{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"},{"label":"Medical Science Apps.","url":"https://skillfed.io/packages/category/scientific-engineering-medical-science-apps"}],"enrichment":{"capability":"Reimplemented data augmentation transforms for deep learning, optimized for CPU performance and designed to work with torch and numpy on individual samples rather than batches.","skillfed_tags":["medical-imaging","data-augmentation","pytorch"],"use_cases":["Augment medical imaging datasets for segmentation tasks while preserving label correspondence","Apply sample-level transforms in medical image analysis pipelines built with nnU-Net","Preprocess images, masks, and keypoints together with type-aware augmentation for detection workflows","Build CPU-optimized data loading pipelines for deep learning without GPU augmentation overhead"],"what_it_does":"batchgeneratorsv2 is a rewrite of the batchgenerators framework for medical image analysis and deep learning pipelines. It provides data augmentation transforms that explicitly handle different data types\u2014images, segmentation masks, pixel-wise regression targets, keypoints, and bounding boxes\u2014with transforms applied at the sample level rather than batch level. The package prioritizes CPU performance and uses PyTorch implementations where possible, though it remains in early development with only a subset of the original transforms currently available.\n\nThe package is built for workflows that need reliable, type-aware augmentation for medical imaging and computer vision tasks. It depends on torch, numpy, and the original batchgenerators package, making it suitable for projects already using PyTorch. The explicit distinction between data types means you don't have to manually manage which transforms apply to which parts of your data.","worth_installing":"Yes, with conditions. Install if you need sample-level, type-aware augmentation for medical imaging or computer vision and are comfortable with an early-stage library (Planning status). The low install friction, active maintenance, and permissive Apache license make it low-risk. However, verify that the currently implemented transforms cover your use case, since the package explicitly covers only a subset of the original batchgenerators."},"id":"batchgeneratorsv2","links":{"html":"https://skillfed.io/packages/batchgeneratorsv2","md":"https://skillfed.io/packages/batchgeneratorsv2.md","pypi":"https://pypi.org/project/batchgeneratorsv2/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-16","license_spdx":null,"license_treatment":"permissive","name":"batchgeneratorsv2","python_support":"supports_current","summary":"Batchgenerators but better"},"popularity":{"monthly_downloads":125259,"position":11828,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.5"}