{"url":"/dataset/rc-49","name":"RC-49","full_name":null,"description_markdown":"RC-49 is a benchmark dataset for generating images conditional on a continuous scalar variable. It is made by rendering 49 3-D chair models from ShapeNet individually. Each chair model is rendered at 899 yaw angles from $0.1^{\\circ}$ to $89.9^{\\circ}$ with a stepsize of $0.1^{\\circ}$. This dataset contains 44,051 RGB images of size $64\\times64$ with corresponding yaw angles as labels. \r\n\r\nNote that in CcGAN, angles are used for training if their last digits are odd. Thus, there are 450 angles in the training set. Moreover, for these 450 training angles, only 25 images for each angle are used for the training.","description_withheld":null,"homepage":"https://github.com/UBCDingXin/cDRE-based_Subsampling_cGANS","introduced_date":"2020-11-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/ccgan-continuous-conditional-generative-1","title":"Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms","first_author":"Xin Ding","url":null},"license":{"name":"For research purpose only","url":null},"modalities":[],"tasks":[{"name":"Image Generation","url":"/task/image-generation","datasets_with_task":"/datasets/task/image-generation"}],"languages":[],"variants":["RC-49"],"data_loaders":[{"repo":"https://github.com/UBCDingXin/cDRE-based_Subsampling_cGANS","url":"https://github.com/UBCDingXin/cDRE-based_Subsampling_cGANS","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-generation-on-rc-49","task":"Image Generation","dataset_variant":"RC-49","rows":2,"metrics":["Intra-FID"],"first_row_in_archive_order":{"model":"cDR-RS","paper":"/paper/efficient-subsampling-for-generating-high","metrics":{"Intra-FID":"0.334"},"code_links":[{"title":"UBCDingXin/cDR-RS","url":"https://github.com/UBCDingXin/cDR-RS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-subsampling-for-generating-high","title":"Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable","date":"2021-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ccgan-continuous-conditional-generative-1","title":"Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms","date":"2020-11-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"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."}