Machine Generated Data
Tags
Amazon
created on 2023-10-23
Ice | 99.7 | |
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Fence | 99.3 | |
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Outdoors | 97.9 | |
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Nature | 97.1 | |
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Person | 94.1 | |
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Person | 93.8 | |
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Person | 93.2 | |
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Adult | 93.2 | |
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Male | 93.2 | |
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Man | 93.2 | |
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Person | 88.8 | |
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Person | 82.5 | |
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Person | 81.6 | |
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Person | 78.1 | |
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Adult | 78.1 | |
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Male | 78.1 | |
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Man | 78.1 | |
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Face | 73.1 | |
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Head | 73.1 | |
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Snow | 71 | |
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Person | 71 | |
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Icicle | 58 | |
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Winter | 58 | |
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Person | 56.5 | |
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Person | 56.3 | |
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Picket | 55.9 | |
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Art | 55.8 | |
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Collage | 55.8 | |
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Yard | 55.4 | |
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Clarifai
created on 2023-10-15
negative | 99.9 | |
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filmstrip | 99.8 | |
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slide | 99.7 | |
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exposed | 99.6 | |
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movie | 99.5 | |
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cinematography | 98.8 | |
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photograph | 97.4 | |
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bobbin | 96.7 | |
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old | 95.8 | |
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retro | 95.6 | |
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dirty | 95.6 | |
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picture frame | 94.9 | |
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tape | 94.3 | |
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collage | 93.9 | |
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man | 92.7 | |
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margin | 92.3 | |
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antique | 92.1 | |
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desktop | 91.4 | |
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graphic design | 89.6 | |
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people | 89.5 | |
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Imagga
created on 2019-02-03
Google
created on 2019-02-03
Photograph | 97.1 | |
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White | 96.5 | |
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Black-and-white | 89 | |
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Snapshot | 86.4 | |
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Text | 85.2 | |
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Monochrome photography | 84.4 | |
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Photography | 81.5 | |
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Stock photography | 74.1 | |
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Room | 71.4 | |
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Monochrome | 64.3 | |
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Negative | 53.5 | |
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Style | 52.5 | |
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Art | 50.2 | |
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Microsoft
created on 2019-02-03
toy | 56 | |
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black and white | 56 | |
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monochrome | 37.9 | |
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wedding | 34.2 | |
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fence | 32.1 | |
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winter | 25.8 | |
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Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/38998282/4522,548,37,48/full/0/native.jpg)
AWS Rekognition
Age | 2-10 |
Gender | Male, 89.9% |
Calm | 94.6% |
Surprised | 6.9% |
Fear | 6% |
Sad | 2.6% |
Confused | 1% |
Happy | 0.8% |
Disgusted | 0.4% |
Angry | 0.3% |
Feature analysis
Categories
Imagga
paintings art | 90.9% | |
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interior objects | 4.5% | |
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streetview architecture | 3.2% | |
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pets animals | 1.3% | |
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Captions
Microsoft
created on 2019-02-03
a close up of a toy store | 56.8% | |
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a group of people in a store | 35.1% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/38998282/4259,77,31,37/full/0/native.jpg)
6
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3090,986,94,27/full/0/native.jpg)
FILM
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3090,985,206,34/full/0/native.jpg)
PAN FILM
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3214,988,81,29/full/0/native.jpg)
PAN
![](https://ids.lib.harvard.edu/ids/iiif/38998282/789,970,318,33/full/0/native.jpg)
SAFETY FILM
![](https://ids.lib.harvard.edu/ids/iiif/38998282/942,972,165,31/full/0/native.jpg)
SAFETY
![](https://ids.lib.harvard.edu/ids/iiif/38998282/4890,569,39,27/full/0/native.jpg)
123
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3210,67,31,31/full/0/native.jpg)
8
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3386,993,44,25/full/0/native.jpg)
18
![](https://ids.lib.harvard.edu/ids/iiif/38998282/2160,64,20,25/full/0/native.jpg)
Z
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3497,991,140,25/full/0/native.jpg)
VAGOY
![](https://ids.lib.harvard.edu/ids/iiif/38998282/5024,1007,115,15/full/0/native.jpg)
WALT
![](https://ids.lib.harvard.edu/ids/iiif/38998282/797,58,3496,954/full/0/native.jpg)
AFETY FILM
P A
9
7
![](https://ids.lib.harvard.edu/ids/iiif/38998282/797,965,125,38/full/0/native.jpg)
FILM
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3269,987,22,25/full/0/native.jpg)
P
![](https://ids.lib.harvard.edu/ids/iiif/38998282/3240,987,22,25/full/0/native.jpg)
A
![](https://ids.lib.harvard.edu/ids/iiif/38998282/4257,80,34,34/full/0/native.jpg)
9
![](https://ids.lib.harvard.edu/ids/iiif/38998282/2159,60,29,36/full/0/native.jpg)
7
![](https://ids.lib.harvard.edu/ids/iiif/38998282/945,967,134,38/full/0/native.jpg)
AFETY