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: Brooklyn Vacation School - Sewing and Mending Lesson.
Date

c. 1900
People

Artist: Unidentified Artist,

Classification

Photographs
Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.84

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: Brooklyn Vacation School - Sewing and Mending Lesson.

People

Artist: Unidentified Artist,

Date

c. 1900

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.84

Machine Generated Data

Tags

Amazon
created on 2019-06-05

School 99.1
Indoors 97.5
Room 97.5
Classroom 97.5
Person 97.5
Human 97.5
Person 94.9
Person 92.9
Person 87.5
Person 81.8
Crowd 81.3
Audience 81.3
College 72.3
Person 71.7
Person 66
Speech 59.7
Person 46.5

Clarifai
created on 2019-06-05

people 99.4
adult 96.4
group 95.3
exhibition 95.1
many 93.4
art 92.9
picture frame 92.8
wear 91.8
museum 90.5
painting 90
woman 89.9
portrait 89.6
man 89.3
print 86.7
bill 86.3
blank 83.6
music 81.8
retro 81.4
illustration 80.7
one 79.7

Imagga
created on 2019-06-05

web site 92.5
background 29.9
vintage 24
screen 23.6
money 23
letter 22
old 20.9
currency 20.6
blackboard 20.5
stamp 20.3
business 20
paper 19.6
mail 19.1
retro 18.8
postmark 18.7
postage 18.7
cash 17.4
bill 17.1
display 16.9
postal 16.7
banking 16.5
envelope 16.4
bank 16.1
finance 16
dollar 15.8
art 15.7
design 14.6
global 14.6
symbol 14.1
grunge 13.6
sign 13.5
exchange 13.4
financial 13.4
post 13.3
office 12.8
circa 12.8
printed 12.8
hundred 12.6
wealth 12.6
black 12
electronic device 12
message 11.9
man 11.4
drawing 11.3
ancient 11.2
culture 11.1
pattern 10.9
aged 10.9
philately 10.9
shows 10.8
communications 10.5
text 10.5
one 10.4
texture 10.4
savings 10.3
rich 10.2
card 10.2
investment 10.1
paint 10
us 9.6
closeup 9.4
world wide web 9.4
frame 9.3
note 9.2
board 9
bills 8.7
museum 8.7
dollars 8.7
pay 8.6
blank 8.6
close 8.6
communication 8.4
dirty 8.1
history 8
market 8
masterpiece 7.9
chalkboard 7.8
paintings 7.8
people 7.8
antique 7.8
space 7.8
brass 7.7
cutting 7.7
payment 7.7
loan 7.7
map 7.6
painted 7.6
fine 7.6
hand 7.6
unique 7.6
hobby 7.6
structure 7.3
success 7.2
icon 7.1

Google
created on 2019-06-05

Photograph 96.7
Text 89.7
Snapshot 86.5
Room 76.5
Photography 70.6
Adaptation 67
Photographic paper 59.4
Stock photography 59.4
Art 58.1
History 57.6
Square 55.4

Microsoft
created on 2019-06-05

person 78.2
box 76.2
old 54
different 40.2
envelope 35.5
picture frame 13.3
sale 10.7

Color Analysis

Face analysis

Amazon

AWS Rekognition

Age 6-13
Gender Female, 53.1%
Surprised 45.1%
Happy 45.1%
Confused 45.1%
Calm 47.7%
Disgusted 45.1%
Angry 45.2%
Sad 51.7%

AWS Rekognition

Age 35-52
Gender Female, 50.2%
Calm 45.2%
Confused 45.2%
Happy 45.2%
Disgusted 46.1%
Sad 46.9%
Angry 51.2%
Surprised 45.2%

AWS Rekognition

Age 27-44
Gender Female, 50.2%
Calm 49.5%
Angry 49.5%
Happy 49.5%
Sad 50.4%
Surprised 49.5%
Confused 49.5%
Disgusted 49.5%

AWS Rekognition

Age 10-15
Gender Male, 50.4%
Disgusted 49.5%
Surprised 49.5%
Sad 49.8%
Angry 49.9%
Happy 49.5%
Confused 49.7%
Calm 49.6%

AWS Rekognition

Age 15-25
Gender Female, 50.4%
Confused 49.5%
Sad 50.3%
Disgusted 49.5%
Angry 49.6%
Happy 49.5%
Calm 49.5%
Surprised 49.5%

