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update model card README.md

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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: 0.5995732574679943
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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
@@ -29,10 +29,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # fl_image_category
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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 fl_image_category_ds dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0728
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- - Accuracy: 0.5996
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  ## Model description
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@@ -60,15 +60,17 @@ 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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- | 1.3061 | 1.0 | 88 | 1.2575 | 0.5349 |
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- | 1.1435 | 2.0 | 176 | 1.1084 | 0.5946 |
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- | 1.079 | 3.0 | 264 | 1.0728 | 0.5996 |
 
 
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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: 0.6216216216216216
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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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  # fl_image_category
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+ This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the fl_image_category_ds dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9667
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+ - Accuracy: 0.6216
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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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+ | 1.274 | 1.0 | 88 | 1.2030 | 0.4986 |
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+ | 1.069 | 2.0 | 176 | 1.0716 | 0.5605 |
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+ | 1.0592 | 3.0 | 264 | 1.0385 | 0.5676 |
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+ | 0.9571 | 4.0 | 352 | 0.9746 | 0.6131 |
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+ | 0.8975 | 5.0 | 440 | 0.9667 | 0.6216 |
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  ### Framework versions