---
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