Human Generated Data

Title

Education, Industrial: United States. New York. New York City. Vacation Schools:New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Vacation Schools: Public School No.32 Brooklyn

Date

1902

People

Artist: Unidentified Artist,

Classification

Photographs

Human Generated Data

Title

Education, Industrial: United States. New York. New York City. Vacation Schools:New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Vacation Schools: Public School No.32 Brooklyn

People

Artist: Unidentified Artist,

Date

1902

Classification

Photographs

Machine Generated Data

Tags

Amazon

Human 99.1
Person 99.1
Person 98.8
Person 98.3
Person 98.2
Person 98.1
Person 98
Person 97.2
Person 97
Person 92.9
Indoors 92.7
Room 92.7
School 92.7
Classroom 92.7
Text 86.6
Person 84.1
Person 84.1
Person 84
Person 81.7
Person 81.5
Person 77.4
Person 75.8
Person 75.7
Person 74.3
Person 74.3
Person 69.5
Person 69.3
Person 65.2
Person 61.8
Page 57.9
Advertisement 57
Person 52.3
Person 48.5
Person 46.9

Clarifai

people 99.5
print 99.4
group 98.8
lithograph 97.7
illustration 97.7
many 97.4
man 96
adult 95.7
art 93.6
no person 92.1
engraving 90.6
military 87.5
room 86.7
vintage 86.3
crowd 84.2
antique 83.9
war 83.6
indoors 82
paper 81.4
group together 81.4

Imagga

sketch 100
drawing 91.2
representation 67.3
vintage 31.4
grunge 28.1
old 27.9
paper 23.6
money 22.1
retro 21.3
business 20.7
antique 18.4
architecture 18
house 17.5
ancient 17.3
aged 17.2
currency 17.1
wheeled vehicle 16.7
finance 16.1
freight car 16
texture 16
bill 15.2
dollar 14.9
symbol 14.8
cash 14.6
bank 14.5
frame 14.2
design 14.1
car 13.8
letter 13.8
building 13.5
art 13.3
blank 12.9
office 12.9
dirty 12.7
history 12.5
sign 12
structure 11.8
financial 11.6
stamp 10.9
postmark 10.8
city 10.8
postage 10.8
wealth 10.8
mail 10.5
post 10.5
wagon 10.5
construction 10.3
vehicle 10.3
note 10.1
envelope 10.1
space 10.1
silhouette 9.9
travel 9.9
style 9.6
worn 9.5
empty 9.4
card 9.4
paint 9.1
graphic 8.8
artistic 8.7
us 8.7
pay 8.6
grungy 8.5
plan 8.5
historical 8.5
black 8.4
savings 8.4
banking 8.3
historic 8.2
container 8.2
message 8.2
pattern 8.2
border 8.1
landmark 8.1
text 7.9
postal 7.8
bills 7.8
wall 7.7
payment 7.7
exchange 7.6
old fashioned 7.6
street 7.4

Google

Photograph 95.9
Text 92.4
Snapshot 84.1
Room 76.5
Collection 76.1
Art 69.5
History 67.6
Adaptation 67
Photography 62.4

Microsoft

person 97.3
indoor 91.3
clothing 63.5
library 63.4
old 53.3
store 32.6
sale 20.1
several 12.9

Face analysis

Amazon

AWS Rekognition

Age 17-27
Gender Female, 50.3%
Angry 49.5%
Sad 50.3%
Disgusted 49.5%
Calm 49.5%
Happy 49.5%
Surprised 49.5%
Confused 49.5%

AWS Rekognition

Age 16-27
Gender Female, 50.3%
Disgusted 49.6%
Happy 49.6%
Confused 49.5%
Sad 50%
Calm 49.8%
Angry 49.6%
Surprised 49.5%

AWS Rekognition

Age 23-38
Gender Female, 50.2%
Disgusted 49.5%
Surprised 49.5%
Confused 49.5%
Sad 50.4%
Angry 49.5%
Calm 49.5%
Happy 49.5%

AWS Rekognition

Age 48-68
Gender Female, 50.2%
Disgusted 49.7%
Happy 49.5%
Calm 49.6%
Angry 49.6%
Confused 49.5%
Sad 50%
Surprised 49.6%

AWS Rekognition

Age 26-43
Gender Female, 50.2%
Sad 49.8%
Surprised 49.5%
Calm 49.7%
Happy 49.7%
Disgusted 49.5%
Confused 49.6%
Angry 49.6%

AWS Rekognition

Age 23-38
Gender Male, 50.3%
Calm 49.6%
Disgusted 49.6%
Angry 49.6%
Sad 50.2%
Happy 49.5%
Confused 49.5%
Surprised 49.5%

AWS Rekognition

Age 23-38
Gender Female, 50.4%
Confused 49.6%
Calm 49.5%
Disgusted 49.6%
Happy 49.7%
Angry 49.6%
Sad 49.7%
Surprised 49.6%

