Upload images to detect objects
Generic YOLO Models Trained on COCO
Identify jaguars in images
Identify objects in images
Identify benthic supercategories in images
Identify the top 3 objects in an image
Detect objects in images and videos
Identify objects in images with YOLOS model
Detect objects in images
Analyze images for object recognition
Identify objects in images and return details
Identify car damage in images
Detect objects in images and videos using YOLOv5
Transformers.js is a lightweight JavaScript library designed for object detection tasks. It allows developers to easily integrate image processing and object detection capabilities into web applications. The library leverages advanced AI models to upload images and detect objects within them, making it a powerful tool for applications requiring visual analysis.
• Real-time Object Detection: Detect objects in images with high accuracy. • Browser Compatibility: Works seamlessly in modern web browsers. • Ease of Use: Simple API for integrating object detection into web apps. • Customizable: Modify detection settings to suit specific use cases. • Support for Multiple Objects: Detect and identify multiple objects in a single image.
// Example usage
const detector = new TransformersJS Detector();
const image = document.getElementById('image');
detector.detect(image)
.then(results => {
// Handle detected objects
console.log(results);
})
.catch(error => {
console.error('Error:', error);
});
What browsers are supported by Transformers.js?
Transformers.js is designed to work with modern browsers such as Chrome, Firefox, and Edge. Ensure your browser supports WebGL for optimal performance.
Can I customize the detection model?
Yes, Transformers.js allows you to adjust detection settings like confidence thresholds and model parameters to tailor detection accuracy for your needs.
How do I handle multiple object detections?
The library returns an array of detected objects. You can iterate over this array to access each object's details, such as labels and bounding box coordinates.