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.

Remove backgrounds from images directly in the browser environment with ease and no additional costs or privacy concerns. Explore an interactive demo.

Setup and customize deep learning environment in seconds.

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.

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

Deep Learning Visualization Toolkit（『飞桨』深度学习可视化工具 ）

《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。

A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

Tengine is a lite, high performance, modular inference engine for embedded device

PyTorch ,ONNX and TensorRT implementation of YOLOv4

An OBS plugin for removing background in portrait images (video), making it easy to replace the background when recording or streaming.

RuVector is a High Performance, Real-Time, Self-Learning Ai, Vector GNN, Memory DB built in Rust.

A lite C++ AI toolkit: 100+ models with MNN, ORT and TRT, including Det, Seg, Stable-Diffusion, Face-Fusion.