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Author: charl-u

docs/README.md

@@ -0,0 +1,47 @@
+[简体中文](README_CN.md)| English
+
+# Tutorials
+
+## Install
+
+- [Install FastDeploy Prebuilt Libraries](en/build_and_install/download_prebuilt_libraries.md)
+- [Build and Install FastDeploy Library on GPU Platform](en/build_and_install/gpu.md)
+- [Build and Install FastDeploy Library on CPU Platform](en/build_and_install/cpu.md)
+- [Build and Install FastDeploy Library on IPU Platform](en/build_and_install/ipu.md)
+- [Build and Install FastDeploy Library on KunlunXin XPU Platform](en/build_and_install/xpu.md)
+- [Build and Install on RV1126 Platform](en/build_and_install/rv1126.md)
+- [Build and Install on RK3588 Platform](en/build_and_install/rknpu2.md)
+- [Build and Install on A311D Platform](en/build_and_install/a311d.md)
+- [Build and Install FastDeploy Library on  Nvidia Jetson Platform](en/build_and_install/jetson.md)
+- [Build and Install FastDeploy Library on Android Platform](en/build_and_install/android.md)
+- [Build and Install FastDeploy Serving Deployment Image](../serving/docs/EN/compile-en.md)
+
+## A Quick Start - Demos
+
+- [Python Deployment Demo](en/quick_start/models/python.md)
+- [C++ Deployment Demo](en/quick_start/models/cpp.md)
+- [A Quick Start on Runtime Python](en/quick_start/runtime/python.md)
+- [A Quick Start on Runtime C++](en/quick_start/runtime/cpp.md)
+
+## API
+
+- [Python API](https://baidu-paddle.github.io/fastdeploy-api/python/html/)
+- [C++ API](https://baidu-paddle.github.io/fastdeploy-api/cpp/html/)
+- [Android Java API](../java/android)
+
+## Performance Optimization
+
+- [Quantization Acceleration](en/quantize.md)
+
+## Frequent Q&As
+
+- [1. How to Change Inference Backends](en/faq/how_to_change_backend.md)
+- [2. How to Use FastDeploy C++ SDK on Windows Platform](en/faq/use_sdk_on_windows.md)
+- [3. How to Use FastDeploy C++ SDK on Android Platform](en/faq/use_cpp_sdk_on_android.md)
+- [4. Tricks of TensorRT](en/faq/tensorrt_tricks.md)
+- [5. How to Develop a New Model](en/faq/develop_a_new_model.md)
+
+## More FastDeploy Deployment Module
+
+- [Deployment AI Model as a Service](../serving)
+- [Benchmark Testing](../benchmark)

docs/README.md

@@ -1,4 +1,4 @@
-[English](README_EN.md) | 简体中文
+[English](README.md) | 简体中文
 
 # 使用文档
 

docs/README_CN.md

@@ -1,4 +1,4 @@
-[English](README_EN.md)| 简体中文
+[English](README.md)| 简体中文
 # 视觉模型预测结果说明
 
 FastDeploy根据视觉模型的任务类型，定义了不同的结构体(`fastdeploy/vision/common/result.h`)来表达模型预测结果，具体如下表所示

docs/README_EN.md

@@ -1,6 +1,6 @@
 English | [中文](matting_result.md)
 
-# MattingResult keying results
+# Matting Result 
 
 The MattingResult code is defined in `fastdeploy/vision/common/result.h`, and is used to indicate the predicted value of alpha transparency predicted and the predicted foreground, etc.
 

