onnx

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

Visualizer for neural network, deep learning and machine learning models

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

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

Open standard for machine learning interoperability

Fast and accurate AI powered file content types detection

Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.

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

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages

Lightning-Fast, On-Device, Multilingual TTS — running natively via ONNX.

LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.

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

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

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

Open source real-time translation app for Android that runs locally

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

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

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

Setup and customize deep learning environment in seconds.

Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark