Software
HEAL-SWIN is an extension of the popular vision-transformer SWIN, capable of operating on high-dimensional spherical data. HEAL-SWIN uses the HEALPix grid and has only minimal computational overhead compared to the baseline SWIN model.
We provide a reference implementation for HEAL-SWIN focused on applications for spherical images, in particular those taken with fisheye lenses. We include a containerized training framework based on PyTorch Lightning and parameter logging with MLFlow. SLURM-based jobscripts for published reference runs and baselines are also available.
This package implements the NTK and NNGP recursions of several group convolutional layers enabling the analysis of the infinite-width training dynamics of equivariant neural networks. The package is written as an extension to the neural-tangents library and is implemented in JAX. Supported symmetry groups are the planar roto-translation group \(C_4 \ltimes \mathbb{R}^2\) and the group of 3D rotations \(\mathrm{SO}(3)\).
