imageseg: Deep Learning Models for Image Segmentation
A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <doi:10.48550/arXiv.1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <doi:10.48550/arXiv.1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.
| Version: | 0.5.0 |
| Imports: | grDevices,keras,magick,magrittr, methods,purrr, stats,tibble,foreach, parallel,doParallel,dplyr |
| Suggests: | R.rsp,testthat |
| Published: | 2022-05-29 |
| DOI: | 10.32614/CRAN.package.imageseg |
| Author: | Juergen Niedballa [aut, cre], Jan Axtner [aut], Leibniz Institute for Zoo and Wildlife Research [cph] |
| Maintainer: | Juergen Niedballa <niedballa at izw-berlin.de> |
| BugReports: | https://github.com/EcoDynIZW/imageseg/issues |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | imageseg results |
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