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This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing models that process both images and text. It provides an on-device inference engine and a vision language model framework to run quantized models locally on mobile and desktop hardware accelerators. The framework features a model quantization toolkit to reduce weight precision for lower memory footprints and increased execution speed on specialized silicon. It also includes an efficient vision encoder utilizing a hybrid encoding system to compress image tokens, which reduces pro
gemma.cpp is a C++ inference engine for Gemma, PaliGemma, and Griffin language models, designed to run directly on-device without Python dependencies. It provides a self-contained runtime that loads quantized model weights and performs text generation on CPU or GPU, along with a model checkpoint converter that transforms PyTorch or Keras checkpoints into a compact binary format for fast loading. The engine supports multiple model architectures, including the Griffin recurrent architecture with gated linear recurrent layers and sliding-window attention for efficient long-sequence handling, as
TengineKit is a mobile computer vision software development kit designed for real-time inference on local hardware. It functions as a neural network engine that executes deep learning models directly on mobile devices, enabling applications to perform complex visual analysis without relying on cloud connectivity. The framework provides specialized tools for detecting and tracking human features, including faces, hands, bodies, and irises, alongside general object detection capabilities. By utilizing a native core runtime and hardware-accelerated execution, the library processes visual data lo
This is a webcam-based client for a local llama.cpp server that enables real-time object detection and vision-language model inference directly from a browser. It captures frames from the user's webcam at configurable intervals and sends them to a locally running inference server for analysis, displaying both detection results and textual scene descriptions as they are produced.
The main features of ngxson/smolvlm-realtime-webcam are: Local Object Detection, Real-Time Object Detection, Vision-Language Models, Vision-Language Inference, Vision-Language Model Analyses, Webcam Frame Captures, llama.cpp Backend Servers, Web Chat Frontends.
Projects with overlapping indexed features include: getstream/vision-agents. apple/ml-fastvlm — This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing… google/gemma.cpp — gemma.cpp is a C++ inference engine for Gemma, PaliGemma, and Griffin language models, designed to run directly… oaid/tenginekit — TengineKit is a mobile computer vision software development kit designed for real-time inference on local hardware. It… cbh123/narrator — Narrator is an artificial intelligence system that converts real-time video feeds into natural language audio… datitran/object_detector_app — This application is a real-time computer vision system designed to identify and label objects within live video feeds,…
