diff --git a/data_management/batch_download_all.py b/data_management/batch_download_all.py
new file mode 100644
index 0000000..21725f3
--- /dev/null
+++ b/data_management/batch_download_all.py
@@ -0,0 +1,298 @@
+"""
+批量下载所有图片到dataset目录
+自动按类别分类到对应文件夹
+"""
+import os
+import sys
+from datetime import datetime
+from tqdm import tqdm
+import requests
+
+# 添加当前目录到路径
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from database import MySQLManager, SQLiteManager
+from utils import ImageDownloader
+from config import IMAGE_URL_PREFIX, DOWNLOAD_TIMEOUT, DOWNLOAD_THREADS
+
+
+def sanitize_folder_name(name: str) -> str:
+    """清理文件夹名称，移除非法字符"""
+    # 保留中文字符、字母、数字、括号、方括号、WP代码
+    import re
+    # 移除或替换非法字符
+    name = name.replace('/', '_').replace('\\', '_')
+    name = name.replace(':', '_').replace('*', '_')
+    name = name.replace('?', '_').replace('"', '_')
+    name = name.replace('<', '_').replace('>', '_')
+    name = name.replace('|', '_')
+    return name.strip()
+
+
+def get_goods_code(goods_name: str) -> str:
+    """从goods_name中提取WP代码（如果存在）"""
+    import re
+    # 匹配WP开头的代码，如 WP2025110700043
+    match = re.search(r'WP\d+', goods_name)
+    if match:
+        return match.group(0)
+    return ''
+
+
+def batch_download_all(
+    datasource: str = '食材',
+    output_dir: str = None,
+    max_images: int = None,
+    skip_existing: bool = True
+):
+    """
+    批量下载所有图片
+    
+    Args:
+        datasource: 数据源（'食材'/'菜品'/'半加工'）
+        output_dir: 输出目录，如果为None则使用dataset目录
+        max_images: 最大下载数量（用于测试），None表示下载全部
+        skip_existing: 是否跳过已存在的文件
+    """
+    print("="*80)
+    print(f"批量下载工具 - 数据源: {datasource}")
+    print("="*80)
+    
+    # 初始化数据库管理器
+    print("\n[1/5] 连接数据库...")
+    mysql_manager = MySQLManager()
+    sqlite_manager = SQLiteManager()
+    
+    # 查询所有图片数据
+    print("\n[2/5] 查询所有图片数据...")
+    all_data = []
+    page = 1
+    page_size = 1000  # 每次查询1000条
+    
+    while True:
+        data_list, total_count = mysql_manager.query_images(
+            datasource=datasource,
+            page=page,
+            page_size=page_size
+        )
+        
+        if not data_list:
+            break
+        
+        all_data.extend(data_list)
+        print(f"  已查询: {len(all_data)} / {total_count} 张图片")
+        
+        if len(all_data) >= total_count:
+            break
+        
+        if max_images and len(all_data) >= max_images:
+            all_data = all_data[:max_images]
+            break
+        
+        page += 1
+    
+    total_images = len(all_data)
+    print(f"\n✓ 共找到 {total_images} 张图片")
+    
+    if total_images == 0:
+        print("没有找到图片，退出")
+        return
+    
+    # 确定输出目录
+    if output_dir is None:
+        # 根据数据源确定目录
+        base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        if datasource == '食材':
+            output_dir = os.path.join(base_dir, 'dataset', 'WholeIngredientRecognition', 'train')
+        elif datasource == '菜品':
+            output_dir = os.path.join(base_dir, 'dataset', 'DishClassification', 'train')
+        elif datasource == '半加工':
+            output_dir = os.path.join(base_dir, 'dataset', 'ProcessedIngredientRecognition', 'train')
+        else:
+            output_dir = os.path.join(base_dir, 'dataset', datasource, 'train')
+    
+    os.makedirs(output_dir, exist_ok=True)
+    print(f"\n[3/5] 输出目录: {output_dir}")
+    
+    # 按类别分组
+    print("\n[4/5] 按类别分组...")
+    category_groups = {}
+    for data in all_data:
+        goods_name = data['goods_name']
+        # 清理文件夹名称
+        folder_name = sanitize_folder_name(goods_name)
+        
+        if folder_name not in category_groups:
+            category_groups[folder_name] = []
+        category_groups[folder_name].append(data)
+    
+    print(f"✓ 共 {len(category_groups)} 个类别")
+    
+    # 显示类别统计
+    print("\n类别统计（前10个）:")
+    sorted_categories = sorted(category_groups.items(), key=lambda x: len(x[1]), reverse=True)
+    for i, (category, items) in enumerate(sorted_categories[:10], 1):
+        print(f"  {i}. {category}: {len(items)} 张")
+    
+    # 下载图片
+    print(f"\n[5/5] 开始下载图片（并发数: {DOWNLOAD_THREADS}）...")
