model_id
stringlengths 12
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|---|---|---|
facebook/mask2former-swin-tiny-ade-semantic |
# Mask2Former
Mask2Former model trained on ADE20k semantic segmentation (tiny-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.
## 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on ADE20k semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic")
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
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
facebook/mask2former-swin-base-IN21k-cityscapes-instance |
# Mask2Former
Mask2Former model trained on Cityscapes instance segmentation (base-IN21k 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.
## Intended uses & limitations
You can use this particular checkpoint for instance 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on Cityscapes instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-instance")
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_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-large-cityscapes-instance |
# Mask2Former
Mask2Former model trained on Cityscapes instance 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.
## Intended uses & limitations
You can use this particular checkpoint for instance 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on Cityscapes instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-cityscapes-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-cityscapes-instance")
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_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-base-IN21k-cityscapes-panoptic |
# Mask2Former
Mask2Former model trained on Cityscapes panoptic segmentation (base-IN21k 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.
## 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on Cityscapes panoptic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-panoptic/")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-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). | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-small-cityscapes-semantic |
# Mask2Former
Mask2Former model trained on Cityscapes semantic segmentation (small-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.
## 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on Cityscapes semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-cityscapes-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-cityscapes-semantic")
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
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-tiny-cityscapes-semantic |
# Mask2Former
Mask2Former model trained on Cityscapes semantic segmentation (tiny-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.
## 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on Cityscapes semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-semantic")
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
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
Xpitfire/segformer-finetuned-segments-cmp-facade |
**Semantic segmentation** is the task of classifying each pixel in an image to a corresponding category. It has a wide range of use cases in fields such as medical imaging, autonomous driving, robotics, etc. For the facade dataset we are interested to classify the front-view of buildings based on 12 distinct classes. The classes are as follows: facade, molding, cornice, pillar, window, door, sill, blind, balcony, shop, deco, and background.
In 2014, [Long et al.](https://arxiv.org/abs/1411.4038) published a fundamental paper that used convolutional neural networks for semantic segmentation. More recent successes in computer vision include the usage of Transformers, therefore also the usage in image classification tasks (i.e. [ViT](https://huggingface.co/blog/fine-tune-vit)). In this turn, Transformers have also been used for semantic segmentation, demonstrating excellent performance on several widely used datasets. We will specifically look at the SegFormer, which is a state-of-the-art model architecture for semantic segmentation introduced in 2021. It has a hierarchical Transformer encoder that does not rely on positional encodings and a simple multi-layer perceptron decoder. In this case, we will use SegFormer to classify street view images of buildings.
More, recently, Yin et al. introduced a novel approach to semantic segmentation that achieves state-of-the-art performance in a zero-shot setting. This means that the model is able to achieve results equivalent to supervised methods on various semantic segmentation datasets, without being trained on these datasets. The approach involves replacing class labels with vector-valued embeddings of short paragraphs that describe the class. This allows for the merging of multiple datasets with different class labels and semantics, resulting in a merged dataset of over 2 million images. The resulting model achieves performance equal to state-of-the-art supervised methods on 7 benchmark datasets, and even outperforms methods when fine-tuned on standard semantic segmentation datasets.
See the full tutorial [here](https://github.com/Xpitfire/SSIW). | [
"unknown",
"background",
"facade",
"window",
"door",
"cornice",
"sill",
"balcony",
"blind",
"molding",
"deco",
"pillar",
"shop"
] |
openmmlab/upernet-convnext-tiny |
# UperNet, ConvNeXt tiny-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
Disclaimer: The team releasing UperNet + ConvNeXt did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-convnext-small |
# UperNet, ConvNeXt small-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
Disclaimer: The team releasing UperNet + ConvNeXt did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-convnext-base |
# UperNet, ConvNeXt base-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
Disclaimer: The team releasing UperNet + ConvNeXt did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-convnext-large |
# UperNet, ConvNeXt large-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
Disclaimer: The team releasing UperNet + ConvNeXt did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-convnext-xlarge |
# UperNet, ConvNeXt xlarge-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
Disclaimer: The team releasing UperNet + ConvNeXt did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-swin-tiny |
# UperNet, Swin Transformer tiny-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030).
Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-swin-small |
# UperNet, Swin Transformer small-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030).
Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-swin-base |
# UperNet, Swin Transformer base-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030).
Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
openmmlab/upernet-swin-large |
# UperNet, Swin Transformer large-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030).
Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
facebook/mask2former-swin-base-IN21k-cityscapes-semantic |
# Mask2Former
Mask2Former model trained on Cityscapes semantic segmentation (base-IN21k, 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.
## 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on Cityscapes semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-semantic")
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
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-base-IN21k-coco-instance |
# Mask2Former
Mask2Former model trained on COCO instance segmentation (base-sized IN21k 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.
## Intended uses & limitations
You can use this particular checkpoint for instance 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 requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on COCO instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-coco-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-coco-instance")
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_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_instance_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",
"motorbike",
"aeroplane",
"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",
"sofa",
"pottedplant",
"bed",
"diningtable",
"toilet",
"tvmonitor",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
] |
andrewljohnson/segformer-b0-finetuned-magic-cards-230117 |
<!-- 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-magic-cards-230117
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.059
- Mean Iou: 0.654
- Mean Accuracy: 0.981
- Overall Accuracy: 0.983
- Accuracy Unlabeled: nan
- Accuracy Front: 0.964
- Accuracy Back: 0.998
- Iou Unlabeled: 0.0
- Iou Front: 0.965
- Iou Back: 0.998
## Model description
Segment magic card front and backs.
## 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
| [
"unlabeled",
"front",
"back"
] |
andrewljohnson/segformer-b5-finetuned-magic-cards-230117 |
<!-- 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-magic-cards-230117
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2096
- Mean Iou: 0.6629
- Mean Accuracy: 0.9944
- Overall Accuracy: 0.9944
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9997
- Accuracy Back: 0.9891
- Iou Unlabeled: 0.0
- Iou Front: 0.9997
- Iou Back: 0.9891
## 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: 1
- eval_batch_size: 1
- 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 Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 0.496 | 0.74 | 20 | 0.4441 | 0.6552 | 0.9838 | 0.9838 | nan | 0.9786 | 0.9890 | 0.0 | 0.9786 | 0.9869 |
| 0.1693 | 1.48 | 40 | 0.4098 | 0.6597 | 0.9897 | 0.9897 | nan | 0.9943 | 0.9851 | 0.0 | 0.9943 | 0.9849 |
| 0.1172 | 2.22 | 60 | 0.2734 | 0.6582 | 0.9874 | 0.9874 | nan | 0.9977 | 0.9770 | 0.0 | 0.9977 | 0.9770 |
| 0.1335 | 2.96 | 80 | 0.2637 | 0.6609 | 0.9914 | 0.9914 | nan | 0.9959 | 0.9869 | 0.0 | 0.9959 | 0.9869 |
| 0.0781 | 3.7 | 100 | 0.5178 | 0.6644 | 0.9966 | 0.9966 | nan | 0.9998 | 0.9933 | 0.0 | 0.9998 | 0.9933 |
| 0.1302 | 4.44 | 120 | 0.2753 | 0.6652 | 0.9978 | 0.9978 | nan | 0.9993 | 0.9962 | 0.0 | 0.9993 | 0.9962 |
| 0.0688 | 5.19 | 140 | 0.1458 | 0.6618 | 0.9926 | 0.9926 | nan | 0.9950 | 0.9903 | 0.0 | 0.9950 | 0.9903 |
| 0.0866 | 5.93 | 160 | 0.1763 | 0.6636 | 0.9954 | 0.9954 | nan | 0.9962 | 0.9946 | 0.0 | 0.9962 | 0.9946 |
| 0.0525 | 6.67 | 180 | 0.1812 | 0.6627 | 0.9941 | 0.9941 | nan | 0.9988 | 0.9895 | 0.0 | 0.9988 | 0.9895 |
| 0.0679 | 7.41 | 200 | 0.2246 | 0.6625 | 0.9937 | 0.9937 | nan | 0.9990 | 0.9884 | 0.0 | 0.9990 | 0.9884 |
| 0.0424 | 8.15 | 220 | 0.2079 | 0.6623 | 0.9934 | 0.9935 | nan | 0.9996 | 0.9873 | 0.0 | 0.9996 | 0.9873 |
| 0.0349 | 8.89 | 240 | 0.1559 | 0.6626 | 0.9939 | 0.9940 | nan | 0.9987 | 0.9892 | 0.0 | 0.9987 | 0.9892 |
| 0.0357 | 9.63 | 260 | 0.2096 | 0.6629 | 0.9944 | 0.9944 | nan | 0.9997 | 0.9891 | 0.0 | 0.9997 | 0.9891 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
| [
"unlabeled",
"front",
"back"
] |
andrewljohnson/segformer-b5-finetuned-magic-cards-230117-2 |
<!-- 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-b5-finetuned-magic-cards-230117-2
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0491
- Mean Iou: 0.6649
- Mean Accuracy: 0.9974
- Overall Accuracy: 0.9972
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9990
- Accuracy Back: 0.9957
- Iou Unlabeled: 0.0
- Iou Front: 0.9990
- Iou Back: 0.9957
## 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: 1
- eval_batch_size: 1
- 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 Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 0.5968 | 0.33 | 20 | 0.4422 | 0.6366 | 0.9701 | 0.9690 | nan | 0.9812 | 0.9590 | 0.0 | 0.9507 | 0.9590 |
| 0.8955 | 0.66 | 40 | 0.2353 | 0.6496 | 0.9819 | 0.9807 | nan | 0.9944 | 0.9695 | 0.0 | 0.9792 | 0.9695 |
| 0.1269 | 0.98 | 60 | 0.1739 | 0.6566 | 0.9922 | 0.9916 | nan | 0.9979 | 0.9866 | 0.0 | 0.9832 | 0.9866 |
| 0.7629 | 1.31 | 80 | 0.1664 | 0.6561 | 0.9915 | 0.9909 | nan | 0.9975 | 0.9856 | 0.0 | 0.9826 | 0.9856 |
| 0.106 | 1.64 | 100 | 0.1005 | 0.6641 | 0.9968 | 0.9967 | nan | 0.9978 | 0.9959 | 0.0 | 0.9966 | 0.9959 |
| 0.3278 | 1.97 | 120 | 0.0577 | 0.6632 | 0.9948 | 0.9947 | nan | 0.9963 | 0.9934 | 0.0 | 0.9963 | 0.9934 |
| 0.061 | 2.3 | 140 | 0.0655 | 0.6642 | 0.9963 | 0.9962 | nan | 0.9972 | 0.9953 | 0.0 | 0.9972 | 0.9953 |
| 0.0766 | 2.62 | 160 | 0.0470 | 0.6635 | 0.9953 | 0.9954 | nan | 0.9940 | 0.9966 | 0.0 | 0.9940 | 0.9966 |
| 0.0664 | 2.95 | 180 | 0.0436 | 0.6617 | 0.9926 | 0.9931 | nan | 0.9877 | 0.9975 | 0.0 | 0.9877 | 0.9975 |
| 0.0655 | 3.28 | 200 | 0.0632 | 0.6649 | 0.9973 | 0.9971 | nan | 0.9994 | 0.9953 | 0.0 | 0.9994 | 0.9953 |
| 0.0356 | 3.61 | 220 | 0.0755 | 0.6661 | 0.9991 | 0.9991 | nan | 0.9992 | 0.9991 | 0.0 | 0.9992 | 0.9991 |
| 0.0516 | 3.93 | 240 | 0.0470 | 0.6643 | 0.9965 | 0.9963 | nan | 0.9987 | 0.9943 | 0.0 | 0.9987 | 0.9943 |
| 0.0517 | 4.26 | 260 | 0.0481 | 0.6645 | 0.9967 | 0.9965 | nan | 0.9989 | 0.9945 | 0.0 | 0.9989 | 0.9945 |
| 0.1886 | 4.59 | 280 | 0.0823 | 0.6659 | 0.9988 | 0.9987 | nan | 0.9999 | 0.9977 | 0.0 | 0.9999 | 0.9977 |
| 0.0453 | 4.92 | 300 | 0.0491 | 0.6649 | 0.9974 | 0.9972 | nan | 0.9990 | 0.9957 | 0.0 | 0.9990 | 0.9957 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
| [
"unlabeled",
"front",
"back"
] |
andrewljohnson/segformer-b5-finetuned-magic-cards-230117-3 |
<!-- 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. -->
# segformer-b5-finetuned-magic-cards-230117-3
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0691
- Mean Iou: 0.6585
- Mean Accuracy: 0.9878
- Overall Accuracy: 0.9912
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9978
- Accuracy Back: 0.9777
- Iou Unlabeled: 0.0
- Iou Front: 0.9978
- Iou Back: 0.9777
## 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: 1
- eval_batch_size: 1
- 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 Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 1.2232 | 0.37 | 20 | 0.4691 | 0.6041 | 0.9201 | 0.9218 | nan | 0.9252 | 0.9150 | 0.0 | 0.9252 | 0.8870 |
| 0.2718 | 0.74 | 40 | 0.1983 | 0.6509 | 0.9764 | 0.9785 | nan | 0.9826 | 0.9702 | 0.0 | 0.9826 | 0.9702 |
| 0.255 | 1.11 | 60 | 0.0939 | 0.6524 | 0.9785 | 0.9794 | nan | 0.9812 | 0.9758 | 0.0 | 0.9812 | 0.9758 |
| 0.1103 | 1.48 | 80 | 0.0682 | 0.6536 | 0.9804 | 0.9813 | nan | 0.9830 | 0.9779 | 0.0 | 0.9830 | 0.9779 |
| 0.1373 | 1.85 | 100 | 0.1260 | 0.6631 | 0.9946 | 0.9961 | nan | 0.9989 | 0.9903 | 0.0 | 0.9989 | 0.9903 |
| 0.0566 | 2.22 | 120 | 0.1558 | 0.6578 | 0.9868 | 0.9912 | nan | 0.9999 | 0.9736 | 0.0 | 0.9999 | 0.9736 |
| 0.1535 | 2.59 | 140 | 0.1330 | 0.6558 | 0.9838 | 0.9883 | nan | 0.9973 | 0.9703 | 0.0 | 0.9973 | 0.9703 |
| 0.0586 | 2.96 | 160 | 0.2317 | 0.6599 | 0.9899 | 0.9933 | nan | 1.0000 | 0.9798 | 0.0 | 1.0000 | 0.9798 |
| 0.0727 | 3.33 | 180 | 0.1018 | 0.6586 | 0.9880 | 0.9919 | nan | 0.9995 | 0.9764 | 0.0 | 0.9995 | 0.9764 |
| 0.3588 | 3.7 | 200 | 0.1151 | 0.6608 | 0.9912 | 0.9939 | nan | 0.9993 | 0.9831 | 0.0 | 0.9993 | 0.9831 |
| 0.0463 | 4.07 | 220 | 0.0538 | 0.6610 | 0.9915 | 0.9934 | nan | 0.9969 | 0.9862 | 0.0 | 0.9969 | 0.9862 |
| 0.046 | 4.44 | 240 | 0.1201 | 0.6581 | 0.9871 | 0.9912 | nan | 0.9991 | 0.9751 | 0.0 | 0.9991 | 0.9751 |
| 0.0468 | 4.81 | 260 | 0.0691 | 0.6585 | 0.9878 | 0.9912 | nan | 0.9978 | 0.9777 | 0.0 | 0.9978 | 0.9777 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
| [
"unlabeled",
"front",
"back"
] |
mraottth/trashbot_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. -->
# trashbot
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locations_pooled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0191
- Mean Iou: 0.3997
- Mean Accuracy: 0.7995
- Overall Accuracy: 0.7995
- Accuracy Unlabeled: nan
- Accuracy Trash: 0.7995
- Iou Unlabeled: 0.0
- Iou Trash: 0.7995
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Iou Unlabeled | Iou Trash |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.0748 | 1.0 | 90 | 0.0386 | 0.3630 | 0.7259 | 0.7259 | nan | 0.7259 | 0.0 | 0.7259 |
| 0.039 | 2.0 | 180 | 0.0242 | 0.3803 | 0.7607 | 0.7607 | nan | 0.7607 | 0.0 | 0.7607 |
| 0.0194 | 3.0 | 270 | 0.0242 | 0.3605 | 0.7210 | 0.7210 | nan | 0.7210 | 0.0 | 0.7210 |
| 0.0112 | 4.0 | 360 | 0.0205 | 0.3995 | 0.7991 | 0.7991 | nan | 0.7991 | 0.0 | 0.7991 |
| 0.0169 | 5.0 | 450 | 0.0192 | 0.4000 | 0.8000 | 0.8000 | nan | 0.8000 | 0.0 | 0.8000 |
| 0.041 | 6.0 | 540 | 0.0196 | 0.3838 | 0.7677 | 0.7677 | nan | 0.7677 | 0.0 | 0.7677 |
| 0.0188 | 7.0 | 630 | 0.0191 | 0.4139 | 0.8277 | 0.8277 | nan | 0.8277 | 0.0 | 0.8277 |
| 0.0073 | 8.0 | 720 | 0.0190 | 0.4069 | 0.8138 | 0.8138 | nan | 0.8138 | 0.0 | 0.8138 |
| 0.025 | 9.0 | 810 | 0.0191 | 0.4087 | 0.8174 | 0.8174 | nan | 0.8174 | 0.0 | 0.8174 |
| 0.006 | 10.0 | 900 | 0.0191 | 0.3997 | 0.7995 | 0.7995 | nan | 0.7995 | 0.0 | 0.7995 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
| [
"unlabeled",
"trash"
] |
mraottth/trashbot |
<!-- 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. -->
# trashbot
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locations_pooled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0189
- Mean Iou: 0.4050
- Mean Accuracy: 0.8101
- Overall Accuracy: 0.8101
- Accuracy Unlabeled: nan
- Accuracy Trash: 0.8101
- Iou Unlabeled: 0.0
- Iou Trash: 0.8101
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Iou Unlabeled | Iou Trash |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.0592 | 1.0 | 90 | 0.0387 | 0.3723 | 0.7446 | 0.7446 | nan | 0.7446 | 0.0 | 0.7446 |
| 0.0402 | 2.0 | 180 | 0.0281 | 0.4123 | 0.8247 | 0.8247 | nan | 0.8247 | 0.0 | 0.8247 |
| 0.0209 | 3.0 | 270 | 0.0246 | 0.3691 | 0.7382 | 0.7382 | nan | 0.7382 | 0.0 | 0.7382 |
| 0.0117 | 4.0 | 360 | 0.0210 | 0.3882 | 0.7763 | 0.7763 | nan | 0.7763 | 0.0 | 0.7763 |
| 0.019 | 5.0 | 450 | 0.0198 | 0.3822 | 0.7644 | 0.7644 | nan | 0.7644 | 0.0 | 0.7644 |
| 0.0445 | 6.0 | 540 | 0.0199 | 0.3771 | 0.7542 | 0.7542 | nan | 0.7542 | 0.0 | 0.7542 |
| 0.0195 | 7.0 | 630 | 0.0191 | 0.4177 | 0.8354 | 0.8354 | nan | 0.8354 | 0.0 | 0.8354 |
| 0.008 | 8.0 | 720 | 0.0191 | 0.4060 | 0.8119 | 0.8119 | nan | 0.8119 | 0.0 | 0.8119 |
| 0.0268 | 9.0 | 810 | 0.0188 | 0.4083 | 0.8166 | 0.8166 | nan | 0.8166 | 0.0 | 0.8166 |
| 0.0061 | 10.0 | 900 | 0.0189 | 0.4050 | 0.8101 | 0.8101 | nan | 0.8101 | 0.0 | 0.8101 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
| [
"unlabeled",
"trash"
] |
s3nh/SegFormer-b0-person-segmentation |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
### Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
## Parameters
```
model = SegformerForSemanticSegmentation.from_pretrained("/notebooks/segformer_5_epoch",
num_labels=2,
id2label=id2label,
label2id=label2id, )
```
## Usage
```python
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
```
<img src = 'data:image/png;base64,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'>
| [
"background",
"person"
] |
s3nh/SegFormer-b4-person-segmentation |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
### Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
## Parameters
```
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b4",
num_labels=2,
id2label=id2label,
label2id=label2id, )
```
## Usage
```python
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
```
<img src = 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'>
#Metric
Todo
#Note
This model was not built by using Huggingface based feature extractor, so automatic api could not work. | [
"background",
"person"
] |
s3nh/SegFormer-b5-person-segm |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
### Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
## Parameters
```
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b5",
num_labels=2,
id2label=id2label,
label2id=label2id, )
```
## Usage
```python
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
```
<img src = 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'>
#Metric
Todo
#Note
This model was not built by using Huggingface based feature extractor, so automatic api could not work. | [
"background",
"person"
] |
DunnBC22/mit-b0-CMP_semantic_seg_with_mps_v2 |
# mit-b0-CMP_semantic_seg_with_mps_v2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0).
It achieves the following results on the evaluation set:
- Loss: 1.0863
- Mean Iou: 0.4097
- Mean Accuracy: 0.5538
- Overall Accuracy: 0.6951
- Per Category Iou:
- Segment 0: 0.5921698801573617
- Segment 1: 0.5795623712718901
- Segment 2: 0.5784812820145221
- Segment 3: 0.2917052691882505
- Segment 4: 0.3792639848157326
- Segment 5: 0.37973303153855376
- Segment 6: 0.4481097636024487
- Segment 7: 0.4354492668218124
- Segment 8: 0.26472453634508664
- Segment 9: 0.4173722023142026
- Segment 10: 0.18166072949276144
- Segment 11: 0.36809541729585366
- Per Category Accuracy:
- Segment 0: 0.6884460857323806
- Segment 1: 0.7851625477616788
- Segment 2: 0.7322992353412343
- Segment 3: 0.45229387721112274
- Segment 4: 0.5829333862769369
- Segment 5: 0.5516333441001092
- Segment 6: 0.5904157921999404
- Segment 7: 0.5288772981353482
- Segment 8: 0.4518224891972707
- Segment 9: 0.571864661897264
- Segment 10: 0.23178753217655862
- Segment 11: 0.47833833709905393
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Segmentation/Trained%2C%20But%20to%20My%20Standard/Center%20for%20Machine%20Perception/Version%202/Center%20for%20Machine%20Perception%20-%20semantic_segmentation_v2.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to use it, but remember that it is at your own risk/peril.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/Xpitfire/cmp_facade
## 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
#### Overall Dataset Metrics
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|
| 1.6807 | 1.0 | 189 | 1.3310 | 0.2226 | 0.3388 | 0.5893 |
| 1.1837 | 2.0 | 378 | 1.1731 | 0.2602 | 0.3876 | 0.6122 |
| 1.0241 | 3.0 | 567 | 1.0485 | 0.2915 | 0.3954 | 0.6393 |
| 0.9353 | 4.0 | 756 | 0.9943 | 0.3054 | 0.4021 | 0.6570 |
| 0.8717 | 5.0 | 945 | 1.0010 | 0.3299 | 0.4440 | 0.6530 |
| 0.8238 | 6.0 | 1134 | 0.9537 | 0.3546 | 0.4771 | 0.6701 |
| 0.7415 | 8.0 | 1512 | 0.9738 | 0.3554 | 0.4634 | 0.6733 |
| 0.7708 | 7.0 | 1323 | 0.9789 | 0.3550 | 0.4837 | 0.6683 |
| 0.7018 | 9.0 | 1701 | 0.9449 | 0.3667 | 0.4802 | 0.6826 |
| 0.682 | 10.0 | 1890 | 0.9422 | 0.3762 | 0.5047 | 0.6805 |
| 0.6503 | 11.0 | 2079 | 0.9889 | 0.3785 | 0.5082 | 0.6729 |
| 0.633 | 12.0 | 2268 | 0.9594 | 0.3901 | 0.5224 | 0.6797 |
| 0.6035 | 13.0 | 2457 | 0.9612 | 0.3939 | 0.5288 | 0.6834 |
| 0.5874 | 14.0 | 2646 | 0.9657 | 0.3939 | 0.5383 | 0.6844 |
| 0.5684 | 15.0 | 2835 | 0.9762 | 0.3950 | 0.5446 | 0.6855 |
| 0.5485 | 16.0 | 3024 | 1.0645 | 0.3794 | 0.5095 | 0.6704 |
| 0.5402 | 17.0 | 3213 | 0.9747 | 0.4044 | 0.5600 | 0.6839 |
| 0.5275 | 18.0 | 3402 | 1.0054 | 0.3944 | 0.5411 | 0.6790 |
| 0.5032 | 19.0 | 3591 | 1.0014 | 0.3973 | 0.5256 | 0.6875 |
| 0.4985 | 20.0 | 3780 | 0.9893 | 0.3990 | 0.5468 | 0.6883 |
| 0.4925 | 21.0 | 3969 | 1.0416 | 0.3955 | 0.5339 | 0.6806 |
| 0.4772 | 22.0 | 4158 | 1.0142 | 0.3969 | 0.5476 | 0.6838 |
| 0.4707 | 23.0 | 4347 | 0.9896 | 0.4077 | 0.5458 | 0.6966 |
| 0.4601 | 24.0 | 4536 | 1.0040 | 0.4104 | 0.5551 | 0.6948 |
| 0.4544 | 25.0 | 4725 | 1.0093 | 0.4093 | 0.5652 | 0.6899 |
| 0.4421 | 26.0 | 4914 | 1.0434 | 0.4064 | 0.5448 | 0.6938 |
| 0.4293 | 27.0 | 5103 | 1.0391 | 0.4076 | 0.5571 | 0.6908 |
| 0.4312 | 28.0 | 5292 | 1.0037 | 0.4100 | 0.5534 | 0.6958 |
| 0.4309 | 29.0 | 5481 | 1.0288 | 0.4101 | 0.5493 | 0.6968 |
| 0.4146 | 30.0 | 5670 | 1.0602 | 0.4062 | 0.5445 | 0.6928 |
| 0.4106 | 31.0 | 5859 | 1.0573 | 0.4113 | 0.5520 | 0.6937 |
| 0.4102 | 32.0 | 6048 | 1.0616 | 0.4043 | 0.5444 | 0.6904 |
| 0.394 | 33.0 | 6237 | 1.0244 | 0.4104 | 0.5587 | 0.6957 |
| 0.3865 | 34.0 | 6426 | 1.0618 | 0.4086 | 0.5468 | 0.6922 |
| 0.3816 | 35.0 | 6615 | 1.0515 | 0.4109 | 0.5587 | 0.6937 |
| 0.3803 | 36.0 | 6804 | 1.0709 | 0.4118 | 0.5507 | 0.6982 |
| 0.3841 | 37.0 | 6993 | 1.0646 | 0.4102 | 0.5423 | 0.7000 |
| 0.383 | 38.0 | 7182 | 1.0769 | 0.4076 | 0.5463 | 0.6981 |
| 0.3831 | 39.0 | 7371 | 1.0821 | 0.4081 | 0.5438 | 0.6949 |
| 0.3701 | 40.0 | 7560 | 1.0971 | 0.4094 | 0.5503 | 0.6939 |
| 0.3728 | 41.0 | 7749 | 1.0850 | 0.4073 | 0.5426 | 0.6955 |
| 0.3693 | 42.0 | 7938 | 1.0969 | 0.4065 | 0.5503 | 0.6922 |
| 0.3627 | 43.0 | 8127 | 1.0932 | 0.4087 | 0.5497 | 0.6948 |
| 0.3707 | 44.0 | 8316 | 1.1095 | 0.4071 | 0.5449 | 0.6950 |
| 0.3715 | 45.0 | 8505 | 1.0884 | 0.4110 | 0.5481 | 0.6962 |
| 0.3637 | 46.0 | 8694 | 1.0893 | 0.4116 | 0.5565 | 0.6948 |
| 0.3581 | 47.0 | 8883 | 1.1164 | 0.4080 | 0.5443 | 0.6938 |
| 0.3595 | 48.0 | 9072 | 1.1264 | 0.4056 | 0.5374 | 0.6942 |
| 0.3604 | 49.0 | 9261 | 1.0948 | 0.4104 | 0.5508 | 0.6953 |
| 0.3541 | 50.0 | 9450 | 1.0863 | 0.4097 | 0.5538 | 0.6951 |
#### Per Category IoU For Each Segment
| Epoch | Segment 0 | Segment 1 | Segment 2 | Segment 3 | Segment 4 | Segment 5 | Segment 6 | Segment 7 | Segment 8 | Segment 9 | Segment 10 | Segment 11 |
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 1.0 | 0.4635 | 0.4905 | 0.4698 | 0.0 | 0.2307 | 0.1515 | 0.2789 | 0.0002 | 0.0250 | 0.3527 | 0.0 | 0.2087 |
| 2.0 | 0.4240 | 0.5249 | 0.5152 | 0.0057 | 0.2636 | 0.2756 | 0.3312 | 0.0575 | 0.0539 | 0.3860 | 0.0 | 0.2854 |
| 3.0 | 0.5442 | 0.5037 | 0.5329 | 0.0412 | 0.3062 | 0.2714 | 0.3820 | 0.1430 | 0.0796 | 0.4007 | 0.0002 | 0.2929 |
| 4.0 | 0.5776 | 0.5289 | 0.5391 | 0.1171 | 0.3137 | 0.2600 | 0.3664 | 0.1527 | 0.1074 | 0.3935 | 0.0002 | 0.3078 |
| 5.0 | 0.4790 | 0.5506 | 0.5472 | 0.1547 | 0.3372 | 0.3297 | 0.4151 | 0.2339 | 0.1709 | 0.4081 | 0.0008 | 0.3314 |
| 6.0 | 0.5572 | 0.5525 | 0.5611 | 0.2076 | 0.3434 | 0.3163 | 0.4103 | 0.3279 | 0.2107 | 0.4191 | 0.0067 | 0.3418 |
| 7.0 | 0.5310 | 0.5634 | 0.5594 | 0.2299 | 0.3424 | 0.3375 | 0.4050 | 0.2883 | 0.2197 | 0.4142 | 0.0316 | 0.3373 |
| 8.0 | 0.5366 | 0.5659 | 0.5550 | 0.2331 | 0.3497 | 0.3334 | 0.4301 | 0.3401 | 0.1989 | 0.4181 | 0.0358 | 0.2680 |
| 9.0 | 0.5798 | 0.5657 | 0.5624 | 0.2368 | 0.3648 | 0.3271 | 0.4250 | 0.3207 | 0.2096 | 0.4236 | 0.0504 | 0.3346 |
| 10.0 | 0.5802 | 0.5622 | 0.5585 | 0.2340 | 0.3793 | 0.3407 | 0.4277 | 0.3801 | 0.2301 | 0.4216 | 0.0640 | 0.3367 |
| 11.0 | 0.5193 | 0.5649 | 0.5605 | 0.2698 | 0.3772 | 0.3526 | 0.4342 | 0.3433 | 0.2415 | 0.4336 | 0.0889 | 0.3562 |
| 12.0 | 0.5539 | 0.5641 | 0.5679 | 0.2658 | 0.3757 | 0.3510 | 0.4257 | 0.3993 | 0.2354 | 0.4338 | 0.1800 | 0.3287 |
| 13.0 | 0.5663 | 0.5666 | 0.5679 | 0.2631 | 0.3726 | 0.3609 | 0.4351 | 0.3759 | 0.2511 | 0.4256 | 0.1737 | 0.3681 |
| 14.0 | 0.5807 | 0.5670 | 0.5679 | 0.2670 | 0.3594 | 0.3605 | 0.4393 | 0.3863 | 0.2406 | 0.4228 | 0.1705 | 0.3652 |
| 15.0 | 0.5800 | 0.5711 | 0.5671 | 0.2825 | 0.3664 | 0.3587 | 0.4408 | 0.4021 | 0.2540 | 0.4246 | 0.1376 | 0.3548 |
| 16.0 | 0.4855 | 0.5683 | 0.5685 | 0.2612 | 0.3832 | 0.3628 | 0.4378 | 0.4056 | 0.2525 | 0.4206 | 0.1242 | 0.2825 |
| 17.0 | 0.5697 | 0.5674 | 0.5687 | 0.2971 | 0.3767 | 0.3741 | 0.4486 | 0.4126 | 0.2489 | 0.4260 | 0.1874 | 0.3757 |
| 18.0 | 0.5341 | 0.5728 | 0.5616 | 0.2827 | 0.3823 | 0.3782 | 0.4298 | 0.4070 | 0.2578 | 0.4195 | 0.1448 | 0.3632 |
| 19.0 | 0.5696 | 0.5739 | 0.5699 | 0.2918 | 0.3717 | 0.3635 | 0.4444 | 0.4122 | 0.2531 | 0.4142 | 0.1659 | 0.3369 |
| 20.0 | 0.5937 | 0.5702 | 0.5630 | 0.2892 | 0.3790 | 0.3757 | 0.4383 | 0.4110 | 0.2592 | 0.4147 | 0.1291 | 0.3653 |
| 21.0 | 0.5336 | 0.5723 | 0.5732 | 0.2843 | 0.3748 | 0.3738 | 0.4383 | 0.3876 | 0.2598 | 0.4170 | 0.1693 | 0.3624 |
| 22.0 | 0.5634 | 0.5752 | 0.5595 | 0.2783 | 0.3833 | 0.3540 | 0.4448 | 0.4054 | 0.2586 | 0.4145 | 0.1597 | 0.3660 |
| 23.0 | 0.6013 | 0.5801 | 0.5794 | 0.2988 | 0.3816 | 0.3736 | 0.4464 | 0.4241 | 0.2633 | 0.4162 | 0.1747 | 0.3530 |
| 24.0 | 0.6061 | 0.5756 | 0.5721 | 0.3086 | 0.3771 | 0.3707 | 0.4459 | 0.4242 | 0.2665 | 0.4104 | 0.1942 | 0.3732 |
| 25.0 | 0.5826 | 0.5745 | 0.5742 | 0.3109 | 0.3765 | 0.3784 | 0.4441 | 0.4184 | 0.2609 | 0.4219 | 0.1930 | 0.3765 |
| 26.0 | 0.5783 | 0.5821 | 0.5770 | 0.2985 | 0.3885 | 0.3582 | 0.4458 | 0.4220 | 0.2717 | 0.4260 | 0.1690 | 0.3600 |
| 27.0 | 0.5764 | 0.5777 | 0.5749 | 0.2868 | 0.3824 | 0.3857 | 0.4450 | 0.4170 | 0.2644 | 0.4295 | 0.1922 | - |
| 28.0 | 0.6023 | 0.5776 | 0.5769 | 0.2964 | 0.3759 | 0.3758 | 0.4464 | 0.4245 | 0.2712 | 0.4083 | 0.1967 | 0.3680 |
| 29.0 | 0.6043 | 0.5814 | 0.5728 | 0.2882 | 0.3867 | 0.3841 | 0.4369 | 0.4254 | 0.2659 | 0.4252 | 0.2106 | 0.3391 |
| 30.0 | 0.5840 | 0.5792 | 0.5750 | 0.2859 | 0.3839 | 0.3786 | 0.4479 | 0.4259 | 0.2664 | 0.3947 | 0.1753 | 0.3780 |
| 31.0 | 0.5819 | 0.5787 | 0.5775 | 0.2882 | 0.3861 | 0.3888 | 0.4522 | 0.4207 | 0.2722 | 0.4277 | 0.2050 | 0.3566 |
| 32.0 | 0.5769 | 0.5774 | 0.5737 | 0.2844 | 0.3762 | 0.3768 | 0.4424 | 0.4331 | 0.2649 | 0.3959 | 0.1748 | 0.3744 |
| 33.0 | 0.6076 | 0.5755 | 0.5774 | 0.2887 | 0.3833 | 0.3803 | 0.4483 | 0.4329 | 0.2687 | 0.4194 | 0.1884 | 0.3547 |
| 34.0 | 0.5729 | 0.5787 | 0.5789 | 0.2853 | 0.3854 | 0.3735 | 0.4469 | 0.4279 | 0.2694 | 0.4240 | 0.1986 | 0.3613 |
| 35.0 | 0.5942 | 0.5769 | 0.5777 | 0.2873 | 0.3867 | 0.3811 | 0.4448 | 0.4281 | 0.2669 | 0.4147 | 0.1956 | 0.3774 |
| 36.0 | 0.6024 | 0.5819 | 0.5782 | 0.2870 | 0.3850 | 0.3781 | 0.4469 | 0.4259 | 0.2696 | 0.4177 | 0.1885 | 0.3802 |
| 37.0 | 0.6099 | 0.5822 | 0.5787 | 0.2920 | 0.3827 | 0.3739 | 0.4416 | 0.4271 | 0.2646 | 0.4200 | 0.1864 | 0.3637 |
| 38.0 | 0.6028 | 0.5823 | 0.5799 | 0.2887 | 0.3828 | 0.3770 | 0.4470 | 0.4238 | 0.2639 | 0.4197 | 0.1617 | 0.3610 |
| 39.0 | 0.5856 | 0.5809 | 0.5772 | 0.2889 | 0.3772 | 0.3683 | 0.4493 | 0.4296 | 0.2665 | 0.4112 | 0.1902 | 0.3723 |
| 40.0 | 0.5830 | 0.5808 | 0.5785 | 0.2947 | 0.3803 | 0.3832 | 0.4496 | 0.4284 | 0.2675 | 0.4111 | 0.1913 | 0.3644 |
| 41.0 | 0.5853 | 0.5827 | 0.5786 | 0.2921 | 0.3809 | 0.3712 | 0.4464 | 0.4330 | 0.2670 | 0.4180 | 0.1631 | 0.3694 |
| 42.0 | 0.5756 | 0.5804 | 0.5766 | 0.2872 | 0.3775 | 0.3786 | 0.4480 | 0.4396 | 0.2669 | 0.4132 | 0.1619 | 0.3729 |
| 43.0 | 0.5872 | 0.5821 | 0.5762 | 0.2896 | 0.3820 | 0.3742 | 0.4499 | 0.4346 | 0.2685 | 0.4164 | 0.1848 | 0.3597 |
| 44.0 | 0.5894 | 0.5823 | 0.5774 | 0.2917 | 0.3801 | 0.3754 | 0.4476 | 0.4287 | 0.2635 | 0.4096 | 0.1911 | 0.3478 |
| 45.0 | 0.5912 | 0.5809 | 0.5791 | 0.2980 | 0.3817 | 0.3750 | 0.4483 | 0.4349 | 0.2677 | 0.4155 | 0.1909 | 0.3686 |
| 46.0 | 0.5922 | 0.5794 | 0.5788 | 0.2952 | 0.3804 | 0.3754 | 0.4487 | 0.4356 | 0.2641 | 0.4159 | 0.2068 | 0.3666 |
| 47.0 | 0.5748 | 0.5822 | 0.5779 | 0.2909 | 0.3849 | 0.3751 | 0.4487 | 0.4350 | 0.2687 | 0.4150 | 0.1785 | 0.3643 |
| 48.0 | 0.5787 | 0.5823 | 0.5789 | 0.2896 | 0.3819 | 0.3750 | 0.4479 | 0.4224 | 0.2665 | 0.4140 | 0.1723 | 0.3580 |
| 49.0 | 0.5878 | 0.5812 | 0.5782 | 0.2930 | 0.3807 | 0.3796 | 0.4482 | 0.4364 | 0.2659 | 0.4139 | 0.1915 | 0.3678 |
| 50.0 | 0.5922 | 0.5796 | 0.5785 | 0.2917 | 0.3793 | 0.3797 | 0.4481 | 0.4354 | 0.2647 | 0.4174 | 0.1817 | 0.3681 |
#### Per Category Accuracy For Each Segment
| Epoch | Segment 0 | Segment 1 | Segment 2 | Segment 3 | Segment 4 | Segment 5 | Segment 6 | Segment 7 | Segment 8 | Segment 9 | Segment 10 | Segment 11 |
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 1.0 | 0.6133 | 0.6847 | 0.7408 | 0.0 | 0.4973 | 0.1720 | 0.4073 | 0.0002 | 0.0255 | 0.6371 | 0.0 | 0.2874 |
| 2.0 | 0.4782 | 0.7844 | 0.6966 | 0.0057 | 0.5735 | 0.3684 | 0.6226 | 0.0577 | 0.0563 | 0.5907 | 0.0 | 0.4168 |
| 3.0 | 0.8126 | 0.6852 | 0.6683 | 0.0420 | 0.4972 | 0.3418 | 0.5121 | 0.1453 | 0.0849 | 0.5882 | 0.0002 | 0.3672 |
| 4.0 | 0.8079 | 0.7362 | 0.6803 | 0.1231 | 0.5129 | 0.3324 | 0.4212 | 0.1554 | 0.1223 | 0.5587 | 0.0002 | 0.3751 |
| 5.0 | 0.5408 | 0.8111 | 0.7439 | 0.1647 | 0.5336 | 0.4720 | 0.5650 | 0.2459 | 0.2127 | 0.6032 | 0.0008 | 0.4343 |
| 6.0 | 0.6870 | 0.7532 | 0.7389 | 0.2428 | 0.5081 | 0.4173 | 0.5923 | 0.3710 | 0.3117 | 0.6181 | 0.0068 | 0.4785 |
| 7.0 | 0.6050 | 0.7961 | 0.7434 | 0.2876 | 0.5835 | 0.4949 | 0.5608 | 0.3103 | 0.3672 | 0.6185 | 0.0345 | 0.4022 |
| 8.0 | 0.6081 | 0.8461 | 0.6598 | 0.3035 | 0.5720 | 0.4540 | 0.5735 | 0.3849 | 0.2642 | 0.5608 | 0.0379 | 0.2962 |
| 9.0 | 0.7241 | 0.7684 | 0.7677 | 0.2958 | 0.5321 | 0.4212 | 0.5547 | 0.3513 | 0.2813 | 0.5645 | 0.0544 | 0.4465 |
| 10.0 | 0.7124 | 0.7649 | 0.7024 | 0.2879 | 0.5535 | 0.4413 | 0.6310 | 0.4960 | 0.3982 | 0.5592 | 0.0724 | 0.4370 |
| 11.0 | 0.5876 | 0.8060 | 0.7296 | 0.3838 | 0.5267 | 0.4983 | 0.5902 | 0.3838 | 0.4151 | 0.5987 | 0.1030 | 0.4756 |
| 12.0 | 0.6497 | 0.7807 | 0.7448 | 0.4018 | 0.5381 | 0.4615 | 0.5849 | 0.4883 | 0.3248 | 0.6063 | 0.2918 | 0.3958 |
| 13.0 | 0.6650 | 0.7792 | 0.7595 | 0.4049 | 0.5501 | 0.4940 | 0.5831 | 0.4375 | 0.3843 | 0.5591 | 0.2578 | 0.4711 |
| 14.0 | 0.6881 | 0.7715 | 0.7076 | 0.4518 | 0.6011 | 0.4900 | 0.6235 | 0.4466 | 0.3627 | 0.5934 | 0.2537 | 0.4702 |
| 15.0 | 0.6690 | 0.7721 | 0.7253 | 0.4607 | 0.6286 | 0.4900 | 0.5936 | 0.4951 | 0.4337 | 0.6295 | 0.1749 | 0.4630 |
| 16.0 | 0.5250 | 0.8335 | 0.7460 | 0.3742 | 0.6114 | 0.4823 | 0.5880 | 0.5021 | 0.4084 | 0.5757 | 0.1498 | 0.3171 |
| 17.0 | 0.6652 | 0.7673 | 0.7058 | 0.4318 | 0.5995 | 0.5137 | 0.6112 | 0.5596 | 0.4548 | 0.5819 | 0.2821 | 0.5465 |
| 18.0 | 0.6012 | 0.8091 | 0.6765 | 0.4561 | 0.5707 | 0.5393 | 0.6255 | 0.5679 | 0.4347 | 0.5567 | 0.1806 | 0.4751 |
| 19.0 | 0.6634 | 0.8079 | 0.6986 | 0.4389 | 0.5274 | 0.4876 | 0.6232 | 0.5022 | 0.3717 | 0.5244 | 0.2232 | 0.4388 |
| 20.0 | 0.7110 | 0.7679 | 0.6952 | 0.4875 | 0.5261 | 0.5549 | 0.6444 | 0.5301 | 0.4512 | 0.5441 | 0.1603 | 0.4888 |
| 21.0 | 0.5945 | 0.8130 | 0.7299 | 0.4511 | 0.5922 | 0.5324 | 0.5643 | 0.4341 | 0.4067 | 0.5834 | 0.2272 | 0.4781 |
| 22.0 | 0.6478 | 0.7921 | 0.6887 | 0.4826 | 0.5784 | 0.4599 | 0.6029 | 0.5938 | 0.4905 | 0.5605 | 0.2094 | 0.4644 |
| 23.0 | 0.7110 | 0.7878 | 0.7192 | 0.4629 | 0.5670 | 0.5061 | 0.5891 | 0.5354 | 0.4442 | 0.5585 | 0.2280 | 0.4401 |
| 24.0 | 0.7277 | 0.7718 | 0.7095 | 0.4789 | 0.5401 | 0.5080 | 0.6040 | 0.5314 | 0.4573 | 0.5414 | 0.2853 | 0.5062 |
| 25.0 | 0.6781 | 0.7703 | 0.7305 | 0.5102 | 0.5954 | 0.5311 | 0.5960 | 0.5286 | 0.4647 | 0.5861 | 0.2676 | 0.5242 |
| 26.0 | 0.6603 | 0.7989 | 0.7349 | 0.4689 | 0.5677 | 0.4620 | 0.6111 | 0.5258 | 0.4556 | 0.5889 | 0.2110 | 0.4530 |
| 27.0 | - | - | - | - | - | - | - | - | - | - | - | - |
| 28.0 | 0.7218 | 0.7735 | 0.7273 | 0.4297 | 0.6001 | 0.5321 | - | - | - | - | - | - |
| 29.0 | 0.7054 | 0.7948 | 0.7009 | 0.4552 | 0.5413 | 0.5357 | 0.5421 | 0.5250 | 0.4701 | 0.5949 | 0.3048 | 0.4213 |
| 30.0 | 0.6744 | 0.8004 | 0.7289 | 0.4421 | 0.5410 | 0.5409 | 0.5822 | 0.5334 | 0.4790 | 0.5028 | 0.2177 | 0.4910 |
| 31.0 | 0.6622 | 0.7858 | 0.7534 | 0.3855 | 0.5707 | 0.5889 | 0.5902 | 0.4979 | 0.4268 | 0.6260 | 0.2735 | 0.4630 |
| 32.0 | 0.6629 | 0.7960 | 0.7345 | 0.4132 | 0.5703 | 0.5450 | 0.5855 | 0.5469 | 0.4371 | 0.5087 | 0.2178 | 0.5147 |
| 33.0 | 0.7279 | 0.7642 | 0.7250 | 0.4999 | 0.5330 | 0.5418 | 0.6148 | 0.5491 | 0.4678 | 0.5808 | 0.2548 | 0.4455 |
| 34.0 | 0.6571 | 0.8002 | 0.7190 | 0.4516 | 0.5621 | 0.5183 | 0.5822 | 0.5444 | 0.3994 | 0.5931 | 0.2752 | 0.4588 |
| 35.0 | 0.6946 | 0.7771 | 0.7289 | 0.4481 | 0.5478 | 0.5396 | 0.5834 | 0.5407 | 0.4980 | 0.5652 | 0.2696 | 0.5116 |
| 36.0 | 0.7040 | 0.7881 | 0.7314 | 0.4432 | 0.5429 | 0.5308 | 0.5705 | 0.5124 | 0.4619 | 0.5667 | 0.2465 | 0.5101 |
| 37.0 | 0.7277 | 0.7884 | 0.7298 | 0.4325 | 0.5471 | 0.5196 | 0.5523 | 0.5073 | 0.4390 | 0.5614 | 0.2453 | 0.4575 |
| 38.0 | 0.7092 | 0.7907 | 0.7297 | 0.4713 | 0.5626 | 0.5483 | 0.5667 | 0.5067 | 0.4552 | 0.5608 | 0.2002 | 0.4545 |
| 39.0 | 0.6763 | 0.8000 | 0.7345 | 0.4678 | 0.5544 | 0.5005 | 0.5818 | 0.5236 | 0.4071 | 0.5436 | 0.2496 | 0.4865 |
| 40.0 | 0.6681 | 0.8020 | 0.7232 | 0.4519 | 0.5724 | 0.5465 | 0.5828 | 0.5132 | 0.4686 | 0.5479 | 0.2589 | 0.4678 |
| 41.0 | 0.6698 | 0.8022 | 0.7318 | 0.4297 | 0.5493 | 0.5160 | 0.5727 | 0.5289 | 0.4574 | 0.5711 | 0.1978 | 0.4842 |
| 42.0 | 0.6542 | 0.7977 | 0.7309 | 0.4450 | 0.5653 | 0.5389 | 0.5874 | 0.5625 | 0.4662 | 0.5561 | 0.1969 | 0.5024 |
| 43.0 | 0.6732 | 0.7995 | 0.7126 | 0.4343 | 0.5636 | 0.5217 | 0.5952 | 0.5608 | 0.4679 | 0.5672 | 0.2449 | 0.4559 |
| 44.0 | 0.6797 | 0.8035 | 0.7234 | 0.4571 | 0.5651 | 0.5352 | 0.5728 | 0.5156 | 0.4591 | 0.5458 | 0.2506 | 0.4307 |
| 45.0 | 0.6866 | 0.7923 | 0.7332 | 0.4349 | 0.5523 | 0.5312 | 0.5855 | 0.5314 | 0.4323 | 0.5653 | 0.2488 | 0.4833 |
| 46.0 | 0.6868 | 0.7856 | 0.7297 | 0.4426 | 0.5763 | 0.5288 | 0.5846 | 0.5331 | 0.4573 | 0.5724 | 0.2999 | 0.4811 |
| 47.0 | 0.6506 | 0.8100 | 0.7248 | 0.4534 | 0.5506 | 0.5230 | 0.5954 | 0.5515 | 0.4251 | 0.5546 | 0.2245 | 0.4677 |
| 48.0 | 0.6590 | 0.8106 | 0.7334 | 0.4353 | 0.5542 | 0.5254 | 0.5813 | 0.4869 | 0.4373 | 0.5611 | 0.2135 | 0.4503 |
| 49.0 | 0.6790 | 0.7967 | 0.7227 | 0.4477 | 0.5612 | 0.5523 | 0.5861 | 0.5460 | 0.4310 | 0.5518 | 0.2535 | 0.4817 |
| 50.0 | 0.6884 | 0.7852 | 0.7323 | 0.4523 | 0.5829 | 0.5516 | 0.5904 | 0.5289 | 0.4518 | 0.5719 | 0.2318 | 0.4783 |
* All values in the above charts are rounded to nearest ten-thousandth.
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1 | [
"background",
"facade",
"window",
"door",
"cornice",
"sill",
"balcony",
"blind",
"molding",
"deco",
"pillar",
"shop"
] |
AlmogM/segformer-b0-finetuned-fish-almogm |
<!-- 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-fish-almogm
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0068
- eval_mean_iou: 0.4831
- eval_mean_accuracy: 1.0000
- eval_overall_accuracy: 1.0000
- eval_accuracy_background: 1.0000
- eval_accuracy_fish: nan
- eval_iou_background: 0.9662
- eval_iou_fish: 0.0
- eval_runtime: 62.449
- eval_samples_per_second: 0.801
- eval_steps_per_second: 0.4
- epoch: 6.46
- step: 640
## 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 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2 | [
"background",
"fish"
] |
yiming19/segformer-b0-finetuned-segments-construction-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-construction-1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yiming19/construction_place dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2796
- Mean Iou: 0.3218
- Mean Accuracy: 0.5305
- Overall Accuracy: 0.9276
- Accuracy Unlabeled: nan
- Accuracy Ruler: 0.8954
- Accuracy Socket: 0.0
- Accuracy Wall: 0.9644
- Accuracy Window: nan
- Accuracy Heater: nan
- Accuracy Floor: 0.6710
- Accuracy Ceiling: 0.0
- Accuracy Skirting: nan
- Accuracy Door: 0.6525
- Accuracy Light: nan
- Iou Unlabeled: nan
- Iou Ruler: 0.7222
- Iou Socket: 0.0
- Iou Wall: 0.9553
- Iou Window: 0.0
- Iou Heater: nan
- Iou Floor: 0.2630
- Iou Ceiling: 0.0
- Iou Skirting: 0.0
- Iou Door: 0.6342
- Iou Light: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Ruler | Accuracy Socket | Accuracy Wall | Accuracy Window | Accuracy Heater | Accuracy Floor | Accuracy Ceiling | Accuracy Skirting | Accuracy Door | Accuracy Light | Iou Unlabeled | Iou Ruler | Iou Socket | Iou Wall | Iou Window | Iou Heater | Iou Floor | Iou Ceiling | Iou Skirting | Iou Door | Iou Light |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:---------------:|:-------------:|:---------------:|:---------------:|:--------------:|:----------------:|:-----------------:|:-------------:|:--------------:|:-------------:|:---------:|:----------:|:--------:|:----------:|:----------:|:---------:|:-----------:|:------------:|:--------:|:---------:|
| 1.8126 | 1.43 | 20 | 2.1233 | 0.1955 | 0.5160 | 0.8448 | nan | 0.8191 | 0.0 | 0.8868 | nan | nan | 0.9618 | 0.0 | nan | 0.4281 | nan | 0.0 | 0.5555 | 0.0 | 0.8845 | 0.0 | 0.0 | 0.2971 | 0.0 | 0.0 | 0.4135 | 0.0 |
| 1.905 | 2.86 | 40 | 1.3611 | 0.1827 | 0.4921 | 0.8505 | nan | 0.9275 | 0.0 | 0.9139 | nan | nan | 0.9627 | 0.0 | nan | 0.1484 | nan | nan | 0.5404 | 0.0 | 0.9095 | 0.0 | 0.0 | 0.2289 | 0.0 | 0.0 | 0.1484 | 0.0 |
| 1.1072 | 4.29 | 60 | 1.0502 | 0.2327 | 0.5517 | 0.8903 | nan | 0.9108 | 0.0 | 0.9266 | nan | nan | 0.9367 | 0.0 | nan | 0.5360 | nan | nan | 0.5301 | 0.0 | 0.9206 | 0.0 | 0.0 | 0.3475 | 0.0 | 0.0 | 0.5284 | 0.0 |
| 1.0076 | 5.71 | 80 | 0.8802 | 0.2744 | 0.5609 | 0.9089 | nan | 0.8208 | 0.0 | 0.9410 | nan | nan | 0.9532 | 0.0 | nan | 0.6505 | nan | nan | 0.5500 | 0.0 | 0.9277 | 0.0 | 0.0 | 0.3688 | 0.0 | 0.0 | 0.6227 | nan |
| 1.5533 | 7.14 | 100 | 0.8991 | 0.2846 | 0.5514 | 0.8878 | nan | 0.8918 | 0.0 | 0.9243 | nan | nan | 0.9591 | 0.0 | nan | 0.5332 | nan | nan | 0.5262 | 0.0 | 0.9169 | 0.0 | nan | 0.3209 | 0.0 | 0.0 | 0.5132 | nan |
| 0.9912 | 8.57 | 120 | 0.9340 | 0.2891 | 0.5652 | 0.8854 | nan | 0.9478 | 0.0 | 0.9151 | nan | nan | 0.9438 | 0.0 | nan | 0.5844 | nan | nan | 0.5059 | 0.0 | 0.9098 | 0.0 | nan | 0.3424 | 0.0 | 0.0 | 0.5544 | nan |
| 0.784 | 10.0 | 140 | 0.7017 | 0.3140 | 0.5984 | 0.9173 | nan | 0.9136 | 0.0 | 0.9305 | nan | nan | 0.8971 | 0.0 | nan | 0.8493 | nan | nan | 0.5324 | 0.0 | 0.9224 | 0.0 | 0.0 | 0.5805 | 0.0 | 0.0 | 0.7909 | nan |
| 0.5636 | 11.43 | 160 | 0.6925 | 0.3573 | 0.5978 | 0.9280 | nan | 0.8714 | 0.0 | 0.9412 | nan | nan | 0.8868 | 0.0 | nan | 0.8876 | nan | nan | 0.5701 | 0.0 | 0.9308 | 0.0 | nan | 0.5638 | 0.0 | 0.0 | 0.7935 | nan |
| 1.0692 | 12.86 | 180 | 0.7313 | 0.2931 | 0.5724 | 0.8981 | nan | 0.9587 | 0.0 | 0.9231 | nan | nan | 0.8880 | 0.0 | nan | 0.6647 | nan | nan | 0.4988 | 0.0 | 0.9182 | 0.0 | nan | 0.3342 | 0.0 | 0.0 | 0.5932 | nan |
| 0.7603 | 14.29 | 200 | 0.6907 | 0.2577 | 0.5744 | 0.9001 | nan | 0.9619 | 0.0 | 0.9251 | nan | nan | 0.8930 | 0.0 | nan | 0.6661 | nan | nan | 0.4939 | 0.0 | 0.9208 | 0.0 | 0.0 | 0.3219 | 0.0 | 0.0 | 0.5824 | nan |
| 0.9509 | 15.71 | 220 | 0.5110 | 0.3682 | 0.6069 | 0.9324 | nan | 0.9355 | 0.0 | 0.9417 | nan | nan | 0.8453 | 0.0 | nan | 0.9191 | nan | nan | 0.5671 | 0.0 | 0.9334 | 0.0 | nan | 0.6050 | 0.0 | 0.0 | 0.8403 | nan |
| 0.4254 | 17.14 | 240 | 0.5925 | 0.2961 | 0.5629 | 0.9023 | nan | 0.9646 | 0.0 | 0.9295 | nan | nan | 0.8261 | 0.0 | nan | 0.6569 | nan | nan | 0.5302 | 0.0 | 0.9243 | 0.0 | nan | 0.3138 | 0.0 | 0.0 | 0.6009 | nan |
| 0.3839 | 18.57 | 260 | 0.4226 | 0.3537 | 0.5479 | 0.9367 | nan | 0.9108 | 0.0 | 0.9540 | nan | nan | 0.5102 | 0.0 | nan | 0.9124 | nan | nan | 0.6643 | 0.0 | 0.9426 | 0.0 | nan | 0.3868 | 0.0 | 0.0 | 0.8361 | nan |
| 0.7441 | 20.0 | 280 | 0.5084 | 0.3533 | 0.5993 | 0.9277 | nan | 0.9691 | 0.0 | 0.9391 | nan | nan | 0.8075 | 0.0 | nan | 0.8801 | nan | nan | 0.5527 | 0.0 | 0.9333 | 0.0 | nan | 0.5197 | 0.0 | 0.0 | 0.8208 | nan |
| 0.4374 | 21.43 | 300 | 0.4683 | 0.3038 | 0.5549 | 0.9173 | nan | 0.9662 | 0.0 | 0.9480 | nan | nan | 0.7594 | 0.0 | nan | 0.6558 | nan | nan | 0.6024 | 0.0 | 0.9419 | 0.0 | nan | 0.2804 | 0.0 | 0.0 | 0.6056 | nan |
| 0.6224 | 22.86 | 320 | 0.4100 | 0.3810 | 0.5960 | 0.9374 | nan | 0.9704 | 0.0 | 0.9460 | nan | nan | 0.7131 | 0.0 | nan | 0.9467 | nan | nan | 0.5986 | 0.0 | 0.9401 | 0.0 | nan | 0.6197 | 0.0 | 0.0 | 0.8898 | nan |
| 0.4473 | 24.29 | 340 | 0.3933 | 0.3368 | 0.5431 | 0.9336 | nan | 0.9212 | 0.0 | 0.9620 | nan | nan | 0.6197 | 0.0 | nan | 0.7556 | nan | nan | 0.7221 | 0.0 | 0.9521 | 0.0 | nan | 0.2958 | 0.0 | 0.0 | 0.7245 | nan |
| 0.3364 | 25.71 | 360 | 0.4336 | 0.2976 | 0.5125 | 0.9134 | nan | 0.9408 | 0.0 | 0.9544 | nan | nan | 0.6075 | 0.0 | nan | 0.5721 | nan | nan | 0.6918 | 0.0 | 0.9481 | 0.0 | nan | 0.1998 | 0.0 | 0.0 | 0.5411 | nan |
| 0.281 | 27.14 | 380 | 0.3795 | 0.3689 | 0.5760 | 0.9420 | nan | 0.9250 | 0.0 | 0.9589 | nan | nan | 0.6859 | 0.0 | nan | 0.8863 | nan | nan | 0.7108 | 0.0 | 0.9518 | 0.0 | nan | 0.4576 | 0.0 | 0.0 | 0.8305 | nan |
| 0.3198 | 28.57 | 400 | 0.4023 | 0.3158 | 0.5143 | 0.9238 | nan | 0.9120 | 0.0 | 0.9610 | nan | nan | 0.5580 | 0.0 | nan | 0.6550 | nan | nan | 0.7210 | 0.0 | 0.9519 | 0.0 | nan | 0.2238 | 0.0 | 0.0 | 0.6293 | nan |
| 0.4624 | 30.0 | 420 | 0.3565 | 0.3770 | 0.5774 | 0.9475 | nan | 0.9408 | 0.0 | 0.9613 | nan | nan | 0.6287 | 0.0 | nan | 0.9337 | nan | nan | 0.6855 | 0.0 | 0.9539 | 0.0 | nan | 0.4943 | 0.0 | 0.0 | 0.8827 | nan |
| 0.2356 | 31.43 | 440 | 0.3940 | 0.3100 | 0.5349 | 0.9221 | nan | 0.9268 | 0.0 | 0.9602 | nan | nan | 0.7187 | 0.0 | nan | 0.6040 | nan | nan | 0.7005 | 0.0 | 0.9536 | 0.0 | nan | 0.2474 | 0.0 | 0.0 | 0.5781 | nan |
| 0.3931 | 32.86 | 460 | 0.3516 | 0.3162 | 0.5570 | 0.9258 | nan | 0.9338 | 0.0 | 0.9598 | nan | nan | 0.8124 | 0.0 | nan | 0.6362 | nan | nan | 0.6824 | 0.0 | 0.9542 | 0.0 | nan | 0.2888 | 0.0 | 0.0 | 0.6040 | nan |
| 0.2431 | 34.29 | 480 | 0.4011 | 0.2955 | 0.5291 | 0.9138 | nan | 0.9242 | 0.0 | 0.9583 | nan | nan | 0.7864 | 0.0 | nan | 0.5058 | nan | nan | 0.6954 | 0.0 | 0.9520 | 0.0 | nan | 0.2331 | 0.0 | 0.0 | 0.4832 | nan |
| 0.2131 | 35.71 | 500 | 0.2847 | 0.3764 | 0.5613 | 0.9487 | nan | 0.8877 | 0.0 | 0.9679 | nan | nan | 0.6103 | 0.0 | nan | 0.9020 | nan | nan | 0.7330 | 0.0 | 0.9571 | 0.0 | nan | 0.4539 | 0.0 | 0.0 | 0.8669 | nan |
| 0.4151 | 37.14 | 520 | 0.3176 | 0.3186 | 0.5239 | 0.9256 | nan | 0.8930 | 0.0 | 0.9640 | nan | nan | 0.6505 | 0.0 | nan | 0.6356 | nan | nan | 0.7251 | 0.0 | 0.9544 | 0.0 | nan | 0.2507 | 0.0 | 0.0 | 0.6187 | nan |
| 0.2408 | 38.57 | 540 | 0.3267 | 0.3071 | 0.5361 | 0.9208 | nan | 0.9264 | 0.0 | 0.9600 | nan | nan | 0.7441 | 0.0 | nan | 0.5859 | nan | nan | 0.6868 | 0.0 | 0.9538 | 0.0 | nan | 0.2526 | 0.0 | 0.0 | 0.5635 | nan |
| 0.2274 | 40.0 | 560 | 0.2875 | 0.3396 | 0.5471 | 0.9349 | nan | 0.9098 | 0.0 | 0.9626 | nan | nan | 0.6456 | 0.0 | nan | 0.7649 | nan | nan | 0.7018 | 0.0 | 0.9547 | 0.0 | nan | 0.3216 | 0.0 | 0.0 | 0.7387 | nan |
| 0.2452 | 41.43 | 580 | 0.2998 | 0.3181 | 0.5357 | 0.9279 | nan | 0.9089 | 0.0 | 0.9642 | nan | nan | 0.6932 | 0.0 | nan | 0.6480 | nan | nan | 0.7057 | 0.0 | 0.9562 | 0.0 | nan | 0.2578 | 0.0 | 0.0 | 0.6252 | nan |
| 0.2922 | 42.86 | 600 | 0.2957 | 0.3131 | 0.5246 | 0.9255 | nan | 0.9056 | 0.0 | 0.9643 | nan | nan | 0.6535 | 0.0 | nan | 0.6242 | nan | nan | 0.7103 | 0.0 | 0.9563 | 0.0 | nan | 0.2347 | 0.0 | 0.0 | 0.6037 | nan |
| 0.3704 | 44.29 | 620 | 0.3290 | 0.3172 | 0.5429 | 0.9247 | nan | 0.9246 | 0.0 | 0.9583 | nan | nan | 0.7123 | 0.0 | nan | 0.6621 | nan | nan | 0.6856 | 0.0 | 0.9527 | 0.0 | nan | 0.2707 | 0.0 | 0.0 | 0.6286 | nan |
| 0.2482 | 45.71 | 640 | 0.2995 | 0.3251 | 0.5368 | 0.9276 | nan | 0.9018 | 0.0 | 0.9617 | nan | nan | 0.6795 | 0.0 | nan | 0.6779 | nan | nan | 0.7154 | 0.0 | 0.9538 | 0.0 | nan | 0.2790 | 0.0 | 0.0 | 0.6528 | nan |
| 0.2798 | 47.14 | 660 | 0.2808 | 0.3323 | 0.5374 | 0.9319 | nan | 0.8938 | 0.0 | 0.9644 | nan | nan | 0.6554 | 0.0 | nan | 0.7110 | nan | nan | 0.7218 | 0.0 | 0.9554 | 0.0 | nan | 0.2919 | 0.0 | 0.0 | 0.6894 | nan |
| 0.2746 | 48.57 | 680 | 0.2695 | 0.3265 | 0.5341 | 0.9299 | nan | 0.8947 | 0.0 | 0.9642 | nan | nan | 0.6597 | 0.0 | nan | 0.6861 | nan | nan | 0.7198 | 0.0 | 0.9554 | 0.0 | nan | 0.2735 | 0.0 | 0.0 | 0.6633 | nan |
| 0.2169 | 50.0 | 700 | 0.2796 | 0.3218 | 0.5305 | 0.9276 | nan | 0.8954 | 0.0 | 0.9644 | nan | nan | 0.6710 | 0.0 | nan | 0.6525 | nan | nan | 0.7222 | 0.0 | 0.9553 | 0.0 | nan | 0.2630 | 0.0 | 0.0 | 0.6342 | nan |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.0.dev0
| [
"unlabeled",
"ruler",
"socket",
"wall",
"window",
"heater",
"floor",
"ceiling",
"skirting",
"door",
"light"
] |
shivalikasingh/video-mask2former-swin-tiny-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-tiny-youtubevis-2021-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-tiny-youtubevis-2021-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
"shark",
"skateboard",
"snake",
"snowboard",
"squirrel",
"surfboard",
"tennis_racket",
"tiger",
"train",
"truck",
"turtle",
"whale",
"zebra"
] |
shivalikasingh/video-mask2former-swin-tiny-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-tiny-youtubevis-2019-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-tiny-youtubevis-2019-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airplane",
"truck",
"zebra",
"tiger",
"elephant",
"snowboard",
"boat",
"shark",
"mouse",
"frog",
"eagle",
"earless_seal",
"tennis_racket"
] |
shivalikasingh/video-mask2former-swin-small-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (small-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-small-youtubevis-2021-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-small-youtubevis-2021-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
"shark",
"skateboard",
"snake",
"snowboard",
"squirrel",
"surfboard",
"tennis_racket",
"tiger",
"train",
"truck",
"turtle",
"whale",
"zebra"
] |
shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (base-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-base-IN21k-youtubevis-2021-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-base-IN21k-youtubevis-2021-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
"shark",
"skateboard",
"snake",
"snowboard",
"squirrel",
"surfboard",
"tennis_racket",
"tiger",
"train",
"truck",
"turtle",
"whale",
"zebra"
] |
shivalikasingh/video-mask2former-swin-large-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-large-youtubevis-2021-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-large-youtubevis-2021-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
"shark",
"skateboard",
"snake",
"snowboard",
"squirrel",
"surfboard",
"tennis_racket",
"tiger",
"train",
"truck",
"turtle",
"whale",
"zebra"
] |
shivalikasingh/video-mask2former-swin-small-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (small-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-small-youtubevis-2019-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-small-youtubevis-2019-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airplane",
"truck",
"zebra",
"tiger",
"elephant",
"snowboard",
"boat",
"shark",
"mouse",
"frog",
"eagle",
"earless_seal",
"tennis_racket"
] |
shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (base-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-base-IN21k-youtubevis-2019-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-base-IN21k-youtubevis-2019-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airplane",
"truck",
"zebra",
"tiger",
"elephant",
"snowboard",
"boat",
"shark",
"mouse",
"frog",
"eagle",
"earless_seal",
"tennis_racket"
] |
shivalikasingh/video-mask2former-swin-large-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
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.
In the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
### How to use
Here is how to use this model:
```python
import torch
import torchvision
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former trained on YouTubeVIS 2021 instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-large-youtubevis-2019-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-large-youtubevis-2019-instance")
file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
video = torchvision.io.read_video(file_path)[0]
video_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
video_input = torch.cat(video_frames)
with torch.no_grad():
outputs = model(**video_input)
# model predicts class_queries_logits of shape `(batch_size, num_queries, num_classes)`
# and masks_queries_logits of shape `(num_queries, batch_size, 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 = image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_video_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airplane",
"truck",
"zebra",
"tiger",
"elephant",
"snowboard",
"boat",
"shark",
"mouse",
"frog",
"eagle",
"earless_seal",
"tennis_racket"
] |
alanoix/segformer_b0_flair_one |
# pretrained model
- https://huggingface.co/nvidia/mit-b0
- SegFormer (b0-sized) encoder pre-trained-only
- SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
# training-set
- you can find the training-set here : https://codalab.lisn.upsaclay.fr/competitions/8769
# training-arguments
- channels : rgb
- batch : 8
- epochs : 8
- learning-rate : 5e-6
- GPU : T4
# results on test-set
- Mean IoU : 59.9
- more information here : https://codalab.lisn.upsaclay.fr/competitions/8769 | [
"none",
"building",
"pervious surface",
"impervious surface",
"bare soil",
"water",
"coniferous",
"deciduous",
"brushwood",
"vineyard",
"herbaceous vegetation",
"agricultural land",
"plowed land"
] |
Efferbach/segformer-finetuned-lane-1k-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-lane-1k-steps
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0548
- Mean Iou: 0.0708
- Mean Accuracy: 0.1236
- Overall Accuracy: 0.1217
- Accuracy Background: nan
- Accuracy Left: 0.1893
- Accuracy Right: 0.0578
- Iou Background: 0.0
- Iou Left: 0.1581
- Iou Right: 0.0544
## 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: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.1 | 1.0 | 308 | 0.0862 | 0.0008 | 0.0013 | 0.0012 | nan | 0.0025 | 0.0 | 0.0 | 0.0025 | 0.0 |
| 0.0596 | 2.0 | 616 | 0.0597 | 0.0712 | 0.1126 | 0.1132 | nan | 0.0940 | 0.1313 | 0.0 | 0.0907 | 0.1228 |
| 0.0506 | 3.0 | 924 | 0.0551 | 0.0682 | 0.1171 | 0.1152 | nan | 0.1805 | 0.0536 | 0.0 | 0.1539 | 0.0508 |
| 0.0494 | 3.25 | 1000 | 0.0548 | 0.0708 | 0.1236 | 0.1217 | nan | 0.1893 | 0.0578 | 0.0 | 0.1581 | 0.0544 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"background",
"left",
"right"
] |
Efferbach/segformer-finetuned-lane-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-lane-10k-steps
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0365
- Mean Iou: 0.4899
- Mean Accuracy: 0.7371
- Overall Accuracy: 0.7371
- Accuracy Background: nan
- Accuracy Left: 0.7394
- Accuracy Right: 0.7348
- Iou Background: 0.0
- Iou Left: 0.7371
- Iou Right: 0.7325
## 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 Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.0792 | 1.0 | 308 | 0.0714 | 0.0148 | 0.0229 | 0.0225 | nan | 0.0373 | 0.0085 | 0.0 | 0.0362 | 0.0083 |
| 0.0437 | 2.0 | 616 | 0.0502 | 0.1687 | 0.2775 | 0.2784 | nan | 0.2492 | 0.3058 | 0.0 | 0.2343 | 0.2718 |
| 0.0326 | 3.0 | 924 | 0.0445 | 0.2614 | 0.4441 | 0.4479 | nan | 0.3134 | 0.5748 | 0.0 | 0.3100 | 0.4742 |
| 0.0224 | 4.0 | 1232 | 0.0370 | 0.4048 | 0.6098 | 0.6100 | nan | 0.6043 | 0.6153 | 0.0 | 0.6031 | 0.6113 |
| 0.0184 | 5.0 | 1540 | 0.0346 | 0.3820 | 0.5858 | 0.5870 | nan | 0.5421 | 0.6295 | 0.0 | 0.5400 | 0.6060 |
| 0.0159 | 6.0 | 1848 | 0.0319 | 0.4367 | 0.6567 | 0.6573 | nan | 0.6343 | 0.6791 | 0.0 | 0.6341 | 0.6760 |
| 0.0139 | 7.0 | 2156 | 0.0317 | 0.4555 | 0.6855 | 0.6860 | nan | 0.6691 | 0.7019 | 0.0 | 0.6680 | 0.6986 |
| 0.0129 | 8.0 | 2464 | 0.0321 | 0.4348 | 0.6533 | 0.6535 | nan | 0.6479 | 0.6588 | 0.0 | 0.6474 | 0.6571 |
| 0.0122 | 9.0 | 2772 | 0.0275 | 0.4541 | 0.6827 | 0.6830 | nan | 0.6710 | 0.6943 | 0.0 | 0.6697 | 0.6927 |
| 0.0111 | 10.0 | 3080 | 0.0305 | 0.4609 | 0.6928 | 0.6927 | nan | 0.6969 | 0.6887 | 0.0 | 0.6963 | 0.6865 |
| 0.011 | 11.0 | 3388 | 0.0286 | 0.4646 | 0.6988 | 0.6991 | nan | 0.6890 | 0.7087 | 0.0 | 0.6883 | 0.7055 |
| 0.0103 | 12.0 | 3696 | 0.0298 | 0.4693 | 0.7058 | 0.7062 | nan | 0.6939 | 0.7177 | 0.0 | 0.6932 | 0.7148 |
| 0.0097 | 13.0 | 4004 | 0.0293 | 0.4717 | 0.7090 | 0.7087 | nan | 0.7184 | 0.6996 | 0.0 | 0.7176 | 0.6975 |
| 0.0093 | 14.0 | 4312 | 0.0330 | 0.4537 | 0.6835 | 0.6836 | nan | 0.6775 | 0.6894 | 0.0 | 0.6768 | 0.6843 |
| 0.009 | 15.0 | 4620 | 0.0331 | 0.4804 | 0.7226 | 0.7226 | nan | 0.7194 | 0.7257 | 0.0 | 0.7178 | 0.7234 |
| 0.0088 | 16.0 | 4928 | 0.0315 | 0.4890 | 0.7355 | 0.7357 | nan | 0.7275 | 0.7435 | 0.0 | 0.7259 | 0.7411 |
| 0.0086 | 17.0 | 5236 | 0.0338 | 0.4813 | 0.7234 | 0.7234 | nan | 0.7224 | 0.7243 | 0.0 | 0.7216 | 0.7223 |
| 0.0085 | 18.0 | 5544 | 0.0348 | 0.4743 | 0.7129 | 0.7126 | nan | 0.7225 | 0.7033 | 0.0 | 0.7217 | 0.7012 |
| 0.0083 | 19.0 | 5852 | 0.0357 | 0.4812 | 0.7245 | 0.7244 | nan | 0.7281 | 0.7210 | 0.0 | 0.7254 | 0.7183 |
| 0.0081 | 20.0 | 6160 | 0.0334 | 0.4829 | 0.7271 | 0.7269 | nan | 0.7337 | 0.7205 | 0.0 | 0.7305 | 0.7182 |
| 0.0079 | 21.0 | 6468 | 0.0359 | 0.4773 | 0.7177 | 0.7177 | nan | 0.7184 | 0.7170 | 0.0 | 0.7174 | 0.7146 |
| 0.0077 | 22.0 | 6776 | 0.0351 | 0.4874 | 0.7332 | 0.7329 | nan | 0.7440 | 0.7223 | 0.0 | 0.7432 | 0.7190 |
| 0.0075 | 23.0 | 7084 | 0.0344 | 0.4855 | 0.7296 | 0.7292 | nan | 0.7437 | 0.7156 | 0.0 | 0.7425 | 0.7141 |
| 0.0077 | 24.0 | 7392 | 0.0362 | 0.4799 | 0.7216 | 0.7216 | nan | 0.7236 | 0.7196 | 0.0 | 0.7223 | 0.7174 |
| 0.0071 | 25.0 | 7700 | 0.0391 | 0.4775 | 0.7179 | 0.7180 | nan | 0.7173 | 0.7186 | 0.0 | 0.7161 | 0.7163 |
| 0.0077 | 26.0 | 8008 | 0.0339 | 0.4895 | 0.7367 | 0.7366 | nan | 0.7405 | 0.7329 | 0.0 | 0.7388 | 0.7297 |
| 0.0069 | 27.0 | 8316 | 0.0344 | 0.4858 | 0.7305 | 0.7305 | nan | 0.7291 | 0.7318 | 0.0 | 0.7278 | 0.7297 |
| 0.0069 | 28.0 | 8624 | 0.0361 | 0.4844 | 0.7283 | 0.7282 | nan | 0.7324 | 0.7243 | 0.0 | 0.7309 | 0.7221 |
| 0.007 | 29.0 | 8932 | 0.0371 | 0.4837 | 0.7273 | 0.7270 | nan | 0.7360 | 0.7186 | 0.0 | 0.7345 | 0.7166 |
| 0.007 | 30.0 | 9240 | 0.0366 | 0.4854 | 0.7305 | 0.7303 | nan | 0.7379 | 0.7231 | 0.0 | 0.7353 | 0.7208 |
| 0.0067 | 31.0 | 9548 | 0.0367 | 0.4866 | 0.7322 | 0.7321 | nan | 0.7357 | 0.7286 | 0.0 | 0.7335 | 0.7263 |
| 0.0068 | 32.0 | 9856 | 0.0364 | 0.4883 | 0.7348 | 0.7347 | nan | 0.7377 | 0.7318 | 0.0 | 0.7355 | 0.7295 |
| 0.0067 | 32.47 | 10000 | 0.0365 | 0.4899 | 0.7371 | 0.7371 | nan | 0.7394 | 0.7348 | 0.0 | 0.7371 | 0.7325 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"background",
"left",
"right"
] |
Efferbach/mobilevit-small-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. -->
# mobilevit-small-10k-steps
This model is a fine-tuned version of [apple/deeplabv3-mobilevit-small](https://huggingface.co/apple/deeplabv3-mobilevit-small) on the Efferbach/lane_master2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0821
- Mean Iou: 0.0
- Mean Accuracy: 0.0
- Overall Accuracy: 0.0
- Accuracy Background: nan
- Accuracy Left: 0.0
- Accuracy Right: 0.0
- Iou Background: 0.0
- Iou Left: 0.0
- Iou Right: 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: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.5041 | 1.0 | 385 | 0.3382 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1553 | 2.0 | 770 | 0.1387 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1019 | 3.0 | 1155 | 0.1037 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0882 | 4.0 | 1540 | 0.0883 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0828 | 5.0 | 1925 | 0.0823 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0807 | 6.0 | 2310 | 0.0820 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0795 | 7.0 | 2695 | 0.0804 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0786 | 8.0 | 3080 | 0.0784 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0777 | 9.0 | 3465 | 0.0786 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0771 | 10.0 | 3850 | 0.0774 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0773 | 11.0 | 4235 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0765 | 12.0 | 4620 | 0.0782 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0757 | 13.0 | 5005 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0756 | 14.0 | 5390 | 0.0774 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0754 | 15.0 | 5775 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0746 | 16.0 | 6160 | 0.0775 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.074 | 17.0 | 6545 | 0.0779 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0736 | 18.0 | 6930 | 0.0792 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0737 | 19.0 | 7315 | 0.0801 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.073 | 20.0 | 7700 | 0.0804 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0729 | 21.0 | 8085 | 0.0805 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0734 | 22.0 | 8470 | 0.0804 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0726 | 23.0 | 8855 | 0.0811 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0726 | 24.0 | 9240 | 0.0816 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0721 | 25.0 | 9625 | 0.0822 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0727 | 25.97 | 10000 | 0.0821 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"background",
"left",
"right"
] |
Efferbach/mobilenet_v2_1-10k-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. -->
# mobilenet_v2_1-10k-steps
This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the Efferbach/lane_master2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0827
- Mean Iou: 0.0
- Mean Accuracy: 0.0
- Overall Accuracy: 0.0
- Accuracy Background: nan
- Accuracy Left: 0.0
- Accuracy Right: 0.0
- Iou Background: 0.0
- Iou Left: 0.0
- Iou Right: 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: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.3253 | 1.0 | 385 | 0.0989 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.128 | 2.0 | 770 | 0.1518 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1212 | 3.0 | 1155 | 0.1852 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.117 | 4.0 | 1540 | 0.1446 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1148 | 5.0 | 1925 | 0.1087 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1167 | 6.0 | 2310 | 0.1502 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1128 | 7.0 | 2695 | 0.0882 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1156 | 8.0 | 3080 | 0.1005 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1164 | 9.0 | 3465 | 0.0844 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1128 | 10.0 | 3850 | 0.1497 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1151 | 11.0 | 4235 | 0.1024 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1112 | 12.0 | 4620 | 0.0869 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1093 | 13.0 | 5005 | 0.0940 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1102 | 14.0 | 5390 | 0.0914 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1111 | 15.0 | 5775 | 0.1047 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1087 | 16.0 | 6160 | 0.1104 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1105 | 17.0 | 6545 | 0.0970 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1083 | 18.0 | 6930 | 0.0868 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1077 | 19.0 | 7315 | 0.1121 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1115 | 20.0 | 7700 | 0.2092 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1102 | 21.0 | 8085 | 0.0850 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1077 | 22.0 | 8470 | 0.1011 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1111 | 23.0 | 8855 | 0.1136 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1099 | 24.0 | 9240 | 0.1001 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1086 | 25.0 | 9625 | 0.0997 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1066 | 25.97 | 10000 | 0.0827 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"background",
"left",
"right"
] |
zklee98/segformer-b1-solarModuleAnomaly-v0.1 |
<!-- 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-b1-solarModuleAnomaly-v0.1
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the zklee98/solarModuleAnomaly dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1547
- Mean Iou: 0.3822
- Mean Accuracy: 0.7643
- Overall Accuracy: 0.7643
- Accuracy Unlabelled: nan
- Accuracy Anomaly: 0.7643
- Iou Unlabelled: 0.0
- Iou Anomaly: 0.7643
## 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 Unlabelled | Accuracy Anomaly | Iou Unlabelled | Iou Anomaly |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.4699 | 0.4 | 20 | 0.6337 | 0.4581 | 0.9162 | 0.9162 | nan | 0.9162 | 0.0 | 0.9162 |
| 0.3129 | 0.8 | 40 | 0.4636 | 0.3704 | 0.7407 | 0.7407 | nan | 0.7407 | 0.0 | 0.7407 |
| 0.2732 | 1.2 | 60 | 0.3164 | 0.3867 | 0.7734 | 0.7734 | nan | 0.7734 | 0.0 | 0.7734 |
| 0.2653 | 1.6 | 80 | 0.3769 | 0.4090 | 0.8180 | 0.8180 | nan | 0.8180 | 0.0 | 0.8180 |
| 0.2232 | 2.0 | 100 | 0.2976 | 0.2479 | 0.4958 | 0.4958 | nan | 0.4958 | 0.0 | 0.4958 |
| 0.5305 | 2.4 | 120 | 0.3151 | 0.3807 | 0.7613 | 0.7613 | nan | 0.7613 | 0.0 | 0.7613 |
| 0.2423 | 2.8 | 140 | 0.3189 | 0.4152 | 0.8305 | 0.8305 | nan | 0.8305 | 0.0 | 0.8305 |
| 0.3341 | 3.2 | 160 | 0.2384 | 0.3861 | 0.7723 | 0.7723 | nan | 0.7723 | 0.0 | 0.7723 |
| 0.2146 | 3.6 | 180 | 0.3200 | 0.4621 | 0.9243 | 0.9243 | nan | 0.9243 | 0.0 | 0.9243 |
| 0.1866 | 4.0 | 200 | 0.2510 | 0.3646 | 0.7291 | 0.7291 | nan | 0.7291 | 0.0 | 0.7291 |
| 0.2861 | 4.4 | 220 | 0.2736 | 0.4202 | 0.8404 | 0.8404 | nan | 0.8404 | 0.0 | 0.8404 |
| 0.2048 | 4.8 | 240 | 0.2410 | 0.3912 | 0.7823 | 0.7823 | nan | 0.7823 | 0.0 | 0.7823 |
| 0.1604 | 5.2 | 260 | 0.2233 | 0.3672 | 0.7344 | 0.7344 | nan | 0.7344 | 0.0 | 0.7344 |
| 0.2756 | 5.6 | 280 | 0.2705 | 0.4494 | 0.8987 | 0.8987 | nan | 0.8987 | 0.0 | 0.8987 |
| 0.1859 | 6.0 | 300 | 0.2211 | 0.4045 | 0.8089 | 0.8089 | nan | 0.8089 | 0.0 | 0.8089 |
| 0.1306 | 6.4 | 320 | 0.2140 | 0.3763 | 0.7525 | 0.7525 | nan | 0.7525 | 0.0 | 0.7525 |
| 0.5508 | 6.8 | 340 | 0.2231 | 0.4185 | 0.8371 | 0.8371 | nan | 0.8371 | 0.0 | 0.8371 |
| 0.1446 | 7.2 | 360 | 0.2139 | 0.3666 | 0.7332 | 0.7332 | nan | 0.7332 | 0.0 | 0.7332 |
| 0.3275 | 7.6 | 380 | 0.2470 | 0.3964 | 0.7928 | 0.7928 | nan | 0.7928 | 0.0 | 0.7928 |
| 0.164 | 8.0 | 400 | 0.2017 | 0.3910 | 0.7819 | 0.7819 | nan | 0.7819 | 0.0 | 0.7819 |
| 0.1864 | 8.4 | 420 | 0.2307 | 0.4408 | 0.8816 | 0.8816 | nan | 0.8816 | 0.0 | 0.8816 |
| 0.1578 | 8.8 | 440 | 0.1869 | 0.3707 | 0.7414 | 0.7414 | nan | 0.7414 | 0.0 | 0.7414 |
| 0.1201 | 9.2 | 460 | 0.2115 | 0.3834 | 0.7667 | 0.7667 | nan | 0.7667 | 0.0 | 0.7667 |
| 0.1783 | 9.6 | 480 | 0.2009 | 0.3747 | 0.7495 | 0.7495 | nan | 0.7495 | 0.0 | 0.7495 |
| 0.1232 | 10.0 | 500 | 0.1797 | 0.3865 | 0.7729 | 0.7729 | nan | 0.7729 | 0.0 | 0.7729 |
| 0.2572 | 10.4 | 520 | 0.1983 | 0.4057 | 0.8115 | 0.8115 | nan | 0.8115 | 0.0 | 0.8115 |
| 0.1209 | 10.8 | 540 | 0.1607 | 0.4274 | 0.8547 | 0.8547 | nan | 0.8547 | 0.0 | 0.8547 |
| 0.1234 | 11.2 | 560 | 0.2260 | 0.4066 | 0.8133 | 0.8133 | nan | 0.8133 | 0.0 | 0.8133 |
| 0.145 | 11.6 | 580 | 0.1963 | 0.3939 | 0.7878 | 0.7878 | nan | 0.7878 | 0.0 | 0.7878 |
| 0.0665 | 12.0 | 600 | 0.1912 | 0.3873 | 0.7747 | 0.7747 | nan | 0.7747 | 0.0 | 0.7747 |
| 0.0826 | 12.4 | 620 | 0.2095 | 0.4186 | 0.8373 | 0.8373 | nan | 0.8373 | 0.0 | 0.8373 |
| 0.1212 | 12.8 | 640 | 0.1732 | 0.4059 | 0.8118 | 0.8118 | nan | 0.8118 | 0.0 | 0.8118 |
| 0.142 | 13.2 | 660 | 0.2086 | 0.4007 | 0.8013 | 0.8013 | nan | 0.8013 | 0.0 | 0.8013 |
| 0.0899 | 13.6 | 680 | 0.1838 | 0.3928 | 0.7856 | 0.7856 | nan | 0.7856 | 0.0 | 0.7856 |
| 0.1857 | 14.0 | 700 | 0.1638 | 0.4157 | 0.8315 | 0.8315 | nan | 0.8315 | 0.0 | 0.8315 |
| 0.0788 | 14.4 | 720 | 0.1736 | 0.4112 | 0.8223 | 0.8223 | nan | 0.8223 | 0.0 | 0.8223 |
| 0.2543 | 14.8 | 740 | 0.1547 | 0.3822 | 0.7643 | 0.7643 | nan | 0.7643 | 0.0 | 0.7643 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"unlabelled",
"anomaly"
] |
mattmdjaga/segformer_b0_clothes | # Segformer B0 fine-tuned for clothes segmentation
SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation.
The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
```python
from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import requests
import matplotlib.pyplot as plt
import torch.nn as nn
extractor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b0_clothes")
model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b0_clothes")
url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
image = Image.open(requests.get(url, stream=True).raw)
inputs = extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits.cpu()
upsampled_logits = nn.functional.interpolate(
logits,
size=image.size[::-1],
mode="bilinear",
align_corners=False,
)
pred_seg = upsampled_logits.argmax(dim=1)[0]
plt.imshow(pred_seg)
``` | [
"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"
] |
Onegafer/segformer-v-mesh-0 |
<!-- 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-v-mesh-0
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the Onegafer/vehicle_segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Mean Iou: 0.4403
- Mean Accuracy: 0.8806
- Overall Accuracy: 0.8806
- Accuracy Background: nan
- Accuracy Windows: 0.8806
- Iou Background: 0.0
- Iou Windows: 0.8806
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Windows | Iou Background | Iou Windows |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.2932 | 0.16 | 20 | 0.3269 | 0.2578 | 0.5156 | 0.5156 | nan | 0.5156 | 0.0 | 0.5156 |
| 0.1417 | 0.31 | 40 | 0.1235 | 0.3790 | 0.7580 | 0.7580 | nan | 0.7580 | 0.0 | 0.7580 |
| 0.0952 | 0.47 | 60 | 0.1245 | 0.4606 | 0.9211 | 0.9211 | nan | 0.9211 | 0.0 | 0.9211 |
| 0.0778 | 0.62 | 80 | 0.0628 | 0.4042 | 0.8084 | 0.8084 | nan | 0.8084 | 0.0 | 0.8084 |
| 0.0448 | 0.78 | 100 | 0.0512 | 0.4161 | 0.8322 | 0.8322 | nan | 0.8322 | 0.0 | 0.8322 |
| 0.0323 | 0.94 | 120 | 0.0435 | 0.4167 | 0.8334 | 0.8334 | nan | 0.8334 | 0.0 | 0.8334 |
| 0.0337 | 1.09 | 140 | 0.0405 | 0.4131 | 0.8262 | 0.8262 | nan | 0.8262 | 0.0 | 0.8262 |
| 0.0586 | 1.25 | 160 | 0.0409 | 0.4509 | 0.9017 | 0.9017 | nan | 0.9017 | 0.0 | 0.9017 |
| 0.0591 | 1.41 | 180 | 0.0404 | 0.4310 | 0.8620 | 0.8620 | nan | 0.8620 | 0.0 | 0.8620 |
| 0.0953 | 1.56 | 200 | 0.0386 | 0.4366 | 0.8732 | 0.8732 | nan | 0.8732 | 0.0 | 0.8732 |
| 0.0607 | 1.72 | 220 | 0.0374 | 0.4414 | 0.8828 | 0.8828 | nan | 0.8828 | 0.0 | 0.8828 |
| 0.0387 | 1.88 | 240 | 0.0360 | 0.4403 | 0.8806 | 0.8806 | nan | 0.8806 | 0.0 | 0.8806 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"background",
"windows"
] |
bilal01/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 pixel_values, the label and the {'pixel_values': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x7FCAFB662B60>, 'label': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1x1 at 0x7FCAFB662B30>} datasets.
It achieves the following results on the evaluation set:
- Loss: 3.5116
- Mean Iou: 0.0268
- Mean Accuracy: 0.0661
- Overall Accuracy: 0.2418
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.0351
- Accuracy Flat-sidewalk: 0.5938
- Accuracy Flat-crosswalk: 0.3236
- Accuracy Flat-cyclinglane: 0.0338
- Accuracy Flat-parkingdriveway: 0.0555
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0006
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0003
- Accuracy Vehicle-car: 0.3388
- Accuracy Vehicle-truck: 0.0016
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.2141
- Accuracy Vehicle-motorcycle: 0.0053
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0888
- Accuracy Construction-building: 0.0391
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0074
- Accuracy Construction-fenceguardrail: 0.0239
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0006
- Accuracy Object-pole: 0.0593
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0665
- Accuracy Nature-vegetation: 0.0846
- Accuracy Nature-terrain: 0.0002
- Accuracy Sky: 0.0030
- Accuracy Void-ground: 0.0635
- Accuracy Void-dynamic: 0.0004
- Accuracy Void-static: 0.0720
- Accuracy Void-unclear: 0.0022
- Iou Unlabeled: 0.0
- Iou Flat-road: 0.0297
- Iou Flat-sidewalk: 0.4826
- Iou Flat-crosswalk: 0.0624
- Iou Flat-cyclinglane: 0.0279
- Iou Flat-parkingdriveway: 0.0203
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0005
- Iou Human-person: 0.0
- Iou Human-rider: 0.0001
- Iou Vehicle-car: 0.1389
- Iou Vehicle-truck: 0.0000
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0013
- Iou Vehicle-motorcycle: 0.0007
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0004
- Iou Construction-building: 0.0383
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0057
- Iou Construction-fenceguardrail: 0.0127
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0001
- Iou Object-pole: 0.0085
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0002
- Iou Nature-vegetation: 0.0818
- Iou Nature-terrain: 0.0002
- Iou Sky: 0.0027
- Iou Void-ground: 0.0115
- Iou Void-dynamic: 0.0001
- Iou Void-static: 0.0102
- Iou Void-unclear: 0.0021
## 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: 0.025
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 3.5028 | 0.01 | 5 | 3.5307 | 0.0194 | 0.0486 | 0.1779 | nan | 0.0150 | 0.4721 | 0.2351 | 0.0249 | 0.0409 | nan | 0.0003 | 0.0 | 0.0003 | 0.1461 | 0.0231 | 0.0 | 0.2163 | 0.0047 | 0.0 | 0.0 | 0.0318 | 0.0223 | 0.0003 | 0.0136 | 0.0166 | 0.0 | nan | 0.0008 | 0.0511 | 0.0 | 0.0665 | 0.0261 | 0.0005 | 0.0010 | 0.0697 | 0.0014 | 0.0720 | 0.0020 | 0.0 | 0.0128 | 0.3979 | 0.0509 | 0.0221 | 0.0166 | 0.0 | 0.0003 | 0.0 | 0.0001 | 0.0769 | 0.0000 | 0.0 | 0.0015 | 0.0003 | 0.0 | 0.0 | 0.0001 | 0.0219 | 0.0001 | 0.0089 | 0.0103 | 0.0 | 0.0 | 0.0001 | 0.0070 | 0.0 | 0.0001 | 0.0257 | 0.0005 | 0.0009 | 0.0109 | 0.0004 | 0.0099 | 0.0019 |
| 3.3613 | 0.03 | 10 | 3.5116 | 0.0268 | 0.0661 | 0.2418 | nan | 0.0351 | 0.5938 | 0.3236 | 0.0338 | 0.0555 | nan | 0.0006 | 0.0 | 0.0003 | 0.3388 | 0.0016 | 0.0 | 0.2141 | 0.0053 | 0.0 | 0.0 | 0.0888 | 0.0391 | 0.0 | 0.0074 | 0.0239 | 0.0 | nan | 0.0006 | 0.0593 | 0.0 | 0.0665 | 0.0846 | 0.0002 | 0.0030 | 0.0635 | 0.0004 | 0.0720 | 0.0022 | 0.0 | 0.0297 | 0.4826 | 0.0624 | 0.0279 | 0.0203 | 0.0 | 0.0005 | 0.0 | 0.0001 | 0.1389 | 0.0000 | 0.0 | 0.0013 | 0.0007 | 0.0 | 0.0 | 0.0004 | 0.0383 | 0.0 | 0.0057 | 0.0127 | 0.0 | 0.0 | 0.0001 | 0.0085 | 0.0 | 0.0002 | 0.0818 | 0.0002 | 0.0027 | 0.0115 | 0.0001 | 0.0102 | 0.0021 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| [
"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"
] |
shehan97/mobilevitv2-1.0-voc-deeplabv3 |
# MobileViTv2 + DeepLabv3 (shehan97/mobilevitv2-1.0-voc-deeplabv3)
<!-- Provide a quick summary of what the model is/does. -->
MobileViTv2 model pre-trained on PASCAL VOC at resolution 512x512.
It was introduced in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari, and first released in [this](https://github.com/apple/ml-cvnets) repository. The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team.
### Model Description
<!-- Provide a longer summary of what this model is. -->
MobileViTv2 is constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
The model in this repo adds a [DeepLabV3](https://arxiv.org/abs/1706.05587) head to the MobileViT backbone for semantic segmentation.
### Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilevitv2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MobileViTv2FeatureExtractor, MobileViTv2ForSemanticSegmentation
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileViTv2FeatureExtractor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
model = MobileViTv2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0)
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The MobileViT + DeepLabV3 model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/) dataset.
### BibTeX entry and citation info
```bibtex
@inproceedings{vision-transformer,
title = {Separable Self-attention for Mobile Vision Transformers},
author = {Sachin Mehta and Mohammad Rastegari},
year = {2022},
URL = {https://arxiv.org/abs/2206.02680}
}
```
| [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor"
] |
matei-dorian/segformer-b0-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-b0-finetuned-human-parsing
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9476
- Mean Iou: 0.0726
- Mean Accuracy: 0.1221
- Overall Accuracy: 0.3575
- Accuracy Background: nan
- Accuracy Hat: 0.0048
- Accuracy Hair: 0.4813
- Accuracy Sunglasses: 0.0
- Accuracy Upper-clothes: 0.9405
- Accuracy Skirt: 0.0000
- Accuracy Pants: 0.0631
- Accuracy Dress: 0.1031
- Accuracy Belt: 0.0
- Accuracy Left-shoe: 0.0011
- Accuracy Right-shoe: 0.0010
- Accuracy Face: 0.4406
- Accuracy Left-leg: 0.0291
- Accuracy Right-leg: 0.0
- Accuracy Left-arm: 0.0
- Accuracy Right-arm: 0.0001
- Accuracy Bag: 0.0114
- Accuracy Scarf: 0.0
- Iou Background: 0.0
- Iou Hat: 0.0043
- Iou Hair: 0.4221
- Iou Sunglasses: 0.0
- Iou Upper-clothes: 0.3239
- Iou Skirt: 0.0000
- Iou Pants: 0.0559
- Iou Dress: 0.0728
- Iou Belt: 0.0
- Iou Left-shoe: 0.0011
- Iou Right-shoe: 0.0009
- Iou Face: 0.3872
- Iou Left-leg: 0.0271
- Iou Right-leg: 0.0
- Iou Left-arm: 0.0
- Iou Right-arm: 0.0001
- Iou Bag: 0.0106
- Iou Scarf: 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: 2
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:|
| 2.5768 | 0.4 | 20 | 2.7812 | 0.0726 | 0.1332 | 0.2876 | nan | 0.0178 | 0.3204 | 0.0004 | 0.5548 | 0.0004 | 0.2555 | 0.2373 | 0.0 | 0.0103 | 0.0003 | 0.5637 | 0.0287 | 0.0302 | 0.0001 | 0.0008 | 0.2435 | 0.0 | 0.0 | 0.0166 | 0.2759 | 0.0001 | 0.2781 | 0.0004 | 0.1710 | 0.1295 | 0.0 | 0.0098 | 0.0003 | 0.3251 | 0.0260 | 0.0248 | 0.0001 | 0.0007 | 0.0491 | 0.0 |
| 2.2093 | 0.8 | 40 | 2.5166 | 0.0563 | 0.1052 | 0.3288 | nan | 0.0 | 0.1994 | 0.0 | 0.9447 | 0.0015 | 0.0435 | 0.1164 | 0.0 | 0.0008 | 0.0000 | 0.4655 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0153 | 0.0 | 0.0 | 0.0 | 0.1946 | 0.0 | 0.3037 | 0.0015 | 0.0417 | 0.0842 | 0.0 | 0.0008 | 0.0000 | 0.3726 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0124 | 0.0 |
| 1.8804 | 1.2 | 60 | 2.0209 | 0.0632 | 0.1110 | 0.3374 | nan | 0.0087 | 0.3724 | 0.0 | 0.9475 | 0.0014 | 0.0162 | 0.0528 | 0.0 | 0.0001 | 0.0008 | 0.4257 | 0.0561 | 0.0001 | 0.0 | 0.0 | 0.0055 | 0.0 | 0.0 | 0.0077 | 0.3472 | 0.0 | 0.3086 | 0.0014 | 0.0156 | 0.0403 | 0.0 | 0.0001 | 0.0008 | 0.3597 | 0.0515 | 0.0001 | 0.0 | 0.0 | 0.0052 | 0.0 |
| 1.8776 | 1.6 | 80 | 2.0016 | 0.0665 | 0.1154 | 0.3454 | nan | 0.0056 | 0.4172 | 0.0 | 0.9412 | 0.0000 | 0.0490 | 0.0697 | 0.0 | 0.0002 | 0.0006 | 0.4349 | 0.0329 | 0.0000 | 0.0 | 0.0000 | 0.0100 | 0.0 | 0.0 | 0.0048 | 0.3791 | 0.0 | 0.3138 | 0.0000 | 0.0438 | 0.0542 | 0.0 | 0.0002 | 0.0006 | 0.3608 | 0.0304 | 0.0000 | 0.0 | 0.0000 | 0.0093 | 0.0 |
| 1.8471 | 2.0 | 100 | 1.9476 | 0.0726 | 0.1221 | 0.3575 | nan | 0.0048 | 0.4813 | 0.0 | 0.9405 | 0.0000 | 0.0631 | 0.1031 | 0.0 | 0.0011 | 0.0010 | 0.4406 | 0.0291 | 0.0 | 0.0 | 0.0001 | 0.0114 | 0.0 | 0.0 | 0.0043 | 0.4221 | 0.0 | 0.3239 | 0.0000 | 0.0559 | 0.0728 | 0.0 | 0.0011 | 0.0009 | 0.3872 | 0.0271 | 0.0 | 0.0 | 0.0001 | 0.0106 | 0.0 |
### 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"
] |
matei-dorian/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"
] |
zho/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: 0.6468
- Mean Iou: 0.2931
- Mean Accuracy: 0.3665
- Overall Accuracy: 0.8121
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.6505
- Accuracy Flat-sidewalk: 0.9345
- Accuracy Flat-crosswalk: 0.9011
- Accuracy Flat-cyclinglane: 0.7895
- Accuracy Flat-parkingdriveway: 0.2382
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.4519
- Accuracy Human-person: 0.5536
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9509
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.7507
- Accuracy Vehicle-caravan: nan
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8681
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.6107
- Accuracy Construction-fenceguardrail: 0.3192
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.5156
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9183
- Accuracy Nature-terrain: 0.8478
- Accuracy Sky: 0.9246
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.1083
- Accuracy Void-static: 0.3940
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.5472
- Iou Flat-sidewalk: 0.8329
- Iou Flat-crosswalk: 0.7961
- Iou Flat-cyclinglane: 0.5266
- Iou Flat-parkingdriveway: 0.2013
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.2863
- Iou Human-person: 0.3887
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.7872
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.4759
- Iou Vehicle-caravan: nan
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.6992
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.3924
- Iou Construction-fenceguardrail: 0.2614
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.3413
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.8182
- Iou Nature-terrain: 0.7517
- Iou Sky: 0.8855
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0963
- Iou Void-static: 0.2896
- 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: 10000
### Training results
| Training Loss | Epoch | Step | Accuracy Construction-bridge | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-fenceguardrail | Accuracy Construction-stairs | Accuracy Construction-tunnel | Accuracy Construction-wall | Accuracy Flat-crosswalk | Accuracy Flat-curb | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Human-person | Accuracy Human-rider | Accuracy Nature-terrain | Accuracy Nature-vegetation | Accuracy Object-pole | Accuracy Object-trafficlight | Accuracy Object-trafficsign | Accuracy Sky | Accuracy Unlabeled | Accuracy Vehicle-bicycle | Accuracy Vehicle-bus | Accuracy Vehicle-car | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Vehicle-motorcycle | Accuracy Vehicle-tramtrain | Accuracy Vehicle-truck | Accuracy Void-dynamic | Accuracy Void-ground | Accuracy Void-static | Accuracy Void-unclear | Iou Construction-bridge | Iou Construction-building | Iou Construction-door | Iou Construction-fenceguardrail | Iou Construction-stairs | Iou Construction-tunnel | Iou Construction-wall | Iou Flat-crosswalk | Iou Flat-curb | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-road | Iou Flat-sidewalk | Iou Human-person | Iou Human-rider | Iou Nature-terrain | Iou Nature-vegetation | Iou Object-pole | Iou Object-trafficlight | Iou Object-trafficsign | Iou Sky | Iou Unlabeled | Iou Vehicle-bicycle | Iou Vehicle-bus | Iou Vehicle-car | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Vehicle-motorcycle | Iou Vehicle-tramtrain | Iou Vehicle-truck | Iou Void-dynamic | Iou Void-ground | Iou Void-static | Iou Void-unclear | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
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| 2.5227 | 1.0 | 107 | 0.0 | 0.8334 | 0.0 | 0.0 | 0.0 | nan | 0.0000 | 0.0 | 0.0 | 0.0416 | 0.0001 | nan | 0.5390 | 0.9293 | 0.0 | 0.0 | 0.2834 | 0.9261 | 0.0 | 0.0 | 0.0 | 0.5133 | nan | 0.0 | 0.0 | 0.8875 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4909 | 0.0 | 0.0 | 0.0 | nan | 0.0000 | 0.0 | 0.0 | 0.0411 | 0.0001 | nan | 0.3808 | 0.7051 | 0.0 | 0.0 | 0.2534 | 0.5904 | 0.0 | 0.0 | 0.0 | 0.5116 | nan | 0.0 | 0.0 | 0.5403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.7749 | 0.1548 | 0.1098 | 0.6606 |
| 1.7544 | 2.0 | 214 | 0.0 | 0.8141 | 0.0 | 0.0 | 0.0 | nan | 0.0024 | 0.0 | 0.0 | 0.2967 | 0.0009 | nan | 0.6039 | 0.9275 | 0.0 | 0.0 | 0.8832 | 0.8157 | 0.0 | 0.0 | 0.0 | 0.7111 | nan | 0.0 | 0.0 | 0.9009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5356 | 0.0 | 0.0 | 0.0 | nan | 0.0024 | 0.0 | 0.0 | 0.2702 | 0.0009 | nan | 0.4296 | 0.7139 | 0.0 | 0.0 | 0.5124 | 0.6367 | 0.0 | 0.0 | 0.0 | 0.7016 | nan | 0.0 | 0.0 | 0.5653 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.4883 | 0.1861 | 0.1365 | 0.6975 |
| 1.523 | 3.0 | 321 | 0.0 | 0.8975 | 0.0 | 0.0 | 0.0 | nan | 0.0009 | 0.0 | 0.0003 | 0.5309 | 0.0063 | nan | 0.4954 | 0.9432 | 0.0 | 0.0 | 0.8476 | 0.8378 | 0.0 | 0.0 | 0.0 | 0.7705 | nan | 0.0 | 0.0 | 0.8567 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5155 | 0.0 | 0.0 | 0.0 | nan | 0.0009 | 0.0 | 0.0003 | 0.4164 | 0.0062 | nan | 0.4161 | 0.7219 | 0.0 | 0.0 | 0.5408 | 0.6765 | 0.0 | 0.0 | 0.0 | 0.7594 | nan | 0.0 | 0.0 | 0.6132 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.2403 | 0.1934 | 0.1459 | 0.7123 |
| 1.2744 | 4.0 | 428 | 0.0 | 0.8602 | 0.0 | 0.0 | 0.0 | nan | 0.0009 | 0.0 | 0.0015 | 0.4753 | 0.0069 | nan | 0.3731 | 0.9792 | 0.0 | 0.0 | 0.7062 | 0.8948 | 0.0 | 0.0 | 0.0 | 0.7488 | nan | 0.0 | 0.0 | 0.8857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5565 | 0.0 | 0.0 | 0.0 | nan | 0.0009 | 0.0 | 0.0015 | 0.4431 | 0.0068 | nan | 0.3413 | 0.6728 | 0.0 | 0.0 | 0.5473 | 0.6788 | 0.0 | 0.0 | 0.0 | 0.7389 | nan | 0.0 | 0.0 | 0.6552 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.1870 | 0.1854 | 0.1451 | 0.7068 |
| 1.1579 | 5.0 | 535 | 0.0 | 0.7388 | 0.0 | 0.0 | 0.0 | nan | 0.0008 | 0.0 | 0.0040 | 0.6937 | 0.0681 | nan | 0.5908 | 0.9639 | 0.0 | 0.0 | 0.5152 | 0.9429 | 0.0 | 0.0 | 0.0 | 0.8365 | nan | 0.0 | 0.0 | 0.9525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5687 | 0.0 | 0.0 | 0.0 | nan | 0.0008 | 0.0 | 0.0039 | 0.5783 | 0.0606 | nan | 0.4884 | 0.7434 | 0.0 | 0.0 | 0.4397 | 0.6660 | 0.0 | 0.0 | 0.0 | 0.8076 | nan | 0.0 | 0.0 | 0.5868 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0435 | 0.1971 | 0.1545 | 0.7340 |
| 1.0928 | 6.0 | 642 | 0.0 | 0.8126 | 0.0 | 0.0 | 0.0 | nan | 0.0127 | 0.1193 | 0.0326 | 0.7981 | 0.1432 | nan | 0.6767 | 0.9152 | 0.0 | 0.0 | 0.8393 | 0.8990 | 0.0115 | 0.0 | 0.0 | 0.8664 | nan | 0.0 | 0.0 | 0.9427 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0048 | 0.0 | 0.0 | 0.6031 | 0.0 | 0.0 | 0.0 | nan | 0.0126 | 0.1193 | 0.0298 | 0.6282 | 0.1206 | nan | 0.5205 | 0.7688 | 0.0 | 0.0 | 0.6037 | 0.6827 | 0.0113 | 0.0 | 0.0 | 0.8312 | nan | 0.0 | 0.0 | 0.5963 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0047 | 0.0 | 0.9777 | 0.2211 | 0.1729 | 0.7531 |
| 1.0371 | 7.0 | 749 | 0.0 | 0.8108 | 0.0 | 0.0 | 0.0 | nan | 0.0145 | 0.2878 | 0.0499 | 0.7673 | 0.1179 | nan | 0.5506 | 0.9510 | 0.0 | 0.0 | 0.8458 | 0.8788 | 0.0158 | 0.0 | 0.0 | 0.8125 | nan | 0.0 | 0.0 | 0.9351 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0032 | 0.0 | 0.0 | 0.5687 | 0.0 | 0.0 | 0.0 | nan | 0.0143 | 0.2871 | 0.0416 | 0.5650 | 0.1067 | nan | 0.4769 | 0.7722 | 0.0 | 0.0 | 0.5986 | 0.6729 | 0.0154 | 0.0 | 0.0 | 0.7949 | nan | 0.0 | 0.0 | 0.5910 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0032 | 0.0 | 0.9290 | 0.2200 | 0.1722 | 0.7457 |
| 0.9645 | 8.0 | 856 | 0.0 | 0.8913 | 0.0 | 0.0 | 0.0 | nan | 0.0530 | 0.3879 | 0.1304 | 0.8027 | 0.1244 | nan | 0.5733 | 0.9459 | 0.0 | 0.0 | 0.8434 | 0.8598 | 0.1344 | 0.0 | 0.0 | 0.8596 | nan | 0.0 | 0.0 | 0.9192 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0196 | 0.0 | 0.0 | 0.5899 | 0.0 | 0.0 | 0.0 | nan | 0.0518 | 0.3362 | 0.0872 | 0.6482 | 0.1137 | nan | 0.4887 | 0.7610 | 0.0 | 0.0 | 0.6153 | 0.7148 | 0.1144 | 0.0 | 0.0 | 0.8278 | nan | 0.0 | 0.0 | 0.6957 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0192 | 0.0 | 0.8855 | 0.2358 | 0.1895 | 0.7593 |
| 0.9171 | 9.0 | 963 | 0.0 | 0.8681 | 0.0 | 0.0 | 0.0 | nan | 0.2267 | 0.2895 | 0.1798 | 0.7741 | 0.2153 | nan | 0.6580 | 0.9264 | 0.0009 | 0.0 | 0.7788 | 0.8887 | 0.1800 | 0.0 | 0.0 | 0.8648 | nan | 0.0 | 0.0 | 0.9422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0689 | 0.0 | 0.0 | 0.6112 | 0.0 | 0.0 | 0.0 | nan | 0.2013 | 0.2859 | 0.1173 | 0.6393 | 0.1769 | nan | 0.5251 | 0.7761 | 0.0009 | 0.0 | 0.6220 | 0.7328 | 0.1391 | 0.0 | 0.0 | 0.8329 | nan | 0.0 | 0.0 | 0.6550 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0622 | 0.0 | 0.8439 | 0.2457 | 0.1993 | 0.7676 |
| 0.8373 | 10.0 | 1070 | 0.0 | 0.8391 | 0.0 | 0.0000 | 0.0 | nan | 0.4409 | 0.3294 | 0.1364 | 0.7858 | 0.1023 | nan | 0.6096 | 0.9644 | 0.0756 | 0.0 | 0.6853 | 0.8993 | 0.1614 | 0.0 | 0.0 | 0.8876 | nan | 0.0 | 0.0 | 0.9315 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0874 | 0.0 | 0.0 | 0.6203 | 0.0 | 0.0000 | 0.0 | nan | 0.2914 | 0.3283 | 0.1050 | 0.6096 | 0.0951 | nan | 0.5427 | 0.7678 | 0.0740 | 0.0 | 0.5665 | 0.7403 | 0.1321 | 0.0 | 0.0 | 0.8500 | nan | 0.0 | 0.0 | 0.6756 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0767 | 0.0 | 0.8317 | 0.2480 | 0.2024 | 0.7710 |
| 0.8375 | 11.0 | 1177 | 0.0 | 0.8248 | 0.0 | 0.0000 | 0.0 | nan | 0.3739 | 0.3951 | 0.2834 | 0.7626 | 0.1777 | nan | 0.4734 | 0.9515 | 0.1276 | 0.0 | 0.7447 | 0.9010 | 0.1872 | 0.0 | 0.0 | 0.9018 | nan | 0.0 | 0.0 | 0.9378 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0591 | 0.0 | 0.0 | 0.6017 | 0.0 | 0.0000 | 0.0 | nan | 0.2379 | 0.3570 | 0.1503 | 0.6432 | 0.1533 | nan | 0.4411 | 0.7743 | 0.1234 | 0.0 | 0.5987 | 0.7041 | 0.1362 | 0.0 | 0.0 | 0.8576 | nan | 0.0 | 0.0 | 0.6553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0518 | 0.0 | 0.8539 | 0.2532 | 0.2027 | 0.7577 |
| 0.8014 | 12.0 | 1284 | 0.0 | 0.8213 | 0.0 | 0.0002 | 0.0 | nan | 0.4219 | 0.5045 | 0.3125 | 0.8556 | 0.2246 | nan | 0.6546 | 0.8896 | 0.2522 | 0.0 | 0.7563 | 0.9184 | 0.2091 | 0.0 | 0.0 | 0.8852 | nan | 0.0 | 0.0 | 0.9338 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1150 | 0.0 | 0.0 | 0.6244 | 0.0 | 0.0002 | 0.0 | nan | 0.2819 | 0.4181 | 0.1371 | 0.5936 | 0.1892 | nan | 0.5497 | 0.7848 | 0.2332 | 0.0 | 0.6418 | 0.7339 | 0.1582 | 0.0 | 0.0 | 0.8537 | nan | 0.0 | 0.0 | 0.6887 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0936 | 0.0 | 0.7821 | 0.2736 | 0.2182 | 0.7698 |
| 0.7598 | 13.0 | 1391 | 0.0 | 0.7520 | 0.0 | 0.0 | 0.0 | nan | 0.5035 | 0.5241 | 0.2865 | 0.8708 | 0.1666 | nan | 0.6404 | 0.8870 | 0.2805 | 0.0 | 0.7662 | 0.9230 | 0.3694 | 0.0 | 0.0 | 0.8932 | nan | 0.0 | 0.0 | 0.9492 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2009 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.0 | 0.0 | nan | 0.3111 | 0.4894 | 0.1504 | 0.5451 | 0.1555 | nan | 0.5227 | 0.7890 | 0.2569 | 0.0 | 0.6171 | 0.7275 | 0.1555 | 0.0 | 0.0 | 0.8569 | nan | 0.0 | 0.0 | 0.6889 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1265 | 0.0 | 0.7959 | 0.2817 | 0.2193 | 0.7653 |
| 0.7333 | 14.0 | 1498 | 0.0 | 0.7852 | 0.0 | 0.0005 | 0.0 | nan | 0.6099 | 0.5852 | 0.3890 | 0.8211 | 0.2961 | nan | 0.6321 | 0.9313 | 0.3684 | 0.0 | 0.6342 | 0.9311 | 0.2435 | 0.0 | 0.0 | 0.8845 | nan | 0.0 | 0.0 | 0.9298 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1712 | 0.0 | 0.0 | 0.6312 | 0.0 | 0.0005 | 0.0 | nan | 0.2920 | 0.4813 | 0.1830 | 0.6730 | 0.2504 | nan | 0.5405 | 0.8112 | 0.3183 | 0.0 | 0.5574 | 0.7360 | 0.1553 | 0.0 | 0.0 | 0.8543 | nan | 0.0 | 0.0 | 0.7520 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1219 | 0.0 | 0.7463 | 0.2879 | 0.2300 | 0.7815 |
| 0.7128 | 15.0 | 1605 | 0.0 | 0.7547 | 0.0 | 0.0126 | 0.0 | nan | 0.6715 | 0.6477 | 0.2623 | 0.8694 | 0.1131 | 0.0 | 0.7576 | 0.9015 | 0.5131 | 0.0 | 0.8870 | 0.8915 | 0.3275 | 0.0 | 0.0 | 0.9177 | nan | 0.0008 | 0.0 | 0.9290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2520 | 0.0 | 0.0 | 0.5980 | 0.0 | 0.0126 | 0.0 | nan | 0.4000 | 0.3362 | 0.1721 | 0.4706 | 0.1069 | 0.0 | 0.6593 | 0.8212 | 0.2914 | 0.0 | 0.6797 | 0.7574 | 0.1981 | 0.0 | 0.0 | 0.8704 | nan | 0.0008 | 0.0 | 0.6431 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1881 | 0.0 | 0.7557 | 0.2942 | 0.2184 | 0.7786 |
| 0.6885 | 16.0 | 1712 | 0.0 | 0.8416 | 0.0 | 0.0086 | 0.0 | nan | 0.5907 | 0.7737 | 0.3100 | 0.7765 | 0.1341 | 0.0 | 0.6753 | 0.9522 | 0.5143 | 0.0 | 0.8466 | 0.8795 | 0.2986 | 0.0 | 0.0 | 0.9155 | nan | 0.0071 | 0.0 | 0.9178 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3074 | 0.0 | 0.0 | 0.6078 | 0.0 | 0.0086 | 0.0 | nan | 0.4106 | 0.3222 | 0.1815 | 0.6082 | 0.1171 | 0.0 | 0.6206 | 0.8253 | 0.2609 | 0.0 | 0.6832 | 0.7692 | 0.1957 | 0.0 | 0.0 | 0.8691 | nan | 0.0071 | 0.0 | 0.6951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2366 | 0.0 | 0.7262 | 0.2954 | 0.2248 | 0.7882 |
| 0.6627 | 17.0 | 1819 | 0.0 | 0.7096 | 0.0 | 0.0181 | 0.0 | nan | 0.7189 | 0.6110 | 0.3654 | 0.8153 | 0.1210 | 0.0 | 0.7156 | 0.9114 | 0.5562 | 0.0 | 0.8788 | 0.9226 | 0.3042 | 0.0 | 0.0 | 0.9273 | nan | 0.0002 | 0.0 | 0.9080 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3069 | 0.0 | 0.0 | 0.5809 | 0.0 | 0.0179 | 0.0 | nan | 0.3488 | 0.3724 | 0.2149 | 0.5069 | 0.1137 | 0.0 | 0.6477 | 0.8079 | 0.2559 | 0.0 | 0.7100 | 0.7595 | 0.1837 | 0.0 | 0.0 | 0.8734 | nan | 0.0002 | 0.0 | 0.7016 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2201 | 0.0 | 0.7429 | 0.2967 | 0.2217 | 0.7786 |
| 0.6954 | 18.0 | 1926 | 0.0 | 0.8919 | 0.0 | 0.0031 | 0.0 | nan | 0.5763 | 0.5167 | 0.3013 | 0.7439 | 0.1958 | 0.0 | 0.7281 | 0.9530 | 0.4080 | 0.0 | 0.8497 | 0.8852 | 0.2874 | 0.0 | 0.0 | 0.8563 | nan | 0.0056 | 0.0 | 0.9222 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3154 | 0.0 | 0.0 | 0.5730 | 0.0 | 0.0031 | 0.0 | nan | 0.3625 | 0.4887 | 0.1980 | 0.6038 | 0.1714 | 0.0 | 0.6684 | 0.8291 | 0.2599 | 0.0 | 0.7176 | 0.7922 | 0.2045 | 0.0 | 0.0 | 0.8322 | nan | 0.0056 | 0.0 | 0.6432 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2459 | 0.0 | 0.6984 | 0.2861 | 0.2303 | 0.7947 |
| 0.6592 | 19.0 | 2033 | 0.0 | 0.8433 | 0.0 | 0.0496 | 0.0 | nan | 0.5622 | 0.6415 | 0.3618 | 0.7738 | 0.1797 | 0.0 | 0.6474 | 0.9741 | 0.6289 | 0.0 | 0.6784 | 0.9279 | 0.3132 | 0.0 | 0.0 | 0.8985 | nan | 0.0019 | 0.0 | 0.9235 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2431 | 0.0 | 0.0 | 0.6155 | 0.0 | 0.0493 | 0.0 | nan | 0.3959 | 0.5424 | 0.2210 | 0.6568 | 0.1504 | 0.0 | 0.6217 | 0.8227 | 0.2586 | 0.0 | 0.6198 | 0.7658 | 0.2117 | 0.0 | 0.0 | 0.8686 | nan | 0.0019 | 0.0 | 0.6541 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1919 | 0.0 | 0.6999 | 0.2924 | 0.2318 | 0.7924 |
| 0.6682 | 20.0 | 2140 | 0.0 | 0.8071 | 0.0 | 0.0796 | 0.0 | nan | 0.5870 | 0.4899 | 0.4985 | 0.7638 | 0.2075 | 0.0 | 0.7505 | 0.9346 | 0.6505 | 0.0 | 0.8297 | 0.9187 | 0.3668 | 0.0 | 0.0 | 0.9157 | nan | 0.0082 | 0.0 | 0.9407 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2163 | 0.0 | 0.0 | 0.6144 | 0.0 | 0.0748 | 0.0 | nan | 0.3846 | 0.4807 | 0.2584 | 0.6083 | 0.1892 | 0.0 | 0.6719 | 0.8371 | 0.2436 | 0.0 | 0.7173 | 0.7842 | 0.1994 | 0.0 | 0.0 | 0.8798 | nan | 0.0082 | 0.0 | 0.6331 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1719 | 0.0 | 0.6756 | 0.3020 | 0.2351 | 0.7976 |
| 0.6249 | 21.0 | 2247 | 0.6678 | 0.2540 | 0.3195 | 0.7981 | nan | 0.6625 | 0.9563 | 0.8027 | 0.7398 | 0.1695 | 0.0 | 0.4050 | 0.7541 | 0.0 | 0.9306 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0473 | nan | 0.0 | 0.8526 | 0.0 | 0.6384 | 0.1242 | 0.0 | nan | 0.0 | 0.3671 | 0.0 | 0.0 | 0.9185 | 0.7725 | 0.8706 | 0.0 | 0.0 | 0.2129 | 0.0 | nan | 0.5746 | 0.8111 | 0.7593 | 0.5842 | 0.1557 | 0.0 | 0.2176 | 0.3250 | 0.0 | 0.7386 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0473 | nan | 0.0 | 0.6693 | 0.0 | 0.3844 | 0.1188 | 0.0 | nan | 0.0 | 0.2479 | 0.0 | 0.0 | 0.7914 | 0.7105 | 0.8285 | 0.0 | 0.0 | 0.1638 | 0.0 |
| 0.6278 | 22.0 | 2354 | 0.6800 | 0.2513 | 0.3216 | 0.7949 | nan | 0.6354 | 0.9558 | 0.8656 | 0.7557 | 0.1401 | 0.0 | 0.4619 | 0.6943 | 0.0 | 0.9333 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0315 | nan | 0.0 | 0.8031 | 0.0 | 0.6422 | 0.1074 | 0.0 | nan | 0.0 | 0.4139 | 0.0 | 0.0 | 0.9114 | 0.8658 | 0.8302 | 0.0 | 0.0 | 0.2446 | 0.0 | nan | 0.5527 | 0.8215 | 0.7864 | 0.5887 | 0.1346 | 0.0 | 0.2336 | 0.3191 | 0.0 | 0.7265 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0315 | nan | 0.0 | 0.6458 | 0.0 | 0.3638 | 0.1048 | 0.0 | nan | 0.0 | 0.2338 | 0.0 | 0.0 | 0.7831 | 0.7282 | 0.8001 | 0.0 | 0.0 | 0.1868 | 0.0 |
| 0.6375 | 23.0 | 2461 | 0.6680 | 0.2563 | 0.3186 | 0.7976 | nan | 0.6355 | 0.9595 | 0.8844 | 0.6403 | 0.2228 | 0.0 | 0.3772 | 0.5620 | 0.0 | 0.9094 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0640 | nan | 0.0 | 0.8615 | 0.0 | 0.6510 | 0.1498 | 0.0 | nan | 0.0 | 0.3834 | 0.0 | 0.0 | 0.9024 | 0.8874 | 0.8627 | 0.0 | 0.0 | 0.2419 | 0.0 | nan | 0.5548 | 0.8086 | 0.7729 | 0.5236 | 0.2018 | 0.0 | 0.2287 | 0.3137 | 0.0 | 0.7398 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0634 | nan | 0.0 | 0.6603 | 0.0 | 0.3896 | 0.1381 | 0.0 | nan | 0.0 | 0.2666 | 0.0 | 0.0 | 0.7881 | 0.7394 | 0.8256 | 0.0 | 0.0 | 0.1871 | 0.0 |
| 0.6202 | 24.0 | 2568 | 0.6866 | 0.2618 | 0.3236 | 0.7961 | nan | 0.6075 | 0.9674 | 0.8360 | 0.6102 | 0.1879 | 0.0 | 0.4285 | 0.5972 | 0.0 | 0.9180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1500 | nan | 0.0 | 0.8830 | 0.0 | 0.6661 | 0.1963 | 0.0 | nan | 0.0 | 0.4180 | 0.0 | 0.0 | 0.8918 | 0.8483 | 0.8660 | 0.0 | 0.0 | 0.2840 | 0.0 | nan | 0.5428 | 0.7997 | 0.7679 | 0.5062 | 0.1644 | 0.0 | 0.2289 | 0.3309 | 0.0 | 0.7596 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1473 | nan | 0.0 | 0.6670 | 0.0 | 0.4004 | 0.1767 | 0.0 | nan | 0.0 | 0.2836 | 0.0 | 0.0 | 0.8076 | 0.7619 | 0.8236 | 0.0 | 0.0 | 0.2079 | 0.0 |
| 0.5627 | 25.0 | 2675 | 0.6950 | 0.2551 | 0.3248 | 0.7883 | nan | 0.6233 | 0.9526 | 0.7145 | 0.7187 | 0.1813 | 0.0 | 0.3959 | 0.7039 | 0.0 | 0.9160 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1183 | nan | 0.0 | 0.8342 | 0.0 | 0.5499 | 0.2476 | 0.0 | nan | 0.0 | 0.4821 | 0.0 | 0.0 | 0.8725 | 0.8618 | 0.8633 | 0.0 | 0.0 | 0.3577 | 0.0 | nan | 0.5503 | 0.7925 | 0.6705 | 0.5845 | 0.1689 | 0.0 | 0.2198 | 0.3385 | 0.0 | 0.7322 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1174 | nan | 0.0 | 0.6527 | 0.0 | 0.3227 | 0.2119 | 0.0 | nan | 0.0 | 0.2422 | 0.0 | 0.0 | 0.7923 | 0.7260 | 0.8255 | 0.0 | 0.0 | 0.2167 | 0.0 |
| 0.5623 | 26.0 | 2782 | 0.6558 | 0.2686 | 0.3385 | 0.8010 | nan | 0.6338 | 0.9493 | 0.8134 | 0.7256 | 0.1979 | 0.0 | 0.4685 | 0.7518 | 0.0 | 0.9364 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2286 | nan | 0.0 | 0.8577 | 0.0 | 0.5809 | 0.2585 | 0.0 | nan | 0.0 | 0.4459 | 0.0 | 0.0 | 0.8951 | 0.8978 | 0.8844 | 0.0 | 0.0192 | 0.2882 | 0.0 | nan | 0.5476 | 0.8200 | 0.7429 | 0.5770 | 0.1837 | 0.0 | 0.2364 | 0.3743 | 0.0 | 0.7396 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2160 | nan | 0.0 | 0.6671 | 0.0 | 0.3646 | 0.2093 | 0.0 | nan | 0.0 | 0.2863 | 0.0 | 0.0 | 0.8023 | 0.7446 | 0.8423 | 0.0 | 0.0185 | 0.2213 | 0.0 |
| 0.5882 | 27.0 | 2889 | 0.6416 | 0.2680 | 0.3280 | 0.8106 | nan | 0.7809 | 0.9232 | 0.8840 | 0.6978 | 0.2374 | 0.0 | 0.4869 | 0.4140 | 0.0 | 0.9242 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2349 | nan | 0.0 | 0.8828 | 0.0 | 0.4518 | 0.2084 | 0.0 | nan | 0.0 | 0.3889 | 0.0 | 0.0 | 0.9206 | 0.8679 | 0.8908 | 0.0 | 0.0 | 0.3012 | 0.0 | nan | 0.6265 | 0.8391 | 0.7529 | 0.6005 | 0.2168 | 0.0 | 0.2675 | 0.2729 | 0.0 | 0.7130 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2226 | nan | 0.0 | 0.6384 | 0.0 | 0.3296 | 0.1915 | 0.0 | nan | 0.0 | 0.2781 | 0.0 | 0.0 | 0.7946 | 0.7640 | 0.8488 | 0.0 | 0.0 | 0.2194 | 0.0 |
| 0.583 | 28.0 | 2996 | 0.6491 | 0.2734 | 0.3417 | 0.8046 | nan | 0.6541 | 0.9605 | 0.8786 | 0.7598 | 0.1411 | 0.0 | 0.4900 | 0.6147 | 0.0 | 0.9432 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3777 | nan | 0.0 | 0.8500 | 0.0 | 0.6605 | 0.2360 | 0.0 | nan | 0.0 | 0.4016 | 0.0 | 0.0 | 0.8786 | 0.8680 | 0.8514 | 0.0 | 0.0716 | 0.2973 | 0.0 | nan | 0.5775 | 0.8311 | 0.7770 | 0.5680 | 0.1357 | 0.0 | 0.2297 | 0.3515 | 0.0 | 0.7436 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3387 | nan | 0.0 | 0.6728 | 0.0 | 0.3790 | 0.2067 | 0.0 | nan | 0.0 | 0.2924 | 0.0 | 0.0 | 0.7950 | 0.7335 | 0.8178 | 0.0 | 0.0647 | 0.2332 | 0.0 |
| 0.5399 | 29.0 | 3103 | 0.6503 | 0.2714 | 0.3437 | 0.8027 | nan | 0.7145 | 0.9360 | 0.8554 | 0.7869 | 0.1668 | 0.0 | 0.4411 | 0.6746 | 0.0 | 0.9579 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3204 | nan | 0.0 | 0.7367 | 0.0 | 0.5891 | 0.2639 | 0.0 | nan | 0.0 | 0.4256 | 0.0 | 0.0 | 0.9170 | 0.9052 | 0.9104 | 0.0 | 0.0836 | 0.3133 | 0.0 | nan | 0.5941 | 0.8288 | 0.7852 | 0.5776 | 0.1580 | 0.0 | 0.2699 | 0.3237 | 0.0 | 0.6720 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2925 | nan | 0.0 | 0.6494 | 0.0 | 0.3454 | 0.2215 | 0.0 | nan | 0.0 | 0.2747 | 0.0 | 0.0 | 0.7852 | 0.7457 | 0.8558 | 0.0 | 0.0774 | 0.2273 | 0.0 |
| 0.5293 | 30.0 | 3210 | 0.6663 | 0.2713 | 0.3395 | 0.8042 | nan | 0.7217 | 0.9318 | 0.8745 | 0.8165 | 0.1842 | 0.0 | 0.3759 | 0.7404 | 0.0 | 0.9308 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3370 | nan | 0.0 | 0.8642 | 0.0 | 0.5393 | 0.2070 | 0.0 | nan | 0.0 | 0.3817 | 0.0 | 0.0 | 0.9030 | 0.7994 | 0.8605 | 0.0 | 0.0136 | 0.3816 | 0.0 | nan | 0.6056 | 0.8248 | 0.7837 | 0.5368 | 0.1772 | 0.0 | 0.2484 | 0.3753 | 0.0 | 0.7504 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3106 | nan | 0.0 | 0.6453 | 0.0 | 0.3263 | 0.1887 | 0.0 | nan | 0.0 | 0.2868 | 0.0 | 0.0 | 0.7993 | 0.7363 | 0.8267 | 0.0 | 0.0130 | 0.2477 | 0.0 |
| 0.5507 | 31.0 | 3317 | 0.6914 | 0.2660 | 0.3290 | 0.7919 | nan | 0.6185 | 0.9644 | 0.6731 | 0.6413 | 0.1576 | 0.0 | 0.3454 | 0.5530 | 0.0 | 0.9147 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5739 | nan | 0.0 | 0.8711 | 0.0 | 0.5920 | 0.3049 | 0.0 | nan | 0.0 | 0.4400 | 0.0 | 0.0 | 0.9047 | 0.7982 | 0.8196 | 0.0 | 0.0041 | 0.3518 | 0.0 | nan | 0.5435 | 0.7910 | 0.6258 | 0.5648 | 0.1434 | 0.0 | 0.2163 | 0.3586 | 0.0 | 0.7603 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4199 | nan | 0.0 | 0.6568 | 0.0 | 0.3419 | 0.2427 | 0.0 | nan | 0.0 | 0.2974 | 0.0 | 0.0 | 0.8016 | 0.7234 | 0.7915 | 0.0 | 0.0040 | 0.2283 | 0.0 |
| 0.5602 | 32.0 | 3424 | 0.6411 | 0.2802 | 0.3472 | 0.8101 | nan | 0.6883 | 0.9485 | 0.8664 | 0.7639 | 0.1489 | 0.0 | 0.5011 | 0.6326 | 0.0 | 0.9104 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5617 | nan | 0.0 | 0.8921 | 0.0 | 0.6268 | 0.2051 | 0.0 | nan | 0.0 | 0.3632 | 0.0 | 0.0 | 0.8960 | 0.8552 | 0.8981 | 0.0 | 0.0221 | 0.3290 | 0.0 | nan | 0.5877 | 0.8330 | 0.7807 | 0.5591 | 0.1386 | 0.0 | 0.2813 | 0.3887 | 0.0 | 0.7831 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4225 | nan | 0.0 | 0.6645 | 0.0 | 0.3730 | 0.1864 | 0.0 | nan | 0.0 | 0.2938 | 0.0 | 0.0 | 0.8000 | 0.7455 | 0.8533 | 0.0 | 0.0216 | 0.2553 | 0.0 |
| 0.5403 | 33.0 | 3531 | 0.6642 | 0.2729 | 0.3431 | 0.8017 | nan | 0.7235 | 0.9123 | 0.8745 | 0.7791 | 0.1617 | 0.0 | 0.4874 | 0.5172 | 0.0 | 0.9381 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5344 | nan | 0.0 | 0.8467 | 0.0 | 0.6245 | 0.1614 | 0.0 | nan | 0.0 | 0.4356 | 0.0 | 0.0 | 0.9141 | 0.8488 | 0.9075 | 0.0 | 0.0052 | 0.3063 | 0.0 | nan | 0.5819 | 0.8258 | 0.7765 | 0.5111 | 0.1504 | 0.0 | 0.2836 | 0.3475 | 0.0 | 0.7294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4025 | nan | 0.0 | 0.6638 | 0.0 | 0.3659 | 0.1505 | 0.0 | nan | 0.0 | 0.3046 | 0.0 | 0.0 | 0.7944 | 0.7435 | 0.8602 | 0.0 | 0.0052 | 0.2349 | 0.0 |
| 0.5168 | 34.0 | 3638 | 0.6402 | 0.2810 | 0.3485 | 0.8095 | nan | 0.7201 | 0.9345 | 0.8740 | 0.7414 | 0.1833 | 0.0 | 0.5538 | 0.5357 | 0.0 | 0.9369 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5640 | nan | 0.0 | 0.8776 | 0.0 | 0.5961 | 0.2626 | 0.0 | nan | 0.0 | 0.4488 | 0.0 | 0.0 | 0.9137 | 0.7841 | 0.8616 | 0.0 | 0.0 | 0.3650 | 0.0 | nan | 0.5901 | 0.8362 | 0.7926 | 0.6243 | 0.1652 | 0.0 | 0.2893 | 0.3653 | 0.0 | 0.7485 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4192 | nan | 0.0 | 0.6649 | 0.0 | 0.3752 | 0.2284 | 0.0 | nan | 0.0 | 0.3013 | 0.0 | 0.0 | 0.7971 | 0.7158 | 0.8280 | 0.0 | 0.0 | 0.2491 | 0.0 |
| 0.522 | 35.0 | 3745 | 0.6674 | 0.2743 | 0.3458 | 0.8002 | nan | 0.5916 | 0.9608 | 0.8505 | 0.7896 | 0.1387 | 0.0 | 0.4421 | 0.7247 | 0.0 | 0.9421 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5275 | nan | 0.0 | 0.8349 | 0.0 | 0.5652 | 0.1952 | 0.0 | nan | 0.0 | 0.4814 | 0.0 | 0.0 | 0.9081 | 0.8478 | 0.8898 | 0.0 | 0.0069 | 0.3697 | 0.0 | nan | 0.5251 | 0.8163 | 0.7812 | 0.5692 | 0.1306 | 0.0 | 0.2611 | 0.3743 | 0.0 | 0.7538 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4358 | nan | 0.0 | 0.6717 | 0.0 | 0.3549 | 0.1812 | 0.0 | nan | 0.0 | 0.2812 | 0.0 | 0.0 | 0.7991 | 0.7471 | 0.8535 | 0.0 | 0.0068 | 0.2358 | 0.0 |
| 0.4947 | 36.0 | 3852 | 0.6619 | 0.2752 | 0.3503 | 0.7991 | nan | 0.6020 | 0.9553 | 0.6755 | 0.7710 | 0.2239 | 0.0 | 0.5168 | 0.6551 | 0.0 | 0.9349 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6691 | nan | 0.0 | 0.8095 | 0.0 | 0.7100 | 0.1976 | 0.0 | nan | 0.0 | 0.4787 | 0.0 | 0.0 | 0.8903 | 0.8914 | 0.8668 | 0.0 | 0.0007 | 0.3623 | 0.0 | nan | 0.5291 | 0.8115 | 0.6361 | 0.5873 | 0.1919 | 0.0 | 0.2904 | 0.4117 | 0.0 | 0.7803 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4348 | nan | 0.0 | 0.6702 | 0.0 | 0.3617 | 0.1812 | 0.0 | nan | 0.0 | 0.2947 | 0.0 | 0.0 | 0.8036 | 0.7365 | 0.8339 | 0.0 | 0.0007 | 0.2507 | 0.0 |
| 0.5073 | 37.0 | 3959 | 0.6782 | 0.2792 | 0.3508 | 0.8019 | nan | 0.6843 | 0.9206 | 0.8269 | 0.7932 | 0.2000 | 0.0 | 0.5293 | 0.6061 | 0.0 | 0.9381 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6202 | nan | 0.0 | 0.8888 | 0.0 | 0.6030 | 0.2416 | 0.0 | nan | 0.0 | 0.3985 | 0.0 | 0.0 | 0.8823 | 0.8329 | 0.8918 | 0.0 | 0.0 | 0.3687 | 0.0 | nan | 0.5649 | 0.8204 | 0.7692 | 0.5226 | 0.1828 | 0.0 | 0.3027 | 0.4019 | 0.0 | 0.7543 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4303 | nan | 0.0 | 0.6624 | 0.0 | 0.3595 | 0.2136 | 0.0 | nan | 0.0 | 0.2976 | 0.0 | 0.0 | 0.8008 | 0.7378 | 0.8484 | 0.0 | 0.0 | 0.2667 | 0.0 |
| 0.4788 | 38.0 | 4066 | 0.6694 | 0.2768 | 0.3467 | 0.8020 | nan | 0.6894 | 0.9371 | 0.8519 | 0.7659 | 0.2090 | 0.0 | 0.4494 | 0.5935 | 0.0 | 0.9331 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6390 | nan | 0.0 | 0.9029 | 0.0 | 0.4947 | 0.2279 | 0.0 | nan | 0.0 | 0.4255 | 0.0 | 0.0 | 0.8438 | 0.8985 | 0.8365 | 0.0 | 0.0 | 0.3976 | 0.0 | nan | 0.5567 | 0.8293 | 0.7865 | 0.5419 | 0.1959 | 0.0 | 0.2915 | 0.3960 | 0.0 | 0.7643 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4261 | nan | 0.0 | 0.6474 | 0.0 | 0.3359 | 0.1974 | 0.0 | nan | 0.0 | 0.3047 | 0.0 | 0.0 | 0.7876 | 0.7159 | 0.8095 | 0.0 | 0.0 | 0.2724 | 0.0 |
| 0.4627 | 39.0 | 4173 | 0.6439 | 0.2840 | 0.3563 | 0.8069 | nan | 0.6652 | 0.9293 | 0.8861 | 0.7534 | 0.2398 | 0.0 | 0.5481 | 0.5694 | 0.0 | 0.9305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6488 | nan | 0.0 | 0.8714 | 0.0 | 0.5817 | 0.3115 | 0.0 | nan | 0.0 | 0.4716 | 0.0 | 0.0 | 0.9060 | 0.8645 | 0.8991 | 0.0 | 0.0123 | 0.3128 | 0.0 | nan | 0.5453 | 0.8303 | 0.7889 | 0.5693 | 0.2107 | 0.0 | 0.3035 | 0.3784 | 0.0 | 0.7531 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4437 | nan | 0.0 | 0.6747 | 0.0 | 0.3647 | 0.2365 | 0.0 | nan | 0.0 | 0.3209 | 0.0 | 0.0 | 0.8070 | 0.7501 | 0.8626 | 0.0 | 0.0123 | 0.2376 | 0.0 |
| 0.4775 | 40.0 | 4280 | 0.6679 | 0.2808 | 0.3499 | 0.8051 | nan | 0.6127 | 0.9570 | 0.8742 | 0.8046 | 0.1980 | 0.0 | 0.4223 | 0.4104 | 0.0 | 0.8918 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7077 | nan | 0.0 | 0.8362 | 0.0 | 0.6999 | 0.3405 | 0.0 | nan | 0.0 | 0.4473 | 0.0 | 0.0 | 0.9272 | 0.7890 | 0.8870 | 0.0 | 0.0348 | 0.3578 | 0.0 | nan | 0.5307 | 0.8250 | 0.7915 | 0.5729 | 0.1789 | 0.0 | 0.2532 | 0.3154 | 0.0 | 0.7855 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4322 | nan | 0.0 | 0.6863 | 0.0 | 0.4071 | 0.2521 | 0.0 | nan | 0.0 | 0.3089 | 0.0 | 0.0 | 0.7975 | 0.7101 | 0.8518 | 0.0 | 0.0332 | 0.2520 | 0.0 |
| 0.4816 | 41.0 | 4387 | 0.6700 | 0.2812 | 0.3491 | 0.8060 | nan | 0.6497 | 0.9430 | 0.8488 | 0.7581 | 0.1492 | 0.0 | 0.5026 | 0.5415 | 0.0 | 0.9317 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5586 | nan | 0.0 | 0.8655 | 0.0 | 0.6495 | 0.3284 | 0.0 | nan | 0.0 | 0.4062 | 0.0 | 0.0 | 0.9026 | 0.8756 | 0.9041 | 0.0 | 0.0154 | 0.3409 | 0.0 | nan | 0.5483 | 0.8245 | 0.7804 | 0.5613 | 0.1444 | 0.0 | 0.2941 | 0.3765 | 0.0 | 0.7657 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4309 | nan | 0.0 | 0.6812 | 0.0 | 0.3456 | 0.2526 | 0.0 | nan | 0.0 | 0.3020 | 0.0 | 0.0 | 0.8013 | 0.7384 | 0.8651 | 0.0 | 0.0147 | 0.2719 | 0.0 |
| 0.4643 | 42.0 | 4494 | 0.6465 | 0.2865 | 0.3603 | 0.8079 | nan | 0.6087 | 0.9460 | 0.8859 | 0.8411 | 0.2736 | 0.0 | 0.5016 | 0.5636 | 0.0 | 0.9311 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6503 | nan | 0.0 | 0.8152 | 0.0 | 0.6211 | 0.3064 | 0.0 | nan | 0.0 | 0.4719 | 0.0 | 0.0 | 0.9130 | 0.8643 | 0.8988 | 0.0 | 0.0386 | 0.3972 | 0.0 | nan | 0.5283 | 0.8363 | 0.7831 | 0.5893 | 0.2376 | 0.0 | 0.2835 | 0.3871 | 0.0 | 0.7808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4435 | nan | 0.0 | 0.6630 | 0.0 | 0.3653 | 0.2468 | 0.0 | nan | 0.0 | 0.3230 | 0.0 | 0.0 | 0.8082 | 0.7553 | 0.8615 | 0.0 | 0.0352 | 0.2410 | 0.0 |
| 0.4758 | 43.0 | 4601 | 0.6531 | 0.2866 | 0.3573 | 0.8033 | nan | 0.6189 | 0.9384 | 0.8678 | 0.7635 | 0.2556 | 0.0 | 0.4631 | 0.5328 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7078 | nan | 0.0 | 0.8840 | 0.0 | 0.5168 | 0.3159 | 0.0 | nan | 0.0 | 0.5012 | 0.0 | 0.0 | 0.9003 | 0.8435 | 0.8800 | 0.0 | 0.1130 | 0.3953 | 0.0 | nan | 0.5198 | 0.8118 | 0.7952 | 0.5642 | 0.2235 | 0.0 | 0.2833 | 0.3642 | 0.0 | 0.7845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4597 | nan | 0.0 | 0.6755 | 0.0 | 0.3530 | 0.2604 | 0.0 | nan | 0.0 | 0.3225 | 0.0 | 0.0 | 0.8104 | 0.7326 | 0.8509 | 0.0 | 0.0804 | 0.2786 | 0.0 |
| 0.4682 | 44.0 | 4708 | 0.6534 | 0.2843 | 0.3584 | 0.8035 | nan | 0.6193 | 0.9309 | 0.8952 | 0.8209 | 0.2108 | 0.0 | 0.4880 | 0.5279 | 0.0 | 0.9208 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6156 | nan | 0.0 | 0.8474 | 0.0 | 0.6475 | 0.3017 | 0.0 | nan | 0.0 | 0.5203 | 0.0 | 0.0 | 0.9113 | 0.8445 | 0.9254 | 0.0 | 0.0324 | 0.4089 | 0.0 | nan | 0.5374 | 0.8204 | 0.7932 | 0.5268 | 0.1915 | 0.0 | 0.2784 | 0.3506 | 0.0 | 0.7789 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4807 | nan | 0.0 | 0.6878 | 0.0 | 0.3576 | 0.2551 | 0.0 | nan | 0.0 | 0.3135 | 0.0 | 0.0 | 0.8111 | 0.7485 | 0.8770 | 0.0 | 0.0241 | 0.2662 | 0.0 |
| 0.4807 | 45.0 | 4815 | 0.6325 | 0.2885 | 0.3653 | 0.8075 | nan | 0.6071 | 0.9223 | 0.8977 | 0.8564 | 0.3516 | 0.0 | 0.5039 | 0.5266 | 0.0 | 0.9433 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6309 | nan | 0.0 | 0.8390 | 0.0 | 0.5600 | 0.3684 | 0.0 | nan | 0.0 | 0.4760 | 0.0 | 0.0 | 0.9242 | 0.8477 | 0.9264 | 0.0 | 0.0706 | 0.4361 | 0.0 | nan | 0.5390 | 0.8355 | 0.7773 | 0.5424 | 0.2623 | 0.0 | 0.2809 | 0.3567 | 0.0 | 0.7695 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4435 | nan | 0.0 | 0.6957 | 0.0 | 0.3710 | 0.2746 | 0.0 | nan | 0.0 | 0.3253 | 0.0 | 0.0 | 0.8070 | 0.7405 | 0.8751 | 0.0 | 0.0603 | 0.2758 | 0.0 |
| 0.4611 | 46.0 | 4922 | 0.6577 | 0.2850 | 0.3588 | 0.8022 | nan | 0.6022 | 0.9292 | 0.8230 | 0.8449 | 0.2449 | 0.0 | 0.4479 | 0.5166 | 0.0 | 0.9396 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6521 | nan | 0.0 | 0.8516 | 0.0 | 0.7020 | 0.3122 | 0.0 | nan | 0.0 | 0.4822 | 0.0 | 0.0 | 0.9015 | 0.8642 | 0.9095 | 0.0 | 0.0737 | 0.3834 | 0.0 | nan | 0.5034 | 0.8172 | 0.7584 | 0.5407 | 0.2171 | 0.0 | 0.2684 | 0.3534 | 0.0 | 0.7740 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4392 | nan | 0.0 | 0.6942 | 0.0 | 0.3877 | 0.2651 | 0.0 | nan | 0.0 | 0.3266 | 0.0 | 0.0 | 0.8136 | 0.7528 | 0.8682 | 0.0 | 0.0684 | 0.2725 | 0.0 |
| 0.3966 | 47.0 | 5029 | 0.6749 | 0.2810 | 0.3530 | 0.7981 | nan | 0.5613 | 0.9379 | 0.7768 | 0.8262 | 0.2161 | 0.0 | 0.4333 | 0.4777 | 0.0 | 0.9410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7133 | nan | 0.0 | 0.8766 | 0.0 | 0.7119 | 0.3548 | 0.0 | nan | 0.0 | 0.3871 | 0.0 | 0.0 | 0.9073 | 0.8326 | 0.8935 | 0.0 | 0.1104 | 0.3367 | 0.0 | nan | 0.4867 | 0.8241 | 0.7219 | 0.4978 | 0.1759 | 0.0 | 0.2653 | 0.3573 | 0.0 | 0.7854 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4599 | nan | 0.0 | 0.6910 | 0.0 | 0.3907 | 0.2610 | 0.0 | nan | 0.0 | 0.3009 | 0.0 | 0.0 | 0.8082 | 0.7419 | 0.8593 | 0.0 | 0.0926 | 0.2732 | 0.0 |
| 0.4672 | 48.0 | 5136 | 0.6660 | 0.2784 | 0.3546 | 0.8021 | nan | 0.7292 | 0.9096 | 0.8990 | 0.8135 | 0.1493 | 0.0 | 0.5230 | 0.5946 | 0.0 | 0.9526 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7375 | nan | 0.0 | 0.8687 | 0.0 | 0.4252 | 0.2353 | 0.0 | nan | 0.0 | 0.4237 | 0.0 | 0.0 | 0.8933 | 0.8270 | 0.9183 | 0.0 | 0.0817 | 0.3646 | 0.0 | nan | 0.5942 | 0.8232 | 0.8036 | 0.5377 | 0.1347 | 0.0 | 0.2647 | 0.3728 | 0.0 | 0.7137 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4297 | nan | 0.0 | 0.6601 | 0.0 | 0.2946 | 0.2127 | 0.0 | nan | 0.0 | 0.3151 | 0.0 | 0.0 | 0.8132 | 0.7409 | 0.8699 | 0.0 | 0.0613 | 0.2675 | 0.0 |
| 0.4622 | 49.0 | 5243 | 0.7150 | 0.2767 | 0.3475 | 0.7951 | nan | 0.6718 | 0.8870 | 0.8975 | 0.8901 | 0.1529 | 0.0 | 0.4453 | 0.5462 | 0.0 | 0.9481 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6509 | nan | 0.0 | 0.9012 | 0.0 | 0.5387 | 0.2114 | 0.0 | nan | 0.0 | 0.4295 | 0.0 | 0.0 | 0.9268 | 0.7997 | 0.9010 | 0.0 | 0.0350 | 0.2863 | 0.0 | nan | 0.5624 | 0.8117 | 0.7961 | 0.4364 | 0.1414 | 0.0 | 0.2702 | 0.3711 | 0.0 | 0.7633 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4582 | nan | 0.0 | 0.6590 | 0.0 | 0.3660 | 0.1902 | 0.0 | nan | 0.0 | 0.3246 | 0.0 | 0.0 | 0.8088 | 0.7464 | 0.8666 | 0.0 | 0.0332 | 0.2487 | 0.0 |
| 0.4145 | 50.0 | 5350 | 0.6807 | 0.2818 | 0.3565 | 0.8008 | nan | 0.6541 | 0.9143 | 0.8871 | 0.8536 | 0.1956 | 0.0 | 0.4524 | 0.5023 | 0.0 | 0.9266 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7110 | nan | 0.0 | 0.8771 | 0.0 | 0.6214 | 0.2828 | 0.0 | nan | 0.0 | 0.4773 | 0.0 | 0.0 | 0.9139 | 0.7822 | 0.9051 | 0.0 | 0.0741 | 0.3770 | 0.0 | nan | 0.5552 | 0.8258 | 0.7814 | 0.4837 | 0.1731 | 0.0 | 0.2705 | 0.3530 | 0.0 | 0.7938 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4623 | nan | 0.0 | 0.6836 | 0.0 | 0.3253 | 0.2389 | 0.0 | nan | 0.0 | 0.3381 | 0.0 | 0.0 | 0.8077 | 0.7193 | 0.8652 | 0.0 | 0.0640 | 0.2760 | 0.0 |
| 0.4544 | 51.0 | 5457 | 0.6710 | 0.2839 | 0.3635 | 0.8006 | nan | 0.6233 | 0.9087 | 0.9049 | 0.8695 | 0.2469 | 0.0 | 0.4528 | 0.5746 | 0.0 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7524 | nan | 0.0 | 0.8690 | 0.0 | 0.5925 | 0.3026 | 0.0 | nan | 0.0 | 0.4862 | 0.0 | 0.0 | 0.9113 | 0.8522 | 0.9246 | 0.0 | 0.0797 | 0.3522 | 0.0 | nan | 0.5369 | 0.8237 | 0.7538 | 0.4608 | 0.2062 | 0.0 | 0.2692 | 0.3838 | 0.0 | 0.7862 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4326 | nan | 0.0 | 0.6889 | 0.0 | 0.3838 | 0.2423 | 0.0 | nan | 0.0 | 0.3336 | 0.0 | 0.0 | 0.8112 | 0.7403 | 0.8791 | 0.0 | 0.0742 | 0.2796 | 0.0 |
| 0.4084 | 52.0 | 5564 | 0.6546 | 0.2867 | 0.3640 | 0.8059 | nan | 0.6423 | 0.9216 | 0.8728 | 0.8610 | 0.1706 | 0.0 | 0.4997 | 0.5610 | 0.0 | 0.9239 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7156 | nan | 0.0 | 0.8634 | 0.0 | 0.6920 | 0.2740 | 0.0 | nan | 0.0 | 0.4887 | 0.0 | 0.0 | 0.9069 | 0.8889 | 0.9000 | 0.0 | 0.0903 | 0.3739 | 0.0 | nan | 0.5431 | 0.8278 | 0.7981 | 0.5189 | 0.1560 | 0.0 | 0.3024 | 0.3737 | 0.0 | 0.7986 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4567 | nan | 0.0 | 0.6880 | 0.0 | 0.3761 | 0.2251 | 0.0 | nan | 0.0 | 0.3343 | 0.0 | 0.0 | 0.8139 | 0.7548 | 0.8646 | 0.0 | 0.0756 | 0.2675 | 0.0 |
| 0.4475 | 53.0 | 5671 | 0.6712 | 0.2818 | 0.3527 | 0.8026 | nan | 0.6170 | 0.9199 | 0.9040 | 0.8414 | 0.2396 | 0.0 | 0.4268 | 0.4352 | 0.0 | 0.9374 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6281 | nan | 0.0 | 0.8676 | 0.0 | 0.6078 | 0.2969 | 0.0 | nan | 0.0 | 0.4899 | 0.0 | 0.0 | 0.9292 | 0.8389 | 0.9008 | 0.0 | 0.0345 | 0.3705 | 0.0 | nan | 0.5299 | 0.8287 | 0.7780 | 0.5304 | 0.1865 | 0.0 | 0.2782 | 0.3138 | 0.0 | 0.7708 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4800 | nan | 0.0 | 0.6830 | 0.0 | 0.3721 | 0.2416 | 0.0 | nan | 0.0 | 0.3294 | 0.0 | 0.0 | 0.7999 | 0.7324 | 0.8688 | 0.0 | 0.0287 | 0.2662 | 0.0 |
| 0.4077 | 54.0 | 5778 | 0.6743 | 0.2885 | 0.3600 | 0.8048 | nan | 0.5791 | 0.9423 | 0.8905 | 0.7810 | 0.2604 | 0.0 | 0.4610 | 0.5324 | 0.0 | 0.9467 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6770 | nan | 0.0 | 0.8826 | 0.0 | 0.5999 | 0.3432 | 0.0 | nan | 0.0 | 0.4846 | 0.0 | 0.0 | 0.9008 | 0.8470 | 0.9224 | 0.0 | 0.0643 | 0.4035 | 0.0 | nan | 0.5145 | 0.8210 | 0.8031 | 0.5666 | 0.1975 | 0.0 | 0.2818 | 0.3555 | 0.0 | 0.7569 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4759 | nan | 0.0 | 0.6745 | 0.0 | 0.3937 | 0.2589 | 0.0 | nan | 0.0 | 0.3445 | 0.0 | 0.0 | 0.8146 | 0.7552 | 0.8771 | 0.0 | 0.0526 | 0.2890 | 0.0 |
| 0.4334 | 55.0 | 5885 | 0.6318 | 0.2919 | 0.3684 | 0.8122 | nan | 0.6590 | 0.9261 | 0.8843 | 0.8552 | 0.2511 | 0.0 | 0.5269 | 0.6052 | 0.0 | 0.9416 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6763 | nan | 0.0 | 0.8438 | 0.0 | 0.6329 | 0.3218 | 0.0 | nan | 0.0 | 0.4795 | 0.0 | 0.0 | 0.9021 | 0.9073 | 0.9129 | 0.0 | 0.0510 | 0.4103 | 0.0 | nan | 0.5659 | 0.8404 | 0.7976 | 0.5330 | 0.2067 | 0.0 | 0.2976 | 0.3918 | 0.0 | 0.7881 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4634 | nan | 0.0 | 0.6961 | 0.0 | 0.4115 | 0.2632 | 0.0 | nan | 0.0 | 0.3295 | 0.0 | 0.0 | 0.8043 | 0.7360 | 0.8742 | 0.0 | 0.0446 | 0.2963 | 0.0 |
| 0.4379 | 56.0 | 5992 | 0.6688 | 0.2871 | 0.3580 | 0.8059 | nan | 0.5682 | 0.9473 | 0.8909 | 0.8684 | 0.1827 | 0.0 | 0.4078 | 0.5539 | 0.0 | 0.9361 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6815 | nan | 0.0 | 0.8838 | 0.0 | 0.6820 | 0.3338 | 0.0 | nan | 0.0 | 0.4720 | 0.0 | 0.0 | 0.9061 | 0.8482 | 0.9133 | 0.0 | 0.0386 | 0.3415 | 0.0 | nan | 0.5081 | 0.8198 | 0.8017 | 0.5046 | 0.1626 | 0.0 | 0.2799 | 0.3793 | 0.0 | 0.7869 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4692 | nan | 0.0 | 0.6965 | 0.0 | 0.4161 | 0.2613 | 0.0 | nan | 0.0 | 0.3389 | 0.0 | 0.0 | 0.8176 | 0.7576 | 0.8727 | 0.0 | 0.0347 | 0.2792 | 0.0 |
| 0.4489 | 57.0 | 6099 | 0.6413 | 0.2898 | 0.3657 | 0.8118 | nan | 0.6336 | 0.9369 | 0.8978 | 0.8637 | 0.2405 | 0.0 | 0.4683 | 0.4792 | 0.0 | 0.9456 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7398 | nan | 0.0 | 0.8757 | 0.0 | 0.6220 | 0.3338 | 0.0 | nan | 0.0 | 0.5178 | 0.0 | 0.0 | 0.8798 | 0.8909 | 0.9242 | 0.0 | 0.0371 | 0.4152 | 0.0 | nan | 0.5641 | 0.8302 | 0.7988 | 0.5222 | 0.2052 | 0.0 | 0.2923 | 0.3509 | 0.0 | 0.7819 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4626 | nan | 0.0 | 0.7010 | 0.0 | 0.4040 | 0.2609 | 0.0 | nan | 0.0 | 0.3240 | 0.0 | 0.0 | 0.8147 | 0.7572 | 0.8780 | 0.0 | 0.0348 | 0.2924 | 0.0 |
| 0.4042 | 58.0 | 6206 | 0.6378 | 0.2905 | 0.3632 | 0.8141 | nan | 0.6889 | 0.9331 | 0.8987 | 0.8277 | 0.1904 | 0.0 | 0.4609 | 0.4760 | 0.0 | 0.9308 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7672 | nan | 0.0 | 0.8689 | 0.0 | 0.6552 | 0.3481 | 0.0 | nan | 0.0 | 0.4860 | 0.0 | 0.0 | 0.9232 | 0.8152 | 0.9071 | 0.0 | 0.0922 | 0.3534 | 0.0 | nan | 0.5797 | 0.8300 | 0.7955 | 0.5497 | 0.1802 | 0.0 | 0.3002 | 0.3608 | 0.0 | 0.7923 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4430 | nan | 0.0 | 0.7010 | 0.0 | 0.3912 | 0.2586 | 0.0 | nan | 0.0 | 0.3330 | 0.0 | 0.0 | 0.8063 | 0.7389 | 0.8722 | 0.0 | 0.0842 | 0.2788 | 0.0 |
| 0.4033 | 59.0 | 6313 | 0.6393 | 0.2901 | 0.3629 | 0.8131 | nan | 0.6851 | 0.9282 | 0.8829 | 0.8307 | 0.1882 | 0.0 | 0.4846 | 0.5244 | 0.0 | 0.9433 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7355 | nan | 0.0 | 0.8673 | 0.0 | 0.6451 | 0.2991 | 0.0 | nan | 0.0 | 0.5054 | 0.0 | 0.0 | 0.9144 | 0.8542 | 0.9130 | 0.0 | 0.0306 | 0.3796 | 0.0 | nan | 0.5736 | 0.8304 | 0.8001 | 0.5264 | 0.1720 | 0.0 | 0.2962 | 0.3684 | 0.0 | 0.7884 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4725 | nan | 0.0 | 0.6993 | 0.0 | 0.3926 | 0.2552 | 0.0 | nan | 0.0 | 0.3409 | 0.0 | 0.0 | 0.8158 | 0.7611 | 0.8778 | 0.0 | 0.0283 | 0.2835 | 0.0 |
| 0.4021 | 60.0 | 6420 | 0.6501 | 0.2886 | 0.3651 | 0.8139 | nan | 0.7362 | 0.9216 | 0.9046 | 0.8150 | 0.1901 | 0.0 | 0.4200 | 0.4985 | 0.0 | 0.9507 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7714 | nan | 0.0 | 0.8632 | 0.0 | 0.6387 | 0.3715 | 0.0 | nan | 0.0 | 0.4586 | 0.0 | 0.0 | 0.9045 | 0.8397 | 0.9113 | 0.0 | 0.1205 | 0.3673 | 0.0 | nan | 0.6146 | 0.8265 | 0.7334 | 0.5541 | 0.1753 | 0.0 | 0.2840 | 0.3505 | 0.0 | 0.7546 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4427 | nan | 0.0 | 0.6866 | 0.0 | 0.3878 | 0.2644 | 0.0 | nan | 0.0 | 0.3435 | 0.0 | 0.0 | 0.8178 | 0.7580 | 0.8759 | 0.0 | 0.0918 | 0.2734 | 0.0 |
| 0.4143 | 61.0 | 6527 | 0.6427 | 0.2897 | 0.3612 | 0.8105 | nan | 0.6811 | 0.9188 | 0.8982 | 0.7937 | 0.2651 | 0.0 | 0.5039 | 0.4599 | 0.0 | 0.9477 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7194 | nan | 0.0 | 0.8837 | 0.0 | 0.5937 | 0.3117 | 0.0 | nan | 0.0 | 0.4858 | 0.0 | 0.0 | 0.9079 | 0.8499 | 0.9188 | 0.0 | 0.0464 | 0.3740 | 0.0 | nan | 0.5727 | 0.8170 | 0.7807 | 0.5701 | 0.2198 | 0.0 | 0.2939 | 0.3411 | 0.0 | 0.7690 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4610 | nan | 0.0 | 0.6847 | 0.0 | 0.3873 | 0.2523 | 0.0 | nan | 0.0 | 0.3447 | 0.0 | 0.0 | 0.8200 | 0.7614 | 0.8782 | 0.0 | 0.0412 | 0.2743 | 0.0 |
| 0.3857 | 62.0 | 6634 | 0.6568 | 0.2875 | 0.3664 | 0.8074 | nan | 0.6878 | 0.9189 | 0.8964 | 0.8039 | 0.1812 | 0.0 | 0.5164 | 0.5660 | 0.0 | 0.9535 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7389 | nan | 0.0 | 0.8524 | 0.0 | 0.6142 | 0.3820 | 0.0 | nan | 0.0 | 0.4951 | 0.0 | 0.0 | 0.8928 | 0.8760 | 0.9272 | 0.0 | 0.0857 | 0.3362 | 0.0 | nan | 0.5667 | 0.8261 | 0.7933 | 0.5405 | 0.1623 | 0.0 | 0.3019 | 0.3736 | 0.0 | 0.7409 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4574 | nan | 0.0 | 0.6916 | 0.0 | 0.3764 | 0.2648 | 0.0 | nan | 0.0 | 0.3324 | 0.0 | 0.0 | 0.8177 | 0.7557 | 0.8809 | 0.0 | 0.0753 | 0.2436 | 0.0 |
| 0.4062 | 63.0 | 6741 | 0.6513 | 0.2914 | 0.3663 | 0.8120 | nan | 0.7112 | 0.9218 | 0.8867 | 0.7747 | 0.2310 | 0.0 | 0.5184 | 0.5408 | 0.0 | 0.9502 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7454 | nan | 0.0 | 0.8541 | 0.0 | 0.5815 | 0.3421 | 0.0 | nan | 0.0 | 0.5055 | 0.0 | 0.0 | 0.9086 | 0.8560 | 0.9291 | 0.0 | 0.0971 | 0.3675 | 0.0 | nan | 0.5784 | 0.8288 | 0.8002 | 0.5326 | 0.2018 | 0.0 | 0.3257 | 0.3750 | 0.0 | 0.7532 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4492 | nan | 0.0 | 0.6895 | 0.0 | 0.3791 | 0.2637 | 0.0 | nan | 0.0 | 0.3276 | 0.0 | 0.0 | 0.8196 | 0.7602 | 0.8842 | 0.0 | 0.0878 | 0.2676 | 0.0 |
| 0.3899 | 64.0 | 6848 | 0.6511 | 0.2897 | 0.3660 | 0.8078 | nan | 0.6784 | 0.9222 | 0.8927 | 0.7620 | 0.2273 | 0.0 | 0.5211 | 0.5469 | 0.0 | 0.9366 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7375 | nan | 0.0 | 0.8515 | 0.0 | 0.6301 | 0.3594 | 0.0 | nan | 0.0 | 0.5137 | 0.0 | 0.0 | 0.9027 | 0.8641 | 0.9136 | 0.0 | 0.0311 | 0.4211 | 0.0 | nan | 0.5682 | 0.8239 | 0.8068 | 0.5166 | 0.2014 | 0.0 | 0.3059 | 0.3793 | 0.0 | 0.7849 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4731 | nan | 0.0 | 0.6751 | 0.0 | 0.3873 | 0.2718 | 0.0 | nan | 0.0 | 0.3411 | 0.0 | 0.0 | 0.8171 | 0.7490 | 0.8788 | 0.0 | 0.0271 | 0.2641 | 0.0 |
| 0.4094 | 65.0 | 6955 | 0.6321 | 0.2906 | 0.3633 | 0.8155 | nan | 0.7419 | 0.9262 | 0.8953 | 0.7420 | 0.2358 | 0.0 | 0.4796 | 0.5340 | 0.0 | 0.9593 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7218 | nan | 0.0 | 0.8464 | 0.0 | 0.5849 | 0.3341 | 0.0 | nan | 0.0 | 0.4942 | 0.0 | 0.0 | 0.9074 | 0.8709 | 0.9111 | 0.0009 | 0.0280 | 0.4123 | 0.0 | nan | 0.6028 | 0.8365 | 0.8011 | 0.5280 | 0.2101 | 0.0 | 0.3052 | 0.3724 | 0.0 | 0.7332 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4604 | nan | 0.0 | 0.6845 | 0.0 | 0.3982 | 0.2645 | 0.0 | nan | 0.0 | 0.3412 | 0.0 | 0.0 | 0.8201 | 0.7577 | 0.8759 | 0.0009 | 0.0255 | 0.2797 | 0.0 |
| 0.3902 | 66.0 | 7062 | 0.6383 | 0.2892 | 0.3622 | 0.8112 | nan | 0.6557 | 0.9316 | 0.8911 | 0.7814 | 0.2329 | 0.0 | 0.5098 | 0.4581 | 0.0 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7239 | nan | 0.0 | 0.8559 | 0.0 | 0.6460 | 0.3358 | 0.0 | nan | 0.0 | 0.5161 | 0.0 | 0.0 | 0.9274 | 0.8429 | 0.8990 | 0.0 | 0.0312 | 0.4118 | 0.0 | nan | 0.5606 | 0.8294 | 0.8023 | 0.5414 | 0.2068 | 0.0 | 0.3016 | 0.3450 | 0.0 | 0.7787 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4684 | nan | 0.0 | 0.6942 | 0.0 | 0.3908 | 0.2621 | 0.0 | nan | 0.0 | 0.3398 | 0.0 | 0.0 | 0.8126 | 0.7445 | 0.8709 | 0.0 | 0.0272 | 0.2774 | 0.0 |
| 0.3735 | 67.0 | 7169 | 0.6484 | 0.2885 | 0.3627 | 0.8076 | nan | 0.6374 | 0.9351 | 0.9035 | 0.7568 | 0.2251 | 0.0 | 0.4998 | 0.4948 | 0.0 | 0.9478 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7209 | nan | 0.0 | 0.8596 | 0.0 | 0.5804 | 0.3791 | 0.0 | nan | 0.0 | 0.4997 | 0.0 | 0.0 | 0.8999 | 0.8741 | 0.9245 | 0.0 | 0.0483 | 0.4185 | 0.0 | nan | 0.5389 | 0.8231 | 0.7871 | 0.5304 | 0.1996 | 0.0 | 0.2827 | 0.3614 | 0.0 | 0.7835 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4719 | nan | 0.0 | 0.6932 | 0.0 | 0.3775 | 0.2770 | 0.0 | nan | 0.0 | 0.3393 | 0.0 | 0.0 | 0.8216 | 0.7540 | 0.8823 | 0.0 | 0.0421 | 0.2668 | 0.0 |
| 0.3888 | 68.0 | 7276 | 0.6295 | 0.2932 | 0.3681 | 0.8124 | nan | 0.6453 | 0.9414 | 0.8924 | 0.7985 | 0.2832 | 0.0 | 0.5193 | 0.6389 | 0.0 | 0.9459 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7338 | nan | 0.0 | 0.8423 | 0.0 | 0.5126 | 0.3179 | 0.0 | nan | 0.0 | 0.5176 | 0.0 | 0.0 | 0.9164 | 0.8300 | 0.9247 | 0.0010 | 0.0627 | 0.4567 | 0.0 | nan | 0.5521 | 0.8326 | 0.7984 | 0.5384 | 0.2291 | 0.0 | 0.3097 | 0.4143 | 0.0 | 0.7877 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4724 | nan | 0.0 | 0.7028 | 0.0 | 0.3784 | 0.2540 | 0.0 | nan | 0.0 | 0.3337 | 0.0 | 0.0 | 0.8172 | 0.7398 | 0.8859 | 0.0010 | 0.0533 | 0.2830 | 0.0 |
| 0.3463 | 69.0 | 7383 | 0.6746 | 0.2916 | 0.3677 | 0.8094 | nan | 0.6515 | 0.9210 | 0.8823 | 0.8440 | 0.1789 | 0.0 | 0.5215 | 0.5737 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7389 | nan | 0.0 | 0.8837 | 0.0 | 0.6300 | 0.3350 | 0.0 | nan | 0.0 | 0.4968 | 0.0 | 0.0 | 0.9032 | 0.8934 | 0.9017 | 0.0 | 0.0703 | 0.4058 | 0.0 | nan | 0.5528 | 0.8245 | 0.7907 | 0.5250 | 0.1632 | 0.0 | 0.3014 | 0.3934 | 0.0 | 0.8010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4788 | nan | 0.0 | 0.6967 | 0.0 | 0.3744 | 0.2605 | 0.0 | nan | 0.0 | 0.3469 | 0.0 | 0.0 | 0.8186 | 0.7642 | 0.8737 | 0.0 | 0.0613 | 0.3051 | 0.0 |
| 0.3702 | 70.0 | 7490 | 0.6890 | 0.2875 | 0.3635 | 0.8012 | nan | 0.5995 | 0.9326 | 0.8853 | 0.8029 | 0.2289 | 0.0 | 0.5002 | 0.5737 | 0.0 | 0.9451 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7417 | nan | 0.0 | 0.8227 | 0.0 | 0.6097 | 0.3263 | 0.0 | nan | 0.0 | 0.5053 | 0.0 | 0.0 | 0.9192 | 0.8235 | 0.9210 | 0.0 | 0.0666 | 0.4292 | 0.0 | nan | 0.5210 | 0.8170 | 0.8010 | 0.5198 | 0.1907 | 0.0 | 0.3010 | 0.3898 | 0.0 | 0.7651 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4836 | nan | 0.0 | 0.6753 | 0.0 | 0.3649 | 0.2576 | 0.0 | nan | 0.0 | 0.3513 | 0.0 | 0.0 | 0.8151 | 0.7466 | 0.8840 | 0.0 | 0.0563 | 0.2598 | 0.0 |
| 0.3642 | 71.0 | 7597 | 0.6835 | 0.2867 | 0.3593 | 0.8038 | nan | 0.6182 | 0.9263 | 0.8897 | 0.8120 | 0.1957 | 0.0 | 0.4355 | 0.5927 | 0.0 | 0.9233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7200 | nan | 0.0 | 0.8870 | 0.0 | 0.6023 | 0.3097 | 0.0 | nan | 0.0 | 0.4994 | 0.0 | 0.0 | 0.9270 | 0.8288 | 0.9199 | 0.0 | 0.0564 | 0.3520 | 0.0 | nan | 0.5306 | 0.8156 | 0.7929 | 0.4950 | 0.1747 | 0.0 | 0.2794 | 0.3891 | 0.0 | 0.8032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4771 | nan | 0.0 | 0.6905 | 0.0 | 0.3674 | 0.2453 | 0.0 | nan | 0.0 | 0.3447 | 0.0 | 0.0 | 0.8116 | 0.7450 | 0.8826 | 0.0 | 0.0496 | 0.2805 | 0.0 |
| 0.36 | 72.0 | 7704 | 0.6669 | 0.2901 | 0.3652 | 0.8075 | nan | 0.6434 | 0.9327 | 0.8960 | 0.7900 | 0.2190 | 0.0 | 0.4746 | 0.5706 | 0.0 | 0.9461 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7405 | nan | 0.0 | 0.8967 | 0.0 | 0.5709 | 0.3347 | 0.0 | nan | 0.0 | 0.5213 | 0.0 | 0.0 | 0.8767 | 0.8656 | 0.9185 | 0.0 | 0.0645 | 0.4230 | 0.0 | nan | 0.5397 | 0.8231 | 0.7948 | 0.5252 | 0.1971 | 0.0 | 0.2832 | 0.3853 | 0.0 | 0.7856 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4817 | nan | 0.0 | 0.6834 | 0.0 | 0.3839 | 0.2617 | 0.0 | nan | 0.0 | 0.3396 | 0.0 | 0.0 | 0.8178 | 0.7627 | 0.8720 | 0.0 | 0.0530 | 0.2933 | 0.0 |
| 0.3973 | 73.0 | 7811 | 0.6383 | 0.2949 | 0.3680 | 0.8186 | nan | 0.7241 | 0.9280 | 0.9008 | 0.7697 | 0.2577 | 0.0 | 0.5086 | 0.5711 | 0.0 | 0.9495 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7286 | nan | 0.0 | 0.8676 | 0.0 | 0.6173 | 0.3238 | 0.0 | nan | 0.0 | 0.5022 | 0.0 | 0.0 | 0.9099 | 0.8670 | 0.9130 | 0.0 | 0.0432 | 0.3933 | 0.0 | nan | 0.5943 | 0.8414 | 0.7925 | 0.5329 | 0.2288 | 0.0 | 0.3133 | 0.3883 | 0.0 | 0.7799 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4800 | nan | 0.0 | 0.6892 | 0.0 | 0.4039 | 0.2600 | 0.0 | nan | 0.0 | 0.3515 | 0.0 | 0.0 | 0.8218 | 0.7658 | 0.8779 | 0.0 | 0.0378 | 0.2778 | 0.0 |
| 0.3552 | 74.0 | 7918 | 0.6462 | 0.2937 | 0.3665 | 0.8151 | nan | 0.6810 | 0.9352 | 0.9009 | 0.7938 | 0.2200 | 0.0 | 0.4290 | 0.5985 | 0.0 | 0.9448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7497 | nan | 0.0 | 0.8762 | 0.0 | 0.6223 | 0.3297 | 0.0 | nan | 0.0 | 0.5028 | 0.0 | 0.0 | 0.9107 | 0.8538 | 0.9194 | 0.0 | 0.0489 | 0.4105 | 0.0 | nan | 0.5681 | 0.8314 | 0.8066 | 0.5452 | 0.1979 | 0.0 | 0.2832 | 0.4003 | 0.0 | 0.7864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4794 | nan | 0.0 | 0.6941 | 0.0 | 0.4007 | 0.2634 | 0.0 | nan | 0.0 | 0.3505 | 0.0 | 0.0 | 0.8197 | 0.7579 | 0.8799 | 0.0 | 0.0428 | 0.2906 | 0.0 |
| 0.3735 | 75.0 | 8025 | 0.6607 | 0.2912 | 0.3658 | 0.8094 | nan | 0.6830 | 0.9221 | 0.8990 | 0.7703 | 0.2393 | 0.0 | 0.4768 | 0.5555 | 0.0 | 0.9397 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7627 | nan | 0.0 | 0.8774 | 0.0 | 0.5842 | 0.3146 | 0.0 | nan | 0.0 | 0.5209 | 0.0 | 0.0 | 0.9052 | 0.8376 | 0.9323 | 0.0006 | 0.0601 | 0.4251 | 0.0 | nan | 0.5616 | 0.8266 | 0.8043 | 0.4916 | 0.2068 | 0.0 | 0.2969 | 0.3852 | 0.0 | 0.7947 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4696 | nan | 0.0 | 0.6919 | 0.0 | 0.3934 | 0.2599 | 0.0 | nan | 0.0 | 0.3454 | 0.0 | 0.0 | 0.8176 | 0.7506 | 0.8838 | 0.0006 | 0.0529 | 0.2857 | 0.0 |
| 0.349 | 76.0 | 8132 | 0.6499 | 0.2920 | 0.3634 | 0.8132 | nan | 0.6815 | 0.9338 | 0.8990 | 0.7476 | 0.2275 | 0.0 | 0.4769 | 0.5225 | 0.0 | 0.9473 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7426 | nan | 0.0 | 0.8829 | 0.0 | 0.6085 | 0.3132 | 0.0 | nan | 0.0 | 0.5296 | 0.0 | 0.0 | 0.9144 | 0.8342 | 0.9098 | 0.0 | 0.0538 | 0.4042 | 0.0 | nan | 0.5611 | 0.8351 | 0.8007 | 0.5302 | 0.1879 | 0.0 | 0.2919 | 0.3759 | 0.0 | 0.7918 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4747 | nan | 0.0 | 0.6961 | 0.0 | 0.4043 | 0.2598 | 0.0 | nan | 0.0 | 0.3443 | 0.0 | 0.0 | 0.8162 | 0.7462 | 0.8769 | 0.0 | 0.0491 | 0.3031 | 0.0 |
| 0.3714 | 77.0 | 8239 | 0.6534 | 0.2926 | 0.3678 | 0.8124 | nan | 0.6790 | 0.9351 | 0.8952 | 0.7512 | 0.2106 | 0.0 | 0.5023 | 0.5752 | 0.0 | 0.9328 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7807 | nan | 0.0 | 0.8562 | 0.0 | 0.6458 | 0.3162 | 0.0 | nan | 0.0 | 0.5232 | 0.0 | 0.0 | 0.9210 | 0.8265 | 0.9273 | 0.0 | 0.0808 | 0.4113 | 0.0 | nan | 0.5593 | 0.8347 | 0.8043 | 0.5370 | 0.1833 | 0.0 | 0.2953 | 0.3971 | 0.0 | 0.7974 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4632 | nan | 0.0 | 0.6987 | 0.0 | 0.3865 | 0.2565 | 0.0 | nan | 0.0 | 0.3415 | 0.0 | 0.0 | 0.8136 | 0.7420 | 0.8860 | 0.0 | 0.0712 | 0.2942 | 0.0 |
| 0.363 | 78.0 | 8346 | 0.6516 | 0.2910 | 0.3632 | 0.8136 | nan | 0.6971 | 0.9296 | 0.8965 | 0.7702 | 0.2131 | 0.0 | 0.4759 | 0.5148 | 0.0 | 0.9332 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7724 | nan | 0.0 | 0.8932 | 0.0 | 0.5626 | 0.3029 | 0.0 | nan | 0.0 | 0.5263 | 0.0 | 0.0 | 0.9160 | 0.8210 | 0.9231 | 0.0 | 0.0554 | 0.4197 | 0.0 | nan | 0.5716 | 0.8385 | 0.7896 | 0.5483 | 0.1777 | 0.0 | 0.2883 | 0.3691 | 0.0 | 0.7908 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4736 | nan | 0.0 | 0.6864 | 0.0 | 0.3961 | 0.2512 | 0.0 | nan | 0.0 | 0.3478 | 0.0 | 0.0 | 0.8160 | 0.7383 | 0.8834 | 0.0 | 0.0501 | 0.2945 | 0.0 |
| 0.3493 | 79.0 | 8453 | 0.6702 | 0.2912 | 0.3685 | 0.8100 | nan | 0.6696 | 0.9258 | 0.9017 | 0.7644 | 0.2376 | 0.0 | 0.4962 | 0.5597 | 0.0 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7711 | nan | 0.0 | 0.8724 | 0.0 | 0.5995 | 0.3210 | 0.0 | nan | 0.0 | 0.5325 | 0.0 | 0.0 | 0.9025 | 0.8466 | 0.9381 | 0.0 | 0.0799 | 0.4247 | 0.0 | nan | 0.5541 | 0.8345 | 0.7881 | 0.5164 | 0.1987 | 0.0 | 0.2920 | 0.3835 | 0.0 | 0.7768 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4737 | nan | 0.0 | 0.6941 | 0.0 | 0.3974 | 0.2577 | 0.0 | nan | 0.0 | 0.3448 | 0.0 | 0.0 | 0.8187 | 0.7431 | 0.8877 | 0.0 | 0.0699 | 0.2870 | 0.0 |
| 0.3792 | 80.0 | 8560 | 0.6412 | 0.2946 | 0.3691 | 0.8157 | nan | 0.6826 | 0.9328 | 0.9031 | 0.7805 | 0.2240 | 0.0 | 0.5004 | 0.5717 | 0.0 | 0.9422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7532 | nan | 0.0 | 0.8790 | 0.0 | 0.6263 | 0.3250 | 0.0 | nan | 0.0 | 0.5130 | 0.0 | 0.0 | 0.9049 | 0.8708 | 0.9215 | 0.0 | 0.0666 | 0.4137 | 0.0 | nan | 0.5668 | 0.8404 | 0.7926 | 0.5316 | 0.1912 | 0.0 | 0.3036 | 0.3948 | 0.0 | 0.7940 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4767 | nan | 0.0 | 0.6963 | 0.0 | 0.3952 | 0.2617 | 0.0 | nan | 0.0 | 0.3547 | 0.0 | 0.0 | 0.8229 | 0.7615 | 0.8830 | 0.0 | 0.0593 | 0.2996 | 0.0 |
| 0.3466 | 81.0 | 8667 | 0.6398 | 0.2949 | 0.3696 | 0.8181 | nan | 0.7198 | 0.9374 | 0.8927 | 0.7518 | 0.1953 | 0.0 | 0.5069 | 0.6073 | 0.0 | 0.9437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7508 | nan | 0.0 | 0.8438 | 0.0 | 0.6477 | 0.3045 | 0.0 | nan | 0.0 | 0.5206 | 0.0 | 0.0 | 0.9149 | 0.8694 | 0.9313 | 0.0 | 0.0794 | 0.4091 | 0.0 | nan | 0.5959 | 0.8409 | 0.8043 | 0.5625 | 0.1746 | 0.0 | 0.2955 | 0.4016 | 0.0 | 0.7887 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4771 | nan | 0.0 | 0.6876 | 0.0 | 0.3937 | 0.2508 | 0.0 | nan | 0.0 | 0.3438 | 0.0 | 0.0 | 0.8203 | 0.7721 | 0.8882 | 0.0 | 0.0703 | 0.2696 | 0.0 |
| 0.3434 | 82.0 | 8774 | 0.6427 | 0.2948 | 0.3702 | 0.8144 | nan | 0.6701 | 0.9388 | 0.8942 | 0.7976 | 0.2036 | 0.0 | 0.4717 | 0.5793 | 0.0 | 0.9421 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7673 | nan | 0.0 | 0.8614 | 0.0 | 0.6617 | 0.3411 | 0.0 | nan | 0.0 | 0.5250 | 0.0 | 0.0 | 0.9065 | 0.8583 | 0.9214 | 0.0 | 0.1155 | 0.3911 | 0.0 | nan | 0.5615 | 0.8356 | 0.8036 | 0.5543 | 0.1765 | 0.0 | 0.2927 | 0.3998 | 0.0 | 0.7927 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4756 | nan | 0.0 | 0.6983 | 0.0 | 0.3912 | 0.2617 | 0.0 | nan | 0.0 | 0.3422 | 0.0 | 0.0 | 0.8220 | 0.7609 | 0.8829 | 0.0 | 0.0994 | 0.2837 | 0.0 |
| 0.3728 | 83.0 | 8881 | 0.6632 | 0.2935 | 0.3712 | 0.8071 | nan | 0.6362 | 0.9181 | 0.8946 | 0.8165 | 0.2796 | 0.0 | 0.4980 | 0.5929 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7612 | nan | 0.0 | 0.8576 | 0.0 | 0.6222 | 0.3247 | 0.0 | nan | 0.0 | 0.5315 | 0.0 | 0.0 | 0.9206 | 0.8297 | 0.9324 | 0.0 | 0.1246 | 0.3953 | 0.0 | nan | 0.5330 | 0.8303 | 0.8021 | 0.5115 | 0.2133 | 0.0 | 0.3082 | 0.4008 | 0.0 | 0.7792 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4707 | nan | 0.0 | 0.6944 | 0.0 | 0.3960 | 0.2571 | 0.0 | nan | 0.0 | 0.3433 | 0.0 | 0.0 | 0.8166 | 0.7505 | 0.8884 | 0.0 | 0.1076 | 0.2874 | 0.0 |
| 0.3449 | 84.0 | 8988 | 0.6665 | 0.2911 | 0.3655 | 0.8080 | nan | 0.6208 | 0.9362 | 0.8933 | 0.7983 | 0.2167 | 0.0 | 0.4705 | 0.5213 | 0.0 | 0.9445 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7528 | nan | 0.0 | 0.8565 | 0.0 | 0.6339 | 0.3453 | 0.0 | nan | 0.0 | 0.5227 | 0.0 | 0.0 | 0.9203 | 0.8327 | 0.9315 | 0.0 | 0.1078 | 0.3915 | 0.0 | nan | 0.5271 | 0.8305 | 0.8038 | 0.5352 | 0.1796 | 0.0 | 0.2901 | 0.3788 | 0.0 | 0.7816 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4767 | nan | 0.0 | 0.6966 | 0.0 | 0.3857 | 0.2623 | 0.0 | nan | 0.0 | 0.3403 | 0.0 | 0.0 | 0.8154 | 0.7512 | 0.8876 | 0.0 | 0.0934 | 0.2779 | 0.0 |
| 0.3677 | 85.0 | 9095 | 0.6600 | 0.2914 | 0.3667 | 0.8089 | nan | 0.6430 | 0.9281 | 0.8959 | 0.7877 | 0.2441 | 0.0 | 0.5011 | 0.5246 | 0.0 | 0.9417 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7416 | nan | 0.0 | 0.8635 | 0.0 | 0.6224 | 0.3337 | 0.0 | nan | 0.0 | 0.5238 | 0.0 | 0.0 | 0.9166 | 0.8404 | 0.9203 | 0.0 | 0.0966 | 0.4086 | 0.0 | nan | 0.5410 | 0.8368 | 0.8012 | 0.5221 | 0.1990 | 0.0 | 0.3032 | 0.3763 | 0.0 | 0.7839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4819 | nan | 0.0 | 0.6880 | 0.0 | 0.3785 | 0.2603 | 0.0 | nan | 0.0 | 0.3469 | 0.0 | 0.0 | 0.8166 | 0.7502 | 0.8825 | 0.0 | 0.0826 | 0.2728 | 0.0 |
| 0.3479 | 86.0 | 9202 | 0.6653 | 0.2925 | 0.3659 | 0.8083 | nan | 0.6215 | 0.9364 | 0.8955 | 0.8062 | 0.2438 | 0.0 | 0.4356 | 0.5749 | 0.0 | 0.9352 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7572 | nan | 0.0 | 0.8647 | 0.0 | 0.5950 | 0.3194 | 0.0 | nan | 0.0 | 0.5181 | 0.0 | 0.0 | 0.9142 | 0.8559 | 0.9196 | 0.0010 | 0.1131 | 0.4024 | 0.0 | nan | 0.5305 | 0.8260 | 0.8026 | 0.5177 | 0.2000 | 0.0 | 0.2845 | 0.3964 | 0.0 | 0.8037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4777 | nan | 0.0 | 0.6850 | 0.0 | 0.3926 | 0.2605 | 0.0 | nan | 0.0 | 0.3443 | 0.0 | 0.0 | 0.8210 | 0.7590 | 0.8827 | 0.0010 | 0.0985 | 0.2760 | 0.0 |
| 0.373 | 87.0 | 9309 | 0.6488 | 0.2953 | 0.3681 | 0.8141 | nan | 0.6465 | 0.9404 | 0.8996 | 0.7934 | 0.2418 | 0.0 | 0.4875 | 0.5646 | 0.0 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7519 | nan | 0.0 | 0.8931 | 0.0 | 0.6325 | 0.3185 | 0.0 | nan | 0.0 | 0.5045 | 0.0 | 0.0 | 0.8982 | 0.8624 | 0.9196 | 0.0000 | 0.1086 | 0.3763 | 0.0 | nan | 0.5479 | 0.8347 | 0.7989 | 0.5439 | 0.2043 | 0.0 | 0.2952 | 0.3956 | 0.0 | 0.8041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4802 | nan | 0.0 | 0.6921 | 0.0 | 0.3919 | 0.2632 | 0.0 | nan | 0.0 | 0.3462 | 0.0 | 0.0 | 0.8219 | 0.7598 | 0.8803 | 0.0000 | 0.0954 | 0.2939 | 0.0 |
| 0.3509 | 88.0 | 9416 | 0.6508 | 0.2938 | 0.3690 | 0.8125 | nan | 0.6480 | 0.9359 | 0.8987 | 0.8023 | 0.2228 | 0.0 | 0.4828 | 0.5941 | 0.0 | 0.9355 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7617 | nan | 0.0 | 0.8669 | 0.0 | 0.5964 | 0.3253 | 0.0 | nan | 0.0 | 0.5218 | 0.0 | 0.0 | 0.9249 | 0.8344 | 0.9275 | 0.0 | 0.1256 | 0.4037 | 0.0 | nan | 0.5517 | 0.8360 | 0.7990 | 0.5289 | 0.1923 | 0.0 | 0.2911 | 0.3969 | 0.0 | 0.7989 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4790 | nan | 0.0 | 0.6967 | 0.0 | 0.3872 | 0.2572 | 0.0 | nan | 0.0 | 0.3400 | 0.0 | 0.0 | 0.8153 | 0.7499 | 0.8866 | 0.0 | 0.1061 | 0.2894 | 0.0 |
| 0.3249 | 89.0 | 9523 | 0.6380 | 0.2947 | 0.3653 | 0.8162 | nan | 0.6541 | 0.9527 | 0.9012 | 0.7578 | 0.2159 | 0.0 | 0.4779 | 0.5541 | 0.0 | 0.9496 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7475 | nan | 0.0 | 0.8613 | 0.0 | 0.6083 | 0.3103 | 0.0 | nan | 0.0 | 0.5111 | 0.0 | 0.0 | 0.9215 | 0.8387 | 0.9247 | 0.0 | 0.1075 | 0.3965 | 0.0 | nan | 0.5525 | 0.8372 | 0.8023 | 0.5649 | 0.1893 | 0.0 | 0.2923 | 0.3918 | 0.0 | 0.7877 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4774 | nan | 0.0 | 0.7001 | 0.0 | 0.3917 | 0.2583 | 0.0 | nan | 0.0 | 0.3406 | 0.0 | 0.0 | 0.8165 | 0.7519 | 0.8854 | 0.0 | 0.0954 | 0.2955 | 0.0 |
| 0.3507 | 90.0 | 9630 | 0.6552 | 0.2931 | 0.3681 | 0.8112 | nan | 0.6412 | 0.9316 | 0.9007 | 0.7940 | 0.2344 | 0.0 | 0.4845 | 0.5679 | 0.0 | 0.9438 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7501 | nan | 0.0 | 0.8788 | 0.0 | 0.6209 | 0.3117 | 0.0 | nan | 0.0 | 0.5239 | 0.0 | 0.0 | 0.9155 | 0.8504 | 0.9231 | 0.0 | 0.1052 | 0.4019 | 0.0 | nan | 0.5432 | 0.8346 | 0.7967 | 0.5219 | 0.1977 | 0.0 | 0.2933 | 0.3922 | 0.0 | 0.7936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4792 | nan | 0.0 | 0.6958 | 0.0 | 0.3913 | 0.2588 | 0.0 | nan | 0.0 | 0.3429 | 0.0 | 0.0 | 0.8188 | 0.7511 | 0.8841 | 0.0 | 0.0910 | 0.2920 | 0.0 |
| 0.3327 | 91.0 | 9737 | 0.6568 | 0.2929 | 0.3687 | 0.8102 | nan | 0.6277 | 0.9380 | 0.8989 | 0.8059 | 0.2578 | 0.0 | 0.4617 | 0.5809 | 0.0 | 0.9460 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7536 | nan | 0.0 | 0.8356 | 0.0 | 0.6285 | 0.3180 | 0.0 | nan | 0.0 | 0.5218 | 0.0 | 0.0 | 0.9181 | 0.8578 | 0.9230 | 0.0004 | 0.0976 | 0.4261 | 0.0 | nan | 0.5366 | 0.8321 | 0.7979 | 0.5259 | 0.2114 | 0.0 | 0.2900 | 0.3969 | 0.0 | 0.7969 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4798 | nan | 0.0 | 0.6966 | 0.0 | 0.3832 | 0.2618 | 0.0 | nan | 0.0 | 0.3398 | 0.0 | 0.0 | 0.8184 | 0.7523 | 0.8857 | 0.0004 | 0.0849 | 0.2836 | 0.0 |
| 0.3428 | 92.0 | 9844 | 0.6481 | 0.2933 | 0.3672 | 0.8120 | nan | 0.6540 | 0.9343 | 0.9003 | 0.7727 | 0.2264 | 0.0 | 0.4777 | 0.5473 | 0.0 | 0.9437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7544 | nan | 0.0 | 0.8720 | 0.0 | 0.6385 | 0.3236 | 0.0 | nan | 0.0 | 0.5132 | 0.0 | 0.0 | 0.9136 | 0.8557 | 0.9224 | 0.0 | 0.1007 | 0.4012 | 0.0 | nan | 0.5486 | 0.8334 | 0.7997 | 0.5315 | 0.1937 | 0.0 | 0.2905 | 0.3891 | 0.0 | 0.7948 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4778 | nan | 0.0 | 0.6974 | 0.0 | 0.3843 | 0.2628 | 0.0 | nan | 0.0 | 0.3480 | 0.0 | 0.0 | 0.8193 | 0.7522 | 0.8844 | 0.0 | 0.0885 | 0.2890 | 0.0 |
| 0.3483 | 93.0 | 9951 | 0.6642 | 0.2923 | 0.3664 | 0.8104 | nan | 0.6314 | 0.9384 | 0.9008 | 0.7929 | 0.2027 | 0.0 | 0.4565 | 0.5687 | 0.0 | 0.9355 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7620 | nan | 0.0 | 0.8702 | 0.0 | 0.6443 | 0.3233 | 0.0 | nan | 0.0 | 0.5056 | 0.0 | 0.0 | 0.9195 | 0.8529 | 0.9224 | 0.0 | 0.1132 | 0.3833 | 0.0 | nan | 0.5395 | 0.8298 | 0.7942 | 0.5268 | 0.1771 | 0.0 | 0.2783 | 0.3974 | 0.0 | 0.8030 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4790 | nan | 0.0 | 0.7001 | 0.0 | 0.3838 | 0.2612 | 0.0 | nan | 0.0 | 0.3438 | 0.0 | 0.0 | 0.8168 | 0.7498 | 0.8846 | 0.0 | 0.0996 | 0.2879 | 0.0 |
| 0.346 | 93.46 | 10000 | 0.6468 | 0.2931 | 0.3665 | 0.8121 | nan | 0.6505 | 0.9345 | 0.9011 | 0.7895 | 0.2382 | 0.0 | 0.4519 | 0.5536 | 0.0 | 0.9509 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7507 | nan | 0.0 | 0.8681 | 0.0 | 0.6107 | 0.3192 | 0.0 | nan | 0.0 | 0.5156 | 0.0 | 0.0 | 0.9183 | 0.8478 | 0.9246 | 0.0 | 0.1083 | 0.3940 | 0.0 | nan | 0.5472 | 0.8329 | 0.7961 | 0.5266 | 0.2013 | 0.0 | 0.2863 | 0.3887 | 0.0 | 0.7872 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4759 | nan | 0.0 | 0.6992 | 0.0 | 0.3924 | 0.2614 | 0.0 | nan | 0.0 | 0.3413 | 0.0 | 0.0 | 0.8182 | 0.7517 | 0.8855 | 0.0 | 0.0963 | 0.2896 | 0.0 |
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
| [
"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"
] |
millionhz/segformer-b0-finetuned-flame |
# SegFormer (b0-sized) model fine-tuned on FLAME
The model was trained for a deep learning project titled [Forest Fire Detection](https://github.com/millionhz/forest-fire-detection).
