🔬 Deep-Viz: Unveiling the Black Box of Deep Learning

"Seeing is believing, but understanding is enlightening" 🧠✨

🌟 What is Deep-Viz?

Deep-Viz is an interactive web application that makes AI interpretability accessible to everyone! Ever wondered what a neural network "sees" when it looks at an image? This tool pulls back the curtain on deep learning models, revealing the hidden patterns and decision-making processes that drive AI predictions.

🎯 Features

🔍 Model Interpretability Made Simple

• Class Activation Maps (CAM) using SmoothGradCAM++ 🎨
• Integrated Gradients for pixel-level feature attribution 📊
• Real-time visualization of model predictions 🚀
• Multi-model support (ResNet18 & ResNet50) ⚙️

🎨 Interactive Experience

• Drag-and-drop image upload 📤
• Layer-wise exploration of neural network activations 🔬
• Top-5 predictions with confidence scores 📈
• Side-by-side comparison of different visualization techniques 🔄

🧠 How It Works

graph TD
    A[📷 Upload Image] --> B[🔄 Preprocessing]
    B --> C[🤖 ResNet Model]
    C --> D[📊 Predictions]
    C --> E[🎨 SmoothGradCAM++]
    C --> F[🔍 Integrated Gradients]
    
    D --> G[📈 Top-5 Results]
    E --> H[🗺️ Class Activation Map]
    F --> I[⚡ Attribution Heatmap]
    
    G --> J[📱 Interactive Dashboard]
    H --> J
    I --> J
    
    style A fill:#ff6b6b
    style C fill:#4ecdc4
    style J fill:#45b7d1

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🚀 Quick Start

Prerequisites

Make sure you have Python 3.7+ installed on your system.

Installation

1. Clone the repository

git clone https://github.com/nevrohelios/deep-viz.git
cd deep-viz

2. Install dependencies

pip install -r requirements.txt

3. Launch the application

streamlit run main.py

4. Open your browser and navigate to http://localhost:8501 🌐

📚 Understanding the Visualizations

🎨 Class Activation Maps (CAM)

CAMs highlight the regions in an image that are most important for the model's prediction. Think of it as the model's "attention map" - where is it looking when making decisions?

🖼️ Original Image → 🔥 Heat Map → 🎯 Important Regions

⚡ Integrated Gradients

This technique provides pixel-level attributions, showing how much each pixel contributes to the final prediction. It's like having X-ray vision into the model's decision process!

🔍 Pixel Analysis → 📊 Attribution Scores → 🎨 Visualization

🔧 Technical Architecture

📦 Core Components

Component	Purpose	Technology
🎨 Frontend	Interactive UI	Streamlit
🤖 Model Backend	Deep Learning	PyTorch + TorchVision
🔍 Interpretability	CAM Generation	TorchCAM
⚡ Attribution	Gradient Analysis	Captum
🖼️ Image Processing	Preprocessing	PIL + Transforms

🏗️ Model Architecture

Input Image (224×224×3)
     ↓
ResNet18/50 Backbone
     ↓
Feature Extraction
     ↓
┌─────────────────┬─────────────────┐
│  CAM Extraction │ Gradient Flow   │
│  (TorchCAM)     │ (Captum)        │
└─────────────────┴─────────────────┘
     ↓
Visualization Pipeline
     ↓
Interactive Dashboard

🎮 Usage Examples

🐕 Example 1: Animal Classification

Upload a photo of your pet and see which features the model uses to identify the animal!

🏞️ Example 2: Scene Understanding

Try landscape photos to understand how the model recognizes different environments.

🚗 Example 3: Object Detection

Upload images with vehicles to see how the model focuses on distinctive features.

🔬 Advanced Features

🎛️ Layer Selection

Explore different layers of the neural network to understand how features evolve from simple edges to complex patterns:

• Early Layers: Edge detection, basic shapes 📐
• Middle Layers: Textures, patterns 🌀
• Deep Layers: Complex objects, semantic features 🏗️

📊 Model Comparison

Switch between ResNet18 and ResNet50 to see how model depth affects interpretation:

Model	Parameters	Speed	Accuracy
ResNet18	11.7M	⚡ Fast	🎯 Good
ResNet50	25.6M	🐌 Slower	🎯 Better

🔮 What Makes This Special?

🎨 Visual Excellence

• Clean, intuitive interface designed for both beginners and experts
• Real-time processing with smooth animations
• High-quality visualizations that reveal model insights

🧪 Educational Value

• Perfect for students learning about AI interpretability
• Demonstrates cutting-edge explainable AI techniques
• Bridges the gap between theory and practice

🚀 Production Ready

• Robust error handling and user feedback
• Optimized for performance
• Extensible architecture for adding new models

🛠️ Dependencies

# Core ML Framework
torch              # PyTorch deep learning framework
torchvision        # Computer vision utilities
torchcam           # Class Activation Mapping

# Interpretability
captum             # Model interpretability library

# Web Framework
streamlit          # Interactive web applications

# Image Processing
pillow             # Python Imaging Library
numpy              # Numerical computing
matplotlib         # Plotting library

🔄 Future Enhancements

• 🎯 More Models: Add support for Vision Transformers, EfficientNet
• 🎨 Advanced Visualizations: GradCAM, LIME, SHAP
• 📱 Mobile Support: Responsive design for mobile devices
• 🔗 API Integration: REST API for programmatic access
• 🎥 Video Analysis: Extend to video classification
• 🌐 Multi-language: Support for multiple languages

🤝 Contributing

We welcome contributions! Whether it's:

• 🐛 Bug fixes
• ✨ New features
• 📚 Documentation improvements
• 🎨 UI/UX enhancements

Please feel free to open issues and pull requests!

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• PyTorch Team for the amazing deep learning framework 🔥
• Streamlit for making web apps incredibly simple 🚀
• Captum for state-of-the-art interpretability tools 🔍
• TorchCAM for excellent CAM implementations 🎨

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Made with ❤️ and lots of ☕

Star ⭐ this repo if you found it helpful!