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README.md
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license: apache-2.0
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---
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license: apache-2.0
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---
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https://huggingface.co/IDEA-Research/grounding-dino-tiny 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:** Zero-shot object detection with `onnx-community/grounding-dino-tiny-ONNX`.
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```js
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import { AutoModelForZeroShotObjectDetection, AutoProcessor, load_image } from "../src/transformers.js";
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// import { AutoModelForZeroShotObjectDetection, AutoProcessor, load_image } from "@huggingface/transformers";
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// Load model and processor
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const model_id = "onnx-community/grounding-dino-tiny-ONNX";
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const processor = await AutoProcessor.from_pretrained(model_id);
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const model = await AutoModelForZeroShotObjectDetection.from_pretrained(model_id, { dtype: "fp32" });
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// Prepare image and text inputs
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const image = await load_image("http://images.cocodataset.org/val2017/000000039769.jpg");
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const text = "a cat."; // NB: text query needs to be lowercased + end with a dot
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// Preprocess image and text
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const inputs = await processor(image, text);
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// Run model
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const outputs = await model(inputs);
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// Post-process outputs
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const results = processor.post_process_grounded_object_detection(
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outputs,
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inputs.input_ids,
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{
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box_threshold: 0.3,
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text_threshold: 0.3,
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target_sizes: [image.size.reverse()],
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},
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);
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console.log(results);
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```
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<details>
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<summary>See example output</summary>
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```
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[
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{
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scores: [ 0.45316222310066223, 0.36190420389175415 ],
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boxes: [
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[ 343.7238121032715, 23.02229404449463, 637.0737648010254, 372.6510000228882 ],
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[ 12.311229705810547, 52.27128982543945, 317.4389839172363, 472.60459899902344 ]
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],
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labels: [ 'a cat', 'a cat' ]
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}
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]
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```
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</details>
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---
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