A model compilation solution for various hardware

Scoreboard for ONNX Backend Compatibility

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

ONNX neural network inference engine

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Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX also delivers a highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions.

🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools

🤗 Optimum Intel: Accelerate inference with Intel optimization tools

A type-safe, lightweight, modern, and performant binding Java binding of Microsoft's ONNX Runtime

Simple and Distributed Machine Learning

Java version of LangChain

Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference

Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf). I don't need a Star, but give me a pull request.

A Rust wrapper for ONNX Runtime

ONNX Runtime Server: The ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference.

pg_onnx: ONNX Runtime integrated with PostgreSQL. Perform ML inference with data in your database.

Official repository of "FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation".