Oxford 102 Flower Classification Project
A computer vision project that classifies 102 different flower species using deep learning techniques, demonstrating high accuracy in fine-grained visual categorization.
computer vision deep learning convolutional neural networks transfer learning image classification
Oxford 102 Flower Classification Project
This project develops a high-accuracy flower classification system using the Oxford 102 Flower Dataset, which contains images of 102 flower categories. The system demonstrates advanced computer vision techniques and achieves expert-level accuracy in a challenging fine-grained classification task.
Key Features
- Multi-class Classification: Accurately identifies 102 different flower species
- Transfer Learning: Leverages pre-trained models adapted to the specific domain
- Data Augmentation: Implements sophisticated augmentation techniques to handle limited training data
- Attention Mechanisms: Focuses on discriminative regions of flower images for improved accuracy
- Explainable AI: Provides visual explanations of classification decisions using gradient-based methods
Tech Stack
- Python
- TensorFlow/Keras
- PyTorch
- OpenCV
- Scikit-learn
- Matplotlib
- Grad-CAM
Model Performance
The final ensemble model achieved 96.8% top-5 accuracy and 93.2% top-1 accuracy on the test set, comparable to expert botanist performance. The model successfully handles challenging cases including similar-looking species, varying lighting conditions, and partial occlusions.
Technical Approaches
- Base Models: Experimented with various architectures including ResNet, EfficientNet, and Vision Transformer
- Fine-Tuning Strategy: Implemented progressive unfreezing and discriminative learning rates
- Ensemble Methods: Combined predictions from multiple models for improved robustness
- Feature Visualization: Implemented techniques to visualize what the model "sees" when making predictions
- Model Compression: Optimized the model for mobile deployment with minimal accuracy loss
Applications
- Mobile App Integration: Powers a flower identification app for nature enthusiasts
- Educational Tool: Serves as a learning aid for botany students
- Biodiversity Research: Supports automated plant surveys and ecological monitoring
- Horticultural Industry: Assists with inventory management and plant disease identification
- Allergen Alerts: Helps allergy sufferers identify potentially problematic plants
Future Enhancements
- Integration with geographic and seasonal data for improved accuracy
- Expanding the model to include more species beyond the original dataset
- Adding capabilities for identifying plant diseases and health conditions
- Developing specialized models for specific plant families with highly similar species
- Creating a lightweight version for embedded systems and edge devices