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

目录

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

训练集：验证集：测试集 （7：2：1）

import os, shutil, randomfrom tqdm import tqdmdef 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_listall_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_pathtest_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 osimport shutilimport randomfrom tqdm import tqdmdef 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_listall_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_pathval_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()