Machine Generated Data
Tags
Amazon
created on 2022-06-04
Transportation | 98.3 | |
| ||
Vehicle | 98.3 | |
| ||
Train | 98.3 | |
| ||
Meal | 97.7 | |
| ||
Food | 97.7 | |
| ||
Railway | 94.4 | |
| ||
Train Track | 94.4 | |
| ||
Rail | 94.4 | |
| ||
Nature | 89.5 | |
| ||
Outdoors | 88.1 | |
| ||
Path | 75.2 | |
| ||
Diner | 69.2 | |
| ||
Restaurant | 69.2 | |
| ||
Land | 57.3 | |
| ||
Walkway | 55.5 | |
|
Imagga
created on 2022-06-04
conveyance | 73 | |
| ||
tramway | 72.9 | |
| ||
streetcar | 53.8 | |
| ||
wheeled vehicle | 46.9 | |
| ||
building | 41.9 | |
| ||
vehicle | 38.6 | |
| ||
train | 32.9 | |
| ||
architecture | 30.7 | |
| ||
car | 27.2 | |
| ||
transportation | 26.9 | |
| ||
travel | 26.1 | |
| ||
city | 25 | |
| ||
structure | 24 | |
| ||
urban | 23.6 | |
| ||
passenger car | 23.3 | |
| ||
station | 23.1 | |
| ||
transport | 21.9 | |
| ||
house | 21.7 | |
| ||
street | 21.2 | |
| ||
old | 19.5 | |
| ||
railroad | 18.7 | |
| ||
railway | 16.7 | |
| ||
greenhouse | 16.2 | |
| ||
rail | 15.7 | |
| ||
road | 15.4 | |
| ||
track | 15.1 | |
| ||
sky | 14 | |
| ||
town | 13.9 | |
| ||
exterior | 12.9 | |
| ||
historic | 12.8 | |
| ||
window | 12.8 | |
| ||
perspective | 11.3 | |
| ||
wall | 11.3 | |
| ||
transit | 10.8 | |
| ||
brick | 10.4 | |
| ||
industrial | 10 | |
| ||
rails | 9.9 | |
| ||
subway | 9.9 | |
| ||
trees | 9.8 | |
| ||
public | 9.7 | |
| ||
metal | 9.7 | |
| ||
home | 9.6 | |
| ||
cold | 9.5 | |
| ||
construction | 9.4 | |
| ||
industry | 9.4 | |
| ||
electric | 9.4 | |
| ||
winter | 9.4 | |
| ||
light | 9.4 | |
| ||
snow | 9.2 | |
| ||
tourism | 9.1 | |
| ||
history | 9 | |
| ||
tracks | 8.9 | |
| ||
rural | 8.8 | |
| ||
residential | 8.6 | |
| ||
outdoor | 8.4 | |
| ||
vintage | 8.3 | |
| ||
park | 8.2 | |
| ||
tourist | 8.2 | |
| ||
new | 8.1 | |
| ||
commute | 7.9 | |
| ||
metro | 7.9 | |
| ||
platform | 7.9 | |
| ||
day | 7.9 | |
| ||
scene | 7.8 | |
| ||
line | 7.7 | |
| ||
roof | 7.6 | |
| ||
traffic | 7.6 | |
| ||
estate | 7.6 | |
| ||
journey | 7.5 | |
| ||
landscape | 7.4 | |
| ||
way | 7.4 | |
| ||
speed | 7.3 | |
| ||
door | 7.2 | |
| ||
landmark | 7.2 | |
| ||
summer | 7.1 | |
| ||
steel | 7.1 | |
|
Google
created on 2022-06-04
Window | 87 | |
| ||
Rolling stock | 85.6 | |
| ||
Railway | 81.5 | |
| ||
Electricity | 80.9 | |
| ||
Door | 79.4 | |
| ||
Train | 79.1 | |
| ||
Building | 78.4 | |
| ||
Tints and shades | 77.1 | |
| ||
Rectangle | 76.6 | |
| ||
Track | 76.2 | |
| ||
Public transport | 76.1 | |
| ||
Railroad car | 75.6 | |
| ||
Monochrome | 74.3 | |
| ||
Passenger car | 73.9 | |
| ||
Monochrome photography | 70.8 | |
| ||
Rolling | 65.6 | |
| ||
Facade | 62.8 | |
| ||
Electrical supply | 59.4 | |
| ||
Vehicle | 54.9 | |
| ||
Snow | 53.4 | |
|
Color Analysis
Feature analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488752/27,225,990,387/full/0/native.jpg)
Train | 98.3% | |
|
Categories
Imagga
cars vehicles | 100% | |
|
Captions
Microsoft
created on 2022-06-04
an old photo of a train | 74.4% | |
