Photos | Laptop Lounge
Jin Yang and a crowd of people gather at a restaurant in Las Vegas for Defcon 22 to work on their laptops, surrounded by chairs and tables.
BLIP-2 Description:
a group of people sitting at tables with laptopsChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
5760w x 3840h - (download 4k)
Usage
Dominant Color:
Location:
machine speaker room couch furniture desk container floor laptop dining consumer hardware travel screen child teen lecture seminar summer court pc indoors bag accessories jewelry workshop chair august iii glasses building cafeteria architecture crowd necklace food monitor mark strip classroom trip audience manufacturing office table jin yang boy canon cup eos restaurant computer flooring handbag school factory off defcon electronics plywood wood
iso
3200
metering mode
5
aperture
f/2.8
focal length
35mm
shutter speed
1/40s
camera make
Canon
camera model
lens model
date
2014-08-09T13:57:58.060000-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(34.18%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.61%)
failure
(-0.29%)
harmonious color
(3.33%)
immersiveness
(0.07%)
interaction
(1.00%)
interesting subject
(-11.79%)
intrusive object presence
(-12.48%)
lively color
(-26.78%)
low light
(28.20%)
noise
(-1.42%)
pleasant camera tilt
(-6.78%)
pleasant composition
(-80.91%)
pleasant lighting
(-38.65%)
pleasant pattern
(5.20%)
pleasant perspective
(9.09%)
pleasant post processing
(3.28%)
pleasant reflection
(-1.78%)
pleasant symmetry
(0.34%)
sharply focused subject
(0.34%)
tastefully blurred
(-27.37%)
well chosen subject
(-12.55%)
well framed subject
(-26.95%)
well timed shot
(24.71%)
all
(-4.61%)
* 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.