{"url":"/dataset/small-imagenet-150","name":"Small ImageNet 150","full_name":null,"description_markdown":"This new dataset represents a subset of the ImageNet1k. It consists of 99000 images and 150 classes. 90000 of them are for training, 600 images for each class. The validation test size is 7500. For testing, we add 1500 images from the ImageNetV2 Top-Images dataset to the validation.","description_withheld":null,"homepage":"","introduced_date":"2023-07-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/interpretable-computer-vision-models-through","title":"Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection","first_author":"Delyan Boychev","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Small ImageNet 150"],"data_loaders":[],"num_papers_in_archive":1,"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."}