Closed whuzs closed 10 months ago
Thanks! The difference may just be in the backbone used, the original NetVlad uses AlexNet backbone and scores a bit lower than the VGG16 I used here.
The Recall@5 and @10 perhaps seem a bit low compared to the @1 recall.
This is not really a code issue though, so will close it.
Hi, Nanne. You have done a great jod on reproducing Net-Vlad!! I have a little problem on recall results, i got similar results as you on Pitts-30k dataset, which are: ====> Calculating recall @ N ====> Recall@1: 0.8567 ====> Recall@5: 0.9507 ====> Recall@10: 0.9713 ====> Recall@20: 0.9840 => Best Recall@5: 0.9508
But in Patch-NetVLAD paper, pitts-30k results is:![image](https://github.com/Nanne/pytorch-NetVlad/assets/38743240/393926b5-4bd3-439a-8119-68fda1c980e8)
The difference is about 3%
Is this result reasonable?