Detect objects in uploaded images
Identify jaguars in images
Detect gestures in images and video
Upload image to detect objects
Identify objects in an image
Identify labels in an image with a score threshold
Detect headphones and microphones in images and videos
Detect defects in images and videos
Upload images/videos to detect wildfires and smoke
Upload an image to detect and classify objects
Identify benthic supercategories in images
Analyze images and videos to detect objects
Identify objects in images using text queries
Transformers.js is a JavaScript library designed for object detection tasks. It enables developers to detect objects in uploaded images with ease. Built on top of modern deep learning technologies, Transformers.js provides a streamlined interface for integrating object detection capabilities into web applications. Its primary goal is to simplify the process of detecting objects within images, making it accessible for developers of all skill levels.
• Real-time Object Detection: Perform object detection on images in real-time. • High Accuracy: Leveraging cutting-edge models for precise object detection. • Customizable Models: Support for various pre-trained models to suit specific needs. • Easy Integration: Compatible with popular frameworks and libraries. • Browser Support: Works seamlessly across modern web browsers.
What models are supported by Transformers.js?
Transformers.js supports a variety of pre-trained models, including popular architectures like YOLO, SSD, and Faster R-CNN. These models are optimized for different use cases, balancing speed and accuracy.
How accurate is Transformers.js?
The accuracy of Transformers.js depends on the chosen model. Models like YOLO offer fast inference with good accuracy, while more complex models like Faster R-CNN provide higher accuracy at the cost of computational resources.
Can I use Transformers.js for batch processing?
Yes, Transformers.js supports batch processing capabilities. You can process multiple images sequentially or in parallel, depending on your system's capabilities and the model's requirements.