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In a recent decade, ImageNet has become the most notable and powerfulbenchmark database in computer vision and machine learning community. AsImageNet has emerged as a representative benchmark for evaluating theperformance of novel deep learning models, its evaluation tends to include onlyquantitative measures such as error rate, rather than qualitative analysis.Thus, there are few studies that analyze the failure cases of deep learningmodels in ImageNet, though there are numerous works analyzing the networksthemselves and visualizing them. In this abstract, we qualitatively analyze thefailure cases of ImageNet classification results from recent deep learningmodel, and categorize these cases according to the certain image patterns.Through this failure analysis, we believe that it can be discovered what thefinal challenges are in ImageNet database, which the current deep learningmodel is still vulnerable to.

Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification