mmpose/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

Arch	Input Size	AP	AP50	AP75	AP (E)	AP (M)	AP (H)	ckpt	log
RTMO-s	640x640	0.673	0.882	0.729	0.737	0.682	0.591	ckpt	log
RTMO-m	640x640	0.711	0.897	0.771	0.774	0.719	0.634	ckpt	log
RTMO-l	640x640	0.732	0.907	0.793	0.792	0.741	0.653	ckpt	log
RTMO-l*	640x640	0.838	0.947	0.893	0.888	0.847	0.772	ckpt	log

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