Method Details
Details for method 'Panoptic-DeepLab w/ SWideRNet [Cityscapes-fine]'
Method overview
| name | Panoptic-DeepLab w/ SWideRNet [Cityscapes-fine] |
| challenge | instance-level semantic labeling |
| details | We revisit the architecture design of Wide Residual Networks. We design a baseline model by incorporating the simple and effective Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets. Its network capacity is further scaled up or down by adjusting the width (i.e., channel size) and depth (i.e., number of layers), resulting in a family of SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such a simple scaling scheme, coupled with grid search, identifies several SWideRNets that significantly advance state-of-the-art performance on panoptic segmentation datasets in both the fast model regime and strong model regime. |
| publication | Scaling Wide Residual Networks for Panoptic Segmentation Liang-Chieh Chen, Huiyu Wang, Siyuan Qiao https://arxiv.org/abs/2011.11675 |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | October, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 37.9881 |
| AP50% | 61.0086 |
| AP100m | 53.6669 |
| AP50m | 55.3675 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 36.7512 | 67.5584 | 56.1119 | 56.1588 |
| rider | 33.1914 | 66.0443 | 48.3469 | 48.7642 |
| car | 57.2325 | 80.6131 | 77.6682 | 80.085 |
| truck | 38.8198 | 49.4483 | 51.8594 | 57.2318 |
| bus | 45.0164 | 56.7782 | 67.0208 | 73.1623 |
| train | 38.8936 | 54.7664 | 53.0612 | 51.2623 |
| motorcycle | 30.1841 | 58.538 | 39.747 | 40.6594 |
| bicycle | 23.8155 | 54.3223 | 35.5198 | 35.6158 |