{"url":"/dataset/sim10k","name":"Sim10k","full_name":null,"description_markdown":"SIM10k is a synthetic dataset containing 10,000 images, which is rendered from the video game Grand Theft Auto V (GTA5).\r\n\r\nSource: [Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation](https://arxiv.org/abs/2003.10275)\r\nImage Source: [https://arxiv.org/pdf/1610.01983.pdf](https://arxiv.org/pdf/1610.01983.pdf)","description_withheld":null,"homepage":"https://fcav.engin.umich.edu/projects/driving-in-the-matrix","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/driving-in-the-matrix-can-virtual-worlds","title":"Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?","first_author":"Matthew Johnson-Roberson","url":null},"license":{"name":"Custom (non-commercial)","url":"https://fcav.engin.umich.edu/projects/driving-in-the-matrix"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Unsupervised Domain Adaptation","url":"/task/unsupervised-domain-adaptation","datasets_with_task":"/datasets/task/unsupervised-domain-adaptation"}],"languages":[],"variants":["SIM10K to Cityscapes","SIM10K to BDD100K","Sim10k"],"data_loaders":[],"num_papers_in_archive":92,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-domain-adaptation-on-sim10k-to-3","task":"Unsupervised Domain Adaptation","dataset_variant":"SIM10K to Cityscapes","rows":13,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"ALDI++","paper":"/paper/align-and-distill-unifying-and-improving","metrics":{"mAP@0.5":"77.8"},"code_links":[{"title":"justinkay/aldi","url":"https://github.com/justinkay/aldi"},{"title":"estrellaxyu/differential-alignment-for-daod","url":"https://github.com/estrellaxyu/differential-alignment-for-daod"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-sim10k-to-2","task":"Unsupervised Domain Adaptation","dataset_variant":"SIM10K to BDD100K","rows":3,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"DDT","paper":"/paper/diffusion-domain-teacher-diffusion-guided","metrics":{"mAP@0.5":"58.3"},"code_links":[{"title":"heboyong/Diffusion-Domain-Teacher","url":"https://github.com/heboyong/Diffusion-Domain-Teacher"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/domain-adaptation-on-sim10k","task":"Domain Adaptation","dataset_variant":"Sim10k","rows":1,"metrics":["mAP"],"first_row_in_archive_order":{"model":"MILA","paper":"/paper/mila-memory-based-instance-level-adaptation-1","metrics":{"mAP":"57.4"},"code_links":[{"title":"hitachi-rd-cv/MILA","url":"https://github.com/hitachi-rd-cv/MILA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/diffusion-domain-teacher-diffusion-guided","title":"Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector","date":"2025-06-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/rt-datr-real-time-unsupervised-domain","title":"RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature Learning","date":"2025-04-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/align-and-distill-unifying-and-improving","title":"Align and Distill: Unifying and Improving Domain Adaptive Object Detection","date":"2024-03-18","rows_on_this_dataset":6,"code_links":2,"syntology":null},{"paper":"/paper/mila-memory-based-instance-level-adaptation-1","title":"MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection","date":"2023-09-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/masked-retraining-teacher-student-framework","title":"Masked Retraining Teacher-Student Framework for Domain Adaptive Object Detection","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/awada-attention-weighted-adversarial-domain","title":"AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection","date":"2022-08-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/to-miss-attend-is-to-misalign-residual-self","title":"To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors","date":"2022-01-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/seeking-similarities-over-differences","title":"Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection","date":"2021-10-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adapting-object-detectors-with-conditional","title":"Adapting Object Detectors with Conditional Domain Normalization","date":"2020-03-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/strong-weak-distribution-alignment-for","title":"Strong-Weak Distribution Alignment for Adaptive Object Detection","date":"2018-12-12","rows_on_this_dataset":1,"code_links":2,"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."}