Category: Articles

I’ve always thought of community as something akin to a party. A thing that forms largely by happenstance, where the people involved are connected through discrete and clear purposes: Socializing, relaxing after a long work week, meeting new people.

So when I found myself in the role of building the Heartbeat community, I initially struggled to see a bigger picture. I had the vague sense that the main priority should be centered on putting the needs and desires of community members first—much in the same way I’d want to be a good host at a party.

Continue reading “Building the Heartbeat Community”

There exist a plethora of articles on quantization, but they generally cover only the surface level theory or provide a simple overview. In this article, I’ll explain how quantization is actually implemented by deep learning frameworks.

Before getting into quantization, it’s good to understand the basic difference between two key concepts: float (floating-point) and int (fixed-point).

Continue reading “Quantization Arithmetic”

A Non-Developer Building Mobile ML?

I’m by no means a developer, engineer, or coder of any kind. Though I do read a lot of tutorials that include code implementations, and occasionally peruse repos to explore what folks are building, the most I’ve ever done is add some CSS style elements to a button within an HTML file.

So on the surface, the idea of me being able to build a cross-platform, mobile-ready machine learning model is kind of absurd.

Continue reading “End-to-End Mobile Machine Learning with Fritz AI Studio: A Non-Developer’s Journey”

Developers using Fritz AI can now track the body positions of people in images and video with Pose Estimation.

Continue reading “Pose Estimation on Android”

Deploying machine learning-based Android apps is gaining prominence and momentum with frameworks like TensorFlow Lite, and there are quite a few articles that describe how to develop mobile apps for computer vision tasks like text classification and image classification.

But there’s very much less that exists about working with audio-based ML tasks in mobile apps, and this blog is meant to address that gap — specifically, I’ll describe the steps and code required to perform audio classification in Android apps.

Continue reading “Audio Classification in an Android App with TensorFlow Lite”

Google’s Colab is a truly innovative product for machine learning. It enables machine engineers to run Notebooks and easily share them with colleagues. Another key advantage is access to GPUs and TPUs.

In this piece, we’ll highlight some of the tips and tricks mentioned during this year’s TF summit. Specifically, these tips will help you in getting the best out of Google’s Colab.

Continue reading “Top 10 Tricks for TensorFlow and Google Colab Users”

In the last piece in this series on developing with Flutter, we looked at how we can implement text recognition using ML Kit, which belongs to the Firebase family.

In this sixth installment of the series, we’ll keep working with ML Kit, this time focusing on implementing image labeling. The application we build will be able to label the contents of an image and provide a confidence level.

Continue reading “Image Labeling in Flutter Using Firebase’s ML Kit”

Among the new capabilities in Lens Studio 3.4, one that might have flown a bit under the radar—especially with the really impressive 3D Body Tracking, Full-Body Segmentation, and enhanced Finger Tracking taking a lot of the spotlight—is the new Asset Library.

Put simply, it’s a new collection of ready-to-use assets, provided by the Snap team and by a few select Official Lens Creators. If you want to get started with a range of different capabilities inside Lens Studio, but don’t want to go elsewhere to build 3D objects, ML models, materials, and more, this new library is where you should start.

Continue reading “Experimenting with Lens Studio’s Asset Library”

This blog is a continuation of our series comparing on-device OCR libraries. Here’s a similar comparison on iOS devices.

Summarizing the results of our comparison on iOS devices, Firebase’s ML Kit was leading by a solid margin against Tesseract OCR. However, outcomes weren’t very similar on Android phones. For this particular test, we used Samsung Galaxy J7 with 2GB RAM and 32GB Memory. Different hardware may result in different results.

Continue reading “Choose the Right On-Device Text Recognition (OCR) SDK on Android Using DeltaML”