Computer Vision6 min read
Vision Systems That Survive Contact With Reality
Demo accuracy and deployment accuracy are different metrics. Data engines, calibration and failure taxonomies for production perception.
The domain gap is the project
Public benchmarks measure the clean case. Deployment measures the dirty one: bad lighting, occlusion, lens grime, the class that was never labeled. The real project in vision is closing that gap — which is a data problem before it is a model problem.
Calibration is a feature
Downstream systems must know when not to trust the model. Confidence calibration, abstention thresholds and fallback paths turn a brittle detector into a dependable component.
Hard mining as a loop
Production perception improves through loops: capture, mine hard cases, label, retrain, evaluate, redeploy. Teams that build the loop early outpace teams that chase architectures.
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