+    
+    success_count = 0
+    failed_count = 0
+    skipped_count = 0
+    error_messages = []
+    
+    # 创建进度条
+    pbar = tqdm(total=total_images, desc="下载进度", unit="张")
+    
+    def download_single(data: dict) -> tuple[bool, str]:
+        """下载单张图片"""
+        try:
+            goods_name = data['goods_name']
+            folder_name = sanitize_folder_name(goods_name)
+            category_dir = os.path.join(output_dir, folder_name)
+            os.makedirs(category_dir, exist_ok=True)
+            
+            # 构建文件名
+            filename = f"img_{data['id']}.jpg"
+            file_path = os.path.join(category_dir, filename)
+            
+            # 检查文件是否已存在
+            if skip_existing and os.path.exists(file_path):
+                return True, "已存在"
+            
+            # 下载图片
+            image_url = data['image_url']
+            if not image_url:
+                return False, "图片URL为空"
+            
+            response = requests.get(image_url, timeout=DOWNLOAD_TIMEOUT, stream=True)
+            response.raise_for_status()
+            
+            # 保存图片
+            with open(file_path, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+            
+            # 记录下载历史
+            sqlite_manager.add_download_record(
+                datasource=data['datasource'],
+                goods_id=data.get('goods_id'),
+                goods_name=goods_name,
+                image_url=image_url,
+                local_path=file_path,
+                file_size=os.path.getsize(file_path)
+            )
+            
+            return True, None
+            
+        except Exception as e:
+            return False, str(e)
+    
+    # 使用线程池下载
+    from concurrent.futures import ThreadPoolExecutor, as_completed
+    
+    with ThreadPoolExecutor(max_workers=DOWNLOAD_THREADS) as executor:
+        # 提交所有任务
+        futures = {executor.submit(download_single, data): data for data in all_data}
+        
+        # 处理结果
+        for future in as_completed(futures):
+            data = futures[future]
+            try:
+                success, error = future.result()
+                if success:
+                    if error == "已存在":
+                        skipped_count += 1
+                    else:
+                        success_count += 1
+                else:
+                    failed_count += 1
+                    error_messages.append(f"{data['goods_name']} (ID: {data['id']}): {error}")
+                
+                pbar.update(1)
+                
+            except Exception as e:
+                failed_count += 1
+                error_messages.append(f"{data['goods_name']} (ID: {data['id']}): {str(e)}")
+                pbar.update(1)
+    
+    pbar.close()
+    
+    # 打印结果
+    print("\n" + "="*80)
+    print("下载完成!")
+    print("="*80)
+    print(f"成功: {success_count} 张")
+    print(f"跳过: {skipped_count} 张（已存在）")
+    print(f"失败: {failed_count} 张")
+    print(f"总计: {total_images} 张")
+    print(f"\n输出目录: {output_dir}")
+    
+    if error_messages:
+        print(f"\n错误信息（前10条）:")
+        for msg in error_messages[:10]:
+            print(f"  - {msg}")
+        if len(error_messages) > 10:
+            print(f"  ... 还有 {len(error_messages) - 10} 条错误")
+    
+    # 关闭数据库连接
+    mysql_manager.close()
+    
+    print("\n✓ 完成!")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='批量下载所有图片到dataset目录')
+    parser.add_argument('--datasource', type=str, default='食材', 
+                       choices=['食材', '菜品', '半加工', 'all'],
+                       help='数据源（默认: 食材，使用 all 下载所有三种）')
+    parser.add_argument('--output', type=str, default=None,
+                       help='输出目录（默认: dataset/{datasource}/train）')
+    parser.add_argument('--max', type=int, default=None,
+                       help='最大下载数量（用于测试）')
+    parser.add_argument('--no-skip', action='store_true',
+                       help='不跳过已存在的文件（重新下载）')
+    
+    args = parser.parse_args()
+    
+    # 如果选择 all，则依次下载所有三种数据源
+    if args.datasource == 'all':
+        datasources = ['食材', '菜品', '半加工']
+        print("\n" + "="*80)
+        print("将依次下载所有三种数据源:")
+        print("  1. 食材 -> WholeIngredientRecognition")
+        print("  2. 菜品 -> DishClassification")
+        print("  3. 半加工 -> ProcessedIngredientRecognition")
+        print("="*80 + "\n")
+        
+        for i, ds in enumerate(datasources, 1):
+            print(f"\n{'='*80}")
+            print(f"[{i}/3] 开始下载: {ds}")
+            print(f"{'='*80}\n")
+            
+            batch_download_all(
+                datasource=ds,
+                output_dir=args.output,
+                max_images=args.max,
+                skip_existing=not args.no_skip
+            )
+            
+            if i < len(datasources):
+                print(f"\n等待5秒后继续下一个数据源...")
+                import time
+                time.sleep(5)
+        
+        print("\n" + "="*80)
+        print("✓ 所有数据源下载完成!")
