Machine Generated Data
Tags
Amazon
created on 2023-10-06
Water | 100 | |
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Waterfront | 100 | |
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Transportation | 99.6 | |
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Vehicle | 99.6 | |
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Watercraft | 99.6 | |
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Port | 99.1 | |
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Boat | 98.2 | |
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Barge | 98.2 | |
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Person | 95.6 | |
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Ferry | 95.2 | |
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Person | 94.6 | |
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Person | 94.4 | |
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Person | 92.4 | |
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Person | 91.9 | |
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Person | 91.7 | |
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Person | 91 | |
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Person | 88.6 | |
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Pier | 87.5 | |
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Boat | 87.3 | |
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Person | 86.8 | |
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Person | 85.8 | |
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Person | 85.5 | |
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Person | 83 | |
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Person | 79.9 | |
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Boat | 75.8 | |
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Person | 70.9 | |
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Person | 66.2 | |
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Tugboat | 63 | |
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Harbor | 57.8 | |
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Appliance | 56.8 | |
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Device | 56.8 | |
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Electrical Device | 56.8 | |
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Steamer | 56.8 | |
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Canal | 56.8 | |
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Outdoors | 56.8 | |
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Person | 55.9 | |
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Sailboat | 55.8 | |
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Cruiser | 55.8 | |
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Military | 55.8 | |
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Navy | 55.8 | |
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Ship | 55.8 | |
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Person | 55.3 | |
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Clarifai
created on 2018-05-11
watercraft | 99.7 | |
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vehicle | 99.4 | |
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transportation system | 99.4 | |
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people | 97.9 | |
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water | 95.4 | |
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ship | 94.8 | |
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group | 91.8 | |
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boat | 91 | |
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no person | 90.8 | |
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group together | 90.7 | |
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pier | 89.4 | |
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river | 85 | |
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harbor | 84.9 | |
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many | 83.9 | |
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sea | 83.8 | |
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industry | 83.7 | |
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warship | 83.4 | |
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cargo ship | 83.3 | |
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barge | 83 | |
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military | 80.7 | |
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Imagga
created on 2023-10-06
vessel | 86 | |
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boat | 71.5 | |
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tugboat | 71.1 | |
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fishing boat | 60.1 | |
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ship | 56.8 | |
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craft | 50.1 | |
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sea | 47.8 | |
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water | 46.8 | |
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harbor | 42.5 | |
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port | 40.6 | |
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fisherman | 39.1 | |
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boats | 37 | |
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ocean | 33.3 | |
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transport | 31.1 | |
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vehicle | 31.1 | |
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travel | 28.2 | |
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marina | 27.8 | |
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dock | 26.4 | |
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tourism | 24.8 | |
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coast | 24.3 | |
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transportation | 23.4 | |
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river | 22.3 | |
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sailing | 21.5 | |
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fishing | 21.2 | |
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sky | 19.2 | |
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nautical | 18.5 | |
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city | 18.3 | |
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pier | 17.9 | |
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tourist | 17.3 | |
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architecture | 16.4 | |
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vacation | 16.4 | |
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sail | 15.6 | |
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conveyance | 15.4 | |
