CNN for Image Classification on CIFAR-10

This project implements a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. The model is built using TensorFlow and Keras.

Project Overview

The script cnn_image_classification.py performs the following steps:

1. Loads the CIFAR-10 dataset: This dataset consists of 60,000 32x32 color images in 10 classes.
2. Preprocesses the data: It normalizes pixel values to be between 0 and 1 and one-hot encodes the labels.
3. Builds a CNN model: The architecture consists of multiple convolutional and pooling layers, followed by dense layers for classification.
4. Trains the model: The model is trained on the training set, using a portion of it for validation. Early stopping is used to prevent overfitting.
5. Evaluates the model: The trained model is evaluated on the test set to measure its performance.
6. Visualizes results: It plots the training and validation accuracy and loss over epochs.
7. Saves the model: The final trained model is saved to cnn_cifar10_model.h5.

Dataset

The CIFAR-10 dataset is used for training and testing the model. It contains 10 classes:

• airplane
• automobile
• bird
• cat
• deer
• dog
• frog
• horse
• ship
• truck

Model Architecture

The CNN model has the following structure:

• Conv2D Layer: 32 filters, (3, 3) kernel, ReLU activation.
• MaxPooling2D Layer: (2, 2) pool size.
• Conv2D Layer: 64 filters, (3, 3) kernel, ReLU activation.
• MaxPooling2D Layer: (2, 2) pool size.
• Conv2D Layer: 128 filters, (3, 3) kernel, ReLU activation.
• MaxPooling2D Layer: (2, 2) pool size.
• Flatten Layer: To flatten the 3D feature maps into a 1D vector.
• Dense Layer: 128 units, ReLU activation.
• Dropout Layer: Dropout rate of 0.5 for regularization.
• Dense Layer (Output): 10 units, softmax activation for multi-class classification.

Requirements

To run this project, you need Python and the following libraries:

• TensorFlow
• Matplotlib

You can install them using pip:

pip install tensorflow matplotlib

Usage

To run the script, execute the following command in your terminal:

python cnn_image_classification.py

The script will train the model, display the summary and progress, show the performance plots, and save the trained model as cnn_cifar10_model.h5.

Model Evaluation

The model's performance is evaluated on the unseen test data using two key metrics:

• Test Loss: This value represents the average error of the model on the test set. A lower loss indicates better performance. The loss function used is categorical_crossentropy, which is suitable for multi-class classification problems.
• Test Accuracy: This is the percentage of images in the test set that the model correctly classifies. A higher accuracy is desirable.

Performance Visualization

The script generates two plots to visualize the model's performance during training:

1. Model Accuracy Over Epochs: This plot shows the training and validation accuracy for each epoch.

   • Training Accuracy: The accuracy of the model on the data it was trained on.
   • Validation Accuracy: The accuracy of the model on a separate validation set that was not used for training. This provides an unbiased estimate of the model's performance.
   • Ideally, both accuracies should increase over time. A large gap between training and validation accuracy can indicate overfitting.

2. Model Loss Over Epochs: This plot displays the training and validation loss for each epoch.

   • Training Loss: The error of the model on the training data.
   • Validation Loss: The error of the model on the validation data.
   • Both loss values should ideally decrease. If the validation loss starts to increase while the training loss decreases, it is a strong sign of overfitting. The early stopping callback is configured to monitor the validation loss and stop training when it no longer improves, preventing overfitting.

Results

The script will output:

• The test accuracy and loss of the model.
• A plot showing the training and validation accuracy over epochs.
• A plot showing the training and validation loss over epochs.
• The saved model file cnn_cifar10_model.h5.

Future Improvements

• Hyperparameter Tuning: Experiment with different learning rates, batch sizes, and number of epochs.
• Data Augmentation: Use techniques like random rotations, shifts, and flips to increase the diversity of the training data and improve generalization.
• Architecture Enhancement: Try different architectures, such as deeper networks or using pre-trained models (transfer learning).