Papers › YOLOv11: An Overview of the Key Architectural Enhancements

YOLOv11: An Overview of the Key Architectural Enhancements

23 Oct 2024arXiv:2410.17725archive 2025-07-28

Rahima Khanam, Muhammad Hussain

This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectural innovations, including the introduction of the C3k2 (Cross Stage Partial with kernel size 2) block, SPPF (Spatial Pyramid Pooling - Fast), and C2PSA (Convolutional block with Parallel Spatial Attention) components, which contribute in improving the models performance in several ways such as enhanced feature extraction. The paper explores YOLOv11's expanded capabilities across various computer vision tasks, including object detection, instance segmentation, pose estimation, and oriented object detection (OBB). We review the model's performance improvements in terms of mean Average Precision (mAP) and computational efficiency compared to its predecessors, with a focus on the trade-off between parameter count and accuracy. Additionally, the study discusses YOLOv11's versatility across different model sizes, from nano to extra-large, catering to diverse application needs from edge devices to high-performance computing environments. Our research provides insights into YOLOv11's position within the broader landscape of object detection and its potential impact on real-time computer vision applications.

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Tasks

Computational Efficiency Instance Segmentation Object Object Detection Oriented Object Detection Pose Estimation Real-Time Object Detection Semantic Segmentation object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11x	FPS (V100, b=1)	88 (T4)	#21 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11x	box AP	54.7	#21 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11l	FPS (V100, b=1)	161 (T4)	#29 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11l	box AP	53.4	#29 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11m	FPS (V100, b=1)	212 (T4)	#47 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11m	box AP	51.5	#47 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11s	FPS (V100, b=1)	400 (T4)	#64 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11s	box AP	47.0	#64 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11n	FPS (V100, b=1)	667 (T4)	#77 of 82	Archive leaderboard	report
Real-Time Object Detection	COCO (Common Objects in Context)	YOLOv11n	box AP	39.5	#77 of 82	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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