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:
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