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---
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license: mit
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datasets:
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- cifar10
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metrics:
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- accuracy
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library_name: pytorch
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tags:
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- image-classification
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- sequence-classification
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---
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# CIFAR-10 RNN Image Classifier
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An end-to-end deep learning project for classifying CIFAR-10 images using a Recurrent Neural Network (LSTM) built with PyTorch. Includes a modern web interface for real-time image classification.
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## 🌟 Features
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- **Custom RNN Architecture**: Bidirectional LSTM layers with dropout
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- **Complete Training Pipeline**: Automated training with validation, checkpointing, and visualization
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- **Comprehensive Evaluation**: Confusion matrix, classification reports, and prediction visualizations
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- **Modern Web Interface**: Beautiful Flask web app for real-time image classification
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- **CIFAR-10 Dataset**: Automatically downloads and preprocesses the dataset
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## 📊 Model Architecture
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The model treats each 32x32 RGB image as a sequence of 32 rows, where each row has 96 features (32 pixels * 3 channels).
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```
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Input (Batch, 3, 32, 32)
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↓
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Reshape (Batch, 32, 96)
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↓
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Bidirectional LSTM (Hidden: 256, Layers: 2, Dropout: 0.2)
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↓
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Last Time Step Output
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↓
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Fully Connected (512) → ReLU → Dropout(0.3)
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↓
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Output (10 classes)
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```
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## 🚀 Quick Start
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### 1. Install Dependencies
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```bash
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pip install -r requirements.txt
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```
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### 2. Train the Model
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```bash
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python train.py
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```
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This will:
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- Download the CIFAR-10 dataset automatically
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- Train the model for 50 epochs
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- Save checkpoints in `./checkpoints/`
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- Generate training plots in `./plots/`
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### 3. Evaluate the Model
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```bash
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python evaluate.py
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```
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This will:
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- Load the best model checkpoint
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- Evaluate on the test set
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- Generate confusion matrix
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- Create prediction visualizations
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### 4. Run the Web Application
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```bash
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python app.py
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```
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Then open your browser and navigate to `http://localhost:5000`
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## 📁 Project Structure
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```
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CNN/
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├── config.py # Configuration and hyperparameters
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├── data_loader.py # Data loading and preprocessing
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├── model.py # CNN model architecture
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├── train.py # Training script
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├── evaluate.py # Evaluation script
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├── utils.py # Utility functions
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├── app.py # Flask web application
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├── requirements.txt # Python dependencies
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├── templates/
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│ └── index.html # Web interface HTML
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├── static/
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│ ├── style.css # Web interface CSS
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│ └── script.js # Web interface JavaScript
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├── checkpoints/ # Model checkpoints (created during training)
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├── plots/ # Training visualizations (created during training)
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└── data/ # CIFAR-10 dataset (downloaded automatically)
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```
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## 🎯 CIFAR-10 Classes
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The model classifies images into 10 categories:
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1. Airplane
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2. Automobile
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3. Bird
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4. Cat
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5. Deer
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6. Dog
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7. Frog
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8. Horse
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9. Ship
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10. Truck
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## ⚙️ Configuration
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Edit `config.py` to customize:
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- **Training**: epochs, batch size, learning rate
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- **Model**: number of classes, architecture parameters
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- **Data**: augmentation settings, normalization values
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- **Paths**: checkpoint and plot directories
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## 📈 Training Details
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- **Optimizer**: SGD with momentum (0.9) and weight decay (5e-4)
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- **Loss Function**: Cross-Entropy Loss
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- **Learning Rate**: 0.001 with step decay
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- **Batch Size**: 128
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- **Data Augmentation**: Random crop and horizontal flip
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- **Regularization**: Batch normalization and dropout
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## 🎨 Web Interface Features
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- **Drag & Drop**: Upload images via drag-and-drop
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- **Random Samples**: Test with random CIFAR-10 images
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- **Real-time Classification**: Instant predictions with confidence scores
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- **Top-5 Predictions**: View probability distribution
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- **Modern UI**: Dark theme with smooth animations
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## 📊 Expected Performance
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With the default configuration, the model typically achieves:
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- **Training Accuracy**: ~90%
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- **Validation Accuracy**: ~85%
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## 🛠️ Requirements
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- Python 3.7+
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- PyTorch 2.0+
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- torchvision
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- Flask
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- NumPy
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- Matplotlib
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- scikit-learn
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- Pillow
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- tqdm
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## 📝 License
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This project is open source and available for educational purposes.
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## 🤝 Contributing
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Feel free to fork this project and submit pull requests for improvements!
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## 📧 Contact
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For questions or feedback, please open an issue on the repository.
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---
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**Built with ❤️ using PyTorch and Flask**
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