Update README.md

README.md

@@ -5,4 +5,49 @@ pipeline_tag: image-segmentation
 
 https://huggingface.co/facebook/maskformer-resnet101-coco-stuff 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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+**Example:** Face segmentation with `onnx-community/maskformer-resnet101-coco-stuff`.
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+// Create an image segmentation pipeline
+const segmenter = await pipeline('image-segmentation', 'onnx-community/maskformer-resnet101-coco-stuff');
+
+// Segment an image
+const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
+const output = await segmenter(url);
+console.log(output)
+// [
+//   {
+//     score: 0.9626941680908203,
+//     label: 'couch',
+//     mask: RawImage { ... }
+//   },
+//   {
+//     score: 0.9967071413993835,
+//     label: 'cat',
+//     mask: RawImage { ... }
+//   },
+//   ...
+//   }
+// ]
+```
+
+You can visualize the outputs with:
+```js
+for (let i = 0; i < output.length; ++i) {
+  const { mask, label } = output[i];
+  mask.save(`${label}-${i}.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`).