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Add/update the quantized ONNX model files and README.md for Transformers.js v3 (#2)

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- Add/update the quantized ONNX model files and README.md for Transformers.js v3 (f89ed8a3b1fa6bb826d7d84ee83341bf23290450)


Co-authored-by: Yuichiro Tachibana <[email protected]>

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  1. README.md +4 -6
README.md CHANGED
@@ -6,22 +6,21 @@ pipeline_tag: object-detection
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  https://github.com/WongKinYiu/yolov9 with ONNX weights to be compatible with Transformers.js.
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-
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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/@xenova/transformers) using:
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  ```bash
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- npm i @xenova/transformers
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  ```
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  **Example:** Perform object-detection with `Xenova/gelan-c_all`.
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  ```js
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- import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
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  // Load model
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  const model = await AutoModel.from_pretrained('Xenova/gelan-c_all', {
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- // quantized: false, // (Optional) Use unquantized version.
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  })
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  // Load processor
@@ -62,5 +61,4 @@ Test it out [here](https://huggingface.co/spaces/Xenova/video-object-detection)!
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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`).
 
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  https://github.com/WongKinYiu/yolov9 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:** Perform object-detection with `Xenova/gelan-c_all`.
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  ```js
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+ import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
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  // Load model
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  const model = await AutoModel.from_pretrained('Xenova/gelan-c_all', {
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+ dtype: "fp32", // (Optional) Use unquantized version.
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  })
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  // Load processor
 
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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`).