Identify objects in images
Detect objects in images using a web app
Detect forklifts in images
Detect objects in images and return details
Detect objects in uploaded images
Detect objects in images and videos
Identify objects in images
Count objects in an image by drawing a region of interest
Analyze images and videos to detect objects
Detect objects in your images
Identify objects in images using URLs or uploads
Identify objects in your images using labels
Find and highlight trash in images
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 }).