{"url":"/dataset/god","name":"GOD","full_name":"Generic Object Decoding","description_markdown":"The Generic Object Decoding (GOD) Dataset is a specialized resource developed for fMRI-based decoding. It aggregates fMRI data gathered through the presentation of images from 200 representative object categories, originating from the 2011 fall release of ImageNet. The training session incorporated 1,200 images (8 per category from 150 distinct object categories). In contrast, the test session included 50 images (one from each of the 50 object categories). It is noteworthy that the categories in the test session were unique from those in the training session and were introduced in a randomized sequence across runs. On five subjects the fMRI scanning was conducted.","description_withheld":null,"homepage":"https://www.nature.com/articles/ncomms15037","introduced_date":"2017-05-22","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"fMRI","url":"/datasets/modality/fmri"}],"tasks":[{"name":"Brain Decoding","url":"/task/brain-decoding","datasets_with_task":"/datasets/task/brain-decoding"},{"name":"Brain Visual Reconstruction","url":"/task/brain-visual-reconstruction","datasets_with_task":"/datasets/task/brain-visual-reconstruction"},{"name":"Brain Visual Reconstruction from fMRI","url":"/task/brain-visual-reconstruction-from-fmri","datasets_with_task":"/datasets/task/brain-visual-reconstruction-from-fmri"}],"languages":[],"variants":["GOD"],"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/brain-visual-reconstruction-from-fmri-on-god","task":"Brain Visual Reconstruction from fMRI","dataset_variant":"GOD","rows":2,"metrics":["50-way-top1-classfication accuract"],"first_row_in_archive_order":{"model":"DC-LDM","paper":"/paper/contrast-attend-and-diffuse-to-decode-high","metrics":{"50-way-top1-classfication accuract":"17.999"},"code_links":[{"title":"soinx0629/vis_dec_neurips","url":"https://github.com/soinx0629/vis_dec_neurips"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/contrast-attend-and-diffuse-to-decode-high","title":"Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities","date":"2023-05-26","rows_on_this_dataset":2,"code_links":1,"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."}