{"id":13576143,"url":"https://github.com/ENSTA-U2IS-AI/torch-uncertainty","last_synced_at":"2025-04-05T05:30:53.009Z","repository":{"id":119126813,"uuid":"596176714","full_name":"ENSTA-U2IS-AI/torch-uncertainty","owner":"ENSTA-U2IS-AI","description":"Open-source framework for uncertainty and deep learning models in PyTorch 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align=\"center\"\u003e\n\n![TorchUncertaintyLogo](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/blob/main/docs/source/_static/images/torch_uncertainty.png)\n\n[![pypi](https://img.shields.io/pypi/v/torch_uncertainty.svg)](https://pypi.python.org/pypi/torch_uncertainty)\n[![tests](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/actions/workflows/run-tests.yml/badge.svg?branch=main\u0026event=push)](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/actions/workflows/run-tests.yml)\n[![Docs](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/actions/workflows/build-docs.yml/badge.svg)](https://torch-uncertainty.github.io/)\n[![PRWelcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/pulls)\n[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\n[![Code Coverage](https://codecov.io/github/ENSTA-U2IS-AI/torch-uncertainty/coverage.svg?branch=master)](https://codecov.io/gh/ENSTA-U2IS-AI/torch-uncertainty)\n[![Downloads](https://static.pepy.tech/badge/torch-uncertainty)](https://pepy.tech/project/torch-uncertainty)\n[![Discord Badge](https://dcbadge.vercel.app/api/server/HMCawt5MJu?compact=true\u0026style=flat)](https://discord.gg/HMCawt5MJu)\n\u003c/div\u003e\n\n_TorchUncertainty_ is a package designed to help you leverage [uncertainty quantification techniques](https://github.com/ENSTA-U2IS-AI/awesome-uncertainty-deeplearning) and make your deep neural networks more reliable. It aims at being collaborative and including as many methods as possible, so reach out to add yours!\n\n:construction: _TorchUncertainty_ is in early development :construction: - expect changes, but reach out and contribute if you are interested in the project! **Please raise an issue if you have any bugs or difficulties and join the [discord server](https://discord.gg/HMCawt5MJu).**\n\n:books: Our webpage and documentation is available here: [torch-uncertainty.github.io](https://torch-uncertainty.github.io). :books:\n\nTorchUncertainty contains the *official implementations* of multiple papers from *major machine-learning and computer vision conferences* and was/will be featured in tutorials at **[WACV](https://wacv2024.thecvf.com/) 2024**, **[HAICON](https://haicon24.de/) 2024** and **[ECCV](https://eccv.ecva.net/) 2024**.\n\n---\n\nThis package provides a multi-level API, including:\n\n- easy-to-use :zap: lightning **uncertainty-aware** training \u0026 evaluation routines for **4 tasks**: classification, probabilistic and pointwise regression, and segmentation.\n- ready-to-train baselines on research datasets, such as ImageNet and CIFAR\n- [pretrained weights](https://huggingface.co/torch-uncertainty) for these baselines on ImageNet and CIFAR ( :construction: work in progress :construction: ).\n- **layers**, **models**, **metrics**, \u0026 **losses** available for use in your networks\n- scikit-learn style post-processing methods such as Temperature Scaling.\n\nHave a look at the [Reference page](https://torch-uncertainty.github.io/references.html) or the [API reference](https://torch-uncertainty.github.io/api.html) for a more exhaustive list of the implemented methods, datasets, metrics, etc.\n\n## :gear: Installation\n\nTorchUncertainty requires Python 3.10 or greater. Install the desired PyTorch version in your environment.\nThen, install the package from PyPI:\n\n```sh\npip install torch-uncertainty\n```\n\nThe installation procedure for contributors is different: have a look at the [contribution page](https://torch-uncertainty.github.io/contributing.html).\n\n## :racehorse: Quickstart\n\nWe make a quickstart available at [torch-uncertainty.github.io/quickstart](https://torch-uncertainty.github.io/quickstart.html).\n\n## :books: Implemented methods\n\nTorchUncertainty currently supports **classification**, **probabilistic** and pointwise **regression**, **segmentation** and **pixelwise regression** (such as monocular depth estimation). It includes the official codes of the following papers:\n\n- *LP-BNN: Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification* - [IEEE TPAMI](https://arxiv.org/abs/2012.02818)\n- *Packed-Ensembles for Efficient Uncertainty Estimation* - [ICLR 2023](https://arxiv.org/abs/2210.09184) - [Tutorial](https://torch-uncertainty.github.io/auto_tutorials/tutorial_pe_cifar10.html)\n- *MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks* - [BMVC 2022](https://arxiv.org/abs/2203.01437)\n\nWe also provide the following methods:\n\n### Baselines\n\nTo date, the following deep learning baselines have been implemented. **Click** :inbox_tray: **on the methods for tutorials**:\n\n- [Deep Ensembles](https://torch-uncertainty.github.io/auto_tutorials/tutorial_from_de_to_pe.html), BatchEnsemble, Masksembles, \u0026 MIMO\n- [MC-Dropout](https://torch-uncertainty.github.io/auto_tutorials/tutorial_mc_dropout.html)\n- [Packed-Ensembles](https://torch-uncertainty.github.io/auto_tutorials/tutorial_from_de_to_pe.html) (see [Blog post](https://medium.com/@adrien.lafage/make-your-neural-networks-more-reliable-with-packed-ensembles-7ad0b737a873))\n- [Variational Bayesian Neural Networks](https://torch-uncertainty.github.io/auto_tutorials/tutorial_bayesian.html)\n- Checkpoint Ensembles \u0026 Snapshot Ensembles\n- Stochastic Weight Averaging \u0026 Stochastic Weight Averaging Gaussian\n- Regression with Beta Gaussian NLL Loss\n- [Deep Evidential Classification](https://torch-uncertainty.github.io/auto_tutorials/tutorial_evidential_classification.html) \u0026 [Regression](https://torch-uncertainty.github.io/auto_tutorials/tutorial_der_cubic.html)\n\n### Augmentation methods\n\nThe following data augmentation methods have been implemented:\n\n- Mixup, MixupIO, RegMixup, WarpingMixup\n\n### Post-processing methods\n\nTo date, the following post-processing methods have been implemented:\n\n- [Temperature](https://torch-uncertainty.github.io/auto_tutorials/tutorial_scaler.html), Vector, \u0026 Matrix scaling\n- [Monte Carlo Batch Normalization](https://torch-uncertainty.github.io/auto_tutorials/tutorial_mc_batch_norm.html)\n- Laplace approximation using the [Laplace library](https://github.com/aleximmer/Laplace)\n\n## Tutorials\n\nCheck out our tutorials at [torch-uncertainty.github.io/auto_tutorials](https://torch-uncertainty.github.io/auto_tutorials/index.html).\n\n## :telescope: Projects using TorchUncertainty\n\nThe following projects use TorchUncertainty:\n\n- *A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors* - [ICLR 2024](https://arxiv.org/abs/2310.08287)\n\n**If you are using TorchUncertainty in your project, please let us know, we will add your project to this list!**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FENSTA-U2IS-AI%2Ftorch-uncertainty","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FENSTA-U2IS-AI%2Ftorch-uncertainty","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FENSTA-U2IS-AI%2Ftorch-uncertainty/lists"}