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segformer-b0-finetuned-segments-toolwear

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  ---
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  license: other
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  tags:
 
 
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  - generated_from_trainer
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  model-index:
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  - name: segformer-b0-finetuned-segments-toolwear
@@ -12,18 +14,16 @@ should probably proofread and complete it, then remove this comment. -->
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  # segformer-b0-finetuned-segments-toolwear
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- This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0406
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- - Mean Iou: 0.3913
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- - Mean Accuracy: 0.7826
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- - Overall Accuracy: 0.7826
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  - Accuracy Unlabeled: nan
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- - Accuracy Tool: nan
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- - Accuracy Wear: 0.7826
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  - Iou Unlabeled: 0.0
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- - Iou Tool: nan
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- - Iou Wear: 0.7826
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  ## Model description
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@@ -52,50 +52,50 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
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- | 0.7907 | 1.18 | 20 | 0.8970 | 0.3905 | 0.7810 | 0.7810 | nan | nan | 0.7810 | 0.0 | nan | 0.7810 |
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- | 0.515 | 2.35 | 40 | 0.4998 | 0.3753 | 0.7506 | 0.7506 | nan | nan | 0.7506 | 0.0 | nan | 0.7506 |
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- | 0.405 | 3.53 | 60 | 0.3773 | 0.4074 | 0.8148 | 0.8148 | nan | nan | 0.8148 | 0.0 | nan | 0.8148 |
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- | 0.3532 | 4.71 | 80 | 0.3191 | 0.4127 | 0.8255 | 0.8255 | nan | nan | 0.8255 | 0.0 | nan | 0.8255 |
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- | 0.2912 | 5.88 | 100 | 0.2693 | 0.4314 | 0.8628 | 0.8628 | nan | nan | 0.8628 | 0.0 | nan | 0.8628 |
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- | 0.2128 | 7.06 | 120 | 0.2297 | 0.4067 | 0.8133 | 0.8133 | nan | nan | 0.8133 | 0.0 | nan | 0.8133 |
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- | 0.1676 | 8.24 | 140 | 0.1849 | 0.4101 | 0.8203 | 0.8203 | nan | nan | 0.8203 | 0.0 | nan | 0.8203 |
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- | 0.1712 | 9.41 | 160 | 0.1446 | 0.3677 | 0.7354 | 0.7354 | nan | nan | 0.7354 | 0.0 | nan | 0.7354 |
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- | 0.1344 | 10.59 | 180 | 0.1265 | 0.3931 | 0.7861 | 0.7861 | nan | nan | 0.7861 | 0.0 | nan | 0.7861 |
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- | 0.1315 | 11.76 | 200 | 0.1023 | 0.3511 | 0.7022 | 0.7022 | nan | nan | 0.7022 | 0.0 | nan | 0.7022 |
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- | 0.109 | 12.94 | 220 | 0.1047 | 0.3986 | 0.7973 | 0.7973 | nan | nan | 0.7973 | 0.0 | nan | 0.7973 |
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- | 0.0985 | 14.12 | 240 | 0.0913 | 0.4042 | 0.8084 | 0.8084 | nan | nan | 0.8084 | 0.0 | nan | 0.8084 |
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- | 0.0711 | 15.29 | 260 | 0.0773 | 0.3192 | 0.6384 | 0.6384 | nan | nan | 0.6384 | 0.0 | nan | 0.6384 |
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- | 0.0636 | 16.47 | 280 | 0.0798 | 0.4138 | 0.8275 | 0.8275 | nan | nan | 0.8275 | 0.0 | nan | 0.8275 |
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- | 0.0619 | 17.65 | 300 | 0.0692 | 0.3770 | 0.7540 | 0.7540 | nan | nan | 0.7540 | 0.0 | nan | 0.7540 |
