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HorcruxNo13
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segformer-b0-finetuned-segments-toolwear

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@@ -14,14 +14,14 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.0316
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- - Mean Iou: 0.4930
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- - Mean Accuracy: 0.9859
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- - Overall Accuracy: 0.9859
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  - Accuracy Unlabeled: nan
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- - Accuracy Outline: 0.9859
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  - Iou Unlabeled: 0.0
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- - Iou Outline: 0.9859
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  ## Model description
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@@ -46,62 +46,55 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 40
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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 Outline | Iou Unlabeled | Iou Outline |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
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- | 0.1277 | 0.8 | 20 | 0.1795 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 |
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- | 0.1026 | 1.6 | 40 | 0.0920 | 0.4764 | 0.9529 | 0.9529 | nan | 0.9529 | 0.0 | 0.9529 |
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- | 0.0934 | 2.4 | 60 | 0.0782 | 0.4859 | 0.9718 | 0.9718 | nan | 0.9718 | 0.0 | 0.9718 |
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- | 0.0682 | 3.2 | 80 | 0.0656 | 0.4862 | 0.9724 | 0.9724 | nan | 0.9724 | 0.0 | 0.9724 |
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- | 0.054 | 4.0 | 100 | 0.0584 | 0.4885 | 0.9769 | 0.9769 | nan | 0.9769 | 0.0 | 0.9769 |
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- | 0.0529 | 4.8 | 120 | 0.0528 | 0.4894 | 0.9787 | 0.9787 | nan | 0.9787 | 0.0 | 0.9787 |
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- | 0.0586 | 5.6 | 140 | 0.0498 | 0.4885 | 0.9771 | 0.9771 | nan | 0.9771 | 0.0 | 0.9771 |
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- | 0.0538 | 6.4 | 160 | 0.0464 | 0.4878 | 0.9756 | 0.9756 | nan | 0.9756 | 0.0 | 0.9756 |
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- | 0.0422 | 7.2 | 180 | 0.0443 | 0.4926 | 0.9851 | 0.9851 | nan | 0.9851 | 0.0 | 0.9851 |
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- | 0.0517 | 8.0 | 200 | 0.0443 | 0.4914 | 0.9828 | 0.9828 | nan | 0.9828 | 0.0 | 0.9828 |
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- | 0.0439 | 8.8 | 220 | 0.0409 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 |
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- | 0.0357 | 9.6 | 240 | 0.0394 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 |
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- | 0.0381 | 10.4 | 260 | 0.0393 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 |
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- | 0.0362 | 11.2 | 280 | 0.0396 | 0.4931 | 0.9863 | 0.9863 | nan | 0.9863 | 0.0 | 0.9863 |
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- | 0.0317 | 12.0 | 300 | 0.0373 | 0.4922 | 0.9844 | 0.9844 | nan | 0.9844 | 0.0 | 0.9844 |
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- | 0.0342 | 12.8 | 320 | 0.0423 | 0.4950 | 0.9899 | 0.9899 | nan | 0.9899 | 0.0 | 0.9899 |
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- | 0.0341 | 13.6 | 340 | 0.0374 | 0.4925 | 0.9849 | 0.9849 | nan | 0.9849 | 0.0 | 0.9849 |
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- | 0.0347 | 14.4 | 360 | 0.0358 | 0.4921 | 0.9842 | 0.9842 | nan | 0.9842 | 0.0 | 0.9842 |
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- | 0.0351 | 15.2 | 380 | 0.0358 | 0.4928 | 0.9855 | 0.9855 | nan | 0.9855 | 0.0 | 0.9855 |
