imageseg: Deep Learning Models for Image Segmentation (original) (raw)
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 |
BugReports: | https://github.com/EcoDynIZW/imageseg/issues |
License: | MIT + file |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | imageseg results |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=imagesegto link to this page.