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YOLOv2 improves upon YOLOv1 through several key modifications: batch normalization, better fine-tuning strategies, anchor boxes with K-means clustering, constrained predictions using sigmoid/exponential functions, passthrough layers for preserving fine-grained features, multi-scale training, and the Darknet-19 backbone. The architecture addresses YOLOv1's high localization error and low recall issues. YOLO9000 extends YOLOv2 to detect over 9,000 object classes by combining COCO and ImageNet datasets using a WordTree hierarchy. The article includes a complete PyTorch implementation of the YOLOv2 architecture from scratch, demonstrating how to build the Darknet-19 backbone, passthrough layer, and detection head.

YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger