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

Vehicle Counting with Analytics using Ultralytics YOLOv8 ???? You can count vehicles in specific zones using YOLOv8, and it allows you to monitor traffic flow and spot congestion points. Analytical line graphs can help visualize traffic movement trends over time. Whether it's for smart cities, toll booths, or parking lots, this technology can be used to deliver the insights needed to enhance operational efficiency. Learn more ?? https://ow.ly/Z3hp50To0sc

?? Exciting News! Ultralytics v8.3.14 is here! ?? We're thrilled to announce the latest release, packed with enhancements to boost your experience: ?? Enhanced Performance: Our TensorRT update optimizes dynamic input handling, ensuring smoother and faster inference. ?? Improved Documentation: We've refined our guides for clearer, more accurate user navigation. ?? Streamlined Codebase: Enjoy quicker execution and simplified maintenance with our code refactor. Dive into the details and see how these updates can elevate your projects: Release Notes https://lnkd.in/dCEbb7qt | Documentation https://lnkd.in/dhJB3qdw Try out the new features and let us know your thoughts. Your feedback is invaluable! ?? #Ultralytics #YOLO #AI #MachineLearning #TensorRT

Tips for model training | Ultralytics documentation ?? The training process enables computer vision models to learn and recognize patterns for making predictions. In my experience, it can be quite challenging and often requires experimenting with various hyperparameter settings to achieve the best results. This guide will help you to learn how to optimize model accuracy and performance. Big thanks to Abirami Vina for the excellent work on this!?? What you can learn ?? ? What is subset and mixed precision training? ? Techniques to consider when handling a large dataset ? Choose between cloud and local training ? How to select the best optimizer? Read more ? https://lnkd.in/dnFPPBTj

New tutorial | Benchmarking Ultralytics YOLO models on GPUs and CPUs! ?? We’ll guide you through the Ultralytics benchmark documentation and explain the key arguments. You'll learn how to benchmark efficiently on both GPUs and CPUs. What's covered: ? Why benchmarking the models is critical for optimizing performance. ? Step-by-step guide to performing benchmarking on GPUs and CPUs. ? Comparisons between CPU and GPU benchmarks. Watch now ?? https://lnkd.in/dyu_dVzD

?? Exciting News! Ultralytics v8.3.13 is here! ?? We're thrilled to announce our latest release, focusing on enhancing the Segment Anything Model (SAM) and improving user experience. Here’s what’s new: ?? Efficiency & Maintainability: Refactored SAM for better prompt handling, making the code easier to maintain and modify. ?? Documentation Clarity: Updated examples for clearer understanding of model functionalities. ?? Visual Clarity: Improved plotting for more intuitive and easily interpretable results. Check out the full release notes and see what’s changed: Release Notes https://lnkd.in/dUSrEjxb Try out the new features and share your feedback with us! Your insights are invaluable. ?? #Ultralytics #YOLO #MachineLearning #AI #Innovation

Try Ultralytics YOLO11 | https://yolo11.com/ ?? Today, we're excited to announce a new feature that allows you to run inference directly, a built-in feature on the website ?? ? No need to download any model files or install libraries. ? You can run inference on your camera feed or upload a video or images. ?? We'll handle everything to display the inference results for you ??

Generate analytical graphs using Ultralytics YOLO11 | Vehicle counts ?? Last week, we added support for YOLO11 models in the analytics solution. The best part is that the same code can work for Ultralytics YOLOv8, YOLO11, and other object detection models i.e. YOLOv9, and YOLOv10. ?? What Ultralytics analytics supports ?? ? You can create bar charts, line graphs, area charts, and pie charts to show your detection results ?? ? The graphs are generated in real-time, so you can see them as they're being created ? ? With every frame, the graphs update automatically based on the data you provide ?? Discover more ?? https://lnkd.in/dxgErrtY

Live inference with Ultralytics YOLO11 is Here! ?? The moment you've been waiting for has arrived—YOLO11 live inference is now available directly on our website! Experience real-time object detection, all without needing any downloads or setups. What's Included: ? Webcam inference: Detect objects live using your webcam. ? Video & image inference: Upload videos or images for instant results. ? Model options: Choose between YOLO11m, YOLO11s, or YOLO11n. ? Customizable options: Adjust confidence and IOU thresholds. Try it now ?? https://yolo11.com/

Fruit Ripeness Identification Using Ultralytics YOLOv8 ?? ?? Recent research utilizing the YOLOv8 model highlights the development of an automated system for identifying the ripeness of fruits. This system uses advanced computer vision techniques to analyze fruit images, allowing for real-time ripeness detection, which can significantly enhance efficiency in agriculture and reduce food waste. Key highlights ?? YOLOv8 is fine-tuned to detect different stages of fruit ripeness with high accuracy in real-time, optimizing harvest timing. ?? The model is trained with diverse fruit datasets, ensuring it works across various fruit types and ripeness conditions. Explore more ?? https://ow.ly/9GKt50To4Kh