Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246451
Length 394 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Duc Haba Duc Haba
Author Profile Icon Duc Haba
Duc Haba
Arrow right icon
View More author details
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

Combining

The techniques or filters in geometric transformations can be readily combined with most image topics. For example, you can mix horizontal flip, cropping, resizing, padding, rotation, translation, and noise injection for many domains, such as people, landscapes, cityscapes, and others.

In addition, taking landscape as a topic, you can combine many filters in photometric transformations, such as darkening, lightening, color saturation, and contrast. Hue shifting and color casting may not apply to landscape photos. However, advanced photographic transformation filters, such as adding rain or snow to landscape images, are acceptable.

There’s more: you can add random erasing to landscape images. As a result, 1,000 landscape images may increase to 200,000 photos for training. That is the power of image augmentation.

Fun challenge

Here is a thought experiment: should you augment the entire image dataset or only a segment?

Data augmentation can generate hundreds...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image