Machine Generated Data
Tags
Amazon
created on 2019-03-22
Wheel | 99.4 | |
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Machine | 99.4 | |
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Human | 98.9 | |
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Person | 98.9 | |
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Apparel | 98.1 | |
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Clothing | 98.1 | |
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Person | 97.1 | |
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Person | 95.4 | |
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Helmet | 91.5 | |
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Truck | 91.5 | |
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Transportation | 91.5 | |
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Vehicle | 91.5 | |
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Person | 90.8 | |
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Tire | 81.5 | |
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Furniture | 71.6 | |
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Wood | 67.8 | |
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Person | 66.6 | |
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Hardhat | 63.2 | |
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Person | 63 | |
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Car Wheel | 62.2 | |
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Spoke | 60.6 | |
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Alloy Wheel | 60.6 | |
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Military Uniform | 60.2 | |
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Military | 60.2 | |
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Chair | 56.8 | |
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Clarifai
created on 2019-03-22
Imagga
created on 2019-03-22
forklift | 81.7 | |
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vehicle | 55.9 | |
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wheeled vehicle | 50.6 | |
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machine | 37.3 | |
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industry | 27.3 | |
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industrial | 26.3 | |
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device | 21.8 | |
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machinery | 21.5 | |
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work | 21.2 | |
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heavy | 21 | |
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power | 20.1 | |
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factory | 19.7 | |
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transportation | 19.7 | |
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equipment | 19 | |
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steel | 18 | |
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building | 17.7 | |
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tractor | 17.3 | |
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conveyance | 17.1 | |
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construction | 17.1 | |
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wheel | 17.1 | |
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metal | 16.9 | |
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old | 16 | |
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architecture | 15.7 | |
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truck | 14.5 | |
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sky | 14 | |
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transport | 13.8 | |
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car | 12.5 | |
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job | 12.4 | |
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iron | 12.1 | |
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tire | 11.8 | |
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dirty | 11.7 | |
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worker | 11.5 | |
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engine | 11.5 | |
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dirt | 11.4 | |
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engineering | 10.5 | |
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typesetting machine | 10.4 | |
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structure | 10.2 | |
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wheels | 9.8 | |
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labor | 9.7 | |
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mechanical | 9.7 | |
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rural | 9.7 | |
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site | 9.4 | |
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energy | 9.2 | |
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city | 9.1 | |
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vintage | 9.1 | |
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environment | 9 | |
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farm | 8.9 | |
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working | 8.8 | |
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mechanic | 8.8 | |
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urban | 8.7 | |
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steam | 8.7 | |
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technical | 8.7 | |
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pollution | 8.6 | |
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plant | 8.5 | |
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travel | 8.4 | |
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gear | 8.4 | |
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historic | 8.2 | |
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printer | 8 | |
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yellow | 7.9 | |
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business | 7.9 | |
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pipe | 7.8 | |
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fuel | 7.7 | |
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stone | 7.6 | |
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technology | 7.4 | |
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heat | 7.4 | |
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road | 7.2 | |
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tank | 7.2 | |
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modern | 7 | |
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Google
created on 2019-03-22
Automotive tire | 90.5 | |
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Motor vehicle | 90.1 | |
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Tire | 86.8 | |
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Wheel | 78.7 | |
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Automotive wheel system | 75.7 | |
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Vehicle | 57.9 | |
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Car | 52.7 | |
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Color Analysis
Face analysis
Amazon
Microsoft

AWS Rekognition
Age | 23-38 |
Gender | Female, 58.2% |
Calm | 11.8% |
Surprised | 0.6% |
Happy | 0.2% |
Sad | 80.8% |
Disgusted | 0.4% |
Angry | 5.3% |
Confused | 0.9% |

AWS Rekognition
Age | 38-59 |
Gender | Male, 50.3% |
Confused | 49.6% |
Calm | 49.6% |
Disgusted | 49.8% |
Surprised | 49.5% |
Sad | 49.8% |
Angry | 49.6% |
Happy | 49.7% |

AWS Rekognition
Age | 4-9 |
Gender | Female, 50.5% |
Disgusted | 50.4% |
Calm | 49.5% |
Surprised | 49.5% |
Happy | 49.5% |
Sad | 49.5% |
Confused | 49.5% |
Angry | 49.5% |

Microsoft Cognitive Services
Age | 24 |
Gender | Male |

Google Vision
Surprise | Very unlikely |
Anger | Very unlikely |
Sorrow | Very unlikely |
Joy | Very unlikely |
Headwear | Unlikely |
Blurred | Very unlikely |
Feature analysis
Categories
Imagga
interior objects | 80.7% | |
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paintings art | 10.4% | |
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streetview architecture | 7% | |
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food drinks | 1% | |
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Captions
Microsoft
created on 2019-03-22
a vintage photo of a truck | 79.7% | |
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a vintage photo of a man in a truck | 68.1% | |
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an old photo of a truck | 68% | |
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