torch-uncertainty/Checkpoints

The Checkpoints dataset as trained and used in A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors 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:

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 - Documentation. Install the desired version of PyTorch and torchvision, for instance with:

pip install torch torchvision

Then, install TorchUncertainty via pip:

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 or on our Discord server.