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:
architecture summer cafeteria boy necklace classroom building defcon food chair child speaker lecture eos iii travel plywood jin yang restaurant furniture flooring court container bag wood manufacturing hardware machine desk glasses indoors factory teen pc school audience off dining mark electronics jewelry screen table accessories room monitor office strip trip handbag august couch canon laptop workshop seminar cup floor computer crowd consumer
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.