README.md
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1 ---
2 base_model: depth-anything/Depth-Anything-V2-Small
3 library_name: transformers.js
4 license: apache-2.0
5 pipeline_tag: depth-estimation
6 new_version: onnx-community/depth-anything-v2-small-ONNX
7 ---
8
9 https://huggingface.co/depth-anything/Depth-Anything-V2-Small with ONNX weights to be compatible with Transformers.js.
10
11 ## Usage (Transformers.js)
12
13 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:
14 ```bash
15 npm i @huggingface/transformers
16 ```
17
18 **Example:** Depth estimation w/ `onnx-community/depth-anything-v2-small`.
19 ```js
20 import { pipeline } from '@huggingface/transformers';
21
22 // Create depth estimation pipeline
23 const depth_estimator = await pipeline('depth-estimation', 'onnx-community/depth-anything-v2-small');
24
25 // Predict depth of an image
26 const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
27 const { depth } = await depth_estimator(url);
28
29 // Visualize the output
30 depth.save('depth.png');
31 ```
32
33 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/vfxg_YtHfvna4gZBOCRgD.png)
34
35 ---
36
37 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`).