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
| { | |
| "_name_or_path": "u2net", | |
| "model_type": "u2net", | |
| "architectures": [ | |
| "U2NetModel" | |
| ], | |
| "transformers.js_config": { | |
| "dtype": "fp32" | |
| }, | |
| "input_name": ["input.1"], | |
| "input_shape": [1, 3, 320, 320], | |
| "output_composite": "1959", | |
| "output_names": [ | |
| "1959", | |
| "1960", | |
| "1961", | |
| "1962", | |
| "1963", | |
| "1964", | |
| "1965" | |
| ], | |
| "output_shape": [1, 320, 320] | |
| } | |