 0.6240 | 0.7764 | 0.6321 | 0.7699 | 0.5491 | 0.3007 | 0.5019 | 0.4872 | 0.8222 | 0.7280 | 0.7474 | 0.7510 | 0.7594 | 0.7764 | 0.3344 | | 0.0452 | 7.76 | 3880 | 0.2026 | 0.6187 | 0.7544 | 0.8228 | nan | 0.8522 | 0.8847 | 0.7730 | 0.8698 | 0.7117 | 0.8773 | 0.7253 | 0.4656 | 0.5983 | 0.6178 | 0.9034 | 0.8591 | 0.8158 | 0.8700 | 0.7850 | 0.8353 | 0.3805 | 0.0 | 0.7390 | 0.8077 | 0.6320 | 0.7788 | 0.6067 | 0.7798 | 0.5474 | 0.3492 | 0.4907 | 0.5241 | 0.8225 | 0.7265 | 0.7491 | 0.7399 | 0.7383 | 0.7763 | 0.3284 | | 0.0709 | 7.8 | 3900 | 0.2129 | 0.6168 | 0.7514 | 0.8228 | nan | 0.8691 | 0.8992 | 0.7177 | 0.8494 | 0.7060 | 0.8733 | 0.7717 | 0.3807 | 0.6129 | 0.5954 | 0.8941 | 0.8474 | 0.8247 | 0.8548 | 0.8000 | 0.8376 | 0.4392 | 0.0 | 0.7570 | 0.8118 | 0.6217 | 0.7666 | 0.6198 | 0.7782 | 0.5532 | 0.3023 | 0.4885 | 0.5096 | 0.8215 | 0.7249 | 0.7523 | 0.7414 | 0.7392 | 0.7806 | 0.3340 | | 0.0445 | 7.84 | 3920 | 0.2112 | 0.6152 | 0.7479 | 0.8208 | nan | 0.8354 | 0.8968 | 0.7225 | 0.8596 | 0.7025 | 0.8762 | 0.7309 | 0.4132 | 0.5801 | 0.6012 | 0.9090 | 0.8403 | 0.8277 | 0.8304 | 0.8310 | 0.8418 | 0.4157 | 0.0 | 0.7408 | 0.8104 | 0.6227 | 0.7721 | 0.5956 | 0.7748 | 0.5390 | 0.3149 | 0.4730 | 0.5026 | 0.8231 | 0.7298 | 0.7588 | 0.7489 | 0.7601 | 0.7798 | 0.3279 | | 0.0684 | 7.88 | 3940 | 0.2073 | 0.6162 | 0.7520 | 0.8212 | nan | 0.8597 | 0.8945 | 0.7100 | 0.8620 | 0.7031 | 0.8764 | 0.7234 | 0.4659 | 0.6066 | 0.6061 | 0.9092 | 0.8544 | 0.8132 | 0.8451 | 0.8228 | 0.8359 | 0.3964 | 0.0 | 0.7482 | 0.8133 | 0.6172 | 0.7733 | 0.5902 | 0.7734 | 0.5329 | 0.3347 | 0.4877 | 0.5064 | 0.8232 | 0.7360 | 0.7534 | 0.7511 | 0.7568 | 0.7794 | 0.3151 | | 0.0461 | 7.92 | 3960 | 0.2076 | 0.6157 | 0.7469 | 0.8250 | nan | 0.8594 | 0.8986 | 0.6659 | 0.8770 | 0.7159 | 0.8692 | 0.7100 | 0.4604 | 0.5639 | 0.5988 | 0.9099 | 0.8512 | 0.8546 | 0.8655 | 0.8396 | 0.8200 | 0.3381 | 0.0 | 0.7589 | 0.8132 | 0.5961 | 0.7836 | 0.5913 | 0.7689 | 0.5409 | 0.3507 | 0.4729 | 0.5124 | 0.8228 | 0.7347 | 0.7600 | 0.7532 | 0.7589 | 0.7715 | 0.2926 | | 0.053 | 7.96 | 3980 | 0.2174 | 0.6106 | 0.7430 | 0.8218 | nan | 0.8738 | 0.8890 | 0.7214 | 0.8702 | 0.7303 | 0.8785 | 0.7177 | 0.3748 | 0.5630 | 0.5626 | 0.9187 | 0.8729 | 0.7903 | 0.8706 | 0.8151 | 0.8118 | 0.3701 | 0.0 | 0.7605 | 0.8134 | 0.6223 | 0.7789 | 0.6070 | 0.7638 | 0.5417 | 0.3003 | 0.4682 | 0.4870 | 0.8225 | 0.7172 | 0.7317 | 0.7506 | 0.7546 | 0.7626 | 0.3087 | | 0.063 | 8.0 | 4000 | 0.2192 | 0.6122 | 0.7454 | 0.8158 | nan | 0.8402 | 0.9083 | 0.7397 | 0.8477 | 0.6991 | 0.8612 | 0.7566 | 0.4382 | 0.5439 | 0.5893 | 0.8825 | 0.8527 | 0.8119 | 0.8538 | 0.8192 | 0.8023 | 0.4255 | 0.0 | 0.7532 | 0.8109 | 0.6301 | 0.7640 | 0.6155 | 0.7572 | 0.5442 | 0.3352 | 0.4609 | 0.5009 | 0.8170 | 0.7196 | 0.7373 | 0.7428 | 0.7510 | 0.7598 | 0.3202 | | 0.0696 | 8.04 | 4020 | 0.2176 | 0.6092 | 0.7437 | 0.8174 | nan | 0.8501 | 0.8994 | 0.7617 | 0.8430 | 0.7154 | 0.8691 | 0.7603 | 0.3024 | 0.5760 | 0.6084 | 0.9050 | 0.7923 | 0.8546 | 0.8203 | 0.8350 | 0.8042 | 0.4453 | 0.0 | 0.7502 | 0.8153 | 0.6318 | 0.7643 | 0.6147 | 0.7635 | 0.5416 | 0.2431 | 0.4767 | 0.5068 | 0.8239 | 0.7155 | 0.7448 | 0.7377 | 0.7574 | 0.7632 | 0.3147 | | 0.0716 | 8.08 | 4040 | 0.2081 | 0.6164 | 0.7460 | 0.8256 | nan | 0.8457 | 0.8875 | 0.7553 | 0.8637 | 0.7370 | 0.8820 | 0.7645 | 0.3343 | 0.6189 | 0.5919 | 0.9100 | 0.8438 | 0.8032 | 0.8669 | 0.7875 | 0.8119 | 0.3787 | 0.0 | 0.7475 | 0.8098 | 0.6328 | 0.7769 | 0.6563 | 0.7717 | 0.5780 | 0.2759 | 0.4884 | 0.5018 | 0.8258 | 0.7273 | 0.7430 | 0.7411 | 0.7404 | 0.7682 | 0.3097 | | 0.0476 | 8.12 | 4060 | 0.2121 | 0.6152 | 0.7445 | 0.8213 | nan | 0.8776 | 0.8912 | 0.7376 | 0.8673 | 0.7077 | 0.8765 | 0.7191 | 0.3780 | 0.5497 | 0.6293 | 0.9055 | 0.8189 | 0.8572 | 0.8369 | 0.8284 | 0.8291 | 0.3461 | 0.0 | 0.7476 | 0.8102 | 0.6290 | 0.7731 | 0.6066 | 0.7692 | 0.5422 | 0.3104 | 0.4700 | 0.5254 | 0.8235 | 0.7342 | 0.7590 | 0.7401 | 0.7570 | 0.7771 | 0.2989 | | 0.0282 | 8.16 | 4080 | 0.2227 | 0.6179 | 0.7503 | 0.8233 | nan | 0.8493 | 0.8953 | 0.7243 | 0.8609 | 0.7054 | 0.8850 | 0.7402 | 0.4689 | 0.5992 | 0.6032 | 0.9098 | 0.8674 | 0.8176 | 0.8304 | 0.8224 | 0.8261 | 0.3496 | 0.0 | 0.7464 | 0.8135 | 0.6221 | 0.7722 | 0.6173 | 0.7711 | 0.5455 | 0.3437 | 0.4873 | 0.5114 | 0.8242 | 0.7384 | 0.7545 | 0.7427 | 0.7597 | 0.7755 | 0.2974 | | 0.066 | 8.2 | 4100 | 0.2196 | 0.6178 | 0.7515 | 0.8234 | nan | 0.8605 | 0.8867 | 0.7520 | 0.8588 | 0.6987 | 0.8821 | 0.7467 | 0.3982 | 0.5296 | 0.7016 | 0.9118 | 0.8113 | 0.8563 | 0.8405 | 0.8277 | 0.8350 | 0.3778 | 0.0 | 0.7526 | 0.8116 | 0.6271 | 0.7691 | 0.6182 | 0.7677 | 0.5441 | 0.3201 | 0.4636 | 0.5442 | 0.8241 | 0.7320 | 0.7581 | 0.7477 | 0.7625 | 0.7792 | 0.2986 | | 0.0778 | 8.24 | 4120 | 0.2207 | 0.6158 | 0.7519 | 0.8196 | nan | 0.8662 | 0.8984 | 0.7375 | 0.8417 | 0.7046 | 0.8827 | 0.7453 | 0.4193 | 0.5823 | 0.6251 | 0.9098 | 0.8391 | 0.8376 | 0.8596 | 0.8218 | 0.8180 | 0.3927 | 0.0 | 0.7475 | 0.8082 | 0.6272 | 0.7637 | 0.6183 | 0.7644 | 0.5440 | 0.3164 | 0.4880 | 0.5196 | 0.8225 | 0.7363 | 0.7576 | 0.7433 | 0.7527 | 0.7708 | 0.3040 | | 0.0467 | 8.28 | 4140 | 0.2348 | 0.6066 | 0.7388 | 0.8137 | nan | 0.8778 | 0.8864 | 0.7390 | 0.8421 | 0.6967 | 0.8328 | 0.7716 | 0.2713 | 0.5635 | 0.6553 | 0.9114 | 0.8487 | 0.8293 | 0.8234 | 0.8276 | 0.8004 | 0.3817 | 0.0 | 0.7537 | 0.8105 | 0.6265 | 0.7509 | 0.5995 | 0.7384 | 0.5331 | 0.2300 | 0.4803 | 0.5344 | 0.8265 | 0.7321 | 0.7541 | 0.7356 | 0.7555 | 0.7611 | 0.2958 | | 0.0729 | 8.32 | 4160 | 0.2377 | 0.6016 | 0.7342 | 0.8092 | nan | 0.8659 | 0.9055 | 0.7306 | 0.8257 | 0.6606 | 0.8228 | 0.7871 | 0.2538 | 0.5477 | 0.6017 | 0.9047 | 0.8374 | 0.8502 | 0.8598 | 0.8237 | 0.8064 | 0.3972 | 0.0 | 0.7492 | 0.8113 | 0.6273 | 0.7462 | 0.5713 | 0.7264 | 0.5335 | 0.2263 | 0.4677 | 0.5076 | 0.8252 | 0.7278 | 0.7542 | 0.7386 | 0.7492 | 0.7609 | 0.3055 | | 0.0314 | 8.36 | 4180 | 0.2331 | 0.6053 | 0.7385 | 0.8127 | nan | 0.8451 | 0.9049 | 0.7290 | 0.8338 | 0.6926 | 0.8417 | 0.7630 | 0.2421 | 0.5876 | 0.6142 | 0.8928 | 0.8661 | 0.8179 | 0.8467 | 0.7945 | 0.8287 | 0.4544 | 0.0 | 0.7498 | 0.8132 | 0.6277 | 0.7472 | 0.6012 | 0.7386 | 0.5316 | 0.2206 | 0.4866 | 0.5158 | 0.8222 | 0.7240 | 0.7455 | 0.7416 | 0.7438 | 0.7740 | 0.3121 | | 0.0417 | 8.4 | 4200 | 0.2297 | 0.6117 | 0.7444 | 0.8154 | nan | 0.8657 | 0.8996 | 0.7438 | 0.8327 | 0.6827 | 0.8573 | 0.7638 | 0.3149 | 0.5832 | 0.6047 | 0.9116 | 0.8488 | 0.8428 | 0.8714 | 0.8068 | 0.8253 | 0.3989 | 0.0 | 0.7633 | 0.8123 | 0.6302 | 0.7487 | 0.6098 | 0.7502 | 0.5344 | 0.2728 | 0.4839 | 0.5104 | 0.8265 | 0.7317 | 0.7573 | 0.7467 | 0.7452 | 0.7725 | 0.3153 | | 0.05 | 8.44 | 4220 | 0.2271 | 0.6161 | 0.7455 | 0.8215 | nan | 0.8631 | 0.9026 | 0.7511 | 0.8575 | 0.6986 | 0.8655 | 0.7491 | 0.3346 | 0.5949 | 0.6104 | 0.8962 | 0.8472 | 0.8347 | 0.8632 | 0.8054 | 0.8265 | 0.3734 | 0.0 | 0.7625 | 0.8138 | 0.6335 | 0.7622 | 0.6207 | 0.7495 | 0.5443 | 0.2922 | 0.4888 | 0.5166 | 0.8246 | 0.7395 | 0.7607 | 0.7435 | 0.7497 | 0.7785 | 0.3096 | | 0.0541 | 8.48 | 4240 | 0.2227 | 0.6164 | 0.7388 | 0.8272 | nan | 0.8478 | 0.9055 | 0.7101 | 0.8879 | 0.6953 | 0.8455 | 0.7449 | 0.2957 | 0.6119 | 0.6019 | 0.9073 | 0.8250 | 0.8470 | 0.8558 | 0.8319 | 0.8216 | 0.3246 | 0.0 | 0.7695 | 0.8174 | 0.6182 | 0.7706 | 0.6331 | 0.7388 | 0.5698 | 0.2683 | 0.4955 | 0.5107 | 0.8253 | 0.7389 | 0.7609 | 0.7467 | 0.7556 | 0.7774 | 0.2987 | | 0.0361 | 8.52 | 4260 | 0.2275 | 0.6168 | 0.7421 | 0.8272 | nan | 0.8536 | 0.8995 | 0.7183 | 0.8799 | 0.7048 | 0.8417 | 0.7611 | 0.3506 | 0.5552 | 0.6506 | 0.9142 | 0.8481 | 0.8468 | 0.8534 | 0.8210 | 0.8173 | 0.2988 | 0.0 | 0.7541 | 0.8131 | 0.6184 | 0.7697 | 0.6383 | 0.7402 | 0.5791 | 0.2963 | 0.4795 | 0.5348 | 0.8234 | 0.7439 | 0.7606 | 0.7444 | 0.7527 | 0.7714 | 0.2818 | | 0.0389 | 8.56 | 4280 | 0.2229 | 0.6198 | 0.7505 | 0.8290 | nan | 0.8848 | 0.8979 | 0.7303 | 0.8820 | 0.6991 | 0.8480 | 0.7562 | 0.4229 | 0.6257 | 0.6414 | 0.9137 | 0.8404 | 0.8390 | 0.8373 | 0.8252 | 0.8380 | 0.2764 | 0.0 | 0.7545 | 0.8152 | 0.6212 | 0.7709 | 0.6410 | 0.7464 | 0.5771 | 0.3138 | 0.5133 | 0.5295 | 0.8244 | 0.7366 | 0.7597 | 0.7498 | 0.7575 | 0.7848 | 0.2609 | | 0.0558 | 8.6 | 4300 | 0.2195 | 0.6148 | 0.7425 | 0.8239 | nan | 0.8746 | 0.8951 | 0.7349 | 0.8719 | 0.7043 | 0.8426 | 0.7614 | 0.3675 | 0.5751 | 0.6383 | 0.9055 | 0.8457 | 0.8271 | 0.8390 | 0.8284 | 0.8273 | 0.2837 | 0.0 | 0.7525 | 0.8152 | 0.6213 | 0.7658 | 0.6393 | 0.7416 | 0.5705 | 0.2937 | 0.4901 | 0.5255 | 0.8239 | 0.7295 | 0.7502 | 0.7522 | 0.7592 | 0.7772 | 0.2592 | | 0.0518 | 8.64 | 4320 | 0.2243 | 0.6100 | 0.7395 | 0.8187 | nan | 0.8573 | 0.8882 | 0.6744 | 0.8370 | 0.7125 | 0.8549 | 0.7943 | 0.3636 | 0.5806 | 0.6465 | 0.9170 | 0.8424 | 0.8206 | 0.8424 | 0.8282 | 0.8064 | 0.3046 | 0.0 | 0.7494 | 0.8109 | 0.5947 | 0.7505 | 0.6412 | 0.7541 | 0.5576 | 0.2835 | 0.4915 | 0.5322 | 0.8231 | 0.7243 | 0.7405 | 0.7508 | 0.7564 | 0.7662 | 0.2525 | | 0.0494 | 8.68 | 4340 | 0.2253 | 0.6195 | 0.7551 | 0.8216 | nan | 0.8610 | 0.8936 | 0.7012 | 0.8349 | 0.7050 | 0.8833 | 0.7699 | 0.4646 | 0.6088 | 0.6337 | 0.9047 | 0.8290 | 0.8393 | 0.8325 | 0.8498 | 0.8475 | 0.3772 | 0.0 | 0.7665 | 0.8150 | 0.6144 | 0.7542 | 0.6231 | 0.7692 | 0.5457 | 0.3459 | 0.5041 | 0.5377 | 0.8237 | 0.7256 | 0.7486 | 0.7537 | 0.7631 | 0.7836 | 0.2767 | | 0.0383 | 8.72 | 4360 | 0.2340 | 0.6198 | 0.7559 | 0.8203 | nan | 0.8520 | 0.9032 | 0.6900 | 0.8180 | 0.7041 | 0.8829 | 0.7936 | 0.4585 | 0.5947 | 0.6318 | 0.9162 | 0.8363 | 0.8426 | 0.8445 | 0.8350 | 0.8253 | 0.4223 | 0.0 | 0.7709 | 0.8145 | 0.6086 | 0.7455 | 0.6360 | 0.7683 | 0.5511 | 0.3486 | 0.4953 | 0.5325 | 0.8247 | 0.7336 | 0.7517 | 0.7556 | 0.7600 | 0.7761 | 0.2841 | | 0.0375 | 8.76 | 4380 | 0.2217 | 0.6190 | 0.7508 | 0.8206 | nan | 0.8522 | 0.8948 | 0.7449 | 0.8423 | 0.7028 | 0.8788 | 0.7530 | 0.3981 | 0.5796 | 0.6369 | 0.9089 | 0.8378 | 0.8432 | 0.8509 | 0.8432 | 0.8251 | 0.3711 | 0.0 | 0.7654 | 0.8135 | 0.6285 | 0.7568 | 0.6153 | 0.7698 | 0.5412 | 0.3309 | 0.4905 | 0.5324 | 0.8261 | 0.7353 | 0.7564 | 0.7579 | 0.7658 | 0.7739 | 0.2821 | | 0.0367 | 8.8 | 4400 | 0.2246 | 0.6145 | 0.7416 | 0.8197 | nan | 0.8334 | 0.9068 | 0.7256 | 0.8322 | 0.7058 | 0.8781 | 0.7749 | 0.3536 | 0.6074 | 0.5935 | 0.9129 | 0.8687 | 0.8181 | 0.8378 | 0.8418 | 0.8087 | 0.3076 | 0.0 | 0.7557 | 0.8191 | 0.6192 | 0.7533 | 0.6236 | 0.7658 | 0.5453 | 0.3045 | 0.4992 | 0.5132 | 0.8244 | 0.7292 | 0.7469 | 0.7588 | 0.7727 | 0.7672 | 0.2636 | | 0.074 | 8.84 | 4420 | 0.2326 | 0.6138 | 0.7419 | 0.8193 | nan | 0.8483 | 0.8924 | 0.7183 | 0.8400 | 0.7080 | 0.8732 | 0.7632 | 0.3716 | 0.5848 | 0.6264 | 0.9149 | 0.8326 | 0.8520 | 0.8216 | 0.8525 | 0.8141 | 0.2978 | 0.0 | 0.7572 | 0.8164 | 0.6165 | 0.7537 | 0.6143 | 0.7643 | 0.5398 | 0.2985 | 0.4928 | 0.5247 | 0.8242 | 0.7314 | 0.7556 | 0.7516 | 0.7737 | 0.7704 | 0.2632 | | 0.038 | 8.88 | 4440 | 0.2314 | 0.6220 | 0.7566 | 0.8234 | nan | 0.8469 | 0.9163 | 0.7150 | 0.8502 | 0.6724 | 0.8791 | 0.7627 | 0.5999 | 0.6023 | 0.6458 | 0.9025 | 0.8408 | 0.8365 | 0.8592 | 0.8411 | 0.8110 | 0.2815 | 0.0 | 0.7603 | 0.8159 | 0.6218 | 0.7619 | 0.6140 | 0.7485 | 0.5529 | 0.4128 | 0.5023 | 0.5385 | 0.8229 | 0.7336 | 0.7566 | 0.7688 | 0.7776 | 0.7600 | 0.2481 | | 0.0486 | 8.92 | 4460 | 0.2265 | 0.6176 | 0.7471 | 0.8218 | nan | 0.8489 | 0.8824 | 0.7234 | 0.8589 | 0.6716 | 0.8823 | 0.7616 | 0.4996 | 0.5337 | 0.6612 | 0.9079 | 0.8436 | 0.8342 | 0.8490 | 0.8480 | 0.8212 | 0.2740 | 0.0 | 0.7505 | 0.8106 | 0.6248 | 0.7667 | 0.6140 | 0.7534 | 0.5585 | 0.3704 | 0.4673 | 0.5368 | 0.8259 | 0.7315 | 0.7565 | 0.7639 | 0.7729 | 0.7609 | 0.2529 | | 0.0589 | 8.96 | 4480 | 0.2335 | 0.6140 | 0.7404 | 0.8184 | nan | 0.8318 | 0.9004 | 0.7298 | 0.8342 | 0.6780 | 0.8726 | 0.7916 | 0.3401 | 0.5608 | 0.6654 | 0.9077 | 0.8507 | 0.8166 | 0.8645 | 0.8000 | 0.8166 | 0.3265 | 0.0 | 0.7470 | 0.8131 | 0.6265 | 0.7503 | 0.6308 | 0.7604 | 0.5579 | 0.2867 | 0.4791 | 0.5382 | 0.8258 | 0.7323 | 0.7529 | 0.7567 | 0.7570 | 0.7584 | 0.2784 | | 0.0928 | 9.0 | 4500 | 0.2234 | 0.6170 | 0.7451 | 0.8261 | nan | 0.8674 | 0.8965 | 0.7326 | 0.8676 | 0.6852 | 0.8826 | 0.7584 | 0.2819 | 0.6002 | 0.6321 | 0.9084 | 0.8186 | 0.8459 | 0.8650 | 0.8278 | 0.8071 | 0.3889 | 0.0 | 0.7652 | 0.8184 | 0.6286 | 0.7709 | 0.6255 | 0.7565 | 0.5666 | 0.2459 | 0.4963 | 0.5297 | 0.8279 | 0.7332 | 0.7606 | 0.7590 | 0.7643 | 0.7585 | 0.2994 | | 0.0573 | 9.04 | 4520 | 0.2235 | 0.6203 | 0.7464 | 0.8278 | nan | 0.8441 | 0.9048 | 0.7557 | 0.8764 | 0.6927 | 0.8730 | 0.7553 | 0.3351 | 0.5800 | 0.6431 | 0.8976 | 0.8401 | 0.8388 | 0.8349 | 0.8396 | 0.8132 | 0.3647 | 0.0 | 0.7572 | 0.8168 | 0.6361 | 0.7754 | 0.6289 | 0.7582 | 0.5684 | 0.2740 | 0.4915 | 0.5384 | 0.8271 | 0.7325 | 0.7539 | 0.7550 | 0.7734 | 0.7672 | 0.3111 | | 0.0523 | 9.08 | 4540 | 0.2217 | 0.6225 | 0.7516 | 0.8277 | nan | 0.8399 | 0.8935 | 0.7553 | 0.8741 | 0.6888 | 0.8867 | 0.7466 | 0.4573 | 0.5676 | 0.6488 | 0.9148 | 0.8437 | 0.8369 | 0.8397 | 0.8095 | 0.8470 | 0.3272 | 0.0 | 0.7538 | 0.8140 | 0.6335 | 0.7796 | 0.6226 | 0.7658 | 0.5625 | 0.3275 | 0.4876 | 0.5414 | 0.8296 | 0.7301 | 0.7501 | 0.7519 | 0.7637 | 0.7855 | 0.3064 | | 0.0372 | 9.12 | 4560 | 0.2219 | 0.6164 | 0.7455 | 0.8271 | nan | 0.8473 | 0.9010 | 0.7986 | 0.8733 | 0.7194 | 0.8896 | 0.7262 | 0.2736 | 0.6386 | 0.5583 | 0.9006 | 0.8329 | 0.8349 | 0.8477 | 0.8408 | 0.8291 | 0.3614 | 0.0 | 0.7641 | 0.8176 | 0.6378 | 0.7793 | 0.6240 | 0.7654 | 0.5526 | 0.2364 | 0.5000 | 0.4922 | 0.8266 | 0.7301 | 0.7500 | 0.7622 | 0.7750 | 0.7774 | 0.3053 | | 0.0338 | 9.16 | 4580 | 0.2194 | 0.6151 | 0.7444 | 0.8235 | nan | 0.8565 | 0.9045 | 0.7748 | 0.8561 | 0.6996 | 0.8831 | 0.7412 | 0.2413 | 0.5597 | 0.6618 | 0.9050 | 0.8354 | 0.8398 | 0.8621 | 0.8231 | 0.8231 | 0.3871 | 0.0 | 0.7564 | 0.8160 | 0.6372 | 0.7708 | 0.6168 | 0.7642 | 0.5501 | 0.2192 | 0.4849 | 0.5388 | 0.8262 | 0.7314 | 0.7486 | 0.7605 | 0.7652 | 0.7703 | 0.3151 | | 0.0257 | 9.2 | 4600 | 0.2310 | 0.6161 | 0.7447 | 0.8228 | nan | 0.8637 | 0.9010 | 0.7017 | 0.8463 | 0.6798 | 0.8983 | 0.7594 | 0.3465 | 0.5853 | 0.6059 | 0.9181 | 0.8553 | 0.8271 | 0.8657 | 0.8445 | 0.8027 | 0.3584 | 0.0 | 0.7591 | 0.8155 | 0.6141 | 0.7665 | 0.6172 | 0.7607 | 0.5539 | 0.2841 | 0.4879 | 0.5133 | 0.8245 | 0.7351 | 0.7456 | 0.7654 | 0.7720 | 0.7611 | 0.3139 | | 0.0361 | 9.24 | 4620 | 0.2252 | 0.6177 | 0.7411 | 0.8252 | nan | 0.8602 | 0.8865 | 0.7017 | 0.8594 | 0.6979 | 0.8903 | 0.7653 | 0.2889 | 0.5940 | 0.6255 | 0.9065 | 0.8491 | 0.8159 | 0.8514 | 0.8225 | 0.8356 | 0.3481 | 0.0 | 0.7640 | 0.8156 | 0.6161 | 0.7685 | 0.6218 | 0.7700 | 0.5514 | 0.2470 | 0.4947 | 0.5280 | 0.8262 | 0.7354 | 0.7478 | 0.7640 | 0.7766 | 0.7753 | 0.3158 | | 0.0347 | 9.28 | 4640 | 0.2147 | 0.6200 | 0.7443 | 0.8232 | nan | 0.8352 | 0.9015 | 0.6848 | 0.8728 | 0.6870 | 0.8730 | 0.7260 | 0.4264 | 0.5880 | 0.6143 | 0.9147 | 0.8463 | 0.8288 | 0.8560 | 0.8371 | 0.8136 | 0.3471 | 0.0 | 0.7601 | 0.8139 | 0.6094 | 0.7736 | 0.6179 | 0.7552 | 0.5541 | 0.3273 | 0.4945 | 0.5242 | 0.8248 | 0.7396 | 0.7573 | 0.7674 | 0.7783 | 0.7595 | 0.3036 | | 0.0448 | 9.32 | 4660 | 0.2202 | 0.6206 | 0.7493 | 0.8224 | nan | 0.8721 | 0.8928 | 0.7461 | 0.8527 | 0.6907 | 0.8672 | 0.7654 | 0.3517 | 0.5951 | 0.6236 | 0.9228 | 0.8334 | 0.8348 | 0.8474 | 0.8318 | 0.8255 | 0.3839 | 0.0 | 0.7636 | 0.8125 | 0.6291 | 0.7633 | 0.6285 | 0.7545 | 0.5555 | 0.2887 | 0.4987 | 0.5293 | 0.8243 | 0.7380 | 0.7602 | 0.7629 | 0.7738 | 0.7699 | 0.3173 | | 0.0461 | 9.36 | 4680 | 0.2219 | 0.6224 | 0.7525 | 0.8226 | nan | 0.8586 | 0.8968 | 0.7592 | 0.8534 | 0.6851 | 0.8649 | 0.7606 | 0.4322 | 0.5740 | 0.6244 | 0.9118 | 0.8247 | 0.8462 | 0.8574 | 0.8488 | 0.8434 | 0.3512 | 0.0 | 0.7621 | 0.8184 | 0.6386 | 0.7572 | 0.6231 | 0.7441 | 0.5521 | 0.3460 | 0.4897 | 0.5255 | 0.8259 | 0.7370 | 0.7613 | 0.7627 | 0.7814 | 0.7692 | 0.3093 | | 0.0426 | 9.4 | 4700 | 0.2204 | 0.6179 | 0.7414 | 0.8235 | nan | 0.8537 | 0.8979 | 0.7061 | 0.8604 | 0.7016 | 0.8757 | 0.7515 | 0.3451 | 0.5808 | 0.6057 | 0.9052 | 0.8406 | 0.8397 | 0.8718 | 0.8247 | 0.8201 | 0.3240 | 0.0 | 0.7597 | 0.8162 | 0.6196 | 0.7614 | 0.6139 | 0.7480 | 0.5505 | 0.2937 | 0.4912 | 0.5175 | 0.8257 | 0.7415 | 0.7624 | 0.7689 | 0.7782 | 0.7715 | 0.3032 | | 0.0298 | 9.44 | 4720 | 0.2229 | 0.6186 | 0.7430 | 0.8269 | nan | 0.8791 | 0.8981 | 0.6689 | 0.8638 | 0.6958 | 0.8800 | 0.7490 | 0.3231 | 0.5789 | 0.6243 | 0.9209 | 0.8405 | 0.8507 | 0.8610 | 0.8479 | 0.8245 | 0.3249 | 0.0 | 0.7687 | 0.8212 | 0.5974 | 0.7684 | 0.6145 | 0.7596 | 0.5552 | 0.2785 | 0.4865 | 0.5197 | 0.8278 | 0.7435 | 0.7616 | 0.7728 | 0.7830 | 0.7728 | 0.3035 | | 0.0396 | 9.48 | 4740 | 0.2194 | 0.6178 | 0.7451 | 0.8249 | nan | 0.8558 | 0.8985 | 0.7329 | 0.8603 | 0.6917 | 0.8656 | 0.7544 | 0.2961 | 0.5915 | 0.6101 | 0.9182 | 0.8229 | 0.8528 | 0.8644 | 0.8321 | 0.8544 | 0.3656 | 0.0 | 0.7584 | 0.8118 | 0.6254 | 0.7689 | 0.6216 | 0.7592 | 0.5630 | 0.2490 | 0.4927 | 0.5189 | 0.8270 | 0.7312 | 0.7581 | 0.7654 | 0.7742 | 0.7813 | 0.3152 | | 0.0376 | 9.52 | 4760 | 0.2246 | 0.6190 | 0.7453 | 0.8293 | nan | 0.8531 | 0.9008 | 0.7675 | 0.8879 | 0.6756 | 0.8653 | 0.7463 | 0.3321 | 0.5628 | 0.6402 | 0.9072 | 0.8465 | 0.8313 | 0.8571 | 0.8365 | 0.8420 | 0.3171 | 0.0 | 0.7661 | 0.8215 | 0.6346 | 0.7783 | 0.6147 | 0.7502 | 0.5700 | 0.2799 | 0.4806 | 0.5286 | 0.8280 | 0.7331 | 0.7529 | 0.7601 | 0.7741 | 0.7820 | 0.2880 | | 0.0352 | 9.56 | 4780 | 0.2257 | 0.6170 | 0.7451 | 0.8236 | nan | 0.8482 | 0.8993 | 0.7699 | 0.8593 | 0.6632 | 0.8646 | 0.7753 | 0.3683 | 0.5849 | 0.6340 | 0.9119 | 0.8564 | 0.8098 | 0.8549 | 0.8404 | 0.8405 | 0.2851 | 0.0 | 0.7635 | 0.8183 | 0.6363 | 0.7651 | 0.6148 | 0.7525 | 0.5637 | 0.3001 | 0.4872 | 0.5264 | 0.8264 | 0.7286 | 0.7449 | 0.7577 | 0.7713 | 0.7812 | 0.2675 | | 0.0542 | 9.6 | 4800 | 0.2272 | 0.6175 | 0.7445 | 0.8233 | nan | 0.8529 | 0.9084 | 0.7263 | 0.8714 | 0.6360 | 0.8556 | 0.7600 | 0.4009 | 0.5835 | 0.6507 | 0.9122 | 0.8492 | 0.8174 | 0.8589 | 0.8306 | 0.8385 | 0.3039 | 0.0 | 0.7682 | 0.8175 | 0.6278 | 0.7656 | 0.5900 | 0.7477 | 0.5562 | 0.3268 | 0.4917 | 0.5386 | 0.8276 | 0.7303 | 0.7475 | 0.7591 | 0.7672 | 0.7832 | 0.2697 | | 0.0597 | 9.64 | 4820 | 0.2243 | 0.6173 | 0.7459 | 0.8209 | nan | 0.8581 | 0.8975 | 0.7392 | 0.8623 | 0.6479 | 0.8756 | 0.7467 | 0.4048 | 0.5571 | 0.6483 | 0.9147 | 0.8412 | 0.8386 | 0.8492 | 0.8357 | 0.8250 | 0.3387 | 0.0 | 0.7632 | 0.8103 | 0.6294 | 0.7655 | 0.5914 | 0.7578 | 0.5482 | 0.3298 | 0.4798 | 0.5331 | 0.8269 | 0.7378 | 0.7562 | 0.7535 | 0.7642 | 0.7758 | 0.2888 | | 0.0855 | 9.68 | 4840 | 0.2361 | 0.6083 | 0.7346 | 0.8123 | nan | 0.8613 | 0.8959 | 0.7404 | 0.8304 | 0.6354 | 0.8824 | 0.7803 | 0.2968 | 0.5771 | 0.6293 | 0.9045 | 0.8358 | 0.8301 | 0.8307 | 0.8167 | 0.8049 | 0.3358 | 0.0 | 0.7646 | 0.8139 | 0.6307 | 0.7473 | 0.5705 | 0.7638 | 0.5246 | 0.2614 | 0.4867 | 0.5266 | 0.8259 | 0.7308 | 0.7483 | 0.7451 | 0.7598 | 0.7597 | 0.2898 | | 0.0563 | 9.72 | 4860 | 0.2289 | 0.6154 | 0.7473 | 0.8178 | nan | 0.8695 | 0.9057 | 0.7158 | 0.8294 | 0.6774 | 0.8696 | 0.7678 | 0.3771 | 0.6135 | 0.5997 | 0.9082 | 0.8485 | 0.8368 | 0.8476 | 0.8450 | 0.8416 | 0.3509 | 0.0 | 0.7647 | 0.8150 | 0.6206 | 0.7501 | 0.6023 | 0.7646 | 0.5346 | 0.3071 | 0.4981 | 0.5153 | 0.8249 | 0.7351 | 0.7524 | 0.7541 | 0.7661 | 0.7764 | 0.2950 | | 0.0694 | 9.76 | 4880 | 0.2276 | 0.6139 | 0.7427 | 0.8186 | nan | 0.8687 | 0.9006 | 0.7177 | 0.8340 | 0.7027 | 0.8921 | 0.7562 | 0.3572 | 0.6075 | 0.5876 | 0.9079 | 0.8441 | 0.8309 | 0.8355 | 0.8477 | 0.8100 | 0.3259 | 0.0 | 0.7621 | 0.8153 | 0.6212 | 0.7563 | 0.6080 | 0.7695 | 0.5397 | 0.2979 | 0.4909 | 0.5043 | 0.8247 | 0.7374 | 0.7541 | 0.7512 | 0.7699 | 0.7609 | 0.2868 | | 0.0377 | 9.8 | 4900 | 0.2148 | 0.6199 | 0.7466 | 0.8302 | nan | 0.8704 | 0.8920 | 0.7402 | 0.8967 | 0.7284 | 0.8958 | 0.6951 | 0.3935 | 0.5583 | 0.6585 | 0.9177 | 0.8135 | 0.8469 | 0.8450 | 0.8428 | 0.8031 | 0.2944 | 0.0 | 0.7683 | 0.8178 | 0.6249 | 0.7924 | 0.6244 | 0.7652 | 0.5623 | 0.3204 | 0.4778 | 0.5333 | 0.8260 | 0.7276 | 0.7532 | 0.7546 | 0.7684 | 0.7598 | 0.2823 | | 0.0534 | 9.84 | 4920 | 0.2119 | 0.6209 | 0.7478 | 0.8312 | nan | 0.8586 | 0.9098 | 0.7091 | 0.8895 | 0.7283 | 0.8786 | 0.7104 | 0.4140 | 0.6087 | 0.6257 | 0.9178 | 0.8457 | 0.8222 | 0.8478 | 0.8429 | 0.8212 | 0.2815 | 0.0 | 0.7662 | 0.8146 | 0.6182 | 0.7906 | 0.6364 | 0.7656 | 0.5698 | 0.3339 | 0.4983 | 0.5214 | 0.8250 | 0.7333 | 0.7485 | 0.7513 | 0.7668 | 0.7670 | 0.2699 | | 0.0596 | 9.88 | 4940 | 0.2095 | 0.6206 | 0.7466 | 0.8317 | nan | 0.8512 | 0.9009 | 0.7502 | 0.8952 | 0.7235 | 0.8842 | 0.7147 | 0.3887 | 0.5872 | 0.6461 | 0.8986 | 0.8318 | 0.8380 | 0.8639 | 0.8334 | 0.8172 | 0.2676 | 0.0 | 0.7584 | 0.8165 | 0.6355 | 0.7917 | 0.6305 | 0.7697 | 0.5682 | 0.3181 | 0.4964 | 0.5364 | 0.8242 | 0.7291 | 0.7487 | 0.7529 | 0.7690 | 0.7622 | 0.2628 | | 0.0443 | 9.92 | 4960 | 0.2174 | 0.6185 | 0.7482 | 0.8271 | nan | 0.8366 | 0.8982 | 0.7238 | 0.8692 | 0.6983 | 0.8825 | 0.7496 | 0.4489 | 0.5994 | 0.6071 | 0.9190 | 0.8424 | 0.8391 | 0.8726 | 0.8129 | 0.8225 | 0.2967 | 0.0 | 0.7460 | 0.8164 | 0.6239 | 0.7827 | 0.6206 | 0.7661 | 0.5694 | 0.3445 | 0.4996 | 0.5204 | 0.8233 | 0.7275 | 0.7472 | 0.7485 | 0.7583 | 0.7631 | 0.2748 | | 0.0366 | 9.96 | 4980 | 0.2141 | 0.6158 | 0.7455 | 0.8229 | nan | 0.8576 | 0.8926 | 0.7784 | 0.8813 | 0.7318 | 0.8717 | 0.7171 | 0.3805 | 0.5959 | 0.6243 | 0.8987 | 0.8323 | 0.8288 | 0.8452 | 0.7942 | 0.7856 | 0.3574 | 0.0 | 0.7498 | 0.8127 | 0.6371 | 0.7817 | 0.6057 | 0.7667 | 0.5537 | 0.3075 | 0.4946 | 0.5219 | 0.8239 | 0.7303 | 0.7494 | 0.7416 | 0.7523 | 0.7455 | 0.3103 | | 0.0378 | 10.0 | 5000 | 0.2255 | 0.6146 | 0.7467 | 0.8205 | nan | 0.8623 | 0.8960 | 0.7348 | 0.8527 | 0.7228 | 0.8557 | 0.7479 | 0.3292 | 0.6004 | 0.6126 | 0.9190 | 0.8466 | 0.8337 | 0.8469 | 0.8429 | 0.7933 | 0.3966 | 0.0 | 0.7603 | 0.8111 | 0.6260 | 0.7673 | 0.6062 | 0.7592 | 0.5469 | 0.2756 | 0.4944 | 0.5214 | 0.8248 | 0.7310 | 0.7492 | 0.7548 | 0.7687 | 0.7511 | 0.3148 | | 0.0412 | 10.04 | 5020 | 0.2159 | 0.6145 | 0.7431 | 0.8224 | nan | 0.8334 | 0.9106 | 0.7136 | 0.8595 | 0.7208 | 0.8631 | 0.7485 | 0.3142 | 0.5852 | 0.6161 | 0.9079 | 0.8300 | 0.8400 | 0.8357 | 0.8431 | 0.7875 | 0.4234 | 0.0 | 0.7569 | 0.8186 | 0.6197 | 0.7697 | 0.6115 | 0.7634 | 0.5506 | 0.2716 | 0.4897 | 0.5235 | 0.8262 | 0.7264 | 0.7453 | 0.7509 | 0.7665 | 0.7481 | 0.3230 | | 0.1179 | 10.08 | 5040 | 0.2071 | 0.6180 | 0.7496 | 0.8239 | nan | 0.8613 | 0.9001 | 0.7407 | 0.8686 | 0.7187 | 0.8611 | 0.7420 | 0.3679 | 0.5785 | 0.6314 | 0.9041 | 0.8371 | 0.8376 | 0.8479 | 0.8274 | 0.8013 | 0.4174 | 0.0 | 0.7594 | 0.8119 | 0.6309 | 0.7763 | 0.6182 | 0.7628 | 0.5625 | 0.2990 | 0.4925 | 0.5347 | 0.8260 | 0.7251 | 0.7413 | 0.7540 | 0.7641 | 0.7537 | 0.3123 | | 0.0377 | 10.12 | 5060 | 0.2216 | 0.6169 | 0.7470 | 0.8237 | nan | 0.8673 | 0.8996 | 0.6967 | 0.8612 | 0.7010 | 0.8703 | 0.7586 | 0.3511 | 0.6051 | 0.6281 | 0.9198 | 0.8355 | 0.8233 | 0.8483 | 0.8428 | 0.7845 | 0.4051 | 0.0 | 0.7629 | 0.8188 | 0.6104 | 0.7721 | 0.6135 | 0.7628 | 0.5605 | 0.2929 | 0.5016 | 0.5304 | 0.8264 | 0.7279 | 0.7409 | 0.7580 | 0.7689 | 0.7454 | 0.3103 | | 0.0445 | 10.16 | 5080 | 0.2239 | 0.6179 | 0.7467 | 0.8240 | nan | 0.8545 | 0.8958 | 0.7701 | 0.8630 | 0.7007 | 0.8702 | 0.7605 | 0.3334 | 0.6025 | 0.6255 | 0.9208 | 0.8364 | 0.8213 | 0.8593 | 0.8240 | 0.7971 | 0.3595 | 0.0 | 0.7640 | 0.8170 | 0.6357 | 0.7734 | 0.6157 | 0.7643 | 0.5647 | 0.2889 | 0.4980 | 0.5305 | 0.8266 | 0.7283 | 0.7441 | 0.7552 | 0.7653 | 0.7550 | 0.2959 | | 0.0397 | 10.2 | 5100 | 0.2256 | 0.6182 | 0.7471 | 0.8257 | nan | 0.8602 | 0.8983 | 0.7746 | 0.8704 | 0.7085 | 0.8777 | 0.7589 | 0.3473 | 0.6178 | 0.5808 | 0.9051 | 0.8427 | 0.8164 | 0.8588 | 0.8123 | 0.7980 | 0.3731 | 0.0 | 0.7645 | 0.8199 | 0.6395 | 0.7753 | 0.6197 | 0.7651 | 0.5659 | 0.2990 | 0.4985 | 0.5049 | 0.8273 | 0.7302 | 0.7465 | 0.7551 | 0.7643 | 0.7549 | 0.2975 | | 0.0335 | 10.24 | 5120 | 0.2156 | 0.6217 | 0.7500 | 0.8290 | nan | 0.8619 | 0.8925 | 0.7266 | 0.8851 | 0.7115 | 0.8604 | 0.7429 | 0.4009 | 0.6091 | 0.6095 | 0.9262 | 0.8338 | 0.8409 | 0.8507 | 0.8471 | 0.8052 | 0.3459 | 0.0 | 0.7638 | 0.8206 | 0.6234 | 0.7827 | 0.6255 | 0.7603 | 0.5746 | 0.3377 | 0.5021 | 0.5211 | 0.8260 | 0.7300 | 0.7501 | 0.7550 | 0.7683 | 0.7577 | 0.2923 | | 0.048 | 10.28 | 5140 | 0.2195 | 0.6206 | 0.7457 | 0.8280 | nan | 0.8531 | 0.9014 | 0.6939 | 0.8842 | 0.7175 | 0.8655 | 0.7404 | 0.4071 | 0.6059 | 0.6196 | 0.9035 | 0.8571 | 0.8085 | 0.8516 | 0.8397 | 0.7988 | 0.3285 | 0.0 | 0.7606 | 0.8163 | 0.6127 | 0.7841 | 0.6295 | 0.7633 | 0.5770 | 0.3358 | 0.5014 | 0.5297 | 0.8262 | 0.7284 | 0.7393 | 0.7531 | 0.7629 | 0.7543 | 0.2961 | | 0.0518 | 10.32 | 5160 | 0.2107 | 0.6254 | 0.7515 | 0.8318 | nan | 0.8493 | 0.8991 | 0.7409 | 0.8845 | 0.7223 | 0.8681 | 0.7451 | 0.3741 | 0.5943 | 0.6483 | 0.9188 | 0.8404 | 0.8384 | 0.8459 | 0.8475 | 0.8103 | 0.3483 | 0.0 | 0.7630 | 0.8202 | 0.6315 | 0.7845 | 0.6292 | 0.7674 | 0.5743 | 0.3200 | 0.5017 | 0.5441 | 0.8304 | 0.7308 | 0.7501 | 0.7621 | 0.7759 | 0.7625 | 0.3086 | | 0.0362 | 10.36 | 5180 | 0.2180 | 0.6204 | 0.7522 | 0.8271 | nan | 0.8772 | 0.8998 | 0.7279 | 0.8720 | 0.7247 | 0.8705 | 0.7520 | 0.3289 | 0.5850 | 0.6485 | 0.9086 | 0.8084 | 0.8411 | 0.8353 | 0.8401 | 0.7921 | 0.4761 | 0.0 | 0.7615 | 0.8145 | 0.6294 | 0.7781 | 0.6348 | 0.7684 | 0.5729 | 0.2863 | 0.4947 | 0.5348 | 0.8298 | 0.7204 | 0.7510 | 0.7587 | 0.7739 | 0.7521 | 0.3060 | | 0.053 | 10.4 | 5200 | 0.2110 | 0.6238 | 0.7492 | 0.8305 | nan | 0.8493 | 0.9017 | 0.7544 | 0.8845 | 0.7309 | 0.8746 | 0.7392 | 0.3401 | 0.5799 | 0.6198 | 0.9038 | 0.8354 | 0.8265 | 0.8521 | 0.8394 | 0.8169 | 0.3874 | 0.0 | 0.7631 | 0.8175 | 0.6393 | 0.7849 | 0.6409 | 0.7671 | 0.5779 | 0.2954 | 0.4893 | 0.5222 | 0.8297 | 0.7329 | 0.7528 | 0.7644 | 0.7747 | 0.7652 | 0.3116 | | 0.0626 | 10.44 | 5220 | 0.2206 | 0.6222 | 0.7489 | 0.8291 | nan | 0.8490 | 0.9027 | 0.7750 | 0.8726 | 0.7048 | 0.8677 | 0.7555 | 0.2887 | 0.5839 | 0.6412 | 0.9092 | 0.8312 | 0.8575 | 0.8582 | 0.8417 | 0.8173 | 0.3746 | 0.0 | 0.7695 | 0.8209 | 0.6442 | 0.7780 | 0.6199 | 0.7659 | 0.5642 | 0.2613 | 0.4979 | 0.5377 | 0.8304 | 0.7308 | 0.7592 | 0.7671 | 0.7764 | 0.7675 | 0.3092 | | 0.0327 | 10.48 | 5240 | 0.2259 | 0.6213 | 0.7494 | 0.8227 | nan | 0.8680 | 0.8990 | 0.7714 | 0.8542 | 0.6356 | 0.8630 | 0.8026 | 0.3758 | 0.5874 | 0.6414 | 0.9155 | 0.8420 | 0.8452 | 0.8468 | 0.8367 | 0.7930 | 0.3618 | 0.0 | 0.7686 | 0.8182 | 0.6428 | 0.7656 | 0.6033 | 0.7590 | 0.5645 | 0.3215 | 0.5024 | 0.5408 | 0.8284 | 0.7342 | 0.7579 | 0.7564 | 0.7711 | 0.7505 | 0.2988 | | 0.0502 | 10.52 | 5260 | 0.2303 | 0.6205 | 0.7532 | 0.8192 | nan | 0.8599 | 0.9073 | 0.7546 | 0.8278 | 0.6260 | 0.8672 | 0.8299 | 0.4034 | 0.6101 | 0.6381 | 0.9083 | 0.8542 | 0.8286 | 0.8677 | 0.8222 | 0.7866 | 0.4117 | 0.0 | 0.7654 | 0.8201 | 0.6399 | 0.7542 | 0.5932 | 0.7602 | 0.5554 | 0.3344 | 0.5066 | 0.5406 | 0.8287 | 0.7359 | 0.7540 | 0.7616 | 0.7674 | 0.7443 | 0.3081 | | 0.0261 | 10.56 | 5280 | 0.2100 | 0.6276 | 0.7585 | 0.8288 | nan | 0.8454 | 0.8949 | 0.7216 | 0.8499 | 0.7262 | 0.8815 | 0.7873 | 0.4518 | 0.5732 | 0.6507 | 0.9106 | 0.8435 | 0.8412 | 0.8713 | 0.8251 | 0.8197 | 0.4006 | 0.0 | 0.7584 | 0.8135 | 0.6246 | 0.7698 | 0.6606 | 0.7626 | 0.5794 | 0.3609 | 0.4907 | 0.5444 | 0.8275 | 0.7360 | 0.7576 | 0.7629 | 0.7689 | 0.7684 | 0.3100 | | 0.0367 | 10.6 | 5300 | 0.2105 | 0.6286 | 0.7600 | 0.8295 | nan | 0.8311 | 0.9008 | 0.7109 | 0.8554 | 0.7369 | 0.8848 | 0.7620 | 0.4867 | 0.6178 | 0.6341 | 0.9151 | 0.8242 | 0.8420 | 0.8563 | 0.8457 | 0.8271 | 0.3897 | 0.0 | 0.7560 | 0.8176 | 0.6200 | 0.7720 | 0.6602 | 0.7608 | 0.5730 | 0.3741 | 0.5059 | 0.5350 | 0.8270 | 0.7342 | 0.7568 | 0.7657 | 0.7747 | 0.7741 | 0.3087 | | 0.0494 | 10.64 | 5320 | 0.2101 | 0.6297 | 0.7626 | 0.8293 | nan | 0.8543 | 0.8998 | 0.7747 | 0.8539 | 0.7431 | 0.8798 | 0.7701 | 0.4706 | 0.5727 | 0.6555 | 0.9018 | 0.8355 | 0.8481 | 0.8455 | 0.8512 | 0.8174 | 0.3906 | 0.0 | 0.7598 | 0.8179 | 0.6454 | 0.7712 | 0.6626 | 0.7614 | 0.5729 | 0.3685 | 0.4892 | 0.5435 | 0.8264 | 0.7346 | 0.7580 | 0.7642 | 0.7773 | 0.7698 | 0.3125 | | 0.032 | 10.68 | 5340 | 0.2092 | 0.6256 | 0.7525 | 0.8288 | nan | 0.8394 | 0.9041 | 0.7387 | 0.8624 | 0.7439 | 0.8801 | 0.7531 | 0.4074 | 0.5851 | 0.6353 | 0.9099 | 0.8306 | 0.8395 | 0.8385 | 0.8394 | 0.8221 | 0.3637 | 0.0 | 0.7606 | 0.8164 | 0.6359 | 0.7755 | 0.6571 | 0.7609 | 0.5714 | 0.3418 | 0.4900 | 0.5332 | 0.8272 | 0.7302 | 0.7549 | 0.7582 | 0.7714 | 0.7749 | 0.3020 | | 0.0815 | 10.72 | 5360 | 0.2096 | 0.6262 | 0.7539 | 0.8322 | nan | 0.8455 | 0.8997 | 0.7519 | 0.8693 | 0.7435 | 0.8937 | 0.7511 | 0.3924 | 0.6073 | 0.5934 | 0.9148 | 0.8577 | 0.8196 | 0.8589 | 0.8448 | 0.8204 | 0.3518 | 0.0 | 0.7650 | 0.8207 | 0.6388 | 0.7816 | 0.6572 | 0.7617 | 0.5773 | 0.3307 | 0.4985 | 0.5189 | 0.8281 | 0.7326 | 0.7490 | 0.7657 | 0.7760 | 0.7720 | 0.2975 | | 0.0529 | 10.76 | 5380 | 0.2130 | 0.6261 | 0.7532 | 0.8287 | nan | 0.8682 | 0.8954 | 0.7341 | 0.8578 | 0.7345 | 0.8630 | 0.7909 | 0.4212 | 0.5998 | 0.6059 | 0.9135 | 0.8545 | 0.8225 | 0.8491 | 0.8459 | 0.8118 | 0.3364 | 0.0 | 0.7678 | 0.8209 | 0.6332 | 0.7739 | 0.6654 | 0.7521 | 0.5824 | 0.3451 | 0.4929 | 0.5194 | 0.8284 | 0.7403 | 0.7554 | 0.7608 | 0.7780 | 0.7618 | 0.2918 | | 0.0348 | 10.8 | 5400 | 0.2174 | 0.6254 | 0.7535 | 0.8270 | nan | 0.8603 | 0.8988 | 0.6850 | 0.8541 | 0.7227 | 0.8434 | 0.7972 | 0.4412 | 0.5788 | 0.6221 | 0.9185 | 0.8593 | 0.8188 | 0.8499 | 0.8463 | 0.8332 | 0.3798 | 0.0 | 0.7677 | 0.8190 | 0.6110 | 0.7707 | 0.6526 | 0.7469 | 0.5817 | 0.3509 | 0.4876 | 0.5275 | 0.8273 | 0.7360 | 0.7504 | 0.7609 | 0.7770 | 0.7691 | 0.3200 | | 0.0424 | 10.84 | 5420 | 0.2128 | 0.6286 | 0.7594 | 0.8323 | nan | 0.8541 | 0.8904 | 0.7261 | 0.8671 | 0.7348 | 0.8700 | 0.7783 | 0.3729 | 0.5723 | 0.6372 | 0.9230 | 0.8561 | 0.8202 | 0.8570 | 0.8394 | 0.8370 | 0.4743 | 0.0 | 0.7685 | 0.8167 | 0.6291 | 0.7790 | 0.6736 | 0.7597 | 0.5928 | 0.3156 | 0.4897 | 0.5383 | 0.8277 | 0.7348 | 0.7485 | 0.7624 | 0.7729 | 0.7761 | 0.3302 | | 0.0443 | 10.88 | 5440 | 0.2127 | 0.6280 | 0.7551 | 0.8318 | nan | 0.8285 | 0.9077 | 0.7122 | 0.8713 | 0.7365 | 0.8675 | 0.7681 | 0.3680 | 0.6071 | 0.6288 | 0.9094 | 0.8384 | 0.8312 | 0.8476 | 0.8344 | 0.8200 | 0.4596 | 0.0 | 0.7680 | 0.8184 | 0.6258 | 0.7799 | 0.6731 | 0.7608 | 0.5919 | 0.3159 | 0.5004 | 0.5312 | 0.8275 | 0.7353 | 0.7551 | 0.7588 | 0.7722 | 0.7686 | 0.3203 | | 0.0725 | 10.92 | 5460 | 0.2146 | 0.6300 | 0.7579 | 0.8328 | nan | 0.8521 | 0.8993 | 0.7243 | 0.8694 | 0.7051 | 0.8731 | 0.7830 | 0.3837 | 0.6255 | 0.6155 | 0.9131 | 0.8557 | 0.8331 | 0.8509 | 0.8339 | 0.8381 | 0.4290 | 0.0 | 0.7717 | 0.8195 | 0.6304 | 0.7799 | 0.6599 | 0.7633 | 0.5904 | 0.3339 | 0.5067 | 0.5259 | 0.8285 | 0.7402 | 0.7604 | 0.7598 | 0.7730 | 0.7798 | 0.3168 | | 0.0516 | 10.96 | 5480 | 0.2144 | 0.6324 | 0.7604 | 0.8355 | nan | 0.8486 | 0.9148 | 0.7692 | 0.8722 | 0.7175 | 0.8743 | 0.7817 | 0.3988 | 0.5847 | 0.6573 | 0.9090 | 0.8564 | 0.8315 | 0.8598 | 0.8257 | 0.8307 | 0.3950 | 0.0 | 0.7761 | 0.8266 | 0.6443 | 0.7816 | 0.6665 | 0.7638 | 0.5937 | 0.3506 | 0.4985 | 0.5469 | 0.8267 | 0.7378 | 0.7569 | 0.7599 | 0.7671 | 0.7812 | 0.3043 | | 0.0542 | 11.0 | 5500 | 0.2133 | 0.6298 | 0.7572 | 0.8331 | nan | 0.8333 | 0.9010 | 0.7820 | 0.8751 | 0.7279 | 0.8879 | 0.7564 | 0.4034 | 0.5703 | 0.6596 | 0.9128 | 0.8320 | 0.8518 | 0.8707 | 0.8125 | 0.8182 | 0.3773 | 0.0 | 0.7640 | 0.8179 | 0.6455 | 0.7836 | 0.6643 | 0.7653 | 0.5916 | 0.3499 | 0.4914 | 0.5451 | 0.8265 | 0.7416 | 0.7649 | 0.7560 | 0.7595 | 0.7752 | 0.2944 | | 0.0404 | 11.04 | 5520 | 0.2140 | 0.6325 | 0.7596 | 0.8365 | nan | 0.8663 | 0.8999 | 0.7718 | 0.8849 | 0.7341 | 0.8879 | 0.7504 | 0.4153 | 0.5794 | 0.6610 | 0.9106 | 0.8534 | 0.8390 | 0.8535 | 0.8246 | 0.8299 | 0.3507 | 0.0 | 0.7760 | 0.8215 | 0.6441 | 0.7857 | 0.6680 | 0.7666 | 0.5918 | 0.3588 | 0.4958 | 0.5472 | 0.8283 | 0.7478 | 0.7633 | 0.7584 | 0.7669 | 0.7815 | 0.2824 | | 0.0451 | 11.08 | 5540 | 0.2135 | 0.6290 | 0.7568 | 0.8327 | nan | 0.8767 | 0.9029 | 0.7418 | 0.8730 | 0.7319 | 0.8768 | 0.7688 | 0.4331 | 0.5868 | 0.6178 | 0.9193 | 0.8594 | 0.8246 | 0.8577 | 0.8235 | 0.8097 | 0.3623 | 0.0 | 0.7778 | 0.8237 | 0.6354 | 0.7786 | 0.6702 | 0.7645 | 0.5908 | 0.3712 | 0.4864 | 0.5227 | 0.8254 | 0.7392 | 0.7573 | 0.7583 | 0.7652 | 0.7654 | 0.2896 | | 0.0477 | 11.12 | 5560 | 0.2149 | 0.6277 | 0.7547 | 0.8314 | nan | 0.8641 | 0.8961 | 0.7527 | 0.8823 | 0.7351 | 0.8723 | 0.7534 | 0.4310 | 0.5857 | 0.6364 | 0.9045 | 0.8509 | 0.8218 | 0.8473 | 0.8141 | 0.8222 | 0.3595 | 0.0 | 0.7712 | 0.8169 | 0.6389 | 0.7823 | 0.6654 | 0.7653 | 0.5867 | 0.3619 | 0.4893 | 0.5323 | 0.8250 | 0.7381 | 0.7556 | 0.7541 | 0.7625 | 0.7697 | 0.2840 | | 0.0373 | 11.16 | 5580 | 0.2104 | 0.6275 | 0.7564 | 0.8315 | nan | 0.8625 | 0.8935 | 0.7551 | 0.8727 | 0.7091 | 0.8845 | 0.7628 | 0.4085 | 0.5838 | 0.6345 | 0.9158 | 0.8496 | 0.8422 | 0.8586 | 0.8232 | 0.8257 | 0.3760 | 0.0 | 0.7754 | 0.8146 | 0.6345 | 0.7790 | 0.6502 | 0.7704 | 0.5822 | 0.3509 | 0.4897 | 0.5328 | 0.8257 | 0.7402 | 0.7627 | 0.7564 | 0.7639 | 0.7744 | 0.2914 | | 0.0359 | 11.2 | 5600 | 0.2093 | 0.6279 | 0.7592 | 0.8310 | nan | 0.8657 | 0.8938 | 0.7299 | 0.8674 | 0.7094 | 0.8825 | 0.7700 | 0.4404 | 0.5819 | 0.6408 | 0.9095 | 0.8462 | 0.8487 | 0.8587 | 0.8144 | 0.8360 | 0.4117 | 0.0 | 0.7685 | 0.8183 | 0.6283 | 0.7763 | 0.6471 | 0.7696 | 0.5786 | 0.3624 | 0.4902 | 0.5349 | 0.8256 | 0.7377 | 0.7633 | 0.7569 | 0.7626 | 0.7809 | 0.3001 | | 0.0549 | 11.24 | 5620 | 0.2080 | 0.6231 | 0.7515 | 0.8279 | nan | 0.8614 | 0.9001 | 0.7025 | 0.8620 | 0.7041 | 0.8777 | 0.7664 | 0.3873 | 0.5975 | 0.6150 | 0.9117 | 0.8499 | 0.8352 | 0.8384 | 0.8368 | 0.8320 | 0.3974 | 0.0 | 0.7629 | 0.8166 | 0.6182 | 0.7750 | 0.6366 | 0.7677 | 0.5736 | 0.3337 | 0.4940 | 0.5205 | 0.8246 | 0.7360 | 0.7591 | 0.7550 | 0.7669 | 0.7764 | 0.2982 | | 0.0512 | 11.28 | 5640 | 0.2108 | 0.6251 | 0.7541 | 0.8314 | nan | 0.8632 | 0.9005 | 0.7296 | 0.8754 | 0.6961 | 0.8890 | 0.7573 | 0.3910 | 0.6054 | 0.6270 | 0.9131 | 0.8557 | 0.8332 | 0.8487 | 0.8269 | 0.8212 | 0.3856 | 0.0 | 0.7650 | 0.8158 | 0.6289 | 0.7828 | 0.6318 | 0.7657 | 0.5804 | 0.3362 | 0.4984 | 0.5274 | 0.8258 | 0.7400 | 0.7589 | 0.7562 | 0.7671 | 0.7733 | 0.2974 | | 0.0329 | 11.32 | 5660 | 0.2129 | 0.6243 | 0.7561 | 0.8279 | nan | 0.8647 | 0.8870 | 0.7665 | 0.8815 | 0.6828 | 0.8908 | 0.7336 | 0.4715 | 0.5886 | 0.6307 | 0.9125 | 0.8491 | 0.8272 | 0.8533 | 0.8241 | 0.8292 | 0.3601 | 0.0 | 0.7595 | 0.8118 | 0.6378 | 0.7854 | 0.6189 | 0.7576 | 0.5749 | 0.3751 | 0.4922 | 0.5265 | 0.8249 | 0.7381 | 0.7567 | 0.7558 | 0.7647 | 0.7755 | 0.2824 | | 0.0624 | 11.36 | 5680 | 0.2176 | 0.6276 | 0.7592 | 0.8322 | nan | 0.8666 | 0.9038 | 0.7484 | 0.8798 | 0.6784 | 0.8929 | 0.7404 | 0.4572 | 0.5800 | 0.6500 | 0.9101 | 0.8476 | 0.8426 | 0.8579 | 0.8419 | 0.8432 | 0.3653 | 0.0 | 0.7675 | 0.8213 | 0.6343 | 0.7865 | 0.6190 | 0.7568 | 0.5758 | 0.3717 | 0.4907 | 0.5365 | 0.8250 | 0.7409 | 0.7612 | 0.7636 | 0.7735 | 0.7825 | 0.2902 | | 0.0438 | 11.4 | 5700 | 0.2145 | 0.6197 | 0.7470 | 0.8276 | nan | 0.8583 | 0.8946 | 0.7420 | 0.8716 | 0.7013 | 0.8881 | 0.7458 | 0.3413 | 0.5784 | 0.6286 | 0.9070 | 0.8443 | 0.8230 | 0.8482 | 0.8484 | 0.8255 | 0.3524 | 0.0 | 0.7606 | 0.8171 | 0.6299 | 0.7809 | 0.6282 | 0.7606 | 0.5730 | 0.2992 | 0.4899 | 0.5293 | 0.8233 | 0.7329 | 0.7486 | 0.7557 | 0.7704 | 0.7716 | 0.2840 | | 0.0357 | 11.44 | 5720 | 0.2165 | 0.6224 | 0.7510 | 0.8290 | nan | 0.8549 | 0.9050 | 0.7354 | 0.8677 | 0.7007 | 0.8893 | 0.7488 | 0.3664 | 0.6073 | 0.6288 | 0.9029 | 0.8525 | 0.8212 | 0.8488 | 0.8396 | 0.8376 | 0.3607 | 0.0 | 0.7648 | 0.8193 | 0.6297 | 0.7792 | 0.6292 | 0.7660 | 0.5687 | 0.3147 | 0.5002 | 0.5310 | 0.8234 | 0.7338 | 0.7487 | 0.7549 | 0.7688 | 0.7767 | 0.2935 | | 0.0476 | 11.48 | 5740 | 0.2198 | 0.6246 | 0.7552 | 0.8284 | nan | 0.8697 | 0.9019 | 0.7674 | 0.8673 | 0.6945 | 0.8877 | 0.7538 | 0.4138 | 0.5647 | 0.6587 | 0.9082 | 0.8418 | 0.8337 | 0.8504 | 0.8338 | 0.8250 | 0.3658 | 0.0 | 0.7700 | 0.8182 | 0.6393 | 0.7769 | 0.6309 | 0.7648 | 0.5706 | 0.3502 | 0.4808 | 0.5358 | 0.8261 | 0.7391 | 0.7571 | 0.7529 | 0.7638 | 0.7699 | 0.2964 | | 0.0467 | 11.52 | 5760 | 0.2185 | 0.6216 | 0.7517 | 0.8282 | nan | 0.8730 | 0.8975 | 0.7627 | 0.8608 | 0.7052 | 0.8941 | 0.7595 | 0.3479 | 0.5988 | 0.6071 | 0.9060 | 0.8564 | 0.8250 | 0.8546 | 0.8256 | 0.8349 | 0.3691 | 0.0 | 0.7690 | 0.8174 | 0.6378 | 0.7752 | 0.6320 | 0.7646 | 0.5660 | 0.3006 | 0.4899 | 0.5149 | 0.8273 | 0.7414 | 0.7564 | 0.7554 | 0.7662 | 0.7761 | 0.2982 | | 0.0433 | 11.56 | 5780 | 0.2111 | 0.6208 | 0.7480 | 0.8278 | nan | 0.8661 | 0.8986 | 0.7574 | 0.8706 | 0.7196 | 0.8946 | 0.7398 | 0.3789 | 0.5838 | 0.5903 | 0.9081 | 0.8516 | 0.8256 | 0.8466 | 0.8307 | 0.8173 | 0.3367 | 0.0 | 0.7611 | 0.8168 | 0.6375 | 0.7805 | 0.6338 | 0.7664 | 0.5661 | 0.3274 | 0.4832 | 0.5056 | 0.8262 | 0.7368 | 0.7542 | 0.7568 | 0.7669 | 0.7684 | 0.2871 | | 0.0304 | 11.6 | 5800 | 0.2154 | 0.6191 | 0.7436 | 0.8277 | nan | 0.8618 | 0.8979 | 0.7209 | 0.8716 | 0.7157 | 0.8946 | 0.7407 | 0.3319 | 0.5662 | 0.6205 | 0.9098 | 0.8442 | 0.8295 | 0.8469 | 0.8375 | 0.8093 | 0.3416 | 0.0 | 0.7636 | 0.8199 | 0.6260 | 0.7789 | 0.6300 | 0.7689 | 0.5623 | 0.2928 | 0.4787 | 0.5193 | 0.8260 | 0.7348 | 0.7550 | 0.7615 | 0.7725 | 0.7631 | 0.2904 | | 0.0456 | 11.64 | 5820 | 0.2215 | 0.6230 | 0.7521 | 0.8266 | nan | 0.8554 | 0.9081 | 0.7498 | 0.8582 | 0.7027 | 0.8900 | 0.7501 | 0.3789 | 0.5846 | 0.6315 | 0.9070 | 0.8620 | 0.8103 | 0.8540 | 0.8338 | 0.8297 | 0.3791 | 0.0 | 0.7671 | 0.8182 | 0.6374 | 0.7732 | 0.6303 | 0.7704 | 0.5589 | 0.3259 | 0.4920 | 0.5315 | 0.8259 | 0.7282 | 0.7434 | 0.7636 | 0.7698 | 0.7745 | 0.3035 | | 0.0395 | 11.68 | 5840 | 0.2156 | 0.6267 | 0.7571 | 0.8288 | nan | 0.8395 | 0.9011 | 0.7391 | 0.8638 | 0.7053 | 0.8887 | 0.7445 | 0.4443 | 0.6131 | 0.6206 | 0.9123 | 0.8457 | 0.8456 | 0.8584 | 0.8299 | 0.8410 | 0.3778 | 0.0 | 0.7617 | 0.8185 | 0.6350 | 0.7767 | 0.6294 | 0.7724 | 0.5599 | 0.3643 | 0.5046 | 0.5287 | 0.8268 | 0.7346 | 0.7577 | 0.7628 | 0.7672 | 0.7783 | 0.3018 | | 0.0391 | 11.72 | 5860 | 0.2238 | 0.6280 | 0.7600 | 0.8292 | nan | 0.8536 | 0.9013 | 0.7666 | 0.8641 | 0.7047 | 0.8927 | 0.7430 | 0.4564 | 0.6096 | 0.6249 | 0.9133 | 0.8358 | 0.8507 | 0.8643 | 0.8406 | 0.8269 | 0.3712 | 0.0 | 0.7668 | 0.8178 | 0.6427 | 0.7769 | 0.6300 | 0.7726 | 0.5607 | 0.3792 | 0.5012 | 0.5286 | 0.8268 | 0.7357 | 0.7595 | 0.7659 | 0.7694 | 0.7724 | 0.2980 | | 0.0346 | 11.76 | 5880 | 0.2191 | 0.6239 | 0.7533 | 0.8262 | nan | 0.8605 | 0.9009 | 0.7339 | 0.8621 | 0.7032 | 0.8922 | 0.7436 | 0.4405 | 0.6021 | 0.6003 | 0.9150 | 0.8546 | 0.8185 | 0.8389 | 0.8408 | 0.8256 | 0.3724 | 0.0 | 0.7648 | 0.8168 | 0.6315 | 0.7751 | 0.6285 | 0.7713 | 0.5581 | 0.3704 | 0.4912 | 0.5119 | 0.8274 | 0.7354 | 0.7498 | 0.7582 | 0.7679 | 0.7729 | 0.2998 | | 0.0428 | 11.8 | 5900 | 0.2230 | 0.6226 | 0.7544 | 0.8254 | nan | 0.8661 | 0.9018 | 0.7239 | 0.8575 | 0.7139 | 0.8850 | 0.7410 | 0.4345 | 0.5995 | 0.6129 | 0.9190 | 0.8587 | 0.8062 | 0.8652 | 0.8263 | 0.8313 | 0.3829 | 0.0 | 0.7703 | 0.8180 | 0.6275 | 0.7721 | 0.6201 | 0.7767 | 0.5547 | 0.3585 | 0.4955 | 0.5214 | 0.8283 | 0.7276 | 0.7427 | 0.7600 | 0.7599 | 0.7759 | 0.2983 | | 0.0376 | 11.84 | 5920 | 0.2248 | 0.6199 | 0.7497 | 0.8238 | nan | 0.8727 | 0.8903 | 0.7110 | 0.8552 | 0.7067 | 0.8816 | 0.7558 | 0.4013 | 0.6114 | 0.6214 | 0.9192 | 0.8479 | 0.8203 | 0.8553 | 0.8205 | 0.8192 | 0.3547 | 0.0 | 0.7625 | 0.8181 | 0.6212 | 0.7665 | 0.6213 | 0.7749 | 0.5510 | 0.3346 | 0.5040 | 0.5291 | 0.8278 | 0.7265 | 0.7457 | 0.7574 | 0.7599 | 0.7695 | 0.2881 | | 0.0359 | 11.88 | 5940 | 0.2267 | 0.6204 | 0.7485 | 0.8251 | nan | 0.8528 | 0.9020 | 0.7108 | 0.8586 | 0.6962 | 0.8801 | 0.7549 | 0.3779 | 0.5934 | 0.6442 | 0.9122 | 0.8469 | 0.8411 | 0.8614 | 0.8156 | 0.8121 | 0.3645 | 0.0 | 0.7618 | 0.8199 | 0.6232 | 0.7681 | 0.6200 | 0.7714 | 0.5553 | 0.3211 | 0.4962 | 0.5359 | 0.8260 | 0.7373 | 0.7558 | 0.7539 | 0.7591 | 0.7666 | 0.2956 | | 0.0345 | 11.92 | 5960 | 0.2250 | 0.6230 | 0.7505 | 0.8262 | nan | 0.8300 | 0.9085 | 0.7175 | 0.8635 | 0.7003 | 0.8771 | 0.7477 | 0.4255 | 0.5867 | 0.6400 | 0.9049 | 0.8476 | 0.8444 | 0.8481 | 0.8222 | 0.8292 | 0.3651 | 0.0 | 0.7581 | 0.8192 | 0.6272 | 0.7707 | 0.6217 | 0.7718 | 0.5576 | 0.3485 | 0.4925 | 0.5330 | 0.8258 | 0.7390 | 0.7586 | 0.7542 | 0.7623 | 0.7761 | 0.2974 | | 0.0374 | 11.96 | 5980 | 0.2186 | 0.6238 | 0.7521 | 0.8256 | nan | 0.8399 | 0.9082 | 0.7109 | 0.8621 | 0.7026 | 0.8810 | 0.7414 | 0.4934 | 0.6159 | 0.5969 | 0.9150 | 0.8529 | 0.8370 | 0.8347 | 0.8388 | 0.8225 | 0.3330 | 0.0 | 0.7612 | 0.8185 | 0.6248 | 0.7701 | 0.6254 | 0.7705 | 0.5559 | 0.3824 | 0.5027 | 0.5153 | 0.8279 | 0.7365 | 0.7563 | 0.7553 | 0.7685 | 0.7736 | 0.2826 | | 0.055 | 12.0 | 6000 | 0.2175 | 0.6251 | 0.7562 | 0.8254 | nan | 0.8404 | 0.9083 | 0.6946 | 0.8566 | 0.7138 | 0.8827 | 0.7373 | 0.4973 | 0.5873 | 0.6403 | 0.9124 | 0.8417 | 0.8445 | 0.8313 | 0.8508 | 0.8241 | 0.3917 | 0.0 | 0.7637 | 0.8192 | 0.6178 | 0.7687 | 0.6261 | 0.7699 | 0.5516 | 0.3656 | 0.4957 | 0.5332 | 0.8288 | 0.7396 | 0.7604 | 0.7583 | 0.7731 | 0.7739 | 0.3052 | | 0.0336 | 12.04 | 6020 | 0.2203 | 0.6265 | 0.7593 | 0.8256 | nan | 0.8568 | 0.8967 | 0.7212 | 0.8569 | 0.7082 | 0.8832 | 0.7463 | 0.4661 | 0.6005 | 0.6372 | 0.9132 | 0.8482 | 0.8314 | 0.8517 | 0.8351 | 0.8277 | 0.4268 | 0.0 | 0.7665 | 0.8147 | 0.6297 | 0.7679 | 0.6285 | 0.7707 | 0.5547 | 0.3604 | 0.5009 | 0.5349 | 0.8294 | 0.7401 | 0.7577 | 0.7606 | 0.7723 | 0.7758 | 0.3115 | | 0.0286 | 12.08 | 6040 | 0.2250 | 0.6271 | 0.7585 | 0.8279 | nan | 0.8574 | 0.8988 | 0.7423 | 0.8675 | 0.7068 | 0.8874 | 0.7407 | 0.4549 | 0.6028 | 0.6397 | 0.9084 | 0.8455 | 0.8280 | 0.8531 | 0.8258 | 0.8309 | 0.4054 | 0.0 | 0.7684 | 0.8179 | 0.6376 | 0.7737 | 0.6257 | 0.7723 | 0.5564 | 0.3632 | 0.5019 | 0.5374 | 0.8298 | 0.7358 | 0.7559 | 0.7609 | 0.7717 | 0.7777 | 0.3018 | | 0.0309 | 12.12 | 6060 | 0.2299 | 0.6233 | 0.7504 | 0.8261 | nan | 0.8402 | 0.9057 | 0.6992 | 0.8646 | 0.6978 | 0.8817 | 0.7436 | 0.4204 | 0.5674 | 0.6546 | 0.9063 | 0.8399 | 0.8438 | 0.8562 | 0.8334 | 0.8201 | 0.3814 | 0.0 | 0.7621 | 0.8182 | 0.6200 | 0.7726 | 0.6221 | 0.7671 | 0.5575 | 0.3449 | 0.4860 | 0.5407 | 0.8272 | 0.7355 | 0.7586 | 0.7605 | 0.7725 | 0.7678 | 0.3055 | | 0.0335 | 12.16 | 6080 | 0.2293 | 0.6213 | 0.7477 | 0.8218 | nan | 0.8327 | 0.8919 | 0.7078 | 0.8492 | 0.6968 | 0.8750 | 0.7630 | 0.4157 | 0.5924 | 0.6157 | 0.9195 | 0.8414 | 0.8426 | 0.8437 | 0.8327 | 0.8217 | 0.3698 | 0.0 | 0.7595 | 0.8158 | 0.6204 | 0.7626 | 0.6262 | 0.7656 | 0.5507 | 0.3408 | 0.5001 | 0.5262 | 0.8277 | 0.7348 | 0.7566 | 0.7532 | 0.7695 | 0.7708 | 0.3035 | | 0.0334 | 12.2 | 6100 | 0.2293 | 0.6235 | 0.7534 | 0.8234 | nan | 0.8485 | 0.9054 | 0.7384 | 0.8482 | 0.7003 | 0.8764 | 0.7602 | 0.4250 | 0.6150 | 0.6245 | 0.9104 | 0.8574 | 0.8285 | 0.8454 | 0.8390 | 0.8127 | 0.3723 | 0.0 | 0.7650 | 0.8190 | 0.6351 | 0.7630 | 0.6232 | 0.7688 | 0.5482 | 0.3464 | 0.5120 | 0.5339 | 0.8277 | 0.7326 | 0.7495 | 0.7546 | 0.7702 | 0.7689 | 0.3058 | | 0.0413 | 12.24 | 6120 | 0.2224 | 0.6251 | 0.7572 | 0.8247 | nan | 0.8724 | 0.9004 | 0.7315 | 0.8544 | 0.7000 | 0.8835 | 0.7461 | 0.4563 | 0.5877 | 0.6541 | 0.9096 | 0.8544 | 0.8243 | 0.8548 | 0.8427 | 0.8206 | 0.3802 | 0.0 | 0.7680 | 0.8162 | 0.6332 | 0.7667 | 0.6181 | 0.7706 | 0.5492 | 0.3710 | 0.4992 | 0.5461 | 0.8275 | 0.7315 | 0.7495 | 0.7578 | 0.7706 | 0.7733 | 0.3027 | | 0.0317 | 12.28 | 6140 | 0.2260 | 0.6248 | 0.7557 | 0.8251 | nan | 0.8752 | 0.8976 | 0.7346 | 0.8549 | 0.7024 | 0.8815 | 0.7496 | 0.4264 | 0.6123 | 0.6348 | 0.9121 | 0.8522 | 0.8280 | 0.8460 | 0.8366 | 0.8302 | 0.3733 | 0.0 | 0.7699 | 0.8190 | 0.6336 | 0.7651 | 0.6137 | 0.7748 | 0.5445 | 0.3573 | 0.5082 | 0.5388 | 0.8289 | 0.7329 | 0.7518 | 0.7561 | 0.7711 | 0.7785 | 0.3026 | | 0.0357 | 12.32 | 6160 | 0.2295 | 0.6233 | 0.7542 | 0.8227 | nan | 0.8614 | 0.9038 | 0.7491 | 0.8420 | 0.7010 | 0.8852 | 0.7599 | 0.4142 | 0.5897 | 0.6365 | 0.9102 | 0.8520 | 0.8247 | 0.8376 | 0.8361 | 0.8309 | 0.3872 | 0.0 | 0.7686 | 0.8170 | 0.6399 | 0.7610 | 0.6167 | 0.7771 | 0.5468 | 0.3442 | 0.4971 | 0.5352 | 0.8287 | 0.7332 | 0.7538 | 0.7543 | 0.7677 | 0.7790 | 0.2991 | | 0.0362 | 12.36 | 6180 | 0.2290 | 0.6259 | 0.7574 | 0.8260 | nan | 0.8600 | 0.9036 | 0.7575 | 0.8503 | 0.6978 | 0.8862 | 0.7541 | 0.3948 | 0.6014 | 0.6476 | 0.9111 | 0.8516 | 0.8385 | 0.8550 | 0.8375 | 0.8296 | 0.3996 | 0.0 | 0.7734 | 0.8207 | 0.6423 | 0.7641 | 0.6160 | 0.7747 | 0.5464 | 0.3382 | 0.5043 | 0.5414 | 0.8295 | 0.7401 | 0.7631 | 0.7609 | 0.7712 | 0.7805 | 0.2992 | | 0.0484 | 12.4 | 6200 | 0.2278 | 0.6258 | 0.7550 | 0.8253 | nan | 0.8532 | 0.9050 | 0.7494 | 0.8537 | 0.6986 | 0.8866 | 0.7488 | 0.4345 | 0.5954 | 0.6401 | 0.9056 | 0.8454 | 0.8465 | 0.8529 | 0.8325 | 0.8251 | 0.3620 | 0.0 | 0.7676 | 0.8179 | 0.6411 | 0.7666 | 0.6182 | 0.7709 | 0.5495 | 0.3644 | 0.5026 | 0.5401 | 0.8280 | 0.7389 | 0.7637 | 0.7584 | 0.7688 | 0.7777 | 0.2906 | | 0.0353 | 12.44 | 6220 | 0.2256 | 0.6294 | 0.7594 | 0.8279 | nan | 0.8645 | 0.9064 | 0.7356 | 0.8636 | 0.7006 | 0.8859 | 0.7354 | 0.4782 | 0.6007 | 0.6601 | 0.9118 | 0.8441 | 0.8395 | 0.8566 | 0.8366 | 0.8307 | 0.3597 | 0.0 | 0.7718 | 0.8221 | 0.6358 | 0.7727 | 0.6177 | 0.7719 | 0.5516 | 0.3942 | 0.5053 | 0.5472 | 0.8294 | 0.7407 | 0.7645 | 0.7622 | 0.7718 | 0.7794 | 0.2904 | | 0.0361 | 12.48 | 6240 | 0.2236 | 0.6265 | 0.7557 | 0.8261 | nan | 0.8510 | 0.9002 | 0.7453 | 0.8569 | 0.6987 | 0.8855 | 0.7481 | 0.4498 | 0.5990 | 0.6371 | 0.9145 | 0.8531 | 0.8317 | 0.8567 | 0.8332 | 0.8344 | 0.3515 | 0.0 | 0.7644 | 0.8185 | 0.6377 | 0.7703 | 0.6220 | 0.7704 | 0.5536 | 0.3703 | 0.5037 | 0.5392 | 0.8296 | 0.7372 | 0.7584 | 0.7628 | 0.7706 | 0.7792 | 0.2882 | | 0.0511 | 12.52 | 6260 | 0.2277 | 0.6253 | 0.7548 | 0.8242 | nan | 0.8476 | 0.8931 | 0.7597 | 0.8480 | 0.7010 | 0.8868 | 0.7604 | 0.4331 | 0.6105 | 0.6158 | 0.9159 | 0.8539 | 0.8393 | 0.8495 | 0.8376 | 0.8202 | 0.3593 | 0.0 | 0.7640 | 0.8184 | 0.6402 | 0.7621 | 0.6255 | 0.7707 | 0.5468 | 0.3641 | 0.5030 | 0.5265 | 0.8306 | 0.7393 | 0.7614 | 0.7625 | 0.7736 | 0.7731 | 0.2929 | | 0.0586 | 12.56 | 6280 | 0.2299 | 0.6242 | 0.7543 | 0.8224 | nan | 0.8597 | 0.8940 | 0.7576 | 0.8428 | 0.7032 | 0.8725 | 0.7691 | 0.4026 | 0.6018 | 0.6374 | 0.9108 | 0.8519 | 0.8347 | 0.8484 | 0.8349 | 0.8241 | 0.3781 | 0.0 | 0.7651 | 0.8190 | 0.6397 | 0.7572 | 0.6239 | 0.7710 | 0.5454 | 0.3396 | 0.5044 | 0.5380 | 0.8308 | 0.7388 | 0.7568 | 0.7571 | 0.7711 | 0.7736 | 0.3037 | | 0.0261 | 12.6 | 6300 | 0.2290 | 0.6244 | 0.7553 | 0.8225 | nan | 0.8704 | 0.8966 | 0.7397 | 0.8438 | 0.7034 | 0.8785 | 0.7639 | 0.4307 | 0.5786 | 0.6540 | 0.9126 | 0.8562 | 0.8144 | 0.8458 | 0.8396 | 0.8254 | 0.3871 | 0.0 | 0.7687 | 0.8189 | 0.6341 | 0.7586 | 0.6231 | 0.7710 | 0.5491 | 0.3626 | 0.4951 | 0.5432 | 0.8300 | 0.7328 | 0.7460 | 0.7561 | 0.7715 | 0.7739 | 0.3039 | | 0.04 | 12.64 | 6320 | 0.2329 | 0.6277 | 0.7594 | 0.8252 | nan | 0.8835 | 0.8976 | 0.7452 | 0.8447 | 0.6999 | 0.8810 | 0.7746 | 0.4545 | 0.5671 | 0.6589 | 0.9143 | 0.8449 | 0.8405 | 0.8459 | 0.8404 | 0.8280 | 0.3883 | 0.0 | 0.7703 | 0.8184 | 0.6348 | 0.7600 | 0.6321 | 0.7721 | 0.5557 | 0.3785 | 0.4905 | 0.5449 | 0.8301 | 0.7400 | 0.7592 | 0.7572 | 0.7744 | 0.7767 | 0.3029 | | 0.0781 | 12.68 | 6340 | 0.2256 | 0.6255 | 0.7551 | 0.8252 | nan | 0.8670 | 0.8966 | 0.7042 | 0.8548 | 0.7049 | 0.8694 | 0.7625 | 0.4641 | 0.5711 | 0.6488 | 0.9170 | 0.8511 | 0.8333 | 0.8529 | 0.8267 | 0.8376 | 0.3752 | 0.0 | 0.7684 | 0.8176 | 0.6185 | 0.7655 | 0.6280 | 0.7709 | 0.5596 | 0.3800 | 0.4920 | 0.5416 | 0.8285 | 0.7369 | 0.7551 | 0.7539 | 0.7680 | 0.7801 | 0.2950 | | 0.0363 | 12.72 | 6360 | 0.2250 | 0.6249 | 0.7525 | 0.8253 | nan | 0.8345 | 0.9020 | 0.7121 | 0.8564 | 0.7004 | 0.8744 | 0.7617 | 0.4431 | 0.5829 | 0.6374 | 0.9145 | 0.8497 | 0.8377 | 0.8500 | 0.8310 | 0.8220 | 0.3834 | 0.0 | 0.7609 | 0.8184 | 0.6233 | 0.7670 | 0.6281 | 0.7684 | 0.5605 | 0.3693 | 0.4970 | 0.5384 | 0.8282 | 0.7373 | 0.7557 | 0.7550 | 0.7704 | 0.7748 | 0.2953 | | 0.0593 | 12.76 | 6380 | 0.2211 | 0.6265 | 0.7593 | 0.8254 | nan | 0.8452 | 0.9015 | 0.7707 | 0.8558 | 0.7028 | 0.8872 | 0.7524 | 0.4794 | 0.5794 | 0.6445 | 0.9071 | 0.8471 | 0.8278 | 0.8500 | 0.8447 | 0.8158 | 0.3967 | 0.0 | 0.7626 | 0.8188 | 0.6433 | 0.7688 | 0.6237 | 0.7701 | 0.5560 | 0.3839 | 0.4912 | 0.5370 | 0.8281 | 0.7371 | 0.7547 | 0.7579 | 0.7748 | 0.7698 | 0.2995 | | 0.0413 | 12.8 | 6400 | 0.2294 | 0.6243 | 0.7537 | 0.8258 | nan | 0.8638 | 0.9019 | 0.7530 | 0.8542 | 0.7002 | 0.8839 | 0.7623 | 0.4083 | 0.5835 | 0.6451 | 0.9085 | 0.8509 | 0.8287 | 0.8441 | 0.8519 | 0.8128 | 0.3597 | 0.0 | 0.7640 | 0.8197 | 0.6395 | 0.7671 | 0.6266 | 0.7705 | 0.5563 | 0.3461 | 0.4931 | 0.5383 | 0.8275 | 0.7364 | 0.7548 | 0.7586 | 0.7787 | 0.7688 | 0.2912 | | 0.0643 | 12.84 | 6420 | 0.2268 | 0.6205 | 0.7460 | 0.8258 | nan | 0.8655 | 0.8930 | 0.7164 | 0.8635 | 0.7005 | 0.8766 | 0.7589 | 0.3734 | 0.5981 | 0.6227 | 0.9131 | 0.8497 | 0.8373 | 0.8601 | 0.8369 | 0.8054 | 0.3110 | 0.0 | 0.7610 | 0.8162 | 0.6249 | 0.7705 | 0.6263 | 0.7691 | 0.5590 | 0.3202 | 0.4985 | 0.5288 | 0.8268 | 0.7389 | 0.7588 | 0.7603 | 0.7768 | 0.7641 | 0.2695 | | 0.0395 | 12.88 | 6440 | 0.2283 | 0.6245 | 0.7517 | 0.8281 | nan | 0.8563 | 0.9066 | 0.7141 | 0.8563 | 0.6995 | 0.8924 | 0.7603 | 0.4074 | 0.5881 | 0.6474 | 0.9126 | 0.8516 | 0.8405 | 0.8606 | 0.8364 | 0.8136 | 0.3343 | 0.0 | 0.7669 | 0.8196 | 0.6246 | 0.7732 | 0.6249 | 0.7803 | 0.5577 | 0.3405 | 0.4975 | 0.5420 | 0.8271 | 0.7388 | 0.7590 | 0.7615 | 0.7773 | 0.7684 | 0.2824 | | 0.0405 | 12.92 | 6460 | 0.2262 | 0.6250 | 0.7532 | 0.8276 | nan | 0.8608 | 0.8982 | 0.7534 | 0.8616 | 0.6956 | 0.8992 | 0.7566 | 0.4366 | 0.5871 | 0.6325 | 0.9096 | 0.8538 | 0.8318 | 0.8533 | 0.8305 | 0.8112 | 0.3330 | 0.0 | 0.7663 | 0.8200 | 0.6393 | 0.7754 | 0.6220 | 0.7795 | 0.5576 | 0.3592 | 0.4954 | 0.5359 | 0.8281 | 0.7382 | 0.7560 | 0.7588 | 0.7745 | 0.7656 | 0.2786 | | 0.0499 | 12.96 | 6480 | 0.2250 | 0.6244 | 0.7539 | 0.8266 | nan | 0.8606 | 0.8939 | 0.7448 | 0.8603 | 0.6943 | 0.8968 | 0.7500 | 0.4342 | 0.5886 | 0.6393 | 0.9134 | 0.8534 | 0.8320 | 0.8542 | 0.8384 | 0.8159 | 0.3458 | 0.0 | 0.7622 | 0.8176 | 0.6369 | 0.7764 | 0.6152 | 0.7770 | 0.5559 | 0.3563 | 0.4988 | 0.5392 | 0.8275 | 0.7375 | 0.7557 | 0.7568 | 0.7745 | 0.7651 | 0.2865 | | 0.0759 | 13.0 | 6500 | 0.2264 | 0.6232 | 0.7504 | 0.8292 | nan | 0.8655 | 0.8962 | 0.7145 | 0.8670 | 0.6965 | 0.8953 | 0.7496 | 0.3597 | 0.6238 | 0.6121 | 0.9137 | 0.8443 | 0.8349 | 0.8655 | 0.8427 | 0.8243 | 0.3505 | 0.0 | 0.7682 | 0.8209 | 0.6255 | 0.7787 | 0.6182 | 0.7777 | 0.5565 | 0.3142 | 0.5113 | 0.5263 | 0.8282 | 0.7378 | 0.7571 | 0.7596 | 0.7752 | 0.7701 | 0.2922 | | 0.0346 | 13.04 | 6520 | 0.2275 | 0.6243 | 0.7520 | 0.8272 | nan | 0.8592 | 0.8995 | 0.7354 | 0.8599 | 0.6961 | 0.8982 | 0.7528 | 0.4045 | 0.6214 | 0.6191 | 0.9039 | 0.8312 | 0.8476 | 0.8599 | 0.8302 | 0.8213 | 0.3431 | 0.0 | 0.7646 | 0.8169 | 0.6353 | 0.7762 | 0.6238 | 0.7778 | 0.5570 | 0.3432 | 0.5123 | 0.5295 | 0.8263 | 0.7357 | 0.7576 | 0.7561 | 0.7707 | 0.7691 | 0.2859 | | 0.0732 | 13.08 | 6540 | 0.2215 | 0.6250 | 0.7543 | 0.8273 | nan | 0.8583 | 0.8961 | 0.7646 | 0.8582 | 0.6937 | 0.9009 | 0.7533 | 0.3897 | 0.6029 | 0.6417 | 0.9080 | 0.8392 | 0.8441 | 0.8565 | 0.8248 | 0.8273 | 0.3642 | 0.0 | 0.7647 | 0.8161 | 0.6412 | 0.7749 | 0.6255 | 0.7769 | 0.5579 | 0.3359 | 0.5064 | 0.5398 | 0.8271 | 0.7387 | 0.7568 | 0.7575 | 0.7706 | 0.7712 | 0.2897 | | 0.0573 | 13.12 | 6560 | 0.2277 | 0.6239 | 0.7521 | 0.8278 | nan | 0.8549 | 0.9003 | 0.7490 | 0.8600 | 0.6969 | 0.8982 | 0.7494 | 0.3698 | 0.6079 | 0.6389 | 0.9136 | 0.8442 | 0.8399 | 0.8518 | 0.8394 | 0.8175 | 0.3537 | 0.0 | 0.7653 | 0.8189 | 0.6380 | 0.7747 | 0.6242 | 0.7777 | 0.5570 | 0.3218 | 0.5059 | 0.5375 | 0.8273 | 0.7397 | 0.7563 | 0.7568 | 0.7733 | 0.7680 | 0.2879 | | 0.0778 | 13.16 | 6580 | 0.2248 | 0.6238 | 0.7522 | 0.8273 | nan | 0.8608 | 0.8912 | 0.7534 | 0.8637 | 0.7025 | 0.8966 | 0.7488 | 0.3889 | 0.5965 | 0.6424 | 0.9154 | 0.8425 | 0.8367 | 0.8508 | 0.8299 | 0.8165 | 0.3509 | 0.0 | 0.7646 | 0.8171 | 0.6389 | 0.7757 | 0.6225 | 0.7797 | 0.5567 | 0.3331 | 0.4992 | 0.5377 | 0.8284 | 0.7414 | 0.7567 | 0.7548 | 0.7699 | 0.7658 | 0.2859 | | 0.0471 | 13.2 | 6600 | 0.2250 | 0.6262 | 0.7549 | 0.8281 | nan | 0.8596 | 0.8987 | 0.7459 | 0.8625 | 0.7008 | 0.8925 | 0.7580 | 0.4383 | 0.5730 | 0.6607 | 0.9067 | 0.8400 | 0.8446 | 0.8613 | 0.8206 | 0.8170 | 0.3530 | 0.0 | 0.7647 | 0.8188 | 0.6374 | 0.7751 | 0.6291 | 0.7785 | 0.5614 | 0.3660 | 0.4895 | 0.5442 | 0.8279 | 0.7419 | 0.7590 | 0.7556 | 0.7670 | 0.7670 | 0.2879 | | 0.0286 | 13.24 | 6620 | 0.2275 | 0.6251 | 0.7541 | 0.8264 | nan | 0.8571 | 0.9003 | 0.7318 | 0.8554 | 0.6998 | 0.8911 | 0.7582 | 0.4283 | 0.6010 | 0.6385 | 0.9097 | 0.8407 | 0.8339 | 0.8515 | 0.8390 | 0.8200 | 0.3629 | 0.0 | 0.7634 | 0.8170 | 0.6323 | 0.7720 | 0.6288 | 0.7760 | 0.5574 | 0.3604 | 0.4999 | 0.5328 | 0.8276 | 0.7388 | 0.7557 | 0.7560 | 0.7715 | 0.7704 | 0.2911 | | 0.0349 | 13.28 | 6640 | 0.2300 | 0.6261 | 0.7560 | 0.8281 | nan | 0.8669 | 0.8957 | 0.7340 | 0.8597 | 0.7003 | 0.8930 | 0.7522 | 0.4288 | 0.6015 | 0.6413 | 0.9139 | 0.8436 | 0.8380 | 0.8504 | 0.8430 | 0.8348 | 0.3542 | 0.0 | 0.7660 | 0.8175 | 0.6332 | 0.7744 | 0.6267 | 0.7768 | 0.5568 | 0.3611 | 0.5022 | 0.5371 | 0.8287 | 0.7393 | 0.7569 | 0.7566 | 0.7738 | 0.7787 | 0.2840 | | 0.0306 | 13.32 | 6660 | 0.2307 | 0.6266 | 0.7564 | 0.8283 | nan | 0.8701 | 0.8999 | 0.7348 | 0.8608 | 0.6980 | 0.8911 | 0.7512 | 0.4234 | 0.6036 | 0.6437 | 0.9131 | 0.8350 | 0.8476 | 0.8492 | 0.8432 | 0.8303 | 0.3638 | 0.0 | 0.7672 | 0.8194 | 0.6338 | 0.7726 | 0.6252 | 0.7740 | 0.5581 | 0.3583 | 0.5038 | 0.5401 | 0.8289 | 0.7399 | 0.7602 | 0.7588 | 0.7746 | 0.7781 | 0.2860 | | 0.0311 | 13.36 | 6680 | 0.2294 | 0.6266 | 0.7570 | 0.8274 | nan | 0.8626 | 0.9014 | 0.7207 | 0.8600 | 0.6993 | 0.8897 | 0.7452 | 0.4604 | 0.5968 | 0.6393 | 0.9114 | 0.8394 | 0.8461 | 0.8619 | 0.8449 | 0.8236 | 0.3669 | 0.0 | 0.7658 | 0.8194 | 0.6288 | 0.7721 | 0.6222 | 0.7735 | 0.5564 | 0.3823 | 0.4997 | 0.5362 | 0.8276 | 0.7403 | 0.7596 | 0.7609 | 0.7734 | 0.7732 | 0.2875 | | 0.0306 | 13.4 | 6700 | 0.2276 | 0.6260 | 0.7567 | 0.8260 | nan | 0.8548 | 0.8992 | 0.7393 | 0.8555 | 0.7020 | 0.8857 | 0.7528 | 0.4614 | 0.6084 | 0.6323 | 0.9099 | 0.8456 | 0.8381 | 0.8640 | 0.8356 | 0.8217 | 0.3570 | 0.0 | 0.7620 | 0.8181 | 0.6351 | 0.7704 | 0.6199 | 0.7759 | 0.5546 | 0.3800 | 0.5050 | 0.5352 | 0.8275 | 0.7395 | 0.7570 | 0.7588 | 0.7712 | 0.7712 | 0.2867 | | 0.039 | 13.44 | 6720 | 0.2236 | 0.6247 | 0.7550 | 0.8256 | nan | 0.8596 | 0.8948 | 0.7488 | 0.8575 | 0.7069 | 0.8837 | 0.7453 | 0.4302 | 0.6010 | 0.6397 | 0.9133 | 0.8442 | 0.8393 | 0.8567 | 0.8473 | 0.8183 | 0.3490 | 0.0 | 0.7612 | 0.8165 | 0.6382 | 0.7708 | 0.6181 | 0.7748 | 0.5527 | 0.3618 | 0.5029 | 0.5384 | 0.8277 | 0.7398 | 0.7570 | 0.7584 | 0.7745 | 0.7701 | 0.2819 | | 0.0432 | 13.48 | 6740 | 0.2253 | 0.6248 | 0.7534 | 0.8265 | nan | 0.8610 | 0.8984 | 0.7402 | 0.8586 | 0.7057 | 0.8851 | 0.7493 | 0.4230 | 0.5866 | 0.6460 | 0.9102 | 0.8378 | 0.8436 | 0.8502 | 0.8424 | 0.8311 | 0.3393 | 0.0 | 0.7616 | 0.8183 | 0.6364 | 0.7708 | 0.6213 | 0.7751 | 0.5551 | 0.3581 | 0.4975 | 0.5416 | 0.8284 | 0.7396 | 0.7572 | 0.7562 | 0.7728 | 0.7769 | 0.2802 | | 0.0561 | 13.52 | 6760 | 0.2249 | 0.6252 | 0.7538 | 0.8258 | nan | 0.8634 | 0.8931 | 0.7255 | 0.8613 | 0.7117 | 0.8817 | 0.7439 | 0.4569 | 0.5847 | 0.6417 | 0.9144 | 0.8428 | 0.8335 | 0.8479 | 0.8433 | 0.8290 | 0.3400 | 0.0 | 0.7629 | 0.8170 | 0.6301 | 0.7711 | 0.6232 | 0.7737 | 0.5549 | 0.3799 | 0.4953 | 0.5382 | 0.8287 | 0.7379 | 0.7536 | 0.7570 | 0.7743 | 0.7745 | 0.2810 | | 0.0274 | 13.56 | 6780 | 0.2255 | 0.6257 | 0.7535 | 0.8276 | nan | 0.8548 | 0.9047 | 0.7118 | 0.8624 | 0.7132 | 0.8827 | 0.7408 | 0.4344 | 0.5899 | 0.6469 | 0.9102 | 0.8451 | 0.8372 | 0.8485 | 0.8431 | 0.8342 | 0.3497 | 0.0 | 0.7653 | 0.8200 | 0.6253 | 0.7725 | 0.6241 | 0.7734 | 0.5553 | 0.3660 | 0.4977 | 0.5403 | 0.8283 | 0.7404 | 0.7565 | 0.7594 | 0.7761 | 0.7769 | 0.2846 | | 0.0351 | 13.6 | 6800 | 0.2265 | 0.6253 | 0.7529 | 0.8267 | nan | 0.8525 | 0.8998 | 0.7031 | 0.8561 | 0.7110 | 0.8882 | 0.7468 | 0.4319 | 0.6002 | 0.6402 | 0.9130 | 0.8394 | 0.8354 | 0.8608 | 0.8389 | 0.8351 | 0.3463 | 0.0 | 0.7637 | 0.8186 | 0.6208 | 0.7706 | 0.6258 | 0.7751 | 0.5545 | 0.3662 | 0.5019 | 0.5370 | 0.8282 | 0.7395 | 0.7572 | 0.7591 | 0.7740 | 0.7785 | 0.2853 | | 0.03 | 13.64 | 6820 | 0.2249 | 0.6277 | 0.7571 | 0.8275 | nan | 0.8542 | 0.9011 | 0.7257 | 0.8581 | 0.7129 | 0.8850 | 0.7441 | 0.4520 | 0.6132 | 0.6366 | 0.9075 | 0.8438 | 0.8373 | 0.8605 | 0.8409 | 0.8409 | 0.3568 | 0.0 | 0.7643 | 0.8179 | 0.6311 | 0.7724 | 0.6256 | 0.7747 | 0.5538 | 0.3791 | 0.5082 | 0.5368 | 0.8279 | 0.7398 | 0.7573 | 0.7594 | 0.7743 | 0.7809 | 0.2953 | | 0.054 | 13.68 | 6840 | 0.2284 | 0.6284 | 0.7573 | 0.8271 | nan | 0.8525 | 0.9009 | 0.7383 | 0.8598 | 0.7058 | 0.8868 | 0.7452 | 0.4685 | 0.6112 | 0.6215 | 0.9114 | 0.8494 | 0.8378 | 0.8523 | 0.8422 | 0.8296 | 0.3611 | 0.0 | 0.7645 | 0.8183 | 0.6366 | 0.7723 | 0.6244 | 0.7750 | 0.5526 | 0.3927 | 0.5061 | 0.5297 | 0.8286 | 0.7404 | 0.7574 | 0.7601 | 0.7754 | 0.7771 | 0.2994 | | 0.0289 | 13.72 | 6860 | 0.2274 | 0.6297 | 0.7595 | 0.8282 | nan | 0.8596 | 0.9078 | 0.7485 | 0.8556 | 0.6974 | 0.8891 | 0.7552 | 0.4650 | 0.6101 | 0.6309 | 0.9094 | 0.8471 | 0.8419 | 0.8515 | 0.8426 | 0.8371 | 0.3631 | 0.0 | 0.7676 | 0.8199 | 0.6401 | 0.7709 | 0.6265 | 0.7744 | 0.5559 | 0.3916 | 0.5079 | 0.5359 | 0.8284 | 0.7406 | 0.7582 | 0.7617 | 0.7749 | 0.7807 | 0.2995 | | 0.0428 | 13.76 | 6880 | 0.2275 | 0.6282 | 0.7584 | 0.8271 | nan | 0.8697 | 0.8965 | 0.7569 | 0.8585 | 0.7026 | 0.8861 | 0.7531 | 0.4632 | 0.5979 | 0.6336 | 0.9129 | 0.8470 | 0.8393 | 0.8537 | 0.8435 | 0.8265 | 0.3511 | 0.0 | 0.7661 | 0.8186 | 0.6414 | 0.7708 | 0.6242 | 0.7755 | 0.5540 | 0.3873 | 0.5025 | 0.5358 | 0.8288 | 0.7410 | 0.7598 | 0.7606 | 0.7745 | 0.7759 | 0.2906 | | 0.0262 | 13.8 | 6900 | 0.2307 | 0.6296 | 0.7619 | 0.8271 | nan | 0.8737 | 0.9034 | 0.7519 | 0.8523 | 0.7033 | 0.8888 | 0.7561 | 0.4930 | 0.5972 | 0.6564 | 0.9107 | 0.8473 | 0.8322 | 0.8615 | 0.8355 | 0.8268 | 0.3629 | 0.0 | 0.7664 | 0.8191 | 0.6413 | 0.7697 | 0.6252 | 0.7771 | 0.5545 | 0.4006 | 0.5030 | 0.5446 | 0.8283 | 0.7406 | 0.7594 | 0.7598 | 0.7721 | 0.7765 | 0.2948 | | 0.034 | 13.84 | 6920 | 0.2306 | 0.6306 | 0.7642 | 0.8280 | nan | 0.8723 | 0.9016 | 0.7533 | 0.8544 | 0.7030 | 0.8925 | 0.7515 | 0.5148 | 0.6102 | 0.6373 | 0.9143 | 0.8519 | 0.8318 | 0.8573 | 0.8446 | 0.8296 | 0.3707 | 0.0 | 0.7661 | 0.8199 | 0.6412 | 0.7711 | 0.6243 | 0.7772 | 0.5539 | 0.4123 | 0.5073 | 0.5376 | 0.8288 | 0.7408 | 0.7590 | 0.7619 | 0.7752 | 0.7782 | 0.2967 | | 0.0364 | 13.88 | 6940 | 0.2279 | 0.6298 | 0.7626 | 0.8268 | nan | 0.8683 | 0.8940 | 0.7468 | 0.8531 | 0.7034 | 0.8918 | 0.7561 | 0.5135 | 0.6046 | 0.6460 | 0.9170 | 0.8475 | 0.8329 | 0.8522 | 0.8421 | 0.8244 | 0.3708 | 0.0 | 0.7630 | 0.8169 | 0.6386 | 0.7703 | 0.6253 | 0.7771 | 0.5546 | 0.4098 | 0.5054 | 0.5407 | 0.8283 | 0.7404 | 0.7592 | 0.7606 | 0.7749 | 0.7758 | 0.2953 | | 0.0521 | 13.92 | 6960 | 0.2275 | 0.6300 | 0.7623 | 0.8271 | nan | 0.8659 | 0.8991 | 0.7397 | 0.8542 | 0.7023 | 0.8863 | 0.7566 | 0.5049 | 0.6100 | 0.6424 | 0.9156 | 0.8527 | 0.8341 | 0.8587 | 0.8405 | 0.8202 | 0.3765 | 0.0 | 0.7651 | 0.8186 | 0.6366 | 0.7705 | 0.6265 | 0.7761 | 0.5561 | 0.4079 | 0.5090 | 0.5414 | 0.8282 | 0.7402 | 0.7585 | 0.7608 | 0.7742 | 0.7734 | 0.2968 | | 0.0303 | 13.96 | 6980 | 0.2276 | 0.6298 | 0.7618 | 0.8277 | nan | 0.8509 | 0.9057 | 0.7412 | 0.8548 | 0.7029 | 0.8858 | 0.7557 | 0.5095 | 0.6060 | 0.6347 | 0.9150 | 0.8610 | 0.8288 | 0.8581 | 0.8385 | 0.8270 | 0.3748 | 0.0 | 0.7652 | 0.8209 | 0.6376 | 0.7709 | 0.6247 | 0.7765 | 0.5552 | 0.4065 | 0.5066 | 0.5384 | 0.8286 | 0.7394 | 0.7566 | 0.7611 | 0.7738 | 0.7768 | 0.2978 | | 0.0313 | 14.0 | 7000 | 0.2287 | 0.6287 | 0.7593 | 0.8268 | nan | 0.8446 | 0.9004 | 0.7461 | 0.8603 | 0.7037 | 0.8831 | 0.7461 | 0.5012 | 0.5885 | 0.6483 | 0.9121 | 0.8581 | 0.8307 | 0.8587 | 0.8352 | 0.8259 | 0.3648 | 0.0 | 0.7627 | 0.8190 | 0.6387 | 0.7721 | 0.6214 | 0.7759 | 0.5527 | 0.4051 | 0.5000 | 0.5437 | 0.8286 | 0.7395 | 0.7566 | 0.7596 | 0.7726 | 0.7751 | 0.2937 | | 0.0295 | 14.04 | 7020 | 0.2245 | 0.6275 | 0.7567 | 0.8264 | nan | 0.8466 | 0.8983 | 0.7551 | 0.8597 | 0.7003 | 0.8906 | 0.7474 | 0.4686 | 0.5751 | 0.6514 | 0.9114 | 0.8521 | 0.8331 | 0.8520 | 0.8400 | 0.8209 | 0.3621 | 0.0 | 0.7624 | 0.8184 | 0.6410 | 0.7722 | 0.6226 | 0.7765 | 0.5533 | 0.3895 | 0.4938 | 0.5430 | 0.8288 | 0.7398 | 0.7575 | 0.7587 | 0.7736 | 0.7730 | 0.2917 | | 0.0335 | 14.08 | 7040 | 0.2252 | 0.6275 | 0.7575 | 0.8272 | nan | 0.8498 | 0.9024 | 0.7555 | 0.8566 | 0.7038 | 0.8934 | 0.7482 | 0.4464 | 0.5949 | 0.6440 | 0.9122 | 0.8530 | 0.8304 | 0.8594 | 0.8340 | 0.8248 | 0.3678 | 0.0 | 0.7637 | 0.8202 | 0.6406 | 0.7716 | 0.6209 | 0.7784 | 0.5514 | 0.3733 | 0.5023 | 0.5410 | 0.8293 | 0.7405 | 0.7581 | 0.7611 | 0.7730 | 0.7750 | 0.2953 | | 0.0308 | 14.12 | 7060 | 0.2311 | 0.6268 | 0.7569 | 0.8266 | nan | 0.8595 | 0.9011 | 0.7570 | 0.8525 | 0.7014 | 0.8929 | 0.7559 | 0.4232 | 0.6013 | 0.6404 | 0.9101 | 0.8513 | 0.8356 | 0.8590 | 0.8357 | 0.8193 | 0.3719 | 0.0 | 0.7655 | 0.8191 | 0.6411 | 0.7699 | 0.6228 | 0.7777 | 0.5522 | 0.3576 | 0.5049 | 0.5401 | 0.8288 | 0.7413 | 0.7598 | 0.7607 | 0.7727 | 0.7719 | 0.2964 | | 0.0901 | 14.16 | 7080 | 0.2238 | 0.6248 | 0.7521 | 0.8257 | nan | 0.8481 | 0.9002 | 0.7428 | 0.8561 | 0.7035 | 0.8920 | 0.7512 | 0.4234 | 0.5920 | 0.6396 | 0.9113 | 0.8482 | 0.8325 | 0.8501 | 0.8319 | 0.8177 | 0.3453 | 0.0 | 0.7600 | 0.8177 | 0.6368 | 0.7711 | 0.6212 | 0.7770 | 0.5519 | 0.3595 | 0.5001 | 0.5397 | 0.8287 | 0.7398 | 0.7578 | 0.7585 | 0.7701 | 0.7704 | 0.2867 | | 0.0342 | 14.2 | 7100 | 0.2280 | 0.6261 | 0.7537 | 0.8276 | nan | 0.8545 | 0.8969 | 0.7266 | 0.8636 | 0.7020 | 0.8895 | 0.7496 | 0.4283 | 0.5876 | 0.6378 | 0.9157 | 0.8480 | 0.8372 | 0.8573 | 0.8374 | 0.8164 | 0.3642 | 0.0 | 0.7629 | 0.8182 | 0.6309 | 0.7742 | 0.6218 | 0.7774 | 0.5560 | 0.3651 | 0.4989 | 0.5398 | 0.8291 | 0.7396 | 0.7580 | 0.7601 | 0.7711 | 0.7713 | 0.2960 | | 0.0464 | 14.24 | 7120 | 0.2264 | 0.6276 | 0.7568 | 0.8275 | nan | 0.8558 | 0.9002 | 0.7335 | 0.8599 | 0.7014 | 0.8886 | 0.7512 | 0.4561 | 0.5957 | 0.6327 | 0.9154 | 0.8503 | 0.8383 | 0.8605 | 0.8382 | 0.8179 | 0.3697 | 0.0 | 0.7631 | 0.8190 | 0.6342 | 0.7730 | 0.6218 | 0.7780 | 0.5556 | 0.3813 | 0.5025 | 0.5375 | 0.8291 | 0.7401 | 0.7586 | 0.7606 | 0.7711 | 0.7717 | 0.2997 | | 0.035 | 14.28 | 7140 | 0.2272 | 0.6279 | 0.7576 | 0.8268 | nan | 0.8576 | 0.8974 | 0.7363 | 0.8581 | 0.7020 | 0.8893 | 0.7512 | 0.4621 | 0.5983 | 0.6404 | 0.9140 | 0.8468 | 0.8391 | 0.8556 | 0.8375 | 0.8194 | 0.3736 | 0.0 | 0.7628 | 0.8179 | 0.6349 | 0.7721 | 0.6215 | 0.7788 | 0.5543 | 0.3825 | 0.5040 | 0.5402 | 0.8294 | 0.7404 | 0.7596 | 0.7597 | 0.7714 | 0.7721 | 0.3005 | | 0.0607 | 14.32 | 7160 | 0.2220 | 0.6271 | 0.7564 | 0.8263 | nan | 0.8558 | 0.9017 | 0.7173 | 0.8597 | 0.7047 | 0.8888 | 0.7371 | 0.4717 | 0.6011 | 0.6354 | 0.9143 | 0.8470 | 0.8350 | 0.8580 | 0.8417 | 0.8224 | 0.3679 | 0.0 | 0.7621 | 0.8178 | 0.6282 | 0.7734 | 0.6183 | 0.7781 | 0.5518 | 0.3886 | 0.5048 | 0.5376 | 0.8283 | 0.7403 | 0.7586 | 0.7595 | 0.7711 | 0.7723 | 0.2973 | | 0.0385 | 14.36 | 7180 | 0.2286 | 0.6258 | 0.7531 | 0.8268 | nan | 0.8600 | 0.9010 | 0.7351 | 0.8591 | 0.7037 | 0.8914 | 0.7511 | 0.4106 | 0.5936 | 0.6409 | 0.9094 | 0.8412 | 0.8394 | 0.8497 | 0.8356 | 0.8172 | 0.3641 | 0.0 | 0.7641 | 0.8184 | 0.6351 | 0.7720 | 0.6222 | 0.7784 | 0.5543 | 0.3494 | 0.5018 | 0.5404 | 0.8295 | 0.7407 | 0.7608 | 0.7580 | 0.7723 | 0.7711 | 0.2966 | | 0.0249 | 14.4 | 7200 | 0.2265 | 0.6277 | 0.7564 | 0.8279 | nan | 0.8588 | 0.9019 | 0.7445 | 0.8581 | 0.7004 | 0.8924 | 0.7549 | 0.4295 | 0.5960 | 0.6456 | 0.9119 | 0.8410 | 0.8413 | 0.8534 | 0.8380 | 0.8246 | 0.3664 | 0.0 | 0.7653 | 0.8200 | 0.6387 | 0.7719 | 0.6239 | 0.7791 | 0.5555 | 0.3618 | 0.5031 | 0.5431 | 0.8301 | 0.7412 | 0.7613 | 0.7592 | 0.7726 | 0.7750 | 0.2969 | | 0.0696 | 14.44 | 7220 | 0.2261 | 0.6251 | 0.7524 | 0.8273 | nan | 0.8546 | 0.9017 | 0.7450 | 0.8596 | 0.7046 | 0.8882 | 0.7522 | 0.3870 | 0.5925 | 0.6459 | 0.9097 | 0.8463 | 0.8409 | 0.8488 | 0.8384 | 0.8196 | 0.3559 | 0.0 | 0.7635 | 0.8198 | 0.6386 | 0.7722 | 0.6209 | 0.7789 | 0.5533 | 0.3322 | 0.5011 | 0.5428 | 0.8294 | 0.7415 | 0.7607 | 0.7582 | 0.7736 | 0.7719 | 0.2937 | | 0.0275 | 14.48 | 7240 | 0.2313 | 0.6257 | 0.7528 | 0.8277 | nan | 0.8543 | 0.9035 | 0.7401 | 0.8612 | 0.7019 | 0.8917 | 0.7480 | 0.3991 | 0.5864 | 0.6421 | 0.9076 | 0.8444 | 0.8442 | 0.8526 | 0.8423 | 0.8194 | 0.3585 | 0.0 | 0.7642 | 0.8200 | 0.6380 | 0.7729 | 0.6208 | 0.7786 | 0.5533 | 0.3423 | 0.4979 | 0.5409 | 0.8290 | 0.7413 | 0.7612 | 0.7601 | 0.7746 | 0.7720 | 0.2953 | | 0.0275 | 14.52 | 7260 | 0.2287 | 0.6262 | 0.7541 | 0.8272 | nan | 0.8583 | 0.8979 | 0.7486 | 0.8614 | 0.7002 | 0.8887 | 0.7501 | 0.4107 | 0.6033 | 0.6407 | 0.9106 | 0.8440 | 0.8423 | 0.8479 | 0.8377 | 0.8219 | 0.3554 | 0.0 | 0.7642 | 0.8185 | 0.6394 | 0.7725 | 0.6219 | 0.7782 | 0.5540 | 0.3498 | 0.5049 | 0.5400 | 0.8294 | 0.7409 | 0.7605 | 0.7583 | 0.7735 | 0.7729 | 0.2929 | | 0.0289 | 14.56 | 7280 | 0.2300 | 0.6261 | 0.7551 | 0.8266 | nan | 0.8598 | 0.9003 | 0.7461 | 0.8573 | 0.7020 | 0.8874 | 0.7491 | 0.4181 | 0.5875 | 0.6462 | 0.9131 | 0.8410 | 0.8484 | 0.8535 | 0.8421 | 0.8187 | 0.3665 | 0.0 | 0.7650 | 0.8185 | 0.6386 | 0.7712 | 0.6206 | 0.7776 | 0.5530 | 0.3536 | 0.4989 | 0.5423 | 0.8287 | 0.7406 | 0.7610 | 0.7598 | 0.7737 | 0.7712 | 0.2960 | | 0.0344 | 14.6 | 7300 | 0.2288 | 0.6249 | 0.7539 | 0.8251 | nan | 0.8592 | 0.8959 | 0.7598 | 0.8538 | 0.7016 | 0.8922 | 0.7515 | 0.4125 | 0.5852 | 0.6357 | 0.9101 | 0.8403 | 0.8440 | 0.8519 | 0.8393 | 0.8194 | 0.3634 | 0.0 | 0.7626 | 0.8163 | 0.6421 | 0.7700 | 0.6202 | 0.7781 | 0.5519 | 0.3484 | 0.4966 | 0.5370 | 0.8285 | 0.7408 | 0.7605 | 0.7584 | 0.7721 | 0.7707 | 0.2945 | | 0.0346 | 14.64 | 7320 | 0.2338 | 0.6268 | 0.7566 | 0.8269 | nan | 0.8557 | 0.9050 | 0.7529 | 0.8560 | 0.7025 | 0.8915 | 0.7463 | 0.4320 | 0.5965 | 0.6417 | 0.9079 | 0.8469 | 0.8410 | 0.8546 | 0.8412 | 0.8252 | 0.3643 | 0.0 | 0.7642 | 0.8193 | 0.6412 | 0.7712 | 0.6191 | 0.7787 | 0.5509 | 0.3614 | 0.5017 | 0.5405 | 0.8284 | 0.7421 | 0.7605 | 0.7596 | 0.7733 | 0.7743 | 0.2955 | | 0.0307 | 14.68 | 7340 | 0.2271 | 0.6265 | 0.7557 | 0.8265 | nan | 0.8564 | 0.9005 | 0.7511 | 0.8552 | 0.7005 | 0.8950 | 0.7497 | 0.4260 | 0.5976 | 0.6397 | 0.9100 | 0.8436 | 0.8409 | 0.8508 | 0.8406 | 0.8237 | 0.3651 | 0.0 | 0.7637 | 0.8184 | 0.6400 | 0.7708 | 0.6203 | 0.7788 | 0.5515 | 0.3581 | 0.5018 | 0.5393 | 0.8290 | 0.7419 | 0.7607 | 0.7594 | 0.7733 | 0.7741 | 0.2952 | | 0.0336 | 14.72 | 7360 | 0.2285 | 0.6264 | 0.7548 | 0.8272 | nan | 0.8554 | 0.9010 | 0.7462 | 0.8625 | 0.7020 | 0.8938 | 0.7406 | 0.4302 | 0.5991 | 0.6354 | 0.9109 | 0.8508 | 0.8367 | 0.8526 | 0.8378 | 0.8187 | 0.3578 | 0.0 | 0.7643 | 0.8186 | 0.6385 | 0.7732 | 0.6186 | 0.7790 | 0.5506 | 0.3647 | 0.5021 | 0.5378 | 0.8292 | 0.7416 | 0.7589 | 0.7596 | 0.7736 | 0.7720 | 0.2933 | | 0.0341 | 14.76 | 7380 | 0.2335 | 0.6266 | 0.7552 | 0.8276 | nan | 0.8532 | 0.9024 | 0.7469 | 0.8566 | 0.6991 | 0.8969 | 0.7516 | 0.4132 | 0.5960 | 0.6437 | 0.9093 | 0.8471 | 0.8419 | 0.8524 | 0.8377 | 0.8259 | 0.3640 | 0.0 | 0.7633 | 0.8190 | 0.6388 | 0.7714 | 0.6215 | 0.7792 | 0.5524 | 0.3511 | 0.5017 | 0.5419 | 0.8290 | 0.7430 | 0.7612 | 0.7601 | 0.7740 | 0.7756 | 0.2950 | | 0.0321 | 14.8 | 7400 | 0.2308 | 0.6264 | 0.7547 | 0.8276 | nan | 0.8541 | 0.9008 | 0.7331 | 0.8581 | 0.7018 | 0.8939 | 0.7496 | 0.4216 | 0.5962 | 0.6428 | 0.9152 | 0.8488 | 0.8427 | 0.8529 | 0.8394 | 0.8179 | 0.3604 | 0.0 | 0.7644 | 0.8196 | 0.6338 | 0.7715 | 0.6202 | 0.7798 | 0.5516 | 0.3569 | 0.5020 | 0.5416 | 0.8294 | 0.7426 | 0.7612 | 0.7601 | 0.7747 | 0.7718 | 0.2938 | | 0.0783 | 14.84 | 7420 | 0.2278 | 0.6258 | 0.7540 | 0.8272 | nan | 0.8556 | 0.9037 | 0.7446 | 0.8563 | 0.7031 | 0.8952 | 0.7497 | 0.3995 | 0.6044 | 0.6390 | 0.9105 | 0.8453 | 0.8387 | 0.8480 | 0.8397 | 0.8223 | 0.3630 | 0.0 | 0.7644 | 0.8195 | 0.6383 | 0.7710 | 0.6198 | 0.7802 | 0.5510 | 0.3419 | 0.5047 | 0.5393 | 0.8294 | 0.7428 | 0.7609 | 0.7590 | 0.7742 | 0.7735 | 0.2945 | | 0.0259 | 14.88 | 7440 | 0.2288 | 0.6263 | 0.7549 | 0.8268 | nan | 0.8559 | 0.8999 | 0.7459 | 0.8566 | 0.7012 | 0.8953 | 0.7488 | 0.4195 | 0.6040 | 0.6386 | 0.9119 | 0.8451 | 0.8358 | 0.8513 | 0.8396 | 0.8231 | 0.3611 | 0.0 | 0.7638 | 0.8185 | 0.6384 | 0.7710 | 0.6206 | 0.7792 | 0.5513 | 0.3552 | 0.5042 | 0.5389 | 0.8296 | 0.7422 | 0.7600 | 0.7592 | 0.7734 | 0.7737 | 0.2937 | | 0.0353 | 14.92 | 7460 | 0.2265 | 0.6264 | 0.7550 | 0.8269 | nan | 0.8508 | 0.9005 | 0.7393 | 0.8568 | 0.7007 | 0.8919 | 0.7503 | 0.4240 | 0.6047 | 0.6397 | 0.9123 | 0.8487 | 0.8358 | 0.8533 | 0.8386 | 0.8246 | 0.3624 | 0.0 | 0.7618 | 0.8188 | 0.6362 | 0.7709 | 0.6215 | 0.7790 | 0.5519 | 0.3581 | 0.5051 | 0.5396 | 0.8293 | 0.7418 | 0.7596 | 0.7598 | 0.7734 | 0.7745 | 0.2943 | | 0.0386 | 14.96 | 7480 | 0.2278 | 0.6261 | 0.7548 | 0.8261 | nan | 0.8536 | 0.8987 | 0.7446 | 0.8552 | 0.7021 | 0.8904 | 0.7532 | 0.4292 | 0.6033 | 0.6383 | 0.9109 | 0.8457 | 0.8402 | 0.8466 | 0.8366 | 0.8216 | 0.3618 | 0.0 | 0.7620 | 0.8177 | 0.6378 | 0.7700 | 0.6215 | 0.7788 | 0.5515 | 0.3583 | 0.5046 | 0.5391 | 0.8293 | 0.7418 | 0.7605 | 0.7575 | 0.7728 | 0.7725 | 0.2940 | | 0.0279 | 15.0 | 7500 | 0.2292 | 0.6258 | 0.7547 | 0.8256 | nan | 0.8561 | 0.8974 | 0.7540 | 0.8553 | 0.7026 | 0.8913 | 0.7525 | 0.4251 | 0.6014 | 0.6374 | 0.9094 | 0.8452 | 0.8343 | 0.8506 | 0.8287 | 0.8232 | 0.3662 | 0.0 | 0.7625 | 0.8171 | 0.6400 | 0.7700 | 0.6211 | 0.7788 | 0.5512 | 0.3564 | 0.5032 | 0.5381 | 0.8294 | 0.7412 | 0.7591 | 0.7579 | 0.7705 | 0.7729 | 0.2956 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

