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swin-tiny-patch4-window7-224-finetuned-RCC

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  1. README.md +17 -18
  2. model.safetensors +1 -1
  3. runs/May24_16-50-18_RTX3090/events.out.tfevents.1716537026.RTX3090.3776938.0 +2 -2
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4721
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- - Accuracy: 0.8110
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- - Precision: 0.8324
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- - Recall: 0.9686
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- - F1: 0.4612
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- - Auc: 0.4922
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  ## Model description
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@@ -57,18 +57,17 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | 0.6033 | 0.9577 | 17 | 0.4558 | 0.8346 | 0.8346 | 1.0 | 0.4549 | 0.5 |
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- | 0.4389 | 1.9718 | 35 | 0.4556 | 0.8346 | 0.8346 | 1.0 | 0.4549 | 0.5 |
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- | 0.4513 | 2.9859 | 53 | 0.4463 | 0.8346 | 0.8346 | 1.0 | 0.4549 | 0.5 |
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- | 0.4127 | 4.0 | 71 | 0.4495 | 0.8281 | 0.8336 | 0.9921 | 0.4530 | 0.4961 |
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- | 0.4069 | 4.9577 | 88 | 0.4574 | 0.8163 | 0.8316 | 0.9780 | 0.4494 | 0.4890 |
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- | 0.4 | 5.9718 | 106 | 0.4532 | 0.8018 | 0.8308 | 0.9575 | 0.4578 | 0.4867 |
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- | 0.3858 | 6.9859 | 124 | 0.4671 | 0.8031 | 0.8338 | 0.9544 | 0.4763 | 0.4970 |
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- | 0.3971 | 8.0 | 142 | 0.4625 | 0.7913 | 0.8326 | 0.9387 | 0.4763 | 0.4931 |
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- | 0.3735 | 8.9577 | 159 | 0.4656 | 0.7966 | 0.8345 | 0.9434 | 0.4842 | 0.4995 |
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- | 0.3633 | 9.5775 | 170 | 0.4721 | 0.8110 | 0.8324 | 0.9686 | 0.4612 | 0.4922 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3565
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+ - Accuracy: 0.9032
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+ - Precision: 0.9032
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+ - Recall: 1.0
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+ - F1: 0.4746
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+ - Auc: 0.5
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---:|
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+ | No log | 0.8571 | 3 | 0.3383 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | No log | 2.0 | 7 | 0.3654 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.3833 | 2.8571 | 10 | 0.3422 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.3833 | 4.0 | 14 | 0.3556 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.3833 | 4.8571 | 17 | 0.3425 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.2492 | 6.0 | 21 | 0.3401 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.2492 | 6.8571 | 24 | 0.3543 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.2492 | 8.0 | 28 | 0.3572 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
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+ | 0.2304 | 8.5714 | 30 | 0.3565 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
 
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  ### Framework versions
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