Closed weihua04 closed 1 year ago
Thank you for your attention!
(2-1) We choose small lamda_0 on CIFAR as we use the entire covariance matrices. On ImageNet, we approximate the covariance matrices by their diagonals to save GPU memory, where we find larger lamda_0 may help(i.e., {1, 2.5, 5, 7.5, 10}). (2-2) We use 7.5/5 for ResNet-152 and ResNet-101, respectively. If this does not work, maybe you can try {1, 2.5, 10} to see how the performance changes.
If you have any other questions, please feel free to reach me.
Thank u very much for open the sourcecode.
I have read the paper, and trained it in vehicle-classification task (with 20 id and 10thousand images)and vehicle-reid task (with 200 id and 10 thousand images) with our own dataset,
and it worked respectively with resnest50 / resnet50 with lamda_0 = 7.5 .
but when i change the backbone with resnet101 ,it didn't work with lamda=7.5,