| 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`. The script corresponding to Tiny-ImageNet also contains a snippet to evaluate the accuracy of a downloaded model. | |
| **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). | |