Introduction Understanding how the brain builds internal representations of the visual world is one of the most fascinating challenges in neuroscience. Over the past decade, deep learning has reshaped computer vision, producing neural networks that not only perform at human-level accuracy on recognition tasks but also seem to process information in ways that resemble our […] The post AI and the Brain: How DINOv3 Models Reveal Insights into Human Visual Processing appeared first on MarkTechP...| MarkTechPost
Introduction Vision Language Models (VLMs) allow both text inputs and visual understanding. However, image resolution is crucial for VLM performance for processing text and chart-rich data. Increasing image resolution creates significant challenges. First, pretrained vision encoders often struggle with high-resolution images due to inefficient pretraining requirements. Running inference on high-resolution images increases computational costs and […] The post Apple Released FastVLM: A Novel ...| MarkTechPost
Robots around the world are about to get a lot smarter as physical AI developers plug in NVIDIA Jetson Thor modules — new robotics computers that can serve as the brains for robotic systems across research and industry. Robots demand rich sensor data and low-latency AI processing. Running real-time robotic applications requires significant AI compute Read Article| NVIDIA Blog
Discover leading visual inspection systems including Roboflow, Zebra, Cognex, Keyence, Basler, and Rockwell, and their key features.| Roboflow Blog
Timing and photography are the heartbeat of marathon events, but manually spotting bib numbers across hours of video is slow, error-prone, and costly. Learn how to automate bib recognition, accurately recording finish-line times and capturing the exact moment each runner crosses.| Roboflow Blog
Discover how to combine Flutter’s elegant cross-platform UI framework with Roboflow’s powerful computer vision platform to build AI-driven applications. Learn how to create a Flutter app that detects and counts Canadian coins.| Roboflow Blog
Large language models and Roboflow make it possible to build computer vision apps in hours. This guide shows how to use LLM coding assistants with Roboflow’s API and Universe models to create and deploy vision AI apps.| Roboflow Blog
Learn how to fine-tune Qwen2.5-VL for document processing using a custom dataset.| Roboflow Blog
AI is key for growth, but the path to success isn’t always obvious. Use our framework to identify strategic vision AI opportunities, design impactful solutions, and successfully deliver a return on investment.| Roboflow Blog
Understand what are pre-trained models, explore top pre-trained models, and learn how to easily use pre-trained models in Roboflow Workflows.| Roboflow Blog
Learn how to use CoreML models trained on Roboflow to control hardware devices with an ESP32 device.| Roboflow Blog
Learn how to train a YOLO11 instance segmentation model with Roboflow.| Roboflow Blog
You don’t need expensive new cameras to start using AI. With Roboflow, you can connect existing IP, CCTV, or even smartphone cameras to powerful computer vision workflows - saving costs, simplifying integration, and unlocking real-time insights.| Roboflow Blog
Manual shelf audits are slow, error-prone, and expensive. Learn how to automatically detect shelf labels, verify prices against POS systems, and catch costly discrepancies in real time.| Roboflow Blog
Roboflow has been accepted into Microsoft's Pegasus Program.| Roboflow Blog
This blog covers top 10 multimodal dataset and where to find multimodal dataset. You will also learn about importance of multimodal dataset in computer vision and tips for using the dataset.| Roboflow Blog
Learn how to take a dataset from Voxel51 into Roboflow, train an RF-DETR model, and deploy it to the cloud, private servers, or edge devices. This step-by-step guide walks you through dataset conversion, model training, workflow testing, and real-world integration.| Roboflow Blog
With this project, we integrate real-time feedback and computer vision to develop a hand-washing steps-tracking system using a Python application and a Roboflow-trained model.| Roboflow Blog
Explore VL-Cogito’s curriculum RL innovations for multimodal reasoning in AI. Boost chart, math, and science problem-solving accuracy| MarkTechPost
The latest Granite vision model recently came in second on the OCRBench leaderboard, and is the best-performing small model on the chart.| IBM Research
IBM’s new vision-language model for enterprise AI can extract knowledge locked away in tables, charts, and other graphics, bringing enterprises closer to automating a range of document understanding tasks.| IBM Research
LiveXiv is updated monthly, to provide a potentially more accurate look at vision-language model performance| IBM Research
The human visual system adapts to a wide range of lighting conditions, from warm sunlight to the cool glow of office fixtures. Yet, a smartphone camera applies numerous system-level processing steps and enhancements. As a result, the same color sample can appear differently under varying illumination or on different devices. In a professional environment, such inconsistency leads to significant waste of time and resources. In this article, the It-Jim mobile app development team explores how s...| It-Jim
