{"url":"/dataset/sixray","name":"SIXray","full_name":null,"description_markdown":"The **SIXray** dataset is constructed by the Pattern Recognition and Intelligent System Development Laboratory, University of Chinese Academy of Sciences. It contains 1,059,231 X-ray images which are collected from some several subway stations. There are six common categories of prohibited items, namely, gun, knife, wrench, pliers, scissors and hammer. It has three subsets called SIXray10, SIXray100 and SIXray1000, There are image-level annotations provided by human security inspectors for the whole dataset. In addition the images in the test set are annotated with a bounding-box for each prohibited item to evaluate the performance of object localization.\n\nSource: [https://github.com/MeioJane/SIXray](https://github.com/MeioJane/SIXray)\nImage Source: [https://github.com/MeioJane/SIXray](https://github.com/MeioJane/SIXray)","description_withheld":null,"homepage":"https://github.com/MeioJane/SIXray","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/sixray-a-large-scale-security-inspection-x","title":"SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images","first_author":"Caijing Miao","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Object Localization","url":"/task/object-localization","datasets_with_task":"/datasets/task/object-localization"}],"languages":[],"variants":["SIXray"],"data_loaders":[{"repo":"https://github.com/MeioJane/SIXray","url":"https://github.com/MeioJane/SIXray","frameworks":[]}],"num_papers_in_archive":39,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-detection-on-sixray","task":"Object Detection","dataset_variant":"SIXray","rows":2,"metrics":["1 in 10 R@5"],"first_row_in_archive_order":{"model":"LRPz","paper":"/paper/towards-best-practice-in-explaining-neural","metrics":{"1 in 10 R@5":"0.01347"},"code_links":[{"title":"sebastian-lapuschkin/lrp_toolbox","url":"https://github.com/sebastian-lapuschkin/lrp_toolbox"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/paint-transformer-feed-forward-neural","title":"Paint Transformer: Feed Forward Neural Painting with Stroke Prediction","date":"2021-08-09","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/towards-best-practice-in-explaining-neural","title":"Towards Best Practice in Explaining Neural Network Decisions with LRP","date":"2019-10-22","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."}