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.: Departments: Press Room
Date

c. 1904
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, 3.2002.324.B.5

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.: Departments: Press Room

People

Artist: Unidentified Artist,

Date

c. 1904

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.324.B.5

Machine Generated Data

Tags

Amazon
created on 2022-06-25

Assembly Line 100
Building 100
Factory 100
Person 99.5
Human 99.5
Person 95.4
Person 94.7
Person 93.2
Train 89.9
Transportation 89.9
Vehicle 89.9
Person 89.1
Person 87.4
Person 84.7
Person 82.1
Portrait 61.1
Photography 61.1
Face 61.1
Photo 61.1

Imagga
created on 2022-06-25

factory 49.9
industrial 46.3
industry 44.4
turbine 43.1
machine 41.7
steel 38.9
power 34.4
device 33.2
accelerator 31.6
modern 30.8
plant 29.5
building 28.6
engineering 28.6
machinery 26.7
pipe 26.2
heavy 25.8
interior 23.9
scientific instrument 23.7
pipes 23.6
loom 23.5
pollution 23.1
urban 22.7
equipment 22.5
textile machine 22.5
metal 21.7
production 21.4
tube 21.2
inside 21.2
energy 21
structure 20.9
waste 20.4
gas 20.2
manufacturing 19.5
heat 19.4
steam 19.4
station 19.3
architecture 18.9
piping 18.8
fuel 18.3
mechanical 17.5
technology 17.1
oil 16.7
environment 16.4
supply 16.4
transportation 16.1
instrument 16
refinery 15.8
valve 15.7
complex 15.5
chemical 15.4
business 14.6
technical 14.5
pipeline 13.8
mechanic 13.7
concrete 13.4
hot 13.4
cyclotron 13.2
economy 13
petrol 12.7
pump 12.7
work 12.7
burn 12.5
system 12.4
electricity 12.3
global 11.8
enterprise 11.8
chimney 11.8
science 11.6
water 11.3
environmental 11.3
construction 11.1
transport 11
robots 10.9
combustible 10.9
smokestack 10.9
tubes 10.8
warming 10.7
engineer 10.7
indoors 10.5
iron 10.3
fabricate 9.9
lines 9.9
facility 9.8
radiation 9.8
cables 9.8
materials 9.8
district 9.7
engine 9.6
design 9.6
stock 9.4
machine made 8.9
constructional 8.9
dumping 8.9
manufactured 8.9
fabrication 8.9
contaminated 8.9
constructor 8.9
poison 8.8
matrix 8.8
tank 8.8
nuclear 8.7
light 8.7
high 8.7
contrast 8.6
area 8.6
estate 8.6
room 8.5
machinist 8.5
reflection 8.1
zone 7.9
manufacture 7.9
stainless 7.8
train 7.7
city 7.5
workshop 7.3
office 7.3
chair 7.2
travel 7

Google
created on 2022-06-25

Motor vehicle 92.2
Black 89.6
Mass production 84.5
Engineering 79.4
Factory 78.1
Industry 77
Machine 75.7
Metal 71.5
Toolroom 70.4
Monochrome 69.7
Auto part 69.2
Steel 65.3
Aircraft engine 58.7
Aerospace engineering 55.9
Monochrome photography 54.4
Automotive wheel system 53.8
Wood 53.4
Vehicle 52
Beam 50.1

Microsoft
created on 2022-06-25

building 99.3
white 73.1
wheel 67
old 64.3
factory 64.3
text 64
auto part 62.1
land vehicle 61.3

Color Analysis

Face analysis

Amazon

Google

AWS Rekognition

Age 22-30
Gender Female, 78.4%
Sad 78.8%
Confused 24%
Calm 21.4%
Surprised 11.2%
Fear 7.1%
Angry 3.6%
Disgusted 2.8%
Happy 1.8%

AWS Rekognition

Age 20-28
Gender Male, 99.9%
Calm 81.7%
Angry 8.6%
Surprised 7%
Fear 6.5%
Confused 3.3%
Sad 2.8%
Happy 0.9%
Disgusted 0.6%

AWS Rekognition

Age 19-27
Gender Male, 98%
Disgusted 69.5%
Calm 19.2%
Surprised 7.8%
Fear 6.9%
Confused 3.4%
Sad 2.5%
Angry 1.1%
Happy 0.4%

AWS Rekognition

Age 21-29
Gender Male, 99.8%
Sad 93.6%
Calm 25.8%
Angry 14.7%
Disgusted 7.1%
Surprised 7.1%
Fear 6.6%
Confused 2.8%
Happy 0.6%

AWS Rekognition

Age 14-22
Gender Male, 66.5%
Happy 28.6%
Calm 25.7%
Angry 17.5%
Sad 12.2%
Surprised 8.1%
Fear 7.3%
Disgusted 5.4%
Confused 3.2%

AWS Rekognition

Age 12-20
Gender Female, 92.6%
Calm 70.7%
Fear 13.8%
Surprised 7.2%
Sad 4.7%
Angry 4.2%
Confused 1.9%
Disgusted 0.9%
Happy 0.7%

AWS Rekognition

Age 12-20
Gender Male, 71.1%
Calm 92.2%
Surprised 6.4%
Fear 6%
Sad 2.7%
Happy 2.5%
Disgusted 1.4%
Angry 1.4%
Confused 0.4%

Google Vision

Surprise Very unlikely
Anger Very unlikely
Sorrow Very unlikely
Joy Very unlikely
Headwear Very unlikely
Blurred Possible

Google Vision

Surprise Very unlikely
Anger Very unlikely
Sorrow Very unlikely
Joy Very unlikely
Headwear Very unlikely
Blurred Very unlikely

Google Vision

Surprise Very unlikely
Anger Very unlikely
Sorrow Very unlikely
Joy Very unlikely
Headwear Very unlikely
Blurred Likely

Feature analysis

Amazon

Person 99.5%
Train 89.9%

Categories

Imagga

streetview architecture 84.1%
interior objects 9.4%
cars vehicles 2.8%
paintings art 1.1%

Text analysis

Amazon

TIDE

Google

DOES IN 30
DOES
IN
30