Hand detection model compression based on channel pruning and knowledge distillation

Haiyan Chen; Wenli Zhao

doi:10.1117/12.2678857

25 May 2023 Hand detection model compression based on channel pruning and knowledge distillation

Haiyan Chen, Wenli Zhao

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127121H (2023) https://doi.org/10.1117/12.2678857
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

Gesture recognition is one of the most intuitive and convenient ways of human-computer interaction. Accurate and realtime hand detection is the key to correct gesture recognition. Deep learning is a mainstream solution to visual tasks such as hand detection and recognition, but detection models based on deep learning usually require large storage resources and computing resources, which seriously hinders its actual deployment and application. Therefore, in order to deploy the hand detection model based on deep convolutional neural network to embedded devices with limited storage resources and computing resources, it is necessary to compress and accelerate the hand detection model. In this paper, YOLOv3 was used as the basic model for hand detection, and a channel pruning method based on local sparse rate attenuation training was proposed to compress and accelerate the model. Meanwhile, knowledge distillation was used instead of fine tuning to conduct distillation training on the pruned model, so as to compensate the accuracy loss caused by channel pruning. The model was trained and tested on the Oxford Hand data set. The results show that the compression method of the hand detection model based on the channel pruning and knowledge distillation training of local sparse rate attenuation can realize the compression acceleration of the model while maintaining a high detection accuracy, which provides support for the deployment of embedded devices of the hand detection model.

Citation Download Citation

Haiyan Chen and Wenli Zhao "Hand detection model compression based on channel pruning and knowledge distillation", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127121H (25 May 2023); https://doi.org/10.1117/12.2678857

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KEYWORDS

Education and training

Performance modeling

Attenuation

Instrument modeling

Object detection

Data modeling

Deep learning

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