{"url":"/dataset/clevrtex","name":"ClevrTex","full_name":null,"description_markdown":"**ClevrTex** is a new benchmark designed as the next challenge to compare, evaluate and analyze algorithms for unsupervised multi-object segmentation. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques.\r\n\r\nImage source: [Karazija et al.](https://arxiv.org/pdf/2111.10265.pdf)","description_withheld":null,"homepage":"https://www.robots.ox.ac.uk/~vgg/data/clevrtex/","introduced_date":"2021-11-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/clevrtex-a-texture-rich-benchmark-for","title":"ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation","first_author":"Laurynas Karazija","url":null},"license":{"name":"CC-BY","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Unsupervised Object Segmentation","url":"/task/unsupervised-object-segmentation","datasets_with_task":"/datasets/task/unsupervised-object-segmentation"}],"languages":[],"variants":["ClevrTex"],"data_loaders":[{"repo":"https://github.com/karazijal/clevrtex-generation","url":"https://github.com/karazijal/clevrtex-generation","frameworks":["pytorch"]}],"num_papers_in_archive":33,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-object-segmentation-on-clevrtex","task":"Unsupervised Object Segmentation","dataset_variant":"ClevrTex","rows":12,"metrics":["mIoU","MSE"],"first_row_in_archive_order":{"model":"AST-Seg-B3-CT","paper":"/paper/unsupervised-multi-object-segmentation-using","metrics":{"MSE":"139±7","mIoU":"79.58±0.54"},"code_links":[{"title":"BrunoSauvalle/AST","url":"https://github.com/BrunoSauvalle/AST"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unsupervised-multi-object-segmentation-using","title":"Unsupervised Multi-object Segmentation Using Attention and Soft-argmax","date":"2022-05-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/clevrtex-a-texture-rich-benchmark-for","title":"ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation","date":"2021-11-19","rows_on_this_dataset":10,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":0,"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":8,"samples_ran":1,"samples_unverified":7,"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."}