Fd-Mobilenet: Improved Mobilenet with a Fast Downsampling Strategy

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 a fast downsampling strategy to Mobile Net framework. In FD-Mobile Net, we perform 32× 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 datasets demonstrate that FD-Mobile Net consistently outperforms MobileNet and achieves comparable results with ShufflieNet under different computational budgets, for instance, surpassing Mobile-Net by 5.5% on the ILSVRC 2012 top-l accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-Mobile Net achieves 1.11× inference speedup over Mobile Net and 1.82× over Shufflie Net under the same complexity.