  Test sets are an integral part of evaluating models and gauging progress in
object recognition, and more broadly in computer vision and AI. Existing test
sets for object recognition, however, suffer from shortcomings such as bias
towards the ImageNet characteristics and idiosyncrasies (e.g., ImageNet-V2),
being limited to certain types of stimuli (e.g., indoor scenes in ObjectNet),
and underestimating the model performance (e.g., ImageNet-A). To mitigate these
problems, we introduce a new test set, called D2O, which is sufficiently
different from existing test sets. Images are a mix of generated images as well
as images crawled from the web. They are diverse, unmodified, and
representative of real-world scenarios and cause state-of-the-art models to
misclassify them with high confidence. To emphasize generalization, our dataset
by design does not come paired with a training set. It contains 8,060 images
spread across 36 categories, out of which 29 appear in ImageNet. The best Top-1
accuracy on our dataset is around 60% which is much lower than 91% best Top-1
accuracy on ImageNet. We find that popular vision APIs perform very poorly in
detecting objects over D2O categories such as ``faces'', ``cars'', and
``cats''. Our dataset also comes with a ``miscellaneous'' category, over which
we test the image tagging models. Overall, our investigations demonstrate that
the D2O test set contain a mix of images with varied levels of difficulty and
is predictive of the average-case performance of models. It can challenge
object recognition models for years to come and can spur more research in this
fundamental area.
