Cosine similarity between CLIP-Reid features of this repo and original repo about yolo_tracking HOT 2 CLOSED

Thanks for the reply. As you rightly said cosine similarity should not be affected by normalization, but anyway I am comparing the normalized features from both models. Need to check the way I am getting the embeddings, I suppose.

from yolo_tracking.

So, my question is, did you retrain or fintune the original CLIP model or directly use it as it is

I used it as it is.

There was a huge difference in terms of cosine similarity (calculated between different IDs). The features from this repo were much better (there was a considerable gap between the same IDs and different IDs) as compared to the features from the original repo (the gap was very low)

The only thing that I can think of is that the features are always normalized when inferring with the ReID models in this repo, as seen here:

https://github.com/mikel-brostrom/yolo_tracking/blob/df424189f658dfeecac16eb67a816bc987271dfa/boxmot/appearance/reid_multibackend.py#L310

but this should not affect the cosine similarity as pre-normalizing features to unit length does not affect the outcome. Cosine similarity measures the cosine of the angle between two vectors, providing a similarity score based on their orientation in space, rather than their size or length.

Also, do you have any suggestions or comments for the real-world problem that we face (different lighting conditions for the same person id) while doing reid on people?

The best results are always achieved by fine-tuning for your specific use-case

from yolo_tracking.