## Model Description
The model is intended to be used for fire detection through image segmentation.
The provided pretrained model was finetuned on the [FLAME](https://dx.doi.org/10.21227/qad6-r683) dataset for 3 epochs with a learning rate of 1e-3 and was able to score an IOU score of **0.745** on the test examples.
# How to use
Here is how to use this model to segment an image:
```python
from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
from PIL import Image
import requests
processor = AutoFeatureExtractor.from_pretrained("millionhz/segformer-b0-finetuned-flame")
model = SegformerForSemanticSegmentation.from_pretrained("millionhz/segformer-b0-finetuned-flame")
url = <add url here>
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
```
## License
The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
| [
"background",
"fire"
] |
bilal01/segformer-b0-finetuned-segments-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-b0-finetuned-segments-test
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the bilal01/stamp-verification-test 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: 5
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| [
"unlabeled",
"stamp"
] |
DiTo97/binarization-segformer-b3 |
# binarization-segformer-b3
This model is a fine-tuned version of [nvidia/segformer-b3-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024)
on the same ensemble of 13 datasets as the [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf) work publicly available
in their GitHub [repository](https://github.com/Leedeng/SauvolaNet#datasets).
It achieves the following results on the evaluation set on DIBCO metrics:
- loss: 0.0743
- DRD: 5.9548
- F-measure: 0.9840
- pseudo F-measure: 0.9740
- PSNR: 16.0119
with PSNR the peak signal-to-noise ratio and DRD the distance reciprocal distortion.
For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
## Model description
This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
This is in contrast to the late trend of adapting classical binarization algorithms with neural networks,
such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf)
as extensions of Otsu's method and Sauvola thresholding algorithm, respectively.
## Intended uses & limitations
TBC
## Training and evaluation data
TBC
## Training procedure
### Training hyperparameters
TBC
### Training results
| training loss | epoch | step | validation loss | DRD | F-measure | pseudo F-measure | PSNR |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:----------------:|:-------:|
| 0.6983 | 0.26 | 10 | 0.7079 | 199.5096 | 0.5945 | 0.5801 | 3.4552 |
| 0.6657 | 0.52 | 20 | 0.6755 | 149.2346 | 0.7006 | 0.6165 | 4.6752 |
| 0.6145 | 0.77 | 30 | 0.6433 | 109.7298 | 0.7831 | 0.6520 | 5.5489 |
| 0.5553 | 1.03 | 40 | 0.5443 | 53.7149 | 0.8952 | 0.8000 | 8.1736 |
| 0.4627 | 1.29 | 50 | 0.4896 | 32.7649 | 0.9321 | 0.8603 | 9.8706 |
| 0.3969 | 1.55 | 60 | 0.4327 | 21.5508 | 0.9526 | 0.8985 | 11.3400 |
| 0.3414 | 1.81 | 70 | 0.3002 | 11.0094 | 0.9732 | 0.9462 | 13.5901 |
| 0.2898 | 2.06 | 80 | 0.2839 | 10.1064 | 0.9748 | 0.9563 | 13.9796 |
| 0.2292 | 2.32 | 90 | 0.2427 | 9.4437 | 0.9761 | 0.9584 | 14.2161 |
| 0.2153 | 2.58 | 100 | 0.2095 | 8.8696 | 0.9771 | 0.9621 | 14.4319 |
| 0.1767 | 2.84 | 110 | 0.1916 | 8.6152 | 0.9776 | 0.9646 | 14.5528 |
| 0.1509 | 3.1 | 120 | 0.1704 | 8.0761 | 0.9791 | 0.9632 | 14.7961 |
| 0.1265 | 3.35 | 130 | 0.1561 | 8.5627 | 0.9784 | 0.9655 | 14.7400 |
| 0.132 | 3.61 | 140 | 0.1318 | 8.1849 | 0.9788 | 0.9670 | 14.8469 |
| 0.1115 | 3.87 | 150 | 0.1317 | 7.8438 | 0.9790 | 0.9657 | 14.9072 |
| 0.0983 | 4.13 | 160 | 0.1273 | 7.9405 | 0.9791 | 0.9673 | 14.9701 |
| 0.1001 | 4.39 | 170 | 0.1234 | 8.4132 | 0.9788 | 0.9691 | 14.8573 |
| 0.0862 | 4.65 | 180 | 0.1147 | 8.0838 | 0.9797 | 0.9678 | 15.0433 |
| 0.0713 | 4.9 | 190 | 0.1134 | 7.6027 | 0.9806 | 0.9687 | 15.2235 |
| 0.0905 | 5.16 | 200 | 0.1061 | 7.2973 | 0.9803 | 0.9699 | 15.1646 |
| 0.0902 | 5.42 | 210 | 0.1061 | 8.4049 | 0.9787 | 0.9699 | 14.8460 |
| 0.0759 | 5.68 | 220 | 0.1062 | 7.7147 | 0.9809 | 0.9695 | 15.2426 |
| 0.0638 | 5.94 | 230 | 0.1019 | 7.7449 | 0.9806 | 0.9695 | 15.2195 |
| 0.0852 | 6.19 | 240 | 0.0962 | 7.0221 | 0.9817 | 0.9693 | 15.4730 |
| 0.0677 | 6.45 | 250 | 0.0961 | 7.2520 | 0.9814 | 0.9710 | 15.3878 |
| 0.0668 | 6.71 | 260 | 0.0972 | 6.6658 | 0.9823 | 0.9689 | 15.6106 |
| 0.0701 | 6.97 | 270 | 0.0909 | 6.9454 | 0.9820 | 0.9713 | 15.5458 |
| 0.0567 | 7.23 | 280 | 0.0925 | 6.5498 | 0.9824 | 0.9718 | 15.5965 |
| 0.0624 | 7.48 | 290 | 0.0899 | 7.3125 | 0.9813 | 0.9717 | 15.3255 |
| 0.0649 | 7.74 | 300 | 0.0932 | 7.4915 | 0.9816 | 0.9684 | 15.5666 |
| 0.0524 | 8.0 | 310 | 0.0905 | 7.1666 | 0.9815 | 0.9711 | 15.4526 |
| 0.0693 | 8.26 | 320 | 0.0901 | 6.5627 | 0.9827 | 0.9704 | 15.7335 |
| 0.0528 | 8.52 | 330 | 0.0845 | 6.6690 | 0.9826 | 0.9734 | 15.5950 |
| 0.0632 | 8.77 | 340 | 0.0822 | 6.2661 | 0.9833 | 0.9723 | 15.8631 |
| 0.0522 | 9.03 | 350 | 0.0844 | 6.0073 | 0.9836 | 0.9715 | 15.9393 |
| 0.0568 | 9.29 | 360 | 0.0817 | 5.9460 | 0.9837 | 0.9721 | 15.9523 |
| 0.057 | 9.55 | 370 | 0.0900 | 7.9726 | 0.9812 | 0.9730 | 15.1229 |
| 0.052 | 9.81 | 380 | 0.0836 | 6.5444 | 0.9822 | 0.9712 | 15.6388 |
| 0.0568 | 10.06 | 390 | 0.0810 | 6.0359 | 0.9836 | 0.9714 | 15.9796 |
| 0.0481 | 10.32 | 400 | 0.0784 | 6.2110 | 0.9835 | 0.9724 | 15.9235 |
| 0.0513 | 10.58 | 410 | 0.0803 | 6.0990 | 0.9835 | 0.9715 | 15.9502 |
| 0.0595 | 10.84 | 420 | 0.0798 | 6.0829 | 0.9835 | 0.9720 | 15.9052 |
| 0.047 | 11.1 | 430 | 0.0779 | 5.8847 | 0.9838 | 0.9725 | 16.0043 |
| 0.0406 | 11.35 | 440 | 0.0802 | 5.7944 | 0.9838 | 0.9713 | 16.0620 |
| 0.0493 | 11.61 | 450 | 0.0781 | 6.0947 | 0.9836 | 0.9731 | 15.9033 |
| 0.064 | 11.87 | 460 | 0.0769 | 6.1257 | 0.9837 | 0.9736 | 15.9080 |
| 0.0622 | 12.13 | 470 | 0.0765 | 6.2964 | 0.9835 | 0.9739 | 15.8188 |
| 0.0457 | 12.39 | 480 | 0.0773 | 5.9826 | 0.9838 | 0.9728 | 16.0119 |
| 0.0447 | 12.65 | 490 | 0.0761 | 5.7977 | 0.9841 | 0.9728 | 16.0900 |
| 0.0515 | 12.9 | 500 | 0.0750 | 5.8569 | 0.9840 | 0.9729 | 16.0633 |
| 0.0357 | 13.16 | 510 | 0.0796 | 5.7990 | 0.9837 | 0.9713 | 16.0818 |
| 0.0503 | 13.42 | 520 | 0.0749 | 5.8323 | 0.9841 | 0.9736 | 16.0510 |
| 0.0508 | 13.68 | 530 | 0.0746 | 6.0361 | 0.9839 | 0.9735 | 15.9709 |
| 0.0533 | 13.94 | 540 | 0.0768 | 6.1596 | 0.9836 | 0.9740 | 15.9193 |
| 0.0503 | 14.19 | 550 | 0.0739 | 5.5900 | 0.9843 | 0.9723 | 16.1883 |
| 0.0515 | 14.45 | 560 | 0.0740 | 5.4660 | 0.9845 | 0.9727 | 16.2745 |
| 0.0502 | 14.71 | 570 | 0.0740 | 5.5895 | 0.9844 | 0.9736 | 16.2054 |
| 0.0401 | 14.97 | 580 | 0.0741 | 5.9694 | 0.9840 | 0.9747 | 15.9603 |
| 0.0495 | 15.23 | 590 | 0.0745 | 5.9136 | 0.9841 | 0.9740 | 16.0458 |
| 0.0413 | 15.48 | 600 | 0.0743 | 5.9548 | 0.9840 | 0.9740 | 16.0119 |
### Framework versions
- transformers 4.31.0
- torch 2.0.0
- datasets 2.13.1
- tokenizers 0.13.3
| [
"background",
"text"
] |
bilal01/segformer-b0-finetuned-segments-stamp-verification |
<!-- 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-stamp-verification
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the bilal01/stamp-verification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0372
- Mean Iou: 0.1908
- Mean Accuracy: 0.3817
- Overall Accuracy: 0.3817
- Accuracy Unlabeled: nan
- Accuracy Stamp: 0.3817
- Iou Unlabeled: 0.0
- Iou Stamp: 0.3817
## Model description
The StampSegNet is a semantic segmentation model fine-tuned on a custom dataset specifically designed for stamp segmentation. It is based on the powerful Hugging Face framework and utilizes deep learning techniques to accurately and efficiently segment stamps from images.
The model has been trained to identify and classify different regions of an image as either belonging to a stamp or not. By leveraging its understanding of stamp-specific features such as intricate designs, borders, and distinct colors, the StampSegNet is capable of producing pixel-level segmentation maps that highlight the exact boundaries of stamps within an image.
## Intended uses & limitations
Stamp Collection Management: The StampSegNet model can be used by stamp collectors and enthusiasts to automatically segment stamps from images. It simplifies the process of organizing and cataloging stamp collections by accurately identifying and isolating stamps, saving time and effort.
E-commerce Platforms: Online marketplaces and auction platforms catering to stamp sellers and buyers can integrate the StampSegNet model to enhance their user experience. Sellers can easily upload images of stamps, and the model can automatically extract and display segmented stamps, facilitating search, categorization, and valuation for potential buyers.
While the StampSegNet exhibits high performance in stamp segmentation, it may encounter challenges in scenarios with heavily damaged or obscured stamps, unusual stamp shapes, or images with poor lighting conditions. Furthermore, as with any AI-based model, biases present in the training data could potentially influence the segmentation results, necessitating careful evaluation and mitigation of any ethical implications.
## Training and evaluation data
The dataset used was taken from kaggle. Link is provided below:
{dataset}(https://www.kaggle.com/datasets/rtatman/stamp-verification-staver-dataset)
We used 60 samples and annotated them on Segments.ai
## Training procedure
Data Collection and Preparation:
Collect a diverse dataset of stamp images along with their corresponding pixel-level annotations, indicating the regions of stamps within the images.
Ensure the dataset includes a wide variety of stamp designs, sizes, colors, backgrounds, and lighting conditions.
Split the dataset into training, validation set
Model Selection and Configuration:
Choose a semantic segmentation model architecture suitable for stamp segmentation tasks.
We used [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) as a pretrained model and fined tuned on it
Configure the model architecture and any necessary hyperparameters, such as learning rate, batch size, and optimizer.
Training:
Train the model on the labeled stamp dataset using the initialized weights.
Use a suitable loss function for semantic segmentation tasks, such as cross-entropy loss or Dice loss.
Perform mini-batch stochastic gradient descent (SGD) or an optimizer like Adam to update the model's parameters.
Monitor the training progress by calculating metrics such as pixel accuracy, mean Intersection over Union (IoU), or F1 score.
### 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 Stamp | Iou Unlabeled | Iou Stamp |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.3384 | 0.83 | 20 | 0.2769 | 0.0335 | 0.0670 | 0.0670 | nan | 0.0670 | 0.0 | 0.0670 |
| 0.2626 | 1.67 | 40 | 0.2201 | 0.1256 | 0.2512 | 0.2512 | nan | 0.2512 | 0.0 | 0.2512 |
| 0.1944 | 2.5 | 60 | 0.1918 | 0.2030 | 0.4060 | 0.4060 | nan | 0.4060 | 0.0 | 0.4060 |
| 0.2665 | 3.33 | 80 | 0.1564 | 0.1574 | 0.3148 | 0.3148 | nan | 0.3148 | 0.0 | 0.3148 |
| 0.1351 | 4.17 | 100 | 0.1194 | 0.1817 | 0.3634 | 0.3634 | nan | 0.3634 | 0.0 | 0.3634 |
| 0.1156 | 5.0 | 120 | 0.1035 | 0.1334 | 0.2668 | 0.2668 | nan | 0.2668 | 0.0 | 0.2668 |
| 0.1103 | 5.83 | 140 | 0.0895 | 0.1819 | 0.3638 | 0.3638 | nan | 0.3638 | 0.0 | 0.3638 |
| 0.0882 | 6.67 | 160 | 0.0746 | 0.0833 | 0.1665 | 0.1665 | nan | 0.1665 | 0.0 | 0.1665 |
| 0.0778 | 7.5 | 180 | 0.0655 | 0.1927 | 0.3854 | 0.3854 | nan | 0.3854 | 0.0 | 0.3854 |
| 0.0672 | 8.33 | 200 | 0.0585 | 0.1327 | 0.2654 | 0.2654 | nan | 0.2654 | 0.0 | 0.2654 |
| 0.0612 | 9.17 | 220 | 0.0615 | 0.1640 | 0.3279 | 0.3279 | nan | 0.3279 | 0.0 | 0.3279 |
| 0.0611 | 10.0 | 240 | 0.0546 | 0.2466 | 0.4933 | 0.4933 | nan | 0.4933 | 0.0 | 0.4933 |
| 0.0537 | 10.83 | 260 | 0.0499 | 0.1129 | 0.2258 | 0.2258 | nan | 0.2258 | 0.0 | 0.2258 |
| 0.0504 | 11.67 | 280 | 0.0502 | 0.1857 | 0.3713 | 0.3713 | nan | 0.3713 | 0.0 | 0.3713 |
| 0.0707 | 12.5 | 300 | 0.0442 | 0.1710 | 0.3419 | 0.3419 | nan | 0.3419 | 0.0 | 0.3419 |
| 0.0508 | 13.33 | 320 | 0.0434 | 0.2003 | 0.4006 | 0.4006 | nan | 0.4006 | 0.0 | 0.4006 |
| 0.0396 | 14.17 | 340 | 0.0420 | 0.1409 | 0.2818 | 0.2818 | nan | 0.2818 | 0.0 | 0.2818 |
| 0.0395 | 15.0 | 360 | 0.0417 | 0.1640 | 0.3280 | 0.3280 | nan | 0.3280 | 0.0 | 0.3280 |
| 0.0387 | 15.83 | 380 | 0.0397 | 0.1827 | 0.3655 | 0.3655 | nan | 0.3655 | 0.0 | 0.3655 |
| 0.0458 | 16.67 | 400 | 0.0387 | 0.1582 | 0.3165 | 0.3165 | nan | 0.3165 | 0.0 | 0.3165 |
| 0.0363 | 17.5 | 420 | 0.0390 | 0.1724 | 0.3449 | 0.3449 | nan | 0.3449 | 0.0 | 0.3449 |
| 0.0401 | 18.33 | 440 | 0.0382 | 0.2018 | 0.4036 | 0.4036 | nan | 0.4036 | 0.0 | 0.4036 |
| 0.0355 | 19.17 | 460 | 0.0382 | 0.2032 | 0.4064 | 0.4064 | nan | 0.4064 | 0.0 | 0.4064 |
| 0.0447 | 20.0 | 480 | 0.0372 | 0.1908 | 0.3817 | 0.3817 | nan | 0.3817 | 0.0 | 0.3817 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3 | [
"unlabeled",
"stamp"
] |
edwardhuang/test-carbonate-segmentation2 |
<!-- 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-carbonate-segmentation2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the edwardhuang/carbonate-thin-sections dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3642
- Mean Iou: 0.2180
- Mean Accuracy: 0.3344
- Overall Accuracy: 0.6454
- Accuracy Micrite: nan
- Accuracy Cement: nan
- Accuracy Peloid/pellet/ooid: nan
- Accuracy Biotic: 0.6660
- Accuracy Scale bar: 0.0028
- Iou Micrite: 0.0
- Iou Cement: nan
- Iou Peloid/pellet/ooid: nan
- Iou Biotic: 0.6511
- Iou Scale bar: 0.0028
## 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 Micrite | Accuracy Cement | Accuracy Peloid/pellet/ooid | Accuracy Biotic | Accuracy Scale bar | Iou Micrite | Iou Cement | Iou Peloid/pellet/ooid | Iou Biotic | Iou Scale bar |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:---------------:|:---------------------------:|:---------------:|:------------------:|:-----------:|:----------:|:----------------------:|:----------:|:-------------:|
| 1.2003 | 2.22 | 20 | 1.5260 | 0.4834 | 0.7334 | 0.9835 | nan | nan | nan | 1.0 | 0.4669 | 0.0 | nan | nan | 0.9834 | 0.4669 |
| 1.2006 | 4.44 | 40 | 0.8923 | 0.6346 | 0.9521 | 0.9498 | nan | nan | nan | 0.9497 | 0.9545 | 0.0 | nan | nan | 0.9494 | 0.9545 |
| 1.4233 | 6.67 | 60 | 1.0240 | 0.4417 | 0.6716 | 0.9793 | nan | nan | nan | 0.9995 | 0.3438 | 0.0 | nan | nan | 0.9814 | 0.3438 |
| 1.1735 | 8.89 | 80 | 0.7964 | 0.5230 | 0.7890 | 0.9437 | nan | nan | nan | 0.9539 | 0.6241 | 0.0 | nan | nan | 0.9449 | 0.6241 |
| 1.0242 | 11.11 | 100 | 0.8747 | 0.5322 | 0.8038 | 0.9849 | nan | nan | nan | 0.9969 | 0.6108 | 0.0 | nan | nan | 0.9859 | 0.6108 |
| 0.9161 | 13.33 | 120 | 0.9217 | 0.5133 | 0.7767 | 0.9831 | nan | nan | nan | 0.9967 | 0.5568 | 0.0 | nan | nan | 0.9832 | 0.5568 |
| 0.8102 | 15.56 | 140 | 0.7069 | 0.5923 | 0.8907 | 0.9490 | nan | nan | nan | 0.9529 | 0.8286 | 0.0 | nan | nan | 0.9484 | 0.8286 |
| 0.5436 | 17.78 | 160 | 0.5149 | 0.3206 | 0.4929 | 0.8806 | nan | nan | nan | 0.9062 | 0.0795 | 0.0 | nan | nan | 0.8823 | 0.0795 |
| 0.8517 | 20.0 | 180 | 0.5646 | 0.3748 | 0.5719 | 0.9200 | nan | nan | nan | 0.9430 | 0.2008 | 0.0 | nan | nan | 0.9236 | 0.2008 |
| 0.4532 | 22.22 | 200 | 0.6128 | 0.3133 | 0.4837 | 0.9188 | nan | nan | nan | 0.9475 | 0.0199 | 0.0 | nan | nan | 0.9201 | 0.0199 |
| 1.3133 | 24.44 | 220 | 0.3006 | 0.2391 | 0.3645 | 0.7064 | nan | nan | nan | 0.7290 | 0.0 | 0.0 | nan | nan | 0.7172 | 0.0 |
| 0.4636 | 26.67 | 240 | 0.3260 | 0.1903 | 0.2901 | 0.5259 | nan | nan | nan | 0.5414 | 0.0388 | 0.0 | nan | nan | 0.5320 | 0.0388 |
| 0.9843 | 28.89 | 260 | 0.3663 | 0.2741 | 0.4182 | 0.6986 | nan | nan | nan | 0.7171 | 0.1193 | 0.0 | nan | nan | 0.7031 | 0.1193 |
| 0.7617 | 31.11 | 280 | 0.3338 | 0.2357 | 0.3627 | 0.7030 | nan | nan | nan | 0.7255 | 0.0 | 0.0 | nan | nan | 0.7072 | 0.0 |
| 1.283 | 33.33 | 300 | 0.3395 | 0.2723 | 0.4176 | 0.7232 | nan | nan | nan | 0.7434 | 0.0919 | 0.0 | nan | nan | 0.7250 | 0.0919 |
| 0.6578 | 35.56 | 320 | 0.3382 | 0.2069 | 0.3170 | 0.6143 | nan | nan | nan | 0.6339 | 0.0 | 0.0 | nan | nan | 0.6207 | 0.0 |
| 0.2129 | 37.78 | 340 | 0.3436 | 0.2288 | 0.3525 | 0.6831 | nan | nan | nan | 0.7049 | 0.0 | 0.0 | nan | nan | 0.6863 | 0.0 |
| 0.7001 | 40.0 | 360 | 0.2998 | 0.2001 | 0.3069 | 0.5771 | nan | nan | nan | 0.5950 | 0.0189 | 0.0 | nan | nan | 0.5813 | 0.0189 |
| 0.3866 | 42.22 | 380 | 0.3162 | 0.1840 | 0.2819 | 0.5464 | nan | nan | nan | 0.5639 | 0.0 | 0.0 | nan | nan | 0.5521 | 0.0 |
| 1.2623 | 44.44 | 400 | 0.3431 | 0.2125 | 0.3254 | 0.6172 | nan | nan | nan | 0.6365 | 0.0142 | 0.0 | nan | nan | 0.6234 | 0.0142 |
| 0.6115 | 46.67 | 420 | 0.2987 | 0.2020 | 0.3095 | 0.5998 | nan | nan | nan | 0.6190 | 0.0 | 0.0 | nan | nan | 0.6060 | 0.0 |
| 0.5802 | 48.89 | 440 | 0.3642 | 0.2180 | 0.3344 | 0.6454 | nan | nan | nan | 0.6660 | 0.0028 | 0.0 | nan | nan | 0.6511 | 0.0028 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
| [
"micrite",
"cement",
"peloid/pellet/ooid",
"biotic",
"scale bar"
] |
apple/mobilevitv2-1.0-voc-deeplabv3 |
# MobileViTv2 + DeepLabv3 (shehan97/mobilevitv2-1.0-voc-deeplabv3)
<!-- Provide a quick summary of what the model is/does. -->
MobileViTv2 model pre-trained on PASCAL VOC at resolution 512x512.
It was introduced in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari, and first released in [this](https://github.com/apple/ml-cvnets) repository. The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team.
### Model Description
<!-- Provide a longer summary of what this model is. -->
MobileViTv2 is constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
The model in this repo adds a [DeepLabV3](https://arxiv.org/abs/1706.05587) head to the MobileViT backbone for semantic segmentation.
### Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilevitv2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MobileViTv2FeatureExtractor, MobileViTv2ForSemanticSegmentation
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileViTv2FeatureExtractor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
model = MobileViTv2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0)
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The MobileViT + DeepLabV3 model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/) dataset.
### BibTeX entry and citation info
```bibtex
@inproceedings{vision-transformer,
title = {Separable Self-attention for Mobile Vision Transformers},
author = {Sachin Mehta and Mohammad Rastegari},
year = {2022},
URL = {https://arxiv.org/abs/2206.02680}
}
```
| [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor"
] |
ArturR01/segformer-b0-example-pytorch-blog |
<!-- 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-example-pytorch-blog
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: 1.2757
- Mean Iou: 0.1462
- Mean Accuracy: 0.2006
- Overall Accuracy: 0.7257
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8968
- Accuracy Flat-sidewalk: 0.9232
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.0013
- Accuracy Flat-parkingdriveway: 0.0024
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8803
- 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: 0.8822
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0000
- 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: 0.8802
- Accuracy Nature-terrain: 0.8441
- Accuracy Sky: 0.9068
- 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: 0.5131
- Iou Flat-sidewalk: 0.7717
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.0013
- Iou Flat-parkingdriveway: 0.0024
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.0
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.5983
- 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: 0.5449
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0000
- 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: 0.7519
- Iou Nature-terrain: 0.5340
- Iou Sky: 0.8151
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 3.0226 | 0.05 | 20 | 3.2451 | 0.0770 | 0.1291 | 0.5814 | nan | 0.3392 | 0.9150 | 0.0007 | 0.0167 | 0.0052 | nan | 0.0281 | 0.0013 | 0.0 | 0.6316 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8880 | 0.0 | 0.0100 | 0.0 | 0.0 | nan | 0.0 | 0.0212 | 0.0 | 0.0 | 0.7776 | 0.2569 | 0.1047 | 0.0036 | 0.0021 | 0.0002 | 0.0 | 0.0 | 0.2887 | 0.6060 | 0.0006 | 0.0156 | 0.0051 | 0.0 | 0.0209 | 0.0011 | 0.0 | 0.4177 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3513 | 0.0 | 0.0087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0109 | 0.0 | 0.0 | 0.6347 | 0.2251 | 0.1037 | 0.0027 | 0.0019 | 0.0002 | 0.0 |
| 2.4643 | 0.1 | 40 | 2.4748 | 0.0979 | 0.1462 | 0.6444 | nan | 0.6454 | 0.9084 | 0.0012 | 0.0002 | 0.0006 | nan | 0.0124 | 0.0000 | 0.0 | 0.6787 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8760 | 0.0 | 0.0119 | 0.0 | 0.0 | nan | 0.0 | 0.0038 | 0.0 | 0.0 | 0.9282 | 0.0365 | 0.4258 | 0.0016 | 0.0 | 0.0 | 0.0 | nan | 0.4331 | 0.6624 | 0.0012 | 0.0002 | 0.0006 | nan | 0.0114 | 0.0000 | 0.0 | 0.4718 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4327 | 0.0 | 0.0115 | 0.0 | 0.0 | nan | 0.0 | 0.0036 | 0.0 | 0.0 | 0.6481 | 0.0359 | 0.4181 | 0.0015 | 0.0 | 0.0 | 0.0 |
| 2.3866 | 0.15 | 60 | 2.0828 | 0.1129 | 0.1636 | 0.6679 | nan | 0.7570 | 0.8891 | 0.0000 | 0.0000 | 0.0002 | nan | 0.0010 | 0.0001 | 0.0 | 0.7980 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8425 | 0.0 | 0.0088 | 0.0 | 0.0 | nan | 0.0 | 0.0006 | 0.0 | 0.0 | 0.9416 | 0.3129 | 0.5187 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | 0.4431 | 0.6874 | 0.0000 | 0.0000 | 0.0002 | nan | 0.0010 | 0.0001 | 0.0 | 0.5410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4735 | 0.0 | 0.0087 | 0.0 | 0.0 | nan | 0.0 | 0.0006 | 0.0 | 0.0 | 0.6784 | 0.2726 | 0.5071 | 0.0000 | 0.0 | 0.0 | 0.0 |
| 2.4998 | 0.2 | 80 | 1.9122 | 0.1276 | 0.1772 | 0.6866 | nan | 0.8098 | 0.8856 | 0.0 | 0.0001 | 0.0 | nan | 0.0004 | 0.0 | 0.0 | 0.8752 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8497 | 0.0 | 0.0073 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9361 | 0.4185 | 0.7115 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | 0.4611 | 0.7099 | 0.0 | 0.0001 | 0.0 | nan | 0.0004 | 0.0 | 0.0 | 0.5317 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5068 | 0.0 | 0.0072 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7000 | 0.3600 | 0.6771 | 0.0000 | 0.0 | 0.0 | 0.0 |
| 1.9775 | 0.25 | 100 | 1.7125 | 0.1344 | 0.1848 | 0.6979 | nan | 0.7868 | 0.9065 | 0.0 | 0.0002 | 0.0000 | nan | 0.0007 | 0.0 | 0.0 | 0.8211 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8331 | 0.0 | 0.0015 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.9286 | 0.6121 | 0.8377 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4865 | 0.7134 | 0.0 | 0.0002 | 0.0000 | nan | 0.0007 | 0.0 | 0.0 | 0.5603 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5044 | 0.0 | 0.0015 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.7089 | 0.4386 | 0.7511 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6408 | 0.3 | 120 | 1.6293 | 0.1379 | 0.1888 | 0.7033 | nan | 0.7671 | 0.9293 | 0.0 | 0.0020 | 0.0000 | nan | 0.0002 | 0.0 | 0.0 | 0.8367 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8499 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8888 | 0.6973 | 0.8808 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4924 | 0.7106 | 0.0 | 0.0020 | 0.0000 | nan | 0.0002 | 0.0 | 0.0 | 0.5812 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5056 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.7306 | 0.4774 | 0.7751 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.0971 | 0.35 | 140 | 1.5878 | 0.1392 | 0.1931 | 0.7067 | nan | 0.8429 | 0.9084 | 0.0 | 0.0003 | 0.0000 | nan | 0.0000 | 0.0 | 0.0 | 0.8886 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8458 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8806 | 0.7458 | 0.8668 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4799 | 0.7350 | 0.0 | 0.0003 | 0.0000 | nan | 0.0000 | 0.0 | 0.0 | 0.5623 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5298 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7340 | 0.4897 | 0.7783 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5524 | 0.4 | 160 | 1.5210 | 0.1416 | 0.1935 | 0.7104 | nan | 0.8431 | 0.9047 | 0.0 | 0.0054 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.8147 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8864 | 0.0 | 0.0011 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8979 | 0.7542 | 0.8898 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4947 | 0.7378 | 0.0 | 0.0054 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.6030 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5067 | 0.0 | 0.0011 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7376 | 0.5122 | 0.7895 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.1125 | 0.45 | 180 | 1.4662 | 0.1381 | 0.1967 | 0.7038 | nan | 0.8346 | 0.9129 | 0.0 | 0.0013 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8720 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8763 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8097 | 0.8918 | 0.8970 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5026 | 0.7394 | 0.0 | 0.0013 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.5807 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5253 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6976 | 0.4392 | 0.7937 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7884 | 0.5 | 200 | 1.3982 | 0.1411 | 0.1928 | 0.7139 | nan | 0.8103 | 0.9245 | 0.0 | 0.0012 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.8626 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8615 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9163 | 0.6946 | 0.9044 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5111 | 0.7331 | 0.0 | 0.0012 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.5772 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5245 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7332 | 0.5028 | 0.7899 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7399 | 0.55 | 220 | 1.4060 | 0.1429 | 0.1965 | 0.7154 | nan | 0.8177 | 0.9351 | 0.0 | 0.0000 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.8868 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8743 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8626 | 0.8036 | 0.9097 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5061 | 0.7372 | 0.0 | 0.0000 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.5900 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5170 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7513 | 0.5264 | 0.8019 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6151 | 0.6 | 240 | 1.3772 | 0.1407 | 0.1920 | 0.7140 | nan | 0.8674 | 0.9061 | 0.0 | 0.0000 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.8325 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8259 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9415 | 0.6687 | 0.9074 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5026 | 0.7512 | 0.0 | 0.0000 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.5943 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5339 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7135 | 0.4782 | 0.7870 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8311 | 0.65 | 260 | 1.3217 | 0.1418 | 0.1945 | 0.7189 | nan | 0.8499 | 0.9251 | 0.0 | 0.0002 | 0.0028 | nan | 0.0 | 0.0 | 0.0 | 0.8839 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8598 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.7105 | 0.8851 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5198 | 0.7509 | 0.0 | 0.0002 | 0.0028 | nan | 0.0 | 0.0 | 0.0 | 0.5533 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5311 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7431 | 0.4986 | 0.7975 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.215 | 0.7 | 280 | 1.3329 | 0.1434 | 0.1977 | 0.7195 | nan | 0.8756 | 0.9182 | 0.0 | 0.0003 | 0.0023 | nan | 0.0 | 0.0 | 0.0 | 0.8858 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9029 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8752 | 0.7868 | 0.8822 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5146 | 0.7624 | 0.0 | 0.0003 | 0.0023 | nan | 0.0 | 0.0 | 0.0 | 0.5919 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5143 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7533 | 0.5041 | 0.8033 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5656 | 0.75 | 300 | 1.2993 | 0.1433 | 0.1973 | 0.7170 | nan | 0.8972 | 0.9030 | 0.0 | 0.0002 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.8611 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8344 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9037 | 0.8070 | 0.9082 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4916 | 0.7608 | 0.0 | 0.0002 | 0.0015 | nan | 0.0 | 0.0 | 0.0 | 0.5982 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5474 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7300 | 0.5160 | 0.7977 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3712 | 0.8 | 320 | 1.2934 | 0.1445 | 0.1984 | 0.7203 | nan | 0.9047 | 0.9056 | 0.0 | 0.0004 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.8724 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8976 | 0.7999 | 0.8984 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4941 | 0.7696 | 0.0 | 0.0004 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.5955 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5460 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7442 | 0.5189 | 0.8093 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1831 | 0.85 | 340 | 1.2771 | 0.1453 | 0.1996 | 0.7217 | nan | 0.9035 | 0.9105 | 0.0 | 0.0010 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8679 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8874 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8812 | 0.8507 | 0.8838 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4996 | 0.7710 | 0.0 | 0.0010 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.6037 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5458 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7443 | 0.5249 | 0.8129 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.343 | 0.9 | 360 | 1.2465 | 0.1449 | 0.1989 | 0.7212 | nan | 0.9086 | 0.9032 | 0.0 | 0.0007 | 0.0022 | nan | 0.0 | 0.0 | 0.0 | 0.8650 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8673 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9040 | 0.8253 | 0.8911 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4947 | 0.7732 | 0.0 | 0.0007 | 0.0022 | nan | 0.0 | 0.0 | 0.0 | 0.5988 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5508 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7403 | 0.5220 | 0.8099 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4857 | 0.95 | 380 | 1.2733 | 0.1453 | 0.2008 | 0.7241 | nan | 0.8789 | 0.9317 | 0.0 | 0.0019 | 0.0035 | nan | 0.0 | 0.0 | 0.0 | 0.8861 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9032 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8505 | 0.8620 | 0.9060 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5280 | 0.7656 | 0.0 | 0.0019 | 0.0035 | nan | 0.0 | 0.0 | 0.0 | 0.5952 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5299 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7500 | 0.5148 | 0.8150 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1595 | 1.0 | 400 | 1.2757 | 0.1462 | 0.2006 | 0.7257 | nan | 0.8968 | 0.9232 | 0.0 | 0.0013 | 0.0024 | nan | 0.0 | 0.0 | 0.0 | 0.8803 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8822 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8802 | 0.8441 | 0.9068 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5131 | 0.7717 | 0.0 | 0.0013 | 0.0024 | nan | 0.0 | 0.0 | 0.0 | 0.5983 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5449 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7519 | 0.5340 | 0.8151 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| [
"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"
] |
thesisabc/segformer-b0-finetuned-segments-sidewalk-2 | # 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)
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2105-15203,
author = {Enze Xie and
Wenhai Wang and
Zhiding Yu and
Anima Anandkumar and
Jose M. Alvarez and
Ping Luo},
title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
Transformers},
journal = {CoRR},
volume = {abs/2105.15203},
year = {2021},
url = {https://arxiv.org/abs/2105.15203},
eprinttype = {arXiv},
eprint = {2105.15203},
timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
"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"
] |
vimassaru/segformer-b0-finetuned-teeth-segmentation |
<!-- 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 vimassaru/teethsegmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1880
- Mean Iou: 0.7311
- Mean Accuracy: 0.8106
- Overall Accuracy: 0.8184
- Accuracy Background: nan
- Accuracy 11: 0.8456
- Accuracy 12: 0.8351
- Accuracy 13: 0.8619
- Accuracy 14: 0.8112
- Accuracy 15: 0.8087
- Accuracy 16: 0.9141
- Accuracy 17: 0.8742
- Accuracy 18: 0.7394
- Accuracy 21: 0.8758
- Accuracy 22: 0.8579
- Accuracy 23: 0.8480
- Accuracy 24: 0.7169
- Accuracy 25: 0.8273
- Accuracy 26: 0.8481
- Accuracy 27: 0.8284
- Accuracy 28: 0.7298
- Accuracy 31: 0.7495
- Accuracy 32: 0.7987
- Accuracy 33: 0.8661
- Accuracy 34: 0.8392
- Accuracy 35: 0.7596
- Accuracy 36: 0.7482
- Accuracy 37: 0.8109
- Accuracy 38: 0.7016
- Accuracy 41: 0.7217
- Accuracy 42: 0.7480
- Accuracy 43: 0.8447
- Accuracy 44: 0.7868
- Accuracy 45: 0.8250
- Accuracy 46: 0.8762
- Accuracy 47: 0.8519
- Accuracy 48: 0.7878
- Iou Background: 0.0
- Iou 11: 0.8226
- Iou 12: 0.8155
- Iou 13: 0.8048
- Iou 14: 0.7807
- Iou 15: 0.7909
- Iou 16: 0.8609
- Iou 17: 0.8145
- Iou 18: 0.6999
- Iou 21: 0.8266
- Iou 22: 0.8160
- Iou 23: 0.8000
- Iou 24: 0.6900
- Iou 25: 0.7760
- Iou 26: 0.8065
- Iou 27: 0.7338
- Iou 28: 0.6771
- Iou 31: 0.6604
- Iou 32: 0.7394
- Iou 33: 0.7977
- Iou 34: 0.7577
- Iou 35: 0.6944
- Iou 36: 0.6774
- Iou 37: 0.7224
- Iou 38: 0.6099
- Iou 41: 0.6166
- Iou 42: 0.6741
- Iou 43: 0.7706
- Iou 44: 0.7386
- Iou 45: 0.7555
- Iou 46: 0.8271
- Iou 47: 0.8210
- Iou 48: 0.7466
## 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: 0.0006
- 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy 11 | Accuracy 12 | Accuracy 13 | Accuracy 14 | Accuracy 15 | Accuracy 16 | Accuracy 17 | Accuracy 18 | Accuracy 21 | Accuracy 22 | Accuracy 23 | Accuracy 24 | Accuracy 25 | Accuracy 26 | Accuracy 27 | Accuracy 28 | Accuracy 31 | Accuracy 32 | Accuracy 33 | Accuracy 34 | Accuracy 35 | Accuracy 36 | Accuracy 37 | Accuracy 38 | Accuracy 41 | Accuracy 42 | Accuracy 43 | Accuracy 44 | Accuracy 45 | Accuracy 46 | Accuracy 47 | Accuracy 48 | Iou Background | Iou 11 | Iou 12 | Iou 13 | Iou 14 | Iou 15 | Iou 16 | Iou 17 | Iou 18 | Iou 21 | Iou 22 | Iou 23 | Iou 24 | Iou 25 | Iou 26 | Iou 27 | Iou 28 | Iou 31 | Iou 32 | Iou 33 | Iou 34 | Iou 35 | Iou 36 | Iou 37 | Iou 38 | Iou 41 | Iou 42 | Iou 43 | Iou 44 | Iou 45 | Iou 46 | Iou 47 | Iou 48 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:--------------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 1.5068 | 2.0 | 20 | 1.5257 | 0.0331 | 0.0872 | 0.1082 | nan | 0.0 | 0.0 | 0.0325 | 0.0 | 0.0 | 0.3861 | 0.7782 | 0.0 | 0.5551 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0025 | 0.5879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1233 | 0.1058 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.2200 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0298 | 0.0 | 0.0 | 0.2256 | 0.2666 | 0.0 | 0.0733 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0025 | 0.1063 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0983 | 0.0956 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.1930 | 0.0 |
| 0.6989 | 4.0 | 40 | 0.6959 | 0.0319 | 0.0406 | 0.0525 | nan | 0.0032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1235 | 0.5046 | 0.0 | 0.0025 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.0821 | 0.2861 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2543 | 0.0 | 0.0 | 0.0032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1124 | 0.3456 | 0.0 | 0.0024 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.0706 | 0.2383 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0395 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2387 | 0.0 |
| 0.5093 | 6.0 | 60 | 0.4954 | 0.1659 | 0.2494 | 0.3064 | nan | 0.4165 | 0.0 | 0.5606 | 0.2388 | 0.2757 | 0.7236 | 0.7757 | 0.0 | 0.6343 | 0.0 | 0.5877 | 0.0009 | 0.0374 | 0.7495 | 0.7771 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0323 | 0.0 | 0.0006 | 0.8019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0436 | 0.0 | 0.0556 | 0.7505 | 0.5182 | 0.0 | 0.2822 | 0.0 | 0.3631 | 0.2026 | 0.2067 | 0.4694 | 0.5250 | 0.0 | 0.3620 | 0.0 | 0.4125 | 0.0009 | 0.0358 | 0.4261 | 0.5167 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0299 | 0.0 | 0.0006 | 0.5306 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0423 | 0.0 | 0.0555 | 0.5479 | 0.4643 |
| 0.3942 | 8.0 | 80 | 0.3680 | 0.3501 | 0.4658 | 0.5243 | nan | 0.5738 | 0.1433 | 0.7262 | 0.5454 | 0.5830 | 0.7900 | 0.8112 | 0.0005 | 0.7442 | 0.0533 | 0.8308 | 0.3071 | 0.5064 | 0.8316 | 0.7153 | 0.0 | 0.0000 | 0.0743 | 0.6079 | 0.4475 | 0.1634 | 0.6588 | 0.7590 | 0.2711 | 0.0 | 0.3319 | 0.4395 | 0.6430 | 0.2279 | 0.7393 | 0.6828 | 0.6962 | 0.0 | 0.4491 | 0.1389 | 0.5260 | 0.4596 | 0.5023 | 0.6223 | 0.5467 | 0.0005 | 0.5418 | 0.0531 | 0.5015 | 0.2715 | 0.4175 | 0.6065 | 0.4828 | 0.0 | 0.0000 | 0.0713 | 0.3230 | 0.3481 | 0.1586 | 0.5433 | 0.5740 | 0.2533 | 0.0 | 0.2423 | 0.3966 | 0.4280 | 0.2200 | 0.6234 | 0.6177 | 0.6324 |
| 0.2784 | 10.0 | 100 | 0.2889 | 0.4636 | 0.5684 | 0.6056 | nan | 0.7515 | 0.3831 | 0.6687 | 0.4978 | 0.5445 | 0.7171 | 0.8213 | 0.3704 | 0.8125 | 0.4238 | 0.7137 | 0.3553 | 0.3746 | 0.6771 | 0.7124 | 0.3304 | 0.2737 | 0.3675 | 0.6784 | 0.5418 | 0.5543 | 0.6343 | 0.7606 | 0.5879 | 0.0628 | 0.4605 | 0.6789 | 0.6545 | 0.4182 | 0.8261 | 0.8022 | 0.7318 | 0.0 | 0.5755 | 0.3346 | 0.5240 | 0.4448 | 0.5151 | 0.6337 | 0.6315 | 0.3573 | 0.5967 | 0.3743 | 0.5367 | 0.2860 | 0.3354 | 0.6064 | 0.5735 | 0.3130 | 0.2389 | 0.3153 | 0.5370 | 0.4762 | 0.4740 | 0.5568 | 0.6436 | 0.4934 | 0.0622 | 0.3368 | 0.5307 | 0.4908 | 0.4042 | 0.7224 | 0.7127 | 0.6656 |
| 0.2464 | 12.0 | 120 | 0.2468 | 0.5400 | 0.6505 | 0.6768 | nan | 0.7948 | 0.5458 | 0.7406 | 0.5603 | 0.6560 | 0.8367 | 0.8329 | 0.4258 | 0.8497 | 0.5678 | 0.7320 | 0.5367 | 0.6036 | 0.7622 | 0.7365 | 0.5251 | 0.4648 | 0.6275 | 0.7353 | 0.5512 | 0.6709 | 0.6991 | 0.7896 | 0.6117 | 0.2989 | 0.5592 | 0.6312 | 0.5789 | 0.6009 | 0.8589 | 0.7659 | 0.6656 | 0.0 | 0.6477 | 0.4822 | 0.5850 | 0.4886 | 0.6094 | 0.7392 | 0.6728 | 0.4202 | 0.6684 | 0.4945 | 0.6069 | 0.4492 | 0.4973 | 0.6686 | 0.6208 | 0.4915 | 0.3555 | 0.4906 | 0.5972 | 0.4926 | 0.5760 | 0.6233 | 0.6776 | 0.5277 | 0.2650 | 0.4418 | 0.5141 | 0.4982 | 0.5567 | 0.7441 | 0.6964 | 0.6216 |
| 0.1842 | 14.0 | 140 | 0.2142 | 0.6089 | 0.7198 | 0.7422 | nan | 0.7984 | 0.6803 | 0.7803 | 0.6313 | 0.7672 | 0.8662 | 0.8918 | 0.6084 | 0.8619 | 0.7294 | 0.8191 | 0.6076 | 0.7007 | 0.8122 | 0.8322 | 0.3832 | 0.5565 | 0.6651 | 0.8472 | 0.7226 | 0.7371 | 0.7750 | 0.7743 | 0.5362 | 0.5420 | 0.6400 | 0.7419 | 0.7320 | 0.6402 | 0.8401 | 0.8309 | 0.6816 | 0.0 | 0.7143 | 0.6368 | 0.7007 | 0.5825 | 0.6365 | 0.7816 | 0.7521 | 0.5826 | 0.7414 | 0.6622 | 0.7151 | 0.5453 | 0.5588 | 0.7077 | 0.6385 | 0.3652 | 0.4556 | 0.5434 | 0.7138 | 0.6374 | 0.6336 | 0.6464 | 0.6580 | 0.4844 | 0.4542 | 0.5349 | 0.6565 | 0.6279 | 0.5893 | 0.7605 | 0.7330 | 0.6423 |
| 0.1488 | 16.0 | 160 | 0.1933 | 0.6322 | 0.7373 | 0.7542 | nan | 0.7908 | 0.6508 | 0.7861 | 0.7352 | 0.7521 | 0.8892 | 0.8725 | 0.6348 | 0.8581 | 0.6927 | 0.8070 | 0.6642 | 0.7642 | 0.8278 | 0.8363 | 0.5789 | 0.6088 | 0.6556 | 0.8139 | 0.6966 | 0.6829 | 0.7853 | 0.7453 | 0.6678 | 0.5757 | 0.6036 | 0.7537 | 0.7381 | 0.7114 | 0.8446 | 0.8103 | 0.7584 | 0.0 | 0.6990 | 0.6113 | 0.7069 | 0.6612 | 0.7097 | 0.7968 | 0.7383 | 0.6079 | 0.7618 | 0.6629 | 0.7209 | 0.5896 | 0.6152 | 0.7371 | 0.6847 | 0.5395 | 0.5130 | 0.5606 | 0.6936 | 0.6153 | 0.5981 | 0.6347 | 0.6535 | 0.5868 | 0.4842 | 0.5324 | 0.6701 | 0.6410 | 0.6346 | 0.7595 | 0.7362 | 0.7052 |
| 0.1416 | 18.0 | 180 | 0.1841 | 0.6533 | 0.7530 | 0.7611 | nan | 0.8485 | 0.7657 | 0.8163 | 0.6475 | 0.6706 | 0.7889 | 0.8295 | 0.8478 | 0.8320 | 0.7455 | 0.7939 | 0.5882 | 0.6691 | 0.7761 | 0.7446 | 0.7167 | 0.5879 | 0.6785 | 0.7867 | 0.7367 | 0.7456 | 0.7522 | 0.7657 | 0.7979 | 0.6336 | 0.7404 | 0.8204 | 0.7517 | 0.6922 | 0.8420 | 0.8470 | 0.8352 | 0.0 | 0.7802 | 0.7301 | 0.7232 | 0.6005 | 0.6380 | 0.7526 | 0.7347 | 0.6939 | 0.7603 | 0.6879 | 0.6913 | 0.5221 | 0.5773 | 0.7374 | 0.6773 | 0.6223 | 0.5234 | 0.5861 | 0.7062 | 0.6492 | 0.6365 | 0.6644 | 0.6850 | 0.6211 | 0.5522 | 0.6502 | 0.7557 | 0.6860 | 0.6374 | 0.7636 | 0.7728 | 0.7395 |
| 0.1346 | 20.0 | 200 | 0.1742 | 0.6462 | 0.7375 | 0.7528 | nan | 0.8037 | 0.7862 | 0.7855 | 0.7327 | 0.7617 | 0.8752 | 0.8049 | 0.5045 | 0.8113 | 0.7420 | 0.8055 | 0.5818 | 0.6962 | 0.7828 | 0.8136 | 0.5866 | 0.5741 | 0.7100 | 0.8119 | 0.7525 | 0.6603 | 0.7224 | 0.8027 | 0.6645 | 0.6230 | 0.7702 | 0.8144 | 0.6772 | 0.7818 | 0.8084 | 0.8461 | 0.7052 | 0.0 | 0.7651 | 0.7384 | 0.7206 | 0.6790 | 0.7135 | 0.7803 | 0.6876 | 0.4954 | 0.7515 | 0.6922 | 0.7354 | 0.5483 | 0.6050 | 0.7345 | 0.6809 | 0.5512 | 0.5261 | 0.6238 | 0.7292 | 0.6495 | 0.5871 | 0.6396 | 0.7011 | 0.5818 | 0.5478 | 0.6583 | 0.7103 | 0.6303 | 0.6758 | 0.7516 | 0.7592 | 0.6734 |
| 0.1042 | 22.0 | 220 | 0.1637 | 0.6758 | 0.7706 | 0.7839 | nan | 0.8440 | 0.8257 | 0.8354 | 0.7495 | 0.7759 | 0.9255 | 0.8405 | 0.5792 | 0.8443 | 0.8035 | 0.8293 | 0.6949 | 0.8221 | 0.8176 | 0.7856 | 0.5664 | 0.6291 | 0.7734 | 0.8161 | 0.7754 | 0.6758 | 0.7612 | 0.7892 | 0.6925 | 0.6765 | 0.7656 | 0.8216 | 0.7656 | 0.7414 | 0.8543 | 0.8211 | 0.7612 | 0.0 | 0.7956 | 0.7843 | 0.7713 | 0.6988 | 0.7373 | 0.8287 | 0.7380 | 0.5596 | 0.7925 | 0.7543 | 0.7762 | 0.6417 | 0.6676 | 0.7463 | 0.6649 | 0.5328 | 0.5629 | 0.6537 | 0.7400 | 0.6748 | 0.6041 | 0.6581 | 0.6950 | 0.6081 | 0.5778 | 0.6579 | 0.7449 | 0.6918 | 0.6812 | 0.7902 | 0.7652 | 0.7059 |
| 0.1014 | 24.0 | 240 | 0.1628 | 0.6739 | 0.7704 | 0.7833 | nan | 0.8358 | 0.8065 | 0.8226 | 0.7126 | 0.7269 | 0.8906 | 0.8694 | 0.6430 | 0.8772 | 0.7933 | 0.8077 | 0.6404 | 0.7989 | 0.8127 | 0.7760 | 0.5488 | 0.7075 | 0.7579 | 0.8430 | 0.7868 | 0.7336 | 0.7986 | 0.7866 | 0.6533 | 0.6949 | 0.6935 | 0.7829 | 0.7970 | 0.7933 | 0.8418 | 0.8350 | 0.7834 | 0.0 | 0.7942 | 0.7717 | 0.7501 | 0.6543 | 0.7011 | 0.8332 | 0.7823 | 0.6183 | 0.8028 | 0.7212 | 0.7369 | 0.5974 | 0.6623 | 0.7291 | 0.6472 | 0.5168 | 0.6019 | 0.6628 | 0.7611 | 0.6877 | 0.6428 | 0.6768 | 0.6912 | 0.5835 | 0.5659 | 0.5941 | 0.7315 | 0.7195 | 0.7043 | 0.7870 | 0.7801 | 0.7295 |
| 0.1026 | 26.0 | 260 | 0.1600 | 0.7036 | 0.7975 | 0.8086 | nan | 0.8306 | 0.8021 | 0.8395 | 0.8163 | 0.8010 | 0.9308 | 0.8947 | 0.7863 | 0.8411 | 0.7763 | 0.8088 | 0.6931 | 0.8100 | 0.8361 | 0.8549 | 0.6659 | 0.6646 | 0.7211 | 0.8081 | 0.7935 | 0.7318 | 0.7776 | 0.8230 | 0.7290 | 0.6925 | 0.7631 | 0.8611 | 0.8405 | 0.7458 | 0.8987 | 0.8776 | 0.8035 | 0.0 | 0.7789 | 0.7732 | 0.7981 | 0.7548 | 0.7578 | 0.8710 | 0.8372 | 0.7335 | 0.7657 | 0.7307 | 0.7706 | 0.6434 | 0.6820 | 0.7926 | 0.7346 | 0.6132 | 0.5936 | 0.6512 | 0.7460 | 0.6843 | 0.6222 | 0.6729 | 0.7212 | 0.6212 | 0.5839 | 0.6754 | 0.8011 | 0.7558 | 0.6898 | 0.8159 | 0.8071 | 0.7388 |
| 0.084 | 28.0 | 280 | 0.1555 | 0.6998 | 0.7893 | 0.7998 | nan | 0.8122 | 0.7962 | 0.8345 | 0.7668 | 0.7207 | 0.8815 | 0.9032 | 0.7212 | 0.8329 | 0.7968 | 0.8244 | 0.7527 | 0.8032 | 0.8308 | 0.8524 | 0.7154 | 0.6876 | 0.7525 | 0.8473 | 0.8036 | 0.7202 | 0.7676 | 0.8123 | 0.7076 | 0.6789 | 0.7105 | 0.8426 | 0.8048 | 0.8069 | 0.8447 | 0.8490 | 0.7757 | 0.0 | 0.7794 | 0.7652 | 0.7627 | 0.6891 | 0.6996 | 0.8404 | 0.8134 | 0.6859 | 0.7800 | 0.7435 | 0.7758 | 0.6849 | 0.7065 | 0.7928 | 0.7470 | 0.6594 | 0.6171 | 0.6737 | 0.7581 | 0.6921 | 0.6345 | 0.6805 | 0.7251 | 0.6076 | 0.5847 | 0.6444 | 0.7949 | 0.7383 | 0.7164 | 0.7882 | 0.7837 | 0.7271 |
| 0.0833 | 30.0 | 300 | 0.1561 | 0.6956 | 0.7859 | 0.7970 | nan | 0.8366 | 0.8484 | 0.8740 | 0.7845 | 0.7260 | 0.9071 | 0.8607 | 0.7272 | 0.8362 | 0.8175 | 0.8326 | 0.7091 | 0.7824 | 0.8454 | 0.8095 | 0.6050 | 0.6899 | 0.7374 | 0.8240 | 0.7946 | 0.7254 | 0.7469 | 0.7926 | 0.7184 | 0.6670 | 0.7099 | 0.8738 | 0.8384 | 0.7947 | 0.8672 | 0.8294 | 0.7376 | 0.0 | 0.7987 | 0.8188 | 0.7940 | 0.7065 | 0.7027 | 0.8353 | 0.7950 | 0.6931 | 0.7850 | 0.7667 | 0.7680 | 0.6546 | 0.6978 | 0.7799 | 0.6885 | 0.5668 | 0.6143 | 0.6660 | 0.7517 | 0.6993 | 0.6363 | 0.6589 | 0.7118 | 0.6149 | 0.5743 | 0.6468 | 0.8047 | 0.7501 | 0.7138 | 0.7978 | 0.7651 | 0.6976 |
| 0.0824 | 32.0 | 320 | 0.1543 | 0.7109 | 0.7960 | 0.8060 | nan | 0.8509 | 0.8384 | 0.8601 | 0.7927 | 0.8072 | 0.8660 | 0.8671 | 0.7814 | 0.8639 | 0.8340 | 0.8483 | 0.7068 | 0.7814 | 0.8166 | 0.8125 | 0.7173 | 0.6564 | 0.7617 | 0.8475 | 0.7745 | 0.7266 | 0.7506 | 0.8202 | 0.6921 | 0.6683 | 0.7198 | 0.8480 | 0.8344 | 0.8031 | 0.8905 | 0.8563 | 0.7774 | 0.0 | 0.8177 | 0.8143 | 0.8060 | 0.7503 | 0.7626 | 0.8317 | 0.8088 | 0.7207 | 0.8040 | 0.7938 | 0.8027 | 0.6677 | 0.6924 | 0.7851 | 0.7154 | 0.6584 | 0.5943 | 0.6837 | 0.7704 | 0.6973 | 0.6296 | 0.6607 | 0.7231 | 0.5996 | 0.5774 | 0.6510 | 0.7985 | 0.7611 | 0.7318 | 0.8271 | 0.8028 | 0.7185 |
| 0.0723 | 34.0 | 340 | 0.1557 | 0.6945 | 0.7807 | 0.7881 | nan | 0.8142 | 0.7941 | 0.8237 | 0.7511 | 0.7073 | 0.8734 | 0.8676 | 0.8159 | 0.8608 | 0.7947 | 0.8292 | 0.7155 | 0.7700 | 0.7827 | 0.8070 | 0.7627 | 0.6978 | 0.7275 | 0.8387 | 0.7683 | 0.7554 | 0.7509 | 0.8002 | 0.6639 | 0.7231 | 0.7017 | 0.8350 | 0.7678 | 0.7631 | 0.8388 | 0.8231 | 0.7584 | 0.0 | 0.7804 | 0.7599 | 0.7532 | 0.6917 | 0.6949 | 0.8422 | 0.8220 | 0.7555 | 0.7997 | 0.7583 | 0.7800 | 0.6642 | 0.6893 | 0.7639 | 0.7098 | 0.6723 | 0.6146 | 0.6639 | 0.7536 | 0.6897 | 0.6526 | 0.6674 | 0.7148 | 0.5882 | 0.5982 | 0.6309 | 0.7618 | 0.6946 | 0.6893 | 0.7850 | 0.7720 | 0.7044 |
| 0.0698 | 36.0 | 360 | 0.1523 | 0.7141 | 0.8005 | 0.8103 | nan | 0.8578 | 0.8162 | 0.8537 | 0.8157 | 0.7768 | 0.8899 | 0.8776 | 0.7987 | 0.8764 | 0.8345 | 0.8257 | 0.7107 | 0.8293 | 0.8590 | 0.8323 | 0.6399 | 0.6852 | 0.7657 | 0.8407 | 0.8026 | 0.7116 | 0.7590 | 0.8231 | 0.7489 | 0.6981 | 0.7221 | 0.8288 | 0.8048 | 0.8054 | 0.8520 | 0.8476 | 0.8261 | 0.0 | 0.8173 | 0.7990 | 0.8041 | 0.7614 | 0.7533 | 0.8548 | 0.8267 | 0.7320 | 0.8152 | 0.7863 | 0.7887 | 0.6712 | 0.7290 | 0.8003 | 0.6995 | 0.5925 | 0.6193 | 0.6904 | 0.7656 | 0.7110 | 0.6409 | 0.6690 | 0.7318 | 0.6404 | 0.5937 | 0.6535 | 0.7776 | 0.7466 | 0.7242 | 0.8047 | 0.8067 | 0.7573 |
| 0.0634 | 38.0 | 380 | 0.1554 | 0.7284 | 0.8209 | 0.8313 | nan | 0.8666 | 0.8745 | 0.8763 | 0.8678 | 0.8072 | 0.9112 | 0.9292 | 0.6655 | 0.8992 | 0.8717 | 0.8572 | 0.7633 | 0.8456 | 0.8279 | 0.8630 | 0.7138 | 0.7027 | 0.8109 | 0.8767 | 0.8415 | 0.7641 | 0.7846 | 0.8168 | 0.7206 | 0.7123 | 0.7586 | 0.8802 | 0.8160 | 0.8257 | 0.8813 | 0.8567 | 0.7810 | 0.0 | 0.8428 | 0.8406 | 0.8132 | 0.7981 | 0.7843 | 0.8678 | 0.8113 | 0.6434 | 0.8351 | 0.8149 | 0.8119 | 0.7135 | 0.7373 | 0.7971 | 0.7440 | 0.6618 | 0.6258 | 0.7111 | 0.8028 | 0.7348 | 0.6718 | 0.6903 | 0.7266 | 0.6194 | 0.6041 | 0.6767 | 0.8105 | 0.7543 | 0.7418 | 0.8154 | 0.8005 | 0.7354 |
| 0.0713 | 40.0 | 400 | 0.1525 | 0.7218 | 0.8123 | 0.8201 | nan | 0.8704 | 0.8448 | 0.8440 | 0.8238 | 0.7767 | 0.8975 | 0.8755 | 0.7267 | 0.8803 | 0.8616 | 0.8490 | 0.7422 | 0.7974 | 0.8279 | 0.8353 | 0.7166 | 0.7667 | 0.7983 | 0.8684 | 0.8377 | 0.7420 | 0.7794 | 0.8101 | 0.7151 | 0.7616 | 0.7408 | 0.8664 | 0.8062 | 0.8180 | 0.8802 | 0.8365 | 0.7950 | 0.0 | 0.8312 | 0.8095 | 0.7830 | 0.7623 | 0.7541 | 0.8481 | 0.8065 | 0.6990 | 0.8168 | 0.8020 | 0.7944 | 0.6918 | 0.7074 | 0.7924 | 0.7342 | 0.6590 | 0.6746 | 0.7122 | 0.7795 | 0.7377 | 0.6698 | 0.6866 | 0.7207 | 0.6159 | 0.6364 | 0.6663 | 0.7890 | 0.7472 | 0.7391 | 0.8140 | 0.7936 | 0.7434 |
| 0.0643 | 42.0 | 420 | 0.1553 | 0.7148 | 0.8042 | 0.8125 | nan | 0.8499 | 0.8216 | 0.8582 | 0.8145 | 0.7421 | 0.9148 | 0.9039 | 0.7032 | 0.8476 | 0.8359 | 0.8361 | 0.6862 | 0.8163 | 0.8320 | 0.8711 | 0.6857 | 0.7295 | 0.7706 | 0.8683 | 0.8590 | 0.7374 | 0.7249 | 0.7984 | 0.7706 | 0.7609 | 0.7409 | 0.8611 | 0.8164 | 0.8064 | 0.8565 | 0.8488 | 0.7650 | 0.0 | 0.8118 | 0.7943 | 0.7992 | 0.7484 | 0.7254 | 0.8550 | 0.8101 | 0.6735 | 0.7970 | 0.7858 | 0.7818 | 0.6519 | 0.7299 | 0.7955 | 0.7341 | 0.6359 | 0.6543 | 0.7031 | 0.7862 | 0.7336 | 0.6665 | 0.6664 | 0.7158 | 0.6325 | 0.6354 | 0.6694 | 0.7977 | 0.7527 | 0.7212 | 0.8083 | 0.8006 | 0.7150 |
| 0.0736 | 44.0 | 440 | 0.1546 | 0.7082 | 0.7925 | 0.8019 | nan | 0.8349 | 0.8097 | 0.8161 | 0.7697 | 0.7851 | 0.8876 | 0.8887 | 0.7442 | 0.8519 | 0.8535 | 0.8236 | 0.7030 | 0.8141 | 0.8471 | 0.8394 | 0.6563 | 0.7174 | 0.7590 | 0.8509 | 0.8080 | 0.7455 | 0.7675 | 0.7895 | 0.7165 | 0.6985 | 0.7191 | 0.8403 | 0.7541 | 0.7865 | 0.8846 | 0.8307 | 0.7672 | 0.0 | 0.7948 | 0.7605 | 0.7549 | 0.7271 | 0.7573 | 0.8537 | 0.8262 | 0.7135 | 0.7982 | 0.7927 | 0.7788 | 0.6656 | 0.7290 | 0.8111 | 0.7311 | 0.6179 | 0.6431 | 0.6932 | 0.7715 | 0.7216 | 0.6715 | 0.6895 | 0.7092 | 0.5991 | 0.5982 | 0.6453 | 0.7688 | 0.7157 | 0.7171 | 0.8114 | 0.7870 | 0.7169 |
| 0.061 | 46.0 | 460 | 0.1509 | 0.7221 | 0.8067 | 0.8162 | nan | 0.8450 | 0.8356 | 0.8496 | 0.7940 | 0.7847 | 0.8876 | 0.9201 | 0.7856 | 0.8733 | 0.8131 | 0.8391 | 0.7294 | 0.8558 | 0.8497 | 0.8405 | 0.7322 | 0.6868 | 0.7573 | 0.8596 | 0.7881 | 0.7321 | 0.7653 | 0.8078 | 0.7268 | 0.6762 | 0.7257 | 0.8676 | 0.8073 | 0.8238 | 0.8874 | 0.8516 | 0.8170 | 0.0 | 0.8206 | 0.8087 | 0.8025 | 0.7576 | 0.7481 | 0.8499 | 0.8369 | 0.7417 | 0.8124 | 0.7749 | 0.7963 | 0.6909 | 0.7448 | 0.8059 | 0.7367 | 0.6721 | 0.6221 | 0.6917 | 0.7695 | 0.7114 | 0.6621 | 0.6866 | 0.7227 | 0.6101 | 0.5882 | 0.6541 | 0.8083 | 0.7590 | 0.7457 | 0.8294 | 0.8115 | 0.7571 |
| 0.0536 | 48.0 | 480 | 0.1521 | 0.7308 | 0.8173 | 0.8245 | nan | 0.8590 | 0.8676 | 0.8617 | 0.8409 | 0.8160 | 0.9215 | 0.8884 | 0.7822 | 0.8956 | 0.8719 | 0.8377 | 0.7602 | 0.8218 | 0.8316 | 0.8560 | 0.7428 | 0.7031 | 0.7863 | 0.8546 | 0.8231 | 0.7696 | 0.7685 | 0.8087 | 0.7229 | 0.7263 | 0.7567 | 0.8379 | 0.8288 | 0.8430 | 0.8626 | 0.8406 | 0.7670 | 0.0 | 0.8344 | 0.8360 | 0.8057 | 0.7941 | 0.7925 | 0.8777 | 0.8388 | 0.7420 | 0.8293 | 0.7991 | 0.7835 | 0.7108 | 0.7494 | 0.8075 | 0.7551 | 0.6756 | 0.6385 | 0.7085 | 0.7825 | 0.7440 | 0.6904 | 0.6901 | 0.7235 | 0.6201 | 0.6148 | 0.6691 | 0.7887 | 0.7640 | 0.7343 | 0.8044 | 0.7917 | 0.7213 |
| 0.076 | 50.0 | 500 | 0.1520 | 0.7336 | 0.8204 | 0.8296 | nan | 0.8641 | 0.8672 | 0.8854 | 0.8506 | 0.8172 | 0.8983 | 0.9032 | 0.7429 | 0.8765 | 0.8368 | 0.8308 | 0.7443 | 0.8053 | 0.8632 | 0.8471 | 0.7114 | 0.7605 | 0.7863 | 0.8809 | 0.8453 | 0.7561 | 0.7871 | 0.8335 | 0.7037 | 0.7512 | 0.7384 | 0.8678 | 0.8357 | 0.7978 | 0.8895 | 0.8628 | 0.8111 | 0.0 | 0.8336 | 0.8387 | 0.8238 | 0.7964 | 0.7937 | 0.8658 | 0.8315 | 0.7116 | 0.8165 | 0.7868 | 0.7900 | 0.6980 | 0.7288 | 0.8131 | 0.7442 | 0.6538 | 0.6699 | 0.7143 | 0.7932 | 0.7474 | 0.6859 | 0.7028 | 0.7366 | 0.6159 | 0.6315 | 0.6707 | 0.8037 | 0.7708 | 0.7340 | 0.8311 | 0.8219 | 0.7517 |
| 0.0562 | 52.0 | 520 | 0.1628 | 0.7150 | 0.8054 | 0.8084 | nan | 0.8622 | 0.8430 | 0.8413 | 0.8382 | 0.8163 | 0.8398 | 0.7818 | 0.8451 | 0.8844 | 0.8439 | 0.8295 | 0.6901 | 0.7747 | 0.8257 | 0.8079 | 0.7877 | 0.7749 | 0.7851 | 0.8438 | 0.8018 | 0.7190 | 0.7503 | 0.7810 | 0.7472 | 0.7554 | 0.7455 | 0.8367 | 0.7893 | 0.8200 | 0.8742 | 0.8448 | 0.7913 | 0.0 | 0.8255 | 0.8175 | 0.8066 | 0.7834 | 0.7810 | 0.8205 | 0.7279 | 0.6485 | 0.8254 | 0.8013 | 0.7820 | 0.6641 | 0.7145 | 0.7787 | 0.7157 | 0.6751 | 0.6732 | 0.7057 | 0.7626 | 0.7167 | 0.6539 | 0.6710 | 0.7056 | 0.6244 | 0.6314 | 0.6625 | 0.7731 | 0.7407 | 0.7382 | 0.8232 | 0.8138 | 0.7333 |
| 0.0682 | 54.0 | 540 | 0.1544 | 0.7273 | 0.8147 | 0.8239 | nan | 0.8486 | 0.8597 | 0.8338 | 0.8060 | 0.7833 | 0.9164 | 0.8783 | 0.6689 | 0.8977 | 0.8613 | 0.8336 | 0.7496 | 0.8304 | 0.8650 | 0.8593 | 0.6824 | 0.7622 | 0.7868 | 0.8478 | 0.8315 | 0.7794 | 0.7738 | 0.8061 | 0.7577 | 0.7475 | 0.7457 | 0.8716 | 0.8245 | 0.8340 | 0.8787 | 0.8517 | 0.7978 | 0.0 | 0.8216 | 0.8116 | 0.7853 | 0.7635 | 0.7660 | 0.8542 | 0.7961 | 0.6473 | 0.8352 | 0.8144 | 0.7992 | 0.7083 | 0.7510 | 0.8180 | 0.7399 | 0.6359 | 0.6697 | 0.7070 | 0.7719 | 0.7462 | 0.6891 | 0.6863 | 0.7279 | 0.6595 | 0.6366 | 0.6769 | 0.8054 | 0.7596 | 0.7396 | 0.8220 | 0.8085 | 0.7471 |
| 0.0478 | 56.0 | 560 | 0.1564 | 0.7253 | 0.8132 | 0.8223 | nan | 0.8486 | 0.8530 | 0.8762 | 0.8190 | 0.7620 | 0.9052 | 0.8812 | 0.6975 | 0.9016 | 0.8451 | 0.8575 | 0.7538 | 0.8509 | 0.8625 | 0.8264 | 0.7102 | 0.7573 | 0.7668 | 0.8688 | 0.8337 | 0.7683 | 0.7906 | 0.7989 | 0.7494 | 0.7500 | 0.7366 | 0.8623 | 0.7946 | 0.7657 | 0.8953 | 0.8399 | 0.7950 | 0.0 | 0.8231 | 0.8223 | 0.8105 | 0.7623 | 0.7461 | 0.8610 | 0.8095 | 0.6731 | 0.8305 | 0.8006 | 0.8120 | 0.7162 | 0.7677 | 0.8179 | 0.7354 | 0.6530 | 0.6614 | 0.7003 | 0.7871 | 0.7482 | 0.6905 | 0.6961 | 0.7200 | 0.6325 | 0.6232 | 0.6626 | 0.7738 | 0.7204 | 0.7057 | 0.8244 | 0.8010 | 0.7470 |
| 0.0512 | 58.0 | 580 | 0.1546 | 0.7298 | 0.8150 | 0.8235 | nan | 0.8586 | 0.8815 | 0.8680 | 0.8305 | 0.8242 | 0.9193 | 0.8879 | 0.7123 | 0.8771 | 0.8630 | 0.8356 | 0.7117 | 0.8269 | 0.8482 | 0.8489 | 0.6943 | 0.7425 | 0.7987 | 0.8770 | 0.8386 | 0.7397 | 0.7332 | 0.8208 | 0.7140 | 0.7405 | 0.7508 | 0.8443 | 0.8103 | 0.8435 | 0.8862 | 0.8513 | 0.7999 | 0.0 | 0.8338 | 0.8388 | 0.8168 | 0.7895 | 0.7902 | 0.8661 | 0.8277 | 0.6841 | 0.8246 | 0.8068 | 0.7864 | 0.6815 | 0.7506 | 0.8171 | 0.7428 | 0.6401 | 0.6576 | 0.7185 | 0.7898 | 0.7393 | 0.6714 | 0.6679 | 0.7291 | 0.6156 | 0.6306 | 0.6758 | 0.7821 | 0.7577 | 0.7551 | 0.8339 | 0.8141 | 0.7489 |
| 0.0509 | 60.0 | 600 | 0.1545 | 0.7353 | 0.8220 | 0.8302 | nan | 0.8592 | 0.8720 | 0.8776 | 0.8462 | 0.8195 | 0.9220 | 0.8871 | 0.7440 | 0.8924 | 0.8589 | 0.8449 | 0.7433 | 0.8409 | 0.8667 | 0.8295 | 0.7048 | 0.7500 | 0.7998 | 0.8735 | 0.8245 | 0.7994 | 0.7692 | 0.8092 | 0.7337 | 0.7343 | 0.7453 | 0.8745 | 0.7983 | 0.8337 | 0.8995 | 0.8459 | 0.8055 | 0.0 | 0.8356 | 0.8372 | 0.8225 | 0.7993 | 0.7965 | 0.8741 | 0.8249 | 0.7122 | 0.8357 | 0.8147 | 0.8049 | 0.7064 | 0.7649 | 0.8106 | 0.7248 | 0.6528 | 0.6648 | 0.7211 | 0.7911 | 0.7476 | 0.7076 | 0.6945 | 0.7237 | 0.6179 | 0.6177 | 0.6697 | 0.7939 | 0.7466 | 0.7526 | 0.8392 | 0.8083 | 0.7503 |
| 0.0435 | 62.0 | 620 | 0.1551 | 0.7266 | 0.8095 | 0.8188 | nan | 0.8571 | 0.8378 | 0.8806 | 0.8244 | 0.7731 | 0.8974 | 0.8916 | 0.7253 | 0.8926 | 0.8779 | 0.8292 | 0.7394 | 0.8426 | 0.8510 | 0.8405 | 0.6897 | 0.7349 | 0.7807 | 0.8512 | 0.8099 | 0.7835 | 0.7475 | 0.8269 | 0.7194 | 0.7203 | 0.7187 | 0.8435 | 0.7898 | 0.8434 | 0.8713 | 0.8395 | 0.7721 | 0.0 | 0.8293 | 0.8170 | 0.8173 | 0.7700 | 0.7571 | 0.8613 | 0.8173 | 0.6964 | 0.8389 | 0.8144 | 0.7866 | 0.7000 | 0.7620 | 0.8155 | 0.7395 | 0.6413 | 0.6535 | 0.7092 | 0.7790 | 0.7303 | 0.6927 | 0.6821 | 0.7387 | 0.6235 | 0.6178 | 0.6549 | 0.7860 | 0.7458 | 0.7499 | 0.8195 | 0.8017 | 0.7275 |
| 0.05 | 64.0 | 640 | 0.1608 | 0.7221 | 0.8067 | 0.8152 | nan | 0.8488 | 0.8286 | 0.8781 | 0.7988 | 0.7907 | 0.9218 | 0.8732 | 0.7222 | 0.8763 | 0.8504 | 0.8118 | 0.7154 | 0.7931 | 0.8343 | 0.8289 | 0.6667 | 0.7748 | 0.7844 | 0.8624 | 0.8260 | 0.7703 | 0.7547 | 0.8016 | 0.7299 | 0.7529 | 0.7077 | 0.8586 | 0.8032 | 0.8272 | 0.8605 | 0.8591 | 0.8017 | 0.0 | 0.8249 | 0.8068 | 0.8109 | 0.7560 | 0.7735 | 0.8627 | 0.8140 | 0.6892 | 0.8176 | 0.7994 | 0.7727 | 0.6770 | 0.7346 | 0.8053 | 0.7320 | 0.6090 | 0.6701 | 0.7123 | 0.7792 | 0.7336 | 0.6902 | 0.6858 | 0.7224 | 0.6134 | 0.6299 | 0.6523 | 0.7845 | 0.7433 | 0.7416 | 0.8153 | 0.8225 | 0.7489 |
| 0.0459 | 66.0 | 660 | 0.1576 | 0.7261 | 0.8120 | 0.8214 | nan | 0.8631 | 0.8474 | 0.8776 | 0.7995 | 0.8026 | 0.9172 | 0.8583 | 0.6941 | 0.8725 | 0.8579 | 0.8357 | 0.6982 | 0.8411 | 0.8560 | 0.8450 | 0.6744 | 0.7390 | 0.7989 | 0.8662 | 0.8403 | 0.7824 | 0.7822 | 0.8095 | 0.7108 | 0.7288 | 0.7710 | 0.8529 | 0.8120 | 0.8185 | 0.8926 | 0.8564 | 0.7811 | 0.0 | 0.8341 | 0.8175 | 0.8080 | 0.7581 | 0.7869 | 0.8494 | 0.7878 | 0.6688 | 0.8230 | 0.8118 | 0.7919 | 0.6771 | 0.7617 | 0.8151 | 0.7318 | 0.6268 | 0.6483 | 0.7184 | 0.7954 | 0.7654 | 0.7075 | 0.6965 | 0.7241 | 0.6160 | 0.6164 | 0.6757 | 0.7775 | 0.7471 | 0.7440 | 0.8312 | 0.8088 | 0.7404 |
| 0.0439 | 68.0 | 680 | 0.1598 | 0.7224 | 0.8018 | 0.8101 | nan | 0.8218 | 0.8233 | 0.8531 | 0.8371 | 0.7939 | 0.9067 | 0.8828 | 0.7340 | 0.8563 | 0.8277 | 0.8418 | 0.7215 | 0.8087 | 0.8318 | 0.8512 | 0.7260 | 0.7475 | 0.7617 | 0.8591 | 0.8351 | 0.7281 | 0.7451 | 0.8040 | 0.6741 | 0.7338 | 0.7377 | 0.8337 | 0.7935 | 0.7990 | 0.8600 | 0.8537 | 0.7724 | 0.0 | 0.8019 | 0.7945 | 0.8109 | 0.7917 | 0.7781 | 0.8646 | 0.8215 | 0.7020 | 0.8158 | 0.7960 | 0.7933 | 0.6862 | 0.7495 | 0.8034 | 0.7442 | 0.6638 | 0.6640 | 0.7041 | 0.7812 | 0.7349 | 0.6623 | 0.6755 | 0.7208 | 0.5887 | 0.6181 | 0.6632 | 0.7688 | 0.7450 | 0.7438 | 0.8133 | 0.8124 | 0.7250 |
| 0.0429 | 70.0 | 700 | 0.1567 | 0.7318 | 0.8169 | 0.8234 | nan | 0.8528 | 0.8642 | 0.8606 | 0.8186 | 0.7842 | 0.9068 | 0.8902 | 0.7569 | 0.8653 | 0.8556 | 0.8600 | 0.7249 | 0.8386 | 0.8292 | 0.8274 | 0.7651 | 0.7225 | 0.7881 | 0.8830 | 0.8518 | 0.7644 | 0.7596 | 0.8094 | 0.7457 | 0.7419 | 0.7706 | 0.8486 | 0.8048 | 0.8377 | 0.8595 | 0.8543 | 0.7989 | 0.0 | 0.8324 | 0.8198 | 0.7989 | 0.7664 | 0.7683 | 0.8598 | 0.8278 | 0.7256 | 0.8254 | 0.8086 | 0.8097 | 0.6964 | 0.7582 | 0.7994 | 0.7423 | 0.6893 | 0.6474 | 0.7181 | 0.8038 | 0.7661 | 0.6991 | 0.6877 | 0.7223 | 0.6216 | 0.6318 | 0.6820 | 0.7805 | 0.7464 | 0.7383 | 0.8097 | 0.8156 | 0.7518 |
| 0.0451 | 72.0 | 720 | 0.1599 | 0.7259 | 0.8108 | 0.8180 | nan | 0.8575 | 0.8445 | 0.8569 | 0.7949 | 0.7508 | 0.9182 | 0.8857 | 0.7585 | 0.8817 | 0.8459 | 0.8278 | 0.7283 | 0.8307 | 0.8325 | 0.8339 | 0.7285 | 0.7605 | 0.7848 | 0.8750 | 0.8350 | 0.7597 | 0.7324 | 0.8046 | 0.7325 | 0.7270 | 0.7560 | 0.8507 | 0.8074 | 0.8130 | 0.8853 | 0.8430 | 0.8029 | 0.0 | 0.8328 | 0.8076 | 0.7840 | 0.7446 | 0.7429 | 0.8682 | 0.8291 | 0.7225 | 0.8281 | 0.8035 | 0.7867 | 0.6942 | 0.7558 | 0.8016 | 0.7417 | 0.6661 | 0.6629 | 0.7118 | 0.7904 | 0.7454 | 0.6875 | 0.6717 | 0.7177 | 0.6107 | 0.6183 | 0.6711 | 0.7747 | 0.7439 | 0.7470 | 0.8309 | 0.8111 | 0.7506 |
| 0.0438 | 74.0 | 740 | 0.1589 | 0.7383 | 0.8252 | 0.8335 | nan | 0.8535 | 0.8510 | 0.8886 | 0.7967 | 0.8137 | 0.9233 | 0.8862 | 0.7682 | 0.8849 | 0.8755 | 0.8550 | 0.7480 | 0.8585 | 0.8498 | 0.8561 | 0.7181 | 0.7650 | 0.7855 | 0.8884 | 0.8541 | 0.7811 | 0.7735 | 0.8343 | 0.6991 | 0.7570 | 0.7538 | 0.8659 | 0.8262 | 0.8308 | 0.9056 | 0.8575 | 0.8012 | 0.0 | 0.8326 | 0.8242 | 0.8009 | 0.7576 | 0.7948 | 0.8759 | 0.8293 | 0.7336 | 0.8340 | 0.8217 | 0.8060 | 0.7140 | 0.7731 | 0.8162 | 0.7444 | 0.6609 | 0.6723 | 0.7180 | 0.7997 | 0.7656 | 0.7070 | 0.6972 | 0.7367 | 0.6091 | 0.6385 | 0.6746 | 0.7887 | 0.7608 | 0.7612 | 0.8432 | 0.8193 | 0.7545 |
| 0.0458 | 76.0 | 760 | 0.1601 | 0.7269 | 0.8121 | 0.8214 | nan | 0.8496 | 0.8397 | 0.8527 | 0.8475 | 0.7941 | 0.9220 | 0.8976 | 0.7672 | 0.8544 | 0.8667 | 0.8357 | 0.7116 | 0.8245 | 0.8729 | 0.8328 | 0.6342 | 0.7355 | 0.7903 | 0.8746 | 0.8412 | 0.7663 | 0.7404 | 0.8199 | 0.6719 | 0.7317 | 0.7733 | 0.8619 | 0.8197 | 0.8283 | 0.8852 | 0.8562 | 0.7875 | 0.0 | 0.8262 | 0.8090 | 0.8018 | 0.7990 | 0.7828 | 0.8765 | 0.8352 | 0.7289 | 0.8138 | 0.8143 | 0.7966 | 0.6834 | 0.7534 | 0.8096 | 0.7147 | 0.5933 | 0.6504 | 0.7155 | 0.7886 | 0.7423 | 0.6852 | 0.6693 | 0.7205 | 0.5887 | 0.6236 | 0.6879 | 0.7929 | 0.7577 | 0.7455 | 0.8230 | 0.8136 | 0.7431 |
| 0.0465 | 78.0 | 780 | 0.1574 | 0.7369 | 0.8213 | 0.8291 | nan | 0.8351 | 0.8481 | 0.8662 | 0.8399 | 0.8210 | 0.9261 | 0.8788 | 0.7511 | 0.8876 | 0.8659 | 0.8343 | 0.7471 | 0.8311 | 0.8514 | 0.8520 | 0.7376 | 0.7532 | 0.8092 | 0.8687 | 0.8439 | 0.7719 | 0.7718 | 0.8254 | 0.7229 | 0.7217 | 0.7521 | 0.8460 | 0.8123 | 0.8303 | 0.8966 | 0.8571 | 0.8238 | 0.0 | 0.8189 | 0.8116 | 0.8060 | 0.7959 | 0.7975 | 0.8742 | 0.8205 | 0.7198 | 0.8357 | 0.8136 | 0.7898 | 0.7078 | 0.7645 | 0.8182 | 0.7533 | 0.6787 | 0.6575 | 0.7309 | 0.7971 | 0.7619 | 0.7030 | 0.6908 | 0.7315 | 0.6232 | 0.6156 | 0.6728 | 0.7805 | 0.7588 | 0.7594 | 0.8367 | 0.8209 | 0.7706 |
| 0.0472 | 80.0 | 800 | 0.1581 | 0.7398 | 0.8240 | 0.8319 | nan | 0.8523 | 0.8478 | 0.8569 | 0.8497 | 0.8148 | 0.9077 | 0.9090 | 0.7707 | 0.8890 | 0.8710 | 0.8457 | 0.7326 | 0.8413 | 0.8534 | 0.8573 | 0.7417 | 0.7604 | 0.7896 | 0.8915 | 0.8587 | 0.7877 | 0.7637 | 0.8249 | 0.7050 | 0.7075 | 0.7400 | 0.8388 | 0.8288 | 0.8473 | 0.9037 | 0.8491 | 0.8298 | 0.0 | 0.8314 | 0.8178 | 0.8091 | 0.8070 | 0.7953 | 0.8692 | 0.8361 | 0.7350 | 0.8355 | 0.8235 | 0.7971 | 0.6969 | 0.7691 | 0.8213 | 0.7577 | 0.6832 | 0.6606 | 0.7202 | 0.8031 | 0.7647 | 0.7041 | 0.6895 | 0.7314 | 0.6169 | 0.6006 | 0.6666 | 0.7819 | 0.7754 | 0.7739 | 0.8431 | 0.8235 | 0.7715 |
| 0.0382 | 82.0 | 820 | 0.1604 | 0.7403 | 0.8266 | 0.8343 | nan | 0.8591 | 0.8582 | 0.8789 | 0.8152 | 0.8205 | 0.9297 | 0.8853 | 0.7376 | 0.9018 | 0.8919 | 0.8719 | 0.7329 | 0.8391 | 0.8647 | 0.8502 | 0.7493 | 0.7837 | 0.8071 | 0.8941 | 0.8678 | 0.7693 | 0.7661 | 0.8292 | 0.7238 | 0.7455 | 0.7454 | 0.8597 | 0.8130 | 0.8221 | 0.8851 | 0.8525 | 0.8015 | 0.0 | 0.8372 | 0.8320 | 0.8087 | 0.7724 | 0.7967 | 0.8729 | 0.8232 | 0.7083 | 0.8480 | 0.8377 | 0.8183 | 0.7049 | 0.7683 | 0.8204 | 0.7546 | 0.6896 | 0.6755 | 0.7341 | 0.8079 | 0.7696 | 0.7004 | 0.6932 | 0.7355 | 0.6306 | 0.6242 | 0.6749 | 0.7846 | 0.7499 | 0.7518 | 0.8345 | 0.8204 | 0.7512 |
| 0.0442 | 84.0 | 840 | 0.1602 | 0.7390 | 0.8225 | 0.8302 | nan | 0.8469 | 0.8636 | 0.8656 | 0.8214 | 0.8119 | 0.9160 | 0.8997 | 0.7620 | 0.8728 | 0.8538 | 0.8407 | 0.7200 | 0.8656 | 0.8623 | 0.8515 | 0.7622 | 0.7776 | 0.7777 | 0.8701 | 0.8395 | 0.7936 | 0.7658 | 0.8260 | 0.7419 | 0.7210 | 0.7438 | 0.8456 | 0.7999 | 0.8217 | 0.8964 | 0.8775 | 0.8064 | 0.0 | 0.8276 | 0.8309 | 0.8056 | 0.7837 | 0.7960 | 0.8768 | 0.8374 | 0.7284 | 0.8277 | 0.8097 | 0.7978 | 0.6942 | 0.7736 | 0.8200 | 0.7589 | 0.6973 | 0.6743 | 0.7147 | 0.7911 | 0.7617 | 0.7151 | 0.6981 | 0.7359 | 0.6366 | 0.6127 | 0.6653 | 0.7748 | 0.7463 | 0.7519 | 0.8417 | 0.8378 | 0.7623 |
| 0.045 | 86.0 | 860 | 0.1615 | 0.7351 | 0.8164 | 0.8236 | nan | 0.8602 | 0.8347 | 0.8650 | 0.8180 | 0.8123 | 0.9033 | 0.8974 | 0.7588 | 0.8922 | 0.8646 | 0.8608 | 0.7367 | 0.8179 | 0.8463 | 0.8300 | 0.7655 | 0.7742 | 0.7765 | 0.8641 | 0.8422 | 0.7663 | 0.7427 | 0.8051 | 0.7022 | 0.7413 | 0.7390 | 0.8461 | 0.8004 | 0.8476 | 0.8903 | 0.8514 | 0.7723 | 0.0 | 0.8395 | 0.8132 | 0.8107 | 0.7807 | 0.7913 | 0.8657 | 0.8293 | 0.7245 | 0.8426 | 0.8242 | 0.8086 | 0.7018 | 0.7566 | 0.8118 | 0.7474 | 0.6984 | 0.6687 | 0.7128 | 0.7916 | 0.7562 | 0.6928 | 0.6757 | 0.7181 | 0.6045 | 0.6275 | 0.6717 | 0.7856 | 0.7561 | 0.7628 | 0.8292 | 0.8143 | 0.7443 |
| 0.0372 | 88.0 | 880 | 0.1615 | 0.7345 | 0.8151 | 0.8228 | nan | 0.8501 | 0.8432 | 0.8601 | 0.8370 | 0.7936 | 0.9159 | 0.8956 | 0.7217 | 0.8874 | 0.8570 | 0.8584 | 0.7030 | 0.8326 | 0.8443 | 0.8394 | 0.7630 | 0.7447 | 0.8013 | 0.8724 | 0.8381 | 0.7680 | 0.7537 | 0.8095 | 0.7261 | 0.7406 | 0.7492 | 0.8459 | 0.7901 | 0.8240 | 0.8649 | 0.8693 | 0.7821 | 0.0 | 0.8303 | 0.8200 | 0.8122 | 0.7979 | 0.7812 | 0.8714 | 0.8228 | 0.6926 | 0.8368 | 0.8187 | 0.8106 | 0.6809 | 0.7613 | 0.8081 | 0.7474 | 0.6928 | 0.6641 | 0.7333 | 0.7950 | 0.7589 | 0.7026 | 0.6846 | 0.7242 | 0.6239 | 0.6259 | 0.6749 | 0.7725 | 0.7395 | 0.7564 | 0.8177 | 0.8299 | 0.7489 |
| 0.0374 | 90.0 | 900 | 0.1635 | 0.7259 | 0.8057 | 0.8148 | nan | 0.8591 | 0.8400 | 0.8580 | 0.7711 | 0.8184 | 0.9109 | 0.8792 | 0.7200 | 0.8711 | 0.8568 | 0.8521 | 0.7213 | 0.8264 | 0.8453 | 0.8146 | 0.7050 | 0.7370 | 0.7954 | 0.8592 | 0.8366 | 0.7650 | 0.7493 | 0.8102 | 0.6756 | 0.6970 | 0.7472 | 0.8469 | 0.7833 | 0.8377 | 0.8697 | 0.8496 | 0.7751 | 0.0 | 0.8369 | 0.8173 | 0.7969 | 0.7483 | 0.7888 | 0.8639 | 0.8139 | 0.6916 | 0.8248 | 0.8155 | 0.8037 | 0.6921 | 0.7614 | 0.8073 | 0.7249 | 0.6526 | 0.6468 | 0.7254 | 0.7886 | 0.7558 | 0.7013 | 0.6802 | 0.7164 | 0.5877 | 0.6011 | 0.6717 | 0.7772 | 0.7363 | 0.7569 | 0.8189 | 0.8122 | 0.7389 |
| 0.0384 | 92.0 | 920 | 0.1662 | 0.7294 | 0.8119 | 0.8203 | nan | 0.8282 | 0.8271 | 0.8567 | 0.8378 | 0.7946 | 0.9053 | 0.8808 | 0.7401 | 0.8701 | 0.8589 | 0.8436 | 0.7213 | 0.8308 | 0.8552 | 0.8439 | 0.6911 | 0.7356 | 0.7934 | 0.8829 | 0.8517 | 0.7629 | 0.7474 | 0.8203 | 0.7053 | 0.7211 | 0.7537 | 0.8616 | 0.8006 | 0.8185 | 0.8969 | 0.8312 | 0.8113 | 0.0 | 0.8096 | 0.8029 | 0.8050 | 0.7889 | 0.7801 | 0.8595 | 0.8151 | 0.7064 | 0.8158 | 0.8083 | 0.7966 | 0.6928 | 0.7656 | 0.8169 | 0.7399 | 0.6401 | 0.6575 | 0.7283 | 0.7965 | 0.7560 | 0.6938 | 0.6824 | 0.7235 | 0.6078 | 0.6164 | 0.6815 | 0.7879 | 0.7477 | 0.7537 | 0.8352 | 0.8107 | 0.7488 |
| 0.0438 | 94.0 | 940 | 0.1668 | 0.7331 | 0.8169 | 0.8256 | nan | 0.8588 | 0.8564 | 0.8736 | 0.8234 | 0.8153 | 0.9055 | 0.8821 | 0.7226 | 0.8809 | 0.8822 | 0.8553 | 0.7457 | 0.8471 | 0.8471 | 0.8523 | 0.7147 | 0.7399 | 0.7916 | 0.8845 | 0.8448 | 0.7651 | 0.7410 | 0.8162 | 0.6957 | 0.7298 | 0.7561 | 0.8573 | 0.8067 | 0.8185 | 0.8912 | 0.8541 | 0.7861 | 0.0 | 0.8350 | 0.8294 | 0.8091 | 0.7862 | 0.7920 | 0.8529 | 0.8077 | 0.6923 | 0.8300 | 0.8309 | 0.8144 | 0.7136 | 0.7743 | 0.8169 | 0.7471 | 0.6616 | 0.6505 | 0.7217 | 0.7928 | 0.7604 | 0.7004 | 0.6766 | 0.7234 | 0.6043 | 0.6203 | 0.6742 | 0.7783 | 0.7471 | 0.7520 | 0.8373 | 0.8209 | 0.7389 |
| 0.036 | 96.0 | 960 | 0.1674 | 0.7379 | 0.8217 | 0.8288 | nan | 0.8562 | 0.8515 | 0.8737 | 0.8185 | 0.8144 | 0.9195 | 0.8892 | 0.7625 | 0.8920 | 0.8553 | 0.8526 | 0.7277 | 0.8312 | 0.8595 | 0.8382 | 0.7206 | 0.7780 | 0.8313 | 0.8830 | 0.8468 | 0.7676 | 0.7622 | 0.8153 | 0.7412 | 0.7288 | 0.7703 | 0.8552 | 0.8082 | 0.8225 | 0.8717 | 0.8556 | 0.7941 | 0.0 | 0.8298 | 0.8227 | 0.8102 | 0.7819 | 0.7973 | 0.8743 | 0.8283 | 0.7266 | 0.8337 | 0.8152 | 0.8044 | 0.6983 | 0.7631 | 0.8144 | 0.7376 | 0.6675 | 0.6867 | 0.7528 | 0.8016 | 0.7655 | 0.7023 | 0.6916 | 0.7313 | 0.6315 | 0.6264 | 0.6847 | 0.7747 | 0.7466 | 0.7519 | 0.8261 | 0.8239 | 0.7469 |
| 0.0336 | 98.0 | 980 | 0.1666 | 0.7321 | 0.8133 | 0.8210 | nan | 0.8512 | 0.8465 | 0.8526 | 0.8285 | 0.8063 | 0.9100 | 0.8760 | 0.7369 | 0.8817 | 0.8537 | 0.8466 | 0.7324 | 0.8159 | 0.8514 | 0.8478 | 0.7347 | 0.7401 | 0.7874 | 0.8621 | 0.8562 | 0.7723 | 0.7436 | 0.8167 | 0.7125 | 0.7452 | 0.7370 | 0.8484 | 0.7910 | 0.8418 | 0.8693 | 0.8446 | 0.7867 | 0.0 | 0.8303 | 0.8223 | 0.8027 | 0.7934 | 0.7914 | 0.8688 | 0.8148 | 0.7023 | 0.8320 | 0.8147 | 0.8049 | 0.6995 | 0.7540 | 0.8128 | 0.7473 | 0.6752 | 0.6575 | 0.7213 | 0.7858 | 0.7661 | 0.6974 | 0.6766 | 0.7266 | 0.6161 | 0.6280 | 0.6708 | 0.7772 | 0.7430 | 0.7540 | 0.8177 | 0.8117 | 0.7435 |
| 0.0402 | 100.0 | 1000 | 0.1703 | 0.7283 | 0.8083 | 0.8167 | nan | 0.8417 | 0.8270 | 0.8618 | 0.8060 | 0.7990 | 0.9187 | 0.8680 | 0.7391 | 0.8604 | 0.8639 | 0.8454 | 0.7151 | 0.8381 | 0.8506 | 0.8240 | 0.7009 | 0.7356 | 0.7958 | 0.8773 | 0.8330 | 0.7522 | 0.7655 | 0.8111 | 0.6956 | 0.7258 | 0.7469 | 0.8437 | 0.7864 | 0.8300 | 0.8780 | 0.8506 | 0.7773 | 0.0 | 0.8190 | 0.8057 | 0.8047 | 0.7723 | 0.7831 | 0.8695 | 0.8148 | 0.7016 | 0.8134 | 0.8207 | 0.7982 | 0.6919 | 0.7658 | 0.8089 | 0.7312 | 0.6512 | 0.6577 | 0.7291 | 0.7901 | 0.7548 | 0.6958 | 0.6875 | 0.7229 | 0.6040 | 0.6209 | 0.6727 | 0.7701 | 0.7373 | 0.7581 | 0.8238 | 0.8180 | 0.7404 |
| 0.0347 | 102.0 | 1020 | 0.1680 | 0.7300 | 0.8095 | 0.8185 | nan | 0.8335 | 0.8526 | 0.8599 | 0.8135 | 0.8154 | 0.9168 | 0.8851 | 0.7336 | 0.8708 | 0.8607 | 0.8366 | 0.7145 | 0.8258 | 0.8478 | 0.8360 | 0.7021 | 0.7329 | 0.7911 | 0.8575 | 0.8391 | 0.7793 | 0.7499 | 0.8254 | 0.7139 | 0.6868 | 0.7281 | 0.8375 | 0.7948 | 0.8374 | 0.8808 | 0.8540 | 0.7907 | 0.0 | 0.8145 | 0.8159 | 0.8009 | 0.7799 | 0.7974 | 0.8760 | 0.8206 | 0.6990 | 0.8228 | 0.8132 | 0.7939 | 0.6895 | 0.7628 | 0.8095 | 0.7383 | 0.6521 | 0.6488 | 0.7220 | 0.7855 | 0.7690 | 0.7114 | 0.6840 | 0.7336 | 0.6189 | 0.5949 | 0.6616 | 0.7669 | 0.7456 | 0.7633 | 0.8265 | 0.8223 | 0.7484 |
| 0.035 | 104.0 | 1040 | 0.1709 | 0.7282 | 0.8072 | 0.8153 | nan | 0.8385 | 0.8355 | 0.8511 | 0.8131 | 0.8032 | 0.9103 | 0.8779 | 0.7489 | 0.8680 | 0.8531 | 0.8395 | 0.7471 | 0.8207 | 0.8457 | 0.8215 | 0.7057 | 0.7264 | 0.7784 | 0.8638 | 0.8373 | 0.7677 | 0.7568 | 0.8065 | 0.7143 | 0.7195 | 0.7515 | 0.8402 | 0.7681 | 0.8156 | 0.8734 | 0.8441 | 0.7879 | 0.0 | 0.8147 | 0.8090 | 0.7994 | 0.7804 | 0.7863 | 0.8696 | 0.8223 | 0.7123 | 0.8177 | 0.8129 | 0.8034 | 0.7119 | 0.7568 | 0.8036 | 0.7286 | 0.6583 | 0.6486 | 0.7185 | 0.7887 | 0.7588 | 0.7049 | 0.6852 | 0.7208 | 0.6119 | 0.6146 | 0.6757 | 0.7664 | 0.7245 | 0.7467 | 0.8158 | 0.8122 | 0.7505 |
| 0.0335 | 106.0 | 1060 | 0.1689 | 0.7300 | 0.8104 | 0.8182 | nan | 0.8563 | 0.8375 | 0.8646 | 0.8213 | 0.8099 | 0.9055 | 0.8828 | 0.7513 | 0.8787 | 0.8536 | 0.8481 | 0.7179 | 0.8123 | 0.8572 | 0.7962 | 0.7255 | 0.7425 | 0.7871 | 0.8624 | 0.8377 | 0.7535 | 0.7514 | 0.8050 | 0.7108 | 0.7213 | 0.7394 | 0.8308 | 0.7975 | 0.8429 | 0.8777 | 0.8517 | 0.8019 | 0.0 | 0.8308 | 0.8139 | 0.8088 | 0.7867 | 0.7939 | 0.8679 | 0.8210 | 0.7128 | 0.8253 | 0.8151 | 0.7990 | 0.6890 | 0.7583 | 0.7943 | 0.7122 | 0.6754 | 0.6602 | 0.7267 | 0.7851 | 0.7517 | 0.6909 | 0.6781 | 0.7182 | 0.6179 | 0.6152 | 0.6650 | 0.7644 | 0.7454 | 0.7630 | 0.8260 | 0.8216 | 0.7548 |
| 0.029 | 108.0 | 1080 | 0.1700 | 0.7333 | 0.8136 | 0.8212 | nan | 0.8593 | 0.8467 | 0.8682 | 0.8181 | 0.8034 | 0.9091 | 0.8770 | 0.7556 | 0.8722 | 0.8573 | 0.8582 | 0.7310 | 0.8322 | 0.8397 | 0.8298 | 0.7147 | 0.7410 | 0.8034 | 0.8671 | 0.8498 | 0.7648 | 0.7557 | 0.8068 | 0.7033 | 0.7165 | 0.7594 | 0.8566 | 0.8019 | 0.8280 | 0.8694 | 0.8471 | 0.7909 | 0.0 | 0.8341 | 0.8231 | 0.8103 | 0.7854 | 0.7882 | 0.8689 | 0.8238 | 0.7129 | 0.8249 | 0.8184 | 0.8094 | 0.7041 | 0.7673 | 0.8042 | 0.7326 | 0.6637 | 0.6579 | 0.7350 | 0.7960 | 0.7669 | 0.7013 | 0.6828 | 0.7193 | 0.6081 | 0.6149 | 0.6780 | 0.7786 | 0.7489 | 0.7559 | 0.8205 | 0.8165 | 0.7464 |
| 0.0417 | 110.0 | 1100 | 0.1772 | 0.7194 | 0.7972 | 0.8058 | nan | 0.8280 | 0.8238 | 0.8445 | 0.7908 | 0.8269 | 0.8967 | 0.8600 | 0.6878 | 0.8638 | 0.8630 | 0.8371 | 0.7040 | 0.8095 | 0.8234 | 0.8274 | 0.6681 | 0.7270 | 0.7865 | 0.8624 | 0.8220 | 0.7584 | 0.7468 | 0.7957 | 0.7270 | 0.7154 | 0.7303 | 0.8290 | 0.7464 | 0.8262 | 0.8746 | 0.8342 | 0.7745 | 0.0 | 0.8075 | 0.7958 | 0.7914 | 0.7650 | 0.8027 | 0.8522 | 0.7956 | 0.6631 | 0.8181 | 0.8149 | 0.7969 | 0.6840 | 0.7509 | 0.7863 | 0.7170 | 0.6226 | 0.6511 | 0.7267 | 0.7909 | 0.7570 | 0.7006 | 0.6756 | 0.7117 | 0.6107 | 0.6136 | 0.6580 | 0.7549 | 0.7100 | 0.7532 | 0.8196 | 0.8043 | 0.7387 |
| 0.0374 | 112.0 | 1120 | 0.1725 | 0.7319 | 0.8114 | 0.8198 | nan | 0.8322 | 0.8258 | 0.8711 | 0.8108 | 0.8157 | 0.9173 | 0.8848 | 0.7720 | 0.8807 | 0.8693 | 0.8443 | 0.7110 | 0.8353 | 0.8411 | 0.8234 | 0.7056 | 0.7309 | 0.7907 | 0.8726 | 0.8280 | 0.7818 | 0.7314 | 0.8230 | 0.6778 | 0.7216 | 0.7462 | 0.8578 | 0.7922 | 0.8357 | 0.8744 | 0.8664 | 0.7935 | 0.0 | 0.8132 | 0.8054 | 0.8010 | 0.7790 | 0.8020 | 0.8777 | 0.8298 | 0.7268 | 0.8316 | 0.8168 | 0.7957 | 0.6886 | 0.7706 | 0.8004 | 0.7338 | 0.6568 | 0.6538 | 0.7290 | 0.7916 | 0.7569 | 0.7091 | 0.6688 | 0.7263 | 0.5925 | 0.6175 | 0.6760 | 0.7787 | 0.7433 | 0.7608 | 0.8271 | 0.8359 | 0.7577 |
| 0.0297 | 114.0 | 1140 | 0.1727 | 0.7286 | 0.8091 | 0.8163 | nan | 0.8480 | 0.8568 | 0.8539 | 0.8107 | 0.8011 | 0.9095 | 0.8637 | 0.7787 | 0.8682 | 0.8601 | 0.8313 | 0.7293 | 0.8333 | 0.8472 | 0.8031 | 0.6997 | 0.7535 | 0.7978 | 0.8637 | 0.8442 | 0.7672 | 0.7536 | 0.7994 | 0.6785 | 0.7323 | 0.7394 | 0.8431 | 0.7923 | 0.8053 | 0.8831 | 0.8481 | 0.7952 | 0.0 | 0.8277 | 0.8237 | 0.8014 | 0.7776 | 0.7850 | 0.8648 | 0.8196 | 0.7270 | 0.8211 | 0.8147 | 0.7931 | 0.7001 | 0.7679 | 0.7956 | 0.7174 | 0.6506 | 0.6663 | 0.7323 | 0.7890 | 0.7627 | 0.7017 | 0.6809 | 0.7092 | 0.5732 | 0.6222 | 0.6654 | 0.7670 | 0.7410 | 0.7481 | 0.8236 | 0.8202 | 0.7549 |
| 0.0294 | 116.0 | 1160 | 0.1741 | 0.7289 | 0.8097 | 0.8181 | nan | 0.8451 | 0.8299 | 0.8669 | 0.8066 | 0.8102 | 0.9189 | 0.8663 | 0.7263 | 0.8787 | 0.8623 | 0.8572 | 0.7096 | 0.8258 | 0.8393 | 0.8254 | 0.7209 | 0.7396 | 0.7924 | 0.8813 | 0.8413 | 0.7707 | 0.7449 | 0.8108 | 0.7085 | 0.7097 | 0.7435 | 0.8482 | 0.7987 | 0.8178 | 0.8761 | 0.8525 | 0.7865 | 0.0 | 0.8209 | 0.8086 | 0.8036 | 0.7730 | 0.7910 | 0.8641 | 0.8041 | 0.6909 | 0.8297 | 0.8162 | 0.8059 | 0.6831 | 0.7628 | 0.7974 | 0.7325 | 0.6700 | 0.6527 | 0.7279 | 0.8020 | 0.7636 | 0.7085 | 0.6812 | 0.7240 | 0.6049 | 0.6047 | 0.6683 | 0.7679 | 0.7448 | 0.7561 | 0.8237 | 0.8200 | 0.7508 |
| 0.0359 | 118.0 | 1180 | 0.1696 | 0.7373 | 0.8183 | 0.8263 | nan | 0.8415 | 0.8324 | 0.8779 | 0.8223 | 0.8145 | 0.9132 | 0.8831 | 0.7632 | 0.8907 | 0.8551 | 0.8537 | 0.7395 | 0.8336 | 0.8347 | 0.8538 | 0.7489 | 0.7662 | 0.7845 | 0.8690 | 0.8365 | 0.7750 | 0.7394 | 0.8282 | 0.7179 | 0.7087 | 0.7534 | 0.8516 | 0.8153 | 0.8305 | 0.8821 | 0.8731 | 0.7971 | 0.0 | 0.8213 | 0.8147 | 0.8140 | 0.7900 | 0.7971 | 0.8686 | 0.8203 | 0.7237 | 0.8391 | 0.8161 | 0.8070 | 0.7056 | 0.7721 | 0.8014 | 0.7469 | 0.6930 | 0.6718 | 0.7250 | 0.7900 | 0.7569 | 0.7040 | 0.6781 | 0.7344 | 0.6230 | 0.6113 | 0.6740 | 0.7783 | 0.7598 | 0.7589 | 0.8341 | 0.8390 | 0.7620 |
| 0.0313 | 120.0 | 1200 | 0.1764 | 0.7273 | 0.8058 | 0.8135 | nan | 0.8369 | 0.8273 | 0.8485 | 0.8146 | 0.7969 | 0.9080 | 0.8679 | 0.7526 | 0.8618 | 0.8506 | 0.8432 | 0.7363 | 0.8113 | 0.8472 | 0.8241 | 0.7444 | 0.7278 | 0.7724 | 0.8587 | 0.8412 | 0.7388 | 0.7400 | 0.8219 | 0.7169 | 0.7267 | 0.7404 | 0.8295 | 0.7807 | 0.8083 | 0.8742 | 0.8474 | 0.7890 | 0.0 | 0.8122 | 0.8035 | 0.7992 | 0.7815 | 0.7815 | 0.8618 | 0.8129 | 0.7130 | 0.8159 | 0.8134 | 0.8015 | 0.7036 | 0.7602 | 0.8034 | 0.7366 | 0.6886 | 0.6460 | 0.7135 | 0.7851 | 0.7406 | 0.6792 | 0.6769 | 0.7315 | 0.6211 | 0.6179 | 0.6664 | 0.7607 | 0.7361 | 0.7435 | 0.8216 | 0.8177 | 0.7533 |
| 0.0329 | 122.0 | 1220 | 0.1771 | 0.7252 | 0.8059 | 0.8141 | nan | 0.8437 | 0.8362 | 0.8540 | 0.7926 | 0.8117 | 0.9154 | 0.8612 | 0.7062 | 0.8569 | 0.8485 | 0.8420 | 0.7121 | 0.8323 | 0.8448 | 0.7997 | 0.7292 | 0.7499 | 0.7871 | 0.8639 | 0.8462 | 0.7443 | 0.7533 | 0.8169 | 0.7038 | 0.7194 | 0.7506 | 0.8452 | 0.7877 | 0.8170 | 0.8761 | 0.8577 | 0.7840 | 0.0 | 0.8204 | 0.8071 | 0.7921 | 0.7643 | 0.7929 | 0.8554 | 0.7942 | 0.6749 | 0.8127 | 0.8085 | 0.8011 | 0.6862 | 0.7614 | 0.7949 | 0.7177 | 0.6742 | 0.6616 | 0.7244 | 0.7864 | 0.7570 | 0.6883 | 0.6835 | 0.7267 | 0.6077 | 0.6149 | 0.6708 | 0.7716 | 0.7383 | 0.7481 | 0.8252 | 0.8227 | 0.7478 |
| 0.0331 | 124.0 | 1240 | 0.1752 | 0.7266 | 0.8066 | 0.8152 | nan | 0.8327 | 0.8351 | 0.8520 | 0.8300 | 0.8021 | 0.9078 | 0.8735 | 0.7416 | 0.8724 | 0.8526 | 0.8503 | 0.7145 | 0.8238 | 0.8628 | 0.8091 | 0.6907 | 0.7477 | 0.7998 | 0.8757 | 0.8473 | 0.7857 | 0.7623 | 0.7796 | 0.6566 | 0.6925 | 0.7452 | 0.8477 | 0.7840 | 0.8092 | 0.8763 | 0.8595 | 0.7925 | 0.0 | 0.8133 | 0.8089 | 0.8030 | 0.7938 | 0.7893 | 0.8603 | 0.8155 | 0.7067 | 0.8229 | 0.8124 | 0.8022 | 0.6871 | 0.7651 | 0.8067 | 0.7168 | 0.6469 | 0.6612 | 0.7356 | 0.8006 | 0.7659 | 0.7079 | 0.6698 | 0.6958 | 0.5808 | 0.5977 | 0.6661 | 0.7631 | 0.7310 | 0.7430 | 0.8245 | 0.8265 | 0.7578 |
| 0.0361 | 126.0 | 1260 | 0.1760 | 0.7281 | 0.8066 | 0.8149 | nan | 0.8466 | 0.8424 | 0.8478 | 0.8209 | 0.8079 | 0.9042 | 0.8718 | 0.7475 | 0.8674 | 0.8598 | 0.8431 | 0.7180 | 0.8312 | 0.8591 | 0.8210 | 0.7164 | 0.7286 | 0.7876 | 0.8620 | 0.8371 | 0.7558 | 0.7635 | 0.8017 | 0.6584 | 0.7283 | 0.7397 | 0.8309 | 0.7813 | 0.8198 | 0.8722 | 0.8533 | 0.7845 | 0.0 | 0.8253 | 0.8165 | 0.7986 | 0.7862 | 0.7933 | 0.8552 | 0.8134 | 0.7104 | 0.8199 | 0.8164 | 0.8013 | 0.6914 | 0.7703 | 0.8109 | 0.7329 | 0.6689 | 0.6540 | 0.7295 | 0.7917 | 0.7571 | 0.6948 | 0.6801 | 0.7140 | 0.5799 | 0.6173 | 0.6632 | 0.7611 | 0.7343 | 0.7483 | 0.8242 | 0.8219 | 0.7448 |
| 0.0312 | 128.0 | 1280 | 0.1742 | 0.7341 | 0.8152 | 0.8232 | nan | 0.8516 | 0.8397 | 0.8645 | 0.8195 | 0.8220 | 0.9142 | 0.8781 | 0.7514 | 0.8924 | 0.8513 | 0.8453 | 0.7260 | 0.8381 | 0.8475 | 0.8443 | 0.7235 | 0.7607 | 0.7856 | 0.8649 | 0.8366 | 0.7681 | 0.7626 | 0.8138 | 0.7074 | 0.7219 | 0.7571 | 0.8486 | 0.7923 | 0.8338 | 0.8693 | 0.8587 | 0.7970 | 0.0 | 0.8313 | 0.8132 | 0.8057 | 0.7875 | 0.8008 | 0.8622 | 0.8182 | 0.7143 | 0.8354 | 0.8092 | 0.7986 | 0.6982 | 0.7714 | 0.8099 | 0.7470 | 0.6739 | 0.6682 | 0.7270 | 0.7921 | 0.7614 | 0.7032 | 0.6868 | 0.7228 | 0.6151 | 0.6166 | 0.6750 | 0.7740 | 0.7450 | 0.7576 | 0.8214 | 0.8268 | 0.7545 |
| 0.0352 | 130.0 | 1300 | 0.1762 | 0.7337 | 0.8138 | 0.8214 | nan | 0.8465 | 0.8373 | 0.8601 | 0.8223 | 0.8076 | 0.9237 | 0.8686 | 0.7449 | 0.8788 | 0.8631 | 0.8411 | 0.7332 | 0.8329 | 0.8459 | 0.8440 | 0.7533 | 0.7380 | 0.8051 | 0.8671 | 0.8474 | 0.7680 | 0.7653 | 0.8178 | 0.7100 | 0.7248 | 0.7397 | 0.8380 | 0.7731 | 0.8290 | 0.8703 | 0.8504 | 0.7949 | 0.0 | 0.8242 | 0.8128 | 0.8075 | 0.7872 | 0.7894 | 0.8733 | 0.8143 | 0.7046 | 0.8274 | 0.8169 | 0.8003 | 0.7033 | 0.7728 | 0.8140 | 0.7518 | 0.6947 | 0.6550 | 0.7367 | 0.7973 | 0.7702 | 0.7038 | 0.6868 | 0.7290 | 0.6183 | 0.6147 | 0.6654 | 0.7641 | 0.7300 | 0.7536 | 0.8197 | 0.8219 | 0.7516 |
| 0.0308 | 132.0 | 1320 | 0.1767 | 0.7336 | 0.8145 | 0.8229 | nan | 0.8483 | 0.8388 | 0.8638 | 0.8193 | 0.8139 | 0.9136 | 0.8830 | 0.7430 | 0.8761 | 0.8595 | 0.8565 | 0.7363 | 0.8355 | 0.8456 | 0.8410 | 0.7363 | 0.7327 | 0.7974 | 0.8752 | 0.8396 | 0.7640 | 0.7673 | 0.8122 | 0.6917 | 0.7051 | 0.7496 | 0.8453 | 0.7955 | 0.8435 | 0.8762 | 0.8540 | 0.8046 | 0.0 | 0.8244 | 0.8181 | 0.8043 | 0.7850 | 0.7983 | 0.8644 | 0.8190 | 0.7058 | 0.8264 | 0.8159 | 0.8095 | 0.7073 | 0.7770 | 0.8103 | 0.7450 | 0.6838 | 0.6462 | 0.7324 | 0.8007 | 0.7607 | 0.6988 | 0.6883 | 0.7210 | 0.6047 | 0.6040 | 0.6700 | 0.7729 | 0.7485 | 0.7593 | 0.8249 | 0.8245 | 0.7580 |
| 0.035 | 134.0 | 1340 | 0.1803 | 0.7278 | 0.8081 | 0.8162 | nan | 0.8446 | 0.8499 | 0.8584 | 0.8092 | 0.7799 | 0.9063 | 0.8774 | 0.7270 | 0.8824 | 0.8659 | 0.8517 | 0.7241 | 0.8169 | 0.8429 | 0.8405 | 0.7277 | 0.7534 | 0.8073 | 0.8616 | 0.8423 | 0.7534 | 0.7434 | 0.8055 | 0.6901 | 0.7149 | 0.7441 | 0.8304 | 0.7820 | 0.8303 | 0.8671 | 0.8533 | 0.7749 | 0.0 | 0.8253 | 0.8253 | 0.8002 | 0.7727 | 0.7689 | 0.8543 | 0.8127 | 0.6947 | 0.8321 | 0.8170 | 0.7998 | 0.6925 | 0.7684 | 0.8080 | 0.7446 | 0.6750 | 0.6590 | 0.7378 | 0.7909 | 0.7509 | 0.6843 | 0.6724 | 0.7162 | 0.6003 | 0.6115 | 0.6695 | 0.7651 | 0.7366 | 0.7534 | 0.8191 | 0.8223 | 0.7365 |
| 0.0277 | 136.0 | 1360 | 0.1799 | 0.7223 | 0.8025 | 0.8122 | nan | 0.8432 | 0.8488 | 0.8584 | 0.8239 | 0.8034 | 0.9104 | 0.8691 | 0.7429 | 0.8737 | 0.8555 | 0.8366 | 0.7177 | 0.8265 | 0.8570 | 0.8051 | 0.6303 | 0.7380 | 0.7781 | 0.8611 | 0.8211 | 0.7401 | 0.7377 | 0.8163 | 0.6755 | 0.7193 | 0.7427 | 0.8413 | 0.7722 | 0.8081 | 0.8594 | 0.8588 | 0.8084 | 0.0 | 0.8236 | 0.8221 | 0.8016 | 0.7879 | 0.7886 | 0.8592 | 0.8099 | 0.7052 | 0.8223 | 0.8106 | 0.7946 | 0.6935 | 0.7682 | 0.8002 | 0.6989 | 0.5919 | 0.6496 | 0.7201 | 0.7830 | 0.7299 | 0.6780 | 0.6707 | 0.7208 | 0.5938 | 0.6121 | 0.6670 | 0.7627 | 0.7224 | 0.7397 | 0.8182 | 0.8300 | 0.7590 |
| 0.0307 | 138.0 | 1380 | 0.1802 | 0.7245 | 0.8013 | 0.8095 | nan | 0.8385 | 0.8174 | 0.8352 | 0.8052 | 0.8034 | 0.8987 | 0.8775 | 0.7357 | 0.8731 | 0.8513 | 0.8348 | 0.7135 | 0.8138 | 0.8448 | 0.8180 | 0.7142 | 0.7243 | 0.7833 | 0.8580 | 0.8321 | 0.7576 | 0.7388 | 0.7974 | 0.7235 | 0.6939 | 0.7228 | 0.8385 | 0.7783 | 0.8230 | 0.8595 | 0.8492 | 0.7873 | 0.0 | 0.8155 | 0.7950 | 0.7886 | 0.7774 | 0.7849 | 0.8558 | 0.8145 | 0.7004 | 0.8249 | 0.8121 | 0.7952 | 0.6879 | 0.7599 | 0.8019 | 0.7250 | 0.6596 | 0.6484 | 0.7282 | 0.7865 | 0.7487 | 0.6937 | 0.6718 | 0.7151 | 0.6187 | 0.6004 | 0.6595 | 0.7698 | 0.7383 | 0.7514 | 0.8129 | 0.8199 | 0.7473 |
| 0.0283 | 140.0 | 1400 | 0.1792 | 0.7326 | 0.8125 | 0.8197 | nan | 0.8466 | 0.8472 | 0.8611 | 0.8056 | 0.8019 | 0.9130 | 0.8852 | 0.7560 | 0.8805 | 0.8542 | 0.8499 | 0.7225 | 0.8237 | 0.8454 | 0.8324 | 0.7328 | 0.7552 | 0.7947 | 0.8646 | 0.8414 | 0.7641 | 0.7516 | 0.8140 | 0.7148 | 0.7387 | 0.7463 | 0.8488 | 0.7753 | 0.8213 | 0.8729 | 0.8411 | 0.7963 | 0.0 | 0.8251 | 0.8231 | 0.8012 | 0.7725 | 0.7882 | 0.8639 | 0.8282 | 0.7158 | 0.8309 | 0.8158 | 0.8031 | 0.6937 | 0.7658 | 0.8063 | 0.7413 | 0.6777 | 0.6618 | 0.7335 | 0.7961 | 0.7604 | 0.7012 | 0.6825 | 0.7285 | 0.6238 | 0.6253 | 0.6738 | 0.7682 | 0.7284 | 0.7490 | 0.8214 | 0.8157 | 0.7530 |
| 0.032 | 142.0 | 1420 | 0.1782 | 0.7359 | 0.8148 | 0.8227 | nan | 0.8447 | 0.8586 | 0.8601 | 0.8306 | 0.8056 | 0.9163 | 0.8769 | 0.7472 | 0.8807 | 0.8624 | 0.8474 | 0.7149 | 0.8277 | 0.8526 | 0.8325 | 0.7280 | 0.7362 | 0.7980 | 0.8824 | 0.8403 | 0.7766 | 0.7512 | 0.8220 | 0.7046 | 0.7182 | 0.7597 | 0.8495 | 0.7981 | 0.8356 | 0.8716 | 0.8525 | 0.7917 | 0.0 | 0.8253 | 0.8349 | 0.8092 | 0.7952 | 0.7917 | 0.8619 | 0.8187 | 0.7090 | 0.8314 | 0.8187 | 0.7992 | 0.6885 | 0.7709 | 0.8074 | 0.7391 | 0.6736 | 0.6597 | 0.7402 | 0.8074 | 0.7667 | 0.7118 | 0.6832 | 0.7323 | 0.6161 | 0.6170 | 0.6827 | 0.7782 | 0.7522 | 0.7633 | 0.8251 | 0.8233 | 0.7493 |
| 0.029 | 144.0 | 1440 | 0.1780 | 0.7373 | 0.8182 | 0.8264 | nan | 0.8570 | 0.8497 | 0.8668 | 0.8319 | 0.8065 | 0.9194 | 0.8787 | 0.7312 | 0.8838 | 0.8607 | 0.8491 | 0.7423 | 0.8263 | 0.8604 | 0.8412 | 0.7344 | 0.7569 | 0.8025 | 0.8743 | 0.8491 | 0.7783 | 0.7626 | 0.8250 | 0.7136 | 0.7273 | 0.7537 | 0.8432 | 0.7922 | 0.8298 | 0.8805 | 0.8544 | 0.8006 | 0.0 | 0.8327 | 0.8304 | 0.8108 | 0.7923 | 0.7925 | 0.8637 | 0.8148 | 0.6967 | 0.8307 | 0.8200 | 0.8057 | 0.7088 | 0.7713 | 0.8156 | 0.7470 | 0.6800 | 0.6636 | 0.7417 | 0.8046 | 0.7709 | 0.7133 | 0.6905 | 0.7341 | 0.6251 | 0.6158 | 0.6745 | 0.7686 | 0.7426 | 0.7584 | 0.8307 | 0.8270 | 0.7567 |
| 0.0289 | 146.0 | 1460 | 0.1787 | 0.7346 | 0.8149 | 0.8229 | nan | 0.8505 | 0.8500 | 0.8619 | 0.8067 | 0.8082 | 0.9105 | 0.8871 | 0.7411 | 0.8737 | 0.8626 | 0.8506 | 0.7233 | 0.8163 | 0.8523 | 0.8352 | 0.7293 | 0.7532 | 0.8141 | 0.8757 | 0.8426 | 0.7655 | 0.7607 | 0.8212 | 0.7049 | 0.7267 | 0.7522 | 0.8479 | 0.8038 | 0.8267 | 0.8764 | 0.8507 | 0.7960 | 0.0 | 0.8249 | 0.8258 | 0.8040 | 0.7770 | 0.7921 | 0.8604 | 0.8229 | 0.7050 | 0.8246 | 0.8199 | 0.8038 | 0.6938 | 0.7631 | 0.8100 | 0.7388 | 0.6730 | 0.6690 | 0.7482 | 0.8026 | 0.7619 | 0.7020 | 0.6885 | 0.7293 | 0.6158 | 0.6216 | 0.6773 | 0.7779 | 0.7508 | 0.7559 | 0.8265 | 0.8222 | 0.7546 |
| 0.0281 | 148.0 | 1480 | 0.1775 | 0.7392 | 0.8203 | 0.8280 | nan | 0.8508 | 0.8442 | 0.8667 | 0.8302 | 0.8155 | 0.9178 | 0.8824 | 0.7545 | 0.8821 | 0.8560 | 0.8632 | 0.7410 | 0.8384 | 0.8568 | 0.8490 | 0.7439 | 0.7587 | 0.8097 | 0.8712 | 0.8525 | 0.7617 | 0.7538 | 0.8246 | 0.7197 | 0.7421 | 0.7511 | 0.8444 | 0.8008 | 0.8240 | 0.8784 | 0.8653 | 0.7978 | 0.0 | 0.8263 | 0.8225 | 0.8069 | 0.7944 | 0.7990 | 0.8648 | 0.8203 | 0.7155 | 0.8299 | 0.8172 | 0.8164 | 0.7091 | 0.7799 | 0.8189 | 0.7499 | 0.6866 | 0.6691 | 0.7456 | 0.8035 | 0.7649 | 0.6946 | 0.6838 | 0.7340 | 0.6297 | 0.6294 | 0.6793 | 0.7739 | 0.7465 | 0.7570 | 0.8306 | 0.8329 | 0.7607 |
| 0.0332 | 150.0 | 1500 | 0.1792 | 0.7336 | 0.8136 | 0.8217 | nan | 0.8568 | 0.8505 | 0.8623 | 0.8151 | 0.8078 | 0.9123 | 0.8824 | 0.7335 | 0.8848 | 0.8571 | 0.8517 | 0.7219 | 0.8249 | 0.8522 | 0.8308 | 0.7083 | 0.7610 | 0.7893 | 0.8641 | 0.8350 | 0.7722 | 0.7547 | 0.8089 | 0.7215 | 0.7347 | 0.7638 | 0.8456 | 0.7854 | 0.8360 | 0.8720 | 0.8656 | 0.7724 | 0.0 | 0.8330 | 0.8240 | 0.8081 | 0.7810 | 0.7891 | 0.8612 | 0.8186 | 0.6966 | 0.8358 | 0.8138 | 0.8052 | 0.6956 | 0.7701 | 0.8101 | 0.7335 | 0.6582 | 0.6690 | 0.7304 | 0.7977 | 0.7621 | 0.7050 | 0.6831 | 0.7228 | 0.6235 | 0.6289 | 0.6834 | 0.7737 | 0.7388 | 0.7567 | 0.8245 | 0.8295 | 0.7468 |
| 0.0291 | 152.0 | 1520 | 0.1799 | 0.7337 | 0.8141 | 0.8214 | nan | 0.8494 | 0.8450 | 0.8653 | 0.8177 | 0.8065 | 0.9123 | 0.8752 | 0.7344 | 0.8787 | 0.8574 | 0.8536 | 0.7175 | 0.8308 | 0.8506 | 0.8196 | 0.7452 | 0.7553 | 0.8132 | 0.8770 | 0.8531 | 0.7556 | 0.7567 | 0.8006 | 0.7159 | 0.7221 | 0.7552 | 0.8497 | 0.7921 | 0.8170 | 0.8732 | 0.8570 | 0.7982 | 0.0 | 0.8265 | 0.8223 | 0.8096 | 0.7854 | 0.7897 | 0.8597 | 0.8126 | 0.6968 | 0.8281 | 0.8128 | 0.8013 | 0.6905 | 0.7758 | 0.8091 | 0.7349 | 0.6885 | 0.6691 | 0.7480 | 0.8043 | 0.7622 | 0.6921 | 0.6835 | 0.7156 | 0.6130 | 0.6203 | 0.6812 | 0.7770 | 0.7415 | 0.7522 | 0.8277 | 0.8274 | 0.7545 |
| 0.0277 | 154.0 | 1540 | 0.1808 | 0.7323 | 0.8116 | 0.8195 | nan | 0.8513 | 0.8379 | 0.8606 | 0.8222 | 0.8142 | 0.9126 | 0.8771 | 0.7298 | 0.8759 | 0.8535 | 0.8461 | 0.7141 | 0.8245 | 0.8472 | 0.8311 | 0.7385 | 0.7372 | 0.7938 | 0.8634 | 0.8418 | 0.7667 | 0.7537 | 0.8095 | 0.7078 | 0.7253 | 0.7529 | 0.8392 | 0.7919 | 0.8226 | 0.8739 | 0.8571 | 0.7972 | 0.0 | 0.8285 | 0.8178 | 0.8094 | 0.7907 | 0.7973 | 0.8612 | 0.8096 | 0.6925 | 0.8260 | 0.8109 | 0.7993 | 0.6875 | 0.7680 | 0.8076 | 0.7380 | 0.6804 | 0.6574 | 0.7350 | 0.7975 | 0.7642 | 0.7014 | 0.6814 | 0.7207 | 0.6152 | 0.6193 | 0.6755 | 0.7714 | 0.7425 | 0.7518 | 0.8257 | 0.8278 | 0.7539 |
| 0.0292 | 156.0 | 1560 | 0.1859 | 0.7238 | 0.8011 | 0.8093 | nan | 0.8294 | 0.8357 | 0.8605 | 0.8069 | 0.7959 | 0.9057 | 0.8540 | 0.7322 | 0.8619 | 0.8489 | 0.8441 | 0.7070 | 0.8227 | 0.8408 | 0.8060 | 0.7021 | 0.7333 | 0.7911 | 0.8673 | 0.8255 | 0.7497 | 0.7470 | 0.8060 | 0.6920 | 0.7086 | 0.7381 | 0.8357 | 0.7860 | 0.8055 | 0.8674 | 0.8495 | 0.7777 | 0.0 | 0.8099 | 0.8138 | 0.8054 | 0.7749 | 0.7821 | 0.8557 | 0.8003 | 0.6927 | 0.8131 | 0.8067 | 0.7957 | 0.6830 | 0.7676 | 0.7967 | 0.7140 | 0.6517 | 0.6539 | 0.7342 | 0.7969 | 0.7492 | 0.6895 | 0.6757 | 0.7184 | 0.6024 | 0.6085 | 0.6675 | 0.7679 | 0.7360 | 0.7407 | 0.8194 | 0.8187 | 0.7420 |
| 0.0279 | 158.0 | 1580 | 0.1847 | 0.7279 | 0.8060 | 0.8142 | nan | 0.8367 | 0.8391 | 0.8552 | 0.8125 | 0.8153 | 0.9116 | 0.8662 | 0.7244 | 0.8689 | 0.8487 | 0.8458 | 0.7036 | 0.8311 | 0.8449 | 0.8109 | 0.7154 | 0.7376 | 0.7914 | 0.8641 | 0.8313 | 0.7614 | 0.7504 | 0.8056 | 0.6889 | 0.7204 | 0.7518 | 0.8379 | 0.7850 | 0.8219 | 0.8749 | 0.8583 | 0.7803 | 0.0 | 0.8173 | 0.8171 | 0.8043 | 0.7854 | 0.7943 | 0.8602 | 0.8086 | 0.6878 | 0.8179 | 0.8071 | 0.7973 | 0.6816 | 0.7720 | 0.8005 | 0.7206 | 0.6633 | 0.6571 | 0.7325 | 0.7954 | 0.7566 | 0.6982 | 0.6781 | 0.7175 | 0.6025 | 0.6164 | 0.6764 | 0.7686 | 0.7371 | 0.7524 | 0.8267 | 0.8253 | 0.7452 |
| 0.0298 | 160.0 | 1600 | 0.1824 | 0.7338 | 0.8144 | 0.8217 | nan | 0.8455 | 0.8396 | 0.8622 | 0.8193 | 0.8132 | 0.9161 | 0.8752 | 0.7435 | 0.8903 | 0.8611 | 0.8510 | 0.7263 | 0.8425 | 0.8508 | 0.8256 | 0.7413 | 0.7524 | 0.8004 | 0.8684 | 0.8540 | 0.7644 | 0.7460 | 0.8022 | 0.7112 | 0.7222 | 0.7507 | 0.8409 | 0.7912 | 0.8231 | 0.8810 | 0.8511 | 0.7977 | 0.0 | 0.8257 | 0.8205 | 0.8048 | 0.7884 | 0.7975 | 0.8641 | 0.8146 | 0.7048 | 0.8350 | 0.8171 | 0.8038 | 0.6999 | 0.7808 | 0.8122 | 0.7373 | 0.6832 | 0.6643 | 0.7409 | 0.8032 | 0.7667 | 0.6943 | 0.6754 | 0.7168 | 0.6070 | 0.6147 | 0.6741 | 0.7693 | 0.7405 | 0.7553 | 0.8296 | 0.8223 | 0.7522 |
| 0.0254 | 162.0 | 1620 | 0.1856 | 0.7297 | 0.8098 | 0.8177 | nan | 0.8487 | 0.8393 | 0.8513 | 0.8164 | 0.8033 | 0.9125 | 0.8814 | 0.7298 | 0.8786 | 0.8559 | 0.8537 | 0.7208 | 0.8270 | 0.8475 | 0.8300 | 0.7144 | 0.7444 | 0.7952 | 0.8683 | 0.8515 | 0.7607 | 0.7373 | 0.8042 | 0.7122 | 0.7206 | 0.7583 | 0.8391 | 0.7885 | 0.8128 | 0.8681 | 0.8472 | 0.7961 | 0.0 | 0.8252 | 0.8109 | 0.7970 | 0.7827 | 0.7869 | 0.8639 | 0.8172 | 0.6932 | 0.8278 | 0.8147 | 0.8044 | 0.6936 | 0.7733 | 0.8055 | 0.7300 | 0.6650 | 0.6587 | 0.7362 | 0.8000 | 0.7630 | 0.6908 | 0.6699 | 0.7170 | 0.6149 | 0.6166 | 0.6797 | 0.7666 | 0.7390 | 0.7466 | 0.8211 | 0.8200 | 0.7496 |
| 0.0275 | 164.0 | 1640 | 0.1851 | 0.7295 | 0.8084 | 0.8160 | nan | 0.8475 | 0.8370 | 0.8551 | 0.8002 | 0.8093 | 0.9130 | 0.8779 | 0.7404 | 0.8736 | 0.8540 | 0.8449 | 0.7173 | 0.8303 | 0.8507 | 0.8253 | 0.7393 | 0.7446 | 0.7915 | 0.8626 | 0.8431 | 0.7646 | 0.7365 | 0.8091 | 0.6926 | 0.7120 | 0.7572 | 0.8406 | 0.7791 | 0.8217 | 0.8647 | 0.8467 | 0.7854 | 0.0 | 0.8251 | 0.8155 | 0.8034 | 0.7728 | 0.7882 | 0.8621 | 0.8137 | 0.7022 | 0.8229 | 0.8112 | 0.7980 | 0.6913 | 0.7744 | 0.8073 | 0.7356 | 0.6850 | 0.6599 | 0.7334 | 0.7958 | 0.7616 | 0.6944 | 0.6678 | 0.7206 | 0.6069 | 0.6129 | 0.6772 | 0.7689 | 0.7345 | 0.7519 | 0.8166 | 0.8184 | 0.7442 |
| 0.029 | 166.0 | 1660 | 0.1840 | 0.7319 | 0.8120 | 0.8197 | nan | 0.8442 | 0.8316 | 0.8567 | 0.8064 | 0.8119 | 0.9196 | 0.8693 | 0.7249 | 0.8771 | 0.8643 | 0.8471 | 0.7223 | 0.8302 | 0.8456 | 0.8353 | 0.7415 | 0.7557 | 0.7994 | 0.8692 | 0.8477 | 0.7721 | 0.7368 | 0.8183 | 0.7002 | 0.7173 | 0.7560 | 0.8483 | 0.7923 | 0.8273 | 0.8705 | 0.8562 | 0.7885 | 0.0 | 0.8225 | 0.8129 | 0.8029 | 0.7779 | 0.7922 | 0.8616 | 0.8062 | 0.6881 | 0.8236 | 0.8180 | 0.8002 | 0.6952 | 0.7754 | 0.8064 | 0.7406 | 0.6888 | 0.6677 | 0.7397 | 0.8004 | 0.7627 | 0.6990 | 0.6710 | 0.7288 | 0.6150 | 0.6170 | 0.6768 | 0.7730 | 0.7413 | 0.7535 | 0.8219 | 0.8229 | 0.7508 |
| 0.0261 | 168.0 | 1680 | 0.1841 | 0.7311 | 0.8108 | 0.8188 | nan | 0.8462 | 0.8403 | 0.8589 | 0.8086 | 0.8042 | 0.9140 | 0.8736 | 0.7250 | 0.8793 | 0.8599 | 0.8408 | 0.7201 | 0.8344 | 0.8519 | 0.8209 | 0.7249 | 0.7517 | 0.7900 | 0.8691 | 0.8414 | 0.7688 | 0.7450 | 0.8147 | 0.7092 | 0.7171 | 0.7593 | 0.8458 | 0.7937 | 0.8229 | 0.8717 | 0.8535 | 0.7894 | 0.0 | 0.8238 | 0.8165 | 0.8046 | 0.7783 | 0.7868 | 0.8603 | 0.8095 | 0.6877 | 0.8267 | 0.8143 | 0.7973 | 0.6947 | 0.7759 | 0.8044 | 0.7286 | 0.6743 | 0.6628 | 0.7353 | 0.8006 | 0.7619 | 0.6998 | 0.6745 | 0.7272 | 0.6214 | 0.6163 | 0.6794 | 0.7738 | 0.7429 | 0.7535 | 0.8226 | 0.8213 | 0.7482 |
| 0.0245 | 170.0 | 1700 | 0.1876 | 0.7285 | 0.8066 | 0.8147 | nan | 0.8357 | 0.8356 | 0.8585 | 0.8069 | 0.8036 | 0.9098 | 0.8707 | 0.7247 | 0.8638 | 0.8570 | 0.8461 | 0.7160 | 0.8180 | 0.8456 | 0.8237 | 0.7374 | 0.7329 | 0.7946 | 0.8684 | 0.8429 | 0.7519 | 0.7456 | 0.8141 | 0.6880 | 0.7154 | 0.7514 | 0.8366 | 0.7872 | 0.8250 | 0.8723 | 0.8485 | 0.7848 | 0.0 | 0.8149 | 0.8165 | 0.8029 | 0.7754 | 0.7869 | 0.8586 | 0.8079 | 0.6861 | 0.8149 | 0.8149 | 0.7974 | 0.6883 | 0.7705 | 0.8049 | 0.7334 | 0.6821 | 0.6529 | 0.7384 | 0.8002 | 0.7576 | 0.6902 | 0.6753 | 0.7257 | 0.6044 | 0.6137 | 0.6751 | 0.7679 | 0.7407 | 0.7562 | 0.8241 | 0.8192 | 0.7435 |
| 0.0256 | 172.0 | 1720 | 0.1836 | 0.7363 | 0.8183 | 0.8267 | nan | 0.8529 | 0.8438 | 0.8728 | 0.8277 | 0.8176 | 0.9211 | 0.8840 | 0.7331 | 0.8799 | 0.8667 | 0.8582 | 0.7325 | 0.8303 | 0.8598 | 0.8328 | 0.7167 | 0.7596 | 0.8124 | 0.8778 | 0.8484 | 0.7648 | 0.7527 | 0.8207 | 0.7065 | 0.7303 | 0.7640 | 0.8577 | 0.7886 | 0.8302 | 0.8848 | 0.8643 | 0.7931 | 0.0 | 0.8307 | 0.8250 | 0.8146 | 0.7947 | 0.7984 | 0.8649 | 0.8164 | 0.6957 | 0.8281 | 0.8225 | 0.8087 | 0.7020 | 0.7792 | 0.8087 | 0.7316 | 0.6679 | 0.6669 | 0.7515 | 0.8068 | 0.7631 | 0.6983 | 0.6814 | 0.7287 | 0.6153 | 0.6229 | 0.6818 | 0.7775 | 0.7394 | 0.7584 | 0.8334 | 0.8303 | 0.7533 |
| 0.0264 | 174.0 | 1740 | 0.1903 | 0.7228 | 0.8005 | 0.8088 | nan | 0.8311 | 0.8224 | 0.8559 | 0.7999 | 0.8008 | 0.9034 | 0.8693 | 0.7272 | 0.8645 | 0.8519 | 0.8333 | 0.7081 | 0.8129 | 0.8380 | 0.8263 | 0.7127 | 0.7399 | 0.7811 | 0.8516 | 0.8377 | 0.7393 | 0.7429 | 0.8033 | 0.6958 | 0.7164 | 0.7352 | 0.8246 | 0.7811 | 0.8105 | 0.8734 | 0.8495 | 0.7764 | 0.0 | 0.8108 | 0.8055 | 0.8013 | 0.7703 | 0.7813 | 0.8546 | 0.8081 | 0.6896 | 0.8169 | 0.8091 | 0.7892 | 0.6828 | 0.7626 | 0.7990 | 0.7291 | 0.6637 | 0.6525 | 0.7266 | 0.7878 | 0.7504 | 0.6811 | 0.6734 | 0.7159 | 0.6009 | 0.6105 | 0.6637 | 0.7580 | 0.7328 | 0.7463 | 0.8242 | 0.8188 | 0.7358 |
| 0.0254 | 176.0 | 1760 | 0.1851 | 0.7329 | 0.8133 | 0.8210 | nan | 0.8454 | 0.8359 | 0.8607 | 0.8075 | 0.8079 | 0.9141 | 0.8796 | 0.7433 | 0.8770 | 0.8540 | 0.8512 | 0.7177 | 0.8306 | 0.8483 | 0.8371 | 0.7307 | 0.7700 | 0.7971 | 0.8656 | 0.8472 | 0.7533 | 0.7532 | 0.8147 | 0.7093 | 0.7321 | 0.7544 | 0.8442 | 0.7939 | 0.8231 | 0.8844 | 0.8553 | 0.7865 | 0.0 | 0.8231 | 0.8168 | 0.8074 | 0.7788 | 0.7869 | 0.8613 | 0.8164 | 0.7033 | 0.8245 | 0.8129 | 0.8027 | 0.6910 | 0.7781 | 0.8075 | 0.7378 | 0.6783 | 0.6715 | 0.7385 | 0.7993 | 0.7594 | 0.6900 | 0.6823 | 0.7255 | 0.6139 | 0.6236 | 0.6791 | 0.7742 | 0.7442 | 0.7569 | 0.8322 | 0.8235 | 0.7451 |
| 0.0308 | 178.0 | 1780 | 0.1852 | 0.7337 | 0.8138 | 0.8218 | nan | 0.8503 | 0.8397 | 0.8655 | 0.8156 | 0.8118 | 0.9154 | 0.8784 | 0.7386 | 0.8773 | 0.8570 | 0.8505 | 0.7158 | 0.8339 | 0.8476 | 0.8361 | 0.7338 | 0.7595 | 0.7961 | 0.8685 | 0.8398 | 0.7659 | 0.7532 | 0.8194 | 0.7005 | 0.7309 | 0.7517 | 0.8436 | 0.7897 | 0.8269 | 0.8776 | 0.8598 | 0.7928 | 0.0 | 0.8268 | 0.8195 | 0.8102 | 0.7848 | 0.7922 | 0.8614 | 0.8154 | 0.7008 | 0.8270 | 0.8153 | 0.8025 | 0.6906 | 0.7764 | 0.8065 | 0.7387 | 0.6817 | 0.6656 | 0.7383 | 0.7991 | 0.7626 | 0.6980 | 0.6813 | 0.7269 | 0.6126 | 0.6227 | 0.6760 | 0.7706 | 0.7422 | 0.7573 | 0.8297 | 0.8275 | 0.7506 |
| 0.0276 | 180.0 | 1800 | 0.1873 | 0.7312 | 0.8109 | 0.8189 | nan | 0.8443 | 0.8352 | 0.8662 | 0.8145 | 0.8125 | 0.9143 | 0.8711 | 0.7360 | 0.8739 | 0.8583 | 0.8472 | 0.7210 | 0.8280 | 0.8502 | 0.8326 | 0.7291 | 0.7501 | 0.7977 | 0.8652 | 0.8431 | 0.7609 | 0.7515 | 0.8124 | 0.7082 | 0.7151 | 0.7460 | 0.8429 | 0.7880 | 0.8217 | 0.8773 | 0.8523 | 0.7820 | 0.0 | 0.8220 | 0.8169 | 0.8089 | 0.7836 | 0.7938 | 0.8607 | 0.8113 | 0.6969 | 0.8228 | 0.8152 | 0.7999 | 0.6928 | 0.7756 | 0.8085 | 0.7378 | 0.6780 | 0.6600 | 0.7379 | 0.7968 | 0.7614 | 0.6951 | 0.6802 | 0.7246 | 0.6142 | 0.6103 | 0.6719 | 0.7691 | 0.7399 | 0.7546 | 0.8261 | 0.8200 | 0.7428 |
| 0.0257 | 182.0 | 1820 | 0.1877 | 0.7298 | 0.8088 | 0.8166 | nan | 0.8449 | 0.8334 | 0.8575 | 0.8103 | 0.8032 | 0.9135 | 0.8732 | 0.7342 | 0.8737 | 0.8544 | 0.8449 | 0.7154 | 0.8283 | 0.8480 | 0.8235 | 0.7249 | 0.7549 | 0.7946 | 0.8629 | 0.8491 | 0.7555 | 0.7506 | 0.8078 | 0.6947 | 0.7270 | 0.7445 | 0.8433 | 0.7819 | 0.8222 | 0.8744 | 0.8508 | 0.7826 | 0.0 | 0.8226 | 0.8132 | 0.8047 | 0.7797 | 0.7859 | 0.8607 | 0.8130 | 0.6960 | 0.8234 | 0.8133 | 0.7987 | 0.6898 | 0.7747 | 0.8050 | 0.7305 | 0.6734 | 0.6642 | 0.7372 | 0.7977 | 0.7624 | 0.6911 | 0.6786 | 0.7205 | 0.6045 | 0.6202 | 0.6722 | 0.7695 | 0.7364 | 0.7548 | 0.8250 | 0.8204 | 0.7434 |
| 0.026 | 184.0 | 1840 | 0.1863 | 0.7317 | 0.8112 | 0.8191 | nan | 0.8458 | 0.8364 | 0.8604 | 0.8130 | 0.8083 | 0.9127 | 0.8757 | 0.7385 | 0.8779 | 0.8557 | 0.8464 | 0.7177 | 0.8292 | 0.8525 | 0.8303 | 0.7248 | 0.7541 | 0.7961 | 0.8657 | 0.8440 | 0.7666 | 0.7510 | 0.8102 | 0.7000 | 0.7253 | 0.7503 | 0.8437 | 0.7840 | 0.8262 | 0.8763 | 0.8556 | 0.7829 | 0.0 | 0.8230 | 0.8154 | 0.8057 | 0.7824 | 0.7899 | 0.8607 | 0.8148 | 0.6988 | 0.8258 | 0.8163 | 0.8009 | 0.6915 | 0.7771 | 0.8084 | 0.7335 | 0.6725 | 0.6633 | 0.7387 | 0.7993 | 0.7628 | 0.6983 | 0.6794 | 0.7224 | 0.6090 | 0.6192 | 0.6759 | 0.7707 | 0.7374 | 0.7561 | 0.8284 | 0.8232 | 0.7439 |
| 0.0289 | 186.0 | 1860 | 0.1875 | 0.7302 | 0.8093 | 0.8173 | nan | 0.8412 | 0.8336 | 0.8576 | 0.8097 | 0.8073 | 0.9110 | 0.8750 | 0.7390 | 0.8742 | 0.8607 | 0.8443 | 0.7199 | 0.8323 | 0.8472 | 0.8270 | 0.7220 | 0.7408 | 0.7988 | 0.8599 | 0.8392 | 0.7653 | 0.7522 | 0.8111 | 0.7013 | 0.7143 | 0.7506 | 0.8392 | 0.7803 | 0.8271 | 0.8773 | 0.8531 | 0.7836 | 0.0 | 0.8196 | 0.8131 | 0.8034 | 0.7796 | 0.7895 | 0.8597 | 0.8148 | 0.6983 | 0.8239 | 0.8167 | 0.7992 | 0.6937 | 0.7756 | 0.8062 | 0.7326 | 0.6707 | 0.6550 | 0.7389 | 0.7958 | 0.7615 | 0.6987 | 0.6796 | 0.7237 | 0.6113 | 0.6111 | 0.6729 | 0.7675 | 0.7358 | 0.7563 | 0.8265 | 0.8213 | 0.7455 |
| 0.0258 | 188.0 | 1880 | 0.1875 | 0.7318 | 0.8117 | 0.8194 | nan | 0.8459 | 0.8376 | 0.8662 | 0.8114 | 0.8108 | 0.9142 | 0.8761 | 0.7311 | 0.8758 | 0.8572 | 0.8502 | 0.7221 | 0.8312 | 0.8503 | 0.8294 | 0.7372 | 0.7509 | 0.7980 | 0.8662 | 0.8423 | 0.7588 | 0.7480 | 0.8105 | 0.7155 | 0.7207 | 0.7486 | 0.8435 | 0.7893 | 0.8208 | 0.8752 | 0.8500 | 0.7879 | 0.0 | 0.8232 | 0.8173 | 0.8062 | 0.7807 | 0.7927 | 0.8608 | 0.8137 | 0.6929 | 0.8254 | 0.8164 | 0.8030 | 0.6946 | 0.7775 | 0.8094 | 0.7381 | 0.6829 | 0.6598 | 0.7387 | 0.7989 | 0.7613 | 0.6952 | 0.6791 | 0.7233 | 0.6162 | 0.6157 | 0.6727 | 0.7695 | 0.7393 | 0.7525 | 0.8261 | 0.8195 | 0.7471 |
| 0.0285 | 190.0 | 1900 | 0.1871 | 0.7306 | 0.8102 | 0.8183 | nan | 0.8448 | 0.8349 | 0.8621 | 0.8193 | 0.8042 | 0.9109 | 0.8756 | 0.7317 | 0.8754 | 0.8574 | 0.8481 | 0.7196 | 0.8264 | 0.8495 | 0.8323 | 0.7198 | 0.7453 | 0.7921 | 0.8648 | 0.8401 | 0.7619 | 0.7456 | 0.8120 | 0.7060 | 0.7283 | 0.7501 | 0.8450 | 0.7884 | 0.8223 | 0.8728 | 0.8511 | 0.7893 | 0.0 | 0.8222 | 0.8149 | 0.8062 | 0.7865 | 0.7884 | 0.8584 | 0.8127 | 0.6937 | 0.8258 | 0.8148 | 0.8006 | 0.6916 | 0.7753 | 0.8076 | 0.7341 | 0.6694 | 0.6588 | 0.7346 | 0.7965 | 0.7587 | 0.6946 | 0.6760 | 0.7239 | 0.6135 | 0.6213 | 0.6749 | 0.7712 | 0.7389 | 0.7522 | 0.8247 | 0.8207 | 0.7475 |
| 0.0334 | 192.0 | 1920 | 0.1871 | 0.7317 | 0.8114 | 0.8194 | nan | 0.8425 | 0.8345 | 0.8608 | 0.8102 | 0.8113 | 0.9146 | 0.8768 | 0.7392 | 0.8772 | 0.8562 | 0.8486 | 0.7204 | 0.8274 | 0.8481 | 0.8296 | 0.7253 | 0.7522 | 0.7959 | 0.8665 | 0.8412 | 0.7626 | 0.7533 | 0.8133 | 0.7042 | 0.7227 | 0.7496 | 0.8450 | 0.7897 | 0.8246 | 0.8783 | 0.8556 | 0.7859 | 0.0 | 0.8203 | 0.8136 | 0.8031 | 0.7806 | 0.7937 | 0.8613 | 0.8163 | 0.7001 | 0.8266 | 0.8148 | 0.8018 | 0.6933 | 0.7761 | 0.8068 | 0.7335 | 0.6733 | 0.6610 | 0.7373 | 0.7990 | 0.7612 | 0.6971 | 0.6811 | 0.7245 | 0.6110 | 0.6171 | 0.6751 | 0.7725 | 0.7410 | 0.7552 | 0.8289 | 0.8235 | 0.7462 |
| 0.0209 | 194.0 | 1940 | 0.1880 | 0.7311 | 0.8105 | 0.8184 | nan | 0.8476 | 0.8353 | 0.8611 | 0.8127 | 0.8063 | 0.9114 | 0.8740 | 0.7454 | 0.8760 | 0.8597 | 0.8502 | 0.7193 | 0.8253 | 0.8481 | 0.8298 | 0.7268 | 0.7446 | 0.7980 | 0.8653 | 0.8433 | 0.7607 | 0.7463 | 0.8123 | 0.7000 | 0.7201 | 0.7481 | 0.8438 | 0.7867 | 0.8235 | 0.8737 | 0.8521 | 0.7870 | 0.0 | 0.8244 | 0.8154 | 0.8046 | 0.7810 | 0.7901 | 0.8597 | 0.8154 | 0.7041 | 0.8272 | 0.8174 | 0.8014 | 0.6915 | 0.7763 | 0.8067 | 0.7342 | 0.6749 | 0.6572 | 0.7390 | 0.7983 | 0.7593 | 0.6943 | 0.6763 | 0.7235 | 0.6095 | 0.6153 | 0.6742 | 0.7707 | 0.7383 | 0.7530 | 0.8255 | 0.8211 | 0.7461 |
| 0.0274 | 196.0 | 1960 | 0.1877 | 0.7308 | 0.8101 | 0.8181 | nan | 0.8445 | 0.8356 | 0.8623 | 0.8141 | 0.8091 | 0.9131 | 0.8760 | 0.7369 | 0.8765 | 0.8584 | 0.8487 | 0.7158 | 0.8265 | 0.8497 | 0.8279 | 0.7256 | 0.7473 | 0.7972 | 0.8635 | 0.8395 | 0.7573 | 0.7462 | 0.8114 | 0.7015 | 0.7210 | 0.7511 | 0.8454 | 0.7854 | 0.8240 | 0.8745 | 0.8523 | 0.7857 | 0.0 | 0.8223 | 0.8166 | 0.8061 | 0.7833 | 0.7915 | 0.8604 | 0.8146 | 0.6981 | 0.8270 | 0.8162 | 0.8000 | 0.6891 | 0.7760 | 0.8073 | 0.7326 | 0.6736 | 0.6598 | 0.7385 | 0.7963 | 0.7567 | 0.6926 | 0.6765 | 0.7229 | 0.6099 | 0.6169 | 0.6753 | 0.7707 | 0.7378 | 0.7541 | 0.8260 | 0.8209 | 0.7453 |
| 0.0259 | 198.0 | 1980 | 0.1877 | 0.7309 | 0.8104 | 0.8184 | nan | 0.8458 | 0.8344 | 0.8619 | 0.8111 | 0.8092 | 0.9141 | 0.8752 | 0.7359 | 0.8780 | 0.8592 | 0.8490 | 0.7169 | 0.8276 | 0.8488 | 0.8287 | 0.7272 | 0.7489 | 0.7987 | 0.8668 | 0.8387 | 0.7594 | 0.7491 | 0.8117 | 0.7003 | 0.7217 | 0.7463 | 0.8436 | 0.7857 | 0.8229 | 0.8764 | 0.8517 | 0.7869 | 0.0 | 0.8231 | 0.8150 | 0.8045 | 0.7807 | 0.7914 | 0.8605 | 0.8139 | 0.6975 | 0.8282 | 0.8169 | 0.8007 | 0.6902 | 0.7764 | 0.8067 | 0.7335 | 0.6750 | 0.6600 | 0.7397 | 0.7981 | 0.7575 | 0.6945 | 0.6781 | 0.7228 | 0.6094 | 0.6163 | 0.6730 | 0.7695 | 0.7376 | 0.7544 | 0.8271 | 0.8207 | 0.7461 |
| 0.0276 | 200.0 | 2000 | 0.1880 | 0.7311 | 0.8106 | 0.8184 | nan | 0.8456 | 0.8351 | 0.8619 | 0.8112 | 0.8087 | 0.9141 | 0.8742 | 0.7394 | 0.8758 | 0.8579 | 0.8480 | 0.7169 | 0.8273 | 0.8481 | 0.8284 | 0.7298 | 0.7495 | 0.7987 | 0.8661 | 0.8392 | 0.7596 | 0.7482 | 0.8109 | 0.7016 | 0.7217 | 0.7480 | 0.8447 | 0.7868 | 0.8250 | 0.8762 | 0.8519 | 0.7878 | 0.0 | 0.8226 | 0.8155 | 0.8048 | 0.7807 | 0.7909 | 0.8609 | 0.8145 | 0.6999 | 0.8266 | 0.8160 | 0.8000 | 0.6900 | 0.7760 | 0.8065 | 0.7338 | 0.6771 | 0.6604 | 0.7394 | 0.7977 | 0.7577 | 0.6944 | 0.6774 | 0.7224 | 0.6099 | 0.6166 | 0.6741 | 0.7706 | 0.7386 | 0.7555 | 0.8271 | 0.8210 | 0.7466 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| [
"background",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"41",
"42",
"43",
"44",
"45",
"46",
"47",
"48"
] |
YIMMYCRUZ/vit-model-ojas |
<!-- 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. -->
# vit-model-ojas
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0099
- Accuracy: 1.0
## Model description
You can manage to segment the images of plant leaves to be able to know if they are healthy or withered.
