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bombshelll
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swin-brain-abnormalities-classification

Image Classification
Transformers
Safetensors
swin
Generated from Trainer
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  1. README.md +19 -20
  2. model.safetensors +1 -1
README.md CHANGED
@@ -1,6 +1,5 @@
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  ---
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  library_name: transformers
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- license: apache-2.0
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  base_model: microsoft/swin-tiny-patch4-window7-224
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  tags:
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  - generated_from_trainer
@@ -18,8 +17,8 @@ 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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1693
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- - Accuracy: 0.9329
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  ## Model description
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@@ -51,23 +50,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.0356 | 1.0 | 6 | 0.6993 | 0.7651 |
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- | 0.732 | 2.0 | 12 | 0.5917 | 0.7718 |
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- | 0.6067 | 3.0 | 18 | 0.4628 | 0.7785 |
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- | 0.5492 | 4.0 | 24 | 0.3543 | 0.9195 |
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- | 0.4017 | 5.0 | 30 | 0.2706 | 0.8993 |
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- | 0.453 | 6.0 | 36 | 0.2359 | 0.9396 |
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- | 0.2814 | 7.0 | 42 | 0.2423 | 0.9195 |
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- | 0.2513 | 8.0 | 48 | 0.2174 | 0.9396 |
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- | 0.2491 | 9.0 | 54 | 0.2138 | 0.9329 |
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- | 0.211 | 10.0 | 60 | 0.1899 | 0.9329 |
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- | 0.1917 | 11.0 | 66 | 0.1761 | 0.9463 |
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- | 0.211 | 12.0 | 72 | 0.1863 | 0.9329 |
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- | 0.1748 | 13.0 | 78 | 0.1747 | 0.9329 |
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- | 0.1605 | 14.0 | 84 | 0.1646 | 0.9530 |
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- | 0.1638 | 15.0 | 90 | 0.1693 | 0.9329 |
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  ### Framework versions
 
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  ---
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  library_name: transformers
 
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  base_model: microsoft/swin-tiny-patch4-window7-224
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  tags:
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  - generated_from_trainer
 
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1730
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+ - Accuracy: 0.9531
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | 0.8641 | 0.9892 | 23 | 0.5282 | 0.7739 |
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+ | 0.4526 | 1.9785 | 46 | 0.2441 | 0.8978 |
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+ | 0.271 | 2.9677 | 69 | 0.1764 | 0.9313 |
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+ | 0.2309 | 4.0 | 93 | 0.1976 | 0.9380 |
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+ | 0.1705 | 4.9892 | 116 | 0.1841 | 0.9414 |
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+ | 0.1789 | 5.9785 | 139 | 0.1264 | 0.9531 |
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+ | 0.1442 | 6.9677 | 162 | 0.2048 | 0.9397 |
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+ | 0.1463 | 8.0 | 186 | 0.1362 | 0.9564 |
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+ | 0.122 | 8.9892 | 209 | 0.1399 | 0.9514 |
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+ | 0.1157 | 9.9785 | 232 | 0.1184 | 0.9615 |
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+ | 0.1089 | 10.9677 | 255 | 0.1848 | 0.9481 |
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+ | 0.1062 | 12.0 | 279 | 0.1380 | 0.9581 |
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+ | 0.0969 | 12.9892 | 302 | 0.1658 | 0.9548 |
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+ | 0.0824 | 13.9785 | 325 | 0.1736 | 0.9548 |
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+ | 0.0768 | 14.8387 | 345 | 0.1730 | 0.9531 |
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  ### Framework versions
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