Method Details
Details for method 'Bilateral_attention_semantic'
Method overview
| name | Bilateral_attention_semantic |
| challenge | pixel-level semantic labeling |
| details | we use bilateral attention mechanism for semantic segmentation |
| publication | Anonymous |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | |
| runtime | 0.0141 s Nvidia Tesla V100 |
| subsampling | no |
| submission date | September, 2020 |
| previous submissions | 1, 2 |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 76.4899 |
| iIoU Classes | 55.9409 |
| IoU Categories | 90.3647 |
| iIoU Categories | 79.4577 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.4219 | - |
| sidewalk | 84.9274 | - |
| building | 92.5435 | - |
| wall | 48.0638 | - |
| fence | 55.4155 | - |
| pole | 65.6648 | - |
| traffic light | 73.8527 | - |
| traffic sign | 77.2465 | - |
| vegetation | 93.257 | - |
| terrain | 71.704 | - |
| sky | 94.9367 | - |
| person | 85.5992 | 69.7131 |
| rider | 68.903 | 47.2872 |
| car | 95.4002 | 89.6569 |
| truck | 62.7195 | 34.7948 |
| bus | 77.5014 | 50.6313 |
| train | 70.9734 | 47.1676 |
| motorcycle | 61.5068 | 44.0372 |
| bicycle | 74.6705 | 64.239 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.6403 | - |
| nature | 92.9416 | - |
| object | 71.969 | - |
| sky | 94.9367 | - |
| construction | 92.9042 | - |
| human | 86.1183 | 70.9903 |
| vehicle | 95.0429 | 87.9251 |