Largest list of models for Core ML (for iOS 11+)

Overview

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.

Awesome PRs Welcome

Models

Image - Metadata/Text

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

Image - Image

Models that transform images.

Text - Metadata/Text

Models that process text data

Miscellaneous

Visualization Tools

Tools that help visualize CoreML Models

Supported formats

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

The Gold

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

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.
Comments
  • Traffic Sign Detection Benchmark

    Traffic Sign Detection Benchmark

    This model should detect and recognize live video traffic signs. If there is a speed limit fo example the app should detect and recognize this shield.

    There is a lot of sources...

    Model URL

    http://btsd.ethz.ch/shareddata/ http://benchmark.ini.rub.de/?section=gtsdb&subsection=news

    Demo URL

    http://www.vision.ee.ethz.ch/~timofter/

    opened by ihyildiz 8
  • Contributing with LocationNet model

    Contributing with LocationNet model

    Model URL

    https://s3.amazonaws.com/aws-bigdata-blog/artifacts/RN1015k500/RN1015k500.mlmodel

    Demo URL

    https://github.com/awslabs/MXNet2CoreML_iOS_sample_app

    Descriptions

    Predict the location where a picture was taken.

    Checklist

    • [x] Only one item is in this pull request
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    opened by menant 7
  • New Model: Fast-Style-Transfer

    New Model: Fast-Style-Transfer

    License

    My Code is MIT, though the model requires an agreement for commercial use

    Summary

    I implemented Fast Style Transfer in CoreML. This is similar to Fast Neural Style, but is TensorFlow. My article explains the process.

    Model URL

    https://github.com/lengstrom/fast-style-transfer

    Demo URL

    https://medium.com/@rambossa/diy-prisma-fast-style-transfer-app-with-coreml-and-tensorflow-817c3b90dacd https://github.com/mdramos/fast-style-transfer-coreml

    Samples

    Here are some CoreML models I created in the process: https://drive.google.com/drive/folders/1CBSanBHbXC5-bJNTTk3-r1WSq56z0eKG?usp=sharing -- just include these in the ios app before running

    Checklist

    • [x] Only one item is in this issue
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable (AFTER INCLUDE THE MLMODELS)
    • [?] Has proper reference
    • [x] If this model takes image as input, an image type is selected instead of multiarray (See https://developer.apple.com/wwdc17/710 if you don't know how to do this)
    opened by backnotprop 5
  • Food101 model

    Food101 model

    Sorry that I don't conform to your issue guide but my issue is just to improve an already existing model in your collection.

    I saw that you added images with the input types and the output types of each model to your README and I therefore wanted to tell you that I finally converted the Food101 CoreML model in a matter where it now accepts CVPixelBuffer instead of MLMultiArray as an input.

    Just a little update :)

    opened by ph1ps 5
  • New Model: DocumentClassification

    New Model: DocumentClassification

    This model is also included in an open-source framework that can be used for document classification.

    License

    MIT

    Summary

    • Classifies documents into one of five categories (Business, Entertainment, Politics, Sports, Technology)
    • Trained with 1,500 articles from the BBC. See "References" in README

    Model URL

    Model Link

    Demo URL

    Demo app is the NewsClassifier iOS app here

    Samples

    Screenshot from the demo app here

    Checklist

    • [x] Only one item is in this issue
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    • [x] If this model takes image as input, an image type is selected instead of multiarray (See https://developer.apple.com/wwdc17/710 if you don't know how to do this)
    opened by toddkramer 4
  • New Model: PoseEstimation

    New Model: PoseEstimation

    License

    Apache License 2.0

    Summary

    edvardHua implements PoseEstimationForMobile estimating human pose from a picture for mobile. And I make demo for that on iOS.

    • Estimate human pose
    • Train AI Challenger dataset that is single person image dataset contain about 20,000

    Model URL

    Model URL

    Demo URL

    Demo URL

    Samples

    Input

    Image[URL]

    Output

    Heatmap[Array<Array<Array>>]

    model |cpm|hourglass -----|-----|----- output | [96, 96, 14] | [48, 48, 14]

    Checklist

    • [x] Only one item is in this issue
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    • [x] If this model takes image as input, an image type is selected instead of multiarray (See https://developer.apple.com/wwdc17/710 if you don't know how to do this)
    opened by tucan9389 3
  • Nudity model

    Nudity model

    License

    BSD-2

    Summary

    This model detects nudity and it's score from an image. The original dataset is Yahoo's OpenNSFW.

    Model URL

    Since this is too big for Github I have a GoogleDrive link: https://drive.google.com/open?id=0B5TjkH3njRqncDJpdDB1Tkl2S2s

    Demo URL

    https://github.com/ph1ps/Nudity-CoreML

    Samples

    Input: Output: SFW (100%), NSFW (0%) Input: Image, Output: NSFW (80%), SFW(20%)

    This gives you back both NSFW and SFW score in order to make people able to decide what their personal threshold is. Let's say there is a picture with NSFW - 70%, some people might consider this as safe but others not. Therefore they can say everything from 70% and down is SFW and everything from 71% to 100% is NSFW.

    Checklist

    • [x] Only one item is in this issue
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    • [x] If this model takes image as input, an image type is selected instead of multiarray (See https://developer.apple.com/wwdc17/710 if you don't know how to do this)
    opened by ph1ps 3
  • Adding in new CoreML Model for Sound Classification

    Adding in new CoreML Model for Sound Classification

    Adding in ESC-10 CoreML Model; trained on the ESC-10 dataset, it performs simple audio event recognition. Example project + blog post are included.

