[Doc] Fix dead links (PaddlePaddle#517)

* first commit for yolov7 * pybind for yolov7 * CPP README.md * CPP README.md * modified yolov7.cc * README.md * python file modify * delete license in fastdeploy/ * repush the conflict part * README.md modified * README.md modified * file path modified * file path modified * file path modified * file path modified * file path modified * README modified * README modified * move some helpers to private * add examples for yolov7 * api.md modified * api.md modified * api.md modified * YOLOv7 * yolov7 release link * yolov7 release link * yolov7 release link * copyright * change some helpers to private * change variables to const and fix documents. * gitignore * Transfer some funtions to private member of class * Transfer some funtions to private member of class * Merge from develop (#9) * Fix compile problem in different python version (PaddlePaddle#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (PaddlePaddle#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (PaddlePaddle#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (PaddlePaddle#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * first commit for yolor * for merge * Develop (PaddlePaddle#11) * Fix compile problem in different python version (PaddlePaddle#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (PaddlePaddle#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (PaddlePaddle#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (PaddlePaddle#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Yolor (PaddlePaddle#16) * Develop (PaddlePaddle#11) (PaddlePaddle#12) * Fix compile problem in different python version (PaddlePaddle#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (PaddlePaddle#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (PaddlePaddle#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (PaddlePaddle#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Develop (PaddlePaddle#13) * Fix compile problem in different python version (PaddlePaddle#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (PaddlePaddle#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (PaddlePaddle#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (PaddlePaddle#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * Develop (PaddlePaddle#14) * Fix compile problem in different python version (PaddlePaddle#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (PaddlePaddle#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (PaddlePaddle#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (PaddlePaddle#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com> * add is_dynamic for YOLO series (PaddlePaddle#22) * modify ppmatting backend and docs * modify ppmatting docs * fix the PPMatting size problem * fix LimitShort's log * retrigger ci * modify PPMatting docs * modify the way for dealing with LimitShort * add python comments for external models * modify resnet c++ comments * modify C++ comments for external models * modify python comments and add result class comments * fix comments compile error * modify result.h comments * first commit for dead links * first commit for dead links * fix docs deadlinks * fix docs deadlinks * fix examples deadlinks * fix examples deadlinks Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com>
felixhjh · Nov 7, 2022 · 3c0f4c1 · 3c0f4c1
1 parent 0bb40ab
commit 3c0f4c1
Show file tree

Hide file tree

Showing 32 changed files with 50 additions and 56 deletions.
diff --git a/docs/api_docs/python/README.md b/docs/api_docs/python/README.md
@@ -2,7 +2,7 @@
 
 This directory help to generate Python API documents for FastDeploy.
 
-1. First, to generate the latest api documents, you need to install the latest FastDeploy, refer [build and install](en/build_and_install) to build FastDeploy python wheel package with the latest code.  
+1. First, to generate the latest api documents, you need to install the latest FastDeploy, refer [build and install](../../cn/build_and_install) to build FastDeploy python wheel package with the latest code.  
 2. After installed FastDeploy in your python environment, there are some dependencies need to install, execute command `pip install -r requirements.txt` in this directory  
 3. Execute command `make html` to generate API documents
 

diff --git a/docs/cn/build_and_install/android.md b/docs/cn/build_and_install/android.md
@@ -102,4 +102,4 @@ make install
 如何使用FastDeploy Android C++ SDK 请参考使用案例文档：  
 - [图像分类Android使用文档](../../../examples/vision/classification/paddleclas/android/README.md)  
 - [目标检测Android使用文档](../../../examples/vision/detection/paddledetection/android/README.md)  
-- [在 Android 通过 JNI 中使用 FastDeploy C++ SDK](../../../../../docs/cn/faq/use_cpp_sdk_on_android.md)
+- [在 Android 通过 JNI 中使用 FastDeploy C++ SDK](../../cn/faq/use_cpp_sdk_on_android.md)
diff --git a/docs/cn/faq/use_sdk_on_windows.md b/docs/cn/faq/use_sdk_on_windows.md
@@ -218,7 +218,7 @@ D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe
 ![image](https://user-images.githubusercontent.com/31974251/192144782-79bccf8f-65d0-4f22-9f41-81751c530319.png)
 
