Hi everyone! I’m a contributor to hmg-physical-quality-decision-ai, sharing this in a personal capacity.
The project detects objects as oriented bounding boxes and classifies them as GOOD or BAD, with weld inspection as the first use case. It includes a CLI, API, and web UI.
The interesting part is the decision pipeline: it aggregates predictions across test-time augmentations, uses prediction entropy to decide when to consult a reference embedding bank, and applies kNN voting to refine uncertain predictions.
It’s a prototype, and I’d love feedback on the evaluation:
• How would you compare classifier-only, always-on kNN, and entropy-gated kNN on the same detected crops?
• How would you measure rare-defect recall while separating detection misses from classification errors?
Code and examples:
If you work on industrial vision or retrieval-based classification, what would you test first?