diff --git a/datasets/vggface2.py b/datasets/vggface2.py
index a36d258c1..f86e3a4b0 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
@@ -99,8 +99,11 @@ 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 torch.cuda.is_available():
+            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 +114,17 @@ 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)
diff --git a/requirements.txt b/requirements.txt
index 95203ea3f..f553bacf0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,7 +6,7 @@ Pillow==10.3.0
 PyYAML==6.0.1
 albumentations==1.4.10
 faiss-cpu==1.8.0
-face-detection==0.2.2
+batch-face>=1.4.0
 h5py==3.11.0
 kornia==0.7.2
 librosa==0.10.2.post1
diff --git a/train.py b/train.py
index f44e1ba53..bd5e49139 100755
--- a/train.py
+++ b/train.py
@@ -1233,8 +1233,14 @@ def _validate(data_loader, model, criterion, loggers, args, epoch=-1, tflogger=N
                     _, class_preds_batch = torch.max(output, 1)
                     class_probs.append(class_probs_batch)
                     class_preds.append(class_preds_batch)
+                if args.kd_relationbased:
+                    stats = (
+                        '',
+                        OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean),
+                                     ('Overall Loss', losses[OVERALL_LOSS_KEY].mean)])
+                    )
 
-                if args.obj_detection:
+                elif args.obj_detection:
                     # Only run compute() if there is at least one new update()
                     if have_mAP:
                         mAP = map_calculator.compute()['map_50']
@@ -1343,12 +1349,8 @@ def update_training_scores_history(perf_scores_history, model, top1, top5, mAP,
     if args.kd_relationbased:
         # Keep perf_scores_history sorted from best to worst based on overall loss
         # overall_loss = student_loss*student_weight + distillation_loss*distillation_weight
-        if not args.sparsity_perf:
-            perf_scores_history.sort(key=operator.attrgetter('vloss', 'epoch'),
-                                     reverse=True)
-        else:
-            perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'vloss', 'epoch'),
-                                     reverse=True)
+        perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'vloss', 'epoch'),
+                                 reverse=True)
         for score in perf_scores_history[:args.num_best_scores]:
             msglogger.info('==> Best [Overall Loss: %f on epoch: %d]',
                            -score.vloss, score.epoch)