SimpleBaseline2D (ECCV'2018)
```bibtex
@inproceedings{xiao2018simple,
title={Simple baselines for human pose estimation and tracking},
author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={466--481},
year={2018}
}
```
ResNet (CVPR'2016)
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
```
COCO (ECCV'2014)
```bibtex
@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
| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: |
| [pose_resnet_50](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-256x192.py) | 256x192 | 0.718 | 0.898 | 0.796 | 0.774 | 0.934 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-256x192-04af38ce_20220923.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-256x192_20220923.log) |
| [pose_resnet_50](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-384x288.py) | 384x288 | 0.731 | 0.900 | 0.799 | 0.782 | 0.937 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-384x288-7b8db90e_20220923.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-384x288_20220923.log) |
| [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192.py) | 256x192 | 0.728 | 0.904 | 0.809 | 0.783 | 0.942 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192-065d3625_20220926.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192_20220926.log) |
| [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb32-210e_coco-384x288.py) | 384x288 | 0.749 | 0.906 | 0.817 | 0.799 | 0.941 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192-065d3625_20220926.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192_20220926.log) |
| [pose_resnet_152](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-256x192.py) | 256x192 | 0.736 | 0.904 | 0.818 | 0.791 | 0.942 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-256x192-0345f330_20220928.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-256x192_20220928.log) |
| [pose_resnet_152](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-384x288.py) | 384x288 | 0.750 | 0.908 | 0.821 | 0.800 | 0.942 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-384x288-7fbb906f_20220927.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-384x288_20220927.log) |