网页设计与网站建设在线考试,杭州正规引流推广公司,建设个人网页,小程序怎么生成1.准备数据集
1.先给出VisDrone2019数据集的下载地址#xff1a;
链接#xff1a;https://pan.baidu.com/s/1e2Q0NgNT-H-Acb2H0Cx8sg 提取码#xff1a;31dl
2.将数据集VisDrone放在datasets目录下面 2.数据集转换程序
1.在根目录下面新建一个.py文件#xff0c;取名叫…1.准备数据集
1.先给出VisDrone2019数据集的下载地址
链接https://pan.baidu.com/s/1e2Q0NgNT-H-Acb2H0Cx8sg 提取码31dl
2.将数据集VisDrone放在datasets目录下面 2.数据集转换程序
1.在根目录下面新建一个.py文件取名叫做visdrone2yolov 2.复制以下代码到这个visdrone2yolov.py文件里面
import os
from pathlib import Pathdef visdrone2yolo(dir):from PIL import Imagefrom tqdm import tqdmdef convert_box(size, box):# Convert VisDrone box to YOLO xywh boxdw 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(parentsTrue, exist_okTrue) # make labels directorypbar tqdm((dir / annotations).glob(*.txt), descfConverting {dir})for f in pbar:img_size Image.open((dir / images / f.name).with_suffix(.jpg)).sizelines []with open(f, r) as file: # read annotation.txtfor row in [x.split(,) for x in file.read().strip().splitlines()]:if row[4] 0: # VisDrone ignored regions class 0continuecls int(row[5]) - 1 # 类别号-1box 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
dir Path(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
3.代码中可能需要修改的地方
将dir的值换成VisDrone数据集的相对路径 然后运行这个程序。
4.数据集转换完毕 转换之后的数据集结构如下 3.准备配置(yaml)文件
1.复制VisDrone到同级文件夹取名叫myVisDrone.yaml 2.配置文件的具体信息如下
# Ultralytics YOLO , AGPL-3.0 license
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Example usage: yolo train dataVisDrone.yaml
# parent
# ├── ultralytics
# └── datasets
# └── VisDrone ← downloads here (2.3 GB)# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/VisDrone # dataset root dir
train: VisDrone2019-DET-train/images # train images (relative to path) 6471 images
val: VisDrone2019-DET-val/images # val images (relative to path) 548 images
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images# Classes
names:0: pedestrian1: people2: bicycle3: car4: van5: truck6: tricycle7: awning-tricycle8: bus9: motor4.开始训练
1.使用yolov8s.pt进行训练
1.复制如下代码打开Terminal粘贴之后开始训练
yolo train modelyolov8s.pt dataultralytics/cfg/datasets/myVisDrone.yaml batch4 epochs100 lr00.01
2.训练过程中遇到如下报错OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized. 可能是因为进程占用的原因重启电脑之后解决顺利训练。 开始训练 3.网络未改进之前使用yolov8s.pt训练的效果 尝试了一下不使用预训练权重开始训练发现还是会默认使用yolov8n.pt
yolov8s训练最好的效果(所有标签) map 0.412
2.使用yolov8l.pt进行训练
yolo train modelyolov8l.pt dataultralytics/cfg/datasets/myVisDrone.yaml batch4 epochs100 lr00.01训练效果