+        print("="*80)
+    else:
+        batch_download_all(
+            datasource=args.datasource,
+            output_dir=args.output,
+            max_images=args.max,
+            skip_existing=not args.no_skip
+        )
diff --git a/data_management/config.py b/data_management/config.py
index f92fdec..e34d863 100644
--- a/data_management/config.py
+++ b/data_management/config.py
@@ -9,7 +9,7 @@ MYSQL_CONFIG = {
     'port': 3308,
     'user': 'root',
     'password': 'Aa135790123',
-    'database': 'yx_terminal',
+    'database': 'yx_terminal_uat',
     'charset': 'utf8mb4'
 }
 
@@ -21,7 +21,7 @@ TABLE_MAPPING = {
 }
 
 # ======================== 图片URL前缀 ========================
-IMAGE_URL_PREFIX = 'http://dev.yixiong-tech.com:8081'
+IMAGE_URL_PREFIX = 'https://dev.yixiong-tech.com:8081'
 
 # ======================== SQLite下载历史数据库 ========================
 # 存储在项目的data_management目录下
diff --git a/data_management/count_images.py b/data_management/count_images.py
new file mode 100644
index 0000000..a64927d
--- /dev/null
+++ b/data_management/count_images.py
@@ -0,0 +1,171 @@
+"""
+统计每个类别的图片数量（包含train/val/test）
+"""
+import os
+from pathlib import Path
+from collections import defaultdict
+
+
+def count_images_by_class(dataset_root: str, output_file: str = None):
+    """
+    统计每个类别的图片数量（包含train/val/test）
+    
+    Args:
+        dataset_root: 数据集根目录（包含train/val/test目录）
+        output_file: 输出文件路径（可选，如果提供则保存到文件）
+    """
+    dataset_root = Path(dataset_root)
+    train_dir = dataset_root / 'train'
+    val_dir = dataset_root / 'val'
+    test_dir = dataset_root / 'test'
+    
+    if not train_dir.exists():
+        print(f"错误: 训练目录不存在: {train_dir}")
+        return
+    
+    print("="*80)
+    print("每个类别的图片数量统计（包含train/val/test）")
+    print("="*80)
+    
+    class_counts = {}
+    class_counts_detail = {}  # 详细统计：train/val/test分别的数量
+    total_images = 0
+    total_train = 0
+    total_val = 0
+    total_test = 0
+    
+    # 获取所有类别文件夹（从train目录）
+    class_folders = sorted([f for f in train_dir.iterdir() if f.is_dir()])
+    
+    # 也检查val和test目录中是否有train中没有的类别
+    all_class_names = set()
+    for folder in class_folders:
+        all_class_names.add(folder.name)
+    
+    if val_dir.exists():
+        for folder in val_dir.iterdir():
+            if folder.is_dir():
+                all_class_names.add(folder.name)
+    
+    if test_dir.exists():
+        for folder in test_dir.iterdir():
+            if folder.is_dir():
+                all_class_names.add(folder.name)
+    
+    # 统计每个类别的图片数量
+    for class_name in sorted(all_class_names):
+        train_count = 0
+        val_count = 0
+        test_count = 0
+        
+        # 统计train目录
+        train_class_dir = train_dir / class_name
+        if train_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                train_count += len(list(train_class_dir.glob(ext)))
+        
+        # 统计val目录
+        val_class_dir = val_dir / class_name
+        if val_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                val_count += len(list(val_class_dir.glob(ext)))
+        
+        # 统计test目录
+        test_class_dir = test_dir / class_name
+        if test_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                test_count += len(list(test_class_dir.glob(ext)))
+        
+        total = train_count + val_count + test_count
+        class_counts[class_name] = total
+        class_counts_detail[class_name] = {
+            'train': train_count,
+            'val': val_count,
+            'test': test_count,
+            'total': total
+        }
+        total_images += total
+        total_train += train_count
+        total_val += val_count
+        total_test += test_count
+    
+    # 按数量排序
+    sorted_classes = sorted(class_counts.items(), key=lambda x: x[1], reverse=True)
+    
+    # 修复文件开头的注释
+    if output_file:
+        # 确保sorted_classes已定义
+        pass
+    
+    # 打印结果
+    print(f"\n总类别数: {len(class_counts)}")
+    print(f"总图片数: {total_images} (训练集: {total_train}, 验证集: {total_val}, 测试集: {total_test})")
+    print(f"平均每类: {total_images // len(class_counts) if len(class_counts) > 0 else 0} 张\n")
+    print("-"*100)
+    print(f"{'类别名称':<50} {'总计':<8} {'训练':<8} {'验证':<8} {'测试':<8} {'状态':<10}")
+    print("-"*100)
+    
+    # 统计样本不足的类别（假设阈值是10）
+    min_threshold = 10
+    insufficient_count = 0
+    
+    for class_name, count in sorted_classes:
+        detail = class_counts_detail[class_name]
+        status = "⚠样本不足" if count < min_threshold else "✓"
+        if count < min_threshold:
+            insufficient_count += 1
+        print(f"{class_name:<50} {detail['total']:<8} {detail['train']:<8} {detail['val']:<8} {detail['test']:<8} {status:<10}")
+    
+    print("-"*100)
+    print(f"\n样本不足的类别数（<{min_threshold}张）: {insufficient_count}")
+    print(f"样本充足的类别数（>={min_threshold}张）: {len(class_counts) - insufficient_count}")
+    
+    # 保存到文件（如果指定）
+    if output_file:
+        output_path = Path(output_file)
+        with open(output_path, 'w', encoding='utf-8') as f:
+            f.write("="*100 + "\n")
+            f.write("每个类别的图片数量统计（包含train/val/test）\n")
+            f.write("="*100 + "\n\n")
+            f.write(f"总类别数: {len(class_counts)}\n")
