lucida

Lucida — general-purpose background removal with soft-alpha mastery Lucida is a BiRefNet-based background-removal / image-matting model fine-tuned to excel where most open models fail: **camouflaged objects, transparent materials (glass), text & logos, VFX glows, and illustrations** — while staying competitive everywhere else. On our 203-image, 9-category benchmark (MAE, lower is better) Lucida leads every model we tested — including a commercial reference — in **camouflage (0.0270)** and illustration (0.0092)**, beats the commercial reference in **text/logo preservation (0.0091 vs 0.0123)** and in **print-design/sticker art (0.0235 — 2x better than every model measured)**, and sets our best-ever **transparency-in-mixed-objects** handling and overall (0.0257)** score — ahead of every model we measured, specialist or commercial, on the 203-image average. Full benchmark, gallery and training recipe: https://github.com/egeorcun/lucida** — or try the live demo. **Changelog note (2026-07-24):** an experimental v13 build was published for a day and then reverted — community testing showed it regressed on real-world layered artwork (poster/collage-style illustrations) that our synthetic design test set does not cover. The current weights are the proven v7. The v13 improvements (reduced background haze on real photos, a transparency milestone) will return in v14 together with the fix. Usage For color decontamination (removing background color fringing) and the full pipeline (CLI, FastAPI service, Docker web UI), see the GitHub repository. Base model & attribution Architecture and initial weights: ZhengPeng7/BiRefNet_HR (MIT). Lucida is a fine-tune; the original copyright notice is preserved. Illustration data includes ToonOut (CC-BY 4.0). Some training datasets (e.g. P3M-10k, COD10K, DIS5K) are distributed for research purposes; see the GitHub README for the full dataset/license table and evaluate suitability for your use case. License MIT (weights and code).