Method Details
Details for method 'Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas]'
Method overview
| name | Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas] |
| 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, Mapillary Vistas Research Edition |
| runtime | n/a |
| subsampling | no |
| submission date | November, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 42.1965 |
| AP50% | 67.4783 |
| AP100m | 57.761 |
| AP50m | 59.6182 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 37.6868 | 69.8007 | 56.6193 | 56.7915 |
| rider | 34.5858 | 69.0087 | 49.9406 | 50.3866 |
| car | 58.1962 | 82.7709 | 77.9262 | 80.2629 |
| truck | 45.0575 | 56.9791 | 59.9592 | 63.3339 |
| bus | 54.762 | 68.9538 | 76.0943 | 83.7102 |
| train | 47.2136 | 69.0065 | 59.0393 | 58.7595 |
| motorcycle | 34.0216 | 65.1602 | 43.809 | 45.1061 |
| bicycle | 26.0487 | 58.1469 | 38.7 | 38.5947 |