diff --git a/VisageSnap/main.py b/VisageSnap/main.py
index fd7a379..f419cd6 100644
--- a/VisageSnap/main.py
+++ b/VisageSnap/main.py
@@ -97,30 +97,32 @@ def _load_labeled(self) -> None: # 미리 주어지는 데이터는 한 사진
                 print("Loading labeled data: {}".format(filename))
                 label = (filename.split(".")[0]).split("-")[0] # 파일 형식은 이름-번호.jpg
                 image = face_recognition.load_image_file(os.path.join(self.labeled_dir, filename))
-                encodings = face_recognition.face_encodings(image)[0]
+                encodings = face_recognition.face_encodings(image)
+                encoding = encodings[0]
 
                 # 만약 두개의 얼굴이 같은 사진에 있다면
-                if len(face_recognition.face_encodings(image)) > 1:
+                if len(encodings) > 1:
                     print("There are more than one face in the image: {}".format(filename))
                     continue
-                
-                # 같은 이름이 있는지 확인
-                face_found = False
-                for i, face in enumerate(self.gen_faces()):
-                    if face.label == label:
-                        print("The label is already in the list: {}".format(filename))
-                        # 동일한 인코딩이 있는지 확인
-                        if np.array_equal(face.encodings, encodings):
-                            print("The encoding is already in the list: {}".format(filename))
-                            continue
-                        self.faces[i].encodings = np.vstack((face.encodings, encodings))
-                        self.faces[i].filenames.append(filename)
-                        face_found = True
-                        break
-                
-                if not face_found:
-                    self.faces.append(Face(label, encodings, [filename]))
 
+                # 만약 같은 얼굴이 있다면
+                FACE_FOUND = False
+                for i, face in enumerate(self.faces): #얼굴 검색
+                    if face.label == label: # 같은 얼굴이라면
+                        for faceEncoding in face.encodings: 
+                            # 인코딩 같은게 있는지 확인
+                            if np.array_equal(faceEncoding, encoding):
+                                print("There is a same face in the image: {}".format(filename))
+                                continue
+
+                            # 인코딩이 다르다면 얼굴에 추가
+                            self.faces[i].encodings.append(encoding)
+                            self.faces[i].filenames.append(filename)
+                            FACE_FOUND = True
+
+                if not FACE_FOUND:
+                    self.faces.append(Face(label, [encoding], [filename]))
+                    
 
     def _load_unlabeled(self) -> None:
         """
@@ -153,6 +155,9 @@ def _load_model(self) -> LabelPropagation:
             return self.model
 
     def _save_model(self) -> None:
+        if not os.path.exists(os.path.join(os.getcwd(), "model")):
+            os.mkdir(os.path.join(os.getcwd(), "model"))
+            
         data = (self.model, self.faces)
         with open(self.model_dir, "wb") as f:
             pickle.dump(data, f)
diff --git a/VisageSnap/utils.py b/VisageSnap/utils.py
index 4a5ff7a..53a44a7 100644
--- a/VisageSnap/utils.py
+++ b/VisageSnap/utils.py
@@ -1,3 +1,5 @@
+import numpy as np
+
 def gen(target: list[any]) -> any:
     """
     This function is a generator.
@@ -6,6 +8,6 @@ def gen(target: list[any]) -> any:
     ----------
     target (list) : target list.
     """
-    assert isinstance(target, list), "target must be a list."
+    assert isinstance(target, list | np.ndarray), "target must be a list or numpy.ndarray."
     for i in target:
         yield i
\ No newline at end of file
diff --git a/tests/model/face_model.pkl b/tests/model/face_model.pkl
index 16adc34..a8b14cc 100644
Binary files a/tests/model/face_model.pkl and b/tests/model/face_model.pkl differ