Exploring Different Image Augmentation Methods in TensorFlow/Keras

Enhancing Deep Learning with Data Augmentation

Data augmentation is a crucial technique in machine learning and deep learning pipelines that helps to enhance the performance and generalization of models. It involves applying various transformations to the existing dataset, thereby creating new examples that are variations of the original data. In the context of image data, data augmentation involves operations like rotation, flipping, zooming, and more. In this article, we’ll delve into different data augmentation methods using the TensorFlow/Keras framework.

Table of Contents:

· 1. Why Data Augmentation?
· 2. Data Augmentation Techniques
∘ 2.1 Method 1: Using ImageDataGenerator function
∘ (1) Horizontal and Vertical Flipping
∘ (2) Rotation
∘ (3) Zooming
∘ (4) Brightness and Contrast Adjustment
∘ (5) Shearing
∘ (6) Channel Shift
∘ (7) Normalization
∘ (8) Implementation Example
∘ 2.2 Method 2: Using Augmentation Layer
∘ (1) RandomFlip Layer
∘ (2) RandomRotation Layer
∘ (3) RandomContrast Layer
∘ (4) RandomBrightness layer
∘ (5) Define a Keras Augmentation layers
∘ (5) Implementation Example
· Conclusion