Author: Fritz

In photograph and video editing, color grading is an important step in the process to enrich the content and make it look more appealing. This is done by adjusting the image/video in specific ways to create a stylistic effect, giving the content a cinematic look. One common way of achieving this consistently across various editing software is by using a LUT (Look Up Table).

Continue reading Color Grading Lenses Using LUTs

Cloud computing is very much a buzzword in the machine learning community, but what does it really mean?

It turns out, cloud computing can be a real lifesaver for your machine learning needs, and it doesn’t need to break the bank.

In this tutorial, you’ll learn how to quickly set up an Amazon EC2 instance for under a dollar and connect to it via SSH.

Now, if none of that made sense, don’t sweat it. This tutorial is intended for beginners, so you don’t need to know anything about Amazon Web Services to follow along.

By the end, you’ll have a computer running in the cloud, which you can connect to and use for almost anything!

Continue reading “MLDeploy and Connect an Amazon EC2 Instance for Cloud Computing with Amazon Web Services”

The recently-concluded CVPR 2020 had quite a large number of contributions in pushing object detection forward. In this piece, we’ll look at a couple of the especially impressive papers.

Continue reading “CVPR 2020: The Top Object Detection Papers”

Sensor fusion is one of the key aspects of self-driving cars.

If we take a look at the 4 main elements of self-driving vehicles, we can categorize sensor fusion a part of both the perception and the localization world. The reason is simple: both these disciplines use multiple sensors to function.

For example, perception is using cameras, LiDARs, and RADARs to detect obstacles’ classes, positions, and velocities with high accuracy. Localization, on the other hand, fuses GPS, LiDAR, and camera data to get an accurate position with centimeter-level accuracy.

Continue reading LiDAR and Camera Fusion in Self-Driving Cars

Today we’re excited to announce the Fritz AI Dataset Collection System, an important piece in our effort to help ML teams and developers generate, collect, and use data for mobile machine learning projects.

Data is a critical component to AI workflows, and the best data comes from the real world, where your models are (or will be) deployed.

For mobile machine learning projects, that means a user’s phone. The new Dataset Collection system makes it easy to gather this data through the same convenient SDK that delivers and runs your models.

Continue reading “Introducing the Fritz AI Dataset Collection System”

When the dust settled on a recent mobile machine learning project, we had accumulated 392 different model checkpoints. With numerous architectures to test, dozens of hyperparameters to sweep, and multiple on-device formats to support, models piled up quickly.

Staying organized and creating efficient workflows were the keys to success. We knew we needed to make this process easier—so we developed a set of command line tools to help.

Continue reading “Organizing mobile machine learning projects with the Fritz CLI”

Apple gave its Vision framework a major boost during WWDC 2019 by adding a lot of advancements. From expanding the number of classes (the term taxonomy is used for this) of its image classification requests to improvements in its face technology and text recognition requests, Apple is bringing in some really interesting improvements in computer vision for iOS.

Continue reading Vision Image Similarity Using Feature Prints in iOS

Though it didn’t get a ton of stage time during the keynote at this year’s WWDC, there’s a lot to be excited about in the latest iteration of Apple’s machine learning framework, Core ML 3.

In this post, I’ll highlight the biggest changes to the software and discuss their implications for developers and machine learning engineers.

Continue reading “What’s new in Core ML 3”