Machine Generated Data
Tags
Amazon
created on 2023-10-12
Road | 99.5 | |
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Machine | 98.8 | |
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Spoke | 98.8 | |
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Architecture | 96.2 | |
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Building | 96.2 | |
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Tarmac | 92.7 | |
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Baby | 91.4 | |
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Person | 91.4 | |
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Wheel | 91.2 | |
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Factory | 89 | |
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Car | 88.7 | |
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Transportation | 88.7 | |
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Vehicle | 88.7 | |
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Wheel | 86.6 | |
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Wheel | 85.4 | |
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Outdoors | 84.4 | |
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Face | 82.6 | |
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Head | 82.6 | |
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Nature | 74.3 | |
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Wheel | 72.2 | |
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Person | 69.6 | |
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Alloy Wheel | 65.8 | |
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Car Wheel | 65.8 | |
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Tire | 65.8 | |
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Person | 61.3 | |
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Wall | 57.8 | |
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Urban | 57.5 | |
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Intersection | 56.2 | |
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Office Building | 55.9 | |
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Indoors | 55.9 | |
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Restaurant | 55.9 | |
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Freeway | 55.6 | |
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Airport | 55.6 | |
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Advertisement | 55.5 | |
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Bus | 55.2 | |
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Warehouse | 55 | |
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Terminal | 55 | |
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Clarifai
created on 2018-08-23
vehicle | 97.7 | |
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people | 97.5 | |
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monochrome | 97.1 | |
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transportation system | 97 | |
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no person | 96.4 | |
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street | 94.3 | |
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architecture | 93.6 | |
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group | 93.6 | |
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railway | 93.1 | |
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city | 91.9 | |
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travel | 91.5 | |
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many | 90.9 | |
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building | 89.9 | |
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train | 89.3 | |
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group together | 89.3 | |
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road | 88.3 | |
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car | 85.9 | |
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war | 85 | |
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outdoors | 84.8 | |
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house | 83.8 | |
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Imagga
created on 2018-08-23
Google
created on 2018-08-23
black | 95.2 | |
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structure | 92.9 | |
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black and white | 92.6 | |
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landmark | 90.2 | |
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urban area | 89.7 | |
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monochrome photography | 86.8 | |
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architecture | 84.8 | |
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night | 84.2 | |
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sky | 78.5 | |
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monochrome | 76.8 | |
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metropolis | 69.1 | |
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building | 64.9 | |
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city | 64.6 | |
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darkness | 53.3 | |
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facade | 50.4 | |
