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Deep Learning for Computer Vision
Deep Learning for Computer Vision

Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

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Deep Learning for Computer Vision

Image Classification

Image classification is the task of classifying a whole image as a single label. For example, an image classification task could label an image as a dog or a cat, given an image is either a dog or a cat. In this chapter, we will see how to use TensorFlow to build such an image classification model and also learn the techniques to improve the accuracy.

We will cover the following topics in this chapter:

  • Training the MNIST model in TensorFlow
  • Training the MNIST model in Keras
  • Other popular image testing datasets
  • The bigger deep learning models
  • Training a model for cats versus dogs
  • Developing real-world applications

Training the MNIST model in TensorFlow

In this section, we will learn about the Modified National Institute of Standards and Technology (MNIST) database data and build a simple classification model. The objective of this section is to learn the general framework for deep learning and use TensorFlow for the same. First, we will build a perceptron or logistic regression model. Then, we will train a CNN to achieve better accuracy. We will also see how TensorBoard helps visualize the training process and understand the parameters. 

The MNIST datasets

The MNIST data has handwritten digits from 0–9 with 60,000 images for training and 10,000 images for testing. This database is widely used to try algorithms...

Training the MNIST model in Keras

In this section, we will use the same model as defined in the previous section using tf.keras APIs. It is better to learn both Keras and layers packages from TensorFlow as they could be seen at several open source codes. The objective of the book is to make you understand various offerings of TensorFlow so that you can build products on top of it. 

"Code is read more often than it is written."

Bearing in mind the preceding quote, you are shown how to implement the same model using various APIs. Open source code of any implementation of the latest algorithms will be a mix of these APIs. Next, we will start with the Keras implementation. 

Preparing the dataset

...

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...

The bigger deep learning models

We will go through several model definitions that have achieved state-of-the-art results in the ImageNet competitions. We will look at them individually on the following topics.

The AlexNet model

Training a model for cats versus dogs

In this section, we will prepare and train a model for predicting cats versus dogs and understand some techniques which increase the accuracy. Most of the image classification problems come into this paradigm. Techniques covered in this section, such as augmentation and transfer learning, are useful for several problems.

Preparing the data

For the purpose of classification, we will download the data from kaggle and store in an appropriate format. Sign up and log in to www.kaggle.com and go to https://www.kaggle.com/c/dogs-vs-cats/data. Download the train.zip and test1.zip files from that page. The train.zip file contains 25,000 images of pet data. We will use only a portion of...

Developing real-world applications

Recognizing cats and dogs is a cool problem but less likely a problem of importance. Real-world applications of image classification used in products may be different. You may have different data, targets, and so on. In this section, you will learn the tips and tricks to tackle such different settings. The factors that should be considered when approaching a new problem are as follows:

  • The number of targets. Is it a 10 class problem or 10,000 class problem?
  • How vast is the intra-class variance? For example, does the different type of cats have to be identified under one class label?
  • How vast is the inter-class variance? For example, do the different cats have to be identified?
  • How big is the data?
  • How balanced is the data? 
  • Is there already a model that is trained with a lot of images?
  • What is the requisite for deployment inference...

Summary

We have covered basic, yet useful models for training classification tasks. We saw a simple model for an MNIST dataset with both Keras and TensorFlow APIs. We also saw how to utilize TensorBoard for watching the training process. Then, we discussed state-of-the-art architectures with some specific applications. Several ways to increase the accuracy such as data augmentation, training on bottleneck layers, and fine-tuning a pre-trained model were also covered. Tips and tricks to train models for new models were also presented.

In the next chapter, we will see how to visualize the deep learning models. We will also deploy the trained models in this chapter for inference. We will also see how to use the trained layers for the application of an image search through an application. Then, we will understand the concept of autoencoders and use it for the dimensionality of...

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Key benefits

  • Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
  • Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more
  • Includes tips on optimizing and improving the performance of your models under various constraints

Description

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.

Who is this book for?

This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

What you will learn

  • Set up an environment for deep learning with Python, TensorFlow, and Keras
  • Define and train a model for image and video classification
  • Use features from a pre-trained Convolutional Neural Network model for image retrieval
  • Understand and implement object detection using the real-world Pedestrian Detection scenario
  • Learn about various problems in image captioning and how to overcome them by training images and text together
  • Implement similarity matching and train a model for face recognition
  • Understand the concept of generative models and use them for image generation
  • Deploy your deep learning models and optimize them for high performance
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Publication date : Jan 23, 2018
Length: 310 pages
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Language : English
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Table of Contents

11 Chapters
Getting Started Chevron down icon Chevron up icon
Image Classification Chevron down icon Chevron up icon
Image Retrieval Chevron down icon Chevron up icon
Object Detection Chevron down icon Chevron up icon
Semantic Segmentation Chevron down icon Chevron up icon
Similarity Learning Chevron down icon Chevron up icon
Image Captioning Chevron down icon Chevron up icon
Generative Models Chevron down icon Chevron up icon
Video Classification Chevron down icon Chevron up icon
Deployment Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.2
(22 Ratings)
5 star 45.5%
4 star 9.1%
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2 star 9.1%
1 star 36.4%
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Neeraj Kumar Feb 11, 2018
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Very well written and easy to understand for beginners.
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shaafi mohamed Feb 03, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A really nice book for people who recently started working with Machine learning or who want to learn machine learning. The book starts with the very fundamental basics of ANN and describes step by step application of popular machine learning packages like tensorflow. It covers all range of AI tools such as CNN, RNN etc tec .. a good book for beginners and also AI specialists. The main good thing is no complex mathematical notations, and no head spinning :-D. The concepts are presented in a simple and easy to understand for any person with a small mathematical background.
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RISHIKESAN V Feb 13, 2018
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This book covers all the fundamental concepts in machine learning and image processing. More practical problems were discussed. It is very useful for engineers who want to excel in an interdisciplinary area.
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Parth Sep 06, 2018
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Good book
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Shiva Sitaraman Feb 10, 2018
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Excellent way to learn Deep learning. This book provides hands on examples in Keras. Definitely a must read for beginners in Deep Learning.
Amazon Verified review Amazon
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