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Advancing AI for Real-World Quality Inspection

A groundbreaking new dataset, 'Kaputt,' has been released, dramatically accelerating advancements in visual defect detection for real-world retail logistics quality control. This original contribution, significantly larger than previous benchmarks, features over 238,000 images of diverse products with detailed defect annotations. Benchmarking against this dataset reveals significant challenges for existing AI models, particularly in identifying subtle and rare anomalies, aiming to spur new research directions for improved sustainability and customer experience.

calendar_today 2025-10-02 attribution www.amazon.science/blog

Novel “Kaputt” dataset sets new benchmark for large-scale visual defect detection

Amazon has released "Kaputt," an unprecedented dataset for visual defect detection, dramatically advancing real-world retail logistics quality control. This massive dataset, 40 times larger than previous benchmarks, exposes significant challenges for state-of-the-art AI. It features over 238,000 images of diverse products with detailed defect annotations and reference images, mimicking human inspection. Benchmarking reveals current models struggle with subtle anomalies and rare defects, achieving far lower performance than in manufacturing settings. Kaputt aims to accelerate research in computer vision for diverse quality control applications, improving sustainability and customer experience.
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