A Novel Collaborative Control Strategy for Enhanced Training of Vehicle Recognition

Fangyi Chen, Chenchen Zhu, Marios Savvides

Abstract: Deep learning methods support vehicular technology in various aspects. How to efficiently and effectively optimize deep learning models remains a challenge. It is known that the learning rate is an important hyper-parameter to optimize models and the batch size is one of the keys to speed up training. This paper empirically studies the principles of scheduling batch size and learning rate during training, and proposes the Collaborative Control Strategy (CCS), which practically improves both classification accuracy and training speed. Instead of stepwise decreasing learning rate and keeping batch size unchanged, we asynchronously adjust batch size and learning rate based on time-division and restart policy. We study and analyze the proposed CCS on general image recognition benchmarks. Compared to traditional training strategy, without bells and whistles CCS decreases error rates by absolute 0.78% and 0.75% on CIFAR-10 and CIFAR-100 respectively, and reduces 40% of training time. For vehicular applications, we demonstrate its advantage on Stanford car-196 dataset with different architectures, showing consistent training speedup and accuracy improvement.