Initial upload: LibreBiRefNetl-matte (BiRefNet, MIT)

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LICENSE

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+MIT License
+
+Copyright (c) 2024 ZhengPeng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LibreBiRefNetl-matte.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f5e9814e1702023d357cffd90bd8a500e5a34a3f73b0297e83167006dde72b9
+size 885083867

NOTICE

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+LibreBiRefNetl-matte weights
+------------------------------------
+
+This product contains weights derived from BiRefNet
+(https://github.com/ZhengPeng7/BiRefNet).
+Copyright (c) 2024 ZhengPeng (Peng Zheng).
+Licensed under the MIT License.
+
+Conversion is a state-dict metadata-wrap: learned parameters are unchanged.
+See weights/convert_birefnet_weights.py in the LibreYOLO source repository.

README.md

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+---
+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.