Machine Generated Data
Tags
Amazon
created on 2022-06-04
Water | 94 | |
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Nature | 89.2 | |
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Outdoors | 88.2 | |
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Waterfront | 85.3 | |
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Transportation | 83.2 | |
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Vehicle | 82.1 | |
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Metropolis | 80.4 | |
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Urban | 80.4 | |
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City | 80.4 | |
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Town | 80.4 | |
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Building | 80.4 | |
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Ship | 77.5 | |
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Pier | 76 | |
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Port | 76 | |
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Dock | 76 | |
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Landscape | 72.4 | |
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Scenery | 63.3 | |
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Watercraft | 61.7 | |
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Vessel | 61.7 | |
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Road | 61.5 | |
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Barge | 57.5 | |
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Boat | 57.5 | |
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Shipwreck | 57.3 | |
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Path | 56.7 | |
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River | 55.5 | |
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Imagga
created on 2022-06-04
Google
created on 2022-06-04
Vehicle | 82.9 | |
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Naval architecture | 82.8 | |
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Urban design | 82.5 | |
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Building | 81.6 | |
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Landscape | 72.7 | |
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House | 72.3 | |
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Monochrome | 70.7 | |
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City | 67.3 | |
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History | 64.9 | |
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Window | 63.9 | |
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Stock photography | 62.4 | |
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Monochrome photography | 61.5 | |
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Art | 61.2 | |
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Slope | 59.3 | |
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Metal | 58.9 | |
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Project | 55.1 | |
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Road | 54.6 | |
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Drawing | 53.5 | |
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Mixed-use | 52.7 | |
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Electricity | 51.2 | |
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Microsoft
created on 2022-06-04
black and white | 87.1 | |
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white | 73 | |
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drawing | 70 | |
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Color Analysis
Categories
Imagga
nature landscape | 57.4% | |
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paintings art | 14.7% | |
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cars vehicles | 6.6% | |
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beaches seaside | 6.2% | |
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sunrises sunsets | 5.6% | |
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macro flowers | 2.3% | |
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food drinks | 2.2% | |
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interior objects | 1.8% | |
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streetview architecture | 1.2% | |
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Captions
Microsoft
created on 2022-06-04
an old photo of a train station | 49.2% | |
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an old photo of a train | 41.6% | |
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an old photo of a train on a steel track | 29.1% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488741/174,337,62,39/full/0/native.jpg)
HAVANA
![](https://ids.lib.harvard.edu/ids/iiif/20488741/183,260,57,35/full/0/native.jpg)
BEEMAN'S
![](https://ids.lib.harvard.edu/ids/iiif/20488741/201,292,37,33/full/0/native.jpg)
PEPSIM
![](https://ids.lib.harvard.edu/ids/iiif/20488741/131,364,23,13/full/0/native.jpg)
ост.
![](https://ids.lib.harvard.edu/ids/iiif/20488741/467,250,38,19/full/0/native.jpg)
GUILDERS
![](https://ids.lib.harvard.edu/ids/iiif/20488741/893,795,66,30/full/0/native.jpg)
9349
![](https://ids.lib.harvard.edu/ids/iiif/20488741/195,282,58,42/full/0/native.jpg)
PEPSIM GI
![](https://ids.lib.harvard.edu/ids/iiif/20488741/229,282,15,14/full/0/native.jpg)
GI
![](https://ids.lib.harvard.edu/ids/iiif/20488741/193,267,15,9/full/0/native.jpg)
CHEW
![](https://ids.lib.harvard.edu/ids/iiif/20488741/475,226,33,14/full/0/native.jpg)
SURWASS RS
![](https://ids.lib.harvard.edu/ids/iiif/20488741/171,198,354,183/full/0/native.jpg)
BE
2280
PEPSIN
ET
HAVANA
KEK
SERINGSA
SAFRASTR
Matan-thoact
P
SUILDERS
![](https://ids.lib.harvard.edu/ids/iiif/20488741/191,282,20,18/full/0/native.jpg)
BE
![](https://ids.lib.harvard.edu/ids/iiif/20488741/476,215,36,15/full/0/native.jpg)
SERINGSA
![](https://ids.lib.harvard.edu/ids/iiif/20488741/463,248,26,17/full/0/native.jpg)
Matan
![](https://ids.lib.harvard.edu/ids/iiif/20488741/488,240,27,18/full/0/native.jpg)
thoact
![](https://ids.lib.harvard.edu/ids/iiif/20488741/482,251,14,14/full/0/native.jpg)
P
![](https://ids.lib.harvard.edu/ids/iiif/20488741/192,287,35,28/full/0/native.jpg)
2280
![](https://ids.lib.harvard.edu/ids/iiif/20488741/201,291,42,38/full/0/native.jpg)
PEPSIN
![](https://ids.lib.harvard.edu/ids/iiif/20488741/191,327,30,16/full/0/native.jpg)
ET
![](https://ids.lib.harvard.edu/ids/iiif/20488741/174,334,70,47/full/0/native.jpg)
HAVANA
![](https://ids.lib.harvard.edu/ids/iiif/20488741/482,200,32,15/full/0/native.jpg)
KEK
![](https://ids.lib.harvard.edu/ids/iiif/20488741/477,226,30,18/full/0/native.jpg)
SAFRASTR
![](https://ids.lib.harvard.edu/ids/iiif/20488741/483,247,10,12/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/20488741/471,251,36,22/full/0/native.jpg)
SUILDERS