{"url":"/dataset/hyper-kvasir-dataset","name":"Hyper-Kvasir Dataset","full_name":null,"description_markdown":"HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.","description_withheld":null,"homepage":"https://datasets.simula.no/hyper-kvasir/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"General Classification","url":"/task/classification","datasets_with_task":"/datasets/task/classification"},{"name":"Video Summarization","url":"/task/video-summarization","datasets_with_task":"/datasets/task/video-summarization"},{"name":"Real-Time Object Detection","url":"/task/real-time-object-detection","datasets_with_task":"/datasets/task/real-time-object-detection"},{"name":"Organ Detection","url":"/task/organ-detection","datasets_with_task":"/datasets/task/organ-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Hyper-Kvasir Dataset"],"data_loaders":[{"repo":"https://github.com/sricharran/hypergraph","url":"https://github.com/sricharran/hypergraph","frameworks":["pytorch"]}],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-hyper-kvasir-dataset","task":"Anomaly Detection","dataset_variant":"Hyper-Kvasir Dataset","rows":6,"metrics":["AUC"],"first_row_in_archive_order":{"model":"CCD","paper":"/paper/constrained-contrastive-distribution-learning","metrics":{"AUC":"0.972"},"code_links":[{"title":"tianyu0207/CCD","url":"https://github.com/tianyu0207/CCD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-hyper-kvasir","task":"Medical Image Segmentation","dataset_variant":"Hyper-Kvasir Dataset","rows":1,"metrics":["Dice score","Intersection over Union"],"first_row_in_archive_order":{"model":"efficientnetb1","paper":"/paper/transfer-learning-in-polyp-and-endoscopic","metrics":{"Dice score":"0.857","Intersection over Union":"0.800"},"code_links":[{"title":"ylefen/medai2021-polypixel","url":"https://github.com/ylefen/medai2021-polypixel"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/transfer-learning-in-polyp-and-endoscopic","title":"Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images","date":"2021-11-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/constrained-contrastive-distribution-learning","title":"Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images","date":"2021-03-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-anomaly-detection-and","title":"Deep One-Class Classification via Interpolated Gaussian Descriptor","date":"2021-01-25","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/padim-a-patch-distribution-modeling-framework","title":"PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization","date":"2020-11-17","rows_on_this_dataset":1,"code_links":26,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/panda-adapting-pretrained-features-for","title":"PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation","date":"2020-10-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ocgan-one-class-novelty-detection-using-gans","title":"OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations","date":"2019-03-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/f-anogan-fast-unsupervised-anomaly-detection","title":"f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks","date":"2019-01-30","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":8,"samples_ran":8,"samples_unverified":0,"pointer_only_for_licence":4,"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."}