使用YOLOv8来训练——心胸肺CT检测数据集，并使用训练好的模型进行预测。

 chest_ct_dataset/

    
 ├── train/
    
 │   ├── images/
    
 │   └── labels/
    
 ├── valid/
    
 │   ├── images/
    
 │   └── labels/
    
 └── data.yaml  # 数据配置文件

 train: ./train/images/  # 训练集图像路径

    
 val: ./valid/images/  # 验证集图像路径
    
 test: ./test/images/  # 测试集图像路径（如果有）
    
  
    
 # Classes
    
 nc: 12  # 类别数量
    
 names: ['infiltrate', 'scarring', 'ild', 'aortic_enlargement', 
    
     'pleural_thickening', 'pleural_effusion', 'calcification', 
    
     'cardiomegaly', 'nodule_mass', 'atelectasis', 
    
     'consolidation', 'pneumothorax']  # 类别名称列表

 import os

    
 import xml.etree.ElementTree as ET
    
 from PIL import Image
    
  
    
 def convert_voc_to_yolo(xml_file, img_size, class_names, output_file):
    
     tree = ET.parse(xml_file)
    
     root = tree.getroot()
    
     
    
     width = int(root.find('size/width').text)
    
     height = int(root.find('size/height').text)
    
     
    
     labels = []
    
     for obj in root.iter('object'):
    
     cls = obj.find('name').text.lower()  # 将类别名转换为小写
    
     if cls not in class_names:
    
         continue
    
     cls_id = class_names.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))
    
     
    
     bb = convert_coordinates(b, width, height, img_size)
    
     labels.append([cls_id, *bb])
    
     
    
     with open(output_file, 'w') as f:
    
     for label in labels:
    
         f.write('%d %.6f %.6f %.6f %.6f\n' % tuple(label))
    
  
    
 def convert_coordinates(box, orig_w, orig_h, target_size):
    
     dw, dh = target_size
    
     x_center = ((box[1] + box[0]) / 2) / orig_w
    
     y_center = ((box[3] + box[2]) / 2) / orig_h
    
     w = (box[1] - box[0]) / orig_w
    
     h = (box[3] - box[2]) / orig_h
    
     
    
     x_center *= dw
    
     y_center *= dh
    
     w *= dw
    
     h *= dh
    
     
    
     return x_center, y_center, w, h
    
  
    
 def main():
    
     class_names = ['infiltrate', 'scarring', 'ild', 'aortic_enlargement', 
    
                'pleural_thickening', 'pleural_effusion', 'calcification', 
    
                'cardiomegaly', 'nodule_mass', 'atelectasis', 
    
                'consolidation', 'pneumothorax']  # 替换为实际类别名称
    
     img_size = (640, 640)  # 输入图像大小
    
     
    
     for phase in ['train', 'valid']:
    
     xml_dir = f'./chest_ct_dataset/{phase}/annotations/'
    
     txt_dir = f'./chest_ct_dataset/{phase}/labels_txt/'
    
     os.makedirs(txt_dir, exist_ok=True)
    
     
    
     for xml_file in os.listdir(xml_dir):
    
         if xml_file.endswith('.xml'):
    
             img_file = xml_file.replace('.xml', '.jpg')
    
             img_path = os.path.join(f'./chest_ct_dataset/{phase}/images/', img_file)
    
             img = Image.open(img_path)
    
             img_width, img_height = img.size
    
             
    
             output_file = os.path.join(txt_dir, img_file.replace('.jpg', '.txt'))
    
             convert_voc_to_yolo(os.path.join(xml_dir, xml_file), img_size, class_names, output_file)
    
  
    
 if __name__ == '__main__':
    
     main()

    pip install ultralytics

 cd path/to/chest_ct_dataset/

    
  
    
 # 下载预训练权重
    
 wget https://github.com/ultralytics/ultralytics/releases/download/v8.0.19/yolov8n.pt
    
  
    
 # 开始训练
    
 ultralytics train model=yolov8n.yaml data=./data.yaml epochs=100 imgsz=640

    ultralytics train model=yolov8s.yaml data=./data.yaml epochs=100 imgsz=640

    ultralytics val model=best.pt data=./data.yaml imgsz=640

    ultralytics predict model=best.pt source=path/to/your/image.jpg imgsz=640