Enhance model card: Add pipeline tag, library name, and sample usage

README.md

@@ -1,9 +1,27 @@
 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
+library_name: pytorch
 ---
 
 # UAGLNet
 
-**Repository:** [https://github.com/Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)
+UAGLNet is an Uncertainty-Aggregated Global-Local Fusion Network designed for building extraction from remote sensing images. It exploits high-quality global-local visual semantics under the guidance of uncertainty modeling, addressing challenges posed by complex structural variations. The network features a novel cooperative encoder (hybrid CNN and transformer layers), an intermediate cooperative interaction block (CIB), a Global-Local Fusion (GLF) module, and an Uncertainty-Aggregated Decoder (UAD) to enhance segmentation accuracy by explicitly estimating pixel-wise uncertainty.
 
-**Paper:** *“UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction”* ([arXiv:2512.12941](https://arxiv.org/abs/2512.12941))
+📄 **Paper:** "[UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction](https://huggingface.co/papers/2512.12941)" ([arXiv:2512.12941](https://arxiv.org/abs/2512.12941))
+💻 **Repository:** [https://github.com/Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)
+
+## Sample Usage
+
+You can quickly reproduce the main results for various datasets by running `Reproduce.py`, which will load the pretrained checkpoints from Hugging Face and perform inference.
+
+```bash
+# To reproduce results on the Inria dataset:
+python Reproduce.py -d Inria
+
+# To reproduce results on the Massachusetts dataset:
+python Reproduce.py -d Mass
+
+# To reproduce results on the WHU dataset:
+python Reproduce.py -d WHU
+```