Human Generated Data

Title

Industrial Problems, Welfare Work: United States. Ohio. Dayton. National Cash Register Company: Welfare Institutions of the National Cash Register Company, Dayton, Ohio.: School for Sales Agents
Date

c. 1903
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, 3.2002.3519.B.2

Human Generated Data

Title

Industrial Problems, Welfare Work: United States. Ohio. Dayton. National Cash Register Company: Welfare Institutions of the National Cash Register Company, Dayton, Ohio.: School for Sales Agents

People

Artist: Unidentified Artist,

Date

c. 1903

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.3519.B.2

Machine Generated Data

Tags

Amazon
created on 2019-06-05

Factory 99.7
Assembly Line 99.7
Building 99.7
Human 99.6
Person 99.6
Person 99.6
Person 99.2
Person 98
Person 96.9
Person 95.6
Person 93.1
Person 91.3
Person 87.9
Person 85.2
Person 84.7
Person 80.3
Chair 79
Furniture 79
Workshop 77.7
Leisure Activities 71.7
Musical Instrument 71.7
Guitar 71.7
Person 66.1
Restaurant 65.2
Cafeteria 65.2
Person 61.1
Crowd 59.8
Manufacturing 58.2

Clarifai
created on 2019-06-05

people 99.9
many 99.8
group 99.4
group together 99
adult 97.9
furniture 97.2
administration 96.3
several 95.6
man 95.2
woman 95.1
education 95.1
desk 92.9
war 92.3
military 91.6
recreation 89.1
teacher 87.9
school 87.9
employee 87
room 86
leader 85.5

Imagga
created on 2019-06-05

passenger 56.1
man 23.5
restaurant 21.2
people 20.6
shop 18.2
table 18.2
room 18.1
work 18
male 15.6
person 15.6
men 15.4
chair 15.1
industry 14.5
interior 14.1
hospital 14.1
mercantile establishment 12.8
old 12.5
adult 12.5
surgeon 12.4
indoors 12.3
worker 11.9
building 11.8
architecture 11.7
working 11.5
vacation 11.5
travel 11.3
sitting 11.2
day 11
industrial 10.9
house 10.9
equipment 10.8
factory 10.6
stall 10.5
home 10.4
smiling 9.4
business 9.1
job 8.8
chairs 8.8
happy 8.8
standing 8.7
lifestyle 8.7
place of business 8.4
boat 8.4
city 8.3
leisure 8.3
inside 8.3
occupation 8.2
outdoors 8.2
furniture 8.1
family 8
water 8
urban 7.9
cafeteria 7.8
machine 7.8
glass 7.8
heavy 7.6
dinner 7.6
senior 7.5
patient 7.5
life 7.4
uniform 7.2
holiday 7.2
medical 7.1
medicine 7
shoe shop 7

Google
created on 2019-06-05

Class 77.4
Classroom 70.7
Room 65.7

Microsoft
created on 2019-06-05

building 99.7
person 98.5
clothing 93.1
people 91.2
outdoor 90.8
man 86.2
table 80.5
group 79.1
furniture 73.2
chair 52.6
black and white 50.3
restaurant 17.8

Color Analysis

Face analysis

Amazon

Microsoft

AWS Rekognition

Age 23-38
Gender Male, 50.6%
Angry 45.2%
Disgusted 45.2%
Surprised 45%
Happy 45.1%
Calm 45.1%
Sad 54.2%
Confused 45.1%

AWS Rekognition

Age 35-52
Gender Male, 54.1%
Happy 45%
Angry 54.6%
Calm 45.2%
Confused 45%
Disgusted 45.1%
Surprised 45%
Sad 45.1%

AWS Rekognition

Age 26-43
Gender Male, 53.7%
Disgusted 45.1%
Happy 45.3%
Surprised 45.2%
Calm 52%
Angry 45.6%
Confused 45.3%
Sad 46.5%

