[Question] Is there currently a way to replace the Yolox detector with another detector?

[aelahi23]

I want to try and run OC-SORT with a few different detectors (YoloV5, F-RCNN, CNN) just to experiment and see how well the detectors perform with OC-SORT.

[noahcao]

Currently, I have not support customized detectors here. But replacing it should be feasible by manipulating the returned model of get_model() here.

[aelahi23]

@noahcao Ah I see thank you very much!

[aelahi23]

I've been trying to replace the model returned by get_model() in demo track but it seems everything is centred around yoloX, is there a way to remove the get_exp() method and just inject a detector and run the tracker with just a random detector? Soon as I have it working with YoloV5 detector I'd be able to make a wrapper to allow others to add their detectors and make a pull request to help with adding support for any detector

[noahcao]

I made a PR to mmtracking to support OC-SORT. As mmtracking is based on mmdetection, you can replace the detector with what you want there.

[noahcao]

Also, it would be great if you can make a PR here to support customized detector. Using an abstract detector wrapper is a good idea. I would appreciate your contribution.

[aelahi23]

I've tried to make a generic wrapper but have realised the problem lies within the function inference in the class predictor, different detectors expect input images in a specific form for example yoloV5 expects a image input in numpy array form but you seem to convert the images into a tensor form which causes the yoloV5 detector to improperly read an image and output bounding boxes as a tensor with the shape [1,229500,6] for a 1080*1920 image when instead yoloV5 should have output a numpy array of [x1,y1,x2,y2,score,class]. removing the tensor conversion causes a further trickle down problem since OCSort.py in the function convert_bbox_to_z and association.py in the fucntion iou_batch. I'm at a loss for how to make a generic wrapper without changing the data types required in OCSort.py and association.py, what would you reckon as a better solution to the generic detector wrapper problem? this seems like the final hurdle if I can solve this I can make a PR for OCsort here by tomorrow with a generic wrapper for other detectors

[noahcao]

Hi, some updates:

There is an implementation is another issue thread. Also, I have supported OC-SORT in mmtracking. You can move to mmtracking to have a more advanced and customized tracking implementation, where of course, customized detector is allowed.