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Issues list

Errors when I try to run train.py
#40 opened Mar 14, 2022 by puppy2000
2
stage 0
#39 opened May 13, 2021 by chen-yuu
transfer learning
#38 opened Feb 11, 2021 by DeccanKing1
Errors when I run train.py
#37 opened Dec 21, 2020 by KrystalCWT
2
Questions about the performance on ImageNet
#35 opened Aug 3, 2020 by jianghaojun
what's the version of torch, torchvision and python?
#34 opened Jul 8, 2020 by 3learning12340
1
i think the num of params for cifar10 residual network is incorrect
#33 opened May 13, 2020 by Prines
During the test, cifar10, the output data structure is incorrect.
#31 opened Mar 31, 2020 by xk88372527
1
What is the meaning of softmax in attention_module.py?
#30 opened Feb 20, 2020 by sydney0zq
Hi，is there any impletation of visualizing the mask? i'm insterest in the mask they showed in the paper,it seems very good
#28 opened Dec 3, 2019 by Danie1Hayes
1
model = ResidualAttentionModel() error with python3
#27 opened Nov 20, 2019 by carol007
1
The error about if __name__ == '__main__': freeze_support()
#26 opened Oct 23, 2019 by November666
1
Focus of the attention mask
#24 opened Sep 26, 2019 by aezco
about the code "out_interp = self.interpolation1(out_middle_2r_blocks) + out_down_residual_blocks1"
#23 opened Aug 27, 2019 by ZachZou-logs
4
Expression of mix attention
#22 opened Jun 14, 2019 by fengshenfeilian
2
Traceback (most recent call last): File "train.py", line 20, in <module> from model.residual_attention_network import ResidualAttentionModel_92_32input_update as ResidualAttentionModel ImportError: No module named model.residual_attention_network
#21 opened Jun 12, 2019 by ZZH2950228
model_92_sgd.pkl is pre_trained for cifar10?
#18 opened May 30, 2019 by jguo16
1
extending it to 3d data
#16 opened May 2, 2019 by Nd-sole
Mixed attention、Channel attention and Spatial attention
#15 opened Apr 9, 2019 by YANYANYEAH
4
It seems that the this code reproduced results can not achieve the results in the original paper ?
#1 opened Mar 20, 2018 by YihangLou
106
ProTip! Follow long discussions with comments:>50.