notebooks | inference | autodistill | maestro

📑 Table of Contents

• 👋 Hello
• 💻 Install
• 🔥 Quickstart
  • Models
  • Annotators
  • Datasets
• 🎬 Tutorials
• 💜 Built with Supervision
• 📚 Documentation
• 🏆 Contribution

👋 Hello

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. 🤝

💻 Install

Pip install the supervision package in a Python>=3.10 environment.

pip install supervision

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

🔥 Quickstart

Models

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.

importsupervisionassvfromPILimportImagefromrfdetrimportRFDETRSmallimage=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.

  importsupervisionassvfromPILimportImagefrominferenceimportget_modelimage=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

Annotators

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

importcv2importsupervisionassvimage=cv2.imread("path/to/image.jpg")
# Assuming detections are obtained from a modeldetections=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 of utils 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 demandpass

👉 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 our how-to guides, end-to-end examples, cheatsheet, and cookbooks!

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.

💜 Built with Supervision

Did you build something cool using supervision? Let us know!

football-players-tracking-25.mp4

traffic_analysis_result.mov

vehicles-step-7-new.mp4

📚 Documentation

Visit our documentation page to learn how supervision can help you build computer vision applications faster and more reliably.

🏆 Contribution

We love your input! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!