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arXiv · 1802.03750

FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy

Zheng Qin·Zhaoning Zhang·Xiaotao Chen·Yuxing Peng
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Abstract

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying an aggressive downsampling strategy to MobileNet framework. In FD-MobileNet, we perform 32$\times$ downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC 2007 datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5.5% on the ILSVRC 2012 top-1 accuracy and 3.6% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11$\times$ inference speedup over MobileNet and 1.82$\times$ over ShuffleNet under the same complexity.

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Zheng Qin, Zhaoning Zhang, Xiaotao Chen, Yuxing Peng. 2018-02-11. FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy. https://arxiv.org/abs/1802.03750

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