[ "background", "hat", "hair", "sunglasses", "upper-clothes", "skirt", "pants", "dress", "belt", "left-shoe", "right-shoe", "face", "left-leg", "right-leg", "left-arm", "right-arm", "bag", "scarf" ]

samitizerxu/segformer-b0-finetuned-kelp-segments-jan-18-10am

# segformer-b0-finetuned-kelp-segments-jan-18-10am This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0197 - Mean Iou: 0.1778 - Mean Accuracy: 0.3556 - Overall Accuracy: 0.3556 - Accuracy Not Kelp: nan - Accuracy Kelp: 0.3556 - Iou Not Kelp: 0.0 - Iou Kelp: 0.3556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Not Kelp | Accuracy Kelp | Iou Not Kelp | Iou Kelp | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:-------------:|:------------:|:--------:| | 0.3836 | 0.05 | 30 | 0.3933 | 0.0106 | 0.0212 | 0.0212 | nan | 0.0212 | 0.0 | 0.0212 | | 0.2552 | 0.1 | 60 | 0.2404 | 0.0002 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 | | 0.2144 | 0.14 | 90 | 0.1817 | 0.0001 | 0.0002 | 0.0002 | nan | 0.0002 | 0.0 | 0.0002 | | 0.1172 | 0.19 | 120 | 0.1304 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.1404 | 0.24 | 150 | 0.0930 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0919 | 0.29 | 180 | 0.0742 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0715 | 0.34 | 210 | 0.0656 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0676 | 0.38 | 240 | 0.0538 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.05 | 0.43 | 270 | 0.0552 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.046 | 0.48 | 300 | 0.0514 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0584 | 0.53 | 330 | 0.0481 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0653 | 0.58 | 360 | 0.0470 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0285 | 0.62 | 390 | 0.0421 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0739 | 0.67 | 420 | 0.0418 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0493 | 0.72 | 450 | 0.0397 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0267 | 0.77 | 480 | 0.0396 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0483 | 0.82 | 510 | 0.0377 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0332 | 0.86 | 540 | 0.0382 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0322 | 0.91 | 570 | 0.0344 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0428 | 0.96 | 600 | 0.0338 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0343 | 1.01 | 630 | 0.0319 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.031 | 1.06 | 660 | 0.0327 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | | 0.0194 | 1.1 | 690 | 0.0328 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | | 0.0451 | 1.15 | 720 | 0.0347 | 0.0012 | 0.0025 | 0.0025 | nan | 0.0025 | 0.0 | 0.0025 | | 0.0571 | 1.2 | 750 | 0.0299 | 0.0006 | 0.0013 | 0.0013 | nan | 0.0013 | 0.0 | 0.0013 | | 0.0572 | 1.25 | 780 | 0.0301 | 0.0086 | 0.0172 | 0.0172 | nan | 0.0172 | 0.0 | 0.0172 | | 0.0563 | 1.3 | 810 | 0.0292 | 0.0072 | 0.0143 | 0.0143 | nan | 0.0143 | 0.0 | 0.0143 | | 0.0183 | 1.34 | 840 | 0.0294 | 0.0005 | 0.0010 | 0.0010 | nan | 0.0010 | 0.0 | 0.0010 | | 0.0145 | 1.39 | 870 | 0.0336 | 0.0140 | 0.0281 | 0.0281 | nan | 0.0281 | 0.0 | 0.0281 | | 0.0275 | 1.44 | 900 | 0.0276 | 0.0015 | 0.0029 | 0.0029 | nan | 0.0029 | 0.0 | 0.0029 | | 0.0445 | 1.49 | 930 | 0.0259 | 0.0083 | 0.0166 | 0.0166 | nan | 0.0166 | 0.0 | 0.0166 | | 0.0101 | 1.54 | 960 | 0.0255 | 0.0462 | 0.0923 | 0.0923 | nan | 0.0923 | 0.0 | 0.0923 | | 0.0245 | 1.58 | 990 | 0.0253 | 0.0062 | 0.0124 | 0.0124 | nan | 0.0124 | 0.0 | 0.0124 | | 0.0146 | 1.63 | 1020 | 0.0270 | 0.0003 | 0.0006 | 0.0006 | nan | 0.0006 | 0.0 | 0.0006 | | 0.0244 | 1.68 | 1050 | 0.0268 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 | | 0.0338 | 1.73 | 1080 | 0.0254 | 0.0022 | 0.0045 | 0.0045 | nan | 0.0045 | 0.0 | 0.0045 | | 0.0232 | 1.78 | 1110 | 0.0255 | 0.0001 | 0.0002 | 0.0002 | nan | 0.0002 | 0.0 | 0.0002 | | 0.0176 | 1.82 | 1140 | 0.0268 | 0.0009 | 0.0017 | 0.0017 | nan | 0.0017 | 0.0 | 0.0017 | | 0.0127 | 1.87 | 1170 | 0.0249 | 0.0095 | 0.0191 | 0.0191 | nan | 0.0191 | 0.0 | 0.0191 | | 0.0327 | 1.92 | 1200 | 0.0238 | 0.0437 | 0.0874 | 0.0874 | nan | 0.0874 | 0.0 | 0.0874 | | 0.0375 | 1.97 | 1230 | 0.0246 | 0.0066 | 0.0133 | 0.0133 | nan | 0.0133 | 0.0 | 0.0133 | | 0.017 | 2.02 | 1260 | 0.0245 | 0.0085 | 0.0169 | 0.0169 | nan | 0.0169 | 0.0 | 0.0169 | | 0.0224 | 2.06 | 1290 | 0.0251 | 0.0028 | 0.0057 | 0.0057 | nan | 0.0057 | 0.0 | 0.0057 | | 0.0174 | 2.11 | 1320 | 0.0240 | 0.0687 | 0.1374 | 0.1374 | nan | 0.1374 | 0.0 | 0.1374 | | 0.0194 | 2.16 | 1350 | 0.0239 | 0.0193 | 0.0386 | 0.0386 | nan | 0.0386 | 0.0 | 0.0386 | | 0.1479 | 2.21 | 1380 | 0.0244 | 0.0073 | 0.0147 | 0.0147 | nan | 0.0147 | 0.0 | 0.0147 | | 0.0161 | 2.26 | 1410 | 0.0229 | 0.0586 | 0.1173 | 0.1173 | nan | 0.1173 | 0.0 | 0.1173 | | 0.0385 | 2.3 | 1440 | 0.0231 | 0.0295 | 0.0590 | 0.0590 | nan | 0.0590 | 0.0 | 0.0590 | | 0.0611 | 2.35 | 1470 | 0.0235 | 0.0199 | 0.0399 | 0.0399 | nan | 0.0399 | 0.0 | 0.0399 | | 0.0204 | 2.4 | 1500 | 0.0225 | 0.0497 | 0.0994 | 0.0994 | nan | 0.0994 | 0.0 | 0.0994 | | 0.0329 | 2.45 | 1530 | 0.0230 | 0.0164 | 0.0327 | 0.0327 | nan | 0.0327 | 0.0 | 0.0327 | | 0.025 | 2.5 | 1560 | 0.0224 | 0.0518 | 0.1036 | 0.1036 | nan | 0.1036 | 0.0 | 0.1036 | | 0.0104 | 2.54 | 1590 | 0.0228 | 0.0244 | 0.0488 | 0.0488 | nan | 0.0488 | 0.0 | 0.0488 | | 0.0096 | 2.59 | 1620 | 0.0233 | 0.1242 | 0.2483 | 0.2483 | nan | 0.2483 | 0.0 | 0.2483 | | 0.005 | 2.64 | 1650 | 0.0221 | 0.0206 | 0.0411 | 0.0411 | nan | 0.0411 | 0.0 | 0.0411 | | 0.0264 | 2.69 | 1680 | 0.0236 | 0.1290 | 0.2581 | 0.2581 | nan | 0.2581 | 0.0 | 0.2581 | | 0.0117 | 2.74 | 1710 | 0.0215 | 0.0665 | 0.1330 | 0.1330 | nan | 0.1330 | 0.0 | 0.1330 | | 0.0096 | 2.78 | 1740 | 0.0229 | 0.0154 | 0.0307 | 0.0307 | nan | 0.0307 | 0.0 | 0.0307 | | 0.0176 | 2.83 | 1770 | 0.0222 | 0.0348 | 0.0696 | 0.0696 | nan | 0.0696 | 0.0 | 0.0696 | | 0.0108 | 2.88 | 1800 | 0.0219 | 0.0528 | 0.1056 | 0.1056 | nan | 0.1056 | 0.0 | 0.1056 | | 0.0189 | 2.93 | 1830 | 0.0217 | 0.0405 | 0.0811 | 0.0811 | nan | 0.0811 | 0.0 | 0.0811 | | 0.0288 | 2.98 | 1860 | 0.0218 | 0.0392 | 0.0784 | 0.0784 | nan | 0.0784 | 0.0 | 0.0784 | | 0.0067 | 3.02 | 1890 | 0.0213 | 0.0399 | 0.0799 | 0.0799 | nan | 0.0799 | 0.0 | 0.0799 | | 0.0104 | 3.07 | 1920 | 0.0225 | 0.0948 | 0.1896 | 0.1896 | nan | 0.1896 | 0.0 | 0.1896 | | 0.0115 | 3.12 | 1950 | 0.0214 | 0.0755 | 0.1510 | 0.1510 | nan | 0.1510 | 0.0 | 0.1510 | | 0.0076 | 3.17 | 1980 | 0.0222 | 0.0823 | 0.1646 | 0.1646 | nan | 0.1646 | 0.0 | 0.1646 | | 0.0444 | 3.22 | 2010 | 0.0213 | 0.0394 | 0.0788 | 0.0788 | nan | 0.0788 | 0.0 | 0.0788 | | 0.021 | 3.26 | 2040 | 0.0220 | 0.0392 | 0.0784 | 0.0784 | nan | 0.0784 | 0.0 | 0.0784 | | 0.0188 | 3.31 | 2070 | 0.0217 | 0.1194 | 0.2388 | 0.2388 | nan | 0.2388 | 0.0 | 0.2388 | | 0.0235 | 3.36 | 2100 | 0.0208 | 0.0560 | 0.1119 | 0.1119 | nan | 0.1119 | 0.0 | 0.1119 | | 0.0112 | 3.41 | 2130 | 0.0214 | 0.0459 | 0.0918 | 0.0918 | nan | 0.0918 | 0.0 | 0.0918 | | 0.0109 | 3.46 | 2160 | 0.0211 | 0.0464 | 0.0928 | 0.0928 | nan | 0.0928 | 0.0 | 0.0928 | | 0.0136 | 3.5 | 2190 | 0.0230 | 0.0174 | 0.0348 | 0.0348 | nan | 0.0348 | 0.0 | 0.0348 | | 0.0194 | 3.55 | 2220 | 0.0217 | 0.0851 | 0.1702 | 0.1702 | nan | 0.1702 | 0.0 | 0.1702 | | 0.0117 | 3.6 | 2250 | 0.0216 | 0.0295 | 0.0589 | 0.0589 | nan | 0.0589 | 0.0 | 0.0589 | | 0.0079 | 3.65 | 2280 | 0.0211 | 0.0368 | 0.0735 | 0.0735 | nan | 0.0735 | 0.0 | 0.0735 | | 0.0074 | 3.7 | 2310 | 0.0212 | 0.0861 | 0.1722 | 0.1722 | nan | 0.1722 | 0.0 | 0.1722 | | 0.0467 | 3.74 | 2340 | 0.0209 | 0.0643 | 0.1286 | 0.1286 | nan | 0.1286 | 0.0 | 0.1286 | | 0.0526 | 3.79 | 2370 | 0.0212 | 0.1275 | 0.2551 | 0.2551 | nan | 0.2551 | 0.0 | 0.2551 | | 0.01 | 3.84 | 2400 | 0.0213 | 0.0363 | 0.0725 | 0.0725 | nan | 0.0725 | 0.0 | 0.0725 | | 0.0426 | 3.89 | 2430 | 0.0212 | 0.1367 | 0.2733 | 0.2733 | nan | 0.2733 | 0.0 | 0.2733 | | 0.0335 | 3.94 | 2460 | 0.0220 | 0.0235 | 0.0470 | 0.0470 | nan | 0.0470 | 0.0 | 0.0470 | | 0.014 | 3.98 | 2490 | 0.0219 | 0.0870 | 0.1740 | 0.1740 | nan | 0.1740 | 0.0 | 0.1740 | | 0.022 | 4.03 | 2520 | 0.0212 | 0.0388 | 0.0775 | 0.0775 | nan | 0.0775 | 0.0 | 0.0775 | | 0.0038 | 4.08 | 2550 | 0.0211 | 0.0691 | 0.1382 | 0.1382 | nan | 0.1382 | 0.0 | 0.1382 | | 0.0164 | 4.13 | 2580 | 0.0210 | 0.0848 | 0.1697 | 0.1697 | nan | 0.1697 | 0.0 | 0.1697 | | 0.0121 | 4.18 | 2610 | 0.0215 | 0.0824 | 0.1648 | 0.1648 | nan | 0.1648 | 0.0 | 0.1648 | | 0.0271 | 4.22 | 2640 | 0.0211 | 0.0877 | 0.1754 | 0.1754 | nan | 0.1754 | 0.0 | 0.1754 | | 0.0046 | 4.27 | 2670 | 0.0200 | 0.0812 | 0.1625 | 0.1625 | nan | 0.1625 | 0.0 | 0.1625 | | 0.02 | 4.32 | 2700 | 0.0214 | 0.0500 | 0.1000 | 0.1000 | nan | 0.1000 | 0.0 | 0.1000 | | 0.0148 | 4.37 | 2730 | 0.0204 | 0.0550 | 0.1100 | 0.1100 | nan | 0.1100 | 0.0 | 0.1100 | | 0.0103 | 4.42 | 2760 | 0.0211 | 0.1222 | 0.2444 | 0.2444 | nan | 0.2444 | 0.0 | 0.2444 | | 0.0687 | 4.46 | 2790 | 0.0212 | 0.1351 | 0.2703 | 0.2703 | nan | 0.2703 | 0.0 | 0.2703 | | 0.0186 | 4.51 | 2820 | 0.0244 | 0.1186 | 0.2373 | 0.2373 | nan | 0.2373 | 0.0 | 0.2373 | | 0.0174 | 4.56 | 2850 | 0.0205 | 0.0487 | 0.0974 | 0.0974 | nan | 0.0974 | 0.0 | 0.0974 | | 0.018 | 4.61 | 2880 | 0.0204 | 0.1213 | 0.2426 | 0.2426 | nan | 0.2426 | 0.0 | 0.2426 | | 0.0224 | 4.66 | 2910 | 0.0201 | 0.1220 | 0.2439 | 0.2439 | nan | 0.2439 | 0.0 | 0.2439 | | 0.0262 | 4.7 | 2940 | 0.0202 | 0.0943 | 0.1885 | 0.1885 | nan | 0.1885 | 0.0 | 0.1885 | | 0.0215 | 4.75 | 2970 | 0.0197 | 0.1166 | 0.2333 | 0.2333 | nan | 0.2333 | 0.0 | 0.2333 | | 0.0159 | 4.8 | 3000 | 0.0203 | 0.1080 | 0.2159 | 0.2159 | nan | 0.2159 | 0.0 | 0.2159 | | 0.018 | 4.85 | 3030 | 0.0200 | 0.1138 | 0.2276 | 0.2276 | nan | 0.2276 | 0.0 | 0.2276 | | 0.0112 | 4.9 | 3060 | 0.0198 | 0.0830 | 0.1661 | 0.1661 | nan | 0.1661 | 0.0 | 0.1661 | | 0.0038 | 4.94 | 3090 | 0.0198 | 0.0605 | 0.1211 | 0.1211 | nan | 0.1211 | 0.0 | 0.1211 | | 0.0076 | 4.99 | 3120 | 0.0193 | 0.1283 | 0.2566 | 0.2566 | nan | 0.2566 | 0.0 | 0.2566 | | 0.0076 | 5.04 | 3150 | 0.0196 | 0.1245 | 0.2491 | 0.2491 | nan | 0.2491 | 0.0 | 0.2491 | | 0.0145 | 5.09 | 3180 | 0.0205 | 0.1699 | 0.3398 | 0.3398 | nan | 0.3398 | 0.0 | 0.3398 | | 0.0079 | 5.14 | 3210 | 0.0199 | 0.1323 | 0.2646 | 0.2646 | nan | 0.2646 | 0.0 | 0.2646 | | 0.0144 | 5.18 | 3240 | 0.0207 | 0.1465 | 0.2931 | 0.2931 | nan | 0.2931 | 0.0 | 0.2931 | | 0.0114 | 5.23 | 3270 | 0.0194 | 0.1094 | 0.2188 | 0.2188 | nan | 0.2188 | 0.0 | 0.2188 | | 0.0255 | 5.28 | 3300 | 0.0209 | 0.1864 | 0.3728 | 0.3728 | nan | 0.3728 | 0.0 | 0.3728 | | 0.0214 | 5.33 | 3330 | 0.0197 | 0.0679 | 0.1357 | 0.1357 | nan | 0.1357 | 0.0 | 0.1357 | | 0.0124 | 5.38 | 3360 | 0.0191 | 0.0968 | 0.1936 | 0.1936 | nan | 0.1936 | 0.0 | 0.1936 | | 0.0301 | 5.42 | 3390 | 0.0199 | 0.0622 | 0.1244 | 0.1244 | nan | 0.1244 | 0.0 | 0.1244 | | 0.0042 | 5.47 | 3420 | 0.0197 | 0.1157 | 0.2313 | 0.2313 | nan | 0.2313 | 0.0 | 0.2313 | | 0.0254 | 5.52 | 3450 | 0.0201 | 0.1449 | 0.2899 | 0.2899 | nan | 0.2899 | 0.0 | 0.2899 | | 0.0112 | 5.57 | 3480 | 0.0230 | 0.2090 | 0.4179 | 0.4179 | nan | 0.4179 | 0.0 | 0.4179 | | 0.0064 | 5.62 | 3510 | 0.0195 | 0.1100 | 0.2199 | 0.2199 | nan | 0.2199 | 0.0 | 0.2199 | | 0.0177 | 5.66 | 3540 | 0.0200 | 0.1095 | 0.2190 | 0.2190 | nan | 0.2190 | 0.0 | 0.2190 | | 0.0204 | 5.71 | 3570 | 0.0193 | 0.1370 | 0.2741 | 0.2741 | nan | 0.2741 | 0.0 | 0.2741 | | 0.0101 | 5.76 | 3600 | 0.0200 | 0.1715 | 0.3430 | 0.3430 | nan | 0.3430 | 0.0 | 0.3430 | | 0.0179 | 5.81 | 3630 | 0.0198 | 0.1837 | 0.3674 | 0.3674 | nan | 0.3674 | 0.0 | 0.3674 | | 0.0398 | 5.86 | 3660 | 0.0219 | 0.1870 | 0.3741 | 0.3741 | nan | 0.3741 | 0.0 | 0.3741 | | 0.006 | 5.9 | 3690 | 0.0206 | 0.0590 | 0.1179 | 0.1179 | nan | 0.1179 | 0.0 | 0.1179 | | 0.0328 | 5.95 | 3720 | 0.0193 | 0.1281 | 0.2562 | 0.2562 | nan | 0.2562 | 0.0 | 0.2562 | | 0.0184 | 6.0 | 3750 | 0.0192 | 0.0955 | 0.1910 | 0.1910 | nan | 0.1910 | 0.0 | 0.1910 | | 0.018 | 6.05 | 3780 | 0.0192 | 0.1283 | 0.2566 | 0.2566 | nan | 0.2566 | 0.0 | 0.2566 | | 0.0074 | 6.1 | 3810 | 0.0214 | 0.0403 | 0.0805 | 0.0805 | nan | 0.0805 | 0.0 | 0.0805 | | 0.0098 | 6.14 | 3840 | 0.0200 | 0.0928 | 0.1855 | 0.1855 | nan | 0.1855 | 0.0 | 0.1855 | | 0.0154 | 6.19 | 3870 | 0.0190 | 0.1304 | 0.2609 | 0.2609 | nan | 0.2609 | 0.0 | 0.2609 | | 0.0197 | 6.24 | 3900 | 0.0189 | 0.1300 | 0.2601 | 0.2601 | nan | 0.2601 | 0.0 | 0.2601 | | 0.0063 | 6.29 | 3930 | 0.0191 | 0.1352 | 0.2704 | 0.2704 | nan | 0.2704 | 0.0 | 0.2704 | | 0.0215 | 6.34 | 3960 | 0.0195 | 0.1572 | 0.3144 | 0.3144 | nan | 0.3144 | 0.0 | 0.3144 | | 0.021 | 6.38 | 3990 | 0.0188 | 0.1590 | 0.3179 | 0.3179 | nan | 0.3179 | 0.0 | 0.3179 | | 0.0193 | 6.43 | 4020 | 0.0195 | 0.1676 | 0.3352 | 0.3352 | nan | 0.3352 | 0.0 | 0.3352 | | 0.0174 | 6.48 | 4050 | 0.0202 | 0.1943 | 0.3886 | 0.3886 | nan | 0.3886 | 0.0 | 0.3886 | | 0.0097 | 6.53 | 4080 | 0.0196 | 0.0611 | 0.1223 | 0.1223 | nan | 0.1223 | 0.0 | 0.1223 | | 0.0043 | 6.58 | 4110 | 0.0196 | 0.0700 | 0.1399 | 0.1399 | nan | 0.1399 | 0.0 | 0.1399 | | 0.0121 | 6.62 | 4140 | 0.0190 | 0.1047 | 0.2093 | 0.2093 | nan | 0.2093 | 0.0 | 0.2093 | | 0.0053 | 6.67 | 4170 | 0.0190 | 0.1374 | 0.2748 | 0.2748 | nan | 0.2748 | 0.0 | 0.2748 | | 0.0234 | 6.72 | 4200 | 0.0197 | 0.1250 | 0.2500 | 0.2500 | nan | 0.2500 | 0.0 | 0.2500 | | 0.0164 | 6.77 | 4230 | 0.0187 | 0.0911 | 0.1822 | 0.1822 | nan | 0.1822 | 0.0 | 0.1822 | | 0.0139 | 6.82 | 4260 | 0.0190 | 0.1242 | 0.2485 | 0.2485 | nan | 0.2485 | 0.0 | 0.2485 | | 0.0431 | 6.86 | 4290 | 0.0194 | 0.1405 | 0.2809 | 0.2809 | nan | 0.2809 | 0.0 | 0.2809 | | 0.0082 | 6.91 | 4320 | 0.0189 | 0.1354 | 0.2707 | 0.2707 | nan | 0.2707 | 0.0 | 0.2707 | | 0.0095 | 6.96 | 4350 | 0.0193 | 0.1231 | 0.2462 | 0.2462 | nan | 0.2462 | 0.0 | 0.2462 | | 0.0062 | 7.01 | 4380 | 0.0195 | 0.1617 | 0.3233 | 0.3233 | nan | 0.3233 | 0.0 | 0.3233 | | 0.0067 | 7.06 | 4410 | 0.0206 | 0.1945 | 0.3890 | 0.3890 | nan | 0.3890 | 0.0 | 0.3890 | | 0.0193 | 7.1 | 4440 | 0.0193 | 0.1563 | 0.3126 | 0.3126 | nan | 0.3126 | 0.0 | 0.3126 | | 0.0482 | 7.15 | 4470 | 0.0197 | 0.1142 | 0.2284 | 0.2284 | nan | 0.2284 | 0.0 | 0.2284 | | 0.6577 | 7.2 | 4500 | 0.0188 | 0.1033 | 0.2066 | 0.2066 | nan | 0.2066 | 0.0 | 0.2066 | | 0.0251 | 7.25 | 4530 | 0.0201 | 0.0845 | 0.1689 | 0.1689 | nan | 0.1689 | 0.0 | 0.1689 | | 0.0059 | 7.3 | 4560 | 0.0191 | 0.1534 | 0.3068 | 0.3068 | nan | 0.3068 | 0.0 | 0.3068 | | 0.0103 | 7.34 | 4590 | 0.0192 | 0.1793 | 0.3586 | 0.3586 | nan | 0.3586 | 0.0 | 0.3586 | | 0.0115 | 7.39 | 4620 | 0.0191 | 0.1000 | 0.2001 | 0.2001 | nan | 0.2001 | 0.0 | 0.2001 | | 0.0096 | 7.44 | 4650 | 0.0194 | 0.0976 | 0.1952 | 0.1952 | nan | 0.1952 | 0.0 | 0.1952 | | 0.0223 | 7.49 | 4680 | 0.0185 | 0.1023 | 0.2046 | 0.2046 | nan | 0.2046 | 0.0 | 0.2046 | | 0.0228 | 7.54 | 4710 | 0.0190 | 0.1669 | 0.3339 | 0.3339 | nan | 0.3339 | 0.0 | 0.3339 | | 0.0449 | 7.58 | 4740 | 0.0183 | 0.1302 | 0.2603 | 0.2603 | nan | 0.2603 | 0.0 | 0.2603 | | 0.0224 | 7.63 | 4770 | 0.0193 | 0.1061 | 0.2122 | 0.2122 | nan | 0.2122 | 0.0 | 0.2122 | | 0.0161 | 7.68 | 4800 | 0.0188 | 0.1251 | 0.2502 | 0.2502 | nan | 0.2502 | 0.0 | 0.2502 | | 0.0106 | 7.73 | 4830 | 0.0194 | 0.1002 | 0.2005 | 0.2005 | nan | 0.2005 | 0.0 | 0.2005 | | 0.0163 | 7.78 | 4860 | 0.0186 | 0.1264 | 0.2528 | 0.2528 | nan | 0.2528 | 0.0 | 0.2528 | | 0.0087 | 7.82 | 4890 | 0.0184 | 0.1355 | 0.2709 | 0.2709 | nan | 0.2709 | 0.0 | 0.2709 | | 0.0247 | 7.87 | 4920 | 0.0195 | 0.2030 | 0.4060 | 0.4060 | nan | 0.4060 | 0.0 | 0.4060 | | 0.0239 | 7.92 | 4950 | 0.0188 | 0.1153 | 0.2306 | 0.2306 | nan | 0.2306 | 0.0 | 0.2306 | | 0.0149 | 7.97 | 4980 | 0.0192 | 0.1265 | 0.2531 | 0.2531 | nan | 0.2531 | 0.0 | 0.2531 | | 0.0121 | 8.02 | 5010 | 0.0191 | 0.1640 | 0.3280 | 0.3280 | nan | 0.3280 | 0.0 | 0.3280 | | 0.0157 | 8.06 | 5040 | 0.0192 | 0.1459 | 0.2917 | 0.2917 | nan | 0.2917 | 0.0 | 0.2917 | | 0.0321 | 8.11 | 5070 | 0.0194 | 0.1636 | 0.3273 | 0.3273 | nan | 0.3273 | 0.0 | 0.3273 | | 0.0228 | 8.16 | 5100 | 0.0185 | 0.1471 | 0.2943 | 0.2943 | nan | 0.2943 | 0.0 | 0.2943 | | 0.0027 | 8.21 | 5130 | 0.0185 | 0.1940 | 0.3881 | 0.3881 | nan | 0.3881 | 0.0 | 0.3881 | | 0.0332 | 8.26 | 5160 | 0.0183 | 0.1219 | 0.2439 | 0.2439 | nan | 0.2439 | 0.0 | 0.2439 | | 0.0099 | 8.3 | 5190 | 0.0183 | 0.1310 | 0.2621 | 0.2621 | nan | 0.2621 | 0.0 | 0.2621 | | 0.0202 | 8.35 | 5220 | 0.0186 | 0.1128 | 0.2257 | 0.2257 | nan | 0.2257 | 0.0 | 0.2257 | | 0.0121 | 8.4 | 5250 | 0.0192 | 0.1444 | 0.2888 | 0.2888 | nan | 0.2888 | 0.0 | 0.2888 | | 0.0058 | 8.45 | 5280 | 0.0201 | 0.0798 | 0.1597 | 0.1597 | nan | 0.1597 | 0.0 | 0.1597 | | 0.014 | 8.5 | 5310 | 0.0186 | 0.1456 | 0.2912 | 0.2912 | nan | 0.2912 | 0.0 | 0.2912 | | 0.0325 | 8.54 | 5340 | 0.0201 | 0.2215 | 0.4430 | 0.4430 | nan | 0.4430 | 0.0 | 0.4430 | | 0.0084 | 8.59 | 5370 | 0.0192 | 0.1044 | 0.2087 | 0.2087 | nan | 0.2087 | 0.0 | 0.2087 | | 0.0125 | 8.64 | 5400 | 0.0185 | 0.1414 | 0.2828 | 0.2828 | nan | 0.2828 | 0.0 | 0.2828 | | 0.0246 | 8.69 | 5430 | 0.0188 | 0.1482 | 0.2965 | 0.2965 | nan | 0.2965 | 0.0 | 0.2965 | | 0.0306 | 8.74 | 5460 | 0.0193 | 0.1833 | 0.3667 | 0.3667 | nan | 0.3667 | 0.0 | 0.3667 | | 0.0145 | 8.78 | 5490 | 0.0186 | 0.1594 | 0.3189 | 0.3189 | nan | 0.3189 | 0.0 | 0.3189 | | 0.0149 | 8.83 | 5520 | 0.0204 | 0.2578 | 0.5157 | 0.5157 | nan | 0.5157 | 0.0 | 0.5157 | | 0.0095 | 8.88 | 5550 | 0.0185 | 0.1725 | 0.3450 | 0.3450 | nan | 0.3450 | 0.0 | 0.3450 | | 0.0157 | 8.93 | 5580 | 0.0189 | 0.1450 | 0.2901 | 0.2901 | nan | 0.2901 | 0.0 | 0.2901 | | 0.0135 | 8.98 | 5610 | 0.0190 | 0.1688 | 0.3376 | 0.3376 | nan | 0.3376 | 0.0 | 0.3376 | | 0.0099 | 9.02 | 5640 | 0.0212 | 0.2304 | 0.4608 | 0.4608 | nan | 0.4608 | 0.0 | 0.4608 | | 0.0232 | 9.07 | 5670 | 0.0191 | 0.2030 | 0.4059 | 0.4059 | nan | 0.4059 | 0.0 | 0.4059 | | 0.0163 | 9.12 | 5700 | 0.0187 | 0.1628 | 0.3255 | 0.3255 | nan | 0.3255 | 0.0 | 0.3255 | | 0.8412 | 9.17 | 5730 | 0.0186 | 0.1743 | 0.3486 | 0.3486 | nan | 0.3486 | 0.0 | 0.3486 | | 0.0189 | 9.22 | 5760 | 0.0192 | 0.1287 | 0.2573 | 0.2573 | nan | 0.2573 | 0.0 | 0.2573 | | 0.0148 | 9.26 | 5790 | 0.0182 | 0.1526 | 0.3051 | 0.3051 | nan | 0.3051 | 0.0 | 0.3051 | | 0.0211 | 9.31 | 5820 | 0.0198 | 0.2222 | 0.4444 | 0.4444 | nan | 0.4444 | 0.0 | 0.4444 | | 0.0185 | 9.36 | 5850 | 0.0197 | 0.2170 | 0.4341 | 0.4341 | nan | 0.4341 | 0.0 | 0.4341 | | 0.0098 | 9.41 | 5880 | 0.0182 | 0.1784 | 0.3568 | 0.3568 | nan | 0.3568 | 0.0 | 0.3568 | | 0.0135 | 9.46 | 5910 | 0.0183 | 0.1618 | 0.3236 | 0.3236 | nan | 0.3236 | 0.0 | 0.3236 | | 0.0123 | 9.5 | 5940 | 0.0186 | 0.1752 | 0.3505 | 0.3505 | nan | 0.3505 | 0.0 | 0.3505 | | 0.0105 | 9.55 | 5970 | 0.0188 | 0.1955 | 0.3910 | 0.3910 | nan | 0.3910 | 0.0 | 0.3910 | | 0.0147 | 9.6 | 6000 | 0.0182 | 0.1437 | 0.2874 | 0.2874 | nan | 0.2874 | 0.0 | 0.2874 | | 0.0181 | 9.65 | 6030 | 0.0181 | 0.1288 | 0.2577 | 0.2577 | nan | 0.2577 | 0.0 | 0.2577 | | 0.0153 | 9.7 | 6060 | 0.0185 | 0.1794 | 0.3588 | 0.3588 | nan | 0.3588 | 0.0 | 0.3588 | | 0.0331 | 9.74 | 6090 | 0.0191 | 0.1037 | 0.2074 | 0.2074 | nan | 0.2074 | 0.0 | 0.2074 | | 0.0105 | 9.79 | 6120 | 0.0187 | 0.1807 | 0.3613 | 0.3613 | nan | 0.3613 | 0.0 | 0.3613 | | 0.0212 | 9.84 | 6150 | 0.0186 | 0.1652 | 0.3304 | 0.3304 | nan | 0.3304 | 0.0 | 0.3304 | | 0.0191 | 9.89 | 6180 | 0.0191 | 0.1921 | 0.3842 | 0.3842 | nan | 0.3842 | 0.0 | 0.3842 | | 0.0163 | 9.94 | 6210 | 0.0191 | 0.1893 | 0.3787 | 0.3787 | nan | 0.3787 | 0.0 | 0.3787 | | 0.054 | 9.98 | 6240 | 0.0190 | 0.1888 | 0.3777 | 0.3777 | nan | 0.3777 | 0.0 | 0.3777 | | 0.0095 | 10.03 | 6270 | 0.0193 | 0.1441 | 0.2881 | 0.2881 | nan | 0.2881 | 0.0 | 0.2881 | | 0.0146 | 10.08 | 6300 | 0.0194 | 0.1614 | 0.3228 | 0.3228 | nan | 0.3228 | 0.0 | 0.3228 | | 0.0189 | 10.13 | 6330 | 0.0189 | 0.1799 | 0.3598 | 0.3598 | nan | 0.3598 | 0.0 | 0.3598 | | 0.01 | 10.18 | 6360 | 0.0184 | 0.2016 | 0.4032 | 0.4032 | nan | 0.4032 | 0.0 | 0.4032 | | 0.0165 | 10.22 | 6390 | 0.0183 | 0.1727 | 0.3453 | 0.3453 | nan | 0.3453 | 0.0 | 0.3453 | | 0.0114 | 10.27 | 6420 | 0.0189 | 0.1329 | 0.2657 | 0.2657 | nan | 0.2657 | 0.0 | 0.2657 | | 0.017 | 10.32 | 6450 | 0.0186 | 0.1636 | 0.3272 | 0.3272 | nan | 0.3272 | 0.0 | 0.3272 | | 0.0109 | 10.37 | 6480 | 0.0184 | 0.1718 | 0.3436 | 0.3436 | nan | 0.3436 | 0.0 | 0.3436 | | 0.0044 | 10.42 | 6510 | 0.0188 | 0.1326 | 0.2652 | 0.2652 | nan | 0.2652 | 0.0 | 0.2652 | | 0.0126 | 10.46 | 6540 | 0.0188 | 0.1786 | 0.3573 | 0.3573 | nan | 0.3573 | 0.0 | 0.3573 | | 0.0044 | 10.51 | 6570 | 0.0191 | 0.1379 | 0.2758 | 0.2758 | nan | 0.2758 | 0.0 | 0.2758 | | 0.0218 | 10.56 | 6600 | 0.0188 | 0.2023 | 0.4046 | 0.4046 | nan | 0.4046 | 0.0 | 0.4046 | | 0.0124 | 10.61 | 6630 | 0.0189 | 0.1895 | 0.3791 | 0.3791 | nan | 0.3791 | 0.0 | 0.3791 | | 0.0056 | 10.66 | 6660 | 0.0189 | 0.1573 | 0.3145 | 0.3145 | nan | 0.3145 | 0.0 | 0.3145 | | 0.0055 | 10.7 | 6690 | 0.0187 | 0.1269 | 0.2538 | 0.2538 | nan | 0.2538 | 0.0 | 0.2538 | | 0.0023 | 10.75 | 6720 | 0.0185 | 0.1908 | 0.3815 | 0.3815 | nan | 0.3815 | 0.0 | 0.3815 | | 0.0517 | 10.8 | 6750 | 0.0181 | 0.1342 | 0.2684 | 0.2684 | nan | 0.2684 | 0.0 | 0.2684 | | 0.032 | 10.85 | 6780 | 0.0188 | 0.1179 | 0.2359 | 0.2359 | nan | 0.2359 | 0.0 | 0.2359 | | 0.0111 | 10.9 | 6810 | 0.0185 | 0.1195 | 0.2391 | 0.2391 | nan | 0.2391 | 0.0 | 0.2391 | | 0.0354 | 10.94 | 6840 | 0.0184 | 0.1911 | 0.3822 | 0.3822 | nan | 0.3822 | 0.0 | 0.3822 | | 0.0051 | 10.99 | 6870 | 0.0190 | 0.1151 | 0.2302 | 0.2302 | nan | 0.2302 | 0.0 | 0.2302 | | 0.0104 | 11.04 | 6900 | 0.0181 | 0.1562 | 0.3123 | 0.3123 | nan | 0.3123 | 0.0 | 0.3123 | | 0.0145 | 11.09 | 6930 | 0.0184 | 0.1641 | 0.3282 | 0.3282 | nan | 0.3282 | 0.0 | 0.3282 | | 0.022 | 11.14 | 6960 | 0.0192 | 0.1011 | 0.2023 | 0.2023 | nan | 0.2023 | 0.0 | 0.2023 | | 0.014 | 11.18 | 6990 | 0.0190 | 0.0986 | 0.1972 | 0.1972 | nan | 0.1972 | 0.0 | 0.1972 | | 0.0195 | 11.23 | 7020 | 0.0193 | 0.1969 | 0.3937 | 0.3937 | nan | 0.3937 | 0.0 | 0.3937 | | 0.0084 | 11.28 | 7050 | 0.0182 | 0.1592 | 0.3184 | 0.3184 | nan | 0.3184 | 0.0 | 0.3184 | | 0.0099 | 11.33 | 7080 | 0.0191 | 0.1812 | 0.3624 | 0.3624 | nan | 0.3624 | 0.0 | 0.3624 | | 0.0165 | 11.38 | 7110 | 0.0198 | 0.1032 | 0.2063 | 0.2063 | nan | 0.2063 | 0.0 | 0.2063 | | 0.0175 | 11.42 | 7140 | 0.0195 | 0.1435 | 0.2870 | 0.2870 | nan | 0.2870 | 0.0 | 0.2870 | | 0.0146 | 11.47 | 7170 | 0.0187 | 0.1509 | 0.3018 | 0.3018 | nan | 0.3018 | 0.0 | 0.3018 | | 0.0106 | 11.52 | 7200 | 0.0186 | 0.1504 | 0.3007 | 0.3007 | nan | 0.3007 | 0.0 | 0.3007 | | 0.0195 | 11.57 | 7230 | 0.0188 | 0.1333 | 0.2667 | 0.2667 | nan | 0.2667 | 0.0 | 0.2667 | | 0.0162 | 11.62 | 7260 | 0.0188 | 0.1397 | 0.2794 | 0.2794 | nan | 0.2794 | 0.0 | 0.2794 | | 0.0198 | 11.66 | 7290 | 0.0184 | 0.1546 | 0.3093 | 0.3093 | nan | 0.3093 | 0.0 | 0.3093 | | 0.0161 | 11.71 | 7320 | 0.0199 | 0.2356 | 0.4712 | 0.4712 | nan | 0.4712 | 0.0 | 0.4712 | | 0.0252 | 11.76 | 7350 | 0.0198 | 0.1594 | 0.3187 | 0.3187 | nan | 0.3187 | 0.0 | 0.3187 | | 0.0122 | 11.81 | 7380 | 0.0195 | 0.1640 | 0.3279 | 0.3279 | nan | 0.3279 | 0.0 | 0.3279 | | 0.0045 | 11.86 | 7410 | 0.0196 | 0.1362 | 0.2724 | 0.2724 | nan | 0.2724 | 0.0 | 0.2724 | | 0.0154 | 11.9 | 7440 | 0.0192 | 0.1514 | 0.3028 | 0.3028 | nan | 0.3028 | 0.0 | 0.3028 | | 0.0153 | 11.95 | 7470 | 0.0189 | 0.1134 | 0.2269 | 0.2269 | nan | 0.2269 | 0.0 | 0.2269 | | 0.015 | 12.0 | 7500 | 0.0189 | 0.1524 | 0.3049 | 0.3049 | nan | 0.3049 | 0.0 | 0.3049 | | 0.011 | 12.05 | 7530 | 0.0190 | 0.1358 | 0.2716 | 0.2716 | nan | 0.2716 | 0.0 | 0.2716 | | 0.0105 | 12.1 | 7560 | 0.0192 | 0.1099 | 0.2198 | 0.2198 | nan | 0.2198 | 0.0 | 0.2198 | | 0.0028 | 12.14 | 7590 | 0.0186 | 0.1551 | 0.3102 | 0.3102 | nan | 0.3102 | 0.0 | 0.3102 | | 0.0088 | 12.19 | 7620 | 0.0182 | 0.1527 | 0.3054 | 0.3054 | nan | 0.3054 | 0.0 | 0.3054 | | 0.0122 | 12.24 | 7650 | 0.0191 | 0.2037 | 0.4074 | 0.4074 | nan | 0.4074 | 0.0 | 0.4074 | | 0.0188 | 12.29 | 7680 | 0.0191 | 0.1690 | 0.3380 | 0.3380 | nan | 0.3380 | 0.0 | 0.3380 | | 0.0108 | 12.34 | 7710 | 0.0197 | 0.1367 | 0.2735 | 0.2735 | nan | 0.2735 | 0.0 | 0.2735 | | 0.0054 | 12.38 | 7740 | 0.0190 | 0.1659 | 0.3319 | 0.3319 | nan | 0.3319 | 0.0 | 0.3319 | | 0.0105 | 12.43 | 7770 | 0.0190 | 0.1749 | 0.3499 | 0.3499 | nan | 0.3499 | 0.0 | 0.3499 | | 0.0131 | 12.48 | 7800 | 0.0194 | 0.1994 | 0.3987 | 0.3987 | nan | 0.3987 | 0.0 | 0.3987 | | 0.0127 | 12.53 | 7830 | 0.0192 | 0.2064 | 0.4128 | 0.4128 | nan | 0.4128 | 0.0 | 0.4128 | | 0.014 | 12.58 | 7860 | 0.0187 | 0.1686 | 0.3372 | 0.3372 | nan | 0.3372 | 0.0 | 0.3372 | | 0.0086 | 12.62 | 7890 | 0.0192 | 0.1871 | 0.3742 | 0.3742 | nan | 0.3742 | 0.0 | 0.3742 | | 0.0089 | 12.67 | 7920 | 0.0187 | 0.1386 | 0.2772 | 0.2772 | nan | 0.2772 | 0.0 | 0.2772 | | 0.0055 | 12.72 | 7950 | 0.0187 | 0.1513 | 0.3026 | 0.3026 | nan | 0.3026 | 0.0 | 0.3026 | | 0.0118 | 12.77 | 7980 | 0.0186 | 0.1484 | 0.2968 | 0.2968 | nan | 0.2968 | 0.0 | 0.2968 | | 0.0059 | 12.82 | 8010 | 0.0185 | 0.1568 | 0.3137 | 0.3137 | nan | 0.3137 | 0.0 | 0.3137 | | 0.0103 | 12.86 | 8040 | 0.0182 | 0.1728 | 0.3456 | 0.3456 | nan | 0.3456 | 0.0 | 0.3456 | | 0.0085 | 12.91 | 8070 | 0.0187 | 0.1903 | 0.3806 | 0.3806 | nan | 0.3806 | 0.0 | 0.3806 | | 0.0145 | 12.96 | 8100 | 0.0185 | 0.1887 | 0.3773 | 0.3773 | nan | 0.3773 | 0.0 | 0.3773 | | 0.0064 | 13.01 | 8130 | 0.0191 | 0.1963 | 0.3925 | 0.3925 | nan | 0.3925 | 0.0 | 0.3925 | | 0.0066 | 13.06 | 8160 | 0.0186 | 0.1888 | 0.3777 | 0.3777 | nan | 0.3777 | 0.0 | 0.3777 | | 0.0139 | 13.1 | 8190 | 0.0183 | 0.1427 | 0.2854 | 0.2854 | nan | 0.2854 | 0.0 | 0.2854 | | 0.0179 | 13.15 | 8220 | 0.0185 | 0.1460 | 0.2921 | 0.2921 | nan | 0.2921 | 0.0 | 0.2921 | | 0.0108 | 13.2 | 8250 | 0.0190 | 0.1738 | 0.3476 | 0.3476 | nan | 0.3476 | 0.0 | 0.3476 | | 0.0112 | 13.25 | 8280 | 0.0196 | 0.2183 | 0.4365 | 0.4365 | nan | 0.4365 | 0.0 | 0.4365 | | 0.0409 | 13.3 | 8310 | 0.0191 | 0.1923 | 0.3846 | 0.3846 | nan | 0.3846 | 0.0 | 0.3846 | | 0.0145 | 13.34 | 8340 | 0.0194 | 0.1946 | 0.3892 | 0.3892 | nan | 0.3892 | 0.0 | 0.3892 | | 0.0077 | 13.39 | 8370 | 0.0195 | 0.2010 | 0.4020 | 0.4020 | nan | 0.4020 | 0.0 | 0.4020 | | 0.0108 | 13.44 | 8400 | 0.0194 | 0.1367 | 0.2734 | 0.2734 | nan | 0.2734 | 0.0 | 0.2734 | | 0.0161 | 13.49 | 8430 | 0.0187 | 0.1856 | 0.3711 | 0.3711 | nan | 0.3711 | 0.0 | 0.3711 | | 0.0101 | 13.54 | 8460 | 0.0185 | 0.1812 | 0.3623 | 0.3623 | nan | 0.3623 | 0.0 | 0.3623 | | 0.0181 | 13.58 | 8490 | 0.0192 | 0.1788 | 0.3576 | 0.3576 | nan | 0.3576 | 0.0 | 0.3576 | | 0.0092 | 13.63 | 8520 | 0.0188 | 0.1516 | 0.3032 | 0.3032 | nan | 0.3032 | 0.0 | 0.3032 | | 0.028 | 13.68 | 8550 | 0.0187 | 0.1690 | 0.3379 | 0.3379 | nan | 0.3379 | 0.0 | 0.3379 | | 0.0176 | 13.73 | 8580 | 0.0185 | 0.1724 | 0.3447 | 0.3447 | nan | 0.3447 | 0.0 | 0.3447 | | 0.0075 | 13.78 | 8610 | 0.0184 | 0.1768 | 0.3537 | 0.3537 | nan | 0.3537 | 0.0 | 0.3537 | | 0.0108 | 13.82 | 8640 | 0.0194 | 0.1717 | 0.3433 | 0.3433 | nan | 0.3433 | 0.0 | 0.3433 | | 0.0209 | 13.87 | 8670 | 0.0192 | 0.1636 | 0.3271 | 0.3271 | nan | 0.3271 | 0.0 | 0.3271 | | 0.0088 | 13.92 | 8700 | 0.0193 | 0.2096 | 0.4192 | 0.4192 | nan | 0.4192 | 0.0 | 0.4192 | | 0.0099 | 13.97 | 8730 | 0.0189 | 0.1995 | 0.3990 | 0.3990 | nan | 0.3990 | 0.0 | 0.3990 | | 0.015 | 14.02 | 8760 | 0.0186 | 0.1647 | 0.3295 | 0.3295 | nan | 0.3295 | 0.0 | 0.3295 | | 0.011 | 14.06 | 8790 | 0.0189 | 0.1676 | 0.3353 | 0.3353 | nan | 0.3353 | 0.0 | 0.3353 | | 0.0081 | 14.11 | 8820 | 0.0194 | 0.1419 | 0.2839 | 0.2839 | nan | 0.2839 | 0.0 | 0.2839 | | 0.0164 | 14.16 | 8850 | 0.0187 | 0.1627 | 0.3254 | 0.3254 | nan | 0.3254 | 0.0 | 0.3254 | | 0.009 | 14.21 | 8880 | 0.0183 | 0.1909 | 0.3817 | 0.3817 | nan | 0.3817 | 0.0 | 0.3817 | | 0.0135 | 14.26 | 8910 | 0.0186 | 0.1640 | 0.3280 | 0.3280 | nan | 0.3280 | 0.0 | 0.3280 | | 0.0286 | 14.3 | 8940 | 0.0188 | 0.1691 | 0.3383 | 0.3383 | nan | 0.3383 | 0.0 | 0.3383 | | 0.0036 | 14.35 | 8970 | 0.0189 | 0.1616 | 0.3231 | 0.3231 | nan | 0.3231 | 0.0 | 0.3231 | | 0.0115 | 14.4 | 9000 | 0.0193 | 0.1359 | 0.2718 | 0.2718 | nan | 0.2718 | 0.0 | 0.2718 | | 0.0107 | 14.45 | 9030 | 0.0189 | 0.1498 | 0.2996 | 0.2996 | nan | 0.2996 | 0.0 | 0.2996 | | 0.0217 | 14.5 | 9060 | 0.0194 | 0.1486 | 0.2972 | 0.2972 | nan | 0.2972 | 0.0 | 0.2972 | | 0.0109 | 14.54 | 9090 | 0.0194 | 0.1554 | 0.3109 | 0.3109 | nan | 0.3109 | 0.0 | 0.3109 | | 0.0104 | 14.59 | 9120 | 0.0195 | 0.1661 | 0.3321 | 0.3321 | nan | 0.3321 | 0.0 | 0.3321 | | 0.0096 | 14.64 | 9150 | 0.0187 | 0.1850 | 0.3700 | 0.3700 | nan | 0.3700 | 0.0 | 0.3700 | | 0.018 | 14.69 | 9180 | 0.0188 | 0.1923 | 0.3847 | 0.3847 | nan | 0.3847 | 0.0 | 0.3847 | | 0.0072 | 14.74 | 9210 | 0.0185 | 0.1775 | 0.3551 | 0.3551 | nan | 0.3551 | 0.0 | 0.3551 | | 0.0106 | 14.78 | 9240 | 0.0186 | 0.1570 | 0.3140 | 0.3140 | nan | 0.3140 | 0.0 | 0.3140 | | 0.0209 | 14.83 | 9270 | 0.0190 | 0.1936 | 0.3872 | 0.3872 | nan | 0.3872 | 0.0 | 0.3872 | | 0.0174 | 14.88 | 9300 | 0.0186 | 0.1920 | 0.3840 | 0.3840 | nan | 0.3840 | 0.0 | 0.3840 | | 0.0109 | 14.93 | 9330 | 0.0184 | 0.1680 | 0.3361 | 0.3361 | nan | 0.3361 | 0.0 | 0.3361 | | 0.019 | 14.98 | 9360 | 0.0189 | 0.1962 | 0.3924 | 0.3924 | nan | 0.3924 | 0.0 | 0.3924 | | 0.0162 | 15.02 | 9390 | 0.0195 | 0.1523 | 0.3045 | 0.3045 | nan | 0.3045 | 0.0 | 0.3045 | | 0.013 | 15.07 | 9420 | 0.0191 | 0.1530 | 0.3059 | 0.3059 | nan | 0.3059 | 0.0 | 0.3059 | | 0.0091 | 15.12 | 9450 | 0.0189 | 0.1871 | 0.3743 | 0.3743 | nan | 0.3743 | 0.0 | 0.3743 | | 0.0022 | 15.17 | 9480 | 0.0195 | 0.1568 | 0.3136 | 0.3136 | nan | 0.3136 | 0.0 | 0.3136 | | 0.0092 | 15.22 | 9510 | 0.0189 | 0.2056 | 0.4112 | 0.4112 | nan | 0.4112 | 0.0 | 0.4112 | | 0.0269 | 15.26 | 9540 | 0.0189 | 0.1692 | 0.3383 | 0.3383 | nan | 0.3383 | 0.0 | 0.3383 | | 0.0194 | 15.31 | 9570 | 0.0191 | 0.1744 | 0.3489 | 0.3489 | nan | 0.3489 | 0.0 | 0.3489 | | 0.0063 | 15.36 | 9600 | 0.0186 | 0.1757 | 0.3513 | 0.3513 | nan | 0.3513 | 0.0 | 0.3513 | | 0.0283 | 15.41 | 9630 | 0.0191 | 0.1858 | 0.3715 | 0.3715 | nan | 0.3715 | 0.0 | 0.3715 | | 0.0051 | 15.46 | 9660 | 0.0196 | 0.1602 | 0.3203 | 0.3203 | nan | 0.3203 | 0.0 | 0.3203 | | 0.003 | 15.5 | 9690 | 0.0195 | 0.1377 | 0.2753 | 0.2753 | nan | 0.2753 | 0.0 | 0.2753 | | 0.0149 | 15.55 | 9720 | 0.0189 | 0.1799 | 0.3597 | 0.3597 | nan | 0.3597 | 0.0 | 0.3597 | | 0.0169 | 15.6 | 9750 | 0.0189 | 0.1772 | 0.3544 | 0.3544 | nan | 0.3544 | 0.0 | 0.3544 | | 0.0125 | 15.65 | 9780 | 0.0193 | 0.1436 | 0.2872 | 0.2872 | nan | 0.2872 | 0.0 | 0.2872 | | 0.0115 | 15.7 | 9810 | 0.0192 | 0.1560 | 0.3121 | 0.3121 | nan | 0.3121 | 0.0 | 0.3121 | | 0.0081 | 15.74 | 9840 | 0.0186 | 0.1693 | 0.3386 | 0.3386 | nan | 0.3386 | 0.0 | 0.3386 | | 0.0183 | 15.79 | 9870 | 0.0192 | 0.1414 | 0.2828 | 0.2828 | nan | 0.2828 | 0.0 | 0.2828 | | 0.0128 | 15.84 | 9900 | 0.0189 | 0.1484 | 0.2967 | 0.2967 | nan | 0.2967 | 0.0 | 0.2967 | | 0.0071 | 15.89 | 9930 | 0.0192 | 0.1309 | 0.2618 | 0.2618 | nan | 0.2618 | 0.0 | 0.2618 | | 0.0112 | 15.94 | 9960 | 0.0194 | 0.1479 | 0.2957 | 0.2957 | nan | 0.2957 | 0.0 | 0.2957 | | 0.002 | 15.98 | 9990 | 0.0191 | 0.1575 | 0.3150 | 0.3150 | nan | 0.3150 | 0.0 | 0.3150 | | 0.0201 | 16.03 | 10020 | 0.0194 | 0.1302 | 0.2604 | 0.2604 | nan | 0.2604 | 0.0 | 0.2604 | | 0.0078 | 16.08 | 10050 | 0.0193 | 0.1424 | 0.2848 | 0.2848 | nan | 0.2848 | 0.0 | 0.2848 | | 0.0288 | 16.13 | 10080 | 0.0195 | 0.1624 | 0.3247 | 0.3247 | nan | 0.3247 | 0.0 | 0.3247 | | 0.0229 | 16.18 | 10110 | 0.0188 | 0.1687 | 0.3373 | 0.3373 | nan | 0.3373 | 0.0 | 0.3373 | | 0.0124 | 16.22 | 10140 | 0.0185 | 0.1783 | 0.3565 | 0.3565 | nan | 0.3565 | 0.0 | 0.3565 | | 0.0098 | 16.27 | 10170 | 0.0190 | 0.1657 | 0.3314 | 0.3314 | nan | 0.3314 | 0.0 | 0.3314 | | 0.0117 | 16.32 | 10200 | 0.0190 | 0.1537 | 0.3073 | 0.3073 | nan | 0.3073 | 0.0 | 0.3073 | | 0.0104 | 16.37 | 10230 | 0.0185 | 0.1686 | 0.3371 | 0.3371 | nan | 0.3371 | 0.0 | 0.3371 | | 0.0131 | 16.42 | 10260 | 0.0191 | 0.1377 | 0.2754 | 0.2754 | nan | 0.2754 | 0.0 | 0.2754 | | 0.0087 | 16.46 | 10290 | 0.0191 | 0.1500 | 0.3000 | 0.3000 | nan | 0.3000 | 0.0 | 0.3000 | | 0.0023 | 16.51 | 10320 | 0.0190 | 0.1774 | 0.3548 | 0.3548 | nan | 0.3548 | 0.0 | 0.3548 | | 0.0132 | 16.56 | 10350 | 0.0189 | 0.1788 | 0.3576 | 0.3576 | nan | 0.3576 | 0.0 | 0.3576 | | 0.0061 | 16.61 | 10380 | 0.0193 | 0.1672 | 0.3344 | 0.3344 | nan | 0.3344 | 0.0 | 0.3344 | | 0.0054 | 16.66 | 10410 | 0.0188 | 0.1780 | 0.3561 | 0.3561 | nan | 0.3561 | 0.0 | 0.3561 | | 0.0228 | 16.7 | 10440 | 0.0188 | 0.1652 | 0.3303 | 0.3303 | nan | 0.3303 | 0.0 | 0.3303 | | 0.0195 | 16.75 | 10470 | 0.0191 | 0.1583 | 0.3166 | 0.3166 | nan | 0.3166 | 0.0 | 0.3166 | | 0.019 | 16.8 | 10500 | 0.0193 | 0.1583 | 0.3166 | 0.3166 | nan | 0.3166 | 0.0 | 0.3166 | | 0.0219 | 16.85 | 10530 | 0.0187 | 0.1671 | 0.3342 | 0.3342 | nan | 0.3342 | 0.0 | 0.3342 | | 0.0146 | 16.9 | 10560 | 0.0189 | 0.2074 | 0.4148 | 0.4148 | nan | 0.4148 | 0.0 | 0.4148 | | 0.0124 | 16.94 | 10590 | 0.0192 | 0.1798 | 0.3597 | 0.3597 | nan | 0.3597 | 0.0 | 0.3597 | | 0.0266 | 16.99 | 10620 | 0.0187 | 0.1631 | 0.3262 | 0.3262 | nan | 0.3262 | 0.0 | 0.3262 | | 0.0066 | 17.04 | 10650 | 0.0194 | 0.1399 | 0.2799 | 0.2799 | nan | 0.2799 | 0.0 | 0.2799 | | 0.0044 | 17.09 | 10680 | 0.0188 | 0.1624 | 0.3248 | 0.3248 | nan | 0.3248 | 0.0 | 0.3248 | | 0.0031 | 17.14 | 10710 | 0.0193 | 0.1989 | 0.3979 | 0.3979 | nan | 0.3979 | 0.0 | 0.3979 | | 0.0056 | 17.18 | 10740 | 0.0188 | 0.1898 | 0.3795 | 0.3795 | nan | 0.3795 | 0.0 | 0.3795 | | 0.0037 | 17.23 | 10770 | 0.0189 | 0.1557 | 0.3114 | 0.3114 | nan | 0.3114 | 0.0 | 0.3114 | | 0.0052 | 17.28 | 10800 | 0.0194 | 0.1391 | 0.2781 | 0.2781 | nan | 0.2781 | 0.0 | 0.2781 | | 0.0151 | 17.33 | 10830 | 0.0191 | 0.1553 | 0.3106 | 0.3106 | nan | 0.3106 | 0.0 | 0.3106 | | 0.0096 | 17.38 | 10860 | 0.0190 | 0.1524 | 0.3047 | 0.3047 | nan | 0.3047 | 0.0 | 0.3047 | | 0.008 | 17.42 | 10890 | 0.0186 | 0.1667 | 0.3335 | 0.3335 | nan | 0.3335 | 0.0 | 0.3335 | | 0.013 | 17.47 | 10920 | 0.0186 | 0.1708 | 0.3416 | 0.3416 | nan | 0.3416 | 0.0 | 0.3416 | | 0.0067 | 17.52 | 10950 | 0.0184 | 0.1808 | 0.3616 | 0.3616 | nan | 0.3616 | 0.0 | 0.3616 | | 0.0101 | 17.57 | 10980 | 0.0187 | 0.1626 | 0.3252 | 0.3252 | nan | 0.3252 | 0.0 | 0.3252 | | 0.0168 | 17.62 | 11010 | 0.0188 | 0.1724 | 0.3447 | 0.3447 | nan | 0.3447 | 0.0 | 0.3447 | | 0.0056 | 17.66 | 11040 | 0.0191 | 0.1582 | 0.3165 | 0.3165 | nan | 0.3165 | 0.0 | 0.3165 | | 0.0062 | 17.71 | 11070 | 0.0194 | 0.1439 | 0.2877 | 0.2877 | nan | 0.2877 | 0.0 | 0.2877 | | 0.0035 | 17.76 | 11100 | 0.0189 | 0.1584 | 0.3168 | 0.3168 | nan | 0.3168 | 0.0 | 0.3168 | | 0.0198 | 17.81 | 11130 | 0.0191 | 0.1671 | 0.3343 | 0.3343 | nan | 0.3343 | 0.0 | 0.3343 | | 0.0018 | 17.86 | 11160 | 0.0195 | 0.1473 | 0.2945 | 0.2945 | nan | 0.2945 | 0.0 | 0.2945 | | 0.0083 | 17.9 | 11190 | 0.0189 | 0.1866 | 0.3732 | 0.3732 | nan | 0.3732 | 0.0 | 0.3732 | | 0.0122 | 17.95 | 11220 | 0.0192 | 0.1647 | 0.3294 | 0.3294 | nan | 0.3294 | 0.0 | 0.3294 | | 0.0099 | 18.0 | 11250 | 0.0192 | 0.1858 | 0.3716 | 0.3716 | nan | 0.3716 | 0.0 | 0.3716 | | 0.0129 | 18.05 | 11280 | 0.0191 | 0.1828 | 0.3656 | 0.3656 | nan | 0.3656 | 0.0 | 0.3656 | | 0.026 | 18.1 | 11310 | 0.0195 | 0.1661 | 0.3322 | 0.3322 | nan | 0.3322 | 0.0 | 0.3322 | | 0.0353 | 18.14 | 11340 | 0.0196 | 0.1703 | 0.3407 | 0.3407 | nan | 0.3407 | 0.0 | 0.3407 | | 0.0104 | 18.19 | 11370 | 0.0196 | 0.1764 | 0.3528 | 0.3528 | nan | 0.3528 | 0.0 | 0.3528 | | 0.0184 | 18.24 | 11400 | 0.0196 | 0.1441 | 0.2881 | 0.2881 | nan | 0.2881 | 0.0 | 0.2881 | | 0.0071 | 18.29 | 11430 | 0.0193 | 0.1693 | 0.3385 | 0.3385 | nan | 0.3385 | 0.0 | 0.3385 | | 0.0359 | 18.34 | 11460 | 0.0194 | 0.1994 | 0.3988 | 0.3988 | nan | 0.3988 | 0.0 | 0.3988 | | 0.0338 | 18.38 | 11490 | 0.0199 | 0.1578 | 0.3156 | 0.3156 | nan | 0.3156 | 0.0 | 0.3156 | | 0.0137 | 18.43 | 11520 | 0.0196 | 0.1741 | 0.3481 | 0.3481 | nan | 0.3481 | 0.0 | 0.3481 | | 0.0098 | 18.48 | 11550 | 0.0193 | 0.1641 | 0.3281 | 0.3281 | nan | 0.3281 | 0.0 | 0.3281 | | 0.013 | 18.53 | 11580 | 0.0193 | 0.1508 | 0.3016 | 0.3016 | nan | 0.3016 | 0.0 | 0.3016 | | 0.008 | 18.58 | 11610 | 0.0192 | 0.1758 | 0.3517 | 0.3517 | nan | 0.3517 | 0.0 | 0.3517 | | 0.0089 | 18.62 | 11640 | 0.0193 | 0.1511 | 0.3023 | 0.3023 | nan | 0.3023 | 0.0 | 0.3023 | | 0.0176 | 18.67 | 11670 | 0.0190 | 0.1646 | 0.3292 | 0.3292 | nan | 0.3292 | 0.0 | 0.3292 | | 0.0091 | 18.72 | 11700 | 0.0193 | 0.1514 | 0.3028 | 0.3028 | nan | 0.3028 | 0.0 | 0.3028 | | 0.0075 | 18.77 | 11730 | 0.0190 | 0.1644 | 0.3288 | 0.3288 | nan | 0.3288 | 0.0 | 0.3288 | | 0.0057 | 18.82 | 11760 | 0.0191 | 0.1789 | 0.3577 | 0.3577 | nan | 0.3577 | 0.0 | 0.3577 | | 0.0063 | 18.86 | 11790 | 0.0188 | 0.1874 | 0.3749 | 0.3749 | nan | 0.3749 | 0.0 | 0.3749 | | 0.0119 | 18.91 | 11820 | 0.0197 | 0.1316 | 0.2632 | 0.2632 | nan | 0.2632 | 0.0 | 0.2632 | | 0.0068 | 18.96 | 11850 | 0.0192 | 0.1963 | 0.3927 | 0.3927 | nan | 0.3927 | 0.0 | 0.3927 | | 0.0179 | 19.01 | 11880 | 0.0194 | 0.1603 | 0.3206 | 0.3206 | nan | 0.3206 | 0.0 | 0.3206 | | 0.0107 | 19.06 | 11910 | 0.0194 | 0.1469 | 0.2939 | 0.2939 | nan | 0.2939 | 0.0 | 0.2939 | | 0.0131 | 19.1 | 11940 | 0.0197 | 0.1692 | 0.3385 | 0.3385 | nan | 0.3385 | 0.0 | 0.3385 | | 0.0093 | 19.15 | 11970 | 0.0198 | 0.1533 | 0.3067 | 0.3067 | nan | 0.3067 | 0.0 | 0.3067 | | 0.0022 | 19.2 | 12000 | 0.0191 | 0.1794 | 0.3588 | 0.3588 | nan | 0.3588 | 0.0 | 0.3588 | | 0.0095 | 19.25 | 12030 | 0.0192 | 0.1663 | 0.3327 | 0.3327 | nan | 0.3327 | 0.0 | 0.3327 | | 0.0129 | 19.3 | 12060 | 0.0188 | 0.1776 | 0.3553 | 0.3553 | nan | 0.3553 | 0.0 | 0.3553 | | 0.0132 | 19.34 | 12090 | 0.0192 | 0.1745 | 0.3491 | 0.3491 | nan | 0.3491 | 0.0 | 0.3491 | | 0.0114 | 19.39 | 12120 | 0.0191 | 0.1755 | 0.3510 | 0.3510 | nan | 0.3510 | 0.0 | 0.3510 | | 0.0117 | 19.44 | 12150 | 0.0195 | 0.1568 | 0.3135 | 0.3135 | nan | 0.3135 | 0.0 | 0.3135 | | 0.0152 | 19.49 | 12180 | 0.0195 | 0.1631 | 0.3263 | 0.3263 | nan | 0.3263 | 0.0 | 0.3263 | | 0.0102 | 19.54 | 12210 | 0.0196 | 0.1699 | 0.3398 | 0.3398 | nan | 0.3398 | 0.0 | 0.3398 | | 0.0424 | 19.58 | 12240 | 0.0192 | 0.1833 | 0.3665 | 0.3665 | nan | 0.3665 | 0.0 | 0.3665 | | 0.0051 | 19.63 | 12270 | 0.0194 | 0.1720 | 0.3439 | 0.3439 | nan | 0.3439 | 0.0 | 0.3439 | | 0.0087 | 19.68 | 12300 | 0.0195 | 0.1760 | 0.3520 | 0.3520 | nan | 0.3520 | 0.0 | 0.3520 | | 0.0116 | 19.73 | 12330 | 0.0196 | 0.1535 | 0.3069 | 0.3069 | nan | 0.3069 | 0.0 | 0.3069 | | 0.0159 | 19.78 | 12360 | 0.0193 | 0.1547 | 0.3095 | 0.3095 | nan | 0.3095 | 0.0 | 0.3095 | | 0.0155 | 19.82 | 12390 | 0.0197 | 0.1531 | 0.3061 | 0.3061 | nan | 0.3061 | 0.0 | 0.3061 | | 0.0193 | 19.87 | 12420 | 0.0199 | 0.1677 | 0.3355 | 0.3355 | nan | 0.3355 | 0.0 | 0.3355 | | 0.0045 | 19.92 | 12450 | 0.0197 | 0.1755 | 0.3510 | 0.3510 | nan | 0.3510 | 0.0 | 0.3510 | | 0.015 | 19.97 | 12480 | 0.0192 | 0.1873 | 0.3747 | 0.3747 | nan | 0.3747 | 0.0 | 0.3747 | | 0.0124 | 20.02 | 12510 | 0.0198 | 0.1526 | 0.3053 | 0.3053 | nan | 0.3053 | 0.0 | 0.3053 | | 0.009 | 20.06 | 12540 | 0.0192 | 0.1834 | 0.3668 | 0.3668 | nan | 0.3668 | 0.0 | 0.3668 | | 0.0152 | 20.11 | 12570 | 0.0197 | 0.1663 | 0.3327 | 0.3327 | nan | 0.3327 | 0.0 | 0.3327 | | 0.0089 | 20.16 | 12600 | 0.0195 | 0.1823 | 0.3647 | 0.3647 | nan | 0.3647 | 0.0 | 0.3647 | | 0.0057 | 20.21 | 12630 | 0.0196 | 0.1693 | 0.3385 | 0.3385 | nan | 0.3385 | 0.0 | 0.3385 | | 0.0091 | 20.26 | 12660 | 0.0194 | 0.1911 | 0.3821 | 0.3821 | nan | 0.3821 | 0.0 | 0.3821 | | 0.0094 | 20.3 | 12690 | 0.0197 | 0.1814 | 0.3628 | 0.3628 | nan | 0.3628 | 0.0 | 0.3628 | | 0.0132 | 20.35 | 12720 | 0.0192 | 0.1724 | 0.3447 | 0.3447 | nan | 0.3447 | 0.0 | 0.3447 | | 0.0136 | 20.4 | 12750 | 0.0189 | 0.1840 | 0.3679 | 0.3679 | nan | 0.3679 | 0.0 | 0.3679 | | 0.004 | 20.45 | 12780 | 0.0191 | 0.1719 | 0.3437 | 0.3437 | nan | 0.3437 | 0.0 | 0.3437 | | 0.001 | 20.5 | 12810 | 0.0191 | 0.1773 | 0.3545 | 0.3545 | nan | 0.3545 | 0.0 | 0.3545 | | 0.0148 | 20.54 | 12840 | 0.0193 | 0.1675 | 0.3351 | 0.3351 | nan | 0.3351 | 0.0 | 0.3351 | | 0.0136 | 20.59 | 12870 | 0.0191 | 0.1812 | 0.3624 | 0.3624 | nan | 0.3624 | 0.0 | 0.3624 | | 0.01 | 20.64 | 12900 | 0.0192 | 0.1713 | 0.3426 | 0.3426 | nan | 0.3426 | 0.0 | 0.3426 | | 0.0071 | 20.69 | 12930 | 0.0193 | 0.1588 | 0.3175 | 0.3175 | nan | 0.3175 | 0.0 | 0.3175 | | 0.0176 | 20.74 | 12960 | 0.0192 | 0.1752 | 0.3504 | 0.3504 | nan | 0.3504 | 0.0 | 0.3504 | | 0.0122 | 20.78 | 12990 | 0.0195 | 0.1603 | 0.3205 | 0.3205 | nan | 0.3205 | 0.0 | 0.3205 | | 0.0087 | 20.83 | 13020 | 0.0196 | 0.1670 | 0.3340 | 0.3340 | nan | 0.3340 | 0.0 | 0.3340 | | 0.0118 | 20.88 | 13050 | 0.0190 | 0.1727 | 0.3454 | 0.3454 | nan | 0.3454 | 0.0 | 0.3454 | | 0.0112 | 20.93 | 13080 | 0.0192 | 0.1669 | 0.3337 | 0.3337 | nan | 0.3337 | 0.0 | 0.3337 | | 0.0195 | 20.98 | 13110 | 0.0191 | 0.1936 | 0.3872 | 0.3872 | nan | 0.3872 | 0.0 | 0.3872 | | 0.0171 | 21.02 | 13140 | 0.0196 | 0.1571 | 0.3142 | 0.3142 | nan | 0.3142 | 0.0 | 0.3142 | | 0.0062 | 21.07 | 13170 | 0.0192 | 0.1928 | 0.3856 | 0.3856 | nan | 0.3856 | 0.0 | 0.3856 | | 0.016 | 21.12 | 13200 | 0.0194 | 0.1548 | 0.3095 | 0.3095 | nan | 0.3095 | 0.0 | 0.3095 | | 0.0103 | 21.17 | 13230 | 0.0198 | 0.1855 | 0.3710 | 0.3710 | nan | 0.3710 | 0.0 | 0.3710 | | 0.0127 | 21.22 | 13260 | 0.0196 | 0.1760 | 0.3521 | 0.3521 | nan | 0.3521 | 0.0 | 0.3521 | | 0.0076 | 21.26 | 13290 | 0.0193 | 0.1732 | 0.3463 | 0.3463 | nan | 0.3463 | 0.0 | 0.3463 | | 0.0178 | 21.31 | 13320 | 0.0196 | 0.1751 | 0.3503 | 0.3503 | nan | 0.3503 | 0.0 | 0.3503 | | 0.0127 | 21.36 | 13350 | 0.0200 | 0.1527 | 0.3055 | 0.3055 | nan | 0.3055 | 0.0 | 0.3055 | | 0.0128 | 21.41 | 13380 | 0.0197 | 0.1580 | 0.3159 | 0.3159 | nan | 0.3159 | 0.0 | 0.3159 | | 0.0081 | 21.46 | 13410 | 0.0190 | 0.1721 | 0.3443 | 0.3443 | nan | 0.3443 | 0.0 | 0.3443 | | 0.0159 | 21.5 | 13440 | 0.0193 | 0.1633 | 0.3267 | 0.3267 | nan | 0.3267 | 0.0 | 0.3267 | | 0.0084 | 21.55 | 13470 | 0.0195 | 0.1530 | 0.3060 | 0.3060 | nan | 0.3060 | 0.0 | 0.3060 | | 0.0227 | 21.6 | 13500 | 0.0192 | 0.1608 | 0.3217 | 0.3217 | nan | 0.3217 | 0.0 | 0.3217 | | 0.0086 | 21.65 | 13530 | 0.0200 | 0.1520 | 0.3039 | 0.3039 | nan | 0.3039 | 0.0 | 0.3039 | | 0.0087 | 21.7 | 13560 | 0.0196 | 0.1685 | 0.3370 | 0.3370 | nan | 0.3370 | 0.0 | 0.3370 | | 0.005 | 21.74 | 13590 | 0.0192 | 0.1762 | 0.3523 | 0.3523 | nan | 0.3523 | 0.0 | 0.3523 | | 0.0072 | 21.79 | 13620 | 0.0196 | 0.1577 | 0.3153 | 0.3153 | nan | 0.3153 | 0.0 | 0.3153 | | 0.0158 | 21.84 | 13650 | 0.0194 | 0.1881 | 0.3762 | 0.3762 | nan | 0.3762 | 0.0 | 0.3762 | | 0.0149 | 21.89 | 13680 | 0.0195 | 0.1565 | 0.3130 | 0.3130 | nan | 0.3130 | 0.0 | 0.3130 | | 0.0028 | 21.94 | 13710 | 0.0190 | 0.1885 | 0.3770 | 0.3770 | nan | 0.3770 | 0.0 | 0.3770 | | 0.0054 | 21.98 | 13740 | 0.0192 | 0.1741 | 0.3481 | 0.3481 | nan | 0.3481 | 0.0 | 0.3481 | | 0.0117 | 22.03 | 13770 | 0.0191 | 0.1779 | 0.3559 | 0.3559 | nan | 0.3559 | 0.0 | 0.3559 | | 0.008 | 22.08 | 13800 | 0.0195 | 0.1800 | 0.3601 | 0.3601 | nan | 0.3601 | 0.0 | 0.3601 | | 0.0072 | 22.13 | 13830 | 0.0193 | 0.1631 | 0.3263 | 0.3263 | nan | 0.3263 | 0.0 | 0.3263 | | 0.0101 | 22.18 | 13860 | 0.0193 | 0.1646 | 0.3292 | 0.3292 | nan | 0.3292 | 0.0 | 0.3292 | | 0.0054 | 22.22 | 13890 | 0.0195 | 0.1703 | 0.3405 | 0.3405 | nan | 0.3405 | 0.0 | 0.3405 | | 0.0091 | 22.27 | 13920 | 0.0197 | 0.1656 | 0.3312 | 0.3312 | nan | 0.3312 | 0.0 | 0.3312 | | 0.0137 | 22.32 | 13950 | 0.0193 | 0.1817 | 0.3635 | 0.3635 | nan | 0.3635 | 0.0 | 0.3635 | | 0.011 | 22.37 | 13980 | 0.0194 | 0.1695 | 0.3391 | 0.3391 | nan | 0.3391 | 0.0 | 0.3391 | | 0.0141 | 22.42 | 14010 | 0.0193 | 0.1778 | 0.3556 | 0.3556 | nan | 0.3556 | 0.0 | 0.3556 | | 0.0087 | 22.46 | 14040 | 0.0193 | 0.1806 | 0.3613 | 0.3613 | nan | 0.3613 | 0.0 | 0.3613 | | 0.037 | 22.51 | 14070 | 0.0189 | 0.1882 | 0.3764 | 0.3764 | nan | 0.3764 | 0.0 | 0.3764 | | 0.0815 | 22.56 | 14100 | 0.0193 | 0.1602 | 0.3204 | 0.3204 | nan | 0.3204 | 0.0 | 0.3204 | | 0.0132 | 22.61 | 14130 | 0.0195 | 0.1702 | 0.3403 | 0.3403 | nan | 0.3403 | 0.0 | 0.3403 | | 0.0042 | 22.66 | 14160 | 0.0191 | 0.1764 | 0.3528 | 0.3528 | nan | 0.3528 | 0.0 | 0.3528 | | 0.0057 | 22.7 | 14190 | 0.0190 | 0.1807 | 0.3614 | 0.3614 | nan | 0.3614 | 0.0 | 0.3614 | | 0.0199 | 22.75 | 14220 | 0.0194 | 0.1628 | 0.3257 | 0.3257 | nan | 0.3257 | 0.0 | 0.3257 | | 0.0103 | 22.8 | 14250 | 0.0200 | 0.1540 | 0.3080 | 0.3080 | nan | 0.3080 | 0.0 | 0.3080 | | 0.0087 | 22.85 | 14280 | 0.0192 | 0.1805 | 0.3610 | 0.3610 | nan | 0.3610 | 0.0 | 0.3610 | | 0.0082 | 22.9 | 14310 | 0.0194 | 0.1516 | 0.3032 | 0.3032 | nan | 0.3032 | 0.0 | 0.3032 | | 0.0186 | 22.94 | 14340 | 0.0194 | 0.1589 | 0.3177 | 0.3177 | nan | 0.3177 | 0.0 | 0.3177 | | 0.0034 | 22.99 | 14370 | 0.0190 | 0.1836 | 0.3671 | 0.3671 | nan | 0.3671 | 0.0 | 0.3671 | | 0.0031 | 23.04 | 14400 | 0.0191 | 0.1798 | 0.3596 | 0.3596 | nan | 0.3596 | 0.0 | 0.3596 | | 0.0171 | 23.09 | 14430 | 0.0195 | 0.1778 | 0.3556 | 0.3556 | nan | 0.3556 | 0.0 | 0.3556 | | 0.0073 | 23.14 | 14460 | 0.0192 | 0.1904 | 0.3808 | 0.3808 | nan | 0.3808 | 0.0 | 0.3808 | | 0.0026 | 23.18 | 14490 | 0.0196 | 0.1704 | 0.3407 | 0.3407 | nan | 0.3407 | 0.0 | 0.3407 | | 0.0067 | 23.23 | 14520 | 0.0192 | 0.1695 | 0.3391 | 0.3391 | nan | 0.3391 | 0.0 | 0.3391 | | 0.0183 | 23.28 | 14550 | 0.0192 | 0.1851 | 0.3703 | 0.3703 | nan | 0.3703 | 0.0 | 0.3703 | | 0.0025 | 23.33 | 14580 | 0.0193 | 0.1735 | 0.3470 | 0.3470 | nan | 0.3470 | 0.0 | 0.3470 | | 0.0055 | 23.38 | 14610 | 0.0194 | 0.1788 | 0.3576 | 0.3576 | nan | 0.3576 | 0.0 | 0.3576 | | 0.0018 | 23.42 | 14640 | 0.0193 | 0.1890 | 0.3780 | 0.3780 | nan | 0.3780 | 0.0 | 0.3780 | | 0.0044 | 23.47 | 14670 | 0.0194 | 0.1809 | 0.3617 | 0.3617 | nan | 0.3617 | 0.0 | 0.3617 | | 0.0209 | 23.52 | 14700 | 0.0197 | 0.1817 | 0.3633 | 0.3633 | nan | 0.3633 | 0.0 | 0.3633 | | 0.0116 | 23.57 | 14730 | 0.0193 | 0.1797 | 0.3594 | 0.3594 | nan | 0.3594 | 0.0 | 0.3594 | | 0.0134 | 23.62 | 14760 | 0.0193 | 0.1807 | 0.3614 | 0.3614 | nan | 0.3614 | 0.0 | 0.3614 | | 0.0067 | 23.66 | 14790 | 0.0194 | 0.1718 | 0.3436 | 0.3436 | nan | 0.3436 | 0.0 | 0.3436 | | 0.0057 | 23.71 | 14820 | 0.0194 | 0.1741 | 0.3482 | 0.3482 | nan | 0.3482 | 0.0 | 0.3482 | | 0.0034 | 23.76 | 14850 | 0.0193 | 0.1679 | 0.3358 | 0.3358 | nan | 0.3358 | 0.0 | 0.3358 | | 0.0176 | 23.81 | 14880 | 0.0191 | 0.2018 | 0.4035 | 0.4035 | nan | 0.4035 | 0.0 | 0.4035 | | 0.0071 | 23.86 | 14910 | 0.0192 | 0.1884 | 0.3768 | 0.3768 | nan | 0.3768 | 0.0 | 0.3768 | | 0.0065 | 23.9 | 14940 | 0.0193 | 0.1744 | 0.3488 | 0.3488 | nan | 0.3488 | 0.0 | 0.3488 | | 0.0188 | 23.95 | 14970 | 0.0199 | 0.1540 | 0.3080 | 0.3080 | nan | 0.3080 | 0.0 | 0.3080 | | 0.0014 | 24.0 | 15000 | 0.0194 | 0.2232 | 0.4464 | 0.4464 | nan | 0.4464 | 0.0 | 0.4464 | | 0.0192 | 24.05 | 15030 | 0.0194 | 0.1709 | 0.3418 | 0.3418 | nan | 0.3418 | 0.0 | 0.3418 | | 0.0064 | 24.1 | 15060 | 0.0193 | 0.1852 | 0.3704 | 0.3704 | nan | 0.3704 | 0.0 | 0.3704 | | 0.0067 | 24.14 | 15090 | 0.0194 | 0.1782 | 0.3563 | 0.3563 | nan | 0.3563 | 0.0 | 0.3563 | | 0.0153 | 24.19 | 15120 | 0.0196 | 0.1827 | 0.3654 | 0.3654 | nan | 0.3654 | 0.0 | 0.3654 | | 0.0072 | 24.24 | 15150 | 0.0196 | 0.1898 | 0.3797 | 0.3797 | nan | 0.3797 | 0.0 | 0.3797 | | 0.0217 | 24.29 | 15180 | 0.0194 | 0.1697 | 0.3394 | 0.3394 | nan | 0.3394 | 0.0 | 0.3394 | | 0.0098 | 24.34 | 15210 | 0.0191 | 0.1801 | 0.3602 | 0.3602 | nan | 0.3602 | 0.0 | 0.3602 | | 0.0031 | 24.38 | 15240 | 0.0191 | 0.1818 | 0.3636 | 0.3636 | nan | 0.3636 | 0.0 | 0.3636 | | 0.0064 | 24.43 | 15270 | 0.0195 | 0.1677 | 0.3354 | 0.3354 | nan | 0.3354 | 0.0 | 0.3354 | | 0.0135 | 24.48 | 15300 | 0.0193 | 0.1727 | 0.3455 | 0.3455 | nan | 0.3455 | 0.0 | 0.3455 | | 0.0048 | 24.53 | 15330 | 0.0193 | 0.1822 | 0.3643 | 0.3643 | nan | 0.3643 | 0.0 | 0.3643 | | 0.0155 | 24.58 | 15360 | 0.0199 | 0.1571 | 0.3142 | 0.3142 | nan | 0.3142 | 0.0 | 0.3142 | | 0.0034 | 24.62 | 15390 | 0.0196 | 0.1758 | 0.3517 | 0.3517 | nan | 0.3517 | 0.0 | 0.3517 | | 0.0057 | 24.67 | 15420 | 0.0192 | 0.1833 | 0.3667 | 0.3667 | nan | 0.3667 | 0.0 | 0.3667 | | 0.0176 | 24.72 | 15450 | 0.0193 | 0.1773 | 0.3545 | 0.3545 | nan | 0.3545 | 0.0 | 0.3545 | | 0.0095 | 24.77 | 15480 | 0.0194 | 0.1799 | 0.3599 | 0.3599 | nan | 0.3599 | 0.0 | 0.3599 | | 0.0166 | 24.82 | 15510 | 0.0191 | 0.1908 | 0.3815 | 0.3815 | nan | 0.3815 | 0.0 | 0.3815 | | 0.0172 | 24.86 | 15540 | 0.0192 | 0.1741 | 0.3482 | 0.3482 | nan | 0.3482 | 0.0 | 0.3482 | | 0.0111 | 24.91 | 15570 | 0.0192 | 0.1702 | 0.3403 | 0.3403 | nan | 0.3403 | 0.0 | 0.3403 | | 0.0075 | 24.96 | 15600 | 0.0196 | 0.1575 | 0.3150 | 0.3150 | nan | 0.3150 | 0.0 | 0.3150 | | 0.0095 | 25.01 | 15630 | 0.0193 | 0.1789 | 0.3579 | 0.3579 | nan | 0.3579 | 0.0 | 0.3579 | | 0.008 | 25.06 | 15660 | 0.0196 | 0.1579 | 0.3158 | 0.3158 | nan | 0.3158 | 0.0 | 0.3158 | | 0.0063 | 25.1 | 15690 | 0.0195 | 0.1660 | 0.3319 | 0.3319 | nan | 0.3319 | 0.0 | 0.3319 | | 0.0103 | 25.15 | 15720 | 0.0195 | 0.1689 | 0.3379 | 0.3379 | nan | 0.3379 | 0.0 | 0.3379 | | 0.0115 | 25.2 | 15750 | 0.0194 | 0.1729 | 0.3457 | 0.3457 | nan | 0.3457 | 0.0 | 0.3457 | | 0.0076 | 25.25 | 15780 | 0.0195 | 0.1731 | 0.3461 | 0.3461 | nan | 0.3461 | 0.0 | 0.3461 | | 0.0375 | 25.3 | 15810 | 0.0196 | 0.1754 | 0.3508 | 0.3508 | nan | 0.3508 | 0.0 | 0.3508 | | 0.0055 | 25.34 | 15840 | 0.0198 | 0.1738 | 0.3475 | 0.3475 | nan | 0.3475 | 0.0 | 0.3475 | | 0.0087 | 25.39 | 15870 | 0.0196 | 0.1677 | 0.3353 | 0.3353 | nan | 0.3353 | 0.0 | 0.3353 | | 0.0099 | 25.44 | 15900 | 0.0193 | 0.1882 | 0.3763 | 0.3763 | nan | 0.3763 | 0.0 | 0.3763 | | 0.0123 | 25.49 | 15930 | 0.0195 | 0.1744 | 0.3488 | 0.3488 | nan | 0.3488 | 0.0 | 0.3488 | | 0.0127 | 25.54 | 15960 | 0.0195 | 0.1818 | 0.3636 | 0.3636 | nan | 0.3636 | 0.0 | 0.3636 | | 0.0074 | 25.58 | 15990 | 0.0196 | 0.1656 | 0.3311 | 0.3311 | nan | 0.3311 | 0.0 | 0.3311 | | 0.0055 | 25.63 | 16020 | 0.0194 | 0.1756 | 0.3511 | 0.3511 | nan | 0.3511 | 0.0 | 0.3511 | | 0.0013 | 25.68 | 16050 | 0.0196 | 0.1834 | 0.3667 | 0.3667 | nan | 0.3667 | 0.0 | 0.3667 | | 0.0149 | 25.73 | 16080 | 0.0197 | 0.1668 | 0.3336 | 0.3336 | nan | 0.3336 | 0.0 | 0.3336 | | 0.0124 | 25.78 | 16110 | 0.0196 | 0.1748 | 0.3497 | 0.3497 | nan | 0.3497 | 0.0 | 0.3497 | | 0.0176 | 25.82 | 16140 | 0.0193 | 0.1926 | 0.3851 | 0.3851 | nan | 0.3851 | 0.0 | 0.3851 | | 0.002 | 25.87 | 16170 | 0.0196 | 0.1635 | 0.3270 | 0.3270 | nan | 0.3270 | 0.0 | 0.3270 | | 0.0074 | 25.92 | 16200 | 0.0195 | 0.1781 | 0.3562 | 0.3562 | nan | 0.3562 | 0.0 | 0.3562 | | 0.0103 | 25.97 | 16230 | 0.0192 | 0.1845 | 0.3689 | 0.3689 | nan | 0.3689 | 0.0 | 0.3689 | | 0.0209 | 26.02 | 16260 | 0.0195 | 0.1596 | 0.3193 | 0.3193 | nan | 0.3193 | 0.0 | 0.3193 | | 0.0499 | 26.06 | 16290 | 0.0196 | 0.1589 | 0.3178 | 0.3178 | nan | 0.3178 | 0.0 | 0.3178 | | 0.0123 | 26.11 | 16320 | 0.0196 | 0.1864 | 0.3728 | 0.3728 | nan | 0.3728 | 0.0 | 0.3728 | | 0.0067 | 26.16 | 16350 | 0.0198 | 0.1725 | 0.3451 | 0.3451 | nan | 0.3451 | 0.0 | 0.3451 | | 0.0081 | 26.21 | 16380 | 0.0194 | 0.1763 | 0.3527 | 0.3527 | nan | 0.3527 | 0.0 | 0.3527 | | 0.004 | 26.26 | 16410 | 0.0196 | 0.1746 | 0.3491 | 0.3491 | nan | 0.3491 | 0.0 | 0.3491 | | 0.0159 | 26.3 | 16440 | 0.0198 | 0.1775 | 0.3551 | 0.3551 | nan | 0.3551 | 0.0 | 0.3551 | | 0.0044 | 26.35 | 16470 | 0.0200 | 0.1773 | 0.3546 | 0.3546 | nan | 0.3546 | 0.0 | 0.3546 | | 0.006 | 26.4 | 16500 | 0.0197 | 0.1687 | 0.3373 | 0.3373 | nan | 0.3373 | 0.0 | 0.3373 | | 0.0115 | 26.45 | 16530 | 0.0196 | 0.1638 | 0.3277 | 0.3277 | nan | 0.3277 | 0.0 | 0.3277 | | 0.0166 | 26.5 | 16560 | 0.0196 | 0.1697 | 0.3394 | 0.3394 | nan | 0.3394 | 0.0 | 0.3394 | | 0.0113 | 26.54 | 16590 | 0.0198 | 0.1709 | 0.3419 | 0.3419 | nan | 0.3419 | 0.0 | 0.3419 | | 0.0092 | 26.59 | 16620 | 0.0197 | 0.1655 | 0.3310 | 0.3310 | nan | 0.3310 | 0.0 | 0.3310 | | 0.0028 | 26.64 | 16650 | 0.0193 | 0.1796 | 0.3592 | 0.3592 | nan | 0.3592 | 0.0 | 0.3592 | | 0.0057 | 26.69 | 16680 | 0.0197 | 0.1637 | 0.3275 | 0.3275 | nan | 0.3275 | 0.0 | 0.3275 | | 0.0041 | 26.74 | 16710 | 0.0194 | 0.1851 | 0.3701 | 0.3701 | nan | 0.3701 | 0.0 | 0.3701 | | 0.0157 | 26.78 | 16740 | 0.0196 | 0.1638 | 0.3277 | 0.3277 | nan | 0.3277 | 0.0 | 0.3277 | | 0.0061 | 26.83 | 16770 | 0.0194 | 0.1873 | 0.3747 | 0.3747 | nan | 0.3747 | 0.0 | 0.3747 | | 0.0068 | 26.88 | 16800 | 0.0196 | 0.1668 | 0.3337 | 0.3337 | nan | 0.3337 | 0.0 | 0.3337 | | 0.0113 | 26.93 | 16830 | 0.0195 | 0.1770 | 0.3540 | 0.3540 | nan | 0.3540 | 0.0 | 0.3540 | | 0.0054 | 26.98 | 16860 | 0.0196 | 0.1800 | 0.3599 | 0.3599 | nan | 0.3599 | 0.0 | 0.3599 | | 0.0091 | 27.02 | 16890 | 0.0196 | 0.1706 | 0.3413 | 0.3413 | nan | 0.3413 | 0.0 | 0.3413 | | 0.0071 | 27.07 | 16920 | 0.0195 | 0.1799 | 0.3598 | 0.3598 | nan | 0.3598 | 0.0 | 0.3598 | | 0.0205 | 27.12 | 16950 | 0.0196 | 0.1749 | 0.3499 | 0.3499 | nan | 0.3499 | 0.0 | 0.3499 | | 0.0092 | 27.17 | 16980 | 0.0198 | 0.1683 | 0.3366 | 0.3366 | nan | 0.3366 | 0.0 | 0.3366 | | 0.0074 | 27.22 | 17010 | 0.0197 | 0.1734 | 0.3468 | 0.3468 | nan | 0.3468 | 0.0 | 0.3468 | | 0.0182 | 27.26 | 17040 | 0.0197 | 0.1706 | 0.3412 | 0.3412 | nan | 0.3412 | 0.0 | 0.3412 | | 0.008 | 27.31 | 17070 | 0.0196 | 0.1755 | 0.3510 | 0.3510 | nan | 0.3510 | 0.0 | 0.3510 | | 0.0046 | 27.36 | 17100 | 0.0195 | 0.1864 | 0.3727 | 0.3727 | nan | 0.3727 | 0.0 | 0.3727 | | 0.0033 | 27.41 | 17130 | 0.0196 | 0.1913 | 0.3826 | 0.3826 | nan | 0.3826 | 0.0 | 0.3826 | | 0.0036 | 27.46 | 17160 | 0.0199 | 0.1749 | 0.3498 | 0.3498 | nan | 0.3498 | 0.0 | 0.3498 | | 0.0162 | 27.5 | 17190 | 0.0195 | 0.1833 | 0.3666 | 0.3666 | nan | 0.3666 | 0.0 | 0.3666 | | 0.0058 | 27.55 | 17220 | 0.0195 | 0.1798 | 0.3596 | 0.3596 | nan | 0.3596 | 0.0 | 0.3596 | | 0.0068 | 27.6 | 17250 | 0.0196 | 0.1746 | 0.3492 | 0.3492 | nan | 0.3492 | 0.0 | 0.3492 | | 0.0089 | 27.65 | 17280 | 0.0196 | 0.1827 | 0.3654 | 0.3654 | nan | 0.3654 | 0.0 | 0.3654 | | 0.014 | 27.7 | 17310 | 0.0198 | 0.1794 | 0.3589 | 0.3589 | nan | 0.3589 | 0.0 | 0.3589 | | 0.013 | 27.74 | 17340 | 0.0197 | 0.1886 | 0.3772 | 0.3772 | nan | 0.3772 | 0.0 | 0.3772 | | 0.005 | 27.79 | 17370 | 0.0196 | 0.1839 | 0.3678 | 0.3678 | nan | 0.3678 | 0.0 | 0.3678 | | 0.0043 | 27.84 | 17400 | 0.0197 | 0.1691 | 0.3381 | 0.3381 | nan | 0.3381 | 0.0 | 0.3381 | | 0.0095 | 27.89 | 17430 | 0.0197 | 0.1657 | 0.3314 | 0.3314 | nan | 0.3314 | 0.0 | 0.3314 | | 0.0061 | 27.94 | 17460 | 0.0196 | 0.1746 | 0.3492 | 0.3492 | nan | 0.3492 | 0.0 | 0.3492 | | 0.0049 | 27.98 | 17490 | 0.0198 | 0.1660 | 0.3321 | 0.3321 | nan | 0.3321 | 0.0 | 0.3321 | | 0.0116 | 28.03 | 17520 | 0.0196 | 0.1853 | 0.3707 | 0.3707 | nan | 0.3707 | 0.0 | 0.3707 | | 0.0172 | 28.08 | 17550 | 0.0196 | 0.1715 | 0.3430 | 0.3430 | nan | 0.3430 | 0.0 | 0.3430 | | 0.008 | 28.13 | 17580 | 0.0198 | 0.1725 | 0.3450 | 0.3450 | nan | 0.3450 | 0.0 | 0.3450 | | 0.0168 | 28.18 | 17610 | 0.0197 | 0.1784 | 0.3569 | 0.3569 | nan | 0.3569 | 0.0 | 0.3569 | | 0.01 | 28.22 | 17640 | 0.0199 | 0.1714 | 0.3427 | 0.3427 | nan | 0.3427 | 0.0 | 0.3427 | | 0.0263 | 28.27 | 17670 | 0.0197 | 0.1725 | 0.3450 | 0.3450 | nan | 0.3450 | 0.0 | 0.3450 | | 0.0035 | 28.32 | 17700 | 0.0197 | 0.1820 | 0.3640 | 0.3640 | nan | 0.3640 | 0.0 | 0.3640 | | 0.0192 | 28.37 | 17730 | 0.0201 | 0.1675 | 0.3350 | 0.3350 | nan | 0.3350 | 0.0 | 0.3350 | | 0.0174 | 28.42 | 17760 | 0.0198 | 0.1819 | 0.3638 | 0.3638 | nan | 0.3638 | 0.0 | 0.3638 | | 0.0049 | 28.46 | 17790 | 0.0195 | 0.1899 | 0.3797 | 0.3797 | nan | 0.3797 | 0.0 | 0.3797 | | 0.0134 | 28.51 | 17820 | 0.0196 | 0.1816 | 0.3632 | 0.3632 | nan | 0.3632 | 0.0 | 0.3632 | | 0.0056 | 28.56 | 17850 | 0.0195 | 0.1682 | 0.3365 | 0.3365 | nan | 0.3365 | 0.0 | 0.3365 | | 0.0017 | 28.61 | 17880 | 0.0195 | 0.1778 | 0.3555 | 0.3555 | nan | 0.3555 | 0.0 | 0.3555 | | 0.0129 | 28.66 | 17910 | 0.0198 | 0.1701 | 0.3401 | 0.3401 | nan | 0.3401 | 0.0 | 0.3401 | | 0.0039 | 28.7 | 17940 | 0.0197 | 0.1762 | 0.3523 | 0.3523 | nan | 0.3523 | 0.0 | 0.3523 | | 0.0089 | 28.75 | 17970 | 0.0197 | 0.1859 | 0.3717 | 0.3717 | nan | 0.3717 | 0.0 | 0.3717 | | 0.0017 | 28.8 | 18000 | 0.0196 | 0.1790 | 0.3579 | 0.3579 | nan | 0.3579 | 0.0 | 0.3579 | | 0.004 | 28.85 | 18030 | 0.0199 | 0.1749 | 0.3498 | 0.3498 | nan | 0.3498 | 0.0 | 0.3498 | | 0.0053 | 28.9 | 18060 | 0.0196 | 0.1767 | 0.3534 | 0.3534 | nan | 0.3534 | 0.0 | 0.3534 | | 0.0026 | 28.94 | 18090 | 0.0198 | 0.1740 | 0.3480 | 0.3480 | nan | 0.3480 | 0.0 | 0.3480 | | 0.0073 | 28.99 | 18120 | 0.0197 | 0.1784 | 0.3568 | 0.3568 | nan | 0.3568 | 0.0 | 0.3568 | | 0.0044 | 29.04 | 18150 | 0.0197 | 0.1825 | 0.3649 | 0.3649 | nan | 0.3649 | 0.0 | 0.3649 | | 0.0099 | 29.09 | 18180 | 0.0197 | 0.1771 | 0.3542 | 0.3542 | nan | 0.3542 | 0.0 | 0.3542 | | 0.0062 | 29.14 | 18210 | 0.0198 | 0.1785 | 0.3570 | 0.3570 | nan | 0.3570 | 0.0 | 0.3570 | | 0.0113 | 29.18 | 18240 | 0.0198 | 0.1730 | 0.3459 | 0.3459 | nan | 0.3459 | 0.0 | 0.3459 | | 0.0097 | 29.23 | 18270 | 0.0197 | 0.1728 | 0.3456 | 0.3456 | nan | 0.3456 | 0.0 | 0.3456 | | 0.0197 | 29.28 | 18300 | 0.0197 | 0.1799 | 0.3599 | 0.3599 | nan | 0.3599 | 0.0 | 0.3599 | | 0.0038 | 29.33 | 18330 | 0.0198 | 0.1745 | 0.3491 | 0.3491 | nan | 0.3491 | 0.0 | 0.3491 | | 0.0068 | 29.38 | 18360 | 0.0196 | 0.1750 | 0.3500 | 0.3500 | nan | 0.3500 | 0.0 | 0.3500 | | 0.0064 | 29.42 | 18390 | 0.0197 | 0.1793 | 0.3587 | 0.3587 | nan | 0.3587 | 0.0 | 0.3587 | | 0.002 | 29.47 | 18420 | 0.0196 | 0.1765 | 0.3531 | 0.3531 | nan | 0.3531 | 0.0 | 0.3531 | | 0.014 | 29.52 | 18450 | 0.0198 | 0.1732 | 0.3463 | 0.3463 | nan | 0.3463 | 0.0 | 0.3463 | | 0.0136 | 29.57 | 18480 | 0.0197 | 0.1743 | 0.3486 | 0.3486 | nan | 0.3486 | 0.0 | 0.3486 | | 0.0123 | 29.62 | 18510 | 0.0197 | 0.1785 | 0.3570 | 0.3570 | nan | 0.3570 | 0.0 | 0.3570 | | 0.0249 | 29.66 | 18540 | 0.0197 | 0.1726 | 0.3452 | 0.3452 | nan | 0.3452 | 0.0 | 0.3452 | | 0.0081 | 29.71 | 18570 | 0.0196 | 0.1818 | 0.3635 | 0.3635 | nan | 0.3635 | 0.0 | 0.3635 | | 0.0028 | 29.76 | 18600 | 0.0197 | 0.1826 | 0.3652 | 0.3652 | nan | 0.3652 | 0.0 | 0.3652 | | 0.0133 | 29.81 | 18630 | 0.0197 | 0.1751 | 0.3502 | 0.3502 | nan | 0.3502 | 0.0 | 0.3502 | | 0.0075 | 29.86 | 18660 | 0.0196 | 0.1764 | 0.3527 | 0.3527 | nan | 0.3527 | 0.0 | 0.3527 | | 0.0231 | 29.9 | 18690 | 0.0197 | 0.1734 | 0.3469 | 0.3469 | nan | 0.3469 | 0.0 | 0.3469 | | 0.0024 | 29.95 | 18720 | 0.0197 | 0.1766 | 0.3531 | 0.3531 | nan | 0.3531 | 0.0 | 0.3531 | | 0.0105 | 30.0 | 18750 | 0.0197 | 0.1778 | 0.3556 | 0.3556 | nan | 0.3556 | 0.0 | 0.3556 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "not_kelp", "kelp" ]

blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_gris-10k-steps

# segformer-finetuned-4ss1st3r_s3gs3m_24Jan_gris-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_gris dataset. It achieves the following results on the evaluation set: - Loss: 0.2539 - Mean Iou: 0.5001 - Mean Accuracy: 0.7682 - Overall Accuracy: 0.9671 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9929 - Accuracy Fallo malla: 0.5837 - Accuracy Fallo adhesivo: 0.8806 - Accuracy Fallo burbuja: 0.6154 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9663 - Iou Fallo malla: 0.5505 - Iou Fallo adhesivo: 0.4321 - Iou Fallo burbuja: 0.5515 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.1186 | 1.0 | 259 | 0.1688 | 0.5045 | 0.6750 | 0.9611 | nan | 0.9940 | 0.5027 | 0.8539 | 0.3494 | 0.0 | 0.9600 | 0.4717 | 0.7563 | 0.3344 | | 0.0669 | 2.0 | 518 | 0.1603 | 0.4270 | 0.7755 | 0.9501 | nan | 0.9685 | 0.7091 | 0.8964 | 0.5282 | 0.0 | 0.9490 | 0.5466 | 0.1627 | 0.4767 | | 0.0608 | 3.0 | 777 | 0.1863 | 0.4142 | 0.7612 | 0.9458 | nan | 0.9703 | 0.5906 | 0.9321 | 0.5517 | 0.0 | 0.9467 | 0.5366 | 0.0891 | 0.4985 | | 0.0551 | 4.0 | 1036 | 0.1654 | 0.4515 | 0.7496 | 0.9620 | nan | 0.9879 | 0.5881 | 0.8763 | 0.5462 | 0.0 | 0.9620 | 0.5560 | 0.2349 | 0.5043 | | 0.0462 | 5.0 | 1295 | 0.2067 | 0.4267 | 0.7598 | 0.9487 | nan | 0.9752 | 0.5450 | 0.8796 | 0.6392 | 0.0 | 0.9502 | 0.5377 | 0.0838 | 0.5620 | | 0.0445 | 6.0 | 1554 | 0.1565 | 0.4557 | 0.7685 | 0.9627 | nan | 0.9873 | 0.5954 | 0.8571 | 0.6343 | 0.0 | 0.9636 | 0.5689 | 0.1837 | 0.5623 | | 0.039 | 7.0 | 1813 | 0.1523 | 0.4576 | 0.8005 | 0.9609 | nan | 0.9817 | 0.6535 | 0.9036 | 0.6630 | 0.0 | 0.9612 | 0.5885 | 0.1643 | 0.5738 | | 0.0367 | 8.0 | 2072 | 0.1954 | 0.4573 | 0.7462 | 0.9614 | nan | 0.9917 | 0.4963 | 0.8762 | 0.6206 | 0.0 | 0.9612 | 0.4850 | 0.2790 | 0.5612 | | 0.0352 | 9.0 | 2331 | 0.2244 | 0.4757 | 0.7542 | 0.9636 | nan | 0.9932 | 0.5098 | 0.8867 | 0.6269 | 0.0 | 0.9629 | 0.5013 | 0.3466 | 0.5674 | | 0.0357 | 10.0 | 2590 | 0.2119 | 0.4687 | 0.7394 | 0.9645 | nan | 0.9934 | 0.5378 | 0.8710 | 0.5552 | 0.0 | 0.9641 | 0.5209 | 0.3377 | 0.5207 | | 0.0352 | 11.0 | 2849 | 0.1957 | 0.4469 | 0.7903 | 0.9584 | nan | 0.9791 | 0.6656 | 0.9237 | 0.5927 | 0.0 | 0.9591 | 0.5829 | 0.1459 | 0.5465 | | 0.032 | 12.0 | 3108 | 0.1811 | 0.4521 | 0.8058 | 0.9594 | nan | 0.9797 | 0.6634 | 0.9338 | 0.6464 | 0.0 | 0.9608 | 0.5929 | 0.1397 | 0.5671 | | 0.0299 | 13.0 | 3367 | 0.2403 | 0.4298 | 0.7596 | 0.9557 | nan | 0.9827 | 0.5553 | 0.9271 | 0.5733 | 0.0 | 0.9572 | 0.5336 | 0.1272 | 0.5311 | | 0.0292 | 14.0 | 3626 | 0.2233 | 0.4667 | 0.7638 | 0.9642 | nan | 0.9900 | 0.5759 | 0.8508 | 0.6385 | 0.0 | 0.9638 | 0.5475 | 0.2511 | 0.5709 | | 0.0264 | 15.0 | 3885 | 0.2382 | 0.4431 | 0.7690 | 0.9594 | nan | 0.9865 | 0.5492 | 0.9139 | 0.6267 | 0.0 | 0.9602 | 0.5326 | 0.1568 | 0.5658 | | 0.0273 | 16.0 | 4144 | 0.2339 | 0.4382 | 0.7751 | 0.9570 | nan | 0.9818 | 0.5876 | 0.9193 | 0.6115 | 0.0 | 0.9584 | 0.5419 | 0.1352 | 0.5554 | | 0.0249 | 17.0 | 4403 | 0.2078 | 0.4950 | 0.7846 | 0.9669 | nan | 0.9925 | 0.5784 | 0.9197 | 0.6477 | 0.0 | 0.9663 | 0.5508 | 0.3921 | 0.5658 | | 0.0242 | 18.0 | 4662 | 0.2495 | 0.4809 | 0.7706 | 0.9645 | nan | 0.9922 | 0.5392 | 0.9007 | 0.6503 | 0.0 | 0.9640 | 0.5147 | 0.3577 | 0.5682 | | 0.0241 | 19.0 | 4921 | 0.2117 | 0.4491 | 0.7954 | 0.9589 | nan | 0.9815 | 0.6243 | 0.9423 | 0.6336 | 0.0 | 0.9597 | 0.5703 | 0.1508 | 0.5647 | | 0.0243 | 20.0 | 5180 | 0.1989 | 0.4754 | 0.8013 | 0.9656 | nan | 0.9875 | 0.6416 | 0.9194 | 0.6568 | 0.0 | 0.9658 | 0.5879 | 0.2482 | 0.5751 | | 0.0251 | 21.0 | 5439 | 0.2095 | 0.4607 | 0.7962 | 0.9629 | nan | 0.9853 | 0.6324 | 0.9337 | 0.6334 | 0.0 | 0.9634 | 0.5732 | 0.2073 | 0.5598 | | 0.0238 | 22.0 | 5698 | 0.2063 | 0.4747 | 0.7927 | 0.9650 | nan | 0.9873 | 0.6383 | 0.9158 | 0.6293 | 0.0 | 0.9645 | 0.5779 | 0.2744 | 0.5569 | | 0.0225 | 23.0 | 5957 | 0.2260 | 0.4656 | 0.7915 | 0.9640 | nan | 0.9880 | 0.6003 | 0.9106 | 0.6672 | 0.0 | 0.9642 | 0.5647 | 0.2241 | 0.5752 | | 0.0231 | 24.0 | 6216 | 0.2454 | 0.4688 | 0.7783 | 0.9645 | nan | 0.9891 | 0.6019 | 0.9197 | 0.6024 | 0.0 | 0.9643 | 0.5591 | 0.2766 | 0.5442 | | 0.0218 | 25.0 | 6475 | 0.2482 | 0.5143 | 0.7752 | 0.9665 | nan | 0.9919 | 0.5896 | 0.9136 | 0.6057 | 0.0 | 0.9655 | 0.5433 | 0.5236 | 0.5390 | | 0.0223 | 26.0 | 6734 | 0.2474 | 0.4648 | 0.7660 | 0.9642 | nan | 0.9903 | 0.5784 | 0.9054 | 0.5898 | 0.0 | 0.9639 | 0.5502 | 0.2768 | 0.5334 | | 0.0238 | 27.0 | 6993 | 0.2475 | 0.4717 | 0.7669 | 0.9651 | nan | 0.9920 | 0.5597 | 0.9019 | 0.6138 | 0.0 | 0.9648 | 0.5379 | 0.3087 | 0.5470 | | 0.021 | 28.0 | 7252 | 0.2490 | 0.4740 | 0.7708 | 0.9649 | nan | 0.9919 | 0.5573 | 0.9116 | 0.6222 | 0.0 | 0.9645 | 0.5362 | 0.3142 | 0.5553 | | 0.0208 | 29.0 | 7511 | 0.2369 | 0.4633 | 0.7669 | 0.9653 | nan | 0.9896 | 0.6134 | 0.8846 | 0.5799 | 0.0 | 0.9652 | 0.5762 | 0.2422 | 0.5327 | | 0.0202 | 30.0 | 7770 | 0.2498 | 0.4863 | 0.7654 | 0.9655 | nan | 0.9930 | 0.5488 | 0.8931 | 0.6267 | 0.0 | 0.9647 | 0.5273 | 0.3811 | 0.5582 | | 0.021 | 31.0 | 8029 | 0.2534 | 0.4799 | 0.7729 | 0.9657 | nan | 0.9915 | 0.5794 | 0.9043 | 0.6164 | 0.0 | 0.9652 | 0.5474 | 0.3368 | 0.5502 | | 0.0202 | 32.0 | 8288 | 0.2626 | 0.4771 | 0.7627 | 0.9653 | nan | 0.9930 | 0.5464 | 0.9014 | 0.6098 | 0.0 | 0.9647 | 0.5272 | 0.3464 | 0.5474 | | 0.0201 | 33.0 | 8547 | 0.2710 | 0.4903 | 0.7673 | 0.9659 | nan | 0.9936 | 0.5431 | 0.8994 | 0.6329 | 0.0 | 0.9653 | 0.5221 | 0.3997 | 0.5645 | | 0.0195 | 34.0 | 8806 | 0.2589 | 0.4915 | 0.7662 | 0.9663 | nan | 0.9930 | 0.5644 | 0.8895 | 0.6177 | 0.0 | 0.9656 | 0.5368 | 0.4014 | 0.5537 | | 0.0194 | 35.0 | 9065 | 0.2304 | 0.5092 | 0.7801 | 0.9675 | nan | 0.9919 | 0.6048 | 0.8941 | 0.6295 | 0.0 | 0.9667 | 0.5615 | 0.4576 | 0.5603 | | 0.0188 | 36.0 | 9324 | 0.2674 | 0.5022 | 0.7629 | 0.9670 | nan | 0.9933 | 0.5783 | 0.8819 | 0.5982 | 0.0 | 0.9662 | 0.5461 | 0.4567 | 0.5418 | | 0.0188 | 37.0 | 9583 | 0.2580 | 0.4897 | 0.7702 | 0.9665 | nan | 0.9925 | 0.5791 | 0.8884 | 0.6207 | 0.0 | 0.9660 | 0.5485 | 0.3793 | 0.5548 | | 0.0192 | 38.0 | 9842 | 0.2556 | 0.5065 | 0.7656 | 0.9673 | nan | 0.9933 | 0.5823 | 0.8739 | 0.6130 | 0.0 | 0.9665 | 0.5494 | 0.4667 | 0.5500 | | 0.019 | 38.61 | 10000 | 0.2539 | 0.5001 | 0.7682 | 0.9671 | nan | 0.9929 | 0.5837 | 0.8806 | 0.6154 | 0.0 | 0.9663 | 0.5505 | 0.4321 | 0.5515 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]

samitizerxu/segformer-b1-kelp-rgb-agg-imgaug-jan-22

# segformer-b1-kelp-rgb-agg-imgaug-jan-22 This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set: - eval_accuracy_kelp: nan - eval_iou_kelp: 0.0 - eval_loss: 0.3223 - eval_mean_iou: 0.0205 - eval_mean_accuracy: 0.0410 - eval_overall_accuracy: 0.0410 - eval_runtime: 62.0057 - eval_samples_per_second: 27.272 - eval_steps_per_second: 3.419 - epoch: 1.16 - step: 570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 40 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "background", "kelp" ]

rohan8020/segformer-b0-finetuned-segments-dots

# segformer-b0-finetuned-segments-dots This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rohan8020/test dataset. It achieves the following results on the evaluation set: - Loss: 0.1328 - Mean Iou: 0.3201 - Mean Accuracy: 0.6402 - Overall Accuracy: 0.6402 - Accuracy Unlabeled: nan - Accuracy Dots: 0.6402 - Iou Unlabeled: 0.0 - Iou Dots: 0.6402 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dots | Iou Unlabeled | Iou Dots | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:| | 0.5642 | 4.0 | 20 | 0.6209 | 0.4838 | 0.9677 | 0.9677 | nan | 0.9677 | 0.0 | 0.9677 | | 0.4154 | 8.0 | 40 | 0.4119 | 0.2969 | 0.5939 | 0.5939 | nan | 0.5939 | 0.0 | 0.5939 | | 0.3246 | 12.0 | 60 | 0.2900 | 0.3123 | 0.6246 | 0.6246 | nan | 0.6246 | 0.0 | 0.6246 | | 0.2898 | 16.0 | 80 | 0.3168 | 0.4260 | 0.8520 | 0.8520 | nan | 0.8520 | 0.0 | 0.8520 | | 0.2419 | 20.0 | 100 | 0.2201 | 0.3446 | 0.6892 | 0.6892 | nan | 0.6892 | 0.0 | 0.6892 | | 0.2042 | 24.0 | 120 | 0.2199 | 0.3213 | 0.6426 | 0.6426 | nan | 0.6426 | 0.0 | 0.6426 | | 0.1662 | 28.0 | 140 | 0.1797 | 0.3002 | 0.6005 | 0.6005 | nan | 0.6005 | 0.0 | 0.6005 | | 0.1757 | 32.0 | 160 | 0.1611 | 0.2919 | 0.5839 | 0.5839 | nan | 0.5839 | 0.0 | 0.5839 | | 0.1473 | 36.0 | 180 | 0.1477 | 0.3219 | 0.6439 | 0.6439 | nan | 0.6439 | 0.0 | 0.6439 | | 0.1645 | 40.0 | 200 | 0.1448 | 0.3267 | 0.6534 | 0.6534 | nan | 0.6534 | 0.0 | 0.6534 | | 0.1576 | 44.0 | 220 | 0.1389 | 0.3377 | 0.6754 | 0.6754 | nan | 0.6754 | 0.0 | 0.6754 | | 0.1381 | 48.0 | 240 | 0.1328 | 0.3201 | 0.6402 | 0.6402 | nan | 0.6402 | 0.0 | 0.6402 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "dots" ]

rohan8020/segformer-b0-finetuned-segments-dots-1

# segformer-b0-finetuned-segments-dots-1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the rohan8020/test dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Mean Iou: 0.0 - Mean Accuracy: nan - Overall Accuracy: nan - Accuracy Unlabeled: nan - Accuracy Dots: nan - Iou Unlabeled: 0.0 - Iou Dots: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dots | Iou Unlabeled | Iou Dots | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:| | 0.0029 | 4.0 | 20 | 0.0122 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0004 | 8.0 | 40 | 0.0010 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0003 | 12.0 | 60 | 0.0004 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0003 | 16.0 | 80 | 0.0003 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0003 | 20.0 | 100 | 0.0002 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0002 | 24.0 | 120 | 0.0002 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 28.0 | 140 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0002 | 32.0 | 160 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 36.0 | 180 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 40.0 | 200 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0002 | 44.0 | 220 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 48.0 | 240 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 52.0 | 260 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 56.0 | 280 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 60.0 | 300 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 64.0 | 320 | 0.0001 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 68.0 | 340 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 72.0 | 360 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 76.0 | 380 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 80.0 | 400 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 84.0 | 420 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 88.0 | 440 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0001 | 92.0 | 460 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 96.0 | 480 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 100.0 | 500 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 104.0 | 520 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 108.0 | 540 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 112.0 | 560 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 116.0 | 580 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 120.0 | 600 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 124.0 | 620 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 128.0 | 640 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 132.0 | 660 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 136.0 | 680 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 140.0 | 700 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 144.0 | 720 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 148.0 | 740 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 152.0 | 760 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 156.0 | 780 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 160.0 | 800 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 164.0 | 820 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 168.0 | 840 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 172.0 | 860 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 176.0 | 880 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 180.0 | 900 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 184.0 | 920 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 188.0 | 940 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 192.0 | 960 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 196.0 | 980 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 200.0 | 1000 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 204.0 | 1020 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 208.0 | 1040 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 212.0 | 1060 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 216.0 | 1080 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 220.0 | 1100 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 224.0 | 1120 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 228.0 | 1140 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 232.0 | 1160 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 236.0 | 1180 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 240.0 | 1200 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 244.0 | 1220 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | | 0.0 | 248.0 | 1240 | 0.0000 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "dots" ]

mrkprc1/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuSegmentation2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3909 - Mean Iou: 0.3084 - Mean Accuracy: 0.9142 - Overall Accuracy: 0.9142 - Accuracy Empty-cell: nan - Accuracy Complete-cell: 0.9142 - Iou Empty-cell: 0.0 - Iou Complete-cell: 0.6169 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Empty-cell | Accuracy Complete-cell | Iou Empty-cell | Iou Complete-cell | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------------:|:--------------:|:-----------------:| | 0.3784 | 10.0 | 20 | 0.4056 | 0.3166 | 0.9695 | 0.9695 | nan | 0.9695 | 0.0 | 0.6333 | | 0.3037 | 20.0 | 40 | 0.4253 | 0.2816 | 0.7997 | 0.7997 | nan | 0.7997 | 0.0 | 0.5631 | | 0.3326 | 30.0 | 60 | 0.4163 | 0.2894 | 0.8280 | 0.8280 | nan | 0.8280 | 0.0 | 0.5789 | | 0.276 | 40.0 | 80 | 0.4037 | 0.2990 | 0.8758 | 0.8758 | nan | 0.8758 | 0.0 | 0.5979 | | 0.2616 | 50.0 | 100 | 0.3909 | 0.3084 | 0.9142 | 0.9142 | nan | 0.9142 | 0.0 | 0.6169 | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "empty-cell", "complete-cell" ]

mrkprc1/segformer-b0-finetuned-sudoku

# segformer-b0-finetuned-sudoku This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuBoundaries2 dataset. It achieves the following results on the evaluation set: - Loss: 1.5465 - Mean Iou: 0.2407 - Mean Accuracy: 0.5 - Overall Accuracy: 0.4814 - Accuracy Unlabelled: 1.0 - Accuracy Sudoku-boundary: 0.0 - Iou Unlabelled: 0.4814 - Iou Sudoku-boundary: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:| | 0.6257 | 2.5 | 20 | 0.7024 | 0.2992 | 0.4856 | 0.4769 | 0.7186 | 0.2525 | 0.3981 | 0.2002 | | 0.6194 | 5.0 | 40 | 0.7513 | 0.2593 | 0.4960 | 0.4797 | 0.9332 | 0.0588 | 0.4633 | 0.0553 | | 0.6134 | 7.5 | 60 | 0.8649 | 0.2428 | 0.4993 | 0.4809 | 0.9921 | 0.0065 | 0.4792 | 0.0065 | | 0.4962 | 10.0 | 80 | 0.9245 | 0.2434 | 0.5006 | 0.4822 | 0.9949 | 0.0063 | 0.4805 | 0.0063 | | 0.5552 | 12.5 | 100 | 0.8606 | 0.2442 | 0.5009 | 0.4826 | 0.9939 | 0.0080 | 0.4804 | 0.0079 | | 0.6282 | 15.0 | 120 | 1.1507 | 0.2407 | 0.5000 | 0.4814 | 1.0000 | 0.0000 | 0.4813 | 0.0000 | | 0.4042 | 17.5 | 140 | 1.0916 | 0.2408 | 0.4997 | 0.4811 | 0.9988 | 0.0007 | 0.4810 | 0.0007 | | 0.8174 | 20.0 | 160 | 0.9731 | 0.2424 | 0.4991 | 0.4807 | 0.9926 | 0.0056 | 0.4792 | 0.0055 | | 0.5353 | 22.5 | 180 | 0.9754 | 0.2409 | 0.4991 | 0.4805 | 0.9964 | 0.0017 | 0.4801 | 0.0017 | | 0.4792 | 25.0 | 200 | 1.6835 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.4244 | 27.5 | 220 | 1.5039 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.376 | 30.0 | 240 | 2.2746 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.4129 | 32.5 | 260 | 2.0116 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.4717 | 35.0 | 280 | 1.8957 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.4229 | 37.5 | 300 | 1.7574 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.5708 | 40.0 | 320 | 2.0764 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.5826 | 42.5 | 340 | 1.6177 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.3765 | 45.0 | 360 | 1.8119 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 0.3704 | 47.5 | 380 | 1.6863 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | | 1.3265 | 50.0 | 400 | 1.5465 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "unlabelled", "sudoku-boundary" ]

unreal-hug/segformer-b0-finetuned-segments-ECHO-jan-25-v2

# segformer-b0-finetuned-segments-ECHO-jan-25-v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset. It achieves the following results on the evaluation set: - Loss: 0.4155 - Mean Iou: 0.3349 - Mean Accuracy: 0.3935 - Overall Accuracy: 0.5591 - Accuracy Unlabeled: nan - Accuracy Lv: 0.6815 - Accuracy Rv: 0.3865 - Accuracy Ra: 0.5805 - Accuracy La: 0.6544 - Accuracy Vs: 0.1155 - Accuracy As: nan - Accuracy Mk: 0.0497 - Accuracy Tk: nan - Accuracy Asd: 0.2779 - Accuracy Vsd: 0.3995 - Accuracy Ak: 0.3959 - Iou Unlabeled: 0.0 - Iou Lv: 0.6626 - Iou Rv: 0.3764 - Iou Ra: 0.5699 - Iou La: 0.6056 - Iou Vs: 0.1108 - Iou As: nan - Iou Mk: 0.0485 - Iou Tk: nan - Iou Asd: 0.2565 - Iou Vsd: 0.3465 - Iou Ak: 0.3718 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Rv | Accuracy Ra | Accuracy La | Accuracy Vs | Accuracy As | Accuracy Mk | Accuracy Tk | Accuracy Asd | Accuracy Vsd | Accuracy Ak | Iou Unlabeled | Iou Lv | Iou Rv | Iou Ra | Iou La | Iou Vs | Iou As | Iou Mk | Iou Tk | Iou Asd | Iou Vsd | Iou Ak | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:------------:|:------------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-------:|:-------:|:------:| | 2.0322 | 0.12 | 20 | 2.2124 | 0.0954 | 0.1885 | 0.3033 | nan | 0.3903 | 0.4680 | 0.0850 | 0.0173 | 0.0 | nan | 0.0011 | nan | 0.0 | 0.1087 | 0.6263 | 0.0 | 0.2970 | 0.2085 | 0.0782 | 0.0172 | 0.0 | nan | 0.0011 | 0.0 | 0.0 | 0.0823 | 0.3647 | | 1.6027 | 0.25 | 40 | 1.5649 | 0.0789 | 0.1168 | 0.2640 | nan | 0.5149 | 0.0061 | 0.0264 | 0.0839 | 0.0 | nan | 0.0 | nan | 0.0001 | 0.0014 | 0.4180 | 0.0 | 0.3418 | 0.0061 | 0.0262 | 0.0787 | 0.0 | nan | 0.0 | nan | 0.0001 | 0.0014 | 0.3342 | | 1.2877 | 0.38 | 60 | 1.2616 | 0.0943 | 0.1296 | 0.2685 | nan | 0.4665 | 0.0053 | 0.0547 | 0.2421 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0050 | 0.3930 | 0.0 | 0.3612 | 0.0053 | 0.0529 | 0.1877 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0050 | 0.3312 | | 1.0981 | 0.5 | 80 | 1.2208 | 0.0967 | 0.1552 | 0.3898 | nan | 0.8151 | 0.0079 | 0.0082 | 0.0794 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4863 | 0.0 | 0.4737 | 0.0079 | 0.0082 | 0.0750 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4020 | | 1.0235 | 0.62 | 100 | 0.9343 | 0.1218 | 0.1888 | 0.4419 | nan | 0.8508 | 0.0102 | 0.0423 | 0.3015 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4947 | 0.0 | 0.5319 | 0.0101 | 0.0418 | 0.2283 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4059 | | 0.8977 | 0.75 | 120 | 0.7806 | 0.1592 | 0.2227 | 0.4764 | nan | 0.8124 | 0.1787 | 0.1188 | 0.4178 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4763 | 0.0 | 0.6151 | 0.1741 | 0.1124 | 0.2995 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3906 | | 0.6932 | 0.88 | 140 | 0.6246 | 0.1262 | 0.1590 | 0.3766 | nan | 0.6794 | 0.2019 | 0.1415 | 0.2810 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.1276 | 0.0 | 0.5674 | 0.1941 | 0.1372 | 0.2414 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.1217 | | 0.6168 | 1.0 | 160 | 0.6124 | 0.1752 | 0.2277 | 0.4717 | nan | 0.7500 | 0.3261 | 0.1491 | 0.4375 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3864 | 0.0 | 0.6106 | 0.2973 | 0.1475 | 0.3522 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3439 | | 0.5758 | 1.12 | 180 | 0.5658 | 0.2037 | 0.2520 | 0.4750 | nan | 0.6646 | 0.3955 | 0.3596 | 0.4433 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4048 | 0.0 | 0.6048 | 0.3705 | 0.3133 | 0.3865 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3623 | | 0.5081 | 1.25 | 200 | 0.5116 | 0.2316 | 0.2993 | 0.5280 | nan | 0.6460 | 0.4867 | 0.4741 | 0.6477 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.4396 | 0.0 | 0.6098 | 0.4523 | 0.3961 | 0.4611 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3965 | | 0.6351 | 1.38 | 220 | 0.4879 | 0.1127 | 0.1324 | 0.2609 | nan | 0.3749 | 0.0902 | 0.2601 | 0.3883 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0783 | 0.0 | 0.3623 | 0.0897 | 0.2510 | 0.3466 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0774 | | 0.6241 | 1.5 | 240 | 0.4593 | 0.2439 | 0.3090 | 0.5686 | nan | 0.7439 | 0.4492 | 0.5367 | 0.6916 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3597 | 0.0 | 0.6995 | 0.4322 | 0.4400 | 0.5265 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3410 | | 0.4315 | 1.62 | 260 | 0.4082 | 0.2175 | 0.2611 | 0.4948 | nan | 0.6811 | 0.3535 | 0.4253 | 0.5871 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.3025 | 0.0 | 0.6398 | 0.3459 | 0.3952 | 0.5052 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.2886 | | 0.5236 | 1.75 | 280 | 0.4651 | 0.1063 | 0.1353 | 0.2191 | nan | 0.2161 | 0.0885 | 0.3687 | 0.4434 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.1015 | 0.0 | 0.2138 | 0.0884 | 0.3282 | 0.3313 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.1012 | | 0.3688 | 1.88 | 300 | 0.4279 | 0.2796 | 0.3459 | 0.6382 | nan | 0.8529 | 0.5705 | 0.5493 | 0.6449 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0051 | 0.4903 | 0.0 | 0.7546 | 0.5277 | 0.5044 | 0.5537 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0051 | 0.4500 | | 0.3659 | 2.0 | 320 | 0.3907 | 0.1881 | 0.2192 | 0.4461 | nan | 0.7156 | 0.1476 | 0.3144 | 0.4135 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0006 | 0.3810 | 0.0 | 0.6851 | 0.1461 | 0.3012 | 0.3919 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0006 | 0.3560 | | 0.3243 | 2.12 | 340 | 0.3846 | 0.2737 | 0.3272 | 0.5846 | nan | 0.7313 | 0.4747 | 0.6435 | 0.7038 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0010 | 0.3904 | 0.0 | 0.7045 | 0.4610 | 0.5733 | 0.6223 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0010 | 0.3752 | | 0.4169 | 2.25 | 360 | 0.4099 | 0.1292 | 0.1475 | 0.2563 | nan | 0.3286 | 0.0968 | 0.3184 | 0.3088 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0005 | 0.2741 | 0.0 | 0.3241 | 0.0965 | 0.3061 | 0.2960 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0005 | 0.2685 | | 0.2951 | 2.38 | 380 | 0.3583 | 0.2277 | 0.2701 | 0.4962 | nan | 0.6695 | 0.2136 | 0.5730 | 0.6784 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0442 | 0.2519 | 0.0 | 0.6409 | 0.2125 | 0.5347 | 0.5967 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0435 | 0.2488 | | 0.3847 | 2.5 | 400 | 0.3565 | 0.2410 | 0.2843 | 0.5032 | nan | 0.6544 | 0.3067 | 0.5888 | 0.6409 | 0.0 | nan | 0.0 | nan | 0.0 | 0.1089 | 0.2594 | 0.0 | 0.6304 | 0.3023 | 0.5347 | 0.5853 | 0.0 | nan | 0.0 | nan | 0.0 | 0.1033 | 0.2535 | | 0.339 | 2.62 | 420 | 0.3715 | 0.3085 | 0.3697 | 0.6227 | nan | 0.7530 | 0.5620 | 0.6411 | 0.6900 | 0.0 | nan | 0.0 | nan | 0.0015 | 0.1337 | 0.5460 | 0.0 | 0.7083 | 0.5347 | 0.5722 | 0.6160 | 0.0 | nan | 0.0 | nan | 0.0015 | 0.1261 | 0.5260 | | 0.7318 | 2.75 | 440 | 0.3574 | 0.2478 | 0.2950 | 0.4525 | nan | 0.5247 | 0.2338 | 0.5171 | 0.6926 | 0.0 | nan | 0.0 | nan | 0.0097 | 0.3424 | 0.3350 | 0.0 | 0.5100 | 0.2322 | 0.4803 | 0.6174 | 0.0 | nan | 0.0 | nan | 0.0097 | 0.3048 | 0.3235 | | 0.2905 | 2.88 | 460 | 0.3609 | 0.1903 | 0.2262 | 0.3935 | nan | 0.4734 | 0.1841 | 0.5925 | 0.5863 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0201 | 0.1799 | 0.0 | 0.4671 | 0.1834 | 0.5348 | 0.5192 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0199 | 0.1786 | | 0.3793 | 3.0 | 480 | 0.3452 | 0.2734 | 0.3213 | 0.5973 | nan | 0.8327 | 0.4635 | 0.5374 | 0.6168 | 0.0 | nan | 0.0 | nan | 0.0263 | 0.0746 | 0.3404 | 0.0 | 0.7723 | 0.4512 | 0.5139 | 0.5700 | 0.0 | nan | 0.0 | nan | 0.0260 | 0.0734 | 0.3270 | | 0.3922 | 3.12 | 500 | 0.3695 | 0.2151 | 0.2604 | 0.3659 | nan | 0.2751 | 0.2847 | 0.6318 | 0.7206 | 0.0 | nan | 0.0 | nan | 0.0045 | 0.1409 | 0.2863 | 0.0 | 0.2726 | 0.2824 | 0.5652 | 0.6103 | 0.0 | nan | 0.0 | nan | 0.0045 | 0.1339 | 0.2824 | | 0.3299 | 3.25 | 520 | 0.3326 | 0.3063 | 0.3610 | 0.6202 | nan | 0.8029 | 0.5001 | 0.5866 | 0.6558 | 0.0 | nan | 0.0 | nan | 0.0422 | 0.1575 | 0.5038 | 0.0 | 0.7639 | 0.4932 | 0.5461 | 0.5948 | 0.0 | nan | 0.0 | nan | 0.0416 | 0.1472 | 0.4762 | | 0.2547 | 3.38 | 540 | 0.3323 | 0.2650 | 0.3121 | 0.5052 | nan | 0.6042 | 0.4311 | 0.6044 | 0.5282 | 0.0 | nan | 0.0 | nan | 0.0813 | 0.1438 | 0.4164 | 0.0 | 0.5882 | 0.4189 | 0.5254 | 0.5006 | 0.0 | nan | 0.0 | nan | 0.0801 | 0.1359 | 0.4010 | | 0.2154 | 3.5 | 560 | 0.3211 | 0.2903 | 0.3397 | 0.5796 | nan | 0.7327 | 0.4341 | 0.6265 | 0.6269 | 0.0 | nan | 0.0 | nan | 0.0340 | 0.1079 | 0.4955 | 0.0 | 0.7034 | 0.4304 | 0.5828 | 0.5874 | 0.0 | nan | 0.0 | nan | 0.0337 | 0.1022 | 0.4634 | | 0.3146 | 3.62 | 580 | 0.3642 | 0.3096 | 0.3854 | 0.5967 | nan | 0.6732 | 0.4518 | 0.7254 | 0.8100 | 0.0 | nan | 0.0 | nan | 0.1293 | 0.2673 | 0.4116 | 0.0 | 0.6557 | 0.4444 | 0.5843 | 0.6517 | 0.0 | nan | 0.0 | nan | 0.1212 | 0.2434 | 0.3957 | | 0.2216 | 3.75 | 600 | 0.3178 | 0.3241 | 0.3818 | 0.5998 | nan | 0.7614 | 0.4294 | 0.5415 | 0.7168 | 0.0 | nan | 0.0 | nan | 0.1378 | 0.4248 | 0.4242 | 0.0 | 0.7254 | 0.4212 | 0.5274 | 0.6520 | 0.0 | nan | 0.0 | nan | 0.1338 | 0.3687 | 0.4125 | | 0.2973 | 3.88 | 620 | 0.3199 | 0.3486 | 0.4127 | 0.6217 | nan | 0.7369 | 0.5178 | 0.5558 | 0.7739 | 0.0 | nan | 0.0 | nan | 0.1965 | 0.4456 | 0.4876 | 0.0 | 0.7072 | 0.4974 | 0.5407 | 0.7010 | 0.0 | nan | 0.0 | nan | 0.1859 | 0.3845 | 0.4692 | | 0.2434 | 4.0 | 640 | 0.3179 | 0.3415 | 0.4057 | 0.6154 | nan | 0.7161 | 0.4582 | 0.6827 | 0.7445 | 0.0 | nan | 0.0 | nan | 0.1295 | 0.3827 | 0.5376 | 0.0 | 0.6869 | 0.4483 | 0.6280 | 0.6769 | 0.0 | nan | 0.0 | nan | 0.1254 | 0.3360 | 0.5134 | | 0.2283 | 4.12 | 660 | 0.3310 | 0.2584 | 0.3073 | 0.5076 | nan | 0.6237 | 0.2267 | 0.6673 | 0.7014 | 0.0 | nan | 0.0 | nan | 0.0718 | 0.1288 | 0.3464 | 0.0 | 0.6078 | 0.2260 | 0.5912 | 0.6270 | 0.0 | nan | 0.0 | nan | 0.0707 | 0.1212 | 0.3401 | | 0.6263 | 4.25 | 680 | 0.3153 | 0.2947 | 0.3436 | 0.5198 | nan | 0.6461 | 0.2824 | 0.5694 | 0.6236 | 0.0 | nan | 0.0 | nan | 0.1512 | 0.3950 | 0.4248 | 0.0 | 0.6244 | 0.2811 | 0.5498 | 0.5852 | 0.0 | nan | 0.0 | nan | 0.1449 | 0.3479 | 0.4140 | | 0.1708 | 4.38 | 700 | 0.3173 | 0.2957 | 0.3435 | 0.5834 | nan | 0.7531 | 0.3902 | 0.5853 | 0.7153 | 0.0 | nan | 0.0 | nan | 0.1331 | 0.1239 | 0.3908 | 0.0 | 0.7188 | 0.3869 | 0.5656 | 0.6612 | 0.0 | nan | 0.0 | nan | 0.1298 | 0.1159 | 0.3788 | | 0.246 | 4.5 | 720 | 0.3138 | 0.2570 | 0.2955 | 0.5052 | nan | 0.6686 | 0.3447 | 0.4552 | 0.5989 | 0.0 | nan | 0.0 | nan | 0.1237 | 0.1230 | 0.3459 | 0.0 | 0.6469 | 0.3416 | 0.4442 | 0.5640 | 0.0 | nan | 0.0 | nan | 0.1213 | 0.1159 | 0.3366 | | 0.3876 | 4.62 | 740 | 0.3084 | 0.3646 | 0.4336 | 0.6321 | nan | 0.7367 | 0.4776 | 0.6536 | 0.7478 | 0.0 | nan | 0.0 | nan | 0.2351 | 0.4973 | 0.5539 | 0.0 | 0.7108 | 0.4700 | 0.6166 | 0.6824 | 0.0 | nan | 0.0 | nan | 0.2208 | 0.4179 | 0.5274 | | 0.4766 | 4.75 | 760 | 0.3251 | 0.2509 | 0.2894 | 0.4716 | nan | 0.6095 | 0.3498 | 0.4348 | 0.4989 | 0.0 | nan | 0.0 | nan | 0.1119 | 0.2020 | 0.3972 | 0.0 | 0.5954 | 0.3434 | 0.4157 | 0.4754 | 0.0 | nan | 0.0 | nan | 0.1092 | 0.1836 | 0.3865 | | 0.4431 | 4.88 | 780 | 0.3052 | 0.3104 | 0.3654 | 0.5781 | nan | 0.7024 | 0.4862 | 0.5150 | 0.7363 | 0.0 | nan | 0.0 | nan | 0.2163 | 0.2456 | 0.3866 | 0.0 | 0.6801 | 0.4736 | 0.5039 | 0.6461 | 0.0 | nan | 0.0 | nan | 0.2021 | 0.2209 | 0.3771 | | 0.2319 | 5.0 | 800 | 0.3104 | 0.3316 | 0.3938 | 0.5875 | nan | 0.6790 | 0.5433 | 0.6711 | 0.6572 | 0.0 | nan | 0.0 | nan | 0.2908 | 0.3349 | 0.3675 | 0.0 | 0.6628 | 0.5265 | 0.5922 | 0.6113 | 0.0 | nan | 0.0 | nan | 0.2656 | 0.2987 | 0.3585 | | 0.3361 | 5.12 | 820 | 0.3125 | 0.3219 | 0.3808 | 0.5905 | nan | 0.7234 | 0.3480 | 0.6026 | 0.7698 | 0.0 | nan | 0.0 | nan | 0.1558 | 0.3461 | 0.4818 | 0.0 | 0.7028 | 0.3453 | 0.5677 | 0.6877 | 0.0 | nan | 0.0 | nan | 0.1498 | 0.3077 | 0.4584 | | 0.412 | 5.25 | 840 | 0.3477 | 0.2427 | 0.2810 | 0.4099 | nan | 0.3970 | 0.2768 | 0.5965 | 0.6918 | 0.0 | nan | 0.0 | nan | 0.1238 | 0.1614 | 0.2814 | 0.0 | 0.3899 | 0.2752 | 0.5703 | 0.6446 | 0.0 | nan | 0.0 | nan | 0.1208 | 0.1494 | 0.2767 | | 0.1799 | 5.38 | 860 | 0.3132 | 0.3444 | 0.4035 | 0.6161 | nan | 0.7651 | 0.4804 | 0.6609 | 0.5953 | 0.0 | nan | 0.0 | nan | 0.2167 | 0.3901 | 0.5227 | 0.0 | 0.7389 | 0.4759 | 0.6240 | 0.5643 | 0.0 | nan | 0.0 | nan | 0.2033 | 0.3414 | 0.4965 | | 0.1716 | 5.5 | 880 | 0.3186 | 0.2805 | 0.3289 | 0.4955 | nan | 0.5685 | 0.3185 | 0.5785 | 0.6888 | 0.0 | nan | 0.0 | nan | 0.1934 | 0.2548 | 0.3577 | 0.0 | 0.5592 | 0.3165 | 0.5487 | 0.6271 | 0.0 | nan | 0.0 | nan | 0.1798 | 0.2295 | 0.3441 | | 0.4388 | 5.62 | 900 | 0.3171 | 0.4098 | 0.4914 | 0.7348 | nan | 0.8788 | 0.6109 | 0.7475 | 0.7979 | 0.0 | nan | 0.0 | nan | 0.2607 | 0.5234 | 0.6032 | 0.0 | 0.8320 | 0.5956 | 0.6842 | 0.7286 | 0.0 | nan | 0.0 | nan | 0.2445 | 0.4481 | 0.5648 | | 0.2632 | 5.75 | 920 | 0.3163 | 0.2697 | 0.3130 | 0.4551 | nan | 0.5145 | 0.3445 | 0.4613 | 0.6042 | 0.0 | nan | 0.0 | nan | 0.1180 | 0.3896 | 0.3846 | 0.0 | 0.5045 | 0.3404 | 0.4568 | 0.5718 | 0.0 | nan | 0.0 | nan | 0.1141 | 0.3416 | 0.3677 | | 0.3024 | 5.88 | 940 | 0.3063 | 0.3883 | 0.4645 | 0.6758 | nan | 0.7778 | 0.6624 | 0.7137 | 0.7013 | 0.0 | nan | 0.0 | nan | 0.3253 | 0.4950 | 0.5054 | 0.0 | 0.7479 | 0.6323 | 0.6434 | 0.6506 | 0.0 | nan | 0.0 | nan | 0.2919 | 0.4251 | 0.4919 | | 0.2551 | 6.0 | 960 | 0.3489 | 0.2261 | 0.2625 | 0.4024 | nan | 0.5011 | 0.1084 | 0.4074 | 0.6709 | 0.0 | nan | 0.0 | nan | 0.1666 | 0.2592 | 0.2489 | 0.0 | 0.4903 | 0.1081 | 0.4042 | 0.6181 | 0.0 | nan | 0.0 | nan | 0.1562 | 0.2369 | 0.2470 | | 0.3281 | 6.12 | 980 | 0.2939 | 0.3635 | 0.4275 | 0.6116 | nan | 0.6803 | 0.5665 | 0.6418 | 0.6806 | 0.0 | nan | 0.0 | nan | 0.2545 | 0.4660 | 0.5579 | 0.0 | 0.6659 | 0.5508 | 0.6049 | 0.6359 | 0.0 | nan | 0.0 | nan | 0.2372 | 0.4071 | 0.5330 | | 0.1372 | 6.25 | 1000 | 0.2998 | 0.3755 | 0.4413 | 0.6450 | nan | 0.7530 | 0.5417 | 0.6673 | 0.7023 | 0.0 | nan | 0.0 | nan | 0.2979 | 0.4450 | 0.5648 | 0.0 | 0.7287 | 0.5300 | 0.6359 | 0.6582 | 0.0 | nan | 0.0 | nan | 0.2737 | 0.3899 | 0.5389 | | 0.3485 | 6.38 | 1020 | 0.3398 | 0.2557 | 0.2941 | 0.4515 | nan | 0.5305 | 0.2957 | 0.5158 | 0.6263 | 0.0 | nan | 0.0 | nan | 0.1937 | 0.2043 | 0.2811 | 0.0 | 0.5206 | 0.2931 | 0.5070 | 0.5912 | 0.0 | nan | 0.0 | nan | 0.1833 | 0.1846 | 0.2769 | | 0.3755 | 6.5 | 1040 | 0.3034 | 0.3526 | 0.4160 | 0.5795 | nan | 0.6346 | 0.4564 | 0.7050 | 0.6986 | 0.0 | nan | 0.0 | nan | 0.3010 | 0.4881 | 0.4598 | 0.0 | 0.6188 | 0.4509 | 0.6639 | 0.6554 | 0.0 | nan | 0.0 | nan | 0.2758 | 0.4166 | 0.4442 | | 0.2617 | 6.62 | 1060 | 0.3166 | 0.2905 | 0.3384 | 0.4820 | nan | 0.5401 | 0.3225 | 0.5981 | 0.6153 | 0.0 | nan | 0.0 | nan | 0.1880 | 0.4060 | 0.3756 | 0.0 | 0.5337 | 0.3212 | 0.5770 | 0.5817 | 0.0 | nan | 0.0 | nan | 0.1774 | 0.3511 | 0.3627 | | 0.2937 | 6.75 | 1080 | 0.3090 | 0.3864 | 0.4585 | 0.7031 | nan | 0.8093 | 0.6687 | 0.7189 | 0.7808 | 0.0 | nan | 0.0 | nan | 0.3271 | 0.2123 | 0.6092 | 0.0 | 0.7810 | 0.6460 | 0.6639 | 0.7094 | 0.0 | nan | 0.0 | nan | 0.2942 | 0.1957 | 0.5738 | | 0.3588 | 6.88 | 1100 | 0.3011 | 0.3653 | 0.4310 | 0.6482 | nan | 0.8132 | 0.4360 | 0.6549 | 0.7523 | 0.0 | nan | 0.0 | nan | 0.3123 | 0.4840 | 0.4267 | 0.0 | 0.7784 | 0.4310 | 0.6235 | 0.6944 | 0.0 | nan | 0.0 | nan | 0.2884 | 0.4223 | 0.4149 | | 0.1613 | 7.0 | 1120 | 0.3302 | 0.2838 | 0.3344 | 0.4622 | nan | 0.4687 | 0.3133 | 0.6402 | 0.6782 | 0.0 | nan | 0.0 | nan | 0.1601 | 0.4001 | 0.3490 | 0.0 | 0.4620 | 0.3100 | 0.5933 | 0.6247 | 0.0 | nan | 0.0 | nan | 0.1525 | 0.3558 | 0.3400 | | 0.4217 | 7.12 | 1140 | 0.3087 | 0.3723 | 0.4451 | 0.6405 | nan | 0.7463 | 0.4361 | 0.7284 | 0.7778 | 0.0 | nan | 0.0 | nan | 0.3229 | 0.4981 | 0.4960 | 0.0 | 0.7258 | 0.4307 | 0.6602 | 0.7060 | 0.0 | nan | 0.0 | nan | 0.2946 | 0.4255 | 0.4800 | | 0.1429 | 7.25 | 1160 | 0.3227 | 0.2794 | 0.3221 | 0.5335 | nan | 0.7381 | 0.3377 | 0.5059 | 0.5342 | 0.0 | nan | 0.0 | nan | 0.2719 | 0.1916 | 0.3198 | 0.0 | 0.7147 | 0.3361 | 0.4943 | 0.5100 | 0.0 | nan | 0.0 | nan | 0.2479 | 0.1761 | 0.3151 | | 0.227 | 7.38 | 1180 | 0.3087 | 0.3749 | 0.4471 | 0.6221 | nan | 0.6645 | 0.5048 | 0.7103 | 0.7872 | 0.0 | nan | 0.0 | nan | 0.3502 | 0.4474 | 0.5594 | 0.0 | 0.6499 | 0.4966 | 0.6631 | 0.7065 | 0.0 | nan | 0.0 | nan | 0.3168 | 0.3915 | 0.5250 | | 0.3733 | 7.5 | 1200 | 0.3304 | 0.2777 | 0.3229 | 0.4832 | nan | 0.5603 | 0.3886 | 0.5612 | 0.5532 | 0.0 | nan | 0.0 | nan | 0.1744 | 0.2915 | 0.3773 | 0.0 | 0.5501 | 0.3824 | 0.5434 | 0.5248 | 0.0 | nan | 0.0 | nan | 0.1655 | 0.2561 | 0.3542 | | 0.3148 | 7.62 | 1220 | 0.3075 | 0.3787 | 0.4500 | 0.6531 | nan | 0.7425 | 0.5909 | 0.7189 | 0.7270 | 0.0 | nan | 0.0 | nan | 0.3257 | 0.4420 | 0.5030 | 0.0 | 0.7152 | 0.5687 | 0.6673 | 0.6700 | 0.0 | nan | 0.0 | nan | 0.2976 | 0.3823 | 0.4863 | | 0.22 | 7.75 | 1240 | 0.3156 | 0.3340 | 0.3934 | 0.5589 | nan | 0.6127 | 0.4262 | 0.6387 | 0.7554 | 0.0 | nan | 0.0 | nan | 0.2170 | 0.4800 | 0.4108 | 0.0 | 0.6002 | 0.4203 | 0.6121 | 0.6848 | 0.0 | nan | 0.0 | nan | 0.2073 | 0.4168 | 0.3984 | | 0.499 | 7.88 | 1260 | 0.3085 | 0.3454 | 0.4092 | 0.6278 | nan | 0.7534 | 0.4363 | 0.7109 | 0.7256 | 0.0 | nan | 0.0 | nan | 0.2525 | 0.2773 | 0.5267 | 0.0 | 0.7296 | 0.4311 | 0.6556 | 0.6668 | 0.0 | nan | 0.0 | nan | 0.2355 | 0.2512 | 0.4848 | | 0.2604 | 8.0 | 1280 | 0.3123 | 0.3504 | 0.4118 | 0.6089 | nan | 0.7175 | 0.4779 | 0.6668 | 0.6668 | 0.0 | nan | 0.0 | nan | 0.2368 | 0.4184 | 0.5218 | 0.0 | 0.6949 | 0.4708 | 0.6402 | 0.6225 | 0.0 | nan | 0.0 | nan | 0.2192 | 0.3649 | 0.4915 | | 0.146 | 8.12 | 1300 | 0.3274 | 0.3036 | 0.3526 | 0.5368 | nan | 0.6413 | 0.4378 | 0.6158 | 0.5674 | 0.0 | nan | 0.0 | nan | 0.1757 | 0.3165 | 0.4191 | 0.0 | 0.6216 | 0.4333 | 0.5946 | 0.5339 | 0.0 | nan | 0.0 | nan | 0.1697 | 0.2810 | 0.4015 | | 0.1103 | 8.25 | 1320 | 0.3339 | 0.2738 | 0.3155 | 0.4762 | nan | 0.5556 | 0.4026 | 0.5015 | 0.5408 | 0.0 | nan | 0.0 | nan | 0.1370 | 0.2938 | 0.4084 | 0.0 | 0.5454 | 0.3969 | 0.4921 | 0.5111 | 0.0 | nan | 0.0 | nan | 0.1330 | 0.2658 | 0.3939 | | 0.1323 | 8.38 | 1340 | 0.3179 | 0.3304 | 0.3865 | 0.5986 | nan | 0.7334 | 0.4769 | 0.6287 | 0.6839 | 0.0 | nan | 0.0 | nan | 0.2152 | 0.3473 | 0.3933 | 0.0 | 0.7022 | 0.4679 | 0.6091 | 0.6261 | 0.0 | nan | 0.0 | nan | 0.2049 | 0.3080 | 0.3863 | | 0.1057 | 8.5 | 1360 | 0.4118 | 0.1949 | 0.2242 | 0.3076 | nan | 0.3007 | 0.1559 | 0.4492 | 0.5135 | 0.0 | nan | 0.0 | nan | 0.1282 | 0.2228 | 0.2472 | 0.0 | 0.2972 | 0.1552 | 0.4441 | 0.4845 | 0.0 | nan | 0.0 | nan | 0.1241 | 0.2028 | 0.2412 | | 0.1248 | 8.62 | 1380 | 0.3228 | 0.4155 | 0.4991 | 0.7269 | nan | 0.8415 | 0.6462 | 0.7622 | 0.7778 | 0.0 | nan | 0.0 | nan | 0.3683 | 0.4851 | 0.6106 | 0.0 | 0.8100 | 0.6271 | 0.6994 | 0.7057 | 0.0 | nan | 0.0 | nan | 0.3280 | 0.4172 | 0.5674 | | 0.1165 | 8.75 | 1400 | 0.3307 | 0.2995 | 0.3485 | 0.5194 | nan | 0.6149 | 0.3102 | 0.5592 | 0.6835 | 0.0 | nan | 0.0 | nan | 0.1857 | 0.3513 | 0.4318 | 0.0 | 0.5982 | 0.3074 | 0.5485 | 0.6349 | 0.0 | nan | 0.0 | nan | 0.1765 | 0.3142 | 0.4156 | | 0.2999 | 8.88 | 1420 | 0.3766 | 0.2329 | 0.2673 | 0.3927 | nan | 0.4223 | 0.2954 | 0.4437 | 0.5620 | 0.0 | nan | 0.0 | nan | 0.1641 | 0.1857 | 0.3327 | 0.0 | 0.4163 | 0.2897 | 0.4411 | 0.5318 | 0.0 | nan | 0.0 | nan | 0.1570 | 0.1704 | 0.3232 | | 0.2005 | 9.0 | 1440 | 0.3224 | 0.3457 | 0.4100 | 0.5800 | nan | 0.6510 | 0.4458 | 0.6701 | 0.6765 | 0.0 | nan | 0.0002 | nan | 0.2510 | 0.4869 | 0.5085 | 0.0 | 0.6327 | 0.4381 | 0.6441 | 0.6348 | 0.0 | nan | 0.0002 | nan | 0.2321 | 0.4092 | 0.4656 | | 0.0952 | 9.12 | 1460 | 0.3368 | 0.2986 | 0.3475 | 0.5230 | nan | 0.5872 | 0.4686 | 0.6328 | 0.6070 | 0.0 | nan | 0.0 | nan | 0.2227 | 0.2252 | 0.3843 | 0.0 | 0.5748 | 0.4534 | 0.5986 | 0.5720 | 0.0 | nan | 0.0 | nan | 0.2114 | 0.2061 | 0.3692 | | 0.3493 | 9.25 | 1480 | 0.3637 | 0.2527 | 0.2909 | 0.4285 | nan | 0.5072 | 0.3061 | 0.4793 | 0.5032 | 0.0 | nan | 0.0 | nan | 0.1539 | 0.3356 | 0.3325 | 0.0 | 0.4971 | 0.3025 | 0.4757 | 0.4801 | 0.0 | nan | 0.0 | nan | 0.1476 | 0.2993 | 0.3245 | | 0.6102 | 9.38 | 1500 | 0.3302 | 0.3325 | 0.3885 | 0.5757 | nan | 0.6527 | 0.5490 | 0.6148 | 0.6472 | 0.0 | nan | 0.0 | nan | 0.2155 | 0.3649 | 0.4522 | 0.0 | 0.6350 | 0.5320 | 0.5993 | 0.6117 | 0.0 | nan | 0.0 | nan | 0.2015 | 0.3228 | 0.4231 | | 0.1355 | 9.5 | 1520 | 0.3136 | 0.3397 | 0.3985 | 0.5917 | nan | 0.7078 | 0.4385 | 0.6197 | 0.6754 | 0.0 | nan | 0.0 | nan | 0.2411 | 0.4128 | 0.4908 | 0.0 | 0.6904 | 0.4342 | 0.6004 | 0.6272 | 0.0 | nan | 0.0 | nan | 0.2228 | 0.3615 | 0.4608 | | 0.2828 | 9.62 | 1540 | 0.3214 | 0.3632 | 0.4329 | 0.6220 | nan | 0.7412 | 0.4203 | 0.6106 | 0.7371 | 0.0 | nan | 0.0004 | nan | 0.3513 | 0.4797 | 0.5558 | 0.0 | 0.7149 | 0.4099 | 0.5925 | 0.6724 | 0.0 | nan | 0.0004 | nan | 0.3130 | 0.4125 | 0.5160 | | 0.2499 | 9.75 | 1560 | 0.3470 | 0.3178 | 0.3744 | 0.5370 | nan | 0.6135 | 0.4310 | 0.6613 | 0.5944 | 0.0 | nan | 0.0001 | nan | 0.3109 | 0.3698 | 0.3889 | 0.0 | 0.5963 | 0.4247 | 0.6161 | 0.5602 | 0.0 | nan | 0.0001 | nan | 0.2767 | 0.3282 | 0.3756 | | 0.3973 | 9.88 | 1580 | 0.3292 | 0.3557 | 0.4222 | 0.6036 | nan | 0.6854 | 0.5253 | 0.6598 | 0.6929 | 0.0 | nan | 0.0 | nan | 0.2791 | 0.5040 | 0.4535 | 0.0 | 0.6632 | 0.5042 | 0.6184 | 0.6434 | 0.0 | nan | 0.0 | nan | 0.2550 | 0.4358 | 0.4374 | | 0.1764 | 10.0 | 1600 | 0.3317 | 0.3493 | 0.4150 | 0.5984 | nan | 0.6897 | 0.4370 | 0.6896 | 0.7262 | 0.0 | nan | 0.0023 | nan | 0.2221 | 0.5208 | 0.4472 | 0.0 | 0.6702 | 0.4293 | 0.6340 | 0.6649 | 0.0 | nan | 0.0023 | nan | 0.2127 | 0.4505 | 0.4294 | | 0.3667 | 10.12 | 1620 | 0.3224 | 0.3385 | 0.3978 | 0.5951 | nan | 0.7262 | 0.4129 | 0.5865 | 0.6864 | 0.0 | nan | 0.0041 | nan | 0.2466 | 0.4033 | 0.5143 | 0.0 | 0.7033 | 0.4053 | 0.5673 | 0.6383 | 0.0 | nan | 0.0041 | nan | 0.2340 | 0.3562 | 0.4768 | | 0.2782 | 10.25 | 1640 | 0.3243 | 0.3675 | 0.4383 | 0.6355 | nan | 0.7304 | 0.5449 | 0.7001 | 0.7129 | 0.0 | nan | 0.0021 | nan | 0.2615 | 0.5006 | 0.4920 | 0.0 | 0.7054 | 0.5218 | 0.6534 | 0.6531 | 0.0 | nan | 0.0021 | nan | 0.2478 | 0.4247 | 0.4667 | | 0.1716 | 10.38 | 1660 | 0.3199 | 0.3531 | 0.4131 | 0.6228 | nan | 0.7347 | 0.5687 | 0.6019 | 0.7168 | 0.0 | nan | 0.0001 | nan | 0.2756 | 0.3677 | 0.4526 | 0.0 | 0.7107 | 0.5562 | 0.5817 | 0.6609 | 0.0 | nan | 0.0001 | nan | 0.2583 | 0.3276 | 0.4354 | | 0.1938 | 10.5 | 1680 | 0.3304 | 0.3369 | 0.4038 | 0.5768 | nan | 0.6403 | 0.3871 | 0.6906 | 0.7228 | 0.0 | nan | 0.0049 | nan | 0.2373 | 0.4005 | 0.5506 | 0.0 | 0.6248 | 0.3827 | 0.6360 | 0.6713 | 0.0 | nan | 0.0049 | nan | 0.2232 | 0.3537 | 0.4722 | | 0.0939 | 10.62 | 1700 | 0.3178 | 0.3848 | 0.4610 | 0.6472 | nan | 0.7358 | 0.5401 | 0.7080 | 0.7452 | 0.0 | nan | 0.0062 | nan | 0.3044 | 0.6229 | 0.4866 | 0.0 | 0.7137 | 0.5308 | 0.6585 | 0.6909 | 0.0 | nan | 0.0062 | nan | 0.2798 | 0.5091 | 0.4593 | | 0.1592 | 10.75 | 1720 | 0.3323 | 0.3312 | 0.3861 | 0.5834 | nan | 0.6894 | 0.6042 | 0.5845 | 0.5805 | 0.0 | nan | 0.0055 | nan | 0.2218 | 0.3658 | 0.4236 | 0.0 | 0.6704 | 0.5786 | 0.5710 | 0.5451 | 0.0 | nan | 0.0055 | nan | 0.2094 | 0.3236 | 0.4085 | | 0.186 | 10.88 | 1740 | 0.3280 | 0.3838 | 0.4597 | 0.6407 | nan | 0.7285 | 0.4968 | 0.6695 | 0.7578 | 0.0 | nan | 0.0145 | nan | 0.4074 | 0.5172 | 0.5460 | 0.0 | 0.7056 | 0.4866 | 0.6377 | 0.6958 | 0.0 | nan | 0.0144 | nan | 0.3540 | 0.4358 | 0.5086 | | 0.124 | 11.0 | 1760 | 0.4089 | 0.2396 | 0.2751 | 0.4128 | nan | 0.5165 | 0.2682 | 0.4594 | 0.4520 | 0.0 | nan | 0.0095 | nan | 0.2177 | 0.2646 | 0.2879 | 0.0 | 0.5080 | 0.2658 | 0.4487 | 0.4315 | 0.0 | nan | 0.0095 | nan | 0.2031 | 0.2473 | 0.2822 | | 0.1084 | 11.12 | 1780 | 0.3512 | 0.3283 | 0.3864 | 0.5415 | nan | 0.5910 | 0.4464 | 0.6008 | 0.6724 | 0.0 | nan | 0.0105 | nan | 0.2345 | 0.4593 | 0.4626 | 0.0 | 0.5774 | 0.4313 | 0.5885 | 0.6218 | 0.0 | nan | 0.0105 | nan | 0.2220 | 0.3926 | 0.4390 | | 0.3364 | 11.25 | 1800 | 0.3514 | 0.3245 | 0.3806 | 0.5425 | nan | 0.6046 | 0.4578 | 0.6162 | 0.6777 | 0.0 | nan | 0.0107 | nan | 0.2340 | 0.4572 | 0.3669 | 0.0 | 0.5876 | 0.4430 | 0.6018 | 0.6301 | 0.0 | nan | 0.0107 | nan | 0.2216 | 0.3951 | 0.3547 | | 0.186 | 11.38 | 1820 | 0.3398 | 0.3337 | 0.3937 | 0.5743 | nan | 0.6718 | 0.3964 | 0.6547 | 0.6960 | 0.0 | nan | 0.0095 | nan | 0.2726 | 0.3967 | 0.4452 | 0.0 | 0.6555 | 0.3905 | 0.6240 | 0.6329 | 0.0 | nan | 0.0095 | nan | 0.2543 | 0.3500 | 0.4209 | | 0.085 | 11.5 | 1840 | 0.3395 | 0.3541 | 0.4172 | 0.5969 | nan | 0.6834 | 0.4750 | 0.6591 | 0.6675 | 0.0029 | nan | 0.0163 | nan | 0.2791 | 0.4399 | 0.5321 | 0.0 | 0.6637 | 0.4648 | 0.6337 | 0.6201 | 0.0029 | nan | 0.0161 | nan | 0.2594 | 0.3801 | 0.5002 | | 0.2861 | 11.62 | 1860 | 0.3575 | 0.3064 | 0.3548 | 0.5451 | nan | 0.6531 | 0.4069 | 0.6701 | 0.6340 | 0.0000 | nan | 0.0111 | nan | 0.2071 | 0.2802 | 0.3303 | 0.0 | 0.6370 | 0.4012 | 0.6428 | 0.5936 | 0.0000 | nan | 0.0110 | nan | 0.1952 | 0.2599 | 0.3230 | | 0.2855 | 11.75 | 1880 | 0.3932 | 0.2415 | 0.2791 | 0.4276 | nan | 0.5475 | 0.2912 | 0.4359 | 0.4478 | 0.0 | nan | 0.0104 | nan | 0.1519 | 0.3073 | 0.3201 | 0.0 | 0.5356 | 0.2855 | 0.4203 | 0.4307 | 0.0 | nan | 0.0104 | nan | 0.1446 | 0.2782 | 0.3099 | | 0.1548 | 11.88 | 1900 | 0.3398 | 0.3513 | 0.4167 | 0.6004 | nan | 0.7097 | 0.3884 | 0.6305 | 0.7354 | 0.0002 | nan | 0.0138 | nan | 0.2828 | 0.4755 | 0.5141 | 0.0 | 0.6920 | 0.3832 | 0.6074 | 0.6691 | 0.0002 | nan | 0.0138 | nan | 0.2669 | 0.4032 | 0.4775 | | 0.123 | 12.0 | 1920 | 0.3279 | 0.3495 | 0.4113 | 0.6009 | nan | 0.7406 | 0.4218 | 0.5964 | 0.6630 | 0.0028 | nan | 0.0183 | nan | 0.2751 | 0.5053 | 0.4784 | 0.0 | 0.7190 | 0.4153 | 0.5867 | 0.6196 | 0.0028 | nan | 0.0181 | nan | 0.2542 | 0.4223 | 0.4568 | | 0.1138 | 12.12 | 1940 | 0.3308 | 0.3983 | 0.4753 | 0.6792 | nan | 0.7682 | 0.5832 | 0.7082 | 0.8045 | 0.0028 | nan | 0.0209 | nan | 0.3537 | 0.4880 | 0.5477 | 0.0 | 0.7424 | 0.5667 | 0.6711 | 0.7307 | 0.0028 | nan | 0.0207 | nan | 0.3242 | 0.4171 | 0.5076 | | 0.1582 | 12.25 | 1960 | 0.3342 | 0.3720 | 0.4410 | 0.6306 | nan | 0.7554 | 0.4633 | 0.6444 | 0.7011 | 0.0062 | nan | 0.0198 | nan | 0.3419 | 0.5214 | 0.5155 | 0.0 | 0.7309 | 0.4556 | 0.6269 | 0.6504 | 0.0062 | nan | 0.0196 | nan | 0.3087 | 0.4391 | 0.4823 | | 0.3449 | 12.38 | 1980 | 0.3976 | 0.2429 | 0.2797 | 0.4225 | nan | 0.5187 | 0.2813 | 0.4234 | 0.5055 | 0.0 | nan | 0.0111 | nan | 0.1548 | 0.2818 | 0.3408 | 0.0 | 0.5069 | 0.2772 | 0.4172 | 0.4855 | 0.0 | nan | 0.0111 | nan | 0.1490 | 0.2573 | 0.3246 | | 0.0296 | 12.5 | 2000 | 0.3332 | 0.3525 | 0.4166 | 0.5979 | nan | 0.6804 | 0.4709 | 0.6924 | 0.6988 | 0.0 | nan | 0.0172 | nan | 0.3403 | 0.3831 | 0.4662 | 0.0 | 0.6615 | 0.4562 | 0.6530 | 0.6470 | 0.0 | nan | 0.0171 | nan | 0.3043 | 0.3389 | 0.4466 | | 0.2308 | 12.62 | 2020 | 0.3418 | 0.3574 | 0.4208 | 0.6123 | nan | 0.7446 | 0.3992 | 0.6491 | 0.7142 | 0.0 | nan | 0.0130 | nan | 0.3085 | 0.4940 | 0.4650 | 0.0 | 0.7205 | 0.3948 | 0.6289 | 0.6646 | 0.0 | nan | 0.0129 | nan | 0.2849 | 0.4238 | 0.4435 | | 0.3632 | 12.75 | 2040 | 0.3847 | 0.2694 | 0.3118 | 0.4605 | nan | 0.5391 | 0.3475 | 0.5106 | 0.5736 | 0.0 | nan | 0.0090 | nan | 0.1930 | 0.3284 | 0.3053 | 0.0 | 0.5278 | 0.3411 | 0.5017 | 0.5453 | 0.0 | nan | 0.0089 | nan | 0.1846 | 0.2916 | 0.2930 | | 0.284 | 12.88 | 2060 | 0.3425 | 0.3836 | 0.4597 | 0.6559 | nan | 0.7536 | 0.5838 | 0.6914 | 0.7272 | 0.0094 | nan | 0.0148 | nan | 0.3443 | 0.5142 | 0.4986 | 0.0 | 0.7278 | 0.5383 | 0.6459 | 0.6707 | 0.0094 | nan | 0.0147 | nan | 0.3187 | 0.4375 | 0.4732 | | 0.1566 | 13.0 | 2080 | 0.3586 | 0.3093 | 0.3628 | 0.5439 | nan | 0.6643 | 0.4048 | 0.5981 | 0.6177 | 0.0075 | nan | 0.0124 | nan | 0.2639 | 0.3493 | 0.3475 | 0.0 | 0.6486 | 0.3917 | 0.5754 | 0.5727 | 0.0075 | nan | 0.0123 | nan | 0.2477 | 0.3058 | 0.3311 | | 0.1545 | 13.12 | 2100 | 0.3630 | 0.3109 | 0.3644 | 0.5329 | nan | 0.6530 | 0.3620 | 0.5186 | 0.6261 | 0.0122 | nan | 0.0107 | nan | 0.1922 | 0.4837 | 0.4209 | 0.0 | 0.6339 | 0.3549 | 0.5053 | 0.5850 | 0.0122 | nan | 0.0106 | nan | 0.1847 | 0.4173 | 0.4049 | | 0.1118 | 13.25 | 2120 | 0.3435 | 0.3579 | 0.4228 | 0.6077 | nan | 0.7231 | 0.4451 | 0.6276 | 0.7012 | 0.0175 | nan | 0.0249 | nan | 0.2535 | 0.5218 | 0.4904 | 0.0 | 0.7009 | 0.4350 | 0.6087 | 0.6502 | 0.0175 | nan | 0.0246 | nan | 0.2404 | 0.4449 | 0.4571 | | 0.0828 | 13.38 | 2140 | 0.3544 | 0.3361 | 0.3956 | 0.5662 | nan | 0.6733 | 0.3762 | 0.6572 | 0.6600 | 0.0125 | nan | 0.0328 | nan | 0.3596 | 0.3828 | 0.4058 | 0.0 | 0.6520 | 0.3665 | 0.6258 | 0.6161 | 0.0125 | nan | 0.0321 | nan | 0.3236 | 0.3448 | 0.3878 | | 0.2605 | 13.5 | 2160 | 0.3451 | 0.3732 | 0.4421 | 0.6309 | nan | 0.7398 | 0.4876 | 0.6322 | 0.7386 | 0.0182 | nan | 0.0378 | nan | 0.3453 | 0.4635 | 0.5161 | 0.0 | 0.7155 | 0.4705 | 0.6104 | 0.6734 | 0.0182 | nan | 0.0369 | nan | 0.3171 | 0.4016 | 0.4886 | | 0.0129 | 13.62 | 2180 | 0.3919 | 0.2765 | 0.3196 | 0.4836 | nan | 0.5955 | 0.3326 | 0.5530 | 0.5408 | 0.0179 | nan | 0.0140 | nan | 0.1846 | 0.2991 | 0.3392 | 0.0 | 0.5800 | 0.3270 | 0.5417 | 0.5094 | 0.0179 | nan | 0.0139 | nan | 0.1754 | 0.2717 | 0.3283 | | 0.1744 | 13.75 | 2200 | 0.3543 | 0.3287 | 0.3864 | 0.5730 | nan | 0.6835 | 0.3695 | 0.6680 | 0.7169 | 0.0132 | nan | 0.0120 | nan | 0.2276 | 0.4016 | 0.3853 | 0.0 | 0.6621 | 0.3663 | 0.6327 | 0.6620 | 0.0132 | nan | 0.0120 | nan | 0.2145 | 0.3531 | 0.3708 | | 0.0863 | 13.88 | 2220 | 0.3536 | 0.3503 | 0.4130 | 0.6052 | nan | 0.7206 | 0.4645 | 0.5874 | 0.6916 | 0.0115 | nan | 0.0200 | nan | 0.3492 | 0.3373 | 0.5352 | 0.0 | 0.6962 | 0.4546 | 0.5755 | 0.6385 | 0.0115 | nan | 0.0198 | nan | 0.3158 | 0.2964 | 0.4949 | | 0.2218 | 14.0 | 2240 | 0.3552 | 0.3527 | 0.4186 | 0.6185 | nan | 0.7443 | 0.4727 | 0.6913 | 0.6287 | 0.0118 | nan | 0.0181 | nan | 0.3010 | 0.3621 | 0.5372 | 0.0 | 0.7196 | 0.4609 | 0.6293 | 0.5846 | 0.0118 | nan | 0.0180 | nan | 0.2818 | 0.3178 | 0.5032 | | 0.1603 | 14.12 | 2260 | 0.3853 | 0.2835 | 0.3305 | 0.4804 | nan | 0.5713 | 0.3329 | 0.5072 | 0.5800 | 0.0061 | nan | 0.0185 | nan | 0.2341 | 0.3506 | 0.3738 | 0.0 | 0.5570 | 0.3257 | 0.4960 | 0.5457 | 0.0061 | nan | 0.0183 | nan | 0.2195 | 0.3115 | 0.3554 | | 0.1556 | 14.25 | 2280 | 0.3580 | 0.3469 | 0.4112 | 0.6041 | nan | 0.7349 | 0.4383 | 0.6388 | 0.6923 | 0.0131 | nan | 0.0196 | nan | 0.3261 | 0.4219 | 0.4161 | 0.0 | 0.7132 | 0.4194 | 0.6082 | 0.6401 | 0.0131 | nan | 0.0194 | nan | 0.2961 | 0.3680 | 0.3921 | | 0.2714 | 14.38 | 2300 | 0.3716 | 0.3215 | 0.3763 | 0.5454 | nan | 0.6469 | 0.3780 | 0.5739 | 0.6474 | 0.0130 | nan | 0.0128 | nan | 0.2433 | 0.4166 | 0.4552 | 0.0 | 0.6285 | 0.3722 | 0.5628 | 0.6024 | 0.0130 | nan | 0.0127 | nan | 0.2288 | 0.3676 | 0.4273 | | 0.2624 | 14.5 | 2320 | 0.3524 | 0.3357 | 0.3931 | 0.5833 | nan | 0.7198 | 0.4113 | 0.6229 | 0.6339 | 0.0164 | nan | 0.0153 | nan | 0.2559 | 0.4207 | 0.4414 | 0.0 | 0.6967 | 0.4056 | 0.6019 | 0.5973 | 0.0164 | nan | 0.0151 | nan | 0.2376 | 0.3674 | 0.4185 | | 0.2223 | 14.62 | 2340 | 0.3570 | 0.3148 | 0.3663 | 0.5565 | nan | 0.6820 | 0.4156 | 0.5894 | 0.6286 | 0.0155 | nan | 0.0149 | nan | 0.2473 | 0.3015 | 0.4022 | 0.0 | 0.6633 | 0.4037 | 0.5782 | 0.5907 | 0.0155 | nan | 0.0148 | nan | 0.2266 | 0.2731 | 0.3817 | | 0.1125 | 14.75 | 2360 | 0.3766 | 0.3027 | 0.3526 | 0.5064 | nan | 0.6058 | 0.3030 | 0.5083 | 0.6290 | 0.0217 | nan | 0.0107 | nan | 0.2239 | 0.4108 | 0.4599 | 0.0 | 0.5916 | 0.2977 | 0.5031 | 0.5910 | 0.0217 | nan | 0.0106 | nan | 0.2101 | 0.3637 | 0.4379 | | 0.1139 | 14.88 | 2380 | 0.3541 | 0.3752 | 0.4445 | 0.6352 | nan | 0.7230 | 0.5186 | 0.7029 | 0.7593 | 0.0249 | nan | 0.0174 | nan | 0.2855 | 0.4914 | 0.4778 | 0.0 | 0.7011 | 0.4986 | 0.6740 | 0.6961 | 0.0249 | nan | 0.0172 | nan | 0.2652 | 0.4230 | 0.4514 | | 0.1841 | 15.0 | 2400 | 0.3596 | 0.3337 | 0.3915 | 0.5678 | nan | 0.6749 | 0.3898 | 0.6395 | 0.6779 | 0.0267 | nan | 0.0162 | nan | 0.2609 | 0.4275 | 0.4104 | 0.0 | 0.6576 | 0.3815 | 0.6209 | 0.6334 | 0.0267 | nan | 0.0161 | nan | 0.2406 | 0.3728 | 0.3871 | | 0.1828 | 15.12 | 2420 | 0.3641 | 0.3060 | 0.3564 | 0.5373 | nan | 0.6464 | 0.3564 | 0.5524 | 0.6620 | 0.0132 | nan | 0.0079 | nan | 0.1817 | 0.3260 | 0.4615 | 0.0 | 0.6299 | 0.3491 | 0.5459 | 0.6195 | 0.0132 | nan | 0.0078 | nan | 0.1742 | 0.2937 | 0.4271 | | 0.0202 | 15.25 | 2440 | 0.3637 | 0.3310 | 0.3910 | 0.5513 | nan | 0.6071 | 0.4158 | 0.6488 | 0.7365 | 0.0339 | nan | 0.0181 | nan | 0.2819 | 0.3906 | 0.3863 | 0.0 | 0.5933 | 0.3991 | 0.6291 | 0.6687 | 0.0339 | nan | 0.0179 | nan | 0.2585 | 0.3465 | 0.3631 | | 0.3244 | 15.38 | 2460 | 0.3752 | 0.3290 | 0.3885 | 0.5348 | nan | 0.5915 | 0.4283 | 0.6163 | 0.6207 | 0.0442 | nan | 0.0320 | nan | 0.2746 | 0.4170 | 0.4722 | 0.0 | 0.5762 | 0.4087 | 0.5914 | 0.5837 | 0.0442 | nan | 0.0315 | nan | 0.2498 | 0.3633 | 0.4410 | | 0.0619 | 15.5 | 2480 | 0.3794 | 0.3052 | 0.3560 | 0.5207 | nan | 0.6266 | 0.3283 | 0.6050 | 0.6392 | 0.0387 | nan | 0.0303 | nan | 0.2255 | 0.3605 | 0.3501 | 0.0 | 0.6104 | 0.3232 | 0.5877 | 0.5951 | 0.0387 | nan | 0.0300 | nan | 0.2125 | 0.3189 | 0.3357 | | 0.0788 | 15.62 | 2500 | 0.3641 | 0.3564 | 0.4204 | 0.6062 | nan | 0.6924 | 0.5027 | 0.6264 | 0.7243 | 0.0330 | nan | 0.0246 | nan | 0.2279 | 0.4379 | 0.5142 | 0.0 | 0.6721 | 0.4863 | 0.6116 | 0.6645 | 0.0330 | nan | 0.0244 | nan | 0.2181 | 0.3780 | 0.4764 | | 0.1819 | 15.75 | 2520 | 0.3730 | 0.3330 | 0.3919 | 0.5496 | nan | 0.6366 | 0.3864 | 0.5794 | 0.6584 | 0.0296 | nan | 0.0193 | nan | 0.2730 | 0.4636 | 0.4804 | 0.0 | 0.6209 | 0.3793 | 0.5687 | 0.6158 | 0.0296 | nan | 0.0190 | nan | 0.2520 | 0.3970 | 0.4476 | | 0.1583 | 15.88 | 2540 | 0.3707 | 0.3437 | 0.4053 | 0.5775 | nan | 0.6838 | 0.4042 | 0.6664 | 0.6669 | 0.0270 | nan | 0.0380 | nan | 0.2653 | 0.4832 | 0.4127 | 0.0 | 0.6658 | 0.3962 | 0.6401 | 0.6213 | 0.0269 | nan | 0.0374 | nan | 0.2491 | 0.4074 | 0.3930 | | 0.0973 | 16.0 | 2560 | 0.3789 | 0.3102 | 0.3645 | 0.5251 | nan | 0.6202 | 0.3226 | 0.6377 | 0.6612 | 0.0240 | nan | 0.0420 | nan | 0.2656 | 0.3646 | 0.3432 | 0.0 | 0.6064 | 0.3155 | 0.6133 | 0.6167 | 0.0240 | nan | 0.0411 | nan | 0.2490 | 0.3193 | 0.3171 | | 0.188 | 16.12 | 2580 | 0.3646 | 0.3456 | 0.4070 | 0.5929 | nan | 0.7021 | 0.4230 | 0.6325 | 0.7055 | 0.0310 | nan | 0.0434 | nan | 0.3221 | 0.3235 | 0.4801 | 0.0 | 0.6798 | 0.4127 | 0.6135 | 0.6518 | 0.0309 | nan | 0.0423 | nan | 0.2943 | 0.2847 | 0.4458 | | 0.0581 | 16.25 | 2600 | 0.4030 | 0.2992 | 0.3506 | 0.4870 | nan | 0.5565 | 0.3320 | 0.6005 | 0.6297 | 0.0621 | nan | 0.0399 | nan | 0.2508 | 0.4016 | 0.2824 | 0.0 | 0.5438 | 0.3243 | 0.5851 | 0.5852 | 0.0610 | nan | 0.0391 | nan | 0.2336 | 0.3470 | 0.2729 | | 0.1891 | 16.38 | 2620 | 0.3766 | 0.3408 | 0.4029 | 0.5618 | nan | 0.6308 | 0.4264 | 0.6309 | 0.6672 | 0.0537 | nan | 0.0358 | nan | 0.2444 | 0.4289 | 0.5077 | 0.0 | 0.6150 | 0.4147 | 0.6043 | 0.6202 | 0.0528 | nan | 0.0353 | nan | 0.2302 | 0.3673 | 0.4679 | | 0.2495 | 16.5 | 2640 | 0.3758 | 0.3481 | 0.4106 | 0.5897 | nan | 0.7078 | 0.4004 | 0.6212 | 0.6981 | 0.0769 | nan | 0.0402 | nan | 0.2365 | 0.4410 | 0.4736 | 0.0 | 0.6873 | 0.3937 | 0.6026 | 0.6416 | 0.0749 | nan | 0.0391 | nan | 0.2227 | 0.3776 | 0.4418 | | 0.153 | 16.62 | 2660 | 0.3899 | 0.2935 | 0.3435 | 0.5074 | nan | 0.6166 | 0.3835 | 0.5905 | 0.5641 | 0.0402 | nan | 0.0290 | nan | 0.2244 | 0.3407 | 0.3025 | 0.0 | 0.6027 | 0.3733 | 0.5639 | 0.5320 | 0.0402 | nan | 0.0286 | nan | 0.2092 | 0.2969 | 0.2883 | | 0.083 | 16.75 | 2680 | 0.3758 | 0.3137 | 0.3669 | 0.5555 | nan | 0.6916 | 0.3837 | 0.5386 | 0.6336 | 0.0400 | nan | 0.0247 | nan | 0.2183 | 0.3063 | 0.4655 | 0.0 | 0.6742 | 0.3761 | 0.5322 | 0.5845 | 0.0400 | nan | 0.0243 | nan | 0.2066 | 0.2715 | 0.4276 | | 0.136 | 16.88 | 2700 | 0.3570 | 0.3606 | 0.4264 | 0.6044 | nan | 0.7070 | 0.4583 | 0.6819 | 0.6820 | 0.0525 | nan | 0.0404 | nan | 0.3045 | 0.4473 | 0.4636 | 0.0 | 0.6892 | 0.4490 | 0.6517 | 0.6349 | 0.0524 | nan | 0.0396 | nan | 0.2758 | 0.3799 | 0.4333 | | 0.0609 | 17.0 | 2720 | 0.3657 | 0.3182 | 0.3719 | 0.5547 | nan | 0.6883 | 0.3931 | 0.5851 | 0.6371 | 0.0482 | nan | 0.0290 | nan | 0.2633 | 0.3435 | 0.3598 | 0.0 | 0.6688 | 0.3841 | 0.5697 | 0.5975 | 0.0482 | nan | 0.0285 | nan | 0.2450 | 0.3019 | 0.3384 | | 0.1483 | 17.12 | 2740 | 0.3847 | 0.3068 | 0.3620 | 0.5117 | nan | 0.5805 | 0.3386 | 0.5781 | 0.6593 | 0.0445 | nan | 0.0343 | nan | 0.2763 | 0.3060 | 0.4403 | 0.0 | 0.5686 | 0.3269 | 0.5604 | 0.6165 | 0.0439 | nan | 0.0335 | nan | 0.2507 | 0.2700 | 0.3978 | | 0.2166 | 17.25 | 2760 | 0.3986 | 0.2853 | 0.3307 | 0.5154 | nan | 0.6550 | 0.3083 | 0.5705 | 0.6325 | 0.0442 | nan | 0.0205 | nan | 0.1784 | 0.2618 | 0.3047 | 0.0 | 0.6390 | 0.3038 | 0.5618 | 0.5883 | 0.0441 | nan | 0.0203 | nan | 0.1703 | 0.2369 | 0.2887 | | 0.096 | 17.38 | 2780 | 0.4041 | 0.2884 | 0.3367 | 0.4939 | nan | 0.6043 | 0.2952 | 0.5538 | 0.6037 | 0.0470 | nan | 0.0231 | nan | 0.2257 | 0.3329 | 0.3444 | 0.0 | 0.5912 | 0.2905 | 0.5458 | 0.5617 | 0.0470 | nan | 0.0228 | nan | 0.2106 | 0.2930 | 0.3217 | | 0.0839 | 17.5 | 2800 | 0.3773 | 0.3516 | 0.4152 | 0.6011 | nan | 0.7099 | 0.4639 | 0.6553 | 0.7115 | 0.0586 | nan | 0.0328 | nan | 0.3030 | 0.3879 | 0.4140 | 0.0 | 0.6884 | 0.4484 | 0.6341 | 0.6528 | 0.0586 | nan | 0.0324 | nan | 0.2784 | 0.3349 | 0.3877 | | 0.1185 | 17.62 | 2820 | 0.3753 | 0.3255 | 0.3816 | 0.5517 | nan | 0.6575 | 0.4173 | 0.6167 | 0.6411 | 0.0711 | nan | 0.0270 | nan | 0.2554 | 0.3842 | 0.3643 | 0.0 | 0.6400 | 0.4044 | 0.6019 | 0.5946 | 0.0708 | nan | 0.0267 | nan | 0.2403 | 0.3319 | 0.3448 | | 0.1155 | 17.75 | 2840 | 0.3742 | 0.3354 | 0.3939 | 0.5575 | nan | 0.6693 | 0.3661 | 0.6355 | 0.6422 | 0.0786 | nan | 0.0323 | nan | 0.2502 | 0.4593 | 0.4115 | 0.0 | 0.6510 | 0.3595 | 0.6193 | 0.5977 | 0.0776 | nan | 0.0320 | nan | 0.2347 | 0.3894 | 0.3928 | | 0.1311 | 17.88 | 2860 | 0.3862 | 0.3306 | 0.3882 | 0.5436 | nan | 0.6268 | 0.3595 | 0.6226 | 0.6840 | 0.0823 | nan | 0.0400 | nan | 0.2510 | 0.4016 | 0.4261 | 0.0 | 0.6125 | 0.3525 | 0.6087 | 0.6301 | 0.0816 | nan | 0.0394 | nan | 0.2348 | 0.3472 | 0.3995 | | 0.0059 | 18.0 | 2880 | 0.3997 | 0.2999 | 0.3509 | 0.5209 | nan | 0.6406 | 0.3559 | 0.6409 | 0.5987 | 0.0524 | nan | 0.0490 | nan | 0.2912 | 0.2650 | 0.2648 | 0.0 | 0.6221 | 0.3495 | 0.6051 | 0.5625 | 0.0523 | nan | 0.0474 | nan | 0.2684 | 0.2353 | 0.2563 | | 0.2505 | 18.12 | 2900 | 0.3948 | 0.3107 | 0.3648 | 0.5266 | nan | 0.6174 | 0.3923 | 0.5960 | 0.6209 | 0.0640 | nan | 0.0383 | nan | 0.2507 | 0.3099 | 0.3937 | 0.0 | 0.6028 | 0.3777 | 0.5739 | 0.5786 | 0.0634 | nan | 0.0375 | nan | 0.2350 | 0.2722 | 0.3660 | | 0.1181 | 18.25 | 2920 | 0.3764 | 0.3401 | 0.3999 | 0.5709 | nan | 0.6792 | 0.4368 | 0.6006 | 0.6382 | 0.0752 | nan | 0.0399 | nan | 0.2711 | 0.3991 | 0.4587 | 0.0 | 0.6604 | 0.4226 | 0.5815 | 0.5978 | 0.0736 | nan | 0.0392 | nan | 0.2526 | 0.3440 | 0.4290 | | 0.1169 | 18.38 | 2940 | 0.3737 | 0.3522 | 0.4141 | 0.5853 | nan | 0.6818 | 0.4810 | 0.6037 | 0.6819 | 0.1025 | nan | 0.0410 | nan | 0.2644 | 0.4201 | 0.4503 | 0.0 | 0.6615 | 0.4636 | 0.5918 | 0.6288 | 0.1004 | nan | 0.0400 | nan | 0.2486 | 0.3615 | 0.4256 | | 0.1075 | 18.5 | 2960 | 0.3985 | 0.3176 | 0.3711 | 0.5438 | nan | 0.6841 | 0.3154 | 0.5765 | 0.6472 | 0.0906 | nan | 0.0435 | nan | 0.2634 | 0.3396 | 0.3801 | 0.0 | 0.6643 | 0.3104 | 0.5673 | 0.6015 | 0.0897 | nan | 0.0423 | nan | 0.2444 | 0.3020 | 0.3537 | | 0.1468 | 18.62 | 2980 | 0.3809 | 0.3592 | 0.4249 | 0.5883 | nan | 0.6884 | 0.4059 | 0.6539 | 0.6849 | 0.0905 | nan | 0.0482 | nan | 0.2846 | 0.4887 | 0.4791 | 0.0 | 0.6685 | 0.3996 | 0.6290 | 0.6350 | 0.0881 | nan | 0.0469 | nan | 0.2632 | 0.4144 | 0.4469 | | 0.1438 | 18.75 | 3000 | 0.4059 | 0.3267 | 0.3847 | 0.5308 | nan | 0.6163 | 0.4193 | 0.5594 | 0.6195 | 0.1263 | nan | 0.0415 | nan | 0.2273 | 0.4443 | 0.4081 | 0.0 | 0.6014 | 0.4035 | 0.5470 | 0.5813 | 0.1224 | nan | 0.0406 | nan | 0.2127 | 0.3774 | 0.3810 | | 0.1021 | 18.88 | 3020 | 0.3904 | 0.3525 | 0.4154 | 0.5957 | nan | 0.7113 | 0.4367 | 0.6382 | 0.7089 | 0.1015 | nan | 0.0432 | nan | 0.2595 | 0.4177 | 0.4213 | 0.0 | 0.6907 | 0.4264 | 0.6212 | 0.6528 | 0.0988 | nan | 0.0422 | nan | 0.2428 | 0.3582 | 0.3923 | | 0.0308 | 19.0 | 3040 | 0.3790 | 0.3502 | 0.4129 | 0.5842 | nan | 0.6850 | 0.4325 | 0.6381 | 0.6733 | 0.0994 | nan | 0.0429 | nan | 0.2607 | 0.4038 | 0.4803 | 0.0 | 0.6662 | 0.4228 | 0.6189 | 0.6261 | 0.0967 | nan | 0.0420 | nan | 0.2443 | 0.3470 | 0.4380 | | 0.2127 | 19.12 | 3060 | 0.3938 | 0.3268 | 0.3842 | 0.5413 | nan | 0.6469 | 0.3713 | 0.5834 | 0.6338 | 0.0856 | nan | 0.0419 | nan | 0.2657 | 0.4172 | 0.4118 | 0.0 | 0.6307 | 0.3641 | 0.5711 | 0.5914 | 0.0838 | nan | 0.0410 | nan | 0.2465 | 0.3570 | 0.3822 | | 0.1228 | 19.25 | 3080 | 0.3930 | 0.3371 | 0.3961 | 0.5707 | nan | 0.6900 | 0.3839 | 0.5852 | 0.6874 | 0.0759 | nan | 0.0337 | nan | 0.2645 | 0.4055 | 0.4391 | 0.0 | 0.6701 | 0.3767 | 0.5733 | 0.6383 | 0.0745 | nan | 0.0331 | nan | 0.2462 | 0.3494 | 0.4094 | | 0.0882 | 19.38 | 3100 | 0.3940 | 0.3372 | 0.3954 | 0.5712 | nan | 0.6829 | 0.4213 | 0.6223 | 0.6689 | 0.0740 | nan | 0.0338 | nan | 0.2622 | 0.3936 | 0.3997 | 0.0 | 0.6636 | 0.4108 | 0.6046 | 0.6226 | 0.0729 | nan | 0.0332 | nan | 0.2445 | 0.3419 | 0.3776 | | 0.0798 | 19.5 | 3120 | 0.4141 | 0.3078 | 0.3595 | 0.5247 | nan | 0.6568 | 0.2977 | 0.5794 | 0.6253 | 0.0788 | nan | 0.0341 | nan | 0.2380 | 0.3701 | 0.3549 | 0.0 | 0.6388 | 0.2941 | 0.5651 | 0.5852 | 0.0775 | nan | 0.0332 | nan | 0.2237 | 0.3256 | 0.3351 | | 0.2337 | 19.62 | 3140 | 0.3981 | 0.3068 | 0.3583 | 0.5271 | nan | 0.6597 | 0.3512 | 0.5748 | 0.5754 | 0.0715 | nan | 0.0304 | nan | 0.2115 | 0.3767 | 0.3733 | 0.0 | 0.6439 | 0.3439 | 0.5569 | 0.5412 | 0.0701 | nan | 0.0299 | nan | 0.2004 | 0.3301 | 0.3515 | | 0.265 | 19.75 | 3160 | 0.3878 | 0.3441 | 0.4051 | 0.5883 | nan | 0.7216 | 0.4309 | 0.6112 | 0.6456 | 0.1042 | nan | 0.0316 | nan | 0.2539 | 0.3974 | 0.4499 | 0.0 | 0.6987 | 0.4186 | 0.5900 | 0.6016 | 0.1009 | nan | 0.0311 | nan | 0.2385 | 0.3425 | 0.4188 | | 0.1612 | 19.88 | 3180 | 0.3921 | 0.3399 | 0.4015 | 0.5622 | nan | 0.6680 | 0.3758 | 0.6031 | 0.6745 | 0.1219 | nan | 0.0368 | nan | 0.2783 | 0.4113 | 0.4439 | 0.0 | 0.6509 | 0.3660 | 0.5870 | 0.6189 | 0.1176 | nan | 0.0361 | nan | 0.2584 | 0.3531 | 0.4113 | | 0.119 | 20.0 | 3200 | 0.3930 | 0.3381 | 0.3973 | 0.5638 | nan | 0.6846 | 0.3830 | 0.6002 | 0.6528 | 0.1236 | nan | 0.0364 | nan | 0.2504 | 0.4254 | 0.4196 | 0.0 | 0.6659 | 0.3747 | 0.5862 | 0.6027 | 0.1201 | nan | 0.0357 | nan | 0.2355 | 0.3642 | 0.3958 | | 0.2127 | 20.12 | 3220 | 0.4055 | 0.3262 | 0.3828 | 0.5438 | nan | 0.6687 | 0.3572 | 0.5735 | 0.6260 | 0.1227 | nan | 0.0452 | nan | 0.2759 | 0.3804 | 0.3954 | 0.0 | 0.6509 | 0.3480 | 0.5618 | 0.5802 | 0.1193 | nan | 0.0442 | nan | 0.2554 | 0.3293 | 0.3729 | | 0.1082 | 20.25 | 3240 | 0.4076 | 0.3233 | 0.3790 | 0.5477 | nan | 0.6622 | 0.3678 | 0.6170 | 0.6725 | 0.1201 | nan | 0.0408 | nan | 0.2368 | 0.3502 | 0.3435 | 0.0 | 0.6445 | 0.3588 | 0.5987 | 0.6188 | 0.1167 | nan | 0.0400 | nan | 0.2242 | 0.3062 | 0.3253 | | 0.1413 | 20.38 | 3260 | 0.3997 | 0.3315 | 0.3894 | 0.5515 | nan | 0.6595 | 0.3799 | 0.5992 | 0.6447 | 0.1206 | nan | 0.0406 | nan | 0.2610 | 0.3701 | 0.4294 | 0.0 | 0.6426 | 0.3715 | 0.5838 | 0.5996 | 0.1176 | nan | 0.0399 | nan | 0.2422 | 0.3200 | 0.3973 | | 0.1308 | 20.5 | 3280 | 0.4210 | 0.3100 | 0.3624 | 0.5150 | nan | 0.6198 | 0.3069 | 0.5631 | 0.6500 | 0.1115 | nan | 0.0402 | nan | 0.2334 | 0.3535 | 0.3831 | 0.0 | 0.6049 | 0.3025 | 0.5540 | 0.6033 | 0.1102 | nan | 0.0393 | nan | 0.2189 | 0.3098 | 0.3574 | | 0.0599 | 20.62 | 3300 | 0.3929 | 0.3457 | 0.4067 | 0.5822 | nan | 0.6963 | 0.4452 | 0.6235 | 0.6543 | 0.1024 | nan | 0.0453 | nan | 0.2641 | 0.3904 | 0.4386 | 0.0 | 0.6756 | 0.4337 | 0.6062 | 0.6087 | 0.1002 | nan | 0.0443 | nan | 0.2460 | 0.3365 | 0.4061 | | 0.0948 | 20.75 | 3320 | 0.3978 | 0.3346 | 0.3933 | 0.5553 | nan | 0.6649 | 0.4108 | 0.6084 | 0.6221 | 0.1142 | nan | 0.0400 | nan | 0.2518 | 0.4197 | 0.4076 | 0.0 | 0.6479 | 0.3990 | 0.5918 | 0.5812 | 0.1109 | nan | 0.0393 | nan | 0.2337 | 0.3621 | 0.3800 | | 0.0418 | 20.88 | 3340 | 0.3987 | 0.3413 | 0.4016 | 0.5691 | nan | 0.6820 | 0.4091 | 0.6041 | 0.6482 | 0.1110 | nan | 0.0344 | nan | 0.2501 | 0.4283 | 0.4476 | 0.0 | 0.6632 | 0.3996 | 0.5894 | 0.6026 | 0.1079 | nan | 0.0337 | nan | 0.2342 | 0.3677 | 0.4142 | | 0.2251 | 21.0 | 3360 | 0.4091 | 0.3067 | 0.3576 | 0.5235 | nan | 0.6628 | 0.3239 | 0.5825 | 0.6036 | 0.1097 | nan | 0.0348 | nan | 0.2448 | 0.3554 | 0.3010 | 0.0 | 0.6446 | 0.3177 | 0.5663 | 0.5655 | 0.1064 | nan | 0.0340 | nan | 0.2288 | 0.3149 | 0.2890 | | 0.0887 | 21.12 | 3380 | 0.3958 | 0.3502 | 0.4144 | 0.5830 | nan | 0.6798 | 0.4381 | 0.6436 | 0.7003 | 0.0985 | nan | 0.0553 | nan | 0.3298 | 0.3692 | 0.4148 | 0.0 | 0.6608 | 0.4252 | 0.6204 | 0.6454 | 0.0957 | nan | 0.0537 | nan | 0.2975 | 0.3220 | 0.3809 | | 0.0981 | 21.25 | 3400 | 0.4053 | 0.3346 | 0.3938 | 0.5521 | nan | 0.6416 | 0.4158 | 0.6278 | 0.6327 | 0.1152 | nan | 0.0472 | nan | 0.2642 | 0.3607 | 0.4387 | 0.0 | 0.6267 | 0.4034 | 0.6096 | 0.5906 | 0.1116 | nan | 0.0460 | nan | 0.2430 | 0.3153 | 0.3998 | | 0.129 | 21.38 | 3420 | 0.4044 | 0.3195 | 0.3724 | 0.5357 | nan | 0.6534 | 0.3715 | 0.5931 | 0.6088 | 0.1166 | nan | 0.0411 | nan | 0.2463 | 0.3438 | 0.3765 | 0.0 | 0.6364 | 0.3635 | 0.5791 | 0.5711 | 0.1137 | nan | 0.0403 | nan | 0.2292 | 0.3042 | 0.3575 | | 0.0612 | 21.5 | 3440 | 0.3903 | 0.3486 | 0.4101 | 0.5821 | nan | 0.6849 | 0.4416 | 0.6240 | 0.6569 | 0.1031 | nan | 0.0424 | nan | 0.2770 | 0.3685 | 0.4925 | 0.0 | 0.6663 | 0.4307 | 0.6044 | 0.6109 | 0.0999 | nan | 0.0416 | nan | 0.2569 | 0.3219 | 0.4536 | | 0.1272 | 21.62 | 3460 | 0.4190 | 0.3084 | 0.3600 | 0.5155 | nan | 0.6333 | 0.3347 | 0.5558 | 0.6046 | 0.0905 | nan | 0.0351 | nan | 0.2328 | 0.4021 | 0.3511 | 0.0 | 0.6178 | 0.3281 | 0.5458 | 0.5679 | 0.0895 | nan | 0.0345 | nan | 0.2190 | 0.3491 | 0.3325 | | 0.0396 | 21.75 | 3480 | 0.4083 | 0.3243 | 0.3801 | 0.5463 | nan | 0.6695 | 0.3636 | 0.6050 | 0.6290 | 0.0988 | nan | 0.0423 | nan | 0.2516 | 0.3892 | 0.3716 | 0.0 | 0.6523 | 0.3566 | 0.5879 | 0.5871 | 0.0963 | nan | 0.0414 | nan | 0.2360 | 0.3372 | 0.3485 | | 0.1612 | 21.88 | 3500 | 0.4034 | 0.3276 | 0.3836 | 0.5571 | nan | 0.6817 | 0.4096 | 0.5779 | 0.6470 | 0.1009 | nan | 0.0430 | nan | 0.2657 | 0.3577 | 0.3690 | 0.0 | 0.6632 | 0.3995 | 0.5630 | 0.6014 | 0.0982 | nan | 0.0420 | nan | 0.2475 | 0.3112 | 0.3502 | | 0.168 | 22.0 | 3520 | 0.3960 | 0.3299 | 0.3866 | 0.5620 | nan | 0.6894 | 0.3956 | 0.5776 | 0.6484 | 0.0963 | nan | 0.0393 | nan | 0.2551 | 0.3656 | 0.4123 | 0.0 | 0.6689 | 0.3867 | 0.5646 | 0.6040 | 0.0935 | nan | 0.0384 | nan | 0.2394 | 0.3177 | 0.3853 | | 0.1447 | 22.12 | 3540 | 0.4078 | 0.3373 | 0.3970 | 0.5641 | nan | 0.6879 | 0.3803 | 0.5834 | 0.6534 | 0.0952 | nan | 0.0500 | nan | 0.2945 | 0.4033 | 0.4248 | 0.0 | 0.6674 | 0.3724 | 0.5691 | 0.6079 | 0.0923 | nan | 0.0486 | nan | 0.2704 | 0.3478 | 0.3974 | | 0.053 | 22.25 | 3560 | 0.4011 | 0.3369 | 0.3956 | 0.5696 | nan | 0.7026 | 0.4088 | 0.5707 | 0.6348 | 0.0921 | nan | 0.0507 | nan | 0.2935 | 0.3820 | 0.4256 | 0.0 | 0.6803 | 0.3990 | 0.5570 | 0.5941 | 0.0895 | nan | 0.0494 | nan | 0.2709 | 0.3313 | 0.3978 | | 0.1187 | 22.38 | 3580 | 0.4053 | 0.3330 | 0.3913 | 0.5555 | nan | 0.6709 | 0.3798 | 0.5832 | 0.6476 | 0.0971 | nan | 0.0440 | nan | 0.2728 | 0.3996 | 0.4266 | 0.0 | 0.6529 | 0.3718 | 0.5682 | 0.6036 | 0.0940 | nan | 0.0429 | nan | 0.2524 | 0.3462 | 0.3977 | | 0.1373 | 22.5 | 3600 | 0.4094 | 0.3264 | 0.3829 | 0.5411 | nan | 0.6504 | 0.3713 | 0.5818 | 0.6355 | 0.1050 | nan | 0.0402 | nan | 0.2590 | 0.4045 | 0.3984 | 0.0 | 0.6343 | 0.3624 | 0.5688 | 0.5951 | 0.1009 | nan | 0.0394 | nan | 0.2407 | 0.3515 | 0.3709 | | 0.144 | 22.62 | 3620 | 0.4051 | 0.3243 | 0.3792 | 0.5460 | nan | 0.6617 | 0.3776 | 0.6009 | 0.6535 | 0.1049 | nan | 0.0384 | nan | 0.2643 | 0.3601 | 0.3512 | 0.0 | 0.6448 | 0.3688 | 0.5872 | 0.6082 | 0.1005 | nan | 0.0377 | nan | 0.2463 | 0.3173 | 0.3320 | | 0.0716 | 22.75 | 3640 | 0.4071 | 0.3209 | 0.3750 | 0.5386 | nan | 0.6443 | 0.3817 | 0.5979 | 0.6464 | 0.1080 | nan | 0.0381 | nan | 0.2470 | 0.3450 | 0.3667 | 0.0 | 0.6288 | 0.3727 | 0.5835 | 0.6006 | 0.1032 | nan | 0.0374 | nan | 0.2315 | 0.3057 | 0.3453 | | 0.0869 | 22.88 | 3660 | 0.4162 | 0.3118 | 0.3636 | 0.5269 | nan | 0.6438 | 0.3654 | 0.5745 | 0.6012 | 0.1058 | nan | 0.0359 | nan | 0.2423 | 0.3257 | 0.3781 | 0.0 | 0.6281 | 0.3557 | 0.5610 | 0.5643 | 0.1014 | nan | 0.0353 | nan | 0.2266 | 0.2902 | 0.3554 | | 0.0846 | 23.0 | 3680 | 0.4079 | 0.3326 | 0.3902 | 0.5584 | nan | 0.6786 | 0.3830 | 0.5896 | 0.6438 | 0.1063 | nan | 0.0384 | nan | 0.2539 | 0.3928 | 0.4253 | 0.0 | 0.6605 | 0.3742 | 0.5765 | 0.5994 | 0.1019 | nan | 0.0376 | nan | 0.2376 | 0.3425 | 0.3957 | | 0.1137 | 23.12 | 3700 | 0.4062 | 0.3270 | 0.3827 | 0.5498 | nan | 0.6647 | 0.3757 | 0.6069 | 0.6502 | 0.1083 | nan | 0.0380 | nan | 0.2465 | 0.3729 | 0.3814 | 0.0 | 0.6474 | 0.3675 | 0.5903 | 0.6042 | 0.1038 | nan | 0.0372 | nan | 0.2317 | 0.3290 | 0.3588 | | 0.109 | 23.25 | 3720 | 0.4160 | 0.3217 | 0.3761 | 0.5445 | nan | 0.6679 | 0.3698 | 0.5816 | 0.6386 | 0.1013 | nan | 0.0424 | nan | 0.2568 | 0.3538 | 0.3723 | 0.0 | 0.6503 | 0.3618 | 0.5679 | 0.5943 | 0.0978 | nan | 0.0415 | nan | 0.2394 | 0.3129 | 0.3514 | | 0.0314 | 23.38 | 3740 | 0.4166 | 0.3156 | 0.3685 | 0.5342 | nan | 0.6615 | 0.3651 | 0.5650 | 0.6126 | 0.1027 | nan | 0.0433 | nan | 0.2581 | 0.3479 | 0.3605 | 0.0 | 0.6445 | 0.3558 | 0.5521 | 0.5746 | 0.0988 | nan | 0.0424 | nan | 0.2393 | 0.3083 | 0.3401 | | 0.1021 | 23.5 | 3760 | 0.4215 | 0.3218 | 0.3774 | 0.5350 | nan | 0.6494 | 0.3625 | 0.5961 | 0.6217 | 0.1038 | nan | 0.0448 | nan | 0.2675 | 0.3971 | 0.3539 | 0.0 | 0.6335 | 0.3532 | 0.5796 | 0.5818 | 0.0997 | nan | 0.0438 | nan | 0.2465 | 0.3460 | 0.3336 | | 0.0086 | 23.62 | 3780 | 0.4121 | 0.3224 | 0.3779 | 0.5430 | nan | 0.6630 | 0.3837 | 0.5842 | 0.6257 | 0.1028 | nan | 0.0432 | nan | 0.2651 | 0.3702 | 0.3634 | 0.0 | 0.6461 | 0.3725 | 0.5697 | 0.5847 | 0.0990 | nan | 0.0423 | nan | 0.2451 | 0.3231 | 0.3413 | | 0.1086 | 23.75 | 3800 | 0.4078 | 0.3309 | 0.3892 | 0.5521 | nan | 0.6630 | 0.3944 | 0.5991 | 0.6368 | 0.1092 | nan | 0.0444 | nan | 0.2721 | 0.3831 | 0.4007 | 0.0 | 0.6464 | 0.3824 | 0.5830 | 0.5932 | 0.1047 | nan | 0.0435 | nan | 0.2511 | 0.3331 | 0.3716 | | 0.1121 | 23.88 | 3820 | 0.4119 | 0.3221 | 0.3780 | 0.5369 | nan | 0.6487 | 0.3695 | 0.5765 | 0.6271 | 0.1155 | nan | 0.0431 | nan | 0.2546 | 0.3730 | 0.3941 | 0.0 | 0.6329 | 0.3588 | 0.5643 | 0.5842 | 0.1105 | nan | 0.0422 | nan | 0.2368 | 0.3252 | 0.3666 | | 0.1118 | 24.0 | 3840 | 0.4161 | 0.3239 | 0.3799 | 0.5430 | nan | 0.6645 | 0.3623 | 0.5649 | 0.6499 | 0.1168 | nan | 0.0422 | nan | 0.2492 | 0.3927 | 0.3769 | 0.0 | 0.6471 | 0.3532 | 0.5559 | 0.6011 | 0.1122 | nan | 0.0413 | nan | 0.2332 | 0.3407 | 0.3547 | | 0.0399 | 24.12 | 3860 | 0.4107 | 0.3313 | 0.3888 | 0.5557 | nan | 0.6795 | 0.3847 | 0.5899 | 0.6415 | 0.1208 | nan | 0.0444 | nan | 0.2652 | 0.3843 | 0.3891 | 0.0 | 0.6607 | 0.3748 | 0.5767 | 0.5956 | 0.1152 | nan | 0.0435 | nan | 0.2462 | 0.3345 | 0.3660 | | 0.0892 | 24.25 | 3880 | 0.4233 | 0.3200 | 0.3743 | 0.5371 | nan | 0.6571 | 0.3608 | 0.5803 | 0.6290 | 0.1144 | nan | 0.0414 | nan | 0.2556 | 0.3617 | 0.3685 | 0.0 | 0.6402 | 0.3525 | 0.5683 | 0.5850 | 0.1098 | nan | 0.0406 | nan | 0.2377 | 0.3184 | 0.3476 | | 0.0504 | 24.38 | 3900 | 0.4126 | 0.3272 | 0.3838 | 0.5469 | nan | 0.6627 | 0.3726 | 0.5902 | 0.6472 | 0.1122 | nan | 0.0434 | nan | 0.2626 | 0.3820 | 0.3810 | 0.0 | 0.6457 | 0.3638 | 0.5776 | 0.5995 | 0.1075 | nan | 0.0425 | nan | 0.2436 | 0.3331 | 0.3583 | | 0.1415 | 24.5 | 3920 | 0.4125 | 0.3313 | 0.3890 | 0.5544 | nan | 0.6739 | 0.3827 | 0.5877 | 0.6506 | 0.1154 | nan | 0.0461 | nan | 0.2685 | 0.3880 | 0.3885 | 0.0 | 0.6554 | 0.3725 | 0.5757 | 0.6023 | 0.1107 | nan | 0.0451 | nan | 0.2488 | 0.3378 | 0.3647 | | 0.0919 | 24.62 | 3940 | 0.4155 | 0.3205 | 0.3752 | 0.5383 | nan | 0.6617 | 0.3538 | 0.5750 | 0.6351 | 0.1112 | nan | 0.0444 | nan | 0.2523 | 0.3764 | 0.3666 | 0.0 | 0.6444 | 0.3457 | 0.5639 | 0.5899 | 0.1069 | nan | 0.0434 | nan | 0.2354 | 0.3296 | 0.3458 | | 0.0443 | 24.75 | 3960 | 0.4101 | 0.3270 | 0.3837 | 0.5447 | nan | 0.6616 | 0.3704 | 0.5699 | 0.6538 | 0.1158 | nan | 0.0460 | nan | 0.2670 | 0.3933 | 0.3753 | 0.0 | 0.6444 | 0.3607 | 0.5605 | 0.6039 | 0.1111 | nan | 0.0450 | nan | 0.2475 | 0.3427 | 0.3542 | | 0.1815 | 24.88 | 3980 | 0.4182 | 0.3204 | 0.3750 | 0.5340 | nan | 0.6499 | 0.3529 | 0.5731 | 0.6316 | 0.1129 | nan | 0.0422 | nan | 0.2545 | 0.3768 | 0.3816 | 0.0 | 0.6337 | 0.3452 | 0.5621 | 0.5873 | 0.1083 | nan | 0.0413 | nan | 0.2370 | 0.3306 | 0.3586 | | 0.005 | 25.0 | 4000 | 0.4155 | 0.3349 | 0.3935 | 0.5591 | nan | 0.6815 | 0.3865 | 0.5805 | 0.6544 | 0.1155 | nan | 0.0497 | nan | 0.2779 | 0.3995 | 0.3959 | 0.0 | 0.6626 | 0.3764 | 0.5699 | 0.6056 | 0.1108 | nan | 0.0485 | nan | 0.2565 | 0.3465 | 0.3718 | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

