roboflow/supervision

👋 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 a Python>=3.9 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, or MMDetection.

import cv2
import supervision as sv
from ultralytics import YOLO

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

len(detections)
# 5

👉 more model connectors

• inference

  Running with Inference requires a Roboflow API KEY.

  import cv2
  import supervision as sv
  from inference import get_model
  
  image = cv2.imread(...)
  model = get_model(model_id="yolov8s-640", 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.

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)

https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce

datasets

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)
  

• 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=...,
  )
  

🎬 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

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.

💜 built with supervision

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

https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4

https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900

https://github.com/roboflow/supervision/assets/26109316/3ac6982f-4943-4108-9b7f-51787ef1a69f

📚 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!