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  • Book Overview & Buying Deep Learning for Computer Vision
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

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

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani
Buy this Book

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
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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1
Getting Started
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Getting Started
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Understanding deep learning
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Deep learning for computer vision
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Development environment setup
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Summary
2
Image Classification
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Image Classification
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Training the MNIST model in TensorFlow
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Training the MNIST model in Keras
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Other popular image testing datasets
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The bigger deep learning models
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Training a model for cats versus dogs
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Developing real-world applications
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Summary
3
Image Retrieval
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Image Retrieval
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Understanding visual features
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Model inference
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Content-based image retrieval
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Summary
4
Object Detection
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Object Detection
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Detecting objects in an image
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Exploring the datasets
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Localizing algorithms
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Detecting objects
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Object detection API
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The YOLO object detection algorithm
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Summary
5
Semantic Segmentation
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Semantic Segmentation
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Predicting pixels
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Datasets
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Algorithms for semantic segmentation
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Ultra-nerve segmentation
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Segmenting satellite images
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Segmenting instances
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Summary
6
Similarity Learning
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Similarity Learning
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Algorithms for similarity learning
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Human face analysis
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Summary
7
Image Captioning
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Image Captioning
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Understanding the problem and datasets
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Understanding natural language processing for image captioning
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Approaches for image captioning and related problems
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Implementing attention-based image captioning
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Summary
8
Generative Models
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Generative Models
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Applications of generative models
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Neural artistic style transfer
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Generative Adversarial Networks
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Visual dialogue model
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Summary
9
Video Classification
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Video Classification
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Understanding and classifying videos
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Extending image-based approaches to videos
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Summary
10
Deployment
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Deployment
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Performance of models
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Deployment in the cloud
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Deployment of models in devices
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Summary
11
Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think

Performance of models

Performance is important for both the training and the deployment of deep learning models. The training usually takes more time due to large data or big model architecture. The resultant models may be a bigger size and hence problematic to use in mobile devices where there is a constraint on RAM. More computing time results in more infrastructure cost. The inference time is critical in video applications. Due to the previously mentioned importance of performance, in this section, we will look at techniques to improve the performance. Reducing the model complexity is an easy option but results in decreasing accuracy. Here, we will focus on methods to improve the performance with an insignificant drop in accuracy. In the next section, we will discuss the option of quantization.  

Quantizing the models

The weights of deep learning models have 32-bit float values. When the weights are quantized to 8-bit, the decrease in accuracy is small and hence cannot be noticed in deployment...

Visually different images
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Tech Concepts
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Programming languages
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Deep Learning for Computer Vision
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