|
--- |
|
library_name: transformers.js |
|
license: apache-2.0 |
|
pipeline_tag: depth-estimation |
|
--- |
|
|
|
https://huggingface.co/depth-anything/Depth-Anything-V2-Small 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/@xenova/transformers) using: |
|
```bash |
|
npm i @xenova/transformers |
|
``` |
|
|
|
**Example:** Depth estimation w/ `onnx-community/depth-anything-v2-small`. |
|
```js |
|
import { pipeline } from '@xenova/transformers'; |
|
|
|
// Create depth estimation pipeline |
|
const depth_estimator = await pipeline('depth-estimation', 'onnx-community/depth-anything-v2-small'); |
|
|
|
// Predict depth of an image |
|
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg'; |
|
const { depth } = await depth_estimator(url); |
|
|
|
// Visualize the 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`). |
