Electronic Thesis and Dissertation Repository

Degree

Master of Science

Program

Computer Science

Supervisor

Ling, Charles X.

Abstract

There has been a rising interest in running high-quality Convolutional Neural Network (CNN) models under strict constraints on memory and computational budget. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and NASNet-A. However, all these architectures are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. Meanwhile, there are few studies that combine efficient models with fast object detection algorithms. This research tries to explore the design of an efficient CNN architecture for both image classification tasks and object detection tasks. We propose an efficient architecture named PeleeNet, which is built with conventional convolution instead. On ImageNet ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy by 0.6% and 11% lower computational cost than MobileNet, the state-of-the-art efficient architecture. It is also important to point out that PeleeNet is of only 66% of the model size of MobileNet and 1/49 size of VGG.

We then propose a real-time object detection system on mobile devices. We combine PeleeNet with Single Shot MultiBox Detector (SSD) method and optimize the architecture for fast speed. Meanwhile, we port SSD to iOS and provide an optimized code implementation. Our proposed detection system, named Pelee, achieves 70.9% mAP on PASCAL VOC2007 dataset at the speed of 17 FPS on iPhone 6s and 23.6 FPS on iPhone 8. Compared to TinyYOLOv2, the most widely used computational efficient object detection system, our proposed Pelee is more accurate (70.9% vs. 57.1%), 2.88 times lower in computational cost and 2.92 times smaller in model size.

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