The market 1501 dataset has train, query and gallery folders, each containing multiple views of people from multiple cameras. I would like to understand how to evaluate a model (trained with triplet loss for example), on this dataset. There are multiple things I unfortunately don't understand about this and I have read papers like the "Strong Baseline" paper and tried to look at some code but found it a bit hard to follow.
If I understand correctly, the basic idea is to take a (batch of) query and find the closest examples in the gallery to it. But in Market-1501, the query and gallery have images with the same camera-id and identity. Won't the model just find that image? 2)Does the model have to identify which camera the gallery image was taken from or just the identity?
Do we calculate features for the whole gallery before evaluating or do we have batches of queries and galleries and look for matches in that set? I would really appreciate some of these details as I'm trying to implement a ReID model myself.
I have also posted this on the AI stack exchange as I'm unsure which one is the correct one for this.