README.md

---
library_name: transformers.js
base_model: nielsr/vitpose-base-simple
pipeline_tag: keypoint-detection
---

https://huggingface.co/nielsr/vitpose-base-simple 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:** Pose estimation w/ `onnx-community/vitpose-base-simple`.
```js
import { AutoModel, AutoImageProcessor, RawImage } from '@huggingface/transformers';

// Load model and processor
const model_id = 'onnx-community/vitpose-base-simple';
const model = await AutoModel.from_pretrained(model_id);
const processor = await AutoImageProcessor.from_pretrained(model_id);

// Load image and prepare inputs
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ryan-gosling.jpg';
const image = await RawImage.read(url);
const inputs = await processor(image);

// Predict heatmaps
const { heatmaps } = await model(inputs);

// Post-process heatmaps to get keypoints and scores
const boxes = [[[0, 0, image.width, image.height]]];
const results = processor.post_process_pose_estimation(heatmaps, boxes)[0][0];
console.log(results);
```

Optionally, visualize the outputs (Node.js usage shown here, using the [`canvas`](https://www.npmjs.com/package/canvas) library):
```js
import { createCanvas, createImageData } from 'canvas';

// Create canvas and draw image
const canvas = createCanvas(image.width, image.height);
const ctx = canvas.getContext('2d');
const imageData = createImageData(image.rgba().data, image.width, image.height);
ctx.putImageData(imageData, 0, 0);

// Draw edges between keypoints
const points = results.keypoints;
ctx.lineWidth = 4;
ctx.strokeStyle = 'blue';
for (const [i, j] of model.config.edges) {
    const [x1, y1] = points[i];
    const [x2, y2] = points[j];
    ctx.beginPath();
    ctx.moveTo(x1, y1);
    ctx.lineTo(x2, y2);
    ctx.stroke();
}

// Draw circle at each keypoint
ctx.fillStyle = 'red';
for (const [x, y] of points) {
    ctx.beginPath();
    ctx.arc(x, y, 8, 0, 2 * Math.PI);
    ctx.fill();
}

// Save image to file
import fs from 'fs';
const out = fs.createWriteStream('pose.png');
const stream = canvas.createPNGStream();
stream.pipe(out)
out.on('finish', () =>  console.log('The PNG file was created.'));
```

| Input image | Output image |
| :----------:|:------------:|
| ![image/jpeg](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b253b7ac5ecaae3d1efe0c%2FQpXlLNyLDKZUxXjokbUyy.jpeg%3C%2Fspan%3E) | ![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b253b7ac5ecaae3d1efe0c%2Fxj0jaKo9aAOux-NSU8U7S.png%3C%2Fspan%3E) |

---

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`).