[ "unlabeled", "lv", "rv", "ra", "la", "vs", "as", "mk", "tk", "asd", "vsd", "ak" ]

mrkprc1/segformer-b1-finetuned-sudoku

# segformer-b1-finetuned-sudoku This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the mrkprc1/SudokuBoundaries2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9826 - Mean Iou: 0.2452 - Mean Accuracy: 0.4999 - Overall Accuracy: 0.4903 - Accuracy Unlabelled: 0.9996 - Accuracy Sudoku-boundary: 0.0001 - Iou Unlabelled: 0.4903 - Iou Sudoku-boundary: 0.0001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:| | 0.6034 | 3.33 | 20 | 0.6951 | 0.3427 | 0.5173 | 0.5149 | 0.6432 | 0.3914 | 0.3940 | 0.2913 | | 0.7796 | 6.67 | 40 | 0.7150 | 0.3049 | 0.5083 | 0.5021 | 0.8309 | 0.1857 | 0.4501 | 0.1597 | | 0.4378 | 10.0 | 60 | 0.9772 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 | | 0.6804 | 13.33 | 80 | 1.1605 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 | | 0.58 | 16.67 | 100 | 0.9787 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 | | 0.6563 | 20.0 | 120 | 1.1860 | 0.2452 | 0.5 | 0.4904 | 1.0 | 0.0 | 0.4904 | 0.0 | | 0.5128 | 23.33 | 140 | 0.8884 | 0.2457 | 0.5002 | 0.4907 | 0.9996 | 0.0009 | 0.4905 | 0.0009 | | 0.5054 | 26.67 | 160 | 0.8746 | 0.2455 | 0.5002 | 0.4907 | 0.9998 | 0.0006 | 0.4905 | 0.0006 | | 0.5532 | 30.0 | 180 | 0.9540 | 0.2452 | 0.5000 | 0.4905 | 1.0 | 0.0000 | 0.4905 | 0.0000 | | 0.3238 | 33.33 | 200 | 0.8916 | 0.2470 | 0.5009 | 0.4914 | 0.9984 | 0.0035 | 0.4905 | 0.0035 | | 0.2964 | 36.67 | 220 | 1.0162 | 0.2453 | 0.5000 | 0.4905 | 1.0000 | 0.0000 | 0.4905 | 0.0000 | | 0.2102 | 40.0 | 240 | 0.9650 | 0.2452 | 0.4998 | 0.4903 | 0.9996 | 0.0001 | 0.4903 | 0.0001 | | 0.623 | 43.33 | 260 | 0.9071 | 0.2461 | 0.5004 | 0.4909 | 0.9991 | 0.0017 | 0.4904 | 0.0017 | | 0.3741 | 46.67 | 280 | 0.9245 | 0.2454 | 0.5000 | 0.4904 | 0.9994 | 0.0006 | 0.4903 | 0.0006 | | 0.5765 | 50.0 | 300 | 0.9826 | 0.2452 | 0.4999 | 0.4903 | 0.9996 | 0.0001 | 0.4903 | 0.0001 | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "unlabelled", "sudoku-boundary" ]

samitizerxu/segformer-b2-kelp-rgb-agg-imgaug-jan-27

# segformer-b2-kelp-rgb-agg-imgaug-jan-27 This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set: - Accuracy Kelp: 0.4483 - Iou Kelp: 0.3982 - Accuracy Bg: 0.9991 - Iou Bg: 0.9954 - Loss: 0.0174 - Mean Iou: 0.6968 - Mean Accuracy: 0.7237 - Overall Accuracy: 0.9955 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Accuracy Kelp | Iou Kelp | Accuracy Bg | Iou Bg | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | |:-------------:|:-----:|:-----:|:-------------:|:--------:|:-----------:|:------:|:---------------:|:--------:|:-------------:|:----------------:| | 0.7557 | 0.06 | 30 | 0.5865 | 0.0073 | 0.4625 | 0.4612 | 0.7403 | 0.2342 | 0.5245 | 0.4633 | | 0.7383 | 0.12 | 60 | 0.5811 | 0.0071 | 0.4541 | 0.4528 | 0.7518 | 0.2300 | 0.5176 | 0.4550 | | 0.7122 | 0.18 | 90 | 0.5460 | 0.0072 | 0.4913 | 0.4898 | 0.7268 | 0.2485 | 0.5187 | 0.4917 | | 0.6843 | 0.24 | 120 | 0.5273 | 0.0074 | 0.5269 | 0.5252 | 0.7031 | 0.2663 | 0.5271 | 0.5269 | | 0.6517 | 0.3 | 150 | 0.5363 | 0.0086 | 0.5842 | 0.5824 | 0.6704 | 0.2955 | 0.5602 | 0.5839 | | 0.5976 | 0.37 | 180 | 0.4706 | 0.0101 | 0.6913 | 0.6888 | 0.5971 | 0.3494 | 0.5809 | 0.6898 | | 0.5402 | 0.43 | 210 | 0.4111 | 0.0125 | 0.7837 | 0.7806 | 0.5262 | 0.3965 | 0.5974 | 0.7812 | | 0.4614 | 0.49 | 240 | 0.3286 | 0.0143 | 0.8518 | 0.8479 | 0.4517 | 0.4311 | 0.5902 | 0.8483 | | 0.4098 | 0.55 | 270 | 0.2838 | 0.0157 | 0.8843 | 0.8801 | 0.4001 | 0.4479 | 0.5841 | 0.8803 | | 0.3617 | 0.61 | 300 | 0.1543 | 0.0166 | 0.9441 | 0.9387 | 0.3193 | 0.4777 | 0.5492 | 0.9388 | | 0.2873 | 0.67 | 330 | 0.0990 | 0.0182 | 0.9701 | 0.9642 | 0.2682 | 0.4912 | 0.5345 | 0.9642 | | 0.3307 | 0.73 | 360 | 0.0594 | 0.0137 | 0.9775 | 0.9713 | 0.2341 | 0.4925 | 0.5185 | 0.9713 | | 0.2259 | 0.79 | 390 | 0.0153 | 0.0080 | 0.9938 | 0.9872 | 0.1845 | 0.4976 | 0.5045 | 0.9872 | | 0.3572 | 0.85 | 420 | 0.0188 | 0.0107 | 0.9949 | 0.9883 | 0.1745 | 0.4995 | 0.5068 | 0.9883 | | 0.216 | 0.91 | 450 | 0.0039 | 0.0033 | 0.9989 | 0.9922 | 0.1289 | 0.4978 | 0.5014 | 0.9922 | | 0.169 | 0.97 | 480 | 0.0080 | 0.0068 | 0.9988 | 0.9922 | 0.1304 | 0.4995 | 0.5034 | 0.9922 | | 0.1336 | 1.03 | 510 | 0.0086 | 0.0077 | 0.9993 | 0.9926 | 0.1179 | 0.5002 | 0.5039 | 0.9926 | | 0.1186 | 1.1 | 540 | 0.0029 | 0.0028 | 0.9999 | 0.9932 | 0.0943 | 0.4980 | 0.5014 | 0.9932 | | 0.1175 | 1.16 | 570 | 0.0070 | 0.0069 | 0.9999 | 0.9932 | 0.0845 | 0.5001 | 0.5035 | 0.9932 | | 0.0977 | 1.22 | 600 | 0.0002 | 0.0002 | 1.0000 | 0.9933 | 0.0659 | 0.4967 | 0.5001 | 0.9933 | | 0.073 | 1.28 | 630 | 0.0319 | 0.0307 | 0.9997 | 0.9932 | 0.0739 | 0.5119 | 0.5158 | 0.9932 | | 0.072 | 1.34 | 660 | 0.0014 | 0.0014 | 1.0000 | 0.9933 | 0.0553 | 0.4973 | 0.5007 | 0.9933 | | 0.0568 | 1.4 | 690 | 0.0027 | 0.0027 | 1.0000 | 0.9933 | 0.0517 | 0.4980 | 0.5013 | 0.9933 | | 0.0418 | 1.46 | 720 | 0.0019 | 0.0019 | 1.0000 | 0.9933 | 0.0458 | 0.4976 | 0.5009 | 0.9933 | | 0.0318 | 1.52 | 750 | 0.0060 | 0.0060 | 1.0000 | 0.9933 | 0.0452 | 0.4997 | 0.5030 | 0.9933 | | 0.0359 | 1.58 | 780 | 0.0707 | 0.0682 | 0.9998 | 0.9935 | 0.0403 | 0.5309 | 0.5352 | 0.9935 | | 0.0438 | 1.64 | 810 | 0.1112 | 0.1055 | 0.9996 | 0.9937 | 0.0362 | 0.5496 | 0.5554 | 0.9937 | | 0.0325 | 1.7 | 840 | 0.1098 | 0.1052 | 0.9997 | 0.9937 | 0.0333 | 0.5495 | 0.5548 | 0.9937 | | 0.022 | 1.76 | 870 | 0.1215 | 0.1173 | 0.9998 | 0.9939 | 0.0318 | 0.5556 | 0.5606 | 0.9939 | | 0.0264 | 1.83 | 900 | 0.0352 | 0.0350 | 1.0000 | 0.9935 | 0.0316 | 0.5143 | 0.5176 | 0.9935 | | 0.0191 | 1.89 | 930 | 0.1969 | 0.1849 | 0.9996 | 0.9942 | 0.0289 | 0.5895 | 0.5982 | 0.9942 | | 0.0268 | 1.95 | 960 | 0.2421 | 0.2221 | 0.9994 | 0.9943 | 0.0284 | 0.6082 | 0.6207 | 0.9943 | | 0.0164 | 2.01 | 990 | 0.1332 | 0.1298 | 0.9998 | 0.9940 | 0.0273 | 0.5619 | 0.5665 | 0.9940 | | 0.0261 | 2.07 | 1020 | 0.3293 | 0.2820 | 0.9989 | 0.9944 | 0.0258 | 0.6382 | 0.6641 | 0.9944 | | 0.0255 | 2.13 | 1050 | 0.1026 | 0.1010 | 0.9999 | 0.9939 | 0.0268 | 0.5474 | 0.5512 | 0.9939 | | 0.0178 | 2.19 | 1080 | 0.3405 | 0.2944 | 0.9989 | 0.9945 | 0.0260 | 0.6445 | 0.6697 | 0.9945 | | 0.0119 | 2.25 | 1110 | 0.3838 | 0.3184 | 0.9986 | 0.9945 | 0.0238 | 0.6564 | 0.6912 | 0.9945 | | 0.0253 | 2.31 | 1140 | 0.3337 | 0.2942 | 0.9991 | 0.9946 | 0.0227 | 0.6444 | 0.6664 | 0.9946 | | 0.0116 | 2.37 | 1170 | 0.2765 | 0.2549 | 0.9994 | 0.9946 | 0.0224 | 0.6247 | 0.6380 | 0.9946 | | 0.0145 | 2.43 | 1200 | 0.1745 | 0.1689 | 0.9998 | 0.9942 | 0.0241 | 0.5816 | 0.5871 | 0.9942 | | 0.0185 | 2.49 | 1230 | 0.2434 | 0.2293 | 0.9996 | 0.9945 | 0.0223 | 0.6119 | 0.6215 | 0.9945 | | 0.0248 | 2.56 | 1260 | 0.2604 | 0.2429 | 0.9995 | 0.9945 | 0.0219 | 0.6187 | 0.6299 | 0.9946 | | 0.0172 | 2.62 | 1290 | 0.2145 | 0.2048 | 0.9997 | 0.9944 | 0.0221 | 0.5996 | 0.6071 | 0.9944 | | 0.0125 | 2.68 | 1320 | 0.3472 | 0.3058 | 0.9991 | 0.9947 | 0.0208 | 0.6502 | 0.6731 | 0.9947 | | 0.0089 | 2.74 | 1350 | 0.3391 | 0.3003 | 0.9991 | 0.9947 | 0.0209 | 0.6475 | 0.6691 | 0.9947 | | 0.0061 | 2.8 | 1380 | 0.3696 | 0.3189 | 0.9989 | 0.9947 | 0.0207 | 0.6568 | 0.6843 | 0.9947 | | 0.0079 | 2.86 | 1410 | 0.6068 | 0.3737 | 0.9958 | 0.9931 | 0.0252 | 0.6834 | 0.8013 | 0.9932 | | 0.01 | 2.92 | 1440 | 0.4511 | 0.3647 | 0.9984 | 0.9947 | 0.0205 | 0.6797 | 0.7248 | 0.9947 | | 0.0112 | 2.98 | 1470 | 0.3328 | 0.2972 | 0.9992 | 0.9947 | 0.0204 | 0.6460 | 0.6660 | 0.9947 | | 0.007 | 3.04 | 1500 | 0.3705 | 0.3214 | 0.9990 | 0.9947 | 0.0203 | 0.6581 | 0.6847 | 0.9947 | | 0.0119 | 3.1 | 1530 | 0.4911 | 0.3717 | 0.9978 | 0.9944 | 0.0207 | 0.6831 | 0.7445 | 0.9944 | | 0.0086 | 3.16 | 1560 | 0.2780 | 0.2584 | 0.9995 | 0.9946 | 0.0200 | 0.6265 | 0.6387 | 0.9946 | | 0.0542 | 3.23 | 1590 | 0.4441 | 0.3631 | 0.9985 | 0.9948 | 0.0196 | 0.6789 | 0.7213 | 0.9948 | | 0.0096 | 3.29 | 1620 | 0.4264 | 0.3542 | 0.9986 | 0.9948 | 0.0195 | 0.6745 | 0.7125 | 0.9948 | | 0.0164 | 3.35 | 1650 | 0.3326 | 0.2997 | 0.9993 | 0.9948 | 0.0195 | 0.6472 | 0.6659 | 0.9948 | | 0.0076 | 3.41 | 1680 | 0.4205 | 0.3515 | 0.9987 | 0.9948 | 0.0197 | 0.6732 | 0.7096 | 0.9948 | | 0.0056 | 3.47 | 1710 | 0.3465 | 0.3085 | 0.9992 | 0.9948 | 0.0194 | 0.6516 | 0.6728 | 0.9948 | | 0.0047 | 3.53 | 1740 | 0.4351 | 0.3550 | 0.9985 | 0.9947 | 0.0193 | 0.6748 | 0.7168 | 0.9947 | | 0.0079 | 3.59 | 1770 | 0.3970 | 0.3436 | 0.9989 | 0.9949 | 0.0190 | 0.6693 | 0.6980 | 0.9949 | | 0.0175 | 3.65 | 1800 | 0.3844 | 0.3369 | 0.9990 | 0.9949 | 0.0189 | 0.6659 | 0.6917 | 0.9949 | | 0.004 | 3.71 | 1830 | 0.3719 | 0.3306 | 0.9992 | 0.9949 | 0.0189 | 0.6628 | 0.6855 | 0.9949 | | 0.0034 | 3.77 | 1860 | 0.4341 | 0.3590 | 0.9986 | 0.9948 | 0.0189 | 0.6769 | 0.7164 | 0.9948 | | 0.0139 | 3.83 | 1890 | 0.3380 | 0.3051 | 0.9993 | 0.9948 | 0.0192 | 0.6500 | 0.6686 | 0.9948 | | 0.0072 | 3.89 | 1920 | 0.4258 | 0.3585 | 0.9987 | 0.9949 | 0.0188 | 0.6767 | 0.7123 | 0.9949 | | 0.0269 | 3.96 | 1950 | 0.2233 | 0.2132 | 0.9997 | 0.9945 | 0.0207 | 0.6038 | 0.6115 | 0.9945 | | 0.006 | 4.02 | 1980 | 0.3002 | 0.2762 | 0.9994 | 0.9947 | 0.0196 | 0.6355 | 0.6498 | 0.9947 | | 0.0034 | 4.08 | 2010 | 0.3036 | 0.2785 | 0.9994 | 0.9947 | 0.0194 | 0.6366 | 0.6515 | 0.9947 | | 0.0039 | 4.14 | 2040 | 0.2260 | 0.2154 | 0.9997 | 0.9945 | 0.0208 | 0.6049 | 0.6129 | 0.9945 | | 0.0085 | 4.2 | 2070 | 0.3807 | 0.3353 | 0.9991 | 0.9949 | 0.0187 | 0.6651 | 0.6899 | 0.9949 | | 0.0064 | 4.26 | 2100 | 0.4472 | 0.3671 | 0.9985 | 0.9948 | 0.0189 | 0.6810 | 0.7229 | 0.9948 | | 0.0072 | 4.32 | 2130 | 0.3930 | 0.3431 | 0.9990 | 0.9949 | 0.0188 | 0.6690 | 0.6960 | 0.9949 | | 0.0285 | 4.38 | 2160 | 0.3110 | 0.2853 | 0.9994 | 0.9948 | 0.0192 | 0.6400 | 0.6552 | 0.9948 | | 0.0106 | 4.44 | 2190 | 0.3427 | 0.3094 | 0.9993 | 0.9949 | 0.0190 | 0.6521 | 0.6710 | 0.9949 | | 0.0139 | 4.5 | 2220 | 0.3754 | 0.3311 | 0.9991 | 0.9949 | 0.0184 | 0.6630 | 0.6873 | 0.9949 | | 0.0109 | 4.56 | 2250 | 0.1866 | 0.1809 | 0.9998 | 0.9943 | 0.0202 | 0.5876 | 0.5932 | 0.9943 | | 0.0143 | 4.62 | 2280 | 0.3296 | 0.3005 | 0.9993 | 0.9948 | 0.0185 | 0.6477 | 0.6645 | 0.9948 | | 0.0061 | 4.69 | 2310 | 0.4646 | 0.3774 | 0.9984 | 0.9948 | 0.0185 | 0.6861 | 0.7315 | 0.9949 | | 0.0039 | 4.75 | 2340 | 0.3154 | 0.2822 | 0.9992 | 0.9946 | 0.0193 | 0.6384 | 0.6573 | 0.9946 | | 0.0059 | 4.81 | 2370 | 0.3287 | 0.3011 | 0.9994 | 0.9949 | 0.0185 | 0.6480 | 0.6640 | 0.9949 | | 0.008 | 4.87 | 2400 | 0.4402 | 0.3721 | 0.9988 | 0.9950 | 0.0182 | 0.6835 | 0.7195 | 0.9950 | | 0.0123 | 4.93 | 2430 | 0.3758 | 0.3328 | 0.9991 | 0.9949 | 0.0186 | 0.6639 | 0.6875 | 0.9949 | | 0.0329 | 4.99 | 2460 | 0.3555 | 0.3223 | 0.9993 | 0.9950 | 0.0183 | 0.6586 | 0.6774 | 0.9950 | | 0.0051 | 5.05 | 2490 | 0.4204 | 0.3602 | 0.9989 | 0.9950 | 0.0182 | 0.6776 | 0.7096 | 0.9950 | | 0.0132 | 5.11 | 2520 | 0.3182 | 0.2873 | 0.9993 | 0.9947 | 0.0190 | 0.6410 | 0.6587 | 0.9947 | | 0.013 | 5.17 | 2550 | 0.3397 | 0.3094 | 0.9993 | 0.9949 | 0.0183 | 0.6522 | 0.6695 | 0.9949 | | 0.0014 | 5.23 | 2580 | 0.2051 | 0.1987 | 0.9998 | 0.9944 | 0.0202 | 0.5965 | 0.6025 | 0.9944 | | 0.0045 | 5.29 | 2610 | 0.4802 | 0.3916 | 0.9985 | 0.9950 | 0.0185 | 0.6933 | 0.7393 | 0.9950 | | 0.0085 | 5.35 | 2640 | 0.3296 | 0.3016 | 0.9994 | 0.9949 | 0.0188 | 0.6482 | 0.6645 | 0.9949 | | 0.0049 | 5.42 | 2670 | 0.3292 | 0.3010 | 0.9994 | 0.9949 | 0.0184 | 0.6479 | 0.6643 | 0.9949 | | 0.0181 | 5.48 | 2700 | 0.3973 | 0.3465 | 0.9990 | 0.9950 | 0.0180 | 0.6707 | 0.6982 | 0.9950 | | 0.0026 | 5.54 | 2730 | 0.4025 | 0.3520 | 0.9990 | 0.9950 | 0.0179 | 0.6735 | 0.7007 | 0.9950 | | 0.0037 | 5.6 | 2760 | 0.2881 | 0.2691 | 0.9995 | 0.9947 | 0.0185 | 0.6319 | 0.6438 | 0.9947 | | 0.0067 | 5.66 | 2790 | 0.4916 | 0.3854 | 0.9981 | 0.9947 | 0.0188 | 0.6901 | 0.7449 | 0.9947 | | 0.0045 | 5.72 | 2820 | 0.4345 | 0.3703 | 0.9988 | 0.9950 | 0.0180 | 0.6827 | 0.7166 | 0.9950 | | 0.0029 | 5.78 | 2850 | 0.4856 | 0.3766 | 0.9980 | 0.9946 | 0.0191 | 0.6856 | 0.7418 | 0.9946 | | 0.0058 | 5.84 | 2880 | 0.3707 | 0.3316 | 0.9992 | 0.9950 | 0.0187 | 0.6633 | 0.6850 | 0.9950 | | 0.0224 | 5.9 | 2910 | 0.5015 | 0.4023 | 0.9983 | 0.9950 | 0.0183 | 0.6986 | 0.7499 | 0.9950 | | 0.0161 | 5.96 | 2940 | 0.4042 | 0.3541 | 0.9990 | 0.9950 | 0.0183 | 0.6746 | 0.7016 | 0.9951 | | 0.0054 | 6.02 | 2970 | 0.4196 | 0.3623 | 0.9989 | 0.9950 | 0.0183 | 0.6787 | 0.7093 | 0.9950 | | 0.0118 | 6.09 | 3000 | 0.4446 | 0.3753 | 0.9988 | 0.9950 | 0.0179 | 0.6852 | 0.7217 | 0.9950 | | 0.0043 | 6.15 | 3030 | 0.2735 | 0.2587 | 0.9996 | 0.9947 | 0.0192 | 0.6267 | 0.6366 | 0.9947 | | 0.0058 | 6.21 | 3060 | 0.2908 | 0.2707 | 0.9995 | 0.9947 | 0.0188 | 0.6327 | 0.6452 | 0.9947 | | 0.0037 | 6.27 | 3090 | 0.3848 | 0.3405 | 0.9991 | 0.9950 | 0.0179 | 0.6677 | 0.6920 | 0.9950 | | 0.0038 | 6.33 | 3120 | 0.3463 | 0.3133 | 0.9993 | 0.9949 | 0.0178 | 0.6541 | 0.6728 | 0.9949 | | 0.0234 | 6.39 | 3150 | 0.4518 | 0.3825 | 0.9988 | 0.9951 | 0.0176 | 0.6888 | 0.7253 | 0.9951 | | 0.0066 | 6.45 | 3180 | 0.3940 | 0.3407 | 0.9989 | 0.9949 | 0.0184 | 0.6678 | 0.6965 | 0.9949 | | 0.0041 | 6.51 | 3210 | 0.4930 | 0.4010 | 0.9985 | 0.9950 | 0.0175 | 0.6980 | 0.7457 | 0.9951 | | 0.0139 | 6.57 | 3240 | 0.5347 | 0.4133 | 0.9980 | 0.9949 | 0.0178 | 0.7041 | 0.7663 | 0.9949 | | 0.0085 | 6.63 | 3270 | 0.4837 | 0.3867 | 0.9983 | 0.9948 | 0.0182 | 0.6908 | 0.7410 | 0.9949 | | 0.0024 | 6.69 | 3300 | 0.3644 | 0.3251 | 0.9992 | 0.9949 | 0.0185 | 0.6600 | 0.6818 | 0.9949 | | 0.0041 | 6.75 | 3330 | 0.3855 | 0.3450 | 0.9992 | 0.9951 | 0.0177 | 0.6700 | 0.6923 | 0.9951 | | 0.0021 | 6.82 | 3360 | 0.3847 | 0.3422 | 0.9992 | 0.9950 | 0.0184 | 0.6686 | 0.6920 | 0.9950 | | 0.0156 | 6.88 | 3390 | 0.3380 | 0.3129 | 0.9995 | 0.9950 | 0.0179 | 0.6540 | 0.6687 | 0.9950 | | 0.0042 | 6.94 | 3420 | 0.5245 | 0.4071 | 0.9981 | 0.9949 | 0.0183 | 0.7010 | 0.7613 | 0.9949 | | 0.0075 | 7.0 | 3450 | 0.4134 | 0.3642 | 0.9991 | 0.9951 | 0.0177 | 0.6797 | 0.7062 | 0.9952 | | 0.0036 | 7.06 | 3480 | 0.3749 | 0.3362 | 0.9992 | 0.9950 | 0.0183 | 0.6656 | 0.6871 | 0.9950 | | 0.0113 | 7.12 | 3510 | 0.4672 | 0.3887 | 0.9986 | 0.9951 | 0.0178 | 0.6919 | 0.7329 | 0.9951 | | 0.0385 | 7.18 | 3540 | 0.2639 | 0.2513 | 0.9997 | 0.9947 | 0.0199 | 0.6230 | 0.6318 | 0.9947 | | 0.0135 | 7.24 | 3570 | 0.3820 | 0.3435 | 0.9992 | 0.9951 | 0.0178 | 0.6693 | 0.6906 | 0.9951 | | 0.0032 | 7.3 | 3600 | 0.3842 | 0.3397 | 0.9991 | 0.9950 | 0.0182 | 0.6673 | 0.6917 | 0.9950 | | 0.0081 | 7.36 | 3630 | 0.1317 | 0.1292 | 0.9999 | 0.9940 | 0.0235 | 0.5616 | 0.5658 | 0.9940 | | 0.0029 | 7.42 | 3660 | 0.3218 | 0.2976 | 0.9995 | 0.9949 | 0.0184 | 0.6462 | 0.6606 | 0.9949 | | 0.0056 | 7.48 | 3690 | 0.5155 | 0.4111 | 0.9983 | 0.9950 | 0.0181 | 0.7031 | 0.7569 | 0.9950 | | 0.0212 | 7.55 | 3720 | 0.4519 | 0.3843 | 0.9988 | 0.9951 | 0.0178 | 0.6897 | 0.7254 | 0.9951 | | 0.0019 | 7.61 | 3750 | 0.4913 | 0.3989 | 0.9984 | 0.9950 | 0.0176 | 0.6970 | 0.7449 | 0.9950 | | 0.0049 | 7.67 | 3780 | 0.3845 | 0.3463 | 0.9993 | 0.9951 | 0.0178 | 0.6707 | 0.6919 | 0.9951 | | 0.0062 | 7.73 | 3810 | 0.4232 | 0.3606 | 0.9988 | 0.9949 | 0.0202 | 0.6778 | 0.7110 | 0.9950 | | 0.0036 | 7.79 | 3840 | 0.3965 | 0.3536 | 0.9992 | 0.9951 | 0.0182 | 0.6743 | 0.6978 | 0.9951 | | 0.008 | 7.85 | 3870 | 0.3852 | 0.3479 | 0.9993 | 0.9951 | 0.0178 | 0.6715 | 0.6923 | 0.9952 | | 0.0037 | 7.91 | 3900 | 0.4829 | 0.3891 | 0.9984 | 0.9949 | 0.0182 | 0.6920 | 0.7406 | 0.9949 | | 0.007 | 7.97 | 3930 | 0.4051 | 0.3582 | 0.9991 | 0.9951 | 0.0181 | 0.6767 | 0.7021 | 0.9951 | | 0.0151 | 8.03 | 3960 | 0.3396 | 0.3099 | 0.9994 | 0.9949 | 0.0187 | 0.6524 | 0.6695 | 0.9949 | | 0.0156 | 8.09 | 3990 | 0.4679 | 0.3832 | 0.9985 | 0.9949 | 0.0183 | 0.6891 | 0.7332 | 0.9949 | | 0.0114 | 8.15 | 4020 | 0.4465 | 0.3838 | 0.9989 | 0.9952 | 0.0178 | 0.6895 | 0.7227 | 0.9952 | | 0.0091 | 8.22 | 4050 | 0.5417 | 0.4095 | 0.9978 | 0.9947 | 0.0181 | 0.7021 | 0.7698 | 0.9948 | | 0.0058 | 8.28 | 4080 | 0.5075 | 0.4157 | 0.9985 | 0.9952 | 0.0173 | 0.7054 | 0.7530 | 0.9952 | | 0.0057 | 8.34 | 4110 | 0.5139 | 0.4156 | 0.9984 | 0.9951 | 0.0171 | 0.7054 | 0.7562 | 0.9951 | | 0.0196 | 8.4 | 4140 | 0.3755 | 0.3357 | 0.9992 | 0.9950 | 0.0176 | 0.6653 | 0.6874 | 0.9950 | | 0.0019 | 8.46 | 4170 | 0.4160 | 0.3638 | 0.9990 | 0.9951 | 0.0175 | 0.6794 | 0.7075 | 0.9951 | | 0.004 | 8.52 | 4200 | 0.3155 | 0.2948 | 0.9995 | 0.9949 | 0.0179 | 0.6449 | 0.6575 | 0.9949 | | 0.015 | 8.58 | 4230 | 0.3264 | 0.3041 | 0.9995 | 0.9950 | 0.0180 | 0.6495 | 0.6630 | 0.9950 | | 0.0024 | 8.64 | 4260 | 0.3284 | 0.3059 | 0.9995 | 0.9950 | 0.0183 | 0.6504 | 0.6639 | 0.9950 | | 0.0367 | 8.7 | 4290 | 0.4347 | 0.3764 | 0.9990 | 0.9952 | 0.0172 | 0.6858 | 0.7168 | 0.9952 | | 0.0126 | 8.76 | 4320 | 0.4370 | 0.3820 | 0.9990 | 0.9952 | 0.0171 | 0.6886 | 0.7180 | 0.9953 | | 0.0046 | 8.82 | 4350 | 0.4161 | 0.3627 | 0.9990 | 0.9951 | 0.0176 | 0.6789 | 0.7076 | 0.9951 | | 0.0041 | 8.88 | 4380 | 0.5022 | 0.4108 | 0.9985 | 0.9951 | 0.0178 | 0.7030 | 0.7503 | 0.9952 | | 0.0082 | 8.95 | 4410 | 0.3560 | 0.3239 | 0.9993 | 0.9950 | 0.0184 | 0.6594 | 0.6776 | 0.9950 | | 0.0072 | 9.01 | 4440 | 0.5267 | 0.4208 | 0.9983 | 0.9951 | 0.0181 | 0.7080 | 0.7625 | 0.9951 | | 0.017 | 9.07 | 4470 | 0.3270 | 0.3061 | 0.9995 | 0.9950 | 0.0178 | 0.6506 | 0.6633 | 0.9950 | | 0.019 | 9.13 | 4500 | 0.4695 | 0.4015 | 0.9989 | 0.9953 | 0.0172 | 0.6984 | 0.7342 | 0.9953 | | 0.0029 | 9.19 | 4530 | 0.4155 | 0.3695 | 0.9992 | 0.9952 | 0.0167 | 0.6824 | 0.7073 | 0.9952 | | 0.0077 | 9.25 | 4560 | 0.4622 | 0.3910 | 0.9988 | 0.9952 | 0.0169 | 0.6931 | 0.7305 | 0.9952 | | 0.0021 | 9.31 | 4590 | 0.5527 | 0.4208 | 0.9979 | 0.9949 | 0.0176 | 0.7079 | 0.7753 | 0.9949 | | 0.0076 | 9.37 | 4620 | 0.3425 | 0.3159 | 0.9994 | 0.9950 | 0.0174 | 0.6555 | 0.6709 | 0.9950 | | 0.0216 | 9.43 | 4650 | 0.4100 | 0.3655 | 0.9992 | 0.9952 | 0.0174 | 0.6804 | 0.7046 | 0.9952 | | 0.0013 | 9.49 | 4680 | 0.3184 | 0.2920 | 0.9994 | 0.9948 | 0.0190 | 0.6434 | 0.6589 | 0.9948 | | 0.0025 | 9.55 | 4710 | 0.4960 | 0.4125 | 0.9986 | 0.9952 | 0.0170 | 0.7039 | 0.7473 | 0.9953 | | 0.0139 | 9.61 | 4740 | 0.4090 | 0.3646 | 0.9992 | 0.9952 | 0.0173 | 0.6799 | 0.7041 | 0.9952 | | 0.0095 | 9.68 | 4770 | 0.4806 | 0.4038 | 0.9987 | 0.9952 | 0.0173 | 0.6995 | 0.7397 | 0.9952 | | 0.0167 | 9.74 | 4800 | 0.6091 | 0.4088 | 0.9967 | 0.9941 | 0.0195 | 0.7014 | 0.8029 | 0.9941 | | 0.0025 | 9.8 | 4830 | 0.4785 | 0.4023 | 0.9987 | 0.9952 | 0.0170 | 0.6987 | 0.7386 | 0.9952 | | 0.0054 | 9.86 | 4860 | 0.2847 | 0.2681 | 0.9996 | 0.9948 | 0.0183 | 0.6315 | 0.6422 | 0.9948 | | 0.0107 | 9.92 | 4890 | 0.3816 | 0.3482 | 0.9994 | 0.9952 | 0.0178 | 0.6717 | 0.6905 | 0.9952 | | 0.0039 | 9.98 | 4920 | 0.3384 | 0.3163 | 0.9995 | 0.9951 | 0.0175 | 0.6557 | 0.6690 | 0.9951 | | 0.0028 | 10.04 | 4950 | 0.3742 | 0.3350 | 0.9992 | 0.9950 | 0.0182 | 0.6650 | 0.6867 | 0.9950 | | 0.006 | 10.1 | 4980 | 0.3311 | 0.3071 | 0.9995 | 0.9950 | 0.0187 | 0.6510 | 0.6653 | 0.9950 | | 0.0077 | 10.16 | 5010 | 0.3679 | 0.3369 | 0.9994 | 0.9951 | 0.0179 | 0.6660 | 0.6836 | 0.9951 | | 0.0015 | 10.22 | 5040 | 0.3777 | 0.3462 | 0.9994 | 0.9952 | 0.0177 | 0.6707 | 0.6885 | 0.9952 | | 0.004 | 10.28 | 5070 | 0.3744 | 0.3442 | 0.9994 | 0.9952 | 0.0178 | 0.6697 | 0.6869 | 0.9952 | | 0.0071 | 10.34 | 5100 | 0.5113 | 0.4167 | 0.9985 | 0.9952 | 0.0176 | 0.7060 | 0.7549 | 0.9952 | | 0.0095 | 10.41 | 5130 | 0.3498 | 0.3191 | 0.9993 | 0.9950 | 0.0182 | 0.6570 | 0.6746 | 0.9950 | | 0.0031 | 10.47 | 5160 | 0.4331 | 0.3818 | 0.9991 | 0.9953 | 0.0175 | 0.6885 | 0.7161 | 0.9953 | | 0.0044 | 10.53 | 5190 | 0.4276 | 0.3762 | 0.9991 | 0.9952 | 0.0173 | 0.6857 | 0.7133 | 0.9952 | | 0.0041 | 10.59 | 5220 | 0.4177 | 0.3680 | 0.9991 | 0.9952 | 0.0183 | 0.6816 | 0.7084 | 0.9952 | | 0.0047 | 10.65 | 5250 | 0.4054 | 0.3648 | 0.9992 | 0.9952 | 0.0175 | 0.6800 | 0.7023 | 0.9953 | | 0.0072 | 10.71 | 5280 | 0.4657 | 0.4011 | 0.9989 | 0.9953 | 0.0172 | 0.6982 | 0.7323 | 0.9953 | | 0.0026 | 10.77 | 5310 | 0.5030 | 0.4209 | 0.9987 | 0.9953 | 0.0172 | 0.7081 | 0.7509 | 0.9954 | | 0.0025 | 10.83 | 5340 | 0.5024 | 0.4142 | 0.9986 | 0.9952 | 0.0177 | 0.7047 | 0.7505 | 0.9952 | | 0.0182 | 10.89 | 5370 | 0.4298 | 0.3824 | 0.9992 | 0.9953 | 0.0174 | 0.6889 | 0.7145 | 0.9953 | | 0.0019 | 10.95 | 5400 | 0.4940 | 0.4172 | 0.9988 | 0.9954 | 0.0175 | 0.7063 | 0.7464 | 0.9954 | | 0.0106 | 11.01 | 5430 | 0.2958 | 0.2791 | 0.9996 | 0.9949 | 0.0188 | 0.6370 | 0.6477 | 0.9949 | | 0.0053 | 11.08 | 5460 | 0.5134 | 0.4229 | 0.9986 | 0.9953 | 0.0161 | 0.7091 | 0.7560 | 0.9953 | | 0.0095 | 11.14 | 5490 | 0.4817 | 0.3820 | 0.9982 | 0.9948 | 0.0181 | 0.6884 | 0.7400 | 0.9948 | | 0.0074 | 11.2 | 5520 | 0.3901 | 0.3532 | 0.9993 | 0.9952 | 0.0178 | 0.6742 | 0.6947 | 0.9952 | | 0.006 | 11.26 | 5550 | 0.4780 | 0.4037 | 0.9988 | 0.9952 | 0.0176 | 0.6995 | 0.7384 | 0.9953 | | 0.0128 | 11.32 | 5580 | 0.3996 | 0.3579 | 0.9992 | 0.9952 | 0.0162 | 0.6765 | 0.6994 | 0.9952 | | 0.0129 | 11.38 | 5610 | 0.5160 | 0.4236 | 0.9985 | 0.9953 | 0.0168 | 0.7094 | 0.7573 | 0.9953 | | 0.0139 | 11.44 | 5640 | 0.4689 | 0.4012 | 0.9989 | 0.9953 | 0.0168 | 0.6982 | 0.7339 | 0.9953 | | 0.0115 | 11.5 | 5670 | 0.3573 | 0.3294 | 0.9994 | 0.9951 | 0.0181 | 0.6623 | 0.6784 | 0.9951 | | 0.0028 | 11.56 | 5700 | 0.3349 | 0.3133 | 0.9995 | 0.9951 | 0.0178 | 0.6542 | 0.6672 | 0.9951 | | 0.0058 | 11.62 | 5730 | 0.5205 | 0.4313 | 0.9986 | 0.9954 | 0.0170 | 0.7134 | 0.7595 | 0.9954 | | 0.0046 | 11.68 | 5760 | 0.4486 | 0.3942 | 0.9991 | 0.9954 | 0.0170 | 0.6948 | 0.7238 | 0.9954 | | 0.004 | 11.74 | 5790 | 0.3965 | 0.3591 | 0.9993 | 0.9952 | 0.0172 | 0.6772 | 0.6979 | 0.9952 | | 0.0222 | 11.81 | 5820 | 0.4083 | 0.3670 | 0.9992 | 0.9953 | 0.0176 | 0.6811 | 0.7038 | 0.9953 | | 0.0048 | 11.87 | 5850 | 0.4489 | 0.3904 | 0.9990 | 0.9953 | 0.0175 | 0.6929 | 0.7239 | 0.9953 | | 0.0025 | 11.93 | 5880 | 0.3839 | 0.3497 | 0.9993 | 0.9952 | 0.0181 | 0.6724 | 0.6916 | 0.9952 | | 0.0127 | 11.99 | 5910 | 0.4677 | 0.4063 | 0.9990 | 0.9954 | 0.0174 | 0.7008 | 0.7334 | 0.9954 | | 0.0201 | 12.05 | 5940 | 0.4691 | 0.4053 | 0.9989 | 0.9954 | 0.0175 | 0.7003 | 0.7340 | 0.9954 | | 0.0181 | 12.11 | 5970 | 0.2999 | 0.2851 | 0.9996 | 0.9949 | 0.0201 | 0.6400 | 0.6498 | 0.9950 | | 0.0043 | 12.17 | 6000 | 0.3289 | 0.3005 | 0.9994 | 0.9948 | 0.0184 | 0.6477 | 0.6641 | 0.9949 | | 0.0108 | 12.23 | 6030 | 0.3651 | 0.3365 | 0.9994 | 0.9952 | 0.0170 | 0.6658 | 0.6823 | 0.9952 | | 0.006 | 12.29 | 6060 | 0.4369 | 0.3833 | 0.9991 | 0.9953 | 0.0171 | 0.6893 | 0.7180 | 0.9953 | | 0.0041 | 12.35 | 6090 | 0.3919 | 0.3542 | 0.9993 | 0.9952 | 0.0174 | 0.6747 | 0.6956 | 0.9952 | | 0.0034 | 12.41 | 6120 | 0.4418 | 0.3914 | 0.9991 | 0.9954 | 0.0168 | 0.6934 | 0.7205 | 0.9954 | | 0.0007 | 12.47 | 6150 | 0.4338 | 0.3864 | 0.9992 | 0.9954 | 0.0167 | 0.6909 | 0.7165 | 0.9954 | | 0.0048 | 12.54 | 6180 | 0.4303 | 0.3839 | 0.9992 | 0.9954 | 0.0174 | 0.6896 | 0.7147 | 0.9954 | | 0.0078 | 12.6 | 6210 | 0.3482 | 0.3178 | 0.9994 | 0.9950 | 0.0185 | 0.6564 | 0.6738 | 0.9950 | | 0.0038 | 12.66 | 6240 | 0.2892 | 0.2742 | 0.9996 | 0.9949 | 0.0184 | 0.6345 | 0.6444 | 0.9949 | | 0.0078 | 12.72 | 6270 | 0.4238 | 0.3712 | 0.9990 | 0.9952 | 0.0180 | 0.6832 | 0.7114 | 0.9952 | | 0.0013 | 12.78 | 6300 | 0.3916 | 0.3584 | 0.9994 | 0.9953 | 0.0175 | 0.6768 | 0.6955 | 0.9953 | | 0.0025 | 12.84 | 6330 | 0.3016 | 0.2810 | 0.9995 | 0.9948 | 0.0195 | 0.6379 | 0.6505 | 0.9948 | | 0.0033 | 12.9 | 6360 | 0.4662 | 0.4071 | 0.9990 | 0.9954 | 0.0168 | 0.7013 | 0.7326 | 0.9954 | | 0.0225 | 12.96 | 6390 | 0.5055 | 0.4238 | 0.9987 | 0.9954 | 0.0169 | 0.7096 | 0.7521 | 0.9954 | | 0.0081 | 13.02 | 6420 | 0.2816 | 0.2664 | 0.9996 | 0.9948 | 0.0184 | 0.6306 | 0.6406 | 0.9948 | | 0.0032 | 13.08 | 6450 | 0.4263 | 0.3805 | 0.9992 | 0.9953 | 0.0175 | 0.6879 | 0.7128 | 0.9953 | | 0.0139 | 13.14 | 6480 | 0.3732 | 0.3428 | 0.9994 | 0.9952 | 0.0157 | 0.6690 | 0.6863 | 0.9952 | | 0.003 | 13.2 | 6510 | 0.3906 | 0.3565 | 0.9994 | 0.9953 | 0.0162 | 0.6759 | 0.6950 | 0.9953 | | 0.0098 | 13.27 | 6540 | 0.4435 | 0.3848 | 0.9990 | 0.9952 | 0.0168 | 0.6900 | 0.7212 | 0.9952 | | 0.0144 | 13.33 | 6570 | 0.4259 | 0.3729 | 0.9990 | 0.9952 | 0.0167 | 0.6840 | 0.7125 | 0.9952 | | 0.007 | 13.39 | 6600 | 0.4175 | 0.3731 | 0.9992 | 0.9953 | 0.0165 | 0.6842 | 0.7084 | 0.9953 | | 0.0012 | 13.45 | 6630 | 0.4576 | 0.4013 | 0.9991 | 0.9954 | 0.0161 | 0.6983 | 0.7283 | 0.9954 | | 0.0032 | 13.51 | 6660 | 0.3585 | 0.3310 | 0.9994 | 0.9951 | 0.0174 | 0.6631 | 0.6790 | 0.9951 | | 0.0077 | 13.57 | 6690 | 0.4255 | 0.3817 | 0.9992 | 0.9954 | 0.0164 | 0.6885 | 0.7124 | 0.9954 | | 0.002 | 13.63 | 6720 | 0.3523 | 0.3248 | 0.9994 | 0.9951 | 0.0175 | 0.6599 | 0.6759 | 0.9951 | | 0.0037 | 13.69 | 6750 | 0.4441 | 0.3920 | 0.9991 | 0.9954 | 0.0164 | 0.6937 | 0.7216 | 0.9954 | | 0.0043 | 13.75 | 6780 | 0.4799 | 0.4152 | 0.9989 | 0.9954 | 0.0165 | 0.7053 | 0.7394 | 0.9955 | | 0.008 | 13.81 | 6810 | 0.4662 | 0.3999 | 0.9989 | 0.9953 | 0.0165 | 0.6976 | 0.7326 | 0.9953 | | 0.0066 | 13.87 | 6840 | 0.4061 | 0.3643 | 0.9992 | 0.9952 | 0.0167 | 0.6798 | 0.7027 | 0.9952 | | 0.0061 | 13.94 | 6870 | 0.3404 | 0.3158 | 0.9995 | 0.9950 | 0.0175 | 0.6554 | 0.6699 | 0.9950 | | 0.0098 | 14.0 | 6900 | 0.5015 | 0.4226 | 0.9987 | 0.9954 | 0.0166 | 0.7090 | 0.7501 | 0.9954 | | 0.0067 | 14.06 | 6930 | 0.4454 | 0.3921 | 0.9991 | 0.9954 | 0.0167 | 0.6937 | 0.7222 | 0.9954 | | 0.0125 | 14.12 | 6960 | 0.4326 | 0.3792 | 0.9990 | 0.9952 | 0.0166 | 0.6872 | 0.7158 | 0.9952 | | 0.0032 | 14.18 | 6990 | 0.4498 | 0.3966 | 0.9991 | 0.9954 | 0.0169 | 0.6960 | 0.7244 | 0.9954 | | 0.0018 | 14.24 | 7020 | 0.3867 | 0.3527 | 0.9993 | 0.9952 | 0.0174 | 0.6740 | 0.6930 | 0.9952 | | 0.0022 | 14.3 | 7050 | 0.5163 | 0.4336 | 0.9987 | 0.9955 | 0.0168 | 0.7145 | 0.7575 | 0.9955 | | 0.0024 | 14.36 | 7080 | 0.4913 | 0.4189 | 0.9988 | 0.9954 | 0.0168 | 0.7071 | 0.7451 | 0.9954 | | 0.0037 | 14.42 | 7110 | 0.4814 | 0.4053 | 0.9987 | 0.9952 | 0.0170 | 0.7003 | 0.7401 | 0.9953 | | 0.0034 | 14.48 | 7140 | 0.4541 | 0.3577 | 0.9982 | 0.9945 | 0.0196 | 0.6761 | 0.7262 | 0.9945 | | 0.005 | 14.54 | 7170 | 0.4347 | 0.3862 | 0.9992 | 0.9953 | 0.0170 | 0.6908 | 0.7169 | 0.9954 | | 0.0019 | 14.6 | 7200 | 0.4984 | 0.4203 | 0.9987 | 0.9954 | 0.0170 | 0.7078 | 0.7486 | 0.9954 | | 0.0034 | 14.67 | 7230 | 0.2893 | 0.2756 | 0.9997 | 0.9949 | 0.0194 | 0.6353 | 0.6445 | 0.9949 | | 0.003 | 14.73 | 7260 | 0.4117 | 0.3725 | 0.9993 | 0.9953 | 0.0171 | 0.6839 | 0.7055 | 0.9953 | | 0.0081 | 14.79 | 7290 | 0.4518 | 0.3980 | 0.9991 | 0.9954 | 0.0169 | 0.6967 | 0.7254 | 0.9954 | | 0.0087 | 14.85 | 7320 | 0.4720 | 0.4094 | 0.9990 | 0.9954 | 0.0170 | 0.7024 | 0.7355 | 0.9954 | | 0.0051 | 14.91 | 7350 | 0.3505 | 0.3257 | 0.9995 | 0.9951 | 0.0173 | 0.6604 | 0.6750 | 0.9951 | | 0.0029 | 14.97 | 7380 | 0.2699 | 0.2504 | 0.9995 | 0.9946 | 0.0183 | 0.6225 | 0.6347 | 0.9946 | | 0.0014 | 15.03 | 7410 | 0.4490 | 0.3937 | 0.9991 | 0.9953 | 0.0150 | 0.6945 | 0.7240 | 0.9954 | | 0.0067 | 15.09 | 7440 | 0.3776 | 0.3474 | 0.9994 | 0.9952 | 0.0162 | 0.6713 | 0.6885 | 0.9952 | | 0.0013 | 15.15 | 7470 | 0.2608 | 0.2514 | 0.9997 | 0.9948 | 0.0191 | 0.6231 | 0.6303 | 0.9948 | | 0.0024 | 15.21 | 7500 | 0.4044 | 0.3670 | 0.9993 | 0.9953 | 0.0172 | 0.6811 | 0.7018 | 0.9953 | | 0.0255 | 15.27 | 7530 | 0.4400 | 0.3799 | 0.9989 | 0.9952 | 0.0169 | 0.6875 | 0.7195 | 0.9952 | | 0.0016 | 15.33 | 7560 | 0.5440 | 0.4439 | 0.9985 | 0.9954 | 0.0168 | 0.7196 | 0.7712 | 0.9954 | | 0.0153 | 15.4 | 7590 | 0.5414 | 0.4439 | 0.9985 | 0.9954 | 0.0165 | 0.7197 | 0.7699 | 0.9954 | | 0.015 | 15.46 | 7620 | 0.3053 | 0.2900 | 0.9996 | 0.9950 | 0.0185 | 0.6425 | 0.6524 | 0.9950 | | 0.0047 | 15.52 | 7650 | 0.5346 | 0.4336 | 0.9984 | 0.9953 | 0.0170 | 0.7144 | 0.7665 | 0.9953 | | 0.0086 | 15.58 | 7680 | 0.5241 | 0.4292 | 0.9985 | 0.9953 | 0.0172 | 0.7122 | 0.7613 | 0.9953 | | 0.0103 | 15.64 | 7710 | 0.3444 | 0.3208 | 0.9995 | 0.9951 | 0.0177 | 0.6580 | 0.6719 | 0.9951 | | 0.0091 | 15.7 | 7740 | 0.3906 | 0.3555 | 0.9993 | 0.9952 | 0.0173 | 0.6753 | 0.6949 | 0.9952 | | 0.0024 | 15.76 | 7770 | 0.3728 | 0.3452 | 0.9995 | 0.9952 | 0.0182 | 0.6702 | 0.6862 | 0.9953 | | 0.0061 | 15.82 | 7800 | 0.4315 | 0.3839 | 0.9992 | 0.9953 | 0.0158 | 0.6896 | 0.7153 | 0.9954 | | 0.0077 | 15.88 | 7830 | 0.4274 | 0.3831 | 0.9992 | 0.9954 | 0.0160 | 0.6892 | 0.7133 | 0.9954 | | 0.0072 | 15.94 | 7860 | 0.4330 | 0.3841 | 0.9991 | 0.9953 | 0.0160 | 0.6897 | 0.7161 | 0.9953 | | 0.009 | 16.0 | 7890 | 0.3586 | 0.3333 | 0.9995 | 0.9952 | 0.0163 | 0.6642 | 0.6790 | 0.9952 | | 0.0092 | 16.06 | 7920 | 0.4129 | 0.3706 | 0.9992 | 0.9953 | 0.0161 | 0.6829 | 0.7060 | 0.9953 | | 0.0032 | 16.13 | 7950 | 0.4836 | 0.4160 | 0.9989 | 0.9954 | 0.0161 | 0.7057 | 0.7413 | 0.9954 | | 0.0062 | 16.19 | 7980 | 0.4115 | 0.3322 | 0.9984 | 0.9944 | 0.0206 | 0.6633 | 0.7049 | 0.9944 | | 0.0093 | 16.25 | 8010 | 0.3616 | 0.3322 | 0.9994 | 0.9951 | 0.0173 | 0.6637 | 0.6805 | 0.9951 | | 0.001 | 16.31 | 8040 | 0.4858 | 0.4150 | 0.9988 | 0.9954 | 0.0161 | 0.7052 | 0.7423 | 0.9954 | | 0.0132 | 16.37 | 8070 | 0.3209 | 0.2952 | 0.9994 | 0.9948 | 0.0188 | 0.6450 | 0.6602 | 0.9949 | | 0.0016 | 16.43 | 8100 | 0.4150 | 0.3742 | 0.9993 | 0.9953 | 0.0166 | 0.6848 | 0.7071 | 0.9953 | | 0.0007 | 16.49 | 8130 | 0.4981 | 0.3988 | 0.9983 | 0.9949 | 0.0185 | 0.6969 | 0.7482 | 0.9950 | | 0.002 | 16.55 | 8160 | 0.3659 | 0.3385 | 0.9995 | 0.9952 | 0.0184 | 0.6668 | 0.6827 | 0.9952 | | 0.0074 | 16.61 | 8190 | 0.4173 | 0.3763 | 0.9993 | 0.9953 | 0.0166 | 0.6858 | 0.7083 | 0.9954 | | 0.0048 | 16.67 | 8220 | 0.3306 | 0.3108 | 0.9996 | 0.9951 | 0.0179 | 0.6529 | 0.6651 | 0.9951 | | 0.0028 | 16.73 | 8250 | 0.4462 | 0.3932 | 0.9991 | 0.9954 | 0.0166 | 0.6943 | 0.7227 | 0.9954 | | 0.0135 | 16.8 | 8280 | 0.3967 | 0.3617 | 0.9993 | 0.9953 | 0.0172 | 0.6785 | 0.6980 | 0.9953 | | 0.0138 | 16.86 | 8310 | 0.3657 | 0.3396 | 0.9995 | 0.9952 | 0.0173 | 0.6674 | 0.6826 | 0.9952 | | 0.0017 | 16.92 | 8340 | 0.3303 | 0.3061 | 0.9995 | 0.9950 | 0.0191 | 0.6505 | 0.6649 | 0.9950 | | 0.0071 | 16.98 | 8370 | 0.3825 | 0.3512 | 0.9994 | 0.9952 | 0.0174 | 0.6732 | 0.6909 | 0.9953 | | 0.0044 | 17.04 | 8400 | 0.4303 | 0.3849 | 0.9992 | 0.9954 | 0.0171 | 0.6901 | 0.7148 | 0.9954 | | 0.0101 | 17.1 | 8430 | 0.4256 | 0.3771 | 0.9991 | 0.9953 | 0.0172 | 0.6862 | 0.7123 | 0.9953 | | 0.0076 | 17.16 | 8460 | 0.4438 | 0.3939 | 0.9991 | 0.9954 | 0.0168 | 0.6947 | 0.7215 | 0.9954 | | 0.0073 | 17.22 | 8490 | 0.5011 | 0.4236 | 0.9988 | 0.9954 | 0.0164 | 0.7095 | 0.7500 | 0.9954 | | 0.0029 | 17.28 | 8520 | 0.2741 | 0.2627 | 0.9997 | 0.9948 | 0.0177 | 0.6288 | 0.6369 | 0.9948 | | 0.0109 | 17.34 | 8550 | 0.4682 | 0.4074 | 0.9990 | 0.9954 | 0.0160 | 0.7014 | 0.7336 | 0.9954 | | 0.0258 | 17.4 | 8580 | 0.4508 | 0.3901 | 0.9989 | 0.9953 | 0.0166 | 0.6927 | 0.7249 | 0.9953 | | 0.0181 | 17.46 | 8610 | 0.4780 | 0.4058 | 0.9988 | 0.9953 | 0.0168 | 0.7006 | 0.7384 | 0.9953 | | 0.0052 | 17.53 | 8640 | 0.3767 | 0.3447 | 0.9994 | 0.9952 | 0.0177 | 0.6699 | 0.6880 | 0.9952 | | 0.0069 | 17.59 | 8670 | 0.3409 | 0.3125 | 0.9994 | 0.9950 | 0.0187 | 0.6537 | 0.6702 | 0.9950 | | 0.004 | 17.65 | 8700 | 0.3400 | 0.3172 | 0.9995 | 0.9951 | 0.0189 | 0.6561 | 0.6697 | 0.9951 | | 0.0061 | 17.71 | 8730 | 0.3931 | 0.3594 | 0.9994 | 0.9953 | 0.0183 | 0.6773 | 0.6962 | 0.9953 | | 0.0038 | 17.77 | 8760 | 0.2883 | 0.2749 | 0.9997 | 0.9949 | 0.0195 | 0.6349 | 0.6440 | 0.9949 | | 0.0021 | 17.83 | 8790 | 0.3568 | 0.3300 | 0.9995 | 0.9951 | 0.0182 | 0.6626 | 0.6781 | 0.9951 | | 0.0019 | 17.89 | 8820 | 0.5072 | 0.4183 | 0.9986 | 0.9952 | 0.0176 | 0.7068 | 0.7529 | 0.9953 | | 0.0006 | 17.95 | 8850 | 0.3592 | 0.3334 | 0.9995 | 0.9952 | 0.0182 | 0.6643 | 0.6793 | 0.9952 | | 0.0031 | 18.01 | 8880 | 0.3860 | 0.3439 | 0.9992 | 0.9950 | 0.0182 | 0.6695 | 0.6926 | 0.9951 | | 0.003 | 18.07 | 8910 | 0.2987 | 0.2766 | 0.9995 | 0.9947 | 0.0198 | 0.6357 | 0.6491 | 0.9948 | | 0.0033 | 18.13 | 8940 | 0.4374 | 0.3876 | 0.9991 | 0.9953 | 0.0173 | 0.6915 | 0.7182 | 0.9954 | | 0.0067 | 18.19 | 8970 | 0.5144 | 0.4266 | 0.9986 | 0.9953 | 0.0176 | 0.7110 | 0.7565 | 0.9954 | | 0.0056 | 18.26 | 9000 | 0.3742 | 0.3451 | 0.9994 | 0.9952 | 0.0179 | 0.6701 | 0.6868 | 0.9952 | | 0.0008 | 18.32 | 9030 | 0.3526 | 0.3308 | 0.9996 | 0.9952 | 0.0178 | 0.6630 | 0.6761 | 0.9952 | | 0.0038 | 18.38 | 9060 | 0.4611 | 0.4051 | 0.9991 | 0.9954 | 0.0176 | 0.7003 | 0.7301 | 0.9955 | | 0.005 | 18.44 | 9090 | 0.4017 | 0.3636 | 0.9993 | 0.9953 | 0.0183 | 0.6794 | 0.7005 | 0.9953 | | 0.0028 | 18.5 | 9120 | 0.4492 | 0.3950 | 0.9991 | 0.9954 | 0.0159 | 0.6952 | 0.7241 | 0.9954 | | 0.0055 | 18.56 | 9150 | 0.5008 | 0.4227 | 0.9988 | 0.9954 | 0.0154 | 0.7090 | 0.7498 | 0.9954 | | 0.0057 | 18.62 | 9180 | 0.3915 | 0.3568 | 0.9993 | 0.9952 | 0.0159 | 0.6760 | 0.6954 | 0.9953 | | 0.0081 | 18.68 | 9210 | 0.4314 | 0.3832 | 0.9991 | 0.9953 | 0.0159 | 0.6892 | 0.7153 | 0.9953 | | 0.0051 | 18.74 | 9240 | 0.3941 | 0.3605 | 0.9994 | 0.9953 | 0.0161 | 0.6779 | 0.6967 | 0.9953 | | 0.0053 | 18.8 | 9270 | 0.3960 | 0.3606 | 0.9993 | 0.9953 | 0.0165 | 0.6779 | 0.6977 | 0.9953 | | 0.0171 | 18.86 | 9300 | 0.4220 | 0.3768 | 0.9992 | 0.9953 | 0.0165 | 0.6861 | 0.7106 | 0.9953 | | 0.0026 | 18.92 | 9330 | 0.4337 | 0.3876 | 0.9992 | 0.9954 | 0.0166 | 0.6915 | 0.7165 | 0.9954 | | 0.0188 | 18.99 | 9360 | 0.4283 | 0.3835 | 0.9992 | 0.9954 | 0.0168 | 0.6894 | 0.7138 | 0.9954 | | 0.0062 | 19.05 | 9390 | 0.4129 | 0.3726 | 0.9993 | 0.9953 | 0.0166 | 0.6840 | 0.7061 | 0.9953 | | 0.0136 | 19.11 | 9420 | 0.4603 | 0.4005 | 0.9990 | 0.9954 | 0.0166 | 0.6979 | 0.7296 | 0.9954 | | 0.005 | 19.17 | 9450 | 0.3971 | 0.3607 | 0.9993 | 0.9953 | 0.0170 | 0.6780 | 0.6982 | 0.9953 | | 0.0106 | 19.23 | 9480 | 0.4597 | 0.3994 | 0.9990 | 0.9953 | 0.0163 | 0.6974 | 0.7293 | 0.9954 | | 0.0045 | 19.29 | 9510 | 0.3873 | 0.3532 | 0.9993 | 0.9952 | 0.0167 | 0.6742 | 0.6933 | 0.9952 | | 0.0015 | 19.35 | 9540 | 0.3104 | 0.2948 | 0.9996 | 0.9950 | 0.0175 | 0.6449 | 0.6550 | 0.9950 | | 0.0049 | 19.41 | 9570 | 0.4511 | 0.3973 | 0.9991 | 0.9954 | 0.0165 | 0.6964 | 0.7251 | 0.9954 | | 0.0023 | 19.47 | 9600 | 0.2654 | 0.2552 | 0.9997 | 0.9948 | 0.0187 | 0.6250 | 0.6326 | 0.9948 | | 0.0079 | 19.53 | 9630 | 0.3844 | 0.3489 | 0.9993 | 0.9952 | 0.0177 | 0.6720 | 0.6918 | 0.9952 | | 0.0112 | 19.59 | 9660 | 0.4717 | 0.4130 | 0.9990 | 0.9955 | 0.0166 | 0.7042 | 0.7353 | 0.9955 | | 0.0021 | 19.66 | 9690 | 0.3977 | 0.3602 | 0.9993 | 0.9952 | 0.0167 | 0.6777 | 0.6985 | 0.9953 | | 0.0056 | 19.72 | 9720 | 0.4466 | 0.3951 | 0.9991 | 0.9954 | 0.0169 | 0.6952 | 0.7229 | 0.9954 | | 0.0089 | 19.78 | 9750 | 0.3270 | 0.3070 | 0.9996 | 0.9950 | 0.0179 | 0.6510 | 0.6633 | 0.9950 | | 0.0076 | 19.84 | 9780 | 0.3947 | 0.3586 | 0.9993 | 0.9952 | 0.0181 | 0.6769 | 0.6970 | 0.9953 | | 0.0012 | 19.9 | 9810 | 0.5196 | 0.4334 | 0.9987 | 0.9954 | 0.0174 | 0.7144 | 0.7592 | 0.9954 | | 0.0074 | 19.96 | 9840 | 0.4607 | 0.4064 | 0.9991 | 0.9955 | 0.0178 | 0.7009 | 0.7299 | 0.9955 | | 0.012 | 20.02 | 9870 | 0.3824 | 0.3508 | 0.9994 | 0.9952 | 0.0186 | 0.6730 | 0.6909 | 0.9952 | | 0.0034 | 20.08 | 9900 | 0.4868 | 0.4136 | 0.9988 | 0.9954 | 0.0176 | 0.7045 | 0.7428 | 0.9954 | | 0.0043 | 20.14 | 9930 | 0.4499 | 0.3988 | 0.9991 | 0.9954 | 0.0181 | 0.6971 | 0.7245 | 0.9954 | | 0.0021 | 20.2 | 9960 | 0.3666 | 0.3397 | 0.9995 | 0.9952 | 0.0162 | 0.6675 | 0.6830 | 0.9952 | | 0.0109 | 20.26 | 9990 | 0.3804 | 0.3483 | 0.9994 | 0.9952 | 0.0156 | 0.6718 | 0.6899 | 0.9952 | | 0.0045 | 20.32 | 10020 | 0.4206 | 0.3782 | 0.9992 | 0.9953 | 0.0152 | 0.6868 | 0.7099 | 0.9954 | | 0.0036 | 20.39 | 10050 | 0.4397 | 0.3924 | 0.9992 | 0.9954 | 0.0157 | 0.6939 | 0.7194 | 0.9954 | | 0.0029 | 20.45 | 10080 | 0.4284 | 0.3800 | 0.9991 | 0.9953 | 0.0155 | 0.6876 | 0.7138 | 0.9953 | | 0.0041 | 20.51 | 10110 | 0.4948 | 0.4209 | 0.9988 | 0.9954 | 0.0159 | 0.7082 | 0.7468 | 0.9954 | | 0.0017 | 20.57 | 10140 | 0.4869 | 0.4188 | 0.9989 | 0.9954 | 0.0157 | 0.7071 | 0.7429 | 0.9955 | | 0.0052 | 20.63 | 10170 | 0.4236 | 0.3800 | 0.9992 | 0.9953 | 0.0160 | 0.6877 | 0.7114 | 0.9954 | | 0.0096 | 20.69 | 10200 | 0.3932 | 0.3608 | 0.9994 | 0.9953 | 0.0161 | 0.6781 | 0.6963 | 0.9953 | | 0.003 | 20.75 | 10230 | 0.4181 | 0.3794 | 0.9993 | 0.9954 | 0.0159 | 0.6874 | 0.7087 | 0.9954 | | 0.0019 | 20.81 | 10260 | 0.3584 | 0.3341 | 0.9995 | 0.9952 | 0.0162 | 0.6646 | 0.6790 | 0.9952 | | 0.0041 | 20.87 | 10290 | 0.3149 | 0.2979 | 0.9996 | 0.9950 | 0.0169 | 0.6465 | 0.6573 | 0.9950 | | 0.0163 | 20.93 | 10320 | 0.4067 | 0.3711 | 0.9994 | 0.9954 | 0.0160 | 0.6833 | 0.7030 | 0.9954 | | 0.0083 | 20.99 | 10350 | 0.4386 | 0.3883 | 0.9991 | 0.9953 | 0.0162 | 0.6918 | 0.7189 | 0.9954 | | 0.0059 | 21.05 | 10380 | 0.4596 | 0.3989 | 0.9990 | 0.9953 | 0.0161 | 0.6971 | 0.7293 | 0.9954 | | 0.0136 | 21.12 | 10410 | 0.4530 | 0.4004 | 0.9991 | 0.9954 | 0.0160 | 0.6979 | 0.7261 | 0.9954 | | 0.0019 | 21.18 | 10440 | 0.3820 | 0.3496 | 0.9994 | 0.9952 | 0.0168 | 0.6724 | 0.6907 | 0.9952 | | 0.0034 | 21.24 | 10470 | 0.5635 | 0.4307 | 0.9979 | 0.9950 | 0.0175 | 0.7128 | 0.7807 | 0.9950 | | 0.0089 | 21.3 | 10500 | 0.4849 | 0.4173 | 0.9989 | 0.9954 | 0.0164 | 0.7064 | 0.7419 | 0.9955 | | 0.0067 | 21.36 | 10530 | 0.3740 | 0.3465 | 0.9995 | 0.9953 | 0.0168 | 0.6709 | 0.6867 | 0.9953 | | 0.0011 | 21.42 | 10560 | 0.4060 | 0.3705 | 0.9994 | 0.9954 | 0.0166 | 0.6829 | 0.7027 | 0.9954 | | 0.0051 | 21.48 | 10590 | 0.4662 | 0.4087 | 0.9990 | 0.9955 | 0.0161 | 0.7021 | 0.7326 | 0.9955 | | 0.0012 | 21.54 | 10620 | 0.4241 | 0.3834 | 0.9993 | 0.9954 | 0.0157 | 0.6894 | 0.7117 | 0.9954 | | 0.0175 | 21.6 | 10650 | 0.3203 | 0.3027 | 0.9996 | 0.9950 | 0.0169 | 0.6489 | 0.6599 | 0.9950 | | 0.0062 | 21.66 | 10680 | 0.4179 | 0.3777 | 0.9993 | 0.9954 | 0.0164 | 0.6865 | 0.7086 | 0.9954 | | 0.001 | 21.72 | 10710 | 0.4611 | 0.4031 | 0.9990 | 0.9954 | 0.0162 | 0.6993 | 0.7301 | 0.9954 | | 0.0007 | 21.78 | 10740 | 0.4893 | 0.4156 | 0.9988 | 0.9954 | 0.0166 | 0.7055 | 0.7440 | 0.9954 | | 0.0074 | 21.85 | 10770 | 0.4030 | 0.3601 | 0.9992 | 0.9952 | 0.0169 | 0.6776 | 0.7011 | 0.9952 | | 0.0064 | 21.91 | 10800 | 0.5096 | 0.4213 | 0.9986 | 0.9953 | 0.0168 | 0.7083 | 0.7541 | 0.9953 | | 0.0052 | 21.97 | 10830 | 0.3540 | 0.3284 | 0.9995 | 0.9951 | 0.0176 | 0.6618 | 0.6767 | 0.9951 | | 0.0075 | 22.03 | 10860 | 0.3808 | 0.3493 | 0.9994 | 0.9952 | 0.0181 | 0.6723 | 0.6901 | 0.9952 | | 0.0013 | 22.09 | 10890 | 0.2467 | 0.2382 | 0.9998 | 0.9947 | 0.0198 | 0.6164 | 0.6232 | 0.9947 | | 0.0055 | 22.15 | 10920 | 0.4700 | 0.4061 | 0.9989 | 0.9954 | 0.0173 | 0.7007 | 0.7345 | 0.9954 | | 0.0061 | 22.21 | 10950 | 0.4901 | 0.4211 | 0.9989 | 0.9955 | 0.0168 | 0.7083 | 0.7445 | 0.9955 | | 0.0004 | 22.27 | 10980 | 0.4764 | 0.4113 | 0.9989 | 0.9954 | 0.0168 | 0.7033 | 0.7377 | 0.9954 | | 0.0138 | 22.33 | 11010 | 0.4446 | 0.3937 | 0.9991 | 0.9954 | 0.0168 | 0.6945 | 0.7219 | 0.9954 | | 0.0094 | 22.39 | 11040 | 0.4146 | 0.3736 | 0.9993 | 0.9953 | 0.0171 | 0.6845 | 0.7069 | 0.9953 | | 0.0019 | 22.45 | 11070 | 0.4256 | 0.3792 | 0.9992 | 0.9953 | 0.0177 | 0.6873 | 0.7124 | 0.9953 | | 0.0061 | 22.52 | 11100 | 0.4614 | 0.4072 | 0.9991 | 0.9955 | 0.0166 | 0.7013 | 0.7303 | 0.9955 | | 0.0248 | 22.58 | 11130 | 0.3462 | 0.3249 | 0.9996 | 0.9952 | 0.0173 | 0.6600 | 0.6729 | 0.9952 | | 0.0038 | 22.64 | 11160 | 0.2986 | 0.2849 | 0.9997 | 0.9950 | 0.0181 | 0.6399 | 0.6491 | 0.9950 | | 0.0086 | 22.7 | 11190 | 0.4815 | 0.4106 | 0.9988 | 0.9953 | 0.0177 | 0.7030 | 0.7402 | 0.9954 | | 0.0024 | 22.76 | 11220 | 0.4619 | 0.4066 | 0.9991 | 0.9955 | 0.0171 | 0.7010 | 0.7305 | 0.9955 | | 0.5688 | 22.82 | 11250 | 0.4511 | 0.3964 | 0.9991 | 0.9954 | 0.0171 | 0.6959 | 0.7251 | 0.9954 | | 0.0245 | 22.88 | 11280 | 0.3592 | 0.3349 | 0.9995 | 0.9952 | 0.0160 | 0.6651 | 0.6794 | 0.9952 | | 0.0042 | 22.94 | 11310 | 0.4520 | 0.3982 | 0.9991 | 0.9954 | 0.0152 | 0.6968 | 0.7256 | 0.9954 | | 0.0071 | 23.0 | 11340 | 0.4718 | 0.3989 | 0.9988 | 0.9952 | 0.0153 | 0.6970 | 0.7353 | 0.9952 | | 0.0072 | 23.06 | 11370 | 0.2602 | 0.2505 | 0.9997 | 0.9948 | 0.0174 | 0.6226 | 0.6300 | 0.9948 | | 0.0018 | 23.12 | 11400 | 0.3847 | 0.3524 | 0.9994 | 0.9952 | 0.0156 | 0.6738 | 0.6921 | 0.9953 | | 0.0059 | 23.18 | 11430 | 0.3958 | 0.3630 | 0.9994 | 0.9953 | 0.0160 | 0.6792 | 0.6976 | 0.9953 | | 0.0061 | 23.25 | 11460 | 0.4973 | 0.4261 | 0.9989 | 0.9955 | 0.0150 | 0.7108 | 0.7481 | 0.9955 | | 0.0064 | 23.31 | 11490 | 0.4901 | 0.4158 | 0.9988 | 0.9954 | 0.0157 | 0.7056 | 0.7444 | 0.9954 | | 0.0038 | 23.37 | 11520 | 0.4829 | 0.4165 | 0.9989 | 0.9954 | 0.0153 | 0.7060 | 0.7409 | 0.9955 | | 0.0071 | 23.43 | 11550 | 0.4206 | 0.3773 | 0.9992 | 0.9953 | 0.0157 | 0.6863 | 0.7099 | 0.9953 | | 0.0038 | 23.49 | 11580 | 0.4850 | 0.4152 | 0.9989 | 0.9954 | 0.0157 | 0.7053 | 0.7419 | 0.9954 | | 0.0067 | 23.55 | 11610 | 0.3487 | 0.3226 | 0.9995 | 0.9951 | 0.0167 | 0.6588 | 0.6741 | 0.9951 | | 0.0153 | 23.61 | 11640 | 0.3981 | 0.3636 | 0.9994 | 0.9953 | 0.0167 | 0.6795 | 0.6988 | 0.9953 | | 0.0051 | 23.67 | 11670 | 0.3383 | 0.3170 | 0.9995 | 0.9951 | 0.0169 | 0.6560 | 0.6689 | 0.9951 | | 0.0046 | 23.73 | 11700 | 0.3682 | 0.3399 | 0.9994 | 0.9952 | 0.0170 | 0.6675 | 0.6838 | 0.9952 | | 0.0067 | 23.79 | 11730 | 0.4416 | 0.3886 | 0.9991 | 0.9953 | 0.0165 | 0.6920 | 0.7203 | 0.9953 | | 0.0064 | 23.85 | 11760 | 0.4353 | 0.3844 | 0.9991 | 0.9953 | 0.0167 | 0.6898 | 0.7172 | 0.9953 | | 0.0154 | 23.91 | 11790 | 0.3648 | 0.3389 | 0.9995 | 0.9952 | 0.0167 | 0.6670 | 0.6821 | 0.9952 | | 0.0024 | 23.98 | 11820 | 0.3661 | 0.3369 | 0.9994 | 0.9951 | 0.0168 | 0.6660 | 0.6828 | 0.9952 | | 0.0033 | 24.04 | 11850 | 0.3889 | 0.3486 | 0.9992 | 0.9951 | 0.0164 | 0.6719 | 0.6941 | 0.9951 | | 0.0008 | 24.1 | 11880 | 0.3108 | 0.2920 | 0.9996 | 0.9949 | 0.0175 | 0.6435 | 0.6552 | 0.9949 | | 0.0122 | 24.16 | 11910 | 0.3765 | 0.3477 | 0.9994 | 0.9952 | 0.0165 | 0.6715 | 0.6880 | 0.9953 | | 0.023 | 24.22 | 11940 | 0.4958 | 0.4147 | 0.9987 | 0.9953 | 0.0159 | 0.7050 | 0.7472 | 0.9953 | | 0.0144 | 24.28 | 11970 | 0.4176 | 0.3776 | 0.9993 | 0.9954 | 0.0159 | 0.6865 | 0.7084 | 0.9954 | | 0.0084 | 24.34 | 12000 | 0.3756 | 0.3454 | 0.9994 | 0.9952 | 0.0162 | 0.6703 | 0.6875 | 0.9952 | | 0.0026 | 24.4 | 12030 | 0.4246 | 0.3801 | 0.9992 | 0.9953 | 0.0158 | 0.6877 | 0.7119 | 0.9954 | | 0.003 | 24.46 | 12060 | 0.4904 | 0.4161 | 0.9988 | 0.9954 | 0.0161 | 0.7057 | 0.7446 | 0.9954 | | 0.0067 | 24.52 | 12090 | 0.4726 | 0.4060 | 0.9989 | 0.9953 | 0.0163 | 0.7007 | 0.7357 | 0.9954 | | 0.01 | 24.58 | 12120 | 0.5012 | 0.4232 | 0.9988 | 0.9954 | 0.0163 | 0.7093 | 0.7500 | 0.9954 | | 0.0047 | 24.65 | 12150 | 0.4273 | 0.3820 | 0.9992 | 0.9953 | 0.0167 | 0.6887 | 0.7132 | 0.9954 | | 0.008 | 24.71 | 12180 | 0.4555 | 0.3992 | 0.9990 | 0.9954 | 0.0165 | 0.6973 | 0.7273 | 0.9954 | | 0.004 | 24.77 | 12210 | 0.4153 | 0.3676 | 0.9991 | 0.9952 | 0.0174 | 0.6814 | 0.7072 | 0.9952 | | 0.0017 | 24.83 | 12240 | 0.4730 | 0.4066 | 0.9989 | 0.9954 | 0.0158 | 0.7010 | 0.7360 | 0.9954 | | 0.0141 | 24.89 | 12270 | 0.4485 | 0.3946 | 0.9991 | 0.9954 | 0.0173 | 0.6950 | 0.7238 | 0.9954 | | 0.018 | 24.95 | 12300 | 0.4337 | 0.3848 | 0.9991 | 0.9953 | 0.0181 | 0.6901 | 0.7164 | 0.9953 | | 0.004 | 25.01 | 12330 | 0.5078 | 0.4322 | 0.9988 | 0.9955 | 0.0171 | 0.7139 | 0.7533 | 0.9955 | | 0.0093 | 25.07 | 12360 | 0.4210 | 0.3783 | 0.9992 | 0.9953 | 0.0172 | 0.6868 | 0.7101 | 0.9954 | | 0.0054 | 25.13 | 12390 | 0.4701 | 0.4077 | 0.9990 | 0.9954 | 0.0174 | 0.7015 | 0.7346 | 0.9954 | | 0.0066 | 25.19 | 12420 | 0.6023 | 0.4358 | 0.9974 | 0.9947 | 0.0189 | 0.7153 | 0.7999 | 0.9948 | | 0.0017 | 25.25 | 12450 | 0.4582 | 0.4010 | 0.9990 | 0.9954 | 0.0182 | 0.6982 | 0.7286 | 0.9954 | | 0.0021 | 25.31 | 12480 | 0.3981 | 0.3630 | 0.9993 | 0.9953 | 0.0184 | 0.6791 | 0.6987 | 0.9953 | | 0.0017 | 25.38 | 12510 | 0.4041 | 0.3635 | 0.9992 | 0.9952 | 0.0181 | 0.6794 | 0.7017 | 0.9952 | | 0.0026 | 25.44 | 12540 | 0.4181 | 0.3758 | 0.9992 | 0.9953 | 0.0184 | 0.6856 | 0.7087 | 0.9953 | | 0.0029 | 25.5 | 12570 | 0.3562 | 0.3329 | 0.9995 | 0.9952 | 0.0191 | 0.6641 | 0.6778 | 0.9952 | | 0.0146 | 25.56 | 12600 | 0.4022 | 0.3673 | 0.9994 | 0.9953 | 0.0185 | 0.6813 | 0.7008 | 0.9953 | | 0.0015 | 25.62 | 12630 | 0.3870 | 0.3565 | 0.9994 | 0.9953 | 0.0166 | 0.6759 | 0.6932 | 0.9953 | | 0.0024 | 25.68 | 12660 | 0.3898 | 0.3570 | 0.9994 | 0.9953 | 0.0171 | 0.6762 | 0.6946 | 0.9953 | | 0.0022 | 25.74 | 12690 | 0.4325 | 0.3888 | 0.9992 | 0.9954 | 0.0176 | 0.6921 | 0.7159 | 0.9954 | | 0.004 | 25.8 | 12720 | 0.4414 | 0.3940 | 0.9992 | 0.9954 | 0.0172 | 0.6947 | 0.7203 | 0.9954 | | 0.0084 | 25.86 | 12750 | 0.4358 | 0.3912 | 0.9992 | 0.9954 | 0.0165 | 0.6933 | 0.7175 | 0.9954 | | 0.0065 | 25.92 | 12780 | 0.3569 | 0.3323 | 0.9995 | 0.9952 | 0.0182 | 0.6638 | 0.6782 | 0.9952 | | 0.0079 | 25.98 | 12810 | 0.4055 | 0.3689 | 0.9993 | 0.9953 | 0.0172 | 0.6821 | 0.7024 | 0.9953 | | 0.005 | 26.04 | 12840 | 0.4212 | 0.3794 | 0.9993 | 0.9954 | 0.0172 | 0.6874 | 0.7102 | 0.9954 | | 0.0007 | 26.11 | 12870 | 0.4125 | 0.3731 | 0.9993 | 0.9953 | 0.0172 | 0.6842 | 0.7059 | 0.9953 | | 0.01 | 26.17 | 12900 | 0.3635 | 0.3363 | 0.9995 | 0.9952 | 0.0181 | 0.6657 | 0.6815 | 0.9952 | | 0.0029 | 26.23 | 12930 | 0.4441 | 0.3959 | 0.9992 | 0.9954 | 0.0172 | 0.6957 | 0.7216 | 0.9955 | | 0.0028 | 26.29 | 12960 | 0.3467 | 0.3259 | 0.9996 | 0.9952 | 0.0181 | 0.6605 | 0.6731 | 0.9952 | | 0.01 | 26.35 | 12990 | 0.3661 | 0.3394 | 0.9995 | 0.9952 | 0.0184 | 0.6673 | 0.6828 | 0.9952 | | 0.0008 | 26.41 | 13020 | 0.4187 | 0.3783 | 0.9993 | 0.9954 | 0.0176 | 0.6868 | 0.7090 | 0.9954 | | 0.0046 | 26.47 | 13050 | 0.3741 | 0.3459 | 0.9994 | 0.9952 | 0.0182 | 0.6705 | 0.6868 | 0.9952 | | 0.0019 | 26.53 | 13080 | 0.3689 | 0.3407 | 0.9994 | 0.9952 | 0.0184 | 0.6680 | 0.6842 | 0.9952 | | 0.0143 | 26.59 | 13110 | 0.3326 | 0.3133 | 0.9996 | 0.9951 | 0.0189 | 0.6542 | 0.6661 | 0.9951 | | 0.0041 | 26.65 | 13140 | 0.4335 | 0.3869 | 0.9992 | 0.9954 | 0.0177 | 0.6911 | 0.7163 | 0.9954 | | 0.005 | 26.71 | 13170 | 0.3742 | 0.3444 | 0.9994 | 0.9952 | 0.0187 | 0.6698 | 0.6868 | 0.9952 | | 0.0019 | 26.77 | 13200 | 0.3227 | 0.3048 | 0.9996 | 0.9950 | 0.0190 | 0.6499 | 0.6611 | 0.9951 | | 0.0006 | 26.84 | 13230 | 0.3938 | 0.3619 | 0.9994 | 0.9953 | 0.0157 | 0.6786 | 0.6966 | 0.9953 | | 0.0096 | 26.9 | 13260 | 0.4391 | 0.3894 | 0.9991 | 0.9954 | 0.0162 | 0.6924 | 0.7191 | 0.9954 | | 0.0028 | 26.96 | 13290 | 0.5010 | 0.4275 | 0.9988 | 0.9955 | 0.0169 | 0.7115 | 0.7499 | 0.9955 | | 0.0067 | 27.02 | 13320 | 0.4395 | 0.3918 | 0.9992 | 0.9954 | 0.0176 | 0.6936 | 0.7193 | 0.9954 | | 0.0124 | 27.08 | 13350 | 0.3646 | 0.3392 | 0.9995 | 0.9952 | 0.0181 | 0.6672 | 0.6820 | 0.9952 | | 0.0016 | 27.14 | 13380 | 0.4036 | 0.3668 | 0.9993 | 0.9953 | 0.0174 | 0.6810 | 0.7015 | 0.9953 | | 0.0069 | 27.2 | 13410 | 0.4628 | 0.4058 | 0.9990 | 0.9954 | 0.0172 | 0.7006 | 0.7309 | 0.9954 | | 0.003 | 27.26 | 13440 | 0.4313 | 0.3868 | 0.9992 | 0.9954 | 0.0162 | 0.6911 | 0.7153 | 0.9954 | | 0.0015 | 27.32 | 13470 | 0.4527 | 0.3979 | 0.9991 | 0.9954 | 0.0165 | 0.6967 | 0.7259 | 0.9954 | | 0.0109 | 27.38 | 13500 | 0.4257 | 0.3809 | 0.9992 | 0.9953 | 0.0169 | 0.6881 | 0.7124 | 0.9954 | | 0.0053 | 27.44 | 13530 | 0.4349 | 0.3895 | 0.9992 | 0.9954 | 0.0170 | 0.6924 | 0.7171 | 0.9954 | | 0.0049 | 27.51 | 13560 | 0.3426 | 0.3183 | 0.9995 | 0.9951 | 0.0175 | 0.6567 | 0.6711 | 0.9951 | | 0.0023 | 27.57 | 13590 | 0.3896 | 0.3572 | 0.9994 | 0.9953 | 0.0163 | 0.6762 | 0.6945 | 0.9953 | | 0.0065 | 27.63 | 13620 | 0.5389 | 0.4445 | 0.9986 | 0.9955 | 0.0162 | 0.7200 | 0.7687 | 0.9955 | | 0.0009 | 27.69 | 13650 | 0.4391 | 0.3864 | 0.9991 | 0.9953 | 0.0177 | 0.6908 | 0.7191 | 0.9953 | | 0.0009 | 27.75 | 13680 | 0.4727 | 0.4134 | 0.9990 | 0.9955 | 0.0177 | 0.7044 | 0.7358 | 0.9955 | | 0.0056 | 27.81 | 13710 | 0.4976 | 0.4227 | 0.9988 | 0.9954 | 0.0174 | 0.7091 | 0.7482 | 0.9954 | | 0.0046 | 27.87 | 13740 | 0.3521 | 0.3289 | 0.9995 | 0.9952 | 0.0183 | 0.6620 | 0.6758 | 0.9952 | | 0.0053 | 27.93 | 13770 | 0.4465 | 0.3968 | 0.9992 | 0.9954 | 0.0175 | 0.6961 | 0.7228 | 0.9954 | | 0.0072 | 27.99 | 13800 | 0.4351 | 0.3902 | 0.9992 | 0.9954 | 0.0177 | 0.6928 | 0.7171 | 0.9954 | | 0.0067 | 28.05 | 13830 | 0.3882 | 0.3585 | 0.9994 | 0.9953 | 0.0176 | 0.6769 | 0.6938 | 0.9953 | | 0.0018 | 28.11 | 13860 | 0.3821 | 0.3486 | 0.9993 | 0.9952 | 0.0177 | 0.6719 | 0.6907 | 0.9952 | | 0.0044 | 28.17 | 13890 | 0.4906 | 0.4144 | 0.9988 | 0.9953 | 0.0182 | 0.7048 | 0.7447 | 0.9953 | | 0.0029 | 28.24 | 13920 | 0.4622 | 0.4051 | 0.9990 | 0.9954 | 0.0173 | 0.7003 | 0.7306 | 0.9954 | | 0.0037 | 28.3 | 13950 | 0.3649 | 0.3392 | 0.9995 | 0.9952 | 0.0182 | 0.6672 | 0.6822 | 0.9952 | | 0.0027 | 28.36 | 13980 | 0.4789 | 0.4142 | 0.9989 | 0.9954 | 0.0176 | 0.7048 | 0.7389 | 0.9955 | | 0.0035 | 28.42 | 14010 | 0.4909 | 0.4227 | 0.9989 | 0.9955 | 0.0174 | 0.7091 | 0.7449 | 0.9955 | | 0.0041 | 28.48 | 14040 | 0.4197 | 0.3795 | 0.9993 | 0.9954 | 0.0179 | 0.6874 | 0.7095 | 0.9954 | | 0.0034 | 28.54 | 14070 | 0.4251 | 0.3841 | 0.9993 | 0.9954 | 0.0177 | 0.6898 | 0.7122 | 0.9954 | | 0.012 | 28.6 | 14100 | 0.4654 | 0.4085 | 0.9991 | 0.9955 | 0.0167 | 0.7020 | 0.7323 | 0.9955 | | 0.0058 | 28.66 | 14130 | 0.3843 | 0.3532 | 0.9994 | 0.9953 | 0.0157 | 0.6742 | 0.6919 | 0.9953 | | 0.0018 | 28.72 | 14160 | 0.4380 | 0.3929 | 0.9992 | 0.9954 | 0.0154 | 0.6942 | 0.7186 | 0.9955 | | 0.0017 | 28.78 | 14190 | 0.4560 | 0.4016 | 0.9991 | 0.9954 | 0.0158 | 0.6985 | 0.7276 | 0.9954 | | 0.0037 | 28.84 | 14220 | 0.4252 | 0.3844 | 0.9993 | 0.9954 | 0.0155 | 0.6899 | 0.7122 | 0.9954 | | 0.0041 | 28.9 | 14250 | 0.4537 | 0.3999 | 0.9991 | 0.9954 | 0.0157 | 0.6977 | 0.7264 | 0.9954 | | 0.0079 | 28.97 | 14280 | 0.3648 | 0.3398 | 0.9995 | 0.9952 | 0.0161 | 0.6675 | 0.6822 | 0.9952 | | 0.0026 | 29.03 | 14310 | 0.4027 | 0.3670 | 0.9993 | 0.9953 | 0.0158 | 0.6811 | 0.7010 | 0.9953 | | 0.0071 | 29.09 | 14340 | 0.4438 | 0.3939 | 0.9991 | 0.9954 | 0.0161 | 0.6946 | 0.7215 | 0.9954 | | 0.0039 | 29.15 | 14370 | 0.4662 | 0.4061 | 0.9990 | 0.9954 | 0.0161 | 0.7007 | 0.7326 | 0.9954 | | 0.0037 | 29.21 | 14400 | 0.3539 | 0.3298 | 0.9995 | 0.9952 | 0.0166 | 0.6625 | 0.6767 | 0.9952 | | 0.0082 | 29.27 | 14430 | 0.4226 | 0.3833 | 0.9993 | 0.9954 | 0.0161 | 0.6894 | 0.7109 | 0.9954 | | 0.0014 | 29.33 | 14460 | 0.4325 | 0.3880 | 0.9992 | 0.9954 | 0.0160 | 0.6917 | 0.7159 | 0.9954 | | 0.0079 | 29.39 | 14490 | 0.4482 | 0.3975 | 0.9991 | 0.9954 | 0.0161 | 0.6965 | 0.7237 | 0.9954 | | 0.0008 | 29.45 | 14520 | 0.3945 | 0.3625 | 0.9994 | 0.9953 | 0.0175 | 0.6789 | 0.6970 | 0.9953 | | 0.0026 | 29.51 | 14550 | 0.4091 | 0.3721 | 0.9993 | 0.9954 | 0.0174 | 0.6837 | 0.7042 | 0.9954 | | 0.0034 | 29.57 | 14580 | 0.4193 | 0.3791 | 0.9993 | 0.9954 | 0.0178 | 0.6872 | 0.7093 | 0.9954 | | 0.0041 | 29.63 | 14610 | 0.4392 | 0.3892 | 0.9991 | 0.9954 | 0.0180 | 0.6923 | 0.7192 | 0.9954 | | 0.0136 | 29.7 | 14640 | 0.4384 | 0.3918 | 0.9992 | 0.9954 | 0.0156 | 0.6936 | 0.7188 | 0.9954 | | 0.004 | 29.76 | 14670 | 0.4131 | 0.3740 | 0.9993 | 0.9953 | 0.0159 | 0.6847 | 0.7062 | 0.9954 | | 0.0045 | 29.82 | 14700 | 0.4490 | 0.3985 | 0.9991 | 0.9954 | 0.0155 | 0.6969 | 0.7241 | 0.9954 | | 0.0013 | 29.88 | 14730 | 0.5070 | 0.4303 | 0.9988 | 0.9955 | 0.0156 | 0.7129 | 0.7529 | 0.9955 | | 0.0097 | 29.94 | 14760 | 0.4140 | 0.3752 | 0.9993 | 0.9954 | 0.0161 | 0.6853 | 0.7066 | 0.9954 | | 0.0175 | 30.0 | 14790 | 0.3519 | 0.3306 | 0.9996 | 0.9952 | 0.0166 | 0.6629 | 0.6758 | 0.9952 | | 0.0063 | 30.06 | 14820 | 0.5193 | 0.4337 | 0.9987 | 0.9954 | 0.0163 | 0.7146 | 0.7590 | 0.9954 | | 0.0017 | 30.12 | 14850 | 0.4257 | 0.3856 | 0.9993 | 0.9954 | 0.0159 | 0.6905 | 0.7125 | 0.9954 | | 0.0021 | 30.18 | 14880 | 0.4579 | 0.4069 | 0.9992 | 0.9955 | 0.0163 | 0.7012 | 0.7285 | 0.9955 | | 0.0119 | 30.24 | 14910 | 0.4442 | 0.3942 | 0.9991 | 0.9954 | 0.0169 | 0.6948 | 0.7217 | 0.9954 | | 0.0032 | 30.3 | 14940 | 0.5246 | 0.4310 | 0.9985 | 0.9953 | 0.0179 | 0.7132 | 0.7616 | 0.9954 | | 0.0001 | 30.37 | 14970 | 0.4919 | 0.4252 | 0.9989 | 0.9955 | 0.0174 | 0.7104 | 0.7454 | 0.9955 | | 0.0031 | 30.43 | 15000 | 0.4361 | 0.3913 | 0.9992 | 0.9954 | 0.0193 | 0.6933 | 0.7177 | 0.9954 | | 0.0195 | 30.49 | 15030 | 0.4252 | 0.3837 | 0.9993 | 0.9954 | 0.0189 | 0.6895 | 0.7122 | 0.9954 | | 0.0053 | 30.55 | 15060 | 0.4938 | 0.4235 | 0.9989 | 0.9955 | 0.0192 | 0.7095 | 0.7464 | 0.9955 | | 0.0077 | 30.61 | 15090 | 0.4642 | 0.4081 | 0.9991 | 0.9955 | 0.0189 | 0.7018 | 0.7316 | 0.9955 | | 0.0024 | 30.67 | 15120 | 0.4152 | 0.3775 | 0.9993 | 0.9954 | 0.0185 | 0.6865 | 0.7073 | 0.9954 | | 0.0148 | 30.73 | 15150 | 0.4397 | 0.3916 | 0.9992 | 0.9954 | 0.0187 | 0.6935 | 0.7194 | 0.9954 | | 0.0036 | 30.79 | 15180 | 0.4450 | 0.3959 | 0.9992 | 0.9954 | 0.0185 | 0.6957 | 0.7221 | 0.9954 | | 0.0029 | 30.85 | 15210 | 0.4752 | 0.4155 | 0.9990 | 0.9955 | 0.0183 | 0.7055 | 0.7371 | 0.9955 | | 0.0094 | 30.91 | 15240 | 0.4414 | 0.3928 | 0.9992 | 0.9954 | 0.0183 | 0.6941 | 0.7203 | 0.9954 | | 0.0027 | 30.97 | 15270 | 0.4677 | 0.4082 | 0.9990 | 0.9954 | 0.0186 | 0.7018 | 0.7334 | 0.9954 | | 0.003 | 31.03 | 15300 | 0.5050 | 0.4270 | 0.9988 | 0.9954 | 0.0185 | 0.7112 | 0.7519 | 0.9954 | | 0.0027 | 31.1 | 15330 | 0.4934 | 0.4153 | 0.9987 | 0.9953 | 0.0190 | 0.7053 | 0.7460 | 0.9953 | | 0.001 | 31.16 | 15360 | 0.4425 | 0.3920 | 0.9991 | 0.9954 | 0.0185 | 0.6937 | 0.7208 | 0.9954 | | 0.0116 | 31.22 | 15390 | 0.4061 | 0.3690 | 0.9993 | 0.9953 | 0.0180 | 0.6822 | 0.7027 | 0.9953 | | 0.0048 | 31.28 | 15420 | 0.4669 | 0.4056 | 0.9990 | 0.9954 | 0.0170 | 0.7005 | 0.7329 | 0.9954 | | 0.0024 | 31.34 | 15450 | 0.4965 | 0.4136 | 0.9986 | 0.9953 | 0.0181 | 0.7044 | 0.7476 | 0.9953 | | 0.0046 | 31.4 | 15480 | 0.4865 | 0.4205 | 0.9989 | 0.9955 | 0.0168 | 0.7080 | 0.7427 | 0.9955 | | 0.0022 | 31.46 | 15510 | 0.4112 | 0.3718 | 0.9993 | 0.9953 | 0.0171 | 0.6836 | 0.7052 | 0.9953 | | 0.0084 | 31.52 | 15540 | 0.4468 | 0.3923 | 0.9991 | 0.9953 | 0.0178 | 0.6938 | 0.7229 | 0.9954 | | 0.0048 | 31.58 | 15570 | 0.3812 | 0.3515 | 0.9994 | 0.9953 | 0.0177 | 0.6734 | 0.6903 | 0.9953 | | 0.0007 | 31.64 | 15600 | 0.4963 | 0.4164 | 0.9987 | 0.9953 | 0.0177 | 0.7058 | 0.7475 | 0.9953 | | 0.0036 | 31.7 | 15630 | 0.4129 | 0.3734 | 0.9993 | 0.9953 | 0.0171 | 0.6843 | 0.7061 | 0.9953 | | 0.0058 | 31.76 | 15660 | 0.4910 | 0.4203 | 0.9989 | 0.9954 | 0.0168 | 0.7079 | 0.7449 | 0.9955 | | 0.0061 | 31.83 | 15690 | 0.4776 | 0.4021 | 0.9987 | 0.9952 | 0.0188 | 0.6987 | 0.7382 | 0.9952 | | 0.0089 | 31.89 | 15720 | 0.4732 | 0.4122 | 0.9990 | 0.9955 | 0.0175 | 0.7038 | 0.7361 | 0.9955 | | 0.0039 | 31.95 | 15750 | 0.4279 | 0.3841 | 0.9992 | 0.9954 | 0.0167 | 0.6897 | 0.7136 | 0.9954 | | 0.0063 | 32.01 | 15780 | 0.4312 | 0.3876 | 0.9992 | 0.9954 | 0.0169 | 0.6915 | 0.7152 | 0.9954 | | 0.0045 | 32.07 | 15810 | 0.4655 | 0.4066 | 0.9990 | 0.9954 | 0.0175 | 0.7010 | 0.7323 | 0.9954 | | 0.0018 | 32.13 | 15840 | 0.4271 | 0.3829 | 0.9992 | 0.9954 | 0.0178 | 0.6892 | 0.7132 | 0.9954 | | 0.0182 | 32.19 | 15870 | 0.4328 | 0.3860 | 0.9992 | 0.9954 | 0.0176 | 0.6907 | 0.7160 | 0.9954 | | 0.0066 | 32.25 | 15900 | 0.4436 | 0.3935 | 0.9991 | 0.9954 | 0.0182 | 0.6944 | 0.7214 | 0.9954 | | 0.0006 | 32.31 | 15930 | 0.4739 | 0.4133 | 0.9990 | 0.9955 | 0.0178 | 0.7044 | 0.7365 | 0.9955 | | 0.0045 | 32.37 | 15960 | 0.4645 | 0.4079 | 0.9991 | 0.9955 | 0.0166 | 0.7017 | 0.7318 | 0.9955 | | 0.0109 | 32.43 | 15990 | 0.4274 | 0.3834 | 0.9992 | 0.9954 | 0.0162 | 0.6894 | 0.7133 | 0.9954 | | 0.0041 | 32.49 | 16020 | 0.3816 | 0.3520 | 0.9994 | 0.9953 | 0.0166 | 0.6736 | 0.6905 | 0.9953 | | 0.0069 | 32.56 | 16050 | 0.4434 | 0.3934 | 0.9991 | 0.9954 | 0.0161 | 0.6944 | 0.7213 | 0.9954 | | 0.006 | 32.62 | 16080 | 0.4011 | 0.3665 | 0.9994 | 0.9953 | 0.0180 | 0.6809 | 0.7002 | 0.9953 | | 0.0011 | 32.68 | 16110 | 0.4647 | 0.4076 | 0.9991 | 0.9955 | 0.0180 | 0.7015 | 0.7319 | 0.9955 | | 0.004 | 32.74 | 16140 | 0.4268 | 0.3837 | 0.9992 | 0.9954 | 0.0186 | 0.6895 | 0.7130 | 0.9954 | | 0.0035 | 32.8 | 16170 | 0.4609 | 0.4083 | 0.9991 | 0.9955 | 0.0181 | 0.7019 | 0.7300 | 0.9955 | | 0.0065 | 32.86 | 16200 | 0.4633 | 0.4065 | 0.9991 | 0.9954 | 0.0177 | 0.7010 | 0.7312 | 0.9955 | | 0.0076 | 32.92 | 16230 | 0.5158 | 0.4170 | 0.9984 | 0.9951 | 0.0193 | 0.7061 | 0.7571 | 0.9952 | | 0.0044 | 32.98 | 16260 | 0.4693 | 0.4100 | 0.9990 | 0.9955 | 0.0178 | 0.7027 | 0.7342 | 0.9955 | | 0.0089 | 33.04 | 16290 | 0.4852 | 0.4169 | 0.9989 | 0.9954 | 0.0182 | 0.7062 | 0.7421 | 0.9954 | | 0.0051 | 33.1 | 16320 | 0.4001 | 0.3625 | 0.9993 | 0.9953 | 0.0195 | 0.6789 | 0.6997 | 0.9953 | | 0.002 | 33.16 | 16350 | 0.4790 | 0.4157 | 0.9990 | 0.9955 | 0.0182 | 0.7056 | 0.7390 | 0.9955 | | 0.0036 | 33.23 | 16380 | 0.4408 | 0.3912 | 0.9991 | 0.9954 | 0.0187 | 0.6933 | 0.7200 | 0.9954 | | 0.0039 | 33.29 | 16410 | 0.4106 | 0.3733 | 0.9993 | 0.9954 | 0.0185 | 0.6843 | 0.7050 | 0.9954 | | 0.0047 | 33.35 | 16440 | 0.4391 | 0.3945 | 0.9992 | 0.9955 | 0.0181 | 0.6950 | 0.7192 | 0.9955 | | 0.0077 | 33.41 | 16470 | 0.4937 | 0.4278 | 0.9990 | 0.9956 | 0.0178 | 0.7117 | 0.7464 | 0.9956 | | 0.0047 | 33.47 | 16500 | 0.4677 | 0.4104 | 0.9991 | 0.9955 | 0.0181 | 0.7029 | 0.7334 | 0.9955 | | 0.0029 | 33.53 | 16530 | 0.4279 | 0.3858 | 0.9993 | 0.9954 | 0.0182 | 0.6906 | 0.7136 | 0.9954 | | 0.0019 | 33.59 | 16560 | 0.4796 | 0.4114 | 0.9989 | 0.9954 | 0.0186 | 0.7034 | 0.7392 | 0.9954 | | 0.0095 | 33.65 | 16590 | 0.4710 | 0.4125 | 0.9990 | 0.9955 | 0.0179 | 0.7040 | 0.7350 | 0.9955 | | 0.0025 | 33.71 | 16620 | 0.4587 | 0.4053 | 0.9991 | 0.9955 | 0.0182 | 0.7004 | 0.7289 | 0.9955 | | 0.0056 | 33.77 | 16650 | 0.4961 | 0.4237 | 0.9988 | 0.9955 | 0.0194 | 0.7096 | 0.7475 | 0.9955 | | 0.0026 | 33.83 | 16680 | 0.4664 | 0.4095 | 0.9991 | 0.9955 | 0.0190 | 0.7025 | 0.7327 | 0.9955 | | 0.0008 | 33.89 | 16710 | 0.4138 | 0.3757 | 0.9993 | 0.9954 | 0.0188 | 0.6855 | 0.7066 | 0.9954 | | 0.0093 | 33.96 | 16740 | 0.4634 | 0.4078 | 0.9991 | 0.9955 | 0.0180 | 0.7017 | 0.7313 | 0.9955 | | 0.0088 | 34.02 | 16770 | 0.4480 | 0.3977 | 0.9991 | 0.9954 | 0.0182 | 0.6966 | 0.7236 | 0.9954 | | 0.004 | 34.08 | 16800 | 0.4497 | 0.3994 | 0.9991 | 0.9954 | 0.0186 | 0.6974 | 0.7244 | 0.9955 | | 0.0074 | 34.14 | 16830 | 0.4425 | 0.3965 | 0.9992 | 0.9955 | 0.0180 | 0.6960 | 0.7209 | 0.9955 | | 0.0039 | 34.2 | 16860 | 0.4196 | 0.3796 | 0.9993 | 0.9954 | 0.0182 | 0.6875 | 0.7095 | 0.9954 | | 0.0008 | 34.26 | 16890 | 0.5240 | 0.4323 | 0.9986 | 0.9954 | 0.0183 | 0.7138 | 0.7613 | 0.9954 | | 0.006 | 34.32 | 16920 | 0.4810 | 0.4149 | 0.9989 | 0.9954 | 0.0176 | 0.7051 | 0.7399 | 0.9954 | | 0.0045 | 34.38 | 16950 | 0.4393 | 0.3918 | 0.9992 | 0.9954 | 0.0181 | 0.6936 | 0.7192 | 0.9954 | | 0.0022 | 34.44 | 16980 | 0.4047 | 0.3701 | 0.9994 | 0.9954 | 0.0180 | 0.6827 | 0.7020 | 0.9954 | | 0.0034 | 34.5 | 17010 | 0.4488 | 0.4005 | 0.9992 | 0.9955 | 0.0179 | 0.6980 | 0.7240 | 0.9955 | | 0.0045 | 34.56 | 17040 | 0.4926 | 0.4229 | 0.9989 | 0.9955 | 0.0180 | 0.7092 | 0.7457 | 0.9955 | | 0.0093 | 34.62 | 17070 | 0.4787 | 0.4095 | 0.9989 | 0.9953 | 0.0189 | 0.7024 | 0.7388 | 0.9954 | | 0.0104 | 34.69 | 17100 | 0.4431 | 0.3949 | 0.9992 | 0.9954 | 0.0184 | 0.6952 | 0.7211 | 0.9954 | | 0.0078 | 34.75 | 17130 | 0.4445 | 0.3946 | 0.9991 | 0.9954 | 0.0185 | 0.6950 | 0.7218 | 0.9954 | | 0.0687 | 34.81 | 17160 | 0.4465 | 0.3940 | 0.9991 | 0.9954 | 0.0191 | 0.6947 | 0.7228 | 0.9954 | | 0.0238 | 34.87 | 17190 | 0.4192 | 0.3795 | 0.9993 | 0.9954 | 0.0187 | 0.6875 | 0.7093 | 0.9954 | | 0.0037 | 34.93 | 17220 | 0.4306 | 0.3873 | 0.9992 | 0.9954 | 0.0187 | 0.6914 | 0.7149 | 0.9954 | | 0.0056 | 34.99 | 17250 | 0.4309 | 0.3861 | 0.9992 | 0.9954 | 0.0186 | 0.6908 | 0.7151 | 0.9954 | | 0.0067 | 35.05 | 17280 | 0.4714 | 0.4125 | 0.9990 | 0.9955 | 0.0172 | 0.7040 | 0.7352 | 0.9955 | | 0.0014 | 35.11 | 17310 | 0.4805 | 0.4171 | 0.9990 | 0.9955 | 0.0163 | 0.7063 | 0.7397 | 0.9955 | | 0.0095 | 35.17 | 17340 | 0.4185 | 0.3798 | 0.9993 | 0.9954 | 0.0163 | 0.6876 | 0.7089 | 0.9954 | | 0.0094 | 35.23 | 17370 | 0.4587 | 0.4050 | 0.9991 | 0.9955 | 0.0164 | 0.7002 | 0.7289 | 0.9955 | | 0.0004 | 35.29 | 17400 | 0.4358 | 0.3914 | 0.9992 | 0.9954 | 0.0163 | 0.6934 | 0.7175 | 0.9954 | | 0.0054 | 35.35 | 17430 | 0.4985 | 0.4226 | 0.9988 | 0.9954 | 0.0163 | 0.7090 | 0.7486 | 0.9954 | | 0.0026 | 35.42 | 17460 | 0.4057 | 0.3708 | 0.9994 | 0.9954 | 0.0168 | 0.6831 | 0.7025 | 0.9954 | | 0.0271 | 35.48 | 17490 | 0.4883 | 0.4182 | 0.9989 | 0.9954 | 0.0173 | 0.7068 | 0.7436 | 0.9954 | | 0.006 | 35.54 | 17520 | 0.4228 | 0.3823 | 0.9993 | 0.9954 | 0.0170 | 0.6889 | 0.7110 | 0.9954 | | 0.0114 | 35.6 | 17550 | 0.4158 | 0.3780 | 0.9993 | 0.9954 | 0.0170 | 0.6867 | 0.7076 | 0.9954 | | 0.0029 | 35.66 | 17580 | 0.4267 | 0.3848 | 0.9993 | 0.9954 | 0.0167 | 0.6901 | 0.7130 | 0.9954 | | 0.007 | 35.72 | 17610 | 0.4375 | 0.3900 | 0.9992 | 0.9954 | 0.0170 | 0.6927 | 0.7183 | 0.9954 | | 0.001 | 35.78 | 17640 | 0.4386 | 0.3920 | 0.9992 | 0.9954 | 0.0169 | 0.6937 | 0.7189 | 0.9954 | | 0.0022 | 35.84 | 17670 | 0.4560 | 0.4036 | 0.9991 | 0.9955 | 0.0173 | 0.6995 | 0.7276 | 0.9955 | | 0.0044 | 35.9 | 17700 | 0.4911 | 0.4245 | 0.9989 | 0.9955 | 0.0168 | 0.7100 | 0.7450 | 0.9955 | | 0.0024 | 35.96 | 17730 | 0.4288 | 0.3862 | 0.9993 | 0.9954 | 0.0170 | 0.6908 | 0.7140 | 0.9954 | | 0.0025 | 36.02 | 17760 | 0.4409 | 0.3935 | 0.9992 | 0.9954 | 0.0177 | 0.6945 | 0.7200 | 0.9954 | | 0.0007 | 36.09 | 17790 | 0.4002 | 0.3664 | 0.9994 | 0.9953 | 0.0170 | 0.6809 | 0.6998 | 0.9954 | | 0.0068 | 36.15 | 17820 | 0.4613 | 0.4075 | 0.9991 | 0.9955 | 0.0167 | 0.7015 | 0.7302 | 0.9955 | | 0.0016 | 36.21 | 17850 | 0.4642 | 0.4079 | 0.9991 | 0.9955 | 0.0165 | 0.7017 | 0.7316 | 0.9955 | | 0.0086 | 36.27 | 17880 | 0.4357 | 0.3909 | 0.9992 | 0.9954 | 0.0167 | 0.6932 | 0.7175 | 0.9954 | | 0.0327 | 36.33 | 17910 | 0.4583 | 0.4054 | 0.9991 | 0.9955 | 0.0169 | 0.7005 | 0.7287 | 0.9955 | | 0.0048 | 36.39 | 17940 | 0.4437 | 0.3969 | 0.9992 | 0.9955 | 0.0167 | 0.6962 | 0.7215 | 0.9955 | | 0.0033 | 36.45 | 17970 | 0.4461 | 0.3994 | 0.9992 | 0.9955 | 0.0166 | 0.6975 | 0.7226 | 0.9955 | | 0.0073 | 36.51 | 18000 | 0.4410 | 0.3950 | 0.9992 | 0.9955 | 0.0162 | 0.6952 | 0.7201 | 0.9955 | | 0.0195 | 36.57 | 18030 | 0.4657 | 0.4091 | 0.9991 | 0.9955 | 0.0162 | 0.7023 | 0.7324 | 0.9955 | | 0.0047 | 36.63 | 18060 | 0.4303 | 0.3871 | 0.9992 | 0.9954 | 0.0167 | 0.6913 | 0.7148 | 0.9954 | | 0.0023 | 36.69 | 18090 | 0.5117 | 0.4306 | 0.9987 | 0.9954 | 0.0168 | 0.7130 | 0.7552 | 0.9955 | | 0.0104 | 36.75 | 18120 | 0.4503 | 0.3998 | 0.9991 | 0.9954 | 0.0166 | 0.6976 | 0.7247 | 0.9955 | | 0.0044 | 36.82 | 18150 | 0.4384 | 0.3919 | 0.9992 | 0.9954 | 0.0170 | 0.6937 | 0.7188 | 0.9954 | | 0.0008 | 36.88 | 18180 | 0.4641 | 0.4074 | 0.9991 | 0.9955 | 0.0167 | 0.7014 | 0.7316 | 0.9955 | | 0.0076 | 36.94 | 18210 | 0.4561 | 0.4008 | 0.9991 | 0.9954 | 0.0172 | 0.6981 | 0.7276 | 0.9954 | | 0.0038 | 37.0 | 18240 | 0.4060 | 0.3706 | 0.9994 | 0.9954 | 0.0171 | 0.6830 | 0.7027 | 0.9954 | | 0.0049 | 37.06 | 18270 | 0.4560 | 0.4028 | 0.9991 | 0.9954 | 0.0172 | 0.6991 | 0.7275 | 0.9955 | | 0.009 | 37.12 | 18300 | 0.4147 | 0.3776 | 0.9993 | 0.9954 | 0.0166 | 0.6865 | 0.7070 | 0.9954 | | 0.0042 | 37.18 | 18330 | 0.4605 | 0.4069 | 0.9991 | 0.9955 | 0.0162 | 0.7012 | 0.7298 | 0.9955 | | 0.0058 | 37.24 | 18360 | 0.4897 | 0.4210 | 0.9989 | 0.9955 | 0.0166 | 0.7083 | 0.7443 | 0.9955 | | 0.0077 | 37.3 | 18390 | 0.4272 | 0.3848 | 0.9993 | 0.9954 | 0.0167 | 0.6901 | 0.7132 | 0.9954 | | 0.0033 | 37.36 | 18420 | 0.4664 | 0.4075 | 0.9990 | 0.9954 | 0.0166 | 0.7015 | 0.7327 | 0.9954 | | 0.0098 | 37.42 | 18450 | 0.4841 | 0.4172 | 0.9989 | 0.9954 | 0.0166 | 0.7063 | 0.7415 | 0.9955 | | 0.0062 | 37.48 | 18480 | 0.4760 | 0.4123 | 0.9990 | 0.9954 | 0.0166 | 0.7038 | 0.7375 | 0.9954 | | 0.0109 | 37.55 | 18510 | 0.4307 | 0.3856 | 0.9992 | 0.9954 | 0.0167 | 0.6905 | 0.7150 | 0.9954 | | 0.0015 | 37.61 | 18540 | 0.4692 | 0.4105 | 0.9990 | 0.9955 | 0.0166 | 0.7030 | 0.7341 | 0.9955 | | 0.001 | 37.67 | 18570 | 0.4478 | 0.3990 | 0.9992 | 0.9955 | 0.0165 | 0.6972 | 0.7235 | 0.9955 | | 0.004 | 37.73 | 18600 | 0.4803 | 0.4152 | 0.9989 | 0.9954 | 0.0170 | 0.7053 | 0.7396 | 0.9955 | | 0.0058 | 37.79 | 18630 | 0.4536 | 0.4029 | 0.9991 | 0.9955 | 0.0166 | 0.6992 | 0.7264 | 0.9955 | | 0.0049 | 37.85 | 18660 | 0.4491 | 0.3995 | 0.9992 | 0.9955 | 0.0166 | 0.6975 | 0.7242 | 0.9955 | | 0.0027 | 37.91 | 18690 | 0.4636 | 0.4083 | 0.9991 | 0.9955 | 0.0165 | 0.7019 | 0.7314 | 0.9955 | | 0.003 | 37.97 | 18720 | 0.4497 | 0.4002 | 0.9992 | 0.9955 | 0.0166 | 0.6978 | 0.7244 | 0.9955 | | 0.0064 | 38.03 | 18750 | 0.4670 | 0.4109 | 0.9991 | 0.9955 | 0.0171 | 0.7032 | 0.7330 | 0.9955 | | 0.0061 | 38.09 | 18780 | 0.4513 | 0.4016 | 0.9992 | 0.9955 | 0.0172 | 0.6985 | 0.7253 | 0.9955 | | 0.0028 | 38.15 | 18810 | 0.4654 | 0.4093 | 0.9991 | 0.9955 | 0.0169 | 0.7024 | 0.7322 | 0.9955 | | 0.0148 | 38.22 | 18840 | 0.4696 | 0.4111 | 0.9990 | 0.9955 | 0.0172 | 0.7033 | 0.7343 | 0.9955 | | 0.0022 | 38.28 | 18870 | 0.4793 | 0.4161 | 0.9990 | 0.9955 | 0.0170 | 0.7058 | 0.7392 | 0.9955 | | 0.0009 | 38.34 | 18900 | 0.4668 | 0.4085 | 0.9990 | 0.9954 | 0.0172 | 0.7020 | 0.7329 | 0.9955 | | 0.0955 | 38.4 | 18930 | 0.4690 | 0.4109 | 0.9990 | 0.9955 | 0.0169 | 0.7032 | 0.7340 | 0.9955 | | 0.0023 | 38.46 | 18960 | 0.4585 | 0.4047 | 0.9991 | 0.9955 | 0.0170 | 0.7001 | 0.7288 | 0.9955 | | 0.0066 | 38.52 | 18990 | 0.4475 | 0.3983 | 0.9992 | 0.9954 | 0.0169 | 0.6969 | 0.7233 | 0.9955 | | 0.0013 | 38.58 | 19020 | 0.4621 | 0.4073 | 0.9991 | 0.9955 | 0.0169 | 0.7014 | 0.7306 | 0.9955 | | 0.0062 | 38.64 | 19050 | 0.4719 | 0.4130 | 0.9990 | 0.9955 | 0.0171 | 0.7042 | 0.7355 | 0.9955 | | 0.0068 | 38.7 | 19080 | 0.4541 | 0.4032 | 0.9991 | 0.9955 | 0.0168 | 0.6994 | 0.7266 | 0.9955 | | 0.0039 | 38.76 | 19110 | 0.4740 | 0.4145 | 0.9990 | 0.9955 | 0.0166 | 0.7050 | 0.7365 | 0.9955 | | 0.0076 | 38.82 | 19140 | 0.4421 | 0.3953 | 0.9992 | 0.9954 | 0.0170 | 0.6954 | 0.7206 | 0.9955 | | 0.003 | 38.88 | 19170 | 0.4267 | 0.3849 | 0.9993 | 0.9954 | 0.0172 | 0.6901 | 0.7130 | 0.9954 | | 0.0044 | 38.95 | 19200 | 0.4539 | 0.4013 | 0.9991 | 0.9954 | 0.0172 | 0.6984 | 0.7265 | 0.9955 | | 0.005 | 39.01 | 19230 | 0.4478 | 0.3977 | 0.9991 | 0.9954 | 0.0173 | 0.6966 | 0.7235 | 0.9954 | | 0.0059 | 39.07 | 19260 | 0.4546 | 0.4024 | 0.9991 | 0.9955 | 0.0173 | 0.6989 | 0.7269 | 0.9955 | | 0.0134 | 39.13 | 19290 | 0.4450 | 0.3965 | 0.9992 | 0.9954 | 0.0171 | 0.6960 | 0.7221 | 0.9955 | | 0.0064 | 39.19 | 19320 | 0.4387 | 0.3928 | 0.9992 | 0.9954 | 0.0172 | 0.6941 | 0.7189 | 0.9954 | | 0.0034 | 39.25 | 19350 | 0.4557 | 0.4029 | 0.9991 | 0.9955 | 0.0172 | 0.6992 | 0.7274 | 0.9955 | | 0.0069 | 39.31 | 19380 | 0.4393 | 0.3931 | 0.9992 | 0.9954 | 0.0172 | 0.6943 | 0.7193 | 0.9954 | | 0.0037 | 39.37 | 19410 | 0.4398 | 0.3933 | 0.9992 | 0.9954 | 0.0174 | 0.6944 | 0.7195 | 0.9954 | | 0.0184 | 39.43 | 19440 | 0.4530 | 0.4012 | 0.9991 | 0.9954 | 0.0174 | 0.6983 | 0.7261 | 0.9955 | | 0.0044 | 39.49 | 19470 | 0.4468 | 0.3975 | 0.9992 | 0.9954 | 0.0172 | 0.6965 | 0.7230 | 0.9955 | | 0.0028 | 39.55 | 19500 | 0.4435 | 0.3953 | 0.9992 | 0.9954 | 0.0173 | 0.6954 | 0.7213 | 0.9954 | | 0.0099 | 39.61 | 19530 | 0.4530 | 0.4009 | 0.9991 | 0.9954 | 0.0174 | 0.6982 | 0.7260 | 0.9955 | | 0.0089 | 39.68 | 19560 | 0.4471 | 0.3977 | 0.9992 | 0.9954 | 0.0174 | 0.6965 | 0.7231 | 0.9955 | | 0.0027 | 39.74 | 19590 | 0.4503 | 0.3996 | 0.9991 | 0.9954 | 0.0172 | 0.6975 | 0.7247 | 0.9955 | | 0.0009 | 39.8 | 19620 | 0.4504 | 0.3996 | 0.9991 | 0.9954 | 0.0173 | 0.6975 | 0.7248 | 0.9955 | | 0.0046 | 39.86 | 19650 | 0.4526 | 0.4008 | 0.9991 | 0.9954 | 0.0173 | 0.6981 | 0.7259 | 0.9955 | | 0.0038 | 39.92 | 19680 | 0.4547 | 0.4021 | 0.9991 | 0.9954 | 0.0174 | 0.6988 | 0.7269 | 0.9955 | | 0.0057 | 39.98 | 19710 | 0.4483 | 0.3982 | 0.9991 | 0.9954 | 0.0174 | 0.6968 | 0.7237 | 0.9955 | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "background", "kelp" ]

