diff --git a/face_alignment/api.py b/face_alignment/api.py
index 2cadf683..2e138c93 100644
--- a/face_alignment/api.py
+++ b/face_alignment/api.py
@@ -247,8 +247,8 @@ def get_landmarks_from_batch(self, image_batch, detected_faces=None):
                 else:
                     pts, pts_img = pts.view(-1, 68, 2) * 4, pts_img.view(-1, 68, 2)
                 landmark_set.append(pts_img.numpy())
-
-            landmark_set = np.concatenate(landmark_set, axis=0)
+            if 0 != len(landmark_set):
+                landmark_set = np.concatenate(landmark_set, axis=0)
             landmarks.append(landmark_set)
         return landmarks
 
diff --git a/face_alignment/detection/blazeface/detect.py b/face_alignment/detection/blazeface/detect.py
index 6258a92c..b1b2dfe1 100644
--- a/face_alignment/detection/blazeface/detect.py
+++ b/face_alignment/detection/blazeface/detect.py
@@ -14,7 +14,7 @@ def detect(net, img, device):
     preds = net.predict_on_image(img)
 
     if 0 == len(preds):
-        return np.zeros((1, 1, 5))
+        return [[]]
 
     shift = np.array([xshift, yshift] * 2)
     scores = preds[:, -1:]
@@ -48,9 +48,6 @@ def batch_detect(net, img_batch, device):
         locs = np.concatenate((pred[:, 1:2], pred[:, 0:1], pred[:, 3:4], pred[:, 2:3]), axis=1)
         bboxlists.append(np.concatenate((locs * orig_size + shift, scores), axis=1))
 
-    if 0 == len(bboxlists):
-        bboxlists = np.zeros((1, 1, 5))
-
     return bboxlists
 
 
diff --git a/face_alignment/detection/sfd/detect.py b/face_alignment/detection/sfd/detect.py
index 35263e4d..1b75e8f1 100644
--- a/face_alignment/detection/sfd/detect.py
+++ b/face_alignment/detection/sfd/detect.py
@@ -22,9 +22,6 @@ def detect(net, img, device):
     # Creates a batch of 1
     img = img.reshape((1,) + img.shape)
 
-    if 'cuda' in device:
-        torch.backends.cudnn.benchmark = True
-
     img = torch.from_numpy(img).float().to(device)
 
     return batch_detect(net, img, device)
@@ -72,10 +69,6 @@ def batch_detect(net, img_batch, device):
         bboxlists.append(bboxlist)
 
     bboxlists = np.array(bboxlists)
-
-    if 0 == len(bboxlists):
-        bboxlists = np.zeros((1, 1, 5))
-
     return bboxlists