[Doc] Fix the version statement for all example docs (PaddlePaddle#654)

* 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 (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <[email protected]> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#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 (#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 <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> * first commit for yolor * for merge * Develop (#11) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <[email protected]> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#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 (#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 <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> * Yolor (#16) * Develop (#11) (#12) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <[email protected]> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#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 (#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 <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> Co-authored-by: Jason <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> * Develop (#13) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <[email protected]> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#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 (#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 <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * Develop (#14) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <[email protected]> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#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 (#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 <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> Co-authored-by: Jason <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> Co-authored-by: Jason <[email protected]> * add is_dynamic for YOLO series (#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 * modify examples' cpp docs for all models Co-authored-by: Jason <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: DefTruth <[email protected]> Co-authored-by: huangjianhui <[email protected]> Co-authored-by: Jason <[email protected]>
ziqi-jin · Nov 22, 2022 · 8fde2b4 · 8fde2b4
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diff --git a/examples/text/ernie-3.0/cpp/README.md b/examples/text/ernie-3.0/cpp/README.md
@@ -12,18 +12,16 @@
 
 ### 快速开始
 
-以下示例展示如何基于FastDeploy库完成ERNIE 3.0 Medium模型在CLUE Benchmark的[AFQMC数据集](https://bj.bcebos.com/paddlenlp/datasets/afqmc_public.zip)上进行文本分类任务的C++预测部署。
+以下示例展示如何基于FastDeploy库完成ERNIE 3.0 Medium模型在CLUE Benchmark的[AFQMC数据集](https://bj.bcebos.com/paddlenlp/datasets/afqmc_public.zip)上进行文本分类任务的C++预测部署。支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
-```bash
-# 下载SDK，编译模型examples代码（SDK中包含了examples代码）
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
 
-cd fastdeploy-linux-x64-gpu-0.7.0/examples/text/ernie-3.0/cpp
+```bash
 mkdir build
 cd build
-# 执行cmake，需要指定FASTDEPLOY_INSTALL_DIR为FastDeploy SDK的目录。
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../fastdeploy-linux-x64-gpu-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 # 下载AFQMC数据集的微调后的ERNIE 3.0模型以及词表

diff --git a/examples/text/uie/cpp/README.md b/examples/text/uie/cpp/README.md
@@ -8,17 +8,15 @@
 - 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)
 
