yolo v3训练自己的数据集

首先是配置yolo v3

这部分参考yolo  v3的官网：https://pjreddie.com/darknet/yolo/

 

Detection Using A Pre-Trained Model

This post will guide you through detecting objects with the YOLO system using a pre-trained model. If you don't already have Darknet installed, you should do that first. Or instead of reading all that just run:

git clone https://github.com/pjreddie/darknet
cd darknet
make

Easy!

You already have the config file for YOLO in the cfg/ subdirectory. You will have to download the pre-trained weight file here (237 MB). Or just run this:

wget https://pjreddie.com/media/files/yolov3.weights

Then run the detector!

./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

You will see some output like this:

layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32  0.299 BFLOPs
    1 conv     64  3 x 3 / 2   416 x 416 x  32   ->   208 x 208 x  64  1.595 BFLOPs
    .......
  105 conv    255  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 255  0.353 BFLOPs
  106 detection
truth_thresh: Using default '1.000000'
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 0.029329 seconds.
dog: 99%
truck: 93%
bicycle: 99%

官网给出的是CPU版本的编译，如果需要使用GPU，则需要修改makefike

 

下面讲解训练自己的数据：

目前使用yolo v3训练自己的数据基本是采用voc格式

voc格式的数据使用labelimg软件进行标注

标注结束后，新建一个文件夹，按照voc数据格式进行存储

例如本人新建了voc2018

 

 

接着，新建三个文件夹

其中Annotations用来存储labelimg生成的xml文件

JPEGImages用来存储原图

ImageSets用来存储训练和测试数据的名称，先面介绍如何生成：

新建train和test文件夹

 

train文件夹存放用于训练的图片

test文件夹用于存放测试的图片

新建一个makeTxt .sh

makeTxt.sh中用于提取训练集和测试集的图片名字（将路径名称换成你自己的）

将路径中的train换成test就可以生成测试集的名字

# /usr/bin/env sh
DATA=/home/dagouzi/darknet/voc/VOCdevkit/VOC2018/train
DATASAVE=/home/dagouzi/darknet/voc/VOCdevkit/VOC2018
echo "Create train.txt..."

find $DATA -name *.jpg | cut -d '/' -f9| cut -c 1-8>>$DATASAVE/train.txt


echo "Done.."

 

将train.txt和test.txt拖入ImageSets/Main文件夹下

接下来下载python脚本用于将xml文件修改成txt文件

wget https://pjreddie.com/media/files/voc_label.py

修改其中的代码（按照您的需求，修改其中的文件名）

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join

sets=[('2018', 'train'), ('2018', 'test')]

classes = ["min", "jun"]


def convert(size, box):
    dw = 1./size[0]
    dh = 1./size[1]
    x = (box[0] + box[1])/2.0
    y = (box[2] + box[3])/2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

def convert_annotation(year, image_id):
    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')

wd = getcwd()

for year, image_set in sets:
    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
    list_file = open('%s_%s.txt'%(year, image_set), 'w')
    for image_id in image_ids:
        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
        convert_annotation(year, image_id)
    list_file.close()

接下来

python voc_label.py

即可生成最终的训练集目录和测试集目录

接下来，修改yolov3的相关文件

• 修改cfg/voc.data文件，进行修改（根据您的目录修改）：

classes= 2
train  = /home/dagouzi/darknet/voc/train.txt
valid  = /home/dagouzi/darknet/voc/test.txt
names = data/voc.names
backup = backup

• 修改data/voc.names文件，进行修改（根据您的类别修改）：

min
jun

• 修改cfg/yolov3-voc.cfg文件，进行修改（根据您的目录修改）：

  [net]
  # Testing
  #batch=1
  #subdivisions=1
  # Training
  batch=32
  subdivisions=16
  width=416
  height=416
  channels=3
  momentum=0.9
  decay=0.0005
  angle=0
  saturation = 1.5
  exposure = 1.5
  hue=.1
  
  learning_rate=0.001
  burn_in=1000
  max_batches = 50200
  policy=steps
  steps=40000,45000
  scales=.1,.1
  
  
  
  [convolutional]
  batch_normalize=1
  filters=32
  size=3
  stride=1
  pad=1
  activation=leaky
  
  # Downsample
  
  [convolutional]
  batch_normalize=1
  filters=64
  size=3
  stride=2
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=32
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=64
  size=3
  stride=1
  pad=1
  activation=leaky
  
  [shortcut]
  from=-3
  activation=linear
  
  # Downsample
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=3
  stride=2
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=64
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=3
  stride=1
  pad=1
  activation=leaky
  
  [shortcut]
  from=-3
  activation=linear
  
  [convolutional]
  batch_normalize=1
  filters=64
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=3
  stride=1
  pad=1
  activation=leaky
  
  [shortcut]
  from=-3
  activation=linear
  
  # Downsample
  
  [convolutional]
  batch_normalize=1
  filters=256
  size=3
  stride=2
  pad=1
  activation=leaky
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  # Downsample
  
  [convolutional]
  batch_normalize=1
  filters=512
  size=3
  stride=2
  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
  
  [shortcut]
  from=-3
  activation=linear
  
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  # Downsample
  
  [convolutional]
  batch_normalize=1
  filters=1024
  size=3
  stride=2
  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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  [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
  
  [shortcut]
  from=-3
  activation=linear
  
  ######################
  
  [convolutional]
  batch_normalize=1
  filters=512
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=1024
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=512
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=1024
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=512
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=1024
  activation=leaky
  
  [convolutional]
  size=1
  stride=1
  pad=1
  filters=21
  activation=linear
  
  [yolo]
  mask = 6,7,8
  anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
  classes=2
  num=9
  jitter=.3
  ignore_thresh = .5
  truth_thresh = 1
  random=1
  
  [route]
  layers = -4
  
  [convolutional]
  batch_normalize=1
  filters=256
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [upsample]
  stride=2
  
  [route]
  layers = -1, 61
  
  
  
  [convolutional]
  batch_normalize=1
  filters=256
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=512
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=256
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=512
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=256
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=512
  activation=leaky
  
  [convolutional]
  size=1
  stride=1
  pad=1
  filters=21
  activation=linear
  
  [yolo]
  mask = 3,4,5
  anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
  classes=2
  num=9
  jitter=.3
  ignore_thresh = .5
  truth_thresh = 1
  random=1
  
  [route]
  layers = -4
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [upsample]
  stride=2
  
  [route]
  layers = -1, 36
  
  
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=256
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=256
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  filters=128
  size=1
  stride=1
  pad=1
  activation=leaky
  
  [convolutional]
  batch_normalize=1
  size=3
  stride=1
  pad=1
  filters=256
  activation=leaky
  
  [convolutional]
  size=1
  stride=1
  pad=1
  filters=21
  activation=linear
  
  [yolo]
  mask = 0,1,2
  anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
  classes=2
  num=9
  jitter=.3
  ignore_thresh = .5
  truth_thresh = 1
  random=1
  

  接着下载预训练模型

wget https://pjreddie.com/media/files/darknet53.conv.74

最后开始进行训练

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74