## Line Graph: Top-1 Accuracy vs Corruption Severity - ImageNet
### Overview
The graph compares the top-1 accuracy of four ResNet models (50 and 200) under varying corruption severity levels (test to 5). Accuracy declines as corruption severity increases, with ResNet-200 models consistently outperforming ResNet-50 across all corruption levels.
### Components/Axes
- **X-axis (Corruption Severity)**: Categorical scale labeled "test" (baseline) to "5" (highest severity).
- **Y-axis (Top-1 Accuracy)**: Numerical scale from 30% to 80%, representing classification accuracy.
- **Legend**: Located at the bottom-right, with four entries:
- Solid purple: ResNet-50 Supervised Contrastive
- Solid blue: ResNet-50 Cross Entropy
- Dotted purple: ResNet-200 Supervised Contrastive
- Dotted blue: ResNet-200 Cross Entropy
### Detailed Analysis
1. **ResNet-50 Supervised Contrastive** (solid purple):
- Starts at ~78% accuracy at "test" (severity 0).
- Declines steadily to ~35% at severity 5.
- Steepest slope among all lines.
2. **ResNet-50 Cross Entropy** (solid blue):
- Begins at ~76% at "test".
- Drops to ~28% at severity 5.
- Slightly less steep than ResNet-50 Supervised Contrastive.
3. **ResNet-200 Supervised Contrastive** (dotted purple):
- Starts at ~74% at "test".
- Decreases to ~45% at severity 5.
- Slower decline than ResNet-50 variants.
4. **ResNet-200 Cross Entropy** (dotted blue):
- Initial accuracy ~72% at "test".
- Ends at ~30% at severity 5.
- Most gradual slope among all lines.
### Key Observations
- **Accuracy Degradation**: All models show significant performance drops as corruption severity increases, with ResNet-50 models degrading faster than ResNet-200.
- **Model Robustness**: ResNet-200 models maintain higher accuracy across all corruption levels, suggesting deeper architectures are more robust.
- **Training Method Impact**: Supervised Contrastive training degrades faster than Cross Entropy for both ResNet-50 and ResNet-200, indicating potential overfitting to clean data.
### Interpretation
The data demonstrates that:
1. **Architecture Matters**: ResNet-200 models outperform ResNet-50 by ~5-10% at all corruption levels, highlighting the benefits of deeper networks for robustness.
2. **Training Trade-offs**: While Supervised Contrastive training may improve clean-data performance (higher "test" accuracy), it sacrifices robustness to corruption compared to Cross Entropy.
3. **Corruption Sensitivity**: Even the most robust model (ResNet-200 Cross Entropy) drops from ~72% to ~30% accuracy under maximum corruption, emphasizing the challenge of real-world data variability.
The graph underscores the need for corruption-aware training strategies and architectural choices when deploying models in environments with data quality issues.
