Machine Generated Data
Tags
Amazon
created on 2023-10-06
City | 99.7 | |
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Architecture | 99.4 | |
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Building | 99.4 | |
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Sign | 98.7 | |
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Symbol | 98.7 | |
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Urban | 98.4 | |
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Neighborhood | 98.3 | |
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Monastery | 97.2 | |
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Metropolis | 95.4 | |
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Indoors | 93.1 | |
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Restaurant | 93.1 | |
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Road | 87.4 | |
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Street | 87.4 | |
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Building | 85.6 | |
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Text | 72.5 | |
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Outdoors | 67.7 | |
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Brick | 57.2 | |
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Shop | 57.1 | |
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Alley | 56.7 | |
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Shelter | 56.5 | |
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Cafeteria | 56.4 | |
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Road Sign | 56.4 | |
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Railway | 56.1 | |
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Terminal | 56.1 | |
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Train | 56.1 | |
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Train Station | 56.1 | |
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Transportation | 56.1 | |
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Vehicle | 56.1 | |
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Cafe | 56.1 | |
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Factory | 55.2 | |
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Awning | 55.2 | |
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Canopy | 55.2 | |
|
Clarifai
created on 2018-05-10
monochrome | 98.7 | |
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street | 98.4 | |
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city | 96.3 | |
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people | 96.2 | |
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outdoors | 95.6 | |
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horizontal plane | 94 | |
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stock | 93.3 | |
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adult | 92.9 | |
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architecture | 92.8 | |
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window | 90.3 | |
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transportation system | 89.1 | |
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text | 88.9 | |
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signalise | 87.3 | |
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business | 85.5 | |
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road | 84.7 | |
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travel | 83.4 | |
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man | 82.7 | |
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illustration | 82.1 | |
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house | 81.3 | |
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town | 79.2 | |
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Imagga
created on 2023-10-06
billboard | 97.3 | |
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signboard | 94.4 | |
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structure | 71.2 | |
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building | 38.3 | |
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architecture | 28.3 | |
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sign | 25.6 | |
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city | 25 | |
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scoreboard | 21 | |
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board | 20.1 | |
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old | 18.8 | |
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business | 17.6 | |
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travel | 16.9 | |
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wall | 16.3 | |
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cinema | 15.8 | |
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office | 15.4 | |
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sky | 15.3 | |
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finance | 15.2 | |
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construction | 13.7 | |
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brick | 13.2 | |
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urban | 13.1 | |
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street | 12.9 | |
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theater | 12.8 | |
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buildings | 12.3 | |
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modern | 11.9 | |
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house | 10.9 | |
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shop | 10.9 | |
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financial | 10.7 | |
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information | 10.6 | |
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text | 10.5 | |
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town | 10.2 | |
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exterior | 10.1 | |
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tower | 9.9 | |
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stall | 9.4 | |
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window | 9.2 | |
