README.md

metadata

license: mit
library_name: libreyolo
pipeline_tag: image-segmentation
tags:
  - background-removal
  - matte
  - dichotomous-image-segmentation
  - birefnet
  - libreyolo

LibreBiRefNetl-matte

BiRefNet background removal (BiRefNet general (Swin-L tier), the quality default), repackaged for LibreYOLO's matte task. Predicts a soft alpha matte at a fixed native 1024x1024.

from libreyolo import LibreYOLO

m = LibreYOLO("LibreBiRefNetl-matte.pt")
res = m.predict("product.jpg")
res[0].matte            # (H, W) float alpha in [0, 1]
res[0].save("cut.png")  # transparent-background PNG

Source

Derived from ZhengPeng7/BiRefNet at commit d83f355. Copyright (c) 2024 ZhengPeng (Peng Zheng). Licensed under the MIT License.

Backbone: Swin Transformer v1 (Swin-L). Training data provenance (upstream): the BiRefNet DIS/General checkpoints are trained on dichotomous-image-segmentation datasets (e.g. DIS5K) under their own academic terms; this repo hosts the author's released weights and does not redistribute training data.

Modifications

State-dict key remapping only (metadata-wrap into the LibreYOLO v1.0 checkpoint schema). Learned parameters are unchanged. Our fp32 forward matches the upstream released weights with max_abs_diff == 0. See weights/convert_birefnet_weights.py in the LibreYOLO source repository.

License

MIT License. See the LICENSE and NOTICE files.