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) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <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.

Package details

AuthorJuergen Niedballa [aut, cre] (<https://orcid.org/0000-0002-9187-2116>), Jan Axtner [aut] (<https://orcid.org/0000-0003-1269-5586>), Leibniz Institute for Zoo and Wildlife Research [cph]
MaintainerJuergen Niedballa <niedballa@izw-berlin.de>
LicenseMIT + file LICENSE
Version0.5.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("imageseg")

Try the imageseg package in your browser

Any scripts or data that you put into this service are public.

imageseg documentation built on May 30, 2022, 1:07 a.m.