Photos | Election Night Restaurant Reception
Hatik and Michaela Conlin join a crowded room of 37 people at a restaurant reception on Election Day 2008. The room is filled with stylish furniture, including a chandelier overhead and tables dressed in crisp white tablecloths.
BLIP-2 Description:
a crowded room with people at tables and a large bannerChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
4368w x 2912h - (download 4k)
Usage
Dominant Color:
architecture reception cafeteria formal flag urban building meal food chair footwear election_day restaurant furniture flooring hatik conlin court shoe waiting room art hall glasses indoors dining interior wear light table room accessories suit cafe lighting home chandelier decor michaela day floor election crowd linen tablecloth
iso
3200
metering mode
5
aperture
f/2.8
focal length
16mm
shutter speed
1/15s
camera make
Canon
camera model
lens model
date
2008-11-04T21:20:07-08:00
tzoffset
-28800
tzname
America/Los_Angeles
overall
(32.93%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.57%)
failure
(-1.49%)
harmonious color
(0.24%)
immersiveness
(0.22%)
interaction
(1.00%)
interesting subject
(-40.67%)
intrusive object presence
(-7.98%)
lively color
(-40.01%)
low light
(97.80%)
noise
(-3.78%)
pleasant camera tilt
(-10.22%)
pleasant composition
(-84.47%)
pleasant lighting
(-51.95%)
pleasant pattern
(2.98%)
pleasant perspective
(-6.00%)
pleasant post processing
(0.29%)
pleasant reflection
(-2.50%)
pleasant symmetry
(0.56%)
sharply focused subject
(0.17%)
tastefully blurred
(-28.15%)
well chosen subject
(7.17%)
well framed subject
(-54.64%)
well timed shot
(17.03%)
all
(-8.39%)
* 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.