imgaug
Image augmentation library for deep neural networks
Decision gist · record as of 2026-08-14
Yes, with caution about maintenance. imgaug is widely used (top 5000 on PyPI, 1351638 monthly downloads) and solves a real problem in machine learning workflows. Install friction is low and the MIT license is unrestrictive. However, the package is dormant—last release was 2020-02-05, and two security vulnerabilities are recorded. For new projects, verify that the vulnerabilities do not affect your use case and that dependencies remain compatible with your Python version. For existing projects already using it, the risk of upgrading may outweigh the benefit.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel.
- Depends on nine common scientific libraries (numpy, scipy, Pillow, scikit-image, opencv-python, matplotlib, imageio, Shapely, six).
- Maintenance is dormant—last release was 2020-02-05, though the repository remains active with a recent commit on 2024-07-30.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows commercial and private use with minimal restrictions, typical for research and production machine learning projects.
last release 2020-02-05 (2382 days) · last repo commit 2024-07-30 · 14,739 stars
2 known vulnerabilities (OSV.dev, 2026-08-14) · 1,351,638 downloads/mo, #4,015 on PyPI
Alternatives
Verify before relying
pip install imgaug
import imgaug.augmenters as iaa
import numpy as np
seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.Affine(rotate=(-10, 45))])
image = numpy.zeros((32, 32, 3), dtype=numpy.uint8)
augmented = seq(image=image)- Current compatibility with Python versions beyond 3.8 (classifiers list 3.8 as latest)
- Compatibility status with numpy 1.18+ and scipy versions released after 2020
- Whether the two known security vulnerabilities (GHSA-g82g-j283-hj97, PYSEC-2026-356) affect typical augmentation workflows
What it is and what it does
imgaug is a Python library that generates augmented versions of input images by applying randomized transformations. It converts a single image into many slightly altered variants—rotated, cropped, with noise added, colors shifted, or perspective changed—to artificially expand training datasets and reduce overfitting in machine learning models.
The library handles not just raw images but also associated annotations: heatmaps, segmentation maps, keypoints, bounding boxes, polygons, and line strings. Transformations automatically align across all these data types (e.g., rotating an image and its bounding boxes by the same angle with zero extra code). It supports probability distributions as parameters, multicore augmentation, and a large collection of augmentation techniques including affine transforms, blurring, color shifts, dropout, and artistic effects.
Use it for
- Expand a small labeled image dataset for training computer vision models by generating variations that reduce overfitting.
- Augment bounding boxes and keypoints alongside images for object detection or pose estimation tasks.
- Apply consistent transformations to segmentation maps and their corresponding images during training.
- Generate synthetic training data by applying random augmentations in parallel across multiple CPU cores.
- Combine multiple augmentation techniques in a sequence with random selection and probability control.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with caution about maintenance.
imgaug is widely used (top 5000 on PyPI, 1351638 monthly downloads) and solves a real problem in machine learning workflows. Install friction is low and the MIT license is unrestrictive. However, the package is dormant—last release was 2020-02-05, and two security vulnerabilities are recorded. For new projects, verify that the vulnerabilities do not affect your use case and that dependencies remain compatible with your Python version. For existing projects already using it, the risk of upgrading may outweigh the benefit.
Install
imgaug on PyPI
Before you install
Low install friction with a pure-Python wheel. Depends on nine common scientific libraries (numpy, scipy, Pillow, scikit-image, opencv-python, matplotlib, imageio, Shapely, six). Maintenance is dormant—last release was 2020-02-05, though the repository remains active with a recent commit on 2024-07-30.
License in practice
MIT license (permissive) allows commercial and private use with minimal restrictions, typical for research and production machine learning projects.
Quickstart
pip install imgaug
import imgaug.augmenters as iaa
import numpy as np
seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.Affine(rotate=(-10, 45))])
image = numpy.zeros((32, 32, 3), dtype=numpy.uint8)
augmented = seq(image=image)Verify before relying
- Current compatibility with Python versions beyond 3.8 (classifiers list 3.8 as latest)
- Compatibility status with numpy 1.18+ and scipy versions released after 2020
- Whether the two known security vulnerabilities (GHSA-g82g-j283-hj97, PYSEC-2026-356) affect typical augmentation workflows
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagessixnumpyscipyPillowmatplotlibscikit-imageopencv-pythonimageioShapely |
| Maintenance | Dormant 2,382 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,351,638 / month, #4,015 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | 2 GHSA-g82g-j283-hj97, PYSEC-2026-356 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Software Development :: Libraries :: Python Modules |
Evidence: imgaug-0.4.0-py2.py3-none-any.whl
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See also albumentations · batchgenerators · ttach · batchgeneratorsv2 · audiomentations · deskew · mtcnn · augmax · torchio · color-operations