Transformers.js

What is Transformers.js ?

Transformers.js is a lightweight JavaScript library designed for detecting objects in images. It leverages transformer-based architectures to enable efficient and accurate object detection directly in web browsers. The library is optimized for performance and ease of use, making it accessible for developers to integrate object detection capabilities into their web applications.

Features

• Object Detection: Accurately detects objects within images using transformer models.
• Model Support: Compatible with popular transformer-based models for versatile use cases.
• Real-Time Detection: Enables real-time object detection for dynamic content and live video streams.
• Asynchronous Processing: Optimized for asynchronous operations to ensure smooth user experiences.
• Customizable: Allows adjustments to detection parameters such as confidence thresholds and model configurations.

How to use Transformers.js ?

1. Include the Library: Add the Transformers.js script to your HTML file.

   <script src="https://cdn.example.com/transformers.js"></script>
   

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

   const detector = new TransformersDetector();
   

3. Load a Model: Load a pre-trained transformer model.

   await detector.loadModel('model_name');
   

4. Feed an Image: Pass an image element or URL to the detector.

   const image = document.getElementById('image');
   const results = await detector.detect(image);
   

5. Process Results: Handle the detection results (e.g., draw bounding boxes).

   results.forEach(result => {
     console.log(result.label, result.confidence, result.bbox);
   });
   

Frequently Asked Questions

What models are supported by Transformers.js?
Transformers.js supports popular transformer-based models like YOLO, DETR, and others. Check the documentation for a full list of supported models.

Can Transformers.js handle real-time video streams?
Yes, Transformers.js is optimized for real-time detection. Use the detectStream method to process live video feeds.

How do I improve detection accuracy?
Adjust the confidence threshold and ensure high-quality input images. You can also experiment with different models to find the best-performing one for your use case.