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segformer-b5-miic-tl

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  1. README.md +23 -23
  2. model.safetensors +1 -1
  3. runs/Jun26_18-07-26_Centauri/events.out.tfevents.1719397958.Centauri.22808.3 +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-b5](https://huggingface.co/nvidia/mit-b5) 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: 0.3163
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- - Mean Iou: 0.3577
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- - Mean Accuracy: 0.7155
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- - Overall Accuracy: 0.7155
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  - Accuracy Unlabeled: nan
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- - Accuracy Circuit: 0.7155
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  - Iou Unlabeled: 0.0
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- - Iou Circuit: 0.7155
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- - Dice Coefficient: 0.7089
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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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- | 0.6189 | 3.12 | 250 | 0.3283 | 0.2955 | 0.5909 | 0.5909 | nan | 0.5909 | 0.0 | 0.5909 | 0.5818 |
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- | 0.5444 | 6.25 | 500 | 0.2823 | 0.3215 | 0.6430 | 0.6430 | nan | 0.6430 | 0.0 | 0.6430 | 0.6240 |
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- | 0.4994 | 9.38 | 750 | 0.2817 | 0.2955 | 0.5911 | 0.5911 | nan | 0.5911 | 0.0 | 0.5911 | 0.5770 |
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- | 0.4729 | 12.5 | 1000 | 0.2364 | 0.3561 | 0.7121 | 0.7121 | nan | 0.7121 | 0.0 | 0.7121 | 0.7067 |
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- | 0.4374 | 15.62 | 1250 | 0.3244 | 0.3609 | 0.7218 | 0.7218 | nan | 0.7218 | 0.0 | 0.7218 | 0.7021 |
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- | 0.4175 | 18.75 | 1500 | 0.2516 | 0.3202 | 0.6404 | 0.6404 | nan | 0.6404 | 0.0 | 0.6404 | 0.6560 |
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- | 0.3884 | 21.88 | 1750 | 0.2744 | 0.3685 | 0.7370 | 0.7370 | nan | 0.7370 | 0.0 | 0.7370 | 0.7078 |
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- | 0.3678 | 25.0 | 2000 | 0.2706 | 0.3896 | 0.7792 | 0.7792 | nan | 0.7792 | 0.0 | 0.7792 | 0.7414 |
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- | 0.3494 | 28.12 | 2250 | 0.3317 | 0.3622 | 0.7245 | 0.7245 | nan | 0.7245 | 0.0 | 0.7245 | 0.7049 |
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- | 0.3319 | 31.25 | 2500 | 0.2490 | 0.3420 | 0.6841 | 0.6841 | nan | 0.6841 | 0.0 | 0.6841 | 0.6905 |
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- | 0.3282 | 34.38 | 2750 | 0.2538 | 0.3893 | 0.7785 | 0.7785 | nan | 0.7785 | 0.0 | 0.7785 | 0.7469 |
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- | 0.3165 | 37.5 | 3000 | 0.3223 | 0.3744 | 0.7488 | 0.7488 | nan | 0.7488 | 0.0 | 0.7488 | 0.7277 |
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- | 0.3092 | 40.62 | 3250 | 0.3375 | 0.3180 | 0.6361 | 0.6361 | nan | 0.6361 | 0.0 | 0.6361 | 0.6374 |
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- | 0.3093 | 43.75 | 3500 | 0.3270 | 0.3417 | 0.6835 | 0.6835 | nan | 0.6835 | 0.0 | 0.6835 | 0.6826 |
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- | 0.3045 | 46.88 | 3750 | 0.3464 | 0.3331 | 0.6662 | 0.6662 | nan | 0.6662 | 0.0 | 0.6662 | 0.6710 |
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- | 0.2995 | 50.0 | 4000 | 0.3163 | 0.3577 | 0.7155 | 0.7155 | nan | 0.7155 | 0.0 | 0.7155 | 0.7089 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the yijisuk/ic-chip-sample dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 0.2247
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+ - Mean Iou: 0.4565
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+ - Mean Accuracy: 0.9129
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+ - Overall Accuracy: 0.9129
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  - Accuracy Unlabeled: nan
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+ - Accuracy Circuit: 0.9129
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  - Iou Unlabeled: 0.0
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+ - Iou Circuit: 0.9129
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+ - Dice Coefficient: 0.8406
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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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+ | 0.2801 | 3.12 | 250 | 0.2305 | 0.4832 | 0.9663 | 0.9663 | nan | 0.9663 | 0.0 | 0.9663 | 0.8527 |
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+ | 0.2785 | 6.25 | 500 | 0.2715 | 0.4800 | 0.9601 | 0.9601 | nan | 0.9601 | 0.0 | 0.9601 | 0.8511 |
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+ | 0.208 | 9.38 | 750 | 0.2681 | 0.4811 | 0.9622 | 0.9622 | nan | 0.9622 | 0.0 | 0.9622 | 0.8538 |
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+ | 0.2042 | 12.5 | 1000 | 0.2959 | 0.4650 | 0.9299 | 0.9299 | nan | 0.9299 | 0.0 | 0.9299 | 0.7879 |
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+ | 0.1649 | 15.62 | 1250 | 0.2407 | 0.4340 | 0.8679 | 0.8679 | nan | 0.8679 | 0.0 | 0.8679 | 0.8150 |
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+ | 0.1353 | 18.75 | 1500 | 0.2530 | 0.4543 | 0.9085 | 0.9085 | nan | 0.9085 | 0.0 | 0.9085 | 0.8336 |
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+ | 0.126 | 21.88 | 1750 | 0.4934 | 0.4559 | 0.9119 | 0.9119 | nan | 0.9119 | 0.0 | 0.9119 | 0.7678 |
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+ | 0.1196 | 25.0 | 2000 | 0.2896 | 0.4604 | 0.9209 | 0.9209 | nan | 0.9209 | 0.0 | 0.9209 | 0.7807 |
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+ | 0.1149 | 28.12 | 2250 | 0.2210 | 0.4634 | 0.9268 | 0.9268 | nan | 0.9268 | 0.0 | 0.9268 | 0.8470 |
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+ | 0.1095 | 31.25 | 2500 | 0.2215 | 0.4534 | 0.9067 | 0.9067 | nan | 0.9067 | 0.0 | 0.9067 | 0.8380 |
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+ | 0.109 | 34.38 | 2750 | 0.2256 | 0.4243 | 0.8487 | 0.8487 | nan | 0.8487 | 0.0 | 0.8487 | 0.8077 |
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+ | 0.1062 | 37.5 | 3000 | 0.2172 | 0.4497 | 0.8994 | 0.8994 | nan | 0.8994 | 0.0 | 0.8994 | 0.8363 |
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+ | 0.1046 | 40.62 | 3250 | 0.2401 | 0.4551 | 0.9102 | 0.9102 | nan | 0.9102 | 0.0 | 0.9102 | 0.8387 |
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+ | 0.1096 | 43.75 | 3500 | 0.2157 | 0.4582 | 0.9164 | 0.9164 | nan | 0.9164 | 0.0 | 0.9164 | 0.8425 |
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+ | 0.1014 | 46.88 | 3750 | 0.2344 | 0.4573 | 0.9146 | 0.9146 | nan | 0.9146 | 0.0 | 0.9146 | 0.8411 |
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+ | 0.1036 | 50.0 | 4000 | 0.2247 | 0.4565 | 0.9129 | 0.9129 | nan | 0.9129 | 0.0 | 0.9129 | 0.8406 |
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  ### Framework versions
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