Repository: likedan/Awesome-CoreML-Models
Branch: master
Commit: f65751092d0b
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Directory structure:
gitextract_o54j4hau/
├── .github/
│ ├── CONTRIBUTING.md
│ └── PULL_REQUEST_TEMPLATE.md
├── .gitignore
├── LICENSE.txt
├── README.md
├── content.json
└── generate_readme.py
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FILE CONTENTS
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FILE: .github/CONTRIBUTING.md
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# Contribution Guidelines
Please ensure your pull request adheres to the following guidelines:
- Search previous suggestions before making a new one, as yours may be a duplicate.
- **Make an individual pull request for each suggestion.**
- Use the following format: `**Model Name** - Short understandable description of what the model does. [Download](LINK) | [Demo](LINK) | [Reference](LINK)`. (Reference is optional)
- Keep descriptions short and simple, but descriptive.
- End all descriptions with a full stop/period.
- Check your spelling and grammar.
- Make sure that your suggestion is positioned as the last item category.
- Make sure your text editor is set to remove trailing whitespace.
#### Models are ineligible if:
- Input(s) and output(s) contains meaningful descriptions
- Author, Description are filled
- Proper references
Your contributions are always welcome! Thank you for your suggestions! :smiley:
================================================
FILE: .github/PULL_REQUEST_TEMPLATE.md
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<!--- Provide a general summary of your changes in the Title above -->
## Model URL
<!--- The model URL -->
## Demo URL
<!--- The demo URL -->
## Descriptions
<!--- Describe what your model do -->
## Checklist
<!--- Go over all the following points, and put an `x` in all the boxes that apply. -->
- [ ] Only one item is in this pull request
- [ ] The model info contains all the required fields
- [ ] The demo project is compilable
- [ ] Has proper reference
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FILE: .gitignore
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================================================
FILE: LICENSE.txt
================================================
The MIT License (MIT)
Copyright (c) 2016 Vinicius Souza
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
<!--
Title: Awesome Core ML Models
Description: A curated list of machine learning models in Core ML format.
Author: Kedan Li
-->
<p align="center">
<img src="images/coreml.png" width="329" height="295"/>
</p>
Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. [The official documentation](https://developer.apple.com/documentation/coreml)
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](https://github.com/likedan/Awesome-CoreML-Models/compare).
Recently, we've included visualization tools. And here's one [Netron](https://lutzroeder.github.io/Netron).
[](https://github.com/sindresorhus/awesome)
[](http://makeapullrequest.com)
# 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](https://github.com/tucan9389/TextDetection-CoreML) | [Reference](https://developer.apple.com/documentation/vision)
* **PhotoAssessment** - Photo Assessment using Core ML and Metal. [Download](https://github.com/yulingtianxia/PhotoAssessment/blob/master/PhotoAssessment-Sample/Sources/NIMANasnet.mlmodel) | [Demo](https://github.com/yulingtianxia/PhotoAssessment) | [Reference](https://arxiv.org/abs/1709.05424)
* **PoseEstimation** - Estimating human pose from a picture for mobile. [Download](https://github.com/edvardHua/PoseEstimationForMobile/tree/master/release) | [Demo](https://github.com/tucan9389/PoseEstimation-CoreML) | [Reference](https://github.com/edvardHua/PoseEstimationForMobile)
* **MobileNet** - Detects the dominant objects present in an image. [Download](https://github.com/hollance/MobileNet-CoreML/raw/master/MobileNet.mlmodel) | [Demo](https://github.com/hollance/MobileNet-CoreML) | [Reference](https://arxiv.org/abs/1704.04861)
* **Places CNN** - Detects the scene of an image from 205 categories such as bedroom, forest, coast etc. [Download](https://github.com/hollance/MobileNet-CoreML/raw/master/MobileNet.mlmodel) | [Demo](https://github.com/chenyi1989/CoreMLDemo) | [Reference](http://places.csail.mit.edu/index.html)
* **Inception v3** - Detects the dominant objects present in an image. [Download](https://github.com/yulingtianxia/Core-ML-Sample/blob/master/CoreMLSample/Inceptionv3.mlmodel) | [Demo](https://github.com/yulingtianxia/Core-ML-Sample/) | [Reference](https://arxiv.org/abs/1512.00567)
