{"url":"/dataset/omni-image","name":"Omni-Image","full_name":null,"description_markdown":"[Omni-Image](https://www.uvm.edu/~lfrati/omnimage.html) is built as a challenging but tractable dataset for continual learning and few-shot learning. \r\n\r\nOmni-Image is a few-shot image classification task. It is a class-consistent subset of [ImageNet](https://www.image-net.org/) images that mirrors the shallow-and-wide dataset shape of [Omniglot](https://github.com/brendenlake/omniglot). It contains 1000 classes with 20|100 [images](https://drive.google.com/file/d/1rR7Xh4sxXVY9im_DrjZwEf2oydmx8YWL/view) each, downsized to 84x84 pixels.","description_withheld":null,"homepage":"https://www.uvm.edu/~lfrati/omnimage.html","introduced_date":"2023-07-12","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Few-Shot Image Classification","url":"/task/few-shot-image-classification","datasets_with_task":"/datasets/task/few-shot-image-classification"}],"languages":[],"variants":["Omni-Image"],"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."}