## 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: 0.0002
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1457 | 3.85 | 500 | 0.0099 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
| [
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
iammartian0/RoadSense_High_Definition_Street_Segmentation |
<!-- 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
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5449
- Mean Iou: 0.3292
- Mean Accuracy: 0.3907
- Overall Accuracy: 0.8555
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8585
- Accuracy Flat-sidewalk: 0.9611
- Accuracy Flat-crosswalk: 0.7673
- Accuracy Flat-cyclinglane: 0.8223
- Accuracy Flat-parkingdriveway: 0.5127
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.4937
- Accuracy Human-person: 0.7164
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9332
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: nan
- Accuracy Vehicle-tramtrain: nan
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.3858
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.9040
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.5848
- Accuracy Construction-fenceguardrail: 0.4417
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.3156
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9413
- Accuracy Nature-terrain: 0.8456
- Accuracy Sky: 0.9600
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.2780
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.7447
- Iou Flat-sidewalk: 0.8755
- Iou Flat-crosswalk: 0.6244
- Iou Flat-cyclinglane: 0.7325
- Iou Flat-parkingdriveway: 0.3997
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.3974
- Iou Human-person: 0.4985
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.7798
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: nan
- Iou Vehicle-tramtrain: nan
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.2904
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.7233
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.4555
- Iou Construction-fenceguardrail: 0.3734
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.2484
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.8451
- Iou Nature-terrain: 0.7346
- Iou Sky: 0.9161
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.2359
- 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: 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 1.4172 | 1.87 | 200 | 1.2183 | 0.1696 | 0.2214 | 0.7509 | nan | 0.8882 | 0.9199 | 0.0 | 0.4200 | 0.0164 | nan | 0.0 | 0.0 | 0.0 | 0.8778 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8448 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9430 | 0.8044 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5435 | 0.8135 | 0.0 | 0.3743 | 0.0160 | nan | 0.0 | 0.0 | 0.0 | 0.6044 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5373 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7516 | 0.6550 | 0.7928 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1152 | 3.74 | 400 | 0.8946 | 0.1947 | 0.2441 | 0.7852 | nan | 0.8535 | 0.9471 | 0.0 | 0.7379 | 0.2453 | nan | 0.0398 | 0.0 | 0.0 | 0.8882 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8746 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0014 | 0.0 | 0.0 | 0.9526 | 0.8285 | 0.9448 | 0.0 | 0.0 | 0.0019 | 0.0 | nan | 0.6355 | 0.8321 | 0.0 | 0.5529 | 0.1940 | nan | 0.0392 | 0.0 | 0.0 | 0.6807 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5913 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0014 | 0.0 | 0.0 | 0.7701 | 0.6777 | 0.8567 | 0.0 | 0.0 | 0.0019 | 0.0 |
| 0.6637 | 5.61 | 600 | 0.7447 | 0.2349 | 0.2841 | 0.8104 | nan | 0.8589 | 0.9451 | 0.4455 | 0.8008 | 0.3753 | nan | 0.3267 | 0.0380 | 0.0 | 0.8920 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9227 | 0.0 | 0.0938 | 0.0 | 0.0 | nan | 0.0 | 0.0167 | 0.0 | 0.0 | 0.9291 | 0.8677 | 0.9557 | 0.0 | 0.0 | 0.0562 | 0.0 | nan | 0.6768 | 0.8543 | 0.4064 | 0.6414 | 0.2914 | nan | 0.2749 | 0.0376 | 0.0 | 0.7268 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6078 | 0.0 | 0.0879 | 0.0 | 0.0 | nan | 0.0 | 0.0164 | 0.0 | 0.0 | 0.8005 | 0.6817 | 0.8918 | 0.0 | 0.0 | 0.0525 | 0.0 |
| 0.673 | 7.48 | 800 | 0.6631 | 0.2691 | 0.3202 | 0.8278 | nan | 0.8387 | 0.9575 | 0.6176 | 0.7938 | 0.4208 | nan | 0.3575 | 0.3977 | 0.0 | 0.9264 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9068 | 0.0 | 0.4035 | 0.0 | 0.0 | nan | 0.0 | 0.1137 | 0.0 | 0.0 | 0.9495 | 0.8165 | 0.9453 | 0.0 | 0.0 | 0.1599 | 0.0 | nan | 0.7042 | 0.8567 | 0.5239 | 0.6600 | 0.3246 | nan | 0.3003 | 0.3212 | 0.0 | 0.7246 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6749 | 0.0 | 0.3113 | 0.0 | 0.0 | nan | 0.0 | 0.1038 | 0.0 | 0.0 | 0.8147 | 0.7070 | 0.9008 | 0.0 | 0.0 | 0.1445 | 0.0 |
| 0.502 | 9.35 | 1000 | 0.6249 | 0.2818 | 0.3371 | 0.8345 | nan | 0.8332 | 0.9538 | 0.7158 | 0.8344 | 0.4079 | nan | 0.4420 | 0.4941 | 0.0 | 0.9275 | 0.0 | nan | nan | 0.0 | 0.0172 | 0.0 | 0.0 | 0.9102 | 0.0 | 0.4787 | 0.0253 | 0.0 | nan | 0.0 | 0.1454 | 0.0 | 0.0 | 0.9460 | 0.8350 | 0.9588 | 0.0 | 0.0 | 0.1887 | 0.0 | nan | 0.7176 | 0.8635 | 0.6035 | 0.6519 | 0.3246 | nan | 0.3545 | 0.3720 | 0.0 | 0.7524 | 0.0 | nan | nan | 0.0 | 0.0172 | 0.0 | 0.0 | 0.6861 | 0.0 | 0.3286 | 0.0250 | 0.0 | nan | 0.0 | 0.1309 | 0.0 | 0.0 | 0.8335 | 0.7300 | 0.9037 | 0.0 | 0.0 | 0.1584 | 0.0 |
| 0.9687 | 11.21 | 1200 | 0.5786 | 0.3093 | 0.3675 | 0.8471 | nan | 0.8703 | 0.9504 | 0.7382 | 0.7705 | 0.5297 | nan | 0.4804 | 0.6250 | 0.0 | 0.9168 | 0.0 | nan | nan | 0.0 | 0.1397 | 0.0 | 0.0 | 0.9228 | 0.0 | 0.5710 | 0.3183 | 0.0 | nan | 0.0 | 0.2252 | 0.0 | 0.0 | 0.9314 | 0.8840 | 0.9536 | 0.0 | 0.0 | 0.1981 | 0.0 | nan | 0.7380 | 0.8743 | 0.5825 | 0.7093 | 0.3829 | nan | 0.3743 | 0.4600 | 0.0 | 0.7727 | 0.0 | nan | nan | 0.0 | 0.1372 | 0.0 | 0.0 | 0.7008 | 0.0 | 0.4315 | 0.2847 | 0.0 | nan | 0.0 | 0.1930 | 0.0 | 0.0 | 0.8397 | 0.7121 | 0.9109 | 0.0 | 0.0 | 0.1761 | 0.0 |
| 0.4681 | 13.08 | 1400 | 0.5759 | 0.3106 | 0.3665 | 0.8462 | nan | 0.8586 | 0.9572 | 0.5158 | 0.8121 | 0.5195 | nan | 0.4539 | 0.6944 | 0.0 | 0.9308 | 0.0 | nan | nan | 0.0 | 0.2759 | 0.0 | 0.0 | 0.9126 | 0.0 | 0.4927 | 0.3145 | 0.0 | nan | 0.0 | 0.2566 | 0.0 | 0.0 | 0.9396 | 0.8736 | 0.9644 | 0.0 | 0.0 | 0.2226 | 0.0 | nan | 0.7134 | 0.8742 | 0.5009 | 0.7146 | 0.4018 | nan | 0.3726 | 0.4661 | 0.0 | 0.7674 | 0.0 | nan | nan | 0.0 | 0.2501 | 0.0 | 0.0 | 0.6997 | 0.0 | 0.3933 | 0.2827 | 0.0 | nan | 0.0 | 0.2137 | 0.0 | 0.0 | 0.8377 | 0.7212 | 0.9109 | 0.0 | 0.0 | 0.1964 | 0.0 |
| 0.5374 | 14.95 | 1600 | 0.5534 | 0.3232 | 0.3823 | 0.8518 | nan | 0.8607 | 0.9545 | 0.7138 | 0.8398 | 0.5129 | nan | 0.4823 | 0.7055 | 0.0 | 0.9225 | 0.0 | nan | nan | 0.0 | 0.3058 | 0.0 | 0.0 | 0.8999 | 0.0 | 0.5436 | 0.3798 | 0.0 | nan | 0.0 | 0.2878 | 0.0 | 0.0 | 0.9485 | 0.8388 | 0.9598 | 0.0 | 0.0 | 0.3145 | 0.0 | nan | 0.7336 | 0.8788 | 0.6094 | 0.7062 | 0.3966 | nan | 0.3854 | 0.4897 | 0.0 | 0.7823 | 0.0 | nan | nan | 0.0 | 0.2782 | 0.0 | 0.0 | 0.7148 | 0.0 | 0.4182 | 0.3304 | 0.0 | nan | 0.0 | 0.2324 | 0.0 | 0.0 | 0.8415 | 0.7356 | 0.9130 | 0.0 | 0.0 | 0.2491 | 0.0 |
| 0.6115 | 16.82 | 1800 | 0.5528 | 0.3266 | 0.3849 | 0.8539 | nan | 0.8521 | 0.9611 | 0.6840 | 0.8291 | 0.5057 | nan | 0.5070 | 0.7165 | 0.0 | 0.9267 | 0.0 | nan | nan | 0.0 | 0.3659 | 0.0 | 0.0 | 0.9007 | 0.0 | 0.5844 | 0.3961 | 0.0 | nan | 0.0 | 0.2827 | 0.0 | 0.0 | 0.9517 | 0.8371 | 0.9602 | 0.0 | 0.0 | 0.2848 | 0.0 | nan | 0.7414 | 0.8721 | 0.6312 | 0.7245 | 0.3979 | nan | 0.3987 | 0.4932 | 0.0 | 0.7799 | 0.0 | nan | nan | 0.0 | 0.2788 | 0.0 | 0.0 | 0.7242 | 0.0 | 0.4542 | 0.3464 | 0.0 | nan | 0.0 | 0.2326 | 0.0 | 0.0 | 0.8384 | 0.7318 | 0.9141 | 0.0 | 0.0 | 0.2386 | 0.0 |
| 0.4766 | 18.69 | 2000 | 0.5449 | 0.3292 | 0.3907 | 0.8555 | nan | 0.8585 | 0.9611 | 0.7673 | 0.8223 | 0.5127 | nan | 0.4937 | 0.7164 | 0.0 | 0.9332 | 0.0 | nan | nan | 0.0 | 0.3858 | 0.0 | 0.0 | 0.9040 | 0.0 | 0.5848 | 0.4417 | 0.0 | nan | 0.0 | 0.3156 | 0.0 | 0.0 | 0.9413 | 0.8456 | 0.9600 | 0.0 | 0.0 | 0.2780 | 0.0 | nan | 0.7447 | 0.8755 | 0.6244 | 0.7325 | 0.3997 | nan | 0.3974 | 0.4985 | 0.0 | 0.7798 | 0.0 | nan | nan | 0.0 | 0.2904 | 0.0 | 0.0 | 0.7233 | 0.0 | 0.4555 | 0.3734 | 0.0 | nan | 0.0 | 0.2484 | 0.0 | 0.0 | 0.8451 | 0.7346 | 0.9161 | 0.0 | 0.0 | 0.2359 | 0.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 | [
"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-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-10k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3966
- Mean Iou: 0.5967
- Mean Accuracy: 0.8460
- Overall Accuracy: 0.9344
- Accuracy Bg: nan
- Accuracy Fallo cohesivo: 0.9510
- Accuracy Fallo malla: 0.8524
- Accuracy Fallo adhesivo: 0.9362
- Accuracy Fallo burbuja: 0.6444
- Iou Bg: 0.0
- Iou Fallo cohesivo: 0.9239
- Iou Fallo malla: 0.7125
- Iou Fallo adhesivo: 0.8335
- Iou Fallo burbuja: 0.5139
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:|
| 0.4796 | 1.0 | 133 | 0.4190 | 0.4518 | 0.6689 | 0.9049 | nan | 0.9277 | 0.8091 | 0.9381 | 0.0008 | 0.0 | 0.8866 | 0.6536 | 0.7179 | 0.0008 |
| 0.2665 | 2.0 | 266 | 0.3667 | 0.5096 | 0.7283 | 0.9001 | nan | 0.9111 | 0.8964 | 0.8731 | 0.2324 | 0.0 | 0.8802 | 0.6013 | 0.8467 | 0.2197 |
| 0.2158 | 3.0 | 399 | 0.3210 | 0.5505 | 0.7807 | 0.9142 | nan | 0.9250 | 0.8685 | 0.9414 | 0.3878 | 0.0 | 0.8952 | 0.6239 | 0.8901 | 0.3432 |
| 0.1737 | 4.0 | 532 | 0.3572 | 0.5370 | 0.7851 | 0.8905 | nan | 0.8905 | 0.9102 | 0.9121 | 0.4277 | 0.0 | 0.8671 | 0.5637 | 0.8777 | 0.3764 |
| 0.1602 | 5.0 | 665 | 0.6273 | 0.4086 | 0.7632 | 0.7743 | nan | 0.7333 | 0.9343 | 0.9685 | 0.4168 | 0.0 | 0.7198 | 0.4460 | 0.5324 | 0.3449 |
| 0.1707 | 6.0 | 798 | 0.3534 | 0.5442 | 0.7953 | 0.9025 | nan | 0.9056 | 0.9031 | 0.9234 | 0.4492 | 0.0 | 0.8812 | 0.5985 | 0.8629 | 0.3783 |
| 0.1376 | 7.0 | 931 | 0.3266 | 0.5513 | 0.7634 | 0.9262 | nan | 0.9434 | 0.8621 | 0.9288 | 0.3195 | 0.0 | 0.9109 | 0.6623 | 0.8866 | 0.2968 |
| 0.1346 | 8.0 | 1064 | 0.4976 | 0.4916 | 0.7900 | 0.8396 | nan | 0.8190 | 0.9133 | 0.9713 | 0.4565 | 0.0 | 0.8041 | 0.4662 | 0.7906 | 0.3970 |
| 0.1319 | 9.0 | 1197 | 0.3650 | 0.5652 | 0.8404 | 0.9043 | nan | 0.9053 | 0.8856 | 0.9593 | 0.6113 | 0.0 | 0.8829 | 0.5992 | 0.8734 | 0.4706 |
| 0.1229 | 10.0 | 1330 | 0.3201 | 0.5666 | 0.7963 | 0.9299 | nan | 0.9435 | 0.8764 | 0.9389 | 0.4265 | 0.0 | 0.9171 | 0.6896 | 0.8499 | 0.3763 |
| 0.1142 | 11.0 | 1463 | 0.3824 | 0.5576 | 0.8204 | 0.9020 | nan | 0.8988 | 0.9231 | 0.9456 | 0.5142 | 0.0 | 0.8795 | 0.6001 | 0.8711 | 0.4374 |
| 0.0983 | 12.0 | 1596 | 0.3133 | 0.5812 | 0.8297 | 0.9293 | nan | 0.9354 | 0.9046 | 0.9558 | 0.5229 | 0.0 | 0.9136 | 0.6969 | 0.8618 | 0.4335 |
| 0.1058 | 13.0 | 1729 | 0.2965 | 0.5860 | 0.8250 | 0.9364 | nan | 0.9528 | 0.8496 | 0.9598 | 0.5378 | 0.0 | 0.9253 | 0.7162 | 0.8502 | 0.4383 |
| 0.1052 | 14.0 | 1862 | 0.2839 | 0.6064 | 0.8275 | 0.9460 | nan | 0.9674 | 0.8517 | 0.9290 | 0.5621 | 0.0 | 0.9355 | 0.7492 | 0.8930 | 0.4540 |
| 0.0911 | 15.0 | 1995 | 0.3245 | 0.5853 | 0.8116 | 0.9368 | nan | 0.9565 | 0.8504 | 0.9298 | 0.5099 | 0.0 | 0.9243 | 0.7171 | 0.8534 | 0.4318 |
| 0.0889 | 16.0 | 2128 | 0.3094 | 0.5969 | 0.8225 | 0.9422 | nan | 0.9615 | 0.8559 | 0.9376 | 0.5351 | 0.0 | 0.9313 | 0.7353 | 0.8726 | 0.4451 |
| 0.0827 | 17.0 | 2261 | 0.4776 | 0.5187 | 0.8195 | 0.8547 | nan | 0.8390 | 0.9163 | 0.9440 | 0.5786 | 0.0 | 0.8207 | 0.4920 | 0.8216 | 0.4590 |
| 0.0939 | 18.0 | 2394 | 0.3923 | 0.5364 | 0.8375 | 0.8948 | nan | 0.8950 | 0.8831 | 0.9437 | 0.6282 | 0.0 | 0.8746 | 0.6268 | 0.7090 | 0.4717 |
| 0.0799 | 19.0 | 2527 | 0.3560 | 0.5776 | 0.8252 | 0.9254 | nan | 0.9337 | 0.8933 | 0.9409 | 0.5331 | 0.0 | 0.9096 | 0.6846 | 0.8519 | 0.4422 |
| 0.075 | 20.0 | 2660 | 0.3803 | 0.5796 | 0.8338 | 0.9194 | nan | 0.9249 | 0.9078 | 0.9238 | 0.5788 | 0.0 | 0.9032 | 0.6459 | 0.8821 | 0.4670 |
| 0.0844 | 21.0 | 2793 | 0.2885 | 0.6170 | 0.8334 | 0.9507 | nan | 0.9757 | 0.8296 | 0.9390 | 0.5892 | 0.0 | 0.9412 | 0.7654 | 0.8933 | 0.4852 |
| 0.0746 | 22.0 | 2926 | 0.3222 | 0.5831 | 0.8160 | 0.9331 | nan | 0.9481 | 0.8685 | 0.9370 | 0.5105 | 0.0 | 0.9193 | 0.7032 | 0.8716 | 0.4215 |
| 0.072 | 23.0 | 3059 | 0.3481 | 0.5878 | 0.8336 | 0.9266 | nan | 0.9357 | 0.8952 | 0.9271 | 0.5764 | 0.0 | 0.9123 | 0.6824 | 0.8720 | 0.4725 |
| 0.0735 | 24.0 | 3192 | 0.3196 | 0.5974 | 0.8403 | 0.9353 | nan | 0.9496 | 0.8666 | 0.9430 | 0.6018 | 0.0 | 0.9225 | 0.7165 | 0.8649 | 0.4832 |
| 0.0674 | 25.0 | 3325 | 0.3407 | 0.5927 | 0.8435 | 0.9282 | nan | 0.9401 | 0.8786 | 0.9246 | 0.6304 | 0.0 | 0.9141 | 0.6844 | 0.8696 | 0.4953 |
| 0.0712 | 26.0 | 3458 | 0.3356 | 0.5906 | 0.8420 | 0.9301 | nan | 0.9405 | 0.8895 | 0.9299 | 0.6080 | 0.0 | 0.9160 | 0.6905 | 0.8743 | 0.4722 |
| 0.072 | 27.0 | 3591 | 0.3491 | 0.5833 | 0.8372 | 0.9286 | nan | 0.9415 | 0.8636 | 0.9425 | 0.6012 | 0.0 | 0.9161 | 0.6966 | 0.8246 | 0.4790 |
| 0.0641 | 28.0 | 3724 | 0.3130 | 0.6087 | 0.8422 | 0.9473 | nan | 0.9697 | 0.8357 | 0.9427 | 0.6208 | 0.0 | 0.9386 | 0.7613 | 0.8599 | 0.4837 |
| 0.0597 | 29.0 | 3857 | 0.3828 | 0.5666 | 0.8394 | 0.9107 | nan | 0.9141 | 0.8934 | 0.9411 | 0.6092 | 0.0 | 0.8924 | 0.6327 | 0.8343 | 0.4735 |
| 0.0648 | 30.0 | 3990 | 0.3435 | 0.6001 | 0.8372 | 0.9403 | nan | 0.9569 | 0.8708 | 0.9276 | 0.5935 | 0.0 | 0.9292 | 0.7312 | 0.8779 | 0.4623 |
| 0.0618 | 31.0 | 4123 | 0.3531 | 0.5963 | 0.8521 | 0.9303 | nan | 0.9450 | 0.8621 | 0.9240 | 0.6773 | 0.0 | 0.9179 | 0.6842 | 0.8730 | 0.5063 |
| 0.0556 | 32.0 | 4256 | 0.3307 | 0.6037 | 0.8417 | 0.9401 | nan | 0.9576 | 0.8637 | 0.9271 | 0.6183 | 0.0 | 0.9298 | 0.7274 | 0.8637 | 0.4974 |
| 0.0616 | 33.0 | 4389 | 0.3510 | 0.5911 | 0.8347 | 0.9298 | nan | 0.9424 | 0.8714 | 0.9388 | 0.5863 | 0.0 | 0.9158 | 0.6914 | 0.8745 | 0.4740 |
| 0.0603 | 34.0 | 4522 | 0.3467 | 0.6022 | 0.8544 | 0.9334 | nan | 0.9487 | 0.8610 | 0.9274 | 0.6807 | 0.0 | 0.9211 | 0.7029 | 0.8738 | 0.5130 |
| 0.0587 | 35.0 | 4655 | 0.3574 | 0.6017 | 0.8407 | 0.9379 | nan | 0.9555 | 0.8541 | 0.9346 | 0.6187 | 0.0 | 0.9269 | 0.7228 | 0.8627 | 0.4962 |
| 0.0557 | 36.0 | 4788 | 0.3871 | 0.5720 | 0.8334 | 0.9178 | nan | 0.9317 | 0.8416 | 0.9374 | 0.6228 | 0.0 | 0.9051 | 0.6479 | 0.8160 | 0.4911 |
| 0.0567 | 37.0 | 4921 | 0.4425 | 0.5656 | 0.8282 | 0.9070 | nan | 0.9114 | 0.8922 | 0.9244 | 0.5848 | 0.0 | 0.8889 | 0.6100 | 0.8575 | 0.4718 |
| 0.0537 | 38.0 | 5054 | 0.3512 | 0.5946 | 0.8392 | 0.9317 | nan | 0.9463 | 0.8649 | 0.9314 | 0.6142 | 0.0 | 0.9187 | 0.6984 | 0.8637 | 0.4921 |
| 0.0559 | 39.0 | 5187 | 0.3676 | 0.5931 | 0.8437 | 0.9273 | nan | 0.9381 | 0.8798 | 0.9323 | 0.6247 | 0.0 | 0.9129 | 0.6779 | 0.8786 | 0.4959 |
| 0.0502 | 40.0 | 5320 | 0.4149 | 0.5518 | 0.8381 | 0.8984 | nan | 0.9011 | 0.8773 | 0.9368 | 0.6370 | 0.0 | 0.8793 | 0.6069 | 0.7741 | 0.4989 |
| 0.0559 | 41.0 | 5453 | 0.4042 | 0.5694 | 0.8342 | 0.9130 | nan | 0.9206 | 0.8721 | 0.9400 | 0.6041 | 0.0 | 0.8971 | 0.6319 | 0.8286 | 0.4896 |
| 0.0523 | 42.0 | 5586 | 0.3669 | 0.5903 | 0.8462 | 0.9286 | nan | 0.9414 | 0.8676 | 0.9337 | 0.6421 | 0.0 | 0.9162 | 0.6883 | 0.8370 | 0.5102 |
| 0.0525 | 43.0 | 5719 | 0.4140 | 0.5704 | 0.8531 | 0.9081 | nan | 0.9110 | 0.8867 | 0.9417 | 0.6729 | 0.0 | 0.8898 | 0.6220 | 0.8366 | 0.5035 |
| 0.0508 | 44.0 | 5852 | 0.3965 | 0.5714 | 0.8396 | 0.9141 | nan | 0.9227 | 0.8800 | 0.9147 | 0.6409 | 0.0 | 0.8989 | 0.6513 | 0.8007 | 0.5060 |
| 0.0507 | 45.0 | 5985 | 0.3793 | 0.5817 | 0.8392 | 0.9196 | nan | 0.9272 | 0.8932 | 0.9214 | 0.6148 | 0.0 | 0.9042 | 0.6627 | 0.8407 | 0.5011 |
| 0.0494 | 46.0 | 6118 | 0.3500 | 0.6020 | 0.8426 | 0.9363 | nan | 0.9524 | 0.8619 | 0.9322 | 0.6240 | 0.0 | 0.9247 | 0.7142 | 0.8653 | 0.5058 |
| 0.0462 | 47.0 | 6251 | 0.3524 | 0.6031 | 0.8435 | 0.9388 | nan | 0.9545 | 0.8668 | 0.9364 | 0.6163 | 0.0 | 0.9274 | 0.7269 | 0.8703 | 0.4909 |
| 0.0486 | 48.0 | 6384 | 0.3876 | 0.5902 | 0.8397 | 0.9308 | nan | 0.9479 | 0.8557 | 0.9161 | 0.6392 | 0.0 | 0.9203 | 0.6928 | 0.8334 | 0.5046 |
| 0.0461 | 49.0 | 6517 | 0.3674 | 0.5933 | 0.8409 | 0.9326 | nan | 0.9482 | 0.8622 | 0.9258 | 0.6274 | 0.0 | 0.9214 | 0.7053 | 0.8367 | 0.5030 |
| 0.0497 | 50.0 | 6650 | 0.4018 | 0.5838 | 0.8374 | 0.9246 | nan | 0.9390 | 0.8519 | 0.9341 | 0.6244 | 0.0 | 0.9102 | 0.6733 | 0.8361 | 0.4992 |
| 0.0491 | 51.0 | 6783 | 0.4036 | 0.5824 | 0.8513 | 0.9198 | nan | 0.9272 | 0.8805 | 0.9403 | 0.6573 | 0.0 | 0.9037 | 0.6712 | 0.8169 | 0.5203 |
| 0.046 | 52.0 | 6916 | 0.3913 | 0.5820 | 0.8395 | 0.9243 | nan | 0.9347 | 0.8771 | 0.9336 | 0.6126 | 0.0 | 0.9105 | 0.6792 | 0.8244 | 0.4960 |
| 0.0488 | 53.0 | 7049 | 0.3441 | 0.6010 | 0.8504 | 0.9362 | nan | 0.9523 | 0.8521 | 0.9457 | 0.6517 | 0.0 | 0.9250 | 0.7225 | 0.8496 | 0.5081 |
| 0.0458 | 54.0 | 7182 | 0.3784 | 0.5977 | 0.8382 | 0.9378 | nan | 0.9603 | 0.8212 | 0.9375 | 0.6337 | 0.0 | 0.9286 | 0.7157 | 0.8387 | 0.5053 |
| 0.0449 | 55.0 | 7315 | 0.3506 | 0.6068 | 0.8493 | 0.9404 | nan | 0.9579 | 0.8554 | 0.9385 | 0.6456 | 0.0 | 0.9300 | 0.7357 | 0.8549 | 0.5132 |
| 0.0482 | 56.0 | 7448 | 0.4005 | 0.5819 | 0.8414 | 0.9249 | nan | 0.9374 | 0.8642 | 0.9337 | 0.6303 | 0.0 | 0.9119 | 0.6831 | 0.8139 | 0.5006 |
| 0.0434 | 57.0 | 7581 | 0.3749 | 0.5914 | 0.8465 | 0.9294 | nan | 0.9423 | 0.8675 | 0.9339 | 0.6421 | 0.0 | 0.9171 | 0.6999 | 0.8265 | 0.5134 |
| 0.0435 | 58.0 | 7714 | 0.4195 | 0.5722 | 0.8400 | 0.9172 | nan | 0.9274 | 0.8700 | 0.9234 | 0.6392 | 0.0 | 0.9025 | 0.6588 | 0.7954 | 0.5044 |
| 0.0442 | 59.0 | 7847 | 0.3975 | 0.5828 | 0.8407 | 0.9257 | nan | 0.9398 | 0.8563 | 0.9312 | 0.6356 | 0.0 | 0.9134 | 0.6866 | 0.8103 | 0.5037 |
| 0.0442 | 60.0 | 7980 | 0.3845 | 0.5929 | 0.8457 | 0.9315 | nan | 0.9459 | 0.8603 | 0.9363 | 0.6404 | 0.0 | 0.9193 | 0.7041 | 0.8308 | 0.5103 |
| 0.0422 | 61.0 | 8113 | 0.3875 | 0.5963 | 0.8465 | 0.9338 | nan | 0.9489 | 0.8616 | 0.9340 | 0.6413 | 0.0 | 0.9226 | 0.7135 | 0.8381 | 0.5072 |
| 0.0436 | 62.0 | 8246 | 0.3859 | 0.6022 | 0.8497 | 0.9385 | nan | 0.9566 | 0.8477 | 0.9382 | 0.6562 | 0.0 | 0.9289 | 0.7300 | 0.8376 | 0.5147 |
| 0.0429 | 63.0 | 8379 | 0.3857 | 0.5956 | 0.8425 | 0.9357 | nan | 0.9534 | 0.8481 | 0.9357 | 0.6327 | 0.0 | 0.9249 | 0.7233 | 0.8283 | 0.5016 |
| 0.0446 | 64.0 | 8512 | 0.3778 | 0.5976 | 0.8495 | 0.9343 | nan | 0.9492 | 0.8602 | 0.9399 | 0.6489 | 0.0 | 0.9232 | 0.7191 | 0.8305 | 0.5153 |
| 0.0429 | 65.0 | 8645 | 0.3889 | 0.5948 | 0.8478 | 0.9330 | nan | 0.9490 | 0.8548 | 0.9325 | 0.6549 | 0.0 | 0.9225 | 0.7075 | 0.8271 | 0.5167 |
| 0.0454 | 66.0 | 8778 | 0.3915 | 0.5941 | 0.8470 | 0.9329 | nan | 0.9490 | 0.8571 | 0.9271 | 0.6547 | 0.0 | 0.9221 | 0.7087 | 0.8278 | 0.5117 |
| 0.0427 | 67.0 | 8911 | 0.3924 | 0.5967 | 0.8455 | 0.9349 | nan | 0.9518 | 0.8520 | 0.9350 | 0.6433 | 0.0 | 0.9247 | 0.7167 | 0.8290 | 0.5133 |
| 0.0425 | 68.0 | 9044 | 0.3990 | 0.5992 | 0.8491 | 0.9358 | nan | 0.9524 | 0.8545 | 0.9355 | 0.6541 | 0.0 | 0.9250 | 0.7187 | 0.8387 | 0.5136 |
| 0.0429 | 69.0 | 9177 | 0.3911 | 0.5909 | 0.8499 | 0.9303 | nan | 0.9451 | 0.8532 | 0.9394 | 0.6619 | 0.0 | 0.9192 | 0.7029 | 0.8178 | 0.5146 |
| 0.0465 | 70.0 | 9310 | 0.3840 | 0.5977 | 0.8481 | 0.9332 | nan | 0.9473 | 0.8700 | 0.9278 | 0.6473 | 0.0 | 0.9215 | 0.7079 | 0.8480 | 0.5110 |
| 0.0436 | 71.0 | 9443 | 0.3862 | 0.5974 | 0.8456 | 0.9351 | nan | 0.9518 | 0.8534 | 0.9359 | 0.6413 | 0.0 | 0.9248 | 0.7162 | 0.8338 | 0.5124 |
| 0.0435 | 72.0 | 9576 | 0.3926 | 0.5952 | 0.8448 | 0.9328 | nan | 0.9484 | 0.8585 | 0.9318 | 0.6405 | 0.0 | 0.9217 | 0.7073 | 0.8386 | 0.5084 |
| 0.0421 | 73.0 | 9709 | 0.3961 | 0.5984 | 0.8467 | 0.9348 | nan | 0.9513 | 0.8564 | 0.9309 | 0.6482 | 0.0 | 0.9243 | 0.7119 | 0.8414 | 0.5143 |
| 0.0409 | 74.0 | 9842 | 0.3973 | 0.5982 | 0.8494 | 0.9341 | nan | 0.9498 | 0.8596 | 0.9306 | 0.6578 | 0.0 | 0.9233 | 0.7094 | 0.8401 | 0.5181 |
| 0.041 | 75.0 | 9975 | 0.3898 | 0.5963 | 0.8476 | 0.9335 | nan | 0.9493 | 0.8561 | 0.9354 | 0.6498 | 0.0 | 0.9227 | 0.7108 | 0.8329 | 0.5153 |
| 0.0436 | 75.19 | 10000 | 0.3966 | 0.5967 | 0.8460 | 0.9344 | nan | 0.9510 | 0.8524 | 0.9362 | 0.6444 | 0.0 | 0.9239 | 0.7125 | 0.8335 | 0.5139 |
### 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"
] |
FashionAI4Wholesale/segformer-b2-finetuned-segments-dresses-071123 |
# Model Card for Model ID
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## Model Details
### Model Description
A SegFormer-b2 model fine tuned with private dress on mannequin datasets
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| [
"unlabeled",
"dress",
"mannequin",
"background"
] |
varcoder/segformer-b4-crack-segmentation-dataset |
<!-- 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-b4-crack-segmentation-dataset
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0594
- Mean Iou: 0.3346
- Mean Accuracy: 0.6691
- Overall Accuracy: 0.6691
- Accuracy Background: nan
- Accuracy Crack: 0.6691
- Iou Background: 0.0
- Iou Crack: 0.6691
## 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 Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.2287 | 0.02 | 100 | 0.2515 | 0.1734 | 0.3468 | 0.3468 | nan | 0.3468 | 0.0 | 0.3468 |
| 0.1792 | 0.04 | 200 | 0.1594 | 0.1671 | 0.3342 | 0.3342 | nan | 0.3342 | 0.0 | 0.3342 |
| 0.1177 | 0.06 | 300 | 0.1762 | 0.1044 | 0.2088 | 0.2088 | nan | 0.2088 | 0.0 | 0.2088 |
| 0.0821 | 0.08 | 400 | 0.1706 | 0.2065 | 0.4130 | 0.4130 | nan | 0.4130 | 0.0 | 0.4130 |
| 0.0666 | 0.1 | 500 | 0.1507 | 0.1931 | 0.3863 | 0.3863 | nan | 0.3863 | 0.0 | 0.3863 |
| 0.0675 | 0.12 | 600 | 0.1374 | 0.3114 | 0.6227 | 0.6227 | nan | 0.6227 | 0.0 | 0.6227 |
| 0.0267 | 0.15 | 700 | 0.1400 | 0.2171 | 0.4342 | 0.4342 | nan | 0.4342 | 0.0 | 0.4342 |
| 0.0192 | 0.17 | 800 | 0.1067 | 0.1594 | 0.3187 | 0.3187 | nan | 0.3187 | 0.0 | 0.3187 |
| 0.0711 | 0.19 | 900 | 0.1002 | 0.2915 | 0.5830 | 0.5830 | nan | 0.5830 | 0.0 | 0.5830 |
| 0.0761 | 0.21 | 1000 | 0.0785 | 0.3099 | 0.6199 | 0.6199 | nan | 0.6199 | 0.0 | 0.6199 |
| 0.0802 | 0.23 | 1100 | 0.0829 | 0.3086 | 0.6173 | 0.6173 | nan | 0.6173 | 0.0 | 0.6173 |
| 0.1058 | 0.25 | 1200 | 0.0895 | 0.2139 | 0.4278 | 0.4278 | nan | 0.4278 | 0.0 | 0.4278 |
| 0.0409 | 0.27 | 1300 | 0.0792 | 0.3237 | 0.6475 | 0.6475 | nan | 0.6475 | 0.0 | 0.6475 |
| 0.063 | 0.29 | 1400 | 0.0739 | 0.3084 | 0.6168 | 0.6168 | nan | 0.6168 | 0.0 | 0.6168 |
| 0.0669 | 0.31 | 1500 | 0.0747 | 0.3326 | 0.6653 | 0.6653 | nan | 0.6653 | 0.0 | 0.6653 |
| 0.1277 | 0.33 | 1600 | 0.0735 | 0.3149 | 0.6297 | 0.6297 | nan | 0.6297 | 0.0 | 0.6297 |
| 0.0388 | 0.35 | 1700 | 0.0708 | 0.2525 | 0.5050 | 0.5050 | nan | 0.5050 | 0.0 | 0.5050 |
| 0.0332 | 0.37 | 1800 | 0.0726 | 0.2908 | 0.5816 | 0.5816 | nan | 0.5816 | 0.0 | 0.5816 |
| 0.0435 | 0.4 | 1900 | 0.0673 | 0.2893 | 0.5786 | 0.5786 | nan | 0.5786 | 0.0 | 0.5786 |
| 0.1297 | 0.42 | 2000 | 0.0698 | 0.3438 | 0.6877 | 0.6877 | nan | 0.6877 | 0.0 | 0.6877 |
| 0.1202 | 0.44 | 2100 | 0.0745 | 0.2899 | 0.5798 | 0.5798 | nan | 0.5798 | 0.0 | 0.5798 |
| 0.0549 | 0.46 | 2200 | 0.0657 | 0.3522 | 0.7044 | 0.7044 | nan | 0.7044 | 0.0 | 0.7044 |
| 0.0223 | 0.48 | 2300 | 0.0808 | 0.2686 | 0.5372 | 0.5372 | nan | 0.5372 | 0.0 | 0.5372 |
| 0.0464 | 0.5 | 2400 | 0.0631 | 0.3221 | 0.6442 | 0.6442 | nan | 0.6442 | 0.0 | 0.6442 |
| 0.0364 | 0.52 | 2500 | 0.0778 | 0.3410 | 0.6820 | 0.6820 | nan | 0.6820 | 0.0 | 0.6820 |
| 0.047 | 0.54 | 2600 | 0.0689 | 0.3489 | 0.6978 | 0.6978 | nan | 0.6978 | 0.0 | 0.6978 |
| 0.0322 | 0.56 | 2700 | 0.0640 | 0.2863 | 0.5727 | 0.5727 | nan | 0.5727 | 0.0 | 0.5727 |
| 0.0453 | 0.58 | 2800 | 0.0574 | 0.3340 | 0.6681 | 0.6681 | nan | 0.6681 | 0.0 | 0.6681 |
| 0.0347 | 0.6 | 2900 | 0.0611 | 0.3289 | 0.6578 | 0.6578 | nan | 0.6578 | 0.0 | 0.6578 |
| 0.0916 | 0.62 | 3000 | 0.0609 | 0.3357 | 0.6714 | 0.6714 | nan | 0.6714 | 0.0 | 0.6714 |
| 0.0523 | 0.65 | 3100 | 0.0557 | 0.3318 | 0.6637 | 0.6637 | nan | 0.6637 | 0.0 | 0.6637 |
| 0.1246 | 0.67 | 3200 | 0.0558 | 0.3294 | 0.6588 | 0.6588 | nan | 0.6588 | 0.0 | 0.6588 |
| 0.0501 | 0.69 | 3300 | 0.0697 | 0.2955 | 0.5910 | 0.5910 | nan | 0.5910 | 0.0 | 0.5910 |
| 0.0312 | 0.71 | 3400 | 0.0604 | 0.3414 | 0.6827 | 0.6827 | nan | 0.6827 | 0.0 | 0.6827 |
| 0.0449 | 0.73 | 3500 | 0.0612 | 0.3305 | 0.6611 | 0.6611 | nan | 0.6611 | 0.0 | 0.6611 |
| 0.0111 | 0.75 | 3600 | 0.0617 | 0.2930 | 0.5860 | 0.5860 | nan | 0.5860 | 0.0 | 0.5860 |
| 0.0206 | 0.77 | 3700 | 0.0627 | 0.3663 | 0.7326 | 0.7326 | nan | 0.7326 | 0.0 | 0.7326 |
| 0.051 | 0.79 | 3800 | 0.0649 | 0.3159 | 0.6318 | 0.6318 | nan | 0.6318 | 0.0 | 0.6318 |
| 0.0243 | 0.81 | 3900 | 0.0600 | 0.3370 | 0.6740 | 0.6740 | nan | 0.6740 | 0.0 | 0.6740 |
| 0.0108 | 0.83 | 4000 | 0.0614 | 0.3595 | 0.7190 | 0.7190 | nan | 0.7190 | 0.0 | 0.7190 |
| 0.0951 | 0.85 | 4100 | 0.0564 | 0.3571 | 0.7142 | 0.7142 | nan | 0.7142 | 0.0 | 0.7142 |
| 0.0731 | 0.87 | 4200 | 0.0597 | 0.3497 | 0.6994 | 0.6994 | nan | 0.6994 | 0.0 | 0.6994 |
| 0.0307 | 0.9 | 4300 | 0.0636 | 0.3468 | 0.6937 | 0.6937 | nan | 0.6937 | 0.0 | 0.6937 |
| 0.1039 | 0.92 | 4400 | 0.0594 | 0.3397 | 0.6795 | 0.6795 | nan | 0.6795 | 0.0 | 0.6795 |
| 0.0083 | 0.94 | 4500 | 0.0606 | 0.3512 | 0.7024 | 0.7024 | nan | 0.7024 | 0.0 | 0.7024 |
| 0.0113 | 0.96 | 4600 | 0.0597 | 0.3288 | 0.6576 | 0.6576 | nan | 0.6576 | 0.0 | 0.6576 |
| 0.0417 | 0.98 | 4700 | 0.0595 | 0.3405 | 0.6811 | 0.6811 | nan | 0.6811 | 0.0 | 0.6811 |
| 0.1944 | 1.0 | 4800 | 0.0594 | 0.3346 | 0.6691 | 0.6691 | nan | 0.6691 | 0.0 | 0.6691 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 | [
"background",
"crack"
] |
mccaly/test2 | # A Large-Scale Benchmark for Food Image Segmentation
By [Xiongwei Wu](http://xiongweiwu.github.io/), [Xin Fu](https://xinfu607.github.io/), Ying Liu, [Ee-Peng Lim](http://www.mysmu.edu/faculty/eplim/), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home/), [Qianru Sun](https://qianrusun.com/).
<div align="center">
<img src="resources/foodseg103.png" width="800"/>
</div>
<br />
## Introduction
We build a new food image dataset FoodSeg103 containing 7,118 images. We annotate these images with 104 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks.
In addition, we propose a multi-modality pre-training approach called ReLeM that explicitly equips a segmentation model with rich and semantic food knowledge.
In this software, we use three popular semantic segmentation methods (i.e., Dilated Convolution based, Feature Pyramid based, and Vision Transformer based) as baselines, and evaluate them as well as ReLeM on our new datasets. We believe that the FoodSeg103 and the pre-trained models using ReLeM can serve as a benchmark to facilitate future works on fine-grained food image understanding.
Please refer our [paper](https://arxiv.org/abs/2105.05409) and our [homepage](https://xiongweiwu.github.io/foodseg103.html) for more details.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Installation
Please refer to [get_started.md](docs/get_started.md#installation) for installation.
## Dataset
Please download the file from [url](https://research.larc.smu.edu.sg/downloads/datarepo/FoodSeg103.zip) and unzip the data in ./data folder (./data/FoodSeg103/), with passwd: LARCdataset9947
## Leaderboard
Please refer to [leaderboard](https://paperswithcode.com/dataset/foodseg103) in paperwithcode website.
## Benchmark and model zoo
:exclamation::exclamation::exclamation: **We have finished the course so the models are available again. Please download the trained models from THIS [link](https://smu-my.sharepoint.com/:u:/g/personal/xwwu_smu_edu_sg/EWBcCC3QrO9LthKX66QCzyoBhFU7PHXKcHhh1lgIC98uKw?e=bHT7vM):eyes: .**
Encoder | Decoder | Crop Size | Batch Size |mIoU | mAcc | Link
--- |:---:|:---:|:---:|:---:|:---:|:---:
R-50 | [FPN](https://arxiv.org/abs/1901.02446) | 512x1024 | 8 | 27.8 | 38.2 | [Model+Config](https://drive.google.com/drive/folders/1CQ5CXxASAoobj7bKqyuvazkeusqMAM4F?usp=sharing)
ReLeM-R-50 | FPN | 512x1024 | 8 | 29.1 | 39.8 | [Model+Config](https://drive.google.com/drive/folders/1m7N2EE8jkX67a0lD6GZ4NQgr4gEcWpDU?usp=sharing)
R-50 | [CCNet](https://arxiv.org/abs/1811.11721) | 512x1024 | 8 | 35.5 | 45.3 | [Model+Config](https://drive.google.com/drive/folders/1pNPbtrGqCq_Zlina2PCs6X8bIvY9ZZxG?usp=sharing)
ReLeM-R-50 | CCNet | 512x1024 | 8 | 36.8 | 47.4 | [Model+Config](https://drive.google.com/drive/folders/1FWwxAsZzDnBbDBEbohqOA8htyWgMLM4U?usp=sharing)
[PVT-S](https://arxiv.org/abs/2102.12122) | FPN | 512x1024 | 8 | 31.3 | 43.0 | Model+Config
ReLeM-PVT-S | FPN | 512x1024 | 8 | 32.0 | 44.1 | Model+Config
[ViT-16/B](https://openreview.net/forum?id=YicbFdNTTy) | [Naive](https://arxiv.org/abs/2012.15840) | 768x768 | 4 | 41.3 | 52.7 | [Model+Config](https://drive.google.com/drive/folders/19b3VG906CA-5kQFaJVk5U6kDxnw9HcWL?usp=sharing)
ReLeM-ViT-16/B | Naive | 768x768 | 4 | 43.9 | 57.0 | [Model+Config](https://drive.google.com/drive/folders/10yKiu8aMeTGphU2CKT2ybeAC3ezgDnXP?usp=sharing)
ViT-16/B | PUP | 768x768 | 4 | 38.5 | 49.1 | Model+Config
ReLeM-ViT-16/B | PUP | 768x768 | 4 | 42.5 | 53.9 | Model+Config
ViT-16/B | [MLA](https://arxiv.org/abs/2012.15840) | 768x768 | 4 | 45.1 | 57.4 | [Model+Config](https://drive.google.com/drive/folders/17Ht1HQDaBJmS0FXaXGjHk0VQNhAJxrlF?usp=sharing)
ReLeM-ViT-16/B | MLA | 768x768 | 4 | 43.3 | 55.9 | [Model+Config](https://drive.google.com/drive/folders/12OlkStefNmELNLo-xJqc-lE-kPZ7DvPV?usp=sharing)
[ViT-16/L](https://openreview.net/forum?id=YicbFdNTTy) | MLA | 768x768 | 4 | 44.5 | 56.6 | [Model+Config](https://drive.google.com/drive/folders/1PS4uh2zktNc0hh-mSLZkRTqgNnkfh7xu?usp=sharing)
[Swin-S](https://arxiv.org/abs/2103.14030) | [UperNet](https://arxiv.org/abs/1807.10221) | 512x1024 | 8 | 41.6 | 53.6 | [Model+Config](https://drive.google.com/drive/folders/1E5fZga8h65dNZCX1m8zywvB8MwrleFNg?usp=sharing)
[Swin-B](https://arxiv.org/abs/2103.14030) | UperNet | 512x1024 | 8 | 41.2 | 53.9 | [Model+Config](https://drive.google.com/drive/folders/1kqOsH51h1pa-88tbFVUV3mmzTNCGzqd0?usp=sharing)
[1] *We do not include the implementation of [swin](https://arxiv.org/abs/2103.14030) in this software. You can use the official [implementation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation) based on our provided models.* \
[2] *We use Step-wise learning policy to train PVT model since we found this policy can yield higher performance, and for other baselines we adopt the default settings.* \
[3] *We use Recipe1M to train ReLeM-PVT-S while other ReLeM models are trained with Recipe1M+ due to time limitation.*
## Train & Test
Train script:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=${PORT:-300} tools/train.py --config [config] --work-dir [work-dir] --launcher pytorch
```
Exmaple:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=${PORT:-300} tools/train.py --config configs/foodnet/SETR_Naive_768x768_80k_base_RM.py --work-dir checkpoints/SETR_Naive_ReLeM --launcher pytorch
```
Test script:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=${PORT:-999} tools/test.py [config] [weights] --launcher pytorch --eval mIoU
```
Example:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=${PORT:-999} tools/test.py checkpoints/SETR_Naive_ReLeM/SETR_Naive_768x768_80k_base_RM.py checkpoints/SETR_Naive_ReLeM/iter_80000.pth --launcher pytorch --eval mIoU
```
## ReLeM
We train recipe information based on the implementation of [im2recipe](https://github.com/torralba-lab/im2recipe-Pytorch) with small modifications, which is trained on [Recipe1M+](http://pic2recipe.csail.mit.edu/) dataset (test images of FoodSeg103 are removed). I may upload the lmdb file later due to the huge datasize (>35G).
It takes about 2~3 weeks to train a ReLeM ViT-Base model with 8 Tesla-V100 cards, so I strongly recommend you use my pre-trained models([link](https://drive.google.com/drive/folders/1LRCHxeMuCXMb68I1XFI8q-aQ2cCyUx_r?usp=sharing)).
## Citation
If you find this project useful in your research, please consider cite:
```latex
@inproceedings{wu2021foodseg,
title={A Large-Scale Benchmark for Food Image Segmentation},
author={Wu, Xiongwei and Fu, Xin and Liu, Ying and Lim, Ee-Peng and Hoi, Steven CH and Sun, Qianru},
booktitle={Proceedings of ACM international conference on Multimedia},
year={2021}
}
```
## Other Issues
If you meet other issues in using the software, you can check the original mmsegmentation (see [doc](https://mmsegmentation.readthedocs.io/) for more details).
## Acknowledgement
The segmentation software in this project was developed mainly by extending the [segmentation](https://github.com/open-mmlab/mmsegmentation/). | [
"background",
"candy",
"egg tart",
"french fries",
"chocolate",
"biscuit",
"popcorn",
"pudding",
"ice cream",
"cheese butter",
"cake",
"wine",
"milkshake",
"coffee",
"juice",
"milk",
"tea",
"almond",
"red beans",
"cashew",
"dried cranberries",
"soy",
"walnut",
"peanut",
"egg",
"apple",
"date",
"apricot",
"avocado",
"banana",
"strawberry",
"cherry",
"blueberry",
"raspberry",
"mango",
"olives",
"peach",
"lemon",
"pear",
"fig",
"pineapple",
"grape",
"kiwi",
"melon",
"orange",
"watermelon",
"steak",
"pork",
"chicken duck",
"sausage",
"fried meat",
"lamb",
"sauce",
"crab",
"fish",
"shellfish",
"shrimp",
"soup",
"bread",
"corn",
"hamburg",
"pizza",
"hanamaki baozi",
"wonton dumplings",
"pasta",
"noodles",
"rice",
"pie",
"tofu",
"eggplant",
"potato",
"garlic",
"cauliflower",
"tomato",
"kelp",
"seaweed",
"spring onion",
"rape",
"ginger",
"okra",
"lettuce",
"pumpkin",
"cucumber",
"white radish",
"carrot",
"asparagus",
"bamboo shoots",
"broccoli",
"celery stick",
"cilantro mint",
"snow peas",
"cabbage",
"bean sprouts",
"onion",
"pepper",
"green beans",
"french beans",
"king oyster mushroom",
"shiitake",
"enoki mushroom",
"oyster mushroom",
"white button mushroom",
"salad",
"other ingredients"
] |
Lexic0n/segformer-b0-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-b0-finetuned-human-parsing
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9476
- Mean Iou: 0.0726
- Mean Accuracy: 0.1221
- Overall Accuracy: 0.3575
- Accuracy Background: nan
- Accuracy Hat: 0.0048
- Accuracy Hair: 0.4813
- Accuracy Sunglasses: 0.0
- Accuracy Upper-clothes: 0.9405
- Accuracy Skirt: 0.0000
- Accuracy Pants: 0.0631
- Accuracy Dress: 0.1031
- Accuracy Belt: 0.0
- Accuracy Left-shoe: 0.0011
- Accuracy Right-shoe: 0.0010
- Accuracy Face: 0.4406
- Accuracy Left-leg: 0.0291
- Accuracy Right-leg: 0.0
- Accuracy Left-arm: 0.0
- Accuracy Right-arm: 0.0001
- Accuracy Bag: 0.0114
- Accuracy Scarf: 0.0
- Iou Background: 0.0
- Iou Hat: 0.0043
- Iou Hair: 0.4221
- Iou Sunglasses: 0.0
- Iou Upper-clothes: 0.3239
- Iou Skirt: 0.0000
- Iou Pants: 0.0559
- Iou Dress: 0.0728
- Iou Belt: 0.0
- Iou Left-shoe: 0.0011
- Iou Right-shoe: 0.0009
- Iou Face: 0.3872
- Iou Left-leg: 0.0271
- Iou Right-leg: 0.0
- Iou Left-arm: 0.0
- Iou Right-arm: 0.0001
- Iou Bag: 0.0106
- Iou Scarf: 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: 2
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:|
| 2.5768 | 0.4 | 20 | 2.7812 | 0.0726 | 0.1332 | 0.2876 | nan | 0.0178 | 0.3204 | 0.0004 | 0.5548 | 0.0004 | 0.2555 | 0.2373 | 0.0 | 0.0103 | 0.0003 | 0.5637 | 0.0287 | 0.0302 | 0.0001 | 0.0008 | 0.2435 | 0.0 | 0.0 | 0.0166 | 0.2759 | 0.0001 | 0.2781 | 0.0004 | 0.1710 | 0.1295 | 0.0 | 0.0098 | 0.0003 | 0.3251 | 0.0260 | 0.0248 | 0.0001 | 0.0007 | 0.0491 | 0.0 |
| 2.2093 | 0.8 | 40 | 2.5166 | 0.0563 | 0.1052 | 0.3288 | nan | 0.0 | 0.1994 | 0.0 | 0.9447 | 0.0015 | 0.0435 | 0.1164 | 0.0 | 0.0008 | 0.0000 | 0.4655 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0153 | 0.0 | 0.0 | 0.0 | 0.1946 | 0.0 | 0.3037 | 0.0015 | 0.0417 | 0.0842 | 0.0 | 0.0008 | 0.0000 | 0.3726 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0124 | 0.0 |
| 1.8804 | 1.2 | 60 | 2.0209 | 0.0632 | 0.1110 | 0.3374 | nan | 0.0087 | 0.3724 | 0.0 | 0.9475 | 0.0014 | 0.0162 | 0.0528 | 0.0 | 0.0001 | 0.0008 | 0.4257 | 0.0561 | 0.0001 | 0.0 | 0.0 | 0.0055 | 0.0 | 0.0 | 0.0077 | 0.3472 | 0.0 | 0.3086 | 0.0014 | 0.0156 | 0.0403 | 0.0 | 0.0001 | 0.0008 | 0.3597 | 0.0515 | 0.0001 | 0.0 | 0.0 | 0.0052 | 0.0 |
| 1.8776 | 1.6 | 80 | 2.0016 | 0.0665 | 0.1154 | 0.3454 | nan | 0.0056 | 0.4172 | 0.0 | 0.9412 | 0.0000 | 0.0490 | 0.0697 | 0.0 | 0.0002 | 0.0006 | 0.4349 | 0.0329 | 0.0000 | 0.0 | 0.0000 | 0.0100 | 0.0 | 0.0 | 0.0048 | 0.3791 | 0.0 | 0.3138 | 0.0000 | 0.0438 | 0.0542 | 0.0 | 0.0002 | 0.0006 | 0.3608 | 0.0304 | 0.0000 | 0.0 | 0.0000 | 0.0093 | 0.0 |
| 1.8471 | 2.0 | 100 | 1.9476 | 0.0726 | 0.1221 | 0.3575 | nan | 0.0048 | 0.4813 | 0.0 | 0.9405 | 0.0000 | 0.0631 | 0.1031 | 0.0 | 0.0011 | 0.0010 | 0.4406 | 0.0291 | 0.0 | 0.0 | 0.0001 | 0.0114 | 0.0 | 0.0 | 0.0043 | 0.4221 | 0.0 | 0.3239 | 0.0000 | 0.0559 | 0.0728 | 0.0 | 0.0011 | 0.0009 | 0.3872 | 0.0271 | 0.0 | 0.0 | 0.0001 | 0.0106 | 0.0 |
### 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"
] |
Isaacks/segformer-finetuned-ihc |
<!-- 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-ihc
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Isaacks/ihc_slide_tissue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0326
- eval_mean_iou: 0.0
- eval_mean_accuracy: nan
- eval_overall_accuracy: nan
- eval_accuracy_background: nan
- eval_accuracy_tissue: nan
- eval_iou_background: 0.0
- eval_iou_tissue: 0.0
- eval_runtime: 19.1281
- eval_samples_per_second: 0.784
- eval_steps_per_second: 0.105
- epoch: 9.15
- step: 183
## 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
| [
"background",
"tissue"
] |
Isaacks/test_push |
<!-- 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_push
This model is a fine-tuned version of [Isaacks/test_push](https://huggingface.co/Isaacks/test_push) on the Isaacks/ihc_slide_tissue 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: 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: 3.0
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.3
- Tokenizers 0.13.3
| [
"background",
"tissue"
] |
univers1123/segformer-b0-scene-parse-150 |
<!-- 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-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3431
- Mean Iou: 0.0959
- Mean Accuracy: 0.1537
- Overall Accuracy: 0.5496
- Per Category Iou: [0.44824978876617866, 0.7548671615728508, 0.7119201505944329, 0.5304481563680256, 0.5684691275095736, 0.33051502835188457, 0.6982393617021276, 0.0, 0.3703529914609331, 0.6659141206351092, 0.028823893043720683, 0.17181416221210322, 0.052153820762502065, 0.0, 0.0, 0.0005543923800536699, 0.40565901784724534, 0.05230759173712194, 0.0, 0.07225859019823891, 0.29980315155352005, nan, 0.003601361102652032, 0.0, 0.0, nan, 0.0, 0.0, 0.38898705304076847, 0.05940808241958817, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0]
- Per Category Accuracy: [0.8427949438202247, 0.9402615186644498, 0.7846678763016725, 0.7286579984703183, 0.8303175022736334, 0.469325820621132, 0.9020126572710594, nan, 0.5974398752913491, 0.9683369330453564, 0.05725843345934362, 0.24220857754209693, 0.12377594986290638, 0.0, 0.0, 0.0005611873291065182, 0.9580213623749935, 0.08566177782535773, 0.0, 0.16335928996064641, 0.43531591571750716, nan, 0.0036190907034607555, 0.0, 0.0, nan, nan, 0.0, 0.45750991876062724, 0.24276243093922653, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 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 | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.9918 | 0.5 | 20 | 4.8969 | 0.0108 | 0.0487 | 0.1875 | [0.18900717264720193, 0.17829851112253592, 0.40144144917749963, 0.1885612981412077, 0.11895876927062042, 0.09866217819019046, 0.0057814729592400894, 0.0, 0.0, 0.0, 0.009622579129617706, 0.022129523898301137, 0.0037298450062015365, 0.0, 0.0, 0.0, 0.06277911646586345, 0.0, 0.0, 0.003906402593851322, 0.012887091043266734, nan, 0.0019786836291242806, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015807537456273512, 0.016491354532320934, 0.0, 0.0, 0.0, nan, 0.001438298321545445, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025794247180438844, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.012904182735093445, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0] | [0.2067212858926342, 0.2463388525747603, 0.8113718838750394, 0.515406462102938, 0.1316758582686337, 0.11907251217424253, 0.007544887960475232, nan, 0.0, 0.0, 0.013315354795213214, 0.22085775420969392, 0.054576315445880666, 0.0, 0.0, 0.0, 0.07673176606105031, 0.0, 0.0, 0.004186552792430713, 0.013544374703761687, nan, 0.0021687933259709673, 0.0, 0.0, nan, nan, 0.0, 0.01809937653504629, 0.30082872928176796, 0.0, nan, 0.0, nan, 0.0019430975470621792, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.3248302818350134, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.05007914807886027, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 4.551 | 1.0 | 40 | 4.5955 | 0.0202 | 0.0640 | 0.3442 | [0.3519414971273263, 0.3937735618735424, 0.42161939446421154, 0.21975617697678057, 0.3809140886893701, 0.09030492572322127, 0.005777833411293457, 0.0, 0.0, 0.0, 0.0, 0.02885598249784122, 0.0, 0.0, 0.0, 0.0, 0.0573680633208358, 0.0, 0.0011308737583006134, 0.006298751950078003, 0.057306667023884476, nan, 0.0014234124996705063, 0.0, 0.0, 0.0, nan, 0.0, 0.017088433502956954, 0.023128390596745027, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0052120890103356235, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0] | [0.5003401997503121, 0.785197975265903, 0.952571789207952, 0.4632600048849975, 0.5973921536125012, 0.09887449654069073, 0.006026290292656446, nan, 0.0, 0.0, 0.0, 0.4399013861754582, 0.0, 0.0, 0.0, 0.0, 0.06849835069898948, 0.0, 0.0098046905639658, 0.0067612827597756005, 0.06144521207072375, nan, 0.0014369918969623589, 0.0, 0.0, nan, nan, 0.0, 0.018779520120914415, 0.07066298342541437, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.01695124459987657, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 4.3293 | 1.5 | 60 | 4.2491 | 0.0352 | 0.0776 | 0.4184 | [0.3871309742960387, 0.45006666311952553, 0.6112315905191344, 0.3032571607536305, 0.44533070206501846, 0.063346836376098, 0.022528980712202534, 0.0, 0.0, 0.0, 0.0, 0.029790687595074403, 0.0019532612486920127, 0.0, 0.0, 0.0, 0.24116048081196448, 0.0, 0.0, 0.0003640114056907116, 0.2558965972702686, nan, 0.0005833454863642993, 0.0, 0.0, nan, 0.0, 0.0, 0.002751498247333308, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7088701622971286, 0.8308114309791149, 0.8838434837488167, 0.5903983055833383, 0.8704499330230141, 0.06874412338436925, 0.02526644174013427, nan, 0.0, 0.0, 0.0, 0.3261233603125872, 0.0036558297427862646, 0.0, 0.0, 0.0, 0.305421749829834, 0.0, 0.0, 0.00037678975131876413, 0.350616893842552, nan, 0.0005854411432068869, 0.0, 0.0, nan, nan, 0.0, 0.0027583600982429624, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.9435 | 2.0 | 80 | 4.1371 | 0.0349 | 0.0760 | 0.4181 | [0.35782571228523724, 0.4339785275779836, 0.33734326770427064, 0.3463785302488506, 0.485148026657255, 0.060373176138162864, 0.25141442893216265, 0.0, 0.0, 0.0, 0.0, 0.03557946863062567, 0.0016987542468856172, 0.0, 0.0, 0.0, 0.07208336234305739, 0.0, 0.0, 0.0, 0.23636984914447706, nan, 0.0012355355980390855, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0013693940431359123, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.5806023720349563, 0.8385180609014508, 0.9781397917324077, 0.6769715154724274, 0.8375259283816657, 0.06956509535905245, 0.3441307230861203, nan, 0.0, 0.0, 0.0, 0.18348218438924552, 0.0019584802193497847, 0.0, 0.0, 0.0, 0.08131315775695062, 0.0, 0.0, 0.0, 0.26825905232466285, nan, 0.0012374096890509201, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0026519337016574587, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.951 | 2.5 | 100 | 3.7658 | 0.0401 | 0.0786 | 0.4477 | [0.405717317146636, 0.4086408628919567, 0.46930568969097103, 0.3717459827442089, 0.4797755033427323, 0.09497839561115337, 0.3326272962505519, 0.0, 0.0, 0.0, 0.0, 0.042534003647694614, 0.0014730878186968838, 0.0, 0.0, 0.0, 0.0077221054763652935, 0.0, 0.0, 0.0, 0.1928527248986324, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.6074812734082397, 0.8766682989045027, 0.9717970968759861, 0.7857572013599547, 0.917421184439835, 0.10295479742341103, 0.40897690494678035, nan, 0.0, 0.0, 0.0, 0.14699041771327565, 0.00169734952343648, 0.0, 0.0, 0.0, 0.007984711241426252, 0.0, 0.0, 0.0, 0.2015282306134467, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.7065 | 3.0 | 120 | 3.6117 | 0.0484 | 0.0779 | 0.4436 | [0.30003355195190606, 0.6100469285116588, 0.7166468505013384, 0.2808355790192315, 0.4914731888407471, 0.004109398150913026, 0.3171473942892336, 0.0, 0.0, 0.0, 0.0, 0.0061059528193994845, 0.0, 0.0, 0.0, 0.0, 0.29060069752832357, 0.0, 0.0, 0.0, 0.17904034375746, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8819553682896379, 0.8053072403033685, 0.8891882297254654, 0.6263173002009267, 0.8730573314039183, 0.005069677365030804, 0.3609246267067045, nan, 0.0, 0.0, 0.0, 0.007326262908177505, 0.0, 0.0, 0.0, 0.0, 0.351196397717158, 0.0, 0.0, 0.0, 0.1831291383594502, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.2097 | 3.5 | 140 | 3.5176 | 0.0525 | 0.0915 | 0.4632 | [0.33578357247667373, 0.5622071180819789, 0.5852978266414613, 0.30513441309066625, 0.47613907301517233, 0.11756483464192244, 0.4294152765583846, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.31985655288181825, 0.0, 0.0, 0.0, 0.2843118911299879, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7485081148564294, 0.7992375930944952, 0.9684679709687598, 0.6516123166120225, 0.9277790969083065, 0.15255694177414147, 0.8402174137113565, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4389758626106079, 0.0, 0.0, 0.0, 0.32737744710799593, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.2266 | 4.0 | 160 | 3.2810 | 0.0575 | 0.0941 | 0.4847 | [0.3992063720223229, 0.4874371519350326, 0.6386934837529401, 0.33463546895473684, 0.5312770971534357, 0.1190245013596048, 0.667785108591032, 0.0, 1.4805712043706462e-05, 0.0, 0.0, 0.0017248288238061224, 0.0, 0.0, 0.0, 0.0, 0.30151870601308806, 0.0, 0.0, 0.0, 0.2782594792704375, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.033659066232356136, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.6934503745318352, 0.9230902817318423, 0.9254102240454402, 0.8022610050614992, 0.8948483721680384, 0.14665927557994302, 0.8533694315222395, nan, 1.4989020542452654e-05, 0.0, 0.0, 0.0023025397711415015, 0.0, 0.0, 0.0, 0.0, 0.35161526781506885, 0.0, 0.0, 0.0, 0.32496445140255376, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.1061878453038674, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.9497 | 4.5 | 180 | 3.1974 | 0.0642 | 0.1033 | 0.5107 | [0.3818606056702946, 0.646717576231877, 0.6893883610101498, 0.35965565930008286, 0.515791148910165, 0.1709952263303293, 0.5905499827668784, 0.0, 0.0, 0.00023758099352051836, 0.0, 0.006708633919955889, 0.0, 0.0, 0.0, 0.0, 0.41776764937787264, 0.0, 0.0, 0.0, 0.3965531191844, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8420318352059926, 0.9034080556631061, 0.9232289365730514, 0.7692872717288791, 0.9074888782362877, 0.22193959891659767, 0.9065753217571756, nan, 0.0, 0.00023758099352051836, 0.0, 0.006791329425993115, 0.0, 0.0, 0.0, 0.0, 0.5687077857479449, 0.0, 0.0, 0.0, 0.563147235827241, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.6589 | 5.0 | 200 | 2.9663 | 0.0613 | 0.1024 | 0.5079 | [0.42472276030924794, 0.5137600415836002, 0.5653029156997235, 0.3946772310334331, 0.48347829428413946, 0.27808625336927223, 0.5484221892074007, 0.0, 0.0, 0.028530701280749877, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40734839451719446, 0.0, 0.0, 0.0, 0.3426947471859925, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7298252184769038, 0.931391919283941, 0.9522049542442411, 0.7744040392154765, 0.9018775956382078, 0.3619644385814727, 0.9283785362367638, nan, 0.0, 0.02853131749460043, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5053144143672443, 0.0, 0.0, 0.0, 0.4407882452637778, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.0452 | 5.5 | 220 | 2.9555 | 0.0726 | 0.1078 | 0.5230 | [0.41914107733008765, 0.6093066343751574, 0.8022820448088637, 0.37087502661273153, 0.5109690014151442, 0.285823996633528, 0.6652541677829964, 0.0, 0.08014894937494459, 0.02611231101511879, 0.0, 0.00470151136389698, 0.0, 0.0, 0.0, 0.0, 0.583424459802292, 0.0, 0.0, 0.009707533967511648, 0.3496014880350035, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0003960101972625795, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7738607990012485, 0.9296880337987109, 0.8900007888923951, 0.8633894394911045, 0.8759447339064831, 0.3812818389772233, 0.8360464237368497, nan, 0.08807548470745179, 0.02611231101511879, 0.0, 0.004767885384686948, 0.0, 0.0, 0.0, 0.0, 0.6983742604324834, 0.0, 0.0, 0.011471154651260152, 0.5210203524697299, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0008839779005524862, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.2101 | 6.0 | 240 | 2.9536 | 0.0660 | 0.1036 | 0.5149 | [0.4210730477967065, 0.6183098808624755, 0.6734301776779076, 0.35971565731735283, 0.4806664840057538, 0.27224325054511417, 0.7002499830387291, 0.0, 0.011102840450618944, 0.14168103448275862, 0.0, 0.04930317875757779, 0.00026014568158168577, 0.0, 0.0, 0.0, 0.34496024621697874, 0.0, 0.0, 0.0071056371387967785, 0.272962729746106, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.003966609436978272, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7978604868913858, 0.8565931969845355, 0.9539720732092143, 0.8333395732936828, 0.9431322829072044, 0.35000070168544845, 0.921994928845797, nan, 0.01330275573142673, 0.14198704103671705, 0.0, 0.05126058237975625, 0.0002611306959133046, 0.0, 0.0, 0.0, 0.4225352112676056, 0.0, 0.0, 0.007849786485807586, 0.3299915257888916, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.007403314917127072, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.1038 | 6.5 | 260 | 2.8714 | 0.0713 | 0.1055 | 0.5121 | [0.333925564423404, 0.752032134611127, 0.736398567000167, 0.33990449010114204, 0.5520605777794296, 0.21890770835546186, 0.7222780908403896, 0.0, 7.871071843208248e-05, 0.07598360127927194, 0.0, 0.060659762997758084, 0.0, 0.0, 0.0, 0.0, 0.5471373300677954, 0.0, 0.0, 0.0, 0.29813009355328596, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.926833645443196, 0.9138335003530189, 0.938908172925213, 0.6401967548640491, 0.8983786641339361, 0.281996856449191, 0.8340674367308234, nan, 8.993412325471592e-05, 0.07645788336933046, 0.0, 0.0660759140385152, 0.0, 0.0, 0.0, 0.0, 0.8070841405309178, 0.0, 0.0, 0.0, 0.3693750628384298, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.7936 | 7.0 | 280 | 2.7721 | 0.0696 | 0.1104 | 0.5252 | [0.4010650933145876, 0.6469693944006977, 0.6760729291497917, 0.3905446292113494, 0.5370746026129916, 0.3149111124917947, 0.4465377195337425, 0.0, 0.0015380273740651898, 0.29528359209671673, 0.0, 0.0016221666885878382, 0.0, 0.0, 0.0, 0.0, 0.4981196507305265, 0.0, 0.0, 0.0028347369074468746, 0.31121040158284957, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0025029983834802105, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8289122971285893, 0.9177295192110597, 0.9145945093089303, 0.7626461710711873, 0.9180707940020877, 0.4089984141908865, 0.9829174941077036, nan, 0.0016338032391273393, 0.3043844492440605, 0.0, 0.0017210903339845568, 0.0, 0.0, 0.0, 0.0, 0.603356196659511, 0.0, 0.0, 0.002867788662815038, 0.4111715955934102, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.005303867403314917, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7945 | 7.5 | 300 | 2.7396 | 0.0686 | 0.1095 | 0.5169 | [0.3938437827362631, 0.5819843893842082, 0.6473757929342038, 0.36969978588499103, 0.5214727294114572, 0.32975030466734545, 0.5638207004122257, 0.0, 0.01157430251082007, 0.20381216057927803, 0.0, 0.0004631023224581471, 0.0, 0.0, 0.0, 0.0, 0.5491695655139486, 0.0, 0.0, 0.0, 0.2884391553243545, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.836646379525593, 0.9250916710319539, 0.8823840328179237, 0.6836981927291982, 0.8937373158132887, 0.43858498112466143, 0.9389056476715981, nan, 0.01230598586535363, 0.20669546436285097, 0.0, 0.00046515954972555586, 0.0, 0.0, 0.0, 0.0, 0.8431200586418137, 0.0, 0.0, 0.0, 0.34882151013314566, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.7413 | 8.0 | 320 | 2.6350 | 0.0787 | 0.1150 | 0.5338 | [0.39834735692948187, 0.6935465113678552, 0.6795960676787267, 0.39310146783541766, 0.49596738777838023, 0.32174356359928624, 0.7683469195364054, 0.0, 0.007555978330533573, 0.47093784418445167, 0.0, 0.0837117604058215, 0.0, 0.0, 0.0, 0.0, 0.4067670846627588, 0.0, 0.0, 0.03581658980557869, 0.3532967032967033, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0038626609442060085, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8854556803995006, 0.8828632108775366, 0.9219233196591985, 0.785858823571361, 0.9486830846150055, 0.39855031786350814, 0.9520851514131204, nan, 0.008393851503773486, 0.4894600431965443, 0.0, 0.08789189692064378, 0.0, 0.0, 0.0, 0.0, 0.5023037855385099, 0.0, 0.0, 0.03690446286527673, 0.4525372362581331, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.007955801104972376, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 3.1279 | 8.5 | 340 | 2.7466 | 0.0803 | 0.1261 | 0.5379 | [0.4732868837154453, 0.5795366043613707, 0.6346097819782593, 0.44734531422398605, 0.5925445006034769, 0.33420482145366226, 0.5418888704389498, 0.0, 0.07466121037607358, 0.5396965892015397, 0.0, 0.0583367158255536, 0.0, 0.0, 0.0, 0.0, 0.5034441615738396, 0.0, 0.0, 0.034642576590730556, 0.38949905000231705, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.013078260869565218, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.7515792759051186, 0.9406003029129751, 0.9365375512780056, 0.8283761305471019, 0.8555873142452657, 0.4673154918113308, 0.9642545472036501, nan, 0.08997159580607206, 0.5662850971922246, 0.0, 0.06016838775700065, 0.0, 0.0, 0.0, 0.0, 0.9499842923713283, 0.0, 0.0, 0.03692539562923888, 0.603608003102423, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.020773480662983426, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.92 | 9.0 | 360 | 2.6091 | 0.0828 | 0.1284 | 0.5504 | [0.40748999734012986, 0.7308994689832818, 0.6897258549098384, 0.4145929905545104, 0.5999534865692469, 0.34933757496681506, 0.6454950667228029, 0.0, 0.06552173666609987, 0.6525195767828322, 0.0, 0.09638426690376392, 0.0, 0.0, 0.0, 0.0, 0.3726748249924363, 0.0, 0.0, 0.02772521596051008, 0.4127414994395235, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9037031835205992, 0.9192924818366092, 0.9290785736825496, 0.8003123545866383, 0.8667919591776391, 0.4330416660819288, 0.9692295006493551, nan, 0.0779728848618387, 0.7073002159827214, 0.0, 0.10589357149502279, 0.0, 0.0, 0.0, 0.0, 0.9351798523482905, 0.0, 0.0, 0.028217365820983, 0.5394338077934016, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.3087 | 9.5 | 380 | 2.6150 | 0.0814 | 0.1198 | 0.5299 | [0.3815018185439071, 0.7057178358063663, 0.7535985879427576, 0.3887717278415141, 0.5256222584858059, 0.34564103730405565, 0.5961295113764614, 0.0, 0.07287089141198226, 0.5505775075987842, 0.0, 0.012498381038725553, 0.0, 0.0, 0.0, 0.0, 0.6003507871321013, 0.0, 0.0, 0.02191998411595354, 0.333831518774682, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0007605753528845938, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9088030586766542, 0.9184341335094632, 0.9228818239192175, 0.6333363641712174, 0.924981295724673, 0.42656160096551915, 0.8384170852544853, nan, 0.10986202606590673, 0.5868466522678186, 0.0, 0.013466368964554842, 0.0, 0.0, 0.0, 0.0, 0.9184904968846537, 0.0, 0.0, 0.023109771414217533, 0.4395242951323557, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0018784530386740331, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1879 | 10.0 | 400 | 2.5502 | 0.0829 | 0.1264 | 0.5403 | [0.42541993453087873, 0.623683983411115, 0.7597304629844778, 0.4205088170462895, 0.5924400522095842, 0.34972647970735005, 0.542108695993839, 0.0, 0.10089223190785503, 0.6533185760423849, 0.0, 0.06435332551516167, 0.0, 0.0, 0.0, 0.0, 0.5496072567949156, 0.0, 0.0, 0.04006614926155156, 0.3497377708876208, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8211766541822721, 0.9566740496959483, 0.9156831808141369, 0.734648360405847, 0.8896447755710964, 0.4752831300784484, 0.9480653340571296, nan, 0.1252557501630056, 0.7776889848812095, 0.0, 0.07147176481533166, 0.0, 0.0, 0.0, 0.0, 0.8636839625111262, 0.0, 0.0, 0.05122247341538977, 0.4568748833000589, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.7904 | 10.5 | 420 | 2.5607 | 0.0847 | 0.1294 | 0.5527 | [0.4089931781192911, 0.6850029861356511, 0.7236868938816361, 0.44801602679367497, 0.6048018807233796, 0.3552823794275764, 0.7384593465045592, 0.0, 0.08126260723073171, 0.6861535314971366, 0.0, 0.07176723173949993, 0.0, 0.0, 0.0, 0.0, 0.5202545409432902, 0.0, 0.0, 0.03092656227494393, 0.31232833178217145, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0070865882197831175, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8964903245942571, 0.9436849477304302, 0.9098295992426633, 0.8017938994581931, 0.8528925545784034, 0.4667366013163619, 0.9616021548969621, nan, 0.10989949861726285, 0.8695032397408208, 0.0, 0.07463484975346543, 0.0, 0.0, 0.0, 0.0, 0.9331771296926541, 0.0, 0.0, 0.03146194423511681, 0.39524295132355686, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0356353591160221, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0716 | 11.0 | 440 | 2.5680 | 0.0847 | 0.1296 | 0.5415 | [0.41481850137628407, 0.7203849284695957, 0.6669945193799962, 0.4362842862741063, 0.5323843818628085, 0.3475532762707128, 0.7414915042691402, 0.0, 0.1676274467627447, 0.6863883910106163, 0.0, 0.07245906603508316, 0.0, 0.0, 0.0, 0.0, 0.42361977248072585, 0.0, 0.0, 0.03842720421848077, 0.3414301980308544, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8675733458177278, 0.9120399366843556, 0.944245029977911, 0.7105906389897683, 0.8862332053527828, 0.47125545560436166, 0.8980959121549656, nan, 0.23361887417466706, 0.860194384449244, 0.0, 0.0779142245790306, 0.0, 0.0, 0.0, 0.0, 0.9558615634326405, 0.0, 0.0, 0.038746546093946246, 0.43930884908722695, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0188 | 11.5 | 460 | 2.5824 | 0.0882 | 0.1300 | 0.5276 | [0.37841752646478294, 0.7626005273875548, 0.7449055407856049, 0.4290295891463663, 0.42924333297292483, 0.3342814382591885, 0.7440181926043109, 0.0, 0.2021076120669866, 0.6599823849517159, 0.0, 0.08257497897785668, 0.0, 0.0, 0.0, 0.0, 0.5392052554536042, 0.0, 0.0, 0.07886073041880408, 0.3480013762317715, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8847003745318353, 0.9254219145011046, 0.8344193751972231, 0.7922913312687979, 0.604714821402171, 0.46469820508862286, 0.879034419256643, nan, 0.2837346643583575, 0.906328293736501, 0.0, 0.10277700251186157, 0.0, 0.0, 0.0, 0.0, 0.9884418032357715, 0.0, 0.0, 0.08624298752407268, 0.566580009479626, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0354 | 12.0 | 480 | 2.5691 | 0.0858 | 0.1256 | 0.5228 | [0.388647954155662, 0.7558385896911918, 0.7917491167908167, 0.41462958598007127, 0.49312579910285853, 0.34302476815200184, 0.6070355141984659, 0.0, 0.17537265388996187, 0.683955223880597, 0.0, 0.06567059698554022, 0.0, 0.0, 0.0, 0.0, 0.5733892374517374, 0.0, 0.0, 0.0970983628377479, 0.2620320855614973, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.010986863532732602, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9077652933832709, 0.8871478351971211, 0.9043310192489744, 0.7432737684546827, 0.7205357262858909, 0.4256459014552956, 0.6939098049186073, nan, 0.2654030922349379, 0.910561555075594, 0.0, 0.06982044841380594, 0.0, 0.0, 0.0, 0.0, 0.9952877113985026, 0.0, 0.0, 0.15270451310391023, 0.3314852850351177, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.027955801104972377, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1633 | 12.5 | 500 | 2.5259 | 0.0848 | 0.1259 | 0.5158 | [0.34497610745189206, 0.7463073107565349, 0.854543520288191, 0.42463992477652146, 0.3850483703078828, 0.3376058072128456, 0.7316751525159396, 0.0, 0.1667267825954894, 0.7156769039332023, 0.0, 0.1001707892767362, 0.0, 0.0, 0.0, 0.0, 0.45019734202157285, 0.0, 0.0, 0.042506510283171685, 0.28340423893583844, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.013840120972909245, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9170614856429463, 0.9196654292026328, 0.9188466393183969, 0.714963959771867, 0.5245171608926084, 0.4617756851958404, 0.8768699022188018, nan, 0.2632821458281809, 0.8571274298056155, 0.0, 0.12140664247837007, 0.0, 0.0, 0.0, 0.0, 0.9839389496832295, 0.0, 0.0, 0.043393619693544334, 0.42290622351809026, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.02983425414364641, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5483 | 13.0 | 520 | 2.4923 | 0.0896 | 0.1335 | 0.5398 | [0.4049279962132016, 0.7282255784140709, 0.8169033163371633, 0.46268063074208626, 0.566813817579484, 0.32839844004056473, 0.7444598645497946, 0.0, 0.19716698176235625, 0.6840045841519319, 0.0, 0.0826003003647286, 0.0, 0.0, 0.0, 0.0, 0.4191962626156142, 0.0, 0.0, 0.06262263410511064, 0.3241681763952101, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.002826652155511216, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8330243445692884, 0.9274802423303801, 0.9277098453770906, 0.7134342780633303, 0.8171125078961162, 0.47830739436125574, 0.9215139250596102, nan, 0.41581041886817904, 0.902354211663067, 0.0, 0.09849753465438646, 0.0, 0.0, 0.0, 0.0, 0.9889653908581602, 0.0, 0.0, 0.08685003767897513, 0.42148427962024043, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.010331491712707183, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.9456 | 13.5 | 540 | 2.4341 | 0.0888 | 0.1350 | 0.5530 | [0.4220915846224215, 0.7247157964309319, 0.830505564319672, 0.4851139449459602, 0.5575769305206115, 0.3589983865973501, 0.6960131553803293, 0.0, 0.17180970380326335, 0.5458583575361728, 0.0, 0.059761036448993575, 0.0, 0.0, 0.0, 0.0, 0.5318637215410267, 0.0005402141790333344, 0.0, 0.0806004398126016, 0.39301167361406775, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8784550561797753, 0.9364921880337987, 0.9157739034395709, 0.7188452151092617, 0.909948434440955, 0.4637123370335546, 0.9510475575314886, nan, 0.2806993876985108, 0.8971058315334773, 0.0, 0.06898316122429994, 0.0, 0.0, 0.0, 0.0, 0.9436619718309859, 0.0006066445419833708, 0.0, 0.10587792012057272, 0.5701133246197377, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1989 | 14.0 | 560 | 2.4261 | 0.0929 | 0.1401 | 0.5574 | [0.43310794022368454, 0.7737883098855011, 0.8060680007048083, 0.489362410594968, 0.610805991884693, 0.3470086891880301, 0.6952947225347309, 0.0, 0.24357075198103878, 0.6835668749605504, 0.0, 0.1061102667862963, 0.0, 0.0, 0.0, 0.0, 0.45462912605112327, 0.0, 0.0, 0.1259496932719323, 0.33998677576659225, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.023312883435582823, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8650249687890137, 0.895028127633407, 0.9563663616282739, 0.7860460223818464, 0.8794078248831823, 0.4683925789746972, 0.9385277161253084, nan, 0.4374620590417519, 0.9356155507559395, 0.0, 0.13477997953297982, 0.0, 0.0, 0.0, 0.0, 0.9957196711869731, 0.0, 0.0, 0.1585866197772754, 0.47265989687315973, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.03988950276243094, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.759 | 14.5 | 580 | 2.4448 | 0.0892 | 0.1338 | 0.5493 | [0.4089707662271852, 0.7381139623935532, 0.8059721309871557, 0.4820201034873249, 0.609475169473329, 0.34888232677538816, 0.7307581018518519, 0.0, 0.18989091820207427, 0.6821615899138637, 0.0, 0.10031948881789138, 0.0, 0.0, 0.0, 0.0, 0.4281578977124959, 0.0, 0.0, 0.035161336998987124, 0.3180350017944225, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.008382156737629792, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8979978152309613, 0.9191857220944268, 0.9098532660145157, 0.7118261511389711, 0.8901667032538719, 0.4567902100846233, 0.9371121906973868, nan, 0.3180745104211165, 0.9185313174946005, 0.0, 0.11684807889105964, 0.0, 0.0, 0.0, 0.0, 0.9912822660872297, 0.0, 0.0, 0.045779954785229844, 0.4327592893153125, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.01596685082872928, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.072 | 15.0 | 600 | 2.5027 | 0.0906 | 0.1397 | 0.5482 | [0.44100427698407574, 0.7311103048313435, 0.7543839224188112, 0.4799902126596585, 0.6074534907993699, 0.31991640494013746, 0.6473536180005114, 0.0, 0.2340784880166992, 0.580419398818412, 0.0, 0.10936251189343482, 0.0, 0.0, 0.0, 0.0, 0.6328263382425094, 0.0, 0.0, 0.0914391513824814, 0.31230421658155033, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03962762162933371, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.756437265917603, 0.9035276265743504, 0.9456847585989271, 0.8358712143497694, 0.8604459457643217, 0.47530418064190183, 0.9568470888963712, nan, 0.3874362029813162, 0.9654643628509719, 0.0, 0.13366359661363847, 0.0, 0.0, 0.0, 0.0, 0.9960992722132049, 0.0, 0.0, 0.1871179770576907, 0.47254499231575775, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.06138121546961326, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.2554 | 15.5 | 620 | 2.4405 | 0.0887 | 0.1312 | 0.5334 | [0.3809029194882498, 0.7608027733964248, 0.7083251720737159, 0.42916048469757884, 0.5873085910806192, 0.3468435260718393, 0.7091692465883562, 0.0, 0.18719278445710874, 0.6750140822402833, 0.0, 0.10760567447568856, 0.0, 0.0, 0.0, 0.0, 0.5381403956788087, 0.0, 0.0, 0.06071958043072803, 0.3247804716408019, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03655427552761218, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9219366416978777, 0.9227984717698773, 0.8130680025244557, 0.6539050563290135, 0.8259001572503147, 0.45392733345495884, 0.8720117639783136, nan, 0.30082214777675353, 0.9058747300215982, 0.0, 0.1386640617731882, 0.0, 0.0, 0.0, 0.0, 0.981988585789832, 0.0, 0.0, 0.07973289793184292, 0.42233170073108023, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.10535911602209945, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0588 | 16.0 | 640 | 2.4574 | 0.0902 | 0.1345 | 0.5342 | [0.41010385424628426, 0.7977529216153805, 0.8340696293159445, 0.48971245825550463, 0.4919749346259328, 0.3272816966190114, 0.6597098182842653, 0.0, 0.2774115424895272, 0.5641758131609281, 0.0, 0.051266510616953684, 0.0, 0.0, 0.0, 0.0, 0.5074328777519622, 0.0, 0.004271625102079277, 0.1204259768046877, 0.3244459964513572, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.00036442367467684196, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8264341448189763, 0.9063033798710912, 0.9167363521615651, 0.802086286171713, 0.69115545380828, 0.48181582160349157, 0.8045269327762852, nan, 0.4446867669432141, 0.9725269978401728, 0.0, 0.05705181877383943, 0.0, 0.0, 0.0, 0.0, 0.9977223938426095, 0.0, 0.005333751666797396, 0.2073599598090932, 0.49375924622610345, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0009392265193370166, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.6455 | 16.5 | 660 | 2.4313 | 0.0867 | 0.1369 | 0.5498 | [0.4264700319086269, 0.6718440123427368, 0.7997752877640454, 0.49715012632283856, 0.5883438717881697, 0.36612418224363996, 0.6399541890021792, 0.0, 0.2783565401956925, 0.6650282147881061, 0.0, 0.0636950146627566, 0.0, 0.0, 0.0, 0.0, 0.33662366696414886, 0.0016348511804587201, 0.0, 0.046494402214828666, 0.42791272943155395, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8620318352059925, 0.9229707108205981, 0.929366519406753, 0.6988416850745497, 0.8974938511094883, 0.4622037133193932, 0.8293604711088511, nan, 0.5140409649931426, 0.8883369330453563, 0.0, 0.07577449065029306, 0.0, 0.0, 0.0, 0.0, 0.9775904497617677, 0.0018199336259501124, 0.0, 0.080151553211086, 0.6211596742455797, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1235 | 17.0 | 680 | 2.4456 | 0.0871 | 0.1344 | 0.5283 | [0.3931975460054607, 0.7312132393646948, 0.752251080518649, 0.48870145905866136, 0.4924137639251856, 0.32257473443500867, 0.6552130418265605, 0.0, 0.23244626064529095, 0.6940257824525398, 0.0, 0.0777659332785768, 0.0, 0.0, 0.0, 0.0, 0.48322164719046834, 0.0, 0.0, 0.08978965642537043, 0.3441371440991089, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07899262429777965, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8845521223470661, 0.9212796365044298, 0.8897404544020195, 0.6964580202210372, 0.6778451778810184, 0.46323519092861054, 0.8177545368964262, nan, 0.416072726727672, 0.9348812095032397, 0.0, 0.10349799981393618, 0.0, 0.0, 0.0, 0.0, 0.9948688413005916, 0.0, 0.0, 0.13966340115548856, 0.545811010729213, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.11419889502762431, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.8286 | 17.5 | 700 | 2.4301 | 0.0920 | 0.1407 | 0.5505 | [0.4526525403911307, 0.7425194076825603, 0.7298432909571285, 0.5114968994416101, 0.5950013338508995, 0.35561215385364114, 0.6371370923132846, 0.0, 0.2790519814464187, 0.6854922770776083, 0.0, 0.11547506230037556, 0.0, 0.0, 0.0, 0.0, 0.5099499129213863, 0.0, 0.0, 0.05601597473277561, 0.38623015388905346, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.10698106201262532, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8729900124843946, 0.924192042271164, 0.8564373619438309, 0.7564632617877308, 0.834356281724467, 0.46144940146231245, 0.8310508558431653, nan, 0.5058869378180483, 0.937451403887689, 0.0, 0.15303749185970789, 0.0, 0.0, 0.0, 0.0, 0.9888606733336824, 0.0, 0.0, 0.08984342292556309, 0.6366143372161498, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.15917127071823203, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.0479 | 18.0 | 720 | 2.4075 | 0.0898 | 0.1414 | 0.5436 | [0.4374213467259567, 0.6982741654601179, 0.8058703485110762, 0.49490116233697407, 0.5458747562993242, 0.34662800424368545, 0.6675288926019943, 0.0, 0.25922904558504306, 0.6564580958665787, 0.0, 0.06828609986504723, 0.0, 0.0, 0.0, 0.0, 0.5524867235515245, 0.004030176361849884, 0.0, 0.06495693639766788, 0.3537035899505954, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.05760272180057577, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8255727215980025, 0.9343384881681737, 0.9236707163142948, 0.75750800943482, 0.7457316171694047, 0.4768549054829701, 0.8564959561324547, nan, 0.5549010349918685, 0.9453563714902807, 0.0, 0.08237975625639594, 0.0, 0.0, 0.0, 0.0, 0.9695926488297817, 0.004317881739999287, 0.0, 0.14855982583940383, 0.5789322494003418, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.24320441988950275, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5913 | 18.5 | 740 | 2.3742 | 0.0890 | 0.1390 | 0.5469 | [0.454543608042716, 0.7561710883076548, 0.6476174668581449, 0.5068105753496513, 0.5979298898988566, 0.31709395674098895, 0.63015072260012, 0.0, 0.27305328355515024, 0.6493838093129658, 0.0, 0.09270533141210374, 0.0, 0.0, 0.0, 0.0, 0.5510527590433066, 0.0022979287796301723, 0.0, 0.10461312981465264, 0.3512229542899809, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.030816640986132512, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8730727215980025, 0.9087019154121211, 0.70795992426633, 0.7923769078678768, 0.8732051735801551, 0.4568919544746481, 0.8377299369885041, nan, 0.43083691196198787, 0.9503023758099352, 0.0, 0.1197088101218718, 0.0, 0.0, 0.0, 0.0, 0.9828655950573328, 0.0026585304928094778, 0.0, 0.2450389349409696, 0.6148255605187941, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.10276243093922652, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.1711 | 19.0 | 760 | 2.4791 | 0.0828 | 0.1303 | 0.5109 | [0.3990185372974579, 0.7262255396409119, 0.6844075233528706, 0.4626738323314395, 0.4562280397881922, 0.3094077256824913, 0.6116278156029165, 0.0, 0.28992189510205096, 0.7007724513972282, 0.0, 0.07657237409565386, 0.0, 0.0, 0.0, 0.0, 0.43612176002746467, 0.0, 0.0, 0.06802583124299515, 0.29350917328309334, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03117790351476407, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8633583021223471, 0.9223785501172934, 0.7493846639318397, 0.6603001242643532, 0.6401767834022517, 0.46511570793044893, 0.7487648509918985, nan, 0.5235589930376, 0.9326781857451404, 0.0, 0.1011722020653084, 0.0, 0.0, 0.0, 0.0, 0.9977093041520498, 0.0, 0.0, 0.13340450473080465, 0.46990218749551155, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.13364640883977902, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.24 | 19.5 | 780 | 2.3946 | 0.0901 | 0.1394 | 0.5429 | [0.4548092562154161, 0.6318103867369477, 0.72163513476081, 0.49794478543154436, 0.5991916108852994, 0.3383029823036611, 0.6064419856631512, 0.0, 0.28341717835243485, 0.7149914761855061, 0.0, 0.1054460128055879, 0.0, 0.0, 0.0, 0.0, 0.5981121440422982, 0.0010851791288836034, 0.0, 0.05393735796858018, 0.3585824286192549, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.007273887382357633, 0.06396948848339815, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8247534332084894, 0.939783235019473, 0.822081098138214, 0.7006298794261376, 0.8882873155892854, 0.46158272169751746, 0.8684935648564891, nan, 0.5053997946504186, 0.8877321814254859, 0.0, 0.13482649548795236, 0.0, 0.0, 0.0, 0.0, 0.9861772867689408, 0.001302501516611355, 0.0, 0.11427195846939629, 0.5384427559858093, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.00861515208766295, 0.23629834254143647, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.232 | 20.0 | 800 | 2.3526 | 0.0948 | 0.1466 | 0.5590 | [0.4425789938260126, 0.6937296397977262, 0.8186584931612056, 0.5324322624613841, 0.599114348804341, 0.35870491493331946, 0.6951078135868305, 0.0, 0.2920867935913111, 0.6704938805785635, 0.0, 0.12268269987831938, 0.0, 0.0, 0.0, 0.0, 0.6003949350977511, 0.02812804473375789, 0.0, 0.07798367783487177, 0.3614614438784472, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.05487663750405036, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8505259051186017, 0.9330459835561528, 0.9250078889239508, 0.7396011060775431, 0.8982756226171649, 0.45928470185385295, 0.9270317256354403, nan, 0.5514385712465619, 0.9371274298056156, 0.0, 0.15945669364592055, 0.0, 0.0, 0.0, 0.0, 0.8397560081679669, 0.03554223316561396, 0.0, 0.1404169806581261, 0.5911264955546299, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.39298342541436465, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6883 | 20.5 | 820 | 2.3970 | 0.0945 | 0.1446 | 0.5412 | [0.4141798017429065, 0.7802581769922772, 0.7493453021254498, 0.5208791649277187, 0.5111936316209107, 0.33498169560972163, 0.676565287832489, 0.0, 0.27079508232840294, 0.6661949111273435, 0.0, 0.17192469151507442, 0.0, 0.0, 0.0, 0.0, 0.6279362521715048, 0.00806402808036795, 0.00468899769436025, 0.11971994860328412, 0.3208761133473644, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.061126428514345084, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8716338951310861, 0.9071987382421938, 0.7968444304196908, 0.7876915890683026, 0.7295921795968837, 0.4658173933788961, 0.9067608517889905, nan, 0.4145063740809857, 0.9455075593952483, 0.0, 0.2514652525816355, 0.0, 0.0, 0.0, 0.0, 0.979409916749568, 0.009510045319915783, 0.02839438387324496, 0.2476973959641631, 0.5759160047685391, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.3380662983425414, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5655 | 21.0 | 840 | 2.3849 | 0.0913 | 0.1451 | 0.5354 | [0.39695922222126684, 0.7761112626124897, 0.7398181021041216, 0.4805221133856404, 0.5447557296135301, 0.31805360331269233, 0.6925109462912826, 0.0, 0.2604956962542102, 0.6627857938970388, 0.0, 0.10330072264341897, 0.0, 0.0, 0.0, 0.0, 0.6444934523198961, 0.025419071173399286, 0.017420612885313224, 0.10974960558281793, 0.27499042512447336, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0692949397844124, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8645162297128589, 0.9115146787528184, 0.8852516566740296, 0.6803517911360472, 0.7708670271626399, 0.46996786280646113, 0.8770485607679569, nan, 0.4590687321536974, 0.9504319654427645, 0.0, 0.1353149130151642, 0.0, 0.0, 0.0, 0.0, 0.9869364888214043, 0.029707740070656248, 0.10581222056631892, 0.23735661056685925, 0.41250736107320857, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.508950276243094, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7399 | 21.5 | 860 | 2.3781 | 0.0906 | 0.1404 | 0.5386 | [0.39331511754852533, 0.7754545465515764, 0.627463766072637, 0.49043222556163374, 0.5995606151388057, 0.3503685192626312, 0.6888485650165912, 0.0, 0.2431310643278037, 0.672429906542056, 0.0, 0.19986591689583932, 0.0, 0.0, 0.0, 0.0, 0.47157075191286174, 0.03745141914968791, 0.006777572396796057, 0.09541783908677053, 0.26727518593644356, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.059640638046389575, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.9091011235955057, 0.9147260117976632, 0.7021891763963395, 0.6874546488596027, 0.8809198471401499, 0.44664033007283493, 0.8373451339595545, nan, 0.40169076151718863, 0.9323974082073434, 0.0, 0.339752535119546, 0.0, 0.0, 0.0, 0.0, 0.9656657416618671, 0.045391285729579275, 0.04486626402070751, 0.17462111697228502, 0.3974405009838703, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.30386740331491713, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0019 | 22.0 | 880 | 2.3395 | 0.0911 | 0.1433 | 0.5513 | [0.4387708086453384, 0.7685412462908012, 0.6613413573765997, 0.5320871602231129, 0.6142786316311354, 0.3506061584145633, 0.6823298195461854, 0.0, 0.2801467728991371, 0.5419867613663816, 0.0, 0.20394669475285615, 0.0, 0.0, 0.0, 0.0, 0.5595260358614912, 0.05133376806539179, 0.008706221182936335, 0.09404473644106541, 0.30720067771084336, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.010004950903539896, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8470755305867665, 0.9216867469879518, 0.7230277690123067, 0.7916833808461743, 0.8742512689786793, 0.476377759378026, 0.8922345374461448, nan, 0.507535730077718, 0.9691144708423326, 0.0, 0.324262722113685, 0.0, 0.0, 0.0, 0.0, 0.9790826744855752, 0.06039681690040324, 0.0516903286532277, 0.23252114209160177, 0.4687675049911667, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.05359116022099448, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.8521 | 22.5 | 900 | 2.3802 | 0.0876 | 0.1388 | 0.5362 | [0.40506840771806973, 0.762763967794697, 0.6071606249102668, 0.49263320443279435, 0.6002160001991933, 0.34321369615548336, 0.6813770136393861, 0.0, 0.24928234799638715, 0.678501833581391, 0.0, 0.09633167687878934, 0.0, 0.0, 0.0, 0.0, 0.5182393876130829, 0.03320218227559274, 0.0051270619705751225, 0.07954303070635713, 0.25899725617360936, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0592231628370207, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8929666042446941, 0.9253792106042317, 0.7172767434521932, 0.6768271049614816, 0.8639672775983263, 0.463926351095331, 0.8379360814682985, nan, 0.41782644213113895, 0.9550755939524838, 0.0, 0.13931528514280397, 0.0, 0.0, 0.0, 0.0, 0.9748023456725483, 0.04050244442065446, 0.03968938740293356, 0.19515615841915768, 0.3728365626301653, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.3679558011049724, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.5347 | 23.0 | 920 | 2.3594 | 0.0896 | 0.1371 | 0.5433 | [0.39949160354707997, 0.75373807284121, 0.7333642959538925, 0.4879978829522525, 0.6027853892837028, 0.350799823813156, 0.6891289925125581, 0.0, 0.24400705662596422, 0.6809165526675787, 0.0, 0.12095083841463415, 0.0, 0.0, 0.0, 0.0, 0.5479324171283401, 0.022911594700760235, 0.0011939628340799852, 0.04572868845080565, 0.2617659886602434, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0573588139827495, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.906206304619226, 0.9375654793085385, 0.791010571158094, 0.7101413618446036, 0.8534772031844309, 0.4582497158173934, 0.8493633571315683, nan, 0.3700639281726136, 0.9460475161987041, 0.0, 0.17715601451297794, 0.0, 0.0, 0.0, 0.0, 0.9373003822189644, 0.027370374335367376, 0.00917719036787199, 0.07443690864941807, 0.3792999439840283, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.34756906077348065, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3155 | 23.5 | 940 | 2.3369 | 0.0918 | 0.1460 | 0.5511 | [0.4381093224801547, 0.8019171553204106, 0.6937287734535938, 0.5437472809894092, 0.6018169036088111, 0.32174778667344434, 0.6733454960577836, 0.0, 0.30267400925831817, 0.5778369520688242, 0.0, 0.20820499281293087, 0.0, 0.0, 0.0, 0.0, 0.5123551354359162, 0.039373532158116634, 0.02494658298933897, 0.09779724088519773, 0.29932684073211047, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.01698146624022003, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8335440074906367, 0.9182462363632222, 0.7863718838750394, 0.7955147165007729, 0.8629122220679089, 0.47966866413124326, 0.8673460272523003, nan, 0.4910028404193928, 0.9748596112311015, 0.0, 0.3301469904177133, 0.0, 0.0, 0.0, 0.0, 0.9762553013246766, 0.04965564000999179, 0.1749156796611499, 0.28001758352172823, 0.43173951136836963, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.09005524861878453, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.3405 | 24.0 | 960 | 2.3595 | 0.0879 | 0.1398 | 0.5410 | [0.43989517732610944, 0.7150986100442155, 0.6360758191098184, 0.5255540722395773, 0.5869049715503682, 0.3234512702432299, 0.7026329783033322, 0.0, 0.30250286746010635, 0.5443713495235167, 0.0, 0.17120688775076143, 0.0, 0.0, 0.0, 0.0, 0.5297883536014967, 0.047709467851342315, 0.0, 0.07997174381054897, 0.25611794662468174, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.008733939656416919, 0.01860040062401344, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8427184769038701, 0.9395156239324026, 0.7314570842537078, 0.697656092608143, 0.8659945074391495, 0.4741148238067839, 0.9170405898480715, nan, 0.5198417159430717, 0.9561771058315335, 0.0, 0.23402176946692715, 0.0, 0.0, 0.0, 0.0, 0.948897848054872, 0.07128073368304606, 0.0, 0.2488277652181194, 0.37850997515188944, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.009144152654449273, 0.11132596685082873, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.333 | 24.5 | 980 | 2.4020 | 0.0880 | 0.1418 | 0.5423 | [0.4585738127457231, 0.6275648067447941, 0.6314083697244316, 0.5392521405984272, 0.5951768537916903, 0.3282610533434779, 0.6981764940769333, 0.0, 0.3272000900292595, 0.7247379174063033, 0.0, 0.14323702298385843, 0.0, 0.0, 0.0, 0.0, 0.3870775652021896, 0.04730719166937846, 0.0, 0.06134763660744217, 0.3309438242948247, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.08457718344245922, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8159987515605494, 0.9603864417974355, 0.7277335121489429, 0.715686012326596, 0.8718297933345579, 0.4631895813744615, 0.9010918785946443, nan, 0.5229669267261731, 0.9347084233261339, 0.0, 0.21422923062610474, 0.0, 0.0, 0.0, 0.0, 0.9274438452274988, 0.062252435499411195, 0.0, 0.12258226576237126, 0.4986426899156888, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.3368508287292818, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2894 | 25.0 | 1000 | 2.3864 | 0.0895 | 0.1462 | 0.5330 | [0.39820238483984177, 0.796022219535693, 0.6640978857302814, 0.5145788754543814, 0.5063150858146147, 0.31625115083674027, 0.6810959828963655, 0.0, 0.26113527257733576, 0.6726899445979198, 0.0, 0.22906033835073447, 0.0, 0.0, 0.0, 0.0, 0.526772925150962, 0.03492519015909281, 0.018926486228376706, 0.08876722490545956, 0.2864895938888456, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.002796029637914162, 0.08532477102852933, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8886953807740324, 0.9091574236454324, 0.7767079520353424, 0.7472976514572091, 0.7041050844268427, 0.4567516173849587, 0.8318479478317037, nan, 0.39870045191896936, 0.9624406047516199, 0.0, 0.4112708158898502, 0.0, 0.0, 0.0, 0.0, 0.9820278548615111, 0.044731113728009135, 0.13679504274845086, 0.23044879845934857, 0.422317337661405, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0030228603816361234, 0.44779005524861876, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1434 | 25.5 | 1020 | 2.3808 | 0.0870 | 0.1430 | 0.5415 | [0.4312894090280853, 0.7514860907423327, 0.6238930671133326, 0.5171519380411355, 0.6101057211497077, 0.3008154566626281, 0.6915124668035739, 0.0, 0.2912826758689131, 0.5666113040414716, 0.0, 0.16547638988290111, 0.00046409096182851837, 0.0, 0.0, 0.0, 0.4371475202267487, 0.05034426295515858, 0.002705062738472636, 0.06979845880260818, 0.38125408011248557, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.025401436479253397, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8541401373283396, 0.9307228915662651, 0.7375433890817292, 0.6814250643161628, 0.8732186137779948, 0.4590636709375921, 0.8838856860144714, nan, 0.4839430117438976, 0.9726133909287257, 0.0, 0.25274444134338075, 0.0005222613918266092, 0.0, 0.0, 0.0, 0.9851955599769622, 0.06692716696998893, 0.022354694485842028, 0.16760864104496356, 0.6542809129167085, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.12270718232044199, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4152 | 26.0 | 1040 | 2.3638 | 0.0936 | 0.1532 | 0.5505 | [0.4390581433407791, 0.7964640058941477, 0.7072400355967855, 0.5218684081011842, 0.5850293135240993, 0.3303222917660343, 0.6725471140032113, 0.0, 0.27670817596368846, 0.6731058554492387, 0.0, 0.20708020499863622, 0.008931439953736426, 0.0, 0.0, 0.0, 0.5095680959455151, 0.03638896025746778, 0.02076316003579855, 0.08472208194458472, 0.2849621516471434, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.12568393434230313, 0.08349886634964586, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8352512484394506, 0.917123123875464, 0.8275757336699274, 0.7732077496741849, 0.8478457602895915, 0.47623391386109437, 0.8749596300393736, nan, 0.4952672167637206, 0.9663066954643629, 0.0, 0.3354963252395572, 0.01814858336597467, 0.0, 0.0, 0.0, 0.9832844651552437, 0.048014131249330905, 0.2001725625539258, 0.21070920204303775, 0.3871421800267153, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.14842244473833366, 0.4598342541436464, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1933 | 26.5 | 1060 | 2.3583 | 0.0945 | 0.1501 | 0.5469 | [0.42529111600331887, 0.6990144112716127, 0.7540950160303118, 0.5036250399233472, 0.6048482256574182, 0.3278464408631659, 0.6501386244117096, 0.0, 0.3309863153710749, 0.6701461061127885, 0.0, 0.16068823815424188, 0.0759493670886076, 0.0, 0.0, 0.0, 0.6276421330982812, 0.03285733530917419, 0.0, 0.06674753570299997, 0.41239939395531733, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.005582040914851246, 0.0798621109529647, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8582896379525593, 0.9401120550253946, 0.8164405175134112, 0.6747073012884627, 0.868055337774572, 0.45685687020222576, 0.9055720852888428, nan, 0.4805405040807608, 0.9539956803455724, 0.0, 0.238277979346916, 0.07833920877399138, 0.0, 0.0, 0.0, 0.9320645059950783, 0.04351782464404239, 0.0, 0.13706773842418152, 0.676342587937894, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.006990364632533535, 0.5362983425414365, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7676 | 27.0 | 1080 | 2.3567 | 0.0926 | 0.1509 | 0.5459 | [0.41386442762131637, 0.7960055724879699, 0.7218561977621083, 0.5168035299390555, 0.5880580526892997, 0.3300573993396712, 0.6781909579446623, 0.0, 0.2845297119281992, 0.7012563536971325, 0.0, 0.1888364952297401, 0.039780521262002745, 0.0, 0.0, 0.0, 0.4978975535168196, 0.04567537266951792, 0.02838107928047968, 0.10232412915208916, 0.279087459984982, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.00678570204599198, 0.07854484909880542, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8624313358302123, 0.9312766187623841, 0.836683496371095, 0.7154007569963327, 0.8227730712196083, 0.46541743267328123, 0.8862632190147668, nan, 0.4713822125293223, 0.9475593952483802, 0.0, 0.28035166061959255, 0.06058232145188667, 0.0, 0.0, 0.0, 0.9547620294256244, 0.05888020554544481, 0.2506078908149659, 0.2675416562002847, 0.40571362911681486, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.007481579444549405, 0.43265193370165744, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.5575 | 27.5 | 1100 | 2.3697 | 0.0901 | 0.1457 | 0.5435 | [0.4330449058471925, 0.7415496484649239, 0.6582991510224409, 0.5107206399916884, 0.5919085961931496, 0.32152780787548696, 0.68747208399237, 0.0, 0.2695065829717953, 0.6896950020291575, 0.0034094166276947215, 0.17989194664865776, 0.023761959835979393, 0.0, 0.0, 0.0, 0.43100931316176777, 0.05545048793403434, 0.00019565120725688115, 0.08220297718419588, 0.35493738896832616, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.023562421824819987, 0.06554660197672603, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8504338327091137, 0.9337022001047669, 0.7716708740927738, 0.7011219448708418, 0.8615346017893384, 0.4684978317919643, 0.8989823334180816, nan, 0.42924807578448787, 0.954341252699784, 0.0054417182345717656, 0.2478137501162899, 0.05901553727640684, 0.0, 0.0, 0.0, 0.9468427666369967, 0.0784712557542019, 0.001725625539257981, 0.1978983504981998, 0.5882826077589303, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.026336671075004724, 0.30154696132596687, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2554 | 28.0 | 1120 | 2.3600 | 0.0908 | 0.1478 | 0.5396 | [0.4245870411963722, 0.7870399701396511, 0.6743924693680554, 0.5037828604923488, 0.6021610759004483, 0.3176424244260717, 0.6686784469885515, 0.0, 0.3011351900484342, 0.664582242501309, 0.0020511317852172, 0.1970743537790876, 0.032291965224037454, 0.0, 0.0, 0.0, 0.46678139857369255, 0.06349432185632616, 0.0, 0.06986375791100168, 0.31471513785167443, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.010739894166640574, 0.07464363872952032, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8389887640449438, 0.9124555879472521, 0.7550844114862733, 0.6772710335692038, 0.8725779643476352, 0.4781074140084483, 0.8861738897401893, nan, 0.5353928247558664, 0.9594816414686825, 0.0033709758975223325, 0.2986091729463206, 0.04413108760934848, 0.0, 0.0, 0.0, 0.9869888475836431, 0.0875887663704814, 0.0, 0.20727622875324458, 0.48120592332993406, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.012960513886264877, 0.4183425414364641, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 2.2107 | 28.5 | 1140 | 2.3688 | 0.0905 | 0.1456 | 0.5420 | [0.41859370389806955, 0.7667516378963897, 0.6755595944797275, 0.5229827746052027, 0.5762823201665176, 0.3283558940003052, 0.7027730340733512, 0.0, 0.28120109546165883, 0.6846517626827171, 0.0, 0.1786280502754033, 0.03361994840928633, 0.0, 0.0, 0.0, 0.4284454994324958, 0.06547736706859063, 0.012462958100453881, 0.06991453779704264, 0.266741758305561, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.06730802868637398, 0.07145529791938318, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8700889513108614, 0.9342488099847405, 0.7711857052698012, 0.7180785914091792, 0.8155400047488699, 0.46050212610690877, 0.890750297191625, nan, 0.4740277746550652, 0.9630669546436285, 0.0, 0.25418643594753, 0.05105105105105105, 0.0, 0.0, 0.0, 0.9734017487826587, 0.09456517860329015, 0.1042434700760844, 0.19042535376371095, 0.3299340735101906, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.07269979217834877, 0.3481767955801105, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7361 | 29.0 | 1160 | 2.3508 | 0.0918 | 0.1483 | 0.5436 | [0.41654288665276806, 0.7925411643174287, 0.6930136805331298, 0.5061774346683199, 0.5923356779044385, 0.3230293544867808, 0.6895139627440876, 0.0, 0.2363788767812238, 0.6377222182874142, 0.0, 0.2168449899419842, 0.033075299085151305, 0.0, 0.0, 0.0, 0.5181226765799256, 0.05391543657743308, 0.012268226822682268, 0.07735822505746048, 0.28361562718329275, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.07860554988996833, 0.08121365560346533, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8848845193508115, 0.9195430113649304, 0.744907699589776, 0.7394870039454378, 0.847944321740416, 0.4446124591268226, 0.8642744745033636, nan, 0.35505991860961844, 0.9723542116630669, 0.0, 0.3459856730858685, 0.03681942812377595, 0.0, 0.0, 0.0, 0.9778653332635217, 0.08459122863362238, 0.10691034590948309, 0.1726115716319183, 0.43146661304453987, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.11067447572265256, 0.45370165745856356, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0032 | 29.5 | 1180 | 2.3543 | 0.0922 | 0.1512 | 0.5443 | [0.42172693105885056, 0.7906304092636438, 0.6995716766885557, 0.5235794280056563, 0.5966555557905998, 0.31724619307417273, 0.6576375643470949, 0.0, 0.2830730969646176, 0.6118519315903495, 0.0, 0.18571861492575403, 0.05163853028798411, 0.0, 0.0, 0.0, 0.5885383948663714, 0.04839595441856208, 0.016447623896516314, 0.08342743868257928, 0.26942911340520653, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.047753564702717244, 0.0753557388848901, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8540121722846442, 0.9050464618397978, 0.7975702114231619, 0.7089807292195949, 0.8845330203260592, 0.46881709867100774, 0.88135698039566, nan, 0.4657613298259025, 0.9820950323974083, 0.0, 0.27081588985021865, 0.06110458284371328, 0.0, 0.0, 0.0, 0.9688203570867585, 0.0754737180173429, 0.14950192171935053, 0.21495855312735493, 0.4059721643709694, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.05365577177404118, 0.526353591160221, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4753 | 30.0 | 1200 | 2.3802 | 0.0906 | 0.1452 | 0.5353 | [0.4182968446268334, 0.7245614420751814, 0.7399438662308484, 0.5222310167082108, 0.5399183829320097, 0.320465614650302, 0.7037323784638214, 0.0, 0.28915533720845393, 0.6621481788079471, 0.007442458767859604, 0.15757803044508464, 0.04727904284060208, 0.0, 0.0, 0.0, 0.480756707265963, 0.045882830155878775, 0.0017206593322928166, 0.06188961172578628, 0.2852105120765736, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.07911695693843154, 0.07258344037351928, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8311267166042446, 0.9383555014006878, 0.8340012622278321, 0.7093176870784684, 0.7601843099130419, 0.461694991369269, 0.8994083653429901, nan, 0.4455186575833202, 0.967451403887689, 0.015602802725674797, 0.21499674388315193, 0.06397702049875963, 0.0, 0.0, 0.0, 0.9759935075134824, 0.07063840416800485, 0.01772688053965017, 0.16369421418404087, 0.43910776611177343, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.09045909692046099, 0.39337016574585637, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1907 | 30.5 | 1220 | 2.3697 | 0.0901 | 0.1445 | 0.5424 | [0.4251599503270035, 0.765785046340057, 0.6581471380414344, 0.5261900558188807, 0.5930662858860665, 0.3183352326985647, 0.6964540996606768, 0.0, 0.30089709108924906, 0.6814073297137216, 0.0, 0.18263983917017215, 0.05210697720319747, 0.0, 0.0, 0.0, 0.3354583969276623, 0.055768695462742206, 0.0005001955309802923, 0.05114433011928644, 0.34335973445510715, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.08411629143768835, 0.05435565940905418, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8500405742821473, 0.916817079281208, 0.7662590722625434, 0.6907439993867011, 0.8838430901702873, 0.4596320361508343, 0.8871152828645837, nan, 0.5097915776693572, 0.95414686825054, 0.0, 0.25144199460414923, 0.06893850372111242, 0.0, 0.0, 0.0, 0.9752997539138175, 0.08221817792527567, 0.004314063848144953, 0.09926316670853219, 0.6002470447984143, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.10757604383147554, 0.13751381215469613, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0148 | 31.0 | 1240 | 2.3644 | 0.0922 | 0.1475 | 0.5320 | [0.39348806932490793, 0.7835452714344651, 0.7081997736482787, 0.4780721893372995, 0.5336730686426202, 0.29837125871030734, 0.6930428493344302, 0.0, 0.29924119164509083, 0.6571159339203156, 0.0, 0.2044692058961942, 0.06489203701588027, 0.0, 0.0, 0.0, 0.4866554648118479, 0.044145581642732315, 0.0, 0.06382143649425778, 0.2935269694364677, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.19617771013814095, 0.07329523564915885, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8640839575530587, 0.9192028036531761, 0.7651585673714105, 0.6830973736898311, 0.75793979687381, 0.4706590229731816, 0.8625497323557504, nan, 0.4755416657298529, 0.9639308855291576, 0.0, 0.3132616987626756, 0.07416111763937851, 0.0, 0.0, 0.0, 0.9781140373841563, 0.06219890803982443, 0.0, 0.15657707443690866, 0.46099708429685593, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.25327791422633666, 0.3783977900552486, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4776 | 31.5 | 1260 | 2.3296 | 0.0927 | 0.1495 | 0.5486 | [0.4542888767818597, 0.7347226902082351, 0.7218922155688623, 0.5378129096513363, 0.583355831137352, 0.3442385557337257, 0.6746452615225272, 0.0, 0.29534572862973707, 0.6409269162210338, 0.0010316812105059536, 0.19597793646577183, 0.035507354033575994, 0.0, 0.0, 0.0, 0.47966749487016436, 0.05985256395442886, 0.0005085775011386064, 0.05393716324508912, 0.2532068763509282, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1537234330214699, 0.08031746031746032, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8223876404494382, 0.9311627417040563, 0.8321749763332281, 0.7307118368482138, 0.8906035096836625, 0.46621384565726876, 0.8951755320245449, nan, 0.49512482106856726, 0.9707343412526998, 0.0017336447472971997, 0.29170155363289607, 0.0624102363232798, 0.0, 0.0, 0.0, 0.9577464788732394, 0.09242408021981943, 0.00525531414228567, 0.1401867202545424, 0.3600677936888672, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.214812015870017, 0.40535911602209945, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6071 | 32.0 | 1280 | 2.3263 | 0.0926 | 0.1478 | 0.5431 | [0.44611914667788144, 0.6906991054003323, 0.6866730381225443, 0.5074964861193935, 0.5781552048719472, 0.32377948368416076, 0.6837998986219906, 0.0, 0.3208871482320843, 0.6827765565497383, 0.0025829619013119556, 0.16798124067363035, 0.04280860702151756, 0.0, 0.0, 0.0, 0.5491364944705348, 0.05461611175296713, 0.0, 0.04500268780538684, 0.2887424974381496, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1499665551839465, 0.07299177849774834, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8343305243445693, 0.9491154599494386, 0.7651940675291891, 0.7267646162156958, 0.8200178306624674, 0.4566568898494183, 0.8991609919672368, nan, 0.5464996889778237, 0.9526133909287257, 0.003948857479954732, 0.2382547213694297, 0.07403055229142186, 0.0, 0.0, 0.0, 0.9489632965076705, 0.0835028369553581, 0.0, 0.11741187306371934, 0.4249457794119759, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.1694313243907047, 0.4468508287292818, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.9074 | 32.5 | 1300 | 2.3673 | 0.0920 | 0.1471 | 0.5351 | [0.3967544010551064, 0.7805696445736764, 0.7306059291494311, 0.50022581423016, 0.5067946749880464, 0.3351793597162349, 0.7093531658204227, 0.0, 0.3347792611677796, 0.6612333363023664, 0.0, 0.1795758966804837, 0.06716556373306945, 0.0, 0.0, 0.0, 0.4484492323879669, 0.045980321574274056, 0.0, 0.09171652606934483, 0.2733365472780724, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.13645581028558554, 0.05806729939603106, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8853901373283396, 0.9281820097023253, 0.8436809719154308, 0.6694193806037072, 0.7217744645201177, 0.45419397392536875, 0.8985494300105134, nan, 0.4985872848138738, 0.9620302375809935, 0.0, 0.2438366359661364, 0.1417939678809244, 0.0, 0.0, 0.0, 0.9818969579559139, 0.06837240837883167, 0.0, 0.25831030729297494, 0.4153225227295578, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.1830719818628377, 0.26027624309392267, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2064 | 33.0 | 1320 | 2.3523 | 0.0919 | 0.1456 | 0.5390 | [0.4014502191538961, 0.7878413260529862, 0.6706802808459629, 0.5061977421770346, 0.5603734397105256, 0.32183266872206184, 0.7139795096759863, 0.0, 0.32093588479813073, 0.6589957542162991, 0.0, 0.18433973921243457, 0.06545315821025668, 0.0, 0.0, 0.0, 0.45807238916454507, 0.04980389715495238, 0.0036028209321340965, 0.0826925852860511, 0.29005297723748164, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.11148026315789474, 0.05935588082356976, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8927543695380774, 0.9379996355934134, 0.7415154622909436, 0.6966131278068679, 0.7923332631456335, 0.4493663780400522, 0.8835146259508414, nan, 0.46116719502964076, 0.9654643628509719, 0.0, 0.2636989487394176, 0.15680898289593942, 0.0, 0.0, 0.0, 0.9679564375098173, 0.07136994611569068, 0.03317907286846027, 0.17987524072678557, 0.40027002570989473, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.12805592291706028, 0.2964088397790055, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.903 | 33.5 | 1340 | 2.3609 | 0.0927 | 0.1487 | 0.5370 | [0.4075733622834502, 0.7542732883284411, 0.6648988920602986, 0.5248570716781542, 0.509697676245945, 0.3181806033068896, 0.6797000421349064, 0.0, 0.3447921257529014, 0.6636860554928405, 0.0, 0.16469173965309977, 0.06162012462497115, 0.0, 0.0, 0.0, 0.4308227696583617, 0.05094559030540425, 0.004221586775181469, 0.08378457218910099, 0.31144272714711513, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.25169195240080827, 0.07541807044410413, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8694319600499376, 0.9455311795385701, 0.7378431681918586, 0.7324358487504925, 0.7119922405257806, 0.4594355642252691, 0.8756948786839737, nan, 0.5100988525904775, 0.9650539956803456, 0.0, 0.24534840450274445, 0.1045828437132785, 0.0, 0.0, 0.0, 0.965809728258024, 0.07426042893337616, 0.03914032473135148, 0.1923930335761534, 0.4532410266722204, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.2965048176837332, 0.3401104972375691, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2941 | 34.0 | 1360 | 2.3475 | 0.0927 | 0.1528 | 0.5455 | [0.4208584637279458, 0.8046875292830021, 0.6522785851757649, 0.5324975634251897, 0.5918016998899351, 0.3248033646493336, 0.676478485049397, 0.0, 0.33967654387607726, 0.5946243841546818, 0.00020294561057636554, 0.20067689467574787, 0.07388928486576384, 0.0, 0.0, 0.0, 0.38386995936230073, 0.049376613390454224, 0.01695264241592313, 0.09430783002474856, 0.3106020836698433, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1503295656087037, 0.08701964014014713, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8805149812734082, 0.916788610016626, 0.7566503628905017, 0.7032809711517719, 0.8671906850468839, 0.45898297711102065, 0.8483395062152561, nan, 0.4811925264743575, 0.9671058315334773, 0.0003370975897522333, 0.3337287189506001, 0.1218174696435566, 0.0, 0.0, 0.0, 0.9805225404471438, 0.0802019769475074, 0.15499254843517138, 0.22494348153730218, 0.44190856469844736, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.2145853013413943, 0.3444198895027624, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.14 | 34.5 | 1380 | 2.3476 | 0.0930 | 0.1484 | 0.5484 | [0.4507045961653645, 0.6939744427313351, 0.640811892188368, 0.5383862308542555, 0.5913912741715294, 0.3223392970447658, 0.6843672644334188, 0.0, 0.37972545527132023, 0.6784252016621853, 0.0, 0.16304582959918415, 0.06193293885601578, 0.0, 0.0, 0.0, 0.38587078842822076, 0.05217989804916869, 0.0, 0.060556068189655976, 0.33883223066477913, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2243663294044018, 0.056978303099948016, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8548813982521848, 0.945048625503906, 0.7189215840959293, 0.7294549305492413, 0.8665455555505777, 0.46162482282442424, 0.8700877488335658, nan, 0.5560177170222812, 0.9591144708423326, 0.0, 0.24541817843520328, 0.12299255777516648, 0.0, 0.0, 0.0, 0.9649850777527619, 0.0865717446383328, 0.0, 0.1360001674621117, 0.505350243454031, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.24383147553372378, 0.23011049723756907, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3208 | 35.0 | 1400 | 2.3157 | 0.0941 | 0.1493 | 0.5541 | [0.4444540497720132, 0.7645915345192558, 0.6473698095654649, 0.5409062259439483, 0.5856129155450979, 0.33085967470669236, 0.703044209235194, 0.0, 0.3400476510847957, 0.6569651741293532, 0.0012611017199280904, 0.16309550359403682, 0.04457278246705168, 0.0, 0.0, 0.0, 0.36734724425949766, 0.05026343639405077, 0.0007944532717030189, 0.07843816285009307, 0.32638752925162456, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.30151397646424116, 0.049744167895238924, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8825561797752809, 0.9378074680574852, 0.7356145471757652, 0.7604140481118772, 0.8518621394106921, 0.45690598818361705, 0.9137560211366806, nan, 0.5038034639626474, 0.9696976241900648, 0.0022633695311935664, 0.22111359196204297, 0.07860033946990469, 0.0, 0.0, 0.0, 0.9637022880779098, 0.07881026299825143, 0.006902502157031924, 0.18965084149711128, 0.5028223431911868, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.33788021915737765, 0.15845303867403315, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.3582 | 35.5 | 1420 | 2.3832 | 0.0909 | 0.1445 | 0.5306 | [0.4051867678695122, 0.7980607418502008, 0.7580956363529654, 0.5205549496763452, 0.4407461812951589, 0.33508630761833824, 0.6726644846774493, 0.0, 0.34557420044197285, 0.6784928828883866, 0.0034325347676846312, 0.17634282975345256, 0.0457234409662312, 0.0, 0.0, 0.0, 0.3998636834450521, 0.04623497669855286, 0.005790456783834268, 0.08466625590470322, 0.2743658403155682, nan, 0.00023949864949372648, 0.0, 0.0, nan, 0.0, 0.0, 0.14829105358633995, 0.04160713176368267, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8635549313358302, 0.9268468011934315, 0.8151980119911644, 0.7514195909795133, 0.6122861328518756, 0.4719115314986598, 0.8713452301603117, nan, 0.5332493948182956, 0.9595032397408207, 0.007006814186992848, 0.27381616894594846, 0.09687948818383602, 0.0, 0.0, 0.0, 0.975273574532698, 0.06726617421403847, 0.05349439171699741, 0.21573306539395462, 0.4105970728064002, nan, 0.00023949864949372648, 0.0, 0.0, nan, nan, 0.0, 0.19459663706782543, 0.1480110497237569, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.386 | 36.0 | 1440 | 2.3824 | 0.0934 | 0.1488 | 0.5302 | [0.39236390712040153, 0.8145851925312122, 0.7558239703806888, 0.4900571399155771, 0.48914028430650736, 0.33919614966813755, 0.6652323001411955, 0.0, 0.3456263625131779, 0.6560158432230317, 0.001601036396079294, 0.18811622781579862, 0.05869991594284113, 0.0, 0.0, 0.0, 0.4797751879268533, 0.046657224133211134, 0.00930281474682576, 0.07758739097589451, 0.3090916412471183, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1549231307719232, 0.07425068119891008, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8881335830212235, 0.9108228756234769, 0.8132888923950773, 0.6788969889517045, 0.696477772152806, 0.45792694051110766, 0.8158373932343381, nan, 0.5430072471914322, 0.9587041036717062, 0.0031542703041101826, 0.3017489999069681, 0.10941376158767463, 0.0, 0.0, 0.0, 0.9866616053196502, 0.06994254719337686, 0.0763197113499098, 0.1891903206899439, 0.4409893282392313, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.21741923294917817, 0.36734806629834255, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.851 | 36.5 | 1460 | 2.3500 | 0.0967 | 0.1573 | 0.5450 | [0.41524299726375297, 0.8009912665364631, 0.6891513755788327, 0.5282791330311297, 0.5382617997822409, 0.3209485427756586, 0.6855615317723598, 0.0, 0.34495694031815277, 0.6450787627917672, 0.00034193392126971464, 0.18070362473347548, 0.05615745756590827, 0.0, 0.0, 0.0, 0.5498109654736161, 0.04774509910281306, 0.01089028271073159, 0.08318980206500814, 0.3353840629016406, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.36103354423668305, 0.07984250543084721, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.860379213483146, 0.9282332543785728, 0.7684206374250552, 0.7450690941895272, 0.7596713423621596, 0.4702380117041133, 0.877598279380742, nan, 0.5499621527231303, 0.9693736501079914, 0.0005778815824323999, 0.2838403572425342, 0.1218174696435566, 0.0, 0.0, 0.0, 0.9784412796481491, 0.07852478321378867, 0.10173346929170915, 0.18906472410617098, 0.5097309797049826, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.385943699225392, 0.48734806629834254, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3517 | 37.0 | 1480 | 2.3574 | 0.0943 | 0.1502 | 0.5440 | [0.4228595354346009, 0.7748181297538302, 0.6949422948852119, 0.5342279860915328, 0.5451333645297145, 0.3215454815990477, 0.6760519655457277, 0.0, 0.3373484782973615, 0.6933557004891509, 0.00421865306403805, 0.17283593452607537, 0.04307984578420965, 0.0, 0.0, 0.0, 0.40725727575039666, 0.04887517311320241, 0.0043186259216079085, 0.07464663207256432, 0.27873249635393826, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.29052538571331477, 0.08985310669244755, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8557943196004993, 0.9320908397294281, 0.7491243294414642, 0.7601234442441714, 0.7828579236686364, 0.4776478100397154, 0.882868706580819, nan, 0.4970284266774588, 0.9551835853131749, 0.007367990176013099, 0.26400130244673925, 0.10066588327457893, 0.0, 0.0, 0.0, 0.9810723074506519, 0.0799700246226314, 0.04125813789316809, 0.18759943062882023, 0.3925426942246097, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.3230304175325902, 0.3440331491712707, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.351 | 37.5 | 1500 | 2.3734 | 0.0904 | 0.1459 | 0.5398 | [0.4358926457706038, 0.767827928071386, 0.6770891494509585, 0.5237361027822186, 0.5741180617280167, 0.3277300764927676, 0.6693217782875592, 0.0, 0.33305621947417646, 0.6817848186433421, 0.019828058775553863, 0.16795366795366795, 0.06148850895439095, 0.0, 0.0, 0.0, 0.3150709636290431, 0.05170151342839647, 0.0038583458742915693, 0.09165746801200231, 0.29477336063242904, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.09638852753515456, 0.05374195633649693, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8666073657927591, 0.9266076593709431, 0.731847585989271, 0.6897794798012483, 0.8391947529467634, 0.45651304433248663, 0.8661091603735338, nan, 0.5284379192241683, 0.9629805615550756, 0.03570826611446871, 0.2569773932458833, 0.1582452017234626, 0.0, 0.0, 0.0, 0.9833368239174826, 0.08435927630874639, 0.03780688681465213, 0.24681821987775265, 0.4466627407609554, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.11758926884564519, 0.10104972375690607, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.9016 | 38.0 | 1520 | 2.3797 | 0.0922 | 0.1468 | 0.5361 | [0.3979803210771621, 0.8215521095152025, 0.7240312356453835, 0.4978367086289089, 0.5282189976939653, 0.32252930323024087, 0.6956390892056101, 0.0, 0.3261938294162919, 0.6670155189426786, 0.0, 0.2158865285041332, 0.04337220080987462, 0.0, 0.0, 0.0, 0.3376964639840277, 0.041506735010132315, 0.013210224784063633, 0.08663169738888706, 0.27028534152603007, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.23038631423964104, 0.046741210839125615, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8922659176029962, 0.9077510419750837, 0.7771655096244873, 0.7171782542730357, 0.7511793773604347, 0.4642596516833434, 0.841818469171093, nan, 0.4669379679384851, 0.963585313174946, 0.0, 0.3869197134617174, 0.1160725943334639, 0.0, 0.0, 0.0, 0.9874469867532332, 0.06212753809370874, 0.11828378696368343, 0.21918697144770996, 0.3689728968875228, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.24832798035140752, 0.10254143646408839, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6249 | 38.5 | 1540 | 2.3389 | 0.0948 | 0.1506 | 0.5476 | [0.44896937443748053, 0.7327870092041515, 0.702833401443945, 0.5179074282523701, 0.5741124663766068, 0.3256582936711242, 0.6919663902831313, 0.0, 0.3850746129757246, 0.6641039748985085, 0.018652642790573826, 0.17898514779259433, 0.05360252503265128, 0.0, 0.0, 0.0, 0.3991114932645457, 0.048388596939961864, 0.0, 0.06818984365021526, 0.2746326684658415, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.28833227198754685, 0.0698983248825294, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8578636079900125, 0.9518485093493065, 0.7326522562322499, 0.7243221174503165, 0.8299837373606139, 0.46163183967890875, 0.8573205340516323, nan, 0.6202156920056059, 0.9645572354211663, 0.03373383737449134, 0.28532886780165595, 0.1286068677373025, 0.0, 0.0, 0.0, 0.9478244934289753, 0.07426042893337616, 0.0, 0.18998576572050574, 0.38148313057466643, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.30795390137918005, 0.290939226519337, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0949 | 39.0 | 1560 | 2.3474 | 0.0965 | 0.1556 | 0.5483 | [0.4252860384788539, 0.800874486291488, 0.7321616393418507, 0.5220628125928689, 0.563952966520061, 0.32862220717670954, 0.6963647914920424, 0.0, 0.3485466160598535, 0.6593487333215776, 0.019300024474668716, 0.18426230540383132, 0.06387929740279237, 0.0, 0.0, 0.0, 0.5084594403754829, 0.049878946007568814, 0.011522930116515343, 0.08760321624528901, 0.26784043386101936, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.3098973395026027, 0.07873112577800342, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8682943196004994, 0.928452467715854, 0.7852832123698328, 0.7360104546078542, 0.8162993759268137, 0.4631895813744615, 0.8962749692501151, nan, 0.5017199901072464, 0.9691144708423326, 0.039873829187835594, 0.2722578844543678, 0.1111111111111111, 0.0, 0.0, 0.0, 0.9614246819205194, 0.07572351282874781, 0.10542003294376029, 0.23695888805157833, 0.4029128305301409, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.323937275647081, 0.46265193370165747, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2577 | 39.5 | 1580 | 2.3334 | 0.0944 | 0.1513 | 0.5470 | [0.4402755503484393, 0.7402331985677358, 0.6773202538216256, 0.5072853858237666, 0.5930683129436178, 0.33015401415690654, 0.7049174176186468, 0.0, 0.357822011528309, 0.6655650925621318, 0.011452232625806693, 0.1390162139974003, 0.07664071894183913, 0.0, 0.0, 0.0, 0.44731726767513147, 0.04801383619727739, 0.0, 0.05874611150993061, 0.31305863769456005, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2304008152173913, 0.07994399179722385, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8585455680399501, 0.9481503518801102, 0.756591195960871, 0.6730278605315376, 0.8680822181702514, 0.47095723928877165, 0.903572483834837, nan, 0.5973649301886368, 0.9613174946004319, 0.022176205725843345, 0.1890408410084659, 0.15811463637550593, 0.0, 0.0, 0.0, 0.9398136028064297, 0.07677621953395425, 0.0, 0.13361383237042618, 0.4668428536546831, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.2563007746079728, 0.4006077348066298, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1701 | 40.0 | 1600 | 2.3302 | 0.0953 | 0.1541 | 0.5490 | [0.4259301389453817, 0.8066144094268355, 0.7156322044500044, 0.5189784024862559, 0.5884532221551915, 0.32656141740077205, 0.7052324253246934, 0.0, 0.3676639641819942, 0.683115567087869, 0.010315416018662519, 0.17511329024589556, 0.06753026120195371, 0.0, 0.0, 0.0, 0.44391109193439166, 0.0498715279315997, 0.006932507840821148, 0.07890224309460545, 0.27097698845413726, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.25831137078080846, 0.07708268849376941, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.867730961298377, 0.9171031953902566, 0.789744398863995, 0.7061905755204573, 0.8827365138814843, 0.4630071431578652, 0.8697441747005752, nan, 0.546507183488095, 0.9609503239740821, 0.020442560978546145, 0.2741185226532701, 0.16607912260086174, 0.0, 0.0, 0.0, 0.9696188282109011, 0.08034471683973879, 0.06675033335947918, 0.21382818387339864, 0.38833431480976116, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.3073871150576233, 0.37149171270718234, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1454 | 40.5 | 1620 | 2.3452 | 0.0937 | 0.1484 | 0.5472 | [0.41590111896925575, 0.7956456544034374, 0.7433866866772062, 0.5075708130433674, 0.5719918487860337, 0.3325156545780296, 0.7216679076693969, 0.0, 0.34159443418760627, 0.692085970298891, 0.0007803923124743727, 0.18136915775078433, 0.05085877283486071, 0.0, 0.0, 0.0, 0.4747165401851638, 0.044398764313752816, 8.663109016563865e-06, 0.0787716253878109, 0.2466963662712683, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1938508839354343, 0.07160791623266269, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.887791822721598, 0.9320481358325552, 0.804970022088987, 0.7098739349724817, 0.8305863062304277, 0.46035828058997713, 0.899078534175319, nan, 0.49087543374478193, 0.9542116630669546, 0.001420625556812983, 0.26218718020280957, 0.10941376158767463, 0.0, 0.0, 0.0, 0.9678386302947798, 0.06821182600007138, 7.84375245117264e-05, 0.18814368249183622, 0.3300777042069431, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.23823918382769696, 0.36082872928176796, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3269 | 41.0 | 1640 | 2.3383 | 0.0968 | 0.1571 | 0.5504 | [0.44117825990446985, 0.7865425366699565, 0.6838256139003621, 0.5341472651761701, 0.5823088792515934, 0.3153129970992795, 0.6957088082357267, 0.0, 0.35667411972151025, 0.655019170442663, 0.02082355913241185, 0.18702265053922051, 0.04257827852744661, 0.0, 0.0, 0.0, 0.4492152762756267, 0.0543782136318288, 0.005244184722801958, 0.08332128659197686, 0.2751865862386259, nan, 0.0033214205051216306, 0.0, 0.0, nan, 0.0, 0.0, 0.42900848740048686, 0.07639193083573487, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8374469413233459, 0.9335982872890427, 0.7516606184916378, 0.741781526508243, 0.847881600817164, 0.4729009079809703, 0.910169107188258, nan, 0.588199144126927, 0.9741252699784018, 0.041679709132936844, 0.2653967810959159, 0.127301214257736, 0.0, 0.0, 0.0, 0.9786114456254255, 0.08849873318345644, 0.05420032943760295, 0.19304194925898016, 0.3865963833790558, nan, 0.003326370131857312, 0.0, 0.0, nan, nan, 0.0, 0.4927640279614585, 0.36613259668508286, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.8185 | 41.5 | 1660 | 2.3574 | 0.0927 | 0.1490 | 0.5449 | [0.4211759415401911, 0.7995945186741249, 0.6978932238632433, 0.5148802388613652, 0.5681032954662072, 0.3273415115720924, 0.709560116667298, 0.0, 0.34431855998331107, 0.6651933866095212, 0.0010200958890135673, 0.17970467088875502, 0.05918037206794284, 0.0, 0.0, 0.0, 0.3746546582488056, 0.048634381455129674, 0.00826594929383612, 0.06770428015564202, 0.28484075132707226, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.26019523473123113, 0.062167810304256964, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8769569288389513, 0.93192998838454, 0.77183654149574, 0.7083478189555733, 0.8184229271854881, 0.4641614157205608, 0.901030035250706, nan, 0.5195344410219515, 0.9654211663066955, 0.001926271941441333, 0.25672155549353426, 0.11463637550594072, 0.0, 0.0, 0.0, 0.9834022723702812, 0.07561645790957428, 0.08290846340889481, 0.14751318764129615, 0.4007727331485285, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.30617797090496884, 0.2087292817679558, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.4443 | 42.0 | 1680 | 2.3533 | 0.0946 | 0.1519 | 0.5481 | [0.4556085209645064, 0.7171990698571566, 0.6706899591548882, 0.5366851043897921, 0.5814569413194934, 0.32244517492484903, 0.7128303140195261, 0.0, 0.3762961100986615, 0.6560768647576836, 0.0015664352847226488, 0.1634929226328134, 0.07514845403044161, 0.0, 0.0, 0.0, 0.4917144503278554, 0.0537456789043014, 0.0, 0.06913935598983319, 0.28430204378382745, nan, 0.0016475121238291371, 0.0, 0.0, nan, 0.0, 0.0, 0.2811459027315123, 0.07812873178887031, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.86375, 0.952699740360307, 0.7202350899337331, 0.7272228075899312, 0.8399698043555202, 0.4587514209130331, 0.899559537961506, nan, 0.538525530049239, 0.9719006479481641, 0.0024559967253376994, 0.23183551958321705, 0.14375244810027418, 0.0, 0.0, 0.0, 0.9383999162259804, 0.08766013631659708, 0.0, 0.17196265594909152, 0.4043922267066917, nan, 0.0016498795854012268, 0.0, 0.0, nan, nan, 0.0, 0.3348573587757415, 0.43375690607734807, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.8899 | 42.5 | 1700 | 2.3360 | 0.0951 | 0.1534 | 0.5492 | [0.42846571344838863, 0.8007632484952961, 0.6826133744392173, 0.5345972402179221, 0.5726169929331992, 0.32029962113543686, 0.7003049806266755, 0.0, 0.36421663255308623, 0.6963148209314396, 0.003372704496021585, 0.18627909013928226, 0.06906686260102865, 0.0, 0.0, 0.0, 0.4428928219783387, 0.0483309094598708, 0.004074604060051903, 0.09843533548822506, 0.27695480393257854, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.25219468979032994, 0.08084125389588646, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8647097378277153, 0.9310033138223973, 0.7406161249605554, 0.7436873886835644, 0.8338477942395313, 0.46626998049314455, 0.8867510942836135, nan, 0.5627777652869274, 0.9561771058315335, 0.005899207820664082, 0.29674853474741836, 0.12273142707925316, 0.0, 0.0, 0.0, 0.965626472590188, 0.07649073974949148, 0.03953251235391011, 0.23203968852047224, 0.389641354150209, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.3549593803136218, 0.3467955801104972, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.8087 | 43.0 | 1720 | 2.3284 | 0.0972 | 0.1525 | 0.5505 | [0.43836696729880237, 0.7567624699912359, 0.6673363858428416, 0.5294658081291947, 0.5873144378689118, 0.3192053517516581, 0.7007850338821053, 0.0, 0.3705301979871153, 0.6521739130434783, 0.004574081111895325, 0.17451251689297895, 0.06082198753022013, 0.0, 0.0, 0.0, 0.38038830433546184, 0.04935740581531204, 0.0, 0.06753617165955196, 0.28932680972698915, nan, 0.0011563613163910894, 0.0, 0.0, nan, 0.0, 0.0, 0.5113006396588486, 0.04920763681664107, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8605243445692884, 0.9427440385359965, 0.7467813190280845, 0.7202411833817376, 0.8547674621770432, 0.46924512679456054, 0.896797201932261, nan, 0.5888061994588963, 0.9732181425485961, 0.008114420553321615, 0.2522792817936552, 0.1248204726465596, 0.0, 0.0, 0.0, 0.9629954447876852, 0.07989865467651572, 0.0, 0.14969019509336012, 0.3983453743734111, nan, 0.0011575768058863446, 0.0, 0.0, nan, nan, 0.0, 0.5436614396372568, 0.1876795580110497, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.8746 | 43.5 | 1740 | 2.3485 | 0.0936 | 0.1477 | 0.5446 | [0.4272191745573527, 0.7504909199535571, 0.6849456177073291, 0.5154349225261836, 0.5841193162988642, 0.32693051310406657, 0.7123769775246045, 0.0, 0.36178099163105865, 0.6689666526971203, 0.0011928429423459245, 0.16990881915548814, 0.07231482641935079, 0.0, 0.0, 0.0, 0.3994246685770762, 0.04632826297499003, 0.0, 0.06954148268216795, 0.2777953386437392, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.22221839120091025, 0.07084158351774922, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00014685048892574548, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8757553058676654, 0.947696267110028, 0.7370108867150521, 0.6910399517918492, 0.8488716953913562, 0.4657086321343868, 0.8679232317957246, nan, 0.5361872428446163, 0.9653347732181425, 0.0019503503407093496, 0.2630709833472881, 0.15067241154197675, 0.0, 0.0, 0.0, 0.946921304780355, 0.07254755022659957, 0.0, 0.15626308297747635, 0.37868233198799245, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.24352918949556018, 0.3024309392265193, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00014685048892574548, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.831 | 44.0 | 1760 | 2.3582 | 0.0946 | 0.1529 | 0.5471 | [0.44862804953591057, 0.7731316248333944, 0.7137798359999303, 0.5272975648440227, 0.5739832784018088, 0.33877220463425345, 0.6904242373379212, 0.0, 0.35824641385185696, 0.6585803739545475, 0.030590522028878193, 0.1564589009798778, 0.08286701777485188, 0.0, 0.0, 0.00017225505627988476, 0.47857741082439215, 0.049684259435257346, 0.0, 0.0840623961777595, 0.2905621537623233, nan, 0.005873482022382729, 0.0, 0.0, nan, 0.0, 0.0, 0.19286701322629465, 0.07456105895753957, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8476591760299625, 0.9429447468512994, 0.808591038182392, 0.6923022066282641, 0.8336529113708554, 0.4681645312039519, 0.9044795195459324, nan, 0.5830354265500521, 0.9745140388768898, 0.06366328766463605, 0.2150200018606382, 0.13147930539234887, 0.0, 0.0, 0.00017313226110733006, 0.9499319336090895, 0.08394889911858117, 0.0, 0.22245248262580591, 0.4135845912988524, nan, 0.0059076333541785864, 0.0, 0.0, nan, nan, 0.0, 0.22150009446438693, 0.4195027624309392, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.7194 | 44.5 | 1780 | 2.3640 | 0.0941 | 0.1514 | 0.5428 | [0.43350567785320054, 0.7731674676783076, 0.7072758223454915, 0.5135186808138796, 0.547413130716231, 0.3328523221188296, 0.7025313371416195, 0.0, 0.3597874653923118, 0.656186049231129, 0.018551556209166084, 0.17703048675137825, 0.05200519330640508, 0.0, 0.0, 0.0002494209869944771, 0.41845855107227503, 0.04923929580525973, 0.0, 0.0882416325448005, 0.28095806107692145, nan, 0.004210693572733773, 0.0, 0.0, nan, 0.0, 0.0, 0.3001928924052702, 0.0747682335502349, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8531023720349563, 0.9457575101919967, 0.7604331019248974, 0.7134948948210112, 0.7740008332922661, 0.47098881513395174, 0.8915542606628232, nan, 0.5902001783693445, 0.9695680345572354, 0.037056656473477646, 0.262140664247837, 0.09413761587674631, 0.0, 0.0, 0.00025074327470716765, 0.9590816273103304, 0.08084430646254862, 0.0, 0.22285020514108683, 0.3822874624764805, nan, 0.004231142807722501, 0.0, 0.0, nan, nan, 0.0, 0.34694880030228603, 0.32883977900552486, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.5284 | 45.0 | 1800 | 2.3472 | 0.0964 | 0.1553 | 0.5461 | [0.4203439092672654, 0.8073401576234241, 0.7243798955613577, 0.5223530028573675, 0.549703015370608, 0.3335615466389159, 0.7053615999830061, 0.0, 0.36481394845791265, 0.6520617809846707, 0.01039811419591409, 0.16680997420464316, 0.05976206359795419, 0.0, 0.0, 0.0005878964589630457, 0.401571427796257, 0.050430558139025965, 0.008387202676400928, 0.09611780899155901, 0.2984873714963755, nan, 0.0018627672738400947, 0.0, 0.0, nan, 0.0, 0.0, 0.4031013001083424, 0.07301291288745726, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00019868007325247918, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8653043071161048, 0.9231756895255882, 0.8054354686020827, 0.7105068452365034, 0.8006281052457093, 0.4700660987692437, 0.9126771983590899, nan, 0.5435093793796044, 0.9720086393088553, 0.01911824901880523, 0.23011442924923248, 0.14035774905340123, 0.0, 0.0, 0.0005910377189526096, 0.9687156395622808, 0.08223602041180458, 0.08416346380108244, 0.2426525998492841, 0.4299584907286385, nan, 0.0018627672738400947, 0.0, 0.0, nan, nan, 0.0, 0.449877196296996, 0.28552486187845305, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.00019868007325247918, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.6914 | 45.5 | 1820 | 2.3321 | 0.0985 | 0.1570 | 0.5504 | [0.44062770914279387, 0.761767946650595, 0.7469356134927128, 0.5301679169197479, 0.5513342308861576, 0.33925065755065226, 0.6870311731837669, 0.0, 0.3704766251548543, 0.6613854686143843, 0.023482004567768357, 0.17349297705785757, 0.048038547649177456, 0.0, 0.0, 0.0005811919178740236, 0.4371920436798586, 0.05251944968520128, 0.0, 0.07658178836605475, 0.28944202056980234, nan, 0.00536928767450185, 0.0, 0.0, nan, 0.0, 0.0, 0.5335586990055466, 0.0678812913945492, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8510736579275905, 0.9427739312638076, 0.8138687283054591, 0.7329546568824088, 0.7962286804861768, 0.47108003424224987, 0.8930522438826626, nan, 0.5961733030555119, 0.971036717062635, 0.043076256290481806, 0.25309331100567495, 0.12886799843321584, 0.0, 0.0, 0.0005850676409833912, 0.945402900675428, 0.08840952075081183, 0.0, 0.17596081386586285, 0.39733995949614354, nan, 0.005388719613608845, 0.0, 0.0, nan, nan, 0.0, 0.6142830153032307, 0.32502762430939225, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.9363 | 46.0 | 1840 | 2.3603 | 0.0957 | 0.1533 | 0.5448 | [0.4307641160941553, 0.7948637865598526, 0.7199057146569164, 0.5296008038661645, 0.5420462362516328, 0.3320313857919698, 0.7084345558217591, 0.0, 0.37007814061503613, 0.6535473021947754, 0.030037394170060253, 0.18495110006140575, 0.0516514213726242, 0.0, 0.0, 0.0002496077592354871, 0.39646599039893343, 0.0522977819099482, 0.0039492332436262875, 0.08375527426160338, 0.2983843452082288, nan, 0.0018980117331634414, 0.0, 0.0, nan, 0.0, 0.0, 0.3670740429766458, 0.05197259285971872, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8532927590511861, 0.9310360534766666, 0.7649850110444936, 0.7348105993749342, 0.785460841983594, 0.4691433824045357, 0.8716681898453229, nan, 0.5828180857521865, 0.9737149028077754, 0.0651802268185211, 0.28721276397804446, 0.12312312312312312, 0.0, 0.0, 0.00025074327470716765, 0.9653777684695534, 0.08430574884915962, 0.0389050121578163, 0.2077576823243741, 0.42707151372391305, nan, 0.0019026837154223824, 0.0, 0.0, nan, nan, 0.0, 0.44602304931041, 0.19906077348066298, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.1896 | 46.5 | 1860 | 2.3470 | 0.0961 | 0.1539 | 0.5448 | [0.4302888708712368, 0.7840775863614363, 0.7197212972507954, 0.5239124080169796, 0.5361995966189897, 0.33269042907597124, 0.686007758583737, 0.0, 0.36299630377477604, 0.6463129034100645, 0.020041912745284815, 0.18784904637894959, 0.04363136176066025, 0.0, 0.0, 0.00018989520158562494, 0.4620590700154913, 0.051365775665998606, 6.591794863343853e-05, 0.08219591952797327, 0.2808638539463431, nan, 0.0025166562909784496, 0.0, 0.0, nan, 0.0, 0.0, 0.4082182774490467, 0.06904881516081106, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0002159566013613904, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8596551186017478, 0.9338957911039242, 0.765592458188703, 0.7349425299651811, 0.7801295635071748, 0.46598930631376567, 0.8797627964185832, nan, 0.564523986180123, 0.9775377969762419, 0.037995714044930295, 0.2902363010512606, 0.10353832092962528, 0.0, 0.0, 0.00019104249501498489, 0.9526414995549505, 0.08139742354494522, 0.0006275001960938112, 0.19362806664992047, 0.39861827269724087, nan, 0.002528041300211557, 0.0, 0.0, nan, nan, 0.0, 0.4692235027394672, 0.3569060773480663, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0002159566013613904, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0699 | 47.0 | 1880 | 2.3703 | 0.0966 | 0.1545 | 0.5362 | [0.39480193878006337, 0.8176080694917801, 0.7219032652986643, 0.505227403004498, 0.5027757960037987, 0.3315813337318605, 0.7109051055395131, 0.0, 0.361499711633177, 0.6554905325530573, 0.0046006010462657215, 0.19404779798587105, 0.05704644062349349, 0.0, 0.0, 0.0005837952588748673, 0.45511712954766076, 0.04679955428995914, 0.011199338690575546, 0.08728850172372749, 0.28654787628263345, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.4469559109919229, 0.07303409090909091, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8836860174781523, 0.9216653950395154, 0.7791811296939097, 0.6976899666786117, 0.7151058863586472, 0.45926715971764176, 0.8847514928296079, nan, 0.5308286680006895, 0.9652699784017279, 0.008957164527702198, 0.3002604893478463, 0.09270139704922313, 0.0, 0.0, 0.0005910377189526096, 0.9757709827739672, 0.07418905898726047, 0.11051847203702252, 0.20723436322532027, 0.3942806256553151, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.5290005667863216, 0.3550828729281768, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.2962 | 47.5 | 1900 | 2.3289 | 0.1004 | 0.1610 | 0.5506 | [0.43813563391067495, 0.7919837442756195, 0.7229490864037844, 0.530693124964866, 0.5619734040268292, 0.32844023814193124, 0.6849341364586738, 0.0, 0.37461864366255104, 0.6442053497825407, 0.03035054317392765, 0.19145526513998173, 0.035979926516712966, 0.0, 0.0, 0.00031904334262891713, 0.5248448757220687, 0.053110580747812254, 0.005331374546833164, 0.09117176661865672, 0.2788984917106411, nan, 0.003947699603905308, 0.0, 0.0, nan, 0.0, 0.0, 0.5592105263157895, 0.07566214768571534, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.840165418227216, 0.929310816043, 0.7848611549384664, 0.7405620599713675, 0.8205464784441627, 0.4759392059727465, 0.9071662692659195, nan, 0.5871274291581416, 0.9789416846652268, 0.05872721581469264, 0.2778630570285608, 0.1048439744091918, 0.0, 0.0, 0.000322384210337787, 0.9621446149013038, 0.08933733005031581, 0.052945329045415324, 0.21598425856150047, 0.38563405771081394, nan, 0.003965033197173916, 0.0, 0.0, nan, nan, 0.0, 0.632722463631211, 0.45265193370165746, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.6174 | 48.0 | 1920 | 2.3525 | 0.0971 | 0.1552 | 0.5450 | [0.4086238986289587, 0.8169527171190324, 0.7147347588187357, 0.5193104998447364, 0.5504920947069307, 0.3318776638002968, 0.7144779541250872, 0.0, 0.36262716401927536, 0.646572972972973, 0.007966049455890372, 0.1963503761004562, 0.055534229046705054, 0.0, 0.0, 0.0007429201478764866, 0.4022438568103258, 0.04821108340987622, 0.008825570478017717, 0.0758365001390305, 0.2995559310508258, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.46843680853833136, 0.0690767784745311, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8733270911360799, 0.9145737012321498, 0.7991401072893657, 0.7125892091474253, 0.8032534238903997, 0.46606649171309483, 0.886551821286479, nan, 0.5329571089177177, 0.9688120950323974, 0.016180684308107197, 0.29930691227090894, 0.1133307220263742, 0.0, 0.0, 0.000752229824121503, 0.9794360961306875, 0.07866752310602006, 0.08479096399717626, 0.17127187473834046, 0.4175918877382474, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.5439637256754204, 0.2720441988950276, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.5171 | 48.5 | 1940 | 2.3309 | 0.0949 | 0.1514 | 0.5467 | [0.4272610254871079, 0.7765229274757001, 0.7065223827054288, 0.521725648381944, 0.5604860198244929, 0.33056051974210365, 0.6943983809185744, 0.0, 0.3654088019294603, 0.6576679731653968, 0.011089910230634277, 0.17749015194147438, 0.05580952380952381, 0.0, 0.0, 0.0008791054223222449, 0.37348308478564973, 0.04977036796996346, 0.0, 0.06882807742697396, 0.28323033982427753, nan, 0.0009555788551634438, 0.0, 0.0, nan, 0.0, 0.0, 0.43712612163509473, 0.05106490021801107, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8619444444444444, 0.9342046826246384, 0.8001577784790155, 0.7139192121247778, 0.8127914842906487, 0.46984155942574063, 0.900624617773777, nan, 0.5552457824643449, 0.9676241900647948, 0.020851893766102428, 0.25674481347102057, 0.11476694085389738, 0.0, 0.0, 0.0008955116953827417, 0.968061155034295, 0.07947043499982158, 0.0, 0.15615841915766557, 0.3745744940608707, nan, 0.0009579945979749059, 0.0, 0.0, nan, nan, 0.0, 0.5301341394294351, 0.16823204419889504, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.3054 | 49.0 | 1960 | 2.3445 | 0.0965 | 0.1510 | 0.5448 | [0.42060555530518123, 0.775225963597999, 0.7057189441614043, 0.5143286771089474, 0.5549782193785344, 0.3272389694170118, 0.7080437975404985, 0.0, 0.3697319477914729, 0.6593277211985636, 0.006849495508311599, 0.1797058129473767, 0.06041899257897156, 0.0, 0.0, 0.0004667182616812491, 0.398030812628838, 0.0480704205053159, 0.0, 0.06379430807210397, 0.3090374985213517, nan, 0.0003989096469649624, 0.0, 0.0, nan, 0.0, 0.0, 0.3985289200936142, 0.05904316085708713, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.873859238451935, 0.9350786890473045, 0.7665785736825497, 0.7055487510273649, 0.8096352778312896, 0.46129503066365407, 0.887802431130565, nan, 0.541575795729628, 0.9676025917926566, 0.012520767619368664, 0.2656758768257512, 0.12012012012012012, 0.0, 0.0, 0.000471636159568244, 0.960429865437981, 0.0773650215894087, 0.0, 0.1433475676128276, 0.4502822343191187, nan, 0.00039916441582287746, 0.0, 0.0, nan, nan, 0.0, 0.45040619686378236, 0.23414364640883978, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 1.0223 | 49.5 | 1980 | 2.3584 | 0.0957 | 0.1510 | 0.5439 | [0.4080709894226452, 0.8048750591212661, 0.7407096572019292, 0.507544480008525, 0.5479745676250415, 0.3399907521254348, 0.7024147379086071, 0.0, 0.36409692514711495, 0.6423189649009936, 0.001532833552309555, 0.19033508092090268, 0.06216861081654295, 0.0, 0.0, 0.0008764396550709975, 0.39762865306209805, 0.04659518329310054, 0.0, 0.058694683342657476, 0.28469598076262453, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.4455286771981303, 0.05693710068296982, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8836360799001248, 0.9253464709499625, 0.811143105080467, 0.6963029126352066, 0.7988741594276267, 0.4540255694177414, 0.8860776889829518, nan, 0.543464412317977, 0.9703455723542117, 0.0028653295128939827, 0.29130616801562936, 0.12247029638333987, 0.0, 0.0, 0.0008895416174135234, 0.9727734436357924, 0.07404631909502908, 0.0, 0.12957380892573056, 0.3809086077876564, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.4934063857925562, 0.22845303867403316, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
| 0.5046 | 50.0 | 2000 | 2.3431 | 0.0959 | 0.1537 | 0.5496 | [0.44824978876617866, 0.7548671615728508, 0.7119201505944329, 0.5304481563680256, 0.5684691275095736, 0.33051502835188457, 0.6982393617021276, 0.0, 0.3703529914609331, 0.6659141206351092, 0.028823893043720683, 0.17181416221210322, 0.052153820762502065, 0.0, 0.0, 0.0005543923800536699, 0.40565901784724534, 0.05230759173712194, 0.0, 0.07225859019823891, 0.29980315155352005, nan, 0.003601361102652032, 0.0, 0.0, nan, 0.0, 0.0, 0.38898705304076847, 0.05940808241958817, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.8427949438202247, 0.9402615186644498, 0.7846678763016725, 0.7286579984703183, 0.8303175022736334, 0.469325820621132, 0.9020126572710594, nan, 0.5974398752913491, 0.9683369330453564, 0.05725843345934362, 0.24220857754209693, 0.12377594986290638, 0.0, 0.0, 0.0005611873291065182, 0.9580213623749935, 0.08566177782535773, 0.0, 0.16335928996064641, 0.43531591571750716, nan, 0.0036190907034607555, 0.0, 0.0, nan, nan, 0.0, 0.45750991876062724, 0.24276243093922653, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
rishitunu/ecc_segformerv1 |
<!-- 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. -->
# ecc_segformerv1
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0351
- Mean Iou: 0.9171
- Mean Accuracy: 0.8041
- Overall Accuracy: 0.8041
- Accuracy Background: nan
- Accuracy Crack: 0.8041
- Iou Background: 0.0
- Iou Crack: 0.9171
## 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
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"crack"
] |
rishitunu/ecc_segformerv2 |
<!-- 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. -->
# ecc_segformerv2
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3478
- Mean Iou: 0.0862
- Mean Accuracy: 0.1924
- Overall Accuracy: 0.1924
- Accuracy Background: nan
- Accuracy Crack: 0.1924
- Iou Background: 0.0
- Iou Crack: 0.1723
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.1019 | 1.0 | 251 | 0.5116 | 0.1490 | 0.3280 | 0.3280 | nan | 0.3280 | 0.0 | 0.2979 |
| 0.0938 | 2.0 | 502 | 0.4725 | 0.1144 | 0.2400 | 0.2400 | nan | 0.2400 | 0.0 | 0.2287 |
| 0.098 | 3.0 | 753 | 0.5117 | 0.1276 | 0.2748 | 0.2748 | nan | 0.2748 | 0.0 | 0.2552 |
| 0.1018 | 4.0 | 1004 | 0.3870 | 0.1053 | 0.2254 | 0.2254 | nan | 0.2254 | 0.0 | 0.2106 |
| 0.0928 | 5.0 | 1255 | 0.2907 | 0.0772 | 0.1630 | 0.1630 | nan | 0.1630 | 0.0 | 0.1544 |
| 0.0936 | 6.0 | 1506 | 0.5220 | 0.1193 | 0.2544 | 0.2544 | nan | 0.2544 | 0.0 | 0.2385 |
| 0.077 | 7.0 | 1757 | 0.1608 | 0.0617 | 0.1308 | 0.1308 | nan | 0.1308 | 0.0 | 0.1235 |
| 0.0963 | 8.0 | 2008 | 0.1756 | 0.0456 | 0.0923 | 0.0923 | nan | 0.0923 | 0.0 | 0.0912 |
| 0.0958 | 9.0 | 2259 | 0.2027 | 0.0862 | 0.1813 | 0.1813 | nan | 0.1813 | 0.0 | 0.1725 |
| 0.0755 | 10.0 | 2510 | 0.2327 | 0.0888 | 0.1832 | 0.1832 | nan | 0.1832 | 0.0 | 0.1776 |
| 0.0632 | 11.0 | 2761 | 0.2169 | 0.0846 | 0.1863 | 0.1863 | nan | 0.1863 | 0.0 | 0.1693 |
| 0.0638 | 12.0 | 3012 | 0.2309 | 0.0852 | 0.1957 | 0.1957 | nan | 0.1957 | 0.0 | 0.1704 |
| 0.0509 | 13.0 | 3263 | 0.3209 | 0.1236 | 0.2910 | 0.2910 | nan | 0.2910 | 0.0 | 0.2472 |
| 0.0497 | 14.0 | 3514 | 0.3274 | 0.1045 | 0.2354 | 0.2354 | nan | 0.2354 | 0.0 | 0.2089 |
| 0.0396 | 15.0 | 3765 | 0.3415 | 0.1005 | 0.2257 | 0.2257 | nan | 0.2257 | 0.0 | 0.2010 |
| 0.0373 | 16.0 | 4016 | 0.3530 | 0.1122 | 0.2486 | 0.2486 | nan | 0.2486 | 0.0 | 0.2244 |
| 0.0388 | 17.0 | 4267 | 0.3312 | 0.0889 | 0.1974 | 0.1974 | nan | 0.1974 | 0.0 | 0.1778 |
| 0.0346 | 18.0 | 4518 | 0.3061 | 0.0903 | 0.2125 | 0.2125 | nan | 0.2125 | 0.0 | 0.1807 |
| 0.0296 | 19.0 | 4769 | 0.3223 | 0.1000 | 0.2315 | 0.2315 | nan | 0.2315 | 0.0 | 0.2000 |
| 0.0311 | 20.0 | 5020 | 0.3458 | 0.0943 | 0.2237 | 0.2237 | nan | 0.2237 | 0.0 | 0.1887 |
| 0.0303 | 21.0 | 5271 | 0.3283 | 0.0975 | 0.2255 | 0.2255 | nan | 0.2255 | 0.0 | 0.1951 |
| 0.0249 | 22.0 | 5522 | 0.3387 | 0.0998 | 0.2327 | 0.2327 | nan | 0.2327 | 0.0 | 0.1996 |
| 0.0298 | 23.0 | 5773 | 0.3332 | 0.0973 | 0.2242 | 0.2242 | nan | 0.2242 | 0.0 | 0.1946 |
| 0.0239 | 24.0 | 6024 | 0.3778 | 0.1146 | 0.2634 | 0.2634 | nan | 0.2634 | 0.0 | 0.2292 |
| 0.0238 | 25.0 | 6275 | 0.3250 | 0.0909 | 0.2081 | 0.2081 | nan | 0.2081 | 0.0 | 0.1818 |
| 0.0242 | 26.0 | 6526 | 0.3826 | 0.1002 | 0.2285 | 0.2285 | nan | 0.2285 | 0.0 | 0.2004 |
| 0.017 | 27.0 | 6777 | 0.3543 | 0.1058 | 0.2367 | 0.2367 | nan | 0.2367 | 0.0 | 0.2115 |
| 0.0241 | 28.0 | 7028 | 0.3491 | 0.0915 | 0.2069 | 0.2069 | nan | 0.2069 | 0.0 | 0.1830 |
| 0.0203 | 29.0 | 7279 | 0.3354 | 0.0899 | 0.2056 | 0.2056 | nan | 0.2056 | 0.0 | 0.1798 |
| 0.0206 | 30.0 | 7530 | 0.3592 | 0.0944 | 0.2165 | 0.2165 | nan | 0.2165 | 0.0 | 0.1888 |
| 0.0211 | 31.0 | 7781 | 0.3200 | 0.0943 | 0.2100 | 0.2100 | nan | 0.2100 | 0.0 | 0.1886 |
| 0.0209 | 32.0 | 8032 | 0.3401 | 0.0850 | 0.1941 | 0.1941 | nan | 0.1941 | 0.0 | 0.1701 |
| 0.0172 | 33.0 | 8283 | 0.3326 | 0.0879 | 0.1986 | 0.1986 | nan | 0.1986 | 0.0 | 0.1759 |
| 0.0187 | 34.0 | 8534 | 0.3343 | 0.0869 | 0.1960 | 0.1960 | nan | 0.1960 | 0.0 | 0.1739 |
| 0.0181 | 35.0 | 8785 | 0.3223 | 0.0824 | 0.1835 | 0.1835 | nan | 0.1835 | 0.0 | 0.1648 |
| 0.0168 | 36.0 | 9036 | 0.3461 | 0.0864 | 0.1933 | 0.1933 | nan | 0.1933 | 0.0 | 0.1727 |
| 0.0169 | 37.0 | 9287 | 0.3438 | 0.0848 | 0.1888 | 0.1888 | nan | 0.1888 | 0.0 | 0.1695 |
| 0.0182 | 38.0 | 9538 | 0.3506 | 0.0865 | 0.1933 | 0.1933 | nan | 0.1933 | 0.0 | 0.1730 |
| 0.0167 | 39.0 | 9789 | 0.3535 | 0.0869 | 0.1946 | 0.1946 | nan | 0.1946 | 0.0 | 0.1739 |
| 0.0174 | 39.84 | 10000 | 0.3478 | 0.0862 | 0.1924 | 0.1924 | nan | 0.1924 | 0.0 | 0.1723 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"crack"
] |
pamixsun/segformer_for_optic_disc_cup_segmentation | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This SegFormer model has undergone specialized fine-tuning on the [REFUGE challenge dataset](https://refuge.grand-challenge.org/),
a public benchmark for semantic segmentation of anatomical structures in retinal fundus images.
The fine-tuning enables expert-level segmentation of the optic disc and optic cup, two critical structures for ophthalmological diagnosis.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Xu Sun](https://pamixsun.github.io)
- **Shared by:** [Xu Sun](https://pamixsun.github.io)
- **Model type:** Image segmentation
- **License:** Apache-2.0
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This pretrained model enables semantic segmentation of key anatomical structures, namely, the optic disc and optic cup, in retinal fundus images.
It takes fundus images as input and outputs the segmentation results.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model has undergone specialized training and fine-tuning exclusively using retinal fundus images,
with the objective to perform semantic segmentation of anatomical structures including the optic disc and optic cup.
Therefore, in order to derive optimal segmentation performance, it is imperative to ensure that only fundus images are entered as inputs to this model.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import cv2
import torch
import numpy as np
from torch import nn
from transformers import AutoImageProcessor, SegformerForSemanticSegmentation
image = cv2.imread('./example.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
processor = AutoImageProcessor.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation")
model = SegformerForSemanticSegmentation.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits.cpu()
upsampled_logits = nn.functional.interpolate(
logits,
size=image.shape[:2],
mode="bilinear",
align_corners=False,
)
pred_disc_cup = upsampled_logits.argmax(dim=1)[0].numpy().astype(np.uint8)
```
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Model Card Contact
- [email protected] | [
"background",
"optic disc",
"optic cup"
] |
baconseason/oneformer_coco_swin_large |
# OneFormer
OneFormer model trained on the COCO dataset (large-sized version, Swin 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 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.
