roboflow/supervision
We write your reusable computer vision tools. 💜
What it solves
Supervision is a toolkit for computer vision that provides the necessary building blocks to build applications around AI models. It removes the need to write repetitive boilerplate code for common tasks like data loading, visualization, and dataset management, allowing developers to focus on the application logic rather than the infrastructure of the vision pipeline.
How it works
The library is designed to be model-agnostic, meaning it can be plugged into any classification, detection, or segmentation model. It provides connectors for popular libraries such as Ultralytics, Transformers, and MMDetection. The toolkit consists of three primary components:
- Models: Connectors that standardize the output of various AI models into a common format (
sv.Detections). - Annotators: Customizable tools for visualizing detections on images or video frames (e.g., drawing bounding boxes).
- Datasets: Utilities for loading, splitting, merging, and saving datasets in multiple formats including COCO, YOLO, and Pascal VOC.
Who it’s for
Computer vision developers and engineers who want to build real-world applications using object detection, tracking, and segmentation models without managing the low-level details of data handling and visualization.
Highlights
- Model Agnostic: Works with any model regardless of the library used to run inference.
- Comprehensive Dataset Utils: Supports loading, splitting, merging, and converting between COCO, YOLO, and Pascal VOC formats.
- Highly Customizable Annotators: Provides a wide range of tools to create professional visualizations of model predictions.
- Real-time Processing: Capable of handling real-time zone counting, speed estimation, and vehicle tracking.