Machine Generated Data
Tags
Amazon
created on 2022-06-04
Transportation | 94.8 | |
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Vehicle | 93.9 | |
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Train | 92.9 | |
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Cable Car | 90.6 | |
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Trolley | 83.7 | |
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Streetcar | 83.7 | |
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Tram | 83.7 | |
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Terminal | 81.7 | |
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Train Station | 64.6 | |
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Railway | 58.4 | |
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Rail | 58.4 | |
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Train Track | 58.4 | |
|
Imagga
created on 2022-06-04
wheeled vehicle | 50.3 | |
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train | 48.2 | |
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vehicle | 47 | |
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conveyance | 40.8 | |
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car | 39.5 | |
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passenger car | 38.7 | |
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transportation | 38.6 | |
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streetcar | 37.9 | |
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tramway | 34.3 | |
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transport | 33.8 | |
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travel | 32.4 | |
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city | 29.9 | |
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railroad | 29.5 | |
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rail | 29.5 | |
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railway | 28.4 | |
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station | 26.8 | |
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architecture | 26.2 | |
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urban | 26.2 | |
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building | 25.9 | |
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track | 20.8 | |
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old | 20.2 | |
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wagon | 16.4 | |
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subway | 15.8 | |
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town | 15.8 | |
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street | 15.6 | |
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transit | 14.8 | |
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engine | 14.4 | |
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locomotive | 14.2 | |
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tunnel | 13.8 | |
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house | 13.5 | |
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window | 13.2 | |
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electric | 13.1 | |
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sky | 12.8 | |
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public | 12.6 | |
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passenger | 12.1 | |
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speed | 11.9 | |
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tracks | 11.8 | |
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power | 11.8 | |
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road | 11.7 | |
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industry | 11.1 | |
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platform | 10.8 | |
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underground | 10.8 | |
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cable | 10.8 | |
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way | 10.5 | |
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traffic | 10.5 | |
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journey | 10.4 | |
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cell | 10.2 | |
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historic | 10.1 | |
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vintage | 9.9 | |
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commuter | 9.9 | |
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metro | 9.9 | |
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roof | 9.5 | |
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motion | 9.4 | |
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construction | 9.4 | |
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wall | 8.9 | |
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river | 8.9 | |
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rails | 8.9 | |
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passage | 8.9 | |
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metal | 8.9 | |
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light | 8.7 | |
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structure | 8.6 | |
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perspective | 8.5 | |
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industrial | 8.2 | |
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history | 8.1 | |
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steel | 8 | |
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tram | 7.9 | |
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tube | 7.8 | |
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cargo | 7.8 | |
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line | 7.7 | |
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move | 7.7 | |
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stone | 7.6 | |
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bridge | 7.6 | |
