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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.
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