Photos | Nightclub TV Time
Natalia Anciso and a group of friends watch music performances and enjoy the urban atmosphere at a local nightclub in 2003.
BLIP-2 Description:
a group of people standing around a televisionChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
480w x 640h - (download 4k)
Usage
Dominant Color:
december necklace pants urban pub frame portrait music speaker bar beverage natalia anciso hat counter performance art old hardware club alcohol junglescene disco winter interior life electronics jewelry screen respect accessories nightclub monitor room night recreation speakers tv photography computer undershirt cinema
Detected Text
date
2003-12-08T16:50:36-08:00
tzoffset
-28800
tzname
America/Los_Angeles
overall
(26.83%)
curation
(50.00%)
highlight visibility
(4.35%)
behavioral
(70.54%)
failure
(-0.61%)
harmonious color
(-0.17%)
immersiveness
(0.20%)
interaction
(1.00%)
interesting subject
(-19.32%)
intrusive object presence
(-11.13%)
lively color
(5.44%)
low light
(96.39%)
noise
(-19.58%)
pleasant camera tilt
(-10.41%)
pleasant composition
(-55.37%)
pleasant lighting
(-39.70%)
pleasant pattern
(4.83%)
pleasant perspective
(-13.11%)
pleasant post processing
(-3.78%)
pleasant reflection
(1.82%)
pleasant symmetry
(0.17%)
sharply focused subject
(0.44%)
tastefully blurred
(-1.27%)
well chosen subject
(-32.13%)
well framed subject
(-13.61%)
well timed shot
(-9.86%)
all
(-7.56%)
* 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.
* NOTE: This image was scaled up from its original size using an AI model called GFP-GAN (Generative Facial Prior), which is a
Generative adversartial network that can be used to repair (or upscale in this case) photos, sometimes the results are a little...
weird.
* 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.