{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"},{"label":"Graphics","url":"https://skillfed.io/packages/category/multimedia-graphics"},{"label":"Video","url":"https://skillfed.io/packages/category/multimedia-video"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"Supervision provides utilities for loading, annotating, and processing computer vision datasets and model outputs\u2014detection, segmentation, and classification results from any model framework.","skillfed_tags":["computer-vision","dataset-utilities","model-agnostic"],"use_cases":["Load and split YOLO or COCO datasets for training, validation, and testing without manual file parsing","Annotate detection results with bounding boxes, masks, or labels for debugging model predictions","Convert datasets between YOLO, COCO, and Pascal VOC formats for compatibility with different training frameworks","Track objects across video frames and calculate metrics like dwell time or speed using zone-based analysis","Merge multiple detection datasets with different class vocabularies into a unified training set"],"what_it_does":"Supervision is a toolkit for building computer vision applications around detection, segmentation, and classification models. It abstracts away common tasks\u2014loading datasets in multiple formats (YOLO, COCO, Pascal VOC), converting between them, splitting and merging datasets, and visualizing model outputs with customizable annotators. The package is model-agnostic and works with any framework; it includes connectors for popular libraries, but you can also pass detections directly if your model already returns the expected format.\n\nThe library handles the plumbing so you focus on application logic rather than format parsing or visualization boilerplate. It depends on standard scientific Python libraries (numpy, scipy, pillow, matplotlib) plus video support (av) and YAML parsing, making it straightforward to integrate into existing pipelines. The package is actively maintained, well-starred, and supports modern Python versions (3.10\u20133.14).","worth_installing":"Yes. Supervision is a mature, actively maintained toolkit with no known vulnerabilities, permissive licensing, and low install friction. It solves real friction points in computer vision workflows\u2014dataset loading, format conversion, and visualization\u2014and integrates cleanly with model frameworks. Install it if you work with detection, segmentation, or classification models and want to avoid reinventing dataset utilities."},"id":"supervision","links":{"html":"https://skillfed.io/packages/supervision","md":"https://skillfed.io/packages/supervision.md","pypi":"https://pypi.org/project/supervision/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":"MIT","license_treatment":"permissive","name":"supervision","python_support":"supports_current","summary":"A set of easy-to-use utils that will come in handy in any Computer Vision project"},"popularity":{"monthly_downloads":1387355,"position":3971,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.30.0"}