ERCNet: Enhancing ReActNet with a Compact ECA Branch

In this paper, we redesign the General Building block of the ReActNet (GBR) in an effort to elevating the accuracy on CIFAR100 image classification dataset, and on PASCAL VOC 07+12 object detection dataset, yet at a lower memory footprint and lower computation cost. The GBR comprises a single Down-sampling Block (DB) and a plurality of Common Blocks (CB). Firstly, we eliminate all the 1x1 Binary Convolutional (BConv) layers of the CBs to reduce the weight parameters as well as the network size. Second, the 1x1 Bconv duplicate of the DB is replaced by the Efficient Channel Attention (ECA) to enrich the representation capacity. Third, a Batch Normalization (BN) unit is added right after the Concatenator of the DB to render the data distribution more suitable for the performance optimization. Finally, the shortcut connection is resided after the RPReLU activation unit so as to balance the information preservation from the shortcut path and information transformation from the residual path. Our experiment shows that the enhanced network (ERCNet) delivers 2.35% higher Top-1 accuracy on CIFAR100 than the original ReActNet yet at around 10% lower memory and 8% lower computation flops. Besides, it generates 81.8% mAP50 under YOLOv8 framework on Pascal VOC 07+12, surpassing the ReActNet by 0.8%.