+            f.write(f"总图片数: {total_images} (训练集: {total_train}, 验证集: {total_val}, 测试集: {total_test})\n")
+            f.write(f"平均每类: {total_images // len(class_counts) if len(class_counts) > 0 else 0} 张\n\n")
+            f.write("-"*100 + "\n")
+            f.write(f"{'类别名称':<50} {'总计':<8} {'训练':<8} {'验证':<8} {'测试':<8} {'状态':<10}\n")
+            f.write("-"*100 + "\n")
+            
+            for class_name, count in sorted_classes:
+                detail = class_counts_detail[class_name]
+                status = "⚠样本不足" if count < min_threshold else "✓"
+                f.write(f"{class_name:<50} {detail['total']:<8} {detail['train']:<8} {detail['val']:<8} {detail['test']:<8} {status:<10}\n")
+            
+            f.write("-"*100 + "\n")
+            f.write(f"\n样本不足的类别数（<{min_threshold}张）: {insufficient_count}\n")
+            f.write(f"样本充足的类别数（>={min_threshold}张）: {len(class_counts) - insufficient_count}\n")
+        
+        print(f"\n✓ 统计结果已保存到: {output_path}")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='统计每个类别的图片数量')
+    parser.add_argument('--dataset', type=str, 
+                       default='dataset/WholeIngredientRecognition',
+                       help='数据集根目录（默认: dataset/WholeIngredientRecognition）')
+    parser.add_argument('--output', type=str, default=None,
+                       help='输出文件路径（可选，保存统计结果到文件）')
+    
+    args = parser.parse_args()
+    
+    # 获取项目根目录
+    script_dir = Path(__file__).parent
+    project_root = script_dir.parent
+    
+    # 处理相对路径
+    if not os.path.isabs(args.dataset):
+        dataset_path = project_root / args.dataset
+    else:
+        dataset_path = Path(args.dataset)
+    
+    count_images_by_class(str(dataset_path), args.output)
diff --git a/data_management/remove_duplicate_images.py b/data_management/remove_duplicate_images.py
new file mode 100644
index 0000000..325eadf
--- /dev/null
+++ b/data_management/remove_duplicate_images.py
@@ -0,0 +1,245 @@
+"""
+检查并删除重复的照片
+通过文件内容（MD5）或文件名来识别重复
+"""
+import os
+import hashlib
+from pathlib import Path
+from collections import defaultdict
+import shutil
+
+
+def calculate_md5(file_path: Path) -> str:
+    """计算文件的MD5值"""
+    hash_md5 = hashlib.md5()
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+
+def find_duplicates_by_content(dataset_root: str, dry_run: bool = True):
+    """
+    通过文件内容（MD5）查找重复照片
+    
+    Args:
+        dataset_root: 数据集根目录
+        dry_run: 是否只是预览，不实际删除
+    """
+    dataset_root = Path(dataset_root)
+    
+    print("="*80)
+    print("检查重复照片（基于文件内容MD5）")
+    print("="*80)
+    print(f"数据集根目录: {dataset_root}")
+    print(f"模式: {'预览模式（不实际删除）' if dry_run else '执行模式'}")
+    print("="*80 + "\n")
+    
+    # 存储所有文件的MD5值
+    md5_to_files = defaultdict(list)
+    
+    # 遍历所有图片文件
+    image_extensions = ['.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG']
+    total_files = 0
+    
+    print("正在计算文件MD5值...")
+    for root, dirs, files in os.walk(dataset_root):
+        for file in files:
+            if any(file.endswith(ext) for ext in image_extensions):
+                file_path = Path(root) / file
+                try:
+                    md5_hash = calculate_md5(file_path)
+                    md5_to_files[md5_hash].append(file_path)
+                    total_files += 1
+                    if total_files % 100 == 0:
+                        print(f"  已处理: {total_files} 个文件...")
+                except Exception as e:
+                    print(f"  ⚠ 处理文件失败 {file_path}: {e}")
+    
+    print(f"\n✓ 共处理 {total_files} 个文件\n")
+    
+    # 找出重复的文件
+    duplicates = {md5: files for md5, files in md5_to_files.items() if len(files) > 1}
+    
+    if not duplicates:
+        print("✓ 没有发现重复的照片")
+        return
+    
+    print(f"发现 {len(duplicates)} 组重复照片:\n")
+    
+    total_duplicates = 0
+    total_to_delete = 0
+    
+    for md5_hash, files in sorted(duplicates.items(), key=lambda x: len(x[1]), reverse=True):
+        # 按路径排序，保留第一个（通常保留train目录的）
+        files_sorted = sorted(files)
+        
+        # 优先保留train目录的文件
+        keep_file = None
+        for f in files_sorted:
+            if 'train' in str(f):
+                keep_file = f
+                break
+        
+        if keep_file is None:
+            keep_file = files_sorted[0]  # 如果没有train目录的，保留第一个
+        
+        files_to_delete = [f for f in files_sorted if f != keep_file]
+        
+        print(f"MD5: {md5_hash[:8]}...")
+        print(f"  保留: {keep_file}")
+        for f in files_to_delete:
+            print(f"  删除: {f}")
+        
+        total_duplicates += len(files)
+        total_to_delete += len(files_to_delete)
+        
+        # 实际删除
+        if not dry_run:
+            for f in files_to_delete:
+                try:
+                    f.unlink()
+                    print(f"    ✓ 已删除: {f.name}")
+                except Exception as e:
+                    print(f"    ✗ 删除失败: {e}")
+        print()
+    
+    print("="*80)
+    print("统计:")
+    print("="*80)
+    print(f"重复文件组数: {len(duplicates)}")
+    print(f"重复文件总数: {total_duplicates}")
+    print(f"将删除文件数: {total_to_delete}")
+    print(f"将保留文件数: {len(duplicates)}")
+    
+    if dry_run:
+        print("\n使用 --execute 参数来实际执行删除操作")
+    else:
+        print("\n✓ 删除完成!")
