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)

Max suppression and anchor boxes

Now we'll solve the problem of choosing the best bounding box from many such boxes generated during the testing time, and the challenge of when a window happens to own more than one bounding box center.

Max suppression

Although during training we assign only one bounding box to a window, the one that owned the center, during testing it can happen that many windows think that they have the center of the best bounding box.

For example, we may have three bounding boxes, and therefore three windows on the center of the bounding boxes, and each of these windows thinks that they have the best bounding box:

But what we'll need is only one bounding box, and preferably the best one. Max suspension...