BasicIRSTD is a PyTorch-based open-source and easy-to-use toolbox for infrared small target detction (IRSTD). This toolbox introduces a simple pipeline to train/test your methods, and builds a benchmark to comprehensively evaluate the performance of existing methods. Our BasicIRSTD can help researchers to get access to infrared small target detction quickly, and facilitates the development of novel methods. Welcome to contribute your own methods to the benchmark.

Note: This repository will be updated on a regular basis. Please stay tuned!

• We provide a PyTorch-based open-source and easy-to-use toolbox for IRSTD.
• We re-implement a number of existing methods on the unified datasets, and develop a benchmark for performance evaluation.
• We share the codes, models and results of existing methods to help researchers better get access to this area.

• Apirl 4, 2022: Pulic The BasicIRSTD ToolBox.

• July 24, 2023: Update BasicIRSTD ToolBox (stable version).

  Code: We fix bugs to ensure the stability of training results.
  Results: We reprodude all models and update the results in table.
  
  

• Python 3
• pytorch 1.2.0 or higher
• numpy, PIL
  
  

• NUST-SIRST   [download]   [paper]
• NUAA-SIRST   [download]   [paper]
• NUDT-SIRST   [download]   [paper]
• IRSTD-1K   [download dir]   [paper]
• NUDT-SIRST-Sea   [download]   [paper]
• IRDST   [download]   [paper]

We used the NUAA-SIRST, NUDT-SIRST, IRSTD-1K for both training and test. Please first download our datasets via Baidu Drive (key:1113) or Google Drive, and place the 3 datasets to the folder ./datasets/. More results will be released soon!

• Our project has the following structure:

  ├──./datasets/
  │    ├── NUAA-SIRST
  │    │    ├── images
  │    │    │    ├── XDU0.png
  │    │    │    ├── XDU1.png
  │    │    │    ├── ...
  │    │    ├── masks
  │    │    │    ├── XDU0.png
  │    │    │    ├── XDU1.png
  │    │    │    ├── ...
  │    │    ├── img_idx
  │    │    │    ├── train_NUAA-SIRST.txt
  │    │    │    ├── test_NUAA-SIRST.txt
  │    ├── NUDT-SIRST
  │    │    ├── images
  │    │    │    ├── 000001.png
  │    │    │    ├── 000002.png
  │    │    │    ├── ...
  │    │    ├── masks
  │    │    │    ├── 000001.png
  │    │    │    ├── 000002.png
  │    │    │    ├── ...
  │    │    ├── img_idx
  │    │    │    ├── train_NUDT-SIRST.txt
  │    │    │    ├── test_NUDT-SIRST.txt
  │    ├── ...  
  

• Run train.py to perform network training. Example for training [model_name] on [dataset_name] datasets:

  $ python train.py --model_name [ACM, ALCNet] --dataset_name [NUAA-SIRST, NUDT-SIRST] --batch_size 16
  

• Checkpoints and Logs will be saved to ./log/, and the ./log/ has the following structure:

  ├──./log/
  │    ├── [dataset_name]
  │    │    ├── [model_name]_eopch400.pth.tar
  

• Run test.py to perform network inference. Example for test [model_name] on [dataset_name] datasets:

  $ python test.py --model_name [ACM, ALCNet] --dataset_name [NUAA-SIRST, NUDT-SIRST] 
  

• The PA/mIoU and PD/FA values of each dataset will be saved to ./test_[current time].txt
  

• Network preditions will be saved to ./results/ that has the following structure:

  ├──./results/
  │    ├── [dataset_name]
  │    │   ├── [model_name]
  │    │   │    ├── XDU0.png
  │    │   │    ├── XDU1.png
  │    │   │    ├── ...
  │    │   │    ├── XDU20.png
  

• Please first put your results on ./results/ that has the following structure:

  ├──./results/
  │    ├── [dataset_name]
  │    │   ├── [method_name]
  │    │   │    ├── XDU0.png
  │    │   │    ├── XDU1.png
  │    │   │    ├── ...
  │    │   │    ├── XDU20.png
  

• Run evaluate.py for direct eevaluation. Example for evaluate [method_name] on [dataset_name] datasets:

  $ python evaluate.py --method_name [ACM, ALCNet] --dataset_name [NUAA-SIRST, NUDT-SIRST] 
  

• The PA/mIoU and PD/FA values of each dataset will be saved to ./eval_[current time].txt
  
  

• Run cal_params.py for parameters and FLOPs calculation. Examples:

  $ python cal_params.py --method_name [ACM, ALCNet]
  

• The parameters and FLOPs of each method will be saved to ./params_[current time].txt
  
  

We benchmark several methods on the above datasets. mIoU, PD and FA metrics under threshold=0.5 are used for quantitative evaluation.

Note: A detailed review of existing IRSTD methods can be referred to Tianfang-Zhang/awesome-infrared-small-targets.

• We provide the result files generated by the aforementioned methods, and researchers can download the results via Baidu Drive (key:1113) and Google Drive.
• The pre-trained models of the aforementioned methods can be downlaoded via Baidu Drive (key:1113) and Google Drive.
  
  

We would like to thank Boyang Li, Ruojing Li, Tianhao Wu and Ting Liu for the helpful discussions and insightful suggestions regarding this repository.

Welcome to raise issues or email to [email protected] for any question regarding our BasicIRSTD.