{"url":"/dataset/sardet-100k","name":"SARDet-100K","full_name":null,"description_markdown":"The SARDet-100K dataset encompasses a total of 116,598 images, and 245,653 instances distributed across six categories: Aircraft, Ship, Car, Bridge, Tank, and Harbor. SARDet100K dataset stands as the first large-scale SAR object detection dataset, comparable in size to the widely used COCO dataset (118K images). The scale and diversity of the SARDet-100K dataset provide researchers with robust training and evaluation for advancing SAR object detection algorithms and techniques, fostering the development of SOTA models in this domain.","description_withheld":null,"homepage":"https://github.com/zcablii/SARDet_100K","introduced_date":"2024-03-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/sardet-100k-towards-open-source-benchmark-and","title":"SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection","first_author":"YuXuan Li","url":null},"license":{"name":"Attribution-NonCommercial 4.0 International","url":"https://github.com/zcablii/SARDet_100K/blob/main/LICENSE"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["SARDet-100K"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-object-detection-on-sardet-100k","task":"2D Object Detection","dataset_variant":"SARDet-100K","rows":13,"metrics":["box mAP","mAP","mAP@50","mAP@75"],"first_row_in_archive_order":{"model":"DenoDet","paper":"/paper/denodet-attention-as-deformable-multi","metrics":{"box mAP":"55.4"},"code_links":[{"title":"zcablii/sardet_100k","url":"https://github.com/zcablii/sardet_100k"},{"title":"grokcv/groksar","url":"https://github.com/grokcv/groksar"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/denodet-attention-as-deformable-multi","title":"DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images","date":"2024-06-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/saratr-x-a-foundation-model-for-synthetic","title":"SARATR-X: Toward Building A Foundation Model for SAR Target Recognition","date":"2024-05-15","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/sardet-100k-towards-open-source-benchmark-and","title":"SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection","date":"2024-03-11","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/sparse-r-cnn-end-to-end-object-detection-with","title":"Sparse R-CNN: End-to-End Object Detection with Learnable Proposals","date":"2020-11-25","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/deformable-detr-deformable-transformers-for-1","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","date":"2020-10-08","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":55,"samples_ran":29,"samples_unverified":26,"pointer_only_for_licence":21,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","rows_on_this_dataset":1,"code_links":87,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":40,"samples_ran":13,"samples_unverified":27,"pointer_only_for_licence":18,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/grid-r-cnn","title":"Grid R-CNN","date":"2018-11-29","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cascade-r-cnn-delving-into-high-quality","title":"Cascade R-CNN: Delving into High Quality Object Detection","date":"2017-12-03","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","rows_on_this_dataset":1,"code_links":234,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":11,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","rows_on_this_dataset":1,"code_links":196,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":124,"samples_ran":59,"samples_unverified":65,"pointer_only_for_licence":42,"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":6,"samples_harvested":235,"samples_ran":114,"samples_unverified":121,"pointer_only_for_licence":89,"papers_with_no_sample_that_ran":1,"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."}