Bump v1.3.0 (#2907)

CITATION.cff

@@ -1,4 +1,4 @@
-cff-version: 1.2.0
+cff-version: 1.3.0
 message: "If you use this software, please cite it as below."
 authors:
   - name: "MMPose Contributors"

README.md

@@ -102,15 +102,7 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
   ![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2)
 
-- Release RTMW models in various sizes. This provides flexibility to select the right model for different speed and accuracy requirements.
-
-  | 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) |
+- Release [RTMW](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw_cocktail14.md) models in various sizes ranging from RTMW-m to RTMW-x. The input sizes include `256x192` and `384x288`. This provides flexibility to select the right model for different speed and accuracy requirements.
 
 - Support inference of [PoseAnything](/projects/pose_anything). Web demo is available [here](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything).
 
@@ -137,15 +129,14 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
 <br/>
 
-- October 12, 2023: MMPose [v1.2.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) has been officially released, with major updates including:
+- January 4, 2024: MMPose [v1.3.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) has been officially released, with major updates including:
 
-  - Support for new datasets: UBody, 300W-LP.
-  - Support for new algorithms: MotionBERT, DWPose, EDPose, Uniformer.
-  - Migration of Associate Embedding, InterNet, YOLOX-Pose algorithms.
-  - Migration of the DeepFashion2 dataset.
-  - Support for Badcase visualization analysis, multi-dataset evaluation, and keypoint visibility prediction features.
+  - Support for new datasets: ExLPose, H3WB
+  - Release of new RTMPose series models: RTMO, RTMW
+  - Support for new algorithm PoseAnything
+  - Enhanced Inferencer with optional progress bar and improved affinity for one-stage methods
 
-  Please check the complete [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) for more details on the updates brought by MMPose v1.2.0!
+  Please check the complete [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) for more details on the updates brought by MMPose v1.3.0!
 
 ## 0.x / 1.x Migration
 

README_CN.md

@@ -100,15 +100,7 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
   ![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2)
 
-- 发布了不同尺寸的 RTMW 模型，满足不同的使用场景
-
-  | 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) |
+- 发布了不同尺寸的 [RTMW](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw_cocktail14.md) 模型，满足不同的使用场景。模型尺寸覆盖从 RTMW-m 到 RTMW-x 的模型，输入图像尺寸包含 256x192 和 384x288
 
 - 支持了 [PoseAnything](/projects/pose_anything) 的推理。[在线试玩](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything)
 
@@ -135,15 +127,14 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
 <br/>
 
-- 2023-10-12：MMPose [v1.2.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) 正式发布了，主要更新包括:
+- 2024-01-04：MMPose [v1.3.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) 正式发布了，主要更新包括:
 
-  - 支持新数据集：UBody、300W-LP。
-  - 支持新算法：MotionBERT、DWPose、EDPose、Uniformer
-  - 迁移 Associate Embedding、InterNet、YOLOX-Pose 算法。
-  - 迁移 DeepFashion2 数据集。
-  - 支持 Badcase 可视化分析、多数据集评测、关键点可见性预测功能。
+  - 支持新数据集：ExLPose、H3WB
+  - 发布 RTMPose 系列新模型：RTMO、RTMW
+  - 支持新算法 PoseAnything
+  - 推理器 Inferencer 支持可选的进度条、提升与单阶段模型的适配性
 
-  请查看完整的 [版本说明](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) 以了解更多 MMPose v1.2.0 带来的更新!
+  请查看完整的 [版本说明](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) 以了解更多 MMPose v1.3.0 带来的更新!
 
 ## 0.x / 1.x 迁移
 

docs/en/faq.md

@@ -19,6 +19,7 @@ Detailed compatible MMPose and MMCV versions are shown as below. Please choose t
 
 | MMPose version |      MMCV/MMEngine version      |
 | :------------: | :-----------------------------: |
+|     1.3.0      |  mmcv>=2.0.1, mmengine>=0.9.0   |
 |     1.2.0      |  mmcv>=2.0.1, mmengine>=0.8.0   |
 |     1.1.0      |  mmcv>=2.0.1, mmengine>=0.8.0   |
 |     1.0.0      |  mmcv>=2.0.0, mmengine>=0.7.0   |

docs/en/notes/changelog.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## **v1.3.0 (04/01/2024)**
+
+Release note: https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0
+
 ## **v1.2.0 (12/10/2023)**
 
 Release note: https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0

docs/zh_cn/faq.md

@@ -19,6 +19,7 @@ Detailed compatible MMPose and MMCV versions are shown as below. Please choose t
 
 | MMPose version |      MMCV/MMEngine version      |
 | :------------: | :-----------------------------: |
+|     1.3.0      |  mmcv>=2.0.1, mmengine>=0.9.0   |
 |     1.2.0      |  mmcv>=2.0.1, mmengine>=0.8.0   |
 |     1.1.0      |  mmcv>=2.0.1, mmengine>=0.8.0   |
 |     1.0.0      |  mmcv>=2.0.0, mmengine>=0.7.0   |

docs/zh_cn/notes/changelog.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## **v1.3.0 (04/01/2024)**
+
+Release note: https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0
+
 ## **v1.2.0 (12/10/2023)**
 
 Release note: https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0

mmpose/__init__.py

@@ -6,7 +6,7 @@
 from .version import __version__, short_version
 
 mmcv_minimum_version = '2.0.0rc4'
-mmcv_maximum_version = '2.2.0'
+mmcv_maximum_version = '2.3.0'
 mmcv_version = digit_version(mmcv.__version__)
 
 mmengine_minimum_version = '0.6.0'

mmpose/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '1.2.0'
+__version__ = '1.3.0'
 short_version = __version__