Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use ZQL9711/RMBG-2-Matting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZQL9711/RMBG-2-Matting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="ZQL9711/RMBG-2-Matting", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("ZQL9711/RMBG-2-Matting", trust_remote_code=True, device_map="auto") - Transformers.js
How to use ZQL9711/RMBG-2-Matting with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'ZQL9711/RMBG-2-Matting'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee673e238f49d6e33f0f14edac67fcafee3deedb35d2a34921abc59a65966a75
- Size of remote file:
- 1.02 GB
- SHA256:
- 5b486f08200f513f460da46dd701db5fbb47d79b4be4b708a19444bcd4e79958
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.