Update README.md

README.md

@@ -22,37 +22,7 @@ UAGLNet addresses the challenges of building extraction from remote sensing imag
 
 <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 clone https://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
+## Main Results
 
 The following table presents the performance of UAGLNet on building extraction benchmarks.
 
@@ -62,19 +32,6 @@ The following table presents the performance of UAGLNet on building extraction b
 | 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:
 ```bibtex
@@ -84,7 +41,4 @@ If you find this project useful in your research, please cite it as:
   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.
+```