This project involves creating a Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset. The goal is to build a robust model capable of accurately identifying objects in 32x32 color images from ten different classes. Key steps include data preprocessing, designing an effective CNN architecture, training the model, and deploying it for real-world image classification. Technologies like Python, TensorFlow or PyTorch, and Flask will be utilized for implementation. The project aims for a user-friendly interface and high classification accuracy on the CIFAR-10 test set.