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Color Analysis
Feature analysis
Categories
Imagga
cars vehicles | 90% | |
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nature landscape | 2.9% | |
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streetview architecture | 2.4% | |
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beaches seaside | 1.5% | |
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sunrises sunsets | 1.1% | |
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paintings art | 0.7% | |
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text visuals | 0.5% | |
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events parties | 0.4% | |
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food drinks | 0.3% | |
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interior objects | 0.1% | |
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Captions
Microsoft
created on 2018-08-23
a black sign with white text on the side of a building | 88.3% | |
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a black sign with white letters on the side of a building | 88.2% | |
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a black sign with white text | 88.1% | |
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Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/11904390/527,415,264,75/full/0/native.jpg)
Camels
![](https://ids.lib.harvard.edu/ids/iiif/11904390/537,553,41,20/full/0/native.jpg)
GLOW
![](https://ids.lib.harvard.edu/ids/iiif/11904390/480,553,98,21/full/0/native.jpg)
GOLDEN GLOW
![](https://ids.lib.harvard.edu/ids/iiif/11904390/480,554,54,20/full/0/native.jpg)
GOLDEN
![](https://ids.lib.harvard.edu/ids/iiif/11904390/963,519,26,16/full/0/native.jpg)
Max
![](https://ids.lib.harvard.edu/ids/iiif/11904390/415,426,20,10/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/11904390/485,434,38,12/full/0/native.jpg)
SMOKE
![](https://ids.lib.harvard.edu/ids/iiif/11904390/764,515,86,37/full/0/native.jpg)
Stores
![](https://ids.lib.harvard.edu/ids/iiif/11904390/414,420,117,17/full/0/native.jpg)
FOR DIGESTION'S SAKE
![](https://ids.lib.harvard.edu/ids/iiif/11904390/881,485,47,8/full/0/native.jpg)
COSTLIER
![](https://ids.lib.harvard.edu/ids/iiif/11904390/503,423,27,9/full/0/native.jpg)
SAKE
![](https://ids.lib.harvard.edu/ids/iiif/11904390/881,485,106,8/full/0/native.jpg)
COSTLIER TOBACCOS
![](https://ids.lib.harvard.edu/ids/iiif/11904390/932,485,55,8/full/0/native.jpg)
TOBACCOS
![](https://ids.lib.harvard.edu/ids/iiif/11904390/438,421,63,15/full/0/native.jpg)
DIGESTION'S
![](https://ids.lib.harvard.edu/ids/iiif/11904390/819,464,13,8/full/0/native.jpg)
YOU
![](https://ids.lib.harvard.edu/ids/iiif/11904390/962,536,55,35/full/0/native.jpg)
LAPRIMA
![](https://ids.lib.harvard.edu/ids/iiif/11904390/428,541,39,11/full/0/native.jpg)
CINCLE
![](https://ids.lib.harvard.edu/ids/iiif/11904390/336,543,21,9/full/0/native.jpg)
FLORIST
![](https://ids.lib.harvard.edu/ids/iiif/11904390/428,541,55,11/full/0/native.jpg)
CINCLE CA
![](https://ids.lib.harvard.edu/ids/iiif/11904390/814,449,29,14/full/0/native.jpg)
CAMELS
![](https://ids.lib.harvard.edu/ids/iiif/11904390/963,519,54,17/full/0/native.jpg)
Max Leh
![](https://ids.lib.harvard.edu/ids/iiif/11904390/471,542,13,6/full/0/native.jpg)
CA
![](https://ids.lib.harvard.edu/ids/iiif/11904390/662,511,189,43/full/0/native.jpg)
Strauss Stores
![](https://ids.lib.harvard.edu/ids/iiif/11904390/990,519,26,16/full/0/native.jpg)
Leh
![](https://ids.lib.harvard.edu/ids/iiif/11904390/806,458,49,18/full/0/native.jpg)
SET YOU ROCATT
![](https://ids.lib.harvard.edu/ids/iiif/11904390/808,467,10,7/full/0/native.jpg)
SET
![](https://ids.lib.harvard.edu/ids/iiif/11904390/285,557,13,4/full/0/native.jpg)
i
![](https://ids.lib.harvard.edu/ids/iiif/11904390/833,458,22,12/full/0/native.jpg)
ROCATT
![](https://ids.lib.harvard.edu/ids/iiif/11904390/301,523,13,7/full/0/native.jpg)
حمد
![](https://ids.lib.harvard.edu/ids/iiif/11904390/662,513,108,38/full/0/native.jpg)
Strauss
![](https://ids.lib.harvard.edu/ids/iiif/11904390/276,520,37,10/full/0/native.jpg)
حمد 100
![](https://ids.lib.harvard.edu/ids/iiif/11904390/275,540,37,11/full/0/native.jpg)
.
![](https://ids.lib.harvard.edu/ids/iiif/11904390/277,521,11,5/full/0/native.jpg)
100
![](https://ids.lib.harvard.edu/ids/iiif/11904390/276,530,33,8/full/0/native.jpg)
NOT
![](https://ids.lib.harvard.edu/ids/iiif/11904390/334,534,24,6/full/0/native.jpg)
HGT
![](https://ids.lib.harvard.edu/ids/iiif/11904390/417,417,432,141/full/0/native.jpg)
Camels
s SAK
FOR DIGEST
CAMELS
Stranca tores
![](https://ids.lib.harvard.edu/ids/iiif/11904390/531,417,251,75/full/0/native.jpg)
Camels
![](https://ids.lib.harvard.edu/ids/iiif/11904390/496,423,9,13/full/0/native.jpg)
s
![](https://ids.lib.harvard.edu/ids/iiif/11904390/505,423,22,14/full/0/native.jpg)
SAK
![](https://ids.lib.harvard.edu/ids/iiif/11904390/417,427,23,13/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/11904390/440,425,40,15/full/0/native.jpg)
DIGEST
![](https://ids.lib.harvard.edu/ids/iiif/11904390/817,454,30,11/full/0/native.jpg)
CAMELS
![](https://ids.lib.harvard.edu/ids/iiif/11904390/666,512,94,43/full/0/native.jpg)
Stranca
![](https://ids.lib.harvard.edu/ids/iiif/11904390/767,516,82,42/full/0/native.jpg)
tores