Machine Generated Data
Tags
Amazon
created on 2022-01-15
Clarifai
created on 2023-10-26
Imagga
created on 2022-01-15
restaurant | 50.9 | |
| ||
building | 27.2 | |
| ||
bottle | 26.4 | |
| ||
interior | 21.2 | |
| ||
glass | 20.6 | |
| ||
structure | 19.8 | |
| ||
indoors | 18.4 | |
| ||
drink | 18.4 | |
| ||
alcohol | 18.3 | |
| ||
counter | 15.4 | |
| ||
shop | 15.3 | |
| ||
table | 14.9 | |
| ||
beverage | 14 | |
| ||
wine | 13.9 | |
| ||
man | 12.8 | |
| ||
business | 12.7 | |
| ||
furniture | 11.6 | |
| ||
kitchen | 10.9 | |
| ||
vintage | 10.5 | |
| ||
technology | 10.4 | |
| ||
party | 10.3 | |
| ||
work | 10.2 | |
| ||
blackboard | 10 | |
| ||
indoor | 10 | |
| ||
vessel | 9.9 | |
| ||
refrigerator | 9.8 | |
| ||
urban | 9.6 | |
| ||
home | 9.6 | |
| ||
light | 9.4 | |
| ||
home appliance | 9.3 | |
| ||
chair | 9.3 | |
| ||
bar | 9.2 | |
| ||
inside | 9.2 | |
| ||
house | 9.2 | |
| ||
people | 8.9 | |
| ||
bartender | 8.8 | |
| ||
equipment | 8.8 | |
| ||
container | 8.6 | |
| ||
empty | 8.6 | |
| ||
luxury | 8.6 | |
| ||
modern | 8.4 | |
| ||
city | 8.3 | |
| ||
digital | 8.1 | |
| ||
celebration | 8 | |
| ||
food | 7.9 | |
| ||
lifestyle | 7.9 | |
| ||
male | 7.8 | |
| ||
color | 7.8 | |
| ||
3d | 7.7 | |
| ||
person | 7.7 | |
| ||
industry | 7.7 | |
| ||
relax | 7.6 | |
| ||
store | 7.6 | |
| ||
relaxation | 7.5 | |
| ||
water | 7.3 | |
| ||
design | 7.3 | |
| ||
liquid | 7.1 | |
|
Google
created on 2022-01-15
Bottle | 86.4 | |
| ||
Black-and-white | 85.7 | |
| ||
Barware | 81.9 | |
| ||
Glass bottle | 78.2 | |
| ||
Musical instrument | 76.4 | |
| ||
Monochrome photography | 75.9 | |
| ||
Monochrome | 74.7 | |
| ||
Display case | 74.5 | |
| ||
Room | 73 | |
| ||
Building | 70.8 | |
| ||
Drink | 69.3 | |
| ||
Font | 68.7 | |
| ||
Machine | 66.6 | |
| ||
Retail | 64.8 | |
| ||
Stock photography | 63.8 | |
| ||
History | 63 | |
| ||
Distilled beverage | 62.5 | |
| ||
Art | 62.1 | |
| ||
Shelf | 57.1 | |
| ||
Liqueur | 56.6 | |
|
Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18815046/306,201,17,21/full/0/native.jpg)
AWS Rekognition
Age | 25-35 |
Gender | Female, 55.7% |
Calm | 91.2% |
Happy | 3.2% |
Sad | 3.1% |
Disgusted | 0.8% |
Fear | 0.6% |
Confused | 0.5% |
Surprised | 0.5% |
Angry | 0.2% |
![](https://ids.lib.harvard.edu/ids/iiif/18815046/296,186,32,37/full/0/native.jpg)
Google Vision
Surprise | Very unlikely |
Anger | Very unlikely |
Sorrow | Very unlikely |
Joy | Very unlikely |
Headwear | Very unlikely |
Blurred | Very unlikely |
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18815046/290,189,49,64/full/0/native.jpg)
Person | 94% | |
|
Categories
Imagga
streetview architecture | 65.5% | |
| ||
interior objects | 20% | |
| ||
paintings art | 13% | |
| ||
text visuals | 1.1% | |
|
Captions
Microsoft
created on 2022-01-15
graphical user interface | 22.3% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/18815046/285,82,114,58/full/0/native.jpg)
MANCHEE
![](https://ids.lib.harvard.edu/ids/iiif/18815046/214,72,190,84/full/0/native.jpg)
_VIN MANCHEE
![](https://ids.lib.harvard.edu/ids/iiif/18815046/275,156,21,18/full/0/native.jpg)
Los
![](https://ids.lib.harvard.edu/ids/iiif/18815046/219,91,63,53/full/0/native.jpg)
_VIN
![](https://ids.lib.harvard.edu/ids/iiif/18815046/587,586,22,9/full/0/native.jpg)
DRY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/330,147,25,5/full/0/native.jpg)
AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/526,464,16,13/full/0/native.jpg)
DO
![](https://ids.lib.harvard.edu/ids/iiif/18815046/355,530,41,15/full/0/native.jpg)
PRIVATE
![](https://ids.lib.harvard.edu/ids/iiif/18815046/648,507,29,17/full/0/native.jpg)
