Photos | Chandelier Shines over Crowded Tent
The chandelier and three lamps light up the crowded tent at Coachella as Ni Hua joins the 24 people gathered around the large screen.
BLIP-2 Description:
a crowd of people in a tent with a large screenChronologically Adjacent
Note: You can also navigate with your arrow keys or swiping.
Metadata
Capture date:
Original Dimensions:
5760w x 3840h - (download 4k)
Usage
Dominant Color:
Location:
architecture reception cafeteria urban pub building food silhouette bar footwear baby beverage lamp hat crowd living restaurant furniture saturday flooring design court shoe bag waiting room hardware alcohol club indoors coachella life bar counter interior electronics screen light accessories room monitor night couch cafe lighting part chandelier back floor computer handbag ni hua cinema
iso
3200
metering mode
5
aperture
f/2.8
focal length
16mm
shutter speed
1/1250s
camera make
Canon
camera model
lens model
date
2014-04-12T17:08:06.910000-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(27.95%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.53%)
failure
(-2.59%)
harmonious color
(0.17%)
immersiveness
(0.46%)
interaction
(1.00%)
interesting subject
(-65.82%)
intrusive object presence
(-4.13%)
lively color
(-27.91%)
low light
(99.95%)
noise
(-3.91%)
pleasant camera tilt
(-8.56%)
pleasant composition
(-83.79%)
pleasant lighting
(-71.68%)
pleasant pattern
(5.42%)
pleasant perspective
(-3.98%)
pleasant post processing
(0.09%)
pleasant reflection
(-0.92%)
pleasant symmetry
(0.71%)
sharply focused subject
(0.12%)
tastefully blurred
(-7.41%)
well chosen subject
(6.21%)
well framed subject
(-57.91%)
well timed shot
(9.42%)
all
(-10.18%)
* 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.