darknet YoloV4手册翻译

Posted henreash

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https://github.com/AlexeyAB/darknet

(对象检测神经网络) – 可用于Linxu和windows的张量计算核心

Yolo v4网页: https://arxiv.org/abs/2004.10934

更多细节: http://pjreddie.com/darknet/yolo/

  1. 改进Improvements in this repository
  2. 如何使用How to use
  3. 如何在Linux下编译How to compile on Linux
  4. 如何在Windows下编译How to compile on Windows
  5. 在MS COCO上训练评估速度和精度Training and Evaluation of speed and accuracy on MS COCO
  6. 如何在多GPU上训练How to train with multi-GPU:
  7. 如何训练自定义对象检测器How to train (to detect your custom objects)
  8. 如何训练tiny-yolo自定义对象检测器How to train tiny-yolo (to detect your custom objects)
  9. 何时需要停止训练When should I stop training
  10. 如何改进对象检测How to improve object detection
  11. 如何标注对象范围框并创建标注文件How to mark bounded boxes of objects and create annotation files
  12. 如何使用Yolo的dll和so库How to use Yolo as DLL and SO libraries

如何在MS COCO评估服务上评估YoloV4的AP

  1. 在MS COCO服务器上下载并解压test-dev2017（ http://images.cocodataset.org/zips/test2017.zip）

Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip

  1. 下载检测任务图片，并替换文件路径

下载检测任务图片列表并替换为实际路径:

 https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt

  1. 下载yolov4.weights文件: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT
  2. 调整cfg/coco.data文件如下所示：

classes= 80

train  = <replace with your path>/trainvalno5k.txt

valid = <replace with your path>/testdev2017.txt

names = data/coco.names

backup = backup

eval=coco

  1. 在./darknet可执行文件同级创建/results目录
  2. 执行验证: ./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights
  3. 将/results/coco_results.json 文件重命名为 detections_test-dev2017_yolov4_results.json，并压缩为detections_test-dev2017_yolov4_results.zip。
  4. 提交detections_test-dev2017_yolov4_results.zip文件到MS COCO的test-dev2019（bbox）评估服务

如何在GPU下评估YoloV4的帧率

  1. 设置Makefile中参数GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1后编译Darknet (Cmake使用同样配置)
  2. 下载 yolov4.weights: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT
  3. 获取任意.avi/.mp4视频文件(分辨率最好不要超过1920x1080以免引起cpu性能瓶颈)
  4. 运行下面两个命令之一，查看平均帧率:
  • 包含video_capturing + NMS + drawing_bboxes: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output
  • 不含video_capturing + NMS + drawing_bboxes: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark

预训练模型

不同cfg-文件对应的权重文件(使用MS COCO 数据集训练):

在RTX 2070 (R)和Tesla V100 (V)上的帧率:

Yolo v3 模型

 

Yolo v2 模型

将下载的模型拷贝到darknet.exe同目录下。

在目录darknet/cfg/中获取cfg-文件

必要条件

 

详细见 https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , Windows 下复制 cudnn.h,cudnn64_7.dll, cudnn64_7.lib 详细见 https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows )

其他框架下的YoloV3

数据集

  • MS COCO: 使用 ./scripts/get_coco_dataset.sh 下载标注好的MS COCO检测数据集
  • OpenImages: 使用python ./scripts/get_openimages_dataset.py 获取标注好的训练检测数据集
  • Pascal VOC: 使用python ./scripts/voc_label.py 获取标注好的Train/Test/Val 检测数据集
  • ILSVRC2012 (ImageNet 分类器): 使用 ./scripts/get_imagenet_train.sh (或 imagenet_label.sh 下载标注好的集合)
  • German/Belgium/Russian/LISA/MASTIF 交通信号检测数据集-见：

 https://github.com/angeligareta/Datasets2Darknet#detection-task

范例结果

https://www.youtube.com/watch?v=MPU2HistivI

其他: https://www.youtube.com/user/pjreddie/videos

本库的改进

  • 添加对windows的支持
  • 添加先进的（State-of-Art）模型: CSP, PRN（医学搜索网络）, EfficientNet
  • 增加层: [conv_lstm], [scale_channels] SE/ASFF/BiFPN, [local_avgpool], [sam], [Gaussian_yolo], [reorg3d] (fixed [reorg]), fixed [batchnorm]
  • 添加训练循环模型的能力(conv-lstm[conv_lstm]/conv-rnn[crnn]层)，可精确检测视频
  • 增加新参数: [net] mixup=1 cutmix=1 mosaic=1 blur=1. 增加新的架构: SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX
  • 增加使用CPU-RAM做GPU处理的训练能力，增加mini_batch_size参数，提升精确度（替换batch-norm）

added the ability for training with GPU-processing using CPU-RAM to increase the mini_batch_size and increase accuracy (instead of batch-norm sync)

improved binary neural network performance 2x-4x times for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg

