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# VIT_AI_image_detector

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0295
- Accuracy: 0.9924

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1686        | 1.0   | 1093  | 0.0843          | 0.9697   |
| 0.1195        | 2.0   | 2187  | 0.0731          | 0.9728   |
| 0.072         | 3.0   | 3281  | 0.0543          | 0.9803   |
| 0.1072        | 4.0   | 4375  | 0.0348          | 0.9884   |
| 0.079         | 5.0   | 5468  | 0.0342          | 0.9886   |
| 0.0681        | 6.0   | 6562  | 0.0317          | 0.9903   |
| 0.0513        | 7.0   | 7656  | 0.0304          | 0.9914   |
| 0.0518        | 8.0   | 8750  | 0.0293          | 0.9916   |
| 0.0674        | 9.0   | 9843  | 0.0295          | 0.9924   |
| 0.058         | 9.99  | 10930 | 0.0313          | 0.9917   |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3

fake real