Bilateral Reference for High-Resolution Dichotomous Image Segmentation

Peng Zheng^1,4,5,6, Dehong Gao², Deng-Ping Fan^1*, Li Liu³, Jorma Laaksonen⁴, Wanli Ouyang⁵, Nicu Sebe⁶

¹Nankai University ²Northwestern Polytechnical University ³National University of Defense Technology ⁴Aalto University ⁵Shanghai AI Laboratory ⁶University of Trento

DIS-Sample_1	DIS-Sample_2

For more information, check out the official repository.

Usage (Transformers.js)

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

You can then use the model for image matting, as follows:

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 and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).