BOLD5000

Brain, Object, Landscape Dataset<br><br>Vision science - particularly machine vision - is being revolutionized by large-scale datasets. State-of-the-art artificial vision models critically depend on large-scale datasets to achieve high performance. In contrast, although large-scale learning models (e.g., AlexNet) have been applied to human neuroimaging data, the stimuli for such neuroimaging experiments include significantly fewer images. The small size of these stimulus sets also translates to limited image diversity. Here we dramatically increase the stimulus set size deployed in an fMRI study of visual scene processing. We scanned four participants in a slow-evented related design that incorporated 4,916 unique scenes. Data was collected over 16 sessions, 15 of which were task-related sessions, plus an additional session for acquiring high resolution anatomical scans. In 8 of the 15 task-related sessions, a functional localizer was run in order to independently define scene-selective cortex. In each scanning session, participants filled out a questionnaire (Daily Intake) about their daily routine, including: current status regarding food and beverage intake, sleep, exercise, ibuprofen, and comfort in the scanner. During BOLD scanning, physiological data (heart rate and respiration) was also acquired.<br>The experiment including 4,803 images presented on a single trial throughout the experiment, and 112 images repeated four times, and one image repeated three times, throughout the experiment, yielding a total of 5,254 stimuli trials. The stimuli were drawn from three datasets: 1) 1000 images from Scene Images (250 scene categories, based on SUN categories, with four exemplars each); 2) 2000 images from the COCO dataset; and 3) 1916 images from the ImageNet dataset. In the experiment, images were presented for 1 second, with 9 seconds of fixation between trials. Participants were asked to judge whether they liked, disliked, or were neutral about the image.<br>In sum, our dataset is unique in three ways: it is 1) significantly larger than existing slow-event neural datasets by an order of magnitude, 2) extremely diverse in stimuli, 3) considerably overlapping with existing computer vision datasets. Our large-scale dataset enables novel neural network training and novel exploration of benchmark computer vision datasets through neuroscience. Finally, the scale advantage of our dataset and the use of a slow event-related design enables, for the first time, joint computer vision and fMRI analyses that span a significant and diverse region of image space using high-performing models. <br><br>Please refer to our website for more details and future news and releases: BOLD5000.org<br>脑、物体与景观数据集 视觉科学，尤其是机器视觉领域，正因大规模数据集的出现迎来革命性变革。当前顶尖的人工视觉模型，其高性能表现完全依赖大规模数据集作为核心支撑。 与之形成对比的是，尽管大规模学习模型（如AlexNet）已被应用于人类神经影像数据，但此类神经成像实验所使用的视觉刺激图像数量却显著偏少，且刺激集规模过小也直接导致图像多样性受限。 本研究大幅扩充了视觉场景加工功能性磁共振成像（functional magnetic resonance imaging，fMRI）实验中所使用的刺激集规模。我们采用慢事件相关设计，对4名被试进行了扫描，实验共纳入4916张独特的场景图像。数据采集共分为16个扫描时段：其中15个为任务相关时段，另有1个时段用于采集高分辨率解剖影像。 在15个任务相关时段中，有8个时段会运行功能定位任务，以独立识别场景选择性脑区。 每个扫描时段中，被试都需填写一份《日常摄入与状态问卷》（Daily Intake），内容涵盖当日饮食、睡眠、运动、布洛芬服用情况以及扫描室内的舒适度感受。 血氧水平依赖（blood oxygen level dependent，BOLD）扫描期间，同时采集了被试的生理数据，包括心率与呼吸信息。 本实验中，共有4803张图像仅在单次试次中呈现，112张图像重复呈现4次，另有1张图像重复呈现3次，总计产生5254个刺激试次。 本次实验的刺激图像源自三个数据集：1）1000张来自场景图像数据集（Scene Images），涵盖250个场景类别（基于SUN分类体系，每个类别包含4张示例图像）；2）2000张来自COCO数据集；3）1916张来自ImageNet数据集。 实验中每张图像呈现时长为1秒，试次之间设置9秒的注视时间。被试需对当前呈现的图像做出喜欢、不喜欢或中立的主观判断。 综上，本数据集具有三大独特优势：其一，规模较现有慢事件相关神经数据集提升了一个数量级；其二，刺激图像多样性极高；其三，与现有计算机视觉数据集存在大量重叠。 本大规模数据集为神经网络训练提供了全新的研究路径，也为通过神经科学手段探索基准计算机视觉数据集开辟了新方向。 最后，依托本数据集的规模优势与慢事件相关实验设计，我们首次实现了基于高性能模型、覆盖大规模多样化图像空间的计算机视觉与功能性磁共振成像联合分析。 欲了解更多细节、后续动态与版本更新，请访问我们的官网：BOLD5000.org