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)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("egeorcun/lucida", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class BiRefNetConfig(PretrainedConfig): | |
| model_type = "SegformerForSemanticSegmentation" | |
| def __init__( | |
| self, | |
| bb_pretrained=False, | |
| **kwargs | |
| ): | |
| self.bb_pretrained = bb_pretrained | |
| super().__init__(**kwargs) | |