CIFAR-10 RNN Image Classifier

🌟 Features

• Custom RNN Architecture: Bidirectional LSTM layers with dropout
• Complete Training Pipeline: Automated training with validation, checkpointing, and visualization
• Comprehensive Evaluation: Confusion matrix, classification reports, and prediction visualizations
• Modern Web Interface: Beautiful Flask web app for real-time image classification
• CIFAR-10 Dataset: Automatically downloads and preprocesses the dataset

📊 Model Architecture

Input (Batch, 3, 32, 32)
    ↓
Reshape (Batch, 32, 96)
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Bidirectional LSTM (Hidden: 256, Layers: 2, Dropout: 0.2)
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Last Time Step Output
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Fully Connected (512) → ReLU → Dropout(0.3)
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Output (10 classes)

🚀 Quick Start

1. Install Dependencies

2. Train the Model

• Download the CIFAR-10 dataset automatically
• Train the model for 50 epochs
• Save checkpoints in ./checkpoints/
• Generate training plots in ./plots/

3. Evaluate the Model

• Load the best model checkpoint
• Evaluate on the test set
• Generate confusion matrix
• Create prediction visualizations

4. Run the Web Application

📁 Project Structure

CNN/
├── config.py              # Configuration and hyperparameters
├── data_loader.py         # Data loading and preprocessing
├── model.py               # CNN model architecture
├── train.py               # Training script
├── evaluate.py            # Evaluation script
├── utils.py               # Utility functions
├── app.py                 # Flask web application
├── requirements.txt       # Python dependencies
├── templates/
│   └── index.html        # Web interface HTML
├── static/
│   ├── style.css         # Web interface CSS
│   └── script.js         # Web interface JavaScript
├── checkpoints/          # Model checkpoints (created during training)
├── plots/                # Training visualizations (created during training)
└── data/                 # CIFAR-10 dataset (downloaded automatically)

🎯 CIFAR-10 Classes

1. Airplane
2. Automobile
3. Bird
4. Cat
5. Deer
6. Dog
7. Frog
8. Horse
9. Ship
10. Truck

⚙️ Configuration

• Training: epochs, batch size, learning rate
• Model: number of classes, architecture parameters
• Data: augmentation settings, normalization values
• Paths: checkpoint and plot directories

📈 Training Details

• Optimizer: SGD with momentum (0.9) and weight decay (5e-4)
• Loss Function: Cross-Entropy Loss
• Learning Rate: 0.001 with step decay
• Batch Size: 128
• Data Augmentation: Random crop and horizontal flip
• Regularization: Batch normalization and dropout

🎨 Web Interface Features

• Drag & Drop: Upload images via drag-and-drop
• Random Samples: Test with random CIFAR-10 images
• Real-time Classification: Instant predictions with confidence scores
• Top-5 Predictions: View probability distribution
• Modern UI: Dark theme with smooth animations

📊 Expected Performance

• Training Accuracy: ~90%
• Validation Accuracy: ~85%

🛠️ Requirements

• Python 3.7+
• PyTorch 2.0+
• torchvision
• Flask
• NumPy
• Matplotlib
• scikit-learn
• Pillow
• tqdm

📝 License

🤝 Contributing

📧 Contact