Damo-YOLO 算法改进：Efficient-RepGFPN 架构解析

The Damo-YOLO algorithm proposes a new Efficient-RepGFPN based on GFPN to meet the design of the neck in real-time object detection, which mainly includes the following improvements:

(1) Different scale features use different channel numbers to flexibly control the expression ability of high-level and low-level features under lightweight computational constraints.

(2) The additional upsampling operation in Queen-Fusion is removed, which greatly reduces the model inference delay with less accuracy degradation.

(3) The original convolution-based feature fusion is improved to CSPNet connection, and the ideas of weight reparameterization and ELAN connection are introduced to improve the model accuracy without increasing more computational complexity.