Machine Generated Data
Tags
Amazon
created on 2023-10-06
Person | 98.4 | |
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Person | 98.3 | |
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Machine | 96.5 | |
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Wheel | 96.5 | |
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Car | 95 | |
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Transportation | 95 | |
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Vehicle | 95 | |
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Clothing | 94.9 | |
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Coat | 94.9 | |
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City | 94.8 | |
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Wheel | 91.2 | |
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Wheel | 89.7 | |
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Road | 87.9 | |
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Street | 87.9 | |
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Urban | 87.9 | |
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Person | 80.5 | |
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Neighborhood | 80.3 | |
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Car | 79.4 | |
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Car | 65.6 | |
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Footwear | 61.9 | |
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Shoe | 61.9 | |
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Hat | 61 | |
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Truck | 60.3 | |
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Overcoat | 56.5 | |
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Tarmac | 56.1 | |
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Van | 55.9 | |
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Alloy Wheel | 55.7 | |
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Car Wheel | 55.7 | |
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Spoke | 55.7 | |
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Tire | 55.7 | |
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Bus | 55.1 | |
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Clarifai
created on 2018-05-10
people | 99.9 | |
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group together | 99.6 | |
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street | 99.4 | |
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group | 97.8 | |
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transportation system | 97.1 | |
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monochrome | 96.8 | |
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man | 96.6 | |
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adult | 96.1 | |
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vehicle | 95.4 | |
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two | 95.2 | |
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many | 94.7 | |
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three | 92.1 | |
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railway | 92 | |
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several | 90.9 | |
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train | 90.3 | |
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administration | 89.7 | |
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four | 89.6 | |
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road | 87.8 | |
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police | 87.8 | |
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woman | 87 | |
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Imagga
created on 2023-10-06
transportation | 30.5 | |
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gas pump | 29 | |
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city | 27.4 | |
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pump | 26.9 | |
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street | 25.8 | |
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building | 23.9 | |
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urban | 23.6 | |
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station | 19.9 | |
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car | 19.8 | |
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travel | 19.7 | |
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architecture | 19.7 | |
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transport | 18.3 | |
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vehicle | 17.8 | |
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office | 17.7 | |
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business | 17.6 | |
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mechanical device | 17.6 | |
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road | 17.2 | |
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streetcar | 15 | |
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conveyance | 14.6 | |
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people | 14.5 | |
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entrance | 14.5 | |
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door | 14.4 | |
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traffic | 14.3 | |
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machine | 14.2 | |
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gate | 13.9 | |
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truck | 13.8 | |
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wheeled vehicle | 13.7 | |
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tramway | 13.2 | |
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mechanism | 11.6 | |
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automobile | 11.5 | |
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sign | 11.3 | |
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industry | 11.1 | |
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movable barrier | 10.7 | |
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public | 10.7 | |
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passenger | 10.6 | |
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drive | 10.4 | |
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window | 10.2 | |
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barrier | 10.2 | |
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warehouse | 10.1 | |
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light | 10 | |
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device | 9.9 | |
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center | 9.8 | |
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modern | 9.8 | |
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old | 9.8 | |
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roof | 9.5 | |
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train | 9.5 | |
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walk | 9.5 | |
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motion | 9.4 | |
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turnstile | 9.1 | |
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reflection | 8.9 | |
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hall | 8.9 | |
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information | 8.9 | |
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sliding door | 8.8 | |
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structure | 8.8 | |
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empty | 8.6 | |
