Learning rate adjustment and optimization of RepVGG network based on warmup strategy
Ying Lin · Applied and Computational Engineering · 2023
Artificial neural networks have developed rapidly in recent years and play an important role in the academic field. In this paper, the RepVGG artificial neural network model is adjusted by the learning rate algorithm, so as to realize the optimization of the model including but not limited to accuracy. The main optimization strategy is to add the warmup strategy based on the learning rate algorithm of the original model so that the model can obtain good prior information on the data early in the training process, so as to converge quickly in the later training. Through a series of tests and simulations, the RepVGG-A0 model improves the Top1 accuracy by about 2.6% to 68.56% and the Top5 accuracy by about 0.38% to 94.32% on imagesetter dataset within 25 training epochs. The precision and recall are improved to 68.43% and 68.63%, respectively.