| name | function | need to be reset recording to your environment | note |
|---|---|---|---|
| Random parameter setting | |||
| seed | Random seed for dataset shuffle | ||
| distributed training setting | |||
| gpus | How many GPU you use in distributed training | ||
| world_size | The number of processes in distributed training | ||
| backend | The platform used for process communication | ||
| init_method | Ip and port of your server | ||
| syncbn | Switch for synchronized batch normalization | ||
| transform setting | |||
| face | Type of face corping | ||
| size | Size of corpped face | ||
| training parameter setting | |||
| debug | Switch of debug mode | ||
| logint | Interval of logging | ||
| modelperiod | Interval of saving model | ||
| valint | Interval of validation | ||
| batch | The size of training batch size | ||
| epochs | The maximum epoch | ||
| net_s | Net architecture for student model | ||
| net_t | Net architecture for teacher model | ||
| traindb | Decide quality and training set segmentation | ["ff-c23-720-140-140"] raw/c23/c40:chosen quality 720:training set size 140:validation set size 140: testing set size |
|
| trainIndex | Choose temper type | 0:Deepfake 1:Face2Face 2:Face Swap 3:Neural Texture |
|
| tagnote | Note part for tag | ||
| dataset setting | |||
| ffpp_faces_df_path | The path of dataset dataframe | ||
| ffpp_faces_dir | The path of dataset | ||
| workers | The process number for dataset loading | ||
| optimizer setting | |||
| lr | Training learning rate | ||
| patience | Adam parameter | ||
| model loading setting | |||
| models_dir | The path of models repository | ||
| mode | Model loading type | 0:choose the best trained model 1: choose the last one 2:choose the one of specific iteration |
|
| index | The model of a specific iteration | ||
| log setting | |||
| log_dir | The path of logs repository |
- use index_dataset.py generating the dataframe of corresponding dataset
- use extract_faces.py transform video into frames and corresponding dataframe
- setting the config.py in folder config
- train your teacher using train_starter.py
- train your expanded model using train_starter.py
- trained model for domain adaptation using train_starter.py
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