update README (#62)

* update README

* update

* update

Co-authored-by: huanghaian <[email protected]>

README.md

@@ -42,8 +42,7 @@ English | [简体中文](README_zh-CN.md)
 
 ## Introduction
 
-MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch. It is
-a part of the [OpenMMLab](https://openmmlab.com/) project.
+MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch and [MMDetection](https://github.com/open-mmlab/mmdetection). It is a part of the [OpenMMLab](https://openmmlab.com/) project.
 
 The master branch works with **PyTorch 1.6+**.
 
@@ -53,20 +52,20 @@ The master branch works with **PyTorch 1.6+**.
 <details open>
 <summary>Major features</summary>
 
-- **Fair and convenient algorithm evaluation**
+- **Unified and convenient benchmark**
 
-  MMYOLO unifies the modules of various YOLO algorithms and provides a unified benchmark process. Users can compare and analyze in a fair and convenient way.
+  MMYOLO unifies the implementation of modules in various YOLO algorithms and provides a unified benchmark. Users can compare and analyze in a fair and convenient way.
 
-- **Detailed introductory and advanced documentation**
+- **Rich and detailed documentation**
 
-  MMYOLO provides a series of documents from getting started, to model deployment, advanced guidelines, and algorithm analysis, making it easy for different users to get started and make extensions quickly.
+  MMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly.
 
 - **Modular Design**
 
-  MMYOLO decompose the framework into different components and users can easily construct a customized model by combining different modules and training and testing strategies.
+  MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies.
 
 <img src="https://user-images.githubusercontent.com/27466624/190986949-01414a91-baae-4228-8828-c59db58dcf36.jpg" alt="BaseModule"/>
-  The picture is provided by RangeKing@GitHub, thank you very much!
+  The figure is contributed by RangeKing@GitHub, thank you very much!
 
 </details>
 
@@ -75,9 +74,9 @@ The master branch works with **PyTorch 1.6+**.
 **v0.1.0** was released on 21/9/2022:
 
 - Unified component interfaces based on [OpenMMLab 2.0](https://github.com/open-mmlab) and [MMDetection 3.0](https://github.com/open-mmlab/mmdetection/tree/3.x)
-- Support for YOLOv5/YOLOX training and deployment, support for YOLOv6 inference and deployment
-- Refactored YOLOX for MMDetection to provide faster training and inference
-- Detailed introductory and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest)
+- Support YOLOv5/YOLOX training, support YOLOv6 inference. Deployment will be supported soon.
+- Refactored YOLOX from MMDetection to accelerate training and inference.
+- Detailed introduction and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest).
 
 For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).
 
@@ -101,11 +100,11 @@ mim install -e .
 
 ## Tutorial
 
-MMYOLO is based on the MMDetection and uses the same code organization and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection.
+MMYOLO is based on MMDetection and adopts the same code structure and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection.
 
-MMYOLO usage is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about [MMDetection User Guide and Advanced Guide](https://mmdetection.readthedocs.io/en/3.x/).
+The usage of MMYOLO is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about [MMDetection User Guide and Advanced Guide](https://mmdetection.readthedocs.io/en/3.x/).
 
-For different sections than MMDetection, we have also prepared user guides and advanced guides, please read our [documentation](https://mmyolo.readthedocs.io/zenh_CN/latest/).
+For different parts from MMDetection, we have also prepared user guides and advanced guides, please read our [documentation](https://mmyolo.readthedocs.io/zenh_CN/latest/).
 
 - User Guides
 

README_zh-CN.md

@@ -42,20 +42,19 @@
 
 ## 简介
 
-MMYOLO 是一个基于 PyTorch 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
+MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
 
 主分支代码目前支持 PyTorch 1.6 以上的版本。
 
 <img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>
-
 <img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
 
 <details open>
 <summary>主要特性</summary>
 
-- **公平便捷的算法评测**
+- **统一便捷的算法评测**
 
-  MMYOLO 统一各类 YOLO 算法模块, 并提供统一评测流程，用户可以公平便捷的进行对比分析。
+  MMYOLO 统一了各类 YOLO 算法模块的实现, 并提供了统一的评测流程，用户可以公平便捷地进行对比分析。
 
 - **丰富的入门和进阶文档**
 
@@ -75,9 +74,9 @@ MMYOLO 是一个基于 PyTorch 的 YOLO 系列算法开源工具箱。它是 [Op
 **v0.1.0** 版本已经在 2022.9.21 发布：
 
 - 基于 [OpenMMLab 2.0](https://github.com/open-mmlab) 和 [MMDetection 3.0](https://github.com/open-mmlab/mmdetection/tree/3.x) 统一了各组件接口。
-- 支持 YOLOv5/YOLOX 训练和部署，支持 YOLOv6 推理和部署
-- 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度
-- 提供了详细入门和进阶教程，详见 [中文教程](https://mmyolo.readthedocs.io/zh_CN/latest)
+- 支持 YOLOv5/YOLOX 训练，支持 YOLOv6 推理。即将支持部署。
+- 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度。
+- 提供了详细入门和进阶教程，详见 [中文教程](https://mmyolo.readthedocs.io/zh_CN/latest)。
 
 发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html)
 
@@ -101,11 +100,11 @@ mim install -e .
 
 ## 教程
 
-MMYOLO 基于 MMDetection 开源库，并且采用相同的代码组织和设计方式。为了更好的使用本开源库，请先阅读 [MMDetection 概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) 对 MMDetection 进行初步的了解。
+MMYOLO 基于 MMDetection 开源库，并且采用相同的代码组织和设计方式。为了更好的使用本开源库，请先阅读 [MMDetection 概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) 对 MMDetection 进行初步地了解。
 
 MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也可以了解 [MMDetection 用户指南和进阶指南](https://mmdetection.readthedocs.io/zh_CN/3.x/) 。
 
-针对和 MMDetection 不同部分，我们也准备了用户指南和进阶指南，请阅读我们的 [文档](https://mmyolo.readthedocs.io/zh_CN/latest/) 。
+针对和 MMDetection 不同的部分，我们也准备了用户指南和进阶指南，请阅读我们的 [文档](https://mmyolo.readthedocs.io/zh_CN/latest/) 。
 
 - 用户指南
 

docs/en/notes/changelog.md

@@ -6,6 +6,6 @@ We have released MMYOLO open source library, which is based on MMEngine, MMCV 2.
 
 ### Highlights
 
-1. Support for YOLOv5/YOLOX training and deployment, support for YOLOv6 inference and deployment.
-2. Refactored YOLOX for MMDetection to provide faster training and inference.
-3. Detailed introductory and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest).
+1. Support YOLOv5/YOLOX training, support YOLOv6 inference. Deployment will be supported soon.
+2. Refactored YOLOX from MMDetection to accelerate training and inference.
+3. Detailed introduction and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest).

docs/zh_cn/notes/changelog.md

@@ -6,6 +6,6 @@
 
 ### 亮点
 
-1. 支持 YOLOv5/YOLOX 训练和部署，支持 YOLOv6 推理和部署
-2. 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度
-3. 提供了详细入门和进阶教程, 包括 YOLOv5 从入门到部署、YOLOv5 算法原理和实现全解析、 特征图可视化等教程
+1. 支持 YOLOv5/YOLOX 训练，支持 YOLOv6 推理。部署即将支持。
+2. 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度。
+3. 提供了详细入门和进阶教程, 包括 YOLOv5 从入门到部署、YOLOv5 算法原理和实现全解析、 特征图可视化等教程。