* **ResNet50** - Detects the dominant objects present in an image. [Download](https://github.com/ytakzk/CoreML-samples/blob/master/CoreML-samples/Resnet50.mlmodel) | [Demo](https://github.com/ytakzk/CoreML-samples) | [Reference](https://arxiv.org/abs/1512.03385)
* **VGG16** - Detects the dominant objects present in an image. [Download](https://docs-assets.developer.apple.com/coreml/models/VGG16.mlmodel) | [Demo](https://github.com/alaphao/CoreMLExample) | [Reference](https://arxiv.org/abs/1409.1556)
* **Car Recognition** - Predict the brand & model of a car. [Download](https://github.com/likedan/Core-ML-Car-Recognition/blob/master/Convert/CarRecognition.mlmodel) | [Demo](https://github.com/ytakzk/CoreML-samples) | [Reference](http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html)
* **YOLO** - Recognize what the objects are inside a given image and where they are in the image. [Download](https://github.com/hollance/YOLO-CoreML-MPSNNGraph/blob/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel) | [Demo](https://github.com/hollance/YOLO-CoreML-MPSNNGraph) | [Reference](http://machinethink.net/blog/object-detection-with-yolo)
* **AgeNet** - Predict a person's age from one's portrait. [Download](https://drive.google.com/file/d/0B1ghKa_MYL6mT1J3T1BEeWx4TWc/view?usp=sharing) | [Demo](https://github.com/cocoa-ai/FacesVisionDemo) | [Reference](http://www.openu.ac.il/home/hassner/projects/cnn_agegender/)
* **GenderNet** - Predict a person's gender from one's portrait. [Download](https://drive.google.com/file/d/0B1ghKa_MYL6mYkNsZHlyc2ZuaFk/view?usp=sharing) | [Demo](https://github.com/cocoa-ai/FacesVisionDemo) | [Reference](http://www.openu.ac.il/home/hassner/projects/cnn_agegender/)
* **MNIST** - Predict handwritten (drawn) digits from images. [Download](https://github.com/ph1ps/MNIST-CoreML/raw/master/MNISTPrediction/MNIST.mlmodel) | [Demo](https://github.com/ph1ps/MNIST-CoreML) | [Reference](http://yann.lecun.com/exdb/mnist/)
* **EmotionNet** - Predict a person's emotion from one's portrait. [Download](https://drive.google.com/file/d/0B1ghKa_MYL6mTlYtRGdXNFlpWDQ/view?usp=sharing) | [Demo](https://github.com/cocoa-ai/FacesVisionDemo) | [Reference](http://www.openu.ac.il/home/hassner/projects/cnn_emotions/)
* **SentimentVision** - Predict positive or negative sentiments from images. [Download](https://drive.google.com/open?id=0B1ghKa_MYL6mZ0dITW5uZlgyNTg) | [Demo](https://github.com/cocoa-ai/SentimentVisionDemo) | [Reference](http://www.sciencedirect.com/science/article/pii/S0262885617300355?via%3Dihub)
* **Food101** - Predict the type of foods from images. [Download](https://drive.google.com/open?id=0B5TjkH3njRqnVjBPZGRZbkNITjA) | [Demo](https://github.com/ph1ps/Food101-CoreML) | [Reference](http://visiir.lip6.fr/explore)
* **Oxford102** - Detect the type of flowers from images. [Download](https://drive.google.com/file/d/0B1ghKa_MYL6meDBHT2NaZGxkNzQ/view?usp=sharing) | [Demo](https://github.com/cocoa-ai/FlowersVisionDemo) | [Reference](http://jimgoo.com/flower-power/)
* **FlickrStyle** - Detect the artistic style of images. [Download](https://drive.google.com/file/d/0B1ghKa_MYL6meDBHT2NaZGxkNzQ/view?usp=sharing) | [Demo](https://github.com/cocoa-ai/StylesVisionDemo) | [Reference](http://sergeykarayev.com/files/1311.3715v3.pdf)
* **RN1015k500** - Predict the location where a picture was taken. [Download](https://s3.amazonaws.com/aws-bigdata-blog/artifacts/RN1015k500/RN1015k500.mlmodel) | [Demo](https://github.com/awslabs/MXNet2CoreML_iOS_sample_app) | [Reference](https://aws.amazon.com/blogs/ai/estimating-the-location-of-images-using-mxnet-and-multimedia-commons-dataset-on-aws-ec2)
* **Nudity** - Classifies an image either as NSFW (nude) or SFW (not nude)
[Download](https://drive.google.com/open?id=0B5TjkH3njRqncDJpdDB1Tkl2S2s) | [Demo](https://github.com/ph1ps/Nudity-CoreML) | [Reference](https://github.com/yahoo/open_nsfw)
* **TextRecognition (ML Kit)** - Recognizing text using ML Kit built-in model in real-time. [Download]() | [Demo](https://github.com/tucan9389/TextRecognition-MLKit) | [Reference](https://firebase.google.com/docs/ml-kit/ios/recognize-text)
* **ImageSegmentation** - Segment the pixels of a camera frame or image into a predefined set of classes. [Download](https://developer.apple.com/machine-learning/models/) | [Demo](https://github.com/tucan9389/ImageSegmentation-CoreML) | [Reference](https://github.com/tensorflow/models/tree/master/research/deeplab)
* **DepthPrediction** - Predict the depth from a single image. [Download](https://developer.apple.com/machine-learning/models/) | [Demo](https://github.com/tucan9389/DepthPrediction-CoreML) | [Reference](https://github.com/iro-cp/FCRN-DepthPrediction)
