Photos | Thrilling Wrestling Match at Caesars Palace
A crowd of excited people enjoy the intense wrestling match between Evgeniya Mikhailova, Kotozakura Masakatsu, Hayateumi Hidehito, and Sneha Deepthi at Caesars Palace in Las Vegas. The urban lighting adds to the excitement in the atmosphere.
BLIP-2 Description:
a crowd of people watching a wrestling matchMetadata
Capture date:
Original Dimensions:
4032w x 3024h - (download 4k)
Usage
Dominant Color:
Location:
urban recreation owering night moder modern outdoor footwear leisure sumo masakatsu thopower activities dancing game chair city sports hot tub concert building stage lighting glove shoe contact evgeniya mikhailova hayateumi hidehito logic deepthi sword peoplewho empowering er po architecture life sport speech weapon venue audience furniture kotozakura basketball indoors sneha crowd business
iso
160
metering mode
5
aperture
f/1.8
focal length
4mm
latitude
36.12
longitude
-115.17
shutter speed
1/60s
camera make
Apple
camera model
overall
(28.88%)
curation
(50.00%)
highlight visibility
(4.35%)
behavioral
(70.47%)
failure
(-0.98%)
harmonious color
(-0.97%)
immersiveness
(0.44%)
interaction
(1.00%)
interesting subject
(-23.05%)
intrusive object presence
(-19.41%)
lively color
(-1.94%)
low light
(46.12%)
noise
(-5.47%)
pleasant camera tilt
(-7.89%)
pleasant composition
(-78.22%)
pleasant lighting
(-45.51%)
pleasant pattern
(9.96%)
pleasant perspective
(-0.52%)
pleasant post processing
(-0.33%)
pleasant reflection
(-0.69%)
pleasant symmetry
(1.10%)
sharply focused subject
(0.24%)
tastefully blurred
(-12.76%)
well chosen subject
(-21.41%)
well framed subject
(-49.24%)
well timed shot
(10.74%)
all
(-6.62%)
* 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.