Photos | Group of Distinguished Guests Pose in Front of Building
Kim Dotcom, Eric Koston, Kim Dong-yeon, and Geoff Kabush stand with 28 other people in front of a beautiful building on a sunny day. The crowd is dressed in a variety of clothing and footwear, including sandals and hats, and they are surrounded by green shrubs and trees.
BLIP-2 Description:
a group of people standing in front of a buildingChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
4368w x 2912h - (download 4k)
Usage
Dominant Color:
architecture pants plant building apc easiest footwear geoff way transportation hat wristwatch lamp outdoors tree outdoor sky kim dotcom shoe bag sandal koston kim dong-yeon today glasses school bus shrub car vehicle college accessories handbag walking kabush stop amercasprinteccom print eric shop crowd
iso
100
metering mode
5
aperture
f/8
focal length
16mm
shutter speed
1/200s
camera make
Canon
camera model
lens model
date
2008-08-06T15:18:04-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(57.18%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.65%)
failure
(-0.02%)
harmonious color
(7.40%)
immersiveness
(0.61%)
interaction
(1.00%)
interesting subject
(40.43%)
intrusive object presence
(-2.27%)
lively color
(24.84%)
low light
(0.27%)
noise
(-0.44%)
pleasant camera tilt
(-4.06%)
pleasant composition
(-2.32%)
pleasant lighting
(24.49%)
pleasant pattern
(10.52%)
pleasant perspective
(24.32%)
pleasant post processing
(0.96%)
pleasant reflection
(0.20%)
pleasant symmetry
(1.37%)
sharply focused subject
(2.20%)
tastefully blurred
(5.16%)
well chosen subject
(-7.36%)
well framed subject
(23.45%)
well timed shot
(3.78%)
all
(11.38%)
* 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.