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Ultralytics YOLO11 - Ultralytics YOLO Docs
Sep 30, 2024 · YOLO11 is the latest iteration in the Ultralytics YOLO series of real-time object detectors, redefining what's possible with cutting-edge accuracy, speed, and efficiency.
ultralytics/ultralytics: Ultralytics YOLO11 - GitHub
Streamline YOLO workflows: Label, train, and deploy effortlessly with Ultralytics HUB. Try now! Track experiments, hyperparameters, and results with Weights & Biases. Free forever, Comet ML lets you save YOLO models, resume training, and interactively visualize predictions. Run YOLO inference up to 6x faster with Neural Magic DeepSparse.
Model Training with Ultralytics YOLO - Ultralytics YOLO Docs
Apr 5, 2025 · Train mode in Ultralytics YOLO11 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. This guide aims to cover all the details you need to get started with training your own …
Model Prediction with Ultralytics YOLO - Ultralytics YOLO Docs
Mar 11, 2025 · In the world of machine learning and computer vision, the process of making sense out of visual data is called 'inference' or 'prediction'. Ultralytics YOLO11 offers a powerful feature known as predict mode that is tailored for high-performance, real-time inference on a wide range of data sources.
Google Colab
YOLOv11 is available in 5 different sizes, ranging from 2.6M to 56.9M parameters, and capable of achieving from 39.5 to 54.7 mAP on the COCO dataset. To fine-tune YOLO11, you need to provide your...
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AlexeyAB/darknet: YOLOv4 / Scaled-YOLOv4 / YOLO - GitHub
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1. More details in articles on medium: Manual: https://github.com/AlexeyAB/darknet/wiki. Discussion:
YOLOv10: Real-Time End-to-End Object Detection - Ultralytics YOLO …
May 25, 2024 · YOLOv10, built on the Ultralytics Python package by researchers at Tsinghua University, introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions.