{"url":"/dataset/fes","name":"FES","full_name":"Fisheye Evaluation Suite","description_markdown":"FES is an indoor dataset that can be used for evaluation of deep learning approaches.\r\nIt consists of 301 top-view fisheye images from an indoor scene.\r\nAnnotations include bounding boxes and instance segmentation masks for 6 classes.","description_withheld":null,"homepage":"https://www.tu-chemnitz.de/etit/dst/forschung/comp_vision/datasets/fes/index.php.en","introduced_date":"2020-11-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-from-theodore-a-synthetic","title":"Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning","first_author":"Tobias Scheck","url":null},"license":{"name":"creative commons 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"},{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"}],"languages":[],"variants":["FES"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}