Machine Generated Data
Tags
Amazon
created on 2022-01-09
Clarifai
created on 2023-10-25
people | 99.5 | |
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vehicle | 99.5 | |
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transportation system | 97.8 | |
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adult | 97.2 | |
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truck | 96.8 | |
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wear | 96.5 | |
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monochrome | 96 | |
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man | 94.8 | |
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one | 93.8 | |
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two | 91.2 | |
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administration | 90.8 | |
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car | 88.3 | |
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no person | 87.4 | |
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three | 86.2 | |
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group | 86.2 | |
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driver | 86.1 | |
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war | 82.7 | |
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group together | 82.3 | |
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military | 81.2 | |
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police | 81 | |
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Imagga
created on 2022-01-09
Google
created on 2022-01-09
Motor vehicle | 90.4 | |
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Vehicle | 88.4 | |
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Adaptation | 79.2 | |
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Working animal | 76.2 | |
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Automotive lighting | 75.8 | |
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Car | 72.6 | |
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Monochrome | 72.4 | |
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Monochrome photography | 69.8 | |
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Automotive exterior | 68.9 | |
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History | 63.9 | |
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Visual arts | 61.7 | |
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Room | 61.5 | |
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Paper product | 61.3 | |
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Advertising | 58.6 | |
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Art | 58.6 | |
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Classic | 57.1 | |
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Vintage clothing | 57 | |
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Photographic paper | 56.7 | |
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Luxury vehicle | 56.1 | |
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Vehicle door | 55.3 | |
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Microsoft
created on 2022-01-09
text | 100 | |
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outdoor | 89.9 | |
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black and white | 82.5 | |
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vehicle | 71.1 | |
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land vehicle | 62.2 | |
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white | 60.9 | |
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fog | 59.3 | |
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sky | 55 | |
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Color Analysis
Feature analysis
Categories
Imagga
streetview architecture | 35.4% | |
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nature landscape | 29.2% | |
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text visuals | 15.7% | |
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beaches seaside | 7.6% | |
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interior objects | 4.4% | |
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paintings art | 4.2% | |
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cars vehicles | 2.3% | |
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Captions
Microsoft
created on 2022-01-09
a vintage photo of a man riding on the back of a truck | 29.3% | |
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a vintage photo of a man | 29.2% | |
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a vintage photo of a truck | 29.1% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18789624/647,780,147,29/full/0/native.jpg)
41409
![](https://ids.lib.harvard.edu/ids/iiif/18789624/375,469,63,9/full/0/native.jpg)
FORD
![](https://ids.lib.harvard.edu/ids/iiif/18789624/706,489,50,16/full/0/native.jpg)
JE
![](https://ids.lib.harvard.edu/ids/iiif/18789624/994,153,14,38/full/0/native.jpg)
OSA
![](https://ids.lib.harvard.edu/ids/iiif/18789624/997,350,13,61/full/0/native.jpg)
NAGOY
![](https://ids.lib.harvard.edu/ids/iiif/18789624/994,256,15,70/full/0/native.jpg)
TOTA'S
![](https://ids.lib.harvard.edu/ids/iiif/18789624/378,471,426,343/full/0/native.jpg)
FORD
4I 4 0 9
![](https://ids.lib.harvard.edu/ids/iiif/18789624/378,471,64,11/full/0/native.jpg)
FORD
![](https://ids.lib.harvard.edu/ids/iiif/18789624/650,785,45,24/full/0/native.jpg)
4I
![](https://ids.lib.harvard.edu/ids/iiif/18789624/703,785,31,26/full/0/native.jpg)
4
![](https://ids.lib.harvard.edu/ids/iiif/18789624/753,782,36,32/full/0/native.jpg)
0
![](https://ids.lib.harvard.edu/ids/iiif/18789624/786,781,18,32/full/0/native.jpg)
9