{"url":"/dataset/meccano","name":"MECCANO","full_name":null,"description_markdown":"The MECCANO dataset is the first dataset of egocentric videos to study human-object interactions in industrial-like settings.\r\nThe MECCANO dataset has been acquired in an industrial-like scenario in which subjects built a toy model of a motorbike. We considered 20 object classes which include the 16 classes categorizing the 49 components, the two tools (screwdriver and wrench), the instructions booklet and a partial_model class.\r\n\r\nAdditional details related to the MECCANO:\r\n\r\n20 different subjects in 2 countries (IT, U.K.)\r\nVideo Acquisition: 1920x1080 at 12.00 fps\r\n11 training videos and 9 validation/test videos\r\n8857 video segments temporally annotated indicating the verbs which describe the actions performed\r\n64349 active objects annotated with bounding boxes\r\n12 verb classes, 20 objects classes and 61 action classes","description_withheld":null,"homepage":"https://iplab.dmi.unict.it/MECCANO/","introduced_date":"2020-10-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-meccano-dataset-understanding-human","title":"The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain","first_author":"Francesco Ragusa","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Action Recognition","url":"/task/action-recognition-in-videos","datasets_with_task":"/datasets/task/action-recognition-in-videos"},{"name":"Object Recognition","url":"/task/object-recognition","datasets_with_task":"/datasets/task/object-recognition"},{"name":"Human-Object Interaction Detection","url":"/task/human-object-interaction-detection","datasets_with_task":"/datasets/task/human-object-interaction-detection"}],"languages":[],"variants":["MECCANO"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/action-recognition-on-meccano","task":"Action Recognition","dataset_variant":"MECCANO","rows":1,"metrics":["Top-1 Accuracy"],"first_row_in_archive_order":{"model":"SlowFast","paper":"/paper/the-meccano-dataset-understanding-human","metrics":{"Top-1 Accuracy":"42.85"},"code_links":[{"title":"fpv-iplab/MECCANO","url":"https://github.com/fpv-iplab/MECCANO"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/human-object-interaction-detection-on-meccano","task":"Human-Object Interaction Detection","dataset_variant":"MECCANO","rows":1,"metrics":["mAP@0.5 role"],"first_row_in_archive_order":{"model":"SlowFast + FasterRCNN","paper":"/paper/the-meccano-dataset-understanding-human","metrics":{"mAP@0.5 role":"25.93"},"code_links":[{"title":"fpv-iplab/MECCANO","url":"https://github.com/fpv-iplab/MECCANO"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-recognition-on-meccano","task":"Object Recognition","dataset_variant":"MECCANO","rows":1,"metrics":["mAP"],"first_row_in_archive_order":{"model":"Faster-RCNN","paper":"/paper/the-meccano-dataset-understanding-human","metrics":{"mAP":"30.39"},"code_links":[{"title":"fpv-iplab/MECCANO","url":"https://github.com/fpv-iplab/MECCANO"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/the-meccano-dataset-understanding-human","title":"The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain","date":"2020-10-12","rows_on_this_dataset":3,"code_links":1,"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."}