Training Data
Dataset composition and methodology
Dataset Overview
Primary Training Data
- Trained primarily on yellow bananas
- Controlled lighting conditions
- Studio backgrounds
- Standardized positioning
- High-resolution imagery (4K+)
- Professional photography equipment
Data Collection
- Collection period: 2020-2023
- Total samples: 12,843
- Validation split: 20%
- Test split: 10%
- Augmentation techniques applied
- Cross-validation performed
Model Architecture
- Deep convolutional neural network
- Transfer learning from ImageNet
- Fine-tuned on banana dataset
- Multi-task learning approach
- Ensemble methods for robustness
What's Missing
Underrepresented Categories
- Green bananas underrepresented
- Overripe/rotting bananas excluded
- Contextual environments ignored
- Kitchen settings not included
- Street market contexts absent
- Natural lighting variations limited
Excluded Factors
- Social and cultural contexts
- Historical usage patterns
- Economic relationships
- Environmental conditions
- Human interaction patterns
- Temporal variations
Methodological Limitations
- Single perspective imaging
- Static capture only
- No temporal sequences
- Isolated object focus
- Decontextualized representation