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ViT_ASVspoof_DF

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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: ViT_ASVspoof_DF
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: validation
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8934108527131783
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+ - name: F1
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+ type: f1
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+ value: 0.8431164853649442
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+ - name: Precision
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+ type: precision
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+ value: 0.7981829517456884
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+ - name: Recall
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+ type: recall
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+ value: 0.8934108527131783
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+ ---
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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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+
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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/bishertello-/uncategorized/runs/q4a21cv3)
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+ # ViT_ASVspoof_DF
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+
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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 the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.8822
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+ - Accuracy: 0.8934
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+ - F1: 0.8431
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+ - Precision: 0.7982
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+ - Recall: 0.8934
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+ - Test: 1
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+ - Auc Roc: 0.3976
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+
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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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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 128
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+ - eval_batch_size: 16
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+ - seed: 42
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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: 500
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+ - num_epochs: 2
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Test | Auc Roc |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----:|:-------:|
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+ | 0.3293 | 0.1078 | 50 | 0.5369 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4810 |
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+ | 0.1251 | 0.2155 | 100 | 0.7074 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5209 |
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+ | 0.0671 | 0.3233 | 150 | 0.8683 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5390 |
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+ | 0.0463 | 0.4310 | 200 | 0.8867 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5820 |
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+ | 0.0365 | 0.5388 | 250 | 0.9675 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.6129 |
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+ | 0.0332 | 0.6466 | 300 | 1.1225 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5544 |
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+ | 0.0788 | 0.7543 | 350 | 1.1081 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5776 |
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+ | 0.0425 | 0.8621 | 400 | 1.4392 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5835 |
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+ | 0.0566 | 0.9698 | 450 | 1.8030 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5043 |
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+ | 0.0821 | 1.0776 | 500 | 1.8901 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.6352 |
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+ | 0.1122 | 1.1853 | 550 | 1.8085 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3735 |
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+ | 0.0446 | 1.2931 | 600 | 1.9759 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3383 |
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+ | 0.0342 | 1.4009 | 650 | 1.9482 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4254 |
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+ | 0.028 | 1.5086 | 700 | 1.9181 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3508 |
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+ | 0.0195 | 1.6164 | 750 | 1.9146 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4860 |
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+ | 0.0107 | 1.7241 | 800 | 1.8752 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4285 |
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+ | 0.0092 | 1.8319 | 850 | 1.8792 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4012 |
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+ | 0.0 | 1.9397 | 900 | 1.8822 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3976 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.3
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+ - Pytorch 2.1.2
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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