Machine Generated Data
Tags
Amazon
created on 2023-10-25
Clarifai
created on 2018-10-06
negative | 100 | |
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exposed | 100 | |
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emulsion | 99.9 | |
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collage | 99.8 | |
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filmstrip | 99.8 | |
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slide | 99.8 | |
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picture frame | 99.6 | |
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photograph | 99.3 | |
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movie | 98.8 | |
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sliding | 98.8 | |
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noisy | 98.7 | |
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moment | 97 | |
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dirty | 96.7 | |
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edge | 96.1 | |
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bobbin | 96.1 | |
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margin | 96 | |
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mess | 95.9 | |
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no person | 95.7 | |
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radiography | 95.7 | |
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cinematography | 95.5 | |
|
Imagga
created on 2018-10-06
Google
created on 2018-10-06
photograph | 95.8 | |
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black | 95.7 | |
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black and white | 93.2 | |
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photography | 82.6 | |
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monochrome photography | 77.1 | |
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picture frame | 70 | |
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monochrome | 67.1 | |
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photographic paper | 57.2 | |
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rectangle | 52.5 | |
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stock photography | 51.8 | |
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font | 50.8 | |
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Color Analysis
Feature analysis
Categories
Imagga
pets animals | 75% | |
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text visuals | 14% | |
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interior objects | 9.6% | |
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nature landscape | 0.4% | |
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food drinks | 0.3% | |
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streetview architecture | 0.3% | |
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paintings art | 0.1% | |
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events parties | 0.1% | |
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cars vehicles | 0.1% | |
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people portraits | 0.1% | |
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Captions
Microsoft
created on 2018-10-06
a flat screen tv | 48.7% | |
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a flat screen monitor | 42.8% | |
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a large flat screen monitor | 37.8% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/38979165/768,181,16,8/full/0/native.jpg)
26
![](https://ids.lib.harvard.edu/ids/iiif/38979165/970,179,16,9/full/0/native.jpg)
27
![](https://ids.lib.harvard.edu/ids/iiif/38979165/375,182,17,9/full/0/native.jpg)
24
![](https://ids.lib.harvard.edu/ids/iiif/38979165/570,181,16,9/full/0/native.jpg)
25
![](https://ids.lib.harvard.edu/ids/iiif/38979165/168,183,16,8/full/0/native.jpg)
23
![](https://ids.lib.harvard.edu/ids/iiif/38979165/657,10,21,6/full/0/native.jpg)
TRI
![](https://ids.lib.harvard.edu/ids/iiif/38979165/689,10,40,7/full/0/native.jpg)
KODAK
![](https://ids.lib.harvard.edu/ids/iiif/38979165/192,12,48,8/full/0/native.jpg)
SAFETY
![](https://ids.lib.harvard.edu/ids/iiif/38979165/865,180,26,9/full/0/native.jpg)
26A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/66,184,23,8/full/0/native.jpg)
22A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/660,180,26,9/full/0/native.jpg)
25A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/265,183,25,8/full/0/native.jpg)
23A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/639,9,90,8/full/0/native.jpg)
KODAK TRI y
![](https://ids.lib.harvard.edu/ids/iiif/38979165/468,181,26,9/full/0/native.jpg)
24A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/596,10,26,8/full/0/native.jpg)
RAN
![](https://ids.lib.harvard.edu/ids/iiif/38979165/561,10,61,8/full/0/native.jpg)
RAN FILM
![](https://ids.lib.harvard.edu/ids/iiif/38979165/561,11,31,7/full/0/native.jpg)
FILM
![](https://ids.lib.harvard.edu/ids/iiif/38979165/640,11,4,5/full/0/native.jpg)
y
![](https://ids.lib.harvard.edu/ids/iiif/38979165/141,13,32,7/full/0/native.jpg)
WILL
![](https://ids.lib.harvard.edu/ids/iiif/38979165/66,179,925,20/full/0/native.jpg)
27
26A
25A
26
25
24A
24
23A
22A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/971,179,20,14/full/0/native.jpg)
27
![](https://ids.lib.harvard.edu/ids/iiif/38979165/868,179,29,15/full/0/native.jpg)
26A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/662,180,28,16/full/0/native.jpg)
25A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/764,180,22,16/full/0/native.jpg)
26
![](https://ids.lib.harvard.edu/ids/iiif/38979165/570,181,20,14/full/0/native.jpg)
25
![](https://ids.lib.harvard.edu/ids/iiif/38979165/470,182,28,12/full/0/native.jpg)
24A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/376,182,21,14/full/0/native.jpg)
24
![](https://ids.lib.harvard.edu/ids/iiif/38979165/265,182,29,14/full/0/native.jpg)
23A
![](https://ids.lib.harvard.edu/ids/iiif/38979165/66,181,28,18/full/0/native.jpg)
22A