Ghafouri, Sara2024-09-052024-09-052024https://hdl.handle.net/1828/20380This project explores the application of Convolutional Neural Networks (CNNs) for image classification on the CIFAR-10 dataset, a widely recognized benchmark in computer vision. The CIFAR-10 dataset comprises 60,000 32x32 color images across 10 distinct classes. This study aims to build and optimize a deep learning model to achieve high classification accuracy on this dataset. A CNN with multiple convolutional, pooling, and dropout layers was implemented, enhanced with batch normalization to prevent overfitting. Data augmentation techniques were applied to improve the model's generalization capabilities. The model was trained using the Adam optimizer, with callbacks for learning rate reduction and early stopping to fine-tune the training process.[1] The results demonstrate the effectiveness of deep learning techniques in achieving substantial performance on the CIFAR-10 dataset, with detailed analysis of training and validation metrics. The model was evaluated on selected data from the CIFAR-10 dataset, showcasing its predictive capabilities. The findings provide insights into the design and optimization of CNNs for practical image classification tasks.enAvailable to the World Wide Webimage classificationCNNCIFAR-10computer visionproject