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b07611031
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leafback-swin-tiny-patch4-window7-224-finetuned

Image Classification
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swin
Generated from Trainer
Eval Results
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  1. README.md +34 -7
  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: 0.9025974025974026
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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: 0.2118
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- - Accuracy: 0.9026
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  ## Model description
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@@ -61,15 +61,42 @@ 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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- | No log | 1.0 | 5 | 0.2907 | 0.8896 |
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- | 0.2113 | 2.0 | 10 | 0.2303 | 0.9091 |
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- | 0.2113 | 3.0 | 15 | 0.2118 | 0.9026 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.961038961038961
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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: 0.0747
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+ - Accuracy: 0.9610
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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: 30
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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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+ | No log | 1.0 | 5 | 0.6616 | 0.6299 |
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+ | 0.6583 | 2.0 | 10 | 0.5232 | 0.7597 |
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+ | 0.6583 | 3.0 | 15 | 0.5043 | 0.7857 |
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+ | 0.3346 | 4.0 | 20 | 0.2879 | 0.8766 |
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+ | 0.3346 | 5.0 | 25 | 0.2424 | 0.9091 |
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+ | 0.1544 | 6.0 | 30 | 0.2217 | 0.8896 |
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+ | 0.1544 | 7.0 | 35 | 0.1466 | 0.9221 |
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+ | 0.088 | 8.0 | 40 | 0.1261 | 0.9481 |
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+ | 0.088 | 9.0 | 45 | 0.1680 | 0.9221 |
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+ | 0.0977 | 10.0 | 50 | 0.1446 | 0.9351 |
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+ | 0.0977 | 11.0 | 55 | 0.1812 | 0.9221 |
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+ | 0.0719 | 12.0 | 60 | 0.1798 | 0.9286 |
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+ | 0.0719 | 13.0 | 65 | 0.1056 | 0.9610 |
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+ | 0.0629 | 14.0 | 70 | 0.1073 | 0.9675 |
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+ | 0.0629 | 15.0 | 75 | 0.1106 | 0.9545 |
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+ | 0.0414 | 16.0 | 80 | 0.1286 | 0.9416 |
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+ | 0.0414 | 17.0 | 85 | 0.0761 | 0.9610 |
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+ | 0.0397 | 18.0 | 90 | 0.0785 | 0.9675 |
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+ | 0.0397 | 19.0 | 95 | 0.0746 | 0.9675 |
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+ | 0.0487 | 20.0 | 100 | 0.0684 | 0.9675 |
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+ | 0.0487 | 21.0 | 105 | 0.0602 | 0.9610 |
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+ | 0.0244 | 22.0 | 110 | 0.0551 | 0.9675 |
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+ | 0.0244 | 23.0 | 115 | 0.0639 | 0.9675 |
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+ | 0.0214 | 24.0 | 120 | 0.0583 | 0.9675 |
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+ | 0.0214 | 25.0 | 125 | 0.0663 | 0.9675 |
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+ | 0.0261 | 26.0 | 130 | 0.1006 | 0.9610 |
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+ | 0.0261 | 27.0 | 135 | 0.0711 | 0.9675 |
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+ | 0.019 | 28.0 | 140 | 0.0629 | 0.9675 |
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+ | 0.019 | 29.0 | 145 | 0.0728 | 0.9610 |
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+ | 0.0237 | 30.0 | 150 | 0.0747 | 0.9610 |
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  ### Framework versions
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