Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Mixup Technique is helpfull? #92

Open
abdikaiym01 opened this issue Jun 9, 2022 · 1 comment
Open

Mixup Technique is helpfull? #92

abdikaiym01 opened this issue Jun 9, 2022 · 1 comment

Comments

@abdikaiym01
Copy link

Hi, have you tested the technique mixup (Mixup: Beyond Empirical Risk Minimization) for the different datasets? Is with this technique models can show better performance on dataset IJBC?

@leondgarse
Copy link
Owner

I only tried the vanilla mixup augment method very earlier, and the results not good. I think it's not compatible with margin loss functions like ArcFace:

  • Mixup will expect a ground truth prediction like, let's say 0.5 mixup, [0, 0, ..., 0.5, ..., 0.5, 0, ...]
  • ArcFace makes a margin for truth predictions, like 0.8 -> 0.4, 0.5 -> 0.1. This makes them pretty low.

Some basic results using EfficientNetV2B0 + MS1MV3:

  • ArcFace: lfw: 0.9975, cfp_fp: 0.975714, agedb_30: 0.976333
  • ArcFace + Mixup: lfw: 0.997833, cfp_fp: 0.973571, agedb_30: 0.974333

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants