Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use mohantesting/remove_background with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohantesting/remove_background with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mohantesting/remove_background", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("mohantesting/remove_background", trust_remote_code=True, dtype="auto") - Transformers.js
How to use mohantesting/remove_background with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'mohantesting/remove_background'); - Notebooks
- Google Colab
- Kaggle
File size: 298 Bytes
ddd449c | 1 2 3 4 5 6 7 8 9 10 11 12 | 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)
|