# 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**.
  
   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**.


We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder **Pretrain**.

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.

```
@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}
}
```

{}
has_space region:us
TAGS
#has_space #region-us 

# 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.
  
   For larger images, you can patch the images with labels using Tools/URL.
2. Get Data List    
   Please refer to Tools/URL to get the train, val, and test csv files.
3. Get Imgs Infos     
   Please refer to Tools/URL 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 URL.
6. [Optional] Run to Test    
   Test the model with checkpoint in URL.


We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder Pretrain.

If you have any questions, please refer to our paper or contact with us by email.



# 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.
  
   For larger images, you can patch the images with labels using Tools/URL.
2. Get Data List    
   Please refer to Tools/URL to get the train, val, and test csv files.
3. Get Imgs Infos     
   Please refer to Tools/URL 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 URL.
6. [Optional] Run to Test    
   Test the model with checkpoint in URL.


We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder Pretrain.

If you have any questions, please refer to our paper or contact with us by email.
TAGS
#has_space #region-us 
 # 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.
  
   For larger images, you can patch the images with labels using Tools/URL.
2. Get Data List    
   Please refer to Tools/URL to get the train, val, and test csv files.
3. Get Imgs Infos     
   Please refer to Tools/URL 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 URL.
6. [Optional] Run to Test    
   Test the model with checkpoint in URL.


We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder Pretrain.

If you have any questions, please refer to our paper or contact with us by email.
passage: TAGS
#has_space #region-us 
# 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.
  
   For larger images, you can patch the images with labels using Tools/URL.
2. Get Data List    
   Please refer to Tools/URL to get the train, val, and test csv files.
3. Get Imgs Infos     
   Please refer to Tools/URL 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 URL.
6. [Optional] Run to Test    
   Test the model with checkpoint in URL.


We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder Pretrain.

If you have any questions, please refer to our paper or contact with us by email.