Commit
·
5cc3657
1
Parent(s):
7247334
v0.1
Browse files- README.md +16 -0
- args.yaml +106 -0
- events.out.tfevents.1727424768.ip-10-192-12-149.2978.0 +3 -0
- images/F1_curve.png +0 -0
- images/PR_curve.png +0 -0
- images/P_curve.png +0 -0
- images/R_curve.png +0 -0
- images/confusion_matrix.png +0 -0
- images/confusion_matrix_normalized.png +0 -0
- images/labels.jpg +0 -0
- images/labels_correlogram.jpg +0 -0
- images/results.png +0 -0
- images/train_batch0.jpg +0 -0
- images/train_batch1.jpg +0 -0
- images/train_batch2.jpg +0 -0
- images/train_batch950.jpg +0 -0
- images/train_batch951.jpg +0 -0
- images/train_batch952.jpg +0 -0
- images/val_batch0_labels.jpg +0 -0
- images/val_batch0_pred.jpg +0 -0
- results.csv +201 -0
- val/F1_curve.png +0 -0
- val/PR_curve.png +0 -0
- val/P_curve.png +0 -0
- val/R_curve.png +0 -0
- val/confusion_matrix.png +0 -0
- val/confusion_matrix_normalized.png +0 -0
- val/val_batch0_labels.jpg +0 -0
- val/val_batch0_pred.jpg +0 -0
- val/val_batch1_labels.jpg +0 -0
- val/val_batch1_pred.jpg +0 -0
- val/val_batch2_labels.jpg +0 -0
- val/val_batch2_pred.jpg +0 -0
- weights/best.onnx +3 -0
- weights/best.pt +3 -0
- weights/best_saved_model/best_float16.tflite +3 -0
- weights/best_saved_model/best_float32.tflite +3 -0
- weights/best_saved_model/fingerprint.pb +3 -0
- weights/best_saved_model/metadata.yaml +14 -0
- weights/best_saved_model/saved_model.pb +3 -0
- weights/best_saved_model/variables/variables.data-00000-of-00001 +0 -0
- weights/best_saved_model/variables/variables.index +0 -0
- weights/last.pt +3 -0
README.md
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---
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license: agpl-3.0
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---
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---
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base_model: Ultralytics/YOLOv8
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license: agpl-3.0
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datasets:
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- openfoodfacts/crop-detection-dataset
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---
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# Crop detection
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When contributors use the mobile app, they are asked to take pictures of the product, then to crop it. To assist users during the process, we create a crop-detection model desin to detect the product edges.
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This model is used within the mobile app.
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<div style="text-align: center;">
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<img src="images/val_batch0_pred.jpg" alt="My Image" width="500">
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</div>
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## Evaluation metric:
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* mAP50_95: 0.895
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args.yaml
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task: detect
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mode: train
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model: models/yolov8n.pt
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data: datasets/data.yaml
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epochs: 200
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time: null
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patience: 100
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batch: 64
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imgsz: 640
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save: true
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save_period: -1
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cache: false
