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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 +14,18 @@ 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: 0.0332
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- - Mean Iou: 0.4969
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- - Mean Accuracy: 0.9938
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- - Overall Accuracy: 0.9938
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  - Accuracy Unlabeled: nan
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- - Accuracy Tool: 0.9938
 
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  - Iou Unlabeled: 0.0
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- - Iou Tool: 0.9938
 
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  ## Model description
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@@ -50,35 +54,35 @@ 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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- | 0.1957 | 1.82 | 20 | 0.3708 | 0.4995 | 0.9991 | 0.9991 | nan | 0.9991 | 0.0 | 0.9991 |
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- | 0.1896 | 3.64 | 40 | 0.1768 | 0.4985 | 0.9970 | 0.9970 | nan | 0.9970 | 0.0 | 0.9970 |
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- | 0.1022 | 5.45 | 60 | 0.0996 | 0.4966 | 0.9933 | 0.9933 | nan | 0.9933 | 0.0 | 0.9933 |
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- | 0.0855 | 7.27 | 80 | 0.0863 | 0.4767 | 0.9535 | 0.9535 | nan | 0.9535 | 0.0 | 0.9535 |
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- | 0.1223 | 9.09 | 100 | 0.0677 | 0.4964 | 0.9927 | 0.9927 | nan | 0.9927 | 0.0 | 0.9927 |
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- | 0.0791 | 10.91 | 120 | 0.0583 | 0.4948 | 0.9896 | 0.9896 | nan | 0.9896 | 0.0 | 0.9896 |
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- | 0.0521 | 12.73 | 140 | 0.0500 | 0.4938 | 0.9876 | 0.9876 | nan | 0.9876 | 0.0 | 0.9876 |
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- | 0.0397 | 14.55 | 160 | 0.0443 | 0.4958 | 0.9916 | 0.9916 | nan | 0.9916 | 0.0 | 0.9916 |
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- | 0.0283 | 16.36 | 180 | 0.0594 | 0.4972 | 0.9943 | 0.9943 | nan | 0.9943 | 0.0 | 0.9943 |
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- | 0.0378 | 18.18 | 200 | 0.0485 | 0.4987 | 0.9974 | 0.9974 | nan | 0.9974 | 0.0 | 0.9974 |
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- | 0.0347 | 20.0 | 220 | 0.0382 | 0.4971 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 |
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- | 0.0245 | 21.82 | 240 | 0.0346 | 0.4966 | 0.9932 | 0.9932 | nan | 0.9932 | 0.0 | 0.9932 |
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- | 0.0425 | 23.64 | 260 | 0.0393 | 0.4961 | 0.9921 | 0.9921 | nan | 0.9921 | 0.0 | 0.9921 |
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- | 0.0293 | 25.45 | 280 | 0.0336 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 |
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- | 0.0247 | 27.27 | 300 | 0.0368 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 |
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- | 0.0287 | 29.09 | 320 | 0.0317 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 |
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- | 0.0254 | 30.91 | 340 | 0.0408 | 0.4966 | 0.9932 | 0.9932 | nan | 0.9932 | 0.0 | 0.9932 |
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- | 0.0347 | 32.73 | 360 | 0.0291 | 0.4965 | 0.9930 | 0.9930 | nan | 0.9930 | 0.0 | 0.9930 |
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- | 0.0174 | 34.55 | 380 | 0.0361 | 0.4978 | 0.9955 | 0.9955 | nan | 0.9955 | 0.0 | 0.9955 |
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- | 0.0191 | 36.36 | 400 | 0.0417 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 |
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- | 0.0234 | 38.18 | 420 | 0.0373 | 0.4974 | 0.9947 | 0.9947 | nan | 0.9947 | 0.0 | 0.9947 |
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- | 0.0306 | 40.0 | 440 | 0.0370 | 0.4969 | 0.9938 | 0.9938 | nan | 0.9938 | 0.0 | 0.9938 |
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- | 0.0178 | 41.82 | 460 | 0.0407 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 |
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- | 0.0152 | 43.64 | 480 | 0.0323 | 0.4968 | 0.9935 | 0.9935 | nan | 0.9935 | 0.0 | 0.9935 |
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- | 0.0181 | 45.45 | 500 | 0.0346 | 0.4974 | 0.9947 | 0.9947 | nan | 0.9947 | 0.0 | 0.9947 |
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- | 0.0155 | 47.27 | 520 | 0.0338 | 0.4971 | 0.9942 | 0.9942 | nan | 0.9942 | 0.0 | 0.9942 |
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- | 0.0223 | 49.09 | 540 | 0.0332 | 0.4969 | 0.9938 | 0.9938 | nan | 0.9938 | 0.0 | 0.9938 |
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  ### Framework versions
 
