Machine Generated Data
Tags
Amazon
created on 2021-12-14
Human | 99.2 | |
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Person | 99.2 | |
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Car | 97.9 | |
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Automobile | 97.9 | |
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Vehicle | 97.9 | |
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Transportation | 97.9 | |
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Fire Hydrant | 90.9 | |
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Hydrant | 90.9 | |
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Watercraft | 88.6 | |
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Vessel | 88.6 | |
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Meal | 75.7 | |
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Food | 75.7 | |
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Person | 68.1 | |
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Cafe | 66.5 | |
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Restaurant | 66.5 | |
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Person | 58.3 | |
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Spoke | 57.4 | |
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Machine | 57.4 | |
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Boat | 57.3 | |
|
Clarifai
created on 2023-10-25
street | 99.7 | |
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restaurant | 99.2 | |
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monochrome | 99.2 | |
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people | 98.4 | |
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bar | 98.4 | |
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vintage | 98 | |
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pub | 97.8 | |
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city | 97.7 | |
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square | 95.9 | |
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architecture | 94.8 | |
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town | 94.2 | |
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black and white | 94.1 | |
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stock | 93.5 | |
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analogue | 93.2 | |
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light | 91.7 | |
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urban | 91.3 | |
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art | 91.2 | |
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old | 90.7 | |
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window | 89.4 | |
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diner | 88.8 | |
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Imagga
created on 2021-12-14
gas pump | 32 | |
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pump | 31.2 | |
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electronic equipment | 24.3 | |
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equipment | 23.9 | |
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mechanical device | 19.3 | |
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architecture | 18.7 | |
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building | 17.7 | |
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house | 17.5 | |
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business | 17 | |
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city | 15.8 | |
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light | 14.7 | |
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cassette deck | 14.5 | |
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interior | 14.1 | |
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mechanism | 14.1 | |
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modern | 14 | |
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home | 13.5 | |
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device | 13.5 | |
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transportation | 13.4 | |
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travel | 13.4 | |
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technology | 13.3 | |
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station | 13.3 | |
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center | 13.1 | |
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television | 12.7 | |
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office | 12.4 | |
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structure | 12.4 | |
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sign | 12 | |
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black | 12 | |
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transport | 11.9 | |
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tape deck | 11.6 | |
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3d | 11.6 | |
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urban | 11.4 | |
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digital | 11.3 | |
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industry | 11.1 | |
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car | 10.9 | |
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design | 10.7 | |
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furniture | 10.6 | |
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computer | 9.8 | |
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digital clock | 9.5 | |
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counter | 9.4 | |
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clock | 9.3 | |
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window | 9.3 | |
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room | 9.1 | |
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shop | 9.1 | |
