Sachin Mehta1, Mohammad Rastegari2, Anat Caspi1, Linda Shapiro1, and Hannaneh Hajishirzi1
University of Washington, Seattle, WA, USA
2 Allen Institute for AI and XNOR.AI
Figure: ESPNet for visual scene understanding on edge devices.
We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet, while its category-wise accuracy is only 8% less. We evaluated EPSNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset. Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet, ShuffleNet, and ENet on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices. Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.
|ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, and Hannaneh Hajishirzi
European Conference on Computer Vision (ECCV), 2018
Qualitative results on the Cityscapes demo videos
Below are the segmentation results produced by ESPNet on the Cityscape's demo videos.
Comparison with efficient convolutional modules
Figure: Comparison between state-of-the-art efficient convolutional modules. Our ESP module outperformed MobileNet and ShuffleNet modules by 7% and 12%, respectively, while learning a similar number of parameters and having comparable network size and inference speed. Furthermore, the ESP module delivered comparable accuracy to ResNext and Inception more efficiently. A basic ResNet module (stack of two 3 × 3 convolutions with a skip-connection) delivered the best performance, but had to learn 6.5× more parameters.
Comparison with state-of-the-art semantic segmentation networks
Figure: Comparison between state-of-the-art semantic segmentation networks. Our ESPNet is fast, has low power consumption, and delivers good category-wise segmentation accuracy.
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This page is adapted from PSPNet.