## Image - Image
*Models that transform images.*
* **HED** - Detect nested edges from a color image. [Download](https://github.com/s1ddok/HED-CoreML/blob/master/HED-CoreML/Models/HED_so.mlmodel) | [Demo](https://github.com/s1ddok/HED-CoreML) | [Reference](http://dl.acm.org/citation.cfm?id=2654889)
* **AnimeScale2x** - Process a bicubic-scaled anime-style artwork [Download](https://github.com/imxieyi/waifu2x-ios/blob/master/waifu2x/models/anime_noise0_model.mlmodel) | [Demo](https://github.com/imxieyi/waifu2x-ios) | [Reference](https://arxiv.org/abs/1501.00092)
## Text - Metadata/Text
*Models that process text data*
* **Sentiment Polarity** - Predict positive or negative sentiments from sentences. [Download](https://github.com/cocoa-ai/SentimentCoreMLDemo/raw/master/SentimentPolarity/Resources/SentimentPolarity.mlmodel) | [Demo](https://github.com/cocoa-ai/SentimentCoreMLDemo) | [Reference](http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/)
* **DocumentClassification** - Classify news articles into 1 of 5 categories. [Download](https://github.com/toddkramer/DocumentClassifier/blob/master/Sources/DocumentClassification.mlmodel) | [Demo](https://github.com/toddkramer/DocumentClassifier) | [Reference](https://github.com/toddkramer/DocumentClassifier/)
* **iMessage Spam Detection** - Detect whether a message is spam. [Download](https://github.com/gkswamy98/imessage-spam-detection/blob/master/MessageClassifier.mlmodel) | [Demo](https://github.com/gkswamy98/imessage-spam-detection/tree/master) | [Reference](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/)
* **NamesDT** - Gender Classification using DecisionTreeClassifier [Download](https://github.com/cocoa-ai/NamesCoreMLDemo/blob/master/Names/Resources/NamesDT.mlmodel) | [Demo](https://github.com/cocoa-ai/NamesCoreMLDemo) | [Reference](http://nlpforhackers.io/)
* **Personality Detection** - Predict personality based on user documents (sentences). [Download](https://github.com/novinfard/profiler-sentiment-analysis/tree/master/ios_app/ProfilerSA/ML%20Models) | [Demo](https://github.com/novinfard/profiler-sentiment-analysis/) | [Reference](https://github.com/novinfard/profiler-sentiment-analysis/blob/master/dissertation-v6.pdf)
* **BERT for Question answering** - Swift Core ML 3 implementation of BERT for Question answering [Download](https://github.com/huggingface/swift-coreml-transformers/blob/master/Resources/BERTSQUADFP16.mlmodel) | [Demo](https://github.com/huggingface/swift-coreml-transformers#-bert) | [Reference](https://github.com/huggingface/pytorch-transformers#run_squadpy-fine-tuning-on-squad-for-question-answering)
* **GPT-2** - OpenAI GPT-2 Text generation (Core ML 3) [Download](https://github.com/huggingface/swift-coreml-transformers/blob/master/Resources/gpt2-512.mlmodel) | [Demo](https://github.com/huggingface/swift-coreml-transformers#-gpt-2) | [Reference](https://github.com/huggingface/pytorch-transformers)
## Miscellaneous
* **Exermote** - Predicts the exercise, when iPhone is worn on right upper arm. [Download](https://github.com/Lausbert/Exermote/tree/master/ExermoteInference) | [Demo](https://github.com/Lausbert/Exermote/tree/master/ExermoteInference) | [Reference](http://lausbert.com/2017/08/03/exermote/)
* **GestureAI** - Recommend an artist based on given location and genre. [Download](https://goo.gl/avdMjD) | [Demo](https://github.com/akimach/GestureAI-CoreML-iOS) | [Reference](https://github.com/akimach/GestureAI-iOS/tree/master/GestureAI)
* **Artists Recommendation** - Recommend an artist based on given location and genre. [Download](https://github.com/agnosticdev/Blog-Examples/blob/master/UsingCoreMLtoCreateASongRecommendationEngine/Artist.mlmodel) | [Demo]() | [Reference](https://www.agnosticdev.com/blog-entry/python/using-scikit-learn-and-coreml-create-music-recommendation-engine)
* **ChordSuggester** - Predicts the most likely next chord based on the entered Chord Progression. [Download](https://github.com/carlosmbe/Mac-CoreML-Chord-Suggester/blob/main/MLChordSuggester.mlpackage.zip) | [Demo](https://github.com/carlosmbe/Mac-CoreML-Chord-Suggester/tree/main) | [Reference](https://medium.com/@huanlui/chordsuggester-i-3a1261d4ea9e)
## Speech Processing
* **Streaming ASR** – Real-time streaming speech recognition engine for iOS. Uses Fast Conformer + CTC, runs fully on device.
[Download](https://github.com/Otosaku/OtosakuStreamingASR-iOS/releases) | [Demo](https://github.com/Otosaku/OtosakuStreamingASR-iOS) | [Reference](https://github.com/Otosaku/OtosakuStreamingASR-iOS)
* **Keyword Spotting (KWS)** – On-device keyword spotting engine using lightweight CRNN architecture, optimized for mobile devices.
