{"url":"/dataset/coco-n-medium","name":"COCO-N Medium","full_name":null,"description_markdown":"COCO-N Medium introduces a stochastic benchmark that simulates common real-world scenarios with noticeable label inaccuracies in the COCO dataset. This benchmark combines class and spatial noises to create a challenging yet realistic evaluation framework for instance segmentation models. It mimics datasets manually annotated by crowd workers, where a moderate level of label noise is expected. By incorporating both class and spatial inaccuracies, COCO-N Medium allows researchers to assess their models' basic robustness to label noise, providing insights into performance in typical real-world applications where perfect annotations are rare. This medium-level benchmark serves as a crucial middle ground, offering a more rigorous test than minimally noisy datasets while remaining within the bounds of commonly encountered data quality issues. COCO-N Medium enables a nuanced evaluation of model performance under realistic conditions, helping identify areas for improvement in handling noisy labels and guiding the development of more robust instance segmentation algorithms.","description_withheld":null,"homepage":"","introduced_date":"2024-06-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-label-noise-in-instance","title":"Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters","first_author":"Eden Grad","url":null},"license":null,"modalities":[],"tasks":[{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Learning with noisy labels","url":"/task/learning-with-noisy-labels","datasets_with_task":"/datasets/task/learning-with-noisy-labels"},{"name":"Benchmarking","url":"/task/benchmarking","datasets_with_task":"/datasets/task/benchmarking"}],"languages":[],"variants":["COCO-N Medium"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/instance-segmentation-on-coco-n-medium","task":"Instance Segmentation","dataset_variant":"COCO-N Medium","rows":1,"metrics":["mIOU"],"first_row_in_archive_order":{"model":"Mask R-CNN ResNet-50 FPN","paper":"/paper/benchmarking-label-noise-in-instance","metrics":{"mIOU":"30.3"},"code_links":[{"title":"eden500/Noisy-Labels-Instance-Segmentation","url":"https://github.com/eden500/Noisy-Labels-Instance-Segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/benchmarking-label-noise-in-instance","title":"Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters","date":"2024-06-16","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"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":2,"samples_ran":2,"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."}