Instructions to use LibreYOLO/LibreFeyNobgl-matte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use LibreYOLO/LibreFeyNobgl-matte with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("LibreYOLO/LibreFeyNobgl-matte", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("LibreYOLO/LibreFeyNobgl-matte") - Notebooks
- Google Colab
- Kaggle
LibreFeyNobgl-matte
FeyNobg background removal, repackaged for LibreYOLO's matte task. Predicts
a soft alpha matte at a fixed native 1024x1024.
from libreyolo import LibreYOLO
m = LibreYOLO("LibreFeyNobgl-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 feyninc/FeyNobg (nobg library), Apache-2.0, Copyright (c) 2026 Feyn Inc. FeyNobg builds on ZhengPeng7/BiRefNet (MIT, Copyright (c) 2024 ZhengPeng).
Backbone: Swin Transformer v1, Swin-L tier with stage 3 deepened from 18 to 24 blocks (263M parameters). Training data provenance (upstream): not disclosed by Feyn Inc.; this repo redistributes the author's released weights under their Apache-2.0 grant and does not redistribute training data.
Modifications
State-dict key remapping only (fused qkv, renamed modules, wrapped into the
LibreYOLO v1.0 checkpoint schema). Learned parameters are unchanged. Our fp32
forward matches the upstream released weights with max_abs_diff == 0
(weights/parity_feynobg.py). See weights/convert_feynobg_weights.py in the
LibreYOLO source repository.
License
Model tree for LibreYOLO/LibreFeyNobgl-matte
Base model
feyninc/FeyNobg