## Requirements

### Environment

- Python 3.8+
- PyTorch 2.0.1+
- CUDA 11.8+
- Ubuntu 22.04 or higher / Windows 10

### Installation
```bash
  conda create --name rscd python=3.8
  conda activate rscd
  conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
  pip install pytorch-lightning==2.0.5
  pip install scikit-image==0.19.3 numpy==1.24.4
  pip install torchmetrics==1.0.1
  pip install -U catalyst==20.09
  pip install albumentations==1.3.1
  pip install einops==0.6.1
  pip install timm==0.6.7
  pip install addict==2.4.0
  pip install soundfile==0.12.1
  pip install ttach==0.0.3
  pip install prettytable==3.8.0
  pip install -U openmim
  pip install triton==2.0.0
  mim install mmcv
  pip install -U fvcore


## Dataset Preparation
  We evaluate our method on three public datasets: **LEVIR-CD**, **WHU-CD**, and **CLCD**.[Download](https://drive.google.com/drive/folders/1zxhJ7v3UPgNsKkdvkYCOW7DdKDAAy_ll) |