## 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_swin_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_swin_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"
] |
rishitunu/ecc_segformerv3 |
<!-- 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. -->
# ecc_segformerv3
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1344
- Mean Iou: 0.0005
- Mean Accuracy: 0.0010
- Overall Accuracy: 0.0010
- Accuracy Background: nan
- Accuracy Crack: 0.0010
- Iou Background: 0.0
- Iou Crack: 0.0010
## 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: 0.0006
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.1306 | 1.0 | 1001 | 0.1114 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.107 | 2.0 | 2002 | 0.1238 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
| 0.1285 | 3.0 | 3003 | 0.1631 | 0.0024 | 0.0049 | 0.0049 | nan | 0.0049 | 0.0 | 0.0048 |
| 0.0887 | 4.0 | 4004 | 0.1083 | 0.0002 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 |
| 0.0828 | 5.0 | 5000 | 0.1344 | 0.0005 | 0.0010 | 0.0010 | nan | 0.0010 | 0.0 | 0.0010 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"crack"
] |
nomsgadded/Segments |
<!-- 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. -->
# Segments
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"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"
] |
rishitunu/ecc_segformer_main |
<!-- 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. -->
# ecc_segformer_main
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset_main dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1918
- Mean Iou: 0.2329
- Mean Accuracy: 0.4658
- Overall Accuracy: 0.4658
- Accuracy Background: nan
- Accuracy Crack: 0.4658
- Iou Background: 0.0
- Iou Crack: 0.4658
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.1069 | 1.0 | 172 | 0.1376 | 0.1660 | 0.3320 | 0.3320 | nan | 0.3320 | 0.0 | 0.3320 |
| 0.0682 | 2.0 | 344 | 0.1327 | 0.2298 | 0.4596 | 0.4596 | nan | 0.4596 | 0.0 | 0.4596 |
| 0.0666 | 3.0 | 516 | 0.2478 | 0.1200 | 0.2401 | 0.2401 | nan | 0.2401 | 0.0 | 0.2401 |
| 0.0639 | 4.0 | 688 | 0.1732 | 0.1538 | 0.3076 | 0.3076 | nan | 0.3076 | 0.0 | 0.3076 |
| 0.0624 | 5.0 | 860 | 0.1027 | 0.2334 | 0.4668 | 0.4668 | nan | 0.4668 | 0.0 | 0.4668 |
| 0.0557 | 6.0 | 1032 | 0.1003 | 0.1851 | 0.3703 | 0.3703 | nan | 0.3703 | 0.0 | 0.3703 |
| 0.0563 | 7.0 | 1204 | 0.1512 | 0.2007 | 0.4014 | 0.4014 | nan | 0.4014 | 0.0 | 0.4014 |
| 0.054 | 8.0 | 1376 | 0.1000 | 0.2401 | 0.4802 | 0.4802 | nan | 0.4802 | 0.0 | 0.4802 |
| 0.0546 | 9.0 | 1548 | 0.0933 | 0.2238 | 0.4475 | 0.4475 | nan | 0.4475 | 0.0 | 0.4475 |
| 0.0498 | 10.0 | 1720 | 0.0964 | 0.2303 | 0.4606 | 0.4606 | nan | 0.4606 | 0.0 | 0.4606 |
| 0.0515 | 11.0 | 1892 | 0.1107 | 0.2258 | 0.4516 | 0.4516 | nan | 0.4516 | 0.0 | 0.4516 |
| 0.0453 | 12.0 | 2064 | 0.0961 | 0.2557 | 0.5115 | 0.5115 | nan | 0.5115 | 0.0 | 0.5115 |
| 0.0431 | 13.0 | 2236 | 0.1027 | 0.2396 | 0.4792 | 0.4792 | nan | 0.4792 | 0.0 | 0.4792 |
| 0.0418 | 14.0 | 2408 | 0.1027 | 0.2521 | 0.5042 | 0.5042 | nan | 0.5042 | 0.0 | 0.5042 |
| 0.0426 | 15.0 | 2580 | 0.1059 | 0.2561 | 0.5123 | 0.5123 | nan | 0.5123 | 0.0 | 0.5123 |
| 0.0377 | 16.0 | 2752 | 0.1193 | 0.2281 | 0.4561 | 0.4561 | nan | 0.4561 | 0.0 | 0.4561 |
| 0.0369 | 17.0 | 2924 | 0.1161 | 0.2486 | 0.4972 | 0.4972 | nan | 0.4972 | 0.0 | 0.4972 |
| 0.036 | 18.0 | 3096 | 0.1058 | 0.2515 | 0.5029 | 0.5029 | nan | 0.5029 | 0.0 | 0.5029 |
| 0.034 | 19.0 | 3268 | 0.1176 | 0.2434 | 0.4868 | 0.4868 | nan | 0.4868 | 0.0 | 0.4868 |
| 0.0337 | 20.0 | 3440 | 0.1162 | 0.2254 | 0.4509 | 0.4509 | nan | 0.4509 | 0.0 | 0.4509 |
| 0.0281 | 21.0 | 3612 | 0.1203 | 0.2213 | 0.4426 | 0.4426 | nan | 0.4426 | 0.0 | 0.4426 |
| 0.0354 | 22.0 | 3784 | 0.1266 | 0.2384 | 0.4768 | 0.4768 | nan | 0.4768 | 0.0 | 0.4768 |
| 0.0323 | 23.0 | 3956 | 0.1223 | 0.2409 | 0.4818 | 0.4818 | nan | 0.4818 | 0.0 | 0.4818 |
| 0.0299 | 24.0 | 4128 | 0.1356 | 0.2195 | 0.4390 | 0.4390 | nan | 0.4390 | 0.0 | 0.4390 |
| 0.0294 | 25.0 | 4300 | 0.1285 | 0.2318 | 0.4636 | 0.4636 | nan | 0.4636 | 0.0 | 0.4636 |
| 0.0295 | 26.0 | 4472 | 0.1274 | 0.2559 | 0.5119 | 0.5119 | nan | 0.5119 | 0.0 | 0.5119 |
| 0.0252 | 27.0 | 4644 | 0.1387 | 0.2413 | 0.4827 | 0.4827 | nan | 0.4827 | 0.0 | 0.4827 |
| 0.029 | 28.0 | 4816 | 0.1468 | 0.2236 | 0.4472 | 0.4472 | nan | 0.4472 | 0.0 | 0.4472 |
| 0.0218 | 29.0 | 4988 | 0.1448 | 0.2433 | 0.4866 | 0.4866 | nan | 0.4866 | 0.0 | 0.4866 |
| 0.0275 | 30.0 | 5160 | 0.1478 | 0.2318 | 0.4635 | 0.4635 | nan | 0.4635 | 0.0 | 0.4635 |
| 0.0233 | 31.0 | 5332 | 0.1377 | 0.2502 | 0.5005 | 0.5005 | nan | 0.5005 | 0.0 | 0.5005 |
| 0.0252 | 32.0 | 5504 | 0.1458 | 0.2399 | 0.4797 | 0.4797 | nan | 0.4797 | 0.0 | 0.4797 |
| 0.0245 | 33.0 | 5676 | 0.1431 | 0.2480 | 0.4960 | 0.4960 | nan | 0.4960 | 0.0 | 0.4960 |
| 0.0225 | 34.0 | 5848 | 0.1562 | 0.2439 | 0.4879 | 0.4879 | nan | 0.4879 | 0.0 | 0.4879 |
| 0.0242 | 35.0 | 6020 | 0.1633 | 0.2323 | 0.4646 | 0.4646 | nan | 0.4646 | 0.0 | 0.4646 |
| 0.0213 | 36.0 | 6192 | 0.1666 | 0.2274 | 0.4549 | 0.4549 | nan | 0.4549 | 0.0 | 0.4549 |
| 0.0256 | 37.0 | 6364 | 0.1665 | 0.2340 | 0.4680 | 0.4680 | nan | 0.4680 | 0.0 | 0.4680 |
| 0.0237 | 38.0 | 6536 | 0.1658 | 0.2410 | 0.4819 | 0.4819 | nan | 0.4819 | 0.0 | 0.4819 |
| 0.0192 | 39.0 | 6708 | 0.1705 | 0.2286 | 0.4572 | 0.4572 | nan | 0.4572 | 0.0 | 0.4572 |
| 0.0198 | 40.0 | 6880 | 0.1688 | 0.2322 | 0.4644 | 0.4644 | nan | 0.4644 | 0.0 | 0.4644 |
| 0.0214 | 41.0 | 7052 | 0.1717 | 0.2315 | 0.4630 | 0.4630 | nan | 0.4630 | 0.0 | 0.4630 |
| 0.0197 | 42.0 | 7224 | 0.1764 | 0.2338 | 0.4677 | 0.4677 | nan | 0.4677 | 0.0 | 0.4677 |
| 0.0187 | 43.0 | 7396 | 0.1764 | 0.2437 | 0.4874 | 0.4874 | nan | 0.4874 | 0.0 | 0.4874 |
| 0.0212 | 44.0 | 7568 | 0.1874 | 0.2259 | 0.4519 | 0.4519 | nan | 0.4519 | 0.0 | 0.4519 |
| 0.0188 | 45.0 | 7740 | 0.1854 | 0.2362 | 0.4725 | 0.4725 | nan | 0.4725 | 0.0 | 0.4725 |
| 0.0188 | 46.0 | 7912 | 0.1772 | 0.2320 | 0.4641 | 0.4641 | nan | 0.4641 | 0.0 | 0.4641 |
| 0.0228 | 47.0 | 8084 | 0.1783 | 0.2385 | 0.4770 | 0.4770 | nan | 0.4770 | 0.0 | 0.4770 |
| 0.0199 | 48.0 | 8256 | 0.1850 | 0.2317 | 0.4634 | 0.4634 | nan | 0.4634 | 0.0 | 0.4634 |
| 0.0202 | 49.0 | 8428 | 0.1872 | 0.2336 | 0.4672 | 0.4672 | nan | 0.4672 | 0.0 | 0.4672 |
| 0.0181 | 50.0 | 8600 | 0.1803 | 0.2405 | 0.4810 | 0.4810 | nan | 0.4810 | 0.0 | 0.4810 |
| 0.0157 | 51.0 | 8772 | 0.1874 | 0.2349 | 0.4697 | 0.4697 | nan | 0.4697 | 0.0 | 0.4697 |
| 0.0162 | 52.0 | 8944 | 0.1889 | 0.2332 | 0.4665 | 0.4665 | nan | 0.4665 | 0.0 | 0.4665 |
| 0.0178 | 53.0 | 9116 | 0.1948 | 0.2357 | 0.4715 | 0.4715 | nan | 0.4715 | 0.0 | 0.4715 |
| 0.0166 | 54.0 | 9288 | 0.1911 | 0.2333 | 0.4666 | 0.4666 | nan | 0.4666 | 0.0 | 0.4666 |
| 0.0193 | 55.0 | 9460 | 0.1959 | 0.2306 | 0.4611 | 0.4611 | nan | 0.4611 | 0.0 | 0.4611 |
| 0.0199 | 56.0 | 9632 | 0.1999 | 0.2330 | 0.4659 | 0.4659 | nan | 0.4659 | 0.0 | 0.4659 |
| 0.0177 | 57.0 | 9804 | 0.1943 | 0.2319 | 0.4639 | 0.4639 | nan | 0.4639 | 0.0 | 0.4639 |
| 0.019 | 58.0 | 9976 | 0.1926 | 0.2327 | 0.4653 | 0.4653 | nan | 0.4653 | 0.0 | 0.4653 |
| 0.0187 | 58.14 | 10000 | 0.1918 | 0.2329 | 0.4658 | 0.4658 | nan | 0.4658 | 0.0 | 0.4658 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"crack"
] |
JCAI2000/segformer-b0-finetuned-segments-sidewalk-2-segformer-tutourial |
<!-- 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-segformer-tutourial
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: 0.5818
- Mean Iou: 0.3011
- Mean Accuracy: 0.3610
- Overall Accuracy: 0.8444
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8364
- Accuracy Flat-sidewalk: 0.9386
- Accuracy Flat-crosswalk: 0.7005
- Accuracy Flat-cyclinglane: 0.7745
- Accuracy Flat-parkingdriveway: 0.4695
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.5463
- Accuracy Human-person: 0.6230
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9128
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: nan
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.4478
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8809
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.5418
- Accuracy Construction-fenceguardrail: 0.5574
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.3278
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9459
- Accuracy Nature-terrain: 0.8600
- Accuracy Sky: 0.9662
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.2230
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.7395
- Iou Flat-sidewalk: 0.8314
- Iou Flat-crosswalk: 0.6251
- Iou Flat-cyclinglane: 0.7160
- Iou Flat-parkingdriveway: 0.3216
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.4206
- Iou Human-person: 0.3351
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.7711
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: nan
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.3854
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.6893
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.4039
- Iou Construction-fenceguardrail: 0.4696
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.2496
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.8583
- Iou Nature-terrain: 0.7294
- Iou Sky: 0.9135
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.1745
- 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: 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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| 2.488 | 0.12 | 50 | 2.3203 | 0.1076 | 0.1555 | 0.6454 | nan | 0.5629 | 0.8917 | 0.0 | 0.1457 | 0.0025 | 0.0 | 0.0001 | 0.0004 | 0.0 | 0.7999 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7386 | 0.0 | 0.0013 | 0.0002 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9632 | 0.0660 | 0.8030 | 0.0001 | 0.0 | 0.0003 | 0.0 | nan | 0.3879 | 0.6486 | 0.0 | 0.1312 | 0.0025 | 0.0 | 0.0001 | 0.0004 | 0.0 | 0.5250 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4739 | 0.0 | 0.0013 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6159 | 0.0553 | 0.7082 | 0.0001 | 0.0 | 0.0003 | 0.0 |
| 1.6299 | 0.25 | 100 | 1.7345 | 0.1218 | 0.1659 | 0.6685 | nan | 0.5674 | 0.9286 | 0.0 | 0.0107 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8298 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8395 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9407 | 0.3829 | 0.8087 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4093 | 0.6497 | 0.0 | 0.0107 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5763 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4921 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7001 | 0.3104 | 0.7495 | 0.0 | 0.0 | 0.0000 | 0.0 |
| 1.4309 | 0.38 | 150 | 1.5198 | 0.1271 | 0.1734 | 0.6897 | nan | 0.7282 | 0.9071 | 0.0 | 0.0079 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8531 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8527 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9456 | 0.3840 | 0.8690 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4687 | 0.7008 | 0.0 | 0.0079 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5573 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5187 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7035 | 0.3237 | 0.7861 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2814 | 0.5 | 200 | 1.3563 | 0.1450 | 0.1908 | 0.7158 | nan | 0.6789 | 0.9448 | 0.0 | 0.2102 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8591 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8588 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9132 | 0.7877 | 0.8520 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4965 | 0.6981 | 0.0 | 0.2093 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6055 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5147 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7683 | 0.5627 | 0.7841 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6945 | 0.62 | 250 | 1.2967 | 0.1469 | 0.1971 | 0.7191 | nan | 0.7823 | 0.8949 | 0.0 | 0.2470 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9344 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8313 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9041 | 0.8094 | 0.9029 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4942 | 0.7283 | 0.0 | 0.2448 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5256 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5211 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7719 | 0.6074 | 0.8072 | 0.0 | 0.0 | 0.0000 | 0.0 |
| 1.5522 | 0.75 | 300 | 1.1943 | 0.1523 | 0.1953 | 0.7277 | nan | 0.7414 | 0.9365 | 0.0 | 0.2922 | 0.0116 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8427 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9099 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9003 | 0.7090 | 0.9066 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5376 | 0.7180 | 0.0 | 0.2825 | 0.0114 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6281 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4879 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7785 | 0.6144 | 0.8143 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7093 | 0.88 | 350 | 1.1594 | 0.1561 | 0.2008 | 0.7326 | nan | 0.6502 | 0.9568 | 0.0 | 0.3765 | 0.0155 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8670 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8770 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9201 | 0.8412 | 0.9199 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5117 | 0.7083 | 0.0 | 0.3374 | 0.0152 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6334 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5252 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7934 | 0.6433 | 0.8275 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1445 | 1.0 | 400 | 1.1201 | 0.1552 | 0.2009 | 0.7320 | nan | 0.7249 | 0.9356 | 0.0 | 0.3739 | 0.0354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7810 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9016 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8938 | 0.8789 | 0.9039 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5260 | 0.7265 | 0.0 | 0.3324 | 0.0341 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6367 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5198 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7766 | 0.5818 | 0.8319 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5762 | 1.12 | 450 | 1.0649 | 0.1578 | 0.2040 | 0.7385 | nan | 0.7259 | 0.9512 | 0.0 | 0.3550 | 0.0466 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8674 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8840 | 0.0 | 0.0099 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8861 | 0.8572 | 0.9454 | 0.0 | 0.0 | 0.0003 | 0.0 | nan | 0.5448 | 0.7259 | 0.0 | 0.3299 | 0.0441 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6460 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5402 | 0.0 | 0.0098 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7752 | 0.5983 | 0.8354 | 0.0 | 0.0 | 0.0003 | 0.0 |
| 1.1724 | 1.25 | 500 | 1.0340 | 0.1636 | 0.2095 | 0.7431 | nan | 0.7165 | 0.9284 | 0.0 | 0.5125 | 0.0790 | 0.0 | 0.0277 | 0.0 | 0.0 | 0.8230 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9039 | 0.0 | 0.0086 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9127 | 0.8649 | 0.9269 | 0.0 | 0.0 | 0.0003 | 0.0 | nan | 0.5347 | 0.7461 | 0.0 | 0.3799 | 0.0718 | 0.0 | 0.0264 | 0.0 | 0.0 | 0.6598 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5231 | 0.0 | 0.0085 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8016 | 0.6426 | 0.8395 | 0.0 | 0.0 | 0.0003 | 0.0 |
| 1.7008 | 1.38 | 550 | 0.9811 | 0.1662 | 0.2101 | 0.7502 | nan | 0.8048 | 0.9259 | 0.0 | 0.3869 | 0.1217 | 0.0 | 0.0361 | 0.0 | 0.0 | 0.8697 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8510 | 0.0 | 0.0281 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9411 | 0.8227 | 0.9355 | 0.0 | 0.0 | 0.0006 | 0.0 | nan | 0.5594 | 0.7493 | 0.0 | 0.3700 | 0.0972 | 0.0 | 0.0346 | 0.0 | 0.0 | 0.6461 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5481 | 0.0 | 0.0272 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7903 | 0.6704 | 0.8245 | 0.0 | 0.0 | 0.0006 | 0.0 |
| 0.5924 | 1.5 | 600 | 0.9687 | 0.1691 | 0.2121 | 0.7520 | nan | 0.7776 | 0.9355 | 0.0 | 0.4147 | 0.1552 | 0.0 | 0.0571 | 0.0 | 0.0 | 0.8155 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9139 | 0.0 | 0.0114 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9159 | 0.8587 | 0.9297 | 0.0 | 0.0 | 0.0010 | 0.0 | nan | 0.5641 | 0.7466 | 0.0 | 0.3827 | 0.1214 | 0.0 | 0.0526 | 0.0 | 0.0 | 0.6719 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5389 | 0.0 | 0.0112 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8124 | 0.6586 | 0.8511 | 0.0 | 0.0 | 0.0010 | 0.0 |
| 1.9075 | 1.62 | 650 | 0.9413 | 0.1721 | 0.2149 | 0.7567 | nan | 0.7972 | 0.9268 | 0.0 | 0.4365 | 0.1483 | 0.0 | 0.1357 | 0.0 | 0.0 | 0.8640 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8842 | 0.0 | 0.0222 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9492 | 0.7697 | 0.9410 | 0.0 | 0.0 | 0.0021 | 0.0 | nan | 0.5851 | 0.7521 | 0.0 | 0.3986 | 0.1187 | 0.0 | 0.1185 | 0.0 | 0.0 | 0.6666 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5508 | 0.0 | 0.0215 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7905 | 0.6580 | 0.8459 | 0.0 | 0.0 | 0.0021 | 0.0 |
| 1.1035 | 1.75 | 700 | 0.9646 | 0.1720 | 0.2138 | 0.7547 | nan | 0.7370 | 0.9564 | 0.0 | 0.4065 | 0.1380 | 0.0 | 0.1129 | 0.0 | 0.0 | 0.8711 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8830 | 0.0 | 0.0262 | 0.0 | 0.0 | nan | 0.0 | 0.0077 | 0.0 | 0.0 | 0.9277 | 0.8131 | 0.9589 | 0.0 | 0.0 | 0.0024 | 0.0 | nan | 0.5860 | 0.7301 | 0.0 | 0.3914 | 0.1090 | 0.0 | 0.1019 | 0.0 | 0.0 | 0.6809 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5383 | 0.0 | 0.0256 | 0.0 | 0.0 | nan | 0.0 | 0.0076 | 0.0 | 0.0 | 0.8127 | 0.6754 | 0.8428 | 0.0 | 0.0 | 0.0024 | 0.0 |
| 0.6372 | 1.88 | 750 | 0.8958 | 0.1813 | 0.2262 | 0.7636 | nan | 0.7484 | 0.9387 | 0.0 | 0.4605 | 0.2077 | 0.0 | 0.2876 | 0.0 | 0.0 | 0.9013 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9166 | 0.0 | 0.0551 | 0.0 | 0.0 | nan | 0.0 | 0.0033 | 0.0 | 0.0 | 0.9241 | 0.8566 | 0.9364 | 0.0 | 0.0 | 0.0030 | 0.0 | nan | 0.5909 | 0.7555 | 0.0 | 0.4116 | 0.1516 | 0.0 | 0.2190 | 0.0 | 0.0 | 0.6771 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5549 | 0.0 | 0.0523 | 0.0 | 0.0 | nan | 0.0 | 0.0033 | 0.0 | 0.0 | 0.8228 | 0.6811 | 0.8775 | 0.0 | 0.0 | 0.0029 | 0.0 |
| 0.5682 | 2.0 | 800 | 0.8480 | 0.1900 | 0.2386 | 0.7738 | nan | 0.7474 | 0.9230 | 0.0 | 0.6150 | 0.3295 | 0.0 | 0.3338 | 0.0 | 0.0 | 0.9118 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9131 | 0.0 | 0.1108 | 0.0 | 0.0 | nan | 0.0 | 0.0085 | 0.0 | 0.0 | 0.9308 | 0.8564 | 0.9532 | 0.0 | 0.0 | 0.0020 | 0.0 | nan | 0.6101 | 0.7761 | 0.0 | 0.5059 | 0.2038 | 0.0 | 0.2501 | 0.0 | 0.0 | 0.6655 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5699 | 0.0 | 0.0966 | 0.0 | 0.0 | nan | 0.0 | 0.0085 | 0.0 | 0.0 | 0.8209 | 0.6909 | 0.8803 | 0.0 | 0.0 | 0.0020 | 0.0 |
| 0.725 | 2.12 | 850 | 0.8578 | 0.1879 | 0.2359 | 0.7747 | nan | 0.7804 | 0.9279 | 0.0 | 0.5716 | 0.2711 | 0.0 | 0.2857 | 0.0 | 0.0 | 0.9158 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8883 | 0.0 | 0.1206 | 0.0 | 0.0 | nan | 0.0 | 0.0090 | 0.0 | 0.0 | 0.9294 | 0.8843 | 0.9572 | 0.0 | 0.0 | 0.0087 | 0.0 | nan | 0.6250 | 0.7717 | 0.0 | 0.5054 | 0.1887 | 0.0 | 0.2134 | 0.0 | 0.0 | 0.6573 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5817 | 0.0 | 0.1049 | 0.0 | 0.0 | nan | 0.0 | 0.0090 | 0.0 | 0.0 | 0.8161 | 0.6510 | 0.8790 | 0.0 | 0.0 | 0.0085 | 0.0 |
| 0.51 | 2.25 | 900 | 0.8616 | 0.1941 | 0.2441 | 0.7678 | nan | 0.6722 | 0.9332 | 0.0 | 0.5630 | 0.4052 | 0.0 | 0.3876 | 0.0 | 0.0 | 0.8768 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8580 | 0.0 | 0.3540 | 0.0 | 0.0 | nan | 0.0 | 0.0175 | 0.0 | 0.0 | 0.9522 | 0.8271 | 0.9557 | 0.0 | 0.0 | 0.0083 | 0.0 | nan | 0.5804 | 0.7650 | 0.0 | 0.4572 | 0.1984 | 0.0 | 0.2696 | 0.0 | 0.0 | 0.6993 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5980 | 0.0 | 0.2558 | 0.0 | 0.0 | nan | 0.0 | 0.0173 | 0.0 | 0.0 | 0.8087 | 0.6755 | 0.8764 | 0.0 | 0.0 | 0.0080 | 0.0 |
| 0.9404 | 2.38 | 950 | 0.8303 | 0.1965 | 0.2458 | 0.7781 | nan | 0.7834 | 0.9231 | 0.0 | 0.5345 | 0.3098 | 0.0 | 0.3520 | 0.0 | 0.0 | 0.9111 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8712 | 0.0 | 0.3564 | 0.0 | 0.0 | nan | 0.0 | 0.0286 | 0.0 | 0.0 | 0.9357 | 0.8818 | 0.9543 | 0.0 | 0.0 | 0.0227 | 0.0 | nan | 0.6167 | 0.7711 | 0.0 | 0.4738 | 0.2072 | 0.0 | 0.2605 | 0.0 | 0.0 | 0.6721 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6204 | 0.0 | 0.2520 | 0.0 | 0.0 | nan | 0.0 | 0.0281 | 0.0 | 0.0 | 0.8179 | 0.6627 | 0.8842 | 0.0 | 0.0 | 0.0213 | 0.0 |
| 0.4844 | 2.5 | 1000 | 0.8113 | 0.1952 | 0.2431 | 0.7762 | nan | 0.7824 | 0.9354 | 0.0 | 0.4556 | 0.3144 | 0.0 | 0.3925 | 0.0 | 0.0 | 0.9023 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8776 | 0.0 | 0.3066 | 0.0 | 0.0 | nan | 0.0 | 0.0409 | 0.0 | 0.0 | 0.9265 | 0.8766 | 0.9575 | 0.0 | 0.0 | 0.0111 | 0.0 | nan | 0.6253 | 0.7656 | 0.0 | 0.4358 | 0.2015 | 0.0 | 0.2825 | 0.0 | 0.0 | 0.6866 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5965 | 0.0 | 0.2222 | 0.0 | 0.0 | nan | 0.0 | 0.0405 | 0.0 | 0.0 | 0.8217 | 0.6857 | 0.8731 | 0.0 | 0.0 | 0.0107 | 0.0 |
| 0.5377 | 2.62 | 1050 | 0.8262 | 0.1955 | 0.2429 | 0.7755 | nan | 0.7408 | 0.9399 | 0.0 | 0.4855 | 0.3249 | 0.0 | 0.4112 | 0.0013 | 0.0 | 0.9104 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8662 | 0.0 | 0.3181 | 0.0 | 0.0 | nan | 0.0 | 0.0144 | 0.0 | 0.0 | 0.9553 | 0.8099 | 0.9541 | 0.0 | 0.0 | 0.0409 | 0.0 | nan | 0.6230 | 0.7603 | 0.0 | 0.4479 | 0.2190 | 0.0 | 0.2781 | 0.0013 | 0.0 | 0.6791 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6198 | 0.0 | 0.2235 | 0.0 | 0.0 | nan | 0.0 | 0.0143 | 0.0 | 0.0 | 0.8064 | 0.6616 | 0.8858 | 0.0 | 0.0 | 0.0363 | 0.0 |
| 0.5133 | 2.75 | 1100 | 0.8080 | 0.1985 | 0.2479 | 0.7775 | nan | 0.7620 | 0.9341 | 0.0 | 0.5057 | 0.3786 | 0.0 | 0.3555 | 0.0025 | 0.0 | 0.9024 | 0.0 | nan | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8409 | 0.0 | 0.4094 | 0.0 | 0.0 | nan | 0.0 | 0.0365 | 0.0 | 0.0 | 0.9372 | 0.8735 | 0.9667 | 0.0 | 0.0 | 0.0286 | 0.0 | nan | 0.6150 | 0.7703 | 0.0 | 0.4541 | 0.2155 | 0.0 | 0.2686 | 0.0025 | 0.0 | 0.6940 | 0.0 | nan | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6231 | 0.0 | 0.2665 | 0.0 | 0.0 | nan | 0.0 | 0.0358 | 0.0 | 0.0 | 0.8177 | 0.6785 | 0.8857 | 0.0 | 0.0 | 0.0258 | 0.0 |
| 0.9712 | 2.88 | 1150 | 0.8097 | 0.1928 | 0.2371 | 0.7741 | nan | 0.8580 | 0.9160 | 0.0 | 0.4079 | 0.2468 | 0.0 | 0.2023 | 0.0377 | 0.0 | 0.9173 | 0.0 | nan | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8780 | 0.0 | 0.2836 | 0.0000 | 0.0 | nan | 0.0 | 0.0766 | 0.0 | 0.0 | 0.9411 | 0.8324 | 0.9629 | 0.0 | 0.0 | 0.0272 | 0.0 | nan | 0.5977 | 0.7718 | 0.0 | 0.3912 | 0.1778 | 0.0 | 0.1669 | 0.0371 | 0.0 | 0.6803 | 0.0 | nan | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6186 | 0.0 | 0.2094 | 0.0000 | 0.0 | nan | 0.0 | 0.0748 | 0.0 | 0.0 | 0.8252 | 0.7069 | 0.8861 | 0.0 | 0.0 | 0.0254 | 0.0 |
| 0.5065 | 3.0 | 1200 | 0.7834 | 0.2004 | 0.2494 | 0.7787 | nan | 0.8491 | 0.8819 | 0.0 | 0.5845 | 0.3527 | 0.0 | 0.3553 | 0.0501 | 0.0 | 0.9198 | 0.0 | nan | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.2436 | 0.0001 | 0.0 | nan | 0.0 | 0.0703 | 0.0 | 0.0 | 0.9449 | 0.8381 | 0.9622 | 0.0 | 0.0 | 0.0309 | 0.0 | nan | 0.6049 | 0.7791 | 0.0 | 0.5258 | 0.2196 | 0.0 | 0.2507 | 0.0488 | 0.0 | 0.6789 | 0.0 | nan | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0 | 0.6144 | 0.0 | 0.1886 | 0.0001 | 0.0 | nan | 0.0 | 0.0683 | 0.0 | 0.0 | 0.8257 | 0.7028 | 0.8773 | 0.0 | 0.0 | 0.0278 | 0.0 |
| 1.7304 | 3.12 | 1250 | 0.7721 | 0.2066 | 0.2535 | 0.7869 | nan | 0.7783 | 0.9325 | 0.0 | 0.5908 | 0.3273 | 0.0 | 0.3851 | 0.0578 | 0.0 | 0.9025 | 0.0 | nan | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8995 | 0.0 | 0.3196 | 0.0018 | 0.0 | nan | 0.0 | 0.0869 | 0.0 | 0.0 | 0.9345 | 0.8763 | 0.9682 | 0.0 | 0.0 | 0.0501 | 0.0 | nan | 0.6235 | 0.7751 | 0.0 | 0.5379 | 0.2078 | 0.0 | 0.2945 | 0.0566 | 0.0 | 0.7051 | 0.0 | nan | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6258 | 0.0 | 0.2442 | 0.0018 | 0.0 | nan | 0.0 | 0.0844 | 0.0 | 0.0 | 0.8338 | 0.7019 | 0.8736 | 0.0 | 0.0 | 0.0437 | 0.0 |
| 0.66 | 3.25 | 1300 | 0.7819 | 0.2112 | 0.2587 | 0.7855 | nan | 0.7894 | 0.9367 | 0.0000 | 0.5252 | 0.3014 | 0.0 | 0.3633 | 0.1003 | 0.0 | 0.9086 | 0.0 | nan | 0.0 | 0.0 | 0.0109 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.4998 | 0.0015 | 0.0 | nan | 0.0 | 0.1663 | 0.0 | 0.0 | 0.9162 | 0.8732 | 0.9374 | 0.0 | 0.0 | 0.0512 | 0.0 | nan | 0.6323 | 0.7652 | 0.0000 | 0.4811 | 0.1876 | 0.0 | 0.2850 | 0.0919 | 0.0 | 0.7142 | 0.0 | nan | 0.0 | 0.0 | 0.0109 | 0.0 | 0.0 | 0.6492 | 0.0 | 0.3148 | 0.0015 | 0.0 | nan | 0.0 | 0.1523 | 0.0 | 0.0 | 0.8382 | 0.6970 | 0.8927 | 0.0 | 0.0 | 0.0448 | 0.0 |
| 0.5002 | 3.38 | 1350 | 0.8003 | 0.2076 | 0.2558 | 0.7818 | nan | 0.7802 | 0.9357 | 0.0 | 0.4772 | 0.2416 | 0.0 | 0.4454 | 0.1361 | 0.0 | 0.9488 | 0.0 | nan | 0.0 | 0.0 | 0.0395 | 0.0 | 0.0 | 0.8563 | 0.0 | 0.3519 | 0.0025 | 0.0 | nan | 0.0 | 0.1423 | 0.0 | 0.0 | 0.9454 | 0.8564 | 0.9638 | 0.0 | 0.0 | 0.0626 | 0.0 | nan | 0.6277 | 0.7656 | 0.0 | 0.4501 | 0.1628 | 0.0 | 0.3162 | 0.1202 | 0.0 | 0.6544 | 0.0 | nan | 0.0 | 0.0 | 0.0389 | 0.0 | 0.0 | 0.6397 | 0.0 | 0.2495 | 0.0025 | 0.0 | nan | 0.0 | 0.1350 | 0.0 | 0.0 | 0.8330 | 0.7017 | 0.8916 | 0.0 | 0.0 | 0.0540 | 0.0 |
| 1.7687 | 3.5 | 1400 | 0.7399 | 0.2183 | 0.2679 | 0.7923 | nan | 0.8339 | 0.9184 | 0.0016 | 0.5828 | 0.2395 | 0.0 | 0.4439 | 0.2259 | 0.0 | 0.9229 | 0.0 | nan | 0.0 | 0.0 | 0.0371 | 0.0 | 0.0 | 0.8792 | 0.0 | 0.4739 | 0.0005 | 0.0 | nan | 0.0 | 0.1879 | 0.0 | 0.0 | 0.9344 | 0.8787 | 0.9571 | 0.0 | 0.0 | 0.0551 | 0.0 | nan | 0.6405 | 0.7799 | 0.0016 | 0.5199 | 0.1753 | 0.0 | 0.3078 | 0.1909 | 0.0 | 0.7149 | 0.0 | nan | 0.0 | 0.0 | 0.0367 | 0.0 | 0.0 | 0.6442 | 0.0 | 0.3149 | 0.0005 | 0.0 | nan | 0.0 | 0.1680 | 0.0 | 0.0 | 0.8413 | 0.7054 | 0.8945 | 0.0 | 0.0 | 0.0493 | 0.0 |
| 0.5413 | 3.62 | 1450 | 0.7299 | 0.2217 | 0.2713 | 0.7940 | nan | 0.8010 | 0.9389 | 0.0110 | 0.5446 | 0.3857 | 0.0 | 0.4218 | 0.2719 | 0.0 | 0.8834 | 0.0 | nan | 0.0 | 0.0 | 0.0176 | 0.0 | 0.0 | 0.8653 | 0.0 | 0.5323 | 0.0011 | 0.0 | nan | 0.0 | 0.1796 | 0.0 | 0.0 | 0.9424 | 0.8469 | 0.9612 | 0.0 | 0.0 | 0.0766 | 0.0 | nan | 0.6441 | 0.7806 | 0.0110 | 0.5222 | 0.2475 | 0.0 | 0.3136 | 0.2070 | 0.0 | 0.7395 | 0.0 | nan | 0.0 | 0.0 | 0.0175 | 0.0 | 0.0 | 0.6481 | 0.0 | 0.3265 | 0.0011 | 0.0 | nan | 0.0 | 0.1629 | 0.0 | 0.0 | 0.8348 | 0.6880 | 0.8869 | 0.0 | 0.0 | 0.0627 | 0.0 |
| 1.2082 | 3.75 | 1500 | 0.6959 | 0.2248 | 0.2729 | 0.7982 | nan | 0.8119 | 0.9199 | 0.0999 | 0.6762 | 0.4158 | 0.0 | 0.3494 | 0.2189 | 0.0 | 0.8810 | 0.0 | nan | 0.0 | 0.0 | 0.0351 | 0.0 | 0.0 | 0.9162 | 0.0 | 0.4066 | 0.0013 | 0.0 | nan | 0.0 | 0.2105 | 0.0 | 0.0 | 0.9437 | 0.8118 | 0.9608 | 0.0 | 0.0 | 0.0740 | 0.0 | nan | 0.6593 | 0.7930 | 0.0999 | 0.5882 | 0.2617 | 0.0 | 0.2944 | 0.1709 | 0.0 | 0.7215 | 0.0 | nan | 0.0 | 0.0 | 0.0349 | 0.0 | 0.0 | 0.6180 | 0.0 | 0.2757 | 0.0013 | 0.0 | nan | 0.0 | 0.1849 | 0.0 | 0.0 | 0.8332 | 0.6978 | 0.8980 | 0.0 | 0.0 | 0.0603 | 0.0 |
| 0.4615 | 3.88 | 1550 | 0.7529 | 0.2224 | 0.2737 | 0.7893 | nan | 0.7573 | 0.9446 | 0.0228 | 0.5131 | 0.3968 | 0.0 | 0.3926 | 0.3763 | 0.0 | 0.8950 | 0.0 | nan | 0.0 | 0.0 | 0.0468 | 0.0 | 0.0 | 0.8758 | 0.0 | 0.4520 | 0.0006 | 0.0 | nan | 0.0 | 0.1823 | 0.0 | 0.0 | 0.9391 | 0.8892 | 0.9592 | 0.0 | 0.0 | 0.1149 | 0.0 | nan | 0.6296 | 0.7708 | 0.0228 | 0.4801 | 0.2393 | 0.0 | 0.2962 | 0.2472 | 0.0 | 0.7382 | 0.0 | nan | 0.0 | 0.0 | 0.0464 | 0.0 | 0.0 | 0.6456 | 0.0 | 0.2974 | 0.0006 | 0.0 | nan | 0.0 | 0.1669 | 0.0 | 0.0 | 0.8403 | 0.7010 | 0.9013 | 0.0 | 0.0 | 0.0922 | 0.0 |
| 2.1925 | 4.0 | 1600 | 0.7518 | 0.2245 | 0.2783 | 0.7905 | nan | 0.6996 | 0.9509 | 0.0877 | 0.6004 | 0.3108 | 0.0 | 0.4915 | 0.4171 | 0.0 | 0.9078 | 0.0 | nan | 0.0 | 0.0 | 0.0642 | 0.0 | 0.0 | 0.8704 | 0.0 | 0.4508 | 0.0048 | 0.0 | nan | 0.0 | 0.1670 | 0.0 | 0.0 | 0.9501 | 0.8806 | 0.9638 | 0.0 | 0.0 | 0.0885 | 0.0 | nan | 0.6114 | 0.7724 | 0.0877 | 0.5295 | 0.2241 | 0.0 | 0.3105 | 0.2501 | 0.0 | 0.7289 | 0.0 | nan | 0.0 | 0.0 | 0.0634 | 0.0 | 0.0 | 0.6496 | 0.0 | 0.2917 | 0.0047 | 0.0 | nan | 0.0 | 0.1524 | 0.0 | 0.0 | 0.8370 | 0.7004 | 0.8949 | 0.0 | 0.0 | 0.0747 | 0.0 |
| 0.8398 | 4.12 | 1650 | 0.7374 | 0.2325 | 0.2856 | 0.7960 | nan | 0.7649 | 0.9445 | 0.0382 | 0.5803 | 0.4033 | 0.0 | 0.4096 | 0.4098 | 0.0 | 0.9223 | 0.0 | nan | 0.0 | 0.0 | 0.0928 | 0.0 | 0.0 | 0.8409 | 0.0 | 0.4956 | 0.0605 | 0.0 | nan | 0.0 | 0.2303 | 0.0 | 0.0 | 0.9465 | 0.8428 | 0.9687 | 0.0 | 0.0 | 0.1883 | 0.0 | nan | 0.6447 | 0.7801 | 0.0382 | 0.5260 | 0.2328 | 0.0 | 0.3170 | 0.2554 | 0.0 | 0.7296 | 0.0 | nan | 0.0 | 0.0 | 0.0916 | 0.0 | 0.0 | 0.6593 | 0.0 | 0.3201 | 0.0596 | 0.0 | nan | 0.0 | 0.1986 | 0.0 | 0.0 | 0.8423 | 0.7166 | 0.8943 | 0.0 | 0.0 | 0.1326 | 0.0 |
| 0.6942 | 4.25 | 1700 | 0.7359 | 0.2283 | 0.2831 | 0.7935 | nan | 0.7659 | 0.9438 | 0.0883 | 0.5150 | 0.3600 | 0.0 | 0.4102 | 0.4644 | 0.0 | 0.9176 | 0.0 | nan | 0.0 | 0.0 | 0.0778 | 0.0 | 0.0 | 0.8748 | 0.0 | 0.4698 | 0.0312 | 0.0 | nan | 0.0 | 0.2085 | 0.0 | 0.0 | 0.9358 | 0.9040 | 0.9597 | 0.0 | 0.0 | 0.1319 | 0.0 | nan | 0.6459 | 0.7740 | 0.0882 | 0.4742 | 0.2239 | 0.0 | 0.3101 | 0.2523 | 0.0 | 0.7376 | 0.0 | nan | 0.0 | 0.0 | 0.0768 | 0.0 | 0.0 | 0.6628 | 0.0 | 0.3118 | 0.0309 | 0.0 | nan | 0.0 | 0.1826 | 0.0 | 0.0 | 0.8401 | 0.6910 | 0.8990 | 0.0 | 0.0 | 0.1043 | 0.0 |
| 0.4449 | 4.38 | 1750 | 0.7015 | 0.2391 | 0.2890 | 0.8038 | nan | 0.7983 | 0.9354 | 0.1658 | 0.5990 | 0.3891 | 0.0 | 0.4565 | 0.3812 | 0.0 | 0.8791 | 0.0 | nan | 0.0 | 0.0 | 0.1167 | 0.0 | 0.0 | 0.9121 | 0.0 | 0.3766 | 0.1337 | 0.0 | nan | 0.0 | 0.2224 | 0.0 | 0.0 | 0.9440 | 0.8811 | 0.9606 | 0.0 | 0.0 | 0.0969 | 0.0 | nan | 0.6789 | 0.7810 | 0.1654 | 0.5370 | 0.2592 | 0.0 | 0.3467 | 0.2309 | 0.0 | 0.7455 | 0.0 | nan | 0.0 | 0.0 | 0.1150 | 0.0 | 0.0 | 0.6470 | 0.0 | 0.2824 | 0.1306 | 0.0 | nan | 0.0 | 0.1949 | 0.0 | 0.0 | 0.8393 | 0.7158 | 0.9010 | 0.0 | 0.0 | 0.0793 | 0.0 |
| 0.3577 | 4.5 | 1800 | 0.7060 | 0.2380 | 0.2900 | 0.8042 | nan | 0.8066 | 0.9443 | 0.1282 | 0.5640 | 0.3782 | 0.0 | 0.4202 | 0.4983 | 0.0 | 0.9135 | 0.0 | nan | 0.0 | 0.0 | 0.1158 | 0.0 | 0.0 | 0.8955 | 0.0 | 0.4212 | 0.1594 | 0.0 | nan | 0.0 | 0.1916 | 0.0 | 0.0 | 0.9433 | 0.8580 | 0.9553 | 0.0 | 0.0 | 0.0871 | 0.0 | nan | 0.6721 | 0.7847 | 0.1280 | 0.5383 | 0.2420 | 0.0 | 0.3305 | 0.2508 | 0.0 | 0.7383 | 0.0 | nan | 0.0 | 0.0 | 0.1131 | 0.0 | 0.0 | 0.6585 | 0.0 | 0.2968 | 0.1542 | 0.0 | nan | 0.0 | 0.1737 | 0.0 | 0.0 | 0.8444 | 0.7157 | 0.9020 | 0.0 | 0.0 | 0.0739 | 0.0 |
| 0.4443 | 4.62 | 1850 | 0.6722 | 0.2509 | 0.3035 | 0.8143 | nan | 0.8160 | 0.9397 | 0.3917 | 0.6472 | 0.3310 | 0.0 | 0.4674 | 0.4023 | 0.0 | 0.9087 | 0.0 | nan | 0.0 | 0.0 | 0.1512 | 0.0 | 0.0 | 0.8799 | 0.0 | 0.4608 | 0.1119 | 0.0 | nan | 0.0 | 0.2761 | 0.0 | 0.0 | 0.9351 | 0.9025 | 0.9701 | 0.0 | 0.0 | 0.1213 | 0.0 | nan | 0.6963 | 0.8011 | 0.3870 | 0.5855 | 0.2383 | 0.0 | 0.3398 | 0.2508 | 0.0 | 0.7382 | 0.0 | nan | 0.0 | 0.0 | 0.1479 | 0.0 | 0.0 | 0.6587 | 0.0 | 0.3103 | 0.1097 | 0.0 | nan | 0.0 | 0.2211 | 0.0 | 0.0 | 0.8499 | 0.7010 | 0.8999 | 0.0 | 0.0 | 0.0938 | 0.0 |
| 1.0 | 4.75 | 1900 | 0.6912 | 0.2484 | 0.3023 | 0.8114 | nan | 0.7584 | 0.9451 | 0.3347 | 0.6729 | 0.3085 | 0.0 | 0.5324 | 0.5104 | 0.0 | 0.8877 | 0.0 | nan | 0.0 | 0.0 | 0.1254 | 0.0 | 0.0 | 0.9136 | 0.0 | 0.4858 | 0.1048 | 0.0 | nan | 0.0 | 0.2013 | 0.0 | 0.0 | 0.9525 | 0.8531 | 0.9638 | 0.0 | 0.0 | 0.1224 | 0.0 | nan | 0.6755 | 0.7882 | 0.3247 | 0.6017 | 0.2234 | 0.0 | 0.3783 | 0.2464 | 0.0 | 0.7472 | 0.0 | nan | 0.0 | 0.0 | 0.1230 | 0.0 | 0.0 | 0.6713 | 0.0 | 0.3498 | 0.1035 | 0.0 | nan | 0.0 | 0.1774 | 0.0 | 0.0 | 0.8385 | 0.6933 | 0.9049 | 0.0 | 0.0 | 0.1010 | 0.0 |
| 0.8306 | 4.88 | 1950 | 0.6655 | 0.2546 | 0.3060 | 0.8163 | nan | 0.8323 | 0.9347 | 0.4204 | 0.6135 | 0.3336 | 0.0 | 0.4604 | 0.4942 | 0.0 | 0.9134 | 0.0 | nan | 0.0 | 0.0 | 0.1336 | 0.0 | 0.0 | 0.9069 | 0.0 | 0.4913 | 0.1178 | 0.0 | nan | 0.0 | 0.2380 | 0.0 | 0.0 | 0.9446 | 0.8621 | 0.9584 | 0.0 | 0.0 | 0.1373 | 0.0 | nan | 0.6862 | 0.7964 | 0.4167 | 0.5900 | 0.2364 | 0.0 | 0.3536 | 0.2819 | 0.0 | 0.7324 | 0.0 | nan | 0.0 | 0.0 | 0.1299 | 0.0 | 0.0 | 0.6780 | 0.0 | 0.3565 | 0.1162 | 0.0 | nan | 0.0 | 0.1977 | 0.0 | 0.0 | 0.8435 | 0.7134 | 0.9057 | 0.0 | 0.0 | 0.1143 | 0.0 |
| 0.5413 | 5.0 | 2000 | 0.6798 | 0.2549 | 0.3053 | 0.8161 | nan | 0.8131 | 0.9486 | 0.3975 | 0.6113 | 0.3320 | 0.0 | 0.4642 | 0.4823 | 0.0 | 0.8911 | 0.0 | nan | 0.0 | 0.0 | 0.1604 | 0.0 | 0.0 | 0.9040 | 0.0 | 0.5306 | 0.1225 | 0.0 | nan | 0.0 | 0.2572 | 0.0 | 0.0 | 0.9378 | 0.8772 | 0.9580 | 0.0 | 0.0 | 0.0807 | 0.0 | nan | 0.6921 | 0.7895 | 0.3820 | 0.5914 | 0.2289 | 0.0 | 0.3709 | 0.2736 | 0.0 | 0.7475 | 0.0 | nan | 0.0 | 0.0 | 0.1553 | 0.0 | 0.0 | 0.6739 | 0.0 | 0.3688 | 0.1197 | 0.0 | nan | 0.0 | 0.2122 | 0.0 | 0.0 | 0.8485 | 0.7278 | 0.9050 | 0.0 | 0.0 | 0.0700 | 0.0 |
| 0.5256 | 5.12 | 2050 | 0.6704 | 0.2561 | 0.3118 | 0.8139 | nan | 0.7844 | 0.9341 | 0.3012 | 0.6551 | 0.4701 | 0.0 | 0.4866 | 0.5064 | 0.0 | 0.9148 | 0.0 | nan | 0.0 | 0.0 | 0.1733 | 0.0 | 0.0 | 0.9118 | 0.0 | 0.5235 | 0.1756 | 0.0 | nan | 0.0 | 0.2735 | 0.0 | 0.0 | 0.9372 | 0.8601 | 0.9652 | 0.0 | 0.0 | 0.1035 | 0.0 | nan | 0.6849 | 0.7987 | 0.2964 | 0.5979 | 0.2575 | 0.0 | 0.3739 | 0.2968 | 0.0 | 0.7392 | 0.0 | nan | 0.0 | 0.0 | 0.1670 | 0.0 | 0.0 | 0.6695 | 0.0 | 0.3530 | 0.1687 | 0.0 | nan | 0.0 | 0.2251 | 0.0 | 0.0 | 0.8533 | 0.7200 | 0.9032 | 0.0 | 0.0 | 0.0889 | 0.0 |
| 1.5354 | 5.25 | 2100 | 0.6749 | 0.2574 | 0.3114 | 0.8129 | nan | 0.7795 | 0.9479 | 0.2673 | 0.6263 | 0.3309 | 0.0 | 0.4880 | 0.5526 | 0.0 | 0.9229 | 0.0 | nan | 0.0 | 0.0 | 0.1946 | 0.0 | 0.0 | 0.9058 | 0.0 | 0.4747 | 0.2891 | 0.0 | nan | 0.0 | 0.2789 | 0.0 | 0.0 | 0.9296 | 0.8926 | 0.9724 | 0.0 | 0.0 | 0.1123 | 0.0 | nan | 0.6664 | 0.7862 | 0.2666 | 0.5673 | 0.2425 | 0.0 | 0.3705 | 0.2926 | 0.0 | 0.7436 | 0.0 | nan | 0.0 | 0.0 | 0.1875 | 0.0 | 0.0 | 0.6739 | 0.0 | 0.3639 | 0.2774 | 0.0 | nan | 0.0 | 0.2256 | 0.0 | 0.0 | 0.8535 | 0.7312 | 0.8955 | 0.0 | 0.0 | 0.0935 | 0.0 |
| 0.9066 | 5.38 | 2150 | 0.6420 | 0.2692 | 0.3288 | 0.8231 | nan | 0.8027 | 0.9289 | 0.5349 | 0.6724 | 0.4873 | 0.0 | 0.5372 | 0.5813 | 0.0 | 0.9129 | 0.0 | nan | 0.0 | 0.0 | 0.1965 | 0.0 | 0.0 | 0.8827 | 0.0 | 0.4897 | 0.2717 | 0.0 | nan | 0.0 | 0.2897 | 0.0 | 0.0 | 0.9517 | 0.8653 | 0.9718 | 0.0 | 0.0 | 0.1459 | 0.0 | nan | 0.7041 | 0.8076 | 0.5014 | 0.6246 | 0.3067 | 0.0 | 0.3802 | 0.2714 | 0.0 | 0.7480 | 0.0 | nan | 0.0 | 0.0 | 0.1893 | 0.0 | 0.0 | 0.6796 | 0.0 | 0.3739 | 0.2635 | 0.0 | nan | 0.0 | 0.2207 | 0.0 | 0.0 | 0.8412 | 0.6891 | 0.8949 | 0.0 | 0.0 | 0.1171 | 0.0 |
| 0.5222 | 5.5 | 2200 | 0.6669 | 0.2669 | 0.3248 | 0.8163 | nan | 0.7671 | 0.9418 | 0.3374 | 0.6925 | 0.4213 | 0.0 | 0.5016 | 0.5665 | 0.0 | 0.9186 | 0.0 | nan | 0.0 | 0.0 | 0.2600 | 0.0 | 0.0 | 0.8706 | 0.0 | 0.5646 | 0.3602 | 0.0 | nan | 0.0 | 0.2850 | 0.0 | 0.0 | 0.9355 | 0.8649 | 0.9657 | 0.0 | 0.0 | 0.1400 | 0.0 | nan | 0.6713 | 0.7977 | 0.3192 | 0.6194 | 0.2542 | 0.0 | 0.3945 | 0.2906 | 0.0 | 0.7521 | 0.0 | nan | 0.0 | 0.0 | 0.2461 | 0.0 | 0.0 | 0.6642 | 0.0 | 0.3736 | 0.3327 | 0.0 | nan | 0.0 | 0.2250 | 0.0 | 0.0 | 0.8543 | 0.7271 | 0.9034 | 0.0 | 0.0 | 0.1157 | 0.0 |
| 0.7196 | 5.62 | 2250 | 0.6619 | 0.2715 | 0.3274 | 0.8231 | nan | 0.8077 | 0.9509 | 0.4463 | 0.6637 | 0.3284 | 0.0 | 0.5066 | 0.5578 | 0.0 | 0.9055 | 0.0 | nan | 0.0 | 0.0 | 0.2320 | 0.0 | 0.0 | 0.8483 | 0.0 | 0.5559 | 0.2812 | 0.0 | nan | 0.0 | 0.3302 | 0.0 | 0.0 | 0.9439 | 0.8596 | 0.9613 | 0.0 | 0.0 | 0.2981 | 0.0 | nan | 0.7066 | 0.7987 | 0.4347 | 0.6179 | 0.2538 | 0.0 | 0.3744 | 0.2811 | 0.0 | 0.7641 | 0.0 | nan | 0.0 | 0.0 | 0.2227 | 0.0 | 0.0 | 0.6750 | 0.0 | 0.3648 | 0.2705 | 0.0 | nan | 0.0 | 0.2409 | 0.0 | 0.0 | 0.8479 | 0.7241 | 0.9075 | 0.0 | 0.0 | 0.2042 | 0.0 |
| 0.4246 | 5.75 | 2300 | 0.6761 | 0.2599 | 0.3149 | 0.8137 | nan | 0.7550 | 0.9532 | 0.3430 | 0.6596 | 0.3628 | 0.0 | 0.4682 | 0.4827 | 0.0 | 0.9109 | 0.0 | nan | 0.0 | 0.0 | 0.2185 | 0.0 | 0.0 | 0.8738 | 0.0 | 0.5448 | 0.2315 | 0.0 | nan | 0.0 | 0.3239 | 0.0 | 0.0 | 0.9358 | 0.8866 | 0.9666 | 0.0 | 0.0 | 0.1596 | 0.0 | nan | 0.6763 | 0.7903 | 0.3378 | 0.6097 | 0.2436 | 0.0 | 0.3451 | 0.2708 | 0.0 | 0.7474 | 0.0 | nan | 0.0 | 0.0 | 0.2118 | 0.0 | 0.0 | 0.6605 | 0.0 | 0.3625 | 0.2241 | 0.0 | nan | 0.0 | 0.2374 | 0.0 | 0.0 | 0.8534 | 0.7154 | 0.9062 | 0.0 | 0.0 | 0.1249 | 0.0 |
| 0.5324 | 5.88 | 2350 | 0.6352 | 0.2711 | 0.3279 | 0.8266 | nan | 0.8153 | 0.9320 | 0.4755 | 0.7299 | 0.5061 | 0.0 | 0.5260 | 0.5623 | 0.0 | 0.9085 | 0.0 | nan | 0.0 | 0.0 | 0.2232 | 0.0 | 0.0 | 0.8919 | 0.0 | 0.4953 | 0.2723 | 0.0 | nan | 0.0 | 0.2771 | 0.0 | 0.0 | 0.9456 | 0.8615 | 0.9550 | 0.0 | 0.0 | 0.1150 | 0.0 | nan | 0.7132 | 0.8098 | 0.4590 | 0.6675 | 0.3239 | 0.0 | 0.3802 | 0.2740 | 0.0 | 0.7602 | 0.0 | nan | 0.0 | 0.0 | 0.2151 | 0.0 | 0.0 | 0.6651 | 0.0 | 0.3498 | 0.2602 | 0.0 | nan | 0.0 | 0.2236 | 0.0 | 0.0 | 0.8521 | 0.7209 | 0.9063 | 0.0 | 0.0 | 0.0948 | 0.0 |
| 0.4106 | 6.0 | 2400 | 0.6168 | 0.2805 | 0.3382 | 0.8286 | nan | 0.8140 | 0.9366 | 0.5348 | 0.7391 | 0.4551 | 0.0 | 0.4434 | 0.5715 | 0.0 | 0.9105 | 0.0 | nan | 0.0 | 0.0 | 0.3385 | 0.0 | 0.0 | 0.8631 | 0.0 | 0.5545 | 0.4411 | 0.0 | nan | 0.0 | 0.2978 | 0.0 | 0.0 | 0.9408 | 0.8805 | 0.9625 | 0.0 | 0.0 | 0.1385 | 0.0 | nan | 0.7068 | 0.8102 | 0.5070 | 0.6757 | 0.2917 | 0.0 | 0.3503 | 0.2908 | 0.0 | 0.7596 | 0.0 | nan | 0.0 | 0.0 | 0.3190 | 0.0 | 0.0 | 0.6776 | 0.0 | 0.3889 | 0.3781 | 0.0 | nan | 0.0 | 0.2281 | 0.0 | 0.0 | 0.8501 | 0.7196 | 0.9076 | 0.0 | 0.0 | 0.1149 | 0.0 |
| 0.5797 | 6.12 | 2450 | 0.6297 | 0.2816 | 0.3393 | 0.8296 | nan | 0.7974 | 0.9383 | 0.4647 | 0.7903 | 0.4007 | 0.0 | 0.5064 | 0.5728 | 0.0 | 0.9213 | 0.0 | nan | 0.0 | 0.0 | 0.3479 | 0.0 | 0.0 | 0.8703 | 0.0 | 0.5732 | 0.4362 | 0.0 | nan | 0.0 | 0.3033 | 0.0 | 0.0 | 0.9375 | 0.8832 | 0.9683 | 0.0 | 0.0 | 0.1466 | 0.0 | nan | 0.6881 | 0.8134 | 0.4564 | 0.6750 | 0.3033 | 0.0 | 0.3764 | 0.3047 | 0.0 | 0.7563 | 0.0 | nan | 0.0 | 0.0 | 0.3199 | 0.0 | 0.0 | 0.6791 | 0.0 | 0.4054 | 0.3939 | 0.0 | nan | 0.0 | 0.2370 | 0.0 | 0.0 | 0.8557 | 0.7179 | 0.9056 | 0.0 | 0.0 | 0.1222 | 0.0 |
| 0.3407 | 6.25 | 2500 | 0.6193 | 0.2818 | 0.3399 | 0.8306 | nan | 0.7883 | 0.9404 | 0.5966 | 0.7679 | 0.4412 | 0.0 | 0.4553 | 0.5684 | 0.0 | 0.9199 | 0.0 | nan | 0.0 | 0.0 | 0.2922 | 0.0 | 0.0 | 0.8774 | 0.0 | 0.5497 | 0.4408 | 0.0 | nan | 0.0 | 0.2956 | 0.0 | 0.0 | 0.9447 | 0.8645 | 0.9658 | 0.0 | 0.0 | 0.1667 | 0.0 | nan | 0.7001 | 0.8173 | 0.5101 | 0.6671 | 0.3084 | 0.0 | 0.3634 | 0.3133 | 0.0 | 0.7495 | 0.0 | nan | 0.0 | 0.0 | 0.2726 | 0.0 | 0.0 | 0.6788 | 0.0 | 0.4024 | 0.4009 | 0.0 | nan | 0.0 | 0.2325 | 0.0 | 0.0 | 0.8498 | 0.7106 | 0.9076 | 0.0 | 0.0 | 0.1343 | 0.0 |
| 0.4585 | 6.38 | 2550 | 0.6187 | 0.2836 | 0.3416 | 0.8315 | nan | 0.8295 | 0.9356 | 0.6898 | 0.6526 | 0.4324 | 0.0 | 0.5160 | 0.5700 | 0.0 | 0.9143 | 0.0 | nan | 0.0 | 0.0 | 0.3278 | 0.0 | 0.0 | 0.8751 | 0.0 | 0.5383 | 0.4159 | 0.0 | nan | 0.0 | 0.2798 | 0.0 | 0.0 | 0.9539 | 0.8512 | 0.9627 | 0.0 | 0.0 | 0.1863 | 0.0 | nan | 0.7158 | 0.8176 | 0.5366 | 0.6200 | 0.3121 | 0.0 | 0.3932 | 0.3097 | 0.0 | 0.7531 | 0.0 | nan | 0.0 | 0.0 | 0.3010 | 0.0 | 0.0 | 0.6800 | 0.0 | 0.4010 | 0.3900 | 0.0 | nan | 0.0 | 0.2275 | 0.0 | 0.0 | 0.8458 | 0.7183 | 0.9100 | 0.0 | 0.0 | 0.1447 | 0.0 |
| 0.6642 | 6.5 | 2600 | 0.6327 | 0.2802 | 0.3352 | 0.8295 | nan | 0.7982 | 0.9375 | 0.5459 | 0.7388 | 0.4326 | 0.0 | 0.5516 | 0.5326 | 0.0 | 0.9274 | 0.0 | nan | 0.0 | 0.0 | 0.2682 | 0.0 | 0.0 | 0.8894 | 0.0 | 0.5355 | 0.3860 | 0.0 | nan | 0.0 | 0.2825 | 0.0 | 0.0 | 0.9413 | 0.8768 | 0.9586 | 0.0 | 0.0 | 0.1224 | 0.0 | nan | 0.7054 | 0.8105 | 0.5189 | 0.6692 | 0.2936 | 0.0 | 0.3925 | 0.3331 | 0.0 | 0.7412 | 0.0 | nan | 0.0 | 0.0 | 0.2434 | 0.0 | 0.0 | 0.6716 | 0.0 | 0.3941 | 0.3668 | 0.0 | nan | 0.0 | 0.2289 | 0.0 | 0.0 | 0.8546 | 0.7260 | 0.9097 | 0.0 | 0.0 | 0.1057 | 0.0 |
| 0.3263 | 6.62 | 2650 | 0.6187 | 0.2829 | 0.3430 | 0.8300 | nan | 0.7874 | 0.9330 | 0.5562 | 0.7970 | 0.4492 | 0.0 | 0.5546 | 0.5622 | 0.0 | 0.9371 | 0.0 | nan | 0.0 | 0.0 | 0.3517 | 0.0 | 0.0 | 0.8711 | 0.0 | 0.5865 | 0.3811 | 0.0 | nan | 0.0 | 0.2937 | 0.0 | 0.0 | 0.9318 | 0.8735 | 0.9725 | 0.0 | 0.0 | 0.1375 | 0.0 | nan | 0.6953 | 0.8209 | 0.5405 | 0.6682 | 0.2987 | 0.0 | 0.4080 | 0.3427 | 0.0 | 0.7412 | 0.0 | nan | 0.0 | 0.0 | 0.3012 | 0.0 | 0.0 | 0.6758 | 0.0 | 0.3928 | 0.3508 | 0.0 | nan | 0.0 | 0.2326 | 0.0 | 0.0 | 0.8543 | 0.7108 | 0.9033 | 0.0 | 0.0 | 0.1166 | 0.0 |
| 0.2918 | 6.75 | 2700 | 0.6222 | 0.2872 | 0.3498 | 0.8277 | nan | 0.7842 | 0.9381 | 0.6136 | 0.7238 | 0.4618 | 0.0 | 0.5182 | 0.5812 | 0.0 | 0.9045 | 0.0 | nan | 0.0 | 0.0 | 0.4309 | 0.0 | 0.0 | 0.8353 | 0.0 | 0.5853 | 0.4976 | 0.0 | nan | 0.0 | 0.3383 | 0.0 | 0.0 | 0.9423 | 0.8831 | 0.9709 | 0.0 | 0.0 | 0.1847 | 0.0 | nan | 0.6937 | 0.8114 | 0.5527 | 0.6574 | 0.3008 | 0.0 | 0.3913 | 0.3018 | 0.0 | 0.7703 | 0.0 | nan | 0.0 | 0.0 | 0.3806 | 0.0 | 0.0 | 0.6696 | 0.0 | 0.3834 | 0.4173 | 0.0 | nan | 0.0 | 0.2506 | 0.0 | 0.0 | 0.8509 | 0.7104 | 0.9029 | 0.0 | 0.0 | 0.1467 | 0.0 |
| 0.8647 | 6.88 | 2750 | 0.6397 | 0.2835 | 0.3383 | 0.8277 | nan | 0.7927 | 0.9560 | 0.5292 | 0.6732 | 0.3404 | 0.0 | 0.4745 | 0.5981 | 0.0 | 0.9258 | 0.0 | nan | 0.0 | 0.0 | 0.3488 | 0.0 | 0.0 | 0.8603 | 0.0 | 0.5484 | 0.5227 | 0.0 | nan | 0.0 | 0.3289 | 0.0 | 0.0 | 0.9511 | 0.8297 | 0.9629 | 0.0 | 0.0 | 0.1840 | 0.0 | nan | 0.6999 | 0.7965 | 0.5125 | 0.6472 | 0.2531 | 0.0 | 0.3711 | 0.3133 | 0.0 | 0.7565 | 0.0 | nan | 0.0 | 0.0 | 0.3100 | 0.0 | 0.0 | 0.6869 | 0.0 | 0.3958 | 0.4342 | 0.0 | nan | 0.0 | 0.2517 | 0.0 | 0.0 | 0.8506 | 0.7355 | 0.9087 | 0.0 | 0.0 | 0.1471 | 0.0 |
| 0.7004 | 7.0 | 2800 | 0.6274 | 0.2874 | 0.3482 | 0.8305 | nan | 0.7888 | 0.9403 | 0.5878 | 0.7560 | 0.4348 | 0.0 | 0.5041 | 0.5891 | 0.0 | 0.9200 | 0.0 | nan | 0.0 | 0.0 | 0.3797 | 0.0 | 0.0 | 0.8462 | 0.0 | 0.5907 | 0.4789 | 0.0 | nan | 0.0 | 0.3490 | 0.0 | 0.0 | 0.9451 | 0.8642 | 0.9649 | 0.0 | 0.0 | 0.2023 | 0.0 | nan | 0.7027 | 0.8129 | 0.5522 | 0.6624 | 0.2862 | 0.0 | 0.3848 | 0.3115 | 0.0 | 0.7656 | 0.0 | nan | 0.0 | 0.0 | 0.3405 | 0.0 | 0.0 | 0.6866 | 0.0 | 0.3918 | 0.4008 | 0.0 | nan | 0.0 | 0.2539 | 0.0 | 0.0 | 0.8526 | 0.7267 | 0.9086 | 0.0 | 0.0 | 0.1584 | 0.0 |
| 0.4585 | 7.12 | 2850 | 0.6071 | 0.2919 | 0.3501 | 0.8403 | nan | 0.8647 | 0.9271 | 0.5548 | 0.7557 | 0.4746 | 0.0 | 0.5295 | 0.5841 | 0.0 | 0.9200 | 0.0 | nan | 0.0 | 0.0 | 0.3762 | 0.0 | 0.0 | 0.9060 | 0.0 | 0.5504 | 0.4677 | 0.0 | nan | 0.0 | 0.3497 | 0.0 | 0.0 | 0.9375 | 0.8628 | 0.9623 | 0.0 | 0.0 | 0.1786 | 0.0 | nan | 0.7391 | 0.8300 | 0.5397 | 0.7073 | 0.3054 | 0.0 | 0.4055 | 0.3090 | 0.0 | 0.7608 | 0.0 | nan | 0.0 | 0.0 | 0.3416 | 0.0 | 0.0 | 0.6935 | 0.0 | 0.4100 | 0.4183 | 0.0 | nan | 0.0 | 0.2544 | 0.0 | 0.0 | 0.8556 | 0.7156 | 0.9094 | 0.0 | 0.0 | 0.1445 | 0.0 |
| 0.5376 | 7.25 | 2900 | 0.6132 | 0.2882 | 0.3473 | 0.8338 | nan | 0.8155 | 0.9337 | 0.6034 | 0.7660 | 0.4710 | 0.0 | 0.4848 | 0.5751 | 0.0 | 0.9343 | 0.0 | nan | 0.0 | 0.0 | 0.3926 | 0.0 | 0.0 | 0.8519 | 0.0 | 0.6031 | 0.4158 | 0.0 | nan | 0.0 | 0.3384 | 0.0 | 0.0 | 0.9504 | 0.8445 | 0.9681 | 0.0 | 0.0 | 0.1651 | 0.0 | nan | 0.7171 | 0.8186 | 0.5670 | 0.6853 | 0.3079 | 0.0 | 0.3894 | 0.3251 | 0.0 | 0.7538 | 0.0 | nan | 0.0 | 0.0 | 0.3447 | 0.0 | 0.0 | 0.6852 | 0.0 | 0.3932 | 0.3749 | 0.0 | nan | 0.0 | 0.2500 | 0.0 | 0.0 | 0.8496 | 0.7157 | 0.9100 | 0.0 | 0.0 | 0.1362 | 0.0 |
| 0.8122 | 7.38 | 2950 | 0.5909 | 0.2962 | 0.3554 | 0.8392 | nan | 0.8091 | 0.9412 | 0.6466 | 0.7718 | 0.4751 | 0.0 | 0.5177 | 0.6016 | 0.0 | 0.9080 | 0.0 | nan | 0.0 | 0.0 | 0.4417 | 0.0 | 0.0 | 0.8859 | 0.0 | 0.5373 | 0.5146 | 0.0 | nan | 0.0 | 0.3196 | 0.0 | 0.0 | 0.9460 | 0.8538 | 0.9662 | 0.0 | 0.0 | 0.2365 | 0.0 | nan | 0.7260 | 0.8224 | 0.5813 | 0.7010 | 0.3154 | 0.0 | 0.3948 | 0.3090 | 0.0 | 0.7724 | 0.0 | nan | 0.0 | 0.0 | 0.3926 | 0.0 | 0.0 | 0.6921 | 0.0 | 0.4100 | 0.4498 | 0.0 | nan | 0.0 | 0.2447 | 0.0 | 0.0 | 0.8537 | 0.7256 | 0.9103 | 0.0 | 0.0 | 0.1782 | 0.0 |
| 0.3453 | 7.5 | 3000 | 0.5974 | 0.2921 | 0.3480 | 0.8385 | nan | 0.8227 | 0.9410 | 0.6648 | 0.7513 | 0.4138 | 0.0 | 0.5276 | 0.5763 | 0.0 | 0.9103 | 0.0 | nan | 0.0 | 0.0 | 0.3788 | 0.0 | 0.0 | 0.8851 | 0.0 | 0.5094 | 0.5078 | 0.0 | nan | 0.0 | 0.2979 | 0.0 | 0.0 | 0.9538 | 0.8589 | 0.9633 | 0.0 | 0.0 | 0.1738 | 0.0 | nan | 0.7255 | 0.8212 | 0.5890 | 0.6919 | 0.3014 | 0.0 | 0.4090 | 0.3098 | 0.0 | 0.7660 | 0.0 | nan | 0.0 | 0.0 | 0.3425 | 0.0 | 0.0 | 0.6849 | 0.0 | 0.3964 | 0.4477 | 0.0 | nan | 0.0 | 0.2358 | 0.0 | 0.0 | 0.8483 | 0.7257 | 0.9112 | 0.0 | 0.0 | 0.1416 | 0.0 |
| 0.4628 | 7.62 | 3050 | 0.5971 | 0.2919 | 0.3514 | 0.8371 | nan | 0.8185 | 0.9402 | 0.6444 | 0.7747 | 0.3999 | 0.0 | 0.5277 | 0.5876 | 0.0 | 0.9099 | 0.0 | nan | 0.0 | 0.0 | 0.4217 | 0.0 | 0.0 | 0.8667 | 0.0 | 0.5486 | 0.5637 | 0.0 | nan | 0.0 | 0.3187 | 0.0 | 0.0 | 0.9444 | 0.8713 | 0.9631 | 0.0 | 0.0 | 0.1452 | 0.0 | nan | 0.7199 | 0.8225 | 0.5822 | 0.6970 | 0.2991 | 0.0 | 0.4052 | 0.3179 | 0.0 | 0.7652 | 0.0 | nan | 0.0 | 0.0 | 0.3749 | 0.0 | 0.0 | 0.6743 | 0.0 | 0.3871 | 0.4440 | 0.0 | nan | 0.0 | 0.2444 | 0.0 | 0.0 | 0.8556 | 0.7193 | 0.9118 | 0.0 | 0.0 | 0.1215 | 0.0 |
| 1.6032 | 7.75 | 3100 | 0.6105 | 0.2925 | 0.3547 | 0.8333 | nan | 0.8138 | 0.9386 | 0.6522 | 0.7084 | 0.4581 | 0.0 | 0.5056 | 0.6305 | 0.0 | 0.9079 | 0.0 | nan | 0.0 | 0.0 | 0.4763 | 0.0 | 0.0 | 0.8483 | 0.0 | 0.5818 | 0.5395 | 0.0 | nan | 0.0 | 0.3222 | 0.0 | 0.0 | 0.9458 | 0.8723 | 0.9676 | 0.0 | 0.0 | 0.1810 | 0.0 | nan | 0.7112 | 0.8156 | 0.5762 | 0.6682 | 0.3138 | 0.0 | 0.3962 | 0.3092 | 0.0 | 0.7700 | 0.0 | nan | 0.0 | 0.0 | 0.4116 | 0.0 | 0.0 | 0.6719 | 0.0 | 0.3836 | 0.4498 | 0.0 | nan | 0.0 | 0.2464 | 0.0 | 0.0 | 0.8550 | 0.7242 | 0.9102 | 0.0 | 0.0 | 0.1482 | 0.0 |
| 0.2408 | 7.88 | 3150 | 0.5932 | 0.2955 | 0.3586 | 0.8381 | nan | 0.8021 | 0.9346 | 0.6598 | 0.7938 | 0.5279 | 0.0 | 0.5633 | 0.6333 | 0.0 | 0.9050 | 0.0 | nan | 0.0 | 0.0 | 0.4273 | 0.0 | 0.0 | 0.8611 | 0.0 | 0.5607 | 0.5245 | 0.0 | nan | 0.0 | 0.3308 | 0.0 | 0.0 | 0.9497 | 0.8486 | 0.9692 | 0.0 | 0.0 | 0.1826 | 0.0 | nan | 0.7185 | 0.8279 | 0.5987 | 0.7105 | 0.3205 | 0.0 | 0.4141 | 0.2957 | 0.0 | 0.7709 | 0.0 | nan | 0.0 | 0.0 | 0.3758 | 0.0 | 0.0 | 0.6796 | 0.0 | 0.3960 | 0.4542 | 0.0 | nan | 0.0 | 0.2473 | 0.0 | 0.0 | 0.8530 | 0.7347 | 0.9100 | 0.0 | 0.0 | 0.1486 | 0.0 |
| 0.5243 | 8.0 | 3200 | 0.5799 | 0.2979 | 0.3597 | 0.8435 | nan | 0.8682 | 0.9230 | 0.6693 | 0.8014 | 0.4804 | 0.0 | 0.5210 | 0.6297 | 0.0 | 0.9214 | 0.0 | nan | 0.0 | 0.0 | 0.4416 | 0.0 | 0.0 | 0.8687 | 0.0 | 0.5426 | 0.5655 | 0.0 | nan | 0.0 | 0.3073 | 0.0 | 0.0 | 0.9424 | 0.8744 | 0.9677 | 0.0 | 0.0 | 0.1860 | 0.0 | nan | 0.7427 | 0.8376 | 0.6361 | 0.7198 | 0.3149 | 0.0 | 0.3952 | 0.3306 | 0.0 | 0.7625 | 0.0 | nan | 0.0 | 0.0 | 0.3724 | 0.0 | 0.0 | 0.6834 | 0.0 | 0.3969 | 0.4529 | 0.0 | nan | 0.0 | 0.2406 | 0.0 | 0.0 | 0.8569 | 0.7294 | 0.9109 | 0.0 | 0.0 | 0.1505 | 0.0 |
| 0.7106 | 8.12 | 3250 | 0.5898 | 0.2938 | 0.3551 | 0.8390 | nan | 0.8028 | 0.9358 | 0.6853 | 0.8185 | 0.4355 | 0.0 | 0.5430 | 0.6255 | 0.0 | 0.9128 | 0.0 | nan | 0.0 | 0.0 | 0.4179 | 0.0 | 0.0 | 0.8797 | 0.0 | 0.5200 | 0.5480 | 0.0 | nan | 0.0 | 0.2941 | 0.0 | 0.0 | 0.9499 | 0.8647 | 0.9643 | 0.0 | 0.0 | 0.1668 | 0.0 | nan | 0.7124 | 0.8312 | 0.6000 | 0.7053 | 0.2940 | 0.0 | 0.4078 | 0.3024 | 0.0 | 0.7677 | 0.0 | nan | 0.0 | 0.0 | 0.3681 | 0.0 | 0.0 | 0.6838 | 0.0 | 0.3989 | 0.4578 | 0.0 | nan | 0.0 | 0.2355 | 0.0 | 0.0 | 0.8532 | 0.7319 | 0.9131 | 0.0 | 0.0 | 0.1374 | 0.0 |
| 0.3865 | 8.25 | 3300 | 0.5789 | 0.2964 | 0.3607 | 0.8419 | nan | 0.8309 | 0.9310 | 0.7078 | 0.7971 | 0.4823 | 0.0 | 0.5610 | 0.6033 | 0.0 | 0.9205 | 0.0 | nan | 0.0 | 0.0 | 0.4831 | 0.0 | 0.0 | 0.8636 | 0.0 | 0.5286 | 0.5814 | 0.0 | nan | 0.0 | 0.3079 | 0.0 | 0.0 | 0.9487 | 0.8607 | 0.9688 | 0.0 | 0.0 | 0.1672 | 0.0 | nan | 0.7273 | 0.8400 | 0.5976 | 0.7206 | 0.3247 | 0.0 | 0.4121 | 0.3307 | 0.0 | 0.7657 | 0.0 | nan | 0.0 | 0.0 | 0.3897 | 0.0 | 0.0 | 0.6828 | 0.0 | 0.3820 | 0.4413 | 0.0 | nan | 0.0 | 0.2445 | 0.0 | 0.0 | 0.8532 | 0.7225 | 0.9115 | 0.0 | 0.0 | 0.1392 | 0.0 |
| 0.5896 | 8.38 | 3350 | 0.6025 | 0.2943 | 0.3538 | 0.8375 | nan | 0.8034 | 0.9395 | 0.6203 | 0.7912 | 0.4404 | 0.0 | 0.5404 | 0.6167 | 0.0 | 0.9121 | 0.0 | nan | 0.0 | 0.0 | 0.4611 | 0.0 | 0.0 | 0.8900 | 0.0 | 0.5326 | 0.5242 | 0.0 | nan | 0.0 | 0.3185 | 0.0 | 0.0 | 0.9474 | 0.8322 | 0.9662 | 0.0 | 0.0 | 0.1859 | 0.0 | nan | 0.7142 | 0.8253 | 0.5802 | 0.6994 | 0.2989 | 0.0 | 0.4128 | 0.3161 | 0.0 | 0.7676 | 0.0 | nan | 0.0 | 0.0 | 0.3812 | 0.0 | 0.0 | 0.6786 | 0.0 | 0.3960 | 0.4637 | 0.0 | nan | 0.0 | 0.2466 | 0.0 | 0.0 | 0.8527 | 0.7224 | 0.9117 | 0.0 | 0.0 | 0.1499 | 0.0 |
| 1.6879 | 8.5 | 3400 | 0.5871 | 0.2966 | 0.3589 | 0.8414 | nan | 0.8057 | 0.9414 | 0.6753 | 0.8033 | 0.4564 | 0.0 | 0.5567 | 0.6269 | 0.0 | 0.9270 | 0.0 | nan | 0.0 | 0.0 | 0.3957 | 0.0 | 0.0 | 0.8722 | 0.0 | 0.5594 | 0.5370 | 0.0 | nan | 0.0 | 0.3143 | 0.0 | 0.0 | 0.9407 | 0.8642 | 0.9686 | 0.0 | 0.0 | 0.2395 | 0.0 | nan | 0.7284 | 0.8327 | 0.6046 | 0.7031 | 0.3060 | 0.0 | 0.4110 | 0.3216 | 0.0 | 0.7609 | 0.0 | nan | 0.0 | 0.0 | 0.3409 | 0.0 | 0.0 | 0.6896 | 0.0 | 0.4035 | 0.4605 | 0.0 | nan | 0.0 | 0.2464 | 0.0 | 0.0 | 0.8586 | 0.7306 | 0.9114 | 0.0 | 0.0 | 0.1818 | 0.0 |
| 0.6102 | 8.62 | 3450 | 0.5856 | 0.3002 | 0.3654 | 0.8415 | nan | 0.8138 | 0.9321 | 0.6827 | 0.7914 | 0.5197 | 0.0 | 0.5760 | 0.6360 | 0.0 | 0.9170 | 0.0 | nan | 0.0 | 0.0 | 0.4329 | 0.0 | 0.0 | 0.8645 | 0.0 | 0.5720 | 0.5589 | 0.0 | nan | 0.0 | 0.3342 | 0.0 | 0.0 | 0.9411 | 0.8703 | 0.9668 | 0.0 | 0.0 | 0.2819 | 0.0 | nan | 0.7285 | 0.8326 | 0.6223 | 0.7048 | 0.3289 | 0.0 | 0.4190 | 0.3168 | 0.0 | 0.7722 | 0.0 | nan | 0.0 | 0.0 | 0.3742 | 0.0 | 0.0 | 0.6903 | 0.0 | 0.3949 | 0.4580 | 0.0 | nan | 0.0 | 0.2568 | 0.0 | 0.0 | 0.8579 | 0.7263 | 0.9125 | 0.0 | 0.0 | 0.2096 | 0.0 |
| 0.5784 | 8.75 | 3500 | 0.5754 | 0.3011 | 0.3608 | 0.8461 | nan | 0.8669 | 0.9306 | 0.6895 | 0.7825 | 0.5161 | 0.0 | 0.5344 | 0.5997 | 0.0 | 0.9154 | 0.0 | nan | 0.0 | 0.0 | 0.4049 | 0.0 | 0.0 | 0.8770 | 0.0 | 0.5577 | 0.5588 | 0.0 | nan | 0.0 | 0.3366 | 0.0 | 0.0 | 0.9399 | 0.8514 | 0.9688 | 0.0 | 0.0 | 0.2159 | 0.0 | nan | 0.7534 | 0.8392 | 0.6345 | 0.7241 | 0.3294 | 0.0 | 0.4154 | 0.3472 | 0.0 | 0.7644 | 0.0 | nan | 0.0 | 0.0 | 0.3513 | 0.0 | 0.0 | 0.6833 | 0.0 | 0.4003 | 0.4627 | 0.0 | nan | 0.0 | 0.2555 | 0.0 | 0.0 | 0.8589 | 0.7317 | 0.9111 | 0.0 | 0.0 | 0.1713 | 0.0 |
| 1.7496 | 8.88 | 3550 | 0.5936 | 0.2977 | 0.3537 | 0.8416 | nan | 0.8368 | 0.9449 | 0.6772 | 0.7436 | 0.4208 | 0.0 | 0.5329 | 0.5842 | 0.0 | 0.9125 | 0.0 | nan | 0.0 | 0.0 | 0.4109 | 0.0 | 0.0 | 0.8839 | 0.0 | 0.5324 | 0.5578 | 0.0 | nan | 0.0 | 0.3259 | 0.0 | 0.0 | 0.9427 | 0.8489 | 0.9690 | 0.0 | 0.0 | 0.1938 | 0.0 | nan | 0.7350 | 0.8241 | 0.6306 | 0.6945 | 0.2953 | 0.0 | 0.4129 | 0.3417 | 0.0 | 0.7659 | 0.0 | nan | 0.0 | 0.0 | 0.3593 | 0.0 | 0.0 | 0.6860 | 0.0 | 0.4051 | 0.4630 | 0.0 | nan | 0.0 | 0.2501 | 0.0 | 0.0 | 0.8582 | 0.7381 | 0.9112 | 0.0 | 0.0 | 0.1560 | 0.0 |
| 0.6411 | 9.0 | 3600 | 0.5782 | 0.3001 | 0.3602 | 0.8450 | nan | 0.8407 | 0.9365 | 0.6861 | 0.7895 | 0.4828 | 0.0 | 0.5688 | 0.5980 | 0.0 | 0.9231 | 0.0 | nan | 0.0 | 0.0 | 0.4029 | 0.0 | 0.0 | 0.8744 | 0.0 | 0.5688 | 0.5442 | 0.0 | nan | 0.0 | 0.3323 | 0.0 | 0.0 | 0.9423 | 0.8588 | 0.9623 | 0.0 | 0.0 | 0.2144 | 0.0 | nan | 0.7472 | 0.8364 | 0.6280 | 0.7149 | 0.3246 | 0.0 | 0.4239 | 0.3428 | 0.0 | 0.7609 | 0.0 | nan | 0.0 | 0.0 | 0.3477 | 0.0 | 0.0 | 0.6867 | 0.0 | 0.4030 | 0.4632 | 0.0 | nan | 0.0 | 0.2531 | 0.0 | 0.0 | 0.8580 | 0.7279 | 0.9144 | 0.0 | 0.0 | 0.1700 | 0.0 |
| 0.2624 | 9.12 | 3650 | 0.5766 | 0.3008 | 0.3624 | 0.8455 | nan | 0.8574 | 0.9342 | 0.6988 | 0.7744 | 0.4983 | 0.0 | 0.5394 | 0.6314 | 0.0 | 0.9148 | 0.0 | nan | 0.0 | 0.0 | 0.4402 | 0.0 | 0.0 | 0.8562 | 0.0 | 0.5510 | 0.5560 | 0.0 | nan | 0.0 | 0.3273 | 0.0 | 0.0 | 0.9469 | 0.8694 | 0.9709 | 0.0 | 0.0 | 0.2311 | 0.0 | nan | 0.7491 | 0.8379 | 0.6241 | 0.7221 | 0.3229 | 0.0 | 0.4173 | 0.3273 | 0.0 | 0.7682 | 0.0 | nan | 0.0 | 0.0 | 0.3773 | 0.0 | 0.0 | 0.6890 | 0.0 | 0.4010 | 0.4651 | 0.0 | nan | 0.0 | 0.2490 | 0.0 | 0.0 | 0.8544 | 0.7306 | 0.9108 | 0.0 | 0.0 | 0.1799 | 0.0 |
| 0.5411 | 9.25 | 3700 | 0.5959 | 0.2991 | 0.3590 | 0.8420 | nan | 0.8243 | 0.9438 | 0.6836 | 0.7438 | 0.4344 | 0.0 | 0.5621 | 0.6202 | 0.0 | 0.9143 | 0.0 | nan | 0.0 | 0.0 | 0.4442 | 0.0 | 0.0 | 0.8901 | 0.0 | 0.5455 | 0.5510 | 0.0 | nan | 0.0 | 0.3440 | 0.0 | 0.0 | 0.9400 | 0.8591 | 0.9693 | 0.0 | 0.0 | 0.2192 | 0.0 | nan | 0.7328 | 0.8259 | 0.6144 | 0.7018 | 0.3058 | 0.0 | 0.4202 | 0.3204 | 0.0 | 0.7710 | 0.0 | nan | 0.0 | 0.0 | 0.3832 | 0.0 | 0.0 | 0.6900 | 0.0 | 0.4063 | 0.4782 | 0.0 | nan | 0.0 | 0.2535 | 0.0 | 0.0 | 0.8589 | 0.7253 | 0.9120 | 0.0 | 0.0 | 0.1724 | 0.0 |
| 0.4142 | 9.38 | 3750 | 0.5848 | 0.3006 | 0.3620 | 0.8433 | nan | 0.8309 | 0.9357 | 0.6855 | 0.7703 | 0.5115 | 0.0 | 0.5599 | 0.6165 | 0.0 | 0.9205 | 0.0 | nan | 0.0 | 0.0 | 0.4350 | 0.0 | 0.0 | 0.8814 | 0.0 | 0.5538 | 0.5682 | 0.0 | nan | 0.0 | 0.3382 | 0.0 | 0.0 | 0.9411 | 0.8575 | 0.9667 | 0.0 | 0.0 | 0.2110 | 0.0 | nan | 0.7370 | 0.8313 | 0.6229 | 0.7160 | 0.3284 | 0.0 | 0.4230 | 0.3381 | 0.0 | 0.7636 | 0.0 | nan | 0.0 | 0.0 | 0.3743 | 0.0 | 0.0 | 0.6905 | 0.0 | 0.4047 | 0.4693 | 0.0 | nan | 0.0 | 0.2534 | 0.0 | 0.0 | 0.8589 | 0.7273 | 0.9130 | 0.0 | 0.0 | 0.1678 | 0.0 |
| 0.1631 | 9.5 | 3800 | 0.5809 | 0.3004 | 0.3613 | 0.8442 | nan | 0.8349 | 0.9352 | 0.6964 | 0.7850 | 0.4969 | 0.0 | 0.5560 | 0.6125 | 0.0 | 0.9205 | 0.0 | nan | 0.0 | 0.0 | 0.4223 | 0.0 | 0.0 | 0.8734 | 0.0 | 0.5475 | 0.5597 | 0.0 | nan | 0.0 | 0.3284 | 0.0 | 0.0 | 0.9443 | 0.8656 | 0.9681 | 0.0 | 0.0 | 0.2162 | 0.0 | nan | 0.7388 | 0.8335 | 0.6233 | 0.7213 | 0.3269 | 0.0 | 0.4226 | 0.3368 | 0.0 | 0.7658 | 0.0 | nan | 0.0 | 0.0 | 0.3673 | 0.0 | 0.0 | 0.6888 | 0.0 | 0.4010 | 0.4662 | 0.0 | nan | 0.0 | 0.2488 | 0.0 | 0.0 | 0.8584 | 0.7272 | 0.9126 | 0.0 | 0.0 | 0.1724 | 0.0 |
| 0.2142 | 9.62 | 3850 | 0.5863 | 0.3005 | 0.3612 | 0.8431 | nan | 0.8182 | 0.9396 | 0.6847 | 0.7896 | 0.4772 | 0.0 | 0.5550 | 0.6190 | 0.0 | 0.9196 | 0.0 | nan | 0.0 | 0.0 | 0.4422 | 0.0 | 0.0 | 0.8868 | 0.0 | 0.5410 | 0.5606 | 0.0 | nan | 0.0 | 0.3348 | 0.0 | 0.0 | 0.9402 | 0.8630 | 0.9681 | 0.0 | 0.0 | 0.2181 | 0.0 | nan | 0.7320 | 0.8301 | 0.6246 | 0.7106 | 0.3221 | 0.0 | 0.4228 | 0.3343 | 0.0 | 0.7649 | 0.0 | nan | 0.0 | 0.0 | 0.3806 | 0.0 | 0.0 | 0.6901 | 0.0 | 0.4065 | 0.4717 | 0.0 | nan | 0.0 | 0.2530 | 0.0 | 0.0 | 0.8602 | 0.7283 | 0.9126 | 0.0 | 0.0 | 0.1703 | 0.0 |
| 0.3553 | 9.75 | 3900 | 0.5858 | 0.3001 | 0.3609 | 0.8427 | nan | 0.8141 | 0.9397 | 0.6925 | 0.7829 | 0.4822 | 0.0 | 0.5729 | 0.6157 | 0.0 | 0.9203 | 0.0 | nan | 0.0 | 0.0 | 0.4383 | 0.0 | 0.0 | 0.8819 | 0.0 | 0.5435 | 0.5580 | 0.0 | nan | 0.0 | 0.3288 | 0.0 | 0.0 | 0.9440 | 0.8589 | 0.9674 | 0.0 | 0.0 | 0.2090 | 0.0 | nan | 0.7311 | 0.8296 | 0.6192 | 0.7106 | 0.3215 | 0.0 | 0.4236 | 0.3390 | 0.0 | 0.7656 | 0.0 | nan | 0.0 | 0.0 | 0.3775 | 0.0 | 0.0 | 0.6901 | 0.0 | 0.4062 | 0.4686 | 0.0 | nan | 0.0 | 0.2500 | 0.0 | 0.0 | 0.8589 | 0.7322 | 0.9129 | 0.0 | 0.0 | 0.1658 | 0.0 |
| 0.2847 | 9.88 | 3950 | 0.5819 | 0.3003 | 0.3605 | 0.8437 | nan | 0.8261 | 0.9406 | 0.6928 | 0.7874 | 0.4766 | 0.0 | 0.5505 | 0.6122 | 0.0 | 0.9229 | 0.0 | nan | 0.0 | 0.0 | 0.4291 | 0.0 | 0.0 | 0.8708 | 0.0 | 0.5581 | 0.5595 | 0.0 | nan | 0.0 | 0.3340 | 0.0 | 0.0 | 0.9449 | 0.8500 | 0.9671 | 0.0 | 0.0 | 0.2120 | 0.0 | nan | 0.7365 | 0.8308 | 0.6246 | 0.7127 | 0.3233 | 0.0 | 0.4204 | 0.3362 | 0.0 | 0.7647 | 0.0 | nan | 0.0 | 0.0 | 0.3718 | 0.0 | 0.0 | 0.6897 | 0.0 | 0.4051 | 0.4663 | 0.0 | nan | 0.0 | 0.2519 | 0.0 | 0.0 | 0.8588 | 0.7346 | 0.9130 | 0.0 | 0.0 | 0.1681 | 0.0 |
| 0.4745 | 10.0 | 4000 | 0.5818 | 0.3011 | 0.3610 | 0.8444 | nan | 0.8364 | 0.9386 | 0.7005 | 0.7745 | 0.4695 | 0.0 | 0.5463 | 0.6230 | 0.0 | 0.9128 | 0.0 | nan | 0.0 | 0.0 | 0.4478 | 0.0 | 0.0 | 0.8809 | 0.0 | 0.5418 | 0.5574 | 0.0 | nan | 0.0 | 0.3278 | 0.0 | 0.0 | 0.9459 | 0.8600 | 0.9662 | 0.0 | 0.0 | 0.2230 | 0.0 | nan | 0.7395 | 0.8314 | 0.6251 | 0.7160 | 0.3216 | 0.0 | 0.4206 | 0.3351 | 0.0 | 0.7711 | 0.0 | nan | 0.0 | 0.0 | 0.3854 | 0.0 | 0.0 | 0.6893 | 0.0 | 0.4039 | 0.4696 | 0.0 | nan | 0.0 | 0.2496 | 0.0 | 0.0 | 0.8583 | 0.7294 | 0.9135 | 0.0 | 0.0 | 0.1745 | 0.0 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"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"
] |
JCAI2000/segformer-b0-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass |
<!-- 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-100by100PNG-50epochs-attempt2-100epochs-backgroundclass
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the JCAI2000/100By100BranchPNG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1491
- Mean Iou: 0.4520
- Mean Accuracy: 0.9041
- Overall Accuracy: 0.9041
- Accuracy Background: nan
- Accuracy Branch: 0.9041
- Iou Background: 0.0
- Iou Branch: 0.9041
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Branch | Iou Background | Iou Branch |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.5951 | 1.05 | 20 | 0.6067 | 0.4897 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 |
| 0.4531 | 2.11 | 40 | 0.4117 | 0.4717 | 0.9435 | 0.9435 | nan | 0.9435 | 0.0 | 0.9435 |
| 0.4396 | 3.16 | 60 | 0.3235 | 0.4750 | 0.9499 | 0.9499 | nan | 0.9499 | 0.0 | 0.9499 |
| 0.2327 | 4.21 | 80 | 0.2603 | 0.4703 | 0.9405 | 0.9405 | nan | 0.9405 | 0.0 | 0.9405 |
| 0.1971 | 5.26 | 100 | 0.2336 | 0.4786 | 0.9572 | 0.9572 | nan | 0.9572 | 0.0 | 0.9572 |
| 0.1768 | 6.32 | 120 | 0.2441 | 0.4776 | 0.9552 | 0.9552 | nan | 0.9552 | 0.0 | 0.9552 |
| 0.2005 | 7.37 | 140 | 0.1811 | 0.4597 | 0.9194 | 0.9194 | nan | 0.9194 | 0.0 | 0.9194 |
| 0.1818 | 8.42 | 160 | 0.2437 | 0.4805 | 0.9610 | 0.9610 | nan | 0.9610 | 0.0 | 0.9610 |
| 0.2163 | 9.47 | 180 | 0.1803 | 0.4774 | 0.9548 | 0.9548 | nan | 0.9548 | 0.0 | 0.9548 |
| 0.1436 | 10.53 | 200 | 0.1897 | 0.4807 | 0.9615 | 0.9615 | nan | 0.9615 | 0.0 | 0.9615 |
| 0.0957 | 11.58 | 220 | 0.1682 | 0.4578 | 0.9157 | 0.9157 | nan | 0.9157 | 0.0 | 0.9157 |
| 0.1614 | 12.63 | 240 | 0.1603 | 0.4422 | 0.8844 | 0.8844 | nan | 0.8844 | 0.0 | 0.8844 |
| 0.0952 | 13.68 | 260 | 0.1732 | 0.4738 | 0.9477 | 0.9477 | nan | 0.9477 | 0.0 | 0.9477 |
| 0.1243 | 14.74 | 280 | 0.1432 | 0.4569 | 0.9139 | 0.9139 | nan | 0.9139 | 0.0 | 0.9139 |
| 0.0943 | 15.79 | 300 | 0.1539 | 0.4725 | 0.9451 | 0.9451 | nan | 0.9451 | 0.0 | 0.9451 |
| 0.0833 | 16.84 | 320 | 0.1176 | 0.4570 | 0.9140 | 0.9140 | nan | 0.9140 | 0.0 | 0.9140 |
| 0.1817 | 17.89 | 340 | 0.1270 | 0.4623 | 0.9246 | 0.9246 | nan | 0.9246 | 0.0 | 0.9246 |
| 0.0939 | 18.95 | 360 | 0.1561 | 0.4715 | 0.9431 | 0.9431 | nan | 0.9431 | 0.0 | 0.9431 |
| 0.0849 | 20.0 | 380 | 0.1496 | 0.4682 | 0.9363 | 0.9363 | nan | 0.9363 | 0.0 | 0.9363 |
| 0.1155 | 21.05 | 400 | 0.1204 | 0.4547 | 0.9094 | 0.9094 | nan | 0.9094 | 0.0 | 0.9094 |
| 0.0507 | 22.11 | 420 | 0.1323 | 0.4667 | 0.9335 | 0.9335 | nan | 0.9335 | 0.0 | 0.9335 |
| 0.0631 | 23.16 | 440 | 0.1219 | 0.4593 | 0.9185 | 0.9185 | nan | 0.9185 | 0.0 | 0.9185 |
| 0.0509 | 24.21 | 460 | 0.1178 | 0.4673 | 0.9346 | 0.9346 | nan | 0.9346 | 0.0 | 0.9346 |
| 0.0594 | 25.26 | 480 | 0.1193 | 0.4568 | 0.9136 | 0.9136 | nan | 0.9136 | 0.0 | 0.9136 |
| 0.0633 | 26.32 | 500 | 0.1321 | 0.4717 | 0.9434 | 0.9434 | nan | 0.9434 | 0.0 | 0.9434 |
| 0.0739 | 27.37 | 520 | 0.1361 | 0.4587 | 0.9174 | 0.9174 | nan | 0.9174 | 0.0 | 0.9174 |
| 0.0894 | 28.42 | 540 | 0.1286 | 0.4658 | 0.9317 | 0.9317 | nan | 0.9317 | 0.0 | 0.9317 |
| 0.0528 | 29.47 | 560 | 0.1296 | 0.4644 | 0.9288 | 0.9288 | nan | 0.9288 | 0.0 | 0.9288 |
| 0.0683 | 30.53 | 580 | 0.1434 | 0.4705 | 0.9410 | 0.9410 | nan | 0.9410 | 0.0 | 0.9410 |
| 0.0343 | 31.58 | 600 | 0.1154 | 0.4598 | 0.9196 | 0.9196 | nan | 0.9196 | 0.0 | 0.9196 |
| 0.0436 | 32.63 | 620 | 0.1417 | 0.4527 | 0.9053 | 0.9053 | nan | 0.9053 | 0.0 | 0.9053 |
| 0.0369 | 33.68 | 640 | 0.1185 | 0.4365 | 0.8730 | 0.8730 | nan | 0.8730 | 0.0 | 0.8730 |
| 0.0537 | 34.74 | 660 | 0.1369 | 0.4660 | 0.9319 | 0.9319 | nan | 0.9319 | 0.0 | 0.9319 |
| 0.0642 | 35.79 | 680 | 0.1351 | 0.4514 | 0.9027 | 0.9027 | nan | 0.9027 | 0.0 | 0.9027 |
| 0.0597 | 36.84 | 700 | 0.1441 | 0.4590 | 0.9180 | 0.9180 | nan | 0.9180 | 0.0 | 0.9180 |
| 0.0382 | 37.89 | 720 | 0.1413 | 0.4568 | 0.9136 | 0.9136 | nan | 0.9136 | 0.0 | 0.9136 |
| 0.0488 | 38.95 | 740 | 0.1369 | 0.4626 | 0.9252 | 0.9252 | nan | 0.9252 | 0.0 | 0.9252 |
| 0.0652 | 40.0 | 760 | 0.1477 | 0.4653 | 0.9306 | 0.9306 | nan | 0.9306 | 0.0 | 0.9306 |
| 0.0376 | 41.05 | 780 | 0.1320 | 0.4579 | 0.9158 | 0.9158 | nan | 0.9158 | 0.0 | 0.9158 |
| 0.0387 | 42.11 | 800 | 0.1298 | 0.4536 | 0.9071 | 0.9071 | nan | 0.9071 | 0.0 | 0.9071 |
| 0.0791 | 43.16 | 820 | 0.1431 | 0.4498 | 0.8997 | 0.8997 | nan | 0.8997 | 0.0 | 0.8997 |
| 0.0304 | 44.21 | 840 | 0.1368 | 0.4426 | 0.8852 | 0.8852 | nan | 0.8852 | 0.0 | 0.8852 |
| 0.0301 | 45.26 | 860 | 0.1523 | 0.4681 | 0.9363 | 0.9363 | nan | 0.9363 | 0.0 | 0.9363 |
| 0.0743 | 46.32 | 880 | 0.1396 | 0.4505 | 0.9009 | 0.9009 | nan | 0.9009 | 0.0 | 0.9009 |
| 0.1028 | 47.37 | 900 | 0.1354 | 0.4463 | 0.8926 | 0.8926 | nan | 0.8926 | 0.0 | 0.8926 |
| 0.0399 | 48.42 | 920 | 0.1497 | 0.4568 | 0.9136 | 0.9136 | nan | 0.9136 | 0.0 | 0.9136 |
| 0.0282 | 49.47 | 940 | 0.1489 | 0.4672 | 0.9343 | 0.9343 | nan | 0.9343 | 0.0 | 0.9343 |
| 0.0266 | 50.53 | 960 | 0.1574 | 0.4564 | 0.9128 | 0.9128 | nan | 0.9128 | 0.0 | 0.9128 |
| 0.0328 | 51.58 | 980 | 0.1540 | 0.4536 | 0.9072 | 0.9072 | nan | 0.9072 | 0.0 | 0.9072 |
| 0.0273 | 52.63 | 1000 | 0.1624 | 0.4572 | 0.9144 | 0.9144 | nan | 0.9144 | 0.0 | 0.9144 |
| 0.0311 | 53.68 | 1020 | 0.1459 | 0.4386 | 0.8771 | 0.8771 | nan | 0.8771 | 0.0 | 0.8771 |
| 0.0481 | 54.74 | 1040 | 0.1607 | 0.4597 | 0.9194 | 0.9194 | nan | 0.9194 | 0.0 | 0.9194 |
| 0.0384 | 55.79 | 1060 | 0.1718 | 0.4596 | 0.9192 | 0.9192 | nan | 0.9192 | 0.0 | 0.9192 |
| 0.0299 | 56.84 | 1080 | 0.1708 | 0.4589 | 0.9178 | 0.9178 | nan | 0.9178 | 0.0 | 0.9178 |
| 0.0315 | 57.89 | 1100 | 0.1458 | 0.4539 | 0.9078 | 0.9078 | nan | 0.9078 | 0.0 | 0.9078 |
| 0.2086 | 58.95 | 1120 | 0.1428 | 0.4590 | 0.9181 | 0.9181 | nan | 0.9181 | 0.0 | 0.9181 |
| 0.0355 | 60.0 | 1140 | 0.1575 | 0.4478 | 0.8957 | 0.8957 | nan | 0.8957 | 0.0 | 0.8957 |
| 0.0236 | 61.05 | 1160 | 0.1610 | 0.4471 | 0.8941 | 0.8941 | nan | 0.8941 | 0.0 | 0.8941 |
| 0.0775 | 62.11 | 1180 | 0.1688 | 0.4478 | 0.8955 | 0.8955 | nan | 0.8955 | 0.0 | 0.8955 |
| 0.026 | 63.16 | 1200 | 0.1513 | 0.4558 | 0.9117 | 0.9117 | nan | 0.9117 | 0.0 | 0.9117 |
| 0.03 | 64.21 | 1220 | 0.1583 | 0.4630 | 0.9260 | 0.9260 | nan | 0.9260 | 0.0 | 0.9260 |
| 0.0255 | 65.26 | 1240 | 0.1595 | 0.4565 | 0.9131 | 0.9131 | nan | 0.9131 | 0.0 | 0.9131 |
| 0.079 | 66.32 | 1260 | 0.1485 | 0.4503 | 0.9005 | 0.9005 | nan | 0.9005 | 0.0 | 0.9005 |
| 0.0366 | 67.37 | 1280 | 0.1658 | 0.4561 | 0.9123 | 0.9123 | nan | 0.9123 | 0.0 | 0.9123 |
| 0.0286 | 68.42 | 1300 | 0.1890 | 0.4667 | 0.9334 | 0.9334 | nan | 0.9334 | 0.0 | 0.9334 |
| 0.0303 | 69.47 | 1320 | 0.1469 | 0.4526 | 0.9052 | 0.9052 | nan | 0.9052 | 0.0 | 0.9052 |
| 0.0215 | 70.53 | 1340 | 0.1559 | 0.4548 | 0.9095 | 0.9095 | nan | 0.9095 | 0.0 | 0.9095 |
| 0.028 | 71.58 | 1360 | 0.1616 | 0.4598 | 0.9195 | 0.9195 | nan | 0.9195 | 0.0 | 0.9195 |
| 0.0228 | 72.63 | 1380 | 0.1445 | 0.4521 | 0.9041 | 0.9041 | nan | 0.9041 | 0.0 | 0.9041 |
| 0.0216 | 73.68 | 1400 | 0.1526 | 0.4542 | 0.9085 | 0.9085 | nan | 0.9085 | 0.0 | 0.9085 |
| 0.0202 | 74.74 | 1420 | 0.1525 | 0.4643 | 0.9285 | 0.9285 | nan | 0.9285 | 0.0 | 0.9285 |
| 0.0297 | 75.79 | 1440 | 0.1471 | 0.4590 | 0.9180 | 0.9180 | nan | 0.9180 | 0.0 | 0.9180 |
| 0.0237 | 76.84 | 1460 | 0.1603 | 0.4604 | 0.9208 | 0.9208 | nan | 0.9208 | 0.0 | 0.9208 |
| 0.0601 | 77.89 | 1480 | 0.1526 | 0.4581 | 0.9161 | 0.9161 | nan | 0.9161 | 0.0 | 0.9161 |
| 0.0299 | 78.95 | 1500 | 0.1625 | 0.4579 | 0.9159 | 0.9159 | nan | 0.9159 | 0.0 | 0.9159 |
| 0.0316 | 80.0 | 1520 | 0.1702 | 0.4593 | 0.9185 | 0.9185 | nan | 0.9185 | 0.0 | 0.9185 |
| 0.0274 | 81.05 | 1540 | 0.1741 | 0.4607 | 0.9214 | 0.9214 | nan | 0.9214 | 0.0 | 0.9214 |
| 0.0274 | 82.11 | 1560 | 0.1609 | 0.4594 | 0.9188 | 0.9188 | nan | 0.9188 | 0.0 | 0.9188 |
| 0.0345 | 83.16 | 1580 | 0.1652 | 0.4581 | 0.9163 | 0.9163 | nan | 0.9163 | 0.0 | 0.9163 |
| 0.018 | 84.21 | 1600 | 0.1645 | 0.4588 | 0.9176 | 0.9176 | nan | 0.9176 | 0.0 | 0.9176 |
| 0.0352 | 85.26 | 1620 | 0.1579 | 0.4588 | 0.9176 | 0.9176 | nan | 0.9176 | 0.0 | 0.9176 |
| 0.0202 | 86.32 | 1640 | 0.1741 | 0.4620 | 0.9239 | 0.9239 | nan | 0.9239 | 0.0 | 0.9239 |
| 0.0315 | 87.37 | 1660 | 0.1587 | 0.4543 | 0.9086 | 0.9086 | nan | 0.9086 | 0.0 | 0.9086 |
| 0.0208 | 88.42 | 1680 | 0.1610 | 0.4579 | 0.9158 | 0.9158 | nan | 0.9158 | 0.0 | 0.9158 |
| 0.0174 | 89.47 | 1700 | 0.1685 | 0.4596 | 0.9193 | 0.9193 | nan | 0.9193 | 0.0 | 0.9193 |
| 0.0278 | 90.53 | 1720 | 0.1698 | 0.4586 | 0.9173 | 0.9173 | nan | 0.9173 | 0.0 | 0.9173 |
| 0.0259 | 91.58 | 1740 | 0.1674 | 0.4593 | 0.9186 | 0.9186 | nan | 0.9186 | 0.0 | 0.9186 |
| 0.0642 | 92.63 | 1760 | 0.1586 | 0.4572 | 0.9144 | 0.9144 | nan | 0.9144 | 0.0 | 0.9144 |
| 0.0543 | 93.68 | 1780 | 0.1636 | 0.4591 | 0.9182 | 0.9182 | nan | 0.9182 | 0.0 | 0.9182 |
| 0.0264 | 94.74 | 1800 | 0.1572 | 0.4586 | 0.9172 | 0.9172 | nan | 0.9172 | 0.0 | 0.9172 |
| 0.0239 | 95.79 | 1820 | 0.1687 | 0.4596 | 0.9191 | 0.9191 | nan | 0.9191 | 0.0 | 0.9191 |
| 0.0238 | 96.84 | 1840 | 0.1595 | 0.4569 | 0.9137 | 0.9137 | nan | 0.9137 | 0.0 | 0.9137 |
| 0.0181 | 97.89 | 1860 | 0.1552 | 0.4552 | 0.9103 | 0.9103 | nan | 0.9103 | 0.0 | 0.9103 |
| 0.0354 | 98.95 | 1880 | 0.1645 | 0.4573 | 0.9146 | 0.9146 | nan | 0.9146 | 0.0 | 0.9146 |
| 0.0897 | 100.0 | 1900 | 0.1491 | 0.4520 | 0.9041 | 0.9041 | nan | 0.9041 | 0.0 | 0.9041 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"branch"
] |
JCAI2000/segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-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-b3-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the JCAI2000/100By100BranchPNG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1497
- Mean Iou: 0.8933
- Mean Accuracy: 0.9531
- Overall Accuracy: 0.9662
- Accuracy Background: 0.9732
- Accuracy Branch: 0.9330
- Iou Background: 0.9597
- Iou Branch: 0.8270
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Branch | Iou Background | Iou Branch |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.2055 | 1.05 | 20 | 0.2925 | 0.8151 | 0.9469 | 0.9320 | 0.9242 | 0.9695 | 0.9183 | 0.7118 |
| 0.1549 | 2.11 | 40 | 0.1328 | 0.8802 | 0.9311 | 0.9628 | 0.9796 | 0.8825 | 0.9561 | 0.8043 |
| 0.0735 | 3.16 | 60 | 0.1178 | 0.8804 | 0.9512 | 0.9613 | 0.9666 | 0.9357 | 0.9538 | 0.8070 |
| 0.0636 | 4.21 | 80 | 0.0844 | 0.8966 | 0.9368 | 0.9686 | 0.9854 | 0.8881 | 0.9629 | 0.8303 |
| 0.0546 | 5.26 | 100 | 0.1099 | 0.8969 | 0.9526 | 0.9676 | 0.9756 | 0.9297 | 0.9614 | 0.8325 |
| 0.0567 | 6.32 | 120 | 0.1012 | 0.8996 | 0.9500 | 0.9688 | 0.9788 | 0.9213 | 0.9629 | 0.8364 |
| 0.0515 | 7.37 | 140 | 0.1137 | 0.8935 | 0.9462 | 0.9668 | 0.9777 | 0.9147 | 0.9605 | 0.8265 |
| 0.052 | 8.42 | 160 | 0.0987 | 0.8914 | 0.9317 | 0.9670 | 0.9858 | 0.8776 | 0.9611 | 0.8217 |
| 0.0358 | 9.47 | 180 | 0.1167 | 0.8978 | 0.9581 | 0.9676 | 0.9726 | 0.9435 | 0.9613 | 0.8344 |
| 0.0254 | 10.53 | 200 | 0.0767 | 0.9111 | 0.9519 | 0.9729 | 0.9840 | 0.9197 | 0.9678 | 0.8545 |
| 0.0483 | 11.58 | 220 | 0.0953 | 0.9037 | 0.9524 | 0.9701 | 0.9795 | 0.9253 | 0.9645 | 0.8429 |
| 0.0285 | 12.63 | 240 | 0.0904 | 0.9026 | 0.9490 | 0.9700 | 0.9811 | 0.9169 | 0.9643 | 0.8409 |
| 0.0389 | 13.68 | 260 | 0.0902 | 0.9025 | 0.9472 | 0.9701 | 0.9821 | 0.9123 | 0.9644 | 0.8406 |
| 0.0473 | 14.74 | 280 | 0.0852 | 0.9084 | 0.9522 | 0.9719 | 0.9823 | 0.9220 | 0.9665 | 0.8502 |
| 0.0266 | 15.79 | 300 | 0.0983 | 0.8985 | 0.9409 | 0.9690 | 0.9839 | 0.8979 | 0.9633 | 0.8337 |
| 0.0233 | 16.84 | 320 | 0.0965 | 0.9052 | 0.9601 | 0.9702 | 0.9756 | 0.9447 | 0.9644 | 0.8460 |
| 0.0257 | 17.89 | 340 | 0.0941 | 0.9039 | 0.9550 | 0.9701 | 0.9781 | 0.9319 | 0.9643 | 0.8434 |
| 0.0352 | 18.95 | 360 | 0.0855 | 0.9043 | 0.9483 | 0.9706 | 0.9824 | 0.9142 | 0.9651 | 0.8435 |
| 0.1941 | 20.0 | 380 | 0.0946 | 0.9045 | 0.9509 | 0.9706 | 0.9809 | 0.9210 | 0.9650 | 0.8441 |
| 0.0325 | 21.05 | 400 | 0.0972 | 0.8973 | 0.9449 | 0.9683 | 0.9807 | 0.9092 | 0.9624 | 0.8323 |
| 0.0159 | 22.11 | 420 | 0.0828 | 0.9081 | 0.9528 | 0.9717 | 0.9817 | 0.9239 | 0.9664 | 0.8498 |
| 0.0175 | 23.16 | 440 | 0.1061 | 0.8995 | 0.9491 | 0.9688 | 0.9793 | 0.9188 | 0.9629 | 0.8360 |
| 0.0281 | 24.21 | 460 | 0.1090 | 0.8969 | 0.9516 | 0.9677 | 0.9761 | 0.9271 | 0.9615 | 0.8323 |
| 0.0177 | 25.26 | 480 | 0.1122 | 0.8983 | 0.9547 | 0.9680 | 0.9750 | 0.9343 | 0.9618 | 0.8347 |
| 0.0228 | 26.32 | 500 | 0.1088 | 0.8957 | 0.9546 | 0.9670 | 0.9736 | 0.9357 | 0.9606 | 0.8307 |
| 0.0348 | 27.37 | 520 | 0.0933 | 0.9059 | 0.9524 | 0.9710 | 0.9808 | 0.9241 | 0.9654 | 0.8464 |
| 0.0177 | 28.42 | 540 | 0.1053 | 0.9025 | 0.9527 | 0.9697 | 0.9787 | 0.9268 | 0.9639 | 0.8411 |
| 0.0182 | 29.47 | 560 | 0.1039 | 0.8992 | 0.9473 | 0.9688 | 0.9802 | 0.9143 | 0.9630 | 0.8355 |
| 0.0171 | 30.53 | 580 | 0.1117 | 0.8991 | 0.9555 | 0.9682 | 0.9750 | 0.9360 | 0.9621 | 0.8361 |
| 0.0275 | 31.58 | 600 | 0.1142 | 0.8935 | 0.9497 | 0.9665 | 0.9754 | 0.9241 | 0.9601 | 0.8268 |
| 0.0186 | 32.63 | 620 | 0.1065 | 0.9024 | 0.9524 | 0.9697 | 0.9788 | 0.9261 | 0.9639 | 0.8408 |
| 0.0173 | 33.68 | 640 | 0.1081 | 0.8986 | 0.9529 | 0.9682 | 0.9764 | 0.9294 | 0.9621 | 0.8351 |
| 0.015 | 34.74 | 660 | 0.1243 | 0.8935 | 0.9530 | 0.9663 | 0.9733 | 0.9327 | 0.9598 | 0.8272 |
| 0.0183 | 35.79 | 680 | 0.1120 | 0.9005 | 0.9500 | 0.9691 | 0.9792 | 0.9209 | 0.9633 | 0.8377 |
| 0.0248 | 36.84 | 700 | 0.1185 | 0.8962 | 0.9517 | 0.9674 | 0.9757 | 0.9277 | 0.9611 | 0.8312 |
| 0.0104 | 37.89 | 720 | 0.1136 | 0.8975 | 0.9506 | 0.9680 | 0.9771 | 0.9241 | 0.9619 | 0.8332 |
| 0.0481 | 38.95 | 740 | 0.1127 | 0.9010 | 0.9528 | 0.9691 | 0.9778 | 0.9277 | 0.9632 | 0.8388 |
| 0.0153 | 40.0 | 760 | 0.1101 | 0.9019 | 0.9537 | 0.9694 | 0.9777 | 0.9297 | 0.9635 | 0.8402 |
| 0.0143 | 41.05 | 780 | 0.1105 | 0.9032 | 0.9558 | 0.9698 | 0.9771 | 0.9345 | 0.9639 | 0.8425 |
| 0.0104 | 42.11 | 800 | 0.1122 | 0.8986 | 0.9428 | 0.9689 | 0.9827 | 0.9028 | 0.9631 | 0.8340 |
| 0.0172 | 43.16 | 820 | 0.1097 | 0.9041 | 0.9540 | 0.9702 | 0.9788 | 0.9291 | 0.9645 | 0.8437 |
| 0.0371 | 44.21 | 840 | 0.1064 | 0.9011 | 0.9503 | 0.9693 | 0.9794 | 0.9212 | 0.9635 | 0.8387 |
| 0.0221 | 45.26 | 860 | 0.1150 | 0.9004 | 0.9515 | 0.9690 | 0.9783 | 0.9247 | 0.9631 | 0.8377 |
| 0.0186 | 46.32 | 880 | 0.1228 | 0.8958 | 0.9518 | 0.9672 | 0.9754 | 0.9282 | 0.9610 | 0.8306 |
| 0.0119 | 47.37 | 900 | 0.1205 | 0.8980 | 0.9525 | 0.9680 | 0.9762 | 0.9288 | 0.9619 | 0.8340 |
| 0.0113 | 48.42 | 920 | 0.1133 | 0.8998 | 0.9502 | 0.9688 | 0.9787 | 0.9216 | 0.9629 | 0.8366 |
| 0.0121 | 49.47 | 940 | 0.1145 | 0.8993 | 0.9490 | 0.9688 | 0.9792 | 0.9188 | 0.9629 | 0.8358 |
| 0.0263 | 50.53 | 960 | 0.1168 | 0.8977 | 0.9542 | 0.9678 | 0.9750 | 0.9334 | 0.9616 | 0.8338 |
| 0.0093 | 51.58 | 980 | 0.1213 | 0.8940 | 0.9534 | 0.9664 | 0.9733 | 0.9334 | 0.9600 | 0.8280 |
| 0.0193 | 52.63 | 1000 | 0.1241 | 0.8971 | 0.9507 | 0.9678 | 0.9769 | 0.9246 | 0.9617 | 0.8326 |
| 0.0139 | 53.68 | 1020 | 0.1263 | 0.8962 | 0.9546 | 0.9672 | 0.9739 | 0.9353 | 0.9609 | 0.8316 |
| 0.012 | 54.74 | 1040 | 0.1252 | 0.8952 | 0.9504 | 0.9671 | 0.9760 | 0.9247 | 0.9609 | 0.8296 |
| 0.008 | 55.79 | 1060 | 0.1219 | 0.8986 | 0.9516 | 0.9683 | 0.9772 | 0.9260 | 0.9623 | 0.8349 |
| 0.0092 | 56.84 | 1080 | 0.1290 | 0.8995 | 0.9552 | 0.9684 | 0.9754 | 0.9349 | 0.9623 | 0.8366 |
| 0.015 | 57.89 | 1100 | 0.1243 | 0.8989 | 0.9545 | 0.9682 | 0.9755 | 0.9335 | 0.9621 | 0.8358 |
| 0.0126 | 58.95 | 1120 | 0.1214 | 0.8977 | 0.9541 | 0.9678 | 0.9751 | 0.9331 | 0.9616 | 0.8337 |
| 0.0212 | 60.0 | 1140 | 0.1298 | 0.8953 | 0.9542 | 0.9669 | 0.9736 | 0.9347 | 0.9605 | 0.8301 |
| 0.0192 | 61.05 | 1160 | 0.1341 | 0.8930 | 0.9518 | 0.9661 | 0.9737 | 0.9299 | 0.9597 | 0.8262 |
| 0.0136 | 62.11 | 1180 | 0.1327 | 0.8970 | 0.9528 | 0.9676 | 0.9754 | 0.9302 | 0.9614 | 0.8325 |
| 0.0131 | 63.16 | 1200 | 0.1233 | 0.8997 | 0.9549 | 0.9685 | 0.9757 | 0.9340 | 0.9624 | 0.8369 |
| 0.0135 | 64.21 | 1220 | 0.1301 | 0.8957 | 0.9542 | 0.9670 | 0.9738 | 0.9345 | 0.9607 | 0.8307 |
| 0.0228 | 65.26 | 1240 | 0.1274 | 0.8979 | 0.9524 | 0.9680 | 0.9762 | 0.9285 | 0.9618 | 0.8339 |
| 0.0138 | 66.32 | 1260 | 0.1336 | 0.8965 | 0.9520 | 0.9675 | 0.9757 | 0.9283 | 0.9613 | 0.8318 |
| 0.0127 | 67.37 | 1280 | 0.1278 | 0.8980 | 0.9519 | 0.9681 | 0.9767 | 0.9271 | 0.9620 | 0.8341 |
| 0.0107 | 68.42 | 1300 | 0.1293 | 0.8970 | 0.9530 | 0.9676 | 0.9753 | 0.9308 | 0.9614 | 0.8327 |
| 0.0278 | 69.47 | 1320 | 0.1413 | 0.8926 | 0.9534 | 0.9659 | 0.9725 | 0.9343 | 0.9593 | 0.8258 |
| 0.0159 | 70.53 | 1340 | 0.1360 | 0.8953 | 0.9522 | 0.9670 | 0.9748 | 0.9296 | 0.9607 | 0.8298 |
| 0.0105 | 71.58 | 1360 | 0.1319 | 0.8972 | 0.9537 | 0.9676 | 0.9750 | 0.9324 | 0.9614 | 0.8330 |
| 0.0168 | 72.63 | 1380 | 0.1343 | 0.8942 | 0.9533 | 0.9665 | 0.9735 | 0.9331 | 0.9601 | 0.8283 |
| 0.0156 | 73.68 | 1400 | 0.1357 | 0.8950 | 0.9516 | 0.9669 | 0.9751 | 0.9281 | 0.9606 | 0.8294 |
| 0.0109 | 74.74 | 1420 | 0.1446 | 0.8905 | 0.9524 | 0.9652 | 0.9719 | 0.9328 | 0.9585 | 0.8226 |
| 0.0168 | 75.79 | 1440 | 0.1339 | 0.8958 | 0.9533 | 0.9671 | 0.9745 | 0.9320 | 0.9608 | 0.8308 |
| 0.0252 | 76.84 | 1460 | 0.1355 | 0.8935 | 0.9532 | 0.9662 | 0.9731 | 0.9333 | 0.9597 | 0.8272 |
| 0.0109 | 77.89 | 1480 | 0.1388 | 0.8932 | 0.9533 | 0.9661 | 0.9729 | 0.9338 | 0.9596 | 0.8267 |
| 0.0109 | 78.95 | 1500 | 0.1404 | 0.8924 | 0.9519 | 0.9659 | 0.9734 | 0.9305 | 0.9594 | 0.8255 |
| 0.0112 | 80.0 | 1520 | 0.1424 | 0.8921 | 0.9535 | 0.9657 | 0.9722 | 0.9349 | 0.9591 | 0.8251 |
| 0.0094 | 81.05 | 1540 | 0.1451 | 0.8924 | 0.9524 | 0.9659 | 0.9730 | 0.9317 | 0.9593 | 0.8254 |
| 0.007 | 82.11 | 1560 | 0.1457 | 0.8931 | 0.9527 | 0.9661 | 0.9732 | 0.9322 | 0.9596 | 0.8266 |
| 0.0119 | 83.16 | 1580 | 0.1424 | 0.8927 | 0.9520 | 0.9661 | 0.9735 | 0.9306 | 0.9595 | 0.8259 |
| 0.0153 | 84.21 | 1600 | 0.1535 | 0.8909 | 0.9530 | 0.9653 | 0.9718 | 0.9342 | 0.9586 | 0.8233 |
| 0.0104 | 85.26 | 1620 | 0.1452 | 0.8921 | 0.9529 | 0.9658 | 0.9725 | 0.9333 | 0.9592 | 0.8251 |
| 0.0101 | 86.32 | 1640 | 0.1503 | 0.8910 | 0.9536 | 0.9653 | 0.9714 | 0.9358 | 0.9586 | 0.8235 |
| 0.009 | 87.37 | 1660 | 0.1508 | 0.8925 | 0.9532 | 0.9659 | 0.9725 | 0.9339 | 0.9593 | 0.8257 |
| 0.0073 | 88.42 | 1680 | 0.1419 | 0.8949 | 0.9528 | 0.9668 | 0.9742 | 0.9315 | 0.9604 | 0.8293 |
| 0.0137 | 89.47 | 1700 | 0.1437 | 0.8942 | 0.9526 | 0.9666 | 0.9739 | 0.9313 | 0.9601 | 0.8282 |
| 0.0061 | 90.53 | 1720 | 0.1474 | 0.8928 | 0.9523 | 0.9660 | 0.9733 | 0.9313 | 0.9595 | 0.8260 |
| 0.0132 | 91.58 | 1740 | 0.1408 | 0.8935 | 0.9522 | 0.9663 | 0.9738 | 0.9306 | 0.9598 | 0.8271 |
| 0.0089 | 92.63 | 1760 | 0.1468 | 0.8933 | 0.9527 | 0.9662 | 0.9734 | 0.9320 | 0.9597 | 0.8268 |
| 0.0141 | 93.68 | 1780 | 0.1458 | 0.8930 | 0.9529 | 0.9661 | 0.9731 | 0.9328 | 0.9596 | 0.8265 |
| 0.0132 | 94.74 | 1800 | 0.1442 | 0.8934 | 0.9528 | 0.9662 | 0.9733 | 0.9324 | 0.9597 | 0.8270 |
| 0.0109 | 95.79 | 1820 | 0.1445 | 0.8928 | 0.9529 | 0.9660 | 0.9730 | 0.9327 | 0.9595 | 0.8262 |
| 0.0248 | 96.84 | 1840 | 0.1391 | 0.8938 | 0.9529 | 0.9664 | 0.9736 | 0.9322 | 0.9599 | 0.8276 |
| 0.0074 | 97.89 | 1860 | 0.1424 | 0.8938 | 0.9531 | 0.9664 | 0.9735 | 0.9327 | 0.9599 | 0.8277 |
| 0.0072 | 98.95 | 1880 | 0.1465 | 0.8931 | 0.9534 | 0.9661 | 0.9728 | 0.9339 | 0.9596 | 0.8266 |
| 0.0183 | 100.0 | 1900 | 0.1497 | 0.8933 | 0.9531 | 0.9662 | 0.9732 | 0.9330 | 0.9597 | 0.8270 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"branch"
] |
Pavarissy/segformer-b0-finetuned-v0 |
<!-- 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-v0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the tontokoton/artery-ultrasound-siit 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: 1e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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
| [
"artery",
"vein",
"nerve",
"muscle1",
"muscle2",
"muscle3",
"muscle4",
"unknown"
] |
Davidadel66/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: 0.9530
- Mean Iou: 0.1712
- Mean Accuracy: 0.2121
- Overall Accuracy: 0.7831
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8549
- Accuracy Flat-sidewalk: 0.9623
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5957
- Accuracy Flat-parkingdriveway: 0.0956
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0075
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9053
- 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: 0.9017
- 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: 0.9152
- Accuracy Nature-terrain: 0.8300
- Accuracy Sky: 0.9299
- 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: 0.6261
- Iou Flat-sidewalk: 0.8045
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5253
- Iou Flat-parkingdriveway: 0.0861
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.0075
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.6945
- 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: 0.5817
- 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: 0.7847
- Iou Nature-terrain: 0.6656
- Iou Sky: 0.8751
- 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: 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 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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| 2.7688 | 0.1 | 20 | 3.0043 | 0.0900 | 0.1326 | 0.6099 | nan | 0.2649 | 0.9602 | 0.0002 | 0.0001 | 0.0040 | nan | 0.0 | 0.0055 | 0.0 | 0.8361 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7673 | 0.0054 | 0.0021 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7635 | 0.0259 | 0.7359 | 0.0 | 0.0036 | 0.0 | 0.0 | nan | 0.2274 | 0.6135 | 0.0002 | 0.0001 | 0.0039 | 0.0 | 0.0 | 0.0055 | 0.0 | 0.5402 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3692 | 0.0018 | 0.0021 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6087 | 0.0247 | 0.6591 | 0.0 | 0.0020 | 0.0 | 0.0 |
| 2.2362 | 0.2 | 40 | 2.2122 | 0.1016 | 0.1476 | 0.6596 | nan | 0.5986 | 0.9497 | 0.0000 | 0.0002 | 0.0010 | nan | 0.0 | 0.0 | 0.0 | 0.8828 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7864 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8463 | 0.0423 | 0.7626 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4075 | 0.6846 | 0.0000 | 0.0002 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5190 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4231 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6493 | 0.0415 | 0.7290 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.9229 | 0.3 | 60 | 1.9323 | 0.1109 | 0.1552 | 0.6790 | nan | 0.7454 | 0.9277 | 0.0002 | 0.0006 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8759 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8641 | 0.0240 | 0.8450 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4388 | 0.7425 | 0.0002 | 0.0006 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.5825 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4447 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6474 | 0.0237 | 0.7787 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8943 | 0.4 | 80 | 1.6697 | 0.1102 | 0.1565 | 0.6866 | nan | 0.6675 | 0.9567 | 0.0 | 0.0005 | 0.0029 | nan | 0.0 | 0.0 | 0.0 | 0.8692 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8957 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8753 | 0.0270 | 0.8696 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4490 | 0.7236 | 0.0 | 0.0005 | 0.0029 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4761 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6626 | 0.0268 | 0.8032 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.9991 | 0.5 | 100 | 1.6000 | 0.1166 | 0.1629 | 0.7001 | nan | 0.8482 | 0.9229 | 0.0 | 0.0024 | 0.0013 | nan | 0.0 | 0.0000 | 0.0 | 0.9172 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8210 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9390 | 0.0378 | 0.8872 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4629 | 0.7702 | 0.0 | 0.0024 | 0.0013 | nan | 0.0 | 0.0000 | 0.0 | 0.5626 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5357 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6573 | 0.0375 | 0.8165 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5725 | 0.6 | 120 | 1.5214 | 0.1250 | 0.1686 | 0.7146 | nan | 0.8401 | 0.9399 | 0.0 | 0.0415 | 0.0017 | nan | 0.0000 | 0.0 | 0.0 | 0.8705 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8933 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9241 | 0.1360 | 0.9159 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4995 | 0.7742 | 0.0 | 0.0413 | 0.0017 | nan | 0.0000 | 0.0 | 0.0 | 0.6375 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6744 | 0.1315 | 0.8355 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4764 | 0.7 | 140 | 1.4602 | 0.1327 | 0.1750 | 0.7245 | nan | 0.8603 | 0.9330 | 0.0 | 0.0371 | 0.0026 | nan | 0.0000 | 0.0 | 0.0 | 0.8552 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9104 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.3764 | 0.8728 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4935 | 0.7879 | 0.0 | 0.0369 | 0.0026 | nan | 0.0000 | 0.0 | 0.0 | 0.6449 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7071 | 0.3529 | 0.8309 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.9994 | 0.8 | 160 | 1.3414 | 0.1439 | 0.1867 | 0.7418 | nan | 0.8373 | 0.9468 | 0.0 | 0.0849 | 0.0051 | nan | 0.0 | 0.0 | 0.0 | 0.8907 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8805 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9226 | 0.6707 | 0.9236 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5211 | 0.7762 | 0.0 | 0.0842 | 0.0051 | nan | 0.0 | 0.0 | 0.0 | 0.6352 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5546 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7427 | 0.5786 | 0.8500 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2919 | 0.9 | 180 | 1.3036 | 0.1401 | 0.1822 | 0.7365 | nan | 0.8860 | 0.9322 | 0.0 | 0.0832 | 0.0067 | nan | 0.0000 | 0.0 | 0.0 | 0.8551 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9462 | 0.5071 | 0.9144 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4969 | 0.8018 | 0.0 | 0.0814 | 0.0067 | nan | 0.0000 | 0.0 | 0.0 | 0.6676 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7185 | 0.4640 | 0.8420 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2882 | 1.0 | 200 | 1.2697 | 0.1466 | 0.1914 | 0.7471 | nan | 0.8344 | 0.9563 | 0.0 | 0.1067 | 0.0068 | nan | 0.0 | 0.0 | 0.0 | 0.9191 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8746 | 0.8138 | 0.9034 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5319 | 0.7814 | 0.0 | 0.1053 | 0.0068 | nan | 0.0 | 0.0 | 0.0 | 0.6284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5583 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7614 | 0.6179 | 0.8480 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1952 | 1.1 | 220 | 1.2088 | 0.1497 | 0.1917 | 0.7499 | nan | 0.8263 | 0.9565 | 0.0 | 0.1384 | 0.0093 | nan | 0.0 | 0.0 | 0.0 | 0.8891 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8945 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9050 | 0.7813 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5518 | 0.7648 | 0.0 | 0.1374 | 0.0092 | nan | 0.0 | 0.0 | 0.0 | 0.6640 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5651 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7604 | 0.6254 | 0.8607 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1901 | 1.2 | 240 | 1.1659 | 0.1508 | 0.1941 | 0.7546 | nan | 0.8903 | 0.9465 | 0.0 | 0.1520 | 0.0136 | nan | 0.0000 | 0.0 | 0.0 | 0.9018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9068 | 0.7657 | 0.9340 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5301 | 0.8032 | 0.0 | 0.1479 | 0.0135 | nan | 0.0000 | 0.0 | 0.0 | 0.6624 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7704 | 0.6415 | 0.8526 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5003 | 1.3 | 260 | 1.1250 | 0.1582 | 0.1977 | 0.7625 | nan | 0.8352 | 0.9605 | 0.0 | 0.3475 | 0.0185 | nan | 0.0000 | 0.0006 | 0.0 | 0.8674 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8876 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9278 | 0.7537 | 0.9255 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5715 | 0.7817 | 0.0 | 0.3299 | 0.0182 | nan | 0.0000 | 0.0006 | 0.0 | 0.6920 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5613 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7639 | 0.6390 | 0.8616 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0678 | 1.4 | 280 | 1.1183 | 0.1595 | 0.2042 | 0.7680 | nan | 0.8673 | 0.9506 | 0.0 | 0.4007 | 0.0276 | nan | 0.0000 | 0.0001 | 0.0 | 0.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8812 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9051 | 0.8502 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5684 | 0.8048 | 0.0 | 0.3824 | 0.0270 | nan | 0.0000 | 0.0001 | 0.0 | 0.6348 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5821 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7698 | 0.6311 | 0.8630 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0902 | 1.5 | 300 | 1.0917 | 0.1642 | 0.2070 | 0.7718 | nan | 0.8710 | 0.9473 | 0.0 | 0.5234 | 0.0382 | nan | 0.0001 | 0.0 | 0.0 | 0.8855 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8980 | 0.8378 | 0.9211 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5662 | 0.8125 | 0.0 | 0.4669 | 0.0370 | nan | 0.0001 | 0.0 | 0.0 | 0.6874 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5623 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7753 | 0.6467 | 0.8642 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0474 | 1.6 | 320 | 1.0803 | 0.1645 | 0.2080 | 0.7736 | nan | 0.8752 | 0.9445 | 0.0 | 0.5435 | 0.0427 | nan | 0.0001 | 0.0001 | 0.0 | 0.9137 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9148 | 0.7931 | 0.9432 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5693 | 0.8150 | 0.0 | 0.4817 | 0.0411 | nan | 0.0001 | 0.0001 | 0.0 | 0.6565 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5710 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7761 | 0.6556 | 0.8619 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.814 | 1.7 | 340 | 1.0579 | 0.1655 | 0.2100 | 0.7740 | nan | 0.8714 | 0.9443 | 0.0 | 0.5910 | 0.0501 | nan | 0.0002 | 0.0 | 0.0 | 0.9037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8918 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8947 | 0.8552 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5724 | 0.8148 | 0.0 | 0.4928 | 0.0478 | nan | 0.0002 | 0.0 | 0.0 | 0.6809 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7711 | 0.6423 | 0.8650 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3638 | 1.8 | 360 | 1.0449 | 0.1641 | 0.2055 | 0.7708 | nan | 0.7817 | 0.9653 | 0.0 | 0.5410 | 0.0607 | nan | 0.0010 | 0.0000 | 0.0 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9180 | 0.7825 | 0.9291 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5960 | 0.7909 | 0.0 | 0.4226 | 0.0572 | nan | 0.0010 | 0.0000 | 0.0 | 0.6764 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5664 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7769 | 0.6636 | 0.8631 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2779 | 1.9 | 380 | 1.0227 | 0.1667 | 0.2074 | 0.7745 | nan | 0.8069 | 0.9631 | 0.0 | 0.5564 | 0.0757 | nan | 0.0019 | 0.0000 | 0.0 | 0.8764 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8981 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9214 | 0.8241 | 0.9197 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6092 | 0.7941 | 0.0 | 0.4529 | 0.0701 | nan | 0.0019 | 0.0000 | 0.0 | 0.6993 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5686 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7724 | 0.6635 | 0.8689 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0811 | 2.0 | 400 | 0.9893 | 0.1672 | 0.2089 | 0.7794 | nan | 0.8730 | 0.9570 | 0.0 | 0.5573 | 0.0561 | nan | 0.0013 | 0.0 | 0.0 | 0.9041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8843 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9289 | 0.7991 | 0.9315 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6005 | 0.8145 | 0.0 | 0.4776 | 0.0532 | nan | 0.0013 | 0.0 | 0.0 | 0.6819 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5802 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7736 | 0.6686 | 0.8671 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.005 | 2.1 | 420 | 0.9977 | 0.1680 | 0.2082 | 0.7783 | nan | 0.8414 | 0.9628 | 0.0 | 0.5530 | 0.0624 | nan | 0.0015 | 0.0 | 0.0 | 0.8958 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8909 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.8101 | 0.9248 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6081 | 0.8005 | 0.0 | 0.4885 | 0.0582 | nan | 0.0014 | 0.0 | 0.0 | 0.6984 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5775 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7744 | 0.6669 | 0.8710 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1406 | 2.2 | 440 | 0.9950 | 0.1688 | 0.2118 | 0.7810 | nan | 0.8863 | 0.9485 | 0.0 | 0.5892 | 0.0719 | nan | 0.0020 | 0.0 | 0.0 | 0.9067 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8789 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9261 | 0.8432 | 0.9362 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5949 | 0.8221 | 0.0 | 0.5206 | 0.0665 | nan | 0.0020 | 0.0 | 0.0 | 0.6859 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5834 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7685 | 0.6580 | 0.8695 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0543 | 2.3 | 460 | 0.9919 | 0.1675 | 0.2111 | 0.7794 | nan | 0.8561 | 0.9568 | 0.0 | 0.5935 | 0.0637 | nan | 0.0044 | 0.0 | 0.0 | 0.9143 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8675 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9190 | 0.8489 | 0.9415 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6062 | 0.8089 | 0.0 | 0.5003 | 0.0600 | nan | 0.0044 | 0.0 | 0.0 | 0.6668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5922 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7687 | 0.6544 | 0.8671 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0831 | 2.4 | 480 | 0.9767 | 0.1686 | 0.2107 | 0.7797 | nan | 0.8387 | 0.9593 | 0.0 | 0.6142 | 0.0727 | nan | 0.0043 | 0.0 | 0.0 | 0.9063 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8895 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9216 | 0.8086 | 0.9375 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6101 | 0.8042 | 0.0 | 0.4901 | 0.0678 | nan | 0.0042 | 0.0 | 0.0 | 0.6846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5854 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7796 | 0.6671 | 0.8714 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0854 | 2.5 | 500 | 0.9623 | 0.1708 | 0.2138 | 0.7830 | nan | 0.8582 | 0.9533 | 0.0 | 0.6350 | 0.1068 | nan | 0.0074 | 0.0 | 0.0 | 0.9125 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8950 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9187 | 0.8375 | 0.9325 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6113 | 0.8175 | 0.0 | 0.5387 | 0.0943 | nan | 0.0073 | 0.0 | 0.0 | 0.6782 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7791 | 0.6587 | 0.8701 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9507 | 2.6 | 520 | 0.9477 | 0.1706 | 0.2132 | 0.7834 | nan | 0.8834 | 0.9526 | 0.0 | 0.5999 | 0.0910 | nan | 0.0063 | 0.0 | 0.0 | 0.9084 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9170 | 0.8497 | 0.9341 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6044 | 0.8194 | 0.0 | 0.5368 | 0.0825 | nan | 0.0062 | 0.0 | 0.0 | 0.6840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5855 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7806 | 0.6592 | 0.8710 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2475 | 2.7 | 540 | 0.9406 | 0.1710 | 0.2129 | 0.7839 | nan | 0.8619 | 0.9584 | 0.0 | 0.6319 | 0.0836 | nan | 0.0067 | 0.0000 | 0.0 | 0.9037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8810 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9258 | 0.8315 | 0.9419 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6158 | 0.8110 | 0.0 | 0.5375 | 0.0770 | nan | 0.0066 | 0.0000 | 0.0 | 0.6905 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5898 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7781 | 0.6668 | 0.8708 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1332 | 2.8 | 560 | 0.9532 | 0.1721 | 0.2141 | 0.7852 | nan | 0.8774 | 0.9543 | 0.0 | 0.6470 | 0.0940 | nan | 0.0065 | 0.0 | 0.0 | 0.9043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8968 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9194 | 0.8224 | 0.9417 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6092 | 0.8193 | 0.0 | 0.5542 | 0.0848 | nan | 0.0064 | 0.0 | 0.0 | 0.6936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5837 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7845 | 0.6707 | 0.8731 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9905 | 2.9 | 580 | 0.9639 | 0.1719 | 0.2137 | 0.7845 | nan | 0.8574 | 0.9590 | 0.0 | 0.6292 | 0.1033 | nan | 0.0097 | 0.0 | 0.0 | 0.9041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8990 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9170 | 0.8356 | 0.9374 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6231 | 0.8120 | 0.0 | 0.5398 | 0.0920 | nan | 0.0096 | 0.0 | 0.0 | 0.6950 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5820 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7835 | 0.6635 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1681 | 3.0 | 600 | 0.9530 | 0.1712 | 0.2121 | 0.7831 | nan | 0.8549 | 0.9623 | 0.0 | 0.5957 | 0.0956 | nan | 0.0075 | 0.0 | 0.0 | 0.9053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9152 | 0.8300 | 0.9299 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6261 | 0.8045 | 0.0 | 0.5253 | 0.0861 | nan | 0.0075 | 0.0 | 0.0 | 0.6945 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5817 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7847 | 0.6656 | 0.8751 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"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"
] |
dbaek111/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-b5](https://huggingface.co/nvidia/mit-b5) 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 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
| [
"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"
] |
dwang-LI/segformer-b-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-b-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: 0.7733
- Mean Iou: 0.2394
- Mean Accuracy: 0.2885
- Overall Accuracy: 0.8145
- Accuarcy Unlabeled: nan
- Accuarcy Flat-road: 0.9002
- Accuarcy Flat-sidewalk: 0.9256
- Accuarcy Flat-crosswalk: 0.6731
- Accuarcy Flat-cyclinglane: 0.7624
- Accuarcy Flat-parkingdriveway: 0.3720
- Accuarcy Flat-railtrack: nan
- Accuarcy Flat-curb: 0.3753
- Accuarcy Human-person: 0.0482
- Accuarcy Human-rider: 0.0
- Accuarcy Vehicle-car: 0.9125
- Accuarcy Vehicle-truck: 0.0
- Accuarcy Vehicle-bus: 0.0
- Accuarcy Vehicle-tramtrain: nan
- Accuarcy Vehicle-motorcycle: 0.0
- Accuarcy Vehicle-bicycle: 0.0
- Accuarcy Vehicle-caravan: 0.0
- Accuarcy Vehicle-cartrailer: 0.0
- Accuarcy Construction-building: 0.8988
- Accuarcy Construction-door: 0.0
- Accuarcy Construction-wall: 0.3240
- Accuarcy Construction-fenceguardrail: 0.0009
- Accuarcy Construction-bridge: 0.0
- Accuarcy Construction-tunnel: nan
- Accuarcy Construction-stairs: 0.0
- Accuarcy Object-pole: 0.0228
- Accuarcy Object-trafficsign: 0.0
- Accuarcy Object-trafficlight: 0.0
- Accuarcy Nature-vegetation: 0.9283
- Accuarcy Nature-terrain: 0.8528
- Accuarcy Sky: 0.9460
- Accuarcy Void-ground: 0.0
- Accuarcy Void-dynamic: 0.0
- Accuarcy Void-static: 0.0014
- Accuarcy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.7132
- Iou Flat-sidewalk: 0.8399
- Iou Flat-crosswalk: 0.5677
- Iou Flat-cyclinglane: 0.6711
- Iou Flat-parkingdriveway: 0.2585
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.3157
- Iou Human-person: 0.0474
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.7025
- 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: 0.6194
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.2615
- Iou Construction-fenceguardrail: 0.0009
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0227
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.7851
- Iou Nature-terrain: 0.7352
- Iou Sky: 0.8791
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0014
- 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuarcy Unlabeled | Accuarcy Flat-road | Accuarcy Flat-sidewalk | Accuarcy Flat-crosswalk | Accuarcy Flat-cyclinglane | Accuarcy Flat-parkingdriveway | Accuarcy Flat-railtrack | Accuarcy Flat-curb | Accuarcy Human-person | Accuarcy Human-rider | Accuarcy Vehicle-car | Accuarcy Vehicle-truck | Accuarcy Vehicle-bus | Accuarcy Vehicle-tramtrain | Accuarcy Vehicle-motorcycle | Accuarcy Vehicle-bicycle | Accuarcy Vehicle-caravan | Accuarcy Vehicle-cartrailer | Accuarcy Construction-building | Accuarcy Construction-door | Accuarcy Construction-wall | Accuarcy Construction-fenceguardrail | Accuarcy Construction-bridge | Accuarcy Construction-tunnel | Accuarcy Construction-stairs | Accuarcy Object-pole | Accuarcy Object-trafficsign | Accuarcy Object-trafficlight | Accuarcy Nature-vegetation | Accuarcy Nature-terrain | Accuarcy Sky | Accuarcy Void-ground | Accuarcy Void-dynamic | Accuarcy Void-static | Accuarcy 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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| 2.2728 | 0.59 | 20 | 2.3946 | 0.1035 | 0.1549 | 0.6540 | nan | 0.6440 | 0.9384 | 0.0 | 0.0006 | 0.0001 | nan | 0.0001 | 0.0 | 0.0 | 0.9243 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6269 | 0.0 | 0.0000 | 0.0002 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9320 | 0.0116 | 0.7234 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4920 | 0.6851 | 0.0 | 0.0006 | 0.0001 | nan | 0.0001 | 0.0 | 0.0 | 0.3557 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4837 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5828 | 0.0115 | 0.7007 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.9006 | 1.18 | 40 | 1.7230 | 0.1153 | 0.1706 | 0.6814 | nan | 0.8635 | 0.8762 | 0.0 | 0.0003 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8614 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8115 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9315 | 0.0405 | 0.9034 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4876 | 0.7405 | 0.0 | 0.0003 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.5225 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5210 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6069 | 0.0399 | 0.7696 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6721 | 1.76 | 60 | 1.4574 | 0.1289 | 0.1783 | 0.6968 | nan | 0.8799 | 0.8822 | 0.0 | 0.0528 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8812 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8573 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9298 | 0.1473 | 0.8959 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4937 | 0.7555 | 0.0 | 0.0519 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.5454 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5547 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6303 | 0.1427 | 0.8205 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4066 | 2.35 | 80 | 1.3422 | 0.1589 | 0.2055 | 0.7457 | nan | 0.8230 | 0.9475 | 0.0 | 0.3015 | 0.0047 | nan | 0.0000 | 0.0 | 0.0 | 0.8977 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9091 | 0.6841 | 0.9322 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6093 | 0.7599 | 0.0 | 0.2787 | 0.0046 | nan | 0.0000 | 0.0 | 0.0 | 0.5489 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5596 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7275 | 0.6092 | 0.8285 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3429 | 2.94 | 100 | 1.1795 | 0.1653 | 0.2103 | 0.7562 | nan | 0.8569 | 0.9495 | 0.0 | 0.3507 | 0.0066 | nan | 0.0000 | 0.0 | 0.0 | 0.8981 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8869 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9026 | 0.7728 | 0.8950 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6153 | 0.7730 | 0.0 | 0.3326 | 0.0065 | nan | 0.0000 | 0.0 | 0.0 | 0.5899 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5742 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7403 | 0.6481 | 0.8448 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2661 | 3.53 | 120 | 1.1008 | 0.1712 | 0.2174 | 0.7629 | nan | 0.8484 | 0.9495 | 0.0 | 0.4917 | 0.0181 | nan | 0.0001 | 0.0 | 0.0 | 0.8996 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9043 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8869 | 0.8036 | 0.9371 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6100 | 0.7894 | 0.0 | 0.4346 | 0.0175 | nan | 0.0001 | 0.0 | 0.0 | 0.6153 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5608 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7533 | 0.6752 | 0.8508 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2166 | 4.12 | 140 | 1.0514 | 0.1771 | 0.2232 | 0.7695 | nan | 0.8815 | 0.9342 | 0.0 | 0.5539 | 0.0713 | nan | 0.0030 | 0.0 | 0.0 | 0.9014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9029 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9068 | 0.8398 | 0.9225 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6195 | 0.7981 | 0.0 | 0.5017 | 0.0642 | nan | 0.0030 | 0.0 | 0.0 | 0.6222 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5694 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7585 | 0.6979 | 0.8546 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0262 | 4.71 | 160 | 1.0025 | 0.1782 | 0.2236 | 0.7665 | nan | 0.9188 | 0.9111 | 0.0 | 0.5462 | 0.1006 | nan | 0.0031 | 0.0 | 0.0 | 0.8814 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8881 | 0.0 | 0.0027 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9260 | 0.8130 | 0.9404 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5776 | 0.8071 | 0.0 | 0.5005 | 0.0888 | nan | 0.0031 | 0.0 | 0.0 | 0.6651 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5803 | 0.0 | 0.0027 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7415 | 0.7028 | 0.8558 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0928 | 5.29 | 180 | 0.9698 | 0.1852 | 0.2308 | 0.7778 | nan | 0.8513 | 0.9428 | 0.0 | 0.6760 | 0.1497 | nan | 0.0419 | 0.0 | 0.0 | 0.8856 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9132 | 0.0 | 0.0056 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9134 | 0.8535 | 0.9219 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6410 | 0.8062 | 0.0 | 0.5617 | 0.1228 | nan | 0.0405 | 0.0 | 0.0 | 0.6597 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5705 | 0.0 | 0.0056 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7603 | 0.7081 | 0.8642 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8736 | 5.88 | 200 | 0.9250 | 0.1906 | 0.2370 | 0.7850 | nan | 0.9149 | 0.9249 | 0.0001 | 0.7226 | 0.1944 | nan | 0.0715 | 0.0027 | 0.0 | 0.8853 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8917 | 0.0 | 0.0153 | 0.0 | 0.0 | nan | 0.0 | 0.0005 | 0.0 | 0.0 | 0.9353 | 0.8470 | 0.9402 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6511 | 0.8250 | 0.0001 | 0.5978 | 0.1516 | nan | 0.0682 | 0.0027 | 0.0 | 0.6817 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5862 | 0.0 | 0.0152 | 0.0 | 0.0 | nan | 0.0 | 0.0005 | 0.0 | 0.0 | 0.7477 | 0.7159 | 0.8635 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.7832 | 6.47 | 220 | 0.8852 | 0.1961 | 0.2421 | 0.7875 | nan | 0.8962 | 0.9385 | 0.0642 | 0.6975 | 0.2064 | nan | 0.1581 | 0.0003 | 0.0 | 0.8995 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9011 | 0.0 | 0.0392 | 0.0 | 0.0 | nan | 0.0 | 0.0009 | 0.0 | 0.0 | 0.8974 | 0.8728 | 0.9342 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6576 | 0.8222 | 0.0624 | 0.6239 | 0.1577 | nan | 0.1421 | 0.0003 | 0.0 | 0.6802 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5989 | 0.0 | 0.0383 | 0.0 | 0.0 | nan | 0.0 | 0.0009 | 0.0 | 0.0 | 0.7547 | 0.6706 | 0.8700 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.7822 | 7.06 | 240 | 0.8621 | 0.2145 | 0.2598 | 0.7992 | nan | 0.8827 | 0.9398 | 0.4415 | 0.7426 | 0.2656 | nan | 0.2218 | 0.0023 | 0.0 | 0.8967 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9092 | 0.0 | 0.0558 | 0.0000 | 0.0 | nan | 0.0 | 0.0020 | 0.0 | 0.0 | 0.9249 | 0.8259 | 0.9429 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6911 | 0.8250 | 0.3902 | 0.6320 | 0.2017 | nan | 0.1950 | 0.0023 | 0.0 | 0.6915 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5886 | 0.0 | 0.0540 | 0.0000 | 0.0 | nan | 0.0 | 0.0020 | 0.0 | 0.0 | 0.7732 | 0.7329 | 0.8703 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.6742 | 7.65 | 260 | 0.8371 | 0.2193 | 0.2667 | 0.8027 | nan | 0.8766 | 0.9312 | 0.3983 | 0.7724 | 0.2975 | nan | 0.2975 | 0.0055 | 0.0 | 0.9111 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9086 | 0.0 | 0.1602 | 0.0001 | 0.0 | nan | 0.0 | 0.0034 | 0.0 | 0.0 | 0.9371 | 0.8321 | 0.9353 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.6894 | 0.8388 | 0.3591 | 0.6398 | 0.2119 | nan | 0.2519 | 0.0055 | 0.0 | 0.6754 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6033 | 0.0 | 0.1492 | 0.0001 | 0.0 | nan | 0.0 | 0.0034 | 0.0 | 0.0 | 0.7671 | 0.7293 | 0.8750 | 0.0 | 0.0 | 0.0000 | 0.0 |
| 0.8116 | 8.24 | 280 | 0.8277 | 0.2314 | 0.2819 | 0.8087 | nan | 0.8894 | 0.9207 | 0.6812 | 0.7773 | 0.3594 | nan | 0.3120 | 0.0109 | 0.0 | 0.9016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8885 | 0.0 | 0.2424 | 0.0005 | 0.0 | nan | 0.0 | 0.0107 | 0.0 | 0.0 | 0.9398 | 0.8575 | 0.9461 | 0.0 | 0.0 | 0.0003 | 0.0 | nan | 0.7112 | 0.8407 | 0.5738 | 0.6399 | 0.2424 | nan | 0.2666 | 0.0108 | 0.0 | 0.6924 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6145 | 0.0 | 0.2148 | 0.0005 | 0.0 | nan | 0.0 | 0.0106 | 0.0 | 0.0 | 0.7579 | 0.7244 | 0.8738 | 0.0 | 0.0 | 0.0003 | 0.0 |
| 0.7791 | 8.82 | 300 | 0.8059 | 0.2255 | 0.2723 | 0.8077 | nan | 0.8684 | 0.9414 | 0.4680 | 0.7998 | 0.2901 | nan | 0.3174 | 0.0107 | 0.0 | 0.8846 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9111 | 0.0 | 0.2193 | 0.0000 | 0.0 | nan | 0.0 | 0.0099 | 0.0 | 0.0 | 0.9290 | 0.8439 | 0.9465 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.7039 | 0.8383 | 0.4188 | 0.6308 | 0.2131 | nan | 0.2698 | 0.0106 | 0.0 | 0.7114 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6008 | 0.0 | 0.1942 | 0.0000 | 0.0 | nan | 0.0 | 0.0099 | 0.0 | 0.0 | 0.7791 | 0.7343 | 0.8760 | 0.0 | 0.0 | 0.0000 | 0.0 |
| 0.7334 | 9.41 | 320 | 0.7962 | 0.2342 | 0.2830 | 0.8117 | nan | 0.8921 | 0.9332 | 0.6837 | 0.7454 | 0.3381 | nan | 0.3264 | 0.0298 | 0.0 | 0.9198 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9018 | 0.0 | 0.2712 | 0.0003 | 0.0 | nan | 0.0 | 0.0182 | 0.0 | 0.0 | 0.9194 | 0.8508 | 0.9434 | 0.0 | 0.0 | 0.0008 | 0.0 | nan | 0.7121 | 0.8388 | 0.5627 | 0.6590 | 0.2316 | nan | 0.2794 | 0.0296 | 0.0 | 0.6884 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6204 | 0.0 | 0.2324 | 0.0003 | 0.0 | nan | 0.0 | 0.0182 | 0.0 | 0.0 | 0.7820 | 0.7278 | 0.8762 | 0.0 | 0.0 | 0.0008 | 0.0 |
| 0.7645 | 10.0 | 340 | 0.7783 | 0.2342 | 0.2809 | 0.8133 | nan | 0.8999 | 0.9347 | 0.5997 | 0.7491 | 0.3278 | nan | 0.3613 | 0.0164 | 0.0 | 0.9043 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9156 | 0.0 | 0.2684 | 0.0003 | 0.0 | nan | 0.0 | 0.0167 | 0.0 | 0.0 | 0.9235 | 0.8454 | 0.9455 | 0.0 | 0.0 | 0.0007 | 0.0 | nan | 0.7218 | 0.8409 | 0.5162 | 0.6738 | 0.2390 | nan | 0.3039 | 0.0162 | 0.0 | 0.7015 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6019 | 0.0 | 0.2260 | 0.0003 | 0.0 | nan | 0.0 | 0.0167 | 0.0 | 0.0 | 0.7860 | 0.7381 | 0.8764 | 0.0 | 0.0 | 0.0007 | 0.0 |
| 0.6792 | 10.59 | 360 | 0.7774 | 0.2358 | 0.2841 | 0.8141 | nan | 0.8954 | 0.9341 | 0.6272 | 0.7826 | 0.3543 | nan | 0.3360 | 0.0300 | 0.0 | 0.9162 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.2909 | 0.0005 | 0.0 | nan | 0.0 | 0.0199 | 0.0 | 0.0 | 0.9226 | 0.8558 | 0.9443 | 0.0 | 0.0 | 0.0010 | 0.0 | nan | 0.7198 | 0.8402 | 0.5426 | 0.6699 | 0.2489 | nan | 0.2900 | 0.0297 | 0.0 | 0.6966 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6186 | 0.0 | 0.2450 | 0.0005 | 0.0 | nan | 0.0 | 0.0199 | 0.0 | 0.0 | 0.7835 | 0.7251 | 0.8784 | 0.0 | 0.0 | 0.0010 | 0.0 |
| 0.8047 | 11.18 | 380 | 0.7734 | 0.2388 | 0.2878 | 0.8147 | nan | 0.8924 | 0.9265 | 0.6512 | 0.7739 | 0.3846 | nan | 0.3762 | 0.0383 | 0.0 | 0.9122 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9053 | 0.0 | 0.3142 | 0.0005 | 0.0 | nan | 0.0 | 0.0216 | 0.0 | 0.0 | 0.9303 | 0.8513 | 0.9427 | 0.0 | 0.0 | 0.0014 | 0.0 | nan | 0.7171 | 0.8421 | 0.5575 | 0.6761 | 0.2609 | nan | 0.3165 | 0.0376 | 0.0 | 0.6982 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6155 | 0.0 | 0.2551 | 0.0005 | 0.0 | nan | 0.0 | 0.0215 | 0.0 | 0.0 | 0.7854 | 0.7377 | 0.8797 | 0.0 | 0.0 | 0.0014 | 0.0 |
| 0.7136 | 11.76 | 400 | 0.7733 | 0.2394 | 0.2885 | 0.8145 | nan | 0.9002 | 0.9256 | 0.6731 | 0.7624 | 0.3720 | nan | 0.3753 | 0.0482 | 0.0 | 0.9125 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8988 | 0.0 | 0.3240 | 0.0009 | 0.0 | nan | 0.0 | 0.0228 | 0.0 | 0.0 | 0.9283 | 0.8528 | 0.9460 | 0.0 | 0.0 | 0.0014 | 0.0 | nan | 0.7132 | 0.8399 | 0.5677 | 0.6711 | 0.2585 | nan | 0.3157 | 0.0474 | 0.0 | 0.7025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6194 | 0.0 | 0.2615 | 0.0009 | 0.0 | nan | 0.0 | 0.0227 | 0.0 | 0.0 | 0.7851 | 0.7352 | 0.8791 | 0.0 | 0.0 | 0.0014 | 0.0 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| [
"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"
] |
TristanPermentier/segformer-b0-scene-parse-150 |
<!-- 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-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6339
- Mean Iou: 0.1200
- Mean Accuracy: 0.1669
- Overall Accuracy: 0.6123
- Per Category Iou: [0.5349505300503856, nan, nan, 0.7662725216601061, nan, 0.5326853699336921, nan, nan, 0.34424006183640854, nan, 0.4690602972950636, nan, 0.0, nan, 0.43864150176543804, nan, nan, 0.0, 0.03116323751411952, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11546494517491812, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.21709821831740012, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2656179069036561, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004383351344800073, nan, nan, nan, nan, nan, nan, 0.27573695030755746, 0.0, 0.0, nan, nan, 0.1730424387328153, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2699536864879483, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
