STT: Building Extraction from Remote Sensing Images with Sparse Token Transformers

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Introduction

This repository is the code implementation of the paper STT: Building Extraction from Remote Sensing Images with Sparse Token Transformers.

This branch has been tested with PyTorch 2.x and CUDA 12.1, supports Python 3.7+, and is compatible with most CUDA versions.

If you find this project helpful, please give us a star ⭐️. Your support is our biggest motivation.

Updates

🌟 2022.10.23 Released the STT project code.

🌟 2022.10.25 Open-sourced the pre-trained models of WHU and INRIA datasets, you can find them on Hugging Face Spaces.

🌟 2024.02.28 Reorganized the project.

Table of Contents

• Introduction
• Updates
• Table of Contents
• Installation
• Dataset Preparation
• STT Model Training
• STT Model Testing
• Citation
• License
• Contact Us

Installation

Dependencies

• Linux or Windows
• Python 3.7+, recommended 3.10
• PyTorch 2.0 or higher, recommended 2.1
• CUDA 11.7 or higher, recommended 12.1

Environment Installation

We recommend using Miniconda for installation. The following commands will create a virtual environment named stt and install PyTorch.

Note: If you are familiar with PyTorch and have already installed it, you can skip to the next section. Otherwise, you can follow the steps below.

Step 0: Install Miniconda.

Step 1: Create a virtual environment named stt and activate it.

conda create -n stt python=3.10 -y
conda activate stt

Step 2: Install PyTorch2.1.x.

Linux/Windows:

pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121

Or

conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia

Step 4: Install other dependencies.

pip install -U pandas opencv-python tqdm scikit-image einops matplotlib

Install STT

Download or clone the STT repository.

git clone [email protected]:KyanChen/STT.git
cd STT

Dataset Preparation

WHU Building Extraction Dataset

Data Download

• Image and label download address: WHU.

Organization

You can also choose other sources to download the data, but you need to organize the dataset in the following format:

${DATASET_ROOT} # Dataset root directory, for example: /home/username/data/WHU
├── train
│   ├── img
│   └── label
├── val
│   ├── img
│   └── label
└── test
    ├── img
    └── label

Note: In the project folder, we provide a folder named Data, which contains an example of the organization of the dataset.

INRIA Building Extraction Dataset

Data Download

• Image and label download address: INRIA.
• You need to use the script 数据切分 to cut the data into a fixed size.

Organization

You can also choose other sources to download the data, but you need to organize the dataset in the above format.

Other Datasets

If you want to use other datasets, you can refer to the above method to prepare the dataset.

Dataset Configuration

• Use the script GetTrainValTestCSV.py to generate training, validation, and test csv files.
• Use the script GetImgMeanStd.py to get the mean and standard deviation of the training set images.

STT Model Training

Train File and Main Parameter Parsing

We provide the training script Train.py. Below we provide an analysis of some of the main parameters.

Parameter Parsing

• line3：os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3' Set the GPU to be used, generally no need to modify.
• line17：backbone: The type of the backbone network, generally no need to modify.
• line22：top_k_s: The number of spatially sparse tokens selected, generally no need to modify.
• line23：top_k_c: The number of channel sparse tokens selected, generally no need to modify.
• line28：BATCH_SIZE: The batch size of a single card, needs to be modified according to the size of the memory.
• line31：DATASET: The path of the training data list, needs to be modified according to the path of the dataset.
• line32：model_path: The save path of the model, generally no need to modify.
• line33：log_path: The save path of the log, generally no need to modify.
• line35：IS_VAL: Whether to verify, generally no need to modify.
• line37：VAL_DATASET: The path of the validation data list, needs to be modified according to the path of the dataset.
• line39：IS_TEST: Whether to test, generally no need to modify.
• line40：TEST_DATASET: The path of the test data list, needs to be modified according to the path of the dataset.
• line45：PRIOR_MEAN: The mean of the training set images, needs to be modified according to the mean of the dataset.
• line46：PRIOR_STD: The standard deviation of the training set images, needs to be modified according to the standard deviation of the dataset.
• line53：load_checkpoint_path: Whether to load the checkpoint, generally empty.
• line55：resume_checkpoint_path: Whether to resume training, generally empty.

Single Card Training

CUDA_VISIBLE_DEVICES=0 python Train.py  # 0 is the GPU number used

Multi-card Training

CUDA_VISIBLE_DEVICES=0,1,2,3 python Train.py  # 0,1,2,3 are the GPU numbers used

STT Model Testing

Single Card Testing

We provide the training script Test.py. You need to modify load_checkpoint_path in line47 of Test.py to the checkpoint file you want to use.

CUDA_VISIBLE_DEVICES=0 python Test.py  # 0 is the GPU number used

Multi-card Testing

CUDA_VISIBLE_DEVICES=0,1,2,3 python Test.py  # 0,1,2,3 are the GPU numbers used

Note: The output results will be saved in log_path.

Citation

If you use the code or performance benchmarks of this project in your research, please refer to the following bibtex to cite TTP.

@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}
}

License

This project is licensed under the Apache 2.0 License.

Contact Us

If you have any other questions❓, please feel free to contact us 👬