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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 +14,18 @@ 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: 0.3547
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- - Mean Iou: 0.3725
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- - Mean Accuracy: 0.7265
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- - Overall Accuracy: 0.8226
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  - Accuracy Unlabeled: nan
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- - Accuracy Tool: 0.6195
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- - Accuracy Wear: 0.8334
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  - Iou Unlabeled: 0.0
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- - Iou Tool: 0.2973
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- - Iou Wear: 0.8202
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  ## Model description
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@@ -54,33 +56,33 @@ 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 | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
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- | 0.7196 | 1.82 | 20 | 0.9873 | 0.2927 | 0.4996 | 0.6806 | nan | 0.2982 | 0.7009 | 0.0 | 0.2025 | 0.6757 |
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- | 0.6004 | 3.64 | 40 | 0.7373 | 0.3312 | 0.6517 | 0.7107 | nan | 0.5861 | 0.7173 | 0.0 | 0.2916 | 0.7019 |
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- | 0.5155 | 5.45 | 60 | 0.6634 | 0.3376 | 0.5621 | 0.6378 | nan | 0.4778 | 0.6463 | 0.0 | 0.3840 | 0.6289 |
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- | 0.4228 | 7.27 | 80 | 0.5380 | 0.3612 | 0.6707 | 0.7661 | nan | 0.5646 | 0.7768 | 0.0 | 0.3241 | 0.7595 |
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- | 0.3216 | 9.09 | 100 | 0.5102 | 0.3466 | 0.6845 | 0.7281 | nan | 0.6361 | 0.7330 | 0.0 | 0.3188 | 0.7209 |
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- | 0.3752 | 10.91 | 120 | 0.4615 | 0.3902 | 0.7013 | 0.8268 | nan | 0.5616 | 0.8409 | 0.0 | 0.3476 | 0.8229 |
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- | 0.3014 | 12.73 | 140 | 0.4504 | 0.4075 | 0.7007 | 0.8311 | nan | 0.5558 | 0.8457 | 0.0 | 0.3949 | 0.8275 |
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- | 0.2183 | 14.55 | 160 | 0.4241 | 0.3708 | 0.7363 | 0.8002 | nan | 0.6653 | 0.8073 | 0.0 | 0.3165 | 0.7959 |
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- | 0.1674 | 16.36 | 180 | 0.4173 | 0.4020 | 0.7433 | 0.8684 | nan | 0.6041 | 0.8824 | 0.0 | 0.3397 | 0.8664 |
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- | 0.2385 | 18.18 | 200 | 0.4716 | 0.3450 | 0.6543 | 0.7462 | nan | 0.5520 | 0.7566 | 0.0 | 0.2941 | 0.7410 |
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- | 0.1588 | 20.0 | 220 | 0.3742 | 0.3820 | 0.7108 | 0.8179 | nan | 0.5917 | 0.8299 | 0.0 | 0.3311 | 0.8149 |
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- | 0.1553 | 21.82 | 240 | 0.3677 | 0.3811 | 0.7312 | 0.8313 | nan | 0.6199 | 0.8426 | 0.0 | 0.3144 | 0.8291 |
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- | 0.1765 | 23.64 | 260 | 0.4131 | 0.3689 | 0.7032 | 0.8024 | nan | 0.5929 | 0.8135 | 0.0 | 0.3082 | 0.7985 |
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- | 0.2516 | 25.45 | 280 | 0.3632 | 0.4142 | 0.7158 | 0.8856 | nan | 0.5270 | 0.9047 | 0.0 | 0.3585 | 0.8841 |
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- | 0.1534 | 27.27 | 300 | 0.3979 | 0.3813 | 0.7191 | 0.8236 | nan | 0.6029 | 0.8354 | 0.0 | 0.3231 | 0.8209 |
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- | 0.1104 | 29.09 | 320 | 0.3787 | 0.3640 | 0.7439 | 0.8044 | nan | 0.6765 | 0.8112 | 0.0 | 0.2911 | 0.8007 |
