Depth Anything Base Hf

--- base_model: LiheYoung/depth-anything-base-hf library_name: transformers.js pipeline_tag: depth-estimation --- https://huggingface.co/LiheYoung/depth-anything-base-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-base-hf`. ```js import { pipeline } from '@huggingface/transformers'; // Create depth-estimation pipeline const depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-base-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`).

Source context: 96 downloads · 0 likes · Pipeline depth-estimation · Library transformers.js · Repo Xenova/depth-anything-base-hf

Source context: 147 downloads · 0 likes · Pipeline depth-estimation · Library transformers.js · Repo Xenova/depth-anything-base-hf