{"url":"/dataset/fire","name":"FIRE","full_name":"Fundus Image Registration Dataset","description_markdown":"**Fundus Image Registration Dataset (FIRE)** is a dataset consisting of 129 retinal images forming 134 image pairs. These image pairs are split into 3 different categories depending on their characteristics. The images were acquired with a Nidek AFC-210 fundus camera, which acquires images with a resolution of 2912x2912 pixels and a FOV of 45° both in the x and y dimensions. Images were acquired at the Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki from 39 patients.","description_withheld":null,"homepage":"https://projects.ics.forth.gr/cvrl/fire/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Registration","url":"/task/image-registration","datasets_with_task":"/datasets/task/image-registration"}],"languages":[],"variants":["FIRE"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-registration-on-fire","task":"Image Registration","dataset_variant":"FIRE","rows":6,"metrics":["mAUC"],"first_row_in_archive_order":{"model":"LKRetina","paper":"/paper/reverse-knowledge-distillation-training-a","metrics":{"mAUC":"0.761"},"code_links":[{"title":"SaharAlmahfouzNasser/MeDAL-Retina","url":"https://github.com/SaharAlmahfouzNasser/MeDAL-Retina"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/reverse-knowledge-distillation-training-a","title":"Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data","date":"2023-07-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-keypoint-detector-and","title":"Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching","date":"2022-07-16","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/glampoints-greedily-learned-accurate-match","title":"GLAMpoints: Greedily Learned Accurate Match points","date":"2019-08-19","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/distinctive-image-features-from-scale","title":"Distinctive Image Features from Scale-Invariant Keypoints","date":"2004-01-05","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"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."}