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LibreYOLO/LibreViTMattes-matte
LibreViTMattes-matte is a image segmentation model from LibreYOLO. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for libreyolo. The card lists the license as other.
NON-COMMERCIAL. This checkpoint was trained on Adobe Composition-1k. Adobe's Deep Image Matting Dataset License Agreement says models may only be used and distributed for non-commercial purposes and may not be sold. P…
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Updated Aug 16, 2026
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From the Hugging Face model README
NON-COMMERCIAL. This checkpoint was trained on Adobe Composition-1k.
Adobe's Deep Image Matting Dataset License Agreement says models may only be
used and distributed for non-commercial purposes and may not be sold.
Permitted distribution must retain the required Deep Image Matting CVPR 2017
attribution. Read LICENSE before use or redistribution.
The restriction applies to this pretrained checkpoint, not to LibreYOLO's MIT code or to independently trained ViTMatte weights with suitable provenance.
LibreViTMatte provides trimap-guided alpha matting under LibreYOLO's existing
matte task. The trimap must use exactly 0/128/255 (or normalized
0/0.5/1) for known background, unknown, and known foreground.
from libreyolo import LibreYOLO
model = LibreYOLO("LibreViTMattes-matte.pt")
result = model("portrait.jpg", trimap="trimap.png")
alpha = result.matte.array
The result is a soft float32 alpha matte on the source canvas. Known background and foreground pixels are forced to exactly zero and one.
6a58ad7646403c1df626fbd746900aec7361ea1dmodel.safetensors, 103,294,572 bytesbda9289db1bb6762d978b42d1c62ae3f34daf7497171a347a1d09657efd788cb20e5e41b601380d65ab7d685520ceba7b94456d097f1fbf2f3b8dc87ab0e27c67d6354e04794f3246bf9a0faf4fead080edeebb6 (Apache-2.0)f48208ceb0d172ab0c1c528bba33cc1159596407 (MIT)Learned tensors are unchanged. Conversion adds LibreYOLO v1 checkpoint metadata. Preprocessing, raw alpha, and constrained-alpha parity with the pinned Transformers reference are bit-exact.
Ning Xu, Brian Price, Scott Cohen, and Thomas Huang. Deep Image Matting. Proceedings of the Conference on Computer Vision and Pattern Recognition, 2017.
See NOTICE for complete model and code provenance.