 （2）其中infer_ppyoloe.cpp的代码可以直接从examples中的代码拷贝过来：  
-- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
+- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
 
 （3）CMakeLists.txt主要包括配置FastDeploy C++ SDK的路径，如果是GPU版本的SDK，还需要配置CUDA_DIRECTORY为CUDA的安装路径，CMakeLists.txt的配置如下：
 

diff --git a/docs/en/faq/use_sdk_on_windows.md b/docs/en/faq/use_sdk_on_windows.md
@@ -179,7 +179,7 @@ D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\third_libs\inst
 
 ![image](https://user-images.githubusercontent.com/31974251/192827842-1f05d435-8a3e-492b-a3b7-d5e88f85f814.png)  
 
-Compile successfully, you can see the exe saved in： 
+Compile successfully, you can see the exe saved in：
 
 ```bat  
 D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe  
@@ -221,7 +221,7 @@ This section is for CMake users and describes how to create CMake projects in Vi
 ![image](https://user-images.githubusercontent.com/31974251/192144782-79bccf8f-65d0-4f22-9f41-81751c530319.png)
 
 （2）The code of infer_ppyoloe.cpp can be copied directly from the code in examples：  
-- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
+- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
 
 （3）CMakeLists.txt mainly includes the configuration of the path of FastDeploy C++ SDK, if it is the GPU version of the SDK, you also need to configure CUDA_DIRECTORY as the installation path of CUDA, the configuration of CMakeLists.txt is as follows：
 
@@ -361,7 +361,7 @@ A brief description of the usage is as follows.
 #### 4.1.2 fastdeploy_init.bat View all dll, lib and include paths in the SDK  
 <div id="CommandLineDeps12"></div>  
 
-Go to the root directory of the SDK and run the show command to view all the dll, lib and include paths in the SDK. In the following command, %cd% means the current directory (the root directory of the SDK). 
+Go to the root directory of the SDK and run the show command to view all the dll, lib and include paths in the SDK. In the following command, %cd% means the current directory (the root directory of the SDK).
 
