SemiBCD

This repository contains the testing code and pretrained models for our paper:

Paper under review
Authors: [Anonymous for review]
Under Review, 2025

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🔧 Environment Setup

We recommend using conda:

conda create -n semibcd python=3.9 -y
conda activate semibcd
pip install -r requirements.txt

📥 Download Backbone Pretrained Weights

SemiBCD uses a ResNet-50 backbone. Download the pretrained checkpoint: 👉 ResNet-50

📥 Download Dataset

1. LEVIR-CD-256

👉 LEVIR-CD-256 Dataset
Extract the downloaded file to the data/LEVIR-CD-256/ folder.

2. WHU-CD-256

👉 WHU-CD-256 Dataset
Extract the downloaded file to the data/WHU-CD-256/ folder.

🚀 Run Testing

1. Download pretrained experiment weights

👉 SemiBCD Experiment Weights

2. Run testing

Run WHU test:

python eval.py --config configs/eval_whu_config.yaml --checkpoint ./best.pth

Run LEVIR test:

python eval.py --config configs/eval_levir_config.yaml --checkpoint ./best.pth

Acknowledgements

SemiBCD is based on SemiCD-VL, SemiVL, UniMatch, APE, and MMSegmentation. We thank their authors for making the source code publicly available.