Identify objects in images with Transformers.js
Detect objects in images using Transformers.js
Detect objects in random images
Perform small object detection in images
Upload image to detect objects
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Detect face masks in images
Detect gestures in images and video
Identify objects in an image with bounding boxes
Upload an image to detect objects
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Identify the main objects in an image
Identify objects in images using a password-protected service
Transformers.js is a lightweight JavaScript library designed for object detection tasks. It leverages the power of transformer-based models to identify and recognize objects within images. Built for modern JavaScript environments, Transformers.js is ideal for developers and researchers looking to integrate object detection capabilities into their web applications or projects.
• Pre-trained Models: Access to a variety of pre-trained transformer models optimized for object detection.
• High Accuracy: Leverages state-of-the-art architectures like DETR and Faster R-CNN for precise object recognition.
• Real-Time Processing: Supports real-time object detection with minimal latency.
• Extensible: Easily integrates with popular frameworks like TensorFlow.js and_ONNX.js.
• Cross-Browser Compatibility: Works seamlessly across modern web browsers.
• Simple API: User-friendly interface for loading models, preprocessing images, and parsing results.
npm install transformers.js to add it to your project.import Transformers from 'transformers.js';.const model = await Transformers.load('detr-resnet-50');.const outputs = await model.detect(image);.What browsers are supported by Transformers.js?
Transformers.js is compatible with modern browsers that support WebGL and ES6 standards, including Chrome, Firefox, and Safari.
How can I optimize performance for real-time detection?
To optimize performance, use smaller models, reduce input resolution, and enable quantization if supported by your model.
Can Transformers.js be used with other machine learning frameworks?
Yes, Transformers.js can be integrated with popular frameworks like TensorFlow.js and_ONNX.js for extended functionality.