{"url":"/dataset/cholect40","name":"CholecT40","full_name":"Cholecystectomy Action Triplet","description_markdown":"CholecT40 is the first endoscopic dataset introduced to enable research on fine-grained action recognition in laparoscopic surgery. \r\n\r\nIt consists of 40 videos of laparoscopic cholecystectomy surgery annotated with triplet information in the form of <instrument, verb, target>. The annotations spans over 128 triplet classes that are composed from 6 classes of surgical instruments, 8 classes of action verbs, and 19 classes of surgical targets.\r\n\r\nThe dataset is used as benchmark for developing deep learning solution for the recognition of surgical activities in the form of a triplet. It is first surgical data science effort to replicate activity recognition in the same level as human-object interaction (HOI) in natural vision tasks.\r\n\r\nThe parent dataset is [CholecT50](https://paperswithcode.com/dataset/cholect50).","description_withheld":null,"homepage":"https://github.com/CAMMA-public/cholect45","introduced_date":"2020-07-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/recognition-of-instrument-tissue-interactions","title":"Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets","first_author":"Chinedu Innocent Nwoye","url":null},"license":{"name":"CC BY-NC-SA 4.0 LICENSE","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"RGB Video","url":"/datasets/modality/rgb-video"}],"tasks":[{"name":"Action Triplet Recognition","url":"/task/action-triplet-recognition","datasets_with_task":"/datasets/task/action-triplet-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":[],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/action-triplet-recognition-on-cholect40","task":"Action Triplet Recognition","dataset_variant":"CholecT40","rows":1,"metrics":["mAP"],"first_row_in_archive_order":{"model":"Tripnet","paper":"/paper/recognition-of-instrument-tissue-interactions","metrics":{"mAP":"18.95"},"code_links":[{"title":"CAMMA-public/cholect50","url":"https://github.com/CAMMA-public/cholect50"},{"title":"camma-public/rendezvous","url":"https://github.com/camma-public/rendezvous"},{"title":"camma-public/tripnet","url":"https://github.com/camma-public/tripnet"},{"title":"camma-public/attention-tripnet","url":"https://github.com/camma-public/attention-tripnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/recognition-of-instrument-tissue-interactions","title":"Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets","date":"2020-07-10","rows_on_this_dataset":1,"code_links":4,"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."}