Swift Tutorial: Native Machine Learning and Machine Vision in iOS 11

It has arrived. With iOS 11, Apple finally introduced a native machine learning and machine vision framework. This opens up a whole host of new possibilities, promising great leaps forward in apps and games of all natures.

Machine learning solutions have been available for a while in the cloud, but these systems require a constant internet connection and oftentimes have a very noticiable delay on iOS for obvious reasons. This also creates a security risk for sensitive data. Some third-party Swift AI systems have begun to take hold inside of a select few apps, but such frameworks never hit the mainstream development community. With Apple’s new introduction at the 2017 WWDC it is likely that many of your favorite apps will see signifigant machine-learning-related updates.

Interested in seeing how you can integrate Apple’s new APIs into your very own apps? It’s easier than you think!

The first thing you’re going to need to do is download the Xcode 9 beta: https://developer.apple.com/download/. Be warned: this is a very large file and will take some time to download. In addition, this early beta version is super buggy and still has a lot of problems (some of which I will discuss later). While Xcode is downloading you can read through the rest of this post so you’re ready to go when it’s finished.

Now head over to the GitHub repo I created for this article and download the Xcode project:

AlexFWulff/Machine-Learning-Demo-iOS_Machine-Learning-Demo-iOS - A small sample to demonstrate the newly-introduced machine learning features in iOS 11_github.com

My sample project will take in an image and spit out likely categorizations with their respective level of confidence. All the calculations are handled on-device utilizing Apple’s new Core ML and Vision frameworks.

The project itself is surprisingly sparse. I want to draw your attention to one file in particular: GoogLeNetPlaces.mlmodel. This file is a trained machine vision model that was created by Google researchers a few years ago. Apple’s new machine learning APIs allow developers to easily access these standardized models inside their iOS apps. When you drag a .mlmodel file into your app Xcode will automatically create a Swift wrapper for it. Some of the model files can be upwards of hundreds of megabytes in size.

Special thanks to Jon Vogel for the photo picker and UITextView implementation.

Unfortunately, Core ML files are not even remotely human-readable like a .plist or .storyboard. Instead, they are just a large collection of bytes that tell the device how to arrange the “neurons” that handle inputs. The more complex a model, the larger its size.

Apple has collected four different trained models for your use. You can find these at https://developer.apple.com/machine-learning/. Apple’s Core ML Tools Python package allows developers to convert preexisting models into the iOS-accessible Core ML format. As the format gains more traction I expect that you will be able to get your hands on trained models for all sorts of use cases.

One of the many bugs: even though the project compiles, the editor still thinks that the Swift wrapper doesn’t exist.

Next open up the ViewController file and take a look at the onImageSelection function. When the user swipes between different photos this method is called, indicating that the algorithm should be run again.

The snippet of code (pictured above) simply tries to create a variable to store the Vision representation of your chosen model. Even if it appears that there’s an error in this section the project should still compile. This is one of the many bugs I’ve found during the brief time that I’ve used Xcode 9 beta.

With support for Core ML models Apple also introduced its own machine vision API: the aptly-named Vision. Vision contains many different machine vision models that can detect faces, barcodes, text, and more. Vision also provides wrappers for image-based Core ML models. Some of these wrappers are specific to certain types of models. For example, the model used in this project accepts an image as the input and returns a descriptive string as the output. As this is a very common thing to do, Apple has included a Vision wrapper for it. For non-image-based models Apple has created a small sample project demonstrating their use. This is completely independent of Vision and solely relies on Core ML.

The next snippet sets up and handles the request. In the project navigator you should see a variety of different images to try out on this model. The image that the model is computing is passed when the user swipes to a new image in the PhotoViewer.

Strangely enough, lower-resolution images seem to have the highest confidences for their most-likely categorization. I’m just an 18-year-old kid so I can’t really explain to you why this happens. If someone reading my article knows why this is the case please leave a response below. I’d love to find out!

The last function takes all of the results and adds them to the UITextView. Scroll down in the UITextView to see all the possibilites that the algorithm can dream up!

Another large bug I noticed that affects this project deals with dragging and dropping files into the project navigator. Don’t even attempt this in Xcode 9 (until the problem is fixed) as it will create huge problems with dependency chains. Just open up the Xcode project in an earlier version of Xcode, select copy items if needed, and confirm.

The last bug that may affect you will sometimes crop up when running your project. If the simulator fails to launch just quit out of both the simulator and Xcode. They should both work for a while until you need to do this again. Enjoy the new look and feel of the simulator as well as a little preview of iOS 11!

Hopefully my sample project gives you a brief overview of just how easy Apple made machine learning in iOS 11. Just drag in a model, do something with the results, and be on your way! The other three models that Apple linked should be compatible with the same VNCoreMLRequest, as they all take in images and output classification information.

Let me know what you thought of this tutorial. To see more of my work check out my website, www.AlexWulff.com, and my Medium page.