Machine Generated Data
Tags
Amazon
created on 2022-06-04
Building | 94.1 | |
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Railway | 91.9 | |
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Train Track | 91.9 | |
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Rail | 91.9 | |
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Transportation | 91.9 | |
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Architecture | 87.4 | |
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Vehicle | 75 | |
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Train | 75 | |
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Handrail | 70.3 | |
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Banister | 70.3 | |
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Pillar | 59.3 | |
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Column | 59.3 | |
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Imagga
created on 2022-06-04
bridge | 82.9 | |
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suspension bridge | 66 | |
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structure | 60.3 | |
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architecture | 34.1 | |
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city | 31.6 | |
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building | 30.6 | |
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urban | 29.7 | |
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construction | 29.1 | |
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support | 26.9 | |
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steel | 20.5 | |
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travel | 20.4 | |
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device | 20.1 | |
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industrial | 20 | |
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sky | 19.9 | |
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river | 18.7 | |
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tower | 17.9 | |
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new | 17 | |
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modern | 16.8 | |
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station | 16.6 | |
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pier | 16 | |
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track | 15.8 | |
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industry | 15.4 | |
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metal | 14.5 | |
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road | 14.5 | |
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landmark | 13.5 | |
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transportation | 13.5 | |
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step | 13.4 | |
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traffic | 13.3 | |
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high | 13 | |
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skyscraper | 13 | |
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glass | 12.5 | |
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skyline | 12.4 | |
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cityscape | 12.3 | |
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window | 11.9 | |
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transport | 11.9 | |
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business | 11.5 | |
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buildings | 11.3 | |
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exterior | 11.1 | |
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railway | 10.8 | |
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office | 10.4 | |
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iron | 10.3 | |
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train | 10.2 | |
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design | 10.1 | |
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street | 10.1 | |
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downtown | 9.6 | |
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perspective | 9.4 | |
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technology | 8.9 | |
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suspension | 8.9 | |
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cable | 8.8 | |
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light | 8.7 | |
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highway | 8.7 | |
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barrier | 8.7 | |
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engineering | 8.6 | |
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power | 8.4 | |
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famous | 8.4 | |
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town | 8.4 | |
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reflection | 8.1 | |
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water | 8 | |
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tunnel | 7.9 | |
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rail | 7.9 | |
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work | 7.9 | |
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line | 7.8 | |
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cables | 7.8 | |
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black | 7.8 | |
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roof | 7.7 | |
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passage | 7.5 | |
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frame | 7.5 | |
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plant | 7.5 | |
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landscape | 7.4 | |
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tourism | 7.4 | |
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tourist | 7.3 | |
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futuristic | 7.2 | |
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night | 7.1 | |
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Google
created on 2022-06-04
Building | 91.5 | |
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Electricity | 79.6 | |
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Rolling | 78.5 | |
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Monochrome | 75.6 | |
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Parallel | 75.5 | |
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Monochrome photography | 74.6 | |
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Railway | 70 | |
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Metal | 69.3 | |
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Urban design | 68.4 | |
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Vehicle | 68.2 | |
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Engineering | 67.3 | |
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Track | 67.3 | |
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Rolling stock | 65.6 | |
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Steel | 65.1 | |
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Transport hub | 63.7 | |
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Stock photography | 63.2 | |
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Machine | 58.6 | |
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Railroad car | 58.5 | |
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History | 58.4 | |
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Factory | 57.4 | |
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Microsoft
created on 2022-06-04
black and white | 89.6 | |
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white | 70.3 | |
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railroad | 23.6 | |
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Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488740/302,90,358,164/full/0/native.jpg)
Train | 75% | |
|
Categories
Imagga
paintings art | 76.8% | |
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nature landscape | 13.5% | |
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streetview architecture | 6.4% | |
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beaches seaside | 1.4% | |
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Captions
Microsoft
created on 2022-06-04
a train traveling down train tracks near a building | 39.4% | |
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a train on a steel track | 33.5% | |
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a train traveling down tracks near a building | 33.4% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488740/0,192,54,27/full/0/native.jpg)
FEED
![](https://ids.lib.harvard.edu/ids/iiif/20488740/746,235,50,38/full/0/native.jpg)
152
![](https://ids.lib.harvard.edu/ids/iiif/20488740/44,789,58,21/full/0/native.jpg)
Main
![](https://ids.lib.harvard.edu/ids/iiif/20488740/819,186,58,30/full/0/native.jpg)
STEWART
![](https://ids.lib.harvard.edu/ids/iiif/20488740/789,180,33,25/full/0/native.jpg)
HUGH
![](https://ids.lib.harvard.edu/ids/iiif/20488740/933,114,80,19/full/0/native.jpg)
AUTOMOBILE
![](https://ids.lib.harvard.edu/ids/iiif/20488740/699,163,42,25/full/0/native.jpg)
CUTTER
![](https://ids.lib.harvard.edu/ids/iiif/20488740/752,265,40,20/full/0/native.jpg)
MAINST
![](https://ids.lib.harvard.edu/ids/iiif/20488740/880,199,17,22/full/0/native.jpg)
&
![](https://ids.lib.harvard.edu/ids/iiif/20488740/738,173,44,22/full/0/native.jpg)
SUCCESSOR
![](https://ids.lib.harvard.edu/ids/iiif/20488740/0,100,38,36/full/0/native.jpg)
AW,
![](https://ids.lib.harvard.edu/ids/iiif/20488740/929,134,17,76/full/0/native.jpg)
BODIES
![](https://ids.lib.harvard.edu/ids/iiif/20488740/11,146,35,31/full/0/native.jpg)
N&
![](https://ids.lib.harvard.edu/ids/iiif/20488740/659,155,268,71/full/0/native.jpg)
J.FRANK CUTTER SUCCESSOR TO HUGH STEWART & CO.