AWS Rekognition

Age 26-43
Gender Male, 50.1%
Calm 49.6%
Surprised 49.6%
Happy 49.6%
Angry 49.6%
Confused 49.7%
Sad 50%
Disgusted 49.6%

AWS Rekognition

Age 9-14
Gender Male, 50.5%
Calm 49.6%
Disgusted 49.6%
Angry 49.8%
Happy 49.5%
Sad 49.7%
Surprised 49.6%
Confused 49.6%

AWS Rekognition

Age 30-47
Gender Female, 53.2%
Angry 45.1%
Happy 45.1%
Confused 45%
Surprised 45.1%
Sad 54.4%
Disgusted 45.1%
Calm 45.2%

AWS Rekognition

Age 20-38
Gender Male, 50.3%
Confused 49.6%
Disgusted 49.5%
Calm 50%
Sad 49.7%
Angry 49.6%
Happy 49.6%
Surprised 49.6%

AWS Rekognition

Age 26-43
Gender Male, 50.2%
Calm 49.7%
Angry 50%
Happy 49.5%
Sad 49.6%
Surprised 49.5%
Confused 49.6%
Disgusted 49.5%

AWS Rekognition

Age 26-43
Gender Female, 50.1%
Confused 49.5%
Calm 49.8%
Disgusted 49.6%
Sad 49.9%
Happy 49.6%
Angry 49.5%
Surprised 49.6%

AWS Rekognition

Age 23-38
Gender Female, 50.1%
Happy 49.5%
Disgusted 49.6%
Angry 49.6%
Confused 49.5%
Calm 49.8%
Surprised 49.6%
Sad 49.9%

AWS Rekognition

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

AWS Rekognition

Age 15-25
Gender Female, 50.3%
Confused 49.5%
Surprised 49.5%
Angry 49.5%
Happy 49.5%
Sad 50.3%
Calm 49.5%
Disgusted 49.5%

AWS Rekognition

Age 12-22
Gender Female, 50.2%
Confused 49.5%
Happy 49.6%
Surprised 49.5%
Calm 49.6%
Angry 49.5%
Sad 50.2%
Disgusted 49.6%

AWS Rekognition

Age 17-27
Gender Female, 50.4%
Sad 50.2%
Disgusted 49.5%
Calm 49.6%
Confused 49.5%
Happy 49.6%
Surprised 49.5%
Angry 49.6%

AWS Rekognition

Age 16-27
Gender Female, 50.4%
Calm 49.6%
Happy 49.5%
Confused 49.5%
Sad 50.3%
Angry 49.5%
Surprised 49.5%
Disgusted 49.5%

AWS Rekognition

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

AWS Rekognition

Age 48-68
Gender Female, 50.5%
Disgusted 49.5%
Confused 49.5%
Happy 49.5%
Calm 50.4%
Surprised 49.5%
Sad 49.5%
Angry 49.5%

AWS Rekognition

Age 14-25
Gender Male, 50.1%
Sad 49.6%
Angry 49.6%
Calm 49.6%
Confused 49.9%
Happy 49.7%
Disgusted 49.6%
Surprised 49.6%

AWS Rekognition

Age 16-27
Gender Female, 50.2%
Disgusted 49.7%
Confused 49.6%
Calm 49.6%
Surprised 49.5%
Happy 49.5%
Sad 49.7%
Angry 49.9%

Feature analysis

Amazon

Person 97.5%

Categories

Imagga

paintings art 78.1%
text visuals 20%

Captions

Microsoft
created on 2019-06-05

a vintage photo of a box 50.7%

Text analysis

Amazon

Needs
City
Examples
York
New
Special
the
of
Adaptation
Examples of the Adaptation of Education to Special City Needs
Publie
Brooklyn
Sehools
Education
to
New York City Publie Sehools 736.61
Lesson
and
Brooklyn Vocotron School- Sewing and Lesson
School-
Sewing
Vocotron
C 195
736.61
OOO

Google

New York City Public Schools Examples of the Adaptation of Education to Special City Needs C 19S Brooklyn Vocation School-Sewing and Mending Lesson
Examples
the
to
Special
C
19S
Brooklyn
Vocation
New
York
City
Public
Schools
of
Adaptation
Education
Needs
School-Sewing
and
Mending
Lesson