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
- 七、更大、更强
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.txt 2 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_name with 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 tqdm def 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').text cls = obj.find('name').text if 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_width if b[3] > size_height: b[3] = size_height txt_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] = 1 if txt_data[1] > 1: txt_data[1] = 1 if txt_data[2] > 1: txt_data[2] = 1 if txt_data[3] > 1: txt_data[3] = 1 yolo_annotations.append(f"{cls_id} {' '.join([str(round(a, 6)) for a in txt_data])}") in_file.close() return yolo_annotations def 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_1 index = 0 img_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() # classes print(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: hat 1: 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
七、更大、更强
用yolov10m训练了一个模型，1280输入。