下载源码和权重文件
源码：https://github.com/AlexeyAB/darknet
权重：https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights
编译环境
修改makefile(打开darknet目录下makefile文件),
根据具体情况修改
GPU=1 # 使用GPU
CUDNN=1 # 使用GPU
CUDNN_HALF=1 # 混合精度训练，用于加速
OPENCV=1 # 使用opencv
AVX=0
OPENMP=0
LIBSO=1 # 生成libdarknet.so，便于python调用darknet模型
ZED_CAMERA=0
ZED_CAMERA_v2_8=0
#ARCH= -gencode arch=compute_35,code=sm_35 \
# -gencode arch=compute_50,code=[sm_50,compute_50] \
# -gencode arch=compute_52,code=[sm_52,compute_52] \
# -gencode arch=compute_61,code=[sm_61,compute_61]
OS := $(shell uname)
# GeForce RTX 3070, 3080, 3090
# ARCH= -gencode arch=compute_86,code=[sm_86,compute_86]
# Kepler GeForce GTX 770, GTX 760, GT 740
# ARCH= -gencode arch=compute_30,code=sm_30
# Tesla A100 (GA100), DGX-A100, RTX 3080
ARCH= -gencode arch=compute_80,code=[sm_80,compute_80]
# Tesla V100
# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]
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NVCC=/usr/local/cuda-11.1/bin/nvcc
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然后直接终端进行编译
sudo make
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就会在当前文件夹生成libdarknet.so文件。
简单测试
./darknet detector test cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights data/dog.jpg # 图片测试
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -ext_output test.mp4 # 视频测试
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -c 0 # 摄像头测试
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训练VOC数据集
通过下面代码划分，创建一个data_spilt.py文件和对应的路径
import os
import random
trainval_percent = 0.8 # 所有数据中用来训练的比例 trainval/all trainval=train+val
train_percent = 1 # trainval用来训练的比例 train/trainval
xmlfilepath = '/home/lqs/Downloads/dataset/VOC/VOC2007/Annotations'
txtsavepath = './VOCdevkit/main'
total_xml = os.listdir(xmlfilepath)
num=len(total_xml)
list=range(num)
tv=int(num*trainval_percent)
tr=int(tv*train_percent)
trainval= random.sample(list,tv)
train=random.sample(trainval,tr)
ftrainval = open(txtsavepath+'/trainval.txt', 'w+') # 训练集数据+验证集数据
ftest = open(txtsavepath+'/test.txt', 'w+') # 测试集
ftrain = open(txtsavepath+'/train.txt', 'w+') # 训练集
fval = open(txtsavepath+'/val.txt', 'w+') # 验证集
for i in list:
name=total_xml[i][:-4]+'\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftrain.write(name)
else:
fval.write(name)
else:
ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()
print('Finished!')
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生成训练文件
修改sets和classes以及里面的关键路径即可，这个代码可自己创建，得到2007_test.txt、2007_train.txt、2007_val.txt三个文件
#---------------------------------------------#
# 运行前一定要修改classes
# 如果生成的2007_train.txt里面没有目标信息
# 那么就是因为classes没有设定正确
#---------------------------------------------#
import xml.etree.ElementTree as ET
from os import getcwd
sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
#-----------------------------------------------------#
# 这里设定的classes顺序要和model_data里的txt一样
#-----------------------------------------------------#
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
def convert_annotation(year, image_id, list_file):
in_file = open('/home/lqs/Downloads/dataset/VOC/VOC%s/Annotations/%s.xml'%(year, image_id), encoding='utf-8')
tree=ET.parse(in_file)
root = tree.getroot()
for obj in root.iter('object'):
difficult = 0
if obj.find('difficult')!=None:
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 = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
wd = getcwd()
for year, image_set in sets:
image_ids = open('/home/lqs/Downloads/dataset/VOC/VOC%s/ImageSets/Main/%s.txt'%(year, image_set), encoding='utf-8').read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w', encoding='utf-8')
for image_id in image_ids:
list_file.write('/home/lqs/Downloads/dataset/VOC%s/JPEGImages/%s.jpg'%(year, image_id))
convert_annotation(year, image_id, list_file)
list_file.write('\n')
list_file.close()
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训练准备
- 修改darknet-master/cfg/voc.data
classes= 20 # 改成自己的类别数
train = /home/pjreddie/data/2007_train.txt # 改成voc_label.py生成的2007_train.txt路径
valid = /home/pjreddie/data/2007_test.txt # 改成voc_label.py生成的2007_test.txt路径
names = data/voc.names # 改成有自己类别的names文件
backup = backup/ # 改为backup/即可
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- 修改darknet-master/cfg/yolov4-tiny.cfg
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64 # 每64张图片更新一次参数
subdivisions=16 # 64张图片分16次放入显卡中，每次4张。因为一次放入过多会内存不足，根据自己显卡性能更改
width=416 # 将输入的图片resize到width×height后，放入网络中训练
height=416 # 只要是32的倍数即可
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
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- 找到如下位置（以yolo为关键字），修改filters和classes，整个文本共有2个filters和2个classes需要修改
[convolutional]
size=1
stride=1
pad=1
filters=255 # 改为3*(classes +5)。不过官网上不建议这种做法，具体可以查官方github 修改地方不止一处！
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=80 # 改为自己的类别数
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
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开始训练
在darknet目录下，使用第一条命令生成预训练权重yolov4-tiny.conv.29，第二条命令开始训练
./darknet partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29 # 生成yolov4-tiny.conv.29文件，用于迁移学习
./darknet detector train cfg/voc.data cfg/yolov4-tiny.cfg yolov4-tiny.conv.29 -map # 训练模型
# -map参数可以在训练过程中对测试集计算map并在chart上显示)
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Error in load_data_detection() - OpenCV
多GPU训练
./darknet detector train [.data] [.cfg] [.weight] -gpus 0, 1, 2 # 多GPU训练
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