Human Generated Data

Title

Miscellaneous [Social Settlements: Canada. Montreal. University Settlement]?
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, 3.2002.3350

Human Generated Data

Title

Miscellaneous [Social Settlements: Canada. Montreal. University Settlement]?

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, 3.2002.3350

Machine Generated Data

Tags

Amazon
created on 2019-06-05

Person 95.2
Human 95.2
Musician 93.6
Musical Instrument 93.6
Person 92.2
Person 91.5
Restaurant 87.9
Person 81.5
Person 80.2
Furniture 79.5
Chair 79.5
People 77.4
Person 76.7
Person 73.1
Person 72
Person 71.5
Table 69.7
Person 67.2
Crowd 63.7
Person 63.6
Room 62.4
Indoors 62.4
Music Band 61.6
Dining Table 61.1
Cafe 59.3
Leisure Activities 58.7
Drummer 55.8
Percussion 55.8
Drum 55.7
Person 55.2
Person 55.2
Cafeteria 55.1
Person 49
Person 48.8
Person 45.1

Clarifai
created on 2019-06-05

people 99.6
group 98.5
adult 97.6
many 96.4
group together 94.8
man 94
woman 91.7
wear 91.5
illustration 90.7
furniture 87.8
room 87.6
crowd 87.3
child 87.2
outfit 84.9
print 84.8
art 83.2
administration 82.3
music 81.2
leader 80.6
chair 80.1

Imagga
created on 2019-06-05

glass 40.7
brass 29.3
wind instrument 23.3
chandelier 21.9
chemistry 19.3
laboratory 19.3
research 19
experiment 18.5
chemical 18.3
biology 18
medical 17.6
decoration 17.6
wine 16.9
lighting fixture 16.7
medicine 16.7
glassware 16.7
musical instrument 16.4
interior 15.9
table 15.9
glasses 15.7
biotechnology 15.7
party 14.6
lab 14.6
set 14.4
celebration 14.3
fixture 13.5
wineglass 13.4
scientific 12.6
container 11.9
science 11.6
beaker 11.3
restaurant 11.3
sketch 11.2
equipment 11.1
cornet 11.1
drawing 10.8
flower 10.8
vase 10.7
development 10.6
luxury 10.3
wedding 10.1
symbol 10.1
drink 10
flask 9.8
champagne 9.7
test 9.6
liquid 9.6
design 9.6
gift 9.5
water 9.3
business 9.1
plant 9
romance 8.9
group 8.9
decor 8.8
napkin 8.8
holiday 8.6
aqua 8.6
elegant 8.6
solution 8.6
leaf 8.6
dinner 8.5
ribbon 8.5
modern 8.4
elegance 8.4
service 8.3
event 8.3
silver 8
agronomy 7.9
weeds 7.9
genetic 7.9
h2o 7.9
boutique 7.8
fluid 7.8
setting 7.7
growing 7.6
engineering 7.6
alcohol 7.6
outfit 7.6
room 7.6
baritone 7.6
sign 7.5
house 7.5
instrument 7.5
tool 7.5
ecology 7.3
food 7.3
metal 7.2
life 7.2
box 7.2
transparent 7.2
romantic 7.1
love 7.1
silverware 7

Google
created on 2019-06-05

Photograph 97
Snapshot 88.2
Room 80.1
Art 73.1
Collection 68.1
Photography 67.8
Adaptation 67
Visual arts 62.5
Stock photography 62.1
Black-and-white 56.4
Interior design 52.6
Family 52.5

Microsoft
created on 2019-06-05

person 96.5
clothing 84.5
man 72.7
group 68.2
room 65.1
gallery 62.3
old 49.9
different 33.8
clothes 22.1
several 13.1

Color Analysis

Face analysis

Amazon

AWS Rekognition

Age 26-43
Gender Male, 50.2%
Sad 50.1%
Surprised 49.5%
Angry 49.6%
Disgusted 49.5%
Confused 49.5%
Happy 49.5%
Calm 49.7%

AWS Rekognition

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

AWS Rekognition

Age 35-52
Gender Female, 50.1%
Happy 49.5%
Disgusted 49.5%
Sad 49.9%
Confused 49.5%
Angry 49.6%
Surprised 49.5%
Calm 49.9%

AWS Rekognition

Age 19-36
Gender Female, 50.4%
Happy 49.7%
Angry 49.5%
Disgusted 49.5%
Confused 49.5%
Sad 50.2%
Calm 49.5%
Surprised 49.5%