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- | 0.0573 | 18.82 | 320 | 0.0608 | 0.3386 | 0.6771 | 0.6771 | nan | nan | 0.6771 | 0.0 | nan | 0.6771 |
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- | 0.0579 | 20.0 | 340 | 0.0609 | 0.3882 | 0.7765 | 0.7765 | nan | nan | 0.7765 | 0.0 | nan | 0.7765 |
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- | 0.0505 | 21.18 | 360 | 0.0552 | 0.3748 | 0.7496 | 0.7496 | nan | nan | 0.7496 | 0.0 | nan | 0.7496 |
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- | 0.0514 | 22.35 | 380 | 0.0606 | 0.4208 | 0.8416 | 0.8416 | nan | nan | 0.8416 | 0.0 | nan | 0.8416 |
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- | 0.0475 | 23.53 | 400 | 0.0513 | 0.3796 | 0.7593 | 0.7593 | nan | nan | 0.7593 | 0.0 | nan | 0.7593 |
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- | 0.0442 | 24.71 | 420 | 0.0526 | 0.4185 | 0.8371 | 0.8371 | nan | nan | 0.8371 | 0.0 | nan | 0.8371 |
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- | 0.0408 | 25.88 | 440 | 0.0526 | 0.4044 | 0.8087 | 0.8087 | nan | nan | 0.8087 | 0.0 | nan | 0.8087 |
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- | 0.0337 | 27.06 | 460 | 0.0485 | 0.3932 | 0.7865 | 0.7865 | nan | nan | 0.7865 | 0.0 | nan | 0.7865 |
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- | 0.0384 | 28.24 | 480 | 0.0463 | 0.4049 | 0.8098 | 0.8098 | nan | nan | 0.8098 | 0.0 | nan | 0.8098 |
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- | 0.0469 | 29.41 | 500 | 0.0459 | 0.3687 | 0.7374 | 0.7374 | nan | nan | 0.7374 | 0.0 | nan | 0.7374 |
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- | 0.0305 | 30.59 | 520 | 0.0444 | 0.3610 | 0.7220 | 0.7220 | nan | nan | 0.7220 | 0.0 | nan | 0.7220 |
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- | 0.0364 | 31.76 | 540 | 0.0461 | 0.4147 | 0.8294 | 0.8294 | nan | nan | 0.8294 | 0.0 | nan | 0.8294 |
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- | 0.034 | 32.94 | 560 | 0.0434 | 0.3907 | 0.7813 | 0.7813 | nan | nan | 0.7813 | 0.0 | nan | 0.7813 |
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- | 0.0276 | 34.12 | 580 | 0.0431 | 0.3880 | 0.7759 | 0.7759 | nan | nan | 0.7759 | 0.0 | nan | 0.7759 |
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- | 0.0281 | 35.29 | 600 | 0.0424 | 0.3761 | 0.7522 | 0.7522 | nan | nan | 0.7522 | 0.0 | nan | 0.7522 |
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- | 0.0264 | 36.47 | 620 | 0.0438 | 0.4045 | 0.8090 | 0.8090 | nan | nan | 0.8090 | 0.0 | nan | 0.8090 |
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- | 0.0269 | 37.65 | 640 | 0.0430 | 0.4041 | 0.8082 | 0.8082 | nan | nan | 0.8082 | 0.0 | nan | 0.8082 |
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- | 0.0245 | 38.82 | 660 | 0.0409 | 0.3803 | 0.7607 | 0.7607 | nan | nan | 0.7607 | 0.0 | nan | 0.7607 |
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- | 0.0241 | 40.0 | 680 | 0.0436 | 0.4147 | 0.8295 | 0.8295 | nan | nan | 0.8295 | 0.0 | nan | 0.8295 |
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- | 0.027 | 41.18 | 700 | 0.0417 | 0.3901 | 0.7803 | 0.7803 | nan | nan | 0.7803 | 0.0 | nan | 0.7803 |
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- | 0.0227 | 42.35 | 720 | 0.0405 | 0.3914 | 0.7828 | 0.7828 | nan | nan | 0.7828 | 0.0 | nan | 0.7828 |
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- | 0.0269 | 43.53 | 740 | 0.0409 | 0.3907 | 0.7814 | 0.7814 | nan | nan | 0.7814 | 0.0 | nan | 0.7814 |
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- | 0.0223 | 44.71 | 760 | 0.0409 | 0.3938 | 0.7877 | 0.7877 | nan | nan | 0.7877 | 0.0 | nan | 0.7877 |
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- | 0.0268 | 45.88 | 780 | 0.0405 | 0.3888 | 0.7776 | 0.7776 | nan | nan | 0.7776 | 0.0 | nan | 0.7776 |
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- | 0.0228 | 47.06 | 800 | 0.0408 | 0.3908 | 0.7817 | 0.7817 | nan | nan | 0.7817 | 0.0 | nan | 0.7817 |
