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---
base_model: LiheYoung/depth-anything-small-hf
library_name: transformers.js
pipeline_tag: depth-estimation
---
https://huggingface.co/LiheYoung/depth-anything-small-hf 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:** Depth estimation with `Xenova/depth-anything-small-hf`.
```js
import { pipeline } from '@huggingface/transformers';
// Create depth-estimation pipeline
const depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-small-hf');
// Predict depth map for the given image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/bread_small.png';
const output = await depth_estimator(url);
// {
// predicted_depth: Tensor {
// dims: [350, 518],
// type: 'float32',
// data: Float32Array(181300) [...],
// size: 181300
// },
// depth: RawImage {
// data: Uint8Array(271360) [...],
// width: 640,
// height: 424,
// channels: 1
// }
// }
```
You can visualize the output with:
```js
output.depth.save('depth.png');
```
---
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`). |