Comments (2)
Hi @belamarib, thanks for your interest in our work. It is possible to apply our method to any dataset (labelled or unlabelled) granted that you build a new dataloader class for the said dataset. I'm planning to update the repo with such a class soon (branch: https://github.com/chi0tzp/FALCO/tree/cvpr23), but I can't make any promises right now. In the meantime, you can simple try to modify the CelebA-HQ class (https://github.com/chi0tzp/FALCO/blob/cvpr23/lib/celebahq.py) to work with your own data.
I will close this issue as soon as I push the aforementioned generic dataset class.
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Thanks for the answer, I will look into It!!
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Related Issues (11)
- Code Release HOT 2
- time per image HOT 1
- What value of m (ID_MARGIN) was used across the paper? & What output scaling to use? HOT 1
- Broken link to download pretrained models HOT 3
- Split test set IDs HOT 1
- Python version / package versions HOT 2
- CelebA-HQ dataset
- Error with download_models.py HOT 1
- CelebA HQ HOT 1
- Face Alignment Errors HOT 2
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