 ```bat
 D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat show %cd%
@@ -504,7 +504,7 @@ copy /Y %FASTDEPLOY_HOME%\third_libs\install\yaml-cpp\lib\*.dll Release\
 copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\bin\*.dll Release\
 copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\bin\*.xml Release\
 copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\3rdparty\tbb\bin\*.dll Release\
-``` 
+```
 Note that if you compile the latest SDK or version >0.2.1 by yourself, the opencv and openvino directory structure has changed and the path needs to be modified appropriately. For example：
 ```bat  
 copy /Y %FASTDEPLOY_HOME%\third_libs\install\opencv\build\x64\vc15\bin\*.dll Release\

diff --git a/docs/en/quantize.md b/docs/en/quantize.md
@@ -27,7 +27,7 @@ FastDeploy基于PaddleSlim, 集成了一键模型量化的工具, 同时, FastDe
 
 ### 用户使用FastDeploy一键模型量化工具来量化模型
 Fastdeploy基于PaddleSlim, 为用户提供了一键模型量化的工具，请参考如下文档进行模型量化.
-- [FastDeploy 一键模型量化](../../tools/quantization/)
+- [FastDeploy 一键模型量化](../../tools/auto_compression/)
 当用户获得产出的量化模型之后，即可以使用FastDeploy来部署量化模型.
 
 

diff --git a/examples/text/ernie-3.0/serving/README.md b/examples/text/ernie-3.0/serving/README.md
@@ -168,4 +168,4 @@ entity: 华夏   label: LOC   pos: [14, 15]
 
 ## 配置修改
 
-当前分类任务(ernie_seqcls_model/config.pbtxt)默认配置在CPU上运行OpenVINO引擎; 序列标注任务默认配置在GPU上运行Paddle引擎。如果要在CPU/GPU或其他推理引擎上运行, 需要修改配置，详情请参考[配置文档](../../../../../serving/docs/zh_CN/model_configuration.md)
+当前分类任务(ernie_seqcls_model/config.pbtxt)默认配置在CPU上运行OpenVINO引擎; 序列标注任务默认配置在GPU上运行Paddle引擎。如果要在CPU/GPU或其他推理引擎上运行, 需要修改配置，详情请参考[配置文档](../../../../serving/docs/zh_CN/model_configuration.md)
diff --git a/examples/vision/README.md b/examples/vision/README.md
@@ -30,4 +30,4 @@ FastDeploy针对飞桨的视觉套件，以及外部热门模型，提供端到
 - 加载模型
 - 调用`predict`接口
 
-FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/runtime/how_to_change_backend.md)。
+FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/cn/faq/how_to_change_backend.md)。
diff --git a/examples/vision/classification/paddleclas/quantize/cpp/README.md b/examples/vision/classification/paddleclas/quantize/cpp/README.md
@@ -8,7 +8,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
 
 ## 以量化后的ResNet50_Vd模型为例, 进行部署
 在本目录执行如下命令即可完成编译,以及量化模型部署.

diff --git a/examples/vision/classification/paddleclas/quantize/python/README.md b/examples/vision/classification/paddleclas/quantize/python/README.md
@@ -8,7 +8,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
 
 
 ## 以量化后的ResNet50_Vd模型为例, 进行部署

diff --git a/examples/vision/classification/paddleclas/web/README.md b/examples/vision/classification/paddleclas/web/README.md
@@ -6,7 +6,7 @@
 
 ## 前端部署图像分类模型
 
-图像分类模型web demo使用[**参考文档**](../../../../examples/application/js/web_demo)
+图像分类模型web demo使用[**参考文档**](../../../../application/js/web_demo/)
 
 
 ## MobileNet js接口
@@ -34,4 +34,3 @@ console.log(res);
 
 - [PaddleClas模型 python部署](../../paddleclas/python/)
 - [PaddleClas模型 C++部署](../cpp/)
-
diff --git a/examples/vision/classification/resnet/cpp/README.md b/examples/vision/classification/resnet/cpp/README.md
@@ -4,8 +4,8 @@
 
 在部署前，需确认以下两个步骤
 
-- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)  
-- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/quick_start)
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
 
 以Linux上 ResNet50 推理为例，在本目录执行如下命令即可完成编译测试
 
@@ -33,7 +33,7 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima
 ```
 
 以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
-- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/compile/how_to_use_sdk_on_windows.md)
+- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
 
 ## ResNet C++接口
 
@@ -74,4 +74,4 @@ fastdeploy::vision::classification::ResNet(
 - [模型介绍](../../)
 - [Python部署](../python)
 - [视觉模型预测结果](../../../../../docs/api/vision_results/)
-- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
+- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
diff --git a/examples/vision/classification/resnet/python/README.md b/examples/vision/classification/resnet/python/README.md
@@ -2,8 +2,8 @@
 
 在部署前，需确认以下两个步骤
 
-- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)  
-- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
 
 本目录下提供`infer.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
 
@@ -69,4 +69,4 @@ fd.vision.classification.ResNet(model_file, params_file, runtime_option=None, mo
 - [ResNet 模型介绍](..)
 - [ResNet C++部署](../cpp)
 - [模型预测结果说明](../../../../../docs/api/vision_results/)
-- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
+- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
diff --git a/examples/vision/detection/paddledetection/quantize/cpp/README.md b/examples/vision/detection/paddledetection/quantize/cpp/README.md
@@ -9,7 +9,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
 
 ## 以量化后的PP-YOLOE-l模型为例, 进行部署
 在本目录执行如下命令即可完成编译,以及量化模型部署.

diff --git a/examples/vision/detection/paddledetection/quantize/python/README.md b/examples/vision/detection/paddledetection/quantize/python/README.md
@@ -8,7 +8,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
 
 
 ## 以量化后的PP-YOLOE-l模型为例, 进行部署

diff --git a/examples/vision/detection/yolov5/quantize/cpp/README.md b/examples/vision/detection/yolov5/quantize/cpp/README.md
@@ -9,7 +9,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
 
