Compared to the previous version, this is an entirely new version (branch v2)!!!

Please use this version directly, as I have almost rewritten all the code to ensure better readability and improved results, as well as to correct some errors in the past code.

git clone https://github.com/JackWoo0831/Yolov7-tracker.git
git checkout v2  # change to v2 branch !!

🙌 If you have any suggestions for adding trackers, please leave a comment in the Issues section with the paper title or link! Everyone is welcome to contribute to making this repo better.

This repo is a toolbox that implements the tracking-by-detection paradigm multi-object tracker. The detector supports:

• YOLOX
• YOLO v7
• YOLO v8,

and the tracker supports:

• SORT
• DeepSORT
• ByteTrack (ECCV2022)
• Bot-SORT (arxiv2206)
• OCSORT (CVPR2023)
• C_BIoU Track (arxiv2211)
• Strong SORT (IEEE TMM 2023)
• Sparse Track (arxiv 2306)

and the reid model supports:

• OSNet
• Extractor from DeepSort

The highlights are:

• Supporting more trackers than MMTracking
• Rewrite multiple trackers with a unified code style, without the need to configure multiple environments for each tracker
• Modular design, which decouples the detector, tracker, reid model and Kalman filter for easy conducting experiments

• [ x ] Add StrongSort and SparseTrack
• [ x ] Add save video function
• [ x ] Add timer function to calculate fps
• [] Add more ReID modules.

The basic env is:

• Ubuntu 18.04
• Python：3.9, Pytorch: 1.12

Run following commond to install other packages:

pip3 install -r requirements.txt

1. YOLOX:

The version of YOLOX is 0.1.0 (same as ByteTrack). To install it, you can clone the ByteTrack repo somewhere, and run:

https://github.com/ifzhang/ByteTrack.git

python3 setup.py develop

2. YOLO v7:

There is no need to execute addtional steps as the repo itself is based on YOLOv7.

3. YOLO v8:

Please run:

pip3 install ultralytics==8.0.94

If you do not want to test on the specific dataset, instead, you only want to run demos, please skip this section.

No matter what dataset you want to test, please organize it in the following way (YOLO style):

dataset_name
     |---images
           |---train
                 |---sequence_name1
                             |---000001.jpg
                             |---000002.jpg ...
           |---val ...
           |---test ...

     |

You can refer to the codes in ./tools to see how to organize the datasets.

Then, you need to prepare a yaml file to indicate the path so that the code can find the images.

Some examples are in tracker/config_files. The important keys are:

DATASET_ROOT: '/data/xxxx/datasets/MOT17'  # your dataset root
SPLIT: test  # train, test or val
CATEGORY_NAMES:  # same in YOLO training
  - 'pedestrian'

CATEGORY_DICT:
  0: 'pedestrian'

Trackers generally do not require parameters to be trained. Please refer to the training methods of different detectors to train YOLOs.

Some references may help you:

• YOLOX: tracker/yolox_utils/train_yolox.py

• YOLO v7:

python train_aux.py --dataset visdrone --workers 8 --device <$GPU_id$> --batch-size 16 --data data/visdrone_all.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights <$YOLO v7 pretrained model path$> --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml

• YOLO v8: tracker/yolov8_utils/train_yolov8.py

If you only want to run a demo:

python tracker/track_demo.py --obj ${video path or images folder path} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path} --save_images

For example:

python tracker/track_demo.py --obj M0203.mp4 --detector yolov8 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt --save_images

If you want to run trackers on dataset:

python tracker/track.py --dataset ${dataset name, related with the yaml file} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path}

For example:

• SORT: python tracker/track.py --dataset uavdt --detector yolov8 --tracker sort --kalman_format sort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt

• DeepSORT: python tracker/track.py --dataset uavdt --detector yolov7 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov7_UAVDT_35epochs_20230507.pt

• ByteTrack: python tracker/track.py --dataset uavdt --detector yolov8 --tracker bytetrack --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt

• OCSort: python tracker/track.py --dataset uavdt --detector yolov8 --tracker ocsort --kalman_format ocsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt

• C-BIoU Track: python tracker/track.py --dataset uavdt --detector yolov8 --tracker c_bioutrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt

• BoT-SORT: python tracker/track.py --dataset uavdt --detector yolox --tracker botsort --kalman_format bot --detector_model_path weights/yolox_m_uavdt_50epochs.pth.tar

• Strong SORT: python tracker/track.py --dataset uavdt --detector yolov8 --tracker strongsort --kalman_format strongsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt

• Sparse Track: python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt

Coming Soon. As an alternative, after obtaining the result txt file, you can use the Easier to use TrackEval repo.