Machine Generated Data
Tags
Amazon
created on 2022-01-23
Person | 97.3 | |
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Human | 97.3 | |
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Person | 93.4 | |
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Wheel | 90.7 | |
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Machine | 90.7 | |
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Transportation | 89.6 | |
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Vehicle | 89.6 | |
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Clothing | 86.7 | |
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Apparel | 86.7 | |
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Car | 83.1 | |
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Automobile | 83.1 | |
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Face | 81 | |
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Chair | 80.7 | |
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Furniture | 80.7 | |
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Street | 79.1 | |
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Building | 79.1 | |
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Urban | 79.1 | |
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Road | 79.1 | |
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City | 79.1 | |
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Town | 79.1 | |
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Shorts | 75.7 | |
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Play | 72 | |
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Kid | 71.7 | |
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Child | 71.7 | |
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Wheel | 69.8 | |
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Outdoors | 69.6 | |
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Path | 69.3 | |
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Portrait | 68.2 | |
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Photography | 68.2 | |
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Photo | 68.2 | |
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Tree | 63.3 | |
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Plant | 63.3 | |
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Girl | 63 | |
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Female | 63 | |
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Person | 61.5 | |
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People | 61.4 | |
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Nature | 59.6 | |
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Pants | 56.9 | |
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Buggy | 56.6 | |
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Boy | 56 | |
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Tarmac | 55.9 | |
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Asphalt | 55.9 | |
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Tire | 55.7 | |
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Clarifai
created on 2023-10-27
people | 100 | |
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vehicle | 99.9 | |
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transportation system | 99.6 | |
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adult | 99.1 | |
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group together | 98.7 | |
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two | 98.5 | |
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group | 97.5 | |
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driver | 96.6 | |
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car | 95.9 | |
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man | 95.8 | |
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one | 94 | |
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three | 92.8 | |
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child | 92.4 | |
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street | 92.3 | |
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monochrome | 91.6 | |
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many | 89.2 | |
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several | 88.5 | |
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administration | 86.9 | |
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engine | 85.9 | |
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woman | 85.3 | |
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Imagga
created on 2022-01-23
barbershop | 48.9 | |
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shop | 39.8 | |
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mercantile establishment | 30.9 | |
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vehicle | 30.9 | |
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car | 29.7 | |
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motor vehicle | 21.7 | |
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transportation | 21.5 | |
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place of business | 20.7 | |
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golf equipment | 19.4 | |
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road | 18.1 | |
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equipment | 17.2 | |
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man | 16.8 | |
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machine | 15.5 | |
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people | 15.1 | |
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transport | 14.6 | |
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sports equipment | 14.6 | |
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drive | 14.2 | |
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working | 14.1 | |
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work | 14.1 | |
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industry | 13.7 | |
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wheeled vehicle | 13.7 | |
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driving | 13.5 | |
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travel | 13.4 | |
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wheel | 13.3 | |
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male | 12.8 | |
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industrial | 12.7 | |
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automobile | 12.4 | |
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building | 11.9 | |
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old | 11.8 | |
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person | 11.8 | |
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auto | 11.5 | |
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computer | 11.3 | |
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chair | 10.7 | |
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engine | 10.6 | |
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establishment | 10.4 | |
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street | 10.1 | |
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speed | 10.1 | |
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danger | 10 | |
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city | 10 | |
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business | 9.7 | |
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sky | 9.6 | |
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room | 9.6 | |
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men | 9.4 | |
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adult | 9.1 | |
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black | 9 | |
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technology | 8.2 | |
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worker | 8.1 | |
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job | 8 | |
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urban | 7.9 | |
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laptop | 7.7 | |
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construction | 7.7 | |
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outdoor | 7.6 | |
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truck | 7.6 | |
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safety | 7.4 | |
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seat | 7.4 | |
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back | 7.3 | |
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engineer | 7.2 | |
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home | 7.2 | |
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steel | 7.1 | |
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day | 7.1 | |
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architecture | 7 | |
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Google
created on 2022-01-23
Tire | 97.9 | |
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Wheel | 97.3 | |
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Land vehicle | 95.8 | |
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Vehicle | 95 | |
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Plant | 92.7 | |
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Motor vehicle | 92.6 | |
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Car | 91.2 | |
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Automotive tire | 90.9 | |
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Automotive design | 81.8 | |
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Fender | 80.4 | |
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Vehicle door | 75.2 | |
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Classic | 74.8 | |
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Classic car | 74.7 | |
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Monochrome | 74.5 | |
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Snapshot | 74.3 | |
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Monochrome photography | 73.9 | |
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Tree | 73.4 | |
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Automotive wheel system | 70.9 | |
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Vintage clothing | 68.6 | |
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Illustration | 68.5 | |
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Microsoft
created on 2022-01-23
text | 98.2 | |
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outdoor | 93.5 | |
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vehicle | 88.1 | |
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land vehicle | 87.3 | |
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wheel | 82.2 | |
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black and white | 81.4 | |
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car | 51.2 | |
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Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20221758/585,203,38,49/full/0/native.jpg)
AWS Rekognition
Age | 13-21 |
Gender | Male, 62.4% |
Sad | 77.3% |
Calm | 20.7% |
Happy | 0.5% |
Angry | 0.4% |
Disgusted | 0.4% |
Fear | 0.3% |
Confused | 0.3% |
Surprised | 0.2% |
![](https://ids.lib.harvard.edu/ids/iiif/20221758/165,213,36,42/full/0/native.jpg)
Google Vision
Surprise | Very unlikely |
Anger | Very unlikely |
Sorrow | Very unlikely |
Joy | Very unlikely |
Headwear | Unlikely |
Blurred | Very unlikely |
Feature analysis
Categories
Imagga
cars vehicles | 99.4% | |
|
Captions
Microsoft
created on 2022-01-23
a person riding on the back of a truck | 54.2% | |
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a person sitting in front of a truck | 54.1% | |
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a person riding on the back of a truck | 46.6% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20221758/130,391,11,6/full/0/native.jpg)
CR
![](https://ids.lib.harvard.edu/ids/iiif/20221758/95,385,12,8/full/0/native.jpg)
-
![](https://ids.lib.harvard.edu/ids/iiif/20221758/990,372,16,43/full/0/native.jpg)
MJIF
![](https://ids.lib.harvard.edu/ids/iiif/20221758/994,713,12,11/full/0/native.jpg)
8
![](https://ids.lib.harvard.edu/ids/iiif/20221758/474,747,263,56/full/0/native.jpg)
13150
![](https://ids.lib.harvard.edu/ids/iiif/20221758/95,385,30,10/full/0/native.jpg)
حية -
![](https://ids.lib.harvard.edu/ids/iiif/20221758/990,372,16,182/full/0/native.jpg)
MJIF YE33AB ОСЛИА
![](https://ids.lib.harvard.edu/ids/iiif/20221758/449,146,18,5/full/0/native.jpg)
CAST
![](https://ids.lib.harvard.edu/ids/iiif/20221758/991,424,14,65/full/0/native.jpg)
YE33AB
![](https://ids.lib.harvard.edu/ids/iiif/20221758/991,500,14,54/full/0/native.jpg)
ОСЛИА
![](https://ids.lib.harvard.edu/ids/iiif/20221758/108,387,16,7/full/0/native.jpg)
حية
![](https://ids.lib.harvard.edu/ids/iiif/20221758/494,133,517,670/full/0/native.jpg)
Bill
I 31S (0
MJIA YT3RA2 02MA
![](https://ids.lib.harvard.edu/ids/iiif/20221758/926,133,27,15/full/0/native.jpg)
Bill
![](https://ids.lib.harvard.edu/ids/iiif/20221758/494,757,65,46/full/0/native.jpg)
I
![](https://ids.lib.harvard.edu/ids/iiif/20221758/556,753,157,49/full/0/native.jpg)
31S
![](https://ids.lib.harvard.edu/ids/iiif/20221758/709,754,24,44/full/0/native.jpg)
(0
![](https://ids.lib.harvard.edu/ids/iiif/20221758/991,373,19,46/full/0/native.jpg)
MJIA
![](https://ids.lib.harvard.edu/ids/iiif/20221758/990,425,22,70/full/0/native.jpg)
YT3RA2
![](https://ids.lib.harvard.edu/ids/iiif/20221758/991,502,21,56/full/0/native.jpg)
02MA