briaai / RMBG-2.0

Overview

RMBG-2.0 is an image segmentation model by BRIA AI for background removal. It predicts an 8-bit alpha matte, not a hard binary mask. Each pixel gets an opacity value from 0 to 255. That matte becomes a clean cutout with smooth edges. It helps you ship consistent product and portrait cutouts without manual masking.

What you can build

• eCommerce product cutouts with soft edges for hair, fur, and semi-transparent objects
• Sticker assets for social posts, chat apps, and thumbnail designs
• Batch background cleanup for catalog photos and marketplace listings
• Preprocessing for design pipelines like background replacement, shadows, and compositing
• Training data prep for vision tasks that need isolated foreground subjects

Inputs

• Upload 1 to 30 images for background removal in a single run.
• Use high-resolution JPEG or PNG for best edge quality.
• If you need transparency in the final asset, start from a PNG when possible.

Outputs

• Returns a background-removed image for each input.
• The output includes an alpha channel derived from the predicted 8-bit matte.
• Edges keep partial transparency, so cutouts blend better on new backgrounds.
• Output size typically matches the original image dimensions.

Recommended settings

• Feed the highest-resolution source you have. Fine details improve with more pixels.
• Prefer PNG inputs when you already have meaningful transparency.
• If you need a hard cutout, post-process the alpha matte with a single threshold (0–255).

Limitations

• Very thin structures can fray. Expect issues with hair, fur, veils, and motion blur.
• Semi-transparent materials can be tricky. Glass, smoke, and water may look imperfect.
• Low-quality inputs reduce accuracy. Heavy JPEG artifacts and low contrast hurt edges.
• Scanned photos, screenshots with compression, and busy patterns can create halos.
• Model weights published for self-hosting are restricted by BRIA AI’s non-commercial license terms.

Safety & compliance

• BRIA AI trains this model on licensed data and markets it for safer commercial workflows.
• Don’t use background removal to mislead viewers about real events or identities.
• Respect image rights and privacy. Get consent for people in photos when required.
• Check your license terms before any commercial deployment of self-hosted weights.