Machine Generated Data
Tags
Amazon
created on 2022-06-04
Tarmac | 95.9 | |
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Asphalt | 95.9 | |
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Human | 93.4 | |
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Person | 93.4 | |
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Architecture | 92.3 | |
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Building | 92.3 | |
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City | 90 | |
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Town | 90 | |
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Urban | 90 | |
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Downtown | 89.8 | |
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Car | 89 | |
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Transportation | 89 | |
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Automobile | 89 | |
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Vehicle | 89 | |
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Road | 88.5 | |
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Path | 87.4 | |
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Intersection | 86.9 | |
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Person | 83.3 | |
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Weather | 79.2 | |
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Nature | 79.2 | |
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Walkway | 76.5 | |
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Spire | 74.8 | |
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Steeple | 74.8 | |
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Tower | 74.8 | |
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Metropolis | 69.3 | |
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Person | 68.8 | |
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Person | 62.5 | |
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Outdoors | 62.5 | |
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Sidewalk | 60.7 | |
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Pavement | 60.7 | |
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Pedestrian | 57 | |
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Person | 54.3 | |
|
Imagga
created on 2022-06-04
Google
created on 2022-06-04
White | 92.2 | |
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Building | 90 | |
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Black | 90 | |
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Road surface | 88.3 | |
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Asphalt | 86.4 | |
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Black-and-white | 86.2 | |
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Style | 83.9 | |
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Thoroughfare | 79.7 | |
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Automotive lighting | 78.5 | |
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Road | 78.4 | |
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Monochrome | 77.5 | |
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Tints and shades | 77.4 | |
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City | 76.5 | |
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Tree | 76.3 | |
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Monochrome photography | 75.1 | |
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Plant | 74.8 | |
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Darkness | 73.7 | |
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Street | 70.8 | |
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Window | 68.4 | |
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Lane | 66 | |
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Microsoft
created on 2022-06-04
black and white | 95.4 | |
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outdoor | 91.9 | |
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sky | 87.6 | |
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street | 84.5 | |
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building | 83.9 | |
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night | 82.4 | |
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light | 74.9 | |
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street light | 74.1 | |
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city | 72.9 | |
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black | 71.4 | |
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tree | 70 | |
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way | 62.8 | |
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monochrome | 58.4 | |
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old | 47.4 | |
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Color Analysis
Feature analysis
Categories
Imagga
cars vehicles | 72% | |
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streetview architecture | 11.9% | |
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nature landscape | 8.4% | |
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interior objects | 2.8% | |
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sunrises sunsets | 2.3% | |
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Captions
Microsoft
created on 2022-06-04
an old photo of a building | 80.4% | |
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an old photo of a large building | 78% | |
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a large building in the rain | 65.9% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20489017/600,781,59,28/full/0/native.jpg)
Mag
![](https://ids.lib.harvard.edu/ids/iiif/20489017/0,432,13,12/full/0/native.jpg)
SALE
![](https://ids.lib.harvard.edu/ids/iiif/20489017/600,781,137,30/full/0/native.jpg)
Mag 23.10
![](https://ids.lib.harvard.edu/ids/iiif/20489017/667,785,69,20/full/0/native.jpg)
23.10
![](https://ids.lib.harvard.edu/ids/iiif/20489017/896,766,75,29/full/0/native.jpg)
4678
![](https://ids.lib.harvard.edu/ids/iiif/20489017/34,434,13,11/full/0/native.jpg)
SILE
![](https://ids.lib.harvard.edu/ids/iiif/20489017/0,409,12,7/full/0/native.jpg)
SANA
![](https://ids.lib.harvard.edu/ids/iiif/20489017/51,32,922,790/full/0/native.jpg)
***********
Met
Mass.Av.opp.Plympton St
PAT
4678
![](https://ids.lib.harvard.edu/ids/iiif/20489017/68,33,78,66/full/0/native.jpg)
***********
![](https://ids.lib.harvard.edu/ids/iiif/20489017/81,382,42,18/full/0/native.jpg)
Met
![](https://ids.lib.harvard.edu/ids/iiif/20489017/52,783,232,25/full/0/native.jpg)
Mass.Av.opp.Plympton
![](https://ids.lib.harvard.edu/ids/iiif/20489017/280,783,27,25/full/0/native.jpg)
St
![](https://ids.lib.harvard.edu/ids/iiif/20489017/384,360,19,75/full/0/native.jpg)
PAT
![](https://ids.lib.harvard.edu/ids/iiif/20489017/906,774,67,23/full/0/native.jpg)
4678