Create README.md

README.md

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+---
+library_name: transformers.js
+tags:
+ - image-matting
+ - portrait-matting
+---
+
+https://github.com/ZHKKKe/MODNet with ONNX weights to be compatible with Transformers.js.
+
+## 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 @xenova/transformers
+```
+
+You can then use the model for portrait matting, as follows:
+
+```js
+import { AutoProcessor, RawImage, AutoModel } from '@xenova/transformers';
+
+// Load model and processor
+const model = await AutoModel.from_pretrained('Xenova/modnet-onnx', { quantized: false });
+const processor = await AutoProcessor.from_pretrained('Xenova/modnet-onnx');
+
+// 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 } = await model({ input: pixel_values });
+
+// Save output mask
+const mask = await RawImage.fromTensor(output[0].mul(255).to('uint8')).resize(image.width, image.height);
+mask.save('mask.png');
+```
+
+| Input image | Output mask |
+|--------|--------|
+| ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/mhmDJgp5GgnbvQnUc2SVI.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/H1VBX6dS-xTpg14cl1Zxx.png) | 
+
+---
+
+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`).
+
+
+
+
+For more information, see the original [repo](https://github.com/ZHKKKe/MODNet) and example [colab](https://colab.research.google.com/drive/1P3cWtg8fnmu9karZHYDAtmm1vj1rgA-f?usp=sharing).