YOLOv12: A Breakdown of the Key Architectural Features

• URL: http://arxiv.org/abs/2502.14740v1
• Date: Thu, 20 Feb 2025 17:08:43 GMT
• Title: YOLOv12: A Breakdown of the Key Architectural Features
• Authors: Mujadded Al Rabbani Alif, Muhammad Hussain,
• Abstract summary: YOLOv12 is a significant advancement in single-stage, real-time object detection.<n>It incorporates an optimised backbone (R-ELAN), 7x7 separable convolutions, and FlashAttention-driven area-based attention.<n>It offers scalable solutions for both latency-sensitive and high-accuracy applications.
• Score: 0.5639904484784127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an architectural analysis of YOLOv12, a significant advancement in single-stage, real-time object detection building upon the strengths of its predecessors while introducing key improvements. The model incorporates an optimised backbone (R-ELAN), 7x7 separable convolutions, and FlashAttention-driven area-based attention, improving feature extraction, enhanced efficiency, and robust detections. With multiple model variants, similar to its predecessors, YOLOv12 offers scalable solutions for both latency-sensitive and high-accuracy applications. Experimental results manifest consistent gains in mean average precision (mAP) and inference speed, making YOLOv12 a compelling choice for applications in autonomous systems, security, and real-time analytics. By achieving an optimal balance between computational efficiency and performance, YOLOv12 sets a new benchmark for real-time computer vision, facilitating deployment across diverse hardware platforms, from edge devices to high-performance clusters.

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