HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch
This work addresses a domain-specific problem for researchers in fields such as astroparticle physics who use hexagonally sampled data, but it is incremental as it extends existing frameworks with specialized operations.
The authors tackled the problem of applying convolutional neural networks to hexagonally sampled data by developing HexagDLy, a PyTorch library that introduces convolution and pooling operations for hexagonal grids, enabling easier access for applications like astroparticle physics.
HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments.