Machine Generated Data
Tags
Amazon
created on 2022-06-04
Railway | 99.2 | |
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Rail | 99.2 | |
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Train Track | 99.2 | |
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Transportation | 99.2 | |
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Wheel | 94.2 | |
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Machine | 94.2 | |
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Truck | 81.6 | |
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Vehicle | 81.6 | |
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Wheel | 75 | |
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Path | 64.6 | |
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Imagga
created on 2022-06-04
tunnel | 100 | |
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passageway | 100 | |
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passage | 100 | |
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way | 71.7 | |
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old | 28.6 | |
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architecture | 23.4 | |
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building | 20.6 | |
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travel | 19.7 | |
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train | 18.3 | |
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landscape | 17.9 | |
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railroad | 16.7 | |
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grunge | 16.2 | |
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sky | 16 | |
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railway | 15.7 | |
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construction | 14.6 | |
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track | 14.4 | |
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transportation | 14.4 | |
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industry | 13.7 | |
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industrial | 13.6 | |
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house | 13.4 | |
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city | 13.3 | |
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road | 12.7 | |
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history | 12.5 | |
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vintage | 12.4 | |
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wall | 12.1 | |
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antique | 12.1 | |
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trees | 11.6 | |
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ancient | 11.2 | |
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transport | 11 | |
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structure | 11 | |
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rail | 10.8 | |
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locomotive | 10.6 | |
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mine | 10.6 | |
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old fashioned | 10.5 | |
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stone | 10.2 | |
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town | 10.2 | |
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tree | 10 | |
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tracks | 9.8 | |
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station | 9.8 | |
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black | 9.6 | |
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winter | 9.4 | |
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street | 9.2 | |
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dirty | 9 | |
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scenery | 9 | |
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outdoors | 9 | |
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snow | 8.9 | |
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rural | 8.8 | |
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dark | 8.4 | |
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frame | 8.3 | |
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tourism | 8.3 | |
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retro | 8.2 | |
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landmark | 8.1 | |
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scenic | 7.9 | |
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urban | 7.9 | |
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corridor | 7.9 | |
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paper | 7.8 | |
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roof | 7.8 | |
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steam | 7.8 | |
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empty | 7.7 | |
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factory | 7.7 | |
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rustic | 7.7 | |
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power | 7.6 | |
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brick | 7.5 | |
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historical | 7.5 | |
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steam locomotive | 7.4 | |
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mountains | 7.4 | |
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grain | 7.4 | |
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light | 7.4 | |
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historic | 7.3 | |
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country | 7 | |
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Google
created on 2022-06-04
White | 92.2 | |
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Nature | 90.1 | |
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Black | 89.9 | |
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Black-and-white | 86.8 | |
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Architecture | 85.7 | |
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Building | 85.5 | |
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Plant | 84.3 | |
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Style | 84 | |
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Asphalt | 81.6 | |
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Woody plant | 80 | |
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Road surface | 79.9 | |
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Window | 78.3 | |
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Road | 77.7 | |
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Electricity | 77.4 | |
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Tints and shades | 77.2 | |
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Monochrome photography | 76.6 | |
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Monochrome | 75.4 | |
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Automotive lighting | 75 | |
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Urban area | 73.4 | |
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City | 73.1 | |
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Color Analysis
Feature analysis
Categories
Imagga
cars vehicles | 99.5% | |
|
Captions
Microsoft
created on 2022-06-04
an old photo of a train | 36.9% | |
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old photo of a train | 31.5% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20489021/309,789,50,24/full/0/native.jpg)
Hall
![](https://ids.lib.harvard.edu/ids/iiif/20489021/628,788,117,26/full/0/native.jpg)
June 2.10
![](https://ids.lib.harvard.edu/ids/iiif/20489021/925,1,61,34/full/0/native.jpg)
1701
![](https://ids.lib.harvard.edu/ids/iiif/20489021/51,786,308,34/full/0/native.jpg)
Flass.AN aly.from copp.City Hall
![](https://ids.lib.harvard.edu/ids/iiif/20489021/58,377,15,10/full/0/native.jpg)
San
![](https://ids.lib.harvard.edu/ids/iiif/20489021/70,418,29,11/full/0/native.jpg)
CAMBRIDA
![](https://ids.lib.harvard.edu/ids/iiif/20489021/920,793,50,19/full/0/native.jpg)
١٦٥١
![](https://ids.lib.harvard.edu/ids/iiif/20489021/233,786,85,33/full/0/native.jpg)
copp.City
![](https://ids.lib.harvard.edu/ids/iiif/20489021/51,786,103,27/full/0/native.jpg)
Flass.AN
![](https://ids.lib.harvard.edu/ids/iiif/20489021/158,790,83,30/full/0/native.jpg)
aly.from
![](https://ids.lib.harvard.edu/ids/iiif/20489021/60,795,53,20/full/0/native.jpg)
Hass
![](https://ids.lib.harvard.edu/ids/iiif/20489021/111,795,11,20/full/0/native.jpg)
.
![](https://ids.lib.harvard.edu/ids/iiif/20489021/117,795,29,20/full/0/native.jpg)
Av
![](https://ids.lib.harvard.edu/ids/iiif/20489021/288,796,29,20/full/0/native.jpg)
rty
![](https://ids.lib.harvard.edu/ids/iiif/20489021/316,796,46,20/full/0/native.jpg)
Hall
![](https://ids.lib.harvard.edu/ids/iiif/20489021/946,8,44,27/full/0/native.jpg)
701
![](https://ids.lib.harvard.edu/ids/iiif/20489021/60,8,930,808/full/0/native.jpg)
Hass. Av. Sly from, opp. rty Hall
June 2. M
701
4101
![](https://ids.lib.harvard.edu/ids/iiif/20489021/157,795,33,20/full/0/native.jpg)
Sly
![](https://ids.lib.harvard.edu/ids/iiif/20489021/194,795,43,20/full/0/native.jpg)
from
![](https://ids.lib.harvard.edu/ids/iiif/20489021/230,795,13,20/full/0/native.jpg)
,
![](https://ids.lib.harvard.edu/ids/iiif/20489021/242,795,36,21/full/0/native.jpg)
opp
![](https://ids.lib.harvard.edu/ids/iiif/20489021/632,799,55,16/full/0/native.jpg)
June
![](https://ids.lib.harvard.edu/ids/iiif/20489021/693,799,28,15/full/0/native.jpg)
2.
![](https://ids.lib.harvard.edu/ids/iiif/20489021/721,799,17,15/full/0/native.jpg)
M
![](https://ids.lib.harvard.edu/ids/iiif/20489021/920,795,49,17/full/0/native.jpg)
4101