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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:

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Akshay 🚀

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373,913 görüntüleme • 3 ay önce •via X (Twitter)

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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 #WorkplaceSafety
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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 #WorkplaceSafety

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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 #AI

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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 #ComputerVision
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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 #ComputerVision

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Meta's SAM 3 is a beast. before: collect data, train custom object detector, use tracker to estimate object motion - days now: track anything with text prompt - seconds real-time object detection will never be the same.
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Meta's SAM 3 is a beast. before: collect data, train custom object detector, use tracker to estimate object motion - days now: track anything with text prompt - seconds real-time object detection will never be the same.

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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 #YOLOE
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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 #YOLOE

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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 #MachineLearning

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We're releasing OVIE, a novel view generation model trained entirely on single images. No multi-view datasets needed. Given a single image, it generates novel views of any scene in real time, running orders of magnitude faster than competing approaches.
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kyutai

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you can just make a drone in threejs and have it fly around map imagery and put a camera on the drone and pipe its feed to a python vision model inference server for detections
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you can just make a drone in threejs and have it fly around map imagery and put a camera on the drone and pipe its feed to a python vision model inference server for detections

patagucci perf papi

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Kirato Hitaka breaks the world record for most skips in a single rope jump with 8 skips in one revolution. It’s almost impossible to catch in real time, but once they slow it down, you realize how insane it actually is.
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Kirato Hitaka breaks the world record for most skips in a single rope jump with 8 skips in one revolution. It’s almost impossible to catch in real time, but once they slow it down, you realize how insane it actually is.

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Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:
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Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

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And the traditional model review video. It will become a figurine one day, but I can only guess how hard it will be to assemble it.🫣
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And the traditional model review video. It will become a figurine one day, but I can only guess how hard it will be to assemble it.🫣

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PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start
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PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start

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I can't believe this.. this AI model can generate 4D physical worlds with text prompt and it's 430,000x faster than real time (43M FPS) it's still in progress and.. will be open sourced soon let's dive in:

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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 #MachineLearning

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NOT SURE HOW DUBAI WILL GLOSS OVER THESE IMAGES AND VIDEOS… It can never be the same old business as usual cosying up to Israel and America. THE ABRAHAM ACCORDS WILL NEED TO BE BURIED SIX FEET UNDER‼️

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Experimenting with super lightweight moving object detection No model, just one pass through a 40x30 10fps monochrome video. Plenty easy for most MCUs to handle

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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 #machinelearning

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Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.
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Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

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