Visualizing Image Augmentation Pipelines with Streamlit
A simple albumentations demo; runs live in your browser
Across all data domains, images have received the greatest amount of attention. Just consider convolutional neural networks: their kernels lend themselves easily to detecting objects in images, regardless of their position. Or, the mighty ImageNet dataset. This million-samples collection had and still has a tremendous impact on the evolution of new network architectures. The sheer amount of samples has led researchers and practitioners (like you and me) to become creative and develop novel solutions. But what if you do not have the resources — time, money, manpower — to work with a dataset that large? Augmentations are the solutions, and that is what we’ll focus on now.
In short, augmentations are simply transformations of the data to improve a machine learning solution’s performance. For example, if we turn an image upside down, we have applied a transformation (horizontal flipping); that is, we have augmented the image. Besides common augmentations such as flipping or rotating a data sample, there are more advanced methods available, including blurring images or making them noisier. What lies behind these transformations is everybody’s favorite: mathematics.
