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Demo-Maker / modules / rtmpose / configs / body_2d_keypoint / rtmo / crowdpose / rtmo_crowdpose.md
RTMO
@misc{lu2023rtmo,
      title={{RTMO}: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation},
      author={Peng Lu and Tao Jiang and Yining Li and Xiangtai Li and Kai Chen and Wenming Yang},
      year={2023},
      eprint={2312.07526},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
CrowdPose (CVPR'2019)
@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

Results on COCO val2017

ArchInput SizeAPAP50AP75AP (E)AP (M)AP (H)ckptlog
RTMO-s640x6400.6730.8820.7290.7370.6820.591ckptlog
RTMO-m640x6400.7110.8970.7710.7740.7190.634ckptlog
RTMO-l640x6400.7320.9070.7930.7920.7410.653ckptlog
RTMO-l*640x6400.8380.9470.8930.8880.8470.772ckptlog

* indicates the model is trained using a combined dataset composed of AI Challenger, COCO, CrowdPose, Halpe, MPII, PoseTrack18 and sub-JHMDB.