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# vit-real-fake-classification-v3

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: 0.0541
- Accuracy: 0.9817
- F1: 0.9834
- Recall: 0.9834
- Precision: 0.9834

## 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: 5e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.2481        | 1.0   | 233  | 0.0846          | 0.9667   | 0.9699 | 0.9737 | 0.9662    |
| 0.1881        | 2.0   | 466  | 0.0773          | 0.9726   | 0.9756 | 0.9912 | 0.9604    |
| 0.1036        | 3.0   | 699  | 0.0691          | 0.9774   | 0.9796 | 0.9815 | 0.9777    |
| 0.0007        | 4.0   | 932  | 0.0698          | 0.9817   | 0.9835 | 0.9854 | 0.9816    |
| 0.0029        | 5.0   | 1165 | 0.0541          | 0.9817   | 0.9834 | 0.9834 | 0.9834    |


### Framework versions

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

fake real