Machine Generated Data
Tags
Amazon
created on 2023-06-15
Clarifai
created on 2023-10-13
monochrome | 99.6 | |
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water | 99.3 | |
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no person | 98.3 | |
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pier | 96 | |
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bridge | 95.8 | |
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boat | 94.7 | |
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black and white | 94.6 | |
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transportation system | 93.7 | |
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harbor | 93.5 | |
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sea | 93.3 | |
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river | 93.1 | |
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vehicle | 92.4 | |
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watercraft | 92.2 | |
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reflection | 92.1 | |
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industry | 90.7 | |
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ship | 90.5 | |
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people | 88 | |
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street | 85.8 | |
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travel | 82.7 | |
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canal | 82 | |
|
Imagga
created on 2023-06-15
Google
created on 2023-06-15
Water | 97.6 | |
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Black-and-white | 83.9 | |
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Lake | 80.1 | |
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Monochrome | 72.9 | |
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Monochrome photography | 72.9 | |
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Machine | 58.4 | |
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Wood | 57.9 | |
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Reflection | 57.6 | |
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Vehicle | 57.4 | |
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Art | 55.8 | |
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Sky | 53 | |
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Dock | 52.8 | |
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Rectangle | 52 | |
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Font | 50.7 | |
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Metal | 50.1 | |
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Color Analysis
Feature analysis
Categories
Imagga
cars vehicles | 39.3% | |
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paintings art | 33.7% | |
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interior objects | 11.2% | |
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streetview architecture | 9.5% | |
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beaches seaside | 2.4% | |
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text visuals | 1.4% | |
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nature landscape | 1.1% | |
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Captions
Microsoft
created on 2023-06-15
a boat is docked next to a body of water | 64.2% | |
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a boat docked next to a body of water | 64.1% | |
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a boat on a body of water | 64% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/497260626/853,432,146,89/full/0/native.jpg)
JOY
![](https://ids.lib.harvard.edu/ids/iiif/497260626/980,547,101,54/full/0/native.jpg)
BOAT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/870,621,34,22/full/0/native.jpg)
ON
![](https://ids.lib.harvard.edu/ids/iiif/497260626/776,525,108,56/full/0/native.jpg)
FAST
![](https://ids.lib.harvard.edu/ids/iiif/497260626/868,618,135,36/full/0/native.jpg)
ON BOARD
![](https://ids.lib.harvard.edu/ids/iiif/497260626/1020,597,65,38/full/0/native.jpg)
BAIT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/923,683,93,31/full/0/native.jpg)
LADIES
![](https://ids.lib.harvard.edu/ids/iiif/497260626/858,675,159,43/full/0/native.jpg)
FOR LADIES
![](https://ids.lib.harvard.edu/ids/iiif/497260626/858,675,57,34/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/497260626/918,621,85,33/full/0/native.jpg)
BOARD
![](https://ids.lib.harvard.edu/ids/iiif/497260626/769,571,223,58/full/0/native.jpg)
RESTAURANT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/791,617,288,84/full/0/native.jpg)
ACCOMMODATIONS
![](https://ids.lib.harvard.edu/ids/iiif/497260626/769,571,317,67/full/0/native.jpg)
RESTAURANT 9 BAIT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/981,596,24,23/full/0/native.jpg)
9
![](https://ids.lib.harvard.edu/ids/iiif/497260626/765,430,681,598/full/0/native.jpg)
JOY
FAST BOAT
RESTAURANT & BAIT
ON BOARD
ACCOMMODATIONS
FOR LADIES
AH
161
![](https://ids.lib.harvard.edu/ids/iiif/497260626/853,436,157,88/full/0/native.jpg)
JOY
![](https://ids.lib.harvard.edu/ids/iiif/497260626/780,531,110,50/full/0/native.jpg)
FAST
![](https://ids.lib.harvard.edu/ids/iiif/497260626/983,552,104,52/full/0/native.jpg)
BOAT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/774,575,214,53/full/0/native.jpg)
RESTAURANT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/988,597,24,34/full/0/native.jpg)
&
![](https://ids.lib.harvard.edu/ids/iiif/497260626/1022,600,70,38/full/0/native.jpg)
BAIT
![](https://ids.lib.harvard.edu/ids/iiif/497260626/870,618,41,31/full/0/native.jpg)
ON
![](https://ids.lib.harvard.edu/ids/iiif/497260626/921,624,84,35/full/0/native.jpg)
BOARD
![](https://ids.lib.harvard.edu/ids/iiif/497260626/800,637,274,58/full/0/native.jpg)
ACCOMMODATIONS
![](https://ids.lib.harvard.edu/ids/iiif/497260626/862,681,54,28/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/497260626/929,687,89,30/full/0/native.jpg)
LADIES
![](https://ids.lib.harvard.edu/ids/iiif/497260626/907,901,325,127/full/0/native.jpg)
AH
![](https://ids.lib.harvard.edu/ids/iiif/497260626/1400,947,47,74/full/0/native.jpg)
161