Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
If you want to know what changed in the latest versions, please refer to the [Release Notes](https://github.com/albumentations-team/albumentations/releases) page.
Image augmentation for machine learning experiments.
Albumentations no longer uses the imgaug library by default. All previous imgaug augmentations in the library are reimplemented in Albumentations with the same API (but you can still install Albumentations with imgaug if you need the old augmentations).
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AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
We started to implement differentiable augmentations as a part of AutoAlbument (a tool that automatically searches for the best augmentation policies for your data) - https://github.com/albumentations-team/autoalbument, it is more a research project.
Data augmentation strategies for object detection? Could you point me to good resources or best practices you know of?
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Albumentations 1.1.0 Was Released
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Optimization for semantic segmentation!
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[N] Facebook AI Open Sources AugLy: A New Python Library For Data Augmentation To Develop Robust Machine Learning Models
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[UPDATE!] Recognize trinkets with Isaac Item Recognizer! And also a few useful features in my newest update.
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