Machine Generated Data
Tags
Amazon
created on 2019-04-05
Vehicle | 96.2 | |
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Military | 96.2 | |
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Military Uniform | 96.2 | |
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Tank | 96.2 | |
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Armored | 96.2 | |
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Transportation | 96.2 | |
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Army | 96.2 | |
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Machine | 77.3 | |
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Train | 72 | |
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Tire | 69.2 | |
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Wheel | 65.5 | |
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Truck | 64.6 | |
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Light | 61.9 | |
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Automobile | 58.9 | |
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Car | 58.9 | |
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Half Track | 55.9 | |
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Motor | 55.7 | |
|
Clarifai
created on 2018-03-22
Imagga
created on 2018-03-22
car | 62.4 | |
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vehicle | 53.6 | |
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jeep | 41.2 | |
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equipment | 33.3 | |
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motor vehicle | 29.8 | |
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tank | 28.4 | |
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wheeled vehicle | 28.3 | |
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old | 27.8 | |
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electronic equipment | 26.8 | |
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engine | 24 | |
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vintage | 23.1 | |
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military vehicle | 23 | |
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tracked vehicle | 22.5 | |
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retro | 22.1 | |
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auto | 22 | |
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radio | 21.5 | |
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transportation | 20.6 | |
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classic | 20.4 | |
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armored vehicle | 20 | |
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metal | 19.3 | |
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antique | 19.2 | |
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wheel | 18.8 | |
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automobile | 18.2 | |
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technology | 17.8 | |
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speed | 16.5 | |
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transport | 16.4 | |
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music | 16.2 | |
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black | 15 | |
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grille | 15 | |
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style | 14.8 | |
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amplifier | 14.8 | |
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radio receiver | 14.5 | |
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receiver | 14.4 | |
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audio | 13.4 | |
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headlight | 13.4 | |
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chrome | 13.2 | |
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object | 13.2 | |
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locomotive | 13.1 | |
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truck | 13 | |
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power | 12.6 | |
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drive | 12.3 | |
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sound | 12.2 | |
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luxury | 12 | |
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device | 11.9 | |
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digital | 11.3 | |
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fast | 11.2 | |
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motor | 11 | |
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race | 10.5 | |
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media | 10.5 | |
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machine | 10.4 | |
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close | 10.3 | |
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set | 10 | |
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road | 9.9 | |
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film | 9.9 | |
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tire | 9.7 | |
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record | 9.7 | |
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shiny | 9.5 | |
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metallic | 9.2 | |
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studio | 9.1 | |
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modern | 9.1 | |
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grate | 8.8 | |
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camera | 8.8 | |
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wheels | 8.8 | |
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listen | 8.7 | |
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show | 8.5 | |
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electronic | 8.4 | |
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entertainment | 8.3 | |
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plastic | 8.3 | |
