BiRefNet — Segmentation AI Model

BiRefNet removes backgrounds by isolating the single most salient subject, holding edges clean through hair, fur and translucent fabric where most cutout tools smear. One subject per pass, one to three seconds.

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Details

  • CategorySegmentation
  • Year2024
  • LicenseMIT

Compliance & Provenance

  • ProviderOpen-source · Specialized
  • EU AI Act RiskMinimal Risk
  • Art. 50 TransparencyNot applicable

Inputs & Outputs

  • ImageInput · image

    Source image — BiRefNet detects the most salient subject (foreground)

  • ImageOutput · image

    Segmentation overlay (subject in color, background black); with extract_foreground enabled, a transparent-background RGBA cutout of the subject instead

  • TextOutput · string

    Segment metadata — connect to Masking-Image-Masker-By-Class with class 'subject' or 'object_0' to obtain a binary mask

Tags

  • segmentation
  • background-removal
  • salient-object
  • high-resolution
  • single-subject
  • gpu-light
  • fast

Alternatives in Segmentation

  • RF-DETR Seg Medium (Scene)

    Real-time instance segmentation on 80 COCO classes (RF-DETR Seg Medium).

  • SAM2 (Scene)

    Segment every object in an image (no labels needed). 10-30s.

  • SAM3 (Scene)

    Language-prompted segmentation (e.g. "person", "red car"). 10-30s/class.

  • SAM3.1 (Scene)

    Language-prompted segmentation — SAM 3.1 Object Multiplex with improved accuracy over SAM3. 10-30s/class.

Resources