This is a framework to gain insight into pytorch deep neural networks for visual recognition tasks.
The README.md
contains only a very brief description - for details, visit the
--> full documentation and apidoc <--
The project is split into the following parts, with the linked jupyter notebooks explaining them:
- [uncertainty](notebooks/Monte-Carlo Uncertainty.ipynb): confidence of predictions
- visualization of network properties.
- base building blocks in a consistent and flexible interface
- [compound](notebooks/Compound Visualization.ipynb) methods implementing 'standard' visualization methods
To follow the examples interactively, clone the repository and run poetry install
.
Then start jupyter with poetry run jupyter notebook
.
- uncertainties
- predictive entropy (total uncertainty).
- mutual information (model uncertainty).
- building blocks
- visualization
From package index: pip install "midnite"
or from source:
git clone https://gitlab.com/luminovo/public/midnite.git
cd midnite
poetry build
pip install dist/midnite-*.whl
We value clean interfaces and well-tested code. If you want to contribute, usually it's best to open an issue first.
We use poetry to manage dependencies.
Please make sure to have the pre-commit hooks installed.
Install pre-commit and then run pre-commit install
to register the hooks with git.
We use make to streamline our development workflow.
Run make help
to see all available commands.
$ make help
help Show this help message
check Run all static checks (like pre-commit hooks)
docs Build all docs
This project is under the MIT license.
Scientific sources: see reference doc page.
This project was developed as a student IDP at luminovo.ai
Code contributors:
- Fabian Huch
- Christina Aigner