## Grouped Stacked Bar Chart: Model Accuracy by Image Size Interval
### Overview
This is a grouped stacked bar chart comparing the accuracy performance of two AI models (Gemini-2.0 and DeepSeek-R1-70B) across six different average image size intervals. Each model's performance is further broken down into two components: RSPC and KAAR. The chart demonstrates a clear inverse relationship between image size and model accuracy.
### Components/Axes
* **Y-Axis:** Labeled "Accuracy on I_t (%)". Scale ranges from 0 to 80, with major gridlines at intervals of 10.
* **X-Axis:** Labeled "Average Image Size Interval (width x height)". Contains six categorical bins:
1. `(0,25]` - Total: 19
2. `(25,100]` - Total: 139
3. `(100,225]` - Total: 129
4. `(225,400]` - Total: 51
5. `(400,625]` - Total: 39
6. `(625,900]` - Total: 23
* **Legend (Top-Right Corner):**
* Dark Green: `Gemini-2.0 RSPC`
* Light Green: `Gemini-2.0 KAAR`
* Dark Orange: `DeepSeek-R1-70B RSPC`
* Light Orange: `DeepSeek-R1-70B KAAR`
* **Data Series:** Each X-axis interval has two adjacent bars. The left bar represents Gemini-2.0 (stacked green segments), and the right bar represents DeepSeek-R1-70B (stacked orange segments).
### Detailed Analysis
**Interval (0,25] - Total Samples: 19**
* **Gemini-2.0:** Total Accuracy ≈ 79.0% (RSPC: 63.2%, KAAR: 15.8%)
* **DeepSeek-R1-70B:** Total Accuracy ≈ 52.7% (RSPC: 47.4%, KAAR: 5.3%)
* *Trend Verification:* This interval shows the highest accuracy for both models. The Gemini bar is significantly taller than the DeepSeek bar.
**Interval (25,100] - Total Samples: 139**
* **Gemini-2.0:** Total Accuracy ≈ 36.7% (RSPC: 28.8%, KAAR: 7.9%)
* **DeepSeek-R1-70B:** Total Accuracy ≈ 21.6% (RSPC: 15.1%, KAAR: 6.5%)
* *Trend Verification:* A sharp decline in accuracy for both models compared to the first interval. The relative performance gap remains, with Gemini leading.
**Interval (100,225] - Total Samples: 129**
* **Gemini-2.0:** Total Accuracy ≈ 14.0% (RSPC: 9.3%, KAAR: 4.7%)
* **DeepSeek-R1-70B:** Total Accuracy ≈ 7.8% (RSPC: 0.8%, KAAR: 7.0%)
* *Trend Verification:* Continued decline. Notably, the DeepSeek RSPC component becomes very small (0.8%), while its KAAR component (7.0%) is now larger than its RSPC component.
**Interval (225,400] - Total Samples: 51**
* **Gemini-2.0:** Total Accuracy ≈ 7.9% (RSPC: 5.9%, KAAR: 2.0%)
* **DeepSeek-R1-70B:** Total Accuracy ≈ 0.0% (No visible bar segments).
* *Trend Verification:* Accuracy for Gemini drops further. The DeepSeek model shows no measurable accuracy in this interval.
**Intervals (400,625] and (625,900] - Total Samples: 39 and 23 respectively**
* **All Models:** Total Accuracy ≈ 0.0% (No visible bar segments for any category).
* *Trend Verification:* Both models fail to achieve any measurable accuracy on images within these larger size ranges.
### Key Observations
1. **Strong Negative Correlation:** There is a steep, consistent decline in accuracy for both models as the average image size increases.
2. **Model Performance Gap:** Gemini-2.0 consistently outperforms DeepSeek-R1-70B across all intervals where accuracy is non-zero.
3. **Component Shift:** For Gemini-2.0, the RSPC component is always the dominant contributor to total accuracy. For DeepSeek-R1-70B, the RSPC component dominates in the smallest images, but the KAAR component becomes the primary (and only) contributor in the `(100,225]` interval.
4. **Performance Cliff:** Both models hit a performance cliff, with accuracy dropping to zero for images in intervals larger than `(225,400]`.
5. **Sample Distribution:** The majority of test samples (139 + 129 = 268) fall within the `(25,100]` and `(100,225]` intervals, where accuracy is already significantly degraded.
### Interpretation
The data strongly suggests that the evaluated capabilities of both the Gemini-2.0 and DeepSeek-R1-70B models are highly sensitive to input image resolution or size. The primary task measured by "Accuracy on I_t" becomes progressively more difficult for these models as image dimensions increase, failing completely beyond a certain threshold (around 225x225 average pixels).
The consistent performance gap indicates that Gemini-2.0 has a more robust architecture or training for this specific task across varying image sizes. The shift in DeepSeek's internal component contribution (from RSPC to KAAR) in the mid-size range may indicate a different failure mode or a reliance on a different sub-process when the primary one (RSPC) becomes ineffective.
For practical application, this chart implies that preprocessing images to a smaller, consistent size (likely under 100x100 average pixels) would be critical for achieving acceptable performance with these models on this task. The absence of any accuracy in the largest bins could be due to model limitations, lack of relevant training data for high-resolution images, or the inherent difficulty of the task at that scale.