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- | 0.0589 | 16.0 | 400 | 0.0346 | 0.4908 | 0.9816 | 0.9816 | nan | 0.9816 | 0.0 | 0.9816 |
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- | 0.0354 | 16.8 | 420 | 0.0353 | 0.4945 | 0.9891 | 0.9891 | nan | 0.9891 | 0.0 | 0.9891 |
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- | 0.0349 | 17.6 | 440 | 0.0346 | 0.4899 | 0.9797 | 0.9797 | nan | 0.9797 | 0.0 | 0.9797 |
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- | 0.0357 | 18.4 | 460 | 0.0340 | 0.4927 | 0.9855 | 0.9855 | nan | 0.9855 | 0.0 | 0.9855 |
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- | 0.032 | 19.2 | 480 | 0.0348 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 |
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- | 0.0365 | 20.0 | 500 | 0.0337 | 0.4924 | 0.9849 | 0.9849 | nan | 0.9849 | 0.0 | 0.9849 |
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- | 0.0361 | 20.8 | 520 | 0.0334 | 0.4932 | 0.9863 | 0.9863 | nan | 0.9863 | 0.0 | 0.9863 |
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- | 0.0411 | 21.6 | 540 | 0.0324 | 0.4921 | 0.9843 | 0.9843 | nan | 0.9843 | 0.0 | 0.9843 |
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- | 0.0335 | 22.4 | 560 | 0.0329 | 0.4932 | 0.9864 | 0.9864 | nan | 0.9864 | 0.0 | 0.9864 |
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- | 0.0285 | 23.2 | 580 | 0.0327 | 0.4924 | 0.9847 | 0.9847 | nan | 0.9847 | 0.0 | 0.9847 |
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- | 0.0339 | 24.0 | 600 | 0.0328 | 0.4913 | 0.9827 | 0.9827 | nan | 0.9827 | 0.0 | 0.9827 |
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- | 0.034 | 24.8 | 620 | 0.0323 | 0.4934 | 0.9869 | 0.9869 | nan | 0.9869 | 0.0 | 0.9869 |
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- | 0.0314 | 25.6 | 640 | 0.0336 | 0.4940 | 0.9880 | 0.9880 | nan | 0.9880 | 0.0 | 0.9880 |
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- | 0.029 | 26.4 | 660 | 0.0324 | 0.4926 | 0.9853 | 0.9853 | nan | 0.9853 | 0.0 | 0.9853 |
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- | 0.0371 | 27.2 | 680 | 0.0324 | 0.4917 | 0.9833 | 0.9833 | nan | 0.9833 | 0.0 | 0.9833 |
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- | 0.0288 | 28.0 | 700 | 0.0322 | 0.4931 | 0.9862 | 0.9862 | nan | 0.9862 | 0.0 | 0.9862 |
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- | 0.0297 | 28.8 | 720 | 0.0320 | 0.4925 | 0.9849 | 0.9849 | nan | 0.9849 | 0.0 | 0.9849 |
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- | 0.0256 | 29.6 | 740 | 0.0321 | 0.4923 | 0.9846 | 0.9846 | nan | 0.9846 | 0.0 | 0.9846 |
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- | 0.033 | 30.4 | 760 | 0.0317 | 0.4926 | 0.9852 | 0.9852 | nan | 0.9852 | 0.0 | 0.9852 |
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- | 0.0251 | 31.2 | 780 | 0.0328 | 0.4943 | 0.9887 | 0.9887 | nan | 0.9887 | 0.0 | 0.9887 |
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- | 0.0286 | 32.0 | 800 | 0.0322 | 0.4938 | 0.9876 | 0.9876 | nan | 0.9876 | 0.0 | 0.9876 |
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- | 0.0273 | 32.8 | 820 | 0.0318 | 0.4930 | 0.9859 | 0.9859 | nan | 0.9859 | 0.0 | 0.9859 |
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- | 0.0289 | 33.6 | 840 | 0.0325 | 0.4937 | 0.9873 | 0.9873 | nan | 0.9873 | 0.0 | 0.9873 |
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- | 0.0279 | 34.4 | 860 | 0.0325 | 0.4937 | 0.9874 | 0.9874 | nan | 0.9874 | 0.0 | 0.9874 |
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- | 0.0284 | 35.2 | 880 | 0.0325 | 0.4940 | 0.9879 | 0.9879 | nan | 0.9879 | 0.0 | 0.9879 |
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- | 0.0229 | 36.0 | 900 | 0.0317 | 0.4931 | 0.9861 | 0.9861 | nan | 0.9861 | 0.0 | 0.9861 |
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- | 0.0256 | 36.8 | 920 | 0.0316 | 0.4927 | 0.9854 | 0.9854 | nan | 0.9854 | 0.0 | 0.9854 |
