###### Prepare dataset # Training dataset mount to /code/train # Public test 2 dataset mount to /code/public_test_2 # Public test 1 dataset mount to /code/public_test cd /code # Split videos to 2 folders fake and real python split_data.py # Extract all even frames from videos python extract_frames.py --config extract_frames_train_config.json python extract_frames.py --config extract_frames_pub1_config.json python extract_frames.py --config extract_frames_pub2_config.json # Split k-fold python split_kfold.py # Detect face cd /code/yolov7-face-main python detect.py ###### Training CUDA_VISIBLE_DEVICES=0 nohup python code/train_teacher.py --exp exp_39 > nohup_exp_39.out & CUDA_VISIBLE_DEVICES=0 nohup python code/train_teacher.py --exp exp_40 > nohup_exp_40.out & CUDA_VISIBLE_DEVICES=0 nohup python code/train_teacher.py --exp exp_43 > nohup_exp_43.out & CUDA_VISIBLE_DEVICES=0 nohup python code/train_teacher.py --exp exp_49 > nohup_exp_49.out &