diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index 6723185..ae03407 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -35,7 +35,7 @@ def build_model(model_name):
 		built deepface model
 	"""
 
-	global model_obj, model_label
+	global model_obj
 
 	models = {
 		'VGG-Face': VGGFace.loadModel,
@@ -48,22 +48,22 @@ def build_model(model_name):
 		'Emotion': Emotion.loadModel,
 		'Age': Age.loadModel,
 		'Gender': Gender.loadModel,
-		'Race': Race.loadModel,
-		'Ensemble': Boosting.loadModel
+		'Race': Race.loadModel
 	}
 
-	if not "model_obj" in globals() or model_label != model_name:
+	if not "model_obj" in globals():
+		model_obj = {}
 
-		model_obj = models.get(model_name)
-
-		if model_obj:
-			model_obj = model_obj()
-			model_label = model_name
-			#print('Using {} model backend'.format(model_name))
+	if not model_name in model_obj.keys():
+		model = models.get(model_name)
+		if model:
+			model = model()
+			model_obj[model_name] = model
+			#print(model_name," built")
 		else:
 			raise ValueError('Invalid model_name passed - {}'.format(model_name))
 
-	return model_obj
+	return model_obj[model_name]
 
 def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True):
 
@@ -123,7 +123,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 
 	if model == None:
 		if model_name == 'Ensemble':
-			models = build_model(model_name)
+			models = Boosting.loadModel()
 		else:
 			model = build_model(model_name)
 			models = {}
@@ -502,7 +502,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 			if model_name == 'Ensemble':
 				print("Ensemble learning enabled")
-				models = build_model(model_name)
+				models = Boosting.loadModel()
 
 			else: #model is not ensemble
 				model = build_model(model_name)
diff --git a/deepface/detectors/FaceDetector.py b/deepface/detectors/FaceDetector.py
index 7c43b5a..dcff5dd 100644
--- a/deepface/detectors/FaceDetector.py
+++ b/deepface/detectors/FaceDetector.py
@@ -6,7 +6,7 @@ from deepface.commons import distance
 
 def build_model(detector_backend):
 
-    global face_detector_obj, face_detector_label
+    global face_detector_obj
 
     backends = {
         'opencv': OpenCvWrapper.build_model,
@@ -16,16 +16,20 @@ def build_model(detector_backend):
         'retinaface': RetinaFaceWrapper.build_model
     }
 
-    if not "face_detector_obj" in globals() or face_detector_label != detector_backend:
-        face_detector_obj = backends.get(detector_backend)
-        face_detector_label = detector_backend
+    if not "face_detector_obj" in globals():
+        face_detector_obj = {}
 
-        if face_detector_obj:
-            face_detector_obj = face_detector_obj()
+    if not detector_backend in face_detector_obj.keys():
+        face_detector = backends.get(detector_backend)
+
+        if face_detector:
+            face_detector = face_detector()
+            face_detector_obj[detector_backend] = face_detector
+            #print(detector_backend," built")
         else:
             raise ValueError("invalid detector_backend passed - " + detector_backend)
 
-    return face_detector_obj
+    return face_detector_obj[detector_backend]
 
 def detect_face(face_detector, detector_backend, img, align = True):
 
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index bd6c699..85262f4 100644
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -168,7 +168,7 @@ dataset = [
 	['dataset/img1.jpg', 'dataset/img2.jpg', True],
 	['dataset/img5.jpg', 'dataset/img6.jpg', True],
 	['dataset/img6.jpg', 'dataset/img7.jpg', True],
-	['dataset/img8.jpg', 'dataset/img9.jpg', True],
+	#['dataset/img8.jpg', 'dataset/img9.jpg', True],
 
 	['dataset/img1.jpg', 'dataset/img11.jpg', True],
 	['dataset/img2.jpg', 'dataset/img11.jpg', True],
@@ -238,10 +238,10 @@ else:
 
 print("Analyze function with passing pre-trained model")
 
-emotion_model = Emotion.loadModel()
-age_model = Age.loadModel()
-gender_model = Gender.loadModel()
-race_model = Race.loadModel()
+emotion_model = DeepFace.build_model("Emotion")
+age_model = DeepFace.build_model("Age")
+gender_model = DeepFace.build_model("Gender")
+race_model = DeepFace.build_model("Race")
 
 facial_attribute_models = {}
 facial_attribute_models["emotion"] = emotion_model
@@ -277,9 +277,9 @@ print("--------------------------")
 print("Pre-trained ensemble method - find")
 
 from deepface import DeepFace
-from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+from deepface.basemodels import Boosting
 
-model = DeepFace.build_model("Ensemble")
+model = Boosting.loadModel()
 df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False)
 
 print(df)