YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS

YOLOv5, released by Glenn Jocher, marks a significant advancement in realtime object detection, offering improved accessibility and performance compared to its predecessors, YOLOv4 and EfficientDet. Unlike earlier versions that utilized the Darknet framework, YOLOv5 is the first in the YOLO family to be implemented natively in PyTorch, enhancing ease of deployment and integration with existing systems. It boasts impressive speed, achieving up to 140 frames per second and maintaining high accuracy with a mean average precision of roughly 0.895 on certain datasets. The model's compact size, with weights files significantly smaller than YOLOv4, facilitates deployment on embedded devices. Various sizes of the model are available, with the smallest being just 27 megabytes. The release underscores the rapid evolution of YOLO models, originally developed by Joseph Redmon, and highlights the role of community feedback in refining new iterations.