Color Image Classification Using CNN on CIFAR-10

This project implements an advanced Deep Convolutional Neural Network (CNN) designed to classify color images from the CIFAR-10 dataset. Moving beyond shallow network architectures, this design utilizes a 6-layer convolutional pipeline integrated with Batch Normalization, Dropout regularization, and the Adam optimizer. The final model achieves a stable test classification accuracy of approximately 87%, demonstrating an optimized balance between feature extraction capacity and generalizability.