Knowledge Commons of Institute of Automation,CAS
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 | |
会议名称 | American Association for AI National Conference(AAAI) |
会议日期 | 2016-2 |
会议地点 | Phoenix, U.S. |
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. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14969 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 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],2016. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
AAAI2016_Shoot to Kn(1243KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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