    Model URL

    Demo URL

    Descriptions

    Checklist

    • [x] Only one item is in this pull request
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    opened by narner 2
  • NamesDT

    NamesDT

    (not the author, just stumbled across this and seems it would fit in Text Analysis here)

    License

    MIT

    Summary

    A Demo application using CoreML framework for predicting gender from first names. See Is it a boy or a girl? An introduction to Machine Learning

    Model URL

    CoreML model was converted from Scikit-learn Pipeline using coremltools python package.

    Demo URL

    NamesCoreMLDemo

    Samples

    In demo README

    Checklist

    • [x] Only one item is in this issue
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    opened by alexcurylo 2
  • Adding image depth prediction model repo

    Adding image depth prediction model repo

    Model URL

    https://github.com/iro-cp/FCRN-DepthPrediction

    Demo URL

    https://github.com/tucan9389/DepthPrediction-CoreML

    Descriptions

    Predict the depth from a single image

    Checklist

    • [x] Only one item is in this pull request
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    opened by tucan9389 1
  • Adding image segmentation and depth prediction model repo

    Adding image segmentation and depth prediction model repo

    Model URL

    https://developer.apple.com/machine-learning/models/

    Demo URL

    https://github.com/tucan9389/ImageSegmentation-CoreML

    | screenshot 1 | screenshot 2 | screenshot 3 | | ---- | ---- | ---- | | | | |

    Descriptions

    Segment the pixels of a camera frame or image into a predefined set of classes.

    Checklist

    • [x] Only one item is in this pull request
    • [x] The model info contains all the required fields
    • [x] The demo project is compilable
    • [x] Has proper reference
    opened by tucan9389 1
Owner
Kedan Li
Co-founder/CEO @ Revery.AI Virtual Dressing Room for Fashion E-Commerce, powered by state-of-the-art AI
Kedan Li
The Habitat-Matterport 3D Research Dataset - the largest-ever dataset of 3D indoor spaces.

Habitat-Matterport 3D Dataset (HM3D) The Habitat-Matterport 3D Research Dataset is the largest-ever dataset of 3D indoor spaces. It consists of 1,000

Meta Research 62 Dec 27, 2022
HMLLDB is a collection of LLDB commands to assist in the debugging of iOS apps.

HMLLDB is a collection of LLDB commands to assist in the debugging of iOS apps. 中文介绍 Features Non-intrusive. Your iOS project does not need to be modi

mao2020 47 Oct 22, 2022
Research shows Google collects 20x more data from Android than Apple collects from iOS. Block this non-consensual telemetry using pihole blocklists.

pihole-antitelemetry Research shows Google collects 20x more data from Android than Apple collects from iOS. Block both using these pihole lists. Proj

Adrian Edwards 290 Jan 9, 2023
Pose estimation for iOS and android using TensorFlow 2.0

?? Mobile 2D Single Person (Or Your Own Object) Pose Estimation for TensorFlow 2.0 This repository is forked from edvardHua/PoseEstimationForMobile wh

tucan9389 165 Nov 16, 2022
An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

알고리즘 스터디 ?? 부스트캠프 웹모바일 6기 iOS 10조의 알고리즘 스터디 입니다. 개인적인 사정 등으로 S034, S055만 참가하였습니다. 스터디 목적 상진: 코테 합격 + 부캠끝나고 아침에 일어나기 위해 필요한 사이클 기완: 꾸준하게 자리에 앉아 공부하기 +

null 2 Jan 11, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Intel Labs 210 Jan 4, 2023
The coda and data for "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach" (ACL '21)

We propose a hierarchical core-fringe learning framework to measure fine-grained domain relevance of terms – the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain.

Jie Huang 14 Oct 21, 2022
Power Core Simulator!

Power Core Simulator Power Core Simulator is a simulator based off the Roblox game "Pinewood Builders Computer Core". In this simulator, you can choos

BananaJeans 1 Nov 13, 2021
arxiv-sanity, but very lite, simply providing the core value proposition of the ability to tag arxiv papers of interest and have the program recommend similar papers.

arxiv-sanity, but very lite, simply providing the core value proposition of the ability to tag arxiv papers of interest and have the program recommend similar papers.

Andrej 671 Dec 31, 2022
Neural networks applied in recognizing guitar chords using python, AutoML.NET with C# and .NET Core

Chord Recognition Demo application The demo application is written in C# with .NETCore. As of July 9, 2020, the only version available is for windows

Andres Mauricio Rondon Patiño 24 Oct 22, 2022
Bravia core script for python

Bravia-Core-Script You need to have a mandatory account If this L3 does not work, try another L3. enjoy

null 5 Dec 26, 2021
🎃 Core identification module of AI powerful point reading system platform.

ppReader-Kernel Intro Core identification module of AI powerful point reading system platform. Usage 硬件: Windows10、GPU:nvdia GTX 1060 、普通RBG相机 软件: con

CrashKing 1 Jan 11, 2022
[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6101 of Semester 1, AY2021-2022, starting from 08/2021. The instructors of this course are Prof. Bo An, Prof. Yu Han, and Dr. Melvin Chen.

AccSrd 1 Sep 22, 2022
AlphaBot2 Pi Core software for interfacing with the various components.

AlphaBot2-Pi-Core AlphaBot2 Pi Core software for interfacing with the various components. This project is currently a W.I.P. I will update this readme

KyleDev 1 Feb 13, 2022
Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Rate Limit Semaphore Rate limit semaphore for async-style (any core) There are t

Yan Kurbatov 4 Jun 21, 2022
[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess

RUCAIBox 26 Dec 19, 2022
A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.

Poisson Image Editing - A Parallel Implementation Jiayi Weng (jiayiwen), Zixu Chen (zixuc) Poisson Image Editing is a technique that can fuse two imag

Jiayi Weng 110 Dec 27, 2022