文章目录
- 一、数据集
- 二、yolov10介绍
- 三、数据voc转换为yolo
- 四、训练
- 五、验证
- 六、数据、模型、训练后的所有文件
寻求帮助请看这里：
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
一、数据集
安全帽佩戴检测
数据集：https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset
基准模型：
二、yolov10介绍
听说过yolov10吗：https://www.jiqizhixin.com/articles/2024-05-28-7
论文：
https://arxiv.org/abs/2405.14458
代码：
https://github.com/THU-MIG/yolov10
三、数据voc转换为yolo
调整一下，整成这样：
VOC2028 # tree -L 1
.
├── images
├── labels
├── test.txt
├── train.txt
├── trainval.txt
└── val.txt2 directories, 4 files写为绝对路径：
# 定义需要处理的文件名列表
file_names = ['test.txt', 'train.txt', 'trainval.txt', 'val.txt']for file_name in file_names:# 打开文件用于读取with open(file_name, 'r') as file:# 读取所有行lines = file.readlines()# 打开（或创建）另一个文件用于写入修改后的内容，这里使用新的文件名表示已修改new_file_name = 'modified_' + file_namewith open(new_file_name, 'w') as new_file:# 遍历每一行并进行修改for line in lines:# 删除行尾的换行符，添加'.jpg'和'images/'，然后再添加回换行符modified_line = '/ssd/xiedong/yolov10/VOC2028/images/' + line.strip() + '.jpg\n'# 将修改后的内容写入新文件new_file.write(modified_line)print("所有文件处理完成。")
转yolo txt：
import traceback
import xml.etree.ElementTree as ET
import os
import shutil
import random
import cv2
import numpy as np
from tqdm import tqdmdef convert_annotation_to_list(xml_filepath, size_width, size_height, classes):in_file = open(xml_filepath, encoding='UTF-8')tree = ET.parse(in_file)root = tree.getroot()# size = root.find('size')# size_width = int(size.find('width').text)# size_height = int(size.find('height').text)yolo_annotations = []# if size_width == 0 or size_height == 0:for obj in root.iter('object'):difficult = obj.find('difficult').textcls = obj.find('name').textif cls not in classes:classes.append(cls)cls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = [float(xmlbox.find('xmin').text),float(xmlbox.find('xmax').text),float(xmlbox.find('ymin').text),float(xmlbox.find('ymax').text)]# 标注越界修正if b[1] > size_width:b[1] = size_widthif b[3] > size_height:b[3] = size_heighttxt_data = [((b[0] + b[1]) / 2.0) / size_width, ((b[2] + b[3]) / 2.0) / size_height,(b[1] - b[0]) / size_width, (b[3] - b[2]) / size_height]# 标注越界修正if txt_data[0] > 1:txt_data[0] = 1if txt_data[1] > 1:txt_data[1] = 1if txt_data[2] > 1:txt_data[2] = 1if txt_data[3] > 1:txt_data[3] = 1yolo_annotations.append(f"{cls_id} {' '.join([str(round(a, 6)) for a in txt_data])}")in_file.close()return yolo_annotationsdef main():classes = []root = r"/ssd/xiedong/yolov10/VOC2028"img_path_1 = os.path.join(root, "images")xml_path_1 = os.path.join(root, "labels")dst_yolo_root_txt = xml_path_1index = 0img_path_1_files = os.listdir(img_path_1)xml_path_1_files = os.listdir(xml_path_1)for img_id in tqdm(img_path_1_files):# 右边的.之前的部分xml_id = img_id.split(".")[0] + ".xml"if xml_id in xml_path_1_files:try:img = cv2.imdecode(np.fromfile(os.path.join(img_path_1, img_id), dtype=np.uint8), 1) # img是矩阵new_txt_name = img_id.split(".")[0] + ".txt"yolo_annotations = convert_annotation_to_list(os.path.join(xml_path_1, img_id.split(".")[0] + ".xml"),img.shape[1],img.shape[0],classes)with open(os.path.join(dst_yolo_root_txt, new_txt_name), 'w') as f:f.write('\n'.join(yolo_annotations))except:traceback.print_exc()# classesprint(f"我已经完成转换 {classes}")if __name__ == '__main__':main()vim voc2028x.yaml
train: /ssd/xiedong/yolov10/VOC2028/modified_train.txt
val: /ssd/xiedong/yolov10/VOC2028/modified_val.txt
test: /ssd/xiedong/yolov10/VOC2028/modified_test.txt# Classes
names:0: hat1: person
四、训练
环境：
git clone https://github.com/THU-MIG/yolov10.git
cd yolov10
conda create -n yolov10 python=3.9 -y
conda activate yolov10
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple some-package
pip install -e . -i https://pypi.tuna.tsinghua.edu.cn/simple some-package训练
yolo detect train data="/ssd/xiedong/yolov10/voc2028x.yaml" model=yolov10s.yaml epochs=200 batch=64 imgsz=640 device=1,3
训练启动后：
训练完成后：
五、验证
yolo val model="/ssd/xiedong/yolov10/runs/detect/train2/weights/best.pt" data="/ssd/xiedong/yolov10/voc2028x.yaml" batch=32 imgsz=640 device=1,3
map50平均达到0.94，已超出基准很多了。
预测：
yolo predict model=yolov10n/s/m/b/l/x.pt
导出：
# End-to-End ONNX
yolo export model=yolov10n/s/m/b/l/x.pt format=onnx opset=13 simplify
# Predict with ONNX
yolo predict model=yolov10n/s/m/b/l/x.onnx# End-to-End TensorRT
yolo export model=yolov10n/s/m/b/l/x.pt format=engine half=True simplify opset=13 workspace=16
# Or
trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
# Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine
demo：
wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt
python app.py
# Please visit http://127.0.0.1:7860
六、数据、模型、训练后的所有文件
yolov10训练安全帽目标监测全部东西，下载看这里：
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
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