Explore image augmentations using a convenient tool

The easy way to understand how image transformations work powered by Albumentations and Streamlit

The interface of the service. Original photo by Binyamin Mellish from Pexels

Image augmentation is an essential concept that became an industry-standard in Computer Vision. It makes ML models more precise and robust, improves generalization, and helps to create high accuracy models with minimal data.

There are ready to use tools to apply different transformations to your training pipeline. My choice is Albumentations library

But to use it effectively, you should understand how it works. In this article, I will tell you how to choose the right set of transformations and present you with a simple tool for the visualization and exploration of different augmentation techniques.

If you are familiar with the “augmentations” concept and already use Albumentations, you can follow this link right now; there you can play with different transformations in the interactive interface and see the result in real-time (like on a gif above), or you can even take the source code and run it locally.

Otherwise, I advise you to continue reading and check it afterward.