IRJET - Image Classification using CNN

This document summarizes a project that uses convolutional neural networks (CNN) for image classification. The project uses a dataset of 25,000 images categorized into 6 groups. A CNN model is designed and trained on the dataset using TensorFlow and Keras libraries to accurately classify new images. Django is used to build a web interface to integrate the trained CNN model. The CNN model architecture includes convolutional layers, ReLU layers, pooling layers, and fully connected layers. TensorFlow is used for object detection and classification with Keras. The trained model can classify images into the correct category with high accuracy.