+
+
+def find_duplicates_by_name(dataset_root: str, dry_run: bool = True):
+    """
+    通过文件名查找重复照片（同一文件名出现在多个目录）
+    
+    Args:
+        dataset_root: 数据集根目录
+        dry_run: 是否只是预览，不实际删除
+    """
+    dataset_root = Path(dataset_root)
+    
+    print("="*80)
+    print("检查重复照片（基于文件名）")
+    print("="*80)
+    print(f"数据集根目录: {dataset_root}")
+    print(f"模式: {'预览模式（不实际删除）' if dry_run else '执行模式'}")
+    print("="*80 + "\n")
+    
+    # 存储文件名到文件路径的映射
+    name_to_files = defaultdict(list)
+    
+    # 遍历所有图片文件
+    image_extensions = ['.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG']
+    
+    for root, dirs, files in os.walk(dataset_root):
+        for file in files:
+            if any(file.endswith(ext) for ext in image_extensions):
+                file_path = Path(root) / file
+                name_to_files[file].append(file_path)
+    
+    # 找出重复的文件名
+    duplicates = {name: files for name, files in name_to_files.items() if len(files) > 1}
+    
+    if not duplicates:
+        print("✓ 没有发现同名重复的照片")
+        return
+    
+    print(f"发现 {len(duplicates)} 组同名重复照片:\n")
+    
+    total_to_delete = 0
+    
+    for file_name, files in sorted(duplicates.items(), key=lambda x: len(x[1]), reverse=True):
+        # 按路径排序，优先保留train目录的
+        files_sorted = sorted(files)
+        
+        keep_file = None
+        for f in files_sorted:
+            if 'train' in str(f):
+                keep_file = f
+                break
+        
+        if keep_file is None:
+            keep_file = files_sorted[0]
+        
+        files_to_delete = [f for f in files_sorted if f != keep_file]
+        
+        print(f"文件名: {file_name}")
+        print(f"  保留: {keep_file}")
+        for f in files_to_delete:
+            print(f"  删除: {f}")
+        
+        total_to_delete += len(files_to_delete)
+        
+        # 实际删除
+        if not dry_run:
+            for f in files_to_delete:
+                try:
+                    f.unlink()
+                    print(f"    ✓ 已删除: {f.name}")
+                except Exception as e:
+                    print(f"    ✗ 删除失败: {e}")
+        print()
+    
+    print("="*80)
+    print(f"将删除文件数: {total_to_delete}")
+    
+    if dry_run:
+        print("\n使用 --execute 参数来实际执行删除操作")
+    else:
+        print("\n✓ 删除完成!")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='检查并删除重复的照片')
+    parser.add_argument('--dataset', type=str, 
+                       default='dataset/WholeIngredientRecognition',
+                       help='数据集根目录（默认: dataset/WholeIngredientRecognition）')
+    parser.add_argument('--method', type=str,
+                       choices=['content', 'name', 'both'],
+                       default='both',
+                       help='检查方法: content(基于MD5), name(基于文件名), both(两种都检查)')
+    parser.add_argument('--dry-run', action='store_true',
+                       help='预览模式，不实际删除')
+    parser.add_argument('--execute', action='store_true',
+                       help='实际执行删除（需要明确指定）')
+    
+    args = parser.parse_args()
+    
+    # 获取项目根目录
+    script_dir = Path(__file__).parent
+    project_root = script_dir.parent
+    
+    # 处理相对路径
+    if not os.path.isabs(args.dataset):
+        if args.dataset.startswith('../'):
+            dataset_path = (script_dir / args.dataset).resolve()
+        else:
+            dataset_path = (project_root / args.dataset).resolve()
+    else:
+        dataset_path = Path(args.dataset).resolve()
+    
+    # 如果没有指定execute，默认是dry-run
+    if not args.execute and not args.dry_run:
+        print("⚠ 警告: 默认是预览模式，不会实际删除")
+        print("使用 --execute 参数来实际执行删除操作\n")
+        args.dry_run = True
+    
+    if args.method in ['content', 'both']:
+        find_duplicates_by_content(str(dataset_path), dry_run=args.dry_run)
+        if args.method == 'both':
+            print("\n" + "="*80 + "\n")
+    
+    if args.method in ['name', 'both']:
+        find_duplicates_by_name(str(dataset_path), dry_run=args.dry_run)
diff --git a/data_management/split_dataset.py b/data_management/split_dataset.py
new file mode 100644
index 0000000..a4b96c3
--- /dev/null
+++ b/data_management/split_dataset.py
@@ -0,0 +1,367 @@
+"""cui
+数据集分割脚本
+1. 将train目录下的图片按70:15:15分割到train/val/test
+2. 对样本量过少的类别进行标记
+"""
+import os
+import shutil
+import random
+from pathlib import Path
+from typing import List, Tuple
+
+
+def split_dataset(
+    dataset_root: str,
+    train_ratio: float = 0.70,
+    val_ratio: float = 0.15,
+    test_ratio: float = 0.15,
+    min_samples_threshold: int = 10,
+    mark_insufficient: bool = True,
+    max_total: int = 120,
+    seed: int = 42
+):
+    """
+    分割数据集
+    
+    Args:
+        dataset_root: 数据集根目录（包含train目录）
+        train_ratio: 训练集比例（默认0.70）
+        val_ratio: 验证集比例（默认0.15）
+        test_ratio: 测试集比例（默认0.15）
+        min_samples_threshold: 最小样本数阈值，低于此值的类别会被标记（默认10）
+        mark_insufficient: 是否在文件夹名称后添加"样本数太少"标记（默认True）
+        max_total: 如果总数超过100，限制到max_total张（默认120）
+        seed: 随机种子（默认42）
+    """
+    # 验证比例
