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Source code and models of GALD net
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We will also release the all the state-of-the-art models and code trained on Cityscape dataset including Deeplabv3, Deeplabv3+, PSPnet, DAnet. All the codes are still in refactoring process.


We propose Global Aggregation then Local Distribution (GALD) scheme to distribute global information to each position adaptively according to the local information around the position. GALD net achieves top performance on Cityscapes dataset. Both source code and models will be available soon. The work was done at DeepMotion AI Research

Comparisons with state-of-the-art models on Cityscapes dataset

Method Conference Backbone mIoU(%)
RefineNet CVPR2017 ResNet-101 73.6
SAC ICCV2017 ResNet-101 78.1
PSPNet CVPR2017 ResNet-101 78.4
DUC-HDC WACV2018 ResNet-101 77.6
AAF ECCV2018 ResNet-101 77.1
BiSeNet ECCV2018 ResNet-101 78.9
PSANet ECCV2018 ResNet-101 80.1
DFN CVPR2018 ResNet-101 79.3
DSSPN CVPR2018 ResNet-101 77.8
DenseASPP CVPR2018 DenseNet-161 80.6
OCNet - ResNet-101 81.7
CCNet - ResNet-101 81.4
GALD-Net - ResNet50 80.8
GALD-Net - ResNet101 81.8
GALD-Net(use coarse data) - ResNet101 82.9
GALD-Net(use Mapillary) - ResNet101 83.3

Detailed Results are shown here (Single Model Result)

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