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This is a web application utilizing EQUINE for neural network uncertainty quantification through a visual user interface. The webapp allows you to upload your own model and data, and let the server retrain the model with EQUINE. The visualization dashboard also allows you to analyze your samples and view uncertainty quantification visualizations to explain model uncertainty.
The EQUINE repository is here https://github.com/mit-ll-responsible-ai/equine
We deployed the frontend application to GitHub pages. You can view a static demo of ScatterUQ here:
https://mit-ll-responsible-ai.github.io/equine-webapp/demo
Our data and analysis script can be found in this release:
https://github.com/mit-ll-responsible-ai/equine-webapp/releases/tag/ScatterUQ-VIS-2023-Data
- Install node packages
cd client
npm i
- Start the development server
npm run dev
- Create a new Anaconda environment
conda create --name equine-webapp python=3.10
- Activate your new environment
conda activate equine-webapp
- Install the necessary packages from pip
pip install -r requirements.txt
- Start the Flask server
python start_server.py
If you'd like to run our flask tests:
- Install pytest
pip install pytest
- Run pytest
pytest
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
© 2023 MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014)
- SPDX-License-Identifier: MIT
This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.
The software/firmware is provided to you on an As-Is basis.