Machine Generated Data
Tags
Amazon
created on 2023-10-06
Path | 100 | |
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Sidewalk | 100 | |
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City | 100 | |
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Road | 100 | |
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Street | 100 | |
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Urban | 100 | |
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Neighborhood | 99.8 | |
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Car | 98.8 | |
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Transportation | 98.8 | |
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Vehicle | 98.8 | |
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Car | 98 | |
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Car | 96.5 | |
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Car | 92.3 | |
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Car | 86.6 | |
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Machine | 81 | |
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Wheel | 81 | |
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Person | 78.3 | |
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Car | 75.7 | |
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Wheel | 75.4 | |
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Outdoors | 75.4 | |
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Wheel | 70.5 | |
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Person | 70 | |
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Wheel | 68.8 | |
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Person | 66.7 | |
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Person | 65.2 | |
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Wheel | 64.4 | |
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Wheel | 60.7 | |
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Tarmac | 57.8 | |
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Advertisement | 57.4 | |
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Intersection | 57.1 | |
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Bus Stop | 57 | |
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Brick | 56.1 | |
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Metropolis | 56 | |
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License Plate | 55.8 | |
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Parking Lot | 55.6 | |
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Text | 55 | |
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Freeway | 55 | |
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Highway | 55 | |
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Clarifai
created on 2018-05-10
street | 99.2 | |
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people | 99 | |
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monochrome | 98.2 | |
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road | 96.3 | |
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transportation system | 94.2 | |
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vehicle | 94.1 | |
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group together | 93.8 | |
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group | 91.5 | |
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adult | 90.9 | |
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man | 88.6 | |
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administration | 87.7 | |
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no person | 86.4 | |
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city | 85.4 | |
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two | 84.9 | |
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black and white | 84.1 | |
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war | 79.5 | |
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building | 78.8 | |
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calamity | 78.3 | |
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architecture | 75.8 | |
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town | 74.9 | |
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Imagga
created on 2023-10-06
gas pump | 56.7 | |
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pump | 44.9 | |
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mechanical device | 36.1 | |
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architecture | 28.2 | |
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city | 24.1 | |
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mechanism | 23.7 | |
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sky | 23 | |
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building | 21.5 | |
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instrument | 21.1 | |
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snow | 21.1 | |
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device | 20.5 | |
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scale | 19.7 | |
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winter | 19.6 | |
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house | 19.2 | |
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old | 18.8 | |
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landscape | 17.8 | |
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street | 17.5 | |
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measuring instrument | 17.5 | |
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structure | 15.7 | |
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chime | 14.8 | |
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travel | 14.8 | |
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sea | 14.1 | |
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town | 13.9 | |
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percussion instrument | 13.8 | |
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cold | 13.8 | |
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landmark | 12.6 | |
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urban | 12.2 | |
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historic | 11.9 | |
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tree | 11.5 | |
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water | 11.3 | |
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outdoors | 11.2 | |
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construction | 11.1 | |
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bridge | 10.9 | |
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tower | 10.7 | |
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tourism | 10.7 | |
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light | 10.7 | |
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trees | 10.7 | |
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window | 10.4 | |
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musical instrument | 10.4 | |
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history | 9.8 | |
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home | 9.6 | |
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block | 9.6 | |
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forklift | 9.4 | |
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guillotine | 9.3 | |
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bell | 9.2 | |
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to | 8.8 | |
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facade | 8.8 | |
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stone | 8.7 | |
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ancient | 8.6 | |
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wall | 8.6 | |
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cityscape | 8.5 | |
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historical | 8.5 | |
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ocean | 8.3 | |
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instrument of execution | 8.2 | |
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vacation | 8.2 | |
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machine | 8 | |
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equipment | 7.7 | |
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weather | 7.7 | |
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frost | 7.7 | |
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pay-phone | 7.6 | |
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ice | 7.4 | |
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billboard | 7.3 | |
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pier | 7.3 | |
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road | 7.2 | |
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season | 7 | |
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Google
created on 2018-05-10
car | 92.4 | |
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black and white | 90.6 | |
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infrastructure | 86.7 | |
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road | 82.5 | |
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lane | 81.8 | |
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snapshot | 81.8 | |
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street | 79.1 | |
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monochrome photography | 76.3 | |
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motor vehicle | 72.3 | |
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asphalt | 70.4 | |
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monochrome | 69.7 | |
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tree | 59.6 | |
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sidewalk | 58.2 | |
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history | 57.3 | |
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angle | 55.2 | |
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road surface | 52.7 | |
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Color Analysis
Feature analysis
Categories
Imagga
streetview architecture | 98.6% | |
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nature landscape | 1.2% | |
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Captions
Microsoft
created on 2018-05-10
a black and white photo of a street | 94.9% | |
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a black and white photo of a city street | 93.4% | |
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a black sign with white text on the side of the street | 92.5% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43163004/766,333,41,26/full/0/native.jpg)
DIP
![](https://ids.lib.harvard.edu/ids/iiif/43163004/657,328,152,81/full/0/native.jpg)
TRIPLE DIP
![](https://ids.lib.harvard.edu/ids/iiif/43163004/694,161,64,20/full/0/native.jpg)
DRUGS
![](https://ids.lib.harvard.edu/ids/iiif/43163004/544,157,19,13/full/0/native.jpg)
ALL
![](https://ids.lib.harvard.edu/ids/iiif/43163004/544,157,53,14/full/0/native.jpg)
ALL MAKES
![](https://ids.lib.harvard.edu/ids/iiif/43163004/564,157,33,13/full/0/native.jpg)
MAKES
![](https://ids.lib.harvard.edu/ids/iiif/43163004/657,330,105,79/full/0/native.jpg)
TRIPLE
![](https://ids.lib.harvard.edu/ids/iiif/43163004/534,131,21,15/full/0/native.jpg)
USED
![](https://ids.lib.harvard.edu/ids/iiif/43163004/551,140,39,17/full/0/native.jpg)
R&G
![](https://ids.lib.harvard.edu/ids/iiif/43163004/730,536,70,74/full/0/native.jpg)
5c
![](https://ids.lib.harvard.edu/ids/iiif/43163004/528,123,90,35/full/0/native.jpg)
- USED an R&G CARS & TRUCKS
![](https://ids.lib.harvard.edu/ids/iiif/43163004/551,123,62,26/full/0/native.jpg)
CARS & TRUCKS
![](https://ids.lib.harvard.edu/ids/iiif/43163004/528,146,13,6/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/43163004/540,146,9,7/full/0/native.jpg)
an
![](https://ids.lib.harvard.edu/ids/iiif/43163004/547,159,264,457/full/0/native.jpg)
ALL MAKE
PLE DIP
5
![](https://ids.lib.harvard.edu/ids/iiif/43163004/547,159,19,14/full/0/native.jpg)
ALL
![](https://ids.lib.harvard.edu/ids/iiif/43163004/566,160,29,13/full/0/native.jpg)
MAKE
![](https://ids.lib.harvard.edu/ids/iiif/43163004/699,336,64,41/full/0/native.jpg)
PLE
![](https://ids.lib.harvard.edu/ids/iiif/43163004/765,335,46,30/full/0/native.jpg)
DIP
![](https://ids.lib.harvard.edu/ids/iiif/43163004/733,537,56,79/full/0/native.jpg)
5