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76 changes: 76 additions & 0 deletions configs/body_2d_keypoint/rtmpose/body8/rtmpose_body8.md
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<!-- [ALGORITHM] -->

<details>
<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-030-58580-8_27">RTMPose (arXiv'2023)</a></summary>

```bibtex
@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}
}
```

</details>

<!-- [BACKBONE] -->

<details>
<summary align="right"><a href="https://arxiv.org/abs/2212.07784">RTMDet (arXiv'2022)</a></summary>

```bibtex
@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}
}
```

</details>

<!-- [DATASET] -->

<details>
<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48">COCO (ECCV'2014)</a></summary>

```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}
}
```

</details>

- Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset.
- `*` denotes model trained on 7 public datasets:
- [AI Challenger](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#aic)
- [MS COCO](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#coco)
- [CrowdPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#crowdpose)
- [MPII](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#mpii)
- [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#sub-jhmdb-dataset)
- [Halpe](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_wholebody_keypoint.html#halpe)
- [PoseTrack18](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#posetrack18)
- `Body8` denotes the addition of the [OCHuman](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#ochuman) dataset, in addition to the 7 datasets mentioned above, for evaluation.

| Config | Input Size | AP<sup><br>(COCO) | [email protected]<sup><br>(Body8) | AUC<sup><br>(Body8) | EPE<sup><br>(Body8) | Params(M) | FLOPS(G) | Download |
| :--------------------------------------------: | :--------: | :---------------: | :---------------------: | :-----------------: | :-----------------: | :-------: | :------: | :-----------------------------------------------: |
| [RTMPose-t\*](/configs/body_2d_keypoint/rtmpose/body8/rtmpose-t_8xb256-210e_body8-256x192.py) | 256x192 | 65.9 | 91.44 | 63.18 | 19.45 | 3.34 | 0.36 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-t_simcc-body7_pt-body7_420e-256x192-026a1439_20230504.pth) |
| [RTMPose-s\*](/configs/body_2d_keypoint/rtmpose/body8/rtmpose-s_8xb256-210e_body8-256x192.py) | 256x192 | 69.7 | 92.45 | 65.15 | 17.85 | 5.47 | 0.68 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-body7_pt-body7_420e-256x192-acd4a1ef_20230504.pth) |
| [RTMPose-m\*](/configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-210e_body8-256x192.py) | 256x192 | 74.9 | 94.25 | 68.59 | 15.12 | 13.59 | 1.93 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth) |
| [RTMPose-l\*](/configs/body_2d_keypoint/rtmpose/body8/rtmpose-l_8xb256-210e_body8-256x192.py) | 256x192 | 76.7 | 95.08 | 70.14 | 13.79 | 27.66 | 4.16 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-256x192-4dba18fc_20230504.pth) |
| [RTMPose-m\*](/configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-210e_body8-384x288.py) | 384x288 | 76.6 | 94.64 | 70.38 | 13.98 | 13.72 | 4.33 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth) |
| [RTMPose-l\*](/configs/body_2d_keypoint/rtmpose/body8/rtmpose-l_8xb256-210e_body8-384x288.py) | 384x288 | 78.3 | 95.36 | 71.58 | 13.08 | 27.79 | 9.35 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-384x288-3f5a1437_20230504.pth) |
93 changes: 93 additions & 0 deletions configs/body_2d_keypoint/rtmpose/body8/rtmpose_body8.yml
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Collections:
- Name: RTMPose
Paper:
Title: "RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose"
URL: https://arxiv.org/abs/2303.07399
README: https://github.com/open-mmlab/mmpose/blob/main/projects/rtmpose/README.md
Models:
- Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-t_8xb256-210e_body8-256x192.py
In Collection: RTMPose
Metadata:
Architecture: &id001
- RTMPose
Training Data: &id002
- AI Challenger
- COCO
- CrowdPose
- MPII
- sub-JHMDB
- Halpe
- PoseTrack18
Name: rtmpose-t_8xb256-210e_body8-256x192
Results:
- Dataset: Body8
Metrics:
AP: 0.659
[email protected]: 0.914
Task: Body 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-t_simcc-body7_pt-body7_420e-256x192-026a1439_20230504.pth
- Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-s_8xb256-210e_body8-256x192.py
In Collection: RTMPose
Metadata:
Architecture: *id001
Training Data: *id002
Name: rtmpose-s_8xb256-210e_body8-256x192
Results:
- Dataset: Body8
Metrics:
AP: 0.697
[email protected]: 0.925
Task: Body 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-body7_pt-body7_420e-256x192-acd4a1ef_20230504.pth
- Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-210e_body8-256x192.py
In Collection: RTMPose
Metadata:
Architecture: *id001
Training Data: *id002
Name: rtmpose-m_8xb256-210e_body8-256x192
Results:
- Dataset: Body8
Metrics:
AP: 0.749
[email protected]: 0.943
Task: Body 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth
- Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-l_8xb256-210e_body8-256x192.py
In Collection: RTMPose
Metadata:
Architecture: *id001
Training Data: *id002
Name: rtmpose-l_8xb256-210e_body8-256x192
Results:
- Dataset: Body8
Metrics:
AP: 0.767
[email protected]: 0.951
Task: Body 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-256x192-4dba18fc_20230504.pth
- Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-210e_body8-384x288.py
In Collection: RTMPose
Metadata:
Architecture: *id001
Training Data: *id002
Name: rtmpose-m_8xb256-210e_body8-384x288
Results:
- Dataset: Body8
Metrics:
AP: 0.766
[email protected]: 0.946
Task: Body 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth
- Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-l_8xb256-210e_body8-384x288.py
In Collection: RTMPose
Metadata:
Architecture: *id001
Training Data: *id002
Name: rtmpose-l_8xb256-210e_body8-384x288
Results:
- Dataset: Body8
Metrics:
AP: 0.783
[email protected]: 0.964
Task: Body 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-384x288-3f5a1437_20230504.pth
2 changes: 1 addition & 1 deletion configs/face_2d_keypoint/rtmpose/README.md
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| Model | Input Size | NME | Details and Download |
| :-------: | :--------: | :--: | :---------------------------------------: |
| RTMPose-m | 256x256 | 1.29 | [rtmpose_lapa.md](./wflw/rtmpose_lapa.md) |
| RTMPose-m | 256x256 | 1.29 | [rtmpose_lapa.md](./lapa/rtmpose_lapa.md) |
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data_root=data_root,
data_mode=data_mode,
ann_file='annotations/lapa_train.json',
data_prefix=dict(img='train/images/'),
data_prefix=dict(img=''),
pipeline=train_pipeline,
))
val_dataloader = dict(
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data_root=data_root,
data_mode=data_mode,
ann_file='annotations/lapa_val.json',
data_prefix=dict(img='val/images/'),
data_prefix=dict(img=''),
test_mode=True,
pipeline=val_pipeline,
))
Expand All @@ -216,7 +216,7 @@
data_root=data_root,
data_mode=data_mode,
ann_file='annotations/lapa_test.json',
data_prefix=dict(img='test/images/'),
data_prefix=dict(img=''),
test_mode=True,
pipeline=val_pipeline,
))
Expand Down
2 changes: 1 addition & 1 deletion configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md
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</details>

