Book Image

Hands-On Computer Vision with Julia

By : Dmitrijs Cudihins
Book Image

Hands-On Computer Vision with Julia

By: Dmitrijs Cudihins

Overview of this book

Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it’s easy to use and lets you write easy-to-compile and efficient machine code. . This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You’ll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you’ll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease.
Table of Contents (11 chapters)
9
Assessments

Reusing your models

The training of neural networks can be a time-consuming process, so we need to track its progress as the process goes on. We need to learn to save and reload a trained model.

Every MXNet model consist of at least two files:

  • model_name-symbol.json: Describes the architecture
  • model_name-epoch.params: Describes the model weights

You can create them yourself or you can download them from the internet. We will cover the process of using pretrained networks in the next chapter.

Saving the model

Saving the model during the training process is done using the mx.do_checkpoint callback. A few important parameters are as follows:

  • prefix: This defines the prefix of the filenames to save the model
  • frequency: The...