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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.
iOS11 demo application for age and gender classification of facial images using Vision and CoreML. This demo is based on the age, gender and emotion neural network classifiers, which were converted from Caffe models to CoreML models using coremltools python package.
A Demo application using CoreML framework for predicting gender from first names. This demo is based on An introduction to Machine Learning tutorial, which describes how to build a classifier able to distinguish between boy and girl names using datasets with the popularity of baby names over the years from The US Social Security Administration.
A Demo application using Vision and CoreML frameworks to detect the most likely sentiment of the given image. This demo is based on the "Fine-tuning CNNs for Visual Sentiment Prediction" neural network classifier, which was converted from original Caffe model to CoreML model using coremltools python package.
This is the Food101 dataset implemented in Apple's new framework called CoreML. The Food101 dataset can predict foods from images. The model was built with Keras 1.2.2 and is a fine-tuned InceptionV3 model. To test this model you can open the Food101Prediction.xcodeproj and run it on your device (iOS 11 and Xcode 9 is required). To test further images just add them to the project and replace my testing with yours.
This is the MNIST dataset implemented in Apple's new framework CoreML. The MNIST dataset can predict handwritten (drawn) digits from an image and outputs a prediction from 0-9. The model was built with Keras 1.2.2. To test this model you can open the MNISTPrediction.xcodeproj and run it on your device (iOS 11 and Xcode 9 is required). To test further images just add them to the project and replace my testing with yours.