diff --git a/ultralytics/models/yolo/detect/val.py b/ultralytics/models/yolo/detect/val.py
index f4109f575..d37aa2b8d 100644
--- a/ultralytics/models/yolo/detect/val.py
+++ b/ultralytics/models/yolo/detect/val.py
@@ -26,6 +26,7 @@ class DetectionValidator(BaseValidator):
         self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
         self.iouv = torch.linspace(0.5, 0.95, 10)  # iou vector for mAP@0.5:0.95
         self.niou = self.iouv.numel()
+        self.lb = []  # for autolabelling
 
     def preprocess(self, batch):
         """Preprocesses batch of images for YOLO training."""
@@ -34,9 +35,13 @@ class DetectionValidator(BaseValidator):
         for k in ['batch_idx', 'cls', 'bboxes']:
             batch[k] = batch[k].to(self.device)
 
-        nb = len(batch['img'])
-        self.lb = [torch.cat([batch['cls'], batch['bboxes']], dim=-1)[batch['batch_idx'] == i]
-                   for i in range(nb)] if self.args.save_hybrid else []  # for autolabelling
+        if self.args.save_hybrid:
+            height, width = batch['img'].shape[2:]
+            nb = len(batch['img'])
+            bboxes = batch['bboxes'] * torch.tensor((width, height, width, height), device=self.device)
+            self.lb = [
+                torch.cat([batch['cls'][batch['batch_idx'] == i], bboxes[batch['batch_idx'] == i]], dim=-1)
+                for i in range(nb)] if self.args.save_hybrid else []  # for autolabelling
 
         return batch
 
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index b3e75ad14..5f65cdf7f 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -225,8 +225,8 @@ def non_max_suppression(
         # Cat apriori labels if autolabelling
         if labels and len(labels[xi]):
             lb = labels[xi]
-            v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
-            v[:, :4] = lb[:, 1:5]  # box
+            v = torch.zeros((len(lb), nc + nm + 4), device=x.device)
+            v[:, :4] = xywh2xyxy(lb[:, 1:5])  # box
             v[range(len(lb)), lb[:, 0].long() + 4] = 1.0  # cls
             x = torch.cat((x, v), 0)