Ultralytics

Ultralytics YOLO11 is Here! 🚀 💙 We proudly unveiled the YOLO11 models last Friday at our annual hybrid event, YOLO Vision 2024. Today, we’re thrilled to share that the YOLO11 models are now available in the Ultralytics Python package! Jing Qiu and Glenn Jocher have done an amazing job on the research and implementation of Ultralytics YOLO11. This launch is a testament to our team’s dedication and hard work over the past few months. Key highlights: ✅ Improved architecture for precise detection and complex tasks. ✅ Faster processing with balanced accuracy. ✅ Higher precision using 22% fewer parameters. ✅ Easily deployable on edge, cloud, and GPU systems. ✅ Handles detection, segmentation, classification, pose, and OBB. 🚀 Run Inference ```yolo predict model="yolo11n.pt"``` Learn more ➡️ https://ow.ly/mKOC50Tyyok

Package identification and segmentation with Ultralytics YOLO11! 📦 Abirami Vina's latest blog explores how computer vision models like Ultralytics YOLO11 can be custom-trained using the Roboflow Package Segmentation Dataset to improve package identification and sorting. Check out Vision AI can reshape logistics automation, helping warehouses track, sort, and even detect damaged packages in real-time! Learn more ➡️ https://ow.ly/kWYs50UVuMP

🚀 Introducing Ultralytics v8.3.73! We're excited to announce the latest update to the Ultralytics platform, bringing advancements that simplify workflows and enhance performance: ✅ Enhanced Containerization: - Now publishing Docker images to GitHub Container Registry (GHCR) and Docker Hub for improved accessibility and metadata clarity. 🐋 ✅ Upgraded NVIDIA Jetson Support: - Added compatibility with PyTorch 2.2.0 and Torchvision 0.17.2, delivering better performance for edge deployments. 🤖 ✅ Improved Learning Resources: - Embedded tutorials, including a new YouTube guide on Package Segmentation, make learning simpler and more visual. 🎥✨ Learn more in the Release Notes https://lnkd.in/dRmcNYSc. 💡 Try it now and let us know your feedback. Together, let's continue innovating! 🌍

New tutorial | The importance of high-quality computer vision datasets! 🚀 Building robust computer vision models starts with one critical component, the dataset. In this tutorial, we’ll dive deep into the key factors that define dataset quality and how they impact model performance. What you’ll learn: ✅ Why dataset quality matters for computer vision models ✅ The top 5 traits of high-quality datasets ✅ Challenges posed by low-quality datasets ✅ Best practices for data splitting in training, validation, and testing Watch now ➡️ https://lnkd.in/dMAnvgye

🚀 Exploring YOLOv11 for Bird Segmentation! 🦜📊  I am excited to share my latest experiment with YOLOv11(Ultralytics), which involves applying segmentation to detect and outline birds in real time! 🏆  🔍 Why this matters?   Bird species identification and tracking are crucial for conservation efforts, ecological studies, and even AI-driven monitoring systems. With YOLOv11’s segmentation capabilities, we can achieve:   ✅ Precise boundary detection 📏   ✅ Real-time performance ⚡   ✅ Better object differentiation 🔄  The potential applications extend to wildlife monitoring, drone-based bird detection, and habitat preservation. Looking forward to optimizing this further! 💡  Would love to hear your thoughts on using YOLOv11 for biodiversity and AI-powered conservation. Let’s discuss this in the comments! ⬇️  #ComputerVision #YOLOv11 #DeepLearning #AI #BirdSegmentation #ObjectDetection #WildlifeTech #MachineLearning 

How can computer vision help in civil engineering? 🚧 Abdelrahman Elgendy's new blog dives into how computer vision models like Ultralytics YOLO11 can streamline processes on construction sites. From identifying construction vehicles and ensuring PPE compliance to detecting material defects, YOLO11 can help reshape how engineers manage efficiency and safety. Learn more ➡️ https://ow.ly/36RI50UUQHf

A Guide to deep dive into Object Detection in 2025! 🌟 Abirami Vina's new guide looks at how object detection is reshaping industries, from self-driving cars to healthcare. With computer vision models like Ultralytics YOLO11, AI-powered vision models are now faster, more accurate, and more efficient than ever. Whether you're a developer, researcher, or business leader, this guide will help you understand how object detection is transforming AI. Learn more ➡️ https://ow.ly/C02450UUQMl

Detect cigarettes and smoke using Ultralytics YOLO11! 🚭 Whether for enhancing public safety, ensuring workplace compliance, or monitoring air quality, YOLO11 brings unparalleled accuracy to real-time cigarette and smoke detection. Leverage this technology to maintain clean, safe, and smoke-free environments. Discover more ➡️ https://ow.ly/5Ee850UGYe2

🚀 New Release Alert: Ultralytics v8.3.72 🚀 We're thrilled to announce our latest update, Ultralytics v8.3.72, packed with improvements for better performance, usability, and reliability! Here's what's new: 🔹 Enhanced NVIDIA Jetson DLA Support - Enjoy seamless inference with explicit DLA core selection and expanded documentation for Jetson devices. Perfect for edge AI and IoT applications! 🔹 Simplified Model Export - Detailed export argument tables to customize formats like TensorRT, ONNX, and CoreML, making deployment smoother than ever. 🔹 Optimized Segmentation & Reliability Fixes - Improved `seg_bbox` visualization performance and enhanced error handling for advanced setups like multi-GPU training. 💡 With these updates, v8.3.72 ensures a streamlined experience for everyone—from edge device users to advanced researchers. 📚 Learn More & Try It Out: - Release Notes: v8.3.72 Release https://lnkd.in/dEiyAHNm - Export Documentation: Export Guide https://lnkd.in/eXu95nA9 ⚡ Upgrade now and share your feedback! Together, we innovate. 🌟

Why computer vision model maintenance is necessary 😉 Training a model i.e. with Ultralytics is just the beginning, regular validation and monitoring are just as important, yet often overlooked. If a model isn’t monitored, changes in visual factors can impact its accuracy. For example, a model trained on daytime images may struggle with nighttime data, leading to incorrect predictions. The steps in model maintenance include: ✅ Regular updates and re-training ✅ Deciding when to retrain your model Offcourse data drift and anomaly detection also play an important role here.🚀 Read the complete article ➡️ https://lnkd.in/dHfNtnRf