Machine Generated Data
Tags
Amazon
created on 2022-01-22
Car | 97.8 | |
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Automobile | 97.8 | |
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Transportation | 97.8 | |
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Vehicle | 97.8 | |
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Car | 96.9 | |
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Tarmac | 95.3 | |
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Asphalt | 95.3 | |
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Person | 85.2 | |
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Human | 85.2 | |
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Sports Car | 84.7 | |
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Car | 79.4 | |
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Road | 75.6 | |
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Race Car | 75.5 | |
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Person | 68.4 | |
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Person | 65.7 | |
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Tire | 65.5 | |
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Urban | 64 | |
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Coupe | 60 | |
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Building | 59.9 | |
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Machine | 59.8 | |
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Person | 59.4 | |
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Intersection | 57.4 | |
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Car Wheel | 56.9 | |
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Wheel | 56.9 | |
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Car | 56.7 | |
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Car | 55.8 | |
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Car | 50.5 | |
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Clarifai
created on 2023-10-26
Imagga
created on 2022-01-22
Google
created on 2022-01-22
Car | 91.2 | |
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Vehicle | 91.2 | |
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Wheel | 87.4 | |
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Motor vehicle | 87 | |
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Tire | 85.2 | |
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Mode of transport | 84.9 | |
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Building | 84.5 | |
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Rectangle | 82.9 | |
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Tints and shades | 76.5 | |
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Font | 73.9 | |
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Monochrome photography | 73 | |
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Automotive lighting | 71.7 | |
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Monochrome | 71.5 | |
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Urban design | 70.6 | |
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History | 64.8 | |
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Stock photography | 64.2 | |
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Room | 62.8 | |
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Asphalt | 61.7 | |
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Road | 61 | |
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Paper product | 60.6 | |
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Color Analysis
Feature analysis
Categories
Imagga
cars vehicles | 86.8% | |
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streetview architecture | 4.3% | |
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beaches seaside | 3.7% | |
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nature landscape | 1.7% | |
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interior objects | 1.6% | |
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text visuals | 1.2% | |
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Captions
Microsoft
created on 2022-01-22
a vintage photo of a street | 90.8% | |
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a vintage photo of a city street | 89.4% | |
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a vintage photo of a building | 89.3% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18823726/996,223,12,27/full/0/native.jpg)
32
![](https://ids.lib.harvard.edu/ids/iiif/18823726/591,298,205,56/full/0/native.jpg)
PURINA
![](https://ids.lib.harvard.edu/ids/iiif/18823726/590,282,399,72/full/0/native.jpg)
PURINA CHO
![](https://ids.lib.harvard.edu/ids/iiif/18823726/828,282,160,56/full/0/native.jpg)
CHO
![](https://ids.lib.harvard.edu/ids/iiif/18823726/770,223,132,40/full/0/native.jpg)
FARM
![](https://ids.lib.harvard.edu/ids/iiif/18823726/587,251,102,31/full/0/native.jpg)
TERRY
![](https://ids.lib.harvard.edu/ids/iiif/18823726/332,341,33,18/full/0/native.jpg)
Coca-Cola
![](https://ids.lib.harvard.edu/ids/iiif/18823726/697,210,16,82/full/0/native.jpg)
HACHER
![](https://ids.lib.harvard.edu/ids/iiif/18823726/994,315,15,169/full/0/native.jpg)
KODVK-SEELA
![](https://ids.lib.harvard.edu/ids/iiif/18823726/340,338,12,6/full/0/native.jpg)
DRINK
![](https://ids.lib.harvard.edu/ids/iiif/18823726/323,335,30,10/full/0/native.jpg)
FRESE DRINK
![](https://ids.lib.harvard.edu/ids/iiif/18823726/324,335,15,8/full/0/native.jpg)
FRESE
![](https://ids.lib.harvard.edu/ids/iiif/18823726/774,401,7,4/full/0/native.jpg)
210
![](https://ids.lib.harvard.edu/ids/iiif/18823726/316,319,18,9/full/0/native.jpg)
SCHUAL
![](https://ids.lib.harvard.edu/ids/iiif/18823726/329,221,683,267/full/0/native.jpg)
ARM
PURINA CHO
Coca-Cola
FREE CHICK M
YT3RA2-XAGO
![](https://ids.lib.harvard.edu/ids/iiif/18823726/814,222,85,44/full/0/native.jpg)
ARM
![](https://ids.lib.harvard.edu/ids/iiif/18823726/585,293,238,70/full/0/native.jpg)
PURINA
![](https://ids.lib.harvard.edu/ids/iiif/18823726/831,281,167,65/full/0/native.jpg)
CHO
![](https://ids.lib.harvard.edu/ids/iiif/18823726/329,343,39,19/full/0/native.jpg)
Coca-Cola
![](https://ids.lib.harvard.edu/ids/iiif/18823726/770,400,20,16/full/0/native.jpg)
FREE
![](https://ids.lib.harvard.edu/ids/iiif/18823726/787,400,14,16/full/0/native.jpg)
CHICK
![](https://ids.lib.harvard.edu/ids/iiif/18823726/798,400,9,15/full/0/native.jpg)
M
![](https://ids.lib.harvard.edu/ids/iiif/18823726/995,317,18,171/full/0/native.jpg)
YT3RA2-XAGO