Create a larger set of images by slightly altering your original images for machine learning projects.
From the README
pip install imgaug
- Optimized for high performance
- Easy to apply augmentations only to some images
- Easy to apply augmentations in random order
- Support for
- Images (full support for uint8, for other dtypes see documentation)
- Heatmaps (float32), Segmentation Maps (int), Masks (bool)
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
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Free · no card · unsubscribe anytimeImage augmentation for machine learning experiments.
imgaug has 14.7k stars on GitHub. It has been forked 2.5k times. imgaug is written mainly in Python. It has been in active development since 2015. imgaug is available under the MIT license. Its main topics are affine-transformation, augment-images, augmentation, bounding-boxes.
What is imgaug?
Image augmentation for machine learning experiments.
Is imgaug open source?
imgaug is an open-source project. It is released under the MIT license.
Is imgaug free?
Yes. imgaug is free and open source — you can use, modify and self-host it.
What license does imgaug use?
imgaug is available under the MIT license.
What language is imgaug written in?
imgaug is written mainly in Python.
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