[Docs] update README.md and overview.md (#7)

README.md

@@ -18,34 +18,27 @@ English | [简体中文](/README_zh-CN.md)
 
 ## Introduction
 
-MMRazor is an open source model compression toolbox based on PyTorch. It is
-a part of the [OpenMMLab](https://openmmlab.com/) project.
+MMRazor is a model compression toolkit for model slimming and AutoML, which includes 3 mainstream technologies:
 
+- Neural Architecture Search (NAS)
+- Pruning
+- Knowledge Distillation (KD)
+- Quantization (in the next release)
 
-<details open><summary>Major features</summary>
+It is a part of the [OpenMMLab](https://openmmlab.com/) project.
 
-- **All in one**
+Major features:
+- **Compatibility**
 
-  MMRazor includes Neural Architecture Search（NAS), Knowledge Distillation（KD), Pruning, and Quantization（coming soon).
-  And it supports the combination of different types of algorithms.
+  MMRazor can be easily applied to various projects in OpenMMLab, due to similar architecture design of OpenMMLab as well as the decoupling of slimming algorithms and vision tasks.
 
-- **General CV Model Compression ToolBox**
+- **Flexibility**
 
-  Thanks to OpenMMLab, the algorithms in MMRazor can be quickly applied to different CV tasks, making the development of model compression algorithms once and for all.
+  Different algorithms, e.g., NAS, pruning and KD, can be incorporated in a plug-n-play manner to build a more powerful system.
 
-- **Decouple model and compression algorithm**
+- **Convenience**
 
-  MMRazor has a variety of built-in automation mechanisms, allowing developers to implement model compression algorithms without modifying the raw model code, such as:
-  - OP can be modified code-free.
-  - Feature maps in the middle layers can be obtained code-free.
-  - Automatically obtain and analyze the connection between nn.Module.
-  - And More ...
-
-- **Flexible and Modular Design**
-
-  We decompose the model compression algorithms into different components, making it much easier and more flexible to build a new algorithm by combining different components.
-
-</details>
+  With better modular design, developers can implement new model compression algorithms with only a few codes, or even by simply modifying config files.
 
 Below is an overview of MMRazor's design and implementation, please refer to [tutorials](/docs/en/tutorials/Tutorial_1_overview.md) for more details.
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README_zh-CN.md

@@ -18,33 +18,30 @@
 
 ## 说明
 
-MMRazor 是一个基于 PyTorch 的模型压缩开源工具箱. 它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分.
+MMRazor是一个可用于模型瘦身和AutoML的模型压缩工具箱，包含了3种主流的技术：
 
+- 网络结构搜索 (NAS)
+- 模型剪枝
+- 知识蒸馏 (KD)
+- 量化 (下个版本发布)
 
-<details open><summary>主要特性</summary>
+MMRazor是[OpenMMLab](https://openmmlab.com/)项目的一部分。
 
-- **多合一**
 
-  MMrazor 实现了4中主流的技术：1）网络结构搜索；2）模型剪枝；3）知识蒸馏；4）量化（很快就会支持），并支持不同算法间的
-  互相组合。
+主要特性
 
-- **通用计算机视觉模型压缩工具包**
+- **兼容性**
 
-  得益于 OpenMMLab 开源生态，MMRazor 中的算法可快速应用到不同的任务上，使模型压缩算法的开发变的一劳永逸。
+  MMRazor和OpenMMLab有着类似的架构设计，并且实现了轻量化算法和视觉任务间轻耦合，因此很容易应用于OpenMMLab中其他的项目。
 
-- **压缩算法和任务模型的解耦**
+- **灵活性**
 
-  MMrazor 具有多种内置的自动化机制，允许开发者在不修改原始模型代码的情况下实现模型压缩算法，比如：
-  - 可以 code-free 地获取模型中间层输出结果；
-  - 可以 code-free 地替换模型中的某些 OP；
-  - 自动获得并分析 nn.Module 间连接
-  - ......
+  多种轻量化算法可以以一种即插即用的方式来组合使用，从而搭建出功能更强大的系统。
 
-- **灵活和模块化设计**
+- **便利性**
 
-  MMRazor 将模型压缩算法分解为不同的组件，使得构建新算法变得更加容易和灵活。
+  得益于更好的模块化设计，开发者仅用修改少量代码，甚至只用修改配置文件即可实现新的轻量化算法。
 
-</details>
 
 下面是MMRazor设计和实现的概括图, 如果想了解更多的细节，请参考 [tutorials](/docs/en/tutorials/Tutorial_1_overview.md)。
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docs/en/tutorials/Tutorial_1_overview.md

@@ -1,14 +1,26 @@
 # Tutorial 1: Overview
 
-MMRazor is an OpenMMLab model compression toolbox to help users slim their models, which includes 4 mainstream technologies: 1）Neural Architecture Search（NAS） 2）Pruning  3）Knowledge Distillation（KD）4）quantization（to do in next stage plan）
+MMRazor is a model compression toolkit for model slimming and AutoML, which includes 3 mainstream technologies:
 
-## Core Features
+- Neural Architecture Search (NAS)
+- Pruning
+- Knowledge Distillation (KD)
+- Quantization (in the next release)
 
-- **Consistency** : MMRazor's design and style are similar to OpenMMLab, so MMRazor is friendly for the users of OpenMMLab other codebases to slim their pre-trained models. You can even quickly apply our pruning and KD algorithms to your models without changing the model definition code.
+It is a part of the [OpenMMLab](https://openmmlab.com/) project.
 
-- **Flexibility** : Several lightweight algorithms are loose coupling in MMRazor，which can be combined to use.
+## Major features:
+- **Compatibility**
 
-- **Universality** : It is loose coupling between lightweight algorithms and tasks. You can easily apply existing algorithms to new tasks.
+  MMRazor can be easily applied to various projects in OpenMMLab, due to similar architecture design of OpenMMLab as well as the decoupling of slimming algorithms and vision tasks.
+
+- **Flexibility**
+
+  Different algorithms, e.g., NAS, pruning and KD, can be incorporated in a plug-n-play manner to build a more powerful system.
+
+- **Convenience**
+
+  With better modular design, developers can implement new model compression algorithms with only a few codes, or even by simply modifying config files.
 
 ## Design and implement