mmpose/configs/face_2d_keypoint/rtmpose/face6/rtmpose_face6.md

RTMPose (arXiv'2023)

@misc{https://doi.org/10.48550/arxiv.2303.07399,
  doi = {10.48550/ARXIV.2303.07399},
  url = {https://arxiv.org/abs/2303.07399},
  author = {Jiang, Tao and Lu, Peng and Zhang, Li and Ma, Ningsheng and Han, Rui and Lyu, Chengqi and Li, Yining and Chen, Kai},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution 4.0 International}
}

RTMDet (arXiv'2022)

@misc{lyu2022rtmdet,
      title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors},
      author={Chengqi Lyu and Wenwei Zhang and Haian Huang and Yue Zhou and Yudong Wang and Yanyi Liu and Shilong Zhang and Kai Chen},
      year={2022},
      eprint={2212.07784},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

COCO (ECCV'2014)

@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

• Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset.
• Face6 and * denote model trained on 6 public datasets:
  • COCO-Wholebody-Face
  • WFLW
  • 300W
  • COFW
  • Halpe
  • LaPa

Config	Input Size	NME (LaPa)	FLOPS (G)	Download
RTMPose-t*	256x256	1.67	0.652	Model
RTMPose-s*	256x256	1.59	1.119	Model
RTMPose-m*	256x256	1.44	2.852	Model