torch-uncertainty 0.1.2

A PyTorch Library for benchmarking and leveraging efficient predictive uncertainty quantification techniques.

TorchUncertainty is a package designed to help you leverage uncertainty quantification techniques and make your neural networks more reliable. It is based on PyTorch Lightning to handle multi-GPU training and inference and automatic logging through tensorboard.

:construction: TorchUncertainty is in early development :construction: - expect massive changes but do reach out to us and contribute if you are interested by the project!

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This package provides a multi-level API, including:

• ready-to-train baselines on research datasets, such as ImageNet and CIFAR
• baselines available for training on your datasets
• pretrained weights for these baselines on ImageNet and CIFAR (work in progress 🚧).
• layers available for use in your networks
• post-processing methods such as temperature scaling

Installation

The package can be installed from PyPI:

pip install torch-uncertainty

To contribute, install the package from source following the instructions of the dedicated page in the documentation and activate the pre-commit hooks with pre-commit install.

Getting Started and Documentation

Please find the documentation at torch-uncertainty.github.io.

A quickstart is available at torch-uncertainty.github.io/quickstart.

Implemented methods

Baselines

To date, the following baselines are implemented:

• Deep Ensembles
• BatchEnsemble
• Masksembles
• Packed-Ensembles (see blog post)

Post-processing methods

To date, the following post-processing methods are implemented:

• Temperature scaling

Tutorials

Awesome Uncertainty repositories

You may find a lot of information about modern uncertainty estimation techniques on the Awesome Uncertainty in Deep Learning.

Other References

This package also contains the official implementation of Packed-Ensembles.

If you find the corresponding models interesting, please consider citing our paper:

@inproceedings{laurent2023packed,
    title={Packed-Ensembles for Efficient Uncertainty Estimation},
    author={Laurent, Olivier and Lafage, Adrien and Tartaglione, Enzo and Daniel, Geoffrey and Martinez, Jean-Marc and Bursuc, Andrei and Franchi, Gianni},
    booktitle={ICLR},
    year={2023}
}