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We write your reusable computer vision tools. 💜

supervision.roboflow.com

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examples		examples
supervision		supervision
test		test
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.pre-commit-config.yaml		.pre-commit-config.yaml
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE.md		LICENSE.md
README.md		README.md
demo.ipynb		demo.ipynb
mkdocs.yml		mkdocs.yml
pyproject.toml		pyproject.toml
release_process.md		release_process.md
tox.ini		tox.ini
uv.lock		uv.lock

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notebooks |inference |autodistill |maestro

👋 hello

We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝

💻 install

Pip install the supervision package in aPython>=3.8 environment.

pip install supervision

Read more about conda, mamba, and installing from source in ourguide.

🔥 quickstart

models

Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have createdconnectors for the most popular libraries like Ultralytics, Transformers, or MMDetection.

importcv2importsupervisionassvfromultralyticsimportYOLOimage=cv2.imread(...)model=YOLO("yolov8s.pt")result=model(image)[0]detections=sv.Detections.from_ultralytics(result)len(detections)# 5

👉 more model connectors

inference

Running withInference requires aRoboflow API KEY.

importcv2importsupervisionassvfrominferenceimportget_modelimage=cv2.imread(...)model=get_model(model_id="yolov8s-640",api_key=<ROBOFLOWAPIKEY>)result=model.infer(image)[0]detections=sv.Detections.from_inference(result)len(detections)# 5

annotators

Supervision offers a wide range of highly customizableannotators, allowing you to compose the perfect visualization for your use case.

importcv2importsupervisionassvimage=cv2.imread(...)detections=sv.Detections(...)box_annotator=sv.BoxAnnotator()annotated_frame=box_annotator.annotate(scene=image.copy(),detections=detections)

supervision-0.16.0-annotators.mp4

datasets

Supervision provides a set ofutils that allow you to load, split, merge, and save datasets in one of the supported formats.

importsupervisionassvfromroboflowimportRoboflowproject=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 demandforpath,image,annotationinds:# loads image on demand

👉 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)

merge

ds_1=sv.DetectionDataset(...)len(ds_1)# 100ds_1.classes# ['dog', 'person']ds_2=sv.DetectionDataset(...)len(ds_2)# 200ds_2.classes# ['cat']ds_merged=sv.DetectionDataset.merge([ds_1,ds_2])len(ds_merged)# 300ds_merged.classes# ['cat', 'dog', 'person']

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=...)

convert

sv.DetectionDataset.from_yolo(images_directory_path=...,annotations_directory_path=...,data_yaml_path=...).as_pascal_voc(images_directory_path=...,annotations_directory_path=...)

🎬 tutorials

Want to learn how to use Supervision? Explore ourhow-to guides,end-to-end examples,cheatsheet, andcookbooks!

Dwell Time Analysis with Computer Vision | Real-Time Stream Processing

Created: 5 Apr 2024

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

Created: 11 Jan 2024

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.