albucore and albumentations

Albucore is an ecosystem sibling to Albumentations, specifically designed to extend and optimize its image transformation capabilities as a high-performance image processing library.

albucore
50
Established
albumentations
47
Emerging
Maintenance 10/25
Adoption 6/25
Maturity 16/25
Community 18/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 21/25
Stars: 23
Forks: 12
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 15,272
Forks: 1,704
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No Package No Dependents
Archived Stale 6m No Package No Dependents

About albucore

albumentations-team/albucore

A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.

Albucore is a high-performance library offering optimized functions for common image processing tasks in computer vision. It takes images as input and applies transformations like arithmetic operations, normalization, or geometric flips, outputting the modified images. This tool is designed for computer vision engineers and researchers who are building and training models.

computer-vision image-processing deep-learning-training data-augmentation model-training-optimization

About albumentations

albumentations-team/albumentations

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

Provides 70+ spatial and pixel-level transforms with a unified API for multiple computer vision tasks (classification, segmentation, object detection, pose estimation) and data types (images, masks, bounding boxes, keypoints). Optimized for speed with native support for PyTorch and TensorFlow pipelines, achieving consistent benchmark performance as the fastest augmentation library. Applies transformations deterministically across all target types simultaneously, ensuring spatial consistency for tasks requiring synchronized augmentation of related annotations.

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