AWS Rekognition

Age 16-27
Gender Male, 50.1%
Angry 49.5%
Happy 49.6%
Disgusted 49.5%
Confused 49.5%
Surprised 49.5%
Sad 50%
Calm 49.9%

AWS Rekognition

Age 35-52
Gender Female, 50.5%
Surprised 49.5%
Happy 49.6%
Confused 49.5%
Sad 50.4%
Calm 49.5%
Disgusted 49.5%
Angry 49.5%

AWS Rekognition

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

AWS Rekognition

Age 45-63
Gender Male, 50.5%
Angry 49.5%
Confused 49.5%
Calm 50.3%
Sad 49.6%
Happy 49.5%
Disgusted 49.5%
Surprised 49.5%

AWS Rekognition

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

AWS Rekognition

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

AWS Rekognition

Age 20-38
Gender Female, 50.2%
Happy 49.5%
Surprised 49.5%
Calm 49.5%
Angry 49.5%
Confused 49.5%
Sad 50.5%
Disgusted 49.5%

AWS Rekognition

Age 15-25
Gender Female, 50.3%
Surprised 49.6%
Confused 49.5%
Happy 49.5%
Angry 49.6%
Calm 50.2%
Sad 49.6%
Disgusted 49.5%

AWS Rekognition

Age 35-52
Gender Female, 50%
Angry 49.6%
Disgusted 49.5%
Confused 49.5%
Calm 49.6%
Happy 49.5%
Surprised 49.5%
Sad 50.2%

AWS Rekognition

Age 15-25
Gender Female, 50.4%
Calm 49.5%
Angry 49.6%
Disgusted 49.5%
Sad 50.1%
Surprised 49.6%
Happy 49.6%
Confused 49.5%

AWS Rekognition

Age 35-52
Gender Female, 50.1%
Sad 49.6%
Surprised 49.7%
Disgusted 49.5%
Calm 50.1%
Happy 49.5%
Confused 49.5%
Angry 49.5%

AWS Rekognition

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

AWS Rekognition

Age 35-55
Gender Female, 50%
Surprised 49.6%
Sad 49.9%
Happy 49.6%
Disgusted 49.5%
Confused 49.6%
Calm 49.7%
Angry 49.6%

AWS Rekognition

Age 35-52
Gender Male, 50.3%
Surprised 49.5%
Angry 49.5%
Disgusted 49.5%
Sad 49.5%
Happy 49.5%
Confused 49.5%
Calm 50.4%

AWS Rekognition

Age 26-43
Gender Male, 50.2%
Angry 49.5%
Surprised 49.5%
Disgusted 49.5%
Happy 49.5%
Calm 49.5%
Sad 50.4%
Confused 49.5%

AWS Rekognition

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

AWS Rekognition

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

AWS Rekognition

Age 10-15
Gender Female, 50.3%
Sad 50.3%
Confused 49.5%
Calm 49.5%
Disgusted 49.5%
Happy 49.5%
Angry 49.5%
Surprised 49.6%

AWS Rekognition

Age 17-27
Gender Male, 50%
Calm 50.2%
Sad 49.7%
Confused 49.5%
Happy 49.5%
Angry 49.5%
Disgusted 49.5%
Surprised 49.5%

AWS Rekognition

Age 26-44
Gender Female, 50.3%
Disgusted 49.5%
Confused 49.5%
Angry 49.5%
Calm 49.6%
Surprised 49.5%
Happy 49.6%
Sad 50.3%

AWS Rekognition

Age 26-43
Gender Female, 50.5%
Happy 49.8%
Angry 49.6%
Calm 49.9%
Confused 49.5%
Surprised 49.6%
Disgusted 49.6%
Sad 49.6%

AWS Rekognition

Age 26-44
Gender Female, 50.3%
Angry 49.5%
Happy 50.3%
Disgusted 49.5%
Sad 49.6%
Confused 49.5%
Calm 49.6%
Surprised 49.5%

AWS Rekognition

Age 26-43
Gender Female, 50.4%
Confused 49.6%
Calm 49.7%
Angry 50%
Sad 49.5%
Happy 49.5%
Disgusted 49.6%
Surprised 49.5%

AWS Rekognition

Age 30-47
Gender Female, 50.5%
Sad 50.2%
Surprised 49.5%
Happy 49.5%
Disgusted 49.5%
Angry 49.6%
Calm 49.5%
Confused 49.6%

AWS Rekognition

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

Feature analysis

Amazon

Person 95.2%

Categories

Imagga

paintings art 96.6%
interior objects 3.3%

Text analysis

Google

XI ML
XI
ML