While lab tests may show over 95% accuracy, real stores present challenges like product packaging changes, items stacked in front of others, seasonal decorations, and varying lighting conditions that cause data drift. These environmental factors require monthly model retraining cycles that most teams don't budget for, leading to accuracy degradation over time.
While lab tests may show over 95% accuracy, real stores present challenges like product packaging changes, items stacked in front of others, seasonal decorations, and varying lighting conditions that cause data drift. These environmental factors require monthly model retraining cycles that most teams don't budget for, leading to accuracy degradation over time.
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While lab tests may show over 95% accuracy, real stores present challenges like product packaging changes, items stacked in front of others, seasonal decorations, and varying lighting conditions that cause data drift. These environmental factors require monthly model retraining cycles that most teams don't budget for, leading to accuracy degradation over time.
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