AI Data for Education, Industrial: United States. New York. New York City. Public Schools, Adaptation to Special City Needs: New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Evening Play Centre.: Public School No.42 Manhattan.: Games and Reading Room. | AI Explorer

Machine Generated Data

Color Analysis

Face analysis

Amazon

AWS Rekognition

Age	26-43
Gender	Male, 50.9%
Calm	46.2%
Sad	48.5%
Confused	45.5%
Happy	47.9%
Angry	45.7%
Surprised	45.4%
Disgusted	45.7%

AWS Rekognition

Age	26-43
Gender	Female, 53.9%
Happy	45.2%
Angry	46.1%
Calm	48.4%
Disgusted	46.8%
Sad	47.9%
Confused	45.3%
Surprised	45.3%

AWS Rekognition

Age	23-38
Gender	Female, 50%
Confused	49.5%
Disgusted	49.5%
Sad	50.2%
Surprised	49.5%
Calm	49.6%
Happy	49.6%
Angry	49.6%

AWS Rekognition

Age	45-66
Gender	Female, 50%
Disgusted	49.8%
Sad	49.9%
Calm	49.5%
Surprised	49.5%
Confused	49.6%
Angry	49.6%
Happy	49.5%

AWS Rekognition

Age	16-27
Gender	Female, 54.8%
Disgusted	45.2%
Sad	53.1%
Calm	45.9%
Surprised	45.2%
Confused	45.3%
Angry	45.2%
Happy	45.1%

AWS Rekognition

Age	26-43
Gender	Female, 50.4%
Sad	50%
Disgusted	49.5%
Calm	49.8%
Surprised	49.5%
Happy	49.6%
Angry	49.5%
Confused	49.5%

AWS Rekognition

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

AWS Rekognition

Age	26-43
Gender	Female, 52.9%
Calm	45.1%
Disgusted	45%
Surprised	45%
Sad	54.7%
Happy	45%
Angry	45.1%
Confused	45%

AWS Rekognition

Age	19-36
Gender	Female, 52.8%
Calm	45.4%
Confused	45.2%
Sad	53.7%
Angry	45.3%
Happy	45.1%
Disgusted	45.2%
Surprised	45.1%

AWS Rekognition

Age	26-43
Gender	Female, 50.5%
Disgusted	49.5%
Sad	50%
Calm	49.7%
Happy	49.5%
Confused	49.6%
Surprised	49.6%
Angry	49.5%

AWS Rekognition

Age	26-43
Gender	Female, 52.1%
Disgusted	45.1%
Sad	46.9%
Surprised	45.1%
Calm	45%
Angry	52.8%
Happy	45%
Confused	45%

AWS Rekognition

Age	20-38
Gender	Female, 50.2%
Calm	49.7%
Confused	49.6%
Sad	49.8%
Angry	49.6%
Happy	49.7%
Disgusted	49.6%
Surprised	49.6%

AWS Rekognition

Age	17-27
Gender	Female, 50.2%
Calm	49.6%
Confused	49.5%
Sad	50.2%
Angry	49.6%
Happy	49.5%
Disgusted	49.5%
Surprised	49.5%

AWS Rekognition

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

AWS Rekognition

Age	26-43
Gender	Male, 50.2%
Angry	45.6%
Surprised	45.3%
Disgusted	45.3%
Happy	45.5%
Sad	52.5%
Confused	45.4%
Calm	45.4%

AWS Rekognition

Age	20-38
Gender	Female, 50.1%
Disgusted	49.6%
Happy	49.5%
Confused	49.6%
Sad	50%
Calm	49.6%
Angry	49.6%
Surprised	49.6%

AWS Rekognition

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

AWS Rekognition

Age	20-38
Gender	Female, 50.2%
Happy	49.6%
Angry	49.7%
Confused	49.6%
Disgusted	49.7%
Sad	49.6%
Calm	49.8%
Surprised	49.6%

AWS Rekognition

Age	12-22
Gender	Male, 50.1%
Happy	49.6%
Angry	49.6%
Calm	49.6%
Disgusted	49.8%
Sad	49.7%
Confused	49.7%
Surprised	49.5%

AWS Rekognition

Age	12-22
Gender	Female, 50.2%
Angry	49.6%
Calm	49.7%
Confused	49.6%
Disgusted	49.5%
Happy	49.5%
Sad	50%
Surprised	49.5%

AWS Rekognition

Age	17-27
Gender	Female, 50.3%
Sad	50%
Confused	49.5%
Angry	49.5%
Surprised	49.5%
Disgusted	49.6%
Happy	49.6%
Calm	49.8%

AWS Rekognition

Age	20-38
Gender	Female, 50.1%
Confused	49.5%
Sad	50.3%
Disgusted	49.5%
Surprised	49.5%
Angry	49.6%
Happy	49.5%
Calm	49.5%

AWS Rekognition

Age	17-27
Gender	Male, 50.2%
Calm	49.6%
Sad	50.3%
Confused	49.5%
Happy	49.6%
Angry	49.5%
Surprised	49.5%
Disgusted	49.5%

AWS Rekognition

Age	26-43
Gender	Female, 50.5%
Disgusted	49.5%
Happy	49.6%
Sad	50.3%
Angry	49.5%
Calm	49.5%
Confused	49.5%
Surprised	49.5%

AWS Rekognition

Age	26-43
Gender	Female, 50.4%
Surprised	49.6%
Calm	49.6%
Sad	49.7%
Happy	49.9%
Angry	49.6%
Confused	49.6%
Disgusted	49.6%

AWS Rekognition

Age	35-52
Gender	Female, 50.5%
Disgusted	49.5%
Sad	49.8%
Happy	49.6%
Confused	49.6%
Surprised	49.6%
Calm	49.8%
Angry	49.6%

AWS Rekognition

Age	26-43
Gender	Female, 50.5%
Calm	49.9%
Happy	49.6%
Surprised	49.5%
Sad	49.7%
Angry	49.6%
Disgusted	49.7%
Confused	49.6%

AWS Rekognition

Age	17-27
Gender	Female, 50.4%
Confused	49.6%
Calm	49.6%
Disgusted	49.7%
Happy	49.7%
Angry	49.7%
Sad	49.8%
Surprised	49.6%

AWS Rekognition

Age	30-47
Gender	Female, 50.5%
Angry	49.5%
Sad	49.9%
Disgusted	49.5%
Calm	49.8%
Happy	49.6%
Surprised	49.5%
Confused	49.5%

AWS Rekognition

Age	23-38
Gender	Male, 50.4%
Surprised	49.6%
Happy	49.6%
Disgusted	49.5%
Calm	49.7%
Confused	49.7%
Sad	49.7%
Angry	49.8%

AWS Rekognition

Age	35-53
Gender	Female, 50.5%
Angry	49.6%
Happy	49.7%
Disgusted	49.6%
Surprised	49.6%
Calm	49.7%
Confused	49.7%
Sad	49.7%

AWS Rekognition

Age	23-38
Gender	Female, 50.1%
Confused	49.5%
Sad	50.2%
Angry	49.6%
Disgusted	49.6%
Calm	49.5%
Surprised	49.5%
Happy	49.5%

AWS Rekognition

Age	15-25
Gender	Female, 50.2%
Calm	49.7%
Confused	49.6%
Happy	49.6%
Sad	49.8%
Surprised	49.6%
Disgusted	49.6%
Angry	49.7%

AWS Rekognition

Age	20-38
Gender	Female, 50.1%
Confused	49.5%
Disgusted	49.5%
Sad	49.6%
Angry	49.6%
Happy	49.5%
Surprised	49.5%
Calm	50.3%

AWS Rekognition

Age	23-38
Gender	Female, 50.5%
Disgusted	49.6%
Surprised	49.6%
Confused	49.6%
Sad	49.7%
Calm	49.8%
Angry	49.5%
Happy	49.7%

Feature analysis

Amazon

Person	98.6%

Captions

Microsoft
created on 2019-06-04

a vintage photo of a group of people in a room	94.3%

a vintage photo of a group of people posing for the camera	82.8%

a vintage photo of a group of people standing in a room	82.7%

Text analysis

Amazon

P.S.42.

CENTRE

P.S.42. EYENINC CENTRE

1401.

EYENINC

FeaRu

FeaRu 216T 1401.

C 5S

216T

Google

P.S.42 EYENINC PLAY CENTRE-CAMEand READING ROOM FEBRUARY 21 1001.

P.S.42

PLAY

CENTRE-CAMEand

ROOM

FEBRUARY

1001.

