Floor plan segmentation using Ultralytics YOLO26! Architectural floor plans... represent rich, unstructured spatial datasets. Applying instance segmentation enables precise identification of structural elements (walls, doors, corridors, and rooms), converting raw CAD images into structured digital assets. These outputs directly support spatial modeling, digital twin integration, and automated facility management. #Construction #AI #MachineLearningshow more

Instance vs semantic segmentation difference | Ultralytics🚀 🧩Instance segmentation... answers: “Which pixels belong to each object?” 🎨Semantic segmentation answers: “Which pixels belong to each class?” #segmentation #MachineLearning #AIshow more

Run promptable segmentation with Ultralytics YOLO26-E! 🧠 Segment objects... using natural language prompts with YOLO26-E, ideal for flexible visual search, rapid annotation, and interactive computer vision workflows. Get started ➡️ #Ultralytics #YOLO26 #AI #Researchshow more

YOLOE-26 released by Ultralytics support text + visual prompts... 🤯 This was a part of the YOLO26 release. It's a zero-shot, promotable model, almost like YOLO-World, but it also offers real-time object segmentation. Highlights👇 #AI #MachineLearning #YOLOEshow more

Railway operators manage thousands of kilometers of track and... critical infrastructure every day!🚆 With Ultralytics YOLO26, teams can automatically monitor assets such as insulators, rail clamps, semaphores, and towers in images and video, improving asset visibility and supporting predictive maintenance workflows. Explore more ➡️ #RailwayOperations #MachineLearning #AIshow more

Count rooftop panels with Ultralytics YOLO26 OBB! 🏠 Use... oriented bounding boxes to detect solar panels from aerial views and capture their orientation, enabling accurate counting for inspections, asset management, and infrastructure analytics. Explore more ➡️ #Ultralytics #YOLO26 #AI #ObjectCounting #ComputerVisionshow more

Pothole detection on the road in real time using... Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AIshow more

Improve crosswalk safety with Ultralytics YOLO26! 🚸 Detect pedestrians,... vehicles, and high-risk interactions in real-time to support safer crossings, smarter alerts, and improved traffic monitoring. Read more ➡️ #Ultralytics #YOLO26 #RoadSafetyshow more

Football players detection + tracking using Ultralytics YOLO26 ⚽️... It took me 30 minutes to train the YOLO26 model on a custom dataset using Ultralytics Platform. Later, I used the object tracker (Bytetrack) for tracking the players; tracking could be improved further, and I am working on it this week. More info 👇 #soccer #football #MachineLearningshow more

Architectural floor plan in a scale of 1:1

Detect worker falls in real time with Ultralytics YOLO26!... 📦 Combine object detection and pose estimation to identify when a person carrying luggage boxes falls, helping logistics teams improve warehouse safety and respond faster to incidents. Get started ➡️ #Ultralytics #YOLO26 #AI #Logistics #WorkplaceSafetyshow more

Over the Reality just hit another major milestone in... the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.show more

People counting in zones using Ultralytics YOLO26 🧑‍🤝‍🧑 In... this demo, people are moving on elevators. I used a custom-trained YOLO26 model to detect and track each person, then applied the object counting in zones solution to count the number of people within each zone. 🙂 #people #retail #MachineLearningshow more

Detect road potholes in real time using Ultralytics YOLO11!... 🕳️ Identify potholes accurately from images or video to support road maintenance, safety monitoring, and smart city infrastructure workflows. Read more ➡️ #AI #SmartCities #Ultralytics #YOLO11show more

Signature detection using Ultralytics YOLOv8 🚀 This technology boosts... security, simplifies document verification, and supports automated processes across different industries ⭐ Learn more ➡️ #AI #yolov8show more

🚨LATEST: INTERLINK UNVEILS NEW PROTOCOL FOR REAL BUSINESSES The... InterLink Foundation of InterLink Labs 👤 + 🌐 has recently announced its own 'Transaction-Backed Digital Assets Protocol'. The protocol is designed to... "connect real-world business activity with on-chain digital asset formation through automated market maker (AMM)-based liquidity infrastructure." Through the protocol, businesses will be able to effectively tokenize their economic activity and turn it into digital assets. "Rather than relying primarily on speculative trading dynamics, the Transaction-Backed Digital Assets Protocol enables digital assets to derive value from real transaction activity occurring within the network."show more

Using floor plans:

Spatial AI ( is building large real-world datasets to... teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.show more

Smart parking pipeline in real-time with Ultralytics YOLO26 🚗... 🅿️ Over the weekend, I built a parking management system that monitors slot occupancy (occupied vs available). In busy parking areas where vehicles partially overlap slots or enter at angles, the system still maintains accurate detection and status updates in real time. More information 👇 #yolo26 #parkingproblem #machinelearningshow more

Diffusion Transformers aren't just generative models, but also powerful... multi-modal encoders. ConceptAttention creates rich heatmaps of text concepts in images from DiT representations. This even works on real images, and can be applied to tasks like segmentation! Demo 👇show more