Method Details
Details for method 'GALD-Net'
Method overview
| name | GALD-Net |
| challenge | pixel-level semantic labeling |
| details | We propose Global Aggregation then Local Distribution (GALD) scheme to distribute global information to each position adaptively according to the local information around the position. (Joint work: Key Laboratory of Machine Perception, School of EECS @Peking University and DeepMotion AI Research ) |
| publication | Global Aggregation then Local Distribution in Fully Convolutional Networks Xiangtai Li, Li Zhang, Ansheng You, Maoke Yang, Kuiyuan Yang, Yunhai Tong BMVC 2019 |
| project page / code | https://github.com/lxtGH/GALD-Net |
| used Cityscapes data | fine annotations, coarse annotations, 16bit, stereo |
| used external data | Mapillary |
| runtime | n/a |
| subsampling | no |
| submission date | March, 2019 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 83.2981 |
| iIoU Classes | 64.5173 |
| IoU Categories | 92.2831 |
| iIoU Categories | 81.9387 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.8097 | - |
| sidewalk | 87.6865 | - |
| building | 94.2001 | - |
| wall | 65.0349 | - |
| fence | 66.7147 | - |
| pole | 73.1498 | - |
| traffic light | 79.2719 | - |
| traffic sign | 82.4421 | - |
| vegetation | 94.1603 | - |
| terrain | 72.9191 | - |
| sky | 96.023 | - |
| person | 88.4498 | 73.702 |
| rider | 76.233 | 56.7356 |
| car | 96.4998 | 91.0737 |
| truck | 79.821 | 52.8011 |
| bus | 89.5861 | 59.6299 |
| train | 87.6622 | 60.4326 |
| motorcycle | 74.0595 | 53.6566 |
| bicycle | 79.9406 | 68.1072 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.7792 | - |
| nature | 93.8373 | - |
| object | 78.2913 | - |
| sky | 96.023 | - |
| construction | 94.4293 | - |
| human | 88.4802 | 74.5157 |
| vehicle | 96.1418 | 89.3617 |