Case Studies | Fritz

Case Studies

Read full case studies about how app developers solve powerful problems with on-device machine learning
 

PlantVillage

Helping farmers in East Africa detect and treat plant disease

PlantVillage

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.
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MDacne

Instant predictions of skin conditions and customized assessments

MDacne

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.
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Superimpose X

Bringing professional photo editing tools to smartphones

Superimpose X

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.
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Momento

Transform photos, live photos, and videos into shareable GIFs

Momento

[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.
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InstaSaber

Transform a piece of paper into a lightsaber

InstaSaber

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.
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One Bite

The definitive list of Best Pizza in America

One Bite

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.
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