  • 合并Convolutional + Batch-norm 为一层，神经网络效率提升约7%

improved neural network performance ~7% by fusing 2 layers into 1: Convolutional + Batch-norm

  • 提升效率:在Makefile或darknet.sln中定义CUDNN_HALF启用张量运算则在Volta/Turing（Tesla V100、Geforce RTX…）GPU上，检测效率x2

 Detection 2x times, on GPU Volta/Turing (Tesla V100, GeForce RTX, ...) using Tensor Cores if CUDNN_HALF defined in the Makefile or darknet.sln

  • 提升效率：使用darknet detector demo检测视频（文件或流）， FullHD1.2倍提速，4K 2倍提速

improved performance ~1.2x times on FullHD, ~2x times on 4K, for detection on the video (file/stream) using darknet detector demo...

  • 提升效率：训练中数据增强速度提升3.5倍（使用opencv SSE/AVX函数替代原有函数）-移除多GPU或GPU Volta训练中的瓶颈

improved performance 3.5 X times of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta

  • 提升效率：在支持AVX指令的Inter CPU上检测和训练效率提升85%

Improved performance of detection and training on Intel CPU with AVX (Yolo v3 ~85%)

  • 当random=1时，在调整网络大小时优化内存分配

optimized memory allocation during network resizing when random=1

  • 优化检测时的GPU初始化过程-直接将batch初始化为1，而不是重新初始化为1

optimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1

  • 增加精确计算mAp、F1、Iou、Precision-Recall功能，命令行：darknet detector map…

added correct calculation of mAP, F1, IoU, Precision-Recall using command darknet detector map…

  • 训练过程中绘制average-Loss 和accuracy-mAP (加-map 标志)chart图

added drawing of chart of average-Loss and accuracy-mAP (-map flag) during training

  • 运行./darknet detector demo ... -json_port 8070 -mjpeg_port 8090 ，可作为JSON、MJPEG服务让软件或浏览器在线获取实时训练结果

run ./darknet detector demo ... -json_port 8070 -mjpeg_port 8090 as JSON and MJPEG server to get results online over the network by using your soft or Web-browser

增加教程 - How to train Yolo v4-v2 (to detect your custom objects)

同时，你也可能对简化库感兴趣-使用INT8-quantization 实现（速度提升30%，mAP降低1%）: https://github.com/AlexeyAB/yolo2_light

如何使用命令行

Linux下使用./darknet 替代darknet.exe, 如:./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights

Linux在根目录查找可执行文件./darknet, Windows下在\\build\\darknet\\x64目录

  • Yolo v4 COCO - 图像: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25
  • 输出对象坐标: darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg
  • Yolo v4 COCO – 视频: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4
  • Yolo v4 COCO - WebCam 0: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0
  • Yolo v4 COCO  net-videocam - Smart WebCam: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg
  • Yolo v4 - 保存结果视频文件 res.avi: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi
  • Yolo v3 Tiny COCO - 视频: darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4
  • JSON 和 MJPEG 服务 让多个软件或浏览器连接到 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 使用 GPU #1: darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4
  • 替代 Yolo v3 COCO - 图像: darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25
  • 在Amazon EC2训练,在Chrome或Firefox浏览器上访问URL: http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090查看mAP & Loss-chart (Darknet编译需要依赖OpenCV): ./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map
  • 186 MB Yolo9000 - 图像: darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights
  • 如果实用cpp api编译app，将data/9k.tree 和 data/coco9k.map文件放到app同目录中
  • 处理图像列表 data/train.txt并将检测结果保存到result.json文件: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt
  • 处理图像列表 data/train.txt 并将结果保存到result.txt文件:
    darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt
  • 伪标记 - 处理图像列表data/new_train.txt ，并将每个图像的检测结果以Yolo训练标注格式保存为到<image_name>.txt (可以用来增加训练数据) : darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
  • 计算描点: darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416
  • 检测精度 mAP@IoU=50: darknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_7000.weights
  • 检测精度 mAP@IoU=75: darknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_7000.weights -iou_thresh 0.75

