linux写物体识别,Linux、Windows下试用DarkNet之YoLo CPU物体识别

た入场券 2023-01-21 15:20 43阅读 0赞

关于YOLO：

YOLO——You Only Look Once

Faster RCNN需要对20k个anchor box进行判断是否是物体，然后再进行物体识别，分成了两步。

YOLO(You Only Look Once)则把物体框的选择与识别进行了结合，一步输出，即变成”You Only Look Once”。

所以识别速度非常快，达到每秒45帧，而在快速版YOLO(Fast YOLO，卷积层更少)中，可以达到每秒155帧。

关于DarkNet：

\#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.

For more information see the \[Darknet project website\](http://pjreddie.com/darknet or https://github.com/pjreddie/darknet).

安装指南：http://pjreddie.com/darknet/install/

这个是我见过安装最简单的开源库了！！！

linux下，解压后直接make就可以了，如果你想实时可视化，开启OPENCV=1，关于这里配置OPENCV时需要注意下：

ifeq ($(OPENCV), 1)

COMMON+= -DOPENCV-I/usr/local/include

CFLAGS+= -DOPENCV

LDFLAGS+= -L/usr/local/lib -lopencv\_core -lopencv\_highgui -lopencv\_imgproc

endif

编译好后，下载训练好的权值数据yolo.weights(我下的是700M左右的)，同时使用了相应的yolo.cfg，从官网下的没运行成功！！！

贴一下yolo.cfg

\[net\]

batch=1

subdivisions=1

height=448

width=448

channels=3

momentum=0.9

decay=0.0005

saturation=1.5

exposure=1.5

hue=.1

learning\_rate=0.0005

policy=steps

steps=200,400,600,20000,30000

scales=2.5,2,2,.1,.1

max\_batches = 40000

\[convolutional\]

batch\_normalize=1

filters=64

size=7

stride=2

pad=1

activation=leaky

\[maxpool\]

size=2

stride=2

\[convolutional\]

batch\_normalize=1

filters=192

size=3

stride=1

pad=1

activation=leaky

\[maxpool\]

size=2

stride=2

\[convolutional\]

batch\_normalize=1

filters=128

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=256

size=3

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

\[maxpool\]

size=2

stride=2

\[convolutional\]

batch\_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=1024

size=3

stride=1

pad=1

activation=leaky

\[maxpool\]

size=2

stride=2

\[convolutional\]

batch\_normalize=1

filters=512

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=1024

size=3

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=512

size=1

stride=1

pad=1

activation=leaky

\[convolutional\]

batch\_normalize=1

filters=1024

size=3

stride=1

pad=1

activation=leaky

\#\#\#\#\#\#\#

\[convolutional\]

batch\_normalize=1

size=3

stride=1

pad=1

filters=1024

activation=leaky

\[convolutional\]

batch\_normalize=1

size=3

stride=2

pad=1

filters=1024

activation=leaky

\[convolutional\]

batch\_normalize=1

size=3

stride=1

pad=1

filters=1024

activation=leaky

\[convolutional\]

batch\_normalize=1

size=3

stride=1

pad=1

filters=1024

activation=leaky

\[local\]

size=3

stride=1

pad=1

filters=256

activation=leaky

\[dropout\]

probability=.5

\[connected\]

output= 1715

activation=linear

\[detection\]

classes=20

coords=4

rescore=1

side=7

num=3

softmax=0

sqrt=1

jitter=.2

object\_scale=1

noobject\_scale=.5

class\_scale=1

coord\_scale=5

键入命令：

$>./darknet yolo test cfg/yolo.cfg yolo.weights data/dog.jpg

![0818b9ca8b590ca3270a3433284dd417.png][]

可见CPU下Debug耗时太长啊！

[0818b9ca8b590ca3270a3433284dd417.png]: /images/20221020/2f5566cb6aed4b5fad2212bf439bb035.png

linux写物体识别,Linux、Windows下试用DarkNet之YoLo CPU物体识别

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