diff --git a/examples/text/ernie-3.0/cpp/README.md b/examples/text/ernie-3.0/cpp/README.md
index 0882a9070..21c02e913 100644
--- 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
 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
index cfb045d8a..7b569eadc 100644
--- 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
index 7d71f4723..066340467 100644
--- 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
index 681d1e3f7..3dced039c 100644
--- 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
index 1180d26c9..fe9c59108 100644
--- 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
index e56a6c1af..de21e6dc3 100755
--- 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
index 5d575457b..bf980355d 100644
--- 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
index 95b640eea..63df0365a 100644
--- 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
index 7e321003f..793fc3938 100644
--- 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
index 640eefb93..f40c20585 100644
--- 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
index d24599942..89bccb487 100644
--- 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
index cc88e3c35..ece3826a5 100644
--- 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
index ebe941a93..2555cee21 100644
--- 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
index cd1c46d78..43ec41580 100644
--- 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
index 00303ffa5..6a8a0d7c0 100644
--- 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
index cc6f7c9b2..5859b7de1 100644
--- 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
diff --git a/examples/vision/detection/yolov7/cpp/README.md b/examples/vision/detection/yolov7/cpp/README.md
index 08c59ac76..2cff4c73b 100644
--- a/examples/vision/detection/yolov7/cpp/README.md
+++ b/examples/vision/detection/yolov7/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yolov7模型文件和测试图片
diff --git a/examples/vision/detection/yolov7/quantize/cpp/README.md b/examples/vision/detection/yolov7/quantize/cpp/README.md
index bad31d532..bf676253a 100644
--- a/examples/vision/detection/yolov7/quantize/cpp/README.md
+++ b/examples/vision/detection/yolov7/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.
+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yolov7量化模型文件和测试图片
diff --git a/examples/vision/detection/yolov7end2end_ort/cpp/README.md b/examples/vision/detection/yolov7end2end_ort/cpp/README.md
index 63747cb32..b5c18f183 100644
--- a/examples/vision/detection/yolov7end2end_ort/cpp/README.md
+++ b/examples/vision/detection/yolov7end2end_ort/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
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-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
-# 如果预编译库还没有支持该模型，请从develop分支源码编译最新的SDK
 
 #下载官方转换好的yolov7模型文件和测试图片
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-ort-nms.onnx
diff --git a/examples/vision/detection/yolov7end2end_trt/cpp/README.md b/examples/vision/detection/yolov7end2end_trt/cpp/README.md
index 24563efba..52baee923 100644
--- a/examples/vision/detection/yolov7end2end_trt/cpp/README.md
+++ b/examples/vision/detection/yolov7end2end_trt/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
 mkdir build
 cd build
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-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
-# 若预编译库没有支持该类 则请自行从源码develop分支编译最新的FastDeploy C++ SDK
 
