[Docs] Refactor readme.md (#984)

* [Docs] Refactor readme.md

* use lowercase

README.md

@@ -11,15 +11,16 @@ English | [简体中文](README_zh-CN.md)
 MMCV is a foundational library for computer vision research and supports many
 research projects as below:
 
-- [MMDetection](https://github.com/open-mmlab/mmdetection): Detection toolbox and benchmark
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): General 3D object detection toolbox and benchmark
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): Semantic segmentation toolbox and benchmark
-- [MMEditing](https://github.com/open-mmlab/mmediting): Image and video editing toolbox
-- [MMPose](https://github.com/open-mmlab/mmpose): Pose estimation toolbox and benchmark
-- [MMAction2](https://github.com/open-mmlab/mmaction2): Action understanding toolbox and benchmark
-- [MMTracking](https://github.com/open-mmlab/mmtracking): Video perception toolbox and benchmark
-- [MMClassification](https://github.com/open-mmlab/mmclassification): Image classification toolbox and benchmark
-- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation 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.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition and understanding toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
 
 It provides the following functionalities.
 
@@ -39,18 +40,12 @@ Note: MMCV requires Python 3.6+.
 
 There are two versions of MMCV:
 
-- **mmcv**: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
 - **mmcv-full**: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.
+- **mmcv**: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
 
-**Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other.
-
-a. Install the lite version.
-
-```python
-pip install mmcv
-```
+**Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full verion is highly recommended if CUDA is avaliable`.
 
-b. Install the full version.
+a. Install the full version.
 
 Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/).
 
@@ -167,6 +162,12 @@ pip install mmcv-full
 
 Note that the local compiling may take up to 10 mins.
 
+b. Install the lite version.
+
+```python
+pip install mmcv
+```
+
 c. Install full version with custom operators for onnxruntime
 
 - Check [here](docs/onnxruntime_op.md) for detailed instruction.

README_zh-CN.md

@@ -10,15 +10,16 @@
 
 MMCV 是一个面向计算机视觉的基础库，它支持了很多开源项目，例如：
 
-- [MMDetection](https://github.com/open-mmlab/mmdetection): 目标检测工具箱
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): 新一代通用 3D 目标检测平台
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): 语义分割工具箱
-- [MMEditing](https://github.com/open-mmlab/mmediting): 图像视频编辑工具箱
-- [MMPose](https://github.com/open-mmlab/mmpose): 姿态估计工具箱
-- [MMAction2](https://github.com/open-mmlab/mmaction2): 新一代视频理解工具箱
-- [MMTracking](https://github.com/open-mmlab/mmtracking): 一体化视频目标感知平台
-- [MMClassification](https://github.com/open-mmlab/mmclassification): 图像分类工具箱
-- [MMOCR](https://github.com/open-mmlab/mmocr): 全流程文字检测识别理解工具包
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
 
 MMCV 提供了如下众多功能：
 
@@ -38,18 +39,12 @@ MMCV 提供了如下众多功能：
 
 MMCV 有两个版本：
 
-- **mmcv**: 精简版，不包含 CUDA 算子但包含其余所有特性和功能，类似 MMCV 1.0 之前的版本。如果你不需要使用 CUDA 算子的话，精简版可以作为一个考虑选项。
 - **mmcv-full**: 完整版，包含所有的特性以及丰富的开箱即用的 CUDA 算子。注意完整版本可能需要更长时间来编译。
+- **mmcv**: 精简版，不包含 CUDA 算子但包含其余所有特性和功能，类似 MMCV 1.0 之前的版本。如果你不需要使用 CUDA 算子的话，精简版可以作为一个考虑选项。
 
-**注意**: 请不要在同一个环境中安装两个版本，否则可能会遇到类似 `ModuleNotFound` 的错误。在安装一个版本之前，需要先卸载另一个。
-
-a. 安装精简版
-
-```python
-pip install mmcv
-```
+**注意**: 请不要在同一个环境中安装两个版本，否则可能会遇到类似 `ModuleNotFound` 的错误。在安装一个版本之前，需要先卸载另一个。`如果CUDA可用，强烈推荐安装mmcv-full`。
 
-b. 安装完整版
+a. 安装完整版
 
 在安装 mmcv-full 之前，请确保 PyTorch 已经成功安装在环境中，可以参考 PyTorch 官方[文档](https://pytorch.org/)。
 
@@ -164,6 +159,12 @@ pip install mmcv-full
 
 但注意本地编译可能会耗时 10 分钟以上。
 
+b. 安装精简版
+
+```python
+pip install mmcv
+```
+
 c. 安装完整版并且编译 onnxruntime 的自定义算子
 
 - 详细的指南请查看 [这里](docs/onnxruntime_op.md)。