docs/api/vision_results/README_EN.md docs/api/vision_results/README.md

@@ -5,17 +5,18 @@ English | [中文](../../cn/faq/develop_a_new_model.md)
 
 | Step        | Description                                                                      | Create or modify the files                |
 |:-----------:|:--------------------------------------------------------------------------------:|:-----------------------------------------:|
-| [1](#step2) | Add a model implementation to the corresponding task module in FastDeploy/vision | resnet.h、resnet.cc、vision.h               |
-| [2](#step4) | Python interface binding via pybind                                              | resnet_pybind.cc、classification_pybind.cc |
-| [3](#step5) | Use Python to call Interface                                                     | resnet.py、\_\_init\_\_.py                 |
+| [1](#step2) | Add a model implementation to the corresponding task module in FastDeploy/vision | resnet.h, resnet.cc, vision.h               |
+| [2](#step4) | Python interface binding via pybind                                              | resnet_pybind.cc, classification_pybind.cc |
+| [3](#step5) | Use Python to call Interface                                                     | resnet.py, \_\_init\_\_.py                 |
 
 After completing the above 3 steps, an external model is integrated.
 
 If you want to contribute your code to FastDeploy, it is very kind of you to add test code, instructions (Readme), and code annotations for the added model in the [test](#test).
 
-## Model Integration
+## Model Integration    <span id="modelsupport"></span>
+
+### Prepare the models  <span id="step1"></span>
 
-### Prepare the models
 
 Before integrating external models, it is important to convert the trained models (.pt, .pdparams, etc.) to the model formats (.onnx, .pdmodel) that FastDeploy supports for deployment. Most open source repositories provide model conversion scripts for developers. As torchvision does not provide conversion scripts, developers can write conversion scripts manually. In this demo, we convert `torchvison.models.resnet50` to `resnet50.onnx` with the following code for your reference.
 
@@ -40,7 +41,7 @@ torch.onnx.export(model,
 
 Running the above script will generate a`resnet50.onnx` file.
 
-### C++
+### C++ <span id="step2"></span>
 
 * Create`resnet.h` file
   * Create a path
@@ -64,7 +65,7 @@ class FASTDEPLOY_DECL ResNet : public FastDeployModel {
   * Create a path
     * FastDeploy/fastdeploy/vision/classification/contrib/resnet.cc (FastDeploy/C++ code/vision/task name/external model name/model name.cc)
   * Create content
-    * Implement the specific logic of the functions declared in `resnet.h` to `resnet.cc`, where `PreProcess` and `PostProcess` need to refer to the official source library for pre- and post-processing logic reproduction. The specific logic of each ResNet function is as follows. For more detailed code, please refer to [resnet.cc](https:// github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-d229d702de28345253a53f2a5839fd2c638f3d32fffa6a7d04d23db9da13a871).
+    * Implement the specific logic of the functions declared in `resnet.h` to `resnet.cc`, where `PreProcess` and `PostProcess` need to refer to the official source library for pre- and post-processing logic reproduction. The specific logic of each ResNet function is as follows. For more detailed code, please refer to [resnet.cc](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-d229d702de28345253a53f2a5839fd2c638f3d32fffa6a7d04d23db9da13a871).
 
 ```C++
 ResNet::ResNet(...) {
@@ -93,7 +94,7 @@ bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
   return true;
 }
 ```
-
+<span id="step3"></span>
 * Add new model file to`vision.h`
   * modify location
     * FastDeploy/fastdeploy/vision.h
@@ -105,7 +106,7 @@ bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
 #endif
 ```
 
-### Pybind
+### Pybind  <span id="step4"></span>
 
 * Create Pybind file
 
@@ -146,7 +147,7 @@ bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
     }
     ```
 
-### Python
+### Python  <span id="step5"></span>
 
 * Create`resnet.py`file
   * Create path
@@ -167,7 +168,7 @@ class ResNet(FastDeployModel):
     def size(self, wh):
         ...
 ```
-
+<span id="step6"></span>
 * Import ResNet classes
   * modify path
     * FastDeploy/python/fastdeploy/vision/classification/\_\_init\_\_.py (FastDeploy/Python code/fastdeploy/vision model/task name/\_\_init\_\_.py)
@@ -177,7 +178,7 @@ class ResNet(FastDeployModel):
 from .contrib.resnet import ResNet
 ```
 
-## Test
+## Test <span id="test"></span>
 
 ### Compile
 
@@ -229,7 +230,7 @@ pip install fastdeploy_gpu_python-Version number-cpxx-cpxxm-system architecture.
 ```
 
