The original version of the following list of visual stimulus sets was compiled by Johanna Margret Sigurdardottir and will be updated as needed. We neither host nor do we provide copies of the stimuli. Reseachers who may wish to use a particular stimulus set should seek further information, including on possible licences, e.g. by following the provided web links, reading the referenced papers, and/or emailing the listed contact person/persons for a particular stimulus set. If you notice an error, know of a stimulus set that should be included, or have any other questions or comments, please contact Heida Maria Sigurdardottir (heidasi(Replace this parenthesis with the @ sign)hi.is). The list is provided as is without any warranty whatsoever.
Table of Contents
Artificial objects
Datasets that contain images of objects and words etc. that do not exist in the real world.
Animacy x Size “Texforms”
Description
The stimulus set consisted of 240 total images with 120 original images of 30 big animals, 30 big objects, 30 small animals, and 30 small objects and their texform counterparts.” The authors “…used a texture synthesis algorithm to create a class of stimuli—texforms— which preserve some mid-level texture and form information from objects while rendering them unrecognizable.
Link
https://konklab.fas.harvard.edu/
License
The dataset is available to download as a ZIP file here. The GIThub repositiories for generating textforms are also available here and here.
Reference(s)
Long, B., Yu, C. P., & Konkle, T. (2018). Mid-level visual features underlie the high-level categorical organization of the ventral stream. Proceedings of the National Academy of Sciences, 115(38), E9015-E9024.
IMAGINE (IMages of AI-Generated Imaginary Novel Entities)
Images of 400 perceptually novel objects created using Generative Adversarial Networks.Link
‘IMAGINE’ (IMages of AI-Generated Imaginary Novel Entities)
License
The article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Reference
Cooper, P. S., Colton, E., Bode, S., & Chong, T. T. J. (2023). Standardised images of novel objects created with generative adversarial networks. Scientific Data, 10(1), 575.
Novel Objects and Unusual Name Database (NOUN)
Description
This set contains 64 made up objects (i.e. they do not exist in real life) but look like real objects. This set also contains pseudowords (words that sound like real English words but don‘t exist), and novel nouns.
Link
http://michaelhout.com/?page_id=759
License
The first and second addition of this database are available for download under the ‘Download’ section of the above link.
Reference(s)
Horst, J. S., & Hout, M. C. (2015). The Novel Object and Unusual Name (NOUN) Database: A collection of novel images for use in experimental research. Behavior Research Methods, 48, 1393-1409. doi: 10.3758/s13428-015-0647-3.
Space Aliens and Nonwords
Description
The dataset consists of pictorial and auditory stimuli that have been developed for use in word learning tasks where the participant learns pairings of novel auditory sound patterns (names) with pictorial depictions of novel objects (referents). The pictorial referents are drawings of “space aliens,” consisting of images that are variants of 144 different aliens. The auditory names are possible nonwords of English; the stimulus set consists of over 2,500 nonword stimuli recorded in a single voice, with controlled onsets, varying from one to seven syllables in length. The pictorial and nonword stimuli can also serve as independent stimulus sets for purposes other than word learning.
License
The full set of these stimuli may be downloaded from www.psychonomic.org/archive/.
Smith, A. (2004a). A new set of norms. Behavior Research Methods, Instruments, and Computers, 3x(x), xxx-xxx.
Smith, A. (2004b). Smith2004norms.txt. Retrieved October 2, 2004 from Psychonomic Society Web Archive: http://www.psychonomic.org/ARCHIVE/.”
Link
https://link.springer.com/article/10.3758/BF03206540
Licence
The dataset is available for download under the ‘Electronic Supplementary Material’ section of the link above.
Reference(s)
Gupta, P., Lipinski, J., Abbs, B., Lin, P. H., Aktunc, E., Ludden, D., Martin, D. & Newman, R. (2004). Space aliens and nonwords: Stimuli for investigating the learning of novel word-meaning pairs. Behavior Research Methods, Instruments, & Computers, 36(4), 599-603. https://doi.org/10.3758/BF03206540
Real Objects
Images of diverse real objects.
Bank of Standardized Stimuli (BOSS)
Description
The BOSS set contains 480 pictures of various real life objects. Each object is shown isolated and not part of any scene but with a white background. The objects are both man-made and natural like vegetables.
