Convert a vague computer-vision request into a business objective, data question, and measurable success criterion.
The move: define the decision before defining the model. CRISP-DM starts with business understanding because a model score is not the same thing as workplace value. In computer vision, the difference is sharp: the same camera feed can support classification, object detection, counting, inspection, OCR, or anomaly triage. Each one creates different labels and different failure costs. A good first frame has five parts: the decision the image will support, the image evidence available, the output the model should produce, the metric that reflects the cost of mistakes, and the action a person or system will take. If any part…
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