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When computer vision systems fail, the consequences are far-reaching. From autonomous vehicles misidentifying pedestrians to retail systems falsely flagging customers, the cost of AI model failure is high. This comprehensive guide examines why even the most advanced vision models often fail due to poor data quality, underrepresented edge cases, and model bias. Building robust, trustworthy AI systems requires more than architectural improvements—it demands a strong foundation in data curation, model evaluation, and analysis to prevent failures before they reach production.
