Machine Generated Data
Tags
Amazon
created on 2019-08-10
Person | 99.4 | |
| ||
Human | 99.4 | |
| ||
Art | 90.2 | |
| ||
Home Decor | 81.3 | |
| ||
Painting | 80.4 | |
| ||
Face | 61.4 | |
| ||
Photography | 61.4 | |
| ||
Photo | 61.4 | |
| ||
Portrait | 61.4 | |
| ||
Text | 58.7 | |
|
Clarifai
created on 2019-08-10
people | 99.5 | |
| ||
art | 98.3 | |
| ||
one | 97.9 | |
| ||
no person | 95.8 | |
| ||
painting | 95.4 | |
| ||
portrait | 95.2 | |
| ||
95.1 | ||
| ||
leader | 94.6 | |
| ||
administration | 93.6 | |
| ||
adult | 93.3 | |
| ||
picture frame | 90.3 | |
| ||
illustration | 87.8 | |
| ||
engraving | 85.9 | |
| ||
antique | 85.1 | |
| ||
man | 84.3 | |
| ||
architecture | 84.3 | |
| ||
retro | 83.8 | |
| ||
Renaissance | 82.4 | |
| ||
decoration | 81.5 | |
| ||
travel | 81.3 | |
|
Imagga
created on 2019-08-10
Google
created on 2019-08-10
Painting | 87.1 | |
| ||
Picture frame | 77.1 | |
| ||
Stock photography | 76.6 | |
| ||
Art | 74 | |
| ||
Illustration | 71 | |
| ||
History | 62.6 | |
| ||
Antique | 59.8 | |
| ||
Portrait | 56.7 | |
| ||
Visual arts | 55 | |
| ||
Collection | 50 | |
|
Color Analysis
Face analysis
Amazon
Microsoft
![](https://ids.lib.harvard.edu/ids/iiif/20463759/347,251,105,169/full/0/native.jpg)
AWS Rekognition
Age | 26-40 |
Gender | Male, 96.6% |
Surprised | 1.5% |
Sad | 4.8% |
Confused | 2.6% |
Angry | 3.3% |
Fear | 1% |
Happy | 0.8% |
Calm | 56.4% |
Disgusted | 29.6% |
![](https://ids.lib.harvard.edu/ids/iiif/20463759/344,278,130,130/full/0/native.jpg)
Microsoft Cognitive Services
Age | 39 |
Gender | Male |
![](https://ids.lib.harvard.edu/ids/iiif/20463759/276,203,201,233/full/0/native.jpg)
Google Vision
Surprise | Very unlikely |
Anger | Very unlikely |
Sorrow | Unlikely |
Joy | Very unlikely |
Headwear | Very unlikely |
Blurred | Very unlikely |
Feature analysis
Categories
Imagga
paintings art | 99.7% | |
|
Captions
Microsoft
created on 2019-08-10
a vintage photo of a person | 79.3% | |
| ||
a vintage photo of a person | 71.6% | |
| ||
an old photo of a person | 71.5% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20463759/108,766,38,9/full/0/native.jpg)
Nantn
![](https://ids.lib.harvard.edu/ids/iiif/20463759/213,861,83,30/full/0/native.jpg)
AFFECAT
![](https://ids.lib.harvard.edu/ids/iiif/20463759/213,859,329,31/full/0/native.jpg)
AFFECAT olympoi
![](https://ids.lib.harvard.edu/ids/iiif/20463759/578,766,41,9/full/0/native.jpg)
Feulerant
![](https://ids.lib.harvard.edu/ids/iiif/20463759/206,913,57,22/full/0/native.jpg)
amoy
![](https://ids.lib.harvard.edu/ids/iiif/20463759/108,765,543,11/full/0/native.jpg)
Nantn drtinere Feulerant 1617
![](https://ids.lib.harvard.edu/ids/iiif/20463759/457,865,83,29/full/0/native.jpg)
olympoi
![](https://ids.lib.harvard.edu/ids/iiif/20463759/522,964,20,11/full/0/native.jpg)
UR
![](https://ids.lib.harvard.edu/ids/iiif/20463759/623,765,28,11/full/0/native.jpg)
1617
![](https://ids.lib.harvard.edu/ids/iiif/20463759/506,767,44,8/full/0/native.jpg)
drtinere
![](https://ids.lib.harvard.edu/ids/iiif/20463759/108,766,550,181/full/0/native.jpg)
Vinte
deineabat
Scalpebnt6
ad Wum
AECTAT
AMOY
![](https://ids.lib.harvard.edu/ids/iiif/20463759/108,766,39,15/full/0/native.jpg)
Vinte
![](https://ids.lib.harvard.edu/ids/iiif/20463759/506,767,50,13/full/0/native.jpg)
deineabat
![](https://ids.lib.harvard.edu/ids/iiif/20463759/564,767,94,15/full/0/native.jpg)
Scalpebnt6
![](https://ids.lib.harvard.edu/ids/iiif/20463759/158,769,19,11/full/0/native.jpg)
ad
![](https://ids.lib.harvard.edu/ids/iiif/20463759/178,769,31,12/full/0/native.jpg)
Wum
![](https://ids.lib.harvard.edu/ids/iiif/20463759/205,857,102,48/full/0/native.jpg)
AECTAT
![](https://ids.lib.harvard.edu/ids/iiif/20463759/198,910,85,37/full/0/native.jpg)
AMOY