Something to remember: YOLOv4 and YOLOv5 have comparable performance, with some benchmarks favoring one over the other depending on specific use cases and settings. Evaluate both models...

In this guide, you'll learn about how YOLOv8 and YOLOv4 Darknet compare on various factors, from weight size to model architecture to FPS.

YOLOv8 uses an anchor-free model with a decoupled head to independently process objectness, classification, and regression tasks. This design allows each branch to focus on its task and improves the model’s overall accuracy.

ABSTRACT. YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8.

Abstract. This paper presents a comprehensive review of the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the architectural advancements, performance improvements, and suitability for edge deployment across these versions.