xception

Xception

This example trains a Xception model on the AVDeepfake1M/AVDeepfake1M++ dataset for classification with video-level labels.

Requirements

• Python
• PyTorch
• PyTorch Lightning
• TIMM
• AVDeepfake1M SDK

Training

python train.py --data_root /path/to/avdeepfake1m --model xception

Output

• Checkpoints: Model checkpoints are saved under ./ckpt/xception/. The last checkpoint is saved as last.ckpt.
• Logs: Training logs (including metrics like train_loss, val_loss, and learning rates) are saved by PyTorch Lightning, typically in a directory named ./lightning_logs/. You can view these logs using TensorBoard (tensorboard --logdir ./lightning_logs).

Inference

After training, you can generate predictions on a dataset subset (train, val, or test) using infer.py. This script will save the predictions to a text file, following the format from the challenge.

python infer.py --data_root /path/to/avdeepfake1m --checkpoint /path/to/your/checkpoint.ckpt --model xception --subset val

The output prediction file will be saved to output/<model_name>_<subset>.txt (e.g., output/xception_val.txt).

Evaluation

python evaluate.py <path_to_prediction_file> <path_to_metadata_json>

For example:

python evaluate.py ./output/xception_val.txt /path/to/avdeepfake1m/val_metadata.json

This will print the AUC score based on your model's predictions.