Machine Generated Data
Tags
Amazon
created on 2019-03-26
Human | 99 | |
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Person | 99 | |
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Person | 99 | |
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Transportation | 98.8 | |
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Automobile | 98.8 | |
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Car | 98.8 | |
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Vehicle | 98.8 | |
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Tire | 96.3 | |
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Machine | 92.8 | |
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Car Wheel | 89.5 | |
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Spoke | 89.5 | |
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Wheel | 77.7 | |
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Alloy Wheel | 77.5 | |
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Hot Rod | 76 | |
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Antique Car | 62.3 | |
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Person | 62 | |
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Sedan | 59.8 | |
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Wheel | 51 | |
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Clarifai
created on 2019-03-26
car | 99.6 | |
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vehicle | 99.5 | |
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transportation system | 98.3 | |
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wheel | 90.7 | |
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monochrome | 88.4 | |
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people | 88.1 | |
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driver | 88 | |
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street | 86.8 | |
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drive | 83.6 | |
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convertible | 81.3 | |
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road | 80.5 | |
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automotive | 78.4 | |
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classic | 76.7 | |
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truck | 75.3 | |
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fast | 73.8 | |
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engine | 72.8 | |
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travel | 71.8 | |
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nostalgia | 68.2 | |
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no person | 68 | |
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hurry | 67.6 | |
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Imagga
created on 2019-03-26
Google
created on 2019-03-26
Motor vehicle | 96.3 | |
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Vehicle | 95.6 | |
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Car | 93.8 | |
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Classic car | 91 | |
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Classic | 72 | |
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Sedan | 61.1 | |
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Coupé | 54.7 | |
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Vintage car | 50.6 | |
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Antique car | 50.2 | |
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Microsoft
created on 2019-03-26
car | 97.1 | |
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road | 96.6 | |
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white | 61.1 | |
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old | 56.2 | |
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vintage | 31.1 | |
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black and white | 10 | |
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monochrome | 4.2 | |
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Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18833094/778,264,26,32/full/0/native.jpg)
AWS Rekognition
Age | 38-57 |
Gender | Male, 50.6% |
Disgusted | 45.3% |
Confused | 45.3% |
Sad | 46% |
Happy | 45.9% |
Surprised | 45.2% |
Calm | 49.4% |
Angry | 47.9% |
![](https://ids.lib.harvard.edu/ids/iiif/18833094/696,258,22,31/full/0/native.jpg)
AWS Rekognition
Age | 19-36 |
Gender | Female, 50.9% |
Calm | 45.5% |
Happy | 45.5% |
Sad | 51.9% |
Angry | 45.4% |
Disgusted | 45.9% |
Confused | 45.5% |
Surprised | 45.3% |
Feature analysis
Categories
Imagga
cars vehicles | 100% | |
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Captions
Microsoft
created on 2019-03-26
a vintage photo of a car | 91.6% | |
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a vintage photo of a person in a car | 85.7% | |
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a vintage photo of a person in a car | 85.6% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18833094/246,326,47,18/full/0/native.jpg)
RESCUE
![](https://ids.lib.harvard.edu/ids/iiif/18833094/246,324,81,22/full/0/native.jpg)
RESCUE UNIT
![](https://ids.lib.harvard.edu/ids/iiif/18833094/295,331,32,22/full/0/native.jpg)
UNIT
![](https://ids.lib.harvard.edu/ids/iiif/18833094/770,515,43,21/full/0/native.jpg)
8737
![](https://ids.lib.harvard.edu/ids/iiif/18833094/360,385,49,12/full/0/native.jpg)
CHANBERLAIN
![](https://ids.lib.harvard.edu/ids/iiif/18833094/360,398,18,12/full/0/native.jpg)
FIRE
![](https://ids.lib.harvard.edu/ids/iiif/18833094/360,397,43,14/full/0/native.jpg)
FIRE DEPT.
![](https://ids.lib.harvard.edu/ids/iiif/18833094/377,399,26,12/full/0/native.jpg)
DEPT.
![](https://ids.lib.harvard.edu/ids/iiif/18833094/251,332,154,85/full/0/native.jpg)
RESCUE UNIT
FIRE DEPT.
![](https://ids.lib.harvard.edu/ids/iiif/18833094/251,332,46,21/full/0/native.jpg)
RESCUE
![](https://ids.lib.harvard.edu/ids/iiif/18833094/299,336,33,20/full/0/native.jpg)
UNIT
![](https://ids.lib.harvard.edu/ids/iiif/18833094/363,401,19,14/full/0/native.jpg)
FIRE
![](https://ids.lib.harvard.edu/ids/iiif/18833094/381,403,24,14/full/0/native.jpg)
DEPT
![](https://ids.lib.harvard.edu/ids/iiif/18833094/399,410,6,7/full/0/native.jpg)
.