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

Image Classification
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swin
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  1. README.md +15 -8
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@@ -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: 0.9011764705882352
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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: 0.2882
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- - Accuracy: 0.9012
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  ## Model description
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@@ -61,20 +61,27 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 3
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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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- | 0.9086 | 1.0 | 30 | 0.5747 | 0.8118 |
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- | 0.5057 | 2.0 | 60 | 0.3523 | 0.8706 |
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- | 0.4594 | 3.0 | 90 | 0.2882 | 0.9012 |
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.39.2
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  - Pytorch 2.2.2+cu121
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  - Datasets 2.18.0
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  - Tokenizers 0.15.2
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9764705882352941
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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: 0.0625
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+ - Accuracy: 0.9765
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 10
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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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+ | 1.506 | 1.0 | 30 | 1.1397 | 0.5976 |
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+ | 0.5645 | 2.0 | 60 | 0.3396 | 0.88 |
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+ | 0.4507 | 3.0 | 90 | 0.1972 | 0.9247 |
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+ | 0.418 | 4.0 | 120 | 0.1484 | 0.9506 |
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+ | 0.3169 | 5.0 | 150 | 0.1866 | 0.92 |
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+ | 0.3346 | 6.0 | 180 | 0.0973 | 0.9718 |
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+ | 0.2823 | 7.0 | 210 | 0.0973 | 0.9694 |
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+ | 0.2711 | 8.0 | 240 | 0.0805 | 0.9671 |
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+ | 0.2638 | 9.0 | 270 | 0.0749 | 0.9718 |
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+ | 0.2755 | 10.0 | 300 | 0.0625 | 0.9765 |
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  ### Framework versions
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+ - Transformers 4.39.3
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  - Pytorch 2.2.2+cu121
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  - Datasets 2.18.0
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  - Tokenizers 0.15.2