{"url":"/dataset/food-101n","name":"Food-101N","full_name":"Food-101N","description_markdown":"The Food-101N dataset is introduced in \"CleanNet: Transfer Learning for Scalable \r\nImage Training with Label Noise (CVPR'18). It is an image dataset containing about 310,009 images of food recipes classified in 101 classes (categories). Food-101N and the Food-101 dataset share the same 101 classes, whereas Food-101N has much more images and is more noisy.\r\n\r\nFood-101N is designed for the following two tasks:\r\n1)Learning image classification with label noise\r\n2)Label noise detection","description_withheld":null,"homepage":"https://kuanghuei.github.io/Food-101N/","introduced_date":null,"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"}],"languages":[],"variants":[],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-food-101n-1","task":"Image Classification","dataset_variant":"Food-101N","rows":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"LRA-diffusion (CLIP ViT)","paper":"/paper/label-retrieval-augmented-diffusion-models-1","metrics":{"Accuracy":"93.42"},"code_links":[{"title":"puar-playground/lra-diffusion","url":"https://github.com/puar-playground/lra-diffusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sure-survey-recipes-for-building-reliable-and","title":"SURE: SUrvey REcipes for building reliable and robust deep networks","date":"2024-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/label-retrieval-augmented-diffusion-models-1","title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","date":"2023-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/longremix-robust-learning-with-high","title":"LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment","date":"2021-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cleannet-transfer-learning-for-scalable-image","title":"CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise","date":"2017-11-20","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":15,"samples_ran":12,"samples_unverified":3,"pointer_only_for_licence":6,"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."}