This repository provides the largest collection of machine learning models converted to Apple's Core ML format, targeting iOS, macOS, tvOS, and watchOS developers. It aims to simplify the integration of advanced AI capabilities into Apple ecosystem applications, offering a wide range of pre-trained models for image analysis, text processing, and more.

How It Works

The project curates and lists models that have been converted to the Core ML format, enabling direct integration into Apple applications without requiring complex conversion pipelines within the app itself. It covers various model architectures and tasks, including object detection, image segmentation, text classification, and sentiment analysis, leveraging Apple's Vision framework and Metal for efficient on-device inference.

Quick Start & Requirements

• Models are provided in .mlmodel format, ready for integration into Xcode projects.
• Requires Xcode and an Apple device for on-device testing and deployment.
• Links to demos and references are provided for individual models.

Highlighted Details

• Extensive library of over 30 pre-converted Core ML models.
• Supports models originally from Caffe, Keras, XGBoost, Scikit-learn, MXNet, LibSVM, and Torch7.
• Includes visualization tools like Netron for model inspection.
• Features models for real-time tasks like text detection and pose estimation.

Maintenance & Community

• Open to community contributions via pull requests for new model conversions.
• The project is community-driven with no explicit mention of core maintainers or sponsorships.

Licensing & Compatibility

• Distributed under the MIT License.
• Permissive licensing allows for commercial use and integration into closed-source applications.

Limitations & Caveats

The repository primarily serves as a curated list and collection of .mlmodel files; it does not provide the original training code or scripts for converting models. Users are responsible for ensuring model compatibility with specific iOS/macOS versions and hardware capabilities.