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Merge pull request #2515 from open-mmlab/dev-1.x
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Dev 1.x
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@Tau-J
Tau-J authored Jul 4, 2023
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include requirements/*.txt
include mmpose/.mim/model-index.yml
include mmpose/.mim/dataset-index.yml
recursive-include mmpose/.mim/configs *.py *.yml
recursive-include mmpose/.mim/tools *.py *.sh
recursive-include mmpose/.mim/demo *.py
55 changes: 31 additions & 24 deletions README.md
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[![actions](https://github.com/open-mmlab/mmpose/workflows/build/badge.svg)](https://github.com/open-mmlab/mmpose/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmpose/branch/latest/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmpose)
[![PyPI](https://img.shields.io/pypi/v/mmpose)](https://pypi.org/project/mmpose/)
[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/blob/master/LICENSE)
[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/blob/main/LICENSE)
[![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/issues)
[![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/issues)

Expand Down Expand Up @@ -63,7 +63,7 @@ English | [简体中文](README_CN.md)
MMPose is an open-source toolbox for pose estimation based on PyTorch.
It is a part of the [OpenMMLab project](https://github.com/open-mmlab).

The master branch works with **PyTorch 1.8+**.
The main branch works with **PyTorch 1.8+**.

https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4

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## What's New

- We are excited to release **YOLOX-Pose**, a One-Stage multi-person pose estimation model based on YOLOX. Checkout our [project page](/projects/yolox-pose/) for more details.
- We are glad to support 3 new datasets:
- (CVPR 2023) [Human-Art](https://github.com/IDEA-Research/HumanArt)
- (CVPR 2022) [Animal Kingdom](https://github.com/sutdcv/Animal-Kingdom)
- (AAAI 2020) [LaPa](https://github.com/JDAI-CV/lapa-dataset/)

![yolox-pose_intro](https://user-images.githubusercontent.com/26127467/226655503-3cee746e-6e42-40be-82ae-6e7cae2a4c7e.jpg)
![image](https://github.com/open-mmlab/mmpose/assets/13503330/c9171dbb-7e7a-4c39-98e3-c92932182efb)

- Welcome to [*projects of MMPose*](/projects/README.md), where you can access to the latest features of MMPose, and share your ideas and codes with the community at once. Contribution to MMPose will be simple and smooth:

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- Build individual projects with full power of MMPose but not bound up with heavy frameworks
- Checkout new projects:
- [RTMPose](/projects/rtmpose/)
- [YOLOX-Pose](/projects/yolox-pose/)
- [YOLOX-Pose](/projects/yolox_pose/)
- [MMPose4AIGC](/projects/mmpose4aigc/)
- [Simple Keypoints](/projects/skps/)
- Become a contributors and make MMPose greater. Start your journey from the [example project](/projects/example_project/)

<br/>

- 2022-04-06: MMPose [v1.0.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.0.0) is officially released, with the main updates including:
- 2023-07-04: MMPose [v1.1.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.1.0) is officially released, with the main updates including:

- Release of [YOLOX-Pose](/projects/yolox-pose/), a One-Stage multi-person pose estimation model based on YOLOX
- Development of [MMPose for AIGC](/projects/mmpose4aigc/) based on RTMPose, generating high-quality skeleton images for Pose-guided AIGC projects
- Support for OpenPose-style skeleton visualization
- More complete and user-friendly [documentation and tutorials](https://mmpose.readthedocs.io/en/latest/overview.html)
- Support new datasets: Human-Art, Animal Kingdom and LaPa.
- Support new config type that is more user-friendly and flexible.
- Improve RTMPose with better performance.
- Migrate 3D pose estimation models on h36m.
- Inference speedup and webcam inference with all demo scripts.

Please refer to the [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.0.0) for more updates brought by MMPose v1.0.0!
Please refer to the [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.1.0) for more updates brought by MMPose v1.1.0!

## 0.x / 1.x Migration

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| HigherHRNet (CVPR 2020) | |
| DeepPose (CVPR 2014) | done |
| RLE (ICCV 2021) | done |
| SoftWingloss (TIP 2021) | |
| VideoPose3D (CVPR 2019) | in progress |
| SoftWingloss (TIP 2021) | done |
| VideoPose3D (CVPR 2019) | done |
| Hourglass (ECCV 2016) | done |
| LiteHRNet (CVPR 2021) | done |
| AdaptiveWingloss (ICCV 2019) | done |
| SimpleBaseline2D (ECCV 2018) | done |
| PoseWarper (NeurIPS 2019) | |
| SimpleBaseline3D (ICCV 2017) | in progress |
| SimpleBaseline3D (ICCV 2017) | done |
| HMR (CVPR 2018) | |
| UDP (CVPR 2020) | done |
| VIPNAS (CVPR 2021) | done |
| Wingloss (CVPR 2018) | |
| Wingloss (CVPR 2018) | done |
| DarkPose (CVPR 2020) | done |
| Associative Embedding (NIPS 2017) | in progress |
| VoxelPose (ECCV 2020) | |
Expand Down Expand Up @@ -214,13 +219,13 @@ A summary can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/lates
- [x] [DeepPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014)
- [x] [CPM](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#cpm-cvpr-2016) (CVPR'2016)
- [x] [Hourglass](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hourglass-eccv-2016) (ECCV'2016)
- [ ] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017)
- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017)
- [ ] [Associative Embedding](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017)
- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018)
- [x] [DSNT](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#dsnt-2018) (ArXiv'2021)
- [x] [HRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019)
- [x] [IPR](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#ipr-eccv-2018) (ECCV'2018)
- [ ] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019)
- [x] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019)
- [x] [HRNetv2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019)
- [x] [MSPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019)
- [x] [SCNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#scnet-cvpr-2020) (CVPR'2020)
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<details close>
<summary><b>Supported techniques:</b></summary>

- [ ] [FPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fpn-cvpr-2017) (CVPR'2017)
- [ ] [FP16](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
- [ ] [Wingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
- [ ] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
- [x] [FPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fpn-cvpr-2017) (CVPR'2017)
- [x] [FP16](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
- [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
- [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [ ] [Albumentations](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#albumentations-information-2020) (Information'2020)
- [ ] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [Albumentations](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#albumentations-information-2020) (Information'2020)
- [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [RLE](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#rle-iccv-2021) (ICCV'2021)

</details>
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- [x] [InterHand2.6M](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020)
- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021)
- [x] [Horse-10](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021)
- [x] [Human-Art](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#human-art-cvpr-2023) \[[homepage](https://idea-research.github.io/HumanArt/)\] (CVPR'2023)
- [x] [LaPa](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#lapa-aaai-2020) \[[homepage](https://github.com/JDAI-CV/lapa-dataset)\] (AAAI'2020)

</details>

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### Model Request

We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/9).
We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/2258).

## Contributing

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