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---
license: apache-2.0
tags:
- vision
- checkpoints
- residual-networks
pretty_name: Checkpoints
---
The Checkpoints dataset as trained and used in [A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors](https://arxiv.org/abs/2310.08287) published at ICLR 2024. All models all trained and uploaded in a float16 format to reduce the memory footprint.
## Usage
### Untar the models
Just untar the desired models available in `models`, for instance with:
```bash
tar -xvf models/cifar10-resnet18/cifar10-resnet18-0-1023.tgz
```
Most of them are regrouped in tar files containing 1024 models each. This will create a new folder containing the models saved as safetensors.
### TorchUncertainty
To load or train models, start by downloading [TorchUncertainty](https://github.com/ENSTA-U2IS-AI/torch-uncertainty) - [Documentation](https://torch-uncertainty.github.io/).
Install the desired version of PyTorch and torchvision, for instance with:
```bash
pip install torch torchvision
```
Then, install TorchUncertainty via pip:
```bash
pip install torch-uncertainty
```
### Loading models
The functions to load the models are available in `scripts`.
**Any questions?** Please feel free to ask in the [GitHub Issues](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/issues) or on our [Discord server](https://discord.gg/HMCawt5MJu).
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