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swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon

Image Classification
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swin
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  1. README.md +9 -14
  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: 0.9930313588850174
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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 [100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification](https://huggingface.co/100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0408
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- - Accuracy: 0.9930
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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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- | 0.2637 | 0.99 | 20 | 0.1274 | 0.9443 |
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- | 0.2582 | 1.98 | 40 | 0.0937 | 0.9756 |
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- | 0.161 | 2.96 | 60 | 0.0924 | 0.9582 |
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- | 0.1535 | 4.0 | 81 | 0.0612 | 0.9861 |
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- | 0.1347 | 4.99 | 101 | 0.0536 | 0.9791 |
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- | 0.1155 | 5.98 | 121 | 0.0408 | 0.9930 |
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- | 0.1306 | 6.96 | 141 | 0.0417 | 0.9930 |
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- | 0.1017 | 8.0 | 162 | 0.0380 | 0.9895 |
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- | 0.0859 | 8.99 | 182 | 0.0417 | 0.9895 |
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- | 0.0897 | 9.88 | 200 | 0.0393 | 0.9895 |
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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: 0.9651567944250871
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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 [100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification](https://huggingface.co/100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0971
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+ - Accuracy: 0.9652
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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: 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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+ | 0.2504 | 0.99 | 20 | 0.1401 | 0.9512 |
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+ | 0.2051 | 1.98 | 40 | 0.1083 | 0.9652 |
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+ | 0.1894 | 2.96 | 60 | 0.0939 | 0.9652 |
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+ | 0.1115 | 4.0 | 81 | 0.0880 | 0.9686 |
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+ | 0.117 | 4.94 | 100 | 0.0971 | 0.9652 |
 
 
 
 
 
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  ### Framework versions
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