| --- |
| 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. |
|
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| ## Usage |
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| ### Tar |
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| Just untar the desired models. This will create a new folder containing the models saved as safetensors. |
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| ### TorchUncertainty |
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| To load or train models, start by downloading [TorchUncertainty](https://github.com/ENSTA-U2IS-AI/torch-uncertainty) - [Documentation](https://torch-uncertainty.github.io/). |
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| Install the desired version of PyTorch and torchvision, for instance with: |
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| ```bash |
| pip install torch torchvision |
| ``` |
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| Then, install TorchUncertainty via pip: |
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| ```bash |
| pip install torch-uncertainty |
| ``` |
|
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| ### Loading models |
| The functions to load the models are available in `scripts`. |
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| **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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