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rembg: Remove Image Backgrounds with Pre-Trained Segmentation Models
Remove the background from an image using pre-trained deep
learning segmentation models ('U-2-Net', 'ISNet', 'BiRefNet' and others)
run through the 'ONNX' Runtime via the 'onnxr' package. Given an image, a
model predicts a foreground alpha matte which is composited into a cutout
with a transparent (or solid-colour) background; optional closed-form alpha
matting (ported from 'pymatting') refines soft edges. An R port of the
Python 'rembg' package (<https://github.com/danielgatis/rembg>). Models are
downloaded on first use and cached in a per-user cache directory.
| Version: |
0.1.1 |
| Imports: |
onnxr, jpeg, png, Matrix, tools, utils |
| Suggests: |
tinytest, openssl |
| Published: |
2026-07-22 |
| DOI: |
10.32614/CRAN.package.rembg (may not be active yet) |
| Author: |
Troy Hernandez 
[aut, cre],
cornball.ai [cph],
Daniel Gatis [cph] (Author of the Python 'rembg' package this is ported
from) |
| Maintainer: |
Troy Hernandez <troy at cornball.ai> |
| BugReports: |
https://github.com/cornball-ai/rembg/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/cornball-ai/rembg |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
rembg results |
Documentation:
Downloads:
Linking:
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https://CRAN.R-project.org/package=rembg
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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