Machine Generated Data
Tags
Amazon
created on 2023-10-25
Clarifai
created on 2019-02-18
no person | 99.1 | |
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health | 96.3 | |
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treatment | 95 | |
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bottle | 94.5 | |
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cure | 93 | |
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glass | 92.7 | |
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medicine | 92.4 | |
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ingredients | 91.1 | |
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isolated | 90.4 | |
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isolate | 90.1 | |
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traditional | 89.5 | |
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food | 88.2 | |
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aromatherapy | 86.6 | |
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healthcare | 86.3 | |
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container | 86.3 | |
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wood | 84.9 | |
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nutrition | 81.7 | |
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relaxation | 80.9 | |
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jar | 80.9 | |
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merchandise | 80 | |
|
Imagga
created on 2019-02-18
bottle | 43.1 | |
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plug | 28.8 | |
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container | 23.3 | |
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food | 23 | |
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glass | 22.6 | |
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brown | 18.4 | |
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liquid | 18.3 | |
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drink | 17.5 | |
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healthy | 16.4 | |
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toiletry | 15.9 | |
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object | 15.4 | |
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health | 15.3 | |
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close | 14.8 | |
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perfume | 14.3 | |
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medicine | 13.2 | |
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pepper | 12.9 | |
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jar | 12.8 | |
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beverage | 12.5 | |
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fresh | 12.4 | |
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vitamin | 12.2 | |
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oil | 12.1 | |
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syrup | 12 | |
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paper | 11.8 | |
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cream | 10.9 | |
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objects | 10.4 | |
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spice | 10.4 | |
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yellow | 9.9 | |
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spa | 9.9 | |
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conserve | 9.6 | |
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aromatherapy | 9.6 | |
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milk | 9.6 | |
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honey | 9.6 | |
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bread | 9.4 | |
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snack | 9.4 | |
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treatment | 9.2 | |
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transparent | 9 | |
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product | 8.9 | |
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diet | 8.9 | |
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bottles | 8.8 | |
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wooden | 8.8 | |
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closeup | 8.8 | |
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coconut | 8.5 | |
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eating | 8.4 | |
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nutrition | 8.4 | |
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pill bottle | 8.4 | |
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cutout | 8.4 | |
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plastic | 8.3 | |
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label | 8.2 | |
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currency | 8.1 | |
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breakfast | 8 | |
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alcohol | 7.9 | |
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lotion | 7.8 | |
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fruit | 7.8 | |
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pharmacy | 7.8 | |
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old | 7.7 | |
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towel | 7.6 | |
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herbal | 7.6 | |
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gourmet | 7.6 | |
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finance | 7.6 | |
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hygiene | 7.6 | |
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page | 7.4 | |
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care | 7.4 | |
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refreshment | 7.3 | |
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group | 7.3 | |
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vintage | 7.2 | |
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ingredient | 7.1 | |
|
Google
created on 2019-02-18
Cork | 68.1 | |
| ||
Beige | 59.9 | |
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Bottle | 55.9 | |
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Label | 53.8 | |
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Packaging and labeling | 50.8 | |
|
Color Analysis
Feature analysis
Categories
Imagga
food drinks | 99.9% | |
|
Captions
Microsoft
created on 2019-02-18
a close up of a bottle | 49.4% | |
| ||
close up of a bottle | 45.5% | |
| ||
a close up of a bottle on a table | 30.8% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20464522/514,767,16,18/full/0/native.jpg)
30
![](https://ids.lib.harvard.edu/ids/iiif/20464522/513,671,16,18/full/0/native.jpg)
20
![](https://ids.lib.harvard.edu/ids/iiif/20464522/514,861,16,19/full/0/native.jpg)
40
![](https://ids.lib.harvard.edu/ids/iiif/20464522/154,607,62,29/full/0/native.jpg)
RAW
![](https://ids.lib.harvard.edu/ids/iiif/20464522/153,600,182,40/full/0/native.jpg)
RAW SIENA
![](https://ids.lib.harvard.edu/ids/iiif/20464522/235,600,100,37/full/0/native.jpg)
SIENA
![](https://ids.lib.harvard.edu/ids/iiif/20464522/513,577,16,18/full/0/native.jpg)
10
![](https://ids.lib.harvard.edu/ids/iiif/20464522/94,677,146,47/full/0/native.jpg)
SPAIN
![](https://ids.lib.harvard.edu/ids/iiif/20464522/92,662,312,70/full/0/native.jpg)
SPAIN 1933
![](https://ids.lib.harvard.edu/ids/iiif/20464522/294,662,109,55/full/0/native.jpg)
1933
![](https://ids.lib.harvard.edu/ids/iiif/20464522/238,603,102,37/full/0/native.jpg)
SIENA
![](https://ids.lib.harvard.edu/ids/iiif/20464522/98,678,63,56/full/0/native.jpg)
PR
![](https://ids.lib.harvard.edu/ids/iiif/20464522/332,669,23,55/full/0/native.jpg)
9
![](https://ids.lib.harvard.edu/ids/iiif/20464522/357,668,23,55/full/0/native.jpg)
3
![](https://ids.lib.harvard.edu/ids/iiif/20464522/98,603,282,131/full/0/native.jpg)
RAW SIENA
PR I N 1 9 3
![](https://ids.lib.harvard.edu/ids/iiif/20464522/156,605,66,36/full/0/native.jpg)
RAW
![](https://ids.lib.harvard.edu/ids/iiif/20464522/173,676,23,55/full/0/native.jpg)
I
![](https://ids.lib.harvard.edu/ids/iiif/20464522/214,674,23,55/full/0/native.jpg)
N
![](https://ids.lib.harvard.edu/ids/iiif/20464522/265,671,47,56/full/0/native.jpg)
1