文章目录[隐藏]

第一步：下载源码

darknet的源码官方链接：https://github.com/AlexeyAB/darknet

我已经搬移到gitee：lishan/darknet

源码项目中有介绍如何在windows上安装darknet

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Requirements for Windows, Linux and macOS

CMake >= 3.18: Download | CMake
Powershell (already installed on windows): Install PowerShell on Windows, Linux, and macOS - PowerShell | Microsoft Docs
CUDA >= 10.2: CUDA Toolkit Archive | NVIDIA Developer (on Linux do Post-installation Actions)
OpenCV >= 2.4: use your preferred package manager (brew, apt), build from source using vcpkg or download from OpenCV official site (on Windows set system variable OpenCV_DIR = C:\opencv\build - where are the include and x64 folders image)
cuDNN >= 8.0.2 cuDNN Archive | NVIDIA Developer (on Linux follow steps described here Installation Guide :: NVIDIA Deep Learning cuDNN Documentation , on Windows follow steps described here Installation Guide :: NVIDIA Deep Learning cuDNN Documentation)
GPU with CC >= 3.0: https://en.wikipedia.org/wiki/CUDA

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第二步：下载必要软件工具

下载CMake、CUDA、Opencv、cuDNN、Visual Studio 2015/1217/2019

使用GPU的前提：保证自己的显卡算力大于等于3.0，否则只能用CPU检测，无需下载CUDA和cuDNN

算力查询网站：CUDA GPUs | NVIDIA Developer

RTX3060的算力在官网查不到，但其算力为8.6

第三步：安装所有依赖并编译darknet

安装Cuda、cuDNN

注意Cuda和CuDNN的版本对应关系

安装cuda和cudnn可参考我的另一篇文章：笔记本RTX3060+Win10_x86_64位搭建Pytorch深度学习本地环境_乐观的lishan的博客-CSDN博客

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cuda10.2、11.0、11.1、11.2 <——>cudnn8.1.0

cuda10.2、11.0、11.1、11.2 <——>cudnn8.1.1

cuda10.2 、11.x<——>cudnn8.2.0

cuda10.2 、11.x<——>cudnn8.2.1

cuda10.2 、11.4<——>cudnn8.2.2

cuda10.2 、11.4<——>cudnn8.2.4

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安装Cmake，下载链接

CMake GUI: Windows win64-x64 Installerhttps://cmake.org/download/

安装OpenCv，假设安装路径为 C:\opencv

新建系统环境变量OpenCV_DIR，值为：

C:\opencv\build\x64\vc14\lib

第三步：编译darknet

打开Cmake-gui，编译

Source code： -> darknet源码文件夹

Binaries -> 编译后输出二进制文件路径（即指定生成的darknet.exe位置）

Configure

Optional platform for generator: x64

Finish

如果没有设置opencv环境变量，或者设置出错，则编译时会出现以下错误：

在cmake中重新指定opencv路径即可

如果出现其他错误，例如cuda出错，一样的操作，直接在cmake中更改对应的环境变量路径即可

重新configure即可

点击Generate

最后点击Generate右侧的Open Project按钮，使用VS 2015/2017/2019打开项目

选择release，x64，点击生成->生成解决方案

出现成功

在darknet\build\darknet\x64\Release下找到darknet.exe即可

复制darknet\build\darknet\x64\Release下所有文件到darknet\build\darknet\x64文件夹下

下载yolov4.weights权值文件到darknet\build\darknet\x64文件夹下

yolov4.weights file 245 MB: yolov4.weights (Google-drive mirror yolov4.weights )

第四步：测试

通过cmd进入darknet\build\darknet\x64文件夹下

测试指定路径的图片

.\darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25

提示输入图片路径：Enter Image Path:

例如输入：data/dog.jpg

测试data文件夹的指定图片

加-ext_output参数可以在控制台显示输出边框坐标信息

.\darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output dog.jpg

测试data文件夹视频xxx.mp4(需要自己准备test.mp4视频文件)

.\darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4

测试本地摄像头视频

.\darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0

测试网络视频

./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg

其他测试命令如下：
- Yolo v4 COCO - image: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25
- Output coordinates of objects: ./darknet detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg
- Yolo v4 COCO - video: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4
- Yolo v4 COCO - WebCam 0: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0
- Yolo v4 COCO for net-videocam - Smart WebCam: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg
- Yolo v4 - save result videofile res.avi: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi
- Yolo v3 Tiny COCO - video: ./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4
- JSON and MJPEG server that allows multiple connections from your soft or Web-browser ip-address:8070 and 8090: ./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
- Yolo v3 Tiny on GPU #1: ./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4
- Alternative method Yolo v3 COCO - image: ./darknet detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25
- Train on Amazon EC2, to see mAP & Loss-chart using URL like: http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090 in the Chrome/Firefox (Darknet should be compiled with OpenCV): ./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map
- 186 MB Yolo9000 - image: ./darknet detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights
- Remember to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
- To process a list of images data/train.txt and save results of detection to result.json file use: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt
- To process a list of images data/train.txt and save results of detection to result.txt use: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt
- Pseudo-labelling - to process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label <image_name>.txt (in this way you can increase the amount of training data) use: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
- To calculate anchors: ./darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416
- To check accuracy mAP@IoU=50: ./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights
- To check accuracy mAP@IoU=75: ./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75