 #下载官方转换好的yolov7模型文件和测试图片
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx
diff --git a/examples/vision/detection/yolox/cpp/README.md b/examples/vision/detection/yolox/cpp/README.md
index e9ee59238..3cc857aec 100644
--- a/examples/vision/detection/yolox/cpp/README.md
+++ b/examples/vision/detection/yolox/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YOLOX模型文件和测试图片
diff --git a/examples/vision/facealign/face_landmark_1000/cpp/README.md b/examples/vision/facealign/face_landmark_1000/cpp/README.md
index 033f54164..755a20664 100644
--- a/examples/vision/facealign/face_landmark_1000/cpp/README.md
+++ b/examples/vision/facealign/face_landmark_1000/cpp/README.md
@@ -7,11 +7,12 @@
 - 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，保证 FastDeploy 版本0.7.0以上(x.x.x >= 0.7.0)支持FaceLandmark1000模型
+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
+# 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
diff --git a/examples/vision/facealign/pfld/cpp/README.md b/examples/vision/facealign/pfld/cpp/README.md
index 4138646d2..d061b010a 100644
--- a/examples/vision/facealign/pfld/cpp/README.md
+++ b/examples/vision/facealign/pfld/cpp/README.md
@@ -7,11 +7,12 @@
 - 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，保证 FastDeploy 版本0.6.0以上(x.x.x >= 0.6.0)支持PFLD模型
+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
+# 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
diff --git a/examples/vision/facealign/pipnet/cpp/README.md b/examples/vision/facealign/pipnet/cpp/README.md
index 3f11bf62b..81778d7d5 100644
--- a/examples/vision/facealign/pipnet/cpp/README.md
+++ b/examples/vision/facealign/pipnet/cpp/README.md
@@ -7,11 +7,12 @@
 - 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，保证 FastDeploy 版本0.7.0以上(x.x.x >= 0.7.0)支持PIPNet模型
+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
+# 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
diff --git a/examples/vision/facedet/retinaface/cpp/README.md b/examples/vision/facedet/retinaface/cpp/README.md
index 9ab11da9a..15d71d9a4 100644
--- a/examples/vision/facedet/retinaface/cpp/README.md
+++ b/examples/vision/facedet/retinaface/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RetinaFace模型文件和测试图片
diff --git a/examples/vision/facedet/scrfd/cpp/README.md b/examples/vision/facedet/scrfd/cpp/README.md
index 669da8c7b..b59acc1ed 100644
--- a/examples/vision/facedet/scrfd/cpp/README.md
+++ b/examples/vision/facedet/scrfd/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SCRFD模型文件和测试图片
diff --git a/examples/vision/facedet/ultraface/cpp/README.md b/examples/vision/facedet/ultraface/cpp/README.md
index 10fa2e95f..8d512af0a 100644
--- a/examples/vision/facedet/ultraface/cpp/README.md
+++ b/examples/vision/facedet/ultraface/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UltraFace模型文件和测试图片
diff --git a/examples/vision/facedet/yolov5face/cpp/README.md b/examples/vision/facedet/yolov5face/cpp/README.md
index 5be8d9ecd..a1df0620d 100644
--- a/examples/vision/facedet/yolov5face/cpp/README.md
+++ b/examples/vision/facedet/yolov5face/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://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-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YOLOv5Face模型文件和测试图片
diff --git a/examples/vision/faceid/adaface/cpp/README.md b/examples/vision/faceid/adaface/cpp/README.md
index 95b939a2e..9c28d584d 100644
--- a/examples/vision/faceid/adaface/cpp/README.md
+++ b/examples/vision/faceid/adaface/cpp/README.md
@@ -8,15 +8,15 @@
 - 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
-# “如果预编译库不包含本模型，请从最新代码编译SDK”
 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
 
 #下载测试图片
diff --git a/examples/vision/faceid/insightface/cpp/README.md b/examples/vision/faceid/insightface/cpp/README.md
index 71a9261cc..341478b7c 100644
--- a/examples/vision/faceid/insightface/cpp/README.md
+++ b/examples/vision/faceid/insightface/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ArcFace模型文件和测试图片
diff --git a/examples/vision/headpose/fsanet/cpp/README.md b/examples/vision/headpose/fsanet/cpp/README.md
index 1a3a51769..51e0a179d 100755
--- a/examples/vision/headpose/fsanet/cpp/README.md
+++ b/examples/vision/headpose/fsanet/cpp/README.md
@@ -7,11 +7,12 @@
 - 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，保证 FastDeploy 版本0.6.0以上(x.x.x >= 0.6.0)支持FSANet模型
+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
+# 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
diff --git a/examples/vision/keypointdetection/det_keypoint_unite/cpp/README.md b/examples/vision/keypointdetection/det_keypoint_unite/cpp/README.md
index a7ccba903..631258127 100644
--- a/examples/vision/keypointdetection/det_keypoint_unite/cpp/README.md
+++ b/examples/vision/keypointdetection/det_keypoint_unite/cpp/README.md
@@ -9,15 +9,15 @@
 - 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)
 
 
-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
-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/keypointdetection/tiny_pose/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PP-TinyPose和PP-PicoDet模型文件和测试图片
diff --git a/examples/vision/keypointdetection/tiny_pose/cpp/README.md b/examples/vision/keypointdetection/tiny_pose/cpp/README.md
index d2a559c34..eba550193 100644
--- a/examples/vision/keypointdetection/tiny_pose/cpp/README.md
+++ b/examples/vision/keypointdetection/tiny_pose/cpp/README.md
@@ -9,15 +9,15 @@
 - 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)
 
 
-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
-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/keypointdetection/tiny_pose/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PP-TinyPose模型文件和测试图片
diff --git a/examples/vision/matting/modnet/cpp/README.md b/examples/vision/matting/modnet/cpp/README.md
index 0fd773361..25f37c107 100644
--- a/examples/vision/matting/modnet/cpp/README.md
+++ b/examples/vision/matting/modnet/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
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-0.7.0.tgz
-
-mkdir build && cd build
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.7.0
+mkdir build
+cd build
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载官方转换好的MODNet模型文件和测试图片
diff --git a/examples/vision/matting/ppmatting/cpp/README.md b/examples/vision/matting/ppmatting/cpp/README.md
index 4ec03d2bf..e8b919f92 100644
--- a/examples/vision/matting/ppmatting/cpp/README.md
+++ b/examples/vision/matting/ppmatting/cpp/README.md
@@ -7,15 +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 PP-Matting 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)下载CPU推理库）
+以Linux上 PP-Matting 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/matting/ppmatting/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PP-Matting模型文件和测试图片
diff --git a/examples/vision/matting/rvm/cpp/README.md b/examples/vision/matting/rvm/cpp/README.md
index c236a717b..d8e00400c 100755
--- a/examples/vision/matting/rvm/cpp/README.md
+++ b/examples/vision/matting/rvm/cpp/README.md
@@ -5,17 +5,17 @@
 - 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 RobustVideoMatting 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)下载CPU推理库）
+以Linux上 RobustVideoMatting 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)
 
