---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-real-fake-cls
  results: []
datasets:
- date3k2/raw_real_fake_images
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/real-fake-classification/runs/3wxs9xk6)
# ViT Real Fake Image Classification

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on [Real & Fake Images](https://huggingface.co/datasets/date3k2/raw_real_fake_images) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Accuracy: 0.9866
- F1: 0.9878
- Recall: 0.9854
- Precision: 0.9902

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1759        | 1.0   | 59   | 0.2212          | 0.9173   | 0.9229 | 0.8978 | 0.9495    |
| 0.1903        | 2.0   | 118  | 0.1047          | 0.9629   | 0.9659 | 0.9503 | 0.9819    |
| 0.0463        | 3.0   | 177  | 0.0824          | 0.9699   | 0.9730 | 0.9834 | 0.9628    |
| 0.0015        | 4.0   | 236  | 0.0763          | 0.9764   | 0.9787 | 0.9825 | 0.9749    |
| 0.0631        | 5.0   | 295  | 0.0794          | 0.9737   | 0.9759 | 0.9640 | 0.9880    |
| 0.0114        | 6.0   | 354  | 0.0582          | 0.9801   | 0.9819 | 0.9786 | 0.9853    |
| 0.0004        | 7.0   | 413  | 0.0662          | 0.9807   | 0.9824 | 0.9796 | 0.9853    |
| 0.0231        | 8.0   | 472  | 0.0713          | 0.9753   | 0.9773 | 0.9659 | 0.9890    |
| 0.0017        | 9.0   | 531  | 0.0518          | 0.9817   | 0.9834 | 0.9796 | 0.9872    |
| 0.0268        | 10.0  | 590  | 0.0385          | 0.9839   | 0.9855 | 0.9903 | 0.9807    |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1