Awesome GitHub RepositoriesClient-Side Inference

TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and server-side environments. It functions as a browser-based model trainer, a WebAssembly inference engine, and a WebGPU accelerated tensor library for low-level linear algebra. The project also includes a model converter to transform Python-based models into optimized formats for JavaScript execution. The library distinguishes itself through a pluggable backend architecture that allows mathematical operations to be executed via CPU, WebGL, or WebGPU. It supports the conversion of Py

Transformers.js is a JavaScript library and web machine learning framework designed to run pretrained transformer models directly in the browser. It serves as a client-side inference engine and a wrapper for the ONNX Runtime, enabling the execution of multimodal AI tasks on user devices without the need for a backend server. The library distinguishes itself by providing a unified toolkit for processing text, image, and audio data locally. This architecture supports privacy-preserving model inference and reduces latency by performing all computations on the client's hardware. Its capabilities

NSFW detection on the client-side via TensorFlow.js

Pose-animator is a system that maps real-time body and face tracking data to 2D vector illustrations. It functions as a skeletal animation engine and motion controller that translates human keypoint recognition into instantaneous SVG path updates. The project enables real-time motion capture from webcam feeds and pose extraction from static images. It utilizes a skeletal rig to link virtual bones to vector character surfaces, allowing for the animation of custom characters and interactive avatars. The tool incorporates client-side machine learning inference for processing camera frames, coor

TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients. The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats

Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a multilayer perceptron model designed for pattern recognition and function approximation. The library includes a standalone inference engine that converts trained models into portable JavaScript functions. This allows predictions to be executed in browser or Node.js environments without requiring the original library dependencies. The system supports persistent model management through JSON serialization for saving and loading network weights. It also provides a streaming mechanism

Keras-js ist eine JavaScript-Inferenz-Engine und ein browserbasiertes Machine-Learning-Framework, das darauf ausgelegt ist, vortrainierte Keras-neuronale Netze auszuführen. Es ermöglicht clientseitige Modell-Inferenz in Webbrowsern oder Node.js-Umgebungen, ohne dass ein Backend-Server erforderlich ist. Die Bibliothek nutzt einen WebGL-Tensor-Beschleuniger, um mathematische Operationen zur Hardwarebeschleunigung auf den Grafikprozessor abzubilden. Um die Reaktionsfähigkeit der Benutzeroberfläche während rechenintensiver Berechnungen aufrechtzuerhalten, integriert sie eine Web-Worker-Inferenz-Runtime, die die Verarbeitung neuronaler Netze in Hintergrund-Threads ausführt. Das System unterstützt das Laden von Modellen über JSON-Konfigurationsdateien und Gewichtungs-Tensoren. Es verwaltet große numerische Arrays unter Verwendung von WebGL-Texturspeicherung, um Hochgeschwindigkeits-Speicherzugriffe während der Tensor-Ausführung zu ermöglichen.