Machine Generated Data
Tags
Amazon
created on 2019-05-31
Vehicle | 99.3 | |
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Automobile | 99.3 | |
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Transportation | 99.3 | |
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Antique Car | 99.2 | |
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Hot Rod | 98.6 | |
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Model T | 95.2 | |
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Tire | 82 | |
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Camera | 80.9 | |
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Electronics | 80.9 | |
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Human | 78.8 | |
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Person | 78.8 | |
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Car | 70.5 | |
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Spoke | 69.5 | |
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Machine | 69.5 | |
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Building | 68.4 | |
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Metropolis | 68.4 | |
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Town | 68.4 | |
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Urban | 68.4 | |
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City | 68.4 | |
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Light | 62.3 | |
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Wheel | 62.3 | |
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Sports Car | 58.8 | |
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Coupe | 58.8 | |
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Car Wheel | 57.2 | |
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Headlight | 57.1 | |
|
Clarifai
created on 2019-05-31
classic | 99.6 | |
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retro | 99.4 | |
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vintage | 98.9 | |
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nostalgia | 98.9 | |
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old | 98.8 | |
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monochrome | 98.3 | |
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antique | 97.6 | |
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vehicle | 97.5 | |
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transportation system | 97.4 | |
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car | 97 | |
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analogue | 96.8 | |
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chrome | 95.8 | |
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luxury | 91.8 | |
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no person | 91.6 | |
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exhibition | 91.1 | |
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rangefinder | 90.9 | |
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mono | 90.3 | |
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obsolete | 90.2 | |
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coverage | 90.1 | |
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machine | 90 | |
|
Imagga
created on 2019-05-31
Google
created on 2019-05-31
Classic | 97.6 | |
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Motor vehicle | 97.4 | |
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Vintage car | 96.8 | |
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Vehicle | 94.7 | |
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Car | 94.4 | |
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Classic car | 93.3 | |
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Antique car | 89.7 | |
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Automotive design | 82.2 | |
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Automotive lighting | 78.5 | |
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Headlamp | 65.6 | |
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Coupé | 61.4 | |
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Family car | 60.7 | |
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Sedan | 60.4 | |
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Fender | 55.3 | |
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Microsoft
created on 2019-05-31
land vehicle | 94.3 | |
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vehicle | 92.2 | |
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auto part | 90.4 | |
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car | 90.3 | |
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wheel | 75.4 | |
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black and white | 70.7 | |
|
Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18741662/419,538,11,15/full/0/native.jpg)
AWS Rekognition
Age | 35-52 |
Gender | Male, 50.3% |
Disgusted | 49.6% |
Sad | 50% |
Happy | 49.5% |
Surprised | 49.5% |
Angry | 49.8% |
Calm | 49.5% |
Confused | 49.5% |
Feature analysis
Categories
Imagga
interior objects | 93.8% | |
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cars vehicles | 3.7% | |
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food drinks | 2% | |
|
Captions
Microsoft
created on 2019-05-31
a car parked in front of a store | 55.3% | |
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a white car in front of a store | 55.2% | |
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a store inside of a car | 55.1% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18741662/633,405,132,27/full/0/native.jpg)
RECORD
![](https://ids.lib.harvard.edu/ids/iiif/18741662/451,321,275,100/full/0/native.jpg)
CONTINENTAL RECORD
![](https://ids.lib.harvard.edu/ids/iiif/18741662/431,378,169,43/full/0/native.jpg)
CONTINENTAL
![](https://ids.lib.harvard.edu/ids/iiif/18741662/52,138,22,17/full/0/native.jpg)
18
![](https://ids.lib.harvard.edu/ids/iiif/18741662/697,568,3,1/full/0/native.jpg)
6.00
![](https://ids.lib.harvard.edu/ids/iiif/18741662/505,1,165,26/full/0/native.jpg)
COMEIROFEAFE
![](https://ids.lib.harvard.edu/ids/iiif/18741662/408,229,51,26/full/0/native.jpg)
AAMIN
![](https://ids.lib.harvard.edu/ids/iiif/18741662/145,92,180,30/full/0/native.jpg)
CETISUECSIISSIIGK
![](https://ids.lib.harvard.edu/ids/iiif/18741662/57,12,541,403/full/0/native.jpg)
CONDITORE
18
NTINENTA
![](https://ids.lib.harvard.edu/ids/iiif/18741662/511,12,87,14/full/0/native.jpg)
CONDITORE
![](https://ids.lib.harvard.edu/ids/iiif/18741662/57,142,20,18/full/0/native.jpg)
18
![](https://ids.lib.harvard.edu/ids/iiif/18741662/469,381,120,34/full/0/native.jpg)
NTINENTA