CSPNet Paper Walkthrough: Just Better, No Tradeoffs

CSPNet (Cross Stage Partial Network) is an architecture designed to make CNN models more lightweight without a corresponding drop in accuracy. It works by splitting the input feature map into two parts, where one part bypasses the main computational block and is later recombined. This Cross Stage Partial mechanism reduces redundant gradient information and significantly lowers computational complexity. The article details different fusion methods, such as "fusion first" and "fusion last," and shows experimental results where applying CSPNet to backbones like DenseNet and ResNeXt improves efficiency.