A global initiative bringing together researchers using the same image database to collect and share behavioral and neuroscience data in object recognition and understanding.

How are we able to make sense of the objects around us, recognize them, and act on them in a meaningful way? These are challenging questions that require a concerted effort across disciplines. The THINGS initiative was born out of the idea that we can overcome these challenges by working on a shared set of objects, allowing us to bridge the gap between brain and behavior, recording techniques, species, and artificial intelligence. Many laboratories around the world have started collecting data with THINGS images. Anyone can be part of the initiative.

THINGS concepts and images

Martin Hebart, Adam Dickter, Alexis Kidder, Wan Kwok, Anna Corriveau, Caitlin Van Wicklin, Chris Baker

A freely available database of 26,107 high quality, manually-curated images of 1,854 diverse object concepts, curated systematically from the everyday American English language and using a large-scale web search. Includes 27 high-level categories, semantic embeddings for all concepts, and more metadata.

THINGS similarity

Martin Hebart, Oliver Contier, Lina Teichmann, Adam Rockter, Charles Zheng, Alexis Kidder, Anna Corriveau, Maryam Vaziri-Pashkam, Francisco Pereira, Chris Baker

More than 4.70 million triplet odd-one-out similarity judgments for 1,854 object images, plus a 66d interpretable embedding. A previous set of 1.46 million triplets served to identify 49 interpretable object dimensions predictive of behavior and similarity (Hebart et al., 2020, Nat Hum Behav). 


Laura Stoinski, Jonas Perkuhn, Martin Hebart

New THINGS metadata, with 53 high-level categories, typicality ratings, nameability scores for all images, size ratings, and ratings along several dimensions (e.g. animacy, manipulability, valence, arousal, preciousness, etc.). In addition, 1,854 license-free images were collected that can be used and reproduced (e.g. in publications) without any restriction.


Oliver Contier, Martin Hebart, Lina Teichmann, Adam Rockter, Charles Zheng, Alexis Kidder, Anna Corriveau, Chris Baker, Maryam Vaziri-Pashkam

Event-related functional MRI data in 3 subjects for 8,640 images (720 categories, 12 images per category), collected over the course of 12 sessions. Includes extensive anatomical scans, population receptive field mapping, and functional localizers. Optimized for studying object recognition with a broad and systematic range of object categories.


Lina Teichmann, Martin Hebart, Oliver Contier, Adam Rockter, Charles Zheng, Alexis Kidder, Anna Corriveau, Maryam Vaziri-Pashkam, Chris Baker

Magnetoencephalography (MEG) data in 4 subjects for 22,248 images (1,854 categories, 12 images per category), collected over the course of 12 sessions. Optimized for studying object recognition with a broad and systematic range of object categories.


Tijl Grootswagers, Ivy Zhou, Amanda Robinson, Martin Hebart, Thomas Carlson

Electroencephalography responses in 50 subjects for 22,248 images (1,854 concepts, 12 images per category), collected in a single session per participant using an RSVP paradigm.


Alessandro Gifford, Kshitij Dwivedi, Gemma Roig, Radoslaw Cichy

Freie Universität Berlin, Goethe Universität, Frankfurt am Main

Raw and preprocessed EEG recordings of 10 participants, each with 82,160 trials spanning 16,740 image conditions coming from the THINGS database.


Lukas Muttenthaler, Martin Hebart

Streamlines the extraction of neural network activations by providing a simple wrapper for extracting activations from a wide range of commonly used deep convolutional neural network architectures.

THINGS memorability

Max Kramer, Martin Hebart, Chris Baker, Wilma Bainbridge

Memorability scores for all 26,107 object images, collected in a large sample of >13,000 participants. Offers a systematic evaluation of memorability across a wide range of natural object images, object concepts, and high-level categories.

THINGS semantic feature norm

Hannes Hansen, Martin Hebart

Semantic feature production norms for all 1,854 object concepts in THINGS, generated with the natural language model GPT-3, developed by OpenAI.


Jaan Aru, Kadi Tulver, Tarun Khajuria

A dataset of "constellation" images that can be used to study inference in human vision and AI. The images are stripped of local details creating a dotted outline of the object that can be inferred from the local pattern. The dataset includes 3533 image sets of a total of 1215 common objects from the THINGS dataset. A selected set of 481 top constellation images and the code to generate more constellation images from photos are also included.

STUFF database and dimensions

Filipp Schmidt, Martin Hebart, Alex Schmid, Roland Fleming

600 images of 200 materials sampled systematically and representatively from the American English language. Includes object dimensions and material similarity matrices identified from >1.8 million similarity judgments.


Marie St-Laurent, CNeuromod

Event-related functional MRI data at 3T in 8 subjects for 4,320 images (720 categories, 6 images per category, 3 repeats per image), using a memory paradigm. Optimized for the study of object recognition for hypothesis-based analyses, data-driven analyses, and representational similarity analysis.

manuscript in preparation

THINGS electrophysiology1

Thomas Reber, Florian Mormann

Direct recordings from human entorhinal cortex, hippocampus, amygdala, and parahippocampal cortex in 23 patients for 1,200 images (150 categories, 8 images each).

data analysis ongoing

THINGS macaque EEG

Siegel Lab

Scalp EEG recordings of 8,640 THINGS images (720 categories, 12 images per category) in 2 macaque monkeys.

data analysis ongoing

THINGS macaque V4

Ratan Murty, Sachi Sanghavi

Electrophysiological recordings of 14,832 THINGS images (1,854 categories, 8 images per category) in area V4 of a macaque monkey.

data collection completed

THINGS macaque visual cortex

Paolo Papale, Pieter Roelfsema

High-channel count electrophysiological recordings of 22,248 images (1,854 categories, 12 images per category) from the macaque visual cortex in two animals.

manuscript in preparation


Avniel Ghuman, LCND team

Intracranial EEG from ventral temporal cortex in human patients.

data collection ongoing



Event-related functional MRI measurements at 3T in a large sample of psychotic patients, presenting 5,568 images from 720 object categories per patient. 

data collection ongoing

Contact Us

Are you using THINGS Images or THINGS Concepts for your research and want to showcase your work here? Please get in touch, we would love to see what THINGS is being used for!

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