Objects

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. 

Artificial objects  

Datasets that contain images of objects and words etc. that do not exist in the real world. 

Artificial objects in different views

Description: This database has 2 object sets, the “amoeboid” set and the “regular set“. One set contains 62 objects, which the creators call “amoeboid“, each is shown from 5 different angels with 20° difference. 31 of these “amoeboids” have a part which you can’t see from all the angles but you can see every part of the other 31 from all angels. The “regular set“ contains 80 objects, 40 of them have parts that can’t be seen from all the angles but the other 40 have parts that can be seen from all 5 positions.

License: Not known.

Link: http://ww2.psy.cuhk.edu.hk/~mael/Stimuli.html

Reference:

Wong, A.C.-N., & Hayward, W.G. (2005). Constraints on view combination: Effects of self-occlusion and difference between familiar views. Journal of Experimental Psychology: Human Perception & Performance, 31, 110-121.

Novel Objects and Unusual Name Database (NOUN)

Description: This set contains 64 made up objects which do not consist in reality but look like real objects. This set also contains pseudowords, which are words that sound like real English words but don‘t exist, and novel nouns.

License: Creative Commons Attribution NonCommercial

Link: http://michaelhout.com/?page_id=759

Reference:

Horst, J. S., & Hout, M. C. (in press). The Novel Object and Unusual Name (NOUN) Database: A collection of novel images for use in experimental research. Behavior Research Methods.

Ziggerins

Description: this database contains pictures of novel objects called “ziggerins” with white background. There are about 6 categories of “ziggerins”, each of which has 12 “ziggerins” which have “similar basic structure but differ in style or the detailed manifestations of the parts… The 12 styles are formed by variations of part along three dimensions: 1) cross section shape, 2) cross-section size change and 3) aspect ratio of the cross section.”

License: Not known.

Link: http://ww2.psy.cuhk.edu.hk/~mael/Stimuli.html

References:.

Wong, A. C.-N., Palmeri, T.J., & Gauthier, I. (2009). Conditions for face-like expertise with objects: Becoming a Ziggerin expert – but which type? Psychological Science, 20(9), 1108-1117.

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.

License: https://creativecommons.org/licenses/by-sa/3.0/

Link: https://sites.google.com/site/mathieubrodeur/Home/boss

Reference: 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 https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtYXRoaWV1YnJvZGV1cnxneDo1MGE3Yzg0MGEyODhkY2Mz

Big and small objects

Set 1

Description: 400 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.

License: Not known

Link: http://konklab.fas.harvard.edu/#

Reference:

Konkle, T., Olivia, A. (2012). A Real_World Size Organization of Object Responses in Occipitotemporal Cortex. Neuron, colume 74( 6), 1114-1124. http://www.sciencedirect.com/science/article/pii/S0896627312004412

Set 2

Description: 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.

License: Not known.

Link: http://konklab.fas.harvard.edu/#

Reference:  Caramazza, A., Konkle, T.(2013). Tripartite Organization of the Ventral Stream by Animacy and Object Size. The Journal of Neuroscience, 33(25), 10235-10242.

https://dash.harvard.edu/bitstream/handle/1/12362624/1781465.pdf?sequence=1

Black and white images of objects

Description: This set contains 299 black and white images of real objects. The images are not part of a scene but black drawings on a white background. They are ordered alphabetically both in English and in French.

License: Not known.

Link: http://leadserv.u-bourgogne.fr/bases/pictures/

References: Bonin, P., Peereman, R., Malardier, N., Méot, A., & Chalard, M. (2003). A new set of 299 pictures for psycholinguistic studies: French norms for name agreement, image agreement, conceptual familiarity, visual complexity, image variability, age of acquisition, and naming latencies. Behavior Research Methods, Instruments, & Computers, 35(1), 158-167.

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.

License: Not known.

Link: http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html

References: If you only use their images then cite:

L.Fei-Fei, R. Fergur and P. Perona. Learning generative visual models from few treining examples: an incremental Beyesian approuch tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model Based Vision. 2004.

If you are using their images and annotations:

L.Fei-Fei, R. Fergus and P. Perona. One-shot learning of object catagories. IEEE Trans. Pattern Recognition and Machine Intelligense. In press.

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.

License: Not known.

Link: http://www.vision.caltech.edu/Image_Datasets/Caltech256/

References: Griffin, G. Holub, AD. Perona, P. The Caltech 256. Caltech Technical Report.

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:

  1. 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.
  2. 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

Giuseppe Toys Dataset

Description: This database contains photos categorized in 4 subsets. The first set, called “Training images“, has 1 to 4 pictures, 61 pictures in all, of each object (mostly stuffed animals and toy vehicles). 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-not toy“, has 26 pictures that do not show any toys from the training set.

License: Not known.

Link: http://www.vision.caltech.edu/pmoreels/Datasets/Giuseppe_Toys_03/#Description

Reference: Not known.

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.

License: Not known.

Link: http://www.vision.caltech.edu/pmoreels/Datasets/Home_Objects_06/

Reference: Not known.

“Massive Memory” Unique Object Images

Description: 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.

License: not known.

Link: http://konklab.fas.harvard.edu/#

References: 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.

License: not known

Link: http://konklab.fas.harvard.edu/#  also on this website http://bradylab.ucsd.edu/stimuli.html

References: 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.

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 

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.

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.

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:

  1. 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.
  2. Konkle, T., & Oliva, A. (2012). A real-world size organization of object responses in occipitotemporal cortex. Neuron, 74(6), 1114-1124.

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 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.

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.

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

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

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.