# deepinv

> DeepInverse is the leading open-source PyTorch-based library for solving imaging inverse problems with deep learning. It provides imaging operators, pretrained reconstruction networks and denoisers, plug-and-play and unfolded optimization, sampling algorithms, training losses and datasets.


deepinverse contributors 2026

## Pages in this subsection

- [Trainer](https://deepinv.org/user_guide/training/trainer.html.md): Training a reconstruction model can be done using the `deepinv.Trainer` class, which can be easily c...
- [Datasets](https://deepinv.org/user_guide/training/datasets.html.md): The datasets module lets you use datasets with DeepInverse, for testing and training.
- [Training Losses](https://deepinv.org/user_guide/training/loss.html.md): This module contains popular training losses for supervised and self-supervised learning,
- [Metrics](https://deepinv.org/user_guide/training/metric.html.md): This module contains popular metrics for inverse problems.
- [Transforms](https://deepinv.org/user_guide/training/transforms.html.md): This module contains different transforms which can be used for data augmentation or together with t...
- [Using Multiple GPUs](https://deepinv.org/user_guide/training/multigpu.html.md): Since all deepinv building blocks inherit from `torch.nn.Module` , they are compatible with torch da...

## Optional

- [Top-level llms.txt](https://deepinv.org/llms.txt): Complete documentation index.