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reflection | 8.1 | |
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lens | 8 | |
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tape | 7.8 | |
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lamp | 7.7 | |
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expensive | 7.7 | |
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heavy | 7.6 | |
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electronics | 7.6 | |
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closeup | 7.4 | |
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sports | 7.4 | |
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computer | 7.2 | |
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broadcasting | 7.2 | |
|
Google
created on 2018-03-22
motor vehicle | 98 | |
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car | 97.6 | |
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vehicle | 97 | |
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military vehicle | 89.1 | |
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transport | 86.8 | |
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black and white | 85.7 | |
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automotive design | 85.5 | |
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armored car | 84.9 | |
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armored car | 82.8 | |
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automotive exterior | 67 | |
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monochrome photography | 60.7 | |
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monochrome | 59.2 | |
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public utility | 50.7 | |
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Microsoft
created on 2018-03-22
military vehicle | 95.1 | |
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old | 81.4 | |
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transport | 75.4 | |
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vintage | 48.4 | |
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Color Analysis
Face analysis
Amazon
Microsoft
![](https://ids.lib.harvard.edu/ids/iiif/16084287/480,167,24,29/full/0/native.jpg)
AWS Rekognition
Age | 26-43 |
Gender | Male, 54% |
Surprised | 45.4% |
Disgusted | 45% |
Happy | 45% |
Sad | 53.5% |
Calm | 45.5% |
Angry | 45.4% |
Confused | 45.2% |
![](https://ids.lib.harvard.edu/ids/iiif/16084287/479,168,25,25/full/0/native.jpg)
Microsoft Cognitive Services
Age | 26 |
Gender | Male |
Feature analysis
Categories
Imagga
cars vehicles | 87.1% | |
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paintings art | 12.4% | |
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food drinks | 0.1% | |
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interior objects | 0.1% | |
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text visuals | 0.1% | |
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streetview architecture | 0.1% | |
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nature landscape | 0.1% | |
|
Captions
Microsoft
created on 2018-03-22
a vintage photo of a truck | 83.3% | |
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a vintage photo of a vehicle | 80.3% | |
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an old photo of a truck | 80.2% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/16084287/516,681,38,13/full/0/native.jpg)
askd
![](https://ids.lib.harvard.edu/ids/iiif/16084287/385,683,71,13/full/0/native.jpg)
stochles
![](https://ids.lib.harvard.edu/ids/iiif/16084287/76,683,47,18/full/0/native.jpg)
poci
![](https://ids.lib.harvard.edu/ids/iiif/16084287/635,681,29,11/full/0/native.jpg)
bach
![](https://ids.lib.harvard.edu/ids/iiif/16084287/196,683,53,15/full/0/native.jpg)
keern:
![](https://ids.lib.harvard.edu/ids/iiif/16084287/256,683,56,20/full/0/native.jpg)
gneds
![](https://ids.lib.harvard.edu/ids/iiif/16084287/109,715,32,18/full/0/native.jpg)
Nry
![](https://ids.lib.harvard.edu/ids/iiif/16084287/710,679,42,15/full/0/native.jpg)
moun
![](https://ids.lib.harvard.edu/ids/iiif/16084287/161,698,41,16/full/0/native.jpg)
uau
![](https://ids.lib.harvard.edu/ids/iiif/16084287/15,723,37,16/full/0/native.jpg)
Mhn.
![](https://ids.lib.harvard.edu/ids/iiif/16084287/14,720,170,20/full/0/native.jpg)
Mhn. Nry /988
![](https://ids.lib.harvard.edu/ids/iiif/16084287/843,679,54,16/full/0/native.jpg)
Banbont
![](https://ids.lib.harvard.edu/ids/iiif/16084287/79,698,48,18/full/0/native.jpg)
AFS
![](https://ids.lib.harvard.edu/ids/iiif/16084287/12,681,57,22/full/0/native.jpg)
Seew:5
![](https://ids.lib.harvard.edu/ids/iiif/16084287/208,698,42,12/full/0/native.jpg)
ites
![](https://ids.lib.harvard.edu/ids/iiif/16084287/146,715,40,15/full/0/native.jpg)
/988
![](https://ids.lib.harvard.edu/ids/iiif/16084287/320,681,57,13/full/0/native.jpg)
muelean
![](https://ids.lib.harvard.edu/ids/iiif/16084287/16,699,954,19/full/0/native.jpg)
Kittlend AFS uau ites Maer
![](https://ids.lib.harvard.edu/ids/iiif/16084287/11,680,914,25/full/0/native.jpg)
Seew:5 poci Me Rsc keern: gneds muelean stochles (twan, askd up my bach to . moun Banbont n.
![](https://ids.lib.harvard.edu/ids/iiif/16084287/17,700,62,17/full/0/native.jpg)
Kittlend
![](https://ids.lib.harvard.edu/ids/iiif/16084287/575,679,48,19/full/0/native.jpg)
up my
![](https://ids.lib.harvard.edu/ids/iiif/16084287/129,679,30,17/full/0/native.jpg)
Me
![](https://ids.lib.harvard.edu/ids/iiif/16084287/459,679,53,19/full/0/native.jpg)
(twan,
![](https://ids.lib.harvard.edu/ids/iiif/16084287/666,681,15,13/full/0/native.jpg)
to
![](https://ids.lib.harvard.edu/ids/iiif/16084287/921,679,50,16/full/0/native.jpg)
Maer
![](https://ids.lib.harvard.edu/ids/iiif/16084287/58,725,47,19/full/0/native.jpg)
wug,
![](https://ids.lib.harvard.edu/ids/iiif/16084287/903,679,22,16/full/0/native.jpg)
n.
![](https://ids.lib.harvard.edu/ids/iiif/16084287/163,681,32,13/full/0/native.jpg)
Rsc
![](https://ids.lib.harvard.edu/ids/iiif/16084287/688,679,21,13/full/0/native.jpg)
.