Exploring the power of lightweight YOLOv4

• Main Authors: Wang, Chien-Yao, Liao, Hong-Yuan Mark, Yeh, I-Hau, Chuang, Yung-Yu, Lin, Youn-Long
• Format: Conference Proceeding
• Language: English
• Subjects: Computational modeling; Learning systems; Neural networks; Object detection; Pipelines; Power demand; Training
• ISSN: 2473-9944
• DOI: 10.1109/ICCVW54120.2021.00092

Research on deep learning has always had two main streams: (1) design a powerful network architecture and train it with existing learning methods to achieve the best results, and (2) design better learning methods so that the existing network architecture can achieve the best capbility after training. In recent years, because mobile device has become popular, the requirement of low power consumption becomes a must. Under the requirement of low power consumption, we hope to design low-cost lightweight networks that can be effectively deployed at the edge, while it must have enough resources to be used and the inference speed must be fast enough. In this work, we set a very ambitious goal of exploring the power of lightweight neural networks. We utilize the analysis of data space, model's representational capacity, and knowledge projection space to construct an automated machine learning pipeline. Through this mechanism, we systematically derive the most suitable knowledge projection space between the data and the model. Our method can indeed automatically find learning strategies suitable for the target model and target application through exploration. Experiment results show that the proposed method can significantly enhance the accuracy of lightweight neural networks for object detection. We directly apply the lightweight model trained by our proposed method to a Jetson Xavier NX embedded module and a Kneron KL720 edge AI SoC as system solutions.