Yolov5 + StrongSORT with OSNet
Introduction
This repository contains a highly configurable two-stage-tracker that adjusts to different deployment scenarios. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to the tracker of your choice. Supported ones at the moment are: StrongSORT OSNet, OCSORT and ByteTrack. They can track any object that your Yolov5 model was trained to detect.
Installation
git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet.git # clone recursively
cd Yolov5_StrongSORT_OSNet
pip install -r requirements.txt # install dependencies
Tutorials
Tracking
$ python track.py
Tracking methods
$ python track.py --tracking-method strongsort
ocsort
bytetrack
Tracking sources
Tracking can be run on most video formats
$ python track.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Select Yolov5 model
There is a clear trade-off between model inference speed and overall performance. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download. These model can be further optimized for you needs by the export.py script
$ python track.py --source 0 --yolo-weights yolov5n.pt --img 640
yolov5s.tflite
yolov5m.pt
yolov5l.onnx
yolov5x.pt --img 1280
...
Select ReID model
The above applies to StrongSORT models as well. Choose a ReID model based on your needs from this ReID model zoo. These model can be further optimized for you needs by the reid_export.py script
$ python track.py --source 0 --reid-weights osnet_x0_25_market1501.pt
mobilenetv2_x1_4_msmt17.engine
resnet50_msmt17.onnx
osnet_x1_0_msmt17.pt
...
Filter tracked classes
By default the tracker tracks all MS COCO classes.
If you want to track a subset of the classes that you model predicts, add their corresponding index after the classes flag,
python track.py --source 0 --yolo-weights yolov5s.pt --classes 16 17 # COCO yolov5 model. Track cats and dogs, only
Here is a list of all the possible objects that a Yolov5 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero
MOT compliant results
Can be saved to your experiment folder runs/track/<yolo_model>_<deep_sort_model>/
by
python track.py --source ... --save-txt
Cite
If you find this project useful in your research, please consider cite:
@misc{yolov5-strongsort-osnet-2022,
title={Real-time multi-camera multi-object tracker using YOLOv5 and StrongSORT with OSNet},
author={Mikel Broström},
howpublished = {\url{https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet}},
year={2022}
}
Contact
For Yolov5 DeepSort OSNet bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: yolov5.deepsort.pytorch@gmail.com