yolov5训练自己的数据集

V2AS问路

yolov5训练自己的数据集

阅读原文时间：2023年07月09日阅读：2

1.安装cuda

　　可以先看看自己的显卡信息，支持哪个cuda版本

　　cuda下载地址：https://developer.nvidia.com/cuda-toolkit-archive

　　我的RTX3060，下载的cuda11.8

　　下载后安装，直接默认安装到底，然后打开cmd，输入nvcc -V

　2.安装cudnn

　　需要安装和cuda版本对应的cudnn

　　地址：https://developer.nvidia.com/rdp/cudnn-archive

　　下载对应的版本，解压替换到cuda安装目录下

3.安装Pytorch

　　我使用的是conda默认的环境，python3.9

　　进入pytorch官网：https://pytorch.org/

　　找到对应的版本下载，我这里不指定torch版本，直接运行

　　pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

　　等待安装完成即可

4.安装标注软件

　　pip install labelImg

　　安装成功后直接运行 labelImg 打开软件

　　open dir打开图片文件夹，change save dir 选择保存的xml文件的文件夹

　　create rectBox去框选需要检测的目标，输入label name

　　标注完自己的数据

　　一个img图片文件夹，一个和图片对应的xml文件夹

5. 将数据集进行分割

　　执行下面代码，即可得到分割好的数据集

import os
import random
import shutil

img_path = 'img'
xml_path = 'xml'

def split_file_name(file_name):
f_name, _ = file_name.split('.')
return f_name

def split_move_file(target_path, save_basic_path, train_scale=0.9):
train_img_path = os.path.join(save_basic_path, 'images/train')
train_xml_path = os.path.join(save_basic_path, 'xml/train')
val_img_path = os.path.join(save_basic_path, 'images/val')
val_xml_path = os.path.join(save_basic_path, 'xml/val')
print(save_basic_path, train_img_path)
if not os.path.exists(train_img_path):
os.makedirs(train_img_path)
if not os.path.exists(train_xml_path):
os.makedirs(train_xml_path)
if not os.path.exists(val_img_path):
os.makedirs(val_img_path)
if not os.path.exists(val_xml_path):
os.makedirs(val_xml_path)

img\_file\_path = os.path.join(target\_path, img\_path)  
file\_list = os.listdir(img\_file\_path)  
# print(file\_list)  
# 得到名字列表  
file\_name\_li = list(map(lambda x: split\_file\_name(x), file\_list))  
random.shuffle(file\_name\_li)  
# print(file\_name\_li)  
train\_ind = int(len(file\_name\_li) \* train\_scale)  
train\_data = file\_name\_li\[:train\_ind\]  
val\_data = file\_name\_li\[train\_ind:\]  
print('total number', len(file\_name\_li))  
print('train number', len(train\_data))  
print('val number', len(val\_data))

for file in train\_data:

    file\_path = os.path.join(img\_file\_path, file+'.jpg')  
    save\_path = os.path.join(train\_img\_path, file+'.jpg')  
    if not os.path.exists(file\_path):  
        file\_path = os.path.join(img\_file\_path, file + '.jpeg')  
        save\_path = os.path.join(train\_img\_path, file + '.jpg')  
        if not os.path.exists(file\_path):  
            file\_path = os.path.join(img\_file\_path, file + '.png')  
            save\_path = os.path.join(train\_img\_path, file + '.png')  
    if os.path.exists(file\_path):  
        shutil.copyfile(file\_path, save\_path)

    # xml文件  
    xml\_file\_path = os.path.join(target\_path, xml\_path)  
    file\_path = os.path.join(xml\_file\_path, file + '.xml')  
    save\_path = os.path.join(train\_xml\_path, file + '.xml')  
    if os.path.exists(file\_path):  
        shutil.copyfile(file\_path, save\_path)  
for file in val\_data:

    file\_path = os.path.join(img\_file\_path, file+'.jpg')  
    save\_path = os.path.join(val\_img\_path, file+'.jpg')  
    if not os.path.exists(file\_path):  
        file\_path = os.path.join(img\_file\_path, file + '.jpeg')  
        save\_path = os.path.join(val\_img\_path, file + '.jpg')  
        if not os.path.exists(file\_path):  
            file\_path = os.path.join(img\_file\_path, file + '.png')  
            save\_path = os.path.join(val\_img\_path, file + '.png')  
    if os.path.exists(file\_path):  
        shutil.copyfile(file\_path, save\_path)

    # xml文件  
    xml\_file\_path = os.path.join(target\_path, xml\_path)  
    file\_path = os.path.join(xml\_file\_path, file + '.xml')  
    save\_path = os.path.join(val\_xml\_path, file + '.xml')  
    if os.path.exists(file\_path):  
        shutil.copyfile(file\_path, save\_path)

if __name__ == '__main__':

target\_path = r'C:\\Users\\mojia\\Desktop\\maizi\\maozi20230326'  
save\_basic\_path = r'C:\\Users\\mojia\\Desktop\\maizi\\maozi20230326\_train\_val'  
if not os.path.exists(save\_basic\_path):  
    os.mkdir(save\_basic\_path)  
scale = 0.9     # 训练集比例  
split\_move\_file(target\_path, save\_basic\_path, scale)

6. 将标注的xml文件转换为txt文件格式

import xml.etree.ElementTree as ET

import os

def convert(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x = (box[0] + box[1]) / 2.0 - 1
y = (box[2] + box[3]) / 2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
if w >= 1:
w = 0.99
if h >= 1:
h = 0.99
return (x, y, w, h)

folder_li = ['train', 'val']
def convert_annotation(rootpath, classes):
labelpath = rootpath + '/labels' # 生成的.txt文件会被保存在labels目录下
if not os.path.exists(labelpath):
os.makedirs(labelpath)
for folder in folder_li:
xmlpath = rootpath + '/xml/'+folder
file_list = os.listdir(xmlpath)
for xmlname in file_list:
xmlfile = os.path.join(xmlpath, xmlname)
with open(xmlfile, "r", encoding='UTF-8') as in_file:
txtname = xmlname[:-4] + '.txt'
# print(txtname)
txtpath = labelpath + '/' + folder
if not os.path.exists(txtpath):
os.makedirs(txtpath)
txtfile = os.path.join(txtpath, txtname)
with open(txtfile, "w+", encoding='UTF-8') as out_file:
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
out_file.truncate()
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')

if __name__ == "__main__":
rootpath = r'C:\Users\mojia\Desktop\maizi\maozi20230326_train_val'
# 数据标签
classes = ['帽子'] # 需要修改
convert_annotation(rootpath, classes)

得到下面这个的目录结构