Human Generated Data

Title

The Stoning of the Elders
Date

c. 1579
People

Artist: Hans Collaert the Elder, Netherlandish 1525/30 - 1580

Artist after: Maerten de Vos, Netherlandish 1532 - 1603

Publisher: Gerard de Jode, Flemish 1509 - 1591
Classification

Prints
Credit Line

Harvard Art Museums/Fogg Museum, Gift of Barbara Ketcham Wheaton in memory of Robert Bradford Wheaton, 2011.613.172

Human Generated Data

Title

The Stoning of the Elders

People

Artist: Hans Collaert the Elder, Netherlandish 1525/30 - 1580

Artist after: Maerten de Vos, Netherlandish 1532 - 1603

Publisher: Gerard de Jode, Flemish 1509 - 1591

Date

c. 1579

Classification

Prints

Credit Line

Harvard Art Museums/Fogg Museum, Gift of Barbara Ketcham Wheaton in memory of Robert Bradford Wheaton, 2011.613.172

Machine Generated Data

Tags

Amazon
created on 2019-06-25

Human 99.1
Person 99.1
Person 97.9
Person 96
Person 93.7
Art 91.6
Person 90.5
Person 86.4
Painting 85.7
Person 83.7
Person 76.3
Person 74.1
Person 65.7
Person 64.2
Archaeology 62.2
Person 56.7
Person 43.8

Clarifai
created on 2019-06-25

print 99.8
painting 99.5
illustration 99.3
art 99.2
people 99.1
lithograph 99
old 96.8
vintage 96.7
group 95.6
card 95.2
man 94.7
antique 94.7
retro 94.1
letter 93.8
ancient 92.7
postcard 92.1
post 91.2
mammal 90.7
collection 88.3
adult 88.2

Imagga
created on 2019-06-25

jigsaw puzzle 46.4
money 37.4
puzzle 36.6
currency 35
cash 33.9
dollar 27.8
vintage 27.3
comic book 26.5
finance 26.2
bank 26
game 26
bill 24.7
paper 23.5
banking 23
dollars 22.2
letter 22
postmark 21.7
stamp 21.4
envelope 21.1
mail 21.1
exchange 21
book jacket 21
wealth 20.6
postage 20.6
postal 20.6
business 20
savings 19.6
note 19.3
old 18.8
financial 17.8
banknote 17.5
bills 16.5
one 16.4
retro 16.4
jacket 16.3
close 16
art 15.9
product 14.8
newspaper 14.3
post 14.3
circa 13.8
printed 13.8
symbol 13.5
wrapping 13.4
ancient 13
shows 12.8
global 12.8
hundred 12.6
states 12.6
us 12.5
pay 12.5
creation 12.4
rich 12.1
culture 12
card 11.9
museum 11.6
painter 11.5
painted 11.4
masterpiece 10.9
philately 10.9
black 10.8
renaissance 10.8
banknotes 10.8
paintings 10.8
print media 10.6
communications 10.5
fine 10.5
united 10.5
office 10.4
icon 10.3
closeup 10.1
investment 10.1
aged 10
post mail 9.9
zigzag 9.9
fame 9.9
known 9.9
stamps 9.9
funds 9.8
market 9.8
delivery 9.7
cutting 9.7
payment 9.6
notes 9.6
loan 9.6
antique 9.5
unique 9.5
economy 9.3
church 9.2
covering 8.9
man 8.7
debt 8.7
change 8.7
daily 8.6
save 8.5
address 7.8
rate 7.8
letters 7.7
artist 7.7
container 7.5
sign 7.5
buy 7.5
book 7.4
message 7.3
religion 7.2

Google
created on 2019-06-25

Art 85.5
Painting 81.6
Tapestry 69.7
Textile 68.4
Picture frame 62.5
Modern art 60.1
Illustration 57.7

Microsoft
created on 2019-06-25

text 98.6
drawing 97.5
person 90.4
cartoon 85.4
book 75.4
stationary 67.9
child art 54.8
painting 43.4
picture frame 41.3
decorated 26.8

Color Analysis

Face analysis

Amazon

AWS Rekognition

Age 26-43
Gender Female, 50.2%
Sad 50.5%
Happy 49.5%
Disgusted 49.5%
Calm 49.5%
Confused 49.5%
Angry 49.5%
Surprised 49.5%

