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:
architecture beer dining table glass door building portrait lager sweater beverage liquor microphone cabinet cupboard nothing furniture nothing_to_do-nothing_to_do design container silvestri danny closet alcohol shirt junglescene indoors glasses electrical dining interior rekognition_c table accessories room lighting device part photography lorenzo de bottle knitwear
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.