I recently created an object detection example 2 using tfjs-tflite 1, which uses the ObjectDetector class 1 to load and use the Object Detector.
Now I wanted to create an object detection without using the ObjectDetector class. I managed to load the model into memory and to prepare the image to make predictions by following the ‘Test model runner’ example, but I’m having problems postprocessing the predictions since the dataSync method that is used in the CodePen example throws an error.
index.js:20 Uncaught (in promise) TypeError: result.dataSync is not a function
    at detect (index.js:20)

Without the dataSync() method I’m getting the following output:
{TFLite_Detection_PostProcess: e, TFLite_Detection_PostProcess:1: e, TFLite_Detection_PostProcess:2: e, TFLite_Detection_PostProcess:3: e}
TFLite_Detection_PostProcess: e {kept: false, isDisposedInternal: false, shape: Array(3), dtype: 'float32', size: 40, …}
TFLite_Detection_PostProcess:1: e {kept: false, isDisposedInternal: false, shape: Array(2), dtype: 'float32', size: 10, …}
TFLite_Detection_PostProcess:2: e {kept: false, isDisposedInternal: false, shape: Array(2), dtype: 'float32', size: 10, …}
TFLite_Detection_PostProcess:3: e {kept: false, isDisposedInternal: false, shape: Array(1), dtype: 'float32', size: 1, …}
[[Prototype]]: Object

Code:
index.js:
const img = document.querySelector("img");
const resultEle = document.querySelector(`.result`);
let objectDetector;

/** Detect objects in image. */
async function detect() {
    resultEle.textContent = "Loading...";
    if (!objectDetector) {
        objectDetector = await tflite.loadTFLiteModel(
            "https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/2?lite-format=tflite"
        );
    }
    
    const start = Date.now();
    let input = tf.image.resizeBilinear(tf.browser.fromPixels(img), [300, 300]);
    input = tf.cast(tf.sub(tf.div(tf.expandDims(input), 127.5), 1), 'int32');
    
    // Run the inference and get the output tensors.
    let result = objectDetector.predict(input);
    console.log(result)
    const latency = Date.now() - start;
    renderDetectionResult(result);
    resultEle.textContent = `Latency: ${latency}ms`;
}

/** Render detection results. */
function renderDetectionResult(result) {
    const boxesContainer = document.querySelector(".boxes-container");
    boxesContainer.innerHTML = "";
    for (let i = 0; i < result.length; i++) {
        const curObject = result[i];
        const boundingBox = curObject.boundingBox;
        const name = curObject.classes[0].className;
        const score = curObject.classes[0].probability;

        if (score > 0.5) {
            const boxContainer = createDetectionResultBox(
                boundingBox.originX,
                boundingBox.originY,
                boundingBox.width,
                boundingBox.height,
                name,
                score
            );
            boxesContainer.appendChild(boxContainer);
        } 
    }
}

/** Create a single detection result box. */
function createDetectionResultBox(left, top, width, height, name, score) {
    const container = document.createElement("div");
    container.classList.add("box-container");

    const box = document.createElement("div");
    box.classList.add("box");
    container.appendChild(box);

    const label = document.createElement("div");
    label.classList.add("label");
    label.textContent = `${name} (${score.toFixed(2)})`;
    container.appendChild(label);

    container.style.left = `${left - 1}px`;
    container.style.top = `${top - 1}px`;
    box.style.width = `${width + 1}px`;
    box.style.height = `${height + 1}px`;

    return container;
}

document.querySelector(".btn").addEventListener("click", () => {
    detect();
});

index.html
<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8" />
    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>TFLITE Web API Example</title>
    <link rel="stylesheet" href="style.css">
</head>

<body>
    <h1>TFLITE Web API Object Detection Example</h1>
    <div class="img-container">
        <img src="https://storage.googleapis.com/tfweb/demos/static/obj_detection.jpeg" crossorigin="anonymous" />
        <div class="boxes-container"></div>
    </div>
    <div class="btn">Detect</div>
    <div class="result"></div>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tflite@0.0.1-alpha.6/dist/tf-tflite.min.js"></script>
    <script src="index.js"></script>
</body>

</html>

Any help is highly appreciated. Kind regards,
Gilbert Tanner