YOLO Issue

I am trying to run the first script at How Beginners Can Quickly Run YOLOv8 for Real-Time Recognition on a Laptop with the following statements direct from the article:

from ultralytics import YOLO 

# Load a pretrained YOLO model (recommended for training)

model = YOLO("yolov8n.pt") 

# Perform object detection on an image using the model

results = model("https://ultralytics.com/images/bus.jpg") 

# Save results to disk

results[0].save(filename=f"result_bus.jpg")

The first three statements run without any issue but the last statement produces the following error:

---------------------------------------------------------------------------

AttributeError Traceback (most recent call last)

/tmp/ipykernel_2417/2241203627.py in <module>

1 # Save results to disk

----> 2 results[0].save(filename=f"result_bus.jpg")

/usr/local/lib/python3.7/dist-packages/ultralytics/utils/__init__.py in __getattr__(self, attr)

134 """Custom attribute access error message with helpful information."""

135 name = self.__class__.__name__

--> 136 raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")

137

138

AttributeError: 'Results' object has no attribute 'save'. See valid attributes below.

A class for storing and manipulating inference results.

Args:

orig_img (numpy.ndarray): The original image as a numpy array.

path (str): The path to the image file.

names (dict): A dictionary of class names.

boxes (torch.tensor, optional): A 2D tensor of bounding box coordinates for each detection.

masks (torch.tensor, optional): A 3D tensor of detection masks, where each mask is a binary image.

probs (torch.tensor, optional): A 1D tensor of probabilities of each class for classification task.

keypoints (List[List[float]], optional): A list of detected keypoints for each object.

Attributes:

orig_img (numpy.ndarray): The original image as a numpy array.

orig_shape (tuple): The original image shape in (height, width) format.

boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.

masks (Masks, optional): A Masks object containing the detection masks.

probs (Probs, optional): A Probs object containing probabilities of each class for classification task.

names (dict): A dictionary of class names.

path (str): The path to the image file.

keypoints (Keypoints, optional): A Keypoints object containing detected keypoints for each object.

speed (dict): A dictionary of preprocess, inference and postprocess speeds in milliseconds per image.

_keys (tuple): A tuple of attribute names for non-empty attributes.

What would be the recommended workaround? Thanks.

Regards.