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Skydata

U-2-Netp is a lightweight version of the U2Net model designed for efficient and effective image segmentation tasks, especially for generating masks. It retains the core architectural design of U2Net while being...

Sourcehuggingface.co/Skydata001/Skydata/tree/f7dd3c8d1531749650259e364d4b7ff1b6adcbf1
TypeOtherVersionRevision f7dd3c8BClasse

Source du modèle

Type
Other
Taille du fichier
4,36 MB
Version
vRevision f7dd3c8
Mis à jour
20 oct. 2026
Téléchargements
18
Source
Hugging Face

Description de la source

U-2-Netp

Model Description

U-2-Netp is a lightweight version of the U2Net model designed for efficient and effective image segmentation tasks, especially for generating masks. It retains the core architectural design of U2Net while being optimized for faster inference times and reduced memory usage.

Usage

Perform mask generation with BritishWerewolf/U-2-Netp.

Example

import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';

const img_url = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png';
const image = await RawImage.read(img_url);

const processor = await AutoProcessor.from_pretrained('BritishWerewolf/U-2-Netp');
const processed = await processor(image);

const model = await AutoModel.from_pretrained('BritishWerewolf/U-2-Netp', {
    dtype: 'fp32',
});

const output = await model({ input: processed.pixel_values });
// {
//   mask: Tensor {
//     dims: [ 1, 320, 320 ],
//     type: 'uint8',
//     data: Uint8Array(102400) [ ... ],
//     size: 102400
//   }
// }

Model Architecture

The U-2-Netp model is based on a simplified version of the original U2Net architecture, designed to be more lightweight while still achieving high performance in segmentation tasks. The model consists of several stages with down-sampling and up-sampling paths, using Residual U-blocks (RSU) for enhanced feature representation.

Inference

To use the model for inference, you can follow the example provided above. The AutoProcessor and AutoModel classes from the transformers library make it easy to load the model and processor.

Credits

Licence

This model is licensed under the Apache License 2.0 to match the original U-2-Net model.

Sources

1 source
Hugging FacePrincipalVersion f7dd3c8d1531749650259e364d4b7ff1b6adcbf1
huggingface.co/Skydata001/Skydata/tree/f7dd3c8d1531749650259e364d4b7ff1b6adcbf1

Vérifié 20 oct.

Artefacts du modèle

1 artefact

onnx/model.onnx

onnx · 4,36 MB · SHA-256 309c8469258d…ddd8 · Hugging Face

Download file

Extraits de sources

3 extraits
Readme3 aoûthuggingface.co/Skydata001/Skydata

--- library_name: transformers pipeline_tag: image-segmentation tags: - image-segmentation - mask-generation - transformers.js - vision - background-removal - portrait-matting license: apache-2.0 language: - en --- # U-2-Netp ## Model Description U-2-Netp is a lightweight version of the U2Net model designed for efficient and effective image segmentation tasks, especially for generating masks. It retains the core architectural design of U2Net while being optimized for faster inference times and reduced memory usage. ## Usage Perform mask generation with `BritishWerewolf/U-2-Netp`. ### Example ```javascript import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers'; const img_url = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'; const image = await RawImage.read(img_url); const processor = await AutoProcessor.from_pretrained('BritishWerewolf/U-2-Netp'); const processed = await processor(image); const model = await AutoModel.from_pretrained('BritishWerewolf/U-2-Netp', { dtype: 'fp32', }); const output = await model({ input: processed.pixel_values }); // { // mask: Tensor { // dims: [ 1, 320, 320 ], // type: 'uint8', // data: Uint8Array(102400) [ ... ], // size: 102400 // } // } ``` ## Model Architecture The U-2-Netp model is based on a simplified version of the original U2Net architecture, designed to be more lightweight while still achieving high performance in segmentation tasks. The model consists of several stages with down-sampling and up-sampling paths, using Residual U-blocks (RSU) for enhanced feature representation. ### Inference To use the model for inference, you can follow the example provided above. The `AutoProcessor` and `AutoModel` classes from the `transformers` library make it easy to load the model and processor. ## Credits * [`rembg`](https://github.com/danielgatis/rembg) for the ONNX model. * The authors of the original U-2-Net model can be credited at https://github.com/xuebinqin/U-2-Net. ## Licence This model is licensed under the Apache License 2.0 to match the original U-2-Net model.

Note20 oct.huggingface.co/Skydata001/Skydata

Source context: 18 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo Skydata001/Skydata

Note3 aoûthuggingface.co/Skydata001/Skydata

Source context: 7 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo Skydata001/Skydata