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ThinNet: An Efficient Convolutional Neural Network for Object Detection

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Sen Cao, Yazhou Liu, Changxin Zhou, Quan-Sen Sun, Pongsak Lasang, Shengmei Shen
24th International Conference on Pattern Recognition (ICPR) 2018, 2018.08

Great advances have been made for the deep networks, but relatively high memory and computation requirements limit their applications in the embedded device. In this paper, we introduce a class of efficient network architecture named ThinNet mainly for object detection applications on memory and computation limited platforms. The new architecture is based on two proposed modules: Front module and Tinier module. The Front module reduce the information loss from raw input images by utilizing more convolution layers with small size filters. The Tinier module use pointwise convolution layers before conventional convolution layer to decrease model size and computation, while ensuring the detection accuracy. Experimental evaluations on ImageNet classification and PASCAL VOC object detection datasets demonstrate the superior performance of ThinNet over other popular models. Our pretrained classification model(ThinNet_C) attains the same top-l and top-5 performance as the classic AlexNet but only with 1/50th the parameters. The detection model also obtains significant improvements over other detection methods, while requiring smaller model size to achieve high performance

Link: https://ieeexplore.ieee.org/document/8545809