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

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results
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  1. README.md +16 -16
  2. model.safetensors +1 -1
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: 0.7272727272727273
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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: 0.6322
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- - Accuracy: 0.7273
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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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- | No log | 0.97 | 9 | 1.0291 | 0.4621 |
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- | 1.0954 | 1.95 | 18 | 0.8322 | 0.6136 |
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- | 0.8859 | 2.92 | 27 | 0.7934 | 0.6364 |
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- | 0.7328 | 4.0 | 37 | 0.7151 | 0.6742 |
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- | 0.6285 | 4.97 | 46 | 0.7614 | 0.6061 |
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- | 0.5817 | 5.95 | 55 | 0.7581 | 0.6439 |
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- | 0.5145 | 6.92 | 64 | 0.6608 | 0.7121 |
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- | 0.4899 | 8.0 | 74 | 0.6711 | 0.6894 |
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- | 0.4372 | 8.97 | 83 | 0.6322 | 0.7273 |
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- | 0.4452 | 9.73 | 90 | 0.6399 | 0.7121 |
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  ### Framework versions
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- - Transformers 4.34.1
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  - Pytorch 2.1.0+cu118
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- - Datasets 2.14.6
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- - Tokenizers 0.14.1
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7289719626168224
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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: 0.6419
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+ - Accuracy: 0.7290
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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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+ | 1.0627 | 1.0 | 15 | 0.9620 | 0.5467 |
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+ | 0.8137 | 2.0 | 30 | 0.7780 | 0.6589 |
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+ | 0.7516 | 3.0 | 45 | 0.7737 | 0.6822 |
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+ | 0.6395 | 4.0 | 60 | 0.7195 | 0.6869 |
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+ | 0.579 | 5.0 | 75 | 0.6742 | 0.7150 |
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+ | 0.5505 | 6.0 | 90 | 0.6526 | 0.7243 |
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+ | 0.5312 | 7.0 | 105 | 0.6616 | 0.7290 |
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+ | 0.4793 | 8.0 | 120 | 0.6470 | 0.7430 |
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+ | 0.4443 | 9.0 | 135 | 0.6375 | 0.7383 |
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+ | 0.4685 | 10.0 | 150 | 0.6419 | 0.7290 |
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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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