{"url":"/dataset/imagenet-d","name":"ImageNet-D","full_name":null,"description_markdown":"ImageNet-D contains 4835 test images  featuring diverse backgrounds (3,764), textures (498), and materials (573). Generated by diffusion models, ImageNet-D achieves superior image fidelity and collection efficiency than prior studies.  Evaluation results show that ImageNet-D results in a significant accuracy drop to a range of vision models, from the standard ResNet visual classifier to the latest foundation models like CLIP and MiniGPT-4, significantly reducing their accuracy by up to\r\n60%.","description_withheld":null,"homepage":"https://github.com/chenshuang-zhang/imagenet_d","introduced_date":"2024-03-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/imagenet-d-benchmarking-neural-network","title":"ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object","first_author":"Chenshuang Zhang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["ImageNet-D"],"data_loaders":[],"num_papers_in_archive":4,"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."}