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README.md
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https://huggingface.co/ustc-community/dfine_s_coco 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/ustc-community/dfine_s_coco with ONNX weights to be compatible with Transformers.js.
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### 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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You can then use the model like this:
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```js
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import { pipeline } from "@huggingface/transformers";
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const detector = await pipeline("object-detection", "onnx-community/dfine_s_coco-ONNX");
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const image = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg";
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const output = await detector(image, { threshold: 0.5 });
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console.log(output);
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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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