OcularSeg 🔥

This repository contains the training/testing codes of our paper entitled "OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios".

Requirements

• Python>=3.8
• Pytorch>=1.13.0
• timm>=0.5

Datasets

• The UBIRIS.v2 dataset is publicly available at http://iris.di.ubi.pt/ubiris2.html.
• The SBVPI and MOBIUS datasets are publicly available at https://sclera.fri.uni-lj.si/datasets.html.
• The ESLD dataset is publicly available at https://www.cjig.cn/zh/article/doi/10.11834/jig.210177/.
• The MMU dataset is publicly available at https://www.kaggle.com/datasets/naureenmohammad/mmu-iris-dataset.
• Our segmentation annotations for UBIRIS.v2, SBVPI, and MMU can be found in Quark Drive.

If you are interested in utilizing our segmentation annotations for your research experiments, please email us for the access code to Quark Drive.

Experiments

1. How to test the model
   • Downloading our trained weight from Google Drive, and move it into ./checkpoints
   • modifying some settings in test.py
   • running test.py
2. How to train the model
   • running train.py

Reference

Some of the codes in this repo are borrowed from:

• TransUNet
• M²SNet
• Expectation-Maximization Attention Networks

Citation

If you find our work useful in your research, please consider citing:

@article{zhang2024ocularseg,
  title={OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios},
  author={Zhang, Yixin and Wang, Caiyong and Li, Haiqing and Sun, Xianyun and Tian, Qichuan and Zhao, Guangzhe},
  journal={Electronics},
  volume={13},
  number={10},
  pages={1967},
  year={2024}
}

Questions

Please contact zhangyixin@stu.bucea.edu.cn (Miss Zhang) or wangcaiyong@bucea.edu.cn (Dr. Wang).