Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
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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:
- dc0b6b93911ae956f34d72352a1613cb0701f87280e7ab8205a200c752efa839
- Size of remote file:
- 234 MB
- SHA256:
- 8bfeb5f93220eb19f6747c217b62cf04342840c4e973f55bf64e9762919f446d
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