Darknet

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

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

Yolo v4 source code: https://github.com/AlexeyAB/darknet

Yolov v4 tiny discussion: https://www.reddit.com/r/MachineLearning/comments/hu7lyt/p_yolov4tiny_speed_1770_fps_tensorrtbatch4/

Useful links: https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7

For more information see the Darknet project website.

For questions or issues please use the Google Group.

一、编译：

1.1 安装opencv：https://blog.csdn.net/qq_31112205/article/details/105161419	

1.2 根据电脑配置设置Makefile：这里默认使用gpu，cudnn，opencv，CUDNN_HALF，LIBSO

[以/trainDarknet-yolov4为根目录]

1.3 make

二、测试：

[以/trainDarknet-yolov4为根目录]

2.1 下载好权重，yolov4.weights放置到./;yolov4.conv.137(训练时使用)放置到./myData/weights/preWeights/

2.2 测试命令：./darknet detect cfg/yolov4.cfg yolov4.weights data/dog.jpg

	或者 ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg

2.3 安装好了opencv会显示检测结果

三、训练(请看下面的更新部分)：

注意：1.在myData/目录下，需要确保myData/Annotations，myData/ImageSets/Main，myData/JPEGImages，myData/weights文件夹存在

      2.根据自己电脑的性能修改batch和subdivisions值，一般来讲电脑性能越好batch值越大，subdivisions值越小，最好是2的整数指数值

3.1 voc数据格式：xml文件放到myData/Annotations，图片放到myData/JPEGImages（文件夹不存在自己新建就行）

3.2 myData.names根据自己的类别填写，要写全

[以/myData为运行根目录]

3.3 执行命令：python dataPrecess.py

[以/trainDarknet-yolov4为根目录]

3.4 训练执行命令：./darknet detector train myData/cfg/myData.data myData/cfg/myYolov4.cfg myData/weights/preWeights/yolov4.conv.137 -gpus 0 -map

四、权重：

链接：https://pan.baidu.com/s/1Dw3-T9fxcPSbmrXH09u6tQ 

提取码：vgib

五、训练(更新20201102):