Machine Generated Data
Tags
Amazon
created on 2022-06-04
Building | 93.1 | |
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Wheel | 92.6 | |
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Machine | 92.6 | |
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Wheel | 86.4 | |
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Factory | 81 | |
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Helicopter | 77.9 | |
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Aircraft | 77.9 | |
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Vehicle | 77.9 | |
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Transportation | 77.9 | |
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Airplane | 71.5 | |
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Car | 60.2 | |
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Automobile | 60.2 | |
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Spoke | 58.7 | |
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Imagga
created on 2022-06-04
freight car | 100 | |
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car | 100 | |
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wheeled vehicle | 100 | |
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vehicle | 74.6 | |
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conveyance | 35.6 | |
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industry | 23 | |
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industrial | 21.8 | |
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transportation | 17.9 | |
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power | 15.9 | |
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steel | 15 | |
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factory | 14.5 | |
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black | 14.4 | |
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machine | 14.3 | |
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water | 13.3 | |
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transport | 12.8 | |
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metal | 12.1 | |
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energy | 11.8 | |
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pipe | 11.7 | |
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station | 10.6 | |
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travel | 10.6 | |
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urban | 10.5 | |
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technology | 10.4 | |
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building | 10.3 | |
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work | 10.2 | |
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equipment | 10.2 | |
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environment | 9.9 | |
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fuel | 9.6 | |
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gas | 9.6 | |
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pollution | 9.6 | |
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sky | 9.6 | |
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ship | 9.5 | |
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construction | 9.4 | |
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oil | 9.3 | |
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old | 9 | |
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plant | 8.9 | |
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pump | 8.8 | |
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sea | 8.6 | |
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heavy | 8.6 | |
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ocean | 8.3 | |
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man | 8.1 | |
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refinery | 7.9 | |
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machinery | 7.8 | |
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dock | 7.8 | |
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modern | 7.7 | |
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harbor | 7.7 | |
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track | 7.7 | |
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shopping cart | 7.7 | |
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speed | 7.3 | |
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protection | 7.3 | |
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architecture | 7 | |
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Google
created on 2022-06-04
Machine tool | 83.2 | |
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Gas | 77.6 | |
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Engineering | 76.6 | |
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Motor vehicle | 75.9 | |
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Machine | 74.9 | |
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Industry | 74.2 | |
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Metal | 71.2 | |
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Factory | 69.6 | |
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Toolroom | 68.5 | |
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Monochrome | 67.9 | |
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Steel | 66.4 | |
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Lathe | 65.5 | |
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Titanium | 63.2 | |
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Monochrome photography | 58 | |
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Rectangle | 53.8 | |
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Still life photography | 52.3 | |
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Science | 52.2 | |
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Room | 50.5 | |
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Microsoft
created on 2022-06-04
black and white | 90.9 | |
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furniture | 89.1 | |
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chair | 73.1 | |
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text | 66.5 | |
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table | 62.6 | |
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Color Analysis
Feature analysis
Amazon
Categories
Imagga
cars vehicles | 99.7% | |
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Captions
Microsoft
created on 2022-06-04
a large machine in a building | 75.7% | |
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a large machine on a table | 55.3% | |
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a machine inside the building | 55.2% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488940/129,785,55,25/full/0/native.jpg)
Square
![](https://ids.lib.harvard.edu/ids/iiif/20488940/424,784,48,23/full/0/native.jpg)
raised
![](https://ids.lib.harvard.edu/ids/iiif/20488940/345,792,37,19/full/0/native.jpg)
gear
![](https://ids.lib.harvard.edu/ids/iiif/20488940/183,785,53,24/full/0/native.jpg)
Shops
![](https://ids.lib.harvard.edu/ids/iiif/20488940/917,784,54,20/full/0/native.jpg)
3985
![](https://ids.lib.harvard.edu/ids/iiif/20488940/56,780,416,35/full/0/native.jpg)
Sullivan Square Shops Wheet latte gear case raised
![](https://ids.lib.harvard.edu/ids/iiif/20488940/56,785,72,29/full/0/native.jpg)
Sullivan
![](https://ids.lib.harvard.edu/ids/iiif/20488940/388,788,35,19/full/0/native.jpg)
case
![](https://ids.lib.harvard.edu/ids/iiif/20488940/700,783,94,25/full/0/native.jpg)
Feb. 5.1904
![](https://ids.lib.harvard.edu/ids/iiif/20488940/235,780,108,31/full/0/native.jpg)
Wheet latte
![](https://ids.lib.harvard.edu/ids/iiif/20488940/57,340,918,474/full/0/native.jpg)
Sullivan Square Shops Whe
gear case raised
LANE
Feb.5.1904
3985
![](https://ids.lib.harvard.edu/ids/iiif/20488940/57,789,72,25/full/0/native.jpg)
Sullivan
![](https://ids.lib.harvard.edu/ids/iiif/20488940/129,788,59,24/full/0/native.jpg)
Square
![](https://ids.lib.harvard.edu/ids/iiif/20488940/183,786,57,25/full/0/native.jpg)
Shops
![](https://ids.lib.harvard.edu/ids/iiif/20488940/239,785,43,25/full/0/native.jpg)
Whe
![](https://ids.lib.harvard.edu/ids/iiif/20488940/347,791,39,21/full/0/native.jpg)
gear
![](https://ids.lib.harvard.edu/ids/iiif/20488940/388,790,38,21/full/0/native.jpg)
case
![](https://ids.lib.harvard.edu/ids/iiif/20488940/425,790,52,20/full/0/native.jpg)
raised
![](https://ids.lib.harvard.edu/ids/iiif/20488940/769,340,49,18/full/0/native.jpg)
LANE
![](https://ids.lib.harvard.edu/ids/iiif/20488940/705,787,94,22/full/0/native.jpg)
Feb.5.1904
![](https://ids.lib.harvard.edu/ids/iiif/20488940/919,787,56,20/full/0/native.jpg)
3985