{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hexagdly-processing-hexagonally-sampled-data","title":"HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch","arxiv_id":"1903.01814","date":"2019-03-05","proceeding":null,"authors":["Constantin Steppa","Tim Lukas Holch"],"abstract":"HexagDLy is a Python-library extending the PyTorch deep learning framework\nwith convolution and pooling operations on hexagonal grids. It aims to ease the\naccess to convolutional neural networks for applications that rely on\nhexagonally sampled data as, for example, commonly found in ground-based\nastroparticle physics experiments.","url_abs":"http://arxiv.org/abs/1903.01814v1","url_pdf":"http://arxiv.org/pdf/1903.01814v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hexagdly-processing-hexagonally-sampled-data","repo_url":"https://github.com/ai4iacts/hexagdly","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.01814","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}