![](https://ids.lib.harvard.edu/ids/iiif/20488740/663,155,41,24/full/0/native.jpg)
J.FRANK
![](https://ids.lib.harvard.edu/ids/iiif/20488740/963,136,17,55/full/0/native.jpg)
TOPS
![](https://ids.lib.harvard.edu/ids/iiif/20488740/894,201,29,26/full/0/native.jpg)
CO.
![](https://ids.lib.harvard.edu/ids/iiif/20488740/993,129,20,75/full/0/native.jpg)
PAINTING
![](https://ids.lib.harvard.edu/ids/iiif/20488740/932,202,75,18/full/0/native.jpg)
UPHOLSTERING
![](https://ids.lib.harvard.edu/ids/iiif/20488740/781,184,9,10/full/0/native.jpg)
TO
![](https://ids.lib.harvard.edu/ids/iiif/20488740/185,783,104,30/full/0/native.jpg)
Combridge
![](https://ids.lib.harvard.edu/ids/iiif/20488740/-5,45,41,36/full/0/native.jpg)
0.
![](https://ids.lib.harvard.edu/ids/iiif/20488740/44,782,439,32/full/0/native.jpg)
Main St. treat Combridge bridge.from vick
![](https://ids.lib.harvard.edu/ids/iiif/20488740/101,788,86,25/full/0/native.jpg)
St. treat
![](https://ids.lib.harvard.edu/ids/iiif/20488740/285,782,155,28/full/0/native.jpg)
bridge.from
![](https://ids.lib.harvard.edu/ids/iiif/20488740/442,791,41,14/full/0/native.jpg)
vick
![](https://ids.lib.harvard.edu/ids/iiif/20488740/948,2,61,24/full/0/native.jpg)
soc
![](https://ids.lib.harvard.edu/ids/iiif/20488740/28,104,14,27/full/0/native.jpg)
,
![](https://ids.lib.harvard.edu/ids/iiif/20488740/15,152,19,26/full/0/native.jpg)
N
![](https://ids.lib.harvard.edu/ids/iiif/20488740/29,151,21,26/full/0/native.jpg)
&
![](https://ids.lib.harvard.edu/ids/iiif/20488740/3,194,53,28/full/0/native.jpg)
FEED
![](https://ids.lib.harvard.edu/ids/iiif/20488740/47,791,57,23/full/0/native.jpg)
Main
![](https://ids.lib.harvard.edu/ids/iiif/20488740/452,789,36,22/full/0/native.jpg)
IGVA
![](https://ids.lib.harvard.edu/ids/iiif/20488740/935,115,80,21/full/0/native.jpg)
AUTOMOBILE
![](https://ids.lib.harvard.edu/ids/iiif/20488740/935,137,14,14/full/0/native.jpg)
B
![](https://ids.lib.harvard.edu/ids/iiif/20488740/750,237,51,39/full/0/native.jpg)
152
![](https://ids.lib.harvard.edu/ids/iiif/20488740/754,266,40,24/full/0/native.jpg)
MAINST
![](https://ids.lib.harvard.edu/ids/iiif/20488740/-8,35,1029,791/full/0/native.jpg)
CO.
AW,
N&
FEED
Main St. Heat Cambridge bodge foress IGVA
AUTOMOBILE
B
P
FRANK CUTTER SUCCESSOR TO HUGH STEWART & CO UPHOLSTERING
TILL 152
MAINST
![](https://ids.lib.harvard.edu/ids/iiif/20488740/-8,50,39,32/full/0/native.jpg)
CO
![](https://ids.lib.harvard.edu/ids/iiif/20488740/26,49,13,31/full/0/native.jpg)
.
![](https://ids.lib.harvard.edu/ids/iiif/20488740/-5,104,43,29/full/0/native.jpg)
AW
![](https://ids.lib.harvard.edu/ids/iiif/20488740/105,791,39,22/full/0/native.jpg)
St.
![](https://ids.lib.harvard.edu/ids/iiif/20488740/139,791,48,22/full/0/native.jpg)
Heat
![](https://ids.lib.harvard.edu/ids/iiif/20488740/187,789,104,24/full/0/native.jpg)
Cambridge
![](https://ids.lib.harvard.edu/ids/iiif/20488740/291,789,68,23/full/0/native.jpg)
bodge
![](https://ids.lib.harvard.edu/ids/iiif/20488740/369,789,73,22/full/0/native.jpg)
foress
![](https://ids.lib.harvard.edu/ids/iiif/20488740/967,166,14,14/full/0/native.jpg)
P
![](https://ids.lib.harvard.edu/ids/iiif/20488740/669,159,36,23/full/0/native.jpg)
FRANK
![](https://ids.lib.harvard.edu/ids/iiif/20488740/700,164,44,26/full/0/native.jpg)
CUTTER
![](https://ids.lib.harvard.edu/ids/iiif/20488740/738,173,47,25/full/0/native.jpg)
SUCCESSOR
![](https://ids.lib.harvard.edu/ids/iiif/20488740/779,183,17,16/full/0/native.jpg)
TO
![](https://ids.lib.harvard.edu/ids/iiif/20488740/789,183,34,23/full/0/native.jpg)
HUGH
![](https://ids.lib.harvard.edu/ids/iiif/20488740/819,190,62,30/full/0/native.jpg)
STEWART
![](https://ids.lib.harvard.edu/ids/iiif/20488740/932,202,77,20/full/0/native.jpg)
UPHOLSTERING
![](https://ids.lib.harvard.edu/ids/iiif/20488740/659,183,84,72/full/0/native.jpg)
TILL