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  "Title": "Deep Learning Models for Image Segmentation",
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  "Maintainer": "Juergen Niedballa <camtrapr@gmail.com>",
  "Description": "A general-purpose workflow for image segmentation using\nTensorFlow models based on the U-Net architecture by\nRonneberger et al. (2015) <doi:10.48550/arXiv.1505.04597> and\nthe U-Net++ architecture by Zhou et al. (2018)\n<doi:10.48550/arXiv.1807.10165>. We provide pre-trained models\nfor assessing canopy density and understory vegetation density\nfrom vegetation photos. In addition, the package provides a\nworkflow for easily creating model input and model\narchitectures for general-purpose image segmentation based on\ngrayscale or color images, both for binary and multi-class\nimage segmentation.",
  "License": "MIT + file LICENSE",
  "BugReports": "https://github.com/EcoDynIZW/imageseg/issues",
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  "Author": "Juergen Niedballa [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-9187-2116>), Jan Axtner [aut]\n(ORCID: <https://orcid.org/0000-0003-1269-5586>), Leibniz\nInstitute for Zoo and Wildlife Research [cph]",
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