Authors| Chris Choy
Authors| Chris Choy
Authors| Chris Choy
Authors| Chris Choy
Authors| Chris Choy
The Neural Radiance Fields (NeRF) proposed an interesting way to represent a 3D scene using an implicit network for high fidelity volumetric rendering. Compared with traditional methods to generate textured 3D mesh and rendering the final mesh, NeRF provides a fully differntiable way to learn geometry, texture, and material properties for specularity, which is very difficult to capture using non-differentiable traditional reconstruction methods.| Chris Choy
Learn how to use Roboflow Workflows to collect and preprocess image training data for use in building a vision model.| Roboflow Blog
Learn what F1 score is, for what it is used, and how to calculate F1 score.| Roboflow Blog
Ignis Energy integrates artificial intelligence to streamline exploration and early decision-making. Its hybrid system combines machine le...| Tech Company News
The D-Robotics RDK X5 is an upgraded AI development board built around the Sunrise X5 octa-core SoC and designed for more demanding ROS-based| CNX Software - Embedded Systems News
Cosmos Reason1 VLM has excellent Physical AI and Embodied Reasoning capabilities that enables it to reason over long video sequences with grounded actions.| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
In this article, we are modifying the Web-DINO 300M architecture for semantic segmentation. We will add a simple segmentation decoder head and train the model for person segmentation. The post Semantic Segmentation using Web-DINO appeared first on DebuggerCafe.| DebuggerCafe
Web-SSL 2.0 is a framework to scale DINOv2 models from 1B to 7B parameters by training them in MC-2B (MetaCLIP-2B) dataset.| DebuggerCafe
Learn how computer vision effectively allows HSE teams to augment and strengthen their worksite safety and operations.| viso.ai
Vision AI enables manufacturers to monitor vehicle safety, to ensure compliance, reduce risk, and improve site operational safety.| viso.ai
NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Read Article| NVIDIA Blog
Learn what transfer learning is and how it is used in computer vision.| Roboflow Blog
We launched the first in a new webinar series lifting the lid on how enterprise teams can get started with computer vision.| viso.ai
Computer vision for detecting issue during 3d printing with automatic notification to Discord and Telegram and pausing the print. This plugin has minimal HW requirements. Recommended hardware is Raspberry pi 5, older version are not supported.| OctoPrint Plugin Repository
Today we are releasing RF-DETR, a state-of-the-art real-time object detection model. Learn more about how RF-DETR works and how to use the model.| Roboflow Blog
Agility Robotics envisions a future in which humanoid, mobile manipulation robots collaborate with people to orchestrate complex industrial tasks. The robots handle mundane, hazardous, and repetitive jobs, empowering human workers to focus on higher-order cognitive functions such as creativity, problem-solving, and strategic thinking that only humans can do. This vision is quickly becoming a reality…| Intel® RealSense™ Depth and Tracking Cameras
The 10 Hottest Computer Vision Trends Shaping 2025| Gramener Blog
Carrying out DINOv2 segmentation experiments for fine-tuning and transfer learning and comparing the results.| DebuggerCafe
Learn how to train a ResNet-50 model for image classification.| Roboflow Blog
Learn what OCR data extraction is and what models you can use to programmatically read the contents of images.| Roboflow Blog
Learn about computer vision and how you can use it to solve problems.| Roboflow Blog
Learn about the latest advancements in AI helping automotive manufacturers modernize their factories and improve productivity.| Roboflow Blog
DINOv2 is a self-supervised computer vision model which learns robust visual features that can be used for downstream tasks.| DebuggerCafe
This article explores computer vision trends and how advances in AI technology will impact industry, businesses, and society.| viso.ai
End-to-end tutorial for detecting and counting objects on a conveyor belt using computer vision.| Roboflow Blog
Master Contrastive Learning with SimCLR and BYOL, theoretical foundations, and step-by-step BYOL implementation of learning representations| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
We break down all current You Only Look Once (YOLO) versions from Joseph Redmon's original release to v9, v10, v11, and beyond.| viso.ai
Intro Since many of my posts were mostly critical and arguably somewhat cynical [1], [2], [3], at least over the last 2-3 years, I decided to switch gears a little and let my audience know I'm actually a very constructive, busy building stuff most of the time, while my ranting on the blog is mostly a side project to vent, since above everything I'm allergic to naive hype and nonsense. Nevertheless I've worked in the so called AI/robotics/perception for at least ten years in industry now (an...| Piekniewski's blog
