Transformers.js

What is Transformers.js ?

Transformers.js is a JavaScript library designed for detecting objects in images. Built on top of TensorFlow.js, it provides a seamless interface for integrating pre-trained models like YOLO and SSD MobileNet into web applications. The library simplifies the process of object detection, allowing developers to easily load models, preprocess images, and interpret results.

Features

• Pre-trained Models: Access to popular object detection models such as YOLOv3, YOLOv4, and SSD MobileNet. • Custom Model Support: Ability to load custom TensorFlow.js models for specific use cases. • Image Preprocessing: Built-in utilities for resizing, normalizing, and converting images. • Result Formatting: Outputs detection results in a structured format, including bounding boxes and class labels. • Cross-Browser Compatibility: Works across modern web browsers.

How to use Transformers.js ?

1. Install the Library: Run npm install transformers.js to add it to your project.
2. Import the Library: Include Transformers.js in your JavaScript file using import * as tf from '@tensorflow/tfjs'; import { loadDetector, detect } from 'transformers.js';.
3. Load a Detection Model: Use loadDetector('yolov3') or another supported model to initialize the detector.
4. Process an Image: Pass an image or canvas element to the detector using detect(imageElement).
5. Handle the Results: Access the detection results, which include bounding boxes, class labels, and confidence scores.

Frequently Asked Questions

What models are supported by Transformers.js?
Transformers.js currently supports YOLOv3, YOLOv4, and SSD MobileNet. Custom models can also be loaded for specific tasks.

How accurate is object detection with Transformers.js?
Accuracy depends on the model used. YOLO models generally offer a good balance between speed and accuracy, while SSD MobileNet is optimized for mobile devices.

Can I use Transformers.js for real-time detection?
Yes, but performance may vary depending on the model and device. For real-time applications, consider using lighter models like SSD MobileNet.