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- | 0.0278 | 37.6 | 940 | 0.0319 | 0.4933 | 0.9867 | 0.9867 | nan | 0.9867 | 0.0 | 0.9867 |
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- | 0.0301 | 38.4 | 960 | 0.0318 | 0.4932 | 0.9865 | 0.9865 | nan | 0.9865 | 0.0 | 0.9865 |
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- | 0.0233 | 39.2 | 980 | 0.0319 | 0.4934 | 0.9868 | 0.9868 | nan | 0.9868 | 0.0 | 0.9868 |
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- | 0.0256 | 40.0 | 1000 | 0.0316 | 0.4930 | 0.9859 | 0.9859 | nan | 0.9859 | 0.0 | 0.9859 |
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  ### Framework versions
 
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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: 0.0799
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+ - Mean Iou: 0.4629
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+ - Mean Accuracy: 0.9258
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+ - Overall Accuracy: 0.9258
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  - Accuracy Unlabeled: nan
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+ - Accuracy Liver: 0.9258
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  - Iou Unlabeled: 0.0
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+ - Iou Liver: 0.9258
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 35
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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 Liver | Iou Unlabeled | Iou Liver |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
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+ | 0.2837 | 0.8 | 20 | 0.3699 | 0.3876 | 0.7752 | 0.7752 | nan | 0.7752 | 0.0 | 0.7752 |
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+ | 0.2264 | 1.6 | 40 | 0.1982 | 0.4222 | 0.8444 | 0.8444 | nan | 0.8444 | 0.0 | 0.8444 |
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+ | 0.1687 | 2.4 | 60 | 0.1594 | 0.3988 | 0.7977 | 0.7977 | nan | 0.7977 | 0.0 | 0.7977 |
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+ | 0.1489 | 3.2 | 80 | 0.1396 | 0.4050 | 0.8100 | 0.8100 | nan | 0.8100 | 0.0 | 0.8100 |
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+ | 0.1111 | 4.0 | 100 | 0.1203 | 0.4223 | 0.8446 | 0.8446 | nan | 0.8446 | 0.0 | 0.8446 |
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+ | 0.1115 | 4.8 | 120 | 0.1160 | 0.4512 | 0.9023 | 0.9023 | nan | 0.9023 | 0.0 | 0.9023 |
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+ | 0.1081 | 5.6 | 140 | 0.1053 | 0.4504 | 0.9009 | 0.9009 | nan | 0.9009 | 0.0 | 0.9009 |
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+ | 0.1111 | 6.4 | 160 | 0.0960 | 0.4526 | 0.9051 | 0.9051 | nan | 0.9051 | 0.0 | 0.9051 |
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+ | 0.0904 | 7.2 | 180 | 0.0954 | 0.4646 | 0.9292 | 0.9292 | nan | 0.9292 | 0.0 | 0.9292 |
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+ | 0.0868 | 8.0 | 200 | 0.0925 | 0.4593 | 0.9187 | 0.9187 | nan | 0.9187 | 0.0 | 0.9187 |
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+ | 0.092 | 8.8 | 220 | 0.0852 | 0.4630 | 0.9261 | 0.9261 | nan | 0.9261 | 0.0 | 0.9261 |
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+ | 0.0686 | 9.6 | 240 | 0.0897 | 0.4631 | 0.9263 | 0.9263 | nan | 0.9263 | 0.0 | 0.9263 |
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+ | 0.0684 | 10.4 | 260 | 0.0939 | 0.4727 | 0.9455 | 0.9455 | nan | 0.9455 | 0.0 | 0.9455 |
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+ | 0.0634 | 11.2 | 280 | 0.0919 | 0.4241 | 0.8483 | 0.8483 | nan | 0.8483 | 0.0 | 0.8483 |
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+ | 0.059 | 12.0 | 300 | 0.0886 | 0.4727 | 0.9455 | 0.9455 | nan | 0.9455 | 0.0 | 0.9455 |
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+ | 0.052 | 12.8 | 320 | 0.0764 | 0.4554 | 0.9108 | 0.9108 | nan | 0.9108 | 0.0 | 0.9108 |