[Download](https://github.com/Otosaku/OtosakuKWS-iOS/releases) | [Demo](https://github.com/Otosaku/OtosakuKWS-iOS) | [Reference](https://github.com/Otosaku/OtosakuKWS-iOS)
# Visualization Tools
*Tools that help visualize CoreML Models*
* [Netron](https://lutzroeder.github.io/Netron)
# Supported formats
*List of model formats that could be converted to Core ML with examples*
* [Caffe](https://apple.github.io/coremltools/generated/coremltools.converters.caffe.convert.html)
* [Keras](https://apple.github.io/coremltools/generated/coremltools.converters.keras.convert.html)
* [XGBoost](https://apple.github.io/coremltools/generated/coremltools.converters.xgboost.convert.html)
* [Scikit-learn](https://apple.github.io/coremltools/generated/coremltools.converters.sklearn.convert.html)
* [MXNet](https://aws.amazon.com/blogs/ai/bring-machine-learning-to-ios-apps-using-apache-mxnet-and-apple-core-ml/)
* [LibSVM](https://apple.github.io/coremltools/generated/coremltools.converters.libsvm.convert.html)
* [Torch7](https://github.com/prisma-ai/torch2coreml)
# The Gold
*Collections of machine learning models that could be converted to Core ML*
* [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) - Big list of models in Caffe format.
* [TensorFlow Models](https://github.com/tensorflow/models) - Models for TensorFlow.
* [TensorFlow Slim Models](https://github.com/tensorflow/models/tree/master/research/slim/README.md) - Another collection of TensorFlow Models.
* [MXNet Model Zoo](https://mxnet.incubator.apache.org/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](https://github.com/MiyainNYC/Visual-Memorability-through-Caffe) Score the memorability of pictures.
* [ILGnet](https://github.com/BestiVictory/ILGnet) The aesthetic evaluation of images.
* [Colorization](https://github.com/richzhang/colorization) Automatic colorization using deep neural networks.
* [Illustration2Vec](https://github.com/rezoo/illustration2vec) Estimating a set of tags and extracting semantic feature vectors from given illustrations.
* [CTPN](https://github.com/tianzhi0549/CTPN) Detecting text in natural image.
* [Image Analogy](https://github.com/msracver/Deep-Image-Analogy) Find semantically-meaningful dense correspondences between two input images.
* [iLID](https://github.com/twerkmeister/iLID) Automatic spoken language identification.
* [Fashion Detection](https://github.com/liuziwei7/fashion-detection) Cloth detection from images.
* [Saliency](https://github.com/imatge-upc/saliency-2016-cvpr) The prediction of salient areas in images has been traditionally addressed with hand-crafted features.
* [Face Detection](https://github.com/DolotovEvgeniy/DeepPyramid) Detect face from image.
* [mtcnn](https://github.com/CongWeilin/mtcnn-caffe) Joint Face Detection and Alignment.
* [deephorizon](https://github.com/scottworkman/deephorizon) Single image horizon line estimation.
# Contributing and License
* [See the guide](https://github.com/likedan/Awesome-CoreML-Models/blob/master/.github/CONTRIBUTING.md)
* Distributed under the MIT license. See LICENSE for more information.
================================================
FILE: content.json
================================================
{
"types": [
"image",
"image-image",
"text",
"miscellaneous"
],
"models": [
{
"name": "TextDetection",
"description": "Detecting text using Vision built-in model in real-time.",
"download_link": "",
"demo_link": "https://github.com/tucan9389/TextDetection-CoreML",
"reference_link": "https://developer.apple.com/documentation/vision",
"type": "image"
},
{
"name": "PhotoAssessment",
"description": "Photo Assessment using Core ML and Metal.",
"download_link": "https://github.com/yulingtianxia/PhotoAssessment/blob/master/PhotoAssessment-Sample/Sources/NIMANasnet.mlmodel",
"demo_link": "https://github.com/yulingtianxia/PhotoAssessment",
"reference_link": "https://arxiv.org/abs/1709.05424",
"type": "image"
},
{
"name": "PoseEstimation",
"description": "Estimating human pose from a picture for mobile.",
"download_link": "https://github.com/edvardHua/PoseEstimationForMobile/tree/master/release",
"demo_link": "https://github.com/tucan9389/PoseEstimation-CoreML",
"reference_link": "https://github.com/edvardHua/PoseEstimationForMobile",
"type": "image"
},
{
"name": "MobileNet",
"description": "Detects the dominant objects present in an image.",
"download_link": "https://github.com/hollance/MobileNet-CoreML/raw/master/MobileNet.mlmodel",
"demo_link": "https://github.com/hollance/MobileNet-CoreML",
"reference_link": "https://arxiv.org/abs/1704.04861",
"type": "image"
},
{
"name": "Places CNN",
"description": "Detects the scene of an image from 205 categories such as bedroom, forest, coast etc.",
"download_link": "https://github.com/hollance/MobileNet-CoreML/raw/master/MobileNet.mlmodel",
"demo_link": "https://github.com/chenyi1989/CoreMLDemo",
"reference_link": "http://places.csail.mit.edu/index.html",
"type": "image"
},
{
"name": "Inception v3",
"description": "Detects the dominant objects present in an image.",
"download_link": "https://github.com/yulingtianxia/Core-ML-Sample/blob/master/CoreMLSample/Inceptionv3.mlmodel",
"demo_link": "https://github.com/yulingtianxia/Core-ML-Sample/",
"reference_link": "https://arxiv.org/abs/1512.00567",
"type": "image"
},
{
"name": "ResNet50",
"description": "Detects the dominant objects present in an image.",
"download_link": "https://github.com/ytakzk/CoreML-samples/blob/master/CoreML-samples/Resnet50.mlmodel",
"demo_link": "https://github.com/ytakzk/CoreML-samples",
"reference_link": "https://arxiv.org/abs/1512.03385",
"type": "image"
},
{
"name": "VGG16",
"description": "Detects the dominant objects present in an image.",
"download_link": "https://docs-assets.developer.apple.com/coreml/models/VGG16.mlmodel",
"demo_link": "https://github.com/alaphao/CoreMLExample",
"reference_link": "https://arxiv.org/abs/1409.1556",
"type": "image"
},
{
"name": "Car Recognition",
"description": "Predict the brand & model of a car.",
"download_link": "https://github.com/likedan/Core-ML-Car-Recognition/blob/master/Convert/CarRecognition.mlmodel",
"demo_link": "https://github.com/ytakzk/CoreML-samples",
"reference_link": "http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html",
"type": "image"
},
{
"name": "YOLO",
"description": "Recognize what the objects are inside a given image and where they are in the image.",
"download_link": "https://github.com/hollance/YOLO-CoreML-MPSNNGraph/blob/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel",
"demo_link": "https://github.com/hollance/YOLO-CoreML-MPSNNGraph",
"reference_link": "http://machinethink.net/blog/object-detection-with-yolo",
"type": "image"
},
{
"name": "AgeNet",
"description": "Predict a person's age from one's portrait.",
"download_link": "https://drive.google.com/file/d/0B1ghKa_MYL6mT1J3T1BEeWx4TWc/view?usp=sharing",
"demo_link": "https://github.com/cocoa-ai/FacesVisionDemo",
"reference_link": "http://www.openu.ac.il/home/hassner/projects/cnn_agegender/",
"type": "image"
},
{
"name": "GenderNet",
"description": "Predict a person's gender from one's portrait.",
"download_link": "https://drive.google.com/file/d/0B1ghKa_MYL6mYkNsZHlyc2ZuaFk/view?usp=sharing",
"demo_link": "https://github.com/cocoa-ai/FacesVisionDemo",
"reference_link": "http://www.openu.ac.il/home/hassner/projects/cnn_agegender/",
"type": "image"
},
{
"name": "MNIST",
"description": "Predict handwritten (drawn) digits from images.",
"download_link": "https://github.com/ph1ps/MNIST-CoreML/raw/master/MNISTPrediction/MNIST.mlmodel",
"demo_link": "https://github.com/ph1ps/MNIST-CoreML",
"reference_link": "http://yann.lecun.com/exdb/mnist/",
"type": "image"
},
{
"name": "EmotionNet",
"description": "Predict a person's emotion from one's portrait.",
"download_link": "https://drive.google.com/file/d/0B1ghKa_MYL6mTlYtRGdXNFlpWDQ/view?usp=sharing",
"demo_link": "https://github.com/cocoa-ai/FacesVisionDemo",
"reference_link": "http://www.openu.ac.il/home/hassner/projects/cnn_emotions/",
"type": "image"
},
{
"name": "HED",
"description": "Detect nested edges from a color image.",
"download_link": "https://github.com/s1ddok/HED-CoreML/blob/master/HED-CoreML/Models/HED_so.mlmodel",
"demo_link": "https://github.com/s1ddok/HED-CoreML",
"reference_link": "http://dl.acm.org/citation.cfm?id=2654889",
"type": "image-image"
},
{
"name": "SentimentVision",
"description": "Predict positive or negative sentiments from images.",
"download_link": "https://drive.google.com/open?id=0B1ghKa_MYL6mZ0dITW5uZlgyNTg",
"demo_link": "https://github.com/cocoa-ai/SentimentVisionDemo",
"reference_link": "http://www.sciencedirect.com/science/article/pii/S0262885617300355?via%3Dihub",
"type": "image"
},
{
"name": "Food101",
"description": "Predict the type of foods from images.",
