[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 (PaddlePaddle#9)

* Fix compile problem in different python version (PaddlePaddle#26)

* fix some usage problem in linux

* Fix compile problem

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

* 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 <[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 (PaddlePaddle#11)

* Fix compile problem in different python version (PaddlePaddle#26)

* fix some usage problem in linux

* Fix compile problem

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

* 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 <[email protected]>
Co-authored-by: root <[email protected]>
Co-authored-by: DefTruth <[email protected]>
Co-authored-by: huangjianhui <[email protected]>

* 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 <[email protected]>

* 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 <[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 (PaddlePaddle#13)

* Fix compile problem in different python version (PaddlePaddle#26)

* fix some usage problem in linux

* Fix compile problem

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

* 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 <[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 (PaddlePaddle#14)

* Fix compile problem in different python version (PaddlePaddle#26)

* fix some usage problem in linux

* Fix compile problem

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

* 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 <[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 (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

* 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]>

Author: 5 people

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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