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Machine learning systems built for production are required to efficiently train, deploy, and update your machine learning models. Various factors have to be considered while deciding on the architecture of each system. Parts of this blog post are based on the Coursera and GCP (Google Cloud Platform) course on building production machine learning systems. Below, I’ll list some of the concerns in building a scalable machine learning system:

Continue reading Building Production Machine Learning Systems

This tutorial is the 9th part in the series, ML Kit for Mobile Developers. If you’re not quite caught up, you can start here:

Earlier this month at Google I/O, the team behind Firebase ML Kit announced the addition of 2 new APIs into their arsenal: object detection and an on-device translation API.

Continue reading Building a real-time object detection app on Android using Firebase ML Kit

In this tutorial, we’ll learn the basics of creating an intro slider using React Native. To do this, we’ll use a React Native module known as react-native-app-walkthrough, and the result will be a simple and configurable app introduction slider.

The detailed explanation of each step for this tutorial is provided below:

First, we need to simply install the react-native-app-walkthrough package. We can do this using NPM (Node Package Manager) or yarn, and for this, we’ll using NPM. Open your NPM console or command prompt within your project directory and enter the following code snippet:

Continue reading Building an App Introduction Slider in React Native

Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few.

We can think of semantic segmentation as image classification at a pixel level. For example, in an image that has many cars, segmentation will label all the objects as car objects. However, a separate class of models known as instance segmentation is able to label the separate instances where an object appears in an image. This kind of segmentation can be very useful in applications that are used to count the number of objects, such as counting the amount of foot traffic in a mall.

Continue reading A 2019 Guide to Semantic Segmentation

This tutorial guides Android developers in preparing the popular library OpenCV (Open Computer Vision) for use. Through a step-by-step guide, the library will be imported into Android Studio (the official IDE for Android).

Upon installation and setup, OpenCV can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.

Continue reading A Guide to Preparing OpenCV for Android

Position Map Regression Networks (PRN) is a method to jointly regress dense alignment and 3D face shape in an end-to-end manner. In this article, I’ll provide a short explanation and discuss its applications in computer vision.

Continue reading 3D Face Reconstruction with Position Map Regression Networks

According to Snapchat’s founders, the smartphone camera has always been at the heart of their app. Over the years, Snapchat has evolved to do much more to your photos than simple image and video messaging.

This giant social media platform is most popular among young people, and the main reason for this is its amazing photo/video editing tools and a wide variety of available camera filters.

Continue reading Creating Snapchat Augmented Reality Lenses in Lens Studio—A Beginner’s Guide

I was inspired by this example of Core ML + ARKit. But I found one significant disadvantage — it doesn’t place annotations on objects automatically. Instead, you need to center the object in your camera view and use your finger to place an annotation.

In my opinion, this destroys user expectations, so I decided to fix that and build a more immersive user experience using object detection in augmented reality.

Continue reading Core ML + ARKit: Annotating Objects in Augmented Reality