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road | 9 | |
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transportation | 9 | |
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new | 8.9 | |
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windows | 8.6 | |
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paper | 8.6 | |
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glass | 8.6 | |
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tall | 8.5 | |
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vintage | 8.3 | |
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retro | 8.2 | |
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symbol | 8.1 | |
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bank | 8.1 | |
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black | 7.8 | |
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signs | 7.8 | |
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downtown | 7.7 | |
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industry | 7.7 | |
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money | 7.7 | |
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traffic | 7.6 | |
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commerce | 7.5 | |
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banking | 7.4 | |
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dirty | 7.2 | |
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currency | 7.2 | |
|
Google
created on 2018-05-10
black and white | 92.1 | |
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monochrome photography | 80.3 | |
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architecture | 80.3 | |
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structure | 78 | |
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history | 70 | |
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monochrome | 67.1 | |
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building | 64 | |
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facade | 57.9 | |
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font | 55.8 | |
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stock photography | 53.5 | |
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brand | 53.2 | |
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Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43161756/40,51,372,636/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/43161756/323,43,653,638/full/0/native.jpg)
Building | 99.4% | |
|
Categories
Imagga
text visuals | 83.9% | |
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paintings art | 13.6% | |
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interior objects | 1.1% | |
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Captions
Microsoft
created on 2018-05-10
a black sign with white text on the side of a building | 94% | |
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a black sign with white letters | 92.3% | |
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a black sign with white text | 92.2% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43161756/462,264,247,68/full/0/native.jpg)
CAKEWALK
![](https://ids.lib.harvard.edu/ids/iiif/43161756/461,260,395,73/full/0/native.jpg)
CAKEWALK CAFE
![](https://ids.lib.harvard.edu/ids/iiif/43161756/724,263,131,62/full/0/native.jpg)
CAFE
![](https://ids.lib.harvard.edu/ids/iiif/43161756/186,55,63,19/full/0/native.jpg)
House
![](https://ids.lib.harvard.edu/ids/iiif/43161756/41,565,30,23/full/0/native.jpg)
IDIO
![](https://ids.lib.harvard.edu/ids/iiif/43161756/553,593,24,11/full/0/native.jpg)
TOR
![](https://ids.lib.harvard.edu/ids/iiif/43161756/394,371,30,17/full/0/native.jpg)
19
![](https://ids.lib.harvard.edu/ids/iiif/43161756/552,592,74,13/full/0/native.jpg)
TOR KNONG
![](https://ids.lib.harvard.edu/ids/iiif/43161756/591,648,61,20/full/0/native.jpg)
SIEKEE
![](https://ids.lib.harvard.edu/ids/iiif/43161756/585,593,42,11/full/0/native.jpg)
KNONG
![](https://ids.lib.harvard.edu/ids/iiif/43161756/564,648,88,20/full/0/native.jpg)
Mr SIEKEE
![](https://ids.lib.harvard.edu/ids/iiif/43161756/565,652,25,14/full/0/native.jpg)
Mr
![](https://ids.lib.harvard.edu/ids/iiif/43161756/85,57,93,21/full/0/native.jpg)
Wholesale
![](https://ids.lib.harvard.edu/ids/iiif/43161756/85,51,262,27/full/0/native.jpg)
Wholesale House (No 10 1ST FL
![](https://ids.lib.harvard.edu/ids/iiif/43161756/581,625,9,12/full/0/native.jpg)
8
![](https://ids.lib.harvard.edu/ids/iiif/43161756/43,523,7,9/full/0/native.jpg)
W
![](https://ids.lib.harvard.edu/ids/iiif/43161756/42,497,15,23/full/0/native.jpg)
all
![](https://ids.lib.harvard.edu/ids/iiif/43161756/315,366,231,27/full/0/native.jpg)
** 19 BMW
![](https://ids.lib.harvard.edu/ids/iiif/43161756/252,56,25,15/full/0/native.jpg)
(No
![](https://ids.lib.harvard.edu/ids/iiif/43161756/497,144,45,44/full/0/native.jpg)
for
![](https://ids.lib.harvard.edu/ids/iiif/43161756/47,550,7,7/full/0/native.jpg)
12
![](https://ids.lib.harvard.edu/ids/iiif/43161756/565,141,48,44/full/0/native.jpg)
As
![](https://ids.lib.harvard.edu/ids/iiif/43161756/42,614,17,12/full/0/native.jpg)
14
![](https://ids.lib.harvard.edu/ids/iiif/43161756/315,368,74,25/full/0/native.jpg)
**
![](https://ids.lib.harvard.edu/ids/iiif/43161756/499,205,39,40/full/0/native.jpg)
of
![](https://ids.lib.harvard.edu/ids/iiif/43161756/40,591,19,19/full/0/native.jpg)
If
![](https://ids.lib.harvard.edu/ids/iiif/43161756/317,666,34,15/full/0/native.jpg)
Jun
![](https://ids.lib.harvard.edu/ids/iiif/43161756/277,51,70,24/full/0/native.jpg)
10 1ST FL
![](https://ids.lib.harvard.edu/ids/iiif/43161756/437,373,109,16/full/0/native.jpg)
BMW
![](https://ids.lib.harvard.edu/ids/iiif/43161756/558,626,11,11/full/0/native.jpg)
16
![](https://ids.lib.harvard.edu/ids/iiif/43161756/325,600,49,13/full/0/native.jpg)
THIS
![](https://ids.lib.harvard.edu/ids/iiif/43161756/560,617,5,3/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/43161756/489,490,53,29/full/0/native.jpg)
ame
![](https://ids.lib.harvard.edu/ids/iiif/43161756/553,580,11,11/full/0/native.jpg)
9
![](https://ids.lib.harvard.edu/ids/iiif/43161756/190,659,59,16/full/0/native.jpg)
rata