YOLOV5 数据集的划分的详细流程（超详细）_yolov5数据集划分

1 数据划分
标注好后的数据集分为两个文件夹，一个文件夹中放置图片另一个文件夹中放置了txt文件。将数据集按照一定的比例进行划分为训练集，测试集，验证集（train、test、val），下述的代码中我按照了 8:1:1 的比例去划分，若想根据自己的需要去划分数据集，修改下述代码中的

代码如下：

import os
import random
from shutil import copyfile
 
def split_dataset(image_folder, txt_folder, output_folder, split_ratio=(0.8, 0.1, 0.1)):
    # Ensure output folders exist
    for dataset in ['train', 'val', 'test']:
        if not os.path.exists(os.path.join(output_folder, dataset, 'images')):
            os.makedirs(os.path.join(output_folder, dataset, 'images'))
        if not os.path.exists(os.path.join(output_folder, dataset, 'txt')):
            os.makedirs(os.path.join(output_folder, dataset, 'txt'))
 
    # Get list of image files
    image_files = [f for f in os.listdir(image_folder) if f.endswith(('.jpg', '.jpeg', '.png'))]
    random.shuffle(image_files)
 
    num_images = len(image_files)
    num_train = int(split_ratio[0] * num_images)
    num_val = int(split_ratio[1] * num_images)
 
    train_images = image_files[:num_train]
    val_images = image_files[num_train:num_train + num_val]
    test_images = image_files[num_train + num_val:]
 
    # Copy images to respective folders
    for dataset, images_list in zip(['train', 'val', 'test'], [train_images, val_images, test_images]):
        for image_file in images_list:
            image_path = os.path.join(image_folder, image_file)
            copyfile(image_path, os.path.join(output_folder, dataset, 'images', image_file))
            txt_file = os.path.splitext(image_file)[0] + '.txt'
            txt_path = os.path.join(txt_folder, txt_file)
 
            # Copy corresponding txt file if exists
            if os.path.exists(txt_path):
                copyfile(txt_path, os.path.join(output_folder, dataset, 'txt', txt_file))
 
if __name__ == "__main__":
    image_folder_path = "D:\PycharmProjects\VOCdevkit\VOC2007\JPEGImages"
    txt_folder_path = "D:\PycharmProjects\VOCdevkit\VOC2007\YOLOLabels"
    output_dataset_path = "D:\PycharmProjects\VOCdevkit"
 
    split_dataset(image_folder_path, txt_folder_path, output_dataset_path)
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上面代码只需要改三个地方：

image_folder_path = "D:\PycharmProjects\VOCdevkit\VOC2007\JPEGImages"
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txt_folder_path = "D:\PycharmProjects\VOCdevkit\VOC2007\YOLOLabels"
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output_dataset_path = "D:\PycharmProjects\VOCdevkit"
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image_folder_path为你保存的图片的文件夹的路径

txt_folder_path 为你保存的txt文件夹的路径

output_dataset_path 为你保存的数据集的文件夹的路径，代码会在改路径下自动生成子文件夹，分别进行测试集，训练集，验证集的存储。
若出现错误可以参考这个网站