Photos | The Epic Crowd at Coachella 2016
Josh Binstock, Millie Knight, Pablo Larrazábal, Sam Fender, and Kazuki Ganaha join the massive crowd at Coachella 2016 for an unforgettable outdoor music experience.
BLIP-2 Description:
a large crowd of people in a parkChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
5760w x 3840h - (download 4k)
Usage
architecture thursday activities urban leisure building knight music speaker footwear festival airplane stage outdoors musical instrument hat transportation millie crowd musician outdoor performance sky shoe bag josh binstock aircraft sam glasses friday indoors audience coachella electronics vehicle performer + group performance accessories musical instrument pablo larrazábal kazuki ganaha recreation band speakers concert handbag fender shelter
iso
800
metering mode
5
aperture
f/2.8
focal length
16mm
shutter speed
1/8000s
camera make
Canon
camera model
lens model
date
2016-04-15T13:01:34.800000-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(39.06%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.53%)
failure
(-0.17%)
harmonious color
(3.52%)
immersiveness
(0.95%)
interaction
(1.00%)
interesting subject
(-41.19%)
intrusive object presence
(-12.16%)
lively color
(-1.05%)
low light
(7.64%)
noise
(-1.39%)
pleasant camera tilt
(-9.59%)
pleasant composition
(-82.47%)
pleasant lighting
(-36.99%)
pleasant pattern
(8.57%)
pleasant perspective
(13.71%)
pleasant post processing
(1.50%)
pleasant reflection
(-4.32%)
pleasant symmetry
(1.32%)
sharply focused subject
(0.20%)
tastefully blurred
(-2.48%)
well chosen subject
(9.94%)
well framed subject
(-51.86%)
well timed shot
(16.69%)
all
(-2.39%)
* 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.