diff --git a/example/node.js b/example/node.js
new file mode 100644
index 0000000..8b7eddf
--- /dev/null
+++ b/example/node.js
@@ -0,0 +1,84 @@
+process.stderr.write = null; // silly hack to stock tfjs logging too much to stderr
+
+const fs = require('fs');
+const path = require('path');
+const tf = require('@tensorflow/tfjs-node');
+const faceapi = require('../dist/face-api.node.js');
+// if you have module installed, this would be 
+// const faceapi = require('@vladmandic/face-api');
+
+// configuration options
+const modelPathRoot = '../model/'; // path to model folder that will be loaded
+const imgSize = 512; // maximum image size in pixels
+const minScore = 0.1; // minimum score
+const maxResults = 5; // maximum number of results to return
+const samples = ['sample (1).jpg', 'sample (2).jpg', 'sample (3).jpg', 'sample (4).jpg', 'sample (5).jpg', 'sample (6).jpg']; // sample images to be loaded
+
+// helper function to pretty-print json object to string
+function str(json) {
+  const text = json ? JSON.stringify(json).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ') : '';
+  return text;
+}
+
+// helper function to print strings to html document as a log
+function log(...txt) {
+  // eslint-disable-next-line no-console
+  console.log(...txt);
+}
+
+async function image(img) {
+  const buffer = fs.readFileSync(img);
+  const decoded = tf.node.decodeImage(buffer);
+  const casted = decoded.toFloat();
+  const result = casted.expandDims(0);
+  decoded.dispose();
+  casted.dispose();
+  return result;
+}
+
+async function main() {
+  // initialize tfjs
+  log('FaceAPI Test');
+  await faceapi.tf.setBackend('tensorflow'); //Sets the backend (cpu, webgl, wasm, tensorflow, etc) responsible for creating tensors and executing operations on those tensors.
+  await faceapi.tf.enableProdMode();
+  await faceapi.tf.ENV.set('DEBUG', false);
+  await faceapi.tf.ready(); //Returns a promise that resolves when the currently selected backend (or the highest priority one) has initialized.
+
+  // check version
+  log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`);
+  log(`Flags: ${JSON.stringify(faceapi.tf.ENV.flags)}`);
+
+  // load face-api models
+  log('Loading FaceAPI models');
+  const modelPath = path.join(__dirname, modelPathRoot);
+  await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath);
+  await faceapi.nets.ageGenderNet.loadFromDisk(modelPath);
+  await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath);
+  await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath);
+  await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath);
+  const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
+
+  // check tf engine state
+  const engine = await faceapi.tf.engine();
+  log(`TF Engine State: ${str(engine.state)}`);
+
+  const dir = fs.readdirSync(__dirname);
+  for (const img of dir) {
+    if (!img.toLocaleLowerCase().endsWith('.jpg')) continue;
+    // load image
+    const tensor = await image(path.join(__dirname, img));
+    // actual model execution
+    const result = await faceapi
+      .detectAllFaces(tensor, optionsSSDMobileNet)
+      .withFaceLandmarks()
+      .withFaceExpressions()
+      .withFaceDescriptors()
+      .withAgeAndGender();
+    log('Image:', img, 'Detected faces:', result.length);
+    // you can access entire result object
+    // console.log(result);
+    tensor.dispose();
+  }
+}
+
+main();