BiRefNet
Produces one high-resolution foreground mask per image, holding edges on thin structures.
- Field
- Cross-domain
- Method
- Deep learning
- Data it takes
- imagery
- License
- MIT
BiRefNet returns the salient foreground of an image at full resolution, with clean boundaries on hair, twigs, antennae and grain edges that coarser segmenters smear.
It takes no prompt and no class list, and it returns a single mask. That suits separating a specimen from its background before measurement, or cutting an object out of a field photograph.
Where the target has to be named, Grounding DINO and SAM 2 are the prompted alternatives in the catalog.
Catalog entry last checked 2026-09-08. All models →
Run BiRefNet on your data
The lab runs this on its own compute, in a container, with the inputs and parameters recorded alongside the result. Initial scoping conversations are free.
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