google-deepmind / google-deepmind/image_obfuscation_benchmark
Comparing Augmentation Methods
Open
- Dominant language
- Python
- Stars
- 27
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
I have a few questions about the "Comparing Augmentation Methods" part of the paper and appreciate your help on them:
1- For this part, the experiments have only been performed on resnet50. Do you have any results for the ViT-based models?
2- Did you train the resent50 from scratch?
3- Are the augmentations applied on all images during the training or a part has gone through CutMix for example?
4- What is the $\alpha$ parameter for MixUp?
(Sharing your augmentation pipeline would be very helpful)
Thanks,
Sara
Contributor guide
Assessment
This issue has not been assessed yet.