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Hi,
Thank you for your great work and sharing your code.
I was able to reproduce mAP scores for ROxford and RParis datasets, also with +1M distractors, both with and without re-ranking. Unfortunately, I cannot reproduce mAP@100 scores for GLDv2 retrieval test set. I am around 0.8 below your scores even without re-ranking. The same applies when I try to reproduce original CVNet - I can reproduce ROP but not GLDv2.
I cannot figure out what I am doing wrong. Is there any special pre-processing of the GLDv2 images which is different to ROP images? For example some image resizing? I noticed that if I keep GLDv2 images in their original size, some of them have their smaller side too small and the extraction with your code crashes. This does not happen with ROP as they are generally larger images with less extreme aspect ratios.
Thank you for your assistance.
The text was updated successfully, but these errors were encountered:
Hi, thank you for your interest in our work! The GLDv2 evaluation pipeline is with my Google collaborators. I have already informed them to provide some support. Stay tuned.
Hi,
Thank you for your great work and sharing your code.
I was able to reproduce mAP scores for ROxford and RParis datasets, also with +1M distractors, both with and without re-ranking. Unfortunately, I cannot reproduce mAP@100 scores for GLDv2 retrieval test set. I am around 0.8 below your scores even without re-ranking. The same applies when I try to reproduce original CVNet - I can reproduce ROP but not GLDv2.
I cannot figure out what I am doing wrong. Is there any special pre-processing of the GLDv2 images which is different to ROP images? For example some image resizing? I noticed that if I keep GLDv2 images in their original size, some of them have their smaller side too small and the extraction with your code crashes. This does not happen with ROP as they are generally larger images with less extreme aspect ratios.
Thank you for your assistance.
The text was updated successfully, but these errors were encountered: