Machine Generated Data
Tags
Amazon
created on 2019-06-04
Clarifai
created on 2019-06-04
Imagga
created on 2019-06-04
freight car | 29.7 | |
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room | 29.4 | |
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table | 23.4 | |
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chair | 23.3 | |
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car | 23.1 | |
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wheeled vehicle | 21.4 | |
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hall | 20 | |
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interior | 19.4 | |
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shop | 18.8 | |
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shoe shop | 18 | |
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classroom | 17.2 | |
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travel | 16.2 | |
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furniture | 16 | |
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business | 15.2 | |
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empty | 14.6 | |
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building | 14.5 | |
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seat | 14.1 | |
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restaurant | 13.7 | |
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vehicle | 13.4 | |
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city | 13.3 | |
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architecture | 13.3 | |
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mercantile establishment | 12.4 | |
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urban | 12.2 | |
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glass | 12 | |
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chairs | 11.7 | |
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tourism | 11.5 | |
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group | 11.3 | |
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structure | 11.1 | |
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dinner | 10.9 | |
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coat hanger | 10.9 | |
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water | 10.7 | |
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window | 10.2 | |
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house | 10 | |
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wood | 10 | |
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modern | 9.8 | |
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landscape | 9.7 | |
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dining | 9.5 | |
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light | 9.3 | |
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nobody | 9.3 | |
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place | 9.3 | |
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floor | 9.3 | |
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design | 9 | |
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people | 8.9 | |
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tables | 8.9 | |
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metal | 8.8 | |
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hanger | 8.7 | |
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place of business | 8.7 | |
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crowd | 8.6 | |
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horizontal | 8.4 | |
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lake | 8.2 | |
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center | 8.2 | |
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decor | 8 | |
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day | 7.8 | |
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station | 7.8 | |
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wall | 7.8 | |
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luxury | 7.7 | |
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industry | 7.7 | |
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old | 7.7 | |
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hotel | 7.6 | |
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eat | 7.5 | |
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outdoors | 7.5 | |
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row | 7.4 | |
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event | 7.4 | |
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equipment | 7.4 | |
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vacation | 7.4 | |
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tourist | 7.2 | |
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support | 7.2 | |
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transportation | 7.2 | |
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steel | 7.1 | |
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work | 7.1 | |
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sea | 7 | |
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sky | 7 | |
|
Google
created on 2019-06-04
Photograph | 96.1 | |
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Text | 94.4 | |
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Snapshot | 86.1 | |
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Room | 76.5 | |
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Photography | 62.4 | |
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History | 54.1 | |
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Art | 50.2 | |
|
Color Analysis
Face analysis
Amazon

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.5% |
Disgusted | 49.5% |
Happy | 49.8% |
Sad | 49.7% |
Calm | 49.7% |
Angry | 49.6% |
Confused | 49.6% |
Surprised | 49.5% |

AWS Rekognition
Age | 27-44 |
Gender | Female, 50.5% |
Surprised | 49.5% |
Disgusted | 49.5% |
Angry | 49.5% |
Calm | 50.3% |
Sad | 49.6% |
Happy | 49.5% |
Confused | 49.5% |

AWS Rekognition
Age | 23-38 |
Gender | Female, 50.3% |
Surprised | 49.5% |
Angry | 49.6% |
Calm | 50% |
Happy | 49.6% |
Sad | 49.7% |
Disgusted | 49.5% |
Confused | 49.6% |

AWS Rekognition
Age | 19-36 |
Gender | Female, 50.4% |
Sad | 49.6% |
Confused | 49.6% |
Disgusted | 49.8% |
Happy | 49.7% |
Angry | 49.7% |
Calm | 49.6% |
Surprised | 49.5% |

AWS Rekognition
Age | 17-27 |
Gender | Male, 50.3% |
Surprised | 49.5% |
Angry | 49.5% |
Calm | 49.5% |
Confused | 49.5% |
Disgusted | 50.3% |
Happy | 49.5% |
Sad | 49.6% |

AWS Rekognition
Age | 16-27 |
Gender | Female, 50.4% |
Sad | 49.9% |
Confused | 49.5% |
Angry | 49.7% |
Happy | 49.5% |
Calm | 49.6% |
Surprised | 49.6% |
Disgusted | 49.7% |

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.4% |
Angry | 49.6% |
Sad | 49.8% |
Disgusted | 49.5% |
Calm | 49.5% |
Confused | 49.7% |
Surprised | 49.6% |
Happy | 49.6% |

AWS Rekognition
Age | 30-47 |
Gender | Female, 50.4% |
Angry | 49.6% |
Happy | 49.5% |
Disgusted | 49.5% |
Surprised | 49.5% |
Calm | 49.9% |
Confused | 49.5% |
Sad | 50% |

AWS Rekognition
Age | 35-52 |
Gender | Male, 50.1% |
Calm | 50% |
Confused | 49.6% |
Sad | 49.7% |
Angry | 49.6% |
Happy | 49.5% |
Disgusted | 49.5% |
Surprised | 49.5% |

AWS Rekognition
Age | 26-43 |
Gender | Female, 50.2% |
Sad | 50.1% |
Confused | 49.5% |
Disgusted | 49.5% |
Happy | 49.6% |
Angry | 49.6% |
Calm | 49.6% |
Surprised | 49.5% |

AWS Rekognition
Age | 23-38 |
Gender | Female, 50.4% |
Calm | 50% |
Happy | 49.6% |
Disgusted | 49.6% |
Confused | 49.5% |
Surprised | 49.6% |
Sad | 49.6% |
Angry | 49.6% |

AWS Rekognition
Age | 20-38 |
Gender | Male, 50.2% |
Sad | 49.5% |
Happy | 49.8% |
Angry | 49.6% |
Calm | 49.9% |
Surprised | 49.6% |
Confused | 49.5% |
Disgusted | 49.6% |
Feature analysis
Categories
Imagga
interior objects | 70.7% | |
| ||
pets animals | 21.3% | |
| ||
paintings art | 3.8% | |
| ||
text visuals | 1.5% | |
|
Captions
Microsoft
created on 2019-06-04
a group of people posing for a photo | 72.3% | |
| ||
a group of people posing for the camera | 72.2% | |
| ||
a group of people posing for a picture | 72.1% | |
|
Text analysis
Amazon

York

Examples

City

Adaptation

New

Needs

the

Education

New York City Public Schools

Examples of the Adaptation of Education to Special City

of

to

Schools

Special

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Public

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Ploycentre

Monhattan

School

C161

Puble

Puble School N: 172 Monhattan

Ed17.13.6.9

N: 172

New York City Publie Schools
Examples of the Adaptation of Education to Special City Needs
L8 ELL735
AYC
Publie Schoel Ne 172 Monhatfan
Evening Ploycentre

New

York

City

Schools

of

Needs

AYC

Publie

172

Ploycentre

Examples

the

Adaptation

Education

to

Special

L8

ELL735

Schoel

Ne

Monhatfan

Evening