{"url":"/dataset/irodent","name":"iRodent","full_name":"iRodent Animal Pose Estimation","description_markdown":"Description: The \"iRodent\" dataset contains rodent species observations obtained using the iNaturalist API, with a focus on Suborder Myomorpha (Taxon ID: 16). The dataset features prominent rodent species like Muskrat, Brown Rat, House Mouse, Black Rat, Hispid Cotton Rat, Meadow Vole, Bank Vole, Deer Mouse, White-footed Mouse, and Striped Field Mouse. The dataset provides manually labeled keypoints for pose estimation and segmentation masks for a subset of images using a Mask R-CNN model.\r\n\r\nIf you use this data, please cite: \r\n\r\nYe, S., Filippova, A., Lauer, J. et al. SuperAnimal pretrained pose estimation models for behavioral analysis. Nat Commun 15, 5165 (2024). https://doi.org/10.1038/s41467-024-48792-2\r\n\r\nCreator: Adaptive Intelligence Lab at EPFL: https://www.mackenziemathislab.org/\r\n\r\nData Format: COCO format\r\nNumber of Images: 443\r\nSpecies: Muskrat, Brown Rat, House Mouse, Black Rat, Hispid Cotton Rat, Meadow Vole, Bank Vole, Deer Mouse, White-footed Mouse, Striped Field Mouse\r\nImage Resolution: Varied (800x600 to 5184x3456 pixels)\r\nAnnotations: Pose keypoints and generated segmentation masks by Tian Qiu and Mackenzie Mathis.\r\nLicense: Apache 2.0\r\nKeywords: animal pose estimation, behaviour analysis, keypoints, rodent\r\n\r\nContact: Mackenzie Mathis\r\nEmail: mackenzie.mathis@epfl.ch","description_withheld":null,"homepage":"https://zenodo.org/records/8250392","introduced_date":"2024-06-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/panoptic-animal-pose-estimators-are-zero-shot","title":"SuperAnimal pretrained pose estimation models for behavioral analysis","first_author":"Shaokai Ye","url":null},"license":{"name":"Apache 2.0","url":"https://zenodo.org/records/8250392"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Pose Estimation","url":"/task/2d-pose-estimation","datasets_with_task":"/datasets/task/2d-pose-estimation"}],"languages":[],"variants":["iRodent"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-pose-estimation-on-irodent","task":"2D Pose Estimation","dataset_variant":"iRodent","rows":8,"metrics":["Average mAP"],"first_row_in_archive_order":{"model":"fine-tuned HRNetw32 pretrained on SuperAnimal (1 fac of data)","paper":"/paper/panoptic-animal-pose-estimators-are-zero-shot","metrics":{"Average mAP":"72.971"},"code_links":[{"title":"AlexEMG/DeepLabCut","url":"https://github.com/AlexEMG/DeepLabCut"},{"title":"DeepLabCut/DeepLabCut","url":"https://github.com/DeepLabCut/DeepLabCut"},{"title":"adaptivemotorcontrollab/modelzoo-figures","url":"https://github.com/adaptivemotorcontrollab/modelzoo-figures"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/panoptic-animal-pose-estimators-are-zero-shot","title":"SuperAnimal pretrained pose estimation models for behavioral analysis","date":"2022-03-14","rows_on_this_dataset":8,"code_links":3,"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."}