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device: null
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workers: 8
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project: null
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name: train2
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exist_ok: false
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pretrained: true
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optimizer: auto
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verbose: true
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seed: 0
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deterministic: true
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single_cls: false
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rect: false
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cos_lr: false
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close_mosaic: 10
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resume: false
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amp: false
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fraction: 1.0
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profile: false
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freeze: null
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multi_scale: false
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overlap_mask: true
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mask_ratio: 4
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dropout: 0.0
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val: true
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split: val
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save_json: false
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save_hybrid: false
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conf: null
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iou: 0.7
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max_det: 300
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half: false
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dnn: false
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plots: true
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source: null
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vid_stride: 1
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stream_buffer: false
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visualize: false
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augment: false
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agnostic_nms: false
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classes: null
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retina_masks: false
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embed: null
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show: false
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save_frames: false
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save_txt: false
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save_conf: false
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save_crop: false
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show_labels: true
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show_conf: true
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show_boxes: true
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line_width: null
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format: torchscript
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keras: false
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optimize: false
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int8: false
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dynamic: false
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simplify: true
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opset: null
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workspace: 4
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nms: false
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lr0: 0.01
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lrf: 0.01
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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warmup_momentum: 0.8
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warmup_bias_lr: 0.1
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box: 7.5
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cls: 0.5
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dfl: 1.5
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pose: 12.0
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kobj: 1.0
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label_smoothing: 0.0
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nbs: 64
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 0.0
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translate: 0.1
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scale: 0.5
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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bgr: 0.0
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mosaic: 1.0
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mixup: 0.0
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copy_paste: 0.0
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auto_augment: randaugment
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erasing: 0.4
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crop_fraction: 1.0
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cfg: null
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tracker: botsort.yaml
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save_dir: runs/detect/train2
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events.out.tfevents.1727424768.ip-10-192-12-149.2978.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:7b1d0785baa20685daf34bf0735f65dd4396a08b79f02d444f466a925ef3f9f9
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size 319181
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images/F1_curve.png
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images/PR_curve.png
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images/P_curve.png
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images/R_curve.png
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images/confusion_matrix.png
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images/confusion_matrix_normalized.png
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images/labels.jpg
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images/labels_correlogram.jpg
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images/results.png
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images/train_batch0.jpg
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images/train_batch1.jpg
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images/train_batch2.jpg
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images/train_batch950.jpg
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images/train_batch951.jpg
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images/train_batch952.jpg
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images/val_batch0_labels.jpg
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images/val_batch0_pred.jpg
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results.csv
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| 1 |
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epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
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| 2 |
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1, 0.80008, 2.5074, 1.3782, 0.00333, 1, 0.57367, 0.35436, 0.43317, 3.1431, 1.1459, 8e-05, 8e-05, 8e-05
