Image augmentation adds more variation to the training dataset and if it is done right, reflects the variation in the real data and therefore helps the model to generalize better.
When training deep networks to classify images, you can sometimes get a significant increase in validation accuracy if you augment the data. This means that you randomly transform the images in certain ways, for example, flipping and zooming.
Image augmentation is helpful for both large and small datasets.
How to use image augmentation
Navigate to the Modeling view on the Peltarion Platform.
Add a Random transformation block.