Roboflow Supervision
MIT-licensed Python CV toolkit — unified
Detectionstype, model adapters, annotators, datasets, tracking, and metrics. "We write your reusable computer vision tools."
45,675+ GitHub stars (2026-06-30 API check); @skalskip92's milestone tweet cited 40K stars and 6.5K dependent OSS projects (7,714 bookmarks). Source: GitHub roboflow/supervision, 2026-06-30; Source: X/@skalskip92, 2026-06-06
Current Version Snapshot
| Field | Snapshot |
|---|---|
| Python package | supervision==0.29.1 |
| Python support | >=3.9 |
| Latest GitHub release | 0.29.1, published 2026-06-23 |
| Default branch | develop, HEAD 934da124f5710fafcdb53e01596f0724ae68d8dd as of 2026-06-30 |
| Repo status | 45,675 stars / 4,055 forks as of 2026-06-30 |
| License | MIT |
Source: PyPI JSON; GitHub API; git ls-remote, 2026-06-30
Core primitives
| Module | Purpose |
|---|---|
sv.Detections |
Common box/mask container; converters from Ultralytics, Transformers, Detectron2, Roboflow Inference, YOLO-NAS, VLMs |
| Annotators | Box/mask/label overlays on images/video |
| Datasets | COCO, YOLO, Pascal VOC load/split/merge |
| Tracking & zones | Persistent IDs, polygon zone counts |
| Metrics | mAP, confusion matrices |
Install: pip install supervision (Python ≥3.9). Docs: supervision.roboflow.com. Source: GitHub README, 2026-06-13
Minimal pattern
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
results = model(source=0)[0]
detections = sv.Detections.from_ultralytics(results)
annotated = sv.BoxAnnotator().annotate(frame, detections)
Source: compiled from supervision.roboflow.com quickstart patterns, 2026-06-13
Ecosystem
Supervision is the shared utility layer in Roboflow's stack — not a trainer. Related repos: inference, autodistill, notebooks.
The basketball demo matters because it shows the shape of a real CV pipeline an agent can reason about: detector outputs, tracking IDs, segmentation overlays, team/color classification, jersey-number recognition, and shot/event classification become structured intermediate artifacts before an LLM ever sees them. For evals, Supervision is the layer that can turn video frames into measurable boxes, labels, zones, counts, and temporal tracks.
Stack fit (Kevin)
- Agent vision demos — normalize model outputs before sending to LLM (count objects, zone triggers) in one Python layer.
- Not on critical Dedalus path unless shipping CV features; prefer when building physical-world or video pipelines.
- Pairs with Security and Review Skills for pulling demo videos, supervision for frame annotation in eval datasets.
Timeline
- 2026-06-30 | Deep-reviewed the bookmark, local demo thumbnail, YouTube tutorial artifact, GitHub repo, Roboflow notebooks link, and PyPI package state. Refreshed current version to
supervision==0.29.1and promoted the durable idea: Supervision is the measurable CV glue layer for physical-world/video agent evals, not just a basketball demo. Source: X/@skalskip92, 2026-06-06; GitHub/PyPI, 2026-06-30 - 2026-06-26 | Refreshed from Kevin's GitHub stars harvest; API snapshot reported 44,971 stars, Python, and topics spanning detection, tracking, datasets, annotators, metrics, and YOLO adapters. Source:
raw/github/kevin-starred-repos-2026-06-26.json; GitHub API, 2026-06-26 - 2026-06-06 | @skalskip92 milestone tweet — 40K stars, 6.5K dependent projects. Source: X/@skalskip92, 2026-06-06
- 2026-06-13 | Wiki page from bookmark absorption. Source: User, 2026-06-13
- 2026-06-13 | Quality remediation: code pattern, module table, stack fit. Source: GitHub + docs, 2026-06-13