一. yolov5 v6.0訓(xùn)練模型

1. 官網(wǎng)下載yolov5 v6.0代碼
   

2. 下載官方預(yù)訓(xùn)練好的模型
   
   

3. 安裝yolov5所需要的庫文件，requirements.txt在下載好的yolov5源代碼中有

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

   

4. 打開yolov5源代碼中的detect.py文件，修改模型的位置
   
   運(yùn)行后可能出現(xiàn)各種錯(cuò)誤，可以去參考網(wǎng)上的教程

二.訓(xùn)練好的yolov5模型轉(zhuǎn)tensorrt引擎

1. 去tensorrtx官網(wǎng)下載代碼
   

2. 將tensorrtx下的yolov5中的gen_wts.py復(fù)制到y(tǒng)olov5源代碼文件夾中
   
   

3. 參考yolov5官方說明，將yolov5模型文件yolov5s.pt轉(zhuǎn)換為yolov5s.wts文件
   

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

   
   

4. 進(jìn)入tensorrtx下的yolov5文件夾，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)

   這里需要注意：
   

5. 在tensorrtx下的yolov5文件夾中，打開cmd，輸入以下代碼

   mkdir build
   cd build 
   cmake ..
   

   

6. 進(jìn)入build文件夾，用vs打開yolov5.sln文件，然后點(diǎn)擊生成，生成解決方案
   
   出現(xiàn)以下這說明生成成功：
   

7. 設(shè)置yolov5為啟動項(xiàng)
   

8. 進(jìn)入yolov5屬性頁，調(diào)試選項(xiàng)設(shè)置如下：
   
   

9. 設(shè)置完成運(yùn)行程序
   
   運(yùn)行可能需要花費(fèi)一定的時(shí)間，出現(xiàn)以下這說明模型轉(zhuǎn)換成功
   

10. 測試生成的模型，在屬性頁面中設(shè)置如下：
    
    出現(xiàn)以下內(nèi)容這說明運(yùn)行成功：