Book Image

Data Augmentation with Python

By : Duc Haba
Book Image

Data Augmentation with Python

By: Duc Haba

Overview of this book

Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.
Table of Contents (17 chapters)
Part 1: Data Augmentation
Part 2: Image Augmentation
Part 3: Text Augmentation
Part 4: Audio Data Augmentation
Part 5: Tabular Data Augmentation

Image biases

Pluto has access to thousands of datasets, and downloading these datasets is as simple as replacing the URL. In particular, he will download the following datasets:

  • The State Farm distracted drivers detection (SFDDD) dataset
  • The Nike shoes dataset
  • The Grapevine leaves dataset

Let’s start with the SFDDD dataset.

State Farm distracted drivers detection

To start, Pluto will slow down and explain every step in downloading the real-world datasets, even though he will use a wrapper function, which seems deceptively simple. Pluto will not write any Python code for programmatically computing the bias fairness matrix values. He relies on your observation to spot the biases in the dataset.

Give Pluto a command to fetch, and he will download and unzip or untar the data to your local disk space. For example, in retrieving data from a competition, ask Pluto to fetch it with the following command:

# fetch real-world data