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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.

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.

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