Augmenting Images for Deep Learning

Using Python to augment data by flipping, adjusting brightness, color jitter and random noise

(source: author)

Data collection can be time-consuming, expensive and, honestly, boring. When our ability to collect data is limited, data augmentation can play an important role. It can help us to build a more robust dataset, reduce overfitting and increase the amount of training data.

We will discuss data augmentation and its benefits. We will also keep things practical. That is by walking through Python code used to augment images. Specifically, we will discuss how to do these augmentations:

• Flipping images
• Adjusting brightness
• Random colour jitter
• Random noise (Gaussian, salt and pepper, and deletion)

To end, we will discuss best practices when it comes to augmenting images. Specifically, how to best validate our model and test it in production. You can also watch the video below for an introduction to the topic: