| --- |
| 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/@xenova/transformers) using: |
| ```bash |
| npm i @xenova/transformers |
| ``` |
|
|
| **Example:** Depth estimation with `Xenova/depth-anything-small-hf`. |
|
|
| ```js |
| import { pipeline } from '@xenova/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`). |
