Photos | Beer in Hand
Lorenzo De Silvestri holding a beer while Danny J looks on, surrounded by furniture and a liquor cabinet.
BLIP-2 Description:
man holding a beerChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
640w x 480h - (download 4k)
Usage
Dominant Color:
lighting knitwear room furniture lorenzo container dining device part danny glass de silvestri nothing beverage shirt cabinet indoors photography accessories bottle glasses building liquor architecture beer cupboard portrait sweater table dining table interior junglescene nothing_to_do-nothing_to_do alcohol electrical closet rekognition_c design door microphone lager
Detected Text
date
2002-08-29T14:23:32-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(29.39%)
curation
(66.09%)
highlight visibility
(5.58%)
behavioral
(70.59%)
failure
(-0.42%)
harmonious color
(-0.74%)
immersiveness
(0.20%)
interaction
(1.00%)
interesting subject
(0.28%)
intrusive object presence
(-38.45%)
lively color
(5.86%)
low light
(7.89%)
noise
(-8.98%)
pleasant camera tilt
(-6.66%)
pleasant composition
(-31.88%)
pleasant lighting
(-20.89%)
pleasant pattern
(2.54%)
pleasant perspective
(-10.88%)
pleasant post processing
(0.21%)
pleasant reflection
(0.13%)
pleasant symmetry
(0.42%)
sharply focused subject
(2.27%)
tastefully blurred
(-5.23%)
well chosen subject
(-17.27%)
well framed subject
(54.00%)
well timed shot
(-3.98%)
all
(-2.61%)
* 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.
* 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.