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2, 0.5867, 1.9363, 1.1718, 0.00333, 1, 0.82976, 0.61493, 0.4634, 3.009, 1.1186, 0.00017911, 0.00017911, 0.00017911
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| 4 |
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3, 0.56338, 1.3547, 1.1606, 0.00333, 1, 0.88863, 0.64351, 0.6416, 3.0523, 1.2866, 0.00027723, 0.00027723, 0.00027723
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| 5 |
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4, 0.58937, 1.1196, 1.1537, 0.91392, 0.65152, 0.90737, 0.60201, 0.93362, 2.6745, 1.6978, 0.00037436, 0.00037436, 0.00037436
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| 6 |
+
5, 0.59195, 0.94609, 1.1737, 0.81707, 0.62121, 0.83961, 0.58676, 0.95035, 2.6987, 1.7313, 0.0004705, 0.0004705, 0.0004705
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| 7 |
+
6, 0.52959, 0.91186, 1.0979, 0.77371, 0.78788, 0.83787, 0.59014, 0.93177, 2.5269, 1.6172, 0.00056565, 0.00056565, 0.00056565
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| 8 |
+
7, 0.53359, 0.8694, 1.1023, 0.71995, 0.34848, 0.70387, 0.39719, 0.77529, 2.9421, 1.5642, 0.0006598, 0.0006598, 0.0006598
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| 9 |
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8, 0.53914, 0.82673, 1.098, 0.8819, 0.78788, 0.92133, 0.63259, 0.96658, 2.6084, 1.8356, 0.00075297, 0.00075297, 0.00075297
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| 10 |
+
9, 0.57903, 0.82612, 1.1099, 0.31538, 0.80303, 0.31444, 0.16938, 1.5251, 3.2544, 2.6845, 0.00084515, 0.00084515, 0.00084515
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| 11 |
+
10, 0.5986, 0.84391, 1.1413, 0.68575, 0.65152, 0.75425, 0.46357, 1.1917, 2.4841, 2.2189, 0.00093634, 0.00093634, 0.00093634
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| 12 |
+
11, 0.60276, 0.83325, 1.146, 0.84456, 0.82327, 0.90469, 0.4426, 1.6111, 2.3716, 2.9511, 0.0010265, 0.0010265, 0.0010265
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| 13 |
+
12, 0.62177, 0.81711, 1.1742, 0.69881, 0.8086, 0.76441, 0.43035, 1.3359, 2.3411, 2.7854, 0.0011157, 0.0011157, 0.0011157
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| 14 |
+
13, 0.63912, 0.7957, 1.1411, 0.34715, 0.82986, 0.38222, 0.2453, 1.4231, 3.3536, 3.3976, 0.001204, 0.001204, 0.001204
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| 15 |
+
14, 0.7276, 0.86252, 1.2411, 0.26262, 0.74242, 0.27547, 0.14673, 1.4135, 3.125, 2.7849, 0.0012912, 0.0012912, 0.0012912
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| 16 |
+
15, 0.68405, 0.82412, 1.2017, 0.49862, 0.43939, 0.46223, 0.23968, 1.5253, 2.717, 2.529, 0.0013774, 0.0013774, 0.0013774
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| 17 |
+
16, 0.71529, 0.81033, 1.2269, 0.18774, 0.45455, 0.15471, 0.07198, 1.7669, 3.2295, 3.0679, 0.0014627, 0.0014627, 0.0014627
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| 18 |
+
17, 0.77918, 0.81687, 1.2627, 0.16241, 0.19101, 0.14965, 0.06821, 2.5086, 3.184, 3.3335, 0.0015469, 0.0015469, 0.0015469
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| 19 |
+
18, 0.76013, 0.79234, 1.2864, 0.68509, 0.5303, 0.5587, 0.27369, 1.7142, 2.3055, 3.0371, 0.0016302, 0.0016302, 0.0016302
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| 20 |
+
19, 0.73189, 0.73936, 1.2275, 0.26179, 0.30303, 0.19232, 0.10241, 2.6906, 3.3375, 3.6926, 0.0017125, 0.0017125, 0.0017125
|
| 21 |
+
20, 0.72712, 0.7435, 1.2419, 0.2059, 0.13636, 0.08186, 0.03823, 2.7905, 3.7717, 3.6293, 0.0017938, 0.0017938, 0.0017938
|
| 22 |
+
21, 0.73077, 0.70411, 1.2535, 0.0161, 0.06061, 0.00536, 0.0008, 4.2476, 3.8557, 5.269, 0.001802, 0.001802, 0.001802
|
| 23 |
+
22, 0.76211, 0.7501, 1.2734, 0.45278, 0.43939, 0.36631, 0.12447, 2.1511, 2.2523, 3.5771, 0.0017921, 0.0017921, 0.0017921
|
