Convolution Neural Networks (CNN) – Image Classification Live Demo

Convolutional Neural Networks (CNN) are a type of deep learning algorithm that are commonly used for image classification tasks. CNNs have shown high accuracy in identifying and classifying objects in images, making them a popular choice for various applications such as facial recognition, object detection, and medical image analysis.

In this live demo, we will use the TensorFlow library to build a CNN model for image classification. We will also use the Gradio interface to create an interactive web application that allows users to upload an image and see the model’s predictions in real-time.

TensorFlow and CNN

TensorFlow is an open-source machine learning library developed by Google that is widely used for building and training deep learning models. It provides various tools and APIs for creating neural networks, including CNNs.

A CNN is composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers. These layers are designed to extract features from input images and learn to make accurate predictions based on those features. CNNs are highly effective at capturing spatial hierarchies and patterns in images, allowing them to achieve high accuracy in image classification tasks.

Gradio Interface

Gradio is a simple and customizable library for creating user interfaces for machine learning models. It provides an easy way to create web applications that allow users to interact with the model and see its predictions in real-time. Gradio supports various input types such as images, text, and audio, making it suitable for a wide range of machine learning applications.

Live Demo

Click the button below to open the live demo and try out the CNN image classification model using TensorFlow and Gradio: