From 575f65ad0ca273f5d5588751c36ce54d9c013388 Mon Sep 17 00:00:00 2001 From: Peng Lu Date: Wed, 3 Jan 2024 13:52:02 +0800 Subject: [PATCH] [Docs] update readme (#2902) --- README.md | 25 +++++++++++++++---------- README_CN.md | 25 +++++++++++++++---------- docs/en/index.rst | 1 + 3 files changed, 31 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index b5a528da0b..d7017e63a3 100644 --- a/README.md +++ b/README.md @@ -98,21 +98,26 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb- ## What's New -- We have added support for two new datasets: +- Release [RTMO](/projects/rtmo), a state-of-the-art real-time method for multi-person pose estimation. - - (CVPR 2023) [UBody](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ubody-cvpr-2023) - - [300W-LP](https://github.com/open-mmlab/mmpose/tree/main/configs/face_2d_keypoint/topdown_heatmap/300wlp) + ![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2) -- Support for four new algorithms: +- Release RTMW models in various sizes. This provides flexibility to select the right model for different speed and accuracy requirements. - - (ICCV 2023) [MotionBERT](https://github.com/open-mmlab/mmpose/tree/main/configs/body_3d_keypoint/motionbert) - - (ICCVW 2023) [DWPose](https://github.com/open-mmlab/mmpose/tree/main/configs/wholebody_2d_keypoint/dwpose) - - (ICLR 2023) [EDPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo/body_2d_keypoint.html#edpose-edpose-on-coco) - - (ICLR 2022) [Uniformer](https://github.com/open-mmlab/mmpose/tree/main/projects/uniformer) + | Arch | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR | Hand AP | Hand AR | Whole AP | Whole AR | ckpt | + | :---------------------------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :---------------------------------------------------------: | + | [rtmw-m](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-m_8xb1024-270e_cocktail14-256x192.py) | 256x192 | 0.676 | 0.747 | 0.671 | 0.794 | 0.783 | 0.854 | 0.491 | 0.604 | 0.582 | 0.673 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-l-m_simcc-cocktail14_270e-256x192-20231122.pth) | + | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb1024-270e_cocktail14-256x192.py) | 256x192 | 0.743 | 0.807 | 0.763 | 0.868 | 0.834 | 0.889 | 0.598 | 0.701 | 0.660 | 0.746 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-256x192-20231122.pth) | + | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb704-270e_cocktail14-256x192.py) | 256x192 | 0.746 | 0.808 | 0.770 | 0.869 | 0.844 | 0.896 | 0.610 | 0.710 | 0.672 | 0.752 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-256x192-13a2546d_20231208.pth) | + | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb320-270e_cocktail14-384x288.py) | 384x288 | 0.761 | 0.824 | 0.793 | 0.885 | 0.884 | 0.921 | 0.663 | 0.752 | 0.701 | 0.780 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-384x288-20231122.pth) | + | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb320-270e_cocktail14-384x288.py) | 384x288 | 0.763 | 0.826 | 0.796 | 0.888 | 0.884 | 0.923 | 0.664 | 0.755 | 0.702 | 0.781 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-384x288-f840f204_20231122.pth) | -- Released the first whole-body pose estimation model, RTMW, with accuracy exceeding 70 AP on COCO-Wholebody. For details, refer to [RTMPose](/projects/rtmpose/). [Try it now!](https://openxlab.org.cn/apps/detail/mmpose/RTMPose) +- Support inference of [PoseAnything](/projects/pose_anything). Web demo is available [here](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything). -![rtmw](https://github.com/open-mmlab/mmpose/assets/13503330/635c4618-c459-45e8-84a5-eb68cf338d00) +- Support for two new datasets: + + - (CVPR 2023) [ExLPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#exlpose-dataset) + - (ICCV 2023) [H3WB](/docs/en/dataset_zoo/3d_wholebody_keypoint.md) - Welcome to use the [*MMPose project*](/projects/README.md). Here, you can discover the latest features and algorithms in MMPose and quickly share your ideas and code implementations with the community. Adding new features to MMPose has become smoother: diff --git a/README_CN.md b/README_CN.md index c8ad583fdc..41789e85d6 100644 --- a/README_CN.md +++ b/README_CN.md @@ -96,21 +96,26 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb- ## 最新进展 -- 我们支持了两个新的数据集: +- 发布了单阶段实时多人姿态估计模型 [RTMO](/projects/rtmo)。