AWS Rekognition

Age 35-52
Gender Male, 53.3%
Confused 45.1%
Disgusted 45.1%
Surprised 45.2%
Sad 46.3%
Angry 45.4%
Happy 45.1%
Calm 52.9%

AWS Rekognition

Age 26-43
Gender Female, 50.3%
Confused 49.5%
Disgusted 49.6%
Surprised 49.5%
Sad 49.9%
Angry 49.8%
Happy 49.7%
Calm 49.5%

AWS Rekognition

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

AWS Rekognition

Age 4-9
Gender Male, 53.7%
Angry 45.5%
Happy 45.3%
Sad 47.6%
Calm 50.2%
Disgusted 45.1%
Confused 45.5%
Surprised 45.8%

AWS Rekognition

Age 45-63
Gender Male, 54.3%
Happy 52.6%
Angry 45.6%
Calm 45%
Disgusted 45.6%
Surprised 45.2%
Confused 45.1%
Sad 45.9%

AWS Rekognition

Age 29-45
Gender Female, 50.2%
Surprised 49.5%
Disgusted 49.5%
Sad 50.4%
Calm 49.5%
Confused 49.5%
Happy 49.5%
Angry 49.5%

AWS Rekognition

Age 4-9
Gender Female, 50.1%
Disgusted 49.6%
Sad 49.7%
Calm 49.7%
Confused 49.6%
Angry 49.7%
Surprised 49.6%
Happy 49.7%

AWS Rekognition

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

AWS Rekognition

Age 60-90
Gender Female, 50.1%
Happy 49.5%
Confused 49.5%
Angry 49.5%
Sad 50.5%
Calm 49.5%
Disgusted 49.5%
Surprised 49.5%

AWS Rekognition

Age 27-44
Gender Male, 50.3%
Happy 49.6%
Surprised 49.6%
Confused 49.5%
Calm 49.7%
Sad 49.8%
Disgusted 49.5%
Angry 49.7%

AWS Rekognition

Age 35-52
Gender Male, 50.3%
Confused 49.6%
Disgusted 49.6%
Angry 49.6%
Happy 49.7%
Sad 49.7%
Calm 49.7%
Surprised 49.7%

AWS Rekognition

Age 38-57
Gender Male, 50.2%
Confused 49.5%
Disgusted 49.5%
Calm 49.5%
Sad 49.9%
Surprised 49.5%
Angry 49.5%
Happy 50%

AWS Rekognition

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

AWS Rekognition

Age 26-43
Gender Male, 51.3%
Sad 49.6%
Disgusted 45.7%
Happy 45.3%
Confused 45.4%
Calm 47.2%
Surprised 45.6%
Angry 46.2%

AWS Rekognition

Age 38-57
Gender Male, 50.1%
Sad 50%
Disgusted 49.7%
Happy 49.5%
Confused 49.5%
Calm 49.6%
Surprised 49.5%
Angry 49.5%

AWS Rekognition

Age 38-59
Gender Male, 50.3%
Disgusted 49.6%
Angry 50%
Calm 49.6%
Sad 49.6%
Confused 49.6%
Happy 49.5%
Surprised 49.6%

AWS Rekognition

Age 38-57
Gender Male, 50.4%
Sad 49.8%
Calm 49.9%
Surprised 49.5%
Angry 49.6%
Happy 49.6%
Confused 49.5%
Disgusted 49.5%

AWS Rekognition

Age 35-52
Gender Male, 50.4%
Sad 50.2%
Calm 49.6%
Disgusted 49.5%
Confused 49.5%
Angry 49.6%
Surprised 49.5%
Happy 49.5%

AWS Rekognition

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

AWS Rekognition

Age 26-43
Gender Female, 50.2%
Confused 49.6%
Disgusted 49.5%
Angry 49.5%
Calm 49.9%
Happy 49.5%
Surprised 49.5%
Sad 49.9%

AWS Rekognition

Age 45-63
Gender Male, 50.5%
Sad 49.9%
Happy 49.5%
Calm 49.5%
Surprised 49.6%
Confused 49.6%
Angry 49.7%
Disgusted 49.7%