"download_link": "https://drive.google.com/open?id=0B5TjkH3njRqnVjBPZGRZbkNITjA",
"demo_link": "https://github.com/ph1ps/Food101-CoreML",
"reference_link": "http://visiir.lip6.fr/explore",
"type": "image"
},
{
"name": "Oxford102",
"description": "Detect the type of flowers from images.",
"download_link": "https://drive.google.com/file/d/0B1ghKa_MYL6meDBHT2NaZGxkNzQ/view?usp=sharing",
"demo_link": "https://github.com/cocoa-ai/FlowersVisionDemo",
"reference_link": "http://jimgoo.com/flower-power/",
"type": "image"
},
{
"name": "FlickrStyle",
"description": "Detect the artistic style of images.",
"download_link": "https://drive.google.com/file/d/0B1ghKa_MYL6meDBHT2NaZGxkNzQ/view?usp=sharing",
"demo_link": "https://github.com/cocoa-ai/StylesVisionDemo",
"reference_link": "http://sergeykarayev.com/files/1311.3715v3.pdf",
"type": "image"
},
{
"name": "RN1015k500",
"description": "Predict the location where a picture was taken.",
"download_link": "https://s3.amazonaws.com/aws-bigdata-blog/artifacts/RN1015k500/RN1015k500.mlmodel",
"demo_link": "https://github.com/awslabs/MXNet2CoreML_iOS_sample_app",
"reference_link": "https://aws.amazon.com/blogs/ai/estimating-the-location-of-images-using-mxnet-and-multimedia-commons-dataset-on-aws-ec2",
"type": "image"
},
{
"name": "Nudity",
"description": "Classifies an image either as NSFW (nude) or SFW (not nude)\n",
"download_link": "https://drive.google.com/open?id=0B5TjkH3njRqncDJpdDB1Tkl2S2s",
"demo_link": "https://github.com/ph1ps/Nudity-CoreML",
"reference_link": "https://github.com/yahoo/open_nsfw",
"type": "image"
},
{
"name": "AnimeScale2x",
"description": "Process a bicubic-scaled anime-style artwork",
"download_link": "https://github.com/imxieyi/waifu2x-ios/blob/master/waifu2x/models/anime_noise0_model.mlmodel",
"demo_link": "https://github.com/imxieyi/waifu2x-ios",
"reference_link": "https://arxiv.org/abs/1501.00092",
"type": "image-image"
},
{
"name": "Sentiment Polarity",
"description": "Predict positive or negative sentiments from sentences.",
"download_link": "https://github.com/cocoa-ai/SentimentCoreMLDemo/raw/master/SentimentPolarity/Resources/SentimentPolarity.mlmodel",
"demo_link": "https://github.com/cocoa-ai/SentimentCoreMLDemo",
"reference_link": "http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/",
"type": "text"
},
{
"name": "DocumentClassification",
"description": "Classify news articles into 1 of 5 categories.",
"download_link": "https://github.com/toddkramer/DocumentClassifier/blob/master/Sources/DocumentClassification.mlmodel",
"demo_link": "https://github.com/toddkramer/DocumentClassifier",
"reference_link": "https://github.com/toddkramer/DocumentClassifier/",
"type": "text"
},
{
"name": "iMessage Spam Detection",
"description": "Detect whether a message is spam.",
"download_link": "https://github.com/gkswamy98/imessage-spam-detection/blob/master/MessageClassifier.mlmodel",
"demo_link": "https://github.com/gkswamy98/imessage-spam-detection/tree/master",
"reference_link": "http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/",
"type": "text"
},
{
"name": "NamesDT",
"description": "Gender Classification using DecisionTreeClassifier",
"download_link": "https://github.com/cocoa-ai/NamesCoreMLDemo/blob/master/Names/Resources/NamesDT.mlmodel",
"demo_link": "https://github.com/cocoa-ai/NamesCoreMLDemo",
"reference_link": "http://nlpforhackers.io/",
"type": "text"
},
{
"name": "Exermote",
"description": "Predicts the exercise, when iPhone is worn on right upper arm.",
"download_link": "https://github.com/Lausbert/Exermote/tree/master/ExermoteInference",
"demo_link": "https://github.com/Lausbert/Exermote/tree/master/ExermoteInference",
"reference_link": "http://lausbert.com/2017/08/03/exermote/",
"type": "miscellaneous"
},
{
"name": "GestureAI",
"description": "Recommend an artist based on given location and genre.",
"download_link": "https://goo.gl/avdMjD",
"demo_link": "https://github.com/akimach/GestureAI-CoreML-iOS",
"reference_link": "https://github.com/akimach/GestureAI-iOS/tree/master/GestureAI",
"type": "miscellaneous"
},
{
"name": "Artists Recommendation",
"description": "Recommend an artist based on given location and genre.",
"download_link": "https://github.com/agnosticdev/Blog-Examples/blob/master/UsingCoreMLtoCreateASongRecommendationEngine/Artist.mlmodel",
"demo_link": "",
"reference_link": "https://www.agnosticdev.com/blog-entry/python/using-scikit-learn-and-coreml-create-music-recommendation-engine",
