👋 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 3.11>=Python>=3.8 environment.

Headless Installation

The headless installation of supervision is designed for environments where graphical user interfaces (GUI) are not needed, making it more lightweight and suitable for server-side or headless applications. To install supervision with the headless version of OpenCV, simply use the following command:

pip install supervision

Desktop Installation

If you require the full version of supervision with GUI support and want to utilize OpenCV's graphical capabilities, you can install the desktop version. This version includes the GUI components of OpenCV, allowing you to interact with images and videos through graphical interfaces. To install supervision with the full version of OpenCV, use the following command:

pip install supervision[desktop]

👉 Install from source

Virtualenv/Pip Installation Method

# clone repository and navigate to root directory
git clone https://github.com/roboflow/supervision.git
cd supervision

# setup python environment and activate it
python3 -m venv venv
source venv/bin/activate

# Headless install
pip install -e "."

# Desktop install
pip install -e ".[desktop]"

Poetry Installation Method

# clone repository and navigate to root directory
git clone https://github.com/roboflow/supervision.git
cd supervision

# setup python environment and activate it
poetry shell

# If you like to speficiy python version 
poetry env use python 3.10 # 3.8,3.9
poetry shell


# Headless install
poetry install

# Desktop install
poetry install --extras "desktop"

🔥 Quickstart

detections processing

>>> import supervision as sv
>>> from ultralytics import YOLO

>>> model = YOLO('yolov8s.pt')
>>> result = model(IMAGE)[0]
>>> detections = sv.Detections.from_yolov8(result)

>>> len(detections)
5

👉 more detections utils

• Easily switch inference pipeline between supported object detection / instance segmentation models

  >>> import supervision as sv
  >>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
  
  >>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
  >>> mask_generator = SamAutomaticMaskGenerator(sam)
  >>> sam_result = mask_generator.generate(IMAGE)
  >>> detections = sv.Detections.from_sam(sam_result=sam_result)
  

• Advanced filtering

  >>> detections = detections[detections.class_id == 0]
  >>> detections = detections[detections.confidence > 0.5]
  >>> detections = detections[detections.area > 1000]
  

• Image annotation

  >>> import supervision as sv
  
  >>> box_annotator = sv.BoxAnnotator()
  >>> annotated_frame = box_annotator.annotate(
  ...     scene=IMAGE,
  ...     detections=detections
  ... )
  

datasets processing

>>> import supervision as sv

>>> dataset = sv.DetectionDataset.from_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... )

>>> dataset.classes
['dog', 'person']

>>> len(dataset)
1000

👉 more dataset utils

• Load object detection / instance segmentation datasets in one of supported formats

  >>> 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='...'
  ... )
  

• Loop over dataset entries

  >>> for name, image, labels in dataset:
  ...     print(labels.xyxy)
  
  array([[404.      , 719.      , 538.      , 884.5     ],
         [155.      , 497.      , 404.      , 833.5     ],
         [ 20.154999, 347.825   , 416.125   , 915.895   ]], dtype=float32)
  

• Split dataset for training, testing and validation

  >>> 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 multiple datasets together

  >>> 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 object detection / instance segmentation datasets in one of supported formats

  >>> 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 labels between supported formats

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

• Load classification datasets in one of supported formats

  >>> cs = sv.ClassificationDataset.from_folder_structure(
  ...     root_directory_path='...'
  ... )
  

• Save classification datasets in one of supported formats

  >>> cs.as_folder_structure(
  ...     root_directory_path='...'
  ... )
  

🎬 tutorials

Accelerate Image Annotation with SAM and Grounding DINO

Created: 20 Apr 2023 | Updated: 20 Apr 2023

Discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8...

SAM - Segment Anything Model by Meta AI: Complete Guide

Created: 11 Apr 2023 | Updated: 11 Apr 2023

Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results...

📚 documentation

Curious how Supervision can help you solve problems on your project? Visit our documentation page!

💜 built with supervision

You built something cool using supervision? Let us know!

🏆 contribution

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