{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"},{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"}],"enrichment":{"capability":"Albucore provides optimized atomic image processing functions that automatically select the fastest implementation (NumPy, OpenCV, NumKong, StringZilla, or PyTorch) based on input characteristics for efficient uint8 and float32 image manipulation.","skillfed_tags":["image-processing","performance-optimization","deep-learning"],"use_cases":["Accelerate image augmentation pipelines in deep learning workflows by routing arithmetic and normalization operations to the fastest available backend.","Preprocess uint8 or float32 images for computer vision models with automatic dtype-aware optimization.","Normalize image batches using precomputed statistics (e.g., ImageNet mean/std) via lookup tables for uint8 or fused NumPy operations for float32.","Apply elementwise transformations (exponential, logarithm, square root) to large float32 image arrays with automatic selection between NumPy and OpenCV.","Build custom image processing pipelines that adapt to input characteristics without manual backend selection."],"what_it_does":"Albucore is a library of optimized image processing functions designed as the foundation for AlbumentationsX, an image augmentation library. It implements arithmetic operations (multiply, add, power, add_weighted, multiply_add), elementwise math (exp, log, sqrt), and normalization functions that work on images in channel-last format with explicit channel dimensions. The core feature is automatic backend routing: each operation benchmarks across NumPy, OpenCV, NumKong, StringZilla, and eligible PyTorch implementations and selects the fastest path for the given input dtype, size, layout, and channel count.\n\nMost functions support uint8 and float32 dtypes; elementwise operations (exp, log, sqrt) are float32-only. Images must follow strict shape conventions: single images as (H, W, C), batches as (N, H, W, C), and 3D volumes as (D, H, W, C), with channel dimension always required even for grayscale. The library depends on numpy, numkong, and stringzilla at runtime, and requires Python 3.10+. Full functionality also requires a pre-installed PyTorch build (CPU, CUDA, or MPS selected separately) and OpenCV.","worth_installing":"Yes, with conditions. Albucore is actively maintained, has no known vulnerabilities, and offers genuine performance value through automatic backend routing for image operations. Install it if you are building image augmentation or preprocessing pipelines and want to avoid manual optimization. The main friction is the mandatory separate PyTorch installation and the requirement for Python 3.10+; ensure your environment meets these prerequisites before installing."},"id":"albucore","links":{"html":"https://skillfed.io/packages/albucore","md":"https://skillfed.io/packages/albucore.md","pypi":"https://pypi.org/project/albucore/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":"MIT","license_treatment":"permissive","name":"albucore","python_support":"supports_current","summary":"High-performance image processing functions for deep learning and computer vision."},"popularity":{"monthly_downloads":3930017,"position":2450,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.13"}