Method Details
Details for method 'Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas + Pseudo-labels]'
Method overview
| name | Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas + Pseudo-labels] |
| 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. Following Naive-Student, this model is additionally trained with pseudo-labels generated from Cityscapes Video and train-extra set (i.e., the coarse annotations are not used, but the images are). |
| 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, video |
| used external data | ImageNet, Mapillary Vistas Research Edition. Cityscapes train-extra set (coarse labels are not used but only images). |
| runtime | n/a |
| subsampling | no |
| submission date | January, 2021 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 43.4345 |
| AP50% | 68.7408 |
| AP100m | 58.8624 |
| AP50m | 60.9078 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 39.3022 | 71.8551 | 58.628 | 58.8941 |
| rider | 34.9208 | 68.9528 | 50.7213 | 51.1363 |
| car | 59.5522 | 83.0367 | 79.8055 | 82.3154 |
| truck | 47.8656 | 59.3632 | 62.7445 | 68.4163 |
| bus | 57.3514 | 72.3257 | 77.6346 | 85.059 |
| train | 45.8594 | 66.9826 | 56.0178 | 55.3642 |
| motorcycle | 35.8183 | 67.241 | 45.5326 | 46.2163 |
| bicycle | 26.8063 | 60.1694 | 39.8146 | 39.8609 |