<!-- [ALGORITHM] -->

<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary>

```bibtex
@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}
}
```

</details>

<!-- [DATASET] -->

<details>
<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-030-58545-7_12">COCO-WholeBody (ECCV'2020)</a></summary>

```bibtex
@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}
```

</details>

Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset

| Arch                                    | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR | Hand AP | Hand AR | Whole AP | Whole AR |                   ckpt                   |                   log                   |
| :-------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :--------------------------------------: | :-------------------------------------: |
| [pose_hrnet_w32](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w32_coco_wholebody_256x192.py) |  256x192   |  0.700  |  0.746  |  0.567  |  0.645  |  0.637  |  0.688  |  0.473  |  0.546  |  0.553   |  0.626   | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_wholebody_256x192-853765cd_20200918.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_wholebody_256x192_20200918.log.json) |
| [pose_hrnet_w32](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w32_coco_wholebody_384x288.py) |  384x288   |  0.701  |  0.773  |  0.586  |  0.692  |  0.727  |  0.783  |  0.516  |  0.604  |  0.586   |  0.674   | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_wholebody_384x288-78cacac3_20200922.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_wholebody_384x288_20200922.log.json) |
| [pose_hrnet_w48](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_256x192.py) |  256x192   |  0.700  |  0.776  |  0.672  |  0.785  |  0.656  |  0.743  |  0.534  |  0.639  |  0.579   |  0.681   | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_256x192-643e18cb_20200922.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_256x192_20200922.log.json) |
| [pose_hrnet_w48](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288.py) |  384x288   |  0.722  |  0.790  |  0.694  |  0.799  |  0.777  |  0.834  |  0.587  |  0.679  |  0.631   |  0.716   | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288-6e061c6a_20200922.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_20200922.log.json) |
