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Data Augmentation with Python

You're reading from   Data Augmentation with Python Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246451
Length 394 pages
Edition 1st Edition
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Author (1):
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Duc Haba Duc Haba
Author Profile Icon Duc Haba
Duc Haba
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Data Augmentation
2. Chapter 1: Data Augmentation Made Easy FREE CHAPTER 3. Chapter 2: Biases in Data Augmentation 4. Part 2: Image Augmentation
5. Chapter 3: Image Augmentation for Classification 6. Chapter 4: Image Augmentation for Segmentation 7. Part 3: Text Augmentation
8. Chapter 5: Text Augmentation 9. Chapter 6: Text Augmentation with Machine Learning 10. Part 4: Audio Data Augmentation
11. Chapter 7: Audio Data Augmentation 12. Chapter 8: Audio Data Augmentation with Spectrogram 13. Part 5: Tabular Data Augmentation
14. Chapter 9: Tabular Data Augmentation 15. Index 16. Other Books You May Enjoy

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
pluto.fetch_kaggle_comp_data...
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