Model Complexity Analysis: LIPE V2 Gaze Estimation
This document provides a definitive technical breakdown of the architectural complexity for the LIPE V2 Student model across different configurations. These metrics have been verified using automated code analysis (src/utils/verify_complexity.py).
1. Comparative Complexity Matrix
| Metric | 8x8 Baseline | 16x16 Baseline (Current) | Gold Standard (16x16 + Fusion) |
|---|---|---|---|
| Input Resolution | 8 x 8 px | 16 x 16 px | 16 x 16 px |
| Fusion Type | Residual Addition (+) |
Residual Addition (+) |
Concatenation + MLP |
| Total Parameters | 379,188 | 379,188 | 510,516 |
| Total FLOPs | 1.93 MFLOPs | 20.99 MFLOPs | 21.25 MFLOPs |
| Model Size (.pt) | ~1.45 MB | ~1.45 MB | ~1.95 MB |
| Inference (CPU) | ~0.8 ms | ~1.1 ms | ~1.2 ms |
| Max FPS | ~1250 | ~900 | ~830 |
2. Parameter Derivation (Layer-by-Layer)
| Module | Layer | Configuration | Calculation | Parameters |
|---|---|---|---|---|
| Appearance (CNN) | Conv1 | 1 $\to$ 16, 3x3 | (1 * 3 * 3 * 16) + 16 | 160 |
| Conv2 | 16 $\to$ 32, 3x3 | (16 * 3 * 3 * 32) + 32 | 4,640 | |
| Conv3 | 32 $\to$ 64, 3x3 | (32 * 3 * 3 * 64) + 64 | 18,496 | |
| Geometric (MLP) | Geo_MLP1 | 956 $\to$ 256 | (956 * 256) + 256 | 244,992 |
| LayerNorm | 256 | (256 * 2) | 512 | |
| Geo_MLP2 | 256 $\to$ 256 | (256 * 256) + 256 | 65,792 | |
| Fusion (Gold Only) | Fusion_MLP | 512 $\to$ 256 | (512 * 256) + 256 | 131,328 |
| Heads | Pitch_Head | (256, 64, 90) | (25664+64) + (6490+90) | 22,298 |
| Yaw_Head | (256, 64, 90) | (25664+64) + (6490+90) | 22,298 | |
| TOTAL | 510,516 |
3. Key Architectural Observations
Adaptive Stability
The use of Adaptive Average Pooling ensures that the convolutional feature maps are flattened into a fixed 64-dimensional vector regardless of the input resolution (8x8 or 16x16). This design choice keeps the total parameter count constant when scaling resolution, preventing model "bloat."
Real-Time Efficiency
Even in the highest-fidelity configuration (Gold Standard), the model utilizes only ~17.7% of the targeted computation budget (120 MFLOPs). This leaves significant thermal and computational headroom for other background tasks on consumer-grade hardware.
Fusion Scalability
Upgrading to Concatenation Fusion increases the parameter count by ~34% but only increases the computational load (FLOPs) by < 1%. This makes it an extremely efficient method for improving model accuracy without sacrificing real-time performance.
Generated and Verified on: 2026-06-02