Computer vision plays a big part in deploying automated visual inspection, making it possible to process the amounts of data from this automation.| AI Accelerator Institute
Computer vision in AR and VR uses digital elements for spatial mapping, object recognition, and creating immersive virtual environments.| viso.ai
This article explores the history of self-supervised learning, introduces DINO Self-Supervised Learning, and shows how to fine-tune DINO for road segmentation| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
As computer vision AI continues to advance, it will bring more sophisticated analysis, smarter training routines and deeper fan engagement.| Griffon Webstudios
Ball tracking is crucial for AI systems to analyze sports effectively, but it's challenging due to factors like the ball's small size, high velocity, complex backgrounds, similar-looking objects, and varying lighting. This tutorial will teach you how to overcome these challenges.| Roboflow Blog
With modern advancements in artificial intelligence and computational power, computer vision has become an integral part of everyday life. Computers’ ability to ‘see’ and interpret the world around them helps in the analysis of the massive amounts of data created in daily operations.| AI Accelerator Institute
Le passage à l'électrique est aussi l'occasion pour les constructeurs automobiles d'optimiser et moderniser leurs processus industriels. Dans...-Intelligence artificielle| www.usine-digitale.fr
The ability to track moving objects across multiple camera feeds is of immense value to us. From baggage monitoring in busy airports to product tracking in large retail stores, there is a strong case for applications of this nature. In principle, this is simple. The tracking system first detects objects entering a camera’s view and […] The post Multi-Camera Object Tracking Using Custom Association Model appeared first on QBurst Blog.| QBurst Blog
Explore top Industry 4.0 companies driving digital manufacturing, enhancing efficiency, and optimizing processes. Read this blog.| Gramener Blog
We dive into the world of AI design tools and examine five leading solutions: Canva, Adobe Photoshop, Beautiful.ai, Decktopus, and Midjourney.| TOPBOTS
Florence-2 is a lightweight vision-language model open-sourced by Microsoft under the MIT license.| Roboflow Blog
Einstein 1 Studio targets those building AI, with the objective of making more functionality available to end users of Salesforce applications.| davidmenninger.ventanaresearch.com
Learn what OpenCV is, what you can do with OpenCV, how OpenCV performs on various tasks when run on CPU vs. GPU, and more.| Roboflow Blog
See how nine different OCR models compare for scene text recognition across industrial domains.| Roboflow Blog
Learn how to monitor retail queues to identify when customers have been waiting for too long.| Roboflow Blog
Training a robust facial keypoint detection model by fine-tuning the pretrained ResNet50 model with the PyTorch framework,| DebuggerCafe
Uniphore’s X Platform offers contact center automation and analytics while targeting a broad range of use cases by employing multiple AI modes.| keithdawson.ventanaresearch.com
Advances in computer vision are enabling enterprises to expand use cases and build the skills necessary to maximize value from unstructured data.| davidmenninger.ventanaresearch.com
Learn how to train a YOLOv9 model on a custom dataset.| Roboflow Blog
YOLOv8 object tracking and counting unveils new dimensions in real-time tacking; explore its mastery in our detailed guide, your key to mastering the tech.| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
The YOLO (You Only Look Once) series of models, renowned for its real-time object detection capabilities, owes much of its effectiveness to its specialized loss functions. In this article, we delve into the various YOLO loss function integral to YOLO's evolution, focusing on their implementation in PyTorch. Our aim is to provide a clear, technical| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
Discover moving object detection using OpenCV, blending contour detection with background subtraction for real-time application in security and traffic.| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
My recent experiences with using WebRTC in a mobile application gave me a chance to get familiar with its capabilities and limitations, namely being reliant ...| spieswl.github.io
In this guide, we evaluate Google's Gemini LMM against several computer vision tasks, from OCR to VQA to zero-shot object detection.| Roboflow Blog
Learn how to use computer vision in your data analytics pipelines.| Roboflow Blog
In this guide, we walk through how to deploy computer vision models (i.e. YOLOv8) offline using Roboflow Inference.| Roboflow Blog
In this guide, we share findings experimenting with GPT-4 with Vision, released by OpenAI in September 2023.| Roboflow Blog
