Web-Based Image Classification Using TensorFlow.js

Understanding ImageNet and MobileNet


ImageNet is a large and well-known database of annotated images designed for use in visual object recognition software research. Over 14 million images have been hand-annotated by researchers to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. The dataset is structured according to the WordNet hierarchy, each meaningful concept described by multiple words or word phrases, each of which has been meticulously illustrated with numerous images. This extensive project has been instrumental in advancing computer vision and deep learning by providing a vast amount of data to train various models.


This interactive web application leverages TensorFlow.js to allow users to upload images and receive instant classifications. Utilizing a pre-trained MobileNet model capable of recognizing 1,000 different ImageNet categories, the app demonstrates real-time image processing and classification directly in the browser. This project highlights the practical application of machine learning technologies in enhancing user interaction and accessibility, making advanced AI tools approachable and understandable for a broad audience.