Machine Generated Data
Tags
Amazon
created on 2022-06-04
Imagga
created on 2022-06-04
subway train | 52.9 | |
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train | 50.8 | |
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building | 39.6 | |
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architecture | 39.5 | |
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public transport | 31.9 | |
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old | 29.3 | |
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conveyance | 24.4 | |
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tunnel | 24.1 | |
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wall | 21.7 | |
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city | 21.6 | |
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garage | 20.9 | |
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corridor | 19.7 | |
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street | 19.3 | |
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light | 18.7 | |
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stone | 17.7 | |
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station | 17.7 | |
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subway station | 17.6 | |
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basement | 17.5 | |
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interior | 16.8 | |
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ancient | 16.4 | |
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structure | 16.1 | |
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dark | 15.9 | |
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urban | 15.7 | |
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terminal | 15.7 | |
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travel | 15.5 | |
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historic | 14.7 | |
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house | 14.4 | |
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night | 14.2 | |
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underground | 13.8 | |
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window | 13.1 | |
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construction | 12.8 | |
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subway | 12.8 | |
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hall | 12.7 | |
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arch | 12.6 | |
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passage | 12.3 | |
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door | 11.9 | |
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road | 11.8 | |
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transportation | 11.7 | |
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inside | 11 | |
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religion | 10.8 | |
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warehouse | 10.8 | |
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history | 10.7 | |
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scene | 10.4 | |
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cinema | 10.3 | |
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track | 10.3 | |
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passageway | 9.9 | |
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facility | 9.9 | |
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brick | 9.4 | |
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church | 9.3 | |
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theater | 8.7 | |
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entrance | 8.7 | |
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medieval | 8.6 | |
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way | 8.6 | |
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historical | 8.5 | |
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floor | 8.4 | |
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town | 8.4 | |
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vintage | 8.3 | |
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room | 8.1 | |
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platform | 8 | |
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ceiling | 7.9 | |
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spooky | 7.8 | |
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empty | 7.7 | |
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lock | 7.7 | |
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concrete | 7.7 | |
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gate | 7.5 | |
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car | 7.5 | |
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monument | 7.5 | |
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lights | 7.4 | |
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column | 7.4 | |
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design | 7.3 | |
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industrial | 7.3 | |
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sky | 7 | |
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Google
created on 2022-06-04
Black | 89.6 | |
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Building | 85.9 | |
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Black-and-white | 85.7 | |
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Line | 81.8 | |
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Tints and shades | 76.9 | |
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Monochrome photography | 75.8 | |
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Monochrome | 74.7 | |
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Room | 70.1 | |
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Event | 69.7 | |
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Rectangle | 67.1 | |
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Ceiling | 67.1 | |
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Stock photography | 65.6 | |
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Symmetry | 62.5 | |
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Light fixture | 62.2 | |
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Arch | 57.9 | |
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Conifer | 57 | |
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Art | 56.3 | |
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Street | 56 | |
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Road | 55.6 | |
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Tree | 54.1 | |
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Microsoft
created on 2022-06-04
black and white | 97.3 | |
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road | 97.2 | |
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building | 80.8 | |
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monochrome | 76.7 | |
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text | 71.6 | |
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street | 50.6 | |
|
Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20489065/128,158,699,385/full/0/native.jpg)
Train | 89.3% | |
|
Categories
Imagga
cars vehicles | 98.1% | |
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pets animals | 1.3% | |
|
Captions
Microsoft
created on 2022-06-04
a road with a building in the background | 91.7% | |
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a building with a store on the side of a road | 88.6% | |
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a road with a building in the rain | 74.6% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20489065/820,373,19,17/full/0/native.jpg)
NO
![](https://ids.lib.harvard.edu/ids/iiif/20489065/819,372,83,21/full/0/native.jpg)
NO PASSING
![](https://ids.lib.harvard.edu/ids/iiif/20489065/842,372,61,20/full/0/native.jpg)
PASSING
![](https://ids.lib.harvard.edu/ids/iiif/20489065/820,413,28,14/full/0/native.jpg)
THIS
![](https://ids.lib.harvard.edu/ids/iiif/20489065/820,413,82,16/full/0/native.jpg)
THIS OPENING
![](https://ids.lib.harvard.edu/ids/iiif/20489065/850,413,51,16/full/0/native.jpg)
OPENING
![](https://ids.lib.harvard.edu/ids/iiif/20489065/359,177,151,42/full/0/native.jpg)
HARVARD
![](https://ids.lib.harvard.edu/ids/iiif/20489065/829,393,64,18/full/0/native.jpg)
THROUGH
![](https://ids.lib.harvard.edu/ids/iiif/20489065/595,370,16,10/full/0/native.jpg)
TR
![](https://ids.lib.harvard.edu/ids/iiif/20489065/474,706,40,12/full/0/native.jpg)
MAY
![](https://ids.lib.harvard.edu/ids/iiif/20489065/588,383,24,11/full/0/native.jpg)
B05
![](https://ids.lib.harvard.edu/ids/iiif/20489065/474,703,98,18/full/0/native.jpg)
MAY 4.1938
![](https://ids.lib.harvard.edu/ids/iiif/20489065/592,402,18,9/full/0/native.jpg)
PCH
![](https://ids.lib.harvard.edu/ids/iiif/20489065/509,703,62,17/full/0/native.jpg)
4.1938
![](https://ids.lib.harvard.edu/ids/iiif/20489065/426,340,43,18/full/0/native.jpg)
9114
![](https://ids.lib.harvard.edu/ids/iiif/20489065/29,705,82,12/full/0/native.jpg)
NATALASS
![](https://ids.lib.harvard.edu/ids/iiif/20489065/836,453,9,5/full/0/native.jpg)
AL
![](https://ids.lib.harvard.edu/ids/iiif/20489065/-8,179,1003,545/full/0/native.jpg)
I
TROLLEY-HARVARD
HARVARD
MAY 4-1938
BOS
NO PASSING
THROUGH
THIS OPENING
23285
![](https://ids.lib.harvard.edu/ids/iiif/20489065/-8,404,27,32/full/0/native.jpg)
I
![](https://ids.lib.harvard.edu/ids/iiif/20489065/120,707,81,15/full/0/native.jpg)
TROLLEY
![](https://ids.lib.harvard.edu/ids/iiif/20489065/201,707,13,15/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/20489065/211,707,81,15/full/0/native.jpg)
HARVARD
![](https://ids.lib.harvard.edu/ids/iiif/20489065/474,707,36,17/full/0/native.jpg)
MAY
![](https://ids.lib.harvard.edu/ids/iiif/20489065/512,706,62,17/full/0/native.jpg)
4-1938
![](https://ids.lib.harvard.edu/ids/iiif/20489065/588,385,28,15/full/0/native.jpg)
BOS
![](https://ids.lib.harvard.edu/ids/iiif/20489065/822,374,22,20/full/0/native.jpg)
NO
![](https://ids.lib.harvard.edu/ids/iiif/20489065/844,374,63,23/full/0/native.jpg)
PASSING
![](https://ids.lib.harvard.edu/ids/iiif/20489065/833,395,62,18/full/0/native.jpg)
THROUGH
![](https://ids.lib.harvard.edu/ids/iiif/20489065/821,413,33,20/full/0/native.jpg)
THIS
![](https://ids.lib.harvard.edu/ids/iiif/20489065/851,414,55,20/full/0/native.jpg)
OPENING
![](https://ids.lib.harvard.edu/ids/iiif/20489065/936,703,59,19/full/0/native.jpg)
23285