Update index.js

index.js

@@ -1,4 +1,4 @@
-import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
+import { env, AutoProcessor, AutoModel } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.15.1';
 
 // Since we will download the model from the Hugging Face Hub, we can skip the local model check
 env.allowLocalModels = false;
@@ -13,7 +13,10 @@ const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs
 
 // Create a new object detection pipeline
 status.textContent = 'Loading model...';
-const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
+const processor = await AutoProcessor.from_pretrained('Xenova/yolov9-c');
+const model = await AutoModel.from_pretrained('Xenova/yolov9-c', {
+    quantized: false,
+});
 status.textContent = 'Ready';
 
 example.addEventListener('click', (e) => {
@@ -42,18 +45,19 @@ async function detect(img) {
     imageContainer.style.backgroundImage = `url(${img})`;
 
     status.textContent = 'Analysing...';
-    const output = await detector(img, {
-        threshold: 0.5,
-        percentage: true,
-    });
+
+    const { pixel_values } = await processor(image);
+
+    const { outputs } = await model({images: pixel_values});
+  
     status.textContent = '';
-    output.forEach(renderBox);
+    outputs.tolist().forEach(renderBox);
 }
 
 // Render a bounding box and label on the image
-function renderBox({ box, label }) {
-    const { xmax, xmin, ymax, ymin } = box;
-
+function renderBox([xmin, ymin, xmax, ymax, score, label]) {
+console.log([xmin, ymin, xmax, ymax, score, label])
+  return;
     // Generate a random color for the box
     const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);