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- | 0.0218 | 48.24 | 820 | 0.0406 | 0.3868 | 0.7736 | 0.7736 | nan | nan | 0.7736 | 0.0 | nan | 0.7736 |
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- | 0.0221 | 49.41 | 840 | 0.0406 | 0.3913 | 0.7826 | 0.7826 | nan | nan | 0.7826 | 0.0 | nan | 0.7826 |
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  ### Framework versions
 
1
  ---
2
  license: other
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  tags:
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+ - vision
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+ - image-segmentation
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  - generated_from_trainer
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  model-index:
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  - name: segformer-b0-finetuned-segments-toolwear
 
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  # segformer-b0-finetuned-segments-toolwear
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the HorcruxNo13/toolwear_complete_tool dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0236
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+ - Mean Iou: 0.4952
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+ - Mean Accuracy: 0.9904
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+ - Overall Accuracy: 0.9904
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  - Accuracy Unlabeled: nan
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+ - Accuracy Tool: 0.9904
 
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  - Iou Unlabeled: 0.0
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+ - Iou Tool: 0.9904
 
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Iou Unlabeled | Iou Tool |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
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+ | 0.1696 | 1.18 | 20 | 0.3490 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 |
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+ | 0.1045 | 2.35 | 40 | 0.0977 | 0.4878 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 |
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+ | 0.0871 | 3.53 | 60 | 0.0650 | 0.4952 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 |
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+ | 0.0542 | 4.71 | 80 | 0.0652 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 |
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+ | 0.0507 | 5.88 | 100 | 0.0573 | 0.4952 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 |
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+ | 0.071 | 7.06 | 120 | 0.0510 | 0.4941 | 0.9883 | 0.9883 | nan | 0.9883 | 0.0 | 0.9883 |
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+ | 0.0456 | 8.24 | 140 | 0.0487 | 0.4892 | 0.9785 | 0.9785 | nan | 0.9785 | 0.0 | 0.9785 |
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+ | 0.0489 | 9.41 | 160 | 0.0430 | 0.4934 | 0.9867 | 0.9867 | nan | 0.9867 | 0.0 | 0.9867 |
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+ | 0.048 | 10.59 | 180 | 0.0409 | 0.4940 | 0.9881 | 0.9881 | nan | 0.9881 | 0.0 | 0.9881 |
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+ | 0.0476 | 11.76 | 200 | 0.0347 | 0.4966 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 |
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+ | 0.0479 | 12.94 | 220 | 0.0367 | 0.4972 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 |
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+ | 0.0242 | 14.12 | 240 | 0.0342 | 0.4962 | 0.9925 | 0.9925 | nan | 0.9925 | 0.0 | 0.9925 |
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+ | 0.0277 | 15.29 | 260 | 0.0305 | 0.4967 | 0.9934 | 0.9934 | nan | 0.9934 | 0.0 | 0.9934 |
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+ | 0.0192 | 16.47 | 280 | 0.0318 | 0.4956 | 0.9913 | 0.9913 | nan | 0.9913 | 0.0 | 0.9913 |
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+ | 0.038 | 17.65 | 300 | 0.0284 | 0.4965 | 0.9929 | 0.9929 | nan | 0.9929 | 0.0 | 0.9929 |
