Book Image

Deep Learning for Computer Vision

By : Rajalingappaa Shanmugamani
Book Image

Deep Learning for Computer Vision

By: Rajalingappaa Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface

Other popular image testing datasets 


The MNIST dataset is the most commonly used dataset for testing the algorithms. But there are other datasets that are used to test image classification algorithms.

The CIFAR dataset

The Canadian Institute for Advanced Research (CIFAR)-10 dataset has 60,000 images with 50,000 images for training and 10,000 images for testing. The number of classes is 10. The image dimension is 32 pixels by 32 pixels. The following are randomly selected images from each of the class:

The images are tiny and just contain one object.  The CIFAR-100 dataset contains the same number of images but with 100 classes. Hence, there are only 600 images per class. Each image comes with a super label and a fine label. This dataset is available at tf.keras.datasets if you wish to experiment.

The Fashion-MNIST dataset

Fashion-MNIST is a dataset created as an alternative to the MNIST dataset. This dataset created as MNIST is considered as too easy and this can be directly replaced with MNIST...