ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, Hannaneh Hajishirzi
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- github.com/sacmehta/ESPNetOfficialIn paperpytorch★ 0
- github.com/PaddlePaddle/PaddleSegpaddle★ 9,319
- github.com/zh320/realtime-semantic-segmentation-pytorchpytorch★ 249
- github.com/sacmehta/3d-espnetpytorch★ 0
- github.com/klintan/lanenet-pytorchpytorch★ 0
- github.com/klintan/pytorch-lanenetpytorch★ 0
- github.com/simuler/ESPNetpaddle★ 0
- github.com/sacmehta/EdgeNetspytorch★ 0
Abstract
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 ESPNet 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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Cityscapes test | ESPNet | Mean IoU (class) | 60.3 | — | Unverified |
| PASCAL VOC 2012 test | ESPNet | Mean IoU | 63.01 | — | Unverified |