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+ | 0.0244 | 18.82 | 320 | 0.0280 | 0.4953 | 0.9906 | 0.9906 | nan | 0.9906 | 0.0 | 0.9906 |
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+ | 0.0273 | 20.0 | 340 | 0.0269 | 0.4955 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
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+ | 0.0174 | 21.18 | 360 | 0.0280 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 |
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+ | 0.0277 | 22.35 | 380 | 0.0270 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 |
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+ | 0.0269 | 23.53 | 400 | 0.0271 | 0.4950 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 |
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+ | 0.0372 | 24.71 | 420 | 0.0252 | 0.4939 | 0.9879 | 0.9879 | nan | 0.9879 | 0.0 | 0.9879 |
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+ | 0.023 | 25.88 | 440 | 0.0263 | 0.4935 | 0.9870 | 0.9870 | nan | 0.9870 | 0.0 | 0.9870 |
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+ | 0.0183 | 27.06 | 460 | 0.0257 | 0.4960 | 0.9920 | 0.9920 | nan | 0.9920 | 0.0 | 0.9920 |
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+ | 0.024 | 28.24 | 480 | 0.0256 | 0.4950 | 0.9900 | 0.9900 | nan | 0.9900 | 0.0 | 0.9900 |
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+ | 0.0145 | 29.41 | 500 | 0.0245 | 0.4956 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
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+ | 0.0158 | 30.59 | 520 | 0.0250 | 0.4947 | 0.9895 | 0.9895 | nan | 0.9895 | 0.0 | 0.9895 |
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+ | 0.0169 | 31.76 | 540 | 0.0247 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 |
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+ | 0.018 | 32.94 | 560 | 0.0237 | 0.4965 | 0.9930 | 0.9930 | nan | 0.9930 | 0.0 | 0.9930 |
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+ | 0.0161 | 34.12 | 580 | 0.0237 | 0.4956 | 0.9913 | 0.9913 | nan | 0.9913 | 0.0 | 0.9913 |
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+ | 0.0191 | 35.29 | 600 | 0.0241 | 0.4951 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 |
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+ | 0.0133 | 36.47 | 620 | 0.0240 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 |
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+ | 0.0118 | 37.65 | 640 | 0.0244 | 0.4949 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 |
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+ | 0.0133 | 38.82 | 660 | 0.0229 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 |
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+ | 0.0198 | 40.0 | 680 | 0.0236 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 |
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+ | 0.0168 | 41.18 | 700 | 0.0234 | 0.4961 | 0.9923 | 0.9923 | nan | 0.9923 | 0.0 | 0.9923 |
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+ | 0.0119 | 42.35 | 720 | 0.0233 | 0.4957 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 |
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+ | 0.0154 | 43.53 | 740 | 0.0243 | 0.4950 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 |
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+ | 0.0126 | 44.71 | 760 | 0.0242 | 0.4949 | 0.9898 | 0.9898 | nan | 0.9898 | 0.0 | 0.9898 |
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+ | 0.0128 | 45.88 | 780 | 0.0243 | 0.4955 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
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+ | 0.0116 | 47.06 | 800 | 0.0239 | 0.4953 | 0.9907 | 0.9907 | nan | 0.9907 | 0.0 | 0.9907 |
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+ | 0.0121 | 48.24 | 820 | 0.0239 | 0.4954 | 0.9909 | 0.9909 | nan | 0.9909 | 0.0 | 0.9909 |
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+ | 0.0164 | 49.41 | 840 | 0.0236 | 0.4952 | 0.9904 | 0.9904 | nan | 0.9904 | 0.0 | 0.9904 |
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  ### Framework versions