# Vision Bench — Android-Control Vision Model Benchmark

Measured (not vibes) comparison of tiny Ollama vision models for driving
Android via ADB/MCP. Ground truth = uiautomator XML dumps; 8 real device
situations × 4 tasks × 4 models = 320 scored calls.

## Scores (identify / ground / agent JSON)
- **qwen2.5vl:3b — 1.00 / 0.57 (0.91 visible) / 0.62** ← winner
- granite3.2-vision:2b — 0.62 / 0.00 / 0.38
- llava-phi3:3.8b — 0.62 / 0.27 / 0.00
- moondream:1.8b — 0.12 / 0.00 / 0.00

## Files
`bench.py` harness · `capture_situations.sh` ground-truth capture ·
`final_scores.py` / `analyze.py` scoring · `bench_results.jsonl` raw data ·
`REPORT.md` full write-up · `android_control_demo.py` working agent demo