{"url":"/dataset/dayton","name":"Dayton","full_name":"Dayton","description_markdown":"The **Dayton** dataset is a dataset for ground-to-aerial (or aerial-to-ground) image translation, or cross-view image synthesis. It contains images of road views and aerial views of roads. There are 76,048 images in total and the train/test split is 55,000/21,048. The images in the original dataset have 354×354 resolution.\n\nSource: [Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation](https://arxiv.org/abs/2002.01048)\nImage Source: [https://arxiv.org/abs/1912.06112](https://arxiv.org/abs/1912.06112)","description_withheld":null,"homepage":"https://github.com/kregmi/cross-view-image-synthesis/blob/master/README.md","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/localizing-and-orienting-street-views-using","title":"Localizing and Orienting Street Views Using Overhead Imagery","first_author":"Nam Vo","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Cross-View Image-to-Image Translation","url":"/task/cross-view-image-to-image-translation","datasets_with_task":"/datasets/task/cross-view-image-to-image-translation"}],"languages":[],"variants":["Dayton (64×64) - aerial-to-ground","Dayton (64x64) - ground-to-aerial","Dayton (256×256) - aerial-to-ground","Dayton (256×256) - ground-to-aerial","Dayton"],"data_loaders":[{"repo":"https://github.com/kregmi/cross-view-image-synthesis","url":"https://github.com/kregmi/cross-view-image-synthesis","frameworks":["pytorch"]}],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/cross-view-image-to-image-translation-on-2","task":"Cross-View Image-to-Image Translation","dataset_variant":"Dayton (256×256) - aerial-to-ground","rows":6,"metrics":["SSIM","KL","PSNR","SD"],"first_row_in_archive_order":{"model":"SelectionGAN","paper":"/paper/multi-channel-attention-selection-gan-with","metrics":{"SSIM":"0.5938"},"code_links":[{"title":"Ha0Tang/SelectionGAN","url":"https://github.com/Ha0Tang/SelectionGAN"},{"title":"Ha0Tang/LocalGlobalGAN","url":"https://github.com/Ha0Tang/LocalGlobalGAN"},{"title":"Ha0Tang/HandGestureRecognition","url":"https://github.com/Ha0Tang/HandGestureRecognition"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cross-view-image-to-image-translation-on","task":"Cross-View Image-to-Image Translation","dataset_variant":"Dayton (64×64) - aerial-to-ground","rows":5,"metrics":["SSIM","KL","LPIPS","PSNR","SD"],"first_row_in_archive_order":{"model":"SelectionGAN","paper":"/paper/multi-channel-attention-selection-gan-with","metrics":{"SSIM":"0.6865"},"code_links":[{"title":"Ha0Tang/SelectionGAN","url":"https://github.com/Ha0Tang/SelectionGAN"},{"title":"Ha0Tang/LocalGlobalGAN","url":"https://github.com/Ha0Tang/LocalGlobalGAN"},{"title":"Ha0Tang/HandGestureRecognition","url":"https://github.com/Ha0Tang/HandGestureRecognition"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cross-view-image-to-image-translation-on-1","task":"Cross-View Image-to-Image Translation","dataset_variant":"Dayton (64x64) - ground-to-aerial","rows":5,"metrics":["SSIM","LPIPS"],"first_row_in_archive_order":{"model":"SelectionGAN","paper":"/paper/multi-channel-attention-selection-gan-with","metrics":{"SSIM":"0.5118"},"code_links":[{"title":"Ha0Tang/SelectionGAN","url":"https://github.com/Ha0Tang/SelectionGAN"},{"title":"Ha0Tang/LocalGlobalGAN","url":"https://github.com/Ha0Tang/LocalGlobalGAN"},{"title":"Ha0Tang/HandGestureRecognition","url":"https://github.com/Ha0Tang/HandGestureRecognition"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cross-view-image-to-image-translation-on-3","task":"Cross-View Image-to-Image Translation","dataset_variant":"Dayton (256×256) - ground-to-aerial","rows":4,"metrics":["SSIM"],"first_row_in_archive_order":{"model":"SelectionGAN","paper":"/paper/multi-channel-attention-selection-gan-with","metrics":{"SSIM":"0.3284"},"code_links":[{"title":"Ha0Tang/SelectionGAN","url":"https://github.com/Ha0Tang/SelectionGAN"},{"title":"Ha0Tang/LocalGlobalGAN","url":"https://github.com/Ha0Tang/LocalGlobalGAN"},{"title":"Ha0Tang/HandGestureRecognition","url":"https://github.com/Ha0Tang/HandGestureRecognition"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/local-class-specific-and-global-image-level","title":"Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation","date":"2019-12-27","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/unified-generative-adversarial-networks-for","title":"Unified Generative Adversarial Networks for Controllable Image-to-Image Translation","date":"2019-12-12","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/multi-channel-attention-selection-gan-with","title":"Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation","date":"2019-04-15","rows_on_this_dataset":4,"code_links":3,"syntology":null},{"paper":"/paper/cross-view-image-synthesis-using-geometry","title":"Cross-view image synthesis using geometry-guided conditional GANs","date":"2018-08-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/cross-view-image-synthesis-using-conditional","title":"Cross-View Image Synthesis using Conditional GANs","date":"2018-03-09","rows_on_this_dataset":7,"code_links":1,"syntology":null},{"paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","rows_on_this_dataset":4,"code_links":192,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":122,"samples_ran":14,"samples_unverified":108,"pointer_only_for_licence":1,"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":1,"samples_harvested":122,"samples_ran":14,"samples_unverified":108,"pointer_only_for_licence":1,"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."}