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DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Alberti Michele, Pondenkandath Vinaychandran, Würsch Marcel, Ingold Rolf, Liwicki Marcus,
Project HisDoc III : Large-Scale Historical Document Classification
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Original article (peer-reviewed)

Journal 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
Publisher IEEE, Niagara Falls
Page(s) 423 - 428
Title of proceedings 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
DOI 10.1109/icfhr-2018.2018.00080

Abstract

We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source, and accessible as Web Service through DIVAServices.
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