AWS Rekognition

Age 17-27
Gender Male, 50.5%
Happy 49.5%
Angry 50.1%
Confused 49.5%
Disgusted 49.6%
Sad 49.8%
Calm 49.5%
Surprised 49.5%

AWS Rekognition

Age 35-53
Gender Female, 50.4%
Calm 49.5%
Disgusted 49.8%
Angry 49.6%
Sad 50%
Happy 49.6%
Confused 49.5%
Surprised 49.5%

AWS Rekognition

Age 27-44
Gender Female, 50.2%
Happy 49.5%
Confused 49.8%
Surprised 49.6%
Angry 49.7%
Calm 49.5%
Sad 49.9%
Disgusted 49.6%

AWS Rekognition

Age 29-45
Gender Female, 50.4%
Happy 49.5%
Angry 49.6%
Confused 49.7%
Calm 49.8%
Sad 49.8%
Disgusted 49.5%
Surprised 49.6%

AWS Rekognition

Age 11-18
Gender Female, 50.5%
Confused 49.6%
Angry 49.6%
Happy 49.6%
Sad 49.9%
Disgusted 49.6%
Surprised 49.6%
Calm 49.8%

AWS Rekognition

Age 16-27
Gender Male, 50.5%
Surprised 49.6%
Angry 49.6%
Calm 49.5%
Happy 49.5%
Sad 49.9%
Disgusted 49.5%
Confused 49.8%

AWS Rekognition

Age 26-43
Gender Female, 50.3%
Angry 49.6%
Sad 50%
Surprised 49.5%
Confused 49.6%
Disgusted 49.5%
Happy 49.6%
Calm 49.6%

AWS Rekognition

Age 26-43
Gender Female, 50.3%
Happy 49.7%
Calm 49.7%
Confused 49.6%
Disgusted 49.6%
Surprised 49.6%
Angry 49.6%
Sad 49.7%

AWS Rekognition

Age 20-38
Gender Male, 50.3%
Surprised 49.5%
Disgusted 49.5%
Angry 49.5%
Calm 49.5%
Sad 50.2%
Happy 49.6%
Confused 49.5%

AWS Rekognition

Age 20-38
Gender Male, 50.3%
Calm 49.6%
Angry 49.7%
Happy 49.6%
Sad 49.8%
Disgusted 49.5%
Confused 49.7%
Surprised 49.6%

AWS Rekognition

Age 38-57
Gender Female, 50.5%
Disgusted 49.6%
Surprised 49.5%
Angry 49.6%
Happy 49.6%
Calm 49.7%
Sad 49.8%
Confused 49.7%

AWS Rekognition

Age 19-36
Gender Female, 50.2%
Disgusted 49.5%
Surprised 49.5%
Angry 49.5%
Happy 49.5%
Calm 49.6%
Sad 50.3%
Confused 49.5%

AWS Rekognition

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

AWS Rekognition

Age 4-9
Gender Female, 50.2%
Disgusted 49.5%
Sad 50.2%
Calm 49.6%
Surprised 49.5%
Confused 49.5%
Angry 49.5%
Happy 49.6%

AWS Rekognition

Age 35-52
Gender Male, 50%
Sad 49.6%
Happy 49.7%
Calm 49.8%
Surprised 49.6%
Angry 49.6%
Disgusted 49.6%
Confused 49.6%

AWS Rekognition

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

AWS Rekognition

Age 26-43
Gender Female, 50.1%
Angry 49.9%
Surprised 49.5%
Calm 49.8%
Sad 49.7%
Happy 49.5%
Disgusted 49.5%
Confused 49.5%

AWS Rekognition

Age 26-43
Gender Female, 50.1%
Surprised 49.5%
Calm 49.6%
Happy 49.6%
Sad 50%
Angry 49.6%
Confused 49.6%
Disgusted 49.8%

Feature analysis

Amazon

Person 99.1%

Captions

Microsoft

a vintage photo of a store 55.6%
a vintage photo of a group of people in front of a store 29%
a vintage photo of a group of people in a store 28.9%

Text analysis

Amazon

York
Needs
Adaptation
Examples
New
Speeial
City
Education
the
Examples of the Adaptation of Education to Speeial City Needs
of
Public
Schools
to
New York City Public Schools 1161.716.7
Fret
Sawing
Vocotion
Vocotion Schoole
Brooklyn
Corpenter Shep Fret Sawing
Pub/
Schoole
Pub/ 5choof N932 Brooklyn
Corpenter
Shep
5choof
N932
1161.716.7
C7

Google

Examples
the
Adaptation
of
Education
Needs
Vacation
Schools
Fret
Sawing
City
Public
Carpenter
Sehool
Ne3z
New York City Public Schools Examples of the Adaptation of Education to Special City Needs 31 heamrLTH Vacation Schools Fret Sawing Carpenter Shep. Public Sehool Ne3z Breoklyn
New
Breoklyn
York
Special
31
heamrLTH
Shep.
to