 本目录下提供`infer.cc`快速完成RobustVideoMatting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
 
 ```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/matting/rvm/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RobustVideoMatting模型文件和测试图片以及视频
diff --git a/examples/vision/ocr/PP-OCRv2/cpp/README.md b/examples/vision/ocr/PP-OCRv2/cpp/README.md
index 9f034c09e..afc35d50b 100644
--- a/examples/vision/ocr/PP-OCRv2/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv2/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)
 
 ```
 mkdir build
 cd build
-wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-gpu-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
 
 
diff --git a/examples/vision/ocr/PP-OCRv3/cpp/README.md b/examples/vision/ocr/PP-OCRv3/cpp/README.md
index 0d397d4cf..369265809 100644
--- a/examples/vision/ocr/PP-OCRv3/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv3/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)
 
 ```
 mkdir build
 cd build
-wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.7.0.tgz
-tar xvf fastdeploy-linux-x64-gpu-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
 
 
diff --git a/examples/vision/segmentation/paddleseg/cpp/README.md b/examples/vision/segmentation/paddleseg/cpp/README.md
index 01ff7b33f..926452ad0 100644
--- a/examples/vision/segmentation/paddleseg/cpp/README.md
+++ b/examples/vision/segmentation/paddleseg/cpp/README.md
@@ -9,15 +9,15 @@
 
 【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
 
-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
-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/segmentation/paddleseg/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Unet模型文件和测试图片
diff --git a/examples/vision/segmentation/paddleseg/quantize/cpp/README.md b/examples/vision/segmentation/paddleseg/quantize/cpp/README.md
index 70e6a839d..34b98790f 100644
--- a/examples/vision/segmentation/paddleseg/quantize/cpp/README.md
+++ b/examples/vision/segmentation/paddleseg/quantize/cpp/README.md
@@ -11,13 +11,14 @@
 - 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.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.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PP_LiteSeg_T_STDC1_cityscapes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PP_LiteSeg_T_STDC1_cityscapes量化模型文件和测试图片
diff --git a/examples/vision/sr/basicvsr/cpp/README.md b/examples/vision/sr/basicvsr/cpp/README.md
index ff5e17a99..802a7ac58 100644
--- a/examples/vision/sr/basicvsr/cpp/README.md
+++ b/examples/vision/sr/basicvsr/cpp/README.md
@@ -6,16 +6,15 @@
 
 - 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BasicVSR推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库）
+以Linux上BasicVSR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）
-# fastdeploy版本 >= 0.7.0
-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/sr/basicvsr/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BasicVSR模型文件和测试视频
diff --git a/examples/vision/sr/edvr/cpp/README.md b/examples/vision/sr/edvr/cpp/README.md
index c40b8cf83..ec917b824 100644
--- a/examples/vision/sr/edvr/cpp/README.md
+++ b/examples/vision/sr/edvr/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EDVR推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库）
+以Linux上EDVR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）
-# fastdeploy版本 >= 0.7.0
-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/sr/edvr/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EDVR模型文件和测试视频
diff --git a/examples/vision/sr/ppmsvsr/cpp/README.md b/examples/vision/sr/ppmsvsr/cpp/README.md
index 2130ffe7e..712264a36 100644
--- a/examples/vision/sr/ppmsvsr/cpp/README.md
+++ b/examples/vision/sr/ppmsvsr/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 PP-MSVSR 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库）
+以Linux上 PP-MSVSR 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）
-# fastdeploy版本 >= 0.7.0
-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/sr/ppmsvsr/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PP-MSVSR模型文件和测试视频
diff --git a/examples/vision/tracking/pptracking/cpp/README.md b/examples/vision/tracking/pptracking/cpp/README.md
index 45b26d0ca..af26e1fff 100644
--- a/examples/vision/tracking/pptracking/cpp/README.md
+++ b/examples/vision/tracking/pptracking/cpp/README.md
@@ -7,15 +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 PP-Tracking 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)下载CPU推理库）
+以Linux上 PP-Tracking 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/tracking/pptracking/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PP-Tracking模型文件和测试视频