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Demo-Maker / modules / rtmpose / configs / body_2d_keypoint / rtmpose / humanart / rtmpose_humanart.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}
}
Human-Art (CVPR'2023)
@inproceedings{ju2023humanart,
    title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes},
    author={Ju, Xuan and Zeng, Ailing and Jianan, Wang and Qiang, Xu and Lei, Zhang},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
    year={2023}}

Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset

ArchInput SizeAPAP50AP75ARAR50ckptlog
rtmpose-t-coco256x1920.1610.2830.1540.2210.373ckptlog
rtmpose-t-humanart-coco256x1920.2490.3950.2560.3230.485ckptlog
rtmpose-s-coco256x1920.1990.3280.1980.2610.418ckptlog
rtmpose-s-humanart-coco256x1920.3110.4620.3230.3810.540ckptlog
rtmpose-m-coco256x1920.2390.3720.2430.3020.455ckptlog
rtmpose-m-humanart-coco256x1920.3550.5030.3770.4170.568ckptlog
rtmpose-l-coco256x1920.2600.3930.2670.3230.472ckptlog
rtmpose-l-humanart-coco256x1920.3780.5210.3990.4420.584ckptlog

Results on Human-Art validation dataset with ground-truth bounding-box

ArchInput SizeAPAP50AP75ARAR50ckptlog
rtmpose-t-coco256x1920.4440.7250.4530.4880.750ckptlog
rtmpose-t-humanart-coco256x1920.6550.8720.7200.6930.890ckptlog
rtmpose-s-coco256x1920.4800.7390.4980.5210.763ckptlog
rtmpose-s-humanart-coco256x1920.6980.8930.7680.7320.903ckptlog
rtmpose-m-coco256x1920.5320.7650.5630.5710.789ckptlog
rtmpose-m-humanart-coco256x1920.7280.8950.7910.7590.906ckptlog
rtmpose-l-coco256x1920.5640.7890.6020.5990.808ckptlog
rtmpose-l-humanart-coco256x1920.7530.9050.8120.7830.915ckptlog

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

ArchInput SizeAPAP50AP75ARAR50ckptlog
rtmpose-t-coco256x1920.6820.8830.7590.7360.920ckptlog
rtmpose-t-humanart-coco256x1920.6650.8750.7390.7210.916ckptlog
rtmpose-s-coco256x1920.7160.8920.7890.7680.929ckptlog
rtmpose-s-humanart-coco256x1920.7060.8880.7800.7590.928ckptlog
rtmpose-m-coco256x1920.7460.8990.8170.7950.935ckptlog
rtmpose-m-humanart-coco256x1920.7250.8920.7950.7750.929ckptlog
rtmpose-l-coco256x1920.7580.9060.8260.8060.942ckptlog
rtmpose-l-humanart-coco256x1920.7480.9010.8160.7960.938ckptlog

Results on COCO val2017 with ground-truth bounding box

ArchInput SizeAPAP50AP75ARAR50ckptlog
rtmpose-t-humanart-coco256x1920.6790.8950.7550.7100.907ckptlog
rtmpose-s-humanart-coco256x1920.7250.9160.7980.7530.925ckptlog
rtmpose-m-humanart-coco256x1920.7440.9160.8180.7700.930ckptlog
rtmpose-l-humanart-coco256x1920.7700.9270.8400.7940.939ckptlog