| ||
an old photo of a train station | 74.3% | |
| ||
old photo of a train | 68.5% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,514,50,13/full/0/native.jpg)
MARATHON
![](https://ids.lib.harvard.edu/ids/iiif/20488752/586,339,51,25/full/0/native.jpg)
5436
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,530,25,12/full/0/native.jpg)
REVERE
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,530,50,12/full/0/native.jpg)
REVERE BEACH
![](https://ids.lib.harvard.edu/ids/iiif/20488752/856,493,33,11/full/0/native.jpg)
GRIND
![](https://ids.lib.harvard.edu/ids/iiif/20488752/876,530,21,12/full/0/native.jpg)
BEACH
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,478,50,16/full/0/native.jpg)
TREADMILL
![](https://ids.lib.harvard.edu/ids/iiif/20488752/914,798,109,25/full/0/native.jpg)
29033
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,472,50,21/full/0/native.jpg)
TREADMILL STRENOOUS
![](https://ids.lib.harvard.edu/ids/iiif/20488752/852,472,39,7/full/0/native.jpg)
STRENOOUS
![](https://ids.lib.harvard.edu/ids/iiif/20488752/303,313,71,23/full/0/native.jpg)
SEACON ST
![](https://ids.lib.harvard.edu/ids/iiif/20488752/756,358,39,22/full/0/native.jpg)
otp
![](https://ids.lib.harvard.edu/ids/iiif/20488752/838,444,54,12/full/0/native.jpg)
ENTER FRONT
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,502,51,11/full/0/native.jpg)
AFTERNOON&EVENIN
![](https://ids.lib.harvard.edu/ids/iiif/20488752/857,495,36,12/full/0/native.jpg)
GRIND
![](https://ids.lib.harvard.edu/ids/iiif/20488752/875,504,8,12/full/0/native.jpg)
&
![](https://ids.lib.harvard.edu/ids/iiif/20488752/848,513,55,21/full/0/native.jpg)
MARATHON
![](https://ids.lib.harvard.edu/ids/iiif/20488752/587,340,436,483/full/0/native.jpg)
5436
att
ENTER FRONT
STALINDOGS
TREADMILL
GRIND
AFTERNOON&EVENING
MARATHON
REVERE BEACH
54 36
29033
![](https://ids.lib.harvard.edu/ids/iiif/20488752/587,340,53,27/full/0/native.jpg)
5436
![](https://ids.lib.harvard.edu/ids/iiif/20488752/759,361,39,23/full/0/native.jpg)
att
![](https://ids.lib.harvard.edu/ids/iiif/20488752/839,447,30,14/full/0/native.jpg)
ENTER
![](https://ids.lib.harvard.edu/ids/iiif/20488752/869,447,28,14/full/0/native.jpg)
FRONT
![](https://ids.lib.harvard.edu/ids/iiif/20488752/852,472,43,15/full/0/native.jpg)
STALINDOGS
![](https://ids.lib.harvard.edu/ids/iiif/20488752/847,479,55,18/full/0/native.jpg)
TREADMILL
![](https://ids.lib.harvard.edu/ids/iiif/20488752/849,504,30,13/full/0/native.jpg)
AFTERNOON
![](https://ids.lib.harvard.edu/ids/iiif/20488752/879,504,22,12/full/0/native.jpg)
EVENING
![](https://ids.lib.harvard.edu/ids/iiif/20488752/849,532,29,15/full/0/native.jpg)
REVERE
![](https://ids.lib.harvard.edu/ids/iiif/20488752/877,532,25,15/full/0/native.jpg)
BEACH
![](https://ids.lib.harvard.edu/ids/iiif/20488752/932,485,22,16/full/0/native.jpg)
54
![](https://ids.lib.harvard.edu/ids/iiif/20488752/954,485,19,16/full/0/native.jpg)
36
![](https://ids.lib.harvard.edu/ids/iiif/20488752/917,805,106,18/full/0/native.jpg)
29033