diff --git a/scripts/convert_to_yolo.py b/scripts/convert_to_yolo.py
deleted file mode 100644
index 53ef36a..0000000
--- a/scripts/convert_to_yolo.py
+++ /dev/null
@@ -1,179 +0,0 @@
-#!/usr/bin/env python3
-"""
-将红外图像数据集转换为YOLO格式
-由于数据集没有标注，使用整个图像作为检测目标（目标检测模式）
-"""
-
-import argparse
-import shutil
-import random
-from pathlib import Path
-import cv2
-
-
-def create_yolo_directories(output_dir: Path) -> dict:
-    """创建YOLO目录结构"""
-    dirs = {
-        "train_images": output_dir / "images" / "train",
-        "val_images": output_dir / "images" / "val",
-        "train_labels": output_dir / "labels" / "train",
-        "val_labels": output_dir / "labels" / "val",
-    }
-    for d in dirs.values():
-        d.mkdir(parents=True, exist_ok=True)
-    return dirs
-
-
-def convert_dataset(input_dir: Path, output_dir: Path,
-                   class_names: list,
-                   train_ratio: float = 0.8,
-                   use_whole_image: bool = True) -> None:
-    """
-    转换数据集为YOLO格式
-
-    Args:
-        input_dir: 输入图像目录（包含类别子目录）
-        output_dir: 输出目录
-        class_names: 类别名称列表
-        train_ratio: 训练集比例
-        use_whole_image: 是否使用整张图像作为检测目标
-    """
-    print("=" * 50)
-    print("转换数据集为YOLO格式")
-    print("=" * 50)
-    print(f"输入目录: {input_dir}")
-    print(f"输出目录: {output_dir}")
-    print(f"使用整图作为目标: {use_whole_image}")
-    print("=" * 50)
-
-    # 创建输出目录
-    dirs = create_yolo_directories(output_dir)
-
-    # 创建类别到ID的映射
-    class_to_id = {name: idx for idx, name in enumerate(class_names)}
-
-    # 收集所有图像文件
-    all_images = []
-    for class_name in class_names:
-        class_dir = input_dir / class_name
-        if not class_dir.exists():
-            print(f"警告: 目录不存在 {class_dir}")
-            continue
-
-        for img_file in class_dir.glob("*.jpg"):
-            all_images.append((img_file, class_name))
-        for img_file in class_dir.glob("*.png"):
-            all_images.append((img_file, class_name))
-        for img_file in class_dir.glob("*.jpeg"):
-            all_images.append((img_file, class_name))
-
-    print(f"找到 {len(all_images)} 张图像")
-
-    if len(all_images) == 0:
-        print("错误: 没有找到图像文件")
-        return
-
-    # 打乱并划分数据集
-    random.seed(42)
-    random.shuffle(all_images)
-
-    train_count = int(len(all_images) * train_ratio)
-    train_images = all_images[:train_count]
-    val_images = all_images[train_count:]
-
-    print(f"训练集: {len(train_images)} 张")
-    print(f"验证集: {len(val_images)} 张")
-
-    # 复制图像并创建标签
-    for img_path, class_name in train_images:
-        copy_and_label(img_path, class_name, dirs["train_images"],
-                      dirs["train_labels"], use_whole_image, class_to_id)
-
-    for img_path, class_name in val_images:
-        copy_and_label(img_path, class_name, dirs["val_images"],
-                      dirs["val_labels"], use_whole_image, class_to_id)
-
-    # 创建dataset.yaml
-    yaml_content = f"""# YOLO数据集配置
-# 红外电力设备故障检测数据集
-
-path: {output_dir.absolute()}
-train: images/train
-val: images/val
-
-nc: {len(class_names)}
-names: {class_names}
-"""
-    with open(output_dir / "dataset.yaml", "w", encoding="utf-8") as f:
-        f.write(yaml_content)
-
-    print("\n" + "=" * 50)
-    print("转换完成!")
-    print(f"配置文件: {output_dir / 'dataset.yaml'}")
-    print("=" * 50)
-
-
-def copy_and_label(img_path: Path, class_name: str,
-                  output_img_dir: Path, output_label_dir: Path,
-                  use_whole_image: bool,
-                  class_to_id: dict) -> None:
-    """复制图像并创建标签文件"""
-    # 复制图像
-    output_img_path = output_img_dir / img_path.name
-    shutil.copy(img_path, output_img_path)
-
-    # 创建标签文件
-    label_path = output_label_dir / f"{img_path.stem}.txt"
-
-    if use_whole_image:
-        # 使用整张图像作为检测目标
-        img = cv2.imread(str(img_path))
-        if img is None:
-            return
-
-        h, w = img.shape[:2]
-
-        # YOLO格式: class_id x_center y_center width height (归一化)
-        # 整图作为目标，使用正确的类别ID
-        class_id = class_to_id.get(class_name, 0)
-        label_content = f"{class_id} 0.5 0.5 1.0 1.0\n"
-
-        with open(label_path, "w") as f:
-            f.write(label_content)
-    else:
-        # 空标签文件（用于后续添加真实标注）
-        with open(label_path, "w") as f:
-            pass
-
-
-def main():
-    parser = argparse.ArgumentParser(description="转换数据集为YOLO格式")
-    parser.add_argument("--input-dir", type=str,
-                       default="data/raw/images",
-                       help="输入图像目录")
-    parser.add_argument("--output-dir", type=str,
-                       default="data/infrared",
-                       help="输出目录")
-    parser.add_argument("--train-ratio", type=float, default=0.8,
-                       help="训练集比例")
-    parser.add_argument("--whole-image", action="store_true",
-                       help="使用整张图像作为检测目标")
-
-    args = parser.parse_args()
-
-    input_dir = Path(args.input_dir)
-    output_dir = Path(args.output_dir)
-
-    if not input_dir.exists():
-        print(f"错误: 输入目录不存在: {input_dir}")
-        return
-
-    # 从目录结构获取类别
-    class_names = [d.name for d in input_dir.iterdir() if d.is_dir()]
-
-    convert_dataset(input_dir, output_dir, class_names,
-                  args.train_ratio, args.whole_image)
-
-
-if __name__ == "__main__":
-    main()