+    if abs(train_ratio + val_ratio + test_ratio - 1.0) > 0.001:
+        raise ValueError(f"比例总和必须为1.0，当前为: {train_ratio + val_ratio + test_ratio}")
+    
+    dataset_root = Path(dataset_root)
+    train_dir = dataset_root / 'train'
+    val_dir = dataset_root / 'val'
+    test_dir = dataset_root / 'test'
+    
+    if not train_dir.exists():
+        raise ValueError(f"训练目录不存在: {train_dir}")
+    
+    # 创建val和test目录
+    val_dir.mkdir(parents=True, exist_ok=True)
+    test_dir.mkdir(parents=True, exist_ok=True)
+    
+    # 设置随机种子
+    random.seed(seed)
+    
+    print("="*80)
+    print(f"数据集分割工具")
+    print("="*80)
+    print(f"数据集根目录: {dataset_root}")
+    print(f"分割比例: 训练集={train_ratio*100:.0f}%, 验证集={val_ratio*100:.0f}%, 测试集={test_ratio*100:.0f}%")
+    print(f"最小样本数阈值: {min_samples_threshold}")
+    print(f"样本不足标记: {'启用' if mark_insufficient else '禁用'}")
+    print("="*80 + "\n")
+    
+    # 检查val和test目录是否已有数据
+    val_has_data = val_dir.exists() and any(val_dir.iterdir())
+    test_has_data = test_dir.exists() and any(test_dir.iterdir())
+    
+    if val_has_data or test_has_data:
+        print("⚠ 检测到验证集或测试集已有数据")
+        print("将检查所有类别的比例，如果不符合70:15:15将重新分配\n")
+    
+    # 获取所有类别文件夹
+    class_folders = [f for f in train_dir.iterdir() if f.is_dir()]
+    total_classes = len(class_folders)
+    
+    print(f"找到 {total_classes} 个类别\n")
+    
+    stats = {
+        'total_classes': total_classes,
+        'processed_classes': 0,
+        'skipped_classes': 0,
+        'sufficient_samples': 0,
+        'insufficient_samples': 0,
+        'total_images': 0,
+        'train_images': 0,
+        'val_images': 0,
+        'test_images': 0,
+        'marked_classes': []
+    }
+    
+    # 处理每个类别
+    for class_folder in sorted(class_folders):
+        class_name = class_folder.name
+        
+        # 收集所有图片（从train/val/test）
+        train_images = []
+        for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+            train_images.extend(list(class_folder.glob(ext)))
+        
+        val_class_dir = val_dir / class_name
+        val_images = []
+        if val_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                val_images.extend(list(val_class_dir.glob(ext)))
+        
+        test_class_dir = test_dir / class_name
+        test_images = []
+        if test_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                test_images.extend(list(test_class_dir.glob(ext)))
+        
+        # 合并所有图片
+        all_images = train_images + val_images + test_images
+        num_images = len(all_images)
+        stats['total_images'] += num_images
+        
+        if num_images == 0:
+            print(f"⚠  {class_name}: 0张图片，跳过")
+            continue
+        
+        # 检查当前比例是否正确
+        current_train = len(train_images)
+        current_val = len(val_images)
+        current_test = len(test_images)
+        
+        # 计算期望的数量
+        expected_train = int(num_images * train_ratio)
+        expected_val = int(num_images * val_ratio)
+        expected_test = num_images - expected_train - expected_val
+        
+        # 检查比例是否接近（允许1-2张的误差）
+        ratio_correct = (
+            abs(current_train - expected_train) <= 2 and
+            abs(current_val - expected_val) <= 2 and
+            abs(current_test - expected_test) <= 2
+        )
+        
+        # 如果总数超过100，即使比例正确也需要限制
+        if ratio_correct and num_images > 0 and num_images <= 100:
+            # 比例正确且总数<=100，跳过
+            stats['skipped_classes'] += 1
+            is_insufficient = num_images < min_samples_threshold
+            if is_insufficient:
+                stats['insufficient_samples'] += 1
+            else:
+                stats['sufficient_samples'] += 1
+            print(f"✓  {class_name}: 总数={num_images}, 比例正确 (train={current_train}, val={current_val}, test={current_test})，跳过")
+            continue
+        
+        # 如果比例正确但总数>100，需要限制
+        if ratio_correct and num_images > 100:
+            print(f"处理 {class_name}: 总数={num_images} > 100, 比例正确但需要限制到{max_total}张")
+            stats['processed_classes'] += 1
+            # 随机选择max_total张
+            random.shuffle(all_images)
+            image_files = all_images[:max_total]
+            num_images = len(image_files)
+            # 重新计算期望数量
+            expected_train = int(num_images * train_ratio)
+            expected_val = int(num_images * val_ratio)
+            expected_test = num_images - expected_train - expected_val
+            print(f"  将重新分配为: train={expected_train}, val={expected_val}, test={expected_test}")
+        elif not ratio_correct:
+            # 比例不正确，需要重新分配
+            stats['processed_classes'] += 1
+            print(f"处理 {class_name}: 总数={num_images}, 当前比例不正确 (train={current_train}, val={current_val}, test={current_test})")
+            
+            # 如果总数超过100，限制到max_total
+            if num_images > 100:
+                print(f"  总数={num_images} > 100, 限制到{max_total}张")
+                random.shuffle(all_images)
+                image_files = all_images[:max_total]
