Code & Data

Open-source implementation and datasets

GitHub Repository

Semi-Supervised ROI Extraction Toolkit - Production-ready Python implementation

⭐ 127 Stars MIT License
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Downloads

Source Code

Complete Python implementation

8 files, 1,320 lines

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Pretrained Model

UNet-EfficientNet-B3 weights

131 MB

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WHU Dataset

Enhanced boundary annotations

2.1 GB

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Docker Image

Ready-to-run container

Latest version

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Quick Installation

Clone and Install

# Clone repository
git clone https://github.com/yourusername/roi-extraction-toolkit.git
cd roi-extraction-toolkit

# Install dependencies
pip install -r requirements.txt

# Configure paths in config.py
nano config.py

# Run training
python main.py

Using Docker

# Pull Docker image
docker pull yourusername/roi-extraction:latest

# Run container
docker run --gpus all -v /data:/data yourusername/roi-extraction

Documentation & Support

Documentation

Complete API documentation and usage guides

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GitHub Issues

Report bugs or request features

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Discussions

Ask questions and share ideas

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Citation

If you use our code or data, please cite:

@inproceedings{yourname2025fixmatch,
  title={FixMatch Meets Remote Sensing},
  author={Your Name and Rajesh Kumar M. and Co-PI Name},
  booktitle={IEEE IGARSS},
  year={2025}
}
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