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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: 0.2168
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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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@@ -48,25 +50,39 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 25
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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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- | 0.9147 | 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
 
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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: 0.1285
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+ - Mean Iou: 0.3499
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+ - Mean Accuracy: 0.6998
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+ - Overall Accuracy: 0.6998
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  - Accuracy Unlabeled: nan
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  - Accuracy Tool: nan
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+ - Accuracy Wear: 0.6998
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  - Iou Unlabeled: 0.0
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  - Iou Tool: nan
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+ - Iou Wear: 0.6998
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 50
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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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+ | 0.8959 | 1.82 | 20 | 0.8677 | 0.4048 | 0.8097 | 0.8097 | nan | nan | 0.8097 | 0.0 | nan | 0.8097 |
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+ | 0.6658 | 3.64 | 40 | 0.6010 | 0.3734 | 0.7468 | 0.7468 | nan | nan | 0.7468 | 0.0 | nan | 0.7468 |
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+ | 0.4389 | 5.45 | 60 | 0.4941 | 0.3634 | 0.7269 | 0.7269 | nan | nan | 0.7269 | 0.0 | nan | 0.7269 |
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+ | 0.3531 | 7.27 | 80 | 0.4390 | 0.3508 | 0.7015 | 0.7015 | nan | nan | 0.7015 | 0.0 | nan | 0.7015 |
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+ | 0.3408 | 9.09 | 100 | 0.3753 | 0.3340 | 0.6679 | 0.6679 | nan | nan | 0.6679 | 0.0 | nan | 0.6679 |
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+ | 0.3266 | 10.91 | 120 | 0.3769 | 0.3761 | 0.7521 | 0.7521 | nan | nan | 0.7521 | 0.0 | nan | 0.7521 |
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+ | 0.2791 | 12.73 | 140 | 0.3491 | 0.3918 | 0.7835 | 0.7835 | nan | nan | 0.7835 | 0.0 | nan | 0.7835 |
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+ | 0.2066 | 14.55 | 160 | 0.2705 | 0.3491 | 0.6981 | 0.6981 | nan | nan | 0.6981 | 0.0 | nan | 0.6981 |
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+ | 0.161 | 16.36 | 180 | 0.2398 | 0.3283 | 0.6567 | 0.6567 | nan | nan | 0.6567 | 0.0 | nan | 0.6567 |
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+ | 0.1558 | 18.18 | 200 | 0.2599 | 0.4021 | 0.8042 | 0.8042 | nan | nan | 0.8042 | 0.0 | nan | 0.8042 |
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+ | 0.128 | 20.0 | 220 | 0.2163 | 0.3387 | 0.6775 | 0.6775 | nan | nan | 0.6775 | 0.0 | nan | 0.6775 |
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+ | 0.11 | 21.82 | 240 | 0.2019 | 0.3599 | 0.7199 | 0.7199 | nan | nan | 0.7199 | 0.0 | nan | 0.7199 |
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+ | 0.1101 | 23.64 | 260 | 0.1905 | 0.3620 | 0.7240 | 0.7240 | nan | nan | 0.7240 | 0.0 | nan | 0.7240 |
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+ | 0.0874 | 25.45 | 280 | 0.1708 | 0.3138 | 0.6276 | 0.6276 | nan | nan | 0.6276 | 0.0 | nan | 0.6276 |
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+ | 0.0815 | 27.27 | 300 | 0.1505 | 0.3191 | 0.6382 | 0.6382 | nan | nan | 0.6382 | 0.0 | nan | 0.6382 |
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+ | 0.082 | 29.09 | 320 | 0.1641 | 0.3520 | 0.7040 | 0.7040 | nan | nan | 0.7040 | 0.0 | nan | 0.7040 |
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+ | 0.0694 | 30.91 | 340 | 0.1456 | 0.3322 | 0.6644 | 0.6644 | nan | nan | 0.6644 | 0.0 | nan | 0.6644 |
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+ | 0.072 | 32.73 | 360 | 0.1416 | 0.3445 | 0.6889 | 0.6889 | nan | nan | 0.6889 | 0.0 | nan | 0.6889 |
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+ | 0.065 | 34.55 | 380 | 0.1348 | 0.3407 | 0.6814 | 0.6814 | nan | nan | 0.6814 | 0.0 | nan | 0.6814 |
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+ | 0.0696 | 36.36 | 400 | 0.1372 | 0.3285 | 0.6569 | 0.6569 | nan | nan | 0.6569 | 0.0 | nan | 0.6569 |
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+ | 0.0666 | 38.18 | 420 | 0.1430 | 0.3636 | 0.7272 | 0.7272 | nan | nan | 0.7272 | 0.0 | nan | 0.7272 |
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+ | 0.0601 | 40.0 | 440 | 0.1222 | 0.3211 | 0.6423 | 0.6423 | nan | nan | 0.6423 | 0.0 | nan | 0.6423 |
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+ | 0.0515 | 41.82 | 460 | 0.1225 | 0.3286 | 0.6572 | 0.6572 | nan | nan | 0.6572 | 0.0 | nan | 0.6572 |
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+ | 0.0558 | 43.64 | 480 | 0.1229 | 0.3375 | 0.6750 | 0.6750 | nan | nan | 0.6750 | 0.0 | nan | 0.6750 |
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+ | 0.07 | 45.45 | 500 | 0.1111 | 0.3057 | 0.6114 | 0.6114 | nan | nan | 0.6114 | 0.0 | nan | 0.6114 |
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+ | 0.0606 | 47.27 | 520 | 0.1251 | 0.3391 | 0.6782 | 0.6782 | nan | nan | 0.6782 | 0.0 | nan | 0.6782 |
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+ | 0.0561 | 49.09 | 540 | 0.1285 | 0.3499 | 0.6998 | 0.6998 | nan | nan | 0.6998 | 0.0 | nan | 0.6998 |
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  ### Framework versions