{"url":"/dataset/cats","name":"CATS","full_name":"Color and Thermal Stereo Benchmark","description_markdown":"A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes.\r\n\r\nSource: [CATS: A Color and Thermal Stereo Benchmark](/paper/cats-a-color-and-thermal-stereo-benchmark)","description_withheld":null,"homepage":"https://bigdatavision.org/CATS","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/cats-a-color-and-thermal-stereo-benchmark","title":"CATS: A Color and Thermal Stereo Benchmark","first_author":"Wayne Treible","url":null},"license":null,"modalities":[],"tasks":[{"name":"Super-Resolution","url":"/task/super-resolution","datasets_with_task":"/datasets/task/super-resolution"},{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Multimodal Unsupervised Image-To-Image Translation","url":"/task/multimodal-unsupervised-image-to-image","datasets_with_task":"/datasets/task/multimodal-unsupervised-image-to-image"},{"name":"Stereo Matching Hand","url":"/task/stereo-matching","datasets_with_task":"/datasets/task/stereo-matching"},{"name":"Stereo Matching","url":"/task/stereo-matching-1","datasets_with_task":"/datasets/task/stereo-matching-1"}],"languages":[],"variants":["Cats-and-Dogs","CATS"],"data_loaders":[{"repo":"https://github.com/GurSergey/datathon_comanda_AA","url":"https://github.com/GurSergey/datathon_comanda_AA","frameworks":[]}],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-cats-and-dogs","task":"Anomaly Detection","dataset_variant":"Cats-and-Dogs","rows":4,"metrics":["ROC AUC"],"first_row_in_archive_order":{"model":"PANDA","paper":"/paper/panda-adapting-pretrained-features-for","metrics":{"ROC AUC":"97.3"},"code_links":[{"title":"talreiss/PANDA","url":"https://github.com/talreiss/PANDA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multimodal-unsupervised-image-to-image","task":"Multimodal Unsupervised Image-To-Image Translation","dataset_variant":"Cats-and-Dogs","rows":3,"metrics":["CIS","IS"],"first_row_in_archive_order":{"model":"MUNIT","paper":"/paper/multimodal-unsupervised-image-to-image","metrics":{"CIS":"1.039","IS":" 1.050"},"code_links":[{"title":"eriklindernoren/PyTorch-GAN","url":"https://github.com/eriklindernoren/PyTorch-GAN"},{"title":"nvlabs/MUNIT","url":"https://github.com/nvlabs/MUNIT"},{"title":"taki0112/MUNIT-Tensorflow","url":"https://github.com/taki0112/MUNIT-Tensorflow"},{"title":"Onr/Council-GAN","url":"https://github.com/Onr/Council-GAN"},{"title":"hyperplane-lab/ACL-GAN","url":"https://github.com/hyperplane-lab/ACL-GAN"},{"title":"yaxingwang/SEMIT","url":"https://github.com/yaxingwang/SEMIT"},{"title":"yaxingwang/SDIT","url":"https://github.com/yaxingwang/SDIT"},{"title":"arobey1/mbrdl","url":"https://github.com/arobey1/mbrdl"},{"title":"AverageName/UI2IT","url":"https://github.com/AverageName/UI2IT"},{"title":"AverageName/Cycle_gan_pytorch","url":"https://github.com/AverageName/Cycle_gan_pytorch"},{"title":"nct_tso_public/laparoscopic-image-2-image-translation","url":"https://gitlab.com/nct_tso_public/laparoscopic-image-2-image-translation"},{"title":"nct_tso_public/surgical-video-sim2real","url":"https://gitlab.com/nct_tso_public/surgical-video-sim2real"},{"title":"yaxingwang/UDIT","url":"https://github.com/yaxingwang/UDIT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/panda-adapting-pretrained-features-for","title":"PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation","date":"2020-10-12","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/multimodal-unsupervised-image-to-image","title":"Multimodal Unsupervised Image-to-Image Translation","date":"2018-04-12","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unpaired-image-to-image-translation-using","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","date":"2017-03-30","rows_on_this_dataset":1,"code_links":190,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":31,"samples_ran":6,"samples_unverified":25,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-image-to-image-translation","title":"Unsupervised Image-to-Image Translation Networks","date":"2017-03-02","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"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":3,"samples_harvested":45,"samples_ran":7,"samples_unverified":38,"pointer_only_for_licence":7,"papers_with_no_sample_that_ran":1,"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."}