batchgeneratorsv2
Batchgenerators but better
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9 and torch installed.
- GPU-based augmentation is not supported; CPU optimization is the design focus.
- Low friction install with three core dependencies (torch, numpy, batchgenerators).
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with attribution and modification notice requirements. No restrictions on redistribution or proprietary applications.
last release 2026-07-16 (29 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 125,259 downloads/mo, #11,828 on PyPI
Alternatives
Verify before relying
pip install batchgeneratorsv2
from batchgeneratorsv2.transforms import SomeTransform
import torch
import numpy as np
# Apply transforms at sample level
transform = SomeTransform()
augmented_sample = transform(sample)- Which specific transforms are currently implemented beyond the description's mention of a 'small subset'
- Performance benchmarks comparing torch-based implementations to the original batchgenerators
- API stability guarantees given the 'work in progress' status and Planning classifier
What it is and 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—images, segmentation masks, pixel-wise regression targets, keypoints, and bounding boxes—with 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
batchgeneratorsv2 on PyPI
Before you install
Low friction install with three core dependencies (torch, numpy, batchgenerators). Active maintenance status with a release 29 days ago. Early development stage (Planning) means the API and feature set may still shift.
Requires Python >=3.9 and torch installed. GPU-based augmentation is not supported; CPU optimization is the design focus.
License in practice
Apache 2.0 permissive license allows commercial and derivative use with attribution and modification notice requirements. No restrictions on redistribution or proprietary applications.
Quickstart
pip install batchgeneratorsv2
from batchgeneratorsv2.transforms import SomeTransform
import torch
import numpy as np
# Apply transforms at sample level
transform = SomeTransform()
augmented_sample = transform(sample)Verify before relying
- Which specific transforms are currently implemented beyond the description's mention of a 'small subset'
- Performance benchmarks comparing torch-based implementations to the original batchgenerators
- API stability guarantees given the 'work in progress' status and Planning classifier
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagestorchnumpybatchgenerators |
| Maintenance | Actively maintained 29 days since the last release |
| First released | |
| Downloads | 125,259 / month, #11,828 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 1 - PlanningIntended Audience :: DevelopersIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: Medical Science Apps. |
Evidence: batchgeneratorsv2-0.3.5-py3-none-any.whl
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