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

Zero-shot capabilities on ImageNet #119

Open
kimihailv opened this issue Feb 2, 2023 · 2 comments
Open

Zero-shot capabilities on ImageNet #119

kimihailv opened this issue Feb 2, 2023 · 2 comments

Comments

@kimihailv
Copy link

Hello, I evaluated ALBEF 14M on ImageNetV2 classification task and it showed relatively low accuracy: top1 – 32.9, top5 - 60.7.
How do you think what reasons of such results? Much smaller training dataset compared to CLIP?

@LiJunnan1992
Copy link
Contributor

Hi @kimihailv , we haven't evaluated this result, but yes the zero-shot performance is largely correlated with the training dataset size.

@shyammarjit
Copy link

shyammarjit commented Mar 15, 2024

Zero-shot capabilities on other datasets (such as dtd, food101, caltech101, sun397 & etc) is much lower as compared to CLIP, MetaCLIP and open_clip methods.

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

3 participants