This is a collection of types and functions that make it a little easier to work with Core ML in Swift.
Some of the things CoreMLHelpers has to offer:
- convert images to
CVPixelBufferobjects and back
MLMultiArrayto image conversion
- handy functions to get top-5 predictions, argmax, and so on
- non-maximum suppression for bounding boxes
Let me know if there's anything else you'd like to see added to this library!
💡 Tip: Get the Core ML Survival Guide
If Core ML is giving you trouble --- or if you want to learn more about using the Core ML and Vision APIs --- then check out my new book Core ML Survival Guide. It has 300+ pages of Core ML tips and tricks.
I wrote the Core ML Survival Guide because the same questions kept coming up on Stack Overflow, on the Apple Developer Forums, and on this GitHub repo. Core ML may appear easy-to-use at first --- but if you want to go beyond the basics, the learning curve suddenly becomes very steep. My goal with this book is to make the advanced features of Core ML accessible to everyone too.
The Core ML Survival Guide currently has over 60 chapters and includes pretty much everything I know about Core ML. As I learn new things I'll keep updating the book, so you'll always have access to the most up-to-date knowledge about Core ML. Cheers!
How to use CoreMLHelpers
The simplest method
Copy the source files from the CoreMLHelpers folder into your project. You probably don't need all of them, so just pick the files you require and ignore the rest.
Note: A lot of the code in CoreMLHelpers is only intended as a demonstration of how to approach a certain problem. There's often more than one way to do it. It's quite likely you will need to customize the code for your particular situation, so use these routines as a starting point.
You can install CoreMLHelpers via Carthage by adding the following line to your Cartfile:
Not sure how this works, I never use CocoaPods.
Read more about Core ML
- proper unit tests
- add more numpy-like functionality to
MultiArray(and fix the bugs!)
CoreMLHelpers is copyright 2017-2020 Matthijs Hollemans and is licensed under the terms of the MIT license.