AWS Rekognition

Age 26-43
Gender Female, 50%
Surprised 45.4%
Confused 45.4%
Angry 46.1%
Happy 45.2%
Disgusted 46.2%
Calm 45.8%
Sad 50.9%

AWS Rekognition

Age 20-38
Gender Female, 50.5%
Sad 49.8%
Angry 49.6%
Happy 49.6%
Calm 49.9%
Confused 49.6%
Disgusted 49.6%
Surprised 49.5%

AWS Rekognition

Age 23-38
Gender Female, 50.4%
Surprised 49.5%
Sad 50.3%
Happy 49.5%
Disgusted 49.5%
Confused 49.5%
Calm 49.6%
Angry 49.5%

AWS Rekognition

Age 45-63
Gender Female, 54.8%
Happy 45.7%
Confused 45.1%
Calm 45.2%
Angry 45.3%
Surprised 45.2%
Sad 53.1%
Disgusted 45.4%

AWS Rekognition

Age 9-14
Gender Female, 50%
Happy 49.5%
Angry 49.5%
Calm 49.8%
Disgusted 49.9%
Surprised 49.5%
Confused 49.5%
Sad 49.8%

AWS Rekognition

Age 23-38
Gender Male, 50.4%
Sad 50.3%
Calm 49.5%
Disgusted 49.6%
Confused 49.5%
Angry 49.5%
Surprised 49.5%
Happy 49.5%

AWS Rekognition

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

AWS Rekognition

Age 35-52
Gender Male, 50.4%
Angry 49.6%
Surprised 49.6%
Sad 49.9%
Calm 49.8%
Disgusted 49.5%
Happy 49.5%
Confused 49.6%

AWS Rekognition

Age 38-59
Gender Male, 50.3%
Calm 49.6%
Angry 49.5%
Happy 49.5%
Sad 50.3%
Disgusted 49.5%
Confused 49.5%
Surprised 49.5%

AWS Rekognition

Age 12-22
Gender Male, 50.4%
Angry 49.7%
Surprised 49.6%
Calm 49.7%
Disgusted 49.6%
Confused 49.6%
Happy 49.5%
Sad 49.7%

AWS Rekognition

Age 23-38
Gender Female, 50.5%
Happy 50%
Confused 49.5%
Sad 49.7%
Calm 49.7%
Disgusted 49.6%
Angry 49.5%
Surprised 49.5%

AWS Rekognition

Age 4-9
Gender Male, 50.1%
Happy 49.5%
Angry 49.5%
Calm 49.5%
Disgusted 49.5%
Surprised 49.5%
Confused 49.5%
Sad 50.3%

AWS Rekognition

Age 35-52
Gender Female, 53.7%
Angry 45.3%
Surprised 46.6%
Calm 48.8%
Disgusted 45.2%
Confused 45.2%
Happy 45.9%
Sad 47.9%

AWS Rekognition

Age 35-52
Gender Female, 50.5%
Confused 49.6%
Disgusted 49.6%
Sad 49.6%
Angry 49.6%
Calm 50%
Surprised 49.6%
Happy 49.6%

Feature analysis

Amazon

Person 99.1%
Painting 85.7%

Categories

Imagga

paintings art 82.4%
events parties 11.1%
text visuals 3%
food drinks 2.2%

Captions

Microsoft
created on 2019-06-25

a painting on the wall 67.9%
a painting in a box 57.5%
a painting of a box 57.4%

Text analysis

Amazon

Mose
an
cruenta
enes
axifque
cadit
plectuntur
feftes
molles
Et
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Falfi
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fouffeir
i3
yan
Ffaue
DaniL
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Jaups
Su
lgnd
chsomi
G

Google

182 Fali plectuntur teftes Jaxiue premuntur, Et cadit in molles poena cruenta fenes. Daniel. i5 Cenid ur auce d ayant conuaineu a ar eSane Pan s. erntr Su Sann, ctont AM pan m レtma
182
Fali
plectuntur
teftes
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premuntur,
Et
cadit
in
molles
poena
cruenta
fenes.
Daniel.
i5
Cenid
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auce
d
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ar
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