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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: 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 (12 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started
Getting Started
Understanding deep learning
Deep learning for computer vision
Development environment setup
Summary
2
Image Classification
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Image Classification
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
Summary
3
Image Retrieval
Image Retrieval
Understanding visual features
Model inference
Content-based image retrieval
Summary
4
Object Detection
Object Detection
Detecting objects in an image
Exploring the datasets
Localizing algorithms
Detecting objects
Object detection API
The YOLO object detection algorithm
Summary
5
Semantic Segmentation
Semantic Segmentation
Predicting pixels
Datasets
Algorithms for semantic segmentation
Ultra-nerve segmentation
Segmenting satellite images
Segmenting instances
Summary
6
Similarity Learning
Similarity Learning
Algorithms for similarity learning
Human face analysis
Summary
7
Image Captioning
Image Captioning
Understanding the problem and datasets
Understanding natural language processing for image captioning
Approaches for image captioning and related problems
Implementing attention-based image captioning
Summary
8
Generative Models
Generative Models
Applications of generative models
Neural artistic style transfer
Generative Adversarial Networks
Visual dialogue model
Summary
9
Video Classification
Video Classification
Understanding and classifying videos
Extending image-based approaches to videos
Summary
10
Deployment
Deployment
Performance of models
Deployment in the cloud
Deployment of models in devices
Summary
11
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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
End of Section 1
Next Section
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