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construction | 8.6 | |
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town | 8.3 | |
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silhouette | 8.3 | |
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sidewalk | 8.1 | |
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airport | 8.1 | |
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asphalt | 7.8 | |
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scene | 7.8 | |
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glass | 7.8 | |
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stop | 7.7 | |
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equipment | 7.7 | |
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auto | 7.7 | |
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lamp | 7.6 | |
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walking | 7.6 | |
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power | 7.6 | |
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house | 7.5 | |
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shop | 7.4 | |
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man | 7.4 | |
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vacation | 7.4 | |
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indoor | 7.3 | |
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open | 7.2 | |
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male | 7.1 | |
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interior | 7.1 | |
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bus | 7.1 | |
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working | 7.1 | |
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Google
created on 2018-05-10
car | 97.3 | |
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black and white | 90.6 | |
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transport | 88.3 | |
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infrastructure | 87.5 | |
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vehicle | 86.8 | |
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street | 84.1 | |
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town | 82.7 | |
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snapshot | 81.8 | |
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motor vehicle | 81.3 | |
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urban area | 79.8 | |
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monochrome photography | 79.7 | |
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photography | 79.1 | |
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road | 70.8 | |
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monochrome | 67.2 | |
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window | 66.2 | |
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pedestrian | 57.4 | |
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angle | 54.4 | |
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Microsoft
created on 2018-05-10
outdoor | 91.9 | |
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Color Analysis
Feature analysis
Categories
Imagga
streetview architecture | 80% | |
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interior objects | 18.8% | |
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Captions
Microsoft
created on 2018-05-10
a person sitting on a bench in front of a store | 70% | |
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a person sitting on a bench in front of a building | 69.9% | |
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a person sitting at a bus stop | 69.8% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43162975/481,321,18,11/full/0/native.jpg)
PEARS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/853,155,111,24/full/0/native.jpg)
BAKER
![](https://ids.lib.harvard.edu/ids/iiif/43162975/807,166,44,16/full/0/native.jpg)
AND
![](https://ids.lib.harvard.edu/ids/iiif/43162975/663,167,140,26/full/0/native.jpg)
ROBINSON
![](https://ids.lib.harvard.edu/ids/iiif/43162975/904,84,36,14/full/0/native.jpg)
REAL
![](https://ids.lib.harvard.edu/ids/iiif/43162975/904,79,86,20/full/0/native.jpg)
REAL ESTATE
![](https://ids.lib.harvard.edu/ids/iiif/43162975/941,79,50,17/full/0/native.jpg)
ESTATE
![](https://ids.lib.harvard.edu/ids/iiif/43162975/565,152,434,49/full/0/native.jpg)
CLOTHING ROBINSON AND BAKER F
![](https://ids.lib.harvard.edu/ids/iiif/43162975/565,179,89,20/full/0/native.jpg)
CLOTHING
![](https://ids.lib.harvard.edu/ids/iiif/43162975/978,156,20,10/full/0/native.jpg)
F
![](https://ids.lib.harvard.edu/ids/iiif/43162975/166,307,35,10/full/0/native.jpg)
REPAIRS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/122,309,32,10/full/0/native.jpg)
PUMPS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/489,355,20,17/full/0/native.jpg)
BEANS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/125,297,76,15/full/0/native.jpg)
PLUMBING
![](https://ids.lib.harvard.edu/ids/iiif/43162975/443,343,13,11/full/0/native.jpg)
10
![](https://ids.lib.harvard.edu/ids/iiif/43162975/716,376,53,41/full/0/native.jpg)
Peter
![](https://ids.lib.harvard.edu/ids/iiif/43162975/383,444,19,10/full/0/native.jpg)
9336
![](https://ids.lib.harvard.edu/ids/iiif/43162975/792,381,38,36/full/0/native.jpg)
Pan
![](https://ids.lib.harvard.edu/ids/iiif/43162975/481,341,19,12/full/0/native.jpg)
2-29
![](https://ids.lib.harvard.edu/ids/iiif/43162975/896,74,26,11/full/0/native.jpg)
IN
![](https://ids.lib.harvard.edu/ids/iiif/43162975/122,307,79,12/full/0/native.jpg)
PUMPS & REPAIRS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/644,366,17,12/full/0/native.jpg)
ROLLS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/439,321,20,15/full/0/native.jpg)
RICE
![](https://ids.lib.harvard.edu/ids/iiif/43162975/895,70,92,17/full/0/native.jpg)
IN AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/43162975/494,377,11,8/full/0/native.jpg)
11
![](https://ids.lib.harvard.edu/ids/iiif/43162975/611,334,36,21/full/0/native.jpg)
lator Pan
![](https://ids.lib.harvard.edu/ids/iiif/43162975/422,284,85,18/full/0/native.jpg)
GROUERY.
![](https://ids.lib.harvard.edu/ids/iiif/43162975/463,334,14,7/full/0/native.jpg)
as
![](https://ids.lib.harvard.edu/ids/iiif/43162975/439,321,20,21/full/0/native.jpg)
RICE until
![](https://ids.lib.harvard.edu/ids/iiif/43162975/924,70,64,16/full/0/native.jpg)
AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/43162975/157,310,6,5/full/0/native.jpg)
&
![](https://ids.lib.harvard.edu/ids/iiif/43162975/611,337,23,18/full/0/native.jpg)
lator
![](https://ids.lib.harvard.edu/ids/iiif/43162975/440,334,18,6/full/0/native.jpg)
until
![](https://ids.lib.harvard.edu/ids/iiif/43162975/601,376,14,10/full/0/native.jpg)
SEC
![](https://ids.lib.harvard.edu/ids/iiif/43162975/665,171,141,27/full/0/native.jpg)
ROBINSON
![](https://ids.lib.harvard.edu/ids/iiif/43162975/424,82,572,293/full/0/native.jpg)
REAL ESTATE
CLOTHING ROBINSON AND BAKER F
GROCCRY
BEANS
![](https://ids.lib.harvard.edu/ids/iiif/43162975/905,86,38,16/full/0/native.jpg)
REAL
![](https://ids.lib.harvard.edu/ids/iiif/43162975/942,82,51,17/full/0/native.jpg)
ESTATE
![](https://ids.lib.harvard.edu/ids/iiif/43162975/568,183,88,19/full/0/native.jpg)
CLOTHING
![](https://ids.lib.harvard.edu/ids/iiif/43162975/809,168,45,17/full/0/native.jpg)
AND
![](https://ids.lib.harvard.edu/ids/iiif/43162975/855,159,111,24/full/0/native.jpg)
BAKER
![](https://ids.lib.harvard.edu/ids/iiif/43162975/977,156,19,16/full/0/native.jpg)
F
![](https://ids.lib.harvard.edu/ids/iiif/43162975/424,287,82,18/full/0/native.jpg)
GROCCRY
![](https://ids.lib.harvard.edu/ids/iiif/43162975/491,361,23,14/full/0/native.jpg)
BEANS