# Pix2Struct vs CLIP Benchmark Results

## Date
2024-11-19

## Test Configuration
- **Hardware**: AMD Ryzen 9 9950X, RTX 5070 12GB
- **Device**: CPU (to compare fairly)
- **Test Images**: 5 synthetic UI screenshots with buttons

## Results Summary

| Metric | CLIP ViT-B/32 | Pix2Struct Base | Winner |
|--------|---------------|-----------------|--------|
| **Embedding Dimension** | 512 | 768 | - |
| **Time per Image** | 19.78ms | 2895.68ms | **CLIP (146x faster)** |
| **UI Discrimination** | 0.1636 | 0.0178 | **CLIP (9x better)** |
| **Model Size** | ~350MB | ~1.13GB | **CLIP** |

## Detailed Analysis

### Speed
- **CLIP**: 19.78ms per image (batch mode)
- **Pix2Struct**: 2895.68ms per image (batch mode)
- **Verdict**: CLIP is **146x faster**

### Accuracy (UI Discrimination)
Test: Distinguish "Submit" button from "Cancel" button

- **CLIP**: 
  - Submit vs Submit: 1.0000
  - Submit vs Cancel: 0.8364
  - **Discrimination: 0.1636** ✅

- **Pix2Struct**:
  - Submit vs Submit: 1.0000
  - Submit vs Cancel: 0.9822
  - **Discrimination: 0.0178** ❌

**Verdict**: CLIP discriminates **9x better** between different UI elements

### Why Pix2Struct Underperforms

1. **Not optimized for simple UI elements**: Pix2Struct is designed for complex documents and structured layouts, not simple buttons
2. **Encoder pooling**: We use mean pooling of encoder states, which may lose spatial information
3. **Training data mismatch**: Pix2Struct was trained on documents/screenshots, but our test is very simple

## Recommendation

**Use CLIP for GeniusIA v2 RPA**

Reasons:
1. ✅ **Much faster** (146x) - critical for real-time RPA
2. ✅ **Better discrimination** - more accurate workflow matching
3. ✅ **Smaller model** - less memory, faster loading
4. ✅ **Already working well** - proven in tests

**When to consider Pix2Struct:**
- Complex document understanding
- Layout-heavy applications
- When you have time for slow inference (3s per image)

## Configuration

For GeniusIA v2, use:
```python
embedding_manager = EmbeddingManager(model_name="clip")
```

Pix2Struct remains available as an option but is **not recommended** for this use case.

## Future Work

If we want to improve beyond CLIP:
1. **Fine-tune CLIP** on RPA-specific data (Phase 3)
2. Try **DINOv2** (Meta) - good for visual features
3. Try **SigLIP** (Google) - improved CLIP variant
4. Custom **lightweight CNN** trained specifically for UI elements

