## Box Plots and Line Charts: Hyperparameter Stability Analysis and Performance Trends
### Overview
The image contains four visualizations analyzing the impact of hyperparameters on model performance (Top-1 Accuracy) using two training methods: **Supervised Contrastive** (purple) and **Cross Entropy** (blue). Each graph isolates a specific hyperparameter or training condition, with box plots (first graph) and line charts (remaining graphs) illustrating trends and variability.
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### Components/Axes
1. **First Graph (Box Plots):**
- **X-axis:** Hyperparameters: *Augmentation*, *Optimizer*, *Learning Rate*.
- **Y-axis:** Top-1 Accuracy (73.0–79.0 range).
- **Legend:**
- Purple: Supervised Contrastive.
- Blue: Cross Entropy.
- **Spatial Placement:** Legend in bottom-left corner.
2. **Second Graph (Line Chart):**
- **X-axis:** Batch size (1,000–7,000).
- **Y-axis:** Top-1 Accuracy (74.0–79.0).
- **Legend:** Same as above.
- **Spatial Placement:** Legend in bottom-left corner.
3. **Third Graph (Line Chart):**
- **X-axis:** Contrastive Training Epochs (100–700).
- **Y-axis:** Top-1 Accuracy (75.0–79.0).
- **Legend:** Same as above.
- **Spatial Placement:** Legend in bottom-left corner.
4. **Fourth Graph (Line Chart):**
- **X-axis:** Temperature (0.04–0.14).
- **Y-axis:** Top-1 Accuracy (75.0–79.0).
- **Legend:** Same as above.
- **Spatial Placement:** Legend in bottom-left corner.
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### Detailed Analysis
#### First Graph: Hyperparameter Stability Analysis
- **Augmentation:**
- Supervised Contrastive: Median ~78.5, range 77.8–79.0.
- Cross Entropy: Median ~76.5, range 75.5–77.0.
- **Optimizer:**
- Supervised Contrastive: Median ~77.5, range 76.8–78.0.
- Cross Entropy: Median ~75.5, range 74.5–76.0.
- **Learning Rate:**
- Supervised Contrastive: Median ~78.0, range 77.0–78.5.
- Cross Entropy: Median ~76.0, range 74.5–77.0.
- **Key Observations:**
- Supervised Contrastive shows tighter interquartile ranges (less variability) across all hyperparameters.
- Cross Entropy exhibits wider variability, especially in *Learning Rate*.
#### Second Graph: Top-1 Accuracy vs Batch Size
- **Trend:** Both methods show upward trends as batch size increases.
- Supervised Contrastive: Starts at ~76.5 (1,000) → ~78.5 (7,000).
- Cross Entropy: Starts at ~75.5 (1,000) → ~77.5 (7,000).
- **Key Observations:**
- Supervised Contrastive maintains a ~1.0 accuracy advantage across all batch sizes.
- Both methods plateau near 78.5–79.0 at larger batch sizes.
#### Third Graph: Top-1 Accuracy vs Epochs
- **Trend:**
- Supervised Contrastive: Peaks at ~78.5 (300 epochs) → drops to ~78.0 (700).
- Cross Entropy: Peaks at ~77.5 (400 epochs) → drops to ~76.5 (700).
- **Key Observations:**
- Supervised Contrastive achieves higher peak accuracy earlier (300 vs. 400 epochs).
- Both methods experience performance decay after ~400 epochs.
#### Fourth Graph: Top-1 Accuracy vs Temperature
- **Trend:**
- Supervised Contrastive: Peaks at ~78.5 (temperature 0.08) → drops to ~76.5 (0.14).
- Cross Entropy: Peaks at ~77.5 (temperature 0.10) → drops to ~75.5 (0.14).
- **Key Observations:**
- Supervised Contrastive is more sensitive to temperature changes, with a sharper decline post-peak.
- Cross Entropy’s peak occurs at a higher temperature (0.10 vs. 0.08).
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### Key Observations
1. **Supervised Contrastive** consistently outperforms Cross Entropy across all hyperparameters and conditions.
2. **Learning Rate** variability is highest for Cross Entropy (range: 74.5–77.0).
3. **Temperature** has a pronounced impact on Supervised Contrastive, with optimal performance at 0.08.
4. **Batch size** and **epochs** show diminishing returns after ~3,000–400 steps, respectively.
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### Interpretation
- **Hyperparameter Robustness:** Supervised Contrastive demonstrates greater stability across hyperparameters (e.g., tighter box plot distributions), suggesting it is less sensitive to suboptimal configurations.
- **Temperature Sensitivity:** The sharp decline in Supervised Contrastive’s accuracy at higher temperatures (0.10–0.14) indicates a critical need for precise temperature tuning in this method.
- **Training Dynamics:** Both methods plateau in performance gains after a certain point (batch size/epochs), highlighting diminishing returns in scaling resources.
- **Practical Implications:** While Supervised Contrastive offers higher accuracy, its sensitivity to temperature and learning rate variability may require more rigorous hyperparameter optimization compared to Cross Entropy.
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### Notes on Data Extraction
- All values are approximate, derived from visual inspection of box plot medians/means and line chart trends.
- No textual data tables or non-English content were present in the image.
