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PROJECT


Data Augmentation for ML Datasets

In this project, we’ll get hands-on experience with data augmentation. We’ll explore different libraries for data augmentation, including OpenCV, TensorFlow, and imgaug. Finally, we’ll learn about advanced augmentation functions in this project.

Data Augmentation for ML Datasets

You will learn to:

Apply data augmentation.

Perform image preprocessing.

Make image augmentation pipelines.

Handle image datasets.

Skills

Data Visualization

Machine Learning Fundamentals

Prerequisites

Intermediate knowledge of image processing

Basic understanding of computer vision

Intermediate knowledge of Python

Basic understanding of TensorFlow

Technologies

Python

OpenCV

Project Description

An important way to increase the performance and outcomes of a machine learning model is through data augmentation. ​​In the case of a computer vision model, it can increase the number of images and help the model to make better generalizations.

We’ll start by making small transformations to images using OpenCV. Next, we’ll make transformations to a batch of images using TensorFlow. Finally, we’ll make a data augmentation pipeline with imgaug that will apply multiple random transformations to an image.

Project Tasks

1

Getting Started

Task 0: Introduction

2

OpenCV

Task 1: Load the Dataset

Task 2: Translate an Image

Task 3: Rotate and Scale an Image

Task 4: Add Perspective

3

Tensorflow

Task 5: Apply Affine Transformations

Task 6: Apply Batch Transformations

4

imgaug

Task 7: Apply Sequential Transformations

Task 8: Add Randomness

Task 9: Apply More Affine Transformations

Congratulations