Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
remove background
background
background-removal
Pytorch
vision
legal liability
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:
- 11b4080f109e2358866a55b58973dc0c6cc64d73edb8c218dff1b006ce03aecb
- Size of remote file:
- 44.4 MB
- SHA256:
- a6648479275dfd0ede0f3a8abc20aa5c437b394681b05e5af6d268250aaf40f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.