{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bold5000-a-public-fmri-dataset-of-5000-images","title":"BOLD5000: A public fMRI dataset of 5000 images","arxiv_id":"1809.01281","date":"2018-09-05","proceeding":null,"authors":["Nadine Chang","John A. Pyles","Abhinav Gupta","Michael J. Tarr","Elissa M. Aminoff"],"abstract":"Vision science, particularly machine vision, has been revolutionized by\nintroducing large-scale image datasets and statistical learning approaches.\nYet, human neuroimaging studies of visual perception still rely on small\nnumbers of images (around 100) due to time-constrained experimental procedures.\nTo apply statistical learning approaches that integrate neuroscience, the\nnumber of images used in neuroimaging must be significantly increased. We\npresent BOLD5000, a human functional MRI (fMRI) study that includes almost\n5,000 distinct images depicting real-world scenes. Beyond dramatically\nincreasing image dataset size relative to prior fMRI studies, BOLD5000 also\naccounts for image diversity, overlapping with standard computer vision\ndatasets by incorporating images from the Scene UNderstanding (SUN), Common\nObjects in Context (COCO), and ImageNet datasets. The scale and diversity of\nthese image datasets, combined with a slow event-related fMRI design, enable\nfine-grained exploration into the neural representation of a wide range of\nvisual features, categories, and semantics. Concurrently, BOLD5000 brings us\ncloser to realizing Marr's dream of a singular vision science - the intertwined\nstudy of biological and computer vision.","url_abs":"http://arxiv.org/abs/1809.01281v1","url_pdf":"http://arxiv.org/pdf/1809.01281v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bold5000-a-public-fmri-dataset-of-5000-images","repo_url":"https://github.com/OpenNeuroDatasets/ds001499","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"bold5000-a-public-fmri-dataset-of-5000-images","repo_url":"https://github.com/mattgorb/cnn_bold5000","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"bold5000-a-public-fmri-dataset-of-5000-images","repo_url":"https://github.com/nchang430/BOLD5000-Scripts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1809.01281","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}