【ML Paper】YOLOv2: part13

This time, I'll introduce the YOLOv2 with the paper by Joseph Redmon and Ali Farhadi. Let's focus and see the difference from yolov1.

This article is part 13. Part 12 is here.

Original Paper: https://arxiv.org/abs/1612.08242

Overview of YOLO Systems

YOLOv2 and YOLO9000 are real-time object detection systems that set new standards in speed and accuracy. YOLOv2 outperforms existing detectors across multiple datasets and allows adjustable image sizes to balance speed and precision.

Expanding Detection with YOLO9000

YOLO9000 extends detection to over 9000 object categories by optimizing detection and classification simultaneously. It utilizes the WordTree structure to integrate data from ImageNet and COCO, reducing the dataset size gap between detection and classification tasks.

YOLO9000 employs the WordTree representation for a richer classification output. It combines datasets through hierarchical classification and uses multi-scale training to enhance performance across various visual tasks, including classification and segmentation.

Conclusion

YOLOv2 and YOLO9000 advance real-time object detection with enhanced flexibility and performance. Their innovative methodologies not only improve detection and classification but also enable future advancements in the field of computer vision.

End

This is the end of the ind¥troducing articles about YOLOv2, thank you for reading!