Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Pytorch
vision
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custom_code
Instructions to use frederikboisen/RMBG-1.4-wardrobe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frederikboisen/RMBG-1.4-wardrobe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="frederikboisen/RMBG-1.4-wardrobe", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("frederikboisen/RMBG-1.4-wardrobe", trust_remote_code=True, dtype="auto") - Transformers.js
How to use frederikboisen/RMBG-1.4-wardrobe with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'frederikboisen/RMBG-1.4-wardrobe'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 080ceffcac72259d9d6a43653294224d09926f8cc55f4ba4422214b5b65a20e2
- Size of remote file:
- 176 MB
- SHA256:
- 8cafcf770b06757c4eaced21b1a88e57fd2b66de01b8045f35f01535ba742e0f
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