CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Shoot to Know What: An Application of Deep Networks on Mobile Devices
Wu JX(吴家祥); Hu QH(胡庆浩); Leng C(冷聪); Cheng J(程健)
2016-02
Conference NameAmerican Association for AI National Conference(AAAI)
Conference Date2016-2
Conference PlacePhoenix, U.S.
AbstractConvolutional neural networks (CNNs) have achieved impressive performance in a wide range of computer vision areas. However, the application on mobile devices remains intractable due to the high computation complexity. In this demo, we propose the Quantized CNN (Q-CNN), an efficient framework for CNN models, to fulfill efficient and accurate image classification on mobile devices. Our Q-CNN framework dramatically accelerates the computation and reduces the storage/memory consumption, so that mobile devices can independently run an ImageNet-scale CNN model. Experiments on the ILSVRC-12 dataset demonstrate 4 ∼ 6× speedup and 15 ∼ 20× compression, with merely one percentage drop in the classification accuracy. Based on the Q-CNN framework, even mobile devices can accurately classify images within one second.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14969
Collection模式识别国家重点实验室_图像与视频分析
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Wu JX,Hu QH,Leng C,et al. Shoot to Know What: An Application of Deep Networks on Mobile Devices[C],2016.
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