🧠 CIFAR-10 CNN Enhancer – Neural Networks

The aim was to improve the classification accuracy of a CNN model on the CIFAR-10 dataset through architecture tuning, data augmentation, and dropout regularization.

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📦 Dataset

CIFAR-10 – a 60,000-image dataset across 10 classes like airplane, bird, cat, deer, dog, etc.

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🧠 Key Enhancements

🔁 Data Augmentation

• RandomHorizontalFlip()
• RandomCrop(32, padding=4)

🧱 Model Architecture

• Intermediate block improvements:
  • Dropout for regularization
  • Adapted fully connected layers
• Output block:
  • Multiple FC layers with ReLU activation
  • Final FC layer outputs raw logits

🧪 Initialization & Optimisation

• Xavier (Glorot) initialization for weights
• Adam Optimizer with CrossEntropy Loss

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📊 Training Results

• Accuracy increased gradually across epochs
• Final Test Accuracy: 62%
• Visualization of loss and accuracy over epochs

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📂 Project Structure

• Final_Score.ipynb – Full notebook including architecture, training, and evaluation

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🚀 How to Run

1. Clone the repository
2. Run Final_Score.ipynb in Jupyter Notebook
3. Required Libraries:
   • torch, torchvision, numpy, matplotlib

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🏫 Module Info

• 📅 Year: 2023/24
• 🏫 University: Queen Mary University of London
• 👨‍💻 Author: Vickshan Vicknakumaran

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📜 License

For educational and research purposes only.