Core ML Models

Source: https://github.com/likedan/Awesome-CoreML-Models at f65751092d0b
License: MIT
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Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation

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.

If you’ve converted a Core ML model, feel free to submit a pull request.

Recently, we’ve included visualization tools. And here’s one Netron.

Models

Image - Metadata/Text

Models that take image data as input and output useful information about the image.

• TextDetection - Detecting text using Vision built-in model in real-time. Download | Demo | Reference
• PhotoAssessment - Photo Assessment using Core ML and Metal. Download | Demo | Reference
• PoseEstimation - Estimating human pose from a picture for mobile. Download | Demo | Reference
• MobileNet - Detects the dominant objects present in an image. Download | Demo | Reference
• Places CNN - Detects the scene of an image from 205 categories such as bedroom, forest, coast etc. Download | Demo | Reference
• Inception v3 - Detects the dominant objects present in an image. Download | Demo | Reference
• ResNet50 - Detects the dominant objects present in an image. Download | Demo | Reference
• VGG16 - Detects the dominant objects present in an image. Download | Demo | Reference
• Car Recognition - Predict the brand & model of a car. Download | Demo | Reference
• YOLO - Recognize what the objects are inside a given image and where they are in the image. Download | Demo | Reference
• AgeNet - Predict a person’s age from one’s portrait. Download | Demo | Reference
• GenderNet - Predict a person’s gender from one’s portrait. Download | Demo | Reference
• MNIST - Predict handwritten (drawn) digits from images. Download | Demo | Reference
• EmotionNet - Predict a person’s emotion from one’s portrait. Download | Demo | Reference
• SentimentVision - Predict positive or negative sentiments from images. Download | Demo | Reference
• Food101 - Predict the type of foods from images. Download | Demo | Reference
• Oxford102 - Detect the type of flowers from images. Download | Demo | Reference
• FlickrStyle - Detect the artistic style of images. Download | Demo | Reference
• RN1015k500 - Predict the location where a picture was taken. Download | Demo | Reference
• Nudity - Classifies an image either as NSFW (nude) or SFW (not nude) Download | Demo | Reference
• TextRecognition (ML Kit) - Recognizing text using ML Kit built-in model in real-time. Download | Demo | Reference
• ImageSegmentation - Segment the pixels of a camera frame or image into a predefined set of classes. Download | Demo | Reference
• DepthPrediction - Predict the depth from a single image. Download | Demo | Reference

Image - Image

Models that transform images.

• HED - Detect nested edges from a color image. Download | Demo | Reference
• AnimeScale2x - Process a bicubic-scaled anime-style artwork Download | Demo | Reference

Text - Metadata/Text

Models that process text data

• Sentiment Polarity - Predict positive or negative sentiments from sentences. Download | Demo | Reference
• DocumentClassification - Classify news articles into 1 of 5 categories. Download | Demo | Reference
• iMessage Spam Detection - Detect whether a message is spam. Download | Demo | Reference
• NamesDT - Gender Classification using DecisionTreeClassifier Download | Demo | Reference
• Personality Detection - Predict personality based on user documents (sentences). Download | Demo | Reference
• BERT for Question answering - Swift Core ML 3 implementation of BERT for Question answering Download | Demo | Reference
• GPT-2 - OpenAI GPT-2 Text generation (Core ML 3) Download | Demo | Reference

Miscellaneous

• Exermote - Predicts the exercise, when iPhone is worn on right upper arm. Download | Demo | Reference
• GestureAI - Recommend an artist based on given location and genre. Download | Demo | Reference
• Artists Recommendation - Recommend an artist based on given location and genre. Download | Demo | Reference
• ChordSuggester - Predicts the most likely next chord based on the entered Chord Progression. Download | Demo | Reference

Speech Processing

• Streaming ASR – Real-time streaming speech recognition engine for iOS. Uses Fast Conformer + CTC, runs fully on device.
  Download | Demo | Reference
• Keyword Spotting (KWS) – On-device keyword spotting engine using lightweight CRNN architecture, optimized for mobile devices.
  Download | Demo | Reference

Visualization Tools

Tools that help visualize CoreML Models

• Netron

Supported formats

List of model formats that could be converted to Core ML with examples

• Caffe
• Keras
• XGBoost
• Scikit-learn
• MXNet
• LibSVM
• Torch7

The Gold

Collections of machine learning models that could be converted to Core ML

• Caffe Model Zoo - Big list of models in Caffe format.
• TensorFlow Models - Models for TensorFlow.
• TensorFlow Slim Models - Another collection of TensorFlow Models.
• MXNet Model Zoo - Collection of MXNet models.

Individual machine learning models that could be converted to Core ML. We’ll keep adjusting the list as they become converted.

• LaMem Score the memorability of pictures.
• ILGnet The aesthetic evaluation of images.
• Colorization Automatic colorization using deep neural networks.
• Illustration2Vec Estimating a set of tags and extracting semantic feature vectors from given illustrations.
• CTPN Detecting text in natural image.
• Image Analogy Find semantically-meaningful dense correspondences between two input images.
• iLID Automatic spoken language identification.
• Fashion Detection Cloth detection from images.
• Saliency The prediction of salient areas in images has been traditionally addressed with hand-crafted features.
• Face Detection Detect face from image.
• mtcnn Joint Face Detection and Alignment.
• deephorizon Single image horizon line estimation.

Contributing and License

• See the guide
• Distributed under the MIT license. See LICENSE for more information.