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Detect and Annotate

Supervision offers a streamlined solution to effortlessly annotate predictions from a range of object detection and segmentation models. This guide demonstrates how to execute inference using the YOLOv8 model with either the Inference or Ultralytics packages. Following this, you'll learn how to import these predictions into Supervision for image annotation purposes.

Run Inference

First, you'll need to obtain predictions from your object detection or segmentation model.

import cv2
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
image = cv2.imread(<PATH TO IMAGE>)
results = model(image)[0]

import cv2
from inference.models.utils import get_roboflow_model

model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
image = cv2.imread(<PATH TO IMAGE>)
results = model.infer(image)[0]

Load Predictions into Supervision

Now that we have predictions from a model, we can load them into Supervision.

We can do so using the sv.Detections.from_ultralytics method, which accepts model results from both detection and segmentation models.

import cv2
import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
image = cv2.imread(<PATH TO IMAGE>)
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)

We can do so using the sv.Detections.from_inference method, which accepts model results from both detection and segmentation models.

import cv2
import supervision as sv
from inference.models.utils import get_roboflow_model

model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
image = cv2.imread(<PATH TO IMAGE>)
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)

You can conveniently load predictions from other computer vision frameworks and libraries using:

Annotate Image

Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the sv.BoundingBoxAnnotator and sv.LabelAnnotator classes. If you are running the segmentation model sv.MaskAnnotator is a drop-in replacement for sv.BoundingBoxAnnotator that will allow you to draw masks instead of boxes.

import cv2
import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
image = cv2.imread(<PATH TO IMAGE>)
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)

bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()

labels = [
    model.model.names[class_id]
    for class_id
    in detections.class_id
]

annotated_image = bounding_box_annotator.annotate(
    scene=image, detections=detections)
annotated_image = label_annotator.annotate(
    scene=annotated_image, detections=detections, labels=labels)

import cv2
import supervision as sv
from inference.models.utils import get_roboflow_model

model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
image = cv2.imread(<PATH TO IMAGE>)
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)

bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated_image = bounding_box_annotator.annotate(
    scene=image, detections=detections)
annotated_image = label_annotator.annotate(
    scene=annotated_image, detections=detections)

Predictions plotted on an image

Display Annotated Image

To display the annotated image in Jupyter Notebook or Google Colab, use the sv.plot_image function.

sv.plot_image(annotated_image)

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