 * C++
-  * Write CmakeLists、C++  code and README.md . Please refer to[cpp/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-afcbe607b796509581f89e38b84190717f1eeda2df0419a2ac9034197ead5f96)
+  * Write CmakeLists、C++  code and README.md . Please refer to [cpp/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-afcbe607b796509581f89e38b84190717f1eeda2df0419a2ac9034197ead5f96)
   * Compile infer.cc
     * Path：FastDeploy/examples/vision/classification/resnet/cpp/
 
@@ -240,7 +241,7 @@ make
 ```
 
 * Python
-  * Please refer to[python/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-5a0d6be8c603a8b81454ac14c17fb93555288d9adf92bbe40454449309700135) for Python code and Readme.md
+  * Please refer to [python/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-5a0d6be8c603a8b81454ac14c17fb93555288d9adf92bbe40454449309700135) for Python code and Readme.md
 
 ### Annotate the Code
 
@@ -249,7 +250,7 @@ make
 To make the code clear for understanding,  developers can annotate the newly-added code.
 
 - C++ code
-  Developers need to add annotations for functions and variables in the resnet.h file, there are three annotating methods as follows, please refer to [resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff- 69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e) for more details.
+  Developers need to add annotations for functions and variables in the resnet.h file, there are three annotating methods as follows, please refer to [resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e) for more details.
 
 ```C++
 /** \brief Predict for the input "im", the result will be saved in "result".

docs/api/vision_results/README_CN.md

@@ -92,7 +92,7 @@ In particular, for the configuration method of the dependency library required b
 
 ### 3.2 SDK usage method 2: Visual Studio 2019 creates sln project using C++ SDK
 
-This section is for non-CMake users and describes how to create a sln project in Visual Studio 2019 to use FastDeploy C++ SDK. CMake users please read the next section directly. In addition, this section is a special thanks to "Awake to the Southern Sky" for his tutorial on FastDeploy: [How to deploy PaddleDetection target detection model on Windows using FastDeploy C++].(https://www.bilibili.com/read/cv18807232)
+This section is for non-CMake users and describes how to create a sln project in Visual Studio 2019 to use FastDeploy C++ SDK. CMake users please read the next section directly. In addition, this section is a special thanks to "Awake to the Southern Sky" for his tutorial on FastDeploy: [How to deploy PaddleDetection target detection model on Windows using FastDeploy C++](https://www.bilibili.com/read/cv18807232).
 
 <div id="VisualStudio2019Sln"></div>  
 
@@ -192,7 +192,7 @@ Compile successfully, you can see the exe saved in：
 D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe  
 ```  
 
-（2）Execute the executable file and get the inference result. First you need to copy all the dlls to the directory where the exe is located. At the same time, you also need to download and extract the pyoloe model files and test images, and then copy them to the directory where the exe is located. Special note, the exe needs to run when the dependency library configuration method, please refer to the section: [various methods to configure the exe to run the required dependency library](#CommandLineDeps)  
+（2）Execute the executable file and get the inference result. First you need to copy all the dlls to the directory where the exe is located. At the same time, you also need to download and extract the pyoloe model files and test images, and then copy them to the directory where the exe is located. Special note, the exe needs to run when the dependency library configuration method, please refer to the section: [various methods to configure the exe to run the required dependency library](#CommandLineDeps).  
 
 ![image](https://user-images.githubusercontent.com/31974251/192829545-3ea36bfc-9a54-492b-984b-2d5d39094d47.png)  
 
@@ -331,7 +331,7 @@ Open the saved image to view the visualization results at：
 <img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
 </div>
 
-Special note, the exe needs to run when the dependency library configuration method, please refer to the section: [a variety of methods to configure the exe to run the required dependency library](#CommandLineDeps)
+Special note, the exe needs to run when the dependency library configuration method, please refer to the section: [a variety of methods to configure the exe to run the required dependency library](#CommandLineDeps).
 
 ## 4. Multiple methods to Configure the Required Dependencies for the Exe Runtime
 <div id="CommandLineDeps"></div>