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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/toolwear_segmentsai dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1291
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+ - Mean Iou: 0.4322
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+ - Mean Accuracy: 0.8644
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+ - Overall Accuracy: 0.8644
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  - Accuracy Unlabeled: nan
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+ - Accuracy Tool: nan
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+ - Accuracy Wear: 0.8644
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  - Iou Unlabeled: 0.0
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+ - Iou Tool: nan
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+ - Iou Wear: 0.8644
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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 | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
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+ | 0.8371 | 1.82 | 20 | 0.9482 | 0.3285 | 0.9854 | 0.9854 | nan | nan | 0.9854 | 0.0 | 0.0 | 0.9854 |
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+ | 0.6335 | 3.64 | 40 | 0.7489 | 0.4996 | 0.9992 | 0.9992 | nan | nan | 0.9992 | 0.0 | nan | 0.9992 |
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+ | 0.5053 | 5.45 | 60 | 0.5400 | 0.4975 | 0.9949 | 0.9949 | nan | nan | 0.9949 | 0.0 | nan | 0.9949 |
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+ | 0.3924 | 7.27 | 80 | 0.4544 | 0.4905 | 0.9810 | 0.9810 | nan | nan | 0.9810 | 0.0 | nan | 0.9810 |
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+ | 0.3419 | 9.09 | 100 | 0.3840 | 0.4727 | 0.9455 | 0.9455 | nan | nan | 0.9455 | 0.0 | nan | 0.9455 |
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+ | 0.3379 | 10.91 | 120 | 0.3407 | 0.4648 | 0.9296 | 0.9296 | nan | nan | 0.9296 | 0.0 | nan | 0.9296 |
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+ | 0.2639 | 12.73 | 140 | 0.3495 | 0.4780 | 0.9559 | 0.9559 | nan | nan | 0.9559 | 0.0 | nan | 0.9559 |
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+ | 0.224 | 14.55 | 160 | 0.2815 | 0.4541 | 0.9081 | 0.9081 | nan | nan | 0.9081 | 0.0 | nan | 0.9081 |
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+ | 0.1725 | 16.36 | 180 | 0.2896 | 0.4599 | 0.9199 | 0.9199 | nan | nan | 0.9199 | 0.0 | nan | 0.9199 |
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+ | 0.1623 | 18.18 | 200 | 0.2540 | 0.4679 | 0.9359 | 0.9359 | nan | nan | 0.9359 | 0.0 | nan | 0.9359 |
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+ | 0.1724 | 20.0 | 220 | 0.2567 | 0.4702 | 0.9404 | 0.9404 | nan | nan | 0.9404 | 0.0 | nan | 0.9404 |
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+ | 0.1503 | 21.82 | 240 | 0.1967 | 0.4459 | 0.8919 | 0.8919 | nan | nan | 0.8919 | 0.0 | nan | 0.8919 |
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+ | 0.1189 | 23.64 | 260 | 0.2153 | 0.4617 | 0.9234 | 0.9234 | nan | nan | 0.9234 | 0.0 | nan | 0.9234 |
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+ | 0.1007 | 25.45 | 280 | 0.1695 | 0.4324 | 0.8648 | 0.8648 | nan | nan | 0.8648 | 0.0 | nan | 0.8648 |
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+ | 0.0921 | 27.27 | 300 | 0.1540 | 0.4346 | 0.8691 | 0.8691 | nan | nan | 0.8691 | 0.0 | nan | 0.8691 |
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+ | 0.0897 | 29.09 | 320 | 0.1657 | 0.4538 | 0.9077 | 0.9077 | nan | nan | 0.9077 | 0.0 | nan | 0.9077 |
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+ | 0.0814 | 30.91 | 340 | 0.1519 | 0.4374 | 0.8749 | 0.8749 | nan | nan | 0.8749 | 0.0 | nan | 0.8749 |
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+ | 0.0729 | 32.73 | 360 | 0.1444 | 0.4430 | 0.8861 | 0.8861 | nan | nan | 0.8861 | 0.0 | nan | 0.8861 |
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+ | 0.0892 | 34.55 | 380 | 0.1283 | 0.4106 | 0.8213 | 0.8213 | nan | nan | 0.8213 | 0.0 | nan | 0.8213 |
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+ | 0.07 | 36.36 | 400 | 0.1442 | 0.4374 | 0.8748 | 0.8748 | nan | nan | 0.8748 | 0.0 | nan | 0.8748 |
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+ | 0.0619 | 38.18 | 420 | 0.1391 | 0.4296 | 0.8592 | 0.8592 | nan | nan | 0.8592 | 0.0 | nan | 0.8592 |
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+ | 0.0563 | 40.0 | 440 | 0.1283 | 0.4402 | 0.8804 | 0.8804 | nan | nan | 0.8804 | 0.0 | nan | 0.8804 |
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+ | 0.0582 | 41.82 | 460 | 0.1275 | 0.4297 | 0.8595 | 0.8595 | nan | nan | 0.8595 | 0.0 | nan | 0.8595 |
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+ | 0.0575 | 43.64 | 480 | 0.1341 | 0.4362 | 0.8724 | 0.8724 | nan | nan | 0.8724 | 0.0 | nan | 0.8724 |
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+ | 0.068 | 45.45 | 500 | 0.1132 | 0.4181 | 0.8362 | 0.8362 | nan | nan | 0.8362 | 0.0 | nan | 0.8362 |
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+ | 0.0595 | 47.27 | 520 | 0.1285 | 0.4316 | 0.8632 | 0.8632 | nan | nan | 0.8632 | 0.0 | nan | 0.8632 |
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+ | 0.0558 | 49.09 | 540 | 0.1291 | 0.4322 | 0.8644 | 0.8644 | nan | nan | 0.8644 | 0.0 | nan | 0.8644 |
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  ### Framework versions