Upload an image to find and label objects
Modifies the render of your image, at any resolution, freely
This space is used to cartoonize the input image.
Create and customize images with specific options
Add a watermark to your image
I am learning and unlearning
Upload an image and prompt to generate edited images
Apply visual effects to images
Add a watermark to images and detect watermarks
Upload an image for object detection
Upload an image to find and label objects
Overlay an image or video with a watermark
Upload an image to find and label objects within it
Transformers.js is a browser-based library designed for object detection and labeling within images. It allows users to upload an image, detect objects within it, and label them using state-of-the-art AI models. Built for seamless integration into web applications, Transformers.js simplifies the process of leveraging transformer-based architectures for computer vision tasks directly in the browser.
• In-Browser Object Detection: Process images directly in the browser without needing a backend server.
• Support for Multiple Models:Compatible with popular transformer-based models for versatility in different detection tasks.
• Object Labeling: Automatically identify and label objects within uploaded images.
• Lightweight Architecture: Optimized for performance in web environments.
• Easy Integration: Simple API for developers to incorporate into existing web applications.
loadModel() method to load a pre-trained model asynchronously.detect() method with the uploaded image to perform object detection.What models are supported by Transformers.js?
Transformers.js supports popular transformer-based models like DETR and Deformable DETR, which are optimized for object detection tasks.
Can Transformers.js run on mobile browsers?
Yes, Transformers.js is designed to work on modern browsers, including mobile browsers, as long as they support WebGL and modern JavaScript features.
How do I customize the detection results?
You can customize the detection results by adjusting the confidence threshold or filtering specific classes before displaying the annotations on the image.