| ---
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| license: mit
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| library_name: libreyolo
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| pipeline_tag: image-segmentation
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| tags:
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| - background-removal
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| - matte
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| - dichotomous-image-segmentation
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| - birefnet
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| - libreyolo
|
| ---
|
|
|
| # LibreBiRefNetl-matte
|
|
|
| BiRefNet background removal (BiRefNet general (Swin-L tier), the quality default), repackaged for LibreYOLO's
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| `matte` task. Predicts a soft alpha matte at a fixed native 1024x1024.
|
|
|
| ```python
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| from libreyolo import LibreYOLO
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|
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| m = LibreYOLO("LibreBiRefNetl-matte.pt")
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| res = m.predict("product.jpg")
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| res[0].matte # (H, W) float alpha in [0, 1]
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| res[0].save("cut.png") # transparent-background PNG
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| ```
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|
|
| ## Source
|
|
|
| Derived from [ZhengPeng7/BiRefNet](https://github.com/ZhengPeng7/BiRefNet)
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| at commit d83f355.
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| Copyright (c) 2024 ZhengPeng (Peng Zheng). Licensed under the MIT License.
|
|
|
| Backbone: Swin Transformer v1 (Swin-L).
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| Training data provenance (upstream): the BiRefNet DIS/General checkpoints are
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| trained on dichotomous-image-segmentation datasets (e.g. DIS5K) under their own
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| academic terms; this repo hosts the author's released weights and does not
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| redistribute training data.
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|
|
| ## Modifications
|
|
|
| State-dict key remapping only (metadata-wrap into the LibreYOLO v1.0 checkpoint
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| schema). Learned parameters are unchanged. Our fp32 forward matches the upstream
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| released weights with `max_abs_diff == 0`. See
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| `weights/convert_birefnet_weights.py` in the
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| [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).
|
|
|
| ## License
|
|
|
| MIT License. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files.
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|
|
