Photos | Ring of Light
Hayateumi Hidehito and Prahlad Singh Patel gather with a crowd of 46 people at Caesars Palace in Las Vegas for an event featuring a ring, lights, and an audience.
BLIP-2 Description:
a group of people standing around a ringMetadata
Capture date:
Original Dimensions:
4032w x 3024h - (download 4k)
Usage
Dominant Color:
Location:
travel accessories winter urban audience lamp shoe leisure lighting trip hayateumi las vegas monitor electronics clark county hidehito mojave palms pro mo tv speech concert december caesars palace crowd furniture pc performance hardware chair vegas caesars apple activities southeast las vegas weapon prahlad singh patel sumo glove screen computer palace south united las states graduation footwear max rekognition_c dr speaker ош paradise dancing iphone strip sword loy laptop nv desert glasses entertainment
iso
250
metering mode
5
aperture
f/2
focal length
6mm
latitude
36.12
longitude
-115.17
shutter speed
1/60s
camera make
Apple
camera model
lens model
overall
(29.05%)
curation
(50.00%)
highlight visibility
(4.35%)
behavioral
(70.50%)
failure
(-0.93%)
harmonious color
(1.11%)
immersiveness
(0.29%)
interaction
(1.00%)
interesting subject
(-30.00%)
intrusive object presence
(-28.25%)
lively color
(-8.76%)
low light
(55.76%)
noise
(-8.69%)
pleasant camera tilt
(-9.42%)
pleasant composition
(-66.21%)
pleasant lighting
(-57.91%)
pleasant pattern
(12.79%)
pleasant perspective
(-5.08%)
pleasant post processing
(2.73%)
pleasant reflection
(-3.75%)
pleasant symmetry
(0.44%)
sharply focused subject
(0.20%)
tastefully blurred
(-9.23%)
well chosen subject
(-30.25%)
well framed subject
(-40.53%)
well timed shot
(-4.42%)
all
(-8.95%)
* NOTE: Amazon Rekognition
detected a celebrity in this image using the
Celebrity Recognition API. The API isn't perfect, but it does give you the MatchConfidence which I display
next to the celebrity's name along with links _↗ to their info.
* WARNING: The title and caption of this image were generated with AI (gpt-3.5-turbo-0301
from
OpenAI) based on a
BLIP-2 image-to-text labeling, tags,
location,
people
and album metadata from the image and are
potentially inaccurate, often hilariously so. If you'd like me to adjust anything,
just reach out.