"type": "miscellaneous"
},
{
"name": "Personality Detection",
"description": "Predict personality based on user documents (sentences).",
"download_link": "https://github.com/novinfard/profiler-sentiment-analysis/tree/master/ios_app/ProfilerSA/ML%20Models",
"demo_link": "https://github.com/novinfard/profiler-sentiment-analysis/",
"reference_link": "https://github.com/novinfard/profiler-sentiment-analysis/blob/master/dissertation-v6.pdf",
"type": "text"
},
{
"name": "BERT for Question answering",
"description": "Swift Core ML 3 implementation of BERT for Question answering",
"download_link": "https://github.com/huggingface/swift-coreml-transformers/blob/master/Resources/BERTSQUADFP16.mlmodel",
"demo_link": "https://github.com/huggingface/swift-coreml-transformers#-bert",
"reference_link": "https://github.com/huggingface/pytorch-transformers#run_squadpy-fine-tuning-on-squad-for-question-answering",
"type": "text"
},
{
"name": "GPT-2",
"description": "OpenAI GPT-2 Text generation (Core ML 3)",
"download_link": "https://github.com/huggingface/swift-coreml-transformers/blob/master/Resources/gpt2-512.mlmodel",
"demo_link": "https://github.com/huggingface/swift-coreml-transformers#-gpt-2",
"reference_link": "https://github.com/huggingface/pytorch-transformers",
"type": "text"
},
{
"name": "TextRecognition (ML Kit)",
"description": "Recognizing text using ML Kit built-in model in real-time.",
"download_link": "",
"demo_link": "https://github.com/tucan9389/TextRecognition-MLKit",
"reference_link": "https://firebase.google.com/docs/ml-kit/ios/recognize-text",
"type": "image"
},
{
"name": "ESC-10",
"description": "Recognize sounds from the ESC-10 sound dataset.",
"download_link": "https://github.com/narner/ESC10-CoreML/blob/master/CreateML%20Project%20And%20Dataset/ESC-10%20Sound%20Classifier.mlproj/Models/ESC-10%20Sound%20Classifier.mlmodel",
"demo_link": "https://github.com/narner/ESC10-CoreML/tree/master/ECS10-CoreML-Demo",
"reference_link": "https://nicholas-arner.squarespace.com/blog/2019/10/29/classification-of-sound-files-on-ios-with-the-soundanalysis-framework",
"type": "miscellaneous"
},
{
"name": "ImageSegmentation",
"description": "Segment the pixels of a camera frame or image into a predefined set of classes.",
"download_link": "https://developer.apple.com/machine-learning/models/",
"demo_link": "https://github.com/tucan9389/ImageSegmentation-CoreML",
"reference_link": "https://github.com/tensorflow/models/tree/master/research/deeplab",
"type": "image"
},
{
"name": "DepthPrediction",
"description": "Predict the depth from a single image.",
"download_link": "https://developer.apple.com/machine-learning/models/",
"demo_link": "https://github.com/tucan9389/DepthPrediction-CoreML",
"reference_link": "https://github.com/iro-cp/FCRN-DepthPrediction",
"type": "image"
},
{
"name": "ChordSuggester",
"description": "Predicts the most likely next chord based on the entered Chord Progression.",
"download_link": "https://github.com/carlosmbe/Mac-CoreML-Chord-Suggester/blob/main/MLChordSuggester.mlpackage.zip",
"demo_link": "https://github.com/carlosmbe/Mac-CoreML-Chord-Suggester",
"reference_link": "https://medium.com/@huanlui/chordsuggester-i-3a1261d4ea9e",
"type": "miscellaneous"
}
]
}
================================================
FILE: generate_readme.py
================================================
import json
with open('content.json', 'r') as f:
data = json.load(f)
model_metadata = {}
for type in data["types"]:
model_metadata[type] = []
for model in data["models"]:
model_metadata[model["type"]].append(model)
def render_model_line(model):
return "* **" + model["name"] + "** - " + model["description"] + " [Download](" + model["download_link"] + ") | [Demo](" + model["demo_link"] + ") | [Reference](" + model["reference_link"] + ")\n"
content = """
<!--
Title: Awesome Core ML Models
Description: A curated list of machine learning models in Core ML format.
Author: Kedan Li
-->
<p align="center">
<img src="images/coreml.png" width="329" height="295"/>
</p>
Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. [The official documentation](https://developer.apple.com/documentation/coreml)
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](https://github.com/likedan/Awesome-CoreML-Models/compare).
Recently, we've included visualization tools. And here's one [Netron](https://lutzroeder.github.io/Netron).
