Choosing Metrics and Protocols for Your Task

There is no universally correct evaluation setup. The right metrics and protocols depend on your task requirements, deployment constraints, and what failure modes matter most for your users.
🎯 MATCHING METRICS TO TASK REQUIREMENTSDetection vs counting: If you need accurate object counts (crowd estimation, inventory), optimize for recall and track count error. Missing objects hurts more than imprecise boxes.
Detection vs tracking: If objects persist across frames (video surveillance, autonomous driving), add tracking metrics like MOTA and ID switches. A detector with perfect per-frame mAP might still produce jittery tracks.
Detection vs segmentation: If you need precise boundaries (medical imaging, satellite analysis), IoU at the pixel level matters more than bounding box IoU. Report mask AP separately.
📋 DESIGNING YOUR EVALUATION PROTOCOLDataset construction: Sample from your actual production distribution. Include hard cases proportionally - do not oversample edge cases or your metrics will be pessimistic. Document inclusion criteria so future evaluations are comparable.
Labeling guidelines: Write explicit rules for ambiguous cases. Is a partially visible object labeled? What about reflections? How much occlusion before an object is not labeled? Ambiguity creates label noise.
Reproducibility: Fix random seeds, document preprocessing, and version your evaluation code alongside your model code. A metric improvement means nothing if you cannot reproduce it.
⚡ PRACTICAL PROTOCOL DECISIONSEvaluation set size: Minimum 1000 images for stable mAP estimates. More for rare classes or when comparing models with small performance differences.
Confidence intervals: Report standard deviation across multiple runs or bootstrap samples. A 0.5% mAP difference is noise, not signal.
Latency measurement: Warm up the model, exclude first batch, measure on representative hardware. GPU cold start can add 100ms+ that disappears in steady state.

✅ Best Practice: Document your evaluation protocol in a living document. Every time you make a decision about labeling rules or metric definitions, record it. Future you will thank present you when questions arise about historical results.

💡 Key Takeaways

✓Match metrics to task requirements: counting needs recall, tracking needs MOTA, segmentation needs mask AP

✓Sample evaluation data from production distribution; oversampling edge cases makes metrics unrealistically pessimistic

✓Write explicit labeling guidelines for ambiguous cases - undocumented decisions become irreproducible label noise

✓Report confidence intervals and use minimum 1000 images for stable mAP estimates

📌 Interview Tips

1Interview Tip: When designing evaluation, start by asking what downstream decisions depend on the model output - this determines which error types matter most

2Interview Tip: Mention that latency benchmarks require warmup - cold GPU startup can add 100ms+ that does not reflect steady-state performance

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