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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: 0.0480
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- - Mean Iou: 0.4892
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- - Mean Accuracy: 0.9785
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- - Overall Accuracy: 0.9785
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  - Accuracy Unlabeled: nan
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- - Accuracy Tool: 0.9785
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  - Iou Unlabeled: 0.0
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- - Iou Tool: 0.9785
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  ## Model description
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@@ -52,33 +52,48 @@ The following hyperparameters were used during training:
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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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- | 0.257 | 1.82 | 20 | 0.4274 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 |
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- | 0.14 | 3.64 | 40 | 0.1857 | 0.4878 | 0.9756 | 0.9756 | nan | 0.9756 | 0.0 | 0.9756 |
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- | 0.0945 | 5.45 | 60 | 0.1383 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 |
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- | 0.0766 | 7.27 | 80 | 0.1041 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 |
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- | 0.0752 | 9.09 | 100 | 0.1055 | 0.4836 | 0.9672 | 0.9672 | nan | 0.9672 | 0.0 | 0.9672 |
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- | 0.0433 | 10.91 | 120 | 0.1180 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
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- | 0.0358 | 12.73 | 140 | 0.0857 | 0.4831 | 0.9662 | 0.9662 | nan | 0.9662 | 0.0 | 0.9662 |
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- | 0.0357 | 14.55 | 160 | 0.0765 | 0.4865 | 0.9730 | 0.9730 | nan | 0.9730 | 0.0 | 0.9730 |
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- | 0.0401 | 16.36 | 180 | 0.0898 | 0.4793 | 0.9587 | 0.9587 | nan | 0.9587 | 0.0 | 0.9587 |
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- | 0.042 | 18.18 | 200 | 0.0755 | 0.4828 | 0.9655 | 0.9655 | nan | 0.9655 | 0.0 | 0.9655 |
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- | 0.0366 | 20.0 | 220 | 0.0744 | 0.4818 | 0.9635 | 0.9635 | nan | 0.9635 | 0.0 | 0.9635 |
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- | 0.0213 | 21.82 | 240 | 0.0708 | 0.4828 | 0.9656 | 0.9656 | nan | 0.9656 | 0.0 | 0.9656 |
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- | 0.0284 | 23.64 | 260 | 0.0684 | 0.4851 | 0.9701 | 0.9701 | nan | 0.9701 | 0.0 | 0.9701 |
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- | 0.0237 | 25.45 | 280 | 0.0625 | 0.4879 | 0.9757 | 0.9757 | nan | 0.9757 | 0.0 | 0.9757 |
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- | 0.0189 | 27.27 | 300 | 0.0603 | 0.4858 | 0.9716 | 0.9716 | nan | 0.9716 | 0.0 | 0.9716 |
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- | 0.026 | 29.09 | 320 | 0.0632 | 0.4860 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 |
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- | 0.0231 | 30.91 | 340 | 0.0662 | 0.4840 | 0.9680 | 0.9680 | nan | 0.9680 | 0.0 | 0.9680 |
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- | 0.0218 | 32.73 | 360 | 0.0563 | 0.4855 | 0.9710 | 0.9710 | nan | 0.9710 | 0.0 | 0.9710 |
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- | 0.0253 | 34.55 | 380 | 0.0627 | 0.4848 | 0.9697 | 0.9697 | nan | 0.9697 | 0.0 | 0.9697 |
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- | 0.0142 | 36.36 | 400 | 0.0621 | 0.4844 | 0.9689 | 0.9689 | nan | 0.9689 | 0.0 | 0.9689 |
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- | 0.0214 | 38.18 | 420 | 0.0668 | 0.4820 | 0.9639 | 0.9639 | nan | 0.9639 | 0.0 | 0.9639 |
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- | 0.0166 | 40.0 | 440 | 0.0555 | 0.4858 | 0.9716 | 0.9716 | nan | 0.9716 | 0.0 | 0.9716 |
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- | 0.0185 | 41.82 | 460 | 0.0545 | 0.4859 | 0.9718 | 0.9718 | nan | 0.9718 | 0.0 | 0.9718 |
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- | 0.0218 | 43.64 | 480 | 0.0500 | 0.4876 | 0.9753 | 0.9753 | nan | 0.9753 | 0.0 | 0.9753 |
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- | 0.0184 | 45.45 | 500 | 0.0481 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 |
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- | 0.018 | 47.27 | 520 | 0.0487 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 |
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- | 0.0254 | 49.09 | 540 | 0.0480 | 0.4892 | 0.9785 | 0.9785 | nan | 0.9785 | 0.0 | 0.9785 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.0223
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+ - Mean Iou: 0.4979
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+ - Mean Accuracy: 0.9957
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+ - Overall Accuracy: 0.9957
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  - Accuracy Unlabeled: nan
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+ - Accuracy Tool: 0.9957
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  - Iou Unlabeled: 0.0
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+ - Iou Tool: 0.9957
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  ## Model description
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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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+ | 0.1534 | 1.18 | 20 | 0.3425 | 0.4977 | 0.9955 | 0.9955 | nan | 0.9955 | 0.0 | 0.9955 |
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+ | 0.091 | 2.35 | 40 | 0.1076 | 0.4948 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 |