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- | 0.1799 | 30.91 | 340 | 0.3654 | 0.3868 | 0.7217 | 0.8257 | nan | 0.6060 | 0.8374 | 0.0 | 0.3378 | 0.8227 |
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- | 0.1069 | 32.73 | 360 | 0.3928 | 0.3524 | 0.7171 | 0.7606 | nan | 0.6687 | 0.7655 | 0.0 | 0.3018 | 0.7554 |
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- | 0.1178 | 34.55 | 380 | 0.3703 | 0.3622 | 0.7259 | 0.8079 | nan | 0.6345 | 0.8172 | 0.0 | 0.2814 | 0.8052 |
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- | 0.1191 | 36.36 | 400 | 0.3636 | 0.3766 | 0.7396 | 0.8264 | nan | 0.6431 | 0.8361 | 0.0 | 0.3069 | 0.8230 |
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- | 0.2008 | 38.18 | 420 | 0.3836 | 0.3685 | 0.7249 | 0.7907 | nan | 0.6516 | 0.7981 | 0.0 | 0.3194 | 0.7860 |
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- | 0.0846 | 40.0 | 440 | 0.3602 | 0.3738 | 0.7285 | 0.8244 | nan | 0.6218 | 0.8352 | 0.0 | 0.2994 | 0.8219 |
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- | 0.1178 | 41.82 | 460 | 0.3631 | 0.3751 | 0.7224 | 0.8311 | nan | 0.6015 | 0.8433 | 0.0 | 0.2964 | 0.8288 |
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- | 0.0806 | 43.64 | 480 | 0.3631 | 0.3678 | 0.7233 | 0.8074 | nan | 0.6297 | 0.8169 | 0.0 | 0.2988 | 0.8045 |
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- | 0.1102 | 45.45 | 500 | 0.3731 | 0.3686 | 0.7113 | 0.8067 | nan | 0.6053 | 0.8174 | 0.0 | 0.3025 | 0.8032 |
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- | 0.0751 | 47.27 | 520 | 0.3671 | 0.3682 | 0.7249 | 0.8117 | nan | 0.6283 | 0.8215 | 0.0 | 0.2959 | 0.8085 |
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- | 0.1272 | 49.09 | 540 | 0.3547 | 0.3725 | 0.7265 | 0.8226 | nan | 0.6195 | 0.8334 | 0.0 | 0.2973 | 0.8202 |
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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_edges dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7517
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+ - Mean Iou: 0.3530
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+ - Mean Accuracy: 0.7066
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+ - Overall Accuracy: 0.7444
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  - Accuracy Unlabeled: nan
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+ - Accuracy Tool: 0.6653
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+ - Accuracy Wear: 0.7480
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  - Iou Unlabeled: 0.0
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+ - Iou Tool: 0.3188
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+ - Iou Wear: 0.7403
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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 | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
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+ | 0.8264 | 1.82 | 20 | 0.9929 | 0.3016 | 0.5119 | 0.6940 | nan | 0.3130 | 0.7109 | 0.0 | 0.2149 | 0.6899 |
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+ | 0.566 | 3.64 | 40 | 0.8390 | 0.3172 | 0.6658 | 0.6561 | nan | 0.6765 | 0.6552 | 0.0 | 0.3052 | 0.6466 |
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+ | 0.5515 | 5.45 | 60 | 0.7996 | 0.3015 | 0.7085 | 0.5831 | nan | 0.8455 | 0.5715 | 0.0 | 0.3365 | 0.5680 |
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+ | 0.496 | 7.27 | 80 | 0.7495 | 0.3370 | 0.7783 | 0.6771 | nan | 0.8889 | 0.6676 | 0.0 | 0.3465 | 0.6645 |
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+ | 0.4945 | 9.09 | 100 | 0.7214 | 0.3106 | 0.6966 | 0.6150 | nan | 0.7858 | 0.6074 | 0.0 | 0.3294 | 0.6025 |
