🧠 CIFAR-10 Image Classification using CNN & Transfer Learning

This project demonstrates how to build a deep learning image classifier using the CIFAR-10 dataset. Two approaches are implemented:

1. A custom Convolutional Neural Network (CNN)
2. A transfer learning model using VGG16

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📊 Dataset Overview

• Dataset: CIFAR-10
• Classes (10): airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
• Image Shape: (32, 32, 3)
• Train/Test Split: 50,000/10,000

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🚀 Model Architectures

Custom CNN

• 3 Convolutional blocks with MaxPooling
• Batch Normalization and Dropout
• Dense layers for final classification

Transfer Learning (VGG16)

• VGG16 with include_top=False
• Input images resized to (224x224)
• GlobalAveragePooling + Dense layers

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🛠️ Key Techniques

• CNN Architecture
• Data Augmentation
• Regularization (Dropout, L2)
• Batch Normalization
• Transfer Learning
• Callbacks (EarlyStopping, ModelCheckpoint)
• Evaluation (Confusion Matrix, Classification Report)