rescuebox-flaskml-0.1.0
This is a repo to consolidate the code from various FlaskML based RescueBox projects that we plan on releasing as 0.1.0
Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (17.0%) to scientific vocabulary
Repository
This is a repo to consolidate the code from various FlaskML based RescueBox projects that we plan on releasing as 0.1.0
Basic Info
- Host: GitHub
- Owner: UMass-Rescue
- License: gpl-3.0
- Language: C++
- Default Branch: main
- Size: 109 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 15
- Releases: 0
Metadata Files
README.md
RescueBox Desktop
![]()
RescueBox Desktop (RBox) is a self-contained binary offering a UI interface to a library of ML models for various forensic applications. To use RescueBox Desktop, start up a model application in the background adhering to the FlaskML interface.
Then, register the model application's IP Host address and port. You can now run the model by specifying its inputs on the UI and analyzing outputs when ready. RBox handles the rest: running the jobs, and interfacing with different ML models. Since RBox is aimed toward forensic analysts, it is designed to operate on local, or drive-mounted storage.
For a review of the project's goals, read What is RescueBox Desktop?. For a view into how RBox-Desktop works, read the architecture section.
Getting Started
Step 1: Download the Latest Release for Windows 10 or 11 Only deployment.
Get the latest release of the binary for your operating system (Windows) from the release page. For Linux, see Additional Instructions for Linux.
Step 2: Deploy the RescueBox Download
Double click the RescueBox-Desktop Setup 1.0.exe. The first input is the install directory. choose any path that exists and has at least 5 GB of free space. The time taken to unzip the files and put them in the install directory is a few minutes. Continue to run the Desktop when prompted. This proces now setups a Python local to RescueBox and starts up the Model Severs . This takes several minutes. It also downloads python modules from internet connection. After Model servers are started the progress bar of the install reaches completion and now the Application is ready to use.
Run the model application, which should provide you with a URL to register with RBox.
Step 3: Using the App
Launch the binary you downloaded in step 1, and register the model application.
You should now be able to see the model application in the app, and be able to run inference tasks on it!

Logs:
the UI shows the logs in reverse order ie: more recent messages first. Files are located here : C:\Users<USERNAME>\AppData\Roaming\RescueBox-Desktop\logs
Troubleshooting:
- First look at the eror in the task execution result or the log in the UI.
- Typical errors are due to : image /video /audio files not readable , perhaps due to unsupported format.
- Other errors are due to not able to read from the input path or write to output path.
- rb_py.log tracks pip and pienv cmdlines , about dependencies for model code.
- main.log is the overall rescuebox UI and Model server logs
- other logs are from python installer.
Development
RescueBox Desktop is built using Electron, React, TypeScript, TailwindCSS, and SQlite (with Sequelize).
RescueBox implments the "Flask-ML" protocol, which is a simple interface for running ML models. See Flask-ML Protocol.
Developer Prerequisites
- Node v18 (newer versions may work)
Developer Install
Clone the repo and install dependencies:
bash
git clone https://github.com/UMass-Rescue/RescueBox-Desktop.git
cd RescueBox-Desktop
npm install
Having issues installing? See this debugging guide
Starting Development
Start the app in the dev environment:
bash
npm start
Packaging for Production
To package apps for the local platform:
bash
npm run package
Docs
See electron-react's docs and guides here
Owner
- Name: UMass Rescue
- Login: UMass-Rescue
- Kind: organization
- Repositories: 27
- Profile: https://github.com/UMass-Rescue
Citation (CITATION.md)
Facial Recognition
If you use deepfake ImageViewer in your research for facial recogntion purposes, please cite these publications:
```
@article{serengil2024lightface,
title = {A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
journal = {Bilisim Teknolojileri Dergisi},
volume = {17},
number = {2},
pages = {95-107},
year = {2024},
doi = {10.17671/gazibtd.1399077},
url = {https://dergipark.org.tr/en/pub/gazibtd/issue/84331/1399077},
publisher = {Gazi University}
}
```
```
@inproceedings{serengil2020lightface,
title = {LightFace: A Hybrid Deep Face Recognition Framework},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
booktitle = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
pages = {23-27},
year = {2020},
doi = {10.1109/ASYU50717.2020.9259802},
url = {https://ieeexplore.ieee.org/document/9259802},
organization = {IEEE}
}
```
```
@inproceedings{serengil2021lightface,
title = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
booktitle = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
pages = {1-4},
year = {2021},
doi = {10.1109/ICEET53442.2021.9659697},
url = {https://ieeexplore.ieee.org/document/9659697/},
organization = {IEEE}
}
```
```
@misc{serengil2023db,
title = {An evaluation of sql and nosql databases for facial recognition pipelines},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
year = {2023},
archivePrefix = {Cambridge Open Engage},
doi = {10.33774/coe-2023-18rcn},
url = {https://www.cambridge.org/engage/coe/article-details/63f3e5541d2d184063d4f569}
}
```
```
@InProceedings{Lanzino_2024_CVPR,
author = {Lanzino, Romeo and Fontana, Federico and Diko, Anxhelo and Marini, Marco Raoul and Cinque, Luigi},
title = {Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2024},
pages = {3771-3780}
}
```
```
@article{guo2022join,
title={Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket},
author={Guo, Nianhui and Bethge, Joseph and Meinel, Christoph and Yang, Haojin},
journal={arXiv preprint arXiv:2211.12933},
year={2022}
}
```
GitHub Events
Total
- Issues event: 23
- Delete event: 16
- Issue comment event: 16
- Member event: 2
- Push event: 54
- Pull request review event: 8
- Pull request event: 37
- Create event: 22
Last Year
- Issues event: 23
- Delete event: 16
- Issue comment event: 16
- Member event: 2
- Push event: 54
- Pull request review event: 8
- Pull request event: 37
- Create event: 22
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 6
- Total pull requests: 13
- Average time to close issues: 22 days
- Average time to close pull requests: 2 days
- Total issue authors: 2
- Total pull request authors: 2
- Average comments per issue: 0.83
- Average comments per pull request: 0.15
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 6
- Pull requests: 13
- Average time to close issues: 22 days
- Average time to close pull requests: 2 days
- Issue authors: 2
- Pull request authors: 2
- Average comments per issue: 0.83
- Average comments per pull request: 0.15
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- sahilx13 (14)
- nb950 (1)
Pull Request Authors
- nb950 (14)
- sahilx13 (4)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- cmake *
- flask-ml *
- psutil *
- einops *
- flask-ml *
- fvcore *
- lightning *
- matplotlib *
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- pandas *
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- retina-face *
- scikit-image *
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- scipy *
- tf-keras *
- timm *
- torch *
- torchinfo *
- torchmetrics *
- torchvision *
- 123 dependencies
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- 113 dependencies
- flask-ml *
- whisper *
- 1625 dependencies
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