diff --git a/net/food_net.py b/net/food_net.py
index 37fd284..d5ebb46 100644
--- a/net/food_net.py
+++ b/net/food_net.py
@@ -1,6 +1,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
 
 
 class FoodCNN(nn.Module):
@@ -10,6 +12,13 @@ class FoodCNN(nn.Module):
     """
     def __init__(self):
         super(FoodCNN, self).__init__()
+        # 图片预处理变换
+        self.preprocess = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+        ])
+        
         # 第一个卷积块
         self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
         self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
@@ -33,6 +42,28 @@ class FoodCNN(nn.Module):
         self.dropout4 = nn.Dropout(0.5)
         self.fc2 = nn.Linear(512, 3)  # 3分类
         
+    def preprocess_image(self, image):
+        """
+        预处理单张图片
+        
+        Args:
+            image: PIL Image 或 numpy array
+            
+        Returns:
+            torch.Tensor: 预处理后的张量，形状为 (1, 3, 32, 32)
+        """
+        if not isinstance(image, Image.Image):
+            # 如果是numpy array，转换为PIL Image
+            if hasattr(image, 'shape'):
+                image = Image.fromarray(image)
+            else:
+                raise ValueError("输入必须是PIL Image或numpy array")
+        
+        # 应用预处理变换
+        processed = self.preprocess(image)
+        # 添加batch维度
+        return processed.unsqueeze(0)
+    
     def forward(self, x):
         # 第一个卷积块
         x = F.relu(self.conv1(x))
@@ -81,4 +112,15 @@ if __name__ == "__main__":
     # 测试前向传播
     dummy_input = torch.randn(1, 3, 32, 32)
     output = model(dummy_input)
-    print(f"输出形状: {output.shape}")
\ No newline at end of file
+    print(f"输出形状: {output.shape}")
+    
+    # 测试图片预处理
+    try:
+        import numpy as np
+        # 创建一个测试图片 (RGB格式)
+        test_image = Image.fromarray(np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8))
+        processed = model.preprocess_image(test_image)
+        print(f"预处理后图片形状: {processed.shape}")
+        print(f"预处理后数值范围: [{processed.min():.3f}, {processed.max():.3f}]")
+    except Exception as e:
+        print(f"预处理测试失败: {e}")
\ No newline at end of file
diff --git a/settings/settings.py b/settings/settings.py
index c92e99a..ea56fa1 100644
--- a/settings/settings.py
+++ b/settings/settings.py
@@ -15,7 +15,7 @@ VAL_DATA_DIR = os.path.join(DATASET_DIR, 'val')
 TEST_DATA_DIR = os.path.join(DATASET_DIR, 'test')
 
 # 模型保存路径
-MODEL_DIR = os.path.join(BASE_DIR, 'model', '06')
+MODEL_DIR = os.path.join(BASE_DIR, 'model', '07')
 BEST_MODEL_PATH = os.path.join(MODEL_DIR, 'best_food_model.pth')
 TRAINING_CURVES_PATH = os.path.join(MODEL_DIR, 'training_curves.png')
 TRAINING_RESULTS_PATH = os.path.join(MODEL_DIR, 'training_results.txt')