Open af00731 opened 6 years ago
have you checked your any results for 224 by 224 images?
referring to table 6, means combination with resnet152 (ft) given in table caption. then what does inception-V3(ft) mean? should'nt it be resnet152(ft)? also, l2 normalization + concatenation means concatenating fg and wofg variations?
Hi,
We merged the labeled and detected images for training. To evaluate the results, we followed the standart protocol of the dataset. We used the original settings for resnet models, you can find the details in the paper. For distance calculation and re-ranking, we did several experiments using different methods including XQDA. But, the results given in the paper are the best. In table 6, Inception-V3(ft)* means the combination of Inception-V3(ft) with resnet152(ft).
Best,
Thanks for answering
can you please mention that which setting have you used for evaluation? Single shot single query or Multishot single query?
Hello, Do you know how to compute the rank1 and mAP? could you give me the code which compute the rank1 and mAP, My mail is pku1401210454@163.com, Thank you very much! @af00731
Hi, I read your human semantic parsing paper for person re-id and trying to reproduce results for cuhk03. For time being I am evaluating results just for re-id without semantic parsing. I have some queries to confirm or ask from you:
1) have you used cuhk03 detected and labelled combined together? as I saw your txt files and it has images > 13164 or your have used any kind of offline augmentation?
2) have you evaluated results for 20 splits and then took the average?
3) for each test split, have you performed single shot single query evaluation taking only one sample in query and gallery?
4) have you changed anything or added any layer in resnet 50 and resnet 152?
5) Have you checked for any other distance metric like XQDA? As many of the papers have told that XQDA always gives better than euclidean.
looking forward to your response. thanks in advance,