MetaFormer
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dqshuai
A PyTorch implementation of "MetaFormer: A Unified Meta Framework for Fine-Grained Recognition". A reference PyTorch implementation of “CoAtNet: Marrying Convolution and Attention for All Data Sizes”
Very nice job! Could you please provide some pre-trained checkpoints? For example, the 92.3% CUB accuracy MetaFormer-1, and the 92.9% CUB accuracy MetaFormer-2? Appreciate for your generosity!
Hi, may I ask where did the meta data come from? Time, latitude and longitude are not provided in the dataset
hi, I have downloaded cub-200 data from the link provided by you, but I don't find meta data,can you tell me how to get meta data, thanks!
I don`t know the cuda version to run this project? Maybe conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch?
I have only one GPU. I have set local_rank=-1 ,but failed to run the code. What do I need to revise to successfully run on one GPU?
Hi, thanks a lot for publishing the code. I had a question: What is the typical training time for inat17?
Thanks for your wonderful work. If it is possible, could you please share your checkpoints on iNaturalist 2018? Thank you very much!
Dear All, This is more a feature request than a bug; anyway, can you test/utilize your method on the DF20 dataset? We include much more metadata within the dataset, thus,...
Do you have a model trained on inat21 without meta, can you provide it to us, thank you