- Per Category Accuracy: [0.7970510497742438, nan, nan, 0.9333683660650987, nan, 0.5709001366216121, nan, nan, 0.5022173025701339, nan, 0.5997946735914005, nan, 0.0, nan, 0.6798811830944017, nan, nan, 0.0, 0.03494731857464189, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18674548490489992, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.4055014699503591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.399427626224171, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005731113077237973, nan, nan, nan, nan, nan, nan, 0.3817023254759232, 0.0, 0.0, nan, nan, 0.18656355727404544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.49311473385204396, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 3.6823 | 1.0 | 20 | 3.5003 | 0.0586 | 0.1033 | 0.4973 | [0.49515664733932946, nan, nan, 0.5775935923663784, nan, 0.04596206382865156, nan, nan, 0.26871056425122297, nan, 0.11369973679410002, nan, 0.0, 0.0, 0.3107990029966113, nan, nan, 0.07862248213125406, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.034315093061257025, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.01068727200680629, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.11023414367115379, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.000786938713592233, nan, nan, nan, nan, nan, nan, 0.05070892559181233, 0.0, 0.0, nan, nan, 0.015040183696900114, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.11313313426108514, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7426263023684041, nan, nan, 0.9727086727777018, nan, 0.05114940356296204, nan, nan, 0.47199142135667094, nan, 0.13174104716468385, nan, 0.0, nan, 0.4854913009051258, nan, nan, 0.08009119658748254, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.08262480087518953, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.011048725239770591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.5815895187334261, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0010960423626975848, nan, nan, nan, nan, nan, nan, 0.06443529825277504, 0.0, 0.0, nan, nan, 0.021105203802158853, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12399654192910954, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 3.4276 | 2.0 | 40 | 3.1882 | 0.0710 | 0.1169 | 0.4890 | [0.43876531073668484, nan, nan, 0.5793460668074677, nan, 0.30489642184557436, nan, nan, 0.32595816708983194, nan, 0.14653798821167174, nan, 0.0, nan, 0.2911432340397004, nan, nan, 0.060054857802800635, 0.002022419470540954, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.024744866592893152, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.07467207433837308, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.12499059372413274, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004522511609125783, nan, nan, nan, nan, nan, nan, 0.09628943874020461, 0.0, 0.0, nan, nan, 0.029982363315696647, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12235712170565591, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.5900893611665498, nan, nan, 0.9783242745736993, nan, 0.3846789392114827, nan, nan, 0.559548929399149, nan, 0.17078325985868711, nan, 0.0, nan, 0.6459378704825733, nan, nan, 0.06118996837537692, 0.002770214277258198, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.06180066406924745, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.10514964576606102, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.47971748890697613, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0059159876926327465, nan, nan, nan, nan, nan, nan, 0.11675000283456353, 0.0, 0.0, nan, nan, 0.0356049621395199, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1275781153513647, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.9493 | 3.0 | 60 | 3.0877 | 0.0780 | 0.1289 | 0.5137 | [0.45940221778653784, nan, nan, 0.6494007938527302, nan, 0.4439837201805287, nan, nan, 0.34639082849373676, nan, 0.22427847777966967, nan, 0.000308090455357693, nan, 0.34777956821568423, nan, nan, 0.004698282190574071, 0.0031407729433002986, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03480888089115643, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0913573016991002, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14217375469266544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.001518325086206108, nan, nan, nan, nan, nan, nan, 0.09802372235537879, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04033963860518523, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.582026866520922, nan, nan, 0.952329573015261, nan, 0.5949736206887242, nan, nan, 0.6362931958905531, nan, 0.3181804456790869, nan, 0.000308090455357693, nan, 0.7024633946812373, nan, nan, 0.0047069206442597634, 0.0040369361903634425, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13068345392779687, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13909104053207383, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.5329640034657495, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0021920847253951697, nan, nan, nan, nan, nan, nan, 0.12659160742428882, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.041218969988884774, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 3.0162 | 4.0 | 80 | 2.8616 | 0.0814 | 0.1232 | 0.5420 | [0.4910465857166263, nan, nan, 0.6443584008757899, nan, 0.45141247019724007, nan, nan, 0.32257705190056235, nan, 0.26924304549583833, nan, 0.0, nan, 0.3331696608404026, nan, nan, 0.0, 0.001900597974698759, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.020747307143488526, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13337046977599973, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.13325950535252862, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0017460871773707102, nan, nan, nan, nan, nan, nan, 0.1319458915051747, 0.0, 0.0, nan, nan, 0.009900990099009901, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06578507371941024, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7132203547582256, nan, nan, 0.8931325481026355, nan, 0.5735731681633843, nan, nan, 0.581002455982566, nan, 0.33111902892686756, nan, 0.0, nan, 0.611482717739854, nan, nan, 0.0, 0.002995146205753522, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03153369287756943, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.19039471781772616, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.37913198729225195, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0021788793957241144, nan, nan, nan, nan, nan, nan, 0.1723074480991417, 0.0, 0.0, nan, nan, 0.01031093926212341, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06681486970482894, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 3.0263 | 5.0 | 100 | 2.7084 | 0.0843 | 0.1324 | 0.5396 | [0.49507208751747933, nan, nan, 0.6203053779437833, nan, 0.4485318235941677, nan, nan, 0.3407940967092522, nan, 0.2978191928406968, nan, 0.0, nan, 0.3557274457037466, nan, nan, 0.0, 0.0022259234469125043, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.026368862501178023, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.12652470809007507, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14366688649397702, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006174898362074405, nan, nan, nan, nan, nan, nan, 0.19123126092560874, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06350128408951938, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.6526707344030444, nan, nan, 0.9749474509023519, nan, 0.5390505607966174, nan, nan, 0.6160157736345083, nan, 0.414004468868893, nan, 0.0, nan, 0.639577053813209, nan, nan, 0.0, 0.003385817449982242, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.05370132238066906, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.23396308255819556, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.45186021477144434, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009045650824672838, nan, nan, nan, nan, nan, nan, 0.2455865845777067, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06412868963813759, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 3.0192 | 6.0 | 120 | 2.5853 | 0.0834 | 0.1228 | 0.5584 | [0.513016303842118, nan, nan, 0.6309294285637532, nan, 0.35611384488558007, nan, nan, 0.2789754627300978, nan, 0.33300786268564675, nan, 0.0, nan, 0.39549797351503974, nan, nan, 0.0, 0.0006107421807796855, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.009196650667414078, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13424914500097224, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.12323683312478671, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004757876929583422, nan, nan, nan, nan, nan, nan, 0.24288385441579335, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06377284193687319, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7580542978289542, nan, nan, 0.9493663014141616, nan, 0.37790185924193903, nan, nan, 0.49498772008717007, nan, 0.3913279787426777, nan, 0.0, nan, 0.6642459982413698, nan, nan, 0.0, 0.0008405351012193679, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.01007619522868165, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2828329076100053, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.22755795940872214, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005942398351974857, nan, nan, nan, nan, nan, nan, 0.31665476149982424, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06400518710633568, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.7891 | 7.0 | 140 | 2.4388 | 0.0948 | 0.1331 | 0.5748 | [0.5154656376508916, nan, nan, 0.6863149964423734, nan, 0.49124725952151366, nan, nan, 0.31201516142142743, nan, 0.35972157545158207, nan, 0.0, nan, 0.39671683424856585, nan, nan, 0.0, 2.370192459627722e-05, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.009904517600114009, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.163059324189356, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14525411857851944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3573847356082313, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07118873794781752, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7711226677412429, nan, nan, 0.9177684357159737, nan, 0.5529665250049951, nan, nan, 0.4886298384586115, nan, 0.44317289691406486, nan, 0.0, nan, 0.594793229711226, nan, nan, 0.0, 3.551556765715639e-05, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.02134234113198856, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.31670200973540896, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.25047916611967336, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.49779470957062033, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07135358774854884, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.3603 | 8.0 | 160 | 2.5459 | 0.0923 | 0.1381 | 0.5408 | [0.4582064985086822, nan, nan, 0.6929509551025913, nan, 0.5612155620675559, nan, nan, 0.3518851291071376, nan, 0.3123848723909702, nan, 0.0, nan, 0.38656074482619035, nan, nan, 0.0, 0.00632781961916225, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.019509625712633233, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.16056645540831488, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14847856488402372, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.003485354097144976, nan, nan, nan, nan, nan, nan, 0.2711320589476366, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04329675572519084, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.6156433337669713, nan, nan, 0.8946001915399062, nan, 0.7932261600686888, nan, nan, 0.5757653325953855, nan, 0.3712482637840449, nan, 0.0, nan, 0.6684194639240202, nan, nan, 0.0, 0.012821119924233456, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.036255110070437406, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3827895320256398, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3233649276656077, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006206504945395962, nan, nan, nan, nan, nan, nan, 0.3861242445888182, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0448314190440904, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 3.0505 | 9.0 | 180 | 2.4342 | 0.0895 | 0.1374 | 0.5334 | [0.4674035619688911, nan, nan, 0.6469130452725848, nan, 0.5407835854397331, nan, nan, 0.30876943112205757, nan, 0.29598515286429283, nan, 0.0, nan, 0.36881671052279913, nan, nan, 0.0, 0.008017672665843675, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03279170249121165, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13950156171504707, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14746426631847753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.013165291208746446, nan, nan, nan, nan, nan, nan, 0.23331071375064993, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.10953377620044287, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.6058283068338183, nan, nan, 0.9748230743398714, nan, 0.6179778921391272, nan, nan, 0.5429381853402054, nan, 0.4526692433117942, nan, 0.0, nan, 0.6640578860565124, nan, nan, 0.0, 0.01460873682964366, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.057827764236224405, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.38476553086895754, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.33507495996009135, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.021339812748425266, nan, nan, nan, nan, nan, nan, 0.30016893998662086, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.11149191058416698, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.3854 | 10.0 | 200 | 2.3098 | 0.0939 | 0.1364 | 0.5669 | [0.5145572933835421, nan, nan, 0.7030505688292765, nan, 0.5830553297262949, nan, nan, 0.33253154963919734, nan, 0.3190317923926545, nan, 0.0, nan, 0.40605592944383456, nan, nan, 0.0, 0.008330498485004311, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03189938344253368, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.15208814114494157, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.11716706900020117, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006876583423814694, nan, nan, nan, nan, nan, nan, 0.23581319835736797, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0643412965149726, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7016591414601333, nan, nan, 0.9361823857912215, nan, 0.6738685516489095, nan, nan, 0.5585526998512574, nan, 0.43041548402681323, nan, 0.0, nan, 0.6699024878930123, nan, nan, 0.0, 0.012465964247661892, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.06970807822966048, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39385030603884524, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2446766613280122, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01103965560500218, nan, nan, nan, nan, nan, nan, 0.2799641711169314, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0656107200197604, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.0245 | 11.0 | 220 | 2.2548 | 0.0955 | 0.1378 | 0.5719 | [0.5220301378484757, nan, nan, 0.6900779538662102, nan, 0.597003770439182, nan, nan, 0.34494446136750745, nan, 0.33220376279018593, nan, 0.0, nan, 0.40090961340118253, nan, nan, 0.0, 0.018837390442916883, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.024534003530502946, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.16223235412939344, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.10163486902644009, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006591395652521519, nan, nan, nan, nan, nan, nan, 0.2809388993745605, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.05083676351290448, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.6982873412593906, nan, nan, 0.9678051268019054, nan, 0.6729127403703363, nan, nan, 0.5562281642395102, nan, 0.4558548221510961, nan, 0.0, nan, 0.6682925975667907, nan, nan, 0.0, 0.029489759677992188, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.06295222923823963, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.4064292255048436, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.17399637671646492, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009798354615922987, nan, nan, nan, nan, nan, nan, 0.34428608682835016, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.05224157095220452, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.1305 | 12.0 | 240 | 2.1615 | 0.0891 | 0.1287 | 0.5754 | [0.52909209668855, nan, nan, 0.6817600912725086, nan, 0.4928497692494535, nan, nan, 0.3053400490721222, nan, 0.30149677514104933, nan, 0.0, nan, 0.44020401115949986, nan, nan, 0.0, 0.02650747496108321, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.009345088921766961, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.14166860984376856, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.06272879674296075, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0049727527815997315, nan, nan, nan, nan, nan, nan, 0.22690293924815086, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07303471847454578, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7419261355764054, nan, nan, 0.9587598412955063, nan, 0.5259392060826318, nan, nan, 0.48901726106056936, nan, 0.3719276526360287, nan, 0.0, nan, 0.7751709414796117, nan, nan, 0.0, 0.04273706641411152, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.011880313993436078, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.38797050460263144, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.09627957045711134, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006061246319014354, nan, nan, nan, nan, nan, nan, 0.27860358062065604, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07521304186735828, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.5724 | 13.0 | 260 | 2.2083 | 0.0989 | 0.1513 | 0.5531 | [0.4800847622248986, nan, nan, 0.6970880808658071, nan, 0.6182665130888115, nan, nan, 0.3425863757467936, nan, 0.3713486637663145, nan, 0.0, nan, 0.3765884611771054, nan, nan, 0.0, 0.04581278147115591, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.07627658417610414, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.17206178086451746, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.13439901892281148, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.021518798527206638, nan, nan, nan, nan, nan, nan, 0.2281, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09575841263170534, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.6012010716147941, nan, nan, 0.9709518538326638, nan, 0.7217455165970958, nan, nan, 0.5542080321007299, nan, 0.6314541940938463, nan, 0.0, nan, 0.6838665365921947, nan, nan, 0.0, 0.08092814016810702, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18847283265838818, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.49998795122656514, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.24745976317378632, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03619580862836241, nan, nan, nan, nan, nan, nan, 0.2844881345170471, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09849326911201679, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.9126 | 14.0 | 280 | 2.1922 | 0.0937 | 0.1380 | 0.5619 | [0.5009904092834208, nan, nan, 0.6698610538672513, nan, 0.4589843172431468, nan, nan, 0.31517044265103583, nan, 0.3104306789045372, nan, 0.0, nan, 0.4116696414534222, nan, nan, 0.0, 0.05665320608660416, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.04817434748097306, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.15597353569249542, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.1482680934947902, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007848093824402574, nan, nan, nan, nan, nan, nan, 0.27710322432627993, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.10443950498618286, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.6797244600413119, nan, nan, 0.9652398602007438, nan, 0.5028917341224627, nan, nan, 0.5103670137327476, nan, 0.37837429796485295, nan, 0.0, nan, 0.7789463092826802, nan, nan, 0.0, 0.09599857937729371, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.07920849087384603, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39425996433563065, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.24882505841888308, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010577469066515246, nan, nan, nan, nan, nan, nan, 0.3560551946211322, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.10735457576880326, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.184 | 15.0 | 300 | 2.0821 | 0.1054 | 0.1492 | 0.5959 | [0.5445153487483887, nan, nan, 0.7363123727181161, nan, 0.6148226937061888, nan, nan, 0.3475879173137814, nan, 0.35835575524286467, nan, 0.0, nan, 0.46660818479509436, nan, nan, 0.0, 0.05904185462228059, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.04270497506100011, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.16342478251978165, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.11624580295233743, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01957395899106045, nan, nan, nan, nan, nan, nan, 0.33294472694199845, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09641039746939899, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7237905464791236, nan, nan, 0.9342327831743387, nan, 0.7085585609910197, nan, nan, 0.5992113182745857, nan, 0.42025484630714416, nan, 0.0, nan, 0.7143757081548819, nan, nan, 0.0, 0.08568722623416597, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.08364201677446596, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.5006867800857873, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.15362197075117495, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03111175670500614, nan, nan, nan, nan, nan, nan, 0.45656881753347617, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.10821909349141658, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.0809 | 16.0 | 320 | 2.0044 | 0.1016 | 0.1366 | 0.6038 | [0.5521032535171966, nan, nan, 0.6849303109873625, nan, 0.5608681131846335, nan, nan, 0.3286185445379954, nan, 0.3117324722344537, nan, 0.0, nan, 0.5007412271537872, nan, nan, 0.0, 0.00896535470186189, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.02470879973127575, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13202960663906452, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.13535477440870933, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006964332796916055, nan, nan, nan, nan, nan, nan, 0.3258174090559218, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.18590681790462252, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8265849734899084, nan, nan, 0.927053146105148, nan, 0.5985484628718619, nan, nan, 0.5087204676744267, nan, 0.3847756507035449, nan, 0.0, nan, 0.6605231268619826, nan, nan, 0.0, 0.011258434947318575, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.04800107479415772, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2624222854113451, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.18541759655525508, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010115282528028312, nan, nan, nan, nan, nan, nan, 0.44154563080376885, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.18824873409904902, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.9142 | 17.0 | 340 | 1.9659 | 0.0994 | 0.1374 | 0.5807 | [0.5146286979691347, nan, nan, 0.6905745271536221, nan, 0.5591977800201816, nan, nan, 0.34355018631476514, nan, 0.3433540309960119, nan, 0.0, nan, 0.44078672618377784, nan, nan, 0.0, 0.018210173845236457, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.02063863329589597, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13463592284853793, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14175516133652527, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007641975925278458, nan, nan, nan, nan, nan, nan, 0.34504392589348754, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.11831867388362652, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7304971289988567, nan, nan, 0.9420685066106143, nan, 0.5985052623620959, nan, nan, 0.5007022034660487, nan, 0.41074340237937074, nan, 0.0, nan, 0.7711024686443236, nan, nan, 0.0, 0.02819936071978217, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03844308389152256, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3037857246132344, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.1818993357313519, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.012122492638028708, nan, nan, nan, nan, nan, nan, 0.4377473156683334, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1295850314931456, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.4201 | 18.0 | 360 | 1.9076 | 0.1058 | 0.1420 | 0.6090 | [0.544585393186956, nan, nan, 0.7251707754198404, nan, 0.5891181339724741, nan, nan, 0.35175747599947543, nan, 0.3768437454607878, nan, 0.0, nan, 0.46080424972867995, nan, nan, 0.0, 0.007712058173094944, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03192860684184432, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.12968165867755327, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.17963202065848935, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006236042242325599, nan, nan, nan, nan, nan, nan, 0.3516538062361807, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.15892993199198577, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8058972658592538, nan, nan, 0.9410890411810798, nan, 0.6437469962145553, nan, nan, 0.5381576671624754, nan, 0.43085331239809166, nan, 0.0, nan, 0.7058406646047238, nan, nan, 0.0, 0.009743104060613236, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.04944053125539796, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.27250710877632656, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.23378055504502848, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008702312253225401, nan, nan, nan, nan, nan, nan, 0.44179507239475263, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.17389156477707793, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.3918 | 19.0 | 380 | 1.9192 | 0.1037 | 0.1492 | 0.6023 | [0.5442973237949046, nan, nan, 0.7038453617346788, nan, 0.5574274701550656, nan, nan, 0.33109252617100604, nan, 0.3536195549984331, nan, 0.0, 0.0, 0.48958630952380955, nan, nan, 0.0, 0.03185425185390533, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.04555193844332643, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.15959564598020237, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.1401469153743555, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007048748992689108, nan, nan, nan, nan, nan, nan, 0.360631823315347, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2158580356918752, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7598110607315368, nan, nan, 0.9670246638723399, nan, 0.6107040063072744, nan, nan, 0.517921754470926, nan, 0.4259013225436319, nan, 0.0, nan, 0.7196428493308893, nan, nan, 0.0, 0.04353024742512134, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.07385371283803235, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.42115282664224785, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.18483997164386798, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009587069341186102, nan, nan, nan, nan, nan, nan, 0.5213556016644557, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.26553044337408915, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.3816 | 20.0 | 400 | 1.9099 | 0.1005 | 0.1444 | 0.5801 | [0.5240378547065215, nan, nan, 0.7084459171029648, nan, 0.3005648391621819, nan, nan, 0.3284451404977932, nan, 0.3888894787696032, nan, 0.0, nan, 0.44350835438817365, nan, nan, 0.0, 0.05071873461975799, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0850628575506369, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.16232786173696878, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.18652889399158057, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009414690469846346, nan, nan, nan, nan, nan, nan, 0.34993112721786757, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.179512063620434, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7640533099803383, nan, nan, 0.9546740712180197, nan, 0.3043043907918113, nan, nan, 0.5266733543187243, nan, 0.5529621353946494, nan, 0.0, nan, 0.6750033903940295, nan, nan, 0.0, 0.07075884929560791, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13726656814386887, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39653718251482, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.25594034710006036, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.013997649451318553, nan, nan, nan, nan, nan, nan, 0.4839053482544758, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.20560083981721625, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.5018 | 21.0 | 420 | 1.8815 | 0.1059 | 0.1428 | 0.5995 | [0.5311212203406633, nan, nan, 0.7177711289807998, nan, 0.42651481922426215, nan, nan, 0.36451006234358574, nan, 0.39474432552608446, nan, 0.0, nan, 0.506004935402564, nan, nan, 0.0, 0.009084462502635261, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0965560638446633, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.12773542545580144, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.15052972972972972, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0073552816083549116, nan, nan, nan, nan, nan, nan, 0.36122781305976226, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22668064473856636, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8159745909259652, nan, nan, 0.8884248952127461, nan, 0.4410826047747363, nan, nan, 0.510878965028192, nan, 0.4610634700163053, nan, 0.0, nan, 0.7544086059137222, nan, nan, 0.0, 0.011222919379661418, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18553634147745812, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2495300978360403, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.18279202877622286, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.011290556868752228, nan, nan, nan, nan, nan, nan, 0.5030329829812805, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2670433493886625, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.301 | 22.0 | 440 | 1.8606 | 0.1127 | 0.1557 | 0.6116 | [0.5454706673579958, nan, nan, 0.740255244679598, nan, 0.5893461091012621, nan, nan, 0.35433112960421975, nan, 0.4003983065708059, nan, 0.0, nan, 0.4706902103290056, nan, nan, 0.0, 0.04674946512433085, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0878654677869828, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.15571204204604946, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.18811653609669202, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009186660517126157, nan, nan, nan, nan, nan, nan, 0.3597557228452911, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22036818018203322, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7677466369254428, nan, nan, 0.9575160756707006, nan, 0.6483370503771945, nan, nan, 0.5502507869521602, nan, 0.4826227429192584, nan, 0.0, nan, 0.7162393311955623, nan, nan, 0.0, 0.062341659760861846, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14310116500009595, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3655356884669141, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2578570115787539, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.012888401758949912, nan, nan, nan, nan, nan, nan, 0.5310044559338754, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2646350500185254, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.2921 | 23.0 | 460 | 1.8113 | 0.1057 | 0.1532 | 0.5913 | [0.5357758165202247, nan, nan, 0.7178963332622186, nan, 0.5757493868768706, nan, nan, 0.34088809244481116, nan, 0.3888587007438775, nan, 0.0, nan, 0.45940927020073186, nan, nan, 0.0, 0.030332067631465825, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11186966640806827, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.1507442372917972, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.16361404207313027, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.012473057193938078, nan, nan, nan, nan, nan, nan, 0.215706225981412, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2064475531245462, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7239290991222531, nan, nan, 0.9626030770761558, nan, 0.6097751953473051, nan, nan, 0.548791033934069, nan, 0.6266380820097832, nan, 0.0, nan, 0.7326969600195987, nan, nan, 0.0, 0.04009707588492956, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.19373164693011918, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.42036965636898166, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2589334943681571, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01971555719888547, nan, nan, nan, nan, nan, nan, 0.26920416794222024, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2633691490675559, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.3405 | 24.0 | 480 | 1.7822 | 0.1085 | 0.1534 | 0.6043 | [0.5414566325029534, nan, nan, 0.7173153879237804, nan, 0.5310126078829097, nan, nan, 0.36009260637040796, nan, 0.42939053817714906, nan, 0.0, nan, 0.4722188917670218, nan, nan, 0.0, 0.015322674081798169, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.09576263304056658, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.16420095890288652, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.15819497832665413, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.00890486944807639, nan, nan, nan, nan, nan, nan, 0.3031977891827872, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.21783933256127247, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7591235857847104, nan, nan, 0.9542138779368415, nan, 0.576780806013511, nan, nan, 0.5692275761873465, nan, 0.5856633854701371, nan, 0.0, nan, 0.7408907768158294, nan, nan, 0.0, 0.018787735290635727, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13832216954877838, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.44317798448117984, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.20314017906372253, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01187159137427866, nan, nan, nan, nan, nan, nan, 0.39185006292731045, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2845807089045325, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.1149 | 25.0 | 500 | 1.7731 | 0.1119 | 0.1561 | 0.5990 | [0.5230321816508672, nan, nan, 0.7190935580028947, nan, 0.5684881046052994, nan, nan, 0.34178000606892306, nan, 0.4634889254146504, nan, 0.0, nan, 0.44083260578388256, nan, nan, 0.0, 0.02177948393993877, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12256739536552475, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.1628205567158558, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.22133767103626448, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008860431486295587, nan, nan, nan, nan, nan, nan, 0.31208343518292186, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.23465598343498908, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7538289710403823, nan, nan, 0.9331196129401376, nan, 0.6125346279086094, nan, nan, 0.5220796291812239, nan, 0.608792801497675, nan, 0.0, nan, 0.7112696697537481, nan, nan, 0.0, 0.026530129039895822, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.19014260215342688, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.34378765241698395, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3036731693228661, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.013997649451318553, nan, nan, nan, nan, nan, nan, 0.43427780990283116, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3219093491416574, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.8525 | 26.0 | 520 | 1.7599 | 0.1083 | 0.1499 | 0.6013 | [0.5330932644142036, nan, nan, 0.7374629122911739, nan, 0.4196436832420096, nan, nan, 0.36779568174556077, nan, 0.45034522075125105, nan, 0.0, nan, 0.46970559257831385, nan, nan, 0.0, 0.02651167431650369, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10133904279732192, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.18001012682076706, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.23047192839707079, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0010134792743488395, nan, nan, nan, nan, nan, nan, 0.2836160821897313, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.20646839213054238, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8119925245031936, nan, nan, 0.9142890013805799, nan, 0.4408612021621855, nan, nan, 0.55742502334913, nan, 0.536686998007126, nan, 0.0, nan, 0.6715692493448884, nan, nan, 0.0, 0.03145495442168817, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.164424313379268, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.36837919899754207, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.35697219523722007, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0014525862638160763, nan, nan, nan, nan, nan, nan, 0.38687256936176967, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.30393973076448066, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.9834 | 27.0 | 540 | 1.7425 | 0.1113 | 0.1546 | 0.6117 | [0.5468403256324604, nan, nan, 0.729015596924764, nan, 0.5298170927901393, nan, nan, 0.34659036444853436, nan, 0.4062670332731743, nan, 0.0, nan, 0.48170900134657657, nan, nan, 0.0, 0.014311797485688203, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12100421456475167, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.1711757925072046, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2214201588085559, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008457097567346056, nan, nan, nan, nan, nan, nan, 0.2971870505293438, 0.0, 0.0, nan, nan, 0.0020491803278688526, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24086284086284085, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7922990114110264, nan, nan, 0.9550098879367172, nan, 0.5779742200957971, nan, nan, 0.5002871078210938, nan, 0.5063862552086479, nan, 0.0, nan, 0.7323994802854056, nan, nan, 0.0, 0.017816976441340122, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1658637698405082, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.35783652224203577, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.30603618032399504, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.012399804561120869, nan, nan, nan, nan, nan, nan, 0.42872206537637336, 0.0, 0.0, nan, nan, 0.0020944095376188173, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.36544399160182783, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.255 | 28.0 | 560 | 1.7499 | 0.1118 | 0.1587 | 0.6071 | [0.5398339756892974, nan, nan, 0.7287949661640746, nan, 0.5465474100530702, nan, nan, 0.35925200783154193, nan, 0.4258153855837521, nan, 0.0, nan, 0.4534640119071021, nan, nan, 0.0, 0.036095939206839234, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11804747854405662, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.17905852894391455, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.23849347211359123, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010316461996506883, nan, nan, nan, nan, nan, nan, 0.2574594613283546, 0.0, 0.0, nan, nan, 0.0006444337038827131, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24188155544359455, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7703357733405677, nan, nan, 0.9543786768821283, nan, 0.5850483035699822, nan, nan, 0.5598187415683697, nan, 0.6128087444894015, nan, 0.0, nan, 0.7064006264573226, nan, nan, 0.0, 0.044986385699064754, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1850565226570447, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3567039375391585, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.41631002704334813, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01505407582500297, nan, nan, nan, nan, nan, nan, 0.33772123768382145, 0.0, 0.0, nan, nan, 0.0006444337038827131, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.32610843522292204, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.0002 | 29.0 | 580 | 1.7234 | 0.1123 | 0.1537 | 0.6115 | [0.5343806524346499, nan, nan, 0.7577406122804516, nan, 0.5857621508154368, nan, nan, 0.35288204865605544, nan, 0.44074177356385946, nan, 0.0, nan, 0.47477546800043285, nan, nan, 0.0, 0.0070126227208976155, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.09253997097454784, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.18628156919220204, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.22707481198047236, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006903668258650132, nan, nan, nan, nan, nan, nan, 0.2662933416415916, 0.0, 0.0, nan, nan, 0.0022658610271903325, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.21873956020201624, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8161723721798985, nan, nan, 0.8926101665402172, nan, 0.6268609969597642, nan, nan, 0.5148984745235048, nan, 0.5249562171628721, nan, 0.0, nan, 0.6910016755108558, nan, nan, 0.0, 0.007991002722860187, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14563460837187878, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3366427297701094, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.4066741932942999, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.00998322923131776, nan, nan, nan, nan, nan, nan, 0.3682551560710682, 0.0, 0.0, nan, nan, 0.002416626389560174, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3436766703717426, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.9107 | 30.0 | 600 | 1.7110 | 0.1109 | 0.1570 | 0.6061 | [0.5409093005757184, nan, nan, 0.7473723058754059, nan, 0.4555303180966537, nan, nan, 0.35164493806392416, nan, 0.4320491085773804, nan, 0.0, nan, 0.4522083206870581, nan, nan, 0.0, 0.049725487125786595, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10475239334604404, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.20116130177364758, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.22513841831253614, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007068769172369218, nan, nan, nan, nan, nan, nan, 0.27485750958849686, 0.0, 0.0, nan, nan, 0.005144694533762058, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.25437975947734176, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7937723231047638, nan, nan, 0.9445218343055435, nan, 0.4700215462542458, nan, nan, 0.5090525441903906, nan, 0.588682891478954, nan, 0.0, nan, 0.703942043948256, nan, nan, 0.0, 0.060151533088670536, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.19929754524691476, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3823557761819847, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.35765484285976845, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009098472143357059, nan, nan, nan, nan, nan, nan, 0.40789369252922436, 0.0, 0.0, nan, nan, 0.005155469631061705, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3774854884525133, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.7442 | 31.0 | 620 | 1.7138 | 0.1100 | 0.1550 | 0.6051 | [0.538370914627848, nan, nan, 0.7400080228868461, nan, 0.5135815722017948, nan, nan, 0.3400483665949275, nan, 0.4300165958900766, nan, 0.0, nan, 0.44986347393882015, nan, nan, 0.0, 0.014369577640396904, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12100166291695197, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.178307206329525, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.24067469406452457, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009674143184204637, nan, nan, nan, nan, nan, nan, 0.2674093087779633, 0.0, 0.0, nan, nan, 0.00589171974522293, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2204903647326626, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7883031954893183, nan, nan, 0.9579234089128245, nan, 0.5528747239217423, nan, nan, 0.5126638763014978, nan, 0.5320218612235038, nan, 0.0, nan, 0.6818279254725772, nan, nan, 0.0, 0.016372676689949094, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.16619004663838935, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.36386090895946793, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3862472759734292, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.014842790550266088, nan, nan, nan, nan, nan, nan, 0.36498973888000724, 0.0, 0.0, nan, nan, 0.005961011760915096, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.39178090650858344, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.0674 | 32.0 | 640 | 1.7114 | 0.1093 | 0.1518 | 0.6065 | [0.5322687824138417, nan, nan, 0.746921322690992, nan, 0.4844228791969084, nan, nan, 0.3539657487456126, nan, 0.4188551233102879, nan, 0.0, nan, 0.47029302899444786, nan, nan, 0.0, 0.01652062987515423, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1012081708348106, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.1694733773920492, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.24721294631389673, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0029394296298533304, nan, nan, nan, nan, nan, nan, 0.272253692089824, 0.0, 0.0, nan, nan, 0.000778816199376947, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22813668906453632, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8149825116950065, nan, nan, 0.936937973408291, nan, 0.5164782944438744, nan, nan, 0.5197758483517244, nan, 0.4902620931215653, nan, 0.0, nan, 0.667006435186603, nan, nan, 0.0, 0.01870486563276903, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.14244861140433374, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3432695551592848, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.37902696458109064, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0038559562639481296, nan, nan, nan, nan, nan, nan, 0.36620293207251947, 0.0, 0.0, nan, nan, 0.0008055421298533913, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.41617265653945906, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.0873 | 33.0 | 660 | 1.6653 | 0.1172 | 0.1696 | 0.6123 | [0.5445668589613619, nan, nan, 0.7524007642536803, nan, 0.6080308328342638, nan, nan, 0.37522726462514705, nan, 0.47058505928341576, nan, 0.0, nan, 0.45211562654289134, nan, nan, 0.0020577643859778054, 0.03698548214138415, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0874434081492265, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2220118553720846, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.25600388486667847, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008400085393133278, nan, nan, nan, nan, nan, nan, 0.2727300819665799, 0.0, 0.0, nan, nan, 0.008752387014640357, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2377502658088699, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7471054660604195, nan, nan, 0.9379485329784455, nan, 0.6798356220603403, nan, nan, 0.5825452281296482, nan, 0.657935261791171, nan, 0.0, nan, 0.7090648199591403, nan, nan, 0.0020592777818636463, 0.04246478039540665, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.20981517379037676, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.42103233890789915, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.45677002651823456, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01195082335230499, nan, nan, nan, nan, nan, nan, 0.40027438575008223, 0.0, 0.0, nan, nan, 0.008860963428387304, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.40734222551562305, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.0019 | 34.0 | 680 | 1.7047 | 0.1082 | 0.1516 | 0.5976 | [0.5252909077146455, nan, nan, 0.7573782099101035, nan, 0.41441033016508255, nan, nan, 0.31164553601714706, nan, 0.4709121646818984, nan, 0.0, nan, 0.4513100730570853, nan, nan, 0.0, 0.026121796557054672, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10214383984573885, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.18893875968482826, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.24868639840372464, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005485878410722834, nan, nan, nan, nan, nan, nan, 0.2657905577984675, 0.0, 0.0, nan, nan, 0.010289694475225581, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22569361498822646, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8073684622454624, nan, nan, 0.9312974962997973, nan, 0.42945086752023676, nan, nan, 0.4385762219378048, nan, 0.571939730660064, nan, 0.0, nan, 0.6894005345885812, nan, nan, 0.0, 0.02940689002012549, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18096846630712243, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.34641428502578436, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3926799170320582, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0075666539015146516, nan, nan, nan, nan, nan, nan, 0.36576074016122995, 0.0, 0.0, nan, nan, 0.010472047688094087, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.40839199703593926, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.9344 | 35.0 | 700 | 1.6748 | 0.1150 | 0.1627 | 0.6151 | [0.5505830958687438, nan, nan, 0.7562419986190323, nan, 0.5978540319640836, nan, nan, 0.32995695112686757, nan, 0.43084969246068283, nan, 0.0, nan, 0.44413403674818086, nan, nan, 0.0, 0.02120224146714213, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11930022573363432, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.19550674411022423, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2520041329675419, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.011823153016352289, nan, nan, nan, nan, nan, nan, 0.2816350837545237, 0.0, 0.0, nan, nan, 0.02907822041291073, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2345825635611859, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7886077997734505, nan, nan, 0.9569563811395381, nan, 0.6730801423456797, nan, nan, 0.5048116503511018, nan, 0.5298327193671115, nan, 0.0, nan, 0.6544510405228644, nan, nan, 0.0, 0.024635965431514147, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.16229391781663244, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39015133259434187, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.37141281802189724, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.017787579066911405, nan, nan, nan, nan, nan, nan, 0.4182455185550529, 0.0, 0.0, nan, nan, 0.03222168519413565, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4939792515746573, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.8097 | 36.0 | 720 | 1.6860 | 0.1136 | 0.1585 | 0.6079 | [0.5370921761611701, nan, nan, 0.7534232274902132, nan, 0.45051163633070723, nan, nan, 0.3470158614454695, nan, 0.4514947692232571, nan, 0.0, nan, 0.4472259023583447, nan, nan, 0.0, 0.02574732931747514, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12124562847150792, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.19888801475595164, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.25768922637500435, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0058514509006449555, nan, nan, nan, nan, nan, nan, 0.27173447017804997, 0.0, 0.0, nan, nan, 0.09133667121832802, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24511241015135193, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8068195399417656, nan, nan, 0.9425286998917924, nan, 0.48357570619333307, nan, nan, 0.5071984503095922, nan, 0.55839724621052, nan, 0.0, nan, 0.6896455178990931, nan, nan, 0.0, 0.029702853083935124, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18098765905993897, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.36247530001445855, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.38847900858560663, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007751528516909425, nan, nan, nan, nan, nan, nan, 0.3808632946698867, 0.0, 0.0, nan, nan, 0.09698727243434832, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.42852290971964924, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.7117 | 37.0 | 740 | 1.7075 | 0.1119 | 0.1543 | 0.6082 | [0.5316191267085415, nan, nan, 0.7572815533980582, nan, 0.4538160469667319, nan, nan, 0.30431906153724625, nan, 0.4471088739555919, nan, 0.0, nan, 0.458622625619912, nan, nan, 0.0, 0.02901237764772904, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13063645830046386, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2073772833894836, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.24302394240109712, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0018472226405244507, nan, nan, nan, nan, nan, nan, 0.3016653313939469, 0.0, 0.0, nan, nan, 0.04694976076555024, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.228434381603097, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8287383565541218, nan, nan, 0.942003208915312, nan, 0.488387162968523, nan, nan, 0.42642775606212596, nan, 0.5283682589528353, nan, 0.0, nan, 0.6432518034708885, nan, nan, 0.0, 0.03315970166923168, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.15891599332092202, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.37601812135524604, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3349961929267204, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.002429780659474164, nan, nan, nan, nan, nan, nan, 0.43973151014206835, 0.0, 0.0, nan, nan, 0.050588045754792976, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4572989996294924, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.8903 | 38.0 | 760 | 1.6940 | 0.1141 | 0.1612 | 0.6022 | [0.5272388914176183, nan, nan, 0.7550644430962281, nan, 0.41146235695229855, nan, nan, 0.3325270522067029, nan, 0.45052910362931103, nan, 0.0, nan, 0.45438585937030007, nan, nan, 0.0, 0.01657318470885357, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12229517410520399, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.20662691942053804, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2590023063760137, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004992486554887694, nan, nan, nan, nan, nan, nan, 0.2898555496110951, 0.0, 0.0, nan, nan, 0.145993083746805, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24471294419777534, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7889726903221508, nan, nan, 0.9519750998121914, nan, 0.43142189077831117, nan, nan, 0.46793040229686256, nan, 0.5804245425448397, nan, 0.0, nan, 0.726935477520594, nan, nan, 0.0, 0.019344145850597846, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1837514154655202, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39544074413224733, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.365610313230236, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006668691483882895, nan, nan, nan, nan, nan, nan, 0.3992879576402826, 0.0, 0.0, nan, nan, 0.1564362816175286, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4883907620106212, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.9552 | 39.0 | 780 | 1.6641 | 0.1162 | 0.1625 | 0.6098 | [0.5310598686250333, nan, nan, 0.7601430490832172, nan, 0.5429095366706864, nan, nan, 0.3445406417268712, nan, 0.4568477729996307, nan, 0.0, nan, 0.46530970057255566, nan, nan, 0.0, 0.014359563469270534, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1130773272197545, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.19405725628857665, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26618730518560496, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008357285392600187, nan, nan, nan, nan, nan, nan, 0.26465747015031377, 0.0, 0.0, nan, nan, 0.08871089617651504, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2501878158721486, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.778973631634001, nan, nan, 0.9464372333677442, nan, 0.58833694237592, nan, nan, 0.4889619149745754, nan, 0.5790053747206957, nan, 0.0, nan, 0.7430518795907028, nan, nan, 0.0, 0.016277968509530012, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18175536917260043, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.37692177936286086, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.394622837188542, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.012056465989673432, nan, nan, nan, nan, nan, nan, 0.3591391997460231, 0.0, 0.0, nan, nan, 0.09457064604478814, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4524206496233173, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.7998 | 40.0 | 800 | 1.6491 | 0.1195 | 0.1656 | 0.6204 | [0.5467238209227602, nan, nan, 0.7600965442912115, nan, 0.582928686810868, nan, nan, 0.36155701416782127, nan, 0.47812036649811807, nan, 0.0, nan, 0.4596212555550058, nan, nan, 0.0, 0.031148168454591535, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1197557612763443, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.20817175700676152, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26782302664655605, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006440314647170281, nan, nan, nan, nan, nan, nan, 0.269295792964432, 0.0, 0.0, nan, nan, 0.08404112650871703, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2449240499941124, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8010172513905502, nan, nan, 0.9341768137212224, nan, 0.6489202572590357, nan, nan, 0.5361859628489398, nan, 0.5695996135032309, nan, 0.0, nan, 0.6908966826634936, nan, nan, 0.0, 0.03564579140523263, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18670709939926683, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3809701672369753, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.4195919867671384, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008887186868620176, nan, nan, nan, nan, nan, nan, 0.37565903602163336, 0.0, 0.0, nan, nan, 0.09086515224746254, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.44954921575892304, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.9118 | 41.0 | 820 | 1.6721 | 0.1160 | 0.1601 | 0.6090 | [0.5327482521002724, nan, nan, 0.7521480816985193, nan, 0.42348318514250716, nan, nan, 0.34787290807664323, nan, 0.46125081085206243, nan, 0.0, nan, 0.46282223820683577, nan, nan, 0.0, 0.05095456865747831, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.125455933497328, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.206567621664828, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.25872563801865195, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.001967741277706409, nan, nan, nan, nan, nan, nan, 0.2845250835300749, 0.0, 0.0, nan, nan, 0.14583025830258303, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.23884811742182516, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8171168569914615, nan, nan, 0.9333372719244786, nan, 0.4405749987849857, nan, nan, 0.4961361513715452, nan, 0.5474968295186907, nan, 0.0, nan, 0.6980711938999156, nan, nan, 0.0, 0.05908606605895584, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.17031648849394468, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.37676514530820765, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3649276656076877, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.002548628626513661, nan, nan, nan, nan, nan, nan, 0.39490005328979444, 0.0, 0.0, nan, nan, 0.15917512485903013, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4622391009015685, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.9207 | 42.0 | 840 | 1.6454 | 0.1206 | 0.1676 | 0.6162 | [0.5379120232752981, nan, nan, 0.7568729914559748, nan, 0.5613482715887681, nan, nan, 0.364717070851165, nan, 0.4756806178795279, nan, 0.0, nan, 0.4550448462618563, nan, nan, 0.0, 0.018526924741625934, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10926136363636364, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.22021430139209439, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26463688376567485, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.00584039493119801, nan, nan, nan, nan, nan, nan, 0.2793764799974909, 0.0, 0.0, nan, nan, 0.16214200477326968, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.25246816795602955, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.793135615538626, nan, nan, 0.9439590303603189, nan, 0.6017345004671055, nan, nan, 0.5147116814832751, nan, 0.5969563379431125, nan, 0.0, nan, 0.6946895492744557, nan, nan, 0.0, 0.020776607079436488, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1845383183309982, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39892283965492314, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.4116890277522514, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.007936403132304199, nan, nan, nan, nan, nan, nan, 0.40398199485243264, 0.0, 0.0, nan, nan, 0.17512485903012728, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4524206496233173, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.7207 | 43.0 | 860 | 1.6300 | 0.1199 | 0.1681 | 0.6145 | [0.5406610748788059, nan, nan, 0.7575992381390396, nan, 0.5760206511115009, nan, nan, 0.36015884868644354, nan, 0.46500148540871594, nan, 0.0, nan, 0.45176054716285924, nan, nan, 0.0, 0.016866204578869986, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10816986644407346, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.212614696784591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26724166862435816, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0068089889710963485, nan, nan, nan, nan, nan, nan, 0.2704699429190119, 0.0, 0.0, nan, nan, 0.14349508782375708, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2593716106290672, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7870149732043419, nan, nan, 0.94244785512618, nan, 0.6205699227250882, nan, nan, 0.5138676536718669, nan, 0.6144090826740746, nan, 0.0, nan, 0.6865569783058529, nan, nan, 0.0, 0.019012667219131054, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.19897126844903365, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.39421176924189116, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.41814792448867066, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.00975873862690982, nan, nan, nan, nan, nan, nan, 0.38520584600383234, 0.0, 0.0, nan, nan, 0.15530852263573386, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4725515623070273, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 2.398 | 44.0 | 880 | 1.6485 | 0.1167 | 0.1611 | 0.6100 | [0.5311814850668972, nan, nan, 0.7555542115237289, nan, 0.49150431547087375, nan, nan, 0.3397112053113447, nan, 0.4711908444169502, nan, 0.0, nan, 0.4483007218044239, nan, nan, 0.0, 0.03373223867894174, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11456464920822017, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2039065290799167, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26244981334084666, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0015820003515556336, nan, nan, nan, nan, nan, nan, 0.2779898701320591, 0.0, 0.0, nan, nan, 0.12441350083245044, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2633676630662313, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8130575818438924, nan, nan, 0.9322520864168357, nan, 0.5200099361172462, nan, nan, 0.4856757411186828, nan, 0.5749743341989251, nan, 0.0, nan, 0.6746446648322083, nan, nan, 0.0, 0.038475198295252755, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.17162159568546917, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3645115427249506, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3913146217869614, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0020204154396714514, nan, nan, nan, nan, nan, nan, 0.37400365091783166, 0.0, 0.0, nan, nan, 0.13243112614789754, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.48558107941212797, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.8206 | 45.0 | 900 | 1.6576 | 0.1195 | 0.1659 | 0.6121 | [0.53605344484101, nan, nan, 0.75744198726446, nan, 0.5238418818047587, nan, nan, 0.34436174115825924, nan, 0.47195355574245396, nan, 0.0, nan, 0.4475797404376424, nan, nan, 0.0, 0.031364722184879165, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11128700706471659, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2101074907833958, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2651975284871611, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.003919791994957454, nan, nan, nan, nan, nan, nan, 0.2761731199707084, 0.0, 0.0, nan, nan, 0.18027666220437305, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2635211174367801, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8053821884760162, nan, nan, 0.9416705016106764, nan, 0.5536685332886928, nan, nan, 0.47402539001694977, nan, 0.6099855063711577, nan, 0.0, nan, 0.6632310673835345, nan, nan, 0.0, 0.035906238901385105, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1807957315317736, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.40720034700467495, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.397799774201171, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005255721209079985, nan, nan, nan, nan, nan, nan, 0.3762939782532286, 0.0, 0.0, nan, nan, 0.19526341227646207, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.49163270347042115, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.7685 | 46.0 | 920 | 1.6461 | 0.1193 | 0.1663 | 0.6126 | [0.5362561433981403, nan, nan, 0.755636163506279, nan, 0.5130186358137029, nan, nan, 0.3490519469206423, nan, 0.4808415970463636, nan, 0.0, nan, 0.4444587000590182, nan, nan, 0.0, 0.020957206264131983, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.116132422490804, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.22112223959036903, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2681155616217824, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0022679869590749853, nan, nan, nan, nan, nan, nan, 0.2762335846152573, 0.0, 0.0, nan, nan, 0.1578549848942598, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2707515143446715, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8006661103407866, nan, nan, 0.9392233927438713, nan, 0.5449744306982822, nan, nan, 0.4989864748002352, nan, 0.6134730358113413, nan, 0.0, nan, 0.6819679159357269, nan, nan, 0.0, 0.023700722149875696, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18238873001554612, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.41417658682346137, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.4022894951033161, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0029579938463163732, nan, nan, nan, nan, nan, nan, 0.37849359955554046, 0.0, 0.0, nan, nan, 0.1683583051393588, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5023465481042362, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.6151 | 47.0 | 940 | 1.6422 | 0.1200 | 0.1665 | 0.6132 | [0.5358835584937998, nan, nan, 0.762341181032713, nan, 0.5249996207965295, nan, nan, 0.3548468717459638, nan, 0.46979561119539376, nan, 0.0, nan, 0.4530588733189128, nan, nan, 0.0, 0.03589317260729502, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11116243437507041, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.21348365421889132, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26835906741072957, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.003541333542608815, nan, nan, nan, nan, nan, nan, 0.2757877170662393, 0.0, 0.0, nan, nan, 0.16579458812976527, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2644075185700632, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.8022811476389466, nan, nan, 0.9352619992288653, nan, 0.5607210165079948, nan, nan, 0.5045210833996333, nan, 0.5937556615737666, nan, 0.0, nan, 0.674386557415776, nan, nan, 0.0, 0.040890256895939385, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1893748920407654, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.3981276206082221, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.41161026071888046, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0047671240112509405, nan, nan, nan, nan, nan, nan, 0.3785049378096761, 0.0, 0.0, nan, nan, 0.17866924440148219, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.48687785599604794, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.8132 | 48.0 | 960 | 1.6362 | 0.1198 | 0.1671 | 0.6154 | [0.537670140668603, nan, nan, 0.7574703715767583, nan, 0.5503381293520359, nan, nan, 0.34713318137716853, nan, 0.475125748502994, nan, 0.0, nan, 0.44345926718928064, nan, nan, 0.0, 0.033251751202633135, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11910377358490566, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2160315408647757, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26936717663421417, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0015017990300881265, nan, nan, nan, nan, nan, nan, 0.27478974836286274, 0.0, 0.0, nan, nan, 0.13573573573573575, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2718448199272799, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7988205045219987, nan, nan, 0.940019402743747, nan, 0.592386990166484, nan, nan, 0.5173267840464907, nan, 0.5989643094389758, nan, 0.0, nan, 0.6741197005953969, nan, nan, 0.0, 0.03731502308511898, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.1822159952401973, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.40074220444358766, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.4068054716832515, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0019015674726319542, nan, nan, nan, nan, nan, nan, 0.3749107112486819, 0.0, 0.0, nan, nan, 0.14564201707749316, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5101580832407064, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 0.532 | 49.0 | 980 | 1.6296 | 0.1197 | 0.1664 | 0.6143 | [0.5366108905436184, nan, nan, 0.7597294770713172, nan, 0.5423197964192679, nan, nan, 0.3459790436344114, nan, 0.4667733595962083, nan, 0.0, nan, 0.4468324476435224, nan, nan, 0.0, 0.036688775081857104, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11978368520371332, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.21549309194854693, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.26910547396528706, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004184269572072527, nan, nan, nan, nan, nan, nan, 0.2764878305832757, 0.0, 0.0, nan, nan, 0.14281490242460082, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2663475049834973, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7993852916321608, nan, nan, 0.9375101055957016, nan, 0.5834660848998018, nan, nan, 0.5171745823100072, nan, 0.5724379491515188, nan, 0.0, nan, 0.67241356682576, nan, nan, 0.0, 0.04138747484313958, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.181525056138802, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.40874259000433755, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.3969070811563, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0054670064838168685, nan, nan, nan, nan, nan, nan, 0.3807385738743948, 0.0, 0.0, nan, nan, 0.1556307394876752, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5033036927257009, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
| 1.0631 | 50.0 | 1000 | 1.6339 | 0.1200 | 0.1669 | 0.6123 | [0.5349505300503856, nan, nan, 0.7662725216601061, nan, 0.5326853699336921, nan, nan, 0.34424006183640854, nan, 0.4690602972950636, nan, 0.0, nan, 0.43864150176543804, nan, nan, 0.0, 0.03116323751411952, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11546494517491812, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.21709821831740012, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2656179069036561, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004383351344800073, nan, nan, nan, nan, nan, nan, 0.27573695030755746, 0.0, 0.0, nan, nan, 0.1730424387328153, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2699536864879483, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] | [0.7970510497742438, nan, nan, 0.9333683660650987, nan, 0.5709001366216121, nan, nan, 0.5022173025701339, nan, 0.5997946735914005, nan, 0.0, nan, 0.6798811830944017, nan, nan, 0.0, 0.03494731857464189, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18674548490489992, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.4055014699503591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.399427626224171, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005731113077237973, nan, nan, nan, nan, nan, nan, 0.3817023254759232, 0.0, 0.0, nan, nan, 0.18656355727404544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.49311473385204396, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3 | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror",
"rug",
"field",
"armchair",
"seat",
"fence",
"desk",
"rock",
"wardrobe",
"lamp",
"bathtub",
"railing",
"cushion",
"base",
"box",
"column",
"signboard",
"chest of drawers",
"counter",
"sand",
"sink",
"skyscraper",
"fireplace",
"refrigerator",
"grandstand",
"path",
"stairs",
"runway",
"case",
"pool table",
"pillow",
"screen door",
"stairway",
"river",
"bridge",
"bookcase",
"blind",
"coffee table",
"toilet",
"flower",
"book",
"hill",
"bench",
"countertop",
"stove",
"palm",
"kitchen island",
"computer",
"swivel chair",
"boat",
"bar",
"arcade machine",
"hovel",
"bus",
"towel",
"light",
"truck",
"tower",
"chandelier",
"awning",
"streetlight",
"booth",
"television receiver",
"airplane",
"dirt track",
"apparel",
"pole",
"land",
"bannister",
"escalator",
"ottoman",
"bottle",
"buffet",
"poster",
"stage",
"van",
"ship",
"fountain",
"conveyer belt",
"canopy",
"washer",
"plaything",
"swimming pool",
"stool",
"barrel",
"basket",
"waterfall",
"tent",
"bag",
"minibike",
"cradle",
"oven",
"ball",
"food",
"step",
"tank",
"trade name",
"microwave",
"pot",
"animal",
"bicycle",
"lake",
"dishwasher",
"screen",
"blanket",
"sculpture",
"hood",
"sconce",
"vase",
"traffic light",
"tray",
"ashcan",
"fan",
"pier",
"crt screen",
"plate",
"monitor",
"bulletin board",
"shower",
"radiator",
"glass",
"clock",
"flag"
] |
dwang-LI/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-b2](https://huggingface.co/nvidia/mit-b2) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7413
- Mean Iou: 0.4159
- Mean Accuracy: 0.4945
- Overall Accuracy: 0.8774
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8388
- Accuracy Flat-sidewalk: 0.9573
- Accuracy Flat-crosswalk: 0.8007
- Accuracy Flat-cyclinglane: 0.8552
- Accuracy Flat-parkingdriveway: 0.5279
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.7175
- Accuracy Human-person: 0.8243
- Accuracy Human-rider: 0.0845
- Accuracy Vehicle-car: 0.9557
- Accuracy Vehicle-truck: 0.1300
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.6660
- Accuracy Vehicle-motorcycle: 0.0883
- Accuracy Vehicle-bicycle: 0.7186
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0195
- Accuracy Construction-building: 0.9361
- Accuracy Construction-door: 0.1788
- Accuracy Construction-wall: 0.6178
- Accuracy Construction-fenceguardrail: 0.3916
- Accuracy Construction-bridge: 0.5516
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.2882
- Accuracy Object-pole: 0.6120
- Accuracy Object-trafficsign: 0.5812
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9537
- Accuracy Nature-terrain: 0.8377
- Accuracy Sky: 0.9844
- Accuracy Void-ground: 0.0132
- Accuracy Void-dynamic: 0.2116
- Accuracy Void-static: 0.4815
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.7575
- Iou Flat-sidewalk: 0.8765
- Iou Flat-crosswalk: 0.6860
- Iou Flat-cyclinglane: 0.7491
- Iou Flat-parkingdriveway: 0.4030
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.5916
- Iou Human-person: 0.6521
- Iou Human-rider: 0.0488
- Iou Vehicle-car: 0.8662
- Iou Vehicle-truck: 0.1122
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.4442
- Iou Vehicle-motorcycle: 0.0883
- Iou Vehicle-bicycle: 0.5874
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0193
- Iou Construction-building: 0.7672
- Iou Construction-door: 0.1600
- Iou Construction-wall: 0.5304
- Iou Construction-fenceguardrail: 0.3282
- Iou Construction-bridge: 0.3920
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.2410
- Iou Object-pole: 0.5084
- Iou Object-trafficsign: 0.3941
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.8872
- Iou Nature-terrain: 0.7611
- Iou Sky: 0.9529
- Iou Void-ground: 0.0098
- Iou Void-dynamic: 0.1155
- Iou Void-static: 0.3780
- 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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| 1.6058 | 0.1 | 20 | 2.0687 | 0.0800 | 0.1356 | 0.6081 | nan | 0.4527 | 0.9100 | 0.0 | 0.0 | 0.0027 | nan | 0.0016 | 0.0075 | 0.0 | 0.8602 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9256 | 0.0 | 0.0197 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8878 | 0.0039 | 0.2678 | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.3225 | 0.6221 | 0.0 | 0.0 | 0.0027 | 0.0 | 0.0015 | 0.0075 | 0.0 | 0.4904 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4424 | 0.0 | 0.0196 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6175 | 0.0039 | 0.2675 | 0.0 | 0.0 | 0.0009 | 0.0 |
| 1.0866 | 0.2 | 40 | 1.2653 | 0.1408 | 0.1825 | 0.6924 | nan | 0.5482 | 0.9667 | 0.0 | 0.0 | 0.0041 | nan | 0.0008 | 0.0357 | 0.0 | 0.8373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9144 | 0.0 | 0.0653 | 0.0 | 0.0 | nan | 0.0 | 0.0022 | 0.0 | 0.0 | 0.9105 | 0.6945 | 0.8432 | 0.0 | 0.0 | 0.0178 | 0.0 | nan | 0.4138 | 0.6618 | 0.0 | 0.0 | 0.0041 | nan | 0.0008 | 0.0352 | 0.0 | 0.6599 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5239 | 0.0 | 0.0645 | 0.0 | 0.0 | nan | 0.0 | 0.0022 | 0.0 | 0.0 | 0.7587 | 0.5554 | 0.8085 | 0.0 | 0.0 | 0.0172 | 0.0 |
| 0.8624 | 0.3 | 60 | 1.0931 | 0.1666 | 0.2119 | 0.7183 | nan | 0.5402 | 0.9777 | 0.0 | 0.2995 | 0.0167 | nan | 0.0017 | 0.1723 | 0.0 | 0.9303 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8316 | 0.0 | 0.0853 | 0.0016 | 0.0 | nan | 0.0 | 0.2050 | 0.0 | 0.0 | 0.9005 | 0.8119 | 0.9414 | 0.0 | 0.0 | 0.0648 | 0.0 | nan | 0.4592 | 0.6670 | 0.0 | 0.2883 | 0.0163 | nan | 0.0017 | 0.1657 | 0.0 | 0.6319 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5774 | 0.0 | 0.0838 | 0.0016 | 0.0 | nan | 0.0 | 0.1763 | 0.0 | 0.0 | 0.7505 | 0.5903 | 0.8594 | 0.0 | 0.0 | 0.0622 | 0.0 |
| 1.1933 | 0.4 | 80 | 0.9780 | 0.1780 | 0.2241 | 0.7326 | nan | 0.5494 | 0.9759 | 0.0 | 0.4403 | 0.0635 | nan | 0.0928 | 0.1458 | 0.0 | 0.9218 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9418 | 0.0 | 0.1182 | 0.0043 | 0.0 | nan | 0.0 | 0.2582 | 0.0 | 0.0 | 0.8457 | 0.8430 | 0.9439 | 0.0 | 0.0 | 0.0278 | 0.0 | nan | 0.4827 | 0.6919 | 0.0 | 0.3890 | 0.0580 | nan | 0.0852 | 0.1389 | 0.0 | 0.6634 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5628 | 0.0 | 0.1040 | 0.0043 | 0.0 | nan | 0.0 | 0.2140 | 0.0 | 0.0 | 0.7901 | 0.6139 | 0.8700 | 0.0 | 0.0 | 0.0276 | 0.0 |
| 0.8678 | 0.5 | 100 | 0.8980 | 0.2055 | 0.2559 | 0.7586 | nan | 0.7035 | 0.9240 | 0.0 | 0.5471 | 0.2248 | nan | 0.1919 | 0.4983 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0122 | 0.0 | 0.0 | 0.8942 | 0.0 | 0.1942 | 0.0415 | 0.0 | nan | 0.0 | 0.3669 | 0.0 | 0.0 | 0.9329 | 0.7145 | 0.9508 | 0.0 | 0.0 | 0.0486 | 0.0 | nan | 0.5343 | 0.7617 | 0.0 | 0.4561 | 0.1464 | nan | 0.1592 | 0.4375 | 0.0 | 0.6418 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0122 | 0.0 | 0.0 | 0.5930 | 0.0 | 0.1698 | 0.0408 | 0.0 | nan | 0.0 | 0.2674 | 0.0 | 0.0 | 0.8039 | 0.6337 | 0.8700 | 0.0 | 0.0 | 0.0478 | 0.0 |
| 0.8691 | 0.6 | 120 | 0.8161 | 0.2164 | 0.2640 | 0.7752 | nan | 0.7915 | 0.9325 | 0.0 | 0.5391 | 0.2122 | nan | 0.1662 | 0.4892 | 0.0 | 0.8716 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1287 | 0.0 | 0.0 | 0.9406 | 0.0 | 0.0777 | 0.1205 | 0.0 | nan | 0.0 | 0.3821 | 0.0 | 0.0 | 0.9227 | 0.8422 | 0.9483 | 0.0 | 0.0 | 0.0846 | 0.0 | nan | 0.5700 | 0.7869 | 0.0 | 0.4869 | 0.1733 | nan | 0.1470 | 0.3997 | 0.0 | 0.7279 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1273 | 0.0 | 0.0 | 0.5747 | 0.0 | 0.0749 | 0.1087 | 0.0 | nan | 0.0 | 0.2806 | 0.0 | 0.0 | 0.8250 | 0.6766 | 0.8837 | 0.0 | 0.0 | 0.0806 | 0.0 |
| 0.5554 | 0.7 | 140 | 0.8194 | 0.2308 | 0.2859 | 0.7661 | nan | 0.5964 | 0.9383 | 0.0 | 0.5685 | 0.2664 | nan | 0.4518 | 0.6607 | 0.0 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1700 | 0.0 | 0.0 | 0.9297 | 0.0 | 0.2699 | 0.1437 | 0.0 | nan | 0.0 | 0.3847 | 0.0 | 0.0 | 0.9183 | 0.8642 | 0.9349 | 0.0 | 0.0 | 0.1238 | 0.0 | nan | 0.4999 | 0.7466 | 0.0 | 0.4657 | 0.1921 | nan | 0.2892 | 0.4987 | 0.0 | 0.7165 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1661 | 0.0 | 0.0 | 0.6027 | 0.0 | 0.2471 | 0.1317 | 0.0 | nan | 0.0 | 0.2981 | 0.0 | 0.0 | 0.8326 | 0.6980 | 0.8876 | 0.0 | 0.0 | 0.1145 | 0.0 |
| 0.794 | 0.8 | 160 | 0.7478 | 0.2403 | 0.2971 | 0.7860 | nan | 0.6689 | 0.9516 | 0.0000 | 0.6487 | 0.2353 | nan | 0.2907 | 0.7860 | 0.0 | 0.9162 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1873 | 0.0 | 0.0 | 0.8967 | 0.0 | 0.4288 | 0.1785 | 0.0 | nan | 0.0 | 0.3825 | 0.0 | 0.0 | 0.9359 | 0.8514 | 0.9644 | 0.0 | 0.0 | 0.1844 | 0.0 | nan | 0.5519 | 0.7755 | 0.0000 | 0.4870 | 0.1750 | nan | 0.2347 | 0.4389 | 0.0 | 0.7562 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1836 | 0.0 | 0.0 | 0.6576 | 0.0 | 0.3686 | 0.1488 | 0.0 | nan | 0.0 | 0.3083 | 0.0 | 0.0 | 0.8332 | 0.7090 | 0.9028 | 0.0 | 0.0 | 0.1575 | 0.0 |
| 0.6339 | 0.9 | 180 | 0.7390 | 0.2411 | 0.2973 | 0.7876 | nan | 0.8110 | 0.9249 | 0.0209 | 0.5771 | 0.3073 | nan | 0.3368 | 0.7894 | 0.0 | 0.8493 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5413 | 0.0 | 0.0 | 0.9288 | 0.0 | 0.1732 | 0.1312 | 0.0 | nan | 0.0 | 0.3136 | 0.0 | 0.0 | 0.9477 | 0.7834 | 0.9502 | 0.0 | 0.0 | 0.1273 | 0.0 | nan | 0.5789 | 0.8126 | 0.0209 | 0.4832 | 0.2278 | nan | 0.2592 | 0.3822 | 0.0 | 0.7646 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4843 | 0.0 | 0.0 | 0.6012 | 0.0 | 0.1659 | 0.1198 | 0.0 | nan | 0.0 | 0.2735 | 0.0 | 0.0 | 0.8253 | 0.7060 | 0.8938 | 0.0 | 0.0 | 0.1164 | 0.0 |
| 0.8587 | 1.0 | 200 | 0.7084 | 0.2675 | 0.3230 | 0.8018 | nan | 0.7580 | 0.9389 | 0.3724 | 0.5680 | 0.3650 | nan | 0.3542 | 0.7193 | 0.0 | 0.9144 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5573 | 0.0 | 0.0 | 0.9250 | 0.0 | 0.3356 | 0.2530 | 0.0 | nan | 0.0 | 0.2509 | 0.0 | 0.0 | 0.9308 | 0.8555 | 0.9539 | 0.0 | 0.0 | 0.2823 | 0.0 | nan | 0.6263 | 0.7931 | 0.3623 | 0.5008 | 0.2209 | nan | 0.2872 | 0.4916 | 0.0 | 0.7620 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4623 | 0.0 | 0.0 | 0.6392 | 0.0 | 0.3075 | 0.1780 | 0.0 | nan | 0.0 | 0.2203 | 0.0 | 0.0 | 0.8510 | 0.7357 | 0.9055 | 0.0 | 0.0 | 0.2151 | 0.0 |
| 0.5614 | 1.1 | 220 | 0.7561 | 0.2601 | 0.3206 | 0.7938 | nan | 0.6713 | 0.9499 | 0.2579 | 0.6298 | 0.3682 | nan | 0.3742 | 0.7721 | 0.0 | 0.9318 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5424 | 0.0 | 0.0 | 0.8845 | 0.0 | 0.4127 | 0.1613 | 0.0 | nan | 0.0 | 0.2406 | 0.0 | 0.0 | 0.9196 | 0.8874 | 0.9724 | 0.0 | 0.0 | 0.2822 | 0.0 | nan | 0.5910 | 0.7841 | 0.2544 | 0.4716 | 0.2352 | nan | 0.2894 | 0.4556 | 0.0 | 0.7663 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4722 | 0.0 | 0.0 | 0.6528 | 0.0 | 0.3492 | 0.1422 | 0.0 | nan | 0.0 | 0.2133 | 0.0 | 0.0 | 0.8336 | 0.6845 | 0.9031 | 0.0 | 0.0 | 0.2248 | 0.0 |
| 0.6716 | 1.2 | 240 | 0.7154 | 0.2718 | 0.3453 | 0.7967 | nan | 0.6683 | 0.9515 | 0.6023 | 0.6991 | 0.3307 | nan | 0.4583 | 0.7933 | 0.0 | 0.9318 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5865 | 0.0 | 0.0 | 0.8739 | 0.0 | 0.3943 | 0.2839 | 0.0 | nan | 0.0 | 0.4104 | 0.0 | 0.0 | 0.8423 | 0.9039 | 0.9740 | 0.0 | 0.0 | 0.3438 | 0.0 | nan | 0.6036 | 0.8231 | 0.5148 | 0.4430 | 0.2387 | nan | 0.3350 | 0.4530 | 0.0 | 0.7768 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4575 | 0.0 | 0.0 | 0.6788 | 0.0 | 0.3633 | 0.1930 | 0.0 | nan | 0.0 | 0.3109 | 0.0 | 0.0 | 0.7773 | 0.5568 | 0.9052 | 0.0 | 0.0 | 0.2673 | 0.0 |
| 0.5977 | 1.3 | 260 | 0.6926 | 0.2792 | 0.3446 | 0.8034 | nan | 0.6946 | 0.9427 | 0.6251 | 0.5124 | 0.4221 | nan | 0.4001 | 0.8085 | 0.0 | 0.8952 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5728 | 0.0 | 0.0 | 0.8277 | 0.0 | 0.6152 | 0.2122 | 0.0 | nan | 0.0 | 0.3902 | 0.0 | 0.0 | 0.9603 | 0.8804 | 0.9529 | 0.0 | 0.0 | 0.3160 | 0.0 | nan | 0.6006 | 0.7857 | 0.5078 | 0.4729 | 0.2514 | nan | 0.3110 | 0.4450 | 0.0 | 0.7790 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4750 | 0.0 | 0.0 | 0.6803 | 0.0 | 0.4791 | 0.1668 | 0.0 | nan | 0.0 | 0.2995 | 0.0 | 0.0 | 0.8278 | 0.7035 | 0.9083 | 0.0 | 0.0 | 0.2401 | 0.0 |
| 0.323 | 1.4 | 280 | 0.6871 | 0.2769 | 0.3368 | 0.8095 | nan | 0.9001 | 0.8911 | 0.5783 | 0.5414 | 0.3860 | nan | 0.2742 | 0.7728 | 0.0 | 0.9198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5951 | 0.0 | 0.0 | 0.9161 | 0.0 | 0.3427 | 0.1982 | 0.0 | nan | 0.0 | 0.3783 | 0.0 | 0.0 | 0.9283 | 0.8808 | 0.9731 | 0.0 | 0.0 | 0.3014 | 0.0 | nan | 0.6163 | 0.8266 | 0.5474 | 0.4864 | 0.2627 | nan | 0.2177 | 0.4875 | 0.0 | 0.7824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4786 | 0.0 | 0.0 | 0.6533 | 0.0 | 0.3035 | 0.1736 | 0.0 | nan | 0.0 | 0.3035 | 0.0 | 0.0 | 0.8537 | 0.7231 | 0.9064 | 0.0 | 0.0 | 0.2376 | 0.0 |
| 0.4141 | 1.5 | 300 | 0.6476 | 0.2867 | 0.3494 | 0.8218 | nan | 0.7932 | 0.9491 | 0.6643 | 0.6611 | 0.2548 | nan | 0.4516 | 0.7783 | 0.0 | 0.9142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6078 | 0.0 | 0.0 | 0.8901 | 0.0 | 0.3304 | 0.2616 | 0.0 | nan | 0.0 | 0.4842 | 0.0 | 0.0 | 0.9445 | 0.7866 | 0.9691 | 0.0 | 0.0 | 0.4399 | 0.0 | nan | 0.6531 | 0.8299 | 0.5304 | 0.5258 | 0.2096 | nan | 0.3576 | 0.4954 | 0.0 | 0.7931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4708 | 0.0 | 0.0 | 0.6738 | 0.0 | 0.2988 | 0.1819 | 0.0 | nan | 0.0 | 0.3425 | 0.0 | 0.0 | 0.8504 | 0.7206 | 0.9123 | 0.0 | 0.0 | 0.3267 | 0.0 |
| 0.3646 | 1.6 | 320 | 0.6528 | 0.2763 | 0.3318 | 0.8139 | nan | 0.7482 | 0.9494 | 0.6282 | 0.6090 | 0.3414 | nan | 0.5601 | 0.7720 | 0.0 | 0.8964 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5823 | 0.0 | 0.0 | 0.9521 | 0.0 | 0.2277 | 0.0532 | 0.0 | nan | 0.0 | 0.3867 | 0.0 | 0.0 | 0.9466 | 0.8266 | 0.9693 | 0.0 | 0.0 | 0.1695 | 0.0 | nan | 0.6420 | 0.8276 | 0.5175 | 0.5250 | 0.2451 | nan | 0.3864 | 0.5556 | 0.0 | 0.7824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5152 | 0.0 | 0.0 | 0.6083 | 0.0 | 0.2015 | 0.0523 | 0.0 | nan | 0.0 | 0.3181 | 0.0 | 0.0 | 0.8566 | 0.7311 | 0.9165 | 0.0 | 0.0 | 0.1596 | 0.0 |
| 0.9788 | 1.7 | 340 | 0.7683 | 0.2612 | 0.3304 | 0.7955 | nan | 0.5996 | 0.9751 | 0.3832 | 0.6502 | 0.2049 | nan | 0.4099 | 0.8274 | 0.0 | 0.9395 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7249 | 0.0 | 0.0 | 0.9074 | 0.0 | 0.2946 | 0.1679 | 0.0 | nan | 0.0 | 0.4672 | 0.0 | 0.0 | 0.9432 | 0.8381 | 0.9654 | 0.0 | 0.0 | 0.2729 | 0.0 | nan | 0.5599 | 0.7533 | 0.3669 | 0.5210 | 0.1659 | nan | 0.3300 | 0.3950 | 0.0 | 0.7744 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3063 | 0.0 | 0.0 | 0.6650 | 0.0 | 0.2691 | 0.1526 | 0.0 | nan | 0.0 | 0.3416 | 0.0 | 0.0 | 0.8595 | 0.7511 | 0.9171 | 0.0 | 0.0 | 0.2307 | 0.0 |
| 0.6605 | 1.8 | 360 | 0.6275 | 0.2884 | 0.3441 | 0.8263 | nan | 0.8420 | 0.9395 | 0.5045 | 0.6022 | 0.2955 | nan | 0.4330 | 0.7870 | 0.0 | 0.8991 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6139 | 0.0 | 0.0 | 0.9320 | 0.0 | 0.5533 | 0.1601 | 0.0 | nan | 0.0 | 0.3684 | 0.0 | 0.0 | 0.9458 | 0.8448 | 0.9551 | 0.0 | 0.0 | 0.3342 | 0.0 | nan | 0.6410 | 0.8340 | 0.4894 | 0.5314 | 0.2450 | nan | 0.3572 | 0.4974 | 0.0 | 0.7906 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4876 | 0.0 | 0.0 | 0.6881 | 0.0 | 0.4509 | 0.1472 | 0.0 | nan | 0.0 | 0.3078 | 0.0 | 0.0 | 0.8575 | 0.7292 | 0.9075 | 0.0 | 0.0 | 0.2676 | 0.0 |
| 0.7524 | 1.9 | 380 | 0.6273 | 0.2919 | 0.3560 | 0.8269 | nan | 0.8081 | 0.9440 | 0.6296 | 0.6118 | 0.3642 | nan | 0.4947 | 0.8006 | 0.0 | 0.9350 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6406 | 0.0 | 0.0 | 0.8665 | 0.0 | 0.4322 | 0.1933 | 0.0 | nan | 0.0 | 0.3940 | 0.0 | 0.0 | 0.9558 | 0.8249 | 0.9678 | 0.0 | 0.0 | 0.5301 | 0.0 | nan | 0.6452 | 0.8301 | 0.5816 | 0.5276 | 0.2781 | nan | 0.3737 | 0.4358 | 0.0 | 0.7892 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4562 | 0.0 | 0.0 | 0.6994 | 0.0 | 0.3982 | 0.1730 | 0.0 | nan | 0.0 | 0.3147 | 0.0 | 0.0 | 0.8553 | 0.7263 | 0.9197 | 0.0 | 0.0 | 0.3361 | 0.0 |
| 1.2079 | 2.0 | 400 | 0.6490 | 0.2897 | 0.3486 | 0.8215 | nan | 0.7698 | 0.9312 | 0.5400 | 0.7152 | 0.5081 | nan | 0.4067 | 0.7781 | 0.0 | 0.8939 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6318 | 0.0 | 0.0 | 0.9512 | 0.0 | 0.4015 | 0.1934 | 0.0 | nan | 0.0 | 0.3986 | 0.0 | 0.0 | 0.9288 | 0.8495 | 0.9746 | 0.0 | 0.0 | 0.2818 | 0.0 | nan | 0.6377 | 0.8292 | 0.5056 | 0.5954 | 0.2895 | nan | 0.3449 | 0.5046 | 0.0 | 0.7963 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5140 | 0.0 | 0.0 | 0.6506 | 0.0 | 0.3486 | 0.1627 | 0.0 | nan | 0.0 | 0.3316 | 0.0 | 0.0 | 0.8600 | 0.7487 | 0.9086 | 0.0 | 0.0 | 0.2415 | 0.0 |
| 0.405 | 2.1 | 420 | 0.6384 | 0.2937 | 0.3622 | 0.8234 | nan | 0.7031 | 0.9520 | 0.6652 | 0.7688 | 0.3272 | nan | 0.4249 | 0.7941 | 0.0 | 0.9555 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6440 | 0.0 | 0.0 | 0.8782 | 0.0 | 0.5263 | 0.2536 | 0.0 | nan | 0.0 | 0.4750 | 0.0 | 0.0 | 0.9257 | 0.8877 | 0.9717 | 0.0 | 0.0204 | 0.4167 | 0.0 | nan | 0.6183 | 0.8256 | 0.5422 | 0.5549 | 0.2588 | nan | 0.3615 | 0.4897 | 0.0 | 0.7693 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4770 | 0.0 | 0.0 | 0.6953 | 0.0 | 0.4538 | 0.1952 | 0.0 | nan | 0.0 | 0.3494 | 0.0 | 0.0 | 0.8578 | 0.7154 | 0.9221 | 0.0 | 0.0178 | 0.2943 | 0.0 |
| 0.3988 | 2.2 | 440 | 0.5934 | 0.2969 | 0.3613 | 0.8282 | nan | 0.8282 | 0.9065 | 0.6606 | 0.7416 | 0.4514 | nan | 0.3391 | 0.8057 | 0.0 | 0.9266 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6561 | 0.0 | 0.0 | 0.9058 | 0.0 | 0.5015 | 0.2832 | 0.0 | nan | 0.0 | 0.3546 | 0.0 | 0.0 | 0.9493 | 0.8867 | 0.9751 | 0.0 | 0.0271 | 0.3634 | 0.0 | nan | 0.6390 | 0.8430 | 0.6169 | 0.5474 | 0.2994 | nan | 0.2954 | 0.4906 | 0.0 | 0.8041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4835 | 0.0 | 0.0 | 0.7002 | 0.0 | 0.4378 | 0.2141 | 0.0 | nan | 0.0 | 0.3096 | 0.0 | 0.0 | 0.8614 | 0.7253 | 0.9219 | 0.0 | 0.0236 | 0.2891 | 0.0 |
| 0.3143 | 2.3 | 460 | 0.6416 | 0.2938 | 0.3609 | 0.8219 | nan | 0.6598 | 0.9464 | 0.7305 | 0.7885 | 0.4049 | nan | 0.5647 | 0.7899 | 0.0 | 0.9416 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6645 | 0.0 | 0.0 | 0.9118 | 0.0 | 0.3080 | 0.1446 | 0.0 | nan | 0.0 | 0.4547 | 0.0013 | 0.0 | 0.9516 | 0.8457 | 0.9714 | 0.0 | 0.0180 | 0.4518 | 0.0 | nan | 0.6057 | 0.8265 | 0.6098 | 0.5589 | 0.2849 | nan | 0.4108 | 0.5416 | 0.0 | 0.7952 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4605 | 0.0 | 0.0 | 0.6670 | 0.0 | 0.2780 | 0.1332 | 0.0 | nan | 0.0 | 0.3446 | 0.0013 | 0.0 | 0.8621 | 0.7526 | 0.9232 | 0.0 | 0.0166 | 0.3277 | 0.0 |
| 0.2928 | 2.4 | 480 | 0.6131 | 0.2982 | 0.3799 | 0.8252 | nan | 0.8027 | 0.9202 | 0.7321 | 0.6574 | 0.5363 | nan | 0.4619 | 0.8093 | 0.0 | 0.9384 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6897 | 0.0 | 0.0 | 0.7726 | 0.0 | 0.6742 | 0.3453 | 0.0 | nan | 0.0 | 0.5261 | 0.0073 | 0.0 | 0.9268 | 0.9278 | 0.9714 | 0.0 | 0.0291 | 0.4292 | 0.0 | nan | 0.6613 | 0.8264 | 0.5482 | 0.6088 | 0.3040 | nan | 0.3857 | 0.4958 | 0.0 | 0.7986 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3815 | 0.0 | 0.0 | 0.6863 | 0.0 | 0.4553 | 0.2230 | 0.0 | nan | 0.0 | 0.3542 | 0.0072 | 0.0 | 0.8510 | 0.6960 | 0.9259 | 0.0 | 0.0247 | 0.3074 | 0.0 |
| 0.4599 | 2.5 | 500 | 0.6091 | 0.3002 | 0.3624 | 0.8349 | nan | 0.7736 | 0.9424 | 0.7071 | 0.7431 | 0.3981 | nan | 0.5366 | 0.7966 | 0.0 | 0.9173 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6335 | 0.0 | 0.0 | 0.9459 | 0.0 | 0.4041 | 0.2116 | 0.0 | nan | 0.0 | 0.4241 | 0.0093 | 0.0 | 0.9330 | 0.9141 | 0.9820 | 0.0 | 0.0143 | 0.3110 | 0.0 | nan | 0.6768 | 0.8398 | 0.5669 | 0.6214 | 0.2799 | nan | 0.4125 | 0.5186 | 0.0 | 0.8087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4910 | 0.0 | 0.0 | 0.6632 | 0.0 | 0.3655 | 0.1828 | 0.0 | nan | 0.0 | 0.3481 | 0.0093 | 0.0 | 0.8663 | 0.7557 | 0.9166 | 0.0 | 0.0129 | 0.2718 | 0.0 |
| 0.4748 | 2.6 | 520 | 0.6341 | 0.2957 | 0.3561 | 0.8299 | nan | 0.8109 | 0.9485 | 0.4931 | 0.6152 | 0.3592 | nan | 0.5488 | 0.8282 | 0.0 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6551 | 0.0 | 0.0 | 0.9281 | 0.0 | 0.4952 | 0.1922 | 0.0 | nan | 0.0 | 0.4332 | 0.0063 | 0.0 | 0.9320 | 0.8761 | 0.9674 | 0.0 | 0.0217 | 0.3563 | 0.0 | nan | 0.6506 | 0.8292 | 0.4840 | 0.5546 | 0.2594 | nan | 0.4223 | 0.4598 | 0.0 | 0.8118 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4897 | 0.0 | 0.0 | 0.6874 | 0.0 | 0.4248 | 0.1705 | 0.0 | nan | 0.0 | 0.3533 | 0.0063 | 0.0 | 0.8680 | 0.7591 | 0.9237 | 0.0 | 0.0187 | 0.2890 | 0.0 |
| 0.3089 | 2.7 | 540 | 0.6322 | 0.3046 | 0.3687 | 0.8342 | nan | 0.7583 | 0.9454 | 0.4860 | 0.7492 | 0.4990 | nan | 0.4709 | 0.8254 | 0.0 | 0.9293 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6323 | 0.0 | 0.0 | 0.8981 | 0.0107 | 0.5664 | 0.2286 | 0.0 | nan | 0.0 | 0.4848 | 0.0162 | 0.0 | 0.9488 | 0.8615 | 0.9829 | 0.0 | 0.0939 | 0.4090 | 0.0 | nan | 0.6480 | 0.8330 | 0.4586 | 0.6005 | 0.3357 | nan | 0.3928 | 0.4786 | 0.0 | 0.8041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5014 | 0.0 | 0.0 | 0.7103 | 0.0106 | 0.4791 | 0.2035 | 0.0 | nan | 0.0 | 0.3768 | 0.0159 | 0.0 | 0.8618 | 0.7505 | 0.9134 | 0.0 | 0.0524 | 0.3200 | 0.0 |
| 1.2466 | 2.8 | 560 | 0.6182 | 0.3016 | 0.3657 | 0.8295 | nan | 0.8185 | 0.9364 | 0.4282 | 0.6726 | 0.4169 | nan | 0.5586 | 0.8235 | 0.0 | 0.9237 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6627 | 0.0 | 0.0 | 0.9047 | 0.0223 | 0.4594 | 0.3392 | 0.0 | nan | 0.0 | 0.4488 | 0.0188 | 0.0 | 0.9480 | 0.7482 | 0.9804 | 0.0000 | 0.0888 | 0.5031 | 0.0 | nan | 0.6578 | 0.8283 | 0.4222 | 0.6020 | 0.3158 | nan | 0.4153 | 0.5162 | 0.0 | 0.8096 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4946 | 0.0 | 0.0 | 0.7006 | 0.0211 | 0.4068 | 0.2277 | 0.0 | nan | 0.0 | 0.3612 | 0.0184 | 0.0 | 0.8493 | 0.6842 | 0.9195 | 0.0000 | 0.0515 | 0.3479 | 0.0 |
| 0.3471 | 2.9 | 580 | 0.6088 | 0.3130 | 0.3846 | 0.8405 | nan | 0.7740 | 0.9325 | 0.8216 | 0.7382 | 0.5086 | nan | 0.5655 | 0.8194 | 0.0 | 0.9507 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6233 | 0.0 | 0.0 | 0.9001 | 0.0216 | 0.5836 | 0.3403 | 0.0 | nan | 0.0 | 0.3942 | 0.0267 | 0.0 | 0.9301 | 0.8750 | 0.9710 | 0.0 | 0.0167 | 0.5157 | 0.0 | nan | 0.6756 | 0.8458 | 0.4939 | 0.6495 | 0.3248 | nan | 0.4361 | 0.5502 | 0.0 | 0.7944 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4773 | 0.0 | 0.0 | 0.7238 | 0.0215 | 0.4953 | 0.2605 | 0.0 | nan | 0.0 | 0.3396 | 0.0261 | 0.0 | 0.8604 | 0.7330 | 0.9247 | 0.0 | 0.0149 | 0.3694 | 0.0 |
| 0.447 | 3.0 | 600 | 0.6063 | 0.3064 | 0.3674 | 0.8408 | nan | 0.7719 | 0.9575 | 0.7510 | 0.7675 | 0.3209 | nan | 0.5208 | 0.8243 | 0.0 | 0.9409 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6379 | 0.0 | 0.0 | 0.8865 | 0.0032 | 0.4287 | 0.1902 | 0.0 | nan | 0.0 | 0.4460 | 0.0301 | 0.0 | 0.9650 | 0.8454 | 0.9800 | 0.0000 | 0.0084 | 0.4821 | 0.0 | nan | 0.6733 | 0.8440 | 0.6036 | 0.6185 | 0.2724 | nan | 0.4230 | 0.4980 | 0.0 | 0.8106 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5051 | 0.0 | 0.0 | 0.7000 | 0.0032 | 0.3955 | 0.1754 | 0.0 | nan | 0.0 | 0.3558 | 0.0298 | 0.0 | 0.8493 | 0.7531 | 0.9206 | 0.0000 | 0.0075 | 0.3646 | 0.0 |
| 0.3025 | 3.1 | 620 | 0.6267 | 0.3100 | 0.3841 | 0.8346 | nan | 0.7058 | 0.9394 | 0.7532 | 0.8210 | 0.4279 | nan | 0.5254 | 0.8072 | 0.0 | 0.9292 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6649 | 0.0 | 0.0 | 0.8850 | 0.0367 | 0.6998 | 0.3119 | 0.0 | nan | 0.0 | 0.4649 | 0.0061 | 0.0 | 0.9281 | 0.8715 | 0.9689 | 0.0019 | 0.0069 | 0.5364 | 0.0 | nan | 0.6314 | 0.8461 | 0.5290 | 0.5602 | 0.3011 | nan | 0.4210 | 0.5059 | 0.0 | 0.8239 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4858 | 0.0 | 0.0 | 0.7287 | 0.0366 | 0.5161 | 0.2431 | 0.0 | nan | 0.0 | 0.3731 | 0.0061 | 0.0 | 0.8683 | 0.7459 | 0.9288 | 0.0018 | 0.0063 | 0.3594 | 0.0 |
| 0.5402 | 3.2 | 640 | 0.6114 | 0.3148 | 0.3742 | 0.8413 | nan | 0.8162 | 0.9555 | 0.7193 | 0.6148 | 0.4400 | nan | 0.5070 | 0.8157 | 0.0 | 0.9328 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6549 | 0.0 | 0.0 | 0.9089 | 0.0828 | 0.4958 | 0.3236 | 0.0 | nan | 0.0 | 0.3724 | 0.0483 | 0.0 | 0.9490 | 0.8336 | 0.9762 | 0.0 | 0.0212 | 0.5066 | 0.0 | nan | 0.6801 | 0.8399 | 0.6177 | 0.5636 | 0.3160 | nan | 0.4129 | 0.5409 | 0.0 | 0.8207 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5088 | 0.0 | 0.0 | 0.7141 | 0.0751 | 0.4367 | 0.2617 | 0.0 | nan | 0.0 | 0.3152 | 0.0471 | 0.0 | 0.8678 | 0.7556 | 0.9279 | 0.0 | 0.0185 | 0.3540 | 0.0 |
| 0.3071 | 3.3 | 660 | 0.6226 | 0.3122 | 0.3814 | 0.8409 | nan | 0.7812 | 0.9437 | 0.7351 | 0.7543 | 0.4146 | nan | 0.5299 | 0.8407 | 0.0 | 0.9495 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7120 | 0.0 | 0.0 | 0.9031 | 0.0202 | 0.4934 | 0.3704 | 0.0 | nan | 0.0 | 0.3671 | 0.0979 | 0.0 | 0.9470 | 0.8862 | 0.9775 | 0.0 | 0.0764 | 0.4034 | 0.0 | nan | 0.6701 | 0.8389 | 0.5992 | 0.6383 | 0.3214 | nan | 0.4274 | 0.5242 | 0.0 | 0.8016 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4534 | 0.0 | 0.0 | 0.7129 | 0.0199 | 0.4292 | 0.2599 | 0.0 | nan | 0.0 | 0.2979 | 0.0930 | 0.0 | 0.8654 | 0.7525 | 0.9216 | 0.0 | 0.0545 | 0.3106 | 0.0 |
| 0.2812 | 3.4 | 680 | 0.5891 | 0.3154 | 0.3853 | 0.8385 | nan | 0.7691 | 0.9259 | 0.7320 | 0.7075 | 0.6098 | nan | 0.6156 | 0.8274 | 0.0 | 0.9228 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6670 | 0.0 | 0.0 | 0.8950 | 0.0003 | 0.7067 | 0.2373 | 0.0 | nan | 0.0 | 0.3845 | 0.0771 | 0.0 | 0.9436 | 0.8482 | 0.9784 | 0.0002 | 0.0573 | 0.4248 | 0.0 | nan | 0.6809 | 0.8459 | 0.6360 | 0.6308 | 0.2685 | nan | 0.4481 | 0.5109 | 0.0 | 0.8121 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4987 | 0.0 | 0.0 | 0.7307 | 0.0003 | 0.5476 | 0.1996 | 0.0 | nan | 0.0 | 0.3162 | 0.0746 | 0.0 | 0.8696 | 0.7530 | 0.9245 | 0.0002 | 0.0378 | 0.3082 | 0.0 |
| 0.4997 | 3.5 | 700 | 0.5982 | 0.3166 | 0.3801 | 0.8448 | nan | 0.8607 | 0.9301 | 0.7868 | 0.6661 | 0.4482 | nan | 0.5705 | 0.7812 | 0.0 | 0.9462 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6700 | 0.0 | 0.0 | 0.9333 | 0.0002 | 0.4569 | 0.2193 | 0.0 | nan | 0.0 | 0.4467 | 0.1375 | 0.0 | 0.9381 | 0.8742 | 0.9743 | 0.0 | 0.0891 | 0.4326 | 0.0 | nan | 0.7075 | 0.8577 | 0.6178 | 0.5973 | 0.2946 | nan | 0.4482 | 0.5701 | 0.0 | 0.8044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4950 | 0.0 | 0.0 | 0.6931 | 0.0002 | 0.4124 | 0.1985 | 0.0 | nan | 0.0 | 0.3601 | 0.1275 | 0.0 | 0.8717 | 0.7650 | 0.9302 | 0.0 | 0.0483 | 0.3308 | 0.0 |
| 0.3472 | 3.6 | 720 | 0.6052 | 0.3213 | 0.3866 | 0.8432 | nan | 0.7653 | 0.9485 | 0.7447 | 0.7379 | 0.4858 | nan | 0.6064 | 0.8020 | 0.0 | 0.9290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6426 | 0.0 | 0.0 | 0.9297 | 0.0 | 0.6149 | 0.3437 | 0.0 | nan | 0.0 | 0.5040 | 0.1435 | 0.0 | 0.9358 | 0.8249 | 0.9786 | 0.0 | 0.1058 | 0.3279 | 0.0 | nan | 0.6746 | 0.8433 | 0.6430 | 0.6389 | 0.3082 | nan | 0.4471 | 0.5350 | 0.0 | 0.8149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5024 | 0.0 | 0.0 | 0.7226 | 0.0 | 0.4948 | 0.2599 | 0.0 | nan | 0.0 | 0.3883 | 0.1319 | 0.0 | 0.8688 | 0.7569 | 0.9257 | 0.0 | 0.0589 | 0.2650 | 0.0 |
| 0.4252 | 3.7 | 740 | 0.6622 | 0.3123 | 0.3891 | 0.8234 | nan | 0.6217 | 0.9484 | 0.7138 | 0.8309 | 0.4263 | nan | 0.5506 | 0.8600 | 0.0 | 0.9525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6234 | 0.0 | 0.0 | 0.8508 | 0.0084 | 0.5880 | 0.3145 | 0.0 | nan | 0.0 | 0.4943 | 0.2299 | 0.0 | 0.9380 | 0.9003 | 0.9850 | 0.0 | 0.2103 | 0.4036 | 0.0 | nan | 0.5607 | 0.8413 | 0.6569 | 0.4842 | 0.3210 | nan | 0.4427 | 0.4777 | 0.0 | 0.7951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4647 | 0.0 | 0.0 | 0.7209 | 0.0084 | 0.5126 | 0.2370 | 0.0 | nan | 0.0 | 0.3795 | 0.2061 | 0.0 | 0.8637 | 0.7505 | 0.9223 | 0.0 | 0.0647 | 0.2846 | 0.0 |
| 0.3308 | 3.8 | 760 | 0.6377 | 0.3127 | 0.3808 | 0.8289 | nan | 0.6214 | 0.9625 | 0.5946 | 0.8099 | 0.4454 | nan | 0.5188 | 0.8494 | 0.0 | 0.9136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6696 | 0.0 | 0.0 | 0.9263 | 0.0024 | 0.6152 | 0.2950 | 0.0 | nan | 0.0027 | 0.4453 | 0.1733 | 0.0 | 0.9520 | 0.8743 | 0.9699 | 0.0004 | 0.1977 | 0.3462 | 0.0 | nan | 0.5763 | 0.8251 | 0.5772 | 0.5411 | 0.3457 | nan | 0.4107 | 0.4836 | 0.0 | 0.8239 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5117 | 0.0 | 0.0 | 0.7262 | 0.0024 | 0.5110 | 0.2294 | 0.0 | nan | 0.0026 | 0.3668 | 0.1574 | 0.0 | 0.8705 | 0.7696 | 0.9339 | 0.0004 | 0.0602 | 0.2801 | 0.0 |
| 0.4693 | 3.9 | 780 | 0.5859 | 0.3216 | 0.3778 | 0.8472 | nan | 0.7901 | 0.9565 | 0.7815 | 0.7387 | 0.4418 | nan | 0.5939 | 0.8066 | 0.0 | 0.9374 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5633 | 0.0 | 0.0 | 0.9404 | 0.0047 | 0.4300 | 0.1846 | 0.0 | nan | 0.0062 | 0.5028 | 0.1681 | 0.0 | 0.9364 | 0.8907 | 0.9751 | 0.0 | 0.0659 | 0.3760 | 0.0 | nan | 0.6844 | 0.8378 | 0.6638 | 0.6608 | 0.3533 | nan | 0.4678 | 0.5651 | 0.0 | 0.8096 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5098 | 0.0 | 0.0 | 0.7056 | 0.0047 | 0.4085 | 0.1746 | 0.0 | nan | 0.0060 | 0.3747 | 0.1550 | 0.0 | 0.8729 | 0.7620 | 0.9327 | 0.0 | 0.0350 | 0.3067 | 0.0 |
| 0.4175 | 4.0 | 800 | 0.5300 | 0.3304 | 0.3990 | 0.8474 | nan | 0.8728 | 0.9146 | 0.8169 | 0.6208 | 0.5492 | nan | 0.5932 | 0.8393 | 0.0 | 0.9432 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6227 | 0.0 | 0.0 | 0.8618 | 0.0183 | 0.7515 | 0.3741 | 0.0 | nan | 0.0 | 0.4608 | 0.2043 | 0.0 | 0.9611 | 0.8383 | 0.9787 | 0.0 | 0.1044 | 0.4406 | 0.0 | nan | 0.7017 | 0.8579 | 0.6370 | 0.5716 | 0.3295 | nan | 0.4682 | 0.5922 | 0.0 | 0.8053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5317 | 0.0 | 0.0 | 0.7427 | 0.0183 | 0.5577 | 0.2733 | 0.0 | nan | 0.0 | 0.3651 | 0.1856 | 0.0 | 0.8612 | 0.7375 | 0.9269 | 0.0 | 0.0746 | 0.3346 | 0.0 |
| 0.2417 | 4.1 | 820 | 0.6240 | 0.3189 | 0.3775 | 0.8361 | nan | 0.8131 | 0.9538 | 0.7682 | 0.4531 | 0.4900 | nan | 0.5241 | 0.8388 | 0.0 | 0.9496 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5933 | 0.0 | 0.0 | 0.9136 | 0.0182 | 0.4895 | 0.2692 | 0.0 | nan | 0.0003 | 0.4741 | 0.2422 | 0.0 | 0.9537 | 0.8414 | 0.9750 | 0.0 | 0.0438 | 0.4760 | 0.0 | nan | 0.6420 | 0.8400 | 0.6500 | 0.4374 | 0.3497 | nan | 0.4397 | 0.5665 | 0.0 | 0.8008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5243 | 0.0 | 0.0 | 0.7188 | 0.0182 | 0.4395 | 0.2415 | 0.0 | nan | 0.0003 | 0.3859 | 0.2129 | 0.0 | 0.8699 | 0.7533 | 0.9329 | 0.0 | 0.0325 | 0.3490 | 0.0 |
| 0.2375 | 4.2 | 840 | 0.5756 | 0.3343 | 0.4028 | 0.8498 | nan | 0.8028 | 0.9399 | 0.7560 | 0.8128 | 0.5208 | nan | 0.5758 | 0.8137 | 0.0 | 0.9266 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6480 | 0.0 | 0.0 | 0.9155 | 0.0155 | 0.5142 | 0.4670 | 0.0 | nan | 0.0 | 0.4630 | 0.3347 | 0.0 | 0.9205 | 0.8892 | 0.9875 | 0.0 | 0.1196 | 0.4656 | 0.0 | nan | 0.7055 | 0.8568 | 0.6719 | 0.6537 | 0.3342 | nan | 0.4664 | 0.5856 | 0.0 | 0.8130 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5293 | 0.0 | 0.0 | 0.7177 | 0.0154 | 0.4507 | 0.2814 | 0.0 | nan | 0.0 | 0.3815 | 0.2734 | 0.0 | 0.8704 | 0.7453 | 0.9204 | 0.0 | 0.0711 | 0.3540 | 0.0 |
| 0.4241 | 4.3 | 860 | 0.5682 | 0.3289 | 0.3927 | 0.8526 | nan | 0.8019 | 0.9477 | 0.7645 | 0.7864 | 0.4614 | nan | 0.6374 | 0.8213 | 0.0 | 0.9414 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6449 | 0.0 | 0.0 | 0.9389 | 0.0015 | 0.5075 | 0.1875 | 0.0 | nan | 0.0060 | 0.4489 | 0.3299 | 0.0 | 0.9467 | 0.8621 | 0.9705 | 0.0 | 0.1149 | 0.4442 | 0.0 | nan | 0.7105 | 0.8518 | 0.6212 | 0.6864 | 0.3408 | nan | 0.4810 | 0.5663 | 0.0 | 0.8198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4875 | 0.0 | 0.0 | 0.7131 | 0.0015 | 0.4607 | 0.1773 | 0.0 | nan | 0.0057 | 0.3741 | 0.2755 | 0.0 | 0.8731 | 0.7626 | 0.9348 | 0.0 | 0.0422 | 0.3375 | 0.0 |
| 0.5282 | 4.4 | 880 | 0.6106 | 0.3241 | 0.3981 | 0.8456 | nan | 0.7704 | 0.9356 | 0.8287 | 0.8018 | 0.5745 | nan | 0.5025 | 0.7925 | 0.0 | 0.9564 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6370 | 0.0 | 0.0 | 0.9097 | 0.0360 | 0.5623 | 0.4363 | 0.0 | nan | 0.0 | 0.4484 | 0.2753 | 0.0 | 0.9503 | 0.7920 | 0.9724 | 0.0009 | 0.1229 | 0.4328 | 0.0 | nan | 0.7081 | 0.8635 | 0.4271 | 0.6627 | 0.3582 | nan | 0.4362 | 0.6102 | 0.0 | 0.8004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4666 | 0.0 | 0.0 | 0.7321 | 0.0353 | 0.4640 | 0.2690 | 0.0 | nan | 0.0 | 0.3772 | 0.2455 | 0.0 | 0.8642 | 0.7079 | 0.9363 | 0.0009 | 0.0663 | 0.3381 | 0.0 |
| 0.3367 | 4.5 | 900 | 0.5852 | 0.3273 | 0.3859 | 0.8544 | nan | 0.8327 | 0.9558 | 0.6822 | 0.7709 | 0.3897 | nan | 0.6299 | 0.7857 | 0.0 | 0.9039 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6906 | 0.0 | 0.0 | 0.9428 | 0.0104 | 0.4495 | 0.3420 | 0.0 | nan | 0.0 | 0.4741 | 0.2323 | 0.0 | 0.9426 | 0.8893 | 0.9785 | 0.0 | 0.0100 | 0.4372 | 0.0 | nan | 0.7217 | 0.8512 | 0.6538 | 0.6746 | 0.3280 | nan | 0.4868 | 0.5682 | 0.0 | 0.8238 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4554 | 0.0 | 0.0 | 0.7065 | 0.0103 | 0.4132 | 0.2616 | 0.0 | nan | 0.0 | 0.3935 | 0.2107 | 0.0 | 0.8720 | 0.7465 | 0.9320 | 0.0 | 0.0089 | 0.3559 | 0.0 |
| 0.1462 | 4.6 | 920 | 0.5898 | 0.3302 | 0.3945 | 0.8517 | nan | 0.8338 | 0.9321 | 0.7807 | 0.7720 | 0.5273 | nan | 0.5959 | 0.8227 | 0.0 | 0.9378 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7047 | 0.0 | 0.0 | 0.9415 | 0.0315 | 0.4600 | 0.3444 | 0.0 | nan | 0.0 | 0.4654 | 0.2423 | 0.0 | 0.9457 | 0.8191 | 0.9787 | 0.0002 | 0.0175 | 0.4721 | 0.0 | nan | 0.7254 | 0.8518 | 0.6417 | 0.6844 | 0.3375 | nan | 0.4761 | 0.5773 | 0.0 | 0.8160 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4945 | 0.0 | 0.0 | 0.7011 | 0.0308 | 0.4212 | 0.2757 | 0.0 | nan | 0.0 | 0.3946 | 0.2157 | 0.0 | 0.8680 | 0.7422 | 0.9321 | 0.0002 | 0.0144 | 0.3644 | 0.0 |
| 0.4018 | 4.7 | 940 | 0.6261 | 0.3313 | 0.4006 | 0.8471 | nan | 0.7361 | 0.9560 | 0.8252 | 0.7443 | 0.4880 | nan | 0.5874 | 0.7623 | 0.0 | 0.9292 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6837 | 0.0 | 0.0 | 0.9315 | 0.0560 | 0.5691 | 0.3433 | 0.0 | nan | 0.0 | 0.4978 | 0.3762 | 0.0 | 0.9313 | 0.9096 | 0.9814 | 0.0004 | 0.0463 | 0.4639 | 0.0 | nan | 0.6786 | 0.8459 | 0.4753 | 0.6694 | 0.3442 | nan | 0.4817 | 0.6111 | 0.0 | 0.8128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4944 | 0.0 | 0.0 | 0.7258 | 0.0547 | 0.4986 | 0.2692 | 0.0 | nan | 0.0 | 0.3990 | 0.3001 | 0.0 | 0.8752 | 0.7656 | 0.9259 | 0.0004 | 0.0314 | 0.3436 | 0.0 |
| 0.4323 | 4.8 | 960 | 0.6071 | 0.3369 | 0.4009 | 0.8527 | nan | 0.8909 | 0.9288 | 0.7706 | 0.7429 | 0.3946 | nan | 0.5634 | 0.8032 | 0.0 | 0.9419 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6484 | 0.0 | 0.0 | 0.8991 | 0.1415 | 0.6081 | 0.2444 | 0.0 | nan | 0.0739 | 0.5198 | 0.4198 | 0.0 | 0.9632 | 0.7166 | 0.9803 | 0.0110 | 0.0255 | 0.5409 | 0.0 | nan | 0.7203 | 0.8627 | 0.6071 | 0.6653 | 0.3017 | nan | 0.4226 | 0.5991 | 0.0 | 0.8170 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4908 | 0.0 | 0.0 | 0.7391 | 0.1318 | 0.5113 | 0.2209 | 0.0 | nan | 0.0611 | 0.4137 | 0.3238 | 0.0 | 0.8530 | 0.6852 | 0.9328 | 0.0093 | 0.0178 | 0.3944 | 0.0 |
| 0.2333 | 4.9 | 980 | 0.6312 | 0.3291 | 0.4049 | 0.8368 | nan | 0.6661 | 0.9599 | 0.7668 | 0.8162 | 0.4021 | nan | 0.5460 | 0.8279 | 0.0 | 0.9377 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6167 | 0.0 | 0.0 | 0.8637 | 0.0490 | 0.7147 | 0.3548 | 0.0 | nan | 0.0487 | 0.4632 | 0.4447 | 0.0 | 0.9449 | 0.8856 | 0.9798 | 0.0024 | 0.2556 | 0.4098 | 0.0 | nan | 0.6061 | 0.8459 | 0.6123 | 0.5262 | 0.3227 | nan | 0.4682 | 0.5298 | 0.0 | 0.8213 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4777 | 0.0 | 0.0 | 0.7401 | 0.0488 | 0.5668 | 0.2560 | 0.0 | nan | 0.0474 | 0.3885 | 0.3212 | 0.0 | 0.8738 | 0.7688 | 0.9317 | 0.0021 | 0.0662 | 0.3102 | 0.0 |
| 0.322 | 5.0 | 1000 | 0.5919 | 0.3324 | 0.3983 | 0.8527 | nan | 0.8795 | 0.9454 | 0.7501 | 0.6501 | 0.4403 | nan | 0.6006 | 0.8655 | 0.0 | 0.9171 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6457 | 0.0 | 0.0 | 0.9125 | 0.0151 | 0.4907 | 0.2739 | 0.0 | nan | 0.0 | 0.5307 | 0.4038 | 0.0 | 0.9539 | 0.8585 | 0.9824 | 0.0017 | 0.2231 | 0.4063 | 0.0 | nan | 0.7358 | 0.8591 | 0.6885 | 0.6124 | 0.2908 | nan | 0.4820 | 0.5026 | 0.0 | 0.8247 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5430 | 0.0 | 0.0 | 0.7227 | 0.0151 | 0.4502 | 0.2349 | 0.0 | nan | 0.0 | 0.4150 | 0.3229 | 0.0 | 0.8710 | 0.7707 | 0.9332 | 0.0017 | 0.0580 | 0.3015 | 0.0 |
| 0.3299 | 5.1 | 1020 | 0.5630 | 0.3381 | 0.4067 | 0.8554 | nan | 0.8362 | 0.9417 | 0.7732 | 0.7712 | 0.4461 | nan | 0.6305 | 0.8050 | 0.0042 | 0.9430 | 0.0011 | 0.0 | 0.0 | 0.0 | 0.6503 | 0.0 | 0.0 | 0.9244 | 0.0045 | 0.5766 | 0.3341 | 0.0 | nan | 0.0 | 0.5153 | 0.4275 | 0.0 | 0.9401 | 0.8734 | 0.9810 | 0.0002 | 0.2593 | 0.3754 | 0.0 | nan | 0.7239 | 0.8683 | 0.6261 | 0.6428 | 0.3037 | nan | 0.5023 | 0.6126 | 0.0030 | 0.8145 | 0.0011 | 0.0 | 0.0 | 0.0 | 0.5157 | 0.0 | 0.0 | 0.7251 | 0.0044 | 0.4858 | 0.2659 | 0.0 | nan | 0.0 | 0.4109 | 0.3378 | 0.0 | 0.8759 | 0.7719 | 0.9339 | 0.0002 | 0.0783 | 0.3135 | 0.0 |
| 0.2486 | 5.2 | 1040 | 0.5781 | 0.3354 | 0.4008 | 0.8553 | nan | 0.8114 | 0.9444 | 0.7999 | 0.8031 | 0.4525 | nan | 0.5980 | 0.7806 | 0.0264 | 0.9194 | 0.0031 | 0.0 | 0.0 | 0.0 | 0.6433 | 0.0 | 0.0 | 0.9335 | 0.0814 | 0.5906 | 0.2020 | 0.0 | nan | 0.0002 | 0.4619 | 0.4333 | 0.0 | 0.9473 | 0.8681 | 0.9758 | 0.0013 | 0.0796 | 0.4692 | 0.0 | nan | 0.7074 | 0.8746 | 0.5776 | 0.6471 | 0.3072 | nan | 0.4838 | 0.5905 | 0.0113 | 0.8288 | 0.0031 | 0.0 | 0.0 | 0.0 | 0.5141 | 0.0 | 0.0 | 0.7319 | 0.0772 | 0.4996 | 0.1780 | 0.0 | nan | 0.0002 | 0.3924 | 0.3248 | 0.0 | 0.8743 | 0.7747 | 0.9360 | 0.0012 | 0.0425 | 0.3553 | 0.0 |
| 0.2187 | 5.3 | 1060 | 0.5951 | 0.3310 | 0.3996 | 0.8521 | nan | 0.8741 | 0.9406 | 0.7747 | 0.6985 | 0.4974 | nan | 0.5988 | 0.7802 | 0.0157 | 0.9384 | 0.0049 | 0.0 | 0.0 | 0.0 | 0.6773 | 0.0 | 0.0 | 0.9064 | 0.1097 | 0.4512 | 0.2733 | 0.0 | nan | 0.0080 | 0.3901 | 0.4226 | 0.0 | 0.9569 | 0.8016 | 0.9778 | 0.0022 | 0.2137 | 0.4736 | 0.0 | nan | 0.7341 | 0.8672 | 0.6043 | 0.6460 | 0.3411 | nan | 0.4676 | 0.5519 | 0.0038 | 0.8196 | 0.0048 | 0.0 | 0.0 | 0.0 | 0.5047 | 0.0 | 0.0 | 0.7232 | 0.1031 | 0.4142 | 0.2260 | 0.0 | nan | 0.0077 | 0.3316 | 0.3239 | 0.0 | 0.8641 | 0.7379 | 0.9360 | 0.0020 | 0.0542 | 0.3228 | 0.0 |
| 1.1803 | 5.4 | 1080 | 0.6495 | 0.3336 | 0.3988 | 0.8391 | nan | 0.6558 | 0.9554 | 0.7805 | 0.8250 | 0.5203 | nan | 0.5736 | 0.8174 | 0.0056 | 0.9396 | 0.0066 | 0.0 | 0.0 | 0.0 | 0.6255 | 0.0 | 0.0 | 0.9273 | 0.0083 | 0.5809 | 0.3201 | 0.0 | nan | 0.0 | 0.5183 | 0.4578 | 0.0 | 0.9433 | 0.8644 | 0.9790 | 0.0010 | 0.0665 | 0.3882 | 0.0 | nan | 0.6035 | 0.8349 | 0.6789 | 0.5849 | 0.3641 | nan | 0.4660 | 0.5953 | 0.0038 | 0.8272 | 0.0064 | 0.0 | 0.0 | 0.0 | 0.5366 | 0.0 | 0.0 | 0.7289 | 0.0081 | 0.4550 | 0.2526 | 0.0 | nan | 0.0 | 0.4133 | 0.3445 | 0.0 | 0.8748 | 0.7729 | 0.9337 | 0.0010 | 0.0502 | 0.3385 | 0.0 |
| 0.2229 | 5.5 | 1100 | 0.5565 | 0.3363 | 0.4115 | 0.8594 | nan | 0.8699 | 0.9352 | 0.8052 | 0.7833 | 0.4652 | nan | 0.6304 | 0.7524 | 0.0693 | 0.9367 | 0.0196 | 0.0 | 0.0 | 0.0 | 0.7243 | 0.0 | 0.0 | 0.9247 | 0.0419 | 0.5269 | 0.3136 | 0.0 | nan | 0.0 | 0.5243 | 0.4829 | 0.0 | 0.9406 | 0.9026 | 0.9807 | 0.0009 | 0.1594 | 0.3776 | 0.0 | nan | 0.7612 | 0.8653 | 0.5482 | 0.6938 | 0.3423 | nan | 0.4929 | 0.5383 | 0.0143 | 0.8332 | 0.0188 | 0.0 | 0.0 | 0.0 | 0.5054 | 0.0 | 0.0 | 0.7323 | 0.0410 | 0.4596 | 0.2366 | 0.0 | nan | 0.0 | 0.4012 | 0.3332 | 0.0 | 0.8753 | 0.7496 | 0.9344 | 0.0009 | 0.0710 | 0.3135 | 0.0 |
| 0.3822 | 5.6 | 1120 | 0.5669 | 0.3459 | 0.4115 | 0.8612 | nan | 0.8962 | 0.9368 | 0.7621 | 0.7845 | 0.5097 | nan | 0.4860 | 0.8278 | 0.0 | 0.9454 | 0.0269 | 0.0 | 0.0 | 0.0 | 0.6764 | 0.0 | 0.0 | 0.9072 | 0.1391 | 0.5740 | 0.2863 | 0.0 | nan | 0.0011 | 0.5018 | 0.4754 | 0.0 | 0.9440 | 0.8610 | 0.9885 | 0.0011 | 0.1953 | 0.4424 | 0.0 | nan | 0.7435 | 0.8646 | 0.7032 | 0.6904 | 0.3584 | nan | 0.4298 | 0.5766 | 0.0 | 0.8297 | 0.0261 | 0.0 | 0.0 | 0.0 | 0.5415 | 0.0 | 0.0 | 0.7357 | 0.1221 | 0.4772 | 0.2503 | 0.0 | nan | 0.0010 | 0.4117 | 0.3376 | 0.0 | 0.8797 | 0.7681 | 0.9209 | 0.0010 | 0.0649 | 0.3353 | 0.0 |
| 0.2649 | 5.7 | 1140 | 0.5898 | 0.3381 | 0.4023 | 0.8563 | nan | 0.7976 | 0.9442 | 0.7650 | 0.7640 | 0.5549 | nan | 0.6973 | 0.8434 | 0.0 | 0.9444 | 0.0094 | 0.0 | 0.0 | 0.0 | 0.6017 | 0.0 | 0.0 | 0.9358 | 0.0022 | 0.5838 | 0.3365 | 0.0 | nan | 0.0000 | 0.4293 | 0.3976 | 0.0 | 0.9468 | 0.8714 | 0.9810 | 0.0012 | 0.0478 | 0.4181 | 0.0 | nan | 0.7151 | 0.8569 | 0.7113 | 0.6623 | 0.3494 | nan | 0.5183 | 0.5293 | 0.0 | 0.8284 | 0.0093 | 0.0 | 0.0 | 0.0 | 0.5048 | 0.0 | 0.0 | 0.7321 | 0.0022 | 0.4675 | 0.2713 | 0.0 | nan | 0.0000 | 0.3827 | 0.3276 | 0.0 | 0.8788 | 0.7549 | 0.9334 | 0.0012 | 0.0408 | 0.3430 | 0.0 |
| 0.36 | 5.8 | 1160 | 0.5610 | 0.3374 | 0.4024 | 0.8560 | nan | 0.8640 | 0.9259 | 0.7671 | 0.7668 | 0.6144 | nan | 0.5556 | 0.8438 | 0.0 | 0.9443 | 0.0083 | 0.0 | 0.0 | 0.0 | 0.6159 | 0.0 | 0.0 | 0.9392 | 0.0182 | 0.5002 | 0.2419 | 0.0 | nan | 0.0076 | 0.5526 | 0.4234 | 0.0 | 0.9436 | 0.8715 | 0.9810 | 0.0 | 0.1199 | 0.3711 | 0.0 | nan | 0.7443 | 0.8662 | 0.6964 | 0.6839 | 0.2997 | nan | 0.4887 | 0.5272 | 0.0 | 0.8304 | 0.0082 | 0.0 | 0.0 | 0.0 | 0.5343 | 0.0 | 0.0 | 0.7226 | 0.0181 | 0.4603 | 0.2237 | 0.0 | nan | 0.0071 | 0.4219 | 0.3267 | 0.0 | 0.8794 | 0.7650 | 0.9339 | 0.0 | 0.0503 | 0.3097 | 0.0 |
| 0.1851 | 5.9 | 1180 | 0.5901 | 0.3316 | 0.4029 | 0.8500 | nan | 0.7504 | 0.9589 | 0.7043 | 0.8477 | 0.4014 | nan | 0.5987 | 0.8495 | 0.0 | 0.9416 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.7332 | 0.0 | 0.0 | 0.9259 | 0.0010 | 0.5587 | 0.3454 | 0.0 | nan | 0.0001 | 0.5006 | 0.4310 | 0.0 | 0.9551 | 0.8353 | 0.9730 | 0.0 | 0.2293 | 0.3470 | 0.0 | nan | 0.6760 | 0.8659 | 0.6777 | 0.5612 | 0.3372 | nan | 0.5049 | 0.4991 | 0.0 | 0.8348 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.4929 | 0.0 | 0.0 | 0.7364 | 0.0010 | 0.4741 | 0.2656 | 0.0 | nan | 0.0001 | 0.4060 | 0.3345 | 0.0 | 0.8720 | 0.7557 | 0.9380 | 0.0 | 0.0757 | 0.2966 | 0.0 |
| 0.2929 | 6.0 | 1200 | 0.5772 | 0.3361 | 0.4214 | 0.8502 | nan | 0.7678 | 0.9437 | 0.8101 | 0.7995 | 0.5434 | nan | 0.6081 | 0.8514 | 0.0 | 0.9448 | 0.0044 | 0.0 | 0.0 | 0.0 | 0.7424 | 0.0 | 0.0 | 0.8567 | 0.1035 | 0.7333 | 0.3929 | 0.0 | nan | 0.1165 | 0.4942 | 0.4598 | 0.0 | 0.9381 | 0.8854 | 0.9776 | 0.0 | 0.1032 | 0.4068 | 0.0 | nan | 0.6929 | 0.8728 | 0.5952 | 0.6048 | 0.3191 | nan | 0.4872 | 0.4926 | 0.0 | 0.8332 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.4247 | 0.0 | 0.0 | 0.7432 | 0.1007 | 0.5006 | 0.3004 | 0.0 | nan | 0.0834 | 0.4060 | 0.3476 | 0.0 | 0.8751 | 0.7607 | 0.9382 | 0.0 | 0.0532 | 0.3191 | 0.0 |
| 0.1837 | 6.1 | 1220 | 0.5619 | 0.3425 | 0.4019 | 0.8599 | nan | 0.8292 | 0.9577 | 0.7582 | 0.7946 | 0.4884 | nan | 0.5736 | 0.8172 | 0.0 | 0.9262 | 0.0073 | 0.0 | 0.0 | 0.0 | 0.6724 | 0.0 | 0.0 | 0.9509 | 0.0249 | 0.4688 | 0.4031 | 0.0 | nan | 0.0112 | 0.4901 | 0.4293 | 0.0 | 0.9458 | 0.8549 | 0.9790 | 0.0001 | 0.0968 | 0.3822 | 0.0 | nan | 0.7390 | 0.8602 | 0.6756 | 0.6938 | 0.3610 | nan | 0.4749 | 0.5803 | 0.0 | 0.8379 | 0.0071 | 0.0 | 0.0 | 0.0 | 0.5735 | 0.0 | 0.0 | 0.7104 | 0.0247 | 0.4235 | 0.2840 | 0.0 | nan | 0.0101 | 0.4083 | 0.3263 | 0.0 | 0.8765 | 0.7763 | 0.9354 | 0.0001 | 0.0555 | 0.3266 | 0.0 |
| 0.2823 | 6.2 | 1240 | 0.5561 | 0.3477 | 0.4153 | 0.8616 | nan | 0.8684 | 0.9325 | 0.7535 | 0.7614 | 0.5858 | nan | 0.6483 | 0.8377 | 0.0 | 0.9540 | 0.0172 | 0.0 | 0.0 | 0.0 | 0.6799 | 0.0 | 0.0 | 0.9022 | 0.0421 | 0.6256 | 0.2876 | 0.0 | nan | 0.0021 | 0.5645 | 0.4458 | 0.0 | 0.9561 | 0.8393 | 0.9661 | 0.0 | 0.1596 | 0.4593 | 0.0 | nan | 0.7564 | 0.8604 | 0.6944 | 0.6911 | 0.3387 | nan | 0.5181 | 0.5511 | 0.0 | 0.8288 | 0.0169 | 0.0 | 0.0 | 0.0 | 0.5345 | 0.0 | 0.0 | 0.7512 | 0.0414 | 0.5198 | 0.2474 | 0.0 | nan | 0.0020 | 0.4409 | 0.3486 | 0.0 | 0.8731 | 0.7634 | 0.9364 | 0.0 | 0.0737 | 0.3371 | 0.0 |
| 0.1737 | 6.3 | 1260 | 0.5944 | 0.3440 | 0.4109 | 0.8589 | nan | 0.8164 | 0.9394 | 0.7514 | 0.8265 | 0.5133 | nan | 0.5731 | 0.8081 | 0.0049 | 0.9528 | 0.0190 | 0.0 | 0.0 | 0.0 | 0.6786 | 0.0 | 0.0 | 0.9077 | 0.0189 | 0.6365 | 0.4641 | 0.0 | nan | 0.0003 | 0.5127 | 0.4451 | 0.0 | 0.9562 | 0.8655 | 0.9817 | 0.0 | 0.0706 | 0.4066 | 0.0 | nan | 0.7103 | 0.8631 | 0.6924 | 0.6440 | 0.3634 | nan | 0.4701 | 0.5896 | 0.0033 | 0.8266 | 0.0181 | 0.0 | 0.0 | 0.0 | 0.4933 | 0.0 | 0.0 | 0.7567 | 0.0189 | 0.5532 | 0.2877 | 0.0 | nan | 0.0003 | 0.4161 | 0.3459 | 0.0 | 0.8718 | 0.7740 | 0.9335 | 0.0 | 0.0414 | 0.3359 | 0.0 |
| 0.2331 | 6.4 | 1280 | 0.5757 | 0.3439 | 0.4080 | 0.8642 | nan | 0.8474 | 0.9577 | 0.7499 | 0.8125 | 0.3469 | nan | 0.6753 | 0.8720 | 0.0 | 0.9422 | 0.0256 | 0.0 | 0.0 | 0.0 | 0.6022 | 0.0 | 0.0 | 0.9147 | 0.0303 | 0.6387 | 0.2659 | 0.0 | nan | 0.0033 | 0.5037 | 0.5161 | 0.0 | 0.9533 | 0.8503 | 0.9761 | 0.0 | 0.1375 | 0.4339 | 0.0 | nan | 0.7464 | 0.8625 | 0.7014 | 0.6901 | 0.2919 | nan | 0.5113 | 0.4932 | 0.0 | 0.8415 | 0.0242 | 0.0 | 0.0 | 0.0 | 0.5310 | 0.0 | 0.0 | 0.7484 | 0.0301 | 0.5440 | 0.2362 | 0.0 | nan | 0.0031 | 0.4189 | 0.3563 | 0.0 | 0.8716 | 0.7666 | 0.9388 | 0.0 | 0.0665 | 0.3304 | 0.0 |
| 0.2482 | 6.5 | 1300 | 0.5652 | 0.3439 | 0.4139 | 0.8565 | nan | 0.8961 | 0.9318 | 0.7939 | 0.6718 | 0.5130 | nan | 0.6420 | 0.8713 | 0.0 | 0.9445 | 0.0252 | 0.0 | 0.0 | 0.0 | 0.6115 | 0.0 | 0.0 | 0.9118 | 0.1801 | 0.5194 | 0.3921 | 0.0 | nan | 0.0787 | 0.5022 | 0.4451 | 0.0 | 0.9485 | 0.8205 | 0.9784 | 0.0000 | 0.1026 | 0.4647 | 0.0 | nan | 0.7433 | 0.8670 | 0.6723 | 0.6234 | 0.3394 | nan | 0.4819 | 0.4912 | 0.0 | 0.8309 | 0.0242 | 0.0 | 0.0 | 0.0 | 0.5364 | 0.0 | 0.0 | 0.7400 | 0.1646 | 0.4697 | 0.2621 | 0.0 | nan | 0.0725 | 0.4230 | 0.3409 | 0.0 | 0.8691 | 0.7309 | 0.9383 | 0.0000 | 0.0509 | 0.3325 | 0.0 |
| 0.2564 | 6.6 | 1320 | 0.5605 | 0.3430 | 0.4096 | 0.8605 | nan | 0.8574 | 0.9411 | 0.7728 | 0.7655 | 0.5374 | nan | 0.6314 | 0.8056 | 0.0 | 0.9448 | 0.0233 | 0.0 | 0.0 | 0.0 | 0.6626 | 0.0 | 0.0 | 0.9296 | 0.0727 | 0.5102 | 0.3465 | 0.0 | nan | 0.0388 | 0.5049 | 0.4320 | 0.0 | 0.9485 | 0.8772 | 0.9762 | 0.0105 | 0.1357 | 0.3834 | 0.0 | nan | 0.7479 | 0.8716 | 0.6437 | 0.6747 | 0.3443 | nan | 0.5198 | 0.5438 | 0.0 | 0.8378 | 0.0228 | 0.0 | 0.0 | 0.0 | 0.5055 | 0.0 | 0.0 | 0.7366 | 0.0677 | 0.4568 | 0.2458 | 0.0 | nan | 0.0316 | 0.4134 | 0.3461 | 0.0 | 0.8717 | 0.7620 | 0.9386 | 0.0074 | 0.0703 | 0.3169 | 0.0 |
| 0.2327 | 6.7 | 1340 | 0.5858 | 0.3514 | 0.4217 | 0.8616 | nan | 0.8699 | 0.9510 | 0.7048 | 0.7627 | 0.4538 | nan | 0.6080 | 0.7268 | 0.1376 | 0.9419 | 0.0359 | 0.0 | 0.0 | 0.0 | 0.7361 | 0.0 | 0.0 | 0.8837 | 0.2082 | 0.6157 | 0.3706 | 0.0 | nan | 0.0909 | 0.4611 | 0.4802 | 0.0 | 0.9503 | 0.8700 | 0.9807 | 0.0210 | 0.1612 | 0.4738 | 0.0 | nan | 0.7492 | 0.8674 | 0.6835 | 0.6819 | 0.3450 | nan | 0.5073 | 0.5297 | 0.0222 | 0.8360 | 0.0342 | 0.0 | 0.0 | 0.0 | 0.5117 | 0.0 | 0.0 | 0.7413 | 0.1671 | 0.4875 | 0.2640 | 0.0 | nan | 0.0776 | 0.4006 | 0.3457 | 0.0 | 0.8775 | 0.7710 | 0.9368 | 0.0109 | 0.0734 | 0.3249 | 0.0 |
| 0.242 | 6.8 | 1360 | 0.5879 | 0.3377 | 0.4038 | 0.8588 | nan | 0.8580 | 0.9459 | 0.6603 | 0.7829 | 0.5026 | nan | 0.6233 | 0.6993 | 0.1826 | 0.9596 | 0.0324 | 0.0 | 0.0 | 0.0 | 0.6813 | 0.0 | 0.0 | 0.8888 | 0.0606 | 0.6100 | 0.2078 | 0.0 | nan | 0.0106 | 0.5106 | 0.4489 | 0.0 | 0.9608 | 0.8217 | 0.9807 | 0.0013 | 0.0424 | 0.4508 | 0.0 | nan | 0.7297 | 0.8711 | 0.6473 | 0.6682 | 0.3661 | nan | 0.5064 | 0.4987 | 0.0245 | 0.8177 | 0.0323 | 0.0 | 0.0 | 0.0 | 0.4811 | 0.0 | 0.0 | 0.7394 | 0.0582 | 0.4697 | 0.1953 | 0.0 | nan | 0.0093 | 0.4198 | 0.3574 | 0.0 | 0.8634 | 0.7439 | 0.9376 | 0.0010 | 0.0280 | 0.3386 | 0.0 |
| 0.2912 | 6.9 | 1380 | 0.6065 | 0.3460 | 0.4119 | 0.8580 | nan | 0.8233 | 0.9491 | 0.7514 | 0.8086 | 0.4890 | nan | 0.6596 | 0.7872 | 0.0 | 0.9363 | 0.0347 | 0.0 | 0.0 | 0.0 | 0.6847 | 0.0 | 0.0 | 0.9520 | 0.0737 | 0.5223 | 0.3596 | 0.0 | nan | 0.0359 | 0.5441 | 0.4222 | 0.0 | 0.9219 | 0.8838 | 0.9741 | 0.0026 | 0.2035 | 0.3599 | 0.0 | nan | 0.7319 | 0.8655 | 0.6413 | 0.6811 | 0.3626 | nan | 0.5255 | 0.5864 | 0.0 | 0.8358 | 0.0330 | 0.0 | 0.0 | 0.0 | 0.5516 | 0.0 | 0.0 | 0.7090 | 0.0674 | 0.4307 | 0.2864 | 0.0 | nan | 0.0250 | 0.4413 | 0.3338 | 0.0 | 0.8745 | 0.7683 | 0.9393 | 0.0022 | 0.0863 | 0.2947 | 0.0 |
| 0.343 | 7.0 | 1400 | 0.5567 | 0.3432 | 0.4134 | 0.8604 | nan | 0.8250 | 0.9528 | 0.7195 | 0.8020 | 0.5162 | nan | 0.6391 | 0.8356 | 0.0060 | 0.9500 | 0.0391 | 0.0 | 0.0 | 0.0 | 0.7280 | 0.0 | 0.0 | 0.8962 | 0.0651 | 0.6283 | 0.3323 | 0.0 | nan | 0.0065 | 0.5047 | 0.4910 | 0.0 | 0.9584 | 0.8309 | 0.9817 | 0.0008 | 0.1522 | 0.3661 | 0.0 | nan | 0.7363 | 0.8592 | 0.6737 | 0.6849 | 0.3638 | nan | 0.5259 | 0.5049 | 0.0037 | 0.8376 | 0.0377 | 0.0 | 0.0 | 0.0 | 0.4747 | 0.0 | 0.0 | 0.7459 | 0.0630 | 0.4846 | 0.2764 | 0.0 | nan | 0.0052 | 0.4170 | 0.3515 | 0.0 | 0.8684 | 0.7582 | 0.9370 | 0.0007 | 0.0725 | 0.3003 | 0.0 |
| 0.2914 | 7.1 | 1420 | 0.5773 | 0.3455 | 0.4101 | 0.8628 | nan | 0.8788 | 0.9472 | 0.7366 | 0.7511 | 0.4820 | nan | 0.6145 | 0.8190 | 0.0 | 0.9357 | 0.0365 | 0.0 | 0.0 | 0.0 | 0.6832 | 0.0 | 0.0 | 0.9259 | 0.0426 | 0.5416 | 0.2700 | 0.0 | nan | 0.0390 | 0.5164 | 0.4809 | 0.0 | 0.9462 | 0.8881 | 0.9822 | 0.0007 | 0.1845 | 0.4210 | 0.0 | nan | 0.7424 | 0.8681 | 0.6758 | 0.6744 | 0.3689 | nan | 0.5043 | 0.5500 | 0.0 | 0.8381 | 0.0355 | 0.0 | 0.0 | 0.0 | 0.5276 | 0.0 | 0.0 | 0.7309 | 0.0420 | 0.4570 | 0.2420 | 0.0 | nan | 0.0278 | 0.4148 | 0.3608 | 0.0 | 0.8780 | 0.7737 | 0.9370 | 0.0007 | 0.0782 | 0.3276 | 0.0 |
| 0.2474 | 7.2 | 1440 | 0.5986 | 0.3456 | 0.4083 | 0.8604 | nan | 0.8241 | 0.9533 | 0.7590 | 0.8042 | 0.4869 | nan | 0.6086 | 0.7678 | 0.0 | 0.9578 | 0.0176 | 0.0 | 0.0 | 0.0 | 0.6546 | 0.0 | 0.0 | 0.9157 | 0.0721 | 0.5568 | 0.2897 | 0.0 | nan | 0.0205 | 0.4932 | 0.4753 | 0.0 | 0.9612 | 0.8364 | 0.9759 | 0.0003 | 0.2280 | 0.4053 | 0.0 | nan | 0.7370 | 0.8694 | 0.6417 | 0.6868 | 0.3282 | nan | 0.5111 | 0.6177 | 0.0 | 0.8225 | 0.0173 | 0.0 | 0.0 | 0.0 | 0.5195 | 0.0 | 0.0 | 0.7384 | 0.0702 | 0.4737 | 0.2533 | 0.0 | nan | 0.0171 | 0.4074 | 0.3697 | 0.0 | 0.8687 | 0.7579 | 0.9399 | 0.0003 | 0.0826 | 0.3284 | 0.0 |
| 0.2505 | 7.3 | 1460 | 0.5985 | 0.3471 | 0.4082 | 0.8566 | nan | 0.7938 | 0.9652 | 0.7313 | 0.7649 | 0.4475 | nan | 0.6260 | 0.8675 | 0.0020 | 0.9491 | 0.0379 | 0.0 | 0.0 | 0.0 | 0.6807 | 0.0 | 0.0 | 0.9081 | 0.1519 | 0.6030 | 0.2748 | 0.0 | nan | 0.0039 | 0.5783 | 0.46 | 0.0 | 0.9587 | 0.8108 | 0.9788 | 0.0001 | 0.0826 | 0.3844 | 0.0 | nan | 0.7066 | 0.8387 | 0.6786 | 0.6773 | 0.3727 | nan | 0.5193 | 0.5468 | 0.0016 | 0.8359 | 0.0354 | 0.0 | 0.0 | 0.0 | 0.5663 | 0.0 | 0.0 | 0.7500 | 0.1320 | 0.4856 | 0.2477 | 0.0 | nan | 0.0034 | 0.4339 | 0.3538 | 0.0 | 0.8699 | 0.7449 | 0.9397 | 0.0001 | 0.0558 | 0.3117 | 0.0 |
| 0.2611 | 7.4 | 1480 | 0.5702 | 0.3476 | 0.4143 | 0.8581 | nan | 0.7707 | 0.9551 | 0.7422 | 0.8445 | 0.4893 | nan | 0.6655 | 0.8344 | 0.0 | 0.9515 | 0.0443 | 0.0 | 0.0 | 0.0 | 0.6860 | 0.0 | 0.0 | 0.9232 | 0.1644 | 0.6029 | 0.3108 | 0.0 | nan | 0.0147 | 0.5112 | 0.4610 | 0.0 | 0.9478 | 0.8592 | 0.9807 | 0.0065 | 0.0864 | 0.4052 | 0.0 | nan | 0.7017 | 0.8700 | 0.6427 | 0.6132 | 0.3791 | nan | 0.5387 | 0.5832 | 0.0 | 0.8422 | 0.0404 | 0.0 | 0.0 | 0.0 | 0.5270 | 0.0 | 0.0 | 0.7452 | 0.1416 | 0.4843 | 0.2549 | 0.0 | nan | 0.0129 | 0.4183 | 0.3607 | 0.0 | 0.8781 | 0.7718 | 0.9388 | 0.0049 | 0.0451 | 0.3281 | 0.0 |
| 0.1479 | 7.5 | 1500 | 0.5701 | 0.3439 | 0.4035 | 0.8595 | nan | 0.8494 | 0.9474 | 0.7318 | 0.7205 | 0.5444 | nan | 0.6775 | 0.8485 | 0.0 | 0.9484 | 0.0252 | 0.0 | 0.0 | 0.0 | 0.6319 | 0.0 | 0.0 | 0.9298 | 0.0287 | 0.5318 | 0.2629 | 0.0 | nan | 0.0034 | 0.4961 | 0.4789 | 0.0 | 0.9603 | 0.8198 | 0.9658 | 0.0029 | 0.0514 | 0.4563 | 0.0 | nan | 0.7411 | 0.8677 | 0.6846 | 0.6476 | 0.3372 | nan | 0.5484 | 0.5975 | 0.0 | 0.8345 | 0.0246 | 0.0 | 0.0 | 0.0 | 0.5414 | 0.0 | 0.0 | 0.7303 | 0.0280 | 0.4573 | 0.2322 | 0.0 | nan | 0.0030 | 0.4152 | 0.3618 | 0.0 | 0.8665 | 0.7385 | 0.9396 | 0.0027 | 0.0370 | 0.3666 | 0.0 |
| 0.2073 | 7.6 | 1520 | 0.6279 | 0.3474 | 0.4175 | 0.8507 | nan | 0.7556 | 0.9561 | 0.7696 | 0.7821 | 0.4914 | nan | 0.6223 | 0.8651 | 0.0 | 0.9547 | 0.0416 | 0.0 | 0.0 | 0.0 | 0.6631 | 0.0 | 0.0 | 0.9289 | 0.1014 | 0.5414 | 0.3211 | 0.0 | nan | 0.1147 | 0.5668 | 0.5320 | 0.0 | 0.9357 | 0.8535 | 0.9849 | 0.0320 | 0.1488 | 0.3977 | 0.0 | nan | 0.6791 | 0.8419 | 0.6776 | 0.6390 | 0.3672 | nan | 0.5206 | 0.5841 | 0.0 | 0.8318 | 0.0399 | 0.0 | 0.0 | 0.0 | 0.5261 | 0.0 | 0.0 | 0.7370 | 0.0923 | 0.4608 | 0.2570 | 0.0 | nan | 0.0822 | 0.4361 | 0.3484 | 0.0 | 0.8778 | 0.7638 | 0.9356 | 0.0195 | 0.0696 | 0.3278 | 0.0 |
| 0.3208 | 7.7 | 1540 | 0.5902 | 0.3443 | 0.4097 | 0.8625 | nan | 0.8514 | 0.9559 | 0.8170 | 0.7319 | 0.4577 | nan | 0.6391 | 0.8315 | 0.0 | 0.9341 | 0.0208 | 0.0 | 0.0 | 0.0 | 0.6960 | 0.0 | 0.0 | 0.9322 | 0.0069 | 0.5405 | 0.3043 | 0.0 | nan | 0.0195 | 0.4643 | 0.5060 | 0.0 | 0.9447 | 0.8907 | 0.9827 | 0.0075 | 0.1400 | 0.4345 | 0.0 | nan | 0.7546 | 0.8646 | 0.6123 | 0.6707 | 0.3476 | nan | 0.5256 | 0.6086 | 0.0 | 0.8453 | 0.0198 | 0.0 | 0.0 | 0.0 | 0.5410 | 0.0 | 0.0 | 0.7307 | 0.0068 | 0.4733 | 0.2653 | 0.0 | nan | 0.0173 | 0.4133 | 0.3411 | 0.0 | 0.8744 | 0.7639 | 0.9379 | 0.0062 | 0.0566 | 0.3404 | 0.0 |
| 0.3173 | 7.8 | 1560 | 0.5700 | 0.3494 | 0.4171 | 0.8633 | nan | 0.8505 | 0.9393 | 0.7827 | 0.8097 | 0.4909 | nan | 0.6613 | 0.8247 | 0.0331 | 0.9473 | 0.0464 | 0.0 | 0.0 | 0.0 | 0.6595 | 0.0 | 0.0 | 0.9329 | 0.0327 | 0.5948 | 0.2566 | 0.0 | nan | 0.0584 | 0.5498 | 0.5224 | 0.0 | 0.9481 | 0.8445 | 0.9784 | 0.0012 | 0.1162 | 0.4649 | 0.0 | nan | 0.7463 | 0.8649 | 0.6431 | 0.6921 | 0.3346 | nan | 0.5205 | 0.5660 | 0.0149 | 0.8430 | 0.0429 | 0.0 | 0.0 | 0.0 | 0.5391 | 0.0 | 0.0 | 0.7422 | 0.0322 | 0.5000 | 0.2370 | 0.0 | nan | 0.0506 | 0.4451 | 0.3526 | 0.0 | 0.8779 | 0.7619 | 0.9406 | 0.0011 | 0.0622 | 0.3703 | 0.0 |
| 0.1863 | 7.9 | 1580 | 0.6211 | 0.3514 | 0.4297 | 0.8592 | nan | 0.8506 | 0.9447 | 0.8257 | 0.7064 | 0.4736 | nan | 0.6324 | 0.8422 | 0.0 | 0.9651 | 0.0536 | 0.0 | 0.0 | 0.0 | 0.6058 | 0.0 | 0.0 | 0.9151 | 0.1508 | 0.5574 | 0.4391 | 0.0 | nan | 0.1478 | 0.5624 | 0.5315 | 0.0 | 0.9451 | 0.8766 | 0.9861 | 0.0010 | 0.3727 | 0.3650 | 0.0 | nan | 0.7333 | 0.8714 | 0.5576 | 0.6480 | 0.3506 | nan | 0.5172 | 0.6009 | 0.0 | 0.8275 | 0.0493 | 0.0 | 0.0 | 0.0 | 0.5273 | 0.0 | 0.0 | 0.7444 | 0.1344 | 0.4871 | 0.3098 | 0.0 | nan | 0.1057 | 0.4511 | 0.3565 | 0.0 | 0.8806 | 0.7541 | 0.9369 | 0.0008 | 0.0878 | 0.3123 | 0.0 |
| 0.2516 | 8.0 | 1600 | 0.5767 | 0.3501 | 0.4204 | 0.8653 | nan | 0.8572 | 0.9601 | 0.7830 | 0.7746 | 0.4109 | nan | 0.6479 | 0.7926 | 0.0650 | 0.9358 | 0.0504 | 0.0 | 0.0 | 0.0 | 0.7195 | 0.0 | 0.0 | 0.9099 | 0.0133 | 0.5805 | 0.3600 | 0.0 | nan | 0.0194 | 0.5341 | 0.5156 | 0.0 | 0.9602 | 0.8242 | 0.9783 | 0.0 | 0.3244 | 0.4364 | 0.0 | nan | 0.7522 | 0.8696 | 0.6293 | 0.6857 | 0.3462 | nan | 0.5311 | 0.5778 | 0.0151 | 0.8472 | 0.0474 | 0.0 | 0.0 | 0.0 | 0.5229 | 0.0 | 0.0 | 0.7538 | 0.0132 | 0.5060 | 0.2932 | 0.0 | nan | 0.0177 | 0.4476 | 0.3751 | 0.0 | 0.8715 | 0.7523 | 0.9414 | 0.0 | 0.0762 | 0.3314 | 0.0 |
| 0.2206 | 8.1 | 1620 | 0.6162 | 0.3546 | 0.4307 | 0.8563 | nan | 0.8299 | 0.9257 | 0.7671 | 0.8026 | 0.4752 | nan | 0.6825 | 0.7886 | 0.0158 | 0.9459 | 0.0448 | 0.0 | 0.0 | 0.0 | 0.6966 | 0.0 | 0.0 | 0.9133 | 0.1368 | 0.6901 | 0.3385 | 0.0 | nan | 0.2352 | 0.5165 | 0.5090 | 0.0 | 0.9412 | 0.8623 | 0.9821 | 0.0369 | 0.2019 | 0.4425 | 0.0 | nan | 0.7445 | 0.8644 | 0.6314 | 0.6645 | 0.3391 | nan | 0.5353 | 0.5417 | 0.0075 | 0.8451 | 0.0427 | 0.0 | 0.0 | 0.0 | 0.4609 | 0.0 | 0.0 | 0.7530 | 0.1226 | 0.5129 | 0.2932 | 0.0 | nan | 0.1710 | 0.4440 | 0.3757 | 0.0 | 0.8807 | 0.7562 | 0.9398 | 0.0078 | 0.0675 | 0.3445 | 0.0 |
| 0.189 | 8.2 | 1640 | 0.5740 | 0.3467 | 0.4147 | 0.8648 | nan | 0.8577 | 0.9535 | 0.7585 | 0.7673 | 0.4784 | nan | 0.6893 | 0.8107 | 0.0637 | 0.9507 | 0.0421 | 0.0 | 0.0 | 0.0 | 0.6907 | 0.0 | 0.0 | 0.9336 | 0.0316 | 0.5791 | 0.2895 | 0.0 | nan | 0.0854 | 0.5458 | 0.5167 | 0.0 | 0.9507 | 0.8159 | 0.9754 | 0.0 | 0.0640 | 0.4187 | 0.0 | nan | 0.7502 | 0.8706 | 0.6532 | 0.6880 | 0.3630 | nan | 0.5298 | 0.4944 | 0.0264 | 0.8465 | 0.0401 | 0.0 | 0.0 | 0.0 | 0.5009 | 0.0 | 0.0 | 0.7409 | 0.0303 | 0.4910 | 0.2531 | 0.0 | nan | 0.0716 | 0.4439 | 0.3748 | 0.0 | 0.8713 | 0.7383 | 0.9429 | 0.0 | 0.0365 | 0.3374 | 0.0 |
| 0.2028 | 8.3 | 1660 | 0.6399 | 0.3438 | 0.4078 | 0.8578 | nan | 0.8066 | 0.9687 | 0.7532 | 0.8068 | 0.3267 | nan | 0.6167 | 0.7656 | 0.0031 | 0.9349 | 0.0451 | 0.0 | 0.0 | 0.0 | 0.7131 | 0.0 | 0.0 | 0.9415 | 0.0212 | 0.5982 | 0.3048 | 0.0 | nan | 0.0864 | 0.5129 | 0.4953 | 0.0 | 0.9371 | 0.8768 | 0.9825 | 0.0 | 0.2933 | 0.2605 | 0.0 | nan | 0.7242 | 0.8444 | 0.7154 | 0.6779 | 0.2892 | nan | 0.5380 | 0.5381 | 0.0014 | 0.8407 | 0.0419 | 0.0 | 0.0 | 0.0 | 0.5174 | 0.0 | 0.0 | 0.7326 | 0.0193 | 0.4840 | 0.2500 | 0.0 | nan | 0.0719 | 0.4295 | 0.3630 | 0.0 | 0.8772 | 0.7656 | 0.9371 | 0.0 | 0.1074 | 0.2369 | 0.0 |
| 0.2783 | 8.4 | 1680 | 0.5473 | 0.3515 | 0.4219 | 0.8664 | nan | 0.8826 | 0.9387 | 0.7571 | 0.8127 | 0.5269 | nan | 0.6741 | 0.8037 | 0.0 | 0.9550 | 0.0350 | 0.0 | 0.0 | 0.0 | 0.6732 | 0.0 | 0.0 | 0.9218 | 0.0816 | 0.5645 | 0.3347 | 0.0 | nan | 0.0116 | 0.5560 | 0.5146 | 0.0 | 0.9369 | 0.8790 | 0.9825 | 0.0009 | 0.2686 | 0.3906 | 0.0 | nan | 0.7608 | 0.8771 | 0.7013 | 0.6885 | 0.3555 | nan | 0.5491 | 0.5221 | 0.0 | 0.8338 | 0.0347 | 0.0 | 0.0 | 0.0 | 0.5370 | 0.0 | 0.0 | 0.7435 | 0.0750 | 0.5028 | 0.2650 | 0.0 | nan | 0.0099 | 0.4431 | 0.3785 | 0.0 | 0.8797 | 0.7654 | 0.9401 | 0.0007 | 0.0687 | 0.3162 | 0.0 |
| 0.199 | 8.5 | 1700 | 0.5928 | 0.3595 | 0.4303 | 0.8670 | nan | 0.8255 | 0.9598 | 0.7983 | 0.8581 | 0.4141 | nan | 0.6636 | 0.8888 | 0.0 | 0.9441 | 0.0413 | 0.0 | 0.0132 | 0.0 | 0.6113 | 0.0 | 0.0 | 0.8697 | 0.2663 | 0.7008 | 0.3577 | 0.0 | nan | 0.2193 | 0.5255 | 0.4762 | 0.0 | 0.9526 | 0.8591 | 0.9803 | 0.0 | 0.0804 | 0.4627 | 0.0 | nan | 0.7395 | 0.8762 | 0.6573 | 0.7119 | 0.3497 | nan | 0.5446 | 0.5005 | 0.0 | 0.8375 | 0.0407 | 0.0 | 0.0131 | 0.0 | 0.5413 | 0.0 | 0.0 | 0.7524 | 0.2323 | 0.4987 | 0.2915 | 0.0 | nan | 0.1353 | 0.4328 | 0.3726 | 0.0 | 0.8722 | 0.7516 | 0.9419 | 0.0 | 0.0540 | 0.3573 | 0.0 |
| 0.1591 | 8.6 | 1720 | 0.5927 | 0.3568 | 0.4274 | 0.8632 | nan | 0.8250 | 0.9475 | 0.7706 | 0.8347 | 0.5623 | nan | 0.6601 | 0.8675 | 0.0 | 0.9547 | 0.0375 | 0.0 | 0.0 | 0.0 | 0.6326 | 0.0 | 0.0 | 0.9278 | 0.1591 | 0.5080 | 0.3138 | 0.0 | nan | 0.2364 | 0.5457 | 0.5151 | 0.0 | 0.9625 | 0.7945 | 0.9821 | 0.0 | 0.2648 | 0.3730 | 0.0 | nan | 0.7411 | 0.8788 | 0.6746 | 0.7028 | 0.3475 | nan | 0.5438 | 0.5751 | 0.0 | 0.8345 | 0.0370 | 0.0 | 0.0 | 0.0 | 0.5548 | 0.0 | 0.0 | 0.7334 | 0.1370 | 0.4510 | 0.2575 | 0.0 | nan | 0.1640 | 0.4431 | 0.3603 | 0.0 | 0.8660 | 0.7312 | 0.9409 | 0.0 | 0.1150 | 0.3292 | 0.0 |
| 0.1553 | 8.7 | 1740 | 0.5695 | 0.3598 | 0.4404 | 0.8679 | nan | 0.8717 | 0.9483 | 0.7673 | 0.7990 | 0.5361 | nan | 0.6620 | 0.7190 | 0.1766 | 0.9412 | 0.0958 | 0.0 | 0.0079 | 0.0 | 0.7929 | 0.0 | 0.0 | 0.8975 | 0.1616 | 0.6552 | 0.3511 | 0.0 | nan | 0.0630 | 0.5369 | 0.5200 | 0.0 | 0.9408 | 0.8779 | 0.9758 | 0.0004 | 0.3845 | 0.4103 | 0.0 | nan | 0.7609 | 0.8754 | 0.6801 | 0.7116 | 0.3771 | nan | 0.5357 | 0.5635 | 0.0429 | 0.8473 | 0.0878 | 0.0 | 0.0078 | 0.0 | 0.4463 | 0.0 | 0.0 | 0.7517 | 0.1464 | 0.5156 | 0.2927 | 0.0 | nan | 0.0538 | 0.4402 | 0.3753 | 0.0 | 0.8760 | 0.7600 | 0.9433 | 0.0003 | 0.0908 | 0.3304 | 0.0 |
| 0.1498 | 8.8 | 1760 | 0.5841 | 0.3633 | 0.4338 | 0.8642 | nan | 0.8380 | 0.9265 | 0.7928 | 0.9074 | 0.5189 | nan | 0.6488 | 0.8137 | 0.0 | 0.9494 | 0.0536 | 0.0 | 0.0085 | 0.0 | 0.7206 | 0.0 | 0.0 | 0.9184 | 0.2066 | 0.6024 | 0.4015 | 0.0 | nan | 0.0487 | 0.5791 | 0.5140 | 0.0 | 0.9414 | 0.8646 | 0.9797 | 0.0054 | 0.1717 | 0.4690 | 0.0 | nan | 0.7476 | 0.8657 | 0.6433 | 0.6603 | 0.3790 | nan | 0.5424 | 0.6239 | 0.0 | 0.8431 | 0.0509 | 0.0 | 0.0084 | 0.0 | 0.5871 | 0.0 | 0.0 | 0.7513 | 0.1678 | 0.5091 | 0.3164 | 0.0 | nan | 0.0414 | 0.4595 | 0.3703 | 0.0 | 0.8827 | 0.7695 | 0.9422 | 0.0033 | 0.0879 | 0.3730 | 0.0 |
| 0.1828 | 8.9 | 1780 | 0.6037 | 0.3609 | 0.4253 | 0.8654 | nan | 0.8656 | 0.9520 | 0.7476 | 0.7937 | 0.4616 | nan | 0.6561 | 0.8421 | 0.0 | 0.9473 | 0.0502 | 0.0 | 0.0 | 0.0 | 0.7023 | 0.0 | 0.0 | 0.8957 | 0.1736 | 0.6450 | 0.3825 | 0.0 | nan | 0.0666 | 0.5406 | 0.5340 | 0.0 | 0.9570 | 0.7497 | 0.9826 | 0.0161 | 0.1262 | 0.5220 | 0.0 | nan | 0.7611 | 0.8645 | 0.7076 | 0.7248 | 0.3637 | nan | 0.5355 | 0.5979 | 0.0 | 0.8396 | 0.0462 | 0.0 | 0.0 | 0.0 | 0.5541 | 0.0 | 0.0 | 0.7555 | 0.1530 | 0.4978 | 0.3036 | 0.0 | nan | 0.0557 | 0.4552 | 0.3779 | 0.0 | 0.8626 | 0.6957 | 0.9399 | 0.0079 | 0.0662 | 0.3827 | 0.0 |
| 0.2588 | 9.0 | 1800 | 0.5867 | 0.3597 | 0.4377 | 0.8588 | nan | 0.8202 | 0.9224 | 0.7975 | 0.8144 | 0.6519 | nan | 0.6872 | 0.7382 | 0.0319 | 0.9508 | 0.0736 | 0.0 | 0.0036 | 0.0 | 0.7372 | 0.0 | 0.0 | 0.9216 | 0.2294 | 0.5725 | 0.3596 | 0.0 | nan | 0.0690 | 0.5450 | 0.5389 | 0.0 | 0.9345 | 0.8713 | 0.9842 | 0.0037 | 0.2376 | 0.5116 | 0.0 | nan | 0.7439 | 0.8635 | 0.6600 | 0.7027 | 0.3202 | nan | 0.5441 | 0.6061 | 0.0136 | 0.8412 | 0.0651 | 0.0 | 0.0036 | 0.0 | 0.4753 | 0.0 | 0.0 | 0.7408 | 0.1843 | 0.4959 | 0.3006 | 0.0 | nan | 0.0559 | 0.4579 | 0.3852 | 0.0 | 0.8822 | 0.7645 | 0.9387 | 0.0026 | 0.0771 | 0.3845 | 0.0 |
| 0.2303 | 9.1 | 1820 | 0.5980 | 0.3532 | 0.4292 | 0.8599 | nan | 0.8094 | 0.9427 | 0.8150 | 0.7852 | 0.4762 | nan | 0.7417 | 0.7367 | 0.0162 | 0.9494 | 0.0567 | 0.0 | 0.0859 | 0.0 | 0.7208 | 0.0 | 0.0 | 0.9223 | 0.0887 | 0.6457 | 0.3506 | 0.0 | nan | 0.0485 | 0.5267 | 0.5245 | 0.0 | 0.9494 | 0.8379 | 0.9815 | 0.0079 | 0.2876 | 0.4272 | 0.0 | nan | 0.7148 | 0.8715 | 0.6250 | 0.6618 | 0.3683 | nan | 0.5072 | 0.6077 | 0.0080 | 0.8445 | 0.0527 | 0.0 | 0.0836 | 0.0 | 0.4327 | 0.0 | 0.0 | 0.7509 | 0.0843 | 0.5323 | 0.2819 | 0.0 | nan | 0.0407 | 0.4408 | 0.3615 | 0.0 | 0.8794 | 0.7633 | 0.9411 | 0.0036 | 0.0975 | 0.3483 | 0.0 |
| 0.1926 | 9.2 | 1840 | 0.6364 | 0.3523 | 0.4171 | 0.8567 | nan | 0.8013 | 0.9537 | 0.7411 | 0.7849 | 0.4775 | nan | 0.6554 | 0.8531 | 0.0 | 0.9392 | 0.0431 | 0.0 | 0.0162 | 0.0 | 0.6634 | 0.0 | 0.0 | 0.9319 | 0.1200 | 0.4821 | 0.3603 | 0.0 | nan | 0.0067 | 0.4962 | 0.5323 | 0.0 | 0.9510 | 0.8484 | 0.9816 | 0.0133 | 0.2473 | 0.4469 | 0.0 | nan | 0.7045 | 0.8678 | 0.6994 | 0.6142 | 0.3742 | nan | 0.5336 | 0.6132 | 0.0 | 0.8493 | 0.0400 | 0.0 | 0.0161 | 0.0 | 0.5747 | 0.0 | 0.0 | 0.7271 | 0.1111 | 0.4372 | 0.3011 | 0.0 | nan | 0.0058 | 0.4273 | 0.3716 | 0.0 | 0.8753 | 0.7585 | 0.9391 | 0.0073 | 0.0719 | 0.3542 | 0.0 |
| 0.1283 | 9.3 | 1860 | 0.6048 | 0.3556 | 0.4276 | 0.8652 | nan | 0.8396 | 0.9455 | 0.7744 | 0.8039 | 0.5487 | nan | 0.6756 | 0.7530 | 0.0430 | 0.9380 | 0.0847 | 0.0 | 0.0 | 0.0 | 0.7757 | 0.0 | 0.0 | 0.9322 | 0.1295 | 0.5577 | 0.2965 | 0.0 | nan | 0.1061 | 0.5510 | 0.5570 | 0.0 | 0.9408 | 0.8590 | 0.9812 | 0.0007 | 0.0445 | 0.5457 | 0.0 | nan | 0.7397 | 0.8676 | 0.7034 | 0.6899 | 0.3817 | nan | 0.5452 | 0.5603 | 0.0131 | 0.8537 | 0.0754 | 0.0 | 0.0 | 0.0 | 0.4386 | 0.0 | 0.0 | 0.7376 | 0.1184 | 0.4767 | 0.2647 | 0.0 | nan | 0.0761 | 0.4534 | 0.3578 | 0.0 | 0.8771 | 0.7525 | 0.9408 | 0.0006 | 0.0278 | 0.4266 | 0.0 |
| 0.242 | 9.4 | 1880 | 0.6183 | 0.3537 | 0.4259 | 0.8652 | nan | 0.8536 | 0.9574 | 0.7653 | 0.7996 | 0.4903 | nan | 0.6016 | 0.8441 | 0.0 | 0.9293 | 0.1477 | 0.0 | 0.0374 | 0.0 | 0.7279 | 0.0 | 0.0 | 0.9335 | 0.0248 | 0.5401 | 0.3316 | 0.0 | nan | 0.0044 | 0.5457 | 0.5598 | 0.0 | 0.9513 | 0.8410 | 0.9830 | 0.0017 | 0.4150 | 0.3431 | 0.0 | nan | 0.7464 | 0.8714 | 0.7064 | 0.6863 | 0.3932 | nan | 0.5200 | 0.5922 | 0.0 | 0.8531 | 0.1256 | 0.0 | 0.0368 | 0.0 | 0.5249 | 0.0 | 0.0 | 0.7372 | 0.0233 | 0.4739 | 0.2831 | 0.0 | nan | 0.0039 | 0.4503 | 0.3367 | 0.0 | 0.8766 | 0.7550 | 0.9400 | 0.0011 | 0.0830 | 0.2982 | 0.0 |
| 0.1308 | 9.5 | 1900 | 0.6292 | 0.3489 | 0.4163 | 0.8593 | nan | 0.7832 | 0.9470 | 0.7857 | 0.8231 | 0.5413 | nan | 0.6989 | 0.8729 | 0.0 | 0.9641 | 0.0627 | 0.0 | 0.0080 | 0.0 | 0.5577 | 0.0 | 0.0 | 0.9156 | 0.0280 | 0.5252 | 0.3589 | 0.0 | nan | 0.0166 | 0.5069 | 0.5345 | 0.0 | 0.9496 | 0.8725 | 0.9817 | 0.0001 | 0.0829 | 0.5056 | 0.0 | nan | 0.7085 | 0.8673 | 0.6933 | 0.6324 | 0.3683 | nan | 0.5443 | 0.5951 | 0.0 | 0.8239 | 0.0577 | 0.0 | 0.0080 | 0.0 | 0.5004 | 0.0 | 0.0 | 0.7442 | 0.0277 | 0.4713 | 0.2897 | 0.0 | nan | 0.0152 | 0.4401 | 0.3716 | 0.0 | 0.8759 | 0.7625 | 0.9409 | 0.0001 | 0.0449 | 0.3823 | 0.0 |
| 0.2915 | 9.6 | 1920 | 0.6007 | 0.3634 | 0.4343 | 0.8576 | nan | 0.8409 | 0.9099 | 0.7642 | 0.8886 | 0.6304 | nan | 0.6444 | 0.8280 | 0.0 | 0.9398 | 0.1026 | 0.0 | 0.1016 | 0.0 | 0.6896 | 0.0 | 0.0 | 0.9341 | 0.1520 | 0.5981 | 0.3620 | 0.0 | nan | 0.0959 | 0.5529 | 0.5185 | 0.0 | 0.9471 | 0.7731 | 0.9769 | 0.0006 | 0.2137 | 0.4315 | 0.0 | nan | 0.7414 | 0.8598 | 0.7123 | 0.6627 | 0.3403 | nan | 0.5468 | 0.6359 | 0.0 | 0.8490 | 0.0906 | 0.0 | 0.0997 | 0.0 | 0.5503 | 0.0 | 0.0 | 0.7453 | 0.1337 | 0.5070 | 0.2869 | 0.0 | nan | 0.0682 | 0.4524 | 0.3722 | 0.0 | 0.8685 | 0.7152 | 0.9420 | 0.0005 | 0.0906 | 0.3574 | 0.0 |
| 0.1619 | 9.7 | 1940 | 0.5878 | 0.3652 | 0.4320 | 0.8669 | nan | 0.8511 | 0.9478 | 0.7936 | 0.7795 | 0.5206 | nan | 0.6435 | 0.8479 | 0.0 | 0.9404 | 0.0867 | 0.0 | 0.0883 | 0.0 | 0.6922 | 0.0 | 0.0 | 0.9222 | 0.1750 | 0.6193 | 0.3488 | 0.0 | nan | 0.1099 | 0.5323 | 0.4732 | 0.0 | 0.9485 | 0.8684 | 0.9788 | 0.0002 | 0.1708 | 0.4845 | 0.0 | nan | 0.7496 | 0.8625 | 0.7050 | 0.6968 | 0.3725 | nan | 0.5267 | 0.5723 | 0.0 | 0.8505 | 0.0783 | 0.0 | 0.0870 | 0.0 | 0.5447 | 0.0 | 0.0 | 0.7519 | 0.1509 | 0.5221 | 0.3030 | 0.0 | nan | 0.0797 | 0.4446 | 0.3724 | 0.0 | 0.8815 | 0.7715 | 0.9416 | 0.0002 | 0.0589 | 0.3618 | 0.0 |
| 0.2238 | 9.8 | 1960 | 0.5750 | 0.3620 | 0.4280 | 0.8640 | nan | 0.8034 | 0.9497 | 0.8037 | 0.8240 | 0.5548 | nan | 0.6514 | 0.7874 | 0.0 | 0.9436 | 0.0789 | 0.0 | 0.0050 | 0.0 | 0.6596 | 0.0 | 0.0 | 0.9175 | 0.1363 | 0.5863 | 0.2951 | 0.0 | nan | 0.1792 | 0.5565 | 0.5238 | 0.0 | 0.9600 | 0.8624 | 0.9827 | 0.0002 | 0.2023 | 0.4319 | 0.0 | nan | 0.7282 | 0.8672 | 0.7068 | 0.6609 | 0.3731 | nan | 0.5422 | 0.6318 | 0.0 | 0.8465 | 0.0714 | 0.0 | 0.0050 | 0.0 | 0.5472 | 0.0 | 0.0 | 0.7508 | 0.1235 | 0.4912 | 0.2610 | 0.0 | nan | 0.1251 | 0.4525 | 0.3775 | 0.0 | 0.8787 | 0.7697 | 0.9406 | 0.0002 | 0.0868 | 0.3471 | 0.0 |
| 0.181 | 9.9 | 1980 | 0.6077 | 0.3659 | 0.4361 | 0.8660 | nan | 0.8058 | 0.9632 | 0.8071 | 0.8176 | 0.4577 | nan | 0.6573 | 0.8114 | 0.0016 | 0.9514 | 0.0731 | 0.0 | 0.0823 | 0.0 | 0.6843 | 0.0 | 0.0 | 0.9176 | 0.2187 | 0.5992 | 0.4228 | 0.0 | nan | 0.0728 | 0.5537 | 0.5348 | 0.0 | 0.9523 | 0.8465 | 0.9807 | 0.0004 | 0.3214 | 0.4197 | 0.0 | nan | 0.7249 | 0.8696 | 0.6807 | 0.6553 | 0.3866 | nan | 0.5360 | 0.6245 | 0.0011 | 0.8409 | 0.0679 | 0.0 | 0.0811 | 0.0 | 0.5402 | 0.0 | 0.0 | 0.7561 | 0.1858 | 0.5035 | 0.3159 | 0.0 | nan | 0.0618 | 0.4582 | 0.3643 | 0.0 | 0.8823 | 0.7655 | 0.9433 | 0.0003 | 0.1145 | 0.3475 | 0.0 |
| 0.1723 | 10.0 | 2000 | 0.5698 | 0.3647 | 0.4371 | 0.8676 | nan | 0.8435 | 0.9415 | 0.7988 | 0.8204 | 0.5753 | nan | 0.6608 | 0.8658 | 0.0004 | 0.9529 | 0.0692 | 0.0 | 0.0399 | 0.0 | 0.6560 | 0.0 | 0.0 | 0.9084 | 0.2922 | 0.6382 | 0.2656 | 0.0 | nan | 0.1818 | 0.5671 | 0.4989 | 0.0 | 0.9476 | 0.8513 | 0.9835 | 0.0000 | 0.1109 | 0.5181 | 0.0 | nan | 0.7495 | 0.8765 | 0.6768 | 0.6880 | 0.3644 | nan | 0.5321 | 0.5254 | 0.0002 | 0.8442 | 0.0641 | 0.0 | 0.0396 | 0.0 | 0.5579 | 0.0 | 0.0 | 0.7536 | 0.2312 | 0.5268 | 0.2439 | 0.0 | nan | 0.1472 | 0.4636 | 0.3572 | 0.0 | 0.8827 | 0.7725 | 0.9408 | 0.0000 | 0.0551 | 0.3764 | 0.0 |
| 0.1796 | 10.1 | 2020 | 0.6002 | 0.3561 | 0.4173 | 0.8698 | nan | 0.8854 | 0.9622 | 0.7805 | 0.7688 | 0.4451 | nan | 0.6674 | 0.7923 | 0.0049 | 0.9383 | 0.0769 | 0.0 | 0.0 | 0.0 | 0.6885 | 0.0 | 0.0 | 0.9317 | 0.1050 | 0.4506 | 0.3630 | 0.0 | nan | 0.0482 | 0.5843 | 0.5063 | 0.0 | 0.9593 | 0.8249 | 0.9777 | 0.0114 | 0.1671 | 0.4151 | 0.0 | nan | 0.7746 | 0.8784 | 0.6935 | 0.6923 | 0.3830 | nan | 0.5434 | 0.5779 | 0.0020 | 0.8516 | 0.0677 | 0.0 | 0.0 | 0.0 | 0.5666 | 0.0 | 0.0 | 0.7279 | 0.1004 | 0.4194 | 0.2902 | 0.0 | nan | 0.0433 | 0.4666 | 0.3417 | 0.0 | 0.8740 | 0.7591 | 0.9439 | 0.0088 | 0.0505 | 0.3390 | 0.0 |
| 0.1796 | 10.2 | 2040 | 0.6191 | 0.3570 | 0.4340 | 0.8510 | nan | 0.7408 | 0.9279 | 0.7743 | 0.8695 | 0.5925 | nan | 0.7097 | 0.7978 | 0.0060 | 0.9488 | 0.1261 | 0.0 | 0.0286 | 0.0 | 0.6876 | 0.0 | 0.0 | 0.9216 | 0.1616 | 0.5833 | 0.3228 | 0.0 | nan | 0.1482 | 0.5801 | 0.5649 | 0.0 | 0.9540 | 0.8119 | 0.9782 | 0.0149 | 0.1834 | 0.4523 | 0.0 | nan | 0.6754 | 0.8756 | 0.7088 | 0.5711 | 0.3412 | nan | 0.5429 | 0.5913 | 0.0021 | 0.8415 | 0.1061 | 0.0 | 0.0284 | 0.0 | 0.5505 | 0.0 | 0.0 | 0.7490 | 0.1510 | 0.4931 | 0.2742 | 0.0 | nan | 0.1126 | 0.4679 | 0.3353 | 0.0 | 0.8774 | 0.7492 | 0.9425 | 0.0090 | 0.0610 | 0.3659 | 0.0 |
| 0.2422 | 10.3 | 2060 | 0.5916 | 0.3663 | 0.4314 | 0.8704 | nan | 0.8751 | 0.9613 | 0.7615 | 0.7939 | 0.4967 | nan | 0.6193 | 0.8196 | 0.0002 | 0.9310 | 0.1808 | 0.0 | 0.0156 | 0.0 | 0.6595 | 0.0 | 0.0 | 0.9255 | 0.2948 | 0.5006 | 0.2877 | 0.0 | nan | 0.1222 | 0.5373 | 0.5146 | 0.0 | 0.9422 | 0.8742 | 0.9779 | 0.0004 | 0.2006 | 0.5132 | 0.0 | nan | 0.7712 | 0.8737 | 0.7154 | 0.7130 | 0.3837 | nan | 0.5275 | 0.5635 | 0.0001 | 0.8538 | 0.1490 | 0.0 | 0.0155 | 0.0 | 0.5508 | 0.0 | 0.0 | 0.7359 | 0.2405 | 0.4500 | 0.2541 | 0.0 | nan | 0.0939 | 0.4606 | 0.3456 | 0.0 | 0.8812 | 0.7705 | 0.9437 | 0.0003 | 0.0603 | 0.3672 | 0.0 |
| 0.2026 | 10.4 | 2080 | 0.5646 | 0.3630 | 0.4326 | 0.8686 | nan | 0.8538 | 0.9433 | 0.7673 | 0.8307 | 0.5518 | nan | 0.6772 | 0.8276 | 0.0011 | 0.9554 | 0.1327 | 0.0 | 0.0814 | 0.0 | 0.6519 | 0.0 | 0.0 | 0.9182 | 0.0747 | 0.5815 | 0.3975 | 0.0 | nan | 0.0813 | 0.5442 | 0.5356 | 0.0 | 0.9545 | 0.8541 | 0.9815 | 0.0002 | 0.2292 | 0.4163 | 0.0 | nan | 0.7587 | 0.8724 | 0.6988 | 0.7286 | 0.3550 | nan | 0.5457 | 0.5709 | 0.0006 | 0.8423 | 0.1143 | 0.0 | 0.0805 | 0.0 | 0.5323 | 0.0 | 0.0 | 0.7452 | 0.0723 | 0.5020 | 0.2997 | 0.0 | nan | 0.0661 | 0.4535 | 0.3444 | 0.0 | 0.8810 | 0.7710 | 0.9428 | 0.0001 | 0.0852 | 0.3510 | 0.0 |
| 0.0985 | 10.5 | 2100 | 0.6142 | 0.3533 | 0.4256 | 0.8627 | nan | 0.8371 | 0.9483 | 0.8197 | 0.7611 | 0.5121 | nan | 0.6438 | 0.8376 | 0.0129 | 0.9141 | 0.1704 | 0.0 | 0.0046 | 0.0 | 0.7401 | 0.0 | 0.0 | 0.9358 | 0.0800 | 0.6162 | 0.3199 | 0.0 | nan | 0.0784 | 0.4951 | 0.4927 | 0.0 | 0.9401 | 0.8604 | 0.9809 | 0.0009 | 0.1092 | 0.5067 | 0.0 | nan | 0.7385 | 0.8643 | 0.6029 | 0.6977 | 0.3564 | nan | 0.5397 | 0.4815 | 0.0045 | 0.8455 | 0.1454 | 0.0 | 0.0046 | 0.0 | 0.5542 | 0.0 | 0.0 | 0.7474 | 0.0779 | 0.5220 | 0.2741 | 0.0 | nan | 0.0691 | 0.4378 | 0.3395 | 0.0 | 0.8814 | 0.7696 | 0.9414 | 0.0006 | 0.0474 | 0.3636 | 0.0 |
| 0.129 | 10.6 | 2120 | 0.5970 | 0.3600 | 0.4274 | 0.8712 | nan | 0.8555 | 0.9481 | 0.8121 | 0.8462 | 0.4839 | nan | 0.6820 | 0.7683 | 0.0 | 0.9493 | 0.0611 | 0.0 | 0.0 | 0.0 | 0.7116 | 0.0 | 0.0 | 0.9261 | 0.0578 | 0.5941 | 0.3243 | 0.0 | nan | 0.2018 | 0.5643 | 0.5253 | 0.0 | 0.9469 | 0.8967 | 0.9830 | 0.0139 | 0.0910 | 0.4330 | 0.0 | nan | 0.7628 | 0.8796 | 0.6506 | 0.7280 | 0.3696 | nan | 0.5534 | 0.5205 | 0.0 | 0.8490 | 0.0568 | 0.0 | 0.0 | 0.0 | 0.5488 | 0.0 | 0.0 | 0.7399 | 0.0564 | 0.5002 | 0.2847 | 0.0 | nan | 0.1699 | 0.4503 | 0.3859 | 0.0 | 0.8830 | 0.7681 | 0.9423 | 0.0101 | 0.0500 | 0.3604 | 0.0 |
| 0.2195 | 10.7 | 2140 | 0.5875 | 0.3673 | 0.4346 | 0.8706 | nan | 0.8730 | 0.9515 | 0.7622 | 0.8346 | 0.5210 | nan | 0.6573 | 0.8155 | 0.0 | 0.9442 | 0.0891 | 0.0 | 0.2017 | 0.0 | 0.7272 | 0.0 | 0.0 | 0.9272 | 0.1671 | 0.5758 | 0.2867 | 0.0 | nan | 0.1521 | 0.5740 | 0.4965 | 0.0 | 0.9551 | 0.7943 | 0.9859 | 0.0004 | 0.2049 | 0.4082 | 0.0 | nan | 0.7686 | 0.8777 | 0.7037 | 0.7166 | 0.3780 | nan | 0.5573 | 0.5395 | 0.0 | 0.8537 | 0.0818 | 0.0 | 0.1935 | 0.0 | 0.5376 | 0.0 | 0.0 | 0.7443 | 0.1575 | 0.5101 | 0.2481 | 0.0 | nan | 0.1143 | 0.4611 | 0.3603 | 0.0 | 0.8727 | 0.7406 | 0.9378 | 0.0004 | 0.0644 | 0.3343 | 0.0 |
| 0.1582 | 10.8 | 2160 | 0.6342 | 0.3674 | 0.4391 | 0.8630 | nan | 0.8124 | 0.9575 | 0.7501 | 0.7968 | 0.5084 | nan | 0.6739 | 0.8201 | 0.0 | 0.9606 | 0.0620 | 0.0 | 0.1560 | 0.0 | 0.6487 | 0.0 | 0.0 | 0.8970 | 0.2414 | 0.6243 | 0.3606 | 0.0 | nan | 0.1296 | 0.5946 | 0.5022 | 0.0 | 0.9500 | 0.8442 | 0.9796 | 0.0014 | 0.3767 | 0.4047 | 0.0 | nan | 0.7290 | 0.8592 | 0.6867 | 0.6946 | 0.3653 | nan | 0.5461 | 0.5826 | 0.0 | 0.8365 | 0.0584 | 0.0 | 0.1430 | 0.0 | 0.5369 | 0.0 | 0.0 | 0.7539 | 0.2097 | 0.5298 | 0.2998 | 0.0 | nan | 0.1009 | 0.4661 | 0.3656 | 0.0 | 0.8808 | 0.7576 | 0.9442 | 0.0009 | 0.0815 | 0.3260 | 0.0 |