Link
https://sites.google.com/site/bosstimuli/
Licence
The dataset is available for download from the following google drive – https://drive.google.com/open?id=1FpnEFkbqe_huRwfsCf7gs5R1zuc1ZOkn
Reference(s)
Brodeur, M. B., Dionne-Dostie, E., Montreuil, T., & Lepage, M. (2010). The bank of standardized stimuli (BOSS), a new set of 480 normative photos of objects to be used as visual stimuli in cognitive research. PloS ONE, 5(5), e10773
Big and small objects
Set 1
Description
The dataset contains 400 pictures of 200 big and 200 small objects. The objects are shown isolated with a white background. Some of these objects can be found in nature, as in plants and fruits, and some are man-made. The objects are categorized in two groups, small objects like glasses and big objects like cars.
Link
https://konklab.fas.harvard.edu/
License
The dataset is available as a .ZIP file here.
Reference(s)
Konkle, T., Olivia, A. (2012). A Real_World Size Organization of Object Responses in Occipitotemporal Cortex. Neuron, colume 74( 6), 1114-1124.
Set 2
Description
The set contains 120 pictures of big and small real objects. The objects are shown isolated with a white background. Some of these objects can be found in nature, as in plants and fruits, and some are man-made. The objects are categorized in two groups, small objects like glasses and big objects like cars.
Link
https://konklab.fas.harvard.edu/
License
The dataset is available as a .ZIP file here.
Reference(s)
Konkle, T., & Caramazza, A. (2013). Tripartite organization of the ventral stream by animacy and object size. Journal of Neuroscience, 33(25), 10235-10242.
Caltech 101
Description
This database contains images of objects from 101 different classes with 40-800 pictures in each class. Most of these classes contain around 50 pictures. Some of these pictures show objects which are a part of a scene but others show them isolated with white or black background.
Link
http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html
License
The images are available for download under the ‘Download’ section of the above link.
Reference(s)
If you only use the images from the dataset, then cite:
Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop (pp. 178-178). IEEE.
If you are using their images and annotations:
Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, 28(4), 594-611.
Caltech 256 database
Description
This dataset contains 30,607 pictures of objects from 256 different classes, both photos of real life objects and drawings of them. Some of the pictures show isolated objects which are not a part of any scenes but other images show objects as a part of a scene. There are at least 80 pictures or more in each category according to their online slide show.
Link
http://www.vision.caltech.edu/Image_Datasets/Caltech256/
License
The images are available for download as a .TAR file here.
Reference(s)
Griffin, G., Holub, A., & Perona, P. (2007). Caltech-256 object category dataset. https://resolver.caltech.edu/CaltechAUTHORS:CNS-TR-2007-001
Giuseppe Toys Dataset
Description
This database contains photos categorized in 4 subsets. The first set, called ‘Training images’, has 1 to 4 pictures of each object (mostly stuffed animals and toy vehicles), with a total of 61 images. The second subset, called ‘Test-Scenes’, has 52 picture of various scenes with many toys from the training set. The third subset, ‘Test Single-Toy’, has 62 photos that show only one toy from the training set. The last subset, ‘Test No-Toy’, has 26 pictures that do not show any toys from the training set.
Link
http://www.vision.caltech.edu/pmoreels/Datasets/Giuseppe_Toys_03/#Description
License
The whole dataset is available in .TAR format here. There are also individual directories for each dataset – the Training Images, Test-Scenes, Test Single-Toy and Test No-Toy
Reference(s)
There is no specific reference for this dataset.
Home Objects
Description
This database contains 224 photos of various objects from someone‘s home. These objects are used in the kitchen, bathroom or living room. They are not shown isolated and are a part of a scene and some pictures contain more than one object.
Link
http://www.vision.caltech.edu/pmoreels/Datasets/Home_Objects_06/
License
The whole dataset is available in .TAR format to download here.
Reference(s)
There is no specific reference for this dataset.
“Massive Memory” Unique Object Images
Description
This dataset contains 2400 pictures of various objects. It also contains 200 pictures which show 2 different objects from the same category and 200 images of the same object twice but in a different pose.
Link
http://konklab.fas.harvard.edu/
License
The dataset is available for download here. There are also individual .ZIP files for the objects which are from the same category but are different here and for those which differ in state and/or pose here.
Reference(s)
Brady, T. F., Konkle, T., Alvarez, G. A., & Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, 105(38), 14325-14329.
“Massive Memory” Object Categories
Description
This database has three subsets. The first includes 200 different classes with 17 pictures of different subtypes in each. The second contains pictures of 240 classes from one to sixteen images of different objects which look the same from the same class. This database also provides information on perceptual and conceptual knowledge for each object.
Link
http://konklab.fas.harvard.edu/
License
The first dataset with 200 object categories is available as a .ZIP file here. The second dataset containing 240 images is available as a .ZIP file here.
Reference(s)
Brady, T. F., Konkle, T., Alvarez, G. A., & Oliva, A. (2010). Conceptual Distincitveness Supports Detailed Visual Long-Term Memory for Real-World Objects. Journal of Experimental Psychology: General 2010, 139(3), 558-578.
MemCat
Description: “We present a new category-based set of 10K images quantified on memorability. The set consists of five broader memorability-relevant semantic categories (animal, sports, food, landscapes, vehicles), with 2K exemplars each, further divided into different subcategories (e.g., bear, pigeon, cat, etc. for animal).” (see link)
License: “Please cite our paper if you find either of them useful for your own work. All materials in this website including images, data, and visualizations, can be used for academic research purpose ONLY. Please also adhere to the terms and conditions of the source datasets: ImageNet, COCO, Open Images Dataset, and SUN.” (see link)
Link: http://gestaltrevision.be/projects/memcat/
References: Goetschalckx, L., & Wagemans, J. (2019). MemCat: A new category-based image set quantified on memorability. PsyArXiv. https://doi.org/10.31234/osf.io/64xfa
Menu –Match Dataset
Description: This database contains 646 pictures of 1386 food items and information on the calories in each meal. These photos were taken of various meals from 3 restaurants: Asian, Italian and a soup restaurant.
License: Not known.
Link: http://neelj.com/projects/menumatch/
References: http://neelj.com/projects/menumatch/menumatch.pdf
Object quartets: State x Exemplar and State x Color
Description: This set shows 100 pictures that show two objects. The objects are of different colors or types and each shown in a different condition. For an example you see two pens that either look alike except one is red but the other is blue or they have the same color but are different types of pens, and you see them with their lid on and off.
License: not known
Link: http://konklab.fas.harvard.edu/#
Reference: Brady, T. F., Konkle, T., Gill, J., Oliva, A., & Alvarez, G. A. (2012). Long-term memory has the same limit on fidelity as working memory. Manuscript submitted for publication.
Objects from distinct categories
Description: This set has 2400 images of various real objects from toys to birds to airplanes. The objects are isolated in each picture with a white background and are not a part of a scene. There are 160 kinds of objects and around 15 pictures of each object. The pictures are organized alphabetically in the set.
License: Not known.
Link: http://konklab.fas.harvard.edu/#
Reference:
Brady, T. F., Konkle, T., Alvarez, G. A., Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. PNAS, 105(38), 14325-14329.
Objects in different states and exemplars (addition to Brady et al., 2013)
Description: In the set there are 150 additional new categories of objects in different exemplars and with different states (2 exemplars x 2 states) to the Brady et al. 2013 stimuli base. Part of these categories also do not overlap with Konkle et al. 2010 stimuli base. Could be used for studying real-world objects and manipulating two different parameters of real-world object.
License: Not known.
Link: https://osf.io/p9gzh/
Reference:
Markov Y., Utochkin I. S., & Brady T.F. (Submitted for Review , 2020). Real-world objects are not stored in bound representations in visual working memory. Preprint: PsyArXiv
Object Size Range
Description: 100 images showing real life objects from buildings, tools to toys with white, plain, background. Along with the images there is information on the objects’ sizes in real life. The objects are ordered from smallest to largest.
License: Not known.
Link: http://konklab.fas.harvard.edu/#
Reference: Konkle, T., & Oliva, A. (2011). Canonical visual size for real-world objects. Journal of experimental psychology: human perception and performance, 37(1), 23 -37.
Object Size Stroop
Description: this database contains 400 real world objects, 200 of them are large in real life but the other 200 are small. They are all in front of a white background. The pictures are of the same sizes but the objects fill the image to a different degree.
License: Not known.
Link: http://konklab.fas.harvard.edu/#
Reference:
- Konkle, T., & Oliva, A. (2012). A familiar-size Stroop effect: real-world size is an automatic property of object representation. Journal of Experimental Psychology: Human Perception and Performance, 38(3), 561.
- Konkle, T., & Oliva, A. (2012). A real-world size organization of object responses in occipitotemporal cortex. Neuron, 74(6), 1114-1124.
Open Images Dataset V6
Description: “Open Images is a dataset of ~9M images annotated with image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.”
License: Not known.
Link: https://storage.googleapis.com/openimages/web/index.html
Reference:
- Krasin I., Duerig T., Alldrin N., Ferrari V., Abu-El-Haija S., Kuznetsova A., Rom H., Uijlings J., Popov S., Kamali S., Malloci M., Pont-Tuset J., Veit A., Belongie S., Gomes V., Gupta A., Sun C., Chechik G., Cai D., Feng Z., Narayanan D., Murphy K.
OpenImages: A public dataset for large-scale multi-label and multi-class image classification, 2017. Available from https://storage.googleapis.com/openimages/web/index.html
Pairs of Objects differing at the examplar level
Description: 200 pictures each of 2 isolated objects, with a white background, which are similar and from same basic category. One picture might e.g. contain 2 mirrors which have different frames or shapes.
License: Not known.
Link: http://konklab.fas.harvard.edu/#
Reference:
Brady, T. F., Konkle, T., Alvarez, G. A., Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. PNAS, 105(38), 14325-14329.
Pairs of object, differing in state or pose
Description: 200 pictures. Each picture shows the same isolated object twice but from different viewpoints. One picture might e.g. show the same chair from two different angles.
License: Not known.
Link: http://konklab.fas.harvard.edu/#
Reference: Brady, T. F., Konkle, T., Alvarez, G. A., Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. PNAS, 105(38), 14325-14329.
PhyloPic
Description: “Free silhouette images of animals, plants, and other life forms”
License: Available for reuse under a Public Domain or Creative Commons license.
Link: http://phylopic.org/
Reference: Mike Keesey, PhyloPic Administrator, email: keesey(Replace this parenthesis with the @ sign)gmail.com
Poporo image set
Description: This database has 800 pictures of pairs of objects. The objects are not shown as a part of any scene but isolated with a white background. These are various real objects from animals, plants to vehicles. This set does not include human faces, letters or other symbols. These object pairs are either rated as semantically related or unrelated.
License: Not known.
Link: http://www.oszillab.net/downloads.php
Reference: Kovalenko, L.Y., Chaumon, M., & Busch, N.A. (2012). A pool of pairs of related objects (POPORO) for investigating visual semantic integration: Behavioral and electrophysiological validation. Brain Topography, 25(3), 272-284. http://link.springer.com/article/10.1007%2Fs10548-011-0216-8
Quality Colour Images with Norms from Seven Psycholinguistic Variables
Description: This set has 360 pictures in all with 120 color pictures in three categories: “Living things“, “Non-living things“, “Nature“. The objects are shown isolated with a white background. Each category has subcategories with pictures in each subcategory. The “Living things” category contains: animals, insects, vegetables, nuts, trees, marine creatures, fruits, body parts, flowers and bird. The “Non-living things” contains musical instruments, buildings, jewellery, food, tools, vehicles, weapons, desk mat, sport games, kitchen tool, clothing, and furniture. The “Nature” category contains: mountain, volcano, wave, cliff, waterfall, ice, iceberg, island, stone, gold, coal, puddle, moon, sun, sea, and a cloud.
License: Creative Commons Attribution
Link: http://dx.plos.org/10.1371/journal.pone.0037527 (see Supporting information for download links)
Reference:
Moreno-Martínez, F. J., & Montoro, P. R. (2012). An ecological alternative to Snodgrass & Vanderwart: 360 high quality colour images with norms for seven psycholinguistic variables. PLoS One, 7(5), e37528
Segmentation evaluation database
Description: This database contains 2 different sets which contain around 200 black and white images of various objects. One dataset contains images which show one object but the other shows two objects. The objects are all a part of a scene. The creators of this database provided a code for the evaluation of the given segmentation algorithm.
License: Depends on the image see website.
Link: http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB
Reference: Not known
THINGS object concept and object image database
Description: The THINGS database comprises 1,854 object concepts and more than 26,000 images reflecting a comprehensive collection of objects nameable in American English. Each object concept comes with at least 12 high-quality standardized object images, with objects embedded in a natural background.
License: https://osf.io/92yhv/
Link: https://osf.io/jum2f/
Reference: Hebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., Van Wicklin, C., & Baker, C. I. (2019). THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE, 14(10). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223792
3D Objects on Turntable
Description: This large set has photos of various object on an automated turntable with a white background, taken from 144 different angels with 5° difference under 3 different lighting conditions. The objects are photographed by 2 cameras.
