  In the realm of resource-constrained mobile vision tasks, the pursuit of
efficiency and performance consistently drives innovation in lightweight
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). While ViTs
excel at capturing global context through self-attention mechanisms, their
deployment in resource-limited environments is hindered by computational
complexity and latency. Conversely, lightweight CNNs are favored for their
parameter efficiency and low latency. This study investigates the complementary
advantages of CNNs and ViTs to develop a versatile vision backbone tailored for
resource-constrained applications. We introduce RepNeXt, a novel model series
integrates multi-scale feature representations and incorporates both serial and
parallel structural reparameterization (SRP) to enhance network depth and width
without compromising inference speed. Extensive experiments demonstrate
RepNeXt's superiority over current leading lightweight CNNs and ViTs, providing
advantageous latency across various vision benchmarks. RepNeXt-M4 matches
RepViT-M1.5's 82.3\% accuracy on ImageNet within 1.5ms on an iPhone 12,
outperforms its AP$^{box}$ by 1.3 on MS-COCO, and reduces parameters by 0.7M.
Codes and models are available at https://github.com/suous/RepNeXt.
