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roboflow supervision
We write your reusable computer vision tools. 💜
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License: MIT License
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- community 90
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We are your essential toolkit for computer vision. From data loading to real-time zone counting, we provide the building blocks so you can focus on building applications around your models. 🤝
Pip install the supervision package in a Python>=3.10 environment.
pip install supervisionRead more about conda, mamba, and installing from source in our guide.
Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created connectors for the most popular libraries like Ultralytics, Transformers, MMDetection, or Inference. Other integrations, like rfdetr, already return sv.Detections directly.
Install the optional dependencies for this example with pip install pillow rfdetr.
import supervision as sv
from PIL import Image
from rfdetr import RFDETRSmall
image = Image.open("path/to/image.jpg")
model = RFDETRSmall()
detections = model.predict(image, threshold=0.5)
len(detections)
# 5👉 more model connectors
-
inference
Running with Inference requires a Roboflow API KEY.
import supervision as sv from PIL import Image from inference import get_model image = Image.open("path/to/image.jpg") model = get_model(model_id="rfdetr-small", api_key="ROBOFLOW_API_KEY") result = model.infer(image)[0] detections = sv.Detections.from_inference(result) len(detections) # 5
Supervision offers a wide range of highly customizable annotators, allowing you to compose the perfect visualization for your use case.
import cv2
import supervision as sv
image = cv2.imread("path/to/image.jpg")
# Assuming detections are obtained from a model
detections = sv.Detections(...)
box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)supervision-0.16.0-annotators.mp4
Supervision provides a set of utils that allow you to load, split, merge, and save datasets in one of the supported formats.
import supervision as sv
from roboflow import Roboflow
project = Roboflow().workspace("WORKSPACE_ID").project("PROJECT_ID")
dataset = project.version("PROJECT_VERSION").download("coco")
ds = sv.DetectionDataset.from_coco(
images_directory_path=f"{dataset.location}/train",
annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)
path, image, annotation = ds[0]
# loads image on demand
for path, image, annotation in ds:
# loads image on demand
pass👉 more dataset utils
-
load
dataset = sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ) dataset = sv.DetectionDataset.from_pascal_voc( images_directory_path=..., annotations_directory_path=..., ) dataset = sv.DetectionDataset.from_coco( images_directory_path=..., annotations_path=..., )
-
split
train_dataset, test_dataset = dataset.split(split_ratio=0.7) test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5) len(train_dataset), len(test_dataset), len(valid_dataset) # (700, 150, 150)
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merge
ds_1 = sv.DetectionDataset(...) len(ds_1) # 100 ds_1.classes # ['dog', 'person'] ds_2 = sv.DetectionDataset(...) len(ds_2) # 200 ds_2.classes # ['cat'] ds_merged = sv.DetectionDataset.merge([ds_1, ds_2]) len(ds_merged) # 300 ds_merged.classes # ['cat', 'dog', 'person']
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save
dataset.as_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ) dataset.as_pascal_voc( images_directory_path=..., annotations_directory_path=..., ) dataset.as_coco( images_directory_path=..., annotations_path=..., )
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convert
sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ).as_pascal_voc( images_directory_path=..., annotations_directory_path=..., )
Want to learn how to use Supervision? Explore our how-to guides, end-to-end examples, cheatsheet, and cookbooks!
Dwell Time Analysis with Computer Vision | Real-Time Stream Processing
Learn how to use computer vision to analyze wait times and optimize processes. This tutorial covers object detection, tracking, and calculating time spent in designated zones. Use these techniques to improve customer experience in retail, traffic management, or other scenarios.
Speed Estimation & Vehicle Tracking | Computer Vision | Open Source
Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.
Did you build something cool using supervision? Let us know!
football-players-tracking-25.mp4
traffic_analysis_result.mov
vehicles-step-7-new.mp4
Visit our documentation page to learn how supervision can help you build computer vision applications faster and more reliably.
We love your input! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (49,863 stars) signals broad adoption.
- Maintained recently (last push 2 weeks ago).
Who should use it
- Developers working primarily with Python
- Teams exploring AI tooling, agents, or ML infrastructure
Potential use cases
- Reference or evaluate Python open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
Strengths
- Recent repository activity
- README present in our index
- Declared license: MIT License
- Substantial fork count (4,754) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest · Active maintenance
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.