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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
@@ -12,16 +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: 0.0341
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- - Mean Iou: 0.4939
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- - Mean Accuracy: 0.9878
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- - Overall Accuracy: 0.9878
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  - Accuracy Unlabeled: nan
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- - Accuracy Tool: 0.9878
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  - Iou Unlabeled: 0.0
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- - Iou Tool: 0.9878
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  ## Model description
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@@ -50,40 +52,28 @@ 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 | Iou Unlabeled | Iou Tool |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
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- | 0.2127 | 1.82 | 20 | 0.3537 | 0.4996 | 0.9991 | 0.9991 | nan | 0.9991 | 0.0 | 0.9991 |
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- | 0.2095 | 3.64 | 40 | 0.1407 | 0.4987 | 0.9974 | 0.9974 | nan | 0.9974 | 0.0 | 0.9974 |
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- | 0.1253 | 5.45 | 60 | 0.1011 | 0.4970 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | 0.9940 |
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- | 0.0812 | 7.27 | 80 | 0.0821 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 |
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- | 0.0841 | 9.09 | 100 | 0.0652 | 0.4926 | 0.9851 | 0.9851 | nan | 0.9851 | 0.0 | 0.9851 |
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- | 0.0574 | 10.91 | 120 | 0.0612 | 0.4930 | 0.9861 | 0.9861 | nan | 0.9861 | 0.0 | 0.9861 |
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- | 0.047 | 12.73 | 140 | 0.0562 | 0.4940 | 0.9880 | 0.9880 | nan | 0.9880 | 0.0 | 0.9880 |
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- | 0.0542 | 14.55 | 160 | 0.0488 | 0.4937 | 0.9874 | 0.9874 | nan | 0.9874 | 0.0 | 0.9874 |
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- | 0.0405 | 16.36 | 180 | 0.0487 | 0.4958 | 0.9916 | 0.9916 | nan | 0.9916 | 0.0 | 0.9916 |
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- | 0.045 | 18.18 | 200 | 0.0484 | 0.4964 | 0.9929 | 0.9929 | nan | 0.9929 | 0.0 | 0.9929 |
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- | 0.0487 | 20.0 | 220 | 0.0412 | 0.4936 | 0.9873 | 0.9873 | nan | 0.9873 | 0.0 | 0.9873 |
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- | 0.0417 | 21.82 | 240 | 0.0397 | 0.4936 | 0.9872 | 0.9872 | nan | 0.9872 | 0.0 | 0.9872 |
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- | 0.0525 | 23.64 | 260 | 0.0393 | 0.4934 | 0.9868 | 0.9868 | nan | 0.9868 | 0.0 | 0.9868 |
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- | 0.0425 | 25.45 | 280 | 0.0381 | 0.4930 | 0.9861 | 0.9861 | nan | 0.9861 | 0.0 | 0.9861 |
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- | 0.0386 | 27.27 | 300 | 0.0393 | 0.4927 | 0.9855 | 0.9855 | nan | 0.9855 | 0.0 | 0.9855 |
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- | 0.0239 | 29.09 | 320 | 0.0387 | 0.4933 | 0.9866 | 0.9866 | nan | 0.9866 | 0.0 | 0.9866 |
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- | 0.0279 | 30.91 | 340 | 0.0369 | 0.4941 | 0.9882 | 0.9882 | nan | 0.9882 | 0.0 | 0.9882 |
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- | 0.0194 | 32.73 | 360 | 0.0368 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 |
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- | 0.0238 | 34.55 | 380 | 0.0370 | 0.4937 | 0.9874 | 0.9874 | nan | 0.9874 | 0.0 | 0.9874 |
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- | 0.0281 | 36.36 | 400 | 0.0347 | 0.4930 | 0.9859 | 0.9859 | nan | 0.9859 | 0.0 | 0.9859 |
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- | 0.0218 | 38.18 | 420 | 0.0351 | 0.4924 | 0.9848 | 0.9848 | nan | 0.9848 | 0.0 | 0.9848 |
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- | 0.0197 | 40.0 | 440 | 0.0354 | 0.4932 | 0.9864 | 0.9864 | nan | 0.9864 | 0.0 | 0.9864 |
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- | 0.0197 | 41.82 | 460 | 0.0343 | 0.4933 | 0.9865 | 0.9865 | nan | 0.9865 | 0.0 | 0.9865 |
