Image Segmentation
Transformers
ONNX
Transformers.js
English
u2net
mask-generation
vision
background-removal
portrait-matting
Instructions to use LEO-LLLL/U-2-Net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LEO-LLLL/U-2-Net with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="LEO-LLLL/U-2-Net")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LEO-LLLL/U-2-Net", dtype="auto") - Transformers.js
How to use LEO-LLLL/U-2-Net with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'LEO-LLLL/U-2-Net'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8449d449b8e34688b11cb2dbf3df40833ecea9ac6faff1e3ffee96dcce696366
- Size of remote file:
- 176 MB
- SHA256:
- 8d10d2f3bb75ae3b6d527c77944fc5e7dcd94b29809d47a739a7a728a912b491
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