Training protocol for RevisitOP

[sonamsingh19]

Hi Filip,
I am new to these datasets and hence the confusion. Usually we have training data with landmark ids , their GT (positive samples) and then separate query images and corresponding positive samples for evaluation.

In these datasets like Oxford5k or ROxford5k, I find the landmark images, and other images for that landmark. For ex, everything starting with all_souls correspond to the all_souls building. But when I see other images containign this tag, it contain people and indoor images which are possibly junk. In the gt files, I see the structure having, all_souls_1_query.txt, all_souls_1_good.txt, all_souls_1_ok.txt, all_souls_1_junk.txt and so on.

While training do we use all_souls_1_good.txt, all_souls_ok.txt and ignore the junk? and cannot use all_souls_1_query.txt?

I just want to make sure I get the standard practice of how to train on this dataset and evaluate properly. I looked in to the PhD thesis too which you linked in another issue, but this training protocol is I am unable to grasp.

Thanks a lot for your patience.

[OrangeCat95]

Do you have any idea on this now? I also don't know how to evaluate with features generated by other methods. By the way, can you share me with the PhD thesis mentioned in your comments? Thank you!