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destination | 7.5 | |
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fast | 7.5 | |
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tourism | 7.4 | |
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technology | 7.4 | |
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inside | 7.4 | |
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tourist | 7.3 | |
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night | 7.1 | |
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interior | 7.1 | |
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modern | 7 | |
|
Google
created on 2022-06-04
Building | 92 | |
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Black | 89.6 | |
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Window | 89.5 | |
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Rolling stock | 87.8 | |
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Door | 87.4 | |
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Black-and-white | 85 | |
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Line | 81.9 | |
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Rectangle | 80.1 | |
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Tints and shades | 77.3 | |
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Facade | 75.2 | |
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Public transport | 74.9 | |
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Railway | 74.3 | |
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Monochrome photography | 73.2 | |
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Monochrome | 72.9 | |
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Electricity | 71.5 | |
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Train | 69.3 | |
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Font | 66 | |
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Stock photography | 63.4 | |
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Automotive lighting | 63.2 | |
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Arch | 61.3 | |
|
Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488755/89,175,901,506/full/0/native.jpg)
Train | 92.9% | |
|
Categories
Imagga
cars vehicles | 86% | |
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interior objects | 8.5% | |
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streetview architecture | 1.9% | |
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paintings art | 1.5% | |
|
Captions
Microsoft
created on 2022-06-04
an old photo of a building | 82.3% | |
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an old photo of a large building | 76.9% | |
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old photo of a building | 76.8% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488755/656,469,85,19/full/0/native.jpg)
SAVE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/673,549,48,17/full/0/native.jpg)
SALE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/651,570,13,12/full/0/native.jpg)
12
![](https://ids.lib.harvard.edu/ids/iiif/20488755/670,586,30,12/full/0/native.jpg)
EXTRA
![](https://ids.lib.harvard.edu/ids/iiif/20488755/650,570,95,19/full/0/native.jpg)
12 STORE buying
![](https://ids.lib.harvard.edu/ids/iiif/20488755/666,572,38,12/full/0/native.jpg)
STORE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/663,518,67,17/full/0/native.jpg)
August
![](https://ids.lib.harvard.edu/ids/iiif/20488755/707,574,38,15/full/0/native.jpg)
buying
![](https://ids.lib.harvard.edu/ids/iiif/20488755/658,534,77,15/full/0/native.jpg)
FURNITURE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/703,587,44,12/full/0/native.jpg)
SAVINGS
![](https://ids.lib.harvard.edu/ids/iiif/20488755/693,489,10,6/full/0/native.jpg)
IN
![](https://ids.lib.harvard.edu/ids/iiif/20488755/818,511,79,24/full/0/native.jpg)
5952
![](https://ids.lib.harvard.edu/ids/iiif/20488755/896,793,119,26/full/0/native.jpg)
31039
![](https://ids.lib.harvard.edu/ids/iiif/20488755/648,586,98,13/full/0/native.jpg)
means EXTRA SAVINGS
![](https://ids.lib.harvard.edu/ids/iiif/20488755/667,495,61,24/full/0/native.jpg)
KANE'S
![](https://ids.lib.harvard.edu/ids/iiif/20488755/723,335,39,20/full/0/native.jpg)
cnp
![](https://ids.lib.harvard.edu/ids/iiif/20488755/864,248,52,16/full/0/native.jpg)
USEY
![](https://ids.lib.harvard.edu/ids/iiif/20488755/648,589,21,8/full/0/native.jpg)
means
![](https://ids.lib.harvard.edu/ids/iiif/20488755/855,262,63,16/full/0/native.jpg)
EVERETT
![](https://ids.lib.harvard.edu/ids/iiif/20488755/298,600,38,26/full/0/native.jpg)
LILA
![](https://ids.lib.harvard.edu/ids/iiif/20488755/657,471,89,20/full/0/native.jpg)
SAVE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/298,336,716,487/full/0/native.jpg)
LILA
СПР
SAVE
KANE'S
August
FURNITURE
SALE
12 STORE buying
mas EXTRA SAVINGS
5902
31039
![](https://ids.lib.harvard.edu/ids/iiif/20488755/717,336,50,23/full/0/native.jpg)
СПР
![](https://ids.lib.harvard.edu/ids/iiif/20488755/668,500,66,25/full/0/native.jpg)
KANE'S
![](https://ids.lib.harvard.edu/ids/iiif/20488755/668,523,60,16/full/0/native.jpg)
August
![](https://ids.lib.harvard.edu/ids/iiif/20488755/660,536,78,16/full/0/native.jpg)
FURNITURE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/674,551,51,19/full/0/native.jpg)
SALE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/650,572,18,17/full/0/native.jpg)
12
![](https://ids.lib.harvard.edu/ids/iiif/20488755/668,573,40,18/full/0/native.jpg)
STORE
![](https://ids.lib.harvard.edu/ids/iiif/20488755/709,575,41,19/full/0/native.jpg)
buying
![](https://ids.lib.harvard.edu/ids/iiif/20488755/650,588,25,15/full/0/native.jpg)
mas
![](https://ids.lib.harvard.edu/ids/iiif/20488755/671,588,35,16/full/0/native.jpg)
EXTRA
![](https://ids.lib.harvard.edu/ids/iiif/20488755/705,588,47,17/full/0/native.jpg)
SAVINGS
![](https://ids.lib.harvard.edu/ids/iiif/20488755/817,511,83,29/full/0/native.jpg)
5902
![](https://ids.lib.harvard.edu/ids/iiif/20488755/904,800,110,23/full/0/native.jpg)
31039