[](https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet/actions/workflows/ci-testing.yml)
## Introduction
This repository contains a highly configurable two-stage-tracker that adjusts to different deployment scenarios. The detections generated by [YOLOv7](https://github.com/WongKinYiu/yolov7), a family of object detection architectures and models pretrained on the COCO dataset, are passed to [StrongSORT](https://github.com/dyhBUPT/StrongSORT)[](https://arxiv.org/pdf/2202.13514.pdf) which combines motion and appearance information based on [OSNet](https://github.com/KaiyangZhou/deep-person-reid)[](https://arxiv.org/abs/1905.00953) in order to tracks the objects. It can track any object that your Yolov7 model was trained to detect.
## Before you run the tracker
1. Clone the repository recursively:
`git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet.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](https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet/blob/main/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
```bash
$ 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
### Yolov7
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 Yolov7 family model for automatic download
```bash
$ python track.py --source 0 --yolo-weights yolov7.pt --img 640
yolov7x.pt --img 640
yolov7-e6e.pt --img 1280
...
```
### StrongSORT
The above applies to StrongSORT models as well. Choose a ReID model based on your needs from this ReID [model zoo](https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO)
```bash
$ python track.py --source 0 --strong-sort-weights osnet_x0_25_market1501.pt
osnet_x0_5_market1501.pt
osnet_x0_75_msmt17.pt
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 MS COCO classes, add their corresponding index after the classes flag
```bash
python track.py --source 0 --yolo-weights yolov7.pt --classes 16 17 # tracks cats and dogs, only
```
[Here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/) is a list of all the possible objects that a Yolov7 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/