Add image-segmentation pipeline tag, PyTorch library, and usage examples (#1)

- Add image-segmentation pipeline tag, PyTorch library, and usage examples (ee9a3baa52b652529eeb43928f736aa7c79ec77f)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,7 @@
 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
+library_name: pytorch
 ---
 
 # UAGLNet
@@ -9,3 +11,79 @@ license: apache-2.0
 **Authors:** [Dstate](https://github.com/Dstate) | **License:** Apache 2.0
 
 **Paper:** *“UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction”* ([arXiv:2512.12941](https://arxiv.org/abs/2512.12941))
+
+🔥 **UAGLNet has been accepted by IEEE TGRS**
+
+We present UAGLNet, which is capable to exploit high-quality global-local visual semantics under the guidance of uncertainty modeling. Specifically, we propose a novel cooperative encoder, which adopts hybrid CNN and transformer layers at different stages to capture the local and global visual semantics, respectively. An intermediate cooperative interaction block (CIB) is designed to narrow the gap between the local and global features when the network becomes deeper. Afterwards, we propose a Global-Local Fusion (GLF) module to complementarily fuse the global and local representations. Moreover, to mitigate the segmentation ambiguity in uncertain regions, we propose an Uncertainty-Aggregated Decoder (UAD) to explicitly estimate the pixel-wise uncertainty to enhance the segmentation accuracy. Extensive experiments demonstrate that our method achieves superior performance to other state-of-the-art methods.
+
+<img width="1000" src="https://github.com/Dstate/UAGLNet/raw/main/assets/architecture2.png">
+
+## Quick Start
+
+### Installation
+
+Clone this repository and create the environment.
+```bash
+git git@github.com:Dstate/UAGLNet.git
+cd UAGLNet
+
+conda create -n uaglnet python=3.8 -y
+conda activate uaglnet
+conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
+pip install -r requirements.txt
+```
+
+### Data Preprocessing
+
+We conduct experiments on the Inria, WHU, and Massachusetts datasets. Detailed guidance for dataset preprocessing is provided here: [DATA_PREPARATION.md](https://github.com/Dstate/UAGLNet/blob/main/assets/DATA_PREPARATION.md).
+
+### Training & Testing
+
+Training and testing examples on the Inria dataset:
+```bash
+# training
+python UAGLNet_train.py -c config/inria/UAGLNet.py
+
+# testing
+python UAGLNet_test.py -c config/inria/UAGLNet.py
+```
+
+### Main Results
+
+The following table presents the performance of UAGLNet on building extraction benchmarks.
+
+| **Benchmark** | **IoU** | **F1** | **P** | **R** | **Weight** |
+| :-------: | :--------: | :--------: | :-----------: | :------: | :------: |
+| Inria | 83.74 | 91.15 | 92.09 | 90.22 | [UAGLNet_Inria](https://huggingface.co/ldxxx/UAGLNet_Inria) |
+| Mass | 76.97 | 86.99 | 88.28 | 85.73 | [UAGLNet_Mass](https://huggingface.co/ldxxx/UAGLNet_Massachusetts) |
+| WHU | 92.07 | 95.87 | 96.21 | 95.54 | [UAGLNet_WHU](https://huggingface.co/ldxxx/UAGLNet_WHU) |
+
+You can quickly reproduce these results by running `Reproduce.py`, which will load the pretrained checkpoints from Hugging Face and perform inference.
+
+```bash
+# Inria
+python Reproduce.py -d Inria
+
+# Massachusetts
+python Reproduce.py -d Mass
+
+# WHU
+python Reproduce.py -d WHU
+```
+
+## Citation
+If you find this project useful in your research, please cite it as:
+```
+@article{UAGLNet,
+  title   = {UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction}, 
+  author  = {Siyuan Yao and Dongxiu Liu and Taotao Li and Shengjie Li and Wenqi Ren and Xiaochun Cao},
+  journal = {arXiv preprint arXiv:2512.12941},
+  year    = {2025}
+}
+```
+
+## Acknowledgement
+This work is built upon [BuildingExtraction](https://github.com/stdcoutzrh/BuildingExtraction), [GeoSeg](https://github.com/WangLibo1995/GeoSeg/tree/main) and [SMT](https://github.com/AFeng-x/SMT). We sincerely appreciate their contributions which provide a clear pipeline and well-organized code.
+
+## License
+This project is licensed under the [Apache License 2.0](https://github.com/Dstate/UAGLNet/blob/main/LICENSE).