Comments (6)
Hi,
Thanks for your interests. We used the FVD code from here.
Also, we made some changes for the released MoCoGAN-HD repo, such as changing DataParallel
to DistributedDataParallel
, and used the repo to train on different datasets to get the released checkpoints, so there might be some differences for the metrics.
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Thanks for your prompt response!
That means the evaluation process I did is same as your implementation?
(FVD between randomly sampled 2048 real videos and fake videos)
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Yes, we did it in the same way :)
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Now I understand the detailed evaluation process!
Thanks again for kind replying!
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Hi, thank you for maintaining the codebase and replying to the issues timely!
I have a follow-up question based on the comments of @bluer555 and @hsi1032 - did you calculate the distance between the 2048 i3d feature vectors of real and fake batch, or calculate the distance using 16 feature vectors and average over 2048/16=128 distances? If it is the former, how did get the standard deviation in the table? Thanks!
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Hi,
We get the distance by 2048 feature vectors and repeat this process 10 times to get the std.
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Related Issues (17)
- Question about the cross-domain video discriminator HOT 1
- how to compute similarity loss in equation (3)? HOT 1
- Incorrect link for the image generator checkpoint on FaceForensics HOT 2
- Usage of UCF-101 dataset HOT 4
- Hyperparameters to train StyleGANv2 on UCF-101 HOT 2
- README should be updated HOT 1
- Did you use any truncation or curation for the released samples? HOT 7
- Did you cut first seconds of the FaceForensics dataset? HOT 2
- Augmentation for training? HOT 1
- Question about Inception score evaluation HOT 1
- Why feed real data into the video discriminator when training G? HOT 1
- About the evaluation code HOT 3
- Question about the way you finetune the generator HOT 1
- How to train on a custom dataset? HOT 2
- Cannot run pca_stats.py HOT 2
- Inference issue using pre-trained models
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