diff --git a/datasets/vggface2.py b/datasets/vggface2.py
index a36d258c1..8d84d6def 100644
--- a/datasets/vggface2.py
+++ b/datasets/vggface2.py
@@ -23,8 +23,8 @@
 from torchvision import transforms
 
 import cv2
-import face_detection
 import kornia.geometry.transform as GT
+from batch_face import RetinaFace
 from PIL import Image
 from skimage import transform as trans
 from tqdm import tqdm
@@ -38,7 +38,7 @@ class VGGFace2(Dataset):
     VGGFace2 Dataset
     """
     def __init__(self, root_dir, d_type, mode, transform=None,
-                 teacher_transform=None, img_size=(112, 112)):
+                 teacher_transform=None, img_size=(112, 112), args=None):
 
         if d_type not in ('test', 'train'):
             raise ValueError("d_type can only be set to 'test' or 'train'")
@@ -47,6 +47,7 @@ def __init__(self, root_dir, d_type, mode, transform=None,
             raise ValueError("mode can only be set to 'detection', 'identification',"
                              "or 'identification_dr'")
 
+        self.device = args.device
         self.root_dir = root_dir
         self.d_type = d_type
         self.transform = transform
@@ -99,8 +100,11 @@ def __extract_gt(self):
         """
         Extracts the ground truth from the dataset
         """
-        detector = face_detection.build_detector("RetinaNetResNet50", confidence_threshold=.5,
-                                                 nms_iou_threshold=.4)
+        if self.device == 'cuda':
+            detector = RetinaFace(gpu_id=torch.cuda.current_device(), network="resnet50")
+        else:
+            detector = RetinaFace(gpu_id=-1, network="resnet50")
+
         img_paths = list(glob.glob(os.path.join(self.d_path + '/**/', '*.jpg'), recursive=True))
         nf_number = 0
         words_count = 0
@@ -111,22 +115,17 @@ def __extract_gt(self):
             boxes = []
             image = cv2.imread(jpg)
 
-            img_max = max(image.shape[0], image.shape[1])
-            if img_max > 1320:
-                continue
-            bboxes, lndmrks = detector.batched_detect_with_landmarks(np.expand_dims(image, 0))
-            bboxes = bboxes[0]
-            lndmrks = lndmrks[0]
+            faces = detector(image)
 
-            if (bboxes.shape[0] == 0) or (lndmrks.shape[0] == 0):
+            if len(faces) == 0:
                 nf_number += 1
                 continue
 
-            for box in bboxes:
+            for face in faces:
+                box = face[0]
                 box = np.clip(box[:4], 0, None)
                 boxes.append(box)
-
-            lndmrks = lndmrks[0]
+            lndmrks = faces[0][1]
 
             dir_name = os.path.dirname(jpg)
             lbl = os.path.relpath(dir_name, self.d_path)
@@ -343,7 +342,7 @@ def VGGFace2_FaceID_get_datasets(data, load_train=True, load_test=True, img_size
 
         train_dataset = VGGFace2(root_dir=data_dir, d_type='train', mode='identification',
                                  transform=train_transform, teacher_transform=teacher_transform,
-                                 img_size=img_size)
+                                 img_size=img_size, args=args)
 
         print(f'Train dataset length: {len(train_dataset)}\n')
     else:
@@ -355,7 +354,7 @@ def VGGFace2_FaceID_get_datasets(data, load_train=True, load_test=True, img_size
 
         test_dataset = VGGFace2(root_dir=data_dir, d_type='test', mode='identification',
                                 transform=test_transform, teacher_transform=teacher_transform,
-                                img_size=img_size)
+                                img_size=img_size, args=args)
 
         print(f'Test dataset length: {len(test_dataset)}\n')
     else:
@@ -378,7 +377,7 @@ def VGGFace2_FaceID_dr_get_datasets(data, load_train=True, load_test=True, img_s
     if load_train:
 
         train_dataset = VGGFace2(root_dir=data_dir, d_type='train', mode='identification_dr',
-                                 transform=train_transform, img_size=img_size)
+                                 transform=train_transform, img_size=img_size, args=args)
 
         print(f'Train dataset length: {len(train_dataset)}\n')
     else:
@@ -389,7 +388,7 @@ def VGGFace2_FaceID_dr_get_datasets(data, load_train=True, load_test=True, img_s
                                             ai8x.normalize(args=args)])
 
         test_dataset = VGGFace2(root_dir=data_dir, d_type='test', mode='identification_dr',
-                                transform=test_transform, img_size=img_size)
+                                transform=test_transform, img_size=img_size, args=args)
 
         print(f'Test dataset length: {len(test_dataset)}\n')
     else:
@@ -409,7 +408,7 @@ def VGGFace2_Facedet_get_datasets(data, load_train=True, load_test=True, img_siz
             ai8x.normalize(args=args)])
 
         train_dataset = VGGFace2(root_dir=data_dir, d_type='train', mode='detection',
-                                 transform=train_transform, img_size=img_size)
+                                 transform=train_transform, img_size=img_size, args=args)
 
         print(f'Train dataset length: {len(train_dataset)}\n')
     else:
@@ -419,7 +418,7 @@ def VGGFace2_Facedet_get_datasets(data, load_train=True, load_test=True, img_siz
         test_transform = transforms.Compose([ai8x.normalize(args=args)])
 
         test_dataset = VGGFace2(root_dir=data_dir, d_type='test', mode='detection',
-                                transform=test_transform, img_size=img_size)
+                                transform=test_transform, img_size=img_size, args=args)
 
         print(f'Test dataset length: {len(test_dataset)}\n')
     else:
diff --git a/requirements.txt b/requirements.txt
index dbf6502ac..a1662a779 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,7 +6,7 @@ Pillow>=7
 PyYAML>=5.1.1
 albumentations>=1.3.0
 faiss-cpu==1.7.4
-face-detection==0.2.2
+batch-face>=1.4.0
 h5py>=3.7.0
 kornia==0.6.8
 librosa>=0.7.2