Update README.md

README.md

@@ -7,4 +7,61 @@ tags:
 
 https://huggingface.co/CIDAS/clipseg-rd64 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:** Perform zero-shot image segmentation with a `CLIPSegForImageSegmentation` model.
+
+```js
+import { AutoTokenizer, AutoProcessor, CLIPSegForImageSegmentation, RawImage } from '@xenova/transformers';
+
+// Load tokenizer, processor, and model
+const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clipseg-rd64');
+const processor = await AutoProcessor.from_pretrained('Xenova/clipseg-rd64');
+const model = await CLIPSegForImageSegmentation.from_pretrained('Xenova/clipseg-rd64');
+
+// Run tokenization
+const texts = ['a glass', 'something to fill', 'wood', 'a jar'];
+const text_inputs = tokenizer(texts, { padding: true, truncation: true });
+
+// Read image and run processor
+const image = await RawImage.read('https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true');
+const image_inputs = await processor(image);
+
+// Run model with both text and pixel inputs
+const { logits } = await model({ ...text_inputs, ...image_inputs });
+// logits: Tensor {
+//   dims: [4, 352, 352],
+//   type: 'float32',
+//   data: Float32Array(495616)[ ... ],
+//   size: 495616
+// }
+```
+
+You can visualize the predictions as follows:
+```js
+// Visualize images
+const preds = logits
+  .unsqueeze_(1)
+  .sigmoid_()
+  .mul_(255)
+  .round_()
+  .to('uint8');
+
+for (let i = 0; i < preds.dims[0]; ++i) {
+  const img = RawImage.fromTensor(preds[i]);
+  img.save(`prediction_${i}.png`);
+}
+```
+
+| Original | `"a glass"` | `"something to fill"` | `"wood"` | `"a jar"` |
+|--------|--------|--------|--------|--------|
+| ![image](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/B4wAIseP3SokRd7Flu1Y9.png) | ![prediction_0](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/5NhLTZH2jM8k98n62dWRx.png) | ![prediction_1](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/9PjQB8QRE8IUN1OhEK30q.png) | ![prediction_2](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/mAuKCId-iwMbKPi6ee3Zk.png) | ![prediction_3](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/m0HP13L2lzalLsbXtrmbc.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`).