ai4iacts/hexagdly
Process hexagonally sampled data with PyTorch
This tool helps researchers and data scientists working with hexagonally sampled data. It takes your hexagonal data and processes it using specialized convolution and pooling methods within a deep learning framework. The output is processed tensor data that respects the unique symmetries of hexagonal grids, allowing for more accurate analysis by those building deep learning models for such datasets.
No commits in the last 6 months. Available on PyPI.
Use this if you need to apply deep learning techniques like convolutions and pooling to data collected on a hexagonal grid, ensuring the operations respect the natural symmetries of that grid.
Not ideal if your primary concern is maximum computational performance, as this tool prioritizes flexibility for prototyping over raw speed.
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98
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17
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 19, 2021
Commits (30d)
0
Dependencies
3
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