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swin-tiny-patch4-window7-224-finetuned-eurosat

Image Classification
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  1. README.md +19 -19
  2. model.safetensors +1 -1
README.md CHANGED
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9913101604278075
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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
@@ -32,8 +32,8 @@ 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: 0.0352
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- - Accuracy: 0.9913
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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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- | 0.2379 | 1.0 | 327 | 0.1448 | 0.9436 |
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- | 0.1352 | 2.0 | 654 | 0.0955 | 0.9643 |
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- | 0.1388 | 3.0 | 981 | 0.1047 | 0.9648 |
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- | 0.1045 | 4.0 | 1309 | 0.0740 | 0.9762 |
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- | 0.144 | 5.0 | 1636 | 0.0479 | 0.9835 |
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- | 0.0888 | 6.0 | 1963 | 0.0358 | 0.9871 |
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- | 0.0893 | 7.0 | 2290 | 0.0542 | 0.9820 |
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- | 0.0743 | 8.0 | 2618 | 0.0536 | 0.9846 |
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- | 0.0532 | 9.0 | 2945 | 0.0654 | 0.9813 |
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- | 0.0358 | 10.0 | 3272 | 0.0507 | 0.9882 |
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- | 0.0257 | 11.0 | 3599 | 0.0287 | 0.9906 |
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- | 0.0118 | 12.0 | 3927 | 0.0355 | 0.9904 |
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- | 0.0274 | 13.0 | 4254 | 0.0275 | 0.9918 |
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- | 0.0442 | 14.0 | 4581 | 0.0321 | 0.9915 |
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- | 0.019 | 14.99 | 4905 | 0.0352 | 0.9913 |
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  ### Framework versions
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  - Transformers 4.35.2
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- - Pytorch 2.1.0+cu118
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  - Datasets 2.15.0
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  - Tokenizers 0.15.0
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9973262032085561
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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: 0.0208
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+ - Accuracy: 0.9973
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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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+ | 0.1091 | 1.0 | 327 | 0.1248 | 0.9541 |
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+ | 0.0474 | 2.0 | 654 | 0.0441 | 0.9880 |
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+ | 0.0072 | 3.0 | 981 | 0.0368 | 0.9918 |
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+ | 0.0072 | 4.0 | 1309 | 0.0331 | 0.9938 |
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+ | 0.0 | 5.0 | 1636 | 0.0306 | 0.9944 |
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+ | 0.0223 | 6.0 | 1963 | 0.0271 | 0.9938 |
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+ | 0.0012 | 7.0 | 2290 | 0.0227 | 0.9971 |
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+ | 0.0701 | 8.0 | 2618 | 0.0516 | 0.9929 |
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+ | 0.0147 | 9.0 | 2945 | 0.0244 | 0.9955 |
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+ | 0.0 | 10.0 | 3272 | 0.0313 | 0.9953 |
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+ | 0.0002 | 11.0 | 3599 | 0.0339 | 0.9967 |
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+ | 0.0001 | 12.0 | 3927 | 0.0268 | 0.9960 |
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+ | 0.0307 | 13.0 | 4254 | 0.0231 | 0.9971 |
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+ | 0.0 | 14.0 | 4581 | 0.0209 | 0.9973 |
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+ | 0.0 | 14.99 | 4905 | 0.0208 | 0.9973 |
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  ### Framework versions
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  - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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  - Datasets 2.15.0
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  - Tokenizers 0.15.0
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