GhostNetV3: Exploring the Training Strategies for Compact Models

arXiv:2404.11202v1 Announce Type: new
Abstract: Compact neural networks are specially designed for applications on edge devices with faster inference speed yet modest performance. However, training strategies of compact models are borrowed from that of conventional models at present, which ignores their difference in model capacity and thus may impede the performance of compact models. In this paper, by systematically investigating the impact of different training ingredients, we introduce a strong training strategy for compact models. We find that the appropriate …

arxiv compact cs.cv strategies training type