Popular GitHub Repositories for Onnx Rest

YOLOv5 ? in PyTorch > ONNX > CoreML > TFLite

Visualizer for neural network, deep learning and machine learning models

ncnn is a high-performance neural network inference framework optimized for the mobile platform

Open standard for machine learning interoperability

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

Unified framework for building enterprise RAG pipelines with small, specialized models

? Scalable embedding, reasoning, ranking for images and sentences with CLIP

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

A collection of pre-trained, state-of-the-art models in the ONNX format

Java version of LangChain

? Yao is an all-in-one application engine that enables developers to create web apps, REST APIs, business applications, and more, with AI as a development partner.

Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, support 11 programming languages

Leading free and open-source face recognition system

Silero VAD: pre-trained enterprise-grade Voice Activity Detector

Setup and customize deep learning environment in seconds.

Effortless data labeling with AI support from Segment Anything and other awesome models.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple