With custom model training notebooks developed by Fritz, the Video Star team has been able to integrate the creation of new styles into their workflow. And with the ability to add tags and metadata to each custom style developed with Fritz, the team can easily manage their distribution.Read the full Case Study
Making you and your friends the stars of awesome music videos.
Create a stabilized style transfer system for creating flawless stylized videos.
Stable style transfer and custom training notebook.
Fritz AI for Mobile, Style Transfer
iOS, Core ML
David Hughes and his team set out to use machine learning to bring this expertise and knowledge into the hands (and smartphones) of farmers themselves. The result was PlantVillage’s Android app Nuru (meaning “light” in Swahili), which serves as an expert assistant for farmers in the field, detecting diseases in Cassava, potato, and African maize – all in real-time and without internet access.Read the full Case Study
Helping farmers in East Africa detect and treat plant disease.
Create a digital assistant to help farmers diagnose crop disease in the field, without an internet connection.
Custom-trained object detection models for Cassava, Potato, and Fall armyworm.
Fritz AI for Mobile, Object Detection
Android, TensorFlow
To incorporate ML into MDacne, Dr. Harth and Oded turned to the Fritz team’s years of ML expertise in creating and integrating a proprietary, protected model to make these predictions on-device and in real-time.Read the full Case Study
Help everyone get clear skin with the world's first fully customized acne treatment kit.
Create a personalized skin assessment and treatment plan utilizing mobile machine learning.
The MDAcne app uses computer vision and machine learning to detect cases of acne and track blemishes over time.
Fritz AI for Mobile, Object Detection, Model Protection
iOS, Core ML
To differentiate his photo editor from the rest, Pankaj focused on 2 specific components not commonly found in most photo editing mobile apps: masking and layering. These features allow users to let their imaginations run free – composite images, incredible double exposures, different worlds blended into one surreal landscape – directly inside the Superimpose X app.Read the full Case Study
Create the best and only image editing app you'll ever need.
Automate tedious image masking tasks with machine learning.
Auto-generate photo masks around people with Image Segmentation.
Fritz AI for Mobile, Image Segmentation
iOS, Core ML
[Momento] decided to implement a machine learning model that would segment out people and allow this dynamic interaction with AR effects. But the journey towards implementation turned out to be more difficult than expected.Read the full Case Study
Help people create and share amazing GIFs in seconds.
Make immersive, fun AR filters and effects.
Use video segmentation to separate people from the background of a scene to composite AR effects more realistically.
Fritz AI for Mobile, Image Segmentation
iOS, Core ML
Andrew and his team realized that to manage a growing library of user-generated content, they’d have to find a way to automatically do some of this work. For the kind of real-time verification One Bite needed, embedding an ML model in the app became an obvious solution.Read the full Case Study
Always find the best slice of pizza.
Create an app with user-generated "one bite" pizza reviews.
Image labeling model to ensure all reviewer photos are of pizza.
Fritz AI for Mobile, Image Labeling
iOS, Core ML
With more than 15,000 app installs in the first couple of weeks, this meant he needed to monitor performance on a bunch of different devices and in different conditions. Without a team of developers at his disposal, Hart needed some help.Read the full Case Study
Create a new entertainment experience allowing everyone to wield an AR saber.
Use computer vision to turn an ordinary piece of paper into a virtual saber and swing it around in realtime.
Deploy and measure custom motion tracking model.
Fritz AI for Mobile, Analytics + Monitoring
iOS, Core ML