diff --git a/toAndroid/toAndroidEmbedding.py b/toAndroid/toAndroidEmbedding.py
index f509169..bd10210 100644
--- a/toAndroid/toAndroidEmbedding.py
+++ b/toAndroid/toAndroidEmbedding.py
@@ -17,8 +17,8 @@ def main():
     # 1. 加载训练好的embedding模型权重
     # base_model = create_resnet50_embedding(embedding_dim=512, pretrained=True)
     base_model = create_mobile_resnet50_embedding(embedding_dim=512, pretrained=True)
-    # model_path = "../model/embedding_20250930_102826/best_embedding_model.pth"
-    model_path = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model.pth"
+    model_path = "../model/DishClassification/cosface_20251105_200551/best_cosface_model.pth"
+    # model_path = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model.pth"
     # model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth"
 
     if not os.path.exists(model_path):
@@ -30,7 +30,12 @@ def main():
     if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:
         # 如果保存的是完整的checkpoint
         base_model.load_state_dict(checkpoint['model_state_dict'])
-        print("✓ 从checkpoint加载模型权重成功")
+        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）
+        base_model.load_state_dict(checkpoint['backbone_state_dict'])
+        print("✓ 检测到CosFace模型格式，使用'backbone_state_dict'加载")
+   
     else:
         # 如果保存的是纯模型权重
         base_model.load_state_dict(checkpoint)
@@ -87,7 +92,8 @@ def main():
         traced_model = torch.jit.trace(mobile_wrapper, single_input)
         
         # 保存模型
-        output_path = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model_mobile.pt"
+        output_path = "../model/DishClassification/cosface_20251105_200551/best_embedding_model_mobile.pt"
+        # output_path = "../model/WholeIngredientRecognition/embedding_20251030_135024/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}")