This repository is an "awesome list" curating TensorFlow Lite models, sample applications, tutorials, and learning resources for developers targeting mobile and edge devices. It aims to showcase community projects, provide easy access to diverse models and implementations, and share knowledge for building on-device ML experiences.

How It Works

The list categorizes TensorFlow Lite resources by task (e.g., computer vision, text, speech) and model type. It links to specific models with their respective app/device implementations, sample code, and official documentation or community tutorials. Key announcements highlight advancements like the MLIR-based converter, Android Support Library, Model Maker, on-device training, and delegates for hardware acceleration.

Quick Start & Requirements

This is a curated list, not a runnable project. To use the models and samples, users will need to follow the links provided for each specific resource, which typically involve TensorFlow Lite setup, model conversion, and platform-specific development (Android, iOS, Flutter).

Highlighted Details

• Extensive categorization of models across computer vision (classification, detection, segmentation, style transfer, generative, pose estimation), text, speech, recommendation, and games.
• Includes links to official TensorFlow Lite models, TensorFlow Hub, and community-contributed models with sample applications.
• Features resources on ML Kit, plugins/SDKs like MediaPipe and Edge Impulse, and tools like Netron for model visualization.
• Provides a comprehensive collection of blog posts, books, videos, and MOOCs for learning and development.

Maintenance & Community

The list is community-driven, encouraging contributions via Pull Requests. It references key contributors and organizations like Google (MediaPipe, TensorFlow team), Hugging Face, and Mozilla. Links to learning resources and specific project repositories are provided.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, as is common for "awesome" lists), but the licensing of individual models and sample applications varies and must be checked at their respective sources. Compatibility for commercial use depends on the licenses of the linked models and tools.

Limitations & Caveats

As a curated list, the quality and maintenance status of individual linked resources can vary. Some links may point to older versions or community projects that are no longer actively maintained. Users must verify the applicability and current state of each resource.