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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: 0.0387
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- - Mean Iou: 0.4153
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- - Mean Accuracy: 0.8306
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- - Overall Accuracy: 0.8306
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  - Accuracy Unlabeled: nan
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- - Accuracy Tool: nan
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- - Accuracy Wear: 0.8306
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  - Iou Unlabeled: 0.0
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- - Iou Tool: nan
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- - Iou Wear: 0.8306
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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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- | 0.8874 | 1.18 | 20 | 0.8947 | 0.4560 | 0.9119 | 0.9119 | nan | nan | 0.9119 | 0.0 | nan | 0.9119 |
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- | 0.5792 | 2.35 | 40 | 0.5701 | 0.2551 | 0.7653 | 0.7653 | nan | nan | 0.7653 | 0.0 | 0.0 | 0.7653 |
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- | 0.5031 | 3.53 | 60 | 0.4364 | 0.4652 | 0.9305 | 0.9305 | nan | nan | 0.9305 | 0.0 | nan | 0.9305 |
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- | 0.4025 | 4.71 | 80 | 0.4218 | 0.4529 | 0.9058 | 0.9058 | nan | nan | 0.9058 | 0.0 | nan | 0.9058 |
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- | 0.3212 | 5.88 | 100 | 0.3115 | 0.4447 | 0.8894 | 0.8894 | nan | nan | 0.8894 | 0.0 | nan | 0.8894 |
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- | 0.2797 | 7.06 | 120 | 0.2646 | 0.3291 | 0.6582 | 0.6582 | nan | nan | 0.6582 | 0.0 | nan | 0.6582 |
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- | 0.2143 | 8.24 | 140 | 0.2223 | 0.4177 | 0.8354 | 0.8354 | nan | nan | 0.8354 | 0.0 | nan | 0.8354 |
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- | 0.1951 | 9.41 | 160 | 0.1815 | 0.4313 | 0.8625 | 0.8625 | nan | nan | 0.8625 | 0.0 | nan | 0.8625 |
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- | 0.1475 | 10.59 | 180 | 0.1571 | 0.4014 | 0.8029 | 0.8029 | nan | nan | 0.8029 | 0.0 | nan | 0.8029 |
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- | 0.1523 | 11.76 | 200 | 0.1386 | 0.4242 | 0.8485 | 0.8485 | nan | nan | 0.8485 | 0.0 | nan | 0.8485 |
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- | 0.1324 | 12.94 | 220 | 0.1127 | 0.4429 | 0.8858 | 0.8858 | nan | nan | 0.8858 | 0.0 | nan | 0.8858 |
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- | 0.0977 | 14.12 | 240 | 0.1064 | 0.4458 | 0.8916 | 0.8916 | nan | nan | 0.8916 | 0.0 | nan | 0.8916 |
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- | 0.0858 | 15.29 | 260 | 0.0915 | 0.4561 | 0.9122 | 0.9122 | nan | nan | 0.9122 | 0.0 | nan | 0.9122 |
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- | 0.0782 | 16.47 | 280 | 0.0934 | 0.4611 | 0.9223 | 0.9223 | nan | nan | 0.9223 | 0.0 | nan | 0.9223 |
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- | 0.0763 | 17.65 | 300 | 0.0757 | 0.4542 | 0.9084 | 0.9084 | nan | nan | 0.9084 | 0.0 | nan | 0.9084 |
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- | 0.0665 | 18.82 | 320 | 0.0718 | 0.4259 | 0.8518 | 0.8518 | nan | nan | 0.8518 | 0.0 | nan | 0.8518 |
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- | 0.0658 | 20.0 | 340 | 0.0636 | 0.3842 | 0.7685 | 0.7685 | nan | nan | 0.7685 | 0.0 | nan | 0.7685 |
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- | 0.0672 | 21.18 | 360 | 0.0590 | 0.4212 | 0.8425 | 0.8425 | nan | nan | 0.8425 | 0.0 | nan | 0.8425 |
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- | 0.05 | 22.35 | 380 | 0.0586 | 0.4502 | 0.9005 | 0.9005 | nan | nan | 0.9005 | 0.0 | nan | 0.9005 |
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- | 0.0525 | 23.53 | 400 | 0.0546 | 0.3913 | 0.7827 | 0.7827 | nan | nan | 0.7827 | 0.0 | nan | 0.7827 |
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- | 0.0451 | 24.71 | 420 | 0.0528 | 0.4383 | 0.8767 | 0.8767 | nan | nan | 0.8767 | 0.0 | nan | 0.8767 |
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- | 0.0407 | 25.88 | 440 | 0.0494 | 0.4337 | 0.8675 | 0.8675 | nan | nan | 0.8675 | 0.0 | nan | 0.8675 |
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- | 0.0462 | 27.06 | 460 | 0.0510 | 0.3397 | 0.6795 | 0.6795 | nan | nan | 0.6795 | 0.0 | nan | 0.6795 |
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- | 0.0376 | 28.24 | 480 | 0.0451 | 0.4271 | 0.8541 | 0.8541 | nan | nan | 0.8541 | 0.0 | nan | 0.8541 |
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- | 0.0349 | 29.41 | 500 | 0.0456 | 0.4173 | 0.8346 | 0.8346 | nan | nan | 0.8346 | 0.0 | nan | 0.8346 |
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- | 0.0406 | 30.59 | 520 | 0.0449 | 0.3863 | 0.7726 | 0.7726 | nan | nan | 0.7726 | 0.0 | nan | 0.7726 |
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- | 0.0333 | 31.76 | 540 | 0.0438 | 0.4361 | 0.8721 | 0.8721 | nan | nan | 0.8721 | 0.0 | nan | 0.8721 |
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- | 0.0331 | 32.94 | 560 | 0.0480 | 0.3417 | 0.6834 | 0.6834 | nan | nan | 0.6834 | 0.0 | nan | 0.6834 |
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- | 0.0756 | 34.12 | 580 | 0.0420 | 0.4362 | 0.8723 | 0.8723 | nan | nan | 0.8723 | 0.0 | nan | 0.8723 |
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- | 0.0295 | 35.29 | 600 | 0.0437 | 0.3674 | 0.7349 | 0.7349 | nan | nan | 0.7349 | 0.0 | nan | 0.7349 |
