{"url":"/dataset/lincolnbeet","name":"Lincolnbeet","full_name":null,"description_markdown":"The Lincolnbeet dataset is an object detection dataset designed to encourage research in the identification of items in environments with high levels of occlusion, and in the development of better approaches to evaluate object detection models in practical scenarios. This dataset was introduced in the paper: \"Towards practical object detection for weed spraying in precision agriculture\". \n\nThe dataset contains 4402 images that contain weed plants and sugar beets which are located with object detection labels. The image size is 1920 x 1080 pixels, and the labels included in the dataset are in COCOjson, XML, and darknets formats.","description_withheld":null,"homepage":"https://github.com/LAR/lincolnbeet_dataset","introduced_date":"2021-09-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/towards-practical-object-detection-for-weed","title":"Towards practical object detection for weed spraying in precision agriculture","first_author":"Adrian Salazar-Gomez","url":null},"license":{"name":"GPL","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Lincolnbeet"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/lincolnbeet-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":1,"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."}