| # STTNet |
| Paper: Building Extraction from Remote Sensing Images with Sparse Token Transformers |
| 1. Prepare Data |
| Prepare data for training, validation, and test phase. All images are with the resolution of $512 \times 512$. Please refer to the directory of **Data**. |
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| For larger images, you can patch the images with labels using **Tools/CutImgSegWithLabel.py**. |
| 2. Get Data List |
| Please refer to **Tools/GetTrainValTestCSV.py** to get the train, val, and test csv files. |
| 3. Get Imgs Infos |
| Please refer to **Tools/GetImgMeanStd.py** to get the mean value and standard deviation of the all image pixels in training set. |
| 4. Modify Model Infos |
| Please modify the model information if you want, or keep the default configuration. |
| 5. Run to Train |
| Train the model in **Main.py**. |
| 6. [Optional] Run to Test |
| Test the model with checkpoint in **Test.py**. |
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| We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder **Pretrain**. |
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| If you have any questions, please refer to [our paper](https://www.mdpi.com/2072-4292/13/21/4441) or contact with us by email. |
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| ``` |
| @Article{rs13214441, |
| AUTHOR = {Chen, Keyan and Zou, Zhengxia and Shi, Zhenwei}, |
| TITLE = {Building Extraction from Remote Sensing Images with Sparse Token Transformers}, |
| JOURNAL = {Remote Sensing}, |
| VOLUME = {13}, |
| YEAR = {2021}, |
| NUMBER = {21}, |
| ARTICLE-NUMBER = {4441}, |
| URL = {https://www.mdpi.com/2072-4292/13/21/4441}, |
| ISSN = {2072-4292}, |
| DOI = {10.3390/rs13214441} |
| } |
| ``` |
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