Machine Generated Data
Tags
Amazon
created on 2022-06-04
Train Station | 99.8 | |
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Terminal | 99.8 | |
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Transportation | 99.8 | |
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Vehicle | 99.8 | |
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Railway | 92.9 | |
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Rail | 92.9 | |
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Train Track | 92.9 | |
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Train | 92.9 | |
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Person | 88.9 | |
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Human | 88.9 | |
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Person | 79 | |
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Subway | 72.9 | |
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Handrail | 55.2 | |
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Banister | 55.2 | |
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Imagga
created on 2022-06-04
Google
created on 2022-06-04
Building | 93.7 | |
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Black | 89.5 | |
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Transport hub | 88.4 | |
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Track | 88 | |
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Mode of transport | 85.3 | |
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Railway | 84.8 | |
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Black-and-white | 83.7 | |
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Rolling stock | 82.9 | |
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Line | 82.8 | |
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Electricity | 80.8 | |
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Parallel | 78.5 | |
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Art | 77.8 | |
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Rectangle | 77.1 | |
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Symmetry | 76.1 | |
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Public transport | 74.2 | |
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Facade | 74 | |
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Window | 73.4 | |
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Monochrome | 72.7 | |
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Monochrome photography | 72.3 | |
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Train station | 71.4 | |
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Microsoft
created on 2022-06-04
black and white | 94.9 | |
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text | 85.5 | |
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building | 78.2 | |
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monochrome | 61.7 | |
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train | 43.4 | |
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Color Analysis
Feature analysis
Categories
Imagga
cars vehicles | 100% | |
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Captions
Microsoft
created on 2022-06-04
a train on a steel track | 67.7% | |
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a close up of a train station | 67.6% | |
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a train on a track | 63.8% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488776/85,309,40,15/full/0/native.jpg)
YEAR
![](https://ids.lib.harvard.edu/ids/iiif/20488776/609,332,37,10/full/0/native.jpg)
TRAIN
![](https://ids.lib.harvard.edu/ids/iiif/20488776/609,332,59,10/full/0/native.jpg)
TRAIN von
![](https://ids.lib.harvard.edu/ids/iiif/20488776/24,309,8,12/full/0/native.jpg)
D
![](https://ids.lib.harvard.edu/ids/iiif/20488776/41,309,14,12/full/0/native.jpg)
LA
![](https://ids.lib.harvard.edu/ids/iiif/20488776/650,333,18,9/full/0/native.jpg)
von
![](https://ids.lib.harvard.edu/ids/iiif/20488776/329,321,27,12/full/0/native.jpg)
OUT
![](https://ids.lib.harvard.edu/ids/iiif/20488776/1,308,54,12/full/0/native.jpg)
S D LA
![](https://ids.lib.harvard.edu/ids/iiif/20488776/403,328,8,9/full/0/native.jpg)
M
![](https://ids.lib.harvard.edu/ids/iiif/20488776/1,308,6,12/full/0/native.jpg)
S
![](https://ids.lib.harvard.edu/ids/iiif/20488776/433,325,7,12/full/0/native.jpg)
E
![](https://ids.lib.harvard.edu/ids/iiif/20488776/405,340,5,4/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/20488776/614,348,50,10/full/0/native.jpg)
SQUARE
![](https://ids.lib.harvard.edu/ids/iiif/20488776/614,344,51,14/full/0/native.jpg)
SQUARE VAN
![](https://ids.lib.harvard.edu/ids/iiif/20488776/646,344,19,6/full/0/native.jpg)
VAN
![](https://ids.lib.harvard.edu/ids/iiif/20488776/54,441,16,5/full/0/native.jpg)
SERVICE
![](https://ids.lib.harvard.edu/ids/iiif/20488776/122,791,38,11/full/0/native.jpg)
alain
![](https://ids.lib.harvard.edu/ids/iiif/20488776/40,441,10,6/full/0/native.jpg)
150
![](https://ids.lib.harvard.edu/ids/iiif/20488776/24,307,658,507/full/0/native.jpg)
D LA YEAR
FOR ANDRE
looking Sty
TRAIN FOR 3
SQUARE
HIL
![](https://ids.lib.harvard.edu/ids/iiif/20488776/24,308,13,18/full/0/native.jpg)
D
![](https://ids.lib.harvard.edu/ids/iiif/20488776/43,308,22,19/full/0/native.jpg)
LA
![](https://ids.lib.harvard.edu/ids/iiif/20488776/86,309,45,20/full/0/native.jpg)
YEAR
![](https://ids.lib.harvard.edu/ids/iiif/20488776/186,424,32,32/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/20488776/203,440,46,46/full/0/native.jpg)
ANDRE
![](https://ids.lib.harvard.edu/ids/iiif/20488776/419,793,65,20/full/0/native.jpg)
looking
![](https://ids.lib.harvard.edu/ids/iiif/20488776/490,795,30,19/full/0/native.jpg)
Sty
![](https://ids.lib.harvard.edu/ids/iiif/20488776/610,332,41,16/full/0/native.jpg)
TRAIN
![](https://ids.lib.harvard.edu/ids/iiif/20488776/672,334,10,14/full/0/native.jpg)
3
![](https://ids.lib.harvard.edu/ids/iiif/20488776/615,346,53,16/full/0/native.jpg)
SQUARE
![](https://ids.lib.harvard.edu/ids/iiif/20488776/623,420,16,12/full/0/native.jpg)
HIL