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@@ -11,15 +11,17 @@ metrics:
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  model-index:
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  - name: vit-real-fake-cls
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  results: []
 
 
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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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  should probably proofread and complete it, then remove this comment. -->
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  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/real-fake-classification/runs/3wxs9xk6)
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- # vit-real-fake-cls
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- This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0398
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  - Accuracy: 0.9866
@@ -27,20 +29,6 @@ It achieves the following results on the evaluation set:
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  - Recall: 0.9854
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  - Precision: 0.9902
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
@@ -74,4 +62,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.41.0
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  - Pytorch 2.3.0+cu121
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  - Datasets 2.19.1
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- - Tokenizers 0.19.1
 
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  model-index:
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  - name: vit-real-fake-cls
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  results: []
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+ datasets:
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+ - date3k2/raw_real_fake_images
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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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  should probably proofread and complete it, then remove this comment. -->
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  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/real-fake-classification/runs/3wxs9xk6)
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+ # ViT Real Fake Image Classification
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on [Real & Fake Images](https://huggingface.co/datasets/date3k2/raw_real_fake_images) dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0398
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  - Accuracy: 0.9866
 
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  - Recall: 0.9854
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  - Precision: 0.9902
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
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  - Transformers 4.41.0
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  - Pytorch 2.3.0+cu121
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  - Datasets 2.19.1
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+ - Tokenizers 0.19.1