Displaying 1 to 8 from 8 results

Awesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)

  •    HTML

We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.

MobileNet-CoreML - The MobileNet neural network using Apple's new CoreML framework

  •    Swift

This is the MobileNet neural network architecture from the paper MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications implemented using Apple's shiny new CoreML framework. This uses the pretrained weights from shicai/MobileNet-Caffe.

YOLO-CoreML-MPSNNGraph - Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API

  •    Swift

This is the source code for my blog post YOLO: Core ML versus MPSNNGraph. YOLO is an object detection network. It can detect multiple objects in an image and puts bounding boxes around these objects. Read my other blog post about YOLO to learn more about how it works.

visual-recognition-coreml - Classify images offline using Watson Visual Recognition and Core ML

  •    Swift

Read this in other languages: δΈ­ε›½, ζ—₯本. Classify images with Watson Visual Recognition and Core ML. The images are classified offline using a deep neural network that is trained by Visual Recognition.

CoreMLHelpers - Types and functions that make it a little easier to work with Core ML in Swift.

  •    Swift

This is a collection of types and functions that make it a little easier to work with Core ML in Swift. Simply add the source files from the CoreMLHelpers folder to your project.

Inception-CoreML - Running Inception-v3 on Core ML

  •    Swift

This is the Inception-v3 neural network running on the shiny new CoreML framework. It uses Inceptionv3.mlmodel from Apple's developer website. It runs from a live video feed and performs a prediction as often as it can manage. If your device becomes too hot, change the setUpCamera() method in ViewController.swift to do videoCapture.fps = 5.

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