Machine Generated Data
Tags
Amazon
created on 2023-10-06
Car | 99.4 | |
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Transportation | 99.4 | |
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Vehicle | 99.4 | |
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Neighborhood | 98.3 | |
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City | 98.2 | |
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Machine | 97.8 | |
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Wheel | 97.8 | |
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Wheel | 96.2 | |
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Wheel | 95.6 | |
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Wheel | 95.4 | |
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Wheel | 95.4 | |
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Wheel | 95.4 | |
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Wheel | 95.3 | |
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Wheel | 94.1 | |
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Urban | 93.7 | |
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Wheel | 93.2 | |
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Car | 90.9 | |
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Wheel | 88.1 | |
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Architecture | 85 | |
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Building | 85 | |
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Factory | 83.3 | |
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Car | 81.1 | |
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Truck | 79 | |
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Antique Car | 77.9 | |
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Model T | 77.9 | |
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Road | 57.8 | |
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Street | 57.8 | |
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Indoors | 57 | |
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Condo | 56.2 | |
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Housing | 56.2 | |
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Postal Office | 56.1 | |
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Hotel | 55.8 | |
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Restaurant | 55.4 | |
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Shop | 55.1 | |
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Clarifai
created on 2018-05-11
vehicle | 99 | |
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people | 98 | |
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street | 96.3 | |
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home | 95 | |
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transportation system | 94.7 | |
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group | 94.4 | |
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group together | 94.3 | |
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car | 92.2 | |
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administration | 91.9 | |
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many | 89.6 | |
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architecture | 86.7 | |
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no person | 86.4 | |
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military | 83.7 | |
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several | 81 | |
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building | 80 | |
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town | 79.9 | |
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outdoors | 79.8 | |
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carriage | 79.6 | |
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house | 79 | |
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adult | 76.2 | |
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Imagga
created on 2023-10-06
building | 54.5 | |
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architecture | 49.8 | |
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model t | 40.9 | |
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motor vehicle | 39.8 | |
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car | 38.7 | |
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city | 34.9 | |
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old | 32.8 | |
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house | 26.2 | |
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town | 24.1 | |
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street | 23 | |
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travel | 22.5 | |
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exterior | 21.2 | |
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sky | 21.1 | |
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urban | 21 | |
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structure | 20.4 | |
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window | 19.7 | |
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tourism | 18.2 | |
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wheeled vehicle | 17.9 | |
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brick | 17.9 | |
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facade | 17.7 | |
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stone | 17.7 | |
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home | 17.6 | |
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historic | 17.4 | |
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balcony | 17 | |
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buildings | 15.1 | |
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center | 14.6 | |
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construction | 14.6 | |
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windows | 14.4 | |
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history | 14.3 | |
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roof | 14.3 | |
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station | 14 | |
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fire station | 13.4 | |
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church | 13 | |
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palace | 12.9 | |
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historical | 12.2 | |
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wall | 12.2 | |
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square | 11.8 | |
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landmark | 11.7 | |
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truck | 11.5 | |
