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:
- fb0f06710ea1d30292d511bbfbf9bb4c5062223a8273c0d500f3e92af7fd9a6a
- Size of remote file:
- 367 MB
- SHA256:
- a813e0eab56c982b71254214f41fa860cc7b565a6f2aab55d1f99f41c646ece1
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