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update model card README.md

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@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0242
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- - Precision: 0.9935
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- - Recall: 0.9934
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  ## Model description
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@@ -45,15 +45,22 @@ 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_steps: 100
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
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- | 0.1176 | 1.0 | 190 | 0.0644 | 0.9836 | 0.9828 |
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- | 0.0515 | 2.0 | 380 | 0.0281 | 0.9934 | 0.9934 |
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- | 0.0189 | 3.0 | 570 | 0.0242 | 0.9935 | 0.9934 |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0189
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+ - Precision: 0.9974
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+ - Recall: 0.9974
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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_steps: 100
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
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+ | 0.1023 | 1.0 | 190 | 0.0528 | 0.9829 | 0.9828 |
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+ | 0.058 | 2.0 | 380 | 0.0375 | 0.9896 | 0.9894 |
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+ | 0.038 | 3.0 | 570 | 0.0359 | 0.9909 | 0.9907 |
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+ | 0.0283 | 4.0 | 760 | 0.0467 | 0.9861 | 0.9854 |
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+ | 0.0326 | 5.0 | 950 | 0.0222 | 0.9934 | 0.9934 |
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+ | 0.0257 | 6.0 | 1140 | 0.0249 | 0.9934 | 0.9934 |
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+ | 0.0259 | 7.0 | 1330 | 0.0296 | 0.9921 | 0.9921 |
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+ | 0.0171 | 8.0 | 1520 | 0.0239 | 0.9948 | 0.9947 |
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+ | 0.0211 | 9.0 | 1710 | 0.0190 | 0.9974 | 0.9974 |
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+ | 0.0054 | 10.0 | 1900 | 0.0189 | 0.9974 | 0.9974 |
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  ### Framework versions