NEY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/287,147,12,6/full/0/native.jpg)
SAY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/774,360,37,20/full/0/native.jpg)
WA
![](https://ids.lib.harvard.edu/ids/iiif/18815046/294,128,29,15/full/0/native.jpg)
With
![](https://ids.lib.harvard.edu/ids/iiif/18815046/586,594,10,6/full/0/native.jpg)
N
![](https://ids.lib.harvard.edu/ids/iiif/18815046/542,506,21,13/full/0/native.jpg)
RICO
![](https://ids.lib.harvard.edu/ids/iiif/18815046/300,146,28,7/full/0/native.jpg)
INCOURTS
![](https://ids.lib.harvard.edu/ids/iiif/18815046/356,504,26,16/full/0/native.jpg)
Ba
![](https://ids.lib.harvard.edu/ids/iiif/18815046/272,146,83,7/full/0/native.jpg)
RECY SAY INCOURTS AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/275,151,90,31/full/0/native.jpg)
Los AnydesCalaf
![](https://ids.lib.harvard.edu/ids/iiif/18815046/356,475,14,6/full/0/native.jpg)
DE
![](https://ids.lib.harvard.edu/ids/iiif/18815046/360,566,6,4/full/0/native.jpg)
de
![](https://ids.lib.harvard.edu/ids/iiif/18815046/638,8,135,29/full/0/native.jpg)
HOAS
![](https://ids.lib.harvard.edu/ids/iiif/18815046/516,502,49,24/full/0/native.jpg)
МИНИО RUM RICO
![](https://ids.lib.harvard.edu/ids/iiif/18815046/527,514,22,10/full/0/native.jpg)
RUM
![](https://ids.lib.harvard.edu/ids/iiif/18815046/10,305,32,142/full/0/native.jpg)
htolt
![](https://ids.lib.harvard.edu/ids/iiif/18815046/566,577,20,15/full/0/native.jpg)
OOM
![](https://ids.lib.harvard.edu/ids/iiif/18815046/564,573,41,24/full/0/native.jpg)
OOM MITTLED
![](https://ids.lib.harvard.edu/ids/iiif/18815046/572,574,32,15/full/0/native.jpg)
MITTLED
![](https://ids.lib.harvard.edu/ids/iiif/18815046/991,638,11,71/full/0/native.jpg)
830N3330
![](https://ids.lib.harvard.edu/ids/iiif/18815046/272,147,12,6/full/0/native.jpg)
RECY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/298,151,66,30/full/0/native.jpg)
AnydesCalaf
![](https://ids.lib.harvard.edu/ids/iiif/18815046/517,502,27,18/full/0/native.jpg)
МИНИО
![](https://ids.lib.harvard.edu/ids/iiif/18815046/635,441,31,12/full/0/native.jpg)
YORK
![](https://ids.lib.harvard.edu/ids/iiif/18815046/565,560,41,21/full/0/native.jpg)
ardap
![](https://ids.lib.harvard.edu/ids/iiif/18815046/369,562,26,11/full/0/native.jpg)
for
![](https://ids.lib.harvard.edu/ids/iiif/18815046/320,530,12,7/full/0/native.jpg)
مع
![](https://ids.lib.harvard.edu/ids/iiif/18815046/369,448,22,35/full/0/native.jpg)
want
![](https://ids.lib.harvard.edu/ids/iiif/18815046/583,512,18,8/full/0/native.jpg)
DADE
![](https://ids.lib.harvard.edu/ids/iiif/18815046/231,94,778,619/full/0/native.jpg)
VIN MANCHEE
cors AGENCY
DO
Ba
NEY
PRIVATES
ON DRY
JA2 83an3330
![](https://ids.lib.harvard.edu/ids/iiif/18815046/231,100,60,49/full/0/native.jpg)
VIN
![](https://ids.lib.harvard.edu/ids/iiif/18815046/295,94,106,55/full/0/native.jpg)
MANCHEE
![](https://ids.lib.harvard.edu/ids/iiif/18815046/291,143,42,18/full/0/native.jpg)
cors
![](https://ids.lib.harvard.edu/ids/iiif/18815046/330,143,29,17/full/0/native.jpg)
AGENCY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/520,466,28,17/full/0/native.jpg)
DO
![](https://ids.lib.harvard.edu/ids/iiif/18815046/362,506,26,20/full/0/native.jpg)
Ba
![](https://ids.lib.harvard.edu/ids/iiif/18815046/651,509,28,18/full/0/native.jpg)
NEY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/359,528,52,24/full/0/native.jpg)
PRIVATES
![](https://ids.lib.harvard.edu/ids/iiif/18815046/566,579,23,15/full/0/native.jpg)
ON
![](https://ids.lib.harvard.edu/ids/iiif/18815046/586,582,26,16/full/0/native.jpg)
DRY
![](https://ids.lib.harvard.edu/ids/iiif/18815046/989,587,21,47/full/0/native.jpg)
JA2
![](https://ids.lib.harvard.edu/ids/iiif/18815046/989,636,21,77/full/0/native.jpg)
83an3330