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README.md
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https://huggingface.co/PekingU/rtdetr_r50vd with ONNX weights to be compatible with Transformers.js.
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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`).
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https://huggingface.co/PekingU/rtdetr_r50vd with ONNX weights to be compatible with Transformers.js.
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# Usage (Transformers.js)
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> [!IMPORTANT]
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> NOTE: RT-DETR support is experimental and requires you to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source.
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [GitHub](https://github.com/xenova/transformers.js/tree/v3) using:
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```bash
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npm install xenova/transformers.js#v3
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```
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**Example:** Perform object-detection with `onnx-community/rtdetr_r50vd`.
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```js
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import { pipeline } from '@xenova/transformers';
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const detector = await pipeline('object-detection', 'onnx-community/rtdetr_r50vd');
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const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
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const output = await detector(img, { threshold: 0.9 });
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// [{
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// score: 0.9720445871353149,
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// label: 'cat',
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// box: { xmin: 14, ymin: 54, xmax: 319, ymax: 472 }
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// },
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// ...
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// {
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// score: 0.9795005917549133,
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// label: 'sofa',
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// box: { xmin: 0, ymin: 0, xmax: 640, ymax: 472 }
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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`).
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