#44th of 22 in ML frameworks and libraries
supervision
Python toolkit for loading, annotating and counting computer vision detections
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- Stars
- 51.2k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
36,341 on 25 Mar 202651,208 on 14 Oct 2026
From GitHub's daily star counts.
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Overview
Supervision is a model-agnostic Python library that wraps detection, classification and segmentation outputs in a common sv.Detections object. It has connectors for Ultralytics, Transformers, MMDetection and Inference, annotators for drawing results, and utilities to load, split, merge and convert YOLO, Pascal VOC and COCO datasets. Tracking, zone counting and speed estimation appear in the linked tutorials.
Who it is for: Python developers building computer vision apps around detection models
Strengths
- Connectors for Ultralytics, Transformers, MMDetection and Inference; rfdetr returns sv.Detections directly
- Dataset utils load, split, merge and convert YOLO, Pascal VOC and COCO
- Configurable annotators such as BoxAnnotator for drawing detections on frames
- Installs with pip; docs, cookbooks and a cheatsheet are available
Weaknesses
- Does not train or run models itself; a separate detection model is required
- Requires Python 3.10 or newer
- Inference and dataset download examples need a Roboflow API key
- Tracking and zone features are shown in video tutorials, not the README text
What it needs
- GPU optional
- Needs Python>=3.10, rfdetr (optional), Roboflow Inference (optional), Roboflow API key (optional)
- Models: Ultralytics, Transformers, MMDetection, Inference, rfdetr
Head to head
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