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yijisuk
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segformer-b1-miic-tl

Image Segmentation
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  1. README.md +23 -23
  2. model.safetensors +1 -1
  3. runs/Jun26_18-07-26_Centauri/events.out.tfevents.1719394925.Centauri.22808.1 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -17,15 +17,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2720
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- - Mean Iou: 0.4437
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- - Mean Accuracy: 0.8874
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- - Overall Accuracy: 0.8874
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  - Accuracy Unlabeled: nan
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- - Accuracy Circuit: 0.8874
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  - Iou Unlabeled: 0.0
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- - Iou Circuit: 0.8874
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- - Dice Coefficient: 0.8076
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  ## Model description
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@@ -56,22 +56,22 @@ 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 Circuit | Iou Unlabeled | Iou Circuit | Dice Coefficient |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
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- | 0.6147 | 3.12 | 250 | 0.5121 | 0.4257 | 0.8515 | 0.8515 | nan | 0.8515 | 0.0 | 0.8515 | 0.7260 |
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- | 0.5757 | 6.25 | 500 | 0.3833 | 0.2639 | 0.5277 | 0.5277 | nan | 0.5277 | 0.0 | 0.5277 | 0.4226 |
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- | 0.5378 | 9.38 | 750 | 0.2758 | 0.4087 | 0.8174 | 0.8174 | nan | 0.8174 | 0.0 | 0.8174 | 0.7551 |
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- | 0.5127 | 12.5 | 1000 | 0.2915 | 0.2967 | 0.5933 | 0.5933 | nan | 0.5933 | 0.0 | 0.5933 | 0.5977 |
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- | 0.4838 | 15.62 | 1250 | 0.3783 | 0.4100 | 0.8200 | 0.8200 | nan | 0.8200 | 0.0 | 0.8200 | 0.7433 |
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- | 0.4763 | 18.75 | 1500 | 0.3150 | 0.4422 | 0.8844 | 0.8844 | nan | 0.8844 | 0.0 | 0.8844 | 0.7948 |
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- | 0.4509 | 21.88 | 1750 | 0.3324 | 0.4464 | 0.8928 | 0.8928 | nan | 0.8928 | 0.0 | 0.8928 | 0.7829 |
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- | 0.4367 | 25.0 | 2000 | 0.2373 | 0.4440 | 0.8880 | 0.8880 | nan | 0.8880 | 0.0 | 0.8880 | 0.8142 |
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- | 0.4236 | 28.12 | 2250 | 0.2457 | 0.4023 | 0.8046 | 0.8046 | nan | 0.8046 | 0.0 | 0.8046 | 0.7656 |
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- | 0.42 | 31.25 | 2500 | 0.2622 | 0.3672 | 0.7343 | 0.7343 | nan | 0.7343 | 0.0 | 0.7343 | 0.7106 |
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- | 0.403 | 34.38 | 2750 | 0.2472 | 0.4470 | 0.8941 | 0.8941 | nan | 0.8941 | 0.0 | 0.8941 | 0.8200 |
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- | 0.3946 | 37.5 | 3000 | 0.3254 | 0.4648 | 0.9295 | 0.9295 | nan | 0.9295 | 0.0 | 0.9295 | 0.8138 |
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- | 0.3848 | 40.62 | 3250 | 0.2792 | 0.4209 | 0.8418 | 0.8418 | nan | 0.8418 | 0.0 | 0.8418 | 0.7813 |
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- | 0.3821 | 43.75 | 3500 | 0.2849 | 0.4217 | 0.8434 | 0.8434 | nan | 0.8434 | 0.0 | 0.8434 | 0.7804 |
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- | 0.3756 | 46.88 | 3750 | 0.2722 | 0.4401 | 0.8801 | 0.8801 | nan | 0.8801 | 0.0 | 0.8801 | 0.8058 |
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- | 0.3727 | 50.0 | 4000 | 0.2720 | 0.4437 | 0.8874 | 0.8874 | nan | 0.8874 | 0.0 | 0.8874 | 0.8076 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2212
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+ - Mean Iou: 0.4723
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+ - Mean Accuracy: 0.9446
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+ - Overall Accuracy: 0.9446
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  - Accuracy Unlabeled: nan
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+ - Accuracy Circuit: 0.9446
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  - Iou Unlabeled: 0.0
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+ - Iou Circuit: 0.9446
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+ - Dice Coefficient: 0.8541
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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 Circuit | Iou Unlabeled | Iou Circuit | Dice Coefficient |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|:----------------:|
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+ | 0.3419 | 3.12 | 250 | 0.2745 | 0.4850 | 0.9701 | 0.9701 | nan | 0.9701 | 0.0 | 0.9701 | 0.8149 |
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+ | 0.2785 | 6.25 | 500 | 0.2789 | 0.4828 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | 0.8285 |
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+ | 0.2549 | 9.38 | 750 | 0.2888 | 0.4721 | 0.9443 | 0.9443 | nan | 0.9443 | 0.0 | 0.9443 | 0.8372 |
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+ | 0.2728 | 12.5 | 1000 | 0.2426 | 0.4699 | 0.9397 | 0.9397 | nan | 0.9397 | 0.0 | 0.9397 | 0.8424 |
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+ | 0.2625 | 15.62 | 1250 | 0.1990 | 0.4632 | 0.9264 | 0.9264 | nan | 0.9264 | 0.0 | 0.9264 | 0.8520 |
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+ | 0.2449 | 18.75 | 1500 | 0.2121 | 0.4706 | 0.9412 | 0.9412 | nan | 0.9412 | 0.0 | 0.9412 | 0.8508 |
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+ | 0.2173 | 21.88 | 1750 | 0.2768 | 0.4780 | 0.9559 | 0.9559 | nan | 0.9559 | 0.0 | 0.9559 | 0.8485 |
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+ | 0.2158 | 25.0 | 2000 | 0.2772 | 0.4643 | 0.9287 | 0.9287 | nan | 0.9287 | 0.0 | 0.9287 | 0.8383 |
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+ | 0.1843 | 28.12 | 2250 | 0.1818 | 0.4671 | 0.9343 | 0.9343 | nan | 0.9343 | 0.0 | 0.9343 | 0.8685 |
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+ | 0.1608 | 31.25 | 2500 | 0.1794 | 0.4591 | 0.9182 | 0.9182 | nan | 0.9182 | 0.0 | 0.9182 | 0.8618 |
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+ | 0.1504 | 34.38 | 2750 | 0.1805 | 0.4586 | 0.9172 | 0.9172 | nan | 0.9172 | 0.0 | 0.9172 | 0.8647 |
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+ | 0.1495 | 37.5 | 3000 | 0.2090 | 0.4773 | 0.9545 | 0.9545 | nan | 0.9545 | 0.0 | 0.9545 | 0.8595 |
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+ | 0.142 | 40.62 | 3250 | 0.2048 | 0.4750 | 0.9500 | 0.9500 | nan | 0.9500 | 0.0 | 0.9500 | 0.8666 |
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+ | 0.1401 | 43.75 | 3500 | 0.2131 | 0.4756 | 0.9512 | 0.9512 | nan | 0.9512 | 0.0 | 0.9512 | 0.8580 |
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+ | 0.1339 | 46.88 | 3750 | 0.2469 | 0.4773 | 0.9546 | 0.9546 | nan | 0.9546 | 0.0 | 0.9546 | 0.8481 |
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+ | 0.1303 | 50.0 | 4000 | 0.2212 | 0.4723 | 0.9446 | 0.9446 | nan | 0.9446 | 0.0 | 0.9446 | 0.8541 |
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  ### Framework versions
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