Shoot to Know What: An Application of Deep Networks on Mobile Devices
文献类型：会议论文
| 作者 | Wu JX(吴家祥) ; Hu QH(胡庆浩); Leng C(冷聪); Cheng J(程健)
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| 出版日期 | 2016-02 |
| 会议日期 | 2016-2 |
| 会议地点 | Phoenix, U.S. |
| 英文摘要 | Convolutional 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14969]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Wu JX,Hu QH,Leng C,et al. Shoot to Know What: An Application of Deep Networks on Mobile Devices[C]. 见:. Phoenix, U.S.. 2016-2. |
入库方式： OAI收割
来源：自动化研究所
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