将分类的类别也应用在训练程序中，更好的解耦。

net/food_net.py

@@ -3,6 +3,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torchvision import transforms
 from PIL import Image
+from settings import settings
 
 
 class FoodCNN(nn.Module):
@@ -50,7 +51,7 @@ class FoodCNN(nn.Module):
         # 全连接层
         self.fc1 = nn.Linear(128 * 4 * 4, 512)
         self.dropout4 = nn.Dropout(0.5)
-        self.fc2 = nn.Linear(512, 3)  # 3分类
+        self.fc2 = nn.Linear(512, settings.NUM_CLASSES)
         
     def preprocess_image(self, image):
         """

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', '10')
+MODEL_DIR = os.path.join(BASE_DIR, 'model', '12')
 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')
@@ -47,7 +47,7 @@ COLOR_JITTER_SATURATION = 0.2
 COLOR_JITTER_HUE = 0.1
 
 # 模型参数
-NUM_CLASSES = 3
+NUM_CLASSES = 4
 
 # 其他配置
 NUM_WORKERS = 0  # Windows下建议设为0

toAndroid/toAndroid.py

@@ -8,7 +8,7 @@ def main():
     
     # 1. 加载训练好的基础模型权重
     base_model = create_food_cnn()
-    model_path = "../model/09/best_food_model.pth"
+    model_path = "../model/10/best_food_model.pth"
     
     if not os.path.exists(model_path):
         print(f"错误：模型文件不存在 {model_path}")
@@ -37,7 +37,7 @@ def main():
         traced_model = torch.jit.trace(mobile_model, example_input)
         
         # 保存模型
-        output_path = "../model/09/best_food_model_mobile.pt"
+        output_path = "../model/10/best_food_model_mobile.pt"
         traced_model.save(output_path)
         print(f"✓ TorchScript模型保存成功: {output_path}")
         

train/train_food_classifier.py

@@ -105,8 +105,8 @@ def test(model, test_loader, device, class_names):
     model.eval()
     correct = 0
     total = 0
-    class_correct = list(0. for i in range(3))
-    class_total = list(0. for i in range(3))
+    class_correct = list(0. for i in range(settings.NUM_CLASSES))
+    class_total = list(0. for i in range(settings.NUM_CLASSES))
     
     with torch.no_grad():
         test_bar = tqdm(test_loader, desc='测试中')
@@ -305,8 +305,8 @@ if __name__ == '__main__':
         f.write("各类别测试准确率:\n")
         f.write("-" * 30 + "\n")
         model.eval()
-        class_correct = list(0. for i in range(3))
-        class_total = list(0. for i in range(3))
+        class_correct = list(0. for i in range(settings.NUM_CLASSES))
+        class_total = list(0. for i in range(settings.NUM_CLASSES))
         
         with torch.no_grad():
             for data, target in test_loader: