{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/2"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/4"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"},{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"},{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Astronomy","url":"https://skillfed.io/packages/category/scientific-engineering-astronomy"},{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"},{"label":"Atmospheric Science","url":"https://skillfed.io/packages/category/scientific-engineering-atmospheric-science"}],"enrichment":{"capability":"Albumentations applies image transformations to training data, supporting classification, segmentation, object detection, and pose estimation with a unified API for images, masks, bounding boxes, and keypoints.","skillfed_tags":["image-augmentation","computer-vision","deep-learning"],"use_cases":["Augment training datasets for image classification to improve model generalization without collecting new labeled data.","Apply consistent transformations to images and bounding boxes for object detection model training.","Generate varied training samples for semantic segmentation by transforming both images and pixel-level masks.","Augment keypoint annotations alongside images for pose estimation and human pose recognition tasks.","Preprocess medical imaging data with specialized augmentations for anomaly detection and diagnostic tasks.","Create synthetic training variations for autonomous driving datasets with coordinated image and bounding-box transforms."],"what_it_does":"Albumentations is a Python library for augmenting training data in computer vision and deep learning tasks. It provides a unified API to apply transformations to images, masks, bounding boxes, and keypoints\u2014supporting classification, semantic and instance segmentation, object detection, and pose estimation. The library separates pixel-level transforms from spatial transforms and applies them consistently across all data types in a single pipeline.\n\nThe package depends on numpy, scipy, PyYAML, pydantic, albucore, opencv-python-headless, typing-extensions, and eval-type-backport. It is classified as Production/Stable and requires Python 3.9 or higher. The last release was 444 days ago.","worth_installing":"Yes. Albumentations is a mature, permissively licensed library with low install friction and no known vulnerabilities. The aging maintenance status (444 days since release) is a minor concern but does not block use for stable augmentation pipelines. Install it if you need a unified, production-grade augmentation API for computer vision tasks."},"id":"albumentations","links":{"html":"https://skillfed.io/packages/albumentations","md":"https://skillfed.io/packages/albumentations.md","pypi":"https://pypi.org/project/albumentations/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-05-27","license_spdx":null,"license_treatment":"permissive","name":"albumentations","python_support":"supports_current","summary":"Fast, flexible, and advanced augmentation library for deep learning, computer vision, and medical imaging. Albumentations offers a wide range of transformations for both 2D (images, masks, bboxes, keypoints) and 3D (volumes, volumetric masks, keypoints) data, with optimized performance and seamless integration into ML workflows."},"popularity":{"monthly_downloads":5497903,"position":2087,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.0.8"}