| 0.141 | 10.9 | 2180 | 0.5708 | 0.3747 | 0.4457 | 0.8710 | nan | 0.8407 | 0.9478 | 0.7710 | 0.8542 | 0.5662 | nan | 0.6839 | 0.8254 | 0.0 | 0.9435 | 0.0333 | 0.0 | 0.0817 | 0.0 | 0.7243 | 0.0 | 0.0008 | 0.9160 | 0.2876 | 0.6263 | 0.4032 | 0.0 | nan | 0.1865 | 0.6024 | 0.5075 | 0.0 | 0.9494 | 0.8427 | 0.9741 | 0.0032 | 0.2235 | 0.4666 | 0.0 | nan | 0.7579 | 0.8806 | 0.6630 | 0.7200 | 0.3798 | nan | 0.5666 | 0.6190 | 0.0 | 0.8524 | 0.0318 | 0.0 | 0.0809 | 0.0 | 0.5689 | 0.0 | 0.0007 | 0.7592 | 0.2481 | 0.5451 | 0.3295 | 0.0 | nan | 0.1405 | 0.4706 | 0.3839 | 0.0 | 0.8806 | 0.7427 | 0.9450 | 0.0021 | 0.0652 | 0.3562 | 0.0 |
| 0.3199 | 11.0 | 2200 | 0.5767 | 0.3700 | 0.4486 | 0.8663 | nan | 0.8247 | 0.9515 | 0.8180 | 0.8596 | 0.5109 | nan | 0.5983 | 0.8435 | 0.0015 | 0.9562 | 0.0444 | 0.0 | 0.1568 | 0.0 | 0.7398 | 0.0 | 0.0 | 0.9145 | 0.3573 | 0.6270 | 0.3562 | 0.0 | nan | 0.1856 | 0.5679 | 0.5520 | 0.0 | 0.9489 | 0.7975 | 0.9869 | 0.0110 | 0.3207 | 0.4260 | 0.0 | nan | 0.7408 | 0.8811 | 0.6150 | 0.7041 | 0.3718 | nan | 0.5332 | 0.5834 | 0.0008 | 0.8431 | 0.0424 | 0.0 | 0.1501 | 0.0 | 0.5590 | 0.0 | 0.0 | 0.7555 | 0.2712 | 0.5466 | 0.2982 | 0.0 | nan | 0.1552 | 0.4603 | 0.3707 | 0.0 | 0.8720 | 0.7199 | 0.9373 | 0.0073 | 0.0800 | 0.3409 | 0.0 |
| 0.3278 | 11.1 | 2220 | 0.6226 | 0.3555 | 0.4225 | 0.8637 | nan | 0.8071 | 0.9370 | 0.7867 | 0.8534 | 0.6282 | nan | 0.6651 | 0.8242 | 0.0 | 0.9478 | 0.0697 | 0.0 | 0.0220 | 0.0 | 0.7002 | 0.0 | 0.0007 | 0.9362 | 0.0432 | 0.5970 | 0.2850 | 0.0 | nan | 0.0440 | 0.5153 | 0.5356 | 0.0 | 0.9503 | 0.8485 | 0.9854 | 0.0018 | 0.0713 | 0.4643 | 0.0 | nan | 0.7277 | 0.8729 | 0.6498 | 0.7103 | 0.3589 | nan | 0.5541 | 0.5788 | 0.0 | 0.8502 | 0.0648 | 0.0 | 0.0218 | 0.0 | 0.5862 | 0.0 | 0.0006 | 0.7406 | 0.0431 | 0.5097 | 0.2525 | 0.0 | nan | 0.0372 | 0.4472 | 0.3750 | 0.0 | 0.8800 | 0.7613 | 0.9395 | 0.0014 | 0.0410 | 0.3711 | 0.0 |
| 0.1683 | 11.2 | 2240 | 0.6238 | 0.3707 | 0.4469 | 0.8674 | nan | 0.8060 | 0.9628 | 0.7842 | 0.8534 | 0.4724 | nan | 0.6818 | 0.8545 | 0.0 | 0.9610 | 0.0733 | 0.0 | 0.2164 | 0.0 | 0.6961 | 0.0 | 0.0 | 0.9128 | 0.3061 | 0.5964 | 0.3623 | 0.0 | nan | 0.2198 | 0.5741 | 0.5310 | 0.0 | 0.9357 | 0.8772 | 0.9830 | 0.0016 | 0.1934 | 0.4458 | 0.0 | nan | 0.7305 | 0.8713 | 0.6423 | 0.7167 | 0.3767 | nan | 0.5533 | 0.5352 | 0.0 | 0.8412 | 0.0703 | 0.0 | 0.2041 | 0.0 | 0.5618 | 0.0 | 0.0 | 0.7557 | 0.2336 | 0.5064 | 0.2999 | 0.0 | nan | 0.1291 | 0.4647 | 0.3650 | 0.0 | 0.8804 | 0.7569 | 0.9428 | 0.0015 | 0.0728 | 0.3509 | 0.0 |
| 0.1605 | 11.3 | 2260 | 0.5795 | 0.3728 | 0.4441 | 0.8687 | nan | 0.8542 | 0.9531 | 0.7614 | 0.8369 | 0.5294 | nan | 0.6406 | 0.7638 | 0.0 | 0.9529 | 0.1167 | 0.0 | 0.3061 | 0.0 | 0.7072 | 0.0 | 0.0 | 0.9357 | 0.1757 | 0.5333 | 0.3332 | 0.0 | nan | 0.2005 | 0.5990 | 0.5629 | 0.0 | 0.9396 | 0.8592 | 0.9558 | 0.0038 | 0.2350 | 0.4551 | 0.0 | nan | 0.7537 | 0.8742 | 0.6982 | 0.7383 | 0.3793 | nan | 0.5505 | 0.6206 | 0.0 | 0.8509 | 0.1002 | 0.0 | 0.2371 | 0.0 | 0.5679 | 0.0 | 0.0 | 0.7413 | 0.1622 | 0.4769 | 0.2774 | 0.0 | nan | 0.1264 | 0.4381 | 0.3432 | 0.0 | 0.8818 | 0.7618 | 0.9269 | 0.0031 | 0.0698 | 0.3487 | 0.0 |
| 0.1396 | 11.4 | 2280 | 0.5767 | 0.3647 | 0.4384 | 0.8682 | nan | 0.8746 | 0.9356 | 0.8180 | 0.8394 | 0.4808 | nan | 0.6852 | 0.7584 | 0.0 | 0.9572 | 0.1009 | 0.0 | 0.3371 | 0.0 | 0.6918 | 0.0 | 0.0 | 0.9334 | 0.0883 | 0.5746 | 0.3300 | 0.0 | nan | 0.1219 | 0.5086 | 0.5141 | 0.0 | 0.9436 | 0.8834 | 0.9812 | 0.0021 | 0.2657 | 0.4033 | 0.0 | nan | 0.7602 | 0.8805 | 0.5760 | 0.7281 | 0.3592 | nan | 0.5368 | 0.6123 | 0.0 | 0.8479 | 0.0859 | 0.0 | 0.2574 | 0.0 | 0.5466 | 0.0 | 0.0 | 0.7417 | 0.0864 | 0.4825 | 0.2789 | 0.0 | nan | 0.0901 | 0.4440 | 0.3503 | 0.0 | 0.8823 | 0.7613 | 0.9418 | 0.0021 | 0.0847 | 0.3331 | 0.0 |
| 0.1348 | 11.5 | 2300 | 0.6258 | 0.3692 | 0.4448 | 0.8655 | nan | 0.8082 | 0.9612 | 0.7932 | 0.8424 | 0.4898 | nan | 0.6678 | 0.8474 | 0.0 | 0.9463 | 0.1012 | 0.0 | 0.5633 | 0.0 | 0.6651 | 0.0 | 0.0 | 0.9174 | 0.1172 | 0.5338 | 0.3249 | 0.0 | nan | 0.1375 | 0.5718 | 0.5233 | 0.0 | 0.9462 | 0.8342 | 0.9834 | 0.0045 | 0.1324 | 0.5217 | 0.0 | nan | 0.7343 | 0.8648 | 0.6512 | 0.7241 | 0.3822 | nan | 0.5542 | 0.5387 | 0.0 | 0.8505 | 0.0884 | 0.0 | 0.3417 | 0.0 | 0.5591 | 0.0 | 0.0 | 0.7463 | 0.1116 | 0.4585 | 0.2749 | 0.0 | nan | 0.1039 | 0.4578 | 0.3698 | 0.0 | 0.8810 | 0.7603 | 0.9383 | 0.0037 | 0.0536 | 0.3669 | 0.0 |
| 0.1144 | 11.6 | 2320 | 0.6251 | 0.3706 | 0.4457 | 0.8656 | nan | 0.8156 | 0.9497 | 0.8051 | 0.8338 | 0.5297 | nan | 0.7026 | 0.8641 | 0.0015 | 0.9475 | 0.0807 | 0.0 | 0.5249 | 0.0 | 0.7219 | 0.0 | 0.0 | 0.9244 | 0.1660 | 0.5631 | 0.2868 | 0.0 | nan | 0.0826 | 0.5438 | 0.5127 | 0.0 | 0.9554 | 0.8453 | 0.9789 | 0.0003 | 0.1870 | 0.4395 | 0.0 | nan | 0.7347 | 0.8709 | 0.6465 | 0.7180 | 0.3731 | nan | 0.5482 | 0.5144 | 0.0008 | 0.8519 | 0.0725 | 0.0 | 0.4376 | 0.0 | 0.5665 | 0.0 | 0.0 | 0.7457 | 0.1464 | 0.4915 | 0.2523 | 0.0 | nan | 0.0738 | 0.4565 | 0.3591 | 0.0 | 0.8778 | 0.7599 | 0.9440 | 0.0003 | 0.0663 | 0.3493 | 0.0 |
| 0.1364 | 11.7 | 2340 | 0.6131 | 0.3674 | 0.4372 | 0.8665 | nan | 0.8147 | 0.9550 | 0.7895 | 0.8548 | 0.4751 | nan | 0.6712 | 0.7707 | 0.0073 | 0.9493 | 0.0768 | 0.0 | 0.0791 | 0.0 | 0.7509 | 0.0 | 0.0 | 0.9269 | 0.2658 | 0.5650 | 0.3656 | 0.0 | nan | 0.1236 | 0.5760 | 0.5266 | 0.0 | 0.9430 | 0.8617 | 0.9851 | 0.0006 | 0.2049 | 0.4518 | 0.0 | nan | 0.7291 | 0.8676 | 0.6741 | 0.6949 | 0.3800 | nan | 0.5491 | 0.5884 | 0.0023 | 0.8533 | 0.0695 | 0.0 | 0.0772 | 0.0 | 0.5511 | 0.0 | 0.0 | 0.7475 | 0.2158 | 0.4930 | 0.2880 | 0.0 | nan | 0.1135 | 0.4663 | 0.3732 | 0.0 | 0.8833 | 0.7754 | 0.9408 | 0.0006 | 0.0709 | 0.3511 | 0.0 |
| 0.2197 | 11.8 | 2360 | 0.5734 | 0.3734 | 0.4465 | 0.8701 | nan | 0.8327 | 0.9541 | 0.7742 | 0.8469 | 0.5193 | nan | 0.6696 | 0.7860 | 0.0435 | 0.9492 | 0.0867 | 0.0 | 0.1161 | 0.0 | 0.7444 | 0.0 | 0.0 | 0.9244 | 0.2077 | 0.6531 | 0.3496 | 0.0 | nan | 0.2229 | 0.5427 | 0.5329 | 0.0 | 0.9429 | 0.8597 | 0.9803 | 0.0501 | 0.2688 | 0.4307 | 0.0 | nan | 0.7428 | 0.8713 | 0.6954 | 0.7325 | 0.3784 | nan | 0.5642 | 0.5273 | 0.0127 | 0.8512 | 0.0784 | 0.0 | 0.1132 | 0.0 | 0.5606 | 0.0 | 0.0 | 0.7609 | 0.1806 | 0.5258 | 0.2898 | 0.0 | nan | 0.1667 | 0.4618 | 0.3730 | 0.0 | 0.8832 | 0.7786 | 0.9455 | 0.0381 | 0.0867 | 0.3316 | 0.0 |
| 0.2554 | 11.9 | 2380 | 0.5918 | 0.3705 | 0.4438 | 0.8689 | nan | 0.8391 | 0.9453 | 0.7625 | 0.8541 | 0.5608 | nan | 0.7264 | 0.8564 | 0.0 | 0.9460 | 0.1626 | 0.0 | 0.1483 | 0.0 | 0.7141 | 0.0 | 0.0 | 0.9265 | 0.1750 | 0.5530 | 0.3455 | 0.0 | nan | 0.1034 | 0.5706 | 0.5443 | 0.0 | 0.9554 | 0.8439 | 0.9802 | 0.0052 | 0.3029 | 0.3817 | 0.0 | nan | 0.7510 | 0.8732 | 0.6893 | 0.7540 | 0.3691 | nan | 0.5639 | 0.5705 | 0.0 | 0.8499 | 0.1376 | 0.0 | 0.1311 | 0.0 | 0.5748 | 0.0 | 0.0 | 0.7445 | 0.1558 | 0.4815 | 0.2829 | 0.0 | nan | 0.0955 | 0.4686 | 0.3581 | 0.0 | 0.8806 | 0.7759 | 0.9447 | 0.0045 | 0.0779 | 0.3222 | 0.0 |
| 0.2711 | 12.0 | 2400 | 0.6319 | 0.3632 | 0.4330 | 0.8671 | nan | 0.8422 | 0.9554 | 0.7821 | 0.8127 | 0.5184 | nan | 0.6174 | 0.6287 | 0.2510 | 0.9395 | 0.1899 | 0.0 | 0.0304 | 0.0 | 0.7609 | 0.0 | 0.0 | 0.9433 | 0.1254 | 0.5463 | 0.3575 | 0.0 | nan | 0.0987 | 0.5525 | 0.4929 | 0.0 | 0.9425 | 0.8727 | 0.9788 | 0.0021 | 0.1966 | 0.4170 | 0.0 | nan | 0.7427 | 0.8662 | 0.6861 | 0.7240 | 0.3806 | nan | 0.5351 | 0.5373 | 0.0305 | 0.8533 | 0.1544 | 0.0 | 0.0300 | 0.0 | 0.5271 | 0.0 | 0.0 | 0.7409 | 0.1153 | 0.4812 | 0.2847 | 0.0 | nan | 0.0876 | 0.4611 | 0.3645 | 0.0 | 0.8860 | 0.7777 | 0.9444 | 0.0015 | 0.0678 | 0.3417 | 0.0 |
| 0.1332 | 12.1 | 2420 | 0.6043 | 0.3666 | 0.4293 | 0.8684 | nan | 0.8262 | 0.9548 | 0.7555 | 0.8478 | 0.5225 | nan | 0.6493 | 0.8324 | 0.0038 | 0.9513 | 0.1307 | 0.0 | 0.0527 | 0.0 | 0.6916 | 0.0 | 0.0 | 0.9388 | 0.1511 | 0.5920 | 0.3121 | 0.0 | nan | 0.0948 | 0.5519 | 0.4701 | 0.0 | 0.9443 | 0.8559 | 0.9821 | 0.0002 | 0.1775 | 0.4472 | 0.0 | nan | 0.7376 | 0.8679 | 0.7048 | 0.7237 | 0.3671 | nan | 0.5495 | 0.5856 | 0.0021 | 0.8509 | 0.1173 | 0.0 | 0.0517 | 0.0 | 0.5722 | 0.0 | 0.0 | 0.7460 | 0.1402 | 0.5015 | 0.2701 | 0.0 | nan | 0.0822 | 0.4603 | 0.3715 | 0.0 | 0.8849 | 0.7750 | 0.9458 | 0.0002 | 0.0680 | 0.3565 | 0.0 |
| 0.2308 | 12.2 | 2440 | 0.5906 | 0.3718 | 0.4328 | 0.8706 | nan | 0.8531 | 0.9513 | 0.7697 | 0.7995 | 0.5138 | nan | 0.6808 | 0.8252 | 0.0 | 0.9505 | 0.1204 | 0.0 | 0.1194 | 0.0 | 0.6586 | 0.0 | 0.0 | 0.9241 | 0.0845 | 0.5784 | 0.3090 | 0.0 | nan | 0.1074 | 0.5745 | 0.5363 | 0.0 | 0.9534 | 0.8744 | 0.9838 | 0.0069 | 0.1622 | 0.5118 | 0.0 | nan | 0.7502 | 0.8699 | 0.7013 | 0.7250 | 0.3777 | nan | 0.5536 | 0.6461 | 0.0 | 0.8491 | 0.1068 | 0.0 | 0.1119 | 0.0 | 0.5816 | 0.0 | 0.0 | 0.7484 | 0.0827 | 0.5018 | 0.2731 | 0.0 | nan | 0.0941 | 0.4679 | 0.3875 | 0.0 | 0.8832 | 0.7777 | 0.9435 | 0.0049 | 0.0723 | 0.3883 | 0.0 |
| 0.1515 | 12.3 | 2460 | 0.5838 | 0.3799 | 0.4509 | 0.8692 | nan | 0.8421 | 0.9520 | 0.7778 | 0.8411 | 0.5345 | nan | 0.6488 | 0.8483 | 0.0140 | 0.9347 | 0.1763 | 0.0 | 0.4235 | 0.0 | 0.6691 | 0.0 | 0.0 | 0.9141 | 0.1371 | 0.6244 | 0.3622 | 0.0 | nan | 0.1605 | 0.5416 | 0.5749 | 0.0 | 0.9604 | 0.8121 | 0.9781 | 0.0010 | 0.2560 | 0.4436 | 0.0 | nan | 0.7502 | 0.8705 | 0.6726 | 0.7230 | 0.3918 | nan | 0.5458 | 0.5629 | 0.0056 | 0.8510 | 0.1486 | 0.0 | 0.3683 | 0.0 | 0.5684 | 0.0 | 0.0 | 0.7557 | 0.1281 | 0.5094 | 0.3033 | 0.0 | nan | 0.1315 | 0.4601 | 0.3689 | 0.0 | 0.8734 | 0.7513 | 0.9462 | 0.0008 | 0.1108 | 0.3598 | 0.0 |
| 0.1225 | 12.4 | 2480 | 0.6349 | 0.3697 | 0.4419 | 0.8653 | nan | 0.8298 | 0.9563 | 0.7270 | 0.8458 | 0.4908 | nan | 0.6935 | 0.8170 | 0.0016 | 0.9535 | 0.1277 | 0.0 | 0.1943 | 0.0 | 0.6818 | 0.0 | 0.0 | 0.9243 | 0.0993 | 0.5907 | 0.3374 | 0.0 | nan | 0.1578 | 0.5889 | 0.5923 | 0.0 | 0.9541 | 0.7663 | 0.9832 | 0.0017 | 0.4623 | 0.3629 | 0.0 | nan | 0.7383 | 0.8689 | 0.6986 | 0.7201 | 0.3735 | nan | 0.5547 | 0.6206 | 0.0008 | 0.8499 | 0.1108 | 0.0 | 0.1761 | 0.0 | 0.5647 | 0.0 | 0.0 | 0.7506 | 0.0919 | 0.5152 | 0.2830 | 0.0 | nan | 0.1368 | 0.4754 | 0.3761 | 0.0 | 0.8704 | 0.7117 | 0.9433 | 0.0012 | 0.0886 | 0.3105 | 0.0 |
| 0.17 | 12.5 | 2500 | 0.6301 | 0.3746 | 0.4515 | 0.8591 | nan | 0.7800 | 0.9414 | 0.7639 | 0.8348 | 0.5670 | nan | 0.6941 | 0.8344 | 0.0025 | 0.9472 | 0.1556 | 0.0 | 0.2321 | 0.0 | 0.7384 | 0.0 | 0.0 | 0.9207 | 0.1489 | 0.5901 | 0.3355 | 0.0 | nan | 0.2611 | 0.5878 | 0.5471 | 0.0 | 0.9567 | 0.8401 | 0.9839 | 0.0020 | 0.3890 | 0.3939 | 0.0 | nan | 0.6970 | 0.8698 | 0.7009 | 0.6399 | 0.3747 | nan | 0.5503 | 0.6201 | 0.0013 | 0.8517 | 0.1344 | 0.0 | 0.2098 | 0.0 | 0.5732 | 0.0 | 0.0 | 0.7501 | 0.1287 | 0.5132 | 0.2866 | 0.0 | nan | 0.2142 | 0.4713 | 0.3932 | 0.0 | 0.8804 | 0.7604 | 0.9405 | 0.0012 | 0.0953 | 0.3289 | 0.0 |
| 0.165 | 12.6 | 2520 | 0.6584 | 0.3661 | 0.4352 | 0.8570 | nan | 0.7368 | 0.9524 | 0.7641 | 0.8569 | 0.5238 | nan | 0.6428 | 0.8209 | 0.0036 | 0.9346 | 0.1399 | 0.0 | 0.1906 | 0.0 | 0.7413 | 0.0 | 0.0 | 0.9404 | 0.2085 | 0.5960 | 0.3233 | 0.0 | nan | 0.1134 | 0.5718 | 0.5039 | 0.0 | 0.9471 | 0.8686 | 0.9802 | 0.0023 | 0.1083 | 0.4567 | 0.0 | nan | 0.6616 | 0.8708 | 0.6893 | 0.5929 | 0.3828 | nan | 0.5459 | 0.5759 | 0.0018 | 0.8532 | 0.1247 | 0.0 | 0.1748 | 0.0 | 0.5620 | 0.0 | 0.0 | 0.7446 | 0.1639 | 0.4943 | 0.2780 | 0.0 | nan | 0.0852 | 0.4645 | 0.3842 | 0.0 | 0.8847 | 0.7794 | 0.9446 | 0.0020 | 0.0760 | 0.3766 | 0.0 |
| 0.1429 | 12.7 | 2540 | 0.6257 | 0.3674 | 0.4410 | 0.8642 | nan | 0.8160 | 0.9534 | 0.7486 | 0.8338 | 0.4912 | nan | 0.6777 | 0.8433 | 0.0 | 0.9496 | 0.1179 | 0.0 | 0.3490 | 0.0 | 0.7247 | 0.0 | 0.0 | 0.9433 | 0.1158 | 0.5400 | 0.3428 | 0.0 | nan | 0.0911 | 0.6048 | 0.5402 | 0.0 | 0.9376 | 0.8745 | 0.9812 | 0.0009 | 0.2446 | 0.3913 | 0.0 | nan | 0.7176 | 0.8700 | 0.6719 | 0.6720 | 0.3812 | nan | 0.5478 | 0.5687 | 0.0 | 0.8470 | 0.1068 | 0.0 | 0.2404 | 0.0 | 0.5742 | 0.0 | 0.0 | 0.7373 | 0.1023 | 0.4654 | 0.2893 | 0.0 | nan | 0.0700 | 0.4721 | 0.3740 | 0.0 | 0.8863 | 0.7742 | 0.9449 | 0.0008 | 0.1089 | 0.3334 | 0.0 |
| 0.2148 | 12.8 | 2560 | 0.6506 | 0.3615 | 0.4361 | 0.8657 | nan | 0.8294 | 0.9461 | 0.7752 | 0.8529 | 0.5457 | nan | 0.6227 | 0.8170 | 0.0 | 0.9457 | 0.1039 | 0.0 | 0.1968 | 0.0 | 0.7317 | 0.0 | 0.0 | 0.9237 | 0.0543 | 0.5955 | 0.3732 | 0.0 | nan | 0.0714 | 0.5452 | 0.5722 | 0.0 | 0.9555 | 0.8497 | 0.9830 | 0.0013 | 0.3009 | 0.3629 | 0.0 | nan | 0.7423 | 0.8720 | 0.6351 | 0.7116 | 0.3801 | nan | 0.5362 | 0.5780 | 0.0 | 0.8514 | 0.0939 | 0.0 | 0.1506 | 0.0 | 0.5538 | 0.0 | 0.0 | 0.7452 | 0.0523 | 0.4998 | 0.3005 | 0.0 | nan | 0.0557 | 0.4536 | 0.3680 | 0.0 | 0.8828 | 0.7690 | 0.9439 | 0.0008 | 0.0857 | 0.3067 | 0.0 |
| 0.1008 | 12.9 | 2580 | 0.6366 | 0.3646 | 0.4308 | 0.8690 | nan | 0.8348 | 0.9596 | 0.7654 | 0.8486 | 0.4781 | nan | 0.6440 | 0.8230 | 0.0 | 0.9401 | 0.0809 | 0.0 | 0.0403 | 0.0 | 0.7241 | 0.0 | 0.0 | 0.9404 | 0.0910 | 0.5811 | 0.3870 | 0.0 | nan | 0.0529 | 0.6005 | 0.5440 | 0.0 | 0.9417 | 0.8558 | 0.9761 | 0.0091 | 0.2742 | 0.3930 | 0.0 | nan | 0.7476 | 0.8739 | 0.6846 | 0.7247 | 0.3771 | nan | 0.5427 | 0.6177 | 0.0 | 0.8530 | 0.0728 | 0.0 | 0.0367 | 0.0 | 0.5974 | 0.0 | 0.0 | 0.7460 | 0.0835 | 0.4990 | 0.3228 | 0.0 | nan | 0.0427 | 0.4805 | 0.3583 | 0.0 | 0.8849 | 0.7654 | 0.9476 | 0.0044 | 0.0760 | 0.3287 | 0.0 |
| 0.1522 | 13.0 | 2600 | 0.6428 | 0.3646 | 0.4295 | 0.8690 | nan | 0.8303 | 0.9632 | 0.7664 | 0.8164 | 0.4636 | nan | 0.6786 | 0.7941 | 0.0266 | 0.9519 | 0.0963 | 0.0 | 0.0264 | 0.0 | 0.7352 | 0.0 | 0.0 | 0.9292 | 0.1689 | 0.5832 | 0.3683 | 0.0 | nan | 0.0134 | 0.5600 | 0.5372 | 0.0 | 0.9532 | 0.8346 | 0.9827 | 0.0022 | 0.2319 | 0.4291 | 0.0 | nan | 0.7495 | 0.8672 | 0.6985 | 0.7028 | 0.3705 | nan | 0.5573 | 0.5974 | 0.0090 | 0.8505 | 0.0855 | 0.0 | 0.0226 | 0.0 | 0.5779 | 0.0 | 0.0 | 0.7493 | 0.1519 | 0.4990 | 0.3153 | 0.0 | nan | 0.0110 | 0.4666 | 0.3700 | 0.0 | 0.8830 | 0.7672 | 0.9455 | 0.0016 | 0.0710 | 0.3461 | 0.0 |
| 0.2057 | 13.1 | 2620 | 0.6362 | 0.3648 | 0.4290 | 0.8681 | nan | 0.8362 | 0.9518 | 0.7971 | 0.8013 | 0.4958 | nan | 0.6809 | 0.8544 | 0.0 | 0.9491 | 0.1131 | 0.0 | 0.0089 | 0.0 | 0.7202 | 0.0 | 0.0 | 0.9329 | 0.0788 | 0.5645 | 0.3541 | 0.0 | nan | 0.0622 | 0.5870 | 0.5498 | 0.0 | 0.9617 | 0.8374 | 0.9785 | 0.0082 | 0.1591 | 0.4444 | 0.0 | nan | 0.7457 | 0.8653 | 0.7014 | 0.7078 | 0.3798 | nan | 0.5451 | 0.6337 | 0.0 | 0.8506 | 0.1009 | 0.0 | 0.0084 | 0.0 | 0.5899 | 0.0 | 0.0 | 0.7449 | 0.0734 | 0.4999 | 0.3024 | 0.0 | nan | 0.0552 | 0.4695 | 0.3761 | 0.0 | 0.8769 | 0.7513 | 0.9487 | 0.0065 | 0.0730 | 0.3664 | 0.0 |
| 0.1275 | 13.2 | 2640 | 0.6206 | 0.3704 | 0.4324 | 0.8716 | nan | 0.8514 | 0.9568 | 0.7906 | 0.8056 | 0.5298 | nan | 0.6604 | 0.8550 | 0.0 | 0.9368 | 0.1211 | 0.0 | 0.0309 | 0.0 | 0.7165 | 0.0 | 0.0 | 0.9430 | 0.1613 | 0.5821 | 0.3947 | 0.0 | nan | 0.0998 | 0.5349 | 0.5008 | 0.0 | 0.9382 | 0.8708 | 0.9835 | 0.0024 | 0.0890 | 0.4824 | 0.0 | nan | 0.7574 | 0.8693 | 0.7222 | 0.7114 | 0.3926 | nan | 0.5542 | 0.5887 | 0.0 | 0.8555 | 0.1079 | 0.0 | 0.0280 | 0.0 | 0.6065 | 0.0 | 0.0 | 0.7420 | 0.1495 | 0.5062 | 0.3216 | 0.0 | nan | 0.0856 | 0.4645 | 0.3651 | 0.0 | 0.8853 | 0.7743 | 0.9452 | 0.0023 | 0.0430 | 0.3754 | 0.0 |
| 0.1347 | 13.3 | 2660 | 0.6095 | 0.3719 | 0.4455 | 0.8700 | nan | 0.8279 | 0.9563 | 0.7853 | 0.8425 | 0.5450 | nan | 0.6901 | 0.8485 | 0.0 | 0.9497 | 0.1046 | 0.0 | 0.0767 | 0.0 | 0.7409 | 0.0 | 0.0 | 0.9110 | 0.2988 | 0.5294 | 0.3394 | 0.0 | nan | 0.1154 | 0.6044 | 0.5436 | 0.0 | 0.9577 | 0.8662 | 0.9823 | 0.0019 | 0.3407 | 0.3988 | 0.0 | nan | 0.7461 | 0.8734 | 0.6989 | 0.7428 | 0.3914 | nan | 0.5611 | 0.5997 | 0.0 | 0.8506 | 0.0949 | 0.0 | 0.0700 | 0.0 | 0.5830 | 0.0 | 0.0 | 0.7483 | 0.2296 | 0.4694 | 0.2874 | 0.0 | nan | 0.0914 | 0.4729 | 0.3638 | 0.0 | 0.8769 | 0.7703 | 0.9457 | 0.0016 | 0.0945 | 0.3362 | 0.0 |
| 0.2485 | 13.4 | 2680 | 0.6268 | 0.3714 | 0.4387 | 0.8683 | nan | 0.8264 | 0.9537 | 0.8050 | 0.8298 | 0.5365 | nan | 0.6674 | 0.8409 | 0.0 | 0.9517 | 0.1271 | 0.0 | 0.0620 | 0.0 | 0.7334 | 0.0 | 0.0001 | 0.9130 | 0.1673 | 0.6948 | 0.2833 | 0.0 | nan | 0.1799 | 0.5789 | 0.4927 | 0.0 | 0.9572 | 0.7666 | 0.9814 | 0.0028 | 0.2146 | 0.4709 | 0.0 | nan | 0.7393 | 0.8683 | 0.6553 | 0.7410 | 0.3897 | nan | 0.5474 | 0.6279 | 0.0 | 0.8533 | 0.1132 | 0.0 | 0.0553 | 0.0 | 0.6019 | 0.0 | 0.0001 | 0.7628 | 0.1513 | 0.5262 | 0.2585 | 0.0 | nan | 0.1395 | 0.4651 | 0.3625 | 0.0 | 0.8740 | 0.7215 | 0.9462 | 0.0020 | 0.1102 | 0.3729 | 0.0 |
| 0.1525 | 13.5 | 2700 | 0.6110 | 0.3650 | 0.4255 | 0.8704 | nan | 0.8474 | 0.9557 | 0.7782 | 0.8435 | 0.5040 | nan | 0.6619 | 0.7982 | 0.0 | 0.9442 | 0.0899 | 0.0 | 0.0353 | 0.0 | 0.6722 | 0.0 | 0.0 | 0.9479 | 0.0813 | 0.5063 | 0.3830 | 0.0 | nan | 0.0530 | 0.5744 | 0.5097 | 0.0 | 0.9437 | 0.8528 | 0.9840 | 0.0021 | 0.1918 | 0.4547 | 0.0 | nan | 0.7505 | 0.8771 | 0.6676 | 0.7346 | 0.3815 | nan | 0.5483 | 0.6471 | 0.0 | 0.8551 | 0.0823 | 0.0 | 0.0322 | 0.0 | 0.5850 | 0.0 | 0.0 | 0.7303 | 0.0762 | 0.4660 | 0.3171 | 0.0 | nan | 0.0483 | 0.4664 | 0.3777 | 0.0 | 0.8836 | 0.7640 | 0.9446 | 0.0016 | 0.0762 | 0.3674 | 0.0 |
| 0.143 | 13.6 | 2720 | 0.6194 | 0.3740 | 0.4436 | 0.8696 | nan | 0.8290 | 0.9541 | 0.7935 | 0.8487 | 0.5119 | nan | 0.6949 | 0.8598 | 0.0002 | 0.9392 | 0.1333 | 0.0 | 0.1574 | 0.0 | 0.6618 | 0.0 | 0.0 | 0.9352 | 0.2334 | 0.5895 | 0.3310 | 0.0 | nan | 0.1549 | 0.5873 | 0.5214 | 0.0 | 0.9487 | 0.8358 | 0.9781 | 0.0015 | 0.2644 | 0.4311 | 0.0 | nan | 0.7467 | 0.8738 | 0.6418 | 0.7283 | 0.3822 | nan | 0.5565 | 0.6359 | 0.0002 | 0.8549 | 0.1172 | 0.0 | 0.1287 | 0.0 | 0.5698 | 0.0 | 0.0 | 0.7493 | 0.1940 | 0.5161 | 0.2898 | 0.0 | nan | 0.1212 | 0.4736 | 0.3769 | 0.0 | 0.8815 | 0.7545 | 0.9493 | 0.0012 | 0.0793 | 0.3459 | 0.0 |
| 0.1094 | 13.7 | 2740 | 0.6446 | 0.3740 | 0.4465 | 0.8647 | nan | 0.8092 | 0.9513 | 0.7723 | 0.8255 | 0.5528 | nan | 0.6324 | 0.8453 | 0.0056 | 0.9468 | 0.1589 | 0.0 | 0.2978 | 0.0000 | 0.6940 | 0.0 | 0.0 | 0.9222 | 0.1008 | 0.6086 | 0.4271 | 0.0 | nan | 0.1750 | 0.5541 | 0.5967 | 0.0 | 0.9486 | 0.8623 | 0.9790 | 0.0019 | 0.1964 | 0.4233 | 0.0 | nan | 0.7344 | 0.8628 | 0.6750 | 0.6872 | 0.3726 | nan | 0.5472 | 0.6229 | 0.0033 | 0.8487 | 0.1334 | 0.0 | 0.2540 | 0.0000 | 0.5686 | 0.0 | 0.0 | 0.7526 | 0.0916 | 0.5157 | 0.3333 | 0.0 | nan | 0.1280 | 0.4655 | 0.3615 | 0.0 | 0.8826 | 0.7633 | 0.9485 | 0.0014 | 0.0656 | 0.3464 | 0.0 |
| 0.1719 | 13.8 | 2760 | 0.6517 | 0.3692 | 0.4442 | 0.8633 | nan | 0.8127 | 0.9523 | 0.7780 | 0.8328 | 0.4678 | nan | 0.6884 | 0.8158 | 0.0169 | 0.9453 | 0.1204 | 0.0 | 0.3134 | 0.0 | 0.7445 | 0.0 | 0.0 | 0.9160 | 0.1944 | 0.5866 | 0.3583 | 0.0 | nan | 0.0522 | 0.5863 | 0.5834 | 0.0 | 0.9586 | 0.7890 | 0.9860 | 0.0052 | 0.2654 | 0.4462 | 0.0 | nan | 0.7298 | 0.8677 | 0.6782 | 0.6843 | 0.3674 | nan | 0.5435 | 0.6222 | 0.0066 | 0.8529 | 0.1059 | 0.0 | 0.2405 | 0.0 | 0.5573 | 0.0 | 0.0 | 0.7502 | 0.1709 | 0.5163 | 0.2980 | 0.0 | nan | 0.0417 | 0.4815 | 0.3531 | 0.0 | 0.8734 | 0.7261 | 0.9391 | 0.0036 | 0.0683 | 0.3350 | 0.0 |
| 0.197 | 13.9 | 2780 | 0.6215 | 0.3729 | 0.4418 | 0.8687 | nan | 0.8668 | 0.9514 | 0.7759 | 0.7972 | 0.5255 | nan | 0.6496 | 0.8175 | 0.0228 | 0.9515 | 0.0731 | 0.0 | 0.2487 | 0.0 | 0.7049 | 0.0 | 0.0 | 0.9290 | 0.1966 | 0.5696 | 0.3480 | 0.0 | nan | 0.1606 | 0.5942 | 0.5603 | 0.0 | 0.9526 | 0.7950 | 0.9848 | 0.0269 | 0.2232 | 0.4112 | 0.0 | nan | 0.7569 | 0.8791 | 0.6367 | 0.7200 | 0.3761 | nan | 0.5464 | 0.6425 | 0.0090 | 0.8494 | 0.0679 | 0.0 | 0.1974 | 0.0 | 0.5789 | 0.0 | 0.0 | 0.7471 | 0.1676 | 0.5024 | 0.2995 | 0.0 | nan | 0.1214 | 0.4876 | 0.3790 | 0.0 | 0.8760 | 0.7302 | 0.9424 | 0.0164 | 0.0679 | 0.3361 | 0.0 |
| 0.1238 | 14.0 | 2800 | 0.6008 | 0.3822 | 0.4546 | 0.8742 | nan | 0.8710 | 0.9515 | 0.7717 | 0.8274 | 0.4743 | nan | 0.7189 | 0.8324 | 0.0504 | 0.9358 | 0.1246 | 0.0 | 0.4430 | 0.0 | 0.7318 | 0.0 | 0.0173 | 0.9097 | 0.0782 | 0.6669 | 0.3868 | 0.0 | nan | 0.1546 | 0.5889 | 0.5536 | 0.0 | 0.9543 | 0.8475 | 0.9784 | 0.0074 | 0.1882 | 0.4838 | 0.0 | nan | 0.7677 | 0.8817 | 0.6589 | 0.7277 | 0.3922 | nan | 0.5545 | 0.6251 | 0.0184 | 0.8534 | 0.1129 | 0.0 | 0.3401 | 0.0 | 0.5678 | 0.0 | 0.0133 | 0.7551 | 0.0757 | 0.5311 | 0.3201 | 0.0 | nan | 0.1346 | 0.4819 | 0.3939 | 0.0 | 0.8806 | 0.7508 | 0.9467 | 0.0057 | 0.0696 | 0.3693 | 0.0 |
| 0.1317 | 14.1 | 2820 | 0.6037 | 0.3756 | 0.4368 | 0.8766 | nan | 0.8728 | 0.9625 | 0.7553 | 0.8525 | 0.4601 | nan | 0.6745 | 0.8292 | 0.0124 | 0.9444 | 0.1426 | 0.0 | 0.0085 | 0.0 | 0.6589 | 0.0 | 0.0052 | 0.9372 | 0.1747 | 0.5779 | 0.3701 | 0.0 | nan | 0.1536 | 0.6059 | 0.5533 | 0.0 | 0.9529 | 0.8101 | 0.9833 | 0.0028 | 0.2206 | 0.4557 | 0.0 | nan | 0.7769 | 0.8838 | 0.7156 | 0.7342 | 0.3727 | nan | 0.5626 | 0.6729 | 0.0066 | 0.8449 | 0.1261 | 0.0 | 0.0054 | 0.0 | 0.5665 | 0.0 | 0.0038 | 0.7449 | 0.1511 | 0.5088 | 0.3111 | 0.0 | nan | 0.1232 | 0.4809 | 0.3857 | 0.0 | 0.8812 | 0.7537 | 0.9452 | 0.0027 | 0.0850 | 0.3737 | 0.0 |
| 0.1561 | 14.2 | 2840 | 0.6731 | 0.3774 | 0.4507 | 0.8659 | nan | 0.8091 | 0.9530 | 0.7726 | 0.8470 | 0.5168 | nan | 0.7039 | 0.8604 | 0.0233 | 0.9451 | 0.1814 | 0.0 | 0.4042 | 0.0002 | 0.7136 | 0.0 | 0.0019 | 0.9294 | 0.1412 | 0.5499 | 0.3167 | 0.0 | nan | 0.2017 | 0.5679 | 0.5189 | 0.0 | 0.9553 | 0.8126 | 0.9828 | 0.0065 | 0.2318 | 0.4767 | 0.0 | nan | 0.7279 | 0.8725 | 0.6738 | 0.6924 | 0.3847 | nan | 0.5622 | 0.6163 | 0.0140 | 0.8548 | 0.1535 | 0.0 | 0.2975 | 0.0002 | 0.5667 | 0.0 | 0.0014 | 0.7453 | 0.1297 | 0.4810 | 0.2752 | 0.0 | nan | 0.1615 | 0.4737 | 0.3750 | 0.0 | 0.8758 | 0.7340 | 0.9463 | 0.0055 | 0.0817 | 0.3751 | 0.0 |
| 0.1034 | 14.3 | 2860 | 0.6550 | 0.3748 | 0.4388 | 0.8693 | nan | 0.8425 | 0.9480 | 0.7923 | 0.8447 | 0.5247 | nan | 0.6776 | 0.7910 | 0.0 | 0.9425 | 0.1608 | 0.0 | 0.0004 | 0.0 | 0.6871 | 0.0 | 0.0 | 0.9286 | 0.1559 | 0.4997 | 0.3733 | 0.0035 | nan | 0.2962 | 0.5880 | 0.5191 | 0.0 | 0.9588 | 0.8267 | 0.9847 | 0.0279 | 0.1601 | 0.5066 | 0.0 | nan | 0.7517 | 0.8791 | 0.6509 | 0.7343 | 0.3776 | nan | 0.5648 | 0.6827 | 0.0 | 0.8562 | 0.1422 | 0.0 | 0.0004 | 0.0 | 0.5781 | 0.0 | 0.0 | 0.7368 | 0.1417 | 0.4471 | 0.3179 | 0.0035 | nan | 0.2035 | 0.4832 | 0.3897 | 0.0 | 0.8790 | 0.7543 | 0.9447 | 0.0138 | 0.0668 | 0.3940 | 0.0 |
| 0.1665 | 14.4 | 2880 | 0.6118 | 0.3831 | 0.4646 | 0.8676 | nan | 0.7911 | 0.9537 | 0.7726 | 0.8726 | 0.4892 | nan | 0.7120 | 0.8720 | 0.0038 | 0.9533 | 0.1108 | 0.0 | 0.1787 | 0.0 | 0.6998 | 0.0 | 0.0 | 0.9148 | 0.3593 | 0.6052 | 0.4110 | 0.0 | nan | 0.3748 | 0.6291 | 0.5697 | 0.0 | 0.9530 | 0.8561 | 0.9818 | 0.0107 | 0.3523 | 0.4381 | 0.0 | nan | 0.7136 | 0.8780 | 0.6737 | 0.6563 | 0.3897 | nan | 0.5721 | 0.6119 | 0.0021 | 0.8477 | 0.0971 | 0.0 | 0.1439 | 0.0 | 0.5845 | 0.0 | 0.0 | 0.7619 | 0.2726 | 0.5318 | 0.3321 | 0.0 | nan | 0.2603 | 0.4942 | 0.3754 | 0.0 | 0.8866 | 0.7676 | 0.9465 | 0.0086 | 0.0947 | 0.3578 | 0.0 |
| 0.1398 | 14.5 | 2900 | 0.6139 | 0.3737 | 0.4408 | 0.8712 | nan | 0.8352 | 0.9550 | 0.7588 | 0.8556 | 0.5251 | nan | 0.6427 | 0.8668 | 0.0036 | 0.9531 | 0.1265 | 0.0 | 0.0435 | 0.0 | 0.6477 | 0.0 | 0.0 | 0.9243 | 0.2127 | 0.6372 | 0.3441 | 0.0 | nan | 0.1601 | 0.5896 | 0.5623 | 0.0 | 0.9568 | 0.8104 | 0.9820 | 0.0034 | 0.2632 | 0.4466 | 0.0 | nan | 0.7429 | 0.8702 | 0.6934 | 0.7224 | 0.3869 | nan | 0.5542 | 0.6216 | 0.0023 | 0.8513 | 0.1085 | 0.0 | 0.0360 | 0.0 | 0.5713 | 0.0 | 0.0 | 0.7667 | 0.1898 | 0.5516 | 0.2950 | 0.0 | nan | 0.1310 | 0.4800 | 0.3634 | 0.0 | 0.8795 | 0.7426 | 0.9460 | 0.0033 | 0.0936 | 0.3543 | 0.0 |
| 0.1276 | 14.6 | 2920 | 0.6433 | 0.3719 | 0.4329 | 0.8700 | nan | 0.8228 | 0.9536 | 0.7414 | 0.8581 | 0.5513 | nan | 0.6904 | 0.8408 | 0.0 | 0.9392 | 0.1324 | 0.0 | 0.0 | 0.0033 | 0.6332 | 0.0 | 0.0 | 0.9406 | 0.1394 | 0.6059 | 0.3639 | 0.0 | nan | 0.1756 | 0.5418 | 0.5289 | 0.0 | 0.9547 | 0.8334 | 0.9822 | 0.0075 | 0.2013 | 0.4118 | 0.0 | nan | 0.7384 | 0.8704 | 0.6956 | 0.7200 | 0.3889 | nan | 0.5758 | 0.6502 | 0.0 | 0.8530 | 0.1164 | 0.0 | 0.0 | 0.0033 | 0.5765 | 0.0 | 0.0 | 0.7459 | 0.1295 | 0.5186 | 0.3150 | 0.0 | nan | 0.1326 | 0.4657 | 0.3834 | 0.0 | 0.8835 | 0.7562 | 0.9467 | 0.0067 | 0.0838 | 0.3460 | 0.0 |
| 0.1534 | 14.7 | 2940 | 0.6143 | 0.3755 | 0.4412 | 0.8690 | nan | 0.8303 | 0.9475 | 0.7472 | 0.8284 | 0.5513 | nan | 0.6966 | 0.8160 | 0.0036 | 0.9502 | 0.1188 | 0.0 | 0.0228 | 0.0112 | 0.6890 | 0.0 | 0.0 | 0.9327 | 0.1902 | 0.6148 | 0.3769 | 0.0 | nan | 0.2047 | 0.6157 | 0.5613 | 0.0 | 0.9489 | 0.8397 | 0.9842 | 0.0051 | 0.1582 | 0.4742 | 0.0 | nan | 0.7388 | 0.8680 | 0.7020 | 0.7018 | 0.3790 | nan | 0.5612 | 0.6487 | 0.0019 | 0.8491 | 0.1049 | 0.0 | 0.0214 | 0.0111 | 0.5808 | 0.0 | 0.0 | 0.7596 | 0.1761 | 0.5272 | 0.3231 | 0.0 | nan | 0.1657 | 0.4738 | 0.3796 | 0.0 | 0.8843 | 0.7596 | 0.9453 | 0.0042 | 0.0823 | 0.3670 | 0.0 |
| 0.7322 | 14.8 | 2960 | 0.5924 | 0.3867 | 0.4634 | 0.8740 | nan | 0.8622 | 0.9550 | 0.7538 | 0.8256 | 0.4927 | nan | 0.6743 | 0.8511 | 0.0127 | 0.9476 | 0.1325 | 0.0 | 0.2133 | 0.0395 | 0.7274 | 0.0 | 0.0 | 0.9184 | 0.3518 | 0.6067 | 0.4630 | 0.0 | nan | 0.2801 | 0.5880 | 0.5689 | 0.0 | 0.9469 | 0.8717 | 0.9805 | 0.0367 | 0.2787 | 0.4502 | 0.0 | nan | 0.7656 | 0.8766 | 0.6694 | 0.7074 | 0.3811 | nan | 0.5560 | 0.6148 | 0.0075 | 0.8546 | 0.1119 | 0.0 | 0.1506 | 0.0395 | 0.5836 | 0.0 | 0.0 | 0.7703 | 0.2928 | 0.5369 | 0.3488 | 0.0 | nan | 0.2015 | 0.4753 | 0.3556 | 0.0 | 0.8884 | 0.7731 | 0.9472 | 0.0223 | 0.0949 | 0.3494 | 0.0 |
| 0.1363 | 14.9 | 2980 | 0.5998 | 0.3843 | 0.4588 | 0.8758 | nan | 0.8613 | 0.9508 | 0.7900 | 0.8233 | 0.5551 | nan | 0.6846 | 0.7923 | 0.1220 | 0.9444 | 0.1305 | 0.0 | 0.2587 | 0.0345 | 0.7305 | 0.0 | 0.0 | 0.9263 | 0.2417 | 0.6455 | 0.3663 | 0.0 | nan | 0.1970 | 0.5860 | 0.5722 | 0.0 | 0.9550 | 0.8504 | 0.9841 | 0.0068 | 0.1978 | 0.4733 | 0.0 | nan | 0.7683 | 0.8822 | 0.6306 | 0.7341 | 0.3911 | nan | 0.5612 | 0.6044 | 0.0279 | 0.8514 | 0.1080 | 0.0 | 0.1795 | 0.0345 | 0.5812 | 0.0 | 0.0 | 0.7676 | 0.2177 | 0.5378 | 0.3020 | 0.0 | nan | 0.1535 | 0.4746 | 0.3613 | 0.0 | 0.8867 | 0.7781 | 0.9441 | 0.0047 | 0.1336 | 0.3809 | 0.0 |
| 0.1537 | 15.0 | 3000 | 0.6302 | 0.3781 | 0.4487 | 0.8742 | nan | 0.8608 | 0.9579 | 0.7474 | 0.8513 | 0.5297 | nan | 0.6613 | 0.8043 | 0.0522 | 0.9385 | 0.1654 | 0.0 | 0.2167 | 0.0953 | 0.7527 | 0.0 | 0.0 | 0.9337 | 0.1895 | 0.5803 | 0.3136 | 0.0 | nan | 0.1148 | 0.6138 | 0.5339 | 0.0 | 0.9526 | 0.8402 | 0.9592 | 0.0053 | 0.2302 | 0.4568 | 0.0 | nan | 0.7672 | 0.8787 | 0.6736 | 0.7405 | 0.3974 | nan | 0.5523 | 0.5813 | 0.0141 | 0.8544 | 0.1415 | 0.0 | 0.1406 | 0.0953 | 0.5765 | 0.0 | 0.0 | 0.7532 | 0.1724 | 0.5052 | 0.2751 | 0.0 | nan | 0.0875 | 0.4545 | 0.3693 | 0.0 | 0.8846 | 0.7687 | 0.9310 | 0.0042 | 0.1148 | 0.3662 | 0.0 |
| 0.1485 | 15.1 | 3020 | 0.6229 | 0.3789 | 0.4409 | 0.8729 | nan | 0.8526 | 0.9580 | 0.7556 | 0.8393 | 0.4950 | nan | 0.6441 | 0.7653 | 0.0076 | 0.9464 | 0.0919 | 0.0 | 0.0298 | 0.0986 | 0.7278 | 0.0 | 0.0 | 0.9373 | 0.2535 | 0.5704 | 0.3935 | 0.0 | nan | 0.1312 | 0.5455 | 0.5439 | 0.0 | 0.9529 | 0.8331 | 0.9853 | 0.0344 | 0.2334 | 0.4811 | 0.0 | nan | 0.7615 | 0.8757 | 0.6904 | 0.7305 | 0.3855 | nan | 0.5535 | 0.6358 | 0.0036 | 0.8538 | 0.0840 | 0.0 | 0.0231 | 0.0986 | 0.5857 | 0.0 | 0.0 | 0.7518 | 0.2238 | 0.5053 | 0.3084 | 0.0 | nan | 0.1041 | 0.4677 | 0.3890 | 0.0 | 0.8814 | 0.7567 | 0.9427 | 0.0189 | 0.1170 | 0.3772 | 0.0 |
| 0.1718 | 15.2 | 3040 | 0.6701 | 0.3798 | 0.4523 | 0.8674 | nan | 0.7972 | 0.9520 | 0.8221 | 0.8217 | 0.5098 | nan | 0.7271 | 0.8380 | 0.0024 | 0.9484 | 0.0892 | 0.0 | 0.2547 | 0.0806 | 0.7163 | 0.0 | 0.0 | 0.9314 | 0.2443 | 0.5898 | 0.4167 | 0.0 | nan | 0.1191 | 0.6087 | 0.5311 | 0.0 | 0.9565 | 0.8495 | 0.9722 | 0.0061 | 0.2044 | 0.4828 | 0.0 | nan | 0.7285 | 0.8674 | 0.6197 | 0.6923 | 0.3759 | nan | 0.5581 | 0.6158 | 0.0015 | 0.8562 | 0.0833 | 0.0 | 0.1950 | 0.0806 | 0.5738 | 0.0 | 0.0 | 0.7618 | 0.2240 | 0.5185 | 0.3276 | 0.0 | nan | 0.1040 | 0.4752 | 0.3956 | 0.0 | 0.8844 | 0.7688 | 0.9478 | 0.0047 | 0.1121 | 0.3815 | 0.0 |
| 0.2132 | 15.3 | 3060 | 0.6335 | 0.3901 | 0.4622 | 0.8752 | nan | 0.8468 | 0.9570 | 0.7840 | 0.8462 | 0.4725 | nan | 0.6814 | 0.8495 | 0.0 | 0.9521 | 0.0949 | 0.0 | 0.4795 | 0.0581 | 0.7033 | 0.0 | 0.0 | 0.9150 | 0.2418 | 0.6258 | 0.4263 | 0.0077 | nan | 0.1338 | 0.5820 | 0.5426 | 0.0 | 0.9475 | 0.8725 | 0.9865 | 0.0032 | 0.2268 | 0.5548 | 0.0 | nan | 0.7638 | 0.8767 | 0.6849 | 0.7075 | 0.3723 | nan | 0.5592 | 0.6402 | 0.0 | 0.8557 | 0.0874 | 0.0 | 0.3410 | 0.0580 | 0.5791 | 0.0 | 0.0 | 0.7701 | 0.2224 | 0.5375 | 0.3389 | 0.0077 | nan | 0.1213 | 0.4724 | 0.3778 | 0.0 | 0.8877 | 0.7703 | 0.9448 | 0.0028 | 0.1137 | 0.3887 | 0.0 |
| 0.1008 | 15.4 | 3080 | 0.6443 | 0.3736 | 0.4415 | 0.8748 | nan | 0.8665 | 0.9538 | 0.7823 | 0.8147 | 0.5270 | nan | 0.6705 | 0.8323 | 0.0113 | 0.9477 | 0.1022 | 0.0 | 0.0254 | 0.0193 | 0.7429 | 0.0 | 0.0 | 0.9372 | 0.1383 | 0.5936 | 0.3546 | 0.0074 | nan | 0.1973 | 0.5904 | 0.5592 | 0.0 | 0.9524 | 0.8656 | 0.9749 | 0.0057 | 0.2072 | 0.4480 | 0.0 | nan | 0.7751 | 0.8771 | 0.7096 | 0.7160 | 0.3638 | nan | 0.5610 | 0.6100 | 0.0053 | 0.8599 | 0.0934 | 0.0 | 0.0221 | 0.0193 | 0.5593 | 0.0 | 0.0 | 0.7542 | 0.1324 | 0.5138 | 0.3004 | 0.0074 | nan | 0.1418 | 0.4798 | 0.3722 | 0.0 | 0.8868 | 0.7715 | 0.9507 | 0.0051 | 0.0993 | 0.3687 | 0.0 |
| 0.0901 | 15.5 | 3100 | 0.6716 | 0.3743 | 0.4341 | 0.8706 | nan | 0.8558 | 0.9587 | 0.6992 | 0.7659 | 0.5143 | nan | 0.6714 | 0.8210 | 0.0 | 0.9460 | 0.1066 | 0.0 | 0.0 | 0.0136 | 0.6662 | 0.0 | 0.0001 | 0.9354 | 0.2046 | 0.5539 | 0.3793 | 0.0 | nan | 0.2436 | 0.5818 | 0.5368 | 0.0 | 0.9485 | 0.8743 | 0.9826 | 0.0010 | 0.1007 | 0.5287 | 0.0 | nan | 0.7455 | 0.8663 | 0.6775 | 0.6980 | 0.3842 | nan | 0.5610 | 0.6267 | 0.0 | 0.8585 | 0.0963 | 0.0 | 0.0 | 0.0136 | 0.5838 | 0.0 | 0.0001 | 0.7477 | 0.1866 | 0.4929 | 0.3108 | 0.0 | nan | 0.1832 | 0.4803 | 0.3828 | 0.0 | 0.8878 | 0.7795 | 0.9468 | 0.0009 | 0.0737 | 0.3941 | 0.0 |
| 0.1319 | 15.6 | 3120 | 0.6274 | 0.3881 | 0.4643 | 0.8720 | nan | 0.8344 | 0.9556 | 0.7932 | 0.8385 | 0.4978 | nan | 0.7199 | 0.8512 | 0.0053 | 0.9580 | 0.0994 | 0.0 | 0.3726 | 0.0014 | 0.6745 | 0.0 | 0.0032 | 0.9289 | 0.2962 | 0.6204 | 0.3558 | 0.0025 | nan | 0.4086 | 0.5790 | 0.5311 | 0.0 | 0.9423 | 0.8488 | 0.9777 | 0.0062 | 0.2870 | 0.4688 | 0.0 | nan | 0.7538 | 0.8741 | 0.6520 | 0.7409 | 0.3659 | nan | 0.5670 | 0.6344 | 0.0029 | 0.8528 | 0.0888 | 0.0 | 0.2982 | 0.0014 | 0.5932 | 0.0 | 0.0024 | 0.7569 | 0.2374 | 0.5227 | 0.3058 | 0.0025 | nan | 0.2161 | 0.4790 | 0.3802 | 0.0 | 0.8840 | 0.7661 | 0.9486 | 0.0043 | 0.1112 | 0.3771 | 0.0 |
| 0.1196 | 15.7 | 3140 | 0.6027 | 0.3772 | 0.4491 | 0.8713 | nan | 0.8343 | 0.9517 | 0.7782 | 0.8481 | 0.4852 | nan | 0.6646 | 0.8781 | 0.0082 | 0.9449 | 0.1244 | 0.0 | 0.1531 | 0.0005 | 0.6859 | 0.0 | 0.0001 | 0.9241 | 0.1114 | 0.6287 | 0.4011 | 0.0004 | nan | 0.2774 | 0.5891 | 0.5923 | 0.0 | 0.9515 | 0.8736 | 0.9861 | 0.0088 | 0.1966 | 0.4716 | 0.0 | nan | 0.7500 | 0.8720 | 0.6315 | 0.7355 | 0.3701 | nan | 0.5496 | 0.6068 | 0.0071 | 0.8624 | 0.1100 | 0.0 | 0.1400 | 0.0005 | 0.6012 | 0.0 | 0.0001 | 0.7640 | 0.1051 | 0.5415 | 0.3113 | 0.0004 | nan | 0.1998 | 0.4782 | 0.3660 | 0.0 | 0.8829 | 0.7651 | 0.9441 | 0.0053 | 0.0993 | 0.3703 | 0.0 |
| 0.2085 | 15.8 | 3160 | 0.6267 | 0.3826 | 0.4602 | 0.8690 | nan | 0.8379 | 0.9526 | 0.7944 | 0.8073 | 0.5268 | nan | 0.6582 | 0.8739 | 0.0182 | 0.9506 | 0.1455 | 0.0 | 0.4964 | 0.0267 | 0.7657 | 0.0 | 0.0 | 0.9212 | 0.0797 | 0.6069 | 0.3732 | 0.0039 | nan | 0.2219 | 0.5556 | 0.5704 | 0.0 | 0.9611 | 0.8149 | 0.9818 | 0.0101 | 0.3218 | 0.4499 | 0.0 | nan | 0.7522 | 0.8662 | 0.6466 | 0.7271 | 0.3761 | nan | 0.5491 | 0.5897 | 0.0104 | 0.8602 | 0.1250 | 0.0 | 0.3505 | 0.0267 | 0.6110 | 0.0 | 0.0 | 0.7596 | 0.0743 | 0.5171 | 0.3070 | 0.0039 | nan | 0.1908 | 0.4781 | 0.3434 | 0.0 | 0.8789 | 0.7513 | 0.9492 | 0.0062 | 0.1302 | 0.3627 | 0.0 |
| 0.1353 | 15.9 | 3180 | 0.6430 | 0.3835 | 0.4563 | 0.8631 | nan | 0.7710 | 0.9483 | 0.7555 | 0.8692 | 0.4833 | nan | 0.7177 | 0.8501 | 0.0091 | 0.9469 | 0.1603 | 0.0 | 0.3588 | 0.0500 | 0.7433 | 0.0 | 0.0 | 0.9276 | 0.0763 | 0.5958 | 0.3994 | 0.0 | nan | 0.2782 | 0.5942 | 0.5438 | 0.0 | 0.9520 | 0.8643 | 0.9798 | 0.0108 | 0.2056 | 0.5113 | 0.0 | nan | 0.6972 | 0.8681 | 0.6930 | 0.6404 | 0.3562 | nan | 0.5509 | 0.6160 | 0.0048 | 0.8632 | 0.1420 | 0.0 | 0.2866 | 0.0500 | 0.6075 | 0.0 | 0.0 | 0.7604 | 0.0732 | 0.5174 | 0.3142 | 0.0 | nan | 0.2496 | 0.4831 | 0.3790 | 0.0 | 0.8853 | 0.7660 | 0.9496 | 0.0087 | 0.1189 | 0.3913 | 0.0 |
| 0.1617 | 16.0 | 3200 | 0.6151 | 0.3882 | 0.4699 | 0.8694 | nan | 0.8348 | 0.9375 | 0.8239 | 0.8528 | 0.6061 | nan | 0.6822 | 0.8461 | 0.0035 | 0.9569 | 0.1134 | 0.0 | 0.2992 | 0.0593 | 0.7013 | 0.0 | 0.0 | 0.9299 | 0.2929 | 0.6016 | 0.3728 | 0.0 | nan | 0.3863 | 0.5991 | 0.5887 | 0.0 | 0.9438 | 0.8419 | 0.9836 | 0.0086 | 0.3252 | 0.4467 | 0.0 | nan | 0.7545 | 0.8715 | 0.7219 | 0.7414 | 0.3574 | nan | 0.5604 | 0.5901 | 0.0020 | 0.8559 | 0.1012 | 0.0 | 0.2251 | 0.0593 | 0.5656 | 0.0 | 0.0 | 0.7552 | 0.2467 | 0.5171 | 0.3168 | 0.0 | nan | 0.2600 | 0.4849 | 0.3788 | 0.0 | 0.8828 | 0.7523 | 0.9466 | 0.0066 | 0.1108 | 0.3571 | 0.0 |
| 0.1087 | 16.1 | 3220 | 0.6156 | 0.3946 | 0.4679 | 0.8739 | nan | 0.8424 | 0.9612 | 0.7933 | 0.8578 | 0.4260 | nan | 0.7008 | 0.8356 | 0.0025 | 0.9456 | 0.1843 | 0.0 | 0.4258 | 0.1187 | 0.7147 | 0.0 | 0.0011 | 0.9300 | 0.2992 | 0.5966 | 0.3569 | 0.0 | nan | 0.4344 | 0.5571 | 0.5288 | 0.0 | 0.9551 | 0.8390 | 0.9786 | 0.0000 | 0.2392 | 0.4465 | 0.0 | nan | 0.7647 | 0.8734 | 0.6996 | 0.7312 | 0.3438 | nan | 0.5670 | 0.6188 | 0.0015 | 0.8613 | 0.1567 | 0.0 | 0.3108 | 0.1187 | 0.6023 | 0.0 | 0.0008 | 0.7553 | 0.2500 | 0.5166 | 0.2998 | 0.0 | nan | 0.2646 | 0.4777 | 0.3785 | 0.0 | 0.8838 | 0.7618 | 0.9511 | 0.0000 | 0.0869 | 0.3505 | 0.0 |
| 0.1534 | 16.2 | 3240 | 0.6361 | 0.3907 | 0.4569 | 0.8695 | nan | 0.8248 | 0.9618 | 0.7594 | 0.8464 | 0.4330 | nan | 0.6412 | 0.7748 | 0.0027 | 0.9560 | 0.1846 | 0.0 | 0.2197 | 0.0492 | 0.6962 | 0.0 | 0.0 | 0.9159 | 0.2669 | 0.5864 | 0.3982 | 0.0130 | nan | 0.4334 | 0.5939 | 0.5933 | 0.0 | 0.9521 | 0.8627 | 0.9699 | 0.0202 | 0.1637 | 0.5010 | 0.0 | nan | 0.7452 | 0.8576 | 0.7096 | 0.7390 | 0.3564 | nan | 0.5582 | 0.6255 | 0.0012 | 0.8545 | 0.1652 | 0.0 | 0.2012 | 0.0492 | 0.5941 | 0.0 | 0.0 | 0.7608 | 0.2184 | 0.5128 | 0.3134 | 0.0129 | nan | 0.3033 | 0.4586 | 0.3854 | 0.0 | 0.8842 | 0.7705 | 0.9393 | 0.0105 | 0.0960 | 0.3782 | 0.0 |
| 0.1424 | 16.3 | 3260 | 0.6442 | 0.3731 | 0.4348 | 0.8686 | nan | 0.8203 | 0.9599 | 0.7388 | 0.8626 | 0.4214 | nan | 0.6717 | 0.8149 | 0.0025 | 0.9475 | 0.1189 | 0.0 | 0.0003 | 0.0073 | 0.6840 | 0.0 | 0.0 | 0.9272 | 0.1288 | 0.6072 | 0.3303 | 0.0014 | nan | 0.3258 | 0.5757 | 0.5233 | 0.0 | 0.9592 | 0.8548 | 0.9727 | 0.0326 | 0.2164 | 0.4083 | 0.0 | nan | 0.7306 | 0.8685 | 0.7158 | 0.7007 | 0.3514 | nan | 0.5668 | 0.6272 | 0.0014 | 0.8631 | 0.1051 | 0.0 | 0.0003 | 0.0073 | 0.5916 | 0.0 | 0.0 | 0.7522 | 0.1159 | 0.5177 | 0.2870 | 0.0014 | nan | 0.2639 | 0.4648 | 0.3685 | 0.0 | 0.8824 | 0.7719 | 0.9418 | 0.0204 | 0.0798 | 0.3408 | 0.0 |
| 0.0966 | 16.4 | 3280 | 0.6606 | 0.3902 | 0.4710 | 0.8664 | nan | 0.8024 | 0.9532 | 0.8037 | 0.8563 | 0.4764 | nan | 0.6834 | 0.8516 | 0.0098 | 0.9516 | 0.1549 | 0.0 | 0.7565 | 0.1112 | 0.6759 | 0.0 | 0.0 | 0.9240 | 0.1866 | 0.5807 | 0.3483 | 0.0 | nan | 0.2897 | 0.5930 | 0.5241 | 0.0 | 0.9495 | 0.8388 | 0.9864 | 0.0284 | 0.2430 | 0.4930 | 0.0 | nan | 0.7277 | 0.8624 | 0.6978 | 0.7045 | 0.3633 | nan | 0.5624 | 0.6023 | 0.0055 | 0.8623 | 0.1326 | 0.0 | 0.3993 | 0.1112 | 0.6022 | 0.0 | 0.0 | 0.7582 | 0.1648 | 0.5129 | 0.3044 | 0.0 | nan | 0.2335 | 0.4796 | 0.3474 | 0.0 | 0.8846 | 0.7624 | 0.9436 | 0.0170 | 0.0817 | 0.3635 | 0.0 |
| 0.1438 | 16.5 | 3300 | 0.6547 | 0.3949 | 0.4663 | 0.8686 | nan | 0.8167 | 0.9584 | 0.7571 | 0.8461 | 0.4767 | nan | 0.6833 | 0.7965 | 0.0007 | 0.9491 | 0.1242 | 0.0 | 0.6012 | 0.1260 | 0.7100 | 0.0 | 0.0 | 0.9347 | 0.2388 | 0.5549 | 0.4005 | 0.0 | nan | 0.2431 | 0.5893 | 0.5809 | 0.0 | 0.9484 | 0.8448 | 0.9847 | 0.0081 | 0.2865 | 0.4624 | 0.0 | nan | 0.7351 | 0.8619 | 0.7252 | 0.7187 | 0.3620 | nan | 0.5654 | 0.6422 | 0.0003 | 0.8603 | 0.1085 | 0.0 | 0.4644 | 0.1260 | 0.6126 | 0.0 | 0.0 | 0.7527 | 0.1927 | 0.4990 | 0.3261 | 0.0 | nan | 0.1837 | 0.4828 | 0.3534 | 0.0 | 0.8866 | 0.7707 | 0.9463 | 0.0061 | 0.0907 | 0.3638 | 0.0 |
| 0.1047 | 16.6 | 3320 | 0.6620 | 0.3902 | 0.4587 | 0.8690 | nan | 0.8165 | 0.9626 | 0.7851 | 0.8390 | 0.4763 | nan | 0.6498 | 0.7912 | 0.0249 | 0.9441 | 0.1145 | 0.0 | 0.4841 | 0.1315 | 0.7786 | 0.0 | 0.0 | 0.9267 | 0.1411 | 0.6071 | 0.3302 | 0.0021 | nan | 0.2145 | 0.5729 | 0.5669 | 0.0 | 0.9516 | 0.8311 | 0.9806 | 0.0048 | 0.2571 | 0.4927 | 0.0 | nan | 0.7392 | 0.8618 | 0.7085 | 0.7183 | 0.3847 | nan | 0.5524 | 0.6180 | 0.0084 | 0.8613 | 0.1008 | 0.0 | 0.4199 | 0.1315 | 0.6120 | 0.0 | 0.0 | 0.7561 | 0.1279 | 0.5092 | 0.2864 | 0.0021 | nan | 0.1764 | 0.4765 | 0.3684 | 0.0 | 0.8822 | 0.7540 | 0.9490 | 0.0033 | 0.0984 | 0.3790 | 0.0 |
| 0.1603 | 16.7 | 3340 | 0.6201 | 0.3909 | 0.4604 | 0.8724 | nan | 0.8357 | 0.9505 | 0.7955 | 0.8510 | 0.5418 | nan | 0.6921 | 0.7958 | 0.0189 | 0.9505 | 0.1030 | 0.0 | 0.4512 | 0.1370 | 0.7554 | 0.0 | 0.0 | 0.9323 | 0.1324 | 0.5851 | 0.2937 | 0.0 | nan | 0.2254 | 0.5794 | 0.5298 | 0.0 | 0.9565 | 0.8437 | 0.9786 | 0.0100 | 0.2826 | 0.5059 | 0.0 | nan | 0.7510 | 0.8757 | 0.7049 | 0.7267 | 0.4078 | nan | 0.5574 | 0.6164 | 0.0068 | 0.8607 | 0.0917 | 0.0 | 0.4070 | 0.1369 | 0.5831 | 0.0 | 0.0 | 0.7535 | 0.1208 | 0.5183 | 0.2661 | 0.0 | nan | 0.1880 | 0.4874 | 0.3682 | 0.0 | 0.8805 | 0.7568 | 0.9503 | 0.0065 | 0.1034 | 0.3822 | 0.0 |
| 0.1489 | 16.8 | 3360 | 0.6502 | 0.3922 | 0.4645 | 0.8689 | nan | 0.8335 | 0.9446 | 0.7831 | 0.8308 | 0.5645 | nan | 0.6905 | 0.7748 | 0.0126 | 0.9532 | 0.1575 | 0.0 | 0.5452 | 0.0976 | 0.7534 | 0.0 | 0.0 | 0.9381 | 0.1427 | 0.5909 | 0.4154 | 0.0 | nan | 0.2283 | 0.5970 | 0.5611 | 0.0 | 0.9464 | 0.8215 | 0.9784 | 0.0080 | 0.1876 | 0.5064 | 0.0 | nan | 0.7445 | 0.8666 | 0.7103 | 0.7164 | 0.3841 | nan | 0.5533 | 0.6011 | 0.0027 | 0.8621 | 0.1280 | 0.0 | 0.4344 | 0.0975 | 0.6057 | 0.0 | 0.0 | 0.7533 | 0.1269 | 0.5119 | 0.3197 | 0.0 | nan | 0.1745 | 0.4893 | 0.3693 | 0.0 | 0.8832 | 0.7496 | 0.9511 | 0.0055 | 0.1152 | 0.3944 | 0.0 |
| 0.1685 | 16.9 | 3380 | 0.6708 | 0.3851 | 0.4575 | 0.8690 | nan | 0.8334 | 0.9582 | 0.7801 | 0.8063 | 0.5133 | nan | 0.6511 | 0.8619 | 0.0251 | 0.9376 | 0.1521 | 0.0 | 0.6353 | 0.0269 | 0.7188 | 0.0 | 0.0 | 0.9257 | 0.1267 | 0.5672 | 0.3617 | 0.0372 | nan | 0.2287 | 0.5864 | 0.5349 | 0.0 | 0.9582 | 0.8384 | 0.9849 | 0.0080 | 0.1262 | 0.4551 | 0.0 | nan | 0.7410 | 0.8653 | 0.6794 | 0.7120 | 0.3925 | nan | 0.5553 | 0.5044 | 0.0111 | 0.8650 | 0.1305 | 0.0 | 0.4608 | 0.0269 | 0.6250 | 0.0 | 0.0 | 0.7559 | 0.1139 | 0.5049 | 0.3013 | 0.0369 | nan | 0.1731 | 0.4805 | 0.3695 | 0.0 | 0.8804 | 0.7613 | 0.9454 | 0.0058 | 0.0652 | 0.3586 | 0.0 |
| 0.1464 | 17.0 | 3400 | 0.6396 | 0.3889 | 0.4638 | 0.8711 | nan | 0.8434 | 0.9486 | 0.8471 | 0.8117 | 0.5337 | nan | 0.6774 | 0.8109 | 0.0009 | 0.9510 | 0.1685 | 0.0 | 0.6241 | 0.0945 | 0.7062 | 0.0 | 0.0 | 0.9337 | 0.0732 | 0.5948 | 0.3886 | 0.0499 | nan | 0.2157 | 0.5693 | 0.5502 | 0.0 | 0.9559 | 0.8320 | 0.9821 | 0.0024 | 0.2056 | 0.4701 | 0.0 | nan | 0.7574 | 0.8809 | 0.6622 | 0.7190 | 0.3777 | nan | 0.5570 | 0.6090 | 0.0005 | 0.8596 | 0.1422 | 0.0 | 0.4428 | 0.0944 | 0.5854 | 0.0 | 0.0 | 0.7552 | 0.0679 | 0.5222 | 0.3214 | 0.0494 | nan | 0.1783 | 0.4779 | 0.3635 | 0.0 | 0.8814 | 0.7449 | 0.9474 | 0.0013 | 0.0831 | 0.3613 | 0.0 |
| 0.1365 | 17.1 | 3420 | 0.6572 | 0.3892 | 0.4633 | 0.8705 | nan | 0.8382 | 0.9572 | 0.8112 | 0.7848 | 0.5338 | nan | 0.6472 | 0.8088 | 0.0071 | 0.9497 | 0.1396 | 0.0 | 0.5627 | 0.0748 | 0.7273 | 0.0 | 0.0 | 0.9303 | 0.1357 | 0.6070 | 0.4103 | 0.0277 | nan | 0.2033 | 0.5932 | 0.5764 | 0.0 | 0.9469 | 0.8637 | 0.9832 | 0.0022 | 0.2535 | 0.4493 | 0.0 | nan | 0.7516 | 0.8673 | 0.6850 | 0.7140 | 0.3789 | nan | 0.5561 | 0.6163 | 0.0036 | 0.8644 | 0.1202 | 0.0 | 0.4187 | 0.0748 | 0.5798 | 0.0 | 0.0 | 0.7616 | 0.1225 | 0.5294 | 0.3379 | 0.0275 | nan | 0.1374 | 0.4880 | 0.3775 | 0.0 | 0.8883 | 0.7683 | 0.9473 | 0.0013 | 0.0840 | 0.3516 | 0.0 |
| 0.1378 | 17.2 | 3440 | 0.6191 | 0.3847 | 0.4545 | 0.8740 | nan | 0.8495 | 0.9487 | 0.7936 | 0.8689 | 0.4940 | nan | 0.6837 | 0.8027 | 0.0011 | 0.9480 | 0.1273 | 0.0 | 0.3769 | 0.0300 | 0.7042 | 0.0 | 0.0 | 0.9262 | 0.1578 | 0.6273 | 0.3385 | 0.0095 | nan | 0.2106 | 0.5912 | 0.5896 | 0.0 | 0.9543 | 0.8506 | 0.9832 | 0.0015 | 0.1770 | 0.4974 | 0.0 | nan | 0.7596 | 0.8815 | 0.6459 | 0.7035 | 0.3778 | nan | 0.5592 | 0.6375 | 0.0007 | 0.8580 | 0.1145 | 0.0 | 0.3289 | 0.0300 | 0.5752 | 0.0 | 0.0 | 0.7606 | 0.1426 | 0.5268 | 0.2930 | 0.0094 | nan | 0.1559 | 0.4862 | 0.3845 | 0.0 | 0.8857 | 0.7672 | 0.9472 | 0.0014 | 0.0916 | 0.3863 | 0.0 |
| 0.1417 | 17.3 | 3460 | 0.6328 | 0.3898 | 0.4660 | 0.8735 | nan | 0.8465 | 0.9501 | 0.7913 | 0.8431 | 0.5206 | nan | 0.6615 | 0.8699 | 0.0089 | 0.9539 | 0.2104 | 0.0 | 0.4512 | 0.0147 | 0.6838 | 0.0 | 0.0 | 0.9255 | 0.2034 | 0.6098 | 0.3673 | 0.0256 | nan | 0.2154 | 0.5991 | 0.6197 | 0.0 | 0.9511 | 0.8755 | 0.9824 | 0.0223 | 0.2381 | 0.4713 | 0.0 | nan | 0.7532 | 0.8780 | 0.6962 | 0.7382 | 0.3778 | nan | 0.5484 | 0.6016 | 0.0075 | 0.8590 | 0.1806 | 0.0 | 0.3458 | 0.0147 | 0.5818 | 0.0 | 0.0 | 0.7599 | 0.1754 | 0.5210 | 0.3029 | 0.0253 | nan | 0.1574 | 0.4859 | 0.3672 | 0.0 | 0.8871 | 0.7619 | 0.9488 | 0.0140 | 0.1032 | 0.3791 | 0.0 |
| 0.1277 | 17.4 | 3480 | 0.6331 | 0.3872 | 0.4582 | 0.8722 | nan | 0.8338 | 0.9512 | 0.7777 | 0.8407 | 0.5404 | nan | 0.7127 | 0.8462 | 0.0131 | 0.9527 | 0.1605 | 0.0 | 0.3180 | 0.0268 | 0.6960 | 0.0 | 0.0 | 0.9278 | 0.2041 | 0.6040 | 0.3440 | 0.0067 | nan | 0.2529 | 0.5973 | 0.5703 | 0.0 | 0.9532 | 0.8474 | 0.9828 | 0.0080 | 0.2203 | 0.4740 | 0.0 | nan | 0.7460 | 0.8775 | 0.7051 | 0.7192 | 0.3927 | nan | 0.5591 | 0.6078 | 0.0072 | 0.8635 | 0.1412 | 0.0 | 0.2497 | 0.0268 | 0.5889 | 0.0 | 0.0 | 0.7585 | 0.1791 | 0.5135 | 0.2937 | 0.0066 | nan | 0.2063 | 0.4870 | 0.3853 | 0.0 | 0.8854 | 0.7689 | 0.9492 | 0.0046 | 0.0924 | 0.3759 | 0.0 |
| 0.162 | 17.5 | 3500 | 0.6330 | 0.3854 | 0.4526 | 0.8730 | nan | 0.8373 | 0.9588 | 0.7677 | 0.8370 | 0.4894 | nan | 0.6701 | 0.8162 | 0.0102 | 0.9564 | 0.1115 | 0.0 | 0.3171 | 0.0528 | 0.6832 | 0.0 | 0.0 | 0.9220 | 0.2651 | 0.6014 | 0.3459 | 0.0011 | nan | 0.1705 | 0.5879 | 0.5539 | 0.0 | 0.9609 | 0.8585 | 0.9792 | 0.0009 | 0.2789 | 0.4478 | 0.0 | nan | 0.7443 | 0.8716 | 0.7125 | 0.7195 | 0.3858 | nan | 0.5647 | 0.6139 | 0.0049 | 0.8575 | 0.0998 | 0.0 | 0.2619 | 0.0528 | 0.5751 | 0.0 | 0.0 | 0.7591 | 0.2179 | 0.5136 | 0.2962 | 0.0010 | nan | 0.1336 | 0.4891 | 0.3825 | 0.0 | 0.8828 | 0.7760 | 0.9499 | 0.0009 | 0.1019 | 0.3647 | 0.0 |
| 0.0967 | 17.6 | 3520 | 0.6176 | 0.3858 | 0.4542 | 0.8752 | nan | 0.8580 | 0.9520 | 0.7998 | 0.8507 | 0.5326 | nan | 0.6841 | 0.8336 | 0.0020 | 0.9521 | 0.1145 | 0.0 | 0.2032 | 0.0751 | 0.6629 | 0.0 | 0.0 | 0.9342 | 0.2073 | 0.5489 | 0.3947 | 0.0218 | nan | 0.2637 | 0.6003 | 0.5635 | 0.0 | 0.9521 | 0.8432 | 0.9831 | 0.0086 | 0.2128 | 0.4805 | 0.0 | nan | 0.7633 | 0.8815 | 0.6620 | 0.7389 | 0.3922 | nan | 0.5692 | 0.6264 | 0.0013 | 0.8571 | 0.1023 | 0.0 | 0.1693 | 0.0751 | 0.5715 | 0.0 | 0.0 | 0.7558 | 0.1879 | 0.4861 | 0.3087 | 0.0216 | nan | 0.2168 | 0.4879 | 0.3836 | 0.0 | 0.8844 | 0.7608 | 0.9469 | 0.0077 | 0.1060 | 0.3815 | 0.0 |
| 0.1003 | 17.7 | 3540 | 0.6420 | 0.3874 | 0.4692 | 0.8713 | nan | 0.8484 | 0.9538 | 0.7812 | 0.8201 | 0.5088 | nan | 0.6856 | 0.8652 | 0.0007 | 0.9537 | 0.1706 | 0.0 | 0.6290 | 0.0624 | 0.7385 | 0.0 | 0.0 | 0.9386 | 0.2066 | 0.5970 | 0.3658 | 0.0267 | nan | 0.2712 | 0.5962 | 0.5393 | 0.0 | 0.9480 | 0.8226 | 0.9837 | 0.0082 | 0.2721 | 0.4197 | 0.0 | nan | 0.7533 | 0.8758 | 0.6771 | 0.6990 | 0.3944 | nan | 0.5631 | 0.5618 | 0.0005 | 0.8596 | 0.1445 | 0.0 | 0.3542 | 0.0624 | 0.5718 | 0.0 | 0.0 | 0.7510 | 0.1778 | 0.4933 | 0.3078 | 0.0262 | nan | 0.2112 | 0.4891 | 0.3718 | 0.0 | 0.8834 | 0.7588 | 0.9469 | 0.0075 | 0.1096 | 0.3450 | 0.0 |
| 0.1321 | 17.8 | 3560 | 0.6472 | 0.3854 | 0.4690 | 0.8700 | nan | 0.8190 | 0.9575 | 0.7796 | 0.8176 | 0.5442 | nan | 0.7002 | 0.8532 | 0.0009 | 0.9549 | 0.2109 | 0.0357 | 0.8843 | 0.1077 | 0.7413 | 0.0 | 0.0 | 0.9250 | 0.1177 | 0.6343 | 0.3388 | 0.0348 | nan | 0.1354 | 0.5508 | 0.4907 | 0.0 | 0.9538 | 0.8184 | 0.9834 | 0.0006 | 0.1281 | 0.4906 | 0.0 | nan | 0.7362 | 0.8674 | 0.6764 | 0.7011 | 0.3939 | nan | 0.5691 | 0.5895 | 0.0007 | 0.8628 | 0.1768 | 0.0357 | 0.3133 | 0.1077 | 0.5636 | 0.0 | 0.0 | 0.7612 | 0.1111 | 0.5214 | 0.2874 | 0.0343 | nan | 0.1176 | 0.4788 | 0.3656 | 0.0 | 0.8833 | 0.7533 | 0.9473 | 0.0006 | 0.0951 | 0.3820 | 0.0 |
| 0.1012 | 17.9 | 3580 | 0.6327 | 0.3945 | 0.4744 | 0.8750 | nan | 0.8444 | 0.9511 | 0.7980 | 0.8497 | 0.5546 | nan | 0.6874 | 0.8387 | 0.0047 | 0.9528 | 0.1516 | 0.0241 | 0.6486 | 0.1604 | 0.7541 | 0.0 | 0.0 | 0.9332 | 0.1317 | 0.6395 | 0.3957 | 0.0456 | nan | 0.1993 | 0.5825 | 0.5292 | 0.0 | 0.9510 | 0.8507 | 0.9821 | 0.0028 | 0.2793 | 0.4377 | 0.0 | nan | 0.7602 | 0.8801 | 0.6560 | 0.7283 | 0.3917 | nan | 0.5599 | 0.6319 | 0.0034 | 0.8636 | 0.1319 | 0.0241 | 0.3515 | 0.1603 | 0.5733 | 0.0 | 0.0 | 0.7636 | 0.1224 | 0.5281 | 0.3166 | 0.0446 | nan | 0.1600 | 0.4877 | 0.3838 | 0.0 | 0.8873 | 0.7671 | 0.9494 | 0.0024 | 0.1333 | 0.3614 | 0.0 |
| 0.1115 | 18.0 | 3600 | 0.6471 | 0.3958 | 0.4775 | 0.8741 | nan | 0.8373 | 0.9557 | 0.8146 | 0.8568 | 0.5123 | nan | 0.6811 | 0.8384 | 0.0147 | 0.9499 | 0.1619 | 0.0286 | 0.6451 | 0.2463 | 0.7429 | 0.0 | 0.0 | 0.9359 | 0.1783 | 0.5939 | 0.3325 | 0.0481 | nan | 0.2130 | 0.5999 | 0.5620 | 0.0 | 0.9495 | 0.8572 | 0.9796 | 0.0047 | 0.2841 | 0.4547 | 0.0 | nan | 0.7578 | 0.8810 | 0.6269 | 0.7312 | 0.3883 | nan | 0.5636 | 0.6215 | 0.0093 | 0.8634 | 0.1373 | 0.0285 | 0.3512 | 0.2463 | 0.5963 | 0.0 | 0.0 | 0.7620 | 0.1596 | 0.5112 | 0.2902 | 0.0468 | nan | 0.1611 | 0.4861 | 0.3712 | 0.0 | 0.8848 | 0.7659 | 0.9499 | 0.0038 | 0.1134 | 0.3581 | 0.0 |
| 0.1348 | 18.1 | 3620 | 0.6481 | 0.3917 | 0.4693 | 0.8706 | nan | 0.8306 | 0.9477 | 0.8035 | 0.8420 | 0.5514 | nan | 0.7045 | 0.8461 | 0.0400 | 0.9511 | 0.1532 | 0.0096 | 0.6127 | 0.1254 | 0.7203 | 0.0001 | 0.0 | 0.9398 | 0.1342 | 0.5573 | 0.3896 | 0.0337 | nan | 0.2287 | 0.5552 | 0.5724 | 0.0 | 0.9505 | 0.8600 | 0.9802 | 0.0028 | 0.2236 | 0.4512 | 0.0 | nan | 0.7478 | 0.8727 | 0.6513 | 0.7269 | 0.3812 | nan | 0.5678 | 0.6342 | 0.0202 | 0.8619 | 0.1277 | 0.0091 | 0.4013 | 0.1253 | 0.5868 | 0.0001 | 0.0 | 0.7536 | 0.1229 | 0.4895 | 0.2892 | 0.0330 | nan | 0.1947 | 0.4772 | 0.3720 | 0.0 | 0.8859 | 0.7637 | 0.9502 | 0.0024 | 0.1190 | 0.3666 | 0.0 |
| 0.1008 | 18.2 | 3640 | 0.6400 | 0.3961 | 0.4778 | 0.8700 | nan | 0.8315 | 0.9466 | 0.7874 | 0.8600 | 0.5177 | nan | 0.6957 | 0.8418 | 0.0366 | 0.9475 | 0.2405 | 0.0 | 0.6111 | 0.1058 | 0.7397 | 0.0 | 0.0009 | 0.9279 | 0.2627 | 0.5572 | 0.3947 | 0.0730 | nan | 0.2279 | 0.6045 | 0.5431 | 0.0 | 0.9459 | 0.8792 | 0.9865 | 0.0192 | 0.2656 | 0.4386 | 0.0 | nan | 0.7440 | 0.8732 | 0.6501 | 0.7160 | 0.3869 | nan | 0.5630 | 0.6315 | 0.0223 | 0.8652 | 0.2004 | 0.0 | 0.4063 | 0.1058 | 0.5690 | 0.0 | 0.0008 | 0.7539 | 0.2115 | 0.4873 | 0.2980 | 0.0709 | nan | 0.1864 | 0.4805 | 0.3760 | 0.0 | 0.8870 | 0.7709 | 0.9467 | 0.0142 | 0.1040 | 0.3519 | 0.0 |
| 0.1345 | 18.3 | 3660 | 0.6472 | 0.3959 | 0.4781 | 0.8706 | nan | 0.8146 | 0.9530 | 0.8235 | 0.8520 | 0.5497 | nan | 0.6968 | 0.8381 | 0.0207 | 0.9516 | 0.1768 | 0.0 | 0.6848 | 0.0982 | 0.7017 | 0.0 | 0.0016 | 0.9232 | 0.2083 | 0.5915 | 0.4181 | 0.0692 | nan | 0.2702 | 0.6141 | 0.5410 | 0.0 | 0.9518 | 0.8357 | 0.9695 | 0.0106 | 0.2471 | 0.4844 | 0.0 | nan | 0.7450 | 0.8758 | 0.6078 | 0.7318 | 0.3818 | nan | 0.5719 | 0.6405 | 0.0114 | 0.8615 | 0.1511 | 0.0 | 0.4094 | 0.0981 | 0.5907 | 0.0 | 0.0014 | 0.7618 | 0.1787 | 0.5066 | 0.3096 | 0.0666 | nan | 0.2119 | 0.4778 | 0.3787 | 0.0 | 0.8857 | 0.7576 | 0.9448 | 0.0082 | 0.1287 | 0.3737 | 0.0 |
| 0.1169 | 18.4 | 3680 | 0.6531 | 0.3883 | 0.4689 | 0.8691 | nan | 0.8289 | 0.9521 | 0.7879 | 0.8127 | 0.5339 | nan | 0.6864 | 0.8635 | 0.0215 | 0.9581 | 0.1386 | 0.0544 | 0.8072 | 0.0718 | 0.6534 | 0.0 | 0.0 | 0.9327 | 0.1959 | 0.5625 | 0.3530 | 0.0056 | nan | 0.2673 | 0.5698 | 0.5440 | 0.0 | 0.9512 | 0.8463 | 0.9864 | 0.0051 | 0.1302 | 0.4835 | 0.0 | nan | 0.7451 | 0.8685 | 0.6699 | 0.7229 | 0.3542 | nan | 0.5663 | 0.5963 | 0.0124 | 0.8566 | 0.1181 | 0.0511 | 0.4035 | 0.0717 | 0.5656 | 0.0 | 0.0 | 0.7549 | 0.1712 | 0.4952 | 0.2960 | 0.0056 | nan | 0.2144 | 0.4820 | 0.3652 | 0.0 | 0.8849 | 0.7623 | 0.9443 | 0.0045 | 0.0663 | 0.3781 | 0.0 |
| 0.1532 | 18.5 | 3700 | 0.6728 | 0.3907 | 0.4725 | 0.8686 | nan | 0.8117 | 0.9581 | 0.7649 | 0.8659 | 0.5009 | nan | 0.6302 | 0.7146 | 0.1802 | 0.9449 | 0.1444 | 0.0 | 0.7378 | 0.1248 | 0.7747 | 0.0 | 0.0 | 0.9387 | 0.1801 | 0.6004 | 0.3819 | 0.0629 | nan | 0.1859 | 0.5827 | 0.5751 | 0.0 | 0.9523 | 0.8323 | 0.9805 | 0.0229 | 0.2482 | 0.4215 | 0.0 | nan | 0.7378 | 0.8700 | 0.6787 | 0.6915 | 0.3825 | nan | 0.5584 | 0.6061 | 0.0625 | 0.8632 | 0.1243 | 0.0 | 0.3961 | 0.1248 | 0.5644 | 0.0 | 0.0 | 0.7546 | 0.1585 | 0.5011 | 0.3042 | 0.0613 | nan | 0.1455 | 0.4883 | 0.3701 | 0.0 | 0.8856 | 0.7606 | 0.9497 | 0.0151 | 0.1017 | 0.3440 | 0.0 |
| 0.0933 | 18.6 | 3720 | 0.6657 | 0.3892 | 0.4705 | 0.8671 | nan | 0.7966 | 0.9503 | 0.7910 | 0.8426 | 0.5041 | nan | 0.7030 | 0.8504 | 0.0036 | 0.9521 | 0.1420 | 0.0 | 0.7654 | 0.0719 | 0.6865 | 0.0 | 0.0 | 0.9344 | 0.1655 | 0.6533 | 0.3030 | 0.0829 | nan | 0.2544 | 0.5910 | 0.5748 | 0.0 | 0.9539 | 0.8573 | 0.9809 | 0.0126 | 0.1971 | 0.4359 | 0.0 | nan | 0.7158 | 0.8649 | 0.6562 | 0.6928 | 0.3857 | nan | 0.5605 | 0.5842 | 0.0018 | 0.8581 | 0.1180 | 0.0 | 0.4258 | 0.0718 | 0.5858 | 0.0 | 0.0 | 0.7634 | 0.1494 | 0.5398 | 0.2672 | 0.0798 | nan | 0.1992 | 0.4898 | 0.3707 | 0.0 | 0.8857 | 0.7744 | 0.9496 | 0.0094 | 0.0952 | 0.3577 | 0.0 |
| 0.1438 | 18.7 | 3740 | 0.6791 | 0.3893 | 0.4663 | 0.8655 | nan | 0.7821 | 0.9529 | 0.7963 | 0.8464 | 0.5156 | nan | 0.6654 | 0.8413 | 0.0087 | 0.9405 | 0.1420 | 0.0 | 0.5114 | 0.1117 | 0.7339 | 0.0 | 0.0 | 0.9284 | 0.2376 | 0.6176 | 0.3744 | 0.1001 | nan | 0.1974 | 0.6001 | 0.5361 | 0.0 | 0.9529 | 0.8762 | 0.9833 | 0.0107 | 0.2175 | 0.4416 | 0.0 | nan | 0.7087 | 0.8600 | 0.6665 | 0.7063 | 0.3783 | nan | 0.5571 | 0.5747 | 0.0045 | 0.8647 | 0.1161 | 0.0 | 0.3470 | 0.1117 | 0.5896 | 0.0 | 0.0 | 0.7619 | 0.2037 | 0.5308 | 0.3091 | 0.0951 | nan | 0.1643 | 0.4884 | 0.3719 | 0.0 | 0.8887 | 0.7752 | 0.9486 | 0.0075 | 0.0816 | 0.3465 | 0.0 |
| 0.0849 | 18.8 | 3760 | 0.6566 | 0.3937 | 0.4671 | 0.8737 | nan | 0.8372 | 0.9555 | 0.7537 | 0.8370 | 0.5635 | nan | 0.6648 | 0.8707 | 0.0169 | 0.9553 | 0.1224 | 0.0 | 0.4110 | 0.0835 | 0.7136 | 0.0 | 0.0012 | 0.9242 | 0.2331 | 0.6116 | 0.4140 | 0.0660 | nan | 0.3150 | 0.5872 | 0.5567 | 0.0 | 0.9522 | 0.8598 | 0.9851 | 0.0051 | 0.1902 | 0.4594 | 0.0 | nan | 0.7514 | 0.8717 | 0.7081 | 0.7288 | 0.3997 | nan | 0.5627 | 0.5637 | 0.0104 | 0.8600 | 0.1056 | 0.0 | 0.3094 | 0.0835 | 0.5931 | 0.0 | 0.0010 | 0.7622 | 0.2070 | 0.5229 | 0.3329 | 0.0644 | nan | 0.2304 | 0.4850 | 0.3810 | 0.0 | 0.8919 | 0.7864 | 0.9474 | 0.0038 | 0.0767 | 0.3569 | 0.0 |
| 0.1116 | 18.9 | 3780 | 0.6471 | 0.3966 | 0.4666 | 0.8774 | nan | 0.8720 | 0.9482 | 0.8046 | 0.8431 | 0.5345 | nan | 0.6738 | 0.8331 | 0.0113 | 0.9485 | 0.0919 | 0.0 | 0.4484 | 0.1166 | 0.7373 | 0.0 | 0.0026 | 0.9275 | 0.1592 | 0.6165 | 0.4077 | 0.0179 | nan | 0.3014 | 0.5796 | 0.5465 | 0.0 | 0.9580 | 0.8396 | 0.9802 | 0.0419 | 0.1764 | 0.5117 | 0.0 | nan | 0.7768 | 0.8863 | 0.6934 | 0.7475 | 0.3869 | nan | 0.5524 | 0.6067 | 0.0064 | 0.8653 | 0.0813 | 0.0 | 0.3712 | 0.1166 | 0.6076 | 0.0 | 0.0022 | 0.7640 | 0.1476 | 0.5426 | 0.3372 | 0.0176 | nan | 0.2389 | 0.4833 | 0.3878 | 0.0 | 0.8879 | 0.7702 | 0.9496 | 0.0196 | 0.0705 | 0.3732 | 0.0 |
| 0.1391 | 19.0 | 3800 | 0.6216 | 0.3908 | 0.4653 | 0.8761 | nan | 0.8496 | 0.9555 | 0.7994 | 0.8339 | 0.5083 | nan | 0.7163 | 0.8324 | 0.0129 | 0.9491 | 0.1400 | 0.0 | 0.5319 | 0.0279 | 0.7549 | 0.0 | 0.0020 | 0.9199 | 0.1396 | 0.6369 | 0.3589 | 0.0829 | nan | 0.2064 | 0.6205 | 0.5965 | 0.0 | 0.9594 | 0.8644 | 0.9757 | 0.0076 | 0.1460 | 0.4598 | 0.0 | nan | 0.7640 | 0.8820 | 0.6720 | 0.7404 | 0.3806 | nan | 0.5694 | 0.5559 | 0.0067 | 0.8639 | 0.1234 | 0.0 | 0.4172 | 0.0279 | 0.5877 | 0.0 | 0.0018 | 0.7649 | 0.1263 | 0.5285 | 0.3136 | 0.0805 | nan | 0.1776 | 0.4837 | 0.3792 | 0.0 | 0.8865 | 0.7708 | 0.9521 | 0.0050 | 0.0771 | 0.3654 | 0.0 |
| 0.1196 | 19.1 | 3820 | 0.6366 | 0.3926 | 0.4722 | 0.8727 | nan | 0.8250 | 0.9484 | 0.8132 | 0.8418 | 0.5690 | nan | 0.7304 | 0.8154 | 0.0102 | 0.9577 | 0.1662 | 0.0002 | 0.6124 | 0.0631 | 0.7290 | 0.0 | 0.0075 | 0.9306 | 0.1782 | 0.6425 | 0.3602 | 0.0330 | nan | 0.2764 | 0.5907 | 0.5957 | 0.0 | 0.9500 | 0.8442 | 0.9833 | 0.0043 | 0.1566 | 0.4757 | 0.0 | nan | 0.7522 | 0.8838 | 0.6799 | 0.7295 | 0.3469 | nan | 0.5803 | 0.5606 | 0.0042 | 0.8591 | 0.1443 | 0.0002 | 0.4289 | 0.0631 | 0.6002 | 0.0 | 0.0067 | 0.7621 | 0.1557 | 0.5209 | 0.3123 | 0.0322 | nan | 0.2066 | 0.4849 | 0.3736 | 0.0 | 0.8885 | 0.7692 | 0.9480 | 0.0035 | 0.1009 | 0.3666 | 0.0 |
| 0.0959 | 19.2 | 3840 | 0.6288 | 0.3935 | 0.4702 | 0.8750 | nan | 0.8575 | 0.9453 | 0.7918 | 0.8497 | 0.5645 | nan | 0.6963 | 0.8299 | 0.0091 | 0.9490 | 0.2160 | 0.0 | 0.4691 | 0.0974 | 0.7566 | 0.0 | 0.0057 | 0.9396 | 0.1275 | 0.5798 | 0.4401 | 0.0737 | nan | 0.2358 | 0.5932 | 0.5844 | 0.0 | 0.9462 | 0.8775 | 0.9824 | 0.0096 | 0.1730 | 0.4441 | 0.0 | nan | 0.7664 | 0.8847 | 0.6672 | 0.7389 | 0.3696 | nan | 0.5766 | 0.5705 | 0.0046 | 0.8629 | 0.1796 | 0.0 | 0.3385 | 0.0973 | 0.5875 | 0.0 | 0.0046 | 0.7543 | 0.1155 | 0.4952 | 0.3465 | 0.0703 | nan | 0.1918 | 0.4893 | 0.3725 | 0.0 | 0.8922 | 0.7813 | 0.9511 | 0.0061 | 0.1146 | 0.3613 | 0.0 |
| 0.0953 | 19.3 | 3860 | 0.6623 | 0.3801 | 0.4474 | 0.8713 | nan | 0.8262 | 0.9593 | 0.7969 | 0.8096 | 0.5081 | nan | 0.6969 | 0.8623 | 0.0131 | 0.9501 | 0.1314 | 0.0012 | 0.2272 | 0.0296 | 0.6862 | 0.0 | 0.0 | 0.9403 | 0.1217 | 0.5800 | 0.3706 | 0.0808 | nan | 0.2683 | 0.5718 | 0.5212 | 0.0 | 0.9522 | 0.8629 | 0.9806 | 0.0062 | 0.1071 | 0.4561 | 0.0 | nan | 0.7435 | 0.8717 | 0.7078 | 0.7134 | 0.3755 | nan | 0.5783 | 0.5435 | 0.0070 | 0.8618 | 0.1100 | 0.0012 | 0.1734 | 0.0295 | 0.5762 | 0.0 | 0.0 | 0.7504 | 0.1110 | 0.4996 | 0.3201 | 0.0776 | nan | 0.2004 | 0.4833 | 0.3712 | 0.0 | 0.8898 | 0.7813 | 0.9505 | 0.0037 | 0.0657 | 0.3646 | 0.0 |
| 0.1373 | 19.4 | 3880 | 0.6685 | 0.3895 | 0.4625 | 0.8714 | nan | 0.8442 | 0.9510 | 0.7916 | 0.8176 | 0.5431 | nan | 0.6855 | 0.8213 | 0.0075 | 0.9520 | 0.1370 | 0.0018 | 0.5377 | 0.0521 | 0.6675 | 0.0 | 0.0017 | 0.9306 | 0.1636 | 0.5739 | 0.3619 | 0.0569 | nan | 0.2615 | 0.5481 | 0.5454 | 0.0 | 0.9551 | 0.8495 | 0.9859 | 0.0112 | 0.2837 | 0.4612 | 0.0 | nan | 0.7560 | 0.8749 | 0.7109 | 0.7168 | 0.3679 | nan | 0.5734 | 0.5998 | 0.0039 | 0.8604 | 0.1156 | 0.0018 | 0.3666 | 0.0521 | 0.5734 | 0.0 | 0.0015 | 0.7527 | 0.1418 | 0.4988 | 0.3208 | 0.0558 | nan | 0.1980 | 0.4756 | 0.3725 | 0.0 | 0.8879 | 0.7700 | 0.9445 | 0.0067 | 0.1031 | 0.3614 | 0.0 |
| 0.0634 | 19.5 | 3900 | 0.6595 | 0.4015 | 0.4852 | 0.8721 | nan | 0.8256 | 0.9453 | 0.8299 | 0.8586 | 0.5898 | nan | 0.6855 | 0.8259 | 0.0306 | 0.9562 | 0.1417 | 0.0069 | 0.6597 | 0.0814 | 0.7130 | 0.0 | 0.0123 | 0.9206 | 0.2307 | 0.6144 | 0.3796 | 0.1257 | nan | 0.3290 | 0.6291 | 0.5818 | 0.0 | 0.9508 | 0.8416 | 0.9838 | 0.0177 | 0.2719 | 0.4868 | 0.0 | nan | 0.7497 | 0.8837 | 0.6989 | 0.7101 | 0.3746 | nan | 0.5735 | 0.6215 | 0.0142 | 0.8605 | 0.1227 | 0.0069 | 0.4337 | 0.0814 | 0.5765 | 0.0 | 0.0110 | 0.7668 | 0.1922 | 0.5229 | 0.3180 | 0.1211 | nan | 0.2457 | 0.4939 | 0.3811 | 0.0 | 0.8884 | 0.7650 | 0.9487 | 0.0094 | 0.1052 | 0.3722 | 0.0 |
| 0.1144 | 19.6 | 3920 | 0.6743 | 0.3943 | 0.4708 | 0.8711 | nan | 0.8363 | 0.9524 | 0.8027 | 0.7968 | 0.5293 | nan | 0.6985 | 0.8225 | 0.1192 | 0.9560 | 0.1437 | 0.0100 | 0.7422 | 0.0896 | 0.6518 | 0.0 | 0.0 | 0.9286 | 0.1067 | 0.6105 | 0.3336 | 0.0772 | nan | 0.2570 | 0.5919 | 0.5545 | 0.0 | 0.9578 | 0.8493 | 0.9787 | 0.0030 | 0.1745 | 0.4916 | 0.0 | nan | 0.7460 | 0.8646 | 0.7031 | 0.7061 | 0.3837 | nan | 0.5698 | 0.6244 | 0.0406 | 0.8595 | 0.1206 | 0.0100 | 0.4231 | 0.0895 | 0.5612 | 0.0 | 0.0 | 0.7606 | 0.0981 | 0.5160 | 0.2920 | 0.0753 | nan | 0.2151 | 0.4904 | 0.3711 | 0.0 | 0.8875 | 0.7760 | 0.9499 | 0.0023 | 0.0951 | 0.3875 | 0.0 |
| 0.1188 | 19.7 | 3940 | 0.6705 | 0.4008 | 0.4736 | 0.8727 | nan | 0.8352 | 0.9605 | 0.7775 | 0.7891 | 0.5159 | nan | 0.6744 | 0.8470 | 0.0115 | 0.9497 | 0.1475 | 0.0024 | 0.6870 | 0.1569 | 0.6805 | 0.0 | 0.0003 | 0.9275 | 0.2195 | 0.6293 | 0.3641 | 0.0765 | nan | 0.2214 | 0.6162 | 0.5561 | 0.0 | 0.9539 | 0.8622 | 0.9766 | 0.0012 | 0.2277 | 0.4884 | 0.0 | nan | 0.7518 | 0.8605 | 0.7179 | 0.7051 | 0.3932 | nan | 0.5742 | 0.6278 | 0.0058 | 0.8634 | 0.1261 | 0.0024 | 0.4491 | 0.1568 | 0.5786 | 0.0 | 0.0002 | 0.7646 | 0.1904 | 0.5319 | 0.3115 | 0.0745 | nan | 0.1672 | 0.5011 | 0.3704 | 0.0 | 0.8884 | 0.7778 | 0.9518 | 0.0011 | 0.1013 | 0.3798 | 0.0 |
| 0.0921 | 19.8 | 3960 | 0.5977 | 0.3969 | 0.4735 | 0.8792 | nan | 0.8769 | 0.9573 | 0.7823 | 0.8502 | 0.4808 | nan | 0.6991 | 0.8520 | 0.0049 | 0.9545 | 0.1090 | 0.0058 | 0.6390 | 0.0450 | 0.6867 | 0.0 | 0.0 | 0.9265 | 0.1901 | 0.6006 | 0.3956 | 0.1275 | nan | 0.2660 | 0.6230 | 0.6010 | 0.0 | 0.9521 | 0.8688 | 0.9846 | 0.0038 | 0.2451 | 0.4230 | 0.0 | nan | 0.7787 | 0.8830 | 0.6752 | 0.7446 | 0.3796 | nan | 0.5851 | 0.6326 | 0.0033 | 0.8611 | 0.0962 | 0.0058 | 0.4192 | 0.0450 | 0.5756 | 0.0 | 0.0 | 0.7626 | 0.1657 | 0.5145 | 0.3129 | 0.1224 | nan | 0.1892 | 0.4940 | 0.3688 | 0.0 | 0.8890 | 0.7783 | 0.9494 | 0.0036 | 0.1144 | 0.3510 | 0.0 |
| 0.0998 | 19.9 | 3980 | 0.6695 | 0.3898 | 0.4654 | 0.8695 | nan | 0.8016 | 0.9558 | 0.7933 | 0.8599 | 0.5248 | nan | 0.7058 | 0.8529 | 0.0 | 0.9482 | 0.1023 | 0.0142 | 0.5224 | 0.0380 | 0.7253 | 0.0 | 0.0163 | 0.9390 | 0.1337 | 0.5630 | 0.3497 | 0.1847 | nan | 0.2445 | 0.5742 | 0.5538 | 0.0 | 0.9543 | 0.8615 | 0.9816 | 0.0138 | 0.2513 | 0.4269 | 0.0 | nan | 0.7260 | 0.8725 | 0.7126 | 0.7037 | 0.3809 | nan | 0.5887 | 0.6069 | 0.0 | 0.8603 | 0.0903 | 0.0136 | 0.3584 | 0.0380 | 0.5701 | 0.0 | 0.0148 | 0.7466 | 0.1170 | 0.4905 | 0.2939 | 0.1709 | nan | 0.1710 | 0.4849 | 0.3728 | 0.0 | 0.8872 | 0.7744 | 0.9510 | 0.0121 | 0.1114 | 0.3524 | 0.0 |
| 0.1001 | 20.0 | 4000 | 0.6360 | 0.3940 | 0.4690 | 0.8747 | nan | 0.8478 | 0.9596 | 0.8029 | 0.8542 | 0.4806 | nan | 0.7011 | 0.8270 | 0.0153 | 0.9524 | 0.1601 | 0.0 | 0.5663 | 0.0927 | 0.7436 | 0.0 | 0.0156 | 0.9355 | 0.1451 | 0.5621 | 0.3605 | 0.1619 | nan | 0.1624 | 0.6078 | 0.5374 | 0.0 | 0.9556 | 0.8211 | 0.9820 | 0.0169 | 0.3125 | 0.4290 | 0.0 | nan | 0.7591 | 0.8734 | 0.6983 | 0.7558 | 0.3819 | nan | 0.5784 | 0.6133 | 0.0099 | 0.8632 | 0.1415 | 0.0 | 0.3731 | 0.0927 | 0.5487 | 0.0 | 0.0145 | 0.7561 | 0.1293 | 0.4910 | 0.3003 | 0.1506 | nan | 0.1298 | 0.4896 | 0.3837 | 0.0 | 0.8829 | 0.7556 | 0.9518 | 0.0153 | 0.1128 | 0.3558 | 0.0 |
| 0.1253 | 20.1 | 4020 | 0.6375 | 0.3945 | 0.4689 | 0.8761 | nan | 0.8562 | 0.9533 | 0.8124 | 0.8497 | 0.5275 | nan | 0.7055 | 0.8616 | 0.0107 | 0.9555 | 0.2256 | 0.0008 | 0.5598 | 0.1015 | 0.7113 | 0.0 | 0.0024 | 0.9286 | 0.1968 | 0.5871 | 0.3271 | 0.1187 | nan | 0.1832 | 0.5976 | 0.5238 | 0.0 | 0.9577 | 0.8331 | 0.9858 | 0.0137 | 0.1747 | 0.4445 | 0.0 | nan | 0.7673 | 0.8788 | 0.6697 | 0.7518 | 0.3957 | nan | 0.5791 | 0.5823 | 0.0078 | 0.8610 | 0.1961 | 0.0008 | 0.3487 | 0.1015 | 0.5737 | 0.0 | 0.0022 | 0.7595 | 0.1723 | 0.5084 | 0.2912 | 0.1149 | nan | 0.1545 | 0.4909 | 0.3803 | 0.0 | 0.8825 | 0.7555 | 0.9488 | 0.0121 | 0.0726 | 0.3626 | 0.0 |
| 0.0963 | 20.2 | 4040 | 0.6343 | 0.3986 | 0.4709 | 0.8758 | nan | 0.8581 | 0.9509 | 0.7889 | 0.8497 | 0.5508 | nan | 0.6903 | 0.8440 | 0.0020 | 0.9561 | 0.1398 | 0.0 | 0.6282 | 0.0328 | 0.6605 | 0.0 | 0.0034 | 0.9246 | 0.2302 | 0.5814 | 0.3759 | 0.1717 | nan | 0.2484 | 0.5943 | 0.5692 | 0.0 | 0.9611 | 0.8160 | 0.9809 | 0.0170 | 0.1377 | 0.5036 | 0.0 | nan | 0.7668 | 0.8783 | 0.6873 | 0.7581 | 0.3905 | nan | 0.5727 | 0.6263 | 0.0016 | 0.8572 | 0.1219 | 0.0 | 0.4296 | 0.0328 | 0.5626 | 0.0 | 0.0032 | 0.7619 | 0.2007 | 0.5207 | 0.2996 | 0.1609 | nan | 0.2098 | 0.4903 | 0.3758 | 0.0 | 0.8799 | 0.7447 | 0.9505 | 0.0132 | 0.0692 | 0.3891 | 0.0 |
| 0.1137 | 20.3 | 4060 | 0.6375 | 0.3946 | 0.4692 | 0.8769 | nan | 0.8613 | 0.9550 | 0.8020 | 0.8515 | 0.5362 | nan | 0.7077 | 0.8397 | 0.0242 | 0.9510 | 0.1386 | 0.0 | 0.6277 | 0.0927 | 0.7196 | 0.0 | 0.0015 | 0.9251 | 0.1790 | 0.6060 | 0.3227 | 0.1148 | nan | 0.1834 | 0.5894 | 0.5724 | 0.0 | 0.9528 | 0.8344 | 0.9837 | 0.0036 | 0.1638 | 0.4759 | 0.0 | nan | 0.7764 | 0.8810 | 0.6866 | 0.7539 | 0.3881 | nan | 0.5824 | 0.5827 | 0.0154 | 0.8616 | 0.1163 | 0.0 | 0.4547 | 0.0926 | 0.5692 | 0.0 | 0.0014 | 0.7587 | 0.1571 | 0.5074 | 0.2865 | 0.1074 | nan | 0.1429 | 0.4889 | 0.3755 | 0.0 | 0.8853 | 0.7612 | 0.9461 | 0.0029 | 0.0755 | 0.3681 | 0.0 |
| 0.1062 | 20.4 | 4080 | 0.6567 | 0.3995 | 0.4775 | 0.8730 | nan | 0.8379 | 0.9542 | 0.8219 | 0.8067 | 0.5245 | nan | 0.7027 | 0.8269 | 0.0109 | 0.9520 | 0.1389 | 0.0 | 0.5794 | 0.1255 | 0.7120 | 0.0 | 0.0142 | 0.9348 | 0.1926 | 0.6014 | 0.4170 | 0.1640 | nan | 0.2494 | 0.6041 | 0.5862 | 0.0 | 0.9498 | 0.8683 | 0.9818 | 0.0056 | 0.2779 | 0.4382 | 0.0 | nan | 0.7553 | 0.8689 | 0.6614 | 0.7353 | 0.3795 | nan | 0.5757 | 0.6132 | 0.0066 | 0.8633 | 0.1199 | 0.0 | 0.4289 | 0.1255 | 0.5749 | 0.0 | 0.0132 | 0.7611 | 0.1679 | 0.5161 | 0.3362 | 0.1469 | nan | 0.1785 | 0.4951 | 0.3797 | 0.0 | 0.8900 | 0.7775 | 0.9491 | 0.0047 | 0.1043 | 0.3541 | 0.0 |
| 0.1041 | 20.5 | 4100 | 0.6890 | 0.4013 | 0.4795 | 0.8670 | nan | 0.7937 | 0.9591 | 0.7886 | 0.8263 | 0.4823 | nan | 0.7058 | 0.8277 | 0.0570 | 0.9562 | 0.2314 | 0.0089 | 0.5893 | 0.0884 | 0.7255 | 0.0 | 0.0212 | 0.9247 | 0.2537 | 0.6046 | 0.3496 | 0.2725 | nan | 0.2060 | 0.5952 | 0.5589 | 0.0 | 0.9555 | 0.8368 | 0.9832 | 0.0022 | 0.2807 | 0.4606 | 0.0 | nan | 0.7252 | 0.8511 | 0.7002 | 0.7143 | 0.3809 | nan | 0.5730 | 0.5817 | 0.0289 | 0.8606 | 0.1952 | 0.0089 | 0.4421 | 0.0883 | 0.5674 | 0.0 | 0.0208 | 0.7613 | 0.2071 | 0.5142 | 0.3038 | 0.2314 | nan | 0.1489 | 0.4961 | 0.3774 | 0.0 | 0.8847 | 0.7648 | 0.9499 | 0.0020 | 0.1005 | 0.3606 | 0.0 |
| 0.1587 | 20.6 | 4120 | 0.6767 | 0.4083 | 0.4812 | 0.8711 | nan | 0.8364 | 0.9537 | 0.7705 | 0.7938 | 0.5484 | nan | 0.6784 | 0.8085 | 0.0284 | 0.9558 | 0.1908 | 0.0 | 0.5209 | 0.2507 | 0.6850 | 0.0 | 0.0004 | 0.9251 | 0.2019 | 0.6493 | 0.3894 | 0.2335 | nan | 0.2594 | 0.5984 | 0.5619 | 0.0 | 0.9466 | 0.8498 | 0.9837 | 0.0110 | 0.2769 | 0.4914 | 0.0 | nan | 0.7442 | 0.8592 | 0.7000 | 0.7254 | 0.3821 | nan | 0.5722 | 0.6206 | 0.0132 | 0.8605 | 0.1656 | 0.0 | 0.4081 | 0.2504 | 0.5632 | 0.0 | 0.0004 | 0.7681 | 0.1775 | 0.5537 | 0.3305 | 0.2045 | nan | 0.1971 | 0.4928 | 0.3788 | 0.0 | 0.8873 | 0.7669 | 0.9484 | 0.0090 | 0.1096 | 0.3747 | 0.0 |
| 0.1263 | 20.7 | 4140 | 0.6792 | 0.4052 | 0.4798 | 0.8693 | nan | 0.8127 | 0.9591 | 0.8023 | 0.8156 | 0.5044 | nan | 0.6911 | 0.8283 | 0.0084 | 0.9539 | 0.1576 | 0.0 | 0.4543 | 0.2301 | 0.6969 | 0.0 | 0.0087 | 0.9293 | 0.1909 | 0.6220 | 0.4202 | 0.2310 | nan | 0.3082 | 0.6124 | 0.5514 | 0.0 | 0.9555 | 0.8294 | 0.9521 | 0.0289 | 0.3463 | 0.4527 | 0.0 | nan | 0.7352 | 0.8585 | 0.6929 | 0.7207 | 0.3930 | nan | 0.5796 | 0.6131 | 0.0048 | 0.8608 | 0.1425 | 0.0 | 0.3678 | 0.2301 | 0.5863 | 0.0 | 0.0082 | 0.7663 | 0.1664 | 0.5473 | 0.3381 | 0.2010 | nan | 0.2321 | 0.4778 | 0.3794 | 0.0 | 0.8840 | 0.7550 | 0.9309 | 0.0198 | 0.1169 | 0.3594 | 0.0 |
| 0.0687 | 20.8 | 4160 | 0.6763 | 0.4026 | 0.4814 | 0.8705 | nan | 0.8374 | 0.9388 | 0.8149 | 0.8092 | 0.6507 | nan | 0.7003 | 0.7479 | 0.0486 | 0.9459 | 0.1209 | 0.0 | 0.4576 | 0.1684 | 0.7734 | 0.0 | 0.0052 | 0.9254 | 0.1351 | 0.6627 | 0.3857 | 0.2935 | nan | 0.3222 | 0.5700 | 0.5956 | 0.0 | 0.9561 | 0.8280 | 0.9860 | 0.0265 | 0.2518 | 0.4466 | 0.0 | nan | 0.7526 | 0.8674 | 0.7003 | 0.7256 | 0.3858 | nan | 0.5779 | 0.5933 | 0.0159 | 0.8653 | 0.1066 | 0.0 | 0.3861 | 0.1684 | 0.5383 | 0.0 | 0.0047 | 0.7688 | 0.1184 | 0.5338 | 0.3312 | 0.2460 | nan | 0.2383 | 0.4819 | 0.3783 | 0.0 | 0.8830 | 0.7539 | 0.9475 | 0.0183 | 0.1309 | 0.3649 | 0.0 |
| 0.1022 | 20.9 | 4180 | 0.6515 | 0.4094 | 0.4840 | 0.8714 | nan | 0.8243 | 0.9531 | 0.8246 | 0.8207 | 0.5230 | nan | 0.7051 | 0.8164 | 0.0200 | 0.9530 | 0.1783 | 0.0 | 0.6442 | 0.2868 | 0.7147 | 0.0 | 0.0004 | 0.9344 | 0.1501 | 0.5645 | 0.3791 | 0.2135 | nan | 0.2752 | 0.6152 | 0.5677 | 0.0 | 0.9529 | 0.8576 | 0.9788 | 0.0186 | 0.2173 | 0.5001 | 0.0 | nan | 0.7452 | 0.8705 | 0.6665 | 0.7204 | 0.3726 | nan | 0.5795 | 0.6388 | 0.0120 | 0.8618 | 0.1561 | 0.0 | 0.4798 | 0.2868 | 0.5968 | 0.0 | 0.0004 | 0.7536 | 0.1331 | 0.4925 | 0.3280 | 0.1859 | nan | 0.2251 | 0.4905 | 0.3779 | 0.0 | 0.8884 | 0.7699 | 0.9526 | 0.0146 | 0.1169 | 0.3848 | 0.0 |
| 0.1278 | 21.0 | 4200 | 0.6634 | 0.4075 | 0.4829 | 0.8711 | nan | 0.8245 | 0.9530 | 0.7866 | 0.8225 | 0.5349 | nan | 0.6884 | 0.8640 | 0.0566 | 0.9489 | 0.2054 | 0.0 | 0.5859 | 0.2362 | 0.7052 | 0.0 | 0.0008 | 0.9347 | 0.1644 | 0.6074 | 0.3896 | 0.2307 | nan | 0.3313 | 0.5614 | 0.5768 | 0.0 | 0.9521 | 0.8605 | 0.9856 | 0.0116 | 0.1722 | 0.4631 | 0.0 | nan | 0.7408 | 0.8674 | 0.7110 | 0.7096 | 0.3785 | nan | 0.5794 | 0.5541 | 0.0353 | 0.8636 | 0.1842 | 0.0 | 0.4354 | 0.2361 | 0.5915 | 0.0 | 0.0008 | 0.7613 | 0.1453 | 0.5112 | 0.3317 | 0.2033 | nan | 0.2664 | 0.4866 | 0.3724 | 0.0 | 0.8884 | 0.7736 | 0.9489 | 0.0089 | 0.0912 | 0.3618 | 0.0 |
| 0.0846 | 21.1 | 4220 | 0.6718 | 0.3986 | 0.4677 | 0.8711 | nan | 0.8204 | 0.9642 | 0.7579 | 0.8309 | 0.4685 | nan | 0.6837 | 0.8341 | 0.0109 | 0.9496 | 0.1461 | 0.0 | 0.4288 | 0.1567 | 0.6752 | 0.0 | 0.0 | 0.9378 | 0.1636 | 0.5715 | 0.3840 | 0.3030 | nan | 0.2801 | 0.6112 | 0.5545 | 0.0 | 0.9499 | 0.8698 | 0.9818 | 0.0113 | 0.1875 | 0.4330 | 0.0 | nan | 0.7372 | 0.8662 | 0.7208 | 0.7000 | 0.3749 | nan | 0.5771 | 0.5614 | 0.0071 | 0.8616 | 0.1265 | 0.0 | 0.3334 | 0.1567 | 0.5866 | 0.0 | 0.0 | 0.7540 | 0.1424 | 0.5010 | 0.3260 | 0.2542 | nan | 0.2147 | 0.4983 | 0.3801 | 0.0 | 0.8891 | 0.7807 | 0.9504 | 0.0093 | 0.0879 | 0.3571 | 0.0 |
| 0.1115 | 21.2 | 4240 | 0.6552 | 0.4076 | 0.4832 | 0.8737 | nan | 0.8317 | 0.9473 | 0.8047 | 0.8574 | 0.5807 | nan | 0.6939 | 0.8171 | 0.0246 | 0.9492 | 0.1527 | 0.0037 | 0.5044 | 0.2308 | 0.6829 | 0.0 | 0.0009 | 0.9300 | 0.1914 | 0.6670 | 0.4029 | 0.3364 | nan | 0.3103 | 0.5589 | 0.5355 | 0.0 | 0.9510 | 0.8408 | 0.9833 | 0.0029 | 0.1955 | 0.4733 | 0.0 | nan | 0.7517 | 0.8763 | 0.6704 | 0.7468 | 0.3714 | nan | 0.5771 | 0.5795 | 0.0150 | 0.8643 | 0.1306 | 0.0037 | 0.3847 | 0.2307 | 0.5843 | 0.0 | 0.0008 | 0.7695 | 0.1615 | 0.5434 | 0.3354 | 0.2658 | nan | 0.2342 | 0.4869 | 0.3854 | 0.0 | 0.8869 | 0.7624 | 0.9506 | 0.0023 | 0.1051 | 0.3660 | 0.0 |
| 0.1084 | 21.3 | 4260 | 0.6892 | 0.3892 | 0.4577 | 0.8640 | nan | 0.7884 | 0.9506 | 0.7385 | 0.8094 | 0.5658 | nan | 0.7285 | 0.8160 | 0.0189 | 0.9520 | 0.1778 | 0.0 | 0.2717 | 0.1533 | 0.6612 | 0.0 | 0.0003 | 0.9476 | 0.1546 | 0.5538 | 0.4004 | 0.1485 | nan | 0.2246 | 0.6010 | 0.5424 | 0.0 | 0.9481 | 0.8537 | 0.9823 | 0.0129 | 0.1972 | 0.4453 | 0.0 | nan | 0.7146 | 0.8556 | 0.7159 | 0.7010 | 0.3691 | nan | 0.5797 | 0.6040 | 0.0121 | 0.8610 | 0.1574 | 0.0 | 0.2095 | 0.1532 | 0.5598 | 0.0 | 0.0002 | 0.7475 | 0.1321 | 0.4993 | 0.3277 | 0.1327 | nan | 0.1811 | 0.4931 | 0.3776 | 0.0 | 0.8874 | 0.7649 | 0.9522 | 0.0083 | 0.0941 | 0.3645 | 0.0 |
| 0.1338 | 21.4 | 4280 | 0.6709 | 0.3973 | 0.4641 | 0.8726 | nan | 0.8313 | 0.9602 | 0.7449 | 0.8214 | 0.5155 | nan | 0.7123 | 0.8303 | 0.0138 | 0.9432 | 0.1363 | 0.0 | 0.4912 | 0.1223 | 0.6385 | 0.0 | 0.0003 | 0.9390 | 0.1575 | 0.6410 | 0.3586 | 0.2166 | nan | 0.2556 | 0.5782 | 0.5075 | 0.0 | 0.9518 | 0.8385 | 0.9821 | 0.0096 | 0.2061 | 0.4482 | 0.0 | nan | 0.7484 | 0.8677 | 0.7121 | 0.7044 | 0.3990 | nan | 0.5868 | 0.5942 | 0.0097 | 0.8629 | 0.1184 | 0.0 | 0.3635 | 0.1222 | 0.5545 | 0.0 | 0.0002 | 0.7618 | 0.1388 | 0.5326 | 0.3045 | 0.1839 | nan | 0.2106 | 0.4879 | 0.3757 | 0.0 | 0.8841 | 0.7657 | 0.9515 | 0.0074 | 0.1076 | 0.3582 | 0.0 |
| 0.0738 | 21.5 | 4300 | 0.6573 | 0.4033 | 0.4748 | 0.8713 | nan | 0.8115 | 0.9567 | 0.8002 | 0.8162 | 0.5514 | nan | 0.7062 | 0.8208 | 0.0280 | 0.9496 | 0.1044 | 0.0029 | 0.4920 | 0.1151 | 0.6800 | 0.0 | 0.0 | 0.9278 | 0.1859 | 0.6386 | 0.3948 | 0.3072 | nan | 0.3047 | 0.6096 | 0.5394 | 0.0 | 0.9550 | 0.8316 | 0.9853 | 0.0043 | 0.1767 | 0.4969 | 0.0 | nan | 0.7366 | 0.8644 | 0.6744 | 0.7142 | 0.3915 | nan | 0.5877 | 0.6245 | 0.0180 | 0.8641 | 0.0928 | 0.0028 | 0.3760 | 0.1150 | 0.5753 | 0.0 | 0.0 | 0.7681 | 0.1669 | 0.5448 | 0.3265 | 0.2540 | nan | 0.2388 | 0.4946 | 0.3862 | 0.0 | 0.8849 | 0.7629 | 0.9491 | 0.0037 | 0.1074 | 0.3806 | 0.0 |
| 0.1029 | 21.6 | 4320 | 0.6469 | 0.4044 | 0.4782 | 0.8737 | nan | 0.8398 | 0.9561 | 0.7955 | 0.8276 | 0.5186 | nan | 0.7033 | 0.7922 | 0.0450 | 0.9536 | 0.1347 | 0.0001 | 0.6867 | 0.1459 | 0.7371 | 0.0 | 0.0003 | 0.9364 | 0.1667 | 0.6232 | 0.3445 | 0.2556 | nan | 0.2941 | 0.5700 | 0.5599 | 0.0 | 0.9584 | 0.8165 | 0.9835 | 0.0045 | 0.1937 | 0.4606 | 0.0 | nan | 0.7543 | 0.8712 | 0.6931 | 0.7240 | 0.3945 | nan | 0.5843 | 0.6372 | 0.0275 | 0.8614 | 0.1184 | 0.0001 | 0.4375 | 0.1459 | 0.5797 | 0.0 | 0.0003 | 0.7593 | 0.1475 | 0.5194 | 0.2930 | 0.2124 | nan | 0.2283 | 0.4873 | 0.3849 | 0.0 | 0.8807 | 0.7483 | 0.9503 | 0.0040 | 0.1232 | 0.3739 | 0.0 |
| 0.1186 | 21.7 | 4340 | 0.6585 | 0.4079 | 0.4883 | 0.8723 | nan | 0.8288 | 0.9558 | 0.7707 | 0.8673 | 0.5224 | nan | 0.6848 | 0.8085 | 0.0581 | 0.9490 | 0.1224 | 0.0 | 0.6958 | 0.0732 | 0.7552 | 0.0 | 0.0 | 0.9204 | 0.2677 | 0.5686 | 0.3612 | 0.3455 | nan | 0.3524 | 0.6461 | 0.5403 | 0.0 | 0.9572 | 0.8196 | 0.9823 | 0.0281 | 0.2567 | 0.4876 | 0.0 | nan | 0.7477 | 0.8743 | 0.7007 | 0.7153 | 0.4094 | nan | 0.5850 | 0.6034 | 0.0355 | 0.8668 | 0.1055 | 0.0 | 0.4602 | 0.0732 | 0.6005 | 0.0 | 0.0 | 0.7550 | 0.2271 | 0.5002 | 0.3071 | 0.2702 | nan | 0.2727 | 0.4949 | 0.3814 | 0.0 | 0.8794 | 0.7426 | 0.9508 | 0.0204 | 0.0945 | 0.3773 | 0.0 |
| 0.0677 | 21.8 | 4360 | 0.6361 | 0.3993 | 0.4769 | 0.8750 | nan | 0.8544 | 0.9537 | 0.7839 | 0.8400 | 0.5455 | nan | 0.6787 | 0.8215 | 0.0144 | 0.9538 | 0.1153 | 0.0 | 0.7256 | 0.0217 | 0.7075 | 0.0 | 0.0 | 0.9299 | 0.1329 | 0.5876 | 0.4313 | 0.2619 | nan | 0.2513 | 0.6280 | 0.5770 | 0.0 | 0.9517 | 0.8389 | 0.9813 | 0.0087 | 0.2031 | 0.4615 | 0.0 | nan | 0.7686 | 0.8791 | 0.6233 | 0.7480 | 0.3951 | nan | 0.5785 | 0.6149 | 0.0087 | 0.8604 | 0.0980 | 0.0 | 0.4816 | 0.0217 | 0.5856 | 0.0 | 0.0 | 0.7597 | 0.1237 | 0.5133 | 0.3455 | 0.2226 | nan | 0.2174 | 0.4940 | 0.3838 | 0.0 | 0.8837 | 0.7542 | 0.9505 | 0.0060 | 0.0886 | 0.3699 | 0.0 |
| 0.0966 | 21.9 | 4380 | 0.6521 | 0.4025 | 0.4823 | 0.8755 | nan | 0.8575 | 0.9535 | 0.7738 | 0.8313 | 0.5383 | nan | 0.6988 | 0.8177 | 0.0173 | 0.9548 | 0.1243 | 0.0 | 0.6781 | 0.1098 | 0.7482 | 0.0 | 0.0 | 0.9375 | 0.1988 | 0.5860 | 0.3846 | 0.2233 | nan | 0.2717 | 0.6065 | 0.6044 | 0.0 | 0.9486 | 0.8565 | 0.9802 | 0.0117 | 0.2642 | 0.4555 | 0.0 | nan | 0.7634 | 0.8766 | 0.6806 | 0.7478 | 0.3922 | nan | 0.5740 | 0.6078 | 0.0107 | 0.8622 | 0.1051 | 0.0 | 0.4432 | 0.1098 | 0.5781 | 0.0 | 0.0 | 0.7588 | 0.1735 | 0.5266 | 0.3227 | 0.1965 | nan | 0.2137 | 0.4956 | 0.3696 | 0.0 | 0.8882 | 0.7733 | 0.9508 | 0.0081 | 0.0910 | 0.3591 | 0.0 |
| 0.1168 | 22.0 | 4400 | 0.6743 | 0.4063 | 0.4888 | 0.8718 | nan | 0.8217 | 0.9470 | 0.8187 | 0.8473 | 0.5740 | nan | 0.6884 | 0.8092 | 0.0404 | 0.9540 | 0.1233 | 0.0 | 0.7544 | 0.1954 | 0.7602 | 0.0 | 0.0011 | 0.9278 | 0.1784 | 0.6134 | 0.3906 | 0.2784 | nan | 0.1975 | 0.6000 | 0.5872 | 0.0 | 0.9548 | 0.8470 | 0.9854 | 0.0077 | 0.2577 | 0.4814 | 0.0 | nan | 0.7449 | 0.8696 | 0.6629 | 0.7342 | 0.3708 | nan | 0.5749 | 0.6200 | 0.0206 | 0.8656 | 0.1045 | 0.0 | 0.4958 | 0.1954 | 0.5853 | 0.0 | 0.0010 | 0.7691 | 0.1619 | 0.5505 | 0.3202 | 0.2488 | nan | 0.1564 | 0.4904 | 0.3779 | 0.0 | 0.8859 | 0.7710 | 0.9489 | 0.0067 | 0.0918 | 0.3755 | 0.0 |
| 0.1196 | 22.1 | 4420 | 0.6573 | 0.4029 | 0.4845 | 0.8736 | nan | 0.8246 | 0.9584 | 0.7864 | 0.8363 | 0.5195 | nan | 0.6933 | 0.7868 | 0.1454 | 0.9523 | 0.1428 | 0.0 | 0.7212 | 0.0582 | 0.7861 | 0.0 | 0.0001 | 0.9330 | 0.2043 | 0.6240 | 0.3923 | 0.2184 | nan | 0.2291 | 0.6001 | 0.5717 | 0.0 | 0.9499 | 0.8588 | 0.9833 | 0.0066 | 0.2396 | 0.4826 | 0.0 | nan | 0.7455 | 0.8696 | 0.6918 | 0.7157 | 0.3893 | nan | 0.5780 | 0.6101 | 0.0592 | 0.8655 | 0.1215 | 0.0 | 0.4956 | 0.0582 | 0.5283 | 0.0 | 0.0001 | 0.7669 | 0.1772 | 0.5426 | 0.3298 | 0.1941 | nan | 0.1912 | 0.4937 | 0.3772 | 0.0 | 0.8886 | 0.7717 | 0.9503 | 0.0059 | 0.0940 | 0.3812 | 0.0 |
| 0.0916 | 22.2 | 4440 | 0.6671 | 0.4016 | 0.4835 | 0.8750 | nan | 0.8546 | 0.9526 | 0.7848 | 0.8181 | 0.5306 | nan | 0.7050 | 0.7740 | 0.0639 | 0.9540 | 0.1298 | 0.0 | 0.7229 | 0.0259 | 0.7771 | 0.0 | 0.0 | 0.9282 | 0.1856 | 0.6414 | 0.4127 | 0.1601 | nan | 0.3675 | 0.6012 | 0.6054 | 0.0 | 0.9586 | 0.8275 | 0.9830 | 0.0156 | 0.2546 | 0.4364 | 0.0 | nan | 0.7593 | 0.8716 | 0.7117 | 0.7337 | 0.3855 | nan | 0.5776 | 0.6147 | 0.0252 | 0.8617 | 0.1121 | 0.0 | 0.4832 | 0.0259 | 0.5267 | 0.0 | 0.0 | 0.7674 | 0.1577 | 0.5443 | 0.3348 | 0.1446 | nan | 0.2485 | 0.4981 | 0.3778 | 0.0 | 0.8851 | 0.7590 | 0.9504 | 0.0125 | 0.1175 | 0.3647 | 0.0 |
| 0.0867 | 22.3 | 4460 | 0.6636 | 0.4042 | 0.4808 | 0.8755 | nan | 0.8381 | 0.9610 | 0.7599 | 0.8121 | 0.5300 | nan | 0.6977 | 0.8275 | 0.0271 | 0.9569 | 0.1220 | 0.0 | 0.7163 | 0.0654 | 0.7150 | 0.0 | 0.0 | 0.9224 | 0.2333 | 0.6599 | 0.3537 | 0.1412 | nan | 0.3349 | 0.5894 | 0.5763 | 0.0 | 0.9542 | 0.8557 | 0.9837 | 0.0079 | 0.2481 | 0.4951 | 0.0 | nan | 0.7549 | 0.8665 | 0.7145 | 0.7176 | 0.4058 | nan | 0.5809 | 0.6203 | 0.0147 | 0.8614 | 0.1082 | 0.0 | 0.4864 | 0.0654 | 0.5410 | 0.0 | 0.0 | 0.7728 | 0.2017 | 0.5630 | 0.3100 | 0.1315 | nan | 0.2410 | 0.4916 | 0.3759 | 0.0 | 0.8892 | 0.7703 | 0.9499 | 0.0067 | 0.1155 | 0.3761 | 0.0 |
| 0.1021 | 22.4 | 4480 | 0.6624 | 0.4058 | 0.4860 | 0.8766 | nan | 0.8609 | 0.9531 | 0.7598 | 0.8181 | 0.5222 | nan | 0.6821 | 0.8110 | 0.1090 | 0.9507 | 0.1304 | 0.0 | 0.7379 | 0.0732 | 0.7369 | 0.0 | 0.0 | 0.9206 | 0.1910 | 0.6836 | 0.4226 | 0.2346 | nan | 0.2167 | 0.6100 | 0.5774 | 0.0 | 0.9527 | 0.8661 | 0.9817 | 0.0058 | 0.2534 | 0.4897 | 0.0 | nan | 0.7584 | 0.8716 | 0.7144 | 0.7334 | 0.3854 | nan | 0.5682 | 0.5957 | 0.0472 | 0.8653 | 0.1130 | 0.0 | 0.4996 | 0.0732 | 0.5125 | 0.0 | 0.0 | 0.7731 | 0.1765 | 0.5758 | 0.3386 | 0.1997 | nan | 0.1902 | 0.4950 | 0.3736 | 0.0 | 0.8902 | 0.7776 | 0.9509 | 0.0048 | 0.1255 | 0.3748 | 0.0 |
| 0.0872 | 22.5 | 4500 | 0.6689 | 0.4063 | 0.4868 | 0.8745 | nan | 0.8574 | 0.9529 | 0.8159 | 0.8035 | 0.5175 | nan | 0.6869 | 0.7413 | 0.2261 | 0.9517 | 0.1434 | 0.0002 | 0.7643 | 0.0877 | 0.6973 | 0.0 | 0.0 | 0.9308 | 0.1619 | 0.5909 | 0.4018 | 0.3157 | nan | 0.2351 | 0.6278 | 0.5717 | 0.0 | 0.9580 | 0.8582 | 0.9820 | 0.0176 | 0.2352 | 0.4442 | 0.0 | nan | 0.7623 | 0.8723 | 0.6897 | 0.7278 | 0.3847 | nan | 0.5636 | 0.6053 | 0.0667 | 0.8630 | 0.1199 | 0.0002 | 0.5100 | 0.0877 | 0.5358 | 0.0 | 0.0 | 0.7563 | 0.1431 | 0.5301 | 0.3409 | 0.2571 | nan | 0.1989 | 0.4989 | 0.3886 | 0.0 | 0.8874 | 0.7713 | 0.9510 | 0.0122 | 0.1084 | 0.3682 | 0.0 |
| 0.0983 | 22.6 | 4520 | 0.7023 | 0.3976 | 0.4705 | 0.8686 | nan | 0.8058 | 0.9578 | 0.8044 | 0.8129 | 0.5313 | nan | 0.6656 | 0.8009 | 0.0983 | 0.9533 | 0.1359 | 0.0 | 0.5326 | 0.0680 | 0.6913 | 0.0 | 0.0016 | 0.9358 | 0.1274 | 0.5976 | 0.3870 | 0.3395 | nan | 0.1922 | 0.6224 | 0.5431 | 0.0 | 0.9529 | 0.8436 | 0.9845 | 0.0118 | 0.2043 | 0.4549 | 0.0 | nan | 0.7301 | 0.8665 | 0.6552 | 0.7167 | 0.3797 | nan | 0.5711 | 0.6217 | 0.0392 | 0.8612 | 0.1163 | 0.0 | 0.4311 | 0.0680 | 0.5479 | 0.0 | 0.0015 | 0.7544 | 0.1128 | 0.5237 | 0.3256 | 0.2716 | nan | 0.1567 | 0.4959 | 0.3898 | 0.0 | 0.8861 | 0.7586 | 0.9494 | 0.0068 | 0.1104 | 0.3756 | 0.0 |
| 0.1209 | 22.7 | 4540 | 0.6888 | 0.4058 | 0.4847 | 0.8721 | nan | 0.8228 | 0.9569 | 0.7871 | 0.8190 | 0.5117 | nan | 0.7215 | 0.8375 | 0.0268 | 0.9549 | 0.1433 | 0.0 | 0.5613 | 0.1067 | 0.6863 | 0.0 | 0.0124 | 0.9308 | 0.1523 | 0.6273 | 0.4237 | 0.4561 | nan | 0.2320 | 0.6087 | 0.5332 | 0.0 | 0.9446 | 0.8729 | 0.9841 | 0.0056 | 0.3310 | 0.4608 | 0.0 | nan | 0.7441 | 0.8699 | 0.6695 | 0.7164 | 0.3835 | nan | 0.5778 | 0.6299 | 0.0147 | 0.8619 | 0.1226 | 0.0 | 0.4366 | 0.1066 | 0.5622 | 0.0 | 0.0117 | 0.7632 | 0.1349 | 0.5324 | 0.3502 | 0.3325 | nan | 0.1801 | 0.4954 | 0.3844 | 0.0 | 0.8894 | 0.7696 | 0.9505 | 0.0040 | 0.1223 | 0.3692 | 0.0 |
| 0.0824 | 22.8 | 4560 | 0.6736 | 0.3993 | 0.4740 | 0.8745 | nan | 0.8310 | 0.9552 | 0.7977 | 0.8584 | 0.5197 | nan | 0.6920 | 0.8539 | 0.0106 | 0.9566 | 0.1252 | 0.0011 | 0.6397 | 0.0409 | 0.6607 | 0.0 | 0.0032 | 0.9340 | 0.1506 | 0.5987 | 0.3985 | 0.3083 | nan | 0.2090 | 0.5958 | 0.5371 | 0.0 | 0.9536 | 0.8469 | 0.9855 | 0.0051 | 0.2170 | 0.4835 | 0.0 | nan | 0.7479 | 0.8744 | 0.6568 | 0.7333 | 0.3959 | nan | 0.5785 | 0.6096 | 0.0067 | 0.8609 | 0.1105 | 0.0011 | 0.4306 | 0.0409 | 0.5658 | 0.0 | 0.0031 | 0.7627 | 0.1352 | 0.5206 | 0.3350 | 0.2573 | nan | 0.1663 | 0.4924 | 0.3826 | 0.0 | 0.8878 | 0.7731 | 0.9471 | 0.0040 | 0.1089 | 0.3872 | 0.0 |
| 0.0955 | 22.9 | 4580 | 0.6769 | 0.3973 | 0.4726 | 0.8750 | nan | 0.8395 | 0.9518 | 0.8094 | 0.8573 | 0.5389 | nan | 0.6844 | 0.8181 | 0.0240 | 0.9536 | 0.1233 | 0.0 | 0.6615 | 0.0566 | 0.6939 | 0.0 | 0.0015 | 0.9378 | 0.1305 | 0.6115 | 0.3674 | 0.2542 | nan | 0.1922 | 0.5850 | 0.5552 | 0.0 | 0.9538 | 0.8550 | 0.9818 | 0.0104 | 0.2114 | 0.4616 | 0.0 | nan | 0.7574 | 0.8781 | 0.6485 | 0.7476 | 0.4006 | nan | 0.5743 | 0.6027 | 0.0124 | 0.8644 | 0.1103 | 0.0 | 0.4160 | 0.0566 | 0.5664 | 0.0 | 0.0014 | 0.7584 | 0.1177 | 0.5146 | 0.3165 | 0.2226 | nan | 0.1647 | 0.4900 | 0.3889 | 0.0 | 0.8875 | 0.7725 | 0.9506 | 0.0059 | 0.1090 | 0.3789 | 0.0 |
| 0.1196 | 23.0 | 4600 | 0.6728 | 0.4035 | 0.4759 | 0.8745 | nan | 0.8255 | 0.9565 | 0.7957 | 0.8567 | 0.5352 | nan | 0.7149 | 0.7660 | 0.0164 | 0.9594 | 0.1322 | 0.0020 | 0.7373 | 0.0622 | 0.6802 | 0.0 | 0.0034 | 0.9197 | 0.1516 | 0.6637 | 0.3872 | 0.2374 | nan | 0.2423 | 0.5818 | 0.5662 | 0.0 | 0.9616 | 0.8030 | 0.9793 | 0.0105 | 0.1726 | 0.5070 | 0.0 | nan | 0.7482 | 0.8715 | 0.6640 | 0.7457 | 0.4033 | nan | 0.5877 | 0.6187 | 0.0062 | 0.8592 | 0.1182 | 0.0020 | 0.4994 | 0.0622 | 0.5617 | 0.0 | 0.0033 | 0.7726 | 0.1381 | 0.5452 | 0.3192 | 0.2098 | nan | 0.1879 | 0.4902 | 0.3920 | 0.0 | 0.8796 | 0.7461 | 0.9529 | 0.0078 | 0.1243 | 0.3965 | 0.0 |
| 0.1144 | 23.1 | 4620 | 0.6609 | 0.4076 | 0.4822 | 0.8764 | nan | 0.8449 | 0.9573 | 0.7680 | 0.8435 | 0.5289 | nan | 0.6911 | 0.8111 | 0.0271 | 0.9566 | 0.1334 | 0.0065 | 0.7120 | 0.1387 | 0.6763 | 0.0 | 0.0017 | 0.9177 | 0.1278 | 0.6233 | 0.4501 | 0.2858 | nan | 0.1839 | 0.6163 | 0.5656 | 0.0 | 0.9523 | 0.8649 | 0.9804 | 0.0073 | 0.2385 | 0.5180 | 0.0 | nan | 0.7512 | 0.8727 | 0.6915 | 0.7513 | 0.4048 | nan | 0.5798 | 0.6187 | 0.0118 | 0.8619 | 0.1208 | 0.0065 | 0.4702 | 0.1386 | 0.5600 | 0.0 | 0.0016 | 0.7692 | 0.1193 | 0.5338 | 0.3470 | 0.2441 | nan | 0.1581 | 0.4946 | 0.3897 | 0.0 | 0.8891 | 0.7725 | 0.9528 | 0.0054 | 0.1337 | 0.3932 | 0.0 |
| 0.1147 | 23.2 | 4640 | 0.6673 | 0.4042 | 0.4842 | 0.8765 | nan | 0.8462 | 0.9556 | 0.7864 | 0.8425 | 0.5362 | nan | 0.7036 | 0.8250 | 0.0328 | 0.9598 | 0.1363 | 0.0 | 0.7146 | 0.0945 | 0.6819 | 0.0 | 0.0 | 0.9310 | 0.1955 | 0.6343 | 0.4073 | 0.2949 | nan | 0.2135 | 0.6039 | 0.5779 | 0.0 | 0.9519 | 0.8486 | 0.9804 | 0.0079 | 0.2781 | 0.4553 | 0.0 | nan | 0.7555 | 0.8744 | 0.6860 | 0.7501 | 0.3970 | nan | 0.5739 | 0.6089 | 0.0172 | 0.8584 | 0.1163 | 0.0 | 0.4415 | 0.0944 | 0.5553 | 0.0 | 0.0 | 0.7657 | 0.1683 | 0.5395 | 0.3418 | 0.2349 | nan | 0.1732 | 0.4906 | 0.3635 | 0.0 | 0.8903 | 0.7753 | 0.9524 | 0.0064 | 0.1280 | 0.3766 | 0.0 |
| 0.0797 | 23.3 | 4660 | 0.6934 | 0.4041 | 0.4799 | 0.8736 | nan | 0.8226 | 0.9545 | 0.7549 | 0.8589 | 0.5554 | nan | 0.6764 | 0.8197 | 0.0164 | 0.9529 | 0.1220 | 0.0 | 0.6507 | 0.0652 | 0.6900 | 0.0 | 0.0 | 0.9273 | 0.1986 | 0.6386 | 0.3753 | 0.2942 | nan | 0.2419 | 0.6171 | 0.5872 | 0.0 | 0.9531 | 0.8602 | 0.9845 | 0.0076 | 0.2604 | 0.4724 | 0.0 | nan | 0.7388 | 0.8706 | 0.6904 | 0.7298 | 0.3955 | nan | 0.5787 | 0.6149 | 0.0105 | 0.8611 | 0.1066 | 0.0 | 0.4802 | 0.0651 | 0.5646 | 0.0 | 0.0 | 0.7635 | 0.1704 | 0.5359 | 0.3274 | 0.2392 | nan | 0.1948 | 0.4917 | 0.3767 | 0.0 | 0.8902 | 0.7752 | 0.9505 | 0.0069 | 0.1253 | 0.3780 | 0.0 |
| 0.0965 | 23.4 | 4680 | 0.7067 | 0.4014 | 0.4765 | 0.8717 | nan | 0.8330 | 0.9523 | 0.7460 | 0.8488 | 0.5634 | nan | 0.6929 | 0.8100 | 0.0555 | 0.9566 | 0.1322 | 0.0 | 0.6135 | 0.1201 | 0.7288 | 0.0 | 0.0 | 0.9314 | 0.1291 | 0.5895 | 0.3716 | 0.3378 | nan | 0.2263 | 0.5871 | 0.5389 | 0.0 | 0.9494 | 0.8527 | 0.9822 | 0.0012 | 0.2427 | 0.4541 | 0.0 | nan | 0.7438 | 0.8718 | 0.6884 | 0.7370 | 0.3878 | nan | 0.5796 | 0.6060 | 0.0254 | 0.8590 | 0.1164 | 0.0 | 0.4471 | 0.1201 | 0.5629 | 0.0 | 0.0 | 0.7508 | 0.1147 | 0.4962 | 0.3251 | 0.2780 | nan | 0.1828 | 0.4891 | 0.3770 | 0.0 | 0.8855 | 0.7589 | 0.9526 | 0.0011 | 0.1225 | 0.3662 | 0.0 |
| 0.1366 | 23.5 | 4700 | 0.6898 | 0.4134 | 0.4982 | 0.8730 | nan | 0.8253 | 0.9597 | 0.7906 | 0.8212 | 0.5107 | nan | 0.6995 | 0.8227 | 0.0240 | 0.9501 | 0.1438 | 0.0149 | 0.8054 | 0.2313 | 0.7367 | 0.0 | 0.0085 | 0.8985 | 0.3260 | 0.6925 | 0.3509 | 0.2820 | nan | 0.3289 | 0.6285 | 0.5403 | 0.0 | 0.9552 | 0.8376 | 0.9854 | 0.0036 | 0.2508 | 0.5173 | 0.0 | nan | 0.7431 | 0.8684 | 0.6978 | 0.7212 | 0.3905 | nan | 0.5702 | 0.6110 | 0.0107 | 0.8661 | 0.1260 | 0.0148 | 0.4738 | 0.2312 | 0.5671 | 0.0 | 0.0082 | 0.7703 | 0.2687 | 0.5575 | 0.3061 | 0.2209 | nan | 0.2486 | 0.4899 | 0.3764 | 0.0 | 0.8827 | 0.7573 | 0.9497 | 0.0033 | 0.1107 | 0.3857 | 0.0 |
| 0.0908 | 23.6 | 4720 | 0.6770 | 0.4098 | 0.4881 | 0.8756 | nan | 0.8416 | 0.9511 | 0.7898 | 0.8537 | 0.5607 | nan | 0.6894 | 0.8402 | 0.0040 | 0.9572 | 0.1218 | 0.0037 | 0.6462 | 0.1959 | 0.6786 | 0.0 | 0.0095 | 0.9199 | 0.2052 | 0.6373 | 0.3794 | 0.3195 | nan | 0.3122 | 0.6081 | 0.5116 | 0.0 | 0.9548 | 0.8582 | 0.9822 | 0.0023 | 0.3116 | 0.4751 | 0.0 | nan | 0.7554 | 0.8787 | 0.6755 | 0.7475 | 0.3776 | nan | 0.5807 | 0.6166 | 0.0025 | 0.8624 | 0.1074 | 0.0037 | 0.4293 | 0.1958 | 0.5660 | 0.0 | 0.0091 | 0.7657 | 0.1809 | 0.5559 | 0.3133 | 0.2630 | nan | 0.2546 | 0.4961 | 0.3786 | 0.0 | 0.8875 | 0.7730 | 0.9517 | 0.0019 | 0.1079 | 0.3751 | 0.0 |
| 0.1014 | 23.7 | 4740 | 0.6535 | 0.4063 | 0.4823 | 0.8735 | nan | 0.8430 | 0.9501 | 0.8058 | 0.8021 | 0.5354 | nan | 0.7165 | 0.8378 | 0.0109 | 0.9548 | 0.1042 | 0.0133 | 0.6325 | 0.0786 | 0.6767 | 0.0 | 0.0003 | 0.9215 | 0.1791 | 0.6628 | 0.3357 | 0.3620 | nan | 0.3204 | 0.6167 | 0.5248 | 0.0 | 0.9541 | 0.8546 | 0.9855 | 0.0132 | 0.2390 | 0.5036 | 0.0 | nan | 0.7544 | 0.8748 | 0.6597 | 0.7311 | 0.3679 | nan | 0.5745 | 0.6423 | 0.0075 | 0.8625 | 0.0925 | 0.0133 | 0.4476 | 0.0785 | 0.5741 | 0.0 | 0.0003 | 0.7645 | 0.1578 | 0.5596 | 0.2964 | 0.2935 | nan | 0.2556 | 0.4971 | 0.3898 | 0.0 | 0.8873 | 0.7724 | 0.9498 | 0.0085 | 0.1011 | 0.3875 | 0.0 |
| 0.102 | 23.8 | 4760 | 0.6747 | 0.4017 | 0.4770 | 0.8742 | nan | 0.8188 | 0.9561 | 0.8030 | 0.8568 | 0.5305 | nan | 0.6926 | 0.8115 | 0.0258 | 0.9537 | 0.0933 | 0.0 | 0.6117 | 0.0918 | 0.7096 | 0.0 | 0.0003 | 0.9281 | 0.1678 | 0.6539 | 0.3376 | 0.3058 | nan | 0.3111 | 0.6180 | 0.5367 | 0.0 | 0.9519 | 0.8692 | 0.9838 | 0.0037 | 0.1622 | 0.4779 | 0.0 | nan | 0.7398 | 0.8785 | 0.6542 | 0.7370 | 0.3722 | nan | 0.5798 | 0.5885 | 0.0108 | 0.8663 | 0.0825 | 0.0 | 0.4224 | 0.0918 | 0.5818 | 0.0 | 0.0003 | 0.7673 | 0.1502 | 0.5593 | 0.2970 | 0.2544 | nan | 0.2420 | 0.4978 | 0.3933 | 0.0 | 0.8884 | 0.7793 | 0.9499 | 0.0026 | 0.0884 | 0.3787 | 0.0 |
| 0.1469 | 23.9 | 4780 | 0.6532 | 0.4060 | 0.4836 | 0.8787 | nan | 0.8692 | 0.9580 | 0.7645 | 0.8406 | 0.5217 | nan | 0.6817 | 0.7427 | 0.0926 | 0.9555 | 0.1089 | 0.0 | 0.5908 | 0.1198 | 0.7532 | 0.0 | 0.0020 | 0.9314 | 0.2335 | 0.6322 | 0.3620 | 0.3100 | nan | 0.3448 | 0.5898 | 0.5816 | 0.0 | 0.9518 | 0.8482 | 0.9816 | 0.0056 | 0.2243 | 0.4756 | 0.0 | nan | 0.7724 | 0.8805 | 0.6834 | 0.7630 | 0.3912 | nan | 0.5737 | 0.5794 | 0.0273 | 0.8663 | 0.0947 | 0.0 | 0.4012 | 0.1198 | 0.5659 | 0.0 | 0.0020 | 0.7664 | 0.2007 | 0.5522 | 0.3059 | 0.2548 | nan | 0.2398 | 0.4910 | 0.3870 | 0.0 | 0.8851 | 0.7671 | 0.9514 | 0.0042 | 0.0953 | 0.3706 | 0.0 |
| 0.0832 | 24.0 | 4800 | 0.6606 | 0.4047 | 0.4815 | 0.8758 | nan | 0.8290 | 0.9556 | 0.7845 | 0.8629 | 0.5526 | nan | 0.6877 | 0.8289 | 0.0126 | 0.9567 | 0.1224 | 0.0 | 0.6626 | 0.1311 | 0.6637 | 0.0 | 0.0090 | 0.9326 | 0.0793 | 0.6616 | 0.3595 | 0.3732 | nan | 0.2827 | 0.6086 | 0.5777 | 0.0 | 0.9524 | 0.8613 | 0.9847 | 0.0140 | 0.2216 | 0.4388 | 0.0 | nan | 0.7495 | 0.8800 | 0.6644 | 0.7370 | 0.3917 | nan | 0.5861 | 0.6419 | 0.0082 | 0.8640 | 0.1073 | 0.0 | 0.4011 | 0.1310 | 0.5753 | 0.0 | 0.0089 | 0.7656 | 0.0747 | 0.5575 | 0.3065 | 0.2953 | nan | 0.2261 | 0.4960 | 0.3872 | 0.0 | 0.8866 | 0.7757 | 0.9502 | 0.0096 | 0.1024 | 0.3698 | 0.0 |
| 0.1054 | 24.1 | 4820 | 0.6621 | 0.4131 | 0.4917 | 0.8779 | nan | 0.8668 | 0.9545 | 0.7446 | 0.8404 | 0.5394 | nan | 0.6960 | 0.8436 | 0.0180 | 0.9535 | 0.1369 | 0.0 | 0.6041 | 0.2740 | 0.6958 | 0.0 | 0.0260 | 0.9239 | 0.2214 | 0.6427 | 0.3805 | 0.2423 | nan | 0.4048 | 0.5851 | 0.5905 | 0.0 | 0.9549 | 0.8283 | 0.9830 | 0.0152 | 0.2682 | 0.5001 | 0.0 | nan | 0.7693 | 0.8823 | 0.6863 | 0.7551 | 0.3808 | nan | 0.5851 | 0.6125 | 0.0100 | 0.8680 | 0.1182 | 0.0 | 0.3845 | 0.2736 | 0.5852 | 0.0 | 0.0255 | 0.7721 | 0.1944 | 0.5505 | 0.3180 | 0.2073 | nan | 0.2891 | 0.4955 | 0.3669 | 0.0 | 0.8834 | 0.7560 | 0.9512 | 0.0100 | 0.1112 | 0.3785 | 0.0 |
| 0.0628 | 24.2 | 4840 | 0.6659 | 0.4126 | 0.4973 | 0.8764 | nan | 0.8482 | 0.9532 | 0.8024 | 0.8445 | 0.5336 | nan | 0.6979 | 0.8672 | 0.0248 | 0.9586 | 0.1514 | 0.0 | 0.6141 | 0.2115 | 0.6955 | 0.0 | 0.0052 | 0.9133 | 0.2168 | 0.6544 | 0.4175 | 0.3947 | nan | 0.3573 | 0.6188 | 0.5859 | 0.0 | 0.9544 | 0.8420 | 0.9826 | 0.0076 | 0.2786 | 0.4811 | 0.0 | nan | 0.7621 | 0.8762 | 0.6918 | 0.7523 | 0.3880 | nan | 0.5765 | 0.5741 | 0.0149 | 0.8643 | 0.1291 | 0.0 | 0.4119 | 0.2112 | 0.5678 | 0.0 | 0.0052 | 0.7720 | 0.1904 | 0.5424 | 0.3399 | 0.3102 | nan | 0.2791 | 0.4959 | 0.3576 | 0.0 | 0.8856 | 0.7586 | 0.9511 | 0.0060 | 0.1135 | 0.3753 | 0.0 |
| 0.0991 | 24.3 | 4860 | 0.6955 | 0.4082 | 0.4875 | 0.8761 | nan | 0.8536 | 0.9518 | 0.8091 | 0.8414 | 0.5575 | nan | 0.6870 | 0.8289 | 0.0324 | 0.9480 | 0.1085 | 0.0 | 0.5813 | 0.1625 | 0.7587 | 0.0 | 0.0232 | 0.9303 | 0.2009 | 0.6298 | 0.3871 | 0.4062 | nan | 0.2810 | 0.5868 | 0.5593 | 0.0 | 0.9548 | 0.8246 | 0.9849 | 0.0032 | 0.2298 | 0.4767 | 0.0 | nan | 0.7655 | 0.8783 | 0.6721 | 0.7540 | 0.3832 | nan | 0.5760 | 0.5723 | 0.0179 | 0.8676 | 0.0964 | 0.0 | 0.4293 | 0.1624 | 0.5609 | 0.0 | 0.0230 | 0.7676 | 0.1764 | 0.5444 | 0.3243 | 0.3140 | nan | 0.2434 | 0.4931 | 0.3775 | 0.0 | 0.8835 | 0.7535 | 0.9503 | 0.0026 | 0.0972 | 0.3749 | 0.0 |
| 0.1244 | 24.4 | 4880 | 0.6913 | 0.4070 | 0.4834 | 0.8750 | nan | 0.8484 | 0.9514 | 0.7952 | 0.8303 | 0.5339 | nan | 0.7220 | 0.8061 | 0.0202 | 0.9484 | 0.1076 | 0.0185 | 0.5105 | 0.1573 | 0.7423 | 0.0 | 0.0147 | 0.9346 | 0.2283 | 0.6113 | 0.3916 | 0.3409 | nan | 0.3059 | 0.6206 | 0.5449 | 0.0 | 0.9550 | 0.8421 | 0.9806 | 0.0050 | 0.2446 | 0.4554 | 0.0 | nan | 0.7589 | 0.8740 | 0.6844 | 0.7453 | 0.3829 | nan | 0.5773 | 0.6150 | 0.0107 | 0.8623 | 0.0949 | 0.0184 | 0.3722 | 0.1573 | 0.5755 | 0.0 | 0.0146 | 0.7645 | 0.1975 | 0.5299 | 0.3193 | 0.2635 | nan | 0.2408 | 0.4983 | 0.3800 | 0.0 | 0.8852 | 0.7619 | 0.9523 | 0.0040 | 0.1092 | 0.3730 | 0.0 |
| 0.1156 | 24.5 | 4900 | 0.6872 | 0.4104 | 0.4857 | 0.8740 | nan | 0.8472 | 0.9539 | 0.8035 | 0.7991 | 0.5354 | nan | 0.6961 | 0.8381 | 0.0211 | 0.9500 | 0.1042 | 0.0164 | 0.5515 | 0.2262 | 0.7248 | 0.0 | 0.0216 | 0.9260 | 0.1495 | 0.6373 | 0.4013 | 0.3315 | nan | 0.3628 | 0.5909 | 0.5302 | 0.0 | 0.9539 | 0.8443 | 0.9846 | 0.0044 | 0.2430 | 0.4948 | 0.0 | nan | 0.7509 | 0.8737 | 0.6822 | 0.7162 | 0.3894 | nan | 0.5797 | 0.6099 | 0.0130 | 0.8638 | 0.0916 | 0.0164 | 0.4414 | 0.2259 | 0.5915 | 0.0 | 0.0215 | 0.7678 | 0.1346 | 0.5359 | 0.3177 | 0.2581 | nan | 0.2672 | 0.4943 | 0.3782 | 0.0 | 0.8868 | 0.7687 | 0.9510 | 0.0034 | 0.1181 | 0.3842 | 0.0 |
| 0.1161 | 24.6 | 4920 | 0.6739 | 0.4109 | 0.4828 | 0.8753 | nan | 0.8585 | 0.9563 | 0.7776 | 0.8028 | 0.5214 | nan | 0.6980 | 0.8283 | 0.0100 | 0.9572 | 0.1064 | 0.0 | 0.5024 | 0.2197 | 0.7194 | 0.0 | 0.0005 | 0.9343 | 0.1388 | 0.6306 | 0.4056 | 0.2956 | nan | 0.4097 | 0.6059 | 0.5564 | 0.0 | 0.9504 | 0.8251 | 0.9853 | 0.0201 | 0.2503 | 0.4829 | 0.0 | nan | 0.7581 | 0.8764 | 0.6901 | 0.7238 | 0.4003 | nan | 0.5772 | 0.6326 | 0.0064 | 0.8622 | 0.0935 | 0.0 | 0.4033 | 0.2191 | 0.5943 | 0.0 | 0.0005 | 0.7664 | 0.1255 | 0.5445 | 0.3369 | 0.2515 | nan | 0.3306 | 0.5028 | 0.3723 | 0.0 | 0.8853 | 0.7556 | 0.9504 | 0.0122 | 0.1017 | 0.3758 | 0.0 |
| 0.0637 | 24.7 | 4940 | 0.6632 | 0.4097 | 0.4835 | 0.8764 | nan | 0.8464 | 0.9557 | 0.8120 | 0.8365 | 0.5221 | nan | 0.6857 | 0.8429 | 0.0299 | 0.9485 | 0.1338 | 0.0 | 0.4987 | 0.1103 | 0.7168 | 0.0 | 0.0032 | 0.9244 | 0.1512 | 0.6448 | 0.3985 | 0.3803 | nan | 0.3515 | 0.6227 | 0.5915 | 0.0 | 0.9588 | 0.8445 | 0.9812 | 0.0259 | 0.1926 | 0.4600 | 0.0 | nan | 0.7592 | 0.8776 | 0.6662 | 0.7446 | 0.4036 | nan | 0.5652 | 0.6249 | 0.0187 | 0.8637 | 0.1150 | 0.0 | 0.4005 | 0.1101 | 0.5944 | 0.0 | 0.0032 | 0.7679 | 0.1344 | 0.5437 | 0.3184 | 0.3012 | nan | 0.2867 | 0.4977 | 0.3855 | 0.0 | 0.8851 | 0.7606 | 0.9509 | 0.0149 | 0.1346 | 0.3814 | 0.0 |
| 0.0985 | 24.8 | 4960 | 0.6682 | 0.4116 | 0.4878 | 0.8794 | nan | 0.8668 | 0.9547 | 0.7974 | 0.8458 | 0.5332 | nan | 0.6825 | 0.8161 | 0.0215 | 0.9582 | 0.1394 | 0.0 | 0.6203 | 0.1289 | 0.6870 | 0.0 | 0.0065 | 0.9294 | 0.1776 | 0.6455 | 0.3973 | 0.4017 | nan | 0.2997 | 0.6134 | 0.5755 | 0.0 | 0.9516 | 0.8513 | 0.9842 | 0.0146 | 0.2445 | 0.4660 | 0.0 | nan | 0.7747 | 0.8812 | 0.6855 | 0.7584 | 0.3930 | nan | 0.5663 | 0.6446 | 0.0122 | 0.8604 | 0.1209 | 0.0 | 0.4040 | 0.1288 | 0.5813 | 0.0 | 0.0065 | 0.7709 | 0.1549 | 0.5461 | 0.3154 | 0.3114 | nan | 0.2470 | 0.4980 | 0.3755 | 0.0 | 0.8899 | 0.7729 | 0.9505 | 0.0092 | 0.1344 | 0.3784 | 0.0 |
| 0.0761 | 24.9 | 4980 | 0.6739 | 0.4110 | 0.4890 | 0.8768 | nan | 0.8640 | 0.9506 | 0.7996 | 0.8113 | 0.5416 | nan | 0.6980 | 0.8474 | 0.0488 | 0.9506 | 0.1134 | 0.0036 | 0.6406 | 0.1765 | 0.7130 | 0.0 | 0.0 | 0.9279 | 0.2156 | 0.6395 | 0.3693 | 0.2851 | nan | 0.3728 | 0.5819 | 0.5480 | 0.0 | 0.9541 | 0.8624 | 0.9829 | 0.0202 | 0.2360 | 0.4945 | 0.0 | nan | 0.7687 | 0.8807 | 0.6984 | 0.7350 | 0.3854 | nan | 0.5674 | 0.6164 | 0.0271 | 0.8653 | 0.0989 | 0.0036 | 0.4341 | 0.1763 | 0.5854 | 0.0 | 0.0 | 0.7705 | 0.1856 | 0.5469 | 0.3020 | 0.2374 | nan | 0.2850 | 0.4921 | 0.3710 | 0.0 | 0.8867 | 0.7714 | 0.9507 | 0.0110 | 0.1154 | 0.3827 | 0.0 |
| 0.0735 | 25.0 | 5000 | 0.6830 | 0.4082 | 0.4872 | 0.8746 | nan | 0.8526 | 0.9559 | 0.8056 | 0.7894 | 0.5199 | nan | 0.6850 | 0.8030 | 0.1254 | 0.9522 | 0.1047 | 0.0 | 0.6198 | 0.1374 | 0.7549 | 0.0 | 0.0015 | 0.9279 | 0.1950 | 0.6612 | 0.3706 | 0.2416 | nan | 0.3857 | 0.6293 | 0.5656 | 0.0 | 0.9538 | 0.8347 | 0.9810 | 0.0132 | 0.2424 | 0.4825 | 0.0 | nan | 0.7567 | 0.8744 | 0.6918 | 0.7206 | 0.3850 | nan | 0.5699 | 0.6128 | 0.0516 | 0.8634 | 0.0923 | 0.0 | 0.4538 | 0.1373 | 0.5654 | 0.0 | 0.0014 | 0.7704 | 0.1717 | 0.5527 | 0.3110 | 0.2065 | nan | 0.2869 | 0.4934 | 0.3816 | 0.0 | 0.8848 | 0.7593 | 0.9523 | 0.0082 | 0.1241 | 0.3836 | 0.0 |
| 0.0938 | 25.1 | 5020 | 0.6645 | 0.4116 | 0.4882 | 0.8781 | nan | 0.8499 | 0.9551 | 0.8119 | 0.8610 | 0.5020 | nan | 0.6796 | 0.8278 | 0.0481 | 0.9580 | 0.1147 | 0.0 | 0.6345 | 0.1092 | 0.7140 | 0.0 | 0.0107 | 0.9178 | 0.1656 | 0.6692 | 0.4067 | 0.3954 | nan | 0.2975 | 0.6107 | 0.5512 | 0.0 | 0.9543 | 0.8624 | 0.9816 | 0.0058 | 0.2392 | 0.4898 | 0.0 | nan | 0.7644 | 0.8812 | 0.6835 | 0.7389 | 0.3873 | nan | 0.5716 | 0.6184 | 0.0260 | 0.8597 | 0.1006 | 0.0 | 0.4904 | 0.1090 | 0.5648 | 0.0 | 0.0104 | 0.7717 | 0.1496 | 0.5551 | 0.3267 | 0.3090 | nan | 0.2399 | 0.5007 | 0.3807 | 0.0 | 0.8883 | 0.7700 | 0.9509 | 0.0040 | 0.1360 | 0.3809 | 0.0 |
| 0.093 | 25.2 | 5040 | 0.6889 | 0.4094 | 0.4870 | 0.8747 | nan | 0.8459 | 0.9616 | 0.7975 | 0.7967 | 0.5299 | nan | 0.6816 | 0.8436 | 0.0369 | 0.9504 | 0.1119 | 0.0 | 0.5495 | 0.1542 | 0.7090 | 0.0 | 0.0110 | 0.9289 | 0.2162 | 0.6300 | 0.3669 | 0.4266 | nan | 0.3106 | 0.6245 | 0.5737 | 0.0 | 0.9493 | 0.8377 | 0.9829 | 0.0034 | 0.2728 | 0.4795 | 0.0 | nan | 0.7568 | 0.8718 | 0.6990 | 0.7256 | 0.3905 | nan | 0.5750 | 0.5860 | 0.0194 | 0.8629 | 0.0962 | 0.0 | 0.4309 | 0.1541 | 0.5898 | 0.0 | 0.0107 | 0.7672 | 0.1901 | 0.5447 | 0.3203 | 0.3227 | nan | 0.2274 | 0.4974 | 0.3704 | 0.0 | 0.8879 | 0.7670 | 0.9509 | 0.0023 | 0.1111 | 0.3720 | 0.0 |
| 0.075 | 25.3 | 5060 | 0.6667 | 0.4123 | 0.4879 | 0.8759 | nan | 0.8341 | 0.9488 | 0.8000 | 0.8593 | 0.5572 | nan | 0.7305 | 0.8057 | 0.0129 | 0.9501 | 0.1211 | 0.0 | 0.5537 | 0.1523 | 0.6850 | 0.0 | 0.0085 | 0.9328 | 0.1743 | 0.6494 | 0.3818 | 0.4368 | nan | 0.3142 | 0.5996 | 0.5596 | 0.0 | 0.9507 | 0.8710 | 0.9842 | 0.0056 | 0.2599 | 0.4734 | 0.0 | nan | 0.7514 | 0.8764 | 0.7078 | 0.7405 | 0.3926 | nan | 0.5763 | 0.6279 | 0.0074 | 0.8605 | 0.1038 | 0.0 | 0.4455 | 0.1521 | 0.5823 | 0.0 | 0.0081 | 0.7689 | 0.1574 | 0.5531 | 0.3275 | 0.3353 | nan | 0.2247 | 0.4981 | 0.3831 | 0.0 | 0.8894 | 0.7808 | 0.9505 | 0.0039 | 0.1115 | 0.3755 | 0.0 |
| 0.0883 | 25.4 | 5080 | 0.6826 | 0.4071 | 0.4811 | 0.8782 | nan | 0.8498 | 0.9660 | 0.7923 | 0.8490 | 0.4725 | nan | 0.6617 | 0.8420 | 0.0195 | 0.9434 | 0.1262 | 0.0 | 0.6498 | 0.1398 | 0.6956 | 0.0 | 0.0 | 0.9290 | 0.1854 | 0.6294 | 0.3749 | 0.3810 | nan | 0.1998 | 0.6090 | 0.5451 | 0.0 | 0.9577 | 0.8428 | 0.9847 | 0.0020 | 0.2837 | 0.4641 | 0.0 | nan | 0.7571 | 0.8752 | 0.7074 | 0.7545 | 0.3956 | nan | 0.5682 | 0.5973 | 0.0116 | 0.8641 | 0.1088 | 0.0 | 0.4206 | 0.1397 | 0.5925 | 0.0 | 0.0 | 0.7698 | 0.1680 | 0.5493 | 0.3142 | 0.3030 | nan | 0.1681 | 0.4992 | 0.3759 | 0.0 | 0.8855 | 0.7756 | 0.9511 | 0.0017 | 0.1063 | 0.3670 | 0.0 |
| 0.0816 | 25.5 | 5100 | 0.6807 | 0.4114 | 0.4955 | 0.8749 | nan | 0.8345 | 0.9478 | 0.8324 | 0.8536 | 0.5572 | nan | 0.7033 | 0.7725 | 0.1826 | 0.9557 | 0.1266 | 0.0 | 0.7428 | 0.0977 | 0.7280 | 0.0 | 0.0050 | 0.9258 | 0.2657 | 0.6480 | 0.3696 | 0.3062 | nan | 0.3317 | 0.6083 | 0.5698 | 0.0 | 0.9562 | 0.8370 | 0.9822 | 0.0077 | 0.2130 | 0.4945 | 0.0 | nan | 0.7530 | 0.8759 | 0.6533 | 0.7513 | 0.4000 | nan | 0.5683 | 0.6028 | 0.0512 | 0.8618 | 0.1103 | 0.0 | 0.5336 | 0.0977 | 0.5827 | 0.0 | 0.0049 | 0.7748 | 0.2246 | 0.5527 | 0.3123 | 0.2552 | nan | 0.2516 | 0.4962 | 0.3681 | 0.0 | 0.8845 | 0.7606 | 0.9528 | 0.0048 | 0.0989 | 0.3816 | 0.0 |
| 0.0916 | 25.6 | 5120 | 0.7149 | 0.4123 | 0.4943 | 0.8733 | nan | 0.8150 | 0.9578 | 0.8043 | 0.8584 | 0.5208 | nan | 0.6757 | 0.7541 | 0.2774 | 0.9568 | 0.1717 | 0.0 | 0.6621 | 0.0629 | 0.7186 | 0.0 | 0.0065 | 0.9310 | 0.2619 | 0.6287 | 0.3896 | 0.3775 | nan | 0.2891 | 0.6290 | 0.5645 | 0.0 | 0.9525 | 0.8389 | 0.9825 | 0.0055 | 0.2428 | 0.4810 | 0.0 | nan | 0.7377 | 0.8732 | 0.6804 | 0.7161 | 0.3912 | nan | 0.5710 | 0.6170 | 0.0662 | 0.8598 | 0.1508 | 0.0 | 0.4993 | 0.0629 | 0.5755 | 0.0 | 0.0064 | 0.7720 | 0.2194 | 0.5453 | 0.3182 | 0.3081 | nan | 0.2384 | 0.4975 | 0.3752 | 0.0 | 0.8870 | 0.7674 | 0.9532 | 0.0039 | 0.1123 | 0.3868 | 0.0 |
| 0.0756 | 25.7 | 5140 | 0.6823 | 0.4106 | 0.4847 | 0.8764 | nan | 0.8456 | 0.9563 | 0.8080 | 0.8325 | 0.5236 | nan | 0.7090 | 0.8032 | 0.0222 | 0.9552 | 0.1291 | 0.0 | 0.6489 | 0.1083 | 0.6883 | 0.0 | 0.0041 | 0.9370 | 0.2403 | 0.6077 | 0.3606 | 0.3683 | nan | 0.3181 | 0.6060 | 0.5516 | 0.0 | 0.9540 | 0.8402 | 0.9832 | 0.0093 | 0.2087 | 0.4923 | 0.0 | nan | 0.7600 | 0.8755 | 0.7023 | 0.7397 | 0.4002 | nan | 0.5797 | 0.6311 | 0.0085 | 0.8632 | 0.1114 | 0.0 | 0.4545 | 0.1082 | 0.5811 | 0.0 | 0.0039 | 0.7603 | 0.2042 | 0.5239 | 0.3056 | 0.2967 | nan | 0.2467 | 0.4979 | 0.3758 | 0.0 | 0.8863 | 0.7610 | 0.9513 | 0.0073 | 0.1088 | 0.3932 | 0.0 |
| 0.0803 | 25.8 | 5160 | 0.6809 | 0.4101 | 0.4872 | 0.8765 | nan | 0.8516 | 0.9534 | 0.7921 | 0.8191 | 0.5660 | nan | 0.6904 | 0.8421 | 0.0473 | 0.9555 | 0.1229 | 0.0 | 0.6922 | 0.0765 | 0.7293 | 0.0 | 0.0033 | 0.9232 | 0.1971 | 0.6275 | 0.3768 | 0.3648 | nan | 0.2874 | 0.5885 | 0.5604 | 0.0 | 0.9569 | 0.8549 | 0.9839 | 0.0082 | 0.2141 | 0.5045 | 0.0 | nan | 0.7598 | 0.8736 | 0.7219 | 0.7322 | 0.4040 | nan | 0.5802 | 0.6153 | 0.0208 | 0.8647 | 0.1069 | 0.0 | 0.4975 | 0.0765 | 0.5739 | 0.0 | 0.0033 | 0.7707 | 0.1722 | 0.5427 | 0.3144 | 0.2992 | nan | 0.2225 | 0.4932 | 0.3685 | 0.0 | 0.8873 | 0.7701 | 0.9497 | 0.0060 | 0.1062 | 0.3894 | 0.0 |
| 0.0942 | 25.9 | 5180 | 0.6411 | 0.4150 | 0.4918 | 0.8804 | nan | 0.8641 | 0.9544 | 0.8129 | 0.8409 | 0.5218 | nan | 0.7097 | 0.8362 | 0.0164 | 0.9534 | 0.1471 | 0.0 | 0.6927 | 0.0914 | 0.7230 | 0.0 | 0.0008 | 0.9245 | 0.2612 | 0.6589 | 0.3817 | 0.3129 | nan | 0.2753 | 0.6200 | 0.5529 | 0.0 | 0.9531 | 0.8723 | 0.9840 | 0.0115 | 0.2889 | 0.4774 | 0.0 | nan | 0.7693 | 0.8800 | 0.7346 | 0.7522 | 0.3931 | nan | 0.5823 | 0.6294 | 0.0100 | 0.8652 | 0.1280 | 0.0 | 0.4820 | 0.0913 | 0.5905 | 0.0 | 0.0008 | 0.7745 | 0.2181 | 0.5536 | 0.3162 | 0.2624 | nan | 0.2308 | 0.5001 | 0.3704 | 0.0 | 0.8909 | 0.7826 | 0.9502 | 0.0081 | 0.1282 | 0.3862 | 0.0 |
| 0.0807 | 26.0 | 5200 | 0.6570 | 0.4128 | 0.4904 | 0.8781 | nan | 0.8506 | 0.9551 | 0.8030 | 0.8504 | 0.5486 | nan | 0.6728 | 0.8390 | 0.0246 | 0.9582 | 0.1389 | 0.0 | 0.7115 | 0.0633 | 0.7197 | 0.0 | 0.0044 | 0.9195 | 0.2165 | 0.6409 | 0.3865 | 0.4242 | nan | 0.2336 | 0.6239 | 0.5434 | 0.0 | 0.9546 | 0.8558 | 0.9826 | 0.0088 | 0.2644 | 0.4987 | 0.0 | nan | 0.7612 | 0.8776 | 0.7084 | 0.7527 | 0.3977 | nan | 0.5819 | 0.6278 | 0.0145 | 0.8635 | 0.1234 | 0.0 | 0.4967 | 0.0632 | 0.5861 | 0.0 | 0.0043 | 0.7734 | 0.1857 | 0.5453 | 0.3198 | 0.3231 | nan | 0.1996 | 0.5033 | 0.3803 | 0.0 | 0.8897 | 0.7765 | 0.9515 | 0.0058 | 0.1138 | 0.3839 | 0.0 |
| 0.0989 | 26.1 | 5220 | 0.6766 | 0.4118 | 0.4929 | 0.8761 | nan | 0.8360 | 0.9501 | 0.7735 | 0.8566 | 0.5484 | nan | 0.7156 | 0.7947 | 0.1476 | 0.9545 | 0.1832 | 0.0 | 0.7047 | 0.0546 | 0.7455 | 0.0 | 0.0060 | 0.9283 | 0.1483 | 0.6515 | 0.4213 | 0.4199 | nan | 0.2458 | 0.6013 | 0.5454 | 0.0 | 0.9561 | 0.8676 | 0.9824 | 0.0061 | 0.2510 | 0.4768 | 0.0 | nan | 0.7478 | 0.8754 | 0.7067 | 0.7406 | 0.3945 | nan | 0.5775 | 0.6051 | 0.0539 | 0.8656 | 0.1609 | 0.0 | 0.4929 | 0.0546 | 0.5695 | 0.0 | 0.0059 | 0.7716 | 0.1330 | 0.5479 | 0.3357 | 0.3199 | nan | 0.2093 | 0.5010 | 0.3795 | 0.0 | 0.8896 | 0.7777 | 0.9525 | 0.0044 | 0.1170 | 0.3885 | 0.0 |
| 0.0975 | 26.2 | 5240 | 0.6686 | 0.4147 | 0.4966 | 0.8766 | nan | 0.8481 | 0.9563 | 0.7830 | 0.8141 | 0.5606 | nan | 0.7064 | 0.8431 | 0.0839 | 0.9516 | 0.2203 | 0.0 | 0.6956 | 0.0612 | 0.7290 | 0.0 | 0.0090 | 0.9187 | 0.2126 | 0.6371 | 0.4440 | 0.4329 | nan | 0.2754 | 0.6159 | 0.5759 | 0.0 | 0.9525 | 0.8437 | 0.9847 | 0.0022 | 0.2114 | 0.5207 | 0.0 | nan | 0.7606 | 0.8729 | 0.7049 | 0.7360 | 0.3947 | nan | 0.5843 | 0.5929 | 0.0388 | 0.8677 | 0.1858 | 0.0 | 0.4800 | 0.0611 | 0.5925 | 0.0 | 0.0081 | 0.7734 | 0.1887 | 0.5505 | 0.3515 | 0.3299 | nan | 0.2270 | 0.5001 | 0.3717 | 0.0 | 0.8893 | 0.7697 | 0.9519 | 0.0017 | 0.0951 | 0.3903 | 0.0 |
| 0.1222 | 26.3 | 5260 | 0.7007 | 0.4112 | 0.4917 | 0.8733 | nan | 0.8112 | 0.9544 | 0.7829 | 0.8609 | 0.5339 | nan | 0.6915 | 0.8542 | 0.0131 | 0.9557 | 0.1905 | 0.0 | 0.7522 | 0.0119 | 0.7064 | 0.0 | 0.0188 | 0.9257 | 0.1751 | 0.6510 | 0.4046 | 0.4786 | nan | 0.2755 | 0.6000 | 0.5597 | 0.0 | 0.9580 | 0.8450 | 0.9834 | 0.0081 | 0.2413 | 0.4900 | 0.0 | nan | 0.7343 | 0.8749 | 0.6988 | 0.6905 | 0.4023 | nan | 0.5853 | 0.6266 | 0.0117 | 0.8660 | 0.1613 | 0.0 | 0.4892 | 0.0119 | 0.6045 | 0.0 | 0.0182 | 0.7709 | 0.1540 | 0.5544 | 0.3348 | 0.3448 | nan | 0.2370 | 0.4994 | 0.3791 | 0.0 | 0.8864 | 0.7669 | 0.9530 | 0.0057 | 0.1116 | 0.3860 | 0.0 |
| 0.1183 | 26.4 | 5280 | 0.6994 | 0.4117 | 0.4921 | 0.8735 | nan | 0.8099 | 0.9557 | 0.8170 | 0.8511 | 0.5359 | nan | 0.7151 | 0.8481 | 0.0280 | 0.9527 | 0.1402 | 0.0 | 0.7074 | 0.0186 | 0.7115 | 0.0 | 0.0120 | 0.9337 | 0.1862 | 0.6441 | 0.4187 | 0.4533 | nan | 0.3415 | 0.6097 | 0.5326 | 0.0 | 0.9519 | 0.8405 | 0.9825 | 0.0120 | 0.2630 | 0.4755 | 0.0 | nan | 0.7376 | 0.8751 | 0.6819 | 0.6959 | 0.4059 | nan | 0.5829 | 0.6393 | 0.0226 | 0.8642 | 0.1216 | 0.0 | 0.4882 | 0.0186 | 0.5983 | 0.0 | 0.0119 | 0.7705 | 0.1645 | 0.5578 | 0.3381 | 0.3383 | nan | 0.2654 | 0.5020 | 0.3845 | 0.0 | 0.8874 | 0.7637 | 0.9527 | 0.0081 | 0.1143 | 0.3839 | 0.0 |
| 0.0751 | 26.5 | 5300 | 0.6749 | 0.4114 | 0.4891 | 0.8763 | nan | 0.8349 | 0.9526 | 0.7899 | 0.8599 | 0.5438 | nan | 0.7032 | 0.8391 | 0.0881 | 0.9516 | 0.1227 | 0.0 | 0.6688 | 0.0251 | 0.7336 | 0.0 | 0.0 | 0.9283 | 0.1750 | 0.6450 | 0.3704 | 0.4431 | nan | 0.2957 | 0.6250 | 0.5594 | 0.0 | 0.9550 | 0.8531 | 0.9841 | 0.0046 | 0.1953 | 0.5032 | 0.0 | nan | 0.7504 | 0.8752 | 0.7089 | 0.7251 | 0.3993 | nan | 0.5903 | 0.6139 | 0.0498 | 0.8656 | 0.1065 | 0.0 | 0.4799 | 0.0251 | 0.5932 | 0.0 | 0.0 | 0.7705 | 0.1554 | 0.5509 | 0.3146 | 0.3413 | nan | 0.2379 | 0.5074 | 0.3865 | 0.0 | 0.8889 | 0.7779 | 0.9511 | 0.0034 | 0.1035 | 0.3907 | 0.0 |
| 0.0861 | 26.6 | 5320 | 0.6729 | 0.4105 | 0.4900 | 0.8744 | nan | 0.8149 | 0.9547 | 0.8105 | 0.8521 | 0.5684 | nan | 0.6942 | 0.8356 | 0.0508 | 0.9531 | 0.1030 | 0.0 | 0.7563 | 0.0308 | 0.7031 | 0.0 | 0.0034 | 0.9281 | 0.2793 | 0.6487 | 0.3257 | 0.4017 | nan | 0.3096 | 0.6137 | 0.5375 | 0.0 | 0.9537 | 0.8597 | 0.9814 | 0.0027 | 0.2138 | 0.4945 | 0.0 | nan | 0.7427 | 0.8728 | 0.6707 | 0.7218 | 0.4035 | nan | 0.5825 | 0.6148 | 0.0303 | 0.8661 | 0.0904 | 0.0 | 0.5141 | 0.0308 | 0.5987 | 0.0 | 0.0034 | 0.7702 | 0.2363 | 0.5536 | 0.2826 | 0.3157 | nan | 0.2324 | 0.5074 | 0.3798 | 0.0 | 0.8882 | 0.7793 | 0.9523 | 0.0021 | 0.1075 | 0.3858 | 0.0 |
| 0.1072 | 26.7 | 5340 | 0.6385 | 0.4165 | 0.5051 | 0.8771 | nan | 0.8402 | 0.9509 | 0.8062 | 0.8605 | 0.5389 | nan | 0.7122 | 0.8531 | 0.0539 | 0.9585 | 0.1279 | 0.0 | 0.8263 | 0.0386 | 0.7015 | 0.0 | 0.0135 | 0.9078 | 0.4718 | 0.6603 | 0.3479 | 0.3799 | nan | 0.3560 | 0.6282 | 0.5538 | 0.0 | 0.9560 | 0.8533 | 0.9839 | 0.0029 | 0.2515 | 0.5266 | 0.0 | nan | 0.7579 | 0.8816 | 0.6728 | 0.7196 | 0.3906 | nan | 0.5818 | 0.6118 | 0.0338 | 0.8640 | 0.1143 | 0.0 | 0.5220 | 0.0386 | 0.5987 | 0.0 | 0.0133 | 0.7761 | 0.3066 | 0.5636 | 0.2985 | 0.3097 | nan | 0.2655 | 0.5059 | 0.3742 | 0.0 | 0.8888 | 0.7756 | 0.9515 | 0.0024 | 0.1193 | 0.3895 | 0.0 |
| 0.075 | 26.8 | 5360 | 0.6882 | 0.4178 | 0.4994 | 0.8770 | nan | 0.8276 | 0.9542 | 0.8144 | 0.8555 | 0.5746 | nan | 0.6765 | 0.8361 | 0.0220 | 0.9521 | 0.1819 | 0.0 | 0.7861 | 0.0414 | 0.7041 | 0.0 | 0.0025 | 0.9256 | 0.3586 | 0.6814 | 0.3652 | 0.3536 | nan | 0.3433 | 0.6089 | 0.5657 | 0.0 | 0.9546 | 0.8467 | 0.9802 | 0.0030 | 0.2599 | 0.5062 | 0.0 | nan | 0.7502 | 0.8759 | 0.6701 | 0.7418 | 0.3962 | nan | 0.5801 | 0.6557 | 0.0151 | 0.8644 | 0.1547 | 0.0 | 0.5061 | 0.0414 | 0.6178 | 0.0 | 0.0024 | 0.7767 | 0.2725 | 0.5582 | 0.3139 | 0.2876 | nan | 0.2507 | 0.5072 | 0.3787 | 0.0 | 0.8898 | 0.7779 | 0.9536 | 0.0026 | 0.1324 | 0.3968 | 0.0 |
| 0.1095 | 26.9 | 5380 | 0.6906 | 0.4114 | 0.4891 | 0.8781 | nan | 0.8369 | 0.9576 | 0.7971 | 0.8501 | 0.5551 | nan | 0.7060 | 0.8135 | 0.0291 | 0.9532 | 0.1185 | 0.0 | 0.6903 | 0.0319 | 0.7166 | 0.0 | 0.0 | 0.9269 | 0.2260 | 0.6456 | 0.3905 | 0.3490 | nan | 0.3174 | 0.6271 | 0.5923 | 0.0 | 0.9549 | 0.8555 | 0.9855 | 0.0044 | 0.2436 | 0.4768 | 0.0 | nan | 0.7561 | 0.8745 | 0.6692 | 0.7556 | 0.4017 | nan | 0.5824 | 0.6476 | 0.0187 | 0.8661 | 0.1019 | 0.0 | 0.4578 | 0.0319 | 0.6070 | 0.0 | 0.0 | 0.7750 | 0.1968 | 0.5555 | 0.3165 | 0.2879 | nan | 0.2456 | 0.5073 | 0.3771 | 0.0 | 0.8899 | 0.7780 | 0.9496 | 0.0039 | 0.1203 | 0.3899 | 0.0 |
| 0.1506 | 27.0 | 5400 | 0.6823 | 0.4105 | 0.4968 | 0.8779 | nan | 0.8466 | 0.9546 | 0.7995 | 0.8469 | 0.5295 | nan | 0.7037 | 0.8394 | 0.0635 | 0.9543 | 0.1163 | 0.0 | 0.7633 | 0.0475 | 0.7615 | 0.0 | 0.0 | 0.9358 | 0.1898 | 0.6437 | 0.3914 | 0.4259 | nan | 0.3250 | 0.6204 | 0.5437 | 0.0 | 0.9495 | 0.8699 | 0.9813 | 0.0103 | 0.3204 | 0.4652 | 0.0 | nan | 0.7623 | 0.8762 | 0.6641 | 0.7556 | 0.3897 | nan | 0.5861 | 0.6245 | 0.0368 | 0.8685 | 0.0992 | 0.0 | 0.4243 | 0.0475 | 0.6058 | 0.0 | 0.0 | 0.7695 | 0.1659 | 0.5516 | 0.3248 | 0.3197 | nan | 0.2507 | 0.5065 | 0.3748 | 0.0 | 0.8914 | 0.7819 | 0.9530 | 0.0080 | 0.1189 | 0.3790 | 0.0 |
| 0.0938 | 27.1 | 5420 | 0.6557 | 0.4105 | 0.4863 | 0.8819 | nan | 0.8763 | 0.9536 | 0.7881 | 0.8415 | 0.5490 | nan | 0.7220 | 0.8324 | 0.0224 | 0.9549 | 0.1229 | 0.0 | 0.7538 | 0.0387 | 0.7186 | 0.0 | 0.0004 | 0.9286 | 0.1472 | 0.6398 | 0.3440 | 0.3325 | nan | 0.2764 | 0.6080 | 0.5649 | 0.0 | 0.9557 | 0.8681 | 0.9825 | 0.0012 | 0.2378 | 0.5020 | 0.0 | nan | 0.7828 | 0.8807 | 0.6887 | 0.7740 | 0.3939 | nan | 0.5933 | 0.6221 | 0.0134 | 0.8674 | 0.1067 | 0.0 | 0.4857 | 0.0387 | 0.6042 | 0.0 | 0.0004 | 0.7721 | 0.1336 | 0.5439 | 0.2990 | 0.2742 | nan | 0.2357 | 0.5013 | 0.3789 | 0.0 | 0.8894 | 0.7802 | 0.9524 | 0.0011 | 0.1293 | 0.3938 | 0.0 |
| 0.0922 | 27.2 | 5440 | 0.6789 | 0.4124 | 0.4937 | 0.8778 | nan | 0.8441 | 0.9528 | 0.7871 | 0.8561 | 0.5597 | nan | 0.7062 | 0.8316 | 0.0127 | 0.9555 | 0.1165 | 0.0 | 0.7839 | 0.0331 | 0.7121 | 0.0 | 0.0036 | 0.9277 | 0.2226 | 0.6475 | 0.3821 | 0.3915 | nan | 0.3323 | 0.6105 | 0.6060 | 0.0 | 0.9571 | 0.8456 | 0.9830 | 0.0124 | 0.2411 | 0.4829 | 0.0 | nan | 0.7593 | 0.8755 | 0.7016 | 0.7540 | 0.3872 | nan | 0.5912 | 0.6258 | 0.0077 | 0.8663 | 0.1008 | 0.0 | 0.4567 | 0.0331 | 0.6051 | 0.0 | 0.0034 | 0.7729 | 0.1901 | 0.5497 | 0.3172 | 0.3099 | nan | 0.2680 | 0.5033 | 0.3734 | 0.0 | 0.8883 | 0.7725 | 0.9521 | 0.0100 | 0.1303 | 0.3909 | 0.0 |
| 0.096 | 27.3 | 5460 | 0.6704 | 0.4060 | 0.4903 | 0.8754 | nan | 0.8254 | 0.9533 | 0.7920 | 0.8735 | 0.5246 | nan | 0.6949 | 0.8410 | 0.0581 | 0.9554 | 0.1050 | 0.0 | 0.7937 | 0.0133 | 0.7730 | 0.0 | 0.0007 | 0.9311 | 0.2743 | 0.6469 | 0.3820 | 0.3244 | nan | 0.2645 | 0.6060 | 0.5833 | 0.0 | 0.9549 | 0.8581 | 0.9833 | 0.0101 | 0.2081 | 0.4576 | 0.0 | nan | 0.7435 | 0.8795 | 0.6874 | 0.7072 | 0.3941 | nan | 0.5839 | 0.5840 | 0.0354 | 0.8667 | 0.0918 | 0.0 | 0.4936 | 0.0133 | 0.5650 | 0.0 | 0.0007 | 0.7718 | 0.2335 | 0.5515 | 0.3144 | 0.2713 | nan | 0.2367 | 0.5008 | 0.3740 | 0.0 | 0.8891 | 0.7737 | 0.9509 | 0.0084 | 0.0967 | 0.3735 | 0.0 |
| 0.0978 | 27.4 | 5480 | 0.6725 | 0.4047 | 0.4838 | 0.8759 | nan | 0.8290 | 0.9558 | 0.8021 | 0.8683 | 0.4995 | nan | 0.7111 | 0.8203 | 0.0517 | 0.9528 | 0.1147 | 0.0 | 0.6795 | 0.0295 | 0.7583 | 0.0 | 0.0 | 0.9286 | 0.2051 | 0.6574 | 0.3569 | 0.3430 | nan | 0.2604 | 0.6237 | 0.5778 | 0.0 | 0.9572 | 0.8412 | 0.9829 | 0.0104 | 0.1870 | 0.4760 | 0.0 | nan | 0.7490 | 0.8809 | 0.6835 | 0.7109 | 0.3761 | nan | 0.5841 | 0.5973 | 0.0304 | 0.8676 | 0.0987 | 0.0 | 0.4418 | 0.0295 | 0.5886 | 0.0 | 0.0 | 0.7723 | 0.1829 | 0.5593 | 0.3080 | 0.2819 | nan | 0.2282 | 0.5039 | 0.3866 | 0.0 | 0.8879 | 0.7694 | 0.9508 | 0.0081 | 0.0898 | 0.3821 | 0.0 |
| 0.1081 | 27.5 | 5500 | 0.6632 | 0.4076 | 0.4835 | 0.8783 | nan | 0.8643 | 0.9516 | 0.7971 | 0.8583 | 0.5222 | nan | 0.7090 | 0.8066 | 0.0313 | 0.9567 | 0.1047 | 0.0 | 0.6207 | 0.0330 | 0.7483 | 0.0 | 0.0 | 0.9300 | 0.2751 | 0.6632 | 0.3831 | 0.2637 | nan | 0.3216 | 0.6242 | 0.5814 | 0.0 | 0.9588 | 0.7810 | 0.9817 | 0.0221 | 0.1954 | 0.4874 | 0.0 | nan | 0.7753 | 0.8867 | 0.6966 | 0.7449 | 0.3831 | nan | 0.5873 | 0.6360 | 0.0203 | 0.8656 | 0.0909 | 0.0 | 0.4102 | 0.0330 | 0.5889 | 0.0 | 0.0 | 0.7705 | 0.2247 | 0.5607 | 0.3196 | 0.2249 | nan | 0.2714 | 0.5076 | 0.3911 | 0.0 | 0.8806 | 0.7286 | 0.9519 | 0.0116 | 0.0938 | 0.3870 | 0.0 |
| 0.0712 | 27.6 | 5520 | 0.6932 | 0.4087 | 0.4852 | 0.8768 | nan | 0.8332 | 0.9596 | 0.8028 | 0.8385 | 0.5178 | nan | 0.7153 | 0.8222 | 0.0493 | 0.9471 | 0.1109 | 0.0 | 0.6470 | 0.0600 | 0.7521 | 0.0 | 0.0019 | 0.9282 | 0.2295 | 0.6720 | 0.3390 | 0.2774 | nan | 0.3487 | 0.6283 | 0.5638 | 0.0 | 0.9566 | 0.8371 | 0.9832 | 0.0143 | 0.2063 | 0.4831 | 0.0 | nan | 0.7533 | 0.8757 | 0.6709 | 0.7481 | 0.3994 | nan | 0.5842 | 0.6202 | 0.0310 | 0.8688 | 0.0967 | 0.0 | 0.4354 | 0.0600 | 0.5986 | 0.0 | 0.0018 | 0.7740 | 0.1988 | 0.5605 | 0.2965 | 0.2358 | nan | 0.2775 | 0.5082 | 0.3916 | 0.0 | 0.8852 | 0.7556 | 0.9511 | 0.0095 | 0.1038 | 0.3855 | 0.0 |
| 0.1027 | 27.7 | 5540 | 0.6726 | 0.4118 | 0.4918 | 0.8777 | nan | 0.8437 | 0.9552 | 0.7899 | 0.8477 | 0.5416 | nan | 0.7039 | 0.8241 | 0.0552 | 0.9555 | 0.1165 | 0.0 | 0.7872 | 0.0404 | 0.7305 | 0.0 | 0.0 | 0.9259 | 0.2684 | 0.6667 | 0.3302 | 0.3230 | nan | 0.3216 | 0.6274 | 0.5729 | 0.0 | 0.9556 | 0.8468 | 0.9843 | 0.0049 | 0.2345 | 0.4851 | 0.0 | nan | 0.7561 | 0.8770 | 0.6958 | 0.7531 | 0.3973 | nan | 0.5829 | 0.6304 | 0.0335 | 0.8641 | 0.1020 | 0.0 | 0.4693 | 0.0404 | 0.5951 | 0.0 | 0.0 | 0.7749 | 0.2252 | 0.5508 | 0.2920 | 0.2629 | nan | 0.2534 | 0.5062 | 0.3897 | 0.0 | 0.8872 | 0.7672 | 0.9510 | 0.0033 | 0.1268 | 0.3899 | 0.0 |
| 0.0892 | 27.8 | 5560 | 0.6352 | 0.4118 | 0.4884 | 0.8813 | nan | 0.8804 | 0.9525 | 0.7938 | 0.8414 | 0.5485 | nan | 0.7087 | 0.8153 | 0.0402 | 0.9562 | 0.1179 | 0.0 | 0.8002 | 0.0301 | 0.6878 | 0.0 | 0.0 | 0.9299 | 0.1701 | 0.6682 | 0.3440 | 0.4775 | nan | 0.2656 | 0.5936 | 0.5302 | 0.0 | 0.9541 | 0.8372 | 0.9861 | 0.0032 | 0.1952 | 0.5014 | 0.0 | nan | 0.7858 | 0.8827 | 0.7003 | 0.7763 | 0.3848 | nan | 0.5858 | 0.6419 | 0.0244 | 0.8634 | 0.1014 | 0.0 | 0.4713 | 0.0301 | 0.5872 | 0.0 | 0.0 | 0.7710 | 0.1535 | 0.5495 | 0.3039 | 0.3359 | nan | 0.2132 | 0.4990 | 0.3878 | 0.0 | 0.8881 | 0.7675 | 0.9497 | 0.0020 | 0.1255 | 0.3961 | 0.0 |
| 0.0857 | 27.9 | 5580 | 0.6976 | 0.4086 | 0.4967 | 0.8745 | nan | 0.8227 | 0.9517 | 0.8055 | 0.8587 | 0.5235 | nan | 0.7266 | 0.8383 | 0.0748 | 0.9593 | 0.1181 | 0.0 | 0.7492 | 0.0193 | 0.7157 | 0.0 | 0.0019 | 0.9173 | 0.2253 | 0.6414 | 0.4203 | 0.4937 | nan | 0.2772 | 0.6241 | 0.5605 | 0.0 | 0.9543 | 0.8592 | 0.9834 | 0.0037 | 0.2642 | 0.5051 | 0.0 | nan | 0.7448 | 0.8754 | 0.6542 | 0.7406 | 0.3817 | nan | 0.5784 | 0.6356 | 0.0402 | 0.8633 | 0.1012 | 0.0 | 0.4460 | 0.0193 | 0.5878 | 0.0 | 0.0018 | 0.7743 | 0.1996 | 0.5449 | 0.3332 | 0.3409 | nan | 0.2216 | 0.5022 | 0.3775 | 0.0 | 0.8887 | 0.7681 | 0.9517 | 0.0029 | 0.1119 | 0.3869 | 0.0 |
| 0.0972 | 28.0 | 5600 | 0.6871 | 0.4086 | 0.4893 | 0.8766 | nan | 0.8299 | 0.9635 | 0.8034 | 0.8568 | 0.4856 | nan | 0.6668 | 0.8288 | 0.0648 | 0.9496 | 0.1159 | 0.0 | 0.6802 | 0.0382 | 0.7206 | 0.0 | 0.0052 | 0.9283 | 0.2539 | 0.6530 | 0.3671 | 0.4558 | nan | 0.2910 | 0.6120 | 0.5659 | 0.0 | 0.9574 | 0.8490 | 0.9824 | 0.0049 | 0.2732 | 0.4544 | 0.0 | nan | 0.7460 | 0.8773 | 0.6448 | 0.7394 | 0.3866 | nan | 0.5750 | 0.6389 | 0.0358 | 0.8679 | 0.1019 | 0.0 | 0.4226 | 0.0382 | 0.5962 | 0.0 | 0.0051 | 0.7747 | 0.2163 | 0.5552 | 0.3197 | 0.3337 | nan | 0.2179 | 0.5055 | 0.3791 | 0.0 | 0.8878 | 0.7706 | 0.9520 | 0.0039 | 0.1103 | 0.3722 | 0.0 |
| 0.0743 | 28.1 | 5620 | 0.6541 | 0.4056 | 0.4848 | 0.8790 | nan | 0.8631 | 0.9537 | 0.8229 | 0.8409 | 0.5408 | nan | 0.6942 | 0.7871 | 0.0371 | 0.9509 | 0.1217 | 0.0 | 0.6734 | 0.0553 | 0.7003 | 0.0 | 0.0110 | 0.9429 | 0.1248 | 0.6635 | 0.3811 | 0.5042 | nan | 0.2089 | 0.6030 | 0.5817 | 0.0 | 0.9534 | 0.8205 | 0.9791 | 0.0077 | 0.2077 | 0.4827 | 0.0 | nan | 0.7753 | 0.8886 | 0.5865 | 0.7629 | 0.3910 | nan | 0.5861 | 0.6408 | 0.0199 | 0.8648 | 0.1062 | 0.0 | 0.4367 | 0.0553 | 0.5790 | 0.0 | 0.0104 | 0.7665 | 0.1147 | 0.5501 | 0.3330 | 0.3413 | nan | 0.1779 | 0.5043 | 0.3899 | 0.0 | 0.8862 | 0.7527 | 0.9532 | 0.0058 | 0.1139 | 0.3876 | 0.0 |
| 0.0942 | 28.2 | 5640 | 0.6635 | 0.4081 | 0.4960 | 0.8790 | nan | 0.8525 | 0.9555 | 0.8179 | 0.8496 | 0.5351 | nan | 0.7077 | 0.8176 | 0.0422 | 0.9503 | 0.1236 | 0.0101 | 0.7732 | 0.0344 | 0.7263 | 0.0024 | 0.0073 | 0.9277 | 0.1708 | 0.6411 | 0.4058 | 0.5404 | nan | 0.2374 | 0.6133 | 0.5887 | 0.0 | 0.9534 | 0.8582 | 0.9830 | 0.0130 | 0.2590 | 0.4756 | 0.0 | nan | 0.7672 | 0.8834 | 0.6328 | 0.7648 | 0.4010 | nan | 0.5878 | 0.6325 | 0.0224 | 0.8678 | 0.1067 | 0.0091 | 0.4179 | 0.0344 | 0.5821 | 0.0024 | 0.0070 | 0.7730 | 0.1557 | 0.5549 | 0.3368 | 0.3462 | nan | 0.2009 | 0.5048 | 0.3854 | 0.0 | 0.8885 | 0.7682 | 0.9517 | 0.0087 | 0.0918 | 0.3746 | 0.0 |
| 0.0886 | 28.3 | 5660 | 0.6853 | 0.4070 | 0.4902 | 0.8768 | nan | 0.8376 | 0.9575 | 0.8021 | 0.8487 | 0.5298 | nan | 0.7247 | 0.8240 | 0.0681 | 0.9529 | 0.1197 | 0.0025 | 0.7321 | 0.0702 | 0.7406 | 0.0 | 0.0094 | 0.9398 | 0.1876 | 0.6085 | 0.3805 | 0.3841 | nan | 0.2739 | 0.6085 | 0.5558 | 0.0 | 0.9502 | 0.8390 | 0.9827 | 0.0155 | 0.2440 | 0.4976 | 0.0 | nan | 0.7588 | 0.8773 | 0.6697 | 0.7653 | 0.3903 | nan | 0.5894 | 0.6166 | 0.0336 | 0.8655 | 0.1039 | 0.0024 | 0.4033 | 0.0702 | 0.5867 | 0.0 | 0.0092 | 0.7629 | 0.1709 | 0.5351 | 0.3236 | 0.2880 | nan | 0.2085 | 0.5044 | 0.3839 | 0.0 | 0.8877 | 0.7654 | 0.9520 | 0.0106 | 0.0980 | 0.3892 | 0.0 |
| 0.0686 | 28.4 | 5680 | 0.6927 | 0.4090 | 0.4976 | 0.8763 | nan | 0.8348 | 0.9564 | 0.8030 | 0.8511 | 0.5345 | nan | 0.7257 | 0.8044 | 0.0892 | 0.9515 | 0.1484 | 0.0079 | 0.7587 | 0.0563 | 0.7866 | 0.0 | 0.0058 | 0.9275 | 0.2435 | 0.6232 | 0.3731 | 0.4607 | nan | 0.2507 | 0.6339 | 0.5748 | 0.0 | 0.9553 | 0.8259 | 0.9828 | 0.0151 | 0.2420 | 0.4988 | 0.0 | nan | 0.7546 | 0.8767 | 0.6618 | 0.7606 | 0.3896 | nan | 0.5871 | 0.6214 | 0.0452 | 0.8665 | 0.1262 | 0.0077 | 0.4076 | 0.0563 | 0.5419 | 0.0 | 0.0057 | 0.7684 | 0.2128 | 0.5385 | 0.3169 | 0.3450 | nan | 0.2047 | 0.5044 | 0.3781 | 0.0 | 0.8854 | 0.7555 | 0.9524 | 0.0115 | 0.1106 | 0.3952 | 0.0 |
| 0.0875 | 28.5 | 5700 | 0.6821 | 0.4057 | 0.4935 | 0.8757 | nan | 0.8305 | 0.9575 | 0.8115 | 0.8588 | 0.5157 | nan | 0.7006 | 0.7422 | 0.2954 | 0.9579 | 0.1075 | 0.0196 | 0.8595 | 0.0428 | 0.7431 | 0.0 | 0.0050 | 0.9338 | 0.1709 | 0.6354 | 0.3660 | 0.3553 | nan | 0.2370 | 0.6056 | 0.5617 | 0.0 | 0.9548 | 0.8313 | 0.9851 | 0.0051 | 0.2237 | 0.4795 | 0.0 | nan | 0.7482 | 0.8784 | 0.6547 | 0.7435 | 0.3981 | nan | 0.5876 | 0.5876 | 0.0877 | 0.8614 | 0.0961 | 0.0196 | 0.4713 | 0.0428 | 0.5644 | 0.0 | 0.0050 | 0.7676 | 0.1565 | 0.5372 | 0.3097 | 0.2924 | nan | 0.2009 | 0.5005 | 0.3819 | 0.0 | 0.8853 | 0.7565 | 0.9506 | 0.0041 | 0.1105 | 0.3831 | 0.0 |
| 0.1119 | 28.6 | 5720 | 0.6901 | 0.4077 | 0.4900 | 0.8763 | nan | 0.8302 | 0.9586 | 0.7965 | 0.8520 | 0.5337 | nan | 0.7242 | 0.8196 | 0.0748 | 0.9513 | 0.1156 | 0.0012 | 0.7790 | 0.0846 | 0.7200 | 0.0 | 0.0159 | 0.9362 | 0.2396 | 0.6415 | 0.3514 | 0.3662 | nan | 0.2889 | 0.6017 | 0.5220 | 0.0 | 0.9530 | 0.8405 | 0.9830 | 0.0093 | 0.2357 | 0.4550 | 0.0 | nan | 0.7512 | 0.8756 | 0.6802 | 0.7477 | 0.3918 | nan | 0.5922 | 0.5992 | 0.0375 | 0.8659 | 0.1026 | 0.0012 | 0.4171 | 0.0845 | 0.5942 | 0.0 | 0.0158 | 0.7687 | 0.2068 | 0.5397 | 0.3038 | 0.2886 | nan | 0.2047 | 0.5016 | 0.3771 | 0.0 | 0.8878 | 0.7668 | 0.9526 | 0.0076 | 0.1130 | 0.3719 | 0.0 |
| 0.065 | 28.7 | 5740 | 0.6797 | 0.4143 | 0.4985 | 0.8774 | nan | 0.8452 | 0.9559 | 0.8006 | 0.8493 | 0.5016 | nan | 0.7138 | 0.8127 | 0.0544 | 0.9576 | 0.1546 | 0.0006 | 0.7711 | 0.1104 | 0.7272 | 0.0 | 0.0075 | 0.9237 | 0.2652 | 0.6554 | 0.4013 | 0.4589 | nan | 0.3079 | 0.6201 | 0.5540 | 0.0 | 0.9557 | 0.8299 | 0.9835 | 0.0105 | 0.2206 | 0.5035 | 0.0 | nan | 0.7559 | 0.8769 | 0.6712 | 0.7529 | 0.4004 | nan | 0.5876 | 0.6260 | 0.0293 | 0.8636 | 0.1338 | 0.0005 | 0.4466 | 0.1104 | 0.5750 | 0.0 | 0.0075 | 0.7745 | 0.2198 | 0.5464 | 0.3310 | 0.3431 | nan | 0.2321 | 0.5006 | 0.3856 | 0.0 | 0.8848 | 0.7497 | 0.9528 | 0.0091 | 0.1076 | 0.3819 | 0.0 |
| 0.0934 | 28.8 | 5760 | 0.6465 | 0.4116 | 0.4904 | 0.8790 | nan | 0.8431 | 0.9547 | 0.7972 | 0.8962 | 0.5132 | nan | 0.7213 | 0.7866 | 0.0628 | 0.9542 | 0.2099 | 0.0010 | 0.6565 | 0.0928 | 0.6967 | 0.0 | 0.0017 | 0.9361 | 0.1630 | 0.6224 | 0.3858 | 0.4652 | nan | 0.2589 | 0.6364 | 0.5783 | 0.0 | 0.9554 | 0.8465 | 0.9811 | 0.0046 | 0.2253 | 0.4466 | 0.0 | nan | 0.7549 | 0.8802 | 0.6827 | 0.7726 | 0.4038 | nan | 0.5928 | 0.6084 | 0.0322 | 0.8658 | 0.1788 | 0.0010 | 0.4057 | 0.0927 | 0.5834 | 0.0 | 0.0017 | 0.7648 | 0.1460 | 0.5400 | 0.3277 | 0.3493 | nan | 0.2143 | 0.5050 | 0.3887 | 0.0 | 0.8878 | 0.7628 | 0.9532 | 0.0042 | 0.1013 | 0.3690 | 0.0 |
| 0.0913 | 28.9 | 5780 | 0.7101 | 0.4135 | 0.4998 | 0.8756 | nan | 0.8296 | 0.9634 | 0.8012 | 0.8352 | 0.4835 | nan | 0.7196 | 0.8435 | 0.0852 | 0.9564 | 0.2459 | 0.0 | 0.6448 | 0.0940 | 0.7343 | 0.0 | 0.0033 | 0.9281 | 0.2470 | 0.6256 | 0.4120 | 0.5362 | nan | 0.2655 | 0.5973 | 0.6031 | 0.0 | 0.9490 | 0.8486 | 0.9847 | 0.0051 | 0.2759 | 0.4760 | 0.0 | nan | 0.7505 | 0.8698 | 0.6877 | 0.7422 | 0.3776 | nan | 0.5863 | 0.5962 | 0.0473 | 0.8671 | 0.2066 | 0.0 | 0.4120 | 0.0940 | 0.5942 | 0.0 | 0.0032 | 0.7700 | 0.2057 | 0.5449 | 0.3383 | 0.3747 | nan | 0.2026 | 0.5004 | 0.3717 | 0.0 | 0.8905 | 0.7700 | 0.9527 | 0.0047 | 0.1002 | 0.3704 | 0.0 |
| 0.072 | 29.0 | 5800 | 0.6968 | 0.4099 | 0.4834 | 0.8762 | nan | 0.8334 | 0.9610 | 0.8071 | 0.8424 | 0.4897 | nan | 0.7077 | 0.8352 | 0.0391 | 0.9520 | 0.1520 | 0.0 | 0.5859 | 0.0863 | 0.7320 | 0.0 | 0.0046 | 0.9333 | 0.1649 | 0.6309 | 0.3745 | 0.4073 | nan | 0.3005 | 0.6135 | 0.5685 | 0.0 | 0.9567 | 0.8291 | 0.9849 | 0.0106 | 0.1745 | 0.4902 | 0.0 | nan | 0.7528 | 0.8734 | 0.6771 | 0.7447 | 0.3839 | nan | 0.5856 | 0.6162 | 0.0255 | 0.8688 | 0.1347 | 0.0 | 0.4398 | 0.0863 | 0.5976 | 0.0 | 0.0046 | 0.7651 | 0.1485 | 0.5473 | 0.3246 | 0.3305 | nan | 0.2484 | 0.4965 | 0.3926 | 0.0 | 0.8861 | 0.7581 | 0.9514 | 0.0094 | 0.0807 | 0.3850 | 0.0 |
| 0.0781 | 29.1 | 5820 | 0.6973 | 0.4131 | 0.4886 | 0.8773 | nan | 0.8343 | 0.9599 | 0.7973 | 0.8468 | 0.5317 | nan | 0.7142 | 0.8298 | 0.0164 | 0.9593 | 0.1801 | 0.0 | 0.6026 | 0.0882 | 0.7129 | 0.0 | 0.0032 | 0.9345 | 0.2456 | 0.6484 | 0.3544 | 0.4435 | nan | 0.3119 | 0.6138 | 0.5755 | 0.0 | 0.9525 | 0.8419 | 0.9814 | 0.0059 | 0.1920 | 0.4572 | 0.0 | nan | 0.7539 | 0.8760 | 0.6968 | 0.7523 | 0.3918 | nan | 0.5919 | 0.6012 | 0.0116 | 0.8643 | 0.1596 | 0.0 | 0.4408 | 0.0882 | 0.5925 | 0.0 | 0.0032 | 0.7668 | 0.2068 | 0.5522 | 0.3123 | 0.3525 | nan | 0.2465 | 0.4988 | 0.3882 | 0.0 | 0.8884 | 0.7642 | 0.9537 | 0.0050 | 0.0883 | 0.3717 | 0.0 |
| 0.0882 | 29.2 | 5840 | 0.6949 | 0.4107 | 0.4873 | 0.8777 | nan | 0.8417 | 0.9601 | 0.7950 | 0.8391 | 0.5112 | nan | 0.7123 | 0.8305 | 0.0195 | 0.9536 | 0.1604 | 0.0 | 0.6562 | 0.0472 | 0.7099 | 0.0 | 0.0 | 0.9325 | 0.1996 | 0.6680 | 0.3698 | 0.5032 | nan | 0.2998 | 0.5804 | 0.5492 | 0.0 | 0.9570 | 0.8352 | 0.9825 | 0.0130 | 0.1928 | 0.4749 | 0.0 | nan | 0.7550 | 0.8757 | 0.6943 | 0.7509 | 0.4055 | nan | 0.5837 | 0.6050 | 0.0133 | 0.8664 | 0.1407 | 0.0 | 0.4336 | 0.0472 | 0.5829 | 0.0 | 0.0 | 0.7708 | 0.1744 | 0.5551 | 0.3162 | 0.3544 | nan | 0.2495 | 0.4980 | 0.3864 | 0.0 | 0.8861 | 0.7591 | 0.9531 | 0.0100 | 0.1005 | 0.3753 | 0.0 |
| 0.0707 | 29.3 | 5860 | 0.6947 | 0.4112 | 0.4895 | 0.8774 | nan | 0.8247 | 0.9609 | 0.7985 | 0.8580 | 0.5161 | nan | 0.7280 | 0.7997 | 0.0293 | 0.9579 | 0.1443 | 0.0 | 0.6550 | 0.0522 | 0.6908 | 0.0 | 0.0013 | 0.9334 | 0.2002 | 0.6496 | 0.3956 | 0.4688 | nan | 0.2862 | 0.6281 | 0.5941 | 0.0 | 0.9545 | 0.8436 | 0.9820 | 0.0123 | 0.2338 | 0.4644 | 0.0 | nan | 0.7498 | 0.8752 | 0.6773 | 0.7486 | 0.4050 | nan | 0.5895 | 0.6463 | 0.0182 | 0.8622 | 0.1285 | 0.0 | 0.4120 | 0.0521 | 0.5773 | 0.0 | 0.0013 | 0.7698 | 0.1752 | 0.5495 | 0.3277 | 0.3557 | nan | 0.2325 | 0.5066 | 0.3845 | 0.0 | 0.8877 | 0.7646 | 0.9535 | 0.0102 | 0.1159 | 0.3829 | 0.0 |
| 0.0808 | 29.4 | 5880 | 0.7035 | 0.4153 | 0.5005 | 0.8777 | nan | 0.8409 | 0.9609 | 0.8013 | 0.8432 | 0.5292 | nan | 0.6840 | 0.8192 | 0.0704 | 0.9569 | 0.1940 | 0.0002 | 0.7892 | 0.0759 | 0.6909 | 0.0 | 0.0115 | 0.9330 | 0.2708 | 0.6312 | 0.3761 | 0.4916 | nan | 0.3595 | 0.6176 | 0.5648 | 0.0 | 0.9512 | 0.8414 | 0.9804 | 0.0128 | 0.2294 | 0.4890 | 0.0 | nan | 0.7548 | 0.8765 | 0.6734 | 0.7502 | 0.4074 | nan | 0.5854 | 0.6300 | 0.0378 | 0.8660 | 0.1712 | 0.0002 | 0.3926 | 0.0759 | 0.5810 | 0.0 | 0.0114 | 0.7675 | 0.2253 | 0.5374 | 0.3222 | 0.3649 | nan | 0.2490 | 0.5043 | 0.3850 | 0.0 | 0.8883 | 0.7650 | 0.9545 | 0.0100 | 0.1134 | 0.3900 | 0.0 |
| 0.0959 | 29.5 | 5900 | 0.6633 | 0.4123 | 0.4992 | 0.8769 | nan | 0.8516 | 0.9532 | 0.8257 | 0.8463 | 0.5411 | nan | 0.7325 | 0.7945 | 0.0996 | 0.9575 | 0.1970 | 0.0 | 0.8361 | 0.0722 | 0.7040 | 0.0 | 0.0015 | 0.9325 | 0.1979 | 0.6232 | 0.3559 | 0.4814 | nan | 0.3650 | 0.6066 | 0.5344 | 0.0 | 0.9574 | 0.7861 | 0.9828 | 0.0202 | 0.2246 | 0.4946 | 0.0 | nan | 0.7671 | 0.8815 | 0.6558 | 0.7607 | 0.4057 | nan | 0.5869 | 0.6192 | 0.0441 | 0.8637 | 0.1692 | 0.0 | 0.4204 | 0.0721 | 0.5866 | 0.0 | 0.0014 | 0.7655 | 0.1765 | 0.5306 | 0.3051 | 0.3484 | nan | 0.2809 | 0.5017 | 0.3784 | 0.0 | 0.8789 | 0.7286 | 0.9534 | 0.0144 | 0.1115 | 0.3860 | 0.0 |
| 0.0771 | 29.6 | 5920 | 0.6884 | 0.4142 | 0.5007 | 0.8764 | nan | 0.8283 | 0.9584 | 0.8123 | 0.8427 | 0.5157 | nan | 0.7270 | 0.8310 | 0.0579 | 0.9523 | 0.2168 | 0.0 | 0.8208 | 0.0862 | 0.7416 | 0.0 | 0.0 | 0.9275 | 0.1550 | 0.6739 | 0.3409 | 0.5263 | nan | 0.3536 | 0.6307 | 0.5499 | 0.0 | 0.9567 | 0.8381 | 0.9828 | 0.0116 | 0.2159 | 0.4682 | 0.0 | nan | 0.7503 | 0.8739 | 0.6630 | 0.7414 | 0.4013 | nan | 0.5855 | 0.6113 | 0.0326 | 0.8692 | 0.1840 | 0.0 | 0.4372 | 0.0862 | 0.5978 | 0.0 | 0.0 | 0.7733 | 0.1416 | 0.5535 | 0.2984 | 0.3676 | nan | 0.2926 | 0.5067 | 0.3830 | 0.0 | 0.8852 | 0.7577 | 0.9536 | 0.0100 | 0.1185 | 0.3791 | 0.0 |
| 0.0705 | 29.7 | 5940 | 0.6747 | 0.4181 | 0.5036 | 0.8780 | nan | 0.8426 | 0.9584 | 0.7923 | 0.8350 | 0.5360 | nan | 0.7188 | 0.8237 | 0.0655 | 0.9586 | 0.2272 | 0.0001 | 0.8067 | 0.0805 | 0.7526 | 0.0 | 0.0 | 0.9323 | 0.2434 | 0.6474 | 0.3673 | 0.5485 | nan | 0.3330 | 0.6053 | 0.5587 | 0.0 | 0.9538 | 0.8479 | 0.9846 | 0.0074 | 0.2233 | 0.4627 | 0.0 | nan | 0.7568 | 0.8740 | 0.6958 | 0.7447 | 0.4053 | nan | 0.5883 | 0.6244 | 0.0361 | 0.8644 | 0.1895 | 0.0001 | 0.4437 | 0.0805 | 0.5893 | 0.0 | 0.0 | 0.7708 | 0.2032 | 0.5473 | 0.3117 | 0.3816 | nan | 0.2713 | 0.5021 | 0.3805 | 0.0 | 0.8884 | 0.7714 | 0.9529 | 0.0068 | 0.1211 | 0.3776 | 0.0 |
| 0.0472 | 29.8 | 5960 | 0.7109 | 0.4086 | 0.4956 | 0.8758 | nan | 0.8353 | 0.9621 | 0.8017 | 0.8276 | 0.5187 | nan | 0.7011 | 0.8241 | 0.1174 | 0.9530 | 0.1418 | 0.0 | 0.7473 | 0.0818 | 0.7646 | 0.0 | 0.0 | 0.9265 | 0.1480 | 0.6075 | 0.4195 | 0.5112 | nan | 0.2423 | 0.6126 | 0.5446 | 0.0 | 0.9582 | 0.8396 | 0.9838 | 0.0135 | 0.3227 | 0.4524 | 0.0 | nan | 0.7546 | 0.8730 | 0.6628 | 0.7390 | 0.4098 | nan | 0.5845 | 0.6204 | 0.0569 | 0.8708 | 0.1223 | 0.0 | 0.4144 | 0.0818 | 0.5821 | 0.0 | 0.0 | 0.7687 | 0.1324 | 0.5337 | 0.3295 | 0.3688 | nan | 0.2083 | 0.5037 | 0.3762 | 0.0 | 0.8858 | 0.7640 | 0.9523 | 0.0120 | 0.1100 | 0.3565 | 0.0 |
| 0.0856 | 29.9 | 5980 | 0.7068 | 0.4075 | 0.4915 | 0.8769 | nan | 0.8328 | 0.9616 | 0.7846 | 0.8604 | 0.5267 | nan | 0.7001 | 0.8317 | 0.0490 | 0.9578 | 0.1086 | 0.0 | 0.7661 | 0.0397 | 0.6910 | 0.0 | 0.0025 | 0.9349 | 0.2551 | 0.6181 | 0.3754 | 0.4470 | nan | 0.2880 | 0.6232 | 0.5678 | 0.0 | 0.9521 | 0.8409 | 0.9837 | 0.0102 | 0.2774 | 0.4412 | 0.0 | nan | 0.7531 | 0.8765 | 0.6674 | 0.7471 | 0.4108 | nan | 0.5886 | 0.6380 | 0.0292 | 0.8636 | 0.0955 | 0.0 | 0.4072 | 0.0397 | 0.5824 | 0.0 | 0.0025 | 0.7642 | 0.2054 | 0.5298 | 0.3210 | 0.3295 | nan | 0.2042 | 0.5070 | 0.3838 | 0.0 | 0.8882 | 0.7660 | 0.9520 | 0.0091 | 0.1154 | 0.3630 | 0.0 |
| 0.0632 | 30.0 | 6000 | 0.6955 | 0.4046 | 0.4834 | 0.8758 | nan | 0.8442 | 0.9537 | 0.7839 | 0.8517 | 0.5396 | nan | 0.7162 | 0.8110 | 0.0400 | 0.9540 | 0.1004 | 0.0 | 0.7228 | 0.0127 | 0.6716 | 0.0 | 0.0072 | 0.9378 | 0.1141 | 0.6040 | 0.3381 | 0.4786 | nan | 0.3070 | 0.6319 | 0.5860 | 0.0 | 0.9547 | 0.8347 | 0.9842 | 0.0080 | 0.2084 | 0.4721 | 0.0 | nan | 0.7577 | 0.8775 | 0.6634 | 0.7512 | 0.3930 | nan | 0.5910 | 0.6374 | 0.0219 | 0.8630 | 0.0879 | 0.0 | 0.4588 | 0.0127 | 0.5791 | 0.0 | 0.0070 | 0.7596 | 0.1049 | 0.5244 | 0.2932 | 0.3452 | nan | 0.2441 | 0.5018 | 0.3876 | 0.0 | 0.8857 | 0.7614 | 0.9505 | 0.0066 | 0.1052 | 0.3755 | 0.0 |
| 0.0753 | 30.1 | 6020 | 0.6949 | 0.4082 | 0.4973 | 0.8758 | nan | 0.8345 | 0.9571 | 0.8362 | 0.8264 | 0.5438 | nan | 0.7323 | 0.8108 | 0.1971 | 0.9566 | 0.1114 | 0.0 | 0.7618 | 0.0325 | 0.7024 | 0.0 | 0.0191 | 0.9319 | 0.1148 | 0.6197 | 0.3594 | 0.5067 | nan | 0.3401 | 0.6437 | 0.5648 | 0.0 | 0.9520 | 0.8443 | 0.9797 | 0.0129 | 0.2513 | 0.4698 | 0.0 | nan | 0.7561 | 0.8772 | 0.6487 | 0.7454 | 0.4034 | nan | 0.5893 | 0.6161 | 0.0728 | 0.8618 | 0.0976 | 0.0 | 0.4558 | 0.0325 | 0.5780 | 0.0 | 0.0188 | 0.7642 | 0.1055 | 0.5314 | 0.3079 | 0.3534 | nan | 0.2505 | 0.5066 | 0.3820 | 0.0 | 0.8864 | 0.7637 | 0.9524 | 0.0108 | 0.1209 | 0.3744 | 0.0 |
| 0.1257 | 30.2 | 6040 | 0.6917 | 0.4070 | 0.4841 | 0.8779 | nan | 0.8493 | 0.9572 | 0.8337 | 0.8431 | 0.5213 | nan | 0.6946 | 0.7666 | 0.1476 | 0.9528 | 0.1400 | 0.0 | 0.6802 | 0.0394 | 0.7341 | 0.0 | 0.0169 | 0.9393 | 0.0977 | 0.6252 | 0.3417 | 0.4498 | nan | 0.2825 | 0.6021 | 0.5489 | 0.0 | 0.9565 | 0.8478 | 0.9835 | 0.0138 | 0.1719 | 0.4552 | 0.0 | nan | 0.7609 | 0.8821 | 0.6538 | 0.7509 | 0.3998 | nan | 0.5869 | 0.6337 | 0.0614 | 0.8635 | 0.1233 | 0.0 | 0.4218 | 0.0394 | 0.5784 | 0.0 | 0.0168 | 0.7609 | 0.0906 | 0.5322 | 0.3005 | 0.3418 | nan | 0.2392 | 0.4983 | 0.3973 | 0.0 | 0.8847 | 0.7660 | 0.9524 | 0.0114 | 0.1011 | 0.3764 | 0.0 |
| 0.1099 | 30.3 | 6060 | 0.6502 | 0.4117 | 0.4958 | 0.8784 | nan | 0.8603 | 0.9469 | 0.8447 | 0.8613 | 0.5677 | nan | 0.6992 | 0.8168 | 0.0617 | 0.9599 | 0.1942 | 0.0 | 0.7344 | 0.0195 | 0.6950 | 0.0 | 0.0363 | 0.9261 | 0.1359 | 0.6417 | 0.3702 | 0.4821 | nan | 0.2626 | 0.6125 | 0.5872 | 0.0 | 0.9514 | 0.8377 | 0.9843 | 0.0076 | 0.2688 | 0.4986 | 0.0 | nan | 0.7764 | 0.8886 | 0.6787 | 0.7535 | 0.3592 | nan | 0.5988 | 0.6382 | 0.0345 | 0.8602 | 0.1687 | 0.0 | 0.4401 | 0.0195 | 0.5770 | 0.0 | 0.0358 | 0.7705 | 0.1235 | 0.5355 | 0.3169 | 0.3607 | nan | 0.2241 | 0.5019 | 0.3836 | 0.0 | 0.8868 | 0.7598 | 0.9519 | 0.0056 | 0.1383 | 0.3852 | 0.0 |
| 0.0772 | 30.4 | 6080 | 0.6619 | 0.4126 | 0.4875 | 0.8790 | nan | 0.8764 | 0.9468 | 0.8257 | 0.8391 | 0.5504 | nan | 0.7166 | 0.8301 | 0.0395 | 0.9459 | 0.1677 | 0.0 | 0.5851 | 0.0885 | 0.7132 | 0.0 | 0.0414 | 0.9361 | 0.1862 | 0.6286 | 0.3409 | 0.3462 | nan | 0.2937 | 0.6241 | 0.5592 | 0.0 | 0.9572 | 0.8257 | 0.9805 | 0.0118 | 0.2612 | 0.4819 | 0.0 | nan | 0.7804 | 0.8881 | 0.7290 | 0.7596 | 0.3569 | nan | 0.5928 | 0.6354 | 0.0243 | 0.8688 | 0.1465 | 0.0 | 0.4213 | 0.0884 | 0.5870 | 0.0 | 0.0406 | 0.7637 | 0.1650 | 0.5315 | 0.3011 | 0.2810 | nan | 0.2343 | 0.5056 | 0.3912 | 0.0 | 0.8844 | 0.7534 | 0.9542 | 0.0080 | 0.1283 | 0.3817 | 0.0 |
| 0.0667 | 30.5 | 6100 | 0.6734 | 0.4156 | 0.4975 | 0.8768 | nan | 0.8659 | 0.9488 | 0.8169 | 0.8425 | 0.5445 | nan | 0.7401 | 0.8129 | 0.1878 | 0.9561 | 0.1335 | 0.0 | 0.6130 | 0.0796 | 0.7363 | 0.0 | 0.0373 | 0.9295 | 0.2599 | 0.5996 | 0.3926 | 0.4041 | nan | 0.3321 | 0.6299 | 0.5870 | 0.0 | 0.9605 | 0.7830 | 0.9819 | 0.0121 | 0.2828 | 0.4500 | 0.0 | nan | 0.7741 | 0.8840 | 0.7218 | 0.7529 | 0.3690 | nan | 0.5993 | 0.6478 | 0.0927 | 0.8646 | 0.1188 | 0.0 | 0.4250 | 0.0795 | 0.5757 | 0.0 | 0.0369 | 0.7654 | 0.2164 | 0.5279 | 0.3266 | 0.3253 | nan | 0.2681 | 0.5043 | 0.3908 | 0.0 | 0.8761 | 0.7178 | 0.9529 | 0.0091 | 0.1090 | 0.3682 | 0.0 |
