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:
pc urban december paradise rekognition_c united winter lamp mo glasses prahlad singh patel south entertainment trip graduation footwear strip leisure sumo ош mojave activities tv states hayateumi clark county dancing vegas chair caesars palace hardware las concert lighting iphone hidehito glove shoe electronics palms palace max las vegas sword desert screen pro performance speaker weapon speech caesars southeast las vegas furniture travel accessories nv audience laptop computer monitor apple loy crowd dr
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 by an AI LLM (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.