#Core ML

Designed to democratize access to machine learning, Turi Create facilitates the easy development of custom models for individuals without technical expertise. This intuitive tool seamlessly integrates complex tasks such as recommendations, object detection, and image classification into applications, supporting various data types including text, images, audio, and video. With scalable data processing on a single machine, it allows exporting models to Core ML for use within Apple's ecosystem, covering iOS, macOS, watchOS, and tvOS. Turi Create offers flexibility and ease of use, with built-in visualizations for data exploration, and supports a wide range of tasks from regression to style transfer, making it ideal for developers across various domains.

Exporters facilitates the conversion of Hugging Face Transformers models to Core ML, ensuring deployment across Apple platforms like macOS and iOS. It offers ready-made configurations for models like BERT and GPT2, supports the ML Program format, and provides options for model optimization and quantization. The package underscores the importance of validation on macOS and suggests pre-optimization with Hugging Face's 'Optimum' for mobile use.

Whisper.rn seamlessly integrates OpenAI's Whisper ASR model into React Native apps. The library supports various platforms and offers high-performance recognition through whisper.cpp. Key features include multi-platform support, detailed installation instructions for iOS and Android, and real-time transcription capabilities. It includes microphone permissions, audio session management, and iOS Core ML support, providing a comprehensive solution for enhancing application speech recognition.

Discover efficient image generation on Apple Silicon using Core ML with Stable Diffusion. This project provides conversion tools for PyTorch to Core ML, facilitating deployment on macOS and iOS. Performance benchmarks highlight device optimizations, including weight compression and attention adjustments for enhanced speed and efficiency. Explore stable image generation with precise model conversion.

Delve into the Apple Neural Engine, a component that improves machine learning model performance on iPhones and iPads. This guide explains the factors affecting ANE efficiency and outlines how to identify and optimize models for ANE use. While Apple's official guidance is scarce, benefit from community insights and experimental findings to troubleshoot and leverage ANE's capabilities effectively.

This project enables Seamless Native Execution of Stable Diffusion on macOS using Apple's Core ML for optimized performance on Apple Silicon Macs. It provides rapid image generation with low memory consumption, functioning entirely offline. Key functions include image-to-image creation, EXIF metadata insertion, and high-resolution conversion, alongside the adaptability of Core ML model integration. The intuitive SwiftUI interface facilitates easy navigation, with strong privacy adherence by keeping all processing local. This solution is perfect for those in search of sophisticated and efficient image creation on newer Mac systems.

Discover a versatile tool designed for precise image annotation, featuring polygon and pixel labeling with Segment Anything models and automated Core ML-based labeling. The tool also includes advanced text recognition capabilities, customizable settings for quick labeling, and a gallery view for easy image management. Export projects in multiple formats like COCO, Labelme, and YOLO, making this solution ideal for enhancing image labeling efficiency.