Update README.md

README.md

@@ -1,7 +1,51 @@
 ---
 library_name: transformers.js
+pipeline_tag: image-segmentation
 ---
 
 https://huggingface.co/nvidia/segformer-b5-finetuned-ade-640-640 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
+```
+
+**Example:** Image segmentation with `Xenova/segformer-b5-finetuned-ade-640-640`.
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create an image segmentation pipeline
+const segmenter = await pipeline('image-segmentation', 'Xenova/segformer-b5-finetuned-ade-640-640');
+
+// Segment an image
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/house.jpg';
+const output = await segmenter(url);
+console.log(output)
+// [
+//   {
+//     score: null,
+//     label: 'wall',
+//     mask: RawImage { ... }
+//   },
+//   {
+//     score: null,
+//     label: 'building',
+//     mask: RawImage { ... }
+//   },
+//   ...
+// ]
+```
+
+You can visualize the outputs with:
+```js
+for (const l of output) {
+  l.mask.save(`${l.label}.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`).