1. yolov8安装

1.1 创建anaconda虚拟环境

比如：名字位torch_py39，因为yolov8 ultralytics安装需要python3.8以上，这里安装python3.9

conda create -n torch_py39 python=3.9

1.2 安装ultralytics

pip install ultralytics

1.3 下载ultralytics源码

源码中有数据配置文件和模型配置文件等，一会训练会用到
下载地址

2. VisDrone数据下载

下载地址

3. 数据集标签转换

from utils.general import download, os, Path

def visdrone2yolo(dir):
    from PIL import Image
    from tqdm import tqdm

    def convert_box(size, box):
        # Convert VisDrone box to YOLO xywh box
        dw = 1. / size[0]
        dh = 1. / size[1]
        return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh

    (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
    pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
    for f in pbar:
        img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
        lines = []
        with open(f, 'r') as file:  # read annotation.txt
            for row in [x.split(',') for x in file.read().strip().splitlines()]:
                if row[4] == '0':  # VisDrone 'ignored regions' class 0
                    continue
                cls = int(row[5]) - 1  # 类别号-1
                box = convert_box(img_size, tuple(map(int, row[:4])))
                lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
                with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
                    fl.writelines(lines)  # write label.txt


if __name__ == '__main__':
    dir = Path('E:\YOLO-datasets\VisDrone')  # datasets文件夹下Visdrone2019文件夹目录
    # Convert
    for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
        visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

4. 准备数据配置文件

VisDrone.yaml位于ultralytics-main\ultralytics\cfg\datasets下
自己可以建一个文件夹，并将VisDrone.yaml拷贝过来
修改配置文件中的path: …/datasets/VisDrone，指向下载数据集的位置

5. 开始训练

指定预训练权重model=yolov8l.pt
指定数据配置所在目录data=E:/code/other/ultralytics-main/VisDrone/VisDrone-2.yaml

yolo train model=yolov8l.pt data=E:/code/other/ultralytics-main/VisDrone/VisDrone-2.yaml batch=8 epochs=100

6. OK等待训练结果吧