Github link with up-to-date code: https://github.com/RolandGao/RegSeg

RegSeg with ImageNet pretraining achieves 79.1 mIOU on Cityscapes test set:
https://www.cityscapes-dataset.com/anonymous-results/?id=77ef08cd9dbf639eea24ca5159d9c691c60d945788b3cdc5ab19bc5d77d83509

RegSeg without ImageNet pretraining achieves 78.3 mIOU on Cityscapes test set:
https://www.cityscapes-dataset.com/anonymous-results/?id=f88876222a7be564973065f111746e9838d4da9268734457cb57b2409cdb9818

The model definitions are in models.py. RegSeg is exp55_decoder26. 
The learnable dilation rates model is L1_decoder26.
