diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 8eb9bc7812d..16bacbbe53d 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.71"
+__version__ = "8.2.72"
 
 import os
 
diff --git a/ultralytics/models/sam2/predict.py b/ultralytics/models/sam2/predict.py
index ca9438a2b7a..f40c0158213 100644
--- a/ultralytics/models/sam2/predict.py
+++ b/ultralytics/models/sam2/predict.py
@@ -102,28 +102,23 @@ def prompt_inference(
         if bboxes is not None:
             bboxes = torch.as_tensor(bboxes, dtype=torch.float32, device=self.device)
             bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
-            bboxes *= r
+            bboxes = bboxes.view(-1, 2, 2) * r
+            bbox_labels = torch.tensor([[2, 3]], dtype=torch.int32, device=bboxes.device).expand(len(bboxes), -1)
+            # NOTE: merge "boxes" and "points" into a single "points" input
+            # (where boxes are added at the beginning) to model.sam_prompt_encoder
+            if points is not None:
+                points = torch.cat([bboxes, points], dim=1)
+                labels = torch.cat([bbox_labels, labels], dim=1)
+            else:
+                points, labels = bboxes, bbox_labels
         if masks is not None:
             masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device).unsqueeze(1)
 
         points = (points, labels) if points is not None else None
-        # TODO: Embed prompts
-        # if bboxes is not None:
-        #     box_coords = bboxes.reshape(-1, 2, 2)
-        #     box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=bboxes.device)
-        #     box_labels = box_labels.repeat(bboxes.size(0), 1)
-        #     # we merge "boxes" and "points" into a single "concat_points" input (where
-        #     # boxes are added at the beginning) to sam_prompt_encoder
-        #     if concat_points is not None:
-        #         concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)
-        #         concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)
-        #         concat_points = (concat_coords, concat_labels)
-        #     else:
-        #         concat_points = (box_coords, box_labels)
 
         sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
             points=points,
-            boxes=bboxes,
+            boxes=None,
             masks=masks,
         )
         # Predict masks