Method Details
Details for method 'Vision Transformer Adapter for Dense Predictions'
Method overview
| name | Vision Transformer Adapter for Dense Predictions |
| challenge | pixel-level semantic labeling |
| details | ViT-Adapter-L, BEiT pre-train, multi-scale testing |
| publication | Vision Transformer Adapter for Dense Predictions Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Yu Qiao https://arxiv.org/abs/2205.08534 |
| project page / code | https://github.com/czczup/ViT-Adapter |
| used Cityscapes data | fine annotations |
| used external data | ImageNet, Mapillary |
| runtime | n/a |
| subsampling | no |
| submission date | May, 2022 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 85.2055 |
| iIoU Classes | 68.2575 |
| IoU Categories | 92.8165 |
| iIoU Categories | 83.4203 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.8827 | - |
| sidewalk | 88.4947 | - |
| building | 94.4966 | - |
| wall | 66.745 | - |
| fence | 70.2345 | - |
| pole | 74.5308 | - |
| traffic light | 80.1767 | - |
| traffic sign | 83.5873 | - |
| vegetation | 94.3988 | - |
| terrain | 73.7165 | - |
| sky | 96.1833 | - |
| person | 89.674 | 75.8518 |
| rider | 79.0404 | 58.9152 |
| car | 96.6793 | 91.497 |
| truck | 85.5161 | 54.622 |
| bus | 94.4201 | 68.165 |
| train | 90.4794 | 66.7375 |
| motorcycle | 79.8667 | 60.3947 |
| bicycle | 81.7815 | 69.8768 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.7941 | - |
| nature | 94.0184 | - |
| object | 79.6169 | - |
| sky | 96.1833 | - |
| construction | 94.8586 | - |
| human | 89.8844 | 76.6379 |
| vehicle | 96.3597 | 90.2027 |