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

Ultralytics YOLO11 vs Ultralytics YOLO26: Which one runs faster?... ⚡ Evaluate inference speed and performance across both models in the ONNX format to understand how YOLO26 improves efficiency for real-time computer vision applications. Read more ➡️ #Ultralytics #YOLO26 #AI #Research #ComputerVisionshow more

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

Monitor patient posture with Ultralytics YOLO26 Pose! Detect keypoints... in real time to identify positions like lying down vs. standing. Useful for patient monitoring, fall-risk alerts, and hospital/elder-care room analytics. Learn more ➡️ #AI #PoseEstimation #HealthTech #YOLO26show more

Launching today: Ultralytics YOLO26! The fastest, most deployable YOLO... ever built. For years, shipping object detection meant extra steps and messy post-processing. YOLO26 changes the game with end-to-end, NMS-free inference, simpler deployment, and strikingly low latency. Built End-to-End. Built for the Edge. 👉 Get started today:show 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

Detect small objects using @Ultralytics YOLO26 + SAHI 😍... A perfect combo when speed isn’t the priority, but accuracy is everything, especially when detecting tiny or distant objects in your images. learn how it works 👇 #AI #objectdetection #trafficanalysisshow 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

Mediapipe is cool, but Ultralytics YOLO26 Pose is on... another level 🔥🔥🔥 I ran a quick comparison between mediapipe and YOLO pose, and while both have their strengths, YOLO26 blew me away. My findings👇 Raw video credit: NVIDIA Team 😀 #AI #PoseEstimation #Peopleshow more

Count pasties in real time with Ultralytics YOLO! Automate... bakery production by detecting and counting pasties, reducing manual errors, and improving packaging efficiency. It's perfect for food processing and smart factory workflows. Read more ➡️ #Industry40show more

Car-to-curb distance estimation using Ultralytics YOLO26. 🚗 In parking... lots, knowing the exact distance between a vehicle and the curb can help prevent minor collisions and improve parking precision. In this demo, a car is detected and tracked, and the system calculates the distance from the curb in real time in pixel space, showcasing how computer vision can turn simple camera feeds into spatial awareness tools. Implementation details 👇 #cardistance #visionai #ROADSAFETYCONCERNshow more

i built an app to search old travel photos... on my computer with natural language queries using free local AI models: - smolVLM to describe the scene and colours - roboflow RF-DETR to detect objects - chromaDB to store metadata labels and run semantic search - python + streamlit for the interface fast and free search has been achieved internallyshow more

Real-time bakery item counting using Ultralytics YOLO26 😍 In... food production lines, counting sounds simple until it isn’t. Items move fast, overlap on conveyors, change orientation, and sometimes partially occlude each other. Whether it’s ice-cream cones on a belt or cream being filled into nests, maintaining accurate counts in real time is critical for quality control and throughput. More info 👇 #Bakery #Retail #MachineLearningshow more

YOLO26新增了实时物体分割功能（real-time object segmentation）。 这个稍微套个壳就是个实时翻译产品，或者做互动对话的机器人也不错。

Ready to get started with visual generative AI? Look... no further. Learn how to run image and video models locally on RTX GPUs using ComfyUI with LTX & BlackForestLabsAI - Unofficial FLUX in our blog below. 👇show more

1. Microsoft - AI For Beginners Curriculum 12-week, 24-lesson... curriculum exploring Artificial Intelligence (AI). Covers Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs included.show more

Real-time object detection will never be the same. Traditional... YOLO needs NMS to remove duplicate boxes; it's slow and inconsistent. YOLO26 skips it entirely: single-pass predictions, faster inference and up to 300 detections per image. Download model:show more

New on Deepvue · Prompt Library with pre-built prompts... for market analysis, themes, stock analysis and earnings · @ mentions to reference any watchlist or screener preset as context · / slash commands to search and run prompts instantly Entire research workflow in one place.show more

No code. No friction. Just pure on-chain magic. Thanks... to xPilot, a breakout project from the AI Hackathon, anyone will be able to create apps and interact with the blockchain using natural language prompts 🔥show more

Coding is cancelled. 😭 AI now lets you build,... edit & deploy any kind of app using natural language prompts, visual editing & no coding. You’d have to be insane to pay a dev for this now. Tutorial + Apps I built:show more

Segment Any 3D Gaussians paper page: Interactive 3D segmentation... in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation. However, existing methods face challenges in either achieving fine-grained, multi-granularity segmentation or contending with substantial computational overhead, inhibiting real-time interaction. In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive segmentation approach that seamlessly blends a 2D segmentation foundation model with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA efficiently embeds multi-granularity 2D segmentation results generated by the segmentation foundation model into 3D Gaussian point features through well-designed contrastive training. Evaluation on existing benchmarks demonstrates that SAGA can achieve competitive performance with state-of-the-art methods. Moreover, SAGA achieves multi-granularity segmentation and accommodates various prompts, including points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation within milliseconds, achieving nearly 1000x acceleration compared to previous SOTA.show more