heroza/segformer-finetuned-biofilm

# segformer-finetuned-biofilm This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the heroza/biofilm dataset. It achieves the following results on the evaluation set: - Loss: 0.3028 - Mean Iou: 0.3319 - Mean Accuracy: 0.6638 - Overall Accuracy: 0.6638 - Accuracy Background: nan - Accuracy Biofilm: 0.6638 - Iou Background: 0.0 - Iou Biofilm: 0.6638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Biofilm | Iou Background | Iou Biofilm | |:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:| | No log | 1.0 | 17 | 0.6476 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 | | No log | 2.0 | 34 | 0.5609 | 0.4991 | 0.9981 | 0.9981 | nan | 0.9981 | 0.0 | 0.9981 | | No log | 3.0 | 51 | 0.4363 | 0.4869 | 0.9738 | 0.9738 | nan | 0.9738 | 0.0 | 0.9738 | | No log | 4.0 | 68 | 0.3583 | 0.3954 | 0.7908 | 0.7908 | nan | 0.7908 | 0.0 | 0.7908 | | No log | 5.0 | 85 | 0.3015 | 0.4016 | 0.8033 | 0.8033 | nan | 0.8033 | 0.0 | 0.8033 | | 0.4792 | 6.0 | 102 | 0.2964 | 0.0504 | 0.1008 | 0.1008 | nan | 0.1008 | 0.0 | 0.1008 | | 0.4792 | 7.0 | 119 | 0.2995 | 0.2103 | 0.4206 | 0.4206 | nan | 0.4206 | 0.0 | 0.4206 | | 0.4792 | 8.0 | 136 | 0.2895 | 0.0417 | 0.0833 | 0.0833 | nan | 0.0833 | 0.0 | 0.0833 | | 0.4792 | 9.0 | 153 | 0.2499 | 0.0562 | 0.1123 | 0.1123 | nan | 0.1123 | 0.0 | 0.1123 | | 0.4792 | 10.0 | 170 | 0.2770 | 0.0570 | 0.1140 | 0.1140 | nan | 0.1140 | 0.0 | 0.1140 | | 0.4792 | 11.0 | 187 | 0.2268 | 0.2041 | 0.4082 | 0.4082 | nan | 0.4082 | 0.0 | 0.4082 | | 0.3238 | 12.0 | 204 | 0.2684 | 0.1290 | 0.2580 | 0.2580 | nan | 0.2580 | 0.0 | 0.2580 | | 0.3238 | 13.0 | 221 | 0.2584 | 0.2469 | 0.4938 | 0.4938 | nan | 0.4938 | 0.0 | 0.4938 | | 0.3238 | 14.0 | 238 | 0.2409 | 0.1139 | 0.2278 | 0.2278 | nan | 0.2278 | 0.0 | 0.2278 | | 0.3238 | 15.0 | 255 | 0.2800 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | | 0.3238 | 16.0 | 272 | 0.2348 | 0.0002 | 0.0004 | 0.0004 | nan | 0.0004 | 0.0 | 0.0004 | | 0.3238 | 17.0 | 289 | 0.2290 | 0.0009 | 0.0018 | 0.0018 | nan | 0.0018 | 0.0 | 0.0018 | | 0.289 | 18.0 | 306 | 0.2077 | 0.2453 | 0.4906 | 0.4906 | nan | 0.4906 | 0.0 | 0.4906 | | 0.289 | 19.0 | 323 | 0.2471 | 0.0117 | 0.0233 | 0.0233 | nan | 0.0233 | 0.0 | 0.0233 | | 0.289 | 20.0 | 340 | 0.2226 | 0.0346 | 0.0692 | 0.0692 | nan | 0.0692 | 0.0 | 0.0692 | | 0.289 | 21.0 | 357 | 0.2539 | 0.0044 | 0.0088 | 0.0088 | nan | 0.0088 | 0.0 | 0.0088 | | 0.289 | 22.0 | 374 | 0.2323 | 0.0029 | 0.0058 | 0.0058 | nan | 0.0058 | 0.0 | 0.0058 | | 0.289 | 23.0 | 391 | 0.2700 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 | | 0.2751 | 24.0 | 408 | 0.2107 | 0.2176 | 0.4351 | 0.4351 | nan | 0.4351 | 0.0 | 0.4351 | | 0.2751 | 25.0 | 425 | 0.1984 | 0.0114 | 0.0228 | 0.0228 | nan | 0.0228 | 0.0 | 0.0228 | | 0.2751 | 26.0 | 442 | 0.2531 | 0.1690 | 0.3379 | 0.3379 | nan | 0.3379 | 0.0 | 0.3379 | | 0.2751 | 27.0 | 459 | 0.2402 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 | | 0.2751 | 28.0 | 476 | 0.2446 | 0.1299 | 0.2597 | 0.2597 | nan | 0.2597 | 0.0 | 0.2597 | | 0.2751 | 29.0 | 493 | 0.2327 | 0.0069 | 0.0139 | 0.0139 | nan | 0.0139 | 0.0 | 0.0139 | | 0.2736 | 30.0 | 510 | 0.2440 | 0.0031 | 0.0061 | 0.0061 | nan | 0.0061 | 0.0 | 0.0061 | | 0.2736 | 31.0 | 527 | 0.2527 | 0.0060 | 0.0119 | 0.0119 | nan | 0.0119 | 0.0 | 0.0119 | | 0.2736 | 32.0 | 544 | 0.2185 | 0.0897 | 0.1795 | 0.1795 | nan | 0.1795 | 0.0 | 0.1795 | | 0.2736 | 33.0 | 561 | 0.2256 | 0.2468 | 0.4936 | 0.4936 | nan | 0.4936 | 0.0 | 0.4936 | | 0.2736 | 34.0 | 578 | 0.2347 | 0.0419 | 0.0837 | 0.0837 | nan | 0.0837 | 0.0 | 0.0837 | | 0.2736 | 35.0 | 595 | 0.2034 | 0.1546 | 0.3091 | 0.3091 | nan | 0.3091 | 0.0 | 0.3091 | | 0.2661 | 36.0 | 612 | 0.2182 | 0.0437 | 0.0873 | 0.0873 | nan | 0.0873 | 0.0 | 0.0873 | | 0.2661 | 37.0 | 629 | 0.1937 | 0.3058 | 0.6116 | 0.6116 | nan | 0.6116 | 0.0 | 0.6116 | | 0.2661 | 38.0 | 646 | 0.2028 | 0.0014 | 0.0028 | 0.0028 | nan | 0.0028 | 0.0 | 0.0028 | | 0.2661 | 39.0 | 663 | 0.1853 | 0.0649 | 0.1298 | 0.1298 | nan | 0.1298 | 0.0 | 0.1298 | | 0.2661 | 40.0 | 680 | 0.2272 | 0.3264 | 0.6527 | 0.6527 | nan | 0.6527 | 0.0 | 0.6527 | | 0.2661 | 41.0 | 697 | 0.2654 | 0.4513 | 0.9027 | 0.9027 | nan | 0.9027 | 0.0 | 0.9027 | | 0.256 | 42.0 | 714 | 0.2408 | 0.3147 | 0.6295 | 0.6295 | nan | 0.6295 | 0.0 | 0.6295 | | 0.256 | 43.0 | 731 | 0.2253 | 0.0708 | 0.1415 | 0.1415 | nan | 0.1415 | 0.0 | 0.1415 | | 0.256 | 44.0 | 748 | 0.2025 | 0.3538 | 0.7076 | 0.7076 | nan | 0.7076 | 0.0 | 0.7076 | | 0.256 | 45.0 | 765 | 0.2271 | 0.0238 | 0.0476 | 0.0476 | nan | 0.0476 | 0.0 | 0.0476 | | 0.256 | 46.0 | 782 | 0.2171 | 0.0911 | 0.1821 | 0.1821 | nan | 0.1821 | 0.0 | 0.1821 | | 0.256 | 47.0 | 799 | 0.2525 | 0.2226 | 0.4453 | 0.4453 | nan | 0.4453 | 0.0 | 0.4453 | | 0.2588 | 48.0 | 816 | 0.2044 | 0.3965 | 0.7929 | 0.7929 | nan | 0.7929 | 0.0 | 0.7929 | | 0.2588 | 49.0 | 833 | 0.2328 | 0.3985 | 0.7969 | 0.7969 | nan | 0.7969 | 0.0 | 0.7969 | | 0.2588 | 50.0 | 850 | 0.2241 | 0.4584 | 0.9168 | 0.9168 | nan | 0.9168 | 0.0 | 0.9168 | | 0.2588 | 51.0 | 867 | 0.1806 | 0.3473 | 0.6946 | 0.6946 | nan | 0.6946 | 0.0 | 0.6946 | | 0.2588 | 52.0 | 884 | 0.2081 | 0.3036 | 0.6071 | 0.6071 | nan | 0.6071 | 0.0 | 0.6071 | | 0.2462 | 53.0 | 901 | 0.2037 | 0.1534 | 0.3068 | 0.3068 | nan | 0.3068 | 0.0 | 0.3068 | | 0.2462 | 54.0 | 918 | 0.2024 | 0.2510 | 0.5021 | 0.5021 | nan | 0.5021 | 0.0 | 0.5021 | | 0.2462 | 55.0 | 935 | 0.2100 | 0.3440 | 0.6880 | 0.6880 | nan | 0.6880 | 0.0 | 0.6880 | | 0.2462 | 56.0 | 952 | 0.1887 | 0.3399 | 0.6799 | 0.6799 | nan | 0.6799 | 0.0 | 0.6799 | | 0.2462 | 57.0 | 969 | 0.2333 | 0.2049 | 0.4097 | 0.4097 | nan | 0.4097 | 0.0 | 0.4097 | | 0.2462 | 58.0 | 986 | 0.2432 | 0.0556 | 0.1112 | 0.1112 | nan | 0.1112 | 0.0 | 0.1112 | | 0.2414 | 59.0 | 1003 | 0.2298 | 0.0935 | 0.1870 | 0.1870 | nan | 0.1870 | 0.0 | 0.1870 | | 0.2414 | 60.0 | 1020 | 0.2172 | 0.1357 | 0.2714 | 0.2714 | nan | 0.2714 | 0.0 | 0.2714 | | 0.2414 | 61.0 | 1037 | 0.1975 | 0.2473 | 0.4945 | 0.4945 | nan | 0.4945 | 0.0 | 0.4945 | | 0.2414 | 62.0 | 1054 | 0.2019 | 0.3072 | 0.6144 | 0.6144 | nan | 0.6144 | 0.0 | 0.6144 | | 0.2414 | 63.0 | 1071 | 0.2331 | 0.2901 | 0.5802 | 0.5802 | nan | 0.5802 | 0.0 | 0.5802 | | 0.2414 | 64.0 | 1088 | 0.2391 | 0.0202 | 0.0405 | 0.0405 | nan | 0.0405 | 0.0 | 0.0405 | | 0.2244 | 65.0 | 1105 | 0.2013 | 0.2051 | 0.4103 | 0.4103 | nan | 0.4103 | 0.0 | 0.4103 | | 0.2244 | 66.0 | 1122 | 0.2076 | 0.2277 | 0.4554 | 0.4554 | nan | 0.4554 | 0.0 | 0.4554 | | 0.2244 | 67.0 | 1139 | 0.2289 | 0.2376 | 0.4752 | 0.4752 | nan | 0.4752 | 0.0 | 0.4752 | | 0.2244 | 68.0 | 1156 | 0.2293 | 0.2633 | 0.5266 | 0.5266 | nan | 0.5266 | 0.0 | 0.5266 | | 0.2244 | 69.0 | 1173 | 0.1907 | 0.2973 | 0.5946 | 0.5946 | nan | 0.5946 | 0.0 | 0.5946 | | 0.2244 | 70.0 | 1190 | 0.2714 | 0.4149 | 0.8298 | 0.8298 | nan | 0.8298 | 0.0 | 0.8298 | | 0.2067 | 71.0 | 1207 | 0.2621 | 0.1077 | 0.2154 | 0.2154 | nan | 0.2154 | 0.0 | 0.2154 | | 0.2067 | 72.0 | 1224 | 0.2028 | 0.3925 | 0.7849 | 0.7849 | nan | 0.7849 | 0.0 | 0.7849 | | 0.2067 | 73.0 | 1241 | 0.2457 | 0.3271 | 0.6543 | 0.6543 | nan | 0.6543 | 0.0 | 0.6543 | | 0.2067 | 74.0 | 1258 | 0.2533 | 0.2758 | 0.5516 | 0.5516 | nan | 0.5516 | 0.0 | 0.5516 | | 0.2067 | 75.0 | 1275 | 0.2223 | 0.2553 | 0.5106 | 0.5106 | nan | 0.5106 | 0.0 | 0.5106 | | 0.2067 | 76.0 | 1292 | 0.2784 | 0.1558 | 0.3116 | 0.3116 | nan | 0.3116 | 0.0 | 0.3116 | | 0.2111 | 77.0 | 1309 | 0.2238 | 0.2742 | 0.5485 | 0.5485 | nan | 0.5485 | 0.0 | 0.5485 | | 0.2111 | 78.0 | 1326 | 0.2486 | 0.3661 | 0.7322 | 0.7322 | nan | 0.7322 | 0.0 | 0.7322 | | 0.2111 | 79.0 | 1343 | 0.2279 | 0.2980 | 0.5961 | 0.5961 | nan | 0.5961 | 0.0 | 0.5961 | | 0.2111 | 80.0 | 1360 | 0.2106 | 0.4261 | 0.8522 | 0.8522 | nan | 0.8522 | 0.0 | 0.8522 | | 0.2111 | 81.0 | 1377 | 0.2611 | 0.1272 | 0.2544 | 0.2544 | nan | 0.2544 | 0.0 | 0.2544 | | 0.2111 | 82.0 | 1394 | 0.2742 | 0.2939 | 0.5879 | 0.5879 | nan | 0.5879 | 0.0 | 0.5879 | | 0.1945 | 83.0 | 1411 | 0.2058 | 0.2690 | 0.5380 | 0.5380 | nan | 0.5380 | 0.0 | 0.5380 | | 0.1945 | 84.0 | 1428 | 0.2239 | 0.2701 | 0.5401 | 0.5401 | nan | 0.5401 | 0.0 | 0.5401 | | 0.1945 | 85.0 | 1445 | 0.3142 | 0.3677 | 0.7355 | 0.7355 | nan | 0.7355 | 0.0 | 0.7355 | | 0.1945 | 86.0 | 1462 | 0.3306 | 0.1677 | 0.3353 | 0.3353 | nan | 0.3353 | 0.0 | 0.3353 | | 0.1945 | 87.0 | 1479 | 0.2530 | 0.2969 | 0.5938 | 0.5938 | nan | 0.5938 | 0.0 | 0.5938 | | 0.1945 | 88.0 | 1496 | 0.2500 | 0.2320 | 0.4640 | 0.4640 | nan | 0.4640 | 0.0 | 0.4640 | | 0.192 | 89.0 | 1513 | 0.2924 | 0.1946 | 0.3891 | 0.3891 | nan | 0.3891 | 0.0 | 0.3891 | | 0.192 | 90.0 | 1530 | 0.3585 | 0.0835 | 0.1670 | 0.1670 | nan | 0.1670 | 0.0 | 0.1670 | | 0.192 | 91.0 | 1547 | 0.3411 | 0.1406 | 0.2813 | 0.2813 | nan | 0.2813 | 0.0 | 0.2813 | | 0.192 | 92.0 | 1564 | 0.2961 | 0.2882 | 0.5764 | 0.5764 | nan | 0.5764 | 0.0 | 0.5764 | | 0.192 | 93.0 | 1581 | 0.2800 | 0.1694 | 0.3387 | 0.3387 | nan | 0.3387 | 0.0 | 0.3387 | | 0.192 | 94.0 | 1598 | 0.3560 | 0.3134 | 0.6269 | 0.6269 | nan | 0.6269 | 0.0 | 0.6269 | | 0.1913 | 95.0 | 1615 | 0.3817 | 0.2125 | 0.4251 | 0.4251 | nan | 0.4251 | 0.0 | 0.4251 | | 0.1913 | 96.0 | 1632 | 0.3245 | 0.0876 | 0.1753 | 0.1753 | nan | 0.1753 | 0.0 | 0.1753 | | 0.1913 | 97.0 | 1649 | 0.3458 | 0.1685 | 0.3369 | 0.3369 | nan | 0.3369 | 0.0 | 0.3369 | | 0.1913 | 98.0 | 1666 | 0.3581 | 0.2758 | 0.5515 | 0.5515 | nan | 0.5515 | 0.0 | 0.5515 | | 0.1913 | 99.0 | 1683 | 0.3175 | 0.2537 | 0.5074 | 0.5074 | nan | 0.5074 | 0.0 | 0.5074 | | 0.1731 | 100.0 | 1700 | 0.2857 | 0.2969 | 0.5939 | 0.5939 | nan | 0.5939 | 0.0 | 0.5939 | | 0.1731 | 101.0 | 1717 | 0.3411 | 0.3170 | 0.6340 | 0.6340 | nan | 0.6340 | 0.0 | 0.6340 | | 0.1731 | 102.0 | 1734 | 0.3221 | 0.2288 | 0.4576 | 0.4576 | nan | 0.4576 | 0.0 | 0.4576 | | 0.1731 | 103.0 | 1751 | 0.2913 | 0.3578 | 0.7156 | 0.7156 | nan | 0.7156 | 0.0 | 0.7156 | | 0.1731 | 104.0 | 1768 | 0.2708 | 0.3599 | 0.7197 | 0.7197 | nan | 0.7197 | 0.0 | 0.7197 | | 0.1731 | 105.0 | 1785 | 0.3456 | 0.2584 | 0.5168 | 0.5168 | nan | 0.5168 | 0.0 | 0.5168 | | 0.1581 | 106.0 | 1802 | 0.2770 | 0.3497 | 0.6995 | 0.6995 | nan | 0.6995 | 0.0 | 0.6995 | | 0.1581 | 107.0 | 1819 | 0.2698 | 0.3113 | 0.6226 | 0.6226 | nan | 0.6226 | 0.0 | 0.6226 | | 0.1581 | 108.0 | 1836 | 0.2393 | 0.3194 | 0.6389 | 0.6389 | nan | 0.6389 | 0.0 | 0.6389 | | 0.1581 | 109.0 | 1853 | 0.3066 | 0.2162 | 0.4324 | 0.4324 | nan | 0.4324 | 0.0 | 0.4324 | | 0.1581 | 110.0 | 1870 | 0.2795 | 0.3652 | 0.7304 | 0.7304 | nan | 0.7304 | 0.0 | 0.7304 | | 0.1581 | 111.0 | 1887 | 0.2847 | 0.3003 | 0.6007 | 0.6007 | nan | 0.6007 | 0.0 | 0.6007 | | 0.1441 | 112.0 | 1904 | 0.2758 | 0.3359 | 0.6718 | 0.6718 | nan | 0.6718 | 0.0 | 0.6718 | | 0.1441 | 113.0 | 1921 | 0.3062 | 0.3258 | 0.6516 | 0.6516 | nan | 0.6516 | 0.0 | 0.6516 | | 0.1441 | 114.0 | 1938 | 0.2293 | 0.3128 | 0.6256 | 0.6256 | nan | 0.6256 | 0.0 | 0.6256 | | 0.1441 | 115.0 | 1955 | 0.2223 | 0.3407 | 0.6813 | 0.6813 | nan | 0.6813 | 0.0 | 0.6813 | | 0.1441 | 116.0 | 1972 | 0.3118 | 0.4092 | 0.8185 | 0.8185 | nan | 0.8185 | 0.0 | 0.8185 | | 0.1441 | 117.0 | 1989 | 0.2838 | 0.2334 | 0.4668 | 0.4668 | nan | 0.4668 | 0.0 | 0.4668 | | 0.1393 | 118.0 | 2006 | 0.3241 | 0.2276 | 0.4551 | 0.4551 | nan | 0.4551 | 0.0 | 0.4551 | | 0.1393 | 119.0 | 2023 | 0.2716 | 0.3810 | 0.7621 | 0.7621 | nan | 0.7621 | 0.0 | 0.7621 | | 0.1393 | 120.0 | 2040 | 0.3328 | 0.3760 | 0.7520 | 0.7520 | nan | 0.7520 | 0.0 | 0.7520 | | 0.1393 | 121.0 | 2057 | 0.3282 | 0.2801 | 0.5602 | 0.5602 | nan | 0.5602 | 0.0 | 0.5602 | | 0.1393 | 122.0 | 2074 | 0.3348 | 0.3117 | 0.6233 | 0.6233 | nan | 0.6233 | 0.0 | 0.6233 | | 0.1393 | 123.0 | 2091 | 0.3851 | 0.2782 | 0.5564 | 0.5564 | nan | 0.5564 | 0.0 | 0.5564 | | 0.1449 | 124.0 | 2108 | 0.3402 | 0.3162 | 0.6323 | 0.6323 | nan | 0.6323 | 0.0 | 0.6323 | | 0.1449 | 125.0 | 2125 | 0.3053 | 0.2355 | 0.4711 | 0.4711 | nan | 0.4711 | 0.0 | 0.4711 | | 0.1449 | 126.0 | 2142 | 0.3265 | 0.2472 | 0.4944 | 0.4944 | nan | 0.4944 | 0.0 | 0.4944 | | 0.1449 | 127.0 | 2159 | 0.4374 | 0.1463 | 0.2927 | 0.2927 | nan | 0.2927 | 0.0 | 0.2927 | | 0.1449 | 128.0 | 2176 | 0.3182 | 0.2567 | 0.5134 | 0.5134 | nan | 0.5134 | 0.0 | 0.5134 | | 0.1449 | 129.0 | 2193 | 0.3109 | 0.3480 | 0.6960 | 0.6960 | nan | 0.6960 | 0.0 | 0.6960 | | 0.1245 | 130.0 | 2210 | 0.3833 | 0.1749 | 0.3498 | 0.3498 | nan | 0.3498 | 0.0 | 0.3498 | | 0.1245 | 131.0 | 2227 | 0.3002 | 0.2612 | 0.5224 | 0.5224 | nan | 0.5224 | 0.0 | 0.5224 | | 0.1245 | 132.0 | 2244 | 0.3869 | 0.3064 | 0.6129 | 0.6129 | nan | 0.6129 | 0.0 | 0.6129 | | 0.1245 | 133.0 | 2261 | 0.3192 | 0.3360 | 0.6720 | 0.6720 | nan | 0.6720 | 0.0 | 0.6720 | | 0.1245 | 134.0 | 2278 | 0.2726 | 0.3727 | 0.7453 | 0.7453 | nan | 0.7453 | 0.0 | 0.7453 | | 0.1245 | 135.0 | 2295 | 0.2383 | 0.3435 | 0.6869 | 0.6869 | nan | 0.6869 | 0.0 | 0.6869 | | 0.1235 | 136.0 | 2312 | 0.2310 | 0.3693 | 0.7386 | 0.7386 | nan | 0.7386 | 0.0 | 0.7386 | | 0.1235 | 137.0 | 2329 | 0.3270 | 0.3227 | 0.6453 | 0.6453 | nan | 0.6453 | 0.0 | 0.6453 | | 0.1235 | 138.0 | 2346 | 0.2932 | 0.3149 | 0.6298 | 0.6298 | nan | 0.6298 | 0.0 | 0.6298 | | 0.1235 | 139.0 | 2363 | 0.2976 | 0.3455 | 0.6911 | 0.6911 | nan | 0.6911 | 0.0 | 0.6911 | | 0.1235 | 140.0 | 2380 | 0.3069 | 0.3496 | 0.6991 | 0.6991 | nan | 0.6991 | 0.0 | 0.6991 | | 0.1235 | 141.0 | 2397 | 0.2529 | 0.2496 | 0.4993 | 0.4993 | nan | 0.4993 | 0.0 | 0.4993 | | 0.1128 | 142.0 | 2414 | 0.2553 | 0.2952 | 0.5904 | 0.5904 | nan | 0.5904 | 0.0 | 0.5904 | | 0.1128 | 143.0 | 2431 | 0.2483 | 0.3372 | 0.6743 | 0.6743 | nan | 0.6743 | 0.0 | 0.6743 | | 0.1128 | 144.0 | 2448 | 0.2817 | 0.3429 | 0.6857 | 0.6857 | nan | 0.6857 | 0.0 | 0.6857 | | 0.1128 | 145.0 | 2465 | 0.2906 | 0.1424 | 0.2847 | 0.2847 | nan | 0.2847 | 0.0 | 0.2847 | | 0.1128 | 146.0 | 2482 | 0.2827 | 0.2326 | 0.4653 | 0.4653 | nan | 0.4653 | 0.0 | 0.4653 | | 0.1128 | 147.0 | 2499 | 0.2806 | 0.2263 | 0.4525 | 0.4525 | nan | 0.4525 | 0.0 | 0.4525 | | 0.1086 | 148.0 | 2516 | 0.3055 | 0.2203 | 0.4406 | 0.4406 | nan | 0.4406 | 0.0 | 0.4406 | | 0.1086 | 149.0 | 2533 | 0.2875 | 0.2702 | 0.5403 | 0.5403 | nan | 0.5403 | 0.0 | 0.5403 | | 0.1086 | 150.0 | 2550 | 0.4335 | 0.2037 | 0.4074 | 0.4074 | nan | 0.4074 | 0.0 | 0.4074 | | 0.1086 | 151.0 | 2567 | 0.3218 | 0.2663 | 0.5326 | 0.5326 | nan | 0.5326 | 0.0 | 0.5326 | | 0.1086 | 152.0 | 2584 | 0.2494 | 0.3017 | 0.6035 | 0.6035 | nan | 0.6035 | 0.0 | 0.6035 | | 0.1059 | 153.0 | 2601 | 0.2533 | 0.3175 | 0.6351 | 0.6351 | nan | 0.6351 | 0.0 | 0.6351 | | 0.1059 | 154.0 | 2618 | 0.3656 | 0.2223 | 0.4447 | 0.4447 | nan | 0.4447 | 0.0 | 0.4447 | | 0.1059 | 155.0 | 2635 | 0.3427 | 0.2402 | 0.4803 | 0.4803 | nan | 0.4803 | 0.0 | 0.4803 | | 0.1059 | 156.0 | 2652 | 0.3269 | 0.2970 | 0.5939 | 0.5939 | nan | 0.5939 | 0.0 | 0.5939 | | 0.1059 | 157.0 | 2669 | 0.3159 | 0.2391 | 0.4783 | 0.4783 | nan | 0.4783 | 0.0 | 0.4783 | | 0.1059 | 158.0 | 2686 | 0.3799 | 0.3644 | 0.7287 | 0.7287 | nan | 0.7287 | 0.0 | 0.7287 | | 0.0998 | 159.0 | 2703 | 0.3057 | 0.2416 | 0.4831 | 0.4831 | nan | 0.4831 | 0.0 | 0.4831 | | 0.0998 | 160.0 | 2720 | 0.3725 | 0.2123 | 0.4245 | 0.4245 | nan | 0.4245 | 0.0 | 0.4245 | | 0.0998 | 161.0 | 2737 | 0.4095 | 0.2383 | 0.4767 | 0.4767 | nan | 0.4767 | 0.0 | 0.4767 | | 0.0998 | 162.0 | 2754 | 0.3927 | 0.3185 | 0.6371 | 0.6371 | nan | 0.6371 | 0.0 | 0.6371 | | 0.0998 | 163.0 | 2771 | 0.3511 | 0.3410 | 0.6820 | 0.6820 | nan | 0.6820 | 0.0 | 0.6820 | | 0.0998 | 164.0 | 2788 | 0.3122 | 0.2452 | 0.4904 | 0.4904 | nan | 0.4904 | 0.0 | 0.4904 | | 0.1008 | 165.0 | 2805 | 0.4119 | 0.2560 | 0.5119 | 0.5119 | nan | 0.5119 | 0.0 | 0.5119 | | 0.1008 | 166.0 | 2822 | 0.4577 | 0.2518 | 0.5035 | 0.5035 | nan | 0.5035 | 0.0 | 0.5035 | | 0.1008 | 167.0 | 2839 | 0.3426 | 0.2673 | 0.5346 | 0.5346 | nan | 0.5346 | 0.0 | 0.5346 | | 0.1008 | 168.0 | 2856 | 0.3069 | 0.3632 | 0.7265 | 0.7265 | nan | 0.7265 | 0.0 | 0.7265 | | 0.1008 | 169.0 | 2873 | 0.4477 | 0.2209 | 0.4417 | 0.4417 | nan | 0.4417 | 0.0 | 0.4417 | | 0.1008 | 170.0 | 2890 | 0.3307 | 0.2525 | 0.5049 | 0.5049 | nan | 0.5049 | 0.0 | 0.5049 | | 0.0998 | 171.0 | 2907 | 0.3338 | 0.2725 | 0.5450 | 0.5450 | nan | 0.5450 | 0.0 | 0.5450 | | 0.0998 | 172.0 | 2924 | 0.3028 | 0.3637 | 0.7275 | 0.7275 | nan | 0.7275 | 0.0 | 0.7275 | | 0.0998 | 173.0 | 2941 | 0.3716 | 0.2877 | 0.5753 | 0.5753 | nan | 0.5753 | 0.0 | 0.5753 | | 0.0998 | 174.0 | 2958 | 0.4544 | 0.3364 | 0.6729 | 0.6729 | nan | 0.6729 | 0.0 | 0.6729 | | 0.0998 | 175.0 | 2975 | 0.3750 | 0.2699 | 0.5399 | 0.5399 | nan | 0.5399 | 0.0 | 0.5399 | | 0.0998 | 176.0 | 2992 | 0.3580 | 0.3064 | 0.6128 | 0.6128 | nan | 0.6128 | 0.0 | 0.6128 | | 0.0944 | 177.0 | 3009 | 0.3519 | 0.2820 | 0.5641 | 0.5641 | nan | 0.5641 | 0.0 | 0.5641 | | 0.0944 | 178.0 | 3026 | 0.4045 | 0.2488 | 0.4975 | 0.4975 | nan | 0.4975 | 0.0 | 0.4975 | | 0.0944 | 179.0 | 3043 | 0.4432 | 0.2521 | 0.5041 | 0.5041 | nan | 0.5041 | 0.0 | 0.5041 | | 0.0944 | 180.0 | 3060 | 0.4220 | 0.1950 | 0.3900 | 0.3900 | nan | 0.3900 | 0.0 | 0.3900 | | 0.0944 | 181.0 | 3077 | 0.3030 | 0.3492 | 0.6983 | 0.6983 | nan | 0.6983 | 0.0 | 0.6983 | | 0.0944 | 182.0 | 3094 | 0.3034 | 0.2901 | 0.5803 | 0.5803 | nan | 0.5803 | 0.0 | 0.5803 | | 0.0934 | 183.0 | 3111 | 0.3266 | 0.2783 | 0.5566 | 0.5566 | nan | 0.5566 | 0.0 | 0.5566 | | 0.0934 | 184.0 | 3128 | 0.3688 | 0.2025 | 0.4051 | 0.4051 | nan | 0.4051 | 0.0 | 0.4051 | | 0.0934 | 185.0 | 3145 | 0.3183 | 0.3159 | 0.6318 | 0.6318 | nan | 0.6318 | 0.0 | 0.6318 | | 0.0934 | 186.0 | 3162 | 0.2926 | 0.3334 | 0.6668 | 0.6668 | nan | 0.6668 | 0.0 | 0.6668 | | 0.0934 | 187.0 | 3179 | 0.3351 | 0.2926 | 0.5851 | 0.5851 | nan | 0.5851 | 0.0 | 0.5851 | | 0.0934 | 188.0 | 3196 | 0.3440 | 0.3410 | 0.6821 | 0.6821 | nan | 0.6821 | 0.0 | 0.6821 | | 0.0835 | 189.0 | 3213 | 0.2707 | 0.3727 | 0.7454 | 0.7454 | nan | 0.7454 | 0.0 | 0.7454 | | 0.0835 | 190.0 | 3230 | 0.2855 | 0.3325 | 0.6649 | 0.6649 | nan | 0.6649 | 0.0 | 0.6649 | | 0.0835 | 191.0 | 3247 | 0.3349 | 0.3753 | 0.7505 | 0.7505 | nan | 0.7505 | 0.0 | 0.7505 | | 0.0835 | 192.0 | 3264 | 0.3246 | 0.3052 | 0.6105 | 0.6105 | nan | 0.6105 | 0.0 | 0.6105 | | 0.0835 | 193.0 | 3281 | 0.3379 | 0.3133 | 0.6266 | 0.6266 | nan | 0.6266 | 0.0 | 0.6266 | | 0.0835 | 194.0 | 3298 | 0.2440 | 0.3287 | 0.6573 | 0.6573 | nan | 0.6573 | 0.0 | 0.6573 | | 0.0886 | 195.0 | 3315 | 0.2616 | 0.3371 | 0.6743 | 0.6743 | nan | 0.6743 | 0.0 | 0.6743 | | 0.0886 | 196.0 | 3332 | 0.3202 | 0.3586 | 0.7173 | 0.7173 | nan | 0.7173 | 0.0 | 0.7173 | | 0.0886 | 197.0 | 3349 | 0.3853 | 0.3157 | 0.6315 | 0.6315 | nan | 0.6315 | 0.0 | 0.6315 | | 0.0886 | 198.0 | 3366 | 0.2670 | 0.3694 | 0.7387 | 0.7387 | nan | 0.7387 | 0.0 | 0.7387 | | 0.0886 | 199.0 | 3383 | 0.2904 | 0.3741 | 0.7483 | 0.7483 | nan | 0.7483 | 0.0 | 0.7483 | | 0.0852 | 200.0 | 3400 | 0.2935 | 0.3762 | 0.7525 | 0.7525 | nan | 0.7525 | 0.0 | 0.7525 | | 0.0852 | 201.0 | 3417 | 0.2990 | 0.2311 | 0.4623 | 0.4623 | nan | 0.4623 | 0.0 | 0.4623 | | 0.0852 | 202.0 | 3434 | 0.4198 | 0.2415 | 0.4830 | 0.4830 | nan | 0.4830 | 0.0 | 0.4830 | | 0.0852 | 203.0 | 3451 | 0.2925 | 0.3272 | 0.6543 | 0.6543 | nan | 0.6543 | 0.0 | 0.6543 | | 0.0852 | 204.0 | 3468 | 0.3397 | 0.3312 | 0.6625 | 0.6625 | nan | 0.6625 | 0.0 | 0.6625 | | 0.0852 | 205.0 | 3485 | 0.3388 | 0.3187 | 0.6374 | 0.6374 | nan | 0.6374 | 0.0 | 0.6374 | | 0.0799 | 206.0 | 3502 | 0.4443 | 0.2287 | 0.4573 | 0.4573 | nan | 0.4573 | 0.0 | 0.4573 | | 0.0799 | 207.0 | 3519 | 0.3457 | 0.2403 | 0.4807 | 0.4807 | nan | 0.4807 | 0.0 | 0.4807 | | 0.0799 | 208.0 | 3536 | 0.2849 | 0.3857 | 0.7714 | 0.7714 | nan | 0.7714 | 0.0 | 0.7714 | | 0.0799 | 209.0 | 3553 | 0.2780 | 0.3685 | 0.7370 | 0.7370 | nan | 0.7370 | 0.0 | 0.7370 | | 0.0799 | 210.0 | 3570 | 0.3077 | 0.4219 | 0.8438 | 0.8438 | nan | 0.8438 | 0.0 | 0.8438 | | 0.0799 | 211.0 | 3587 | 0.3165 | 0.3990 | 0.7980 | 0.7980 | nan | 0.7980 | 0.0 | 0.7980 | | 0.0814 | 212.0 | 3604 | 0.2359 | 0.3532 | 0.7065 | 0.7065 | nan | 0.7065 | 0.0 | 0.7065 | | 0.0814 | 213.0 | 3621 | 0.2817 | 0.3445 | 0.6890 | 0.6890 | nan | 0.6890 | 0.0 | 0.6890 | | 0.0814 | 214.0 | 3638 | 0.2271 | 0.3884 | 0.7767 | 0.7767 | nan | 0.7767 | 0.0 | 0.7767 | | 0.0814 | 215.0 | 3655 | 0.2554 | 0.3485 | 0.6970 | 0.6970 | nan | 0.6970 | 0.0 | 0.6970 | | 0.0814 | 216.0 | 3672 | 0.3288 | 0.2850 | 0.5700 | 0.5700 | nan | 0.5700 | 0.0 | 0.5700 | | 0.0814 | 217.0 | 3689 | 0.3606 | 0.2887 | 0.5775 | 0.5775 | nan | 0.5775 | 0.0 | 0.5775 | | 0.076 | 218.0 | 3706 | 0.3455 | 0.2865 | 0.5730 | 0.5730 | nan | 0.5730 | 0.0 | 0.5730 | | 0.076 | 219.0 | 3723 | 0.2988 | 0.2831 | 0.5662 | 0.5662 | nan | 0.5662 | 0.0 | 0.5662 | | 0.076 | 220.0 | 3740 | 0.2780 | 0.3819 | 0.7639 | 0.7639 | nan | 0.7639 | 0.0 | 0.7639 | | 0.076 | 221.0 | 3757 | 0.3546 | 0.3373 | 0.6745 | 0.6745 | nan | 0.6745 | 0.0 | 0.6745 | | 0.076 | 222.0 | 3774 | 0.2463 | 0.3351 | 0.6701 | 0.6701 | nan | 0.6701 | 0.0 | 0.6701 | | 0.076 | 223.0 | 3791 | 0.2828 | 0.3337 | 0.6673 | 0.6673 | nan | 0.6673 | 0.0 | 0.6673 | | 0.0695 | 224.0 | 3808 | 0.2713 | 0.3310 | 0.6620 | 0.6620 | nan | 0.6620 | 0.0 | 0.6620 | | 0.0695 | 225.0 | 3825 | 0.2579 | 0.3943 | 0.7886 | 0.7886 | nan | 0.7886 | 0.0 | 0.7886 | | 0.0695 | 226.0 | 3842 | 0.2866 | 0.3328 | 0.6656 | 0.6656 | nan | 0.6656 | 0.0 | 0.6656 | | 0.0695 | 227.0 | 3859 | 0.2017 | 0.3899 | 0.7799 | 0.7799 | nan | 0.7799 | 0.0 | 0.7799 | | 0.0695 | 228.0 | 3876 | 0.2404 | 0.3488 | 0.6977 | 0.6977 | nan | 0.6977 | 0.0 | 0.6977 | | 0.0695 | 229.0 | 3893 | 0.2647 | 0.3062 | 0.6124 | 0.6124 | nan | 0.6124 | 0.0 | 0.6124 | | 0.0711 | 230.0 | 3910 | 0.3399 | 0.3073 | 0.6146 | 0.6146 | nan | 0.6146 | 0.0 | 0.6146 | | 0.0711 | 231.0 | 3927 | 0.2394 | 0.2819 | 0.5637 | 0.5637 | nan | 0.5637 | 0.0 | 0.5637 | | 0.0711 | 232.0 | 3944 | 0.2756 | 0.2701 | 0.5402 | 0.5402 | nan | 0.5402 | 0.0 | 0.5402 | | 0.0711 | 233.0 | 3961 | 0.3077 | 0.1907 | 0.3815 | 0.3815 | nan | 0.3815 | 0.0 | 0.3815 | | 0.0711 | 234.0 | 3978 | 0.2459 | 0.3408 | 0.6817 | 0.6817 | nan | 0.6817 | 0.0 | 0.6817 | | 0.0711 | 235.0 | 3995 | 0.2907 | 0.3307 | 0.6613 | 0.6613 | nan | 0.6613 | 0.0 | 0.6613 | | 0.0708 | 236.0 | 4012 | 0.2833 | 0.3640 | 0.7280 | 0.7280 | nan | 0.7280 | 0.0 | 0.7280 | | 0.0708 | 237.0 | 4029 | 0.3665 | 0.2503 | 0.5006 | 0.5006 | nan | 0.5006 | 0.0 | 0.5006 | | 0.0708 | 238.0 | 4046 | 0.3428 | 0.2794 | 0.5589 | 0.5589 | nan | 0.5589 | 0.0 | 0.5589 | | 0.0708 | 239.0 | 4063 | 0.3150 | 0.2842 | 0.5684 | 0.5684 | nan | 0.5684 | 0.0 | 0.5684 | | 0.0708 | 240.0 | 4080 | 0.2640 | 0.3057 | 0.6114 | 0.6114 | nan | 0.6114 | 0.0 | 0.6114 | | 0.0708 | 241.0 | 4097 | 0.2757 | 0.3047 | 0.6095 | 0.6095 | nan | 0.6095 | 0.0 | 0.6095 | | 0.0723 | 242.0 | 4114 | 0.3195 | 0.2267 | 0.4534 | 0.4534 | nan | 0.4534 | 0.0 | 0.4534 | | 0.0723 | 243.0 | 4131 | 0.2558 | 0.3521 | 0.7042 | 0.7042 | nan | 0.7042 | 0.0 | 0.7042 | | 0.0723 | 244.0 | 4148 | 0.2594 | 0.3304 | 0.6608 | 0.6608 | nan | 0.6608 | 0.0 | 0.6608 | | 0.0723 | 245.0 | 4165 | 0.3000 | 0.2973 | 0.5946 | 0.5946 | nan | 0.5946 | 0.0 | 0.5946 | | 0.0723 | 246.0 | 4182 | 0.2605 | 0.2818 | 0.5635 | 0.5635 | nan | 0.5635 | 0.0 | 0.5635 | | 0.0723 | 247.0 | 4199 | 0.4257 | 0.2287 | 0.4573 | 0.4573 | nan | 0.4573 | 0.0 | 0.4573 | | 0.071 | 248.0 | 4216 | 0.2179 | 0.3327 | 0.6654 | 0.6654 | nan | 0.6654 | 0.0 | 0.6654 | | 0.071 | 249.0 | 4233 | 0.2834 | 0.3390 | 0.6780 | 0.6780 | nan | 0.6780 | 0.0 | 0.6780 | | 0.071 | 250.0 | 4250 | 0.2524 | 0.3294 | 0.6587 | 0.6587 | nan | 0.6587 | 0.0 | 0.6587 | | 0.071 | 251.0 | 4267 | 0.2615 | 0.3814 | 0.7628 | 0.7628 | nan | 0.7628 | 0.0 | 0.7628 | | 0.071 | 252.0 | 4284 | 0.3364 | 0.3251 | 0.6502 | 0.6502 | nan | 0.6502 | 0.0 | 0.6502 | | 0.0698 | 253.0 | 4301 | 0.2771 | 0.3535 | 0.7069 | 0.7069 | nan | 0.7069 | 0.0 | 0.7069 | | 0.0698 | 254.0 | 4318 | 0.2185 | 0.3216 | 0.6432 | 0.6432 | nan | 0.6432 | 0.0 | 0.6432 | | 0.0698 | 255.0 | 4335 | 0.3342 | 0.2726 | 0.5452 | 0.5452 | nan | 0.5452 | 0.0 | 0.5452 | | 0.0698 | 256.0 | 4352 | 0.3268 | 0.2718 | 0.5436 | 0.5436 | nan | 0.5436 | 0.0 | 0.5436 | | 0.0698 | 257.0 | 4369 | 0.2967 | 0.2964 | 0.5929 | 0.5929 | nan | 0.5929 | 0.0 | 0.5929 | | 0.0698 | 258.0 | 4386 | 0.2684 | 0.3280 | 0.6559 | 0.6559 | nan | 0.6559 | 0.0 | 0.6559 | | 0.0669 | 259.0 | 4403 | 0.3126 | 0.2626 | 0.5251 | 0.5251 | nan | 0.5251 | 0.0 | 0.5251 | | 0.0669 | 260.0 | 4420 | 0.2726 | 0.3176 | 0.6353 | 0.6353 | nan | 0.6353 | 0.0 | 0.6353 | | 0.0669 | 261.0 | 4437 | 0.3289 | 0.2687 | 0.5374 | 0.5374 | nan | 0.5374 | 0.0 | 0.5374 | | 0.0669 | 262.0 | 4454 | 0.2902 | 0.3025 | 0.6050 | 0.6050 | nan | 0.6050 | 0.0 | 0.6050 | | 0.0669 | 263.0 | 4471 | 0.2427 | 0.3462 | 0.6925 | 0.6925 | nan | 0.6925 | 0.0 | 0.6925 | | 0.0669 | 264.0 | 4488 | 0.3117 | 0.2864 | 0.5727 | 0.5727 | nan | 0.5727 | 0.0 | 0.5727 | | 0.062 | 265.0 | 4505 | 0.2793 | 0.3098 | 0.6195 | 0.6195 | nan | 0.6195 | 0.0 | 0.6195 | | 0.062 | 266.0 | 4522 | 0.2447 | 0.3196 | 0.6391 | 0.6391 | nan | 0.6391 | 0.0 | 0.6391 | | 0.062 | 267.0 | 4539 | 0.2943 | 0.2910 | 0.5821 | 0.5821 | nan | 0.5821 | 0.0 | 0.5821 | | 0.062 | 268.0 | 4556 | 0.2506 | 0.2764 | 0.5527 | 0.5527 | nan | 0.5527 | 0.0 | 0.5527 | | 0.062 | 269.0 | 4573 | 0.2507 | 0.3380 | 0.6760 | 0.6760 | nan | 0.6760 | 0.0 | 0.6760 | | 0.062 | 270.0 | 4590 | 0.2783 | 0.2740 | 0.5480 | 0.5480 | nan | 0.5480 | 0.0 | 0.5480 | | 0.0565 | 271.0 | 4607 | 0.2896 | 0.3476 | 0.6952 | 0.6952 | nan | 0.6952 | 0.0 | 0.6952 | | 0.0565 | 272.0 | 4624 | 0.2855 | 0.3333 | 0.6665 | 0.6665 | nan | 0.6665 | 0.0 | 0.6665 | | 0.0565 | 273.0 | 4641 | 0.2183 | 0.3395 | 0.6790 | 0.6790 | nan | 0.6790 | 0.0 | 0.6790 | | 0.0565 | 274.0 | 4658 | 0.2132 | 0.3567 | 0.7134 | 0.7134 | nan | 0.7134 | 0.0 | 0.7134 | | 0.0565 | 275.0 | 4675 | 0.2632 | 0.3130 | 0.6260 | 0.6260 | nan | 0.6260 | 0.0 | 0.6260 | | 0.0565 | 276.0 | 4692 | 0.2548 | 0.2878 | 0.5756 | 0.5756 | nan | 0.5756 | 0.0 | 0.5756 | | 0.0585 | 277.0 | 4709 | 0.2379 | 0.3666 | 0.7333 | 0.7333 | nan | 0.7333 | 0.0 | 0.7333 | | 0.0585 | 278.0 | 4726 | 0.3023 | 0.2528 | 0.5056 | 0.5056 | nan | 0.5056 | 0.0 | 0.5056 | | 0.0585 | 279.0 | 4743 | 0.2598 | 0.3065 | 0.6130 | 0.6130 | nan | 0.6130 | 0.0 | 0.6130 | | 0.0585 | 280.0 | 4760 | 0.2836 | 0.3839 | 0.7679 | 0.7679 | nan | 0.7679 | 0.0 | 0.7679 | | 0.0585 | 281.0 | 4777 | 0.2904 | 0.2997 | 0.5994 | 0.5994 | nan | 0.5994 | 0.0 | 0.5994 | | 0.0585 | 282.0 | 4794 | 0.2363 | 0.3810 | 0.7621 | 0.7621 | nan | 0.7621 | 0.0 | 0.7621 | | 0.0622 | 283.0 | 4811 | 0.2126 | 0.3643 | 0.7286 | 0.7286 | nan | 0.7286 | 0.0 | 0.7286 | | 0.0622 | 284.0 | 4828 | 0.2295 | 0.3264 | 0.6528 | 0.6528 | nan | 0.6528 | 0.0 | 0.6528 | | 0.0622 | 285.0 | 4845 | 0.2663 | 0.2939 | 0.5879 | 0.5879 | nan | 0.5879 | 0.0 | 0.5879 | | 0.0622 | 286.0 | 4862 | 0.3011 | 0.2987 | 0.5974 | 0.5974 | nan | 0.5974 | 0.0 | 0.5974 | | 0.0622 | 287.0 | 4879 | 0.2626 | 0.3025 | 0.6051 | 0.6051 | nan | 0.6051 | 0.0 | 0.6051 | | 0.0622 | 288.0 | 4896 | 0.2773 | 0.2663 | 0.5326 | 0.5326 | nan | 0.5326 | 0.0 | 0.5326 | | 0.0608 | 289.0 | 4913 | 0.2915 | 0.2532 | 0.5064 | 0.5064 | nan | 0.5064 | 0.0 | 0.5064 | | 0.0608 | 290.0 | 4930 | 0.2915 | 0.2240 | 0.4480 | 0.4480 | nan | 0.4480 | 0.0 | 0.4480 | | 0.0608 | 291.0 | 4947 | 0.2605 | 0.3051 | 0.6103 | 0.6103 | nan | 0.6103 | 0.0 | 0.6103 | | 0.0608 | 292.0 | 4964 | 0.3391 | 0.2572 | 0.5145 | 0.5145 | nan | 0.5145 | 0.0 | 0.5145 | | 0.0608 | 293.0 | 4981 | 0.2621 | 0.2815 | 0.5629 | 0.5629 | nan | 0.5629 | 0.0 | 0.5629 | | 0.0608 | 294.0 | 4998 | 0.2932 | 0.3072 | 0.6144 | 0.6144 | nan | 0.6144 | 0.0 | 0.6144 | | 0.0536 | 295.0 | 5015 | 0.3546 | 0.2762 | 0.5524 | 0.5524 | nan | 0.5524 | 0.0 | 0.5524 | | 0.0536 | 296.0 | 5032 | 0.2652 | 0.2772 | 0.5545 | 0.5545 | nan | 0.5545 | 0.0 | 0.5545 | | 0.0536 | 297.0 | 5049 | 0.2781 | 0.2515 | 0.5030 | 0.5030 | nan | 0.5030 | 0.0 | 0.5030 | | 0.0536 | 298.0 | 5066 | 0.2777 | 0.2666 | 0.5333 | 0.5333 | nan | 0.5333 | 0.0 | 0.5333 | | 0.0536 | 299.0 | 5083 | 0.2573 | 0.2863 | 0.5726 | 0.5726 | nan | 0.5726 | 0.0 | 0.5726 | | 0.0572 | 300.0 | 5100 | 0.2443 | 0.3174 | 0.6348 | 0.6348 | nan | 0.6348 | 0.0 | 0.6348 | | 0.0572 | 301.0 | 5117 | 0.2724 | 0.3107 | 0.6214 | 0.6214 | nan | 0.6214 | 0.0 | 0.6214 | | 0.0572 | 302.0 | 5134 | 0.2443 | 0.3254 | 0.6509 | 0.6509 | nan | 0.6509 | 0.0 | 0.6509 | | 0.0572 | 303.0 | 5151 | 0.2556 | 0.2854 | 0.5708 | 0.5708 | nan | 0.5708 | 0.0 | 0.5708 | | 0.0572 | 304.0 | 5168 | 0.2798 | 0.3086 | 0.6171 | 0.6171 | nan | 0.6171 | 0.0 | 0.6171 | | 0.0572 | 305.0 | 5185 | 0.2805 | 0.3136 | 0.6271 | 0.6271 | nan | 0.6271 | 0.0 | 0.6271 | | 0.057 | 306.0 | 5202 | 0.1925 | 0.3286 | 0.6572 | 0.6572 | nan | 0.6572 | 0.0 | 0.6572 | | 0.057 | 307.0 | 5219 | 0.2125 | 0.4029 | 0.8058 | 0.8058 | nan | 0.8058 | 0.0 | 0.8058 | | 0.057 | 308.0 | 5236 | 0.2009 | 0.3365 | 0.6731 | 0.6731 | nan | 0.6731 | 0.0 | 0.6731 | | 0.057 | 309.0 | 5253 | 0.2251 | 0.3493 | 0.6986 | 0.6986 | nan | 0.6986 | 0.0 | 0.6986 | | 0.057 | 310.0 | 5270 | 0.2298 | 0.3558 | 0.7116 | 0.7116 | nan | 0.7116 | 0.0 | 0.7116 | | 0.057 | 311.0 | 5287 | 0.2192 | 0.2883 | 0.5767 | 0.5767 | nan | 0.5767 | 0.0 | 0.5767 | | 0.0522 | 312.0 | 5304 | 0.2349 | 0.3551 | 0.7102 | 0.7102 | nan | 0.7102 | 0.0 | 0.7102 | | 0.0522 | 313.0 | 5321 | 0.1832 | 0.4190 | 0.8379 | 0.8379 | nan | 0.8379 | 0.0 | 0.8379 | | 0.0522 | 314.0 | 5338 | 0.2119 | 0.3985 | 0.7971 | 0.7971 | nan | 0.7971 | 0.0 | 0.7971 | | 0.0522 | 315.0 | 5355 | 0.2038 | 0.4374 | 0.8747 | 0.8747 | nan | 0.8747 | 0.0 | 0.8747 | | 0.0522 | 316.0 | 5372 | 0.2124 | 0.3713 | 0.7427 | 0.7427 | nan | 0.7427 | 0.0 | 0.7427 | | 0.0522 | 317.0 | 5389 | 0.1980 | 0.3498 | 0.6995 | 0.6995 | nan | 0.6995 | 0.0 | 0.6995 | | 0.0523 | 318.0 | 5406 | 0.2483 | 0.3521 | 0.7042 | 0.7042 | nan | 0.7042 | 0.0 | 0.7042 | | 0.0523 | 319.0 | 5423 | 0.2873 | 0.3058 | 0.6116 | 0.6116 | nan | 0.6116 | 0.0 | 0.6116 | | 0.0523 | 320.0 | 5440 | 0.2366 | 0.3530 | 0.7060 | 0.7060 | nan | 0.7060 | 0.0 | 0.7060 | | 0.0523 | 321.0 | 5457 | 0.2613 | 0.3360 | 0.6720 | 0.6720 | nan | 0.6720 | 0.0 | 0.6720 | | 0.0523 | 322.0 | 5474 | 0.2392 | 0.3401 | 0.6802 | 0.6802 | nan | 0.6802 | 0.0 | 0.6802 | | 0.0523 | 323.0 | 5491 | 0.2279 | 0.3464 | 0.6928 | 0.6928 | nan | 0.6928 | 0.0 | 0.6928 | | 0.05 | 324.0 | 5508 | 0.2718 | 0.2724 | 0.5449 | 0.5449 | nan | 0.5449 | 0.0 | 0.5449 | | 0.05 | 325.0 | 5525 | 0.2367 | 0.3879 | 0.7758 | 0.7758 | nan | 0.7758 | 0.0 | 0.7758 | | 0.05 | 326.0 | 5542 | 0.2976 | 0.3504 | 0.7008 | 0.7008 | nan | 0.7008 | 0.0 | 0.7008 | | 0.05 | 327.0 | 5559 | 0.2424 | 0.3214 | 0.6429 | 0.6429 | nan | 0.6429 | 0.0 | 0.6429 | | 0.05 | 328.0 | 5576 | 0.2852 | 0.3042 | 0.6084 | 0.6084 | nan | 0.6084 | 0.0 | 0.6084 | | 0.05 | 329.0 | 5593 | 0.2473 | 0.2739 | 0.5479 | 0.5479 | nan | 0.5479 | 0.0 | 0.5479 | | 0.0552 | 330.0 | 5610 | 0.2461 | 0.3971 | 0.7943 | 0.7943 | nan | 0.7943 | 0.0 | 0.7943 | | 0.0552 | 331.0 | 5627 | 0.2122 | 0.3252 | 0.6503 | 0.6503 | nan | 0.6503 | 0.0 | 0.6503 | | 0.0552 | 332.0 | 5644 | 0.2318 | 0.2996 | 0.5993 | 0.5993 | nan | 0.5993 | 0.0 | 0.5993 | | 0.0552 | 333.0 | 5661 | 0.2186 | 0.3774 | 0.7547 | 0.7547 | nan | 0.7547 | 0.0 | 0.7547 | | 0.0552 | 334.0 | 5678 | 0.2682 | 0.2615 | 0.5230 | 0.5230 | nan | 0.5230 | 0.0 | 0.5230 | | 0.0552 | 335.0 | 5695 | 0.2490 | 0.3225 | 0.6451 | 0.6451 | nan | 0.6451 | 0.0 | 0.6451 | | 0.0524 | 336.0 | 5712 | 0.2334 | 0.3464 | 0.6927 | 0.6927 | nan | 0.6927 | 0.0 | 0.6927 | | 0.0524 | 337.0 | 5729 | 0.2234 | 0.3504 | 0.7008 | 0.7008 | nan | 0.7008 | 0.0 | 0.7008 | | 0.0524 | 338.0 | 5746 | 0.2413 | 0.3737 | 0.7473 | 0.7473 | nan | 0.7473 | 0.0 | 0.7473 | | 0.0524 | 339.0 | 5763 | 0.2368 | 0.3699 | 0.7399 | 0.7399 | nan | 0.7399 | 0.0 | 0.7399 | | 0.0524 | 340.0 | 5780 | 0.2442 | 0.3809 | 0.7619 | 0.7619 | nan | 0.7619 | 0.0 | 0.7619 | | 0.0524 | 341.0 | 5797 | 0.2208 | 0.3740 | 0.7481 | 0.7481 | nan | 0.7481 | 0.0 | 0.7481 | | 0.0513 | 342.0 | 5814 | 0.2509 | 0.3849 | 0.7698 | 0.7698 | nan | 0.7698 | 0.0 | 0.7698 | | 0.0513 | 343.0 | 5831 | 0.2194 | 0.3553 | 0.7106 | 0.7106 | nan | 0.7106 | 0.0 | 0.7106 | | 0.0513 | 344.0 | 5848 | 0.2536 | 0.3990 | 0.7980 | 0.7980 | nan | 0.7980 | 0.0 | 0.7980 | | 0.0513 | 345.0 | 5865 | 0.2803 | 0.3740 | 0.7479 | 0.7479 | nan | 0.7479 | 0.0 | 0.7479 | | 0.0513 | 346.0 | 5882 | 0.2732 | 0.3944 | 0.7889 | 0.7889 | nan | 0.7889 | 0.0 | 0.7889 | | 0.0513 | 347.0 | 5899 | 0.2759 | 0.3588 | 0.7176 | 0.7176 | nan | 0.7176 | 0.0 | 0.7176 | | 0.0504 | 348.0 | 5916 | 0.2941 | 0.3472 | 0.6945 | 0.6945 | nan | 0.6945 | 0.0 | 0.6945 | | 0.0504 | 349.0 | 5933 | 0.2170 | 0.3646 | 0.7292 | 0.7292 | nan | 0.7292 | 0.0 | 0.7292 | | 0.0504 | 350.0 | 5950 | 0.2464 | 0.3842 | 0.7685 | 0.7685 | nan | 0.7685 | 0.0 | 0.7685 | | 0.0504 | 351.0 | 5967 | 0.2394 | 0.3697 | 0.7395 | 0.7395 | nan | 0.7395 | 0.0 | 0.7395 | | 0.0504 | 352.0 | 5984 | 0.2645 | 0.3719 | 0.7438 | 0.7438 | nan | 0.7438 | 0.0 | 0.7438 | | 0.0497 | 353.0 | 6001 | 0.2226 | 0.3932 | 0.7865 | 0.7865 | nan | 0.7865 | 0.0 | 0.7865 | | 0.0497 | 354.0 | 6018 | 0.2579 | 0.3718 | 0.7436 | 0.7436 | nan | 0.7436 | 0.0 | 0.7436 | | 0.0497 | 355.0 | 6035 | 0.2149 | 0.3905 | 0.7810 | 0.7810 | nan | 0.7810 | 0.0 | 0.7810 | | 0.0497 | 356.0 | 6052 | 0.2202 | 0.3803 | 0.7606 | 0.7606 | nan | 0.7606 | 0.0 | 0.7606 | | 0.0497 | 357.0 | 6069 | 0.2281 | 0.3398 | 0.6795 | 0.6795 | nan | 0.6795 | 0.0 | 0.6795 | | 0.0497 | 358.0 | 6086 | 0.2857 | 0.3210 | 0.6419 | 0.6419 | nan | 0.6419 | 0.0 | 0.6419 | | 0.0464 | 359.0 | 6103 | 0.2393 | 0.3573 | 0.7146 | 0.7146 | nan | 0.7146 | 0.0 | 0.7146 | | 0.0464 | 360.0 | 6120 | 0.2901 | 0.2873 | 0.5747 | 0.5747 | nan | 0.5747 | 0.0 | 0.5747 | | 0.0464 | 361.0 | 6137 | 0.2689 | 0.3335 | 0.6669 | 0.6669 | nan | 0.6669 | 0.0 | 0.6669 | | 0.0464 | 362.0 | 6154 | 0.3531 | 0.2828 | 0.5655 | 0.5655 | nan | 0.5655 | 0.0 | 0.5655 | | 0.0464 | 363.0 | 6171 | 0.2432 | 0.4020 | 0.8039 | 0.8039 | nan | 0.8039 | 0.0 | 0.8039 | | 0.0464 | 364.0 | 6188 | 0.2345 | 0.3927 | 0.7855 | 0.7855 | nan | 0.7855 | 0.0 | 0.7855 | | 0.0513 | 365.0 | 6205 | 0.3075 | 0.3593 | 0.7185 | 0.7185 | nan | 0.7185 | 0.0 | 0.7185 | | 0.0513 | 366.0 | 6222 | 0.2853 | 0.3391 | 0.6782 | 0.6782 | nan | 0.6782 | 0.0 | 0.6782 | | 0.0513 | 367.0 | 6239 | 0.3308 | 0.3121 | 0.6243 | 0.6243 | nan | 0.6243 | 0.0 | 0.6243 | | 0.0513 | 368.0 | 6256 | 0.2364 | 0.3805 | 0.7610 | 0.7610 | nan | 0.7610 | 0.0 | 0.7610 | | 0.0513 | 369.0 | 6273 | 0.2420 | 0.3628 | 0.7257 | 0.7257 | nan | 0.7257 | 0.0 | 0.7257 | | 0.0513 | 370.0 | 6290 | 0.2562 | 0.3500 | 0.7000 | 0.7000 | nan | 0.7000 | 0.0 | 0.7000 | | 0.0491 | 371.0 | 6307 | 0.2455 | 0.3431 | 0.6863 | 0.6863 | nan | 0.6863 | 0.0 | 0.6863 | | 0.0491 | 372.0 | 6324 | 0.2793 | 0.3599 | 0.7198 | 0.7198 | nan | 0.7198 | 0.0 | 0.7198 | | 0.0491 | 373.0 | 6341 | 0.2232 | 0.3733 | 0.7466 | 0.7466 | nan | 0.7466 | 0.0 | 0.7466 | | 0.0491 | 374.0 | 6358 | 0.2899 | 0.3353 | 0.6706 | 0.6706 | nan | 0.6706 | 0.0 | 0.6706 | | 0.0491 | 375.0 | 6375 | 0.2351 | 0.3562 | 0.7124 | 0.7124 | nan | 0.7124 | 0.0 | 0.7124 | | 0.0491 | 376.0 | 6392 | 0.2447 | 0.3252 | 0.6504 | 0.6504 | nan | 0.6504 | 0.0 | 0.6504 | | 0.0477 | 377.0 | 6409 | 0.2346 | 0.3344 | 0.6689 | 0.6689 | nan | 0.6689 | 0.0 | 0.6689 | | 0.0477 | 378.0 | 6426 | 0.2468 | 0.3630 | 0.7259 | 0.7259 | nan | 0.7259 | 0.0 | 0.7259 | | 0.0477 | 379.0 | 6443 | 0.3616 | 0.3322 | 0.6645 | 0.6645 | nan | 0.6645 | 0.0 | 0.6645 | | 0.0477 | 380.0 | 6460 | 0.2846 | 0.3387 | 0.6775 | 0.6775 | nan | 0.6775 | 0.0 | 0.6775 | | 0.0477 | 381.0 | 6477 | 0.3733 | 0.2779 | 0.5558 | 0.5558 | nan | 0.5558 | 0.0 | 0.5558 | | 0.0477 | 382.0 | 6494 | 0.3140 | 0.3178 | 0.6356 | 0.6356 | nan | 0.6356 | 0.0 | 0.6356 | | 0.0446 | 383.0 | 6511 | 0.3926 | 0.3009 | 0.6018 | 0.6018 | nan | 0.6018 | 0.0 | 0.6018 | | 0.0446 | 384.0 | 6528 | 0.2901 | 0.3070 | 0.6140 | 0.6140 | nan | 0.6140 | 0.0 | 0.6140 | | 0.0446 | 385.0 | 6545 | 0.3247 | 0.3514 | 0.7027 | 0.7027 | nan | 0.7027 | 0.0 | 0.7027 | | 0.0446 | 386.0 | 6562 | 0.3177 | 0.3340 | 0.6681 | 0.6681 | nan | 0.6681 | 0.0 | 0.6681 | | 0.0446 | 387.0 | 6579 | 0.2939 | 0.3542 | 0.7084 | 0.7084 | nan | 0.7084 | 0.0 | 0.7084 | | 0.0446 | 388.0 | 6596 | 0.2585 | 0.3410 | 0.6820 | 0.6820 | nan | 0.6820 | 0.0 | 0.6820 | | 0.0489 | 389.0 | 6613 | 0.3240 | 0.3161 | 0.6321 | 0.6321 | nan | 0.6321 | 0.0 | 0.6321 | | 0.0489 | 390.0 | 6630 | 0.2895 | 0.3698 | 0.7396 | 0.7396 | nan | 0.7396 | 0.0 | 0.7396 | | 0.0489 | 391.0 | 6647 | 0.2994 | 0.3214 | 0.6428 | 0.6428 | nan | 0.6428 | 0.0 | 0.6428 | | 0.0489 | 392.0 | 6664 | 0.2779 | 0.3668 | 0.7336 | 0.7336 | nan | 0.7336 | 0.0 | 0.7336 | | 0.0489 | 393.0 | 6681 | 0.2841 | 0.3098 | 0.6197 | 0.6197 | nan | 0.6197 | 0.0 | 0.6197 | | 0.0489 | 394.0 | 6698 | 0.3211 | 0.2982 | 0.5964 | 0.5964 | nan | 0.5964 | 0.0 | 0.5964 | | 0.0449 | 395.0 | 6715 | 0.3262 | 0.2988 | 0.5977 | 0.5977 | nan | 0.5977 | 0.0 | 0.5977 | | 0.0449 | 396.0 | 6732 | 0.3133 | 0.3101 | 0.6202 | 0.6202 | nan | 0.6202 | 0.0 | 0.6202 | | 0.0449 | 397.0 | 6749 | 0.3241 | 0.3381 | 0.6762 | 0.6762 | nan | 0.6762 | 0.0 | 0.6762 | | 0.0449 | 398.0 | 6766 | 0.3566 | 0.3000 | 0.5999 | 0.5999 | nan | 0.5999 | 0.0 | 0.5999 | | 0.0449 | 399.0 | 6783 | 0.2994 | 0.3600 | 0.7199 | 0.7199 | nan | 0.7199 | 0.0 | 0.7199 | | 0.0463 | 400.0 | 6800 | 0.2672 | 0.3268 | 0.6536 | 0.6536 | nan | 0.6536 | 0.0 | 0.6536 | | 0.0463 | 401.0 | 6817 | 0.2945 | 0.3565 | 0.7131 | 0.7131 | nan | 0.7131 | 0.0 | 0.7131 | | 0.0463 | 402.0 | 6834 | 0.2970 | 0.3397 | 0.6794 | 0.6794 | nan | 0.6794 | 0.0 | 0.6794 | | 0.0463 | 403.0 | 6851 | 0.2687 | 0.3577 | 0.7154 | 0.7154 | nan | 0.7154 | 0.0 | 0.7154 | | 0.0463 | 404.0 | 6868 | 0.2877 | 0.3387 | 0.6774 | 0.6774 | nan | 0.6774 | 0.0 | 0.6774 | | 0.0463 | 405.0 | 6885 | 0.2600 | 0.3489 | 0.6977 | 0.6977 | nan | 0.6977 | 0.0 | 0.6977 | | 0.0443 | 406.0 | 6902 | 0.3077 | 0.3165 | 0.6330 | 0.6330 | nan | 0.6330 | 0.0 | 0.6330 | | 0.0443 | 407.0 | 6919 | 0.2634 | 0.3436 | 0.6873 | 0.6873 | nan | 0.6873 | 0.0 | 0.6873 | | 0.0443 | 408.0 | 6936 | 0.2766 | 0.3393 | 0.6786 | 0.6786 | nan | 0.6786 | 0.0 | 0.6786 | | 0.0443 | 409.0 | 6953 | 0.2781 | 0.3096 | 0.6191 | 0.6191 | nan | 0.6191 | 0.0 | 0.6191 | | 0.0443 | 410.0 | 6970 | 0.2749 | 0.3357 | 0.6714 | 0.6714 | nan | 0.6714 | 0.0 | 0.6714 | | 0.0443 | 411.0 | 6987 | 0.3368 | 0.3182 | 0.6364 | 0.6364 | nan | 0.6364 | 0.0 | 0.6364 | | 0.0429 | 412.0 | 7004 | 0.3096 | 0.3016 | 0.6032 | 0.6032 | nan | 0.6032 | 0.0 | 0.6032 | | 0.0429 | 413.0 | 7021 | 0.3003 | 0.3402 | 0.6804 | 0.6804 | nan | 0.6804 | 0.0 | 0.6804 | | 0.0429 | 414.0 | 7038 | 0.2831 | 0.3491 | 0.6983 | 0.6983 | nan | 0.6983 | 0.0 | 0.6983 | | 0.0429 | 415.0 | 7055 | 0.3294 | 0.2715 | 0.5430 | 0.5430 | nan | 0.5430 | 0.0 | 0.5430 | | 0.0429 | 416.0 | 7072 | 0.2549 | 0.3333 | 0.6665 | 0.6665 | nan | 0.6665 | 0.0 | 0.6665 | | 0.0429 | 417.0 | 7089 | 0.3011 | 0.2956 | 0.5911 | 0.5911 | nan | 0.5911 | 0.0 | 0.5911 | | 0.0394 | 418.0 | 7106 | 0.2614 | 0.3257 | 0.6514 | 0.6514 | nan | 0.6514 | 0.0 | 0.6514 | | 0.0394 | 419.0 | 7123 | 0.3040 | 0.3185 | 0.6369 | 0.6369 | nan | 0.6369 | 0.0 | 0.6369 | | 0.0394 | 420.0 | 7140 | 0.2870 | 0.3443 | 0.6885 | 0.6885 | nan | 0.6885 | 0.0 | 0.6885 | | 0.0394 | 421.0 | 7157 | 0.2593 | 0.3536 | 0.7072 | 0.7072 | nan | 0.7072 | 0.0 | 0.7072 | | 0.0394 | 422.0 | 7174 | 0.2808 | 0.3458 | 0.6915 | 0.6915 | nan | 0.6915 | 0.0 | 0.6915 | | 0.0394 | 423.0 | 7191 | 0.2958 | 0.3261 | 0.6522 | 0.6522 | nan | 0.6522 | 0.0 | 0.6522 | | 0.0432 | 424.0 | 7208 | 0.2818 | 0.3377 | 0.6753 | 0.6753 | nan | 0.6753 | 0.0 | 0.6753 | | 0.0432 | 425.0 | 7225 | 0.3045 | 0.3385 | 0.6769 | 0.6769 | nan | 0.6769 | 0.0 | 0.6769 | | 0.0432 | 426.0 | 7242 | 0.3077 | 0.3070 | 0.6139 | 0.6139 | nan | 0.6139 | 0.0 | 0.6139 | | 0.0432 | 427.0 | 7259 | 0.3225 | 0.3227 | 0.6455 | 0.6455 | nan | 0.6455 | 0.0 | 0.6455 | | 0.0432 | 428.0 | 7276 | 0.3013 | 0.3387 | 0.6774 | 0.6774 | nan | 0.6774 | 0.0 | 0.6774 | | 0.0432 | 429.0 | 7293 | 0.2909 | 0.3348 | 0.6697 | 0.6697 | nan | 0.6697 | 0.0 | 0.6697 | | 0.0419 | 430.0 | 7310 | 0.3022 | 0.3250 | 0.6501 | 0.6501 | nan | 0.6501 | 0.0 | 0.6501 | | 0.0419 | 431.0 | 7327 | 0.2724 | 0.3467 | 0.6934 | 0.6934 | nan | 0.6934 | 0.0 | 0.6934 | | 0.0419 | 432.0 | 7344 | 0.3426 | 0.3265 | 0.6529 | 0.6529 | nan | 0.6529 | 0.0 | 0.6529 | | 0.0419 | 433.0 | 7361 | 0.2962 | 0.3380 | 0.6760 | 0.6760 | nan | 0.6760 | 0.0 | 0.6760 | | 0.0419 | 434.0 | 7378 | 0.2787 | 0.3242 | 0.6484 | 0.6484 | nan | 0.6484 | 0.0 | 0.6484 | | 0.0419 | 435.0 | 7395 | 0.3095 | 0.3365 | 0.6730 | 0.6730 | nan | 0.6730 | 0.0 | 0.6730 | | 0.0398 | 436.0 | 7412 | 0.3047 | 0.3122 | 0.6243 | 0.6243 | nan | 0.6243 | 0.0 | 0.6243 | | 0.0398 | 437.0 | 7429 | 0.3225 | 0.3024 | 0.6048 | 0.6048 | nan | 0.6048 | 0.0 | 0.6048 | | 0.0398 | 438.0 | 7446 | 0.3274 | 0.2866 | 0.5731 | 0.5731 | nan | 0.5731 | 0.0 | 0.5731 | | 0.0398 | 439.0 | 7463 | 0.3423 | 0.2367 | 0.4735 | 0.4735 | nan | 0.4735 | 0.0 | 0.4735 | | 0.0398 | 440.0 | 7480 | 0.3609 | 0.2558 | 0.5117 | 0.5117 | nan | 0.5117 | 0.0 | 0.5117 | | 0.0398 | 441.0 | 7497 | 0.2948 | 0.3113 | 0.6227 | 0.6227 | nan | 0.6227 | 0.0 | 0.6227 | | 0.0455 | 442.0 | 7514 | 0.3299 | 0.2566 | 0.5132 | 0.5132 | nan | 0.5132 | 0.0 | 0.5132 | | 0.0455 | 443.0 | 7531 | 0.2974 | 0.3005 | 0.6011 | 0.6011 | nan | 0.6011 | 0.0 | 0.6011 | | 0.0455 | 444.0 | 7548 | 0.3208 | 0.2764 | 0.5529 | 0.5529 | nan | 0.5529 | 0.0 | 0.5529 | | 0.0455 | 445.0 | 7565 | 0.2894 | 0.3427 | 0.6854 | 0.6854 | nan | 0.6854 | 0.0 | 0.6854 | | 0.0455 | 446.0 | 7582 | 0.2981 | 0.3060 | 0.6119 | 0.6119 | nan | 0.6119 | 0.0 | 0.6119 | | 0.0455 | 447.0 | 7599 | 0.2535 | 0.3309 | 0.6618 | 0.6618 | nan | 0.6618 | 0.0 | 0.6618 | | 0.0434 | 448.0 | 7616 | 0.2865 | 0.2696 | 0.5392 | 0.5392 | nan | 0.5392 | 0.0 | 0.5392 | | 0.0434 | 449.0 | 7633 | 0.2940 | 0.3166 | 0.6332 | 0.6332 | nan | 0.6332 | 0.0 | 0.6332 | | 0.0434 | 450.0 | 7650 | 0.3000 | 0.3000 | 0.6001 | 0.6001 | nan | 0.6001 | 0.0 | 0.6001 | | 0.0434 | 451.0 | 7667 | 0.3164 | 0.2847 | 0.5694 | 0.5694 | nan | 0.5694 | 0.0 | 0.5694 | | 0.0434 | 452.0 | 7684 | 0.2465 | 0.3287 | 0.6575 | 0.6575 | nan | 0.6575 | 0.0 | 0.6575 | | 0.0412 | 453.0 | 7701 | 0.2836 | 0.3172 | 0.6344 | 0.6344 | nan | 0.6344 | 0.0 | 0.6344 | | 0.0412 | 454.0 | 7718 | 0.3480 | 0.2801 | 0.5603 | 0.5603 | nan | 0.5603 | 0.0 | 0.5603 | | 0.0412 | 455.0 | 7735 | 0.3047 | 0.3197 | 0.6394 | 0.6394 | nan | 0.6394 | 0.0 | 0.6394 | | 0.0412 | 456.0 | 7752 | 0.3430 | 0.3214 | 0.6428 | 0.6428 | nan | 0.6428 | 0.0 | 0.6428 | | 0.0412 | 457.0 | 7769 | 0.3213 | 0.3358 | 0.6717 | 0.6717 | nan | 0.6717 | 0.0 | 0.6717 | | 0.0412 | 458.0 | 7786 | 0.2413 | 0.3538 | 0.7076 | 0.7076 | nan | 0.7076 | 0.0 | 0.7076 | | 0.0399 | 459.0 | 7803 | 0.2633 | 0.3588 | 0.7176 | 0.7176 | nan | 0.7176 | 0.0 | 0.7176 | | 0.0399 | 460.0 | 7820 | 0.2632 | 0.3204 | 0.6409 | 0.6409 | nan | 0.6409 | 0.0 | 0.6409 | | 0.0399 | 461.0 | 7837 | 0.2530 | 0.3445 | 0.6890 | 0.6890 | nan | 0.6890 | 0.0 | 0.6890 | | 0.0399 | 462.0 | 7854 | 0.2841 | 0.3305 | 0.6610 | 0.6610 | nan | 0.6610 | 0.0 | 0.6610 | | 0.0399 | 463.0 | 7871 | 0.3084 | 0.3473 | 0.6945 | 0.6945 | nan | 0.6945 | 0.0 | 0.6945 | | 0.0399 | 464.0 | 7888 | 0.2717 | 0.3447 | 0.6893 | 0.6893 | nan | 0.6893 | 0.0 | 0.6893 | | 0.0368 | 465.0 | 7905 | 0.3134 | 0.3144 | 0.6288 | 0.6288 | nan | 0.6288 | 0.0 | 0.6288 | | 0.0368 | 466.0 | 7922 | 0.2957 | 0.3273 | 0.6546 | 0.6546 | nan | 0.6546 | 0.0 | 0.6546 | | 0.0368 | 467.0 | 7939 | 0.3295 | 0.3207 | 0.6413 | 0.6413 | nan | 0.6413 | 0.0 | 0.6413 | | 0.0368 | 468.0 | 7956 | 0.2908 | 0.3230 | 0.6461 | 0.6461 | nan | 0.6461 | 0.0 | 0.6461 | | 0.0368 | 469.0 | 7973 | 0.2739 | 0.3202 | 0.6405 | 0.6405 | nan | 0.6405 | 0.0 | 0.6405 | | 0.0368 | 470.0 | 7990 | 0.2764 | 0.3203 | 0.6406 | 0.6406 | nan | 0.6406 | 0.0 | 0.6406 | | 0.0368 | 471.0 | 8007 | 0.3048 | 0.3294 | 0.6587 | 0.6587 | nan | 0.6587 | 0.0 | 0.6587 | | 0.0368 | 472.0 | 8024 | 0.2829 | 0.3251 | 0.6502 | 0.6502 | nan | 0.6502 | 0.0 | 0.6502 | | 0.0368 | 473.0 | 8041 | 0.2717 | 0.3276 | 0.6551 | 0.6551 | nan | 0.6551 | 0.0 | 0.6551 | | 0.0368 | 474.0 | 8058 | 0.2902 | 0.3323 | 0.6647 | 0.6647 | nan | 0.6647 | 0.0 | 0.6647 | | 0.0368 | 475.0 | 8075 | 0.2730 | 0.3443 | 0.6886 | 0.6886 | nan | 0.6886 | 0.0 | 0.6886 | | 0.0368 | 476.0 | 8092 | 0.2629 | 0.3312 | 0.6624 | 0.6624 | nan | 0.6624 | 0.0 | 0.6624 | | 0.0399 | 477.0 | 8109 | 0.2836 | 0.3352 | 0.6704 | 0.6704 | nan | 0.6704 | 0.0 | 0.6704 | | 0.0399 | 478.0 | 8126 | 0.2831 | 0.3203 | 0.6405 | 0.6405 | nan | 0.6405 | 0.0 | 0.6405 | | 0.0399 | 479.0 | 8143 | 0.2949 | 0.3243 | 0.6487 | 0.6487 | nan | 0.6487 | 0.0 | 0.6487 | | 0.0399 | 480.0 | 8160 | 0.2918 | 0.3236 | 0.6471 | 0.6471 | nan | 0.6471 | 0.0 | 0.6471 | | 0.0399 | 481.0 | 8177 | 0.3012 | 0.3402 | 0.6805 | 0.6805 | nan | 0.6805 | 0.0 | 0.6805 | | 0.0399 | 482.0 | 8194 | 0.2733 | 0.3648 | 0.7297 | 0.7297 | nan | 0.7297 | 0.0 | 0.7297 | | 0.0369 | 483.0 | 8211 | 0.2584 | 0.3431 | 0.6862 | 0.6862 | nan | 0.6862 | 0.0 | 0.6862 | | 0.0369 | 484.0 | 8228 | 0.2849 | 0.3199 | 0.6397 | 0.6397 | nan | 0.6397 | 0.0 | 0.6397 | | 0.0369 | 485.0 | 8245 | 0.2702 | 0.3411 | 0.6822 | 0.6822 | nan | 0.6822 | 0.0 | 0.6822 | | 0.0369 | 486.0 | 8262 | 0.2605 | 0.3625 | 0.7251 | 0.7251 | nan | 0.7251 | 0.0 | 0.7251 | | 0.0369 | 487.0 | 8279 | 0.2785 | 0.3134 | 0.6269 | 0.6269 | nan | 0.6269 | 0.0 | 0.6269 | | 0.0369 | 488.0 | 8296 | 0.2773 | 0.3261 | 0.6521 | 0.6521 | nan | 0.6521 | 0.0 | 0.6521 | | 0.0383 | 489.0 | 8313 | 0.2727 | 0.3387 | 0.6774 | 0.6774 | nan | 0.6774 | 0.0 | 0.6774 | | 0.0383 | 490.0 | 8330 | 0.2823 | 0.3219 | 0.6438 | 0.6438 | nan | 0.6438 | 0.0 | 0.6438 | | 0.0383 | 491.0 | 8347 | 0.2764 | 0.3363 | 0.6727 | 0.6727 | nan | 0.6727 | 0.0 | 0.6727 | | 0.0383 | 492.0 | 8364 | 0.2678 | 0.3299 | 0.6598 | 0.6598 | nan | 0.6598 | 0.0 | 0.6598 | | 0.0383 | 493.0 | 8381 | 0.2692 | 0.3311 | 0.6621 | 0.6621 | nan | 0.6621 | 0.0 | 0.6621 | | 0.0383 | 494.0 | 8398 | 0.2727 | 0.3456 | 0.6911 | 0.6911 | nan | 0.6911 | 0.0 | 0.6911 | | 0.0394 | 495.0 | 8415 | 0.2802 | 0.3402 | 0.6804 | 0.6804 | nan | 0.6804 | 0.0 | 0.6804 | | 0.0394 | 496.0 | 8432 | 0.3122 | 0.3183 | 0.6367 | 0.6367 | nan | 0.6367 | 0.0 | 0.6367 | | 0.0394 | 497.0 | 8449 | 0.3404 | 0.3141 | 0.6281 | 0.6281 | nan | 0.6281 | 0.0 | 0.6281 | | 0.0394 | 498.0 | 8466 | 0.2993 | 0.3081 | 0.6162 | 0.6162 | nan | 0.6162 | 0.0 | 0.6162 | | 0.0394 | 499.0 | 8483 | 0.3233 | 0.3339 | 0.6679 | 0.6679 | nan | 0.6679 | 0.0 | 0.6679 | | 0.0367 | 500.0 | 8500 | 0.2986 | 0.3229 | 0.6458 | 0.6458 | nan | 0.6458 | 0.0 | 0.6458 | | 0.0367 | 501.0 | 8517 | 0.3184 | 0.3021 | 0.6042 | 0.6042 | nan | 0.6042 | 0.0 | 0.6042 | | 0.0367 | 502.0 | 8534 | 0.3128 | 0.3209 | 0.6419 | 0.6419 | nan | 0.6419 | 0.0 | 0.6419 | | 0.0367 | 503.0 | 8551 | 0.3056 | 0.2974 | 0.5949 | 0.5949 | nan | 0.5949 | 0.0 | 0.5949 | | 0.0367 | 504.0 | 8568 | 0.3157 | 0.3028 | 0.6056 | 0.6056 | nan | 0.6056 | 0.0 | 0.6056 | | 0.0367 | 505.0 | 8585 | 0.3014 | 0.3058 | 0.6115 | 0.6115 | nan | 0.6115 | 0.0 | 0.6115 | | 0.0391 | 506.0 | 8602 | 0.2977 | 0.3321 | 0.6643 | 0.6643 | nan | 0.6643 | 0.0 | 0.6643 | | 0.0391 | 507.0 | 8619 | 0.2992 | 0.3053 | 0.6105 | 0.6105 | nan | 0.6105 | 0.0 | 0.6105 | | 0.0391 | 508.0 | 8636 | 0.3318 | 0.3268 | 0.6536 | 0.6536 | nan | 0.6536 | 0.0 | 0.6536 | | 0.0391 | 509.0 | 8653 | 0.2701 | 0.3549 | 0.7097 | 0.7097 | nan | 0.7097 | 0.0 | 0.7097 | | 0.0391 | 510.0 | 8670 | 0.2745 | 0.3606 | 0.7212 | 0.7212 | nan | 0.7212 | 0.0 | 0.7212 | | 0.0391 | 511.0 | 8687 | 0.2766 | 0.3344 | 0.6687 | 0.6687 | nan | 0.6687 | 0.0 | 0.6687 | | 0.0386 | 512.0 | 8704 | 0.2634 | 0.3547 | 0.7094 | 0.7094 | nan | 0.7094 | 0.0 | 0.7094 | | 0.0386 | 513.0 | 8721 | 0.2668 | 0.3444 | 0.6888 | 0.6888 | nan | 0.6888 | 0.0 | 0.6888 | | 0.0386 | 514.0 | 8738 | 0.2762 | 0.3647 | 0.7295 | 0.7295 | nan | 0.7295 | 0.0 | 0.7295 | | 0.0386 | 515.0 | 8755 | 0.2736 | 0.3652 | 0.7304 | 0.7304 | nan | 0.7304 | 0.0 | 0.7304 | | 0.0386 | 516.0 | 8772 | 0.2756 | 0.3618 | 0.7236 | 0.7236 | nan | 0.7236 | 0.0 | 0.7236 | | 0.0386 | 517.0 | 8789 | 0.2841 | 0.3720 | 0.7440 | 0.7440 | nan | 0.7440 | 0.0 | 0.7440 | | 0.0338 | 518.0 | 8806 | 0.2861 | 0.3610 | 0.7220 | 0.7220 | nan | 0.7220 | 0.0 | 0.7220 | | 0.0338 | 519.0 | 8823 | 0.2724 | 0.3430 | 0.6861 | 0.6861 | nan | 0.6861 | 0.0 | 0.6861 | | 0.0338 | 520.0 | 8840 | 0.2911 | 0.3522 | 0.7044 | 0.7044 | nan | 0.7044 | 0.0 | 0.7044 | | 0.0338 | 521.0 | 8857 | 0.2740 | 0.3668 | 0.7335 | 0.7335 | nan | 0.7335 | 0.0 | 0.7335 | | 0.0338 | 522.0 | 8874 | 0.2657 | 0.3511 | 0.7023 | 0.7023 | nan | 0.7023 | 0.0 | 0.7023 | | 0.0338 | 523.0 | 8891 | 0.2856 | 0.3520 | 0.7041 | 0.7041 | nan | 0.7041 | 0.0 | 0.7041 | | 0.0369 | 524.0 | 8908 | 0.2662 | 0.3424 | 0.6848 | 0.6848 | nan | 0.6848 | 0.0 | 0.6848 | | 0.0369 | 525.0 | 8925 | 0.2772 | 0.3398 | 0.6796 | 0.6796 | nan | 0.6796 | 0.0 | 0.6796 | | 0.0369 | 526.0 | 8942 | 0.2844 | 0.3388 | 0.6776 | 0.6776 | nan | 0.6776 | 0.0 | 0.6776 | | 0.0369 | 527.0 | 8959 | 0.2786 | 0.3548 | 0.7096 | 0.7096 | nan | 0.7096 | 0.0 | 0.7096 | | 0.0369 | 528.0 | 8976 | 0.2524 | 0.3508 | 0.7016 | 0.7016 | nan | 0.7016 | 0.0 | 0.7016 | | 0.0369 | 529.0 | 8993 | 0.2647 | 0.3509 | 0.7017 | 0.7017 | nan | 0.7017 | 0.0 | 0.7017 | | 0.0361 | 530.0 | 9010 | 0.2829 | 0.3421 | 0.6842 | 0.6842 | nan | 0.6842 | 0.0 | 0.6842 | | 0.0361 | 531.0 | 9027 | 0.2837 | 0.3338 | 0.6676 | 0.6676 | nan | 0.6676 | 0.0 | 0.6676 | | 0.0361 | 532.0 | 9044 | 0.2799 | 0.3444 | 0.6888 | 0.6888 | nan | 0.6888 | 0.0 | 0.6888 | | 0.0361 | 533.0 | 9061 | 0.2871 | 0.3425 | 0.6850 | 0.6850 | nan | 0.6850 | 0.0 | 0.6850 | | 0.0361 | 534.0 | 9078 | 0.2889 | 0.3486 | 0.6971 | 0.6971 | nan | 0.6971 | 0.0 | 0.6971 | | 0.0361 | 535.0 | 9095 | 0.2979 | 0.3557 | 0.7115 | 0.7115 | nan | 0.7115 | 0.0 | 0.7115 | | 0.0367 | 536.0 | 9112 | 0.2985 | 0.3555 | 0.7109 | 0.7109 | nan | 0.7109 | 0.0 | 0.7109 | | 0.0367 | 537.0 | 9129 | 0.2993 | 0.3417 | 0.6835 | 0.6835 | nan | 0.6835 | 0.0 | 0.6835 | | 0.0367 | 538.0 | 9146 | 0.2988 | 0.3512 | 0.7023 | 0.7023 | nan | 0.7023 | 0.0 | 0.7023 | | 0.0367 | 539.0 | 9163 | 0.3023 | 0.3459 | 0.6918 | 0.6918 | nan | 0.6918 | 0.0 | 0.6918 | | 0.0367 | 540.0 | 9180 | 0.2876 | 0.3139 | 0.6277 | 0.6277 | nan | 0.6277 | 0.0 | 0.6277 | | 0.0367 | 541.0 | 9197 | 0.2854 | 0.3197 | 0.6394 | 0.6394 | nan | 0.6394 | 0.0 | 0.6394 | | 0.0393 | 542.0 | 9214 | 0.2806 | 0.3452 | 0.6904 | 0.6904 | nan | 0.6904 | 0.0 | 0.6904 | | 0.0393 | 543.0 | 9231 | 0.2664 | 0.3571 | 0.7142 | 0.7142 | nan | 0.7142 | 0.0 | 0.7142 | | 0.0393 | 544.0 | 9248 | 0.2923 | 0.3503 | 0.7006 | 0.7006 | nan | 0.7006 | 0.0 | 0.7006 | | 0.0393 | 545.0 | 9265 | 0.2719 | 0.3553 | 0.7105 | 0.7105 | nan | 0.7105 | 0.0 | 0.7105 | | 0.0393 | 546.0 | 9282 | 0.2820 | 0.3617 | 0.7234 | 0.7234 | nan | 0.7234 | 0.0 | 0.7234 | | 0.0393 | 547.0 | 9299 | 0.2697 | 0.3491 | 0.6982 | 0.6982 | nan | 0.6982 | 0.0 | 0.6982 | | 0.0354 | 548.0 | 9316 | 0.2908 | 0.3322 | 0.6645 | 0.6645 | nan | 0.6645 | 0.0 | 0.6645 | | 0.0354 | 549.0 | 9333 | 0.2798 | 0.3340 | 0.6680 | 0.6680 | nan | 0.6680 | 0.0 | 0.6680 | | 0.0354 | 550.0 | 9350 | 0.2844 | 0.3518 | 0.7037 | 0.7037 | nan | 0.7037 | 0.0 | 0.7037 | | 0.0354 | 551.0 | 9367 | 0.2983 | 0.3524 | 0.7047 | 0.7047 | nan | 0.7047 | 0.0 | 0.7047 | | 0.0354 | 552.0 | 9384 | 0.2837 | 0.3202 | 0.6405 | 0.6405 | nan | 0.6405 | 0.0 | 0.6405 | | 0.0376 | 553.0 | 9401 | 0.2840 | 0.3301 | 0.6603 | 0.6603 | nan | 0.6603 | 0.0 | 0.6603 | | 0.0376 | 554.0 | 9418 | 0.2836 | 0.3496 | 0.6993 | 0.6993 | nan | 0.6993 | 0.0 | 0.6993 | | 0.0376 | 555.0 | 9435 | 0.2829 | 0.3432 | 0.6864 | 0.6864 | nan | 0.6864 | 0.0 | 0.6864 | | 0.0376 | 556.0 | 9452 | 0.2956 | 0.3330 | 0.6659 | 0.6659 | nan | 0.6659 | 0.0 | 0.6659 | | 0.0376 | 557.0 | 9469 | 0.2928 | 0.3373 | 0.6745 | 0.6745 | nan | 0.6745 | 0.0 | 0.6745 | | 0.0376 | 558.0 | 9486 | 0.2903 | 0.3444 | 0.6888 | 0.6888 | nan | 0.6888 | 0.0 | 0.6888 | | 0.0409 | 559.0 | 9503 | 0.3086 | 0.3323 | 0.6646 | 0.6646 | nan | 0.6646 | 0.0 | 0.6646 | | 0.0409 | 560.0 | 9520 | 0.3257 | 0.3188 | 0.6377 | 0.6377 | nan | 0.6377 | 0.0 | 0.6377 | | 0.0409 | 561.0 | 9537 | 0.3098 | 0.3307 | 0.6614 | 0.6614 | nan | 0.6614 | 0.0 | 0.6614 | | 0.0409 | 562.0 | 9554 | 0.2905 | 0.3315 | 0.6631 | 0.6631 | nan | 0.6631 | 0.0 | 0.6631 | | 0.0409 | 563.0 | 9571 | 0.2886 | 0.3368 | 0.6736 | 0.6736 | nan | 0.6736 | 0.0 | 0.6736 | | 0.0409 | 564.0 | 9588 | 0.2952 | 0.3354 | 0.6709 | 0.6709 | nan | 0.6709 | 0.0 | 0.6709 | | 0.0336 | 565.0 | 9605 | 0.3089 | 0.3303 | 0.6605 | 0.6605 | nan | 0.6605 | 0.0 | 0.6605 | | 0.0336 | 566.0 | 9622 | 0.3004 | 0.3305 | 0.6611 | 0.6611 | nan | 0.6611 | 0.0 | 0.6611 | | 0.0336 | 567.0 | 9639 | 0.3005 | 0.3417 | 0.6835 | 0.6835 | nan | 0.6835 | 0.0 | 0.6835 | | 0.0336 | 568.0 | 9656 | 0.2944 | 0.3374 | 0.6748 | 0.6748 | nan | 0.6748 | 0.0 | 0.6748 | | 0.0336 | 569.0 | 9673 | 0.2885 | 0.3375 | 0.6750 | 0.6750 | nan | 0.6750 | 0.0 | 0.6750 | | 0.0336 | 570.0 | 9690 | 0.2940 | 0.3365 | 0.6731 | 0.6731 | nan | 0.6731 | 0.0 | 0.6731 | | 0.0379 | 571.0 | 9707 | 0.2970 | 0.3249 | 0.6498 | 0.6498 | nan | 0.6498 | 0.0 | 0.6498 | | 0.0379 | 572.0 | 9724 | 0.3117 | 0.3362 | 0.6723 | 0.6723 | nan | 0.6723 | 0.0 | 0.6723 | | 0.0379 | 573.0 | 9741 | 0.3093 | 0.3406 | 0.6813 | 0.6813 | nan | 0.6813 | 0.0 | 0.6813 | | 0.0379 | 574.0 | 9758 | 0.3059 | 0.3450 | 0.6901 | 0.6901 | nan | 0.6901 | 0.0 | 0.6901 | | 0.0379 | 575.0 | 9775 | 0.2997 | 0.3425 | 0.6849 | 0.6849 | nan | 0.6849 | 0.0 | 0.6849 | | 0.0379 | 576.0 | 9792 | 0.3000 | 0.3420 | 0.6840 | 0.6840 | nan | 0.6840 | 0.0 | 0.6840 | | 0.0355 | 577.0 | 9809 | 0.3034 | 0.3334 | 0.6669 | 0.6669 | nan | 0.6669 | 0.0 | 0.6669 | | 0.0355 | 578.0 | 9826 | 0.3058 | 0.3415 | 0.6831 | 0.6831 | nan | 0.6831 | 0.0 | 0.6831 | | 0.0355 | 579.0 | 9843 | 0.2928 | 0.3354 | 0.6708 | 0.6708 | nan | 0.6708 | 0.0 | 0.6708 | | 0.0355 | 580.0 | 9860 | 0.2982 | 0.3291 | 0.6582 | 0.6582 | nan | 0.6582 | 0.0 | 0.6582 | | 0.0355 | 581.0 | 9877 | 0.3069 | 0.3263 | 0.6526 | 0.6526 | nan | 0.6526 | 0.0 | 0.6526 | | 0.0355 | 582.0 | 9894 | 0.3015 | 0.3348 | 0.6696 | 0.6696 | nan | 0.6696 | 0.0 | 0.6696 | | 0.0339 | 583.0 | 9911 | 0.2990 | 0.3225 | 0.6450 | 0.6450 | nan | 0.6450 | 0.0 | 0.6450 | | 0.0339 | 584.0 | 9928 | 0.3005 | 0.3259 | 0.6518 | 0.6518 | nan | 0.6518 | 0.0 | 0.6518 | | 0.0339 | 585.0 | 9945 | 0.3164 | 0.3330 | 0.6661 | 0.6661 | nan | 0.6661 | 0.0 | 0.6661 | | 0.0339 | 586.0 | 9962 | 0.3049 | 0.3255 | 0.6510 | 0.6510 | nan | 0.6510 | 0.0 | 0.6510 | | 0.0339 | 587.0 | 9979 | 0.3079 | 0.3267 | 0.6534 | 0.6534 | nan | 0.6534 | 0.0 | 0.6534 | | 0.0339 | 588.0 | 9996 | 0.3019 | 0.3333 | 0.6667 | 0.6667 | nan | 0.6667 | 0.0 | 0.6667 | | 0.0328 | 588.24 | 10000 | 0.3028 | 0.3319 | 0.6638 | 0.6638 | nan | 0.6638 | 0.0 | 0.6638 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.15.1

[ "background", "biofilm" ]

blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps

# segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_rojo dataset. It achieves the following results on the evaluation set: - Loss: 1.2098 - Mean Iou: 0.3648 - Mean Accuracy: 0.6821 - Overall Accuracy: 0.6947 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.7354 - Accuracy Fallo malla: 0.6052 - Accuracy Fallo adhesivo: 0.9884 - Accuracy Fallo burbuja: 0.3995 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.5920 - Iou Fallo malla: 0.5774 - Iou Fallo adhesivo: 0.2950 - Iou Fallo burbuja: 0.3598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.5778 | 1.0 | 114 | 0.8590 | 0.2588 | 0.5626 | 0.6271 | nan | 0.3415 | 0.9120 | 0.9748 | 0.0221 | 0.0 | 0.3339 | 0.6211 | 0.3168 | 0.0220 | | 0.3326 | 2.0 | 228 | 0.6845 | 0.3570 | 0.6755 | 0.7131 | nan | 0.5232 | 0.8953 | 0.9911 | 0.2921 | 0.0 | 0.5003 | 0.6924 | 0.3417 | 0.2503 | | 0.2636 | 3.0 | 342 | 0.6662 | 0.3896 | 0.7107 | 0.7344 | nan | 0.7666 | 0.6622 | 0.9838 | 0.4304 | 0.0 | 0.6088 | 0.6188 | 0.4014 | 0.3191 | | 0.2505 | 4.0 | 456 | 0.7666 | 0.3732 | 0.7408 | 0.6807 | nan | 0.4141 | 0.9417 | 0.9778 | 0.6297 | 0.0 | 0.4065 | 0.6276 | 0.4337 | 0.3980 | | 0.2306 | 5.0 | 570 | 0.4680 | 0.4690 | 0.7389 | 0.8099 | nan | 0.7461 | 0.8649 | 0.9742 | 0.3705 | 0.0 | 0.6711 | 0.7095 | 0.6349 | 0.3294 | | 0.1998 | 6.0 | 684 | 0.5711 | 0.4449 | 0.7494 | 0.7824 | nan | 0.8528 | 0.6732 | 0.9865 | 0.4850 | 0.0 | 0.6781 | 0.6338 | 0.5320 | 0.3807 | | 0.2062 | 7.0 | 798 | 0.6403 | 0.4070 | 0.7437 | 0.7283 | nan | 0.5736 | 0.8683 | 0.9881 | 0.5447 | 0.0 | 0.5300 | 0.6452 | 0.4613 | 0.3987 | | 0.182 | 8.0 | 912 | 0.5934 | 0.4344 | 0.7309 | 0.7770 | nan | 0.8171 | 0.7036 | 0.9840 | 0.4190 | 0.0 | 0.6640 | 0.6485 | 0.4916 | 0.3681 | | 0.178 | 9.0 | 1026 | 0.7158 | 0.3811 | 0.6915 | 0.7313 | nan | 0.7292 | 0.6984 | 0.9913 | 0.3472 | 0.0 | 0.6148 | 0.6404 | 0.3348 | 0.3153 | | 0.1568 | 10.0 | 1140 | 0.5892 | 0.4169 | 0.6970 | 0.7873 | nan | 0.8088 | 0.7398 | 0.9855 | 0.2538 | 0.0 | 0.6770 | 0.6664 | 0.5004 | 0.2407 | | 0.1576 | 11.0 | 1254 | 0.6419 | 0.4228 | 0.7177 | 0.7652 | nan | 0.7970 | 0.7001 | 0.9805 | 0.3931 | 0.0 | 0.6509 | 0.6318 | 0.4701 | 0.3614 | | 0.1667 | 12.0 | 1368 | 0.6563 | 0.4060 | 0.7369 | 0.7605 | nan | 0.7409 | 0.7517 | 0.9871 | 0.4681 | 0.0 | 0.6326 | 0.6731 | 0.4103 | 0.3139 | | 0.1436 | 13.0 | 1482 | 0.9148 | 0.3864 | 0.7079 | 0.7187 | nan | 0.6666 | 0.7400 | 0.9900 | 0.4352 | 0.0 | 0.6025 | 0.6632 | 0.2829 | 0.3835 | | 0.1469 | 14.0 | 1596 | 0.6680 | 0.4166 | 0.7216 | 0.7689 | nan | 0.7843 | 0.7225 | 0.9861 | 0.3936 | 0.0 | 0.6703 | 0.6608 | 0.3946 | 0.3571 | | 0.1288 | 15.0 | 1710 | 0.8170 | 0.3765 | 0.6849 | 0.7164 | nan | 0.8269 | 0.5509 | 0.9859 | 0.3759 | 0.0 | 0.6242 | 0.5368 | 0.3815 | 0.3398 | | 0.1243 | 16.0 | 1824 | 0.8197 | 0.4034 | 0.7169 | 0.7375 | nan | 0.8456 | 0.5776 | 0.9842 | 0.4602 | 0.0 | 0.6517 | 0.5582 | 0.4078 | 0.3991 | | 0.1208 | 17.0 | 1938 | 0.7927 | 0.3848 | 0.6774 | 0.7295 | nan | 0.8592 | 0.5460 | 0.9810 | 0.3233 | 0.0 | 0.6359 | 0.5256 | 0.4647 | 0.2978 | | 0.115 | 18.0 | 2052 | 1.1226 | 0.3376 | 0.6484 | 0.6727 | nan | 0.7053 | 0.5900 | 0.9905 | 0.3079 | 0.0 | 0.5659 | 0.5688 | 0.2673 | 0.2860 | | 0.1138 | 19.0 | 2166 | 0.8244 | 0.4055 | 0.7099 | 0.7446 | nan | 0.8200 | 0.6248 | 0.9833 | 0.4115 | 0.0 | 0.6364 | 0.5964 | 0.4287 | 0.3659 | | 0.1144 | 20.0 | 2280 | 0.5964 | 0.4493 | 0.7179 | 0.8034 | nan | 0.8594 | 0.7188 | 0.9808 | 0.3127 | 0.0 | 0.6995 | 0.6608 | 0.5990 | 0.2873 | | 0.108 | 21.0 | 2394 | 0.6545 | 0.4418 | 0.7348 | 0.7902 | nan | 0.8263 | 0.7241 | 0.9835 | 0.4053 | 0.0 | 0.6832 | 0.6653 | 0.5023 | 0.3582 | | 0.109 | 22.0 | 2508 | 0.9552 | 0.3775 | 0.6990 | 0.7058 | nan | 0.6835 | 0.6906 | 0.9894 | 0.4325 | 0.0 | 0.5907 | 0.6391 | 0.2756 | 0.3819 | | 0.0987 | 23.0 | 2622 | 0.7971 | 0.3974 | 0.7133 | 0.7453 | nan | 0.7451 | 0.7124 | 0.9871 | 0.4084 | 0.0 | 0.6281 | 0.6560 | 0.3577 | 0.3452 | | 0.0977 | 24.0 | 2736 | 0.9783 | 0.3718 | 0.6984 | 0.7001 | nan | 0.5950 | 0.7793 | 0.9916 | 0.4276 | 0.0 | 0.5491 | 0.6786 | 0.2620 | 0.3692 | | 0.0954 | 25.0 | 2850 | 0.9562 | 0.3856 | 0.6981 | 0.7352 | nan | 0.7102 | 0.7294 | 0.9904 | 0.3623 | 0.0 | 0.6355 | 0.6603 | 0.2988 | 0.3332 | | 0.0928 | 26.0 | 2964 | 0.9185 | 0.3787 | 0.6870 | 0.7355 | nan | 0.7815 | 0.6491 | 0.9847 | 0.3327 | 0.0 | 0.6569 | 0.6184 | 0.3151 | 0.3028 | | 0.0918 | 27.0 | 3078 | 0.9617 | 0.3845 | 0.6916 | 0.7175 | nan | 0.8211 | 0.5605 | 0.9809 | 0.4037 | 0.0 | 0.6123 | 0.5462 | 0.3994 | 0.3648 | | 0.0801 | 28.0 | 3192 | 1.1167 | 0.3672 | 0.6811 | 0.7091 | nan | 0.7352 | 0.6393 | 0.9927 | 0.3570 | 0.0 | 0.6151 | 0.6141 | 0.2816 | 0.3250 | | 0.0852 | 29.0 | 3306 | 0.8549 | 0.4217 | 0.7108 | 0.7596 | nan | 0.8684 | 0.6040 | 0.9848 | 0.3862 | 0.0 | 0.6576 | 0.5808 | 0.5146 | 0.3553 | | 0.0816 | 30.0 | 3420 | 0.9536 | 0.3902 | 0.7034 | 0.7366 | nan | 0.7752 | 0.6573 | 0.9885 | 0.3926 | 0.0 | 0.6415 | 0.6301 | 0.3274 | 0.3517 | | 0.0876 | 31.0 | 3534 | 1.0597 | 0.3873 | 0.7065 | 0.7158 | nan | 0.7490 | 0.6374 | 0.9920 | 0.4475 | 0.0 | 0.6117 | 0.6160 | 0.3051 | 0.4035 | | 0.0811 | 32.0 | 3648 | 0.8829 | 0.4038 | 0.7077 | 0.7569 | nan | 0.7949 | 0.6829 | 0.9860 | 0.3669 | 0.0 | 0.6442 | 0.6498 | 0.3943 | 0.3304 | | 0.0789 | 33.0 | 3762 | 0.9615 | 0.4002 | 0.7104 | 0.7436 | nan | 0.7890 | 0.6575 | 0.9884 | 0.4066 | 0.0 | 0.6344 | 0.6308 | 0.3702 | 0.3658 | | 0.0752 | 34.0 | 3876 | 0.7799 | 0.4297 | 0.7280 | 0.7806 | nan | 0.8279 | 0.6991 | 0.9873 | 0.3975 | 0.0 | 0.6787 | 0.6605 | 0.4458 | 0.3634 | | 0.0731 | 35.0 | 3990 | 0.9285 | 0.4061 | 0.7025 | 0.7531 | nan | 0.8595 | 0.5987 | 0.9898 | 0.3619 | 0.0 | 0.6579 | 0.5797 | 0.4600 | 0.3330 | | 0.0752 | 36.0 | 4104 | 0.9218 | 0.4112 | 0.7276 | 0.7463 | nan | 0.7632 | 0.6926 | 0.9880 | 0.4667 | 0.0 | 0.6393 | 0.6507 | 0.3462 | 0.4200 | | 0.0701 | 37.0 | 4218 | 0.8808 | 0.4105 | 0.7184 | 0.7562 | nan | 0.8090 | 0.6635 | 0.9893 | 0.4119 | 0.0 | 0.6569 | 0.6342 | 0.3876 | 0.3740 | | 0.0717 | 38.0 | 4332 | 1.1090 | 0.3748 | 0.6881 | 0.7166 | nan | 0.7554 | 0.6334 | 0.9905 | 0.3729 | 0.0 | 0.6272 | 0.6069 | 0.2969 | 0.3433 | | 0.0716 | 39.0 | 4446 | 0.9456 | 0.4018 | 0.7064 | 0.7528 | nan | 0.8217 | 0.6418 | 0.9872 | 0.3747 | 0.0 | 0.6638 | 0.6131 | 0.3863 | 0.3456 | | 0.069 | 40.0 | 4560 | 0.8462 | 0.4157 | 0.7038 | 0.7697 | nan | 0.8656 | 0.6316 | 0.9856 | 0.3324 | 0.0 | 0.6750 | 0.6041 | 0.4917 | 0.3078 | | 0.07 | 41.0 | 4674 | 0.9715 | 0.3843 | 0.6886 | 0.7393 | nan | 0.8006 | 0.6353 | 0.9896 | 0.3289 | 0.0 | 0.6420 | 0.6104 | 0.3633 | 0.3056 | | 0.0649 | 42.0 | 4788 | 0.9114 | 0.3997 | 0.7066 | 0.7592 | nan | 0.7682 | 0.7185 | 0.9917 | 0.3478 | 0.0 | 0.6613 | 0.6728 | 0.3449 | 0.3196 | | 0.0665 | 43.0 | 4902 | 1.1847 | 0.3662 | 0.6812 | 0.6981 | nan | 0.7131 | 0.6389 | 0.9912 | 0.3817 | 0.0 | 0.5853 | 0.6122 | 0.2832 | 0.3504 | | 0.0646 | 44.0 | 5016 | 1.1242 | 0.3744 | 0.6906 | 0.7086 | nan | 0.6930 | 0.6870 | 0.9891 | 0.3932 | 0.0 | 0.5902 | 0.6495 | 0.2770 | 0.3555 | | 0.0662 | 45.0 | 5130 | 1.1017 | 0.3605 | 0.6735 | 0.7023 | nan | 0.7333 | 0.6261 | 0.9906 | 0.3439 | 0.0 | 0.5997 | 0.5996 | 0.2906 | 0.3126 | | 0.0644 | 46.0 | 5244 | 1.2989 | 0.3470 | 0.6600 | 0.6735 | nan | 0.6567 | 0.6473 | 0.9917 | 0.3445 | 0.0 | 0.5607 | 0.6182 | 0.2377 | 0.3185 | | 0.0595 | 47.0 | 5358 | 1.0764 | 0.3833 | 0.6982 | 0.7241 | nan | 0.7650 | 0.6389 | 0.9932 | 0.3957 | 0.0 | 0.6345 | 0.6134 | 0.3071 | 0.3618 | | 0.0603 | 48.0 | 5472 | 1.0871 | 0.3692 | 0.6813 | 0.7128 | nan | 0.7153 | 0.6718 | 0.9884 | 0.3497 | 0.0 | 0.6079 | 0.6388 | 0.2797 | 0.3197 | | 0.0591 | 49.0 | 5586 | 1.1054 | 0.3800 | 0.6956 | 0.7171 | nan | 0.7103 | 0.6866 | 0.9892 | 0.3963 | 0.0 | 0.6116 | 0.6458 | 0.2816 | 0.3609 | | 0.0612 | 50.0 | 5700 | 1.1061 | 0.3652 | 0.6768 | 0.7087 | nan | 0.7394 | 0.6340 | 0.9903 | 0.3435 | 0.0 | 0.6027 | 0.6074 | 0.3009 | 0.3147 | | 0.0609 | 51.0 | 5814 | 0.9938 | 0.3742 | 0.6850 | 0.7206 | nan | 0.7555 | 0.6433 | 0.9890 | 0.3523 | 0.0 | 0.6210 | 0.6121 | 0.3143 | 0.3235 | | 0.058 | 52.0 | 5928 | 1.0391 | 0.3745 | 0.6836 | 0.7248 | nan | 0.7691 | 0.6374 | 0.9901 | 0.3379 | 0.0 | 0.6275 | 0.6082 | 0.3257 | 0.3109 | | 0.0559 | 53.0 | 6042 | 0.9916 | 0.3922 | 0.7033 | 0.7373 | nan | 0.8044 | 0.6249 | 0.9902 | 0.3938 | 0.0 | 0.6429 | 0.6003 | 0.3644 | 0.3537 | | 0.0572 | 54.0 | 6156 | 1.0124 | 0.3801 | 0.6907 | 0.7262 | nan | 0.7721 | 0.6371 | 0.9885 | 0.3650 | 0.0 | 0.6284 | 0.6052 | 0.3326 | 0.3341 | | 0.0558 | 55.0 | 6270 | 1.0856 | 0.3692 | 0.6823 | 0.7120 | nan | 0.7232 | 0.6604 | 0.9894 | 0.3565 | 0.0 | 0.6094 | 0.6255 | 0.2864 | 0.3246 | | 0.058 | 56.0 | 6384 | 1.0581 | 0.3837 | 0.6998 | 0.7212 | nan | 0.7353 | 0.6668 | 0.9910 | 0.4062 | 0.0 | 0.6126 | 0.6269 | 0.3125 | 0.3666 | | 0.0518 | 57.0 | 6498 | 1.0176 | 0.3933 | 0.7060 | 0.7362 | nan | 0.7857 | 0.6440 | 0.9884 | 0.4060 | 0.0 | 0.6395 | 0.6127 | 0.3489 | 0.3655 | | 0.0537 | 58.0 | 6612 | 1.2001 | 0.3676 | 0.6853 | 0.6947 | nan | 0.7737 | 0.5607 | 0.9884 | 0.4184 | 0.0 | 0.6003 | 0.5391 | 0.3221 | 0.3764 | | 0.0552 | 59.0 | 6726 | 0.9751 | 0.3940 | 0.7068 | 0.7314 | nan | 0.8019 | 0.6139 | 0.9870 | 0.4244 | 0.0 | 0.6353 | 0.5871 | 0.3662 | 0.3816 | | 0.0538 | 60.0 | 6840 | 1.0382 | 0.3782 | 0.6909 | 0.7203 | nan | 0.7216 | 0.6813 | 0.9895 | 0.3714 | 0.0 | 0.6093 | 0.6389 | 0.3011 | 0.3418 | | 0.0528 | 61.0 | 6954 | 1.1785 | 0.3662 | 0.6819 | 0.7019 | nan | 0.7278 | 0.6310 | 0.9904 | 0.3784 | 0.0 | 0.5966 | 0.6013 | 0.2914 | 0.3419 | | 0.0531 | 62.0 | 7068 | 1.1054 | 0.3685 | 0.6852 | 0.7026 | nan | 0.7290 | 0.6310 | 0.9899 | 0.3911 | 0.0 | 0.5969 | 0.5981 | 0.2961 | 0.3514 | | 0.0522 | 63.0 | 7182 | 1.1271 | 0.3717 | 0.6871 | 0.7094 | nan | 0.7268 | 0.6496 | 0.9906 | 0.3816 | 0.0 | 0.6069 | 0.6148 | 0.2905 | 0.3460 | | 0.0507 | 64.0 | 7296 | 1.0440 | 0.3734 | 0.6825 | 0.7242 | nan | 0.7678 | 0.6380 | 0.9884 | 0.3359 | 0.0 | 0.6279 | 0.6043 | 0.3272 | 0.3076 | | 0.0519 | 65.0 | 7410 | 1.1191 | 0.3727 | 0.6884 | 0.7102 | nan | 0.7264 | 0.6517 | 0.9911 | 0.3843 | 0.0 | 0.6028 | 0.6156 | 0.2978 | 0.3472 | | 0.0502 | 66.0 | 7524 | 1.0089 | 0.3917 | 0.7036 | 0.7408 | nan | 0.7555 | 0.6898 | 0.9896 | 0.3794 | 0.0 | 0.6413 | 0.6472 | 0.3261 | 0.3437 | | 0.051 | 67.0 | 7638 | 1.2112 | 0.3672 | 0.6806 | 0.7083 | nan | 0.7352 | 0.6378 | 0.9899 | 0.3593 | 0.0 | 0.6078 | 0.6085 | 0.2918 | 0.3279 | | 0.0508 | 68.0 | 7752 | 1.1584 | 0.3702 | 0.6860 | 0.7052 | nan | 0.7202 | 0.6477 | 0.9888 | 0.3875 | 0.0 | 0.5956 | 0.6155 | 0.2902 | 0.3495 | | 0.048 | 69.0 | 7866 | 1.1363 | 0.3773 | 0.6922 | 0.7165 | nan | 0.7297 | 0.6628 | 0.9895 | 0.3865 | 0.0 | 0.6158 | 0.6289 | 0.2901 | 0.3518 | | 0.0483 | 70.0 | 7980 | 1.1489 | 0.3749 | 0.6916 | 0.7103 | nan | 0.7398 | 0.6367 | 0.9889 | 0.4011 | 0.0 | 0.6074 | 0.6080 | 0.2994 | 0.3598 | | 0.0495 | 71.0 | 8094 | 1.1470 | 0.3774 | 0.6943 | 0.7102 | nan | 0.7454 | 0.6295 | 0.9891 | 0.4131 | 0.0 | 0.6059 | 0.6032 | 0.3053 | 0.3724 | | 0.0472 | 72.0 | 8208 | 1.2749 | 0.3597 | 0.6782 | 0.6864 | nan | 0.7291 | 0.5930 | 0.9891 | 0.4017 | 0.0 | 0.5899 | 0.5704 | 0.2771 | 0.3612 | | 0.0486 | 73.0 | 8322 | 1.1217 | 0.3773 | 0.6946 | 0.7117 | nan | 0.7549 | 0.6224 | 0.9882 | 0.4128 | 0.0 | 0.6094 | 0.5946 | 0.3150 | 0.3678 | | 0.051 | 74.0 | 8436 | 1.1895 | 0.3724 | 0.6889 | 0.7069 | nan | 0.7432 | 0.6247 | 0.9888 | 0.3990 | 0.0 | 0.6052 | 0.5959 | 0.3021 | 0.3590 | | 0.0472 | 75.0 | 8550 | 1.2084 | 0.3677 | 0.6847 | 0.7009 | nan | 0.7179 | 0.6399 | 0.9905 | 0.3904 | 0.0 | 0.5979 | 0.6078 | 0.2808 | 0.3522 | | 0.0481 | 76.0 | 8664 | 1.1778 | 0.3688 | 0.6841 | 0.7049 | nan | 0.7395 | 0.6244 | 0.9899 | 0.3824 | 0.0 | 0.6024 | 0.5950 | 0.2996 | 0.3469 | | 0.0462 | 77.0 | 8778 | 1.2409 | 0.3693 | 0.6863 | 0.7015 | nan | 0.7278 | 0.6297 | 0.9900 | 0.3975 | 0.0 | 0.5964 | 0.5990 | 0.2918 | 0.3593 | | 0.0464 | 78.0 | 8892 | 1.2724 | 0.3606 | 0.6792 | 0.6877 | nan | 0.7119 | 0.6158 | 0.9905 | 0.3986 | 0.0 | 0.5825 | 0.5857 | 0.2770 | 0.3578 | | 0.0477 | 79.0 | 9006 | 1.2107 | 0.3629 | 0.6797 | 0.6936 | nan | 0.7322 | 0.6063 | 0.9898 | 0.3905 | 0.0 | 0.5928 | 0.5791 | 0.2889 | 0.3540 | | 0.0452 | 80.0 | 9120 | 1.1745 | 0.3721 | 0.6889 | 0.7059 | nan | 0.7548 | 0.6087 | 0.9899 | 0.4022 | 0.0 | 0.6080 | 0.5820 | 0.3085 | 0.3620 | | 0.0447 | 81.0 | 9234 | 1.2787 | 0.3599 | 0.6776 | 0.6876 | nan | 0.7199 | 0.6063 | 0.9902 | 0.3938 | 0.0 | 0.5857 | 0.5788 | 0.2786 | 0.3566 | | 0.0481 | 82.0 | 9348 | 1.2049 | 0.3658 | 0.6836 | 0.6947 | nan | 0.7515 | 0.5865 | 0.9887 | 0.4078 | 0.0 | 0.5956 | 0.5627 | 0.3044 | 0.3660 | | 0.0444 | 83.0 | 9462 | 1.1427 | 0.3746 | 0.6930 | 0.7051 | nan | 0.7520 | 0.6100 | 0.9883 | 0.4215 | 0.0 | 0.6042 | 0.5824 | 0.3100 | 0.3763 | | 0.0481 | 84.0 | 9576 | 1.1876 | 0.3669 | 0.6848 | 0.6968 | nan | 0.7358 | 0.6094 | 0.9895 | 0.4046 | 0.0 | 0.5944 | 0.5818 | 0.2947 | 0.3636 | | 0.046 | 85.0 | 9690 | 1.2264 | 0.3628 | 0.6799 | 0.6928 | nan | 0.7348 | 0.6015 | 0.9885 | 0.3948 | 0.0 | 0.5906 | 0.5746 | 0.2930 | 0.3560 | | 0.0472 | 86.0 | 9804 | 1.2377 | 0.3659 | 0.6828 | 0.6967 | nan | 0.7287 | 0.6176 | 0.9890 | 0.3959 | 0.0 | 0.5926 | 0.5876 | 0.2913 | 0.3577 | | 0.0465 | 87.0 | 9918 | 1.2037 | 0.3644 | 0.6841 | 0.6903 | nan | 0.7176 | 0.6150 | 0.9893 | 0.4146 | 0.0 | 0.5859 | 0.5856 | 0.2808 | 0.3697 | | 0.0475 | 87.72 | 10000 | 1.2098 | 0.3648 | 0.6821 | 0.6947 | nan | 0.7354 | 0.6052 | 0.9884 | 0.3995 | 0.0 | 0.5920 | 0.5774 | 0.2950 | 0.3598 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]

blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_negro-10k-steps

# segformer-finetuned-4ss1st3r_s3gs3m_24Jan_negro-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_negro dataset. It achieves the following results on the evaluation set: - Loss: 0.2142 - Mean Iou: 0.5724 - Mean Accuracy: 0.7571 - Overall Accuracy: 0.9468 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9826 - Accuracy Fallo malla: 0.7246 - Accuracy Fallo adhesivo: 0.9679 - Accuracy Fallo burbuja: 0.3533 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9368 - Iou Fallo malla: 0.6678 - Iou Fallo adhesivo: 0.9310 - Iou Fallo burbuja: 0.3263 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.1902 | 1.0 | 219 | 0.2615 | 0.5072 | 0.7247 | 0.9038 | nan | 0.9203 | 0.7991 | 0.9401 | 0.2393 | 0.0 | 0.8853 | 0.5556 | 0.9006 | 0.1944 | | 0.1367 | 2.0 | 438 | 0.2067 | 0.5492 | 0.7602 | 0.9293 | nan | 0.9599 | 0.7487 | 0.9317 | 0.4004 | 0.0 | 0.9160 | 0.6120 | 0.8988 | 0.3195 | | 0.1066 | 3.0 | 657 | 0.1963 | 0.5659 | 0.7814 | 0.9313 | nan | 0.9520 | 0.8022 | 0.9545 | 0.4169 | 0.0 | 0.9175 | 0.6270 | 0.9267 | 0.3584 | | 0.1102 | 4.0 | 876 | 0.1595 | 0.5782 | 0.7756 | 0.9444 | nan | 0.9727 | 0.7669 | 0.9693 | 0.3934 | 0.0 | 0.9336 | 0.6828 | 0.9326 | 0.3422 | | 0.1114 | 5.0 | 1095 | 0.1678 | 0.5772 | 0.7950 | 0.9378 | nan | 0.9619 | 0.7778 | 0.9756 | 0.4648 | 0.0 | 0.9255 | 0.6534 | 0.9151 | 0.3922 | | 0.0897 | 6.0 | 1314 | 0.1726 | 0.5811 | 0.7976 | 0.9420 | nan | 0.9701 | 0.7598 | 0.9723 | 0.4881 | 0.0 | 0.9307 | 0.6613 | 0.9170 | 0.3965 | | 0.0788 | 7.0 | 1533 | 0.2096 | 0.5491 | 0.7253 | 0.9342 | nan | 0.9898 | 0.5936 | 0.9381 | 0.3797 | 0.0 | 0.9235 | 0.5698 | 0.9149 | 0.3374 | | 0.0788 | 8.0 | 1752 | 0.1574 | 0.5774 | 0.7733 | 0.9465 | nan | 0.9726 | 0.7858 | 0.9675 | 0.3673 | 0.0 | 0.9359 | 0.6914 | 0.9264 | 0.3331 | | 0.0855 | 9.0 | 1971 | 0.1970 | 0.5406 | 0.7141 | 0.9380 | nan | 0.9866 | 0.6305 | 0.9708 | 0.2687 | 0.0 | 0.9274 | 0.5984 | 0.9224 | 0.2548 | | 0.0761 | 10.0 | 2190 | 0.1903 | 0.5564 | 0.7479 | 0.9382 | nan | 0.9746 | 0.7050 | 0.9737 | 0.3383 | 0.0 | 0.9268 | 0.6272 | 0.9182 | 0.3098 | | 0.0686 | 11.0 | 2409 | 0.1910 | 0.5562 | 0.7435 | 0.9393 | nan | 0.9827 | 0.6605 | 0.9738 | 0.3572 | 0.0 | 0.9285 | 0.6156 | 0.9209 | 0.3160 | | 0.062 | 12.0 | 2628 | 0.2038 | 0.5453 | 0.7399 | 0.9334 | nan | 0.9728 | 0.6739 | 0.9811 | 0.3317 | 0.0 | 0.9214 | 0.6013 | 0.9035 | 0.3001 | | 0.0586 | 13.0 | 2847 | 0.1914 | 0.5471 | 0.7342 | 0.9402 | nan | 0.9758 | 0.7103 | 0.9814 | 0.2693 | 0.0 | 0.9290 | 0.6397 | 0.9150 | 0.2517 | | 0.0531 | 14.0 | 3066 | 0.1747 | 0.5716 | 0.7689 | 0.9449 | nan | 0.9701 | 0.7945 | 0.9588 | 0.3522 | 0.0 | 0.9339 | 0.6815 | 0.9280 | 0.3147 | | 0.0522 | 15.0 | 3285 | 0.1933 | 0.5591 | 0.7399 | 0.9454 | nan | 0.9810 | 0.7222 | 0.9744 | 0.2820 | 0.0 | 0.9351 | 0.6603 | 0.9355 | 0.2645 | | 0.059 | 16.0 | 3504 | 0.1897 | 0.5691 | 0.7878 | 0.9384 | nan | 0.9499 | 0.8594 | 0.9809 | 0.3608 | 0.0 | 0.9252 | 0.6741 | 0.9159 | 0.3303 | | 0.0503 | 17.0 | 3723 | 0.1895 | 0.5652 | 0.7795 | 0.9365 | nan | 0.9588 | 0.7866 | 0.9808 | 0.3917 | 0.0 | 0.9238 | 0.6508 | 0.9004 | 0.3511 | | 0.0518 | 18.0 | 3942 | 0.2131 | 0.5533 | 0.7332 | 0.9402 | nan | 0.9807 | 0.6877 | 0.9645 | 0.2998 | 0.0 | 0.9294 | 0.6248 | 0.9334 | 0.2790 | | 0.0439 | 19.0 | 4161 | 0.2168 | 0.5565 | 0.7411 | 0.9388 | nan | 0.9801 | 0.6828 | 0.9567 | 0.3448 | 0.0 | 0.9278 | 0.6194 | 0.9234 | 0.3121 | | 0.0459 | 20.0 | 4380 | 0.2688 | 0.5266 | 0.7127 | 0.9266 | nan | 0.9824 | 0.5567 | 0.9841 | 0.3277 | 0.0 | 0.9149 | 0.5329 | 0.8866 | 0.2987 | | 0.043 | 21.0 | 4599 | 0.2395 | 0.5542 | 0.7409 | 0.9369 | nan | 0.9821 | 0.6444 | 0.9745 | 0.3625 | 0.0 | 0.9258 | 0.5974 | 0.9228 | 0.3248 | | 0.0436 | 22.0 | 4818 | 0.1790 | 0.5736 | 0.7750 | 0.9441 | nan | 0.9706 | 0.7783 | 0.9694 | 0.3819 | 0.0 | 0.9331 | 0.6772 | 0.9143 | 0.3433 | | 0.0443 | 23.0 | 5037 | 0.1843 | 0.5683 | 0.7613 | 0.9442 | nan | 0.9756 | 0.7470 | 0.9716 | 0.3511 | 0.0 | 0.9335 | 0.6684 | 0.9177 | 0.3219 | | 0.0402 | 24.0 | 5256 | 0.2048 | 0.5666 | 0.7535 | 0.9429 | nan | 0.9800 | 0.7089 | 0.9706 | 0.3544 | 0.0 | 0.9324 | 0.6457 | 0.9302 | 0.3246 | | 0.0399 | 25.0 | 5475 | 0.2102 | 0.5651 | 0.7524 | 0.9430 | nan | 0.9830 | 0.6875 | 0.9754 | 0.3637 | 0.0 | 0.9327 | 0.6412 | 0.9231 | 0.3287 | | 0.0404 | 26.0 | 5694 | 0.1993 | 0.5792 | 0.7815 | 0.9460 | nan | 0.9690 | 0.8035 | 0.9697 | 0.3837 | 0.0 | 0.9351 | 0.6876 | 0.9289 | 0.3443 | | 0.0388 | 27.0 | 5913 | 0.2024 | 0.5681 | 0.7501 | 0.9470 | nan | 0.9821 | 0.7343 | 0.9605 | 0.3236 | 0.0 | 0.9370 | 0.6715 | 0.9322 | 0.3001 | | 0.0369 | 28.0 | 6132 | 0.1830 | 0.5701 | 0.7553 | 0.9481 | nan | 0.9779 | 0.7698 | 0.9608 | 0.3126 | 0.0 | 0.9379 | 0.6871 | 0.9323 | 0.2931 | | 0.0373 | 29.0 | 6351 | 0.2162 | 0.5682 | 0.7535 | 0.9438 | nan | 0.9828 | 0.7011 | 0.9639 | 0.3665 | 0.0 | 0.9335 | 0.6482 | 0.9239 | 0.3352 | | 0.0348 | 30.0 | 6570 | 0.2126 | 0.5640 | 0.7479 | 0.9435 | nan | 0.9813 | 0.7097 | 0.9623 | 0.3384 | 0.0 | 0.9330 | 0.6537 | 0.9197 | 0.3135 | | 0.0354 | 31.0 | 6789 | 0.2025 | 0.5626 | 0.7467 | 0.9469 | nan | 0.9795 | 0.7453 | 0.9725 | 0.2896 | 0.0 | 0.9368 | 0.6762 | 0.9285 | 0.2716 | | 0.0344 | 32.0 | 7008 | 0.1973 | 0.5786 | 0.7739 | 0.9469 | nan | 0.9734 | 0.7828 | 0.9698 | 0.3695 | 0.0 | 0.9364 | 0.6853 | 0.9326 | 0.3389 | | 0.0333 | 33.0 | 7227 | 0.2199 | 0.5722 | 0.7624 | 0.9438 | nan | 0.9817 | 0.7045 | 0.9696 | 0.3940 | 0.0 | 0.9334 | 0.6481 | 0.9287 | 0.3510 | | 0.0345 | 34.0 | 7446 | 0.2052 | 0.5791 | 0.7724 | 0.9465 | nan | 0.9799 | 0.7347 | 0.9736 | 0.4015 | 0.0 | 0.9363 | 0.6698 | 0.9311 | 0.3582 | | 0.0326 | 35.0 | 7665 | 0.2176 | 0.5758 | 0.7629 | 0.9462 | nan | 0.9835 | 0.7124 | 0.9689 | 0.3868 | 0.0 | 0.9362 | 0.6595 | 0.9345 | 0.3490 | | 0.034 | 36.0 | 7884 | 0.2247 | 0.5717 | 0.7557 | 0.9453 | nan | 0.9841 | 0.7033 | 0.9661 | 0.3694 | 0.0 | 0.9352 | 0.6533 | 0.9331 | 0.3369 | | 0.0324 | 37.0 | 8103 | 0.1957 | 0.5797 | 0.7736 | 0.9490 | nan | 0.9763 | 0.7801 | 0.9725 | 0.3657 | 0.0 | 0.9390 | 0.6963 | 0.9299 | 0.3333 | | 0.0332 | 38.0 | 8322 | 0.1996 | 0.5770 | 0.7644 | 0.9478 | nan | 0.9826 | 0.7310 | 0.9696 | 0.3743 | 0.0 | 0.9379 | 0.6741 | 0.9336 | 0.3393 | | 0.0332 | 39.0 | 8541 | 0.2129 | 0.5638 | 0.7423 | 0.9449 | nan | 0.9845 | 0.7021 | 0.9616 | 0.3212 | 0.0 | 0.9348 | 0.6514 | 0.9328 | 0.3001 | | 0.03 | 40.0 | 8760 | 0.2283 | 0.5694 | 0.7539 | 0.9441 | nan | 0.9840 | 0.6931 | 0.9686 | 0.3699 | 0.0 | 0.9339 | 0.6464 | 0.9277 | 0.3387 | | 0.0319 | 41.0 | 8979 | 0.2013 | 0.5741 | 0.7624 | 0.9471 | nan | 0.9804 | 0.7416 | 0.9670 | 0.3606 | 0.0 | 0.9370 | 0.6760 | 0.9277 | 0.3300 | | 0.0361 | 42.0 | 9198 | 0.2094 | 0.5709 | 0.7568 | 0.9463 | nan | 0.9810 | 0.7317 | 0.9663 | 0.3483 | 0.0 | 0.9362 | 0.6689 | 0.9279 | 0.3216 | | 0.0304 | 43.0 | 9417 | 0.2098 | 0.5731 | 0.7586 | 0.9468 | nan | 0.9821 | 0.7282 | 0.9666 | 0.3575 | 0.0 | 0.9368 | 0.6700 | 0.9295 | 0.3293 | | 0.0303 | 44.0 | 9636 | 0.2155 | 0.5705 | 0.7554 | 0.9470 | nan | 0.9814 | 0.7329 | 0.9702 | 0.3370 | 0.0 | 0.9369 | 0.6718 | 0.9301 | 0.3137 | | 0.03 | 45.0 | 9855 | 0.2183 | 0.5703 | 0.7541 | 0.9464 | nan | 0.9825 | 0.7229 | 0.9677 | 0.3435 | 0.0 | 0.9364 | 0.6657 | 0.9311 | 0.3181 | | 0.0301 | 45.66 | 10000 | 0.2142 | 0.5724 | 0.7571 | 0.9468 | nan | 0.9826 | 0.7246 | 0.9679 | 0.3533 | 0.0 | 0.9368 | 0.6678 | 0.9310 | 0.3263 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]

Mohamad-Jaallouk/segformer-b0-finetuned-segments-sidewalk-oct-22

# segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.8820 - eval_mean_iou: 0.1648 - eval_mean_accuracy: 0.2025 - eval_overall_accuracy: 0.7805 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8435 - eval_accuracy_flat-sidewalk: 0.9378 - eval_accuracy_flat-crosswalk: 0.0 - eval_accuracy_flat-cyclinglane: 0.5809 - eval_accuracy_flat-parkingdriveway: 0.0715 - eval_accuracy_flat-railtrack: 0.0 - eval_accuracy_flat-curb: 0.0041 - eval_accuracy_human-person: 0.0 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.8730 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: 0.0 - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.0 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.8780 - eval_accuracy_construction-door: 0.0 - eval_accuracy_construction-wall: 0.0000 - eval_accuracy_construction-fenceguardrail: 0.0 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: 0.0 - eval_accuracy_construction-stairs: 0.0 - eval_accuracy_object-pole: 0.0 - eval_accuracy_object-trafficsign: 0.0 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9399 - eval_accuracy_nature-terrain: 0.8232 - eval_accuracy_sky: 0.9347 - eval_accuracy_void-ground: 0.0 - eval_accuracy_void-dynamic: 0.0 - eval_accuracy_void-static: 0.0 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.5426 - eval_iou_flat-sidewalk: 0.8046 - eval_iou_flat-crosswalk: 0.0 - eval_iou_flat-cyclinglane: 0.5502 - eval_iou_flat-parkingdriveway: 0.0678 - eval_iou_flat-railtrack: 0.0 - eval_iou_flat-curb: 0.0041 - eval_iou_human-person: 0.0 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.6930 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: 0.0 - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.0 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.6055 - eval_iou_construction-door: 0.0 - eval_iou_construction-wall: 0.0000 - eval_iou_construction-fenceguardrail: 0.0 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: 0.0 - eval_iou_construction-stairs: 0.0 - eval_iou_object-pole: 0.0 - eval_iou_object-trafficsign: 0.0 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.7900 - eval_iou_nature-terrain: 0.7063 - eval_iou_sky: 0.8381 - eval_iou_void-ground: 0.0 - eval_iou_void-dynamic: 0.0 - eval_iou_void-static: 0.0 - eval_iou_void-unclear: 0.0 - eval_runtime: 21.9758 - eval_samples_per_second: 9.101 - eval_steps_per_second: 0.592 - epoch: 0.4 - step: 20 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

IrshadG/segformer-b0-finetuned-segments-sidewalk-123

# segformer-b0-finetuned-segments-sidewalk-123 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5603 - eval_mean_iou: 0.3194 - eval_mean_accuracy: 0.3797 - eval_overall_accuracy: 0.8649 - eval_accuracy_unlabeled: nan - eval_accuracy_flat-road: 0.8652 - eval_accuracy_flat-sidewalk: 0.9642 - eval_accuracy_flat-crosswalk: 0.6083 - eval_accuracy_flat-cyclinglane: 0.7173 - eval_accuracy_flat-parkingdriveway: 0.5189 - eval_accuracy_flat-railtrack: 0.0 - eval_accuracy_flat-curb: 0.6089 - eval_accuracy_human-person: 0.8421 - eval_accuracy_human-rider: 0.0 - eval_accuracy_vehicle-car: 0.9347 - eval_accuracy_vehicle-truck: 0.0 - eval_accuracy_vehicle-bus: 0.0 - eval_accuracy_vehicle-tramtrain: 0.0 - eval_accuracy_vehicle-motorcycle: 0.0 - eval_accuracy_vehicle-bicycle: 0.7877 - eval_accuracy_vehicle-caravan: 0.0 - eval_accuracy_vehicle-cartrailer: 0.0 - eval_accuracy_construction-building: 0.9288 - eval_accuracy_construction-door: 0.0851 - eval_accuracy_construction-wall: 0.4696 - eval_accuracy_construction-fenceguardrail: 0.4756 - eval_accuracy_construction-bridge: 0.0 - eval_accuracy_construction-tunnel: nan - eval_accuracy_construction-stairs: 0.0047 - eval_accuracy_object-pole: 0.3599 - eval_accuracy_object-trafficsign: 0.0028 - eval_accuracy_object-trafficlight: 0.0 - eval_accuracy_nature-vegetation: 0.9419 - eval_accuracy_nature-terrain: 0.8921 - eval_accuracy_sky: 0.9773 - eval_accuracy_void-ground: 0.0003 - eval_accuracy_void-dynamic: 0.1852 - eval_accuracy_void-static: 0.3603 - eval_accuracy_void-unclear: 0.0 - eval_iou_unlabeled: nan - eval_iou_flat-road: 0.7774 - eval_iou_flat-sidewalk: 0.8659 - eval_iou_flat-crosswalk: 0.4889 - eval_iou_flat-cyclinglane: 0.6488 - eval_iou_flat-parkingdriveway: 0.4072 - eval_iou_flat-railtrack: 0.0 - eval_iou_flat-curb: 0.4944 - eval_iou_human-person: 0.6064 - eval_iou_human-rider: 0.0 - eval_iou_vehicle-car: 0.8283 - eval_iou_vehicle-truck: 0.0 - eval_iou_vehicle-bus: 0.0 - eval_iou_vehicle-tramtrain: 0.0 - eval_iou_vehicle-motorcycle: 0.0 - eval_iou_vehicle-bicycle: 0.5105 - eval_iou_vehicle-caravan: 0.0 - eval_iou_vehicle-cartrailer: 0.0 - eval_iou_construction-building: 0.7458 - eval_iou_construction-door: 0.0760 - eval_iou_construction-wall: 0.4006 - eval_iou_construction-fenceguardrail: 0.3288 - eval_iou_construction-bridge: 0.0 - eval_iou_construction-tunnel: nan - eval_iou_construction-stairs: 0.0047 - eval_iou_object-pole: 0.2789 - eval_iou_object-trafficsign: 0.0027 - eval_iou_object-trafficlight: 0.0 - eval_iou_nature-vegetation: 0.8781 - eval_iou_nature-terrain: 0.8170 - eval_iou_sky: 0.9417 - eval_iou_void-ground: 0.0002 - eval_iou_void-dynamic: 0.1706 - eval_iou_void-static: 0.2686 - eval_iou_void-unclear: 0.0 - eval_runtime: 246.8492 - eval_samples_per_second: 0.81 - eval_steps_per_second: 0.405 - epoch: 15.45 - step: 6180 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0 - Pytorch 2.2.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1

[ "unlabeled", "flat-road", "flat-sidewalk", "flat-crosswalk", "flat-cyclinglane", "flat-parkingdriveway", "flat-railtrack", "flat-curb", "human-person", "human-rider", "vehicle-car", "vehicle-truck", "vehicle-bus", "vehicle-tramtrain", "vehicle-motorcycle", "vehicle-bicycle", "vehicle-caravan", "vehicle-cartrailer", "construction-building", "construction-door", "construction-wall", "construction-fenceguardrail", "construction-bridge", "construction-tunnel", "construction-stairs", "object-pole", "object-trafficsign", "object-trafficlight", "nature-vegetation", "nature-terrain", "sky", "void-ground", "void-dynamic", "void-static", "void-unclear" ]

sam1120/safety-utcustom-train-SF-RGBD-b5

# safety-utcustom-train-SF-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0867 - Mean Iou: 0.7280 - Mean Accuracy: 0.7762 - Overall Accuracy: 0.9818 - Accuracy Unlabeled: nan - Accuracy Safe: 0.5578 - Accuracy Unsafe: 0.9947 - Iou Unlabeled: nan - Iou Safe: 0.4745 - Iou Unsafe: 0.9814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |:-------------:|:------:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:| | 0.789 | 0.91 | 10 | 0.0203 | nan | 0.8957 | 0.0095 | 0.0 | 0.8722 | 0.9555 | 0.4580 | 0.2939 | 0.8698 | | 0.7579 | 1.82 | 20 | 0.0117 | nan | 0.9614 | 0.0069 | 0.0 | 0.9338 | 0.8322 | 0.4866 | 0.3136 | 0.9334 | | 0.7103 | 2.73 | 30 | 0.0051 | nan | 0.9893 | 0.0043 | 0.0 | 0.9604 | 0.6729 | 0.4972 | 0.3216 | 0.9602 | | 0.676 | 3.64 | 40 | 0.0021 | nan | 0.9969 | 0.0020 | 0.0 | 0.9675 | 0.5336 | 0.4995 | 0.3232 | 0.9675 | | 0.5955 | 4.55 | 50 | 0.0001 | nan | 0.9993 | 0.0001 | 0.0 | 0.9698 | 0.4440 | 0.4997 | 0.3233 | 0.9698 | | 0.5691 | 5.45 | 60 | 0.0000 | nan | 0.9997 | 0.0000 | 0.0 | 0.9702 | 0.3812 | 0.4999 | 0.3234 | 0.9702 | | 0.5067 | 6.36 | 70 | 0.0 | nan | 0.9996 | 0.0 | 0.0 | 0.9701 | 0.3590 | 0.4998 | 0.3234 | 0.9701 | | 0.4656 | 7.27 | 80 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9703 | 0.3247 | 0.4999 | 0.3234 | 0.9703 | | 0.4227 | 8.18 | 90 | 0.0 | nan | 0.9998 | 0.0 | 0.0 | 0.9702 | 0.3171 | 0.4999 | 0.3234 | 0.9702 | | 0.3898 | 9.09 | 100 | 0.0004 | nan | 0.9996 | 0.0004 | 0.0 | 0.9701 | 0.3122 | 0.5000 | 0.3235 | 0.9701 | | 0.3513 | 10.0 | 110 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9703 | 0.2876 | 0.4999 | 0.3234 | 0.9703 | | 0.4157 | 10.91 | 120 | 0.0000 | nan | 0.9998 | 0.0000 | 0.0 | 0.9703 | 0.2820 | 0.4999 | 0.3234 | 0.9703 | | 0.3317 | 11.82 | 130 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9703 | 0.2693 | 0.4999 | 0.3234 | 0.9703 | | 0.321 | 12.73 | 140 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9704 | 0.2647 | 0.4999 | 0.3235 | 0.9704 | | 0.2887 | 13.64 | 150 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9704 | 0.2539 | 0.5000 | 0.3235 | 0.9704 | | 0.3008 | 14.55 | 160 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9704 | 0.2536 | 0.5000 | 0.3235 | 0.9704 | | 0.2853 | 15.45 | 170 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9704 | 0.2397 | 0.5000 | 0.3235 | 0.9704 | | 0.2684 | 16.36 | 180 | 0.0 | nan | 0.9999 | 0.0 | 0.0 | 0.9704 | 0.2321 | 0.5000 | 0.3235 | 0.9704 | | 0.2585 | 17.27 | 190 | 0.0000 | nan | 0.9999 | 0.0000 | 0.0 | 0.9704 | 0.2208 | 0.5000 | 0.3235 | 0.9704 | | 0.2088 | 18.18 | 200 | 0.0084 | nan | 0.9997 | 0.0083 | 0.0 | 0.9704 | 0.2011 | 0.5041 | 0.3262 | 0.9704 | | 0.2518 | 19.09 | 210 | 0.0468 | nan | 0.9989 | 0.0451 | 0.0 | 0.9707 | 0.2026 | 0.5228 | 0.3386 | 0.9707 | | 0.218 | 20.0 | 220 | 0.0879 | nan | 0.9984 | 0.0834 | nan | 0.9714 | 0.1889 | 0.5431 | 0.5274 | 0.9715 | | 0.2046 | 20.91 | 230 | 0.1931 | nan | 0.9969 | 0.1752 | nan | 0.9730 | 0.1847 | 0.5950 | 0.5741 | 0.9732 | | 0.2147 | 21.82 | 240 | 0.2042 | nan | 0.9968 | 0.1850 | nan | 0.9733 | 0.1766 | 0.6005 | 0.5791 | 0.9734 | | 0.188 | 22.73 | 250 | 0.2020 | nan | 0.9972 | 0.1849 | nan | 0.9735 | 0.1726 | 0.5996 | 0.5792 | 0.9737 | | 0.2175 | 23.64 | 260 | 0.1898 | nan | 0.9974 | 0.1748 | nan | 0.9734 | 0.1706 | 0.5936 | 0.5741 | 0.9735 | | 0.2059 | 24.55 | 270 | 0.3006 | nan | 0.9962 | 0.2670 | nan | 0.9754 | 0.1689 | 0.6484 | 0.6212 | 0.9756 | | 0.1776 | 25.45 | 280 | 0.2870 | nan | 0.9967 | 0.2587 | nan | 0.9755 | 0.1612 | 0.6418 | 0.6171 | 0.9757 | | 0.1585 | 26.36 | 290 | 0.4254 | nan | 0.9944 | 0.3593 | nan | 0.9773 | 0.1537 | 0.7099 | 0.6683 | 0.9776 | | 0.1588 | 27.27 | 300 | 0.2798 | nan | 0.9970 | 0.2548 | nan | 0.9756 | 0.1527 | 0.6384 | 0.6152 | 0.9758 | | 0.153 | 28.18 | 310 | 0.4288 | nan | 0.9946 | 0.3646 | nan | 0.9776 | 0.1452 | 0.7117 | 0.6711 | 0.9779 | | 0.1623 | 29.09 | 320 | 0.4401 | nan | 0.9945 | 0.3726 | nan | 0.9778 | 0.1442 | 0.7173 | 0.6752 | 0.9781 | | 0.1603 | 30.0 | 330 | 0.4050 | nan | 0.9958 | 0.3562 | nan | 0.9781 | 0.1407 | 0.7004 | 0.6671 | 0.9784 | | 0.1694 | 30.91 | 340 | 0.4585 | nan | 0.9948 | 0.3911 | nan | 0.9786 | 0.1343 | 0.7266 | 0.6849 | 0.9789 | | 0.1585 | 31.82 | 350 | 0.3861 | nan | 0.9962 | 0.3433 | nan | 0.9779 | 0.1353 | 0.6912 | 0.6606 | 0.9782 | | 0.1342 | 32.73 | 360 | 0.4963 | nan | 0.9939 | 0.4132 | nan | 0.9789 | 0.1338 | 0.7451 | 0.6961 | 0.9792 | | 0.1358 | 33.64 | 370 | 0.5048 | nan | 0.9937 | 0.4182 | nan | 0.9789 | 0.1342 | 0.7493 | 0.6986 | 0.9793 | | 0.1493 | 34.55 | 380 | 0.4809 | nan | 0.9946 | 0.4080 | nan | 0.9791 | 0.1297 | 0.7377 | 0.6936 | 0.9794 | | 0.1435 | 35.45 | 390 | 0.5658 | nan | 0.9923 | 0.4518 | nan | 0.9794 | 0.1271 | 0.7791 | 0.7156 | 0.9797 | | 0.1305 | 36.36 | 400 | 0.4157 | nan | 0.9968 | 0.3758 | nan | 0.9793 | 0.1225 | 0.7062 | 0.6776 | 0.9796 | | 0.1496 | 37.27 | 410 | 0.5385 | nan | 0.9934 | 0.4420 | nan | 0.9796 | 0.1237 | 0.7659 | 0.7108 | 0.9799 | | 0.1445 | 38.18 | 420 | 0.5763 | nan | 0.9924 | 0.4615 | nan | 0.9798 | 0.1207 | 0.7843 | 0.7206 | 0.9801 | | 0.1307 | 39.09 | 430 | 0.4853 | nan | 0.9956 | 0.4244 | nan | 0.9803 | 0.1194 | 0.7404 | 0.7023 | 0.9806 | | 0.1379 | 40.0 | 440 | 0.5722 | nan | 0.9922 | 0.4557 | nan | 0.9795 | 0.1174 | 0.7822 | 0.7176 | 0.9798 | | 0.1202 | 40.91 | 450 | 0.5399 | nan | 0.9943 | 0.4544 | nan | 0.9805 | 0.1143 | 0.7671 | 0.7175 | 0.9809 | | 0.1239 | 41.82 | 460 | 0.5580 | nan | 0.9932 | 0.4558 | nan | 0.9800 | 0.1150 | 0.7756 | 0.7179 | 0.9803 | | 0.1183 | 42.73 | 470 | 0.4777 | nan | 0.9961 | 0.4236 | nan | 0.9805 | 0.1129 | 0.7369 | 0.7021 | 0.9808 | | 0.1202 | 43.64 | 480 | 0.5933 | nan | 0.9928 | 0.4793 | nan | 0.9806 | 0.1119 | 0.7930 | 0.7300 | 0.9810 | | 0.1276 | 44.55 | 490 | 0.5425 | nan | 0.9942 | 0.4561 | nan | 0.9806 | 0.1131 | 0.7683 | 0.7183 | 0.9809 | | 0.1172 | 45.45 | 500 | 0.6272 | nan | 0.9898 | 0.4700 | nan | 0.9787 | 0.1135 | 0.8085 | 0.7244 | 0.9791 | | 0.1288 | 46.36 | 510 | 0.4236 | nan | 0.9974 | 0.3898 | nan | 0.9802 | 0.1105 | 0.7105 | 0.6850 | 0.9804 | | 0.1185 | 47.27 | 520 | 0.6035 | nan | 0.9914 | 0.4711 | nan | 0.9796 | 0.1130 | 0.7975 | 0.7254 | 0.9800 | | 0.1045 | 48.18 | 530 | 0.5750 | nan | 0.9930 | 0.4679 | nan | 0.9804 | 0.1102 | 0.7840 | 0.7241 | 0.9807 | | 0.1211 | 49.09 | 540 | 0.5812 | nan | 0.9929 | 0.4715 | nan | 0.9804 | 0.1069 | 0.7870 | 0.7260 | 0.9808 | | 0.1206 | 50.0 | 550 | 0.5221 | nan | 0.9953 | 0.4528 | nan | 0.9811 | 0.1071 | 0.7587 | 0.7169 | 0.9814 | | 0.1193 | 50.91 | 560 | 0.4956 | nan | 0.9961 | 0.4398 | nan | 0.9811 | 0.1053 | 0.7459 | 0.7105 | 0.9814 | | 0.1116 | 51.82 | 570 | 0.5257 | nan | 0.9951 | 0.4528 | nan | 0.9809 | 0.1043 | 0.7604 | 0.7169 | 0.9812 | | 0.1218 | 52.73 | 580 | 0.5936 | nan | 0.9922 | 0.4724 | nan | 0.9801 | 0.1078 | 0.7929 | 0.7262 | 0.9804 | | 0.1284 | 53.64 | 590 | 0.5872 | nan | 0.9924 | 0.4696 | nan | 0.9801 | 0.1054 | 0.7898 | 0.7248 | 0.9804 | | 0.096 | 54.55 | 600 | 0.5451 | nan | 0.9942 | 0.4580 | nan | 0.9806 | 0.1028 | 0.7697 | 0.7193 | 0.9809 | | 0.1091 | 55.45 | 610 | 0.6014 | nan | 0.9917 | 0.4725 | nan | 0.9798 | 0.1022 | 0.7965 | 0.7261 | 0.9802 | | 0.1068 | 56.36 | 620 | 0.4926 | nan | 0.9962 | 0.4374 | nan | 0.9810 | 0.1015 | 0.7444 | 0.7092 | 0.9813 | | 0.106 | 57.27 | 630 | 0.5713 | nan | 0.9937 | 0.4731 | nan | 0.9809 | 0.1011 | 0.7825 | 0.7270 | 0.9812 | | 0.1009 | 58.18 | 640 | 0.4512 | nan | 0.9969 | 0.4089 | nan | 0.9805 | 0.1028 | 0.7240 | 0.6947 | 0.9807 | | 0.1018 | 59.09 | 650 | 0.6053 | nan | 0.9919 | 0.4779 | nan | 0.9801 | 0.1022 | 0.7986 | 0.7290 | 0.9805 | | 0.1012 | 60.0 | 660 | 0.5167 | nan | 0.9949 | 0.4427 | nan | 0.9805 | 0.1016 | 0.7558 | 0.7116 | 0.9808 | | 0.1052 | 60.91 | 670 | 0.5464 | nan | 0.9943 | 0.4604 | nan | 0.9808 | 0.0999 | 0.7703 | 0.7206 | 0.9811 | | 0.1229 | 61.82 | 680 | 0.5706 | nan | 0.9939 | 0.4750 | nan | 0.9810 | 0.0993 | 0.7822 | 0.7280 | 0.9814 | | 0.0963 | 62.73 | 690 | 0.5746 | nan | 0.9936 | 0.4754 | nan | 0.9809 | 0.0974 | 0.7841 | 0.7282 | 0.9813 | | 0.1115 | 63.64 | 700 | 0.5239 | nan | 0.9955 | 0.4562 | nan | 0.9813 | 0.0974 | 0.7597 | 0.7187 | 0.9816 | | 0.1025 | 64.55 | 710 | 0.5845 | nan | 0.9935 | 0.4813 | nan | 0.9811 | 0.0964 | 0.7890 | 0.7312 | 0.9814 | | 0.0916 | 65.45 | 720 | 0.5493 | nan | 0.9947 | 0.4685 | nan | 0.9813 | 0.0962 | 0.7720 | 0.7249 | 0.9816 | | 0.1055 | 66.36 | 730 | 0.5273 | nan | 0.9953 | 0.4571 | nan | 0.9812 | 0.0947 | 0.7613 | 0.7191 | 0.9815 | | 0.1081 | 67.27 | 740 | 0.6093 | nan | 0.9919 | 0.4813 | nan | 0.9802 | 0.0964 | 0.8006 | 0.7308 | 0.9806 | | 0.1039 | 68.18 | 750 | 0.5405 | nan | 0.9945 | 0.4573 | nan | 0.9807 | 0.0950 | 0.7675 | 0.7190 | 0.9811 | | 0.106 | 69.09 | 760 | 0.5564 | nan | 0.9943 | 0.4682 | nan | 0.9810 | 0.0939 | 0.7753 | 0.7246 | 0.9813 | | 0.0912 | 70.0 | 770 | 0.5377 | nan | 0.9949 | 0.4612 | nan | 0.9811 | 0.0936 | 0.7663 | 0.7212 | 0.9814 | | 0.0951 | 70.91 | 780 | 0.5600 | nan | 0.9941 | 0.4689 | nan | 0.9809 | 0.0938 | 0.7771 | 0.7249 | 0.9813 | | 0.0998 | 71.82 | 790 | 0.5573 | nan | 0.9944 | 0.4705 | nan | 0.9812 | 0.0928 | 0.7759 | 0.7258 | 0.9815 | | 0.0889 | 72.73 | 800 | 0.5398 | nan | 0.9949 | 0.4628 | nan | 0.9812 | 0.0931 | 0.7674 | 0.7220 | 0.9815 | | 0.0906 | 73.64 | 810 | 0.5151 | nan | 0.9958 | 0.4528 | nan | 0.9813 | 0.0928 | 0.7555 | 0.7171 | 0.9816 | | 0.0911 | 74.55 | 820 | 0.5682 | nan | 0.9938 | 0.4722 | nan | 0.9809 | 0.0924 | 0.7810 | 0.7265 | 0.9812 | | 0.0907 | 75.45 | 830 | 0.4864 | nan | 0.9965 | 0.4365 | nan | 0.9812 | 0.0929 | 0.7415 | 0.7089 | 0.9815 | | 0.1117 | 76.36 | 840 | 0.5239 | nan | 0.9956 | 0.4576 | nan | 0.9814 | 0.0934 | 0.7598 | 0.7195 | 0.9817 | | 0.0812 | 77.27 | 850 | 0.5279 | nan | 0.9956 | 0.4605 | nan | 0.9814 | 0.0915 | 0.7617 | 0.7210 | 0.9817 | | 0.0888 | 78.18 | 860 | 0.5615 | nan | 0.9942 | 0.4720 | nan | 0.9811 | 0.0915 | 0.7778 | 0.7266 | 0.9814 | | 0.09 | 79.09 | 870 | 0.5414 | nan | 0.9948 | 0.4628 | nan | 0.9811 | 0.0920 | 0.7681 | 0.7220 | 0.9814 | | 0.1052 | 80.0 | 880 | 0.5866 | nan | 0.9932 | 0.4790 | nan | 0.9808 | 0.0917 | 0.7899 | 0.7299 | 0.9812 | | 0.0867 | 80.91 | 890 | 0.5252 | nan | 0.9955 | 0.4573 | nan | 0.9813 | 0.0912 | 0.7603 | 0.7193 | 0.9816 | | 0.0942 | 81.82 | 900 | 0.5091 | nan | 0.9959 | 0.4490 | nan | 0.9813 | 0.0925 | 0.7525 | 0.7152 | 0.9815 | | 0.0917 | 82.73 | 910 | 0.5454 | nan | 0.9950 | 0.4682 | nan | 0.9814 | 0.0908 | 0.7702 | 0.7248 | 0.9817 | | 0.103 | 83.64 | 920 | 0.5452 | nan | 0.9949 | 0.4672 | nan | 0.9813 | 0.0912 | 0.7701 | 0.7243 | 0.9816 | | 0.0939 | 84.55 | 930 | 0.5539 | nan | 0.9947 | 0.4717 | nan | 0.9814 | 0.0900 | 0.7743 | 0.7265 | 0.9817 | | 0.0892 | 85.45 | 940 | 0.5330 | nan | 0.9954 | 0.4635 | nan | 0.9815 | 0.0900 | 0.7642 | 0.7225 | 0.9818 | | 0.0899 | 86.36 | 950 | 0.5756 | nan | 0.9938 | 0.4778 | nan | 0.9811 | 0.0905 | 0.7847 | 0.7295 | 0.9814 | | 0.0877 | 87.27 | 960 | 0.5771 | nan | 0.9937 | 0.4787 | nan | 0.9811 | 0.0893 | 0.7854 | 0.7299 | 0.9814 | | 0.0851 | 88.18 | 970 | 0.5087 | nan | 0.9961 | 0.4512 | nan | 0.9814 | 0.0897 | 0.7524 | 0.7163 | 0.9817 | | 0.0857 | 89.09 | 980 | 0.5363 | nan | 0.9953 | 0.4644 | nan | 0.9814 | 0.0894 | 0.7658 | 0.7229 | 0.9817 | | 0.0821 | 90.0 | 990 | 0.5333 | nan | 0.9953 | 0.4623 | nan | 0.9814 | 0.0895 | 0.7643 | 0.7218 | 0.9817 | | 0.0931 | 90.91 | 1000 | 0.5581 | nan | 0.9944 | 0.4718 | nan | 0.9812 | 0.0895 | 0.7763 | 0.7265 | 0.9815 | | 0.0787 | 91.82 | 1010 | 0.5525 | nan | 0.9946 | 0.4689 | nan | 0.9812 | 0.0889 | 0.7735 | 0.7251 | 0.9815 | | 0.0865 | 92.73 | 1020 | 0.5659 | nan | 0.9941 | 0.4746 | nan | 0.9812 | 0.0883 | 0.7800 | 0.7279 | 0.9815 | | 0.0939 | 93.64 | 1030 | 0.5583 | nan | 0.9945 | 0.4723 | nan | 0.9813 | 0.0891 | 0.7764 | 0.7268 | 0.9816 | | 0.0874 | 94.55 | 1040 | 0.5258 | nan | 0.9955 | 0.4580 | nan | 0.9813 | 0.0893 | 0.7607 | 0.7197 | 0.9816 | | 0.0927 | 95.45 | 1050 | 0.5319 | nan | 0.9953 | 0.4608 | nan | 0.9813 | 0.0894 | 0.7636 | 0.7211 | 0.9816 | | 0.0808 | 96.36 | 1060 | 0.5444 | nan | 0.9949 | 0.4665 | nan | 0.9813 | 0.0897 | 0.7696 | 0.7239 | 0.9816 | | 0.0924 | 97.27 | 1070 | 0.5445 | nan | 0.9950 | 0.4671 | nan | 0.9814 | 0.0892 | 0.7697 | 0.7243 | 0.9817 | | 0.08 | 98.18 | 1080 | 0.5522 | nan | 0.9947 | 0.4703 | nan | 0.9813 | 0.0884 | 0.7735 | 0.7258 | 0.9816 | | 0.0798 | 99.09 | 1090 | 0.0880 | 0.7300 | 0.7842 | 0.9815 | nan | 0.5745 | 0.9939 | nan | 0.4788 | 0.9812 | | 0.0789 | 100.0 | 1100 | 0.0877 | 0.7231 | 0.7668 | 0.9817 | nan | 0.5383 | 0.9952 | nan | 0.4647 | 0.9814 | | 0.0801 | 100.91 | 1110 | 0.0885 | 0.7232 | 0.7677 | 0.9816 | nan | 0.5404 | 0.9951 | nan | 0.4650 | 0.9813 | | 0.1043 | 101.82 | 1120 | 0.0891 | 0.7242 | 0.7697 | 0.9816 | nan | 0.5445 | 0.9950 | nan | 0.4670 | 0.9813 | | 0.0893 | 102.73 | 1130 | 0.0882 | 0.7263 | 0.7728 | 0.9817 | nan | 0.5508 | 0.9949 | nan | 0.4712 | 0.9814 | | 0.0923 | 103.64 | 1140 | 0.0892 | 0.7134 | 0.7504 | 0.9815 | nan | 0.5048 | 0.9960 | nan | 0.4457 | 0.9812 | | 0.0915 | 104.55 | 1150 | 0.0884 | 0.7293 | 0.7795 | 0.9817 | nan | 0.5646 | 0.9944 | nan | 0.4772 | 0.9814 | | 0.0859 | 105.45 | 1160 | 0.0880 | 0.7340 | 0.7941 | 0.9815 | nan | 0.5949 | 0.9932 | nan | 0.4869 | 0.9811 | | 0.0872 | 106.36 | 1170 | 0.0872 | 0.7298 | 0.7815 | 0.9817 | nan | 0.5688 | 0.9942 | nan | 0.4783 | 0.9814 | | 0.0845 | 107.27 | 1180 | 0.0881 | 0.7310 | 0.7843 | 0.9817 | nan | 0.5746 | 0.9940 | nan | 0.4806 | 0.9813 | | 0.0842 | 108.18 | 1190 | 0.0869 | 0.7285 | 0.7766 | 0.9818 | nan | 0.5584 | 0.9947 | nan | 0.4755 | 0.9815 | | 0.0906 | 109.09 | 1200 | 0.0875 | 0.7277 | 0.7754 | 0.9818 | nan | 0.5560 | 0.9947 | nan | 0.4740 | 0.9815 | | 0.0953 | 110.0 | 1210 | 0.0878 | 0.7289 | 0.7777 | 0.9818 | nan | 0.5608 | 0.9946 | nan | 0.4764 | 0.9815 | | 0.0988 | 110.91 | 1220 | 0.0880 | 0.7303 | 0.7809 | 0.9818 | nan | 0.5674 | 0.9944 | nan | 0.4790 | 0.9815 | | 0.0894 | 111.82 | 1230 | 0.0869 | 0.7300 | 0.7801 | 0.9818 | nan | 0.5657 | 0.9945 | nan | 0.4785 | 0.9815 | | 0.0788 | 112.73 | 1240 | 0.0868 | 0.7283 | 0.7758 | 0.9818 | nan | 0.5569 | 0.9948 | nan | 0.4750 | 0.9815 | | 0.0793 | 113.64 | 1250 | 0.0870 | 0.7281 | 0.7758 | 0.9818 | nan | 0.5569 | 0.9947 | nan | 0.4747 | 0.9815 | | 0.084 | 114.55 | 1260 | 0.0874 | 0.7295 | 0.7809 | 0.9817 | nan | 0.5675 | 0.9943 | nan | 0.4777 | 0.9814 | | 0.0832 | 115.45 | 1270 | 0.0875 | 0.7277 | 0.7760 | 0.9817 | nan | 0.5574 | 0.9946 | nan | 0.4739 | 0.9814 | | 0.0833 | 116.36 | 1280 | 0.0873 | 0.7274 | 0.7755 | 0.9817 | nan | 0.5563 | 0.9947 | nan | 0.4735 | 0.9814 | | 0.0786 | 117.27 | 1290 | 0.0867 | 0.7277 | 0.7754 | 0.9818 | nan | 0.5561 | 0.9947 | nan | 0.4740 | 0.9815 | | 0.0839 | 118.18 | 1300 | 0.0865 | 0.7285 | 0.7779 | 0.9817 | nan | 0.5613 | 0.9945 | nan | 0.4755 | 0.9814 | | 0.0847 | 119.09 | 1310 | 0.0877 | 0.7293 | 0.7816 | 0.9816 | nan | 0.5691 | 0.9941 | nan | 0.4773 | 0.9813 | | 0.0933 | 120.0 | 1320 | 0.0867 | 0.7280 | 0.7762 | 0.9818 | nan | 0.5578 | 0.9947 | nan | 0.4745 | 0.9814 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/safety-utcustom-train-SF-RGB-b0

# safety-utcustom-train-SF-RGB-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3221 - Mean Iou: 0.7557 - Mean Accuracy: 0.8092 - Overall Accuracy: 0.9835 - Accuracy Unlabeled: nan - Accuracy Safe: 0.6240 - Accuracy Unsafe: 0.9945 - Iou Unlabeled: nan - Iou Safe: 0.5281 - Iou Unsafe: 0.9832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 1.2069 | 1.0 | 10 | 1.1287 | 0.0406 | 0.3613 | 0.1117 | nan | 0.6267 | 0.0960 | 0.0 | 0.0261 | 0.0958 | | 1.196 | 2.0 | 20 | 1.1408 | 0.0465 | 0.3971 | 0.1274 | nan | 0.6837 | 0.1105 | 0.0 | 0.0290 | 0.1104 | | 1.1866 | 3.0 | 30 | 1.1441 | 0.0662 | 0.4586 | 0.1826 | nan | 0.7519 | 0.1653 | 0.0 | 0.0335 | 0.1652 | | 1.1701 | 4.0 | 40 | 1.1350 | 0.1016 | 0.5469 | 0.2805 | nan | 0.8301 | 0.2638 | 0.0 | 0.0410 | 0.2637 | | 1.1467 | 5.0 | 50 | 1.1285 | 0.1325 | 0.6266 | 0.3646 | nan | 0.9052 | 0.3481 | 0.0 | 0.0496 | 0.3481 | | 1.1126 | 6.0 | 60 | 1.0914 | 0.1933 | 0.7318 | 0.5257 | nan | 0.9508 | 0.5128 | 0.0 | 0.0673 | 0.5127 | | 1.0735 | 7.0 | 70 | 1.0392 | 0.2462 | 0.8075 | 0.6582 | nan | 0.9662 | 0.6489 | 0.0 | 0.0900 | 0.6487 | | 1.0335 | 8.0 | 80 | 1.0015 | 0.2783 | 0.8456 | 0.7301 | nan | 0.9683 | 0.7228 | 0.0 | 0.1122 | 0.7226 | | 1.0088 | 9.0 | 90 | 0.9502 | 0.3061 | 0.8736 | 0.7884 | nan | 0.9643 | 0.7830 | 0.0 | 0.1359 | 0.7825 | | 0.9993 | 10.0 | 100 | 0.9158 | 0.3246 | 0.8886 | 0.8232 | nan | 0.9581 | 0.8191 | 0.0 | 0.1556 | 0.8183 | | 0.9114 | 11.0 | 110 | 0.8472 | 0.3562 | 0.9061 | 0.8732 | nan | 0.9411 | 0.8711 | 0.0 | 0.1990 | 0.8697 | | 0.9027 | 12.0 | 120 | 0.8073 | 0.3687 | 0.9085 | 0.8909 | nan | 0.9271 | 0.8898 | 0.0 | 0.2182 | 0.8881 | | 0.8775 | 13.0 | 130 | 0.7756 | 0.3819 | 0.9011 | 0.9086 | nan | 0.8931 | 0.9090 | 0.0 | 0.2394 | 0.9062 | | 0.8532 | 14.0 | 140 | 0.7544 | 0.3883 | 0.9005 | 0.9156 | nan | 0.8844 | 0.9166 | 0.0 | 0.2513 | 0.9135 | | 0.7509 | 15.0 | 150 | 0.7137 | 0.4039 | 0.8965 | 0.9311 | nan | 0.8597 | 0.9333 | 0.0 | 0.2824 | 0.9294 | | 0.7711 | 16.0 | 160 | 0.6837 | 0.4131 | 0.8959 | 0.9394 | nan | 0.8497 | 0.9422 | 0.0 | 0.3014 | 0.9379 | | 0.7163 | 17.0 | 170 | 0.6573 | 0.4230 | 0.8859 | 0.9467 | nan | 0.8212 | 0.9505 | 0.0 | 0.3234 | 0.9454 | | 0.6609 | 18.0 | 180 | 0.6698 | 0.4200 | 0.8889 | 0.9449 | nan | 0.8294 | 0.9484 | 0.0 | 0.3163 | 0.9436 | | 0.7237 | 19.0 | 190 | 0.6465 | 0.4236 | 0.8821 | 0.9479 | nan | 0.8121 | 0.9520 | 0.0 | 0.3241 | 0.9467 | | 0.6264 | 20.0 | 200 | 0.6300 | 0.4293 | 0.8776 | 0.9520 | nan | 0.7985 | 0.9566 | 0.0 | 0.3372 | 0.9508 | | 0.6711 | 21.0 | 210 | 0.6050 | 0.4391 | 0.8731 | 0.9576 | nan | 0.7833 | 0.9630 | 0.0 | 0.3605 | 0.9567 | | 0.626 | 22.0 | 220 | 0.5855 | 0.4409 | 0.8742 | 0.9585 | nan | 0.7846 | 0.9637 | 0.0 | 0.3653 | 0.9575 | | 0.6103 | 23.0 | 230 | 0.5651 | 0.4474 | 0.8671 | 0.9623 | nan | 0.7658 | 0.9683 | 0.0 | 0.3807 | 0.9615 | | 0.6462 | 24.0 | 240 | 0.5621 | 0.4489 | 0.8643 | 0.9631 | nan | 0.7592 | 0.9693 | 0.0 | 0.3844 | 0.9623 | | 0.5442 | 25.0 | 250 | 0.5460 | 0.4563 | 0.8592 | 0.9668 | nan | 0.7450 | 0.9735 | 0.0 | 0.4028 | 0.9660 | | 0.6764 | 26.0 | 260 | 0.5673 | 0.4544 | 0.8646 | 0.9657 | nan | 0.7571 | 0.9721 | 0.0 | 0.3983 | 0.9650 | | 0.6471 | 27.0 | 270 | 0.5412 | 0.4586 | 0.8561 | 0.9679 | nan | 0.7374 | 0.9749 | 0.0 | 0.4087 | 0.9672 | | 0.5589 | 28.0 | 280 | 0.5427 | 0.4573 | 0.8601 | 0.9671 | nan | 0.7465 | 0.9738 | 0.0 | 0.4057 | 0.9663 | | 0.6512 | 29.0 | 290 | 0.5264 | 0.4600 | 0.8567 | 0.9681 | nan | 0.7384 | 0.9751 | 0.0 | 0.4126 | 0.9674 | | 0.6146 | 30.0 | 300 | 0.5321 | 0.4616 | 0.8619 | 0.9688 | nan | 0.7482 | 0.9755 | 0.0 | 0.4167 | 0.9681 | | 0.4938 | 31.0 | 310 | 0.5025 | 0.4751 | 0.8475 | 0.9744 | nan | 0.7127 | 0.9823 | 0.0 | 0.4515 | 0.9738 | | 0.4868 | 32.0 | 320 | 0.4836 | 0.4781 | 0.8342 | 0.9761 | nan | 0.6833 | 0.9851 | 0.0 | 0.4586 | 0.9756 | | 0.6315 | 33.0 | 330 | 0.4918 | 0.4739 | 0.8479 | 0.9740 | nan | 0.7139 | 0.9819 | 0.0 | 0.4483 | 0.9735 | | 0.5529 | 34.0 | 340 | 0.4879 | 0.4753 | 0.8414 | 0.9749 | nan | 0.6995 | 0.9832 | 0.0 | 0.4516 | 0.9743 | | 0.4592 | 35.0 | 350 | 0.4826 | 0.4764 | 0.8364 | 0.9754 | nan | 0.6887 | 0.9842 | 0.0 | 0.4542 | 0.9749 | | 0.5904 | 36.0 | 360 | 0.4611 | 0.4859 | 0.8159 | 0.9793 | nan | 0.6423 | 0.9896 | 0.0 | 0.4789 | 0.9789 | | 0.4804 | 37.0 | 370 | 0.4654 | 0.4796 | 0.8359 | 0.9764 | nan | 0.6865 | 0.9853 | 0.0 | 0.4627 | 0.9760 | | 0.4701 | 38.0 | 380 | 0.4625 | 0.4846 | 0.8251 | 0.9784 | nan | 0.6623 | 0.9880 | 0.0 | 0.4758 | 0.9779 | | 0.4729 | 39.0 | 390 | 0.4536 | 0.4838 | 0.8231 | 0.9783 | nan | 0.6582 | 0.9881 | 0.0 | 0.4736 | 0.9779 | | 0.4219 | 40.0 | 400 | 0.4514 | 0.4838 | 0.8305 | 0.9779 | nan | 0.6738 | 0.9872 | 0.0 | 0.4739 | 0.9775 | | 0.6494 | 41.0 | 410 | 0.4425 | 0.4892 | 0.8162 | 0.9801 | nan | 0.6420 | 0.9904 | 0.0 | 0.4878 | 0.9797 | | 0.4616 | 42.0 | 420 | 0.4390 | 0.7316 | 0.8225 | 0.9794 | nan | 0.6558 | 0.9892 | nan | 0.4842 | 0.9790 | | 0.4408 | 43.0 | 430 | 0.4419 | 0.7358 | 0.8272 | 0.9797 | nan | 0.6652 | 0.9893 | nan | 0.4923 | 0.9793 | | 0.4532 | 44.0 | 440 | 0.4371 | 0.7375 | 0.8274 | 0.9800 | nan | 0.6651 | 0.9896 | nan | 0.4954 | 0.9796 | | 0.5015 | 45.0 | 450 | 0.4376 | 0.7364 | 0.8276 | 0.9798 | nan | 0.6659 | 0.9894 | nan | 0.4933 | 0.9794 | | 0.4965 | 46.0 | 460 | 0.4201 | 0.7405 | 0.8137 | 0.9812 | nan | 0.6357 | 0.9918 | nan | 0.5002 | 0.9809 | | 0.4837 | 47.0 | 470 | 0.4281 | 0.7378 | 0.8279 | 0.9800 | nan | 0.6662 | 0.9896 | nan | 0.4961 | 0.9796 | | 0.4221 | 48.0 | 480 | 0.4288 | 0.7371 | 0.8227 | 0.9802 | nan | 0.6553 | 0.9901 | nan | 0.4944 | 0.9798 | | 0.4491 | 49.0 | 490 | 0.4152 | 0.7371 | 0.8074 | 0.9811 | nan | 0.6228 | 0.9920 | nan | 0.4935 | 0.9808 | | 0.4121 | 50.0 | 500 | 0.4159 | 0.7367 | 0.8063 | 0.9811 | nan | 0.6205 | 0.9921 | nan | 0.4927 | 0.9808 | | 0.4727 | 51.0 | 510 | 0.4199 | 0.7354 | 0.8095 | 0.9807 | nan | 0.6274 | 0.9915 | nan | 0.4905 | 0.9804 | | 0.5323 | 52.0 | 520 | 0.4079 | 0.7383 | 0.8074 | 0.9813 | nan | 0.6227 | 0.9922 | nan | 0.4957 | 0.9809 | | 0.409 | 53.0 | 530 | 0.4103 | 0.7392 | 0.8161 | 0.9809 | nan | 0.6409 | 0.9913 | nan | 0.4978 | 0.9805 | | 0.6391 | 54.0 | 540 | 0.4063 | 0.7406 | 0.8133 | 0.9813 | nan | 0.6349 | 0.9918 | nan | 0.5003 | 0.9809 | | 0.3905 | 55.0 | 550 | 0.4000 | 0.7409 | 0.8122 | 0.9814 | nan | 0.6325 | 0.9920 | nan | 0.5007 | 0.9810 | | 0.4138 | 56.0 | 560 | 0.4028 | 0.7398 | 0.8183 | 0.9809 | nan | 0.6455 | 0.9911 | nan | 0.4990 | 0.9805 | | 0.3977 | 57.0 | 570 | 0.3865 | 0.7372 | 0.7912 | 0.9821 | nan | 0.5884 | 0.9941 | nan | 0.4926 | 0.9818 | | 0.4186 | 58.0 | 580 | 0.3845 | 0.7416 | 0.7994 | 0.9822 | nan | 0.6050 | 0.9937 | nan | 0.5014 | 0.9819 | | 0.4921 | 59.0 | 590 | 0.3881 | 0.7427 | 0.8102 | 0.9817 | nan | 0.6278 | 0.9925 | nan | 0.5039 | 0.9814 | | 0.3953 | 60.0 | 600 | 0.3823 | 0.7429 | 0.8027 | 0.9822 | nan | 0.6119 | 0.9935 | nan | 0.5039 | 0.9819 | | 0.4263 | 61.0 | 610 | 0.3841 | 0.7420 | 0.8075 | 0.9818 | nan | 0.6222 | 0.9928 | nan | 0.5026 | 0.9815 | | 0.3798 | 62.0 | 620 | 0.3763 | 0.7446 | 0.8054 | 0.9823 | nan | 0.6174 | 0.9934 | nan | 0.5072 | 0.9820 | | 0.4208 | 63.0 | 630 | 0.3724 | 0.7437 | 0.7919 | 0.9829 | nan | 0.5888 | 0.9949 | nan | 0.5047 | 0.9826 | | 0.3627 | 64.0 | 640 | 0.3760 | 0.7466 | 0.8111 | 0.9822 | nan | 0.6292 | 0.9930 | nan | 0.5112 | 0.9819 | | 0.4156 | 65.0 | 650 | 0.3669 | 0.7478 | 0.8018 | 0.9829 | nan | 0.6092 | 0.9943 | nan | 0.5130 | 0.9826 | | 0.468 | 66.0 | 660 | 0.3706 | 0.7508 | 0.8145 | 0.9826 | nan | 0.6359 | 0.9932 | nan | 0.5193 | 0.9823 | | 0.4547 | 67.0 | 670 | 0.3692 | 0.7512 | 0.8189 | 0.9824 | nan | 0.6451 | 0.9927 | nan | 0.5204 | 0.9821 | | 0.3604 | 68.0 | 680 | 0.3691 | 0.7520 | 0.8152 | 0.9827 | nan | 0.6371 | 0.9933 | nan | 0.5215 | 0.9824 | | 0.4476 | 69.0 | 690 | 0.3679 | 0.7516 | 0.8195 | 0.9825 | nan | 0.6463 | 0.9927 | nan | 0.5210 | 0.9821 | | 0.3535 | 70.0 | 700 | 0.3589 | 0.7522 | 0.8097 | 0.9831 | nan | 0.6255 | 0.9939 | nan | 0.5217 | 0.9827 | | 0.3539 | 71.0 | 710 | 0.3572 | 0.7526 | 0.8091 | 0.9831 | nan | 0.6242 | 0.9941 | nan | 0.5224 | 0.9828 | | 0.3675 | 72.0 | 720 | 0.3589 | 0.7518 | 0.8100 | 0.9830 | nan | 0.6261 | 0.9939 | nan | 0.5209 | 0.9827 | | 0.4148 | 73.0 | 730 | 0.3536 | 0.7504 | 0.8093 | 0.9828 | nan | 0.6249 | 0.9937 | nan | 0.5182 | 0.9825 | | 0.3941 | 74.0 | 740 | 0.3538 | 0.7497 | 0.8099 | 0.9827 | nan | 0.6263 | 0.9936 | nan | 0.5169 | 0.9824 | | 0.4264 | 75.0 | 750 | 0.3595 | 0.7469 | 0.8197 | 0.9818 | nan | 0.6473 | 0.9920 | nan | 0.5123 | 0.9814 | | 0.3815 | 76.0 | 760 | 0.3525 | 0.7492 | 0.8097 | 0.9827 | nan | 0.6258 | 0.9935 | nan | 0.5162 | 0.9823 | | 0.3459 | 77.0 | 770 | 0.3443 | 0.7452 | 0.7926 | 0.9831 | nan | 0.5901 | 0.9951 | nan | 0.5076 | 0.9828 | | 0.3794 | 78.0 | 780 | 0.3538 | 0.7501 | 0.8154 | 0.9825 | nan | 0.6377 | 0.9930 | nan | 0.5180 | 0.9821 | | 0.3761 | 79.0 | 790 | 0.3525 | 0.7483 | 0.8169 | 0.9821 | nan | 0.6412 | 0.9925 | nan | 0.5147 | 0.9818 | | 0.3612 | 80.0 | 800 | 0.3495 | 0.7513 | 0.8128 | 0.9828 | nan | 0.6321 | 0.9934 | nan | 0.5201 | 0.9824 | | 0.405 | 81.0 | 810 | 0.3466 | 0.7502 | 0.8148 | 0.9825 | nan | 0.6365 | 0.9931 | nan | 0.5182 | 0.9822 | | 0.4289 | 82.0 | 820 | 0.3458 | 0.7498 | 0.8092 | 0.9828 | nan | 0.6247 | 0.9937 | nan | 0.5171 | 0.9824 | | 0.3523 | 83.0 | 830 | 0.3435 | 0.7503 | 0.8112 | 0.9827 | nan | 0.6288 | 0.9935 | nan | 0.5183 | 0.9824 | | 0.4254 | 84.0 | 840 | 0.3403 | 0.7495 | 0.8000 | 0.9832 | nan | 0.6052 | 0.9947 | nan | 0.5160 | 0.9829 | | 0.3399 | 85.0 | 850 | 0.3355 | 0.7492 | 0.8003 | 0.9832 | nan | 0.6059 | 0.9947 | nan | 0.5155 | 0.9829 | | 0.3251 | 86.0 | 860 | 0.3395 | 0.7503 | 0.8028 | 0.9832 | nan | 0.6111 | 0.9945 | nan | 0.5178 | 0.9829 | | 0.3748 | 87.0 | 870 | 0.3400 | 0.7502 | 0.8117 | 0.9827 | nan | 0.6299 | 0.9934 | nan | 0.5181 | 0.9824 | | 0.4398 | 88.0 | 880 | 0.3450 | 0.7527 | 0.8197 | 0.9826 | nan | 0.6466 | 0.9928 | nan | 0.5231 | 0.9822 | | 0.3782 | 89.0 | 890 | 0.3454 | 0.7547 | 0.8180 | 0.9829 | nan | 0.6426 | 0.9933 | nan | 0.5268 | 0.9826 | | 0.4318 | 90.0 | 900 | 0.3424 | 0.7541 | 0.8162 | 0.9830 | nan | 0.6390 | 0.9934 | nan | 0.5255 | 0.9826 | | 0.3428 | 91.0 | 910 | 0.3327 | 0.7541 | 0.8124 | 0.9832 | nan | 0.6309 | 0.9939 | nan | 0.5253 | 0.9828 | | 0.4303 | 92.0 | 920 | 0.3364 | 0.7525 | 0.8108 | 0.9830 | nan | 0.6277 | 0.9939 | nan | 0.5223 | 0.9827 | | 0.3624 | 93.0 | 930 | 0.3277 | 0.7531 | 0.8063 | 0.9834 | nan | 0.6182 | 0.9945 | nan | 0.5231 | 0.9830 | | 0.3418 | 94.0 | 940 | 0.3315 | 0.7548 | 0.8125 | 0.9833 | nan | 0.6311 | 0.9940 | nan | 0.5267 | 0.9829 | | 0.321 | 95.0 | 950 | 0.3266 | 0.7541 | 0.8070 | 0.9835 | nan | 0.6195 | 0.9945 | nan | 0.5251 | 0.9831 | | 0.3152 | 96.0 | 960 | 0.3265 | 0.7531 | 0.8025 | 0.9836 | nan | 0.6101 | 0.9949 | nan | 0.5230 | 0.9833 | | 0.3153 | 97.0 | 970 | 0.3263 | 0.7537 | 0.8048 | 0.9835 | nan | 0.6149 | 0.9947 | nan | 0.5243 | 0.9832 | | 0.3158 | 98.0 | 980 | 0.3299 | 0.7553 | 0.8139 | 0.9832 | nan | 0.6340 | 0.9939 | nan | 0.5278 | 0.9829 | | 0.3162 | 99.0 | 990 | 0.3248 | 0.7546 | 0.8076 | 0.9835 | nan | 0.6207 | 0.9945 | nan | 0.5260 | 0.9832 | | 0.3748 | 100.0 | 1000 | 0.3238 | 0.7553 | 0.8077 | 0.9836 | nan | 0.6208 | 0.9946 | nan | 0.5274 | 0.9833 | | 0.3598 | 101.0 | 1010 | 0.3221 | 0.7544 | 0.8096 | 0.9833 | nan | 0.6250 | 0.9943 | nan | 0.5257 | 0.9830 | | 0.3245 | 102.0 | 1020 | 0.3247 | 0.7527 | 0.8156 | 0.9828 | nan | 0.6380 | 0.9933 | nan | 0.5228 | 0.9825 | | 0.3527 | 103.0 | 1030 | 0.3275 | 0.7537 | 0.8193 | 0.9827 | nan | 0.6456 | 0.9930 | nan | 0.5250 | 0.9824 | | 0.5087 | 104.0 | 1040 | 0.3221 | 0.7559 | 0.8105 | 0.9835 | nan | 0.6266 | 0.9944 | nan | 0.5287 | 0.9832 | | 0.3331 | 105.0 | 1050 | 0.3183 | 0.7560 | 0.8064 | 0.9837 | nan | 0.6180 | 0.9948 | nan | 0.5285 | 0.9834 | | 0.324 | 106.0 | 1060 | 0.3198 | 0.7561 | 0.8090 | 0.9836 | nan | 0.6235 | 0.9946 | nan | 0.5289 | 0.9833 | | 0.3512 | 107.0 | 1070 | 0.3194 | 0.7549 | 0.8052 | 0.9836 | nan | 0.6155 | 0.9949 | nan | 0.5265 | 0.9833 | | 0.3274 | 108.0 | 1080 | 0.3185 | 0.7569 | 0.8122 | 0.9835 | nan | 0.6301 | 0.9943 | nan | 0.5306 | 0.9832 | | 0.335 | 109.0 | 1090 | 0.3177 | 0.7554 | 0.8081 | 0.9836 | nan | 0.6217 | 0.9946 | nan | 0.5276 | 0.9832 | | 0.3581 | 110.0 | 1100 | 0.3204 | 0.7568 | 0.8146 | 0.9834 | nan | 0.6352 | 0.9940 | nan | 0.5306 | 0.9831 | | 0.3307 | 111.0 | 1110 | 0.3216 | 0.7571 | 0.8138 | 0.9835 | nan | 0.6335 | 0.9941 | nan | 0.5310 | 0.9832 | | 0.3162 | 112.0 | 1120 | 0.3227 | 0.7575 | 0.8181 | 0.9833 | nan | 0.6425 | 0.9937 | nan | 0.5320 | 0.9830 | | 0.3687 | 113.0 | 1130 | 0.3188 | 0.7567 | 0.8124 | 0.9835 | nan | 0.6306 | 0.9942 | nan | 0.5302 | 0.9832 | | 0.4099 | 114.0 | 1140 | 0.3151 | 0.7550 | 0.8063 | 0.9836 | nan | 0.6178 | 0.9947 | nan | 0.5266 | 0.9833 | | 0.3283 | 115.0 | 1150 | 0.3152 | 0.7557 | 0.8088 | 0.9836 | nan | 0.6232 | 0.9945 | nan | 0.5281 | 0.9832 | | 0.3118 | 116.0 | 1160 | 0.3180 | 0.7556 | 0.8097 | 0.9835 | nan | 0.6249 | 0.9944 | nan | 0.5280 | 0.9832 | | 0.3233 | 117.0 | 1170 | 0.3164 | 0.7551 | 0.8070 | 0.9836 | nan | 0.6192 | 0.9947 | nan | 0.5269 | 0.9833 | | 0.3401 | 118.0 | 1180 | 0.3192 | 0.7562 | 0.8122 | 0.9834 | nan | 0.6303 | 0.9942 | nan | 0.5292 | 0.9831 | | 0.3867 | 119.0 | 1190 | 0.3199 | 0.7566 | 0.8160 | 0.9833 | nan | 0.6382 | 0.9938 | nan | 0.5302 | 0.9830 | | 0.3217 | 120.0 | 1200 | 0.3221 | 0.7557 | 0.8092 | 0.9835 | nan | 0.6240 | 0.9945 | nan | 0.5281 | 0.9832 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps

# segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_all dataset. It achieves the following results on the evaluation set: - Loss: 0.3095 - Mean Iou: 0.5513 - Mean Accuracy: 0.7874 - Overall Accuracy: 0.9260 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9668 - Accuracy Fallo malla: 0.6808 - Accuracy Fallo adhesivo: 0.9727 - Accuracy Fallo burbuja: 0.5291 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9167 - Iou Fallo malla: 0.6189 - Iou Fallo adhesivo: 0.7307 - Iou Fallo burbuja: 0.4903 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.1378 | 1.0 | 783 | 0.2677 | 0.4895 | 0.7143 | 0.9122 | nan | 0.9724 | 0.5531 | 0.9663 | 0.3654 | 0.0 | 0.9038 | 0.5327 | 0.6757 | 0.3351 | | 0.1117 | 2.0 | 1566 | 0.2305 | 0.5289 | 0.7978 | 0.9246 | nan | 0.9507 | 0.7727 | 0.9705 | 0.4974 | 0.0 | 0.9214 | 0.6808 | 0.5876 | 0.4549 | | 0.0881 | 3.0 | 2349 | 0.2041 | 0.5556 | 0.7867 | 0.9354 | nan | 0.9712 | 0.7391 | 0.9389 | 0.4975 | 0.0 | 0.9273 | 0.6790 | 0.7323 | 0.4394 | | 0.0878 | 4.0 | 3132 | 0.1984 | 0.5584 | 0.8003 | 0.9346 | nan | 0.9556 | 0.8247 | 0.9602 | 0.4606 | 0.0 | 0.9261 | 0.6935 | 0.7373 | 0.4352 | | 0.0895 | 5.0 | 3915 | 0.2841 | 0.5246 | 0.8086 | 0.9088 | nan | 0.9137 | 0.8834 | 0.9719 | 0.4652 | 0.0 | 0.8964 | 0.6309 | 0.6593 | 0.4365 | | 0.0773 | 6.0 | 4698 | 0.2547 | 0.5652 | 0.7823 | 0.9336 | nan | 0.9775 | 0.6843 | 0.9384 | 0.5291 | 0.0 | 0.9251 | 0.6378 | 0.7820 | 0.4813 | | 0.0667 | 7.0 | 5481 | 0.2726 | 0.5609 | 0.7932 | 0.9295 | nan | 0.9741 | 0.6609 | 0.9689 | 0.5689 | 0.0 | 0.9203 | 0.6202 | 0.7548 | 0.5093 | | 0.0678 | 8.0 | 6264 | 0.2950 | 0.5276 | 0.8002 | 0.9175 | nan | 0.9443 | 0.7561 | 0.9713 | 0.5292 | 0.0 | 0.9089 | 0.6570 | 0.5900 | 0.4822 | | 0.0653 | 9.0 | 7047 | 0.2712 | 0.5467 | 0.7682 | 0.9288 | nan | 0.9690 | 0.6971 | 0.9641 | 0.4425 | 0.0 | 0.9189 | 0.6330 | 0.7588 | 0.4228 | | 0.0646 | 10.0 | 7830 | 0.2841 | 0.5499 | 0.7819 | 0.9272 | nan | 0.9681 | 0.6840 | 0.9688 | 0.5068 | 0.0 | 0.9178 | 0.6243 | 0.7345 | 0.4728 | | 0.057 | 11.0 | 8613 | 0.3373 | 0.5257 | 0.7782 | 0.9166 | nan | 0.9593 | 0.6555 | 0.9739 | 0.5242 | 0.0 | 0.9075 | 0.6040 | 0.6319 | 0.4848 | | 0.0591 | 12.0 | 9396 | 0.3082 | 0.5504 | 0.7900 | 0.9247 | nan | 0.9656 | 0.6776 | 0.9705 | 0.5463 | 0.0 | 0.9148 | 0.6172 | 0.7182 | 0.5019 | | 0.053 | 12.77 | 10000 | 0.3095 | 0.5513 | 0.7874 | 0.9260 | nan | 0.9668 | 0.6808 | 0.9727 | 0.5291 | 0.0 | 0.9167 | 0.6189 | 0.7307 | 0.4903 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

[ "bg", "fallo cohesivo", "fallo malla", "fallo adhesivo", "fallo burbuja" ]

PostsDesert/segformer-b5-finetuned-segments-instryde-foot-test

# segformer-b5-finetuned-segments-instryde-foot-test This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 - Mean Iou: 0.4800 - Mean Accuracy: 0.9599 - Overall Accuracy: 0.9599 - Per Category Iou: [0.0, 0.9599216842864238] - Per Category Accuracy: [nan, 0.9599216842864238] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:| | 0.1024 | 0.27 | 20 | 0.2085 | 0.4534 | 0.9067 | 0.9067 | [0.0, 0.9067344993758137] | [nan, 0.9067344993758137] | | 0.0431 | 0.53 | 40 | 0.0487 | 0.4604 | 0.9207 | 0.9207 | [0.0, 0.9207331455341442] | [nan, 0.9207331455341442] | | 0.0354 | 0.8 | 60 | 0.0319 | 0.4577 | 0.9155 | 0.9155 | [0.0, 0.9154662028576415] | [nan, 0.9154662028576415] | | 0.0389 | 1.07 | 80 | 0.0276 | 0.4629 | 0.9257 | 0.9257 | [0.0, 0.9257162800419576] | [nan, 0.9257162800419576] | | 0.0208 | 1.33 | 100 | 0.0244 | 0.4702 | 0.9404 | 0.9404 | [0.0, 0.9403945317069335] | [nan, 0.9403945317069335] | | 0.0241 | 1.6 | 120 | 0.0212 | 0.4703 | 0.9406 | 0.9406 | [0.0, 0.9406131407017349] | [nan, 0.9406131407017349] | | 0.0167 | 1.87 | 140 | 0.0208 | 0.4761 | 0.9521 | 0.9521 | [0.0, 0.9521215619420916] | [nan, 0.9521215619420916] | | 0.0156 | 2.13 | 160 | 0.0205 | 0.4612 | 0.9224 | 0.9224 | [0.0, 0.9224359945462809] | [nan, 0.9224359945462809] | | 0.0156 | 2.4 | 180 | 0.0208 | 0.4734 | 0.9468 | 0.9468 | [0.0, 0.9467575875538612] | [nan, 0.9467575875538612] | | 0.0167 | 2.67 | 200 | 0.0182 | 0.4833 | 0.9667 | 0.9667 | [0.0, 0.9666659635383208] | [nan, 0.9666659635383208] | | 0.0145 | 2.93 | 220 | 0.0243 | 0.4351 | 0.8702 | 0.8702 | [0.0, 0.8702122233110058] | [nan, 0.8702122233110058] | | 0.0114 | 3.2 | 240 | 0.0176 | 0.4686 | 0.9373 | 0.9373 | [0.0, 0.93726765603217] | [nan, 0.93726765603217] | | 0.0155 | 3.47 | 260 | 0.0161 | 0.4770 | 0.9541 | 0.9541 | [0.0, 0.9540767701096305] | [nan, 0.9540767701096305] | | 0.0158 | 3.73 | 280 | 0.0169 | 0.4684 | 0.9368 | 0.9368 | [0.0, 0.9368239181251786] | [nan, 0.9368239181251786] | | 0.0114 | 4.0 | 300 | 0.0162 | 0.4777 | 0.9554 | 0.9554 | [0.0, 0.9554348305492647] | [nan, 0.9554348305492647] | | 0.0112 | 4.27 | 320 | 0.0159 | 0.4839 | 0.9678 | 0.9678 | [0.0, 0.9677532556440432] | [nan, 0.9677532556440432] | | 0.0131 | 4.53 | 340 | 0.0154 | 0.4811 | 0.9622 | 0.9622 | [0.0, 0.9622032718479555] | [nan, 0.9622032718479555] | | 0.0101 | 4.8 | 360 | 0.0156 | 0.4683 | 0.9367 | 0.9367 | [0.0, 0.9366846987126999] | [nan, 0.9366846987126999] | | 0.0102 | 5.07 | 380 | 0.0152 | 0.4758 | 0.9517 | 0.9517 | [0.0, 0.9516509773164403] | [nan, 0.9516509773164403] | | 0.0101 | 5.33 | 400 | 0.0169 | 0.4884 | 0.9768 | 0.9768 | [0.0, 0.9768393358121804] | [nan, 0.9768393358121804] | | 0.0082 | 5.6 | 420 | 0.0150 | 0.4761 | 0.9522 | 0.9522 | [0.0, 0.9522462074215836] | [nan, 0.9522462074215836] | | 0.01 | 5.87 | 440 | 0.0152 | 0.4788 | 0.9576 | 0.9576 | [0.0, 0.9575745140264517] | [nan, 0.9575745140264517] | | 0.0098 | 6.13 | 460 | 0.0148 | 0.4783 | 0.9565 | 0.9565 | [0.0, 0.9565489693736469] | [nan, 0.9565489693736469] | | 0.0088 | 6.4 | 480 | 0.0153 | 0.4795 | 0.9591 | 0.9591 | [0.0, 0.959051850601846] | [nan, 0.959051850601846] | | 0.0091 | 6.67 | 500 | 0.0152 | 0.4828 | 0.9656 | 0.9656 | [0.0, 0.965590177169167] | [nan, 0.965590177169167] | | 0.0102 | 6.93 | 520 | 0.0149 | 0.4800 | 0.9599 | 0.9599 | [0.0, 0.9599216842864238] | [nan, 0.9599216842864238] | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "unlabeled", "foot" ]

sam1120/safety-utcustom-train-SF-RGB-b5

# safety-utcustom-train-SF-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2236 - Mean Iou: 0.5051 - Mean Accuracy: 0.8441 - Overall Accuracy: 0.9820 - Accuracy Unlabeled: nan - Accuracy Safe: 0.6977 - Accuracy Unsafe: 0.9906 - Iou Unlabeled: 0.0 - Iou Safe: 0.5337 - Iou Unsafe: 0.9816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 130 ### Training results | Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |:-------------:|:------:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:| | 1.2239 | 0.91 | 10 | 0.3992 | nan | 0.2951 | 0.0314 | 0.0 | 0.2939 | 1.1103 | 0.3472 | 0.1084 | 0.2982 | | 1.1948 | 1.82 | 20 | 0.5219 | nan | 0.3705 | 0.0440 | 0.0 | 0.3689 | 1.0963 | 0.4462 | 0.1376 | 0.3750 | | 1.1661 | 2.73 | 30 | 0.5863 | nan | 0.4988 | 0.0647 | 0.0 | 0.4961 | 1.0516 | 0.5426 | 0.1870 | 0.5014 | | 1.1112 | 3.64 | 40 | 0.5459 | nan | 0.5794 | 0.0900 | 0.0 | 0.5754 | 1.0048 | 0.5626 | 0.2218 | 0.5784 | | 1.0907 | 4.55 | 50 | 0.5993 | nan | 0.6367 | 0.1094 | 0.0 | 0.6321 | 0.9690 | 0.6180 | 0.2472 | 0.6356 | | 1.047 | 5.45 | 60 | 0.6692 | nan | 0.6699 | 0.1159 | 0.0 | 0.6656 | 0.9437 | 0.6695 | 0.2605 | 0.6699 | | 1.0112 | 6.36 | 70 | 0.6673 | nan | 0.7189 | 0.1349 | 0.0 | 0.7137 | 0.9084 | 0.6931 | 0.2829 | 0.7173 | | 0.9925 | 7.27 | 80 | 0.6842 | nan | 0.7665 | 0.1452 | 0.0 | 0.7605 | 0.8647 | 0.7254 | 0.3019 | 0.7641 | | 0.9395 | 8.18 | 90 | 0.6818 | nan | 0.7921 | 0.1620 | 0.0 | 0.7856 | 0.8319 | 0.7369 | 0.3159 | 0.7888 | | 0.8902 | 9.09 | 100 | 0.6806 | nan | 0.8142 | 0.1770 | 0.0 | 0.8072 | 0.8014 | 0.7474 | 0.3281 | 0.8102 | | 0.9057 | 10.0 | 110 | 0.6984 | nan | 0.8179 | 0.1733 | 0.0 | 0.8109 | 0.7867 | 0.7581 | 0.3281 | 0.8143 | | 0.8321 | 10.91 | 120 | 0.6744 | nan | 0.8494 | 0.1862 | 0.0 | 0.8413 | 0.7440 | 0.7619 | 0.3425 | 0.8442 | | 0.8152 | 11.82 | 130 | 0.6688 | nan | 0.8590 | 0.2006 | 0.0 | 0.8507 | 0.7270 | 0.7639 | 0.3504 | 0.8534 | | 0.7929 | 12.73 | 140 | 0.6660 | nan | 0.8657 | 0.2085 | 0.0 | 0.8572 | 0.7045 | 0.7658 | 0.3553 | 0.8598 | | 0.7568 | 13.64 | 150 | 0.6571 | nan | 0.8838 | 0.2185 | 0.0 | 0.8748 | 0.6744 | 0.7704 | 0.3644 | 0.8771 | | 0.7085 | 14.55 | 160 | 0.6519 | nan | 0.8934 | 0.2260 | 0.0 | 0.8842 | 0.6556 | 0.7727 | 0.3701 | 0.8863 | | 0.7147 | 15.45 | 170 | 0.6561 | nan | 0.8964 | 0.2283 | 0.0 | 0.8872 | 0.6509 | 0.7762 | 0.3718 | 0.8893 | | 0.6991 | 16.36 | 180 | 0.6620 | nan | 0.8964 | 0.2267 | 0.0 | 0.8874 | 0.6502 | 0.7792 | 0.3714 | 0.8895 | | 0.6357 | 17.27 | 190 | 0.6612 | nan | 0.9051 | 0.2411 | 0.0 | 0.8960 | 0.6230 | 0.7831 | 0.3790 | 0.8979 | | 0.6815 | 18.18 | 200 | 0.6484 | nan | 0.9178 | 0.2594 | 0.0 | 0.9082 | 0.5993 | 0.7831 | 0.3892 | 0.9098 | | 0.6398 | 19.09 | 210 | 0.6414 | nan | 0.9258 | 0.2682 | 0.0 | 0.9159 | 0.5785 | 0.7836 | 0.3947 | 0.9174 | | 0.5845 | 20.0 | 220 | 0.6426 | nan | 0.9286 | 0.2698 | 0.0 | 0.9187 | 0.5641 | 0.7856 | 0.3962 | 0.9202 | | 0.6062 | 20.91 | 230 | 0.6520 | nan | 0.9252 | 0.2641 | 0.0 | 0.9156 | 0.5693 | 0.7886 | 0.3932 | 0.9171 | | 0.6071 | 21.82 | 240 | 0.6592 | nan | 0.9283 | 0.2675 | 0.0 | 0.9188 | 0.5627 | 0.7937 | 0.3955 | 0.9203 | | 0.6209 | 22.73 | 250 | 0.6619 | nan | 0.9300 | 0.2724 | 0.0 | 0.9205 | 0.5632 | 0.7959 | 0.3977 | 0.9220 | | 0.5609 | 23.64 | 260 | 0.6505 | nan | 0.9379 | 0.2868 | 0.0 | 0.9281 | 0.5416 | 0.7942 | 0.4050 | 0.9294 | | 0.5752 | 24.55 | 270 | 0.6412 | nan | 0.9451 | 0.2983 | 0.0 | 0.9350 | 0.5141 | 0.7932 | 0.4111 | 0.9362 | | 0.6004 | 25.45 | 280 | 0.6492 | nan | 0.9412 | 0.2907 | 0.0 | 0.9313 | 0.5255 | 0.7952 | 0.4073 | 0.9326 | | 0.5524 | 26.36 | 290 | 0.6588 | nan | 0.9387 | 0.2868 | 0.0 | 0.9291 | 0.5314 | 0.7987 | 0.4053 | 0.9304 | | 0.5758 | 27.27 | 300 | 0.6544 | nan | 0.9423 | 0.2913 | 0.0 | 0.9326 | 0.5268 | 0.7984 | 0.4080 | 0.9338 | | 0.5598 | 28.18 | 310 | 0.6605 | nan | 0.9408 | 0.2897 | 0.0 | 0.9312 | 0.5240 | 0.8006 | 0.4070 | 0.9325 | | 0.5505 | 29.09 | 320 | 0.6582 | nan | 0.9421 | 0.2959 | 0.0 | 0.9324 | 0.5165 | 0.8002 | 0.4094 | 0.9337 | | 0.5754 | 30.0 | 330 | 0.6578 | nan | 0.9433 | 0.2959 | 0.0 | 0.9336 | 0.5145 | 0.8005 | 0.4098 | 0.9348 | | 0.5284 | 30.91 | 340 | 0.6719 | nan | 0.9411 | 0.2941 | 0.0 | 0.9318 | 0.5175 | 0.8065 | 0.4086 | 0.9331 | | 0.5463 | 31.82 | 350 | 0.6684 | nan | 0.9448 | 0.3020 | 0.0 | 0.9354 | 0.5016 | 0.8066 | 0.4125 | 0.9367 | | 0.4923 | 32.73 | 360 | 0.6688 | nan | 0.9463 | 0.3066 | 0.0 | 0.9369 | 0.4947 | 0.8075 | 0.4145 | 0.9381 | | 0.4922 | 33.64 | 370 | 0.6685 | nan | 0.9504 | 0.3165 | 0.0 | 0.9409 | 0.4738 | 0.8094 | 0.4191 | 0.9420 | | 0.4976 | 34.55 | 380 | 0.6748 | nan | 0.9535 | 0.3233 | 0.0 | 0.9443 | 0.4663 | 0.8142 | 0.4225 | 0.9453 | | 0.4922 | 35.45 | 390 | 0.6509 | nan | 0.9653 | 0.3484 | 0.0 | 0.9552 | 0.4295 | 0.8081 | 0.4345 | 0.9560 | | 0.4608 | 36.36 | 400 | 0.6580 | nan | 0.9637 | 0.3507 | 0.0 | 0.9538 | 0.4434 | 0.8109 | 0.4348 | 0.9547 | | 0.4836 | 37.27 | 410 | 0.6522 | nan | 0.9662 | 0.3588 | 0.0 | 0.9561 | 0.4328 | 0.8092 | 0.4383 | 0.9569 | | 0.459 | 38.18 | 420 | 0.6477 | nan | 0.9691 | 0.3632 | 0.0 | 0.9588 | 0.4211 | 0.8084 | 0.4407 | 0.9596 | | 0.4528 | 39.09 | 430 | 0.6593 | nan | 0.9668 | 0.3574 | 0.0 | 0.9569 | 0.4239 | 0.8131 | 0.4381 | 0.9577 | | 0.4202 | 40.0 | 440 | 0.6572 | nan | 0.9689 | 0.3650 | 0.0 | 0.9590 | 0.4141 | 0.8130 | 0.4413 | 0.9597 | | 0.4805 | 40.91 | 450 | 0.6470 | nan | 0.9724 | 0.3754 | 0.0 | 0.9621 | 0.4012 | 0.8097 | 0.4458 | 0.9628 | | 0.4611 | 41.82 | 460 | 0.6525 | nan | 0.9718 | 0.3716 | 0.0 | 0.9617 | 0.4025 | 0.8122 | 0.4444 | 0.9624 | | 0.4339 | 42.73 | 470 | 0.6487 | nan | 0.9726 | 0.3744 | 0.0 | 0.9624 | 0.3951 | 0.8107 | 0.4456 | 0.9631 | | 0.4361 | 43.64 | 480 | 0.6448 | nan | 0.9740 | 0.3769 | 0.0 | 0.9636 | 0.3946 | 0.8094 | 0.4468 | 0.9643 | | 0.4416 | 44.55 | 490 | 0.6447 | nan | 0.9746 | 0.3783 | 0.0 | 0.9642 | 0.3871 | 0.8097 | 0.4475 | 0.9649 | | 0.4524 | 45.45 | 500 | 0.6589 | nan | 0.9712 | 0.3701 | 0.0 | 0.9612 | 0.4025 | 0.8151 | 0.4438 | 0.9620 | | 0.4319 | 46.36 | 510 | 0.6730 | nan | 0.9673 | 0.3594 | 0.0 | 0.9578 | 0.4169 | 0.8202 | 0.4391 | 0.9586 | | 0.4224 | 47.27 | 520 | 0.6603 | nan | 0.9712 | 0.3716 | 0.0 | 0.9613 | 0.3986 | 0.8158 | 0.4443 | 0.9620 | | 0.4333 | 48.18 | 530 | 0.6650 | nan | 0.9703 | 0.3724 | 0.0 | 0.9605 | 0.4038 | 0.8176 | 0.4443 | 0.9612 | | 0.3916 | 49.09 | 540 | 0.6624 | nan | 0.9724 | 0.3781 | 0.0 | 0.9626 | 0.3968 | 0.8174 | 0.4469 | 0.9633 | | 0.4803 | 50.0 | 550 | 0.6680 | nan | 0.9726 | 0.3809 | 0.0 | 0.9629 | 0.3942 | 0.8203 | 0.4479 | 0.9636 | | 0.3543 | 50.91 | 560 | 0.6473 | nan | 0.9777 | 0.3952 | 0.0 | 0.9673 | 0.3697 | 0.8125 | 0.4542 | 0.9680 | | 0.3684 | 51.82 | 570 | 0.6515 | nan | 0.9772 | 0.3951 | 0.0 | 0.9670 | 0.3708 | 0.8143 | 0.4540 | 0.9676 | | 0.4004 | 52.73 | 580 | 0.6437 | nan | 0.9793 | 0.4014 | 0.0 | 0.9688 | 0.3585 | 0.8115 | 0.4567 | 0.9694 | | 0.3656 | 53.64 | 590 | 0.6559 | nan | 0.9780 | 0.4010 | 0.0 | 0.9679 | 0.3654 | 0.8169 | 0.4563 | 0.9685 | | 0.3918 | 54.55 | 600 | 0.6432 | nan | 0.9809 | 0.4115 | 0.0 | 0.9704 | 0.3527 | 0.8121 | 0.4606 | 0.9709 | | 0.3741 | 55.45 | 610 | 0.6393 | nan | 0.9827 | 0.4185 | 0.0 | 0.9720 | 0.3361 | 0.8110 | 0.4635 | 0.9726 | | 0.3656 | 56.36 | 620 | 0.6540 | nan | 0.9807 | 0.4147 | 0.0 | 0.9705 | 0.3473 | 0.8174 | 0.4617 | 0.9710 | | 0.3341 | 57.27 | 630 | 0.6258 | nan | 0.9845 | 0.4247 | 0.0 | 0.9734 | 0.3335 | 0.8052 | 0.4660 | 0.9739 | | 0.3669 | 58.18 | 640 | 0.6495 | nan | 0.9815 | 0.4190 | 0.0 | 0.9712 | 0.3395 | 0.8155 | 0.4634 | 0.9717 | | 0.3347 | 59.09 | 650 | 0.6612 | nan | 0.9800 | 0.4174 | 0.0 | 0.9700 | 0.3416 | 0.8206 | 0.4625 | 0.9706 | | 0.4287 | 60.0 | 660 | 0.6673 | nan | 0.9797 | 0.4185 | 0.0 | 0.9699 | 0.3419 | 0.8235 | 0.4628 | 0.9705 | | 0.3838 | 60.91 | 670 | 0.6611 | nan | 0.9812 | 0.4227 | 0.0 | 0.9712 | 0.3381 | 0.8211 | 0.4646 | 0.9718 | | 0.352 | 61.82 | 680 | 0.6407 | nan | 0.9845 | 0.4318 | 0.0 | 0.9738 | 0.3216 | 0.8126 | 0.4685 | 0.9743 | | 0.3343 | 62.73 | 690 | 0.6499 | nan | 0.9837 | 0.4311 | 0.0 | 0.9733 | 0.3275 | 0.8168 | 0.4681 | 0.9738 | | 0.3443 | 63.64 | 700 | 0.6528 | nan | 0.9836 | 0.4324 | 0.0 | 0.9733 | 0.3273 | 0.8182 | 0.4686 | 0.9738 | | 0.3183 | 64.55 | 710 | 0.6456 | nan | 0.9848 | 0.4367 | 0.0 | 0.9743 | 0.3155 | 0.8152 | 0.4703 | 0.9748 | | 0.3346 | 65.45 | 720 | 0.6517 | nan | 0.9841 | 0.4356 | 0.0 | 0.9738 | 0.3212 | 0.8179 | 0.4698 | 0.9743 | | 0.3225 | 66.36 | 730 | 0.6367 | nan | 0.9863 | 0.4432 | 0.0 | 0.9755 | 0.3052 | 0.8115 | 0.4729 | 0.9759 | | 0.3792 | 67.27 | 740 | 0.6381 | nan | 0.9861 | 0.4429 | 0.0 | 0.9753 | 0.3037 | 0.8121 | 0.4728 | 0.9758 | | 0.3177 | 68.18 | 750 | 0.6345 | nan | 0.9865 | 0.4446 | 0.0 | 0.9756 | 0.2989 | 0.8105 | 0.4734 | 0.9761 | | 0.3295 | 69.09 | 760 | 0.6404 | nan | 0.9859 | 0.4426 | 0.0 | 0.9752 | 0.3064 | 0.8131 | 0.4726 | 0.9757 | | 0.3847 | 70.0 | 770 | 0.6429 | nan | 0.9857 | 0.4439 | 0.0 | 0.9751 | 0.3054 | 0.8143 | 0.4730 | 0.9756 | | 0.3406 | 70.91 | 780 | 0.6443 | nan | 0.9862 | 0.4476 | 0.0 | 0.9756 | 0.3075 | 0.8152 | 0.4744 | 0.9761 | | 0.3847 | 71.82 | 790 | 0.6343 | nan | 0.9877 | 0.4546 | 0.0 | 0.9769 | 0.2911 | 0.8110 | 0.4772 | 0.9773 | | 0.3292 | 72.73 | 800 | 0.6328 | nan | 0.9881 | 0.4567 | 0.0 | 0.9771 | 0.2905 | 0.8105 | 0.4779 | 0.9776 | | 0.3156 | 73.64 | 810 | 0.6318 | nan | 0.9882 | 0.4579 | 0.0 | 0.9773 | 0.2865 | 0.8100 | 0.4784 | 0.9777 | | 0.3106 | 74.55 | 820 | 0.6333 | nan | 0.9884 | 0.4600 | 0.0 | 0.9775 | 0.2812 | 0.8109 | 0.4792 | 0.9779 | | 0.3004 | 75.45 | 830 | 0.6232 | nan | 0.9893 | 0.4632 | 0.0 | 0.9781 | 0.2798 | 0.8063 | 0.4804 | 0.9785 | | 0.3336 | 76.36 | 840 | 0.6485 | nan | 0.9872 | 0.4593 | 0.0 | 0.9768 | 0.2954 | 0.8178 | 0.4787 | 0.9772 | | 0.299 | 77.27 | 850 | 0.6490 | nan | 0.9874 | 0.4613 | 0.0 | 0.9769 | 0.2909 | 0.8182 | 0.4794 | 0.9774 | | 0.292 | 78.18 | 860 | 0.6497 | nan | 0.9875 | 0.4629 | 0.0 | 0.9771 | 0.2853 | 0.8186 | 0.4800 | 0.9775 | | 0.2922 | 79.09 | 870 | 0.6586 | nan | 0.9866 | 0.4601 | 0.0 | 0.9765 | 0.2917 | 0.8226 | 0.4789 | 0.9770 | | 0.3583 | 80.0 | 880 | 0.6515 | nan | 0.9876 | 0.4644 | 0.0 | 0.9772 | 0.2876 | 0.8195 | 0.4805 | 0.9776 | | 0.293 | 80.91 | 890 | 0.6465 | nan | 0.9882 | 0.4674 | 0.0 | 0.9777 | 0.2767 | 0.8173 | 0.4817 | 0.9781 | | 0.3287 | 81.82 | 900 | 0.6518 | nan | 0.9876 | 0.4652 | 0.0 | 0.9773 | 0.2858 | 0.8197 | 0.4808 | 0.9777 | | 0.3067 | 82.73 | 910 | 0.6528 | nan | 0.9875 | 0.4654 | 0.0 | 0.9772 | 0.2861 | 0.8202 | 0.4809 | 0.9776 | | 0.3374 | 83.64 | 920 | 0.6577 | nan | 0.9870 | 0.4631 | 0.0 | 0.9768 | 0.2869 | 0.8224 | 0.4800 | 0.9773 | | 0.3171 | 84.55 | 930 | 0.6442 | nan | 0.9887 | 0.4714 | 0.0 | 0.9781 | 0.2706 | 0.8164 | 0.4832 | 0.9785 | | 0.3156 | 85.45 | 940 | 0.6321 | nan | 0.9899 | 0.4757 | 0.0 | 0.9789 | 0.2708 | 0.8110 | 0.4849 | 0.9793 | | 0.2749 | 86.36 | 950 | 0.6518 | nan | 0.9887 | 0.4765 | 0.0 | 0.9783 | 0.2760 | 0.8202 | 0.4850 | 0.9787 | | 0.2725 | 87.27 | 960 | 0.6681 | nan | 0.9876 | 0.4765 | 0.0 | 0.9777 | 0.2780 | 0.8279 | 0.4847 | 0.9782 | | 0.2948 | 88.18 | 970 | 0.6565 | nan | 0.9891 | 0.4845 | 0.0 | 0.9788 | 0.2636 | 0.8228 | 0.4878 | 0.9793 | | 0.2972 | 89.09 | 980 | 0.6722 | nan | 0.9879 | 0.4829 | 0.0 | 0.9782 | 0.2770 | 0.8301 | 0.4870 | 0.9786 | | 0.3101 | 90.0 | 990 | 0.6711 | nan | 0.9882 | 0.4859 | 0.0 | 0.9784 | 0.2765 | 0.8297 | 0.4881 | 0.9788 | | 0.2874 | 90.91 | 1000 | 0.6689 | nan | 0.9888 | 0.4899 | 0.0 | 0.9789 | 0.2690 | 0.8288 | 0.4896 | 0.9793 | | 0.275 | 91.82 | 1010 | 0.6542 | nan | 0.9901 | 0.4947 | 0.0 | 0.9798 | 0.2593 | 0.8221 | 0.4915 | 0.9802 | | 0.2711 | 92.73 | 1020 | 0.6673 | nan | 0.9893 | 0.4957 | 0.0 | 0.9794 | 0.2608 | 0.8283 | 0.4917 | 0.9798 | | 0.2691 | 93.64 | 1030 | 0.6819 | nan | 0.9884 | 0.4954 | 0.0 | 0.9789 | 0.2609 | 0.8352 | 0.4915 | 0.9794 | | 0.274 | 94.55 | 1040 | 0.6722 | nan | 0.9895 | 0.5007 | 0.0 | 0.9797 | 0.2542 | 0.8309 | 0.4935 | 0.9801 | | 0.27 | 95.45 | 1050 | 0.6436 | nan | 0.9919 | 0.5087 | 0.0 | 0.9812 | 0.2357 | 0.8177 | 0.4966 | 0.9816 | | 0.255 | 96.36 | 1060 | 0.6671 | nan | 0.9903 | 0.5074 | 0.0 | 0.9804 | 0.2460 | 0.8287 | 0.4959 | 0.9808 | | 0.2756 | 97.27 | 1070 | 0.6634 | nan | 0.9909 | 0.5113 | 0.0 | 0.9809 | 0.2411 | 0.8271 | 0.4974 | 0.9812 | | 0.2473 | 98.18 | 1080 | 0.6724 | nan | 0.9904 | 0.5116 | 0.0 | 0.9806 | 0.2447 | 0.8314 | 0.4974 | 0.9810 | | 0.266 | 99.09 | 1090 | 0.6778 | nan | 0.9901 | 0.5118 | 0.0 | 0.9804 | 0.2455 | 0.8339 | 0.4974 | 0.9808 | | 0.2682 | 100.0 | 1100 | 0.6651 | nan | 0.9911 | 0.5156 | 0.0 | 0.9811 | 0.2359 | 0.8281 | 0.4989 | 0.9815 | | 0.2607 | 100.91 | 1110 | 0.6739 | nan | 0.9905 | 0.5144 | 0.0 | 0.9808 | 0.2376 | 0.8322 | 0.4984 | 0.9812 | | 0.2506 | 101.82 | 1120 | 0.6727 | nan | 0.9907 | 0.5153 | 0.0 | 0.9809 | 0.2380 | 0.8317 | 0.4987 | 0.9813 | | 0.2729 | 102.73 | 1130 | 0.6802 | nan | 0.9902 | 0.5146 | 0.0 | 0.9806 | 0.2375 | 0.8352 | 0.4984 | 0.9810 | | 0.2348 | 103.64 | 1140 | 0.6731 | nan | 0.9908 | 0.5172 | 0.0 | 0.9810 | 0.2335 | 0.8319 | 0.4994 | 0.9814 | | 0.2409 | 104.55 | 1150 | 0.6781 | nan | 0.9904 | 0.5164 | 0.0 | 0.9808 | 0.2385 | 0.8343 | 0.4991 | 0.9812 | | 0.2737 | 105.45 | 1160 | 0.6774 | nan | 0.9906 | 0.5177 | 0.0 | 0.9809 | 0.2357 | 0.8340 | 0.4995 | 0.9813 | | 0.2857 | 106.36 | 1170 | 0.6659 | nan | 0.9915 | 0.5203 | 0.0 | 0.9815 | 0.2304 | 0.8287 | 0.5006 | 0.9818 | | 0.2503 | 107.27 | 1180 | 0.6822 | nan | 0.9902 | 0.5172 | 0.0 | 0.9807 | 0.2397 | 0.8362 | 0.4993 | 0.9811 | | 0.2524 | 108.18 | 1190 | 0.6741 | nan | 0.9909 | 0.5199 | 0.0 | 0.9812 | 0.2294 | 0.8325 | 0.5004 | 0.9816 | | 0.2722 | 109.09 | 1200 | 0.6928 | nan | 0.9894 | 0.5145 | 0.0 | 0.9802 | 0.2461 | 0.8411 | 0.4982 | 0.9806 | | 0.2761 | 110.0 | 1210 | 0.2530 | 0.4979 | 0.8435 | 0.9804 | nan | 0.6981 | 0.9889 | 0.0 | 0.5136 | 0.9799 | | 0.2595 | 110.91 | 1220 | 0.2386 | 0.5002 | 0.8395 | 0.9811 | nan | 0.6891 | 0.9900 | 0.0 | 0.5197 | 0.9808 | | 0.2877 | 111.82 | 1230 | 0.2260 | 0.5022 | 0.8345 | 0.9819 | nan | 0.6779 | 0.9911 | 0.0 | 0.5251 | 0.9815 | | 0.2307 | 112.73 | 1240 | 0.2219 | 0.5031 | 0.8317 | 0.9822 | nan | 0.6717 | 0.9916 | 0.0 | 0.5275 | 0.9818 | | 0.2559 | 113.64 | 1250 | 0.2382 | 0.5011 | 0.8413 | 0.9812 | nan | 0.6927 | 0.9900 | 0.0 | 0.5224 | 0.9808 | | 0.2668 | 114.55 | 1260 | 0.2275 | 0.5024 | 0.8395 | 0.9816 | nan | 0.6883 | 0.9906 | 0.0 | 0.5258 | 0.9813 | | 0.2269 | 115.45 | 1270 | 0.2212 | 0.5038 | 0.8356 | 0.9821 | nan | 0.6798 | 0.9913 | 0.0 | 0.5296 | 0.9818 | | 0.2713 | 116.36 | 1280 | 0.2265 | 0.5041 | 0.8365 | 0.9821 | nan | 0.6817 | 0.9913 | 0.0 | 0.5305 | 0.9818 | | 0.2351 | 117.27 | 1290 | 0.2113 | 0.5064 | 0.8255 | 0.9832 | nan | 0.6580 | 0.9931 | 0.0 | 0.5363 | 0.9828 | | 0.2306 | 118.18 | 1300 | 0.2258 | 0.5053 | 0.8354 | 0.9824 | nan | 0.6791 | 0.9917 | 0.0 | 0.5337 | 0.9821 | | 0.2371 | 119.09 | 1310 | 0.2234 | 0.5057 | 0.8342 | 0.9826 | nan | 0.6765 | 0.9919 | 0.0 | 0.5348 | 0.9822 | | 0.277 | 120.0 | 1320 | 0.2263 | 0.5061 | 0.8340 | 0.9827 | nan | 0.6760 | 0.9920 | 0.0 | 0.5359 | 0.9823 | | 0.2121 | 120.91 | 1330 | 0.2191 | 0.5065 | 0.8337 | 0.9828 | nan | 0.6752 | 0.9921 | 0.0 | 0.5370 | 0.9824 | | 0.2426 | 121.82 | 1340 | 0.2237 | 0.5055 | 0.8400 | 0.9822 | nan | 0.6889 | 0.9912 | 0.0 | 0.5345 | 0.9819 | | 0.2429 | 122.73 | 1350 | 0.2106 | 0.5069 | 0.8340 | 0.9829 | nan | 0.6757 | 0.9922 | 0.0 | 0.5383 | 0.9825 | | 0.244 | 123.64 | 1360 | 0.2133 | 0.5062 | 0.8395 | 0.9824 | nan | 0.6876 | 0.9914 | 0.0 | 0.5364 | 0.9821 | | 0.2287 | 124.55 | 1370 | 0.2126 | 0.5068 | 0.8371 | 0.9827 | nan | 0.6823 | 0.9918 | 0.0 | 0.5381 | 0.9823 | | 0.2223 | 125.45 | 1380 | 0.1988 | 0.5083 | 0.8305 | 0.9833 | nan | 0.6681 | 0.9929 | 0.0 | 0.5420 | 0.9830 | | 0.2275 | 126.36 | 1390 | 0.2164 | 0.5061 | 0.8409 | 0.9823 | nan | 0.6906 | 0.9912 | 0.0 | 0.5363 | 0.9820 | | 0.2378 | 127.27 | 1400 | 0.2201 | 0.5062 | 0.8405 | 0.9824 | nan | 0.6898 | 0.9913 | 0.0 | 0.5365 | 0.9820 | | 0.2385 | 128.18 | 1410 | 0.2286 | 0.5046 | 0.8455 | 0.9818 | nan | 0.7006 | 0.9903 | 0.0 | 0.5324 | 0.9814 | | 0.2787 | 129.09 | 1420 | 0.2157 | 0.5063 | 0.8405 | 0.9824 | nan | 0.6896 | 0.9913 | 0.0 | 0.5369 | 0.9820 | | 0.2377 | 130.0 | 1430 | 0.2236 | 0.5051 | 0.8441 | 0.9820 | nan | 0.6977 | 0.9906 | 0.0 | 0.5337 | 0.9816 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/safety-utcustom-train-SF-RGBD-b0