| 24 |
+
23, 0.69578, 0.70559, 1.2007, 0.21431, 0.37879, 0.16552, 0.05218, 2.5806, 2.9601, 3.5978, 0.0017822, 0.0017822, 0.0017822
|
| 25 |
+
24, 0.71667, 0.66429, 1.206, 0.5666, 0.36364, 0.41359, 0.1633, 2.3022, 2.6837, 3.918, 0.0017723, 0.0017723, 0.0017723
|
| 26 |
+
25, 0.79609, 0.69014, 1.2662, 0.41326, 0.57576, 0.37733, 0.10664, 2.5646, 2.6698, 4.8492, 0.0017624, 0.0017624, 0.0017624
|
| 27 |
+
26, 0.72127, 0.6673, 1.2144, 0.43899, 0.54545, 0.39921, 0.12739, 2.566, 2.1014, 3.9268, 0.0017525, 0.0017525, 0.0017525
|
| 28 |
+
27, 0.72534, 0.67433, 1.2511, 0.28733, 0.28788, 0.24242, 0.07859, 2.1649, 3.6741, 3.0685, 0.0017426, 0.0017426, 0.0017426
|
| 29 |
+
28, 0.8216, 0.70782, 1.3075, 0.64505, 0.84848, 0.76413, 0.47347, 1.3983, 1.2964, 2.382, 0.0017327, 0.0017327, 0.0017327
|
| 30 |
+
29, 0.77898, 0.622, 1.2697, 0.51584, 0.83947, 0.60953, 0.36652, 1.3264, 1.5723, 2.3053, 0.0017228, 0.0017228, 0.0017228
|
| 31 |
+
30, 0.68374, 0.59116, 1.1982, 0.22922, 0.65152, 0.21973, 0.11423, 1.5033, 2.5704, 2.7141, 0.0017129, 0.0017129, 0.0017129
|
| 32 |
+
31, 0.72088, 0.6394, 1.2498, 0.67796, 0.60606, 0.7011, 0.42724, 1.2639, 1.5365, 2.185, 0.001703, 0.001703, 0.001703
|
| 33 |
+
32, 0.65734, 0.58037, 1.1882, 0.8806, 0.89398, 0.9421, 0.58566, 1.1777, 1.0107, 2.0217, 0.0016931, 0.0016931, 0.0016931
|
| 34 |
+
33, 0.69687, 0.57151, 1.2086, 0.61437, 0.79662, 0.77936, 0.45006, 1.28, 1.3621, 2.1727, 0.0016832, 0.0016832, 0.0016832
|
| 35 |
+
34, 0.63298, 0.55263, 1.1527, 0.48806, 0.57576, 0.52962, 0.26443, 1.5251, 2.4744, 2.2987, 0.0016733, 0.0016733, 0.0016733
|
| 36 |
+
35, 0.64935, 0.5466, 1.1611, 0.73757, 0.72395, 0.7347, 0.45371, 1.2821, 1.546, 1.964, 0.0016634, 0.0016634, 0.0016634
|
| 37 |
+
36, 0.66643, 0.54368, 1.1951, 0.81485, 0.86689, 0.87531, 0.60532, 1.0617, 1.0005, 1.8746, 0.0016535, 0.0016535, 0.0016535
|
| 38 |
+
37, 0.71783, 0.60031, 1.2366, 0.83394, 0.76097, 0.83176, 0.45825, 1.4509, 1.4438, 2.6654, 0.0016436, 0.0016436, 0.0016436
|
| 39 |
+
38, 0.67606, 0.56904, 1.1896, 0.82962, 0.88536, 0.91302, 0.52986, 1.2797, 0.97537, 2.2031, 0.0016337, 0.0016337, 0.0016337
|
| 40 |
+
39, 0.63007, 0.53471, 1.1824, 0.76972, 0.81036, 0.84636, 0.53444, 1.2108, 1.2007, 2.0489, 0.0016238, 0.0016238, 0.0016238
|
| 41 |
+
40, 0.59933, 0.49837, 1.1399, 0.83028, 0.88954, 0.91639, 0.59459, 1.1109, 1.0232, 1.8535, 0.0016139, 0.0016139, 0.0016139
|
| 42 |
+
41, 0.58657, 0.50587, 1.1306, 0.78695, 0.81818, 0.85638, 0.46199, 1.5078, 1.3214, 2.2371, 0.001604, 0.001604, 0.001604
|
| 43 |
+
42, 0.58787, 0.49984, 1.1026, 0.74599, 0.84549, 0.85283, 0.56335, 1.0847, 1.1177, 1.8151, 0.0015941, 0.0015941, 0.0015941
|
| 44 |
+
43, 0.60393, 0.49923, 1.1534, 0.81517, 0.87879, 0.93193, 0.67574, 0.94539, 0.92722, 1.6757, 0.0015842, 0.0015842, 0.0015842
|
| 45 |
+
44, 0.60281, 0.51043, 1.1483, 0.87212, 0.93939, 0.95318, 0.64296, 1.1676, 1.0025, 1.9105, 0.0015743, 0.0015743, 0.0015743
|
| 46 |
+
45, 0.59533, 0.48075, 1.1224, 0.9072, 0.95455, 0.9644, 0.70813, 0.99002, 1.2562, 1.7691, 0.0015644, 0.0015644, 0.0015644
|
| 47 |
+
46, 0.57836, 0.45649, 1.0997, 0.74038, 0.63636, 0.72829, 0.43961, 1.291, 1.5848, 2.1095, 0.0015545, 0.0015545, 0.0015545
|
| 48 |
+
47, 0.63255, 0.47323, 1.1685, 0.6213, 0.60606, 0.69617, 0.39339, 1.5978, 1.9947, 2.5419, 0.0015446, 0.0015446, 0.0015446
|
| 49 |
+
48, 0.6489, 0.52005, 1.1531, 0.68511, 0.78788, 0.75841, 0.40318, 1.4756, 1.7208, 2.3241, 0.0015347, 0.0015347, 0.0015347
|
| 50 |
+
49, 0.63901, 0.48939, 1.1723, 0.55967, 0.75758, 0.60679, 0.31261, 1.5644, 2.4083, 2.5732, 0.0015248, 0.0015248, 0.0015248
|
| 51 |
+
50, 0.54167, 0.46672, 1.0984, 0.80361, 0.86804, 0.92121, 0.5701, 1.2114, 1.3277, 2.0499, 0.0015149, 0.0015149, 0.0015149
|
| 52 |
+
51, 0.53548, 0.44225, 1.0877, 0.84499, 0.90851, 0.94733, 0.60985, 1.2671, 1.1979, 2.034, 0.001505, 0.001505, 0.001505
|
| 53 |
+
