This repository contains a Python implementation of our paper MS-Former.

• Prepare the data:

  • Download datasets LEVIR, BCDD-BGMix, SYSU, and GVLM
  • Crop LEVIR datasets into 256x256 patches. Generate patch-level annotations for LEVIR, BCDD-BGMix, SYSU, and GVLM datasets.
  • Generate list file as ls -R ./label/* > test.txt
  • Prepare datasets into the following structure and set their path in train.py and test.py

  ├─Train
      ├─A        ...jpg/png
      ├─B        ...jpg/png
      ├─label    ...jpg/png
      └─list     ...txt
  ├─Test
      ├─A
      ├─B
      ├─label
      └─list
  

• Prerequisites for Python:

  • Creating a virtual environment in the terminal: conda create -n MS-Former python=3.8
  • Installing necessary packages: pip install -r requirements.txt

• Train/Test

  • sh train.sh
  • sh test.sh

Please cite our paper if you find the work useful:

@article{li2023ms,
    title={MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations},
    author={Li, Zhenglai and Tang, Chang and Liu, Xinwang and Li, Changdong and Li, Xianju and Zhang, Wei},
    journal={arXiv preprint arXiv:2311.09726},
    year={2023}
}