YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

• URL: http://arxiv.org/abs/2009.05697v2
• Date: Wed, 30 Dec 2020 15:55:43 GMT
• Title: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
• Authors: Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, Yanzhi Wang
• Abstract summary: We propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20.
• Score: 38.98949683262209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14$\times$ compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5$\times$ speedup. Source code is at: \url{https://github.com/nightsnack/YOLObile}.

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