Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
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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.
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
Unified framework for building enterprise RAG pipelines with small, specialized models
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
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
Open source real-time translation app for Android that runs locally
Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.
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
State of the Art Natural Language Processing
1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
PyTorch to Keras model convertor
Learn to sing the songs you love. A free, open-source singing trainer for Mac that listens to the music you play.
A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
Your users download a 4GB AI model, the connection drops at 3.8GB, verifyfetch resumes from 3.8GB and verifies every byte. Drop-in integrity verification for Transformers.js, WebLLM, and any large file in the browser.