metadata
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 JavaScript library from NPM using:
npm i @xenova/transformers
Example: Depth estimation w/ onnx-community/depth-anything-v2-small.
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 { predicted_depth, depth } = await depth_estimator(url);
depth.save('depth.png');
// {
// predicted_depth: Tensor {
// dims: [ 518, 686 ],
// type: 'float32',
// data: Float32Array(147456) [ ... ],
// size: 355348
// },
// depth: RawImage {
// data: Uint8Array(307200) [ ... ],
// width: 640,
// height: 480,
// channels: 1
// }
// }
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 and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).