The performance of a deep learning model is influenced by large datasets and diversity of the dataset. But, there might be situations where the dataset is simply not large enough or diverse enough. In such cases, data augmentation is used. Data augmentation is a technique that enables you to significantly increase the diversity of data available for training models, without actually collecting new data. Although deep learning models come with inbuilt methods to augment the data, these can be inefficient or lacking some required functionality.

In this article, we will learn about an augmentation package for machine learning specifically using the PyTorch framework called Albumentation.

What is albumentation library?

Albumentation is a fast image augmentation library and easy to use with other libraries as a wrapper. The package is written on NumPy, OpenCV, and imgaug. What makes this library different is the number of data augmentation techniques that are available.

#albumentation #data augmentation #deep learning #image augmentation #python