A curated collection of machine learning models in Core ML format for iOS, macOS, tvOS, and watchOS developers.
Awesome Core ML Models is a curated repository of machine learning models pre-converted into Apple's Core ML format. It provides iOS, macOS, tvOS, and watchOS developers with a large collection of ready-to-use models for tasks like image classification, object detection, text analysis, and more, eliminating the need to convert models themselves.
Apple platform developers (iOS, macOS, tvOS, watchOS) who want to integrate machine learning features into their apps without building models from scratch or handling complex conversions.
It offers the largest centralized collection of Core ML models, each with associated demos and references, significantly reducing development time and complexity for adding ML capabilities to Apple ecosystem applications.
Largest list of models for Core ML (for iOS 11+)
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Offers a wide range of pre-trained models for diverse tasks like image classification, pose estimation, sentiment analysis, and more, categorized clearly in the README with over 30 entries.
All models are already in .mlmodel format, eliminating the need for developers to handle conversion using tools like coremltools, saving significant time and effort.
Each model listing includes download links, demo applications, and reference papers, providing practical examples and context for integration and learning.
Welcomes pull requests for new models, fostering a growing collection that stays updated with contributions, as highlighted by the PRs welcome badge and contribution guide.
The collection relies on community contributions, so newer or cutting-edge models may not be added promptly, and some demo links or references could be outdated or broken.
Models are provided as-is in Core ML format, making fine-tuning or modification difficult without accessing and reconverting the original source models, which requires additional ML expertise.
All models are in Core ML format, restricting use to Apple platforms only and offering no support for cross-platform deployment, which can be a lock-in for broader projects.