Yolox

All models are YOLOX-X (depth=1.33, width=1.25), input size 800×1440 (except VisDrone: 896×1600), single pedestrian class (except VisDrone: 10 classes). Size ~756 MB (VisDrone: 806 MB).

Origin & Citation

The DanceTrack and MOT20 YOLOX-X weights were trained following the procedure described in:

Focusing on Tracks for Online Multi-Object Tracking Kyujin Shim, Kangwook Ko, Yujin Yang, Changick Kim Korea Advanced Institute of Science and Technology (KAIST) CVPR 2025

The SportsMOT YOLOX-X weights were trained following the procedure described in:

Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports Hsiang-Wei Huang, Cheng-Yen Yang, Jiacheng Sun, Pyong-Kun Kim, Kwang-Ju Kim, Kyoungoh Lee, Chung-I Huang, Jenq-Neng Hwang Information Processing Lab, University of Washington; ETRI; National Center for High-Performance Computing arXiv:2306.13074 (2023)

The VisDrone YOLOX-X weights were trained following the procedure described in:

Uncertainty-aware Unsupervised Multi-Object Tracking Kai Liu, Sheng Jin, Zhihang Fu, Ze Chen, Rongxin Jiang, Jieping Ye Zhejiang University; Alibaba DAMO Academy arXiv:2307.15409 (2023)

The MOT17 YOLOX-X weights follow the ByteTrack-style training recipe originally proposed in:

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 (2021)

Usage with BoxMOT

These models are automatically downloaded when running BoxMOT benchmarks:

# MOT17 evaluation (auto-downloads yolox_x_MOT17_ablation.pt)
boxmot eval --benchmark mot17 --split ablation --tracker boosttrack

# SportsMOT evaluation (auto-downloads yolox_x_sportsmot.pt)
boxmot eval --benchmark sportsmot --split ablation --tracker boosttrack

Or use directly in Python:

from boxmot import Boxmot

# SportsMOT with YOLOX-X detector
tracker = Boxmot(detector="yolox_x_sportsmot", tracker="boosttrack")
results = tracker.track(source="path/to/video.mp4")

License

The YOLOX architecture is released under the Apache 2.0 License. Weights are provided for research purposes. Please cite the original papers when using these models.

@inproceedings{shim2025tracktrack,
  title={Focusing on Tracks for Online Multi-Object Tracking},
  author={Shim, Kyujin and Ko, Kangwook and Yang, Yujin and Kim, Changick},
  booktitle={CVPR},
  year={2025}
}

@article{huang2023deep,
  title={Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports},
  author={Huang, Hsiang-Wei and Yang, Cheng-Yen and Sun, Jiacheng and Kim, Pyong-Kun and Kim, Kwang-Ju and Lee, Kyoungoh and Huang, Chung-I and Hwang, Jenq-Neng},
  journal={arXiv preprint arXiv:2306.13074},
  year={2023}
}

@article{liu2023u2mot,
  title={Uncertainty-aware Unsupervised Multi-Object Tracking},
  author={Liu, Kai and Jin, Sheng and Fu, Zhihang and Chen, Ze and Jiang, Rongxin and Ye, Jieping},
  journal={arXiv preprint arXiv:2307.15409},
  year={2023}
}

@article{zhang2021bytetrack,
  title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
  author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
  journal={arXiv preprint arXiv:2110.06864},
  year={2021}
}

@article{ge2021yolox,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}

yolox_x_MOT17_test.pt

pt · 756 MB · SHA-256 e3945f3523fd…cfba · Hugging Face