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Machine Learning Projects for Mobile Applications
Machine Learning Projects for Mobile Applications

Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML

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Machine Learning Projects for Mobile Applications

CNN Based Age and Gender Identification Using Core ML

In this chapter, we are going to build an iOS application to detect the age, gender, and emotion of a person from the camera feed or from the user's photo gallery. We will use existing data models that were built for the same purpose using the Caffe machine learning (ML) library, and convert those models into Core ML models for the ease of use in our application. We will discuss more how Convolutional Neural Networks (CNNs) work in terms of predicting age, gender, and emotion with the sample application. 

This application can be useful with multiple use cases. A few cases are as follows:

  • Finding what kind of photos you capture by parsing all the photos from your gallery
  • Understanding the customer entering a location (hospital, restaurant, and so on)
  • Figuring out the right marketing data by actually capturing...

Age, gender, and emotion prediction

This chapter is going to cover a complete iOS application using Core ML models to detect age, gender, and emotion from a photo taken using an iPhone camera or from a photo in a user's phone gallery.

Core ML enables developers to install and run pre-trained models on a device, and this has its own advantages. Since Core ML lives in the local device, it is not necessary to call a cloud service in order to get the prediction results. This improves the communication latency and also saves data bandwidth. The other crucial benefit of Core ML is privacy. You don't need to send your data to a third party in order to get the results picked for you. The main downside of having an offline model is that the model cannot be updated, and so it cannot be improved with newer inputs. Furthermore, a few models might increase memory footprints, since...

Convolutional Neural Networks 

One of the earliest applications of neural networks was demonstrated with Optical Character Recognition (OCR), but they were limited by time, computational resources, and other challenges faced when training bigger networks.

CNN is a part of feedforward neural networks, which are influenced by biological processes. This works in the same way that neurons work in the brain, as well as the connectivity patterns between them. These neurons will respond to stimuli that are only in a specific region in the visual field, known as the receptive field. When multiple neurons overlap each other, they will cover the whole visual field. The following diagram shows the CNN architecture:

CNN has an input layer and one output layer, as well as multiple hidden layers. These hidden layers consist of pooling layers, convolutional layers,...

The implementation on iOS using Core ML

It is now time to jump into the coding part of the application. We are using a model developed using the Caffe deep learning framework by Berkeley AI Research (BAIR) team as well as the community of contributors. As a first step, we need to convert the existing Caffe models into Core ML models to be utilized in our application:

//Downloading Age and Gender models 
wget
http://www.openu.ac.il/home/hassner/projects/cnn_agegender/cnn_age_gen
der_models_and_data.0.0.2.zip
unzip -a cnn_age_gender_models_and_data.0.0.2.zip

Now, go to the extracted folder and convert the model into a Core ML model:

import coremltools

folder = 'cnn_age_gender_models_and_data.0.0.2'

coreml_model = coremltools.converters.caffe.convert(
(folder + '/age_net.caffemodel', folder + '/deploy_age.prototxt'),
image_input_names = &apos...

Summary

In this chapter, we learned to build a complete iOS application from scratch. We also learned how to convert a Caffe model into a Core ML model. Now we know how to import a Core ML model into an iOS app and get the predicted results from the model. By doing this, we save data bandwidth without using the internet, and the data remains on a local device without affecting user privacy.

In the next chapter, with this knowledge, we'll move on to building an application to apply an artistic style to an existing image through neural networks.

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Key benefits

  • Explore machine learning using classification, analytics, and detection tasks.
  • Work with image, text and video datasets to delve into real-world tasks
  • Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite

Description

Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google’s ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN. By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.

Who is this book for?

Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.

What you will learn

  • Demystify the machine learning landscape on mobile
  • Age and gender detection using TensorFlow Lite and Core ML
  • Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning
  • Create a digit classifier using adversarial learning
  • Build a cross-platform application with face filters using OpenCV
  • Classify food using deep CNNs and TensorFlow Lite on iOS

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Publication date : Oct 31, 2018
Length: 246 pages
Edition : 1st
Language : English
ISBN-13 : 9781788998468
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Product Details

Publication date : Oct 31, 2018
Length: 246 pages
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Language : English
ISBN-13 : 9781788998468
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Tools :

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Table of Contents

10 Chapters
Mobile Landscapes in Machine Learning Chevron down icon Chevron up icon
CNN Based Age and Gender Identification Using Core ML Chevron down icon Chevron up icon
Applying Neural Style Transfer on Photos Chevron down icon Chevron up icon
Deep Diving into the ML Kit with Firebase Chevron down icon Chevron up icon
A Snapchat-Like AR Filter on Android Chevron down icon Chevron up icon
Handwritten Digit Classifier Using Adversarial Learning Chevron down icon Chevron up icon
Face-Swapping with Your Friends Using OpenCV Chevron down icon Chevron up icon
Classifying Food Using Transfer Learning Chevron down icon Chevron up icon
What's Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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