Machine Generated Data
Tags
Amazon
created on 2023-10-25
Clarifai
created on 2018-10-06
Imagga
created on 2018-10-06
film | 67.6 | |
| ||
photographic paper | 49.3 | |
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negative | 44.7 | |
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x-ray film | 39.7 | |
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photographic equipment | 32.9 | |
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bride | 24.1 | |
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wedding | 20.2 | |
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dress | 19 | |
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human | 16.5 | |
| ||
hand | 12.9 | |
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pretty | 12.6 | |
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veil | 11.8 | |
| ||
elegance | 11.7 | |
| ||
portrait | 11.6 | |
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medical | 11.5 | |
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love | 11 | |
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x ray | 10.8 | |
| ||
marriage | 10.4 | |
| ||
ray | 10.4 | |
| ||
celebration | 10.4 | |
| ||
adult | 10.3 | |
| ||
hair | 10.3 | |
| ||
person | 10.2 | |
| ||
flower | 10 | |
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care | 9.9 | |
| ||
attractive | 9.8 | |
| ||
health | 9.7 | |
| ||
medicine | 9.7 | |
| ||
style | 9.6 | |
| ||
people | 9.5 | |
| ||
glass | 9.3 | |
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fashion | 9 | |
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design | 9 | |
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romantic | 8.9 | |
| ||
gown | 8.8 | |
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bridal | 8.7 | |
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married | 8.6 | |
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holiday | 8.6 | |
| ||
bouquet | 8.5 | |
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clean | 8.3 | |
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equipment | 8.2 | |
| ||
lady | 8.1 | |
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body | 8 | |
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x | 7.9 | |
| ||
face | 7.8 | |
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spine | 7.8 | |
| ||
model | 7.8 | |
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exam | 7.7 | |
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floral | 7.7 | |
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hospital | 7.5 | |
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doctor | 7.5 | |
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happy | 7.5 | |
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holding | 7.4 | |
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purity | 7.4 | |
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retro | 7.4 | |
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business | 7.3 | |
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color | 7.2 | |
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lifestyle | 7.2 | |
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looking | 7.2 | |
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cute | 7.2 | |
|
Google
created on 2018-10-06
black and white | 94.9 | |
| ||
monochrome photography | 91.6 | |
| ||
photography | 82.5 | |
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monochrome | 72.3 | |
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water | 57.7 | |
|
Microsoft
created on 2018-10-06
indoor | 92.3 | |
|
Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/44806689/37,17,125,54/full/0/native.jpg)
Person | 78.5% | |
|
Categories
Imagga
paintings art | 99.9% | |
|
Captions
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/44806689/181,122,8,12/full/0/native.jpg)
16
![](https://ids.lib.harvard.edu/ids/iiif/44806689/184,518,8,12/full/0/native.jpg)
14
![](https://ids.lib.harvard.edu/ids/iiif/44806689/187,923,8,12/full/0/native.jpg)
12
![](https://ids.lib.harvard.edu/ids/iiif/44806689/183,326,8,12/full/0/native.jpg)
15
![](https://ids.lib.harvard.edu/ids/iiif/44806689/15,981,8,31/full/0/native.jpg)
KODA
![](https://ids.lib.harvard.edu/ids/iiif/44806689/12,533,8,47/full/0/native.jpg)
SAFETY
![](https://ids.lib.harvard.edu/ids/iiif/44806689/7,186,8,33/full/0/native.jpg)
FILM
![](https://ids.lib.harvard.edu/ids/iiif/44806689/185,727,8,12/full/0/native.jpg)
13
![](https://ids.lib.harvard.edu/ids/iiif/44806689/186,819,8,21/full/0/native.jpg)
12A
![](https://ids.lib.harvard.edu/ids/iiif/44806689/8,93,8,21/full/0/native.jpg)
TRI
![](https://ids.lib.harvard.edu/ids/iiif/44806689/182,220,8,21/full/0/native.jpg)
15A
![](https://ids.lib.harvard.edu/ids/iiif/44806689/12,477,8,38/full/0/native.jpg)
KODAK
![](https://ids.lib.harvard.edu/ids/iiif/44806689/7,151,8,67/full/0/native.jpg)
PAN FILM
![](https://ids.lib.harvard.edu/ids/iiif/44806689/184,615,8,23/full/0/native.jpg)
13A
![](https://ids.lib.harvard.edu/ids/iiif/44806689/180,16,8,21/full/0/native.jpg)
16A
![](https://ids.lib.harvard.edu/ids/iiif/44806689/7,43,9,90/full/0/native.jpg)
KODAK TRI x
![](https://ids.lib.harvard.edu/ids/iiif/44806689/8,151,8,26/full/0/native.jpg)
PAN
![](https://ids.lib.harvard.edu/ids/iiif/44806689/9,127,7,6/full/0/native.jpg)
x
![](https://ids.lib.harvard.edu/ids/iiif/44806689/184,416,8,20/full/0/native.jpg)
14.5
![](https://ids.lib.harvard.edu/ids/iiif/44806689/13,600,8,34/full/0/native.jpg)
TELM
![](https://ids.lib.harvard.edu/ids/iiif/44806689/182,122,15,521/full/0/native.jpg)
13А
14
5
16
![](https://ids.lib.harvard.edu/ids/iiif/44806689/185,617,12,26/full/0/native.jpg)
13А
![](https://ids.lib.harvard.edu/ids/iiif/44806689/184,517,13,17/full/0/native.jpg)
14
![](https://ids.lib.harvard.edu/ids/iiif/44806689/183,327,12,11/full/0/native.jpg)
5
![](https://ids.lib.harvard.edu/ids/iiif/44806689/182,122,12,17/full/0/native.jpg)
16