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- | 0.0325 | 36.47 | 620 | 0.0409 | 0.4087 | 0.8174 | 0.8174 | nan | nan | 0.8174 | 0.0 | nan | 0.8174 |
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- | 0.0299 | 37.65 | 640 | 0.0405 | 0.4150 | 0.8299 | 0.8299 | nan | nan | 0.8299 | 0.0 | nan | 0.8299 |
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- | 0.0384 | 38.82 | 660 | 0.0416 | 0.3690 | 0.7380 | 0.7380 | nan | nan | 0.7380 | 0.0 | nan | 0.7380 |
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- | 0.0269 | 40.0 | 680 | 0.0393 | 0.4356 | 0.8713 | 0.8713 | nan | nan | 0.8713 | 0.0 | nan | 0.8713 |
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- | 0.025 | 41.18 | 700 | 0.0389 | 0.3976 | 0.7952 | 0.7952 | nan | nan | 0.7952 | 0.0 | nan | 0.7952 |
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- | 0.0256 | 42.35 | 720 | 0.0392 | 0.3729 | 0.7459 | 0.7459 | nan | nan | 0.7459 | 0.0 | nan | 0.7459 |
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- | 0.0303 | 43.53 | 740 | 0.0400 | 0.3869 | 0.7738 | 0.7738 | nan | nan | 0.7738 | 0.0 | nan | 0.7738 |
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- | 0.0244 | 44.71 | 760 | 0.0389 | 0.4022 | 0.8044 | 0.8044 | nan | nan | 0.8044 | 0.0 | nan | 0.8044 |
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- | 0.03 | 45.88 | 780 | 0.0387 | 0.4003 | 0.8006 | 0.8006 | nan | nan | 0.8006 | 0.0 | nan | 0.8006 |
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- | 0.0238 | 47.06 | 800 | 0.0384 | 0.4073 | 0.8147 | 0.8147 | nan | nan | 0.8147 | 0.0 | nan | 0.8147 |
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- | 0.0278 | 48.24 | 820 | 0.0394 | 0.4151 | 0.8302 | 0.8302 | nan | nan | 0.8302 | 0.0 | nan | 0.8302 |
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- | 0.0281 | 49.41 | 840 | 0.0387 | 0.4153 | 0.8306 | 0.8306 | nan | nan | 0.8306 | 0.0 | nan | 0.8306 |
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  ### Framework versions
 
1
  ---
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  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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+ | 0.0708 | 7.06 | 120 | 0.0510 | 0.4941 | 0.9883 | 0.9883 | nan | 0.9883 | 0.0 | 0.9883 |
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+ | 0.0455 | 8.24 | 140 | 0.0487 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 |
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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 | 0.9880 | 0.9880 | nan | 0.9880 | 0.0 | 0.9880 |
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+ | 0.0476 | 11.76 | 200 | 0.0347 | 0.4965 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 |
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+ | 0.048 | 12.94 | 220 | 0.0366 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 |
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+ | 0.0242 | 14.12 | 240 | 0.0342 | 0.4963 | 0.9926 | 0.9926 | nan | 0.9926 | 0.0 | 0.9926 |
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+ | 0.0276 | 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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+ | 0.0382 | 17.65 | 300 | 0.0285 | 0.4964 | 0.9929 | 0.9929 | nan | 0.9929 | 0.0 | 0.9929 |
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+ | 0.0244 | 18.82 | 320 | 0.0282 | 0.4952 | 0.9904 | 0.9904 | nan | 0.9904 | 0.0 | 0.9904 |
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+ | 0.0272 | 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 | 0.0281 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 |
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+ | 0.0276 | 22.35 | 380 | 0.0270 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 |
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+ | 0.0268 | 23.53 | 400 | 0.0272 | 0.4950 | 0.9900 | 0.9900 | nan | 0.9900 | 0.0 | 0.9900 |
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+ | 0.0373 | 24.71 | 420 | 0.0253 | 0.4939 | 0.9878 | 0.9878 | nan | 0.9878 | 0.0 | 0.9878 |
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+ | 0.023 | 25.88 | 440 | 0.0264 | 0.4935 | 0.9869 | 0.9869 | nan | 0.9869 | 0.0 | 0.9869 |
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+ | 0.0183 | 27.06 | 460 | 0.0257 | 0.4960 | 0.9919 | 0.9919 | nan | 0.9919 | 0.0 | 0.9919 |
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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 | 0.0246 | 0.4956 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
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+ | 0.0158 | 30.59 | 520 | 0.0251 | 0.4947 | 0.9894 | 0.9894 | nan | 0.9894 | 0.0 | 0.9894 |
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+ | 0.017 | 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 | 0.0238 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 |
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+ | 0.0191 | 35.29 | 600 | 0.0241 | 0.4950 | 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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+ | 0.0199 | 40.0 | 680 | 0.0236 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 |
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+ | 0.0167 | 41.18 | 700 | 0.0234 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 |
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+ | 0.0119 | 42.35 | 720 | 0.0234 | 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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+ | 0.0165 | 49.41 | 840 | 0.0236 | 0.4952 | 0.9904 | 0.9904 | nan | 0.9904 | 0.0 | 0.9904 |
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  ### Framework versions