  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}.
