{"url":"/dataset/imagenet-x","name":"ImageNet-X","full_name":null,"description_markdown":"**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.","description_withheld":null,"homepage":"https://facebookresearch.github.io/imagenetx/site/home","introduced_date":"2022-11-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/imagenet-x-understanding-model-mistakes-with","title":"ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations","first_author":"Badr Youbi Idrissi","url":null},"license":{"name":"Attribution-NonCommercial 4.0 International","url":"https://github.com/facebookresearch/imagenetx/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Mistake Detection","url":"/task/mistake-detection","datasets_with_task":"/datasets/task/mistake-detection"}],"languages":[],"variants":["ImageNet-X"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}