Comments (5)
Thanks a lot
from combining-efficientnet-and-vision-transformers-for-video-deepfake-detection.
This part has been hugely inspired by Selim Seferbekov work: https://github.com/selimsef/dfdc_deepfake_challenge/blob/master/preprocessing/face_detector.py
We maintained the frame resize to be totally comparable, anyway, we think that it would not affect the result. Maybe you can try to remove it and see what happens.
from combining-efficientnet-and-vision-transformers-for-video-deepfake-detection.
I try to cancel the resize operator and a error come like the follow picture. Maybe if you don't resize the computer doesn't have enough resource to execute the code?
from combining-efficientnet-and-vision-transformers-for-video-deepfake-detection.
Can you provide the full stack trace?
from combining-efficientnet-and-vision-transformers-for-video-deepfake-detection.
Sorry for replying so late. In the end ,I didn't change it. But I have another question. I use MTCNN to extract face.The thresholds is set as same as yours. In this situation, I find some videos such as NT/808_829.mp4 can not extract enough faces as the paper mentioned. And I try to decrease the thresholds. But some frames are not face. What should I do?
from combining-efficientnet-and-vision-transformers-for-video-deepfake-detection.
Related Issues (20)
- Test set labels HOT 2
- Multiprocesssing Error HOT 2
- In DFDC data set, there will be moving face images in some videos. How do you deal with it?
- In DFDC data set, there will be moving face images in some videos. How do you deal with it? HOT 1
- Grouping of faces corresponding to same person for inference HOT 5
- Details on training and testing HOT 6
- DFDC Pre-processing timeline HOT 1
- no faces detected when run the detect_faces.py HOT 2
- Combining EfficientNet and Vision Transformers HOT 1
- About Cuda HOT 1
- Validation set HOT 9
- test AUC HOT 3
- AttributeError: 'EfficientNet' object has no attribute 'delete_blocks' HOT 1
- Inquiry Regarding Dataset Architecture and Organization HOT 4
- Error Encountered in Pretrained Model Evaluation
- Custom test set
- test the model in ff++
- ff++dataset structure issue
- ff++dataset structure issue HOT 1
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