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Overview 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. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of computer vision tasks. |
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Key Features Enhanced Feature Extraction: YOLO11 employs an improved backbone and neck architecture, which enhances feature extraction capabilities for more precise object detection and complex task performance. Optimized for Efficiency and Speed: YOLO11 introduces refined architectural designs and optimized training pipelines, delivering faster processing speeds and maintaining an optimal balance between accuracy and performance. Greater Accuracy with Fewer Parameters: With advancements in model design, YOLO11m achieves a higher mean Average Precision (mAP) on the COCO dataset while using 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy. Adaptability Across Environments: YOLO11 can be seamlessly deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs, ensuring maximum flexibility. Broad Range of Supported Tasks: Whether it's object detection, instance segmentation, image classification, pose estimation, or oriented object detection (OBB), YOLO11 is designed to cater to a diverse set of computer vision challenges. |
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What are the key improvements in Ultralytics YOLO11 compared to previous versions? Ultralytics YOLO11 introduces several significant advancements over its predecessors. Key improvements include: Enhanced Feature Extraction: YOLO11 employs an improved backbone and neck architecture, enhancing feature extraction capabilities for more precise object detection. Optimized Efficiency and Speed: Refined architectural designs and optimized training pipelines deliver faster processing speeds while maintaining a balance between accuracy and performance. Greater Accuracy with Fewer Parameters: YOLO11m achieves higher mean Average Precision (mAP) on the COCO dataset with 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy. Adaptability Across Environments: YOLO11 can be deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs. Broad Range of Supported Tasks: YOLO11 supports diverse computer vision tasks such as object detection, instance segmentation, image classification, pose estimation, and oriented object detection (OBB). |
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