Machine Generated Data
Tags
Amazon
created on 2019-06-05
Clarifai
created on 2019-06-05
Imagga
created on 2019-06-05
web site | 100 | |
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sewage system | 31 | |
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facility | 29.2 | |
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sky | 27.4 | |
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architecture | 23.4 | |
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building | 22.6 | |
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city | 19.1 | |
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structure | 18.1 | |
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sea | 18.1 | |
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old | 17.4 | |
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sunset | 17.1 | |
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water | 16 | |
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travel | 15.5 | |
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silhouette | 14.9 | |
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clouds | 14.4 | |
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landscape | 14.1 | |
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grunge | 13.6 | |
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landmark | 12.6 | |
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ocean | 12.5 | |
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summer | 12.2 | |
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business | 12.2 | |
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sun | 12.1 | |
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design | 11.8 | |
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night | 11.6 | |
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retro | 11.5 | |
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urban | 11.4 | |
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scene | 11.3 | |
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construction | 11.1 | |
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beach | 11.1 | |
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house | 10.9 | |
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coast | 10.8 | |
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skyline | 10.5 | |
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empty | 10.3 | |
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paper | 10.2 | |
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reflection | 10.2 | |
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space | 10.1 | |
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dirty | 10 | |
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vintage | 9.9 | |
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tourism | 9.9 | |
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vacation | 9.8 | |
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cityscape | 9.5 | |
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frame | 9.3 | |
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tower | 9.2 | |
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exterior | 9.2 | |
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street | 9.2 | |
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wood | 9.2 | |
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drawing | 9.1 | |
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horizon | 9 | |
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downtown | 8.7 | |
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buildings | 8.5 | |
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destination | 8.4 | |
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outdoors | 8.2 | |
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tourist | 8.2 | |
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scenery | 8.1 | |
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light | 8 | |
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river | 8 | |
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wooden | 7.9 | |
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scenic | 7.9 | |
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mist | 7.7 | |
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blank | 7.7 | |
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modern | 7.7 | |
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dusk | 7.6 | |
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roof | 7.6 | |
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bridge | 7.6 | |
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mountains | 7.4 | |
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transport | 7.3 | |
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graphic | 7.3 | |
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holiday | 7.2 | |
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Google
created on 2019-06-05
Photograph | 95 | |
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Text | 91.6 | |
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Room | 71.4 | |
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Adaptation | 67 | |
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Architecture | 65.5 | |
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Illustration | 64 | |
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Photography | 62.4 | |
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House | 59.3 | |
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Photographic paper | 58.2 | |
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Art | 58.1 | |
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History | 57.6 | |
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Paper product | 55.7 | |
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Paper | 54.3 | |
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Microsoft
created on 2019-06-05
indoor | 85.6 | |
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sky | 84.2 | |
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house | 78.2 | |
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screenshot | 34.6 | |
|
Color Analysis
Categories
Imagga
text visuals | 38.4% | |
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paintings art | 26.7% | |
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nature landscape | 17.8% | |
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beaches seaside | 8.6% | |
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streetview architecture | 6.5% | |
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Captions
Microsoft
created on 2019-06-05
a screen shot of a computer | 75.5% | |
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a sign on the screen | 68.6% | |
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a screen shot of a monitor | 65.2% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43313971/405,104,66,13/full/0/native.jpg)
Almshouses
![](https://ids.lib.harvard.edu/ids/iiif/43313971/562,104,44,16/full/0/native.jpg)
County,
![](https://ids.lib.harvard.edu/ids/iiif/43313971/473,104,17,13/full/0/native.jpg)
of
![](https://ids.lib.harvard.edu/ids/iiif/43313971/405,101,236,17/full/0/native.jpg)
Almshouses of Seheneetady County, N.Y.
![](https://ids.lib.harvard.edu/ids/iiif/43313971/491,104,68,14/full/0/native.jpg)
Seheneetady
![](https://ids.lib.harvard.edu/ids/iiif/43313971/820,213,34,16/full/0/native.jpg)
28
![](https://ids.lib.harvard.edu/ids/iiif/43313971/289,473,28,8/full/0/native.jpg)
WOMENS
![](https://ids.lib.harvard.edu/ids/iiif/43313971/609,104,31,15/full/0/native.jpg)
N.Y.
![](https://ids.lib.harvard.edu/ids/iiif/43313971/702,477,40,8/full/0/native.jpg)
FEARVRY
![](https://ids.lib.harvard.edu/ids/iiif/43313971/292,106,455,385/full/0/native.jpg)
Almshouses of Scheneetady County, N. Y.
iiii
WOMENS BULDNG
REAR VEW
![](https://ids.lib.harvard.edu/ids/iiif/43313971/409,108,65,12/full/0/native.jpg)
Almshouses
![](https://ids.lib.harvard.edu/ids/iiif/43313971/476,106,15,16/full/0/native.jpg)
of
![](https://ids.lib.harvard.edu/ids/iiif/43313971/495,108,66,15/full/0/native.jpg)
Scheneetady
![](https://ids.lib.harvard.edu/ids/iiif/43313971/566,110,43,13/full/0/native.jpg)
County,
![](https://ids.lib.harvard.edu/ids/iiif/43313971/614,109,14,13/full/0/native.jpg)
N.
![](https://ids.lib.harvard.edu/ids/iiif/43313971/629,110,14,12/full/0/native.jpg)
Y.
![](https://ids.lib.harvard.edu/ids/iiif/43313971/574,337,45,19/full/0/native.jpg)
iiii
![](https://ids.lib.harvard.edu/ids/iiif/43313971/292,477,29,9/full/0/native.jpg)
WOMENS
![](https://ids.lib.harvard.edu/ids/iiif/43313971/322,477,31,9/full/0/native.jpg)
BULDNG
![](https://ids.lib.harvard.edu/ids/iiif/43313971/704,481,24,10/full/0/native.jpg)
REAR
![](https://ids.lib.harvard.edu/ids/iiif/43313971/727,481,20,10/full/0/native.jpg)
VEW