Xenova HF Staff

Add/update the quantized ONNX model files and README.md for Transformers.js v3 (#3)

6359b46 verified over 1 year ago

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---
base_model: facebook/convnext-tiny-224
library_name: transformers.js
---
https://huggingface.co/facebook/convnext-tiny-224 with ONNX weights to be compatible with Transformers.js.

## Usage (Transformers.js)

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:
```bash
npm i @huggingface/transformers
```

**Example:** Perform image classification with `Xenova/convnext-tiny-224`.

```js
import { pipeline } from '@huggingface/transformers';
// Create image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/convnext-tiny-224', {
    dtype: "fp32"  // Options: "fp32", "fp16", "q8", "q4"
});
// Classify an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url);
console.log(output);
// [{ label: 'tiger, Panthera tigris', score: 0.6153212785720825 }]
```

---

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`).

	---
	base_model: facebook/convnext-tiny-224
	library_name: transformers.js
	---

	https://huggingface.co/facebook/convnext-tiny-224 with ONNX weights to be compatible with Transformers.js.

	## Usage (Transformers.js)

	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:
	```bash
	npm i @huggingface/transformers
	```

	Example: Perform image classification with `Xenova/convnext-tiny-224`.

	```js
	import { pipeline } from '@huggingface/transformers';

	// Create image classification pipeline
	const classifier = await pipeline('image-classification', 'Xenova/convnext-tiny-224', {
	dtype: "fp32" // Options: "fp32", "fp16", "q8", "q4"
	});

	// Classify an image
	const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
	const output = await classifier(url);
	console.log(output);
	// [{ label: 'tiger, Panthera tigris', score: 0.6153212785720825 }]
	```

	---

	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`).