Results on COCO-WholeBody-Face val set
Results on LaPa val set

| Arch | Input Size | NME | ckpt | log |
| :------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :------------------------------------------------------------: |
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3 changes: 2 additions & 1 deletion configs/face_2d_keypoint/topdown_regression/README.md
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| Model | Input Size | NME | ckpt | log |
| :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: |
| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_wflw-256x256.py) | 256x256 | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) |
| [ResNet-50+SoftWingLoss](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_softwingloss_8xb64-210e_wflw-256x256.py) | 256x256 | 4.67 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss_20211212.log.json) |
| [ResNet-50+WingLoss](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_wingloss_8xb64-210e_wflw-256x256.py) | 256x256 | 4.67 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_wingloss-f82a5e53_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_wingloss_20210303.log.json) |
| [ResNet-50+SoftWingLoss](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_softwingloss_8xb64-210e_wflw-256x256.py) | 256x256 | 4.44 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss_20211212.log.json) |
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<!-- [ALGORITHM] -->

<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2014/html/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.html">DeepPose (CVPR'2014)</a></summary>

```bibtex
@inproceedings{toshev2014deeppose,
title={Deeppose: Human pose estimation via deep neural networks},
author={Toshev, Alexander and Szegedy, Christian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={1653--1660},
year={2014}
}
```

</details>

<!-- [BACKBONE] -->

<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html">ResNet (CVPR'2016)</a></summary>

```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}
}
```

</details>

<!-- [ALGORITHM] -->

<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2018/html/Feng_Wing_Loss_for_CVPR_2018_paper.html">Wingloss (CVPR'2018)</a></summary>

```bibtex
@inproceedings{feng2018wing,
title={Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks},
author={Feng, Zhen-Hua and Kittler, Josef and Awais, Muhammad and Huber, Patrik and Wu, Xiao-Jun},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on},
year={2018},
pages ={2235-2245},
organization={IEEE}
}
```

</details>

<!-- [DATASET] -->

<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Look_at_Boundary_CVPR_2018_paper.html">WFLW (CVPR'2018)</a></summary>

```bibtex
@inproceedings{wu2018look,
title={Look at boundary: A boundary-aware face alignment algorithm},
author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={2129--2138},
year={2018}
}
```

</details>

Results on WFLW dataset

The model is trained on WFLW train set.

| Model | Input Size | NME | ckpt | log |
| :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: |
| [ResNet-50+WingLoss](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_wingloss_8xb64-210e_wflw-256x256.py) | 256x256 | 4.67 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_wingloss-f82a5e53_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_wingloss_20210303.log.json) |
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Models:
- Config: configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_wingloss_8xb64-210e_wflw-256x256.py
In Collection: ResNet
Metadata:
Architecture:
- DeepPose
- ResNet
- WingLoss
Training Data: WFLW
Name: td-reg_res50_wingloss_8xb64-210e_wflw-256x256
Results:
- Dataset: WFLW
Metrics:
NME: 4.67
Task: Face 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_wingloss-f82a5e53_20210303.pth
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