# safety-utcustom-train-SF-RGBD-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1043 - Mean Iou: 0.7188 - Mean Accuracy: 0.7607 - Overall Accuracy: 0.9815 - Accuracy Unlabeled: nan - Accuracy Safe: 0.5261 - Accuracy Unsafe: 0.9953 - Iou Unlabeled: nan - Iou Safe: 0.4564 - Iou Unsafe: 0.9812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 130 ### Training results | Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |:-------------:|:-----:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:| | 1.0084 | 1.0 | 10 | 0.0368 | nan | 0.7845 | 0.0163 | 0.0 | 0.7666 | 1.0688 | 0.4107 | 0.2610 | 0.7625 | | 0.8483 | 2.0 | 20 | 0.0002 | nan | 0.9980 | 0.0002 | 0.0 | 0.9687 | 0.8740 | 0.4991 | 0.3230 | 0.9686 | | 0.7058 | 3.0 | 30 | 0.0009 | nan | 0.9930 | 0.0009 | 0.0 | 0.9641 | 0.7416 | 0.4969 | 0.3217 | 0.9637 | | 0.578 | 4.0 | 40 | 0.0007 | nan | 0.9953 | 0.0007 | 0.0 | 0.9662 | 0.5969 | 0.4980 | 0.3223 | 0.9659 | | 0.5531 | 5.0 | 50 | 0.0061 | nan | 0.9974 | 0.0059 | 0.0 | 0.9682 | 0.5068 | 0.5018 | 0.3247 | 0.9681 | | 0.4786 | 6.0 | 60 | 0.0097 | nan | 0.9961 | 0.0092 | 0.0 | 0.9671 | 0.4575 | 0.5029 | 0.3254 | 0.9670 | | 0.4681 | 7.0 | 70 | 0.0067 | nan | 0.9983 | 0.0064 | 0.0 | 0.9690 | 0.4382 | 0.5025 | 0.3251 | 0.9690 | | 0.4139 | 8.0 | 80 | 0.0017 | nan | 0.9980 | 0.0016 | 0.0 | 0.9686 | 0.3973 | 0.4998 | 0.3234 | 0.9686 | | 0.4275 | 9.0 | 90 | 0.0077 | nan | 0.9994 | 0.0076 | nan | 0.9701 | 0.3983 | 0.5036 | 0.4888 | 0.9701 | | 0.3975 | 10.0 | 100 | 0.0008 | nan | 0.9998 | 0.0008 | 0.0 | 0.9702 | 0.3398 | 0.5003 | 0.3237 | 0.9702 | | 0.4325 | 11.0 | 110 | 0.0941 | nan | 0.9993 | 0.0919 | 0.0 | 0.9725 | 0.3785 | 0.5467 | 0.3548 | 0.9725 | | 0.3239 | 12.0 | 120 | 0.0772 | nan | 0.9995 | 0.0759 | 0.0 | 0.9722 | 0.3338 | 0.5383 | 0.3493 | 0.9722 | | 0.3733 | 13.0 | 130 | 0.0763 | nan | 0.9995 | 0.0751 | nan | 0.9722 | 0.3013 | 0.5379 | 0.5236 | 0.9722 | | 0.3165 | 14.0 | 140 | 0.0800 | nan | 0.9994 | 0.0786 | nan | 0.9722 | 0.2849 | 0.5397 | 0.5254 | 0.9723 | | 0.3329 | 15.0 | 150 | 0.1118 | nan | 0.9990 | 0.1083 | nan | 0.9727 | 0.3002 | 0.5554 | 0.5405 | 0.9728 | | 0.3214 | 16.0 | 160 | 0.0908 | nan | 0.9995 | 0.0892 | nan | 0.9726 | 0.2725 | 0.5451 | 0.5309 | 0.9726 | | 0.2744 | 17.0 | 170 | 0.1573 | nan | 0.9986 | 0.1503 | nan | 0.9736 | 0.2896 | 0.5780 | 0.5620 | 0.9737 | | 0.2948 | 18.0 | 180 | 0.1330 | nan | 0.9989 | 0.1282 | nan | 0.9732 | 0.2564 | 0.5659 | 0.5507 | 0.9733 | | 0.2653 | 19.0 | 190 | 0.1732 | nan | 0.9987 | 0.1660 | nan | 0.9742 | 0.2518 | 0.5860 | 0.5701 | 0.9743 | | 0.3026 | 20.0 | 200 | 0.1408 | nan | 0.9990 | 0.1364 | nan | 0.9735 | 0.2531 | 0.5699 | 0.5550 | 0.9737 | | 0.2649 | 21.0 | 210 | 0.1802 | nan | 0.9986 | 0.1722 | nan | 0.9743 | 0.2384 | 0.5894 | 0.5732 | 0.9744 | | 0.2431 | 22.0 | 220 | 0.1993 | nan | 0.9983 | 0.1890 | nan | 0.9746 | 0.2390 | 0.5988 | 0.5818 | 0.9747 | | 0.2608 | 23.0 | 230 | 0.2317 | nan | 0.9981 | 0.2181 | nan | 0.9753 | 0.2355 | 0.6149 | 0.5967 | 0.9755 | | 0.223 | 24.0 | 240 | 0.1697 | nan | 0.9989 | 0.1637 | nan | 0.9743 | 0.2290 | 0.5843 | 0.5690 | 0.9744 | | 0.2448 | 25.0 | 250 | 0.2141 | nan | 0.9985 | 0.2037 | nan | 0.9751 | 0.2262 | 0.6063 | 0.5894 | 0.9753 | | 0.2547 | 26.0 | 260 | 0.2737 | nan | 0.9978 | 0.2555 | nan | 0.9763 | 0.2281 | 0.6357 | 0.6159 | 0.9764 | | 0.2266 | 27.0 | 270 | 0.2391 | nan | 0.9981 | 0.2252 | nan | 0.9755 | 0.2191 | 0.6186 | 0.6004 | 0.9757 | | 0.2357 | 28.0 | 280 | 0.2227 | nan | 0.9985 | 0.2122 | nan | 0.9754 | 0.2218 | 0.6106 | 0.5938 | 0.9756 | | 0.2563 | 29.0 | 290 | 0.1852 | nan | 0.9988 | 0.1782 | nan | 0.9746 | 0.2096 | 0.5920 | 0.5764 | 0.9748 | | 0.226 | 30.0 | 300 | 0.2844 | nan | 0.9977 | 0.2643 | nan | 0.9764 | 0.2121 | 0.6410 | 0.6203 | 0.9766 | | 0.2221 | 31.0 | 310 | 0.2718 | nan | 0.9978 | 0.2533 | nan | 0.9761 | 0.2016 | 0.6348 | 0.6147 | 0.9763 | | 0.2317 | 32.0 | 320 | 0.2649 | nan | 0.9982 | 0.2499 | nan | 0.9763 | 0.2008 | 0.6315 | 0.6131 | 0.9765 | | 0.2643 | 33.0 | 330 | 0.3254 | nan | 0.9976 | 0.3014 | nan | 0.9775 | 0.1989 | 0.6615 | 0.6394 | 0.9777 | | 0.2118 | 34.0 | 340 | 0.3347 | nan | 0.9977 | 0.3117 | nan | 0.9779 | 0.1901 | 0.6662 | 0.6448 | 0.9782 | | 0.2133 | 35.0 | 350 | 0.3619 | nan | 0.9976 | 0.3350 | nan | 0.9785 | 0.1917 | 0.6797 | 0.6568 | 0.9788 | | 0.2064 | 36.0 | 360 | 0.3401 | nan | 0.9978 | 0.3174 | nan | 0.9782 | 0.1860 | 0.6690 | 0.6478 | 0.9784 | | 0.2341 | 37.0 | 370 | 0.2704 | nan | 0.9983 | 0.2557 | nan | 0.9766 | 0.1775 | 0.6343 | 0.6162 | 0.9768 | | 0.2093 | 38.0 | 380 | 0.3552 | nan | 0.9928 | 0.2874 | nan | 0.9737 | 0.1934 | 0.6740 | 0.6306 | 0.9740 | | 0.1958 | 39.0 | 390 | 0.3001 | nan | 0.9980 | 0.2818 | nan | 0.9772 | 0.1755 | 0.6491 | 0.6295 | 0.9774 | | 0.1886 | 40.0 | 400 | 0.3881 | nan | 0.9969 | 0.3522 | nan | 0.9787 | 0.1768 | 0.6925 | 0.6654 | 0.9789 | | 0.1734 | 41.0 | 410 | 0.3948 | nan | 0.9973 | 0.3626 | nan | 0.9793 | 0.1745 | 0.6960 | 0.6709 | 0.9795 | | 0.1795 | 42.0 | 420 | 0.4168 | nan | 0.9970 | 0.3789 | nan | 0.9796 | 0.1710 | 0.7069 | 0.6793 | 0.9798 | | 0.222 | 43.0 | 430 | 0.4041 | nan | 0.9972 | 0.3700 | nan | 0.9794 | 0.1706 | 0.7007 | 0.6747 | 0.9797 | | 0.1831 | 44.0 | 440 | 0.4044 | nan | 0.9972 | 0.3708 | nan | 0.9795 | 0.1687 | 0.7008 | 0.6752 | 0.9797 | | 0.1935 | 45.0 | 450 | 0.4347 | nan | 0.9964 | 0.3889 | nan | 0.9796 | 0.1711 | 0.7155 | 0.6842 | 0.9798 | | 0.1728 | 46.0 | 460 | 0.4208 | nan | 0.9969 | 0.3819 | nan | 0.9796 | 0.1714 | 0.7088 | 0.6808 | 0.9799 | | 0.1742 | 47.0 | 470 | 0.3898 | nan | 0.9974 | 0.3590 | nan | 0.9792 | 0.1670 | 0.6936 | 0.6691 | 0.9794 | | 0.2064 | 48.0 | 480 | 0.4209 | nan | 0.9970 | 0.3827 | nan | 0.9797 | 0.1683 | 0.7089 | 0.6812 | 0.9799 | | 0.1946 | 49.0 | 490 | 0.3746 | nan | 0.9976 | 0.3471 | nan | 0.9790 | 0.1659 | 0.6861 | 0.6630 | 0.9792 | | 0.1836 | 50.0 | 500 | 0.4487 | nan | 0.9965 | 0.4020 | nan | 0.9800 | 0.1618 | 0.7226 | 0.6910 | 0.9803 | | 0.1786 | 51.0 | 510 | 0.4327 | nan | 0.9966 | 0.3896 | nan | 0.9797 | 0.1595 | 0.7147 | 0.6846 | 0.9800 | | 0.1867 | 52.0 | 520 | 0.4540 | nan | 0.9966 | 0.4083 | nan | 0.9803 | 0.1555 | 0.7253 | 0.6943 | 0.9806 | | 0.1824 | 53.0 | 530 | 0.4386 | nan | 0.9966 | 0.3942 | nan | 0.9798 | 0.1564 | 0.7176 | 0.6870 | 0.9801 | | 0.1494 | 54.0 | 540 | 0.4920 | nan | 0.9956 | 0.4299 | nan | 0.9804 | 0.1540 | 0.7438 | 0.7052 | 0.9807 | | 0.1583 | 55.0 | 550 | 0.4558 | nan | 0.9964 | 0.4075 | nan | 0.9802 | 0.1502 | 0.7261 | 0.6939 | 0.9804 | | 0.1648 | 56.0 | 560 | 0.4791 | nan | 0.9958 | 0.4208 | nan | 0.9802 | 0.1523 | 0.7374 | 0.7005 | 0.9805 | | 0.1993 | 57.0 | 570 | 0.4586 | nan | 0.9964 | 0.4103 | nan | 0.9803 | 0.1502 | 0.7275 | 0.6953 | 0.9805 | | 0.2243 | 58.0 | 580 | 0.3920 | nan | 0.9973 | 0.3599 | nan | 0.9792 | 0.1474 | 0.6946 | 0.6695 | 0.9794 | | 0.1551 | 59.0 | 590 | 0.4687 | nan | 0.9961 | 0.4157 | nan | 0.9803 | 0.1445 | 0.7324 | 0.6980 | 0.9805 | | 0.1666 | 60.0 | 600 | 0.4460 | nan | 0.9964 | 0.3986 | nan | 0.9799 | 0.1444 | 0.7212 | 0.6892 | 0.9801 | | 0.1632 | 61.0 | 610 | 0.5120 | nan | 0.9951 | 0.4411 | nan | 0.9805 | 0.1504 | 0.7535 | 0.7108 | 0.9808 | | 0.1589 | 62.0 | 620 | 0.4059 | nan | 0.9971 | 0.3704 | nan | 0.9794 | 0.1430 | 0.7015 | 0.6749 | 0.9796 | | 0.1454 | 63.0 | 630 | 0.4835 | nan | 0.9959 | 0.4260 | nan | 0.9805 | 0.1423 | 0.7397 | 0.7032 | 0.9808 | | 0.1635 | 64.0 | 640 | 0.4902 | nan | 0.9957 | 0.4299 | nan | 0.9805 | 0.1424 | 0.7430 | 0.7052 | 0.9808 | | 0.1515 | 65.0 | 650 | 0.4775 | nan | 0.9962 | 0.4239 | nan | 0.9806 | 0.1422 | 0.7368 | 0.7022 | 0.9808 | | 0.151 | 66.0 | 660 | 0.4718 | nan | 0.9962 | 0.4195 | nan | 0.9804 | 0.1423 | 0.7340 | 0.7000 | 0.9807 | | 0.166 | 67.0 | 670 | 0.4721 | nan | 0.9963 | 0.4208 | nan | 0.9805 | 0.1427 | 0.7342 | 0.7007 | 0.9808 | | 0.1561 | 68.0 | 680 | 0.4916 | nan | 0.9959 | 0.4332 | nan | 0.9807 | 0.1420 | 0.7437 | 0.7070 | 0.9810 | | 0.1501 | 69.0 | 690 | 0.4906 | nan | 0.9958 | 0.4311 | nan | 0.9806 | 0.1437 | 0.7432 | 0.7058 | 0.9809 | | 0.1598 | 70.0 | 700 | 0.3445 | nan | 0.9977 | 0.3204 | nan | 0.9782 | 0.1379 | 0.6711 | 0.6493 | 0.9784 | | 0.1431 | 71.0 | 710 | 0.4898 | nan | 0.9960 | 0.4325 | nan | 0.9807 | 0.1400 | 0.7429 | 0.7066 | 0.9810 | | 0.164 | 72.0 | 720 | 0.4698 | nan | 0.9964 | 0.4196 | nan | 0.9805 | 0.1347 | 0.7331 | 0.7001 | 0.9808 | | 0.1555 | 73.0 | 730 | 0.5271 | nan | 0.9937 | 0.4364 | nan | 0.9796 | 0.1368 | 0.7604 | 0.7080 | 0.9799 | | 0.1924 | 74.0 | 740 | 0.4638 | nan | 0.9965 | 0.4159 | nan | 0.9805 | 0.1312 | 0.7301 | 0.6982 | 0.9808 | | 0.1612 | 75.0 | 750 | 0.5052 | nan | 0.9956 | 0.4409 | nan | 0.9808 | 0.1340 | 0.7504 | 0.7108 | 0.9811 | | 0.1234 | 76.0 | 760 | 0.5301 | nan | 0.9946 | 0.4501 | nan | 0.9806 | 0.1354 | 0.7624 | 0.7153 | 0.9809 | | 0.1679 | 77.0 | 770 | 0.4644 | nan | 0.9964 | 0.4156 | nan | 0.9804 | 0.1323 | 0.7304 | 0.6980 | 0.9807 | | 0.1375 | 78.0 | 780 | 0.4804 | nan | 0.9961 | 0.4263 | nan | 0.9806 | 0.1355 | 0.7383 | 0.7035 | 0.9809 | | 0.1839 | 79.0 | 790 | 0.5070 | nan | 0.9955 | 0.4422 | nan | 0.9808 | 0.1319 | 0.7512 | 0.7115 | 0.9811 | | 0.155 | 80.0 | 800 | 0.4846 | nan | 0.9961 | 0.4295 | nan | 0.9807 | 0.1298 | 0.7403 | 0.7051 | 0.9810 | | 0.1219 | 81.0 | 810 | 0.4671 | nan | 0.9963 | 0.4167 | nan | 0.9804 | 0.1302 | 0.7317 | 0.6986 | 0.9807 | | 0.1218 | 82.0 | 820 | 0.4864 | nan | 0.9960 | 0.4300 | nan | 0.9807 | 0.1313 | 0.7412 | 0.7054 | 0.9810 | | 0.138 | 83.0 | 830 | 0.5097 | nan | 0.9955 | 0.4445 | nan | 0.9809 | 0.1318 | 0.7526 | 0.7127 | 0.9812 | | 0.1399 | 84.0 | 840 | 0.5067 | nan | 0.9957 | 0.4441 | nan | 0.9810 | 0.1290 | 0.7512 | 0.7126 | 0.9813 | | 0.1455 | 85.0 | 850 | 0.5024 | nan | 0.9957 | 0.4404 | nan | 0.9809 | 0.1277 | 0.7491 | 0.7106 | 0.9811 | | 0.1466 | 86.0 | 860 | 0.4920 | nan | 0.9959 | 0.4341 | nan | 0.9808 | 0.1243 | 0.7440 | 0.7074 | 0.9811 | | 0.1769 | 87.0 | 870 | 0.5737 | nan | 0.9924 | 0.4592 | nan | 0.9797 | 0.1317 | 0.7831 | 0.7194 | 0.9800 | | 0.1453 | 88.0 | 880 | 0.3341 | nan | 0.9978 | 0.3115 | nan | 0.9780 | 0.1254 | 0.6659 | 0.6447 | 0.9782 | | 0.133 | 89.0 | 890 | 0.5257 | nan | 0.9950 | 0.4518 | nan | 0.9809 | 0.1283 | 0.7603 | 0.7163 | 0.9812 | | 0.1288 | 90.0 | 900 | 0.5049 | nan | 0.9957 | 0.4420 | nan | 0.9809 | 0.1221 | 0.7503 | 0.7115 | 0.9812 | | 0.1318 | 91.0 | 910 | 0.4838 | nan | 0.9961 | 0.4290 | nan | 0.9807 | 0.1219 | 0.7400 | 0.7049 | 0.9810 | | 0.1211 | 92.0 | 920 | 0.5355 | nan | 0.9950 | 0.4596 | nan | 0.9811 | 0.1242 | 0.7652 | 0.7203 | 0.9814 | | 0.1137 | 93.0 | 930 | 0.5135 | nan | 0.9958 | 0.4517 | nan | 0.9813 | 0.1181 | 0.7547 | 0.7165 | 0.9816 | | 0.1312 | 94.0 | 940 | 0.4775 | nan | 0.9963 | 0.4262 | nan | 0.9807 | 0.1199 | 0.7369 | 0.7035 | 0.9810 | | 0.1591 | 95.0 | 950 | 0.5115 | nan | 0.9956 | 0.4473 | nan | 0.9810 | 0.1182 | 0.7536 | 0.7142 | 0.9813 | | 0.1207 | 96.0 | 960 | 0.5206 | nan | 0.9956 | 0.4544 | nan | 0.9812 | 0.1156 | 0.7581 | 0.7178 | 0.9815 | | 0.1203 | 97.0 | 970 | 0.5054 | nan | 0.9958 | 0.4439 | nan | 0.9810 | 0.1165 | 0.7506 | 0.7124 | 0.9813 | | 0.1196 | 98.0 | 980 | 0.5296 | nan | 0.9953 | 0.4585 | nan | 0.9812 | 0.1131 | 0.7624 | 0.7199 | 0.9815 | | 0.1304 | 99.0 | 990 | 0.5269 | nan | 0.9953 | 0.4568 | nan | 0.9812 | 0.1155 | 0.7611 | 0.7190 | 0.9815 | | 0.1058 | 100.0 | 1000 | 0.5163 | nan | 0.9955 | 0.4496 | nan | 0.9810 | 0.1144 | 0.7559 | 0.7153 | 0.9813 | | 0.1135 | 101.0 | 1010 | 0.4934 | nan | 0.9961 | 0.4368 | nan | 0.9809 | 0.1113 | 0.7447 | 0.7089 | 0.9812 | | 0.1116 | 102.0 | 1020 | 0.5878 | nan | 0.9932 | 0.4799 | nan | 0.9808 | 0.1128 | 0.7905 | 0.7304 | 0.9812 | | 0.1036 | 103.0 | 1030 | 0.4826 | nan | 0.9963 | 0.4304 | nan | 0.9809 | 0.1078 | 0.7394 | 0.7056 | 0.9811 | | 0.1195 | 104.0 | 1040 | 0.4364 | nan | 0.9966 | 0.3930 | nan | 0.9798 | 0.1110 | 0.7165 | 0.6864 | 0.9801 | | 0.1205 | 105.0 | 1050 | 0.5793 | nan | 0.9934 | 0.4762 | nan | 0.9808 | 0.1120 | 0.7864 | 0.7285 | 0.9812 | | 0.1453 | 106.0 | 1060 | 0.4707 | nan | 0.9964 | 0.4205 | nan | 0.9806 | 0.1110 | 0.7336 | 0.7005 | 0.9808 | | 0.0965 | 107.0 | 1070 | 0.5638 | nan | 0.9941 | 0.4723 | nan | 0.9811 | 0.1091 | 0.7789 | 0.7267 | 0.9814 | | 0.1058 | 108.0 | 1080 | 0.4881 | nan | 0.9962 | 0.4337 | nan | 0.9809 | 0.1085 | 0.7422 | 0.7073 | 0.9812 | | 0.1163 | 109.0 | 1090 | 0.5128 | nan | 0.9957 | 0.4493 | nan | 0.9811 | 0.1077 | 0.7542 | 0.7152 | 0.9814 | | 0.1145 | 110.0 | 1100 | 0.5228 | nan | 0.9954 | 0.4547 | nan | 0.9812 | 0.1081 | 0.7591 | 0.7179 | 0.9815 | | 0.1031 | 111.0 | 1110 | 0.5522 | nan | 0.9945 | 0.4673 | nan | 0.9811 | 0.1073 | 0.7733 | 0.7242 | 0.9814 | | 0.1042 | 112.0 | 1120 | 0.5490 | nan | 0.9947 | 0.4669 | nan | 0.9812 | 0.1064 | 0.7718 | 0.7241 | 0.9815 | | 0.1119 | 113.0 | 1130 | 0.5064 | nan | 0.9958 | 0.4449 | nan | 0.9811 | 0.1063 | 0.7511 | 0.7130 | 0.9813 | | 0.1116 | 114.0 | 1140 | 0.5172 | nan | 0.9956 | 0.4520 | nan | 0.9812 | 0.1074 | 0.7564 | 0.7166 | 0.9815 | | 0.1063 | 115.0 | 1150 | 0.5163 | nan | 0.9956 | 0.4511 | nan | 0.9812 | 0.1072 | 0.7560 | 0.7161 | 0.9814 | | 0.1054 | 116.0 | 1160 | 0.4994 | nan | 0.9960 | 0.4408 | nan | 0.9810 | 0.1065 | 0.7477 | 0.7109 | 0.9813 | | 0.1613 | 117.0 | 1170 | 0.5251 | nan | 0.9955 | 0.4576 | nan | 0.9813 | 0.1060 | 0.7603 | 0.7195 | 0.9816 | | 0.1542 | 118.0 | 1180 | 0.5454 | nan | 0.9947 | 0.4649 | nan | 0.9812 | 0.1058 | 0.7701 | 0.7230 | 0.9815 | | 0.1226 | 119.0 | 1190 | 0.5469 | nan | 0.9947 | 0.4658 | nan | 0.9812 | 0.1064 | 0.7708 | 0.7235 | 0.9815 | | 0.1295 | 120.0 | 1200 | 0.5437 | nan | 0.9948 | 0.4646 | nan | 0.9812 | 0.1060 | 0.7693 | 0.7229 | 0.9815 | | 0.1438 | 121.0 | 1210 | 0.1076 | 0.7084 | 0.7435 | 0.9812 | nan | 0.4909 | 0.9962 | nan | 0.4358 | 0.9809 | | 0.1391 | 122.0 | 1220 | 0.1081 | 0.7221 | 0.7683 | 0.9814 | nan | 0.5417 | 0.9948 | nan | 0.4630 | 0.9811 | | 0.1756 | 123.0 | 1230 | 0.1041 | 0.7233 | 0.7710 | 0.9814 | nan | 0.5473 | 0.9947 | nan | 0.4655 | 0.9811 | | 0.1174 | 124.0 | 1240 | 0.1029 | 0.7189 | 0.7614 | 0.9815 | nan | 0.5275 | 0.9953 | nan | 0.4566 | 0.9812 | | 0.1025 | 125.0 | 1250 | 0.1043 | 0.7100 | 0.7470 | 0.9812 | nan | 0.4980 | 0.9959 | nan | 0.4391 | 0.9809 | | 0.0997 | 126.0 | 1260 | 0.1038 | 0.7211 | 0.7638 | 0.9816 | nan | 0.5323 | 0.9953 | nan | 0.4609 | 0.9813 | | 0.1768 | 127.0 | 1270 | 0.1037 | 0.7204 | 0.7617 | 0.9817 | nan | 0.5279 | 0.9955 | nan | 0.4594 | 0.9814 | | 0.1527 | 128.0 | 1280 | 0.1027 | 0.7167 | 0.7564 | 0.9815 | nan | 0.5171 | 0.9956 | nan | 0.4522 | 0.9812 | | 0.1269 | 129.0 | 1290 | 0.1041 | 0.7178 | 0.7583 | 0.9815 | nan | 0.5211 | 0.9955 | nan | 0.4543 | 0.9812 | | 0.0968 | 130.0 | 1300 | 0.1043 | 0.7188 | 0.7607 | 0.9815 | nan | 0.5261 | 0.9953 | nan | 0.4564 | 0.9812 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5

# INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0472 - Mean Iou: 0.8728 - Mean Accuracy: 0.9195 - Overall Accuracy: 0.9919 - Accuracy Unlabeled: nan - Accuracy Safe: 0.8426 - Accuracy Unsafe: 0.9964 - Iou Unlabeled: nan - Iou Safe: 0.7540 - Iou Unsafe: 0.9917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 1.2199 | 2.0 | 20 | 1.1474 | 0.0765 | 0.4399 | 0.2168 | nan | 0.6770 | 0.2028 | 0.0 | 0.0279 | 0.2014 | | 1.1542 | 4.0 | 40 | 1.0616 | 0.1558 | 0.6082 | 0.4365 | nan | 0.7908 | 0.4257 | 0.0 | 0.0436 | 0.4237 | | 1.0324 | 6.0 | 60 | 0.9140 | 0.2569 | 0.6886 | 0.7091 | nan | 0.6667 | 0.7104 | 0.0 | 0.0672 | 0.7036 | | 0.8058 | 8.0 | 80 | 0.7629 | 0.2970 | 0.6780 | 0.8115 | nan | 0.5360 | 0.8199 | 0.0 | 0.0823 | 0.8086 | | 0.681 | 10.0 | 100 | 0.5545 | 0.3510 | 0.6964 | 0.9135 | nan | 0.4657 | 0.9271 | 0.0 | 0.1407 | 0.9123 | | 0.5248 | 12.0 | 120 | 0.4153 | 0.3738 | 0.6747 | 0.9462 | nan | 0.3861 | 0.9633 | 0.0 | 0.1757 | 0.9456 | | 0.3372 | 14.0 | 140 | 0.3050 | 0.3896 | 0.6548 | 0.9628 | nan | 0.3276 | 0.9821 | 0.0 | 0.2064 | 0.9624 | | 0.2818 | 16.0 | 160 | 0.2346 | 0.4313 | 0.7331 | 0.9703 | nan | 0.4810 | 0.9852 | 0.0 | 0.3239 | 0.9699 | | 0.2081 | 18.0 | 180 | 0.1802 | 0.4762 | 0.7973 | 0.9782 | nan | 0.6050 | 0.9896 | 0.0 | 0.4509 | 0.9778 | | 0.141 | 20.0 | 200 | 0.1362 | 0.4968 | 0.7945 | 0.9830 | nan | 0.5943 | 0.9948 | 0.0 | 0.5078 | 0.9827 | | 0.0963 | 22.0 | 220 | 0.1032 | 0.7409 | 0.7652 | 0.9841 | nan | 0.5326 | 0.9979 | nan | 0.4978 | 0.9839 | | 0.0842 | 24.0 | 240 | 0.0770 | 0.7840 | 0.8200 | 0.9863 | nan | 0.6432 | 0.9968 | nan | 0.5818 | 0.9861 | | 0.0702 | 26.0 | 260 | 0.0669 | 0.7836 | 0.8193 | 0.9863 | nan | 0.6417 | 0.9968 | nan | 0.5812 | 0.9861 | | 0.0706 | 28.0 | 280 | 0.0671 | 0.8065 | 0.8593 | 0.9872 | nan | 0.7234 | 0.9953 | nan | 0.6261 | 0.9870 | | 0.0747 | 30.0 | 300 | 0.0551 | 0.7808 | 0.7980 | 0.9870 | nan | 0.5971 | 0.9988 | nan | 0.5748 | 0.9867 | | 0.057 | 32.0 | 320 | 0.0492 | 0.8267 | 0.8736 | 0.9888 | nan | 0.7511 | 0.9961 | nan | 0.6648 | 0.9886 | | 0.0435 | 34.0 | 340 | 0.0507 | 0.7956 | 0.8134 | 0.9878 | nan | 0.6280 | 0.9988 | nan | 0.6035 | 0.9876 | | 0.0326 | 36.0 | 360 | 0.0418 | 0.8422 | 0.8895 | 0.9898 | nan | 0.7830 | 0.9961 | nan | 0.6947 | 0.9896 | | 0.0262 | 38.0 | 380 | 0.0420 | 0.8280 | 0.8550 | 0.9895 | nan | 0.7120 | 0.9979 | nan | 0.6667 | 0.9893 | | 0.0268 | 40.0 | 400 | 0.0392 | 0.8407 | 0.8822 | 0.9899 | nan | 0.7676 | 0.9967 | nan | 0.6918 | 0.9897 | | 0.0395 | 42.0 | 420 | 0.0466 | 0.5436 | 0.8370 | 0.9889 | nan | 0.6755 | 0.9984 | 0.0 | 0.6422 | 0.9887 | | 0.0279 | 44.0 | 440 | 0.0439 | 0.8321 | 0.8946 | 0.9887 | nan | 0.7945 | 0.9947 | nan | 0.6758 | 0.9885 | | 0.0468 | 46.0 | 460 | 0.0360 | 0.8480 | 0.8894 | 0.9904 | nan | 0.7822 | 0.9967 | nan | 0.7059 | 0.9901 | | 0.0233 | 48.0 | 480 | 0.0376 | 0.8507 | 0.8962 | 0.9905 | nan | 0.7960 | 0.9964 | nan | 0.7113 | 0.9902 | | 0.0288 | 50.0 | 500 | 0.0386 | 0.8404 | 0.8845 | 0.9898 | nan | 0.7725 | 0.9964 | nan | 0.6913 | 0.9896 | | 0.0266 | 52.0 | 520 | 0.0361 | 0.8455 | 0.8768 | 0.9905 | nan | 0.7560 | 0.9976 | nan | 0.7008 | 0.9902 | | 0.0241 | 54.0 | 540 | 0.0367 | 0.8504 | 0.9039 | 0.9902 | nan | 0.8122 | 0.9957 | nan | 0.7109 | 0.9900 | | 0.0239 | 56.0 | 560 | 0.0414 | 0.8401 | 0.8809 | 0.9899 | nan | 0.7650 | 0.9967 | nan | 0.6906 | 0.9896 | | 0.0221 | 58.0 | 580 | 0.0375 | 0.8536 | 0.8971 | 0.9907 | nan | 0.7977 | 0.9966 | nan | 0.7167 | 0.9905 | | 0.0324 | 60.0 | 600 | 0.0390 | 0.8566 | 0.9017 | 0.9908 | nan | 0.8069 | 0.9964 | nan | 0.7226 | 0.9906 | | 0.0264 | 62.0 | 620 | 0.0405 | 0.8506 | 0.9088 | 0.9901 | nan | 0.8224 | 0.9952 | nan | 0.7114 | 0.9899 | | 0.0146 | 64.0 | 640 | 0.0356 | 0.8627 | 0.9158 | 0.9911 | nan | 0.8358 | 0.9958 | nan | 0.7346 | 0.9909 | | 0.0252 | 66.0 | 660 | 0.0310 | 0.8667 | 0.9010 | 0.9917 | nan | 0.8046 | 0.9974 | nan | 0.7418 | 0.9915 | | 0.0155 | 68.0 | 680 | 0.0359 | 0.8567 | 0.9056 | 0.9908 | nan | 0.8152 | 0.9961 | nan | 0.7229 | 0.9905 | | 0.0169 | 70.0 | 700 | 0.0490 | 0.8476 | 0.9182 | 0.9896 | nan | 0.8422 | 0.9941 | nan | 0.7058 | 0.9894 | | 0.0142 | 72.0 | 720 | 0.0357 | 0.8442 | 0.8771 | 0.9903 | nan | 0.7568 | 0.9974 | nan | 0.6982 | 0.9901 | | 0.0244 | 74.0 | 740 | 0.0400 | 0.8523 | 0.9070 | 0.9903 | nan | 0.8183 | 0.9956 | nan | 0.7146 | 0.9901 | | 0.016 | 76.0 | 760 | 0.0302 | 0.8644 | 0.9037 | 0.9915 | nan | 0.8105 | 0.9970 | nan | 0.7376 | 0.9913 | | 0.0137 | 78.0 | 780 | 0.0325 | 0.8664 | 0.9118 | 0.9915 | nan | 0.8271 | 0.9965 | nan | 0.7415 | 0.9913 | | 0.0115 | 80.0 | 800 | 0.0347 | 0.8678 | 0.9162 | 0.9915 | nan | 0.8362 | 0.9962 | nan | 0.7443 | 0.9913 | | 0.0117 | 82.0 | 820 | 0.0320 | 0.8697 | 0.9084 | 0.9918 | nan | 0.8197 | 0.9971 | nan | 0.7478 | 0.9916 | | 0.0108 | 84.0 | 840 | 0.0348 | 0.8691 | 0.9192 | 0.9916 | nan | 0.8423 | 0.9961 | nan | 0.7468 | 0.9913 | | 0.0101 | 86.0 | 860 | 0.0342 | 0.8683 | 0.9081 | 0.9917 | nan | 0.8192 | 0.9970 | nan | 0.7452 | 0.9915 | | 0.0085 | 88.0 | 880 | 0.0441 | 0.8639 | 0.9214 | 0.9911 | nan | 0.8474 | 0.9954 | nan | 0.7370 | 0.9908 | | 0.009 | 90.0 | 900 | 0.0428 | 0.8619 | 0.9086 | 0.9912 | nan | 0.8209 | 0.9963 | nan | 0.7330 | 0.9909 | | 0.009 | 92.0 | 920 | 0.0444 | 0.8620 | 0.9089 | 0.9912 | nan | 0.8215 | 0.9963 | nan | 0.7331 | 0.9909 | | 0.0089 | 94.0 | 940 | 0.0410 | 0.8645 | 0.9147 | 0.9913 | nan | 0.8334 | 0.9961 | nan | 0.7380 | 0.9910 | | 0.0091 | 96.0 | 960 | 0.0418 | 0.8663 | 0.9155 | 0.9914 | nan | 0.8349 | 0.9962 | nan | 0.7413 | 0.9912 | | 0.0079 | 98.0 | 980 | 0.0398 | 0.8629 | 0.9085 | 0.9913 | nan | 0.8205 | 0.9965 | nan | 0.7348 | 0.9910 | | 0.0084 | 100.0 | 1000 | 0.0497 | 0.8553 | 0.9109 | 0.9905 | nan | 0.8262 | 0.9955 | nan | 0.7204 | 0.9903 | | 0.0088 | 102.0 | 1020 | 0.0399 | 0.8558 | 0.9058 | 0.9907 | nan | 0.8156 | 0.9960 | nan | 0.7212 | 0.9905 | | 0.0089 | 104.0 | 1040 | 0.0388 | 0.8678 | 0.9225 | 0.9914 | nan | 0.8494 | 0.9957 | nan | 0.7444 | 0.9912 | | 0.008 | 106.0 | 1060 | 0.0449 | 0.8622 | 0.9225 | 0.9909 | nan | 0.8498 | 0.9952 | nan | 0.7337 | 0.9907 | | 0.0084 | 108.0 | 1080 | 0.0429 | 0.8687 | 0.9233 | 0.9914 | nan | 0.8510 | 0.9957 | nan | 0.7462 | 0.9912 | | 0.0084 | 110.0 | 1100 | 0.0405 | 0.8687 | 0.9169 | 0.9916 | nan | 0.8375 | 0.9963 | nan | 0.7460 | 0.9914 | | 0.007 | 112.0 | 1120 | 0.0544 | 0.8620 | 0.9180 | 0.9910 | nan | 0.8404 | 0.9956 | nan | 0.7333 | 0.9907 | | 0.0079 | 114.0 | 1140 | 0.0501 | 0.8602 | 0.9176 | 0.9908 | nan | 0.8399 | 0.9954 | nan | 0.7299 | 0.9906 | | 0.0084 | 116.0 | 1160 | 0.0508 | 0.8605 | 0.9212 | 0.9908 | nan | 0.8473 | 0.9951 | nan | 0.7304 | 0.9905 | | 0.0113 | 118.0 | 1180 | 0.0511 | 0.8601 | 0.9228 | 0.9907 | nan | 0.8507 | 0.9950 | nan | 0.7298 | 0.9905 | | 0.0076 | 120.0 | 1200 | 0.0556 | 0.8602 | 0.9264 | 0.9906 | nan | 0.8582 | 0.9947 | nan | 0.7299 | 0.9904 | | 0.0081 | 122.0 | 1220 | 0.0471 | 0.8665 | 0.9256 | 0.9912 | nan | 0.8559 | 0.9953 | nan | 0.7420 | 0.9910 | | 0.0054 | 124.0 | 1240 | 0.0504 | 0.8652 | 0.9174 | 0.9913 | nan | 0.8389 | 0.9959 | nan | 0.7394 | 0.9910 | | 0.0054 | 126.0 | 1260 | 0.0502 | 0.8666 | 0.9209 | 0.9913 | nan | 0.8461 | 0.9957 | nan | 0.7420 | 0.9911 | | 0.0092 | 128.0 | 1280 | 0.0540 | 0.8642 | 0.9223 | 0.9911 | nan | 0.8492 | 0.9954 | nan | 0.7376 | 0.9908 | | 0.007 | 130.0 | 1300 | 0.0533 | 0.8637 | 0.9207 | 0.9911 | nan | 0.8459 | 0.9955 | nan | 0.7366 | 0.9908 | | 0.0063 | 132.0 | 1320 | 0.0526 | 0.8644 | 0.9259 | 0.9910 | nan | 0.8567 | 0.9951 | nan | 0.7380 | 0.9908 | | 0.0101 | 134.0 | 1340 | 0.0462 | 0.8653 | 0.9067 | 0.9915 | nan | 0.8166 | 0.9968 | nan | 0.7393 | 0.9913 | | 0.0183 | 136.0 | 1360 | 0.0516 | 0.5675 | 0.9024 | 0.9904 | nan | 0.8089 | 0.9959 | 0.0 | 0.7123 | 0.9901 | | 0.0102 | 138.0 | 1380 | 0.0388 | 0.8366 | 0.8716 | 0.9898 | nan | 0.7460 | 0.9972 | nan | 0.6837 | 0.9896 | | 0.0277 | 140.0 | 1400 | 0.0649 | 0.8159 | 0.9590 | 0.9850 | nan | 0.9313 | 0.9866 | nan | 0.6472 | 0.9846 | | 0.0169 | 142.0 | 1420 | 0.0340 | 0.8444 | 0.9148 | 0.9894 | nan | 0.8355 | 0.9941 | nan | 0.6996 | 0.9891 | | 0.0359 | 144.0 | 1440 | 0.0314 | 0.8667 | 0.8987 | 0.9918 | nan | 0.7997 | 0.9976 | nan | 0.7419 | 0.9916 | | 0.0117 | 146.0 | 1460 | 0.0307 | 0.8517 | 0.8869 | 0.9908 | nan | 0.7765 | 0.9973 | nan | 0.7129 | 0.9905 | | 0.0097 | 148.0 | 1480 | 0.0323 | 0.8757 | 0.9070 | 0.9924 | nan | 0.8162 | 0.9977 | nan | 0.7593 | 0.9922 | | 0.0063 | 150.0 | 1500 | 0.0302 | 0.8808 | 0.9155 | 0.9926 | nan | 0.8335 | 0.9975 | nan | 0.7692 | 0.9924 | | 0.0085 | 152.0 | 1520 | 0.0352 | 0.8697 | 0.9113 | 0.9918 | nan | 0.8258 | 0.9968 | nan | 0.7478 | 0.9916 | | 0.0078 | 154.0 | 1540 | 0.0428 | 0.8649 | 0.9190 | 0.9912 | nan | 0.8423 | 0.9957 | nan | 0.7388 | 0.9910 | | 0.0056 | 156.0 | 1560 | 0.0340 | 0.8709 | 0.9170 | 0.9918 | nan | 0.8376 | 0.9965 | nan | 0.7502 | 0.9916 | | 0.0063 | 158.0 | 1580 | 0.0359 | 0.8661 | 0.9201 | 0.9913 | nan | 0.8445 | 0.9958 | nan | 0.7412 | 0.9911 | | 0.0083 | 160.0 | 1600 | 0.0375 | 0.8684 | 0.9186 | 0.9915 | nan | 0.8410 | 0.9961 | nan | 0.7456 | 0.9913 | | 0.0065 | 162.0 | 1620 | 0.0370 | 0.8699 | 0.9210 | 0.9916 | nan | 0.8459 | 0.9960 | nan | 0.7484 | 0.9914 | | 0.0063 | 164.0 | 1640 | 0.0388 | 0.8699 | 0.9228 | 0.9916 | nan | 0.8498 | 0.9959 | nan | 0.7484 | 0.9913 | | 0.0056 | 166.0 | 1660 | 0.0386 | 0.8702 | 0.9238 | 0.9916 | nan | 0.8517 | 0.9958 | nan | 0.7491 | 0.9914 | | 0.0049 | 168.0 | 1680 | 0.0394 | 0.8703 | 0.9199 | 0.9917 | nan | 0.8436 | 0.9962 | nan | 0.7491 | 0.9914 | | 0.0054 | 170.0 | 1700 | 0.0400 | 0.8704 | 0.9195 | 0.9917 | nan | 0.8428 | 0.9962 | nan | 0.7494 | 0.9915 | | 0.0046 | 172.0 | 1720 | 0.0398 | 0.8728 | 0.9187 | 0.9919 | nan | 0.8410 | 0.9965 | nan | 0.7539 | 0.9917 | | 0.0058 | 174.0 | 1740 | 0.0402 | 0.8711 | 0.9166 | 0.9918 | nan | 0.8367 | 0.9965 | nan | 0.7507 | 0.9916 | | 0.005 | 176.0 | 1760 | 0.0400 | 0.8720 | 0.9196 | 0.9918 | nan | 0.8428 | 0.9963 | nan | 0.7525 | 0.9916 | | 0.0061 | 178.0 | 1780 | 0.0417 | 0.8714 | 0.9226 | 0.9917 | nan | 0.8492 | 0.9960 | nan | 0.7513 | 0.9915 | | 0.0061 | 180.0 | 1800 | 0.0407 | 0.8731 | 0.9249 | 0.9918 | nan | 0.8538 | 0.9960 | nan | 0.7545 | 0.9916 | | 0.0065 | 182.0 | 1820 | 0.0420 | 0.8712 | 0.9235 | 0.9917 | nan | 0.8511 | 0.9959 | nan | 0.7509 | 0.9914 | | 0.0045 | 184.0 | 1840 | 0.0421 | 0.8718 | 0.9250 | 0.9917 | nan | 0.8541 | 0.9959 | nan | 0.7522 | 0.9915 | | 0.0056 | 186.0 | 1860 | 0.0435 | 0.8703 | 0.9157 | 0.9917 | nan | 0.8349 | 0.9965 | nan | 0.7490 | 0.9915 | | 0.0059 | 188.0 | 1880 | 0.0436 | 0.8707 | 0.9191 | 0.9917 | nan | 0.8419 | 0.9963 | nan | 0.7499 | 0.9915 | | 0.0042 | 190.0 | 1900 | 0.0436 | 0.8707 | 0.9210 | 0.9917 | nan | 0.8458 | 0.9961 | nan | 0.7499 | 0.9915 | | 0.006 | 192.0 | 1920 | 0.0426 | 0.8697 | 0.9193 | 0.9916 | nan | 0.8425 | 0.9962 | nan | 0.7480 | 0.9914 | | 0.0053 | 194.0 | 1940 | 0.0447 | 0.8697 | 0.9199 | 0.9916 | nan | 0.8437 | 0.9961 | nan | 0.7480 | 0.9914 | | 0.0044 | 196.0 | 1960 | 0.0441 | 0.8710 | 0.9238 | 0.9916 | nan | 0.8516 | 0.9959 | nan | 0.7505 | 0.9914 | | 0.0049 | 198.0 | 1980 | 0.0453 | 0.8693 | 0.9219 | 0.9915 | nan | 0.8479 | 0.9959 | nan | 0.7473 | 0.9913 | | 0.0059 | 200.0 | 2000 | 0.0444 | 0.8726 | 0.9233 | 0.9918 | nan | 0.8506 | 0.9961 | nan | 0.7537 | 0.9916 | | 0.005 | 202.0 | 2020 | 0.0447 | 0.8717 | 0.9256 | 0.9917 | nan | 0.8555 | 0.9958 | nan | 0.7519 | 0.9914 | | 0.005 | 204.0 | 2040 | 0.0451 | 0.8711 | 0.9227 | 0.9917 | nan | 0.8494 | 0.9960 | nan | 0.7507 | 0.9915 | | 0.0043 | 206.0 | 2060 | 0.0458 | 0.8707 | 0.9220 | 0.9916 | nan | 0.8480 | 0.9960 | nan | 0.7499 | 0.9914 | | 0.0043 | 208.0 | 2080 | 0.0462 | 0.8696 | 0.9221 | 0.9916 | nan | 0.8482 | 0.9959 | nan | 0.7479 | 0.9913 | | 0.0062 | 210.0 | 2100 | 0.0451 | 0.8715 | 0.9211 | 0.9917 | nan | 0.8461 | 0.9962 | nan | 0.7515 | 0.9915 | | 0.0056 | 212.0 | 2120 | 0.0470 | 0.8706 | 0.9213 | 0.9917 | nan | 0.8466 | 0.9961 | nan | 0.7498 | 0.9914 | | 0.0049 | 214.0 | 2140 | 0.0480 | 0.8679 | 0.9229 | 0.9914 | nan | 0.8500 | 0.9957 | nan | 0.7447 | 0.9912 | | 0.0038 | 216.0 | 2160 | 0.0474 | 0.8700 | 0.9194 | 0.9916 | nan | 0.8427 | 0.9962 | nan | 0.7486 | 0.9914 | | 0.0043 | 218.0 | 2180 | 0.0472 | 0.8693 | 0.9231 | 0.9915 | nan | 0.8503 | 0.9958 | nan | 0.7474 | 0.9913 | | 0.005 | 220.0 | 2200 | 0.0471 | 0.8695 | 0.9156 | 0.9917 | nan | 0.8348 | 0.9964 | nan | 0.7475 | 0.9915 | | 0.0041 | 222.0 | 2220 | 0.0472 | 0.8719 | 0.9187 | 0.9918 | nan | 0.8411 | 0.9964 | nan | 0.7522 | 0.9916 | | 0.0041 | 224.0 | 2240 | 0.0471 | 0.8720 | 0.9219 | 0.9918 | nan | 0.8477 | 0.9962 | nan | 0.7525 | 0.9916 | | 0.005 | 226.0 | 2260 | 0.0479 | 0.8720 | 0.9191 | 0.9918 | nan | 0.8418 | 0.9964 | nan | 0.7524 | 0.9916 | | 0.0041 | 228.0 | 2280 | 0.0489 | 0.8706 | 0.9183 | 0.9917 | nan | 0.8403 | 0.9963 | nan | 0.7498 | 0.9915 | | 0.005 | 230.0 | 2300 | 0.0472 | 0.8728 | 0.9195 | 0.9919 | nan | 0.8426 | 0.9964 | nan | 0.7540 | 0.9917 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5

# INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0506 - Mean Iou: 0.8519 - Mean Accuracy: 0.9125 - Overall Accuracy: 0.9902 - Accuracy Unlabeled: nan - Accuracy Safe: 0.8300 - Accuracy Unsafe: 0.9950 - Iou Unlabeled: nan - Iou Safe: 0.7138 - Iou Unsafe: 0.9899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 1.4012 | 2.0 | 20 | 1.1653 | 0.0379 | 0.0923 | 0.0926 | nan | 0.0920 | 0.0926 | 0.0 | 0.0214 | 0.0923 | | 1.2461 | 4.0 | 40 | 0.9899 | 0.2255 | 0.3670 | 0.6307 | nan | 0.0868 | 0.6473 | 0.0 | 0.0379 | 0.6386 | | 1.0596 | 6.0 | 60 | 0.7738 | 0.2703 | 0.4188 | 0.7941 | nan | 0.0199 | 0.8177 | 0.0 | 0.0143 | 0.7967 | | 0.8267 | 8.0 | 80 | 0.6767 | 0.2902 | 0.4512 | 0.8507 | nan | 0.0265 | 0.8758 | 0.0 | 0.0183 | 0.8522 | | 0.7282 | 10.0 | 100 | 0.5637 | 0.3086 | 0.4776 | 0.9098 | nan | 0.0183 | 0.9370 | 0.0 | 0.0149 | 0.9110 | | 0.5124 | 12.0 | 120 | 0.4667 | 0.3254 | 0.5053 | 0.9512 | nan | 0.0314 | 0.9792 | 0.0 | 0.0247 | 0.9516 | | 0.3126 | 14.0 | 140 | 0.3585 | 0.3325 | 0.5147 | 0.9662 | nan | 0.0349 | 0.9945 | 0.0 | 0.0313 | 0.9663 | | 0.2862 | 16.0 | 160 | 0.2890 | 0.3346 | 0.5168 | 0.9703 | nan | 0.0349 | 0.9988 | 0.0 | 0.0336 | 0.9703 | | 0.2374 | 18.0 | 180 | 0.2102 | 0.3647 | 0.5637 | 0.9725 | nan | 0.1291 | 0.9982 | 0.0 | 0.1218 | 0.9724 | | 0.1583 | 20.0 | 200 | 0.1730 | 0.6293 | 0.6574 | 0.9761 | nan | 0.3186 | 0.9961 | nan | 0.2827 | 0.9759 | | 0.1082 | 22.0 | 220 | 0.1317 | 0.6306 | 0.6566 | 0.9765 | nan | 0.3166 | 0.9966 | nan | 0.2849 | 0.9763 | | 0.1025 | 24.0 | 240 | 0.1116 | 0.6494 | 0.6766 | 0.9777 | nan | 0.3565 | 0.9967 | nan | 0.3212 | 0.9775 | | 0.1158 | 26.0 | 260 | 0.0965 | 0.7200 | 0.7978 | 0.9791 | nan | 0.6051 | 0.9905 | nan | 0.4612 | 0.9787 | | 0.0882 | 28.0 | 280 | 0.0857 | 0.7356 | 0.7857 | 0.9822 | nan | 0.5769 | 0.9946 | nan | 0.4893 | 0.9819 | | 0.07 | 30.0 | 300 | 0.0829 | 0.6717 | 0.6934 | 0.9799 | nan | 0.3890 | 0.9979 | nan | 0.3637 | 0.9797 | | 0.0911 | 32.0 | 320 | 0.0677 | 0.7680 | 0.8244 | 0.9843 | nan | 0.6545 | 0.9944 | nan | 0.5521 | 0.9840 | | 0.0807 | 34.0 | 340 | 0.0696 | 0.7779 | 0.8716 | 0.9834 | nan | 0.7528 | 0.9904 | nan | 0.5727 | 0.9830 | | 0.0531 | 36.0 | 360 | 0.0611 | 0.7761 | 0.8781 | 0.9829 | nan | 0.7668 | 0.9895 | nan | 0.5698 | 0.9825 | | 0.0407 | 38.0 | 380 | 0.0567 | 0.7828 | 0.8396 | 0.9854 | nan | 0.6846 | 0.9945 | nan | 0.5805 | 0.9851 | | 0.0449 | 40.0 | 400 | 0.0639 | 0.7725 | 0.8200 | 0.9851 | nan | 0.6446 | 0.9954 | nan | 0.5602 | 0.9848 | | 0.0932 | 42.0 | 420 | 0.0503 | 0.7726 | 0.7983 | 0.9861 | nan | 0.5987 | 0.9979 | nan | 0.5593 | 0.9858 | | 0.0362 | 44.0 | 440 | 0.0634 | 0.7553 | 0.8670 | 0.9805 | nan | 0.7464 | 0.9876 | nan | 0.5306 | 0.9801 | | 0.0324 | 46.0 | 460 | 0.0501 | 0.8024 | 0.8615 | 0.9867 | nan | 0.7284 | 0.9946 | nan | 0.6184 | 0.9864 | | 0.036 | 48.0 | 480 | 0.0454 | 0.8010 | 0.8454 | 0.9872 | nan | 0.6947 | 0.9961 | nan | 0.6151 | 0.9869 | | 0.0356 | 50.0 | 500 | 0.0495 | 0.8061 | 0.8760 | 0.9866 | nan | 0.7585 | 0.9936 | nan | 0.6260 | 0.9863 | | 0.0333 | 52.0 | 520 | 0.0483 | 0.7743 | 0.8128 | 0.9856 | nan | 0.6292 | 0.9964 | nan | 0.5632 | 0.9853 | | 0.0277 | 54.0 | 540 | 0.0445 | 0.7714 | 0.7932 | 0.9862 | nan | 0.5880 | 0.9983 | nan | 0.5569 | 0.9859 | | 0.0298 | 56.0 | 560 | 0.0460 | 0.8034 | 0.8518 | 0.9872 | nan | 0.7078 | 0.9957 | nan | 0.6198 | 0.9869 | | 0.0256 | 58.0 | 580 | 0.0416 | 0.8181 | 0.8548 | 0.9886 | nan | 0.7126 | 0.9970 | nan | 0.6479 | 0.9883 | | 0.0336 | 60.0 | 600 | 0.0442 | 0.7957 | 0.8168 | 0.9877 | nan | 0.6351 | 0.9984 | nan | 0.6039 | 0.9875 | | 0.0283 | 62.0 | 620 | 0.0425 | 0.8141 | 0.8812 | 0.9873 | nan | 0.7684 | 0.9940 | nan | 0.6413 | 0.9870 | | 0.0198 | 64.0 | 640 | 0.0455 | 0.8059 | 0.8401 | 0.9879 | nan | 0.6830 | 0.9971 | nan | 0.6242 | 0.9876 | | 0.0181 | 66.0 | 660 | 0.0444 | 0.8144 | 0.8733 | 0.9876 | nan | 0.7519 | 0.9948 | nan | 0.6415 | 0.9873 | | 0.0188 | 68.0 | 680 | 0.0456 | 0.8179 | 0.8696 | 0.9881 | nan | 0.7436 | 0.9955 | nan | 0.6479 | 0.9878 | | 0.0165 | 70.0 | 700 | 0.0431 | 0.8208 | 0.8985 | 0.9875 | nan | 0.8040 | 0.9930 | nan | 0.6544 | 0.9872 | | 0.0184 | 72.0 | 720 | 0.0421 | 0.8165 | 0.8785 | 0.9876 | nan | 0.7625 | 0.9945 | nan | 0.6457 | 0.9874 | | 0.0336 | 74.0 | 740 | 0.0441 | 0.8081 | 0.8792 | 0.9867 | nan | 0.7650 | 0.9935 | nan | 0.6298 | 0.9864 | | 0.0165 | 76.0 | 760 | 0.0374 | 0.8200 | 0.8555 | 0.9887 | nan | 0.7139 | 0.9971 | nan | 0.6515 | 0.9885 | | 0.0127 | 78.0 | 780 | 0.0402 | 0.8222 | 0.8780 | 0.9882 | nan | 0.7608 | 0.9952 | nan | 0.6563 | 0.9880 | | 0.0152 | 80.0 | 800 | 0.0430 | 0.8230 | 0.8687 | 0.9886 | nan | 0.7413 | 0.9961 | nan | 0.6576 | 0.9883 | | 0.0143 | 82.0 | 820 | 0.0410 | 0.8087 | 0.8422 | 0.9881 | nan | 0.6873 | 0.9972 | nan | 0.6297 | 0.9878 | | 0.0134 | 84.0 | 840 | 0.0335 | 0.8429 | 0.8893 | 0.9899 | nan | 0.7823 | 0.9962 | nan | 0.6962 | 0.9897 | | 0.0122 | 86.0 | 860 | 0.0396 | 0.8312 | 0.8749 | 0.9892 | nan | 0.7534 | 0.9964 | nan | 0.6734 | 0.9890 | | 0.0126 | 88.0 | 880 | 0.0405 | 0.8341 | 0.8805 | 0.9893 | nan | 0.7649 | 0.9962 | nan | 0.6791 | 0.9891 | | 0.0121 | 90.0 | 900 | 0.0400 | 0.8390 | 0.8810 | 0.9898 | nan | 0.7654 | 0.9966 | nan | 0.6884 | 0.9895 | | 0.0104 | 92.0 | 920 | 0.0372 | 0.8453 | 0.8990 | 0.9899 | nan | 0.8024 | 0.9956 | nan | 0.7010 | 0.9896 | | 0.0128 | 94.0 | 940 | 0.0394 | 0.8411 | 0.8893 | 0.9897 | nan | 0.7825 | 0.9961 | nan | 0.6927 | 0.9895 | | 0.0124 | 96.0 | 960 | 0.0409 | 0.8395 | 0.8948 | 0.9895 | nan | 0.7943 | 0.9954 | nan | 0.6899 | 0.9892 | | 0.0095 | 98.0 | 980 | 0.0413 | 0.8258 | 0.8903 | 0.9882 | nan | 0.7863 | 0.9944 | nan | 0.6637 | 0.9880 | | 0.0147 | 100.0 | 1000 | 0.0468 | 0.8181 | 0.9044 | 0.9870 | nan | 0.8167 | 0.9922 | nan | 0.6496 | 0.9867 | | 0.0125 | 102.0 | 1020 | 0.0379 | 0.8213 | 0.8961 | 0.9876 | nan | 0.7989 | 0.9933 | nan | 0.6553 | 0.9873 | | 0.0142 | 104.0 | 1040 | 0.0328 | 0.8449 | 0.9154 | 0.9894 | nan | 0.8366 | 0.9941 | nan | 0.7006 | 0.9892 | | 0.0101 | 106.0 | 1060 | 0.0428 | 0.8407 | 0.9144 | 0.9891 | nan | 0.8351 | 0.9937 | nan | 0.6927 | 0.9888 | | 0.0097 | 108.0 | 1080 | 0.0397 | 0.8296 | 0.8847 | 0.9888 | nan | 0.7740 | 0.9953 | nan | 0.6707 | 0.9885 | | 0.01 | 110.0 | 1100 | 0.0384 | 0.8457 | 0.8935 | 0.9901 | nan | 0.7910 | 0.9961 | nan | 0.7016 | 0.9898 | | 0.0084 | 112.0 | 1120 | 0.0385 | 0.8421 | 0.8874 | 0.9899 | nan | 0.7784 | 0.9963 | nan | 0.6945 | 0.9896 | | 0.0086 | 114.0 | 1140 | 0.0413 | 0.8488 | 0.8882 | 0.9905 | nan | 0.7795 | 0.9969 | nan | 0.7074 | 0.9903 | | 0.0112 | 116.0 | 1160 | 0.0427 | 0.8459 | 0.8942 | 0.9901 | nan | 0.7924 | 0.9961 | nan | 0.7020 | 0.9898 | | 0.0132 | 118.0 | 1180 | 0.0407 | 0.8510 | 0.9011 | 0.9904 | nan | 0.8062 | 0.9960 | nan | 0.7118 | 0.9901 | | 0.0084 | 120.0 | 1200 | 0.0432 | 0.8510 | 0.9015 | 0.9903 | nan | 0.8071 | 0.9959 | nan | 0.7118 | 0.9901 | | 0.008 | 122.0 | 1220 | 0.0431 | 0.8504 | 0.9077 | 0.9901 | nan | 0.8202 | 0.9953 | nan | 0.7109 | 0.9899 | | 0.0069 | 124.0 | 1240 | 0.0424 | 0.8522 | 0.8982 | 0.9905 | nan | 0.8001 | 0.9963 | nan | 0.7141 | 0.9903 | | 0.006 | 126.0 | 1260 | 0.0447 | 0.8537 | 0.9114 | 0.9904 | nan | 0.8275 | 0.9953 | nan | 0.7173 | 0.9901 | | 0.0123 | 128.0 | 1280 | 0.0464 | 0.8529 | 0.9102 | 0.9903 | nan | 0.8250 | 0.9954 | nan | 0.7157 | 0.9901 | | 0.0073 | 130.0 | 1300 | 0.0441 | 0.8520 | 0.9025 | 0.9904 | nan | 0.8090 | 0.9959 | nan | 0.7139 | 0.9902 | | 0.0066 | 132.0 | 1320 | 0.0447 | 0.8524 | 0.9086 | 0.9903 | nan | 0.8217 | 0.9954 | nan | 0.7148 | 0.9901 | | 0.0063 | 134.0 | 1340 | 0.0434 | 0.8546 | 0.9077 | 0.9905 | nan | 0.8197 | 0.9957 | nan | 0.7189 | 0.9903 | | 0.0068 | 136.0 | 1360 | 0.0475 | 0.8518 | 0.9090 | 0.9902 | nan | 0.8226 | 0.9953 | nan | 0.7135 | 0.9900 | | 0.0056 | 138.0 | 1380 | 0.0458 | 0.8549 | 0.9122 | 0.9905 | nan | 0.8291 | 0.9954 | nan | 0.7195 | 0.9902 | | 0.007 | 140.0 | 1400 | 0.0455 | 0.8554 | 0.9126 | 0.9905 | nan | 0.8298 | 0.9954 | nan | 0.7205 | 0.9903 | | 0.0064 | 142.0 | 1420 | 0.0476 | 0.8542 | 0.9047 | 0.9906 | nan | 0.8133 | 0.9960 | nan | 0.7180 | 0.9903 | | 0.0065 | 144.0 | 1440 | 0.0437 | 0.8556 | 0.9107 | 0.9906 | nan | 0.8258 | 0.9956 | nan | 0.7210 | 0.9903 | | 0.005 | 146.0 | 1460 | 0.0455 | 0.8551 | 0.9098 | 0.9905 | nan | 0.8239 | 0.9956 | nan | 0.7198 | 0.9903 | | 0.005 | 148.0 | 1480 | 0.0458 | 0.8539 | 0.9084 | 0.9905 | nan | 0.8212 | 0.9956 | nan | 0.7175 | 0.9902 | | 0.0048 | 150.0 | 1500 | 0.0462 | 0.8558 | 0.9041 | 0.9907 | nan | 0.8121 | 0.9962 | nan | 0.7211 | 0.9905 | | 0.0063 | 152.0 | 1520 | 0.0453 | 0.8560 | 0.9175 | 0.9904 | nan | 0.8400 | 0.9950 | nan | 0.7217 | 0.9902 | | 0.006 | 154.0 | 1540 | 0.0473 | 0.8531 | 0.9073 | 0.9904 | nan | 0.8190 | 0.9956 | nan | 0.7160 | 0.9902 | | 0.0043 | 156.0 | 1560 | 0.0448 | 0.8562 | 0.9100 | 0.9906 | nan | 0.8243 | 0.9957 | nan | 0.7220 | 0.9904 | | 0.0049 | 158.0 | 1580 | 0.0480 | 0.8518 | 0.9137 | 0.9901 | nan | 0.8324 | 0.9949 | nan | 0.7138 | 0.9899 | | 0.0065 | 160.0 | 1600 | 0.0475 | 0.8556 | 0.9095 | 0.9906 | nan | 0.8233 | 0.9957 | nan | 0.7209 | 0.9903 | | 0.0052 | 162.0 | 1620 | 0.0479 | 0.8531 | 0.9087 | 0.9904 | nan | 0.8218 | 0.9955 | nan | 0.7161 | 0.9901 | | 0.0063 | 164.0 | 1640 | 0.0488 | 0.8571 | 0.9115 | 0.9907 | nan | 0.8273 | 0.9956 | nan | 0.7238 | 0.9904 | | 0.0053 | 166.0 | 1660 | 0.0514 | 0.8515 | 0.9152 | 0.9901 | nan | 0.8357 | 0.9948 | nan | 0.7132 | 0.9898 | | 0.0046 | 168.0 | 1680 | 0.0476 | 0.8540 | 0.9040 | 0.9906 | nan | 0.8119 | 0.9960 | nan | 0.7177 | 0.9903 | | 0.0039 | 170.0 | 1700 | 0.0483 | 0.5699 | 0.9121 | 0.9905 | nan | 0.8289 | 0.9954 | 0.0 | 0.7195 | 0.9902 | | 0.0044 | 172.0 | 1720 | 0.0494 | 0.8550 | 0.9114 | 0.9905 | nan | 0.8273 | 0.9954 | nan | 0.7197 | 0.9902 | | 0.0051 | 174.0 | 1740 | 0.0503 | 0.8556 | 0.9103 | 0.9906 | nan | 0.8250 | 0.9956 | nan | 0.7208 | 0.9903 | | 0.0041 | 176.0 | 1760 | 0.0499 | 0.8545 | 0.9118 | 0.9904 | nan | 0.8283 | 0.9954 | nan | 0.7188 | 0.9902 | | 0.0049 | 178.0 | 1780 | 0.0525 | 0.8541 | 0.9066 | 0.9905 | nan | 0.8174 | 0.9958 | nan | 0.7179 | 0.9903 | | 0.0048 | 180.0 | 1800 | 0.0496 | 0.8556 | 0.9165 | 0.9904 | nan | 0.8380 | 0.9951 | nan | 0.7210 | 0.9902 | | 0.008 | 182.0 | 1820 | 0.0487 | 0.8528 | 0.9085 | 0.9904 | nan | 0.8215 | 0.9955 | nan | 0.7155 | 0.9901 | | 0.0041 | 184.0 | 1840 | 0.0506 | 0.8519 | 0.9125 | 0.9902 | nan | 0.8300 | 0.9950 | nan | 0.7138 | 0.9899 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/safety-utcustom-train-SF30-RGB-b0

# safety-utcustom-train-SF30-RGB-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Loss: 0.7492 - Mean Iou: 0.3878 - Mean Accuracy: 0.8431 - Overall Accuracy: 0.9233 - Accuracy Unlabeled: nan - Accuracy Safe: 0.7575 - Accuracy Unsafe: 0.9287 - Iou Unlabeled: 0.0 - Iou Safe: 0.2418 - Iou Unsafe: 0.9214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 1.1527 | 5.0 | 10 | 1.1085 | 0.0590 | 0.4585 | 0.1664 | nan | 0.7704 | 0.1465 | 0.0 | 0.0307 | 0.1464 | | 1.1326 | 10.0 | 20 | 1.1091 | 0.0963 | 0.6082 | 0.2699 | nan | 0.9695 | 0.2470 | 0.0 | 0.0419 | 0.2470 | | 1.0981 | 15.0 | 30 | 1.0980 | 0.1530 | 0.6989 | 0.4242 | nan | 0.9922 | 0.4055 | 0.0 | 0.0535 | 0.4055 | | 1.086 | 20.0 | 40 | 1.0822 | 0.1916 | 0.7515 | 0.5256 | nan | 0.9927 | 0.5103 | 0.0 | 0.0644 | 0.5103 | | 1.0466 | 25.0 | 50 | 1.0541 | 0.2226 | 0.7909 | 0.6043 | nan | 0.9902 | 0.5917 | 0.0 | 0.0761 | 0.5917 | | 1.0533 | 30.0 | 60 | 1.0249 | 0.2444 | 0.8167 | 0.6580 | nan | 0.9863 | 0.6472 | 0.0 | 0.0861 | 0.6471 | | 0.9779 | 35.0 | 70 | 1.0010 | 0.2607 | 0.8322 | 0.6966 | nan | 0.9771 | 0.6874 | 0.0 | 0.0951 | 0.6871 | | 0.9161 | 40.0 | 80 | 0.9695 | 0.2808 | 0.8487 | 0.7412 | nan | 0.9635 | 0.7339 | 0.0 | 0.1091 | 0.7334 | | 0.9843 | 45.0 | 90 | 0.9403 | 0.3004 | 0.8631 | 0.7823 | nan | 0.9494 | 0.7768 | 0.0 | 0.1254 | 0.7759 | | 0.9568 | 50.0 | 100 | 0.9071 | 0.3176 | 0.8663 | 0.8169 | nan | 0.9191 | 0.8135 | 0.0 | 0.1412 | 0.8117 | | 0.8443 | 55.0 | 110 | 0.8627 | 0.3403 | 0.8656 | 0.8576 | nan | 0.8742 | 0.8570 | 0.0 | 0.1672 | 0.8537 | | 0.8765 | 60.0 | 120 | 0.8488 | 0.3450 | 0.8625 | 0.8657 | nan | 0.8591 | 0.8659 | 0.0 | 0.1729 | 0.8620 | | 0.899 | 65.0 | 130 | 0.8429 | 0.3481 | 0.8629 | 0.8705 | nan | 0.8548 | 0.8710 | 0.0 | 0.1772 | 0.8669 | | 0.7713 | 70.0 | 140 | 0.8085 | 0.3632 | 0.8497 | 0.8939 | nan | 0.8026 | 0.8969 | 0.0 | 0.1983 | 0.8912 | | 0.8505 | 75.0 | 150 | 0.7821 | 0.3762 | 0.8465 | 0.9102 | nan | 0.7786 | 0.9145 | 0.0 | 0.2208 | 0.9079 | | 0.7352 | 80.0 | 160 | 0.7841 | 0.3819 | 0.8392 | 0.9173 | nan | 0.7557 | 0.9226 | 0.0 | 0.2304 | 0.9153 | | 0.7205 | 85.0 | 170 | 0.7502 | 0.3974 | 0.8400 | 0.9325 | nan | 0.7413 | 0.9388 | 0.0 | 0.2613 | 0.9309 | | 0.711 | 90.0 | 180 | 0.7417 | 0.3962 | 0.8428 | 0.9313 | nan | 0.7484 | 0.9373 | 0.0 | 0.2591 | 0.9296 | | 0.7855 | 95.0 | 190 | 0.7281 | 0.4003 | 0.8439 | 0.9343 | nan | 0.7473 | 0.9404 | 0.0 | 0.2683 | 0.9327 | | 0.7632 | 100.0 | 200 | 0.7494 | 0.3883 | 0.8419 | 0.9237 | nan | 0.7545 | 0.9293 | 0.0 | 0.2430 | 0.9219 | | 0.8145 | 105.0 | 210 | 0.7495 | 0.3862 | 0.8412 | 0.9219 | nan | 0.7551 | 0.9274 | 0.0 | 0.2387 | 0.9201 | | 0.8217 | 110.0 | 220 | 0.7355 | 0.3933 | 0.8422 | 0.9282 | nan | 0.7502 | 0.9341 | 0.0 | 0.2533 | 0.9265 | | 0.7784 | 115.0 | 230 | 0.7258 | 0.4088 | 0.8411 | 0.9413 | nan | 0.7340 | 0.9481 | 0.0 | 0.2864 | 0.9400 | | 0.8349 | 120.0 | 240 | 0.7492 | 0.3878 | 0.8431 | 0.9233 | nan | 0.7575 | 0.9287 | 0.0 | 0.2418 | 0.9214 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/safety-utcustom-train-SF30-RGB-b5

# safety-utcustom-train-SF30-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Accuracy Safe: 0.8299 - Accuracy Unlabeled: nan - Accuracy Unsafe: 0.9036 - Iou Safe: 0.3480 - Iou Unlabeled: 0.0 - Iou Unsafe: 0.8996 - Loss: 0.5783 - Mean Accuracy: 0.8668 - Mean Iou: 0.4158 - Overall Accuracy: 0.9013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |:-------------:|:-----:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:| | 1.0614 | 5.0 | 10 | 0.1904 | nan | 0.5439 | 0.0682 | 0.0 | 0.5350 | 1.0385 | 0.3672 | 0.2011 | 0.5327 | | 1.0269 | 10.0 | 20 | 0.4801 | nan | 0.5773 | 0.1795 | 0.0 | 0.5719 | 0.9975 | 0.5287 | 0.2505 | 0.5742 | | 1.0005 | 15.0 | 30 | 0.6270 | nan | 0.6316 | 0.2261 | 0.0 | 0.6269 | 0.9428 | 0.6293 | 0.2843 | 0.6315 | | 0.9716 | 20.0 | 40 | 0.6870 | nan | 0.6802 | 0.2529 | 0.0 | 0.6756 | 0.8918 | 0.6836 | 0.3095 | 0.6804 | | 0.9255 | 25.0 | 50 | 0.7339 | nan | 0.7081 | 0.2805 | 0.0 | 0.7037 | 0.8542 | 0.7210 | 0.3281 | 0.7089 | | 0.9256 | 30.0 | 60 | 0.7705 | nan | 0.7229 | 0.2781 | 0.0 | 0.7189 | 0.8330 | 0.7467 | 0.3324 | 0.7244 | | 0.8167 | 35.0 | 70 | 0.7622 | nan | 0.7349 | 0.3004 | 0.0 | 0.7311 | 0.8114 | 0.7485 | 0.3438 | 0.7358 | | 0.7927 | 40.0 | 80 | 0.7776 | nan | 0.7594 | 0.3154 | 0.0 | 0.7559 | 0.7793 | 0.7685 | 0.3571 | 0.7600 | | 0.8227 | 45.0 | 90 | 0.8020 | nan | 0.7821 | 0.3152 | 0.0 | 0.7789 | 0.7574 | 0.7920 | 0.3647 | 0.7827 | | 0.81 | 50.0 | 100 | 0.8114 | nan | 0.7983 | 0.3140 | 0.0 | 0.7955 | 0.7370 | 0.8049 | 0.3698 | 0.7987 | | 0.7198 | 55.0 | 110 | 0.8002 | nan | 0.8194 | 0.3303 | 0.0 | 0.8162 | 0.7118 | 0.8098 | 0.3822 | 0.8188 | | 0.7523 | 60.0 | 120 | 0.7877 | nan | 0.8482 | 0.3457 | 0.0 | 0.8443 | 0.6832 | 0.8179 | 0.3967 | 0.8462 | | 0.7239 | 65.0 | 130 | 0.8112 | nan | 0.8485 | 0.3197 | 0.0 | 0.8453 | 0.6745 | 0.8298 | 0.3883 | 0.8473 | | 0.6235 | 70.0 | 140 | 0.7906 | nan | 0.8686 | 0.3507 | 0.0 | 0.8649 | 0.6419 | 0.8296 | 0.4052 | 0.8662 | | 0.6887 | 75.0 | 150 | 0.7951 | nan | 0.8758 | 0.3568 | 0.0 | 0.8720 | 0.6302 | 0.8354 | 0.4096 | 0.8732 | | 0.6079 | 80.0 | 160 | 0.8069 | nan | 0.8879 | 0.3561 | 0.0 | 0.8841 | 0.6120 | 0.8474 | 0.4134 | 0.8853 | | 0.6022 | 85.0 | 170 | 0.8126 | nan | 0.9062 | 0.3699 | 0.0 | 0.9020 | 0.5849 | 0.8594 | 0.4240 | 0.9032 | | 0.5748 | 90.0 | 180 | 0.8053 | nan | 0.9047 | 0.3793 | 0.0 | 0.9005 | 0.5802 | 0.8550 | 0.4266 | 0.9016 | | 0.6228 | 95.0 | 190 | 0.8164 | nan | 0.9050 | 0.3624 | 0.0 | 0.9007 | 0.5793 | 0.8607 | 0.4210 | 0.9022 | | 0.5332 | 100.0 | 200 | 0.8214 | nan | 0.9134 | 0.3623 | 0.0 | 0.9091 | 0.5616 | 0.8674 | 0.4238 | 0.9105 | | 0.6655 | 105.0 | 210 | 0.8262 | nan | 0.9072 | 0.3572 | 0.0 | 0.9031 | 0.5688 | 0.8667 | 0.4201 | 0.9046 | | 0.5835 | 110.0 | 220 | 0.8233 | nan | 0.9092 | 0.3599 | 0.0 | 0.9050 | 0.5653 | 0.8662 | 0.4216 | 0.9064 | | 0.5764 | 115.0 | 230 | 0.8099 | nan | 0.9165 | 0.3783 | 0.0 | 0.9120 | 0.5460 | 0.8632 | 0.4301 | 0.9131 | | 0.5621 | 120.0 | 240 | 0.8299 | nan | 0.9036 | 0.3480 | 0.0 | 0.8996 | 0.5783 | 0.8668 | 0.4158 | 0.9013 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/safety-utcustom-train-SF30-RGBD-b5

# safety-utcustom-train-SF30-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Mean Iou: 0.6486 - Mean Accuracy: 0.7199 - Overall Accuracy: 0.9704 - Accuracy Unlabeled: nan - Accuracy Safe: 0.4523 - Accuracy Unsafe: 0.9874 - Iou Unlabeled: nan - Iou Safe: 0.3271 - Iou Unsafe: 0.9700 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 0.8758 | 5.0 | 10 | 0.9831 | 0.3415 | 0.6100 | 0.9154 | nan | 0.2839 | 0.9362 | 0.0 | 0.1099 | 0.9147 | | 0.7637 | 10.0 | 20 | 0.7236 | 0.3771 | 0.6275 | 0.9582 | nan | 0.2745 | 0.9806 | 0.0 | 0.1735 | 0.9578 | | 0.6698 | 15.0 | 30 | 0.5510 | 0.3789 | 0.6286 | 0.9593 | nan | 0.2755 | 0.9818 | 0.0 | 0.1776 | 0.9590 | | 0.5935 | 20.0 | 40 | 0.4632 | 0.3822 | 0.6388 | 0.9591 | nan | 0.2967 | 0.9809 | 0.0 | 0.1877 | 0.9588 | | 0.5108 | 25.0 | 50 | 0.4239 | 0.3814 | 0.6492 | 0.9560 | nan | 0.3214 | 0.9769 | 0.0 | 0.1887 | 0.9556 | | 0.4597 | 30.0 | 60 | 0.4134 | 0.3845 | 0.6422 | 0.9596 | nan | 0.3034 | 0.9811 | 0.0 | 0.1943 | 0.9592 | | 0.4307 | 35.0 | 70 | 0.3918 | 0.3900 | 0.6516 | 0.9594 | nan | 0.3229 | 0.9803 | 0.0 | 0.2111 | 0.9590 | | 0.367 | 40.0 | 80 | 0.3578 | 0.3885 | 0.6600 | 0.9582 | nan | 0.3415 | 0.9784 | 0.0 | 0.2077 | 0.9577 | | 0.3249 | 45.0 | 90 | 0.3395 | 0.3921 | 0.6587 | 0.9607 | nan | 0.3360 | 0.9813 | 0.0 | 0.2161 | 0.9603 | | 0.292 | 50.0 | 100 | 0.3124 | 0.3969 | 0.6622 | 0.9633 | nan | 0.3408 | 0.9837 | 0.0 | 0.2280 | 0.9629 | | 0.2766 | 55.0 | 110 | 0.2820 | 0.4078 | 0.6878 | 0.9644 | nan | 0.3925 | 0.9831 | 0.0 | 0.2594 | 0.9639 | | 0.2347 | 60.0 | 120 | 0.2673 | 0.6169 | 0.7000 | 0.9641 | nan | 0.4181 | 0.9820 | nan | 0.2701 | 0.9636 | | 0.226 | 65.0 | 130 | 0.2350 | 0.6280 | 0.6854 | 0.9698 | nan | 0.3818 | 0.9891 | nan | 0.2865 | 0.9694 | | 0.3262 | 70.0 | 140 | 0.2354 | 0.6338 | 0.7125 | 0.9674 | nan | 0.4402 | 0.9848 | nan | 0.3006 | 0.9670 | | 0.1991 | 75.0 | 150 | 0.2231 | 0.6363 | 0.7169 | 0.9676 | nan | 0.4492 | 0.9846 | nan | 0.3056 | 0.9671 | | 0.2106 | 80.0 | 160 | 0.2089 | 0.6399 | 0.7152 | 0.9688 | nan | 0.4444 | 0.9860 | nan | 0.3114 | 0.9683 | | 0.1995 | 85.0 | 170 | 0.1969 | 0.6493 | 0.7179 | 0.9709 | nan | 0.4478 | 0.9880 | nan | 0.3281 | 0.9704 | | 0.1981 | 90.0 | 180 | 0.1909 | 0.6503 | 0.7136 | 0.9716 | nan | 0.4381 | 0.9892 | nan | 0.3293 | 0.9712 | | 0.1875 | 95.0 | 190 | 0.1965 | 0.6473 | 0.7231 | 0.9697 | nan | 0.4598 | 0.9864 | nan | 0.3254 | 0.9692 | | 0.2088 | 100.0 | 200 | 0.1952 | 0.6486 | 0.7199 | 0.9704 | nan | 0.4523 | 0.9874 | nan | 0.3271 | 0.9700 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/safety-utcustom-train-SF30-RGBD-b0

# safety-utcustom-train-SF30-RGBD-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set: - Loss: 0.3227 - Mean Iou: 0.5786 - Mean Accuracy: 0.6222 - Overall Accuracy: 0.9658 - Accuracy Unlabeled: nan - Accuracy Safe: 0.2552 - Accuracy Unsafe: 0.9891 - Iou Unlabeled: nan - Iou Safe: 0.1917 - Iou Unsafe: 0.9655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 0.9925 | 5.0 | 10 | 1.0612 | 0.3101 | 0.5355 | 0.8847 | nan | 0.1625 | 0.9085 | 0.0 | 0.0462 | 0.8841 | | 0.8589 | 10.0 | 20 | 0.9441 | 0.3303 | 0.5181 | 0.9537 | nan | 0.0529 | 0.9833 | 0.0 | 0.0373 | 0.9537 | | 0.7016 | 15.0 | 30 | 0.7764 | 0.3274 | 0.5069 | 0.9654 | nan | 0.0172 | 0.9965 | 0.0 | 0.0169 | 0.9654 | | 0.6093 | 20.0 | 40 | 0.6213 | 0.3339 | 0.5219 | 0.9603 | nan | 0.0538 | 0.9901 | 0.0 | 0.0415 | 0.9603 | | 0.5281 | 25.0 | 50 | 0.5431 | 0.3355 | 0.5213 | 0.9650 | nan | 0.0476 | 0.9951 | 0.0 | 0.0417 | 0.9649 | | 0.5077 | 30.0 | 60 | 0.5043 | 0.3361 | 0.5231 | 0.9638 | nan | 0.0524 | 0.9938 | 0.0 | 0.0444 | 0.9638 | | 0.5197 | 35.0 | 70 | 0.4579 | 0.3379 | 0.5249 | 0.9657 | nan | 0.0543 | 0.9956 | 0.0 | 0.0481 | 0.9656 | | 0.4477 | 40.0 | 80 | 0.4340 | 0.3395 | 0.5271 | 0.9662 | nan | 0.0583 | 0.9960 | 0.0 | 0.0523 | 0.9661 | | 0.4371 | 45.0 | 90 | 0.4033 | 0.3407 | 0.5287 | 0.9669 | nan | 0.0607 | 0.9967 | 0.0 | 0.0553 | 0.9669 | | 0.3972 | 50.0 | 100 | 0.3975 | 0.3420 | 0.5292 | 0.9686 | nan | 0.0600 | 0.9985 | 0.0 | 0.0574 | 0.9686 | | 0.4101 | 55.0 | 110 | 0.3777 | 0.5215 | 0.5381 | 0.9691 | nan | 0.0778 | 0.9983 | nan | 0.0740 | 0.9690 | | 0.3528 | 60.0 | 120 | 0.3625 | 0.5360 | 0.5587 | 0.9668 | nan | 0.1229 | 0.9945 | nan | 0.1054 | 0.9667 | | 0.3552 | 65.0 | 130 | 0.3733 | 0.5550 | 0.5829 | 0.9671 | nan | 0.1726 | 0.9932 | nan | 0.1430 | 0.9669 | | 0.3798 | 70.0 | 140 | 0.3444 | 0.5598 | 0.5753 | 0.9722 | nan | 0.1515 | 0.9991 | nan | 0.1476 | 0.9720 | | 0.3235 | 75.0 | 150 | 0.3461 | 0.5651 | 0.6041 | 0.9650 | nan | 0.2187 | 0.9895 | nan | 0.1656 | 0.9647 | | 0.3457 | 80.0 | 160 | 0.3335 | 0.5638 | 0.5880 | 0.9695 | nan | 0.1806 | 0.9954 | nan | 0.1582 | 0.9693 | | 0.318 | 85.0 | 170 | 0.3334 | 0.5739 | 0.6114 | 0.9667 | nan | 0.2321 | 0.9908 | nan | 0.1814 | 0.9665 | | 0.32 | 90.0 | 180 | 0.3307 | 0.5779 | 0.6112 | 0.9684 | nan | 0.2299 | 0.9926 | nan | 0.1877 | 0.9681 | | 0.3122 | 95.0 | 190 | 0.3263 | 0.5778 | 0.6175 | 0.9667 | nan | 0.2447 | 0.9904 | nan | 0.1891 | 0.9664 | | 0.3554 | 100.0 | 200 | 0.3227 | 0.5786 | 0.6222 | 0.9658 | nan | 0.2552 | 0.9891 | nan | 0.1917 | 0.9655 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "safe", "unsafe" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_1

# dropoff-utcustom-train-SF-RGB-b0_1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.5626 - Mean Iou: 0.4261 - Mean Accuracy: 0.7046 - Overall Accuracy: 0.9598 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4247 - Accuracy Undropoff: 0.9846 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3192 - Iou Undropoff: 0.9590 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.1029 | 3.33 | 10 | 1.0852 | 0.1637 | 0.3955 | 0.4522 | nan | 0.3333 | 0.4577 | 0.0 | 0.0410 | 0.4501 | | 1.0856 | 6.67 | 20 | 1.0764 | 0.1911 | 0.5086 | 0.5025 | nan | 0.5153 | 0.5019 | 0.0 | 0.0761 | 0.4972 | | 1.0755 | 10.0 | 30 | 1.0611 | 0.2252 | 0.6367 | 0.5749 | nan | 0.7045 | 0.5688 | 0.0 | 0.1104 | 0.5652 | | 1.0285 | 13.33 | 40 | 1.0382 | 0.2622 | 0.7487 | 0.6568 | nan | 0.8494 | 0.6479 | 0.0 | 0.1420 | 0.6445 | | 0.9935 | 16.67 | 50 | 1.0151 | 0.2893 | 0.7814 | 0.7201 | nan | 0.8486 | 0.7141 | 0.0 | 0.1580 | 0.7099 | | 0.9927 | 20.0 | 60 | 0.9834 | 0.3160 | 0.7963 | 0.7816 | nan | 0.8124 | 0.7801 | 0.0 | 0.1735 | 0.7744 | | 0.938 | 23.33 | 70 | 0.9585 | 0.3308 | 0.8084 | 0.8127 | nan | 0.8036 | 0.8131 | 0.0 | 0.1860 | 0.8065 | | 0.9169 | 26.67 | 80 | 0.9376 | 0.3457 | 0.8169 | 0.8376 | nan | 0.7943 | 0.8396 | 0.0 | 0.2048 | 0.8324 | | 0.8814 | 30.0 | 90 | 0.9003 | 0.3624 | 0.8086 | 0.8691 | nan | 0.7421 | 0.8750 | 0.0 | 0.2220 | 0.8651 | | 0.8618 | 33.33 | 100 | 0.8894 | 0.3669 | 0.8184 | 0.8761 | nan | 0.7550 | 0.8817 | 0.0 | 0.2287 | 0.8720 | | 0.8388 | 36.67 | 110 | 0.8618 | 0.3774 | 0.8096 | 0.8926 | nan | 0.7187 | 0.9006 | 0.0 | 0.2431 | 0.8892 | | 0.8878 | 40.0 | 120 | 0.8269 | 0.3929 | 0.7937 | 0.9140 | nan | 0.6618 | 0.9257 | 0.0 | 0.2671 | 0.9116 | | 0.8066 | 43.33 | 130 | 0.8074 | 0.4014 | 0.7955 | 0.9225 | nan | 0.6562 | 0.9348 | 0.0 | 0.2839 | 0.9202 | | 0.8084 | 46.67 | 140 | 0.7919 | 0.4023 | 0.7932 | 0.9248 | nan | 0.6487 | 0.9376 | 0.0 | 0.2844 | 0.9226 | | 0.7415 | 50.0 | 150 | 0.7707 | 0.4068 | 0.7850 | 0.9309 | nan | 0.6249 | 0.9451 | 0.0 | 0.2913 | 0.9290 | | 0.7508 | 53.33 | 160 | 0.7326 | 0.4154 | 0.7660 | 0.9415 | nan | 0.5735 | 0.9585 | 0.0 | 0.3063 | 0.9400 | | 0.7312 | 56.67 | 170 | 0.7126 | 0.4196 | 0.7636 | 0.9449 | nan | 0.5646 | 0.9625 | 0.0 | 0.3155 | 0.9435 | | 0.6442 | 60.0 | 180 | 0.6869 | 0.4255 | 0.7500 | 0.9509 | nan | 0.5296 | 0.9704 | 0.0 | 0.3268 | 0.9497 | | 0.6633 | 63.33 | 190 | 0.6765 | 0.4286 | 0.7524 | 0.9525 | nan | 0.5328 | 0.9719 | 0.0 | 0.3343 | 0.9513 | | 0.7247 | 66.67 | 200 | 0.6557 | 0.4307 | 0.7335 | 0.9568 | nan | 0.4886 | 0.9785 | 0.0 | 0.3364 | 0.9558 | | 0.6133 | 70.0 | 210 | 0.6369 | 0.4298 | 0.7279 | 0.9573 | nan | 0.4761 | 0.9796 | 0.0 | 0.3330 | 0.9564 | | 0.6309 | 73.33 | 220 | 0.6309 | 0.4298 | 0.7437 | 0.9547 | nan | 0.5123 | 0.9752 | 0.0 | 0.3356 | 0.9536 | | 0.6373 | 76.67 | 230 | 0.6094 | 0.4276 | 0.7197 | 0.9577 | nan | 0.4585 | 0.9808 | 0.0 | 0.3262 | 0.9568 | | 0.8436 | 80.0 | 240 | 0.6195 | 0.4341 | 0.7438 | 0.9569 | nan | 0.5101 | 0.9776 | 0.0 | 0.3463 | 0.9559 | | 0.6172 | 83.33 | 250 | 0.6207 | 0.4323 | 0.7384 | 0.9570 | nan | 0.4987 | 0.9782 | 0.0 | 0.3409 | 0.9560 | | 0.6048 | 86.67 | 260 | 0.5949 | 0.4272 | 0.7136 | 0.9586 | nan | 0.4449 | 0.9824 | 0.0 | 0.3237 | 0.9578 | | 0.7887 | 90.0 | 270 | 0.6007 | 0.4308 | 0.7282 | 0.9580 | nan | 0.4760 | 0.9803 | 0.0 | 0.3353 | 0.9571 | | 0.605 | 93.33 | 280 | 0.5883 | 0.4284 | 0.7157 | 0.9589 | nan | 0.4489 | 0.9825 | 0.0 | 0.3271 | 0.9581 | | 0.5964 | 96.67 | 290 | 0.5872 | 0.4277 | 0.7134 | 0.9590 | nan | 0.4439 | 0.9828 | 0.0 | 0.3251 | 0.9581 | | 0.6097 | 100.0 | 300 | 0.5903 | 0.4300 | 0.7240 | 0.9582 | nan | 0.4669 | 0.9810 | 0.0 | 0.3325 | 0.9573 | | 0.5886 | 103.33 | 310 | 0.5710 | 0.4250 | 0.7035 | 0.9594 | nan | 0.4227 | 0.9843 | 0.0 | 0.3162 | 0.9586 | | 0.6079 | 106.67 | 320 | 0.5695 | 0.4277 | 0.7112 | 0.9594 | nan | 0.4390 | 0.9835 | 0.0 | 0.3245 | 0.9586 | | 0.8054 | 110.0 | 330 | 0.5746 | 0.4308 | 0.7237 | 0.9588 | nan | 0.4657 | 0.9816 | 0.0 | 0.3344 | 0.9579 | | 0.5496 | 113.33 | 340 | 0.5631 | 0.4285 | 0.7129 | 0.9595 | nan | 0.4424 | 0.9835 | 0.0 | 0.3269 | 0.9587 | | 0.6271 | 116.67 | 350 | 0.5761 | 0.4302 | 0.7214 | 0.9589 | nan | 0.4608 | 0.9819 | 0.0 | 0.3326 | 0.9580 | | 0.5511 | 120.0 | 360 | 0.5626 | 0.4261 | 0.7046 | 0.9598 | nan | 0.4247 | 0.9846 | 0.0 | 0.3192 | 0.9590 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_2

# dropoff-utcustom-train-SF-RGB-b0_2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.6222 - Mean Iou: 0.4086 - Mean Accuracy: 0.6638 - Overall Accuracy: 0.9583 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3408 - Accuracy Undropoff: 0.9869 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.2682 - Iou Undropoff: 0.9576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.1857 | 3.33 | 10 | 1.1215 | 0.0852 | 0.2039 | 0.0995 | nan | 0.3183 | 0.0894 | 0.0 | 0.1663 | 0.0893 | | 1.1597 | 6.67 | 20 | 1.1165 | 0.1437 | 0.3568 | 0.2326 | nan | 0.4930 | 0.2206 | 0.0 | 0.2108 | 0.2203 | | 1.1528 | 10.0 | 30 | 1.1040 | 0.1938 | 0.5140 | 0.3464 | nan | 0.6978 | 0.3301 | 0.0 | 0.2517 | 0.3297 | | 1.0852 | 13.33 | 40 | 1.0896 | 0.2243 | 0.6289 | 0.4219 | nan | 0.8561 | 0.4018 | 0.0 | 0.2717 | 0.4011 | | 1.0388 | 16.67 | 50 | 1.0511 | 0.2700 | 0.6748 | 0.5498 | nan | 0.8120 | 0.5377 | 0.0 | 0.2744 | 0.5357 | | 1.0426 | 20.0 | 60 | 1.0089 | 0.3147 | 0.6787 | 0.6640 | nan | 0.6949 | 0.6625 | 0.0 | 0.2857 | 0.6583 | | 0.9621 | 23.33 | 70 | 0.9921 | 0.3374 | 0.7060 | 0.7392 | nan | 0.6695 | 0.7424 | 0.0 | 0.2760 | 0.7361 | | 0.925 | 26.67 | 80 | 0.9464 | 0.3591 | 0.7031 | 0.7964 | nan | 0.6007 | 0.8054 | 0.0 | 0.2807 | 0.7965 | | 0.8872 | 30.0 | 90 | 0.8993 | 0.3858 | 0.7074 | 0.8676 | nan | 0.5316 | 0.8831 | 0.0 | 0.2888 | 0.8686 | | 0.8751 | 33.33 | 100 | 0.8974 | 0.3896 | 0.7177 | 0.8817 | nan | 0.5379 | 0.8976 | 0.0 | 0.2866 | 0.8822 | | 0.8571 | 36.67 | 110 | 0.8501 | 0.4028 | 0.7162 | 0.9122 | nan | 0.5011 | 0.9312 | 0.0 | 0.2953 | 0.9131 | | 0.8866 | 40.0 | 120 | 0.8434 | 0.4072 | 0.7240 | 0.9252 | nan | 0.5032 | 0.9448 | 0.0 | 0.2963 | 0.9254 | | 0.8127 | 43.33 | 130 | 0.7922 | 0.4142 | 0.7089 | 0.9404 | nan | 0.4548 | 0.9629 | 0.0 | 0.3025 | 0.9402 | | 0.8062 | 46.67 | 140 | 0.7917 | 0.4123 | 0.7103 | 0.9432 | nan | 0.4548 | 0.9658 | 0.0 | 0.2943 | 0.9425 | | 0.7512 | 50.0 | 150 | 0.7646 | 0.4142 | 0.7059 | 0.9478 | nan | 0.4404 | 0.9713 | 0.0 | 0.2955 | 0.9470 | | 0.7554 | 53.33 | 160 | 0.7497 | 0.4161 | 0.7001 | 0.9510 | nan | 0.4248 | 0.9754 | 0.0 | 0.2981 | 0.9502 | | 0.7468 | 56.67 | 170 | 0.7326 | 0.4177 | 0.6989 | 0.9535 | nan | 0.4195 | 0.9782 | 0.0 | 0.3005 | 0.9527 | | 0.6506 | 60.0 | 180 | 0.7184 | 0.4173 | 0.6992 | 0.9541 | nan | 0.4196 | 0.9789 | 0.0 | 0.2987 | 0.9533 | | 0.6761 | 63.33 | 190 | 0.7037 | 0.4142 | 0.6884 | 0.9546 | nan | 0.3964 | 0.9805 | 0.0 | 0.2886 | 0.9539 | | 0.7245 | 66.67 | 200 | 0.6960 | 0.4122 | 0.6821 | 0.9553 | nan | 0.3824 | 0.9818 | 0.0 | 0.2820 | 0.9545 | | 0.6514 | 70.0 | 210 | 0.6755 | 0.4104 | 0.6705 | 0.9573 | nan | 0.3559 | 0.9852 | 0.0 | 0.2746 | 0.9566 | | 0.6433 | 73.33 | 220 | 0.6804 | 0.4180 | 0.6954 | 0.9556 | nan | 0.4100 | 0.9809 | 0.0 | 0.2991 | 0.9548 | | 0.6686 | 76.67 | 230 | 0.6608 | 0.4107 | 0.6694 | 0.9578 | nan | 0.3531 | 0.9858 | 0.0 | 0.2749 | 0.9571 | | 0.9091 | 80.0 | 240 | 0.6701 | 0.4160 | 0.6922 | 0.9557 | nan | 0.4031 | 0.9813 | 0.0 | 0.2930 | 0.9549 | | 0.6346 | 83.33 | 250 | 0.6725 | 0.4166 | 0.6904 | 0.9563 | nan | 0.3987 | 0.9821 | 0.0 | 0.2944 | 0.9555 | | 0.6303 | 86.67 | 260 | 0.6460 | 0.4090 | 0.6670 | 0.9576 | nan | 0.3481 | 0.9858 | 0.0 | 0.2702 | 0.9569 | | 0.8923 | 90.0 | 270 | 0.6550 | 0.4131 | 0.6799 | 0.9568 | nan | 0.3760 | 0.9837 | 0.0 | 0.2832 | 0.9561 | | 0.6334 | 93.33 | 280 | 0.6468 | 0.4100 | 0.6708 | 0.9572 | nan | 0.3566 | 0.9851 | 0.0 | 0.2734 | 0.9565 | | 0.6242 | 96.67 | 290 | 0.6483 | 0.4106 | 0.6728 | 0.9572 | nan | 0.3607 | 0.9848 | 0.0 | 0.2754 | 0.9565 | | 0.7401 | 100.0 | 300 | 0.6470 | 0.4129 | 0.6796 | 0.9569 | nan | 0.3755 | 0.9838 | 0.0 | 0.2825 | 0.9561 | | 0.6148 | 103.33 | 310 | 0.6242 | 0.4081 | 0.6633 | 0.9582 | nan | 0.3397 | 0.9868 | 0.0 | 0.2668 | 0.9575 | | 0.6345 | 106.67 | 320 | 0.6287 | 0.4093 | 0.6670 | 0.9579 | nan | 0.3478 | 0.9862 | 0.0 | 0.2708 | 0.9573 | | 0.8711 | 110.0 | 330 | 0.6396 | 0.4130 | 0.6782 | 0.9572 | nan | 0.3720 | 0.9843 | 0.0 | 0.2826 | 0.9565 | | 0.5812 | 113.33 | 340 | 0.6266 | 0.4101 | 0.6689 | 0.9580 | nan | 0.3517 | 0.9861 | 0.0 | 0.2731 | 0.9573 | | 0.6503 | 116.67 | 350 | 0.6384 | 0.4130 | 0.6775 | 0.9573 | nan | 0.3706 | 0.9845 | 0.0 | 0.2824 | 0.9566 | | 0.5923 | 120.0 | 360 | 0.6222 | 0.4086 | 0.6638 | 0.9583 | nan | 0.3408 | 0.9869 | 0.0 | 0.2682 | 0.9576 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_3

# dropoff-utcustom-train-SF-RGB-b0_3 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3958 - Mean Iou: 0.6134 - Mean Accuracy: 0.6480 - Overall Accuracy: 0.9627 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3026 - Accuracy Undropoff: 0.9933 - Iou Unlabeled: nan - Iou Dropoff: 0.2645 - Iou Undropoff: 0.9622 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.1015 | 3.33 | 10 | 1.0990 | 0.1184 | 0.4572 | 0.3294 | nan | 0.5975 | 0.3170 | 0.0 | 0.0427 | 0.3124 | | 1.0478 | 6.67 | 20 | 1.0756 | 0.2121 | 0.7082 | 0.5654 | nan | 0.8648 | 0.5515 | 0.0 | 0.0879 | 0.5482 | | 1.0451 | 10.0 | 30 | 1.0269 | 0.2846 | 0.8053 | 0.7334 | nan | 0.8842 | 0.7264 | 0.0 | 0.1313 | 0.7226 | | 0.9095 | 13.33 | 40 | 0.9476 | 0.3360 | 0.7905 | 0.8411 | nan | 0.7349 | 0.8460 | 0.0 | 0.1723 | 0.8358 | | 0.8091 | 16.67 | 50 | 0.8425 | 0.3858 | 0.7645 | 0.9167 | nan | 0.5975 | 0.9315 | 0.0 | 0.2429 | 0.9145 | | 0.8094 | 20.0 | 60 | 0.7489 | 0.4090 | 0.7445 | 0.9417 | nan | 0.5281 | 0.9608 | 0.0 | 0.2866 | 0.9403 | | 0.6945 | 23.33 | 70 | 0.7005 | 0.4148 | 0.7472 | 0.9453 | nan | 0.5298 | 0.9646 | 0.0 | 0.3004 | 0.9440 | | 0.6337 | 26.67 | 80 | 0.6331 | 0.6267 | 0.7334 | 0.9499 | nan | 0.4958 | 0.9709 | nan | 0.3047 | 0.9488 | | 0.603 | 30.0 | 90 | 0.5726 | 0.6222 | 0.6935 | 0.9559 | nan | 0.4057 | 0.9814 | nan | 0.2894 | 0.9551 | | 0.5903 | 33.33 | 100 | 0.5841 | 0.6248 | 0.7151 | 0.9526 | nan | 0.4546 | 0.9757 | nan | 0.2980 | 0.9516 | | 0.5514 | 36.67 | 110 | 0.5157 | 0.6227 | 0.6818 | 0.9585 | nan | 0.3781 | 0.9854 | nan | 0.2875 | 0.9578 | | 0.6464 | 40.0 | 120 | 0.5141 | 0.6240 | 0.6889 | 0.9575 | nan | 0.3941 | 0.9836 | nan | 0.2912 | 0.9568 | | 0.5198 | 43.33 | 130 | 0.4890 | 0.4141 | 0.6762 | 0.9591 | nan | 0.3657 | 0.9866 | 0.0 | 0.2838 | 0.9585 | | 0.5077 | 46.67 | 140 | 0.4855 | 0.4118 | 0.6719 | 0.9588 | nan | 0.3572 | 0.9866 | 0.0 | 0.2773 | 0.9581 | | 0.4817 | 50.0 | 150 | 0.4710 | 0.6182 | 0.6733 | 0.9587 | nan | 0.3602 | 0.9864 | nan | 0.2784 | 0.9580 | | 0.4713 | 53.33 | 160 | 0.4669 | 0.6196 | 0.6683 | 0.9603 | nan | 0.3479 | 0.9887 | nan | 0.2795 | 0.9597 | | 0.4516 | 56.67 | 170 | 0.4486 | 0.4107 | 0.6586 | 0.9612 | nan | 0.3265 | 0.9906 | 0.0 | 0.2715 | 0.9606 | | 0.4059 | 60.0 | 180 | 0.4361 | 0.6136 | 0.6548 | 0.9612 | nan | 0.3187 | 0.9909 | nan | 0.2665 | 0.9606 | | 0.4142 | 63.33 | 190 | 0.4267 | 0.6115 | 0.6503 | 0.9615 | nan | 0.3089 | 0.9917 | nan | 0.2621 | 0.9610 | | 0.4393 | 66.67 | 200 | 0.4188 | 0.6035 | 0.6354 | 0.9623 | nan | 0.2768 | 0.9940 | nan | 0.2452 | 0.9618 | | 0.4071 | 70.0 | 210 | 0.4224 | 0.6137 | 0.6528 | 0.9617 | nan | 0.3138 | 0.9917 | nan | 0.2663 | 0.9612 | | 0.4009 | 73.33 | 220 | 0.4205 | 0.6136 | 0.6540 | 0.9614 | nan | 0.3167 | 0.9912 | nan | 0.2664 | 0.9608 | | 0.4043 | 76.67 | 230 | 0.4148 | 0.6132 | 0.6514 | 0.9619 | nan | 0.3108 | 0.9920 | nan | 0.2651 | 0.9613 | | 0.6302 | 80.0 | 240 | 0.4116 | 0.6133 | 0.6513 | 0.9619 | nan | 0.3105 | 0.9921 | nan | 0.2653 | 0.9614 | | 0.3859 | 83.33 | 250 | 0.4113 | 0.6141 | 0.6543 | 0.9615 | nan | 0.3174 | 0.9913 | nan | 0.2673 | 0.9609 | | 0.3791 | 86.67 | 260 | 0.4033 | 0.6042 | 0.6361 | 0.9623 | nan | 0.2782 | 0.9940 | nan | 0.2465 | 0.9619 | | 0.5716 | 90.0 | 270 | 0.4088 | 0.6168 | 0.6575 | 0.9617 | nan | 0.3237 | 0.9913 | nan | 0.2724 | 0.9612 | | 0.3803 | 93.33 | 280 | 0.4024 | 0.6171 | 0.6565 | 0.9621 | nan | 0.3211 | 0.9918 | nan | 0.2727 | 0.9615 | | 0.371 | 96.67 | 290 | 0.3979 | 0.6166 | 0.6539 | 0.9625 | nan | 0.3154 | 0.9925 | nan | 0.2713 | 0.9620 | | 0.3656 | 100.0 | 300 | 0.3992 | 0.6204 | 0.6615 | 0.9621 | nan | 0.3316 | 0.9913 | nan | 0.2793 | 0.9615 | | 0.3674 | 103.33 | 310 | 0.3930 | 0.6110 | 0.6433 | 0.9630 | nan | 0.2925 | 0.9941 | nan | 0.2594 | 0.9625 | | 0.378 | 106.67 | 320 | 0.3925 | 0.6124 | 0.6459 | 0.9629 | nan | 0.2981 | 0.9937 | nan | 0.2623 | 0.9624 | | 0.5766 | 110.0 | 330 | 0.3965 | 0.6192 | 0.6594 | 0.9621 | nan | 0.3272 | 0.9916 | nan | 0.2768 | 0.9616 | | 0.3513 | 113.33 | 340 | 0.3927 | 0.6161 | 0.6523 | 0.9627 | nan | 0.3118 | 0.9928 | nan | 0.2701 | 0.9622 | | 0.3731 | 116.67 | 350 | 0.3975 | 0.6200 | 0.6613 | 0.9620 | nan | 0.3315 | 0.9912 | nan | 0.2785 | 0.9614 | | 0.3489 | 120.0 | 360 | 0.3958 | 0.6134 | 0.6480 | 0.9627 | nan | 0.3026 | 0.9933 | nan | 0.2645 | 0.9622 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_4

# dropoff-utcustom-train-SF-RGB-b0_4 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3032 - Mean Iou: 0.6301 - Mean Accuracy: 0.6710 - Overall Accuracy: 0.9634 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3502 - Accuracy Undropoff: 0.9918 - Iou Unlabeled: nan - Iou Dropoff: 0.2973 - Iou Undropoff: 0.9628 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0311 | 3.33 | 10 | 1.0742 | 0.2063 | 0.6373 | 0.5492 | nan | 0.7339 | 0.5406 | 0.0 | 0.0848 | 0.5342 | | 0.9741 | 6.67 | 20 | 1.0151 | 0.3072 | 0.8067 | 0.7686 | nan | 0.8485 | 0.7649 | 0.0 | 0.1619 | 0.7596 | | 0.9441 | 10.0 | 30 | 0.9345 | 0.3432 | 0.8327 | 0.8408 | nan | 0.8239 | 0.8416 | 0.0 | 0.1947 | 0.8348 | | 0.8222 | 13.33 | 40 | 0.8358 | 0.3643 | 0.8236 | 0.8773 | nan | 0.7646 | 0.8825 | 0.0 | 0.2199 | 0.8731 | | 0.7243 | 16.67 | 50 | 0.7135 | 0.3924 | 0.7838 | 0.9194 | nan | 0.6350 | 0.9325 | 0.0 | 0.2603 | 0.9170 | | 0.7213 | 20.0 | 60 | 0.6358 | 0.4054 | 0.7528 | 0.9374 | nan | 0.5502 | 0.9554 | 0.0 | 0.2805 | 0.9359 | | 0.5836 | 23.33 | 70 | 0.5604 | 0.4211 | 0.7412 | 0.9505 | nan | 0.5115 | 0.9708 | 0.0 | 0.3139 | 0.9493 | | 0.5285 | 26.67 | 80 | 0.5227 | 0.4281 | 0.7570 | 0.9519 | nan | 0.5432 | 0.9708 | 0.0 | 0.3335 | 0.9507 | | 0.4955 | 30.0 | 90 | 0.4478 | 0.4191 | 0.6945 | 0.9581 | nan | 0.4052 | 0.9837 | 0.0 | 0.2999 | 0.9573 | | 0.4646 | 33.33 | 100 | 0.4537 | 0.4215 | 0.6998 | 0.9584 | nan | 0.4161 | 0.9835 | 0.0 | 0.3069 | 0.9576 | | 0.4356 | 36.67 | 110 | 0.4454 | 0.4224 | 0.7105 | 0.9569 | nan | 0.4402 | 0.9808 | 0.0 | 0.3112 | 0.9560 | | 0.4829 | 40.0 | 120 | 0.4099 | 0.4196 | 0.6901 | 0.9593 | nan | 0.3947 | 0.9854 | 0.0 | 0.3002 | 0.9585 | | 0.4051 | 43.33 | 130 | 0.3911 | 0.6267 | 0.6784 | 0.9607 | nan | 0.3687 | 0.9881 | nan | 0.2933 | 0.9600 | | 0.3916 | 46.67 | 140 | 0.3841 | 0.4183 | 0.6897 | 0.9586 | nan | 0.3946 | 0.9847 | 0.0 | 0.2969 | 0.9579 | | 0.3713 | 50.0 | 150 | 0.3788 | 0.4248 | 0.7001 | 0.9600 | nan | 0.4149 | 0.9853 | 0.0 | 0.3150 | 0.9593 | | 0.359 | 53.33 | 160 | 0.3719 | 0.6254 | 0.6761 | 0.9607 | nan | 0.3639 | 0.9883 | nan | 0.2908 | 0.9601 | | 0.3459 | 56.67 | 170 | 0.3610 | 0.6245 | 0.6774 | 0.9601 | nan | 0.3673 | 0.9876 | nan | 0.2895 | 0.9594 | | 0.3099 | 60.0 | 180 | 0.3455 | 0.6246 | 0.6687 | 0.9620 | nan | 0.3468 | 0.9905 | nan | 0.2879 | 0.9614 | | 0.3124 | 63.33 | 190 | 0.3436 | 0.6277 | 0.6763 | 0.9615 | nan | 0.3634 | 0.9892 | nan | 0.2946 | 0.9608 | | 0.3283 | 66.67 | 200 | 0.3344 | 0.6237 | 0.6607 | 0.9634 | nan | 0.3286 | 0.9928 | nan | 0.2845 | 0.9629 | | 0.2974 | 70.0 | 210 | 0.3412 | 0.6312 | 0.6817 | 0.9616 | nan | 0.3746 | 0.9888 | nan | 0.3014 | 0.9609 | | 0.3003 | 73.33 | 220 | 0.3322 | 0.6320 | 0.6877 | 0.9607 | nan | 0.3881 | 0.9872 | nan | 0.3041 | 0.9600 | | 0.2968 | 76.67 | 230 | 0.3289 | 0.6344 | 0.6807 | 0.9628 | nan | 0.3712 | 0.9902 | nan | 0.3066 | 0.9622 | | 0.4415 | 80.0 | 240 | 0.3333 | 0.6320 | 0.6800 | 0.9622 | nan | 0.3705 | 0.9896 | nan | 0.3024 | 0.9615 | | 0.2836 | 83.33 | 250 | 0.3271 | 0.6287 | 0.6757 | 0.9619 | nan | 0.3617 | 0.9897 | nan | 0.2960 | 0.9613 | | 0.2762 | 86.67 | 260 | 0.3203 | 0.6263 | 0.6673 | 0.9629 | nan | 0.3429 | 0.9916 | nan | 0.2903 | 0.9623 | | 0.3901 | 90.0 | 270 | 0.3186 | 0.6290 | 0.6787 | 0.9614 | nan | 0.3685 | 0.9889 | nan | 0.2971 | 0.9608 | | 0.2755 | 93.33 | 280 | 0.3086 | 0.6283 | 0.6693 | 0.9631 | nan | 0.3468 | 0.9917 | nan | 0.2940 | 0.9625 | | 0.2652 | 96.67 | 290 | 0.3099 | 0.6302 | 0.6779 | 0.9620 | nan | 0.3661 | 0.9896 | nan | 0.2991 | 0.9614 | | 0.2627 | 100.0 | 300 | 0.3056 | 0.6294 | 0.6728 | 0.9627 | nan | 0.3548 | 0.9909 | nan | 0.2966 | 0.9622 | | 0.2647 | 103.33 | 310 | 0.3036 | 0.6292 | 0.6689 | 0.9635 | nan | 0.3458 | 0.9921 | nan | 0.2954 | 0.9629 | | 0.2697 | 106.67 | 320 | 0.3043 | 0.6298 | 0.6713 | 0.9632 | nan | 0.3510 | 0.9916 | nan | 0.2970 | 0.9626 | | 0.3878 | 110.0 | 330 | 0.3037 | 0.6297 | 0.6740 | 0.9626 | nan | 0.3573 | 0.9907 | nan | 0.2973 | 0.9620 | | 0.2521 | 113.33 | 340 | 0.3013 | 0.6300 | 0.6714 | 0.9633 | nan | 0.3513 | 0.9916 | nan | 0.2974 | 0.9627 | | 0.2663 | 116.67 | 350 | 0.3060 | 0.6298 | 0.6766 | 0.9621 | nan | 0.3634 | 0.9899 | nan | 0.2981 | 0.9615 | | 0.2507 | 120.0 | 360 | 0.3032 | 0.6301 | 0.6710 | 0.9634 | nan | 0.3502 | 0.9918 | nan | 0.2973 | 0.9628 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_5

# dropoff-utcustom-train-SF-RGB-b0_5 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2543 - Mean Iou: 0.6541 - Mean Accuracy: 0.6937 - Overall Accuracy: 0.9665 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3944 - Accuracy Undropoff: 0.9930 - Iou Unlabeled: nan - Iou Dropoff: 0.3424 - Iou Undropoff: 0.9659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.2123 | 3.33 | 10 | 1.1206 | 0.0793 | 0.1898 | 0.1888 | nan | 0.1908 | 0.1887 | 0.0 | 0.0494 | 0.1886 | | 1.0927 | 6.67 | 20 | 1.0985 | 0.2196 | 0.5875 | 0.5351 | nan | 0.6450 | 0.5300 | 0.0 | 0.1290 | 0.5298 | | 1.0578 | 10.0 | 30 | 0.9786 | 0.3662 | 0.7562 | 0.8622 | nan | 0.6400 | 0.8725 | 0.0 | 0.2367 | 0.8621 | | 0.788 | 13.33 | 40 | 0.7940 | 0.4289 | 0.7505 | 0.9456 | nan | 0.5365 | 0.9646 | 0.0 | 0.3398 | 0.9468 | | 0.6353 | 16.67 | 50 | 0.6206 | 0.4182 | 0.6840 | 0.9583 | nan | 0.3830 | 0.9850 | 0.0 | 0.2966 | 0.9581 | | 0.6944 | 20.0 | 60 | 0.5213 | 0.4211 | 0.6766 | 0.9623 | nan | 0.3631 | 0.9901 | 0.0 | 0.3014 | 0.9620 | | 0.5046 | 23.33 | 70 | 0.4765 | 0.4239 | 0.6796 | 0.9634 | nan | 0.3683 | 0.9910 | 0.0 | 0.3090 | 0.9628 | | 0.4684 | 26.67 | 80 | 0.4643 | 0.3982 | 0.6347 | 0.9598 | nan | 0.2779 | 0.9914 | 0.0 | 0.2352 | 0.9593 | | 0.4401 | 30.0 | 90 | 0.4483 | 0.4110 | 0.6507 | 0.9632 | nan | 0.3077 | 0.9936 | 0.0 | 0.2703 | 0.9627 | | 0.4268 | 33.33 | 100 | 0.4366 | 0.6489 | 0.7001 | 0.9638 | nan | 0.4108 | 0.9895 | nan | 0.3347 | 0.9632 | | 0.3939 | 36.67 | 110 | 0.4027 | 0.4272 | 0.6798 | 0.9650 | nan | 0.3670 | 0.9927 | 0.0 | 0.3171 | 0.9644 | | 0.4472 | 40.0 | 120 | 0.4159 | 0.6428 | 0.6896 | 0.9638 | nan | 0.3887 | 0.9905 | nan | 0.3225 | 0.9632 | | 0.3618 | 43.33 | 130 | 0.3765 | 0.6325 | 0.6671 | 0.9650 | nan | 0.3402 | 0.9939 | nan | 0.3006 | 0.9644 | | 0.3456 | 46.67 | 140 | 0.3671 | 0.6395 | 0.6816 | 0.9643 | nan | 0.3715 | 0.9917 | nan | 0.3153 | 0.9637 | | 0.3352 | 50.0 | 150 | 0.3572 | 0.6431 | 0.6839 | 0.9650 | nan | 0.3755 | 0.9923 | nan | 0.3218 | 0.9644 | | 0.3143 | 53.33 | 160 | 0.3451 | 0.6351 | 0.6702 | 0.9651 | nan | 0.3467 | 0.9938 | nan | 0.3056 | 0.9646 | | 0.3009 | 56.67 | 170 | 0.3357 | 0.6449 | 0.6941 | 0.9636 | nan | 0.3984 | 0.9898 | nan | 0.3267 | 0.9630 | | 0.2765 | 60.0 | 180 | 0.3188 | 0.6458 | 0.6934 | 0.9641 | nan | 0.3965 | 0.9903 | nan | 0.3282 | 0.9634 | | 0.2703 | 63.33 | 190 | 0.3179 | 0.6385 | 0.6732 | 0.9656 | nan | 0.3525 | 0.9940 | nan | 0.3119 | 0.9650 | | 0.2746 | 66.67 | 200 | 0.3067 | 0.6385 | 0.6702 | 0.9662 | nan | 0.3456 | 0.9949 | nan | 0.3113 | 0.9656 | | 0.2516 | 70.0 | 210 | 0.2992 | 0.6569 | 0.6968 | 0.9667 | nan | 0.4008 | 0.9929 | nan | 0.3477 | 0.9661 | | 0.2503 | 73.33 | 220 | 0.2999 | 0.6671 | 0.7198 | 0.9659 | nan | 0.4497 | 0.9899 | nan | 0.3689 | 0.9652 | | 0.2443 | 76.67 | 230 | 0.2816 | 0.6439 | 0.6750 | 0.9668 | nan | 0.3547 | 0.9952 | nan | 0.3215 | 0.9663 | | 0.3757 | 80.0 | 240 | 0.2907 | 0.6593 | 0.7063 | 0.9659 | nan | 0.4215 | 0.9911 | nan | 0.3535 | 0.9652 | | 0.2306 | 83.33 | 250 | 0.2767 | 0.6439 | 0.6807 | 0.9658 | nan | 0.3680 | 0.9935 | nan | 0.3226 | 0.9652 | | 0.2216 | 86.67 | 260 | 0.2792 | 0.6583 | 0.7018 | 0.9663 | nan | 0.4115 | 0.9920 | nan | 0.3509 | 0.9657 | | 0.3202 | 90.0 | 270 | 0.2681 | 0.6425 | 0.6789 | 0.9657 | nan | 0.3642 | 0.9936 | nan | 0.3199 | 0.9652 | | 0.2174 | 93.33 | 280 | 0.2633 | 0.6467 | 0.6860 | 0.9657 | nan | 0.3791 | 0.9928 | nan | 0.3284 | 0.9651 | | 0.2086 | 96.67 | 290 | 0.2658 | 0.6476 | 0.6900 | 0.9652 | nan | 0.3880 | 0.9920 | nan | 0.3306 | 0.9646 | | 0.2042 | 100.0 | 300 | 0.2651 | 0.6486 | 0.6898 | 0.9655 | nan | 0.3873 | 0.9923 | nan | 0.3322 | 0.9649 | | 0.2071 | 103.33 | 310 | 0.2597 | 0.6445 | 0.6792 | 0.9662 | nan | 0.3643 | 0.9941 | nan | 0.3233 | 0.9657 | | 0.2097 | 106.67 | 320 | 0.2596 | 0.6615 | 0.7062 | 0.9665 | nan | 0.4206 | 0.9918 | nan | 0.3571 | 0.9658 | | 0.3118 | 110.0 | 330 | 0.2557 | 0.6516 | 0.6928 | 0.9659 | nan | 0.3931 | 0.9924 | nan | 0.3380 | 0.9653 | | 0.1956 | 113.33 | 340 | 0.2517 | 0.6494 | 0.6865 | 0.9664 | nan | 0.3794 | 0.9936 | nan | 0.3331 | 0.9658 | | 0.201 | 116.67 | 350 | 0.2570 | 0.6573 | 0.7032 | 0.9658 | nan | 0.4151 | 0.9913 | nan | 0.3494 | 0.9651 | | 0.1952 | 120.0 | 360 | 0.2543 | 0.6541 | 0.6937 | 0.9665 | nan | 0.3944 | 0.9930 | nan | 0.3424 | 0.9659 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_6

