List CoreML-Models

A List CoreML-Models projects and resources. Feel free to contribute!

With Core ML, you can integrate trained machine learning models into your app.

CoreML-Models is the result of applying a machine learning algorithm to a set of training data. The model makes predictions based on new input data. For example, a model that's been trained on a region's historical house prices may be able to predict a house's price when given the number of bedrooms and bathrooms.

• Projects
• Articles
• Resources

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Projects

• Exermote - Predicts the exercise, when iPhone is worn on right upper arm.

• SentimentPolarity - iOS11 demo application for sentiment polarity analysis.

• SentimentPolarity - iOS11 demo application for sentiment polarity analysis.

• imessage-spam-detection - Simple iMessage app to detect whether a message is spam.

• FNS-Candy - Convert Torch7 models into Apple CoreML format.

• HED_so - Holistically-Nested Edge Detection. Side outputs

• Nudity - Classifies an image either as NSFW (nude) or SFW (not nude)

• RN1015k500 - Predict the location where a picture was taken.

• CNNEmotions - Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

• Inceptionv3 - Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, person etc. The top-5 error from the original publication is 5.6%.

• GoogLeNetPlaces - Detects the scene of an image from 205 categories such as airport, bedroom, forest, coast etc.

• DocumentClassifier - DocumentClassifier is a Swift framework for classifying documents into one of five categories (Business, Entertainment, Politics, Sports, and Technology). It uses a CoreML model trained with 1,500 news articles from the BBC.

• CoreML-in-ARKit - Simple project to detect objects and display 3D labels above them in AR.

• Food101-CoreML - A CoreML model which classifies images of food.

• UnsplashExplorer-CoreML - This app takes a random photo from Unsplash and make predictions about what is inside with Core ML framework using InceptionV3 model.

• ObjectDetectionCoreML - Compare CoreML models.

• ImageDetector-Machine-Learning - This uses iOS 11's CoreML combined with Google's Inceptionv3 Model to detect the contents of an image taken in the camera or selected from the library.

• VisualSentimentCNN - From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction.

• Tutorial_coreml - Integrating Machine Learning models into your app.

• CoreML-in-ARKit - Simple project to detect objects and display 3D labels above them in AR. This serves as a basic template for an ARKit project to use CoreML.

• Artists Recommendation - Recommend a artist based on given location and genre.

• NamesDT - Predict whether a name is for male or female.

• FlickrStyle - Detect the artistic style of images.

• Oxford102 - Detect the type of flowers from images.

• Food101 - Predict the type of foods from images.

• SentimentVision - Predict positive or negative sentiments from images.

• HED - Detect nested edges from a color image. Models.

• FacesVisionDemo - Gender Classification from first names.

• Sentiment Polarity - Predict positive or negative sentiments from sentences.

• MNIST - Predict handwritten (drawn) digits from images.

• EmotionNet - Predict a person's emotion from one's portrait.

• GenderNet - Predict a person's gender from one's portrait.

• AgeNet - Predict a person's age from one's portrait.

• YOLO - Recognize what the objects are inside a given image and where they are in the image.

• Car Recognition - Detects the dominant objects present in an image.

• Car VGG16 - Detects the dominant objects present in an image.

• ResNet50 - Predict the brand & model of a car.

• Inception v3 - Detects the dominant objects present in an image.

• Places CNN - Detects the scene of an image from 205 categories such as bedroom, forest, coast etc.

• MobileNet - Detects the dominant objects present in an image.

Articles

• Core ML in depth by Apple
• Coreml-Introduction by Appcoda
• Machine Learning in iOS Using Core ML by BignerdRanch
• CoreML: A Quick Walkthrough by Apple Nick Bourdakos

Resources

• Official CoreML Documentation

• Apple Machine Learning

• Vision Framework

• Getting a Core ML Model

• Integrating a Core ML Model into Your App

• Welcoming Core ML

• Model Zoo

Utilities to Work with Core ML

• Torch7 - Torch7 -> CoreML

• MXNet - Bring Machine Learning to iOS apps using Apache MXNet and Apple Core ML

• coremltools - Core ML is an Apple framework that allows developers to easily integrate machine learning (ML) models into apps. Core ML is availiable on iOS, watchOS, macOS, and tvOS. Core ML introduces a public file format (.mlmodel) for a broad set of ML methods including deep neural networks (convolutional and recurrent), tree ensembles (boosted trees, random forest, decision trees), and generalized linear models. Core ML models directly integrate into apps within Xcode.

• TensorFlow - This repository contains machine learning models implemented in TensorFlow. The models are maintained by their respective authors. To propose a model for inclusion, please submit a pull request.

• Caffe - Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data: check out the model zoo!

• TensorFlow Slim - TF-slim is a new lightweight high-level API of TensorFlow (tensorflow.contrib.slim) for defining, training and evaluating complex models.

Twitter

• Follow us Twitter - Follow us on the next on twitter.

Contributing

Your contributions are always welcome! To add, remove, or change things on the list: Submit a pull request. See contribution.md for guidelines.