ETTR123 / RegSeg

The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation"

RegSeg

The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation"

Paper: arxiv

D block

Decoder

Setup

Install the dependencies in requirements.txt by using pip and virtualenv.

Download Cityscapes

go to https://www.cityscapes-dataset.com, create an account, and download gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip. You can delete the test images to save some space if you don't want to submit to the competition. Name the directory cityscapes_dataset. Make sure that you have downloaded the required python packages and run

CITYSCAPES_DATASET=cityscapes_dataset csCreateTrainIdLabelImgs

There are 19 classes.

Results from paper

To see the ablation studies results from the paper, go here.

Usage

To visualize your model, go to show.py. To train, validate, benchmark, and save the results of your model, go to train.py.

Results on Cityscapes server

RegSeg (exp48_decoder26, 30FPS): 78.3

Larger RegSeg (exp53_decoder29, 20 FPS): 79.5

Citation

If you find our work helpful, please consider citing our paper.

@article{gao2021rethink,
  title={Rethink Dilated Convolution for Real-time Semantic Segmentation},
  author={Gao, Roland},
  journal={arXiv preprint arXiv:2111.09957},
  year={2021}
}