Inference on custom data

[AlexMuresan]

The way I understand this algorithm would work on custom data is as follows:

1. I run a tracker + object detector over my video and create "galleries" for each id identified by the tracker.
   • If I have an id switch because of occlusions or something similar I will have two galleries of the same person with two different ids
2. I save each crop identified by the object detector as a separate image in order to use as "queries"
3. Load a pretrained model (e.g efficientnet_v2_optimizer_45.pth) and run inference.py
   • This will calculate the features of each "gallery" I previously created as well as for each "query"
4. See to which of the galleries each "query" was "matched" and if it's more than one gallery assume there was an id switch there and that those galleries should belong to the same id.

Let me know if any of this makes sense or I should approach the problem differently. Thank you!