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information | 8.8 | |
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entrance | 8.7 | |
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display | 8.7 | |
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terminal | 8.6 | |
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glass | 8.6 | |
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power | 8.4 | |
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old | 8.4 | |
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service | 8.3 | |
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cassette player | 8.2 | |
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automobile | 7.7 | |
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lamp | 7.6 | |
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living | 7.6 | |
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network | 7.4 | |
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water | 7.3 | |
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steel | 7.1 | |
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button | 7 | |
|
Google
created on 2021-12-14
Car | 94.2 | |
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Vehicle | 92 | |
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Building | 90.7 | |
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Automotive lighting | 89.9 | |
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Motor vehicle | 88.4 | |
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Window | 88.2 | |
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Rectangle | 84 | |
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Black-and-white | 83 | |
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Tints and shades | 77.4 | |
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Automotive exterior | 77 | |
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Kit car | 74.3 | |
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Monochrome photography | 74.3 | |
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Font | 74.1 | |
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Monochrome | 73.9 | |
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Personal luxury car | 72.8 | |
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Automotive design | 71.8 | |
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Classic car | 70.7 | |
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Vehicle door | 68.7 | |
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Family car | 66.7 | |
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Room | 65.9 | |
|
Microsoft
created on 2021-12-14
text | 99.6 | |
| ||
black and white | 95.2 | |
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street | 92.8 | |
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monochrome | 79.7 | |
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city | 69 | |
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Color Analysis
Feature analysis
Amazon
Categories
Imagga
cars vehicles | 100% | |
|
Captions
Microsoft
created on 2021-12-14
a store front at night | 87.8% | |
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a screen shot of a store window | 70% | |
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a store front at day | 69.9% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18781876/669,371,42,11/full/0/native.jpg)
BARBER
![](https://ids.lib.harvard.edu/ids/iiif/18781876/462,431,21,16/full/0/native.jpg)
7up
![](https://ids.lib.harvard.edu/ids/iiif/18781876/458,454,16,6/full/0/native.jpg)
your
![](https://ids.lib.harvard.edu/ids/iiif/18781876/178,197,44,31/full/0/native.jpg)
LUNCHEON
![](https://ids.lib.harvard.edu/ids/iiif/18781876/669,369,77,13/full/0/native.jpg)
BARBER SHOP
![](https://ids.lib.harvard.edu/ids/iiif/18781876/390,229,67,21/full/0/native.jpg)
Coca-Cola
![](https://ids.lib.harvard.edu/ids/iiif/18781876/177,175,46,33/full/0/native.jpg)
FOUNTAIN
![](https://ids.lib.harvard.edu/ids/iiif/18781876/717,370,29,12/full/0/native.jpg)
SHOP
![](https://ids.lib.harvard.edu/ids/iiif/18781876/475,421,18,5/full/0/native.jpg)
ACTION
![](https://ids.lib.harvard.edu/ids/iiif/18781876/458,461,18,6/full/0/native.jpg)
thirst
![](https://ids.lib.harvard.edu/ids/iiif/18781876/453,421,40,6/full/0/native.jpg)
GET REAL ACTION
![](https://ids.lib.harvard.edu/ids/iiif/18781876/681,338,49,20/full/0/native.jpg)
VALENTI
![](https://ids.lib.harvard.edu/ids/iiif/18781876/699,357,18,10/full/0/native.jpg)
184
![](https://ids.lib.harvard.edu/ids/iiif/18781876/458,461,32,6/full/0/native.jpg)
thirst away
![](https://ids.lib.harvard.edu/ids/iiif/18781876/453,422,20,5/full/0/native.jpg)
GET REAL
![](https://ids.lib.harvard.edu/ids/iiif/18781876/674,327,64,44/full/0/native.jpg)
VALENTI B
![](https://ids.lib.harvard.edu/ids/iiif/18781876/178,241,47,35/full/0/native.jpg)
CocaCola
![](https://ids.lib.harvard.edu/ids/iiif/18781876/725,349,8,8/full/0/native.jpg)
B
![](https://ids.lib.harvard.edu/ids/iiif/18781876/475,461,14,6/full/0/native.jpg)
away
![](https://ids.lib.harvard.edu/ids/iiif/18781876/59,162,21,15/full/0/native.jpg)
NOV
![](https://ids.lib.harvard.edu/ids/iiif/18781876/693,441,17,5/full/0/native.jpg)
mart
![](https://ids.lib.harvard.edu/ids/iiif/18781876/56,172,28,22/full/0/native.jpg)
VIN
![](https://ids.lib.harvard.edu/ids/iiif/18781876/395,227,68,33/full/0/native.jpg)
Coca-Cola
![](https://ids.lib.harvard.edu/ids/iiif/18781876/679,340,56,22/full/0/native.jpg)
ALENTI
![](https://ids.lib.harvard.edu/ids/iiif/18781876/103,226,646,162/full/0/native.jpg)
Coca-Cola
1. Coca-Cola
ALENTI
BARBER GHOR
![](https://ids.lib.harvard.edu/ids/iiif/18781876/216,236,31,31/full/0/native.jpg)
1.
![](https://ids.lib.harvard.edu/ids/iiif/18781876/673,371,43,17/full/0/native.jpg)
BARBER
![](https://ids.lib.harvard.edu/ids/iiif/18781876/720,370,29,16/full/0/native.jpg)
GHOR