| 0.0914 | 30.6 | 6120 | 0.6724 | 0.4115 | 0.4907 | 0.8772 | nan | 0.8487 | 0.9539 | 0.8272 | 0.8489 | 0.5201 | nan | 0.7346 | 0.8388 | 0.0661 | 0.9553 | 0.1135 | 0.0 | 0.6458 | 0.0374 | 0.7067 | 0.0 | 0.0146 | 0.9313 | 0.2638 | 0.6162 | 0.3748 | 0.4614 | nan | 0.2940 | 0.6111 | 0.5433 | 0.0 | 0.9517 | 0.8375 | 0.9826 | 0.0122 | 0.2292 | 0.4805 | 0.0 | nan | 0.7623 | 0.8822 | 0.6902 | 0.7479 | 0.3890 | nan | 0.5967 | 0.6272 | 0.0419 | 0.8660 | 0.1011 | 0.0 | 0.4417 | 0.0374 | 0.5728 | 0.0 | 0.0145 | 0.7672 | 0.2226 | 0.5318 | 0.3195 | 0.3483 | nan | 0.2485 | 0.5019 | 0.3951 | 0.0 | 0.8840 | 0.7441 | 0.9528 | 0.0088 | 0.0989 | 0.3731 | 0.0 |
| 0.0661 | 30.7 | 6140 | 0.7170 | 0.4061 | 0.4897 | 0.8731 | nan | 0.8210 | 0.9529 | 0.8176 | 0.8697 | 0.5278 | nan | 0.7301 | 0.8420 | 0.0351 | 0.9562 | 0.1284 | 0.0 | 0.6482 | 0.0361 | 0.7150 | 0.0 | 0.0179 | 0.9353 | 0.2255 | 0.6233 | 0.3457 | 0.5291 | nan | 0.2833 | 0.6036 | 0.5790 | 0.0 | 0.9537 | 0.8027 | 0.9850 | 0.0134 | 0.2334 | 0.4607 | 0.0 | nan | 0.7420 | 0.8802 | 0.6737 | 0.7257 | 0.3774 | nan | 0.5939 | 0.6169 | 0.0226 | 0.8641 | 0.1118 | 0.0 | 0.4206 | 0.0361 | 0.5798 | 0.0 | 0.0178 | 0.7671 | 0.1958 | 0.5363 | 0.2960 | 0.3592 | nan | 0.2366 | 0.4952 | 0.3927 | 0.0 | 0.8805 | 0.7368 | 0.9513 | 0.0107 | 0.1042 | 0.3705 | 0.0 |
| 0.0741 | 30.8 | 6160 | 0.7035 | 0.4059 | 0.4835 | 0.8739 | nan | 0.8332 | 0.9534 | 0.8044 | 0.8594 | 0.5470 | nan | 0.6966 | 0.8466 | 0.0264 | 0.9513 | 0.1178 | 0.0 | 0.6525 | 0.0279 | 0.6862 | 0.0 | 0.0111 | 0.9339 | 0.2435 | 0.6108 | 0.3731 | 0.4133 | nan | 0.2855 | 0.6059 | 0.5583 | 0.0 | 0.9556 | 0.8048 | 0.9822 | 0.0117 | 0.2116 | 0.4694 | 0.0 | nan | 0.7500 | 0.8788 | 0.6764 | 0.7474 | 0.3757 | nan | 0.5922 | 0.6360 | 0.0169 | 0.8647 | 0.1031 | 0.0 | 0.4324 | 0.0278 | 0.5708 | 0.0 | 0.0111 | 0.7611 | 0.2063 | 0.5166 | 0.3174 | 0.3225 | nan | 0.2321 | 0.4967 | 0.3917 | 0.0 | 0.8810 | 0.7370 | 0.9529 | 0.0099 | 0.1061 | 0.3742 | 0.0 |
| 0.058 | 30.9 | 6180 | 0.6949 | 0.4057 | 0.4874 | 0.8758 | nan | 0.8500 | 0.9529 | 0.8062 | 0.8529 | 0.5494 | nan | 0.7168 | 0.8530 | 0.0539 | 0.9533 | 0.1434 | 0.0 | 0.6919 | 0.0333 | 0.7170 | 0.0 | 0.0029 | 0.9345 | 0.1742 | 0.5842 | 0.3866 | 0.4719 | nan | 0.2287 | 0.6061 | 0.5550 | 0.0 | 0.9526 | 0.8203 | 0.9840 | 0.0032 | 0.2581 | 0.4611 | 0.0 | nan | 0.7621 | 0.8822 | 0.6865 | 0.7517 | 0.3810 | nan | 0.5951 | 0.6290 | 0.0340 | 0.8645 | 0.1196 | 0.0 | 0.4348 | 0.0333 | 0.5701 | 0.0 | 0.0029 | 0.7594 | 0.1569 | 0.5025 | 0.3272 | 0.3516 | nan | 0.1977 | 0.4968 | 0.3827 | 0.0 | 0.8834 | 0.7426 | 0.9522 | 0.0028 | 0.1132 | 0.3673 | 0.0 |
| 0.0769 | 31.0 | 6200 | 0.7021 | 0.4053 | 0.4841 | 0.8771 | nan | 0.8492 | 0.9555 | 0.8176 | 0.8517 | 0.5460 | nan | 0.6977 | 0.8309 | 0.0346 | 0.9528 | 0.1484 | 0.0 | 0.6787 | 0.0428 | 0.7329 | 0.0 | 0.0019 | 0.9380 | 0.1775 | 0.5846 | 0.3760 | 0.3971 | nan | 0.2414 | 0.5944 | 0.5549 | 0.0 | 0.9527 | 0.8520 | 0.9845 | 0.0045 | 0.2571 | 0.4371 | 0.0 | nan | 0.7645 | 0.8833 | 0.6655 | 0.7506 | 0.3847 | nan | 0.5952 | 0.6331 | 0.0236 | 0.8665 | 0.1226 | 0.0 | 0.4309 | 0.0428 | 0.5769 | 0.0 | 0.0018 | 0.7567 | 0.1589 | 0.5088 | 0.3230 | 0.3165 | nan | 0.2053 | 0.4955 | 0.3887 | 0.0 | 0.8882 | 0.7661 | 0.9518 | 0.0039 | 0.1047 | 0.3583 | 0.0 |
| 0.1001 | 31.1 | 6220 | 0.6853 | 0.4093 | 0.4854 | 0.8775 | nan | 0.8416 | 0.9577 | 0.8128 | 0.8535 | 0.5338 | nan | 0.7194 | 0.8196 | 0.0204 | 0.9562 | 0.1343 | 0.0 | 0.6562 | 0.0496 | 0.7100 | 0.0 | 0.0097 | 0.9281 | 0.1991 | 0.6091 | 0.3812 | 0.4154 | nan | 0.2431 | 0.6048 | 0.5839 | 0.0 | 0.9558 | 0.8400 | 0.9842 | 0.0073 | 0.2275 | 0.4778 | 0.0 | nan | 0.7583 | 0.8790 | 0.6821 | 0.7485 | 0.3949 | nan | 0.5976 | 0.6516 | 0.0143 | 0.8642 | 0.1142 | 0.0 | 0.4418 | 0.0496 | 0.5859 | 0.0 | 0.0096 | 0.7646 | 0.1751 | 0.5216 | 0.3246 | 0.3335 | nan | 0.1983 | 0.4977 | 0.3874 | 0.0 | 0.8879 | 0.7664 | 0.9515 | 0.0064 | 0.1118 | 0.3783 | 0.0 |
| 0.0825 | 31.2 | 6240 | 0.7171 | 0.4077 | 0.4846 | 0.8742 | nan | 0.8166 | 0.9577 | 0.8072 | 0.8654 | 0.5347 | nan | 0.7027 | 0.8295 | 0.0439 | 0.9538 | 0.1191 | 0.0 | 0.6460 | 0.0475 | 0.7103 | 0.0 | 0.0139 | 0.9326 | 0.1952 | 0.6114 | 0.3621 | 0.3947 | nan | 0.2462 | 0.6264 | 0.5722 | 0.0 | 0.9531 | 0.8397 | 0.9826 | 0.0105 | 0.2558 | 0.4754 | 0.0 | nan | 0.7417 | 0.8761 | 0.7062 | 0.7212 | 0.3914 | nan | 0.5990 | 0.6442 | 0.0283 | 0.8653 | 0.1020 | 0.0 | 0.4481 | 0.0475 | 0.5948 | 0.0 | 0.0138 | 0.7624 | 0.1714 | 0.5208 | 0.3110 | 0.3095 | nan | 0.2061 | 0.5013 | 0.3833 | 0.0 | 0.8875 | 0.7596 | 0.9522 | 0.0089 | 0.1172 | 0.3768 | 0.0 |
| 0.0719 | 31.3 | 6260 | 0.7227 | 0.4029 | 0.4776 | 0.8724 | nan | 0.8252 | 0.9572 | 0.8001 | 0.8361 | 0.5346 | nan | 0.7171 | 0.8383 | 0.0841 | 0.9517 | 0.1249 | 0.0004 | 0.6682 | 0.0438 | 0.7044 | 0.0 | 0.0090 | 0.9348 | 0.1377 | 0.5864 | 0.3301 | 0.3852 | nan | 0.2310 | 0.5876 | 0.5513 | 0.0 | 0.9574 | 0.8105 | 0.9836 | 0.0052 | 0.1920 | 0.4961 | 0.0 | nan | 0.7465 | 0.8720 | 0.6935 | 0.7138 | 0.3899 | nan | 0.5954 | 0.6378 | 0.0479 | 0.8665 | 0.1055 | 0.0004 | 0.4430 | 0.0438 | 0.5924 | 0.0 | 0.0089 | 0.7560 | 0.1232 | 0.5036 | 0.2936 | 0.3103 | nan | 0.1915 | 0.4936 | 0.3802 | 0.0 | 0.8833 | 0.7456 | 0.9516 | 0.0045 | 0.1095 | 0.3888 | 0.0 |
| 0.0706 | 31.4 | 6280 | 0.7254 | 0.4055 | 0.4860 | 0.8726 | nan | 0.8178 | 0.9526 | 0.8173 | 0.8460 | 0.5517 | nan | 0.7147 | 0.8187 | 0.0855 | 0.9556 | 0.1163 | 0.0048 | 0.6842 | 0.0582 | 0.7201 | 0.0 | 0.0240 | 0.9390 | 0.1200 | 0.5932 | 0.3625 | 0.4347 | nan | 0.2568 | 0.5897 | 0.5849 | 0.0 | 0.9546 | 0.8502 | 0.9816 | 0.0098 | 0.2429 | 0.4652 | 0.0 | nan | 0.7442 | 0.8756 | 0.6588 | 0.7151 | 0.3918 | nan | 0.5943 | 0.6401 | 0.0474 | 0.8644 | 0.0997 | 0.0046 | 0.4438 | 0.0581 | 0.5896 | 0.0 | 0.0238 | 0.7558 | 0.1079 | 0.5100 | 0.3098 | 0.3399 | nan | 0.2168 | 0.4966 | 0.3814 | 0.0 | 0.8874 | 0.7607 | 0.9527 | 0.0083 | 0.1194 | 0.3773 | 0.0 |
| 0.0874 | 31.5 | 6300 | 0.7268 | 0.4020 | 0.4789 | 0.8717 | nan | 0.8095 | 0.9606 | 0.8034 | 0.8561 | 0.5209 | nan | 0.7001 | 0.8063 | 0.0546 | 0.9517 | 0.1006 | 0.0039 | 0.6399 | 0.0730 | 0.7356 | 0.0 | 0.0078 | 0.9383 | 0.1055 | 0.5675 | 0.3776 | 0.4526 | nan | 0.2018 | 0.5976 | 0.5706 | 0.0 | 0.9536 | 0.8297 | 0.9801 | 0.0118 | 0.2338 | 0.4798 | 0.0 | nan | 0.7348 | 0.8737 | 0.6748 | 0.7096 | 0.3915 | nan | 0.5888 | 0.6465 | 0.0313 | 0.8675 | 0.0893 | 0.0037 | 0.4096 | 0.0730 | 0.5995 | 0.0 | 0.0078 | 0.7556 | 0.0964 | 0.5046 | 0.3131 | 0.3490 | nan | 0.1735 | 0.4953 | 0.3811 | 0.0 | 0.8872 | 0.7585 | 0.9528 | 0.0095 | 0.1093 | 0.3771 | 0.0 |
| 0.1078 | 31.6 | 6320 | 0.7038 | 0.4033 | 0.4803 | 0.8737 | nan | 0.8193 | 0.9549 | 0.8142 | 0.8598 | 0.5280 | nan | 0.7177 | 0.8195 | 0.0581 | 0.9601 | 0.1067 | 0.0096 | 0.6746 | 0.0319 | 0.7294 | 0.0 | 0.0 | 0.9362 | 0.1320 | 0.6057 | 0.3791 | 0.3374 | nan | 0.2643 | 0.5980 | 0.5489 | 0.0 | 0.9565 | 0.8236 | 0.9834 | 0.0105 | 0.2318 | 0.4768 | 0.0 | nan | 0.7444 | 0.8757 | 0.6466 | 0.7298 | 0.4080 | nan | 0.5887 | 0.6389 | 0.0335 | 0.8631 | 0.0937 | 0.0093 | 0.4737 | 0.0319 | 0.5699 | 0.0 | 0.0 | 0.7627 | 0.1200 | 0.5175 | 0.3115 | 0.2830 | nan | 0.2302 | 0.4973 | 0.3836 | 0.0 | 0.8837 | 0.7454 | 0.9522 | 0.0086 | 0.1224 | 0.3814 | 0.0 |
| 0.0711 | 31.7 | 6340 | 0.7174 | 0.4066 | 0.4866 | 0.8736 | nan | 0.8243 | 0.9516 | 0.8095 | 0.8656 | 0.5432 | nan | 0.7135 | 0.8475 | 0.0550 | 0.9579 | 0.1080 | 0.0546 | 0.7416 | 0.0321 | 0.7189 | 0.0 | 0.0 | 0.9370 | 0.1802 | 0.6009 | 0.3674 | 0.3297 | nan | 0.2785 | 0.6281 | 0.5601 | 0.0 | 0.9530 | 0.8254 | 0.9845 | 0.0107 | 0.2219 | 0.4711 | 0.0 | nan | 0.7459 | 0.8751 | 0.6467 | 0.7309 | 0.3992 | nan | 0.5923 | 0.6307 | 0.0327 | 0.8658 | 0.0929 | 0.0520 | 0.4931 | 0.0321 | 0.5788 | 0.0 | 0.0 | 0.7610 | 0.1603 | 0.5105 | 0.3171 | 0.2766 | nan | 0.2344 | 0.5053 | 0.3758 | 0.0 | 0.8858 | 0.7519 | 0.9518 | 0.0092 | 0.1202 | 0.3818 | 0.0 |
| 0.0753 | 31.8 | 6360 | 0.7175 | 0.4085 | 0.4870 | 0.8750 | nan | 0.8346 | 0.9524 | 0.7899 | 0.8642 | 0.5514 | nan | 0.7120 | 0.8405 | 0.0501 | 0.9590 | 0.1016 | 0.0091 | 0.6982 | 0.0294 | 0.6857 | 0.0 | 0.0012 | 0.9302 | 0.1419 | 0.6116 | 0.3700 | 0.5232 | nan | 0.2718 | 0.6032 | 0.5396 | 0.0 | 0.9547 | 0.8306 | 0.9835 | 0.0090 | 0.2432 | 0.4908 | 0.0 | nan | 0.7515 | 0.8752 | 0.6752 | 0.7407 | 0.3913 | nan | 0.5938 | 0.6172 | 0.0311 | 0.8641 | 0.0885 | 0.0088 | 0.5006 | 0.0294 | 0.5643 | 0.0 | 0.0012 | 0.7671 | 0.1301 | 0.5224 | 0.3169 | 0.3806 | nan | 0.2300 | 0.5022 | 0.3785 | 0.0 | 0.8872 | 0.7572 | 0.9525 | 0.0075 | 0.1284 | 0.3792 | 0.0 |
| 0.0705 | 31.9 | 6380 | 0.7062 | 0.4112 | 0.4900 | 0.8763 | nan | 0.8427 | 0.9512 | 0.7884 | 0.8599 | 0.5317 | nan | 0.7283 | 0.8216 | 0.0484 | 0.9527 | 0.1100 | 0.0072 | 0.6203 | 0.0666 | 0.7012 | 0.0 | 0.0029 | 0.9403 | 0.1995 | 0.6033 | 0.3720 | 0.5112 | nan | 0.3261 | 0.6278 | 0.5613 | 0.0 | 0.9518 | 0.8619 | 0.9833 | 0.0094 | 0.2489 | 0.4499 | 0.0 | nan | 0.7558 | 0.8745 | 0.6857 | 0.7456 | 0.3975 | nan | 0.5863 | 0.6238 | 0.0294 | 0.8666 | 0.0955 | 0.0069 | 0.4483 | 0.0666 | 0.5581 | 0.0 | 0.0029 | 0.7610 | 0.1724 | 0.5200 | 0.3221 | 0.3774 | nan | 0.2558 | 0.5087 | 0.3873 | 0.0 | 0.8909 | 0.7734 | 0.9525 | 0.0079 | 0.1195 | 0.3665 | 0.0 |
| 0.0822 | 32.0 | 6400 | 0.6943 | 0.4123 | 0.4897 | 0.8763 | nan | 0.8249 | 0.9565 | 0.8046 | 0.8503 | 0.5427 | nan | 0.7243 | 0.8296 | 0.0390 | 0.9608 | 0.0953 | 0.0 | 0.6325 | 0.0404 | 0.6859 | 0.0 | 0.0005 | 0.9273 | 0.2207 | 0.6202 | 0.4016 | 0.4459 | nan | 0.3713 | 0.6071 | 0.5696 | 0.0 | 0.9564 | 0.8529 | 0.9832 | 0.0115 | 0.2157 | 0.4988 | 0.0 | nan | 0.7500 | 0.8751 | 0.6614 | 0.7480 | 0.4049 | nan | 0.5941 | 0.6398 | 0.0240 | 0.8622 | 0.0844 | 0.0 | 0.4865 | 0.0404 | 0.5522 | 0.0 | 0.0005 | 0.7696 | 0.1895 | 0.5334 | 0.3299 | 0.3518 | nan | 0.2886 | 0.5030 | 0.3887 | 0.0 | 0.8885 | 0.7667 | 0.9526 | 0.0091 | 0.1140 | 0.3856 | 0.0 |
| 0.0668 | 32.1 | 6420 | 0.7170 | 0.4117 | 0.4908 | 0.8756 | nan | 0.8303 | 0.9569 | 0.8044 | 0.8574 | 0.5256 | nan | 0.7134 | 0.8253 | 0.0717 | 0.9551 | 0.1055 | 0.0004 | 0.6658 | 0.0383 | 0.6925 | 0.0 | 0.0016 | 0.9343 | 0.2526 | 0.6060 | 0.3833 | 0.4895 | nan | 0.3324 | 0.6097 | 0.5615 | 0.0 | 0.9505 | 0.8486 | 0.9849 | 0.0083 | 0.2182 | 0.4814 | 0.0 | nan | 0.7487 | 0.8756 | 0.6546 | 0.7416 | 0.4008 | nan | 0.5996 | 0.6438 | 0.0427 | 0.8637 | 0.0916 | 0.0004 | 0.4797 | 0.0383 | 0.5574 | 0.0 | 0.0016 | 0.7635 | 0.2075 | 0.5271 | 0.3201 | 0.3666 | nan | 0.2627 | 0.4996 | 0.3834 | 0.0 | 0.8883 | 0.7686 | 0.9515 | 0.0066 | 0.1079 | 0.3810 | 0.0 |
| 0.109 | 32.2 | 6440 | 0.6935 | 0.4073 | 0.4896 | 0.8756 | nan | 0.8346 | 0.9530 | 0.7909 | 0.8605 | 0.5453 | nan | 0.7193 | 0.8166 | 0.1420 | 0.9557 | 0.1090 | 0.0 | 0.6780 | 0.0411 | 0.7133 | 0.0 | 0.0003 | 0.9332 | 0.1200 | 0.6218 | 0.3600 | 0.4940 | nan | 0.2634 | 0.6039 | 0.5729 | 0.0 | 0.9543 | 0.8559 | 0.9829 | 0.0058 | 0.2842 | 0.4566 | 0.0 | nan | 0.7505 | 0.8765 | 0.6700 | 0.7381 | 0.3989 | nan | 0.6023 | 0.6275 | 0.0790 | 0.8640 | 0.0946 | 0.0 | 0.4644 | 0.0411 | 0.5404 | 0.0 | 0.0003 | 0.7660 | 0.1098 | 0.5411 | 0.3056 | 0.3728 | nan | 0.2263 | 0.4973 | 0.3782 | 0.0 | 0.8881 | 0.7704 | 0.9521 | 0.0049 | 0.1110 | 0.3630 | 0.0 |
| 0.1092 | 32.3 | 6460 | 0.6913 | 0.4103 | 0.4887 | 0.8780 | nan | 0.8523 | 0.9547 | 0.7885 | 0.8480 | 0.5476 | nan | 0.7248 | 0.8243 | 0.0524 | 0.9527 | 0.1199 | 0.0 | 0.6414 | 0.0525 | 0.6923 | 0.0 | 0.0012 | 0.9293 | 0.1572 | 0.6457 | 0.3509 | 0.5211 | nan | 0.2821 | 0.6106 | 0.5562 | 0.0 | 0.9529 | 0.8452 | 0.9834 | 0.0110 | 0.2626 | 0.4776 | 0.0 | nan | 0.7633 | 0.8792 | 0.6827 | 0.7520 | 0.3946 | nan | 0.5972 | 0.6374 | 0.0305 | 0.8661 | 0.1029 | 0.0 | 0.4491 | 0.0525 | 0.5630 | 0.0 | 0.0012 | 0.7682 | 0.1397 | 0.5439 | 0.3047 | 0.3765 | nan | 0.2310 | 0.5023 | 0.3822 | 0.0 | 0.8886 | 0.7707 | 0.9526 | 0.0090 | 0.1141 | 0.3733 | 0.0 |
| 0.0882 | 32.4 | 6480 | 0.6950 | 0.4128 | 0.4911 | 0.8775 | nan | 0.8384 | 0.9547 | 0.8099 | 0.8561 | 0.5512 | nan | 0.7151 | 0.8269 | 0.0289 | 0.9541 | 0.0924 | 0.0 | 0.6422 | 0.0898 | 0.6895 | 0.0 | 0.0008 | 0.9313 | 0.2086 | 0.6159 | 0.4136 | 0.4909 | nan | 0.2832 | 0.6301 | 0.5775 | 0.0 | 0.9506 | 0.8589 | 0.9833 | 0.0161 | 0.2169 | 0.4886 | 0.0 | nan | 0.7574 | 0.8779 | 0.6794 | 0.7450 | 0.4003 | nan | 0.5922 | 0.6370 | 0.0167 | 0.8665 | 0.0826 | 0.0 | 0.4593 | 0.0898 | 0.5692 | 0.0 | 0.0008 | 0.7670 | 0.1782 | 0.5370 | 0.3388 | 0.3688 | nan | 0.2250 | 0.5074 | 0.3948 | 0.0 | 0.8908 | 0.7754 | 0.9528 | 0.0123 | 0.1047 | 0.3829 | 0.0 |
| 0.1078 | 32.5 | 6500 | 0.7008 | 0.4077 | 0.4851 | 0.8768 | nan | 0.8260 | 0.9568 | 0.8089 | 0.8641 | 0.5325 | nan | 0.7276 | 0.8352 | 0.0344 | 0.9569 | 0.0890 | 0.0 | 0.6498 | 0.0799 | 0.7141 | 0.0 | 0.0003 | 0.9332 | 0.1337 | 0.6248 | 0.4004 | 0.4473 | nan | 0.2084 | 0.6083 | 0.5986 | 0.0 | 0.9546 | 0.8545 | 0.9826 | 0.0117 | 0.2128 | 0.4765 | 0.0 | nan | 0.7537 | 0.8767 | 0.6901 | 0.7304 | 0.4085 | nan | 0.5935 | 0.6295 | 0.0208 | 0.8652 | 0.0800 | 0.0 | 0.4612 | 0.0799 | 0.5707 | 0.0 | 0.0003 | 0.7653 | 0.1212 | 0.5288 | 0.3344 | 0.3514 | nan | 0.1775 | 0.5027 | 0.3925 | 0.0 | 0.8896 | 0.7701 | 0.9528 | 0.0095 | 0.1122 | 0.3797 | 0.0 |
| 0.0839 | 32.6 | 6520 | 0.6931 | 0.4070 | 0.4811 | 0.8762 | nan | 0.8256 | 0.9578 | 0.7687 | 0.8671 | 0.5476 | nan | 0.7239 | 0.8142 | 0.0359 | 0.9583 | 0.0924 | 0.0 | 0.6538 | 0.0468 | 0.6923 | 0.0 | 0.0052 | 0.9296 | 0.1578 | 0.6324 | 0.3887 | 0.4013 | nan | 0.2183 | 0.6161 | 0.5845 | 0.0 | 0.9555 | 0.8465 | 0.9810 | 0.0069 | 0.2266 | 0.4620 | 0.0 | nan | 0.7481 | 0.8773 | 0.7198 | 0.7139 | 0.4052 | nan | 0.5951 | 0.6356 | 0.0227 | 0.8628 | 0.0832 | 0.0 | 0.4647 | 0.0468 | 0.5635 | 0.0 | 0.0052 | 0.7671 | 0.1405 | 0.5332 | 0.3266 | 0.3282 | nan | 0.1798 | 0.5013 | 0.3971 | 0.0 | 0.8898 | 0.7736 | 0.9531 | 0.0060 | 0.1144 | 0.3709 | 0.0 |
| 0.0993 | 32.7 | 6540 | 0.7215 | 0.4052 | 0.4845 | 0.8741 | nan | 0.8252 | 0.9563 | 0.7900 | 0.8293 | 0.5540 | nan | 0.7042 | 0.8283 | 0.1096 | 0.9620 | 0.0997 | 0.0 | 0.6786 | 0.0450 | 0.6849 | 0.0 | 0.0052 | 0.9311 | 0.1770 | 0.6198 | 0.3774 | 0.3792 | nan | 0.1926 | 0.6116 | 0.5805 | 0.0 | 0.9537 | 0.8576 | 0.9825 | 0.0017 | 0.2979 | 0.4689 | 0.0 | nan | 0.7450 | 0.8710 | 0.6980 | 0.7201 | 0.4009 | nan | 0.5858 | 0.6205 | 0.0619 | 0.8594 | 0.0887 | 0.0 | 0.4599 | 0.0450 | 0.5572 | 0.0 | 0.0052 | 0.7674 | 0.1559 | 0.5378 | 0.3225 | 0.3122 | nan | 0.1615 | 0.5010 | 0.3915 | 0.0 | 0.8896 | 0.7746 | 0.9524 | 0.0015 | 0.1138 | 0.3657 | 0.0 |
| 0.0767 | 32.8 | 6560 | 0.7154 | 0.4119 | 0.4878 | 0.8752 | nan | 0.8259 | 0.9534 | 0.8095 | 0.8543 | 0.5497 | nan | 0.6975 | 0.7829 | 0.1343 | 0.9474 | 0.1097 | 0.0 | 0.6268 | 0.0537 | 0.7137 | 0.0 | 0.0033 | 0.9335 | 0.1615 | 0.6321 | 0.4182 | 0.4522 | nan | 0.2498 | 0.6249 | 0.5742 | 0.0 | 0.9536 | 0.8550 | 0.9824 | 0.0084 | 0.2087 | 0.4922 | 0.0 | nan | 0.7454 | 0.8740 | 0.6896 | 0.7329 | 0.3946 | nan | 0.5824 | 0.6412 | 0.0629 | 0.8653 | 0.0960 | 0.0 | 0.4605 | 0.0537 | 0.5902 | 0.0 | 0.0033 | 0.7651 | 0.1420 | 0.5385 | 0.3385 | 0.3546 | nan | 0.2151 | 0.5038 | 0.3986 | 0.0 | 0.8898 | 0.7714 | 0.9525 | 0.0068 | 0.1229 | 0.3897 | 0.0 |
| 0.07 | 32.9 | 6580 | 0.7151 | 0.4086 | 0.4868 | 0.8742 | nan | 0.8154 | 0.9566 | 0.8130 | 0.8613 | 0.5386 | nan | 0.7003 | 0.8201 | 0.0959 | 0.9565 | 0.1221 | 0.0079 | 0.6187 | 0.0545 | 0.7487 | 0.0 | 0.0009 | 0.9347 | 0.2350 | 0.6063 | 0.3738 | 0.4407 | nan | 0.2614 | 0.6001 | 0.5478 | 0.0 | 0.9557 | 0.8371 | 0.9858 | 0.0061 | 0.2000 | 0.4813 | 0.0 | nan | 0.7412 | 0.8754 | 0.6716 | 0.7269 | 0.3948 | nan | 0.5877 | 0.6248 | 0.0466 | 0.8617 | 0.1046 | 0.0078 | 0.4182 | 0.0545 | 0.6030 | 0.0 | 0.0009 | 0.7639 | 0.1969 | 0.5336 | 0.3179 | 0.3477 | nan | 0.2122 | 0.4997 | 0.3894 | 0.0 | 0.8867 | 0.7627 | 0.9507 | 0.0050 | 0.1037 | 0.3853 | 0.0 |
| 0.0869 | 33.0 | 6600 | 0.7291 | 0.4098 | 0.4886 | 0.8733 | nan | 0.8000 | 0.9569 | 0.8014 | 0.8654 | 0.5538 | nan | 0.7208 | 0.8246 | 0.0684 | 0.9506 | 0.1201 | 0.0254 | 0.6151 | 0.0426 | 0.7512 | 0.0 | 0.0015 | 0.9316 | 0.2126 | 0.6254 | 0.3746 | 0.4522 | nan | 0.2562 | 0.6426 | 0.5521 | 0.0 | 0.9547 | 0.8444 | 0.9837 | 0.0108 | 0.2265 | 0.4712 | 0.0 | nan | 0.7315 | 0.8745 | 0.6940 | 0.7067 | 0.4021 | nan | 0.5896 | 0.6237 | 0.0401 | 0.8644 | 0.1045 | 0.0251 | 0.4398 | 0.0425 | 0.6079 | 0.0 | 0.0015 | 0.7688 | 0.1816 | 0.5406 | 0.3177 | 0.3504 | nan | 0.1928 | 0.5069 | 0.3942 | 0.0 | 0.8878 | 0.7669 | 0.9524 | 0.0084 | 0.1167 | 0.3818 | 0.0 |
| 0.0907 | 33.1 | 6620 | 0.7083 | 0.4140 | 0.4938 | 0.8760 | nan | 0.8313 | 0.9492 | 0.7967 | 0.8513 | 0.5814 | nan | 0.7147 | 0.8239 | 0.0723 | 0.9481 | 0.1296 | 0.0329 | 0.6339 | 0.0548 | 0.7398 | 0.0 | 0.0030 | 0.9302 | 0.2007 | 0.6677 | 0.3820 | 0.4066 | nan | 0.3267 | 0.6150 | 0.5558 | 0.0 | 0.9557 | 0.8548 | 0.9816 | 0.0128 | 0.2743 | 0.4734 | 0.0 | nan | 0.7510 | 0.8790 | 0.6874 | 0.7512 | 0.3778 | nan | 0.5872 | 0.6345 | 0.0395 | 0.8648 | 0.1112 | 0.0328 | 0.4481 | 0.0548 | 0.5977 | 0.0 | 0.0030 | 0.7735 | 0.1747 | 0.5569 | 0.3216 | 0.3227 | nan | 0.2508 | 0.5051 | 0.3969 | 0.0 | 0.8878 | 0.7636 | 0.9535 | 0.0094 | 0.1310 | 0.3795 | 0.0 |
| 0.0662 | 33.2 | 6640 | 0.7039 | 0.4131 | 0.4916 | 0.8772 | nan | 0.8298 | 0.9586 | 0.8088 | 0.8580 | 0.5375 | nan | 0.7101 | 0.7987 | 0.1955 | 0.9538 | 0.1375 | 0.0 | 0.6408 | 0.0635 | 0.7458 | 0.0 | 0.0037 | 0.9299 | 0.1396 | 0.6343 | 0.3837 | 0.4410 | nan | 0.2617 | 0.6124 | 0.5711 | 0.0 | 0.9535 | 0.8593 | 0.9840 | 0.0078 | 0.2362 | 0.4741 | 0.0 | nan | 0.7508 | 0.8788 | 0.6696 | 0.7440 | 0.3979 | nan | 0.5928 | 0.6328 | 0.0921 | 0.8642 | 0.1173 | 0.0 | 0.4648 | 0.0635 | 0.5916 | 0.0 | 0.0037 | 0.7705 | 0.1264 | 0.5459 | 0.3223 | 0.3503 | nan | 0.2286 | 0.5051 | 0.3944 | 0.0 | 0.8893 | 0.7750 | 0.9522 | 0.0059 | 0.1141 | 0.3754 | 0.0 |
| 0.0726 | 33.3 | 6660 | 0.7065 | 0.4084 | 0.4842 | 0.8759 | nan | 0.8363 | 0.9559 | 0.8071 | 0.8274 | 0.5425 | nan | 0.7215 | 0.8516 | 0.0306 | 0.9505 | 0.1148 | 0.0 | 0.6430 | 0.0477 | 0.6927 | 0.0 | 0.0025 | 0.9344 | 0.1697 | 0.6262 | 0.3146 | 0.4589 | nan | 0.2954 | 0.6111 | 0.5492 | 0.0 | 0.9557 | 0.8535 | 0.9834 | 0.0044 | 0.2195 | 0.4939 | 0.0 | nan | 0.7537 | 0.8756 | 0.6706 | 0.7371 | 0.4011 | nan | 0.5870 | 0.6198 | 0.0206 | 0.8659 | 0.0991 | 0.0 | 0.4766 | 0.0477 | 0.5819 | 0.0 | 0.0025 | 0.7675 | 0.1532 | 0.5420 | 0.2811 | 0.3503 | nan | 0.2542 | 0.5009 | 0.3853 | 0.0 | 0.8869 | 0.7699 | 0.9523 | 0.0035 | 0.1047 | 0.3782 | 0.0 |
| 0.0765 | 33.4 | 6680 | 0.7081 | 0.4149 | 0.4950 | 0.8765 | nan | 0.8328 | 0.9578 | 0.8029 | 0.8399 | 0.5425 | nan | 0.7116 | 0.8201 | 0.0353 | 0.9547 | 0.1215 | 0.0 | 0.6405 | 0.0628 | 0.7235 | 0.0 | 0.0085 | 0.9322 | 0.1502 | 0.6530 | 0.4228 | 0.6183 | nan | 0.3093 | 0.6097 | 0.5750 | 0.0 | 0.9516 | 0.8377 | 0.9840 | 0.0098 | 0.2520 | 0.4802 | 0.0 | nan | 0.7528 | 0.8742 | 0.6729 | 0.7404 | 0.4038 | nan | 0.5890 | 0.6490 | 0.0240 | 0.8631 | 0.1061 | 0.0 | 0.4733 | 0.0628 | 0.5778 | 0.0 | 0.0085 | 0.7719 | 0.1361 | 0.5468 | 0.3464 | 0.4091 | nan | 0.2539 | 0.5036 | 0.3912 | 0.0 | 0.8882 | 0.7625 | 0.9521 | 0.0076 | 0.1290 | 0.3809 | 0.0 |
| 0.0897 | 33.5 | 6700 | 0.7159 | 0.4094 | 0.4878 | 0.8751 | nan | 0.8256 | 0.9557 | 0.8043 | 0.8559 | 0.5403 | nan | 0.7293 | 0.8425 | 0.0251 | 0.9597 | 0.0810 | 0.0 | 0.6896 | 0.0414 | 0.7049 | 0.0 | 0.0086 | 0.9321 | 0.1699 | 0.6306 | 0.3758 | 0.5154 | nan | 0.2817 | 0.6137 | 0.5449 | 0.0 | 0.9536 | 0.8209 | 0.9839 | 0.0041 | 0.2244 | 0.4935 | 0.0 | nan | 0.7496 | 0.8754 | 0.6659 | 0.7466 | 0.3971 | nan | 0.5921 | 0.6412 | 0.0174 | 0.8602 | 0.0730 | 0.0 | 0.4721 | 0.0414 | 0.5749 | 0.0 | 0.0086 | 0.7695 | 0.1523 | 0.5378 | 0.3204 | 0.3785 | nan | 0.2446 | 0.5059 | 0.3870 | 0.0 | 0.8839 | 0.7483 | 0.9526 | 0.0031 | 0.1198 | 0.3810 | 0.0 |
| 0.0776 | 33.6 | 6720 | 0.7226 | 0.4115 | 0.4950 | 0.8749 | nan | 0.8214 | 0.9543 | 0.8080 | 0.8569 | 0.5551 | nan | 0.7005 | 0.8607 | 0.0413 | 0.9565 | 0.0964 | 0.0 | 0.6873 | 0.0378 | 0.7132 | 0.0 | 0.0026 | 0.9314 | 0.2169 | 0.6416 | 0.3864 | 0.5312 | nan | 0.3206 | 0.6181 | 0.5986 | 0.0 | 0.9525 | 0.8474 | 0.9843 | 0.0084 | 0.2475 | 0.4630 | 0.0 | nan | 0.7465 | 0.8753 | 0.6633 | 0.7468 | 0.3910 | nan | 0.5916 | 0.6261 | 0.0278 | 0.8626 | 0.0849 | 0.0 | 0.4609 | 0.0378 | 0.5827 | 0.0 | 0.0026 | 0.7684 | 0.1841 | 0.5377 | 0.3260 | 0.3794 | nan | 0.2578 | 0.5090 | 0.3896 | 0.0 | 0.8880 | 0.7629 | 0.9518 | 0.0058 | 0.1302 | 0.3758 | 0.0 |
| 0.1109 | 33.7 | 6740 | 0.7219 | 0.4140 | 0.4975 | 0.8759 | nan | 0.8275 | 0.9576 | 0.8050 | 0.8541 | 0.5187 | nan | 0.7415 | 0.8474 | 0.0535 | 0.9539 | 0.1190 | 0.0 | 0.7124 | 0.0666 | 0.7268 | 0.0 | 0.0017 | 0.9352 | 0.1979 | 0.6278 | 0.3975 | 0.5485 | nan | 0.3189 | 0.6067 | 0.5829 | 0.0 | 0.9535 | 0.8316 | 0.9827 | 0.0100 | 0.2786 | 0.4640 | 0.0 | nan | 0.7494 | 0.8761 | 0.6653 | 0.7520 | 0.3944 | nan | 0.5889 | 0.6479 | 0.0342 | 0.8640 | 0.1020 | 0.0 | 0.4684 | 0.0666 | 0.5976 | 0.0 | 0.0017 | 0.7665 | 0.1717 | 0.5348 | 0.3320 | 0.3754 | nan | 0.2489 | 0.5057 | 0.3869 | 0.0 | 0.8872 | 0.7600 | 0.9523 | 0.0073 | 0.1342 | 0.3753 | 0.0 |
| 0.0714 | 33.8 | 6760 | 0.7040 | 0.4191 | 0.5104 | 0.8769 | nan | 0.8311 | 0.9574 | 0.8046 | 0.8543 | 0.5370 | nan | 0.7318 | 0.8065 | 0.3629 | 0.9544 | 0.1247 | 0.0 | 0.7249 | 0.1480 | 0.7440 | 0.0 | 0.0020 | 0.9248 | 0.2265 | 0.6234 | 0.4050 | 0.5126 | nan | 0.2951 | 0.6125 | 0.5795 | 0.0 | 0.9579 | 0.8385 | 0.9825 | 0.0048 | 0.3099 | 0.4775 | 0.0 | nan | 0.7546 | 0.8781 | 0.6648 | 0.7504 | 0.4058 | nan | 0.5879 | 0.6292 | 0.1124 | 0.8647 | 0.1069 | 0.0 | 0.4684 | 0.1479 | 0.5998 | 0.0 | 0.0020 | 0.7721 | 0.1987 | 0.5353 | 0.3365 | 0.3703 | nan | 0.2205 | 0.5051 | 0.3877 | 0.0 | 0.8865 | 0.7618 | 0.9521 | 0.0035 | 0.1365 | 0.3713 | 0.0 |
| 0.0615 | 33.9 | 6780 | 0.7146 | 0.4125 | 0.4896 | 0.8775 | nan | 0.8301 | 0.9632 | 0.7929 | 0.8508 | 0.5108 | nan | 0.7121 | 0.8431 | 0.0684 | 0.9536 | 0.1265 | 0.0 | 0.7271 | 0.0748 | 0.6713 | 0.0 | 0.0 | 0.9263 | 0.2143 | 0.6247 | 0.3687 | 0.4975 | nan | 0.2420 | 0.6167 | 0.5793 | 0.0 | 0.9579 | 0.8454 | 0.9836 | 0.0041 | 0.1795 | 0.5033 | 0.0 | nan | 0.7504 | 0.8762 | 0.6669 | 0.7404 | 0.4170 | nan | 0.5865 | 0.6454 | 0.0385 | 0.8639 | 0.1077 | 0.0 | 0.4739 | 0.0748 | 0.5780 | 0.0 | 0.0 | 0.7720 | 0.1918 | 0.5342 | 0.3126 | 0.3662 | nan | 0.1871 | 0.5051 | 0.3988 | 0.0 | 0.8858 | 0.7658 | 0.9516 | 0.0035 | 0.1193 | 0.3869 | 0.0 |
| 0.0893 | 34.0 | 6800 | 0.7000 | 0.4168 | 0.4974 | 0.8783 | nan | 0.8374 | 0.9617 | 0.7923 | 0.8609 | 0.5329 | nan | 0.7031 | 0.7904 | 0.1178 | 0.9539 | 0.1435 | 0.0022 | 0.8039 | 0.0996 | 0.7168 | 0.0 | 0.0020 | 0.9357 | 0.2202 | 0.6263 | 0.3937 | 0.5509 | nan | 0.2026 | 0.6028 | 0.5671 | 0.0 | 0.9493 | 0.8424 | 0.9830 | 0.0082 | 0.2195 | 0.4952 | 0.0 | nan | 0.7542 | 0.8793 | 0.6748 | 0.7430 | 0.4192 | nan | 0.5891 | 0.6673 | 0.0697 | 0.8621 | 0.1203 | 0.0022 | 0.4841 | 0.0995 | 0.5736 | 0.0 | 0.0020 | 0.7679 | 0.1916 | 0.5293 | 0.3269 | 0.3909 | nan | 0.1635 | 0.5054 | 0.3861 | 0.0 | 0.8889 | 0.7676 | 0.9519 | 0.0068 | 0.1331 | 0.3868 | 0.0 |
| 0.0643 | 34.1 | 6820 | 0.7305 | 0.4052 | 0.4840 | 0.8741 | nan | 0.8217 | 0.9635 | 0.8015 | 0.8551 | 0.4787 | nan | 0.7021 | 0.8348 | 0.0715 | 0.9554 | 0.1245 | 0.0 | 0.6547 | 0.0495 | 0.7125 | 0.0 | 0.0079 | 0.9311 | 0.2483 | 0.5820 | 0.3625 | 0.4930 | nan | 0.1410 | 0.6551 | 0.5725 | 0.0 | 0.9555 | 0.8247 | 0.9831 | 0.0033 | 0.2233 | 0.4796 | 0.0 | nan | 0.7460 | 0.8727 | 0.6596 | 0.7339 | 0.3828 | nan | 0.5858 | 0.6495 | 0.0449 | 0.8623 | 0.1080 | 0.0 | 0.4414 | 0.0495 | 0.5549 | 0.0 | 0.0079 | 0.7635 | 0.2121 | 0.5171 | 0.3097 | 0.3602 | nan | 0.1169 | 0.5045 | 0.3949 | 0.0 | 0.8853 | 0.7568 | 0.9522 | 0.0027 | 0.1130 | 0.3797 | 0.0 |
| 0.0835 | 34.2 | 6840 | 0.7173 | 0.4094 | 0.4836 | 0.8773 | nan | 0.8376 | 0.9589 | 0.7892 | 0.8537 | 0.5312 | nan | 0.7159 | 0.8236 | 0.0417 | 0.9520 | 0.1248 | 0.0 | 0.6412 | 0.0239 | 0.6903 | 0.0 | 0.0034 | 0.9344 | 0.1832 | 0.6327 | 0.3625 | 0.5151 | nan | 0.2228 | 0.6314 | 0.5617 | 0.0 | 0.9543 | 0.8310 | 0.9842 | 0.0093 | 0.1743 | 0.4925 | 0.0 | nan | 0.7552 | 0.8780 | 0.6802 | 0.7442 | 0.4123 | nan | 0.5862 | 0.6551 | 0.0259 | 0.8620 | 0.1086 | 0.0 | 0.4597 | 0.0239 | 0.5638 | 0.0 | 0.0034 | 0.7681 | 0.1653 | 0.5335 | 0.3098 | 0.3808 | nan | 0.1794 | 0.5121 | 0.3907 | 0.0 | 0.8856 | 0.7535 | 0.9520 | 0.0069 | 0.1149 | 0.3910 | 0.0 |
| 0.0742 | 34.3 | 6860 | 0.7048 | 0.4105 | 0.4888 | 0.8782 | nan | 0.8362 | 0.9591 | 0.8005 | 0.8552 | 0.5264 | nan | 0.7207 | 0.8449 | 0.0987 | 0.9547 | 0.1075 | 0.0 | 0.6571 | 0.0278 | 0.6951 | 0.0 | 0.0081 | 0.9278 | 0.1670 | 0.6597 | 0.3732 | 0.5320 | nan | 0.2193 | 0.6198 | 0.5725 | 0.0 | 0.9523 | 0.8574 | 0.9834 | 0.0013 | 0.1958 | 0.4896 | 0.0 | nan | 0.7562 | 0.8790 | 0.6689 | 0.7413 | 0.4090 | nan | 0.5854 | 0.6317 | 0.0561 | 0.8609 | 0.0946 | 0.0 | 0.4557 | 0.0278 | 0.5638 | 0.0 | 0.0081 | 0.7737 | 0.1521 | 0.5446 | 0.3168 | 0.3893 | nan | 0.1820 | 0.5130 | 0.3914 | 0.0 | 0.8888 | 0.7689 | 0.9520 | 0.0010 | 0.1353 | 0.3876 | 0.0 |
| 0.1168 | 34.4 | 6880 | 0.6841 | 0.4150 | 0.4977 | 0.8794 | nan | 0.8529 | 0.9527 | 0.8020 | 0.8523 | 0.5574 | nan | 0.7265 | 0.8365 | 0.0637 | 0.9550 | 0.1016 | 0.0 | 0.6778 | 0.0272 | 0.7031 | 0.0 | 0.0212 | 0.9231 | 0.2925 | 0.6511 | 0.3985 | 0.5207 | nan | 0.2553 | 0.6245 | 0.5977 | 0.0 | 0.9545 | 0.8385 | 0.9839 | 0.0018 | 0.2349 | 0.5202 | 0.0 | nan | 0.7699 | 0.8812 | 0.6835 | 0.7501 | 0.3954 | nan | 0.5866 | 0.6470 | 0.0383 | 0.8615 | 0.0893 | 0.0 | 0.4509 | 0.0272 | 0.5621 | 0.0 | 0.0212 | 0.7771 | 0.2466 | 0.5537 | 0.3301 | 0.3799 | nan | 0.2045 | 0.5060 | 0.3862 | 0.0 | 0.8881 | 0.7639 | 0.9515 | 0.0015 | 0.1364 | 0.3917 | 0.0 |
| 0.0595 | 34.5 | 6900 | 0.7130 | 0.4101 | 0.4904 | 0.8775 | nan | 0.8442 | 0.9532 | 0.8013 | 0.8490 | 0.5569 | nan | 0.7149 | 0.8149 | 0.1256 | 0.9551 | 0.1126 | 0.0 | 0.6683 | 0.0307 | 0.7014 | 0.0 | 0.0049 | 0.9346 | 0.2040 | 0.6351 | 0.3813 | 0.4835 | nan | 0.2342 | 0.6111 | 0.5948 | 0.0 | 0.9543 | 0.8439 | 0.9837 | 0.0032 | 0.2197 | 0.4776 | 0.0 | nan | 0.7597 | 0.8772 | 0.6766 | 0.7504 | 0.3995 | nan | 0.5850 | 0.6392 | 0.0631 | 0.8600 | 0.0989 | 0.0 | 0.4336 | 0.0307 | 0.5614 | 0.0 | 0.0049 | 0.7709 | 0.1801 | 0.5459 | 0.3220 | 0.3608 | nan | 0.1852 | 0.5028 | 0.3971 | 0.0 | 0.8874 | 0.7644 | 0.9520 | 0.0026 | 0.1309 | 0.3810 | 0.0 |
| 0.0885 | 34.6 | 6920 | 0.7065 | 0.4110 | 0.4956 | 0.8773 | nan | 0.8436 | 0.9571 | 0.7963 | 0.8438 | 0.5365 | nan | 0.7003 | 0.8339 | 0.1525 | 0.9518 | 0.1117 | 0.0 | 0.6927 | 0.0622 | 0.7234 | 0.0 | 0.0065 | 0.9294 | 0.2014 | 0.6350 | 0.3897 | 0.5032 | nan | 0.2568 | 0.6164 | 0.5865 | 0.0 | 0.9536 | 0.8522 | 0.9817 | 0.0117 | 0.2515 | 0.4782 | 0.0 | nan | 0.7598 | 0.8766 | 0.6939 | 0.7435 | 0.4051 | nan | 0.5845 | 0.6021 | 0.0802 | 0.8648 | 0.0967 | 0.0 | 0.4335 | 0.0622 | 0.5517 | 0.0 | 0.0065 | 0.7722 | 0.1793 | 0.5491 | 0.3233 | 0.3673 | nan | 0.1997 | 0.5005 | 0.3978 | 0.0 | 0.8879 | 0.7661 | 0.9532 | 0.0080 | 0.1146 | 0.3710 | 0.0 |
| 0.0715 | 34.7 | 6940 | 0.7091 | 0.4125 | 0.4943 | 0.8776 | nan | 0.8471 | 0.9560 | 0.7982 | 0.8358 | 0.5498 | nan | 0.7191 | 0.8264 | 0.0810 | 0.9588 | 0.1080 | 0.0 | 0.6942 | 0.0434 | 0.6989 | 0.0 | 0.0072 | 0.9267 | 0.2175 | 0.6463 | 0.3871 | 0.4835 | nan | 0.3013 | 0.6419 | 0.5906 | 0.0 | 0.9537 | 0.8299 | 0.9838 | 0.0065 | 0.2428 | 0.4815 | 0.0 | nan | 0.7602 | 0.8772 | 0.7021 | 0.7450 | 0.4044 | nan | 0.5904 | 0.6210 | 0.0448 | 0.8598 | 0.0944 | 0.0 | 0.4384 | 0.0434 | 0.5587 | 0.0 | 0.0072 | 0.7725 | 0.1907 | 0.5470 | 0.3270 | 0.3721 | nan | 0.2330 | 0.5032 | 0.4042 | 0.0 | 0.8870 | 0.7600 | 0.9526 | 0.0048 | 0.1173 | 0.3813 | 0.0 |
| 0.0908 | 34.8 | 6960 | 0.7034 | 0.4133 | 0.4963 | 0.8781 | nan | 0.8463 | 0.9567 | 0.7999 | 0.8399 | 0.5551 | nan | 0.7234 | 0.8263 | 0.1250 | 0.9508 | 0.1135 | 0.0 | 0.6795 | 0.0507 | 0.7067 | 0.0 | 0.0101 | 0.9316 | 0.2536 | 0.6314 | 0.3883 | 0.4968 | nan | 0.2776 | 0.6215 | 0.5755 | 0.0 | 0.9553 | 0.8408 | 0.9832 | 0.0066 | 0.2792 | 0.4559 | 0.0 | nan | 0.7646 | 0.8796 | 0.6948 | 0.7483 | 0.4008 | nan | 0.5927 | 0.6159 | 0.0623 | 0.8653 | 0.0994 | 0.0 | 0.4262 | 0.0507 | 0.5722 | 0.0 | 0.0101 | 0.7701 | 0.2134 | 0.5379 | 0.3254 | 0.3697 | nan | 0.2032 | 0.5077 | 0.4076 | 0.0 | 0.8870 | 0.7637 | 0.9529 | 0.0047 | 0.1300 | 0.3709 | 0.0 |
| 0.0926 | 34.9 | 6980 | 0.7226 | 0.4129 | 0.5021 | 0.8743 | nan | 0.8084 | 0.9567 | 0.7951 | 0.8701 | 0.5444 | nan | 0.7083 | 0.8245 | 0.1514 | 0.9543 | 0.1348 | 0.0 | 0.6805 | 0.0463 | 0.7060 | 0.0 | 0.0069 | 0.9234 | 0.2647 | 0.6287 | 0.3968 | 0.5927 | nan | 0.2616 | 0.6449 | 0.5906 | 0.0 | 0.9519 | 0.8647 | 0.9833 | 0.0077 | 0.2975 | 0.4714 | 0.0 | nan | 0.7343 | 0.8782 | 0.6921 | 0.6995 | 0.4072 | nan | 0.5899 | 0.6290 | 0.0722 | 0.8616 | 0.1157 | 0.0 | 0.4273 | 0.0463 | 0.5762 | 0.0 | 0.0069 | 0.7722 | 0.2214 | 0.5357 | 0.3314 | 0.3707 | nan | 0.1966 | 0.5107 | 0.3979 | 0.0 | 0.8896 | 0.7743 | 0.9526 | 0.0057 | 0.1434 | 0.3734 | 0.0 |
| 0.072 | 35.0 | 7000 | 0.7112 | 0.4118 | 0.4961 | 0.8770 | nan | 0.8373 | 0.9527 | 0.7939 | 0.8673 | 0.5509 | nan | 0.7249 | 0.8349 | 0.1411 | 0.9566 | 0.1426 | 0.0 | 0.6715 | 0.0284 | 0.7095 | 0.0 | 0.0032 | 0.9373 | 0.2113 | 0.6021 | 0.3573 | 0.5783 | nan | 0.2289 | 0.6315 | 0.5659 | 0.0 | 0.9492 | 0.8623 | 0.9810 | 0.0086 | 0.2546 | 0.4921 | 0.0 | nan | 0.7609 | 0.8859 | 0.6978 | 0.7222 | 0.3807 | nan | 0.5923 | 0.6311 | 0.0732 | 0.8622 | 0.1218 | 0.0 | 0.4430 | 0.0284 | 0.5737 | 0.0 | 0.0032 | 0.7643 | 0.1859 | 0.5229 | 0.3097 | 0.3805 | nan | 0.1885 | 0.5066 | 0.4033 | 0.0 | 0.8896 | 0.7731 | 0.9540 | 0.0062 | 0.1348 | 0.3822 | 0.0 |
| 0.0878 | 35.1 | 7020 | 0.7032 | 0.4121 | 0.4950 | 0.8796 | nan | 0.8632 | 0.9551 | 0.8148 | 0.8522 | 0.5301 | nan | 0.7068 | 0.8316 | 0.0810 | 0.9546 | 0.1448 | 0.0 | 0.6698 | 0.0374 | 0.7077 | 0.0 | 0.0 | 0.9315 | 0.1499 | 0.6055 | 0.3846 | 0.6106 | nan | 0.2612 | 0.6163 | 0.5929 | 0.0 | 0.9509 | 0.8546 | 0.9852 | 0.0106 | 0.2482 | 0.4902 | 0.0 | nan | 0.7732 | 0.8875 | 0.6897 | 0.7523 | 0.3764 | nan | 0.5895 | 0.6392 | 0.0493 | 0.8625 | 0.1203 | 0.0 | 0.4445 | 0.0374 | 0.5728 | 0.0 | 0.0 | 0.7643 | 0.1360 | 0.5188 | 0.3272 | 0.3897 | nan | 0.2108 | 0.5050 | 0.3999 | 0.0 | 0.8897 | 0.7721 | 0.9513 | 0.0080 | 0.1363 | 0.3830 | 0.0 |
| 0.0617 | 35.2 | 7040 | 0.7085 | 0.4076 | 0.4862 | 0.8779 | nan | 0.8469 | 0.9541 | 0.8175 | 0.8613 | 0.5417 | nan | 0.7101 | 0.8428 | 0.0342 | 0.9524 | 0.1063 | 0.0 | 0.6526 | 0.0122 | 0.7009 | 0.0 | 0.0007 | 0.9350 | 0.1259 | 0.5991 | 0.3807 | 0.5674 | nan | 0.2156 | 0.6201 | 0.5787 | 0.0 | 0.9548 | 0.8446 | 0.9818 | 0.0100 | 0.2173 | 0.4933 | 0.0 | nan | 0.7655 | 0.8837 | 0.6960 | 0.7419 | 0.3874 | nan | 0.5899 | 0.6424 | 0.0234 | 0.8619 | 0.0929 | 0.0 | 0.4448 | 0.0122 | 0.5773 | 0.0 | 0.0007 | 0.7608 | 0.1156 | 0.5165 | 0.3232 | 0.3777 | nan | 0.1875 | 0.5068 | 0.4089 | 0.0 | 0.8878 | 0.7641 | 0.9526 | 0.0079 | 0.1262 | 0.3872 | 0.0 |
| 0.0636 | 35.3 | 7060 | 0.7015 | 0.4130 | 0.4922 | 0.8782 | nan | 0.8488 | 0.9566 | 0.8009 | 0.8527 | 0.5343 | nan | 0.7245 | 0.8251 | 0.0319 | 0.9549 | 0.1212 | 0.0 | 0.6995 | 0.0283 | 0.6915 | 0.0 | 0.0028 | 0.9273 | 0.2412 | 0.6259 | 0.3709 | 0.5330 | nan | 0.2623 | 0.6272 | 0.5727 | 0.0 | 0.9555 | 0.8255 | 0.9827 | 0.0109 | 0.2392 | 0.5022 | 0.0 | nan | 0.7659 | 0.8814 | 0.6998 | 0.7451 | 0.3928 | nan | 0.5907 | 0.6495 | 0.0203 | 0.8597 | 0.1045 | 0.0 | 0.4582 | 0.0283 | 0.5761 | 0.0 | 0.0028 | 0.7697 | 0.2071 | 0.5300 | 0.3199 | 0.3712 | nan | 0.2169 | 0.5065 | 0.4021 | 0.0 | 0.8852 | 0.7539 | 0.9524 | 0.0086 | 0.1298 | 0.3871 | 0.0 |
| 0.0551 | 35.4 | 7080 | 0.6996 | 0.4136 | 0.4917 | 0.8791 | nan | 0.8576 | 0.9591 | 0.8023 | 0.8473 | 0.5101 | nan | 0.7156 | 0.8227 | 0.0597 | 0.9540 | 0.1384 | 0.0 | 0.6836 | 0.0255 | 0.7013 | 0.0 | 0.0024 | 0.9314 | 0.2454 | 0.6274 | 0.3617 | 0.5197 | nan | 0.2522 | 0.6283 | 0.5832 | 0.0 | 0.9528 | 0.8283 | 0.9866 | 0.0045 | 0.2473 | 0.4846 | 0.0 | nan | 0.7706 | 0.8814 | 0.7086 | 0.7500 | 0.3846 | nan | 0.5886 | 0.6455 | 0.0359 | 0.8613 | 0.1172 | 0.0 | 0.4454 | 0.0255 | 0.5828 | 0.0 | 0.0024 | 0.7678 | 0.2096 | 0.5277 | 0.3131 | 0.3811 | nan | 0.2117 | 0.5064 | 0.4019 | 0.0 | 0.8859 | 0.7581 | 0.9508 | 0.0038 | 0.1357 | 0.3816 | 0.0 |
| 0.0555 | 35.5 | 7100 | 0.7008 | 0.4108 | 0.4897 | 0.8788 | nan | 0.8481 | 0.9579 | 0.8094 | 0.8626 | 0.5052 | nan | 0.7268 | 0.8382 | 0.0548 | 0.9542 | 0.1216 | 0.0 | 0.6768 | 0.0288 | 0.7086 | 0.0 | 0.0040 | 0.9293 | 0.1859 | 0.6175 | 0.3534 | 0.5334 | nan | 0.2739 | 0.6032 | 0.5648 | 0.0 | 0.9600 | 0.8306 | 0.9847 | 0.0082 | 0.2428 | 0.4855 | 0.0 | nan | 0.7684 | 0.8847 | 0.6922 | 0.7430 | 0.3884 | nan | 0.5942 | 0.6428 | 0.0347 | 0.8619 | 0.1038 | 0.0 | 0.4355 | 0.0288 | 0.5760 | 0.0 | 0.0040 | 0.7669 | 0.1662 | 0.5237 | 0.3013 | 0.3811 | nan | 0.2261 | 0.5022 | 0.4037 | 0.0 | 0.8831 | 0.7549 | 0.9516 | 0.0065 | 0.1401 | 0.3809 | 0.0 |
| 0.0685 | 35.6 | 7120 | 0.6991 | 0.4137 | 0.4954 | 0.8780 | nan | 0.8426 | 0.9550 | 0.8109 | 0.8636 | 0.5350 | nan | 0.7355 | 0.8334 | 0.0566 | 0.9551 | 0.1427 | 0.0012 | 0.7120 | 0.0213 | 0.6881 | 0.0 | 0.0188 | 0.9347 | 0.2164 | 0.6212 | 0.3516 | 0.5650 | nan | 0.2964 | 0.6247 | 0.5762 | 0.0 | 0.9528 | 0.8363 | 0.9829 | 0.0074 | 0.2304 | 0.4844 | 0.0 | nan | 0.7640 | 0.8856 | 0.6964 | 0.7336 | 0.3824 | nan | 0.5942 | 0.6517 | 0.0340 | 0.8610 | 0.1194 | 0.0012 | 0.4459 | 0.0213 | 0.5836 | 0.0 | 0.0187 | 0.7668 | 0.1879 | 0.5232 | 0.3031 | 0.3881 | nan | 0.2392 | 0.5056 | 0.4001 | 0.0 | 0.8858 | 0.7562 | 0.9529 | 0.0062 | 0.1420 | 0.3893 | 0.0 |
| 0.0688 | 35.7 | 7140 | 0.7115 | 0.4137 | 0.4982 | 0.8749 | nan | 0.8133 | 0.9552 | 0.7908 | 0.8666 | 0.5457 | nan | 0.7425 | 0.8438 | 0.0803 | 0.9552 | 0.1694 | 0.0015 | 0.7415 | 0.0262 | 0.6994 | 0.0 | 0.0347 | 0.9313 | 0.1861 | 0.6127 | 0.3975 | 0.5372 | nan | 0.2925 | 0.6242 | 0.5672 | 0.0 | 0.9508 | 0.8592 | 0.9825 | 0.0076 | 0.2294 | 0.4985 | 0.0 | nan | 0.7406 | 0.8803 | 0.7048 | 0.7144 | 0.3771 | nan | 0.5912 | 0.6374 | 0.0471 | 0.8628 | 0.1437 | 0.0015 | 0.4355 | 0.0262 | 0.5822 | 0.0 | 0.0345 | 0.7694 | 0.1668 | 0.5288 | 0.3207 | 0.3802 | nan | 0.2327 | 0.5057 | 0.3945 | 0.0 | 0.8890 | 0.7673 | 0.9535 | 0.0067 | 0.1502 | 0.3926 | 0.0 |
| 0.0911 | 35.8 | 7160 | 0.7127 | 0.4171 | 0.5055 | 0.8749 | nan | 0.8198 | 0.9536 | 0.7962 | 0.8677 | 0.5529 | nan | 0.7179 | 0.8123 | 0.2586 | 0.9532 | 0.1314 | 0.0 | 0.7348 | 0.0632 | 0.7108 | 0.0 | 0.0530 | 0.9332 | 0.2061 | 0.6238 | 0.4007 | 0.5218 | nan | 0.3449 | 0.6127 | 0.5598 | 0.0 | 0.9534 | 0.8369 | 0.9825 | 0.0082 | 0.2718 | 0.4932 | 0.0 | nan | 0.7420 | 0.8851 | 0.6962 | 0.7035 | 0.3731 | nan | 0.5890 | 0.6483 | 0.1169 | 0.8648 | 0.1145 | 0.0 | 0.4173 | 0.0632 | 0.5896 | 0.0 | 0.0524 | 0.7712 | 0.1831 | 0.5377 | 0.3297 | 0.3778 | nan | 0.2610 | 0.5062 | 0.3907 | 0.0 | 0.8873 | 0.7594 | 0.9537 | 0.0071 | 0.1381 | 0.3888 | 0.0 |
| 0.0745 | 35.9 | 7180 | 0.6970 | 0.4162 | 0.5017 | 0.8765 | nan | 0.8326 | 0.9566 | 0.7924 | 0.8683 | 0.5136 | nan | 0.7218 | 0.7968 | 0.3063 | 0.9562 | 0.1121 | 0.0 | 0.6826 | 0.0618 | 0.7158 | 0.0 | 0.0413 | 0.9271 | 0.2218 | 0.6374 | 0.3751 | 0.4786 | nan | 0.3344 | 0.6221 | 0.5697 | 0.0 | 0.9569 | 0.8356 | 0.9849 | 0.0138 | 0.2779 | 0.4618 | 0.0 | nan | 0.7538 | 0.8827 | 0.7012 | 0.7188 | 0.3717 | nan | 0.5949 | 0.6410 | 0.1112 | 0.8610 | 0.0984 | 0.0 | 0.4344 | 0.0618 | 0.5806 | 0.0 | 0.0409 | 0.7695 | 0.1922 | 0.5333 | 0.3247 | 0.3578 | nan | 0.2487 | 0.5049 | 0.3946 | 0.0 | 0.8862 | 0.7612 | 0.9519 | 0.0110 | 0.1510 | 0.3789 | 0.0 |
| 0.098 | 36.0 | 7200 | 0.7067 | 0.4167 | 0.5020 | 0.8761 | nan | 0.8272 | 0.9580 | 0.7997 | 0.8615 | 0.5146 | nan | 0.7204 | 0.7786 | 0.3260 | 0.9567 | 0.1210 | 0.0 | 0.7143 | 0.0673 | 0.7219 | 0.0 | 0.0364 | 0.9272 | 0.1989 | 0.6440 | 0.3668 | 0.4954 | nan | 0.3236 | 0.6110 | 0.5818 | 0.0 | 0.9562 | 0.8395 | 0.9854 | 0.0191 | 0.2414 | 0.4706 | 0.0 | nan | 0.7499 | 0.8770 | 0.6894 | 0.7338 | 0.3880 | nan | 0.5896 | 0.6261 | 0.1040 | 0.8598 | 0.1053 | 0.0 | 0.4644 | 0.0673 | 0.5807 | 0.0 | 0.0362 | 0.7711 | 0.1764 | 0.5369 | 0.3151 | 0.3816 | nan | 0.2475 | 0.5046 | 0.3927 | 0.0 | 0.8861 | 0.7613 | 0.9518 | 0.0145 | 0.1437 | 0.3786 | 0.0 |
| 0.0913 | 36.1 | 7220 | 0.6997 | 0.4150 | 0.4988 | 0.8783 | nan | 0.8518 | 0.9561 | 0.8063 | 0.8402 | 0.5311 | nan | 0.7216 | 0.7947 | 0.2903 | 0.9534 | 0.1148 | 0.0 | 0.6703 | 0.0730 | 0.7298 | 0.0 | 0.0158 | 0.9356 | 0.1644 | 0.6500 | 0.3514 | 0.4716 | nan | 0.3353 | 0.6106 | 0.5744 | 0.0 | 0.9499 | 0.8522 | 0.9845 | 0.0146 | 0.2490 | 0.4688 | 0.0 | nan | 0.7663 | 0.8818 | 0.6878 | 0.7574 | 0.3797 | nan | 0.5869 | 0.6227 | 0.1008 | 0.8619 | 0.1000 | 0.0 | 0.4523 | 0.0730 | 0.5852 | 0.0 | 0.0157 | 0.7675 | 0.1473 | 0.5364 | 0.3086 | 0.3620 | nan | 0.2524 | 0.5042 | 0.3920 | 0.0 | 0.8884 | 0.7666 | 0.9527 | 0.0105 | 0.1416 | 0.3776 | 0.0 |
| 0.0637 | 36.2 | 7240 | 0.6977 | 0.4142 | 0.4944 | 0.8788 | nan | 0.8588 | 0.9543 | 0.8064 | 0.8406 | 0.5492 | nan | 0.7120 | 0.8186 | 0.1689 | 0.9555 | 0.1137 | 0.0 | 0.6676 | 0.0581 | 0.7305 | 0.0 | 0.0312 | 0.9309 | 0.1872 | 0.6187 | 0.3752 | 0.4368 | nan | 0.2872 | 0.6168 | 0.5789 | 0.0 | 0.9575 | 0.8339 | 0.9822 | 0.0152 | 0.2486 | 0.4868 | 0.0 | nan | 0.7745 | 0.8842 | 0.6891 | 0.7590 | 0.3856 | nan | 0.5883 | 0.6318 | 0.0757 | 0.8617 | 0.1009 | 0.0 | 0.4530 | 0.0581 | 0.5816 | 0.0 | 0.0312 | 0.7674 | 0.1672 | 0.5317 | 0.3163 | 0.3459 | nan | 0.2318 | 0.5034 | 0.3969 | 0.0 | 0.8852 | 0.7579 | 0.9540 | 0.0101 | 0.1300 | 0.3808 | 0.0 |
| 0.0577 | 36.3 | 7260 | 0.6996 | 0.4130 | 0.4876 | 0.8784 | nan | 0.8582 | 0.9553 | 0.7909 | 0.8403 | 0.5440 | nan | 0.7311 | 0.8018 | 0.1001 | 0.9540 | 0.1164 | 0.0 | 0.6541 | 0.0438 | 0.6995 | 0.0 | 0.0241 | 0.9389 | 0.1828 | 0.6238 | 0.3535 | 0.4863 | nan | 0.2532 | 0.6019 | 0.5866 | 0.0 | 0.9520 | 0.8268 | 0.9827 | 0.0112 | 0.1905 | 0.5003 | 0.0 | nan | 0.7702 | 0.8815 | 0.7048 | 0.7527 | 0.3851 | nan | 0.5930 | 0.6534 | 0.0464 | 0.8612 | 0.1019 | 0.0 | 0.4455 | 0.0438 | 0.5897 | 0.0 | 0.0240 | 0.7643 | 0.1630 | 0.5292 | 0.3078 | 0.3715 | nan | 0.2076 | 0.5011 | 0.3996 | 0.0 | 0.8853 | 0.7536 | 0.9532 | 0.0087 | 0.1253 | 0.3925 | 0.0 |
| 0.0702 | 36.4 | 7280 | 0.6983 | 0.4149 | 0.4925 | 0.8794 | nan | 0.8620 | 0.9546 | 0.8033 | 0.8408 | 0.5509 | nan | 0.7143 | 0.8175 | 0.0806 | 0.9553 | 0.1160 | 0.0 | 0.6629 | 0.0396 | 0.6983 | 0.0 | 0.0254 | 0.9273 | 0.2064 | 0.6429 | 0.3873 | 0.4986 | nan | 0.2752 | 0.6216 | 0.5645 | 0.0 | 0.9558 | 0.8278 | 0.9822 | 0.0105 | 0.2456 | 0.4927 | 0.0 | nan | 0.7758 | 0.8830 | 0.6970 | 0.7579 | 0.3846 | nan | 0.5898 | 0.6602 | 0.0442 | 0.8605 | 0.1009 | 0.0 | 0.4327 | 0.0396 | 0.5747 | 0.0 | 0.0254 | 0.7718 | 0.1811 | 0.5403 | 0.3225 | 0.3797 | nan | 0.2263 | 0.5026 | 0.3968 | 0.0 | 0.8848 | 0.7530 | 0.9532 | 0.0078 | 0.1448 | 0.3873 | 0.0 |
| 0.0964 | 36.5 | 7300 | 0.7178 | 0.4104 | 0.4857 | 0.8780 | nan | 0.8624 | 0.9553 | 0.8127 | 0.8304 | 0.5364 | nan | 0.7039 | 0.8188 | 0.0519 | 0.9557 | 0.1239 | 0.0 | 0.6651 | 0.0256 | 0.6564 | 0.0 | 0.0216 | 0.9335 | 0.2001 | 0.6143 | 0.3561 | 0.4989 | nan | 0.2867 | 0.6079 | 0.5412 | 0.0 | 0.9570 | 0.8218 | 0.9826 | 0.0098 | 0.2261 | 0.4856 | 0.0 | nan | 0.7748 | 0.8830 | 0.6690 | 0.7528 | 0.3793 | nan | 0.5862 | 0.6547 | 0.0308 | 0.8591 | 0.1060 | 0.0 | 0.4377 | 0.0256 | 0.5692 | 0.0 | 0.0215 | 0.7674 | 0.1777 | 0.5325 | 0.3070 | 0.3708 | nan | 0.2353 | 0.5018 | 0.3911 | 0.0 | 0.8832 | 0.7496 | 0.9528 | 0.0068 | 0.1247 | 0.3816 | 0.0 |
| 0.0779 | 36.6 | 7320 | 0.7006 | 0.4113 | 0.4883 | 0.8802 | nan | 0.8665 | 0.9604 | 0.7948 | 0.8530 | 0.4945 | nan | 0.6943 | 0.8122 | 0.0683 | 0.9578 | 0.1127 | 0.0 | 0.6658 | 0.0479 | 0.6622 | 0.0 | 0.0275 | 0.9319 | 0.1801 | 0.6153 | 0.3630 | 0.5298 | nan | 0.2535 | 0.6365 | 0.5624 | 0.0 | 0.9528 | 0.8414 | 0.9845 | 0.0084 | 0.2596 | 0.4869 | 0.0 | nan | 0.7764 | 0.8868 | 0.6850 | 0.7540 | 0.3797 | nan | 0.5877 | 0.6487 | 0.0401 | 0.8588 | 0.0975 | 0.0 | 0.4239 | 0.0478 | 0.5621 | 0.0 | 0.0273 | 0.7669 | 0.1615 | 0.5356 | 0.3141 | 0.3862 | nan | 0.2107 | 0.5080 | 0.3909 | 0.0 | 0.8876 | 0.7632 | 0.9520 | 0.0057 | 0.1236 | 0.3791 | 0.0 |
| 0.0819 | 36.7 | 7340 | 0.6940 | 0.4133 | 0.4937 | 0.8800 | nan | 0.8635 | 0.9556 | 0.8034 | 0.8453 | 0.5412 | nan | 0.7088 | 0.8302 | 0.0977 | 0.9553 | 0.1181 | 0.0 | 0.6735 | 0.0520 | 0.6906 | 0.0024 | 0.0285 | 0.9300 | 0.1750 | 0.6211 | 0.3797 | 0.4705 | nan | 0.2997 | 0.6162 | 0.5961 | 0.0 | 0.9550 | 0.8468 | 0.9837 | 0.0117 | 0.2575 | 0.4882 | 0.0 | nan | 0.7777 | 0.8874 | 0.6822 | 0.7598 | 0.3861 | nan | 0.5925 | 0.6507 | 0.0582 | 0.8616 | 0.1026 | 0.0 | 0.4242 | 0.0520 | 0.5613 | 0.0024 | 0.0280 | 0.7668 | 0.1571 | 0.5304 | 0.3237 | 0.3636 | nan | 0.2547 | 0.5030 | 0.3807 | 0.0 | 0.8867 | 0.7599 | 0.9527 | 0.0080 | 0.1316 | 0.3807 | 0.0 |
| 0.0808 | 36.8 | 7360 | 0.7044 | 0.4140 | 0.4945 | 0.8787 | nan | 0.8521 | 0.9593 | 0.8008 | 0.8295 | 0.5274 | nan | 0.7146 | 0.8379 | 0.0985 | 0.9559 | 0.1124 | 0.0 | 0.7056 | 0.0324 | 0.7043 | 0.0 | 0.0238 | 0.9325 | 0.2648 | 0.6339 | 0.3744 | 0.4287 | nan | 0.3486 | 0.6128 | 0.5930 | 0.0 | 0.9544 | 0.8436 | 0.9831 | 0.0106 | 0.2225 | 0.4661 | 0.0 | nan | 0.7659 | 0.8791 | 0.6990 | 0.7507 | 0.3942 | nan | 0.5905 | 0.6398 | 0.0591 | 0.8624 | 0.0988 | 0.0 | 0.4251 | 0.0324 | 0.5589 | 0.0 | 0.0237 | 0.7672 | 0.2195 | 0.5346 | 0.3235 | 0.3453 | nan | 0.2728 | 0.5047 | 0.3876 | 0.0 | 0.8877 | 0.7607 | 0.9530 | 0.0081 | 0.1244 | 0.3778 | 0.0 |
| 0.0728 | 36.9 | 7380 | 0.7084 | 0.4142 | 0.4939 | 0.8775 | nan | 0.8526 | 0.9574 | 0.7959 | 0.8252 | 0.5435 | nan | 0.7087 | 0.8486 | 0.0754 | 0.9540 | 0.1073 | 0.0 | 0.7192 | 0.0265 | 0.6834 | 0.0 | 0.0597 | 0.9352 | 0.2742 | 0.6030 | 0.3712 | 0.4912 | nan | 0.3306 | 0.6073 | 0.5723 | 0.0 | 0.9549 | 0.8389 | 0.9843 | 0.0061 | 0.1954 | 0.4826 | 0.0 | nan | 0.7638 | 0.8777 | 0.7027 | 0.7416 | 0.3910 | nan | 0.5880 | 0.6377 | 0.0475 | 0.8630 | 0.0950 | 0.0 | 0.4225 | 0.0265 | 0.5725 | 0.0 | 0.0590 | 0.7648 | 0.2240 | 0.5250 | 0.3208 | 0.3676 | nan | 0.2636 | 0.5029 | 0.3931 | 0.0 | 0.8882 | 0.7638 | 0.9525 | 0.0050 | 0.1131 | 0.3814 | 0.0 |
| 0.1764 | 37.0 | 7400 | 0.7037 | 0.4157 | 0.4940 | 0.8778 | nan | 0.8468 | 0.9595 | 0.7919 | 0.8447 | 0.5265 | nan | 0.6947 | 0.8410 | 0.0717 | 0.9525 | 0.1123 | 0.0 | 0.6746 | 0.0423 | 0.6907 | 0.0 | 0.0719 | 0.9325 | 0.2086 | 0.6238 | 0.3980 | 0.5253 | nan | 0.3076 | 0.6047 | 0.5729 | 0.0 | 0.9522 | 0.8394 | 0.9850 | 0.0059 | 0.2324 | 0.4974 | 0.0 | nan | 0.7613 | 0.8786 | 0.7001 | 0.7416 | 0.3913 | nan | 0.5885 | 0.6472 | 0.0451 | 0.8637 | 0.0987 | 0.0 | 0.4451 | 0.0423 | 0.5728 | 0.0 | 0.0705 | 0.7681 | 0.1829 | 0.5288 | 0.3336 | 0.3785 | nan | 0.2514 | 0.5018 | 0.3933 | 0.0 | 0.8883 | 0.7619 | 0.9517 | 0.0048 | 0.1275 | 0.3840 | 0.0 |
| 0.0895 | 37.1 | 7420 | 0.6999 | 0.4126 | 0.4867 | 0.8771 | nan | 0.8503 | 0.9592 | 0.7848 | 0.8346 | 0.5228 | nan | 0.7162 | 0.8293 | 0.0593 | 0.9567 | 0.1175 | 0.0 | 0.6605 | 0.0387 | 0.6881 | 0.0 | 0.0442 | 0.9354 | 0.1439 | 0.6318 | 0.3631 | 0.4673 | nan | 0.3297 | 0.6239 | 0.5750 | 0.0 | 0.9561 | 0.8125 | 0.9823 | 0.0063 | 0.2213 | 0.4635 | 0.0 | nan | 0.7630 | 0.8783 | 0.7080 | 0.7374 | 0.3937 | nan | 0.5928 | 0.6596 | 0.0380 | 0.8603 | 0.1037 | 0.0 | 0.4432 | 0.0387 | 0.5748 | 0.0 | 0.0438 | 0.7654 | 0.1303 | 0.5292 | 0.3127 | 0.3603 | nan | 0.2676 | 0.5057 | 0.3996 | 0.0 | 0.8828 | 0.7418 | 0.9535 | 0.0050 | 0.1345 | 0.3791 | 0.0 |
| 0.0634 | 37.2 | 7440 | 0.7077 | 0.4125 | 0.4917 | 0.8772 | nan | 0.8502 | 0.9576 | 0.7968 | 0.8249 | 0.5279 | nan | 0.7244 | 0.8363 | 0.0593 | 0.9579 | 0.1177 | 0.0 | 0.7277 | 0.0377 | 0.6814 | 0.0 | 0.0241 | 0.9348 | 0.1902 | 0.6227 | 0.3439 | 0.4986 | nan | 0.3346 | 0.6298 | 0.5920 | 0.0 | 0.9556 | 0.8270 | 0.9823 | 0.0088 | 0.2083 | 0.4821 | 0.0 | nan | 0.7617 | 0.8781 | 0.7008 | 0.7425 | 0.3955 | nan | 0.5903 | 0.6593 | 0.0376 | 0.8607 | 0.1038 | 0.0 | 0.4193 | 0.0377 | 0.5790 | 0.0 | 0.0239 | 0.7656 | 0.1680 | 0.5331 | 0.3008 | 0.3671 | nan | 0.2706 | 0.5073 | 0.3960 | 0.0 | 0.8836 | 0.7501 | 0.9531 | 0.0067 | 0.1227 | 0.3856 | 0.0 |
| 0.0869 | 37.3 | 7460 | 0.7203 | 0.4136 | 0.4989 | 0.8760 | nan | 0.8378 | 0.9566 | 0.8226 | 0.8214 | 0.5284 | nan | 0.7195 | 0.8496 | 0.0744 | 0.9566 | 0.1154 | 0.0 | 0.7636 | 0.0293 | 0.6973 | 0.0 | 0.0175 | 0.9256 | 0.2485 | 0.6449 | 0.3697 | 0.5119 | nan | 0.3441 | 0.6346 | 0.5931 | 0.0 | 0.9548 | 0.8344 | 0.9848 | 0.0120 | 0.2231 | 0.4933 | 0.0 | nan | 0.7570 | 0.8759 | 0.6561 | 0.7358 | 0.3974 | nan | 0.5874 | 0.6473 | 0.0484 | 0.8628 | 0.1012 | 0.0 | 0.4442 | 0.0293 | 0.5714 | 0.0 | 0.0173 | 0.7723 | 0.2101 | 0.5453 | 0.3139 | 0.3701 | nan | 0.2793 | 0.5088 | 0.3939 | 0.0 | 0.8855 | 0.7551 | 0.9520 | 0.0085 | 0.1230 | 0.3860 | 0.0 |
| 0.0597 | 37.4 | 7480 | 0.7213 | 0.4157 | 0.4955 | 0.8759 | nan | 0.8351 | 0.9555 | 0.8265 | 0.8139 | 0.5288 | nan | 0.7252 | 0.8324 | 0.0834 | 0.9548 | 0.1119 | 0.0 | 0.6856 | 0.0357 | 0.6952 | 0.0 | 0.0188 | 0.9232 | 0.2232 | 0.6509 | 0.4116 | 0.4663 | nan | 0.3377 | 0.6356 | 0.5721 | 0.0 | 0.9563 | 0.8457 | 0.9834 | 0.0146 | 0.2253 | 0.5080 | 0.0 | nan | 0.7572 | 0.8750 | 0.6576 | 0.7290 | 0.3910 | nan | 0.5886 | 0.6493 | 0.0514 | 0.8631 | 0.0988 | 0.0 | 0.4646 | 0.0357 | 0.5736 | 0.0 | 0.0186 | 0.7752 | 0.1952 | 0.5484 | 0.3319 | 0.3610 | nan | 0.2802 | 0.5078 | 0.4058 | 0.0 | 0.8867 | 0.7619 | 0.9530 | 0.0098 | 0.1402 | 0.3920 | 0.0 |
| 0.1193 | 37.5 | 7500 | 0.7207 | 0.4120 | 0.4897 | 0.8770 | nan | 0.8432 | 0.9606 | 0.8109 | 0.8180 | 0.5229 | nan | 0.7120 | 0.8275 | 0.1034 | 0.9543 | 0.1219 | 0.0 | 0.6833 | 0.0345 | 0.6969 | 0.0 | 0.0102 | 0.9327 | 0.1481 | 0.6324 | 0.3758 | 0.4912 | nan | 0.2921 | 0.6100 | 0.5838 | 0.0 | 0.9552 | 0.8455 | 0.9850 | 0.0088 | 0.2433 | 0.4661 | 0.0 | nan | 0.7599 | 0.8764 | 0.6587 | 0.7355 | 0.4006 | nan | 0.5885 | 0.6496 | 0.0616 | 0.8628 | 0.1051 | 0.0 | 0.4668 | 0.0345 | 0.5765 | 0.0 | 0.0101 | 0.7681 | 0.1350 | 0.5379 | 0.3189 | 0.3620 | nan | 0.2479 | 0.5048 | 0.3974 | 0.0 | 0.8879 | 0.7671 | 0.9516 | 0.0066 | 0.1352 | 0.3757 | 0.0 |
| 0.0614 | 37.6 | 7520 | 0.6856 | 0.4164 | 0.4986 | 0.8806 | nan | 0.8557 | 0.9552 | 0.8226 | 0.8593 | 0.5294 | nan | 0.7159 | 0.8328 | 0.1112 | 0.9576 | 0.1192 | 0.0 | 0.6645 | 0.0129 | 0.6900 | 0.0 | 0.0179 | 0.9247 | 0.1651 | 0.6610 | 0.4152 | 0.5309 | nan | 0.3175 | 0.6542 | 0.5826 | 0.0 | 0.9500 | 0.8539 | 0.9860 | 0.0117 | 0.2712 | 0.4871 | 0.0 | nan | 0.7709 | 0.8876 | 0.6646 | 0.7462 | 0.3925 | nan | 0.5944 | 0.6430 | 0.0640 | 0.8613 | 0.1034 | 0.0 | 0.4655 | 0.0129 | 0.5776 | 0.0 | 0.0176 | 0.7756 | 0.1487 | 0.5568 | 0.3333 | 0.3772 | nan | 0.2724 | 0.5143 | 0.3912 | 0.0 | 0.8902 | 0.7694 | 0.9512 | 0.0086 | 0.1547 | 0.3807 | 0.0 |
| 0.0606 | 37.7 | 7540 | 0.6941 | 0.4182 | 0.5017 | 0.8805 | nan | 0.8650 | 0.9560 | 0.7933 | 0.8414 | 0.5326 | nan | 0.7177 | 0.8293 | 0.2166 | 0.9546 | 0.1315 | 0.0 | 0.6449 | 0.0166 | 0.7135 | 0.0 | 0.0131 | 0.9264 | 0.1896 | 0.6603 | 0.3691 | 0.5088 | nan | 0.3542 | 0.6202 | 0.5767 | 0.0 | 0.9502 | 0.8751 | 0.9825 | 0.0109 | 0.3413 | 0.4621 | 0.0 | nan | 0.7733 | 0.8840 | 0.6919 | 0.7561 | 0.3889 | nan | 0.5943 | 0.6378 | 0.1103 | 0.8633 | 0.1141 | 0.0 | 0.4581 | 0.0166 | 0.5770 | 0.0 | 0.0129 | 0.7770 | 0.1652 | 0.5553 | 0.3172 | 0.3632 | nan | 0.2839 | 0.5131 | 0.3938 | 0.0 | 0.8873 | 0.7691 | 0.9542 | 0.0079 | 0.1494 | 0.3677 | 0.0 |
| 0.0705 | 37.8 | 7560 | 0.7229 | 0.4164 | 0.4947 | 0.8785 | nan | 0.8464 | 0.9580 | 0.8100 | 0.8481 | 0.5249 | nan | 0.7054 | 0.8062 | 0.1369 | 0.9560 | 0.1133 | 0.0 | 0.6437 | 0.0216 | 0.7081 | 0.0 | 0.0221 | 0.9333 | 0.1667 | 0.6620 | 0.3972 | 0.4877 | nan | 0.3476 | 0.6257 | 0.5574 | 0.0 | 0.9519 | 0.8326 | 0.9843 | 0.0128 | 0.2983 | 0.4736 | 0.0 | nan | 0.7628 | 0.8790 | 0.6833 | 0.7575 | 0.3933 | nan | 0.5903 | 0.6564 | 0.0770 | 0.8623 | 0.0997 | 0.0 | 0.4658 | 0.0216 | 0.5761 | 0.0 | 0.0218 | 0.7726 | 0.1472 | 0.5519 | 0.3323 | 0.3607 | nan | 0.2752 | 0.5137 | 0.3994 | 0.0 | 0.8860 | 0.7577 | 0.9535 | 0.0095 | 0.1437 | 0.3755 | 0.0 |
| 0.0537 | 37.9 | 7580 | 0.7356 | 0.4172 | 0.5007 | 0.8764 | nan | 0.8459 | 0.9600 | 0.8097 | 0.8236 | 0.5127 | nan | 0.7044 | 0.8155 | 0.2175 | 0.9523 | 0.1212 | 0.0 | 0.6535 | 0.0820 | 0.7624 | 0.0 | 0.0245 | 0.9178 | 0.2273 | 0.6465 | 0.3677 | 0.4937 | nan | 0.3210 | 0.6487 | 0.5791 | 0.0 | 0.9585 | 0.8132 | 0.9828 | 0.0122 | 0.2730 | 0.4941 | 0.0 | nan | 0.7605 | 0.8744 | 0.6860 | 0.7425 | 0.3914 | nan | 0.5853 | 0.6235 | 0.1081 | 0.8668 | 0.1056 | 0.0 | 0.4666 | 0.0820 | 0.5846 | 0.0 | 0.0237 | 0.7741 | 0.1965 | 0.5475 | 0.3153 | 0.3550 | nan | 0.2637 | 0.5131 | 0.3937 | 0.0 | 0.8822 | 0.7447 | 0.9532 | 0.0093 | 0.1260 | 0.3765 | 0.0 |
| 0.0794 | 38.0 | 7600 | 0.7181 | 0.4144 | 0.4909 | 0.8764 | nan | 0.8466 | 0.9593 | 0.7987 | 0.8244 | 0.5173 | nan | 0.6945 | 0.8151 | 0.1500 | 0.9550 | 0.1158 | 0.0 | 0.6362 | 0.0640 | 0.7349 | 0.0 | 0.0074 | 0.9308 | 0.2132 | 0.6209 | 0.3703 | 0.4600 | nan | 0.3048 | 0.6224 | 0.5582 | 0.0 | 0.9569 | 0.8241 | 0.9826 | 0.0143 | 0.2317 | 0.5001 | 0.0 | nan | 0.7596 | 0.8736 | 0.6989 | 0.7417 | 0.3967 | nan | 0.5822 | 0.6178 | 0.0823 | 0.8657 | 0.1009 | 0.0 | 0.4681 | 0.0640 | 0.5913 | 0.0 | 0.0073 | 0.7699 | 0.1893 | 0.5379 | 0.3136 | 0.3458 | nan | 0.2566 | 0.5099 | 0.3935 | 0.0 | 0.8832 | 0.7484 | 0.9532 | 0.0109 | 0.1155 | 0.3826 | 0.0 |
| 0.0976 | 38.1 | 7620 | 0.7275 | 0.4167 | 0.4948 | 0.8778 | nan | 0.8452 | 0.9596 | 0.7945 | 0.8375 | 0.5127 | nan | 0.7134 | 0.8046 | 0.2441 | 0.9569 | 0.1222 | 0.0 | 0.6225 | 0.0448 | 0.7151 | 0.0 | 0.0046 | 0.9291 | 0.1728 | 0.6484 | 0.3735 | 0.5232 | nan | 0.3094 | 0.6309 | 0.5690 | 0.0 | 0.9544 | 0.8394 | 0.9851 | 0.0144 | 0.2225 | 0.4840 | 0.0 | nan | 0.7599 | 0.8755 | 0.7011 | 0.7472 | 0.3964 | nan | 0.5875 | 0.6215 | 0.1186 | 0.8636 | 0.1064 | 0.0 | 0.4711 | 0.0448 | 0.5874 | 0.0 | 0.0046 | 0.7716 | 0.1564 | 0.5438 | 0.3163 | 0.3747 | nan | 0.2580 | 0.5110 | 0.3998 | 0.0 | 0.8870 | 0.7616 | 0.9522 | 0.0109 | 0.1265 | 0.3795 | 0.0 |
| 0.1365 | 38.2 | 7640 | 0.7119 | 0.4121 | 0.4928 | 0.8766 | nan | 0.8474 | 0.9524 | 0.8074 | 0.8526 | 0.5495 | nan | 0.6898 | 0.8332 | 0.0948 | 0.9565 | 0.1132 | 0.0 | 0.6853 | 0.0279 | 0.7159 | 0.0 | 0.0181 | 0.9358 | 0.1758 | 0.6504 | 0.3769 | 0.5242 | nan | 0.2627 | 0.6129 | 0.5666 | 0.0 | 0.9530 | 0.8232 | 0.9847 | 0.0102 | 0.2972 | 0.4530 | 0.0 | nan | 0.7633 | 0.8785 | 0.6964 | 0.7575 | 0.3707 | nan | 0.5854 | 0.6259 | 0.0489 | 0.8638 | 0.0986 | 0.0 | 0.4741 | 0.0279 | 0.5917 | 0.0 | 0.0180 | 0.7694 | 0.1586 | 0.5437 | 0.3192 | 0.3748 | nan | 0.2147 | 0.5088 | 0.3942 | 0.0 | 0.8850 | 0.7495 | 0.9525 | 0.0080 | 0.1414 | 0.3649 | 0.0 |
| 0.083 | 38.3 | 7660 | 0.6949 | 0.4127 | 0.4875 | 0.8774 | nan | 0.8514 | 0.9562 | 0.7999 | 0.8520 | 0.5273 | nan | 0.6936 | 0.8133 | 0.0601 | 0.9558 | 0.1203 | 0.0 | 0.6775 | 0.0249 | 0.6821 | 0.0 | 0.0110 | 0.9317 | 0.2019 | 0.6379 | 0.3723 | 0.4744 | nan | 0.2766 | 0.6250 | 0.5635 | 0.0 | 0.9551 | 0.8210 | 0.9834 | 0.0084 | 0.2563 | 0.4670 | 0.0 | nan | 0.7648 | 0.8777 | 0.7051 | 0.7562 | 0.3775 | nan | 0.5838 | 0.6511 | 0.0339 | 0.8629 | 0.1044 | 0.0 | 0.4645 | 0.0249 | 0.5882 | 0.0 | 0.0109 | 0.7678 | 0.1786 | 0.5393 | 0.3193 | 0.3608 | nan | 0.2288 | 0.5063 | 0.3969 | 0.0 | 0.8832 | 0.7467 | 0.9530 | 0.0070 | 0.1366 | 0.3761 | 0.0 |
| 0.0565 | 38.4 | 7680 | 0.7109 | 0.4145 | 0.4937 | 0.8779 | nan | 0.8473 | 0.9554 | 0.7978 | 0.8475 | 0.5370 | nan | 0.7277 | 0.8299 | 0.0863 | 0.9572 | 0.1351 | 0.0 | 0.7158 | 0.0126 | 0.6839 | 0.0 | 0.0220 | 0.9303 | 0.1862 | 0.6312 | 0.3700 | 0.4975 | nan | 0.3249 | 0.6206 | 0.5891 | 0.0 | 0.9540 | 0.8441 | 0.9846 | 0.0100 | 0.2242 | 0.4755 | 0.0 | nan | 0.7633 | 0.8766 | 0.7151 | 0.7567 | 0.3933 | nan | 0.5880 | 0.6438 | 0.0540 | 0.8617 | 0.1155 | 0.0 | 0.4699 | 0.0126 | 0.5720 | 0.0 | 0.0219 | 0.7696 | 0.1657 | 0.5374 | 0.3103 | 0.3687 | nan | 0.2571 | 0.5025 | 0.3951 | 0.0 | 0.8852 | 0.7603 | 0.9523 | 0.0079 | 0.1289 | 0.3780 | 0.0 |
| 0.0839 | 38.5 | 7700 | 0.7159 | 0.4131 | 0.4910 | 0.8775 | nan | 0.8444 | 0.9584 | 0.8049 | 0.8463 | 0.5358 | nan | 0.7017 | 0.8419 | 0.0834 | 0.9532 | 0.1406 | 0.0 | 0.7470 | 0.0308 | 0.6798 | 0.0014 | 0.0332 | 0.9384 | 0.1592 | 0.6145 | 0.3729 | 0.5277 | nan | 0.2538 | 0.6062 | 0.5783 | 0.0 | 0.9537 | 0.8386 | 0.9830 | 0.0098 | 0.2066 | 0.4680 | 0.0 | nan | 0.7614 | 0.8756 | 0.7064 | 0.7559 | 0.3953 | nan | 0.5875 | 0.6396 | 0.0518 | 0.8645 | 0.1198 | 0.0 | 0.4784 | 0.0308 | 0.5764 | 0.0014 | 0.0330 | 0.7649 | 0.1447 | 0.5283 | 0.3164 | 0.3755 | nan | 0.2125 | 0.5022 | 0.3936 | 0.0 | 0.8854 | 0.7581 | 0.9531 | 0.0079 | 0.1206 | 0.3774 | 0.0 |
| 0.0716 | 38.6 | 7720 | 0.7162 | 0.4187 | 0.5017 | 0.8788 | nan | 0.8526 | 0.9551 | 0.8050 | 0.8509 | 0.5184 | nan | 0.7209 | 0.8335 | 0.1556 | 0.9543 | 0.1366 | 0.0 | 0.7470 | 0.0510 | 0.7137 | 0.0 | 0.0399 | 0.9287 | 0.1901 | 0.6347 | 0.3853 | 0.5274 | nan | 0.3012 | 0.6383 | 0.5748 | 0.0 | 0.9536 | 0.8538 | 0.9817 | 0.0128 | 0.2525 | 0.4866 | 0.0 | nan | 0.7630 | 0.8781 | 0.7102 | 0.7565 | 0.3939 | nan | 0.5860 | 0.6298 | 0.0866 | 0.8660 | 0.1174 | 0.0 | 0.4762 | 0.0510 | 0.5783 | 0.0 | 0.0391 | 0.7713 | 0.1708 | 0.5383 | 0.3233 | 0.3755 | nan | 0.2561 | 0.5111 | 0.3920 | 0.0 | 0.8877 | 0.7657 | 0.9540 | 0.0100 | 0.1305 | 0.3816 | 0.0 |
| 0.0676 | 38.7 | 7740 | 0.7302 | 0.4197 | 0.5022 | 0.8779 | nan | 0.8427 | 0.9615 | 0.8072 | 0.8324 | 0.5191 | nan | 0.7091 | 0.8190 | 0.1884 | 0.9549 | 0.1308 | 0.0 | 0.7349 | 0.0611 | 0.7321 | 0.0 | 0.0403 | 0.9298 | 0.2212 | 0.6389 | 0.3802 | 0.5425 | nan | 0.3131 | 0.6210 | 0.5711 | 0.0 | 0.9553 | 0.8361 | 0.9837 | 0.0132 | 0.2581 | 0.4737 | 0.0 | nan | 0.7592 | 0.8737 | 0.7083 | 0.7446 | 0.4115 | nan | 0.5851 | 0.6418 | 0.1032 | 0.8648 | 0.1133 | 0.0 | 0.4610 | 0.0611 | 0.5807 | 0.0 | 0.0396 | 0.7703 | 0.1939 | 0.5360 | 0.3207 | 0.3842 | nan | 0.2520 | 0.5080 | 0.3975 | 0.0 | 0.8857 | 0.7559 | 0.9533 | 0.0114 | 0.1337 | 0.3807 | 0.0 |
| 0.0372 | 38.8 | 7760 | 0.7275 | 0.4183 | 0.4979 | 0.8781 | nan | 0.8438 | 0.9619 | 0.8070 | 0.8256 | 0.5068 | nan | 0.7279 | 0.8360 | 0.1696 | 0.9537 | 0.1189 | 0.0 | 0.7087 | 0.0612 | 0.7082 | 0.0 | 0.0407 | 0.9307 | 0.2306 | 0.6416 | 0.3363 | 0.4796 | nan | 0.3186 | 0.6241 | 0.5594 | 0.0 | 0.9526 | 0.8501 | 0.9834 | 0.0108 | 0.2527 | 0.4908 | 0.0 | nan | 0.7588 | 0.8734 | 0.7065 | 0.7448 | 0.4069 | nan | 0.5842 | 0.6397 | 0.0891 | 0.8659 | 0.1040 | 0.0 | 0.4616 | 0.0612 | 0.5898 | 0.0 | 0.0400 | 0.7725 | 0.2028 | 0.5434 | 0.2970 | 0.3622 | nan | 0.2571 | 0.5070 | 0.3964 | 0.0 | 0.8866 | 0.7624 | 0.9531 | 0.0095 | 0.1274 | 0.3815 | 0.0 |
| 0.1237 | 38.9 | 7780 | 0.7390 | 0.4173 | 0.4961 | 0.8774 | nan | 0.8399 | 0.9606 | 0.8099 | 0.8417 | 0.5393 | nan | 0.7047 | 0.8089 | 0.1372 | 0.9554 | 0.1194 | 0.0 | 0.7419 | 0.0579 | 0.7088 | 0.0 | 0.0401 | 0.9338 | 0.2093 | 0.6252 | 0.3535 | 0.5579 | nan | 0.2733 | 0.6027 | 0.5453 | 0.0 | 0.9556 | 0.8218 | 0.9823 | 0.0119 | 0.2484 | 0.4868 | 0.0 | nan | 0.7578 | 0.8762 | 0.6966 | 0.7555 | 0.4077 | nan | 0.5903 | 0.6490 | 0.0734 | 0.8654 | 0.1039 | 0.0 | 0.4925 | 0.0579 | 0.5851 | 0.0 | 0.0395 | 0.7697 | 0.1866 | 0.5410 | 0.3043 | 0.3889 | nan | 0.2289 | 0.5039 | 0.3952 | 0.0 | 0.8828 | 0.7474 | 0.9537 | 0.0093 | 0.1161 | 0.3756 | 0.0 |
| 0.0878 | 39.0 | 7800 | 0.7379 | 0.4135 | 0.4932 | 0.8779 | nan | 0.8465 | 0.9608 | 0.8103 | 0.8408 | 0.5221 | nan | 0.7165 | 0.8213 | 0.1609 | 0.9555 | 0.1149 | 0.0 | 0.6741 | 0.0498 | 0.7193 | 0.0 | 0.0385 | 0.9362 | 0.1633 | 0.6127 | 0.3813 | 0.5541 | nan | 0.2256 | 0.6193 | 0.5516 | 0.0 | 0.9528 | 0.8320 | 0.9839 | 0.0110 | 0.2697 | 0.4585 | 0.0 | nan | 0.7617 | 0.8781 | 0.6954 | 0.7586 | 0.4065 | nan | 0.5940 | 0.6398 | 0.0858 | 0.8650 | 0.0996 | 0.0 | 0.4466 | 0.0498 | 0.5844 | 0.0 | 0.0380 | 0.7653 | 0.1480 | 0.5314 | 0.3164 | 0.3876 | nan | 0.1939 | 0.5069 | 0.3963 | 0.0 | 0.8841 | 0.7475 | 0.9528 | 0.0084 | 0.1212 | 0.3684 | 0.0 |
| 0.062 | 39.1 | 7820 | 0.7108 | 0.4157 | 0.4923 | 0.8784 | nan | 0.8438 | 0.9597 | 0.7949 | 0.8504 | 0.5365 | nan | 0.7155 | 0.8291 | 0.0994 | 0.9575 | 0.1482 | 0.0 | 0.6562 | 0.0225 | 0.6960 | 0.0 | 0.0527 | 0.9367 | 0.1711 | 0.6192 | 0.3725 | 0.5414 | nan | 0.2670 | 0.6328 | 0.5576 | 0.0 | 0.9509 | 0.8439 | 0.9822 | 0.0065 | 0.2352 | 0.4735 | 0.0 | nan | 0.7621 | 0.8780 | 0.7092 | 0.7552 | 0.4032 | nan | 0.5936 | 0.6374 | 0.0592 | 0.8633 | 0.1272 | 0.0 | 0.4597 | 0.0225 | 0.5862 | 0.0 | 0.0517 | 0.7645 | 0.1543 | 0.5282 | 0.3163 | 0.3868 | nan | 0.2224 | 0.5101 | 0.4008 | 0.0 | 0.8880 | 0.7637 | 0.9543 | 0.0051 | 0.1229 | 0.3766 | 0.0 |
| 0.1004 | 39.2 | 7840 | 0.7033 | 0.4185 | 0.4942 | 0.8791 | nan | 0.8559 | 0.9565 | 0.7930 | 0.8491 | 0.5355 | nan | 0.7101 | 0.8301 | 0.1027 | 0.9562 | 0.1725 | 0.0 | 0.6637 | 0.0242 | 0.7070 | 0.0 | 0.0722 | 0.9286 | 0.1791 | 0.6424 | 0.3857 | 0.5214 | nan | 0.2739 | 0.6100 | 0.5467 | 0.0 | 0.9586 | 0.8115 | 0.9831 | 0.0036 | 0.2308 | 0.5100 | 0.0 | nan | 0.7675 | 0.8797 | 0.7109 | 0.7577 | 0.3964 | nan | 0.5891 | 0.6448 | 0.0609 | 0.8643 | 0.1490 | 0.0 | 0.4624 | 0.0242 | 0.5880 | 0.0 | 0.0703 | 0.7721 | 0.1616 | 0.5424 | 0.3222 | 0.3832 | nan | 0.2333 | 0.5032 | 0.3985 | 0.0 | 0.8826 | 0.7440 | 0.9536 | 0.0029 | 0.1356 | 0.3931 | 0.0 |
| 0.0602 | 39.3 | 7860 | 0.7216 | 0.4169 | 0.4948 | 0.8780 | nan | 0.8340 | 0.9595 | 0.8077 | 0.8507 | 0.5321 | nan | 0.7220 | 0.8371 | 0.1258 | 0.9539 | 0.1473 | 0.0 | 0.6460 | 0.0272 | 0.7104 | 0.0 | 0.0401 | 0.9283 | 0.2397 | 0.6438 | 0.3579 | 0.4705 | nan | 0.2827 | 0.6228 | 0.5608 | 0.0 | 0.9569 | 0.8349 | 0.9816 | 0.0061 | 0.2532 | 0.4996 | 0.0 | nan | 0.7584 | 0.8766 | 0.6849 | 0.7489 | 0.3992 | nan | 0.5886 | 0.6289 | 0.0706 | 0.8663 | 0.1302 | 0.0 | 0.4607 | 0.0272 | 0.5851 | 0.0 | 0.0398 | 0.7749 | 0.2118 | 0.5500 | 0.3089 | 0.3623 | nan | 0.2385 | 0.5067 | 0.3993 | 0.0 | 0.8862 | 0.7622 | 0.9543 | 0.0048 | 0.1298 | 0.3845 | 0.0 |
| 0.0722 | 39.4 | 7880 | 0.7279 | 0.4158 | 0.4944 | 0.8793 | nan | 0.8447 | 0.9610 | 0.8107 | 0.8430 | 0.5297 | nan | 0.7024 | 0.8319 | 0.1052 | 0.9593 | 0.1157 | 0.0 | 0.6465 | 0.0196 | 0.7043 | 0.0 | 0.0278 | 0.9274 | 0.2287 | 0.6582 | 0.3815 | 0.4803 | nan | 0.2805 | 0.6389 | 0.5961 | 0.0 | 0.9555 | 0.8343 | 0.9837 | 0.0073 | 0.2698 | 0.4762 | 0.0 | nan | 0.7635 | 0.8777 | 0.6774 | 0.7572 | 0.4057 | nan | 0.5842 | 0.6384 | 0.0617 | 0.8630 | 0.1021 | 0.0 | 0.4500 | 0.0196 | 0.5784 | 0.0 | 0.0277 | 0.7755 | 0.2023 | 0.5536 | 0.3216 | 0.3683 | nan | 0.2345 | 0.5113 | 0.4037 | 0.0 | 0.8876 | 0.7632 | 0.9530 | 0.0058 | 0.1393 | 0.3805 | 0.0 |
| 0.0719 | 39.5 | 7900 | 0.7208 | 0.4149 | 0.4961 | 0.8786 | nan | 0.8398 | 0.9592 | 0.8124 | 0.8508 | 0.5282 | nan | 0.7058 | 0.8316 | 0.1139 | 0.9600 | 0.1281 | 0.0 | 0.6649 | 0.0164 | 0.7065 | 0.0 | 0.0230 | 0.9300 | 0.1827 | 0.6428 | 0.4054 | 0.5390 | nan | 0.2653 | 0.6270 | 0.5696 | 0.0 | 0.9535 | 0.8449 | 0.9823 | 0.0082 | 0.2985 | 0.4852 | 0.0 | nan | 0.7593 | 0.8784 | 0.6730 | 0.7511 | 0.4080 | nan | 0.5888 | 0.6418 | 0.0683 | 0.8619 | 0.1105 | 0.0 | 0.4541 | 0.0164 | 0.5749 | 0.0 | 0.0230 | 0.7736 | 0.1657 | 0.5479 | 0.3315 | 0.3771 | nan | 0.2269 | 0.5095 | 0.4005 | 0.0 | 0.8888 | 0.7629 | 0.9537 | 0.0065 | 0.1402 | 0.3813 | 0.0 |
| 0.0846 | 39.6 | 7920 | 0.6984 | 0.4183 | 0.4961 | 0.8808 | nan | 0.8589 | 0.9569 | 0.8112 | 0.8473 | 0.5346 | nan | 0.7155 | 0.8169 | 0.0974 | 0.9561 | 0.1416 | 0.0 | 0.6767 | 0.0101 | 0.7005 | 0.0 | 0.0254 | 0.9290 | 0.1983 | 0.6502 | 0.3968 | 0.5309 | nan | 0.2897 | 0.6273 | 0.5516 | 0.0 | 0.9538 | 0.8456 | 0.9819 | 0.0081 | 0.2572 | 0.5060 | 0.0 | nan | 0.7752 | 0.8828 | 0.6751 | 0.7567 | 0.4072 | nan | 0.5912 | 0.6550 | 0.0613 | 0.8624 | 0.1209 | 0.0 | 0.4808 | 0.0101 | 0.5745 | 0.0 | 0.0253 | 0.7752 | 0.1782 | 0.5485 | 0.3296 | 0.3791 | nan | 0.2458 | 0.5081 | 0.4058 | 0.0 | 0.8873 | 0.7594 | 0.9539 | 0.0062 | 0.1392 | 0.3899 | 0.0 |
| 0.0809 | 39.7 | 7940 | 0.7169 | 0.4148 | 0.4923 | 0.8793 | nan | 0.8536 | 0.9586 | 0.8105 | 0.8475 | 0.5322 | nan | 0.6982 | 0.8256 | 0.0994 | 0.9558 | 0.1317 | 0.0 | 0.6672 | 0.0141 | 0.7055 | 0.0 | 0.0191 | 0.9298 | 0.1881 | 0.6427 | 0.3488 | 0.5112 | nan | 0.2933 | 0.6179 | 0.5871 | 0.0 | 0.9564 | 0.8229 | 0.9850 | 0.0119 | 0.2337 | 0.5052 | 0.0 | nan | 0.7701 | 0.8802 | 0.6757 | 0.7586 | 0.4029 | nan | 0.5886 | 0.6480 | 0.0595 | 0.8637 | 0.1131 | 0.0 | 0.4732 | 0.0141 | 0.5779 | 0.0 | 0.0190 | 0.7734 | 0.1697 | 0.5488 | 0.3030 | 0.3724 | nan | 0.2432 | 0.5065 | 0.4009 | 0.0 | 0.8848 | 0.7553 | 0.9522 | 0.0083 | 0.1235 | 0.3883 | 0.0 |
| 0.0945 | 39.8 | 7960 | 0.7017 | 0.4152 | 0.4958 | 0.8797 | nan | 0.8576 | 0.9532 | 0.8158 | 0.8498 | 0.5588 | nan | 0.7138 | 0.8261 | 0.1482 | 0.9577 | 0.1459 | 0.0 | 0.6522 | 0.0102 | 0.7086 | 0.0 | 0.0185 | 0.9304 | 0.1772 | 0.6265 | 0.3742 | 0.5060 | nan | 0.2929 | 0.6277 | 0.5883 | 0.0 | 0.9563 | 0.8431 | 0.9832 | 0.0089 | 0.2584 | 0.4771 | 0.0 | nan | 0.7751 | 0.8855 | 0.6741 | 0.7566 | 0.3922 | nan | 0.5920 | 0.6459 | 0.0836 | 0.8614 | 0.1255 | 0.0 | 0.4616 | 0.0102 | 0.5758 | 0.0 | 0.0184 | 0.7704 | 0.1604 | 0.5422 | 0.3159 | 0.3706 | nan | 0.2470 | 0.5085 | 0.3991 | 0.0 | 0.8875 | 0.7653 | 0.9526 | 0.0060 | 0.1240 | 0.3785 | 0.0 |
| 0.0709 | 39.9 | 7980 | 0.6824 | 0.4177 | 0.4990 | 0.8802 | nan | 0.8637 | 0.9545 | 0.8064 | 0.8450 | 0.5502 | nan | 0.7172 | 0.8241 | 0.1955 | 0.9567 | 0.1354 | 0.0 | 0.6952 | 0.0083 | 0.7013 | 0.0 | 0.0200 | 0.9256 | 0.1924 | 0.6498 | 0.3677 | 0.5193 | nan | 0.3254 | 0.6198 | 0.5777 | 0.0 | 0.9556 | 0.8344 | 0.9831 | 0.0078 | 0.2394 | 0.4968 | 0.0 | nan | 0.7781 | 0.8862 | 0.7001 | 0.7581 | 0.3762 | nan | 0.5914 | 0.6247 | 0.1057 | 0.8610 | 0.1174 | 0.0 | 0.4881 | 0.0083 | 0.5716 | 0.0 | 0.0199 | 0.7752 | 0.1735 | 0.5509 | 0.3146 | 0.3802 | nan | 0.2705 | 0.5070 | 0.3950 | 0.0 | 0.8869 | 0.7625 | 0.9528 | 0.0055 | 0.1265 | 0.3797 | 0.0 |
| 0.0547 | 40.0 | 8000 | 0.7111 | 0.4160 | 0.4967 | 0.8793 | nan | 0.8592 | 0.9570 | 0.7988 | 0.8468 | 0.5238 | nan | 0.7063 | 0.8328 | 0.1482 | 0.9541 | 0.1329 | 0.0 | 0.6587 | 0.0168 | 0.7095 | 0.0 | 0.0289 | 0.9318 | 0.2070 | 0.6181 | 0.3898 | 0.5362 | nan | 0.3203 | 0.6264 | 0.5822 | 0.0 | 0.9551 | 0.8464 | 0.9843 | 0.0083 | 0.2529 | 0.4631 | 0.0 | nan | 0.7693 | 0.8781 | 0.7153 | 0.7588 | 0.3972 | nan | 0.5880 | 0.6141 | 0.0841 | 0.8626 | 0.1162 | 0.0 | 0.4551 | 0.0168 | 0.5758 | 0.0 | 0.0288 | 0.7695 | 0.1828 | 0.5380 | 0.3252 | 0.3743 | nan | 0.2568 | 0.5091 | 0.3966 | 0.0 | 0.8883 | 0.7678 | 0.9528 | 0.0061 | 0.1174 | 0.3673 | 0.0 |
| 0.0858 | 40.1 | 8020 | 0.7221 | 0.4135 | 0.4925 | 0.8782 | nan | 0.8552 | 0.9559 | 0.7976 | 0.8474 | 0.5309 | nan | 0.7098 | 0.8381 | 0.1045 | 0.9552 | 0.1035 | 0.0 | 0.6336 | 0.0115 | 0.7002 | 0.0 | 0.0388 | 0.9335 | 0.2127 | 0.5985 | 0.3998 | 0.5091 | nan | 0.3033 | 0.6293 | 0.5819 | 0.0 | 0.9555 | 0.8360 | 0.9838 | 0.0103 | 0.2591 | 0.4654 | 0.0 | nan | 0.7694 | 0.8781 | 0.7211 | 0.7589 | 0.3944 | nan | 0.5895 | 0.6134 | 0.0608 | 0.8625 | 0.0920 | 0.0 | 0.4409 | 0.0115 | 0.5837 | 0.0 | 0.0385 | 0.7643 | 0.1876 | 0.5244 | 0.3306 | 0.3740 | nan | 0.2456 | 0.5079 | 0.3983 | 0.0 | 0.8871 | 0.7570 | 0.9532 | 0.0073 | 0.1129 | 0.3659 | 0.0 |
| 0.0695 | 40.2 | 8040 | 0.7099 | 0.4159 | 0.4970 | 0.8800 | nan | 0.8637 | 0.9564 | 0.7999 | 0.8444 | 0.5332 | nan | 0.7073 | 0.8490 | 0.1056 | 0.9560 | 0.1068 | 0.0 | 0.6443 | 0.0205 | 0.7023 | 0.0 | 0.0346 | 0.9285 | 0.2501 | 0.6405 | 0.3662 | 0.5070 | nan | 0.3411 | 0.6337 | 0.5808 | 0.0 | 0.9531 | 0.8505 | 0.9833 | 0.0080 | 0.2765 | 0.4612 | 0.0 | nan | 0.7759 | 0.8817 | 0.7130 | 0.7633 | 0.3899 | nan | 0.5911 | 0.6022 | 0.0613 | 0.8624 | 0.0944 | 0.0 | 0.4481 | 0.0205 | 0.5844 | 0.0 | 0.0342 | 0.7705 | 0.2157 | 0.5417 | 0.3178 | 0.3717 | nan | 0.2610 | 0.5101 | 0.3980 | 0.0 | 0.8885 | 0.7637 | 0.9531 | 0.0057 | 0.1230 | 0.3653 | 0.0 |
| 0.0796 | 40.3 | 8060 | 0.7107 | 0.4157 | 0.4954 | 0.8796 | nan | 0.8609 | 0.9568 | 0.7905 | 0.8493 | 0.5125 | nan | 0.7184 | 0.8283 | 0.1212 | 0.9543 | 0.1012 | 0.0 | 0.6666 | 0.0362 | 0.7047 | 0.0 | 0.0299 | 0.9276 | 0.1844 | 0.6379 | 0.3452 | 0.5341 | nan | 0.3662 | 0.6248 | 0.5844 | 0.0 | 0.9572 | 0.8399 | 0.9828 | 0.0140 | 0.2255 | 0.4967 | 0.0 | nan | 0.7721 | 0.8809 | 0.7172 | 0.7599 | 0.3866 | nan | 0.5904 | 0.6015 | 0.0668 | 0.8629 | 0.0898 | 0.0 | 0.4694 | 0.0362 | 0.5856 | 0.0 | 0.0297 | 0.7715 | 0.1673 | 0.5417 | 0.3024 | 0.3677 | nan | 0.2845 | 0.5088 | 0.3984 | 0.0 | 0.8865 | 0.7622 | 0.9529 | 0.0097 | 0.1214 | 0.3776 | 0.0 |
| 0.0724 | 40.4 | 8080 | 0.7106 | 0.4169 | 0.5013 | 0.8799 | nan | 0.8624 | 0.9575 | 0.7998 | 0.8468 | 0.5267 | nan | 0.7205 | 0.8300 | 0.1562 | 0.9553 | 0.1078 | 0.0 | 0.6866 | 0.0474 | 0.7193 | 0.0 | 0.0365 | 0.9303 | 0.1972 | 0.6227 | 0.3779 | 0.5839 | nan | 0.3352 | 0.6225 | 0.5880 | 0.0 | 0.9522 | 0.8457 | 0.9839 | 0.0096 | 0.2625 | 0.4761 | 0.0 | nan | 0.7742 | 0.8807 | 0.7120 | 0.7606 | 0.3870 | nan | 0.5935 | 0.6092 | 0.0852 | 0.8635 | 0.0951 | 0.0 | 0.4542 | 0.0474 | 0.5808 | 0.0 | 0.0362 | 0.7702 | 0.1771 | 0.5395 | 0.3223 | 0.3795 | nan | 0.2676 | 0.5092 | 0.3938 | 0.0 | 0.8893 | 0.7672 | 0.9525 | 0.0073 | 0.1162 | 0.3706 | 0.0 |
| 0.0861 | 40.5 | 8100 | 0.7126 | 0.4154 | 0.4968 | 0.8788 | nan | 0.8509 | 0.9591 | 0.8039 | 0.8473 | 0.5250 | nan | 0.7123 | 0.8259 | 0.1234 | 0.9525 | 0.1220 | 0.0 | 0.6810 | 0.0406 | 0.7185 | 0.0 | 0.0481 | 0.9290 | 0.2089 | 0.6287 | 0.3642 | 0.5502 | nan | 0.2832 | 0.6238 | 0.5661 | 0.0 | 0.9556 | 0.8417 | 0.9821 | 0.0075 | 0.2778 | 0.4699 | 0.0 | nan | 0.7683 | 0.8775 | 0.7027 | 0.7596 | 0.3931 | nan | 0.5943 | 0.6091 | 0.0731 | 0.8649 | 0.1067 | 0.0 | 0.4585 | 0.0406 | 0.5824 | 0.0 | 0.0475 | 0.7699 | 0.1858 | 0.5358 | 0.3135 | 0.3763 | nan | 0.2335 | 0.5090 | 0.3943 | 0.0 | 0.8875 | 0.7648 | 0.9533 | 0.0059 | 0.1189 | 0.3664 | 0.0 |
| 0.0864 | 40.6 | 8120 | 0.7126 | 0.4158 | 0.4996 | 0.8781 | nan | 0.8529 | 0.9577 | 0.7999 | 0.8500 | 0.5235 | nan | 0.7092 | 0.8165 | 0.1371 | 0.9546 | 0.1320 | 0.0 | 0.7268 | 0.0451 | 0.7072 | 0.0 | 0.0315 | 0.9346 | 0.1732 | 0.6225 | 0.3816 | 0.6257 | nan | 0.2730 | 0.6131 | 0.5802 | 0.0 | 0.9530 | 0.8303 | 0.9835 | 0.0093 | 0.2946 | 0.4686 | 0.0 | nan | 0.7672 | 0.8786 | 0.7101 | 0.7576 | 0.3883 | nan | 0.5942 | 0.6285 | 0.0779 | 0.8631 | 0.1131 | 0.0 | 0.4647 | 0.0451 | 0.5853 | 0.0 | 0.0312 | 0.7671 | 0.1563 | 0.5319 | 0.3204 | 0.4060 | nan | 0.2306 | 0.5065 | 0.3911 | 0.0 | 0.8866 | 0.7560 | 0.9531 | 0.0071 | 0.1212 | 0.3685 | 0.0 |
| 0.0489 | 40.7 | 8140 | 0.7001 | 0.4168 | 0.4990 | 0.8786 | nan | 0.8581 | 0.9576 | 0.7935 | 0.8392 | 0.5069 | nan | 0.7259 | 0.7895 | 0.2352 | 0.9557 | 0.1464 | 0.0 | 0.7005 | 0.0433 | 0.6987 | 0.0 | 0.0159 | 0.9282 | 0.1917 | 0.6173 | 0.3791 | 0.5618 | nan | 0.2642 | 0.6416 | 0.5948 | 0.0 | 0.9565 | 0.8391 | 0.9837 | 0.0108 | 0.2517 | 0.4799 | 0.0 | nan | 0.7651 | 0.8791 | 0.7217 | 0.7586 | 0.3793 | nan | 0.5944 | 0.6412 | 0.0998 | 0.8614 | 0.1255 | 0.0 | 0.4618 | 0.0433 | 0.5817 | 0.0 | 0.0158 | 0.7689 | 0.1718 | 0.5362 | 0.3195 | 0.3800 | nan | 0.2271 | 0.5068 | 0.3928 | 0.0 | 0.8861 | 0.7587 | 0.9531 | 0.0083 | 0.1205 | 0.3787 | 0.0 |
| 0.0885 | 40.8 | 8160 | 0.7055 | 0.4151 | 0.4967 | 0.8789 | nan | 0.8546 | 0.9578 | 0.8063 | 0.8368 | 0.5227 | nan | 0.7276 | 0.8306 | 0.1052 | 0.9568 | 0.1536 | 0.0 | 0.7151 | 0.0256 | 0.6851 | 0.0 | 0.0230 | 0.9323 | 0.1821 | 0.6141 | 0.3939 | 0.5730 | nan | 0.2549 | 0.6408 | 0.5897 | 0.0 | 0.9506 | 0.8533 | 0.9838 | 0.0067 | 0.2379 | 0.4792 | 0.0 | nan | 0.7664 | 0.8800 | 0.7130 | 0.7574 | 0.3786 | nan | 0.5950 | 0.6417 | 0.0597 | 0.8608 | 0.1306 | 0.0 | 0.4675 | 0.0256 | 0.5791 | 0.0 | 0.0230 | 0.7671 | 0.1637 | 0.5323 | 0.3301 | 0.3915 | nan | 0.2119 | 0.5075 | 0.3876 | 0.0 | 0.8901 | 0.7687 | 0.9532 | 0.0054 | 0.1181 | 0.3782 | 0.0 |
| 0.0666 | 40.9 | 8180 | 0.6983 | 0.4148 | 0.4943 | 0.8804 | nan | 0.8615 | 0.9583 | 0.8135 | 0.8462 | 0.5189 | nan | 0.7181 | 0.8330 | 0.0987 | 0.9506 | 0.1547 | 0.0 | 0.6725 | 0.0495 | 0.7100 | 0.0 | 0.0287 | 0.9267 | 0.1776 | 0.6343 | 0.3780 | 0.5432 | nan | 0.2443 | 0.6137 | 0.5769 | 0.0 | 0.9584 | 0.8384 | 0.9828 | 0.0066 | 0.2333 | 0.4880 | 0.0 | nan | 0.7760 | 0.8835 | 0.6975 | 0.7577 | 0.3847 | nan | 0.5952 | 0.6365 | 0.0593 | 0.8659 | 0.1333 | 0.0 | 0.4577 | 0.0495 | 0.5792 | 0.0 | 0.0286 | 0.7722 | 0.1602 | 0.5420 | 0.3217 | 0.3705 | nan | 0.2078 | 0.5065 | 0.3912 | 0.0 | 0.8859 | 0.7595 | 0.9537 | 0.0053 | 0.1169 | 0.3758 | 0.0 |
| 0.0927 | 41.0 | 8200 | 0.7110 | 0.4152 | 0.4938 | 0.8796 | nan | 0.8595 | 0.9572 | 0.8074 | 0.8472 | 0.5385 | nan | 0.7135 | 0.8222 | 0.0817 | 0.9568 | 0.1376 | 0.0 | 0.6614 | 0.0422 | 0.7135 | 0.0 | 0.0282 | 0.9262 | 0.1968 | 0.6167 | 0.3808 | 0.5397 | nan | 0.2615 | 0.6287 | 0.5838 | 0.0 | 0.9574 | 0.8236 | 0.9837 | 0.0121 | 0.2220 | 0.5007 | 0.0 | nan | 0.7749 | 0.8832 | 0.7024 | 0.7589 | 0.3841 | nan | 0.5957 | 0.6435 | 0.0512 | 0.8635 | 0.1196 | 0.0 | 0.4658 | 0.0422 | 0.5789 | 0.0 | 0.0281 | 0.7691 | 0.1761 | 0.5324 | 0.3221 | 0.3748 | nan | 0.2213 | 0.5070 | 0.3918 | 0.0 | 0.8855 | 0.7553 | 0.9531 | 0.0094 | 0.1149 | 0.3822 | 0.0 |
| 0.0784 | 41.1 | 8220 | 0.7319 | 0.4160 | 0.4940 | 0.8790 | nan | 0.8561 | 0.9583 | 0.8089 | 0.8384 | 0.5278 | nan | 0.7092 | 0.8246 | 0.0814 | 0.9545 | 0.1391 | 0.0 | 0.6316 | 0.0590 | 0.7229 | 0.0 | 0.0238 | 0.9329 | 0.2112 | 0.6189 | 0.3859 | 0.5190 | nan | 0.2873 | 0.6168 | 0.5891 | 0.0 | 0.9543 | 0.8355 | 0.9833 | 0.0165 | 0.2376 | 0.4825 | 0.0 | nan | 0.7691 | 0.8808 | 0.6975 | 0.7617 | 0.3839 | nan | 0.5908 | 0.6494 | 0.0511 | 0.8644 | 0.1202 | 0.0 | 0.4527 | 0.0590 | 0.5800 | 0.0 | 0.0237 | 0.7681 | 0.1869 | 0.5344 | 0.3263 | 0.3720 | nan | 0.2333 | 0.5073 | 0.3929 | 0.0 | 0.8865 | 0.7570 | 0.9537 | 0.0123 | 0.1167 | 0.3791 | 0.0 |
| 0.0667 | 41.2 | 8240 | 0.7222 | 0.4126 | 0.4903 | 0.8783 | nan | 0.8543 | 0.9550 | 0.8106 | 0.8437 | 0.5405 | nan | 0.7182 | 0.8463 | 0.0717 | 0.9550 | 0.1303 | 0.0 | 0.6477 | 0.0313 | 0.7111 | 0.0 | 0.0306 | 0.9294 | 0.1755 | 0.6283 | 0.3626 | 0.4902 | nan | 0.2610 | 0.6342 | 0.5772 | 0.0 | 0.9563 | 0.8257 | 0.9828 | 0.0121 | 0.2199 | 0.4890 | 0.0 | nan | 0.7670 | 0.8804 | 0.6984 | 0.7617 | 0.3820 | nan | 0.5916 | 0.6406 | 0.0455 | 0.8624 | 0.1137 | 0.0 | 0.4502 | 0.0313 | 0.5807 | 0.0 | 0.0305 | 0.7701 | 0.1591 | 0.5380 | 0.3146 | 0.3684 | nan | 0.2147 | 0.5078 | 0.3928 | 0.0 | 0.8845 | 0.7513 | 0.9536 | 0.0095 | 0.1195 | 0.3833 | 0.0 |
| 0.0719 | 41.3 | 8260 | 0.7150 | 0.4129 | 0.4889 | 0.8786 | nan | 0.8482 | 0.9565 | 0.8046 | 0.8534 | 0.5374 | nan | 0.7099 | 0.8436 | 0.0664 | 0.9558 | 0.1160 | 0.0 | 0.6357 | 0.0288 | 0.7091 | 0.0 | 0.0459 | 0.9284 | 0.1771 | 0.6290 | 0.3729 | 0.4730 | nan | 0.2386 | 0.6250 | 0.5816 | 0.0 | 0.9566 | 0.8394 | 0.9835 | 0.0035 | 0.2339 | 0.4895 | 0.0 | nan | 0.7638 | 0.8790 | 0.7074 | 0.7540 | 0.3897 | nan | 0.5946 | 0.6470 | 0.0429 | 0.8617 | 0.1015 | 0.0 | 0.4443 | 0.0288 | 0.5846 | 0.0 | 0.0458 | 0.7694 | 0.1605 | 0.5365 | 0.3197 | 0.3608 | nan | 0.2000 | 0.5086 | 0.3954 | 0.0 | 0.8862 | 0.7583 | 0.9526 | 0.0030 | 0.1288 | 0.3859 | 0.0 |
| 0.058 | 41.4 | 8280 | 0.7249 | 0.4149 | 0.4906 | 0.8788 | nan | 0.8595 | 0.9577 | 0.8047 | 0.8316 | 0.5300 | nan | 0.7112 | 0.8323 | 0.0734 | 0.9546 | 0.1188 | 0.0 | 0.6529 | 0.0564 | 0.7018 | 0.0 | 0.0388 | 0.9337 | 0.2297 | 0.6200 | 0.3705 | 0.4951 | nan | 0.2212 | 0.6194 | 0.5791 | 0.0 | 0.9535 | 0.8400 | 0.9823 | 0.0089 | 0.2406 | 0.4811 | 0.0 | nan | 0.7669 | 0.8796 | 0.7100 | 0.7539 | 0.3916 | nan | 0.5929 | 0.6481 | 0.0462 | 0.8639 | 0.1034 | 0.0 | 0.4556 | 0.0564 | 0.5855 | 0.0 | 0.0385 | 0.7658 | 0.1999 | 0.5280 | 0.3195 | 0.3666 | nan | 0.1890 | 0.5092 | 0.3935 | 0.0 | 0.8874 | 0.7598 | 0.9533 | 0.0071 | 0.1239 | 0.3806 | 0.0 |
| 0.0713 | 41.5 | 8300 | 0.7246 | 0.4159 | 0.4938 | 0.8796 | nan | 0.8617 | 0.9582 | 0.8017 | 0.8346 | 0.5269 | nan | 0.7055 | 0.8484 | 0.0846 | 0.9547 | 0.1224 | 0.0 | 0.6546 | 0.0629 | 0.7093 | 0.0 | 0.0433 | 0.9283 | 0.2270 | 0.6212 | 0.3884 | 0.5291 | nan | 0.2168 | 0.6255 | 0.5767 | 0.0 | 0.9544 | 0.8536 | 0.9821 | 0.0084 | 0.2440 | 0.4769 | 0.0 | nan | 0.7708 | 0.8794 | 0.7099 | 0.7561 | 0.3945 | nan | 0.5920 | 0.6368 | 0.0546 | 0.8646 | 0.1063 | 0.0 | 0.4508 | 0.0629 | 0.5847 | 0.0 | 0.0426 | 0.7672 | 0.1970 | 0.5285 | 0.3282 | 0.3829 | nan | 0.1803 | 0.5096 | 0.3944 | 0.0 | 0.8888 | 0.7674 | 0.9534 | 0.0067 | 0.1231 | 0.3763 | 0.0 |
| 0.0749 | 41.6 | 8320 | 0.7235 | 0.4159 | 0.4939 | 0.8791 | nan | 0.8596 | 0.9574 | 0.7982 | 0.8473 | 0.5339 | nan | 0.7065 | 0.8321 | 0.1159 | 0.9565 | 0.1260 | 0.0 | 0.6461 | 0.0717 | 0.7121 | 0.0 | 0.0399 | 0.9331 | 0.2266 | 0.6165 | 0.3872 | 0.5393 | nan | 0.2080 | 0.6241 | 0.5663 | 0.0 | 0.9519 | 0.8333 | 0.9848 | 0.0066 | 0.2445 | 0.4781 | 0.0 | nan | 0.7714 | 0.8796 | 0.7094 | 0.7562 | 0.3935 | nan | 0.5940 | 0.6356 | 0.0654 | 0.8632 | 0.1084 | 0.0 | 0.4528 | 0.0717 | 0.5806 | 0.0 | 0.0393 | 0.7653 | 0.1969 | 0.5249 | 0.3302 | 0.3871 | nan | 0.1723 | 0.5094 | 0.3931 | 0.0 | 0.8876 | 0.7604 | 0.9525 | 0.0054 | 0.1249 | 0.3762 | 0.0 |
| 0.0797 | 41.7 | 8340 | 0.7274 | 0.4130 | 0.4909 | 0.8792 | nan | 0.8519 | 0.9562 | 0.8035 | 0.8531 | 0.5439 | nan | 0.7088 | 0.8430 | 0.0806 | 0.9565 | 0.1222 | 0.0 | 0.6614 | 0.0473 | 0.7077 | 0.0 | 0.0384 | 0.9308 | 0.1828 | 0.6351 | 0.3837 | 0.5151 | nan | 0.1893 | 0.6149 | 0.5878 | 0.0 | 0.9549 | 0.8449 | 0.9851 | 0.0052 | 0.2324 | 0.4736 | 0.0 | nan | 0.7700 | 0.8795 | 0.7001 | 0.7552 | 0.3956 | nan | 0.5961 | 0.6341 | 0.0483 | 0.8633 | 0.1057 | 0.0 | 0.4576 | 0.0472 | 0.5786 | 0.0 | 0.0380 | 0.7683 | 0.1633 | 0.5348 | 0.3249 | 0.3814 | nan | 0.1590 | 0.5099 | 0.3968 | 0.0 | 0.8878 | 0.7646 | 0.9524 | 0.0043 | 0.1245 | 0.3759 | 0.0 |
| 0.0992 | 41.8 | 8360 | 0.7200 | 0.4129 | 0.4919 | 0.8783 | nan | 0.8496 | 0.9564 | 0.8070 | 0.8435 | 0.5330 | nan | 0.7220 | 0.8368 | 0.0732 | 0.9572 | 0.1298 | 0.0 | 0.6709 | 0.0622 | 0.7007 | 0.0 | 0.0269 | 0.9303 | 0.1829 | 0.6236 | 0.3705 | 0.5537 | nan | 0.1950 | 0.6268 | 0.5977 | 0.0 | 0.9553 | 0.8440 | 0.9844 | 0.0053 | 0.2268 | 0.4750 | 0.0 | nan | 0.7641 | 0.8773 | 0.6907 | 0.7587 | 0.3943 | nan | 0.5929 | 0.6437 | 0.0429 | 0.8629 | 0.1115 | 0.0 | 0.4596 | 0.0622 | 0.5825 | 0.0 | 0.0267 | 0.7682 | 0.1647 | 0.5321 | 0.3166 | 0.3840 | nan | 0.1642 | 0.5102 | 0.3949 | 0.0 | 0.8868 | 0.7622 | 0.9528 | 0.0044 | 0.1231 | 0.3788 | 0.0 |
| 0.0582 | 41.9 | 8380 | 0.7296 | 0.4136 | 0.4924 | 0.8785 | nan | 0.8458 | 0.9592 | 0.8061 | 0.8447 | 0.5136 | nan | 0.7207 | 0.8342 | 0.0641 | 0.9576 | 0.1245 | 0.0 | 0.6799 | 0.0590 | 0.7184 | 0.0 | 0.0293 | 0.9313 | 0.1980 | 0.6216 | 0.3837 | 0.5488 | nan | 0.2023 | 0.6359 | 0.5885 | 0.0 | 0.9538 | 0.8449 | 0.9838 | 0.0088 | 0.2121 | 0.4875 | 0.0 | nan | 0.7637 | 0.8770 | 0.6863 | 0.7567 | 0.3963 | nan | 0.5920 | 0.6472 | 0.0403 | 0.8635 | 0.1081 | 0.0 | 0.4642 | 0.0590 | 0.5765 | 0.0 | 0.0291 | 0.7673 | 0.1775 | 0.5307 | 0.3274 | 0.3845 | nan | 0.1720 | 0.5113 | 0.3951 | 0.0 | 0.8879 | 0.7644 | 0.9531 | 0.0069 | 0.1164 | 0.3815 | 0.0 |
| 0.0701 | 42.0 | 8400 | 0.7337 | 0.4166 | 0.4941 | 0.8794 | nan | 0.8473 | 0.9594 | 0.8017 | 0.8461 | 0.5233 | nan | 0.7117 | 0.8309 | 0.0828 | 0.9545 | 0.1145 | 0.0 | 0.6676 | 0.0605 | 0.7218 | 0.0 | 0.0342 | 0.9306 | 0.2053 | 0.6414 | 0.3719 | 0.5456 | nan | 0.2417 | 0.6270 | 0.5795 | 0.0 | 0.9551 | 0.8547 | 0.9828 | 0.0081 | 0.2259 | 0.4839 | 0.0 | nan | 0.7642 | 0.8767 | 0.7027 | 0.7549 | 0.3968 | nan | 0.5939 | 0.6486 | 0.0503 | 0.8654 | 0.1005 | 0.0 | 0.4616 | 0.0605 | 0.5804 | 0.0 | 0.0339 | 0.7705 | 0.1820 | 0.5421 | 0.3226 | 0.3825 | nan | 0.2008 | 0.5129 | 0.4000 | 0.0 | 0.8890 | 0.7714 | 0.9532 | 0.0065 | 0.1244 | 0.3819 | 0.0 |
| 0.0728 | 42.1 | 8420 | 0.7088 | 0.4176 | 0.4970 | 0.8802 | nan | 0.8586 | 0.9580 | 0.7946 | 0.8437 | 0.5241 | nan | 0.7327 | 0.8391 | 0.0650 | 0.9594 | 0.1225 | 0.0 | 0.6719 | 0.0386 | 0.7056 | 0.0 | 0.0373 | 0.9302 | 0.2239 | 0.6361 | 0.3748 | 0.5825 | nan | 0.2644 | 0.6285 | 0.5914 | 0.0 | 0.9539 | 0.8399 | 0.9820 | 0.0074 | 0.2436 | 0.4938 | 0.0 | nan | 0.7709 | 0.8803 | 0.7146 | 0.7559 | 0.3943 | nan | 0.5952 | 0.6516 | 0.0407 | 0.8623 | 0.1078 | 0.0 | 0.4596 | 0.0386 | 0.5840 | 0.0 | 0.0372 | 0.7724 | 0.1968 | 0.5428 | 0.3211 | 0.3926 | nan | 0.2153 | 0.5124 | 0.3964 | 0.0 | 0.8873 | 0.7598 | 0.9536 | 0.0059 | 0.1290 | 0.3856 | 0.0 |
| 0.0779 | 42.2 | 8440 | 0.7205 | 0.4161 | 0.4943 | 0.8797 | nan | 0.8508 | 0.9577 | 0.7977 | 0.8524 | 0.5320 | nan | 0.7101 | 0.8392 | 0.0628 | 0.9558 | 0.1263 | 0.0 | 0.6522 | 0.0314 | 0.7007 | 0.0 | 0.0404 | 0.9291 | 0.2000 | 0.6533 | 0.3771 | 0.5758 | nan | 0.2348 | 0.6217 | 0.5855 | 0.0 | 0.9555 | 0.8560 | 0.9833 | 0.0088 | 0.2664 | 0.4606 | 0.0 | nan | 0.7671 | 0.8793 | 0.7060 | 0.7545 | 0.3968 | nan | 0.5946 | 0.6539 | 0.0420 | 0.8647 | 0.1110 | 0.0 | 0.4508 | 0.0314 | 0.5844 | 0.0 | 0.0401 | 0.7719 | 0.1772 | 0.5459 | 0.3214 | 0.3899 | nan | 0.1958 | 0.5120 | 0.4020 | 0.0 | 0.8878 | 0.7681 | 0.9529 | 0.0068 | 0.1305 | 0.3751 | 0.0 |
| 0.0871 | 42.3 | 8460 | 0.7242 | 0.4185 | 0.4984 | 0.8793 | nan | 0.8523 | 0.9558 | 0.8010 | 0.8471 | 0.5390 | nan | 0.7165 | 0.8376 | 0.0732 | 0.9575 | 0.1268 | 0.0 | 0.6554 | 0.0401 | 0.7135 | 0.0 | 0.0391 | 0.9302 | 0.2009 | 0.6376 | 0.3938 | 0.5685 | nan | 0.2904 | 0.6257 | 0.6011 | 0.0 | 0.9547 | 0.8443 | 0.9831 | 0.0072 | 0.2709 | 0.4865 | 0.0 | nan | 0.7665 | 0.8795 | 0.7045 | 0.7569 | 0.3943 | nan | 0.5916 | 0.6578 | 0.0482 | 0.8641 | 0.1112 | 0.0 | 0.4562 | 0.0401 | 0.5822 | 0.0 | 0.0388 | 0.7715 | 0.1783 | 0.5404 | 0.3289 | 0.3985 | nan | 0.2423 | 0.5129 | 0.3986 | 0.0 | 0.8878 | 0.7631 | 0.9532 | 0.0056 | 0.1355 | 0.3838 | 0.0 |
| 0.0671 | 42.4 | 8480 | 0.7098 | 0.4188 | 0.4982 | 0.8806 | nan | 0.8619 | 0.9552 | 0.8057 | 0.8452 | 0.5423 | nan | 0.7258 | 0.8334 | 0.0872 | 0.9566 | 0.1294 | 0.0 | 0.6649 | 0.0448 | 0.7137 | 0.0 | 0.0332 | 0.9312 | 0.1886 | 0.6284 | 0.3881 | 0.5432 | nan | 0.3220 | 0.6299 | 0.5896 | 0.0 | 0.9543 | 0.8488 | 0.9821 | 0.0093 | 0.2349 | 0.4914 | 0.0 | nan | 0.7764 | 0.8833 | 0.6977 | 0.7602 | 0.3922 | nan | 0.5932 | 0.6581 | 0.0533 | 0.8642 | 0.1131 | 0.0 | 0.4540 | 0.0448 | 0.5776 | 0.0 | 0.0331 | 0.7703 | 0.1689 | 0.5359 | 0.3266 | 0.3886 | nan | 0.2650 | 0.5127 | 0.3999 | 0.0 | 0.8876 | 0.7635 | 0.9537 | 0.0070 | 0.1350 | 0.3869 | 0.0 |
| 0.0736 | 42.5 | 8500 | 0.7222 | 0.4162 | 0.4933 | 0.8795 | nan | 0.8571 | 0.9566 | 0.8087 | 0.8463 | 0.5427 | nan | 0.7186 | 0.8216 | 0.0681 | 0.9545 | 0.1284 | 0.0 | 0.6721 | 0.0528 | 0.7124 | 0.0 | 0.0335 | 0.9342 | 0.1955 | 0.6150 | 0.3653 | 0.5562 | nan | 0.2725 | 0.6109 | 0.5731 | 0.0 | 0.9558 | 0.8340 | 0.9829 | 0.0079 | 0.2154 | 0.4945 | 0.0 | nan | 0.7733 | 0.8818 | 0.6834 | 0.7600 | 0.3922 | nan | 0.5932 | 0.6659 | 0.0427 | 0.8649 | 0.1118 | 0.0 | 0.4561 | 0.0528 | 0.5790 | 0.0 | 0.0332 | 0.7670 | 0.1743 | 0.5256 | 0.3162 | 0.3880 | nan | 0.2272 | 0.5090 | 0.3999 | 0.0 | 0.8859 | 0.7582 | 0.9532 | 0.0062 | 0.1276 | 0.3903 | 0.0 |
| 0.0666 | 42.6 | 8520 | 0.7110 | 0.4198 | 0.4967 | 0.8812 | nan | 0.8616 | 0.9569 | 0.7996 | 0.8491 | 0.5364 | nan | 0.7060 | 0.8177 | 0.0932 | 0.9571 | 0.1263 | 0.0 | 0.6658 | 0.0576 | 0.7184 | 0.0 | 0.0473 | 0.9285 | 0.2135 | 0.6561 | 0.3734 | 0.5421 | nan | 0.2644 | 0.6309 | 0.5902 | 0.0 | 0.9542 | 0.8551 | 0.9831 | 0.0090 | 0.2125 | 0.4895 | 0.0 | nan | 0.7743 | 0.8819 | 0.6957 | 0.7576 | 0.3946 | nan | 0.5919 | 0.6597 | 0.0575 | 0.8641 | 0.1115 | 0.0 | 0.4605 | 0.0576 | 0.5757 | 0.0 | 0.0468 | 0.7745 | 0.1876 | 0.5488 | 0.3221 | 0.3951 | nan | 0.2200 | 0.5136 | 0.4034 | 0.0 | 0.8891 | 0.7712 | 0.9533 | 0.0069 | 0.1312 | 0.3877 | 0.0 |
| 0.0695 | 42.7 | 8540 | 0.7218 | 0.4189 | 0.4977 | 0.8780 | nan | 0.8364 | 0.9574 | 0.7969 | 0.8431 | 0.5503 | nan | 0.7173 | 0.8123 | 0.1159 | 0.9577 | 0.1220 | 0.0 | 0.6804 | 0.0625 | 0.7212 | 0.0 | 0.0555 | 0.9310 | 0.1974 | 0.6558 | 0.3836 | 0.5621 | nan | 0.2385 | 0.6290 | 0.6051 | 0.0 | 0.9517 | 0.8515 | 0.9845 | 0.0038 | 0.2094 | 0.4955 | 0.0 | nan | 0.7579 | 0.8747 | 0.7015 | 0.7469 | 0.3890 | nan | 0.5928 | 0.6626 | 0.0668 | 0.8629 | 0.1075 | 0.0 | 0.4649 | 0.0625 | 0.5803 | 0.0 | 0.0551 | 0.7735 | 0.1761 | 0.5460 | 0.3289 | 0.4016 | nan | 0.1946 | 0.5145 | 0.4018 | 0.0 | 0.8895 | 0.7713 | 0.9526 | 0.0032 | 0.1358 | 0.3916 | 0.0 |
| 0.0572 | 42.8 | 8560 | 0.7331 | 0.4175 | 0.4938 | 0.8775 | nan | 0.8462 | 0.9556 | 0.7954 | 0.8294 | 0.5532 | nan | 0.7014 | 0.8166 | 0.1012 | 0.9509 | 0.1225 | 0.0 | 0.6679 | 0.0907 | 0.7107 | 0.0 | 0.0320 | 0.9310 | 0.1957 | 0.6430 | 0.3670 | 0.5316 | nan | 0.2325 | 0.6179 | 0.5918 | 0.0 | 0.9551 | 0.8517 | 0.9839 | 0.0061 | 0.2207 | 0.5003 | 0.0 | nan | 0.7595 | 0.8746 | 0.7054 | 0.7463 | 0.3845 | nan | 0.5869 | 0.6623 | 0.0572 | 0.8663 | 0.1078 | 0.0 | 0.4633 | 0.0906 | 0.5868 | 0.0 | 0.0318 | 0.7716 | 0.1738 | 0.5426 | 0.3163 | 0.3871 | nan | 0.1916 | 0.5119 | 0.3987 | 0.0 | 0.8883 | 0.7697 | 0.9529 | 0.0050 | 0.1348 | 0.3928 | 0.0 |
| 0.0852 | 42.9 | 8580 | 0.7294 | 0.4218 | 0.5035 | 0.8781 | nan | 0.8474 | 0.9539 | 0.7988 | 0.8363 | 0.5591 | nan | 0.7173 | 0.8223 | 0.1199 | 0.9574 | 0.1374 | 0.0 | 0.6863 | 0.0966 | 0.7122 | 0.0002 | 0.0356 | 0.9282 | 0.2153 | 0.6422 | 0.3889 | 0.5794 | nan | 0.3187 | 0.6249 | 0.5866 | 0.0 | 0.9541 | 0.8453 | 0.9840 | 0.0073 | 0.2534 | 0.5031 | 0.0 | nan | 0.7629 | 0.8772 | 0.7008 | 0.7526 | 0.3834 | nan | 0.5882 | 0.6610 | 0.0666 | 0.8639 | 0.1194 | 0.0 | 0.4654 | 0.0966 | 0.5804 | 0.0002 | 0.0351 | 0.7734 | 0.1897 | 0.5443 | 0.3258 | 0.4037 | nan | 0.2523 | 0.5123 | 0.3962 | 0.0 | 0.8888 | 0.7665 | 0.9529 | 0.0059 | 0.1388 | 0.3924 | 0.0 |
| 0.088 | 43.0 | 8600 | 0.7267 | 0.4198 | 0.4991 | 0.8778 | nan | 0.8440 | 0.9545 | 0.8047 | 0.8429 | 0.5587 | nan | 0.7048 | 0.8152 | 0.1090 | 0.9560 | 0.1211 | 0.0 | 0.7106 | 0.0746 | 0.6981 | 0.0001 | 0.0267 | 0.9307 | 0.2232 | 0.6422 | 0.3654 | 0.5411 | nan | 0.3333 | 0.6245 | 0.5647 | 0.0 | 0.9534 | 0.8454 | 0.9850 | 0.0061 | 0.2331 | 0.5013 | 0.0 | nan | 0.7621 | 0.8776 | 0.6877 | 0.7496 | 0.3866 | nan | 0.5884 | 0.6568 | 0.0592 | 0.8628 | 0.1055 | 0.0 | 0.4838 | 0.0746 | 0.5828 | 0.0001 | 0.0265 | 0.7720 | 0.1964 | 0.5455 | 0.3158 | 0.3866 | nan | 0.2690 | 0.5124 | 0.3965 | 0.0 | 0.8881 | 0.7673 | 0.9523 | 0.0047 | 0.1319 | 0.3907 | 0.0 |
| 0.065 | 43.1 | 8620 | 0.7317 | 0.4196 | 0.5005 | 0.8781 | nan | 0.8451 | 0.9537 | 0.8098 | 0.8427 | 0.5527 | nan | 0.7069 | 0.8223 | 0.1230 | 0.9560 | 0.1239 | 0.0 | 0.6888 | 0.0726 | 0.7129 | 0.0 | 0.0271 | 0.9294 | 0.2125 | 0.6537 | 0.3741 | 0.5544 | nan | 0.3373 | 0.6257 | 0.5650 | 0.0 | 0.9528 | 0.8529 | 0.9834 | 0.0072 | 0.2298 | 0.5000 | 0.0 | nan | 0.7622 | 0.8785 | 0.6837 | 0.7518 | 0.3855 | nan | 0.5875 | 0.6530 | 0.0684 | 0.8638 | 0.1080 | 0.0 | 0.4804 | 0.0725 | 0.5771 | 0.0 | 0.0269 | 0.7726 | 0.1868 | 0.5450 | 0.3229 | 0.3885 | nan | 0.2678 | 0.5116 | 0.3946 | 0.0 | 0.8892 | 0.7700 | 0.9534 | 0.0056 | 0.1319 | 0.3878 | 0.0 |
| 0.0697 | 43.2 | 8640 | 0.7204 | 0.4192 | 0.5005 | 0.8785 | nan | 0.8482 | 0.9530 | 0.8100 | 0.8455 | 0.5573 | nan | 0.7103 | 0.8250 | 0.1150 | 0.9551 | 0.1219 | 0.0 | 0.6710 | 0.0804 | 0.7151 | 0.0 | 0.0266 | 0.9303 | 0.2002 | 0.6498 | 0.3774 | 0.5495 | nan | 0.3482 | 0.6393 | 0.5789 | 0.0 | 0.9557 | 0.8447 | 0.9829 | 0.0081 | 0.2347 | 0.4812 | 0.0 | nan | 0.7656 | 0.8803 | 0.6869 | 0.7549 | 0.3853 | nan | 0.5889 | 0.6460 | 0.0617 | 0.8646 | 0.1058 | 0.0 | 0.4654 | 0.0803 | 0.5804 | 0.0 | 0.0264 | 0.7723 | 0.1779 | 0.5491 | 0.3224 | 0.3888 | nan | 0.2797 | 0.5128 | 0.3962 | 0.0 | 0.8874 | 0.7630 | 0.9534 | 0.0062 | 0.1306 | 0.3827 | 0.0 |
| 0.0618 | 43.3 | 8660 | 0.7165 | 0.4183 | 0.4983 | 0.8792 | nan | 0.8572 | 0.9548 | 0.8042 | 0.8479 | 0.5460 | nan | 0.7072 | 0.8212 | 0.1021 | 0.9566 | 0.1165 | 0.0 | 0.6709 | 0.0647 | 0.7131 | 0.0 | 0.0285 | 0.9315 | 0.2115 | 0.6370 | 0.3804 | 0.5576 | nan | 0.3268 | 0.6234 | 0.5779 | 0.0 | 0.9542 | 0.8376 | 0.9834 | 0.0093 | 0.2454 | 0.4792 | 0.0 | nan | 0.7710 | 0.8820 | 0.7006 | 0.7550 | 0.3833 | nan | 0.5902 | 0.6422 | 0.0543 | 0.8639 | 0.1019 | 0.0 | 0.4641 | 0.0647 | 0.5811 | 0.0 | 0.0281 | 0.7700 | 0.1853 | 0.5414 | 0.3240 | 0.3977 | nan | 0.2670 | 0.5098 | 0.3917 | 0.0 | 0.8875 | 0.7613 | 0.9529 | 0.0073 | 0.1270 | 0.3791 | 0.0 |
| 0.06 | 43.4 | 8680 | 0.7109 | 0.4201 | 0.5011 | 0.8800 | nan | 0.8595 | 0.9558 | 0.7967 | 0.8494 | 0.5272 | nan | 0.7195 | 0.8216 | 0.1296 | 0.9587 | 0.1180 | 0.0 | 0.6750 | 0.0609 | 0.7230 | 0.0 | 0.0352 | 0.9277 | 0.2070 | 0.6379 | 0.3972 | 0.5523 | nan | 0.3292 | 0.6208 | 0.5823 | 0.0 | 0.9550 | 0.8361 | 0.9826 | 0.0137 | 0.2559 | 0.5083 | 0.0 | nan | 0.7730 | 0.8823 | 0.7029 | 0.7538 | 0.3909 | nan | 0.5893 | 0.6437 | 0.0670 | 0.8638 | 0.1036 | 0.0 | 0.4610 | 0.0609 | 0.5810 | 0.0 | 0.0346 | 0.7733 | 0.1834 | 0.5459 | 0.3286 | 0.4014 | nan | 0.2719 | 0.5098 | 0.3906 | 0.0 | 0.8872 | 0.7607 | 0.9534 | 0.0097 | 0.1288 | 0.3893 | 0.0 |
| 0.0637 | 43.5 | 8700 | 0.7112 | 0.4197 | 0.5019 | 0.8798 | nan | 0.8598 | 0.9564 | 0.7924 | 0.8452 | 0.5278 | nan | 0.7139 | 0.8459 | 0.0673 | 0.9563 | 0.1162 | 0.0 | 0.7044 | 0.0499 | 0.7242 | 0.0046 | 0.0234 | 0.9320 | 0.2177 | 0.6367 | 0.4050 | 0.5829 | nan | 0.3565 | 0.6146 | 0.5795 | 0.0 | 0.9508 | 0.8419 | 0.9832 | 0.0151 | 0.2453 | 0.5112 | 0.0 | nan | 0.7701 | 0.8810 | 0.7087 | 0.7526 | 0.3938 | nan | 0.5881 | 0.6374 | 0.0410 | 0.8654 | 0.1019 | 0.0 | 0.4544 | 0.0498 | 0.5856 | 0.0044 | 0.0231 | 0.7726 | 0.1907 | 0.5461 | 0.3340 | 0.4087 | nan | 0.2842 | 0.5077 | 0.3904 | 0.0 | 0.8887 | 0.7634 | 0.9534 | 0.0104 | 0.1279 | 0.3934 | 0.0 |
| 0.0686 | 43.6 | 8720 | 0.7090 | 0.4197 | 0.4989 | 0.8799 | nan | 0.8557 | 0.9581 | 0.7838 | 0.8390 | 0.5234 | nan | 0.7065 | 0.8386 | 0.0559 | 0.9563 | 0.1346 | 0.0 | 0.7263 | 0.0440 | 0.7063 | 0.0053 | 0.0168 | 0.9253 | 0.2019 | 0.6655 | 0.4033 | 0.5193 | nan | 0.3487 | 0.6270 | 0.5874 | 0.0 | 0.9547 | 0.8525 | 0.9830 | 0.0137 | 0.2328 | 0.4980 | 0.0 | nan | 0.7640 | 0.8780 | 0.7104 | 0.7531 | 0.4014 | nan | 0.5854 | 0.6436 | 0.0343 | 0.8650 | 0.1170 | 0.0 | 0.4675 | 0.0440 | 0.5847 | 0.0052 | 0.0166 | 0.7773 | 0.1781 | 0.5546 | 0.3319 | 0.3880 | nan | 0.2757 | 0.5094 | 0.3956 | 0.0 | 0.8885 | 0.7679 | 0.9534 | 0.0100 | 0.1391 | 0.3922 | 0.0 |
| 0.0807 | 43.7 | 8740 | 0.7229 | 0.4180 | 0.4983 | 0.8792 | nan | 0.8466 | 0.9586 | 0.7929 | 0.8472 | 0.5371 | nan | 0.7049 | 0.8364 | 0.0621 | 0.9543 | 0.1221 | 0.0 | 0.7106 | 0.0559 | 0.7133 | 0.0062 | 0.0340 | 0.9336 | 0.1828 | 0.6558 | 0.3936 | 0.5337 | nan | 0.3272 | 0.6369 | 0.6002 | 0.0 | 0.9521 | 0.8448 | 0.9834 | 0.0054 | 0.2294 | 0.4832 | 0.0 | nan | 0.7613 | 0.8771 | 0.7048 | 0.7507 | 0.4042 | nan | 0.5878 | 0.6479 | 0.0383 | 0.8658 | 0.1062 | 0.0 | 0.4355 | 0.0559 | 0.5919 | 0.0061 | 0.0334 | 0.7728 | 0.1631 | 0.5500 | 0.3294 | 0.3916 | nan | 0.2608 | 0.5128 | 0.3933 | 0.0 | 0.8887 | 0.7658 | 0.9533 | 0.0044 | 0.1359 | 0.3871 | 0.0 |
| 0.0557 | 43.8 | 8760 | 0.7293 | 0.4202 | 0.5003 | 0.8787 | nan | 0.8414 | 0.9593 | 0.7976 | 0.8454 | 0.5346 | nan | 0.7111 | 0.8324 | 0.0655 | 0.9556 | 0.1376 | 0.0 | 0.7028 | 0.0795 | 0.7066 | 0.0112 | 0.0297 | 0.9321 | 0.2095 | 0.6408 | 0.3838 | 0.5562 | nan | 0.3337 | 0.6187 | 0.5912 | 0.0 | 0.9529 | 0.8491 | 0.9826 | 0.0117 | 0.2366 | 0.4989 | 0.0 | nan | 0.7594 | 0.8760 | 0.7044 | 0.7460 | 0.4061 | nan | 0.5894 | 0.6491 | 0.0391 | 0.8650 | 0.1192 | 0.0 | 0.4528 | 0.0795 | 0.5932 | 0.0110 | 0.0292 | 0.7729 | 0.1842 | 0.5456 | 0.3261 | 0.3934 | nan | 0.2579 | 0.5118 | 0.3937 | 0.0 | 0.8887 | 0.7667 | 0.9542 | 0.0090 | 0.1334 | 0.3893 | 0.0 |
| 0.0655 | 43.9 | 8780 | 0.7311 | 0.4198 | 0.5014 | 0.8783 | nan | 0.8393 | 0.9554 | 0.8100 | 0.8526 | 0.5444 | nan | 0.7158 | 0.8314 | 0.0777 | 0.9564 | 0.1426 | 0.0 | 0.7048 | 0.0661 | 0.7165 | 0.0070 | 0.0355 | 0.9295 | 0.2021 | 0.6563 | 0.3785 | 0.5421 | nan | 0.3485 | 0.6098 | 0.5809 | 0.0 | 0.9562 | 0.8432 | 0.9819 | 0.0113 | 0.2569 | 0.4911 | 0.0 | nan | 0.7591 | 0.8774 | 0.6880 | 0.7491 | 0.4010 | nan | 0.5890 | 0.6512 | 0.0459 | 0.8645 | 0.1234 | 0.0 | 0.4569 | 0.0661 | 0.5894 | 0.0069 | 0.0348 | 0.7745 | 0.1777 | 0.5502 | 0.3229 | 0.3875 | nan | 0.2705 | 0.5105 | 0.3950 | 0.0 | 0.8871 | 0.7637 | 0.9543 | 0.0087 | 0.1385 | 0.3887 | 0.0 |
| 0.1089 | 44.0 | 8800 | 0.7312 | 0.4192 | 0.4999 | 0.8783 | nan | 0.8397 | 0.9578 | 0.8063 | 0.8582 | 0.5364 | nan | 0.6964 | 0.8238 | 0.0701 | 0.9568 | 0.1412 | 0.0 | 0.6988 | 0.0710 | 0.7186 | 0.0024 | 0.0395 | 0.9293 | 0.1825 | 0.6419 | 0.3726 | 0.5495 | nan | 0.3361 | 0.6296 | 0.5986 | 0.0 | 0.9542 | 0.8373 | 0.9846 | 0.0106 | 0.2464 | 0.5059 | 0.0 | nan | 0.7582 | 0.8774 | 0.6870 | 0.7482 | 0.4026 | nan | 0.5888 | 0.6556 | 0.0403 | 0.8647 | 0.1220 | 0.0 | 0.4713 | 0.0709 | 0.5898 | 0.0024 | 0.0388 | 0.7742 | 0.1633 | 0.5487 | 0.3173 | 0.3890 | nan | 0.2694 | 0.5097 | 0.3926 | 0.0 | 0.8871 | 0.7616 | 0.9533 | 0.0082 | 0.1322 | 0.3913 | 0.0 |
| 0.0651 | 44.1 | 8820 | 0.7258 | 0.4167 | 0.4960 | 0.8781 | nan | 0.8389 | 0.9580 | 0.8069 | 0.8540 | 0.5310 | nan | 0.7010 | 0.8305 | 0.0672 | 0.9582 | 0.1303 | 0.0 | 0.6921 | 0.0439 | 0.7103 | 0.0 | 0.0331 | 0.9301 | 0.1987 | 0.6444 | 0.3661 | 0.5154 | nan | 0.3231 | 0.6235 | 0.5817 | 0.0 | 0.9546 | 0.8372 | 0.9849 | 0.0090 | 0.2459 | 0.5016 | 0.0 | nan | 0.7581 | 0.8772 | 0.6853 | 0.7509 | 0.4018 | nan | 0.5875 | 0.6505 | 0.0400 | 0.8634 | 0.1134 | 0.0 | 0.4602 | 0.0439 | 0.5862 | 0.0 | 0.0328 | 0.7733 | 0.1758 | 0.5474 | 0.3146 | 0.3825 | nan | 0.2584 | 0.5083 | 0.3952 | 0.0 | 0.8866 | 0.7597 | 0.9530 | 0.0068 | 0.1321 | 0.3900 | 0.0 |
| 0.0692 | 44.2 | 8840 | 0.7330 | 0.4161 | 0.4947 | 0.8773 | nan | 0.8412 | 0.9553 | 0.8049 | 0.8450 | 0.5478 | nan | 0.7073 | 0.8187 | 0.0795 | 0.9572 | 0.1261 | 0.0 | 0.6916 | 0.0548 | 0.7093 | 0.0029 | 0.0275 | 0.9332 | 0.1765 | 0.6301 | 0.3681 | 0.5453 | nan | 0.2985 | 0.6152 | 0.5738 | 0.0 | 0.9541 | 0.8444 | 0.9853 | 0.0108 | 0.2370 | 0.4879 | 0.0 | nan | 0.7584 | 0.8765 | 0.6907 | 0.7500 | 0.3961 | nan | 0.5871 | 0.6576 | 0.0452 | 0.8634 | 0.1096 | 0.0 | 0.4611 | 0.0548 | 0.5893 | 0.0028 | 0.0274 | 0.7684 | 0.1583 | 0.5353 | 0.3184 | 0.3885 | nan | 0.2442 | 0.5093 | 0.3966 | 0.0 | 0.8871 | 0.7617 | 0.9526 | 0.0082 | 0.1301 | 0.3866 | 0.0 |
| 0.0654 | 44.3 | 8860 | 0.7268 | 0.4164 | 0.4958 | 0.8779 | nan | 0.8438 | 0.9578 | 0.7944 | 0.8479 | 0.5362 | nan | 0.6987 | 0.8252 | 0.0859 | 0.9576 | 0.1255 | 0.0 | 0.6884 | 0.0533 | 0.7208 | 0.0038 | 0.0228 | 0.9300 | 0.1571 | 0.6303 | 0.4273 | 0.5702 | nan | 0.2575 | 0.6223 | 0.5944 | 0.0 | 0.9520 | 0.8468 | 0.9850 | 0.0115 | 0.2333 | 0.4859 | 0.0 | nan | 0.7596 | 0.8765 | 0.6991 | 0.7497 | 0.3995 | nan | 0.5865 | 0.6540 | 0.0503 | 0.8638 | 0.1088 | 0.0 | 0.4636 | 0.0533 | 0.5866 | 0.0037 | 0.0227 | 0.7688 | 0.1423 | 0.5312 | 0.3468 | 0.4065 | nan | 0.2179 | 0.5087 | 0.3934 | 0.0 | 0.8891 | 0.7646 | 0.9523 | 0.0087 | 0.1321 | 0.3839 | 0.0 |
| 0.0886 | 44.4 | 8880 | 0.7317 | 0.4162 | 0.4946 | 0.8776 | nan | 0.8453 | 0.9574 | 0.7868 | 0.8436 | 0.5339 | nan | 0.7076 | 0.8323 | 0.0835 | 0.9565 | 0.1189 | 0.0 | 0.6836 | 0.0644 | 0.7184 | 0.0029 | 0.0224 | 0.9334 | 0.1645 | 0.6164 | 0.4076 | 0.5555 | nan | 0.2833 | 0.6216 | 0.5878 | 0.0 | 0.9531 | 0.8455 | 0.9836 | 0.0136 | 0.2147 | 0.4873 | 0.0 | nan | 0.7597 | 0.8757 | 0.7058 | 0.7446 | 0.4026 | nan | 0.5884 | 0.6438 | 0.0485 | 0.8646 | 0.1042 | 0.0 | 0.4628 | 0.0644 | 0.5891 | 0.0028 | 0.0223 | 0.7662 | 0.1479 | 0.5247 | 0.3425 | 0.3939 | nan | 0.2349 | 0.5086 | 0.3960 | 0.0 | 0.8889 | 0.7651 | 0.9531 | 0.0099 | 0.1246 | 0.3835 | 0.0 |
| 0.0592 | 44.5 | 8900 | 0.7325 | 0.4189 | 0.4975 | 0.8784 | nan | 0.8458 | 0.9586 | 0.7892 | 0.8429 | 0.5300 | nan | 0.7085 | 0.8330 | 0.1017 | 0.9566 | 0.1281 | 0.0 | 0.6934 | 0.0763 | 0.7256 | 0.0006 | 0.0309 | 0.9290 | 0.2149 | 0.6268 | 0.3929 | 0.5390 | nan | 0.2751 | 0.6182 | 0.5806 | 0.0 | 0.9533 | 0.8541 | 0.9831 | 0.0123 | 0.2190 | 0.5009 | 0.0 | nan | 0.7606 | 0.8758 | 0.7049 | 0.7442 | 0.4042 | nan | 0.5897 | 0.6434 | 0.0576 | 0.8656 | 0.1122 | 0.0 | 0.4635 | 0.0763 | 0.5883 | 0.0005 | 0.0305 | 0.7700 | 0.1886 | 0.5313 | 0.3367 | 0.3915 | nan | 0.2296 | 0.5086 | 0.3965 | 0.0 | 0.8897 | 0.7701 | 0.9537 | 0.0092 | 0.1255 | 0.3859 | 0.0 |
| 0.0756 | 44.6 | 8920 | 0.7285 | 0.4177 | 0.4947 | 0.8778 | nan | 0.8449 | 0.9569 | 0.7980 | 0.8439 | 0.5414 | nan | 0.7156 | 0.8249 | 0.0876 | 0.9550 | 0.1230 | 0.0 | 0.6976 | 0.0827 | 0.7212 | 0.0011 | 0.0269 | 0.9353 | 0.2019 | 0.6128 | 0.3814 | 0.5312 | nan | 0.2838 | 0.6041 | 0.5666 | 0.0 | 0.9534 | 0.8478 | 0.9831 | 0.0133 | 0.2039 | 0.4928 | 0.0 | nan | 0.7604 | 0.8769 | 0.6981 | 0.7464 | 0.4036 | nan | 0.5917 | 0.6492 | 0.0511 | 0.8661 | 0.1076 | 0.0 | 0.4700 | 0.0827 | 0.5898 | 0.0011 | 0.0267 | 0.7655 | 0.1780 | 0.5223 | 0.3287 | 0.3834 | nan | 0.2358 | 0.5075 | 0.3975 | 0.0 | 0.8880 | 0.7659 | 0.9536 | 0.0100 | 0.1227 | 0.3856 | 0.0 |
| 0.0552 | 44.7 | 8940 | 0.7286 | 0.4178 | 0.4960 | 0.8785 | nan | 0.8500 | 0.9568 | 0.7997 | 0.8454 | 0.5380 | nan | 0.7104 | 0.8261 | 0.0826 | 0.9550 | 0.1311 | 0.0 | 0.6737 | 0.0857 | 0.7207 | 0.0013 | 0.0228 | 0.9298 | 0.1966 | 0.6192 | 0.3907 | 0.5334 | nan | 0.2912 | 0.6324 | 0.5819 | 0.0 | 0.9558 | 0.8433 | 0.9824 | 0.0117 | 0.2209 | 0.4822 | 0.0 | nan | 0.7627 | 0.8784 | 0.6998 | 0.7509 | 0.4015 | nan | 0.5918 | 0.6423 | 0.0479 | 0.8664 | 0.1136 | 0.0 | 0.4565 | 0.0857 | 0.5902 | 0.0013 | 0.0225 | 0.7685 | 0.1739 | 0.5288 | 0.3294 | 0.3864 | nan | 0.2423 | 0.5103 | 0.3982 | 0.0 | 0.8870 | 0.7651 | 0.9538 | 0.0089 | 0.1235 | 0.3812 | 0.0 |
| 0.0666 | 44.8 | 8960 | 0.7386 | 0.4175 | 0.4952 | 0.8780 | nan | 0.8404 | 0.9579 | 0.8046 | 0.8503 | 0.5445 | nan | 0.7113 | 0.8195 | 0.1001 | 0.9529 | 0.1387 | 0.0 | 0.6556 | 0.0980 | 0.7253 | 0.0010 | 0.0275 | 0.9326 | 0.1886 | 0.6285 | 0.3736 | 0.5390 | nan | 0.2726 | 0.6155 | 0.5677 | 0.0 | 0.9554 | 0.8414 | 0.9833 | 0.0083 | 0.2342 | 0.4780 | 0.0 | nan | 0.7598 | 0.8779 | 0.6886 | 0.7473 | 0.4052 | nan | 0.5924 | 0.6417 | 0.0559 | 0.8671 | 0.1196 | 0.0 | 0.4493 | 0.0980 | 0.5946 | 0.0010 | 0.0273 | 0.7685 | 0.1679 | 0.5325 | 0.3207 | 0.3926 | nan | 0.2302 | 0.5093 | 0.3996 | 0.0 | 0.8867 | 0.7644 | 0.9536 | 0.0063 | 0.1233 | 0.3797 | 0.0 |
| 0.062 | 44.9 | 8980 | 0.7354 | 0.4167 | 0.4943 | 0.8776 | nan | 0.8392 | 0.9559 | 0.8056 | 0.8510 | 0.5501 | nan | 0.7168 | 0.8244 | 0.0703 | 0.9562 | 0.1388 | 0.0 | 0.6559 | 0.0825 | 0.7042 | 0.0 | 0.0273 | 0.9347 | 0.2042 | 0.6299 | 0.3684 | 0.5330 | nan | 0.2867 | 0.6248 | 0.5721 | 0.0 | 0.9543 | 0.8418 | 0.9828 | 0.0084 | 0.2265 | 0.4720 | 0.0 | nan | 0.7588 | 0.8780 | 0.6875 | 0.7494 | 0.4034 | nan | 0.5932 | 0.6421 | 0.0418 | 0.8650 | 0.1199 | 0.0 | 0.4503 | 0.0825 | 0.5912 | 0.0 | 0.0270 | 0.7679 | 0.1794 | 0.5320 | 0.3182 | 0.3908 | nan | 0.2400 | 0.5106 | 0.3981 | 0.0 | 0.8870 | 0.7627 | 0.9540 | 0.0062 | 0.1210 | 0.3772 | 0.0 |
| 0.0723 | 45.0 | 9000 | 0.7471 | 0.4173 | 0.4954 | 0.8778 | nan | 0.8333 | 0.9563 | 0.8064 | 0.8629 | 0.5388 | nan | 0.7114 | 0.8171 | 0.0703 | 0.9572 | 0.1278 | 0.0 | 0.6566 | 0.0568 | 0.7102 | 0.0 | 0.0346 | 0.9322 | 0.1989 | 0.6334 | 0.3889 | 0.5232 | nan | 0.3184 | 0.6227 | 0.5818 | 0.0 | 0.9517 | 0.8613 | 0.9836 | 0.0112 | 0.2188 | 0.4869 | 0.0 | nan | 0.7549 | 0.8780 | 0.6770 | 0.7456 | 0.4055 | nan | 0.5931 | 0.6419 | 0.0425 | 0.8642 | 0.1121 | 0.0 | 0.4577 | 0.0568 | 0.5839 | 0.0 | 0.0343 | 0.7700 | 0.1761 | 0.5356 | 0.3285 | 0.3927 | nan | 0.2656 | 0.5089 | 0.3986 | 0.0 | 0.8898 | 0.7708 | 0.9535 | 0.0083 | 0.1270 | 0.3819 | 0.0 |
| 0.0648 | 45.1 | 9020 | 0.7314 | 0.4180 | 0.4975 | 0.8780 | nan | 0.8368 | 0.9564 | 0.8026 | 0.8635 | 0.5396 | nan | 0.7064 | 0.8212 | 0.0805 | 0.9556 | 0.1300 | 0.0 | 0.6996 | 0.0558 | 0.7156 | 0.0 | 0.0289 | 0.9301 | 0.2240 | 0.6456 | 0.3784 | 0.5095 | nan | 0.3363 | 0.6191 | 0.5911 | 0.0 | 0.9550 | 0.8447 | 0.9817 | 0.0094 | 0.2181 | 0.4845 | 0.0 | nan | 0.7572 | 0.8784 | 0.6826 | 0.7447 | 0.4042 | nan | 0.5951 | 0.6395 | 0.0479 | 0.8653 | 0.1139 | 0.0 | 0.4665 | 0.0558 | 0.5832 | 0.0 | 0.0287 | 0.7728 | 0.1957 | 0.5406 | 0.3217 | 0.3863 | nan | 0.2713 | 0.5095 | 0.3938 | 0.0 | 0.8876 | 0.7644 | 0.9542 | 0.0069 | 0.1283 | 0.3792 | 0.0 |
| 0.0623 | 45.2 | 9040 | 0.7278 | 0.4188 | 0.4995 | 0.8778 | nan | 0.8387 | 0.9556 | 0.8078 | 0.8564 | 0.5497 | nan | 0.7147 | 0.8184 | 0.0845 | 0.9571 | 0.1374 | 0.0 | 0.7057 | 0.0565 | 0.7144 | 0.0 | 0.0271 | 0.9300 | 0.2166 | 0.6403 | 0.3961 | 0.5277 | nan | 0.3465 | 0.6230 | 0.5861 | 0.0 | 0.9538 | 0.8378 | 0.9831 | 0.0104 | 0.2284 | 0.4816 | 0.0 | nan | 0.7591 | 0.8794 | 0.6766 | 0.7506 | 0.3999 | nan | 0.5965 | 0.6427 | 0.0491 | 0.8646 | 0.1196 | 0.0 | 0.4744 | 0.0565 | 0.5865 | 0.0 | 0.0269 | 0.7725 | 0.1910 | 0.5386 | 0.3288 | 0.3946 | nan | 0.2760 | 0.5099 | 0.3924 | 0.0 | 0.8870 | 0.7582 | 0.9538 | 0.0074 | 0.1293 | 0.3795 | 0.0 |
| 0.0933 | 45.3 | 9060 | 0.7315 | 0.4165 | 0.4958 | 0.8770 | nan | 0.8354 | 0.9562 | 0.8112 | 0.8543 | 0.5486 | nan | 0.7124 | 0.8203 | 0.0950 | 0.9562 | 0.1240 | 0.0 | 0.6678 | 0.0667 | 0.7217 | 0.0 | 0.0277 | 0.9344 | 0.1949 | 0.6166 | 0.3834 | 0.5225 | nan | 0.3350 | 0.6123 | 0.5716 | 0.0 | 0.9552 | 0.8340 | 0.9832 | 0.0097 | 0.2344 | 0.4819 | 0.0 | nan | 0.7565 | 0.8785 | 0.6719 | 0.7522 | 0.4025 | nan | 0.5940 | 0.6437 | 0.0542 | 0.8649 | 0.1081 | 0.0 | 0.4599 | 0.0667 | 0.5888 | 0.0 | 0.0274 | 0.7680 | 0.1737 | 0.5282 | 0.3233 | 0.3892 | nan | 0.2649 | 0.5078 | 0.3946 | 0.0 | 0.8857 | 0.7566 | 0.9535 | 0.0070 | 0.1277 | 0.3796 | 0.0 |
| 0.0677 | 45.4 | 9080 | 0.7344 | 0.4174 | 0.4972 | 0.8772 | nan | 0.8379 | 0.9557 | 0.8075 | 0.8528 | 0.5457 | nan | 0.7049 | 0.8222 | 0.1007 | 0.9576 | 0.1268 | 0.0 | 0.6637 | 0.0536 | 0.7270 | 0.0 | 0.0301 | 0.9329 | 0.1934 | 0.6329 | 0.3872 | 0.5320 | nan | 0.3338 | 0.6185 | 0.5816 | 0.0 | 0.9532 | 0.8411 | 0.9840 | 0.0124 | 0.2396 | 0.4811 | 0.0 | nan | 0.7567 | 0.8781 | 0.6787 | 0.7525 | 0.3997 | nan | 0.5912 | 0.6507 | 0.0581 | 0.8639 | 0.1107 | 0.0 | 0.4616 | 0.0536 | 0.5893 | 0.0 | 0.0297 | 0.7699 | 0.1720 | 0.5350 | 0.3262 | 0.3926 | nan | 0.2656 | 0.5087 | 0.3940 | 0.0 | 0.8867 | 0.7585 | 0.9531 | 0.0086 | 0.1308 | 0.3809 | 0.0 |
| 0.0562 | 45.5 | 9100 | 0.7293 | 0.4182 | 0.4980 | 0.8775 | nan | 0.8373 | 0.9568 | 0.8015 | 0.8531 | 0.5431 | nan | 0.7127 | 0.8264 | 0.0872 | 0.9570 | 0.1405 | 0.0 | 0.6721 | 0.0682 | 0.7243 | 0.0 | 0.0273 | 0.9280 | 0.2170 | 0.6294 | 0.3873 | 0.5305 | nan | 0.3120 | 0.6229 | 0.5872 | 0.0 | 0.9560 | 0.8360 | 0.9832 | 0.0102 | 0.2377 | 0.4911 | 0.0 | nan | 0.7569 | 0.8778 | 0.6855 | 0.7522 | 0.4006 | nan | 0.5921 | 0.6538 | 0.0508 | 0.8642 | 0.1226 | 0.0 | 0.4610 | 0.0682 | 0.5914 | 0.0 | 0.0270 | 0.7720 | 0.1898 | 0.5368 | 0.3270 | 0.3875 | nan | 0.2549 | 0.5103 | 0.3902 | 0.0 | 0.8855 | 0.7555 | 0.9537 | 0.0074 | 0.1266 | 0.3816 | 0.0 |
| 0.0722 | 45.6 | 9120 | 0.7235 | 0.4166 | 0.4948 | 0.8775 | nan | 0.8413 | 0.9552 | 0.7981 | 0.8550 | 0.5459 | nan | 0.7196 | 0.8248 | 0.0916 | 0.9560 | 0.1451 | 0.0 | 0.6633 | 0.0771 | 0.7236 | 0.0 | 0.0392 | 0.9341 | 0.1956 | 0.6222 | 0.3749 | 0.5074 | nan | 0.2789 | 0.6205 | 0.5841 | 0.0 | 0.9543 | 0.8399 | 0.9834 | 0.0040 | 0.2227 | 0.4759 | 0.0 | nan | 0.7596 | 0.8782 | 0.6895 | 0.7517 | 0.3923 | nan | 0.5944 | 0.6488 | 0.0515 | 0.8644 | 0.1259 | 0.0 | 0.4444 | 0.0771 | 0.5943 | 0.0 | 0.0388 | 0.7686 | 0.1723 | 0.5313 | 0.3210 | 0.3871 | nan | 0.2354 | 0.5105 | 0.3925 | 0.0 | 0.8865 | 0.7586 | 0.9536 | 0.0031 | 0.1195 | 0.3787 | 0.0 |
| 0.0855 | 45.7 | 9140 | 0.7305 | 0.4143 | 0.4917 | 0.8777 | nan | 0.8419 | 0.9569 | 0.8012 | 0.8498 | 0.5367 | nan | 0.7175 | 0.8233 | 0.0903 | 0.9561 | 0.1382 | 0.0 | 0.6537 | 0.0677 | 0.7246 | 0.0 | 0.0283 | 0.9335 | 0.1604 | 0.6322 | 0.3685 | 0.5063 | nan | 0.2680 | 0.6185 | 0.5825 | 0.0 | 0.9568 | 0.8389 | 0.9840 | 0.0062 | 0.2316 | 0.4611 | 0.0 | nan | 0.7593 | 0.8777 | 0.6863 | 0.7509 | 0.3992 | nan | 0.5939 | 0.6446 | 0.0520 | 0.8650 | 0.1202 | 0.0 | 0.4408 | 0.0677 | 0.5886 | 0.0 | 0.0282 | 0.7690 | 0.1451 | 0.5347 | 0.3159 | 0.3871 | nan | 0.2261 | 0.5104 | 0.3969 | 0.0 | 0.8861 | 0.7610 | 0.9529 | 0.0048 | 0.1205 | 0.3735 | 0.0 |
| 0.1003 | 45.8 | 9160 | 0.7352 | 0.4134 | 0.4917 | 0.8775 | nan | 0.8407 | 0.9570 | 0.8053 | 0.8416 | 0.5420 | nan | 0.7239 | 0.8304 | 0.0839 | 0.9577 | 0.1438 | 0.0 | 0.6563 | 0.0487 | 0.7149 | 0.0 | 0.0286 | 0.9311 | 0.1542 | 0.6253 | 0.3811 | 0.5337 | nan | 0.2558 | 0.6163 | 0.5718 | 0.0 | 0.9561 | 0.8423 | 0.9833 | 0.0079 | 0.2251 | 0.4753 | 0.0 | nan | 0.7589 | 0.8770 | 0.6796 | 0.7526 | 0.4003 | nan | 0.5916 | 0.6395 | 0.0499 | 0.8648 | 0.1252 | 0.0 | 0.4420 | 0.0487 | 0.5860 | 0.0 | 0.0284 | 0.7701 | 0.1400 | 0.5330 | 0.3208 | 0.3949 | nan | 0.2163 | 0.5095 | 0.3961 | 0.0 | 0.8868 | 0.7634 | 0.9535 | 0.0060 | 0.1182 | 0.3753 | 0.0 |
| 0.0856 | 45.9 | 9180 | 0.7275 | 0.4160 | 0.4940 | 0.8780 | nan | 0.8410 | 0.9569 | 0.8037 | 0.8525 | 0.5318 | nan | 0.7125 | 0.8325 | 0.0641 | 0.9577 | 0.1498 | 0.0 | 0.6451 | 0.0527 | 0.7146 | 0.0 | 0.0274 | 0.9315 | 0.1914 | 0.6320 | 0.3802 | 0.5320 | nan | 0.2852 | 0.6298 | 0.5780 | 0.0 | 0.9526 | 0.8491 | 0.9825 | 0.0073 | 0.2231 | 0.4918 | 0.0 | nan | 0.7590 | 0.8777 | 0.6857 | 0.7505 | 0.3972 | nan | 0.5930 | 0.6430 | 0.0397 | 0.8646 | 0.1305 | 0.0 | 0.4510 | 0.0527 | 0.5873 | 0.0 | 0.0272 | 0.7714 | 0.1695 | 0.5386 | 0.3233 | 0.3932 | nan | 0.2411 | 0.5101 | 0.3956 | 0.0 | 0.8883 | 0.7657 | 0.9541 | 0.0055 | 0.1176 | 0.3792 | 0.0 |
| 0.0818 | 46.0 | 9200 | 0.7458 | 0.4147 | 0.4941 | 0.8772 | nan | 0.8414 | 0.9570 | 0.7964 | 0.8409 | 0.5371 | nan | 0.7188 | 0.8320 | 0.0784 | 0.9571 | 0.1535 | 0.0 | 0.6523 | 0.0480 | 0.7226 | 0.0 | 0.0183 | 0.9319 | 0.1858 | 0.6348 | 0.3898 | 0.5330 | nan | 0.2789 | 0.6231 | 0.5943 | 0.0 | 0.9558 | 0.8330 | 0.9843 | 0.0110 | 0.2373 | 0.4636 | 0.0 | nan | 0.7578 | 0.8759 | 0.6976 | 0.7506 | 0.3975 | nan | 0.5916 | 0.6398 | 0.0482 | 0.8652 | 0.1325 | 0.0 | 0.4373 | 0.0480 | 0.5842 | 0.0 | 0.0181 | 0.7708 | 0.1639 | 0.5377 | 0.3260 | 0.3928 | nan | 0.2324 | 0.5116 | 0.3963 | 0.0 | 0.8858 | 0.7563 | 0.9532 | 0.0081 | 0.1199 | 0.3716 | 0.0 |
| 0.0727 | 46.1 | 9220 | 0.7385 | 0.4162 | 0.4947 | 0.8773 | nan | 0.8434 | 0.9572 | 0.7878 | 0.8435 | 0.5369 | nan | 0.7161 | 0.8285 | 0.0850 | 0.9556 | 0.1520 | 0.0 | 0.6477 | 0.0513 | 0.7257 | 0.0 | 0.0171 | 0.9305 | 0.2151 | 0.6339 | 0.3868 | 0.5109 | nan | 0.2958 | 0.6302 | 0.5912 | 0.0 | 0.9548 | 0.8314 | 0.9839 | 0.0083 | 0.2354 | 0.4753 | 0.0 | nan | 0.7582 | 0.8760 | 0.7016 | 0.7485 | 0.3972 | nan | 0.5919 | 0.6417 | 0.0512 | 0.8663 | 0.1306 | 0.0 | 0.4412 | 0.0513 | 0.5872 | 0.0 | 0.0169 | 0.7718 | 0.1877 | 0.5386 | 0.3260 | 0.3906 | nan | 0.2423 | 0.5121 | 0.3961 | 0.0 | 0.8860 | 0.7552 | 0.9534 | 0.0062 | 0.1189 | 0.3750 | 0.0 |
| 0.0575 | 46.2 | 9240 | 0.7399 | 0.4176 | 0.4962 | 0.8774 | nan | 0.8411 | 0.9561 | 0.7987 | 0.8454 | 0.5384 | nan | 0.7186 | 0.8241 | 0.0766 | 0.9531 | 0.1534 | 0.0 | 0.6517 | 0.0703 | 0.7375 | 0.0 | 0.0285 | 0.9308 | 0.2062 | 0.6480 | 0.3815 | 0.5007 | nan | 0.3178 | 0.6272 | 0.5761 | 0.0 | 0.9552 | 0.8348 | 0.9836 | 0.0087 | 0.2379 | 0.4757 | 0.0 | nan | 0.7579 | 0.8761 | 0.6950 | 0.7505 | 0.3951 | nan | 0.5925 | 0.6410 | 0.0465 | 0.8681 | 0.1319 | 0.0 | 0.4437 | 0.0703 | 0.5958 | 0.0 | 0.0279 | 0.7733 | 0.1823 | 0.5453 | 0.3248 | 0.3814 | nan | 0.2603 | 0.5126 | 0.3966 | 0.0 | 0.8860 | 0.7557 | 0.9535 | 0.0066 | 0.1182 | 0.3746 | 0.0 |
| 0.0972 | 46.3 | 9260 | 0.7251 | 0.4175 | 0.4958 | 0.8779 | nan | 0.8462 | 0.9550 | 0.7966 | 0.8451 | 0.5448 | nan | 0.7146 | 0.8252 | 0.0723 | 0.9575 | 0.1456 | 0.0 | 0.6676 | 0.0647 | 0.7245 | 0.0 | 0.0273 | 0.9313 | 0.1948 | 0.6500 | 0.3861 | 0.5225 | nan | 0.3129 | 0.6088 | 0.5722 | 0.0 | 0.9545 | 0.8397 | 0.9830 | 0.0075 | 0.2369 | 0.4783 | 0.0 | nan | 0.7610 | 0.8766 | 0.7007 | 0.7536 | 0.3934 | nan | 0.5942 | 0.6424 | 0.0442 | 0.8653 | 0.1253 | 0.0 | 0.4524 | 0.0647 | 0.5900 | 0.0 | 0.0269 | 0.7730 | 0.1726 | 0.5446 | 0.3287 | 0.3898 | nan | 0.2568 | 0.5100 | 0.3965 | 0.0 | 0.8871 | 0.7584 | 0.9537 | 0.0058 | 0.1184 | 0.3745 | 0.0 |
| 0.0528 | 46.4 | 9280 | 0.7302 | 0.4166 | 0.4933 | 0.8784 | nan | 0.8492 | 0.9565 | 0.7981 | 0.8473 | 0.5424 | nan | 0.6950 | 0.8226 | 0.0683 | 0.9571 | 0.1401 | 0.0 | 0.6675 | 0.0655 | 0.7211 | 0.0 | 0.0256 | 0.9300 | 0.1869 | 0.6525 | 0.3817 | 0.5119 | nan | 0.2976 | 0.6154 | 0.5809 | 0.0 | 0.9544 | 0.8396 | 0.9842 | 0.0088 | 0.2052 | 0.4807 | 0.0 | nan | 0.7626 | 0.8770 | 0.6993 | 0.7526 | 0.3962 | nan | 0.5926 | 0.6401 | 0.0423 | 0.8654 | 0.1211 | 0.0 | 0.4546 | 0.0655 | 0.5869 | 0.0 | 0.0253 | 0.7730 | 0.1662 | 0.5443 | 0.3279 | 0.3840 | nan | 0.2467 | 0.5094 | 0.3965 | 0.0 | 0.8873 | 0.7608 | 0.9531 | 0.0066 | 0.1164 | 0.3777 | 0.0 |
| 0.0629 | 46.5 | 9300 | 0.7256 | 0.4175 | 0.4942 | 0.8785 | nan | 0.8492 | 0.9565 | 0.7925 | 0.8456 | 0.5273 | nan | 0.7210 | 0.8175 | 0.0515 | 0.9588 | 0.1461 | 0.0 | 0.6703 | 0.0769 | 0.7161 | 0.0 | 0.0221 | 0.9329 | 0.1864 | 0.6420 | 0.3898 | 0.5235 | nan | 0.2992 | 0.6153 | 0.5840 | 0.0 | 0.9537 | 0.8408 | 0.9846 | 0.0105 | 0.2214 | 0.4793 | 0.0 | nan | 0.7628 | 0.8771 | 0.7040 | 0.7518 | 0.3966 | nan | 0.5926 | 0.6505 | 0.0332 | 0.8643 | 0.1260 | 0.0 | 0.4539 | 0.0769 | 0.5916 | 0.0 | 0.0219 | 0.7715 | 0.1656 | 0.5414 | 0.3303 | 0.3870 | nan | 0.2515 | 0.5090 | 0.3982 | 0.0 | 0.8873 | 0.7597 | 0.9529 | 0.0079 | 0.1177 | 0.3779 | 0.0 |
| 0.0586 | 46.6 | 9320 | 0.7344 | 0.4168 | 0.4927 | 0.8779 | nan | 0.8464 | 0.9545 | 0.8018 | 0.8483 | 0.5394 | nan | 0.7172 | 0.8116 | 0.0486 | 0.9574 | 0.1387 | 0.0 | 0.6651 | 0.0812 | 0.7010 | 0.0 | 0.0252 | 0.9338 | 0.1746 | 0.6301 | 0.3853 | 0.5186 | nan | 0.3050 | 0.6162 | 0.5729 | 0.0 | 0.9552 | 0.8382 | 0.9841 | 0.0090 | 0.2169 | 0.4890 | 0.0 | nan | 0.7616 | 0.8774 | 0.6929 | 0.7521 | 0.3965 | nan | 0.5919 | 0.6575 | 0.0306 | 0.8647 | 0.1198 | 0.0 | 0.4507 | 0.0812 | 0.5933 | 0.0 | 0.0249 | 0.7696 | 0.1567 | 0.5370 | 0.3266 | 0.3832 | nan | 0.2577 | 0.5076 | 0.3988 | 0.0 | 0.8866 | 0.7574 | 0.9530 | 0.0069 | 0.1191 | 0.3816 | 0.0 |
| 0.0568 | 46.7 | 9340 | 0.7314 | 0.4166 | 0.4932 | 0.8782 | nan | 0.8463 | 0.9563 | 0.8034 | 0.8480 | 0.5337 | nan | 0.7172 | 0.8189 | 0.0663 | 0.9556 | 0.1397 | 0.0 | 0.6488 | 0.0903 | 0.7265 | 0.0 | 0.0278 | 0.9337 | 0.1693 | 0.6203 | 0.3956 | 0.5112 | nan | 0.2894 | 0.6188 | 0.5791 | 0.0 | 0.9555 | 0.8414 | 0.9835 | 0.0048 | 0.2222 | 0.4803 | 0.0 | nan | 0.7618 | 0.8772 | 0.6908 | 0.7524 | 0.3994 | nan | 0.5930 | 0.6552 | 0.0414 | 0.8665 | 0.1207 | 0.0 | 0.4390 | 0.0903 | 0.5985 | 0.0 | 0.0274 | 0.7683 | 0.1518 | 0.5324 | 0.3318 | 0.3791 | nan | 0.2433 | 0.5085 | 0.3998 | 0.0 | 0.8870 | 0.7603 | 0.9532 | 0.0039 | 0.1184 | 0.3791 | 0.0 |
| 0.0611 | 46.8 | 9360 | 0.7297 | 0.4186 | 0.4960 | 0.8783 | nan | 0.8479 | 0.9551 | 0.8031 | 0.8471 | 0.5461 | nan | 0.7064 | 0.8101 | 0.0863 | 0.9576 | 0.1442 | 0.0 | 0.6391 | 0.0892 | 0.7285 | 0.0 | 0.0249 | 0.9313 | 0.1826 | 0.6372 | 0.3967 | 0.5232 | nan | 0.3067 | 0.6147 | 0.5874 | 0.0 | 0.9550 | 0.8416 | 0.9838 | 0.0089 | 0.2377 | 0.4788 | 0.0 | nan | 0.7624 | 0.8777 | 0.6896 | 0.7528 | 0.3999 | nan | 0.5928 | 0.6580 | 0.0516 | 0.8651 | 0.1244 | 0.0 | 0.4500 | 0.0892 | 0.5927 | 0.0 | 0.0246 | 0.7715 | 0.1615 | 0.5397 | 0.3325 | 0.3890 | nan | 0.2582 | 0.5085 | 0.3975 | 0.0 | 0.8872 | 0.7598 | 0.9531 | 0.0068 | 0.1216 | 0.3773 | 0.0 |
| 0.1038 | 46.9 | 9380 | 0.7274 | 0.4177 | 0.4942 | 0.8783 | nan | 0.8465 | 0.9568 | 0.7992 | 0.8453 | 0.5452 | nan | 0.7094 | 0.8165 | 0.0852 | 0.9587 | 0.1454 | 0.0 | 0.6590 | 0.0907 | 0.7254 | 0.0 | 0.0270 | 0.9278 | 0.1996 | 0.6471 | 0.3748 | 0.4610 | nan | 0.2958 | 0.6211 | 0.5866 | 0.0 | 0.9559 | 0.8365 | 0.9833 | 0.0078 | 0.2260 | 0.4811 | 0.0 | nan | 0.7621 | 0.8770 | 0.6919 | 0.7533 | 0.4018 | nan | 0.5925 | 0.6552 | 0.0508 | 0.8641 | 0.1258 | 0.0 | 0.4486 | 0.0907 | 0.5881 | 0.0 | 0.0267 | 0.7738 | 0.1758 | 0.5457 | 0.3221 | 0.3668 | nan | 0.2496 | 0.5085 | 0.3966 | 0.0 | 0.8863 | 0.7590 | 0.9533 | 0.0059 | 0.1182 | 0.3766 | 0.0 |
| 0.0721 | 47.0 | 9400 | 0.7309 | 0.4177 | 0.4954 | 0.8785 | nan | 0.8496 | 0.9579 | 0.7981 | 0.8473 | 0.5252 | nan | 0.7008 | 0.8264 | 0.0810 | 0.9573 | 0.1389 | 0.0 | 0.6520 | 0.0924 | 0.7349 | 0.0 | 0.0209 | 0.9318 | 0.1881 | 0.6388 | 0.3893 | 0.5309 | nan | 0.2970 | 0.6226 | 0.5797 | 0.0 | 0.9539 | 0.8383 | 0.9824 | 0.0103 | 0.2231 | 0.4843 | 0.0 | nan | 0.7617 | 0.8773 | 0.6923 | 0.7515 | 0.4019 | nan | 0.5902 | 0.6515 | 0.0488 | 0.8656 | 0.1199 | 0.0 | 0.4441 | 0.0924 | 0.5927 | 0.0 | 0.0206 | 0.7722 | 0.1676 | 0.5419 | 0.3287 | 0.3841 | nan | 0.2496 | 0.5102 | 0.3969 | 0.0 | 0.8872 | 0.7594 | 0.9538 | 0.0076 | 0.1178 | 0.3788 | 0.0 |
| 0.0765 | 47.1 | 9420 | 0.7436 | 0.4173 | 0.4947 | 0.8780 | nan | 0.8395 | 0.9568 | 0.7953 | 0.8553 | 0.5420 | nan | 0.7069 | 0.8268 | 0.0823 | 0.9524 | 0.1397 | 0.0 | 0.6590 | 0.0776 | 0.7365 | 0.0 | 0.0291 | 0.9325 | 0.1797 | 0.6467 | 0.3971 | 0.5116 | nan | 0.2776 | 0.6242 | 0.5679 | 0.0 | 0.9525 | 0.8448 | 0.9835 | 0.0085 | 0.2178 | 0.4879 | 0.0 | nan | 0.7576 | 0.8766 | 0.6852 | 0.7475 | 0.4050 | nan | 0.5935 | 0.6497 | 0.0490 | 0.8680 | 0.1209 | 0.0 | 0.4457 | 0.0776 | 0.5973 | 0.0 | 0.0285 | 0.7724 | 0.1608 | 0.5445 | 0.3343 | 0.3842 | nan | 0.2379 | 0.5114 | 0.3974 | 0.0 | 0.8881 | 0.7620 | 0.9534 | 0.0065 | 0.1186 | 0.3802 | 0.0 |
| 0.0778 | 47.2 | 9440 | 0.7376 | 0.4153 | 0.4919 | 0.8779 | nan | 0.8420 | 0.9571 | 0.7947 | 0.8531 | 0.5307 | nan | 0.7137 | 0.8216 | 0.0704 | 0.9525 | 0.1361 | 0.0 | 0.6425 | 0.0739 | 0.7302 | 0.0 | 0.0221 | 0.9352 | 0.1703 | 0.6248 | 0.3884 | 0.5102 | nan | 0.2715 | 0.6262 | 0.5748 | 0.0 | 0.9541 | 0.8482 | 0.9821 | 0.0113 | 0.2260 | 0.4762 | 0.0 | nan | 0.7585 | 0.8773 | 0.6851 | 0.7481 | 0.4032 | nan | 0.5925 | 0.6512 | 0.0433 | 0.8679 | 0.1177 | 0.0 | 0.4427 | 0.0739 | 0.5972 | 0.0 | 0.0217 | 0.7690 | 0.1527 | 0.5360 | 0.3284 | 0.3733 | nan | 0.2334 | 0.5107 | 0.3990 | 0.0 | 0.8876 | 0.7631 | 0.9538 | 0.0084 | 0.1165 | 0.3760 | 0.0 |
| 0.0621 | 47.3 | 9460 | 0.7279 | 0.4137 | 0.4915 | 0.8774 | nan | 0.8375 | 0.9566 | 0.8020 | 0.8541 | 0.5428 | nan | 0.7096 | 0.8224 | 0.0794 | 0.9576 | 0.1345 | 0.0 | 0.6602 | 0.0616 | 0.7244 | 0.0 | 0.0222 | 0.9324 | 0.1667 | 0.6315 | 0.3798 | 0.5007 | nan | 0.2676 | 0.6308 | 0.5884 | 0.0 | 0.9567 | 0.8354 | 0.9810 | 0.0079 | 0.2086 | 0.4761 | 0.0 | nan | 0.7575 | 0.8770 | 0.6783 | 0.7480 | 0.4013 | nan | 0.5942 | 0.6488 | 0.0470 | 0.8652 | 0.1168 | 0.0 | 0.4413 | 0.0616 | 0.5908 | 0.0 | 0.0220 | 0.7701 | 0.1501 | 0.5382 | 0.3234 | 0.3744 | nan | 0.2276 | 0.5094 | 0.3971 | 0.0 | 0.8862 | 0.7601 | 0.9539 | 0.0060 | 0.1150 | 0.3776 | 0.0 |
| 0.0558 | 47.4 | 9480 | 0.7360 | 0.4143 | 0.4952 | 0.8770 | nan | 0.8385 | 0.9552 | 0.8044 | 0.8521 | 0.5415 | nan | 0.7143 | 0.8293 | 0.0805 | 0.9582 | 0.1368 | 0.0 | 0.6804 | 0.0656 | 0.7297 | 0.0 | 0.0222 | 0.9316 | 0.1784 | 0.6315 | 0.3912 | 0.5249 | nan | 0.2854 | 0.6190 | 0.5898 | 0.0 | 0.9560 | 0.8343 | 0.9832 | 0.0095 | 0.2359 | 0.4682 | 0.0 | nan | 0.7570 | 0.8768 | 0.6773 | 0.7489 | 0.3980 | nan | 0.5937 | 0.6465 | 0.0482 | 0.8650 | 0.1187 | 0.0 | 0.4391 | 0.0656 | 0.5893 | 0.0 | 0.0220 | 0.7709 | 0.1589 | 0.5394 | 0.3281 | 0.3818 | nan | 0.2384 | 0.5090 | 0.3943 | 0.0 | 0.8860 | 0.7567 | 0.9533 | 0.0072 | 0.1149 | 0.3720 | 0.0 |
| 0.0726 | 47.5 | 9500 | 0.7314 | 0.4160 | 0.4960 | 0.8772 | nan | 0.8422 | 0.9544 | 0.7934 | 0.8513 | 0.5438 | nan | 0.7173 | 0.8243 | 0.0799 | 0.9579 | 0.1533 | 0.0 | 0.7087 | 0.0749 | 0.7255 | 0.0 | 0.0160 | 0.9311 | 0.2048 | 0.6372 | 0.3851 | 0.5228 | nan | 0.2714 | 0.6266 | 0.5853 | 0.0 | 0.9553 | 0.8319 | 0.9832 | 0.0115 | 0.2038 | 0.4784 | 0.0 | nan | 0.7578 | 0.8768 | 0.6892 | 0.7488 | 0.3959 | nan | 0.5926 | 0.6495 | 0.0482 | 0.8648 | 0.1313 | 0.0 | 0.4538 | 0.0749 | 0.5859 | 0.0 | 0.0158 | 0.7718 | 0.1794 | 0.5413 | 0.3257 | 0.3844 | nan | 0.2306 | 0.5098 | 0.3921 | 0.0 | 0.8859 | 0.7547 | 0.9535 | 0.0087 | 0.1108 | 0.3771 | 0.0 |
| 0.0779 | 47.6 | 9520 | 0.7363 | 0.4140 | 0.4934 | 0.8770 | nan | 0.8399 | 0.9569 | 0.7949 | 0.8506 | 0.5383 | nan | 0.7144 | 0.8275 | 0.0841 | 0.9571 | 0.1461 | 0.0 | 0.6956 | 0.0652 | 0.7286 | 0.0 | 0.0193 | 0.9326 | 0.1878 | 0.6376 | 0.3843 | 0.5204 | nan | 0.2447 | 0.6152 | 0.5780 | 0.0 | 0.9556 | 0.8259 | 0.9832 | 0.0079 | 0.2296 | 0.4659 | 0.0 | nan | 0.7575 | 0.8757 | 0.6880 | 0.7494 | 0.3984 | nan | 0.5922 | 0.6447 | 0.0499 | 0.8652 | 0.1262 | 0.0 | 0.4449 | 0.0652 | 0.5913 | 0.0 | 0.0191 | 0.7713 | 0.1667 | 0.5406 | 0.3256 | 0.3877 | nan | 0.2083 | 0.5086 | 0.3915 | 0.0 | 0.8857 | 0.7537 | 0.9534 | 0.0062 | 0.1125 | 0.3698 | 0.0 |
| 0.0684 | 47.7 | 9540 | 0.7313 | 0.4147 | 0.4948 | 0.8773 | nan | 0.8394 | 0.9563 | 0.7983 | 0.8544 | 0.5386 | nan | 0.7088 | 0.8248 | 0.0835 | 0.9565 | 0.1451 | 0.0 | 0.6814 | 0.0755 | 0.7316 | 0.0 | 0.0193 | 0.9340 | 0.1910 | 0.6379 | 0.3899 | 0.5246 | nan | 0.2466 | 0.6246 | 0.5960 | 0.0 | 0.9549 | 0.8363 | 0.9820 | 0.0070 | 0.2391 | 0.4575 | 0.0 | nan | 0.7574 | 0.8760 | 0.6850 | 0.7473 | 0.3987 | nan | 0.5922 | 0.6459 | 0.0498 | 0.8657 | 0.1249 | 0.0 | 0.4395 | 0.0755 | 0.5940 | 0.0 | 0.0191 | 0.7708 | 0.1686 | 0.5411 | 0.3285 | 0.3841 | nan | 0.2108 | 0.5105 | 0.3936 | 0.0 | 0.8873 | 0.7602 | 0.9538 | 0.0055 | 0.1153 | 0.3687 | 0.0 |
| 0.0506 | 47.8 | 9560 | 0.7419 | 0.4159 | 0.4964 | 0.8773 | nan | 0.8391 | 0.9555 | 0.7997 | 0.8506 | 0.5418 | nan | 0.7197 | 0.8267 | 0.0754 | 0.9557 | 0.1434 | 0.0 | 0.6838 | 0.0789 | 0.7314 | 0.0 | 0.0211 | 0.9315 | 0.2033 | 0.6406 | 0.3975 | 0.5284 | nan | 0.2621 | 0.6212 | 0.5835 | 0.0 | 0.9555 | 0.8379 | 0.9829 | 0.0084 | 0.2466 | 0.4632 | 0.0 | nan | 0.7574 | 0.8760 | 0.6866 | 0.7498 | 0.3988 | nan | 0.5916 | 0.6472 | 0.0461 | 0.8665 | 0.1237 | 0.0 | 0.4367 | 0.0789 | 0.5946 | 0.0 | 0.0208 | 0.7718 | 0.1779 | 0.5414 | 0.3328 | 0.3886 | nan | 0.2215 | 0.5104 | 0.3945 | 0.0 | 0.8871 | 0.7591 | 0.9536 | 0.0066 | 0.1176 | 0.3700 | 0.0 |
| 0.088 | 47.9 | 9580 | 0.7372 | 0.4154 | 0.4948 | 0.8770 | nan | 0.8372 | 0.9557 | 0.7968 | 0.8555 | 0.5386 | nan | 0.7190 | 0.8235 | 0.0752 | 0.9572 | 0.1330 | 0.0 | 0.6780 | 0.0705 | 0.7250 | 0.0 | 0.0221 | 0.9338 | 0.2007 | 0.6348 | 0.3928 | 0.5277 | nan | 0.2733 | 0.6200 | 0.5778 | 0.0 | 0.9557 | 0.8324 | 0.9817 | 0.0100 | 0.2410 | 0.4647 | 0.0 | nan | 0.7565 | 0.8758 | 0.6889 | 0.7478 | 0.3999 | nan | 0.5922 | 0.6489 | 0.0459 | 0.8654 | 0.1161 | 0.0 | 0.4375 | 0.0705 | 0.5903 | 0.0 | 0.0219 | 0.7702 | 0.1762 | 0.5390 | 0.3303 | 0.3902 | nan | 0.2294 | 0.5097 | 0.3954 | 0.0 | 0.8866 | 0.7577 | 0.9539 | 0.0076 | 0.1170 | 0.3704 | 0.0 |
| 0.0708 | 48.0 | 9600 | 0.7499 | 0.4138 | 0.4932 | 0.8768 | nan | 0.8380 | 0.9567 | 0.8049 | 0.8498 | 0.5414 | nan | 0.7080 | 0.8187 | 0.0876 | 0.9575 | 0.1376 | 0.0 | 0.6679 | 0.0770 | 0.7211 | 0.0 | 0.0208 | 0.9348 | 0.1811 | 0.6242 | 0.3758 | 0.5263 | nan | 0.2632 | 0.6166 | 0.5870 | 0.0 | 0.9565 | 0.8334 | 0.9838 | 0.0097 | 0.2509 | 0.4512 | 0.0 | nan | 0.7566 | 0.8760 | 0.6799 | 0.7510 | 0.4006 | nan | 0.5910 | 0.6498 | 0.0504 | 0.8651 | 0.1189 | 0.0 | 0.4321 | 0.0770 | 0.5895 | 0.0 | 0.0206 | 0.7681 | 0.1609 | 0.5345 | 0.3214 | 0.3879 | nan | 0.2187 | 0.5091 | 0.3942 | 0.0 | 0.8860 | 0.7582 | 0.9531 | 0.0074 | 0.1174 | 0.3658 | 0.0 |
| 0.0619 | 48.1 | 9620 | 0.7347 | 0.4134 | 0.4906 | 0.8774 | nan | 0.8400 | 0.9564 | 0.7991 | 0.8501 | 0.5427 | nan | 0.7101 | 0.8264 | 0.0870 | 0.9591 | 0.1333 | 0.0 | 0.6645 | 0.0631 | 0.7117 | 0.0 | 0.0173 | 0.9342 | 0.1844 | 0.6364 | 0.3685 | 0.4912 | nan | 0.2683 | 0.6247 | 0.5788 | 0.0 | 0.9545 | 0.8391 | 0.9843 | 0.0087 | 0.2019 | 0.4636 | 0.0 | nan | 0.7576 | 0.8762 | 0.6888 | 0.7512 | 0.4006 | nan | 0.5913 | 0.6446 | 0.0503 | 0.8633 | 0.1157 | 0.0 | 0.4366 | 0.0631 | 0.5840 | 0.0 | 0.0172 | 0.7691 | 0.1639 | 0.5377 | 0.3190 | 0.3767 | nan | 0.2234 | 0.5091 | 0.3951 | 0.0 | 0.8869 | 0.7603 | 0.9529 | 0.0067 | 0.1134 | 0.3731 | 0.0 |
| 0.0679 | 48.2 | 9640 | 0.7443 | 0.4146 | 0.4932 | 0.8774 | nan | 0.8398 | 0.9563 | 0.8032 | 0.8523 | 0.5367 | nan | 0.7114 | 0.8243 | 0.0852 | 0.9570 | 0.1333 | 0.0 | 0.6632 | 0.0695 | 0.7156 | 0.0 | 0.0205 | 0.9321 | 0.1847 | 0.6359 | 0.3747 | 0.5341 | nan | 0.2829 | 0.6160 | 0.5802 | 0.0 | 0.9557 | 0.8381 | 0.9843 | 0.0100 | 0.2119 | 0.4738 | 0.0 | nan | 0.7574 | 0.8765 | 0.6845 | 0.7502 | 0.4017 | nan | 0.5913 | 0.6466 | 0.0493 | 0.8652 | 0.1155 | 0.0 | 0.4376 | 0.0695 | 0.5849 | 0.0 | 0.0204 | 0.7699 | 0.1649 | 0.5374 | 0.3215 | 0.3882 | nan | 0.2340 | 0.5089 | 0.3947 | 0.0 | 0.8867 | 0.7605 | 0.9530 | 0.0075 | 0.1149 | 0.3748 | 0.0 |
| 0.0938 | 48.3 | 9660 | 0.7429 | 0.4140 | 0.4931 | 0.8774 | nan | 0.8406 | 0.9563 | 0.8019 | 0.8513 | 0.5320 | nan | 0.7138 | 0.8390 | 0.0770 | 0.9569 | 0.1339 | 0.0 | 0.6531 | 0.0543 | 0.7198 | 0.0 | 0.0187 | 0.9321 | 0.1847 | 0.6366 | 0.3761 | 0.5390 | nan | 0.2840 | 0.6273 | 0.5741 | 0.0 | 0.9549 | 0.8357 | 0.9836 | 0.0111 | 0.2154 | 0.4776 | 0.0 | nan | 0.7576 | 0.8767 | 0.6863 | 0.7500 | 0.4008 | nan | 0.5908 | 0.6389 | 0.0470 | 0.8657 | 0.1162 | 0.0 | 0.4395 | 0.0543 | 0.5858 | 0.0 | 0.0185 | 0.7704 | 0.1651 | 0.5387 | 0.3217 | 0.3894 | nan | 0.2368 | 0.5099 | 0.3934 | 0.0 | 0.8866 | 0.7584 | 0.9532 | 0.0083 | 0.1141 | 0.3750 | 0.0 |
| 0.0591 | 48.4 | 9680 | 0.7427 | 0.4138 | 0.4931 | 0.8771 | nan | 0.8375 | 0.9563 | 0.8043 | 0.8503 | 0.5410 | nan | 0.7166 | 0.8399 | 0.0708 | 0.9560 | 0.1297 | 0.0 | 0.6557 | 0.0510 | 0.7179 | 0.0 | 0.0270 | 0.9321 | 0.1910 | 0.6275 | 0.3767 | 0.5428 | nan | 0.2835 | 0.6128 | 0.5744 | 0.0 | 0.9556 | 0.8359 | 0.9835 | 0.0084 | 0.2141 | 0.4868 | 0.0 | nan | 0.7571 | 0.8764 | 0.6832 | 0.7505 | 0.4016 | nan | 0.5924 | 0.6416 | 0.0450 | 0.8665 | 0.1128 | 0.0 | 0.4379 | 0.0510 | 0.5873 | 0.0 | 0.0268 | 0.7699 | 0.1697 | 0.5351 | 0.3212 | 0.3897 | nan | 0.2340 | 0.5080 | 0.3918 | 0.0 | 0.8863 | 0.7582 | 0.9532 | 0.0065 | 0.1118 | 0.3760 | 0.0 |
| 0.0523 | 48.5 | 9700 | 0.7421 | 0.4144 | 0.4949 | 0.8775 | nan | 0.8384 | 0.9572 | 0.8026 | 0.8506 | 0.5378 | nan | 0.7151 | 0.8431 | 0.0752 | 0.9573 | 0.1406 | 0.0 | 0.6618 | 0.0523 | 0.7143 | 0.0 | 0.0241 | 0.9290 | 0.1937 | 0.6402 | 0.3804 | 0.5604 | nan | 0.2732 | 0.6196 | 0.5871 | 0.0 | 0.9550 | 0.8367 | 0.9846 | 0.0083 | 0.2182 | 0.4810 | 0.0 | nan | 0.7572 | 0.8761 | 0.6848 | 0.7501 | 0.4023 | nan | 0.5922 | 0.6393 | 0.0462 | 0.8657 | 0.1215 | 0.0 | 0.4336 | 0.0523 | 0.5862 | 0.0 | 0.0239 | 0.7723 | 0.1720 | 0.5407 | 0.3235 | 0.3985 | nan | 0.2255 | 0.5089 | 0.3921 | 0.0 | 0.8870 | 0.7597 | 0.9527 | 0.0065 | 0.1145 | 0.3759 | 0.0 |
| 0.0637 | 48.6 | 9720 | 0.7331 | 0.4134 | 0.4923 | 0.8774 | nan | 0.8411 | 0.9564 | 0.8016 | 0.8486 | 0.5389 | nan | 0.7131 | 0.8436 | 0.0734 | 0.9559 | 0.1355 | 0.0 | 0.6678 | 0.0479 | 0.7143 | 0.0 | 0.0212 | 0.9321 | 0.1742 | 0.6388 | 0.3770 | 0.5414 | nan | 0.2821 | 0.6092 | 0.5614 | 0.0 | 0.9559 | 0.8377 | 0.9840 | 0.0100 | 0.2209 | 0.4709 | 0.0 | nan | 0.7578 | 0.8764 | 0.6842 | 0.7507 | 0.4014 | nan | 0.5907 | 0.6393 | 0.0455 | 0.8661 | 0.1174 | 0.0 | 0.4289 | 0.0479 | 0.5867 | 0.0 | 0.0211 | 0.7708 | 0.1560 | 0.5389 | 0.3215 | 0.3930 | nan | 0.2359 | 0.5076 | 0.3937 | 0.0 | 0.8865 | 0.7603 | 0.9530 | 0.0076 | 0.1152 | 0.3744 | 0.0 |
| 0.0684 | 48.7 | 9740 | 0.7413 | 0.4147 | 0.4926 | 0.8777 | nan | 0.8393 | 0.9575 | 0.7960 | 0.8495 | 0.5399 | nan | 0.7155 | 0.8305 | 0.0608 | 0.9568 | 0.1343 | 0.0 | 0.6793 | 0.0566 | 0.7062 | 0.0 | 0.0236 | 0.9330 | 0.1737 | 0.6419 | 0.3874 | 0.5362 | nan | 0.2728 | 0.6194 | 0.5792 | 0.0 | 0.9542 | 0.8375 | 0.9835 | 0.0097 | 0.2096 | 0.4797 | 0.0 | nan | 0.7577 | 0.8758 | 0.6888 | 0.7504 | 0.4032 | nan | 0.5920 | 0.6493 | 0.0382 | 0.8655 | 0.1165 | 0.0 | 0.4411 | 0.0566 | 0.5861 | 0.0 | 0.0234 | 0.7713 | 0.1561 | 0.5405 | 0.3279 | 0.3945 | nan | 0.2280 | 0.5106 | 0.3945 | 0.0 | 0.8875 | 0.7615 | 0.9533 | 0.0074 | 0.1156 | 0.3780 | 0.0 |
| 0.0569 | 48.8 | 9760 | 0.7418 | 0.4147 | 0.4920 | 0.8776 | nan | 0.8406 | 0.9572 | 0.7979 | 0.8524 | 0.5363 | nan | 0.7072 | 0.8246 | 0.0664 | 0.9582 | 0.1298 | 0.0 | 0.6691 | 0.0599 | 0.7100 | 0.0 | 0.0233 | 0.9336 | 0.1790 | 0.6340 | 0.3755 | 0.5214 | nan | 0.2883 | 0.6211 | 0.5849 | 0.0 | 0.9546 | 0.8381 | 0.9827 | 0.0090 | 0.2097 | 0.4801 | 0.0 | nan | 0.7578 | 0.8763 | 0.6864 | 0.7495 | 0.4025 | nan | 0.5912 | 0.6513 | 0.0409 | 0.8647 | 0.1129 | 0.0 | 0.4435 | 0.0599 | 0.5852 | 0.0 | 0.0232 | 0.7699 | 0.1597 | 0.5369 | 0.3219 | 0.3894 | nan | 0.2395 | 0.5099 | 0.3934 | 0.0 | 0.8873 | 0.7619 | 0.9536 | 0.0069 | 0.1146 | 0.3783 | 0.0 |
| 0.0643 | 48.9 | 9780 | 0.7366 | 0.4135 | 0.4904 | 0.8775 | nan | 0.8419 | 0.9566 | 0.7893 | 0.8522 | 0.5418 | nan | 0.7185 | 0.8334 | 0.0726 | 0.9567 | 0.1313 | 0.0 | 0.6664 | 0.0590 | 0.7157 | 0.0 | 0.0185 | 0.9318 | 0.1687 | 0.6233 | 0.3777 | 0.5091 | nan | 0.2657 | 0.6215 | 0.5780 | 0.0 | 0.9570 | 0.8320 | 0.9825 | 0.0083 | 0.1973 | 0.4853 | 0.0 | nan | 0.7581 | 0.8762 | 0.6937 | 0.7500 | 0.4024 | nan | 0.5925 | 0.6464 | 0.0436 | 0.8658 | 0.1139 | 0.0 | 0.4394 | 0.0590 | 0.5873 | 0.0 | 0.0184 | 0.7697 | 0.1521 | 0.5328 | 0.3214 | 0.3823 | nan | 0.2255 | 0.5103 | 0.3930 | 0.0 | 0.8863 | 0.7600 | 0.9536 | 0.0065 | 0.1109 | 0.3795 | 0.0 |
| 0.0618 | 49.0 | 9800 | 0.7324 | 0.4133 | 0.4908 | 0.8776 | nan | 0.8385 | 0.9577 | 0.8009 | 0.8520 | 0.5329 | nan | 0.7132 | 0.8277 | 0.0697 | 0.9596 | 0.1262 | 0.0 | 0.6673 | 0.0580 | 0.7046 | 0.0 | 0.0220 | 0.9325 | 0.1735 | 0.6305 | 0.3849 | 0.5274 | nan | 0.2633 | 0.6253 | 0.5768 | 0.0 | 0.9547 | 0.8406 | 0.9843 | 0.0085 | 0.1999 | 0.4747 | 0.0 | nan | 0.7577 | 0.8761 | 0.6846 | 0.7503 | 0.4020 | nan | 0.5919 | 0.6485 | 0.0424 | 0.8634 | 0.1100 | 0.0 | 0.4368 | 0.0580 | 0.5832 | 0.0 | 0.0219 | 0.7696 | 0.1555 | 0.5351 | 0.3255 | 0.3876 | nan | 0.2220 | 0.5101 | 0.3954 | 0.0 | 0.8875 | 0.7621 | 0.9528 | 0.0066 | 0.1131 | 0.3773 | 0.0 |
| 0.0722 | 49.1 | 9820 | 0.7347 | 0.4152 | 0.4928 | 0.8777 | nan | 0.8403 | 0.9565 | 0.7873 | 0.8557 | 0.5384 | nan | 0.7119 | 0.8282 | 0.0710 | 0.9564 | 0.1294 | 0.0 | 0.6734 | 0.0744 | 0.7173 | 0.0 | 0.0147 | 0.9324 | 0.1844 | 0.6363 | 0.3878 | 0.5176 | nan | 0.2736 | 0.6280 | 0.5896 | 0.0 | 0.9558 | 0.8401 | 0.9836 | 0.0111 | 0.2020 | 0.4727 | 0.0 | nan | 0.7581 | 0.8762 | 0.6974 | 0.7481 | 0.4016 | nan | 0.5941 | 0.6497 | 0.0431 | 0.8656 | 0.1120 | 0.0 | 0.4399 | 0.0744 | 0.5860 | 0.0 | 0.0146 | 0.7702 | 0.1637 | 0.5370 | 0.3277 | 0.3850 | nan | 0.2310 | 0.5107 | 0.3964 | 0.0 | 0.8870 | 0.7609 | 0.9530 | 0.0084 | 0.1158 | 0.3774 | 0.0 |
| 0.0848 | 49.2 | 9840 | 0.7302 | 0.4154 | 0.4924 | 0.8779 | nan | 0.8416 | 0.9570 | 0.7898 | 0.8545 | 0.5362 | nan | 0.7096 | 0.8272 | 0.0683 | 0.9567 | 0.1272 | 0.0 | 0.6605 | 0.0652 | 0.7112 | 0.0 | 0.0269 | 0.9322 | 0.1873 | 0.6383 | 0.3877 | 0.5232 | nan | 0.2786 | 0.6276 | 0.5780 | 0.0 | 0.9544 | 0.8408 | 0.9845 | 0.0039 | 0.2122 | 0.4777 | 0.0 | nan | 0.7582 | 0.8762 | 0.6952 | 0.7485 | 0.4011 | nan | 0.5941 | 0.6516 | 0.0409 | 0.8653 | 0.1102 | 0.0 | 0.4407 | 0.0652 | 0.5879 | 0.0 | 0.0267 | 0.7704 | 0.1661 | 0.5384 | 0.3281 | 0.3896 | nan | 0.2317 | 0.5104 | 0.3957 | 0.0 | 0.8876 | 0.7614 | 0.9526 | 0.0032 | 0.1171 | 0.3778 | 0.0 |
| 0.0643 | 49.3 | 9860 | 0.7355 | 0.4159 | 0.4939 | 0.8777 | nan | 0.8423 | 0.9557 | 0.7939 | 0.8495 | 0.5429 | nan | 0.7175 | 0.8320 | 0.0748 | 0.9574 | 0.1283 | 0.0 | 0.6710 | 0.0728 | 0.7084 | 0.0 | 0.0211 | 0.9312 | 0.1863 | 0.6321 | 0.3859 | 0.5260 | nan | 0.2964 | 0.6212 | 0.5756 | 0.0 | 0.9555 | 0.8396 | 0.9838 | 0.0094 | 0.2111 | 0.4841 | 0.0 | nan | 0.7584 | 0.8765 | 0.6940 | 0.7514 | 0.4006 | nan | 0.5930 | 0.6498 | 0.0438 | 0.8652 | 0.1112 | 0.0 | 0.4418 | 0.0728 | 0.5869 | 0.0 | 0.0209 | 0.7707 | 0.1661 | 0.5366 | 0.3264 | 0.3899 | nan | 0.2456 | 0.5099 | 0.3942 | 0.0 | 0.8869 | 0.7603 | 0.9532 | 0.0072 | 0.1160 | 0.3788 | 0.0 |
| 0.0671 | 49.4 | 9880 | 0.7447 | 0.4160 | 0.4946 | 0.8777 | nan | 0.8400 | 0.9564 | 0.8003 | 0.8508 | 0.5393 | nan | 0.7193 | 0.8202 | 0.0841 | 0.9561 | 0.1303 | 0.0 | 0.6808 | 0.0803 | 0.7160 | 0.0 | 0.0180 | 0.9329 | 0.1805 | 0.6364 | 0.3888 | 0.5383 | nan | 0.2899 | 0.6208 | 0.5842 | 0.0 | 0.9553 | 0.8362 | 0.9844 | 0.0111 | 0.1967 | 0.4782 | 0.0 | nan | 0.7581 | 0.8765 | 0.6879 | 0.7514 | 0.4014 | nan | 0.5924 | 0.6533 | 0.0488 | 0.8656 | 0.1124 | 0.0 | 0.4460 | 0.0803 | 0.5854 | 0.0 | 0.0179 | 0.7700 | 0.1612 | 0.5372 | 0.3270 | 0.3889 | nan | 0.2425 | 0.5099 | 0.3944 | 0.0 | 0.8867 | 0.7602 | 0.9529 | 0.0084 | 0.1167 | 0.3799 | 0.0 |
| 0.0706 | 49.5 | 9900 | 0.7401 | 0.4150 | 0.4933 | 0.8775 | nan | 0.8401 | 0.9563 | 0.7972 | 0.8519 | 0.5382 | nan | 0.7231 | 0.8251 | 0.0681 | 0.9572 | 0.1265 | 0.0 | 0.6689 | 0.0728 | 0.7077 | 0.0 | 0.0200 | 0.9336 | 0.1805 | 0.6236 | 0.3934 | 0.5249 | nan | 0.2937 | 0.6237 | 0.5834 | 0.0 | 0.9554 | 0.8395 | 0.9831 | 0.0091 | 0.2201 | 0.4694 | 0.0 | nan | 0.7584 | 0.8766 | 0.6901 | 0.7510 | 0.4012 | nan | 0.5928 | 0.6518 | 0.0408 | 0.8652 | 0.1098 | 0.0 | 0.4336 | 0.0728 | 0.5861 | 0.0 | 0.0199 | 0.7688 | 0.1606 | 0.5332 | 0.3284 | 0.3879 | nan | 0.2446 | 0.5098 | 0.3961 | 0.0 | 0.8869 | 0.7609 | 0.9535 | 0.0070 | 0.1169 | 0.3748 | 0.0 |
| 0.1163 | 49.6 | 9920 | 0.7314 | 0.4154 | 0.4931 | 0.8776 | nan | 0.8406 | 0.9555 | 0.7982 | 0.8534 | 0.5451 | nan | 0.7131 | 0.8257 | 0.0790 | 0.9569 | 0.1210 | 0.0 | 0.6700 | 0.0738 | 0.7103 | 0.0 | 0.0224 | 0.9322 | 0.1839 | 0.6400 | 0.3738 | 0.5067 | nan | 0.3024 | 0.6231 | 0.5802 | 0.0 | 0.9561 | 0.8347 | 0.9831 | 0.0082 | 0.2098 | 0.4802 | 0.0 | nan | 0.7584 | 0.8768 | 0.6893 | 0.7502 | 0.4007 | nan | 0.5928 | 0.6520 | 0.0460 | 0.8651 | 0.1058 | 0.0 | 0.4382 | 0.0738 | 0.5842 | 0.0 | 0.0223 | 0.7704 | 0.1638 | 0.5387 | 0.3210 | 0.3845 | nan | 0.2505 | 0.5099 | 0.3950 | 0.0 | 0.8865 | 0.7604 | 0.9534 | 0.0063 | 0.1171 | 0.3788 | 0.0 |
| 0.0655 | 49.7 | 9940 | 0.7432 | 0.4168 | 0.4966 | 0.8777 | nan | 0.8411 | 0.9569 | 0.7993 | 0.8503 | 0.5401 | nan | 0.7121 | 0.8312 | 0.0845 | 0.9536 | 0.1329 | 0.0 | 0.6689 | 0.0911 | 0.7217 | 0.0 | 0.0183 | 0.9332 | 0.1866 | 0.6354 | 0.3938 | 0.5485 | nan | 0.2954 | 0.6268 | 0.5871 | 0.0 | 0.9534 | 0.8387 | 0.9836 | 0.0106 | 0.2143 | 0.4811 | 0.0 | nan | 0.7581 | 0.8764 | 0.6886 | 0.7514 | 0.4024 | nan | 0.5921 | 0.6494 | 0.0486 | 0.8673 | 0.1144 | 0.0 | 0.4376 | 0.0911 | 0.5913 | 0.0 | 0.0181 | 0.7701 | 0.1660 | 0.5373 | 0.3309 | 0.3936 | nan | 0.2439 | 0.5109 | 0.3925 | 0.0 | 0.8879 | 0.7621 | 0.9533 | 0.0080 | 0.1168 | 0.3782 | 0.0 |
| 0.0654 | 49.8 | 9960 | 0.7503 | 0.4162 | 0.4953 | 0.8776 | nan | 0.8410 | 0.9559 | 0.8005 | 0.8522 | 0.5450 | nan | 0.7082 | 0.8301 | 0.0806 | 0.9556 | 0.1303 | 0.0 | 0.6670 | 0.0746 | 0.7186 | 0.0 | 0.0215 | 0.9323 | 0.1870 | 0.6362 | 0.3906 | 0.5446 | nan | 0.3029 | 0.6134 | 0.5720 | 0.0 | 0.9558 | 0.8356 | 0.9824 | 0.0106 | 0.2257 | 0.4792 | 0.0 | nan | 0.7582 | 0.8768 | 0.6872 | 0.7508 | 0.4013 | nan | 0.5918 | 0.6502 | 0.0475 | 0.8662 | 0.1127 | 0.0 | 0.4361 | 0.0745 | 0.5863 | 0.0 | 0.0213 | 0.7704 | 0.1661 | 0.5377 | 0.3288 | 0.3947 | nan | 0.2522 | 0.5096 | 0.3940 | 0.0 | 0.8870 | 0.7608 | 0.9537 | 0.0080 | 0.1186 | 0.3769 | 0.0 |
| 0.0668 | 49.9 | 9980 | 0.7339 | 0.4160 | 0.4957 | 0.8778 | nan | 0.8397 | 0.9574 | 0.8012 | 0.8533 | 0.5329 | nan | 0.7149 | 0.8324 | 0.0808 | 0.9581 | 0.1315 | 0.0 | 0.6686 | 0.0743 | 0.7097 | 0.0 | 0.0236 | 0.9318 | 0.1824 | 0.6321 | 0.3937 | 0.5660 | nan | 0.2925 | 0.6250 | 0.5827 | 0.0 | 0.9539 | 0.8401 | 0.9840 | 0.0107 | 0.2145 | 0.4754 | 0.0 | nan | 0.7578 | 0.8765 | 0.6865 | 0.7502 | 0.4021 | nan | 0.5921 | 0.6487 | 0.0468 | 0.8647 | 0.1139 | 0.0 | 0.4381 | 0.0743 | 0.5855 | 0.0 | 0.0234 | 0.7708 | 0.1630 | 0.5373 | 0.3288 | 0.3982 | nan | 0.2423 | 0.5108 | 0.3945 | 0.0 | 0.8876 | 0.7623 | 0.9531 | 0.0081 | 0.1176 | 0.3773 | 0.0 |
| 0.0611 | 50.0 | 10000 | 0.7413 | 0.4159 | 0.4945 | 0.8774 | nan | 0.8388 | 0.9573 | 0.8007 | 0.8552 | 0.5279 | nan | 0.7175 | 0.8243 | 0.0845 | 0.9557 | 0.1300 | 0.0 | 0.6660 | 0.0883 | 0.7186 | 0.0 | 0.0195 | 0.9361 | 0.1788 | 0.6178 | 0.3916 | 0.5516 | nan | 0.2882 | 0.6120 | 0.5812 | 0.0 | 0.9537 | 0.8377 | 0.9844 | 0.0132 | 0.2116 | 0.4815 | 0.0 | nan | 0.7575 | 0.8765 | 0.6860 | 0.7491 | 0.4030 | nan | 0.5916 | 0.6521 | 0.0488 | 0.8662 | 0.1122 | 0.0 | 0.4442 | 0.0883 | 0.5874 | 0.0 | 0.0193 | 0.7672 | 0.1600 | 0.5304 | 0.3282 | 0.3920 | nan | 0.2410 | 0.5084 | 0.3941 | 0.0 | 0.8872 | 0.7611 | 0.9529 | 0.0098 | 0.1155 | 0.3780 | 0.0 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| [
"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"
] |
RintaroMisaka/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.
