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+# C++部署
+
+## 准备预测库
+
+参考编译文档[FastDeploy编译](../compile/README.md)进行编译，或直接使用如下预编译库
+
+| 编译库 | 平台 | 支持设备 | 说明 |
+|:------ | :---- | :------- | :----- |
+|[fastdeploy-linux-x64-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz) | Linux | CPU | 集成ONNXRuntime |
+|[fastdeploy-linux-x64-gpu-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-gpu-0.0.3.tgz) | Linux | CPU/GPU | 集成ONNXRuntime, TensorRT |
+|[fastdeploy-osx-x86_64-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-osx-x86_64-0.0.3.tgz) | Mac OSX Intel CPU | CPU | 集成ONNXRuntime |
+|[fastdeploy-osx-arm64-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-osx-arm64-0.0.3.tgz) | Mac OSX M1 CPU | CPU | 集成ONNXRuntime |
+
+
+## 使用
+
+FastDeploy提供了多种领域内的模型，可快速完成模型的部署，本文档以YOLOv5在Linux上的部署为例
+
+```
+# 下载库并解压
+wget https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz
+tar xvf fastdeploy-linux-x64-0.0.3.tgz
+
+# 下载模型和测试图片
+wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.onnx
+wget https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg
+```
+
+### YOLOv5预测代码
+
+准备如下`yolov5.cc`代码
+```
+#include "fastdeploy/vision.h"
+
+int main() {
+  typedef vis = fastdeploy::vision;
+
+  auto model = vis::ultralytics::YOLOv5("yolov5s.onnx"); // 加载模型
+
+  if (!model.Initialized()) { // 判断模型是否初始化成功
+    std::cerr << "Initialize failed." << std::endl;
+    return -1;
+  }
+
+  cv::Mat im = cv::imread("bus.jpg"); // 读入图片
+
+  vis::DetectionResult res;
+  if (!model.Predict(&im, &res)) { // 预测图片
+    std::cerr << "Prediction failed." << std::endl;
+    return -1;
+  }
+
+  std::cout << res.Str() << std::endl; // 输出检测结果
+  return 0;
+}
+```
+
+### 编译代码
+
+编译前先完成CMakeLists.txt的开发，在`yolov5.cc`同级目录创建`CMakeLists.txt`文件，内容如下
+```
+PROJECT(yolov5_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.16)
+# 在低版本ABI环境中，可通过如下代码进行兼容性编译
+# add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+
+# 在下面指定下载解压后的fastdeploy库路径
+set(FASTDEPLOY_INSTALL_DIR /ssd1/download/fastdeploy-linux-x64-0.0.3/)
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(yolov5_demo ${PROJECT_SOURCE_DIR}/yolov5.cc)
+message(${FASTDEPLOY_LIBS})
+# 添加FastDeploy库依赖
+target_link_libraries(yolov5_demo ${FASTDEPLOY_LIBS})
+~
+```
+
+此时当前目录结构如下所示
+```
+- demo_directory
+|___fastdeploy-linux-x64-0.0.3/ # 预测库解压
+|___yolov5.cc                   # 示例代码
+|___CMakeLists.txt              # cmake文件
+|___yolov5s.onnx                # 模型文件
+|___bus.jpeg                    # 测试图片
+```
+
+执行如下命令进行编译
+```
+cmake .
+make -j
+```
+编译后可执行二进制即为当前目录下的`yolov5_demo`，使用如下命令执行
+```
+./yolov5_demo
+```
+
+即会加载模型进行推理，得到结果如下
+```
+DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
+223.395126,403.948669, 345.337189, 867.339050, 0.856906, 0
+668.301758,400.781372, 808.441772, 882.534973, 0.829716, 0
+50.210758,398.571289, 243.123383, 905.016846, 0.805375, 0
+23.768217,214.979355, 802.627869, 778.840820, 0.756311, 5
+0.737200,552.281006, 78.617218, 890.945007, 0.363471, 0
+```