在安卓智能手机中使用网络video-camera mjpeg-stream

  1. 下载Android phone mjpeg-stream 软件: IP Webcam / Smart WebCam
  2. 使用wifi(through a WiFi-router)或usb将安卓连接到计算机
  3. 在手机上启动Smart WebCam
  4. 替换下面的地址为手机Smart WebCam APP中的地址，并启动:
  • Yolo v4 COCO-model: darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0

如何在Linux下编译 (使用cmake)

CMakeLists.txt 文件尝试寻找以安装的可选依赖，如CUDA、cudn、ZED并据此进行编译。同时创建darkent共享对象库文件用于编码开发。

在clone的源码库中:

mkdir build-release
cd build-release
cmake ..
make
make install

如何在Linux下编译 (使用 make)

在darknet目录中执行make. make前,可以在Makefile中设置如下选项: link

  • GPU=1 使用CUDA来build，进而使用GPU加速(CUDA包含在/usr/local/cuda)
  • CUDNN=1 使用cuDNN v5-v7来build，进而使用GPU加速训练(cuDNN 包含在/usr/local/cudnn)
  • CUDNN_HALF=1 使用Tensor Cores 来build，在Titan V / Tesla V100 / DGX-2 及更新硬件上检测提速3倍，训练提速2倍
  • OPENCV=1使用 OpenCV 4.x/3.x/2.4.x来build -可在网络摄像头或web-cams上检测视频文件和视频流
  • DEBUG=1 编译Yolo的debug版本
  • OPENMP=1 to build with OpenMP support to accelerate Yolo by using multi-core CPU
  • LIBSO=1 编译darknet.so动态库及使用这个库的可执行文件uselib.尝试运行：LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4。如何使用这个so库，见C++范例: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp 或: LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4
  • ZED_CAMERA=1使用ZED-3D-camera 支持来build(依赖ZED SDK的安装), 然后执行LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera

在linux下运行本文中的darknet范例，将./darknet 替换darknet.exe, i.e. use this command: ./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights

如何在Windows下编译(使用CMake-GUI)

如果安装了VS2015/2017/2019,CUDA >10.0,cuDNN > 7.0,OpenCV>2.4，推荐采用此方法。

注意：确保安装好CUDA和OPENCV

CMake-GUI如下图所示：

  1. Configure
  2. 选择生成平台（设置为x64）Optional platform for generator (Set: x64)
  3. Finish
  4. Generate
  5. Open Project
  6. 设置为x64&Release  Set: x64 & Release
  7. Build
  8. Build solution

如何在Windows下编译(使用vcpkg)

vcpkg是自动下载依赖包的工具

如果已经安装了Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, OpenCV > 2.4,推荐使用CMake-GUI方式。

否则，按如下步骤：

  1. 安装并升级VS2017的最新版，确保安装全部的补丁（如不确定可再次运行安装程序）。如果是全新安装，可从Visual Studio Community下载。
  2. 安装CUDA和cuDNN
  3. 安装git和cmake.并确保都加入到了PATH环境变量中。 Make sure they are on the Path at least for the current account
  4. 安装vcpkg并安装测试库，确保所有组件ok，例如运行cvpkg install opengl
  5. 定义VCPKG_ROOT环境变量，指向vcpkg的安装目录
  6. 定义环境变量VCPKG_DEFAULT_TRIPLET，值为x64-windows
  7. 打开Powershell并输入如下命令： 
    PS \\>                  cd $env:VCPKG_ROOT
PS Code\\vcpkg>         .\\vcpkg install pthreads opencv[ffmpeg] #replace with opencv[cuda,ffmpeg] in case you want to use cuda-accelerated openCV

     

  8. 打开Powershell，进入到darknet目录，运行命令.\\build.ps1。如果要使用vs，可以在build后看到cmake生成的两个解决方案文件，一个是build_win_debug，一个是build_win_release，包含了与当前系统匹配的所有配置信息