 ## 快速开始
-以Linux上uie-base模型推理为例，在本目录执行如下命令即可完成编译测试。
+以Linux上uie-base模型推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)。
 
 ```
-#下载SDK，编译模型examples代码（SDK中包含了examples代码）
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
-
-cd fastdeploy-linux-x64-gpu-0.7.0/examples/text/uie/cpp
 mkdir build
 cd build
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../fastdeploy-linux-x64-gpu-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 # 下载uie-base模型以及词表

diff --git a/examples/vision/classification/paddleclas/cpp/README.md b/examples/vision/classification/paddleclas/cpp/README.md
@@ -7,16 +7,15 @@
 - 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_vd推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上ResNet50_vd推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
-#下载SDK，编译模型examples代码（SDK中包含了examples代码）
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
-cd fastdeploy-linux-x64-gpu-0.7.0/examples/vision/classification/paddleclas/cpp
 mkdir build
 cd build
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 # 下载ResNet50_vd模型文件和测试图片

diff --git a/examples/vision/classification/paddleclas/quantize/cpp/README.md b/examples/vision/classification/paddleclas/quantize/cpp/README.md
@@ -10,14 +10,15 @@
 - 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.)
 
-## 以量化后的ResNet50_Vd模型为例, 进行部署
+## 以量化后的ResNet50_Vd模型为例, 进行部署，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 在本目录执行如下命令即可完成编译,以及量化模型部署.
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载FastDeloy提供的ResNet50_Vd量化模型文件和测试图片

diff --git a/examples/vision/classification/resnet/cpp/README.md b/examples/vision/classification/resnet/cpp/README.md
@@ -7,16 +7,15 @@
 - 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
+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，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
-#下载SDK，编译模型examples代码（SDK中包含了examples代码）
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.2.1.tgz
-tar xvf fastdeploy-linux-x64-gpu-0.2.1.tgz
-cd fastdeploy-linux-x64-gpu-0.2.1/examples/vision/classification/resnet/cpp
 mkdir build
 cd build
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.1
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 # 下载ResNet模型文件和测试图片

diff --git a/examples/vision/classification/yolov5cls/cpp/README.md b/examples/vision/classification/yolov5cls/cpp/README.md
@@ -7,14 +7,15 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的yolov5模型文件和测试图片

diff --git a/examples/vision/detection/nanodet_plus/cpp/README.md b/examples/vision/detection/nanodet_plus/cpp/README.md
@@ -7,14 +7,15 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的NanoDetPlus模型文件和测试图片

diff --git a/examples/vision/detection/paddledetection/cpp/README.md b/examples/vision/detection/paddledetection/cpp/README.md
@@ -7,17 +7,17 @@
 - 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上推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 以ppyoloe为例进行推理部署
 
-#下载SDK，编译模型examples代码（SDK中包含了examples代码）
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-gpu-0.7.0.tgz
-cd fastdeploy-linux-x64-gpu-0.7.0/examples/vision/detection/paddledetection/cpp
-mkdir build && cd build
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.7.0
+mkdir build
+cd build
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 # 下载PPYOLOE模型文件和测试图片

diff --git a/examples/vision/detection/paddledetection/quantize/cpp/README.md b/examples/vision/detection/paddledetection/quantize/cpp/README.md
@@ -11,14 +11,15 @@
 - 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.)
 
-## 以量化后的PP-YOLOE-l模型为例, 进行部署
+## 以量化后的PP-YOLOE-l模型为例, 进行部署。支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 在本目录执行如下命令即可完成编译,以及量化模型部署.
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载FastDeloy提供的ppyoloe_crn_l_300e_coco量化模型文件和测试图片

diff --git a/examples/vision/detection/scaledyolov4/cpp/README.md b/examples/vision/detection/scaledyolov4/cpp/README.md
@@ -7,14 +7,15 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的ScaledYOLOv4模型文件和测试图片

diff --git a/examples/vision/detection/yolor/cpp/README.md b/examples/vision/detection/yolor/cpp/README.md
@@ -7,14 +7,15 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的YOLOR模型文件和测试图片

diff --git a/examples/vision/detection/yolov5/cpp/README.md b/examples/vision/detection/yolov5/cpp/README.md
@@ -7,16 +7,16 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
-
 #下载官方转换好的yolov5模型文件和测试图片
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

diff --git a/examples/vision/detection/yolov5/quantize/cpp/README.md b/examples/vision/detection/yolov5/quantize/cpp/README.md
@@ -12,13 +12,14 @@
 - 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.
+在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载FastDeloy提供的yolov5s量化模型文件和测试图片

diff --git a/examples/vision/detection/yolov5lite/cpp/README.md b/examples/vision/detection/yolov5lite/cpp/README.md
@@ -7,14 +7,15 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的YOLOv5Lite模型文件和测试图片

diff --git a/examples/vision/detection/yolov6/cpp/README.md b/examples/vision/detection/yolov6/cpp/README.md
@@ -7,14 +7,15 @@
 - 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CPU推理为例，在本目录执行如下命令即可完成编译测试
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的YOLOv6模型文件和测试图片

diff --git a/examples/vision/detection/yolov6/quantize/cpp/README.md b/examples/vision/detection/yolov6/quantize/cpp/README.md
@@ -12,13 +12,14 @@
 - 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.
+在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
 ```bash
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载FastDeloy提供的yolov6s量化模型文件和测试图片