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 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.