| --- |
| license: mit |
| pipeline_tag: image-segmentation |
| base_model: ZhengPeng7/BiRefNet_HR |
| datasets: |
| - joelseytre/toonout |
| tags: |
| - background-removal |
| - image-matting |
| - BiRefNet |
| - transparency |
| - camouflage |
| - text-preservation |
| - illustration |
| - rgba |
| library_name: transformers |
| --- |
| |
| # 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 191-image, 8-category benchmark (MAE, lower is better) Lucida leads every |
| model we tested — including a commercial reference — in **camouflage (0.0273)** and |
| **illustration (0.0095)**, matches the commercial reference in **text/logo |
| preservation (0.0126)**, and sets our best-ever **transparency (0.0376)** and |
| **overall (0.0304)** scores. Full benchmark, gallery and training recipe: |
| **https://github.com/egeorcun/lucida** — or try the [live demo](https://huggingface.co/spaces/egeorcun/lucida-demo). |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from PIL import Image |
| from torchvision import transforms |
| from transformers import AutoModelForImageSegmentation |
| |
| model = AutoModelForImageSegmentation.from_pretrained( |
| "egeorcun/lucida", trust_remote_code=True, dtype=torch.float32) |
| model.eval() |
| |
| t = transforms.Compose([ |
| transforms.Resize((1024, 1024)), |
| transforms.ToTensor(), |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
| ]) |
| img = Image.open("input.jpg").convert("RGB") |
| with torch.no_grad(): |
| preds = model(t(img).unsqueeze(0))[-1].sigmoid() |
| alpha = transforms.functional.resize(preds[0], img.size[::-1]).squeeze(0) |
| rgba = img.copy() |
| rgba.putalpha(Image.fromarray((alpha.numpy() * 255).astype("uint8"))) |
| rgba.save("output.png") |
| ``` |
|
|
| 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](https://huggingface.co/ZhengPeng7/BiRefNet_HR) (MIT). Lucida is a fine-tune; the original copyright notice is preserved. |
| - Illustration data includes [ToonOut](https://huggingface.co/datasets/joelseytre/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). |
|
|
