Human Generated Data

Title

Education, Industrial: United States. New York. New York City. Public Schools, Adaptation to Special Needs: New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Public School No. 37 Manhattan: Housekeeping-Serving.
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, 3.2002.82.1

Human Generated Data

Title

Education, Industrial: United States. New York. New York City. Public Schools, Adaptation to Special Needs: New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Public School No. 37 Manhattan: Housekeeping-Serving.

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, 3.2002.82.1

Machine Generated Data

Tags

Amazon
created on 2019-06-05

Furniture 98.8
Chair 98.8
Ceiling Fan 97.6
Appliance 97.6
Person 97.4
Human 97.4
Person 96.7
Room 92.5
Indoors 92.5
Person 88.8
Reception 82.2
Reception Room 82.2
Waiting Room 82.2
Person 77.4
Classroom 74.7
School 74.7
Table 73.8
Person 73.5
Chair 71.5
Person 70.3
Person 66.3
Clinic 65.9
Tablecloth 62.1
Hall 58.8
Restaurant 58.7
Dining Table 57
Person 42.9

Clarifai
created on 2019-06-05

people 100
group 99.6
many 99.4
adult 97.2
group together 97.2
furniture 96.3
chair 95.5
man 94.8
room 94.3
woman 94.2
leader 93.9
administration 92.4
child 91.4
wear 88.7
education 87.8
several 87.1
military 85.9
crowd 83.8
print 81.7
seat 80.9

Imagga
created on 2019-06-05

room 72.7
classroom 59.5
interior 37.1
chair 36.4
table 29.4
furniture 25.9
bass 24
bowed stringed instrument 23.2
modern 23.1
stringed instrument 22.3
indoors 21.1
musical instrument 19.9
violin 18.9
floor 18.6
house 18.4
people 17.8
male 17
decor 16.8
home 16.7
empty 16.3
wood 15.8
men 15.4
design 15.2
business 14
seat 13.9
window 13.7
chairs 13.7
man 13.4
restaurant 13.3
indoor 12.8
person 12.5
life 12.3
teacher 12.2
light 12
inside 12
adult 11.8
tables 11.8
elegance 11.7
architecture 11.7
glass 11.7
office 11.2
style 11.1
wall 11.1
decoration 10.9
lifestyle 10.8
comfortable 10.5
urban 10.5
luxury 10.3
building 10.1
wooden 9.7
apartment 9.6
scene 9.5
dining 9.5
living 9.5
work 9.4
shop 9.4
nobody 9.3
dinner 9.3
kitchen 8.9
group 8.9
hall 8.8
women 8.7
3d 8.5
relax 8.4
relaxation 8.4
plant 8.2
music 8.1
cabinet 7.9
render 7.8
space 7.7
travel 7.7
class 7.7
lamp 7.6
desk 7.6
salon 7.5
city 7.5
oboe 7.4
barbershop 7.3
food 7.2
board 7.2
cafeteria 7.2
businessman 7.1

Google
created on 2019-06-05

Photograph 95.8
Snapshot 85.4
Room 81.5
Event 67
Adaptation 67
History 57.6
Family 57.3

Microsoft
created on 2019-06-05

wall 97.4
person 96.9
clothing 95.2
indoor 93.9
woman 84.9
table 77.9
furniture 76
chair 54.8
room 46
several 11.3

Color Analysis

Face analysis

Amazon

AWS Rekognition

Age 35-52
Gender Female, 52.4%
Confused 45.4%
Surprised 45.4%
Angry 45.8%
Calm 46.3%
Sad 45.9%
Happy 46%
Disgusted 50.3%

AWS Rekognition

Age 48-68
Gender Male, 50.6%
Disgusted 46.5%
Calm 45.7%
Confused 45.2%
Happy 49.4%
Sad 47.5%
Angry 45.4%
Surprised 45.3%

AWS Rekognition

Age 20-38
Gender Female, 50.3%
Calm 49.7%
Angry 49.6%
Happy 49.5%
Sad 50.1%
Surprised 49.5%
Confused 49.5%
Disgusted 49.5%

AWS Rekognition

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

AWS Rekognition

Age 35-52
Gender Female, 50.3%
Angry 49.6%
Sad 50.2%
Surprised 49.5%
Happy 49.5%
Calm 49.6%
Disgusted 49.5%
Confused 49.5%

AWS Rekognition

Age 35-52
Gender Female, 54.1%
Confused 45.2%
Disgusted 45.6%
Surprised 45.5%
Sad 47.9%
Angry 45.4%
Happy 47.5%
Calm 47.9%

AWS Rekognition

Age 17-27
Gender Female, 50.4%
Calm 49.5%
Sad 50.3%
Surprised 49.5%
Disgusted 49.5%
Angry 49.5%
Happy 49.6%
Confused 49.5%

AWS Rekognition

Age 60-90
Gender Female, 50%
Disgusted 49.6%
Confused 49.5%
Calm 49.5%
Angry 49.6%
Happy 49.5%
Sad 50.3%
Surprised 49.5%

AWS Rekognition

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

AWS Rekognition

Age 35-52
Gender Male, 50.3%
Disgusted 49.8%
Sad 49.6%
Calm 49.6%
Confused 49.6%
Happy 49.5%
Surprised 49.5%
Angry 49.9%

AWS Rekognition

Age 20-38
Gender Female, 50.4%
Calm 49.6%
Angry 49.5%
Sad 50.3%
Happy 49.5%
Confused 49.5%
Surprised 49.5%
Disgusted 49.5%

AWS Rekognition

Age 11-18
Gender Female, 50.1%
Confused 49.6%
Angry 49.5%
Disgusted 49.5%
Surprised 49.6%
Sad 50.2%
Calm 49.6%
Happy 49.5%

AWS Rekognition

Age 23-38
Gender Female, 50.4%
Disgusted 49.6%
Confused 49.6%
Calm 49.6%
Angry 49.6%
Happy 49.8%
Sad 49.7%
Surprised 49.6%

AWS Rekognition

Age 20-38
Gender Female, 53.3%
Surprised 45.4%
Sad 46.4%
Calm 47.1%
Confused 45.3%
Disgusted 47.7%
Happy 46.2%
Angry 46.8%

AWS Rekognition

Age 19-36
Gender Female, 50.1%
Angry 49.6%
Disgusted 49.9%
Sad 49.7%
Calm 49.6%
Happy 49.6%
Surprised 49.6%
Confused 49.6%

AWS Rekognition

Age 23-38
Gender Male, 50.2%
Confused 49.6%
Surprised 49.6%
Sad 49.9%
Calm 49.6%
Happy 49.5%
Angry 49.8%
Disgusted 49.5%

AWS Rekognition

Age 20-38
Gender Male, 50.2%
Sad 49.6%
Disgusted 50.1%
Confused 49.5%
Happy 49.7%
Calm 49.5%
Surprised 49.5%
Angry 49.5%

Feature analysis

Amazon

Chair 98.8%
Ceiling Fan 97.6%
Person 97.4%

Text analysis

Amazon

Serving
PS.31
1O

Google

Cio P.S.3 MAN HousekeepingServing
Cio
P.S.3
MAN
HousekeepingServing