text: Title: Results differ when using cv2 vs pillow
  Body: ### Search before asking
  
  - [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar bug report.
  
  
  ### Bug
  
  From [this comment](https://github.com/roboflow/supervision/issues/1038#issuecomment-2018147877), I understand supervision doesn't change channel order, and the issue I highlight here is likely addressed by documentation. I observe that if I open an image with cv2 or with pillow the predictions are different. The model was trained using ultraltics which I believe also uses cv2, so when I use pillow the channels order is changed. I suggest adding a note to the docs to check which library was used in training, then use that with supervision. Comparisons below:
  
  cv2:
  ![image](https://github.com/user-attachments/assets/fd2692aa-d869-4a9a-abfa-d304c342f38a)
  
  pillow:
  ![Pasted Graphic 5](https://github.com/user-attachments/assets/73a0e0e7-096f-46e3-805f-15798d7c7bd9)
  
  
  ### Environment
  
  _No response_
  
  ### Minimal Reproducible Example
  
  ```python
  image_path = "my.png"
  
  # change
  image = cv2.imread(image_path)
  # image = np.array(Image.open(image_path))
  
  def callback(image_slice: np.ndarray) -> sv.Detections:
      result = model(image_slice)[0]
      return sv.Detections.from_ultralytics(result)
  
  slicer = sv.InferenceSlicer(callback = callback)
  detections = slicer(image)
  detections = detections[detections.class_id == 1]
  
  box_annotator = sv.BoxAnnotator()
  label_annotator = sv.LabelAnnotator()
  
  annotated_image = box_annotator.annotate(scene=image, detections=detections)
  annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
  ```
  
  ### Additional
  
  _No response_
  
  ### Are you willing to submit a PR?
  
  - [ ] Yes I'd like to help by submitting a PR!