Abstract
Many high-performance networks were not designed with lightweight application scenarios in mind from the outset, which has greatly restricted their scope of application. This paper takes ConvNeXt as the research object and significantly reduces the parameter scale and network complexity of ConvNeXt by integrating the Cross Stage Partial Connections mechanism and a series of optimized designs. The new network is named E-ConvNeXt, which can maintain high accuracy performance under different complexity configurations. The three core innovations 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: E-ConvNeXt-mini reaches 78.3% Top-1 accuracy at 0.9GFLOPs. E-ConvNeXt-small reaches 81.9% Top-1 accuracy at 3.1GFLOPs. Transfer learning tests on object detection tasks further confirm its generalization capability.
Abstract (translated)
许多高性能网络最初并未考虑轻量级应用场景的需求，这极大地限制了它们的应用范围。本文以ConvNeXt为研究对象，在集成Cross Stage Partial Connections（CSP）机制和一系列优化设计的基础上，大幅减少了ConvNeXt的参数规模与网络复杂度，并将新网络命名为E-ConvNeXt。该网络能够在不同的复杂度配置下保持高精度性能。 E-ConvNeXt的三大核心创新包括： 1. 将Cross Stage Partial Network（CSPNet）与ConvNeXt相结合并调整网络结构，最多可减少模型的80%网络复杂度； 2. 优化Stem和Block结构，增强模型的特征表达能力和操作效率； 3. 使用通道注意力机制替代Layer Scale。 在ImageNet分类任务上的实验验证表明了E-ConvNeXt优秀的精度与效率平衡：E-ConvNeXt-mini版本以0.9GFLOPs达到78.3%的Top-1准确率，而E-ConvNeXt-small版本则以3.1GFLOPs实现了81.9%的Top-1准确率。在目标检测任务上的迁移学习测试进一步证实了其泛化能力。
URL
https://arxiv.org/abs/2508.20955
