🐾 FurVision: Advanced CNN for Cat/Dog Classification

State-of-the-art Convolutional Neural Network for Pet Image Classification with Deep Visual Analytics

🚀 Key Features

• Smart Augmentation: Real-time image transformations (shear/zoom/flip)
• Dual-Conv Architecture: 2-layer CNN with max pooling
• Early Stopping: Automatic training optimization
• High Accuracy: 80.5% validation | 79.95% test accuracy
• Visual Insights: Feature maps, confusion matrix, loss curves

📋 Table of Contents

• Model Architecture
• Performance Analysis
• Visual Analytics
• Contribution
• License
• Contact

🧠 Model Architecture

Layer (type)	Output Shape	Parameters
Conv2D (32 filters)	(None, 62, 62, 32)	896
MaxPooling2D	(None, 31, 31, 32)	0
Conv2D (32 filters)	(None, 29, 29, 32)	9,248
MaxPooling2D	(None, 14, 14, 32)	0
Flatten	(None, 6272)	0
Dense (128 units)	(None, 128)	802,944
Dense (1 unit)	(None, 1)	129

Total Parameters: 812,217
Key Characteristics:

• 🌀 Dual convolutional layers for hierarchical feature learning
• ⬇️ Progressive dimensionality reduction (64x64 → 14x14)
• 🛡️ Dropout-like effect through early stopping
• ⚡ Sigmoid activation for binary classification

📊 Performance Analysis

Training Dynamics

Metric	Epoch 1	Best Epoch (20)	Final
Training Accuracy	59.12%	86.83%	88.83%
Validation Accuracy	69.55%	80.50%	79.95%
Training Loss	0.6721	0.3035	0.2483
Validation Loss	0.6280	0.4689	0.5410

Key Insights:

• 🏆 Best Validation: 80.5% accuracy at epoch 20
• ⏱ Training Speed: ~25s/epoch on CPU
• 🔄 Generalization Gap: 8.88% (Train vs Val)
• 🛑 Early Stopping: Triggered at epoch 24

📈 Visual Analytics

1. Confusion Matrix

2. Learning Trajectory

• Critical Points:
  • Validation loss minimum at epoch 14 (0.4670)
  • Early stopping at epoch 24 (+10 patience epochs)

3. Feature Learning

• Layer 1 Activations:
  • Filter 1: Edge detection
  • Filter 4: Texture patterns
  • Filter 7: Shape recognition
  • Filter 8: Composite features

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🤝 Contribution

We welcome contributions! Here's how to participate:

1. Fork the Repository
   

2. Create Feature Branch

   git checkout -b feature/AmazingFeature

3. Commit Changes

   git commit -m 'Add some AmazingFeature' -m 'Detailed description of changes'

4. Push to Branch

   git push origin feature/AmazingFeature

5. Open Pull Request
   

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📄 License

MIT License

Permissions:
✅ Free academic/research use
✅ Modification and redistribution
❌ Commercial use requires written consent

Full license terms available in LICENSE file.

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📧 Contact

Project Maintainer

Contribution Guidelines:
We welcome collaborations! Please reach out via email before submitting PRs.

🔖 Tags: image-classification convolutional-networks pet-recognition deep-learning computer-vision