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# finetuned-ai-real-cifake

This model is a fine-tuned version of [ongtrandong2/ai_vs_real_image_detection](https://huggingface.co/ongtrandong2/ai_vs_real_image_detection) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0622
- Accuracy: 0.9756

## 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: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4286        | 0.3617 | 200  | 0.4256          | 0.8549   |
| 0.3499        | 0.7233 | 400  | 0.1693          | 0.9353   |
| 0.2664        | 1.0850 | 600  | 0.3467          | 0.8690   |
| 0.2303        | 1.4467 | 800  | 0.4582          | 0.8398   |
| 0.1553        | 1.8083 | 1000 | 0.2135          | 0.9186   |
| 0.1751        | 2.1700 | 1200 | 0.0793          | 0.9715   |
| 0.1383        | 2.5316 | 1400 | 0.0638          | 0.9753   |
| 0.1375        | 2.8933 | 1600 | 0.0622          | 0.9756   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0

ai real