RFENet

This repo contains the the implementation of Our IJCAI-2023 work: RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation.

paper

arxiv

Any Suggestions/Questions/Pull Requests are welcome.

The master branch works with PyTorch 1.10.1 .

Dataset Preparation

Dataloaders for Trans10k, PMD, and GSD are available in datasets. Details of preparing each dataset can be found at DATASETS.md

Model Checkpoint

Pretrained Backbones

The pretrained backbones we use is the same as that used in the EBLNet paper, which you can find in the README file of EBLNet.

Trained Models and CKPTs

Here are the trained models reported in our paper, you can use them to evaluate.

Dataset	Backbone	mIoU	Model
Trans10k	ResNet50 (os8)	91.25	Link
GSD	ResNeXt101	86.50	Link
PMD	ResNeXt101	73.56	Link

Evaluation

After downloading the trained models, you could easily evaluate the models with the scripts located in scripts/test directory. For example, when evaluating the RFENet on Trans10k dataset:

sh scripts/test/test_Trans10k_R50_os8_RFENet.sh {path_of_checkpoint} {path_to_save_results}

Note that, when computing the mean IoU, we do not include the background.

During evaluation, if you don't want to save images during evaluating for visualization, all you need to do is remove args: dump_images in the test scripts.

Training

You could easily evaluate the models with the scripts located in scripts/train directory. For example, when training RFENet on the Trans10k dataset:

sh scripts/train/train_Trans10k_R50_os8_RFENet.sh {path_to_save_results}

Note that, all our models are trained on 4 V-100 GPUs with 32G memory.

Citation

If you find this repo is helpful to your research. Please consider cite our work.

@inproceedings{DBLP:conf/ijcai/FanWWWYM23,
  author       = {Ke Fan and
                  Changan Wang and
                  Yabiao Wang and
                  Chengjie Wang and
                  Ran Yi and
                  Lizhuang Ma},
  title        = {RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation},
  booktitle    = {Proceedings of the Thirty-Second International Joint Conference on
                  Artificial Intelligence, {IJCAI} 2023, 19th-25th August 2023, Macao,
                  SAR, China},
  pages        = {717--725},
  publisher    = {ijcai.org},
  year         = {2023},
  url          = {https://doi.org/10.24963/ijcai.2023/80},
  doi          = {10.24963/ijcai.2023/80},
  timestamp    = {Mon, 28 Aug 2023 17:23:07 +0200},
  biburl       = {https://dblp.org/rec/conf/ijcai/FanWWWYM23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Acknowledgement

This repo is based on EBLNet repo and NVIDIA segmentation repo. We fully thank their open-sourced code.