{"url":"/dataset/sa-det-100k","name":"SA-Det-100k","full_name":null,"description_markdown":"SA-Det-100k is a large-scale class-agnostic object detection dataset for Research Purposes only. The dataset is based on a subset of SA-1B (see LICENSE), and all objects belong to the same category objects. Because it contains a large number of scenarios but does not provide class-specific annotations, we believe it may be a good choice to pre-training models for a variety of downstream tasks with different categories. The dataset contains about 100k images, and each image is resized using opencv-python so that the larger one of their height and width is 1333, which is consistent with the data augmentation commonly used to train COCO. \r\nFor example project based on this dataset, please see Relation-DETR (https://github.com/xiuqhou/Relation-DETR).","description_withheld":null,"homepage":"https://huggingface.co/datasets/xiuqhou/SA-Det-100k","introduced_date":"2024-07-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/relation-detr-exploring-explicit-position","title":"Relation DETR: Exploring Explicit Position Relation Prior for Object Detection","first_author":"Xiuquan Hou","url":null},"license":{"name":"sa-1b-dataset-research-license","url":"https://huggingface.co/datasets/xiuqhou/SA-Det-100k/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SA-Det-100k"],"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/object-detection-on-sa-det-100k","task":"Object Detection","dataset_variant":"SA-Det-100k","rows":2,"metrics":["AP","AP50","AP75","APS","APM","APL"],"first_row_in_archive_order":{"model":"Relation-DETR (ResNet50 1x)","paper":"/paper/relation-detr-exploring-explicit-position","metrics":{"AP":"45.0","AP50":"53.1","AP75":"48.9","APL":"62.9","APM":"44.4","APS":"6.0"},"code_links":[{"title":"xiuqhou/relation-detr","url":"https://github.com/xiuqhou/relation-detr"},{"title":"xiuqhou/Salience-DETR","url":"https://github.com/xiuqhou/Salience-DETR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/relation-detr-exploring-explicit-position","title":"Relation DETR: Exploring Explicit Position Relation Prior for Object Detection","date":"2024-07-16","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dino-detr-with-improved-denoising-anchor-1","title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","date":"2022-03-07","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":7,"samples_unverified":8,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":16,"samples_ran":8,"samples_unverified":8,"pointer_only_for_licence":5,"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."}