FeyNobg

Install

FeyNobg needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

The checkpoint downloads from the LibreYOLO organization on Hugging Face on first use and is cached locally, the same as any other family, though it is not yet listed in the Checkpoints table on this page.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreFeyNobgl-matte.pt")result = model(SAMPLE_IMAGE, save=True) matte = result.matteprint(matte.array.shape, matte.array.dtype)

libreyolo predict model=LibreFeyNobgl-matte.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreFeyNobgl-matte.pt")result = model(SAMPLE_IMAGE) # RGBA (H, W, 4) uint8: source RGB plus the matte as an alpha channel.rgba = result.cutout()result.save("subject.png")

A matte result carries no boxes; result.matte is a dense (H, W) float32 array in [0, 1], 1 fully foreground and 0 fully background. Unlike a binary mask, the soft matte keeps anti-aliased edge detail such as hair and fur. result.cutout() composites the source image with that alpha channel into an RGBA array, and result.save(path) (or save=True on the predict call) writes it straight to a transparent-background PNG. The model runs at a fixed native 1024x1024 canvas; a different resolution is not supported, because the Swin backbone's relative-position tables are tied to it, and a mismatch interpolates them badly rather than raising an error. See prediction for sources, streaming and result handling.

Variants

One published size, l, a Swin-L tier backbone. FeyNobg takes BiRefNet's architecture and deepens its third Swin stage from 18 to 24 blocks before retraining, so the LibreYOLO port reuses BiRefNet's forward path, preprocessing and single-logit output contract; predict, validate and checkpoint handling behave the same as the birefnet family.

Validate

val() reports two metrics over a paired image/matte folder, both in [0, 1] and independent of resolution: MAE, the mean absolute error against the ground-truth alpha (lower is better), and S-measure (Fan et al., ICCV 2017), a structural similarity that credits preserving the subject's shape and holes, which pixel MAE alone misses (higher is better). Validation drives the model's own predict, so it uses the family's exact preprocessing.

from libreyolo import LibreYOLO model = LibreYOLO("LibreFeyNobgl-matte.pt") # A directory containing images/ and an auto-detected matte directory# (mattes/, matte/, gt/, masks/, mask/ or alpha/) also works in place# of a dataset YAML.metrics = model.val(data="my-matte-dataset/") print(metrics["metrics/MAE"])print(metrics["metrics/Smeasure"])

Validation is inference-only. The upstream nobg library ships Apache-2.0 training code; fine-tuning today means training there and converting the result with LibreYOLO's own conversion script, not calling train() on this family, which raises rather than running a partial trainer.

Licensing

Citation

@software{nobg,
  title={nobg: Open Source Background Removal Models for Image and Video Matting},
  author={Hichri, Hafedh},
  year={2026},
  url={https://github.com/feyninc/nobg},
  license={Apache-2.0},
}