liangbh6
liangbh6
@Cysu @huanghoujing Hi, I maintain standard Market1501 evaluation set splits and train the model with the setting of TriNet(i.e. one GPU, 18 identities x 4 instances/id), but I get rank1...
I have tried to increase the number of identities and got a better rank1, but I have the doubt that if I want to utilize the reproduced result for comparison,...
Yes, I want to reproduce the results of the paper you mentioned. Seems that a longer training time help sometimes. Thanks a lot!
@OysterEleven Have you figured out why? I even get ~40% rank1. It is a little confusing.
@OysterEleven And I find that however I use 224x224 or 299x299 or 256x256, the size of the layer before classifier is 1536...
Well, I check out several versions of GoogLeNet and find the Inception here cannot match any of them.
@Simon4john pytorch. So, the reason is the differences between pytorch and caffe? If I want to reproduce your results using pytorch, do you have some suggestion, about the learning rate,...
@Simon4Yan Excellent work! Thanks a lot.
I got the same problem with pytorch=0.3.1 too. Have you found the solution?
Actually I have an interest in mAP score of TriNet on cuhk03.