Machine Generated Data
Tags
Amazon
created on 2019-06-05
Clarifai
created on 2019-06-05
Imagga
created on 2019-06-05
window | 24 | |
| ||
vintage | 19.8 | |
| ||
old | 19.5 | |
| ||
art | 18.3 | |
| ||
history | 15.2 | |
| ||
architecture | 14.8 | |
| ||
building | 14.5 | |
| ||
newspaper | 14.2 | |
| ||
retro | 13.9 | |
| ||
antique | 13.5 | |
| ||
room | 13.2 | |
| ||
drawing | 12.7 | |
| ||
interior | 12.4 | |
| ||
product | 12 | |
| ||
boutique | 11.8 | |
| ||
black | 11.4 | |
| ||
historic | 11 | |
| ||
house | 10.9 | |
| ||
man | 10.7 | |
| ||
ancient | 10.4 | |
| ||
historical | 10.3 | |
| ||
monument | 10.3 | |
| ||
culture | 10.2 | |
| ||
grunge | 10.2 | |
| ||
office | 10.2 | |
| ||
creation | 10 | |
| ||
paint | 10 | |
| ||
sculpture | 9.7 | |
| ||
business | 9.7 | |
| ||
home | 9.6 | |
| ||
wall | 9.4 | |
| ||
frame | 9.3 | |
| ||
sketch | 9.3 | |
| ||
travel | 9.1 | |
| ||
web site | 9.1 | |
| ||
door | 9 | |
| ||
design | 9 | |
| ||
people | 8.9 | |
| ||
symbol | 8.7 | |
| ||
case | 8.6 | |
| ||
paper | 8.6 | |
| ||
city | 8.3 | |
| ||
brass | 8 | |
| ||
light | 8 | |
| ||
barbershop | 7.9 | |
| ||
shop | 7.8 | |
| ||
men | 7.7 | |
| ||
silhouette | 7.4 | |
| ||
famous | 7.4 | |
| ||
tourism | 7.4 | |
| ||
brown | 7.4 | |
| ||
aged | 7.2 | |
| ||
book jacket | 7.1 | |
| ||
male | 7.1 | |
| ||
indoors | 7 | |
|
Google
created on 2019-06-05
Photograph | 96.3 | |
| ||
Snapshot | 86.1 | |
| ||
Text | 85.2 | |
| ||
Picture frame | 67.5 | |
| ||
Room | 65.7 | |
| ||
Photography | 62.4 | |
| ||
Gentleman | 57.3 | |
| ||
Square | 50.6 | |
| ||
Art | 50.2 | |
|
Color Analysis
Face analysis
Amazon

AWS Rekognition
Age | 26-43 |
Gender | Female, 54.2% |
Happy | 49.5% |
Disgusted | 45.7% |
Sad | 47.4% |
Surprised | 45.6% |
Confused | 45.2% |
Calm | 45.9% |
Angry | 45.7% |

AWS Rekognition
Age | 20-38 |
Gender | Female, 50.1% |
Angry | 49.9% |
Confused | 49.5% |
Calm | 49.6% |
Surprised | 49.5% |
Sad | 49.9% |
Happy | 49.6% |
Disgusted | 49.5% |

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.3% |
Sad | 49.6% |
Happy | 49.5% |
Surprised | 49.5% |
Disgusted | 49.5% |
Confused | 49.5% |
Calm | 49.5% |
Angry | 50.4% |

AWS Rekognition
Age | 26-43 |
Gender | Male, 50.3% |
Sad | 49.5% |
Calm | 50.2% |
Angry | 49.5% |
Disgusted | 49.7% |
Surprised | 49.5% |
Confused | 49.5% |
Happy | 49.5% |

AWS Rekognition
Age | 14-25 |
Gender | Male, 50.5% |
Angry | 49.7% |
Surprised | 49.6% |
Sad | 49.9% |
Disgusted | 49.7% |
Confused | 49.6% |
Happy | 49.6% |
Calm | 49.5% |

AWS Rekognition
Age | 26-43 |
Gender | Male, 50.1% |
Surprised | 49.5% |
Disgusted | 49.5% |
Calm | 49.5% |
Confused | 49.5% |
Angry | 49.5% |
Sad | 50.3% |
Happy | 49.5% |
Feature analysis
Categories
Imagga
paintings art | 81.2% | |
| ||
streetview architecture | 9.1% | |
| ||
text visuals | 4.5% | |
| ||
interior objects | 3.2% | |
| ||
pets animals | 1.4% | |
|
Captions
Text analysis
Amazon

Needs

York

Adaptation

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Examples

Edueation

Publio

Examples of the Adaptation of Edueation to Special City Needs

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New York City Public Shools
Examples of the Adaptation of Education to Special City Needs
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New

York

City

Public

Shools

Examples

of

the

Adaptation

Education

to

Special

Needs

4G.nר

5

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weaxon

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Waroa

Class

frem

Wodleigh

High

School

Manhattan

M

Nafural

His

tory,

Manhattan.