{"url":"/dataset/swinyseg","name":"SWINySEG","full_name":"Singapore Whole sky Nychthemeron Image SEGmentation Database","description_markdown":"The SWINySEG dataset contains 6768 daytime- and nighttime-images of sky/cloud patches along with their corresponding binary ground truth maps. The images in the SWINySeg dataset are taken from two of our earlier sky/cloud image segmentation datasets -- SWIMSEG and SWINSEG.  All images were captured in Singapore using WAHRSIS, a calibrated ground-based whole sky imager, over a period of 12 months from January to December 2016. The ground truth annotation was done in consultation with experts from Singapore Meteorological Services.\r\n\r\nSource: [CloudSegNet: A deep network for nychthemeron cloud image segmentation](https://stefan.winkler.site/Publications/grsl2019.pdf)","description_withheld":null,"homepage":"http://vintage.winklerbros.net/swinyseg.html","introduced_date":"2019-04-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/cloudsegnet-a-deep-network-for-nychthemeron","title":"CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation","first_author":"Soumyabrata Dev","url":null},"license":{"name":"Creative Commons License","url":"https://creativecommons.org/licenses/by-nc/4.0/"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["SWINySEG"],"data_loaders":[{"repo":"https://github.com/fvisin/dataset_loaders","url":"https://github.com/fvisin/dataset_loaders","frameworks":["pytorch"]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-swinyseg","task":"Semantic Segmentation","dataset_variant":"SWINySEG","rows":1,"metrics":["Average Precision","Average Recall","F1-Score","MCC","Mean IoU"],"first_row_in_archive_order":{"model":"ACLNet","paper":"/paper/aclnet-an-attention-and-clustering-based","metrics":{"Average Precision":"0.959","Average Recall":"0.979","F1-Score":"0.968","MCC":"0.960","Mean IoU":"0.993"},"code_links":[{"title":"ckmvigil/aclnet","url":"https://github.com/ckmvigil/aclnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/aclnet-an-attention-and-clustering-based","title":"ACLNet: An Attention and Clustering-based Cloud Segmentation Network","date":"2022-07-13","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."}