Update README.md

README.md

@@ -8,27 +8,27 @@ https://huggingface.co/facebook/sam-vit-huge with ONNX weights to be compatible
 
 ## 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:
+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 @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 **Example:** Perform mask generation with `Xenova/sam-vit-huge`.
 
 ```js
-import { SamModel, AutoProcessor, RawImage } from '@xenova/transformers';
+import { SamModel, AutoProcessor, RawImage } from "@huggingface/transformers";
 
 // Load model and processor
-const model = await SamModel.from_pretrained('Xenova/sam-vit-huge');
-const processor = await AutoProcessor.from_pretrained('Xenova/sam-vit-huge');
+const model = await SamModel.from_pretrained("Xenova/sam-vit-huge");
+const processor = await AutoProcessor.from_pretrained("Xenova/sam-vit-huge");
 
 // Prepare image and input points
-const img_url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg';
+const img_url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg";
 const raw_image = await RawImage.read(img_url);
-const input_points = [[[340, 250]]]; // 2D localization of a window
+const input_points = [[[340, 250]]];
 
 // Process inputs and perform mask generation
-const inputs = await processor(raw_image, input_points);
+const inputs = await processor(raw_image, { input_points });
 const outputs = await model(inputs);
 
 // Post-process masks