Human Generated Data

Title

Industrial Problems, Welfare Work: United States: Welfare and Educational Work for the Families of Employees: Families of employees shortly after their arrival in America. In mill-towns where public institutions are inefficient the welfare work of industrial corporations is of especial value in developing the children of immigrants.

Date

c. 1903

People

Artist: Unidentified Artist,

Classification

Photographs

Human Generated Data

Title

Industrial Problems, Welfare Work: United States: Welfare and Educational Work for the Families of Employees: Families of employees shortly after their arrival in America. In mill-towns where public institutions are inefficient the welfare work of industrial corporations is of especial value in developing the children of immigrants.

People

Artist: Unidentified Artist,

Date

c. 1903

Classification

Photographs

Machine Generated Data

Tags

Amazon

Human 99.6
Person 99.6
Person 98.9
Person 98.9
Person 98.6
People 98.3
Family 98
Person 96.3
Clothing 80.8
Apparel 80.8
Urban 68.1
Building 61.5
Housing 60.2
Door 59.7

Clarifai

people 99.9
one 98.5
child 97.4
adult 97.4
wear 94.4
portrait 92.5
man 92.1
woman 92
two 91.2
home 88.1
family 85.3
furniture 85
group 84
administration 82.1
canine 81.5
sit 81.5
military 80.8
doorway 78.4
war 78.1
outfit 77.8

Imagga

city 26.6
street 24.8
man 24.2
urban 21.8
people 18.4
male 17.4
person 17.2
adult 15.6
fashion 13.6
wall 12.5
portrait 12.3
building 12.3
action 12.1
support 12
dirty 11.8
cleaner 11.6
sill 11.2
black 11
device 10.6
walking 10.4
men 10.3
posing 9.8
sidewalk 9.6
walk 9.5
model 9.3
teenager 9.1
pose 9.1
world 9
outdoors 9
structural member 8.9
cool 8.9
architecture 8.8
hair 8.7
women 8.7
lifestyle 8.7
moving 8.6
motion 8.6
outdoor 8.4
town 8.3
human 8.2
life 8.2
alone 8.2
one 8.2
exercise 8.2
style 8.2
business 7.9
boy 7.8
jump 7.7
elegance 7.6
dark 7.5
performer 7.4
danger 7.3
sport 7.2
brick 7.2

Google

Microsoft

clothing 97.1
person 94.8
outdoor 94.1
footwear 85.5
smile 83.1
child 82.8
toddler 82.4
baby 69.1
human face 68.4
boy 56.5

Face analysis

Amazon

Google

AWS Rekognition

Age 26-43
Gender Female, 53.5%
Calm 46.4%
Angry 45.5%
Happy 49.9%
Sad 45.7%
Surprised 45.8%
Confused 45.5%
Disgusted 46.2%

AWS Rekognition

Age 35-52
Gender Female, 54.6%
Calm 45.3%
Happy 45%
Confused 45.2%
Sad 54.3%
Angry 45.1%
Surprised 45.1%
Disgusted 45%

AWS Rekognition

Age 4-9
Gender Female, 51.4%
Angry 45.2%
Surprised 45%
Calm 45.1%
Happy 45%
Confused 45.3%
Disgusted 45.1%
Sad 54.3%

AWS Rekognition

Age 9-14
Gender Female, 55%
Calm 45.3%
Confused 45%
Happy 54.6%
Surprised 45%
Angry 45%
Disgusted 45%
Sad 45%

AWS Rekognition

Age 11-18
Gender Female, 54.7%
Surprised 45%
Happy 54.6%
Confused 45%
Calm 45.1%
Disgusted 45.1%
Angry 45%
Sad 45.1%

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 Possible
Headwear Very unlikely
Blurred Very unlikely

Google Vision

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

Google Vision

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

Google Vision

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

Feature analysis

Amazon

Person 99.6%

Captions

Microsoft

a person standing in front of a building 87.9%
a person standing in front of a building 85.3%
a man and a woman standing in front of a building 76%

Text analysis

Google

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