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: Public School No. 147 Manhattan.: Vacation School - Chair Caning.

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, 3.2002.1458.1

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: Public School No. 147 Manhattan.: Vacation School - Chair Caning.

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, 3.2002.1458.1

Machine Generated Data

Tags

Amazon
created on 2019-06-04

Classroom 99.5
School 99.5
Indoors 99.5
Room 99.5
Person 99.1
Human 99.1
Person 99
Person 98.5
Person 98.1
Chair 96.1
Furniture 96.1
Person 91.6
Person 90.9
Person 90.1
Person 77.5
Person 74.7
Building 70.6
Factory 70.6
Person 69.2
Person 65.7
Person 50.2
Person 42.9

Clarifai
created on 2019-06-04

people 100
many 99.7
group 99.6
adult 98.6
group together 98.3
room 97.2
furniture 96.8
administration 96.2
seat 94.5
education 94.4
war 94.1
man 93.9
military 93.3
several 93.2
woman 92.8
vehicle 92.7
leader 92.6
chair 92.5
instrument 92.1
print 91.5

Imagga
created on 2019-06-04

room 89.2
interior 62.8
table 55
chair 52.9
classroom 51.9
furniture 44.4
house 39.3
restaurant 36.9
modern 33.7
decor 33.6
home 31.9
dining 29.5
floor 28.8
design 27.6
kitchen 25.8
indoors 25.5
chairs 23.5
wood 23.4
window 22.9
empty 22.3
luxury 22.3
architecture 21.1
glass 21
indoor 21
seat 20.7
dinner 19.5
tables 18.7
cafeteria 18.5
inside 18.4
decoration 18.1
structure 17.4
style 17.1
building 17
light 16.7
apartment 16.3
contemporary 16
elegance 16
counter 15.7
residential 15.3
comfortable 15.3
lamp 15.3
stool 14.8
drink 14.2
food 14
wall 13.8
hall 13.6
3d 13.2
lunch 12.9
stove 12.8
plant 12.7
place 12.1
cabinet 11.8
eat 11.8
wooden 11.4
bar 11.1
nobody 10.9
domestic 10.9
city 10.8
tile 10.6
urban 10.5
scene 10.4
render 10.4
office 10.4
service 10.2
relaxation 10.1
cabinets 9.9
sink 9.8
steel 9.7
patio 9.7
metal 9.7
center 9.6
hotel 9.6
living 9.5
space 9.3
coffee 9.3
meal 9.1
oven 8.8
residence 8.8
sofa 8.6
elegant 8.6
decorate 8.6
relax 8.4
life 8.4
area 8.4
wine 8.3
cook 8.2
shop 8.1
dining room 7.9
refrigerator 7.9
day 7.9
diner 7.8
party 7.7
setting 7.7
lights 7.4
desk 7.4
barbershop 7.3
business 7.3

Google
created on 2019-06-04

Microsoft
created on 2019-06-04

indoor 97.2
person 93.7
chair 93.6
table 92
old 88.9
clothing 87.8
room 79.9
restaurant 22.5
cluttered 19.4
furniture 17.9
several 11.2

Color Analysis

Face analysis

Amazon

Microsoft

AWS Rekognition

Age 15-25
Gender Male, 54.2%
Calm 52.1%
Confused 45%
Sad 47.6%
Angry 45.1%
Happy 45.1%
Disgusted 45%
Surprised 45%

AWS Rekognition

Age 20-38
Gender Female, 52.6%
Calm 46.3%
Happy 48%
Disgusted 45.3%
Confused 45.4%
Surprised 45.5%
Sad 49.2%
Angry 45.4%

AWS Rekognition

Age 15-25
Gender Male, 51%
Sad 49.3%
Confused 45.3%
Disgusted 45.2%
Happy 47%
Angry 45.3%
Calm 47.7%
Surprised 45.4%

AWS Rekognition

Age 29-45
Gender Male, 50.1%
Confused 49.5%
Sad 50.4%
Disgusted 49.5%
Surprised 49.5%
Angry 49.5%
Happy 49.5%
Calm 49.5%

AWS Rekognition

Age 10-15
Gender Male, 50.1%
Calm 45.2%
Disgusted 45.1%
Angry 45.6%
Sad 53.9%
Happy 45.1%
Confused 45.1%
Surprised 45.1%

AWS Rekognition

Age 26-43
Gender Female, 50.1%
Calm 49.7%
Happy 49.5%
Sad 50.2%
Surprised 49.5%
Disgusted 49.5%
Angry 49.5%
Confused 49.5%

AWS Rekognition

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

AWS Rekognition

Age 14-25
Gender Male, 50.3%
Disgusted 49.5%
Sad 49.8%
Calm 49.9%
Happy 49.6%
Confused 49.6%
Surprised 49.6%
Angry 49.6%

AWS Rekognition

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

AWS Rekognition

Age 15-25
Gender Female, 50.3%
Confused 49.6%
Disgusted 49.5%
Sad 50%
Angry 49.5%
Happy 49.5%
Surprised 49.5%
Calm 49.8%

AWS Rekognition

Age 16-27
Gender Female, 50.5%
Happy 49.6%
Disgusted 49.5%
Surprised 49.5%
Angry 49.5%
Calm 49.6%
Sad 50.1%
Confused 49.6%

AWS Rekognition

Age 23-38
Gender Female, 50.2%
Surprised 49.6%
Angry 49.7%
Happy 49.7%
Sad 49.8%
Calm 49.6%
Confused 49.6%
Disgusted 49.6%

AWS Rekognition

Age 14-25
Gender Female, 50.2%
Calm 49.6%
Disgusted 49.6%
Surprised 49.6%
Sad 49.8%
Happy 49.6%
Angry 49.8%
Confused 49.6%

AWS Rekognition

Age 30-47
Gender Female, 52.7%
Surprised 45.7%
Confused 45.9%
Angry 45.5%
Disgusted 45.5%
Sad 47.7%
Happy 46.2%
Calm 48.4%

Microsoft Cognitive Services

Age 22
Gender Female

Feature analysis

Amazon

Person 99.1%
Chair 96.1%

Categories

Text analysis

Amazon

S.4MANHATTAN.
c g

Google

P.S.141MANHATTAN.
P.S.141MANHATTAN.