Open standard for machine learning interoperability
-
Updated
Jul 16, 2024 - Python
Open standard for machine learning interoperability
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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.
🚀🚀🚀 A collection of some awesome public YOLO object detection series projects.
Java version of LangChain
A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
Deep learning model converter for PaddlePaddle. (『飞桨』深度学习模型转换工具)
Running ONNX models in vanilla Kotlin
Speech-to-text, text-to-speech, and speaker recognition using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, C/C++, Python, Kotlin, C#, Go, NodeJS, Java, Swift, Dart, JavaScript, Flutter
Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
YOLO telegram bot for object detection and instance segmentation, implemented in Python + OpenCV + ONNXRuntime
Face recognition library which can be called from Python, integrated various useful functions.
The Java library to run Deep Learning models
Neural Network Compression Framework for enhanced OpenVINO™ inference
Web AI
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Add a description, image, and links to the onnx topic page so that developers can more easily learn about it.
To associate your repository with the onnx topic, visit your repo's landing page and select "manage topics."