mmpose/configs/body_2d_keypoint/topdown_heatmap/posetrack18/hrnet_posetrack18.md

HRNet (CVPR'2019)

@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}

PoseTrack18 (CVPR'2018)

@inproceedings{andriluka2018posetrack,
  title={Posetrack: A benchmark for human pose estimation and tracking},
  author={Andriluka, Mykhaylo and Iqbal, Umar and Insafutdinov, Eldar and Pishchulin, Leonid and Milan, Anton and Gall, Juergen and Schiele, Bernt},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5167--5176},
  year={2018}
}

Results on PoseTrack2018 val with ground-truth bounding boxes

Arch	Input Size	Head	Shou	Elb	Wri	Hip	Knee	Ankl	Total	ckpt	log
pose_hrnet_w32	256x192	86.2	89.0	84.5	79.2	82.3	82.5	78.7	83.4	ckpt	log
pose_hrnet_w32	384x288	87.1	89.0	85.1	80.2	80.6	82.8	79.6	83.7	ckpt	log
pose_hrnet_w48	256x192	88.3	90.2	86.0	81.0	80.7	83.3	80.6	84.6	ckpt	log
pose_hrnet_w48	384x288	87.8	90.0	86.2	81.3	81.0	83.4	80.9	84.6	ckpt	log

The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18.

Results on PoseTrack2018 val with MMDetection pre-trained Cascade R-CNN (X-101-64x4d-FPN) human detector

Arch	Input Size	Head	Shou	Elb	Wri	Hip	Knee	Ankl	Total	ckpt	log
pose_hrnet_w32	256x192	78.0	82.9	79.5	73.8	76.9	76.6	70.2	76.9	ckpt	log
pose_hrnet_w32	384x288	79.9	83.6	80.4	74.5	74.8	76.1	70.5	77.3	ckpt	log
pose_hrnet_w48	256x192	80.1	83.4	80.6	74.8	74.3	76.8	70.5	77.4	ckpt	log
pose_hrnet_w48	384x288	80.2	83.8	80.9	75.2	74.7	76.7	71.7	77.8	ckpt	log

The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18.