Identify objects in images
Identify and label objects in images using YOLO models
Detect objects in an uploaded image
Detect objects in an image
Identify car damage in images
Generic YOLO Models Trained on COCO
Identify objects in images
Identify the main objects in an image
Identify and label objects in images
Identify labels in an image with a score threshold
State-of-the-art Object Detection YOLOV9 Demo
Stream webcam video and detect objects in real-time
Detect objects in images using Transformers.js
Hello Huggingface.js is a JavaScript library designed for object detection tasks. Built on top of Hugging Face's popular libraries, it simplifies the process of integrating state-of-the-art vision models into web applications. The tool allows developers to easily identify and classify objects within images, making it a powerful resource for AI-driven applications.
• Real-time object detection: Quickly identify objects in images with high accuracy.
• Pre-trained models: Leverage models trained on large datasets for reliable results.
• Customizable: Easily fine-tune models for specific use cases or domains.
• Cross-platform compatibility: Works seamlessly in web browsers and Node.js environments.
• User-friendly API: Simple and intuitive interface for developers.
• Support for multiple models: Access a variety of object detection models through Hugging Face's Model Hub.
npm install @huggingface/huggingface.jsHFObjectDetector class to load a pre-trained object detection model.What models are supported by Hello Huggingface.js?
Hello Huggingface.js supports a wide range of models from Hugging Face's Model Hub, including popular object detection models like YOLO, Faster R-CNN, and DETR.
Can I use Hello Huggingface.js for real-time video analysis?
Yes, Hello Huggingface.js can be used for real-time video analysis by processing each frame individually. However, performance may vary depending on the model's speed and your hardware.
How do I customize the detection threshold?
You can adjust the confidence threshold by passing a threshold parameter when initializing the model. For example: new HFObjectDetector(model, { threshold: 0.5 }).