Machine Generated Data
Tags
Amazon
created on 2023-10-06
Boy | 99.4 | |
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Child | 99.4 | |
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Male | 99.4 | |
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Person | 99.4 | |
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Boy | 99.2 | |
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Child | 99.2 | |
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Male | 99.2 | |
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Person | 99.2 | |
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Airport | 99.2 | |
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Back | 99.1 | |
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Body Part | 99.1 | |
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Head | 72.2 | |
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Aircraft | 71.5 | |
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Transportation | 71.5 | |
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Vehicle | 71.5 | |
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Person | 66.5 | |
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Airplane | 64.7 | |
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Barbershop | 64.5 | |
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Indoors | 64.5 | |
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Terminal | 62.1 | |
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Sitting | 56.6 | |
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Airfield | 56.3 | |
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Landing | 56.2 | |
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Biplane | 56 | |
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Architecture | 55.7 | |
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Building | 55.7 | |
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Factory | 55.7 | |
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Manufacturing | 55.7 | |
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Photography | 55.7 | |
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Worker | 55.5 | |
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Clothing | 55.3 | |
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Shirt | 55.3 | |
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Outdoors | 55.1 | |
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Clarifai
created on 2018-05-11
people | 99.9 | |
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adult | 99.3 | |
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two | 99 | |
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vehicle | 98.4 | |
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man | 98.4 | |
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group | 98.3 | |
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one | 97.2 | |
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watercraft | 96.9 | |
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woman | 96.8 | |
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recreation | 96.3 | |
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transportation system | 96.2 | |
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group together | 95.9 | |
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several | 94.8 | |
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three | 94.1 | |
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four | 93.8 | |
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furniture | 93.5 | |
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education | 91.3 | |
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wear | 90.3 | |
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many | 89.1 | |
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five | 89.1 | |
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Imagga
created on 2023-10-06
Google
created on 2018-05-11
photograph | 94.8 | |
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black and white | 90.8 | |
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snapshot | 81.8 | |
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vehicle | 79.6 | |
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monochrome photography | 77.9 | |
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monochrome | 62.1 | |
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Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43158941/829,327,61,84/full/0/native.jpg)
AWS Rekognition
Age | 12-20 |
Gender | Male, 78.5% |
Calm | 91.1% |
Surprised | 6.5% |
Fear | 5.9% |
Sad | 5% |
Confused | 1.7% |
Disgusted | 0.2% |
Angry | 0.2% |
Happy | 0.2% |
Feature analysis
Categories
Imagga
people portraits | 84.2% | |
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interior objects | 10.6% | |
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food drinks | 2% | |
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events parties | 1.2% | |
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Captions
Microsoft
created on 2018-05-11
a group of people sitting on a bench | 78.8% | |
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a group of people sitting on a boat | 67.1% | |
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a group of people sitting in a boat | 67% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43158941/184,246,110,27/full/0/native.jpg)
MELLON
![](https://ids.lib.harvard.edu/ids/iiif/43158941/171,192,44,27/full/0/native.jpg)
ICE
![](https://ids.lib.harvard.edu/ids/iiif/43158941/171,188,130,31/full/0/native.jpg)
ICE COLD
![](https://ids.lib.harvard.edu/ids/iiif/43158941/236,188,65,28/full/0/native.jpg)
COLD
![](https://ids.lib.harvard.edu/ids/iiif/43158941/194,220,101,28/full/0/native.jpg)
VATER-
![](https://ids.lib.harvard.edu/ids/iiif/43158941/174,191,133,207/full/0/native.jpg)
ICE COLD
VATER
104
![](https://ids.lib.harvard.edu/ids/iiif/43158941/174,196,45,26/full/0/native.jpg)
ICE
![](https://ids.lib.harvard.edu/ids/iiif/43158941/238,191,69,28/full/0/native.jpg)
COLD
![](https://ids.lib.harvard.edu/ids/iiif/43158941/200,223,89,31/full/0/native.jpg)
VATER
![](https://ids.lib.harvard.edu/ids/iiif/43158941/185,370,43,28/full/0/native.jpg)
104