EYENINC

READING

Title Education, Industrial: United States. New York. New York City. Public Schools, Adaptation to Special City Needs: New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Evening Play Centre.: Public School No.42 Manhattan.: Games and Reading Room.	Date 1901
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, 3.2002.1476.1

School	99.2

Classroom	99.1

Room	99.1

Indoors	99.1

Human	98.6

Person	98.6

Person	98.2

Person	98.2

Person	98.2

Person	96.5

Person	96.2

Person	95.3

Person	94.3

Workshop	94.3

Crowd	93.4

Audience	93.4

Person	87.1

Person	86.2

Person	82

Person	74.7

Person	67.6

Speech	65.7

People	64.2

Person	62.4

Clinic	60

Person	50.4

Person	50.3

Person	48.7

people	100

many	99.9

group	99.6

group together	98.2

administration	96.6

adult	96.5

man	94.1

leader	90.2

education	89.8

military	89.4

woman	88.6

chair	86.7

school	86.3

teacher	83.5

sit	81.9

several	81.2

child	80.8

crowd	80.3

furniture	79.7

elementary school	77

classroom	100

room	100

interior	61

chair	57.8

table	57.3

restaurant	46.1

furniture	45.3

cafeteria	37.1

floor	32.5

house	31.8

chairs	28.4

wood	27.5

decor	27.4

modern	26.6

home	26.3

design	24.8

dining	23.8

building	23.7

empty	23.2

luxury	23.1

architecture	22.6

seat	22.2

window	22

indoor	20.1

teacher	19.7

indoors	18.5

living	18

inside	17.5

decoration	17.4

sofa	17.2

comfortable	17.2

lamp	17.2

tables	16.7

structure	16.6

dinner	16

contemporary	16

style	15.6

hall	15.5

residential	15.3

glass	14.8

light	14

hotel	13.4

food	12.7

carpet	12.6

elegance	12.6

decorate	12.4

estate	12.3

wall	12

stool	11.9

couch	11.6

office	11.5

apartment	11.5

educator	11.5

plant	11.2

relax	10.9

eat	10.9

nobody	10.9

space	10.9

kitchen	10.8

residence	10.7

real	10.4

professional	9.9

rug	9.9

wooden	9.7

lighting	9.7

urban	9.6

lunch	9.6

desk	9.5

elegant	9.4

bar	9.2

drink	9.2

people	8.9

counter	8.9

upscale	8.9

armchair	8.8

marble	8.7

work	8.6

day	8.6

person	8.6

business	8.5

place	8.4

relaxation	8.4

meal	8.1

family	8

stove	7.9

fireplace	7.9

hardwood	7.9

education	7.8

scene	7.8

party	7.7

class	7.7

life	7.7

expensive	7.7

school	7.3

Photograph	95

Room	83.1

Snapshot	82

Table	67.2

Event	62.7

Classroom	59.7

Sitting	54.1

Vintage clothing	53.5

Furniture	53.3

Art	50.2

old	95.6

person	86.5

people	74.9

group	72

furniture	53.5

table	51.7

posing	41.1

vintage	33.6

several	12.8

Human Generated Data

Title

Education, Industrial: United States. New York. New York City. Public Schools, Adaptation to Special City Needs: New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Evening Play Centre.: Public School No.42 Manhattan.: Games and Reading Room.

Date

1901

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, 3.2002.1476.1

Human Generated Data

Title

Education, Industrial: United States. New York. New York City. Public Schools, Adaptation to Special City Needs: New York City Public Schools. Examples of the Adaptation of Education to Special City Needs: Evening Play Centre.: Public School No.42 Manhattan.: Games and Reading Room.

People

Artist: Unidentified Artist,

Date

1901

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.1476.1

Machine Generated Data

Tags

Amazoncreated on 2019-06-04

Clarifaicreated on 2019-06-04

Imaggacreated on 2019-06-04

Googlecreated on 2019-06-04

Microsoftcreated on 2019-06-04

Color Analysis

Face analysis

Amazon

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

Feature analysis

Amazon

Categories

Imagga

Captions

Microsoftcreated on 2019-06-04

Text analysis

Amazon

Google

Amazon
created on 2019-06-04

Clarifai
created on 2019-06-04

Imagga
created on 2019-06-04

Google
created on 2019-06-04

Microsoft
created on 2019-06-04

Microsoft
created on 2019-06-04