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+ | 0.0558 | 13.6 | 340 | 0.0769 | 0.4629 | 0.9258 | 0.9258 | nan | 0.9258 | 0.0 | 0.9258 |
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+ | 0.0594 | 14.4 | 360 | 0.0770 | 0.4616 | 0.9231 | 0.9231 | nan | 0.9231 | 0.0 | 0.9231 |
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+ | 0.0641 | 15.2 | 380 | 0.0844 | 0.4709 | 0.9417 | 0.9417 | nan | 0.9417 | 0.0 | 0.9417 |
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+ | 0.0645 | 16.0 | 400 | 0.0790 | 0.4632 | 0.9263 | 0.9263 | nan | 0.9263 | 0.0 | 0.9263 |
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+ | 0.0545 | 16.8 | 420 | 0.0776 | 0.4610 | 0.9220 | 0.9220 | nan | 0.9220 | 0.0 | 0.9220 |
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+ | 0.056 | 17.6 | 440 | 0.0780 | 0.4541 | 0.9082 | 0.9082 | nan | 0.9082 | 0.0 | 0.9082 |
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+ | 0.0472 | 18.4 | 460 | 0.0742 | 0.4595 | 0.9189 | 0.9189 | nan | 0.9189 | 0.0 | 0.9189 |
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+ | 0.0478 | 19.2 | 480 | 0.0806 | 0.4690 | 0.9380 | 0.9380 | nan | 0.9380 | 0.0 | 0.9380 |
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+ | 0.0523 | 20.0 | 500 | 0.0741 | 0.4550 | 0.9100 | 0.9100 | nan | 0.9100 | 0.0 | 0.9100 |
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+ | 0.0401 | 20.8 | 520 | 0.0794 | 0.4637 | 0.9274 | 0.9274 | nan | 0.9274 | 0.0 | 0.9274 |
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+ | 0.041 | 21.6 | 540 | 0.0772 | 0.4631 | 0.9262 | 0.9262 | nan | 0.9262 | 0.0 | 0.9262 |
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+ | 0.0386 | 22.4 | 560 | 0.0795 | 0.4620 | 0.9240 | 0.9240 | nan | 0.9240 | 0.0 | 0.9240 |
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+ | 0.0386 | 23.2 | 580 | 0.0761 | 0.4616 | 0.9232 | 0.9232 | nan | 0.9232 | 0.0 | 0.9232 |
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+ | 0.0628 | 24.0 | 600 | 0.0778 | 0.4636 | 0.9271 | 0.9271 | nan | 0.9271 | 0.0 | 0.9271 |
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+ | 0.0387 | 24.8 | 620 | 0.0782 | 0.4613 | 0.9226 | 0.9226 | nan | 0.9226 | 0.0 | 0.9226 |
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+ | 0.0422 | 25.6 | 640 | 0.0778 | 0.4616 | 0.9233 | 0.9233 | nan | 0.9233 | 0.0 | 0.9233 |
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+ | 0.0392 | 26.4 | 660 | 0.0838 | 0.4696 | 0.9393 | 0.9393 | nan | 0.9393 | 0.0 | 0.9393 |
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+ | 0.04 | 27.2 | 680 | 0.0809 | 0.4658 | 0.9315 | 0.9315 | nan | 0.9315 | 0.0 | 0.9315 |
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+ | 0.0341 | 28.0 | 700 | 0.0822 | 0.4667 | 0.9335 | 0.9335 | nan | 0.9335 | 0.0 | 0.9335 |
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+ | 0.0329 | 28.8 | 720 | 0.0797 | 0.4639 | 0.9278 | 0.9278 | nan | 0.9278 | 0.0 | 0.9278 |
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+ | 0.0373 | 29.6 | 740 | 0.0769 | 0.4582 | 0.9163 | 0.9163 | nan | 0.9163 | 0.0 | 0.9163 |
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+ | 0.0366 | 30.4 | 760 | 0.0804 | 0.4632 | 0.9264 | 0.9264 | nan | 0.9264 | 0.0 | 0.9264 |
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+ | 0.0432 | 31.2 | 780 | 0.0793 | 0.4587 | 0.9174 | 0.9174 | nan | 0.9174 | 0.0 | 0.9174 |
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+ | 0.0328 | 32.0 | 800 | 0.0838 | 0.4688 | 0.9377 | 0.9377 | nan | 0.9377 | 0.0 | 0.9377 |
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+ | 0.0377 | 32.8 | 820 | 0.0805 | 0.4643 | 0.9286 | 0.9286 | nan | 0.9286 | 0.0 | 0.9286 |
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+ | 0.0327 | 33.6 | 840 | 0.0784 | 0.4614 | 0.9228 | 0.9228 | nan | 0.9228 | 0.0 | 0.9228 |
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+ | 0.032 | 34.4 | 860 | 0.0799 | 0.4629 | 0.9258 | 0.9258 | nan | 0.9258 | 0.0 | 0.9258 |
 
 
 
 
 
 
 
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  ### Framework versions