+                num_images = len(image_files)
+                # 重新计算期望数量
+                expected_train = int(num_images * train_ratio)
+                expected_val = int(num_images * val_ratio)
+                expected_test = num_images - expected_train - expected_val
+            else:
+                image_files = all_images
+            print(f"  将重新分配为: train={expected_train}, val={expected_val}, test={expected_test}")
+        
+        
+        # 检查样本数是否足够
+        is_insufficient = num_images < min_samples_threshold
+        
+        if is_insufficient:
+            stats['insufficient_samples'] += 1
+            if mark_insufficient:
+                # 标记样本不足的类别
+                marked_name = f"{class_name}_样本数太少"
+                stats['marked_classes'].append((class_name, num_images, marked_name))
+        else:
+            stats['sufficient_samples'] += 1
+        
+        # 确定最终的类别名称（如果需要标记）
+        final_class_name = f"{class_name}_样本数太少" if (is_insufficient and mark_insufficient) else class_name
+        
+        # 如果还没打乱（总数<=100的情况），现在打乱
+        if num_images == len(all_images):
+            random.shuffle(image_files)
+        
+        # 计算分割点
+        n_train = int(num_images * train_ratio)
+        n_val = int(num_images * val_ratio)
+        # 剩余的全部给test，确保所有图片都被分配
+        
+        # 分割图片（重新分配）
+        new_train_images = image_files[:n_train]
+        new_val_images = image_files[n_train:n_train + n_val]
+        new_test_images = image_files[n_train + n_val:]
+        
+        # 创建目标目录
+        train_target = train_dir / final_class_name
+        val_target = val_dir / final_class_name
+        test_target = test_dir / final_class_name
+        
+        train_target.mkdir(exist_ok=True)
+        val_target.mkdir(exist_ok=True)
+        test_target.mkdir(exist_ok=True)
+        
+        # 先移动图片到正确的位置（在删除之前）
+        moved_to_train = 0
+        for img in new_train_images:
+            if not img.exists():
+                continue
+            target = train_target / img.name
+            if img.parent != train_target:
+                if target.exists():
+                    target.unlink()
+                try:
+                    shutil.move(str(img), str(target))
+                    moved_to_train += 1
+                except Exception as e:
+                    print(f"    ⚠ 移动失败 {img}: {e}")
+        
+        moved_to_val = 0
+        for img in new_val_images:
+            if not img.exists():
+                continue
+            target = val_target / img.name
+            if img.parent != val_target:
+                if target.exists():
+                    target.unlink()
+                try:
+                    shutil.move(str(img), str(target))
+                    moved_to_val += 1
+                except Exception as e:
+                    print(f"    ⚠ 移动失败 {img}: {e}")
+        
+        moved_to_test = 0
+        for img in new_test_images:
+            if not img.exists():
+                continue
+            target = test_target / img.name
+            if img.parent != test_target:
+                if target.exists():
+                    target.unlink()
+                try:
+                    shutil.move(str(img), str(target))
+                    moved_to_test += 1
+                except Exception as e:
+                    print(f"    ⚠ 移动失败 {img}: {e}")
+        
+        # 然后清理所有目录（删除不在新分配列表中的图片）
+        # 收集所有原始图片路径
+        original_images = set(train_images + val_images + test_images)
+        new_images = set(new_train_images + new_val_images + new_test_images)
+        images_to_delete = original_images - new_images
+        
+        for img in images_to_delete:
+            if img.exists():
+                try:
+                    img.unlink()
+                except Exception as e:
+                    pass
+        
+        # 处理样本不足标记（如果需要）
+        if mark_insufficient and is_insufficient:
+            # 需要重命名train目录的文件夹
+            if class_folder != train_target:
+                # 如果原文件夹还存在且不为空，需要移动剩余文件
+                if class_folder.exists() and any(class_folder.iterdir()):
+                    for item in class_folder.iterdir():
+                        if item.is_file():
+                            shutil.move(str(item), train_target / item.name)
+                # 删除原文件夹（如果为空）
+                try:
+                    if class_folder.exists() and not any(class_folder.iterdir()):
+                        class_folder.rmdir()
+                except Exception as e:
+                    print(f"  警告: 删除原文件夹失败: {e}")
+        
+        # 更新统计
+        stats['train_images'] += len(new_train_images)
+        stats['val_images'] += len(new_val_images)
+        stats['test_images'] += len(new_test_images)
+        
+        # 打印进度
+        status = "⚠样本不足" if is_insufficient else "✓"
+        print(f"{status} {class_name}: 总计={num_images}, 训练={len(new_train_images)}, 验证={len(new_val_images)}, 测试={len(new_test_images)}")
+    
+    # 打印总结
+    print("\n" + "="*80)
+    print("分割完成!")
