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《Enet: A deep neural network architecture for real-time semantic segmentation》
—待写
作者：AdamPaszke ,etc
单位：华沙大学&普渡大学
发表会议及时间：CVPR 2016
Submission history
From: Adam Paszke [view email]
[v1] Tue, 7 Jun 2016 14:09:27 UTC (2,824 KB)
https://arxiv.org/abs/1606.02147
AbstractThe ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18× faster, requires 75× less FLOPs, has 79× less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster. 一 论文导读 二 论文精读 三 代码实现 四 问题思索