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swin-tiny-patch4-window7-224-eurosat

Image Classification
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PyTorch
swin
Generated from Trainer
Eval Results
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update model card README.md

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  1. README.md +14 -9
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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: 0.9775132275132276
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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: 0.0918
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- - Accuracy: 0.9775
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  ## Model description
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@@ -60,17 +60,22 @@ 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: 5
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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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- | 0.6233 | 0.98 | 33 | 0.3001 | 0.9101 |
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- | 0.1958 | 1.98 | 66 | 0.1287 | 0.9643 |
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- | 0.1212 | 2.98 | 99 | 0.1109 | 0.9709 |
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- | 0.0734 | 3.98 | 132 | 0.1179 | 0.9643 |
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- | 0.0457 | 4.98 | 165 | 0.0918 | 0.9775 |
 
 
 
 
 
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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: 0.9814814814814815
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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: 0.0719
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+ - Accuracy: 0.9815
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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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+ | 1.2075 | 0.98 | 33 | 0.5666 | 0.8519 |
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+ | 0.2022 | 1.98 | 66 | 0.2523 | 0.9127 |
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+ | 0.1206 | 2.98 | 99 | 0.1576 | 0.9497 |
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+ | 0.0897 | 3.98 | 132 | 0.1421 | 0.9563 |
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+ | 0.0564 | 4.98 | 165 | 0.1114 | 0.9656 |
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+ | 0.0475 | 5.98 | 198 | 0.0678 | 0.9815 |
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+ | 0.0332 | 6.98 | 231 | 0.0819 | 0.9775 |
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+ | 0.0234 | 7.98 | 264 | 0.0679 | 0.9802 |
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+ | 0.0126 | 8.98 | 297 | 0.0684 | 0.9828 |
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+ | 0.0306 | 9.98 | 330 | 0.0719 | 0.9815 |
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  ### Framework versions