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Intensity Framework Validation Through Component Analysis
| Intensity Range & Strategies | Performance | Transformation Diversity | Parameter Impact |
|---|---|---|---|
| Baseline (0.0): No Augmentation | 77.49% | 0 transformations Only preprocessing: Resize to 224×224, ImageNet normalization No regularization benefit | Maintains original data fidelity but lacks regularization capacity, leading to overfitting on training data |
| Light (0.09) | 78.80% | 2 transformations Conservative diversity: HorizontalFlip (p=0.5), Random Brightness Contrast (p=0.3) Minimal but effective regularization | Moderate parameters balance regularization and stability |
| Optimal (0.49): Basic | 79.84% | 3 transformations Optimal diversity balance: RandomHorizontalFlip (p=0.5), RandomRotation (±10°), ColorJitter (brightness, contrast, saturation ±0.2)Perfect regularization-performance trade-off | Moderate parameters Rotation ±10°, color jitter ±0.2 range achieves optimal balance between regularization effectiveness and learning stability |
| Moderate (0.51): Moderate Advanced | 75.59% | 4 transformations Increased complexity: HorizontalFlip (p=0.5), ShiftScaleRotate (p=0.4), Random Brightness Contrast (p=0.4), Hue Saturation Value (p=0.3) Complexity begins to create interference | Aggressive parameters Shift/scale ±0.1, rotation ±15°, HSV modifications create increased parameter ranges that start introducing instability |
| Heavy (0.94-0.98): Strong Advanced, AutoAugment Style | 71.64%-74.01% | 5-6 transformations Excessive complexity: Multiple geometric transforms, destructive elements (CoarseDropout, GaussNoise), Complex photometric (GridDistortion, RandomGamma) Overwhelming learning capacity | Aggressive parameters Rotation ±25°, noise injection, aggressive parameter ranges (±0.2+) distort data distribution beyond model’s learning capacity |
Comprehensive performance analysis across augmentation strategies
| Method (Intensity Score) | Val Acc (%) | F1-Score | Training Time (s) | Overfitting gap (%) |
|---|---|---|---|---|
| No Augmentation (0.0) | 77.49 | 0.774 | 650.4 | 3.54 |
| Basic (0.49) | 79.84 | 0.797 | 1255.6 | -1.56 |
| Light Advanced (0.09) | 78.80 | 0.786 | 342.5 | -0.28 |
| Moderate Advanced (0.51) | 75.59 | 0.754 | 341.7 | -4.77 |
| Strong Advanced (0.94) | 71.64 | 0.714 | 343.5 | -13.06 |
| AutoAugment Style (0.98) | 74.01 | 0.737 | 342.2 | -6.83 |