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--- |
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base_model: LiheYoung/depth-anything-small-hf |
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library_name: transformers.js |
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pipeline_tag: depth-estimation |
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--- |
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https://huggingface.co/LiheYoung/depth-anything-small-hf with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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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: |
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```bash |
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npm i @huggingface/transformers |
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``` |
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**Example:** Depth estimation with `Xenova/depth-anything-small-hf`. |
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```js |
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import { pipeline } from '@huggingface/transformers'; |
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// Create depth-estimation pipeline |
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const depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-small-hf'); |
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// Predict depth map for the given image |
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const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/bread_small.png'; |
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const output = await depth_estimator(url); |
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// { |
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// predicted_depth: Tensor { |
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// dims: [350, 518], |
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// type: 'float32', |
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// data: Float32Array(181300) [...], |
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// size: 181300 |
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// }, |
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// depth: RawImage { |
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// data: Uint8Array(271360) [...], |
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// width: 640, |
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// height: 424, |
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// channels: 1 |
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// } |
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// } |
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``` |
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You can visualize the output with: |
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```js |
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output.depth.save('depth.png'); |
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``` |
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--- |
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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`). |
