Non-Standard Echo State Networks for Video Door State Monitoring

Metadata

• Author: Peter Steiner,
• Azarakhsh Jalalvand, Peter Birkholz
• Conference: International Joint Conference ont Neural Networks (IJCNN ) 2023, Queensland, Australia
• Weblink: https://github.com/TUD-STKS/VideoDoorStateMonitoring

Summary and Contents

This is the repository containing the code to reproduce the content of the research paper "Non-Standard Echo State Networks for Video Door State Monitoring". It contains a Jupyter notebook, pre-trained models and the code to obtain all Figures and results in the paper.

We propose to use the following structure of this README:

• File list:
  • The file list contains all files provided in the repository together with a short description.
• Usage:
  • How can users get started with your research code. This contains setting up a installing packages in a virtual environment venv and running one main.py that includes your main code.
  • Very important and often forgotten: How can the data to reproduce the results be obtained?
  • In case of a Jupyter notebook, it is strongly recommended to add a link to Binder.
• Referencing:
  • How can your work be cited? Ideally, provide a bibtex entry of the paper.

File list

• The following scripts are provided in this repository
  • scripts/run.sh: UNIX Bash script to reproduce the results in the paper.
  • scripts/run_jupyter-lab.sh: UNIX Bash script to start the Jupyter Notebook for the paper.
  • scripts/run.bat: Windows batch script to reproduce the results in the paper.
  • scripts/run_jupyter-lab.bat: Windows batch script to start the Jupyter Notebook for the paper.
• The following python scripts and modules are provided in src
  • src/dataset: Module for dataset handling.
  • src/input_to_node: Custom InputToNode building blocks for the KM-ESN and simple ESN architectures.
  • src/node_to_node: Custom NodeToNode building blocks for the simple ESN architectures.
  • src/main.py: The main script to train all models.
  • Video_Door_State_Monitoring.ipynb: The Jupyter notebook to reproduce all results.
• requirements.txt: Text file containing all required Python modules to be installed.
• README.md: The README displayed here.
• LICENSE: Textfile containing the license for this source code. You can find
• data/: The directory data is prepared to hold the dataset. Please request the dataset from the authors.
• results/: The directory results is prepared to hold the trained models. Please request the pre-trained models from the authors.
• .gitignore: Command file for Github to ignore specific files.

Usage

The easiest way to reproduce the results is to run the Notebook online using Binder.

To run the scripts or to start the Jupyter Notebook locally, at first, please ensure that you have a valid Python distribution installed on your system. Here, at least Python 3.8 is required.

You can then call run_jupyter-lab.ps1 or run_jupyter-lab.sh. This will install a new Python venv, which is our recommended way of getting started.

The pre-trained models that are part of this repository can be re-trained using the script main.py. The easiest way to reproduce the results is to either download and extract this Github repository in the desired directory, open a Linux Shell and call run.sh or open a Windows PowerShell and call run.ps1.

In that way, again, a Python venv is created, where all required packages (specified by requirements.txt) are installed. Afterwards, the script main.py is excecuted with all default arguments activated in order to reproduce all results in the paper.

If you want to suppress any options, simply remove the particular option.

License and Referencing

This program is licensed under the BSD 3-Clause License. If you in any way use this code for research that results in publications, please cite our original article

@inproceedings{steiner2023non-standard,
  author    = {Steiner, Peter and Jalalvand, Azarakhsh and Birkholz, Peter},
  booktitle = {2023 International Joint Conference on Neural Networks (IJCNN)},
  title     = {Non-Standard Echo State Networks for Video Door State Monitoring},
  year      = {2023},
  doi       = {...},
  pages     = {1--8},
}

Appendix

For any questions, do not hesitate to open an issue or to drop a line to Peter Steiner