Machine Generated Data
Tags
Amazon
created on 2019-06-04
Clarifai
created on 2019-06-04
Imagga
created on 2019-06-04
envelope | 100 | |
| ||
container | 100 | |
| ||
money | 32.3 | |
| ||
paper | 29.8 | |
| ||
currency | 29.6 | |
| ||
cash | 27.5 | |
| ||
finance | 26.2 | |
| ||
bank | 23.3 | |
| ||
banking | 22.1 | |
| ||
business | 20.7 | |
| ||
bill | 19 | |
| ||
old | 18.8 | |
| ||
box | 18.8 | |
| ||
note | 17.5 | |
| ||
investment | 17.4 | |
| ||
wealth | 17.1 | |
| ||
card | 16.2 | |
| ||
financial | 16 | |
| ||
dollar | 15.8 | |
| ||
vintage | 15.7 | |
| ||
retro | 15.6 | |
| ||
savings | 14.9 | |
| ||
ancient | 13.8 | |
| ||
banknote | 13.6 | |
| ||
insulating material | 13.4 | |
| ||
economy | 13 | |
| ||
blank | 12.9 | |
| ||
bills | 12.6 | |
| ||
exchange | 12.4 | |
| ||
close | 11.4 | |
| ||
grunge | 11.1 | |
| ||
letter | 11 | |
| ||
aged | 10.9 | |
| ||
symbol | 10.8 | |
| ||
texture | 10.4 | |
| ||
building material | 10 | |
| ||
dollars | 9.7 | |
| ||
payment | 9.6 | |
| ||
loan | 9.6 | |
| ||
antique | 9.5 | |
| ||
gift | 9.5 | |
| ||
color | 9.5 | |
| ||
buy | 9.4 | |
| ||
mousetrap | 9.2 | |
| ||
frame | 9.2 | |
| ||
present | 9.1 | |
| ||
carton | 8.7 | |
| ||
hundred | 8.7 | |
| ||
objects | 8.7 | |
| ||
pay | 8.6 | |
| ||
hand | 8.4 | |
| ||
sign | 8.3 | |
| ||
stamp | 8.2 | |
| ||
trap | 8.1 | |
| ||
gambling | 7.8 | |
| ||
king | 7.8 | |
| ||
play | 7.8 | |
| ||
change | 7.7 | |
| ||
notes | 7.7 | |
| ||
7.7 | ||
| ||
design | 7.5 | |
| ||
birthday | 7.4 | |
| ||
gold | 7.4 | |
| ||
brown | 7.4 | |
| ||
package | 7.3 | |
| ||
yellow | 7.3 | |
| ||
valentine | 7.3 | |
| ||
board | 7.3 | |
| ||
success | 7.2 | |
| ||
collection | 7.2 | |
| ||
game | 7.1 | |
|
Google
created on 2019-06-04
Text | 91.6 | |
| ||
Art | 72.1 | |
| ||
Stock photography | 67.6 | |
| ||
Adaptation | 67 | |
| ||
Illustration | 60.2 | |
| ||
Painting | 59.3 | |
| ||
Visual arts | 59.3 | |
|
Color Analysis
Face analysis
Amazon

AWS Rekognition
Age | 23-38 |
Gender | Male, 50.1% |
Sad | 49.6% |
Surprised | 49.7% |
Disgusted | 49.5% |
Calm | 49.7% |
Happy | 49.6% |
Confused | 49.9% |
Angry | 49.5% |

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.2% |
Surprised | 49.6% |
Confused | 49.6% |
Sad | 49.9% |
Disgusted | 49.6% |
Calm | 49.6% |
Angry | 49.6% |
Happy | 49.7% |

AWS Rekognition
Age | 20-38 |
Gender | Female, 50% |
Sad | 49.6% |
Calm | 49.5% |
Disgusted | 50.1% |
Happy | 49.5% |
Confused | 49.5% |
Angry | 49.7% |
Surprised | 49.5% |

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.2% |
Angry | 49.6% |
Happy | 49.6% |
Disgusted | 49.6% |
Sad | 49.8% |
Confused | 49.5% |
Calm | 49.8% |
Surprised | 49.6% |

AWS Rekognition
Age | 11-18 |
Gender | Female, 50.4% |
Sad | 49.7% |
Confused | 49.6% |
Calm | 49.9% |
Disgusted | 49.5% |
Happy | 49.6% |
Angry | 49.6% |
Surprised | 49.6% |

AWS Rekognition
Age | 16-27 |
Gender | Male, 50.4% |
Disgusted | 50.4% |
Confused | 49.5% |
Surprised | 49.5% |
Calm | 49.5% |
Happy | 49.5% |
Angry | 49.5% |
Sad | 49.5% |

AWS Rekognition
Age | 15-25 |
Gender | Female, 50.4% |
Disgusted | 49.8% |
Sad | 49.7% |
Surprised | 49.6% |
Calm | 49.6% |
Angry | 49.7% |
Happy | 49.6% |
Confused | 49.5% |

AWS Rekognition
Age | 35-52 |
Gender | Female, 50.4% |
Calm | 49.5% |
Disgusted | 49.5% |
Angry | 49.6% |
Sad | 50.3% |
Happy | 49.5% |
Confused | 49.5% |
Surprised | 49.5% |

AWS Rekognition
Age | 20-38 |
Gender | Female, 50% |
Surprised | 49.5% |
Calm | 49.5% |
Happy | 49.5% |
Sad | 50.3% |
Angry | 49.6% |
Confused | 49.5% |
Disgusted | 49.5% |

AWS Rekognition
Age | 30-47 |
Gender | Male, 50.2% |
Disgusted | 49.5% |
Confused | 49.6% |
Angry | 49.6% |
Calm | 49.6% |
Surprised | 49.6% |
Happy | 49.5% |
Sad | 50.1% |

AWS Rekognition
Age | 26-44 |
Gender | Female, 50.5% |
Calm | 49.6% |
Happy | 49.7% |
Surprised | 49.5% |
Sad | 49.7% |
Angry | 49.7% |
Disgusted | 49.6% |
Confused | 49.6% |

AWS Rekognition
Age | 19-36 |
Gender | Female, 50.5% |
Happy | 49.8% |
Disgusted | 49.6% |
Sad | 49.6% |
Confused | 49.5% |
Angry | 49.5% |
Surprised | 49.7% |
Calm | 49.7% |

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.4% |
Calm | 49.5% |
Disgusted | 49.6% |
Surprised | 49.6% |
Sad | 50.1% |
Happy | 49.5% |
Angry | 49.6% |
Confused | 49.6% |
Feature analysis
Categories
Imagga
paintings art | 65.6% | |
| ||
text visuals | 31.9% | |
| ||
food drinks | 1.6% | |
|
Captions
Microsoft
created on 2019-06-04
a display in a room | 33.6% | |
|
Text analysis
Amazon

European

Standards

Conditions

Immigration

Before

Living

Environment Before Immigration

Standards of Living in European Cities

Naples,

in

of

Environment

Soeial

Cities

Soeial Conditions in Naples, Italy : 1905

1905

Italy :

55111430

Environment Before Immigration
Standards of Living in European Cities
Social Conditions in Naples, Italy: 1905
TA59, HAPOLU-Machersais naneletangsana dal vnrs
STREET SCENE NAPLES

Environment

Before

Immigration

Standards

of

Living

in

European

Cities

Social

Conditions

Naples,

Italy:

1905

TA59,

HAPOLU-Machersais

naneletangsana

dal

vnrs

STREET

SCENE

NAPLES