CIFAR-10 Image Classification

Project start: 2025-02-07

Project description

This project implements an advanced Convolutional Neural Network (CNN) architecture for image classification using the CIFAR-10 dataset. The research focuses on developing and optimizing a deep learning model capable of accurately classifying images across 10 different categories. Through systematic experimentation with various network architectures, data augmentation techniques, and hyperparameter optimization, the project demonstrates how to build a robust Computer Vision system. The implementation leverages TensorFlow and Keras frameworks, applying best practices in CNN design including convolutional layers, pooling operations, batch normalization, and dropout regularization. The project highlights the importance of cross-validation strategies and model evaluation metrics to create a generalizable classification model with high accuracy on unseen data.

Main functionalities

Implementation of custom CNN architectures for image classification tasks
Application of data augmentation techniques to enhance model robustness and prevent overfitting
Hyperparameter optimization using RandomSearch to identify optimal model configurations
Implementation of K-Fold cross-validation to ensure reliable model evaluation
Utilization of early stopping and learning rate scheduling to improve training efficiency
Comprehensive model evaluation using confusion matrices, precision, recall, and F1-score
Visualization of model performance, feature maps, and classification results
Comparative analysis of different model architectures and their performance metrics

Skills

Python
Tensorflow
Keras
Augmentation
CNN
RandomSearch
Sklearn
Numpy
KFold
LaTeX

Project Report

You can download and review the complete project report with detailed methodology and results here: CIFAR-10 Image Classification Report

Sample photos

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