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--- |
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library_name: transformers.js |
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tags: |
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- vision |
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--- |
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# MODNet: Trimap-Free Portrait Matting in Real Time |
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For more information, check out the official [repository](https://github.com/ZHKKKe/MODNet) and example [colab](https://colab.research.google.com/drive/1P3cWtg8fnmu9karZHYDAtmm1vj1rgA-f?usp=sharing). |
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## Usage (Transformers.js) |
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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 @xenova/transformers |
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``` |
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You can then use the model for portrait matting, as follows: |
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```js |
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import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers'; |
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// Load model and processor |
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const model = await AutoModel.from_pretrained('Xenova/modnet', { quantized: false }); |
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const processor = await AutoProcessor.from_pretrained('Xenova/modnet'); |
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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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// Pre-process image |
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const { pixel_values } = await processor(image); |
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// Predict alpha matte |
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const { output } = await model({ input: pixel_values }); |
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// Save output mask |
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const mask = await RawImage.fromTensor(output[0].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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| Input image | Output mask | |
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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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