diff --git a/src/transformers/models/oneformer/image_processing_oneformer.py b/src/transformers/models/oneformer/image_processing_oneformer.py
index d9b0c0168682ab..9f865f8efd9b94 100644
--- a/src/transformers/models/oneformer/image_processing_oneformer.py
+++ b/src/transformers/models/oneformer/image_processing_oneformer.py
@@ -1244,8 +1244,8 @@ def post_process_instance_segmentation(
             # if this is panoptic segmentation, we only keep the "thing" classes
             if task_type == "panoptic":
                 keep = torch.zeros_like(scores_per_image).bool()
-                for i, lab in enumerate(labels_per_image):
-                    keep[i] = lab in self.metadata["thing_ids"]
+                for j, lab in enumerate(labels_per_image):
+                    keep[j] = lab in self.metadata["thing_ids"]
 
                 scores_per_image = scores_per_image[keep]
                 labels_per_image = labels_per_image[keep]
@@ -1258,8 +1258,8 @@ def post_process_instance_segmentation(
                 continue
 
             if "ade20k" in self.class_info_file and not is_demo and "instance" in task_type:
-                for i in range(labels_per_image.shape[0]):
-                    labels_per_image[i] = self.metadata["thing_ids"].index(labels_per_image[i].item())
+                for j in range(labels_per_image.shape[0]):
+                    labels_per_image[j] = self.metadata["thing_ids"].index(labels_per_image[j].item())
 
             # Get segmentation map and segment information of batch item
             target_size = target_sizes[i] if target_sizes is not None else None
diff --git a/tests/models/oneformer/test_image_processing_oneformer.py b/tests/models/oneformer/test_image_processing_oneformer.py
index 4a9e560463adf0..abec659a8bfc87 100644
--- a/tests/models/oneformer/test_image_processing_oneformer.py
+++ b/tests/models/oneformer/test_image_processing_oneformer.py
@@ -295,6 +295,19 @@ def test_post_process_instance_segmentation(self):
                 el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
             )
 
+        segmentation_with_opts = image_processor.post_process_instance_segmentation(
+            outputs,
+            threshold=0,
+            target_sizes=[(1, 4) for _ in range(self.image_processor_tester.batch_size)],
+            task_type="panoptic",
+        )
+        self.assertTrue(len(segmentation_with_opts) == self.image_processor_tester.batch_size)
+        for el in segmentation_with_opts:
+            self.assertTrue("segmentation" in el)
+            self.assertTrue("segments_info" in el)
+            self.assertEqual(type(el["segments_info"]), list)
+            self.assertEqual(el["segmentation"].shape, (1, 4))
+
     def test_post_process_panoptic_segmentation(self):
         image_processor = self.image_processing_class(
             num_labels=self.image_processor_tester.num_classes,