{"url":"/dataset/ufpr-admr-v1-dataset","name":"UFPR-ADMR-v1","full_name":null,"description_markdown":"This dataset contains 2,000 dial meter images obtained on-site by employees of the Energy Company of Paraná (Copel), which serves more than 4 million consuming units in the Brazilian state of Paraná. The images were acquired with many different cameras and are available in the JPG format with 320×640 or 640×320 pixels (depending on the camera orientation). \r\n\r\nThe dataset is split into three sets: training (1200 images), validation (400 images), and testing (400 images). Every image has the following annotations available in a .txt file: the counter’s corners (x1, y1), (x2, y2), (x3, y3), (x4, y4). The corners can be used to rectify the counter patch and represent, respectively, the top-left, top-right, bottom-right, and bottom-left corners. For each dial, the current position (x, y, w, h) and the corresponding reading (pointed values and final reading). All counters of the dataset (regardless of meter type) have 4 or 5 dials; thus, 9,097 dials were manually annotated.\r\n\r\nSource: [Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines](/paper/deep-learning-for-image-based-automatic-dial)","description_withheld":null,"homepage":"https://web.inf.ufpr.br/vri/databases/ufpr-admr/","introduced_date":"2020-09-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-learning-for-image-based-automatic-dial","title":"Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines","first_author":"Gabriel Salomon","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Meter Reading","url":"/task/meter-reading","datasets_with_task":"/datasets/task/meter-reading"},{"name":"Image-based Automatic Meter Reading","url":"/task/image-based-automatic-meter-reading","datasets_with_task":"/datasets/task/image-based-automatic-meter-reading"},{"name":"Dial Meter Reading","url":"/task/dial-meter-reading","datasets_with_task":"/datasets/task/dial-meter-reading"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["UFPR-ADMR-v1"],"data_loaders":[{"repo":"https://github.com/abdoub560/Flutter_date_and_time_picker","url":"https://github.com/abdoub560/Flutter_date_and_time_picker","frameworks":["tf"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/meter-reading-on-ufpr-admr-v1-dataset","task":"Meter Reading","dataset_variant":"UFPR-ADMR-v1","rows":11,"metrics":["Rank-1 Recognition Rate"],"first_row_in_archive_order":{"model":"Faster-RCNN (ResNeXt-101)","paper":"/paper/deep-learning-for-image-based-automatic-dial","metrics":{"Rank-1 Recognition Rate":"75.25"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deep-learning-for-image-based-automatic-dial","title":"Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines","date":"2020-05-06","rows_on_this_dataset":11,"code_links":0,"syntology":null}],"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."}