yolov5s用自己的数据集进行训练模型

https://blog.csdn.net/qq_40770527/article/details/124143214
自动化分训练集和验证集以及测试集的代码如下；

import os
import shutil
import random

# 保证随机可复现
random.seed(0)


# def mk_dir(file_path):
# if os.path.exists(file_path):
# # 如果文件夹存在，则先删除原文件夹在重新创建
# shutil.rmtree(file_path)
# os.makedirs(file_path)

def split_data(file_path, new_file_path, train_rate, val_rate, test_rate):
    # yolov5训练自己数据集时 准备了images图片文件夹和txt标签文件夹；但是
    # 需要分割训练集、验证集、测试集3个文件夹，每个文件夹有images和labels
    # 2个文件夹;此方法可以把imags和labels总文件夹，分割成3个文件夹;
    # file_path ='images 文件夹'
    # xmlpath= 'txt文件夹'
    # new_file_path='保存的新地址'

        eachclass_image = []
        for image in os.listdir(file_path):
            eachclass_image.append(image)
        total = len(eachclass_image)
        random.shuffle(eachclass_image)
        train_images = eachclass_image[0:int(train_rate * total)]  # 注意左闭右开
        val_images = eachclass_image[int(train_rate * total):int((train_rate + val_rate) * total)]  # 注意左闭右开
        test_images = eachclass_image[int((train_rate + val_rate) * total):]

        #训练集
        for image in train_images:
            print(image)
            old_path = file_path + '/' + image
            new_path1 = new_file_path + '/' + 'train' + '/' + 'images'
            if not os.path.exists(new_path1):
                os.makedirs(new_path1)
            new_path = new_path1 + '/' + image
            # print(new_path)
            shutil.copy(old_path, new_path)
        new_name = os.listdir(new_file_path + '/' + 'train' + '/' + 'images')
        # print(new_name[1][:-4])
        for im in new_name:
            old_xmlpath = xmlpath + '/' + im[:-3] + 'txt'
            print('old',old_xmlpath)
            new_xmlpath1 = new_file_path + '/' + 'train' + '/' + 'labels'
            if not os.path.exists(new_xmlpath1):
                os.makedirs(new_xmlpath1)
            new_xmlpath = new_xmlpath1 + '/' + im[:-3] + 'txt'
            print('xml name',new_xmlpath)
            if not os.path.exists(f'{
      old_xmlpath}'):
                open(f'{
      old_xmlpath}', 'w')
            shutil.copy(old_xmlpath, new_xmlpath)

        #验证集
        for image in val_images:
            old_path = file_path + '/' + image
            new_path1 = new_file_path + '/' + 'val' + '/' + 'images'
            if not os.path.exists(new_path1):
                os.makedirs(new_path1)
            new_path = new_path1 + '/' + image
            shutil.copy(old_path, new_path)
        new_name = os.listdir(new_file_path + '/' + 'val' + '/' + 'images')
        for im in new_name:
            old_xmlpath = xmlpath + '/' + im[:-3] + 'txt'
            new_xmlpath1 = new_file_path + '/' + 'val' + '/' + 'labels'
            if not os.path.exists(new_xmlpath1):
                os.makedirs(new_xmlpath1)
            new_xmlpath = new_xmlpath1 + '/' + im[:-3] + 'txt'
            if not os.path.exists(f'{
      old_xmlpath}'):
                open(f'{
      old_xmlpath}', 'w')
            shutil.copy(old_xmlpath, new_xmlpath)

        #测试集
        for image in test_images:
            old_path = file_path + '/' + image
            new_path1 = new_file_path + '/' + 'test' + '/' + 'images'
            if not os.path.exists(new_path1):
                os.makedirs(new_path1)
            new_path = new_path1 + '/' + image
            shutil.copy(old_path, new_path)
        new_name = os.listdir(new_file_path + '/' + 'test' + '/' + 'images')
        for im in new_name:
            old_xmlpath = xmlpath + '/' + im[:-3] + 'txt'
            new_xmlpath1 = new_file_path + '/' + 'test' + '/' + 'labels'
            if not os.path.exists(new_xmlpath1):
                os.makedirs(new_xmlpath1)
            new_xmlpath = new_xmlpath1 + '/' + im[:-3] + 'txt'
            if not os.path.exists(f'{
      old_xmlpath}'):
                open(f'{
      old_xmlpath}', 'w')
            shutil.copy(old_xmlpath, new_xmlpath)
        print('ok')

if __name__ == '__main__':
    file_path = "./dropout-nosise-salt/img"
    xmlpath = './dropout-nosise-salt/labels'
    new_file_path = "./dropout-nosise-salt/train-val"
    split_data(file_path, new_file_path, train_rate=0.7, val_rate=0.2, test_rate=0.1)