{"url":"/dataset/rwth-phoenix-handshapes-dev-set","name":"RWTH-PHOENIX Handshapes dev set","full_name":"RWTH-PHOENIX-Weather 2014 MS Handshapes dev set","description_markdown":"We manually labelled 3359 images from the RWTH-PHOENIX-Weather 2014 Development set. \r\n\r\nSome of the 45 encountered pose-independent hand shape classes are depicted in Figure 1. They show the large intra-class variance and the strong similarity between several classes. The hand shapes occur with different frequency in the data. The distribution of counts per class can be verified in Figure 2 showing that the top 14 hand shapes explain 90% of the annotated samples.\r\n\r\n\r\nFor our works on hand shape recognition we follow the hand shape taxonomy by the danish sign language lexicon team (Jette H. Kristoffersen and Thomas Troelsgård, Center for Tegnsprog, Denmark http://www.tegnsprog.dk), which amounts to over 60 different hand shapes, often with very subtle differences such as a flexed versus straight thumb. The employed classes are shown in Table1.","description_withheld":null,"homepage":"https://www-i6.informatik.rwth-aachen.de/~koller/1miohands-data/","introduced_date":"2016-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-hand-how-to-train-a-cnn-on-1-million","title":"Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled","first_author":"Oscar Koller","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Hand Gesture Recognition","url":"/task/hand-gesture-recognition","datasets_with_task":"/datasets/task/hand-gesture-recognition"}],"languages":[{"name":"German","url":"/datasets/language/german"},{"name":"German Sign Language","url":"/datasets/language/german-sign-language"}],"variants":["RWTH-PHOENIX Handshapes dev set"],"data_loaders":[{"repo":"https://github.com/midusi/handshape_datasets","url":"https://github.com/midusi/handshape_datasets","frameworks":[]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hand-gesture-recognition-on-rwth-phoenix","task":"Hand Gesture Recognition","dataset_variant":"RWTH-PHOENIX Handshapes dev set","rows":2,"metrics":["Accuracy "],"first_row_in_archive_order":{"model":"DenseNet","paper":"/paper/a-comparison-of-small-sample-methods-for","metrics":{"Accuracy ":"96.05"},"code_links":[{"title":"midusi/cacic2019-handshapes","url":"https://github.com/midusi/cacic2019-handshapes"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-comparison-of-small-sample-methods-for","title":"A comparison of small sample methods for Handshape Recognition","date":"2023-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-study-of-convolutional-architectures-for","title":"A Study of Convolutional Architectures for Handshape Recognition applied to Sign Language","date":"2017-10-01","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."}