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1.代码下载
2.数据集准备#xff08;.xml转.txt)
#xff08;1#xff09;修改图像文件名
#xff08;2#xff09;图片和标签文件数量不对应#xff0c;解决办法
#xff08;3#xff09;.xml转.txt
#xff08;4#xff09;.txt文件随机划分出对应的训练…目录
1.代码下载
2.数据集准备.xml转.txt)
1修改图像文件名
2图片和标签文件数量不对应解决办法
3.xml转.txt
4.txt文件随机划分出对应的训练集、测试集、验证集
3.训练数据集
1修改.yaml文件 2修改网络参数 3训练中断 1.代码下载
论文地址https://arxiv.org/abs/2207.02696
论文代码下载地址mirrors / WongKinYiu / yolov7 · GitCode
2.数据集准备.xml转.txt)
1修改图像文件名
制作数据集运用到的一些功能代码_桦拾的博客-CSDN博客
2图片和标签文件数量不对应解决办法
通过以下代码进行对比后输出多余文件名自己再根据实际情况和需要进行增删文件python 两个文件夹里的文件名对比_inspur秃头哥的博客-CSDN博客
# -*- coding: utf-8 -*-
import ospath1 r./train
path2 r./train_xmldef file_name(image_dir,xml_dir):jpg_list []xml_list []for root, dirs, files in os.walk(image_dir):for file in files:jpg_list.append(os.path.splitext(file)[0])for root, dirs, files in os.walk(xml_dir):for file in files:xml_list.append(os.path.splitext(file)[0])print(len(jpg_list))diff set(xml_list).difference(set(jpg_list)) # 差集在a中但不在b中的元素for name in diff:print(no jpg, name .xml)diff2 set(jpg_list).difference(set(xml_list)) # 差集在b中但不在a中的元素print(len(diff2))for name in diff2:print(no xml, name .jpg)
if __name__ __main__:file_name(path1,path2)3.xml转.txt
需增加或减少数据集种类只需修改classes列表、if语句部分以及print部分对应文件夹目录也需修改
将xml转化为yolov5的训练格式txt - 知乎
import os
from glob import glob
import xml.etree.ElementTree as ETxml_dir rF:\datasets\tomato_dataset\xml####xml文件夹
output_txt_dir rF:\datasets\tomato_dataset\txt####输出yolo所对应格式的文件夹###进行归一化操作
def convert(size, box):dw 1. / (size[0])dh 1. / (size[1])x (box[0] box[1]) / 2.0 - 1y (box[2] box[3]) / 2.0 - 1w box[1] - box[0]h box[3] - box[2]x x * dww w * dwy y * dhh h * dhreturn (x, y, w, h)def load_xml():####这里是你自己的分类classes [bruise,crack,blackspot, rot]xml_list glob(os.path.join(xml_dir, *.xml))# print(len(xml_list), xml_list)count_pictures {}count_detection {}count 0class_num0 0class_num1 0class_num2 0class_num3 0class_num4 0class_num5 0for file in xml_list:count count 1imgName file.split(\\)[-1][:-4] # 文件名不包含后缀imglabel os.path.join(output_txt_dir, imgName .txt) # 创建TXT文件夹路径加文件名加.TXT# print(imglabel)out_file open(imglabel, w, encodingUTF-8) # 以写入的方式打开TXTtree ET.parse(file)root tree.getroot()for child in root:if child.tag size:w child[0].texth child[1].textif child.tag object:x_min child[4][0].texty_min child[4][1].textx_max child[4][2].texty_max child[4][3].textbox convert((int(w), int(h)), (int(x_min), int(x_max), int(y_min), int(y_max)))label child[0].textif label in classes:##按照上面的顺序填写标签如果超过这些自己增加复制就行了if label bruise:label 0class_num0 1out_file.write(str(label) .join([str(round(a, 6)) for a in box]) \n) # 把内容写入TXT中if label crack:label 1class_num1 1out_file.write(str(label) .join([str(round(a, 6)) for a in box]) \n)if label blackspot:label 2class_num2 1out_file.write(str(label) .join([str(round(a, 6)) for a in box]) \n)if label rot:label 3class_num3 1out_file.write(str(label) .join([str(round(a, 6)) for a in box]) \n)print(ALL:, count, bruise:, class_num0, crack:, class_num1, blackspot:, class_num2, rot:, class_num3)return len(xml_list), classes, count_pictures, count_detection # return 用在函数内部表示当调用该函数时if __name__ __main__:classes load_xml()print(classes)
4.txt文件随机划分出对应的训练集、测试集、验证集
此代码生成相应的 train.txt; val.txt; test.txt 文件。 YOLOv7保姆级教程个人踩坑无数----训练自己的数据集_AmbitionToFree的博客-CSDN博客
# 将图片和标注数据按比例切分为 训练集和测试集
# 直接划分txt文件jpg文件
#### 强调 路径中不能出现中文否则报错找不到文件
import shutil
