{"url":"/dataset/obj-mda","name":"OBJ-MDA","full_name":null,"description_markdown":"The dataset contains images of 16 artworks included in the cultural site “Galleria Regionale di Palazzo Bellomo2”. The collection covers different types of artworks, as well as books, sculptures and paintings. The dataset three domains:\r\ni) synthetic images generated from a 3D model of the cultural site and automatically labeled\r\nduring the generation process;\r\n ii) real images collected by 10 visitors with a HoloLens device and manually labeled;\r\niii) realimages collected by the same visitors with a GoPro and manually labeled.","description_withheld":null,"homepage":"https://github.com/fpv-iplab/STMDA-RetinaNet","introduced_date":"2022-09-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-multi-camera-unsupervised-domain-adaptation","title":"A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[{"name":"Multi-target Domain Adaptation","url":"/task/multi-target-domain-adaptation","datasets_with_task":"/datasets/task/multi-target-domain-adaptation"}],"languages":[],"variants":["OBJ-MDA"],"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/multi-target-domain-adaptation-on-obj-mda","task":"Multi-target Domain Adaptation","dataset_variant":"OBJ-MDA","rows":1,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"STMDA-RetinaNet","paper":"/paper/a-multi-camera-unsupervised-domain-adaptation","metrics":{"mAP@0.5":"66.64"},"code_links":[{"title":"fpv-iplab/STMDA-RetinaNet","url":"https://github.com/fpv-iplab/STMDA-RetinaNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-multi-camera-unsupervised-domain-adaptation","title":"A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training","date":null,"rows_on_this_dataset":1,"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."}