【ML Paper】YOLOv2: part1

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.

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

1. Overview

Authors published the YOLO9000(as known v2) model that can detect over 9000 object categories.

Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy.

YOLO v2 Performances:
At 67 FPS, gets 76.8 mAP on VOC 2007.
At 40 FPS, gets 78.6 mAP.
These results outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster.

The authors develop a joint training method for YOLO9000, using both COCO and ImageNet datasets. This allows YOLO9000 to detect classes without labeled detection data, achieving 19.7 mAP on ImageNet with 44 classes and 16.0 mAP on 156 additional classes. YOLO9000 can recognize over 9,000 categories while maintaining real-time performance.