# Supervision — Reusable Computer Vision Tools by Roboflow

> A Python library of composable building blocks for detecting, tracking, classifying, and annotating objects in images and video streams.

## Install

Save in your project root:

# Supervision — Reusable Computer Vision Tools by Roboflow

## Quick Use
```bash
pip install supervision
python -c "import supervision as sv; print(sv.__version__)"
```

## Introduction
Supervision is a Python library that provides reusable utilities for processing computer vision model outputs. Instead of writing custom post-processing code for every detection model, you compose Supervision's annotators, trackers, and filters to build complete vision pipelines.

## What Supervision Does
- Provides annotators for bounding boxes, masks, labels, heatmaps, and tracking trails on images and video
- Includes object tracking algorithms (ByteTrack, SORT) that work with any detector output
- Offers filtering and zone-based counting for line crossing and polygon region analytics
- Converts between detection formats from YOLO, Detectron2, SAM, and other model families
- Handles video I/O with frame-by-frame or batch processing pipelines

## Architecture Overview
Supervision centers on a Detections data class that normalizes outputs from any model into a consistent format (xyxy boxes, masks, confidence, class IDs, tracker IDs). Annotators and trackers consume Detections objects, and all components are stateless and composable so you can chain them freely.

## Self-Hosting & Configuration
- Install from PyPI with optional extras for video codecs
- No server or GPU required for annotation and tracking logic
- Pair with any detection model (Ultralytics, Grounding DINO, SAM, etc.)
- Use the VideoSink and get_video_frames_generator helpers for video pipelines
- Integrates with Roboflow Inference for managed model hosting, but works fully standalone

## Key Features
- Model-agnostic: normalizes outputs from 15+ detection and segmentation frameworks
- Rich annotation toolkit with 10+ built-in visual styles
- Zone-based analytics for counting objects entering, leaving, or occupying a region
- Video utilities handle frame extraction, FPS control, and output encoding
- Actively maintained with frequent releases and growing community

## Comparison with Similar Tools
- **OpenCV** — low-level image operations; Supervision provides higher-level vision pipeline components
- **Ultralytics** — bundles a specific model (YOLO); Supervision is model-agnostic post-processing
- **Detectron2** — Meta's detection framework; Supervision complements any framework as a utility layer
- **MMDetection** — full detection toolbox; Supervision focuses on downstream processing and visualization
- **DeepSORT** — tracking only; Supervision combines tracking with annotation, filtering, and counting

## FAQ
**Q: Does Supervision train or run detection models?**
A: No. Supervision handles everything after inference: annotation, tracking, filtering, and counting. Bring your own model.

**Q: What detection formats are supported?**
A: YOLO, Detectron2, SAM, Grounding DINO, PaddleDetection, and any custom model that outputs boxes or masks.

**Q: Can I use it for real-time video?**
A: Yes. The library is lightweight enough for real-time pipelines when paired with a fast detector.

**Q: Is GPU required?**
A: Not for Supervision itself. GPU is only needed if your upstream detection model requires it.

## Sources
- https://github.com/roboflow/supervision
- https://supervision.roboflow.com/

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Source: https://tokrepo.com/en/workflows/4a9bbd36-416b-11f1-9bc6-00163e2b0d79
Author: AI Open Source