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marine | 15.3 | |
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bay | 15.1 | |
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coastline | 15.1 | |
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summer | 14.2 | |
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old | 14 | |
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industry | 13.7 | |
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town | 13 | |
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moored | 12.9 | |
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house | 12.6 | |
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village | 12.5 | |
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building | 12.1 | |
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canal | 11.9 | |
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industrial | 11.8 | |
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beach | 11.8 | |
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quay | 11.8 | |
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mast | 11.8 | |
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maritime | 11.8 | |
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ships | 11.8 | |
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cruise | 11.7 | |
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traditional | 11.7 | |
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cargo | 11.7 | |
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urban | 11.4 | |
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landscape | 11.2 | |
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fish | 11 | |
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wharf | 10.8 | |
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freight | 10.8 | |
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holiday | 10.8 | |
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outdoors | 10.5 | |
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sunny | 10.4 | |
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coastal | 9.7 | |
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seaside | 9.7 | |
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shore | 9.3 | |
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boating | 8.9 | |
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anchor | 8.9 | |
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scenic | 8.8 | |
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colorful | 8.6 | |
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bridge | 8.5 | |
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outdoor | 8.4 | |
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island | 8.3 | |
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anchored | 7.9 | |
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ferry | 7.9 | |
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sailboat | 7.9 | |
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navigation | 7.7 | |
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trip | 7.6 | |
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destination | 7.5 | |
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famous | 7.5 | |
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business | 7.3 | |
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scenery | 7.2 | |
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day | 7.1 | |
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wooden | 7.1 | |
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Google
created on 2018-05-11
water transportation | 92.4 | |
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waterway | 90.5 | |
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black and white | 86 | |
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watercraft | 84.6 | |
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transport | 84.2 | |
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boat | 82.9 | |
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ferry | 80 | |
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ship | 75.8 | |
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monochrome photography | 67.2 | |
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motor ship | 64.1 | |
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monochrome | 56.6 | |
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channel | 52.7 | |
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submarine chaser | 50.9 | |
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Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43160007/593,310,8,11/full/0/native.jpg)
AWS Rekognition
Age | 12-20 |
Gender | Male, 96.6% |
Calm | 62.1% |
Angry | 13.2% |
Fear | 8.5% |
Surprised | 7% |
Sad | 6.8% |
Confused | 3.4% |
Disgusted | 3.1% |
Happy | 2.5% |
Feature analysis
Categories
Imagga
cars vehicles | 69.2% | |
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streetview architecture | 24.7% | |
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beaches seaside | 2.1% | |
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interior objects | 1.8% | |
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paintings art | 1.4% | |
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Captions
Microsoft
created on 2018-05-11
a boat is docked next to a body of water | 88.4% | |
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a black and white photo of a boat | 88.3% | |
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an old photo of a boat | 88.2% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/43160007/825,44,52,34/full/0/native.jpg)
BOARD,
![](https://ids.lib.harvard.edu/ids/iiif/43160007/795,55,33,24/full/0/native.jpg)
DOCK
![](https://ids.lib.harvard.edu/ids/iiif/43160007/381,382,54,15/full/0/native.jpg)
SIPSEY
![](https://ids.lib.harvard.edu/ids/iiif/43160007/476,279,49,36/full/0/native.jpg)
LINE
![](https://ids.lib.harvard.edu/ids/iiif/43160007/412,258,113,59/full/0/native.jpg)
BARGE LINE
![](https://ids.lib.harvard.edu/ids/iiif/43160007/413,258,64,44/full/0/native.jpg)
BARGE
![](https://ids.lib.harvard.edu/ids/iiif/43160007/567,300,86,34/full/0/native.jpg)
MB-29
![](https://ids.lib.harvard.edu/ids/iiif/43160007/170,140,28,12/full/0/native.jpg)
SIPSET
![](https://ids.lib.harvard.edu/ids/iiif/43160007/794,18,197,66/full/0/native.jpg)
DOCK BOARD, TOULOUSE
![](https://ids.lib.harvard.edu/ids/iiif/43160007/877,20,113,45/full/0/native.jpg)
TOULOUSE
![](https://ids.lib.harvard.edu/ids/iiif/43160007/880,37,41,32/full/0/native.jpg)
TOU
![](https://ids.lib.harvard.edu/ids/iiif/43160007/383,37,538,363/full/0/native.jpg)
BOARD, TOU
SIPSEY
![](https://ids.lib.harvard.edu/ids/iiif/43160007/827,46,55,36/full/0/native.jpg)
BOARD,
![](https://ids.lib.harvard.edu/ids/iiif/43160007/383,384,57,16/full/0/native.jpg)
SIPSEY