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cff-version: 1.2.0
message: 'To cite package "rembg" in publications use:'
type: software
license: MIT
title: 'rembg: Remove Image Backgrounds with Pre-Trained Segmentation Models'
version: 0.1.1
abstract: 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.
authors:
- family-names: Hernandez
  given-names: Troy
  email: troy@cornball.ai
  orcid: https://orcid.org/0009-0005-4248-604X
repository: https://cornball-ai.r-universe.dev
repository-code: https://github.com/cornball-ai/rembg
commit: a55bc85cab9771412019f62e838eac0a061adb25
url: https://github.com/cornball-ai/rembg
date-released: '2026-07-13'
contact:
- family-names: Hernandez
  given-names: Troy
  email: troy@cornball.ai
  orcid: https://orcid.org/0009-0005-4248-604X