Method Details
Details for method 'Fast-SCNN'
Method overview
| name | Fast-SCNN |
| challenge | pixel-level semantic labeling |
| details | The encoder-decoder framework is state-of-the-art for offline semantic image segmentation. Since the rise in autonomous systems, real-time computation is increasingly desirable. In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation on embedded devices with low memory. Building on existing two-branch methods for fast segmentation, we introduce our `learning to downsample' module which computes low-level features for multiple resolution branches simultaneously. Our network combines spatial detail at high resolution with deep features extracted at lower resolution, yielding an accuracy of 68.0% mean intersection over union at 123.5 frames per second on Cityscapes. We also show that large scale pre-training is unnecessary. We thoroughly validate our metric in experiments with ImageNet pre-training and the coarse labeled data of Cityscapes. Finally, we show even faster computation with competitive results on subsampled inputs, without any network modifications. |
| publication | Fast-SCNN: Fast Semantic Segmentation Network Rudra PK Poudel, Stephan Liwicki, Roberto Cipolla https://arxiv.org/abs/1902.04502 |
| project page / code | |
| used Cityscapes data | fine annotations, coarse annotations |
| used external data | |
| runtime | 0.0081 s Nvidia Titan Xp (Pascal) |
| subsampling | no |
| submission date | November, 2018 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 68.0156 |
| iIoU Classes | 37.9056 |
| IoU Categories | 84.7353 |
| iIoU Categories | 63.4613 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 97.9461 | - |
| sidewalk | 81.5856 | - |
| building | 89.6942 | - |
| wall | 46.3715 | - |
| fence | 48.6384 | - |
| pole | 48.3227 | - |
| traffic light | 53.0512 | - |
| traffic sign | 60.5445 | - |
| vegetation | 90.7149 | - |
| terrain | 67.175 | - |
| sky | 94.3191 | - |
| person | 73.9914 | 45.1417 |
| rider | 54.5843 | 25.5122 |
| car | 92.9909 | 83.255 |
| truck | 57.4255 | 20.045 |
| bus | 65.468 | 32.0083 |
| train | 58.217 | 34.2284 |
| motorcycle | 50.0359 | 20.7084 |
| bicycle | 61.2194 | 42.3458 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.1956 | - |
| nature | 90.3145 | - |
| object | 55.0954 | - |
| sky | 94.3191 | - |
| construction | 89.6207 | - |
| human | 74.1565 | 46.1312 |
| vehicle | 91.4451 | 80.7914 |