import random
import os# 原始路径
image_original_path rE:\0_net_code\datasets\images
label_original_path rE:\0_net_code\datasets\txtcur_path os.getcwd()# 训练集路径
train_image_path os.path.join(cur_path, datasets/images/train/)
train_label_path os.path.join(cur_path, datasets/labels/train/)
print(----------)
# 验证集路径
val_image_path os.path.join(cur_path, datasets/images/val/)
val_label_path os.path.join(cur_path, datasets/labels/val/)
print(----------)
# 测试集路径
test_image_path os.path.join(cur_path, datasets/images/test/)
test_label_path os.path.join(cur_path, datasets/labels/test/)
print(----------)
# 训练集目录
list_train os.path.join(cur_path, datasets/train.txt)
list_val os.path.join(cur_path, datasets/val.txt)
list_test os.path.join(cur_path, datasets/test.txt)
print(----------)
train_percent 0.8
val_percent 0.1
test_percent 0.1
print(----------)def del_file(path):for i in os.listdir(path):file_data path \\ ios.remove(file_data)def mkdir():if not os.path.exists(train_image_path):os.makedirs(train_image_path)else:del_file(train_image_path)if not os.path.exists(train_label_path):os.makedirs(train_label_path)else:del_file(train_label_path)if not os.path.exists(val_image_path):os.makedirs(val_image_path)else:del_file(val_image_path)if not os.path.exists(val_label_path):os.makedirs(val_label_path)else:del_file(val_label_path)if not os.path.exists(test_image_path):os.makedirs(test_image_path)else:del_file(test_image_path)if not os.path.exists(test_label_path):os.makedirs(test_label_path)else:del_file(test_label_path)def clearfile():if os.path.exists(list_train):os.remove(list_train)if os.path.exists(list_val):os.remove(list_val)if os.path.exists(list_test):os.remove(list_test)def main():mkdir()clearfile()file_train open(list_train, w)file_val open(list_val, w)file_test open(list_test, w)total_txt os.listdir(label_original_path)num_txt len(total_txt)list_all_txt range(num_txt)num_train int(num_txt * train_percent)num_val int(num_txt * val_percent)num_test num_txt - num_train - num_valtrain random.sample(list_all_txt, num_train)# train从list_all_txt取出num_train个元素# 所以list_all_txt列表只剩下了这些元素val_test [i for i in list_all_txt if not i in train]# 再从val_test取出num_val个元素val_test剩下的元素就是testval random.sample(val_test, num_val)print(训练集数目{}, 验证集数目{}, 测试集数目{}.format(len(train), len(val), len(val_test) - len(val)))for i in list_all_txt:name total_txt[i][:-4]srcImage image_original_path \\ name .jpgsrcLabel label_original_path \\ name .txtif i in train:dst_train_Image train_image_path name .jpgdst_train_Label train_label_path name .txtshutil.copyfile(srcImage, dst_train_Image)shutil.copyfile(srcLabel, dst_train_Label)file_train.write(dst_train_Image \n)elif i in val:dst_val_Image val_image_path name .jpgdst_val_Label val_label_path name .txtshutil.copyfile(srcImage, dst_val_Image)shutil.copyfile(srcLabel, dst_val_Label)file_val.write(dst_val_Image \n)else:dst_test_Image test_image_path name .jpgdst_test_Label test_label_path name .txtshutil.copyfile(srcImage, dst_test_Image)shutil.copyfile(srcLabel, dst_test_Label)file_test.write(dst_test_Image \n)file_train.close()file_val.close()file_test.close()if __name__ __main__:main()
3.训练数据集
1修改.yaml文件
根据自己数据集情况修改数据集文件路径、数据集种类、标签名 2修改网络参数
根据GPU情况修改参数注意权重文件路径、yaml文件路径 3训练中断
若训练中断可从最新的epcho开始训练将resume参数的default改为True即可。
