Developers using Fritz AI can now add tags and metadata to on-device machine learning models. Models can be queried by tags and loaded dynamically via the iOS and Android SDKs, giving you more control over distribution and usage.
Deliver models to users based on hardware, location, software environment, or any other attribute.
Continue reading “Distributing on-device machine learning models with tags and metadata”
Neural networks have many practical use cases, from identifying street signs and people in self-driving cars to recognizing text and transcribing audio.
But one of the more fun use cases is applying the “style” of one image to another. Neural networks can be trained to tease apart the content of a painting from the style of a painting.
The results can then be applied to any image. The result is a set of fun photo filters that replicate famous paintings.
Artificial Intelligence coupled with Apple’s latest tech allows you to create captivating artwork using pictures from your iPhone or iPad.
I downloaded Looq on a Saturday morning a couple weeks ago. That day, I’d written a long list of chores I needed to take care of—cleaning a stack of dishes, laundry, a trip to the store.
There are now a bunch of off-the-shelf tools for training artistic style transfer models and thousands of open source implementations. Most use a variation of the network architecture described by Johnson et al to perform fast, feed-forward stylization.
As a result, the majority of the style transfer models you find are the same size: 7MB. That’s not an unreasonably large asset to add to your application, but it’s also not insignificant.
Research suggests that neural networks are often way larger than they need to be—that many of the millions of weights they contain are insignificant and needlessly precise. So I wondered: What’s the smallest model I can create that still reliably performs style transfer?
Continue reading “Creating a 17 KB style transfer model with layer pruning and quantization”
With the A11 Bionic’s Neural Engine in last year’s iPhone X, Apple introduced its first chip for hardware-accelerated AI. This year, Apple promised huge performance increases to Core ML in their iOS 12 update. They claim models run 9X faster and use 1/10th the energy on the new A12 Bionic processor. Early reports suggested that Apple has delivered, but I wanted to be sure.
In this article, we’ll take a moment to look at some of the interesting things that transpired in 2018 in the machine learning world. We’ll look at some of the top open source projects as ranked by Mybridge, major developments in machine learning frameworks, and some of the things to look forward to in 2019.
Continue reading 2018 Year-in-Review: Machine Learning Open Source Projects & Frameworks
Image labeling techniques can be used to obtain insights into the contents of an image. Some of the use cases for this are moderation of content as well as automatic metadata generation.
Using Fritz AI, we can do this on Android, identifying the contents of an image or even a video frame. In this piece, we’ll see how we can label images from our phone’s gallery.
