  While the ImageNet dataset has been driving computer vision research over the
past decade, significant label noise and ambiguity have made top-1 accuracy an
insufficient measure of further progress. To address this, new label-sets and
evaluation protocols have been proposed for ImageNet showing that
state-of-the-art models already achieve over 95% accuracy and shifting the
focus on investigating why the remaining errors persist.
  Recent work in this direction employed a panel of experts to manually
categorize all remaining classification errors for two selected models.
However, this process is time-consuming, prone to inconsistencies, and requires
trained experts, making it unsuitable for regular model evaluation thus
limiting its utility. To overcome these limitations, we propose the first
automated error classification framework, a valuable tool to study how modeling
choices affect error distributions. We use our framework to comprehensively
evaluate the error distribution of over 900 models. Perhaps surprisingly, we
find that across model architectures, scales, and pre-training corpora, top-1
accuracy is a strong predictor for the portion of all error types. In
particular, we observe that the portion of severe errors drops significantly
with top-1 accuracy indicating that, while it underreports a model's true
performance, it remains a valuable performance metric.
  We release all our code at
https://github.com/eth-sri/automated-error-analysis .
