onnxruntime-inference-examples

Inference framework

Repository providing examples for using ONNX Runtime (ORT) to perform machine learning inferencing.

Examples for using ONNX Runtime for machine learning inferencing.

GitHub

1k stars
38 watching
342 forks
Language: C++
last commit: 2 months ago

Related projects:

Repository Description Stars
microsoft/onnxruntime-training-examples Accelerates training of large transformer models by providing optimized kernels and memory optimizations. 317
microsoft/onnxruntime A cross-platform, high-performance machine learning accelerator 14,990
mlcommons/inference Measures the performance of deep learning models in various deployment scenarios. 1,256
xboot/libonnx An onnx inference engine for embedded devices with hardware acceleration support 589
alrevuelta/connxr An embedded device-friendly C ONNX runtime with zero dependencies 196
triton-inference-server/client Client libraries and examples for communicating with Triton using various programming languages 579
tpoisonooo/llama.onnx A project providing onnx models and tools for inference with LLaMa transformer model on various devices 356
kraiskil/onnx2c Generates C code from ONNX files for efficient neural network inference on microcontrollers 234
emergentorder/onnx-scala An API and backend for running ONNX models in Scala 3 using typeful, functional deep learning and classical machine learning. 138
sassoftware/enlighten-apply Provides code and materials to apply SAS machine learning techniques in software development 127
nilseuropa/ros_ncnn A ROS wrapper for NCNN, allowing developers to integrate high-performance neural network inference into ROS projects 63
oramasearch/onnx-go A Go package that allows developers to import pre-trained neural network models without being tied to a framework or library. 726
roboflow/inference A platform for deploying and fine-tuning computer vision models in production-ready environments. 1,401
microsoft/0xdeca10b A framework for hosting and training machine learning models on a blockchain, enabling secure sharing and prediction without requiring users to pay for data or model updates. 559