---
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.

```python
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](https://github.com/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](https://github.com/LibreYOLO/libreyolo).

## License

MIT License. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files.