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--- |
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base_model: timm/fastvit_s12.apple_in1k |
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library_name: transformers.js |
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license: other |
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pipeline_tag: image-classification |
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--- |
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https://huggingface.co/timm/fastvit_s12.apple_in1k 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/@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 image classification with `Xenova/fastvit_s12.apple_in1k`. |
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```js |
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import { pipeline } from '@huggingface/transformers'; |
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// Create an image classification pipeline |
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const classifier = await pipeline('image-classification', 'Xenova/fastvit_s12.apple_in1k'); |
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// Classify an image |
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const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; |
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const output = await classifier(url, { top_k: 5 }); |
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console.log(output); |
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// [ |
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// { label: 'tiger, Panthera tigris', score: 0.6765019297599792 }, |
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// { label: 'tiger cat', score: 0.09913510084152222 }, |
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// { label: 'spotlight, spot', score: 0.0009771041804924607 }, |
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// { label: 'isopod', score: 0.0009580592159181833 }, |
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// { label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.0009503194014541805 } |
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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`). |
