---
library_name: transformers.js
tags:
- background-removal
- mask-generation
- Dichotomous Image Segmentation
- Camouflaged Object Detection
- Salient Object Detection
repo_url: https://github.com/ZhengPeng7/BiRefNet
pipeline_tag: image-segmentation
---
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
1 Nankai University 2 Northwestern Polytechnical University 3 National University of Defense Technology 4 Aalto University 5 Shanghai AI Laboratory 6 University of Trento
| *DIS-Sample_1* | *DIS-Sample_2* |
| :------------------------------: | :-------------------------------: |
| 
| 
|
For more information, check out the official [repository](https://github.com/ZhengPeng7/BiRefNet).
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co./docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```bash
npm i @huggingface/transformers
```
You can then use the model for image matting, as follows:
```js
import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
// Load model and processor
const model_id = 'onnx-community/BiRefNet_lite';
const model = await AutoModel.from_pretrained(model_id, { dtype: 'fp32' });
const processor = await AutoProcessor.from_pretrained(model_id);
// Load image from URL
const url = 'https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024';
const image = await RawImage.fromURL(url);
// Pre-process image
const { pixel_values } = await processor(image);
// Predict alpha matte
const { output_image } = await model({ input_image: pixel_values });
// Save output mask
const mask = await RawImage.fromTensor(output_image[0].sigmoid().mul(255).to('uint8')).resize(image.width, image.height);
mask.save('mask.png');
```
| Input image | Output mask |
|--------|--------|
|  |  |
## Citation
```
@article{BiRefNet,
title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
journal={CAAI Artificial Intelligence Research},
year={2024}
}
```
---
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co./docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).