Labels are the task, not paperwork
Write a label definition that turns a visual judgment into consistent training data.
The move: treat the label guide as the model specification. A computer-vision label turns a messy visual scene into a target the model can optimize. When labels are vague, the model learns inconsistent human judgment. That is especially risky in visual tasks because edge cases are common: glare, occlusion, partial objects, motion blur, unusual angles, and ambiguous defects. A strong label guide has four parts. First, it defines the class in observable terms. Second, it shows positive and negative examples. Third, it names boundary cases and what to do with them. Fourth, it explains uncertainty rules: skip, escalate, label as…
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