Machine Generated Data
Tags
Amazon
created on 2019-05-30
Town | 99.6 | |
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Downtown | 99.6 | |
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Building | 99.6 | |
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City | 99.6 | |
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Urban | 99.6 | |
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Architecture | 98.2 | |
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Metropolis | 97 | |
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Path | 91.1 | |
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Tower | 88.4 | |
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Spire | 88.4 | |
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Steeple | 88.4 | |
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Car | 86.2 | |
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Vehicle | 86.2 | |
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Transportation | 86.2 | |
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Automobile | 86.2 | |
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Road | 84.2 | |
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Human | 82.7 | |
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Pedestrian | 82.7 | |
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Handrail | 72.6 | |
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Banister | 72.6 | |
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Walkway | 68 | |
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Car | 67.2 | |
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Person | 67.1 | |
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Sidewalk | 62.9 | |
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Pavement | 62.9 | |
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Intersection | 60.1 | |
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Town Square | 57.4 | |
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Plaza | 57.4 | |
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Person | 49.2 | |
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Clarifai
created on 2019-05-30
people | 96.5 | |
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monochrome | 95.8 | |
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street | 94.9 | |
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no person | 93.8 | |
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architecture | 93.6 | |
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building | 92.4 | |
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city | 91.8 | |
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outdoors | 89.2 | |
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travel | 88.6 | |
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many | 87 | |
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road | 86.1 | |
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group | 85.2 | |
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vehicle | 84.7 | |
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square | 83.3 | |
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transportation system | 79.9 | |
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group together | 79.1 | |
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town | 77.5 | |
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old | 77.4 | |
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man | 77.3 | |
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business | 76.4 | |
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Imagga
created on 2019-05-30
Google
created on 2019-05-30
White | 96.5 | |
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Photograph | 96.1 | |
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Black | 94.8 | |
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Black-and-white | 94 | |
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Snapshot | 87.5 | |
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Monochrome photography | 87 | |
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Architecture | 86.9 | |
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Monochrome | 82.6 | |
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City | 79.8 | |
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Building | 77.8 | |
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Photography | 77 | |
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Stock photography | 75.6 | |
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Metropolis | 74.3 | |
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Line | 73 | |
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Street | 72.9 | |
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Downtown | 64.1 | |
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Urban design | 58.4 | |
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Thoroughfare | 57.7 | |
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Style | 57.4 | |
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Road | 52.7 | |
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Microsoft
created on 2019-05-30
building | 94.8 | |
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sky | 90.7 | |
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black and white | 89.9 | |
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skyscraper | 88.2 | |
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street | 79.8 | |
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black | 66.5 | |
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way | 61.3 | |
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city | 55 | |
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Color Analysis
Feature analysis
Categories
Imagga
streetview architecture | 97.8% | |
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Captions
Microsoft
created on 2019-05-30
a black and white photo of a city street | 92.1% | |
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a black and white photo of a street | 91.4% | |
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a black and white photo of a city | 90.3% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/15331803/904,207,103,30/full/0/native.jpg)
INTERN
![](https://ids.lib.harvard.edu/ids/iiif/15331803/389,278,42,16/full/0/native.jpg)
TERS
![](https://ids.lib.harvard.edu/ids/iiif/15331803/835,242,55,17/full/0/native.jpg)
SUPPER
![](https://ids.lib.harvard.edu/ids/iiif/15331803/376,297,53,15/full/0/native.jpg)
ANUTST
![](https://ids.lib.harvard.edu/ids/iiif/15331803/607,319,35,10/full/0/native.jpg)
EDGAR
![](https://ids.lib.harvard.edu/ids/iiif/15331803/606,318,78,13/full/0/native.jpg)
EDGAR HOOVER'S
![](https://ids.lib.harvard.edu/ids/iiif/15331803/951,230,64,20/full/0/native.jpg)
CAS
![](https://ids.lib.harvard.edu/ids/iiif/15331803/643,317,42,13/full/0/native.jpg)
HOOVER'S
![](https://ids.lib.harvard.edu/ids/iiif/15331803/784,348,33,18/full/0/native.jpg)
hela
![](https://ids.lib.harvard.edu/ids/iiif/15331803/931,334,86,23/full/0/native.jpg)
Wfel
![](https://ids.lib.harvard.edu/ids/iiif/15331803/621,263,387,60/full/0/native.jpg)
oL ao SUPPER CAS
![](https://ids.lib.harvard.edu/ids/iiif/15331803/380,371,49,20/full/0/native.jpg)
Uetas
![](https://ids.lib.harvard.edu/ids/iiif/15331803/626,288,171,35/full/0/native.jpg)
oL ao
![](https://ids.lib.harvard.edu/ids/iiif/15331803/924,373,41,10/full/0/native.jpg)
0DA
![](https://ids.lib.harvard.edu/ids/iiif/15331803/376,219,54,23/full/0/native.jpg)
MitE
![](https://ids.lib.harvard.edu/ids/iiif/15331803/377,213,613,122/full/0/native.jpg)
INTER
TERS
ANUTst
EDGAR
![](https://ids.lib.harvard.edu/ids/iiif/15331803/911,213,79,28/full/0/native.jpg)
INTER
![](https://ids.lib.harvard.edu/ids/iiif/15331803/390,280,38,22/full/0/native.jpg)
TERS
![](https://ids.lib.harvard.edu/ids/iiif/15331803/377,298,54,19/full/0/native.jpg)
ANUTst
![](https://ids.lib.harvard.edu/ids/iiif/15331803/615,322,29,13/full/0/native.jpg)
EDGAR