diff --git a/classifier/embedding_food_classifier_app.py b/classifier/embedding_food_classifier_app.py
index 6c5538b..5d7e3d3 100644
--- a/classifier/embedding_food_classifier_app.py
+++ b/classifier/embedding_food_classifier_app.py
@@ -65,16 +65,16 @@ class EmbeddingFoodClassifierApp:
             BASE_DIR = os.path.dirname(os.path.abspath(__file__))
             # model_path = "../model/embedding_20251011_133653/best_embedding_model.pth"
             # model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth")
-            model_path = os.path.join(BASE_DIR, "../model/ProcessedIngredientRecognition/embedding_20251029_170904/best_embedding_model.pth")
+            # model_path = os.path.join(BASE_DIR, "../model/ProcessedIngredientRecognition/embedding_20251029_170904/best_embedding_model.pth")
             # model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/embedding_20251024_091151/best_embedding_model.pth")
-            # model_path = os.path.join(BASE_DIR, "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth")
+            model_path = os.path.join(BASE_DIR, "../model/DishClassification/cosface_20251105_200551/best_cosface_model.pth")
 
             # FAISS索引目录
             # index_dir = "../faiss_vector_db/faiss_index"
             # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index")
-            index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/ProcessedIngredientRecognition/faiss_index")
+            # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/ProcessedIngredientRecognition/faiss_index")
             # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index")
-            # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index")
+            index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index")
 
             if os.path.exists(model_path) and os.path.exists(index_dir):
                 # 1. 加载embedding模型
@@ -91,6 +91,10 @@ class EmbeddingFoodClassifierApp:
                 if isinstance(checkpoint, dict):
                     if 'model_state_dict' in checkpoint:
                         self.model.load_state_dict(checkpoint['model_state_dict'])
+                    elif 'backbone_state_dict' in checkpoint:
+                        # CosFace格式：使用backbone_state_dict（只加载backbone部分）
+                        self.model.load_state_dict(checkpoint['backbone_state_dict'])
+                        print("检测到CosFace模型格式，使用'backbone_state_dict'加载")
                     elif 'state_dict' in checkpoint:
                         self.model.load_state_dict(checkpoint['state_dict'])
                     else:
diff --git a/faiss_vector_db/build_faiss_index.py b/faiss_vector_db/build_faiss_index.py
index c105ca0..4b73f67 100644
--- a/faiss_vector_db/build_faiss_index.py
+++ b/faiss_vector_db/build_faiss_index.py
@@ -75,13 +75,25 @@ class FAISSIndexBuilder:
             # 处理不同的保存格式
             if isinstance(checkpoint, dict):
                 if 'model_state_dict' in checkpoint:
+                    # Triplet格式：使用model_state_dict
                     self.model.load_state_dict(checkpoint['model_state_dict'])
+                    print("检测到Triplet模型格式，使用'model_state_dict'加载")
+                elif 'backbone_state_dict' in checkpoint:
+                    # CosFace格式：使用backbone_state_dict（只加载backbone部分）
+                    self.model.load_state_dict(checkpoint['backbone_state_dict'])
+                    print("检测到CosFace模型格式，使用'backbone_state_dict'加载")
                 elif 'state_dict' in checkpoint:
+                    # 通用格式：使用state_dict
                     self.model.load_state_dict(checkpoint['state_dict'])
+                    print("检测到通用模型格式，使用'state_dict'加载")
                 else:
+                    # 直接加载整个checkpoint作为state_dict
                     self.model.load_state_dict(checkpoint)
+                    print("使用checkpoint直接加载")
             else:
+                # checkpoint本身就是state_dict
                 self.model.load_state_dict(checkpoint)
+                print("checkpoint为state_dict格式，直接加载")
             
             # 移动到设备并设置为评估模式
             self.model.to(self.device)
@@ -408,6 +420,10 @@ class FAISSSearcher:
         if isinstance(checkpoint, dict):
             if 'model_state_dict' in checkpoint:
                 self.model.load_state_dict(checkpoint['model_state_dict'])
+            elif 'backbone_state_dict' in checkpoint:
+                # CosFace格式：使用backbone_state_dict（只加载backbone部分）
+                self.model.load_state_dict(checkpoint['backbone_state_dict'])
+                print("检测到CosFace模型格式，使用'backbone_state_dict'加载")
             elif 'state_dict' in checkpoint:
                 self.model.load_state_dict(checkpoint['state_dict'])
             else:
@@ -480,16 +496,17 @@ def main():
     """主函数"""
     # 配置参数
     # MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
-    MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251103_172012/best_embedding_model.pth"
+    # MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251103_172012/best_embedding_model.pth"
     # MODEL_PATH = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model.pth"
-    # MODEL_PATH = "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth"
-    TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train"
+    # MODEL_PATH = "../model/DishClassification/cosface_20251105_200551/best_embedding_model.pth"
+    MODEL_PATH = "../model/DishClassification/cosface_20251105_200551/best_cosface_model.pth"
+    # TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train"
     # TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
-    # TRAIN_DIR = "../dataset/DishClassification/train"
+    TRAIN_DIR = "../dataset/DishClassification/train"
 
-    OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index"
+    # OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index"
     # OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
-    # OUTPUT_DIR = "DishClassification/faiss_index"
+    OUTPUT_DIR = "DishClassification/faiss_index"
     BATCH_SIZE = 16
     INDEX_TYPE = 'flat'  # 'flat', 'ivf', 'hnsw'
     EMBEDDING_DIM = 512
diff --git a/faiss_vector_db/visualize_embeddings.py b/faiss_vector_db/visualize_embeddings.py
index cab4355..aacfecc 100644
--- a/faiss_vector_db/visualize_embeddings.py
+++ b/faiss_vector_db/visualize_embeddings.py
@@ -162,12 +162,12 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] =
 
 def main():
     parser = argparse.ArgumentParser(description="可视化高维 embedding 并进行坍塌诊断")
-    # parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径")
+    parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径")
     # parser.add_argument("--embeddings", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
-    parser.add_argument("--embeddings", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
-    # parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径")
+    # parser.add_argument("--embeddings", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
+    parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径")
     # parser.add_argument("--labels", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
-    parser.add_argument("--labels", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
+    # parser.add_argument("--labels", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
     parser.add_argument("--method", type=str, default="pca", choices=["pca", "tsne", "umap"], help="降维方法")
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--max_points", type=int, default=None, help="抽样上限，避免t-SNE/UMAP过慢；None为全量")