roboflow/supervision: We write your reusable computer vision tools. 💜 · GitHub

notebooks | inference | autodistill | maestro

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

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 supervision

Read 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

• 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 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

• 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)
  # 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']

• 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=...,
  )

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

Speed Estimation & Vehicle Tracking | Computer Vision | Open Source

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

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!