ByteTrack is the SOTA tracker in MOT benchmarks with strong detector YOLOX and a simple association strategy only based on motion information.

Motion information (IoU distance) is efficient and effective in short-term tracking, but can not be used for recovering targets after long-time disappear or conditions with moving camera.

So it is important to enhance ByteTrack with a ReID module for long-term tracking, improving the performance under other challenging conditions, such as moving camera.

Some code is borrowed from FairMOT

For now, the results are trained on half of MOT17 and tested on the other half of MOT17. And the performance is lower than the original performance.

Any issue and suggestions are welcome!

tracking results using tracking strategy of ByteTrack, with detection head and ReID head trained together

tracking results using tracking strategy of FairMOT, with detection head and ReID head trained together

Modifications, TODOs and Performance


  • Enhanced ByteTrack with a ReID module (head) following the paradigm of FairMOT.
  • Add a classifier for supervised training of ReID head.
  • Using uncertainty loss in FairMOT for the balance of detection and ReID tasks.
  • Tracking strategy is borrowed from FairMOT


  • support more datasets
  • single class –> multiple class
  • other loss functions for better ReID performance
  • other strategies for multiple tasks balance
  • … …

The following contents is original README in ByteTrack.



ByteTrack is a simple, fast and strong multi-object tracker.

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Zehuan Yuan, Ping Luo, Wenyu Liu, Xinggang Wang

arXiv 2110.06864

Demo Links

Google Colab demo Huggingface Demo Original Paper: ByteTrack
Open In Colab Hugging Face Spaces arXiv 2110.06864


Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 scores ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU.

Tracking performance

Results on MOT challenge test set

MOT17 80.3 77.3 63.1 53.2% 14.5% 25491 83721 2196 29.6
MOT20 77.8 75.2 61.3 69.2% 9.5% 26249 87594 1223 13.7

Visualization results on MOT challenge test set


1. Installing on the host machine

Step1. Install ByteTrack.

git clone
cd ByteTrack
pip3 install -r requirements.txt
python3 develop

Step2. Install pycocotools.

pip3 install cython; pip3 install 'git+'

Step3. Others

pip3 install cython_bbox

2. Docker build

docker build -t bytetrack:latest .

# Startup sample
mkdir -p pretrained && \
mkdir -p YOLOX_outputs && \
xhost +local: && \
docker run --gpus all -it --rm \
-v $PWD/pretrained:/workspace/ByteTrack/pretrained \
-v $PWD/datasets:/workspace/ByteTrack/datasets \
-v $PWD/YOLOX_outputs:/workspace/ByteTrack/YOLOX_outputs \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
--privileged \

Data preparation

Download MOT17, MOT20, CrowdHuman, Cityperson, ETHZ and put them under
/datasets in the following structure:

   |        └——————train
   |        └——————test
   |         └——————Crowdhuman_train
   |         └——————Crowdhuman_val
   |         └——————annotation_train.odgt
   |         └——————annotation_val.odgt
   |        └——————train
   |        └——————test
   |        └——————images
   |        └——————labels_with_ids

Then, you need to turn the datasets to COCO format and mix different training data:

<div class="highlight highlight-source-shell position-relative overflow-auto" data-snippet-clipboard-copy-content="cd
python3 tools/
python3 tools/
python3 tools/
python3 tools/
python3 tools/”>

cd <ByteTrack_HOME>
python3 tools/
python3 tools/
python3 tools/
python3 tools/
python3 tools/