Machine Generated Data
Tags
Amazon
created on 2022-06-04
Imagga
created on 2022-06-04
road | 26.2 | |
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city | 24.9 | |
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sky | 23.6 | |
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landscape | 20.1 | |
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travel | 19.7 | |
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architecture | 19.7 | |
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car | 19.6 | |
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transportation | 18.8 | |
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submarine | 18.1 | |
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track | 17.9 | |
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building | 17.8 | |
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intersection | 17.6 | |
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snow | 17.3 | |
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ship | 16.7 | |
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highway | 16.4 | |
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structure | 15.8 | |
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urban | 15.7 | |
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street | 15.6 | |
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wheeled vehicle | 15.5 | |
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vehicle | 15.2 | |
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night | 15.1 | |
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traffic | 14.3 | |
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speed | 13.7 | |
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streetcar | 13.5 | |
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light | 13.4 | |
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cityscape | 13.3 | |
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submersible | 13.2 | |
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warship | 13.1 | |
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industry | 12.8 | |
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transport | 12.8 | |
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clouds | 12.7 | |
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town | 12.1 | |
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trees | 11.6 | |
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water | 11.3 | |
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industrial | 10.9 | |
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bridge | 10.8 | |
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mountain | 10.8 | |
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tramway | 10.6 | |
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lights | 10.2 | |
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mountains | 10.2 | |
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vessel | 10.1 | |
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old | 9.8 | |
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hills | 9.7 | |
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driving | 9.7 | |
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line | 9.6 | |
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truck | 9.5 | |
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scene | 9.5 | |
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drive | 9.5 | |
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day | 9.4 | |
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tunnel | 9.4 | |
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hill | 9.4 | |
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winter | 9.4 | |
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outdoor | 9.2 | |
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tourism | 9.1 | |
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weather | 8.8 | |
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downtown | 8.7 | |
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cloud | 8.6 | |
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sea | 8.6 | |
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way | 8.6 | |
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skyline | 8.6 | |
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conveyance | 8.4 | |
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tower | 8.1 | |
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business | 7.9 | |
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asphalt | 7.8 | |
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aerial | 7.8 | |
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summer | 7.7 | |
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military vehicle | 7.5 | |
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field | 7.5 | |
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dark | 7.5 | |
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outdoors | 7.5 | |
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land | 7.4 | |
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metal | 7.2 | |
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history | 7.2 | |
|
Google
created on 2022-06-04
Building | 90.2 | |
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Infrastructure | 88.9 | |
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Road surface | 88 | |
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Asphalt | 87.6 | |
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Automotive lighting | 84.4 | |
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Black-and-white | 84.3 | |
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Thoroughfare | 79.2 | |
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Monochrome photography | 77.4 | |
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City | 77.2 | |
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Monochrome | 76.1 | |
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Road | 76 | |
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Sky | 74.8 | |
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Font | 74.6 | |
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Midnight | 70.2 | |
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Darkness | 69.9 | |
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Electricity | 67.7 | |
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Lane | 66.5 | |
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Street | 63.8 | |
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Stock photography | 63.7 | |
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Landscape | 62.4 | |
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Color Analysis
Categories
Imagga
cars vehicles | 80% | |
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nature landscape | 10.8% | |
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streetview architecture | 5.6% | |
|
Captions
Microsoft
created on 2022-06-04
a large ship in the background | 83.2% | |
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an old photo of a large ship in the background | 74.9% | |
| ||
a large ship parked on a train track | 47.5% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20489117/261,742,50,23/full/0/native.jpg)
N'LY
![](https://ids.lib.harvard.edu/ids/iiif/20489117/388,743,117,23/full/0/native.jpg)
PEABODY
![](https://ids.lib.harvard.edu/ids/iiif/20489117/511,743,42,25/full/0/native.jpg)
SQ.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/121,742,55,18/full/0/native.jpg)
SEC.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/44,739,132,24/full/0/native.jpg)
D.R.T.- SEC.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/261,742,292,26/full/0/native.jpg)
N'LY VIEW PEABODY SQ.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/44,739,72,24/full/0/native.jpg)
D.R.T.-
![](https://ids.lib.harvard.edu/ids/iiif/20489117/417,260,10,3/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/20489117/322,744,57,18/full/0/native.jpg)
VIEW
![](https://ids.lib.harvard.edu/ids/iiif/20489117/128,763,117,20/full/0/native.jpg)
NO.222.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/417,259,53,9/full/0/native.jpg)
- - Preming -
![](https://ids.lib.harvard.edu/ids/iiif/20489117/731,764,62,29/full/0/native.jpg)
day
![](https://ids.lib.harvard.edu/ids/iiif/20489117/430,285,20,5/full/0/native.jpg)
DYERS
![](https://ids.lib.harvard.edu/ids/iiif/20489117/417,279,33,7/full/0/native.jpg)
CLEANSEUS
![](https://ids.lib.harvard.edu/ids/iiif/20489117/730,762,185,30/full/0/native.jpg)
day (01.223)
![](https://ids.lib.harvard.edu/ids/iiif/20489117/436,259,24,9/full/0/native.jpg)
Preming
![](https://ids.lib.harvard.edu/ids/iiif/20489117/801,762,114,30/full/0/native.jpg)
(01.223)
![](https://ids.lib.harvard.edu/ids/iiif/20489117/46,258,527,538/full/0/native.jpg)
D.R.T. SEC.
NO.222.
Jill Our Pressing the
Waldemovar
CLEANERS
www.
N'LY VIEYY PEABODY SQ.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/46,742,73,23/full/0/native.jpg)
D.R.T.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/123,743,50,23/full/0/native.jpg)
SEC
![](https://ids.lib.harvard.edu/ids/iiif/20489117/171,744,11,21/full/0/native.jpg)
.
![](https://ids.lib.harvard.edu/ids/iiif/20489117/129,765,110,21/full/0/native.jpg)
NO.222
![](https://ids.lib.harvard.edu/ids/iiif/20489117/417,260,15,9/full/0/native.jpg)
Jill
![](https://ids.lib.harvard.edu/ids/iiif/20489117/429,260,12,9/full/0/native.jpg)
Our
![](https://ids.lib.harvard.edu/ids/iiif/20489117/438,260,26,11/full/0/native.jpg)
Pressing
![](https://ids.lib.harvard.edu/ids/iiif/20489117/461,261,14,9/full/0/native.jpg)
the
![](https://ids.lib.harvard.edu/ids/iiif/20489117/411,265,59,16/full/0/native.jpg)
Waldemovar
![](https://ids.lib.harvard.edu/ids/iiif/20489117/418,281,38,10/full/0/native.jpg)
CLEANERS
![](https://ids.lib.harvard.edu/ids/iiif/20489117/477,593,24,13/full/0/native.jpg)
www
![](https://ids.lib.harvard.edu/ids/iiif/20489117/262,746,57,24/full/0/native.jpg)
N'LY
![](https://ids.lib.harvard.edu/ids/iiif/20489117/322,746,63,24/full/0/native.jpg)
VIEYY
![](https://ids.lib.harvard.edu/ids/iiif/20489117/389,746,120,24/full/0/native.jpg)
PEABODY
![](https://ids.lib.harvard.edu/ids/iiif/20489117/513,746,37,24/full/0/native.jpg)
SQ