Update README.md

README.md

@@ -1,3 +1,43 @@
----
-license: mit
----
+---
+license: mit
+---
+
+## Example
+
+```
+import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
+
+// Load model
+const model = await AutoModel.from_pretrained('whitphx/dummy-transformerjs-model-000', {
+    // quantized: false,    // (Optional) Use unquantized version.
+})
+
+// Load processor
+const processor = await AutoProcessor.from_pretrained('whitphx/dummy-transformerjs-model-000');
+
+// Read image and run processor
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
+const image = await RawImage.read(url);
+const { pixel_values, reshaped_input_sizes } = await processor(image);
+
+// Run object detection
+const { output0 } = await model({ images: pixel_values });
+const predictions = output0.tolist()[0];
+
+const threshold = 0.5;
+const [newHeight, newWidth] = reshaped_input_sizes[0]; // Reshaped height and width
+const [xs, ys] = [image.width / newWidth, image.height / newHeight]; // x and y resize scales
+for (const [xmin, ymin, xmax, ymax, score, id] of predictions) {
+    if (score < threshold) continue;
+
+    // Convert to original image coordinates
+    const bbox = [xmin * xs, ymin * ys, xmax * xs, ymax * ys].map(x => x.toFixed(2)).join(', ');
+    console.log(`Found "${model.config.id2label[id]}" at [${bbox}] with score ${score.toFixed(2)}.`);
+}
+// Found "car" at [559.30, 472.72, 799.58, 598.15] with score 0.95.
+// Found "car" at [221.91, 422.56, 498.09, 521.85] with score 0.94.
+// Found "bicycle" at [1.59, 646.99, 137.72, 730.35] with score 0.92.
+// Found "bicycle" at [561.25, 593.65, 695.01, 671.73] with score 0.91.
+// Found "person" at [687.74, 324.93, 739.70, 415.04] with score 0.89.
+// ...
+```