All-day-CityScapes-segmentation

All-day Semantic Segmentation & All-day CityScapes dataset

This is the official implementation of our work entitled as Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation, accepted by IEEE Transactions on Image Processing.

Dataset Download

Please download All-day CityScapes from: [https://isis-data.science.uva.nl/cv/1ADcityscape.zip]

For CopyRight issue, we only provide the rendered samples on both training and validation set of the original CityScapes.

All the sample name and data folder organization from All-day CityScapes is the same as the original CityScapes.

Source Code & Implementation

The proposed interactive intrinsic-extrinsic learning can be embedded into a variety of CNN and ViT based segmentation models.

Here we provide the source code that is implemented on DDRNet-23 backbone, which is: 1) simple and easy to config; 2) most of the experiments in this paper conduct on. This implementation is highly based on the DDRNet source code. The original implementation of DDRNet can be found in this page.

Please follow the below steps to run the AO-SegNet (DDRNet-23 based backbone).

Step 1: Configuration

Follow the original DDRNet-23 to prepare all the packages and data folder.

Step 2: Train the Model

python train.py --data_pth D:/alldaycityscapes --nclass 19

Step 3: Evaluation

python eval.py

Citation and Reference

If you find this project useful, please cite:

@ARTICLE{Bi2023AD,
  author={Bi, Qi and You, Shaodi and Gevers, Theo},
  journal={IEEE Transactions on Image Processing}, 
  title={Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation}, 
  year={2023},
  volume={32},
  number={},
  pages={3821-3835},
  doi={10.1109/TIP.2023.3290469}}