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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.84251968503937
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ 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 the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4542
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- - Accuracy: 0.8425
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6766 | 1.0 | 18 | 0.4542 | 0.8425 |
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- | 0.4078 | 2.0 | 36 | 0.3918 | 0.8425 |
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- | 0.4251 | 3.0 | 54 | 0.3993 | 0.8425 |
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- | 0.3648 | 4.0 | 72 | 0.3716 | 0.8386 |
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- | 0.3474 | 5.0 | 90 | 0.3802 | 0.8346 |
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- | 0.367 | 6.0 | 108 | 0.3757 | 0.8346 |
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  ### Framework versions
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- - Transformers 4.22.2
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  - Pytorch 1.12.1+cu113
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- - Datasets 2.5.1
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- - Tokenizers 0.12.1
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7296969696969697
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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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 imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5135
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+ - Accuracy: 0.7297
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6197 | 1.0 | 58 | 0.6089 | 0.6558 |
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+ | 0.5984 | 2.0 | 116 | 0.5503 | 0.7103 |
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+ | 0.5664 | 3.0 | 174 | 0.5392 | 0.7321 |
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+ | 0.5645 | 4.0 | 232 | 0.5388 | 0.7261 |
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+ | 0.5467 | 5.0 | 290 | 0.5143 | 0.7321 |
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+ | 0.5316 | 6.0 | 348 | 0.5135 | 0.7297 |
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  ### Framework versions
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+ - Transformers 4.23.1
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  - Pytorch 1.12.1+cu113
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+ - Datasets 2.5.2
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+ - Tokenizers 0.13.1