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# vit-real-fake-classification-v4

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: 0.0585
- Accuracy: 0.9796
- F1: 0.9815
- Recall: 0.9815
- Precision: 0.9815

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1295        | 1.0   | 233  | 0.2414          | 0.9151   | 0.9280 | 0.9912 | 0.8723    |
| 0.4466        | 2.0   | 466  | 0.1042          | 0.9646   | 0.9680 | 0.9718 | 0.9643    |
| 0.3302        | 3.0   | 699  | 0.0667          | 0.9764   | 0.9786 | 0.9776 | 0.9795    |
| 0.0003        | 4.0   | 932  | 0.0995          | 0.9731   | 0.9758 | 0.9796 | 0.9720    |
| 0.0002        | 5.0   | 1165 | 0.0585          | 0.9796   | 0.9815 | 0.9815 | 0.9815    |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

fake real