Awesome Open Source
Awesome Open Source

Awesome Core ML models Awesome

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

MNIST

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

Food101

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

SentimentPolarity

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

VisualSentimentCNN

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

AgeNet

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

GenderNet

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

CNNEmotions

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

NamesDT

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

Oxford102

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

FlickrStyle

Model: FlickrStyle
Description: Image Style Classification
Author: Sergey Karayev
Reference: Recognizing Image Style
Example: StylesVisionDemo

Model Demonstration App

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



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