52, 0.56225, 0.47008, 1.1412, 0.87591, 0.85564, 0.95779, 0.67816, 1.0263, 1.1075, 1.9492, 0.0014951, 0.0014951, 0.0014951
|
| 54 |
+
53, 0.56491, 0.44781, 1.1302, 0.96634, 0.89394, 0.9756, 0.70506, 0.96822, 0.73799, 1.7274, 0.0014852, 0.0014852, 0.0014852
|
| 55 |
+
54, 0.56503, 0.41255, 1.1421, 0.72897, 0.81818, 0.83232, 0.56706, 1.0363, 1.2832, 1.8343, 0.0014753, 0.0014753, 0.0014753
|
| 56 |
+
55, 0.52337, 0.41373, 1.0696, 0.86015, 0.90909, 0.92014, 0.67956, 0.91228, 0.78177, 1.6163, 0.0014654, 0.0014654, 0.0014654
|
| 57 |
+
56, 0.55012, 0.44265, 1.09, 0.90832, 0.80303, 0.94129, 0.70639, 0.95232, 0.75839, 1.6959, 0.0014555, 0.0014555, 0.0014555
|
| 58 |
+
57, 0.55584, 0.42328, 1.1116, 0.96339, 0.93939, 0.9848, 0.73545, 0.93717, 0.6524, 1.8342, 0.0014456, 0.0014456, 0.0014456
|
| 59 |
+
58, 0.53118, 0.4373, 1.0914, 0.92679, 0.90909, 0.9731, 0.701, 0.91935, 0.70073, 1.6371, 0.0014357, 0.0014357, 0.0014357
|
| 60 |
+
59, 0.53571, 0.43987, 1.0942, 0.92544, 0.90909, 0.96938, 0.70839, 1.0035, 0.82196, 1.7007, 0.0014258, 0.0014258, 0.0014258
|
| 61 |
+
60, 0.52264, 0.45802, 1.1037, 0.94155, 0.93939, 0.96689, 0.71511, 0.97366, 0.72765, 1.6901, 0.0014159, 0.0014159, 0.0014159
|
| 62 |
+
61, 0.5058, 0.41218, 1.0787, 0.85948, 0.98485, 0.97449, 0.73847, 0.92347, 0.63828, 1.7042, 0.001406, 0.001406, 0.001406
|
| 63 |
+
62, 0.52545, 0.42698, 1.0813, 0.66405, 0.59091, 0.70342, 0.4714, 1.2133, 1.8906, 1.9772, 0.0013961, 0.0013961, 0.0013961
|
| 64 |
+
63, 0.57361, 0.42475, 1.1125, 0.73748, 0.63848, 0.7396, 0.45552, 1.4643, 1.0861, 2.2425, 0.0013862, 0.0013862, 0.0013862
|
| 65 |
+
64, 0.53551, 0.40875, 1.1077, 0.88195, 0.95455, 0.95445, 0.65041, 1.0713, 0.72506, 1.7926, 0.0013763, 0.0013763, 0.0013763
|
| 66 |
+
65, 0.47998, 0.38197, 1.0467, 0.65667, 0.75758, 0.77028, 0.50277, 1.1534, 1.2452, 1.8874, 0.0013664, 0.0013664, 0.0013664
|
| 67 |
+
66, 0.48802, 0.37588, 1.0529, 0.86905, 0.78788, 0.85076, 0.60201, 1.0576, 0.97203, 1.837, 0.0013565, 0.0013565, 0.0013565
|
| 68 |
+
67, 0.50432, 0.39397, 1.0526, 0.969, 0.94723, 0.98226, 0.71157, 0.94353, 0.79835, 1.7105, 0.0013466, 0.0013466, 0.0013466
|
| 69 |
+
68, 0.49997, 0.39655, 1.0763, 0.9483, 0.93939, 0.98463, 0.72438, 0.97741, 0.68675, 1.8006, 0.0013367, 0.0013367, 0.0013367
|
| 70 |
+
69, 0.53723, 0.39957, 1.1103, 0.95208, 0.95455, 0.98005, 0.65645, 1.2032, 0.72604, 2.0467, 0.0013268, 0.0013268, 0.0013268
|
| 71 |
+
70, 0.50324, 0.37262, 1.0796, 0.91699, 0.98485, 0.98212, 0.73242, 0.9087, 0.5984, 1.6499, 0.0013169, 0.0013169, 0.0013169
|
| 72 |
+
71, 0.488, 0.38622, 1.0756, 0.98096, 0.93939, 0.98955, 0.7461, 0.86533, 0.59217, 1.5832, 0.001307, 0.001307, 0.001307
|
| 73 |
+
72, 0.44812, 0.36384, 1.0319, 0.83785, 0.80303, 0.88704, 0.53443, 1.2193, 0.98505, 2.0019, 0.0012971, 0.0012971, 0.0012971
|
| 74 |
+
73, 0.49083, 0.36915, 1.0609, 0.94218, 0.98756, 0.99091, 0.76736, 0.82591, 0.68828, 1.5764, 0.0012872, 0.0012872, 0.0012872
|
| 75 |
+
74, 0.50042, 0.36713, 1.0434, 0.93201, 0.9697, 0.98098, 0.7523, 0.84331, 0.80972, 1.623, 0.0012773, 0.0012773, 0.0012773
|
| 76 |
+
75, 0.48471, 0.3708, 1.094, 0.9114, 0.9697, 0.97766, 0.65443, 1.0285, 0.71543, 1.8793, 0.0012674, 0.0012674, 0.0012674
|
| 77 |
+
76, 0.46457, 0.38107, 1.0436, 0.92857, 0.98478, 0.97217, 0.73683, 0.83556, 0.68087, 1.5151, 0.0012575, 0.0012575, 0.0012575
|
| 78 |
+
77, 0.48411, 0.41395, 1.0873, 0.91462, 0.93939, 0.96429, 0.68997, 0.86588, 0.77793, 1.5627, 0.0012476, 0.0012476, 0.0012476
|
| 79 |
+
78, 0.45523, 0.362, 1.0327, 0.89999, 0.95446, 0.94452, 0.72233, 0.87581, 0.78715, 1.6511, 0.0012377, 0.0012377, 0.0012377
|
| 80 |
+
79, 0.44884, 0.35391, 1.0407, 0.89551, 0.909, 0.93428, 0.72793, 0.88259, 0.65329, 1.6021, 0.0012278, 0.0012278, 0.0012278
|
| 81 |
+
80, 0.45595, 0.34567, 1.0504, 0.94196, 0.98367, 0.98477, 0.76205, 0.84019, 0.623, 1.5456, 0.0012179, 0.0012179, 0.0012179
|
| 82 |
+
81, 0.4716, 0.38245, 1.0596, 0.90255, 0.92424, 0.94545, 0.73079, 0.83417, 0.65875, 1.5099, 0.001208, 0.001208, 0.001208
