diff --git a/tests/test_cli.py b/tests/test_cli.py
index 9582dde8f1..3eadf3c24e 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -101,7 +101,7 @@ def test_mobilesam():
     model.predict(source, points=[900, 370], labels=[1])
 
     # Predict a segment based on a box prompt
-    model.predict(source, bboxes=[439, 437, 524, 709])
+    model.predict(source, bboxes=[439, 437, 524, 709], save=True)
 
     # Predict all
     # model(source)
diff --git a/ultralytics/models/sam/predict.py b/ultralytics/models/sam/predict.py
index 8ecb069eeb..686ef70c63 100644
--- a/ultralytics/models/sam/predict.py
+++ b/ultralytics/models/sam/predict.py
@@ -450,16 +450,18 @@ class Predictor(BasePredictor):
 
         results = []
         for masks, orig_img, img_path in zip([pred_masks], orig_imgs, self.batch[0]):
-            if pred_bboxes is not None:
-                pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
-                cls = torch.arange(len(pred_masks), dtype=torch.int32, device=pred_masks.device)
-                pred_bboxes = torch.cat([pred_bboxes, pred_scores[:, None], cls[:, None]], dim=-1)
-
             if len(masks) == 0:
                 masks = None
             else:
                 masks = ops.scale_masks(masks[None].float(), orig_img.shape[:2], padding=False)[0]
                 masks = masks > self.model.mask_threshold  # to bool
+                if pred_bboxes is not None:
+                    pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
+                else:
+                    pred_bboxes = batched_mask_to_box(masks)
+                # NOTE: SAM models do not return cls info. This `cls` here is just a placeholder for consistency.
+                cls = torch.arange(len(pred_masks), dtype=torch.int32, device=pred_masks.device)
+                pred_bboxes = torch.cat([pred_bboxes, pred_scores[:, None], cls[:, None]], dim=-1)
             results.append(Results(orig_img, path=img_path, names=names, masks=masks, boxes=pred_bboxes))
         # Reset segment-all mode.
         self.segment_all = False