AI Data for Defectives, Feeble-minded: United States. Massachusetts. Waverly. School for Feeble-minded: Massachusetts School for the Feeble-Minded.: Brush Making. | AI Explorer

Unable to open [object Object]: HTTP 0 attempting to load TileSource

Human Generated Data

Title Defectives, Feeble-minded: United States. Massachusetts. Waverly. School for Feeble-minded: Massachusetts School for the Feeble-Minded.: Brush Making.	Date c. 1903
People Artist: William A. Webster, American active mid-19th-early 20th century	Classification Photographs
Credit Line Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.313.3

Human Generated Data

Title

Defectives, Feeble-minded: United States. Massachusetts. Waverly. School for Feeble-minded: Massachusetts School for the Feeble-Minded.: Brush Making.

People

Artist: William A. Webster, American active mid-19th-early 20th century

Date

c. 1903

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.313.3

Machine Generated Data

Color Analysis

Face analysis

Amazon

Google

AWS Rekognition

Age	29-45
Gender	Male, 54.8%
Sad	52.4%
Disgusted	45.2%
Surprised	45.3%
Happy	45.2%
Confused	45.9%
Angry	45.5%
Calm	45.6%

Feature analysis

Amazon

Person

Person	99.5%

Captions

Microsoft
created on 2019-06-05

a group of people standing in a kitchen	93.2%

a group of people standing in a kitchen preparing food	90.3%

a group of people standing around a counter in a kitchen	90.2%

Text analysis

Google

TANWNERAS

Person	99.5

Human	99.5

Person	99.3

Person	99.1

Person	97.8

Person	97.4

Restaurant	97.4

Person	96.1

Person	95.6

Person	95.2

Person	92.6

Person	90.7

Cafeteria	90.4

Person	84.9

Building	80

Pub	79.4

Apparel	70.6

Clothing	70.6

Meal	70.2

Food	70.2

Shelf	65.9

Cafe	64.3

Person	61.8

Person	61

Bar Counter	60.7

Person	60.5

Worker	59.6

Factory	56.6

people	99.9

many	98.8

group together	98.6

group	98.5

furniture	98.5

adult	98.4

administration	97.8

room	96.9

man	96

woman	90.2

leader	88.5

education	87.9

desk	87.2

military	86.9

home	86.8

several	86.3

war	85.5

chair	85.3

wear	83.2

child	82.6

shop	51.6

mercantile establishment	36.6

place of business	24.2

bakery	20.3

interior	20.3

architecture	19.5

old	19.5

building	18.1

house	17.5

table	17.3

room	17.1

chair	14.9

ancient	14.7

butcher shop	14.5

restaurant	14.3

city	14.1

history	13.4

sculpture	13.4

counter	13.2

home	12.8

people	12.3

barbershop	12.2

man	12.1

establishment	12

historic	11.9

window	11.8

wood	11.7

life	11.3

travel	11.3

art	10.7

indoors	10.5

wall	10.3

tourism	9.9

religion	9.9

wooden	9.7

urban	9.6

statue	9.5

sitting	9.4

monument	9.3

vintage	9.3

food	9.3

kitchen	8.9

decor	8.8

light	8.7

dining	8.6

glass	8.6

culture	8.5

dinner	8.5

stone	8.4

shoe shop	8.4

inside	8.3

structure	8.3

landmark	8.1

furniture	8

decoration	8

classroom	7.8

modern	7.7

customer	7.6

temple	7.6

store	7.6

famous	7.4

floor	7.4

tradition	7.4

retro	7.4

design	7.3

indoor	7.3

marble	7.2

antique	7.2

holiday	7.2

person	7.2

family	7.1

love	7.1

Building	69.2

Room	65.7

Black-and-white	56.4

History	54.1

Factory	52.3

table	98.8

person	96.6

clothing	94.1

kitchen	92.3

furniture	89.5

man	74.4

counter	58.5

preparing	51.5

cooking	47.3

Age	26-43
Gender	Male, 54.7%
Sad	46%
Disgusted	45.1%
Surprised	45.1%
Angry	45.2%
Happy	45.1%
Calm	53.4%
Confused	45.2%

Age	26-43
Gender	Female, 52%
Disgusted	45.1%
Confused	45.1%
Surprised	45.1%
Calm	46.3%
Happy	45.1%
Angry	45.3%
Sad	53%

Age	35-52
Gender	Male, 53.1%
Disgusted	45.3%
Angry	45.2%
Sad	54%
Surprised	45.1%
Happy	45.1%
Calm	45.2%
Confused	45.2%

Age	26-43
Gender	Female, 50.1%
Sad	48.5%
Happy	45.6%
Surprised	45.5%
Disgusted	45.5%
Angry	48.3%
Calm	46.2%
Confused	45.5%

Age	35-52
Gender	Male, 51.6%
Angry	45.8%
Disgusted	45.2%
Sad	53.7%
Confused	45.2%
Happy	45%
Surprised	45.1%
Calm	45%

Age	16-27
Gender	Male, 51.5%
Angry	49.8%
Surprised	45.4%
Disgusted	45.8%
Happy	45%
Calm	45.6%
Sad	47.6%
Confused	45.7%

Age	11-18
Gender	Male, 50.2%
Surprised	45.5%
Sad	45.5%
Happy	45.6%
Disgusted	46.2%
Confused	45.7%
Calm	50.3%
Angry	46.1%

Age	26-43
Gender	Male, 50.1%
Angry	45.3%
Calm	52%
Sad	46.6%
Confused	45.3%
Disgusted	45.3%
Happy	45.3%
Surprised	45.3%

Age	26-43
Gender	Female, 50.2%
Disgusted	45.1%
Calm	45.1%
Surprised	45.1%
Sad	54.4%
Confused	45.1%
Angry	45.2%
Happy	45.1%

Age	27-44
Gender	Female, 54.5%
Sad	47.6%
Calm	48.8%
Disgusted	46.2%
Happy	45.4%
Confused	45.5%
Angry	45.9%
Surprised	45.6%

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

Person	99.3%

Person	99.1%

Person	97.8%

Age	57-77
Gender	Male, 53.6%
Angry	45.5%
Surprised	45.1%
Disgusted	49.1%
Happy	45.2%
Calm	45.1%
Sad	49.8%
Confused	45.2%

Human Generated Data

Title

Defectives, Feeble-minded: United States. Massachusetts. Waverly. School for Feeble-minded: Massachusetts School for the Feeble-Minded.: Brush Making.

Date

c. 1903

People

Artist: William A. Webster, American active mid-19th-early 20th century

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.313.3

Human Generated Data

Title

Defectives, Feeble-minded: United States. Massachusetts. Waverly. School for Feeble-minded: Massachusetts School for the Feeble-Minded.: Brush Making.

People

Artist: William A. Webster, American active mid-19th-early 20th century

Date

c. 1903

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.313.3

Machine Generated Data

Tags

Amazoncreated on 2019-06-05

Clarifaicreated on 2019-06-05

Imaggacreated on 2019-06-05

Googlecreated on 2019-06-05

Microsoftcreated on 2019-06-05

Color Analysis

Face analysis

Amazon

Google

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

Google Vision

Feature analysis

Amazon

Categories

Imagga

Captions

Microsoftcreated on 2019-06-05

Text analysis

Google

Amazon
created on 2019-06-05

Clarifai
created on 2019-06-05

Imagga
created on 2019-06-05

Google
created on 2019-06-05

Microsoft
created on 2019-06-05

Microsoft
created on 2019-06-05