diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 9c7efe4e..1550f75a 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -535,17 +535,25 @@ class Detections:
                 data=data,
             )
         elif "segments_info" in transformers_results:
-            
             segments_info = transformers_results["segments_info"]
 
             if "segmentation" in transformers_results:
-                scores = np.array([segment['score'] for segment in segments_info])
-                class_ids = np.array([segment['label_id'] for segment in segments_info])
-                segmentation_array = transformers_results["segmentation"].cpu().detach().numpy()
-                masks = np.array([(segmentation_array == segment['id']).astype(bool) for segment in segments_info])
+                scores = np.array([segment["score"] for segment in segments_info])
+                class_ids = np.array([segment["label_id"] for segment in segments_info])
+                segmentation_array = (
+                    transformers_results["segmentation"].cpu().detach().numpy()
+                )
+                masks = np.array(
+                    [
+                        (segmentation_array == segment["id"]).astype(bool)
+                        for segment in segments_info
+                    ]
+                )
 
                 if id2label is not None:
-                    class_names = np.array([id2label[class_id] for class_id in class_ids])
+                    class_names = np.array(
+                        [id2label[class_id] for class_id in class_ids]
+                    )
                     data[CLASS_NAME_DATA_FIELD] = class_names
 
                 return cls(
@@ -554,15 +562,24 @@ class Detections:
                     confidence=scores,
                     class_id=class_ids,
                     data=data,
-            )
+                )
 
             elif "png_string" in transformers_results:
-                class_ids = np.array([segment['category_id'] for segment in segments_info])
+                class_ids = np.array(
+                    [segment["category_id"] for segment in segments_info]
+                )
                 segmentation_array = png_to_mask(transformers_results["png_string"])
-                masks = np.array([(segmentation_array == segment['id']).astype(bool) for segment in segments_info])
+                masks = np.array(
+                    [
+                        (segmentation_array == segment["id"]).astype(bool)
+                        for segment in segments_info
+                    ]
+                )
 
                 if id2label is not None:
-                    class_names = np.array([id2label[class_id] for class_id in class_ids])
+                    class_names = np.array(
+                        [id2label[class_id] for class_id in class_ids]
+                    )
                     data[CLASS_NAME_DATA_FIELD] = class_names
 
                 return cls(
@@ -570,7 +587,7 @@ class Detections:
                     mask=masks,
                     class_id=class_ids,
                     data=data,
-            ) 
+                )
         else:
             raise NotImplementedError(
                 "Only object detection and semantic segmentation results are supported."
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 0884d0d5..00b3693a 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -1,10 +1,10 @@
+import io
 from itertools import chain
 from typing import Dict, List, Optional, Tuple, Union
 
 import cv2
 import numpy as np
 import numpy.typing as npt
-import io
 from PIL import Image
 
 from supervision.config import CLASS_NAME_DATA_FIELD
@@ -1003,6 +1003,7 @@ def cross_product(anchors: np.ndarray, vector: Vector) -> np.ndarray:
     vector_start = np.array([vector.start.x, vector.start.y])
     return np.cross(vector_at_zero, anchors - vector_start)
 
+
 def png_to_mask(png_string):
     """
     Convert a PNG byte string to a binary mask array.
@@ -1015,6 +1016,6 @@ def png_to_mask(png_string):
       of the image.
     """
     image = Image.open(io.BytesIO(png_string))
-    mask = np.array(image, dtype= np.uint8)
+    mask = np.array(image, dtype=np.uint8)
 
-    return mask[:,:,0]
+    return mask[:, :, 0]