Machine Generated Data
Tags
Amazon
created on 2022-01-09
Bird | 96.9 | |
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Animal | 96.9 | |
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Nature | 95.3 | |
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Water | 94.2 | |
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Landscape | 93.5 | |
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Outdoors | 93.5 | |
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Airport | 90.2 | |
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Airfield | 90.2 | |
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Waterfront | 90 | |
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Bird | 85 | |
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Bird | 84.1 | |
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Scenery | 82.1 | |
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Bird | 80.7 | |
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Bird | 78.6 | |
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Bird | 78 | |
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Bird | 77.5 | |
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Bird | 76.6 | |
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Harbor | 73.9 | |
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Pier | 73.9 | |
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Dock | 73.9 | |
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Port | 73.9 | |
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Bird | 72.8 | |
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Building | 72.7 | |
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Bird | 72.5 | |
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Bird | 72 | |
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Sea | 71.6 | |
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Ocean | 71.6 | |
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Urban | 69.8 | |
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Road | 67.4 | |
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Bird | 66.7 | |
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Bird | 65.3 | |
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Aerial View | 63 | |
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Bird | 61.8 | |
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Shoreline | 60.6 | |
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Airplane | 60.5 | |
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Vehicle | 60.5 | |
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Aircraft | 60.5 | |
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Transportation | 60.5 | |
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Coast | 58.2 | |
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Landing | 57.5 | |
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Weather | 56.3 | |
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Clarifai
created on 2023-10-26
Imagga
created on 2022-01-09
Google
created on 2022-01-09
Black | 89.7 | |
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Organism | 85.4 | |
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Font | 84.5 | |
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Black-and-white | 84.1 | |
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Style | 83.8 | |
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Rectangle | 82.7 | |
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Line | 81.5 | |
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Monochrome | 77 | |
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Monochrome photography | 76.4 | |
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Snapshot | 74.3 | |
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Art | 66.4 | |
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Illustration | 66 | |
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Stock photography | 64.9 | |
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Room | 62.3 | |
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Photographic paper | 60.7 | |
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Urban design | 60.6 | |
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Slope | 59.2 | |
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Visual arts | 57.6 | |
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Painting | 55.7 | |
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Still life photography | 54.3 | |
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Microsoft
created on 2022-01-09
text | 99.9 | |
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ship | 69.4 | |
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old | 61.8 | |
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black and white | 53.3 | |
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image | 34.7 | |
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Color Analysis
Feature analysis
Categories
Imagga
beaches seaside | 47.7% | |
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streetview architecture | 23.7% | |
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nature landscape | 14.4% | |
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text visuals | 5.2% | |
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cars vehicles | 4.9% | |
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interior objects | 1.7% | |
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Captions
Microsoft
created on 2022-01-09
a screenshot of a video game | 37.9% | |
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an old photo of a building | 37.8% | |
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old photo of a building | 37.7% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18792470/552,772,104,29/full/0/native.jpg)
365
![](https://ids.lib.harvard.edu/ids/iiif/18792470/552,772,209,30/full/0/native.jpg)
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![](https://ids.lib.harvard.edu/ids/iiif/18792470/671,774,89,26/full/0/native.jpg)
35.
![](https://ids.lib.harvard.edu/ids/iiif/18792470/990,167,13,23/full/0/native.jpg)
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![](https://ids.lib.harvard.edu/ids/iiif/18792470/987,245,18,169/full/0/native.jpg)
YT37A3-XAOOX
![](https://ids.lib.harvard.edu/ids/iiif/18792470/732,659,84,20/full/0/native.jpg)
GABY
![](https://ids.lib.harvard.edu/ids/iiif/18792470/555,773,116,33/full/0/native.jpg)
365
![](https://ids.lib.harvard.edu/ids/iiif/18792470/678,774,94,33/full/0/native.jpg)
35.
![](https://ids.lib.harvard.edu/ids/iiif/18792470/987,245,23,98/full/0/native.jpg)
YT3RA8-
![](https://ids.lib.harvard.edu/ids/iiif/18792470/988,371,23,46/full/0/native.jpg)
AGOX
![](https://ids.lib.harvard.edu/ids/iiif/18792470/555,245,455,562/full/0/native.jpg)
啡
365 35.
YT3RA8- AGOX
![](https://ids.lib.harvard.edu/ids/iiif/18792470/708,472,36,12/full/0/native.jpg)
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