A new study reveals how the brain compensates for rapid eye movements, maintaining a stable visual perception despite dynamic visual input. Researchers found that this stability mechanism breaks down for non-rigid motion like rotating vortices.
Microsoft has released the OmniParser model on HuggingFace, a vision-based tool designed to parse UI screenshots into structured elements, enhancing intelligent GUI automation across platforms without relying on additional contextual data.
Simon Willison explains how to use the mistral.rs library in Rust to run the Llama Vision model on a Mac M2 laptop. He provides a detailed example and discusses the memory usage and GPU utilization.
Meta releases Llama 3.2, which features small and medium-sized vision LLMs (11B and 90B) alongside lightweight text-only models (1B and 3B). It also introduces the Llama Stack Distribution.
This project demonstrates how to use the ESP32-CAM to capture an image of a vehicle's license plate, send it to a cloud server for recognition, and display the recognized number plate on an OLED screen. The project includes setup instructions, code, and component details.
MLX-VLM: A package for running Vision LLMs on Mac using MLX.
A DIY project that uses a Raspberry Pi and computer vision to detect and notify about stray cat sightings, with an added feature to deter birds from approaching the cat food.
This article explores how to incorporate images into a RAG (Retrieval-Augmented Generation) knowledgebase using Large Language Models (LLMs) with vision capabilities. It provides a step-by-step guide to collecting, uploading, and transcribing images for a richer and more detailed knowledgebase.
This article guides readers through running the latest YOLO v10 object detection model on different hardware, specifically on a Raspberry Pi 5, and a computer. The article discusses the importance of computer vision in ML applications, the versatility of YOLO, and the cross-platform code presented in the article.
TrashAI is a platform that utilizes Artificial Intelligence to identify and sort waste, aiming to increase recycling rates and decrease contamination. By partnering with waste facilities and municipalities, TrashAI hopes to make a significant impact on waste management and contribute to a sustainable future.
