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segformer-b0-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.1719392861.Centauri.22808.0 +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-b0](https://huggingface.co/nvidia/mit-b0) 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: 0.3532
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- - Mean Iou: 0.4001
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- - Mean Accuracy: 0.8002
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- - Overall Accuracy: 0.8002
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  - Accuracy Unlabeled: nan
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- - Accuracy Circuit: 0.8002
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  - Iou Unlabeled: 0.0
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- - Iou Circuit: 0.8002
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- - Dice Coefficient: 0.7456
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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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- | 0.5722 | 3.12 | 250 | 0.4296 | 0.3809 | 0.7618 | 0.7618 | nan | 0.7618 | 0.0 | 0.7618 | 0.6766 |
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- | 0.547 | 6.25 | 500 | 0.3983 | 0.3370 | 0.6739 | 0.6739 | nan | 0.6739 | 0.0 | 0.6739 | 0.6065 |
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- | 0.5147 | 9.38 | 750 | 0.3643 | 0.3487 | 0.6974 | 0.6974 | nan | 0.6974 | 0.0 | 0.6974 | 0.6477 |
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- | 0.5083 | 12.5 | 1000 | 0.3505 | 0.3006 | 0.6012 | 0.6012 | nan | 0.6012 | 0.0 | 0.6012 | 0.5586 |
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- | 0.4818 | 15.62 | 1250 | 0.3184 | 0.4400 | 0.8799 | 0.8799 | nan | 0.8799 | 0.0 | 0.8799 | 0.7758 |
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- | 0.4664 | 18.75 | 1500 | 0.3622 | 0.4347 | 0.8693 | 0.8693 | nan | 0.8693 | 0.0 | 0.8693 | 0.7755 |
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- | 0.4504 | 21.88 | 1750 | 0.3279 | 0.4327 | 0.8654 | 0.8654 | nan | 0.8654 | 0.0 | 0.8654 | 0.7792 |
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- | 0.4427 | 25.0 | 2000 | 0.3168 | 0.4386 | 0.8771 | 0.8771 | nan | 0.8771 | 0.0 | 0.8771 | 0.7840 |
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- | 0.4336 | 28.12 | 2250 | 0.2790 | 0.4100 | 0.8200 | 0.8200 | nan | 0.8200 | 0.0 | 0.8200 | 0.7636 |
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- | 0.4226 | 31.25 | 2500 | 0.3237 | 0.4148 | 0.8295 | 0.8295 | nan | 0.8295 | 0.0 | 0.8295 | 0.7641 |
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- | 0.4155 | 34.38 | 2750 | 0.3336 | 0.4169 | 0.8339 | 0.8339 | nan | 0.8339 | 0.0 | 0.8339 | 0.7664 |
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- | 0.4082 | 37.5 | 3000 | 0.3787 | 0.4267 | 0.8533 | 0.8533 | nan | 0.8533 | 0.0 | 0.8533 | 0.7760 |
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- | 0.403 | 40.62 | 3250 | 0.3541 | 0.3693 | 0.7387 | 0.7387 | nan | 0.7387 | 0.0 | 0.7387 | 0.6942 |
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- | 0.398 | 43.75 | 3500 | 0.3361 | 0.3864 | 0.7728 | 0.7728 | nan | 0.7728 | 0.0 | 0.7728 | 0.7244 |
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- | 0.3943 | 46.88 | 3750 | 0.3599 | 0.4053 | 0.8106 | 0.8106 | nan | 0.8106 | 0.0 | 0.8106 | 0.7519 |
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- | 0.3951 | 50.0 | 4000 | 0.3532 | 0.4001 | 0.8002 | 0.8002 | nan | 0.8002 | 0.0 | 0.8002 | 0.7456 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) 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: 0.3415
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+ - Mean Iou: 0.4569
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+ - Mean Accuracy: 0.9138
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+ - Overall Accuracy: 0.9138
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  - Accuracy Unlabeled: nan
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+ - Accuracy Circuit: 0.9138
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  - Iou Unlabeled: 0.0
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+ - Iou Circuit: 0.9138
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+ - Dice Coefficient: 0.8323
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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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+ | 0.3496 | 3.12 | 250 | 0.3203 | 0.4832 | 0.9665 | 0.9665 | nan | 0.9665 | 0.0 | 0.9665 | 0.8163 |
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+ | 0.2808 | 6.25 | 500 | 0.3289 | 0.4814 | 0.9629 | 0.9629 | nan | 0.9629 | 0.0 | 0.9629 | 0.8271 |
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+ | 0.2582 | 9.38 | 750 | 0.3404 | 0.4670 | 0.9339 | 0.9339 | nan | 0.9339 | 0.0 | 0.9339 | 0.8327 |
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+ | 0.2791 | 12.5 | 1000 | 0.3033 | 0.4591 | 0.9181 | 0.9181 | nan | 0.9181 | 0.0 | 0.9181 | 0.8300 |
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+ | 0.2668 | 15.62 | 1250 | 0.3117 | 0.4559 | 0.9118 | 0.9118 | nan | 0.9118 | 0.0 | 0.9118 | 0.8285 |
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+ | 0.2531 | 18.75 | 1500 | 0.2652 | 0.4686 | 0.9373 | 0.9373 | nan | 0.9373 | 0.0 | 0.9373 | 0.8432 |
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+ | 0.2326 | 21.88 | 1750 | 0.3256 | 0.4604 | 0.9208 | 0.9208 | nan | 0.9208 | 0.0 | 0.9208 | 0.8315 |
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+ | 0.2361 | 25.0 | 2000 | 0.3129 | 0.4656 | 0.9313 | 0.9313 | nan | 0.9313 | 0.0 | 0.9313 | 0.8400 |
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+ | 0.2167 | 28.12 | 2250 | 0.3135 | 0.4558 | 0.9116 | 0.9116 | nan | 0.9116 | 0.0 | 0.9116 | 0.8290 |
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+ | 0.2133 | 31.25 | 2500 | 0.3132 | 0.4560 | 0.9120 | 0.9120 | nan | 0.9120 | 0.0 | 0.9120 | 0.8219 |
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+ | 0.1769 | 34.38 | 2750 | 0.3200 | 0.4441 | 0.8882 | 0.8882 | nan | 0.8882 | 0.0 | 0.8882 | 0.8176 |
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+ | 0.1899 | 37.5 | 3000 | 0.3342 | 0.4612 | 0.9224 | 0.9224 | nan | 0.9224 | 0.0 | 0.9224 | 0.8363 |
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+ | 0.1765 | 40.62 | 3250 | 0.3445 | 0.4625 | 0.9249 | 0.9249 | nan | 0.9249 | 0.0 | 0.9249 | 0.8369 |
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+ | 0.1739 | 43.75 | 3500 | 0.3235 | 0.4608 | 0.9216 | 0.9216 | nan | 0.9216 | 0.0 | 0.9216 | 0.8373 |
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+ | 0.1639 | 46.88 | 3750 | 0.3527 | 0.4591 | 0.9181 | 0.9181 | nan | 0.9181 | 0.0 | 0.9181 | 0.8342 |
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+ | 0.1734 | 50.0 | 4000 | 0.3415 | 0.4569 | 0.9138 | 0.9138 | nan | 0.9138 | 0.0 | 0.9138 | 0.8323 |
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  ### Framework versions
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