Paper: arxiv
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D block
Decoder
Install the dependencies in requirements.txt by using pip and virtualenv.
go to https://www.cityscapes-dataset.com, create an account, and download gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip. Unzip both of them and put them in a directory called cityscapes_dataset. The cityscapes_dataset directory should be inside the RegSeg directory. If you put the dataset somewhere else, you can set the config field
config["dataset_dir"]="the location of your dataset"
You can delete the test images to save some space if you don't want to submit to the competition. Make sure that you have downloaded the required python packages and run
CITYSCAPES_DATASET=cityscapes_dataset csCreateTrainIdLabelImgs
There are 19 classes.
To see the ablation studies results from the paper, go here.
To visualize your model, go to show.py.
To see the model definitions and do some speed tests, go to model.py.
To train, validate, benchmark, and save the results of your model, go to train.py.
RegSeg (exp48_decoder26, 30FPS)
Larger RegSeg (exp53_decoder29, 20 FPS)
| Run | RegSeg model weights | DDRNet-23 model weights |
|---|---|---|
| run1 | 77.76 | 77.84 |
| run2 | 78.85 | 78.07 |
| run3 | 78.07 | 77.53 |
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}
}