Onnx
Showing 35 of 35 projects
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A visualizer for neural network, deep learning, and machine learning models across multiple frameworks.
An open standard format for representing machine learning models to enable interoperability between frameworks.
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
A unified deep learning toolkit for describing neural networks as computational graphs, supporting feed-forward DNNs, CNNs, and RNNs/LSTMs.
A Rust-based deep learning framework and tensor library optimized for flexibility, efficiency, and cross-platform portability.
A next-generation Kaldi-based toolkit for offline speech-to-text, text-to-speech, and audio processing across 12 languages and diverse hardware.
A high-performance CNN-based face detection library written in pure C++ with SIMD optimizations, achieving up to 1000 FPS.
An open-source Java library that simplifies integrating LLMs into Java applications through a unified API and comprehensive toolbox.
A PyTorch implementation of YOLOv3 for real-time object detection, supporting export to ONNX, CoreML, and TFLite.
An open-source Android app for real-time, offline voice translation between multiple languages using on-device AI models.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A comprehensive toolset for converting, visualizing, and managing deep learning models across multiple frameworks like TensorFlow, PyTorch, and Caffe.
A self-learning vector database with graph intelligence, local AI, and PostgreSQL integration, built for real-time adaptation.
A Rust-native port of Hugging Face Transformers providing ready-to-use NLP pipelines and transformer models like BERT, GPT2, and T5.
A deep learning framework for research, development, and production with flexible Python API and C++ core.
A framework for running deep neural network models directly in web browsers using ONNX format with WebGPU, WebGL, and WebAssembly backends.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
A high-level Deep Learning API for JVM and Android developers, written in Kotlin and inspired by Keras.
An accelerated machine learning framework for Go, offering a PyTorch/Jax/TensorFlow-like experience with support for CPUs, GPUs, TPUs, and WASM.
A Go interface for importing and executing pre-trained ONNX neural network models without framework dependencies.
Convert PyTorch models to Keras (TensorFlow backend) for deployment and interoperability.
A lightweight, portable pure C99 ONNX inference engine for embedded devices with hardware acceleration support.
A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.
A distributed Spark/Scala implementation of Isolation Forest and Extended Isolation Forest algorithms for scalable unsupervised outlier detection.
A pure C99 ONNX runtime with zero dependencies, designed for embedded devices and old hardware.
A fork of OpenAI's Whisper speech recognition models optimized with OpenVINO backend for faster CPU inference.
A type-safe, functional ONNX API and backend for deep learning and classical machine learning in Scala 3.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
A deep similarity learning-based type inference tool for Python that provides ML-powered type auto-completion.
A curated collection of machine-learned models for autonomous driving tasks like perception, prediction, and planning.
Push-to-talk voice dictation for Linux with 100% local transcription, multilingual support, and speaker diarization.
A JavaScript/TypeScript library for accelerated tensor computation and ONNX model execution in the browser.
Interactive tutorial for learning face detection and recognition with hands-on code samples using local AI.
A thread-safe vector database for model inference built on LMDB.
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