Yolov5 + Deep Sort with PyTorch

Introduction

This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algorithm which tracks the objects. It can track any object that your Yolov5 model was trained to detect.

Tutorials

• Yolov5 training on Custom Data (link to external repository) 
• DeepSort deep descriptor training (link to external repository) 
• Yolov5 deep_sort pytorch evaluation 

Before you run the tracker

1. Clone the repository recursively:

git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git

If you already cloned and forgot to use --recurse-submodules you can run git submodule update --init

2. Make sure that you fulfill all the requirements: Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install, run:

pip install -r requirements.txt

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 object detection and ReID model

Yolov5

There is a clear trade-off between model inference speed and accuracy. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download

$ python track.py --source 0 --yolo_model yolov5n.pt --img 640
                                          yolov5s.pt
                                          yolov5m.pt
                                          yolov5l.pt 
                                          yolov5x.pt --img 1280
                                            ...

DeepSort

Choose a ReID model based on your needs from this ReID model zoo

$ python track.py --source 0 --deep_sort_model osnet_x1_0
                                               nasnsetmobile
                                               resnext101_32x8d
                                            ...

Filter tracked classes

By default the tracker tracks all MS COCO classes.

If you only want to track persons I recommend you to get these weights for increased performance

python3 track.py --source 0 --yolo_model yolov5/weights/crowdhuman_yolov5m.pt --classes 0  # tracks persons, only

If you want to track a subset of the MS COCO classes, add their corresponding index after the classes flag

python3 track.py --source 0 --yolo_model yolov5s.pt --classes 16 17  # tracks 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 inference/output by

python3 track.py --source ... --save-txt

Cite

If you find this project useful in your research, please consider cite:

@misc{yolov5deepsort2020,
    title={Real-time multi-object tracker using YOLOv5 and deep sort},
    author={Mikel Broström},
    howpublished = {\url{https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch}},
    year={2020}
}