|
| 83 |
+
82, 0.45816, 0.35499, 1.0412, 0.91542, 0.98396, 0.98427, 0.77265, 0.75338, 0.62003, 1.4043, 0.0011981, 0.0011981, 0.0011981
|
| 84 |
+
83, 0.46293, 0.35918, 1.0441, 0.92779, 0.97337, 0.98641, 0.70579, 0.87356, 0.7218, 1.6675, 0.0011882, 0.0011882, 0.0011882
|
| 85 |
+
84, 0.46013, 0.38669, 1.0622, 0.93835, 0.9697, 0.98342, 0.75294, 0.78135, 0.78854, 1.5327, 0.0011783, 0.0011783, 0.0011783
|
| 86 |
+
85, 0.44193, 0.36697, 1.0377, 0.9679, 1, 0.99319, 0.79343, 0.73563, 0.4954, 1.4205, 0.0011684, 0.0011684, 0.0011684
|
| 87 |
+
86, 0.4208, 0.33152, 1.0337, 0.97044, 0.99479, 0.99396, 0.78654, 0.7666, 0.50683, 1.5557, 0.0011585, 0.0011585, 0.0011585
|
| 88 |
+
87, 0.46829, 0.34155, 1.0532, 0.93456, 0.95455, 0.982, 0.73836, 0.81451, 0.62981, 1.5051, 0.0011486, 0.0011486, 0.0011486
|
| 89 |
+
88, 0.44766, 0.34791, 1.0391, 0.94057, 0.95922, 0.98704, 0.73048, 0.88448, 0.58566, 1.6009, 0.0011387, 0.0011387, 0.0011387
|
| 90 |
+
89, 0.41898, 0.32985, 1.0099, 0.93869, 0.9697, 0.98171, 0.71504, 0.87074, 0.59796, 1.5421, 0.0011288, 0.0011288, 0.0011288
|
| 91 |
+
90, 0.43301, 0.36118, 1.0422, 0.92518, 0.98485, 0.98633, 0.76634, 0.77171, 0.58183, 1.4363, 0.0011189, 0.0011189, 0.0011189
|
| 92 |
+
91, 0.42219, 0.32601, 1.0364, 0.98481, 0.98217, 0.99381, 0.80857, 0.74414, 0.45744, 1.4351, 0.001109, 0.001109, 0.001109
|
| 93 |
+
92, 0.43338, 0.35044, 1.0405, 0.98319, 1, 0.9944, 0.78891, 0.7571, 0.49663, 1.4856, 0.0010991, 0.0010991, 0.0010991
|
| 94 |
+
93, 0.4475, 0.34287, 1.0387, 0.98263, 0.98485, 0.9944, 0.76333, 0.77815, 0.48069, 1.5322, 0.0010892, 0.0010892, 0.0010892
|
| 95 |
+
94, 0.44224, 0.32963, 1.0353, 0.97009, 0.98273, 0.99197, 0.83974, 0.67285, 0.47394, 1.3451, 0.0010793, 0.0010793, 0.0010793
|
| 96 |
+
95, 0.43957, 0.33643, 1.0255, 0.95545, 0.98485, 0.99197, 0.79329, 0.73639, 0.54345, 1.4317, 0.0010694, 0.0010694, 0.0010694
|
| 97 |
+
96, 0.47503, 0.37144, 1.1173, 0.9324, 0.95455, 0.97441, 0.7383, 0.83224, 0.55005, 1.5953, 0.0010595, 0.0010595, 0.0010595
|
| 98 |
+
97, 0.41468, 0.31004, 1.0203, 0.99833, 1, 0.995, 0.77359, 0.80554, 0.46526, 1.5296, 0.0010496, 0.0010496, 0.0010496
|
| 99 |
+
98, 0.4114, 0.30433, 0.99849, 0.96951, 1, 0.99117, 0.80771, 0.70996, 0.46241, 1.4212, 0.0010397, 0.0010397, 0.0010397
|
| 100 |
+
99, 0.40579, 0.30221, 1.0046, 0.95652, 0.99993, 0.99151, 0.8228, 0.66275, 0.45685, 1.3876, 0.0010298, 0.0010298, 0.0010298
|
| 101 |
+
100, 0.45183, 0.33926, 1.032, 0.96781, 1, 0.99412, 0.84619, 0.6371, 0.4336, 1.3506, 0.0010199, 0.0010199, 0.0010199
|
| 102 |
+
101, 0.42307, 0.32213, 1.0169, 0.96945, 1, 0.9944, 0.81278, 0.73405, 0.54025, 1.4567, 0.00101, 0.00101, 0.00101
|
| 103 |
+
102, 0.42144, 0.31165, 1.0199, 0.99065, 1, 0.995, 0.82798, 0.6272, 0.37959, 1.3217, 0.0010001, 0.0010001, 0.0010001
|
| 104 |
+
103, 0.39929, 0.31633, 1.0099, 0.9557, 0.98059, 0.99297, 0.8104, 0.69087, 0.42959, 1.4674, 0.0009902, 0.0009902, 0.0009902
|
| 105 |
+
104, 0.40323, 0.30844, 1.0111, 0.98507, 0.99981, 0.99172, 0.81073, 0.72716, 0.41959, 1.4678, 0.0009803, 0.0009803, 0.0009803
|
| 106 |
+
105, 0.42462, 0.30658, 1.0233, 0.97974, 0.98485, 0.99425, 0.80733, 0.72001, 0.41311, 1.464, 0.0009704, 0.0009704, 0.0009704
|
| 107 |
+
106, 0.43156, 0.31527, 1.0162, 0.98484, 0.98458, 0.99235, 0.79084, 0.76511, 0.5266, 1.4428, 0.0009605, 0.0009605, 0.0009605
|
| 108 |
+
107, 0.45813, 0.34014, 1.0342, 0.88452, 0.92843, 0.97163, 0.75832, 0.78862, 0.6958, 1.5362, 0.0009506, 0.0009506, 0.0009506
|
| 109 |
+
108, 0.44184, 0.33477, 1.0618, 0.94249, 0.99337, 0.9876, 0.78322, 0.77452, 0.56784, 1.5171, 0.0009407, 0.0009407, 0.0009407
|
| 110 |
+
109, 0.41007, 0.31218, 1.0098, 0.96723, 0.89434, 0.98317, 0.78901, 0.76647, 0.71795, 1.4452, 0.0009308, 0.0009308, 0.0009308
|
| 111 |
+
110, 0.39786, 0.3197, 1.012, 0.95647, 0.99888, 0.99354, 0.82507, 0.65425, 0.4716, 1.2733, 0.0009209, 0.0009209, 0.0009209
