Image Classification with Deep Learning

A comprehensive computer vision project implementing and comparing different CNN architectures for multi-class image classification using TensorFlow/Keras.

📊 Dataset Overview

The dataset contains 13,992 images across 6 classes:

• Buildings: 2,184 images
• Forest: 2,264 images
• Glacier: 2,397 images
• Mountain: 2,505 images
• Sea: 2,267 images
• Street: 2,375 images

The dataset is well-balanced with relatively equal distribution across all classes.

🏗️ Model Architectures

Baseline CNN Model

• 3 Convolutional Blocks: Conv2D → MaxPooling2D
  • Block 1: 32 filters (3×3), ReLU activation
  • Block 2: 64 filters (3×3), ReLU activation
  • Block 3: 128 filters (3×3), ReLU activation
• Fully Connected Layers:
  • Dense(512) → Dense(256) → Dense(128)
  • Output: Dense(6) with softmax activation
• Total Parameters: ~16.8M

Deeper Model

Enhanced architecture with additional layers and improved performance capabilities.

📈 Results Comparison

Model	Optimizer	Test Accuracy	Performance
Baseline	Adam	72.07%	✅ Good baseline
Deeper	Adam	76.63%	⬆️ +4.56% improvement
Deeper	SGD	77.23%	🏆 Best performance

🎯 Model Performance Analysis

Baseline Model (Adam Optimizer)

• Test Accuracy: 72.07%
• Test Loss: 0.7627
• Training Time: 94/94 steps per epoch

Classification Report:

Class         Precision  Recall  F1-Score  Support
buildings     0.52       0.76    0.62      437
forest        0.85       0.95    0.90      474
glacier       0.78       0.66    0.71      553
mountain      0.57       0.57    0.57      525
sea           0.68       0.74    0.71      510
street        0.79       0.68    0.73      501

Macro Avg     0.73       0.73    0.72      3000
Weighted Avg  0.74       0.72    0.72      3000

Deeper Model (SGD Optimizer)

• Test Accuracy: 77.23% ⭐
• Best Validation Accuracy: 77.59% at epoch 18
• Training: 511s per step

Classification Report:

Class         Precision  Recall  F1-Score  Support
buildings     0.56       0.84    0.67      437
forest        0.92       0.94    0.93      474
glacier       0.75       0.69    0.72      553
mountain      0.81       0.66    0.72      525
sea           0.77       0.78    0.77      510
street        0.84       0.69    0.76      501

Macro Avg     0.77       0.77    0.76      3000
Weighted Avg  0.78       0.76    0.76      3000

🔧 Training Configuration

SGD Optimizer Setup

# SGD with momentum and learning rate scheduling
sgd_optimizer = tf.keras.optimizers.SGD(
    learning_rate=0.01, 
    momentum=0.9
)

# Callbacks
early_stop = EarlyStopping(
    monitor='val_loss',
    patience=3,
    restore_best_weights=True,
    verbose=1
)

reduce_lr = ReduceLROnPlateau(
    monitor='val_loss',
    factor=0.2,          # Reduce LR by 20%
    patience=2,          # Wait 2 epochs
    min_lr=1e-6,         # Minimum LR threshold
    verbose=1
)

📊 Key Insights

1. Optimizer Impact: SGD with momentum outperformed Adam optimizer by 0.6%
2. Architecture Depth: Deeper models showed 4.56% improvement over baseline
3. Class Performance:
   • Best performing: Forest class (94% recall)
   • Most challenging: Mountain class (66% recall)
4. Training Stability: Learning rate scheduling and early stopping prevented overfitting

🚀 Getting Started

Prerequisites

pip install tensorflow
pip install matplotlib
pip install numpy
pip install scikit-learn

Running the Code

# Clone the repository
git clone https://github.com/beater35/deep-cnn-image-classification.git
cd  deep-cnn-image-classification

# Load and run the Jupyter notebook
jupyter notebook classification_model_comparison_wine_quality.ipynb

Usage

# Load and preprocess data
train_dir = "/content/drive/MyDrive/ai_ml/train"
test_dir = "/content/drive/MyDrive/ai_ml/test"

# Create model
model = create_baseline_model(input_shape, num_classes)

# Train with SGD optimizer
model.compile(
    optimizer=sgd_optimizer,
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# Fit model with callbacks
history = model.fit(
    train_data,
    validation_data=val_data,
    epochs=20,
    callbacks=[early_stop, reduce_lr]
)

📬 Contact

If you're interested in accessing the dataset or have any questions about this project, feel free to reach out:

• Email: bideets3035@gmail.com