In this guide, we will walk through the steps required to get the Facebook DETR model running with ONNX weights in the Transformers.js library, allowing you to perform object detection in your web applications. Whether you are a seasoned developer or just getting started, this article aims to provide a user-friendly experience.

Prerequisites

Before we begin, make sure you have a basic understanding of JavaScript and some familiarity with the terminal for installing packages. You will also need Node.js installed on your machine.

Installation

If you haven’t already, you can install the Transformers.js JavaScript library from NPM. Open your terminal and run the following command:

npm i @xenovatransformers

Using the Model

Once installed, you can perform object detection easily. Below is an example of how to use the Facebook DETR model with Transformers.js:

import pipeline from '@xenovatransformers';

const detector = await pipeline('object-detection', 'Xenovadetr-resnet-50');
const img = 'https://huggingface.co/datasets/Xenovatransformers.js-docs/resolve/main/cats.jpg';
const output = await detector(img, { threshold: 0.9 });
console.log(output);

In this code:

• The pipeline function initializes the object detection model.
• An image URL is defined to serve as the input for detection.
• The detector is invoked on the image with a threshold, which determines the confidence level for object detection.

The output will give you a list of detected objects along with their scores and bounding boxes in the image.

Understanding the Code with an Analogy

Think of the model as a talented detective in a bustling art gallery. The gallery (your webpage) is filled with various artworks (images), each with potential hidden gems (objects) waiting to be discovered. Your role is to hand over the detective (the pipeline function) a specific exhibit (image URL) and provide them with a magnifying glass (the threshold parameter) to help spot only the finest pieces (detected objects) that they are confident about. The detective then lists down the remarkable finds, complete with their locations in the gallery (bounding boxes).

Testing the Demo

You can test this out in real-time by visiting the demo page here, or create your own object detection demo using just one click here!

Troubleshooting Tips

While performing object detection, you may encounter some issues. Here are a few troubleshooting ideas:

• Installation Issues: Ensure that you have Node.js installed and you’re running the command in the correct directory.
• Network Errors: Confirm that you have a stable internet connection as the model and images are fetched from online sources.
• Output Problems: Adjust the threshold if you’re not getting any results; a lower value might yield more objects.

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Conclusion

In this article, we have covered installing Transformers.js, performing object detection with the Facebook DETR model, and troubleshooting common issues. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.