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.
Table of Contents (8 chapters)

Resolving object localization

In this section, we'll look at an interesting solution to solving the object localization and detection problem. In addition, we'll learn how to label data and modify the prediction layer based on the need of the localization problem. We'll extend this to landmark detection, which should enable us to do fascinating things, such as detect a person smiling.

We're familiar with the image classification problem; we need to determine whether the image contains any of the desired classes, such as whether the object is a car or a person. Apart from image recognition, we also want to find the position of the object or localize the object by marking it with a bounding box:

We'll also extend the idea of localization to multiple objects during the course of this chapter.

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