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

Requesting pre-trained model variants trained on grayscale ImageNet #217

Open
aisosalo opened this issue Dec 14, 2021 · 9 comments
Open

Comments

@aisosalo
Copy link

Would it be possible to have also greyscale ImageNet weights for the models along the lines described in Xie & Richmond?

Xie, Y. and Richmond, D., “Pre-training on grayscale ImageNet improves medical image classification,” in [Proceedings of the European Conference on Computer Vision (ECCV) Workshops], 476–484, Springer (September 2019).

@Fzz123
Copy link

Fzz123 commented Dec 14, 2021 via email

@chetana348
Copy link

Hello, I am looking for ImageNet grayscale weights as well.

@Fzz123
Copy link

Fzz123 commented Jul 21, 2023 via email

@eatmore18
Copy link

Hello, I'm interested in obtaining ImageNet grayscale weights, particularly for resnet50. How can I access them?

@Fzz123
Copy link

Fzz123 commented Nov 21, 2023 via email

@chetana348
Copy link

Hello, I'm interested in obtaining ImageNet grayscale weights, particularly for resnet50. How can I access them?

I think you can try the radImageNet if you are working on medical images
https://github.com/BMEII-AI/RadImageNet

@NavodPeiris
Copy link

@chetana348 @Fzz123 @aisosalo
i have weights of mobilenet models which are trained with greyscale imagenet dataset.

checkout this repo: https://github.com/Navodplayer1/MobileNet_96x96_greyscale_weights

@Fzz123
Copy link

Fzz123 commented Jan 25, 2024 via email

@chetana348
Copy link

@Navodplayer1
This is awesome. Thnx a lot.

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

5 participants