E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections

Many high-performance networks are not designed with lightweight application scenarios in mind from the outset, which has greatly restricted their scope of application. This paper focuses on ConvNeXt as the research subject. It incorporates the Cross Stage Partial Connections mechanism and a series of optimizations. These improvements significantly reduce the parameter scale and network complexity. The new network is named E-ConvNeXt, which can maintain high accuracy performance under different complexity configurations. The three core contributions of E-ConvNeXt are : (1) Integrating the Cross Stage Partial Network (CSPNet) with ConvNeXt and adjusting the network structure, which reduces the model's network complexity by up to 80\%; (2) Optimizing the Stem and Block structures to enhance the model's feature expression capability and operational efficiency; (3) Replacing Layer Scale with channel attention. Experimental validation on ImageNet classification demonstrates E-ConvNeXt's superior accuracy-efficiency balance. Transfer learning tests on object detection tasks further confirm its generalization capability.