Image Augmentations with Albumentations

Image Augmentations with Albumentations

Learn how to Implement Image Augmentation for Your Deep Learning Models. In this article, we’ll see how this can be done using the open-source Albumentation package.

One of the biggest problems in the deep learning field is obtaining enough training data. As we know, deep learning models perform better with more training data. Very little data could lead to poor performance as well as overfitting. This problem is addressed via image augmentation: This is a technique used to generate more training samples from existing data. In this article, we’ll see how this can be done using the open-source Albumentation package.

Image Augmentation

With image augmentation, various transformations are applied to the original data in order to generate new data. This can be flipping or shearing the image. Other ways of doing this include blurring or cropping the image. This technique is applied to classification, segmentation, pose estimation, and object detection tasks. It helps to prevent overfitting as well as to improve a model’s performance.


The Albumentations package provides a variety of techniques for performing image augmentations. I have seen it being widely used in Kaggle competitions. It is also used in industry, deep learning research, and open-source projects. The tool is loved for its performance and speed. It uses NumPy and Open CV for data processing. At the moment, Albumentations supports 60 image augmentations. The tool also allows developers to easily add new augmentations and use them in their machine learning pipeline.

deep-learning model-performance heartbeat image-augmentation

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