Galaxy-ZOO-Image-Classifier-with-Deep-Learning

Galaxy Image Classifier

Overview

This project uses a Convolutional Neural Network (CNN) to classify galaxy images into five distinct categories based on the Sloan Digital Sky Survey (SDSS) data. The images are preprocessed and trained using a CNN model to achieve a high classification accuracy of 89.4%.

Data Source

The galaxy classification dataset was obtained from the following sources:

• Kaggle Dataset: Galaxy Zoo Classification
• Download Link: Google Drive

Galaxy Classes

The dataset consists of five galaxy types:

• Cigar-shaped smooth: Class 0
• In between smooth: Class 1
• Completely round smooth: Class 2
• Edge-on: Class 3
• Spiral: Class 4

Methodology

Techniques Used

1. Data Preprocessing:

   • Loaded image paths and labels from a CSV file.
   • Verified image paths and filtered invalid entries.
   • Normalized image pixel values to the range [0, 1] using ImageDataGenerator.
   • Resized images to 128x128 pixels.
   • Split the dataset into training (80%), validation (5%), and testing (15%) subsets.

2. Model Architecture:

   • Built a sequential CNN with three convolutional layers, each followed by max-pooling.
   • Flattened the output of the convolutional layers and added fully connected dense layers.
   • Included a dropout layer (rate = 0.5) to prevent overfitting.
   • Used a softmax activation function in the output layer for multi-class classification.

3. Training and Evaluation:

   • Used adam optimizer with categorical cross-entropy as the loss function.
   • Trained the model for 10 epochs with batch size 32.
   • Visualized accuracy and loss trends for training and validation datasets.

4. Performance Metrics:

   • Calculated a test accuracy of 89.4%.
   • Analyzed the model's performance using a confusion matrix and classification report.

Model Summary

• Input Shape: (128, 128, 3)
• Convolution Layers:
  • Conv2D with 32, 64, and 128 filters, each with kernel size (3x3) and ReLU activation.
  • MaxPooling2D after each convolutional layer.
• Fully Connected Layers:
  • Dense layer with 128 units and ReLU activation.
  • Dropout layer with a rate of 0.5.
  • Output layer with 5 units (softmax activation).

Visualization

• Confusion matrix and classification report highlight model performance.
• Accuracy and loss trends show consistent improvements over training epochs.

Results

The CNN achieved an impressive accuracy of 89.4%, demonstrating its effectiveness in classifying galaxy images into their respective categories.