Machine Generated Data
Tags
Amazon
created on 2019-11-15
Clarifai
created on 2019-11-15
people | 98.6 | |
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adult | 95.2 | |
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no person | 94.2 | |
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transportation system | 93.8 | |
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vehicle | 92.5 | |
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monochrome | 92.4 | |
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train | 91.7 | |
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group | 90.2 | |
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railway | 88.4 | |
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man | 87.3 | |
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one | 86.7 | |
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group together | 86.6 | |
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airport | 85.6 | |
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street | 85.5 | |
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subway system | 84.5 | |
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indoors | 83.9 | |
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woman | 81.7 | |
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aircraft | 81.6 | |
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room | 81.3 | |
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war | 80.9 | |
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Imagga
created on 2019-11-15
train | 53.3 | |
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subway train | 48.5 | |
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transportation | 33.1 | |
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public transport | 32.2 | |
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station | 29.5 | |
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conveyance | 27.8 | |
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car | 27.5 | |
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transport | 25.5 | |
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subway station | 22.3 | |
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terminal | 22 | |
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vehicle | 21.9 | |
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travel | 18.3 | |
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building | 15.4 | |
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road | 15.3 | |
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business | 15.2 | |
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equipment | 14.9 | |
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passenger | 14.6 | |
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garage | 13.9 | |
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automobile | 13.4 | |
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city | 13.3 | |
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drive | 13.2 | |
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urban | 13.1 | |
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industry | 12.8 | |
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structure | 11.9 | |
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facility | 11.5 | |
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old | 11.1 | |
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technology | 11.1 | |
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speed | 11 | |
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architecture | 10.9 | |
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track | 10.9 | |
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subway | 10.8 | |
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electronic equipment | 10.6 | |
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traffic | 10.4 | |
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journey | 10.3 | |
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door | 10 | |
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industrial | 10 | |
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railroad | 9.8 | |
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public | 9.7 | |
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engine | 9.6 | |
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black | 9.6 | |
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house | 9.2 | |
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metro | 8.9 | |
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move | 8.6 | |
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auto | 8.6 | |
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street | 8.3 | |
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hut | 8.1 | |
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shelter | 7.8 | |
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railway | 7.8 | |
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motion | 7.7 | |
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fast | 7.5 | |
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wheeled vehicle | 7.5 | |
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exterior | 7.4 | |
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light | 7.3 | |
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bus | 7.3 | |
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home | 7.2 | |
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interior | 7.1 | |
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truck | 7 | |
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Google
created on 2019-11-15
Photograph | 95.9 | |
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White | 95.5 | |
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Black | 94.5 | |
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Black-and-white | 90.3 | |
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Snapshot | 87.5 | |
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Monochrome | 77.2 | |
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Photography | 75.7 | |
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Monochrome photography | 64.7 | |
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Street | 59.7 | |
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Style | 53.5 | |
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Building | 50.8 | |
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Signage | 50.7 | |
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Microsoft
created on 2019-11-15
text | 97.8 | |
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person | 93.3 | |
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clothing | 79 | |
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black and white | 77.5 | |
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man | 77.1 | |
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Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/13159582/116,328,72,203/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/185,293,65,163/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/444,315,60,157/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/371,287,46,153/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/267,315,51,161/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/406,293,50,151/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/336,284,47,147/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/303,292,40,135/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/510,291,17,46/full/0/native.jpg)
![](https://ids.lib.harvard.edu/ids/iiif/13159582/151,291,36,39/full/0/native.jpg)
Person | 99.1% | |
|
Categories
Imagga
cars vehicles | 54.9% | |
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interior objects | 40.9% | |
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streetview architecture | 2.8% | |
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Captions
Microsoft
created on 2019-11-15
a group of people standing in front of a building | 67.9% | |
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a group of people sitting at a train station | 26.9% | |
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a group of people walking in front of a building | 26.8% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/13159582/665,203,289,43/full/0/native.jpg)
HIDALGO
![](https://ids.lib.harvard.edu/ids/iiif/13159582/178,208,779,84/full/0/native.jpg)
HIDALGO
Sheke
![](https://ids.lib.harvard.edu/ids/iiif/13159582/668,208,289,43/full/0/native.jpg)
HIDALGO
![](https://ids.lib.harvard.edu/ids/iiif/13159582/178,278,35,14/full/0/native.jpg)
Sheke