Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+license: apache-2.0
+tags:
+- computer-vision
+- image-matching
+- overlap-detection
+- feature-extraction
+datasets:
+- SSSSphinx/SCoDe
+---
+
+# SCoDe: Scale-aware Co-visible Region Detection for Image Matching
+
+<div align="center">
+
+[![Paper](https://img.shields.io/badge/Paper-ScienceDirect-green)](https://www.sciencedirect.com/science/article/abs/pii/S0924271625003260)
+[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.isprsjprs.2025.08.015-orange)](https://doi.org/10.1016/j.isprsjprs.2025.08.015)
+[![Project Page](https://img.shields.io/badge/Project-Website-blue)](https://xupan.top/Projects/scode)
+[![GitHub](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/SSSSphinx/SCoDe)
+
+</div>
+
+## Overview
+
+SCoDe is a scale-aware co-visible region detection model designed for robust image matching. It detects overlapping regions between image pairs while being invariant to scale variations, making it particularly effective for structure-from-motion and 3D reconstruction tasks.
+
+This model is built upon the CCOE (Co-visible region detection with Overlap Estimation) architecture and has been trained on the MegaDepth dataset.
+
+## Model Details
+
+- **Architecture**: CCOE-based transformer with multi-scale attention
+- **Backbone**: ResNet-50
+- **Input Size**: 1024×1024 (configurable)
+- **Training Dataset**: MegaDepth
+- **Framework**: PyTorch
+
+### Key Features
+
+- Scale-aware overlap region detection
+- Rotation-invariant matching capabilities
+- End-to-end trainable pipeline
+- Compatible with various feature extractors (SIFT, SuperPoint, D2-Net, R2D2, DISK)
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch torchvision
+git clone https://github.com/SSSSphinx/SCoDe.git
+cd SCoDe
+pip install -r requirements.txt
+```
+
+### Quick Start
+
+```python
+import torch
+from src.config.default import get_cfg_defaults
+from src.model import CCOE
+
+# Load configuration
+cfg = get_cfg_defaults()
+cfg.merge_from_file('configs/scode_config.py')
+
+# Initialize model
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = CCOE(cfg.CCOE).eval().to(device)
+
+# Load pre-trained weights
+model.load_state_dict(torch.load('weights/scode.pth', map_location=device))
+
+# Model is ready for inference
+with torch.no_grad():
+    # Process image pair (example)
+    image1 = torch.randn(1, 3, 1024, 1024).to(device)
+    image2 = torch.randn(1, 3, 1024, 1024).to(device)
+    output = model({'image1': image1, 'image2': image2})
+```
+
+### Training
+
+```bash
+# Single GPU training
+python train_scode.py --num_workers 4 --epoch 15 --batch_size 4 --validation --learning_rate 1e-5
+
+# Multi-GPU distributed training (4 GPUs)
+python -m torch.distributed.launch --nproc_per_node 4 --master_port=29501 train_scode.py \
+    --num_workers 4 --epoch 15 --batch_size 4 --validation --learning_rate 1e-5
+```
+
+### Evaluation
+
+#### Rotation Invariance Evaluation
+```bash
+python rot_inv_eval.py \
+    --extractors superpoint d2net r2d2 disk \
+    --image_pairs path/to/image/pairs \
+    --output_dir outputs/scode_rot_eval
+```
+
+#### Pose Estimation Evaluation
+```bash
+python eval_pose_estimation.py \
+    --results_dir outputs/megadepth_results \
+    --dataset megadepth
+```
+
+#### Radar Evaluation
+```bash
+python eval_radar.py \
+    --results_dir outputs/radar_results
+```
+
+## Configuration
+
+Main configuration files:
+- [`configs/scode_config.py`](configs/scode_config.py) - SCoDe model configuration
+- [`src/config/default.py`](src/config/default.py) - Default configuration template
+
+### Key Parameters
+
+```python
+# Training
+cfg.DATASET.TRAIN.IMAGE_SIZE = [1024, 1024]
+cfg.DATASET.TRAIN.BATCH_SIZE = 4
+cfg.DATASET.TRAIN.PAIRS_LENGTH = 128000
+
+# Validation
+cfg.DATASET.VAL.IMAGE_SIZE = [1024, 1024]
+
+# Model
+cfg.CCOE.BACKBONE.NUM_LAYERS = 50
+cfg.CCOE.BACKBONE.STRIDE = 32
+cfg.CCOE.CCA.DEPTH = [2, 2, 2, 2]
+cfg.CCOE.CCA.NUM_HEADS = [8, 8, 8, 8]
+```
+
+## Dataset
+
+The model is trained on the [MegaDepth](https://github.com/zhengqili/MegaDepth) dataset with scale-aware pair generation.
+
+Dataset preparation:
+```bash
+python dataset_preparation.py \
+    --base_path dataset/megadepth/MegaDepth \
+    --num_per_scene 5000
+```
+
+Validation pairs are automatically generated and evaluated during training.
+
+## Model Performance
+
+SCoDe demonstrates strong performance on:
+- **Rotation Invariance**: Robust to image rotations up to 360°
+- **Scale Invariance**: Effective across multiple image scales
+- **Pose Estimation**: Improved camera pose estimation on MegaDepth benchmark
+- **Feature Matching**: Enhanced matching accuracy with various feature extractors
+
+## Supported Feature Extractors
+
+The model works seamlessly with:
+- SIFT (with brute-force matcher)
+- SuperPoint (with NN matcher)
+- D2-Net
+- R2D2
+- DISK
+
+## Citation
+
+If you find this project useful in your research, please cite our paper:
+
+```bibtex
+@article{pan2025scale,
+  title={Scale-aware co-visible region detection for image matching},
+  author={Pan, Xu and Xia, Zimin and Zheng, Xianwei},
+  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
+  volume={229},
+  pages={122--137},
+  year={2025},
+  publisher={Elsevier}
+}
+```
+
+## License
+
+This project is licensed under the Apache-2.0 License. See the LICENSE file for details.
+
+## Acknowledgments
+
+- [MegaDepth](https://github.com/zhengqili/MegaDepth) - Dataset and benchmarks
+- [OETR](https://github.com/TencentYoutuResearch/ImageMatching-OETR) - Model initialization strategies
+- PyTorch team for the excellent framework
+
+## Contact
+
+For questions or issues, please visit the [GitHub repository](https://github.com/SSSSphinx/SCoDe) or contact the authors.
+
+---
+
+**Paper**: [Scale-aware Co-visible Region Detection for Image Matching](https://www.sciencedirect.com/science/article/abs/pii/S0924271625003260)  
+**Project Page**: [https://xupan.top/Projects/scode](https://xupan.top/Projects/scode)