AWS Rekognition

Age 30-47
Gender Male, 54.4%
Calm 45.2%
Confused 45.1%
Disgusted 45%
Happy 45%
Angry 45.5%
Sad 54.1%
Surprised 45%

AWS Rekognition

Age 20-38
Gender Male, 51.2%
Angry 47.2%
Happy 45.3%
Surprised 45.7%
Disgusted 45.3%
Sad 46.8%
Confused 45.7%
Calm 49%

AWS Rekognition

Age 26-43
Gender Female, 53.3%
Angry 45.4%
Calm 45.4%
Surprised 45.2%
Disgusted 45.1%
Happy 45.1%
Sad 53.6%
Confused 45.2%

AWS Rekognition

Age 35-52
Gender Male, 51.6%
Surprised 45.9%
Confused 45.3%
Angry 45.4%
Happy 46.7%
Disgusted 45.2%
Sad 45.9%
Calm 50.6%

AWS Rekognition

Age 35-52
Gender Male, 51.7%
Disgusted 47.4%
Sad 45.9%
Calm 46%
Confused 46%
Happy 47.3%
Surprised 46.2%
Angry 46.2%

AWS Rekognition

Age 35-52
Gender Male, 54.8%
Calm 51.9%
Sad 46.6%
Surprised 45.1%
Disgusted 45.1%
Angry 45.7%
Happy 45.1%
Confused 45.4%

AWS Rekognition

Age 26-43
Gender Male, 53.5%
Sad 51.1%
Happy 45.5%
Angry 45.4%
Calm 47.3%
Surprised 45.3%
Confused 45.3%
Disgusted 45.1%

AWS Rekognition

Age 26-43
Gender Male, 50.5%
Confused 49.5%
Sad 49.9%
Angry 49.6%
Calm 49.9%
Happy 49.5%
Disgusted 49.5%
Surprised 49.5%

AWS Rekognition

Age 26-43
Gender Male, 50.4%
Sad 49.7%
Disgusted 49.6%
Happy 49.6%
Surprised 49.6%
Angry 49.6%
Calm 49.9%
Confused 49.5%

AWS Rekognition

Age 35-55
Gender Female, 50.2%
Surprised 49.6%
Disgusted 49.6%
Calm 49.6%
Confused 49.5%
Angry 49.7%
Sad 49.6%
Happy 49.8%

AWS Rekognition

Age 35-55
Gender Female, 50%
Angry 49.5%
Disgusted 49.5%
Sad 50.5%
Calm 49.5%
Happy 49.5%
Surprised 49.5%
Confused 49.5%

AWS Rekognition

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

AWS Rekognition

Age 26-43
Gender Male, 50.3%
Confused 49.6%
Calm 49.7%
Angry 49.6%
Surprised 49.6%
Sad 49.6%
Happy 49.9%
Disgusted 49.6%

AWS Rekognition

Age 27-44
Gender Male, 50.4%
Sad 49.8%
Confused 49.5%
Angry 49.7%
Calm 49.9%
Surprised 49.5%
Happy 49.5%
Disgusted 49.5%

AWS Rekognition

Age 45-63
Gender Male, 50.5%
Angry 49.5%
Disgusted 50.2%
Sad 49.5%
Happy 49.5%
Confused 49.5%
Surprised 49.5%
Calm 49.7%

AWS Rekognition

Age 16-27
Gender Male, 50.5%
Surprised 49.5%
Happy 49.5%
Sad 49.6%
Angry 49.5%
Disgusted 49.5%
Calm 50.3%
Confused 49.5%

AWS Rekognition

Age 14-23
Gender Female, 50.5%
Calm 50.1%
Angry 49.5%
Sad 49.7%
Confused 49.6%
Surprised 49.5%
Happy 49.5%
Disgusted 49.5%

Microsoft Cognitive Services

Age 50
Gender Male

Feature analysis

Amazon

Person 99.6%
Chair 79%
Guitar 71.7%

Categories

Imagga

people portraits 59.6%
events parties 39.6%

Text analysis

Google

GE Ary
GE
Ary