如何在windows下编译（传统方式）

  1. If you have CUDA 10.0, cuDNN 7.4 and OpenCV 3.x (with paths: C:\\opencv_3.0\\opencv\\build\\include & C:\\opencv_3.0\\opencv\\build\\x64\\vc14\\lib), then open build\\darknet\\darknet.sln, set x64 and Release https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable CUDNN with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg

1.1. Find files opencv_world320.dll and opencv_ffmpeg320_64.dll (or opencv_world340.dll and opencv_ffmpeg340_64.dll) in C:\\opencv_3.0\\opencv\\build\\x64\\vc14\\bin and put it near with darknet.exe

1.2 Check that there are bin and include folders in the C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.0 if aren't, then copy them to this folder from the path where is CUDA installed

1.3. To install CUDNN (speedup neural network), do the following:

1.4. If you want to build without CUDNN then: open \\darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: CUDNN;

  1. If you have other version of CUDA (not 10.0) then open build\\darknet\\darknet.vcxproj by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version. Then open \\darknet.sln -> (right click on project) -> properties -> CUDA C/C++ -> Device and remove there ;compute_75,sm_75. Then do step 1
  2. If you don't have GPU, but have OpenCV 3.0 (with paths: C:\\opencv_3.0\\opencv\\build\\include & C:\\opencv_3.0\\opencv\\build\\x64\\vc14\\lib), then open build\\darknet\\darknet_no_gpu.sln, set x64 and Release, and do the: Build -> Build darknet_no_gpu
  3. If you have OpenCV 2.4.13 instead of 3.0 then you should change paths after \\darknet.sln is opened

4.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: C:\\opencv_2.4.13\\opencv\\build\\include

4.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: C:\\opencv_2.4.13\\opencv\\build\\x64\\vc14\\lib

  1. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: \\darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: CUDNN_HALF;

Note: CUDA must be installed only after Visual Studio has been installed.

How to compile (custom):

Also, you can to create your own darknet.sln & darknet.vcxproj, this example for CUDA 9.1 and OpenCV 3.0

Then add to your created project:

  • (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:

C:\\opencv_3.0\\opencv\\build\\include;..\\..\\3rdparty\\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\\include

  • (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
  • add to project:
    • all .c files
    • all .cu files
    • file http_stream.cpp from \\src directory
    • file darknet.h from \\include directory
  • (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:

C:\\opencv_3.0\\opencv\\build\\x64\\vc14\\lib;$(CUDA_PATH)\\lib\\$(PlatformName);$(CUDNN)\\lib\\x64;%(AdditionalLibraryDirectories)

  • (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:

..\\..\\3rdparty\\lib\\x64\\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)

  • (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions

OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)

  • compile to .exe (X64 & Release) and put .dll-s near with .exe: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg
    • pthreadVC2.dll, pthreadGC2.dll from \\3rdparty\\dll\\x64
    • cusolver64_91.dll, curand64_91.dll, cudart64_91.dll, cublas64_91.dll - 91 for CUDA 9.1 or your version, from C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.1\\bin
    • For OpenCV 3.2: opencv_world320.dll and opencv_ffmpeg320_64.dll from C:\\opencv_3.0\\opencv\\build\\x64\\vc14\\bin
    • For OpenCV 2.4.13: opencv_core2413.dll, opencv_highgui2413.dll and opencv_ffmpeg2413_64.dll from C:\\opencv_2.4.13\\opencv\\build\\x64\\vc14\\bin

使用多GPU训练:

  1. 首先在1GPU下训练1000次: darknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137
  2. 然后停止训练，使用部分训练模型/backup/yolov4_1000.weights 继续使用多gpu（如4个gpu）进行训练: darknet.exe detector train cfg/coco.data cfg/yolov4.cfg /backup/yolov4_1000.weights -gpus 0,1,2,3

数据集较小时应该减少学习速率，如4gpu训练时设置learning_rate=0.00025（learning_rate = 0.001 / GPUs）。同时在cfg-文件中将burn_in和max_batches变为原来的4倍。如burn_in从1000变为4000.如果policy-steps则steps参数也要做同样变更。

Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set learning_rate = 0.00025 (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times burn_in =以上是关于darknet YoloV4手册翻译的主要内容，如果未能解决你的问题，请参考以下文章

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