Open adriarm opened 1 month ago
Hi,
thanks for the submission! I'll try to add the models in the next days.
Perfect, thank you so much! When do you think the models will be able to appear on the leaderboard? Please let me know if there are any issues.
Paper Information
The focus of the paper is on efficient robust training using gradient regularisation, and showing it is surprisingly effective on ImageNet.
Leaderboard Claim(s)
Our model can be instantiated and benchmarked by:
data_dir
,arch
andckpt_location
inbenchmark_model.py
benchmark_model.py
The models use the Swin architecture from the old timm==0.6.7 version. If it is preferred for me to write the pull request, please let me know what the best way to implement it is.
Model 1
Model 2
Model Zoo:
timm
. If not, I added the link to the architecture implementation so that it can be added.