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.

Which object detection models work with Supervision?¶

Supervision is model agnostic. sv.Detections includes converters for Ultralytics YOLO, Roboflow Inference, Hugging Face Transformers outputs, SAM, Detectron2, MMDetection, 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(). 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, and Pascal VOC. Use DetectionDataset.from_yolo(), DetectionDataset.from_coco(), or DetectionDataset.from_pascal_voc() 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?¶

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.