Comments (2)
Thanks for the interest in our paper.
For your question, it means 'discarding the down-sampling operation between stage3 and stage4' in section 3.1 of the paper.
So, you are right :)
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Thank you for your response !
So, 'discarding the down-sampling operation between stage3 and stage4' is changing the last stride of Resnet layer4 from 2 into 1 ? That is, last stride = 1 ?
These days I reimplement your great work with pytorch. When evaluating in CUB-200-2011, I just get 62% Recall@1. I believe I miss some important details.
So, some questions raising:
- Batch sample: shuffer all samples and get 128 samples in a batch? or use P-K sampling format(P-classes, K samples per class)
- I find that without L2 norm and FC after GD( like another issue said) could get higher performance(can't still reach your proposed results in CUB-200). Do you know the reasons about this?
- Could you share some training tricks?
Looking forward to your guidance.
Thank you so much!
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