YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger

YOLOv2 improves upon its predecessor, YOLOv1, by addressing issues of high localization error and low recall through several key modifications. These changes include adding batch normalization for training stability, a better fine-tuning process for higher resolution images, and replacing fully-connected layers to make the network fully convolutional. The model adopts anchor boxes, with dimensions determined by k-means clustering on the dataset's bounding boxes, to improve prediction accuracy. Further enhancements like the Darknet-19 backbone, a passthrough layer for fine-grained features, and a joint training method led to the YOLO9000 model, capable of detecting over 9000 object categories.