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cityscape | 11.4 | |
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ancient | 11.2 | |
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houses | 10.7 | |
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culture | 10.3 | |
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office | 10.1 | |
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road | 9.9 | |
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facility | 9.9 | |
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architectural | 9.6 | |
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fire engine | 9.2 | |
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traditional | 9.2 | |
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warehouse | 8.7 | |
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antique | 8.7 | |
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estate | 8.6 | |
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snow | 8.5 | |
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place | 8.4 | |
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public house | 8.3 | |
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tourist | 8.2 | |
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aged | 8.1 | |
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religion | 8.1 | |
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tower | 8.1 | |
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hall | 8 | |
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apartment | 7.7 | |
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outdoor | 7.7 | |
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lamp | 7.6 | |
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door | 7.6 | |
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famous | 7.4 | |
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trees | 7.1 | |
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Google
created on 2018-05-11
motor vehicle | 97.4 | |
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car | 95.1 | |
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transport | 91.2 | |
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vehicle | 88.6 | |
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town | 84.2 | |
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black and white | 78.1 | |
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house | 70.5 | |
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facade | 64.7 | |
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history | 59.1 | |
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building | 58.2 | |
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family car | 56.7 | |
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downtown | 56.4 | |
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compact car | 54.8 | |
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classic | 54.4 | |
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monochrome photography | 52.2 | |
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sedan | 52 | |
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Microsoft
created on 2018-05-11
building | 99.9 | |
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road | 99.9 | |
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outdoor | 99.7 | |
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street | 75.2 | |
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city | 50.8 | |
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government building | 34.1 | |
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apartment building | 26.4 | |
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Color Analysis
Feature analysis
Categories
Imagga
streetview architecture | 100% | |
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Captions
Microsoft
created on 2018-05-11
a white and black truck parked in front of a building | 89.6% | |
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a truck is parked in front of a building | 89.5% | |
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a truck is parked on the side of a building | 89.4% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43157959/480,306,42,10/full/0/native.jpg)
SECOND
![](https://ids.lib.harvard.edu/ids/iiif/43157959/731,212,44,15/full/0/native.jpg)
WARS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/729,195,61,14/full/0/native.jpg)
FOREIGN
![](https://ids.lib.harvard.edu/ids/iiif/43157959/716,195,75,14/full/0/native.jpg)
of FOREIGN
![](https://ids.lib.harvard.edu/ids/iiif/43157959/716,195,8,12/full/0/native.jpg)
of
![](https://ids.lib.harvard.edu/ids/iiif/43157959/715,177,78,16/full/0/native.jpg)
VETERANS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/485,341,32,11/full/0/native.jpg)
BANK
![](https://ids.lib.harvard.edu/ids/iiif/43157959/990,380,19,13/full/0/native.jpg)
GR
![](https://ids.lib.harvard.edu/ids/iiif/43157959/270,320,54,12/full/0/native.jpg)
NATIONAL
![](https://ids.lib.harvard.edu/ids/iiif/43157959/72,388,46,9/full/0/native.jpg)
RAILWAY
![](https://ids.lib.harvard.edu/ids/iiif/43157959/72,388,149,13/full/0/native.jpg)
RAILWAY EXPRESS AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/43157959/123,389,50,11/full/0/native.jpg)
EXPRESS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/177,389,44,11/full/0/native.jpg)
AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/43157959/87,456,106,16/full/0/native.jpg)
RAILWAY EXPRESS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/157,576,19,8/full/0/native.jpg)
SEIM
![](https://ids.lib.harvard.edu/ids/iiif/43157959/90,179,708,297/full/0/native.jpg)
RANS
df FOREIGN
WARS
COND
AL
SECOND
ATIONA
BANK
RAILWAY EXPREsS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/755,179,43,19/full/0/native.jpg)
RANS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/717,196,12,13/full/0/native.jpg)
df
![](https://ids.lib.harvard.edu/ids/iiif/43157959/729,196,67,20/full/0/native.jpg)
FOREIGN
![](https://ids.lib.harvard.edu/ids/iiif/43157959/733,214,47,16/full/0/native.jpg)
WARS
![](https://ids.lib.harvard.edu/ids/iiif/43157959/293,304,30,13/full/0/native.jpg)
COND
![](https://ids.lib.harvard.edu/ids/iiif/43157959/312,322,14,13/full/0/native.jpg)
AL
![](https://ids.lib.harvard.edu/ids/iiif/43157959/482,306,42,17/full/0/native.jpg)
SECOND
![](https://ids.lib.harvard.edu/ids/iiif/43157959/481,325,42,13/full/0/native.jpg)
ATIONA
![](https://ids.lib.harvard.edu/ids/iiif/43157959/487,342,34,14/full/0/native.jpg)
BANK
![](https://ids.lib.harvard.edu/ids/iiif/43157959/90,458,49,14/full/0/native.jpg)
RAILWAY
![](https://ids.lib.harvard.edu/ids/iiif/43157959/144,461,53,15/full/0/native.jpg)
EXPREsS