yolo_tracking VS cosine_metric_learning

You can use YOLOv5 with the StrongSORT. We have been using it for human detection and tracking. It works really well and YOLOv5 in general really easy to use and implement out of the box. here is the repo that we are using.

Try looking into DeepSort, which uses a deep association metric in addition to the traditional SORT algorithm to kind of improve upon the ID reassignment issue. However, I suspect you would have to come up with your own re-id model since you have a unique object you're trying to detect. Here's the paper . I've had decent results using https://github.com/mikel-brostrom/Yolov5_DeepSort_OSNet as an out of the box implementation for coco object. It's written in PyTorch.

https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch I see this combination mentioned a decent amount

https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/blob/master/deep_sort_pytorch/deep_sort/sort/tracker.py.

Hi! I have run into DeepSort but I think I am missing something. Once I opened this issue https://github.com/nwojke/cosine_metric_learning/issues/103 while trying to using it, but I think i was not so clear and the guy from DeepSort misunderstanded me. I saw that in DeepSort to train the appearance descriptor they use datasets which uses different views of the same object, for example the same white car will have both pictures in the training and in the test set. Instead I just have one photo for each instance. I think but I am not sure that this framework does not suit my problem due to this. Maybe I will post another question about this since It is a doubt that I am bringing with me.

I require the use of Deep SORT for tracking simulated vehicles, which I will need to retrain a model for since the original model is trained on person re-identification. I am trying to follow the original github repository for this https://github.com/nwojke/cosine_metric_learning but can't figure out how the dataset should be constructed.