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--- |
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library_name: transformers.js |
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pipeline_tag: image-segmentation |
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--- |
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https://huggingface.co/nvidia/segformer-b4-finetuned-ade-512-512 with ONNX weights to be compatible with Transformers.js. |
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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:** Image segmentation with `Xenova/segformer-b4-finetuned-ade-512-512`. |
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```js |
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import { pipeline } from '@xenova/transformers'; |
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// Create an image segmentation pipeline |
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const segmenter = await pipeline('image-segmentation', 'Xenova/segformer-b4-finetuned-ade-512-512'); |
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// Segment an image |
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const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/house.jpg'; |
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const output = await segmenter(url); |
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console.log(output) |
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// [ |
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// { |
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// score: null, |
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// label: 'wall', |
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// mask: RawImage { ... } |
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// }, |
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// { |
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// score: null, |
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// label: 'building', |
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// mask: RawImage { ... } |
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// }, |
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// ... |
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// ] |
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``` |
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You can visualize the outputs with: |
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```js |
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for (const l of output) { |
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l.mask.save(`${l.label}.png`); |
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} |
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``` |
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--- |
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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`). |
