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

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  1. README.md +18 -18
  2. model.safetensors +1 -1
  3. runs/Dec15_15-45-40_3f9e287c326f/events.out.tfevents.1702655141.3f9e287c326f.594.0 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9973262032085561
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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
@@ -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: 0.0208
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- - Accuracy: 0.9973
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  ## Model description
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@@ -67,21 +67,21 @@ The following hyperparameters were used during training:
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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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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9966577540106952
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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: 0.0271
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+ - Accuracy: 0.9967
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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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+ | 0.0898 | 1.0 | 327 | 0.0707 | 0.9757 |
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+ | 0.0221 | 2.0 | 654 | 0.0278 | 0.9920 |
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+ | 0.06 | 3.0 | 981 | 0.0345 | 0.9913 |
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+ | 0.0094 | 4.0 | 1309 | 0.0300 | 0.9947 |
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+ | 0.0004 | 5.0 | 1636 | 0.0398 | 0.9942 |
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+ | 0.0035 | 6.0 | 1963 | 0.0136 | 0.9975 |
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+ | 0.0246 | 7.0 | 2290 | 0.0339 | 0.9940 |
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+ | 0.0012 | 8.0 | 2618 | 0.0316 | 0.9958 |
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+ | 0.0 | 9.0 | 2945 | 0.0302 | 0.9964 |
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+ | 0.0 | 10.0 | 3272 | 0.0201 | 0.9973 |
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+ | 0.0003 | 11.0 | 3599 | 0.0222 | 0.9955 |
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+ | 0.0 | 12.0 | 3927 | 0.0218 | 0.9962 |
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+ | 0.0001 | 13.0 | 4254 | 0.0293 | 0.9962 |
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+ | 0.0002 | 14.0 | 4581 | 0.0272 | 0.9962 |
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+ | 0.0 | 14.99 | 4905 | 0.0271 | 0.9967 |
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  ### Framework versions
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