How to plot and visualize YOLOv9 predictions

A common task in working with computer vision models is visualizing model predictions. Being able to qualitatively visualize predictions is useful in in model development, testing, and work to prepare a model for production.

Using the supervision Python package, you can plot and visualize

YOLOv9

predictions in a few lines of code. In this guide, we will show how to plot and visualize model predictions.

We will:

1. Install supervision
2. Load data
3. Plot predictions with a supervision Annotator

Without further ado, let's get started!

Step #1: Install supervision

First, install the supervision pip package:

pip install supervision

Once you have installed supervision, you are ready to load your data and start writing logic to filter detections.

Step #2: Load Data

First, we are going to load our dataset into a supervision.DetectionDataset() object. This object will contain information about all the images in a dataset. You can load datasets from many different model types, from YOLO to MMDetection. For this guide, we will use the

YOLOv9

data loader.

You can load data using the following code:
‍


import cv2
import inference
import supervision as sv

annotator = sv.BoxAnnotator()

def render(predictions, image):
    classes = {item["class_id"]: item["class"] for item in predictions["predictions"]}

    detections = sv.Detections.from_roboflow(predictions)

    print(predictions)

    image = annotator.annotate(
        scene=image, detections=detections, labels=[classes[i] for i in detections.class_id]
    )

    cv2.imshow("Prediction", image)
    cv2.waitKey(1)


inference.Stream(
    source="webcam",
    model="microsoft-coco/9",
    output_channel_order="BGR",
    use_main_thread=True,
    on_prediction=render,
    api_key="api_key"
)

Above, replace "microsoft-coco/9" with the model ID of a YOLOv9 model hosted on Roboflow.

To upload a model to Roboflow, first install the Roboflow Python package:

pip install roboflow

Then, create a new Python file and paste in the following code:


from roboflow import Roboflow

rf = Roboflow(api_key="API_KEY")
project = rf.workspace().project("PROJECT_ID")
project.version(DATASET_VERSION).deploy(model_type="yolov8", model_path=f"{HOME}/runs/detect/train/")

In the code above, add your API key and the path to the model weights you want to upload. Learn how to retrieve your API key. Your weights will be uploaded to Roboflow. Your model will shortly be accessible over an API, and available for use in Inference. To learn more about uploading model weights to Roboflow, check out our full guide to uploading weights to Roboflow.

Replace the ... with the response object from your model.

Step #3: Plot Detections

Supervision has two annotators that let you visualize detections from a computer vision model:

1. BoxAnnotator, to plot bounding boxes.
2. MaskAnnotator, to plot segmentation masks.

You can use the following code to plot bounding boxes:


annotated_frame = box_annotator.annotate(
	scene=image.copy(),
	detections=detections,
	labels=labels
)

sv.plot_image(image=annotated_frame, size=(16, 16))

To plot segmentation masks, replace BoundingBoxAnnotator() with MaskAnnotator(). This will use the segmentation mask annotator for visualizing detections.

Next steps

supervision provides an extensive range of functionalities for working with computer vision models. With supervision, you can:

1. Process and filter detections and segmentation masks from a range of popular models (YOLOv5, Ultralytics YOLOv8, MMDetection, and more).
2. Process and filter classifications.
3. Compute confusion matrices.

And more! To learn about the full range of functionality in supervision, check out the supervision documentation.

Learn how to plot detections for other models

Below, you can find our guides on how to plot detections for other computer vision models.

DeepSparse Segmentation

DeepSparse Object Detection

YOLOv8 Object Detection

Detectron2

YOLOv5