# dropoff-utcustom-train-SF-RGB-b0_6 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1833 - Mean Iou: 0.6595 - Mean Accuracy: 0.7018 - Overall Accuracy: 0.9666 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4113 - Accuracy Undropoff: 0.9924 - Iou Unlabeled: nan - Iou Dropoff: 0.3531 - Iou Undropoff: 0.9660 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.1234 | 3.33 | 10 | 1.0973 | 0.1779 | 0.5629 | 0.3723 | nan | 0.7720 | 0.3538 | 0.0 | 0.1801 | 0.3536 | | 0.975 | 6.67 | 20 | 1.0260 | 0.3499 | 0.8180 | 0.8109 | nan | 0.8259 | 0.8102 | 0.0 | 0.2428 | 0.8069 | | 0.9464 | 10.0 | 30 | 0.8130 | 0.4297 | 0.7456 | 0.9502 | nan | 0.5212 | 0.9700 | 0.0 | 0.3385 | 0.9507 | | 0.6167 | 13.33 | 40 | 0.6001 | 0.4451 | 0.7438 | 0.9617 | nan | 0.5048 | 0.9829 | 0.0 | 0.3743 | 0.9610 | | 0.4818 | 16.67 | 50 | 0.4629 | 0.4491 | 0.7237 | 0.9666 | nan | 0.4573 | 0.9902 | 0.0 | 0.3815 | 0.9659 | | 0.4733 | 20.0 | 60 | 0.4379 | 0.4335 | 0.7067 | 0.9630 | nan | 0.4256 | 0.9879 | 0.0 | 0.3383 | 0.9623 | | 0.3843 | 23.33 | 70 | 0.4073 | 0.4310 | 0.6872 | 0.9652 | nan | 0.3821 | 0.9922 | 0.0 | 0.3283 | 0.9646 | | 0.3579 | 26.67 | 80 | 0.3731 | 0.4354 | 0.6999 | 0.9651 | nan | 0.4090 | 0.9908 | 0.0 | 0.3418 | 0.9644 | | 0.3212 | 30.0 | 90 | 0.3655 | 0.6589 | 0.7129 | 0.9647 | nan | 0.4366 | 0.9892 | nan | 0.3538 | 0.9640 | | 0.3088 | 33.33 | 100 | 0.3306 | 0.6310 | 0.6689 | 0.9641 | nan | 0.3451 | 0.9928 | nan | 0.2985 | 0.9635 | | 0.2825 | 36.67 | 110 | 0.3253 | 0.6633 | 0.7103 | 0.9663 | nan | 0.4293 | 0.9912 | nan | 0.3609 | 0.9657 | | 0.3029 | 40.0 | 120 | 0.3130 | 0.6556 | 0.7079 | 0.9645 | nan | 0.4264 | 0.9895 | nan | 0.3474 | 0.9638 | | 0.252 | 43.33 | 130 | 0.2898 | 0.6703 | 0.7310 | 0.9652 | nan | 0.4740 | 0.9880 | nan | 0.3762 | 0.9645 | | 0.2395 | 46.67 | 140 | 0.2843 | 0.6587 | 0.7088 | 0.9653 | nan | 0.4275 | 0.9902 | nan | 0.3527 | 0.9646 | | 0.2308 | 50.0 | 150 | 0.2744 | 0.6481 | 0.6870 | 0.9659 | nan | 0.3811 | 0.9930 | nan | 0.3309 | 0.9653 | | 0.2125 | 53.33 | 160 | 0.2579 | 0.6555 | 0.7028 | 0.9653 | nan | 0.4147 | 0.9909 | nan | 0.3464 | 0.9647 | | 0.1953 | 56.67 | 170 | 0.2551 | 0.6549 | 0.7054 | 0.9647 | nan | 0.4209 | 0.9899 | nan | 0.3458 | 0.9641 | | 0.1743 | 60.0 | 180 | 0.2377 | 0.6393 | 0.6768 | 0.9651 | nan | 0.3605 | 0.9931 | nan | 0.3140 | 0.9646 | | 0.17 | 63.33 | 190 | 0.2342 | 0.6564 | 0.7002 | 0.9660 | nan | 0.4086 | 0.9918 | nan | 0.3474 | 0.9654 | | 0.173 | 66.67 | 200 | 0.2296 | 0.6629 | 0.7095 | 0.9664 | nan | 0.4277 | 0.9913 | nan | 0.3602 | 0.9657 | | 0.1487 | 70.0 | 210 | 0.2152 | 0.6525 | 0.6861 | 0.9673 | nan | 0.3777 | 0.9946 | nan | 0.3383 | 0.9667 | | 0.1501 | 73.33 | 220 | 0.2179 | 0.6593 | 0.7019 | 0.9665 | nan | 0.4116 | 0.9923 | nan | 0.3527 | 0.9659 | | 0.1419 | 76.67 | 230 | 0.2055 | 0.6605 | 0.7057 | 0.9663 | nan | 0.4199 | 0.9916 | nan | 0.3553 | 0.9656 | | 0.2049 | 80.0 | 240 | 0.2060 | 0.6563 | 0.7004 | 0.9659 | nan | 0.4091 | 0.9917 | nan | 0.3472 | 0.9653 | | 0.1339 | 83.33 | 250 | 0.2006 | 0.6514 | 0.6921 | 0.9660 | nan | 0.3916 | 0.9926 | nan | 0.3375 | 0.9654 | | 0.1262 | 86.67 | 260 | 0.1963 | 0.6559 | 0.7033 | 0.9654 | nan | 0.4158 | 0.9908 | nan | 0.3470 | 0.9647 | | 0.179 | 90.0 | 270 | 0.1907 | 0.6549 | 0.6976 | 0.9660 | nan | 0.4032 | 0.9921 | nan | 0.3445 | 0.9654 | | 0.1216 | 93.33 | 280 | 0.1901 | 0.6561 | 0.6994 | 0.9661 | nan | 0.4068 | 0.9920 | nan | 0.3468 | 0.9655 | | 0.1144 | 96.67 | 290 | 0.1917 | 0.6565 | 0.7017 | 0.9658 | nan | 0.4119 | 0.9915 | nan | 0.3478 | 0.9652 | | 0.1095 | 100.0 | 300 | 0.1900 | 0.6621 | 0.7108 | 0.9659 | nan | 0.4309 | 0.9907 | nan | 0.3590 | 0.9653 | | 0.1144 | 103.33 | 310 | 0.1848 | 0.6595 | 0.6994 | 0.9670 | nan | 0.4058 | 0.9930 | nan | 0.3526 | 0.9664 | | 0.1144 | 106.67 | 320 | 0.1849 | 0.6585 | 0.7011 | 0.9665 | nan | 0.4100 | 0.9922 | nan | 0.3512 | 0.9658 | | 0.1574 | 110.0 | 330 | 0.1852 | 0.6592 | 0.7025 | 0.9664 | nan | 0.4128 | 0.9921 | nan | 0.3526 | 0.9658 | | 0.1085 | 113.33 | 340 | 0.1819 | 0.6595 | 0.7016 | 0.9667 | nan | 0.4108 | 0.9924 | nan | 0.3530 | 0.9660 | | 0.1099 | 116.67 | 350 | 0.1856 | 0.6602 | 0.7057 | 0.9662 | nan | 0.4198 | 0.9915 | nan | 0.3548 | 0.9656 | | 0.1048 | 120.0 | 360 | 0.1833 | 0.6595 | 0.7018 | 0.9666 | nan | 0.4113 | 0.9924 | nan | 0.3531 | 0.9660 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b0_7

# dropoff-utcustom-train-SF-RGB-b0_7 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1457 - Mean Iou: 0.6795 - Mean Accuracy: 0.7207 - Overall Accuracy: 0.9691 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4481 - Accuracy Undropoff: 0.9932 - Iou Unlabeled: nan - Iou Dropoff: 0.3907 - Iou Undropoff: 0.9684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.1505 | 3.33 | 10 | 1.1103 | 0.1106 | 0.6036 | 0.2919 | nan | 0.9456 | 0.2616 | 0.0 | 0.0703 | 0.2616 | | 0.9635 | 6.67 | 20 | 1.0114 | 0.3710 | 0.8470 | 0.8737 | nan | 0.8177 | 0.8763 | 0.0 | 0.2435 | 0.8694 | | 0.9358 | 10.0 | 30 | 0.8242 | 0.4206 | 0.7727 | 0.9440 | nan | 0.5848 | 0.9606 | 0.0 | 0.3194 | 0.9425 | | 0.579 | 13.33 | 40 | 0.5703 | 0.4525 | 0.7615 | 0.9633 | nan | 0.5402 | 0.9829 | 0.0 | 0.3951 | 0.9624 | | 0.4411 | 16.67 | 50 | 0.4166 | 0.4529 | 0.7380 | 0.9667 | nan | 0.4872 | 0.9889 | 0.0 | 0.3928 | 0.9659 | | 0.4311 | 20.0 | 60 | 0.3843 | 0.6678 | 0.7156 | 0.9667 | nan | 0.4400 | 0.9911 | nan | 0.3695 | 0.9661 | | 0.3437 | 23.33 | 70 | 0.3590 | 0.4347 | 0.6956 | 0.9655 | nan | 0.3995 | 0.9918 | 0.0 | 0.3392 | 0.9649 | | 0.3136 | 26.67 | 80 | 0.3198 | 0.6259 | 0.6622 | 0.9638 | nan | 0.3312 | 0.9931 | nan | 0.2885 | 0.9633 | | 0.2682 | 30.0 | 90 | 0.2919 | 0.6187 | 0.6470 | 0.9648 | nan | 0.2984 | 0.9957 | nan | 0.2730 | 0.9643 | | 0.2521 | 33.33 | 100 | 0.2957 | 0.6448 | 0.6845 | 0.9653 | nan | 0.3764 | 0.9926 | nan | 0.3248 | 0.9648 | | 0.2287 | 36.67 | 110 | 0.2747 | 0.6800 | 0.7256 | 0.9685 | nan | 0.4591 | 0.9921 | nan | 0.3922 | 0.9678 | | 0.2203 | 40.0 | 120 | 0.2537 | 0.7108 | 0.7687 | 0.9706 | nan | 0.5472 | 0.9902 | nan | 0.4517 | 0.9699 | | 0.1964 | 43.33 | 130 | 0.2356 | 0.6689 | 0.7054 | 0.9686 | nan | 0.4167 | 0.9941 | nan | 0.3699 | 0.9680 | | 0.1776 | 46.67 | 140 | 0.2205 | 0.6729 | 0.7137 | 0.9684 | nan | 0.4343 | 0.9931 | nan | 0.3780 | 0.9677 | | 0.1675 | 50.0 | 150 | 0.2061 | 0.6809 | 0.7244 | 0.9689 | nan | 0.4562 | 0.9926 | nan | 0.3936 | 0.9682 | | 0.148 | 53.33 | 160 | 0.1954 | 0.6924 | 0.7418 | 0.9694 | nan | 0.4920 | 0.9915 | nan | 0.4160 | 0.9687 | | 0.1364 | 56.67 | 170 | 0.1915 | 0.6869 | 0.7415 | 0.9681 | nan | 0.4928 | 0.9902 | nan | 0.4064 | 0.9674 | | 0.1171 | 60.0 | 180 | 0.1776 | 0.7206 | 0.7816 | 0.9714 | nan | 0.5734 | 0.9899 | nan | 0.4706 | 0.9707 | | 0.1169 | 63.33 | 190 | 0.1754 | 0.6580 | 0.6853 | 0.9689 | nan | 0.3741 | 0.9965 | nan | 0.3476 | 0.9684 | | 0.1178 | 66.67 | 200 | 0.1676 | 0.6783 | 0.7233 | 0.9684 | nan | 0.4545 | 0.9922 | nan | 0.3888 | 0.9677 | | 0.1016 | 70.0 | 210 | 0.1670 | 0.6633 | 0.6985 | 0.9682 | nan | 0.4025 | 0.9944 | nan | 0.3590 | 0.9676 | | 0.1025 | 73.33 | 220 | 0.1648 | 0.6789 | 0.7154 | 0.9696 | nan | 0.4366 | 0.9943 | nan | 0.3888 | 0.9690 | | 0.0956 | 76.67 | 230 | 0.1607 | 0.6684 | 0.7103 | 0.9677 | nan | 0.4279 | 0.9927 | nan | 0.3697 | 0.9671 | | 0.1443 | 80.0 | 240 | 0.1611 | 0.6747 | 0.7134 | 0.9688 | nan | 0.4332 | 0.9937 | nan | 0.3811 | 0.9682 | | 0.0902 | 83.33 | 250 | 0.1600 | 0.6713 | 0.7060 | 0.9691 | nan | 0.4174 | 0.9946 | nan | 0.3740 | 0.9685 | | 0.0846 | 86.67 | 260 | 0.1559 | 0.6772 | 0.7263 | 0.9677 | nan | 0.4613 | 0.9912 | nan | 0.3874 | 0.9670 | | 0.1166 | 90.0 | 270 | 0.1587 | 0.6615 | 0.6984 | 0.9677 | nan | 0.4030 | 0.9939 | nan | 0.3559 | 0.9671 | | 0.0825 | 93.33 | 280 | 0.1538 | 0.6684 | 0.7068 | 0.9682 | nan | 0.4199 | 0.9936 | nan | 0.3692 | 0.9676 | | 0.0769 | 96.67 | 290 | 0.1527 | 0.6649 | 0.7033 | 0.9679 | nan | 0.4130 | 0.9936 | nan | 0.3626 | 0.9673 | | 0.0722 | 100.0 | 300 | 0.1473 | 0.6832 | 0.7247 | 0.9694 | nan | 0.4563 | 0.9932 | nan | 0.3976 | 0.9688 | | 0.0779 | 103.33 | 310 | 0.1465 | 0.6809 | 0.7200 | 0.9695 | nan | 0.4462 | 0.9937 | nan | 0.3930 | 0.9689 | | 0.0771 | 106.67 | 320 | 0.1494 | 0.6673 | 0.7052 | 0.9682 | nan | 0.4167 | 0.9937 | nan | 0.3670 | 0.9676 | | 0.1082 | 110.0 | 330 | 0.1479 | 0.6753 | 0.7182 | 0.9683 | nan | 0.4438 | 0.9926 | nan | 0.3830 | 0.9677 | | 0.0726 | 113.33 | 340 | 0.1451 | 0.6765 | 0.7159 | 0.9689 | nan | 0.4384 | 0.9935 | nan | 0.3846 | 0.9683 | | 0.0743 | 116.67 | 350 | 0.1469 | 0.6814 | 0.7249 | 0.9689 | nan | 0.4571 | 0.9927 | nan | 0.3946 | 0.9683 | | 0.0703 | 120.0 | 360 | 0.1457 | 0.6795 | 0.7207 | 0.9691 | nan | 0.4481 | 0.9932 | nan | 0.3907 | 0.9684 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_1

# dropoff-utcustom-train-SF-RGBD-b0_1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4979 - Mean Iou: 0.4170 - Mean Accuracy: 0.6846 - Overall Accuracy: 0.9603 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3839 - Accuracy Undropoff: 0.9853 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.2914 - Iou Undropoff: 0.9597 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0495 | 5.0 | 10 | 1.0890 | 0.1852 | 0.3572 | 0.4990 | nan | 0.2026 | 0.5119 | 0.0 | 0.0474 | 0.5081 | | 0.9941 | 10.0 | 20 | 1.0479 | 0.3452 | 0.8357 | 0.8479 | nan | 0.8225 | 0.8490 | 0.0 | 0.1931 | 0.8425 | | 0.9448 | 15.0 | 30 | 0.9839 | 0.3790 | 0.8217 | 0.9010 | nan | 0.7351 | 0.9082 | 0.0 | 0.2390 | 0.8980 | | 0.8912 | 20.0 | 40 | 0.9041 | 0.3845 | 0.7150 | 0.9247 | nan | 0.4863 | 0.9437 | 0.0 | 0.2303 | 0.9233 | | 0.8458 | 25.0 | 50 | 0.7997 | 0.3835 | 0.6687 | 0.9326 | nan | 0.3808 | 0.9565 | 0.0 | 0.2188 | 0.9316 | | 0.8299 | 30.0 | 60 | 0.7387 | 0.3751 | 0.6333 | 0.9326 | nan | 0.3068 | 0.9597 | 0.0 | 0.1934 | 0.9318 | | 0.7518 | 35.0 | 70 | 0.6810 | 0.3791 | 0.6322 | 0.9404 | nan | 0.2961 | 0.9683 | 0.0 | 0.1975 | 0.9397 | | 0.6943 | 40.0 | 80 | 0.6322 | 0.3703 | 0.6069 | 0.9422 | nan | 0.2411 | 0.9726 | 0.0 | 0.1691 | 0.9417 | | 0.6617 | 45.0 | 90 | 0.6071 | 0.3780 | 0.6240 | 0.9454 | nan | 0.2734 | 0.9746 | 0.0 | 0.1892 | 0.9449 | | 0.634 | 50.0 | 100 | 0.5932 | 0.3765 | 0.6106 | 0.9497 | nan | 0.2407 | 0.9805 | 0.0 | 0.1802 | 0.9494 | | 0.6157 | 55.0 | 110 | 0.5829 | 0.3982 | 0.6538 | 0.9524 | nan | 0.3281 | 0.9795 | 0.0 | 0.2425 | 0.9520 | | 0.5814 | 60.0 | 120 | 0.5708 | 0.4038 | 0.6699 | 0.9533 | nan | 0.3608 | 0.9790 | 0.0 | 0.2586 | 0.9528 | | 0.5988 | 65.0 | 130 | 0.5575 | 0.3974 | 0.6456 | 0.9569 | nan | 0.3061 | 0.9851 | 0.0 | 0.2357 | 0.9564 | | 0.5583 | 70.0 | 140 | 0.5530 | 0.4224 | 0.7075 | 0.9576 | nan | 0.4346 | 0.9803 | 0.0 | 0.3103 | 0.9570 | | 0.5596 | 75.0 | 150 | 0.5264 | 0.4034 | 0.6522 | 0.9598 | nan | 0.3167 | 0.9877 | 0.0 | 0.2510 | 0.9593 | | 0.5524 | 80.0 | 160 | 0.5392 | 0.4208 | 0.7109 | 0.9567 | nan | 0.4429 | 0.9790 | 0.0 | 0.3065 | 0.9560 | | 0.5294 | 85.0 | 170 | 0.5257 | 0.4161 | 0.6913 | 0.9582 | nan | 0.4002 | 0.9824 | 0.0 | 0.2909 | 0.9576 | | 0.5477 | 90.0 | 180 | 0.5178 | 0.4207 | 0.6962 | 0.9591 | nan | 0.4095 | 0.9829 | 0.0 | 0.3035 | 0.9584 | | 0.528 | 95.0 | 190 | 0.5185 | 0.4183 | 0.6939 | 0.9590 | nan | 0.4047 | 0.9831 | 0.0 | 0.2965 | 0.9584 | | 0.5144 | 100.0 | 200 | 0.5004 | 0.4153 | 0.6788 | 0.9604 | nan | 0.3716 | 0.9860 | 0.0 | 0.2859 | 0.9599 | | 0.5313 | 105.0 | 210 | 0.5032 | 0.4199 | 0.7005 | 0.9585 | nan | 0.4191 | 0.9819 | 0.0 | 0.3020 | 0.9578 | | 0.5172 | 110.0 | 220 | 0.4993 | 0.4188 | 0.6931 | 0.9591 | nan | 0.4030 | 0.9832 | 0.0 | 0.2978 | 0.9585 | | 0.5124 | 115.0 | 230 | 0.4999 | 0.4167 | 0.6828 | 0.9606 | nan | 0.3799 | 0.9858 | 0.0 | 0.2901 | 0.9600 | | 0.5025 | 120.0 | 240 | 0.4979 | 0.4170 | 0.6846 | 0.9603 | nan | 0.3839 | 0.9853 | 0.0 | 0.2914 | 0.9597 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_2

# dropoff-utcustom-train-SF-RGBD-b0_2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4274 - Mean Iou: 0.6102 - Mean Accuracy: 0.6603 - Overall Accuracy: 0.9607 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3326 - Accuracy Undropoff: 0.9879 - Iou Unlabeled: nan - Iou Dropoff: 0.2602 - Iou Undropoff: 0.9601 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0555 | 5.0 | 10 | 1.0734 | 0.2254 | 0.4211 | 0.6018 | nan | 0.2240 | 0.6182 | 0.0 | 0.0622 | 0.6140 | | 0.9825 | 10.0 | 20 | 1.0261 | 0.2992 | 0.6380 | 0.7780 | nan | 0.4852 | 0.7907 | 0.0 | 0.1170 | 0.7807 | | 0.8991 | 15.0 | 30 | 0.8985 | 0.3231 | 0.5517 | 0.8892 | nan | 0.1836 | 0.9198 | 0.0 | 0.0776 | 0.8917 | | 0.8191 | 20.0 | 40 | 0.7413 | 0.3270 | 0.5262 | 0.9299 | nan | 0.0858 | 0.9665 | 0.0 | 0.0513 | 0.9296 | | 0.7562 | 25.0 | 50 | 0.6268 | 0.3259 | 0.5130 | 0.9436 | nan | 0.0433 | 0.9826 | 0.0 | 0.0343 | 0.9435 | | 0.7395 | 30.0 | 60 | 0.5872 | 0.3235 | 0.5073 | 0.9498 | nan | 0.0246 | 0.9900 | 0.0 | 0.0206 | 0.9498 | | 0.7272 | 35.0 | 70 | 0.5820 | 0.3379 | 0.5415 | 0.9411 | nan | 0.1055 | 0.9774 | 0.0 | 0.0729 | 0.9409 | | 0.6525 | 40.0 | 80 | 0.5571 | 0.3445 | 0.5451 | 0.9498 | nan | 0.1036 | 0.9865 | 0.0 | 0.0839 | 0.9496 | | 0.6161 | 45.0 | 90 | 0.5465 | 0.3480 | 0.5480 | 0.9528 | nan | 0.1064 | 0.9895 | 0.0 | 0.0914 | 0.9526 | | 0.6131 | 50.0 | 100 | 0.5379 | 0.3712 | 0.5917 | 0.9555 | nan | 0.1949 | 0.9885 | 0.0 | 0.1584 | 0.9551 | | 0.579 | 55.0 | 110 | 0.5229 | 0.3892 | 0.6411 | 0.9536 | nan | 0.3002 | 0.9819 | 0.0 | 0.2146 | 0.9530 | | 0.5133 | 60.0 | 120 | 0.5113 | 0.3962 | 0.6596 | 0.9541 | nan | 0.3384 | 0.9808 | 0.0 | 0.2352 | 0.9535 | | 0.535 | 65.0 | 130 | 0.4925 | 0.3981 | 0.6566 | 0.9561 | nan | 0.3299 | 0.9833 | 0.0 | 0.2386 | 0.9555 | | 0.4866 | 70.0 | 140 | 0.4717 | 0.5993 | 0.6516 | 0.9584 | nan | 0.3169 | 0.9863 | nan | 0.2407 | 0.9579 | | 0.5119 | 75.0 | 150 | 0.4712 | 0.5976 | 0.6513 | 0.9578 | nan | 0.3171 | 0.9856 | nan | 0.2380 | 0.9572 | | 0.5034 | 80.0 | 160 | 0.4737 | 0.6120 | 0.6840 | 0.9562 | nan | 0.3872 | 0.9808 | nan | 0.2686 | 0.9554 | | 0.4503 | 85.0 | 170 | 0.4496 | 0.6103 | 0.6618 | 0.9604 | nan | 0.3361 | 0.9875 | nan | 0.2607 | 0.9598 | | 0.4653 | 90.0 | 180 | 0.4617 | 0.6201 | 0.6907 | 0.9580 | nan | 0.3992 | 0.9822 | nan | 0.2830 | 0.9572 | | 0.4375 | 95.0 | 190 | 0.4412 | 0.6090 | 0.6592 | 0.9605 | nan | 0.3305 | 0.9878 | nan | 0.2580 | 0.9599 | | 0.4306 | 100.0 | 200 | 0.4355 | 0.6120 | 0.6653 | 0.9602 | nan | 0.3436 | 0.9870 | nan | 0.2643 | 0.9597 | | 0.4456 | 105.0 | 210 | 0.4414 | 0.6178 | 0.6756 | 0.9601 | nan | 0.3653 | 0.9860 | nan | 0.2760 | 0.9595 | | 0.4435 | 110.0 | 220 | 0.4387 | 0.6150 | 0.6681 | 0.9608 | nan | 0.3489 | 0.9873 | nan | 0.2699 | 0.9602 | | 0.4263 | 115.0 | 230 | 0.4348 | 0.6156 | 0.6692 | 0.9607 | nan | 0.3512 | 0.9872 | nan | 0.2711 | 0.9602 | | 0.4123 | 120.0 | 240 | 0.4274 | 0.6102 | 0.6603 | 0.9607 | nan | 0.3326 | 0.9879 | nan | 0.2602 | 0.9601 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_3

# dropoff-utcustom-train-SF-RGBD-b0_3 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3666 - Mean Iou: 0.6400 - Mean Accuracy: 0.7120 - Overall Accuracy: 0.9610 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4404 - Accuracy Undropoff: 0.9836 - Iou Unlabeled: nan - Iou Dropoff: 0.3196 - Iou Undropoff: 0.9603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0352 | 5.0 | 10 | 1.0676 | 0.2560 | 0.5776 | 0.7142 | nan | 0.4286 | 0.7266 | 0.0 | 0.0589 | 0.7090 | | 0.9564 | 10.0 | 20 | 0.9743 | 0.3355 | 0.5576 | 0.9248 | nan | 0.1571 | 0.9581 | 0.0 | 0.0822 | 0.9243 | | 0.8577 | 15.0 | 30 | 0.8504 | 0.3318 | 0.5283 | 0.9409 | nan | 0.0782 | 0.9784 | 0.0 | 0.0545 | 0.9407 | | 0.7512 | 20.0 | 40 | 0.6972 | 0.3270 | 0.5122 | 0.9527 | nan | 0.0318 | 0.9926 | 0.0 | 0.0283 | 0.9526 | | 0.6955 | 25.0 | 50 | 0.5761 | 0.3259 | 0.5099 | 0.9545 | nan | 0.0250 | 0.9948 | 0.0 | 0.0234 | 0.9544 | | 0.6691 | 30.0 | 60 | 0.5209 | 0.3360 | 0.5271 | 0.9525 | nan | 0.0632 | 0.9911 | 0.0 | 0.0557 | 0.9524 | | 0.626 | 35.0 | 70 | 0.5297 | 0.3408 | 0.5362 | 0.9505 | nan | 0.0844 | 0.9881 | 0.0 | 0.0719 | 0.9503 | | 0.5544 | 40.0 | 80 | 0.5263 | 0.3616 | 0.5757 | 0.9521 | nan | 0.1652 | 0.9862 | 0.0 | 0.1330 | 0.9518 | | 0.5316 | 45.0 | 90 | 0.4825 | 0.3836 | 0.6353 | 0.9506 | nan | 0.2915 | 0.9792 | 0.0 | 0.2009 | 0.9500 | | 0.4929 | 50.0 | 100 | 0.4763 | 0.3958 | 0.6588 | 0.9530 | nan | 0.3378 | 0.9797 | 0.0 | 0.2352 | 0.9524 | | 0.468 | 55.0 | 110 | 0.4583 | 0.4077 | 0.6974 | 0.9528 | nan | 0.4188 | 0.9759 | 0.0 | 0.2713 | 0.9519 | | 0.429 | 60.0 | 120 | 0.4268 | 0.3985 | 0.6526 | 0.9575 | nan | 0.3199 | 0.9852 | 0.0 | 0.2386 | 0.9569 | | 0.4211 | 65.0 | 130 | 0.3988 | 0.3951 | 0.6406 | 0.9584 | nan | 0.2939 | 0.9872 | 0.0 | 0.2275 | 0.9578 | | 0.3926 | 70.0 | 140 | 0.4085 | 0.4102 | 0.6780 | 0.9587 | nan | 0.3718 | 0.9842 | 0.0 | 0.2726 | 0.9581 | | 0.4006 | 75.0 | 150 | 0.3944 | 0.6077 | 0.6574 | 0.9604 | nan | 0.3269 | 0.9879 | nan | 0.2555 | 0.9599 | | 0.3978 | 80.0 | 160 | 0.3881 | 0.6216 | 0.6875 | 0.9591 | nan | 0.3912 | 0.9838 | nan | 0.2848 | 0.9585 | | 0.3553 | 85.0 | 170 | 0.3877 | 0.6333 | 0.7077 | 0.9595 | nan | 0.4329 | 0.9824 | nan | 0.3079 | 0.9588 | | 0.3637 | 90.0 | 180 | 0.4004 | 0.6428 | 0.7273 | 0.9594 | nan | 0.4741 | 0.9805 | nan | 0.3270 | 0.9586 | | 0.3416 | 95.0 | 190 | 0.3835 | 0.6403 | 0.7166 | 0.9604 | nan | 0.4507 | 0.9825 | nan | 0.3210 | 0.9596 | | 0.342 | 100.0 | 200 | 0.3634 | 0.6371 | 0.7061 | 0.9611 | nan | 0.4279 | 0.9842 | nan | 0.3137 | 0.9604 | | 0.3393 | 105.0 | 210 | 0.3740 | 0.6429 | 0.7217 | 0.9604 | nan | 0.4614 | 0.9820 | nan | 0.3262 | 0.9596 | | 0.3535 | 110.0 | 220 | 0.3771 | 0.6423 | 0.7199 | 0.9605 | nan | 0.4575 | 0.9823 | nan | 0.3249 | 0.9597 | | 0.3159 | 115.0 | 230 | 0.3710 | 0.6423 | 0.7167 | 0.9610 | nan | 0.4502 | 0.9832 | nan | 0.3243 | 0.9603 | | 0.3278 | 120.0 | 240 | 0.3666 | 0.6400 | 0.7120 | 0.9610 | nan | 0.4404 | 0.9836 | nan | 0.3196 | 0.9603 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_4

# dropoff-utcustom-train-SF-RGBD-b0_4 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3688 - Mean Iou: 0.3485 - Mean Accuracy: 0.5433 - Overall Accuracy: 0.9606 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0881 - Accuracy Undropoff: 0.9984 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.0851 - Iou Undropoff: 0.9604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.2008 | 5.0 | 10 | 1.0960 | 0.1205 | 0.4461 | 0.2825 | nan | 0.6246 | 0.2677 | 0.0 | 0.0943 | 0.2671 | | 1.0485 | 10.0 | 20 | 1.0952 | 0.1603 | 0.6272 | 0.4049 | nan | 0.8696 | 0.3848 | 0.0 | 0.0965 | 0.3843 | | 0.9156 | 15.0 | 30 | 1.0312 | 0.3080 | 0.5963 | 0.8333 | nan | 0.3377 | 0.8548 | 0.0 | 0.0924 | 0.8317 | | 0.7435 | 20.0 | 40 | 0.9448 | 0.3221 | 0.5508 | 0.8937 | nan | 0.1769 | 0.9248 | 0.0 | 0.0733 | 0.8930 | | 0.7336 | 25.0 | 50 | 0.7446 | 0.3191 | 0.4998 | 0.9461 | nan | 0.0129 | 0.9866 | 0.0 | 0.0113 | 0.9461 | | 0.6585 | 30.0 | 60 | 0.6397 | 0.3183 | 0.4981 | 0.9534 | nan | 0.0014 | 0.9948 | 0.0 | 0.0013 | 0.9534 | | 0.583 | 35.0 | 70 | 0.5785 | 0.3181 | 0.4978 | 0.9537 | nan | 0.0006 | 0.9951 | 0.0 | 0.0005 | 0.9537 | | 0.5324 | 40.0 | 80 | 0.5458 | 0.3182 | 0.4980 | 0.9545 | nan | 0.0002 | 0.9958 | 0.0 | 0.0002 | 0.9545 | | 0.5155 | 45.0 | 90 | 0.5347 | 0.3186 | 0.4987 | 0.9558 | nan | 0.0001 | 0.9973 | 0.0 | 0.0001 | 0.9558 | | 0.4874 | 50.0 | 100 | 0.4954 | 0.3179 | 0.4976 | 0.9537 | nan | 0.0 | 0.9951 | 0.0 | 0.0 | 0.9537 | | 0.4716 | 55.0 | 110 | 0.4646 | 0.3185 | 0.4985 | 0.9555 | nan | 0.0 | 0.9969 | 0.0 | 0.0 | 0.9555 | | 0.4441 | 60.0 | 120 | 0.4426 | 0.3185 | 0.4985 | 0.9555 | nan | 0.0 | 0.9970 | 0.0 | 0.0 | 0.9555 | | 0.4659 | 65.0 | 130 | 0.4345 | 0.3189 | 0.4991 | 0.9567 | nan | 0.0 | 0.9982 | 0.0 | 0.0 | 0.9567 | | 0.4758 | 70.0 | 140 | 0.4221 | 0.3181 | 0.4978 | 0.9543 | nan | 0.0 | 0.9957 | 0.0 | 0.0 | 0.9543 | | 0.4208 | 75.0 | 150 | 0.4029 | 0.3190 | 0.4993 | 0.9571 | nan | 0.0 | 0.9987 | 0.0 | 0.0 | 0.9571 | | 0.4395 | 80.0 | 160 | 0.4170 | 0.3207 | 0.5016 | 0.9559 | nan | 0.0062 | 0.9971 | 0.0 | 0.0062 | 0.9559 | | 0.3981 | 85.0 | 170 | 0.3992 | 0.3214 | 0.5027 | 0.9574 | nan | 0.0067 | 0.9987 | 0.0 | 0.0066 | 0.9574 | | 0.3983 | 90.0 | 180 | 0.3965 | 0.3282 | 0.5125 | 0.9560 | nan | 0.0288 | 0.9963 | 0.0 | 0.0285 | 0.9560 | | 0.398 | 95.0 | 190 | 0.3747 | 0.3272 | 0.5112 | 0.9569 | nan | 0.0251 | 0.9973 | 0.0 | 0.0249 | 0.9568 | | 0.3767 | 100.0 | 200 | 0.3722 | 0.3301 | 0.5155 | 0.9574 | nan | 0.0336 | 0.9975 | 0.0 | 0.0330 | 0.9573 | | 0.3797 | 105.0 | 210 | 0.3781 | 0.3334 | 0.5204 | 0.9583 | nan | 0.0429 | 0.9980 | 0.0 | 0.0420 | 0.9582 | | 0.373 | 110.0 | 220 | 0.3744 | 0.3409 | 0.5317 | 0.9593 | nan | 0.0654 | 0.9980 | 0.0 | 0.0636 | 0.9591 | | 0.372 | 115.0 | 230 | 0.3700 | 0.3440 | 0.5364 | 0.9599 | nan | 0.0746 | 0.9983 | 0.0 | 0.0723 | 0.9598 | | 0.3629 | 120.0 | 240 | 0.3688 | 0.3485 | 0.5433 | 0.9606 | nan | 0.0881 | 0.9984 | 0.0 | 0.0851 | 0.9604 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_5

# dropoff-utcustom-train-SF-RGBD-b0_5 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2608 - Mean Iou: 0.6161 - Mean Accuracy: 0.6630 - Overall Accuracy: 0.9623 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3365 - Accuracy Undropoff: 0.9894 - Iou Unlabeled: nan - Iou Dropoff: 0.2705 - Iou Undropoff: 0.9617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.9263 | 5.0 | 10 | 1.0370 | 0.2869 | 0.7147 | 0.7632 | nan | 0.6618 | 0.7675 | 0.0 | 0.1042 | 0.7565 | | 0.8069 | 10.0 | 20 | 0.8622 | 0.4857 | 0.5062 | 0.9589 | nan | 0.0125 | 0.9999 | nan | 0.0124 | 0.9589 | | 0.6851 | 15.0 | 30 | 0.6490 | 0.4876 | 0.5081 | 0.9586 | nan | 0.0167 | 0.9995 | nan | 0.0165 | 0.9586 | | 0.5882 | 20.0 | 40 | 0.4739 | 0.3253 | 0.5085 | 0.9586 | nan | 0.0177 | 0.9994 | 0.0 | 0.0174 | 0.9585 | | 0.53 | 25.0 | 50 | 0.4153 | 0.3375 | 0.5274 | 0.9584 | nan | 0.0573 | 0.9975 | 0.0 | 0.0542 | 0.9583 | | 0.5009 | 30.0 | 60 | 0.4275 | 0.3835 | 0.6488 | 0.9475 | nan | 0.3230 | 0.9746 | 0.0 | 0.2037 | 0.9468 | | 0.4699 | 35.0 | 70 | 0.3819 | 0.4158 | 0.6985 | 0.9578 | nan | 0.4157 | 0.9813 | 0.0 | 0.2904 | 0.9570 | | 0.3946 | 40.0 | 80 | 0.3563 | 0.6183 | 0.6844 | 0.9585 | nan | 0.3854 | 0.9834 | nan | 0.2787 | 0.9579 | | 0.3788 | 45.0 | 90 | 0.3259 | 0.6292 | 0.7011 | 0.9593 | nan | 0.4196 | 0.9827 | nan | 0.2998 | 0.9585 | | 0.3412 | 50.0 | 100 | 0.3392 | 0.6170 | 0.6933 | 0.9562 | nan | 0.4066 | 0.9801 | nan | 0.2785 | 0.9555 | | 0.3326 | 55.0 | 110 | 0.3214 | 0.6279 | 0.6914 | 0.9606 | nan | 0.3977 | 0.9851 | nan | 0.2958 | 0.9600 | | 0.2954 | 60.0 | 120 | 0.3119 | 0.6261 | 0.6847 | 0.9613 | nan | 0.3831 | 0.9864 | nan | 0.2915 | 0.9607 | | 0.3006 | 65.0 | 130 | 0.2853 | 0.5900 | 0.6223 | 0.9625 | nan | 0.2513 | 0.9934 | nan | 0.2180 | 0.9621 | | 0.2715 | 70.0 | 140 | 0.3021 | 0.6314 | 0.6903 | 0.9620 | nan | 0.3938 | 0.9867 | nan | 0.3014 | 0.9614 | | 0.276 | 75.0 | 150 | 0.2950 | 0.6243 | 0.6783 | 0.9619 | nan | 0.3690 | 0.9877 | nan | 0.2873 | 0.9613 | | 0.2622 | 80.0 | 160 | 0.2843 | 0.6134 | 0.6651 | 0.9608 | nan | 0.3426 | 0.9876 | nan | 0.2665 | 0.9602 | | 0.2395 | 85.0 | 170 | 0.2752 | 0.6050 | 0.6495 | 0.9613 | nan | 0.3094 | 0.9895 | nan | 0.2493 | 0.9608 | | 0.2597 | 90.0 | 180 | 0.2813 | 0.6296 | 0.6874 | 0.9620 | nan | 0.3879 | 0.9869 | nan | 0.2979 | 0.9614 | | 0.2294 | 95.0 | 190 | 0.2747 | 0.6106 | 0.6575 | 0.9615 | nan | 0.3259 | 0.9890 | nan | 0.2602 | 0.9609 | | 0.2303 | 100.0 | 200 | 0.2606 | 0.6040 | 0.6462 | 0.9616 | nan | 0.3023 | 0.9902 | nan | 0.2468 | 0.9611 | | 0.2335 | 105.0 | 210 | 0.2606 | 0.6080 | 0.6515 | 0.9619 | nan | 0.3130 | 0.9901 | nan | 0.2547 | 0.9614 | | 0.2322 | 110.0 | 220 | 0.2619 | 0.6167 | 0.6631 | 0.9624 | nan | 0.3366 | 0.9896 | nan | 0.2715 | 0.9619 | | 0.2116 | 115.0 | 230 | 0.2618 | 0.6183 | 0.6660 | 0.9624 | nan | 0.3427 | 0.9893 | nan | 0.2747 | 0.9618 | | 0.2099 | 120.0 | 240 | 0.2608 | 0.6161 | 0.6630 | 0.9623 | nan | 0.3365 | 0.9894 | nan | 0.2705 | 0.9617 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_6

# dropoff-utcustom-train-SF-RGBD-b0_6 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2353 - Mean Iou: 0.6539 - Mean Accuracy: 0.7065 - Overall Accuracy: 0.9662 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4233 - Accuracy Undropoff: 0.9897 - Iou Unlabeled: nan - Iou Dropoff: 0.3423 - Iou Undropoff: 0.9656 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.9975 | 5.0 | 10 | 1.0470 | 0.2819 | 0.6747 | 0.7186 | nan | 0.6267 | 0.7226 | 0.0 | 0.1290 | 0.7167 | | 0.8329 | 10.0 | 20 | 0.8435 | 0.3211 | 0.5026 | 0.9526 | nan | 0.0117 | 0.9934 | 0.0 | 0.0106 | 0.9526 | | 0.6857 | 15.0 | 30 | 0.6184 | 0.3191 | 0.4994 | 0.9567 | nan | 0.0006 | 0.9981 | 0.0 | 0.0006 | 0.9567 | | 0.5913 | 20.0 | 40 | 0.4793 | 0.3193 | 0.4997 | 0.9573 | nan | 0.0005 | 0.9988 | 0.0 | 0.0005 | 0.9573 | | 0.5299 | 25.0 | 50 | 0.4529 | 0.3488 | 0.5442 | 0.9596 | nan | 0.0911 | 0.9973 | 0.0 | 0.0869 | 0.9595 | | 0.4922 | 30.0 | 60 | 0.4037 | 0.4352 | 0.6983 | 0.9671 | nan | 0.4051 | 0.9915 | 0.0 | 0.3390 | 0.9666 | | 0.4769 | 35.0 | 70 | 0.4161 | 0.4090 | 0.7560 | 0.9426 | nan | 0.5524 | 0.9595 | 0.0 | 0.2858 | 0.9412 | | 0.3916 | 40.0 | 80 | 0.3343 | 0.6320 | 0.6946 | 0.9614 | nan | 0.4036 | 0.9856 | nan | 0.3033 | 0.9608 | | 0.3567 | 45.0 | 90 | 0.3372 | 0.6374 | 0.7140 | 0.9598 | nan | 0.4458 | 0.9821 | nan | 0.3157 | 0.9591 | | 0.3234 | 50.0 | 100 | 0.3074 | 0.6402 | 0.6883 | 0.9652 | nan | 0.3863 | 0.9903 | nan | 0.3157 | 0.9646 | | 0.3181 | 55.0 | 110 | 0.3043 | 0.6396 | 0.7138 | 0.9606 | nan | 0.4446 | 0.9830 | nan | 0.3194 | 0.9599 | | 0.2584 | 60.0 | 120 | 0.3069 | 0.6450 | 0.7204 | 0.9613 | nan | 0.4576 | 0.9831 | nan | 0.3294 | 0.9605 | | 0.2566 | 65.0 | 130 | 0.2824 | 0.6431 | 0.7063 | 0.9630 | nan | 0.4263 | 0.9863 | nan | 0.3239 | 0.9623 | | 0.2353 | 70.0 | 140 | 0.2763 | 0.6470 | 0.7046 | 0.9645 | nan | 0.4212 | 0.9880 | nan | 0.3301 | 0.9638 | | 0.2368 | 75.0 | 150 | 0.2644 | 0.6474 | 0.6973 | 0.9658 | nan | 0.4044 | 0.9902 | nan | 0.3296 | 0.9652 | | 0.2225 | 80.0 | 160 | 0.2673 | 0.6462 | 0.7089 | 0.9635 | nan | 0.4313 | 0.9866 | nan | 0.3296 | 0.9629 | | 0.1976 | 85.0 | 170 | 0.2568 | 0.6449 | 0.7057 | 0.9637 | nan | 0.4244 | 0.9870 | nan | 0.3268 | 0.9630 | | 0.1981 | 90.0 | 180 | 0.2572 | 0.6444 | 0.7110 | 0.9626 | nan | 0.4365 | 0.9855 | nan | 0.3269 | 0.9619 | | 0.1857 | 95.0 | 190 | 0.2503 | 0.6504 | 0.7027 | 0.9658 | nan | 0.4157 | 0.9897 | nan | 0.3356 | 0.9652 | | 0.1826 | 100.0 | 200 | 0.2345 | 0.6509 | 0.6984 | 0.9666 | nan | 0.4059 | 0.9909 | nan | 0.3357 | 0.9660 | | 0.1818 | 105.0 | 210 | 0.2484 | 0.6506 | 0.7160 | 0.9637 | nan | 0.4458 | 0.9862 | nan | 0.3381 | 0.9630 | | 0.1919 | 110.0 | 220 | 0.2343 | 0.6526 | 0.6996 | 0.9669 | nan | 0.4080 | 0.9912 | nan | 0.3389 | 0.9663 | | 0.17 | 115.0 | 230 | 0.2377 | 0.6535 | 0.7065 | 0.9661 | nan | 0.4235 | 0.9896 | nan | 0.3416 | 0.9655 | | 0.1739 | 120.0 | 240 | 0.2353 | 0.6539 | 0.7065 | 0.9662 | nan | 0.4233 | 0.9897 | nan | 0.3423 | 0.9656 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b0_7

# dropoff-utcustom-train-SF-RGBD-b0_7 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2075 - Mean Iou: 0.6372 - Mean Accuracy: 0.6861 - Overall Accuracy: 0.9647 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3822 - Accuracy Undropoff: 0.9900 - Iou Unlabeled: nan - Iou Dropoff: 0.3104 - Iou Undropoff: 0.9641 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.9508 | 5.0 | 10 | 1.0263 | 0.3104 | 0.5474 | 0.8717 | nan | 0.1937 | 0.9011 | 0.0 | 0.0605 | 0.8706 | | 0.7814 | 10.0 | 20 | 0.7568 | 0.4971 | 0.5339 | 0.9361 | nan | 0.0952 | 0.9726 | nan | 0.0584 | 0.9359 | | 0.642 | 15.0 | 30 | 0.5907 | 0.5134 | 0.5443 | 0.9494 | nan | 0.1026 | 0.9861 | nan | 0.0777 | 0.9492 | | 0.5118 | 20.0 | 40 | 0.4804 | 0.3658 | 0.5923 | 0.9513 | nan | 0.2006 | 0.9839 | 0.0 | 0.1464 | 0.9509 | | 0.4581 | 25.0 | 50 | 0.4405 | 0.3715 | 0.5915 | 0.9569 | nan | 0.1930 | 0.9900 | 0.0 | 0.1578 | 0.9565 | | 0.4213 | 30.0 | 60 | 0.4146 | 0.3828 | 0.6136 | 0.9580 | nan | 0.2379 | 0.9892 | 0.0 | 0.1910 | 0.9575 | | 0.3571 | 35.0 | 70 | 0.3750 | 0.3846 | 0.6180 | 0.9578 | nan | 0.2474 | 0.9887 | 0.0 | 0.1963 | 0.9574 | | 0.3205 | 40.0 | 80 | 0.3478 | 0.5777 | 0.6202 | 0.9576 | nan | 0.2522 | 0.9882 | nan | 0.1982 | 0.9571 | | 0.3114 | 45.0 | 90 | 0.3461 | 0.3895 | 0.6423 | 0.9541 | nan | 0.3022 | 0.9824 | 0.0 | 0.2150 | 0.9535 | | 0.2747 | 50.0 | 100 | 0.3253 | 0.5875 | 0.6357 | 0.9575 | nan | 0.2847 | 0.9867 | nan | 0.2180 | 0.9570 | | 0.2593 | 55.0 | 110 | 0.3083 | 0.5967 | 0.6599 | 0.9552 | nan | 0.3377 | 0.9820 | nan | 0.2387 | 0.9546 | | 0.2293 | 60.0 | 120 | 0.2762 | 0.5966 | 0.6389 | 0.9606 | nan | 0.2880 | 0.9898 | nan | 0.2331 | 0.9601 | | 0.2306 | 65.0 | 130 | 0.2655 | 0.6016 | 0.6587 | 0.9577 | nan | 0.3326 | 0.9848 | nan | 0.2462 | 0.9571 | | 0.2118 | 70.0 | 140 | 0.2446 | 0.6039 | 0.6509 | 0.9605 | nan | 0.3133 | 0.9886 | nan | 0.2479 | 0.9600 | | 0.2038 | 75.0 | 150 | 0.2395 | 0.6164 | 0.6708 | 0.9607 | nan | 0.3547 | 0.9870 | nan | 0.2727 | 0.9601 | | 0.1895 | 80.0 | 160 | 0.2196 | 0.6254 | 0.6721 | 0.9636 | nan | 0.3542 | 0.9900 | nan | 0.2878 | 0.9630 | | 0.1681 | 85.0 | 170 | 0.2176 | 0.6302 | 0.6829 | 0.9630 | nan | 0.3773 | 0.9884 | nan | 0.2979 | 0.9624 | | 0.1612 | 90.0 | 180 | 0.2175 | 0.6334 | 0.6870 | 0.9633 | nan | 0.3857 | 0.9884 | nan | 0.3042 | 0.9627 | | 0.1545 | 95.0 | 190 | 0.2140 | 0.6337 | 0.6816 | 0.9644 | nan | 0.3732 | 0.9900 | nan | 0.3035 | 0.9638 | | 0.1551 | 100.0 | 200 | 0.2134 | 0.6357 | 0.6891 | 0.9637 | nan | 0.3896 | 0.9886 | nan | 0.3083 | 0.9631 | | 0.1508 | 105.0 | 210 | 0.2090 | 0.6359 | 0.6865 | 0.9642 | nan | 0.3837 | 0.9894 | nan | 0.3083 | 0.9636 | | 0.1536 | 110.0 | 220 | 0.2057 | 0.6346 | 0.6801 | 0.9650 | nan | 0.3694 | 0.9908 | nan | 0.3048 | 0.9644 | | 0.1392 | 115.0 | 230 | 0.2083 | 0.6387 | 0.6890 | 0.9646 | nan | 0.3883 | 0.9896 | nan | 0.3133 | 0.9640 | | 0.1446 | 120.0 | 240 | 0.2075 | 0.6372 | 0.6861 | 0.9647 | nan | 0.3822 | 0.9900 | nan | 0.3104 | 0.9641 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_1

# dropoff-utcustom-train-SF-RGBD-b5_1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3428 - Mean Iou: 0.4792 - Mean Accuracy: 0.5000 - Overall Accuracy: 0.9583 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0001 - Accuracy Undropoff: 0.9999 - Iou Unlabeled: nan - Iou Dropoff: 0.0001 - Iou Undropoff: 0.9583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.8047 | 5.0 | 10 | 0.9867 | 0.2744 | 0.6315 | 0.7475 | nan | 0.5049 | 0.7581 | 0.0 | 0.0812 | 0.7422 | | 0.7528 | 10.0 | 20 | 0.8526 | 0.3461 | 0.5957 | 0.9213 | nan | 0.2406 | 0.9508 | 0.0 | 0.1178 | 0.9205 | | 0.7087 | 15.0 | 30 | 0.7023 | 0.3450 | 0.5533 | 0.9467 | nan | 0.1243 | 0.9824 | 0.0 | 0.0887 | 0.9464 | | 0.6601 | 20.0 | 40 | 0.6251 | 0.3381 | 0.5390 | 0.9462 | nan | 0.0948 | 0.9832 | 0.0 | 0.0684 | 0.9460 | | 0.6274 | 25.0 | 50 | 0.5828 | 0.3286 | 0.5178 | 0.9486 | nan | 0.0479 | 0.9876 | 0.0 | 0.0374 | 0.9485 | | 0.5929 | 30.0 | 60 | 0.5478 | 0.3257 | 0.5122 | 0.9488 | nan | 0.0359 | 0.9884 | 0.0 | 0.0284 | 0.9487 | | 0.5672 | 35.0 | 70 | 0.5237 | 0.3240 | 0.5088 | 0.9494 | nan | 0.0283 | 0.9893 | 0.0 | 0.0227 | 0.9493 | | 0.5454 | 40.0 | 80 | 0.4966 | 0.4856 | 0.5072 | 0.9529 | nan | 0.0212 | 0.9933 | nan | 0.0183 | 0.9528 | | 0.5261 | 45.0 | 90 | 0.4700 | 0.3234 | 0.5062 | 0.9553 | nan | 0.0163 | 0.9960 | 0.0 | 0.0149 | 0.9552 | | 0.5012 | 50.0 | 100 | 0.4576 | 0.4832 | 0.5041 | 0.9563 | nan | 0.0107 | 0.9974 | nan | 0.0101 | 0.9563 | | 0.4875 | 55.0 | 110 | 0.4430 | 0.4811 | 0.5018 | 0.9566 | nan | 0.0058 | 0.9978 | nan | 0.0056 | 0.9565 | | 0.4622 | 60.0 | 120 | 0.4328 | 0.4800 | 0.5007 | 0.9570 | nan | 0.0031 | 0.9983 | nan | 0.0030 | 0.9570 | | 0.4394 | 65.0 | 130 | 0.4179 | 0.4796 | 0.5004 | 0.9572 | nan | 0.0021 | 0.9986 | nan | 0.0021 | 0.9572 | | 0.4352 | 70.0 | 140 | 0.4048 | 0.4795 | 0.5002 | 0.9573 | nan | 0.0016 | 0.9988 | nan | 0.0016 | 0.9573 | | 0.426 | 75.0 | 150 | 0.3881 | 0.4796 | 0.5003 | 0.9577 | nan | 0.0015 | 0.9992 | nan | 0.0014 | 0.9577 | | 0.4175 | 80.0 | 160 | 0.3794 | 0.4797 | 0.5004 | 0.9579 | nan | 0.0014 | 0.9994 | nan | 0.0014 | 0.9579 | | 0.4087 | 85.0 | 170 | 0.3742 | 0.3196 | 0.5002 | 0.9577 | nan | 0.0012 | 0.9992 | 0.0 | 0.0012 | 0.9577 | | 0.3887 | 90.0 | 180 | 0.3645 | 0.4792 | 0.4999 | 0.9581 | nan | 0.0003 | 0.9996 | nan | 0.0003 | 0.9581 | | 0.3799 | 95.0 | 190 | 0.3540 | 0.4791 | 0.4999 | 0.9581 | nan | 0.0001 | 0.9997 | nan | 0.0001 | 0.9581 | | 0.376 | 100.0 | 200 | 0.3511 | 0.4792 | 0.4999 | 0.9582 | nan | 0.0001 | 0.9998 | nan | 0.0001 | 0.9582 | | 0.3677 | 105.0 | 210 | 0.3452 | 0.4792 | 0.4999 | 0.9582 | nan | 0.0001 | 0.9998 | nan | 0.0001 | 0.9582 | | 0.358 | 110.0 | 220 | 0.3437 | 0.4792 | 0.4999 | 0.9582 | nan | 0.0001 | 0.9998 | nan | 0.0001 | 0.9582 | | 0.3997 | 115.0 | 230 | 0.3434 | 0.4792 | 0.5000 | 0.9583 | nan | 0.0001 | 0.9999 | nan | 0.0001 | 0.9583 | | 0.3769 | 120.0 | 240 | 0.3428 | 0.4792 | 0.5000 | 0.9583 | nan | 0.0001 | 0.9999 | nan | 0.0001 | 0.9583 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_2

# dropoff-utcustom-train-SF-RGBD-b5_2 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4198 - Mean Iou: 0.3194 - Mean Accuracy: 0.4998 - Overall Accuracy: 0.9558 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0023 - Accuracy Undropoff: 0.9972 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.0022 - Iou Undropoff: 0.9558 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.989 | 5.0 | 10 | 1.0190 | 0.2162 | 0.5831 | 0.5879 | nan | 0.5779 | 0.5883 | 0.0 | 0.0657 | 0.5829 | | 0.9092 | 10.0 | 20 | 0.8686 | 0.3164 | 0.5199 | 0.8922 | nan | 0.1137 | 0.9260 | 0.0 | 0.0539 | 0.8953 | | 0.8483 | 15.0 | 30 | 0.7438 | 0.3256 | 0.5234 | 0.9219 | nan | 0.0888 | 0.9581 | 0.0 | 0.0545 | 0.9224 | | 0.7856 | 20.0 | 40 | 0.6571 | 0.3182 | 0.5013 | 0.9336 | nan | 0.0297 | 0.9728 | 0.0 | 0.0210 | 0.9335 | | 0.7459 | 25.0 | 50 | 0.6144 | 0.3164 | 0.4980 | 0.9324 | nan | 0.0242 | 0.9718 | 0.0 | 0.0168 | 0.9324 | | 0.7027 | 30.0 | 60 | 0.5861 | 0.3168 | 0.4975 | 0.9351 | nan | 0.0202 | 0.9748 | 0.0 | 0.0151 | 0.9353 | | 0.6827 | 35.0 | 70 | 0.5568 | 0.3171 | 0.4975 | 0.9391 | nan | 0.0159 | 0.9791 | 0.0 | 0.0122 | 0.9391 | | 0.6362 | 40.0 | 80 | 0.5405 | 0.3179 | 0.4982 | 0.9424 | nan | 0.0138 | 0.9827 | 0.0 | 0.0112 | 0.9425 | | 0.6098 | 45.0 | 90 | 0.5192 | 0.3174 | 0.4971 | 0.9449 | nan | 0.0087 | 0.9855 | 0.0 | 0.0073 | 0.9449 | | 0.5946 | 50.0 | 100 | 0.5025 | 0.3179 | 0.4978 | 0.9475 | nan | 0.0072 | 0.9883 | 0.0 | 0.0062 | 0.9477 | | 0.5868 | 55.0 | 110 | 0.4943 | 0.3179 | 0.4976 | 0.9490 | nan | 0.0052 | 0.9900 | 0.0 | 0.0046 | 0.9491 | | 0.5557 | 60.0 | 120 | 0.4798 | 0.3184 | 0.4983 | 0.9505 | nan | 0.0051 | 0.9915 | 0.0 | 0.0045 | 0.9506 | | 0.5327 | 65.0 | 130 | 0.4736 | 0.3184 | 0.4983 | 0.9514 | nan | 0.0041 | 0.9925 | 0.0 | 0.0038 | 0.9514 | | 0.525 | 70.0 | 140 | 0.4657 | 0.3187 | 0.4987 | 0.9526 | nan | 0.0038 | 0.9937 | 0.0 | 0.0035 | 0.9526 | | 0.5266 | 75.0 | 150 | 0.4528 | 0.3190 | 0.4992 | 0.9534 | nan | 0.0037 | 0.9946 | 0.0 | 0.0034 | 0.9535 | | 0.5139 | 80.0 | 160 | 0.4538 | 0.3189 | 0.4991 | 0.9533 | nan | 0.0037 | 0.9945 | 0.0 | 0.0035 | 0.9534 | | 0.5128 | 85.0 | 170 | 0.4460 | 0.3192 | 0.4995 | 0.9543 | nan | 0.0033 | 0.9956 | 0.0 | 0.0031 | 0.9543 | | 0.4901 | 90.0 | 180 | 0.4371 | 0.3192 | 0.4995 | 0.9548 | nan | 0.0029 | 0.9961 | 0.0 | 0.0027 | 0.9548 | | 0.4767 | 95.0 | 190 | 0.4325 | 0.3193 | 0.4997 | 0.9552 | nan | 0.0029 | 0.9965 | 0.0 | 0.0027 | 0.9552 | | 0.4692 | 100.0 | 200 | 0.4272 | 0.3193 | 0.4997 | 0.9556 | nan | 0.0024 | 0.9970 | 0.0 | 0.0023 | 0.9556 | | 0.4632 | 105.0 | 210 | 0.4251 | 0.3193 | 0.4996 | 0.9556 | nan | 0.0023 | 0.9969 | 0.0 | 0.0023 | 0.9556 | | 0.4626 | 110.0 | 220 | 0.4236 | 0.3193 | 0.4997 | 0.9556 | nan | 0.0024 | 0.9970 | 0.0 | 0.0024 | 0.9556 | | 0.4837 | 115.0 | 230 | 0.4216 | 0.3194 | 0.4998 | 0.9558 | nan | 0.0023 | 0.9972 | 0.0 | 0.0023 | 0.9558 | | 0.4809 | 120.0 | 240 | 0.4198 | 0.3194 | 0.4998 | 0.9558 | nan | 0.0023 | 0.9972 | 0.0 | 0.0022 | 0.9558 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_3

# dropoff-utcustom-train-SF-RGBD-b5_3 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2768 - Mean Iou: 0.3194 - Mean Accuracy: 0.4999 - Overall Accuracy: 0.9578 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0006 - Accuracy Undropoff: 0.9993 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.0006 - Iou Undropoff: 0.9578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0992 | 5.0 | 10 | 1.0599 | 0.1938 | 0.4241 | 0.5281 | nan | 0.3106 | 0.5376 | 0.0 | 0.0540 | 0.5273 | | 1.0188 | 10.0 | 20 | 0.9493 | 0.2781 | 0.4808 | 0.7846 | nan | 0.1494 | 0.8122 | 0.0 | 0.0476 | 0.7868 | | 0.9218 | 15.0 | 30 | 0.8130 | 0.3074 | 0.4913 | 0.8851 | nan | 0.0618 | 0.9209 | 0.0 | 0.0364 | 0.8858 | | 0.8411 | 20.0 | 40 | 0.7253 | 0.3089 | 0.4866 | 0.9038 | nan | 0.0315 | 0.9416 | 0.0 | 0.0221 | 0.9047 | | 0.7583 | 25.0 | 50 | 0.6719 | 0.3097 | 0.4890 | 0.9069 | nan | 0.0331 | 0.9448 | 0.0 | 0.0216 | 0.9076 | | 0.688 | 30.0 | 60 | 0.6303 | 0.3109 | 0.4883 | 0.9170 | nan | 0.0207 | 0.9559 | 0.0 | 0.0149 | 0.9179 | | 0.6279 | 35.0 | 70 | 0.5919 | 0.3139 | 0.4918 | 0.9276 | nan | 0.0164 | 0.9671 | 0.0 | 0.0133 | 0.9283 | | 0.5533 | 40.0 | 80 | 0.5375 | 0.3168 | 0.4961 | 0.9377 | nan | 0.0144 | 0.9777 | 0.0 | 0.0125 | 0.9380 | | 0.5116 | 45.0 | 90 | 0.5111 | 0.3176 | 0.4970 | 0.9442 | nan | 0.0093 | 0.9847 | 0.0 | 0.0083 | 0.9445 | | 0.4801 | 50.0 | 100 | 0.4696 | 0.3183 | 0.4981 | 0.9492 | nan | 0.0062 | 0.9901 | 0.0 | 0.0057 | 0.9492 | | 0.4744 | 55.0 | 110 | 0.4317 | 0.3187 | 0.4987 | 0.9543 | nan | 0.0018 | 0.9956 | 0.0 | 0.0017 | 0.9543 | | 0.4494 | 60.0 | 120 | 0.3991 | 0.3189 | 0.4991 | 0.9555 | nan | 0.0013 | 0.9969 | 0.0 | 0.0012 | 0.9555 | | 0.386 | 65.0 | 130 | 0.3737 | 0.3189 | 0.4990 | 0.9565 | nan | 0.0000 | 0.9980 | 0.0 | 0.0000 | 0.9565 | | 0.3674 | 70.0 | 140 | 0.3538 | 0.3191 | 0.4994 | 0.9567 | nan | 0.0007 | 0.9981 | 0.0 | 0.0007 | 0.9567 | | 0.3601 | 75.0 | 150 | 0.3413 | 0.3192 | 0.4995 | 0.9573 | nan | 0.0002 | 0.9988 | 0.0 | 0.0002 | 0.9573 | | 0.3626 | 80.0 | 160 | 0.3225 | 0.3193 | 0.4996 | 0.9569 | nan | 0.0009 | 0.9984 | 0.0 | 0.0009 | 0.9569 | | 0.3331 | 85.0 | 170 | 0.3163 | 0.3195 | 0.5000 | 0.9576 | nan | 0.0009 | 0.9991 | 0.0 | 0.0009 | 0.9576 | | 0.319 | 90.0 | 180 | 0.3004 | 0.3200 | 0.5008 | 0.9577 | nan | 0.0024 | 0.9991 | 0.0 | 0.0024 | 0.9577 | | 0.3163 | 95.0 | 190 | 0.2931 | 0.3198 | 0.5004 | 0.9575 | nan | 0.0020 | 0.9989 | 0.0 | 0.0020 | 0.9575 | | 0.3185 | 100.0 | 200 | 0.2920 | 0.3194 | 0.4999 | 0.9577 | nan | 0.0006 | 0.9992 | 0.0 | 0.0006 | 0.9577 | | 0.3122 | 105.0 | 210 | 0.2831 | 0.3194 | 0.4999 | 0.9578 | nan | 0.0005 | 0.9994 | 0.0 | 0.0005 | 0.9578 | | 0.3218 | 110.0 | 220 | 0.2788 | 0.3195 | 0.5000 | 0.9576 | nan | 0.0009 | 0.9991 | 0.0 | 0.0009 | 0.9576 | | 0.3037 | 115.0 | 230 | 0.2752 | 0.3194 | 0.4999 | 0.9577 | nan | 0.0006 | 0.9992 | 0.0 | 0.0006 | 0.9577 | | 0.3319 | 120.0 | 240 | 0.2768 | 0.3194 | 0.4999 | 0.9578 | nan | 0.0006 | 0.9993 | 0.0 | 0.0006 | 0.9578 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_4

# dropoff-utcustom-train-SF-RGBD-b5_4 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2351 - Mean Iou: 0.4792 - Mean Accuracy: 0.5 - Overall Accuracy: 0.9584 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.0 - Accuracy Undropoff: 1.0 - Iou Unlabeled: nan - Iou Dropoff: 0.0 - Iou Undropoff: 0.9584 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0114 | 5.0 | 10 | 1.0037 | 0.2459 | 0.4345 | 0.7074 | nan | 0.1368 | 0.7322 | 0.0 | 0.0286 | 0.7089 | | 0.9088 | 10.0 | 20 | 0.8245 | 0.3119 | 0.5046 | 0.8887 | nan | 0.0857 | 0.9235 | 0.0 | 0.0460 | 0.8897 | | 0.8029 | 15.0 | 30 | 0.6620 | 0.3157 | 0.4998 | 0.9214 | nan | 0.0399 | 0.9596 | 0.0 | 0.0253 | 0.9219 | | 0.6935 | 20.0 | 40 | 0.5662 | 0.3154 | 0.4959 | 0.9309 | nan | 0.0214 | 0.9704 | 0.0 | 0.0151 | 0.9311 | | 0.635 | 25.0 | 50 | 0.5018 | 0.3175 | 0.4978 | 0.9401 | nan | 0.0153 | 0.9803 | 0.0 | 0.0121 | 0.9404 | | 0.5579 | 30.0 | 60 | 0.4701 | 0.3178 | 0.4978 | 0.9422 | nan | 0.0131 | 0.9825 | 0.0 | 0.0111 | 0.9423 | | 0.5086 | 35.0 | 70 | 0.4403 | 0.3181 | 0.4977 | 0.9459 | nan | 0.0088 | 0.9866 | 0.0 | 0.0080 | 0.9461 | | 0.472 | 40.0 | 80 | 0.4328 | 0.3177 | 0.4971 | 0.9471 | nan | 0.0063 | 0.9879 | 0.0 | 0.0059 | 0.9473 | | 0.4484 | 45.0 | 90 | 0.4136 | 0.3184 | 0.4981 | 0.9506 | nan | 0.0046 | 0.9916 | 0.0 | 0.0044 | 0.9508 | | 0.4026 | 50.0 | 100 | 0.4013 | 0.3186 | 0.4985 | 0.9516 | nan | 0.0043 | 0.9926 | 0.0 | 0.0042 | 0.9517 | | 0.3873 | 55.0 | 110 | 0.3621 | 0.3189 | 0.4991 | 0.9557 | nan | 0.0010 | 0.9971 | 0.0 | 0.0009 | 0.9557 | | 0.3549 | 60.0 | 120 | 0.3479 | 0.3189 | 0.4992 | 0.9564 | nan | 0.0004 | 0.9979 | 0.0 | 0.0004 | 0.9564 | | 0.3358 | 65.0 | 130 | 0.3282 | 0.3191 | 0.4994 | 0.9571 | nan | 0.0001 | 0.9986 | 0.0 | 0.0001 | 0.9571 | | 0.3146 | 70.0 | 140 | 0.3141 | 0.3193 | 0.4996 | 0.9577 | nan | 0.0000 | 0.9993 | 0.0 | 0.0000 | 0.9577 | | 0.3116 | 75.0 | 150 | 0.2941 | 0.3194 | 0.4999 | 0.9582 | nan | 0.0 | 0.9998 | 0.0 | 0.0 | 0.9582 | | 0.3151 | 80.0 | 160 | 0.2809 | 0.3195 | 0.5000 | 0.9584 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9584 | | 0.2778 | 85.0 | 170 | 0.2750 | 0.3195 | 0.5000 | 0.9584 | nan | 0.0 | 1.0000 | 0.0 | 0.0 | 0.9584 | | 0.2753 | 90.0 | 180 | 0.2615 | 0.3195 | 0.5000 | 0.9584 | nan | 0.0 | 1.0000 | 0.0 | 0.0 | 0.9584 | | 0.2809 | 95.0 | 190 | 0.2547 | 0.4792 | 0.5 | 0.9584 | nan | 0.0 | 1.0 | nan | 0.0 | 0.9584 | | 0.2606 | 100.0 | 200 | 0.2464 | 0.4792 | 0.5 | 0.9584 | nan | 0.0 | 1.0 | nan | 0.0 | 0.9584 | | 0.2563 | 105.0 | 210 | 0.2459 | 0.4792 | 0.5 | 0.9584 | nan | 0.0 | 1.0 | nan | 0.0 | 0.9584 | | 0.2454 | 110.0 | 220 | 0.2393 | 0.4792 | 0.5 | 0.9584 | nan | 0.0 | 1.0 | nan | 0.0 | 0.9584 | | 0.2707 | 115.0 | 230 | 0.2368 | 0.4792 | 0.5 | 0.9584 | nan | 0.0 | 1.0 | nan | 0.0 | 0.9584 | | 0.2433 | 120.0 | 240 | 0.2351 | 0.4792 | 0.5 | 0.9584 | nan | 0.0 | 1.0 | nan | 0.0 | 0.9584 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_5

# dropoff-utcustom-train-SF-RGBD-b5_5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2636 - Mean Iou: 0.4256 - Mean Accuracy: 0.6832 - Overall Accuracy: 0.9656 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3752 - Accuracy Undropoff: 0.9912 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3118 - Iou Undropoff: 0.9650 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.1487 | 5.0 | 10 | 1.0250 | 0.2562 | 0.6276 | 0.6778 | nan | 0.5730 | 0.6823 | 0.0 | 0.0971 | 0.6714 | | 1.0128 | 10.0 | 20 | 0.9030 | 0.3142 | 0.6730 | 0.8268 | nan | 0.5053 | 0.8407 | 0.0 | 0.1195 | 0.8231 | | 0.8561 | 15.0 | 30 | 0.7359 | 0.3520 | 0.6913 | 0.8949 | nan | 0.4692 | 0.9133 | 0.0 | 0.1632 | 0.8928 | | 0.7551 | 20.0 | 40 | 0.6534 | 0.3634 | 0.6999 | 0.9090 | nan | 0.4719 | 0.9280 | 0.0 | 0.1829 | 0.9072 | | 0.6236 | 25.0 | 50 | 0.5938 | 0.3710 | 0.7001 | 0.9189 | nan | 0.4614 | 0.9388 | 0.0 | 0.1955 | 0.9173 | | 0.4977 | 30.0 | 60 | 0.5293 | 0.3850 | 0.6987 | 0.9341 | nan | 0.4420 | 0.9555 | 0.0 | 0.2222 | 0.9329 | | 0.4188 | 35.0 | 70 | 0.4859 | 0.3935 | 0.6941 | 0.9425 | nan | 0.4231 | 0.9650 | 0.0 | 0.2390 | 0.9415 | | 0.3532 | 40.0 | 80 | 0.4278 | 0.4019 | 0.6823 | 0.9519 | nan | 0.3881 | 0.9764 | 0.0 | 0.2547 | 0.9511 | | 0.3187 | 45.0 | 90 | 0.3914 | 0.4098 | 0.6873 | 0.9560 | nan | 0.3942 | 0.9804 | 0.0 | 0.2742 | 0.9553 | | 0.2631 | 50.0 | 100 | 0.3647 | 0.4134 | 0.6918 | 0.9575 | nan | 0.4020 | 0.9815 | 0.0 | 0.2835 | 0.9567 | | 0.2565 | 55.0 | 110 | 0.3424 | 0.4141 | 0.6895 | 0.9585 | nan | 0.3962 | 0.9829 | 0.0 | 0.2846 | 0.9578 | | 0.2259 | 60.0 | 120 | 0.3127 | 0.4178 | 0.6853 | 0.9613 | nan | 0.3843 | 0.9863 | 0.0 | 0.2926 | 0.9607 | | 0.2263 | 65.0 | 130 | 0.2920 | 0.4202 | 0.6822 | 0.9632 | nan | 0.3757 | 0.9886 | 0.0 | 0.2981 | 0.9626 | | 0.1961 | 70.0 | 140 | 0.2755 | 0.4218 | 0.6769 | 0.9649 | nan | 0.3627 | 0.9911 | 0.0 | 0.3009 | 0.9644 | | 0.1897 | 75.0 | 150 | 0.2726 | 0.4232 | 0.6803 | 0.9650 | nan | 0.3698 | 0.9908 | 0.0 | 0.3052 | 0.9645 | | 0.1863 | 80.0 | 160 | 0.2762 | 0.4241 | 0.6830 | 0.9649 | nan | 0.3756 | 0.9904 | 0.0 | 0.3079 | 0.9643 | | 0.1656 | 85.0 | 170 | 0.2730 | 0.4241 | 0.6809 | 0.9653 | nan | 0.3708 | 0.9911 | 0.0 | 0.3076 | 0.9648 | | 0.1745 | 90.0 | 180 | 0.2740 | 0.4241 | 0.6821 | 0.9651 | nan | 0.3736 | 0.9907 | 0.0 | 0.3079 | 0.9645 | | 0.1726 | 95.0 | 190 | 0.2779 | 0.4242 | 0.6854 | 0.9645 | nan | 0.3809 | 0.9898 | 0.0 | 0.3085 | 0.9639 | | 0.158 | 100.0 | 200 | 0.2661 | 0.4248 | 0.6808 | 0.9656 | nan | 0.3701 | 0.9915 | 0.0 | 0.3094 | 0.9651 | | 0.19 | 105.0 | 210 | 0.2667 | 0.4240 | 0.6790 | 0.9656 | nan | 0.3664 | 0.9916 | 0.0 | 0.3070 | 0.9651 | | 0.1533 | 110.0 | 220 | 0.2696 | 0.4258 | 0.6843 | 0.9655 | nan | 0.3777 | 0.9910 | 0.0 | 0.3126 | 0.9649 | | 0.1644 | 115.0 | 230 | 0.2690 | 0.4261 | 0.6855 | 0.9654 | nan | 0.3803 | 0.9908 | 0.0 | 0.3136 | 0.9648 | | 0.1594 | 120.0 | 240 | 0.2636 | 0.4256 | 0.6832 | 0.9656 | nan | 0.3752 | 0.9912 | 0.0 | 0.3118 | 0.9650 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_6

# dropoff-utcustom-train-SF-RGBD-b5_6 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1429 - Mean Iou: 0.6443 - Mean Accuracy: 0.6853 - Overall Accuracy: 0.9669 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3782 - Accuracy Undropoff: 0.9925 - Iou Unlabeled: nan - Iou Dropoff: 0.3223 - Iou Undropoff: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.159 | 5.0 | 10 | 1.0040 | 0.2283 | 0.5676 | 0.6267 | nan | 0.5031 | 0.6321 | 0.0 | 0.0644 | 0.6203 | | 0.8345 | 10.0 | 20 | 0.7480 | 0.3236 | 0.5320 | 0.9158 | nan | 0.1134 | 0.9506 | 0.0 | 0.0555 | 0.9154 | | 0.5406 | 15.0 | 30 | 0.5477 | 0.3223 | 0.5049 | 0.9513 | nan | 0.0179 | 0.9918 | 0.0 | 0.0157 | 0.9513 | | 0.3695 | 20.0 | 40 | 0.4590 | 0.3215 | 0.5036 | 0.9519 | nan | 0.0146 | 0.9926 | 0.0 | 0.0125 | 0.9519 | | 0.3053 | 25.0 | 50 | 0.3790 | 0.3196 | 0.5001 | 0.9565 | nan | 0.0023 | 0.9979 | 0.0 | 0.0022 | 0.9565 | | 0.2436 | 30.0 | 60 | 0.3303 | 0.4812 | 0.5020 | 0.9568 | nan | 0.0059 | 0.9981 | nan | 0.0056 | 0.9568 | | 0.2148 | 35.0 | 70 | 0.2739 | 0.4794 | 0.5002 | 0.9580 | nan | 0.0008 | 0.9996 | nan | 0.0008 | 0.9580 | | 0.1983 | 40.0 | 80 | 0.2348 | 0.5079 | 0.5284 | 0.9595 | nan | 0.0582 | 0.9986 | nan | 0.0564 | 0.9594 | | 0.1784 | 45.0 | 90 | 0.2178 | 0.6064 | 0.6440 | 0.9631 | nan | 0.2960 | 0.9920 | nan | 0.2501 | 0.9626 | | 0.1631 | 50.0 | 100 | 0.1943 | 0.6223 | 0.6811 | 0.9607 | nan | 0.3760 | 0.9861 | nan | 0.2846 | 0.9601 | | 0.1468 | 55.0 | 110 | 0.1759 | 0.6206 | 0.6731 | 0.9617 | nan | 0.3583 | 0.9879 | nan | 0.2801 | 0.9611 | | 0.1353 | 60.0 | 120 | 0.1657 | 0.6014 | 0.6335 | 0.9639 | nan | 0.2731 | 0.9939 | nan | 0.2393 | 0.9635 | | 0.1474 | 65.0 | 130 | 0.1590 | 0.5943 | 0.6228 | 0.9641 | nan | 0.2505 | 0.9951 | nan | 0.2249 | 0.9637 | | 0.1172 | 70.0 | 140 | 0.1562 | 0.6272 | 0.6662 | 0.9653 | nan | 0.3400 | 0.9924 | nan | 0.2896 | 0.9648 | | 0.1169 | 75.0 | 150 | 0.1538 | 0.6302 | 0.6696 | 0.9656 | nan | 0.3467 | 0.9925 | nan | 0.2954 | 0.9651 | | 0.1263 | 80.0 | 160 | 0.1540 | 0.6372 | 0.6784 | 0.9661 | nan | 0.3645 | 0.9922 | nan | 0.3089 | 0.9656 | | 0.1028 | 85.0 | 170 | 0.1512 | 0.6462 | 0.6948 | 0.9659 | nan | 0.3992 | 0.9904 | nan | 0.3271 | 0.9653 | | 0.1163 | 90.0 | 180 | 0.1493 | 0.6469 | 0.6932 | 0.9663 | nan | 0.3953 | 0.9911 | nan | 0.3280 | 0.9658 | | 0.0998 | 95.0 | 190 | 0.1481 | 0.6457 | 0.6894 | 0.9666 | nan | 0.3869 | 0.9918 | nan | 0.3253 | 0.9661 | | 0.0997 | 100.0 | 200 | 0.1465 | 0.6454 | 0.6893 | 0.9665 | nan | 0.3869 | 0.9917 | nan | 0.3247 | 0.9660 | | 0.0998 | 105.0 | 210 | 0.1473 | 0.6488 | 0.6937 | 0.9668 | nan | 0.3958 | 0.9916 | nan | 0.3313 | 0.9662 | | 0.1003 | 110.0 | 220 | 0.1437 | 0.6401 | 0.6774 | 0.9671 | nan | 0.3614 | 0.9934 | nan | 0.3136 | 0.9666 | | 0.0932 | 115.0 | 230 | 0.1434 | 0.6469 | 0.6898 | 0.9669 | nan | 0.3876 | 0.9920 | nan | 0.3275 | 0.9664 | | 0.0942 | 120.0 | 240 | 0.1429 | 0.6443 | 0.6853 | 0.9669 | nan | 0.3782 | 0.9925 | nan | 0.3223 | 0.9664 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGBD-b5_7

# dropoff-utcustom-train-SF-RGBD-b5_7 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1296 - Mean Iou: 0.6242 - Mean Accuracy: 0.6623 - Overall Accuracy: 0.9652 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3319 - Accuracy Undropoff: 0.9926 - Iou Unlabeled: nan - Iou Dropoff: 0.2838 - Iou Undropoff: 0.9647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.9278 | 5.0 | 10 | 0.8454 | 0.3197 | 0.5545 | 0.8788 | nan | 0.2009 | 0.9082 | 0.0 | 0.0807 | 0.8785 | | 0.5551 | 10.0 | 20 | 0.4668 | 0.3221 | 0.5042 | 0.9540 | nan | 0.0135 | 0.9948 | 0.0 | 0.0122 | 0.9540 | | 0.3667 | 15.0 | 30 | 0.3354 | 0.3218 | 0.5035 | 0.9570 | nan | 0.0088 | 0.9982 | 0.0 | 0.0085 | 0.9570 | | 0.2402 | 20.0 | 40 | 0.2678 | 0.5985 | 0.6492 | 0.9587 | nan | 0.3116 | 0.9868 | nan | 0.2388 | 0.9582 | | 0.1562 | 25.0 | 50 | 0.2101 | 0.6240 | 0.6719 | 0.9631 | nan | 0.3544 | 0.9895 | nan | 0.2854 | 0.9625 | | 0.1159 | 30.0 | 60 | 0.1704 | 0.6262 | 0.6641 | 0.9654 | nan | 0.3353 | 0.9928 | nan | 0.2875 | 0.9650 | | 0.0869 | 35.0 | 70 | 0.1443 | 0.6380 | 0.6817 | 0.9657 | nan | 0.3720 | 0.9915 | nan | 0.3108 | 0.9652 | | 0.079 | 40.0 | 80 | 0.1350 | 0.6072 | 0.6360 | 0.9654 | nan | 0.2766 | 0.9953 | nan | 0.2494 | 0.9650 | | 0.0647 | 45.0 | 90 | 0.1370 | 0.5800 | 0.6031 | 0.9643 | nan | 0.2090 | 0.9971 | nan | 0.1959 | 0.9640 | | 0.0587 | 50.0 | 100 | 0.1336 | 0.6276 | 0.6796 | 0.9628 | nan | 0.3707 | 0.9885 | nan | 0.2929 | 0.9622 | | 0.0575 | 55.0 | 110 | 0.1313 | 0.6189 | 0.6531 | 0.9654 | nan | 0.3126 | 0.9937 | nan | 0.2729 | 0.9649 | | 0.0527 | 60.0 | 120 | 0.1298 | 0.6252 | 0.6655 | 0.9648 | nan | 0.3391 | 0.9920 | nan | 0.2860 | 0.9643 | | 0.0491 | 65.0 | 130 | 0.1313 | 0.6110 | 0.6492 | 0.9635 | nan | 0.3063 | 0.9920 | nan | 0.2589 | 0.9631 | | 0.0441 | 70.0 | 140 | 0.1295 | 0.6103 | 0.6429 | 0.9648 | nan | 0.2919 | 0.9939 | nan | 0.2562 | 0.9643 | | 0.0426 | 75.0 | 150 | 0.1233 | 0.6271 | 0.6633 | 0.9659 | nan | 0.3333 | 0.9933 | nan | 0.2887 | 0.9654 | | 0.0477 | 80.0 | 160 | 0.1286 | 0.6255 | 0.6629 | 0.9655 | nan | 0.3328 | 0.9929 | nan | 0.2861 | 0.9650 | | 0.039 | 85.0 | 170 | 0.1265 | 0.6380 | 0.6824 | 0.9656 | nan | 0.3735 | 0.9913 | nan | 0.3109 | 0.9650 | | 0.0378 | 90.0 | 180 | 0.1309 | 0.6185 | 0.6543 | 0.9650 | nan | 0.3154 | 0.9932 | nan | 0.2725 | 0.9645 | | 0.0362 | 95.0 | 190 | 0.1266 | 0.6311 | 0.6715 | 0.9655 | nan | 0.3508 | 0.9922 | nan | 0.2973 | 0.9650 | | 0.0394 | 100.0 | 200 | 0.1307 | 0.6274 | 0.6635 | 0.9659 | nan | 0.3337 | 0.9934 | nan | 0.2894 | 0.9655 | | 0.0362 | 105.0 | 210 | 0.1271 | 0.6366 | 0.6789 | 0.9658 | nan | 0.3661 | 0.9918 | nan | 0.3080 | 0.9653 | | 0.0361 | 110.0 | 220 | 0.1274 | 0.6317 | 0.6736 | 0.9653 | nan | 0.3554 | 0.9918 | nan | 0.2987 | 0.9648 | | 0.0353 | 115.0 | 230 | 0.1290 | 0.6216 | 0.6579 | 0.9652 | nan | 0.3228 | 0.9931 | nan | 0.2784 | 0.9647 | | 0.0344 | 120.0 | 240 | 0.1296 | 0.6242 | 0.6623 | 0.9652 | nan | 0.3319 | 0.9926 | nan | 0.2838 | 0.9647 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b5_1

# dropoff-utcustom-train-SF-RGB-b5_1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.6279 - Mean Iou: 0.4054 - Mean Accuracy: 0.7471 - Overall Accuracy: 0.8860 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5956 - Accuracy Undropoff: 0.8986 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3318 - Iou Undropoff: 0.8843 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0071 | 5.0 | 10 | 1.0206 | 0.1745 | 0.2748 | 0.5034 | nan | 0.0255 | 0.5241 | 0.0 | 0.0147 | 0.5087 | | 0.9688 | 10.0 | 20 | 0.9873 | 0.2140 | 0.3486 | 0.5771 | nan | 0.0992 | 0.5979 | 0.0 | 0.0582 | 0.5838 | | 0.9406 | 15.0 | 30 | 0.9313 | 0.2613 | 0.4446 | 0.6655 | nan | 0.2038 | 0.6855 | 0.0 | 0.1135 | 0.6705 | | 0.9278 | 20.0 | 40 | 0.8851 | 0.2930 | 0.5149 | 0.7111 | nan | 0.3009 | 0.7289 | 0.0 | 0.1648 | 0.7142 | | 0.8956 | 25.0 | 50 | 0.8563 | 0.3118 | 0.5642 | 0.7358 | nan | 0.3770 | 0.7514 | 0.0 | 0.1985 | 0.7370 | | 0.8674 | 30.0 | 60 | 0.8260 | 0.3303 | 0.6086 | 0.7664 | nan | 0.4366 | 0.7807 | 0.0 | 0.2246 | 0.7664 | | 0.8438 | 35.0 | 70 | 0.8149 | 0.3347 | 0.6355 | 0.7671 | nan | 0.4921 | 0.7790 | 0.0 | 0.2381 | 0.7660 | | 0.8309 | 40.0 | 80 | 0.7881 | 0.3459 | 0.6472 | 0.7847 | nan | 0.4972 | 0.7972 | 0.0 | 0.2539 | 0.7839 | | 0.8069 | 45.0 | 90 | 0.7640 | 0.3567 | 0.6617 | 0.8041 | nan | 0.5063 | 0.8170 | 0.0 | 0.2668 | 0.8033 | | 0.7779 | 50.0 | 100 | 0.7486 | 0.3637 | 0.6792 | 0.8145 | nan | 0.5316 | 0.8268 | 0.0 | 0.2778 | 0.8132 | | 0.7695 | 55.0 | 110 | 0.7354 | 0.3684 | 0.6936 | 0.8214 | nan | 0.5542 | 0.8329 | 0.0 | 0.2858 | 0.8195 | | 0.7568 | 60.0 | 120 | 0.7164 | 0.3757 | 0.7032 | 0.8365 | nan | 0.5577 | 0.8486 | 0.0 | 0.2924 | 0.8347 | | 0.7285 | 65.0 | 130 | 0.6976 | 0.3836 | 0.7119 | 0.8484 | nan | 0.5630 | 0.8608 | 0.0 | 0.3042 | 0.8467 | | 0.7217 | 70.0 | 140 | 0.6922 | 0.3857 | 0.7217 | 0.8499 | nan | 0.5817 | 0.8616 | 0.0 | 0.3091 | 0.8480 | | 0.7095 | 75.0 | 150 | 0.6708 | 0.3926 | 0.7287 | 0.8624 | nan | 0.5828 | 0.8745 | 0.0 | 0.3172 | 0.8605 | | 0.6944 | 80.0 | 160 | 0.6637 | 0.3951 | 0.7320 | 0.8660 | nan | 0.5858 | 0.8781 | 0.0 | 0.3212 | 0.8641 | | 0.6878 | 85.0 | 170 | 0.6632 | 0.3942 | 0.7397 | 0.8673 | nan | 0.6005 | 0.8788 | 0.0 | 0.3175 | 0.8652 | | 0.6868 | 90.0 | 180 | 0.6468 | 0.3998 | 0.7391 | 0.8756 | nan | 0.5902 | 0.8880 | 0.0 | 0.3257 | 0.8739 | | 0.6581 | 95.0 | 190 | 0.6444 | 0.4003 | 0.7421 | 0.8776 | nan | 0.5942 | 0.8899 | 0.0 | 0.3249 | 0.8759 | | 0.6587 | 100.0 | 200 | 0.6383 | 0.4026 | 0.7427 | 0.8814 | nan | 0.5914 | 0.8940 | 0.0 | 0.3281 | 0.8797 | | 0.6525 | 105.0 | 210 | 0.6334 | 0.4032 | 0.7434 | 0.8825 | nan | 0.5918 | 0.8951 | 0.0 | 0.3289 | 0.8808 | | 0.658 | 110.0 | 220 | 0.6345 | 0.4026 | 0.7451 | 0.8811 | nan | 0.5968 | 0.8934 | 0.0 | 0.3285 | 0.8793 | | 0.6575 | 115.0 | 230 | 0.6300 | 0.4050 | 0.7463 | 0.8851 | nan | 0.5948 | 0.8977 | 0.0 | 0.3314 | 0.8835 | | 0.6625 | 120.0 | 240 | 0.6279 | 0.4054 | 0.7471 | 0.8860 | nan | 0.5956 | 0.8986 | 0.0 | 0.3318 | 0.8843 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b5_2

# dropoff-utcustom-train-SF-RGB-b5_2 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4848 - Mean Iou: 0.4257 - Mean Accuracy: 0.7972 - Overall Accuracy: 0.9466 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.6343 - Accuracy Undropoff: 0.9601 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3321 - Iou Undropoff: 0.9451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0108 | 5.0 | 10 | 1.0721 | 0.1514 | 0.5401 | 0.4205 | nan | 0.6706 | 0.4096 | 0.0 | 0.0494 | 0.4047 | | 0.9654 | 10.0 | 20 | 0.9802 | 0.2190 | 0.6570 | 0.5944 | nan | 0.7253 | 0.5887 | 0.0 | 0.0745 | 0.5826 | | 0.9175 | 15.0 | 30 | 0.9047 | 0.2553 | 0.7350 | 0.6792 | nan | 0.7960 | 0.6741 | 0.0 | 0.0973 | 0.6686 | | 0.9052 | 20.0 | 40 | 0.8427 | 0.2812 | 0.7661 | 0.7377 | nan | 0.7971 | 0.7351 | 0.0 | 0.1146 | 0.7290 | | 0.8555 | 25.0 | 50 | 0.7970 | 0.3063 | 0.7827 | 0.7900 | nan | 0.7748 | 0.7906 | 0.0 | 0.1357 | 0.7832 | | 0.8291 | 30.0 | 60 | 0.7543 | 0.3289 | 0.7891 | 0.8332 | nan | 0.7410 | 0.8372 | 0.0 | 0.1586 | 0.8282 | | 0.7923 | 35.0 | 70 | 0.7327 | 0.3375 | 0.7961 | 0.8471 | nan | 0.7405 | 0.8517 | 0.0 | 0.1701 | 0.8425 | | 0.7724 | 40.0 | 80 | 0.6994 | 0.3529 | 0.7968 | 0.8719 | nan | 0.7149 | 0.8787 | 0.0 | 0.1906 | 0.8682 | | 0.7215 | 45.0 | 90 | 0.6675 | 0.3694 | 0.7935 | 0.8954 | nan | 0.6824 | 0.9047 | 0.0 | 0.2157 | 0.8926 | | 0.6907 | 50.0 | 100 | 0.6521 | 0.3742 | 0.7998 | 0.9000 | nan | 0.6904 | 0.9091 | 0.0 | 0.2252 | 0.8973 | | 0.6768 | 55.0 | 110 | 0.6260 | 0.3850 | 0.8022 | 0.9118 | nan | 0.6827 | 0.9217 | 0.0 | 0.2455 | 0.9094 | | 0.659 | 60.0 | 120 | 0.6010 | 0.3965 | 0.7973 | 0.9244 | nan | 0.6586 | 0.9359 | 0.0 | 0.2671 | 0.9224 | | 0.6265 | 65.0 | 130 | 0.5847 | 0.4005 | 0.7992 | 0.9276 | nan | 0.6592 | 0.9393 | 0.0 | 0.2757 | 0.9258 | | 0.6134 | 70.0 | 140 | 0.5673 | 0.4060 | 0.8022 | 0.9316 | nan | 0.6611 | 0.9433 | 0.0 | 0.2881 | 0.9297 | | 0.5864 | 75.0 | 150 | 0.5401 | 0.4132 | 0.7961 | 0.9383 | nan | 0.6410 | 0.9511 | 0.0 | 0.3029 | 0.9366 | | 0.5686 | 80.0 | 160 | 0.5289 | 0.4153 | 0.7974 | 0.9395 | nan | 0.6424 | 0.9524 | 0.0 | 0.3080 | 0.9379 | | 0.5597 | 85.0 | 170 | 0.5386 | 0.4114 | 0.8079 | 0.9350 | nan | 0.6692 | 0.9465 | 0.0 | 0.3011 | 0.9331 | | 0.5718 | 90.0 | 180 | 0.5080 | 0.4210 | 0.7947 | 0.9438 | nan | 0.6321 | 0.9573 | 0.0 | 0.3208 | 0.9423 | | 0.517 | 95.0 | 190 | 0.5026 | 0.4222 | 0.7956 | 0.9445 | nan | 0.6332 | 0.9580 | 0.0 | 0.3236 | 0.9430 | | 0.5252 | 100.0 | 200 | 0.4990 | 0.4232 | 0.7969 | 0.9450 | nan | 0.6354 | 0.9584 | 0.0 | 0.3261 | 0.9435 | | 0.5174 | 105.0 | 210 | 0.4951 | 0.4223 | 0.8012 | 0.9437 | nan | 0.6457 | 0.9567 | 0.0 | 0.3249 | 0.9422 | | 0.5217 | 110.0 | 220 | 0.4882 | 0.4238 | 0.7993 | 0.9450 | nan | 0.6404 | 0.9582 | 0.0 | 0.3280 | 0.9435 | | 0.5224 | 115.0 | 230 | 0.4846 | 0.4258 | 0.7968 | 0.9467 | nan | 0.6333 | 0.9603 | 0.0 | 0.3321 | 0.9452 | | 0.5399 | 120.0 | 240 | 0.4848 | 0.4257 | 0.7972 | 0.9466 | nan | 0.6343 | 0.9601 | 0.0 | 0.3321 | 0.9451 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b5_3

# dropoff-utcustom-train-SF-RGB-b5_3 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3770 - Mean Iou: 0.4572 - Mean Accuracy: 0.7822 - Overall Accuracy: 0.9640 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5839 - Accuracy Undropoff: 0.9805 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4086 - Iou Undropoff: 0.9631 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.3135 | 5.0 | 10 | 1.2008 | 0.0546 | 0.2586 | 0.1227 | nan | 0.4069 | 0.1103 | 0.0 | 0.0535 | 0.1102 | | 1.2309 | 10.0 | 20 | 1.1294 | 0.1176 | 0.3397 | 0.2490 | nan | 0.4388 | 0.2407 | 0.0 | 0.1129 | 0.2400 | | 1.1346 | 15.0 | 30 | 1.0395 | 0.2171 | 0.4865 | 0.5022 | nan | 0.4694 | 0.5036 | 0.0 | 0.1524 | 0.4989 | | 1.1088 | 20.0 | 40 | 0.9755 | 0.2608 | 0.5521 | 0.6176 | nan | 0.4808 | 0.6235 | 0.0 | 0.1661 | 0.6163 | | 1.007 | 25.0 | 50 | 0.9197 | 0.2895 | 0.5959 | 0.6775 | nan | 0.5068 | 0.6849 | 0.0 | 0.1923 | 0.6763 | | 0.9145 | 30.0 | 60 | 0.8635 | 0.3162 | 0.6299 | 0.7335 | nan | 0.5168 | 0.7429 | 0.0 | 0.2156 | 0.7329 | | 0.8745 | 35.0 | 70 | 0.8070 | 0.3398 | 0.6784 | 0.7808 | nan | 0.5667 | 0.7901 | 0.0 | 0.2404 | 0.7791 | | 0.8088 | 40.0 | 80 | 0.7442 | 0.3667 | 0.7191 | 0.8290 | nan | 0.5993 | 0.8389 | 0.0 | 0.2730 | 0.8272 | | 0.7184 | 45.0 | 90 | 0.6956 | 0.3832 | 0.7513 | 0.8603 | nan | 0.6323 | 0.8702 | 0.0 | 0.2915 | 0.8580 | | 0.6908 | 50.0 | 100 | 0.6751 | 0.3931 | 0.7592 | 0.8748 | nan | 0.6332 | 0.8853 | 0.0 | 0.3067 | 0.8728 | | 0.643 | 55.0 | 110 | 0.6101 | 0.4134 | 0.7714 | 0.9108 | nan | 0.6194 | 0.9234 | 0.0 | 0.3308 | 0.9094 | | 0.6014 | 60.0 | 120 | 0.5971 | 0.4166 | 0.7826 | 0.9189 | nan | 0.6339 | 0.9313 | 0.0 | 0.3324 | 0.9175 | | 0.5685 | 65.0 | 130 | 0.5595 | 0.4304 | 0.7946 | 0.9328 | nan | 0.6439 | 0.9453 | 0.0 | 0.3599 | 0.9314 | | 0.5172 | 70.0 | 140 | 0.5344 | 0.4373 | 0.8010 | 0.9406 | nan | 0.6488 | 0.9532 | 0.0 | 0.3727 | 0.9393 | | 0.4757 | 75.0 | 150 | 0.4963 | 0.4434 | 0.7997 | 0.9490 | nan | 0.6368 | 0.9626 | 0.0 | 0.3822 | 0.9479 | | 0.4288 | 80.0 | 160 | 0.4599 | 0.4488 | 0.7936 | 0.9556 | nan | 0.6169 | 0.9702 | 0.0 | 0.3918 | 0.9546 | | 0.4124 | 85.0 | 170 | 0.4710 | 0.4469 | 0.7989 | 0.9540 | nan | 0.6296 | 0.9681 | 0.0 | 0.3876 | 0.9529 | | 0.4995 | 90.0 | 180 | 0.4209 | 0.4537 | 0.7883 | 0.9606 | nan | 0.6004 | 0.9762 | 0.0 | 0.4015 | 0.9597 | | 0.3815 | 95.0 | 190 | 0.4287 | 0.4524 | 0.7919 | 0.9595 | nan | 0.6090 | 0.9748 | 0.0 | 0.3988 | 0.9586 | | 0.3764 | 100.0 | 200 | 0.4245 | 0.4529 | 0.7913 | 0.9600 | nan | 0.6073 | 0.9753 | 0.0 | 0.3998 | 0.9590 | | 0.4074 | 105.0 | 210 | 0.4096 | 0.4542 | 0.7894 | 0.9613 | nan | 0.6018 | 0.9769 | 0.0 | 0.4021 | 0.9603 | | 0.3975 | 110.0 | 220 | 0.4107 | 0.4538 | 0.7905 | 0.9610 | nan | 0.6045 | 0.9765 | 0.0 | 0.4013 | 0.9601 | | 0.3598 | 115.0 | 230 | 0.3918 | 0.4558 | 0.7863 | 0.9627 | nan | 0.5939 | 0.9787 | 0.0 | 0.4057 | 0.9618 | | 0.3709 | 120.0 | 240 | 0.3770 | 0.4572 | 0.7822 | 0.9640 | nan | 0.5839 | 0.9805 | 0.0 | 0.4086 | 0.9631 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b5_4

# dropoff-utcustom-train-SF-RGB-b5_4 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2242 - Mean Iou: 0.4568 - Mean Accuracy: 0.7402 - Overall Accuracy: 0.9696 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4899 - Accuracy Undropoff: 0.9904 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4016 - Iou Undropoff: 0.9690 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.9465 | 5.0 | 10 | 0.9974 | 0.2695 | 0.5001 | 0.6771 | nan | 0.3071 | 0.6931 | 0.0 | 0.1261 | 0.6824 | | 0.8558 | 10.0 | 20 | 0.8237 | 0.3822 | 0.7119 | 0.8664 | nan | 0.5434 | 0.8804 | 0.0 | 0.2787 | 0.8678 | | 0.7585 | 15.0 | 30 | 0.6801 | 0.4232 | 0.7487 | 0.9194 | nan | 0.5625 | 0.9349 | 0.0 | 0.3494 | 0.9202 | | 0.715 | 20.0 | 40 | 0.6076 | 0.4298 | 0.7663 | 0.9232 | nan | 0.5952 | 0.9375 | 0.0 | 0.3661 | 0.9233 | | 0.6145 | 25.0 | 50 | 0.5298 | 0.4398 | 0.7760 | 0.9380 | nan | 0.5994 | 0.9527 | 0.0 | 0.3819 | 0.9375 | | 0.5355 | 30.0 | 60 | 0.4821 | 0.4426 | 0.7749 | 0.9428 | nan | 0.5918 | 0.9581 | 0.0 | 0.3857 | 0.9422 | | 0.4619 | 35.0 | 70 | 0.4266 | 0.4493 | 0.7716 | 0.9524 | nan | 0.5743 | 0.9688 | 0.0 | 0.3962 | 0.9517 | | 0.4367 | 40.0 | 80 | 0.3941 | 0.4519 | 0.7738 | 0.9568 | nan | 0.5742 | 0.9734 | 0.0 | 0.3997 | 0.9559 | | 0.3839 | 45.0 | 90 | 0.3801 | 0.4528 | 0.7796 | 0.9577 | nan | 0.5853 | 0.9738 | 0.0 | 0.4017 | 0.9567 | | 0.3164 | 50.0 | 100 | 0.3549 | 0.4543 | 0.7785 | 0.9608 | nan | 0.5797 | 0.9773 | 0.0 | 0.4030 | 0.9599 | | 0.3018 | 55.0 | 110 | 0.3327 | 0.4573 | 0.7731 | 0.9639 | nan | 0.5650 | 0.9812 | 0.0 | 0.4087 | 0.9631 | | 0.2646 | 60.0 | 120 | 0.3127 | 0.4590 | 0.7703 | 0.9658 | nan | 0.5571 | 0.9835 | 0.0 | 0.4121 | 0.9650 | | 0.2378 | 65.0 | 130 | 0.2958 | 0.4628 | 0.7728 | 0.9673 | nan | 0.5607 | 0.9850 | 0.0 | 0.4217 | 0.9666 | | 0.2076 | 70.0 | 140 | 0.2778 | 0.4675 | 0.7729 | 0.9693 | nan | 0.5586 | 0.9871 | 0.0 | 0.4340 | 0.9686 | | 0.1951 | 75.0 | 150 | 0.2648 | 0.4666 | 0.7719 | 0.9692 | nan | 0.5567 | 0.9871 | 0.0 | 0.4314 | 0.9685 | | 0.1734 | 80.0 | 160 | 0.2522 | 0.4673 | 0.7643 | 0.9703 | nan | 0.5397 | 0.9890 | 0.0 | 0.4322 | 0.9696 | | 0.1569 | 85.0 | 170 | 0.2436 | 0.4660 | 0.7603 | 0.9703 | nan | 0.5312 | 0.9894 | 0.0 | 0.4282 | 0.9697 | | 0.1691 | 90.0 | 180 | 0.2411 | 0.4647 | 0.7624 | 0.9697 | nan | 0.5363 | 0.9885 | 0.0 | 0.4250 | 0.9690 | | 0.1498 | 95.0 | 190 | 0.2335 | 0.4623 | 0.7537 | 0.9699 | nan | 0.5179 | 0.9895 | 0.0 | 0.4176 | 0.9692 | | 0.1478 | 100.0 | 200 | 0.2281 | 0.4585 | 0.7420 | 0.9700 | nan | 0.4934 | 0.9906 | 0.0 | 0.4062 | 0.9693 | | 0.1407 | 105.0 | 210 | 0.2278 | 0.4615 | 0.7501 | 0.9701 | nan | 0.5102 | 0.9900 | 0.0 | 0.4151 | 0.9694 | | 0.1397 | 110.0 | 220 | 0.2305 | 0.4610 | 0.7512 | 0.9698 | nan | 0.5129 | 0.9896 | 0.0 | 0.4140 | 0.9691 | | 0.1317 | 115.0 | 230 | 0.2265 | 0.4576 | 0.7430 | 0.9695 | nan | 0.4959 | 0.9901 | 0.0 | 0.4038 | 0.9689 | | 0.1548 | 120.0 | 240 | 0.2242 | 0.4568 | 0.7402 | 0.9696 | nan | 0.4899 | 0.9904 | 0.0 | 0.4016 | 0.9690 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b5_6

# dropoff-utcustom-train-SF-RGB-b5_6 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Mean Iou: 0.6980 - Mean Accuracy: 0.7503 - Overall Accuracy: 0.9714 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5091 - Accuracy Undropoff: 0.9915 - Iou Unlabeled: nan - Iou Dropoff: 0.4253 - Iou Undropoff: 0.9708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 1.0694 | 5.0 | 10 | 1.0190 | 0.2533 | 0.6371 | 0.6676 | nan | 0.6038 | 0.6703 | 0.0 | 0.0976 | 0.6624 | | 0.8457 | 10.0 | 20 | 0.7681 | 0.4126 | 0.7662 | 0.9307 | nan | 0.5867 | 0.9457 | 0.0 | 0.3078 | 0.9300 | | 0.6049 | 15.0 | 30 | 0.5718 | 0.4362 | 0.7527 | 0.9568 | nan | 0.5301 | 0.9753 | 0.0 | 0.3527 | 0.9561 | | 0.5206 | 20.0 | 40 | 0.4181 | 0.4522 | 0.7468 | 0.9662 | nan | 0.5076 | 0.9861 | 0.0 | 0.3909 | 0.9656 | | 0.3478 | 25.0 | 50 | 0.3144 | 0.4603 | 0.7376 | 0.9709 | nan | 0.4832 | 0.9920 | 0.0 | 0.4105 | 0.9705 | | 0.2023 | 30.0 | 60 | 0.2893 | 0.4654 | 0.7612 | 0.9701 | nan | 0.5332 | 0.9891 | 0.0 | 0.4267 | 0.9695 | | 0.1367 | 35.0 | 70 | 0.2351 | 0.6813 | 0.7176 | 0.9715 | nan | 0.4407 | 0.9946 | nan | 0.3916 | 0.9710 | | 0.1272 | 40.0 | 80 | 0.2364 | 0.6824 | 0.7217 | 0.9713 | nan | 0.4495 | 0.9939 | nan | 0.3941 | 0.9707 | | 0.0929 | 45.0 | 90 | 0.2536 | 0.4704 | 0.7617 | 0.9718 | nan | 0.5326 | 0.9909 | 0.0 | 0.4401 | 0.9712 | | 0.0756 | 50.0 | 100 | 0.2253 | 0.6950 | 0.7479 | 0.9710 | nan | 0.5045 | 0.9912 | nan | 0.4197 | 0.9704 | | 0.0756 | 55.0 | 110 | 0.2305 | 0.7043 | 0.7606 | 0.9716 | nan | 0.5305 | 0.9908 | nan | 0.4375 | 0.9710 | | 0.0721 | 60.0 | 120 | 0.2213 | 0.6964 | 0.7448 | 0.9716 | nan | 0.4974 | 0.9922 | nan | 0.4218 | 0.9711 | | 0.0683 | 65.0 | 130 | 0.2338 | 0.7047 | 0.7631 | 0.9715 | nan | 0.5359 | 0.9904 | nan | 0.4385 | 0.9708 | | 0.0642 | 70.0 | 140 | 0.2314 | 0.7046 | 0.7637 | 0.9714 | nan | 0.5373 | 0.9902 | nan | 0.4385 | 0.9707 | | 0.0623 | 75.0 | 150 | 0.2205 | 0.7013 | 0.7565 | 0.9714 | nan | 0.5222 | 0.9909 | nan | 0.4317 | 0.9708 | | 0.0601 | 80.0 | 160 | 0.2209 | 0.6983 | 0.7496 | 0.9715 | nan | 0.5075 | 0.9917 | nan | 0.4257 | 0.9709 | | 0.0557 | 85.0 | 170 | 0.2067 | 0.6982 | 0.7463 | 0.9719 | nan | 0.5003 | 0.9923 | nan | 0.4252 | 0.9713 | | 0.0571 | 90.0 | 180 | 0.2354 | 0.7022 | 0.7603 | 0.9712 | nan | 0.5302 | 0.9904 | nan | 0.4339 | 0.9706 | | 0.0544 | 95.0 | 190 | 0.2240 | 0.7010 | 0.7562 | 0.9714 | nan | 0.5215 | 0.9909 | nan | 0.4311 | 0.9708 | | 0.0553 | 100.0 | 200 | 0.2204 | 0.6968 | 0.7454 | 0.9717 | nan | 0.4987 | 0.9922 | nan | 0.4225 | 0.9711 | | 0.0525 | 105.0 | 210 | 0.2332 | 0.7050 | 0.7625 | 0.9716 | nan | 0.5344 | 0.9906 | nan | 0.4390 | 0.9710 | | 0.0524 | 110.0 | 220 | 0.2371 | 0.7033 | 0.7605 | 0.9715 | nan | 0.5304 | 0.9906 | nan | 0.4359 | 0.9708 | | 0.0513 | 115.0 | 230 | 0.2333 | 0.6987 | 0.7519 | 0.9714 | nan | 0.5125 | 0.9913 | nan | 0.4267 | 0.9707 | | 0.0537 | 120.0 | 240 | 0.2315 | 0.6980 | 0.7503 | 0.9714 | nan | 0.5091 | 0.9915 | nan | 0.4253 | 0.9708 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]

sam1120/dropoff-utcustom-train-SF-RGB-b5_7

# dropoff-utcustom-train-SF-RGB-b5_7 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1841 - Mean Iou: 0.7025 - Mean Accuracy: 0.7532 - Overall Accuracy: 0.9721 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5145 - Accuracy Undropoff: 0.9919 - Iou Unlabeled: nan - Iou Dropoff: 0.4336 - Iou Undropoff: 0.9715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| | 0.8255 | 5.0 | 10 | 0.7949 | 0.4128 | 0.7856 | 0.9393 | nan | 0.6179 | 0.9533 | 0.0 | 0.3007 | 0.9377 | | 0.4434 | 10.0 | 20 | 0.4247 | 0.4471 | 0.7066 | 0.9705 | nan | 0.4187 | 0.9944 | 0.0 | 0.3714 | 0.9700 | | 0.2107 | 15.0 | 30 | 0.2726 | 0.6711 | 0.7003 | 0.9715 | nan | 0.4046 | 0.9961 | nan | 0.3713 | 0.9710 | | 0.1678 | 20.0 | 40 | 0.2388 | 0.6801 | 0.7343 | 0.9691 | nan | 0.4782 | 0.9904 | nan | 0.3917 | 0.9685 | | 0.0972 | 25.0 | 50 | 0.1849 | 0.6764 | 0.7096 | 0.9715 | nan | 0.4241 | 0.9952 | nan | 0.3818 | 0.9709 | | 0.0604 | 30.0 | 60 | 0.2019 | 0.4644 | 0.7568 | 0.9704 | nan | 0.5239 | 0.9897 | 0.0 | 0.4236 | 0.9697 | | 0.0497 | 35.0 | 70 | 0.1793 | 0.6838 | 0.7345 | 0.9700 | nan | 0.4775 | 0.9914 | nan | 0.3983 | 0.9694 | | 0.0492 | 40.0 | 80 | 0.2000 | 0.4639 | 0.7567 | 0.9702 | nan | 0.5239 | 0.9896 | 0.0 | 0.4223 | 0.9695 | | 0.0409 | 45.0 | 90 | 0.1893 | 0.7030 | 0.7778 | 0.9696 | nan | 0.5687 | 0.9869 | nan | 0.4372 | 0.9688 | | 0.0328 | 50.0 | 100 | 0.1842 | 0.7040 | 0.7715 | 0.9704 | nan | 0.5545 | 0.9885 | nan | 0.4382 | 0.9697 | | 0.0332 | 55.0 | 110 | 0.1781 | 0.7015 | 0.7563 | 0.9715 | nan | 0.5216 | 0.9910 | nan | 0.4322 | 0.9709 | | 0.0314 | 60.0 | 120 | 0.1732 | 0.6890 | 0.7305 | 0.9717 | nan | 0.4675 | 0.9935 | nan | 0.4068 | 0.9711 | | 0.0318 | 65.0 | 130 | 0.1786 | 0.6971 | 0.7477 | 0.9715 | nan | 0.5037 | 0.9918 | nan | 0.4233 | 0.9709 | | 0.0291 | 70.0 | 140 | 0.1814 | 0.7119 | 0.7687 | 0.9725 | nan | 0.5466 | 0.9909 | nan | 0.4521 | 0.9718 | | 0.0273 | 75.0 | 150 | 0.1755 | 0.7101 | 0.7677 | 0.9722 | nan | 0.5446 | 0.9907 | nan | 0.4487 | 0.9715 | | 0.0274 | 80.0 | 160 | 0.1786 | 0.7006 | 0.7494 | 0.9720 | nan | 0.5066 | 0.9922 | nan | 0.4297 | 0.9714 | | 0.0248 | 85.0 | 170 | 0.1741 | 0.7029 | 0.7526 | 0.9722 | nan | 0.5131 | 0.9921 | nan | 0.4341 | 0.9716 | | 0.0248 | 90.0 | 180 | 0.1832 | 0.7050 | 0.7595 | 0.9719 | nan | 0.5278 | 0.9912 | nan | 0.4387 | 0.9713 | | 0.0242 | 95.0 | 190 | 0.1808 | 0.7028 | 0.7539 | 0.9720 | nan | 0.5160 | 0.9918 | nan | 0.4341 | 0.9714 | | 0.024 | 100.0 | 200 | 0.1796 | 0.7022 | 0.7501 | 0.9723 | nan | 0.5077 | 0.9925 | nan | 0.4327 | 0.9717 | | 0.0231 | 105.0 | 210 | 0.1835 | 0.7137 | 0.7731 | 0.9724 | nan | 0.5557 | 0.9905 | nan | 0.4556 | 0.9717 | | 0.0238 | 110.0 | 220 | 0.1823 | 0.7046 | 0.7565 | 0.9721 | nan | 0.5214 | 0.9917 | nan | 0.4376 | 0.9715 | | 0.0228 | 115.0 | 230 | 0.1833 | 0.7009 | 0.7504 | 0.9720 | nan | 0.5088 | 0.9921 | nan | 0.4305 | 0.9714 | | 0.0255 | 120.0 | 240 | 0.1841 | 0.7025 | 0.7532 | 0.9721 | nan | 0.5145 | 0.9919 | nan | 0.4336 | 0.9715 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "unlabeled", "dropoff", "undropoff" ]