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+ | 0.4392 | 10.91 | 120 | 0.7105 | 0.3012 | 0.7519 | 0.5990 | nan | 0.9191 | 0.5848 | 0.0 | 0.3198 | 0.5839 |
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+ | 0.3211 | 12.73 | 140 | 0.7570 | 0.3470 | 0.7008 | 0.7352 | nan | 0.6632 | 0.7384 | 0.0 | 0.3116 | 0.7292 |
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+ | 0.2289 | 14.55 | 160 | 0.9477 | 0.3748 | 0.7214 | 0.7566 | nan | 0.6830 | 0.7598 | 0.0 | 0.3718 | 0.7527 |
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+ | 0.4674 | 16.36 | 180 | 0.8172 | 0.3637 | 0.7442 | 0.7533 | nan | 0.7344 | 0.7541 | 0.0 | 0.3437 | 0.7476 |
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+ | 0.3226 | 18.18 | 200 | 0.8199 | 0.3238 | 0.7286 | 0.6845 | nan | 0.7769 | 0.6804 | 0.0 | 0.2939 | 0.6777 |
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+ | 0.1706 | 20.0 | 220 | 0.7336 | 0.3410 | 0.6894 | 0.7096 | nan | 0.6673 | 0.7115 | 0.0 | 0.3185 | 0.7044 |
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+ | 0.2786 | 21.82 | 240 | 0.9254 | 0.3662 | 0.7577 | 0.7864 | nan | 0.7264 | 0.7891 | 0.0 | 0.3164 | 0.7821 |
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+ | 0.1685 | 23.64 | 260 | 0.8291 | 0.3435 | 0.7685 | 0.7294 | nan | 0.8113 | 0.7258 | 0.0 | 0.3082 | 0.7224 |
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+ | 0.1649 | 25.45 | 280 | 0.7200 | 0.3303 | 0.7133 | 0.6593 | nan | 0.7723 | 0.6543 | 0.0 | 0.3394 | 0.6516 |
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+ | 0.1481 | 27.27 | 300 | 0.8155 | 0.3531 | 0.7558 | 0.7434 | nan | 0.7695 | 0.7422 | 0.0 | 0.3206 | 0.7385 |
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+ | 0.1476 | 29.09 | 320 | 0.7374 | 0.3455 | 0.6734 | 0.7252 | nan | 0.6169 | 0.7300 | 0.0 | 0.3153 | 0.7211 |
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+ | 0.2284 | 30.91 | 340 | 0.7254 | 0.3265 | 0.6989 | 0.6766 | nan | 0.7233 | 0.6745 | 0.0 | 0.3099 | 0.6695 |
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+ | 0.1212 | 32.73 | 360 | 0.8022 | 0.3591 | 0.7252 | 0.7662 | nan | 0.6804 | 0.7700 | 0.0 | 0.3153 | 0.7620 |
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+ | 0.1284 | 34.55 | 380 | 0.7345 | 0.3449 | 0.7044 | 0.7331 | nan | 0.6731 | 0.7357 | 0.0 | 0.3062 | 0.7284 |
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+ | 0.1685 | 36.36 | 400 | 0.7581 | 0.3275 | 0.7357 | 0.6991 | nan | 0.7757 | 0.6957 | 0.0 | 0.2910 | 0.6915 |
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+ | 0.1018 | 38.18 | 420 | 0.7303 | 0.3401 | 0.6575 | 0.7173 | nan | 0.5921 | 0.7228 | 0.0 | 0.3069 | 0.7133 |
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+ | 0.1405 | 40.0 | 440 | 0.7375 | 0.3555 | 0.7301 | 0.7475 | nan | 0.7111 | 0.7491 | 0.0 | 0.3234 | 0.7431 |
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+ | 0.08 | 41.82 | 460 | 0.7449 | 0.3561 | 0.7047 | 0.7457 | nan | 0.6598 | 0.7495 | 0.0 | 0.3265 | 0.7417 |
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+ | 0.1311 | 43.64 | 480 | 0.7680 | 0.3552 | 0.7205 | 0.7444 | nan | 0.6945 | 0.7466 | 0.0 | 0.3257 | 0.7398 |
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+ | 0.1235 | 45.45 | 500 | 0.7589 | 0.3523 | 0.7117 | 0.7398 | nan | 0.6811 | 0.7424 | 0.0 | 0.3218 | 0.7352 |
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+ | 0.1169 | 47.27 | 520 | 0.7676 | 0.3535 | 0.6952 | 0.7529 | nan | 0.6320 | 0.7583 | 0.0 | 0.3110 | 0.7494 |
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+ | 0.14 | 49.09 | 540 | 0.7517 | 0.3530 | 0.7066 | 0.7444 | nan | 0.6653 | 0.7480 | 0.0 | 0.3188 | 0.7403 |
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  ### Framework versions