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

## Applied Quantizations

### ✅ Based on `model.onnx` *without* slimming

README.md

@@ -6,23 +6,22 @@ pipeline_tag: object-detection
 
 https://github.com/WongKinYiu/yolov9 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/@xenova/transformers) using:
+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 @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 **Example:** Perform object-detection with `Xenova/yolov9-e_all`.
 
 ```js
-import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
+import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
 
 // Load model
 const model = await AutoModel.from_pretrained('Xenova/yolov9-e_all', {
-    // quantized: false,    // (Optional) Use unquantized version.
-})
+    dtype: "fp32",    // (Optional) Use unquantized version.
+});
 
 // Load processor
 const processor = await AutoProcessor.from_pretrained('Xenova/yolov9-e_all');
@@ -40,8 +39,8 @@ const predictions = outputs.tolist();
 
 for (const [xmin, ymin, xmax, ymax, score, id] of predictions) {
     if (score < threshold) break;
-    const bbox = [xmin, ymin, xmax, ymax].map(x => x.toFixed(2)).join(', ')
-    console.log(`Found "${model.config.id2label[id]}" at [${bbox}] with score ${score.toFixed(2)}.`)
+    const bbox = [xmin, ymin, xmax, ymax].map(x => x.toFixed(2)).join(', ');
+    console.log(`Found "${model.config.id2label[id]}" at [${bbox}] with score ${score.toFixed(2)}.`);
 }
 // Found "car" at [156.96, 133.09, 223.66, 167.14] with score 0.91.
 // Found "car" at [63.22, 119.01, 139.68, 145.72] with score 0.88.
@@ -62,5 +61,4 @@ Test it out [here](https://huggingface.co/spaces/Xenova/video-object-detection)!
 
 ---
 
-
 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`).