This repository provides a comprehensive collection of pre-trained AI models optimized for the ailia SDK, targeting developers and researchers seeking efficient on-device inference across diverse platforms. It offers a vast array of models for tasks ranging from computer vision and natural language processing to audio analysis, enabling rapid integration of state-of-the-art AI capabilities into applications.

How It Works

The ailia SDK is a cross-platform, high-speed inference engine that leverages GPU acceleration via Vulkan and Metal. It provides a unified C++ API and supports bindings for Unity (C#), Python, Rust, Flutter (Dart), and JNI. This collection comprises models exported from various frameworks like PyTorch, TensorFlow, Keras, Darknet, and OpenVINO, ensuring broad compatibility and performance.

Quick Start & Requirements

• Installation: Models are typically used via the ailia SDK. Refer to the ailia SDK documentation for installation and usage.
• Prerequisites: Ailia SDK requires specific build environments depending on the target platform. Model compatibility varies, with most requiring ailia SDK version 1.2.x or later.
• Try: Google Colaboratory is available for trying models.

Highlighted Details

• Over 377 pre-trained models available, with frequent updates adding new architectures and capabilities.
• Supports a wide range of AI tasks including object detection (YOLO variants, SSD, etc.), image segmentation, NLP (BERT, GPT variants), audio processing, and more.
• Optimized for cross-platform deployment on Windows, macOS, Linux, iOS, Android, Jetson, and Raspberry Pi.
• Models are exported from popular frameworks like PyTorch, TensorFlow, Keras, and OpenVINO.

Maintenance & Community

The project is actively maintained, with recent updates including models like YOLOv11-seg, GPT-SoVITS v3, and Qwen2-VL. Community engagement and support channels are not explicitly detailed in the README.

Licensing & Compatibility

The README does not specify a license for the models or the ailia SDK itself. Users should verify licensing terms for commercial use and integration into closed-source projects.

Limitations & Caveats

The README does not detail specific limitations or known issues for individual models. Users must consult model-specific documentation or test thoroughly for performance and accuracy on their target hardware and use cases. The absence of a clear license could be a significant adoption blocker.