Metadata-Version: 2.1
Name: hexagdly
Version: 2.0.2
Summary: Utilising CNNs for hexagonally sampled data with PyTorch
Home-page: https://github.com/ai4iacts/hexagdly
Author: T.L. Holch, C. Steppa
Author-email: holchtim@physik.hu-berlin.de, steppa@uni-potsdam.de
License: MIT
Keywords: hexagonal convolution
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: numpy
Provides-Extra: dev
Requires-Dist: scipy ; extra == 'dev'
Requires-Dist: matplotlib ; extra == 'dev'
Requires-Dist: jupyter ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'

# HexagDLy

HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It can be used to build convolutional neural networks for applications that rely on hexagonally sampled data. More information is avialable on [GitHub](https://github.com/ai4iacts/hexagdly "HexagDLy - Project Page")






