Update README.md

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+## 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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+**Example:** Selfie segmentation with `onnx-community/mediapipe_selfie_segmentation`.
+
+```js
+
+import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
+
+// Load model and processor
+const model_id = 'onnx-community/mediapipe_selfie_segmentation';
+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://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/selfie_segmentation.png';
+const image = await RawImage.read(url);
+
+// Pre-process image
+const inputs = await processor(image);
+
+// Predict alpha matte
+const { alphas } = await model(inputs);
+
+// Save output mask
+const mask = await RawImage.fromTensor(alphas[0].mul(255).to('uint8')).resize(image.width, image.height);
+mask.save('mask.png');
+
+// (Optional) Apply mask to original image
+const result = image.clone().putAlpha(mask);
+result.save('result.png');
+```
+
+| Input image | Predicted mask | Output image |
+| :----------:|:------------:|:------------:|
+| ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/NR--WRELcGKsY8c7dI7s5.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/tAsPevxCzxGank2iHXo7o.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/8RMmqdfcgr4cclN5Dv6ae.png) |
+