License: Not know.
Link: http://www.vision.caltech.edu/pmoreels/Datasets/TurntableObjects/index.html
References:
- Moreels, P., & Perona, P. (2005, October). Evaluation of features detectors and descriptors based on 3D objects. In Tenth IEEE International Conference on Computer Vision, 1, 800-807.
- Moreels, P., & Perona, P. (2007). Evaluation of features detectors and descriptors based on 3d objects. International Journal of Computer Vision, 73(3), 263-284.
100 Exemplar Pairs
Description: 100 pictures. Each showing 2 object which would be categorized as the same but have a different look. The objects are not shown as a part of a scene but with a white background.
License: Not known.
Link: http://bradylab.ucsd.edu/stimuli.html
Reference:
Brady, T. F., Konkle, T., Alvarez, G. A. and Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, USA, 105 (38), 14325-14329. http://www.pnas.org/content/105/38/14325.full
100 Objects in 2 states x 2 colors
Description: This set contains 400 pictures in all of 100 different objects. The objects are shown isolated with a white background. There are 4 picture of each object, in 2 of the pictures the object is in the same colour but in different state and the other 2 are of the same object in another colour in 2 different states. For example there are 2 pictures of the same black and blue bag. In one photo the bag is closed but in the other one the bag is open. Then there is another image of a bag which looks just the same but is yellow and black and in one picture that same bag is open but in another on the bag is closed.
License: Not known.
Link: http://bradylab.ucsd.edu/stimuli.html
Reference: Brady, T. F., Konkle, T., Alvarez, G.A., and Oliva, A. (2013). Real-world objects are not represented as bound units: Independent forgetting of different object details from visual memory. Journal of Experimental Psychology: General, 142(3), 791-808.
100 Objects in 2 states x 2 exemplars
Description: This set contains 400 pictures in all of 100 different objects. In each picture one object is shown isolated, that is it is not part of any scene, with a white background. There are 2 different examples of each object and 2 photos of the same object but in a different state or position. For example one category is of books with 4 different pictures. 2 of those pictures are of the same book but in one picture the book is open while in another image the book is closed. The other two pictures in the same category are of another book, one picture of the book open the other closed and so forth.
License: Not known.
Link: http://bradylab.ucsd.edu/stimuli.html
Reference: Brady, T. F., Konkle, T., Alvarez, G.A., and Oliva, A. (2013). Real-world objects are not represented as bound units: Independent forgetting of different object details from visual memory. Journal of Experimental Psychology: General, 142(3), 791-808.
100 State Pairs
Description: 100 pictures. Each picture shows the same object twice in different conditions, e.g. a CD player open and closed. The objects are shown isolated with a white background.
License: Not known.
Link: http://bradylab.ucsd.edu/stimuli.html
Reference:
Brady, T. F., Konkle, T., Alvarez, G. A. and Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, USA, 105 (38), 14325-14329. http://www.pnas.org/content/105/38/14325.full
540 Objects designed to be color-rotated, plus code to rotate them
Description: “this set contains 540 objects with a code to change their color and turn them so you can see them from various angles.”
License: Not known
Link: http://bradylab.ucsd.edu/stimuli.html
Reference:
Brady, T. F., Konkle, T.F., Gill, J., Oliva, A. and Alvarez, G.A. (2013). Visual long-term memory has the same limit on fidelity as visual working memory. Psychological Science, 24(6), 981-990.
2400 Unique objects
Description: 2400 images of isolated various objects with a white background.
License: Not known.
Link: http://bradylab.ucsd.edu/stimuli.html
Reference:
Brady, T. F., Konkle, T., Alvarez, G. A. and Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, USA, 105 (38), 14325-14329. http://www.pnas.org/content/105/38/14325.full
Objects that move
STL-10 dataset
Description: this database contains 10 categories of animals and moving vehicles. The categories are airplane, truck, car, ship, monkey, horse, dog, deer, cat and bird. There are 500 images for training and 800 test pictures in each category. The images are 96×96 pixels and in color.
License: Not known.
Link: https://cs.stanford.edu/~acoates/stl10/
Reference:
Adam Coates, Honglak Lee, Andrew Y. Ng An Analysis of Single Layer Networks in Unsupervised Feature Learning AISTATS, 2011.