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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
@@ -12,18 +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: 0.2940
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- - Mean Iou: 0.4104
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- - Mean Accuracy: 0.8207
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- - Overall Accuracy: 0.8207
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  - Accuracy Unlabeled: nan
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  - Accuracy Tool: nan
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- - Accuracy Wear: 0.8207
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  - Iou Unlabeled: 0.0
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  - Iou Tool: nan
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- - Iou Wear: 0.8207
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  ## Model description
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@@ -54,19 +56,19 @@ 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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- | 0.843 | 1.82 | 20 | 0.8832 | 0.4637 | 0.9274 | 0.9274 | nan | nan | 0.9274 | 0.0 | nan | 0.9274 |
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- | 0.6849 | 3.64 | 40 | 0.5914 | 0.4361 | 0.8722 | 0.8722 | nan | nan | 0.8722 | 0.0 | nan | 0.8722 |
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- | 0.5085 | 5.45 | 60 | 0.5178 | 0.4628 | 0.9256 | 0.9256 | nan | nan | 0.9256 | 0.0 | nan | 0.9256 |
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- | 0.4027 | 7.27 | 80 | 0.5099 | 0.4598 | 0.9195 | 0.9195 | nan | nan | 0.9195 | 0.0 | nan | 0.9195 |
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- | 0.3536 | 9.09 | 100 | 0.4262 | 0.4365 | 0.8730 | 0.8730 | nan | nan | 0.8730 | 0.0 | nan | 0.8730 |
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- | 0.3657 | 10.91 | 120 | 0.3891 | 0.4228 | 0.8457 | 0.8457 | nan | nan | 0.8457 | 0.0 | nan | 0.8457 |
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- | 0.3234 | 12.73 | 140 | 0.4221 | 0.4377 | 0.8754 | 0.8754 | nan | nan | 0.8754 | 0.0 | nan | 0.8754 |
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- | 0.2874 | 14.55 | 160 | 0.3355 | 0.4098 | 0.8197 | 0.8197 | nan | nan | 0.8197 | 0.0 | nan | 0.8197 |
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- | 0.2335 | 16.36 | 180 | 0.3570 | 0.4266 | 0.8531 | 0.8531 | nan | nan | 0.8531 | 0.0 | nan | 0.8531 |
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- | 0.2167 | 18.18 | 200 | 0.3238 | 0.4404 | 0.8808 | 0.8808 | nan | nan | 0.8808 | 0.0 | nan | 0.8808 |
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- | 0.2201 | 20.0 | 220 | 0.3103 | 0.4185 | 0.8370 | 0.8370 | nan | nan | 0.8370 | 0.0 | nan | 0.8370 |
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- | 0.205 | 21.82 | 240 | 0.2881 | 0.4115 | 0.8230 | 0.8230 | nan | nan | 0.8230 | 0.0 | nan | 0.8230 |
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- | 0.241 | 23.64 | 260 | 0.2940 | 0.4104 | 0.8207 | 0.8207 | nan | nan | 0.8207 | 0.0 | nan | 0.8207 |
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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: 0.2168
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+ - Mean Iou: 0.3007
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+ - Mean Accuracy: 0.6014
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+ - Overall Accuracy: 0.6014
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  - Accuracy Unlabeled: nan
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  - Accuracy Tool: nan
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+ - Accuracy Wear: 0.6014
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  - Iou Unlabeled: 0.0
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  - Iou Tool: nan
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+ - Iou Wear: 0.6014
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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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+ | 0.9147 | 1.82 | 20 | 0.9308 | 0.3010 | 0.9029 | 0.9029 | nan | nan | 0.9029 | 0.0 | 0.0 | 0.9029 |
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+ | 0.6788 | 3.64 | 40 | 0.6521 | 0.2589 | 0.7768 | 0.7768 | nan | nan | 0.7768 | 0.0 | 0.0 | 0.7768 |
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+ | 0.4559 | 5.45 | 60 | 0.4600 | 0.2663 | 0.7989 | 0.7989 | nan | nan | 0.7989 | 0.0 | 0.0 | 0.7989 |
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+ | 0.3799 | 7.27 | 80 | 0.3767 | 0.2061 | 0.6182 | 0.6182 | nan | nan | 0.6182 | 0.0 | 0.0 | 0.6182 |
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+ | 0.4438 | 9.09 | 100 | 0.3259 | 0.3479 | 0.6958 | 0.6958 | nan | nan | 0.6958 | 0.0 | nan | 0.6958 |
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+ | 0.3534 | 10.91 | 120 | 0.3008 | 0.3057 | 0.6114 | 0.6114 | nan | nan | 0.6114 | 0.0 | nan | 0.6114 |
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+ | 0.3332 | 12.73 | 140 | 0.2805 | 0.3631 | 0.7261 | 0.7261 | nan | nan | 0.7261 | 0.0 | nan | 0.7261 |
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+ | 0.2543 | 14.55 | 160 | 0.2659 | 0.2927 | 0.5853 | 0.5853 | nan | nan | 0.5853 | 0.0 | nan | 0.5853 |
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+ | 0.2746 | 16.36 | 180 | 0.2324 | 0.2724 | 0.5449 | 0.5449 | nan | nan | 0.5449 | 0.0 | nan | 0.5449 |
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+ | 0.2532 | 18.18 | 200 | 0.2409 | 0.3597 | 0.7194 | 0.7194 | nan | nan | 0.7194 | 0.0 | nan | 0.7194 |
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+ | 0.2353 | 20.0 | 220 | 0.2369 | 0.3070 | 0.6139 | 0.6139 | nan | nan | 0.6139 | 0.0 | nan | 0.6139 |
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+ | 0.2192 | 21.82 | 240 | 0.2210 | 0.3041 | 0.6083 | 0.6083 | nan | nan | 0.6083 | 0.0 | nan | 0.6083 |
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+ | 0.2469 | 23.64 | 260 | 0.2168 | 0.3007 | 0.6014 | 0.6014 | nan | nan | 0.6014 | 0.0 | nan | 0.6014 |
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  ### Framework versions