+    print("="*80)
+    print(f"总类别数: {stats['total_classes']}")
+    print(f"已处理: {stats['processed_classes']} 个类别（比例不正确，已重新分配）")
+    print(f"已跳过: {stats['skipped_classes']} 个类别（比例正确）")
+    print(f"样本充足: {stats['sufficient_samples']} 个类别")
+    print(f"样本不足: {stats['insufficient_samples']} 个类别")
+    print(f"\n总图片数: {stats['total_images']}")
+    print(f"训练集: {stats['train_images']} 张 ({stats['train_images']/stats['total_images']*100:.1f}%)")
+    print(f"验证集: {stats['val_images']} 张 ({stats['val_images']/stats['total_images']*100:.1f}%)")
+    print(f"测试集: {stats['test_images']} 张 ({stats['test_images']/stats['total_images']*100:.1f}%)")
+    
+    if stats['marked_classes']:
+        print(f"\n已标记的类别（样本数 < {min_samples_threshold}）:")
+        for old_name, count, new_name in stats['marked_classes']:
+            print(f"  {old_name} ({count}张) -> {new_name}")
+    
+    print("\n✓ 完成!")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='数据集分割工具')
+    parser.add_argument('--dataset', type=str, 
+                       default='dataset/WholeIngredientRecognition',
+                       help='数据集根目录（默认: dataset/WholeIngredientRecognition）')
+    parser.add_argument('--train-ratio', type=float, default=0.70,
+                       help='训练集比例（默认: 0.70）')
+    parser.add_argument('--val-ratio', type=float, default=0.15,
+                       help='验证集比例（默认: 0.15）')
+    parser.add_argument('--test-ratio', type=float, default=0.15,
+                       help='测试集比例（默认: 0.15）')
+    parser.add_argument('--min-samples', type=int, default=10,
+                       help='最小样本数阈值，低于此值会被标记（默认: 10）')
+    parser.add_argument('--no-mark', action='store_true',
+                       help='不标记样本不足的类别')
+    parser.add_argument('--seed', type=int, default=42,
+                       help='随机种子（默认: 42）')
+    parser.add_argument('--max-total', type=int, default=120,
+                       help='如果总数超过100，限制到max_total张（默认: 120）')
+    
+    args = parser.parse_args()
+    
+    # 获取项目根目录
+    script_dir = Path(__file__).parent
+    project_root = script_dir.parent
+    
+    # 处理相对路径
+    if not os.path.isabs(args.dataset):
+        dataset_path = project_root / args.dataset
+    else:
+        dataset_path = Path(args.dataset)
+    
+    split_dataset(
+        dataset_root=str(dataset_path),
+        train_ratio=args.train_ratio,
+        val_ratio=args.val_ratio,
+        test_ratio=args.test_ratio,
+        min_samples_threshold=args.min_samples,
+        mark_insufficient=not args.no_mark,
+        max_total=args.max_total,
+        seed=args.seed
+    )
diff --git a/faiss_vector_db/build_faiss_index.py b/faiss_vector_db/build_faiss_index.py
index adf6315..c9f6ca7 100644
--- a/faiss_vector_db/build_faiss_index.py
+++ b/faiss_vector_db/build_faiss_index.py
@@ -556,7 +556,14 @@ def main():
     # MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
     # MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251103_172012/best_embedding_model.pth"
     # MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20251113_160103/best_cosface_model.pth"
-    MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20251204_140816/model_s64.0_m0.4.pth"
+    #老索引
+    #MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20251204_140816/model_s64.0_m0.4.pth"
+    #新索引
+    #MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20260118_220318/best_cosface_model.pth"
+    #新索引
+    #MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model.pth"
+    #grid_search最佳模型 s=68, m=0.42 1.22
+    MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20260122_112545/best_model_s68.0_m0.42.pth"
     # MODEL_PATH = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
     # MODEL_PATH = "../model/DishClassification/cosface_20251111_153649/best_cosface_model.pth"
     # TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train"
diff --git a/toAndroid/toAndroidEmbedding.py b/toAndroid/toAndroidEmbedding.py
index 4bdafbf..43195f8 100644
--- a/toAndroid/toAndroidEmbedding.py
+++ b/toAndroid/toAndroidEmbedding.py
@@ -19,7 +19,10 @@ def main():
     base_model = create_mobile_resnet50_embedding(embedding_dim=512, pretrained=True)
     # model_path = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
     # model_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth"
-    model_path = "../model/WholeIngredientRecognition/grid_search_20251204_140816/model_s64.0_m0.4.pth"
+    #原本路径，原本模型
+    #model_path = "../model/WholeIngredientRecognition/grid_search_20251204_140816/model_s64.0_m0.4.pth"
+    #新路径，新模型 （模型不可用）
+    model_path = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model.pth"
     # model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth"
 
     if not os.path.exists(model_path):
@@ -94,12 +97,15 @@ def main():
         
         # 保存模型,一定不要乱改，防止模型覆盖!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
         # output_path = "../model/DishClassification/grid_search_20251121_102723/best_embedding_model_mobile.pt"
-        output_path = "../model/WholeIngredientRecognition/grid_search_20251204_140816/best_embedding_model_mobile.pt"
+        #原本路径
+        #output_path = "../model/WholeIngredientRecognition/grid_search_20251204_140816/best_embedding_model_mobile.pt"
+        #新路径
+        output_path = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model_mobile.pt"
         # output_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_embedding_model_mobile.pt"
         # output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt"
         traced_model.save(output_path)
         print(f"✓ TorchScript模型保存成功: {output_path}")
-        
+
         # 验证保存的模型
         loaded_model = torch.jit.load(output_path)
         with torch.no_grad():