This repository is merged with https://github.com/jfzhang95/pytorch-deeplab-xception and is success of https://github.com/sunggukcha/deeplabv3plus-bdd100k-drivablearea. Please follow installation policies of the repositories above.

For BDD100k/drivable_area semantic segmentation, I added

1. bdd100k drivable area dataloader, and training/val/test scripts.
2. prediction visualization for both color (visual result) and id (greyscale png file for submission).
3. added Group Noramlization.
4. deeplabv3 which is without deeplabv3+ decoder, but with aspp only.
5. WRN as backbone is added (original code from mapillary@github)
6. additional visualization that marks corrects, missed and wrong pixels.
7. IBN-Net by github.com/XingangPan/IBN-Net/
8. EfficientNet added which is implemented by https://github.com/lukemelas/EfficientNet-PyTorch.
9. Feature Pyramid Networks(FPNs) for semantic segmentation added (version: Panoptic Feature Pyramid Networks).

For more detail, please visit the repositories above.

Single 12GB GPU

IGN-a-16 denotes instance group normalization with channel-grouping number 16, replacing BN of IBNNet-a with GN16. Group-16 denotes group normalization with channel-grouping number 16.