相比 RTMPose 在多人场景下性能更优 + + ![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2) - - (CVPR 2023) [UBody](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ubody-cvpr-2023) - - [300W-LP](https://github.com/open-mmlab/mmpose/tree/main/configs/face_2d_keypoint/topdown_heatmap/300wlp) +- 发布了不同尺寸的 RTMW 模型，满足不同的使用场景 -- 支持四个新算法： + | Arch | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR | Hand AP | Hand AR | Whole AP | Whole AR | ckpt | + | :---------------------------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :---------------------------------------------------------: | + | [rtmw-m](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-m_8xb1024-270e_cocktail14-256x192.py) | 256x192 | 0.676 | 0.747 | 0.671 | 0.794 | 0.783 | 0.854 | 0.491 | 0.604 | 0.582 | 0.673 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-l-m_simcc-cocktail14_270e-256x192-20231122.pth) | + | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb1024-270e_cocktail14-256x192.py) | 256x192 | 0.743 | 0.807 | 0.763 | 0.868 | 0.834 | 0.889 | 0.598 | 0.701 | 0.660 | 0.746 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-256x192-20231122.pth) | + | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb704-270e_cocktail14-256x192.py) | 256x192 | 0.746 | 0.808 | 0.770 | 0.869 | 0.844 | 0.896 | 0.610 | 0.710 | 0.672 | 0.752 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-256x192-13a2546d_20231208.pth) | + | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb320-270e_cocktail14-384x288.py) | 384x288 | 0.761 | 0.824 | 0.793 | 0.885 | 0.884 | 0.921 | 0.663 | 0.752 | 0.701 | 0.780 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-384x288-20231122.pth) | + | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb320-270e_cocktail14-384x288.py) | 384x288 | 0.763 | 0.826 | 0.796 | 0.888 | 0.884 | 0.923 | 0.664 | 0.755 | 0.702 | 0.781 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-384x288-f840f204_20231122.pth) | - - (ICCV 2023) [MotionBERT](https://github.com/open-mmlab/mmpose/tree/main/configs/body_3d_keypoint/motionbert) - - (ICCVW 2023) [DWPose](https://github.com/open-mmlab/mmpose/tree/main/configs/wholebody_2d_keypoint/dwpose) - - (ICLR 2023) [EDPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo/body_2d_keypoint.html#edpose-edpose-on-coco) - - (ICLR 2022) [Uniformer](https://github.com/open-mmlab/mmpose/tree/main/projects/uniformer) +- 支持了 [PoseAnything](/projects/pose_anything) 的推理。[在线试玩](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything) -- 发布首个在 COCO-Wholebody 上精度超过 70 AP 的全身姿态估计模型 RTMW，具体请参考 [RTMPose](/projects/rtmpose/)。[在线试玩](https://openxlab.org.cn/apps/detail/mmpose/RTMPose) +- 我们支持了两个新的数据集: -![rtmw](https://github.com/open-mmlab/mmpose/assets/13503330/635c4618-c459-45e8-84a5-eb68cf338d00) + - (CVPR 2023) [ExLPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#exlpose-dataset) + - (ICCV 2023) [H3WB](/docs/en/dataset_zoo/3d_wholebody_keypoint.md) - 欢迎使用 [*MMPose 项目*](/projects/README.md)。在这里，您可以发现 MMPose 中的最新功能和算法，并且可以通过最快的方式与社区分享自己的创意和代码实现。向 MMPose 中添加新功能从此变得简单丝滑： diff --git a/docs/en/index.rst b/docs/en/index.rst index cc3782925e..2c96751d7a 100644 --- a/docs/en/index.rst +++ b/docs/en/index.rst @@ -81,6 +81,7 @@ You can change the documentation language at the lower-left corner of the page. dataset_zoo/2d_animal_keypoint.md dataset_zoo/3d_body_keypoint.md dataset_zoo/3d_hand_keypoint.md + dataset_zoo/3d_wholebody_keypoint.md .. toctree:: :maxdepth: 1