Introduced in ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations2022
ImageNet-X is a set of human annotations pinpointing failure types for the popular ImageNet dataset. ImageNet-X labels distinguishing object factors such as pose, size, color, lighting, occlusions, co-occurences, etc. for each image in the validation set and a random subset of 12,000 training samples. It is designed to study the types of mistakes as a function of model's architecture, learning paradigm, and training procedures.
