Frequently Asked Questions

What is Supervision?

Supervision is an open-source Python library by Roboflow for computer vision workflows. It provides a unified Detections class with converters for supported object detection, segmentation, and VLM outputs.

How do I install Supervision?

Install the base package with:

pip install supervision

Use the metrics extra when you need optional metric dependencies:

pip install "supervision[metrics]"

Sample asset utilities are part of the base package under supervision.assets.

Supervision does not install OpenCV. Its image, drawing, and file-video APIs use the included fallback when cv2 is unavailable, and automatically use a compatible cv2 already present in your environment. See the OpenCV migration guide when upgrading an existing environment or choosing an OpenCV wheel yourself.

Which object detection models work with Supervision?

Supervision is model agnostic. RF-DETR works natively — its predict method returns an sv.Detections object directly, no conversion needed. sv.Detections also includes converters for Roboflow Inference, Hugging Face Transformers outputs, SAM, Detectron2, MMDetection, Ultralytics YOLO, YOLO-NAS, PaddleDet, NCNN, Azure AI Vision, and VLM parsers including Florence-2, PaliGemma, Qwen VL, Gemini, DeepSeek VL 2, and Moondream. Keypoint outputs have separate sv.KeyPoints converters, including MediaPipe.

What can I do with Supervision?

You can annotate images and video, filter detections, track objects, count objects in zones or across lines, load and convert datasets, evaluate models with detection metrics, and export predictions for downstream analysis.

How do I track objects across video frames?

Assign persistent tracker IDs before visualization. The built-in sv.ByteTrack wrapper accepts Detections through update_with_detections(), but it is deprecated in favor of ByteTrackTracker from the external trackers package. After tracking, combine the output with annotators such as sv.TraceAnnotator, sv.BoxAnnotator, and sv.LabelAnnotator.

What dataset formats does Supervision support?

For detection datasets, Supervision supports YOLO, COCO JSON, Pascal VOC, CreateML, and LabelMe. Use DetectionDataset.from_yolo(), DetectionDataset.from_coco(), DetectionDataset.from_pascal_voc(), DetectionDataset.from_createml(), or DetectionDataset.from_labelme() to load datasets, and the matching as_* methods to export them.

How do I count objects in a zone?

Use sv.PolygonZone for arbitrary polygon regions and sv.LineZone for line-crossing counts. Line crossing requires detections.tracker_id, so run a tracker before calling the line zone trigger.

How do I benchmark a model?

Install supervision[metrics], then use supervision.metrics.mean_average_precision.MeanAveragePrecision for mAP and sv.ConfusionMatrix for confusion matrices. Accumulate predictions and ground-truth Detections, then call compute() to calculate metrics.

Is Supervision free to use?

Yes. Supervision is free and open source under the MIT license.

How do I process frames from a webcam with supervision?

Supervision does not support live camera capture. Manage the capture device yourself with cv2.VideoCapture, which works regardless of which OpenCV wheel (opencv-python or opencv-python-headless) is installed, and pass individual frames to supervision annotators:

import cv2  # requires: pip install opencv-python (or opencv-python-headless)
import supervision as sv

cap = cv2.VideoCapture(0)
annotator = sv.BoxAnnotator()

while True:
    ret, frame = cap.read()
    if not ret:
        break
    # run your detector, then annotate:
    # annotated = annotator.annotate(frame, detections)

cap.release()

Where is the source code?

The source code is available at github.com/roboflow/supervision.