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- | 0.0231 | 43.64 | 480 | 0.0345 | 0.4931 | 0.9862 | 0.9862 | nan | 0.9862 | 0.0 | 0.9862 |
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- | 0.0223 | 45.45 | 500 | 0.0346 | 0.4938 | 0.9875 | 0.9875 | nan | 0.9875 | 0.0 | 0.9875 |
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- | 0.0184 | 47.27 | 520 | 0.0340 | 0.4927 | 0.9854 | 0.9854 | nan | 0.9854 | 0.0 | 0.9854 |
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- | 0.0202 | 49.09 | 540 | 0.0341 | 0.4939 | 0.9878 | 0.9878 | nan | 0.9878 | 0.0 | 0.9878 |
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  ### Framework versions
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  - Transformers 4.28.0
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- - Pytorch 2.0.1+cu118
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- - Datasets 2.14.5
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  - Tokenizers 0.13.3
 
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  ---
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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/new_wear dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0737
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+ - Mean Iou: 0.3080
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+ - Mean Accuracy: 0.6160
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+ - Overall Accuracy: 0.6160
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  - Accuracy Unlabeled: nan
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+ - Accuracy Wear: 0.6160
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  - Iou Unlabeled: 0.0
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+ - Iou Wear: 0.6160
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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 Wear | Iou Unlabeled | Iou Wear |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
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+ | 0.4694 | 3.33 | 20 | 0.4857 | 0.3178 | 0.6356 | 0.6356 | nan | 0.6356 | 0.0 | 0.6356 |
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+ | 0.3038 | 6.67 | 40 | 0.2805 | 0.3408 | 0.6816 | 0.6816 | nan | 0.6816 | 0.0 | 0.6816 |
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+ | 0.2303 | 10.0 | 60 | 0.2080 | 0.3408 | 0.6816 | 0.6816 | nan | 0.6816 | 0.0 | 0.6816 |
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+ | 0.1935 | 13.33 | 80 | 0.1870 | 0.3420 | 0.6841 | 0.6841 | nan | 0.6841 | 0.0 | 0.6841 |
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+ | 0.1697 | 16.67 | 100 | 0.1507 | 0.3405 | 0.6810 | 0.6810 | nan | 0.6810 | 0.0 | 0.6810 |
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+ | 0.1406 | 20.0 | 120 | 0.1377 | 0.3437 | 0.6874 | 0.6874 | nan | 0.6874 | 0.0 | 0.6874 |
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+ | 0.1363 | 23.33 | 140 | 0.1156 | 0.3301 | 0.6601 | 0.6601 | nan | 0.6601 | 0.0 | 0.6601 |
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+ | 0.117 | 26.67 | 160 | 0.1019 | 0.3376 | 0.6753 | 0.6753 | nan | 0.6753 | 0.0 | 0.6753 |
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+ | 0.0972 | 30.0 | 180 | 0.0935 | 0.3264 | 0.6529 | 0.6529 | nan | 0.6529 | 0.0 | 0.6529 |
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+ | 0.1076 | 33.33 | 200 | 0.0901 | 0.3292 | 0.6584 | 0.6584 | nan | 0.6584 | 0.0 | 0.6584 |
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+ | 0.0868 | 36.67 | 220 | 0.0806 | 0.3218 | 0.6436 | 0.6436 | nan | 0.6436 | 0.0 | 0.6436 |
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+ | 0.0866 | 40.0 | 240 | 0.0766 | 0.3183 | 0.6367 | 0.6367 | nan | 0.6367 | 0.0 | 0.6367 |
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+ | 0.0757 | 43.33 | 260 | 0.0750 | 0.3082 | 0.6165 | 0.6165 | nan | 0.6165 | 0.0 | 0.6165 |
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+ | 0.077 | 46.67 | 280 | 0.0750 | 0.3104 | 0.6207 | 0.6207 | nan | 0.6207 | 0.0 | 0.6207 |
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+ | 0.0765 | 50.0 | 300 | 0.0737 | 0.3080 | 0.6160 | 0.6160 | nan | 0.6160 | 0.0 | 0.6160 |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.28.0
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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  - Tokenizers 0.13.3