一. yolov5 v6.0训练模型

1. 官网下载yolov5 v6.0代码
   

2. 下载官方预训练好的模型
   
   

3. 安装yolov5所需要的库文件，requirements.txt在下载好的yolov5源代码中有

   pip install -r C:\Users\10001540\Downloads\yolov5-6.0\requirements.txt
   

   

4. 打开yolov5源代码中的detect.py文件，修改模型的位置
   
   运行后可能出现各种错误，可以去参考网上的教程

二.训练好的yolov5模型转tensorrt引擎

1. 去tensorrtx官网下载代码
   

2. 将tensorrtx下的yolov5中的gen_wts.py复制到yolov5源代码文件夹中
   
   

3. 参考yolov5官方说明，将yolov5模型文件yolov5s.pt转换为yolov5s.wts文件
   

   python gen_wts.py -w weights/yolov5s.pt -o yolov5s.wts
   

   
   

4. 进入tensorrtx下的yolov5文件夹，修改里面的CMakeList.txt，如下：

   cmake_minimum_required(VERSION 2.6)project(yolov5) #1
   set(OpenCV_DIR "D:\\Program Files\\opencv\\build")  #2
   set(OpenCV_INCLUDE_DIRS ${OpenCV_DIR}\\include) #3
   set(OpenCV_LIB_DIRS ${OpenCV_DIR}\\x64\\vc15\\lib) #4
   set(OpenCV_Debug_LIBS "opencv_world454d.lib") #5
   set(OpenCV_Release_LIBS "opencv_world454.lib") #6
   set(TRT_DIR "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\TensorRT-8.2.3.0")  #7
   set(TRT_INCLUDE_DIRS ${TRT_DIR}\\include) #8
   set(TRT_LIB_DIRS ${TRT_DIR}\\lib) #9
   set(Dirent_INCLUDE_DIRS "Z:\\code\\dirent-master\\include") #10add_definitions(-std=c++11)
   add_definitions(-DAPI_EXPORTS)option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
   set(CMAKE_CXX_STANDARD 11)
   set(CMAKE_BUILD_TYPE Debug)set(THREADS_PREFER_PTHREAD_FLAG ON)
   find_package(Threads)# setup CUDA
   find_package(CUDA REQUIRED)
   message(STATUS "    libraries: ${CUDA_LIBRARIES}")
   message(STATUS "    include path: ${CUDA_INCLUDE_DIRS}")include_directories(${CUDA_INCLUDE_DIRS})####
   enable_language(CUDA)  # add this line, then no need to setup cuda path in vs
   ####
   include_directories(${PROJECT_SOURCE_DIR}/include) #11
   include_directories(${TRT_INCLUDE_DIRS}) #12
   link_directories(${TRT_LIB_DIRS}) #13
   include_directories(${OpenCV_INCLUDE_DIRS}) #14
   link_directories(${OpenCV_LIB_DIRS}) #15
   include_directories(${Dirent_INCLUDE_DIRS}) #16# -D_MWAITXINTRIN_H_INCLUDED for solving error: identifier "__builtin_ia32_mwaitx" is undefined
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -D_MWAITXINTRIN_H_INCLUDED")# setup opencv
   find_package(OpenCV QUIETNO_MODULENO_DEFAULT_PATHNO_CMAKE_PATHNO_CMAKE_ENVIRONMENT_PATHNO_SYSTEM_ENVIRONMENT_PATHNO_CMAKE_PACKAGE_REGISTRYNO_CMAKE_BUILDS_PATHNO_CMAKE_SYSTEM_PATHNO_CMAKE_SYSTEM_PACKAGE_REGISTRY
   )message(STATUS "OpenCV library status:")
   message(STATUS "    version: ${OpenCV_VERSION}")
   message(STATUS "    lib path: ${OpenCV_LIB_DIRS}")
   message(STATUS "    Debug libraries: ${OpenCV_Debug_LIBS}")
   message(STATUS "    Release libraries: ${OpenCV_Release_LIBS}")
   message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")add_executable(yolov5 ${PROJECT_SOURCE_DIR}/yolov5.cpp ${PROJECT_SOURCE_DIR}/common.hpp ${PROJECT_SOURCE_DIR}/yololayer.cu ${PROJECT_SOURCE_DIR}/yololayer.h ${PROJECT_SOURCE_DIR}/preprocess.cu ${PROJECT_SOURCE_DIR}/preprocess.h)   #17target_link_libraries(yolov5 "nvinfer" "nvinfer_plugin") #18
   target_link_libraries(yolov5 debug ${OpenCV_Debug_LIBS}) #19
   target_link_libraries(yolov5 optimized ${OpenCV_Release_LIBS}) #20
   target_link_libraries(yolov5 ${CUDA_LIBRARIES}) #21
   target_link_libraries(yolov5 Threads::Threads)  if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
   set(CMAKE_CUDA_ARCHITECTURES 70 75 80 86)
   endif(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)

   这里需要注意：
   

5. 在tensorrtx下的yolov5文件夹中，打开cmd，输入以下代码

   mkdir build
   cd build 
   cmake ..
   

   

6. 进入build文件夹，用vs打开yolov5.sln文件，然后点击生成，生成解决方案
   
   出现以下这说明生成成功：
   

7. 设置yolov5为启动项
   

8. 进入yolov5属性页，调试选项设置如下：
   
   

9. 设置完成运行程序
   
   运行可能需要花费一定的时间，出现以下这说明模型转换成功
   

10. 测试生成的模型，在属性页面中设置如下：
    
    出现以下内容这说明运行成功：