Photos | Gathering in the Housing Complex
Valentino Campitelli (middle) and his friends hang out in the living room of their housing complex. The architecture and clothing styles of the early 2000s reflect in the photo.
BLIP-2 Description:
a group of people standing in a roomMetadata
Capture date:
Original Dimensions:
480w x 640h - (download 4k)
Usage
Dominant Color:
pants handbag speaker junglescene old hardware indoors valentino campitelli music table speakers cup proper computer shirt housing portrait hostel wood living screen room staircase handrail belt building hat architecture couch house wristwatch monitor accessories electronics photography bag jason flooring stairs footwear fence floor jeans shoe furniture lighting
Detected Text
overall
(17.35%)
curation
(67.82%)
highlight visibility
(5.71%)
behavioral
(70.40%)
failure
(-1.03%)
harmonious color
(-1.06%)
immersiveness
(0.81%)
interaction
(1.00%)
interesting subject
(-75.34%)
intrusive object presence
(-6.86%)
lively color
(-4.85%)
low light
(62.06%)
noise
(-11.79%)
pleasant camera tilt
(-14.49%)
pleasant composition
(-78.47%)
pleasant lighting
(-53.91%)
pleasant pattern
(5.93%)
pleasant perspective
(-17.30%)
pleasant post processing
(2.38%)
pleasant reflection
(1.15%)
pleasant symmetry
(0.32%)
sharply focused subject
(0.15%)
tastefully blurred
(-6.11%)
well chosen subject
(-7.18%)
well framed subject
(-66.36%)
well timed shot
(-13.84%)
all
(-11.18%)
* 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.