BOLD5000: A public fMRI dataset of 5000 images

TL;DR

BOLD5000 is a large-scale, diverse fMRI dataset of nearly 5,000 real-world scene images designed to advance the integration of neuroscience and computer vision through extensive neural imaging data.

Contribution

It introduces a significantly larger and more diverse fMRI dataset of visual perception, bridging the gap between neuroscience and computer vision research.

Findings

Enables detailed analysis of neural responses to diverse visual features.

Facilitates comparison between biological and computer vision representations.

Supports large-scale statistical learning approaches in neuroimaging.

Abstract

Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies of visual perception still rely on small numbers of images (around 100) due to time-constrained experimental procedures. To apply statistical learning approaches that integrate neuroscience, the number of images used in neuroimaging must be significantly increased. We present BOLD5000, a human functional MRI (fMRI) study that includes almost 5,000 distinct images depicting real-world scenes. Beyond dramatically increasing image dataset size relative to prior fMRI studies, BOLD5000 also accounts for image diversity, overlapping with standard computer vision datasets by incorporating images from the Scene UNderstanding (SUN), Common Objects in Context (COCO), and ImageNet datasets. The scale and diversity of these image datasets, combined with a slow event-related fMRI design, enable fine-grained exploration into the neural representation of a wide range of visual features, categories, and semantics. Concurrently, BOLD5000 brings us closer to realizing Marr's dream of a singular vision science - the intertwined study of biological and computer vision.