Popular GitHub Repositories for Craft Onnx

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/

Java version of LangChain

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

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

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

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

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

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

Setup and customize deep learning environment in seconds.

Nexa SDK is a comprehensive toolkit for supporting GGML and ONNX models. It supports text generation, image generation, vision-language models (VLM), Audio Language Model, auto-speech-recognition (ASR), and text-to-speech (TTS) capabilities.

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

Simple and Distributed Machine Learning