This toolbox is used to evaluate the performance of video polyp segmentation task.
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Prerequisites of environment:
python -m pip install opencv-python tdqm prettytable scikit-learn
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Running the evaluation:
sh ./eval.sh
By running the script, results of all models on SUN-SEG dataset will be evaluated simultaneously. If you want to evaluate the specific models, please modify the
$MODEL_NAMES
variable ineval.sh
which is corresponding to the argument--model_lst
. Note that the modified model name should be the same to the folder name under./data/Pred/
. Invps_evaluator.py
, you can specify--metric_list
to decide the applying metrics.--txt_name
denotes the folder name of evaluation result.--data_lst
and--check_integrity
represent the used dataset and the integrity examination of result maps and ground truth.
If you have found our work useful, please use the following reference to cite this project:
@article{ji2022vps,
title={Deep Learning for Video Polyp Segmentation: A Comprehensive Study},
author={Ji, Ge-Peng and Xiao, Guobao and Chou, Yu-Cheng and Fan, Deng-Ping and Zhao, Kai and Chen, Geng and Gool, Luc Van},
journal={arXiv},
year={2022}
}
@inproceedings{ji2021pnsnet,
title={Progressively Normalized Self-Attention Network for Video Polyp Segmentation},
author={Ji, Ge-Peng and Chou, Yu-Cheng and Fan, Deng-Ping and Chen, Geng and Jha, Debesh and Fu, Huazhu and Shao, Ling},
booktitle={MICCAI},
pages={142--152},
year={2021}
}