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