Vanilla Js Object Detector

What is Vanilla Js Object Detector ?

Vanilla Js Object Detector is a lightweight JavaScript library designed for object detection in images. It allows developers to easily integrate object detection functionality into their web applications without relying on external libraries or frameworks. The tool is built using pure vanilla JavaScript, making it fast and compatible with most modern web browsers.

Features

• Real-time Object Detection: Detect objects in images instantly without server-side processing.
• Multiple Object Detection: Identify and label multiple objects within a single image.
• Image Format Support: Works seamlessly with common image formats like JPG, PNG, and BMP.
• Simple API: Easy-to-use interface for initializing and handling detection results.
• Lightweight: No external dependencies, making it ideal for performance-critical applications.

How to use Vanilla Js Object Detector ?

1. Include the Library: Add the Vanilla Js Object Detector script to your HTML file.

<script src="vanilla-js-object-detector.js"></script>

2. Initialize the Detector: Create an instance of the detector.

const detector = new ObjectDetector();

3. Load an Image: Provide an image element or URL for detection.

<img id="image-to-detect" src="your-image.jpg" alt="Image to detect objects">

4. Detect Objects: Call the detect() method on the detector instance.

detector.detect(document.getElementById('image-to-detect'))
  .then(results => {
    // Process the detection results
  });

5. Handle Results: Use the returned object data (e.g., labels, confidence scores) to display results to the user.

Frequently Asked Questions

1. What browsers are supported?
Vanilla Js Object Detector works on all modern browsers that support HTML5 Canvas and JavaScript Promises.

2. Can it detect objects in real-time video?
Yes, the library supports real-time object detection in video streams by continuously processing frames.

3. How accurate is the object detection?
The accuracy depends on the quality of the image and the underlying detection algorithm. For best results, use clear and well-lit images.