| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: Deepfakes_detection |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Deepfakes_detection |
| |
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3242 |
| - Accuracy: 0.9222 |
| - Auc: 0.9998 |
| - F1 Fake: 0.9278 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 0.0002 |
| - train_batch_size: 256 |
| - eval_batch_size: 512 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 5 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | F1 Fake | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:| |
| | No log | 1.0 | 11 | 0.3630 | 0.8579 | 0.9527 | 0.8454 | |
| | No log | 2.0 | 22 | 0.2680 | 0.9114 | 0.9863 | 0.9166 | |
| | No log | 3.0 | 33 | 0.3072 | 0.9123 | 0.9879 | 0.9178 | |
| | No log | 4.0 | 44 | 0.2917 | 0.914 | 0.988 | 0.9193 | |
| | 0.0568 | 5.0 | 55 | 0.2840 | 0.9132 | 0.988 | 0.9182 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.5.4 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.4 |
| - Tokenizers 0.22.2 |
| |
