Fine tuning about practical-rife

[hzwer]

https://github.com/megvii-research/ECCV2022-RIFE
I think if you can train original RIFE, it will be not hard to finetune practical-RIFE v4.6. If you meet any problem, just raise the issue.

[laugam]

Thank you for your reply.
The train and evaluate functions that you defined in the file train.py don't seem to be compatible with the new files RIFE_HDv3.py and IFNet_HDv3.py, for instance the loss functions are different.
Do you have any suggestions on how to modify those functions?

Many thanks.

[xsp1]

Hello, I am also trying to retrain or fine-tune the v4.6 model on my own data set recently. I also found that RIFE_HDv3.py and IFNet_HDv3.py are different from the original version. I modified a lot of codes, such as removing After using loss_cons, I found that the effect is very poor after successful training. Did you fine-tune successfully? How did you solve these problems? Thanks!

[laugam]

Hello, unfortunately I did not manage to train/fine-tune the v4.6 model, I'm using the model as it is for the moment.
Maybe you will find useful the comments on this issue: hzwer/ECCV2022-RIFE#293 (comment)

I asked for the training code there but they didn't reply yet.
Please let me know if you succeed in this.
Thanks!