Supervision

// Overview

Supervision is a Python library from Roboflow that gives you reusable building blocks for computer vision tasks. It handles the work that surrounds a model: drawing detections on frames, loading and converting datasets, counting objects in zones, and more, so you can focus on your application instead of plumbing.

It is designed to be model agnostic. You plug in any classification, detection, or segmentation model, including Ultralytics, Transformers, MMDetection, RF-DETR, and Roboflow Inference, and convert their output into a common `sv.Detections` format that the rest of the toolkit understands.

As a computer vision tool, it sits between your model and your output. It is a fit for developers building real-time video pipelines, dataset workflows, or annotated visualizations who want consistent, customizable utilities rather than rewriting the same glue code for each model.

// What it does

• Model-agnostic `sv.Detections` format with connectors for Ultralytics, Transformers, MMDetection, RF-DETR, and Roboflow Inference
• Customizable annotators (such as `BoxAnnotator`) for composing detection and segmentation visualizations
• Dataset utilities to load, split, merge, save, and convert between COCO, YOLO, and Pascal VOC formats
• On-demand image loading when iterating over a `DetectionDataset`
• Real-time video helpers including zone counting and stream processing for tasks like dwell-time analysis

// Getting started

Install the package into a Python 3.9 or newer environment, then plug in a model and start annotating detections.

pip install supervision

pip install pillow rfdetr

import supervision as sv
from PIL import Image
from rfdetr import RFDETRSmall

image = Image.open(...)
model = RFDETRSmall()
detections = model.predict(image, threshold=0.5)

len(detections)
# 5

import cv2
import supervision as sv

image = cv2.imread(...)
detections = sv.Detections(...)

box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Draw boxes, masks, and labels on images or video frames from any detection model with a consistent API
• Convert and merge object-detection datasets between COCO, YOLO, and Pascal VOC formats
• Build real-time video analytics like zone counting and dwell-time analysis on a live stream
• Standardize output from different models (Ultralytics, Transformers, RF-DETR, Inference) into one Detections format

// How Supervision compares

Supervision alongside other open-source vision & understanding tools AI/TLDR tracks, ranked by GitHub stars.