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+ | 0.0827 | 3.53 | 60 | 0.0828 | 0.4965 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 |
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+ | 0.0729 | 4.71 | 80 | 0.0795 | 0.4967 | 0.9934 | 0.9934 | nan | 0.9934 | 0.0 | 0.9934 |
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+ | 0.0825 | 5.88 | 100 | 0.0606 | 0.4910 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 |
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+ | 0.0604 | 7.06 | 120 | 0.0546 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 |
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+ | 0.0575 | 8.24 | 140 | 0.0460 | 0.4942 | 0.9884 | 0.9884 | nan | 0.9884 | 0.0 | 0.9884 |
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+ | 0.0592 | 9.41 | 160 | 0.0450 | 0.4906 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 |
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+ | 0.0478 | 10.59 | 180 | 0.0400 | 0.4981 | 0.9962 | 0.9962 | nan | 0.9962 | 0.0 | 0.9962 |
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+ | 0.046 | 11.76 | 200 | 0.0403 | 0.4982 | 0.9964 | 0.9964 | nan | 0.9964 | 0.0 | 0.9964 |
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+ | 0.0535 | 12.94 | 220 | 0.0340 | 0.4971 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 |
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+ | 0.0317 | 14.12 | 240 | 0.0332 | 0.4975 | 0.9949 | 0.9949 | nan | 0.9949 | 0.0 | 0.9949 |
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+ | 0.0352 | 15.29 | 260 | 0.0328 | 0.4982 | 0.9964 | 0.9964 | nan | 0.9964 | 0.0 | 0.9964 |
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+ | 0.0258 | 16.47 | 280 | 0.0295 | 0.4963 | 0.9926 | 0.9926 | nan | 0.9926 | 0.0 | 0.9926 |
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+ | 0.0218 | 17.65 | 300 | 0.0265 | 0.4968 | 0.9935 | 0.9935 | nan | 0.9935 | 0.0 | 0.9935 |
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+ | 0.026 | 18.82 | 320 | 0.0284 | 0.4979 | 0.9958 | 0.9958 | nan | 0.9958 | 0.0 | 0.9958 |
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+ | 0.026 | 20.0 | 340 | 0.0267 | 0.4971 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 |
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+ | 0.02 | 21.18 | 360 | 0.0242 | 0.4967 | 0.9935 | 0.9935 | nan | 0.9935 | 0.0 | 0.9935 |
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+ | 0.0255 | 22.35 | 380 | 0.0270 | 0.4975 | 0.9949 | 0.9949 | nan | 0.9949 | 0.0 | 0.9949 |
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+ | 0.0282 | 23.53 | 400 | 0.0240 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 |
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+ | 0.0188 | 24.71 | 420 | 0.0244 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 |
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+ | 0.0196 | 25.88 | 440 | 0.0226 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 |
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+ | 0.0165 | 27.06 | 460 | 0.0235 | 0.4968 | 0.9937 | 0.9937 | nan | 0.9937 | 0.0 | 0.9937 |
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+ | 0.02 | 28.24 | 480 | 0.0245 | 0.4981 | 0.9962 | 0.9962 | nan | 0.9962 | 0.0 | 0.9962 |
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+ | 0.0213 | 29.41 | 500 | 0.0225 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 |
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+ | 0.0174 | 30.59 | 520 | 0.0221 | 0.4970 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | 0.9940 |
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+ | 0.0163 | 31.76 | 540 | 0.0226 | 0.4975 | 0.9951 | 0.9951 | nan | 0.9951 | 0.0 | 0.9951 |
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+ | 0.0242 | 32.94 | 560 | 0.0236 | 0.4978 | 0.9956 | 0.9956 | nan | 0.9956 | 0.0 | 0.9956 |
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+ | 0.0195 | 34.12 | 580 | 0.0217 | 0.4976 | 0.9953 | 0.9953 | nan | 0.9953 | 0.0 | 0.9953 |
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+ | 0.0134 | 35.29 | 600 | 0.0220 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 |
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+ | 0.0192 | 36.47 | 620 | 0.0216 | 0.4974 | 0.9947 | 0.9947 | nan | 0.9947 | 0.0 | 0.9947 |
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+ | 0.0138 | 37.65 | 640 | 0.0219 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 |
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+ | 0.0147 | 38.82 | 660 | 0.0215 | 0.4973 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 |
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+ | 0.0208 | 40.0 | 680 | 0.0219 | 0.4979 | 0.9958 | 0.9958 | nan | 0.9958 | 0.0 | 0.9958 |
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+ | 0.0152 | 41.18 | 700 | 0.0211 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 |
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+ | 0.0145 | 42.35 | 720 | 0.0214 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 |
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+ | 0.0138 | 43.53 | 740 | 0.0217 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 |
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+ | 0.0122 | 44.71 | 760 | 0.0218 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 |
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+ | 0.0201 | 45.88 | 780 | 0.0220 | 0.4976 | 0.9953 | 0.9953 | nan | 0.9953 | 0.0 | 0.9953 |
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+ | 0.0147 | 47.06 | 800 | 0.0219 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 |
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+ | 0.0131 | 48.24 | 820 | 0.0213 | 0.4975 | 0.9950 | 0.9950 | nan | 0.9950 | 0.0 | 0.9950 |
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+ | 0.016 | 49.41 | 840 | 0.0223 | 0.4979 | 0.9957 | 0.9957 | nan | 0.9957 | 0.0 | 0.9957 |
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  ### Framework versions