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) . 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.


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0.4.0 by Juergen Niedballa, a month ago

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Authors: Juergen Niedballa [aut, cre] , Jan Axtner [aut] , Leibniz Institute for Zoo and Wildlife Research [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports grDevices, keras, magick, magrittr, methods, purrr, stats, tibble, foreach, parallel, doParallel, dplyr

Suggests R.rsp, testthat

See at CRAN