[](https://github.com/sindresorhus/awesome)
[](http://makeapullrequest.com)
# Models
## Image - Metadata/Text
*Models that takes image data as input and output useful information about the image.*
"""
for model in model_metadata["image"]:
content += render_model_line(model)
content += """
## Image - Image
*Models that transform image.*
"""
for model in model_metadata["image-image"]:
content += render_model_line(model)
content += """
## Text - Metadata/Text
*Models that process text data*
"""
for model in model_metadata["text"]:
content += render_model_line(model)
content += "## Miscellaneous\n"
for model in model_metadata["miscellaneous"]:
content += render_model_line(model)
content += """
# Visualization Tools
*Tools that helps visualize CoreML Models*
* [Netron](https://lutzroeder.github.io/Netron)
# Supported formats
*List of model formats that could be converted to Core ML with examples*
* [Caffe](https://apple.github.io/coremltools/generated/coremltools.converters.caffe.convert.html)
* [Keras](https://apple.github.io/coremltools/generated/coremltools.converters.keras.convert.html)
* [XGBoost](https://apple.github.io/coremltools/generated/coremltools.converters.xgboost.convert.html)
* [Scikit-learn](https://apple.github.io/coremltools/generated/coremltools.converters.sklearn.convert.html)
* [MXNet](https://aws.amazon.com/blogs/ai/bring-machine-learning-to-ios-apps-using-apache-mxnet-and-apple-core-ml/)
* [LibSVM](https://apple.github.io/coremltools/generated/coremltools.converters.libsvm.convert.html)
* [Torch7](https://github.com/prisma-ai/torch2coreml)
# The Gold
*Collections of machine learning models that could be converted to Core ML*
* [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) - Big list of models in Caffe format.
* [TensorFlow Models](https://github.com/tensorflow/models) - Models for TensorFlow.
* [TensorFlow Slim Models](https://github.com/tensorflow/models/tree/master/research/slim/README.md) - Another collection of TensorFlow Models.
* [MXNet Model Zoo](https://mxnet.incubator.apache.org/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](https://github.com/MiyainNYC/Visual-Memorability-through-Caffe) Score the memorability of pictures.
* [ILGnet](https://github.com/BestiVictory/ILGnet) The aesthetic evaluation of images.
* [Colorization](https://github.com/richzhang/colorization) Automatic colorization using deep neural networks.
* [Illustration2Vec](https://github.com/rezoo/illustration2vec) Estimating a set of tags and extracting semantic feature vectors from given illustrations.
* [CTPN](https://github.com/tianzhi0549/CTPN) Detecting text in natural image.
* [Image Analogy](https://github.com/msracver/Deep-Image-Analogy) Find semantically-meaningful dense correspondences between two input images.
* [iLID](https://github.com/twerkmeister/iLID) Automatic spoken language identification.
* [Fashion Detection](https://github.com/liuziwei7/fashion-detection) Cloth detection from images.
* [Saliency](https://github.com/imatge-upc/saliency-2016-cvpr) The prediction of salient areas in images has been traditionally addressed with hand-crafted features.
* [Face Detection](https://github.com/DolotovEvgeniy/DeepPyramid) Detect face from image.
* [mtcnn](https://github.com/CongWeilin/mtcnn-caffe) Joint Face Detection and Alignment.
* [deephorizon](https://github.com/scottworkman/deephorizon) Single image horizon line estimation.
# Contributing and License
* [See the guide](https://github.com/likedan/Awesome-CoreML-Models/blob/master/.github/CONTRIBUTING.md)
* Distributed under the MIT license. See LICENSE for more information.
"""
with open("README.md", 'w') as out:
out.write(content + '\n')
gitextract_o54j4hau/ ├── .github/ │ ├── CONTRIBUTING.md │ └── PULL_REQUEST_TEMPLATE.md ├── .gitignore ├── LICENSE.txt ├── README.md ├── content.json └── generate_readme.py
SYMBOL INDEX (1 symbols across 1 files) FILE: generate_readme.py function render_model_line (line 13) | def render_model_line(model):
Condensed preview — 7 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (45K chars).
[
{
"path": ".github/CONTRIBUTING.md",
"chars": 926,
"preview": "# Contribution Guidelines\n\nPlease ensure your pull request adheres to the following guidelines:\n\n- Search previous sugge"
},
{
"path": ".github/PULL_REQUEST_TEMPLATE.md",
"chars": 465,
"preview": "<!--- Provide a general summary of your changes in the Title above -->\n\n\n## Model URL\n<!--- The model URL -->\n\n## Demo U"
},
{
"path": ".gitignore",
"chars": 1455,
"preview": "# Xcode\n#\n# gitignore contributors: remember to update Global/Xcode.gitignore, Objective-C.gitignore & Swift.gitignore\n\n"
},
{
"path": "LICENSE.txt",
"chars": 1081,
"preview": "The MIT License (MIT)\n\nCopyright (c) 2016 Vinicius Souza\n\nPermission is hereby granted, free of charge, to any person ob"
},
{
"path": "README.md",
"chars": 15462,
"preview": "\n\n<!--\nTitle: Awesome Core ML Models\nDescription: A curated list of machine learning models in Core ML format.\nAuthor: K"
},
{
"path": "content.json",
"chars": 18230,
"preview": "{\n \"types\": [\n \"image\",\n \"image-image\",\n \"text\",\n \"miscellaneous\"\n "
},
{
"path": "generate_readme.py",
"chars": 5349,
"preview": "import json\n\nwith open('content.json', 'r') as f:\n data = json.load(f)\n\nmodel_metadata = {}\nfor type in data[\"types\"]"
}
]
About this extraction
This page contains the full source code of the likedan/Awesome-CoreML-Models GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 7 files (42.0 KB), approximately 10.8k tokens, and a symbol index with 1 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.