supervision
A set of easy-to-use utils that will come in handy in any Computer Vision project
Decision gist · record as of 2026-08-14
Yes. Supervision is a mature, actively maintained toolkit with no known vulnerabilities, permissive licensing, and low install friction. It solves real friction points in computer vision workflows—dataset loading, format conversion, and visualization—and integrates cleanly with model frameworks. Install it if you work with detection, segmentation, or classification models and want to avoid reinventing dataset utilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10
- Low install friction with a pure-Python wheel.
- Active maintenance (last commit 2026-08-14, released 2026-08-04) and a large community (49398 stars).
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal restrictions.
last release 2026-08-04 (10 days) · last repo commit 2026-08-14 · 49,398 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,387,355 downloads/mo, #3,971 on PyPI
Alternatives
Verify before relying
pip install supervision
import supervision as sv
from pillow import Image
image = Image.open("path/to/image.jpg")
detections = sv.Detections(...)
box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)- Whether the package works with models outside the listed connectors without custom adapter code
- Performance characteristics when handling large datasets or high-resolution video streams
- Whether all dataset format conversions preserve annotation fidelity across YOLO, Pascal VOC, and COCO
What it is and what it does
Supervision is a toolkit for building computer vision applications around detection, segmentation, and classification models. It abstracts away common tasks—loading datasets in multiple formats (YOLO, COCO, Pascal VOC), converting between them, splitting and merging datasets, and visualizing model outputs with customizable annotators. The package is model-agnostic and works with any framework; it includes connectors for popular libraries, but you can also pass detections directly if your model already returns the expected format.
The library handles the plumbing so you focus on application logic rather than format parsing or visualization boilerplate. It depends on standard scientific Python libraries (numpy, scipy, pillow, matplotlib) plus video support (av) and YAML parsing, making it straightforward to integrate into existing pipelines. The package is actively maintained, well-starred, and supports modern Python versions (3.10–3.14).
Use it for
- Load and split YOLO or COCO datasets for training, validation, and testing without manual file parsing
- Annotate detection results with bounding boxes, masks, or labels for debugging model predictions
- Convert datasets between YOLO, COCO, and Pascal VOC formats for compatibility with different training frameworks
- Track objects across video frames and calculate metrics like dwell time or speed using zone-based analysis
- Merge multiple detection datasets with different class vocabularies into a unified training set
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Supervision is a mature, actively maintained toolkit with no known vulnerabilities, permissive licensing, and low install friction. It solves real friction points in computer vision workflows—dataset loading, format conversion, and visualization—and integrates cleanly with model frameworks. Install it if you work with detection, segmentation, or classification models and want to avoid reinventing dataset utilities.
Install
supervision on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance (last commit 2026-08-14, released 2026-08-04) and a large community (49398 stars). Ten runtime dependencies are all well-established libraries.
Requires Python >= 3.10
License in practice
MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal restrictions.
Quickstart
pip install supervision
import supervision as sv
from pillow import Image
image = Image.open("path/to/image.jpg")
detections = sv.Detections(...)
box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)Verify before relying
- Whether the package works with models outside the listed connectors without custom adapter code
- Performance characteristics when handling large datasets or high-resolution video streams
- Whether all dataset format conversions preserve annotation fidelity across YOLO, Pascal VOC, and COCO
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesavdefusedxmlmatplotlibnumpypillowpydeprecatepyyamlrequestsscipytqdm |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,387,355 / month, #3,971 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Multimedia :: GraphicsTopic :: Multimedia :: VideoTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Software DevelopmentTyping :: Typed |
Evidence: supervision-0.30.0-py3-none-any.whl
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See also roboflow · controlnet-aux · inference-models · supervisely · pycocotools · ultralytics · inference-cli · rf100vl · icevision · tensorflow-datasets