pris-cv / an-erudite-fgvc-model Goto Github PK
View Code? Open in Web Editor NEWCode release for Your “An Erudite Fine-Grained Visual Classification Model (CVPR 2023)"
License: MIT License
Code release for Your “An Erudite Fine-Grained Visual Classification Model (CVPR 2023)"
License: MIT License
Thanks for your kindly sharing code. There is an issue, when running the code with two or more cards (using dataparallel), the error happens as follows. Could you please share your solution? It would be very helpful. Single card is ok, but it is two slow for training.
Besides, could you please share your paper in a arxiv version? Then more details can be found and your excellent work can be spread more fluently. Thank you very much.
Traceback (most recent call last):
File "main.py", line 440, in
train(epoch)
File "main.py", line 341, in train
task_model.adapt(adaptation_loss) # computes gradient, update task_model in-place
File "/home/sunhongbo/anaconda3/envs/u_modal/lib/python3.6/site-packages/learn2learn/algorithms/maml.py", line 169, in adapt
self.module = maml_update(self.module, self.lr, gradients)
UnboundLocalError: local variable 'gradients' referenced before assignment
Thanks for your great work! I noticed only mean acc is provided in Table 2. Is it possible share the detailed performance per datasets under training separately and joint training on 4 datasets?
Or is this detailed performance per datasets on training separately ?
Looking forward to your reply, thanks!
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