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segformer-b0-finetuned-segments-toolwear

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update model card README.md

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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: 0.0737
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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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@@ -52,21 +54,21 @@ 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 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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  ---
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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: 0.1003
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+ - Mean Iou: 0.3700
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+ - Mean Accuracy: 0.7400
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+ - Overall Accuracy: 0.7400
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  - Accuracy Unlabeled: nan
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+ - Accuracy Wear: 0.7400
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  - Iou Unlabeled: 0.0
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+ - Iou Wear: 0.7400
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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 Wear | Iou Unlabeled | Iou Wear |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
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+ | 0.6115 | 3.33 | 20 | 0.6156 | 0.4098 | 0.8197 | 0.8197 | nan | 0.8197 | 0.0 | 0.8197 |
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+ | 0.3737 | 6.67 | 40 | 0.3901 | 0.4130 | 0.8261 | 0.8261 | nan | 0.8261 | 0.0 | 0.8261 |
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+ | 0.2937 | 10.0 | 60 | 0.2764 | 0.3966 | 0.7932 | 0.7932 | nan | 0.7932 | 0.0 | 0.7932 |
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+ | 0.2457 | 13.33 | 80 | 0.2804 | 0.3957 | 0.7914 | 0.7914 | nan | 0.7914 | 0.0 | 0.7914 |
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+ | 0.2099 | 16.67 | 100 | 0.2116 | 0.4119 | 0.8238 | 0.8238 | nan | 0.8238 | 0.0 | 0.8238 |
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+ | 0.1894 | 20.0 | 120 | 0.1894 | 0.3775 | 0.7549 | 0.7549 | nan | 0.7549 | 0.0 | 0.7549 |
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+ | 0.1551 | 23.33 | 140 | 0.1515 | 0.3809 | 0.7619 | 0.7619 | nan | 0.7619 | 0.0 | 0.7619 |
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+ | 0.1511 | 26.67 | 160 | 0.1347 | 0.3719 | 0.7438 | 0.7438 | nan | 0.7438 | 0.0 | 0.7438 |
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+ | 0.1299 | 30.0 | 180 | 0.1218 | 0.3716 | 0.7432 | 0.7432 | nan | 0.7432 | 0.0 | 0.7432 |
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+ | 0.1284 | 33.33 | 200 | 0.1275 | 0.3694 | 0.7389 | 0.7389 | nan | 0.7389 | 0.0 | 0.7389 |
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+ | 0.1415 | 36.67 | 220 | 0.1096 | 0.3919 | 0.7839 | 0.7839 | nan | 0.7839 | 0.0 | 0.7839 |
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+ | 0.1032 | 40.0 | 240 | 0.1030 | 0.3766 | 0.7531 | 0.7531 | nan | 0.7531 | 0.0 | 0.7531 |
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+ | 0.1076 | 43.33 | 260 | 0.0994 | 0.3818 | 0.7635 | 0.7635 | nan | 0.7635 | 0.0 | 0.7635 |
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+ | 0.1024 | 46.67 | 280 | 0.1024 | 0.3767 | 0.7534 | 0.7534 | nan | 0.7534 | 0.0 | 0.7534 |
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+ | 0.1038 | 50.0 | 300 | 0.1003 | 0.3700 | 0.7400 | 0.7400 | nan | 0.7400 | 0.0 | 0.7400 |
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  ### Framework versions