supervision/docs/how_to/detect_and_annotate.md

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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](https://github.com/roboflow/inference) or
[Ultralytics](https://github.com/ultralytics/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.

=== "Ultralytics"

    ```python
    import cv2
    from ultralytics import YOLO

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

=== "Inference"

    ```python
    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.

=== "Ultralytics"

    We can do so using the [`sv.Detections.from_ultralytics`](detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.

    ```python
    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)
    ```

=== "Inference"

    We can do so using the [`sv.Detections.from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.

    ```python
    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:

- [`from_deepsparse`](detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
- [`from_detectron2`](detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
- [`from_mmdetection`](detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
- [`from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
- [`from_sam`](detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
- [`from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
- [`from_yolo_nas`](detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))

## 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`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.

=== "Ultralytics"

    ```python
    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)
    ```

=== "Inference"

    ```python
    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](https://media.roboflow.com/supervision_annotate_example.png)

## Display Annotated Image

To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](utils/notebook/#supervision.utils.notebook.plot_image) function.

```python
sv.plot_image(annotated_image)
```