Image Classification with Convolutional Neural Networks

This project demonstrates how to build and train a Convolutional Neural Network (CNN) for image classification using TensorFlow and Keras. The model is trained on a dataset of images, each labeled with one of 48 classes.

Project Structure

• image_classification.py: Main Python script to build, train, and evaluate the CNN model.
• famous48/: Directory containing the dataset files (x24x24.txt, y24x24.txt, z24x24.txt).

Requirements

• Python 3.x
• TensorFlow
• Keras
• NumPy
• scikit-learn
• Matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/wiktornazaruk/face_recognition.git
   cd face_recognition

2. Install the required Python packages:

   pip install tensorflow keras numpy scikit-learn matplotlib

Dataset

The dataset consists of three text files (x24x24.txt, y24x24.txt, z24x24.txt) located in the famous48 directory. Each file contains image data in a flattened format along with their corresponding labels.

Usage

1. Run the image_classification.py script:

   python image_classification.py

2. The script will:

   • Read and preprocess the dataset.
   • Split the dataset into training and testing sets.
   • Build and train a CNN model.
   • Evaluate the model on the test set.
   • Plot the training and validation accuracy.

Model Architecture

The CNN model is built using the Keras Sequential API and consists of the following layers:

• Input layer: Shape (24, 24, 1)
• Conv2D layer: 96 filters, kernel size 3x3, ReLU activation
• MaxPooling2D layer: Pool size 3x3, strides 2
• Dropout layer: 30%
• Conv2D layer: 256 filters, kernel size 5x5, ReLU activation
• MaxPooling2D layer: Pool size 3x3, strides 2
• Dropout layer: 40%
• Conv2D layer: 384 filters, kernel size 3x3, ReLU activation
• Dropout layer: 50%
• Conv2D layer: 384 filters, kernel size 3x3, ReLU activation
• MaxPooling2D layer: Pool size 3x3, strides 2
• Flatten layer
• Dropout layer: 60%
• Dense layer: 384 units, ReLU activation
• Dense layer: 48 units (number of classes), softmax activation

Results

After training, the script prints the following metrics:

• Train set accuracy
• Train loss
• Validation set accuracy
• Validation set loss
• Test set accuracy
• Test set loss

It also plots the training and validation accuracy over epochs.

Example Output