Image Segmentation
Transformers
Safetensors
background-removal
image-matting
BiRefNet
transparency
camouflage
text-preservation
illustration
rgba
custom_code
Instructions to use egeorcun/lucida with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egeorcun/lucida with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="egeorcun/lucida", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("egeorcun/lucida", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from egeorcun/lucida: direct link, hf CLI and curl.
- Browser
- Download file 367 Bytes
-
https://huggingface.co/egeorcun/lucida/resolve/main/config.json
- Command line
-
hf download hf://egeorcun/lucida/config.json
-
curl -L -o config.json https://huggingface.co/egeorcun/lucida/resolve/main/config.json
367 Bytes
| { | |
| "_name_or_path": "egeorcun/lucida", | |
| "architectures": [ | |
| "BiRefNet" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "BiRefNet_config.BiRefNetConfig", | |
| "AutoModelForImageSegmentation": "birefnet.BiRefNet" | |
| }, | |
| "custom_pipelines": { | |
| "image-segmentation": { | |
| "pt": [ | |
| "AutoModelForImageSegmentation" | |
| ], | |
| "tf": [], | |
| "type": "image" | |
| } | |
| }, | |
| "bb_pretrained": false | |
| } |