Table of contents

• Introduction
• Importing libraries and data
• Utility functions
• Creating torch dataset
• Transforms
  • CenterCrop
  • ColorJitter
  • Grayscale
  • Pad
  • RandomAffine
  • RandomApply
  • RandomChoice
  • RandomCrop
  • RandomResizedCrop
  • RandomHorizontalFlip
  • RandomVerticalFlip
• Conclusion
• Reference

Introduction

Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset.

Training deep learning neural network models on more data can result in more skillful models, and the augmentation techniques can create variations of the images that can improve the ability of the fit models to generalize what they have learned to new images.

Here I am trying to introduce few of the image augmentation techniques that are available in pytorch.

Imporing libraries and data