|
| 112 |
+
111, 0.4186, 0.31447, 1.017, 0.95508, 0.96651, 0.98732, 0.79153, 0.73117, 0.49189, 1.401, 0.000911, 0.000911, 0.000911
|
| 113 |
+
112, 0.37861, 0.31225, 1.0004, 0.9711, 0.9697, 0.98514, 0.83449, 0.64682, 0.44273, 1.2962, 0.0009011, 0.0009011, 0.0009011
|
| 114 |
+
113, 0.40929, 0.30042, 1.0051, 0.94525, 0.93939, 0.94636, 0.79466, 0.71521, 0.49716, 1.3651, 0.0008912, 0.0008912, 0.0008912
|
| 115 |
+
114, 0.39454, 0.31299, 1.0131, 0.95887, 0.93939, 0.95031, 0.76939, 0.73673, 0.5105, 1.4143, 0.0008813, 0.0008813, 0.0008813
|
| 116 |
+
115, 0.37089, 0.29189, 0.99644, 0.95122, 0.98485, 0.98436, 0.78275, 0.72298, 0.5806, 1.3574, 0.0008714, 0.0008714, 0.0008714
|
| 117 |
+
116, 0.40011, 0.28763, 1.0216, 0.97983, 1, 0.99082, 0.81949, 0.65596, 0.47255, 1.2827, 0.0008615, 0.0008615, 0.0008615
|
| 118 |
+
117, 0.42434, 0.31053, 1.0091, 1, 0.99961, 0.995, 0.79575, 0.7281, 0.50181, 1.4063, 0.0008516, 0.0008516, 0.0008516
|
| 119 |
+
118, 0.3614, 0.2865, 0.97785, 0.97744, 0.98485, 0.99339, 0.74391, 0.85632, 0.54221, 1.6579, 0.0008417, 0.0008417, 0.0008417
|
| 120 |
+
119, 0.41173, 0.31532, 1.0239, 0.94985, 0.93939, 0.97983, 0.69627, 0.92963, 0.58322, 1.684, 0.0008318, 0.0008318, 0.0008318
|
| 121 |
+
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val/F1_curve.png
ADDED
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val/PR_curve.png
ADDED
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val/P_curve.png
ADDED
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val/R_curve.png
ADDED
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val/confusion_matrix.png
ADDED
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val/confusion_matrix_normalized.png
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val/val_batch0_labels.jpg
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val/val_batch0_pred.jpg
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val/val_batch1_pred.jpg
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val/val_batch2_labels.jpg
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val/val_batch2_pred.jpg
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weights/best.onnx
ADDED
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weights/best.pt
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weights/best_saved_model/fingerprint.pb
ADDED
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weights/best_saved_model/metadata.yaml
ADDED
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|
|
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|
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|
| 1 |
+
description: Ultralytics best model trained on datasets/data.yaml
|
| 2 |
+
author: Ultralytics
|
| 3 |
+
date: '2024-09-27T11:04:25.282742'
|
| 4 |
+
version: 8.2.94
|
| 5 |
+
license: AGPL-3.0 License (https://ultralytics.com/license)
|
| 6 |
+
docs: https://docs.ultralytics.com
|
| 7 |
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stride: 32
|
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task: detect
|
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batch: 1
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imgsz:
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- 640
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- 640
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names:
|
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0: product
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weights/best_saved_model/saved_model.pb
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Binary file (145 Bytes). View file
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weights/last.pt
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:ca5868a594bfa1351e9cbf7186e6e37709afd53c312b6b9f3767ff2aab36d32d
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| 3 |
+
size 6267491
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