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## 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) |
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