 ## 以量化后的YOLOv5s模型为例, 进行部署
 在本目录执行如下命令即可完成编译,以及量化模型部署.

diff --git a/examples/vision/detection/yolov5/quantize/python/README.md b/examples/vision/detection/yolov5/quantize/python/README.md
@@ -8,7 +8,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
 
 
 ## 以量化后的YOLOv5s模型为例, 进行部署

diff --git a/examples/vision/detection/yolov6/quantize/cpp/README.md b/examples/vision/detection/yolov6/quantize/cpp/README.md
@@ -9,7 +9,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
 
 ## 以量化后的YOLOv6s模型为例, 进行部署
 在本目录执行如下命令即可完成编译,以及量化模型部署.

diff --git a/examples/vision/detection/yolov6/quantize/python/README.md b/examples/vision/detection/yolov6/quantize/python/README.md
@@ -8,7 +8,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
 
 ## 以量化后的YOLOv6s模型为例, 进行部署
 ```bash

diff --git a/examples/vision/detection/yolov7/python/README_EN.md b/examples/vision/detection/yolov7/python/README_EN.md
@@ -4,8 +4,8 @@ English | [简体中文](README.md)
 
 Two steps before deployment:
 
-- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/docs_en/environment.md)
-- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/docs_en/quick_start)
+- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
 
 
 This doc provides a quick `infer.py` demo of YOLOv7 deployment on CPU/GPU, and accelerated GPU deployment by TensorRT. Run the following command:
@@ -21,7 +21,7 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 
 # CPU Inference
 python infer.py --model yolov7.onnx --image 000000014439.jpg --device cpu
-# GPU 
+# GPU
 python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu
 # GPU上使用TensorRT推理
 python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_trt True
@@ -51,18 +51,18 @@ YOLOv7 model loading and initialisation, with model_file being the exported ONNX
 > ```python
 > YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
 > ```
-> 
+>
 > Model prediction interface with direct output of detection results from the image input.
-> 
+>
 > **Parameters**
-> 
+>
 > > * **image_data**(np.ndarray): Input image. Images need to be in HWC or BGR format
 > > * **conf_threshold**(float): Filter threshold for detection box confidence
 > > * **nms_iou_threshold**(float): iou thresholds during NMS processing
 
 > **Return**
-> 
-> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/docs_en/api/vision_results/)
+>
+> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/api/vision_results/)
 
 ### Class Member Variables
 
@@ -80,5 +80,5 @@ Users can modify the following pre-processing parameters for their needs. This w
 
 - [YOLOv7 Model Introduction](..)
 - [YOLOv7 C++ Deployment](../cpp)
-- [Vision Model Results](../../../../../docs/docs_en/api/vision_results/)
-- [how to change inference backend](../../../../../docs/docs_en/runtime/how_to_change_inference_backend.md)
+- [Vision Model Results](../../../../../docs/api/vision_results/)
+- [how to change inference backend](../../../../../docs/en/faq/how_to_change_backend.md)
diff --git a/examples/vision/detection/yolov7/quantize/cpp/README.md b/examples/vision/detection/yolov7/quantize/cpp/README.md
@@ -9,7 +9,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
 
 ## 以量化后的YOLOv7模型为例, 进行部署
 在本目录执行如下命令即可完成编译,以及量化模型部署.

diff --git a/examples/vision/detection/yolov7/quantize/python/README.md b/examples/vision/detection/yolov7/quantize/python/README.md
@@ -8,7 +8,7 @@
 
 ### 量化模型准备
 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
 
 ## 以量化后的YOLOv7模型为例, 进行部署
 ```bash

diff --git a/examples/vision/keypointdetection/det_keypoint_unite/python/README.md b/examples/vision/keypointdetection/det_keypoint_unite/python/README.md
@@ -71,4 +71,4 @@ PPTinyPosePipeline模型加载和初始化，其中det_model是使用`fd.vision.
 - [Pipeline 模型介绍](..)
 - [Pipeline C++部署](../cpp)
 - [模型预测结果说明](../../../../../docs/api/vision_results/)
-- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
+- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)