Book Image

Hands-On Java Deep Learning for Computer Vision

By : Klevis Ramo
Book Image

Hands-On Java Deep Learning for Computer Vision

By: Klevis Ramo

Overview of this book

Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The book is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy.

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Table of Contents (8 chapters)
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
Introduction to Computer Vision and Training Neural Networks
Introduction to Computer Vision and Training Neural Networks
The computer vision state
Exploring neural networks
How does a neural network learn?
Organizing data and applications
Effective training techniques
Optimizing algorithms
Configuring the training parameters of the neural network
Representing images and outputs
Building a handwritten digit recognizer
Summary
2
Convolutional Neural Network Architectures
Convolutional Neural Network Architectures
Understanding edge detection
Building a Java edge detection application
Convolution on RGB images
Working with convolutional layers' parameters
Pooling layers
Building and training a Convolution Neural Network
Improving the handwritten digit recognition application
Summary
3
Transfer Learning and Deep CNN Architectures
Transfer Learning and Deep CNN Architectures
Working with classical networks
Using residual networks for image recognition
The power of 1 x 1 convolutions and the inception network
Applying transfer learning
Neural networks
Building an animal image classification – using transfer learning and VGG-16 architecture
Summary
4
Real-Time Object Detection
Real-Time Object Detection
Resolving object localization
Object detection with the sliding window solution
Convolutional sliding window
Detecting objects with the YOLO algorithm
Max suppression and anchor boxes
Building a real-time video, car, and pedestrian detection application
Summary
5
Creating Art with Neural Style Transfer
Creating Art with Neural Style Transfer
What are convolution network layers learning?
Neural style transfer
Applying content cost function
Applying style cost function
Building a neural network that produces art
Summary
6
Face Recognition
Face Recognition
Problems in face detection
Differentiating inputs with Siamese networks
Exploring triplet loss
Binary classification
Building a face recognition Java application
Summary
7
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Building a face recognition Java application

We are going through the code details of building a Java face recognition application, and by the end of this section, we shall be able to create a live demo version of the recognition application.

Let's begin with exploring the code by creating a basic network:

Training the model for face recognition is time consuming and hard. To take care of this, we shall be using transfer recognition and to obtain pre-trained weights. The time model we choose is based on the inception network GoogLeNet, and this will be used to obtain the encodings or the activations of the last layer. Post this, instead of calculating the distance between them directly, we shall normalize the encodings using the L2-norm and only after this, we shall use the distance between the images.

Notice that we are not using the squared distance but rather just...

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