## 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 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
| [
"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"
] |
ayoubkirouane/Segments-Sidewalk-SegFormer-B0 |
## Model Details
+ **Model Name**: Segments-Sidewalk-SegFormer-B0
+ **Model Type**: Semantic Segmentation
+ **Base Model**: nvidia/segformer-b0-finetuned-ade-512-512
+ **Fine-Tuning Dataset**: Sidewalk-Semantic
## Model Description
The **Segments-Sidewalk-SegFormer-B0** model is a semantic segmentation model fine-tuned on the **sidewalk-semantic** dataset. It is based on the **SegFormer (b0-sized)** architecture and has been adapted for the task of segmenting sidewalk images into various classes, such as road surfaces, pedestrians, vehicles, and more.
## Model Architecture
The model architecture is based on SegFormer, which utilizes a **hierarchical Transformer Encoder and a lightweight all-MLP decoder head**. This architecture has been proven effective in semantic segmentation tasks, and fine-tuning on the 'sidewalk-semantic' dataset allows it to learn to segment sidewalk images accurately.
## Intended Uses
The **Segments-Sidewalk-SegFormer-B0** model can be used for various applications in the context of sidewalk image analysis and understanding.
**Some of the intended use cases include**
+ **Semantic Segmentation**: Use the model to perform pixel-level classification of sidewalk images, enabling the identification of different objects and features in the images, such as road surfaces, pedestrians, vehicles, and construction elements.
+ **Urban Planning**: The model can assist in urban planning tasks by providing detailed information about sidewalk infrastructure, helping city planners make informed decisions.
+ **Autonomous Navigation**: Deploy the model in autonomous vehicles or robots to enhance their understanding of the sidewalk environment, aiding in safe navigation.
## Limitations
+ **Resolution Dependency**: The model's performance may be sensitive to the resolution of the input images. Fine-tuning was performed at a specific resolution, so using significantly different resolutions may require additional adjustments.
+ **Hardware Requirements**: Inference with deep learning models can be computationally intensive, requiring access to GPUs or other specialized hardware for real-time or efficient processing.
## Ethical Considerations
When using and deploying the **Segments-Sidewalk-SegFormer-B0** model, consider the following ethical considerations:
+ **Bias and Fairness**: Carefully evaluate the dataset for biases that may be present and address them to avoid unfair or discriminatory outcomes in predictions, especially when dealing with human-related classes (e.g., pedestrians).
+ **Privacy**: Be mindful of privacy concerns when processing sidewalk images, as they may contain personally identifiable information or capture private locations. Appropriate data anonymization and consent mechanisms should be in place.
+ **Transparency**: Clearly communicate the model's capabilities and limitations to end-users and stakeholders, ensuring they understand the model's potential errors and uncertainties.
+ **Regulatory Compliance**: Adhere to local and national regulations regarding the collection and processing of sidewalk images, especially if the data involves public spaces or private property.
+ **Accessibility**: Ensure that the model's outputs and applications are accessible to individuals with disabilities and do not exclude any user group.
## Usage
```python
# Load model directly
from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation
extractor = AutoFeatureExtractor.from_pretrained("ayoubkirouane/Segments-Sidewalk-SegFormer-B0")
model = SegformerForSemanticSegmentation.from_pretrained("ayoubkirouane/Segments-Sidewalk-SegFormer-B0")
``` | [
"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"
] |
JCAI2000/segformerb5-finetuned-largerImages |
<!-- 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. -->
# segformerb5-finetuned-largerImages
This model is a fine-tuned version of [JCAI2000/segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-2](https://huggingface.co/JCAI2000/segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-2) on the JCAI2000/LargerImagesLabelled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0724
- Mean Iou: 0.7754
- Mean Accuracy: 0.8589
- Overall Accuracy: 0.9828
- Accuracy Background: 0.9910
- Accuracy Branch: 0.7269
- Iou Background: 0.9824
- Iou Branch: 0.5684
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Branch | Iou Background | Iou Branch |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.0872 | 1.18 | 20 | 0.0678 | 0.6870 | 0.7241 | 0.9784 | 0.9954 | 0.4528 | 0.9781 | 0.3958 |
| 0.0848 | 2.35 | 40 | 0.0577 | 0.7333 | 0.7908 | 0.9806 | 0.9932 | 0.5884 | 0.9802 | 0.4864 |
| 0.0549 | 3.53 | 60 | 0.0634 | 0.7485 | 0.8857 | 0.9773 | 0.9834 | 0.7879 | 0.9767 | 0.5203 |
| 0.0653 | 4.71 | 80 | 0.0493 | 0.7662 | 0.8346 | 0.9827 | 0.9926 | 0.6767 | 0.9823 | 0.5500 |
| 0.0596 | 5.88 | 100 | 0.0476 | 0.7497 | 0.7920 | 0.9828 | 0.9955 | 0.5885 | 0.9825 | 0.5168 |
| 0.0458 | 7.06 | 120 | 0.0478 | 0.7636 | 0.8357 | 0.9823 | 0.9921 | 0.6793 | 0.9819 | 0.5452 |
| 0.0285 | 8.24 | 140 | 0.0458 | 0.7758 | 0.8574 | 0.9829 | 0.9913 | 0.7235 | 0.9825 | 0.5691 |
| 0.0341 | 9.41 | 160 | 0.0466 | 0.7670 | 0.8376 | 0.9827 | 0.9923 | 0.6829 | 0.9823 | 0.5517 |
| 0.0369 | 10.59 | 180 | 0.0491 | 0.7699 | 0.8731 | 0.9813 | 0.9885 | 0.7576 | 0.9809 | 0.5589 |
| 0.0352 | 11.76 | 200 | 0.0465 | 0.7731 | 0.8551 | 0.9826 | 0.9911 | 0.7191 | 0.9822 | 0.5640 |
| 0.0477 | 12.94 | 220 | 0.0462 | 0.7721 | 0.8415 | 0.9832 | 0.9926 | 0.6905 | 0.9828 | 0.5615 |
| 0.0404 | 14.12 | 240 | 0.0493 | 0.7704 | 0.8734 | 0.9814 | 0.9886 | 0.7583 | 0.9809 | 0.5599 |
| 0.0221 | 15.29 | 260 | 0.0458 | 0.7798 | 0.8719 | 0.9828 | 0.9901 | 0.7536 | 0.9823 | 0.5772 |
| 0.0263 | 16.47 | 280 | 0.0450 | 0.7778 | 0.8509 | 0.9835 | 0.9923 | 0.7096 | 0.9831 | 0.5726 |
| 0.0364 | 17.65 | 300 | 0.0489 | 0.7756 | 0.8537 | 0.9830 | 0.9917 | 0.7158 | 0.9827 | 0.5686 |
| 0.02 | 18.82 | 320 | 0.0493 | 0.7713 | 0.8474 | 0.9828 | 0.9918 | 0.7031 | 0.9824 | 0.5602 |
| 0.0193 | 20.0 | 340 | 0.0481 | 0.7786 | 0.8694 | 0.9827 | 0.9903 | 0.7484 | 0.9823 | 0.5749 |
| 0.0133 | 21.18 | 360 | 0.0486 | 0.7756 | 0.8552 | 0.9830 | 0.9915 | 0.7189 | 0.9826 | 0.5686 |
| 0.0163 | 22.35 | 380 | 0.0492 | 0.7768 | 0.8632 | 0.9828 | 0.9907 | 0.7357 | 0.9824 | 0.5713 |
| 0.0252 | 23.53 | 400 | 0.0510 | 0.7725 | 0.8605 | 0.9823 | 0.9904 | 0.7306 | 0.9819 | 0.5632 |
| 0.0178 | 24.71 | 420 | 0.0509 | 0.7770 | 0.8665 | 0.9826 | 0.9904 | 0.7427 | 0.9822 | 0.5719 |
| 0.0167 | 25.88 | 440 | 0.0516 | 0.7748 | 0.8615 | 0.9826 | 0.9906 | 0.7323 | 0.9822 | 0.5675 |
| 0.0332 | 27.06 | 460 | 0.0507 | 0.7702 | 0.8422 | 0.9829 | 0.9922 | 0.6921 | 0.9825 | 0.5578 |
| 0.021 | 28.24 | 480 | 0.0522 | 0.7710 | 0.8482 | 0.9827 | 0.9916 | 0.7048 | 0.9823 | 0.5597 |
| 0.0284 | 29.41 | 500 | 0.0536 | 0.7762 | 0.8643 | 0.9826 | 0.9905 | 0.7380 | 0.9822 | 0.5702 |
| 0.0174 | 30.59 | 520 | 0.0535 | 0.7739 | 0.8591 | 0.9826 | 0.9908 | 0.7274 | 0.9822 | 0.5657 |
| 0.0228 | 31.76 | 540 | 0.0527 | 0.7765 | 0.8578 | 0.9830 | 0.9913 | 0.7243 | 0.9826 | 0.5703 |
| 0.0347 | 32.94 | 560 | 0.0530 | 0.7754 | 0.8534 | 0.9830 | 0.9917 | 0.7151 | 0.9826 | 0.5681 |
| 0.0182 | 34.12 | 580 | 0.0554 | 0.7764 | 0.8670 | 0.9825 | 0.9902 | 0.7437 | 0.9821 | 0.5706 |
| 0.0139 | 35.29 | 600 | 0.0521 | 0.7786 | 0.8540 | 0.9834 | 0.9920 | 0.7159 | 0.9830 | 0.5742 |
| 0.0121 | 36.47 | 620 | 0.0549 | 0.7787 | 0.8728 | 0.9826 | 0.9899 | 0.7557 | 0.9822 | 0.5752 |
| 0.0191 | 37.65 | 640 | 0.0560 | 0.7766 | 0.8678 | 0.9825 | 0.9902 | 0.7454 | 0.9821 | 0.5711 |
| 0.0216 | 38.82 | 660 | 0.0553 | 0.7745 | 0.8543 | 0.9829 | 0.9914 | 0.7172 | 0.9825 | 0.5665 |
| 0.0135 | 40.0 | 680 | 0.0569 | 0.7738 | 0.8640 | 0.9823 | 0.9902 | 0.7379 | 0.9819 | 0.5658 |
| 0.0167 | 41.18 | 700 | 0.0566 | 0.7765 | 0.8619 | 0.9828 | 0.9908 | 0.7330 | 0.9824 | 0.5707 |
| 0.0224 | 42.35 | 720 | 0.0570 | 0.7768 | 0.8680 | 0.9825 | 0.9902 | 0.7458 | 0.9821 | 0.5714 |
| 0.0188 | 43.53 | 740 | 0.0575 | 0.7768 | 0.8630 | 0.9828 | 0.9907 | 0.7353 | 0.9824 | 0.5713 |
| 0.0338 | 44.71 | 760 | 0.0565 | 0.7783 | 0.8634 | 0.9830 | 0.9909 | 0.7359 | 0.9826 | 0.5741 |
| 0.0122 | 45.88 | 780 | 0.0585 | 0.7788 | 0.8656 | 0.9829 | 0.9907 | 0.7404 | 0.9825 | 0.5750 |
| 0.0119 | 47.06 | 800 | 0.0587 | 0.7774 | 0.8639 | 0.9828 | 0.9907 | 0.7371 | 0.9824 | 0.5725 |
| 0.0086 | 48.24 | 820 | 0.0594 | 0.7777 | 0.8567 | 0.9832 | 0.9916 | 0.7218 | 0.9828 | 0.5726 |
| 0.0094 | 49.41 | 840 | 0.0597 | 0.7766 | 0.8627 | 0.9828 | 0.9907 | 0.7347 | 0.9823 | 0.5708 |
| 0.0107 | 50.59 | 860 | 0.0619 | 0.7773 | 0.8624 | 0.9829 | 0.9909 | 0.7338 | 0.9825 | 0.5722 |
| 0.0175 | 51.76 | 880 | 0.0605 | 0.7752 | 0.8588 | 0.9828 | 0.9910 | 0.7266 | 0.9824 | 0.5681 |
| 0.0139 | 52.94 | 900 | 0.0620 | 0.7786 | 0.8675 | 0.9828 | 0.9905 | 0.7446 | 0.9824 | 0.5748 |
| 0.02 | 54.12 | 920 | 0.0633 | 0.7764 | 0.8628 | 0.9827 | 0.9907 | 0.7348 | 0.9823 | 0.5704 |
| 0.0294 | 55.29 | 940 | 0.0637 | 0.7762 | 0.8584 | 0.9829 | 0.9912 | 0.7256 | 0.9825 | 0.5699 |
| 0.0175 | 56.47 | 960 | 0.0639 | 0.7789 | 0.8717 | 0.9827 | 0.9900 | 0.7534 | 0.9822 | 0.5755 |
| 0.008 | 57.65 | 980 | 0.0640 | 0.7797 | 0.8667 | 0.9830 | 0.9907 | 0.7428 | 0.9826 | 0.5768 |
| 0.0132 | 58.82 | 1000 | 0.0652 | 0.7754 | 0.8657 | 0.9825 | 0.9902 | 0.7412 | 0.9820 | 0.5687 |
| 0.0339 | 60.0 | 1020 | 0.0640 | 0.7785 | 0.8664 | 0.9828 | 0.9906 | 0.7421 | 0.9824 | 0.5746 |
| 0.0144 | 61.18 | 1040 | 0.0618 | 0.7804 | 0.8577 | 0.9835 | 0.9919 | 0.7236 | 0.9831 | 0.5778 |
| 0.0206 | 62.35 | 1060 | 0.0653 | 0.7767 | 0.8636 | 0.9827 | 0.9907 | 0.7366 | 0.9823 | 0.5710 |
| 0.0165 | 63.53 | 1080 | 0.0651 | 0.7774 | 0.8602 | 0.9830 | 0.9912 | 0.7293 | 0.9826 | 0.5722 |
| 0.0175 | 64.71 | 1100 | 0.0648 | 0.7758 | 0.8568 | 0.9829 | 0.9913 | 0.7222 | 0.9825 | 0.5690 |
| 0.0104 | 65.88 | 1120 | 0.0669 | 0.7771 | 0.8618 | 0.9829 | 0.9909 | 0.7327 | 0.9825 | 0.5717 |
| 0.0191 | 67.06 | 1140 | 0.0662 | 0.7779 | 0.8696 | 0.9826 | 0.9901 | 0.7490 | 0.9822 | 0.5737 |
| 0.0123 | 68.24 | 1160 | 0.0668 | 0.7775 | 0.8591 | 0.9830 | 0.9913 | 0.7270 | 0.9826 | 0.5723 |
| 0.0127 | 69.41 | 1180 | 0.0676 | 0.7772 | 0.8637 | 0.9828 | 0.9907 | 0.7366 | 0.9824 | 0.5720 |
| 0.0092 | 70.59 | 1200 | 0.0673 | 0.7778 | 0.8699 | 0.9826 | 0.9901 | 0.7496 | 0.9822 | 0.5735 |
| 0.0101 | 71.76 | 1220 | 0.0680 | 0.7761 | 0.8694 | 0.9824 | 0.9899 | 0.7489 | 0.9820 | 0.5703 |
| 0.0204 | 72.94 | 1240 | 0.0676 | 0.7772 | 0.8640 | 0.9828 | 0.9907 | 0.7373 | 0.9824 | 0.5721 |
| 0.008 | 74.12 | 1260 | 0.0685 | 0.7768 | 0.8661 | 0.9826 | 0.9904 | 0.7417 | 0.9822 | 0.5714 |
| 0.0124 | 75.29 | 1280 | 0.0676 | 0.7776 | 0.8648 | 0.9828 | 0.9907 | 0.7390 | 0.9824 | 0.5729 |
| 0.0134 | 76.47 | 1300 | 0.0689 | 0.7770 | 0.8672 | 0.9826 | 0.9903 | 0.7441 | 0.9822 | 0.5718 |
| 0.0082 | 77.65 | 1320 | 0.0688 | 0.7755 | 0.8621 | 0.9826 | 0.9907 | 0.7336 | 0.9822 | 0.5687 |
| 0.0125 | 78.82 | 1340 | 0.0698 | 0.7761 | 0.8655 | 0.9826 | 0.9904 | 0.7407 | 0.9822 | 0.5701 |
| 0.0064 | 80.0 | 1360 | 0.0707 | 0.7760 | 0.8611 | 0.9827 | 0.9908 | 0.7314 | 0.9823 | 0.5696 |
| 0.0131 | 81.18 | 1380 | 0.0709 | 0.7765 | 0.8645 | 0.9827 | 0.9905 | 0.7384 | 0.9822 | 0.5707 |
| 0.0099 | 82.35 | 1400 | 0.0696 | 0.7769 | 0.8665 | 0.9826 | 0.9904 | 0.7427 | 0.9822 | 0.5717 |
| 0.0174 | 83.53 | 1420 | 0.0704 | 0.7757 | 0.8612 | 0.9827 | 0.9908 | 0.7317 | 0.9823 | 0.5692 |
| 0.0122 | 84.71 | 1440 | 0.0710 | 0.7754 | 0.8595 | 0.9827 | 0.9910 | 0.7281 | 0.9823 | 0.5685 |
| 0.0136 | 85.88 | 1460 | 0.0705 | 0.7765 | 0.8632 | 0.9827 | 0.9907 | 0.7356 | 0.9823 | 0.5706 |
| 0.0115 | 87.06 | 1480 | 0.0711 | 0.7763 | 0.8638 | 0.9827 | 0.9906 | 0.7370 | 0.9823 | 0.5703 |
| 0.0074 | 88.24 | 1500 | 0.0709 | 0.7763 | 0.8606 | 0.9828 | 0.9910 | 0.7302 | 0.9824 | 0.5702 |
| 0.0073 | 89.41 | 1520 | 0.0707 | 0.7767 | 0.8595 | 0.9829 | 0.9911 | 0.7278 | 0.9825 | 0.5708 |
| 0.0073 | 90.59 | 1540 | 0.0713 | 0.7756 | 0.8556 | 0.9830 | 0.9914 | 0.7197 | 0.9826 | 0.5687 |
| 0.0123 | 91.76 | 1560 | 0.0716 | 0.7763 | 0.8623 | 0.9827 | 0.9907 | 0.7339 | 0.9823 | 0.5702 |
| 0.0161 | 92.94 | 1580 | 0.0712 | 0.7762 | 0.8601 | 0.9828 | 0.9910 | 0.7293 | 0.9824 | 0.5700 |
| 0.0061 | 94.12 | 1600 | 0.0718 | 0.7754 | 0.8597 | 0.9827 | 0.9909 | 0.7284 | 0.9823 | 0.5685 |
| 0.0063 | 95.29 | 1620 | 0.0725 | 0.7751 | 0.8598 | 0.9827 | 0.9909 | 0.7288 | 0.9823 | 0.5679 |
| 0.0124 | 96.47 | 1640 | 0.0722 | 0.7750 | 0.8633 | 0.9825 | 0.9905 | 0.7361 | 0.9821 | 0.5680 |
| 0.0123 | 97.65 | 1660 | 0.0722 | 0.7751 | 0.8598 | 0.9827 | 0.9909 | 0.7287 | 0.9823 | 0.5680 |
| 0.0185 | 98.82 | 1680 | 0.0717 | 0.7753 | 0.8593 | 0.9827 | 0.9910 | 0.7277 | 0.9823 | 0.5683 |
| 0.0152 | 100.0 | 1700 | 0.0724 | 0.7754 | 0.8589 | 0.9828 | 0.9910 | 0.7269 | 0.9824 | 0.5684 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"branch"
] |
Manduzamzam/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 Manduzamzam/practice2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6829
- Mean Iou: 0.0140
- Mean Accuracy: 0.0279
- Overall Accuracy: 0.0279
- Accuracy Background: nan
- Accuracy Object: 0.0279
- Iou Background: 0.0
- Iou Object: 0.0279
## 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 Background | Accuracy Object | Iou Background | Iou Object |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| No log | 0.71 | 10 | 0.6829 | 0.0140 | 0.0279 | 0.0279 | nan | 0.0279 | 0.0 | 0.0279 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.14.5
- Tokenizers 0.14.0
| [
"background",
"object"
] |
JCAI2000/segformerb5-largeImages |
<!-- 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. -->
# segformerb5-largeImages
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the JCAI2000/LargerImagesLabelled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1156
- Mean Iou: 0.7785
- Mean Accuracy: 0.8298
- Overall Accuracy: 0.9767
- Accuracy Background: 0.9925
- Accuracy Branch: 0.6671
- Iou Background: 0.9759
- Iou Branch: 0.5812
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Branch | Iou Background | Iou Branch |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.2671 | 1.18 | 20 | 0.2779 | 0.4834 | 0.5075 | 0.9509 | 0.9985 | 0.0165 | 0.9508 | 0.0160 |
| 0.122 | 2.35 | 40 | 0.1772 | 0.6522 | 0.6931 | 0.9632 | 0.9922 | 0.3940 | 0.9625 | 0.3419 |
| 0.0671 | 3.53 | 60 | 0.1086 | 0.7392 | 0.8603 | 0.9658 | 0.9772 | 0.7435 | 0.9646 | 0.5138 |
| 0.0979 | 4.71 | 80 | 0.0860 | 0.7552 | 0.8493 | 0.9705 | 0.9836 | 0.7150 | 0.9695 | 0.5409 |
| 0.0749 | 5.88 | 100 | 0.0727 | 0.7601 | 0.8116 | 0.9746 | 0.9921 | 0.6311 | 0.9738 | 0.5465 |
| 0.032 | 7.06 | 120 | 0.0721 | 0.7535 | 0.8016 | 0.9741 | 0.9927 | 0.6106 | 0.9733 | 0.5338 |
| 0.0337 | 8.24 | 140 | 0.0719 | 0.7745 | 0.8530 | 0.9743 | 0.9873 | 0.7187 | 0.9733 | 0.5757 |
| 0.0398 | 9.41 | 160 | 0.0704 | 0.7732 | 0.8302 | 0.9756 | 0.9913 | 0.6690 | 0.9748 | 0.5715 |
| 0.0374 | 10.59 | 180 | 0.0724 | 0.7583 | 0.7995 | 0.9752 | 0.9941 | 0.6050 | 0.9744 | 0.5422 |
| 0.0334 | 11.76 | 200 | 0.0724 | 0.7721 | 0.8231 | 0.9760 | 0.9924 | 0.6537 | 0.9752 | 0.5690 |
| 0.025 | 12.94 | 220 | 0.0731 | 0.7725 | 0.8192 | 0.9763 | 0.9932 | 0.6452 | 0.9755 | 0.5694 |
| 0.0336 | 14.12 | 240 | 0.0699 | 0.7793 | 0.8334 | 0.9765 | 0.9919 | 0.6748 | 0.9757 | 0.5828 |
| 0.0321 | 15.29 | 260 | 0.0697 | 0.7825 | 0.8395 | 0.9767 | 0.9915 | 0.6875 | 0.9759 | 0.5891 |
| 0.0216 | 16.47 | 280 | 0.0752 | 0.7701 | 0.8176 | 0.9760 | 0.9930 | 0.6421 | 0.9752 | 0.5650 |
| 0.0178 | 17.65 | 300 | 0.0743 | 0.7753 | 0.8296 | 0.9761 | 0.9918 | 0.6674 | 0.9753 | 0.5752 |
| 0.0206 | 18.82 | 320 | 0.0717 | 0.7881 | 0.8488 | 0.9771 | 0.9909 | 0.7066 | 0.9763 | 0.5999 |
| 0.0162 | 20.0 | 340 | 0.0786 | 0.7694 | 0.8141 | 0.9761 | 0.9935 | 0.6347 | 0.9754 | 0.5634 |
| 0.0306 | 21.18 | 360 | 0.0785 | 0.7785 | 0.8275 | 0.9768 | 0.9929 | 0.6622 | 0.9760 | 0.5809 |
| 0.0179 | 22.35 | 380 | 0.0769 | 0.7816 | 0.8414 | 0.9764 | 0.9909 | 0.6919 | 0.9756 | 0.5876 |
| 0.0152 | 23.53 | 400 | 0.0776 | 0.7842 | 0.8461 | 0.9766 | 0.9906 | 0.7016 | 0.9758 | 0.5926 |
| 0.0245 | 24.71 | 420 | 0.0820 | 0.7725 | 0.8164 | 0.9765 | 0.9937 | 0.6390 | 0.9758 | 0.5692 |
| 0.0248 | 25.88 | 440 | 0.0829 | 0.7772 | 0.8268 | 0.9766 | 0.9928 | 0.6608 | 0.9759 | 0.5786 |
| 0.0176 | 27.06 | 460 | 0.0818 | 0.7761 | 0.8271 | 0.9764 | 0.9925 | 0.6617 | 0.9756 | 0.5767 |
| 0.0135 | 28.24 | 480 | 0.0816 | 0.7805 | 0.8384 | 0.9764 | 0.9913 | 0.6854 | 0.9756 | 0.5855 |
| 0.0343 | 29.41 | 500 | 0.0852 | 0.7777 | 0.8310 | 0.9764 | 0.9921 | 0.6699 | 0.9756 | 0.5798 |
| 0.0147 | 30.59 | 520 | 0.0851 | 0.7792 | 0.8367 | 0.9763 | 0.9913 | 0.6820 | 0.9755 | 0.5829 |
| 0.0119 | 31.76 | 540 | 0.0880 | 0.7800 | 0.8337 | 0.9767 | 0.9920 | 0.6754 | 0.9759 | 0.5842 |
| 0.0143 | 32.94 | 560 | 0.0899 | 0.7749 | 0.8241 | 0.9764 | 0.9928 | 0.6555 | 0.9756 | 0.5743 |
| 0.0122 | 34.12 | 580 | 0.0886 | 0.7810 | 0.8374 | 0.9766 | 0.9916 | 0.6832 | 0.9758 | 0.5863 |
| 0.0135 | 35.29 | 600 | 0.0908 | 0.7727 | 0.8206 | 0.9762 | 0.9930 | 0.6482 | 0.9755 | 0.5699 |
| 0.0203 | 36.47 | 620 | 0.0913 | 0.7758 | 0.8267 | 0.9764 | 0.9925 | 0.6608 | 0.9756 | 0.5759 |
| 0.0109 | 37.65 | 640 | 0.0898 | 0.7803 | 0.8337 | 0.9767 | 0.9921 | 0.6753 | 0.9759 | 0.5847 |
| 0.0141 | 38.82 | 660 | 0.0936 | 0.7774 | 0.8280 | 0.9766 | 0.9926 | 0.6634 | 0.9758 | 0.5790 |
| 0.0087 | 40.0 | 680 | 0.0903 | 0.7830 | 0.8493 | 0.9762 | 0.9898 | 0.7088 | 0.9753 | 0.5908 |
| 0.0099 | 41.18 | 700 | 0.0930 | 0.7779 | 0.8284 | 0.9766 | 0.9926 | 0.6641 | 0.9759 | 0.5799 |
| 0.0149 | 42.35 | 720 | 0.0908 | 0.7799 | 0.8320 | 0.9767 | 0.9923 | 0.6717 | 0.9760 | 0.5838 |
| 0.0168 | 43.53 | 740 | 0.0897 | 0.7864 | 0.8496 | 0.9768 | 0.9904 | 0.7087 | 0.9759 | 0.5969 |
| 0.0281 | 44.71 | 760 | 0.0954 | 0.7760 | 0.8259 | 0.9765 | 0.9927 | 0.6591 | 0.9757 | 0.5762 |
| 0.0102 | 45.88 | 780 | 0.0942 | 0.7819 | 0.8382 | 0.9767 | 0.9916 | 0.6849 | 0.9759 | 0.5879 |
| 0.0087 | 47.06 | 800 | 0.0948 | 0.7843 | 0.8422 | 0.9769 | 0.9913 | 0.6931 | 0.9761 | 0.5926 |
| 0.0166 | 48.24 | 820 | 0.0981 | 0.7777 | 0.8280 | 0.9766 | 0.9926 | 0.6634 | 0.9759 | 0.5796 |
| 0.0236 | 49.41 | 840 | 0.0972 | 0.7770 | 0.8274 | 0.9765 | 0.9926 | 0.6622 | 0.9758 | 0.5782 |
| 0.0168 | 50.59 | 860 | 0.0994 | 0.7751 | 0.8218 | 0.9766 | 0.9932 | 0.6505 | 0.9758 | 0.5743 |
| 0.017 | 51.76 | 880 | 0.0991 | 0.7779 | 0.8281 | 0.9767 | 0.9926 | 0.6635 | 0.9759 | 0.5799 |
| 0.0111 | 52.94 | 900 | 0.0994 | 0.7778 | 0.8266 | 0.9767 | 0.9929 | 0.6603 | 0.9760 | 0.5797 |
| 0.0202 | 54.12 | 920 | 0.0985 | 0.7845 | 0.8380 | 0.9772 | 0.9921 | 0.6839 | 0.9764 | 0.5926 |
| 0.0142 | 55.29 | 940 | 0.1025 | 0.7762 | 0.8240 | 0.9767 | 0.9931 | 0.6548 | 0.9759 | 0.5766 |
| 0.01 | 56.47 | 960 | 0.0997 | 0.7808 | 0.8346 | 0.9767 | 0.9920 | 0.6771 | 0.9759 | 0.5857 |
| 0.0127 | 57.65 | 980 | 0.1028 | 0.7797 | 0.8317 | 0.9767 | 0.9923 | 0.6712 | 0.9759 | 0.5835 |
| 0.0069 | 58.82 | 1000 | 0.1011 | 0.7834 | 0.8400 | 0.9768 | 0.9915 | 0.6885 | 0.9760 | 0.5907 |
| 0.0109 | 60.0 | 1020 | 0.1059 | 0.7775 | 0.8282 | 0.9766 | 0.9925 | 0.6638 | 0.9758 | 0.5792 |
| 0.0087 | 61.18 | 1040 | 0.1037 | 0.7793 | 0.8308 | 0.9767 | 0.9924 | 0.6692 | 0.9759 | 0.5826 |
| 0.0125 | 62.35 | 1060 | 0.1056 | 0.7784 | 0.8279 | 0.9768 | 0.9928 | 0.6630 | 0.9760 | 0.5808 |
| 0.0084 | 63.53 | 1080 | 0.1066 | 0.7803 | 0.8330 | 0.9768 | 0.9922 | 0.6737 | 0.9760 | 0.5847 |
| 0.0183 | 64.71 | 1100 | 0.1056 | 0.7806 | 0.8340 | 0.9767 | 0.9921 | 0.6759 | 0.9760 | 0.5853 |
| 0.0106 | 65.88 | 1120 | 0.1076 | 0.7768 | 0.8257 | 0.9766 | 0.9929 | 0.6586 | 0.9759 | 0.5778 |
| 0.0072 | 67.06 | 1140 | 0.1103 | 0.7771 | 0.8278 | 0.9765 | 0.9925 | 0.6630 | 0.9758 | 0.5784 |
| 0.0112 | 68.24 | 1160 | 0.1070 | 0.7799 | 0.8315 | 0.9768 | 0.9924 | 0.6705 | 0.9760 | 0.5838 |
| 0.0149 | 69.41 | 1180 | 0.1089 | 0.7778 | 0.8284 | 0.9766 | 0.9926 | 0.6642 | 0.9758 | 0.5797 |
| 0.0147 | 70.59 | 1200 | 0.1087 | 0.7805 | 0.8325 | 0.9768 | 0.9924 | 0.6727 | 0.9760 | 0.5850 |
| 0.013 | 71.76 | 1220 | 0.1081 | 0.7803 | 0.8331 | 0.9767 | 0.9922 | 0.6741 | 0.9760 | 0.5846 |
| 0.013 | 72.94 | 1240 | 0.1097 | 0.7789 | 0.8304 | 0.9767 | 0.9924 | 0.6683 | 0.9759 | 0.5818 |
| 0.0115 | 74.12 | 1260 | 0.1104 | 0.7773 | 0.8269 | 0.9766 | 0.9927 | 0.6610 | 0.9759 | 0.5787 |
| 0.0102 | 75.29 | 1280 | 0.1097 | 0.7795 | 0.8323 | 0.9767 | 0.9922 | 0.6725 | 0.9759 | 0.5831 |
| 0.0133 | 76.47 | 1300 | 0.1101 | 0.7808 | 0.8355 | 0.9767 | 0.9919 | 0.6791 | 0.9759 | 0.5857 |
| 0.013 | 77.65 | 1320 | 0.1111 | 0.7814 | 0.8358 | 0.9768 | 0.9919 | 0.6797 | 0.9760 | 0.5867 |
| 0.0068 | 78.82 | 1340 | 0.1107 | 0.7814 | 0.8362 | 0.9767 | 0.9919 | 0.6805 | 0.9759 | 0.5869 |
| 0.0036 | 80.0 | 1360 | 0.1136 | 0.7789 | 0.8313 | 0.9766 | 0.9923 | 0.6703 | 0.9758 | 0.5820 |
| 0.0163 | 81.18 | 1380 | 0.1123 | 0.7809 | 0.8347 | 0.9767 | 0.9920 | 0.6773 | 0.9760 | 0.5858 |
| 0.0065 | 82.35 | 1400 | 0.1117 | 0.7811 | 0.8356 | 0.9767 | 0.9919 | 0.6794 | 0.9759 | 0.5862 |
| 0.018 | 83.53 | 1420 | 0.1121 | 0.7811 | 0.8360 | 0.9767 | 0.9918 | 0.6802 | 0.9759 | 0.5864 |
| 0.0122 | 84.71 | 1440 | 0.1123 | 0.7803 | 0.8346 | 0.9766 | 0.9919 | 0.6772 | 0.9759 | 0.5847 |
| 0.0085 | 85.88 | 1460 | 0.1139 | 0.7783 | 0.8300 | 0.9766 | 0.9924 | 0.6676 | 0.9758 | 0.5808 |
| 0.0074 | 87.06 | 1480 | 0.1130 | 0.7820 | 0.8364 | 0.9768 | 0.9919 | 0.6808 | 0.9760 | 0.5879 |
| 0.0124 | 88.24 | 1500 | 0.1141 | 0.7801 | 0.8332 | 0.9767 | 0.9921 | 0.6743 | 0.9759 | 0.5843 |
| 0.0114 | 89.41 | 1520 | 0.1152 | 0.7783 | 0.8301 | 0.9766 | 0.9924 | 0.6678 | 0.9758 | 0.5808 |
| 0.0113 | 90.59 | 1540 | 0.1153 | 0.7784 | 0.8302 | 0.9766 | 0.9924 | 0.6680 | 0.9758 | 0.5811 |
| 0.0076 | 91.76 | 1560 | 0.1153 | 0.7778 | 0.8286 | 0.9766 | 0.9925 | 0.6647 | 0.9758 | 0.5797 |
| 0.0128 | 92.94 | 1580 | 0.1149 | 0.7785 | 0.8308 | 0.9766 | 0.9923 | 0.6694 | 0.9758 | 0.5813 |
| 0.0046 | 94.12 | 1600 | 0.1154 | 0.7781 | 0.8298 | 0.9766 | 0.9923 | 0.6673 | 0.9758 | 0.5803 |
| 0.0091 | 95.29 | 1620 | 0.1143 | 0.7792 | 0.8318 | 0.9766 | 0.9922 | 0.6713 | 0.9759 | 0.5826 |
| 0.0121 | 96.47 | 1640 | 0.1153 | 0.7784 | 0.8302 | 0.9766 | 0.9924 | 0.6681 | 0.9758 | 0.5810 |
| 0.0082 | 97.65 | 1660 | 0.1151 | 0.7787 | 0.8308 | 0.9766 | 0.9923 | 0.6694 | 0.9758 | 0.5815 |
| 0.0094 | 98.82 | 1680 | 0.1155 | 0.7784 | 0.8295 | 0.9766 | 0.9925 | 0.6664 | 0.9759 | 0.5808 |
| 0.0067 | 100.0 | 1700 | 0.1156 | 0.7785 | 0.8298 | 0.9767 | 0.9925 | 0.6671 | 0.9759 | 0.5812 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
| [
"background",
"branch"
] |
twdent/segformer-b0-finetuned-robot-hiking |
<!-- 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-robot-hiking
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the twdent/Hiking dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1458
- Mean Iou: 0.6158
- Mean Accuracy: 0.9603
- Overall Accuracy: 0.9619
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9546
- Accuracy Non-traversable: 0.9661
- Iou Unlabeled: 0.0
- Iou Traversable: 0.9044
- Iou Non-traversable: 0.9429
## 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 Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:|
| 0.5363 | 1.33 | 20 | 0.7433 | 0.5581 | 0.9246 | 0.9153 | nan | 0.9573 | 0.8919 | 0.0 | 0.8030 | 0.8712 |
| 0.3689 | 2.67 | 40 | 0.4666 | 0.5567 | 0.9281 | 0.9137 | nan | 0.9791 | 0.8771 | 0.0 | 0.8030 | 0.8670 |
| 0.2706 | 4.0 | 60 | 0.3204 | 0.5880 | 0.9479 | 0.9407 | nan | 0.9732 | 0.9226 | 0.0 | 0.8550 | 0.9089 |
| 0.2338 | 5.33 | 80 | 0.2881 | 0.5985 | 0.9504 | 0.9497 | nan | 0.9527 | 0.9481 | 0.0 | 0.8719 | 0.9236 |
| 0.2068 | 6.67 | 100 | 0.2556 | 0.9022 | 0.9521 | 0.9521 | nan | 0.9522 | 0.9521 | nan | 0.8770 | 0.9273 |
| 0.1764 | 8.0 | 120 | 0.2401 | 0.6024 | 0.9539 | 0.9528 | nan | 0.9577 | 0.9500 | 0.0 | 0.8792 | 0.9280 |
| 0.2639 | 9.33 | 140 | 0.2588 | 0.5937 | 0.9504 | 0.9455 | nan | 0.9680 | 0.9329 | 0.0 | 0.8646 | 0.9166 |
| 0.1813 | 10.67 | 160 | 0.2124 | 0.6030 | 0.9526 | 0.9530 | nan | 0.9513 | 0.9540 | 0.0 | 0.8801 | 0.9287 |
| 0.1407 | 12.0 | 180 | 0.1938 | 0.6055 | 0.9518 | 0.9554 | nan | 0.9388 | 0.9647 | 0.0 | 0.8836 | 0.9328 |
| 0.13 | 13.33 | 200 | 0.1881 | 0.6062 | 0.9524 | 0.9558 | nan | 0.9403 | 0.9644 | 0.0 | 0.8854 | 0.9333 |
| 0.107 | 14.67 | 220 | 0.2092 | 0.5967 | 0.9530 | 0.9474 | nan | 0.9725 | 0.9334 | 0.0 | 0.8708 | 0.9194 |
| 0.1282 | 16.0 | 240 | 0.1803 | 0.6065 | 0.9536 | 0.9555 | nan | 0.9471 | 0.9602 | 0.0 | 0.8869 | 0.9328 |
| 0.146 | 17.33 | 260 | 0.1912 | 0.6028 | 0.9559 | 0.9519 | nan | 0.9700 | 0.9418 | 0.0 | 0.8814 | 0.9269 |
| 0.1011 | 18.67 | 280 | 0.1769 | 0.6079 | 0.9598 | 0.9561 | nan | 0.9727 | 0.9468 | 0.0 | 0.8907 | 0.9330 |
| 0.1124 | 20.0 | 300 | 0.1580 | 0.6135 | 0.9582 | 0.9608 | nan | 0.9491 | 0.9673 | 0.0 | 0.8995 | 0.9411 |
| 0.0801 | 21.33 | 320 | 0.1614 | 0.6113 | 0.9582 | 0.9588 | nan | 0.9563 | 0.9602 | 0.0 | 0.8960 | 0.9380 |
| 0.0831 | 22.67 | 340 | 0.1540 | 0.6130 | 0.9608 | 0.9601 | nan | 0.9633 | 0.9584 | 0.0 | 0.8994 | 0.9396 |
| 0.0599 | 24.0 | 360 | 0.1641 | 0.6098 | 0.9584 | 0.9576 | nan | 0.9614 | 0.9554 | 0.0 | 0.8935 | 0.9358 |
| 0.0955 | 25.33 | 380 | 0.1711 | 0.6084 | 0.9597 | 0.9562 | nan | 0.9720 | 0.9474 | 0.0 | 0.8917 | 0.9334 |
| 0.0667 | 26.67 | 400 | 0.1618 | 0.6109 | 0.9574 | 0.9583 | nan | 0.9543 | 0.9605 | 0.0 | 0.8954 | 0.9373 |
| 0.0783 | 28.0 | 420 | 0.1640 | 0.6089 | 0.9589 | 0.9568 | nan | 0.9665 | 0.9513 | 0.0 | 0.8924 | 0.9343 |
| 0.0743 | 29.33 | 440 | 0.1512 | 0.6145 | 0.9582 | 0.9612 | nan | 0.9478 | 0.9686 | 0.0 | 0.9016 | 0.9419 |
| 0.0775 | 30.67 | 460 | 0.1574 | 0.6131 | 0.9583 | 0.9598 | nan | 0.9528 | 0.9637 | 0.0 | 0.8995 | 0.9398 |
| 0.0773 | 32.0 | 480 | 0.1464 | 0.6157 | 0.9610 | 0.9621 | nan | 0.9573 | 0.9647 | 0.0 | 0.9043 | 0.9428 |
| 0.0575 | 33.33 | 500 | 0.1600 | 0.6085 | 0.9568 | 0.9564 | nan | 0.9583 | 0.9554 | 0.0 | 0.8912 | 0.9343 |
| 0.0729 | 34.67 | 520 | 0.1540 | 0.6105 | 0.9569 | 0.9577 | nan | 0.9541 | 0.9597 | 0.0 | 0.8946 | 0.9369 |
| 0.1409 | 36.0 | 540 | 0.1557 | 0.6112 | 0.9584 | 0.9586 | nan | 0.9575 | 0.9593 | 0.0 | 0.8962 | 0.9376 |
| 0.0543 | 37.33 | 560 | 0.1607 | 0.6103 | 0.9546 | 0.9581 | nan | 0.9422 | 0.9670 | 0.0 | 0.8938 | 0.9373 |
| 0.063 | 38.67 | 580 | 0.1622 | 0.6099 | 0.9558 | 0.9574 | nan | 0.9504 | 0.9613 | 0.0 | 0.8933 | 0.9364 |
| 0.0549 | 40.0 | 600 | 0.1543 | 0.6118 | 0.9571 | 0.9590 | nan | 0.9503 | 0.9639 | 0.0 | 0.8969 | 0.9386 |
| 0.0766 | 41.33 | 620 | 0.1481 | 0.6139 | 0.9575 | 0.9606 | nan | 0.9467 | 0.9684 | 0.0 | 0.9005 | 0.9412 |
| 0.0616 | 42.67 | 640 | 0.1485 | 0.6151 | 0.9600 | 0.9614 | nan | 0.9550 | 0.9649 | 0.0 | 0.9032 | 0.9422 |
| 0.0872 | 44.0 | 660 | 0.1511 | 0.6144 | 0.9607 | 0.9609 | nan | 0.9602 | 0.9612 | 0.0 | 0.9022 | 0.9409 |
| 0.0677 | 45.33 | 680 | 0.1510 | 0.6139 | 0.9599 | 0.9604 | nan | 0.9580 | 0.9618 | 0.0 | 0.9012 | 0.9405 |
| 0.1075 | 46.67 | 700 | 0.1506 | 0.6145 | 0.9606 | 0.9610 | nan | 0.9592 | 0.9619 | 0.0 | 0.9024 | 0.9411 |
| 0.0485 | 48.0 | 720 | 0.1450 | 0.6159 | 0.9595 | 0.9621 | nan | 0.9506 | 0.9685 | 0.0 | 0.9043 | 0.9433 |
| 0.0972 | 49.33 | 740 | 0.1458 | 0.6158 | 0.9603 | 0.9619 | nan | 0.9546 | 0.9661 | 0.0 | 0.9044 | 0.9429 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
| [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b1-finetuned-Hiking |
<!-- 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-Hiking
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the twdent/Hiking dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1129
- Mean Iou: 0.6261
- Mean Accuracy: 0.9707
- Overall Accuracy: 0.9700
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9730
- Accuracy Non-traversable: 0.9684
- Iou Unlabeled: 0.0
- Iou Traversable: 0.9226
- Iou Non-traversable: 0.9557
## 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 Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:|
| 0.5526 | 1.33 | 20 | 0.7232 | 0.5814 | 0.9404 | 0.9333 | nan | 0.9630 | 0.9177 | 0.0 | 0.8433 | 0.9009 |
| 0.3768 | 2.67 | 40 | 0.3616 | 0.5929 | 0.9504 | 0.9453 | nan | 0.9666 | 0.9341 | 0.0 | 0.8605 | 0.9183 |
| 0.3271 | 4.0 | 60 | 0.2683 | 0.6080 | 0.9606 | 0.9571 | nan | 0.9718 | 0.9494 | 0.0 | 0.8883 | 0.9358 |
| 0.2377 | 5.33 | 80 | 0.2754 | 0.5869 | 0.9256 | 0.9429 | nan | 0.8706 | 0.9807 | 0.0 | 0.8415 | 0.9191 |
| 0.2242 | 6.67 | 100 | 0.2299 | 0.6027 | 0.9447 | 0.9545 | nan | 0.9134 | 0.9760 | 0.0 | 0.8739 | 0.9341 |
| 0.2458 | 8.0 | 120 | 0.1939 | 0.6207 | 0.9604 | 0.9667 | nan | 0.9402 | 0.9805 | 0.0 | 0.9107 | 0.9513 |
| 0.1541 | 9.33 | 140 | 0.1988 | 0.6121 | 0.9654 | 0.9599 | nan | 0.9831 | 0.9477 | 0.0 | 0.8967 | 0.9395 |
| 0.1448 | 10.67 | 160 | 0.1722 | 0.6202 | 0.9677 | 0.9662 | nan | 0.9725 | 0.9629 | 0.0 | 0.9112 | 0.9494 |
| 0.1533 | 12.0 | 180 | 0.2112 | 0.5951 | 0.9570 | 0.9454 | nan | 0.9941 | 0.9199 | 0.0 | 0.8676 | 0.9176 |
| 0.107 | 13.33 | 200 | 0.1658 | 0.6139 | 0.9626 | 0.9614 | nan | 0.9665 | 0.9587 | 0.0 | 0.8992 | 0.9426 |
| 0.109 | 14.67 | 220 | 0.1342 | 0.6267 | 0.9714 | 0.9712 | nan | 0.9724 | 0.9705 | 0.0 | 0.9231 | 0.9569 |
| 0.1092 | 16.0 | 240 | 0.1448 | 0.6173 | 0.9690 | 0.9636 | nan | 0.9860 | 0.9519 | 0.0 | 0.9065 | 0.9453 |
| 0.0971 | 17.33 | 260 | 0.1282 | 0.6216 | 0.9691 | 0.9673 | nan | 0.9747 | 0.9635 | 0.0 | 0.9136 | 0.9512 |
| 0.1448 | 18.67 | 280 | 0.1504 | 0.6155 | 0.9661 | 0.9619 | nan | 0.9795 | 0.9526 | 0.0 | 0.9032 | 0.9434 |
| 0.0797 | 20.0 | 300 | 0.1312 | 0.6209 | 0.9669 | 0.9666 | nan | 0.9680 | 0.9659 | 0.0 | 0.9124 | 0.9503 |
| 0.0766 | 21.33 | 320 | 0.1164 | 0.6251 | 0.9667 | 0.9696 | nan | 0.9574 | 0.9760 | 0.0 | 0.9198 | 0.9555 |
| 0.0822 | 22.67 | 340 | 0.1365 | 0.6171 | 0.9638 | 0.9639 | nan | 0.9635 | 0.9641 | 0.0 | 0.9050 | 0.9464 |
| 0.075 | 24.0 | 360 | 0.1401 | 0.6160 | 0.9679 | 0.9621 | nan | 0.9862 | 0.9495 | 0.0 | 0.9046 | 0.9433 |
| 0.0684 | 25.33 | 380 | 0.1317 | 0.6190 | 0.9687 | 0.9645 | nan | 0.9822 | 0.9552 | 0.0 | 0.9099 | 0.9472 |
| 0.0767 | 26.67 | 400 | 0.1293 | 0.6195 | 0.9699 | 0.9651 | nan | 0.9851 | 0.9547 | 0.0 | 0.9107 | 0.9478 |
| 0.0576 | 28.0 | 420 | 0.1195 | 0.6236 | 0.9701 | 0.9679 | nan | 0.9771 | 0.9631 | 0.0 | 0.9180 | 0.9529 |
| 0.0596 | 29.33 | 440 | 0.1179 | 0.6248 | 0.9717 | 0.9692 | nan | 0.9794 | 0.9639 | 0.0 | 0.9204 | 0.9541 |
| 0.0564 | 30.67 | 460 | 0.1110 | 0.6264 | 0.9719 | 0.9701 | nan | 0.9777 | 0.9661 | 0.0 | 0.9233 | 0.9559 |
| 0.0496 | 32.0 | 480 | 0.1063 | 0.6284 | 0.9726 | 0.9714 | nan | 0.9761 | 0.9690 | 0.0 | 0.9271 | 0.9581 |
| 0.0722 | 33.33 | 500 | 0.1073 | 0.6272 | 0.9712 | 0.9711 | nan | 0.9716 | 0.9708 | 0.0 | 0.9244 | 0.9573 |
| 0.0465 | 34.67 | 520 | 0.1228 | 0.6220 | 0.9692 | 0.9669 | nan | 0.9763 | 0.9620 | 0.0 | 0.9150 | 0.9510 |
| 0.0655 | 36.0 | 540 | 0.1142 | 0.6245 | 0.9704 | 0.9689 | nan | 0.9752 | 0.9656 | 0.0 | 0.9196 | 0.9540 |
| 0.0516 | 37.33 | 560 | 0.1197 | 0.6238 | 0.9687 | 0.9684 | nan | 0.9696 | 0.9677 | 0.0 | 0.9181 | 0.9533 |
| 0.0774 | 38.67 | 580 | 0.1114 | 0.6266 | 0.9706 | 0.9704 | nan | 0.9712 | 0.9700 | 0.0 | 0.9234 | 0.9565 |
| 0.0572 | 40.0 | 600 | 0.1124 | 0.6261 | 0.9707 | 0.9700 | nan | 0.9730 | 0.9684 | 0.0 | 0.9227 | 0.9558 |
| 0.0554 | 41.33 | 620 | 0.1116 | 0.6273 | 0.9718 | 0.9709 | nan | 0.9747 | 0.9688 | 0.0 | 0.9248 | 0.9570 |
| 0.0438 | 42.67 | 640 | 0.1192 | 0.6259 | 0.9707 | 0.9694 | nan | 0.9746 | 0.9667 | 0.0 | 0.9225 | 0.9551 |
| 0.0486 | 44.0 | 660 | 0.1186 | 0.6248 | 0.9709 | 0.9689 | nan | 0.9775 | 0.9643 | 0.0 | 0.9203 | 0.9540 |
| 0.0582 | 45.33 | 680 | 0.1194 | 0.6250 | 0.9705 | 0.9691 | nan | 0.9751 | 0.9660 | 0.0 | 0.9209 | 0.9542 |
| 0.0643 | 46.67 | 700 | 0.1157 | 0.6252 | 0.9706 | 0.9692 | nan | 0.9750 | 0.9662 | 0.0 | 0.9209 | 0.9546 |
| 0.057 | 48.0 | 720 | 0.1181 | 0.6251 | 0.9708 | 0.9691 | nan | 0.9763 | 0.9654 | 0.0 | 0.9210 | 0.9543 |
| 0.0468 | 49.33 | 740 | 0.1129 | 0.6261 | 0.9707 | 0.9700 | nan | 0.9730 | 0.9684 | 0.0 | 0.9226 | 0.9557 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
| [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b5-finetuned-Hiking |
<!-- 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-Hiking
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the twdent/Hiking dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1401
- Mean Iou: 0.6237
- Mean Accuracy: 0.9673
- Overall Accuracy: 0.9683
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9641
- Accuracy Non-traversable: 0.9705
- Iou Unlabeled: 0.0
- Iou Traversable: 0.9178
- Iou Non-traversable: 0.9532
## 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 Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:|
| 0.2675 | 1.33 | 20 | 0.2742 | 0.6058 | 0.9616 | 0.9550 | nan | 0.9826 | 0.9406 | 0.0 | 0.8853 | 0.9321 |
| 0.1418 | 2.67 | 40 | 0.1827 | 0.6073 | 0.9562 | 0.9566 | nan | 0.9548 | 0.9575 | 0.0 | 0.8858 | 0.9360 |
| 0.0949 | 4.0 | 60 | 0.1561 | 0.6002 | 0.9382 | 0.9523 | nan | 0.8931 | 0.9832 | 0.0 | 0.8692 | 0.9314 |
| 0.0684 | 5.33 | 80 | 0.1364 | 0.6135 | 0.9556 | 0.9614 | nan | 0.9369 | 0.9742 | 0.0 | 0.8967 | 0.9437 |
| 0.0627 | 6.67 | 100 | 0.1289 | 0.6122 | 0.9506 | 0.9610 | nan | 0.9177 | 0.9836 | 0.0 | 0.8928 | 0.9438 |
| 0.0625 | 8.0 | 120 | 0.1097 | 0.6208 | 0.9610 | 0.9658 | nan | 0.9458 | 0.9762 | 0.0 | 0.9113 | 0.9510 |
| 0.0371 | 9.33 | 140 | 0.1361 | 0.6130 | 0.9551 | 0.9610 | nan | 0.9361 | 0.9741 | 0.0 | 0.8959 | 0.9431 |
| 0.0409 | 10.67 | 160 | 0.1239 | 0.6194 | 0.9615 | 0.9653 | nan | 0.9494 | 0.9737 | 0.0 | 0.9086 | 0.9494 |
| 0.0457 | 12.0 | 180 | 0.0993 | 0.6281 | 0.9715 | 0.9713 | nan | 0.9723 | 0.9707 | 0.0 | 0.9264 | 0.9579 |
| 0.0368 | 13.33 | 200 | 0.1354 | 0.6146 | 0.9563 | 0.9617 | nan | 0.9389 | 0.9737 | 0.0 | 0.8993 | 0.9446 |
| 0.0667 | 14.67 | 220 | 0.1208 | 0.6171 | 0.9565 | 0.9644 | nan | 0.9316 | 0.9815 | 0.0 | 0.9032 | 0.9482 |
| 0.029 | 16.0 | 240 | 0.0946 | 0.6291 | 0.9695 | 0.9724 | nan | 0.9606 | 0.9785 | 0.0 | 0.9276 | 0.9596 |
| 0.0467 | 17.33 | 260 | 0.1188 | 0.6224 | 0.9655 | 0.9676 | nan | 0.9589 | 0.9721 | 0.0 | 0.9151 | 0.9522 |
| 0.0449 | 18.67 | 280 | 0.1201 | 0.6212 | 0.9638 | 0.9667 | nan | 0.9545 | 0.9731 | 0.0 | 0.9125 | 0.9511 |
| 0.0353 | 20.0 | 300 | 0.1285 | 0.6234 | 0.9687 | 0.9681 | nan | 0.9706 | 0.9668 | 0.0 | 0.9174 | 0.9527 |
| 0.025 | 21.33 | 320 | 0.1292 | 0.6204 | 0.9641 | 0.9659 | nan | 0.9582 | 0.9699 | 0.0 | 0.9114 | 0.9500 |
| 0.0244 | 22.67 | 340 | 0.1352 | 0.6208 | 0.9665 | 0.9664 | nan | 0.9667 | 0.9662 | 0.0 | 0.9124 | 0.9501 |
| 0.035 | 24.0 | 360 | 0.1260 | 0.6252 | 0.9699 | 0.9693 | nan | 0.9718 | 0.9681 | 0.0 | 0.9211 | 0.9544 |
| 0.0295 | 25.33 | 380 | 0.1190 | 0.6244 | 0.9669 | 0.9688 | nan | 0.9607 | 0.9730 | 0.0 | 0.9190 | 0.9543 |
| 0.032 | 26.67 | 400 | 0.1258 | 0.6253 | 0.9694 | 0.9695 | nan | 0.9693 | 0.9695 | 0.0 | 0.9211 | 0.9547 |
| 0.0241 | 28.0 | 420 | 0.1255 | 0.6230 | 0.9658 | 0.9678 | nan | 0.9593 | 0.9723 | 0.0 | 0.9164 | 0.9527 |
| 0.0246 | 29.33 | 440 | 0.1273 | 0.6238 | 0.9675 | 0.9683 | nan | 0.9651 | 0.9699 | 0.0 | 0.9179 | 0.9534 |
| 0.0214 | 30.67 | 460 | 0.1321 | 0.6233 | 0.9670 | 0.9675 | nan | 0.9652 | 0.9687 | 0.0 | 0.9171 | 0.9527 |
| 0.0236 | 32.0 | 480 | 0.1289 | 0.6241 | 0.9687 | 0.9685 | nan | 0.9695 | 0.9679 | 0.0 | 0.9189 | 0.9534 |
| 0.0238 | 33.33 | 500 | 0.1309 | 0.6234 | 0.9664 | 0.9680 | nan | 0.9612 | 0.9716 | 0.0 | 0.9172 | 0.9529 |
| 0.0204 | 34.67 | 520 | 0.1271 | 0.6249 | 0.9681 | 0.9693 | nan | 0.9643 | 0.9719 | 0.0 | 0.9201 | 0.9547 |
| 0.0243 | 36.0 | 540 | 0.1264 | 0.6248 | 0.9679 | 0.9693 | nan | 0.9636 | 0.9723 | 0.0 | 0.9196 | 0.9547 |
| 0.0259 | 37.33 | 560 | 0.1305 | 0.6226 | 0.9656 | 0.9679 | nan | 0.9582 | 0.9730 | 0.0 | 0.9154 | 0.9525 |
| 0.0341 | 38.67 | 580 | 0.1277 | 0.6245 | 0.9674 | 0.9690 | nan | 0.9623 | 0.9725 | 0.0 | 0.9192 | 0.9543 |
| 0.0275 | 40.0 | 600 | 0.1369 | 0.6221 | 0.9653 | 0.9672 | nan | 0.9590 | 0.9715 | 0.0 | 0.9147 | 0.9516 |
| 0.0303 | 41.33 | 620 | 0.1380 | 0.6235 | 0.9674 | 0.9681 | nan | 0.9650 | 0.9698 | 0.0 | 0.9175 | 0.9530 |
| 0.0207 | 42.67 | 640 | 0.1389 | 0.6237 | 0.9677 | 0.9682 | nan | 0.9662 | 0.9692 | 0.0 | 0.9180 | 0.9531 |
| 0.0231 | 44.0 | 660 | 0.1369 | 0.6243 | 0.9679 | 0.9688 | nan | 0.9652 | 0.9707 | 0.0 | 0.9190 | 0.9538 |
| 0.0249 | 45.33 | 680 | 0.1379 | 0.6237 | 0.9672 | 0.9683 | nan | 0.9640 | 0.9705 | 0.0 | 0.9179 | 0.9532 |
| 0.0382 | 46.67 | 700 | 0.1384 | 0.6239 | 0.9677 | 0.9685 | nan | 0.9650 | 0.9704 | 0.0 | 0.9182 | 0.9534 |
| 0.0238 | 48.0 | 720 | 0.1420 | 0.6230 | 0.9668 | 0.9677 | nan | 0.9640 | 0.9697 | 0.0 | 0.9166 | 0.9524 |
| 0.0212 | 49.33 | 740 | 0.1401 | 0.6237 | 0.9673 | 0.9683 | nan | 0.9641 | 0.9705 | 0.0 | 0.9178 | 0.9532 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
| [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b5-finetuned-HikingHD |
<!-- 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-HikingHD
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the twdent/HikingHD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1077
- Mean Iou: 0.6364
- Mean Accuracy: 0.9770
- Overall Accuracy: 0.9771
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9758
- Accuracy Non-traversable: 0.9781
- Iou Unlabeled: 0.0
- Iou Traversable: 0.9493
- Iou Non-traversable: 0.9600
- Local Testing:
- Average inference time: 0.9943107657962376
## 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 Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:|
| 0.1595 | 1.33 | 20 | 0.1789 | 0.9314 | 0.9657 | 0.9649 | nan | 0.9727 | 0.9588 | nan | 0.9240 | 0.9388 |
| 0.3593 | 2.67 | 40 | 0.1137 | 0.9429 | 0.9731 | 0.9709 | nan | 0.9911 | 0.9551 | nan | 0.9372 | 0.9485 |
| 0.1002 | 4.0 | 60 | 0.0979 | 0.9363 | 0.9661 | 0.9677 | nan | 0.9531 | 0.9791 | nan | 0.9282 | 0.9444 |
| 0.0348 | 5.33 | 80 | 0.0933 | 0.9442 | 0.9713 | 0.9717 | nan | 0.9675 | 0.9750 | nan | 0.9375 | 0.9509 |
| 0.0374 | 6.67 | 100 | 0.0884 | 0.9459 | 0.9714 | 0.9727 | nan | 0.9611 | 0.9817 | nan | 0.9391 | 0.9528 |
| 0.0447 | 8.0 | 120 | 0.0886 | 0.9491 | 0.9737 | 0.9743 | nan | 0.9684 | 0.9789 | nan | 0.9430 | 0.9553 |
| 0.0464 | 9.33 | 140 | 0.0790 | 0.9564 | 0.9783 | 0.9780 | nan | 0.9804 | 0.9762 | nan | 0.9514 | 0.9614 |
| 0.0421 | 10.67 | 160 | 0.0868 | 0.9540 | 0.9764 | 0.9768 | nan | 0.9733 | 0.9796 | nan | 0.9485 | 0.9596 |
| 0.0253 | 12.0 | 180 | 0.0887 | 0.9530 | 0.9756 | 0.9763 | nan | 0.9700 | 0.9812 | nan | 0.9472 | 0.9587 |
| 0.0364 | 13.33 | 200 | 0.0960 | 0.9494 | 0.9733 | 0.9745 | nan | 0.9638 | 0.9829 | nan | 0.9431 | 0.9558 |
| 0.0276 | 14.67 | 220 | 0.0980 | 0.9470 | 0.9717 | 0.9732 | nan | 0.9595 | 0.9840 | nan | 0.9402 | 0.9538 |
| 0.0279 | 16.0 | 240 | 0.0914 | 0.9534 | 0.9761 | 0.9765 | nan | 0.9725 | 0.9796 | nan | 0.9478 | 0.9590 |
| 0.026 | 17.33 | 260 | 0.0886 | 0.9557 | 0.9778 | 0.9777 | nan | 0.9792 | 0.9764 | nan | 0.9506 | 0.9609 |
| 0.0228 | 18.67 | 280 | 0.0888 | 0.9547 | 0.9775 | 0.9771 | nan | 0.9804 | 0.9745 | nan | 0.9495 | 0.9599 |
| 0.0259 | 20.0 | 300 | 0.0984 | 0.9505 | 0.9742 | 0.9750 | nan | 0.9679 | 0.9806 | nan | 0.9444 | 0.9565 |
| 0.0306 | 21.33 | 320 | 0.0890 | 0.9542 | 0.9763 | 0.9769 | nan | 0.9716 | 0.9811 | nan | 0.9487 | 0.9598 |
| 0.0305 | 22.67 | 340 | 0.0967 | 0.6352 | 0.9752 | 0.9762 | nan | 0.9669 | 0.9834 | 0.0 | 0.9468 | 0.9586 |
| 0.0219 | 24.0 | 360 | 0.0983 | 0.9538 | 0.9764 | 0.9767 | nan | 0.9735 | 0.9792 | nan | 0.9483 | 0.9593 |
| 0.023 | 25.33 | 380 | 0.0940 | 0.6368 | 0.9771 | 0.9774 | nan | 0.9743 | 0.9799 | 0.0 | 0.9499 | 0.9606 |
| 0.0217 | 26.67 | 400 | 0.0973 | 0.6360 | 0.9767 | 0.9768 | nan | 0.9758 | 0.9776 | 0.0 | 0.9486 | 0.9595 |
| 0.0267 | 28.0 | 420 | 0.1023 | 0.6360 | 0.9770 | 0.9768 | nan | 0.9792 | 0.9749 | 0.0 | 0.9487 | 0.9593 |
| 0.0202 | 29.33 | 440 | 0.0955 | 0.6376 | 0.9783 | 0.9780 | nan | 0.9802 | 0.9764 | 0.0 | 0.9514 | 0.9615 |
| 0.0225 | 30.67 | 460 | 0.1016 | 0.6360 | 0.9763 | 0.9768 | nan | 0.9727 | 0.9800 | 0.0 | 0.9484 | 0.9595 |
| 0.0288 | 32.0 | 480 | 0.1026 | 0.6354 | 0.9756 | 0.9763 | nan | 0.9697 | 0.9815 | 0.0 | 0.9473 | 0.9588 |
| 0.0209 | 33.33 | 500 | 0.0977 | 0.6370 | 0.9771 | 0.9776 | nan | 0.9735 | 0.9808 | 0.0 | 0.9502 | 0.9609 |
| 0.0202 | 34.67 | 520 | 0.1005 | 0.6367 | 0.9772 | 0.9773 | nan | 0.9762 | 0.9782 | 0.0 | 0.9497 | 0.9604 |
| 0.0194 | 36.0 | 540 | 0.1032 | 0.6365 | 0.9771 | 0.9772 | nan | 0.9766 | 0.9776 | 0.0 | 0.9495 | 0.9601 |
| 0.0165 | 37.33 | 560 | 0.1013 | 0.6373 | 0.9777 | 0.9778 | nan | 0.9769 | 0.9785 | 0.0 | 0.9508 | 0.9612 |
| 0.0226 | 38.67 | 580 | 0.1005 | 0.6367 | 0.9771 | 0.9773 | nan | 0.9752 | 0.9790 | 0.0 | 0.9497 | 0.9604 |
| 0.0206 | 40.0 | 600 | 0.1032 | 0.6369 | 0.9773 | 0.9775 | nan | 0.9757 | 0.9789 | 0.0 | 0.9501 | 0.9607 |
| 0.016 | 41.33 | 620 | 0.1007 | 0.6373 | 0.9776 | 0.9777 | nan | 0.9761 | 0.9790 | 0.0 | 0.9506 | 0.9611 |
| 0.012 | 42.67 | 640 | 0.1048 | 0.6364 | 0.9768 | 0.9771 | nan | 0.9748 | 0.9789 | 0.0 | 0.9493 | 0.9601 |
| 0.0261 | 44.0 | 660 | 0.1073 | 0.6364 | 0.9770 | 0.9771 | nan | 0.9760 | 0.9780 | 0.0 | 0.9493 | 0.9600 |
| 0.0125 | 45.33 | 680 | 0.1088 | 0.6357 | 0.9761 | 0.9765 | nan | 0.9727 | 0.9795 | 0.0 | 0.9479 | 0.9591 |
| 0.0259 | 46.67 | 700 | 0.1073 | 0.6365 | 0.9770 | 0.9772 | nan | 0.9760 | 0.9780 | 0.0 | 0.9494 | 0.9601 |
| 0.0173 | 48.0 | 720 | 0.1052 | 0.6366 | 0.9770 | 0.9772 | nan | 0.9755 | 0.9785 | 0.0 | 0.9495 | 0.9602 |
| 0.0162 | 49.33 | 740 | 0.1077 | 0.6364 | 0.9770 | 0.9771 | nan | 0.9758 | 0.9781 | 0.0 | 0.9493 | 0.9600 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
| [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b0-finetuned-HikingHD |
<!-- 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-HikingHD
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the twdent/HikingHD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1189
- Mean Iou: 0.9224
- Mean Accuracy: 0.9627
- Overall Accuracy: 0.9622
- Accuracy Traversable: 0.9645
- Accuracy Non-traversable: 0.9608
- Iou Traversable: 0.9032
- Iou Non-traversable: 0.9415
## 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 Traversable | Accuracy Non-traversable | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:------------------------:|:---------------:|:-------------------:|
| 0.1555 | 1.33 | 20 | 0.3462 | 0.8817 | 0.9484 | 0.9401 | 0.9792 | 0.9176 | 0.8568 | 0.9067 |
| 0.1168 | 2.67 | 40 | 0.1551 | 0.8998 | 0.9529 | 0.9503 | 0.9628 | 0.9431 | 0.8764 | 0.9233 |
| 0.1054 | 4.0 | 60 | 0.1566 | 0.8910 | 0.9527 | 0.9452 | 0.9807 | 0.9247 | 0.8675 | 0.9146 |
| 0.0775 | 5.33 | 80 | 0.1892 | 0.8645 | 0.9415 | 0.9304 | 0.9830 | 0.9000 | 0.8378 | 0.8912 |
| 0.1111 | 6.67 | 100 | 0.1369 | 0.9015 | 0.9515 | 0.9514 | 0.9520 | 0.9511 | 0.8776 | 0.9255 |
| 0.0737 | 8.0 | 120 | 0.1358 | 0.9005 | 0.9503 | 0.9510 | 0.9476 | 0.9529 | 0.8761 | 0.9249 |
| 0.0908 | 9.33 | 140 | 0.1186 | 0.9097 | 0.9565 | 0.9556 | 0.9599 | 0.9532 | 0.8878 | 0.9316 |
| 0.0654 | 10.67 | 160 | 0.1177 | 0.9182 | 0.9624 | 0.9599 | 0.9719 | 0.9529 | 0.8986 | 0.9377 |
| 0.0871 | 12.0 | 180 | 0.1220 | 0.9105 | 0.9546 | 0.9563 | 0.9482 | 0.9609 | 0.8880 | 0.9330 |
| 0.0493 | 13.33 | 200 | 0.1237 | 0.9126 | 0.9559 | 0.9573 | 0.9504 | 0.9613 | 0.8907 | 0.9346 |
| 0.0643 | 14.67 | 220 | 0.1232 | 0.9107 | 0.9503 | 0.9567 | 0.9265 | 0.9741 | 0.8868 | 0.9345 |
| 0.0491 | 16.0 | 240 | 0.1199 | 0.9140 | 0.9573 | 0.9580 | 0.9545 | 0.9600 | 0.8926 | 0.9355 |
| 0.0556 | 17.33 | 260 | 0.1114 | 0.9199 | 0.9613 | 0.9609 | 0.9628 | 0.9598 | 0.9001 | 0.9396 |
| 0.0484 | 18.67 | 280 | 0.1137 | 0.9189 | 0.9628 | 0.9603 | 0.9720 | 0.9535 | 0.8995 | 0.9383 |
| 0.0607 | 20.0 | 300 | 0.1230 | 0.9163 | 0.9625 | 0.9588 | 0.9762 | 0.9488 | 0.8966 | 0.9359 |
| 0.044 | 21.33 | 320 | 0.1349 | 0.9077 | 0.9567 | 0.9545 | 0.9648 | 0.9485 | 0.8858 | 0.9297 |
| 0.0426 | 22.67 | 340 | 0.1313 | 0.9070 | 0.9563 | 0.9541 | 0.9646 | 0.9481 | 0.8850 | 0.9291 |
| 0.0269 | 24.0 | 360 | 0.1143 | 0.9226 | 0.9668 | 0.9620 | 0.9850 | 0.9487 | 0.9046 | 0.9406 |
| 0.0593 | 25.33 | 380 | 0.1038 | 0.9235 | 0.9616 | 0.9629 | 0.9570 | 0.9662 | 0.9041 | 0.9428 |
| 0.0321 | 26.67 | 400 | 0.1136 | 0.9179 | 0.9598 | 0.9599 | 0.9595 | 0.9602 | 0.8976 | 0.9383 |
| 0.0752 | 28.0 | 420 | 0.1196 | 0.9194 | 0.9627 | 0.9606 | 0.9705 | 0.9548 | 0.9000 | 0.9388 |
| 0.0812 | 29.33 | 440 | 0.1253 | 0.9216 | 0.9665 | 0.9615 | 0.9854 | 0.9477 | 0.9035 | 0.9398 |
| 0.0329 | 30.67 | 460 | 0.1023 | 0.9294 | 0.9671 | 0.9657 | 0.9725 | 0.9618 | 0.9120 | 0.9467 |
| 0.035 | 32.0 | 480 | 0.0969 | 0.9282 | 0.9658 | 0.9651 | 0.9686 | 0.9631 | 0.9104 | 0.9460 |
| 0.0332 | 33.33 | 500 | 0.1086 | 0.9231 | 0.9620 | 0.9626 | 0.9598 | 0.9643 | 0.9038 | 0.9424 |
| 0.0343 | 34.67 | 520 | 0.0962 | 0.9312 | 0.9689 | 0.9666 | 0.9774 | 0.9603 | 0.9145 | 0.9480 |
| 0.0337 | 36.0 | 540 | 0.1072 | 0.9251 | 0.9649 | 0.9635 | 0.9703 | 0.9595 | 0.9067 | 0.9434 |
| 0.0367 | 37.33 | 560 | 0.1033 | 0.9302 | 0.9692 | 0.9660 | 0.9809 | 0.9574 | 0.9135 | 0.9470 |
| 0.0327 | 38.67 | 580 | 0.1014 | 0.9312 | 0.9681 | 0.9666 | 0.9734 | 0.9627 | 0.9143 | 0.9482 |
| 0.0293 | 40.0 | 600 | 0.1202 | 0.9207 | 0.9622 | 0.9613 | 0.9656 | 0.9588 | 0.9012 | 0.9401 |
| 0.0272 | 41.33 | 620 | 0.1113 | 0.9246 | 0.9634 | 0.9633 | 0.9637 | 0.9631 | 0.9058 | 0.9433 |
| 0.0304 | 42.67 | 640 | 0.1070 | 0.9253 | 0.9643 | 0.9637 | 0.9668 | 0.9619 | 0.9069 | 0.9438 |
| 0.037 | 44.0 | 660 | 0.1120 | 0.9228 | 0.9629 | 0.9624 | 0.9648 | 0.9610 | 0.9037 | 0.9419 |
| 0.0323 | 45.33 | 680 | 0.1132 | 0.9213 | 0.9615 | 0.9617 | 0.9609 | 0.9621 | 0.9016 | 0.9409 |
| 0.0281 | 46.67 | 700 | 0.1203 | 0.9199 | 0.9616 | 0.9609 | 0.9643 | 0.9589 | 0.9002 | 0.9396 |
| 0.0339 | 48.0 | 720 | 0.1124 | 0.9253 | 0.9646 | 0.9637 | 0.9683 | 0.9610 | 0.9070 | 0.9437 |
| 0.0289 | 49.33 | 740 | 0.1189 | 0.9224 | 0.9627 | 0.9622 | 0.9645 | 0.9608 | 0.9032 | 0.9415 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"traversable",
"non-traversable"
] |
giuseppemartino/model1 |
<!-- 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. -->
# model1
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the giuseppemartino/i-SAID_custom_or_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1646
- Mean Iou: 0.2689
- Mean Accuracy: 0.3089
- Overall Accuracy: 0.3928
- Accuracy Background: nan
- Accuracy Ship: 0.7889
- Accuracy Small-vehicle: 0.3939
- Accuracy Tennis-court: 0.6399
- Accuracy Helicopter: nan
- Accuracy Basketball-court: 0.0
- Accuracy Ground-track-field: 0.4337
- Accuracy Swimming-pool: 0.6049
- Accuracy Harbor: 0.3386
- Accuracy Soccer-ball-field: 0.2551
- Accuracy Plane: 0.0001
- Accuracy Storage-tank: 0.0
- Accuracy Baseball-diamond: 0.5217
- Accuracy Large-vehicle: 0.3477
- Accuracy Bridge: 0.0
- Accuracy Roundabout: 0.0
- Iou Background: 0.0
- Iou Ship: 0.6137
- Iou Small-vehicle: 0.3354
- Iou Tennis-court: 0.6399
- Iou Helicopter: nan
- Iou Basketball-court: 0.0
- Iou Ground-track-field: 0.4084
- Iou Swimming-pool: 0.6049
- Iou Harbor: 0.3165
- Iou Soccer-ball-field: 0.2514
- Iou Plane: 0.0001
- Iou Storage-tank: 0.0
- Iou Baseball-diamond: 0.5217
- Iou Large-vehicle: 0.3418
- 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: 840
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------------:|:----------------------:|:---------------:|:--------------------------:|:--------------:|:---------------------:|:-------------------------:|:----------------------:|:---------------:|:-------------------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------------------:|:----------------------:|:-----------------:|:----------:|:---------------------:|:---------:|:----------------:|:--------------------:|:-----------------:|:----------:|:--------------:|
| 1.1466 | 1.0 | 105 | 0.3419 | 0.0260 | 0.0279 | 0.0687 | nan | 0.0068 | 0.0036 | 0.3562 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0240 | 0.0 | 0.0 | 0.0 | 0.0067 | 0.0036 | 0.3562 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0240 | 0.0 | 0.0 |
| 0.3289 | 2.0 | 210 | 0.2301 | 0.1252 | 0.1441 | 0.2674 | nan | 0.5316 | 0.1793 | 0.6775 | nan | 0.0 | 0.0324 | 0.1854 | 0.1185 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2923 | 0.0 | 0.0 | 0.0 | 0.4189 | 0.1612 | 0.6752 | nan | 0.0 | 0.0321 | 0.1854 | 0.1157 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2898 | 0.0 | 0.0 |
| 0.1819 | 3.0 | 315 | 0.1965 | 0.1611 | 0.1937 | 0.3286 | nan | 0.7305 | 0.2842 | 0.4229 | nan | 0.0 | 0.3566 | 0.2424 | 0.1707 | 0.0739 | 0.0 | 0.0 | 0.0 | 0.4300 | 0.0 | 0.0 | 0.0 | 0.5605 | 0.2492 | 0.4229 | nan | 0.0 | 0.2817 | 0.2424 | 0.1637 | 0.0738 | 0.0 | 0.0 | 0.0 | 0.4223 | 0.0 | 0.0 |
| 0.1505 | 4.0 | 420 | 0.1760 | 0.1987 | 0.2352 | 0.3689 | nan | 0.7552 | 0.3079 | 0.5796 | nan | 0.0 | 0.4515 | 0.4367 | 0.2065 | 0.1437 | 0.0 | 0.0 | 0.0 | 0.4115 | 0.0 | 0.0 | 0.0 | 0.5715 | 0.2762 | 0.5790 | nan | 0.0 | 0.3752 | 0.4367 | 0.1957 | 0.1435 | 0.0 | 0.0 | 0.0 | 0.4029 | 0.0 | 0.0 |
| 0.1269 | 5.0 | 525 | 0.1688 | 0.2239 | 0.2616 | 0.3561 | nan | 0.8249 | 0.3133 | 0.5309 | nan | 0.0 | 0.3966 | 0.6398 | 0.2513 | 0.1975 | 0.0003 | 0.0 | 0.1336 | 0.3738 | 0.0 | 0.0 | 0.0 | 0.6006 | 0.2833 | 0.5309 | nan | 0.0 | 0.3711 | 0.6398 | 0.2378 | 0.1957 | 0.0003 | 0.0 | 0.1336 | 0.3661 | 0.0 | 0.0 |
| 0.1012 | 6.0 | 630 | 0.1763 | 0.2563 | 0.3036 | 0.3830 | nan | 0.7977 | 0.4801 | 0.6774 | nan | 0.0 | 0.4913 | 0.7772 | 0.2993 | 0.2702 | 0.0 | 0.0 | 0.2024 | 0.2541 | 0.0 | 0.0 | 0.0 | 0.6060 | 0.3488 | 0.6774 | nan | 0.0 | 0.4359 | 0.7767 | 0.2816 | 0.2638 | 0.0 | 0.0 | 0.2024 | 0.2515 | 0.0 | 0.0 |
| 0.0996 | 7.0 | 735 | 0.1687 | 0.2515 | 0.2906 | 0.3644 | nan | 0.7947 | 0.3775 | 0.5884 | nan | 0.0 | 0.4452 | 0.5756 | 0.2734 | 0.2140 | 0.0 | 0.0 | 0.4769 | 0.3225 | 0.0 | 0.0 | 0.0 | 0.6093 | 0.3246 | 0.5884 | nan | 0.0 | 0.4081 | 0.5756 | 0.2599 | 0.2128 | 0.0 | 0.0 | 0.4769 | 0.3174 | 0.0 | 0.0 |
| 0.0945 | 8.0 | 840 | 0.1646 | 0.2689 | 0.3089 | 0.3928 | nan | 0.7889 | 0.3939 | 0.6399 | nan | 0.0 | 0.4337 | 0.6049 | 0.3386 | 0.2551 | 0.0001 | 0.0 | 0.5217 | 0.3477 | 0.0 | 0.0 | 0.0 | 0.6137 | 0.3354 | 0.6399 | nan | 0.0 | 0.4084 | 0.6049 | 0.3165 | 0.2514 | 0.0001 | 0.0 | 0.5217 | 0.3418 | 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"
] |
RyuNumchon/segformer-b0-finetuned-v0 |
<!-- 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-v0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the tontokoton/artery-ultrasound-siit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1250
- Mean Iou: 0.5989
- Mean Accuracy: 0.7803
- Overall Accuracy: 0.9315
- Accuracy Artery: nan
- Accuracy Vein: 0.8041
- Accuracy Nerve: 0.7428
- Accuracy Muscle1: 0.6254
- Accuracy Muscle2: 0.9488
- Accuracy Muscle3: nan
- Accuracy Muscle4: nan
- Accuracy Unknown: nan
- Iou Artery: 0.0
- Iou Vein: 0.7805
- Iou Nerve: 0.7054
- Iou Muscle1: 0.5651
- Iou Muscle2: 0.9432
- Iou Muscle3: nan
- Iou Muscle4: nan
- Iou Unknown: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Artery | Accuracy Vein | Accuracy Nerve | Accuracy Muscle1 | Accuracy Muscle2 | Accuracy Muscle3 | Accuracy Muscle4 | Accuracy Unknown | Iou Artery | Iou Vein | Iou Nerve | Iou Muscle1 | Iou Muscle2 | Iou Muscle3 | Iou Muscle4 | Iou Unknown |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------:|:-------------:|:--------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------:|:--------:|:---------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 1.5981 | 0.25 | 10 | 1.9054 | 0.1100 | 0.2407 | 0.8771 | nan | 0.0 | 0.0010 | 0.0 | 0.9616 | nan | nan | nan | 0.0 | 0.0 | 0.0007 | 0.0 | 0.8789 | 0.0 | 0.0 | 0.0 |
| 1.3165 | 0.5 | 20 | 1.5810 | 0.1254 | 0.2400 | 0.8755 | nan | 0.0 | 0.0 | 0.0 | 0.9599 | nan | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8775 | 0.0 | nan | 0.0 |
| 1.3184 | 0.75 | 30 | 1.3830 | 0.1284 | 0.2460 | 0.8974 | nan | 0.0 | 0.0 | 0.0 | 0.9839 | nan | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8987 | 0.0 | nan | 0.0 |
| 1.2067 | 1.0 | 40 | 1.1315 | 0.1309 | 0.2493 | 0.8770 | nan | 0.0368 | 0.0006 | 0.0 | 0.9598 | nan | nan | nan | 0.0 | 0.0363 | 0.0006 | 0.0 | 0.8797 | 0.0 | nan | 0.0 |
| 1.0176 | 1.25 | 50 | 1.0023 | 0.1613 | 0.2607 | 0.8512 | nan | 0.1131 | 0.0017 | 0.0 | 0.9280 | nan | nan | nan | 0.0 | 0.1081 | 0.0017 | 0.0 | 0.8583 | nan | nan | 0.0 |
| 0.8938 | 1.5 | 60 | 0.9674 | 0.2064 | 0.2783 | 0.8904 | nan | 0.1438 | 0.0 | 0.0 | 0.9696 | nan | nan | nan | 0.0 | 0.1383 | 0.0 | 0.0 | 0.8937 | nan | nan | nan |
| 0.8061 | 1.75 | 70 | 0.9469 | 0.2357 | 0.3240 | 0.8960 | nan | 0.3288 | 0.0 | 0.0 | 0.9672 | nan | nan | nan | 0.0 | 0.2740 | 0.0 | 0.0 | 0.9046 | nan | nan | nan |
| 0.936 | 2.0 | 80 | 0.9020 | 0.2702 | 0.3954 | 0.9096 | nan | 0.6123 | 0.0 | 0.0 | 0.9691 | nan | nan | nan | 0.0 | 0.4288 | 0.0 | 0.0 | 0.9223 | nan | nan | nan |
| 0.8633 | 2.25 | 90 | 0.7953 | 0.2578 | 0.3520 | 0.8981 | nan | 0.4406 | 0.0031 | 0.0 | 0.9642 | nan | nan | nan | 0.0 | 0.3816 | 0.0031 | 0.0 | 0.9044 | nan | nan | nan |
| 0.6906 | 2.5 | 100 | 0.8386 | 0.2606 | 0.3572 | 0.9189 | nan | 0.4401 | 0.0014 | 0.0 | 0.9871 | nan | nan | nan | 0.0 | 0.3769 | 0.0014 | 0.0 | 0.9248 | nan | nan | nan |
| 0.7863 | 2.75 | 110 | 0.7661 | 0.2831 | 0.4056 | 0.9112 | nan | 0.6438 | 0.0096 | 0.0 | 0.9691 | nan | nan | nan | 0.0 | 0.4827 | 0.0096 | 0.0 | 0.9231 | nan | nan | nan |
| 0.6937 | 3.0 | 120 | 0.6903 | 0.2875 | 0.4062 | 0.9096 | nan | 0.6577 | 0.0 | 0.0 | 0.9670 | nan | nan | nan | 0.0 | 0.5187 | 0.0 | 0.0 | 0.9190 | nan | nan | nan |
| 0.6985 | 3.25 | 130 | 0.6297 | 0.2878 | 0.4463 | 0.8957 | nan | 0.8255 | 0.0160 | 0.0 | 0.9435 | nan | nan | nan | 0.0 | 0.5071 | 0.0160 | 0.0 | 0.9158 | nan | nan | nan |
| 0.5591 | 3.5 | 140 | 0.6197 | 0.3199 | 0.4766 | 0.9248 | nan | 0.8379 | 0.0965 | 0.0 | 0.9721 | nan | nan | nan | 0.0 | 0.5623 | 0.0951 | 0.0 | 0.9420 | nan | nan | nan |
| 0.5853 | 3.75 | 150 | 0.6285 | 0.3065 | 0.4339 | 0.9282 | nan | 0.7321 | 0.0200 | 0.0 | 0.9833 | nan | nan | nan | 0.0 | 0.5746 | 0.0200 | 0.0 | 0.9380 | nan | nan | nan |
| 0.5948 | 4.0 | 160 | 0.5402 | 0.3175 | 0.4721 | 0.9164 | nan | 0.8473 | 0.0782 | 0.0 | 0.9631 | nan | nan | nan | 0.0 | 0.5788 | 0.0770 | 0.0 | 0.9317 | nan | nan | nan |
| 0.475 | 4.25 | 170 | 0.5844 | 0.3309 | 0.4869 | 0.9353 | nan | 0.8472 | 0.1178 | 0.0 | 0.9825 | nan | nan | nan | 0.0 | 0.5887 | 0.1143 | 0.0 | 0.9517 | nan | nan | nan |
| 0.5397 | 4.5 | 180 | 0.5073 | 0.3293 | 0.4707 | 0.9099 | nan | 0.8099 | 0.1165 | 0.0 | 0.9564 | nan | nan | nan | 0.0 | 0.6117 | 0.1138 | 0.0 | 0.9209 | nan | nan | nan |
| 0.4519 | 4.75 | 190 | 0.4611 | 0.3390 | 0.5135 | 0.8874 | nan | 0.9137 | 0.2166 | 0.0001 | 0.9236 | nan | nan | nan | 0.0 | 0.5848 | 0.2061 | 0.0001 | 0.9041 | nan | nan | nan |
| 0.4307 | 5.0 | 200 | 0.4340 | 0.3485 | 0.4834 | 0.9122 | nan | 0.7946 | 0.1816 | 0.0 | 0.9574 | nan | nan | nan | 0.0 | 0.6460 | 0.1729 | 0.0 | 0.9235 | nan | nan | nan |
| 0.4245 | 5.25 | 210 | 0.4282 | 0.3766 | 0.5516 | 0.9264 | nan | 0.9083 | 0.3351 | 0.0004 | 0.9626 | nan | nan | nan | 0.0 | 0.6405 | 0.2981 | 0.0004 | 0.9440 | nan | nan | nan |
| 0.3845 | 5.5 | 220 | 0.3944 | 0.3649 | 0.4975 | 0.9234 | nan | 0.7745 | 0.2466 | 0.0006 | 0.9685 | nan | nan | nan | 0.0 | 0.6545 | 0.2296 | 0.0006 | 0.9400 | nan | nan | nan |
| 0.5122 | 5.75 | 230 | 0.3684 | 0.3942 | 0.5587 | 0.8972 | nan | 0.8431 | 0.4204 | 0.0413 | 0.9300 | nan | nan | nan | 0.0 | 0.6684 | 0.3514 | 0.0413 | 0.9099 | nan | nan | nan |
| 0.4236 | 6.0 | 240 | 0.3650 | 0.3936 | 0.5529 | 0.9369 | nan | 0.8553 | 0.3606 | 0.0202 | 0.9754 | nan | nan | nan | 0.0 | 0.6715 | 0.3239 | 0.0202 | 0.9526 | nan | nan | nan |
| 0.3275 | 6.25 | 250 | 0.3393 | 0.3903 | 0.5420 | 0.9139 | nan | 0.7951 | 0.4081 | 0.0133 | 0.9514 | nan | nan | nan | 0.0 | 0.6684 | 0.3406 | 0.0133 | 0.9292 | nan | nan | nan |
| 0.3005 | 6.5 | 260 | 0.3286 | 0.3970 | 0.5450 | 0.9188 | nan | 0.7425 | 0.4352 | 0.0447 | 0.9578 | nan | nan | nan | 0.0 | 0.6427 | 0.3591 | 0.0444 | 0.9387 | nan | nan | nan |
| 0.2943 | 6.75 | 270 | 0.3019 | 0.4104 | 0.5770 | 0.9175 | nan | 0.8720 | 0.4117 | 0.0734 | 0.9507 | nan | nan | nan | 0.0 | 0.6773 | 0.3649 | 0.0733 | 0.9366 | nan | nan | nan |
| 0.2736 | 7.0 | 280 | 0.3538 | 0.4001 | 0.5627 | 0.9369 | nan | 0.8595 | 0.3494 | 0.0671 | 0.9748 | nan | nan | nan | 0.0 | 0.6601 | 0.3197 | 0.0671 | 0.9537 | nan | nan | nan |
| 0.2989 | 7.25 | 290 | 0.3069 | 0.4238 | 0.5728 | 0.9236 | nan | 0.7635 | 0.4661 | 0.1014 | 0.9601 | nan | nan | nan | 0.0 | 0.6849 | 0.3929 | 0.1006 | 0.9406 | nan | nan | nan |
| 0.2383 | 7.5 | 300 | 0.2756 | 0.4346 | 0.6040 | 0.8856 | nan | 0.8046 | 0.6093 | 0.0903 | 0.9119 | nan | nan | nan | 0.0 | 0.6795 | 0.4990 | 0.0895 | 0.9051 | nan | nan | nan |
| 0.2803 | 7.75 | 310 | 0.3289 | 0.4315 | 0.5812 | 0.9451 | nan | 0.7226 | 0.4786 | 0.1392 | 0.9845 | nan | nan | nan | 0.0 | 0.6625 | 0.4020 | 0.1368 | 0.9564 | nan | nan | nan |
| 0.2266 | 8.0 | 320 | 0.2588 | 0.4478 | 0.6102 | 0.9257 | nan | 0.8408 | 0.4894 | 0.1535 | 0.9572 | nan | nan | nan | 0.0 | 0.7105 | 0.4321 | 0.1512 | 0.9455 | nan | nan | nan |
| 0.2947 | 8.25 | 330 | 0.2424 | 0.4488 | 0.6058 | 0.8874 | nan | 0.8346 | 0.4993 | 0.1745 | 0.9148 | nan | nan | nan | 0.0 | 0.7149 | 0.4517 | 0.1719 | 0.9054 | nan | nan | nan |
| 0.3069 | 8.5 | 340 | 0.2433 | 0.5001 | 0.6775 | 0.9427 | nan | 0.8486 | 0.5763 | 0.3154 | 0.9698 | nan | nan | nan | 0.0 | 0.7323 | 0.4985 | 0.3107 | 0.9589 | nan | nan | nan |
| 0.2954 | 8.75 | 350 | 0.2371 | 0.4665 | 0.6307 | 0.9049 | nan | 0.8528 | 0.5362 | 0.2023 | 0.9314 | nan | nan | nan | 0.0 | 0.7269 | 0.4852 | 0.1988 | 0.9215 | nan | nan | nan |
| 0.2287 | 9.0 | 360 | 0.2437 | 0.4764 | 0.6633 | 0.9248 | nan | 0.8853 | 0.5762 | 0.2419 | 0.9498 | nan | nan | nan | 0.0 | 0.7139 | 0.4883 | 0.2368 | 0.9432 | nan | nan | nan |
| 0.2449 | 9.25 | 370 | 0.2466 | 0.4698 | 0.6213 | 0.9463 | nan | 0.7620 | 0.4747 | 0.2664 | 0.9820 | nan | nan | nan | 0.0 | 0.7141 | 0.4142 | 0.2578 | 0.9632 | nan | nan | nan |
| 0.2171 | 9.5 | 380 | 0.2179 | 0.5171 | 0.7060 | 0.8975 | nan | 0.8653 | 0.6024 | 0.4399 | 0.9166 | nan | nan | nan | 0.0 | 0.7210 | 0.5251 | 0.4279 | 0.9113 | nan | nan | nan |
| 0.2345 | 9.75 | 390 | 0.2432 | 0.4915 | 0.6525 | 0.9360 | nan | 0.7856 | 0.5085 | 0.3486 | 0.9671 | nan | nan | nan | 0.0 | 0.7231 | 0.4468 | 0.3367 | 0.9511 | nan | nan | nan |
| 0.1929 | 10.0 | 400 | 0.2028 | 0.5067 | 0.6812 | 0.9085 | nan | 0.8078 | 0.5964 | 0.3884 | 0.9323 | nan | nan | nan | 0.0 | 0.7194 | 0.5208 | 0.3679 | 0.9257 | nan | nan | nan |
| 0.2294 | 10.25 | 410 | 0.1962 | 0.5284 | 0.7170 | 0.9115 | nan | 0.8341 | 0.6590 | 0.4435 | 0.9314 | nan | nan | nan | 0.0 | 0.7272 | 0.5626 | 0.4262 | 0.9260 | nan | nan | nan |
| 0.183 | 10.5 | 420 | 0.1962 | 0.5029 | 0.6601 | 0.9130 | nan | 0.7631 | 0.5437 | 0.3927 | 0.9411 | nan | nan | nan | 0.0 | 0.7233 | 0.4884 | 0.3753 | 0.9276 | nan | nan | nan |
| 0.2132 | 10.75 | 430 | 0.1966 | 0.5624 | 0.7620 | 0.9211 | nan | 0.8301 | 0.7474 | 0.5331 | 0.9376 | nan | nan | nan | 0.0 | 0.7485 | 0.6333 | 0.4969 | 0.9333 | nan | nan | nan |
| 0.1768 | 11.0 | 440 | 0.1950 | 0.5154 | 0.6800 | 0.9276 | nan | 0.7467 | 0.5792 | 0.4381 | 0.9558 | nan | nan | nan | 0.0 | 0.7149 | 0.5164 | 0.4004 | 0.9453 | nan | nan | nan |
| 0.1955 | 11.25 | 450 | 0.1961 | 0.5556 | 0.7492 | 0.9456 | nan | 0.8563 | 0.6371 | 0.5364 | 0.9670 | nan | nan | nan | 0.0 | 0.7464 | 0.5632 | 0.5077 | 0.9606 | nan | nan | nan |
| 0.1763 | 11.5 | 460 | 0.1816 | 0.5177 | 0.6941 | 0.9121 | nan | 0.8779 | 0.4815 | 0.4816 | 0.9354 | nan | nan | nan | 0.0 | 0.7402 | 0.4623 | 0.4584 | 0.9278 | nan | nan | nan |
| 0.1361 | 11.75 | 470 | 0.1843 | 0.5485 | 0.7446 | 0.9183 | nan | 0.7590 | 0.7921 | 0.4904 | 0.9370 | nan | nan | nan | 0.0 | 0.7226 | 0.6360 | 0.4513 | 0.9327 | nan | nan | nan |
| 0.1613 | 12.0 | 480 | 0.1754 | 0.5565 | 0.7312 | 0.9283 | nan | 0.7660 | 0.6440 | 0.5633 | 0.9514 | nan | nan | nan | 0.0 | 0.7384 | 0.5899 | 0.5115 | 0.9428 | nan | nan | nan |
| 0.2158 | 12.25 | 490 | 0.1851 | 0.5537 | 0.7317 | 0.8648 | nan | 0.8204 | 0.6881 | 0.5401 | 0.8783 | nan | nan | nan | 0.0 | 0.7617 | 0.6330 | 0.4983 | 0.8754 | nan | nan | nan |
| 0.1542 | 12.5 | 500 | 0.2025 | 0.5759 | 0.7662 | 0.9560 | nan | 0.7910 | 0.7072 | 0.5887 | 0.9781 | nan | nan | nan | 0.0 | 0.7568 | 0.6179 | 0.5360 | 0.9690 | nan | nan | nan |
| 0.19 | 12.75 | 510 | 0.1733 | 0.5551 | 0.7325 | 0.8911 | nan | 0.8147 | 0.6676 | 0.5398 | 0.9081 | nan | nan | nan | 0.0 | 0.7613 | 0.6099 | 0.5021 | 0.9025 | nan | nan | nan |
| 0.1584 | 13.0 | 520 | 0.1832 | 0.5297 | 0.6916 | 0.9464 | nan | 0.7672 | 0.6176 | 0.4067 | 0.9748 | nan | nan | nan | 0.0 | 0.7395 | 0.5588 | 0.3863 | 0.9638 | nan | nan | nan |
| 0.1311 | 13.25 | 530 | 0.1566 | 0.5695 | 0.7565 | 0.9197 | nan | 0.7986 | 0.7456 | 0.5444 | 0.9374 | nan | nan | nan | 0.0 | 0.7532 | 0.6615 | 0.5001 | 0.9326 | nan | nan | nan |
| 0.1742 | 13.5 | 540 | 0.1611 | 0.5855 | 0.7788 | 0.9321 | nan | 0.8407 | 0.7155 | 0.6102 | 0.9489 | nan | nan | nan | 0.0 | 0.7771 | 0.6482 | 0.5582 | 0.9442 | nan | nan | nan |
| 0.146 | 13.75 | 550 | 0.1588 | 0.5743 | 0.7598 | 0.9196 | nan | 0.7929 | 0.7125 | 0.5960 | 0.9378 | nan | nan | nan | 0.0 | 0.7529 | 0.6457 | 0.5403 | 0.9327 | nan | nan | nan |
| 0.1326 | 14.0 | 560 | 0.1627 | 0.5573 | 0.7314 | 0.9237 | nan | 0.7425 | 0.6936 | 0.5431 | 0.9462 | nan | nan | nan | 0.0 | 0.7240 | 0.6261 | 0.4978 | 0.9384 | nan | nan | nan |
| 0.1385 | 14.25 | 570 | 0.1553 | 0.5832 | 0.7734 | 0.9125 | nan | 0.8122 | 0.7620 | 0.5919 | 0.9275 | nan | nan | nan | 0.0 | 0.7699 | 0.6833 | 0.5388 | 0.9239 | nan | nan | nan |
| 0.1419 | 14.5 | 580 | 0.1574 | 0.5828 | 0.7701 | 0.9399 | nan | 0.8022 | 0.7043 | 0.6144 | 0.9596 | nan | nan | nan | 0.0 | 0.7680 | 0.6404 | 0.5527 | 0.9531 | nan | nan | nan |
| 0.1342 | 14.75 | 590 | 0.1489 | 0.5806 | 0.7682 | 0.9216 | nan | 0.8378 | 0.6775 | 0.6187 | 0.9387 | nan | nan | nan | 0.0 | 0.7803 | 0.6314 | 0.5573 | 0.9339 | nan | nan | nan |
| 0.1342 | 15.0 | 600 | 0.1547 | 0.5712 | 0.7491 | 0.9141 | nan | 0.8159 | 0.7522 | 0.4975 | 0.9310 | nan | nan | nan | 0.0 | 0.7762 | 0.6817 | 0.4725 | 0.9258 | nan | nan | nan |
| 0.128 | 15.25 | 610 | 0.1530 | 0.5747 | 0.7581 | 0.9353 | nan | 0.8365 | 0.7411 | 0.5012 | 0.9536 | nan | nan | nan | 0.0 | 0.7886 | 0.6657 | 0.4709 | 0.9483 | nan | nan | nan |
| 0.1104 | 15.5 | 620 | 0.1558 | 0.5653 | 0.7379 | 0.9336 | nan | 0.7945 | 0.6492 | 0.5520 | 0.9560 | nan | nan | nan | 0.0 | 0.7639 | 0.6000 | 0.5143 | 0.9484 | nan | nan | nan |
| 0.12 | 15.75 | 630 | 0.1485 | 0.5813 | 0.7676 | 0.9294 | nan | 0.7847 | 0.7600 | 0.5779 | 0.9476 | nan | nan | nan | 0.0 | 0.7613 | 0.6758 | 0.5265 | 0.9431 | nan | nan | nan |
| 0.1347 | 16.0 | 640 | 0.1434 | 0.5641 | 0.7391 | 0.9182 | nan | 0.8150 | 0.6400 | 0.5631 | 0.9382 | nan | nan | nan | 0.0 | 0.7737 | 0.6006 | 0.5140 | 0.9321 | nan | nan | nan |
| 0.109 | 16.25 | 650 | 0.1480 | 0.5865 | 0.7738 | 0.9127 | nan | 0.8339 | 0.7099 | 0.6234 | 0.9280 | nan | nan | nan | 0.0 | 0.7806 | 0.6626 | 0.5660 | 0.9235 | nan | nan | nan |
| 0.1117 | 16.5 | 660 | 0.1521 | 0.5878 | 0.7706 | 0.9448 | nan | 0.7946 | 0.7264 | 0.5967 | 0.9649 | nan | nan | nan | 0.0 | 0.7701 | 0.6647 | 0.5462 | 0.9581 | nan | nan | nan |
| 0.1269 | 16.75 | 670 | 0.1433 | 0.5767 | 0.7571 | 0.9299 | nan | 0.7746 | 0.7047 | 0.5989 | 0.9502 | nan | nan | nan | 0.0 | 0.7537 | 0.6502 | 0.5357 | 0.9440 | nan | nan | nan |
| 0.1078 | 17.0 | 680 | 0.1433 | 0.5857 | 0.7701 | 0.9284 | nan | 0.7843 | 0.7396 | 0.6097 | 0.9467 | nan | nan | nan | 0.0 | 0.7651 | 0.6720 | 0.5516 | 0.9397 | nan | nan | nan |
| 0.0928 | 17.25 | 690 | 0.1410 | 0.5961 | 0.7853 | 0.9266 | nan | 0.8229 | 0.7606 | 0.6157 | 0.9421 | nan | nan | nan | 0.0 | 0.7881 | 0.6982 | 0.5568 | 0.9375 | nan | nan | nan |
| 0.1062 | 17.5 | 700 | 0.1401 | 0.5792 | 0.7581 | 0.9345 | nan | 0.8318 | 0.6907 | 0.5561 | 0.9538 | nan | nan | nan | 0.0 | 0.7834 | 0.6469 | 0.5168 | 0.9487 | nan | nan | nan |
| 0.0892 | 17.75 | 710 | 0.1397 | 0.5895 | 0.7750 | 0.9105 | nan | 0.8335 | 0.7268 | 0.6146 | 0.9252 | nan | nan | nan | 0.0 | 0.7852 | 0.6837 | 0.5582 | 0.9206 | nan | nan | nan |
| 0.1024 | 18.0 | 720 | 0.1386 | 0.5857 | 0.7709 | 0.9293 | nan | 0.8107 | 0.7295 | 0.5966 | 0.9470 | nan | nan | nan | 0.0 | 0.7716 | 0.6812 | 0.5330 | 0.9430 | nan | nan | nan |
| 0.1266 | 18.25 | 730 | 0.1450 | 0.5682 | 0.7451 | 0.9093 | nan | 0.7798 | 0.6337 | 0.6379 | 0.9290 | nan | nan | nan | 0.0 | 0.7504 | 0.6035 | 0.5638 | 0.9234 | nan | nan | nan |
| 0.1108 | 18.5 | 740 | 0.1410 | 0.5877 | 0.7731 | 0.9386 | nan | 0.7968 | 0.7035 | 0.6338 | 0.9581 | nan | nan | nan | 0.0 | 0.7687 | 0.6467 | 0.5720 | 0.9510 | nan | nan | nan |
| 0.1119 | 18.75 | 750 | 0.1401 | 0.5560 | 0.7230 | 0.9187 | nan | 0.7988 | 0.6046 | 0.5475 | 0.9410 | nan | nan | nan | 0.0 | 0.7616 | 0.5775 | 0.5059 | 0.9351 | nan | nan | nan |
| 0.0933 | 19.0 | 760 | 0.1395 | 0.6052 | 0.8009 | 0.9353 | nan | 0.8338 | 0.7599 | 0.6595 | 0.9505 | nan | nan | nan | 0.0 | 0.7911 | 0.6947 | 0.5951 | 0.9449 | nan | nan | nan |
| 0.2262 | 19.25 | 770 | 0.1358 | 0.5938 | 0.7834 | 0.9333 | nan | 0.8059 | 0.7249 | 0.6520 | 0.9509 | nan | nan | nan | 0.0 | 0.7735 | 0.6695 | 0.5810 | 0.9451 | nan | nan | nan |
| 0.1088 | 19.5 | 780 | 0.1320 | 0.5950 | 0.7832 | 0.9282 | nan | 0.8218 | 0.7150 | 0.6510 | 0.9449 | nan | nan | nan | 0.0 | 0.7825 | 0.6718 | 0.5808 | 0.9398 | nan | nan | nan |
| 0.0846 | 19.75 | 790 | 0.1349 | 0.5920 | 0.7806 | 0.9180 | nan | 0.7860 | 0.7802 | 0.6226 | 0.9336 | nan | nan | nan | 0.0 | 0.7626 | 0.7109 | 0.5574 | 0.9290 | nan | nan | nan |
| 0.0877 | 20.0 | 800 | 0.1390 | 0.5918 | 0.7821 | 0.9331 | nan | 0.8089 | 0.7612 | 0.6081 | 0.9500 | nan | nan | nan | 0.0 | 0.7748 | 0.6904 | 0.5490 | 0.9446 | nan | nan | nan |
| 0.1505 | 20.25 | 810 | 0.1366 | 0.5643 | 0.7310 | 0.9188 | nan | 0.8080 | 0.6375 | 0.5389 | 0.9397 | nan | nan | nan | 0.0 | 0.7736 | 0.6140 | 0.5002 | 0.9335 | nan | nan | nan |
| 0.0957 | 20.5 | 820 | 0.1355 | 0.6131 | 0.8118 | 0.9325 | nan | 0.8188 | 0.8154 | 0.6669 | 0.9461 | nan | nan | nan | 0.0 | 0.7920 | 0.7390 | 0.5935 | 0.9410 | nan | nan | nan |
| 0.1408 | 20.75 | 830 | 0.1360 | 0.5871 | 0.7707 | 0.9138 | nan | 0.7967 | 0.7719 | 0.5848 | 0.9294 | nan | nan | nan | 0.0 | 0.7665 | 0.7133 | 0.5296 | 0.9259 | nan | nan | nan |
| 0.0954 | 21.0 | 840 | 0.1412 | 0.5768 | 0.7489 | 0.9452 | nan | 0.7956 | 0.6830 | 0.5493 | 0.9676 | nan | nan | nan | 0.0 | 0.7677 | 0.6480 | 0.5074 | 0.9611 | nan | nan | nan |
| 0.093 | 21.25 | 850 | 0.1364 | 0.5938 | 0.7855 | 0.9095 | nan | 0.8280 | 0.7745 | 0.6169 | 0.9227 | nan | nan | nan | 0.0 | 0.7861 | 0.7078 | 0.5568 | 0.9182 | nan | nan | nan |
| 0.15 | 21.5 | 860 | 0.1346 | 0.5892 | 0.7706 | 0.9376 | nan | 0.8296 | 0.6965 | 0.6002 | 0.9563 | nan | nan | nan | 0.0 | 0.7859 | 0.6598 | 0.5508 | 0.9493 | nan | nan | nan |
| 0.0734 | 21.75 | 870 | 0.1349 | 0.5788 | 0.7588 | 0.9215 | nan | 0.7738 | 0.7021 | 0.6186 | 0.9407 | nan | nan | nan | 0.0 | 0.7498 | 0.6585 | 0.5509 | 0.9349 | nan | nan | nan |
| 0.0992 | 22.0 | 880 | 0.1342 | 0.5839 | 0.7685 | 0.9141 | nan | 0.7920 | 0.7441 | 0.6076 | 0.9305 | nan | nan | nan | 0.0 | 0.7593 | 0.6888 | 0.5444 | 0.9270 | nan | nan | nan |
| 0.0699 | 22.25 | 890 | 0.1349 | 0.5949 | 0.7859 | 0.9218 | nan | 0.8082 | 0.7646 | 0.6335 | 0.9371 | nan | nan | nan | 0.0 | 0.7758 | 0.6991 | 0.5677 | 0.9320 | nan | nan | nan |
| 0.0692 | 22.5 | 900 | 0.1325 | 0.5990 | 0.7881 | 0.9348 | nan | 0.8269 | 0.7526 | 0.6216 | 0.9512 | nan | nan | nan | 0.0 | 0.7874 | 0.6978 | 0.5645 | 0.9455 | nan | nan | nan |
| 0.0855 | 22.75 | 910 | 0.1346 | 0.5718 | 0.7462 | 0.9241 | nan | 0.7901 | 0.7139 | 0.5369 | 0.9438 | nan | nan | nan | 0.0 | 0.7595 | 0.6672 | 0.4940 | 0.9384 | nan | nan | nan |
| 0.1123 | 23.0 | 920 | 0.1305 | 0.6031 | 0.7903 | 0.9221 | nan | 0.8226 | 0.7347 | 0.6667 | 0.9372 | nan | nan | nan | 0.0 | 0.7866 | 0.6986 | 0.5980 | 0.9322 | nan | nan | nan |
| 0.1032 | 23.25 | 930 | 0.1338 | 0.5981 | 0.7827 | 0.9276 | nan | 0.8256 | 0.7335 | 0.6277 | 0.9439 | nan | nan | nan | 0.0 | 0.7896 | 0.6922 | 0.5709 | 0.9380 | nan | nan | nan |
| 0.0827 | 23.5 | 940 | 0.1316 | 0.5831 | 0.7617 | 0.9225 | nan | 0.7890 | 0.7441 | 0.5734 | 0.9404 | nan | nan | nan | 0.0 | 0.7618 | 0.6964 | 0.5211 | 0.9361 | nan | nan | nan |
| 0.0935 | 23.75 | 950 | 0.1284 | 0.5808 | 0.7593 | 0.9183 | nan | 0.7925 | 0.7156 | 0.5927 | 0.9363 | nan | nan | nan | 0.0 | 0.7659 | 0.6757 | 0.5313 | 0.9312 | nan | nan | nan |
| 0.0686 | 24.0 | 960 | 0.1286 | 0.5898 | 0.7710 | 0.9299 | nan | 0.8111 | 0.7004 | 0.6243 | 0.9482 | nan | nan | nan | 0.0 | 0.7813 | 0.6639 | 0.5621 | 0.9416 | nan | nan | nan |
| 0.0846 | 24.25 | 970 | 0.1293 | 0.5764 | 0.7524 | 0.9232 | nan | 0.7797 | 0.6874 | 0.5993 | 0.9432 | nan | nan | nan | 0.0 | 0.7566 | 0.6487 | 0.5388 | 0.9379 | nan | nan | nan |
| 0.1122 | 24.5 | 980 | 0.1326 | 0.5892 | 0.7699 | 0.9315 | nan | 0.7961 | 0.7212 | 0.6121 | 0.9501 | nan | nan | nan | 0.0 | 0.7689 | 0.6802 | 0.5527 | 0.9441 | nan | nan | nan |
| 0.1055 | 24.75 | 990 | 0.1332 | 0.6078 | 0.7999 | 0.9242 | nan | 0.8298 | 0.7853 | 0.6466 | 0.9378 | nan | nan | nan | 0.0 | 0.7905 | 0.7294 | 0.5858 | 0.9334 | nan | nan | nan |
| 0.075 | 25.0 | 1000 | 0.1309 | 0.5961 | 0.7801 | 0.9318 | nan | 0.8068 | 0.7844 | 0.5807 | 0.9484 | nan | nan | nan | 0.0 | 0.7772 | 0.7249 | 0.5342 | 0.9440 | nan | nan | nan |
| 0.0844 | 25.25 | 1010 | 0.1276 | 0.5851 | 0.7637 | 0.9198 | nan | 0.7883 | 0.7191 | 0.6097 | 0.9377 | nan | nan | nan | 0.0 | 0.7653 | 0.6790 | 0.5487 | 0.9322 | nan | nan | nan |
| 0.0898 | 25.5 | 1020 | 0.1282 | 0.6144 | 0.8161 | 0.9363 | nan | 0.8163 | 0.8330 | 0.6652 | 0.9498 | nan | nan | nan | 0.0 | 0.7872 | 0.7476 | 0.5917 | 0.9456 | nan | nan | nan |
| 0.0939 | 25.75 | 1030 | 0.1289 | 0.5882 | 0.7745 | 0.9172 | nan | 0.7838 | 0.7803 | 0.6008 | 0.9333 | nan | nan | nan | 0.0 | 0.7602 | 0.7155 | 0.5360 | 0.9293 | nan | nan | nan |
| 0.106 | 26.0 | 1040 | 0.1279 | 0.5970 | 0.7799 | 0.9366 | nan | 0.8110 | 0.7394 | 0.6147 | 0.9544 | nan | nan | nan | 0.0 | 0.7855 | 0.6939 | 0.5584 | 0.9474 | nan | nan | nan |
| 0.0731 | 26.25 | 1050 | 0.1255 | 0.5828 | 0.7595 | 0.9260 | nan | 0.7886 | 0.7105 | 0.5939 | 0.9452 | nan | nan | nan | 0.0 | 0.7652 | 0.6719 | 0.5372 | 0.9396 | nan | nan | nan |
| 0.1029 | 26.5 | 1060 | 0.1295 | 0.6009 | 0.7905 | 0.9391 | nan | 0.8098 | 0.7815 | 0.6150 | 0.9558 | nan | nan | nan | 0.0 | 0.7805 | 0.7169 | 0.5572 | 0.9500 | nan | nan | nan |
| 0.074 | 26.75 | 1070 | 0.1315 | 0.6024 | 0.7962 | 0.9162 | nan | 0.8273 | 0.7903 | 0.6380 | 0.9292 | nan | nan | nan | 0.0 | 0.7873 | 0.7265 | 0.5730 | 0.9254 | nan | nan | nan |
| 0.1074 | 27.0 | 1080 | 0.1287 | 0.5949 | 0.7808 | 0.9344 | nan | 0.8235 | 0.7171 | 0.6309 | 0.9519 | nan | nan | nan | 0.0 | 0.7852 | 0.6772 | 0.5651 | 0.9472 | nan | nan | nan |
| 0.1263 | 27.25 | 1090 | 0.1286 | 0.5856 | 0.7657 | 0.9226 | nan | 0.7733 | 0.7113 | 0.6369 | 0.9413 | nan | nan | nan | 0.0 | 0.7528 | 0.6755 | 0.5642 | 0.9355 | nan | nan | nan |
| 0.0636 | 27.5 | 1100 | 0.1297 | 0.6070 | 0.7964 | 0.9322 | nan | 0.8155 | 0.7751 | 0.6472 | 0.9476 | nan | nan | nan | 0.0 | 0.7871 | 0.7224 | 0.5833 | 0.9423 | nan | nan | nan |
| 0.0561 | 27.75 | 1110 | 0.1304 | 0.5967 | 0.7823 | 0.9297 | nan | 0.8114 | 0.7522 | 0.6194 | 0.9463 | nan | nan | nan | 0.0 | 0.7798 | 0.7037 | 0.5584 | 0.9416 | nan | nan | nan |
| 0.0735 | 28.0 | 1120 | 0.1287 | 0.5878 | 0.7701 | 0.9221 | nan | 0.8128 | 0.7150 | 0.6133 | 0.9392 | nan | nan | nan | 0.0 | 0.7811 | 0.6752 | 0.5483 | 0.9342 | nan | nan | nan |
| 0.0785 | 28.25 | 1130 | 0.1311 | 0.5968 | 0.7838 | 0.9360 | nan | 0.8067 | 0.7526 | 0.6223 | 0.9534 | nan | nan | nan | 0.0 | 0.7808 | 0.6982 | 0.5576 | 0.9473 | nan | nan | nan |
| 0.0934 | 28.5 | 1140 | 0.1275 | 0.6008 | 0.7891 | 0.9258 | nan | 0.8110 | 0.7626 | 0.6415 | 0.9413 | nan | nan | nan | 0.0 | 0.7811 | 0.7133 | 0.5734 | 0.9364 | nan | nan | nan |
| 0.079 | 28.75 | 1150 | 0.1290 | 0.5940 | 0.7772 | 0.9319 | nan | 0.7815 | 0.7548 | 0.6225 | 0.9500 | nan | nan | nan | 0.0 | 0.7610 | 0.7027 | 0.5622 | 0.9442 | nan | nan | nan |
| 0.0597 | 29.0 | 1160 | 0.1260 | 0.6038 | 0.7936 | 0.9262 | nan | 0.8107 | 0.7829 | 0.6396 | 0.9411 | nan | nan | nan | 0.0 | 0.7806 | 0.7277 | 0.5738 | 0.9368 | nan | nan | nan |
| 0.0646 | 29.25 | 1170 | 0.1237 | 0.6037 | 0.7929 | 0.9271 | nan | 0.8202 | 0.7670 | 0.6422 | 0.9422 | nan | nan | nan | 0.0 | 0.7858 | 0.7218 | 0.5729 | 0.9380 | nan | nan | nan |
| 0.0581 | 29.5 | 1180 | 0.1311 | 0.5926 | 0.7739 | 0.9272 | nan | 0.8069 | 0.7047 | 0.6390 | 0.9450 | nan | nan | nan | 0.0 | 0.7788 | 0.6745 | 0.5713 | 0.9384 | nan | nan | nan |
| 0.0666 | 29.75 | 1190 | 0.1280 | 0.5925 | 0.7750 | 0.9314 | nan | 0.8076 | 0.7085 | 0.6343 | 0.9496 | nan | nan | nan | 0.0 | 0.7780 | 0.6742 | 0.5659 | 0.9442 | nan | nan | nan |
| 0.0883 | 30.0 | 1200 | 0.1273 | 0.5947 | 0.7794 | 0.9221 | nan | 0.8191 | 0.7195 | 0.6407 | 0.9384 | nan | nan | nan | 0.0 | 0.7858 | 0.6780 | 0.5759 | 0.9337 | nan | nan | nan |
| 0.0839 | 30.25 | 1210 | 0.1331 | 0.5921 | 0.7714 | 0.9349 | nan | 0.8139 | 0.7057 | 0.6126 | 0.9535 | nan | nan | nan | 0.0 | 0.7840 | 0.6679 | 0.5616 | 0.9472 | nan | nan | nan |
| 0.0767 | 30.5 | 1220 | 0.1301 | 0.5948 | 0.7797 | 0.9263 | nan | 0.8107 | 0.7438 | 0.6216 | 0.9428 | nan | nan | nan | 0.0 | 0.7816 | 0.6926 | 0.5624 | 0.9373 | nan | nan | nan |
| 0.06 | 30.75 | 1230 | 0.1288 | 0.6006 | 0.7874 | 0.9217 | nan | 0.8126 | 0.7691 | 0.6313 | 0.9367 | nan | nan | nan | 0.0 | 0.7868 | 0.7181 | 0.5662 | 0.9319 | nan | nan | nan |
| 0.1277 | 31.0 | 1240 | 0.1262 | 0.6044 | 0.7896 | 0.9389 | nan | 0.8150 | 0.7755 | 0.6125 | 0.9555 | nan | nan | nan | 0.0 | 0.7898 | 0.7248 | 0.5574 | 0.9500 | nan | nan | nan |
| 0.0702 | 31.25 | 1250 | 0.1257 | 0.6054 | 0.7919 | 0.9371 | nan | 0.8156 | 0.7733 | 0.6251 | 0.9534 | nan | nan | nan | 0.0 | 0.7865 | 0.7274 | 0.5649 | 0.9482 | nan | nan | nan |
| 0.0665 | 31.5 | 1260 | 0.1285 | 0.5981 | 0.7795 | 0.9312 | nan | 0.8190 | 0.7311 | 0.6196 | 0.9483 | nan | nan | nan | 0.0 | 0.7877 | 0.6987 | 0.5621 | 0.9422 | nan | nan | nan |
| 0.0579 | 31.75 | 1270 | 0.1287 | 0.6023 | 0.7905 | 0.9365 | nan | 0.8239 | 0.7493 | 0.6360 | 0.9530 | nan | nan | nan | 0.0 | 0.7917 | 0.7080 | 0.5648 | 0.9471 | nan | nan | nan |
| 0.0898 | 32.0 | 1280 | 0.1264 | 0.5927 | 0.7750 | 0.9243 | nan | 0.8108 | 0.7344 | 0.6136 | 0.9411 | nan | nan | nan | 0.0 | 0.7817 | 0.6990 | 0.5470 | 0.9357 | nan | nan | nan |
| 0.0785 | 32.25 | 1290 | 0.1331 | 0.6062 | 0.7942 | 0.9447 | nan | 0.8155 | 0.7564 | 0.6428 | 0.9620 | nan | nan | nan | 0.0 | 0.7904 | 0.7101 | 0.5755 | 0.9552 | nan | nan | nan |
| 0.0684 | 32.5 | 1300 | 0.1257 | 0.5981 | 0.7863 | 0.9245 | nan | 0.8043 | 0.7894 | 0.6117 | 0.9398 | nan | nan | nan | 0.0 | 0.7742 | 0.7343 | 0.5463 | 0.9358 | nan | nan | nan |
| 0.0802 | 32.75 | 1310 | 0.1287 | 0.5966 | 0.7797 | 0.9400 | nan | 0.7983 | 0.7626 | 0.5997 | 0.9582 | nan | nan | nan | 0.0 | 0.7729 | 0.7117 | 0.5464 | 0.9521 | nan | nan | nan |
| 0.0768 | 33.0 | 1320 | 0.1236 | 0.6074 | 0.7970 | 0.9299 | nan | 0.8187 | 0.7681 | 0.6562 | 0.9450 | nan | nan | nan | 0.0 | 0.7893 | 0.7194 | 0.5888 | 0.9393 | nan | nan | nan |
| 0.0779 | 33.25 | 1330 | 0.1254 | 0.5993 | 0.7830 | 0.9331 | nan | 0.8223 | 0.7322 | 0.6275 | 0.9501 | nan | nan | nan | 0.0 | 0.7897 | 0.6970 | 0.5655 | 0.9444 | nan | nan | nan |
| 0.0785 | 33.5 | 1340 | 0.1293 | 0.5949 | 0.7803 | 0.9250 | nan | 0.8038 | 0.7556 | 0.6204 | 0.9413 | nan | nan | nan | 0.0 | 0.7729 | 0.7105 | 0.5548 | 0.9363 | nan | nan | nan |
| 0.0678 | 33.75 | 1350 | 0.1284 | 0.6084 | 0.8006 | 0.9314 | nan | 0.8205 | 0.7821 | 0.6537 | 0.9462 | nan | nan | nan | 0.0 | 0.7888 | 0.7267 | 0.5856 | 0.9410 | nan | nan | nan |
| 0.0668 | 34.0 | 1360 | 0.1281 | 0.5968 | 0.7788 | 0.9330 | nan | 0.8029 | 0.7376 | 0.6240 | 0.9507 | nan | nan | nan | 0.0 | 0.7767 | 0.6987 | 0.5637 | 0.9447 | nan | nan | nan |
| 0.0624 | 34.25 | 1370 | 0.1293 | 0.5922 | 0.7727 | 0.9330 | nan | 0.8148 | 0.7268 | 0.5982 | 0.9510 | nan | nan | nan | 0.0 | 0.7835 | 0.6879 | 0.5445 | 0.9453 | nan | nan | nan |
| 0.087 | 34.5 | 1380 | 0.1268 | 0.6089 | 0.7989 | 0.9317 | nan | 0.8302 | 0.7630 | 0.6556 | 0.9466 | nan | nan | nan | 0.0 | 0.7974 | 0.7176 | 0.5877 | 0.9417 | nan | nan | nan |
| 0.1505 | 34.75 | 1390 | 0.1295 | 0.5960 | 0.7778 | 0.9349 | nan | 0.8007 | 0.7235 | 0.6339 | 0.9532 | nan | nan | nan | 0.0 | 0.7757 | 0.6886 | 0.5681 | 0.9473 | nan | nan | nan |
| 0.0651 | 35.0 | 1400 | 0.1272 | 0.5947 | 0.7780 | 0.9317 | nan | 0.7943 | 0.7450 | 0.6234 | 0.9495 | nan | nan | nan | 0.0 | 0.7690 | 0.7010 | 0.5595 | 0.9441 | nan | nan | nan |
| 0.0518 | 35.25 | 1410 | 0.1263 | 0.6011 | 0.7860 | 0.9313 | nan | 0.8104 | 0.7499 | 0.6356 | 0.9479 | nan | nan | nan | 0.0 | 0.7840 | 0.7053 | 0.5736 | 0.9424 | nan | nan | nan |
| 0.0937 | 35.5 | 1420 | 0.1249 | 0.6085 | 0.7972 | 0.9337 | nan | 0.8323 | 0.7622 | 0.6455 | 0.9489 | nan | nan | nan | 0.0 | 0.8009 | 0.7138 | 0.5841 | 0.9438 | nan | nan | nan |
| 0.0587 | 35.75 | 1430 | 0.1243 | 0.5959 | 0.7765 | 0.9292 | nan | 0.8148 | 0.7462 | 0.5989 | 0.9462 | nan | nan | nan | 0.0 | 0.7875 | 0.7022 | 0.5490 | 0.9407 | nan | nan | nan |
| 0.0519 | 36.0 | 1440 | 0.1258 | 0.5898 | 0.7681 | 0.9319 | nan | 0.7902 | 0.7263 | 0.6052 | 0.9508 | nan | nan | nan | 0.0 | 0.7680 | 0.6870 | 0.5493 | 0.9447 | nan | nan | nan |
| 0.0655 | 36.25 | 1450 | 0.1271 | 0.5969 | 0.7758 | 0.9319 | nan | 0.8096 | 0.7110 | 0.6326 | 0.9500 | nan | nan | nan | 0.0 | 0.7832 | 0.6838 | 0.5739 | 0.9435 | nan | nan | nan |
| 0.054 | 36.5 | 1460 | 0.1251 | 0.6118 | 0.8001 | 0.9307 | nan | 0.8335 | 0.7646 | 0.6570 | 0.9453 | nan | nan | nan | 0.0 | 0.8008 | 0.7262 | 0.5920 | 0.9401 | nan | nan | nan |
| 0.0978 | 36.75 | 1470 | 0.1288 | 0.6031 | 0.7890 | 0.9390 | nan | 0.8111 | 0.7736 | 0.6156 | 0.9558 | nan | nan | nan | 0.0 | 0.7844 | 0.7240 | 0.5568 | 0.9505 | nan | nan | nan |
| 0.0862 | 37.0 | 1480 | 0.1267 | 0.5814 | 0.7569 | 0.9204 | nan | 0.7738 | 0.7223 | 0.5922 | 0.9393 | nan | nan | nan | 0.0 | 0.7537 | 0.6862 | 0.5323 | 0.9348 | nan | nan | nan |
| 0.0855 | 37.25 | 1490 | 0.1279 | 0.6086 | 0.7958 | 0.9416 | nan | 0.8167 | 0.7609 | 0.6471 | 0.9584 | nan | nan | nan | 0.0 | 0.7897 | 0.7183 | 0.5823 | 0.9527 | nan | nan | nan |
| 0.0806 | 37.5 | 1500 | 0.1247 | 0.6065 | 0.7941 | 0.9274 | nan | 0.8189 | 0.7541 | 0.6606 | 0.9427 | nan | nan | nan | 0.0 | 0.7906 | 0.7133 | 0.5914 | 0.9374 | nan | nan | nan |
| 0.0883 | 37.75 | 1510 | 0.1258 | 0.5954 | 0.7737 | 0.9328 | nan | 0.8100 | 0.7040 | 0.6294 | 0.9512 | nan | nan | nan | 0.0 | 0.7844 | 0.6758 | 0.5720 | 0.9450 | nan | nan | nan |
| 0.0661 | 38.0 | 1520 | 0.1236 | 0.6027 | 0.7868 | 0.9311 | nan | 0.8211 | 0.7581 | 0.6208 | 0.9472 | nan | nan | nan | 0.0 | 0.7907 | 0.7149 | 0.5657 | 0.9422 | nan | nan | nan |
| 0.0575 | 38.25 | 1530 | 0.1231 | 0.6090 | 0.7970 | 0.9339 | nan | 0.8245 | 0.7703 | 0.6440 | 0.9493 | nan | nan | nan | 0.0 | 0.7954 | 0.7226 | 0.5830 | 0.9440 | nan | nan | nan |
| 0.0637 | 38.5 | 1540 | 0.1255 | 0.5997 | 0.7823 | 0.9360 | nan | 0.8067 | 0.7342 | 0.6347 | 0.9538 | nan | nan | nan | 0.0 | 0.7822 | 0.6970 | 0.5714 | 0.9479 | nan | nan | nan |
| 0.0531 | 38.75 | 1550 | 0.1267 | 0.5879 | 0.7631 | 0.9303 | nan | 0.7893 | 0.7018 | 0.6117 | 0.9498 | nan | nan | nan | 0.0 | 0.7680 | 0.6724 | 0.5555 | 0.9437 | nan | nan | nan |
| 0.0673 | 39.0 | 1560 | 0.1249 | 0.6035 | 0.7880 | 0.9322 | nan | 0.8120 | 0.7355 | 0.6555 | 0.9490 | nan | nan | nan | 0.0 | 0.7860 | 0.6999 | 0.5885 | 0.9433 | nan | nan | nan |
| 0.0781 | 39.25 | 1570 | 0.1230 | 0.6086 | 0.7967 | 0.9327 | nan | 0.8232 | 0.7645 | 0.6510 | 0.9481 | nan | nan | nan | 0.0 | 0.7938 | 0.7196 | 0.5864 | 0.9431 | nan | nan | nan |
| 0.0499 | 39.5 | 1580 | 0.1241 | 0.6003 | 0.7827 | 0.9297 | nan | 0.8099 | 0.7283 | 0.6458 | 0.9468 | nan | nan | nan | 0.0 | 0.7855 | 0.6954 | 0.5795 | 0.9409 | nan | nan | nan |
| 0.0888 | 39.75 | 1590 | 0.1244 | 0.6016 | 0.7852 | 0.9318 | nan | 0.8149 | 0.7420 | 0.6355 | 0.9486 | nan | nan | nan | 0.0 | 0.7887 | 0.7038 | 0.5727 | 0.9427 | nan | nan | nan |
| 0.0696 | 40.0 | 1600 | 0.1248 | 0.5966 | 0.7777 | 0.9328 | nan | 0.8083 | 0.7433 | 0.6089 | 0.9503 | nan | nan | nan | 0.0 | 0.7828 | 0.7037 | 0.5517 | 0.9447 | nan | nan | nan |
| 0.0635 | 40.25 | 1610 | 0.1281 | 0.5987 | 0.7805 | 0.9363 | nan | 0.8046 | 0.7398 | 0.6235 | 0.9542 | nan | nan | nan | 0.0 | 0.7809 | 0.7025 | 0.5619 | 0.9484 | nan | nan | nan |
| 0.066 | 40.5 | 1620 | 0.1228 | 0.6070 | 0.7953 | 0.9286 | nan | 0.8158 | 0.7630 | 0.6586 | 0.9439 | nan | nan | nan | 0.0 | 0.7888 | 0.7209 | 0.5866 | 0.9389 | nan | nan | nan |
| 0.0692 | 40.75 | 1630 | 0.1244 | 0.6071 | 0.7927 | 0.9330 | nan | 0.8190 | 0.7486 | 0.6542 | 0.9491 | nan | nan | nan | 0.0 | 0.7926 | 0.7115 | 0.5877 | 0.9435 | nan | nan | nan |
| 0.0678 | 41.0 | 1640 | 0.1256 | 0.6038 | 0.7869 | 0.9310 | nan | 0.8069 | 0.7425 | 0.6504 | 0.9477 | nan | nan | nan | 0.0 | 0.7851 | 0.7066 | 0.5853 | 0.9419 | nan | nan | nan |
| 0.0934 | 41.25 | 1650 | 0.1252 | 0.6024 | 0.7856 | 0.9328 | nan | 0.8046 | 0.7388 | 0.6490 | 0.9499 | nan | nan | nan | 0.0 | 0.7825 | 0.7034 | 0.5816 | 0.9443 | nan | nan | nan |
| 0.0552 | 41.5 | 1660 | 0.1271 | 0.5990 | 0.7805 | 0.9344 | nan | 0.7972 | 0.7469 | 0.6259 | 0.9522 | nan | nan | nan | 0.0 | 0.7758 | 0.7079 | 0.5649 | 0.9465 | nan | nan | nan |
| 0.0547 | 41.75 | 1670 | 0.1263 | 0.5966 | 0.7762 | 0.9295 | nan | 0.8027 | 0.7420 | 0.6133 | 0.9469 | nan | nan | nan | 0.0 | 0.7790 | 0.7055 | 0.5574 | 0.9411 | nan | nan | nan |
| 0.0626 | 42.0 | 1680 | 0.1257 | 0.6049 | 0.7899 | 0.9307 | nan | 0.8194 | 0.7639 | 0.6300 | 0.9464 | nan | nan | nan | 0.0 | 0.7913 | 0.7223 | 0.5697 | 0.9413 | nan | nan | nan |
| 0.0974 | 42.25 | 1690 | 0.1267 | 0.6092 | 0.7969 | 0.9360 | nan | 0.8232 | 0.7758 | 0.6371 | 0.9516 | nan | nan | nan | 0.0 | 0.7946 | 0.7293 | 0.5760 | 0.9463 | nan | nan | nan |
| 0.0722 | 42.5 | 1700 | 0.1258 | 0.5978 | 0.7799 | 0.9288 | nan | 0.8040 | 0.7434 | 0.6264 | 0.9459 | nan | nan | nan | 0.0 | 0.7791 | 0.7065 | 0.5631 | 0.9403 | nan | nan | nan |
| 0.0543 | 42.75 | 1710 | 0.1270 | 0.5938 | 0.7727 | 0.9304 | nan | 0.7973 | 0.7354 | 0.6096 | 0.9484 | nan | nan | nan | 0.0 | 0.7748 | 0.7001 | 0.5512 | 0.9427 | nan | nan | nan |
| 0.1652 | 43.0 | 1720 | 0.1258 | 0.5998 | 0.7831 | 0.9302 | nan | 0.8054 | 0.7611 | 0.6190 | 0.9469 | nan | nan | nan | 0.0 | 0.7814 | 0.7180 | 0.5583 | 0.9414 | nan | nan | nan |
| 0.0646 | 43.25 | 1730 | 0.1253 | 0.6046 | 0.7894 | 0.9326 | nan | 0.8174 | 0.7607 | 0.6306 | 0.9488 | nan | nan | nan | 0.0 | 0.7907 | 0.7208 | 0.5682 | 0.9434 | nan | nan | nan |
| 0.0681 | 43.5 | 1740 | 0.1268 | 0.6009 | 0.7819 | 0.9299 | nan | 0.8176 | 0.7320 | 0.6315 | 0.9467 | nan | nan | nan | 0.0 | 0.7909 | 0.7025 | 0.5702 | 0.9409 | nan | nan | nan |
| 0.0622 | 43.75 | 1750 | 0.1262 | 0.6021 | 0.7842 | 0.9300 | nan | 0.8191 | 0.7421 | 0.6291 | 0.9465 | nan | nan | nan | 0.0 | 0.7918 | 0.7083 | 0.5698 | 0.9408 | nan | nan | nan |
| 0.0577 | 44.0 | 1760 | 0.1254 | 0.6013 | 0.7847 | 0.9277 | nan | 0.8112 | 0.7499 | 0.6337 | 0.9439 | nan | nan | nan | 0.0 | 0.7859 | 0.7118 | 0.5705 | 0.9385 | nan | nan | nan |
| 0.0729 | 44.25 | 1770 | 0.1246 | 0.6054 | 0.7913 | 0.9342 | nan | 0.8119 | 0.7571 | 0.6456 | 0.9506 | nan | nan | nan | 0.0 | 0.7875 | 0.7158 | 0.5789 | 0.9449 | nan | nan | nan |
| 0.0467 | 44.5 | 1780 | 0.1257 | 0.5994 | 0.7821 | 0.9333 | nan | 0.7977 | 0.7464 | 0.6334 | 0.9508 | nan | nan | nan | 0.0 | 0.7762 | 0.7073 | 0.5684 | 0.9450 | nan | nan | nan |
| 0.1224 | 44.75 | 1790 | 0.1246 | 0.5999 | 0.7835 | 0.9293 | nan | 0.8019 | 0.7509 | 0.6351 | 0.9461 | nan | nan | nan | 0.0 | 0.7785 | 0.7108 | 0.5694 | 0.9408 | nan | nan | nan |
| 0.0531 | 45.0 | 1800 | 0.1259 | 0.5965 | 0.7766 | 0.9305 | nan | 0.8022 | 0.7423 | 0.6140 | 0.9481 | nan | nan | nan | 0.0 | 0.7788 | 0.7046 | 0.5565 | 0.9425 | nan | nan | nan |
| 0.0628 | 45.25 | 1810 | 0.1260 | 0.6041 | 0.7887 | 0.9346 | nan | 0.8118 | 0.7595 | 0.6323 | 0.9512 | nan | nan | nan | 0.0 | 0.7873 | 0.7166 | 0.5708 | 0.9456 | nan | nan | nan |
| 0.0579 | 45.5 | 1820 | 0.1251 | 0.6053 | 0.7910 | 0.9327 | nan | 0.8162 | 0.7640 | 0.6352 | 0.9488 | nan | nan | nan | 0.0 | 0.7901 | 0.7201 | 0.5728 | 0.9435 | nan | nan | nan |
| 0.0622 | 45.75 | 1830 | 0.1239 | 0.6046 | 0.7914 | 0.9307 | nan | 0.8142 | 0.7689 | 0.6362 | 0.9464 | nan | nan | nan | 0.0 | 0.7873 | 0.7229 | 0.5714 | 0.9413 | nan | nan | nan |
| 0.0474 | 46.0 | 1840 | 0.1246 | 0.6021 | 0.7858 | 0.9314 | nan | 0.8119 | 0.7497 | 0.6336 | 0.9480 | nan | nan | nan | 0.0 | 0.7856 | 0.7109 | 0.5717 | 0.9424 | nan | nan | nan |
| 0.0622 | 46.25 | 1850 | 0.1252 | 0.6014 | 0.7842 | 0.9321 | nan | 0.8083 | 0.7335 | 0.6457 | 0.9493 | nan | nan | nan | 0.0 | 0.7833 | 0.7008 | 0.5794 | 0.9435 | nan | nan | nan |
| 0.0564 | 46.5 | 1860 | 0.1252 | 0.6006 | 0.7827 | 0.9312 | nan | 0.8037 | 0.7417 | 0.6371 | 0.9483 | nan | nan | nan | 0.0 | 0.7804 | 0.7059 | 0.5742 | 0.9426 | nan | nan | nan |
| 0.0555 | 46.75 | 1870 | 0.1243 | 0.6018 | 0.7852 | 0.9305 | nan | 0.8067 | 0.7456 | 0.6415 | 0.9472 | nan | nan | nan | 0.0 | 0.7823 | 0.7082 | 0.5769 | 0.9417 | nan | nan | nan |
| 0.0654 | 47.0 | 1880 | 0.1260 | 0.6045 | 0.7876 | 0.9367 | nan | 0.8092 | 0.7452 | 0.6422 | 0.9539 | nan | nan | nan | 0.0 | 0.7860 | 0.7081 | 0.5801 | 0.9480 | nan | nan | nan |
| 0.0649 | 47.25 | 1890 | 0.1250 | 0.6007 | 0.7826 | 0.9323 | nan | 0.8064 | 0.7456 | 0.6289 | 0.9494 | nan | nan | nan | 0.0 | 0.7829 | 0.7070 | 0.5697 | 0.9439 | nan | nan | nan |
| 0.0688 | 47.5 | 1900 | 0.1264 | 0.6032 | 0.7862 | 0.9347 | nan | 0.8080 | 0.7513 | 0.6339 | 0.9518 | nan | nan | nan | 0.0 | 0.7846 | 0.7114 | 0.5737 | 0.9461 | nan | nan | nan |
| 0.0601 | 47.75 | 1910 | 0.1251 | 0.6039 | 0.7889 | 0.9332 | nan | 0.8070 | 0.7630 | 0.6360 | 0.9497 | nan | nan | nan | 0.0 | 0.7833 | 0.7184 | 0.5733 | 0.9443 | nan | nan | nan |
| 0.0626 | 48.0 | 1920 | 0.1241 | 0.6041 | 0.7890 | 0.9323 | nan | 0.8101 | 0.7548 | 0.6426 | 0.9487 | nan | nan | nan | 0.0 | 0.7852 | 0.7141 | 0.5780 | 0.9433 | nan | nan | nan |
| 0.0545 | 48.25 | 1930 | 0.1240 | 0.6056 | 0.7919 | 0.9324 | nan | 0.8130 | 0.7646 | 0.6417 | 0.9485 | nan | nan | nan | 0.0 | 0.7870 | 0.7209 | 0.5772 | 0.9432 | nan | nan | nan |
| 0.052 | 48.5 | 1940 | 0.1239 | 0.6027 | 0.7874 | 0.9309 | nan | 0.8087 | 0.7524 | 0.6411 | 0.9474 | nan | nan | nan | 0.0 | 0.7834 | 0.7129 | 0.5751 | 0.9420 | nan | nan | nan |
| 0.0672 | 48.75 | 1950 | 0.1258 | 0.6008 | 0.7821 | 0.9351 | nan | 0.8084 | 0.7285 | 0.6387 | 0.9529 | nan | nan | nan | 0.0 | 0.7844 | 0.6974 | 0.5753 | 0.9468 | nan | nan | nan |
| 0.132 | 49.0 | 1960 | 0.1251 | 0.6006 | 0.7833 | 0.9317 | nan | 0.8070 | 0.7450 | 0.6325 | 0.9488 | nan | nan | nan | 0.0 | 0.7823 | 0.7075 | 0.5701 | 0.9432 | nan | nan | nan |
| 0.0759 | 49.25 | 1970 | 0.1245 | 0.6014 | 0.7852 | 0.9306 | nan | 0.8074 | 0.7518 | 0.6345 | 0.9472 | nan | nan | nan | 0.0 | 0.7826 | 0.7117 | 0.5712 | 0.9417 | nan | nan | nan |
| 0.0519 | 49.5 | 1980 | 0.1249 | 0.6029 | 0.7875 | 0.9323 | nan | 0.8084 | 0.7526 | 0.6401 | 0.9489 | nan | nan | nan | 0.0 | 0.7837 | 0.7127 | 0.5749 | 0.9433 | nan | nan | nan |
| 0.052 | 49.75 | 1990 | 0.1252 | 0.5998 | 0.7819 | 0.9323 | nan | 0.8042 | 0.7501 | 0.6240 | 0.9495 | nan | nan | nan | 0.0 | 0.7805 | 0.7103 | 0.5643 | 0.9439 | nan | nan | nan |
| 0.0575 | 50.0 | 2000 | 0.1250 | 0.5989 | 0.7803 | 0.9315 | nan | 0.8041 | 0.7428 | 0.6254 | 0.9488 | nan | nan | nan | 0.0 | 0.7805 | 0.7054 | 0.5651 | 0.9432 | nan | nan | nan |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
| [
"artery",
"vein",
"nerve",
"muscle1",
"muscle2",
"muscle3",
"muscle4",
"unknown"
] |
twdent/segformer-b1-finetuned-HikingHD |
<!-- 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-HikingHD
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the twdent/HikingHD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1067
- Mean Iou: 0.9379
- Mean Accuracy: 0.9665
- Overall Accuracy: 0.9684
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9485
- Accuracy Non-traversable: 0.9845
- Iou Unlabeled: nan
- Iou Traversable: 0.9305
- Iou Non-traversable: 0.9452
- Local Tests:
- Average inference time: 0.2622481801774767
## 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 Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:|
| 0.3796 | 1.67 | 20 | 0.5835 | 0.6174 | 0.9605 | 0.9617 | nan | 0.9488 | 0.9721 | 0.0 | 0.9180 | 0.9343 |
| 0.3086 | 3.33 | 40 | 0.2597 | 0.9230 | 0.9589 | 0.9605 | nan | 0.9439 | 0.9739 | nan | 0.9143 | 0.9318 |
| 0.2717 | 5.0 | 60 | 0.2202 | 0.9386 | 0.9681 | 0.9687 | nan | 0.9626 | 0.9736 | nan | 0.9321 | 0.9451 |
| 0.2655 | 6.67 | 80 | 0.2127 | 0.9334 | 0.9658 | 0.9659 | nan | 0.9646 | 0.9670 | nan | 0.9267 | 0.9402 |
| 0.1603 | 8.33 | 100 | 0.1699 | 0.9383 | 0.9677 | 0.9686 | nan | 0.9601 | 0.9753 | nan | 0.9316 | 0.9450 |
| 0.2 | 10.0 | 120 | 0.1692 | 0.9289 | 0.9609 | 0.9637 | nan | 0.9342 | 0.9876 | nan | 0.9200 | 0.9378 |
| 0.1613 | 11.67 | 140 | 0.1389 | 0.9399 | 0.9676 | 0.9695 | nan | 0.9498 | 0.9853 | nan | 0.9328 | 0.9470 |
| 0.185 | 13.33 | 160 | 0.1612 | 0.9217 | 0.9566 | 0.9600 | nan | 0.9254 | 0.9878 | nan | 0.9116 | 0.9318 |
| 0.251 | 15.0 | 180 | 0.1461 | 0.9277 | 0.9603 | 0.9631 | nan | 0.9340 | 0.9865 | nan | 0.9187 | 0.9368 |
| 0.1038 | 16.67 | 200 | 0.1401 | 0.9248 | 0.9581 | 0.9616 | nan | 0.9258 | 0.9904 | nan | 0.9149 | 0.9346 |
| 0.0628 | 18.33 | 220 | 0.1556 | 0.9195 | 0.9548 | 0.9588 | nan | 0.9171 | 0.9924 | nan | 0.9086 | 0.9303 |
| 0.077 | 20.0 | 240 | 0.1439 | 0.9213 | 0.9561 | 0.9598 | nan | 0.9220 | 0.9902 | nan | 0.9110 | 0.9317 |
| 0.0714 | 21.67 | 260 | 0.1267 | 0.9344 | 0.9641 | 0.9666 | nan | 0.9404 | 0.9878 | nan | 0.9263 | 0.9425 |
| 0.081 | 23.33 | 280 | 0.1097 | 0.9397 | 0.9672 | 0.9694 | nan | 0.9470 | 0.9874 | nan | 0.9324 | 0.9470 |
| 0.09 | 25.0 | 300 | 0.1063 | 0.9402 | 0.9679 | 0.9696 | nan | 0.9522 | 0.9836 | nan | 0.9332 | 0.9472 |
| 0.0737 | 26.67 | 320 | 0.1045 | 0.9395 | 0.9674 | 0.9692 | nan | 0.9502 | 0.9845 | nan | 0.9323 | 0.9466 |
| 0.1173 | 28.33 | 340 | 0.1019 | 0.9427 | 0.9702 | 0.9708 | nan | 0.9644 | 0.9760 | nan | 0.9365 | 0.9488 |
| 0.0535 | 30.0 | 360 | 0.1132 | 0.9387 | 0.9674 | 0.9688 | nan | 0.9549 | 0.9799 | nan | 0.9317 | 0.9456 |
| 0.0693 | 31.67 | 380 | 0.1182 | 0.9340 | 0.9637 | 0.9664 | nan | 0.9389 | 0.9886 | nan | 0.9258 | 0.9422 |
| 0.0649 | 33.33 | 400 | 0.1108 | 0.9374 | 0.9662 | 0.9681 | nan | 0.9483 | 0.9841 | nan | 0.9300 | 0.9448 |
| 0.1581 | 35.0 | 420 | 0.1107 | 0.9368 | 0.9658 | 0.9678 | nan | 0.9473 | 0.9844 | nan | 0.9293 | 0.9443 |
| 0.0711 | 36.67 | 440 | 0.1011 | 0.9414 | 0.9690 | 0.9702 | nan | 0.9578 | 0.9801 | nan | 0.9348 | 0.9479 |
| 0.0743 | 38.33 | 460 | 0.1026 | 0.9400 | 0.9676 | 0.9695 | nan | 0.9500 | 0.9853 | nan | 0.9329 | 0.9471 |
| 0.0602 | 40.0 | 480 | 0.1029 | 0.9407 | 0.9681 | 0.9699 | nan | 0.9521 | 0.9841 | nan | 0.9337 | 0.9476 |
| 0.0768 | 41.67 | 500 | 0.1059 | 0.9386 | 0.9670 | 0.9688 | nan | 0.9502 | 0.9837 | nan | 0.9314 | 0.9458 |
| 0.0494 | 43.33 | 520 | 0.1076 | 0.9375 | 0.9663 | 0.9682 | nan | 0.9484 | 0.9842 | nan | 0.9302 | 0.9449 |
| 0.0359 | 45.0 | 540 | 0.1097 | 0.9369 | 0.9659 | 0.9679 | nan | 0.9473 | 0.9844 | nan | 0.9294 | 0.9444 |
| 0.0799 | 46.67 | 560 | 0.1070 | 0.9379 | 0.9666 | 0.9684 | nan | 0.9493 | 0.9838 | nan | 0.9306 | 0.9452 |
| 0.0685 | 48.33 | 580 | 0.1075 | 0.9378 | 0.9665 | 0.9684 | nan | 0.9489 | 0.9841 | nan | 0.9305 | 0.9452 |
| 0.0437 | 50.0 | 600 | 0.1067 | 0.9379 | 0.9665 | 0.9684 | nan | 0.9485 | 0.9845 | nan | 0.9305 | 0.9452 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
| [
"unlabeled",
"traversable",
"non-traversable"
] |
SpotLab/MobileViT_DeepLabv3 |
_Forked from [apple/deeplabv3-mobilevit-xx-small](https://huggingface.co/apple/deeplabv3-mobilevit-xx-small)_
# MobileViT + DeepLabV3 (extra extra small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](https://github.com/apple/ml-cvnets). The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
MobileViT is a light-weight, low latency convolutional neural network that combines MobileNetV2-style layers with a new block that replaces local processing in convolutions with global processing using transformers. As with ViT (Vision Transformer), the image data is converted into flattened patches before it is processed by the transformer layers. Afterwards, the patches are "unflattened" back into feature maps. This allows the MobileViT-block to be placed anywhere inside a CNN. MobileViT does not require any positional embeddings.
The model in this repo adds a [DeepLabV3](https://arxiv.org/abs/1706.05587) head to the MobileViT backbone for semantic segmentation.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilevit) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0)
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The MobileViT + DeepLabV3 model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/) dataset.
## Training procedure
### Preprocessing
At inference time, images are center-cropped at 512x512. Pixels are normalized to the range [0, 1]. Images are expected to be in BGR pixel order, not RGB.
### Pretraining
The MobileViT networks are trained from scratch for 300 epochs on ImageNet-1k on 8 NVIDIA GPUs with an effective batch size of 1024 and learning rate warmup for 3k steps, followed by cosine annealing. Also used were label smoothing cross-entropy loss and L2 weight decay. Training resolution varies from 160x160 to 320x320, using multi-scale sampling.
To obtain the DeepLabV3 model, MobileViT was fine-tuned on the PASCAL VOC dataset using 4 NVIDIA A100 GPUs.
## Evaluation results
| Model | PASCAL VOC mIOU | # params | URL |
|-------------------|-----------------|-----------|-----------------------------------------------------------|
| **MobileViT-XXS** | **73.6** | **1.9 M** | https://huggingface.co/apple/deeplabv3-mobilevit-xx-small |
| MobileViT-XS | 77.1 | 2.9 M | https://huggingface.co/apple/deeplabv3-mobilevit-x-small |
| MobileViT-S | 79.1 | 6.4 M | https://huggingface.co/apple/deeplabv3-mobilevit-small |
### BibTeX entry and citation info
```bibtex
@inproceedings{vision-transformer,
title = {MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer},
author = {Sachin Mehta and Mohammad Rastegari},
year = {2022},
URL = {https://arxiv.org/abs/2110.02178}
}
```
| [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor"
] |
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