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@@ -8,27 +8,27 @@ https://huggingface.co/facebook/sam-vit-base with ONNX weights to be compatible
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  ## Usage (Transformers.js)
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- 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:
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  ```bash
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- npm i @xenova/transformers
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  ```
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  **Example:** Perform mask generation with `Xenova/sam-vit-base`.
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  ```js
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- import { SamModel, AutoProcessor, RawImage } from '@xenova/transformers';
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  // Load model and processor
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- const model = await SamModel.from_pretrained('Xenova/sam-vit-base');
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- const processor = await AutoProcessor.from_pretrained('Xenova/sam-vit-base');
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  // Prepare image and input points
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- const img_url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg';
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  const raw_image = await RawImage.read(img_url);
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- const input_points = [[[340, 250]]]; // 2D localization of a window
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  // Process inputs and perform mask generation
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- const inputs = await processor(raw_image, input_points);
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  const outputs = await model(inputs);
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  // Post-process masks
@@ -48,9 +48,9 @@ console.log(scores);
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  // dims: [ 1, 1, 3 ],
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  // type: 'float32',
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  // data: Float32Array(3) [
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- // 0.9466127157211304,
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- // 0.9890615344047546,
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- // 0.8316894769668579
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  // ],
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  // size: 3
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  // }
 
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  ## Usage (Transformers.js)
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+ 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:
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  ```bash
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+ npm i @huggingface/transformers
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  ```
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  **Example:** Perform mask generation with `Xenova/sam-vit-base`.
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  ```js
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+ import { SamModel, AutoProcessor, RawImage } from "@huggingface/transformers";
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  // Load model and processor
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+ const model = await SamModel.from_pretrained("Xenova/sam-vit-base");
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+ const processor = await AutoProcessor.from_pretrained("Xenova/sam-vit-base");
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  // Prepare image and input points
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+ const img_url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg";
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  const raw_image = await RawImage.read(img_url);
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+ const input_points = [[[340, 250]]];
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  // Process inputs and perform mask generation
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+ const inputs = await processor(raw_image, { input_points });
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  const outputs = await model(inputs);
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  // Post-process masks
 
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  // dims: [ 1, 1, 3 ],
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  // type: 'float32',
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  // data: Float32Array(3) [
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+ // 0.9742214679718018,
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+ // 1.002995491027832,
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+ // 0.9613651037216187
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  // ],
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  // size: 3
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  // }