Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use briaai/RMBG-1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briaai/RMBG-1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True, dtype="auto") - Transformers.js
How to use briaai/RMBG-1.4 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'briaai/RMBG-1.4'); - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 805f50e7943f8be300f41f002e14420fbac3d2e5398b0aa796ca6537bcb7df68
- Size of remote file:
- 2.16 MB
- SHA256:
- 43a9453f567d9bff7fe4481205575bbf302499379047ee6073247315452ba8fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.