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First of all, thank you for your awesome idea! I really like the neat way you combine attention and part recognition. Congratulations on your inspiring work!
I followed the README docs and successfully reproduced the experiment result in your paper. However, the fact that the code works on a customized version of MXNet and the computation being done with symbol instead of gluon is not quite favorable. I noticed at the very end of your README file that you are planning to release a PyTorch version. Is there an expected data for that?
The text was updated successfully, but these errors were encountered:
First of all, thank you for your awesome idea! I really like the neat way you combine attention and part recognition. Congratulations on your inspiring work!
I followed the README docs and successfully reproduced the experiment result in your paper. However, the fact that the code works on a customized version of MXNet and the computation being done with symbol instead of gluon is not quite favorable. I noticed at the very end of your README file that you are planning to release a PyTorch version. Is there an expected data for that?
The text was updated successfully, but these errors were encountered: