Machine Generated Data
Tags
Amazon
created on 2022-01-09
Clarifai
created on 2023-10-25
people | 99.6 | |
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portrait | 98.9 | |
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book series | 98.7 | |
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one | 97.5 | |
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man | 96.8 | |
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adult | 96.4 | |
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eyeglasses | 91.1 | |
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administration | 90.3 | |
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newspaper | 89.3 | |
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writer | 88.9 | |
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leader | 86.8 | |
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elderly | 86.6 | |
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furniture | 86.2 | |
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eyewear | 85.4 | |
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education | 84.9 | |
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physics | 84.8 | |
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school | 83 | |
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sit | 82.5 | |
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wear | 82.3 | |
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monochrome | 80.7 | |
|
Imagga
created on 2022-01-09
Google
created on 2022-01-09
Font | 80.7 | |
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Tie | 80.1 | |
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Blazer | 78.2 | |
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Eyewear | 78.1 | |
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Collar | 77.7 | |
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Suit | 76.8 | |
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Writing | 71.2 | |
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White-collar worker | 69.7 | |
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Formal wear | 68.5 | |
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Sitting | 68.1 | |
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Blackboard | 66.8 | |
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Chalk | 66.2 | |
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Monochrome photography | 65.1 | |
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Signature | 64.7 | |
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Vintage clothing | 64.1 | |
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Stock photography | 64.1 | |
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Office instrument | 64 | |
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Monochrome | 63.7 | |
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Room | 63.4 | |
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Reading | 61.9 | |
|
Color Analysis
Face analysis
Amazon
Microsoft
![](https://ids.lib.harvard.edu/ids/iiif/18791038/425,219,158,199/full/0/native.jpg)
AWS Rekognition
Age | 53-61 |
Gender | Male, 99.8% |
Confused | 55.7% |
Calm | 23.7% |
Surprised | 8.6% |
Sad | 7.2% |
Angry | 2% |
Disgusted | 1.7% |
Fear | 0.7% |
Happy | 0.4% |
![](https://ids.lib.harvard.edu/ids/iiif/18791038/411,276,153,153/full/0/native.jpg)
Microsoft Cognitive Services
Age | 67 |
Gender | Male |
![](https://ids.lib.harvard.edu/ids/iiif/18791038/395,145,241,280/full/0/native.jpg)
Google Vision
Surprise | Very unlikely |
Anger | Very unlikely |
Sorrow | Very unlikely |
Joy | Very unlikely |
Headwear | Very unlikely |
Blurred | Very unlikely |
Feature analysis
Categories
Imagga
paintings art | 87.9% | |
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streetview architecture | 6.6% | |
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people portraits | 2.5% | |
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interior objects | 1.2% | |
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Captions
Microsoft
created on 2022-01-09
J. Robert Oppenheimer wearing a suit and tie sitting in a chair | 94.6% | |
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J. Robert Oppenheimer wearing a suit and tie sitting down | 94.5% | |
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J. Robert Oppenheimer sitting on a table | 81.1% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18791038/32,286,41,61/full/0/native.jpg)
e
![](https://ids.lib.harvard.edu/ids/iiif/18791038/218,636,54,87/full/0/native.jpg)
UNIVERSIT
![](https://ids.lib.harvard.edu/ids/iiif/18791038/234,637,52,77/full/0/native.jpg)
CALIFORN
![](https://ids.lib.harvard.edu/ids/iiif/18791038/262,638,63,94/full/0/native.jpg)
Radiation
![](https://ids.lib.harvard.edu/ids/iiif/18791038/356,682,13,20/full/0/native.jpg)
TAIS
![](https://ids.lib.harvard.edu/ids/iiif/18791038/144,260,23,38/full/0/native.jpg)
X
![](https://ids.lib.harvard.edu/ids/iiif/18791038/32,127,394,220/full/0/native.jpg)
e X - )
![](https://ids.lib.harvard.edu/ids/iiif/18791038/289,641,59,83/full/0/native.jpg)
Labore
![](https://ids.lib.harvard.edu/ids/iiif/18791038/751,965,51,20/full/0/native.jpg)
Indial
![](https://ids.lib.harvard.edu/ids/iiif/18791038/703,966,40,21/full/0/native.jpg)
Aubrey
![](https://ids.lib.harvard.edu/ids/iiif/18791038/689,959,112,35/full/0/native.jpg)
d. Aubrey Indial
![](https://ids.lib.harvard.edu/ids/iiif/18791038/690,968,19,14/full/0/native.jpg)
d.
![](https://ids.lib.harvard.edu/ids/iiif/18791038/342,701,13,27/full/0/native.jpg)
ONCY
![](https://ids.lib.harvard.edu/ids/iiif/18791038/354,704,6,11/full/0/native.jpg)
ON
![](https://ids.lib.harvard.edu/ids/iiif/18791038/351,689,6,9/full/0/native.jpg)
8
![](https://ids.lib.harvard.edu/ids/iiif/18791038/292,127,134,129/full/0/native.jpg)
- )
![](https://ids.lib.harvard.edu/ids/iiif/18791038/231,673,35,47/full/0/native.jpg)
CAL
![](https://ids.lib.harvard.edu/ids/iiif/18791038/231,653,573,338/full/0/native.jpg)
4. Aulre, Todal
CAL
Radion
![](https://ids.lib.harvard.edu/ids/iiif/18791038/686,967,29,24/full/0/native.jpg)
4.
![](https://ids.lib.harvard.edu/ids/iiif/18791038/711,966,42,25/full/0/native.jpg)
Aulre,
![](https://ids.lib.harvard.edu/ids/iiif/18791038/753,964,51,25/full/0/native.jpg)
Todal
![](https://ids.lib.harvard.edu/ids/iiif/18791038/270,654,42,62/full/0/native.jpg)
Radion