diff --git a/src/transformers/models/maskformer/feature_extraction_maskformer.py b/src/transformers/models/maskformer/feature_extraction_maskformer.py
index fce59b0a4b8b..ca7c08f23fa5 100644
--- a/src/transformers/models/maskformer/feature_extraction_maskformer.py
+++ b/src/transformers/models/maskformer/feature_extraction_maskformer.py
@@ -538,7 +538,6 @@ def post_process_panoptic_segmentation(
                     # create the area, since bool we just need to sum :)
                     mask_k_area = mask_k.sum()
                     # this is the area of all the stuff in query k
-                    # TODO not 100%, why are the taking the k query here????
                     original_area = (mask_probs[k] >= 0.5).sum()
 
                     mask_does_exist = mask_k_area > 0 and original_area > 0
@@ -565,5 +564,5 @@ def post_process_panoptic_segmentation(
                             )
                             if is_stuff:
                                 stuff_memory_list[pred_class] = current_segment_id
-                results.append({"segmentation": segmentation, "segments": segments})
+            results.append({"segmentation": segmentation, "segments": segments})
         return results
diff --git a/tests/maskformer/test_modeling_maskformer.py b/tests/maskformer/test_modeling_maskformer.py
index f2e1f56f0f5b..67151ead6ff8 100644
--- a/tests/maskformer/test_modeling_maskformer.py
+++ b/tests/maskformer/test_modeling_maskformer.py
@@ -404,3 +404,23 @@ def test_with_annotations_and_loss(self):
             outputs = model(**inputs)
 
         self.assertTrue(outputs.loss is not None)
+
+    def test_panoptic_segmentation(self):
+        model = MaskFormerForInstanceSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval()
+        feature_extractor = self.default_feature_extractor
+
+        inputs = feature_extractor(
+            [np.zeros((3, 384, 384)), np.zeros((3, 384, 384))],
+            annotations=[
+                {"masks": np.random.rand(10, 384, 384).astype(np.float32), "labels": np.zeros(10).astype(np.int64)},
+                {"masks": np.random.rand(10, 384, 384).astype(np.float32), "labels": np.zeros(10).astype(np.int64)},
+            ],
+            return_tensors="pt",
+        )
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        panoptic_segmentation = feature_extractor.post_process_panoptic_segmentation(outputs)
+
+        self.assertTrue(len(panoptic_segmentation) == 2)