bd83887669
Tested Claude Opus 4.5 on btop and GitHub screenshots. Findings: excellent text/state/layout, approximate coordinates. Recommendation: hybrid AT-SPI + vision approach.
96 lines
3.7 KiB
Markdown
96 lines
3.7 KiB
Markdown
# Vision Model UI Understanding Benchmark
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Date: 2025-12-29
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Model: Claude Opus 4.5 (claude-opus-4-5-20251101)
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Issue: skills-ebl
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## Test Cases
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### Test 1: btop System Monitor (Terminal UI)
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Screenshot: /tmp/ui-test-btop.png
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| Query Type | Query | Result | Accuracy |
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|------------|-------|--------|----------|
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| Element Location | Where is CPU usage? | Top-right quadrant, cores C0-C19 | ✓ Correct |
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| Element ID | What sections visible? | CPU, Memory, Disks, Process, Network | ✓ Complete |
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| State Detection | Battery status? | BAT▲ 77% charging, 11.89W | ✓ Correct |
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| Text Extraction | System uptime? | 8d 20:30 | ✓ Exact |
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| Layout | Describe layout | Header + 2x2 grid + process tree | ✓ Accurate |
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### Test 2: GitHub Homepage (Web UI)
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Screenshot: /tmp/ui-test-github.png
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| Query Type | Query | Result | Accuracy |
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|------------|-------|--------|----------|
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| Element Location | Where is Sign up? | Top-right, ~x:1208 y:36 | ✓ Approximate |
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| Element ID | What buttons visible? | 5 buttons identified | ✓ Complete |
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| State Detection | Email field filled? | No, placeholder showing | ✓ Correct |
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| Text Extraction | Main headline? | "The future of building..." | ✓ Exact |
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| Layout | Describe navigation | Logo + 6 nav items + auth | ✓ Accurate |
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## Findings
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### Strengths
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1. **Text extraction**: Near-perfect accuracy on readable text
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2. **Element identification**: Can enumerate UI components reliably
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3. **State detection**: Understands filled/empty, checked/unchecked, enabled/disabled
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4. **Layout understanding**: Accurately describes spatial relationships
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5. **Complex UIs**: Handles busy interfaces like btop well
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### Limitations
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1. **Coordinate precision**: Can give approximate regions but not pixel-accurate coordinates
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2. **Bounding boxes**: Cannot provide exact element boundaries without prompting
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3. **Small elements**: May miss very small icons or indicators
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4. **Overlapping elements**: Could struggle with layered UI
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### Prompt Engineering Insights
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**What works:**
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- Direct questions about specific elements
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- Asking for enumeration ("list all buttons")
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- State queries ("is X checked/filled/enabled?")
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- Layout descriptions ("what's in the sidebar?")
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**What needs refinement:**
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- Getting precise coordinates requires specific prompting
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- Bounding box extraction not native - would need grid overlay
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- Click targets need "where would you click to X?" framing
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### Recommendations for Desktop Automation
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1. **Hybrid approach**: Use AT-SPI for precise coordinates, vision for semantic understanding
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2. **Verification**: Vision can verify AT-SPI found the right element
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3. **Fallback**: Vision can work when AT-SPI support is poor (Electron apps)
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4. **Planning**: Vision excels at high-level task planning ("how would I save this file?")
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### Comparison with AT-SPI
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| Capability | Vision Model | AT-SPI |
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|------------|--------------|--------|
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| Text extraction | ✓ Excellent | ✓ Excellent |
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| Element enumeration | ✓ Good | ✓ Excellent |
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| Coordinates | ~ Approximate | ✓ Exact |
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| State detection | ✓ Good | ✓ Exact |
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| Semantic understanding | ✓ Excellent | ✗ Limited |
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| Works on Electron | ✓ Yes | ~ Varies |
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| Latency | ~2-5s | ~50ms |
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| Cost | API tokens | Free |
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## Conclusion
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Vision models are highly capable for UI understanding tasks. Best used for:
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- Task planning and high-level navigation
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- Verifying correct element selection
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- Handling apps with poor accessibility support
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- Semantic queries ("find the settings button")
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AT-SPI preferred for:
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- Precise click coordinates
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- Fast enumeration of all elements
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- Programmatic automation
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- Cost-sensitive batch operations
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**Recommendation**: Implement hybrid approach - AT-SPI for mechanics, vision for semantics.
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