对yolov5的数据集进行划分【训练集、验证集、测试集】7：2:1和【训练集、验证集】8:2-Toy模板网

这篇具有很好参考价值的文章主要介绍了对yolov5的数据集进行划分【训练集、验证集、测试集】7：2:1和【训练集、验证集】8:2。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

训练集：验证集：测试集（7：2：1）

训练集：验证集（8：2）

参考的这位博主：

(487条消息) YOLOv5数据集划分脚本(train、val、test)_yolov5 val_叱咤风云灬龙的博客-CSDN博客文章来源地址https://www.toymoban.com/news/detail-532806.html

训练集：验证集：测试集（7：2：1）

import os, shutil, random
from tqdm import tqdm

def split_img(img_path, label_path, split_list):
    try :   
        Data = 'DataSet'
        # Data是你要将要创建的文件夹路径（路径一定是相对于你当前的这个脚本而言的）
        os.mkdir(Data)

        train_img_dir = Data + '/images/train'
        val_img_dir = Data + '/images/val'
        test_img_dir = Data + '/images/test'

        train_label_dir = Data + '/labels/train'
        val_label_dir = Data + '/labels/val'
        test_label_dir = Data + '/labels/test'

        # 创建文件夹
        os.makedirs(train_img_dir)
        os.makedirs(train_label_dir)
        os.makedirs(val_img_dir)
        os.makedirs(val_label_dir)
        os.makedirs(test_img_dir)
        os.makedirs(test_label_dir)

    except:
        print('文件目录已存在')
        
    train, val, test = split_list
    all_img = os.listdir(img_path)
    all_img_path = [os.path.join(img_path, img) for img in all_img]
    # all_label = os.listdir(label_path)
    # all_label_path = [os.path.join(label_path, label) for label in all_label]
    train_img = random.sample(all_img_path, int(train * len(all_img_path)))
    train_img_copy = [os.path.join(train_img_dir, img.split('\\')[-1]) for img in train_img]
    train_label = [toLabelPath(img, label_path) for img in train_img]
    train_label_copy = [os.path.join(train_label_dir, label.split('\\')[-1]) for label in train_label]
    for i in tqdm(range(len(train_img)), desc='train ', ncols=80, unit='img'):
        _copy(train_img[i], train_img_dir)
        _copy(train_label[i], train_label_dir)
        all_img_path.remove(train_img[i])
    val_img = random.sample(all_img_path, int(val / (val + test) * len(all_img_path)))
    val_label = [toLabelPath(img, label_path) for img in val_img]
    for i in tqdm(range(len(val_img)), desc='val ', ncols=80, unit='img'):
        _copy(val_img[i], val_img_dir)
        _copy(val_label[i], val_label_dir)
        all_img_path.remove(val_img[i])
    test_img = all_img_path
    test_label = [toLabelPath(img, label_path) for img in test_img]
    for i in tqdm(range(len(test_img)), desc='test ', ncols=80, unit='img'):
        _copy(test_img[i], test_img_dir)
        _copy(test_label[i], test_label_dir)


def _copy(from_path, to_path):
    shutil.copy(from_path, to_path)

def toLabelPath(img_path, label_path):
    img = img_path.split('\\')[-1]
    label = img.split('.jpg')[0] + '.txt'
    return os.path.join(label_path, label)

def main():
    img_path = 你的图片存放的路径（路径一定是相对于你当前的这个脚本文件而言的）
    label_path = 你的txt文件存放的路径（路径一定是相对于你当前的这个脚本文件而言的）
    split_list = [0.7, 0.2, 0.1]	# 数据集划分比例[train:val:test]
    split_img(img_path, label_path, split_list)

if __name__ == '__main__':
    main()

训练集：验证集（8：2）


import os
import shutil
import random
from tqdm import tqdm

def split_img(img_path, label_path, split_list):
    try:  # 创建数据集文件夹
        Data = 'DataSet2parts'
        os.mkdir(Data)

        train_img_dir = Data + '/images/train'
        val_img_dir = Data + '/images/val'
        # test_img_dir = Data + '/images/test'

        train_label_dir = Data + '/labels/train'
        val_label_dir = Data + '/labels/val'
        # test_label_dir = Data + '/labels/test'

        # 创建文件夹
        os.makedirs(train_img_dir)
        os.makedirs(train_label_dir)
        os.makedirs(val_img_dir)
        os.makedirs(val_label_dir)
        # os.makedirs(test_img_dir)
        # os.makedirs(test_label_dir)

    except:
        print('文件目录已存在')

    train, val = split_list
    all_img = os.listdir(img_path)
    all_img_path = [os.path.join(img_path, img) for img in all_img]
    # all_label = os.listdir(label_path)
    # all_label_path = [os.path.join(label_path, label) for label in all_label]
    train_img = random.sample(all_img_path, int(train * len(all_img_path)))
    train_img_copy = [os.path.join(train_img_dir, img.split('\\')[-1]) for img in train_img]
    train_label = [toLabelPath(img, label_path) for img in train_img]
    train_label_copy = [os.path.join(train_label_dir, label.split('\\')[-1]) for label in train_label]
    for i in tqdm(range(len(train_img)), desc='train ', ncols=80, unit='img'):
        _copy(train_img[i], train_img_dir)
        _copy(train_label[i], train_label_dir)
        all_img_path.remove(train_img[i])
    val_img = all_img_path
    val_label = [toLabelPath(img, label_path) for img in val_img]
    for i in tqdm(range(len(val_img)), desc='val ', ncols=80, unit='img'):
        _copy(val_img[i], val_img_dir)
        _copy(val_label[i], val_label_dir)


def _copy(from_path, to_path):
    shutil.copy(from_path, to_path)


def toLabelPath(img_path, label_path):
    img = img_path.split('\\')[-1]
    label = img.split('.jpg')[0] + '.txt'
    return os.path.join(label_path, label)


def main():
    img_path = 'datasets/500'
    label_path = 'datasets/txtresults'
    split_list = [0.8, 0.2]  # 数据集划分比例[train:val]
    split_img(img_path, label_path, split_list)


if __name__ == '__main__':
    main()

到了这里，关于对yolov5的数据集进行划分【训练集、验证集、测试集】7：2:1和【训练集、验证集】8:2的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！