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
txt文件里有标签索引和归一化后的坐标和宽高信息
7.下载yolov5源码
直接将代码下载到本地，我下载的时v7.0
下载版本对应的与训练模型
8.修改训练的数据集路径及参数
修改data/coco128.yaml,给出数据集的路径
修改models/yolov5s.yaml,注意我训练时用的yolov5s.pt。这里主要将标签数改成一样的，nc字段改为1个，我只标了一个。
修改train.py，这个我只将device改为0，也就是启用GPU训练，其他参数没有改变，或者在运行train.py时传入参数也一样。
直接运行 python train.py
9.查看训练结果
可以查看损失函数，准确率等信息
训练好的结果在run/train文件夹下面，找到最新的文件夹
可以运行tensorboard --logdir=C:\Users\mojia\Desktop\yolov5-master\runs\train\exp14 通过浏览器查看运行的结果
训练好的权重参数保存在weights文件夹下面
10.进行预测
修改detect.py文件， 修改使用的权重文件，和检测的目标文件
运行 python detect.py
结果保存在/runs/detect路径下最新的文件夹里
标签：xml,yolov5,join,训练,数据,train,file,path,os From： https://www.cnblogs.com/moon3496694/p/17258921.html