Awesome

Awesome Core ML models

This repository has a collection of Open Source machine learning models which work with Apples Core ML standard.

Apple has published some of their own models. They can be downloaded here. Those published models are: SqueezeNet, Places205-GoogLeNet, ResNet50, Inception v3, VGG16 and will not be republished in this repository.

Contributing

If you want your model added simply create a pull request with your repository and model added. In order to keep the quality of this repository you have to conform to this project structure (taken from @hollance).

├── Convert
    ├── coreml.py
    ├── mobilenet_deploy.prototxt
    └── synset_words.txt

There has to be a Convert directory with a Python script and additional data to reproduce this model on your own. If your model requires a huge amount of space please include a script which downloads those files.

├── MobileNetCoreML
│   ├── *.swift
├── MobileNetCoreML.xcodeproj
│   ├── project.pbxproj
│   └── project.xcworkspace
│       └── contents.xcworkspacedata
├── README.markdown

You also have to have an Xcode project where the user can test the model (sample data included would be nice).

This is a template for the README to copy:

### Name of your model
**Model:** [Model.mlmodel](link for downloading) <br />
**Description:** Short description <br />
**Author:** [Author](https://github.com/author) <br />
**Reference:** [Name of reference](URL to reference) <br />
**Example:** [Your example project](URL to example project) <br />

Models

MobileNet

Model: MobileNet.mlmodel <br /> Description: Object detection, finegrain classification, face attributes and large scale geo-localization <br /> Author: Matthijs Hollemans <br /> Reference: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications <br /> Example: MobileNet-CoreML <br />

MNIST

Model: MNIST.mlmodel <br /> Description: Handwritten digit classification <br /> Author: Philipp Gabriel <br /> Reference: MNIST handwritten digit database <br /> Example: MNIST-CoreML <br />

Food101

Model: Food101.mlmodel <br /> Description: Food classification <br /> Author: Philipp Gabriel <br /> Reference: UPMC Food-101 <br /> Example: Food101-CoreML <br />

SentimentPolarity

Model: SentimentPolarity <br /> Description: Sentiment Polarity Analysis <br /> Author: Vadym Markov <br /> Reference: Epinions.com reviews dataset <br /> Example: SentimentCoreMLDemo <br />

VisualSentimentCNN

Model: VisualSentimentCNN <br /> Description: Visual Sentiment Prediction <br /> Author: Image Processing Group - BarcelonaTECH - UPC <br /> Reference: From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction <br /> Example: SentimentVisionDemo <br />

AgeNet

Model: AgeNet <br /> Description: Age Classification <br /> Author: Gil Levi and Tal Hassner <br /> Reference: Age and Gender Classification using Convolutional Neural Networks <br /> Example: FacesVisionDemo <br />

GenderNet

Model: GenderNet <br /> Description: Gender Classification <br /> Author: Gil Levi and Tal Hassner <br /> Reference: Age and Gender Classification using Convolutional Neural Networks <br /> Example: FacesVisionDemo <br />

CNNEmotions

Model: CNNEmotions <br /> Description: Emotion Recognition <br /> Author: Gil Levi and Tal Hassner <br /> Reference: Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns <br /> Example: FacesVisionDemo <br />

NamesDT

Model: NamesDT <br /> Description: Gender Classification from first names <br /> Author: http://nlpforhackers.io <br /> Reference: Is it a boy or a girl? An introduction to Machine Learning <br /> Example: NamesCoreMLDemo <br />

Oxford102

Model: Oxford102 <br /> Description: Flower Classification <br /> Author: Jimmie Goode <br /> Reference: Classifying images in the Oxford 102 flower dataset with CNNs <br /> Example: FlowersVisionDemo <br />

FlickrStyle

Model: FlickrStyle <br /> Description: Image Style Classification <br /> Author: Sergey Karayev <br /> Reference: Recognizing Image Style <br /> Example: StylesVisionDemo <br />

Model Demonstration App

Description: Discover, download, on-device-compile & launch different image processing CoreML models on iOS. <br /> Author: Eugene Bokhan <br /> Source: Awesome ML <br /> Lincese: BSD 3-Clause <br />