https://github.com/himudigonda/openforensics-deepfake-challenge

https://github.com/himudigonda/openforensics-deepfake-challenge

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• Host: GitHub
• URL: https://github.com/himudigonda/openforensics-deepfake-challenge
• Owner: himudigonda
• License: mit
• Created: 2024-08-20T17:58:34.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-26T03:58:54.000Z (3 months ago)
• Last Synced: 2024-08-26T05:01:06.315Z (3 months ago)
• Language: Python
• Size: 7.81 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Deepfake Detection with Swin Transformer

This repository contains code for deepfake detection using the Swin Transformer architecture. The project leverages PyTorch and PyTorch Lightning for training and evaluation.

## Dataset Structure

The dataset should be organized as follows:

```

/data/

    train/

        Fake/

            fake_0.jpg

            fake_1.jpg

            ...

        Real/

            real_0.jpg

            real_1.jpg

            ...

    val/

        Fake/

            ...

        Real/

            ...

    test/

        Fake/

            ...

        Real/

            ...

```

## Requirements

* Python 3.9+

* PyTorch 

* PyTorch Lightning

* timm (PyTorch Image Models)

* albumentations

* torchmetrics

* Other dependencies listed in `requirements.txt`

Install the required packages using:

```bash

pip install -r requirements.txt

```

## Configuration

The project's settings are defined in `configs/config.yaml`. You can modify parameters such as:

* `data_root`: Path to the dataset root directory

* `model_name`: Swin Transformer model name from `timm`

* `batch_size`, `num_epochs`, `learning_rate`, etc.

* `train_augmentations` and `val_augmentations`: Albumentations transformations

## Training

To train the model, run:

```bash

python3 train.py

```

Training progress and metrics will be logged to TensorBoard. Checkpoints will be saved in the `checkpoints` directory.

## Evaluation

To evaluate a trained model on the validation set, use:

```bash

python3 eval.py

```

Evaluation metrics will be printed to the console.

## Key Files

* `train.py`: Main training script.

* `eval.py`: Evaluation script.

* `swin_base.py`: Defines the Swin Transformer model.

* `dataloader.py`: Handles dataset loading and augmentations.

* `augmentations.py`: Defines image transformations.

* `metrics.py`: Calculates evaluation metrics.

* `configs/config.yaml`: Project configuration file.

## Acknowledgements

* The Swin Transformer implementation is based on the `timm` library.

* Albumentations is used for image augmentations.

* PyTorch Lightning simplifies training and experimentation.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).