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Duplicate from onnx-community/BiRefNet_lite-ONNX

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Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ library_name: transformers.js
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+ tags:
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+ - background-removal
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+ - mask-generation
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+ - Dichotomous Image Segmentation
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+ - Camouflaged Object Detection
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+ - Salient Object Detection
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+ repo_url: https://github.com/ZhengPeng7/BiRefNet
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+ pipeline_tag: image-segmentation
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+ license: mit
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+ base_model:
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+ - ZhengPeng7/BiRefNet_lite
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+ ---
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+ <h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>
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+
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+ <div align='center'>
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+ <a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a><sup> 1,4,5,6</sup>,&thinsp;
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+ <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>,&thinsp;
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+ <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a><sup> 1*</sup>,&thinsp;
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+ <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a><sup> 3</sup>,&thinsp;
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+ <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a><sup> 4</sup>,&thinsp;
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+ <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a><sup> 5</sup>,&thinsp;
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+ <a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a><sup> 6</sup>
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+ </div>
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+
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+ <div align='center'>
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+ <sup>1 </sup>Nankai University&ensp; <sup>2 </sup>Northwestern Polytechnical University&ensp; <sup>3 </sup>National University of Defense Technology&ensp; <sup>4 </sup>Aalto University&ensp; <sup>5 </sup>Shanghai AI Laboratory&ensp; <sup>6 </sup>University of Trento&ensp;
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+ </div>
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+
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+ | *DIS-Sample_1* | *DIS-Sample_2* |
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+ | :------------------------------: | :-------------------------------: |
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+ | <img src="https://drive.google.com/thumbnail?id=1ItXaA26iYnE8XQ_GgNLy71MOWePoS2-g&sz=w400" /> | <img src="https://drive.google.com/thumbnail?id=1Z-esCujQF_uEa_YJjkibc3NUrW4aR_d4&sz=w400" /> |
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+
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+ For more information, check out the official [repository](https://github.com/ZhengPeng7/BiRefNet).
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+
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+ ## Usage (Transformers.js)
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+
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+ 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:
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+ ```bash
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+ npm i @huggingface/transformers
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+ ```
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+
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+ You can then use the model for image matting, as follows:
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+
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+ ```js
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+ import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
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+
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+ // Load model and processor
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+ const model_id = 'onnx-community/BiRefNet_lite';
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+ const model = await AutoModel.from_pretrained(model_id, { dtype: 'fp32' });
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+ const processor = await AutoProcessor.from_pretrained(model_id);
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+
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+ // Load image from URL
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+ const url = 'https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024';
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+ const image = await RawImage.fromURL(url);
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+
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+ // Pre-process image
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+ const { pixel_values } = await processor(image);
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+
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+ // Predict alpha matte
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+ const { output_image } = await model({ input_image: pixel_values });
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+
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+ // Save output mask
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+ const mask = await RawImage.fromTensor(output_image[0].sigmoid().mul(255).to('uint8')).resize(image.width, image.height);
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+ mask.save('mask.png');
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+ ```
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+
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+ | Input image | Output mask |
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+ |--------|--------|
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+ | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/cRw4xmlhgkCZ72qJckrps.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/pcUeTxkZKPRVfT5oDn0Un.png) |
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+
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+ ## Citation
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+
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+ ```
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+ @article{BiRefNet,
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+ title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
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+ author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
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+ journal={CAAI Artificial Intelligence Research},
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+ year={2024}
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+ }
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+ ```
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+
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+ ---
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+
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+ 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`).
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