diff --git a/.gitignore b/.gitignore
index 4b36b2533d64e6e2c0a197ee37eedcd1c81127f5..e0562287bd0e251f039f98173d35d6ad1db21e99 100644
--- a/.gitignore
+++ b/.gitignore
@@ -108,4 +108,5 @@ temp/
 *.dat
 nohup.out
 *.webp
+*.webm
 .DS_Store
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 0ec4dc7a6930c94627c97c8bfd284a45868a5a48..5cecf2f230dcc3d5557384d405cca443efc3dec3 100644
--- a/Makefile
+++ b/Makefile
@@ -7,4 +7,4 @@ test:
 	PYTHONPATH=. pytest tests/test_file_utils.py
 	PYTHONPATH=. pytest tests/test_facenet.py
 	PYTHONPATH=. pytest tests/test_anti_spoofing_two_stream_vit.py
-	#PYTHONPATH=. pytest tests/test_deepfake_detection_sifdnet.py
+	PYTHONPATH=. pytest tests/test_deepfake_detection_sifdnet.py
diff --git a/app/recognition/anti_spoofing_two_stream_vit.py b/app/recognition/anti_spoofing_two_stream_vit.py
index de812b568261fb84f6f0d2187554b64040bfcf4c..32ba58a5fb298fdd51b638f7ca2ae8b64b477b2f 100644
--- a/app/recognition/anti_spoofing_two_stream_vit.py
+++ b/app/recognition/anti_spoofing_two_stream_vit.py
@@ -20,9 +20,8 @@ class AntiSpoofingTwoStreamVit(FaceModel):
     def __init__(self):
         super().__init__()
         self.model = vit_base_resnet50_224_in21k(pretrained=False, in_chans=3, num_classes=2)
+        self.model_loaded = False
         self.transform = create_anti_spoofing_test_transform(model_cfg=self.model.default_cfg)
-        if config.get_config('ANTI_SPOOFING') == 'true':
-            self.create_model()
 
     def init_app(self, app):
         pass
@@ -39,11 +38,14 @@ class AntiSpoofingTwoStreamVit(FaceModel):
             if gpu_memory >= 4 * 1024 * 1024 * 1024:
                 self.model.cuda()
         self.model.eval()
+        self.model_loaded = True
 
     def get_embeddings(self, x: numpy.ndarray) -> numpy.ndarray:
         pass
 
     def get_prediction(self, x: numpy.ndarray, **kwargs) -> numpy.ndarray:
+        if not self.model_loaded and config.get_config('ANTI_SPOOFING') == 'true':
+            self.create_model()
         msr = kwargs.pop('msr')
         transformed = self.transform(image=x, mask=msr)
         x = transformed["image"]
diff --git a/app/recognition/deepfake_detection_sifdnet.py b/app/recognition/deepfake_detection_sifdnet.py
index a038eab854c3fdcf61a8f81e1591ccd5db84e3c4..bf021755d74b12fec36e1742d5728a1dcc233789 100644
--- a/app/recognition/deepfake_detection_sifdnet.py
+++ b/app/recognition/deepfake_detection_sifdnet.py
@@ -20,9 +20,8 @@ class DeepfakeDetectionSIFDNet(FaceModel):
     def __init__(self):
         super().__init__()
         self.model = sifdnet()
+        self.model_loaded = False
         self.transform = create_deepfake_detection_test_transform(model_cfg=self.model.default_cfg)
-        if config.get_config('DEEPFAKE_DETECTION') == 'true':
-            self.create_model()
 
     def init_app(self, app):
         pass
@@ -38,6 +37,7 @@ class DeepfakeDetectionSIFDNet(FaceModel):
             if gpu_memory >= 1 * 1024 * 1024 * 1024:
                 self.model.cuda()
         self.model.eval()
+        self.model_loaded = True
 
     def get_embeddings(self, x: numpy.ndarray) -> numpy.ndarray:
         pass
@@ -46,6 +46,8 @@ class DeepfakeDetectionSIFDNet(FaceModel):
         pass
 
     def get_predictions(self, x: numpy.ndarray, **kwargs) -> (float, numpy.ndarray):
+        if not self.model_loaded and config.get_config('DEEPFAKE_DETECTION') == 'true':
+            self.create_model()
         transformed = self.transform(image=x)
         x = transformed["image"]
         x = x.unsqueeze(0)
diff --git a/data/video/video_tracked.webm b/data/video/video_tracked.webm
deleted file mode 100644
index 578fcdd0316a0f465768a00f86a55729a1384fa7..0000000000000000000000000000000000000000
Binary files a/data/video/video_tracked.webm and /dev/null differ
diff --git a/tests/test_deepfake_detection_sifdnet.py b/tests/test_deepfake_detection_sifdnet.py
index a5a452498b6b2747c60519eb9a578cc45d2ab179..0fe90153aa6c212ccaf6516617b9da1db93177d9 100644
--- a/tests/test_deepfake_detection_sifdnet.py
+++ b/tests/test_deepfake_detection_sifdnet.py
@@ -1,3 +1,5 @@
+import collections
+import itertools
 import unittest
 
 import cv2
@@ -17,11 +19,13 @@ class DeepfakeDetectionSIFDNetTestCase(unittest.TestCase):
         deepfake_detection_sifdnet = DeepfakeDetectionSIFDNet.instance()
         deepfake_detection_sifdnet.create_model()
 
-        frames, _ = file_utils.get_video_frames_and_fps(video_path)
+        frames, fps = file_utils.get_video_frames_and_fps(video_path)
         detector = FacenetDetector(landmarks=False)
         mask_frames = []
         fake_probs = []
-        for index, frame in frames.items():
+        sliced = itertools.islice(frames.items(), int(fps))
+        # sliced_o = collections.OrderedDict(sliced)
+        for index, frame in sliced:
             # h, w = frame.shape[:2]
             # if index > 50:
             #     break