---
license: mit
library_name: libreyolo
pipeline_tag: image-segmentation
tags:
  - background-removal
  - matte
  - dichotomous-image-segmentation
  - ben2
  - libreyolo
---

# LibreBEN2b-matte

BEN2 Base background removal, repackaged for LibreYOLO's `matte` task. It
predicts a soft alpha matte at a fixed native 1024x1024 resolution.

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreBEN2b-matte.pt")
result = model.predict("product.jpg")
result[0].matte.array      # (H, W) float alpha in [0, 1]
result[0].save("cut.png")  # transparent-background PNG
```

## Source

Derived from [PramaLLC/BEN2](https://github.com/PramaLLC/BEN2) at commit
`2c99a5da477b5523585bfa5c893888a6e818a8f6`, using the released checkpoint from
[PramaLLC/BEN2](https://huggingface.co/PramaLLC/BEN2) at revision
`e48a20765fb421d19dcdb0bf3cc61e802ca5ec8f`.
Copyright (c) 2025 Prama LLC. Licensed under the MIT License.

Training data provenance (upstream): DIS5K and Prama LLC's proprietary 22K
segmentation dataset. This repository redistributes only the released MIT
checkpoint and does not redistribute training data.

## Modifications

State-dict metadata wrap only. Learned parameters are unchanged. The native
LibreYOLO fp32 forward matches the released BEN2 Base network with
`max_abs_diff == 0` for batch sizes 1 and 2. See
`weights/convert_ben2_weights.py` in the
[LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).

The port returns raw logits for LibreYOLO's shared matte postprocessing and
does not include BEN2's optional media or foreground-refinement helpers.

## License

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