[Docs] Update repo list

README.md

@@ -329,21 +329,21 @@ This project is released under the [Apache 2.0 license](LICENSE).
 - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
 - [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
+- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
 - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
-- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
-- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
 - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
 - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
 - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

README_zh-CN.md

@@ -327,26 +327,26 @@ runner.train()
 ## OpenMMLab 的其他项目
 
 - [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMLab 项目、算法、模型的统一入口
-- [MMCV](https://github.com/open-mmlab/mmcv/tree/dev-2.x): OpenMMLab 计算机视觉基础库
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
 - [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
-- [MMClassification](https://github.com/open-mmlab/mmclassification/tree/dev-1.x): OpenMMLab 图像分类工具箱
-- [MMDetection](https://github.com/open-mmlab/mmdetection/tree/dev-3.x): OpenMMLab 目标检测工具箱
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d/tree/dev-1.x): OpenMMLab 新一代通用 3D 目标检测平台
-- [MMRotate](https://github.com/open-mmlab/mmrotate/tree/dev-1.x): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
+- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
 - [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x): OpenMMLab 语义分割工具箱
-- [MMOCR](https://github.com/open-mmlab/mmocr/tree/dev-1.x): OpenMMLab 全流程文字检测识别理解工具包
-- [MMPose](https://github.com/open-mmlab/mmpose/tree/dev-1.x): OpenMMLab 姿态估计工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
-- [MMSelfSup](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x): OpenMMLab 自监督学习工具箱与测试基准
-- [MMRazor](https://github.com/open-mmlab/mmrazor/tree/dev-1.x): OpenMMLab 模型压缩工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
 - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
-- [MMAction2](https://github.com/open-mmlab/mmaction2/tree/dev-1.x): OpenMMLab 新一代视频理解工具箱
-- [MMTracking](https://github.com/open-mmlab/mmtracking/tree/dev-1.x): OpenMMLab 一体化视频目标感知平台
-- [MMFlow](https://github.com/open-mmlab/mmflow/tree/dev-1.x): OpenMMLab 光流估计工具箱与测试基准
-- [MMEditing](https://github.com/open-mmlab/mmediting/tree/dev-1.x): OpenMMLab 图像视频编辑工具箱
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration/tree/dev-1.x): OpenMMLab 图片视频生成模型工具箱
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
 
 ## 欢迎加入 OpenMMLab 社区
 

docs/en/advanced_tutorials/registry.md

@@ -1,6 +1,6 @@
 # Registry
 
-OpenMMLab supports a rich collection of algorithms and datasets, therefore, many modules with similar functionality are implemented. For example, the implementations of `ResNet` and `SE-ResNet` are based on the classes `ResNet` and `SEResNet`, respectively, which have similar functions and interfaces and belong to the model components of the algorithm library. To manage these functionally similar modules, MMEngine implements the [registry](mmengine.registry.Registry). Most of the algorithm libraries in OpenMMLab use `registry` to manage their modules, including [MMDetection](https://github.com/open-mmlab/mmdetection), [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), [MMPretrain](https://github.com/open-mmlab/mmpretrain) and [MMEditing](https://github.com/open-mmlab/mmediting), etc.
+OpenMMLab supports a rich collection of algorithms and datasets, therefore, many modules with similar functionality are implemented. For example, the implementations of `ResNet` and `SE-ResNet` are based on the classes `ResNet` and `SEResNet`, respectively, which have similar functions and interfaces and belong to the model components of the algorithm library. To manage these functionally similar modules, MMEngine implements the [registry](mmengine.registry.Registry). Most of the algorithm libraries in OpenMMLab use `registry` to manage their modules, including [MMDetection](https://github.com/open-mmlab/mmdetection), [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), [MMPretrain](https://github.com/open-mmlab/mmpretrain) and [MMagic](https://github.com/open-mmlab/MMagic), etc.
 
 ## What is a registry
 

docs/zh_cn/advanced_tutorials/registry.md

@@ -1,6 +1,6 @@
 # 注册器（Registry）
 
-OpenMMLab 的算法库支持了丰富的算法和数据集，因此实现了很多功能相近的模块。例如 ResNet 和 SE-ResNet 的算法实现分别基于 `ResNet` 和 `SEResNet` 类，这些类有相似的功能和接口，都属于算法库中的模型组件。为了管理这些功能相似的模块，MMEngine 实现了 [注册器](mmengine.registry.Registry)。OpenMMLab 大多数算法库均使用注册器来管理它们的代码模块，包括 [MMDetection](https://github.com/open-mmlab/mmdetection)， [MMDetection3D](https://github.com/open-mmlab/mmdetection3d)，[MMPretrain](https://github.com/open-mmlab/mmpretrain) 和 [MMEditing](https://github.com/open-mmlab/mmediting) 等。
+OpenMMLab 的算法库支持了丰富的算法和数据集，因此实现了很多功能相近的模块。例如 ResNet 和 SE-ResNet 的算法实现分别基于 `ResNet` 和 `SEResNet` 类，这些类有相似的功能和接口，都属于算法库中的模型组件。为了管理这些功能相似的模块，MMEngine 实现了 [注册器](mmengine.registry.Registry)。OpenMMLab 大多数算法库均使用注册器来管理它们的代码模块，包括 [MMDetection](https://github.com/open-mmlab/mmdetection)， [MMDetection3D](https://github.com/open-mmlab/mmdetection3d)，[MMPretrain](https://github.com/open-mmlab/mmpretrain) 和 [MMagic](https://github.com/open-mmlab/mmagic) 等。
 
 ## 什么是注册器
 

mmengine/config/utils.py

@@ -27,6 +27,7 @@
     'mmselfsup': 'mmselfsup',
     'mmyolo': 'mmyolo',
     'mmpretrain': 'mmpretrain',
+    'mmagic': 'mmagic',
 }
 
 # PKG2PROJECT is not a proper name to represent the mapping between module name