InPeerReview/HA2F

🛠️ Requirements

Environment

• Linux system,
• Python 3.8+, recommended 3.10
• PyTorch 2.0 or higher, recommended 2.1.0
• CUDA 11.7 or higher, recommended 12.1

Environment Installation

It is recommended to use Miniconda for installation. The following commands will create a virtual environment named stnr and install PyTorch. In the following installation steps, the default installed CUDA version is 12.1. If your CUDA version is not 12.1, please modify it according to the actual situation.

# Create conda environment
conda create -n stnr python=3.8 -y
conda activate stnr

# Install PyTorch
pip install -r requirements.txt

📁 Dataset Preparation

We evaluate our method on five remote sensing change detection datasets: WHU-CD, LEVIR-CD, SYSU-CD.

Dataset	Link
WHU-CD	Download
LEVIR-CD	Download
SYSU-CD	Download

Example of Training on LEVIR-CD Dataset

python main.py --file_root LEVIR --max_steps 80000 --model_type small --batch_size 16 --lr 2e-4 --gpu_id 0

Example of Training on LEVIR-CD Dataset

python eval.py --file_root LEVIR --max_steps 80000 --model_type small --batch_size 16 --lr 2e-4 --gpu_id 0

📂 DATA Structure

├─Train
      ├─A          jpg/png
      ├─B          jpg/png
      └─label      jpg/png
  ├─Val
      ├─A 
      ├─B
      └─label
  ├─Test
      ├─A
      ├─B
      └─label

🙏 Acknowledgement

We sincerely thank the following works for their contributions:

• ChangeViT – A state-of-the-art method for remote sensing change detection that inspired and influenced parts of this work.